diff --git a/API_scheme.md b/API_scheme.md new file mode 100644 index 000000000..800a04741 --- /dev/null +++ b/API_scheme.md @@ -0,0 +1,49 @@ +

PINA Code Structure

+ + +Here is a high-level overview of PINA’s main modules. For full details, refer to the +documentation. + +```mermaid +flowchart TB + PINA["

pina

The basic module including `Condition`, LabelTensor, `Graph` and `Trainer` API"] + + subgraph R1[" "] + direction LR + PROB["

pina.problem

Module for defining problems via base class inheritance"] + MODEL["

pina.model

Module for built-in PyTorch models full architectures"] + SOLVER["

pina.solver

Module for built-in solvers and abstract interfaces"] + CALLBACK["

pina.callback

Module for built-in callbacks to integrate training pipelines"] + end + + subgraph R2[" "] + direction LR + DOMAIN["

pina.domain

Module for defining geometries and set operations"] + BLOCK["

pina.block

Module for built-in PyTorch models layers only"] + OPTIM["

pina.optim

Module for build or import optimizers and schedulers"] + DATA["

pina.data

Module for DataModules for data processing"] + end + + subgraph R3[" "] + direction LR + OPERATOR["

pina.operator

Module for differential operators"] + ADAPT["

pina.adaptive_function

Module for PyTorch learnable activations"] + LOSS["

pina.loss

Module for losses and weighting strategies"] + CONDITION["

pina.condition

Module for model training constraints"] + end + + PINA --> PROB + PINA --> MODEL + PINA --> SOLVER + PINA --> CALLBACK + + PROB --> DOMAIN + MODEL --> BLOCK + SOLVER --> OPTIM + CALLBACK --> DATA + + DOMAIN --> OPERATOR + BLOCK --> ADAPT + OPTIM --> LOSS + DATA --> CONDITION +``` \ No newline at end of file diff --git a/README.md b/README.md index 81a256d70..3369cc5d5 100644 --- a/README.md +++ b/README.md @@ -4,38 +4,87 @@ Copyright Contributors to the Pyro project. SPDX-License-Identifier: Apache-2.0 --> - - - - - -
- - PINA logo - - -

- A Unified Framework for Scientific Machine Learning -

-
- - ------------------------------------------ - -[![pages-build-deployment](https://github.com/mathLab/PINA/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/mathLab/PINA/actions/workflows/pages/pages-build-deployment) -[![Version](https://img.shields.io/pypi/v/pina-mathlab?label=version&logo=pypi)](https://pypi.org/project/pina-mathlab/) -[![Downloads](https://img.shields.io/pypi/dm/pina-mathlab?label=downloads&logo=pypi)](https://pypi.org/project/pina-mathlab/) -[![JOSS](https://img.shields.io/badge/JOSS-10.21105/JOSS.05352-blue?logo=open-access)](https://joss.theoj.org/papers/10.21105/joss.05352) -[![LICENSE](https://img.shields.io/github/license/mathLab/PINA)](https://github.com/mathLab/PINA/blob/main/LICENSE.rst) - - -[Getting Started](https://github.com/mathLab/PINA/tree/master/tutorials#pina-tutorials) | -[Documentation](https://mathlab.github.io/PINA/) | -[Contributing](https://github.com/mathLab/PINA/blob/master/CONTRIBUTING.md) - -**PINA** is an open-source Python library designed to simplify and accelerate the development of Scientific Machine Learning (SciML) solutions. Built on top of [PyTorch](https://pytorch.org/), [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), and [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/), PINA provides an intuitive framework for defining, experimenting with, and solving complex problems using Neural Networks, Physics-Informed Neural Networks (PINNs), Neural Operators, and more. +
+

+ + + + + PyPI downloads + +
+ + + + + +

+ + PINA + + +

+ A Unified Framework for Scientific Machine Learning +

+ + + +

+ PINA is an open-source Python library designed to simplify and accelerate the development of + Scientific Machine Learning (SciML) solutions, including PINNs, Neural Operators, + data-driven modeling, and more. +

+ +

+

Built on top of

+ + + + + + + + + + +

+
+
+ + +

News & Announcements

+ +
+ +
+ +

+ Want the full history? + See the Releases page. +

+ +
+ +

What's PINA

+ +PINA provides an intuitive framework for defining, experimenting with, and solving complex problems using Neural Networks, Physics-Informed Neural Networks (PINNs), Neural Operators, and more. - **Modular Architecture**: Designed with modularity in mind and relying on powerful yet composable abstractions, PINA allows users to easily plug, replace, or extend components, making experimentation and customization straightforward. @@ -45,51 +94,128 @@ SPDX-License-Identifier: Apache-2.0 -## Installation +PINA pipeline -### Installing a stable PINA release -**Install using pip:** -```sh -pip install "pina-mathlab" -``` -**Install from source:** -```sh -git clone https://github.com/mathLab/PINA -cd PINA -git checkout master -pip install . -``` +
-**Install with extra packages:** +
+ +

Installation

+        + + + + -To install extra dependencies required to run tests or tutorials directories, please use the following command: -```sh -pip install "pina-mathlab[extras]" -``` -Available extras include: -* `dev` for development purpuses, use this if you want to [Contribute](https://github.com/mathLab/PINA/blob/master/CONTRIBUTING.md#contributing-to-pina). -* `test` for running test locally. -* `doc` for building documentation locally. -* `tutorial` for running [Tutorials](https://github.com/mathLab/PINA/tree/master/tutorials#pina-tutorials). +

Install a stable release

-## Quick Tour for New Users -Solving a differential problem in **PINA** follows the *four steps pipeline*: +
pip install "pina-mathlab"
-1. Define the problem to be solved with its constraints using the [Problem API](https://mathlab.github.io/PINA/_rst/_code.html#problems). +

Install from source

-2. Design your model using PyTorch, or for graph-based problems, leverage PyTorch Geometric to build Graph Neural Networks. You can also import models directly from the [Model API](https://mathlab.github.io/PINA/_rst/_code.html#models). +
git clone https://github.com/mathLab/PINA
+cd PINA
+git checkout master
+pip install .
+
+ +Install with extra dependencies + +

+To install additional packages required for development, tests, docs, or tutorials: +

+ +
pip install "pina-mathlab[extras]"
+ +

Available extras:

+ + + +
+
+ + +
+ +

Getting started with PINA

+        + + +      + + + + + +

+Solving a differential problem in PINA follows a clean four-step pipeline: +

+ +
    +
  1. + Define the problem and constraints using the + Problem API. +
    2. +
  2. + Design your model using PyTorch, PyTorch Geometric, or import from the + Model API. +
    3. +
  3. + Select or build a Solver using the + Solver API. +
    4. +
  4. + Train with the + Trainer API, + powered by PyTorch Lightning. +
    5. +
+ +```mermaid +flowchart LR + STEP1["

Problem and Data

Define the mathematical problem
Identify constraints or import data"] + STEP2["

Model Design

Build a PyTorch module Choose or customize a model"] + STEP3["

Solver Selection

Use available solvers or define your own strategy"] + STEP4["

Training

Optimize the model with PyTorch Lightning"] + + STEP1 e1@--> STEP2 + STEP2 e2@--> STEP3 + STEP3 e3@--> STEP4 + e1@{ animate: true } + e2@{ animate: true } + e3@{ animate: true } +``` -3. Select or build a Solver for the Problem, e.g., supervised solvers, or physics-informed (e.g., PINN) solvers. [PINA Solvers](https://mathlab.github.io/PINA/_rst/_code.html#solvers) are modular and can be used as-is or customized. +

+Want to dive deeper? Check out the official +Tutorials. +

-4. Train the model using the [Trainer API](https://mathlab.github.io/PINA/_rst/trainer.html) class, built on PyTorch Lightning, which supports efficient, scalable training with advanced features. +
+
-Do you want to learn more about it? Look at our [Tutorials](https://github.com/mathLab/PINA/tree/master/tutorials#pina-tutorials). +
+ +

PINA by Examples

+        + + + + -### Solve Data Driven Problems -Data driven modelling aims to learn a function that given some input data gives an output (e.g. regression, classification, ...). In PINA you can easily do this by: -```python +
+

Data-Driven Modeling Example

 + +```python import torch from pina import Trainer from pina.model import FeedForward @@ -101,16 +227,28 @@ target_tensor = input_tensor.pow(3) # Step 1. Define problem problem = SupervisedProblem(input_tensor, target_tensor) -# Step 2. Design model (you can use your favourite torch.nn.Module in here) -model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64]) -# Step 3. Define Solver -solver = SupervisedSolver(problem, model, use_lt=False) + +# Step 2. Define model +model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64]) + +# Step 3. Define solver +solver = SupervisedSolver(problem, model, use_lt=False) + # Step 4. Train -trainer = Trainer(solver, max_epochs=1000, accelerator='gpu') +trainer = Trainer(solver, max_epochs=1000, accelerator="gpu") trainer.train() ``` -### Solve Physics Informed Problems -Physics-informed modeling aims to learn functions that not only fit data, but also satisfy known physical laws, such as differential equations or boundary conditions. For example, the following differential problem: +
+ +
+ +
+ +

Physics-Informed Example

 + +

+Consider the following differential problem: +

$$ \begin{cases} @@ -118,8 +256,9 @@ $$ u(x=0) &= 1 \end{cases} $$ - -in PINA, can be easily implemented by: +

+In PINA, this can be implemented as: +

```python from pina import Trainer, Condition @@ -135,7 +274,6 @@ def ode_equation(input_, output_): u = output_.extract(["u"]) return u_x - u -# build the problem class SimpleODE(SpatialProblem): output_variables = ["u"] spatial_domain = CartesianDomain({"x": [0, 1]}) @@ -151,52 +289,81 @@ class SimpleODE(SpatialProblem): # Step 1. Define problem problem = SimpleODE() problem.discretise_domain(n=100, mode="grid", domains=["D", "x0"]) -# Step 2. Design model (you can use your favourite torch.nn.Module in here) -model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64]) -# Step 3. Define Solver -solver = PINN(problem, model) -# Step 4. Train -trainer = Trainer(solver, max_epochs=1000, accelerator='gpu') -trainer.train() -``` - -## Application Programming Interface -Here's a quick look at PINA's main module. For a better experience and full details, check out the [documentation](https://mathlab.github.io/PINA/). - - - - - -## Contributing and Community - -We would love to develop PINA together with our community! Best way to get started is to select any issue from the [`good-first-issue` label](https://github.com/mathLab/PINA/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22). If you would like to contribute, please review our [Contributing Guide](CONTRIBUTING.md) for all relevant details. - -We warmly thank all the contributors that have supported PINA so far: - - Contributors - - -Made with [contrib.rocks](https://contrib.rocks). - -## Citation -If **PINA** has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: +# Step 2. Define model +model = FeedForward(input_dimensions=1, output_dimensions=1, layers=[64, 64]) -``` -Coscia, D., Ivagnes, A., Demo, N., & Rozza, G. (2023). Physics-Informed Neural networks for Advanced modeling. Journal of Open Source Software, 8(87), 5352. -``` +# Step 3. Define solver +solver = PINN(problem, model) -Or in BibTex format -``` -@article{coscia2023physics, - title={Physics-Informed Neural networks for Advanced modeling}, - author={Coscia, Dario and Ivagnes, Anna and Demo, Nicola and Rozza, Gianluigi}, - journal={Journal of Open Source Software}, - volume={8}, - number={87}, - pages={5352}, - year={2023} - } +# Step 4. Train +trainer = Trainer(solver, max_epochs=1000, accelerator="gpu") +trainer.train() ``` +
+
+
+ +
+ +

Contributing & Community

+        + + +

+We would love to develop PINA together with the community. +A great place to start is the list of + + good-first-issue + +issues. +

+ +

+If you would like to contribute, please read the +Contributing Guide. +

+ +

+ + Contributors + +

+ +

+ Made with contrib.rocks. +

+ +
+
+ +
+ +

Citation

+        + + + + +

+If PINA has been significant in your research and you would like to acknowledge it, please cite: +

+ +
Coscia, D., Ivagnes, A., Demo, N., & Rozza, G. (2023).
+Physics-Informed Neural networks for Advanced modeling.
+Journal of Open Source Software, 8(87), 5352.
+ +

Or in BibTeX format:

+ +
@article{coscia2023physics,
+  title={Physics-Informed Neural networks for Advanced modeling},
+  author={Coscia, Dario and Ivagnes, Anna and Demo, Nicola and Rozza, Gianluigi},
+  journal={Journal of Open Source Software},
+  volume={8},
+  number={87},
+  pages={5352},
+  year={2023}
+}
+
diff --git a/SECURITY.md b/SECURITY.md index b1dfe91f8..a425740c2 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -10,6 +10,7 @@ Security fixes are given priority and might be enough to cause a new version to | Version | Supported | | ------- | ------------------ | +| 0.3 | ✅ | | 0.2 | ✅ | | 0.1 | ✅ | diff --git a/docs/source/_rst/_code.rst b/docs/source/_rst/_code.rst index 64d88bc8b..6b2111946 100644 --- a/docs/source/_rst/_code.rst +++ b/docs/source/_rst/_code.rst @@ -15,17 +15,20 @@ The pipeline to solve differential equations with PINA follows just five steps: 2. Generate data using built in `Geometrical Domains`_, or load high level simulation results as :doc:`LabelTensor ` 3. Choose or build one or more `Models`_ to solve the problem 4. Choose a solver across PINA available `Solvers`_, or build one using the :doc:`SolverInterface ` - 5. Train the model with the PINA :doc:`Trainer `, enhance the train with `Callbacks`_ + 5. Train the model with the PINA :doc:`Trainer `, enhance the train with `Callbacks`_ -Trainer, Dataset and Datamodule --------------------------------- +Trainer, Data Loader and Data Module +---------------------------------------- .. toctree:: :titlesonly: Trainer - Dataset - DataModule + Data Module + Single-Batch Data Loader + Aggregator + Creator + Condition Subset Data Types ------------ @@ -52,37 +55,65 @@ Conditions .. toctree:: :titlesonly: - ConditionInterface + Condition Interface + Base Condition Condition - DataCondition - DomainEquationCondition - InputEquationCondition - InputTargetCondition + Data Condition + Domain Equation Condition + Graph Time Series Condition + Input Equation Condition + Input Target Condition + Time Series Condition + +Batch and Data Managers +-------------------------- +.. toctree:: + :titlesonly: + + Batch Manager + Data Manager Interface + Data Manager + Graph Data Manager + Tensor Data Manager Solvers --------------- +------------------------ .. toctree:: :titlesonly: - SolverInterface - SingleSolverInterface - MultiSolverInterface - SupervisedSolverInterface - DeepEnsembleSolverInterface - PINNInterface - PINN - GradientPINN - CausalPINN - CompetitivePINN - SelfAdaptivePINN - RBAPINN - DeepEnsemblePINN - SupervisedSolver - DeepEnsembleSupervisedSolver - ReducedOrderModelSolver - GAROM + Solver Interface + Base Solver + Single-Model Solver + Multi-Model Solver + Ensemble Solver + Supervised Single-Model Solver + Supervised Ensemble Solver + Physics-Informed Single-Model Solver + Physics-Informed Ensemble Solver + Autoregressive Single-Model Solver + Autoregressive Ensemble Solver + Self-Adaptive Physics-Informed Solver + Competitive Physics-Informed Solver + Gradient Physics-Informed Single-Model Solver + RBA Physics-Informed Single-Model Solver + Causal Physics-Informed Single-Model Solver + +Mixins +------------------------ + +.. toctree:: + :titlesonly: + Single-Model Mixin + Multi-Model Mixin + Ensemble Mixin + Condition Aggregator Mixin + Manual Optimization Mixin + Physics-Informed Mixin + Autoregressive Mixin + Gradient-Enhanced Mixin + Residual-Based Attention Mixin Models ------------ @@ -108,6 +139,8 @@ Models PirateNet EquivariantGraphNeuralOperator SINDy + Vectorized Spline + Kolmogorov-Arnold Network Blocks ------------- @@ -126,6 +159,7 @@ Blocks Continuous Convolution Block Orthogonal Block PirateNet Block + KAN Block Message Passing ------------------- @@ -157,31 +191,32 @@ Optimizers and Schedulers .. toctree:: :titlesonly: - Optimizer - Scheduler - TorchOptimizer - TorchScheduler + Optimizer Interface + Scheduler Interface + Torch Optimizer + Torch Scheduler -Adaptive Activation Functions +Adaptive Functions ------------------------------- .. toctree:: :titlesonly: - Adaptive Function Interface - Adaptive ReLU - Adaptive Sigmoid - Adaptive Tanh - Adaptive SiLU - Adaptive Mish - Adaptive ELU - Adaptive CELU - Adaptive GELU - Adaptive Softmin - Adaptive Softmax - Adaptive SIREN - Adaptive Exp + Adaptive Function Interface + Base Adaptive Function + Adaptive CELU + Adaptive ELU + Adaptive Exp + Adaptive GELU + Adaptive Mish + Adaptive ReLU + Adaptive Sigmoid + Adaptive SiLU + Adaptive SIREN + Adaptive Softmax + Adaptive Softmin + Adaptive Tanh Equations and Differential Operators @@ -190,39 +225,60 @@ Equations and Differential Operators .. toctree:: :titlesonly: - EquationInterface + Equation Interface + Base Equation Equation - SystemEquation - Equation Factory + System Equation Differential Operators +Equation Zoo +--------------------------------------- + +.. toctree:: + :titlesonly: + + Acoustic Wave Equation + Advection Equation + Allen-Cahn Equation + Burgers' Equation + Diffusion-Reaction Equation + Fixed Flux + Fixed Gradient + Fixed Laplacian + Fixed Value + Helmholtz Equation + Poisson Equation + + Problems -------------- .. toctree:: :titlesonly: - AbstractProblem + ProblemInterface + BaseProblem InverseProblem ParametricProblem SpatialProblem TimeDependentProblem -Problems Zoo +Problem Zoo -------------- .. toctree:: :titlesonly: - AcousticWaveProblem - AdvectionProblem - AllenCahnProblem - DiffusionReactionProblem - HelmholtzProblem - InversePoisson2DSquareProblem - Poisson2DSquareProblem - SupervisedProblem + Acoustic Wave Problem + Advection Problem + Allen-Cahn Problem + Burgers' Problem + Diffusion-Reaction Problem + Helmholtz Problem + Inverse Poisson 2D Square Problem + Poisson 2D Square Problem + Supervised Problem Geometrical Domains @@ -258,23 +314,37 @@ Callbacks Switch Optimizer Switch Scheduler - Normalizer Data - PINA Progress Bar - Metric Tracker Refinement Interface + Base Refinement R3 Refinement + Data Normalizer + Metric Tracker + PINA Progress Bar + + +Losses +--------- -Losses and Weightings ---------------------- +.. toctree:: + :titlesonly: + + DualLossInterface + BaseDualLoss + LpLoss + PowerLoss + SinkhornLoss + + +Weighting Schemas +-------------------- .. toctree:: :titlesonly: - LossInterface - LpLoss - PowerLoss - WeightingInterface - ScalarWeighting - NeuralTangentKernelWeighting - SelfAdaptiveWeighting - LinearWeighting \ No newline at end of file + Weighting Interface + Base Weighting + Linear Weighting + Neural-Tangent-Kernel Weighting + No Weighting + Scalar Weighting + Self-Adaptive Weighting diff --git a/docs/source/_rst/adaptive_function/AdaptiveActivationFunctionInterface.rst b/docs/source/_rst/adaptive_function/AdaptiveActivationFunctionInterface.rst deleted file mode 100644 index cf8b6551d..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveActivationFunctionInterface.rst +++ /dev/null @@ -1,8 +0,0 @@ -AdaptiveActivationFunctionInterface -======================================= - -.. currentmodule:: pina.adaptive_function.adaptive_function_interface - -.. automodule:: pina.adaptive_function.adaptive_function_interface - :members: - :show-inheritance: diff --git a/docs/source/_rst/adaptive_function/AdaptiveCELU.rst b/docs/source/_rst/adaptive_function/AdaptiveCELU.rst deleted file mode 100644 index c4d6d5429..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveCELU.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveCELU -============ - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveCELU - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveELU.rst b/docs/source/_rst/adaptive_function/AdaptiveELU.rst deleted file mode 100644 index aab273b08..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveELU.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveELU -=========== - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveELU - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveExp.rst b/docs/source/_rst/adaptive_function/AdaptiveExp.rst deleted file mode 100644 index a7ee52b20..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveExp.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveExp -=========== - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveExp - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveGELU.rst b/docs/source/_rst/adaptive_function/AdaptiveGELU.rst deleted file mode 100644 index b4aef14dc..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveGELU.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveGELU -============ - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveGELU - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveMish.rst b/docs/source/_rst/adaptive_function/AdaptiveMish.rst deleted file mode 100644 index d006df054..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveMish.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveMish -============ - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveMish - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveReLU.rst b/docs/source/_rst/adaptive_function/AdaptiveReLU.rst deleted file mode 100644 index d0fe4de68..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveReLU.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveReLU -============ - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveReLU - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveSIREN.rst b/docs/source/_rst/adaptive_function/AdaptiveSIREN.rst deleted file mode 100644 index 9f132547b..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveSIREN.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveSIREN -============= - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveSIREN - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveSiLU.rst b/docs/source/_rst/adaptive_function/AdaptiveSiLU.rst deleted file mode 100644 index 722678611..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveSiLU.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveSiLU -============ - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveSiLU - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveSigmoid.rst b/docs/source/_rst/adaptive_function/AdaptiveSigmoid.rst deleted file mode 100644 index 6002ffb31..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveSigmoid.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveSigmoid -=============== - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveSigmoid - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveSoftmax.rst b/docs/source/_rst/adaptive_function/AdaptiveSoftmax.rst deleted file mode 100644 index c2b4c9f09..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveSoftmax.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveSoftmax -=============== - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveSoftmax - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveSoftmin.rst b/docs/source/_rst/adaptive_function/AdaptiveSoftmin.rst deleted file mode 100644 index 5189cb391..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveSoftmin.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveSoftmin -=============== - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveSoftmin - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/AdaptiveTanh.rst b/docs/source/_rst/adaptive_function/AdaptiveTanh.rst deleted file mode 100644 index 9a9b380a3..000000000 --- a/docs/source/_rst/adaptive_function/AdaptiveTanh.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdaptiveTanh -============ - -.. currentmodule:: pina.adaptive_function.adaptive_function - -.. autoclass:: AdaptiveTanh - :members: - :show-inheritance: - :inherited-members: AdaptiveActivationFunctionInterface diff --git a/docs/source/_rst/adaptive_function/adaptive_celu.rst b/docs/source/_rst/adaptive_function/adaptive_celu.rst new file mode 100644 index 000000000..b04bcf42b --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_celu.rst @@ -0,0 +1,9 @@ +Adaptive CELU +================== +.. currentmodule:: pina.adaptive_function.adaptive_celu + +.. automodule:: pina._src.adaptive_function.adaptive_celu + +.. autoclass:: pina._src.adaptive_function.adaptive_celu.AdaptiveCELU + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_elu.rst b/docs/source/_rst/adaptive_function/adaptive_elu.rst new file mode 100644 index 000000000..e758b20b3 --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_elu.rst @@ -0,0 +1,9 @@ +Adaptive ELU +============================= +.. currentmodule:: pina.adaptive_function.adaptive_elu + +.. automodule:: pina._src.adaptive_function.adaptive_elu + +.. autoclass:: pina._src.adaptive_function.adaptive_elu.AdaptiveELU + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_exp.rst b/docs/source/_rst/adaptive_function/adaptive_exp.rst new file mode 100644 index 000000000..3feeb6192 --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_exp.rst @@ -0,0 +1,9 @@ +Adaptive Exp +============================= +.. currentmodule:: pina.adaptive_function.adaptive_exp + +.. automodule:: pina._src.adaptive_function.adaptive_exp + +.. autoclass:: pina._src.adaptive_function.adaptive_exp.AdaptiveExp + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_function_interface.rst b/docs/source/_rst/adaptive_function/adaptive_function_interface.rst new file mode 100644 index 000000000..e7859c0d2 --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_function_interface.rst @@ -0,0 +1,9 @@ +Adaptive Function Interface +============================= +.. currentmodule:: pina.adaptive_function.adaptive_function_interface + +.. automodule:: pina._src.adaptive_function.adaptive_function_interface + +.. autoclass:: pina._src.adaptive_function.adaptive_function_interface.AdaptiveFunctionInterface + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_gelu.rst b/docs/source/_rst/adaptive_function/adaptive_gelu.rst new file mode 100644 index 000000000..a07960373 --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_gelu.rst @@ -0,0 +1,9 @@ +Adaptive GELU +============================= +.. currentmodule:: pina.adaptive_function.adaptive_gelu + +.. automodule:: pina._src.adaptive_function.adaptive_gelu + +.. autoclass:: pina._src.adaptive_function.adaptive_gelu.AdaptiveGELU + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_mish.rst b/docs/source/_rst/adaptive_function/adaptive_mish.rst new file mode 100644 index 000000000..f56c911fb --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_mish.rst @@ -0,0 +1,9 @@ +Adaptive Mish +============================= +.. currentmodule:: pina.adaptive_function.adaptive_mish + +.. automodule:: pina._src.adaptive_function.adaptive_mish + +.. autoclass:: pina._src.adaptive_function.adaptive_mish.AdaptiveMish + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_relu.rst b/docs/source/_rst/adaptive_function/adaptive_relu.rst new file mode 100644 index 000000000..a2032f344 --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_relu.rst @@ -0,0 +1,9 @@ +Adaptive ReLU +============================= +.. currentmodule:: pina.adaptive_function.adaptive_relu + +.. automodule:: pina._src.adaptive_function.adaptive_relu + +.. autoclass:: pina._src.adaptive_function.adaptive_relu.AdaptiveReLU + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_sigmoid.rst b/docs/source/_rst/adaptive_function/adaptive_sigmoid.rst new file mode 100644 index 000000000..8aef91c0d --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_sigmoid.rst @@ -0,0 +1,9 @@ +Adaptive Sigmoid +============================= +.. currentmodule:: pina.adaptive_function.adaptive_sigmoid + +.. automodule:: pina._src.adaptive_function.adaptive_sigmoid + +.. autoclass:: pina._src.adaptive_function.adaptive_sigmoid.AdaptiveSigmoid + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_silu.rst b/docs/source/_rst/adaptive_function/adaptive_silu.rst new file mode 100644 index 000000000..2d22dcf20 --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_silu.rst @@ -0,0 +1,9 @@ +Adaptive SiLU +============================= +.. currentmodule:: pina.adaptive_function.adaptive_silu + +.. automodule:: pina._src.adaptive_function.adaptive_silu + +.. autoclass:: pina._src.adaptive_function.adaptive_silu.AdaptiveSiLU + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_siren.rst b/docs/source/_rst/adaptive_function/adaptive_siren.rst new file mode 100644 index 000000000..167cd79ff --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_siren.rst @@ -0,0 +1,9 @@ +Adaptive SIREN +============================= +.. currentmodule:: pina.adaptive_function.adaptive_siren + +.. automodule:: pina._src.adaptive_function.adaptive_siren + +.. autoclass:: pina._src.adaptive_function.adaptive_siren.AdaptiveSIREN + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_softmax.rst b/docs/source/_rst/adaptive_function/adaptive_softmax.rst new file mode 100644 index 000000000..8797acae9 --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_softmax.rst @@ -0,0 +1,9 @@ +Adaptive Softmax +============================= +.. currentmodule:: pina.adaptive_function.adaptive_softmax + +.. automodule:: pina._src.adaptive_function.adaptive_softmax + +.. autoclass:: pina._src.adaptive_function.adaptive_softmax.AdaptiveSoftmax + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_softmin.rst b/docs/source/_rst/adaptive_function/adaptive_softmin.rst new file mode 100644 index 000000000..72ed8ae1f --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_softmin.rst @@ -0,0 +1,9 @@ +Adaptive Softmin +============================= +.. currentmodule:: pina.adaptive_function.adaptive_softmin + +.. automodule:: pina._src.adaptive_function.adaptive_softmin + +.. autoclass:: pina._src.adaptive_function.adaptive_softmin.AdaptiveSoftmin + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/adaptive_tanh.rst b/docs/source/_rst/adaptive_function/adaptive_tanh.rst new file mode 100644 index 000000000..dbd9e4313 --- /dev/null +++ b/docs/source/_rst/adaptive_function/adaptive_tanh.rst @@ -0,0 +1,9 @@ +Adaptive Tanh +============================= +.. currentmodule:: pina.adaptive_function.adaptive_tanh + +.. automodule:: pina._src.adaptive_function.adaptive_tanh + +.. autoclass:: pina._src.adaptive_function.adaptive_tanh.AdaptiveTanh + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/adaptive_function/base_adaptive_function.rst b/docs/source/_rst/adaptive_function/base_adaptive_function.rst new file mode 100644 index 000000000..6b1e6cee7 --- /dev/null +++ b/docs/source/_rst/adaptive_function/base_adaptive_function.rst @@ -0,0 +1,9 @@ +Base Adaptive Function +============================= +.. currentmodule:: pina.adaptive_function.base_adaptive_function + +.. automodule:: pina._src.adaptive_function.base_adaptive_function + +.. autoclass:: pina._src.adaptive_function.base_adaptive_function.BaseAdaptiveFunction + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/callback/optim/switch_optimizer.rst b/docs/source/_rst/callback/optim/switch_optimizer.rst index 635e79a18..13b7db7ad 100644 --- a/docs/source/_rst/callback/optim/switch_optimizer.rst +++ b/docs/source/_rst/callback/optim/switch_optimizer.rst @@ -2,6 +2,8 @@ Switch Optimizer ===================== .. currentmodule:: pina.callback.optim.switch_optimizer +.. automodule:: pina._src.callback.optim.switch_optimizer + :show-inheritance: .. autoclass:: SwitchOptimizer :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/callback/optim/switch_scheduler.rst b/docs/source/_rst/callback/optim/switch_scheduler.rst index 3176904da..42d5e6be0 100644 --- a/docs/source/_rst/callback/optim/switch_scheduler.rst +++ b/docs/source/_rst/callback/optim/switch_scheduler.rst @@ -2,6 +2,8 @@ Switch Scheduler ===================== .. currentmodule:: pina.callback.optim.switch_scheduler +.. automodule:: pina._src.callback.optim.switch_scheduler + :show-inheritance: .. autoclass:: SwitchScheduler :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/callback/processing/data_normalizer.rst b/docs/source/_rst/callback/processing/data_normalizer.rst new file mode 100644 index 000000000..358d2f472 --- /dev/null +++ b/docs/source/_rst/callback/processing/data_normalizer.rst @@ -0,0 +1,9 @@ +Data Normalizer +======================= +.. currentmodule:: pina.callback.processing.data_normalizer + +.. automodule:: pina._src.callback.processing.data_normalizer + +.. autoclass:: pina._src.callback.processing.data_normalizer.DataNormalizer + :members: + :show-inheritance: diff --git a/docs/source/_rst/callback/processing/metric_tracker.rst b/docs/source/_rst/callback/processing/metric_tracker.rst index f21cc7730..22d7cc229 100644 --- a/docs/source/_rst/callback/processing/metric_tracker.rst +++ b/docs/source/_rst/callback/processing/metric_tracker.rst @@ -2,6 +2,9 @@ Metric Tracker ================== .. currentmodule:: pina.callback.processing.metric_tracker -.. autoclass:: MetricTracker +.. automodule:: pina._src.callback.processing.metric_tracker + +.. autoclass:: pina._src.callback.processing.metric_tracker.MetricTracker :members: - :show-inheritance: \ No newline at end of file + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/callback/processing/normalizer_data_callback.rst b/docs/source/_rst/callback/processing/normalizer_data_callback.rst deleted file mode 100644 index a44f0c402..000000000 --- a/docs/source/_rst/callback/processing/normalizer_data_callback.rst +++ /dev/null @@ -1,7 +0,0 @@ -Normalizer Data -======================= - -.. currentmodule:: pina.callback.processing.normalizer_data_callback -.. autoclass:: NormalizerDataCallback - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/callback/processing/pina_progress_bar.rst b/docs/source/_rst/callback/processing/pina_progress_bar.rst index 1d42ad120..9c64678eb 100644 --- a/docs/source/_rst/callback/processing/pina_progress_bar.rst +++ b/docs/source/_rst/callback/processing/pina_progress_bar.rst @@ -2,6 +2,8 @@ PINA Progress Bar ================== .. currentmodule:: pina.callback.processing.pina_progress_bar -.. autoclass:: PINAProgressBar +.. automodule:: pina._src.callback.processing.pina_progress_bar + +.. autoclass:: pina._src.callback.processing.pina_progress_bar.PINAProgressBar :members: - :show-inheritance: \ No newline at end of file + :show-inheritance: diff --git a/docs/source/_rst/callback/refinement/base_refinement.rst b/docs/source/_rst/callback/refinement/base_refinement.rst new file mode 100644 index 000000000..5f8eaf218 --- /dev/null +++ b/docs/source/_rst/callback/refinement/base_refinement.rst @@ -0,0 +1,7 @@ +Base Refinement +======================= + +.. currentmodule:: pina.callback.refinement.base_refinement +.. autoclass:: pina._src.callback.refinement.base_refinement.BaseRefinement + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/callback/refinement/r3_refinement.rst b/docs/source/_rst/callback/refinement/r3_refinement.rst index eb3bfebf2..0d787c840 100644 --- a/docs/source/_rst/callback/refinement/r3_refinement.rst +++ b/docs/source/_rst/callback/refinement/r3_refinement.rst @@ -1,7 +1,7 @@ -Refinments callbacks +R3 Refinement ======================= -.. currentmodule:: pina.callback.refinement -.. autoclass:: R3Refinement +.. currentmodule:: pina.callback.refinement.r3_refinement +.. autoclass:: pina._src.callback.refinement.r3_refinement.R3Refinement :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/callback/refinement/refinement_interface.rst b/docs/source/_rst/callback/refinement/refinement_interface.rst index 5e02f2dc3..1af845800 100644 --- a/docs/source/_rst/callback/refinement/refinement_interface.rst +++ b/docs/source/_rst/callback/refinement/refinement_interface.rst @@ -1,7 +1,7 @@ Refinement Interface ======================= -.. currentmodule:: pina.callback.refinement -.. autoclass:: RefinementInterface +.. currentmodule:: pina.callback.refinement.refinement_interface +.. autoclass:: pina._src.callback.refinement.refinement_interface.RefinementInterface :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/base_condition.rst b/docs/source/_rst/condition/base_condition.rst new file mode 100644 index 000000000..2ba4113bd --- /dev/null +++ b/docs/source/_rst/condition/base_condition.rst @@ -0,0 +1,9 @@ +Base Condition +================ +.. currentmodule:: pina.condition.base_condition + +.. automodule:: pina._src.condition.base_condition + +.. autoclass:: pina._src.condition.base_condition.BaseCondition + :members: + :show-inheritance: diff --git a/docs/source/_rst/condition/condition.rst b/docs/source/_rst/condition/condition.rst index 51edfafff..0f8070506 100644 --- a/docs/source/_rst/condition/condition.rst +++ b/docs/source/_rst/condition/condition.rst @@ -1,7 +1,9 @@ -Conditions +Condition ============= .. currentmodule:: pina.condition.condition -.. autoclass:: Condition +.. automodule:: pina._src.condition.condition + +.. autoclass:: pina._src.condition.condition.Condition :members: - :show-inheritance: \ No newline at end of file + :show-inheritance: diff --git a/docs/source/_rst/condition/condition_interface.rst b/docs/source/_rst/condition/condition_interface.rst index 88459629b..a81de1afa 100644 --- a/docs/source/_rst/condition/condition_interface.rst +++ b/docs/source/_rst/condition/condition_interface.rst @@ -1,7 +1,9 @@ -ConditionInterface +Condition Interface ====================== .. currentmodule:: pina.condition.condition_interface -.. autoclass:: ConditionInterface +.. automodule:: pina._src.condition.condition_interface + +.. autoclass:: pina._src.condition.condition_interface.ConditionInterface :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/data_condition.rst b/docs/source/_rst/condition/data_condition.rst index b7c322ea1..d614fbb7b 100644 --- a/docs/source/_rst/condition/data_condition.rst +++ b/docs/source/_rst/condition/data_condition.rst @@ -1,15 +1,9 @@ -Data Conditions +Data Condition ================== .. currentmodule:: pina.condition.data_condition -.. autoclass:: DataCondition - :members: - :show-inheritance: - -.. autoclass:: GraphDataCondition - :members: - :show-inheritance: +.. automodule:: pina._src.condition.data_condition -.. autoclass:: TensorDataCondition +.. autoclass:: pina._src.condition.data_condition.DataCondition :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/domain_equation_condition.rst b/docs/source/_rst/condition/domain_equation_condition.rst index 505c8b839..2c372f13f 100644 --- a/docs/source/_rst/condition/domain_equation_condition.rst +++ b/docs/source/_rst/condition/domain_equation_condition.rst @@ -2,6 +2,8 @@ Domain Equation Condition =========================== .. currentmodule:: pina.condition.domain_equation_condition -.. autoclass:: DomainEquationCondition +.. automodule:: pina._src.condition.domain_equation_condition + +.. autoclass:: pina._src.condition.domain_equation_condition.DomainEquationCondition :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/graph_time_series_condition.rst b/docs/source/_rst/condition/graph_time_series_condition.rst new file mode 100644 index 000000000..6314980fb --- /dev/null +++ b/docs/source/_rst/condition/graph_time_series_condition.rst @@ -0,0 +1,10 @@ +Graph Time Series Condition +============================= + +.. currentmodule:: pina.condition.graph_time_series_condition + +.. automodule:: pina._src.condition.graph_time_series_condition + +.. autoclass:: pina._src.condition.graph_time_series_condition.GraphTimeSeriesCondition + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/input_equation_condition.rst b/docs/source/_rst/condition/input_equation_condition.rst index 4f5450e93..da0a48476 100644 --- a/docs/source/_rst/condition/input_equation_condition.rst +++ b/docs/source/_rst/condition/input_equation_condition.rst @@ -2,14 +2,8 @@ Input Equation Condition =========================== .. currentmodule:: pina.condition.input_equation_condition -.. autoclass:: InputEquationCondition - :members: - :show-inheritance: - -.. autoclass:: InputTensorEquationCondition - :members: - :show-inheritance: +.. automodule:: pina._src.condition.input_equation_condition -.. autoclass:: InputGraphEquationCondition +.. autoclass:: pina._src.condition.input_equation_condition.InputEquationCondition :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/input_target_condition.rst b/docs/source/_rst/condition/input_target_condition.rst index 960b7d6f4..da8333714 100644 --- a/docs/source/_rst/condition/input_target_condition.rst +++ b/docs/source/_rst/condition/input_target_condition.rst @@ -2,22 +2,8 @@ Input Target Condition =========================== .. currentmodule:: pina.condition.input_target_condition -.. autoclass:: InputTargetCondition - :members: - :show-inheritance: - -.. autoclass:: TensorInputTensorTargetCondition - :members: - :show-inheritance: +.. automodule:: pina._src.condition.input_target_condition -.. autoclass:: TensorInputGraphTargetCondition +.. autoclass:: pina._src.condition.input_target_condition.InputTargetCondition :members: :show-inheritance: - -.. autoclass:: GraphInputTensorTargetCondition - :members: - :show-inheritance: - -.. autoclass:: GraphInputGraphTargetCondition - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/condition/time_series_condition.rst b/docs/source/_rst/condition/time_series_condition.rst new file mode 100644 index 000000000..49a5f8795 --- /dev/null +++ b/docs/source/_rst/condition/time_series_condition.rst @@ -0,0 +1,10 @@ +Time Series Condition +======================= + +.. currentmodule:: pina.condition.time_series_condition + +.. automodule:: pina._src.condition.time_series_condition + +.. autoclass:: pina._src.condition.time_series_condition.TimeSeriesCondition + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/aggregator.rst b/docs/source/_rst/data/aggregator.rst new file mode 100644 index 000000000..738a57524 --- /dev/null +++ b/docs/source/_rst/data/aggregator.rst @@ -0,0 +1,9 @@ +Aggregator +================ +.. currentmodule:: pina.data.aggregator + +.. automodule:: pina._src.data.aggregator + +.. autoclass:: pina._src.data.aggregator._Aggregator + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/condition_subset.rst b/docs/source/_rst/data/condition_subset.rst new file mode 100644 index 000000000..84c032dc8 --- /dev/null +++ b/docs/source/_rst/data/condition_subset.rst @@ -0,0 +1,9 @@ +Condition Subset +================ +.. currentmodule:: pina.data.condition_subset + +.. automodule:: pina._src.data.condition_subset + +.. autoclass:: pina._src.data.condition_subset._ConditionSubset + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/creator.rst b/docs/source/_rst/data/creator.rst new file mode 100644 index 000000000..5d836292d --- /dev/null +++ b/docs/source/_rst/data/creator.rst @@ -0,0 +1,9 @@ +Creator +======= +.. currentmodule:: pina.data.creator + +.. automodule:: pina._src.data.creator + +.. autoclass:: pina._src.data.creator._Creator + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/data_module.rst b/docs/source/_rst/data/data_module.rst index b7ffb14e0..e31dae2b9 100644 --- a/docs/source/_rst/data/data_module.rst +++ b/docs/source/_rst/data/data_module.rst @@ -2,14 +2,6 @@ DataModule ====================== .. currentmodule:: pina.data.data_module -.. autoclass:: Collator +.. autoclass:: pina._src.data.data_module.DataModule :members: :show-inheritance: - -.. autoclass:: PinaDataModule - :members: - :show-inheritance: - -.. autoclass:: PinaSampler - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/dataset.rst b/docs/source/_rst/data/dataset.rst deleted file mode 100644 index b49b41db1..000000000 --- a/docs/source/_rst/data/dataset.rst +++ /dev/null @@ -1,19 +0,0 @@ -Dataset -====================== -.. currentmodule:: pina.data.dataset - -.. autoclass:: PinaDataset - :members: - :show-inheritance: - -.. autoclass:: PinaDatasetFactory - :members: - :show-inheritance: - -.. autoclass:: PinaGraphDataset - :members: - :show-inheritance: - -.. autoclass:: PinaTensorDataset - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/manager/batch_manager.rst b/docs/source/_rst/data/manager/batch_manager.rst new file mode 100644 index 000000000..5d7c36650 --- /dev/null +++ b/docs/source/_rst/data/manager/batch_manager.rst @@ -0,0 +1,9 @@ +Batch Manager +====================== +.. currentmodule:: pina.data.manager.batch_manager + +.. automodule:: pina._src.data.manager.batch_manager + +.. autoclass:: pina._src.data.manager.batch_manager._BatchManager + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/manager/data_manager.rst b/docs/source/_rst/data/manager/data_manager.rst new file mode 100644 index 000000000..9b32b8242 --- /dev/null +++ b/docs/source/_rst/data/manager/data_manager.rst @@ -0,0 +1,9 @@ +Data Manager +====================== +.. currentmodule:: pina.data.manager.data_manager + +.. automodule:: pina._src.data.manager.data_manager + +.. autoclass:: pina._src.data.manager.data_manager._DataManager + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/manager/data_manager_interface.rst b/docs/source/_rst/data/manager/data_manager_interface.rst new file mode 100644 index 000000000..e4a502abf --- /dev/null +++ b/docs/source/_rst/data/manager/data_manager_interface.rst @@ -0,0 +1,9 @@ +Data Manager Interface +========================= +.. currentmodule:: pina.data.manager.data_manager_interface + +.. automodule:: pina._src.data.manager.data_manager_interface + +.. autoclass:: pina._src.data.manager.data_manager_interface._DataManagerInterface + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/manager/graph_data_manager.rst b/docs/source/_rst/data/manager/graph_data_manager.rst new file mode 100644 index 000000000..bbbf23a52 --- /dev/null +++ b/docs/source/_rst/data/manager/graph_data_manager.rst @@ -0,0 +1,9 @@ +Graph Data Manager +====================== +.. currentmodule:: pina.data.manager.graph_data_manager + +.. automodule:: pina._src.data.manager.graph_data_manager + +.. autoclass:: pina._src.data.manager.graph_data_manager._GraphDataManager + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/manager/tensor_data_manager.rst b/docs/source/_rst/data/manager/tensor_data_manager.rst new file mode 100644 index 000000000..f8bb06028 --- /dev/null +++ b/docs/source/_rst/data/manager/tensor_data_manager.rst @@ -0,0 +1,9 @@ +Tensor Data Manager +====================== +.. currentmodule:: pina.data.manager.tensor_data_manager + +.. automodule:: pina._src.data.manager.tensor_data_manager + +.. autoclass:: pina._src.data.manager.tensor_data_manager._TensorDataManager + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/data/single_batch_data_loader.rst b/docs/source/_rst/data/single_batch_data_loader.rst new file mode 100644 index 000000000..7c1debb92 --- /dev/null +++ b/docs/source/_rst/data/single_batch_data_loader.rst @@ -0,0 +1,9 @@ +Single-Batch Data Loader +=========================== +.. currentmodule:: pina.data.single_batch_data_loader + +.. automodule:: pina._src.data.single_batch_data_loader + +.. autoclass:: pina._src.data.single_batch_data_loader._SingleBatchDataLoader + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/domain/base_domain.rst b/docs/source/_rst/domain/base_domain.rst index e6b9ce88c..3850ba4fa 100644 --- a/docs/source/_rst/domain/base_domain.rst +++ b/docs/source/_rst/domain/base_domain.rst @@ -2,8 +2,8 @@ BaseDomain =========== .. currentmodule:: pina.domain.base_domain -.. automodule:: pina.domain.base_domain +.. automodule:: pina._src.domain.base_domain -.. autoclass:: BaseDomain +.. autoclass:: pina._src.domain.base_domain.BaseDomain :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/domain/base_operation.rst b/docs/source/_rst/domain/base_operation.rst index cfa145f03..122048d81 100644 --- a/docs/source/_rst/domain/base_operation.rst +++ b/docs/source/_rst/domain/base_operation.rst @@ -2,8 +2,8 @@ BaseOperation ============== .. currentmodule:: pina.domain.base_operation -.. automodule:: pina.domain.base_operation +.. automodule:: pina._src.domain.base_operation -.. autoclass:: BaseOperation +.. autoclass:: pina._src.domain.base_operation.BaseOperation :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/domain/cartesian_domain.rst b/docs/source/_rst/domain/cartesian_domain.rst index 15491be8c..bc2afec03 100644 --- a/docs/source/_rst/domain/cartesian_domain.rst +++ b/docs/source/_rst/domain/cartesian_domain.rst @@ -2,9 +2,9 @@ CartesianDomain ====================== .. currentmodule:: pina.domain.cartesian_domain -.. automodule:: pina.domain.cartesian_domain +.. automodule:: pina._src.domain.cartesian_domain -.. autoclass:: CartesianDomain +.. autoclass:: pina._src.domain.cartesian_domain.CartesianDomain :members: :show-inheritance: :noindex: diff --git a/docs/source/_rst/domain/difference.rst b/docs/source/_rst/domain/difference.rst index 0167c3062..91ffd4ec9 100644 --- a/docs/source/_rst/domain/difference.rst +++ b/docs/source/_rst/domain/difference.rst @@ -2,8 +2,8 @@ Difference ====================== .. currentmodule:: pina.domain.difference -.. automodule:: pina.domain.difference +.. automodule:: pina._src.domain.difference -.. autoclass:: Difference +.. autoclass:: pina._src.domain.difference.Difference :members: :show-inheritance: diff --git a/docs/source/_rst/domain/domain_interface.rst b/docs/source/_rst/domain/domain_interface.rst index 898896ba3..96594a23b 100644 --- a/docs/source/_rst/domain/domain_interface.rst +++ b/docs/source/_rst/domain/domain_interface.rst @@ -2,8 +2,8 @@ DomainInterface ================ .. currentmodule:: pina.domain.domain_interface -.. automodule:: pina.domain.domain_interface +.. automodule:: pina._src.domain.domain_interface -.. autoclass:: DomainInterface +.. autoclass:: pina._src.domain.domain_interface.DomainInterface :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/domain/ellipsoid_domain.rst b/docs/source/_rst/domain/ellipsoid_domain.rst index 4a9799e29..2cbc5f7ec 100644 --- a/docs/source/_rst/domain/ellipsoid_domain.rst +++ b/docs/source/_rst/domain/ellipsoid_domain.rst @@ -2,9 +2,9 @@ EllipsoidDomain ====================== .. currentmodule:: pina.domain.ellipsoid_domain -.. automodule:: pina.domain.ellipsoid_domain +.. automodule:: pina._src.domain.ellipsoid_domain -.. autoclass:: EllipsoidDomain +.. autoclass:: pina._src.domain.ellipsoid_domain.EllipsoidDomain :members: :show-inheritance: :noindex: diff --git a/docs/source/_rst/domain/exclusion.rst b/docs/source/_rst/domain/exclusion.rst index f624122ae..040b48416 100644 --- a/docs/source/_rst/domain/exclusion.rst +++ b/docs/source/_rst/domain/exclusion.rst @@ -2,8 +2,8 @@ Exclusion ====================== .. currentmodule:: pina.domain.exclusion -.. automodule:: pina.domain.exclusion +.. automodule:: pina._src.domain.exclusion -.. autoclass:: Exclusion +.. autoclass:: pina._src.domain.exclusion.Exclusion :members: :show-inheritance: diff --git a/docs/source/_rst/domain/intersection.rst b/docs/source/_rst/domain/intersection.rst index fade1d042..666fe0f00 100644 --- a/docs/source/_rst/domain/intersection.rst +++ b/docs/source/_rst/domain/intersection.rst @@ -2,8 +2,8 @@ Intersection ====================== .. currentmodule:: pina.domain.intersection -.. automodule:: pina.domain.intersection +.. automodule:: pina._src.domain.intersection -.. autoclass:: Intersection +.. autoclass:: pina._src.domain.intersection.Intersection :members: :show-inheritance: diff --git a/docs/source/_rst/domain/operation_interface.rst b/docs/source/_rst/domain/operation_interface.rst index 0acd393dc..42e92fbe8 100644 --- a/docs/source/_rst/domain/operation_interface.rst +++ b/docs/source/_rst/domain/operation_interface.rst @@ -2,8 +2,8 @@ OperationInterface ====================== .. currentmodule:: pina.domain.operation_interface -.. automodule:: pina.domain.operation_interface +.. automodule:: pina._src.domain.operation_interface -.. autoclass:: OperationInterface +.. autoclass:: pina._src.domain.operation_interface.OperationInterface :members: :show-inheritance: diff --git a/docs/source/_rst/domain/simplex_domain.rst b/docs/source/_rst/domain/simplex_domain.rst index 5f1d31c9b..0aba5f912 100644 --- a/docs/source/_rst/domain/simplex_domain.rst +++ b/docs/source/_rst/domain/simplex_domain.rst @@ -2,9 +2,9 @@ SimplexDomain ====================== .. currentmodule:: pina.domain.simplex_domain -.. automodule:: pina.domain.simplex_domain +.. automodule:: pina._src.domain.simplex_domain -.. autoclass:: SimplexDomain +.. autoclass:: pina._src.domain.simplex_domain.SimplexDomain :members: :show-inheritance: :noindex: diff --git a/docs/source/_rst/domain/union.rst b/docs/source/_rst/domain/union.rst index 614bb351c..fc5ff92a9 100644 --- a/docs/source/_rst/domain/union.rst +++ b/docs/source/_rst/domain/union.rst @@ -2,8 +2,8 @@ Union ====================== .. currentmodule:: pina.domain.union -.. automodule:: pina.domain.union +.. automodule:: pina._src.domain.union -.. autoclass:: Union +.. autoclass:: pina._src.domain.union.Union :members: :show-inheritance: diff --git a/docs/source/_rst/equation/base_equation.rst b/docs/source/_rst/equation/base_equation.rst new file mode 100644 index 000000000..5bb98901f --- /dev/null +++ b/docs/source/_rst/equation/base_equation.rst @@ -0,0 +1,7 @@ +Base Equation +==================== + +.. currentmodule:: pina.equation.base_equation +.. autoclass:: pina._src.equation.base_equation.BaseEquation + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/equation/equation.rst b/docs/source/_rst/equation/equation.rst index 33e19c957..edb350090 100644 --- a/docs/source/_rst/equation/equation.rst +++ b/docs/source/_rst/equation/equation.rst @@ -2,6 +2,6 @@ Equation ========== .. currentmodule:: pina.equation.equation -.. autoclass:: Equation +.. autoclass:: pina._src.equation.equation.Equation :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/equation/equation_factory.rst b/docs/source/_rst/equation/equation_factory.rst deleted file mode 100644 index 86390c6bd..000000000 --- a/docs/source/_rst/equation/equation_factory.rst +++ /dev/null @@ -1,43 +0,0 @@ -Equation Factory -================== - -.. currentmodule:: pina.equation.equation_factory -.. autoclass:: FixedValue - :members: - :show-inheritance: - -.. autoclass:: FixedGradient - :members: - :show-inheritance: - -.. autoclass:: FixedFlux - :members: - :show-inheritance: - -.. autoclass:: FixedLaplacian - :members: - :show-inheritance: - -.. autoclass:: Laplace - :members: - :show-inheritance: - -.. autoclass:: Advection - :members: - :show-inheritance: - -.. autoclass:: AllenCahn - :members: - :show-inheritance: - -.. autoclass:: DiffusionReaction - :members: - :show-inheritance: - -.. autoclass:: Helmholtz - :members: - :show-inheritance: - -.. autoclass:: Poisson - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/equation/equation_interface.rst b/docs/source/_rst/equation/equation_interface.rst index cde7b0012..f16502831 100644 --- a/docs/source/_rst/equation/equation_interface.rst +++ b/docs/source/_rst/equation/equation_interface.rst @@ -2,6 +2,6 @@ Equation Interface ==================== .. currentmodule:: pina.equation.equation_interface -.. autoclass:: EquationInterface +.. autoclass:: pina._src.equation.equation_interface.EquationInterface :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/equation/system_equation.rst b/docs/source/_rst/equation/system_equation.rst index 33c931cd9..88d1554f8 100644 --- a/docs/source/_rst/equation/system_equation.rst +++ b/docs/source/_rst/equation/system_equation.rst @@ -2,6 +2,6 @@ System Equation ================= .. currentmodule:: pina.equation.system_equation -.. autoclass:: SystemEquation +.. autoclass:: pina._src.equation.system_equation.SystemEquation :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/equation/zoo/acoustic_wave_equation.rst b/docs/source/_rst/equation/zoo/acoustic_wave_equation.rst new file mode 100644 index 000000000..5bc19d920 --- /dev/null +++ b/docs/source/_rst/equation/zoo/acoustic_wave_equation.rst @@ -0,0 +1,7 @@ +AcousticWaveEquation +===================== +.. currentmodule:: pina.equation.zoo.acoustic_wave_equation + +.. automodule:: pina._src.equation.zoo.acoustic_wave_equation + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/advection_equation.rst b/docs/source/_rst/equation/zoo/advection_equation.rst new file mode 100644 index 000000000..4386b3a3d --- /dev/null +++ b/docs/source/_rst/equation/zoo/advection_equation.rst @@ -0,0 +1,7 @@ +Advection Equation +===================== +.. currentmodule:: pina.equation.zoo.advection_equation + +.. automodule:: pina._src.equation.zoo.advection_equation + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/allen_cahn_equation.rst b/docs/source/_rst/equation/zoo/allen_cahn_equation.rst new file mode 100644 index 000000000..fff220811 --- /dev/null +++ b/docs/source/_rst/equation/zoo/allen_cahn_equation.rst @@ -0,0 +1,7 @@ +Allen Cahn Equation +===================== +.. currentmodule:: pina.equation.zoo.allen_cahn_equation + +.. automodule:: pina._src.equation.zoo.allen_cahn_equation + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/burgers_equation.rst b/docs/source/_rst/equation/zoo/burgers_equation.rst new file mode 100644 index 000000000..8f478621f --- /dev/null +++ b/docs/source/_rst/equation/zoo/burgers_equation.rst @@ -0,0 +1,7 @@ +Burgers' Equation +==================== +.. currentmodule:: pina.equation.zoo.burgers_equation + +.. automodule:: pina._src.equation.zoo.burgers_equation + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/diffusion_reaction_equation.rst b/docs/source/_rst/equation/zoo/diffusion_reaction_equation.rst new file mode 100644 index 000000000..d45143074 --- /dev/null +++ b/docs/source/_rst/equation/zoo/diffusion_reaction_equation.rst @@ -0,0 +1,7 @@ +Diffusion Reaction Equation +============================== +.. currentmodule:: pina.equation.zoo.diffusion_reaction_equation + +.. automodule:: pina._src.equation.zoo.diffusion_reaction_equation + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/fixed_flux.rst b/docs/source/_rst/equation/zoo/fixed_flux.rst new file mode 100644 index 000000000..9b81db4b2 --- /dev/null +++ b/docs/source/_rst/equation/zoo/fixed_flux.rst @@ -0,0 +1,7 @@ +Fixed Flux +===================== +.. currentmodule:: pina.equation.zoo.fixed_flux + +.. automodule:: pina._src.equation.zoo.fixed_flux + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/fixed_gradient.rst b/docs/source/_rst/equation/zoo/fixed_gradient.rst new file mode 100644 index 000000000..f8da5dea8 --- /dev/null +++ b/docs/source/_rst/equation/zoo/fixed_gradient.rst @@ -0,0 +1,7 @@ +Fixed Gradient +===================== +.. currentmodule:: pina.equation.zoo.fixed_gradient + +.. automodule:: pina._src.equation.zoo.fixed_gradient + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/fixed_laplacian.rst b/docs/source/_rst/equation/zoo/fixed_laplacian.rst new file mode 100644 index 000000000..3123918a6 --- /dev/null +++ b/docs/source/_rst/equation/zoo/fixed_laplacian.rst @@ -0,0 +1,7 @@ +Fixed Laplacian +===================== +.. currentmodule:: pina.equation.zoo.fixed_laplacian + +.. automodule:: pina._src.equation.zoo.fixed_laplacian + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/fixed_value.rst b/docs/source/_rst/equation/zoo/fixed_value.rst new file mode 100644 index 000000000..29eaa0521 --- /dev/null +++ b/docs/source/_rst/equation/zoo/fixed_value.rst @@ -0,0 +1,7 @@ +Fixed Value +===================== +.. currentmodule:: pina.equation.zoo.fixed_value + +.. automodule:: pina._src.equation.zoo.fixed_value + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/helmholtz_equation.rst b/docs/source/_rst/equation/zoo/helmholtz_equation.rst new file mode 100644 index 000000000..7728b60ed --- /dev/null +++ b/docs/source/_rst/equation/zoo/helmholtz_equation.rst @@ -0,0 +1,9 @@ +Helmholtz Equation +===================== +.. currentmodule:: pina.equation.zoo.helmholtz_equation + +.. automodule:: pina._src.equation.zoo.helmholtz_equation + +.. autoclass:: pina._src.equation.zoo.helmholtz_equation.HelmholtzEquation + :members: + :show-inheritance: diff --git a/docs/source/_rst/equation/zoo/poisson_equation.rst b/docs/source/_rst/equation/zoo/poisson_equation.rst new file mode 100644 index 000000000..f23796450 --- /dev/null +++ b/docs/source/_rst/equation/zoo/poisson_equation.rst @@ -0,0 +1,9 @@ +Poisson Equation +===================== +.. currentmodule:: pina.equation.zoo.poisson_equation + +.. automodule:: pina._src.equation.zoo.poisson_equation + +.. autoclass:: pina._src.equation.zoo.poisson_equation.PoissonEquation + :members: + :show-inheritance: diff --git a/docs/source/_rst/graph/graph.rst b/docs/source/_rst/graph/graph.rst index 1921f83e0..58180f50f 100644 --- a/docs/source/_rst/graph/graph.rst +++ b/docs/source/_rst/graph/graph.rst @@ -3,7 +3,7 @@ Graph .. currentmodule:: pina.graph -.. autoclass:: Graph +.. autoclass:: pina._src.core.graph.Graph :members: :private-members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/graph/graph_builder.rst b/docs/source/_rst/graph/graph_builder.rst index 2508aecb7..f576fe7c7 100644 --- a/docs/source/_rst/graph/graph_builder.rst +++ b/docs/source/_rst/graph/graph_builder.rst @@ -3,7 +3,7 @@ GraphBuilder .. currentmodule:: pina.graph -.. autoclass:: GraphBuilder +.. autoclass:: pina._src.core.graph.GraphBuilder :members: :private-members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/graph/knn_graph.rst b/docs/source/_rst/graph/knn_graph.rst index 8ef0b190b..e31a004ab 100644 --- a/docs/source/_rst/graph/knn_graph.rst +++ b/docs/source/_rst/graph/knn_graph.rst @@ -3,7 +3,7 @@ KNNGraph .. currentmodule:: pina.graph -.. autoclass:: KNNGraph +.. autoclass:: pina._src.core.graph.KNNGraph :members: :private-members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/graph/label_batch.rst b/docs/source/_rst/graph/label_batch.rst index 7cd4d2684..5a68bde60 100644 --- a/docs/source/_rst/graph/label_batch.rst +++ b/docs/source/_rst/graph/label_batch.rst @@ -3,7 +3,7 @@ LabelBatch .. currentmodule:: pina.graph -.. autoclass:: LabelBatch +.. autoclass:: pina._src.core.graph.LabelBatch :members: :private-members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/graph/radius_graph.rst b/docs/source/_rst/graph/radius_graph.rst index 7414d2dc1..9db9fb174 100644 --- a/docs/source/_rst/graph/radius_graph.rst +++ b/docs/source/_rst/graph/radius_graph.rst @@ -3,7 +3,7 @@ RadiusGraph .. currentmodule:: pina.graph -.. autoclass:: RadiusGraph +.. autoclass:: pina._src.core.graph.RadiusGraph :members: :private-members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/label_tensor.rst b/docs/source/_rst/label_tensor.rst index 9eb227369..1b750ad97 100644 --- a/docs/source/_rst/label_tensor.rst +++ b/docs/source/_rst/label_tensor.rst @@ -2,8 +2,11 @@ LabelTensor =========== .. currentmodule:: pina.label_tensor +.. automodule:: pina._src.core.label_tensor + :no-members: -.. autoclass:: LabelTensor + +.. autoclass:: pina._src.core.label_tensor.LabelTensor :members: :private-members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/base_dual_loss.rst b/docs/source/_rst/loss/base_dual_loss.rst new file mode 100644 index 000000000..8037f894b --- /dev/null +++ b/docs/source/_rst/loss/base_dual_loss.rst @@ -0,0 +1,9 @@ +Base Dual Loss +================ +.. currentmodule:: pina.loss.base_dual_loss + +.. automodule:: pina._src.loss.base_dual_loss + +.. autoclass:: pina._src.loss.base_dual_loss.BaseDualLoss + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/dual_loss_interface.rst b/docs/source/_rst/loss/dual_loss_interface.rst new file mode 100644 index 000000000..a6a005914 --- /dev/null +++ b/docs/source/_rst/loss/dual_loss_interface.rst @@ -0,0 +1,9 @@ +Dual Loss Interface +=================== +.. currentmodule:: pina.loss.dual_loss_interface + +.. automodule:: pina._src.loss.dual_loss_interface + +.. autoclass:: pina._src.loss.dual_loss_interface.DualLossInterface + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/linear_weighting.rst b/docs/source/_rst/loss/linear_weighting.rst deleted file mode 100644 index 16e6232d0..000000000 --- a/docs/source/_rst/loss/linear_weighting.rst +++ /dev/null @@ -1,9 +0,0 @@ -LinearWeighting -============================= -.. currentmodule:: pina.loss.linear_weighting - -.. automodule:: pina.loss.linear_weighting - -.. autoclass:: LinearWeighting - :members: - :show-inheritance: diff --git a/docs/source/_rst/loss/loss_interface.rst b/docs/source/_rst/loss/loss_interface.rst deleted file mode 100644 index 8ff78c01e..000000000 --- a/docs/source/_rst/loss/loss_interface.rst +++ /dev/null @@ -1,9 +0,0 @@ -LossInterface -=============== -.. currentmodule:: pina.loss.loss_interface - -.. automodule:: pina.loss.loss_interface - -.. autoclass:: LossInterface - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/lp_loss.rst b/docs/source/_rst/loss/lp_loss.rst new file mode 100644 index 000000000..4924d3445 --- /dev/null +++ b/docs/source/_rst/loss/lp_loss.rst @@ -0,0 +1,10 @@ +Lp Loss +=============== +.. currentmodule:: pina.loss.lp_loss + +.. automodule:: pina._src.loss.lp_loss + :no-members: + +.. autoclass:: pina._src.loss.lp_loss.LpLoss + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/lploss.rst b/docs/source/_rst/loss/lploss.rst deleted file mode 100644 index 37dfdfe3c..000000000 --- a/docs/source/_rst/loss/lploss.rst +++ /dev/null @@ -1,7 +0,0 @@ -LpLoss -=============== -.. currentmodule:: pina.loss.lp_loss - -.. autoclass:: LpLoss - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/ntk_weighting.rst b/docs/source/_rst/loss/ntk_weighting.rst deleted file mode 100644 index 6d9d8816d..000000000 --- a/docs/source/_rst/loss/ntk_weighting.rst +++ /dev/null @@ -1,9 +0,0 @@ -NeuralTangentKernelWeighting -============================= -.. currentmodule:: pina.loss.ntk_weighting - -.. automodule:: pina.loss.ntk_weighting - -.. autoclass:: NeuralTangentKernelWeighting - :members: - :show-inheritance: diff --git a/docs/source/_rst/loss/power_loss.rst b/docs/source/_rst/loss/power_loss.rst new file mode 100644 index 000000000..a0258c20f --- /dev/null +++ b/docs/source/_rst/loss/power_loss.rst @@ -0,0 +1,9 @@ +Power Loss +==================== +.. currentmodule:: pina.loss.power_loss + +.. automodule:: pina._src.loss.power_loss + +.. autoclass:: pina._src.loss.power_loss.PowerLoss + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/powerloss.rst b/docs/source/_rst/loss/powerloss.rst deleted file mode 100644 index e4dee43b8..000000000 --- a/docs/source/_rst/loss/powerloss.rst +++ /dev/null @@ -1,7 +0,0 @@ -PowerLoss -==================== -.. currentmodule:: pina.loss.power_loss - -.. autoclass:: PowerLoss - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/scalar_weighting.rst b/docs/source/_rst/loss/scalar_weighting.rst deleted file mode 100644 index 5ee82a785..000000000 --- a/docs/source/_rst/loss/scalar_weighting.rst +++ /dev/null @@ -1,9 +0,0 @@ -ScalarWeighting -=================== -.. currentmodule:: pina.loss.scalar_weighting - -.. automodule:: pina.loss.scalar_weighting - -.. autoclass:: ScalarWeighting - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/self_adaptive_weighting.rst b/docs/source/_rst/loss/self_adaptive_weighting.rst deleted file mode 100644 index cd1daed1f..000000000 --- a/docs/source/_rst/loss/self_adaptive_weighting.rst +++ /dev/null @@ -1,9 +0,0 @@ -SelfAdaptiveWeighting -============================= -.. currentmodule:: pina.loss.self_adaptive_weighting - -.. automodule:: pina.loss.self_adaptive_weighting - -.. autoclass:: SelfAdaptiveWeighting - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/loss/sinkhorn_loss.rst b/docs/source/_rst/loss/sinkhorn_loss.rst new file mode 100644 index 000000000..17aa370ad --- /dev/null +++ b/docs/source/_rst/loss/sinkhorn_loss.rst @@ -0,0 +1,11 @@ +Sinkhorn Loss +=============== + +.. currentmodule:: pina.loss.sinkhorn_loss + +.. automodule:: pina._src.loss.sinkhorn_loss + :no-members: + +.. autoclass:: pina._src.loss.sinkhorn_loss.SinkhornLoss + :members: + :show-inheritance: diff --git a/docs/source/_rst/loss/weighting_interface.rst b/docs/source/_rst/loss/weighting_interface.rst deleted file mode 100644 index 2b0fa1bdc..000000000 --- a/docs/source/_rst/loss/weighting_interface.rst +++ /dev/null @@ -1,9 +0,0 @@ -WeightingInterface -=================== -.. currentmodule:: pina.loss.weighting_interface - -.. automodule:: pina.loss.weighting_interface - -.. autoclass:: WeightingInterface - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/average_neural_operator.rst b/docs/source/_rst/model/average_neural_operator.rst index 02211e9a8..a54107620 100644 --- a/docs/source/_rst/model/average_neural_operator.rst +++ b/docs/source/_rst/model/average_neural_operator.rst @@ -2,6 +2,6 @@ Averaging Neural Operator ============================== .. currentmodule:: pina.model.average_neural_operator -.. autoclass:: AveragingNeuralOperator +.. autoclass:: pina._src.model.average_neural_operator.AveragingNeuralOperator :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/block/average_neural_operator_block.rst b/docs/source/_rst/model/block/average_neural_operator_block.rst index 0072ec9d0..1e38fc215 100644 --- a/docs/source/_rst/model/block/average_neural_operator_block.rst +++ b/docs/source/_rst/model/block/average_neural_operator_block.rst @@ -2,7 +2,7 @@ Averaging Neural Operator Block ================================== .. currentmodule:: pina.model.block.average_neural_operator_block -.. autoclass:: AVNOBlock +.. autoclass:: pina._src.model.block.average_neural_operator_block.AVNOBlock :members: :show-inheritance: :noindex: diff --git a/docs/source/_rst/model/block/convolution.rst b/docs/source/_rst/model/block/convolution.rst index 4033d5d56..bd0d32e71 100644 --- a/docs/source/_rst/model/block/convolution.rst +++ b/docs/source/_rst/model/block/convolution.rst @@ -2,7 +2,7 @@ Continuous Convolution Block =============================== .. currentmodule:: pina.model.block.convolution_2d -.. autoclass:: ContinuousConvBlock +.. autoclass:: pina._src.model.block.convolution_2d.ContinuousConvBlock :members: :show-inheritance: :noindex: diff --git a/docs/source/_rst/model/block/convolution_interface.rst b/docs/source/_rst/model/block/convolution_interface.rst index f8e61c16c..c6708ca94 100644 --- a/docs/source/_rst/model/block/convolution_interface.rst +++ b/docs/source/_rst/model/block/convolution_interface.rst @@ -2,7 +2,7 @@ Continuous Convolution Interface ================================== .. currentmodule:: pina.model.block.convolution -.. autoclass:: BaseContinuousConv +.. autoclass:: pina._src.model.block.convolution.BaseContinuousConv :members: :show-inheritance: :noindex: diff --git a/docs/source/_rst/model/block/enhanced_linear.rst b/docs/source/_rst/model/block/enhanced_linear.rst index d08cf79bf..92e8d5581 100644 --- a/docs/source/_rst/model/block/enhanced_linear.rst +++ b/docs/source/_rst/model/block/enhanced_linear.rst @@ -2,7 +2,7 @@ EnhancedLinear Block ===================== .. currentmodule:: pina.model.block.residual -.. autoclass:: EnhancedLinear +.. autoclass:: pina._src.model.block.residual.EnhancedLinear :members: :show-inheritance: :noindex: \ No newline at end of file diff --git a/docs/source/_rst/model/block/fourier_block.rst b/docs/source/_rst/model/block/fourier_block.rst index c0fff4deb..9b601bb3d 100644 --- a/docs/source/_rst/model/block/fourier_block.rst +++ b/docs/source/_rst/model/block/fourier_block.rst @@ -3,14 +3,14 @@ Fourier Neural Operator Block .. currentmodule:: pina.model.block.fourier_block -.. autoclass:: FourierBlock1D +.. autoclass:: pina._src.model.block.fourier_block.FourierBlock1D :members: :show-inheritance: -.. autoclass:: FourierBlock2D +.. autoclass:: pina._src.model.block.fourier_block.FourierBlock2D :members: :show-inheritance: -.. autoclass:: FourierBlock3D +.. autoclass:: pina._src.model.block.fourier_block.FourierBlock3D :members: :show-inheritance: diff --git a/docs/source/_rst/model/block/fourier_embedding.rst b/docs/source/_rst/model/block/fourier_embedding.rst index 77eb3960c..48c8df41c 100644 --- a/docs/source/_rst/model/block/fourier_embedding.rst +++ b/docs/source/_rst/model/block/fourier_embedding.rst @@ -2,7 +2,7 @@ Fourier Feature Embedding ======================================= .. currentmodule:: pina.model.block.embedding -.. autoclass:: FourierFeatureEmbedding +.. autoclass:: pina._src.model.block.embedding.FourierFeatureEmbedding :members: :show-inheritance: diff --git a/docs/source/_rst/model/block/gno_block.rst b/docs/source/_rst/model/block/gno_block.rst index 19a532bab..8ce3f2f30 100644 --- a/docs/source/_rst/model/block/gno_block.rst +++ b/docs/source/_rst/model/block/gno_block.rst @@ -2,7 +2,7 @@ Graph Neural Operator Block =============================== .. currentmodule:: pina.model.block.gno_block -.. autoclass:: GNOBlock +.. autoclass:: pina._src.model.block.gno_block.GNOBlock :members: :show-inheritance: :noindex: diff --git a/docs/source/_rst/model/block/kan_block.rst b/docs/source/_rst/model/block/kan_block.rst new file mode 100644 index 000000000..95ca239eb --- /dev/null +++ b/docs/source/_rst/model/block/kan_block.rst @@ -0,0 +1,7 @@ +KANBlock +======================= +.. currentmodule:: pina.model.block.kan_block + +.. autoclass:: pina._src.model.block.kan_block.KANBlock + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/block/low_rank_block.rst b/docs/source/_rst/model/block/low_rank_block.rst index 366068f79..83c7a11a0 100644 --- a/docs/source/_rst/model/block/low_rank_block.rst +++ b/docs/source/_rst/model/block/low_rank_block.rst @@ -2,7 +2,7 @@ Low Rank Neural Operator Block ================================= .. currentmodule:: pina.model.block.low_rank_block -.. autoclass:: LowRankBlock +.. autoclass:: pina._src.model.block.low_rank_block.LowRankBlock :members: :show-inheritance: :noindex: diff --git a/docs/source/_rst/model/block/message_passing/deep_tensor_network_block.rst b/docs/source/_rst/model/block/message_passing/deep_tensor_network_block.rst index 30121e5a6..51482496a 100644 --- a/docs/source/_rst/model/block/message_passing/deep_tensor_network_block.rst +++ b/docs/source/_rst/model/block/message_passing/deep_tensor_network_block.rst @@ -2,7 +2,7 @@ Deep Tensor Network Block ================================== .. currentmodule:: pina.model.block.message_passing.deep_tensor_network_block -.. autoclass:: DeepTensorNetworkBlock +.. autoclass:: pina._src.model.block.message_passing.deep_tensor_network_block.DeepTensorNetworkBlock :members: :show-inheritance: :noindex: diff --git a/docs/source/_rst/model/block/message_passing/en_equivariant_network_block.rst b/docs/source/_rst/model/block/message_passing/en_equivariant_network_block.rst index e2755c665..09966ea0a 100644 --- a/docs/source/_rst/model/block/message_passing/en_equivariant_network_block.rst +++ b/docs/source/_rst/model/block/message_passing/en_equivariant_network_block.rst @@ -2,7 +2,7 @@ E(n) Equivariant Network Block ================================== .. currentmodule:: pina.model.block.message_passing.en_equivariant_network_block -.. autoclass:: EnEquivariantNetworkBlock +.. autoclass:: pina._src.model.block.message_passing.en_equivariant_network_block.EnEquivariantNetworkBlock :members: :show-inheritance: :noindex: \ No newline at end of file diff --git a/docs/source/_rst/model/block/message_passing/equivariant_graph_neural_operator_block.rst b/docs/source/_rst/model/block/message_passing/equivariant_graph_neural_operator_block.rst index 8d047f84e..b61c4f430 100644 --- a/docs/source/_rst/model/block/message_passing/equivariant_graph_neural_operator_block.rst +++ b/docs/source/_rst/model/block/message_passing/equivariant_graph_neural_operator_block.rst @@ -2,6 +2,6 @@ EquivariantGraphNeuralOperatorBlock ===================================== .. currentmodule:: pina.model.block.message_passing.equivariant_graph_neural_operator_block -.. autoclass:: EquivariantGraphNeuralOperatorBlock +.. autoclass:: pina._src.model.block.message_passing.equivariant_graph_neural_operator_block.EquivariantGraphNeuralOperatorBlock :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/block/message_passing/interaction_network_block.rst b/docs/source/_rst/model/block/message_passing/interaction_network_block.rst index ffac307e2..a4c86e562 100644 --- a/docs/source/_rst/model/block/message_passing/interaction_network_block.rst +++ b/docs/source/_rst/model/block/message_passing/interaction_network_block.rst @@ -2,7 +2,7 @@ Interaction Network Block ================================== .. currentmodule:: pina.model.block.message_passing.interaction_network_block -.. autoclass:: InteractionNetworkBlock +.. autoclass:: pina._src.model.block.message_passing.interaction_network_block.InteractionNetworkBlock :members: :show-inheritance: :noindex: \ No newline at end of file diff --git a/docs/source/_rst/model/block/message_passing/radial_field_network_block.rst b/docs/source/_rst/model/block/message_passing/radial_field_network_block.rst index e05203f33..bb66ee770 100644 --- a/docs/source/_rst/model/block/message_passing/radial_field_network_block.rst +++ b/docs/source/_rst/model/block/message_passing/radial_field_network_block.rst @@ -2,7 +2,7 @@ Radial Field Network Block ================================== .. currentmodule:: pina.model.block.message_passing.radial_field_network_block -.. autoclass:: RadialFieldNetworkBlock +.. autoclass:: pina._src.model.block.message_passing.radial_field_network_block.RadialFieldNetworkBlock :members: :show-inheritance: :noindex: \ No newline at end of file diff --git a/docs/source/_rst/model/block/orthogonal.rst b/docs/source/_rst/model/block/orthogonal.rst index 21d12998a..a9fc727fb 100644 --- a/docs/source/_rst/model/block/orthogonal.rst +++ b/docs/source/_rst/model/block/orthogonal.rst @@ -2,6 +2,6 @@ Orthogonal Block ====================== .. currentmodule:: pina.model.block.orthogonal -.. autoclass:: OrthogonalBlock +.. autoclass:: pina._src.model.block.orthogonal.OrthogonalBlock :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/block/pbc_embedding.rst b/docs/source/_rst/model/block/pbc_embedding.rst index f469644af..e79ae9514 100644 --- a/docs/source/_rst/model/block/pbc_embedding.rst +++ b/docs/source/_rst/model/block/pbc_embedding.rst @@ -2,7 +2,7 @@ Periodic Boundary Condition Embedding ======================================= .. currentmodule:: pina.model.block.embedding -.. autoclass:: PeriodicBoundaryEmbedding +.. autoclass:: pina._src.model.block.embedding.PeriodicBoundaryEmbedding :members: :show-inheritance: diff --git a/docs/source/_rst/model/block/pirate_network_block.rst b/docs/source/_rst/model/block/pirate_network_block.rst index 5d0428a68..f534d3cb0 100644 --- a/docs/source/_rst/model/block/pirate_network_block.rst +++ b/docs/source/_rst/model/block/pirate_network_block.rst @@ -2,7 +2,7 @@ PirateNet Block ======================================= .. currentmodule:: pina.model.block.pirate_network_block -.. autoclass:: PirateNetBlock +.. autoclass:: pina._src.model.block.pirate_network_block.PirateNetBlock :members: :show-inheritance: diff --git a/docs/source/_rst/model/block/pod_block.rst b/docs/source/_rst/model/block/pod_block.rst index 4b66e2c97..98fadbb1e 100644 --- a/docs/source/_rst/model/block/pod_block.rst +++ b/docs/source/_rst/model/block/pod_block.rst @@ -2,6 +2,6 @@ Proper Orthogonal Decomposition Block ============================================ .. currentmodule:: pina.model.block.pod_block -.. autoclass:: PODBlock +.. autoclass:: pina._src.model.block.pod_block.PODBlock :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/block/rbf_block.rst b/docs/source/_rst/model/block/rbf_block.rst index 545f14d08..b8997d21b 100644 --- a/docs/source/_rst/model/block/rbf_block.rst +++ b/docs/source/_rst/model/block/rbf_block.rst @@ -2,6 +2,6 @@ Radias Basis Function Block ============================= .. currentmodule:: pina.model.block.rbf_block -.. autoclass:: RBFBlock +.. autoclass:: pina._src.model.block.rbf_block.RBFBlock :members: :show-inheritance: diff --git a/docs/source/_rst/model/block/residual.rst b/docs/source/_rst/model/block/residual.rst index 69741c74c..d0e478563 100644 --- a/docs/source/_rst/model/block/residual.rst +++ b/docs/source/_rst/model/block/residual.rst @@ -2,6 +2,6 @@ Residual Block =================== .. currentmodule:: pina.model.block.residual -.. autoclass:: ResidualBlock +.. autoclass:: pina._src.model.block.residual.ResidualBlock :members: :show-inheritance: diff --git a/docs/source/_rst/model/block/spectral.rst b/docs/source/_rst/model/block/spectral.rst index 3c80f3dd8..1ee0e1d19 100644 --- a/docs/source/_rst/model/block/spectral.rst +++ b/docs/source/_rst/model/block/spectral.rst @@ -2,14 +2,14 @@ Spectral Convolution Block ============================ .. currentmodule:: pina.model.block.spectral -.. autoclass:: SpectralConvBlock1D +.. autoclass:: pina._src.model.block.spectral.SpectralConvBlock1D :members: :show-inheritance: -.. autoclass:: SpectralConvBlock2D +.. autoclass:: pina._src.model.block.spectral.SpectralConvBlock2D :members: :show-inheritance: -.. autoclass:: SpectralConvBlock3D +.. autoclass:: pina._src.model.block.spectral.SpectralConvBlock3D :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/deeponet.rst b/docs/source/_rst/model/deeponet.rst index 0ca08242d..eef25dcae 100644 --- a/docs/source/_rst/model/deeponet.rst +++ b/docs/source/_rst/model/deeponet.rst @@ -2,6 +2,6 @@ DeepONet =========== .. currentmodule:: pina.model.deeponet -.. autoclass:: DeepONet +.. autoclass:: pina._src.model.deeponet.DeepONet :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/equivariant_graph_neural_operator.rst b/docs/source/_rst/model/equivariant_graph_neural_operator.rst index a11edcc00..e100f5c1e 100644 --- a/docs/source/_rst/model/equivariant_graph_neural_operator.rst +++ b/docs/source/_rst/model/equivariant_graph_neural_operator.rst @@ -2,6 +2,6 @@ EquivariantGraphNeuralOperator ================================= .. currentmodule:: pina.model.equivariant_graph_neural_operator -.. autoclass:: EquivariantGraphNeuralOperator +.. autoclass:: pina._src.model.equivariant_graph_neural_operator.EquivariantGraphNeuralOperator :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/feed_forward.rst b/docs/source/_rst/model/feed_forward.rst index 2dea8e550..be75ed70b 100644 --- a/docs/source/_rst/model/feed_forward.rst +++ b/docs/source/_rst/model/feed_forward.rst @@ -2,6 +2,6 @@ FeedForward ====================== .. currentmodule:: pina.model.feed_forward -.. autoclass:: FeedForward +.. autoclass:: pina._src.model.feed_forward.FeedForward :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/fourier_integral_kernel.rst b/docs/source/_rst/model/fourier_integral_kernel.rst index b1fb484fe..dba63c429 100644 --- a/docs/source/_rst/model/fourier_integral_kernel.rst +++ b/docs/source/_rst/model/fourier_integral_kernel.rst @@ -2,6 +2,6 @@ FourierIntegralKernel ========================= .. currentmodule:: pina.model.fourier_neural_operator -.. autoclass:: FourierIntegralKernel +.. autoclass:: pina._src.model.fourier_neural_operator.FourierIntegralKernel :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/fourier_neural_operator.rst b/docs/source/_rst/model/fourier_neural_operator.rst index e77494fd0..14cb52667 100644 --- a/docs/source/_rst/model/fourier_neural_operator.rst +++ b/docs/source/_rst/model/fourier_neural_operator.rst @@ -2,6 +2,6 @@ FNO =========== .. currentmodule:: pina.model.fourier_neural_operator -.. autoclass:: FNO +.. autoclass:: pina._src.model.fourier_neural_operator.FNO :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/graph_neural_operator.rst b/docs/source/_rst/model/graph_neural_operator.rst index fbb8600e5..7f7b7ed6b 100644 --- a/docs/source/_rst/model/graph_neural_operator.rst +++ b/docs/source/_rst/model/graph_neural_operator.rst @@ -2,6 +2,6 @@ GraphNeuralOperator ======================= .. currentmodule:: pina.model.graph_neural_operator -.. autoclass:: GraphNeuralOperator +.. autoclass:: pina._src.model.graph_neural_operator.GraphNeuralOperator :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/graph_neural_operator_integral_kernel.rst b/docs/source/_rst/model/graph_neural_operator_integral_kernel.rst index cf15a31a5..45f78c366 100644 --- a/docs/source/_rst/model/graph_neural_operator_integral_kernel.rst +++ b/docs/source/_rst/model/graph_neural_operator_integral_kernel.rst @@ -2,6 +2,6 @@ GraphNeuralKernel ======================= .. currentmodule:: pina.model.graph_neural_operator -.. autoclass:: GraphNeuralKernel +.. autoclass:: pina._src.model.graph_neural_operator.GraphNeuralKernel :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/kernel_neural_operator.rst b/docs/source/_rst/model/kernel_neural_operator.rst index d693afac5..75a39b223 100644 --- a/docs/source/_rst/model/kernel_neural_operator.rst +++ b/docs/source/_rst/model/kernel_neural_operator.rst @@ -2,6 +2,6 @@ KernelNeuralOperator ======================= .. currentmodule:: pina.model.kernel_neural_operator -.. autoclass:: KernelNeuralOperator +.. autoclass:: pina._src.model.kernel_neural_operator.KernelNeuralOperator :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/kolmogorov_arnold_network.rst b/docs/source/_rst/model/kolmogorov_arnold_network.rst new file mode 100644 index 000000000..0211611f4 --- /dev/null +++ b/docs/source/_rst/model/kolmogorov_arnold_network.rst @@ -0,0 +1,7 @@ +KolmogorovArnoldNetwork +=========================== +.. currentmodule:: pina.model.kolmogorov_arnold_network + +.. autoclass:: pina._src.model.kolmogorov_arnold_network.KolmogorovArnoldNetwork + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/low_rank_neural_operator.rst b/docs/source/_rst/model/low_rank_neural_operator.rst index 22fe7cc93..e0362d144 100644 --- a/docs/source/_rst/model/low_rank_neural_operator.rst +++ b/docs/source/_rst/model/low_rank_neural_operator.rst @@ -2,6 +2,6 @@ Low Rank Neural Operator ============================== .. currentmodule:: pina.model.low_rank_neural_operator -.. autoclass:: LowRankNeuralOperator +.. autoclass:: pina._src.model.low_rank_neural_operator.LowRankNeuralOperator :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/mionet.rst b/docs/source/_rst/model/mionet.rst index fe6281710..1888d911e 100644 --- a/docs/source/_rst/model/mionet.rst +++ b/docs/source/_rst/model/mionet.rst @@ -2,6 +2,6 @@ MIONet =========== .. currentmodule:: pina.model.deeponet -.. autoclass:: MIONet +.. autoclass:: pina._src.model.deeponet.MIONet :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/multi_feed_forward.rst b/docs/source/_rst/model/multi_feed_forward.rst index aa79580ee..458173ced 100644 --- a/docs/source/_rst/model/multi_feed_forward.rst +++ b/docs/source/_rst/model/multi_feed_forward.rst @@ -2,6 +2,6 @@ MultiFeedForward ================== .. currentmodule:: pina.model.multi_feed_forward -.. autoclass:: MultiFeedForward +.. autoclass:: pina._src.model.multi_feed_forward.MultiFeedForward :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/pirate_network.rst b/docs/source/_rst/model/pirate_network.rst index 5b374c247..a60449a6c 100644 --- a/docs/source/_rst/model/pirate_network.rst +++ b/docs/source/_rst/model/pirate_network.rst @@ -2,6 +2,6 @@ PirateNet ======================= .. currentmodule:: pina.model.pirate_network -.. autoclass:: PirateNet +.. autoclass:: pina._src.model.pirate_network.PirateNet :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/residual_feed_forward.rst b/docs/source/_rst/model/residual_feed_forward.rst index 66d83a42c..d8ce08152 100644 --- a/docs/source/_rst/model/residual_feed_forward.rst +++ b/docs/source/_rst/model/residual_feed_forward.rst @@ -2,6 +2,6 @@ ResidualFeedForward ====================== .. currentmodule:: pina.model.feed_forward -.. autoclass:: ResidualFeedForward +.. autoclass:: pina._src.model.feed_forward.ResidualFeedForward :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/sindy.rst b/docs/source/_rst/model/sindy.rst index bd507603b..f07ca6d30 100644 --- a/docs/source/_rst/model/sindy.rst +++ b/docs/source/_rst/model/sindy.rst @@ -2,6 +2,6 @@ SINDy ======================= .. currentmodule:: pina.model.sindy -.. autoclass:: SINDy +.. autoclass:: pina._src.model.sindy.SINDy :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/spline.rst b/docs/source/_rst/model/spline.rst index aa7450b70..278a95d3b 100644 --- a/docs/source/_rst/model/spline.rst +++ b/docs/source/_rst/model/spline.rst @@ -2,6 +2,6 @@ Spline ======== .. currentmodule:: pina.model.spline -.. autoclass:: Spline +.. autoclass:: pina._src.model.spline.Spline :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/spline_surface.rst b/docs/source/_rst/model/spline_surface.rst index 6bbf137d8..9b204cd22 100644 --- a/docs/source/_rst/model/spline_surface.rst +++ b/docs/source/_rst/model/spline_surface.rst @@ -2,6 +2,6 @@ Spline Surface ================ .. currentmodule:: pina.model.spline_surface -.. autoclass:: SplineSurface +.. autoclass:: pina._src.model.spline_surface.SplineSurface :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/model/vectorized_spline.rst b/docs/source/_rst/model/vectorized_spline.rst new file mode 100644 index 000000000..08522bc54 --- /dev/null +++ b/docs/source/_rst/model/vectorized_spline.rst @@ -0,0 +1,7 @@ +VectorizedSpline +======================= +.. currentmodule:: pina.model.vectorized_spline + +.. autoclass:: pina._src.model.vectorized_spline.VectorizedSpline + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/operator.rst b/docs/source/_rst/operator.rst index 42746a6f8..fe0ad0398 100644 --- a/docs/source/_rst/operator.rst +++ b/docs/source/_rst/operator.rst @@ -3,6 +3,7 @@ Operators .. currentmodule:: pina.operator -.. automodule:: pina.operator + +.. automodule:: pina._src.core.operator :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/optim/optimizer_interface.rst b/docs/source/_rst/optim/optimizer_interface.rst index 88c18e8f5..23a933bae 100644 --- a/docs/source/_rst/optim/optimizer_interface.rst +++ b/docs/source/_rst/optim/optimizer_interface.rst @@ -1,7 +1,7 @@ -Optimizer -============ +Optimizer Interface +===================== .. currentmodule:: pina.optim.optimizer_interface -.. autoclass:: Optimizer +.. autoclass:: pina._src.optim.optimizer_interface.OptimizerInterface :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/optim/scheduler_interface.rst b/docs/source/_rst/optim/scheduler_interface.rst index ab8ee292e..03b3e83f7 100644 --- a/docs/source/_rst/optim/scheduler_interface.rst +++ b/docs/source/_rst/optim/scheduler_interface.rst @@ -1,7 +1,7 @@ -Scheduler -============= +Scheduler Interface +===================== .. currentmodule:: pina.optim.scheduler_interface -.. autoclass:: Scheduler +.. autoclass:: pina._src.optim.scheduler_interface.SchedulerInterface :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/optim/torch_optimizer.rst b/docs/source/_rst/optim/torch_optimizer.rst index 3e6c9d912..54bfe9a3a 100644 --- a/docs/source/_rst/optim/torch_optimizer.rst +++ b/docs/source/_rst/optim/torch_optimizer.rst @@ -1,7 +1,7 @@ -TorchOptimizer +Torch Optimizer =============== .. currentmodule:: pina.optim.torch_optimizer -.. autoclass:: TorchOptimizer +.. autoclass:: pina._src.optim.torch_optimizer.TorchOptimizer :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/optim/torch_scheduler.rst b/docs/source/_rst/optim/torch_scheduler.rst index 5c3e4df36..59260533e 100644 --- a/docs/source/_rst/optim/torch_scheduler.rst +++ b/docs/source/_rst/optim/torch_scheduler.rst @@ -1,7 +1,7 @@ -TorchScheduler +Torch Scheduler =============== .. currentmodule:: pina.optim.torch_scheduler -.. autoclass:: TorchScheduler +.. autoclass:: pina._src.optim.torch_scheduler.TorchScheduler :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/problem/abstract_problem.rst b/docs/source/_rst/problem/abstract_problem.rst deleted file mode 100644 index 143909e1b..000000000 --- a/docs/source/_rst/problem/abstract_problem.rst +++ /dev/null @@ -1,9 +0,0 @@ -AbstractProblem -=============== -.. currentmodule:: pina.problem.abstract_problem - -.. automodule:: pina.problem.abstract_problem - -.. autoclass:: AbstractProblem - :members: - :show-inheritance: diff --git a/docs/source/_rst/problem/base_problem.rst b/docs/source/_rst/problem/base_problem.rst new file mode 100644 index 000000000..2261a90f7 --- /dev/null +++ b/docs/source/_rst/problem/base_problem.rst @@ -0,0 +1,9 @@ +Base Problem +=============== +.. currentmodule:: pina.problem.base_problem + +.. automodule:: pina._src.problem.base_problem + +.. autoclass:: pina._src.problem.base_problem.BaseProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/inverse_problem.rst b/docs/source/_rst/problem/inverse_problem.rst index 5ce306ffc..4b5de05cb 100644 --- a/docs/source/_rst/problem/inverse_problem.rst +++ b/docs/source/_rst/problem/inverse_problem.rst @@ -2,8 +2,8 @@ InverseProblem ============== .. currentmodule:: pina.problem.inverse_problem -.. automodule:: pina.problem.inverse_problem +.. automodule:: pina._src.problem.inverse_problem -.. autoclass:: InverseProblem +.. autoclass:: pina._src.problem.inverse_problem.InverseProblem :members: :show-inheritance: diff --git a/docs/source/_rst/problem/parametric_problem.rst b/docs/source/_rst/problem/parametric_problem.rst index 8f217fbbe..1a5e83490 100644 --- a/docs/source/_rst/problem/parametric_problem.rst +++ b/docs/source/_rst/problem/parametric_problem.rst @@ -2,8 +2,8 @@ ParametricProblem ==================== .. currentmodule:: pina.problem.parametric_problem -.. automodule:: pina.problem.parametric_problem +.. automodule:: pina._src.problem.parametric_problem -.. autoclass:: ParametricProblem +.. autoclass:: pina._src.problem.parametric_problem.ParametricProblem :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/problem/problem_interface.rst b/docs/source/_rst/problem/problem_interface.rst new file mode 100644 index 000000000..08136e23c --- /dev/null +++ b/docs/source/_rst/problem/problem_interface.rst @@ -0,0 +1,9 @@ +ProblemInterface +=================== +.. currentmodule:: pina.problem.problem_interface + +.. automodule:: pina._src.problem.problem_interface + +.. autoclass:: pina._src.problem.problem_interface.ProblemInterface + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/spatial_problem.rst b/docs/source/_rst/problem/spatial_problem.rst index 90ec6ec3c..757243ef1 100644 --- a/docs/source/_rst/problem/spatial_problem.rst +++ b/docs/source/_rst/problem/spatial_problem.rst @@ -2,8 +2,8 @@ SpatialProblem ============== .. currentmodule:: pina.problem.spatial_problem -.. automodule:: pina.problem.spatial_problem +.. automodule:: pina._src.problem.spatial_problem -.. autoclass:: SpatialProblem +.. autoclass:: pina._src.problem.spatial_problem.SpatialProblem :members: :show-inheritance: diff --git a/docs/source/_rst/problem/time_dependent_problem.rst b/docs/source/_rst/problem/time_dependent_problem.rst index db94121c2..dda1e07f1 100644 --- a/docs/source/_rst/problem/time_dependent_problem.rst +++ b/docs/source/_rst/problem/time_dependent_problem.rst @@ -2,8 +2,8 @@ TimeDependentProblem ==================== .. currentmodule:: pina.problem.time_dependent_problem -.. automodule:: pina.problem.time_dependent_problem +.. automodule:: pina._src.problem.time_dependent_problem -.. autoclass:: TimeDependentProblem +.. autoclass:: pina._src.problem.time_dependent_problem.TimeDependentProblem :members: :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/acoustic_wave.rst b/docs/source/_rst/problem/zoo/acoustic_wave.rst deleted file mode 100644 index 4a9489667..000000000 --- a/docs/source/_rst/problem/zoo/acoustic_wave.rst +++ /dev/null @@ -1,9 +0,0 @@ -AcousticWaveProblem -===================== -.. currentmodule:: pina.problem.zoo.acoustic_wave - -.. automodule:: pina.problem.zoo.acoustic_wave - -.. autoclass:: AcousticWaveProblem - :members: - :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/acoustic_wave_problem.rst b/docs/source/_rst/problem/zoo/acoustic_wave_problem.rst new file mode 100644 index 000000000..c6acb93f1 --- /dev/null +++ b/docs/source/_rst/problem/zoo/acoustic_wave_problem.rst @@ -0,0 +1,9 @@ +AcousticWaveProblem +===================== +.. currentmodule:: pina.problem.zoo.acoustic_wave_problem + +.. automodule:: pina._src.problem.zoo.acoustic_wave_problem + +.. autoclass:: pina._src.problem.zoo.acoustic_wave_problem.AcousticWaveProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/advection.rst b/docs/source/_rst/problem/zoo/advection.rst deleted file mode 100644 index b83cc9d99..000000000 --- a/docs/source/_rst/problem/zoo/advection.rst +++ /dev/null @@ -1,9 +0,0 @@ -AdvectionProblem -================== -.. currentmodule:: pina.problem.zoo.advection - -.. automodule:: pina.problem.zoo.advection - -.. autoclass:: AdvectionProblem - :members: - :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/advection_problem.rst b/docs/source/_rst/problem/zoo/advection_problem.rst new file mode 100644 index 000000000..df37679cb --- /dev/null +++ b/docs/source/_rst/problem/zoo/advection_problem.rst @@ -0,0 +1,9 @@ +AdvectionProblem +================== +.. currentmodule:: pina.problem.zoo.advection_problem + +.. automodule:: pina._src.problem.zoo.advection_problem + +.. autoclass:: pina._src.problem.zoo.advection_problem.AdvectionProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/allen_cahn.rst b/docs/source/_rst/problem/zoo/allen_cahn.rst deleted file mode 100644 index ada3465d1..000000000 --- a/docs/source/_rst/problem/zoo/allen_cahn.rst +++ /dev/null @@ -1,9 +0,0 @@ -AllenCahnProblem -================== -.. currentmodule:: pina.problem.zoo.allen_cahn - -.. automodule:: pina.problem.zoo.allen_cahn - -.. autoclass:: AllenCahnProblem - :members: - :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/allen_cahn_problem.rst b/docs/source/_rst/problem/zoo/allen_cahn_problem.rst new file mode 100644 index 000000000..463be3a55 --- /dev/null +++ b/docs/source/_rst/problem/zoo/allen_cahn_problem.rst @@ -0,0 +1,9 @@ +AllenCahnProblem +================== +.. currentmodule:: pina.problem.zoo.allen_cahn_problem + +.. automodule:: pina._src.problem.zoo.allen_cahn_problem + +.. autoclass:: pina._src.problem.zoo.allen_cahn_problem.AllenCahnProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/burgers_problem.rst b/docs/source/_rst/problem/zoo/burgers_problem.rst new file mode 100644 index 000000000..75151d8d8 --- /dev/null +++ b/docs/source/_rst/problem/zoo/burgers_problem.rst @@ -0,0 +1,9 @@ +Burgers' Problem +===================== +.. currentmodule:: pina.problem.zoo.burgers_problem + +.. automodule:: pina._src.problem.zoo.burgers_problem + +.. autoclass:: pina._src.problem.zoo.burgers_problem.BurgersProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/diffusion_reaction.rst b/docs/source/_rst/problem/zoo/diffusion_reaction.rst deleted file mode 100644 index 0cad0fd67..000000000 --- a/docs/source/_rst/problem/zoo/diffusion_reaction.rst +++ /dev/null @@ -1,9 +0,0 @@ -DiffusionReactionProblem -========================= -.. currentmodule:: pina.problem.zoo.diffusion_reaction - -.. automodule:: pina.problem.zoo.diffusion_reaction - -.. autoclass:: DiffusionReactionProblem - :members: - :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/diffusion_reaction_problem.rst b/docs/source/_rst/problem/zoo/diffusion_reaction_problem.rst new file mode 100644 index 000000000..307a56c52 --- /dev/null +++ b/docs/source/_rst/problem/zoo/diffusion_reaction_problem.rst @@ -0,0 +1,9 @@ +DiffusionReactionProblem +========================= +.. currentmodule:: pina.problem.zoo.diffusion_reaction_problem + +.. automodule:: pina._src.problem.zoo.diffusion_reaction_problem + +.. autoclass:: pina._src.problem.zoo.diffusion_reaction_problem.DiffusionReactionProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/helmholtz.rst b/docs/source/_rst/problem/zoo/helmholtz.rst deleted file mode 100644 index af4ec7dbc..000000000 --- a/docs/source/_rst/problem/zoo/helmholtz.rst +++ /dev/null @@ -1,9 +0,0 @@ -HelmholtzProblem -================== -.. currentmodule:: pina.problem.zoo.helmholtz - -.. automodule:: pina.problem.zoo.helmholtz - -.. autoclass:: HelmholtzProblem - :members: - :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/helmholtz_problem.rst b/docs/source/_rst/problem/zoo/helmholtz_problem.rst new file mode 100644 index 000000000..952578a2b --- /dev/null +++ b/docs/source/_rst/problem/zoo/helmholtz_problem.rst @@ -0,0 +1,9 @@ +HelmholtzProblem +================== +.. currentmodule:: pina.problem.zoo.helmholtz_problem + +.. automodule:: pina._src.problem.zoo.helmholtz_problem + +.. autoclass:: pina._src.problem.zoo.helmholtz_problem.HelmholtzProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/inverse_poisson_2d_square.rst b/docs/source/_rst/problem/zoo/inverse_poisson_2d_square.rst deleted file mode 100644 index 727c17b47..000000000 --- a/docs/source/_rst/problem/zoo/inverse_poisson_2d_square.rst +++ /dev/null @@ -1,9 +0,0 @@ -InversePoisson2DSquareProblem -============================== -.. currentmodule:: pina.problem.zoo.inverse_poisson_2d_square - -.. automodule:: pina.problem.zoo.inverse_poisson_2d_square - -.. autoclass:: InversePoisson2DSquareProblem - :members: - :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/inverse_poisson_problem.rst b/docs/source/_rst/problem/zoo/inverse_poisson_problem.rst new file mode 100644 index 000000000..503eb21bf --- /dev/null +++ b/docs/source/_rst/problem/zoo/inverse_poisson_problem.rst @@ -0,0 +1,9 @@ +InversePoisson2DSquareProblem +============================== +.. currentmodule:: pina.problem.zoo.inverse_poisson_problem + +.. automodule:: pina._src.problem.zoo.inverse_poisson_problem + +.. autoclass:: pina._src.problem.zoo.inverse_poisson_problem.InversePoisson2DSquareProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/poisson_2d_square.rst b/docs/source/_rst/problem/zoo/poisson_2d_square.rst deleted file mode 100644 index 718c33ccc..000000000 --- a/docs/source/_rst/problem/zoo/poisson_2d_square.rst +++ /dev/null @@ -1,9 +0,0 @@ -Poisson2DSquareProblem -======================== -.. currentmodule:: pina.problem.zoo.poisson_2d_square - -.. automodule:: pina.problem.zoo.poisson_2d_square - -.. autoclass:: Poisson2DSquareProblem - :members: - :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/poisson_problem.rst b/docs/source/_rst/problem/zoo/poisson_problem.rst new file mode 100644 index 000000000..a480a8953 --- /dev/null +++ b/docs/source/_rst/problem/zoo/poisson_problem.rst @@ -0,0 +1,9 @@ +Poisson2DSquareProblem +======================== +.. currentmodule:: pina.problem.zoo.poisson_problem + +.. automodule:: pina._src.problem.zoo.poisson_problem + +.. autoclass:: pina._src.problem.zoo.poisson_problem.Poisson2DSquareProblem + :members: + :show-inheritance: diff --git a/docs/source/_rst/problem/zoo/supervised_problem.rst b/docs/source/_rst/problem/zoo/supervised_problem.rst index aad7d5aa5..6bf368376 100644 --- a/docs/source/_rst/problem/zoo/supervised_problem.rst +++ b/docs/source/_rst/problem/zoo/supervised_problem.rst @@ -2,8 +2,8 @@ SupervisedProblem ================== .. currentmodule:: pina.problem.zoo.supervised_problem -.. automodule:: pina.problem.zoo.supervised_problem +.. automodule:: pina._src.problem.zoo.supervised_problem -.. autoclass:: SupervisedProblem +.. autoclass:: pina._src.problem.zoo.supervised_problem.SupervisedProblem :members: :show-inheritance: diff --git a/docs/source/_rst/solver/autoregressive_ensemble_solver.rst b/docs/source/_rst/solver/autoregressive_ensemble_solver.rst new file mode 100644 index 000000000..ba90c826f --- /dev/null +++ b/docs/source/_rst/solver/autoregressive_ensemble_solver.rst @@ -0,0 +1,10 @@ +Autoregressive Ensemble Solver +================================= +.. currentmodule:: pina.solver.autoregressive_ensemble_solver + +.. automodule:: pina._src.solver.autoregressive_ensemble_solver + +.. autoclass:: pina._src.solver.autoregressive_ensemble_solver.AutoregressiveEnsembleSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/autoregressive_single_model_solver.rst b/docs/source/_rst/solver/autoregressive_single_model_solver.rst new file mode 100644 index 000000000..217c1ff59 --- /dev/null +++ b/docs/source/_rst/solver/autoregressive_single_model_solver.rst @@ -0,0 +1,10 @@ +Autoregressive Single Model Solver +====================================== +.. currentmodule:: pina.solver.autoregressive_single_model_solver + +.. automodule:: pina._src.solver.autoregressive_single_model_solver + +.. autoclass:: pina._src.solver.autoregressive_single_model_solver.AutoregressiveSingleModelSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/base_solver.rst b/docs/source/_rst/solver/base_solver.rst new file mode 100644 index 000000000..939b94311 --- /dev/null +++ b/docs/source/_rst/solver/base_solver.rst @@ -0,0 +1,10 @@ +Base Solver +================================= +.. currentmodule:: pina.solver.base_solver + +.. automodule:: pina._src.solver.base_solver + +.. autoclass:: pina._src.solver.base_solver.BaseSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/causal_physics_informed_single_model_solver.rst b/docs/source/_rst/solver/causal_physics_informed_single_model_solver.rst new file mode 100644 index 000000000..811231ae1 --- /dev/null +++ b/docs/source/_rst/solver/causal_physics_informed_single_model_solver.rst @@ -0,0 +1,11 @@ +Causal Physics Informed Single Model Solver +================================================= + +.. currentmodule:: pina.solver.causal_physics_informed_single_model_solver + +.. automodule:: pina._src.solver.causal_physics_informed_single_model_solver + +.. autoclass:: pina._src.solver.causal_physics_informed_single_model_solver.CausalPhysicsInformedSingleModelSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/competitive_physics_informed_solver.rst b/docs/source/_rst/solver/competitive_physics_informed_solver.rst new file mode 100644 index 000000000..9138dca85 --- /dev/null +++ b/docs/source/_rst/solver/competitive_physics_informed_solver.rst @@ -0,0 +1,11 @@ +Competitive Physics-Informed Solver +======================================= + +.. currentmodule:: pina.solver.competitive_physics_informed_solver + +.. automodule:: pina._src.solver.competitive_physics_informed_solver + +.. autoclass:: pina._src.solver.competitive_physics_informed_solver.CompetitivePhysicsInformedSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/ensemble_solver.rst b/docs/source/_rst/solver/ensemble_solver.rst new file mode 100644 index 000000000..1031422c0 --- /dev/null +++ b/docs/source/_rst/solver/ensemble_solver.rst @@ -0,0 +1,10 @@ +Ensemble Solver +================================= +.. currentmodule:: pina.solver.ensemble_solver + +.. automodule:: pina._src.solver.ensemble_solver + +.. autoclass:: pina._src.solver.ensemble_solver.EnsembleSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/ensemble_solver/ensemble_pinn.rst b/docs/source/_rst/solver/ensemble_solver/ensemble_pinn.rst deleted file mode 100644 index 2e42dcf0d..000000000 --- a/docs/source/_rst/solver/ensemble_solver/ensemble_pinn.rst +++ /dev/null @@ -1,8 +0,0 @@ -DeepEnsemblePINN -================== -.. currentmodule:: pina.solver.ensemble_solver.ensemble_pinn - -.. autoclass:: DeepEnsemblePINN - :show-inheritance: - :members: - diff --git a/docs/source/_rst/solver/ensemble_solver/ensemble_solver_interface.rst b/docs/source/_rst/solver/ensemble_solver/ensemble_solver_interface.rst deleted file mode 100644 index 664bb8c8f..000000000 --- a/docs/source/_rst/solver/ensemble_solver/ensemble_solver_interface.rst +++ /dev/null @@ -1,8 +0,0 @@ -DeepEnsembleSolverInterface -============================= -.. currentmodule:: pina.solver.ensemble_solver.ensemble_solver_interface - -.. autoclass:: DeepEnsembleSolverInterface - :show-inheritance: - :members: - diff --git a/docs/source/_rst/solver/ensemble_solver/ensemble_supervised.rst b/docs/source/_rst/solver/ensemble_solver/ensemble_supervised.rst deleted file mode 100644 index 575b28594..000000000 --- a/docs/source/_rst/solver/ensemble_solver/ensemble_supervised.rst +++ /dev/null @@ -1,8 +0,0 @@ -DeepEnsembleSupervisedSolver -============================= -.. currentmodule:: pina.solver.ensemble_solver.ensemble_supervised - -.. autoclass:: DeepEnsembleSupervisedSolver - :show-inheritance: - :members: - diff --git a/docs/source/_rst/solver/garom.rst b/docs/source/_rst/solver/garom.rst deleted file mode 100644 index 0e5820f6f..000000000 --- a/docs/source/_rst/solver/garom.rst +++ /dev/null @@ -1,7 +0,0 @@ -GAROM -====== -.. currentmodule:: pina.solver.garom - -.. autoclass:: GAROM - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/gradient_physics_informed_single_model_solver.rst b/docs/source/_rst/solver/gradient_physics_informed_single_model_solver.rst new file mode 100644 index 000000000..b602d2277 --- /dev/null +++ b/docs/source/_rst/solver/gradient_physics_informed_single_model_solver.rst @@ -0,0 +1,11 @@ +Gradient Physics Informed Single Model Solver +================================================= + +.. currentmodule:: pina.solver.gradient_physics_informed_single_model_solver + +.. automodule:: pina._src.solver.gradient_physics_informed_single_model_solver + +.. autoclass:: pina._src.solver.gradient_physics_informed_single_model_solver.GradientPhysicsInformedSingleModelSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/mixin/autoregressive_mixin.rst b/docs/source/_rst/solver/mixin/autoregressive_mixin.rst new file mode 100644 index 000000000..fee7df2ac --- /dev/null +++ b/docs/source/_rst/solver/mixin/autoregressive_mixin.rst @@ -0,0 +1,11 @@ +Autoregressive Mixin +================================= + +.. currentmodule:: pina.solver.mixin.autoregressive_mixin + +.. automodule:: pina._src.solver.mixin.autoregressive_mixin + +.. autoclass:: pina._src.solver.mixin.autoregressive_mixin.AutoregressiveMixin + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/mixin/condition_aggregator_mixin.rst b/docs/source/_rst/solver/mixin/condition_aggregator_mixin.rst new file mode 100644 index 000000000..4868edff1 --- /dev/null +++ b/docs/source/_rst/solver/mixin/condition_aggregator_mixin.rst @@ -0,0 +1,11 @@ +Condition Aggregator Mixin +================================= + +.. currentmodule:: pina.solver.mixin.condition_aggregator_mixin + +.. automodule:: pina._src.solver.mixin.condition_aggregator_mixin + +.. autoclass:: pina._src.solver.mixin.condition_aggregator_mixin.ConditionAggregatorMixin + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/mixin/ensemble_mixin.rst b/docs/source/_rst/solver/mixin/ensemble_mixin.rst new file mode 100644 index 000000000..d3548e745 --- /dev/null +++ b/docs/source/_rst/solver/mixin/ensemble_mixin.rst @@ -0,0 +1,11 @@ +Ensemble Mixin +================================= + +.. currentmodule:: pina.solver.mixin.ensemble_mixin + +.. automodule:: pina._src.solver.mixin.ensemble_mixin + +.. autoclass:: pina._src.solver.mixin.ensemble_mixin.EnsembleMixin + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/mixin/gradient_enhanced_mixin.rst b/docs/source/_rst/solver/mixin/gradient_enhanced_mixin.rst new file mode 100644 index 000000000..f9ab310b6 --- /dev/null +++ b/docs/source/_rst/solver/mixin/gradient_enhanced_mixin.rst @@ -0,0 +1,11 @@ +Gradient-Enhanced Mixin +================================= + +.. currentmodule:: pina.solver.mixin.gradient_enhanced_mixin + +.. automodule:: pina._src.solver.mixin.gradient_enhanced_mixin + +.. autoclass:: pina._src.solver.mixin.gradient_enhanced_mixin.GradientEnhancedMixin + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/mixin/manual_optimization_mixin.rst b/docs/source/_rst/solver/mixin/manual_optimization_mixin.rst new file mode 100644 index 000000000..5974aa222 --- /dev/null +++ b/docs/source/_rst/solver/mixin/manual_optimization_mixin.rst @@ -0,0 +1,11 @@ +Manual Optimization Mixin +================================= + +.. currentmodule:: pina.solver.mixin.manual_optimization_mixin + +.. automodule:: pina._src.solver.mixin.manual_optimization_mixin + +.. autoclass:: pina._src.solver.mixin.manual_optimization_mixin.ManualOptimizationMixin + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/mixin/multi_model_mixin.rst b/docs/source/_rst/solver/mixin/multi_model_mixin.rst new file mode 100644 index 000000000..0302d1883 --- /dev/null +++ b/docs/source/_rst/solver/mixin/multi_model_mixin.rst @@ -0,0 +1,11 @@ +Multi-Model Mixin +================================= + +.. currentmodule:: pina.solver.mixin.multi_model_mixin + +.. automodule:: pina._src.solver.mixin.multi_model_mixin + +.. autoclass:: pina._src.solver.mixin.multi_model_mixin.MultiModelMixin + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/mixin/physics_informed_mixin.rst b/docs/source/_rst/solver/mixin/physics_informed_mixin.rst new file mode 100644 index 000000000..8503d9cbc --- /dev/null +++ b/docs/source/_rst/solver/mixin/physics_informed_mixin.rst @@ -0,0 +1,11 @@ +Physics-Informed Mixin +================================= + +.. currentmodule:: pina.solver.mixin.physics_informed_mixin + +.. automodule:: pina._src.solver.mixin.physics_informed_mixin + +.. autoclass:: pina._src.solver.mixin.physics_informed_mixin.PhysicsInformedMixin + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/mixin/residual_based_attention_mixin.rst b/docs/source/_rst/solver/mixin/residual_based_attention_mixin.rst new file mode 100644 index 000000000..768c108a8 --- /dev/null +++ b/docs/source/_rst/solver/mixin/residual_based_attention_mixin.rst @@ -0,0 +1,11 @@ +Residual-Based Attention Mixin +================================= + +.. currentmodule:: pina.solver.mixin.residual_based_attention_mixin + +.. automodule:: pina._src.solver.mixin.residual_based_attention_mixin + +.. autoclass:: pina._src.solver.mixin.residual_based_attention_mixin.ResidualBasedAttentionMixin + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/mixin/single_model_mixin.rst b/docs/source/_rst/solver/mixin/single_model_mixin.rst new file mode 100644 index 000000000..c7c665793 --- /dev/null +++ b/docs/source/_rst/solver/mixin/single_model_mixin.rst @@ -0,0 +1,11 @@ +Single-Model Mixin +================================= + +.. currentmodule:: pina.solver.mixin.single_model_mixin + +.. automodule:: pina._src.solver.mixin.single_model_mixin + +.. autoclass:: pina._src.solver.mixin.single_model_mixin.SingleModelMixin + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/multi_model_solver.rst b/docs/source/_rst/solver/multi_model_solver.rst new file mode 100644 index 000000000..37e1cf4df --- /dev/null +++ b/docs/source/_rst/solver/multi_model_solver.rst @@ -0,0 +1,10 @@ +Multi Model Solver +================================= +.. currentmodule:: pina.solver.multi_model_solver + +.. automodule:: pina._src.solver.multi_model_solver + +.. autoclass:: pina._src.solver.multi_model_solver.MultiModelSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/multi_solver_interface.rst b/docs/source/_rst/solver/multi_solver_interface.rst deleted file mode 100644 index 7f68c83a4..000000000 --- a/docs/source/_rst/solver/multi_solver_interface.rst +++ /dev/null @@ -1,8 +0,0 @@ -MultiSolverInterface -====================== -.. currentmodule:: pina.solver.solver - -.. autoclass:: MultiSolverInterface - :show-inheritance: - :members: - diff --git a/docs/source/_rst/solver/physics_informed_ensemble_solver.rst b/docs/source/_rst/solver/physics_informed_ensemble_solver.rst new file mode 100644 index 000000000..726da79f8 --- /dev/null +++ b/docs/source/_rst/solver/physics_informed_ensemble_solver.rst @@ -0,0 +1,10 @@ +Physics Informed Ensemble Solver +================================= +.. currentmodule:: pina.solver.physics_informed_ensemble_solver + +.. automodule:: pina._src.solver.physics_informed_ensemble_solver + +.. autoclass:: pina._src.solver.physics_informed_ensemble_solver.PhysicsInformedEnsembleSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/physics_informed_single_model_solver.rst b/docs/source/_rst/solver/physics_informed_single_model_solver.rst new file mode 100644 index 000000000..38f5952d6 --- /dev/null +++ b/docs/source/_rst/solver/physics_informed_single_model_solver.rst @@ -0,0 +1,10 @@ +Physics Informed Single Model Solver +======================================= +.. currentmodule:: pina.solver.physics_informed_single_model_solver + +.. automodule:: pina._src.solver.physics_informed_single_model_solver + +.. autoclass:: pina._src.solver.physics_informed_single_model_solver.PhysicsInformedSingleModelSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/physics_informed_solver/causal_pinn.rst b/docs/source/_rst/solver/physics_informed_solver/causal_pinn.rst deleted file mode 100644 index 6fab9ef0e..000000000 --- a/docs/source/_rst/solver/physics_informed_solver/causal_pinn.rst +++ /dev/null @@ -1,7 +0,0 @@ -CausalPINN -============== -.. currentmodule:: pina.solver.physics_informed_solver.causal_pinn - -.. autoclass:: CausalPINN - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/physics_informed_solver/competitive_pinn.rst b/docs/source/_rst/solver/physics_informed_solver/competitive_pinn.rst deleted file mode 100644 index 372cb0f3d..000000000 --- a/docs/source/_rst/solver/physics_informed_solver/competitive_pinn.rst +++ /dev/null @@ -1,7 +0,0 @@ -CompetitivePINN -================= -.. currentmodule:: pina.solver.physics_informed_solver.competitive_pinn - -.. autoclass:: CompetitivePINN - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/physics_informed_solver/gradient_pinn.rst b/docs/source/_rst/solver/physics_informed_solver/gradient_pinn.rst deleted file mode 100644 index 66a490013..000000000 --- a/docs/source/_rst/solver/physics_informed_solver/gradient_pinn.rst +++ /dev/null @@ -1,7 +0,0 @@ -GradientPINN -============== -.. currentmodule:: pina.solver.physics_informed_solver.gradient_pinn - -.. autoclass:: GradientPINN - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/physics_informed_solver/pinn.rst b/docs/source/_rst/solver/physics_informed_solver/pinn.rst deleted file mode 100644 index fdc31253b..000000000 --- a/docs/source/_rst/solver/physics_informed_solver/pinn.rst +++ /dev/null @@ -1,7 +0,0 @@ -PINN -====== -.. currentmodule:: pina.solver.physics_informed_solver.pinn - -.. autoclass:: PINN - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/physics_informed_solver/pinn_interface.rst b/docs/source/_rst/solver/physics_informed_solver/pinn_interface.rst deleted file mode 100644 index 2242cf8b4..000000000 --- a/docs/source/_rst/solver/physics_informed_solver/pinn_interface.rst +++ /dev/null @@ -1,7 +0,0 @@ -PINNInterface -================= -.. currentmodule:: pina.solver.physics_informed_solver.pinn_interface - -.. autoclass:: PINNInterface - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/physics_informed_solver/rba_pinn.rst b/docs/source/_rst/solver/physics_informed_solver/rba_pinn.rst deleted file mode 100644 index cf94b6df0..000000000 --- a/docs/source/_rst/solver/physics_informed_solver/rba_pinn.rst +++ /dev/null @@ -1,7 +0,0 @@ -RBAPINN -======== -.. currentmodule:: pina.solver.physics_informed_solver.rba_pinn - -.. autoclass:: RBAPINN - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/physics_informed_solver/self_adaptive_pinn.rst b/docs/source/_rst/solver/physics_informed_solver/self_adaptive_pinn.rst deleted file mode 100644 index 2290059bd..000000000 --- a/docs/source/_rst/solver/physics_informed_solver/self_adaptive_pinn.rst +++ /dev/null @@ -1,7 +0,0 @@ -SelfAdaptivePINN -================== -.. currentmodule:: pina.solver.physics_informed_solver.self_adaptive_pinn - -.. autoclass:: SelfAdaptivePINN - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/rba_physics_informed_single_model_solver.rst b/docs/source/_rst/solver/rba_physics_informed_single_model_solver.rst new file mode 100644 index 000000000..7765d2d95 --- /dev/null +++ b/docs/source/_rst/solver/rba_physics_informed_single_model_solver.rst @@ -0,0 +1,11 @@ +RBA Physics-Informed Single-Model Solver +================================================= + +.. currentmodule:: pina.solver.rba_physics_informed_single_model_solver + +.. automodule:: pina._src.solver.rba_physics_informed_single_model_solver + +.. autoclass:: pina._src.solver.rba_physics_informed_single_model_solver.RBAPhysicsInformedSingleModelSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/self_adaptive_physics_informed_solver.rst b/docs/source/_rst/solver/self_adaptive_physics_informed_solver.rst new file mode 100644 index 000000000..901520e1b --- /dev/null +++ b/docs/source/_rst/solver/self_adaptive_physics_informed_solver.rst @@ -0,0 +1,11 @@ +Self-Adaptive Physics-Informed Solver +======================================= + +.. currentmodule:: pina.solver.self_adaptive_physics_informed_solver + +.. automodule:: pina._src.solver.self_adaptive_physics_informed_solver + +.. autoclass:: pina._src.solver.self_adaptive_physics_informed_solver.SelfAdaptivePhysicsInformedSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/single_model_solver.rst b/docs/source/_rst/solver/single_model_solver.rst new file mode 100644 index 000000000..7bb4857d5 --- /dev/null +++ b/docs/source/_rst/solver/single_model_solver.rst @@ -0,0 +1,10 @@ +Single Model Solver +================================= +.. currentmodule:: pina.solver.single_model_solver + +.. automodule:: pina._src.solver.single_model_solver + +.. autoclass:: pina._src.solver.single_model_solver.SingleModelSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/single_solver_interface.rst b/docs/source/_rst/solver/single_solver_interface.rst deleted file mode 100644 index 5b85f11b5..000000000 --- a/docs/source/_rst/solver/single_solver_interface.rst +++ /dev/null @@ -1,8 +0,0 @@ -SingleSolverInterface -====================== -.. currentmodule:: pina.solver.solver - -.. autoclass:: SingleSolverInterface - :show-inheritance: - :members: - diff --git a/docs/source/_rst/solver/solver_interface.rst b/docs/source/_rst/solver/solver_interface.rst index 9bb11783e..b9f4b9e66 100644 --- a/docs/source/_rst/solver/solver_interface.rst +++ b/docs/source/_rst/solver/solver_interface.rst @@ -1,8 +1,10 @@ -SolverInterface -================= -.. currentmodule:: pina.solver.solver +Solver Interface +================================= +.. currentmodule:: pina.solver.solver_interface -.. autoclass:: SolverInterface - :show-inheritance: +.. automodule:: pina._src.solver.solver_interface + +.. autoclass:: pina._src.solver.solver_interface.SolverInterface :members: - + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/supervised_ensemble_solver.rst b/docs/source/_rst/solver/supervised_ensemble_solver.rst new file mode 100644 index 000000000..23f276640 --- /dev/null +++ b/docs/source/_rst/solver/supervised_ensemble_solver.rst @@ -0,0 +1,10 @@ +Supervised Ensemble Solver +================================= +.. currentmodule:: pina.solver.supervised_ensemble_solver + +.. automodule:: pina._src.solver.supervised_ensemble_solver + +.. autoclass:: pina._src.solver.supervised_ensemble_solver.SupervisedEnsembleSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/supervised_single_model_solver.rst b/docs/source/_rst/solver/supervised_single_model_solver.rst new file mode 100644 index 000000000..13c3b2fc0 --- /dev/null +++ b/docs/source/_rst/solver/supervised_single_model_solver.rst @@ -0,0 +1,11 @@ +Supervised Single Model Solver +================================= + +.. currentmodule:: pina.solver.supervised_single_model_solver + +.. automodule:: pina._src.solver.supervised_single_model_solver + +.. autoclass:: pina._src.solver.supervised_single_model_solver.SupervisedSingleModelSolver + :members: + :show-inheritance: + :noindex: diff --git a/docs/source/_rst/solver/supervised_solver/reduced_order_model.rst b/docs/source/_rst/solver/supervised_solver/reduced_order_model.rst deleted file mode 100644 index 878014c29..000000000 --- a/docs/source/_rst/solver/supervised_solver/reduced_order_model.rst +++ /dev/null @@ -1,7 +0,0 @@ -ReducedOrderModelSolver -========================== -.. currentmodule:: pina.solver.supervised_solver.reduced_order_model - -.. autoclass:: ReducedOrderModelSolver - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/supervised_solver/supervised.rst b/docs/source/_rst/solver/supervised_solver/supervised.rst deleted file mode 100644 index 60ffdf828..000000000 --- a/docs/source/_rst/solver/supervised_solver/supervised.rst +++ /dev/null @@ -1,7 +0,0 @@ -SupervisedSolver -=================== -.. currentmodule:: pina.solver.supervised_solver.supervised - -.. autoclass:: SupervisedSolver - :members: - :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/solver/supervised_solver/supervised_solver_interface.rst b/docs/source/_rst/solver/supervised_solver/supervised_solver_interface.rst deleted file mode 100644 index 4903a18dd..000000000 --- a/docs/source/_rst/solver/supervised_solver/supervised_solver_interface.rst +++ /dev/null @@ -1,8 +0,0 @@ -SupervisedSolverInterface -========================== -.. currentmodule:: pina.solver.supervised_solver.supervised_solver_interface - -.. autoclass:: SupervisedSolverInterface - :show-inheritance: - :members: - diff --git a/docs/source/_rst/trainer.rst b/docs/source/_rst/trainer.rst index 2582b6da9..8e5a99a38 100644 --- a/docs/source/_rst/trainer.rst +++ b/docs/source/_rst/trainer.rst @@ -3,6 +3,6 @@ Trainer .. automodule:: pina.trainer -.. autoclass:: Trainer +.. autoclass:: pina._src.core.trainer.Trainer :members: :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/weighting/base_weighting.rst b/docs/source/_rst/weighting/base_weighting.rst new file mode 100644 index 000000000..c8544697d --- /dev/null +++ b/docs/source/_rst/weighting/base_weighting.rst @@ -0,0 +1,9 @@ +BaseWeighting +=================== +.. currentmodule:: pina.weighting.base_weighting + +.. automodule:: pina._src.weighting.base_weighting + +.. autoclass:: pina._src.weighting.base_weighting.BaseWeighting + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/weighting/linear_weighting.rst b/docs/source/_rst/weighting/linear_weighting.rst new file mode 100644 index 000000000..1941bbe80 --- /dev/null +++ b/docs/source/_rst/weighting/linear_weighting.rst @@ -0,0 +1,11 @@ +LinearWeighting +============================= + +.. currentmodule:: pina.weighting + +.. automodule:: pina._src.weighting.linear_weighting + :no-members: + +.. autoclass:: pina._src.weighting.linear_weighting.LinearWeighting + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/weighting/no_weighting.rst b/docs/source/_rst/weighting/no_weighting.rst new file mode 100644 index 000000000..f6794eb5c --- /dev/null +++ b/docs/source/_rst/weighting/no_weighting.rst @@ -0,0 +1,9 @@ +No Weighting +=================== +.. currentmodule:: pina.weighting.no_weighting + +.. automodule:: pina._src.weighting.no_weighting + +.. autoclass:: pina._src.weighting.no_weighting._NoWeighting + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/weighting/ntk_weighting.rst b/docs/source/_rst/weighting/ntk_weighting.rst new file mode 100644 index 000000000..acee58fa2 --- /dev/null +++ b/docs/source/_rst/weighting/ntk_weighting.rst @@ -0,0 +1,9 @@ +NeuralTangentKernelWeighting +============================= +.. currentmodule:: pina.weighting.ntk_weighting + +.. automodule:: pina._src.weighting.ntk_weighting + +.. autoclass:: pina._src.weighting.ntk_weighting.NeuralTangentKernelWeighting + :members: + :show-inheritance: diff --git a/docs/source/_rst/weighting/scalar_weighting.rst b/docs/source/_rst/weighting/scalar_weighting.rst new file mode 100644 index 000000000..712425086 --- /dev/null +++ b/docs/source/_rst/weighting/scalar_weighting.rst @@ -0,0 +1,9 @@ +ScalarWeighting +=================== +.. currentmodule:: pina.weighting.scalar_weighting + +.. automodule:: pina._src.weighting.scalar_weighting + +.. autoclass:: pina._src.weighting.scalar_weighting.ScalarWeighting + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/weighting/self_adaptive_weighting.rst b/docs/source/_rst/weighting/self_adaptive_weighting.rst new file mode 100644 index 000000000..32ed13aba --- /dev/null +++ b/docs/source/_rst/weighting/self_adaptive_weighting.rst @@ -0,0 +1,9 @@ +SelfAdaptiveWeighting +============================= +.. currentmodule:: pina.weighting.self_adaptive_weighting + +.. automodule:: pina._src.weighting.self_adaptive_weighting + +.. autoclass:: pina._src.weighting.self_adaptive_weighting.SelfAdaptiveWeighting + :members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/_rst/weighting/weighting_interface.rst b/docs/source/_rst/weighting/weighting_interface.rst new file mode 100644 index 000000000..19cf34b42 --- /dev/null +++ b/docs/source/_rst/weighting/weighting_interface.rst @@ -0,0 +1,9 @@ +WeightingInterface +=================== +.. currentmodule:: pina.weighting.weighting_interface + +.. automodule:: pina._src.weighting.weighting_interface + +.. autoclass:: pina._src.weighting.weighting_interface.WeightingInterface + :members: + :show-inheritance: \ No newline at end of file diff --git a/pina/__init__.py b/pina/__init__.py index 2cbe7f3bb..cafab2d31 100644 --- a/pina/__init__.py +++ b/pina/__init__.py @@ -1,18 +1,37 @@ -"""Module for the Pina library.""" +""" +A specialized framework for Scientific Machine Learning (SciML), providing +tools for Physics-Informed Neural Networks (PINNs), Neural Operators, +and data-driven physical modeling. +""" __all__ = [ - "Trainer", "LabelTensor", + "Trainer", "Condition", - "PinaDataModule", + "DataModule", "Graph", - "SolverInterface", - "MultiSolverInterface", ] -from .label_tensor import LabelTensor -from .graph import Graph -from .solver import SolverInterface, MultiSolverInterface -from .trainer import Trainer -from .condition.condition import Condition -from .data import PinaDataModule +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.graph import Graph +from pina._src.core.trainer import Trainer +from pina._src.condition.condition import Condition +from pina._src.data.data_module import DataModule + +# Back-compatibility with version 0.2, to be removed soon +import warnings + +_DEPRECATED_IMPORTS = {"PinaDataModule": "DataModule"} + + +def __getattr__(name): + if name in _DEPRECATED_IMPORTS: + + warnings.warn( + f"Importing '{name}' from 'pina' is deprecated; use " + f"pina.{_DEPRECATED_IMPORTS[name]} instead.", + DeprecationWarning, + stacklevel=2, + ) + + return globals()[_DEPRECATED_IMPORTS[name]] diff --git a/pina/_src/__init__.py b/pina/_src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/adaptive_function/__init__.py b/pina/_src/adaptive_function/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/adaptive_function/adaptive_celu.py b/pina/_src/adaptive_function/adaptive_celu.py new file mode 100644 index 000000000..bb460933c --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_celu.py @@ -0,0 +1,77 @@ +"""Module for the Adaptive CELU activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveCELU(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`~torch.nn.CELU` activation. + + This module extends the standard CELU by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function :math:`\text{CELU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + the corresponding adaptive activation + :math:`\text{CELU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is + defined as: + + .. math:: + \text{CELU}_{\text{adaptive}}({x})=\alpha\,\text{CELU}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + The CELU function is defined elementwise as: + + .. math:: + \text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1)) + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveCELU` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.CELU() diff --git a/pina/_src/adaptive_function/adaptive_elu.py b/pina/_src/adaptive_function/adaptive_elu.py new file mode 100644 index 000000000..12b40fa46 --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_elu.py @@ -0,0 +1,80 @@ +"""Module for the Adaptive ELU activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveELU(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`~torch.nn.ELU` activation. + + This module extends the standard ELU by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function :math:`\text{ELU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + the corresponding adaptive activation + :math:`\text{ELU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is + defined as: + + .. math:: + \text{ELU}_{\text{adaptive}}({x}) = \alpha\,\text{ELU}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + The ELU function is defined elementwise as: + + .. math:: + \text{ELU}(x) = \begin{cases} + x, & \text{ if }x > 0\\ + \exp(x) - 1, & \text{ if }x \leq 0 + \end{cases} + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveELU` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.ELU() diff --git a/pina/_src/adaptive_function/adaptive_exp.py b/pina/_src/adaptive_function/adaptive_exp.py new file mode 100644 index 000000000..c6484f8c9 --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_exp.py @@ -0,0 +1,73 @@ +"""Module for the Adaptive Exp activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveExp(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :obj:`~torch.exp` activation. + + This module extends the standard exponential function by introducing + learnable scaling and shifting parameters applied to both the input and the + output. + + Given the function :math:`\text{exp}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + the corresponding adaptive activation + :math:`\text{exp}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` + is defined as: + + .. math:: + \text{exp}_{\text{adaptive}}({x}) = \alpha\,\text{exp}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveExp` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.exp diff --git a/pina/_src/adaptive_function/adaptive_function_interface.py b/pina/_src/adaptive_function/adaptive_function_interface.py new file mode 100644 index 000000000..d53694bcd --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_function_interface.py @@ -0,0 +1,50 @@ +"""Module for the Adaptive Function Interface.""" + +from abc import ABCMeta, abstractmethod + + +class AdaptiveFunctionInterface(metaclass=ABCMeta): + """ + Abstract interface for all adaptive functions. + """ + + @abstractmethod + def forward(self, x): + """ + Compute the transformation of the adaptive function on the input. + + :param x: The input tensor to evaluate the adaptive function. + :type x: torch.Tensor | LabelTensor + :return: The output of the adaptive function. + :rtype: torch.Tensor | LabelTensor + """ + + @property + @abstractmethod + def alpha(self): + """ + The output scaling parameter of the adaptive function. + + :return: The alpha parameter. + :rtype: torch.nn.Parameter | torch.Tensor + """ + + @property + @abstractmethod + def beta(self): + """ + The input scaling parameter of the adaptive function. + + :return: The beta parameter. + :rtype: torch.nn.Parameter | torch.Tensor + """ + + @property + @abstractmethod + def gamma(self): + """ + The input shifting parameter of the adaptive function. + + :return: The gamma parameter. + :rtype: torch.nn.Parameter | torch.Tensor + """ diff --git a/pina/_src/adaptive_function/adaptive_gelu.py b/pina/_src/adaptive_function/adaptive_gelu.py new file mode 100644 index 000000000..148d43d52 --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_gelu.py @@ -0,0 +1,78 @@ +"""Module for the Adaptive GELU activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveGELU(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`~torch.nn.GELU` activation. + + This module extends the standard GELU by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function :math:`\text{GELU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + the corresponding adaptive activation + :math:`\text{GELU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is + defined as: + + .. math:: + \text{GELU}_{\text{adaptive}}({x})=\alpha\,\text{GELU}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + The GELU function is defined elementwise as: + + .. math:: + \text{GELU}(x)=0.5*x*(1+\text{Tanh}(\sqrt{2 / \pi}*(x+0.044715*x^3))) + + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveGELU` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.GELU() diff --git a/pina/_src/adaptive_function/adaptive_mish.py b/pina/_src/adaptive_function/adaptive_mish.py new file mode 100644 index 000000000..1c7278a1e --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_mish.py @@ -0,0 +1,77 @@ +"""Module for the Adaptive Mish activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveMish(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`~torch.nn.Mish` activation. + + This module extends the standard Mish by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function :math:`\text{Mish}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + the corresponding adaptive activation + :math:`\text{Mish}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is + defined as: + + .. math:: + \text{Mish}_{\text{adaptive}}({x})=\alpha\,\text{Mish}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + The Mish function is defined elementwise as: + + .. math:: + \text{Mish}(x) = x * \text{Tanh}(x) + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveMish` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.Mish() diff --git a/pina/_src/adaptive_function/adaptive_relu.py b/pina/_src/adaptive_function/adaptive_relu.py new file mode 100644 index 000000000..bd8ec0879 --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_relu.py @@ -0,0 +1,78 @@ +"""Module for the Adaptive ReLU activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveReLU(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`torch.nn.ReLU` activation. + + This module extends the standard ReLU by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function :math:`\text{ReLU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + the corresponding adaptive activation + :math:`\text{ReLU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is + defined as: + + .. math:: + \text{ReLU}_{\text{adaptive}}(x) = + \alpha \, \text{ReLU}(\beta x + \gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + The ReLU function is defined elementwise as: + + .. math:: + \text{ReLU}(x) = \max(0, x). + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveReLU` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.ReLU() diff --git a/pina/_src/adaptive_function/adaptive_sigmoid.py b/pina/_src/adaptive_function/adaptive_sigmoid.py new file mode 100644 index 000000000..c88eafab2 --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_sigmoid.py @@ -0,0 +1,79 @@ +"""Module for the Adaptive Sigmoid activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveSigmoid(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`~torch.nn.Sigmoid` activation. + + This module extends the standard Sigmoid by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function + :math:`\text{Sigmoid}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, the + corresponding adaptive activation + :math:`\text{Sigmoid}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` + is defined as: + + .. math:: + \text{Sigmoid}_{\text{adaptive}}({x})= + \alpha\,\text{Sigmoid}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + The Sigmoid function is defined elementwise as: + + .. math:: + \text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)} + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveSigmoid` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.Sigmoid() diff --git a/pina/_src/adaptive_function/adaptive_silu.py b/pina/_src/adaptive_function/adaptive_silu.py new file mode 100644 index 000000000..d35b867a6 --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_silu.py @@ -0,0 +1,79 @@ +"""Module for the Adaptive SiLU activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveSiLU(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`~torch.nn.SiLU` activation. + + This module extends the standard SiLU by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function :math:`\text{SiLU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + the corresponding adaptive activation + :math:`\text{SiLU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is + defined as: + + .. math:: + \text{SiLU}_{\text{adaptive}}({x})=\alpha\,\text{SiLU}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + The SiLU function is defined elementwise as: + + .. math:: + \text{SiLU}(x) = x * \sigma(x), + + where :math:`\sigma(x)` is the logistic sigmoid function. + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveSiLU` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.SiLU() diff --git a/pina/_src/adaptive_function/adaptive_siren.py b/pina/_src/adaptive_function/adaptive_siren.py new file mode 100644 index 000000000..dfb42b4b9 --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_siren.py @@ -0,0 +1,72 @@ +"""Module for the Adaptive SIREN activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveSIREN(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :obj:`~torch.sin` activation. + + This module extends the standard SIREN by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function :math:`\text{sin}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + the corresponding adaptive activation + :math:`\text{sin}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` + is defined as: + + .. math:: + \text{sin}_{\text{adaptive}}({x}) = \alpha\,\text{sin}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveSIREN` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.sin diff --git a/pina/_src/adaptive_function/adaptive_softmax.py b/pina/_src/adaptive_function/adaptive_softmax.py new file mode 100644 index 000000000..7f2ad156f --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_softmax.py @@ -0,0 +1,79 @@ +"""Module for the Adaptive Softmax activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveSoftmax(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`~torch.nn.Softmax` activation. + + This module extends the standard Softmax by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function + :math:`\text{Softmax}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, the + corresponding adaptive activation + :math:`\text{Softmax}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` + is defined as: + + .. math:: + \text{Softmax}_{\text{adaptive}}({x})=\alpha\, + \text{Softmax}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + The Softmax function is defined elementwise as: + + .. math:: + \text{Softmax}(x_i) = \frac{\exp(x_i)}{\sum_j \exp(x_j)} + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveSoftmax` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.Softmax(dim=-1) diff --git a/pina/_src/adaptive_function/adaptive_softmin.py b/pina/_src/adaptive_function/adaptive_softmin.py new file mode 100644 index 000000000..b07e27bbf --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_softmin.py @@ -0,0 +1,79 @@ +"""Module for the Adaptive Softmin activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveSoftmin(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`~torch.nn.Softmin` activation. + + This module extends the standard Softmin by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function + :math:`\text{Softmin}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, the + corresponding adaptive activation + :math:`\text{Softmin}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` + is defined as: + + .. math:: + \text{Softmin}_{\text{adaptive}}({x})=\alpha\, + \text{Softmin}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + The Softmin function is defined elementwise as: + + .. math:: + \text{Softmin}(x_i) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)} + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveSoftmin` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.Softmin(dim=-1) diff --git a/pina/_src/adaptive_function/adaptive_tanh.py b/pina/_src/adaptive_function/adaptive_tanh.py new file mode 100644 index 000000000..513f4b3f0 --- /dev/null +++ b/pina/_src/adaptive_function/adaptive_tanh.py @@ -0,0 +1,72 @@ +"""Module for the Adaptive Tanh activation function.""" + +import torch +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, +) + + +class AdaptiveTanh(BaseAdaptiveFunction): + r""" + Adaptive, trainable variant of the :class:`~torch.nn.Tanh` activation. + + This module extends the standard Tanh by introducing learnable scaling + and shifting parameters applied to both the input and the output. + + Given the function :math:`\text{Tanh}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, + the corresponding adaptive activation + :math:`\text{Tanh}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is + defined as: + + .. math:: + \text{Tanh}_{\text{adaptive}}({x})=\alpha\,\text{Tanh}(\beta{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`AdaptiveTanh` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__(alpha, beta, gamma, fixed) + self._func = torch.nn.Tanh() diff --git a/pina/_src/adaptive_function/base_adaptive_function.py b/pina/_src/adaptive_function/base_adaptive_function.py new file mode 100644 index 000000000..c391d308a --- /dev/null +++ b/pina/_src/adaptive_function/base_adaptive_function.py @@ -0,0 +1,178 @@ +"""Module for the Adaptive Function base class.""" + +import torch +from pina._src.core.utils import check_consistency +from pina._src.adaptive_function.adaptive_function_interface import ( + AdaptiveFunctionInterface, +) + + +class BaseAdaptiveFunction(torch.nn.Module, AdaptiveFunctionInterface): + r""" + Base class for all adaptive functions, implementing common functionality. + + This class extends a standard :class:`torch.nn.Module` activation function + into a trainable adaptive form. It implements the common mechanism used to + scale and shift both the input and the output of a given activation + function. + + Given a function :math:`f:\mathbb{R}^n\rightarrow\mathbb{R}^m`, the adaptive + function :math:`f_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^m` + is defined as: + + .. math:: + f_{\text{adaptive}}(\mathbf{x}) = \alpha\,f(\beta\mathbf{x}+\gamma), + + where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are learnable + parameters controlling output scaling, input scaling, and input shifting, + respectively. + + All specific adaptive functions should inherit from this class and implement + the abstract methods declared in the interface. + + This class is not meant to be instantiated directly. + + .. seealso:: + + **Original reference**: Godfrey, L. B., Gashler, M. S. (2015). + *A continuum among logarithmic, linear, and exponential functions, + and its potential to improve generalization in neural networks.* + 7th international joint conference on knowledge discovery, knowledge + engineering and knowledge management (IC3K), Vol. 1. + DOI: `arXiv preprint arXiv:1602.01321. + `_. + + **Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020). + *Adaptive activation functions accelerate convergence in deep and + physics-informed neural networks*. + Journal of Computational Physics, 404. + DOI: `JCP 10.1016 `_. + """ + + def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): + """ + Initialization of the :class:`BaseAdaptiveFunction` class. + + :param alpha: The output scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type alpha: int | float + :param beta: The input scaling parameter of the adaptive function. + If ``None``, it is initialized to ``1``. Default is ``None``. + :type beta: int | float + :param gamma: The input shifting parameter of the adaptive function. + If ``None``, it is initialized to ``0``. Default is ``None``. + :type gamma: int | float + :param fixed: The names of parameters to keep fixed during training. + These parameters will not be optimized and will have + ``requires_grad=False``. Available options are ``"alpha"``, + ``"beta"``, and ``"gamma"``. If ``None``, all parameters are + trainable. Default is ``None``. + :type fixed: str | list[str] + :raises ValueError: If alpha, when provided, is not a number. + :raises ValueError: If beta, when provided, is not a number. + :raises ValueError: If gamma, when provided, is not a number. + :raises ValueError: If fixed, when provided, is neither a string nor a + list of strings. + :raises ValueError: If fixed contains invalid parameter names. + """ + super().__init__() + + # Set default values for alpha, beta, gamma if they are None + alpha = 1.0 if alpha is None else alpha + beta = 1.0 if beta is None else beta + gamma = 0.0 if gamma is None else gamma + + # Check consistency + check_consistency(alpha, (int, float)) + check_consistency(beta, (int, float)) + check_consistency(gamma, (int, float)) + + # Process fixed parameters + if fixed is not None: + check_consistency(fixed, str) + fixed = {fixed} if isinstance(fixed, str) else set(fixed) + else: + fixed = set() + + # Validate fixed parameter names + invalid_names = fixed - {"alpha", "beta", "gamma"} + if invalid_names: + raise ValueError( + f"Invalid fixed parameter name(s): {sorted(invalid_names)}. " + "Available options are 'alpha', 'beta', and 'gamma'." + ) + + # Register either a trainable parameter or a fixed buffer + def _register_adaptive_param(name, value): + """ + Helper function to register an adaptive parameter as either a + trainable parameter or a fixed buffer, depending on whether it is + specified in the ``fixed`` argument. + """ + # Convert value to tensor + tensor = torch.tensor(value, dtype=torch.float32) + + # Register as buffer if fixed, otherwise as parameter + if name in fixed: + self.register_buffer(f"_{name}", tensor) + else: + setattr(self, f"_{name}", torch.nn.Parameter(tensor)) + + # Register parameters + _register_adaptive_param("alpha", alpha) + _register_adaptive_param("beta", beta) + _register_adaptive_param("gamma", gamma) + + # Initialize the adaptive function to None, to be set by subclasses + self._func = None + + def forward(self, x): + """ + Compute the transformation of the adaptive function on the input. + + :param x: The input tensor to evaluate the adaptive function. + :type x: torch.Tensor | LabelTensor + :raises RuntimeError: If the adaptive function has not been set. + :raises RuntimeError: If the adaptive function is not callable. + :return: The output of the adaptive function. + :rtype: torch.Tensor | LabelTensor + """ + # Raise an error if the adaptive function has not been set + if self._func is None: + raise RuntimeError("The adaptive function has not been set.") + + # Raise an error if the adaptive function is not callable + if not callable(self._func): + raise RuntimeError("The adaptive function is not callable.") + + return self.alpha * (self._func(self.beta * x + self.gamma)) + + @property + def alpha(self): + """ + The output scaling parameter of the adaptive function. + + :return: The alpha parameter. + :rtype: torch.nn.Parameter | torch.Tensor + """ + return self._alpha + + @property + def beta(self): + """ + The input scaling parameter of the adaptive function. + + :return: The beta parameter. + :rtype: torch.nn.Parameter | torch.Tensor + """ + return self._beta + + @property + def gamma(self): + """ + The input shifting parameter of the adaptive function. + + :return: The gamma parameter. + :rtype: torch.nn.Parameter | torch.Tensor + """ + return self._gamma diff --git a/pina/_src/callback/__init__.py b/pina/_src/callback/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/callback/optim/__init__.py b/pina/_src/callback/optim/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/callback/optim/switch_optimizer.py b/pina/_src/callback/optim/switch_optimizer.py similarity index 57% rename from pina/callback/optim/switch_optimizer.py rename to pina/_src/callback/optim/switch_optimizer.py index 3072b7c2e..36561fa28 100644 --- a/pina/callback/optim/switch_optimizer.py +++ b/pina/_src/callback/optim/switch_optimizer.py @@ -1,27 +1,36 @@ """Module for the SwitchOptimizer callback.""" from lightning.pytorch.callbacks import Callback -from ...optim import TorchOptimizer -from ...utils import check_consistency +from pina._src.optim.optimizer_interface import OptimizerInterface +from pina._src.core.utils import check_consistency, check_positive_integer class SwitchOptimizer(Callback): """ - PINA Implementation of a Lightning Callback to switch optimizer during - training. + Lightning callback for dynamically replacing optimizers during training. + + This callback enables switching to one or more new optimizers at a specified + epoch without restarting the training loop. It is particularly useful for + staged optimization strategies (e.g., coarse-to-fine training or optimizer + warm-up phases), where different optimizers are applied sequentially. + + At the target epoch, the provided optimizers are hooked to the model + parameters and replace the current optimizers in both the PINA solver and + the Lightning trainer strategy. """ def __init__(self, new_optimizers, epoch_switch): """ - This callback allows switching between different optimizers during - training, enabling the exploration of multiple optimization strategies - without interrupting the training process. + Initialization of the :class:`SwitchOptimizer` class. :param new_optimizers: The model optimizers to switch to. Can be a single :class:`torch.optim.Optimizer` instance or a list of them for multiple model solver. - :type new_optimizers: pina.optim.TorchOptimizer | list + :type new_optimizers: pina.optim.OptimizerInterface | list :param int epoch_switch: The epoch at which the optimizer switch occurs. + :raises AssertionError: If ``epoch_switch`` is not a positive integer. + :raises ValueError: If any of the provided optimizers are not instances + of :class:`pina.optim.OptimizerInterface`. Example: >>> optimizer = TorchOptimizer(torch.optim.Adam, lr=0.01) @@ -31,19 +40,14 @@ def __init__(self, new_optimizers, epoch_switch): """ super().__init__() - # Check if epoch_switch is greater than 1 - if epoch_switch < 1: - raise ValueError("epoch_switch must be greater than one.") + # Check consistency + check_positive_integer(epoch_switch, strict=True) + check_consistency(new_optimizers, OptimizerInterface) # If new_optimizers is not a list, convert it to a list if not isinstance(new_optimizers, list): new_optimizers = [new_optimizers] - # Check consistency - check_consistency(epoch_switch, int) - for optimizer in new_optimizers: - check_consistency(optimizer, TorchOptimizer) - # Store the new optimizers and epoch switch self._new_optimizers = new_optimizers self._epoch_switch = epoch_switch @@ -52,9 +56,9 @@ def on_train_epoch_start(self, trainer, __): """ Switch the optimizer at the start of the specified training epoch. - :param lightning.pytorch.Trainer trainer: The trainer object managing - the training process. - :param _: Placeholder argument (not used). + :param Trainer trainer: The trainer object managing the training + process. + :param __: Placeholder argument, not used. """ # Check if the current epoch matches the switch epoch if trainer.current_epoch == self._epoch_switch: diff --git a/pina/callback/optim/switch_scheduler.py b/pina/_src/callback/optim/switch_scheduler.py similarity index 65% rename from pina/callback/optim/switch_scheduler.py rename to pina/_src/callback/optim/switch_scheduler.py index 3641f4ee4..61284fb50 100644 --- a/pina/callback/optim/switch_scheduler.py +++ b/pina/_src/callback/optim/switch_scheduler.py @@ -1,30 +1,31 @@ """Module for the SwitchScheduler callback.""" from lightning.pytorch.callbacks import Callback -from ...optim import TorchScheduler -from ...utils import check_consistency, check_positive_integer +from pina._src.optim.scheduler_interface import SchedulerInterface +from pina._src.core.utils import check_consistency, check_positive_integer class SwitchScheduler(Callback): """ - Callback to switch scheduler during training. + Lightning callback for dynamically replacing schedulers during training. + + This callback enables switching to new scheduler(s) at a specified epoch + without interrupting the training loop. It is useful for staged training + strategies where different learning rate policies are applied sequentially. """ def __init__(self, new_schedulers, epoch_switch): """ - This callback allows switching between different schedulers during - training, enabling the exploration of multiple optimization strategies - without interrupting the training process. + Initialization of the :class:`SwitchScheduler` class. :param new_schedulers: The scheduler or list of schedulers to switch to. Use a single scheduler for single-model solvers, or a list of schedulers when working with multiple models. - :type new_schedulers: pina.optim.TorchScheduler | - list[pina.optim.TorchScheduler] + :type new_schedulers: SchedulerInterface | list[SchedulerInterface] :param int epoch_switch: The epoch at which the scheduler switch occurs. - :raise AssertionError: If epoch_switch is less than 1. - :raise ValueError: If each scheduler in ``new_schedulers`` is not an - instance of :class:`pina.optim.TorchScheduler`. + :raises AssertionError: If ``epoch_switch`` is not a positive integer. + :raises ValueError: If any of the provided schedulers are not instances + of :class:`pina.optim.SchedulerInterface`. Example: >>> scheduler = TorchScheduler( @@ -36,17 +37,14 @@ def __init__(self, new_schedulers, epoch_switch): """ super().__init__() - # Check if epoch_switch is greater than 1 - check_positive_integer(epoch_switch - 1, strict=True) + # Check consistency + check_positive_integer(epoch_switch, strict=True) + check_consistency(new_schedulers, SchedulerInterface) # If new_schedulers is not a list, convert it to a list if not isinstance(new_schedulers, list): new_schedulers = [new_schedulers] - # Check consistency - for scheduler in new_schedulers: - check_consistency(scheduler, TorchScheduler) - # Store the new schedulers and epoch switch self._new_schedulers = new_schedulers self._epoch_switch = epoch_switch @@ -55,9 +53,9 @@ def on_train_epoch_start(self, trainer, __): """ Switch the scheduler at the start of the specified training epoch. - :param lightning.pytorch.Trainer trainer: The trainer object managing + :param Trainer trainer: The trainer object managing the training process. - :param __: Placeholder argument (not used). + :param __: Placeholder argument, not used. """ # Check if the current epoch matches the switch epoch if trainer.current_epoch == self._epoch_switch: diff --git a/pina/_src/callback/processing/__init__.py b/pina/_src/callback/processing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/callback/processing/data_normalizer.py b/pina/_src/callback/processing/data_normalizer.py new file mode 100644 index 000000000..515ed51d7 --- /dev/null +++ b/pina/_src/callback/processing/data_normalizer.py @@ -0,0 +1,206 @@ +"""Module for the Data Normalizer callback.""" + +from typing import Callable +import torch +from lightning.pytorch import Callback +from pina._src.core.utils import check_consistency +from pina._src.core.label_tensor import LabelTensor +from pina._src.condition.condition import InputTargetCondition + + +class DataNormalizer(Callback): + r""" + Callback for dataset normalization on input-target conditions. + + This callback computes and applies a normalization transform to either + input or target tensors within a dataset. The transformation is defined as: + + .. math:: + + x_{\text{norm}} = \frac{x - \mu}{\sigma}, + + where :math:`\mu` and :math:`\sigma` are computed using the provided + ``shift_fn`` and ``scale_fn`` functions, respectively. Normalization + parameters are estimated from the training dataset and then applied in-place + to the selected datasets depending on the chosen stage. + + .. note:: + + This callback ignores all conditions that are not instances of + :class:`~pina.condition.InputTargetCondition`. + + :Example: + + >>> DataNormalizer( + ... scale_fn=torch.std, + ... shift_fn=torch.mean, + ... stage="all", + ... apply_to="input", + ... ) + """ + + # Define valid options for stage and apply_to parameters + _VALID_STAGES = {"train", "validate", "test", "all"} + _VALID_APPLY_TO = {"input", "target"} + + def __init__( + self, + scale_fn=torch.std, + shift_fn=torch.mean, + stage="all", + apply_to="input", + ): + """ + Initialization of the :class:`DataNormalizer` class. + + :param Callable scale_fn: The function used to compute the scaling + factor. Default is ``torch.std``. + :param Callable shift_fn: The function used to compute the shifting + factor. Default is ``torch.mean``. + :param str stage: The stage during which normalization is applied. + Available options are ``"train"``, ``"validate"``, ``"test"``, and + ``"all"``. Default is ``"all"``. + :param str apply_to: Specifies whether normalization is applied to + ``"input"`` or ``"target"`` tensors. Default is ``"input"``. + :raises ValueError: If ``scale_fn`` is not Callable. + :raises ValueError: If ``shift_fn`` is not Callable. + :raises ValueError: If ``stage`` is invalid. + :raises ValueError: If ``apply_to`` is invalid. + """ + super().__init__() + + # Check consistency + check_consistency(scale_fn, Callable) + check_consistency(shift_fn, Callable) + check_consistency(stage, str) + check_consistency(apply_to, str) + + # Validate stage parameter + if stage not in self._VALID_STAGES: + raise ValueError( + "Invalid value for 'stage'. Available options are " + f"{self._VALID_STAGES}. Got {stage}." + ) + + # Validate apply_to parameter + if apply_to not in self._VALID_APPLY_TO: + raise ValueError( + "Invalid value for 'apply_to'. Available options are " + f"{self._VALID_APPLY_TO}. Got {apply_to}." + ) + + # Initialize attributes + self.scale_fn = scale_fn + self.shift_fn = shift_fn + self.stage = stage + self.apply_to = apply_to + self._normalizer = {} + self._normalized_conditions = set() + + def setup(self, trainer, pl_module, stage): + """ + Compute and apply normalization during the setup phase. + + :param Trainer trainer: The trainer instance managing the execution. + :param BaseSolver pl_module: The solver module being executed. + :param str stage: Current execution stage. + :raises NotImplementedError: If the dataset is graph-based and + therefore unsupported. + """ + # Check if any condition contains graph-based data + if any( + hasattr(ds.condition.data, "graph_key") + for ds in trainer.datamodule.train_datasets.values() + ): + raise NotImplementedError( + "DataNormalizer is not compatible with graph-based datasets." + ) + + # Extract input-target conditions + conditions_to_normalize = [ + name + for name, cond in pl_module.problem.conditions.items() + if isinstance(cond, InputTargetCondition) + ] + + # Extract the dictionary of all datasets + dataset = trainer.datamodule.train_datasets + + # Compute scale and shift parameters if not already computed + if not self.normalizer: + + # Iterate over conditions and compute normalization parameters + for cond in conditions_to_normalize: + pts = self._get_data(dataset, cond) + shift = self.shift_fn(pts) + scale = self.scale_fn(pts) + + self._normalizer[cond] = { + "shift": shift, + "scale": scale, + } + + # Apply normalization to training datasets + if stage == "fit" and self.stage in ["train", "all"]: + self.normalize_dataset(trainer.datamodule.train_datasets) + + if stage == "fit" and self.stage in ["validate", "all"]: + self.normalize_dataset(trainer.datamodule.val_datasets) + + if stage == "test" and self.stage in ["test", "all"]: + self.normalize_dataset(trainer.datamodule.test_datasets) + + return super().setup(trainer, pl_module, stage) + + def normalize_dataset(self, dataset): + """ + Apply normalization to all datasets in-place. + + Each condition is updated using precomputed normalization parameters. + The transformation preserves tensor types. + + :param dict dataset: The mapping between condition names and their + associated dataset subsets. + """ + # Iterate over conditions and apply normalization + for cond, norm_params in self.normalizer.items(): + if cond in self._normalized_conditions: + continue + + # Extract the points to normalize and the normalization parameters + data_container = getattr(dataset[cond].condition, self.apply_to) + points = data_container.data + scale = norm_params["scale"] + shift = norm_params["shift"] + + # Apply normalization + scaled_pts = (points - shift) / scale + if isinstance(data_container, LabelTensor): + scaled_pts = LabelTensor(scaled_pts, data_container.labels) + + # Update the dataset in-place + data_container.data = scaled_pts + self._normalized_conditions.add(cond) + + def _get_data(self, dataset, cond): + """ + Extract the selected data field from the dataset for a given condition. + + :param dict dataset: The mapping between condition names and their + associated dataset subsets. + :param str cond: The condition name. + :return: The selected input or target data. + :rtype: torch.Tensor + """ + return getattr(dataset[cond].condition, self.apply_to).data + + @property + def normalizer(self): + """ + The dictionary mapping each condition to its corresponding ``shift`` and + ``scale`` values. + + :return: The dictionary of normalization parameters. + :rtype: dict + """ + return self._normalizer diff --git a/pina/_src/callback/processing/metric_tracker.py b/pina/_src/callback/processing/metric_tracker.py new file mode 100644 index 000000000..360a5aacb --- /dev/null +++ b/pina/_src/callback/processing/metric_tracker.py @@ -0,0 +1,107 @@ +"""Module for the Metric Tracker.""" + +import copy +import torch +from lightning.pytorch.callbacks import Callback +from pina._src.core.utils import check_consistency + + +class MetricTracker(Callback): + """ + Callback for collecting selected metrics logged during training. + """ + + def __init__(self, metrics_to_track=None): + """ + Initialization of the :class:`MetricTracker` class. + + :param metrics_to_track: The names of the metrics to collect. If + ``None``, defaults to ``["train_loss"]`` when no batch size is + available, otherwise to ``["train_loss_epoch"]``. Default is + ``None``. + :type metrics_to_track: str | list[str] + :raises ValueError: If any of the provided metric names are not strings. + """ + super().__init__() + + # Check consistency + if metrics_to_track is not None: + check_consistency(metrics_to_track, str) + + # Convert to list if a single string is provided + if isinstance(metrics_to_track, str): + metrics_to_track = [metrics_to_track] + + # Initialize the collection list and store the metrics to track + self.metrics_to_track = metrics_to_track + self._collection = [] + + def setup(self, trainer, pl_module, stage): + """ + Configure the metrics to track before execution starts. + + When a batch size is provided (i.e. ``trainer.batch_size`` is not + ``None``), metric names are expanded to match Lightning's logging + convention: for each metric ``m``, both ``m_step`` and ``m_epoch`` are + tracked. For example, ``"train_loss"`` becomes + ``["train_loss_step", "train_loss_epoch"]``. + + :param Trainer trainer: The trainer instance managing the execution. + :param BaseSolver pl_module: The solver module being executed. + :param str stage: Current execution stage. + """ + # Set default metrics to train_loss if no batch size is available + if self.metrics_to_track is None: + self.metrics_to_track = ["train_loss"] + + # If a batch size is provided, expand metric names to match convention + if trainer.batch_size is not None: + self.metrics_to_track = [ + f"{metric}_{suffix}" + for metric in self.metrics_to_track + for suffix in ("step", "epoch") + ] + + return super().setup(trainer, pl_module, stage) + + def on_train_epoch_end(self, trainer, __): + """ + Store the selected logged metrics at the end of each training epoch. + + :param Trainer trainer: The trainer instance managing the execution. + :param __: Placeholder argument, not used. + """ + # Only collect metrics after the first epoch to ensure they are logged + if trainer.current_epoch > 0: + + # Collect the metrics that are being tracked + tracked_metrics = { + k: v + for k, v in trainer.logged_metrics.items() + if k in self.metrics_to_track + } + self._collection.append(copy.deepcopy(tracked_metrics)) + + @property + def metrics(self): + """ + Return the collected metrics stacked over the tracked epochs. + + :return: The dictionary mapping each metric name to a tensor containing + its values across epochs. Returns an empty dictionary if no metrics + have been collected. + :rtype: dict[str, torch.Tensor] + """ + if not self._collection: + return {} + + # Identify the common keys across all collected metric dictionaries + common_keys = set(self._collection[0]).intersection( + *self._collection[1:] + ) + + return { + k: torch.stack([dic[k] for dic in self._collection]) + for k in common_keys + if k in self.metrics_to_track + } diff --git a/pina/_src/callback/processing/pina_progress_bar.py b/pina/_src/callback/processing/pina_progress_bar.py new file mode 100644 index 000000000..bde274052 --- /dev/null +++ b/pina/_src/callback/processing/pina_progress_bar.py @@ -0,0 +1,116 @@ +"""Module for the Processing Callbacks.""" + +from lightning.pytorch.callbacks import TQDMProgressBar +from lightning.pytorch.callbacks.progress.progress_bar import ( + get_standard_metrics, +) +from pina._src.core.utils import check_consistency + + +class PINAProgressBar(TQDMProgressBar): + """ + Custom progress bar callback for PINA training workflows. + + This callback extends the default Lightning progress bar by filtering the + displayed metrics. + + Metrics can refer either to condition-specific losses, identified by the + names assigned to the problem conditions, or to global losses. Global losses + are selected using ``"train"``, ``"val"``, or ``"test"``, and are internally + expanded to the corresponding logged loss metrics. + """ + + GLOBAL_LOSS_KEYS = ("train", "val", "test") + + BAR_FORMAT = ( + "{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, " + "{rate_noinv_fmt}{postfix}]" + ) + + def __init__(self, metrics="val", **kwargs): + """ + Initialization of the :class:`PINAProgressBar`. + + :param metrics: The names of the metrics to be shown in the progress + bar. Each entry can be either a key of a condition defined in the + problem or one of the global loss keys: ``"train"``, ``"val"``, or + ``"test"``. These global keys are internally expanded to the + corresponding logged loss names. Default is ``"val"``. + :type metrics: str | list(str) | tuple(str) + :param dict kwargs: Additional keyword arguments passed to + :class:`lightning.pytorch.callbacks.TQDMProgressBar`. + :raises TypeError: If ``metrics`` contains non-string elements. + """ + super().__init__(**kwargs) + + # Check consistency + check_consistency(metrics, str) + + # Convert to list if a single string is provided + if isinstance(metrics, str): + metrics = [metrics] + + # Store the sorted metrics for later use in get_metrics + self._sorted_metrics = sorted(metrics) + + def get_metrics(self, trainer, __): + """ + Retrieve and filter metrics to be displayed in the progress bar. + + This method combines standard Lightning metrics with user-selected + progress bar metrics, retaining only the metrics specified at + initialization. + + :param Trainer trainer: The trainer managing the training loop. + :param __: Placeholder argument, not used. + :return: Dictionary containing the metrics to display. + :rtype: dict + + .. note:: + This method overrides the default Lightning behavior. It can be + further customized by subclassing. + """ + # Retrieve standard metrics and user-selected progress bar metrics + standard_metrics = get_standard_metrics(trainer) + progress_bar_metrics = trainer.progress_bar_metrics + + # Filter progress bar metrics to include only specified keys + if progress_bar_metrics: + progress_bar_metrics = { + key: progress_bar_metrics[key] + for key in progress_bar_metrics + if key in self._sorted_metrics + } + + return {**standard_metrics, **progress_bar_metrics} + + def setup(self, trainer, pl_module, stage): + """ + Configure the metrics to track before execution starts. + + The requested metrics must be either names assigned to problem + conditions or global loss keys. The accepted global loss keys are + ``"train"``, ``"val"``, and ``"test"``. + + :param Trainer trainer: The trainer instance managing the execution. + :param BaseSolver pl_module: The solver module being executed. + :param str stage: Current execution stage. + :raises KeyError: If a metric key is neither a condition key nor one of + ``"train"``, ``"val"``, or ``"test"``. + """ + # Get the condition keys from the problem + condition_keys = trainer.solver.problem.conditions.keys() + for key in self._sorted_metrics: + if key not in condition_keys and key not in self.GLOBAL_LOSS_KEYS: + raise KeyError( + f"Key '{key}' is not a valid metric. It must be either a " + f"problem condition key or one of {self.GLOBAL_LOSS_KEYS}." + ) + + # Add the appropriate suffix to the metric names based on batch size + suffix = "_loss_epoch" if trainer.batch_size is not None else "_loss" + self._sorted_metrics = [ + metric + suffix for metric in self._sorted_metrics + ] + + return super().setup(trainer, pl_module, stage) diff --git a/pina/_src/callback/refinement/__init__.py b/pina/_src/callback/refinement/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/callback/refinement/base_refinement.py b/pina/_src/callback/refinement/base_refinement.py new file mode 100644 index 000000000..640528975 --- /dev/null +++ b/pina/_src/callback/refinement/base_refinement.py @@ -0,0 +1,155 @@ +"""Module for the Base Refinement class.""" + +from pina._src.solver.physics_informed_single_model_solver import ( + PhysicsInformedSingleModelSolver, +) +from lightning.pytorch import Callback +from pina._src.core.utils import check_consistency, check_positive_integer +from pina._src.callback.refinement.refinement_interface import ( + RefinementInterface, +) + + +class BaseRefinement(Callback, RefinementInterface): + """ + Base class for all refinement strategies, implementing common functionality. + + A refinement strategy is responsible for dynamically updating the training + dataset during optimization, typically by resampling points in the domain + based on model behavior (e.g., error-driven refinement). + + All specific refinement strategies should inherit from this class and + implement its abstract methods. + + This class is not meant to be instantiated directly. + """ + + def __init__(self, sample_every, condition_to_update=None): + """ + Initialization of the :class:`BaseRefinement` class. + + :param int sample_every: The number of epochs between successive + refinement steps. + :param condition_to_update: The condition(s) to be updated during + refinement. If ``None``, all conditions associated with a domain are + updated. Default is ``None``. + :type condition_to_update: str | list[str] | tuple[str] + :raises AssertionError: If ``sample_every`` is not a positive integer. + :raises ValueError: If ``condition_to_update``, when provided, is not a + string or an iterable of strings. + """ + # Check consistency + check_positive_integer(sample_every, strict=True) + if condition_to_update is not None: + if isinstance(condition_to_update, str): + condition_to_update = [condition_to_update] + check_consistency([condition_to_update], (list, tuple)) + check_consistency(condition_to_update, str) + + # Initialize attributes + self._condition_to_update = condition_to_update + self.sample_every = sample_every + self._initial_population_size = None + self._dataset = None + + def on_train_start(self, trainer, solver): + """ + This method is called once before training begins and is typically used + to initialize datasets, sampling conditions, or internal state. + + :param Trainer trainer: The trainer managing the training loop. + :param BaseSolver solver: The solver associated with the trainer. + :raise RuntimeError: If the solver is not physics-informed (i.e., does + not implement PINNInterface). + :raise RuntimeError: If any of the specified conditions do not exist in + the problem. + :raise RuntimeError: If any of the specified conditions do not have a + 'domain' attribute for sampling. + """ + # Check solver consistency + if not isinstance(solver, PhysicsInformedSingleModelSolver): + raise RuntimeError( + "Refinement strategies require a physics-informed solver. " + f"Got '{type(solver).__name__}'." + ) + + # Initialize conditions to update if not provided + if self._condition_to_update is None: + self._condition_to_update = [ + name + for name, cond in solver.problem.conditions.items() + if hasattr(cond, "domain") + ] + + # Validate conditions and solver + for cond in self._condition_to_update: + + # Check if condition exists in the problem + if cond not in solver.problem.conditions: + raise RuntimeError( + f"Unknown condition '{cond}'. Available conditions: " + f"{list(solver.problem.conditions.keys())}." + ) + + # Check if condition has a domain to sample from + if not hasattr(solver.problem.conditions[cond], "domain"): + raise RuntimeError( + f"Condition '{cond}' has no 'domain' attribute and cannot " + "be used for sampling." + ) + + # Initialize dataset and compute initial population size + self._dataset = trainer.datamodule.train_datasets + self._initial_population_size = { + cond: self.dataset[cond].dataset_length + for cond in self._condition_to_update + } + + def on_train_epoch_end(self, trainer, solver): + """ + Apply refinement at the end of a training epoch. + + This method is invoked after each epoch and can update the dataset based + on the current state of the model. + + :param Trainer trainer: The trainer managing the training loop. + :param BaseSolver solver: The solver associated with the trainer. + """ + # Store current epoch + epoch = trainer.current_epoch + + # Sample if it's time to refine + if epoch % self.sample_every == 0 and epoch != 0: + + # Update points for each condition to update + for name in self._condition_to_update: + + current_points = solver.problem.conditions[name].data.input + new_points = self.sample(current_points, name, solver) + solver.problem.conditions[name].data.input = new_points + + @property + def dataset(self): + """ + The training datasets managed by the refinement strategy. + + The dataset is stored as a dictionary whose keys are condition names and + whose values are the corresponding dataset subsets. The content of this + dictionary can be updated dynamically during refinement. + + :return: The mapping between condition names and dataset subsets. + :rtype: dict + """ + return self._dataset + + @property + def initial_population_size(self): + """ + Initial size of the sampled dataset for each condition before any + refinement is applied. + + :return: A mapping between each condition name and its initial number + of sampled points. + :rtype: dict[str, int] + """ + return self._initial_population_size diff --git a/pina/_src/callback/refinement/r3_refinement.py b/pina/_src/callback/refinement/r3_refinement.py new file mode 100644 index 000000000..1e52c4b4b --- /dev/null +++ b/pina/_src/callback/refinement/r3_refinement.py @@ -0,0 +1,105 @@ +"""Module for the R3Refinement callback.""" + +import torch +from pina._src.core.utils import check_consistency +from pina._src.core.label_tensor import LabelTensor +from pina._src.loss.dual_loss_interface import DualLossInterface +from pina._src.callback.refinement.base_refinement import BaseRefinement + + +class R3Refinement(BaseRefinement): + """ + Refinement strategy based on the R3 (Retain-Resample-Release) algorithm. + + This method adaptively updates collocation points by retaining points with + high residuals, resampling new points in the domain, releasing points with + low residuals. + + The objective is to concentrate sampling in regions where the PDE residual + is large, improving training efficiency and solution accuracy. + + .. seealso:: + + **Original Reference**: Daw, Arka, et al. (2023). + *Mitigating Propagation Failures in Physics-informed Neural Networks + using Retain-Resample-Release (R3) Sampling*. + DOI: `10.48550/arXiv.2207.02338 + `_ + + :Example: + + >>> r3 = R3Refinement(sample_every=5) + """ + + def __init__( + self, + sample_every, + residual_loss=torch.nn.L1Loss, + condition_to_update=None, + ): + """ + Initialization of the :class:`R3Refinement` class. + + :param int sample_every: The number of epochs between successive + refinement steps. + :param residual_loss: The loss used to evaluate residual magnitude. Must + be a subclass of :class:`torch.nn.Module` or + :class:`pina.loss.DualLossInterface`. + Default is :class:`torch.nn.L1Loss`. + :type residual_loss: DualLossInterface | torch.nn.modules.loss._Loss + :param condition_to_update: The condition(s) to be updated during + refinement. If ``None``, all conditions associated with a domain are + updated. Default is ``None``. + :type condition_to_update: str | list[str] | tuple[str] + :raises ValueError: If the condition_to_update is neither a string nor + an iterable of strings. + :raises ValueError: If the residual_loss is not a valid loss class. + """ + super().__init__(sample_every, condition_to_update) + + # Check consistency + check_consistency( + residual_loss, + (DualLossInterface, torch.nn.modules.loss._Loss), + subclass=True, + ) + + # Store the loss function for computing residuals during sampling + self.loss_fn = residual_loss(reduction="none") + + def sample(self, current_points, condition_name, solver): + """ + Generate new sample points for a given condition. + + :param LabelTensor current_points: The existing points in the domain. + :param str condition_name: The identifier of the condition to refine. + :param BaseSolver solver: The solver used for sampling decisions. + :return: Newly sampled points. + :rtype: LabelTensor + """ + # Retrieve condition and current points + device = solver.trainer.strategy.root_device + condition = solver.problem.conditions[condition_name] + current_points = current_points.to(device).requires_grad_(True) + + # Compute residuals for the given condition + target = condition.evaluate({"input": current_points}, solver) + residuals = self.loss_fn(target, torch.zeros_like(target)).mean( + dim=tuple(range(1, target.ndim)) + ) + + # Retrieve domain and initial population size + domain_name = solver.problem.conditions[condition_name].domain + domain = solver.problem.domains[domain_name] + num_old_points = self.initial_population_size[condition_name] + + # Select points with residual above the mean + mask = (residuals >= residuals.mean()).flatten() + high_residual_pts = current_points[mask] + high_residual_pts.labels = current_points.labels + + # Sample new points to maintain the initial population size + num_new_pts = max(num_old_points - len(high_residual_pts), 0) + samples = domain.sample(num_new_pts, "random").to(device) + + return LabelTensor.cat([high_residual_pts, samples]) diff --git a/pina/_src/callback/refinement/refinement_interface.py b/pina/_src/callback/refinement/refinement_interface.py new file mode 100644 index 000000000..320d526af --- /dev/null +++ b/pina/_src/callback/refinement/refinement_interface.py @@ -0,0 +1,69 @@ +"""Module for the Refinement Interface.""" + +from abc import ABCMeta, abstractmethod + + +class RefinementInterface(metaclass=ABCMeta): + """ + Abstract interface for all refinement strategies. + """ + + @abstractmethod + def on_train_start(self, trainer, solver): + """ + This method is called once before training begins and is typically used + to initialize datasets, sampling conditions, or internal state. + + :param Trainer trainer: The trainer managing the training loop. + :param BaseSolver solver: The solver associated with the trainer. + """ + + @abstractmethod + def on_train_epoch_end(self, trainer, solver): + """ + Apply refinement at the end of a training epoch. + + This method is invoked after each epoch and can update the dataset based + on the current state of the model. + + :param Trainer trainer: The trainer managing the training loop. + :param BaseSolver solver: The solver associated with the trainer. + """ + + @abstractmethod + def sample(self, current_points, condition_name, solver): + """ + Generate new sample points for a given condition. + + :param LabelTensor current_points: The existing points in the domain. + :param str condition_name: The identifier of the condition to refine. + :param BaseSolver solver: The solver used for sampling decisions. + :return: Newly sampled points. + :rtype: LabelTensor + """ + + @property + @abstractmethod + def dataset(self): + """ + The training datasets managed by the refinement strategy. + + The dataset is stored as a dictionary whose keys are condition names and + whose values are the corresponding dataset subsets. The content of this + dictionary can be updated dynamically during refinement. + + :return: The mapping between condition names and dataset subsets. + :rtype: dict + """ + + @property + @abstractmethod + def initial_population_size(self): + """ + Initial size of the sampled dataset for each condition before any + refinement is applied. + + :return: A mapping between each condition name and its initial number + of sampled points. + :rtype: dict[str, int] + """ diff --git a/pina/_src/condition/__init__.py b/pina/_src/condition/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/condition/base_condition.py b/pina/_src/condition/base_condition.py new file mode 100644 index 000000000..9adf19b2d --- /dev/null +++ b/pina/_src/condition/base_condition.py @@ -0,0 +1,153 @@ +"""Module for the Base Condition class.""" + +from functools import partial +import torch +from torch_geometric.data import Batch +from torch.utils.data import DataLoader +from pina._src.condition.condition_interface import ConditionInterface +from pina._src.core.graph import LabelBatch +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency +from pina._src.data.single_batch_data_loader import _SingleBatchDataLoader +from pina._src.problem.problem_interface import ProblemInterface + + +class BaseCondition(ConditionInterface): + """ + Base class for all conditions, implementing common functionality. + + All specific condition types should inherit from this class and implement + the abstract methods of + :class:`~pina.condition.condition_interface.ConditionInterface`. + + This class is not meant to be instantiated directly. + """ + + # Available collate functions for automatic batching + collate_fn_dict = { + "tensor": torch.stack, + "label_tensor": LabelTensor.stack, + "graph": LabelBatch.from_data_list, + "data": Batch.from_data_list, + } + + def __init__(self, **kwargs): + """ + Initialization of the :class:`BaseCondition` class. + + :param dict kwargs: The keyword arguments representing the data to be + stored in the condition. + """ + super().__init__() + self.data = self.store_data(**kwargs) + self.has_custom_dataloader_fn = False + + def __len__(self): + """ + Return the number of data points in the condition. + + :return: The number of data points. + :rtype: int + """ + return len(self.data) + + def __getitem__(self, idx): + """ + Return the data point at the specified index. + + :param int idx: The index of the data point to retrieve. + :return: The data point at the specified index. + :rtype: Any + """ + return self.data[idx] + + def create_dataloader( + self, dataset, batch_size, automatic_batching, **kwargs + ): + """ + Create the DataLoader for the condition. + + :param _ConditionSubset dataset: The dataset for the DataLoader. + :param int batch_size: The batch size for the DataLoader. + :param bool automatic_batching: Whether to use automatic batching. + :param dict kwargs: Additional keyword arguments for the DataLoader. + :return: The DataLoader for the condition. + :rtype: torch.utils.data.DataLoader + """ + # If batching the entire dataset, return a _SingleBatchDataLoader + if batch_size == len(dataset): + return _SingleBatchDataLoader(dataset) + + # Otherwise, return a regular DataLoader with the appropriate collate + return DataLoader( + dataset=dataset, + collate_fn=( + partial(self.collate_fn, condition=self) + if not automatic_batching + else self.automatic_batching_collate_fn + ), + batch_size=batch_size, + **kwargs, + ) + + def switch_dataloader_fn(self, create_dataloader_fn): + """ + Switch the dataloader function for the condition. + + :param Callable create_dataloader_fn: The new dataloader function to use + for the condition. + :return: The new dataloader function for the condition. + :rtype: Callable + """ + self.has_custom_dataloader_fn = True + self.create_dataloader = create_dataloader_fn + + @classmethod + def automatic_batching_collate_fn(cls, batch): + """ + Collate function for automatic batching to be used in the DataLoader. + + :param list batch: A list of items from the dataset. + :return: A collated batch. + :rtype: dict + """ + # If the batch is empty, return an empty dictionary + if not batch: + return {} + + # Otherwise, collate the batch using the appropriate collate function + instance_class = batch[0].__class__ + return instance_class.create_batch(batch) + + @staticmethod + def collate_fn(batch, condition): + """ + Collate function for custom batching to be used in the DataLoader. + + :param list batch: A list of items from the dataset. + :param BaseCondition condition: The condition instance. + :return: A collated batch. + :rtype: dict + """ + return condition.data[batch].to_batch() + + @property + def problem(self): + """ + The problem associated with this condition. + + :return: The problem associated with this condition. + :rtype: BaseProblem + """ + return self._problem + + @problem.setter + def problem(self, value): + """ + Set the problem associated with this condition. + + :param BaseProblem value: The problem to associate with this condition. + :raises ValueError: If the problem is not an instance of BaseProblem. + """ + check_consistency(value, ProblemInterface) + self._problem = value diff --git a/pina/_src/condition/condition.py b/pina/_src/condition/condition.py new file mode 100644 index 000000000..69875a6a8 --- /dev/null +++ b/pina/_src/condition/condition.py @@ -0,0 +1,167 @@ +"""Module for the Condition class.""" + +from pina._src.condition.input_equation_condition import InputEquationCondition +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.condition.time_series_condition import TimeSeriesCondition +from pina._src.condition.graph_time_series_condition import ( + GraphTimeSeriesCondition, +) +from pina._src.condition.data_condition import DataCondition +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) + + +class Condition: + """ + The :class:`Condition` class is a core component of the PINA framework that + provides a unified interface to define heterogeneous constraints that must + be satisfied by a :class:`~pina.problem.base_problem.BaseProblem`. + + It encapsulates all types of constraints - physical, boundary, initial, or + data-driven - that the solver must satisfy during training. The specific + behavior is inferred from the arguments passed to the constructor. + + Multiple types of conditions can be used within the same problem, allowing + for a high degree of flexibility in defining complex problems. + + The :class:`Condition` class behavior specializes internally based on the + arguments provided during instantiation. Depending on the specified keyword + arguments, the class automatically selects the appropriate internal + implementation. + + Available `Condition` types: + + - :class:`~pina.condition.input_target_condition.InputTargetCondition`: + represents a supervised condition defined by both ``input`` and ``target`` + data. The model is trained to reproduce the ``target`` values given the + ``input``. Supported data types include :class:`torch.Tensor`, + :class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`, or + :class:`~torch_geometric.data.Data`. The class automatically selects the + appropriate implementation based on the types of ``input`` and ``target``. + + - :class:`~pina.condition.domain_equation_condition.DomainEquationCondition` + : represents a general physics-informed condition defined by a ``domain`` + and an ``equation``. The model learns to minimize the equation residual + through evaluations performed at points sampled from the specified domain. + + - :class:`~pina.condition.input_equation_condition.InputEquationCondition`: + represents a general physics-informed condition defined by ``input`` + points and an ``equation``. The model learns to minimize the equation + residual through evaluations performed at the provided ``input``. + Supported data types for the ``input`` include :class:`~pina.graph.Graph` + or :class:`~pina.label_tensor.LabelTensor`. The class automatically + selects the appropriate implementation based on the types of the + ``input``. + + - :class:`~pina.condition.time_series_condition.TimeSeriesCondition`: + represents a condition designed for time series data, where the model is + trained to capture temporal dependencies and dynamics. It is defined by an + ``input`` tensor of shape ``[trajectories, time_steps, *features]`` + containing time series data. Supported data types for the ``input`` + include class:`~pina.label_tensor.LabelTensor` or :class:`torch.Tensor`. + The class automatically selects the appropriate implementation based on + the type of the ``input``. + + - :class:`~pina.condition.data_condition.DataCondition`: represents an + unsupervised, data-driven condition defined by the ``input`` only. + The model is trained using a custom unsupervised loss determined by the + chosen :class:`~pina.solver.base_solver.BaseSolver`, while leveraging the + provided data during training. Optional ``conditional_variables`` can be + specified when the model depends on additional parameters. + Supported data types include :class:`~pina.label_tensor.LabelTensor`, + :class:`torch.Tensor`, :class:`~torch_geometric.data.Data`, or + :class:`~pina.graph.Graph`. The class automatically selects the + appropriate implementation based on the type of the ``input``. + + .. note:: + + The user should always instantiate :class:`Condition` directly, without + manually creating subclass instances. Please refer to the specific + :class:`Condition` classes for implementation details. + + :Example: + + >>> from pina import Condition + + >>> # Example of InputTargetCondition signature + >>> condition = Condition(input=input, target=target) + + >>> # Example of DomainEquationCondition signature + >>> condition = Condition(domain=domain, equation=equation) + + >>> # Example of InputEquationCondition signature + >>> condition = Condition(input=input, equation=equation) + + >>> # Example of TimeSeriesCondition signature + >>> condition = Condition( + ... input=input, n_windows=n_windows, unroll_length=unroll_length + ... ) + + >>> # Example of DataCondition signature + >>> condition = Condition(input=data, conditional_variables=cond_vars) + """ + + # Internal specifications for condition types, used for dispatching + # Each tuple contains: (condition class, required kwargs, optional kwargs) + _SPECS = ( + (InputTargetCondition, {"input", "target"}, set()), + (InputEquationCondition, {"input", "equation"}, set()), + (DomainEquationCondition, {"domain", "equation"}, set()), + (DataCondition, {"input"}, {"conditional_variables"}), + ( + TimeSeriesCondition, + {"input", "n_windows", "unroll_length"}, + {"randomize"}, + ), + ( + GraphTimeSeriesCondition, + {"input", "n_windows", "unroll_length"}, + {"key", "randomize"}, + ), + ) + + # Compute the set of all available keyword arguments (optional + required) + available_kwargs = sorted(set().union(*(rq | op for _, rq, op in _SPECS))) + + def __new__(cls, *args, **kwargs): + """ + Instantiate the appropriate :class:`Condition` object based on the + keyword arguments passed. + + :param tuple args: The positional arguments (should be empty). + :param dict kwargs: The keyword arguments corresponding to the + parameters of the specific :class:`Condition` type to instantiate. + :raises ValueError: If unexpected positional arguments are provided. + :raises ValueError: If the keyword arguments do not match any valid + signature for the available condition types. + :return: The appropriate :class:`Condition` object. + :rtype: ConditionInterface + """ + # Ensure no positional arguments are provided + if args: + raise ValueError( + "Condition takes only keyword arguments. " + f"Available arguments are: {cls.available_kwargs}." + ) + + # Iterate through the specifications to find a matching condition type + for condition_cls, required, optional in cls._SPECS: + + # Find allowed keys for condition type + allowed = required | optional + + # Check if the provided keys match the required and optional keys + if required <= set(kwargs) <= allowed: + return condition_cls(**kwargs) + + # If no valid signature is found, prepare a list of valid signatures + valid_signatures = [ + sorted(required | optional) for _, required, optional in cls._SPECS + ] + + # If no valid signature is found, raise an error + raise ValueError( + f"Invalid keyword arguments {sorted(set(kwargs))}. " + f"Valid signatures are: {valid_signatures}." + ) diff --git a/pina/_src/condition/condition_interface.py b/pina/_src/condition/condition_interface.py new file mode 100644 index 000000000..baa6a5d99 --- /dev/null +++ b/pina/_src/condition/condition_interface.py @@ -0,0 +1,133 @@ +"""Module for the Condition interface.""" + +from abc import ABCMeta, abstractmethod + + +class ConditionInterface(metaclass=ABCMeta): + """ + Abstract interface for all conditions. + + Refer to :class:`pina.condition.condition.Condition` for a thorough + description of all available conditions and how to instantiate them. + """ + + @abstractmethod + def __len__(self): + """ + Return the number of data points in the condition. + + :return: The number of data points. + :rtype: int + """ + + @abstractmethod + def __getitem__(self, idx): + """ + Return the data point at the specified index. + + :param int idx: The index of the data point to retrieve. + :return: The data point at the specified index. + :rtype: Any + """ + + @abstractmethod + def store_data(self, **kwargs): + """ + Store the data for the condition in a suitable format. + + :param dict kwargs: The keyword arguments containing the data to be + stored. + :return: The stored data in a suitable format. + :rtype: Any + """ + + @abstractmethod + def create_dataloader( + self, dataset, batch_size, automatic_batching, **kwargs + ): + """ + Create the DataLoader for the condition. + + :param _ConditionSubset dataset: The dataset for the DataLoader. + :param int batch_size: The batch size for the DataLoader. + :param bool automatic_batching: Whether to use automatic batching. + :param dict kwargs: Additional keyword arguments for the DataLoader. + :return: The DataLoader for the condition. + :rtype: torch.utils.data.DataLoader + """ + + @abstractmethod + def evaluate(self, batch, solver): + """ + Evaluate the residual of the condition on the given batch using the + solver. + + This method computes the non-aggregated, element-wise residual of the + condition. A forward pass of the solver's model is performed on the + input samples, and the condition residual is evaluated accordingly. + + The returned tensor is not reduced, preserving the per-sample residual + values. + + :param dict batch: The batch containing the data required by the + condition evaluation. + :param BaseSolver solver: The solver used to perform the forward pass + and compute the residual. The solver provides access to the model + and its parameters, which may be necessary for evaluating the + condition residual. + :return: The non-aggregated residual tensor. + :rtype: torch.Tensor | LabelTensor + """ + + @abstractmethod + def switch_dataloader_fn(self, create_dataloader_fn): + """ + Switch the dataloader function for the condition. + + :param Callable create_dataloader_fn: The new dataloader function to use + for the condition. + :return: The new dataloader function for the condition. + :rtype: Callable + """ + + @classmethod + @abstractmethod + def automatic_batching_collate_fn(cls, batch): + """ + Collate function for automatic batching to be used in the DataLoader. + + :param list batch: A list of items from the dataset. + :return: A collated batch. + :rtype: dict + """ + + @staticmethod + @abstractmethod + def collate_fn(batch, condition): + """ + Collate function for custom batching to be used in the DataLoader. + + :param list batch: A list of items from the dataset. + :param BaseCondition condition: The condition instance. + :return: A collated batch. + :rtype: dict + """ + + @property + @abstractmethod + def problem(self): + """ + The problem associated with this condition. + + :return: The problem associated with this condition. + :rtype: BaseProblem + """ + + @problem.setter + @abstractmethod + def problem(self, value): + """ + Set the problem associated with this condition. + + :param BaseProblem value: The problem to associate with this condition. + """ diff --git a/pina/_src/condition/data_condition.py b/pina/_src/condition/data_condition.py new file mode 100644 index 000000000..ee6ee4c76 --- /dev/null +++ b/pina/_src/condition/data_condition.py @@ -0,0 +1,133 @@ +"""Module for the Data Condition class.""" + +import torch +from torch_geometric.data import Data +from pina._src.condition.base_condition import BaseCondition +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.graph import Graph +from pina._src.data.manager.data_manager import _DataManager +from pina._src.core.utils import check_consistency + + +class DataCondition(BaseCondition): + """ + The class :class:`DataCondition` defines an unsupervised condition based on + ``input`` data. This condition is typically used in data-driven problems, + where the model is trained using a custom unsupervised loss determined by + the chosen :class:`~pina.solver.base_solver.BaseSolver`, while leveraging + the provided data during training. Optional ``conditional_variables`` can be + specified when the model depends on additional parameters. + + :Example: + + >>> from pina import Condition, LabelTensor + >>> import torch + + >>> pts = LabelTensor(torch.randn(100, 2), labels=["x", "y"]) + >>> cond_vars = LabelTensor(torch.randn(100, 1), labels=["w"]) + >>> condition = Condition(input=pts, conditional_variables=cond_vars) + """ + + # Available fields, input and conditional variables data types + __fields__ = ["input", "conditional_variables"] + _avail_input_cls = (torch.Tensor, LabelTensor, Data, Graph) + _avail_conditional_variables_cls = (torch.Tensor, LabelTensor) + + def __new__(cls, input, conditional_variables=None): + """ + Check the types of ``input`` and ``conditional_variables`` and + instantiate an instance of :class:`DataCondition` accordingly. + + :param input: The input data associated with the condition. + :type input: torch.Tensor | LabelTensor | Graph | + Data | list[Graph] | list[Data] | tuple[Graph] | tuple[Data] + :param conditional_variables: The conditional variables associated with + the condition. Default is ``None``. + :type conditional_variables: torch.Tensor | LabelTensor + :raises ValueError: If ``input`` is not of type :class:`torch.Tensor`, + :class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`, + or :class:`~torch_geometric.data.Data`, nor is it a list or tuple of + :class:`~pina.graph.Graph` or :class:`~torch_geometric.data.Data`. + :raises ValueError: If ``conditional_variables`` is not of type + :class:`torch.Tensor` or :class:`~pina.label_tensor.LabelTensor`. + :return: A new instance of :class:`DataCondition`. + :rtype: DataCondition + """ + # Check input type - if iterable, ensure it is either Data or Graph + if isinstance(input, (list, tuple)): + check_consistency(input, (Data, Graph)) + else: + check_consistency(input, cls._avail_input_cls) + + # Check conditional_variables type + if conditional_variables is not None: + check_consistency( + conditional_variables, cls._avail_conditional_variables_cls + ) + + return super().__new__(cls) + + def store_data(self, **kwargs): + """ + Store the input data and the conditional variables in a dictionary-like + structure. + + :param dict kwargs: The keyword arguments containing the data to be + stored. + :return: A dictionary-like structure containing the stored data. + :rtype: _DataManager + """ + # Store input and conditional variables in a dictionary-like structure + data_dict = {"input": kwargs.get("input")} + cond_vars = kwargs.get("conditional_variables", None) + if cond_vars is not None: + data_dict["conditional_variables"] = cond_vars + + return _DataManager(**data_dict) + + def evaluate(self, batch, solver): + """ + Evaluate the residual of the condition on the given batch using the + solver. + + This method computes the non-aggregated, element-wise residual of the + condition. A forward pass of the solver's model is performed on the + input samples, and the condition residual is evaluated accordingly. + + The returned tensor is not reduced, preserving the per-sample residual + values. + + :param dict batch: The batch containing the data required by the + condition evaluation. + :param BaseSolver solver: The solver used to perform the forward pass + and compute the residual. The solver provides access to the model + and its parameters, which may be necessary for evaluating the + condition residual. + :return: The non-aggregated residual tensor. + :rtype: torch.Tensor | LabelTensor + """ + return solver.forward(batch["input"]) + + @property + def conditional_variables(self): + """ + The conditional variables associated with the condition. + + :return: The conditional variables. + :rtype: torch.Tensor | LabelTensor | None + """ + if hasattr(self.data, "conditional_variables"): + return self.data.conditional_variables + + return None + + @property + def input(self): + """ + The input data associated with the condition. + + :return: The input data. + :rtype: torch.Tensor | LabelTensor | Graph | Data | + list[Graph] | list[Data] | tuple[Graph] | tuple[Data] + """ + return self.data.input diff --git a/pina/_src/condition/domain_equation_condition.py b/pina/_src/condition/domain_equation_condition.py new file mode 100644 index 000000000..4641d3933 --- /dev/null +++ b/pina/_src/condition/domain_equation_condition.py @@ -0,0 +1,153 @@ +"""Module for the Domain-Equation Condition class.""" + +from pina._src.condition.base_condition import BaseCondition +from pina._src.domain.domain_interface import DomainInterface +from pina._src.equation.base_equation import BaseEquation +from pina._src.core.utils import check_consistency + + +class DomainEquationCondition(BaseCondition): + """ + The class :class:`DomainEquationCondition` defines a condition based on a + ``domain`` and an ``equation``. This condition is typically used in + physics-informed problems, where the model is trained to satisfy a given + ``equation`` over a specified ``domain``. The ``domain`` is used to sample + points where the ``equation`` residual is evaluated and minimized during + training. + + :Example: + + >>> from pina.domain import CartesianDomain + >>> from pina.equation import Equation + >>> from pina import Condition + + >>> # Equation to be satisfied over the domain: # x^2 + y^2 - 1 = 0 + >>> def dummy_equation(pts): + ... return pts["x"]**2 + pts["y"]**2 - 1 + + >>> domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) + >>> condition = Condition(domain=domain, equation=Equation(dummy_equation)) + """ + + # Available fields, domain and equation data types + __fields__ = ["domain", "equation"] + _avail_domain_cls = (DomainInterface, str) + _avail_equation_cls = BaseEquation + + def __len__(self): + """ + Return the number of data points in the condition. + + :raises NotImplementedError: Always raised since the number of points is + determined by the domain sampling strategy and is not fixed. + """ + raise NotImplementedError( + "The number of data points in a DomainEquationCondition is not " + "fixed and is determined by the domain sampling strategy. " + "Therefore, the :meth:`__len__` method is not implemented for this " + "condition." + ) + + def __getitem__(self, idx): + """ + Return the data point at the specified index. + + :raises NotImplementedError: Always raised since the data points are not + stored in a list-like structure and cannot be accessed by index. + """ + raise NotImplementedError( + "Data points in a DomainEquationCondition are not stored in a " + "list-like structure and cannot be accessed by index. Therefore, " + "the :meth:`__getitem__` method is not implemented for this " + "condition." + ) + + def store_data(self, **kwargs): + """ + Store the domain and the equation for the condition. It sets the + attributes ``domain`` and ``equation`` of the condition instance based + on the provided keyword arguments. + + :param dict kwargs: The keyword arguments containing the data to be + stored. + """ + # Store domain and equation as attributes of the condition instance + setattr(self, "domain", kwargs.get("domain")) + setattr(self, "equation", kwargs.get("equation")) + + def evaluate(self, batch, solver): + """ + Evaluate the residual of the condition on the given batch using the + solver. + + This method computes the non-aggregated, element-wise residual of the + condition. A forward pass of the solver's model is performed on the + input samples, and the condition residual is evaluated accordingly. + + The returned tensor is not reduced, preserving the per-sample residual + values. + + :param dict batch: The batch containing the data required by the + condition evaluation. + :param BaseSolver solver: The solver used to perform the forward pass + and compute the residual. The solver provides access to the model + and its parameters, which may be necessary for evaluating the + condition residual. + :raises NotImplementedError: Always raised since any domain-equation + condition is transformed into an input-equation condition before + evaluation, and the residual is computed using the input-equation + condition's evaluation method. + """ + raise NotImplementedError( + "Domain-equation conditions are transformed into input-equation " + "conditions before evaluation, and the residual is computed using " + "the input-equation condition's evaluation method. Therefore, the " + "evaluate method is not implemented for domain-equation conditions." + ) + + @property + def equation(self): + """ + The equation associated with the condition. + + :return: The equation. + :rtype: BaseEquation + """ + return self._equation + + @equation.setter + def equation(self, value): + """ + Set the equation associated with this condition. + + :param BaseEquation value: The equation to associate with the condition. + :raises ValueError: If ``value`` is not an instance of + :class:`~pina.equation.base_equation.BaseEquation`. + """ + # Check consistency + check_consistency(value, self._avail_equation_cls) + self._equation = value + + @property + def domain(self): + """ + The domain associated with the condition. + + :return: The domain. + :rtype: DomainInterface + """ + return self._domain + + @domain.setter + def domain(self, value): + """ + Set the domain associated with this condition. + + :param DomainInterface value: The domain to associate with the + condition. + :raises ValueError: If ``value`` is neither a string nor an instance of + :class:`~pina.domain.domain_interface.DomainInterface`. + """ + # Check consistency + check_consistency(value, self._avail_domain_cls) + self._domain = value diff --git a/pina/_src/condition/graph_time_series_condition.py b/pina/_src/condition/graph_time_series_condition.py new file mode 100644 index 000000000..77ece43d6 --- /dev/null +++ b/pina/_src/condition/graph_time_series_condition.py @@ -0,0 +1,147 @@ +"""Module for the TimeSeriesCondition class.""" + +import torch +from pina._src.core.utils import check_consistency, check_positive_integer +from pina._src.data.manager.data_manager import _DataManager +from pina._src.condition.time_series_condition import TimeSeriesCondition +from pina._src.core.label_tensor import LabelTensor +from pina._src.condition.base_condition import BaseCondition +from torch_geometric.data import Data +from pina._src.core.graph import Graph + + +class GraphTimeSeriesCondition(TimeSeriesCondition): + """ + The :class:`TimeSeriesCondition` class represents an autoregressive time + series condition defined by temporal ``input`` data. The input is expected + to have shape ``[trajectories, time_steps, *features]``, where the second + dimension corresponds to the temporal evolution of each trajectory. + + During training, the condition automatically extracts overlapping temporal + windows from the trajectories. The parameter ``unroll_length`` defines the + number of consecutive time steps contained in each temporal window, while + ``n_windows`` controls how many temporal windows are created from the + available trajectories. + + Internally, the unrolled data is stored as a tensor of shape + ``[trajectories, n_windows, unroll_length, *features]``. + + Supported data types include :class:`~pina.label_tensor.LabelTensor` and + :class:`torch.Tensor`. + + :Example: + + >>> from pina import Condition, LabelTensor + >>> import torch + + >>> data = LabelTensor(torch.rand(5, 10, 2), labels=["u", "v"]) + >>> condition = Condition(input=data, unroll_length=5, n_windows=3) + """ + + # Available fields and input data types + __fields__ = ["input", "unroll_length", "n_windows", "key", "randomize"] + _avail_input_cls = (Data, Graph) + + def __new__(cls, input, n_windows, unroll_length, key="x", randomize=False): + # Check consistency + check_consistency(input, cls._avail_input_cls) + check_consistency(randomize, bool) + check_consistency(key, str) + check_positive_integer(n_windows, strict=True) + check_positive_integer(unroll_length, strict=True) + + return BaseCondition.__new__(cls) + + def store_data(self, **kwargs): + """ + Store the unrolled time-series input data. + + The method extracts the time-series input data and creates the temporal + windows based on the specified ``unroll_length`` and ``n_windows``. + + :param dict kwargs: The keyword arguments containing the data to be + stored. + :return: A dictionary-like structure containing the stored data. + :rtype: _DataManager + """ + # Extract unrolling parameters from kwargs + unroll_length = kwargs.get("unroll_length") + n_windows = kwargs.get("n_windows") + randomize = kwargs.get("randomize", False) + key = kwargs.get("key", "x") + graph = kwargs.get("input") + + # Create unrolled windows from the input data + if not hasattr(graph, key): + raise ValueError( + f"The provided graph does not have the specified key '{key}'." + ) + + unrolled_data = self._unroll( + data=graph.__getattribute__(key), + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + graph.__setattr__(key, unrolled_data) + + return _DataManager(input=graph) + + def evaluate(self, batch, solver): + """ + Evaluate the residual of the condition on the given batch using the + solver. + + This method computes the per-step residuals through autoregressive + unrolling. A forward pass of the solver's model is performed at each + time step, and the per-step residuals (predicted - target) are + returned as a stacked tensor. + + The returned tensor preserves all per-step residual values without + reduction or loss aggregation. + + :param dict batch: The batch containing the data required by the + condition evaluation. + :param SolverInterface solver: The solver used to perform the forward + pass and compute the residual. The solver provides access to the + model and its parameters, which may be necessary for evaluating the + condition residual. + :raises ValueError: If the input tensor in the batch has less than 4 + dimensions. + :return: The stacked per-step residual tensor of shape + ``[time_steps - 1, trajectories, windows, *features]``. + :rtype: torch.Tensor | LabelTensor + """ + # Raise error if input tensor does not have at least 4 dimensions + if batch["input"].x.dim() < 4: + raise ValueError( + "The provided input tensor must have at least 4 dimensions:" + " [trajectories, windows, time_steps, *features]." + f" Got shape {batch['input'].shape}." + ) + + # Copy the kwargs to avoid modifying the original settings + kwargs = solver._kwargs.copy() + + # Extract the initial state and initialize the step-wise residuals list + current_state = batch["input"].x[:, :, 0, :] + residuals = [] + + # Iterate over the time steps + for step in range(1, batch["input"].x.shape[2]): + + # Pre-process, forward, and post-process the current state + processed_input = solver.preprocess_step(current_state, **kwargs) + output = solver.forward(processed_input) + predicted_state = solver.postprocess_step(output, **kwargs) + + # Retrieve the target and compute the step-wise residual + target_state = batch["input"].x[:, :, step, :] + step_residual = predicted_state - target_state + residuals.append(step_residual) + + # Update the current state for the next iteration + current_state = predicted_state + + # Stack the step-wise residuals + return torch.stack(residuals).as_subclass(torch.Tensor) diff --git a/pina/_src/condition/input_equation_condition.py b/pina/_src/condition/input_equation_condition.py new file mode 100644 index 000000000..8682f7af7 --- /dev/null +++ b/pina/_src/condition/input_equation_condition.py @@ -0,0 +1,136 @@ +"""Module for the Input-Equation Condition class.""" + +from pina._src.condition.base_condition import BaseCondition +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.graph import Graph +from pina._src.equation.base_equation import BaseEquation +from pina._src.data.manager.data_manager import _DataManager +from pina._src.core.utils import check_consistency + + +class InputEquationCondition(BaseCondition): + """ + The class :class:`InputEquationCondition` defines a condition based on + ``input`` data and an ``equation``. This condition is typically used in + physics-informed problems, where the model is trained to satisfy a given + ``equation`` through the evaluation of the residual performed at the + provided ``input``. + + :Example: + + >>> from pina import Condition, LabelTensor + >>> from pina.equation import Equation + >>> import torch + + >>> # Equation to be satisfied over the input points: # x^2 + y^2 - 1 = 0 + >>> def dummy_equation(pts): + ... return pts["x"]**2 + pts["y"]**2 - 1 + + >>> pts = LabelTensor(torch.randn(100, 2), labels=["x", "y"]) + >>> condition = Condition(input=pts, equation=Equation(dummy_equation)) + """ + + # Available fields, input and equation data types + __fields__ = ["input", "equation"] + _avail_input_cls = (LabelTensor, Graph) + _avail_equation_cls = BaseEquation + + def __new__(cls, input, equation): + """ + Check the types of ``input`` and ``equation`` and instantiate an + instance of :class:`InputEquationCondition` accordingly. + + :param input: The input data associated with the condition. + :type input: LabelTensor | Graph | list[Graph] | tuple[Graph] + :param BaseEquation equation: The equation associated with the + condition. + :raises ValueError: If ``input`` is not an instance of + :class:`~pina.label_tensor.LabelTensor`, or + :class:`~pina.graph.Graph`, nor a list or tuple of + :class:`~pina.graph.Graph`. + :raises ValueError: If ``equation`` is not an instance of + :class:`~pina.equation.base_equation.BaseEquation`. + :return: A new instance of :class:`InputEquationCondition`. + :rtype: InputEquationCondition + """ + # Check input type - equation is checked in the setter + if isinstance(input, (list, tuple)): + check_consistency(input, Graph) + else: + check_consistency(input, cls._avail_input_cls) + + return super().__new__(cls) + + def store_data(self, **kwargs): + """ + Store the input data in a dictionary-like structure. + + :param dict kwargs: The keyword arguments containing the data to be + stored. + :return: A dictionary-like structure containing the stored data. + :rtype: _DataManager + """ + # Save the equation as an attribute of the condition instance + setattr(self, "equation", kwargs.pop("equation")) + + return _DataManager(**kwargs) + + @property + def input(self): + """ + The input data associated with the condition. + + :return: The input data. + :rtype: LabelTensor | Graph | list[Graph] | tuple[Graph] + """ + return self.data.input + + @property + def equation(self): + """ + The equation associated with the condition. + + :return: The equation. + :rtype: BaseEquation + """ + return self._equation + + @equation.setter + def equation(self, value): + """ + Set the equation associated with this condition. + + :param BaseEquation value: The equation to associate with the condition. + :raises ValueError: If ``value`` is not an instance of + :class:`~pina.equation.base_equation.BaseEquation`. + """ + # Check consistency + check_consistency(value, self._avail_equation_cls) + self._equation = value + + def evaluate(self, batch, solver): + """ + Evaluate the residual of the condition on the given batch using the + solver. + + This method computes the non-aggregated, element-wise residual of the + condition. A forward pass of the solver's model is performed on the + input samples, and the condition residual is evaluated accordingly. + + The returned tensor is not reduced, preserving the per-sample residual + values. + + :param dict batch: The batch containing the data required by the + condition evaluation. + :param BaseSolver solver: The solver used to perform the forward pass + and compute the residual. The solver provides access to the model + and its parameters, which may be necessary for evaluating the + condition residual. + :return: The non-aggregated residual tensor. + :rtype: LabelTensor + """ + # Compute residuals + samples = batch["input"].requires_grad_(True) + return self.equation.residual( + samples, solver.forward(samples), solver._params + ) diff --git a/pina/_src/condition/input_target_condition.py b/pina/_src/condition/input_target_condition.py new file mode 100644 index 000000000..ead8cee3c --- /dev/null +++ b/pina/_src/condition/input_target_condition.py @@ -0,0 +1,129 @@ +"""Module for the Input-Target Condition class.""" + +import torch +from torch_geometric.data import Data +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.graph import Graph +from pina._src.condition.base_condition import BaseCondition +from pina._src.data.manager.data_manager import _DataManager +from pina._src.core.utils import check_consistency + + +class InputTargetCondition(BaseCondition): + """ + The :class:`InputTargetCondition` class represents a supervised condition + defined by both ``input`` and ``target`` data. The model is trained to + reproduce the ``target`` values given the ``input``. Supported data types + include :class:`torch.Tensor`, :class:`~pina.label_tensor.LabelTensor`, + :class:`~pina.graph.Graph`, or :class:`~torch_geometric.data.Data`. + + :Example: + + >>> from pina import Condition, LabelTensor + >>> from pina.graph import Graph + >>> import torch + + >>> pos = LabelTensor(torch.randn(100, 2), labels=["x", "y"]) + >>> edge_index = torch.randint(0, 100, (2, 300)) + >>> graph = Graph(pos=pos, edge_index=edge_index) + + >>> input = LabelTensor(torch.randn(100, 2), labels=["x", "y"]) + >>> condition = Condition(input=input, target=graph) + """ + + # Available fields, input, and target data types + __fields__ = ["input", "target"] + _avail_input_cls = (torch.Tensor, LabelTensor, Data, Graph) + _avail_target_cls = (torch.Tensor, LabelTensor, Data, Graph) + + def __new__(cls, input, target): + """ + Check the types of ``input`` and ``target`` data and instantiate an + instance of :class:`InputTargetCondition` accordingly. + + :param input: The input data associated with the condition. + :type input: torch.Tensor | LabelTensor | Graph | + Data | list[Graph] | list[Data] | tuple[Graph] | tuple[Data] + :param target: The target data associated with the condition. + :type target: torch.Tensor | LabelTensor | Graph | + Data | list[Graph] | list[Data] | tuple[Graph] | tuple[Data] + :raises ValueError: If ``input`` is not of type :class:`torch.Tensor`, + :class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`, + or :class:`~torch_geometric.data.Data`, nor is it a list or tuple of + :class:`~pina.graph.Graph` or :class:`~torch_geometric.data.Data`. + :raises ValueError: If ``target`` is not of type :class:`torch.Tensor`, + :class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`, + or :class:`~torch_geometric.data.Data`, nor is it a list or tuple of + :class:`~pina.graph.Graph` or :class:`~torch_geometric.data.Data`. + :return: A new instance of :class:`InputTargetCondition`. + :rtype: InputTargetCondition + """ + # Check input type - if iterable, ensure it is either Data or Graph + if isinstance(input, (list, tuple)): + check_consistency(input, (Data, Graph)) + else: + check_consistency(input, cls._avail_input_cls) + + # Check target type - if iterable, ensure it is either Data or Graph + if isinstance(target, (list, tuple)): + check_consistency(target, (Data, Graph)) + else: + check_consistency(target, cls._avail_target_cls) + + return super().__new__(cls) + + def store_data(self, **kwargs): + """ + Store the input and target data in a dictionary-like structure. + + :param dict kwargs: The keyword arguments containing the data to be + stored. + :return: A dictionary-like structure containing the stored data. + :rtype: _DataManager + """ + return _DataManager(**kwargs) + + def evaluate(self, batch, solver): + """ + Evaluate the residual of the condition on the given batch using the + solver. + + This method computes the non-aggregated, element-wise residual of the + condition. A forward pass of the solver's model is performed on the + input samples, and the condition residual is evaluated accordingly. + + The returned tensor is not reduced, preserving the per-sample residual + values. + + :param dict batch: The batch containing the data required by the + condition evaluation. + :param BaseSolver solver: The solver used to perform the forward pass + and compute the residual. The solver provides access to the model + and its parameters, which may be necessary for evaluating the + condition residual. + :return: The non-aggregated residual tensor. + :rtype: torch.Tensor | LabelTensor + """ + return solver.forward(batch["input"]) - batch["target"] + + @property + def input(self): + """ + The input data associated with the condition. + + :return: The input data. + :rtype: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | + list[Data] | tuple[Graph] | tuple[Data] + """ + return self.data.input + + @property + def target(self): + """ + The target data associated with the condition. + + :return: The target data. + :rtype: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | + list[Data] | tuple[Graph] | tuple[Data] + """ + return self.data.target diff --git a/pina/_src/condition/time_series_condition.py b/pina/_src/condition/time_series_condition.py new file mode 100644 index 000000000..28b38eaa6 --- /dev/null +++ b/pina/_src/condition/time_series_condition.py @@ -0,0 +1,247 @@ +"""Module for the TimeSeriesCondition class.""" + +import torch +from pina._src.core.utils import check_consistency, check_positive_integer +from pina._src.data.manager.data_manager import _DataManager +from pina._src.condition.base_condition import BaseCondition +from pina._src.core.label_tensor import LabelTensor + + +class TimeSeriesCondition(BaseCondition): + """ + The :class:`TimeSeriesCondition` class represents an autoregressive time + series condition defined by temporal ``input`` data. The input is expected + to have shape ``[trajectories, time_steps, *features]``, where the second + dimension corresponds to the temporal evolution of each trajectory. + + During training, the condition automatically extracts overlapping temporal + windows from the trajectories. The parameter ``unroll_length`` defines the + number of consecutive time steps contained in each temporal window, while + ``n_windows`` controls how many temporal windows are created from the + available trajectories. + + Internally, the unrolled data is stored as a tensor of shape + ``[trajectories, n_windows, unroll_length, *features]``. + + Supported data types include :class:`~pina.label_tensor.LabelTensor` and + :class:`torch.Tensor`. + + :Example: + + >>> from pina import Condition, LabelTensor + >>> import torch + + >>> data = LabelTensor(torch.rand(5, 10, 2), labels=["u", "v"]) + >>> condition = Condition(input=data, unroll_length=5, n_windows=3) + """ + + # Available fields and input data types + __fields__ = ["input", "unroll_length", "n_windows", "randomize"] + _avail_input_cls = (torch.Tensor, LabelTensor) + + def __new__(cls, input, n_windows, unroll_length, randomize=False): + """ + Validate the input data and time-series parameters. + + :param input: The temporal input data. + :type input: torch.Tensor | LabelTensor + :param int n_windows: The maximum number of temporal windows to extract. + :param int unroll_length: The number of time steps in each window. + :param bool randomize: If ``True``, randomly permute the valid starting + indices before selecting the windows. Default is ``False``. + :raises ValueError: If ``input`` is not of type :class:`torch.Tensor` or + :class:`~pina.label_tensor.LabelTensor`. + :raises AssertionError: If ``unroll_length`` is not a positive integer. + :raises AssertionError: If ``n_windows`` is not a positive integer. + :raises ValueError: If ``randomize`` is not a boolean value. + :raises ValueError: If ``input`` has fewer than three dimensions. + :raises ValueError: If ``unroll_length`` is lower than 2. + :return: A new :class:`TimeSeriesCondition` instance. + :rtype: TimeSeriesCondition + """ + # Check consistency + check_consistency(input, cls._avail_input_cls) + check_consistency(randomize, bool) + check_positive_integer(n_windows, strict=True) + check_positive_integer(unroll_length, strict=True) + + # Validate input + if input.dim() < 3: + raise ValueError( + "The provided data tensor must have at least 3 dimensions: " + f"[trajectories, time, *features]. Got shape {input.shape}." + ) + + # Validate unroll_length + if unroll_length < 2: + raise ValueError( + f"unroll_length must be strictly greater than 1 to create " + f" temporal windows. Got unroll_length={unroll_length}." + ) + + return super().__new__(cls) + + def store_data(self, **kwargs): + """ + Store the unrolled time-series input data. + + The method extracts the time-series input data and creates the temporal + windows based on the specified ``unroll_length`` and ``n_windows``. + + :param dict kwargs: The keyword arguments containing the data to be + stored. + :return: A dictionary-like structure containing the stored data. + :rtype: _DataManager + """ + # Extract unrolling parameters from kwargs + unroll_length = kwargs.get("unroll_length") + n_windows = kwargs.get("n_windows") + randomize = kwargs.get("randomize", False) + data = kwargs.get("input") + + # Create unrolled windows from the input data + unrolled_data = self._unroll( + data=data, + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + + # Preserve labels if the input data is a LabelTensor + if isinstance(data, LabelTensor): + unrolled_data = unrolled_data.as_subclass(LabelTensor) + unrolled_data.labels = data.labels + + return _DataManager(input=unrolled_data) + + def _unroll(self, data, n_windows, unroll_length, randomize): + """ + Build temporal windows from time-series data. + + Given data with shape ``[trajectories, time_steps, *features]``, this + method returns a tensor of overlapping temporal windows with shape + ``[trajectories, windows, unroll_length, *features]``. + + :param data: The temporal data tensor to be unrolled. + :type data: torch.Tensor | LabelTensor + :param int n_windows: The maximum number of temporal windows to extract. + :param int unroll_length: The number of time steps in each window. + :param bool randomize: If ``True``, starting indices are randomly + permuted before applying ``n_windows``. Default is ``True``. + :raises ValueError: If ``unroll_length`` is greater than the number of + time steps in the data. + :return: A tensor of unrolled windows. + :rtype: torch.Tensor | LabelTensor + """ + # Store the number of time steps in the data + time_steps = data.shape[1] + + # Compute the last valid starting index for unroll windows + last_idx = time_steps - unroll_length + + # Raise error if unroll_length is greater than time_steps + if last_idx < 0: + raise ValueError( + f"Cannot create unroll windows: unroll_length {unroll_length} " + f"exceeds the available number of time steps {time_steps}." + ) + + # Extract starting indices + start_indices = torch.arange(last_idx + 1) + + # Randomly permute starting indices if randomize is True + if randomize: + start_indices = start_indices[torch.randperm(len(start_indices))] + + # Raise error if n_windows is greater than the number of valid windows + if len(start_indices) < n_windows: + raise ValueError( + f"Cannot create {n_windows} unroll windows with the selected " + f"unroll_length {unroll_length} from data with {time_steps} " + f"time steps. Only {len(start_indices)} valid windows are " + "available." + ) + + # Limit the number of windows to n_windows + start_indices = start_indices[:n_windows] + + # Create unroll windows by slicing the input data at the starting idx + windows = [data[:, s : s + unroll_length] for s in start_indices] + + if isinstance(data, LabelTensor): + # Preserve labels if the input data is a LabelTensor + unrolled_data = torch.stack(windows, dim=1).as_subclass(LabelTensor) + unrolled_data.labels = data.labels + else: + unrolled_data = torch.stack(windows, dim=1) + + return unrolled_data + + def evaluate(self, batch, solver): + """ + Evaluate the residual of the condition on the given batch using the + solver. + + This method computes the per-step residuals through autoregressive + unrolling. A forward pass of the solver's model is performed at each + time step, and the per-step residuals (predicted - target) are + returned as a stacked tensor. + + The returned tensor preserves all per-step residual values without + reduction or loss aggregation. + + :param dict batch: The batch containing the data required by the + condition evaluation. + :param BaseSolver solver: The solver used to perform the forward pass + and compute the residual. The solver provides access to the model + and its parameters, which may be necessary for evaluating the + condition residual. + :raises ValueError: If the input tensor in the batch has less than 4 + dimensions. + :return: The stacked per-step residual tensor of shape + ``[time_steps - 1, trajectories, windows, *features]``. + :rtype: torch.Tensor | LabelTensor + """ + # Raise error if input tensor does not have at least 4 dimensions + if batch["input"].dim() < 4: + raise ValueError( + "The provided input tensor must have at least 4 dimensions:" + " [trajectories, windows, time_steps, *features]." + f" Got shape {batch['input'].shape}." + ) + + # Copy the kwargs to avoid modifying the original settings + kwargs = solver._kwargs.copy() + + # Extract the initial state and initialize the step-wise residuals list + current_state = batch["input"][:, :, 0] + residuals = [] + + # Iterate over the time steps + for step in range(1, batch["input"].shape[2]): + + # Pre-process, forward, and post-process the current state + processed_input = solver.preprocess_step(current_state, **kwargs) + output = solver.forward(processed_input) + predicted_state = solver.postprocess_step(output, **kwargs) + + # Retrieve the target and compute the step-wise residual + target_state = batch["input"][:, :, step] + step_residual = predicted_state - target_state + residuals.append(step_residual) + + # Update the current state for the next iteration + current_state = predicted_state + + # Stack the step-wise residuals + return torch.stack(residuals).as_subclass(torch.Tensor) + + @property + def input(self): + """ + The unrolled temporal input data. + + :return: The input data. + :rtype: torch.Tensor | LabelTensor + """ + return self.data.input diff --git a/pina/_src/core/__init__.py b/pina/_src/core/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/core/graph.py b/pina/_src/core/graph.py new file mode 100644 index 000000000..4b0a2fcb0 --- /dev/null +++ b/pina/_src/core/graph.py @@ -0,0 +1,421 @@ +"""Module to build Graph objects and perform operations on them.""" + +import torch +from torch_geometric.data import Data, Batch +from torch_geometric.utils import to_undirected +from torch_geometric.utils.loop import remove_self_loops +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency, is_function + + +class Graph(Data): + """ + Extends :class:`~torch_geometric.data.Data` class to include additional + checks and functionalities. + """ + + def __new__( + cls, + **kwargs, + ): + """ + Create a new instance of the :class:`~pina.graph.Graph` class by + checking the consistency of the input data and storing the attributes. + + :param dict kwargs: Parameters used to initialize the + :class:`~pina.graph.Graph` object. + :return: A new instance of the :class:`~pina.graph.Graph` class. + :rtype: Graph + """ + # create class instance + instance = Data.__new__(cls) + + # check the consistency of types defined in __init__, the others are not + # checked (as in pyg Data object) + instance._check_type_consistency(**kwargs) + + return instance + + def __init__( + self, + x=None, + edge_index=None, + pos=None, + edge_attr=None, + undirected=False, + **kwargs, + ): + """ + Initialize the object by setting the node features, edge index, + edge attributes, and positions. The edge index is preprocessed to make + the graph undirected if required. For more details, see the + :meth:`torch_geometric.data.Data` + + :param x: Optional tensor of node features ``(N, F)`` where ``F`` is the + number of features per node. + :type x: torch.Tensor, LabelTensor + :param torch.Tensor edge_index: A tensor of shape ``(2, E)`` + representing the indices of the graph's edges. + :param pos: A tensor of shape ``(N, D)`` representing the positions of + ``N`` points in ``D``-dimensional space. + :type pos: torch.Tensor | LabelTensor + :param edge_attr: Optional tensor of edge_featured ``(E, F')`` where + ``F'`` is the number of edge features + :type edge_attr: torch.Tensor | LabelTensor + :param bool undirected: Whether to make the graph undirected + :param dict kwargs: Additional keyword arguments passed to the + :class:`~torch_geometric.data.Data` class constructor. + """ + # preprocessing + self._preprocess_edge_index(edge_index, undirected) + + # calling init + super().__init__( + x=x, edge_index=edge_index, edge_attr=edge_attr, pos=pos, **kwargs + ) + + def _check_type_consistency(self, **kwargs): + """ + Check the consistency of the types of the input data. + + :param dict kwargs: Attributes to be checked for consistency. + """ + # default types, specified in cls.__new__, by default they are Nont + # if specified in **kwargs they get override + x, pos, edge_index, edge_attr = None, None, None, None + if "pos" in kwargs: + pos = kwargs["pos"] + self._check_pos_consistency(pos) + if "edge_index" in kwargs: + edge_index = kwargs["edge_index"] + self._check_edge_index_consistency(edge_index) + if "x" in kwargs: + x = kwargs["x"] + # self._check_x_consistency(x, pos) + if "edge_attr" in kwargs: + edge_attr = kwargs["edge_attr"] + self._check_edge_attr_consistency(edge_attr, edge_index) + if "undirected" in kwargs: + undirected = kwargs["undirected"] + check_consistency(undirected, bool) + + @staticmethod + def _check_pos_consistency(pos): + """ + Check if the position tensor is consistent. + :param torch.Tensor pos: The position tensor. + :raises ValueError: If the position tensor is not consistent. + """ + if pos is not None: + check_consistency(pos, (torch.Tensor, LabelTensor)) + if pos.ndim != 2: + raise ValueError("pos must be a 2D tensor.") + + @staticmethod + def _check_edge_index_consistency(edge_index): + """ + Check if the edge index is consistent. + + :param torch.Tensor edge_index: The edge index tensor. + :raises ValueError: If the edge index tensor is not consistent. + """ + check_consistency(edge_index, (torch.Tensor, LabelTensor)) + if edge_index.ndim != 2: + raise ValueError("edge_index must be a 2D tensor.") + if edge_index.size(0) != 2: + raise ValueError("edge_index must have shape [2, num_edges].") + + @staticmethod + def _check_edge_attr_consistency(edge_attr, edge_index): + """ + Check if the edge attribute tensor is consistent in type and shape + with the edge index. + + :param edge_attr: The edge attribute tensor. + :type edge_attr: torch.Tensor | LabelTensor + :param torch.Tensor edge_index: The edge index tensor. + :raises ValueError: If the edge attribute tensor is not consistent. + """ + if edge_attr is not None: + check_consistency(edge_attr, (torch.Tensor, LabelTensor)) + if edge_attr.ndim != 2: + raise ValueError("edge_attr must be a 2D tensor.") + if edge_attr.size(0) != edge_index.size(1): + raise ValueError( + "edge_attr must have shape " + "[num_edges, num_edge_features], expected " + f"num_edges {edge_index.size(1)} " + f"got {edge_attr.size(0)}." + ) + + @staticmethod + def _check_x_consistency(x, pos=None): + """ + Check if the input tensor x is consistent with the position tensor + `pos`. + + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :param pos: The position tensor. + :type pos: torch.Tensor | LabelTensor + :raises ValueError: If the input tensor is not consistent. + """ + if x is not None: + check_consistency(x, (torch.Tensor, LabelTensor)) + if x.ndim != 2: + raise ValueError("x must be a 2D tensor.") + if pos is not None: + if x.size(0) != pos.size(0): + raise ValueError("Inconsistent number of nodes.") + + @staticmethod + def _preprocess_edge_index(edge_index, undirected): + """ + Preprocess the edge index to make the graph undirected (if required). + + :param torch.Tensor edge_index: The edge index. + :param bool undirected: Whether the graph is undirected. + :return: The preprocessed edge index. + :rtype: torch.Tensor + """ + if undirected: + edge_index = to_undirected(edge_index) + return edge_index + + def extract(self, labels, attr="x"): + """ + Perform extraction of labels from the attribute specified by `attr`. + + :param labels: Labels to extract + :type labels: list[str] | tuple[str] | str | dict + :return: Batch object with extraction performed on x + :rtype: PinaBatch + """ + # Extract labels from LabelTensor object + tensor = getattr(self, attr).extract(labels) + # Set the extracted tensor as the new attribute + setattr(self, attr, tensor) + return self + + +class GraphBuilder: + """ + A class that allows an easy definition of :class:`Graph` instances. + """ + + def __new__( + cls, + pos, + edge_index, + x=None, + edge_attr=False, + custom_edge_func=None, + loop=True, + **kwargs, + ): + """ + Compute the edge attributes and create a new instance of the + :class:`~pina.graph.Graph` class. + + :param pos: A tensor of shape ``(N, D)`` representing the positions of + ``N`` points in ``D``-dimensional space. + :type pos: torch.Tensor or LabelTensor + :param edge_index: A tensor of shape ``(2, E)`` representing the indices + of the graph's edges. + :type edge_index: torch.Tensor + :param x: Optional tensor of node features of shape ``(N, F)``, where + ``F`` is the number of features per node. + :type x: torch.Tensor | LabelTensor, optional + :param bool edge_attr: Whether to compute the edge attributes. + :param custom_edge_func: A custom function to compute edge attributes. + If provided, overrides ``edge_attr``. + :type custom_edge_func: Callable, optional + :param bool loop: Whether to include self-loops. + :param kwargs: Additional keyword arguments passed to the + :class:`~pina.graph.Graph` class constructor. + :return: A :class:`~pina.graph.Graph` instance constructed using the + provided information. + :rtype: Graph + """ + if not loop: + edge_index = remove_self_loops(edge_index)[0] + edge_attr = cls._create_edge_attr( + pos, edge_index, edge_attr, custom_edge_func or cls._build_edge_attr + ) + return Graph( + x=x, + edge_index=edge_index, + edge_attr=edge_attr, + pos=pos, + **kwargs, + ) + + @staticmethod + def _create_edge_attr(pos, edge_index, edge_attr, func): + """ + Create the edge attributes based on the input parameters. + + :param pos: Positions of the points. + :type pos: torch.Tensor | LabelTensor + :param torch.Tensor edge_index: Edge indices. + :param bool edge_attr: Whether to compute the edge attributes. + :param Callable func: Function to compute the edge attributes. + :raises ValueError: If ``func`` is not a function. + :return: The edge attributes. + :rtype: torch.Tensor | LabelTensor | None + """ + check_consistency(edge_attr, bool) + if edge_attr: + if is_function(func): + return func(pos, edge_index) + raise ValueError("custom_edge_func must be a function.") + return None + + @staticmethod + def _build_edge_attr(pos, edge_index): + """ + Default function to compute the edge attributes. + + :param pos: Positions of the points. + :type pos: torch.Tensor | LabelTensor + :param torch.Tensor edge_index: Edge indices. + :return: The edge attributes. + :rtype: torch.Tensor + """ + return ( + (pos[edge_index[0]] - pos[edge_index[1]]) + .abs() + .as_subclass(torch.Tensor) + ) + + +class RadiusGraph(GraphBuilder): + """ + Extends the :class:`~pina.graph.GraphBuilder` class to compute + ``edge_index`` based on a radius. Each point is connected to all the points + within the radius. + """ + + def __new__(cls, pos, radius, **kwargs): + """ + Instantiate the :class:`~pina.graph.Graph` class by computing the + ``edge_index`` based on the radius provided. + + :param pos: A tensor of shape ``(N, D)`` representing the positions of + ``N`` points in ``D``-dimensional space. + :type pos: torch.Tensor | LabelTensor + :param float radius: The radius within which points are connected. + :param dict kwargs: The additional keyword arguments to be passed to + :class:`GraphBuilder` and :class:`Graph` classes. + :return: A :class:`~pina.graph.Graph` instance with the computed + ``edge_index``. + :rtype: Graph + """ + edge_index = cls.compute_radius_graph(pos, radius) + return super().__new__(cls, pos=pos, edge_index=edge_index, **kwargs) + + @staticmethod + def compute_radius_graph(points, radius): + """ + Computes the ``edge_index`` based on the radius. Each point is connected + to all the points within the radius. + + :param points: A tensor of shape ``(N, D)`` representing the positions + of ``N`` points in ``D``-dimensional space. + :type points: torch.Tensor | LabelTensor + :param float radius: The radius within which points are connected. + :return: A tensor of shape ``(2, E)``, with ``E`` number of edges, + representing the edge indices of the graph. + :rtype: torch.Tensor + """ + dist = torch.cdist(points, points, p=2) + return ( + torch.nonzero(dist <= radius, as_tuple=False) + .t() + .as_subclass(torch.Tensor) + ) + + +class KNNGraph(GraphBuilder): + """ + Extends the :class:`~pina.graph.GraphBuilder` class to compute + ``edge_index`` based on a K-nearest neighbors algorithm. + """ + + def __new__(cls, pos, neighbours, **kwargs): + """ + Instantiate the :class:`~pina.graph.Graph` class by computing the + ``edge_index`` based on the K-nearest neighbors algorithm. + + :param pos: A tensor of shape ``(N, D)`` representing the positions of + ``N`` points in ``D``-dimensional space. + :type pos: torch.Tensor | LabelTensor + :param int neighbours: The number of nearest neighbors to consider when + building the graph. + :param dict kwargs: The additional keyword arguments to be passed to + :class:`GraphBuilder` and :class:`Graph` classes. + + :return: A :class:`~pina.graph.Graph` instance with the computed + ``edge_index``. + :rtype: Graph + """ + + edge_index = cls.compute_knn_graph(pos, neighbours) + return super().__new__(cls, pos=pos, edge_index=edge_index, **kwargs) + + @staticmethod + def compute_knn_graph(points, neighbours): + """ + Computes the ``edge_index`` based on the K-nearest neighbors algorithm. + + :param points: A tensor of shape ``(N, D)`` representing the positions + of ``N`` points in ``D``-dimensional space. + :type points: torch.Tensor | LabelTensor + :param int neighbours: The number of nearest neighbors to consider when + building the graph. + :return: A tensor of shape ``(2, E)``, with ``E`` number of edges, + representing the edge indices of the graph. + :rtype: torch.Tensor + """ + dist = torch.cdist(points, points, p=2) + knn_indices = torch.topk(dist, k=neighbours, largest=False).indices + row = torch.arange(points.size(0)).repeat_interleave(neighbours) + col = knn_indices.flatten() + return torch.stack([row, col], dim=0).as_subclass(torch.Tensor) + + +class LabelBatch(Batch): + """ + Extends the :class:`~torch_geometric.data.Batch` class to include + :class:`~pina.label_tensor.LabelTensor` objects. + """ + + @classmethod + def from_data_list(cls, data_list): + """ + Create a Batch object from a list of :class:`~torch_geometric.data.Data` + or :class:`~pina.graph.Graph` objects. + + :param data_list: List of :class:`~torch_geometric.data.Data` or + :class:`~pina.graph.Graph` objects. + :type data_list: list[Data] | list[Graph] + :return: A :class:`~torch_geometric.data.Batch` object containing + the input data. + :rtype: :class:`~torch_geometric.data.Batch` + """ + # Store the labels of Data/Graph objects (all data have the same labels) + # If the data do not contain labels, labels is an empty dictionary, + # therefore the labels are not stored + labels = { + k: v.labels + for k, v in data_list[0].items() + if isinstance(v, LabelTensor) + } + + # Create a Batch object from the list of Data objects + batch = super().from_data_list(data_list) + + # Put the labels back in the Batch object + for k, v in labels.items(): + batch[k].labels = v + return batch diff --git a/pina/_src/core/label_tensor.py b/pina/_src/core/label_tensor.py new file mode 100644 index 000000000..41bccc6fc --- /dev/null +++ b/pina/_src/core/label_tensor.py @@ -0,0 +1,753 @@ +"""Module for LabelTensor""" + +from copy import copy, deepcopy +import torch +from torch import Tensor + + +class LabelTensor(torch.Tensor): + """ + Extension of the :class:`torch.Tensor` class that includes labels for + each dimension. + """ + + @staticmethod + def __new__(cls, x, labels, *args, **kwargs): + """ + Create a new instance of the :class:`~pina.label_tensor.LabelTensor` + class. + + :param torch.Tensor x: :class:`torch.tensor` instance to be casted as a + :class:`~pina.label_tensor.LabelTensor`. + :param labels: Labels to assign to the tensor. + :type labels: str | list[str] | dict + :return: The instance of the :class:`~pina.label_tensor.LabelTensor` + class. + :rtype: LabelTensor + """ + + if isinstance(x, LabelTensor): + return x + return super().__new__(cls, x, *args, **kwargs) + + @property + def tensor(self): + """ + Returns the tensor part of the :class:`~pina.label_tensor.LabelTensor` + object. + + :return: Tensor part of the :class:`~pina.label_tensor.LabelTensor`. + :rtype: torch.Tensor + """ + + return self.as_subclass(Tensor) + + def __init__(self, x, labels): + """ + Initialize the :class:`~pina.label_tensor.LabelTensor` instance, by + checking the consistency of the labels and the tensor. Specifically, the + labels must match the following conditions: + + - At each dimension, the number of labels must match the size of the \ + dimension. + - At each dimension, the labels must be unique. + + The labels can be passed in the following formats: + + :Example: + >>> from pina import LabelTensor + >>> tensor = LabelTensor( + >>> torch.rand((2000, 3)), + ... {1: {"name": "space", "dof": ['a', 'b', 'c']}}) + >>> tensor = LabelTensor( + >>> torch.rand((2000, 3)), + ... ["a", "b", "c"]) + + The keys of the dictionary are the dimension indices, and the values are + dictionaries containing the labels and the name of the dimension. If + the labels are passed as a list, these are assigned to the last + dimension. + + :param torch.Tensor x: The tensor to be casted as a + :class:`~pina.label_tensor.LabelTensor`. + :param labels: Labels to assign to the tensor. + :type labels: str | list[str] | dict + :raises ValueError: If the labels are not consistent with the tensor. + """ + super().__init__() + if labels is not None: + self.labels = labels + else: + self._labels = {} + + @property + def full_labels(self): + """ + Returns the full labels of the tensor, even for the dimensions that are + not labeled. + + :return: The full labels of the tensor + :rtype: dict + """ + to_return_dict = {} + shape_tensor = self.shape + for i, value in enumerate(shape_tensor): + if i in self._labels: + to_return_dict[i] = self._labels[i] + else: + to_return_dict[i] = {"dof": range(value), "name": i} + return to_return_dict + + @property + def stored_labels(self): + """ + Returns the labels stored inside the instance. + + :return: The labels stored inside the instance. + :rtype: dict + """ + return self._labels + + @property + def labels(self): + """ + Returns the labels of the last dimension of the instance. + + :return: labels of last dimension + :rtype: list + """ + if self.ndim - 1 in self._labels: + return self._labels[self.ndim - 1]["dof"] + return None + + @labels.setter + def labels(self, labels): + """ + Set labels stored insider the instance by checking the type of the + input labels and handling it accordingly. The following types are + accepted: + + - **list**: The list of labels is assigned to the last dimension. + - **dict**: The dictionary of labels is assigned to the tensor. + - **str**: The string is assigned to the last dimension. + + :param labels: Labels to assign to the class variable _labels. + :type labels: str | list[str] | dict + """ + + if not hasattr(self, "_labels"): + self._labels = {} + if isinstance(labels, dict): + self._init_labels_from_dict(labels) + elif isinstance(labels, (list, range)): + self._init_labels_from_list(labels) + elif isinstance(labels, str): + labels = [labels] + self._init_labels_from_list(labels) + else: + raise ValueError("labels must be list, dict or string.") + + def _init_labels_from_dict(self, labels): + """ + Store the internal label representation according to the values + passed as input. + + :param dict labels: The label(s) to update. + :raises ValueError: If the dof list contains duplicates or the number of + dof does not match the tensor shape. + """ + + tensor_shape = self.shape + + def validate_dof(dof_list, dim_size): + """Validate the 'dof' list for uniqueness and size.""" + if len(dof_list) != len(set(dof_list)): + raise ValueError("dof must be unique") + if len(dof_list) != dim_size: + raise ValueError( + f"Number of dof ({len(dof_list)}) does not match " + f"tensor shape ({dim_size})" + ) + + for dim, label in labels.items(): + if isinstance(label, dict): + if "name" not in label: + label["name"] = dim + if "dof" not in label: + label["dof"] = range(tensor_shape[dim]) + if "dof" in label and "name" in label: + dof = label["dof"] + dof_list = dof if isinstance(dof, (list, range)) else [dof] + if not isinstance(dof_list, (list, range)): + raise ValueError( + f"'dof' should be a list or range, not" + f" {type(dof_list)}" + ) + validate_dof(dof_list, tensor_shape[dim]) + else: + raise ValueError( + "Labels dictionary must contain either " + " both 'name' and 'dof' keys" + ) + else: + raise ValueError( + f"Invalid label format for {dim}: Expected " + f"list or dictionary, got {type(label)}" + ) + + # Assign validated label data to internal labels + self._labels[dim] = label + + def _init_labels_from_list(self, labels): + """ + Given a list of dof, this method update the internal label + representation by assigning the dof to the last dimension. + + :param labels: The label(s) to update. + :type labels: list + """ + + # Create a dict with labels + last_dim_labels = { + self.ndim - 1: {"dof": labels, "name": self.ndim - 1} + } + self._init_labels_from_dict(last_dim_labels) + + def extract(self, labels_to_extract): + """ + Extract the subset of the original tensor by returning all the positions + corresponding to the passed ``label_to_extract``. If + ``label_to_extract`` is a dictionary, the keys are the dimension names + and the values are the labels to extract. If a single label or a list + of labels is passed, the last dimension is considered. + + :Example: + >>> from pina import LabelTensor + >>> labels = {1: {'dof': ["a", "b", "c"], 'name': 'space'}} + >>> tensor = LabelTensor(torch.rand((2000, 3)), labels) + >>> tensor.extract("a") + >>> tensor.extract(["a", "b"]) + >>> tensor.extract({"space": ["a", "b"]}) + + :param labels_to_extract: The label(s) to extract. + :type labels_to_extract: str | list[str] | tuple[str] | dict + :return: The extracted tensor with the updated labels. + :rtype: LabelTensor + + :raises TypeError: Labels are not ``str``, ``list[str]`` or ``dict`` + properly setted. + :raises ValueError: Label to extract is not in the labels ``list``. + """ + + def get_label_indices(dim_labels, labels_te): + if isinstance(labels_te, (int, str)): + labels_te = [labels_te] + return ( + [dim_labels.index(label) for label in labels_te] + if len(labels_te) > 1 + else slice( + dim_labels.index(labels_te[0]), + dim_labels.index(labels_te[0]) + 1, + ) + ) + + # Ensure labels_to_extract is a list or dict + if isinstance(labels_to_extract, (str, int)): + labels_to_extract = [labels_to_extract] + + labels = copy(self._labels) + + # Get the dimension names and the respective dimension index + dim_names = {labels[dim]["name"]: dim for dim in labels} + ndim = super().ndim + tensor = self.tensor.as_subclass(torch.Tensor) + + # Convert list/tuple to a dict for the last dimension if applicable + if isinstance(labels_to_extract, (list, tuple)): + last_dim = ndim - 1 + dim_name = labels[last_dim]["name"] + labels_to_extract = {dim_name: list(labels_to_extract)} + + # Validate the labels_to_extract type + if not isinstance(labels_to_extract, dict): + raise ValueError( + "labels_to_extract must be a string, list, or dictionary." + ) + + # Perform the extraction for each specified dimension + for dim_name, labels_te in labels_to_extract.items(): + if dim_name not in dim_names: + raise ValueError( + f"Cannot extract labels for dimension '{dim_name}' as it is" + f" not present in the original labels." + ) + + idx_dim = dim_names[dim_name] + dim_labels = labels[idx_dim]["dof"] + indices = get_label_indices(dim_labels, labels_te) + + extractor = [slice(None)] * ndim + extractor[idx_dim] = indices + tensor = tensor[tuple(extractor)] + + labels[idx_dim] = {"dof": labels_te, "name": dim_name} + + return LabelTensor(tensor, labels) + + def __str__(self): + """ + The string representation of the + :class:`~pina.label_tensor.LabelTensor`. + + :return: String representation of the + :class:`~pina.label_tensor.LabelTensor` instance. + :rtype: str + """ + + s = "" + for key, value in self._labels.items(): + s += f"{key}: {value}\n" + s += "\n" + s += self.tensor.__str__() + return s + + @staticmethod + def cat(tensors, dim=0): + """ + Concatenate a list of tensors along a specified dimension. For more + details, see :meth:`torch.cat`. + + :param list[LabelTensor] tensors: + :class:`~pina.label_tensor.LabelTensor` instances to concatenate + :param int dim: Dimensions on which you want to perform the operation + (default is 0) + :return: A new :class:`LabelTensor` instance obtained by concatenating + the input instances. + + :rtype: LabelTensor + :raises ValueError: either number dof or dimensions names differ. + """ + + if not tensors: + return [] # Handle empty list + if len(tensors) == 1: + return tensors[0] # Return single tensor as-is + + # Perform concatenation + cat_tensor = torch.cat(tensors, dim=dim) + tensors_labels = [tensor.stored_labels for tensor in tensors] + + # Check label consistency across tensors, excluding the + # concatenation dimension + for key in tensors_labels[0]: + if key != dim: + if any( + tensors_labels[i][key] != tensors_labels[0][key] + for i in range(len(tensors_labels)) + ): + raise RuntimeError( + f"Tensors must have the same labels along all " + f"dimensions except {dim}." + ) + + # Copy and update the 'dof' for the concatenation dimension + cat_labels = {k: copy(v) for k, v in tensors_labels[0].items()} + + # Update labels if the concatenation dimension has labels + if dim in tensors[0].stored_labels: + if dim in cat_labels: + cat_dofs = [label[dim]["dof"] for label in tensors_labels] + cat_labels[dim]["dof"] = sum(cat_dofs, []) + else: + cat_labels = tensors[0].stored_labels + + # Assign updated labels to the concatenated tensor + cat_tensor._labels = cat_labels + return cat_tensor + + @staticmethod + def stack(tensors): + """ + Stacks a list of tensors along a new dimension. For more details, see + :meth:`torch.stack`. + + :param list[LabelTensor] tensors: A list of tensors to stack. + All tensors must have the same shape. + :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained + by stacking the input tensors. + :rtype: LabelTensor + """ + + # Perform stacking in torch + new_tensor = torch.stack(tensors) + + # Increase labels keys by 1 + labels = tensors[0]._labels + labels = {key + 1: value for key, value in labels.items()} + new_tensor._labels = labels + return new_tensor + + def requires_grad_(self, mode=True): + """ + Override the :meth:`~torch.Tensor.requires_grad_` method to handle + the labels in the new tensor. + For more details, see :meth:`~torch.Tensor.requires_grad_`. + + :param bool mode: A boolean value indicating whether the tensor should + track gradients.If `True`, the tensor will track gradients; + if `False`, it will not. + :return: The :class:`~pina.label_tensor.LabelTensor` itself with the + updated ``requires_grad`` state and retained labels. + :rtype: LabelTensor + """ + + lt = super().requires_grad_(mode) + lt._labels = self._labels + return lt + + @property + def dtype(self): + """ + Give the ``dtype`` of the tensor. For more details, see + :meth:`torch.dtype`. + + :return: The data type of the tensor. + :rtype: torch.dtype + """ + + return super().dtype + + def to(self, *args, **kwargs): + """ + Performs Tensor dtype and/or device conversion. For more details, see + :meth:`torch.Tensor.to`. + + :return: A new :class:`~pina.label_tensor.LabelTensor` instance with the + updated dtype and/or device and retained labels. + :rtype: LabelTensor + """ + + lt = super().to(*args, **kwargs) + lt._labels = self._labels + return lt + + def clone(self, *args, **kwargs): + """ + Clone the :class:`~pina.label_tensor.LabelTensor`. For more details, see + :meth:`torch.Tensor.clone`. + + :return: A new :class:`~pina.label_tensor.LabelTensor` instance with the + same data and labels but allocated in a different memory location. + :rtype: LabelTensor + """ + + out = LabelTensor( + super().clone(*args, **kwargs), deepcopy(self._labels) + ) + return out + + def append(self, tensor, mode="std"): + """ + Appends a given tensor to the current tensor along the last dimension. + This method supports two types of appending operations: + + 1. **Standard append** ("std"): Concatenates the input tensor with the \ + current tensor along the last dimension. + 2. **Cross append** ("cross"): Creates a cross-product of the current \ + tensor and the input tensor. + + :param tensor: The tensor to append to the current tensor. + :type tensor: LabelTensor + :param mode: The append mode to use. Defaults to ``st``. + :type mode: str, optional + :return: A new :class:`LabelTensor` instance obtained by appending the + input tensor. + :rtype: LabelTensor + + :raises ValueError: If the mode is not "std" or "cross". + """ + + if mode == "std": + # Call cat on last dimension + new_label_tensor = LabelTensor.cat( + [self, tensor], dim=self.ndim - 1 + ) + return new_label_tensor + if mode == "cross": + # Crete tensor and call cat on last dimension + tensor1 = self + tensor2 = tensor + n1 = tensor1.shape[0] + n2 = tensor2.shape[0] + tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels) + tensor2 = LabelTensor( + tensor2.repeat_interleave(n1, dim=0), labels=tensor2.labels + ) + new_label_tensor = LabelTensor.cat( + [tensor1, tensor2], dim=self.ndim - 1 + ) + return new_label_tensor + raise ValueError('mode must be either "std" or "cross"') + + @staticmethod + def vstack(tensors): + """ + Stack tensors vertically. For more details, see :meth:`torch.vstack`. + + :param list of LabelTensor label_tensors: The + :class:`~pina.label_tensor.LabelTensor` instances to stack. They + need to have equal labels. + :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained + by stacking the input tensors vertically. + :rtype: LabelTensor + """ + + return LabelTensor.cat(tensors, dim=0) + + # This method is used to update labels + def _update_single_label(self, index, dim): + """ + Update the labels of the tensor based on the index (or list of indices). + + :param index: Index of dof to retain. + :type index: int | slice | list[int] | tuple[int] | torch.Tensor + :param int dim: Dimension of the indexes in the original tensor. + :return: The updated labels for the specified dimension. + :rtype: list[int] + :raises: ValueError: If the index type is not supported. + """ + old_dof = self._labels[dim]["dof"] + # Handle slicing + if isinstance(index, slice): + new_dof = old_dof[index] + # Handle single integer index + elif isinstance(index, int): + new_dof = [old_dof[index]] + # Handle lists or tensors + elif isinstance(index, (list, torch.Tensor)): + # Handle list of bools + if isinstance(index, torch.Tensor) and index.dtype == torch.bool: + index = index.nonzero().squeeze() + new_dof = ( + [old_dof[i] for i in index] + if isinstance(old_dof, list) + else index + ) + else: + raise NotImplementedError( + f"Unsupported index type: {type(index)}. Expected slice, int, " + f"list, or torch.Tensor." + ) + return new_dof + + def __getitem__(self, index): + """ + Override the __getitem__ method to handle the labels of the + :class:`~pina.label_tensor.LabelTensor` instance. It first performs + __getitem__ operation on the :class:`torch.Tensor` part of the instance, + then updates the labels based on the index. + + :param index: The index used to access the item + :type index: int | str | tuple of int | list ot int | torch.Tensor + :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained + `__getitem__` operation on :class:`torch.Tensor` part of the + instance, with the updated labels. + :rtype: LabelTensor + + :raises KeyError: If an invalid label index is provided. + :raises IndexError: If an invalid index is accessed in the tensor. + """ + + # Handle string index + if isinstance(index, str) or ( + isinstance(index, (tuple, list)) + and all(isinstance(i, str) for i in index) + ): + return self.extract(index) + + # Retrieve selected tensor and labels + selected_tensor = super().__getitem__(index) + if not hasattr(self, "_labels"): + return selected_tensor + + original_labels = self._labels + updated_labels = copy(original_labels) + + # Ensure the index is iterable + if not isinstance(index, tuple): + index = [index] + + # Update labels based on the index + offset = 0 + removed = 0 + for dim, idx in enumerate(index): + if dim in original_labels: + if isinstance(idx, int): + # Compute the working dimension considering the removed + # dimensions due to int index on a non labled dimension + dim_ = dim - removed + selected_tensor = selected_tensor.unsqueeze(dim_) + if idx != slice(None): + # Update the labels for the selected dimension + updated_labels[offset] = { + "dof": self._update_single_label(idx, dim), + "name": original_labels[dim]["name"], + } + else: + # Adjust label keys if dimension is reduced (case of integer + # index on a non-labeled dimension) + if isinstance(idx, int): + updated_labels = { + key - 1 if key > dim else key: value + for key, value in updated_labels.items() + } + removed += 1 + continue + offset += 1 + + # Update the selected tensor's labels + selected_tensor._labels = updated_labels + return selected_tensor + + def sort_labels(self, dim=None): + """ + Sort the labels along the specified dimension and apply. It applies the + same sorting to the tensor part of the instance. + + :param int dim: The dimension along which to sort the labels. + If ``None``, the last dimension is used. + :return: A new tensor with sorted labels along the specified dimension. + :rtype: LabelTensor + """ + + def arg_sort(lst): + return sorted(range(len(lst)), key=lambda x: lst[x]) + + if dim is None: + dim = self.ndim - 1 + if self.shape[dim] == 1: + return self + labels = self.stored_labels[dim]["dof"] + sorted_index = arg_sort(labels) + # Define an indexer to sort the tensor along the specified dimension + indexer = [slice(None)] * self.ndim + # Assigned the sorted index to the specified dimension + indexer[dim] = sorted_index + return self[tuple(indexer)] + + def __deepcopy__(self, memo): + """ + Creates a deep copy of the object. For more details, see + :meth:`copy.deepcopy`. + + :param memo: LabelTensor object to be copied. + :type memo: LabelTensor + :return: A deep copy of the original LabelTensor object. + :rtype: LabelTensor + """ + + cls = self.__class__ + result = cls(deepcopy(self.tensor), deepcopy(self.stored_labels)) + return result + + def permute(self, *dims): + """ + Permutes the dimensions of the tensor and the associated labels + accordingly. For more details, see :meth:`torch.Tensor.permute`. + + :param dims: The dimensions to permute the tensor to. + :type dims: tuple[int] | list[int] + :return: A new object with permuted dimensions and reordered labels. + :rtype: LabelTensor + """ + # Call the base class permute method + tensor = super().permute(*dims) + + # Update lables + labels = self._labels + keys_list = list(*dims) + labels = {keys_list.index(k): v for k, v in labels.items()} + + # Assign labels to the new tensor + tensor._labels = labels + return tensor + + def detach(self): + """ + Detaches the tensor from the computation graph and retains the stored + labels. For more details, see :meth:`torch.Tensor.detach`. + + :return: A new tensor detached from the computation graph. + :rtype: LabelTensor + """ + + lt = super().detach() + + # Copy the labels to the new tensor only if present + if hasattr(self, "_labels"): + lt._labels = self.stored_labels + return lt + + @staticmethod + def summation(tensors): + """ + Computes the summation of a list of + :class:`~pina.label_tensor.LabelTensor` instances. + + + :param list[LabelTensor] tensors: A list of tensors to sum. All + tensors must have the same shape and labels. + :return: A new `LabelTensor` containing the element-wise sum of the + input tensors. + :rtype: LabelTensor + + :raises ValueError: If the input `tensors` list is empty. + :raises RuntimeError: If the tensors have different shapes and/or + mismatched labels. + """ + + if not tensors: + raise ValueError("The tensors list must not be empty.") + + if len(tensors) == 1: + return tensors[0] + + # Initialize result tensor and labels + data = torch.zeros_like(tensors[0].tensor).to(tensors[0].device) + last_dim_labels = [] + + # Accumulate tensors + for tensor in tensors: + data += tensor.tensor + last_dim_labels.append(tensor.labels) + + # Construct last dimension labels + last_dim_labels = ["+".join(items) for items in zip(*last_dim_labels)] + + # Update the labels for the resulting tensor + labels = {k: copy(v) for k, v in tensors[0].stored_labels.items()} + labels[tensors[0].ndim - 1] = { + "dof": last_dim_labels, + "name": tensors[0].name, + } + + return LabelTensor(data, labels) + + def reshape(self, *shape): + """ + Override the reshape method to update the labels of the tensor. + For more details, see :meth:`torch.Tensor.reshape`. + + :param tuple of int shape: The new shape of the tensor. + :return: A new :class:`~pina.label_tensor.LabelTensor` instance with the + updated shape and labels. + :rtype: LabelTensor + """ + + # As for now the reshape method is used only in the context of the + # dataset, the labels are not + tensor = super().reshape(*shape) + if not hasattr(self, "_labels") or shape != (-1, *self.shape[2:]): + return tensor + tensor.labels = self.labels + return tensor diff --git a/pina/_src/core/operator.py b/pina/_src/core/operator.py new file mode 100644 index 000000000..8ed28c3a6 --- /dev/null +++ b/pina/_src/core/operator.py @@ -0,0 +1,482 @@ +"""Module for vectorized differential operators implementation. + +Differential operators are used to define differential problems and are +implemented to run efficiently on various accelerators, including CPU, GPU, TPU, +and MPS. + +Each differential operator takes the following inputs: +- A tensor on which the operator is applied. +- A tensor with respect to which the operator is computed. +- The names of the output variables for which the operator is evaluated. +- The names of the variables with respect to which the operator is computed. + +Each differential operator has its fast version, which performs no internal +checks on input and output tensors. For these methods, the user is always +required to specify both ``components`` and ``d`` as lists of strings. +""" + +import torch +from pina._src.core.label_tensor import LabelTensor + + +def _check_values(output_, input_, components, d): + """ + Perform checks on arguments of differential operators. + + :param LabelTensor output_: The output tensor on which the operator is + computed. + :param LabelTensor input_: The input tensor with respect to which the + operator is computed. + :param components: The names of the output variables for which to compute + the operator. It must be a subset of the output labels. + If ``None``, all output variables are considered. Default is ``None``. + :type components: str | list[str] + :param d: The names of the input variables with respect to which the + operator is computed. It must be a subset of the input labels. + If ``None``, all input variables are considered. Default is ``None``. + :type d: str | list[str] + :raises TypeError: If the input tensor is not a LabelTensor. + :raises TypeError: If the output tensor is not a LabelTensor. + :raises RuntimeError: If derivative labels are missing from the ``input_``. + :raises RuntimeError: If component labels are missing from the ``output_``. + :return: The components and d lists. + :rtype: tuple[list[str], list[str]] + """ + # Check if the input is a LabelTensor + if not isinstance(input_, LabelTensor): + raise TypeError("Input must be a LabelTensor.") + + # Check if the output is a LabelTensor + if not isinstance(output_, LabelTensor): + raise TypeError("Output must be a LabelTensor.") + + # If no labels are provided, use all labels + d = d or input_.labels + components = components or output_.labels + + # Convert to list if not already + d = d if isinstance(d, list) else [d] + components = components if isinstance(components, list) else [components] + + # Check if all labels are present in the input tensor + if not all(di in input_.labels for di in d): + raise RuntimeError("Derivative labels missing from input tensor.") + + # Check if all labels are present in the output tensor + if not all(c in output_.labels for c in components): + raise RuntimeError("Component label missing from output tensor.") + + return components, d + + +def _scalar_grad(output_, input_, d): + """ + Compute the gradient of a scalar-valued ``output_``. + + :param LabelTensor output_: The output tensor on which the gradient is + computed. It must be a column tensor. + :param LabelTensor input_: The input tensor with respect to which the + gradient is computed. + :param list[str] d: The names of the input variables with respect to + which the gradient is computed. It must be a subset of the input + labels. If ``None``, all input variables are considered. + :return: The computed gradient tensor. + :rtype: LabelTensor + """ + grad_out = torch.autograd.grad( + outputs=output_, + inputs=input_, + grad_outputs=torch.ones_like(output_), + create_graph=True, + retain_graph=True, + allow_unused=True, + )[0] + + return grad_out[..., [input_.labels.index(i) for i in d]] + + +def _scalar_laplacian(output_, input_, d): + """ + Compute the laplacian of a scalar-valued ``output_``. + + :param LabelTensor output_: The output tensor on which the laplacian is + computed. It must be a column tensor. + :param LabelTensor input_: The input tensor with respect to which the + laplacian is computed. + :param list[str] d: The names of the input variables with respect to + which the laplacian is computed. It must be a subset of the input + labels. If ``None``, all input variables are considered. + :return: The computed laplacian tensor. + :rtype: LabelTensor + """ + first_grad = fast_grad( + output_=output_, input_=input_, components=output_.labels, d=d + ) + second_grad = fast_grad( + output_=first_grad, input_=input_, components=first_grad.labels, d=d + ) + labels_to_extract = [f"d{c}d{d_}" for c, d_ in zip(first_grad.labels, d)] + return torch.sum( + second_grad.extract(labels_to_extract), dim=-1, keepdim=True + ) + + +def fast_grad(output_, input_, components, d): + """ + Compute the gradient of the ``output_`` with respect to the ``input``. + + Unlike ``grad``, this function performs no internal checks on input and + output tensors. The user is required to specify both ``components`` and + ``d`` as lists of strings. It is designed to enhance computation speed. + + This operator supports both vector-valued and scalar-valued functions with + one or multiple input coordinates. + + :param LabelTensor output_: The output tensor on which the gradient is + computed. + :param LabelTensor input_: The input tensor with respect to which the + gradient is computed. + :param list[str] components: The names of the output variables for which to + compute the gradient. It must be a subset of the output labels. + :param list[str] d: The names of the input variables with respect to which + the gradient is computed. It must be a subset of the input labels. + :return: The computed gradient tensor. + :rtype: LabelTensor + """ + # Scalar gradient + if output_.shape[-1] == 1: + return LabelTensor( + _scalar_grad(output_=output_, input_=input_, d=d), + labels=[f"d{output_.labels[0]}d{i}" for i in d], + ) + + # Vector gradient + grads = torch.cat( + [ + _scalar_grad(output_=output_.extract(c), input_=input_, d=d) + for c in components + ], + dim=-1, + ) + + return LabelTensor( + grads, labels=[f"d{c}d{i}" for c in components for i in d] + ) + + +def fast_div(output_, input_, components, d): + """ + Compute the divergence of the ``output_`` with respect to ``input``. + + Unlike ``div``, this function performs no internal checks on input and + output tensors. The user is required to specify both ``components`` and + ``d`` as lists of strings. It is designed to enhance computation speed. + + This operator supports vector-valued functions with multiple input + coordinates. + + :param LabelTensor output_: The output tensor on which the divergence is + computed. + :param LabelTensor input_: The input tensor with respect to which the + divergence is computed. + :param list[str] components: The names of the output variables for which to + compute the divergence. It must be a subset of the output labels. + :param list[str] d: The names of the input variables with respect to which + the divergence is computed. It must be a subset of the input labels. + :rtype: LabelTensor + """ + grad_out = fast_grad( + output_=output_, input_=input_, components=components, d=d + ) + tensors_to_sum = [ + grad_out.extract(f"d{c}d{d_}") for c, d_ in zip(components, d) + ] + + return LabelTensor.summation(tensors_to_sum) + + +def fast_laplacian(output_, input_, components, d, method="std"): + """ + Compute the laplacian of the ``output_`` with respect to ``input``. + + Unlike ``laplacian``, this function performs no internal checks on input and + output tensors. The user is required to specify both ``components`` and + ``d`` as lists of strings. It is designed to enhance computation speed. + + This operator supports both vector-valued and scalar-valued functions with + one or multiple input coordinates. + + :param LabelTensor output_: The output tensor on which the laplacian is + computed. + :param LabelTensor input_: The input tensor with respect to which the + laplacian is computed. + :param list[str] components: The names of the output variables for which to + compute the laplacian. It must be a subset of the output labels. + :param list[str] d: The names of the input variables with respect to which + the laplacian is computed. It must be a subset of the input labels. + :param str method: The method used to compute the Laplacian. Available + methods are ``std`` and ``divgrad``. The ``std`` method computes the + trace of the Hessian matrix, while the ``divgrad`` method computes the + divergence of the gradient. Default is ``std``. + :return: The computed laplacian tensor. + :rtype: LabelTensor + :raises ValueError: If the passed method is neither ``std`` nor ``divgrad``. + """ + # Scalar laplacian + if output_.shape[-1] == 1: + return LabelTensor( + _scalar_laplacian(output_=output_, input_=input_, d=d), + labels=[f"dd{c}" for c in components], + ) + + # Initialize the result tensor and its labels + labels = [f"dd{c}" for c in components] + result = torch.empty( + input_.shape[0], len(components), device=output_.device + ) + + # Vector laplacian + if method == "std": + result = torch.cat( + [ + _scalar_laplacian( + output_=output_.extract(c), input_=input_, d=d + ) + for c in components + ], + dim=-1, + ) + + elif method == "divgrad": + grads = fast_grad( + output_=output_, input_=input_, components=components, d=d + ) + result = torch.cat( + [ + fast_div( + output_=grads, + input_=input_, + components=[f"d{c}d{i}" for i in d], + d=d, + ) + for c in components + ], + dim=-1, + ) + + else: + raise ValueError( + "Invalid method. Available methods are ``std`` and ``divgrad``." + ) + + return LabelTensor(result, labels=labels) + + +def fast_advection(output_, input_, velocity_field, components, d): + """ + Perform the advection operation on the ``output_`` with respect to the + ``input``. This operator supports vector-valued functions with multiple + input coordinates. + + Unlike ``advection``, this function performs no internal checks on input and + output tensors. The user is required to specify both ``components`` and + ``d`` as lists of strings. It is designed to enhance computation speed. + + :param LabelTensor output_: The output tensor on which the advection is + computed. It includes both the velocity and the quantity to be advected. + :param LabelTensor input_: the input tensor with respect to which advection + is computed. + :param list[str] velocity_field: The name of the output variables used as + velocity field. It must be chosen among the output labels. + :param list[str] components: The names of the output variables for which to + compute the advection. It must be a subset of the output labels. + :param list[str] d: The names of the input variables with respect to which + the advection is computed. It must be a subset of the input labels. + :return: The computed advection tensor. + :rtype: LabelTensor + """ + # Add a dimension to the velocity field for following operations + velocity = output_.extract(velocity_field).unsqueeze(-1) + + # Compute the gradient + grads = fast_grad( + output_=output_, input_=input_, components=components, d=d + ) + + # Reshape into [..., len(filter_components), len(d)] + tmp = grads.reshape(*output_.shape[:-1], len(components), len(d)) + + # Transpose to [..., len(d), len(filter_components)] + tmp = tmp.transpose(-1, -2) + + adv = (tmp * velocity).sum(dim=tmp.tensor.ndim - 2) + return LabelTensor(adv, labels=[f"adv_{c}" for c in components]) + + +def grad(output_, input_, components=None, d=None): + """ + Compute the gradient of the ``output_`` with respect to the ``input``. + + This operator supports both vector-valued and scalar-valued functions with + one or multiple input coordinates. + + :param LabelTensor output_: The output tensor on which the gradient is + computed. + :param LabelTensor input_: The input tensor with respect to which the + gradient is computed. + :param components: The names of the output variables for which to compute + the gradient. It must be a subset of the output labels. + If ``None``, all output variables are considered. Default is ``None``. + :type components: str | list[str] + :param d: The names of the input variables with respect to which the + gradient is computed. It must be a subset of the input labels. + If ``None``, all input variables are considered. Default is ``None``. + :type d: str | list[str] + :raises TypeError: If the input tensor is not a LabelTensor. + :raises TypeError: If the output tensor is not a LabelTensor. + :raises RuntimeError: If derivative labels are missing from the ``input_``. + :raises RuntimeError: If component labels are missing from the ``output_``. + :return: The computed gradient tensor. + :rtype: LabelTensor + """ + components, d = _check_values( + output_=output_, input_=input_, components=components, d=d + ) + return fast_grad(output_=output_, input_=input_, components=components, d=d) + + +def div(output_, input_, components=None, d=None): + """ + Compute the divergence of the ``output_`` with respect to ``input``. + + This operator supports vector-valued functions with multiple input + coordinates. + + :param LabelTensor output_: The output tensor on which the divergence is + computed. + :param LabelTensor input_: The input tensor with respect to which the + divergence is computed. + :param components: The names of the output variables for which to compute + the divergence. It must be a subset of the output labels. + If ``None``, all output variables are considered. Default is ``None``. + :type components: str | list[str] + :param d: The names of the input variables with respect to which the + divergence is computed. It must be a subset of the input labels. + If ``None``, all input variables are considered. Default is ``None``. + :type components: str | list[str] + :raises TypeError: If the input tensor is not a LabelTensor. + :raises TypeError: If the output tensor is not a LabelTensor. + :raises ValueError: If the length of ``components`` and ``d`` do not match. + :return: The computed divergence tensor. + :rtype: LabelTensor + """ + components, d = _check_values( + output_=output_, input_=input_, components=components, d=d + ) + + # Components and d must be of the same length + if len(components) != len(d): + raise ValueError( + "Divergence requires components and d to be of the same length." + ) + + return fast_div(output_=output_, input_=input_, components=components, d=d) + + +def laplacian(output_, input_, components=None, d=None, method="std"): + """ + Compute the laplacian of the ``output_`` with respect to ``input``. + + This operator supports both vector-valued and scalar-valued functions with + one or multiple input coordinates. + + :param LabelTensor output_: The output tensor on which the laplacian is + computed. + :param LabelTensor input_: The input tensor with respect to which the + laplacian is computed. + :param components: The names of the output variables for which to + compute the laplacian. It must be a subset of the output labels. + If ``None``, all output variables are considered. Default is ``None``. + :type components: str | list[str] + :param d: The names of the input variables with respect to which + the laplacian is computed. It must be a subset of the input labels. + If ``None``, all input variables are considered. Default is ``None``. + :type d: str | list[str] + :param str method: The method used to compute the Laplacian. Available + methods are ``std`` and ``divgrad``. The ``std`` method computes the + trace of the Hessian matrix, while the ``divgrad`` method computes the + divergence of the gradient. Default is ``std``. + :raises TypeError: If the input tensor is not a LabelTensor. + :raises TypeError: If the output tensor is not a LabelTensor. + :raises ValueError: If the passed method is neither ``std`` nor ``divgrad``. + :return: The computed laplacian tensor. + :rtype: LabelTensor + """ + components, d = _check_values( + output_=output_, input_=input_, components=components, d=d + ) + + return fast_laplacian( + output_=output_, + input_=input_, + components=components, + d=d, + method=method, + ) + + +def advection(output_, input_, velocity_field, components=None, d=None): + """ + Perform the advection operation on the ``output_`` with respect to the + ``input``. This operator supports vector-valued functions with multiple + input coordinates. + + :param LabelTensor output_: The output tensor on which the advection is + computed. It includes both the velocity and the quantity to be advected. + :param LabelTensor input_: the input tensor with respect to which advection + is computed. + :param velocity_field: The name of the output variables used as velocity + field. It must be chosen among the output labels. + :type velocity_field: str | list[str] + :param components: The names of the output variables for which to compute + the advection. It must be a subset of the output labels. + If ``None``, all output variables are considered. Default is ``None``. + :type components: str | list[str] + :param d: The names of the input variables with respect to which the + advection is computed. It must be a subset of the input labels. + If ``None``, all input variables are considered. Default is ``None``. + :type d: str | list[str] + :raises TypeError: If the input tensor is not a LabelTensor. + :raises TypeError: If the output tensor is not a LabelTensor. + :raises RuntimeError: If the velocity field is not a subset of the output + labels. + :raises RuntimeError: If the dimensionality of the velocity field does not + match that of the input tensor. + :return: The computed advection tensor. + :rtype: LabelTensor + """ + components, d = _check_values( + output_=output_, input_=input_, components=components, d=d + ) + + # Map velocity_field to a list if it is a string + if isinstance(velocity_field, str): + velocity_field = [velocity_field] + + # Check if all the velocity_field labels are present in the output labels + if not all(vi in output_.labels for vi in velocity_field): + raise RuntimeError("Velocity labels missing from output tensor.") + + # Check if the velocity has the same dimensionality as the input tensor + if len(velocity_field) != len(d): + raise RuntimeError( + "Velocity dimensionality does not match input dimensionality." + ) + + return fast_advection( + output_=output_, + input_=input_, + velocity_field=velocity_field, + components=components, + d=d, + ) diff --git a/pina/_src/core/trainer.py b/pina/_src/core/trainer.py new file mode 100644 index 000000000..0b89ab168 --- /dev/null +++ b/pina/_src/core/trainer.py @@ -0,0 +1,289 @@ +"""Trainer utilities built on top of the PyTorch Lightning Trainer class.""" + +import warnings +import torch +import lightning +from pina._src.solver.mixin.physics_informed_mixin import PhysicsInformedMixin +from pina._src.solver.base_solver import BaseSolver +from pina._src.data.data_module import DataModule +from pina._src.core.utils import ( + check_consistency, + custom_warning_format, + check_positive_integer, +) + +# Set custom warning format and filter warnings +warnings.formatwarning = custom_warning_format +warnings.filterwarnings("always", category=UserWarning) + + +class Trainer(lightning.pytorch.Trainer): + """ + PINA-specific extension of :class:`lightning.pytorch.Trainer`. + + The trainer configures solver execution, dataset splitting, batching, + logging, device placement for unknown parameters, and gradient tracking + requirements for physics-informed solvers. + """ + + # Available batching modes + _AVAIL_BATCHING_MODES = { + "common_batch_size", + "proportional", + "separate_conditions", + } + + def __init__( + self, + solver, + batch_size=None, + train_size=1.0, + test_size=0.0, + val_size=0.0, + batching_mode="common_batch_size", + automatic_batching=False, + num_workers=0, + pin_memory=False, + shuffle=True, + **kwargs, + ): + """ + Initialization of the :class:`Trainer` class. + + :param BaseSolver solver: The solver used to train, validate, and test + the associated problem. + :param int batch_size: The number of samples per batch. If ``None``, the + entire dataset is processed as a single batch. Default is ``None``. + :param float train_size: The fraction of samples assigned to the + training split. Must belong to the interval ``[0, 1]``. + Default is ``1.0``. + :param float val_size: The fraction of samples assigned to the + validation split. Must belong to the interval ``[0, 1]``. + Default is ``0.0``. + :param float test_size: The fraction of samples assigned to the test + split. Must belong to the interval ``[0, 1]``. Default is ``0.0``. + :param str batching_mode: The strategy used to aggregate batches across + dataloaders. Available options are ``"common_batch_size"`` for + uniform batch sizes across conditions, ``"proportional"`` for batch + sizes proportional to dataset sizes, and ``"separate_conditions"`` + for iterating through each condition separately. + Default is ``"common_batch_size"``. + :param bool automatic_batching: Whether PyTorch automatic batching + should be enabled. If ``True``, dataset elements are retrieved + individually and collated into batches by the dataloader. + If ``False``, entire subsets are retrieved directly from the + condition object. Default is ``False``. + :param int num_workers: The number of worker processes used by + dataloaders. Default is ``0`` for sequential loading. + :param bool pin_memory: Whether pinned memory should be enabled during + data loading. Default is ``False``. + :param bool shuffle: Whether condition samples should be shuffled before + splitting. Default is ``True``. + :param dict kwargs: Additional keyword arguments forwarded to the + Lightning trainer. + :raises ValueError: If ``solver`` is not a PINA solver. + :raises ValueError: If ``train_size``, ``val_size``, or ``test_size`` is + not a float in the interval ``[0, 1]``. + :raises ValueError: If the sum of ``train_size``, ``val_size``, and + ``test_size`` is not equal to 1. + :raises ValueError: If ``automatic_batching``, ``pin_memory``, or + ``shuffle`` is not a boolean. + :raises AssertionError: If ``num_workers`` is a negative integer. + :raises ValueError: If ``batch_size``, when provided, is not a positive + integer. + :raises ValueError: If ``batching_mode`` is not one of the available + options. + :raises UserWarning: If the provided ``batching_mode`` is incompatible + with the ``batch_size``. + :raises RuntimeError: If any domain in the problem has not been + discretised. + """ + # Backward compatibility: compile has been removed + if "compile" in kwargs: + warnings.warn( + "`compile` is deprecated and no longer used. Compilation is " + "now disabled and the argument will be ignored.", + DeprecationWarning, + stacklevel=2, + ) + kwargs.pop("compile") + + # Check consistency + check_consistency(solver, BaseSolver) + check_consistency(train_size, float) + check_consistency(test_size, float) + check_consistency(val_size, float) + check_consistency(automatic_batching, bool) + check_consistency(pin_memory, bool) + check_consistency(shuffle, bool) + check_positive_integer(num_workers, strict=False) + if batch_size is not None: + check_positive_integer(batch_size, strict=True) + + # Check that train_size, test_size and val_size sum to 1 + total = train_size + val_size + test_size + if not torch.isclose(torch.tensor(total), torch.tensor(1.0)): + raise ValueError( + "`train_size`, `val_size`, and `test_size` must sum to 1." + ) + + # Check consistency + if batching_mode not in self._AVAIL_BATCHING_MODES: + raise ValueError( + f"Invalid batching mode '{batching_mode}'. " + f"Expected one of: {sorted(self._AVAIL_BATCHING_MODES)}." + ) + + # Set inference mode to false when usiing physics-informed mixin + if isinstance(solver, PhysicsInformedMixin): + kwargs["inference_mode"] = False + + # Set log_every_n_steps to 0 if batch_size is None, otherwise default + kwargs["log_every_n_steps"] = ( + 0 if batch_size is None else kwargs.get("log_every_n_steps", 50) + ) + + # Set default value for enable_progress_bar to True if not provided + kwargs.setdefault("enable_progress_bar", True) + + # Initialize the parent class with the provided keyword arguments + super().__init__(**kwargs) + + # Raise warning if batch size and batching mode are incompatible + if batch_size is None and batching_mode != "common_batch_size": + warnings.warn( + f"Batching mode '{batching_mode}' is ignored when the batch " + "size is None. Setting batching_mode to 'common_batch_size'.", + UserWarning, + ) + + # Set batching mode to common_batch_size if incompatible + batching_mode = "common_batch_size" + + # Raise warning if batch size and batching mode are incompatible + if ( + batch_size is not None + and batching_mode == "proportional" + and batch_size <= len(solver.problem.conditions) + ): + warnings.warn( + "Batching mode 'proportional' requires the batch size to be " + "larger than the number of conditions. Setting batching_mode " + "to 'common_batch_size'.", + UserWarning, + ) + + # Set batching mode to common_batch_size if incompatible + batching_mode = "common_batch_size" + + # Initialize the class attributes + self.solver = solver + self.batch_size = batch_size + + # Move the unknown parameters to the correct device + self._move_to_device() + + # Check that all domains are discretised, otherwise raise an error + if not self.solver.problem.are_all_domains_discretised: + + # Get the list of sampled domains from the problem + sampled_domains = self.solver.problem.discretised_domains + + # Create a status message for each domain + status = "\n".join( + f" - Domain '{name}': " + f"{'sampled' if name in sampled_domains else 'not sampled'}" + for name in self.solver.problem.domains + ) + + # Raise an error with the status of each domain + raise RuntimeError( + "Cannot create the Trainer because some domains have not been " + f"sampled. Domain status:\n{status}" + ) + + # Create the data module + self.data_module = DataModule( + problem=self.solver.problem, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batch_size=self.batch_size, + batching_mode=batching_mode, + automatic_batching=automatic_batching, + num_workers=num_workers, + pin_memory=pin_memory, + shuffle=shuffle, + ) + + # Set logging kwargs + self.logging_kwargs = { + "sync_dist": bool( + len(self._accelerator_connector._parallel_devices) > 1 + ), + "on_step": bool(kwargs["log_every_n_steps"] > 0), + "prog_bar": bool(kwargs["enable_progress_bar"]), + "on_epoch": True, + } + + def _move_to_device(self): + """ + Move problem unknown parameters to the trainer device. + + If the associated problem defines ``unknown_parameters``, each parameter + is moved to the first device configured by the Lightning accelerator + connector. + """ + # Get the device from the accelerator connector + device = self._accelerator_connector._parallel_devices[0] + + # Get the problem instance from the solver + problem = self.solver.problem + + # Move the unknown parameters to the correct device if they exist + if hasattr(problem, "unknown_parameters"): + for key in problem.unknown_parameters: + problem.unknown_parameters[key] = torch.nn.Parameter( + problem.unknown_parameters[key].data.to(device) + ) + + def train(self, **kwargs): + """ + Fit the solver using the trainer data module. + + :param dict kwargs: Additional keyword arguments forwarded to the + Lightning trainer ``fit`` method. + :return: Result returned by Lightning's ``fit`` method. + :rtype: Any + """ + return super().fit(self.solver, datamodule=self.data_module, **kwargs) + + def test(self, **kwargs): + """ + Test the solver using the trainer data module. + + :param dict kwargs: Additional keyword arguments forwarded to the + Lightning trainer ``test`` method. + :return: Result returned by Lightning's ``test`` method. + :rtype: Any + """ + return super().test(self.solver, datamodule=self.data_module, **kwargs) + + @property + def solver(self): + """ + Return the solver attached to the trainer. + + :return: The solver used by the trainer. + :rtype: BaseSolver + """ + return self._solver + + @solver.setter + def solver(self, solver): + """ + Set the solver attached to the trainer. + + :param BaseSolver solver: The solver instance to attach. + """ + self._solver = solver diff --git a/pina/_src/core/type_checker.py b/pina/_src/core/type_checker.py new file mode 100644 index 000000000..e8c908ac9 --- /dev/null +++ b/pina/_src/core/type_checker.py @@ -0,0 +1,93 @@ +"""Module for enforcing type hints in Python functions.""" + +import inspect +import typing +import logging + + +def enforce_types(func): + """ + Function decorator to enforce type hints at runtime. + + This decorator checks the types of the arguments and of the return value of + the decorated function against the type hints specified in the function + signature. If the types do not match, a TypeError is raised. + Type checking is only performed when the logging level is set to `DEBUG`. + + :param Callable func: The function to be decorated. + :return: The decorated function with enforced type hints. + :rtype: Callable + + :Example: + + >>> @enforce_types + def dummy_function(a: int, b: float) -> float: + ... return a+b + + # This always works. + dummy_function(1, 2.0) + + # This raises a TypeError for the second argument, if logging is set to + # `DEBUG`. + dummy_function(1, "Hello, world!") + + + >>> @enforce_types + def dummy_function2(a: int, right: bool) -> float: + ... if right: + ... return float(a) + ... else: + ... return "Hello, world!" + + # This always works. + dummy_function2(1, right=True) + + # This raises a TypeError for the return value if logging is set to + # `DEBUG`. + dummy_function2(1, right=False) + """ + + def wrapper(*args, **kwargs): + """ + Wrapper function to enforce type hints. + + :param tuple args: Positional arguments passed to the function. + :param dict kwargs: Keyword arguments passed to the function. + :raises TypeError: If the argument or return type does not match the + specified type hints. + :return: The result of the decorated function. + :rtype: Any + """ + level = logging.getLevelName(logging.getLogger().getEffectiveLevel()) + + # Enforce type hints only in debug mode + if level != "DEBUG": + return func(*args, **kwargs) + + # Get the type hints for the function arguments + hints = typing.get_type_hints(func) + sig = inspect.signature(func) + bound = sig.bind(*args, **kwargs) + bound.apply_defaults() + + for arg_name, arg_value in bound.arguments.items(): + expected_type = hints.get(arg_name) + if expected_type and not isinstance(arg_value, expected_type): + raise TypeError( + f"Argument '{arg_name}' must be {expected_type.__name__}, " + f"but got {type(arg_value).__name__}!" + ) + + # Get the type hints for the return values + return_type = hints.get("return") + result = func(*args, **kwargs) + + if return_type and not isinstance(result, return_type): + raise TypeError( + f"Return value must be {return_type.__name__}, " + f"but got {type(result).__name__}!" + ) + + return result + + return wrapper diff --git a/pina/_src/core/utils.py b/pina/_src/core/utils.py new file mode 100644 index 000000000..d0226ea83 --- /dev/null +++ b/pina/_src/core/utils.py @@ -0,0 +1,270 @@ +"""Module for utility functions.""" + +import types +from functools import reduce +import torch + +from pina._src.core.label_tensor import LabelTensor + + +# Codacy error unused parameters +def custom_warning_format( + message, category, filename, lineno, file=None, line=None +): + """ + Custom warning formatting function. + + :param str message: The warning message. + :param Warning category: The warning category. + :param str filename: The filename where the warning is raised. + :param int lineno: The line number where the warning is raised. + :param str file: The file object where the warning is raised. + Default is None. + :param int line: The line where the warning is raised. + :return: The formatted warning message. + :rtype: str + """ + return f"{filename}: {category.__name__}: {message}\n" + + +def check_consistency(object_, object_instance, subclass=False): + """ + Check if an object maintains inheritance consistency. + + This function checks whether a given object is an instance of a specified + class or, if ``subclass=True``, whether it is a subclass of the specified + class. + + :param object: The object to check. + :type object: Iterable | Object + :param Object object_instance: The expected parent class. + :param bool subclass: If True, checks whether ``object_`` is a subclass + of ``object_instance`` instead of an instance. Default is ``False``. + :raises ValueError: If ``object_`` does not inherit from ``object_instance`` + as expected. + """ + if not isinstance(object_, (list, set, tuple)): + object_ = [object_] + + for obj in object_: + is_class = isinstance(obj, type) + expected_type_name = ( + object_instance.__name__ + if isinstance(object_instance, type) + else str(object_instance) + ) + + if subclass: + if not is_class: + raise ValueError( + f"You passed {repr(obj)} " + f"(an instance of {type(obj).__name__}), " + f"but a {expected_type_name} class was expected. " + f"Please pass a {expected_type_name} class or a " + "derived one." + ) + if not issubclass(obj, object_instance): + raise ValueError( + f"You passed {obj.__name__} class, but a " + f"{expected_type_name} class was expected. " + f"Please pass a {expected_type_name} class or a " + "derived one." + ) + else: + if is_class: + raise ValueError( + f"You passed {obj.__name__} class, but a " + f"{expected_type_name} instance was expected. " + f"Please pass a {expected_type_name} instance." + ) + if not isinstance(obj, object_instance): + raise ValueError( + f"You passed {repr(obj)} " + f"(an instance of {type(obj).__name__}), " + f"but a {expected_type_name} instance was expected. " + f"Please pass a {expected_type_name} instance." + ) + + +def labelize_forward(forward, input_variables, output_variables): + """ + Decorator to enable or disable the use of + :class:`~pina.label_tensor.LabelTensor` during the forward pass. + + :param Callable forward: The forward function of a :class:`torch.nn.Module`. + :param list[str] input_variables: The names of the input variables of a + :class:`~pina.problem.base_problem.BaseProblem`. + :param list[str] output_variables: The names of the output variables of a + :class:`~pina.problem.base_problem.BaseProblem`. + :return: The decorated forward function. + :rtype: Callable + """ + + def wrapper(x, *args, **kwargs): + """ + Decorated forward function. + + :param LabelTensor x: The labelized input of the forward pass of an + instance of :class:`torch.nn.Module`. + :param Iterable args: Additional positional arguments passed to + ``forward`` method. + :param dict kwargs: Additional keyword arguments passed to + ``forward`` method. + :return: The labelized output of the forward pass of an instance of + :class:`torch.nn.Module`. + :rtype: LabelTensor + """ + x = x.extract(input_variables) + output = forward(x, *args, **kwargs) + # keep it like this, directly using LabelTensor(...) raises errors + # when compiling the code + output = output.as_subclass(LabelTensor) + output.labels = output_variables + return output + + return wrapper + + +def merge_tensors(tensors): + """ + Merge a list of :class:`~pina.label_tensor.LabelTensor` instances into a + single :class:`~pina.label_tensor.LabelTensor` tensor, by applying + iteratively the cartesian product. + + :param list[LabelTensor] tensors: The list of tensors to merge. + :raises ValueError: If the list of tensors is empty. + :return: The merged tensor. + :rtype: LabelTensor + """ + if tensors: + return reduce(merge_two_tensors, tensors[1:], tensors[0]) + raise ValueError("Expected at least one tensor") + + +def merge_two_tensors(tensor1, tensor2): + """ + Merge two :class:`~pina.label_tensor.LabelTensor` instances into a single + :class:`~pina.label_tensor.LabelTensor` tensor, by applying the cartesian + product. + + :param LabelTensor tensor1: The first tensor to merge. + :param LabelTensor tensor2: The second tensor to merge. + :return: The merged tensor. + :rtype: LabelTensor + """ + n1 = tensor1.shape[0] + n2 = tensor2.shape[0] + + tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels) + tensor2 = LabelTensor( + tensor2.repeat_interleave(n1, dim=0), labels=tensor2.labels + ) + return tensor1.append(tensor2) + + +def torch_lhs(n, dim): + """ + The Latin Hypercube Sampling torch routine, sampling in :math:`[0, 1)`$. + + :param int n: The number of points to sample. + :param int dim: The number of dimensions of the sampling space. + :raises TypeError: If `n` or `dim` are not integers. + :raises ValueError: If `dim` is less than 1. + :return: The sampled points. + :rtype: torch.tensor + """ + + if not isinstance(n, int): + raise TypeError("number of point n must be int") + + if not isinstance(dim, int): + raise TypeError("dim must be int") + + if dim < 1: + raise ValueError("dim must be greater than one") + + samples = torch.rand(size=(n, dim)) + + perms = torch.tile(torch.arange(1, n + 1), (dim, 1)) + + for row in range(dim): + idx_perm = torch.randperm(perms.shape[-1]) + perms[row, :] = perms[row, idx_perm] + + perms = perms.T + + samples = (perms - samples) / n + + return samples + + +def is_function(f): + """ + Check if the given object is a function or a lambda. + + :param Object f: The object to be checked. + :return: ``True`` if ``f`` is a function, ``False`` otherwise. + :rtype: bool + """ + return callable(f) + + +def chebyshev_roots(n): + """ + Compute the roots of the Chebyshev polynomial of degree ``n``. + + :param int n: The number of roots to return. + :return: The roots of the Chebyshev polynomials. + :rtype: torch.Tensor + """ + pi = torch.acos(torch.zeros(1)).item() * 2 + k = torch.arange(n) + nodes = torch.sort(torch.cos(pi * (k + 0.5) / n))[0] + return nodes + + +def check_positive_integer(value, strict=True): + """ + Check if the value is a positive integer. + + :param int value: The value to check. + :param bool strict: If True, the value must be strictly positive. + Default is True. + :raises AssertionError: If the value is not a positive integer. + """ + if strict: + assert ( + isinstance(value, int) and value > 0 + ), f"Expected a strictly positive integer, got {value}." + else: + assert ( + isinstance(value, int) and value >= 0 + ), f"Expected a non-negative integer, got {value}." + + +def in_range(value, range_vals, strict=True): + """ + Check if a value is within a specified range. + + :param int value: The integer value to check. + :param list[int] range_vals: A list of two integers representing the range + limits. The first element specifies the lower bound, and the second + specifies the upper bound. + :param bool strict: If True, the value must be strictly positive. + Default is True. + :return: True if the value satisfies the range condition, False otherwise. + :rtype: bool + """ + # Validate inputs + check_consistency(value, (float, int)) + check_consistency(range_vals, (float, int)) + assert ( + isinstance(range_vals, list) and len(range_vals) == 2 + ), "range_vals must be a list of two integers [lower, upper]" + lower, upper = range_vals + + # Check the range + if strict: + return lower < value < upper + + return lower <= value <= upper diff --git a/pina/_src/data/__init__.py b/pina/_src/data/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/data/aggregator.py b/pina/_src/data/aggregator.py new file mode 100644 index 000000000..d6e149a3f --- /dev/null +++ b/pina/_src/data/aggregator.py @@ -0,0 +1,87 @@ +"""Utility class for aggregating multiple dataloaders into a single iterable.""" + + +class _Aggregator: + """ + Aggregate multiple dataloaders into a unified iterable object. + + The aggregator combines batches produced by multiple dataloaders according + to the selected batching strategy. It is primarily used to coordinate the + iteration of multiple training conditions within a single training loop. + """ + + def __init__(self, dataloaders, batching_mode): + """ + Initialization of the :class:`_Aggregator` class. + + :param dict[str, DataLoader] dataloaders: The mapping between condition + names and their corresponding dataloaders. + :param str batching_mode: The strategy used to aggregate batches across + dataloaders. Available options are ``"common_batch_size"`` for + uniform batch sizes across conditions, ``"proportional"`` for batch + sizes proportional to dataset sizes, and ``"separate_conditions"`` + for iterating through each condition separately. + :raises NotImplementedError: If the selected batching mode is not yet + implemented. + """ + # Raise not implemented error for separate_conditions mode + if batching_mode == "separate_conditions": + raise NotImplementedError( + "Batching mode 'separate_conditions' is not implemented yet." + ) + + # Initialize attributes + self.dataloaders = dataloaders + self.batching_mode = batching_mode + + def __len__(self): + """ + Return the length of the aggregated dataloader. The length is determined + by the number of iterations required to exhaust the dataloaders based on + the selected batching mode. + + For ``"separate_conditions"``, the total number of iterations is the sum + of the lengths of all dataloaders. For all other batching modes, the + length corresponds to the maximum length among the aggregated + dataloaders. + + :return: The length of the aggregated dataloader. + :rtype: int + """ + # Separate conditions case + if self.batching_mode == "separate_conditions": + return sum(len(dl) for dl in self.dataloaders.values()) + + return max(len(dl) for dl in self.dataloaders.values()) + + def __iter__(self): + """ + Iterate over the aggregated dataloaders. + + At each iteration, a dictionary containing one batch per dataloader is + yielded. If a dataloader is exhausted before the others, its iterator is + restarted automatically to ensure continuous batch generation. + + :yield: The dictionary mapping each condition name to its batch. + :rtype: Iterator[dict[str, Any]] + """ + # Initialize iterators for each dataloader + iterators = {name: iter(dl) for name, dl in self.dataloaders.items()} + + # Iterate until the maximum number of iterations is reached + for _ in range(len(self)): + batch = {} + + # Generate a batch for each dataloader + for name, dataloader in self.dataloaders.items(): + + # Attempt to get the next batch from the dataloader's iterator + try: + batch[name] = next(iterators[name]) + + # Restart the iterator if it is exhausted + except StopIteration: + iterators[name] = iter(dataloader) + batch[name] = next(iterators[name]) + + yield batch diff --git a/pina/_src/data/condition_subset.py b/pina/_src/data/condition_subset.py new file mode 100644 index 000000000..068e833a2 --- /dev/null +++ b/pina/_src/data/condition_subset.py @@ -0,0 +1,101 @@ +"""Utilities for handling condition dataset subsets.""" + +from torch_geometric.data import Batch +from pina._src.core.graph import LabelBatch, Graph + + +class _ConditionSubset: + """ + Wrapper around a condition dataset restricted to a subset of indices. + + The class behaves similarly to :class:`torch.utils.data.Subset` and supports + cyclic indexing together with optional automatic batching. + """ + + def __init__(self, condition, indices, automatic_batching): + """ + Initialization of the :class:`_ConditionSubset` class. + + :param BaseCondition condition: The underlying condition. + :param list[int] indices: The list of indices identifying the subset + samples. + :param bool automatic_batching: Whether dataset items should be returned + directly or as raw indices. + """ + super().__init__() + + # Initialize the class attributes + self.condition = condition + self.indices = indices + self.automatic_batching = automatic_batching + + # Actual number of samples contained in the subset + self.dataset_length = len(self.indices) + + # Effective iterable length used and modified during batching + self.iterable_length = self.dataset_length + + def __len__(self): + """ + Return the effective iterable length of the subset. + + :return: The number of accessible elements in the subset. + :rtype: int + """ + return self.iterable_length + + def __getitem__(self, idx): + """ + Retrieve an element from the subset. + + If the requested index exceeds the actual dataset size, cyclic indexing + is applied through modulo wrapping. When automatic batching is disabled, + the raw dataset index is returned instead of the corresponding sample. + + :param int idx: The position of the element inside the subset. + :return: The dataset sample or raw dataset index depending on the + batching configuration. + :rtype: dict | int + """ + # Apply cyclic indexing if the requested index exceeds the subset length + if idx >= self.dataset_length: + idx = idx % self.dataset_length + + # Fetch the corresponding dataset index from the list of indices + idx = self.indices[idx] + + # Return the raw dataset index if automatic batching is disabled + if not self.automatic_batching: + return idx + + return self.condition[idx] + + def get_all_data(self): + """ + Retrieve and aggregate all subset samples. + + If the returned data contains a ``"data"`` field composed of graph + objects, the samples are merged into a single batched graph structure + using the appropriate batching implementation. + + :return: The aggregated subset data. + :rtype: dict + """ + # Fetch the data corresponding to the subset indices + data = self.condition[self.indices] + + # Data as a list of graph objects merged into a single batched graph + if "data" in data and isinstance(data["data"], list): + + # Define the batching function + batch_fn = ( + LabelBatch.from_data_list + if isinstance(data["data"][0], Graph) + else Batch.from_data_list + ) + + # Merge the list of graph objects into a single batched graph + data["data"] = batch_fn(data["data"]) + data = {"input": data["data"], "target": data["data"].y} + + return data diff --git a/pina/_src/data/creator.py b/pina/_src/data/creator.py new file mode 100644 index 000000000..4a5e3207b --- /dev/null +++ b/pina/_src/data/creator.py @@ -0,0 +1,240 @@ +"""Module for creating dataloaders for multiple conditions.""" + +import torch +from torch.utils.data.distributed import DistributedSampler + + +class _Creator: + """ + Utility class for creating data loaders associated with multiple conditions. + + The class supports different batching strategies to adapt data loading + behavior to specific training requirements + """ + + def __init__( + self, + batching_mode, + batch_size, + shuffle, + automatic_batching, + num_workers, + pin_memory, + conditions, + ): + """ + Initialization of the :class:`_Creator` class. + + :param str batching_mode: The strategy used to aggregate batches across + data loaders. Available options are ``"common_batch_size"`` for + uniform batch sizes across conditions, ``"proportional"`` for batch + sizes proportional to dataset sizes, and ``"separate_conditions"`` + for iterating through each condition separately. + :param int batch_size: Batch size configuration used by the selected + batching strategy. For ``"common_batch_size"``, the same batch size + is assigned to all conditions. For ``"proportional"``, this value + represents the total batch size distributed proportionally across + conditions. For ``"separate_conditions"``, this value is applied + independently to each condition and capped by the corresponding + dataset size. + :param bool shuffle: Whether samples should be shuffled during loading. + :param bool automatic_batching: Whether automatic batching should be + enabled in the data loaders. + :param int num_workers: The number of worker processes used for data + loading. + :param bool pin_memory: Whether data loaders should pin memory. + :param dict[str, BaseCondition] conditions: The mapping between + condition names and condition objects responsible for data loader + creation. + """ + # Initialize attributes + self.batching_mode = batching_mode + self.batch_size = batch_size + self.shuffle = shuffle + self.automatic_batching = automatic_batching + self.num_workers = num_workers + self.pin_memory = pin_memory + self.conditions = conditions + + def __call__(self, datasets): + """ + Create data loaders for all provided datasets. + + Batch sizes are computed according to the selected batching mode, and a + dedicated data loader is created for each condition. + + :param dict[str, _ConditionSubset] datasets: The mapping between + condition names and datasets. + :return: The mapping between condition names and the corresponding + data loaders. + :rtype: dict[str, DataLoader] + """ + # Compute batch sizes per condition based on batching_mode + batch_sizes = self._compute_batch_sizes(datasets) + dataloaders = {} + + # If common_batch_size mode, ensure all datasets have the same length + if self.batching_mode == "common_batch_size": + iterable_length = max(len(dataset) for dataset in datasets.values()) + + # Iterate through datasets and create dataloaders + for name, dataset in datasets.items(): + + # If common_batch_size mode, set max_len for datasets + if ( + self.batching_mode == "common_batch_size" + and dataset.dataset_length != batch_sizes[name] + ): + dataset.iterable_length = iterable_length + + # Create dataloader for the current condition + dataloaders[name] = self.conditions[name].create_dataloader( + dataset=dataset, + batch_size=batch_sizes[name], + automatic_batching=self.automatic_batching, + sampler=self._define_sampler(dataset, self.shuffle), + num_workers=self.num_workers, + pin_memory=self.pin_memory, + ) + + return dataloaders + + def _define_sampler(self, dataset, shuffle): + """ + Define the sampling strategy for a dataset. + + Distributed training uses :class:`DistributedSampler`, while + non-distributed execution uses either :class:`RandomSampler` or + :class:`SequentialSampler` depending on ``shuffle``. + + :param _ConditionSubset dataset: The dataset associated with the + sampler. + :param bool shuffle: Whether samples should be shuffled during loading. + :return: The configured sampler instance. + :rtype: Sampler + """ + # Distributed training case + if torch.distributed.is_initialized(): + return DistributedSampler(dataset, shuffle=shuffle) + + # Non-distributed training case - shuffle True + if shuffle: + return torch.utils.data.RandomSampler(dataset) + + # Non-distributed training case - shuffle False + return torch.utils.data.SequentialSampler(dataset) + + def _compute_batch_sizes(self, datasets): + """ + Compute batch sizes for each dataset according to the selected batching + mode. + + :param dict[str, _ConditionSubset] datasets: The mapping between + condition names and datasets. + :return: The mapping between condition names and computed batch sizes. + :rtype: dict[str, int] + """ + # Common batch size mode + if self.batching_mode == "common_batch_size": + + # Compute batch size + batch_size = ( + max(dataset.dataset_length for dataset in datasets.values()) + if self.batch_size is None + else self.batch_size + ) + + return { + name: min(batch_size, len(dataset)) + for name, dataset in datasets.items() + } + + # Proportional batch size mode + if self.batching_mode == "proportional": + return self._compute_proportional_batch_sizes(datasets) + + # Separate conditions mode + return { + name: ( + len(dataset) + if self.batch_size is None + else min(self.batch_size, len(dataset)) + ) + for name, dataset in datasets.items() + } + + def _compute_proportional_batch_sizes(self, datasets): + """ + Compute batch sizes proportionally to dataset sizes. + + Each dataset receives a fraction of the total batch size proportional to + its number of samples, while ensuring that each dataset contributes at + least one sample. + + :param dict[str, _ConditionSubset] datasets: The mapping between + condition names and datasets. + :return: The mapping between condition names and proportional batch + sizes. + :rtype: dict[str, int] + """ + # Compute the sizes of each dataset + dataset_sizes = { + name: len(dataset) for name, dataset in datasets.items() + } + + # Determine the total number of elements across all datasets + total_size = sum(dataset_sizes.values()) + + # Compute the batch sizes + batch_sizes = { + name: max(1, int(self.batch_size * (size / total_size))) + for name, size in dataset_sizes.items() + } + + # Compute assigned batch size and difference with the total batch size + assigned_batch_size = sum(batch_sizes.values()) + difference = self.batch_size - assigned_batch_size + + # If difference > 0, distribute to datasets with more than 1 sample + if difference > 0: + + # Sort datasets by size in descending order + sorted_datasets = sorted( + dataset_sizes, + key=lambda name: dataset_sizes[name], + reverse=True, + ) + + # Distribute to datasets with more than 1 sample + for name in sorted_datasets: + + # Stop distribution when the difference is fully allocated + if difference == 0: + break + + # Distribute to datasets with more than 1 sample + if dataset_sizes[name] > 1: + batch_sizes[name] += 1 + difference -= 1 + + # If difference < 0, reduce from datasets with more than 1 sample + if difference < 0: + + # Sort batches by size in descending order + sorted_batches = sorted( + batch_sizes, key=lambda name: batch_sizes[name], reverse=True + ) + + # Reduce from datasets with more than 1 sample + for name in sorted_batches: + + # Stop reduction when the difference is fully allocated + if difference == 0: + break + + # Reduce from datasets with more than 1 sample + if batch_sizes[name] > 1: + batch_sizes[name] -= 1 + difference += 1 + + return batch_sizes diff --git a/pina/_src/data/data_module.py b/pina/_src/data/data_module.py new file mode 100644 index 000000000..c5d3804a5 --- /dev/null +++ b/pina/_src/data/data_module.py @@ -0,0 +1,278 @@ +""" +Utilities for creating and managing datasets and dataloaders. + +This module defines a custom extension of the Lighting DataModule used to handle +dataset splitting, batching, and dataloader creation for PINA conditions. +""" + +import warnings +import torch +from lightning.pytorch import LightningDataModule +from pina._src.data.condition_subset import _ConditionSubset +from pina._src.data.aggregator import _Aggregator +from pina._src.data.creator import _Creator + + +class DataModule(LightningDataModule): + """ + An extension of the Lightning data module for managing PINA condition + datasets. + + The data module handles train/validation/test dataset splitting, condition + subset creation, dataloader construction, and batching coordination across + multiple conditions. + + Dataset splitting is performed independently for each condition, and the + resulting subsets are wrapped into :class:`_ConditionSubset` objects. + Dataloaders are then created and aggregated according to the selected + batching strategy. + """ + + def __init__( + self, + problem, + train_size, + val_size, + test_size, + batch_size, + batching_mode, + automatic_batching, + shuffle, + num_workers, + pin_memory, + ): + """ + Initialization of the :class:`DataModule` class. + + :param BaseProblem problem: The problem containing the conditions and + sampled data used to construct datasets and dataloaders. + :param float train_size: The fraction of samples assigned to the + training split. Must belong to the interval ``[0, 1]``. + :param float val_size: The fraction of samples assigned to the + validation split. Must belong to the interval ``[0, 1]``. + :param float test_size: The fraction of samples assigned to the test + split. Must belong to the interval ``[0, 1]``. + :param int batch_size: The number of samples per batch. If ``None``, the + entire dataset is processed as a single batch. + :param str batching_mode: The strategy used to aggregate batches across + dataloaders. Available options are ``"common_batch_size"`` for + uniform batch sizes across conditions, ``"proportional"`` for batch + sizes proportional to dataset sizes, and ``"separate_conditions"`` + for iterating through each condition separately. + :param bool automatic_batching: Whether PyTorch automatic batching + should be enabled. If ``True``, dataset elements are retrieved + individually and collated into batches by the dataloader. + If ``False``, entire subsets are retrieved directly from the + condition object. + :param bool shuffle: Whether condition samples should be shuffled before + splitting. + :param int num_workers: The number of worker processes used by + dataloaders. + :param bool pin_memory: Whether pinned memory should be enabled during + data loading. + :raises UserWarning: If ``num_workers`` is set to non-default value + while ``batch_size`` is None. + :raises UserWarning: If ``pin_memory`` is set to ``True`` while + ``batch_size`` is None. + """ + super().__init__() + + # Initialize the attributes -- consistency checked in trainer + self.problem = problem + self.batch_size = batch_size + self.batching_mode = batching_mode + self.automatic_batching = automatic_batching + self.shuffle = shuffle + self.num_workers = num_workers + self.pin_memory = pin_memory + + # If batch size is None, num_workers has no effect + if batch_size is None and num_workers != 0: + warnings.warn("num_workers has no effect when batch_size is None.") + self.num_workers = 0 + + # If batch size is None, pin_memory has no effect + if batch_size is None and pin_memory: + warnings.warn("pin_memory has no effect when batch_size is None.") + self.pin_memory = False + + # Move domain discretisation into conditions subsets + self.problem.move_discretisation_into_conditions() + + # If no splits are defined, use the default dataloaders + if train_size == 0: + self.train_dataloader = super().train_dataloader + if val_size == 0: + self.val_dataloader = super().val_dataloader + if test_size == 0: + self.test_dataloader = super().test_dataloader + + # Otherwise, create the condition splits and initialize the creator + self._create_condition_splits(train_size, test_size) + self.creator = _Creator( + batching_mode=self.batching_mode, + batch_size=self.batch_size, + shuffle=self.shuffle, + automatic_batching=self.automatic_batching, + num_workers=self.num_workers, + pin_memory=self.pin_memory, + conditions=self.problem.conditions, + ) + + def _create_condition_splits(self, train_size, test_size): + """ + Create train/validation/test index splits for each condition. + + Samples belonging to each condition are optionally shuffled before being + partitioned into train, validation, and test subsets according to the + specified split fractions. + + :param float train_size: The fraction of samples assigned to the + training split. Must belong to the interval ``[0, 1]``. + :param float test_size: The fraction of samples assigned to the test + split. Must belong to the interval ``[0, 1]``. + """ + # Initialize the dictionary to store the split idx for each condition + self.split_idxs = {} + + # Iterate through conditions and create the splits + for condition_name, condition in self.problem.conditions.items(): + + # Get the total number of samples for the current condition + condition_length = len(condition) + + # Generate shuffled or sequential indices for the condition samples + indices = ( + torch.randperm(condition_length).tolist() + if self.shuffle + else list(range(condition_length)) + ) + + # Compute the split indices for train, validation, and test subsets + train_end = int(train_size * condition_length) + test_end = train_end + int(test_size * condition_length) + + # Store the computed split indices in the dictionary + self.split_idxs[condition_name] = { + "train": indices[:train_end], + "test": indices[train_end:test_end], + "val": indices[test_end:], + } + + def setup(self, stage=None): + """ + Create dataset subsets for the requested execution stage. + + Depending on the selected stage, it initializes the ``train_datasets``, + the ``val_datasets``, or the ``test_datasets`` attributes. Each dataset + is represented as a mapping between condition names and + :class:`_ConditionSubset` instances. + + :param str stage: The execution stage. Available options are ``"fit"`` + for training/validation and ``"test"`` for testing. If ``None``, both + training/validation and testing datasets are created. + Default is ``None``. + :raises ValueError: If the provided stage is invalid. + """ + # Validate the stage argument + if stage not in ("fit", "test", None): + raise ValueError( + f"Invalid stage. Got {stage}, expected either 'fit' or 'test'." + ) + + # Fit stage: create training and validation datasets + if stage in ("fit", None): + + # Train dataset + self.train_datasets = { + name: _ConditionSubset( + condition, + self.split_idxs[name]["train"], + automatic_batching=self.automatic_batching, + ) + for name, condition in self.problem.conditions.items() + if len(self.split_idxs[name]["train"]) > 0 + } + + # Validation dataset + self.val_datasets = { + name: _ConditionSubset( + condition, + self.split_idxs[name]["val"], + automatic_batching=self.automatic_batching, + ) + for name, condition in self.problem.conditions.items() + if len(self.split_idxs[name]["val"]) > 0 + } + + # Test stage: create testing dataset + if stage in ("test", None): + + # Test dataset + self.test_datasets = { + name: _ConditionSubset( + condition, + self.split_idxs[name]["test"], + automatic_batching=self.automatic_batching, + ) + for name, condition in self.problem.conditions.items() + if len(self.split_idxs[name]["test"]) > 0 + } + + def transfer_batch_to_device(self, batch, device, _): + """ + Transfer a batch to the target device. + + The method transfers all condition batches contained in the aggregated + batch dictionary to the specified device. + + :param dict batch: The mapping between the condition names and the + condition batches. + :param torch.device device: The target device. + :param _: Placeholder argument, not used. + :return: A list of tuples containing condition names and transferred + batches. + :rtype: list[tuple[str, Any]] + """ + return [ + (condition_name, condition.to(device)) + for condition_name, condition in batch.items() + ] + + def train_dataloader(self): + """ + Create the aggregated train dataloader. + + :return: The aggregated dataloader coordinating all train condition + dataloaders. + :rtype: _Aggregator + """ + return _Aggregator( + self.creator(self.train_datasets), + batching_mode=self.batching_mode, + ) + + def val_dataloader(self): + """ + Create the aggregated validation dataloader. + + :return: The aggregated dataloader coordinating all validation condition + dataloaders. + :rtype: _Aggregator + """ + return _Aggregator( + self.creator(self.val_datasets), batching_mode=self.batching_mode + ) + + def test_dataloader(self): + """ + Create the aggregated test dataloader. + + :return: The aggregated dataloader coordinating all test condition + dataloaders. + :rtype: _Aggregator + """ + return _Aggregator( + self.creator(self.test_datasets), + batching_mode=self.batching_mode, + ) diff --git a/pina/_src/data/manager/batch_manager.py b/pina/_src/data/manager/batch_manager.py new file mode 100644 index 000000000..cdea44616 --- /dev/null +++ b/pina/_src/data/manager/batch_manager.py @@ -0,0 +1,47 @@ +"""Module for the Batch Manager class.""" + + +class _BatchManager(dict): + """ + Dict-like container for batched data with attribute-style access and + convenience methods for device placement. + """ + + def to(self, device): + """ + Move all compatible values in the batch to the specified device. + + :param device: The target device. + :type device: torch.device | str + :return: The updated batch manager. + :rtype: _BatchManager + """ + for key, value in self.items(): + if hasattr(value, "to"): + moved_value = value.to(device) + self[key] = moved_value + + return self + + def __getattribute__(self, name): + """ + Provide attribute-style access to dictionary keys. + + :param str name: The name of the attribute to retrieve. + :raises AttributeError: If the attribute is not found as a standard + attribute or a dictionary key. + :return: The value associated with the attribute name. + :rtype: Any + """ + # First, attempt to retrieve the attribute using the standard method. + try: + return super().__getattribute__(name) + + # If not found, attempt to retrieve the attribute as a dictionary key. + except AttributeError: + try: + return self[name] + except KeyError: + raise AttributeError( + f"'BatchManager' object has no attribute '{name}'" + ) diff --git a/pina/_src/data/manager/data_manager.py b/pina/_src/data/manager/data_manager.py new file mode 100644 index 000000000..3fd976d1d --- /dev/null +++ b/pina/_src/data/manager/data_manager.py @@ -0,0 +1,50 @@ +"""Module for the Data Manager factory class.""" + +import torch +from pina._src.core.label_tensor import LabelTensor +from pina._src.equation.base_equation import BaseEquation +from pina._src.data.manager.graph_data_manager import _GraphDataManager +from pina._src.data.manager.tensor_data_manager import _TensorDataManager + + +class _DataManager: + """ + Factory class for data manager implementations. + + This class dispatches object creation to either + :class:`~pina.data.manager.tensor_data_manager._TensorDataManager` or + :class:`~pina.data.manager.graph_data_manager._GraphDataManager` depending + on the types of the provided keyword arguments. + """ + + def __new__(cls, **kwargs): + """ + Create the appropriate data manager implementation based on the provided + keyword arguments. + + If all values in ``kwargs`` are instances of :class:`torch.Tensor`, + :class:`~pina.label_tensor.LabelTensor`, or + :class:`~pina.equation.base_equation.BaseEquation`, an instance of + :class:`~pina.data.manager.tensor_data_manager._TensorDataManager` is + created. Otherwise, an instance of + :class:`~pina.data.manager.graph_data_manager._GraphDataManager` is + created. + + :param dict kwargs: The keyword arguments for the data manager. + :return: A concrete data manager instance. + :rtype: _TensorDataManager | _GraphDataManager + """ + # Guard subclass instantiation + if cls is not _DataManager: + return super().__new__(cls) + + # Check if there are only tensors / equations + is_tensor_only = all( + isinstance(v, (torch.Tensor, LabelTensor, BaseEquation)) + for v in kwargs.values() + ) + + # Choose the appropriate subclass + subclass = _TensorDataManager if is_tensor_only else _GraphDataManager + + return subclass(**kwargs) diff --git a/pina/_src/data/manager/data_manager_interface.py b/pina/_src/data/manager/data_manager_interface.py new file mode 100644 index 000000000..41b841e39 --- /dev/null +++ b/pina/_src/data/manager/data_manager_interface.py @@ -0,0 +1,53 @@ +"""Module for the Data Manager interface.""" + +from abc import ABCMeta, abstractmethod + + +class _DataManagerInterface(metaclass=ABCMeta): + """ + Abstract interface for all data managers. + """ + + @abstractmethod + def __len__(self): + """ + Return the number of samples in the data manager. + + :return: The number of samples. + :rtype: int + """ + + @abstractmethod + def __getitem__(self, idx): + """ + Return the item at the specified indices. + + :param idx: The indices of the data point to retrieve. + :type idx: int | slice | list[int] | torch.Tensor + :return: A new :class:`_DataManager` instance containing the + selected data items. + :rtype: _DataManager + """ + + @abstractmethod + def to_batch(self): + """ + Create a batch from the current data manager. + + :return: A new :class:`~pina.condition.data_manager._DataManager` + instance with batched data. + :rtype: _DataManager + """ + + @staticmethod + @abstractmethod + def create_batch(items): + """ + Create a batch from a list of :class:`_DataManager` items. + + :param list[_DataManager] items: A list of + :class:`_DataManager` items to batch. + :return: A new instance of :class:`_DataManager` containing the + batched data. + :rtype: _DataManager + """ diff --git a/pina/_src/data/manager/graph_data_manager.py b/pina/_src/data/manager/graph_data_manager.py new file mode 100644 index 000000000..660c75f83 --- /dev/null +++ b/pina/_src/data/manager/graph_data_manager.py @@ -0,0 +1,246 @@ +"""Module for the Graph-Data Manager class.""" + +import torch +from torch_geometric.data import Data +from torch_geometric.data.batch import Batch +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.graph import Graph, LabelBatch +from pina._src.data.manager.batch_manager import _BatchManager +from pina._src.data.manager.data_manager_interface import _DataManagerInterface + + +class _GraphDataManager(_DataManagerInterface): + """ + Data manager for graph-based data. It handles inputs stored as + :class:`Graph`, :class:`Data`, or lists / tuples of these types. + """ + + def __init__(self, **kwargs): + """ + Initialization of the :class:`_GraphDataManager` class. + + :param dict kwargs: The keyword arguments for the graph data manager. + """ + # Initialize keys + self.keys = list(kwargs.keys()) + + # Find graph-based data + self.graph_key = next( + k + for k, v in kwargs.items() + if isinstance(v, (Graph, Data, list, tuple)) + ) + + # Find tensor data + self.keys = [ + k + for k in self.keys + if k != self.graph_key + and isinstance(kwargs[k], (torch.Tensor, LabelTensor)) + ] + + # Prepare graphs and assign tensors + self.data = self._prepare_graphs(kwargs) + + def __len__(self): + """ + Return the number of samples in the graph data manager. + + :return: The number of samples. + :rtype: int + """ + return len(self.data) + + def __getitem__(self, idx): + """ + Return the item at the specified indices. + + :param idx: The indices of the graphs to retrieve. + :type idx: int | slice | list[int] | torch.Tensor + :raises TypeError: If an index with invalid type is passed. + :return: A new :class:`_GraphDataManager` instance containing the + selected graphs. + :rtype: _GraphDataManager + """ + # Selection for integers or slices + if isinstance(idx, (int, slice)): + selected = self.data[idx] + + # Selection for lists or tensors + elif isinstance(idx, (list, torch.Tensor)): + selected = [self.data[i] for i in idx] + + # Raise TypeError if index type is invalid + else: + raise TypeError(f"Invalid index type: {type(idx)}") + + # Ensure selected is a list + if not isinstance(selected, list): + selected = [selected] + + return _GraphDataManager._init_from_graphs_list( + selected, graph_key=self.graph_key, keys=self.keys + ) + + def __getattr__(self, name): + """ + Provide dynamic access to stored graph and tensor data. + + If ``name`` corresponds to the graph key, return the list of graph + objects. If it matches a tensor key, retrieve the corresponding + tensors from all graphs and stack them along the batch dimension. + + :param str name: The name of the attribute to access. + :return: The requested graph data or stacked tensor values. + :rtype: torch.Tensor | LabelTensor | list[Graph] | list[Data] + """ + # Stack tensors from all graph if name is a tensor key + if name in self.keys: + tensors = [getattr(g, name) for g in self.data] + batch_fn = ( + LabelTensor.stack + if isinstance(tensors[0], LabelTensor) + else torch.stack + ) + return batch_fn(tensors) + + # Otherwise, return graphs + if name == self.graph_key: + return self.data if len(self.data) > 1 else self.data[0] + + return super().__getattribute__(name) + + def _prepare_graphs(self, kwargs): + """ + Attach tensor data to the corresponding graph objects. + + :param kwargs: The keyword arguments containing graph data and + associated tensor features. + :raises ValueError: If the number of graphs does not match the number of + samples in the tensor of features to associate. + :return: A list of graphs with the corresponding tensors assigned. + :rtype: list[Graph] | list[Data] + """ + # Get graph-based data and store in a list + graphs = kwargs.pop(self.graph_key) + if not isinstance(graphs, (list, tuple)): + graphs = [graphs] + + # Iterate of items + for name, tensor in kwargs.items(): + + # Verify the consistency between the number of graphs and samples + if len(graphs) != tensor.shape[0]: + raise ValueError( + f"Number of graphs ({len(graphs)}) does not match " + f"number of samples for key '{name}' " + f"({kwargs[name].shape[0]})." + ) + + # Assign tensors to graphs + for i, g in enumerate(graphs): + setattr(g, name, tensor[i]) + + return graphs + + def to_batch(self): + """ + Create a batch from the current graph data manager. + + :return: A new instance of :class:`_BatchManager` with batched data. + :rtype: _BatchManager + """ + # Define the batch function + batching_fn = ( + LabelBatch.from_data_list + if isinstance(self.data[0], Graph) + else Batch.from_data_list + ) + + # Create the batch manager + batch_data = _BatchManager() + batched_graph = batching_fn(self.data) + for k in self.keys: + if k == self.graph_key: + continue + batch_data[k] = getattr(batched_graph, k) + delattr(batched_graph, k) + batch_data[self.graph_key] = batched_graph + + return batch_data + + @staticmethod + def create_batch(items): + """ + Create a batch from a list of :class:`_GraphDataManager` items. + + :param list[_GraphDataManager] items: A list of + :class:`_GraphDataManager` items to batch. + :return: A new instance of :class:`_BatchManager` containing the batched + data. + :rtype: _BatchManager + """ + # Return None if no items are provided + if not items: + return None + + # Retrieve the first _GraphDataManager of the list and corresponding key + first = items[0] + graph_key = first.graph_key + + # Initialize the batch manager + batch_data = _BatchManager() + + # Define batch function + batching_fn = ( + LabelBatch.from_data_list + if isinstance(first.data[0], Graph) + else Batch.from_data_list + ) + + # Batch over graphs + batched_graph = batching_fn([item.data[0] for item in items]) + + # Use a set for O(1) lookups if keys are large + keys_to_transfer = set(first.keys) + if graph_key in keys_to_transfer: + keys_to_transfer.remove(graph_key) + + # Iterate over the keys of the _GraphDataManager + for k in keys_to_transfer: + + # Extract values + val = getattr(batched_graph, k, None) + if val is not None: + batch_data[k] = val + delattr(batched_graph, k) + + # Assign key to batch + batch_data[graph_key] = batched_graph + + return batch_data + + @classmethod + def _init_from_graphs_list(cls, graphs, graph_key, keys): + """ + Create a :class:`_GraphDataManager` instance directly from a list of + graph objects. + + This method bypasses the standard initialization logic and is used + internally to construct new instances (e.g., subsets) from already + processed graph data. + + :param list graphs: A list of graph objects. + :param str graph_key: The name of the attribute used to store the + graphs. + :param list keys: A list of tensor keys associated with the graphs. + :return: A new instance of :class:`_GraphDataManager`. + :rtype: _GraphDataManager + """ + # Create a new instance without calling __init__ + obj = _GraphDataManager.__new__(_GraphDataManager) + obj.graph_key = graph_key + obj.keys = keys + obj.data = graphs + + return obj diff --git a/pina/_src/data/manager/tensor_data_manager.py b/pina/_src/data/manager/tensor_data_manager.py new file mode 100644 index 000000000..2e530c40f --- /dev/null +++ b/pina/_src/data/manager/tensor_data_manager.py @@ -0,0 +1,110 @@ +"""Module for the Tensor-Data Manager class.""" + +import torch +from pina._src.core.label_tensor import LabelTensor +from pina._src.data.manager.batch_manager import _BatchManager +from pina._src.data.manager.data_manager_interface import _DataManagerInterface + + +class _TensorDataManager(_DataManagerInterface): + """ + Data manager for tensor-based data. It handles inputs stored as + :class:`torch.Tensor` or :class:`~pina.label_tensor.LabelTensor`. + """ + + def __init__(self, **kwargs): + """ + Initialization of the :class:`_TensorDataManager` class. + + :param dict kwargs: The keyword arguments for the tensor data manager. + """ + self.keys = list(kwargs.keys()) + self.data = kwargs + + # Set attributes from kwargs + for k, v in kwargs.items(): + setattr(self, k, v) + + def __len__(self): + """ + Return the number of samples in the tensor data manager. + + :return: The number of samples. + :rtype: int + """ + return self.data[self.keys[0]].shape[0] + + def __getitem__(self, idx): + """ + Return the item at the specified indices. + + :param idx: The indices of the data point to retrieve. + :type idx: int | slice | list[int] | torch.Tensor + :return: A new :class:`_TensorDataManager` instance containing the + selected data items. + :rtype: _TensorDataManager + """ + # Get data at selected indices + new_data = { + k: (self.data[k][idx] if k in self.keys else self.data[k]) + for k in self.keys + } + + return _TensorDataManager(**new_data) + + def to_batch(self): + """ + Create a batch from the current tensor data manager. + + :return: A new instance of :class:`_BatchManager` with batched data. + :rtype: _BatchManager + """ + # Create the batch manager + batch_data = _BatchManager() + for k in self.keys: + batch_data[k] = self.data[k] + + return batch_data + + @staticmethod + def create_batch(items): + """ + Create a batch from a list of :class:`_TensorDataManager` items. + + :param list[_TensorDataManager] items: A list of + :class:`_TensorDataManager` items to batch. + :return: A new instance of :class:`_BatchManager` containing the batched + data. + :rtype: _BatchManager + """ + # Return None if no items are provided + if not items: + return None + + # Retrieve the first _TensorDataManager of the list + first = items[0] + + # Initialize the batch manager + batch_data = _BatchManager() + + # Iterate over the keys of the _TensorDataManager + for k in first.keys: + + # Extract values and a sample used to determine the batch function + vals = [it.data[k] for it in items] + sample = vals[0] + + # Define the batch function based on the data type + if isinstance(sample, (torch.Tensor, LabelTensor)): + batch_fn = ( + LabelTensor.stack + if isinstance(sample, LabelTensor) + else torch.stack + ) + batch_data[k] = batch_fn(vals) + + # If no tensor is provided, just take the first value + else: + batch_data[k] = sample + + return batch_data diff --git a/pina/_src/data/single_batch_data_loader.py b/pina/_src/data/single_batch_data_loader.py new file mode 100644 index 000000000..bec4cf93e --- /dev/null +++ b/pina/_src/data/single_batch_data_loader.py @@ -0,0 +1,106 @@ +"""Module for the Single-Batch Data Loader class.""" + +import torch + + +class _SingleBatchDataLoader: + """ + Data loader wrapper that returns the entire dataset as a single batch. + + This utility is intended for cases where mini-batching is disabled (e.g. + ``batch_size=None``). The loader yields exactly one batch per iteration. + + In distributed environments, the dataset is automatically partitioned across + processes according to the current rank and world size. Each process + receives only its corresponding subset of data. + + In non-distributed environments, the full dataset is returned. + """ + + def __init__(self, dataset): + """ + Initialization of the :class:`_SingleBatchDataLoader` class. + + In distributed training, the dataset indices are split across processes + using the current rank and world size, so that each process receives + only its corresponding subset of data. + + In non-distributed training, the full dataset is loaded. + + The resulting data is converted into a single batch and stored + internally. + + :param dataset: Dataset object. + :raises RuntimeError: If the dataset size is smaller than the number of + distributed processes. + """ + # Initialize the flag to track if the batch has been yielded + self._has_yielded = False + + # Distributed training + if ( + torch.distributed.is_available() + and torch.distributed.is_initialized() + ): + # Get rank and world_size + rank = torch.distributed.get_rank() + world_size = torch.distributed.get_world_size() + + # Raise runtime error if the dataset is smaller than the world size + if len(dataset) < world_size: + raise RuntimeError( + "Dataset size is smaller than the distributed world size. " + "Increase the dataset size or use a single GPU." + ) + + # Select dataset idx assigned to the current distributed process + idx, i = [], rank + while i < len(dataset): + idx.append(i) + i += world_size + + # Fetch the process-specific subset + self.dataset = dataset.fetch_from_idx_list(idx).to_batch() + + # Non-distributed training + else: + self.dataset = dataset.get_all_data().to_batch() + + def __iter__(self): + """ + Return the data loader iterator. + + :return: The data loader instance itself. + :rtype: _SingleBatchDataLoader + """ + # Reset the flag to yield the batch again if iterator is restarted + self._has_yielded = False + return self + + def __len__(self): + """ + Return the number of batches produced by the data loader. + + Since the entire dataset is returned as a single batch, the length is + always ``1``. + + :return: The number of batches. + :rtype: int + """ + return 1 + + def __next__(self): + """ + Return the next batch. + + :return: The dataset converted into a single batch. + :rtype: _BatchManager + """ + # Yield the batch only once per iteration + if self._has_yielded: + raise StopIteration + + # Set the flag to indicate that the batch has been yielded + self._has_yielded = True + + return self.dataset diff --git a/pina/_src/domain/__init__.py b/pina/_src/domain/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/domain/base_domain.py b/pina/_src/domain/base_domain.py similarity index 94% rename from pina/domain/base_domain.py rename to pina/_src/domain/base_domain.py index c7bef9700..d3cea3848 100644 --- a/pina/domain/base_domain.py +++ b/pina/_src/domain/base_domain.py @@ -2,8 +2,8 @@ from copy import deepcopy from abc import ABCMeta -from .domain_interface import DomainInterface -from ..utils import check_consistency, check_positive_integer +from pina._src.domain.domain_interface import DomainInterface +from pina._src.core.utils import check_consistency, check_positive_integer class BaseDomain(DomainInterface, metaclass=ABCMeta): @@ -103,8 +103,17 @@ def update(self, domain): f"with domain of type {type(domain)}." ) - # Update fixed and ranged variables + # Create a deepcopy of the current domain updated = deepcopy(self) + + # Remove keys that change category + for key in domain.fixed: + updated.range.pop(key, None) + + for key in domain.range: + updated.fixed.pop(key, None) + + # Update fixed and ranged variables updated.fixed.update(domain.fixed) updated.range.update(domain.range) diff --git a/pina/domain/base_operation.py b/pina/_src/domain/base_operation.py similarity index 97% rename from pina/domain/base_operation.py rename to pina/_src/domain/base_operation.py index 8261ae431..ff83e1551 100644 --- a/pina/domain/base_operation.py +++ b/pina/_src/domain/base_operation.py @@ -2,9 +2,9 @@ from copy import deepcopy from abc import ABCMeta -from .operation_interface import OperationInterface -from .base_domain import BaseDomain -from ..utils import check_consistency +from pina._src.domain.operation_interface import OperationInterface +from pina._src.domain.base_domain import BaseDomain +from pina._src.core.utils import check_consistency class BaseOperation(OperationInterface, BaseDomain, metaclass=ABCMeta): diff --git a/pina/domain/cartesian_domain.py b/pina/_src/domain/cartesian_domain.py similarity index 97% rename from pina/domain/cartesian_domain.py rename to pina/_src/domain/cartesian_domain.py index 3333a8fc3..089e3377c 100644 --- a/pina/domain/cartesian_domain.py +++ b/pina/_src/domain/cartesian_domain.py @@ -1,10 +1,10 @@ """Module for the Cartesian Domain.""" import torch -from .base_domain import BaseDomain -from .union import Union -from ..utils import torch_lhs, chebyshev_roots, check_consistency -from ..label_tensor import LabelTensor +from pina._src.domain.base_domain import BaseDomain +from pina._src.domain.union import Union +from pina._src.core.utils import torch_lhs, chebyshev_roots, check_consistency +from pina._src.core.label_tensor import LabelTensor class CartesianDomain(BaseDomain): diff --git a/pina/domain/difference.py b/pina/_src/domain/difference.py similarity index 96% rename from pina/domain/difference.py rename to pina/_src/domain/difference.py index 76807b035..ce87920e5 100644 --- a/pina/domain/difference.py +++ b/pina/_src/domain/difference.py @@ -1,8 +1,8 @@ """Module for the Difference operation.""" -from .base_operation import BaseOperation -from ..label_tensor import LabelTensor -from ..utils import check_consistency +from pina._src.domain.base_operation import BaseOperation +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency class Difference(BaseOperation): diff --git a/pina/domain/domain_interface.py b/pina/_src/domain/domain_interface.py similarity index 100% rename from pina/domain/domain_interface.py rename to pina/_src/domain/domain_interface.py diff --git a/pina/domain/ellipsoid_domain.py b/pina/_src/domain/ellipsoid_domain.py similarity index 98% rename from pina/domain/ellipsoid_domain.py rename to pina/_src/domain/ellipsoid_domain.py index ecb08e37c..402ec29a8 100644 --- a/pina/domain/ellipsoid_domain.py +++ b/pina/_src/domain/ellipsoid_domain.py @@ -2,9 +2,9 @@ from copy import deepcopy import torch -from .base_domain import BaseDomain -from ..label_tensor import LabelTensor -from ..utils import check_consistency +from pina._src.domain.base_domain import BaseDomain +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency class EllipsoidDomain(BaseDomain): diff --git a/pina/domain/exclusion.py b/pina/_src/domain/exclusion.py similarity index 97% rename from pina/domain/exclusion.py rename to pina/_src/domain/exclusion.py index 59205f3a8..914e17086 100644 --- a/pina/domain/exclusion.py +++ b/pina/_src/domain/exclusion.py @@ -1,9 +1,9 @@ """Module for the Exclusion set-operation.""" import random -from .base_operation import BaseOperation -from ..label_tensor import LabelTensor -from ..utils import check_consistency +from pina._src.domain.base_operation import BaseOperation +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency class Exclusion(BaseOperation): diff --git a/pina/domain/intersection.py b/pina/_src/domain/intersection.py similarity index 96% rename from pina/domain/intersection.py rename to pina/_src/domain/intersection.py index 105575df1..1b004556e 100644 --- a/pina/domain/intersection.py +++ b/pina/_src/domain/intersection.py @@ -1,9 +1,9 @@ """Module for the Intersection operation.""" import random -from .base_operation import BaseOperation -from ..label_tensor import LabelTensor -from ..utils import check_consistency +from pina._src.domain.base_operation import BaseOperation +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency class Intersection(BaseOperation): diff --git a/pina/domain/operation_interface.py b/pina/_src/domain/operation_interface.py similarity index 92% rename from pina/domain/operation_interface.py rename to pina/_src/domain/operation_interface.py index 9be458972..357556105 100644 --- a/pina/domain/operation_interface.py +++ b/pina/_src/domain/operation_interface.py @@ -1,7 +1,7 @@ """Module for the Operation Interface.""" from abc import ABCMeta, abstractmethod -from .domain_interface import DomainInterface +from pina._src.domain.domain_interface import DomainInterface class OperationInterface(DomainInterface, metaclass=ABCMeta): diff --git a/pina/domain/simplex_domain.py b/pina/_src/domain/simplex_domain.py similarity index 98% rename from pina/domain/simplex_domain.py rename to pina/_src/domain/simplex_domain.py index 9e3a3e58f..5dff002ce 100644 --- a/pina/domain/simplex_domain.py +++ b/pina/_src/domain/simplex_domain.py @@ -2,9 +2,9 @@ from copy import deepcopy import torch -from .base_domain import BaseDomain -from ..label_tensor import LabelTensor -from ..utils import check_consistency +from pina._src.domain.base_domain import BaseDomain +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency class SimplexDomain(BaseDomain): diff --git a/pina/domain/union.py b/pina/_src/domain/union.py similarity index 95% rename from pina/domain/union.py rename to pina/_src/domain/union.py index df094bb82..eff137df3 100644 --- a/pina/domain/union.py +++ b/pina/_src/domain/union.py @@ -1,9 +1,9 @@ """Module for the Union operation.""" import random -from .base_operation import BaseOperation -from ..label_tensor import LabelTensor -from ..utils import check_consistency +from pina._src.domain.base_operation import BaseOperation +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency class Union(BaseOperation): diff --git a/pina/_src/equation/__init__.py b/pina/_src/equation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/equation/base_equation.py b/pina/_src/equation/base_equation.py new file mode 100644 index 000000000..4fff8dd3b --- /dev/null +++ b/pina/_src/equation/base_equation.py @@ -0,0 +1,67 @@ +"""Module for the Base Equation.""" + +from abc import ABCMeta, abstractmethod +import torch + + +class BaseEquation(metaclass=ABCMeta): + """ + Base class for all equations, implementing common functionality. + + Equations are fundamental components in PINA, representing mathematical + constraints that must be satisfied by the model outputs. They can be passed + to :class:`~pina.condition.condition.Condition` objects to define the + conditions under which the model is trained. + + All specific equation types should inherit from this class and implement its + abstract methods. + + This class is not meant to be instantiated directly. + """ + + @abstractmethod + def residual(self, input_, output_, params_): + """ + Evaluate the equation residual at the given inputs. + + :param LabelTensor input_: The input points where the residual is + computed. + :param LabelTensor output_: The output tensor, potentially produced by a + :class:`torch.nn.Module` instance. + :param dict params_: An optional dictionary of unknown parameters, used + in :class:`~pina.problem.inverse_problem.InverseProblem` settings. + If the equation is not related to an inverse problem, this should be + set to ``None``. Default is ``None``. + :return: The residual values of the equation. + :rtype: LabelTensor + """ + + def to(self, device): + """ + Move all tensor attributes to the specified device. + + :param torch.device device: The target device to move the tensors to. + :return: The instance moved to the specified device. + :rtype: BaseEquation + """ + # Iterate over all attributes of the Equation + for key, val in self.__dict__.items(): + + # Move tensors in dictionaries to the specified device + if isinstance(val, dict): + self.__dict__[key] = { + k: v.to(device) if torch.is_tensor(v) else v + for k, v in val.items() + } + + # Move tensors in lists to the specified device + elif isinstance(val, list): + self.__dict__[key] = [ + v.to(device) if torch.is_tensor(v) else v for v in val + ] + + # Move tensor attributes to the specified device + elif torch.is_tensor(val): + self.__dict__[key] = val.to(device) + + return self diff --git a/pina/_src/equation/equation.py b/pina/_src/equation/equation.py new file mode 100644 index 000000000..d10da2bbe --- /dev/null +++ b/pina/_src/equation/equation.py @@ -0,0 +1,65 @@ +"""Module for the Equation.""" + +import inspect +from pina._src.equation.base_equation import BaseEquation + + +class Equation(BaseEquation): + """ + Implementation of the Equation class, representing a single mathematical + equation to be satisfied by the model outputs. + + It can be passed to a :class:`~pina.condition.condition.Condition` object to + define the conditions under which the model is trained. + """ + + def __init__(self, equation): + """ + Initialization of the :class:`Equation` class. + + :param Callable equation: A callable function used to compute the + residual of a mathematical equation. + :raises ValueError: If the equation is not a callable function. + """ + # Check consistency + if not callable(equation): + raise ValueError(f"Expected a callable function, got {equation}") + + # Compute the signature length + sig = inspect.signature(equation) + self.__len_sig = len(sig.parameters) + self.__equation = equation + + def residual(self, input_, output_, params_=None): + """ + Evaluate the equation residual at the given inputs. + + :param LabelTensor input_: The input points where the residual is + computed. + :param LabelTensor output_: The output tensor, potentially produced by a + :class:`torch.nn.Module` instance. + :param dict params_: An optional dictionary of unknown parameters, used + in :class:`~pina.problem.inverse_problem.InverseProblem` settings. + If the equation is not related to an inverse problem, this should be + set to ``None``. Default is ``None``. + :raises RuntimeError: If the underlying equation signature is neither of + length 2 for direct problems nor of length 3 for inverse problems. + :return: The residual values of the equation. + :rtype: LabelTensor + """ + # Move the equation to the input_ device + self.to(input_.device) + + # Evaluate the equation for direct problems + if self.__len_sig == 2: + return self.__equation(input_, output_) + + # Evaluate the equation for inverse problems + if self.__len_sig == 3: + return self.__equation(input_, output_, params_) + + # Raise an error if the signature length is unexpected + raise RuntimeError( + f"Unexpected number of arguments in equation: {self.__len_sig}. " + "Expected either 2 for direct problems, or 3 for inverse problems." + ) diff --git a/pina/_src/equation/equation_interface.py b/pina/_src/equation/equation_interface.py new file mode 100644 index 000000000..fa59de678 --- /dev/null +++ b/pina/_src/equation/equation_interface.py @@ -0,0 +1,36 @@ +"""Module for the Equation Interface.""" + +from abc import ABCMeta, abstractmethod + + +class EquationInterface(metaclass=ABCMeta): + """ + Abstract interface for all equations. + """ + + @abstractmethod + def residual(self, input_, output_, params_=None): + """ + Evaluate the equation residual at the given inputs. + + :param LabelTensor input_: The input points where the residual is + computed. + :param LabelTensor output_: The output tensor, potentially produced by a + :class:`torch.nn.Module` instance. + :param dict params_: An optional dictionary of unknown parameters, used + in :class:`~pina.problem.inverse_problem.InverseProblem` settings. + If the equation is not related to an inverse problem, this should be + set to ``None``. Default is ``None``. + :return: The residual values of the equation. + :rtype: LabelTensor + """ + + @abstractmethod + def to(self, device): + """ + Move all tensor attributes to the specified device. + + :param torch.device device: The target device to move the tensors to. + :return: The instance moved to the specified device. + :rtype: EquationInterface + """ diff --git a/pina/_src/equation/system_equation.py b/pina/_src/equation/system_equation.py new file mode 100644 index 000000000..7d3bdafd4 --- /dev/null +++ b/pina/_src/equation/system_equation.py @@ -0,0 +1,118 @@ +"""Module for the System of Equation.""" + +from typing import Callable +import torch +from pina._src.equation.base_equation import BaseEquation +from pina._src.core.utils import check_consistency +from pina._src.equation.equation import Equation + + +class SystemEquation(BaseEquation): + """ + Implementation of the SystemEquation class, representing a system of + mathematical equation to be satisfied by the model outputs. It is useful for + multi-component outputs or coupled problems, where multiple constraints must + be evaluated together. + + It can be passed to a :class:`~pina.condition.condition.Condition` object to + define the conditions under which the model is trained. + + Each equation in the system must be either an instance of + :class:`~pina.equation.equation.Equation`, or a callable function. + + Residuals are computed independently for each equation and then aggregated + using an optional reduction (e.g., ``mean``, ``sum``). The final result is + returned as a single :class:`~pina.LabelTensor`. + + :Example: + + >>> pts = LabelTensor(torch.rand(10, 2), labels=["x", "y"]) + >>> pts.requires_grad = True + >>> output_ = torch.pow(pts, 2) + >>> output_.labels = ["u", "v"] + >>> system_equation = SystemEquation( + ... [ + ... FixedValue(value=1.0, components=["u"]), + ... FixedGradient(value=0.0, components=["v"], d=["y"]), + ... ], + ... reduction="mean", + ... ) + >>> residual = system_equation.residual(pts, output_) + """ + + def __init__(self, list_equation, reduction=None): + """ + Initialization of the :class:`SystemEquation` class. + + :param list_equation: The list of equations used for the computation of + the residuals. Each element of the list can be either a callable + function or a :class:`~pina.equation.equation.Equation` instance. + :type list_equation: list[Callable] | list[Equation] + :param reduction: The method used to combine the residuals from each + equation. Available options are: ``None``, ``"mean"``, ``"sum"``, or + a custom callable. If ``None``, no reduction is applied. If + ``"mean"``, the residuals are averaged. If ``"sum"``, the residuals + are summed. If a callable is provided, it is used as a custom + reduction (no validation is performed). + :raises ValueError: If the list of equations is not a list. + :raises ValueError: If any element of the list of equations is not a + callable function or a :class:`~pina.equation.equation.Equation` + instance. + :raises ValueError: If an invalid reduction method is used. + """ + # Check consistency + check_consistency([list_equation], list) + check_consistency(list_equation, (Callable, Equation)) + + # Convert all callable functions to Equation instances, if necessary + self.equations = [ + equation if isinstance(equation, Equation) else Equation(equation) + for equation in list_equation + ] + + # Validate and set the reduction method + if reduction == "mean": + self.reduction = torch.mean + elif reduction == "sum": + self.reduction = torch.sum + elif (reduction is None) or callable(reduction): + self.reduction = reduction + else: + raise ValueError( + "Invalid reduction method. Available options include: None, " + "'mean', 'sum', or a custom callable." + ) + + def residual(self, input_, output_, params_=None): + """ + Evaluate each equation residual from the system of equations at the + given inputs and aggregate it according to the specified ``reduction``. + + :param LabelTensor input_: The input points where the residual is + computed. + :param LabelTensor output_: The output tensor, potentially produced by a + :class:`torch.nn.Module` instance. + :param dict params_: An optional dictionary of unknown parameters, used + in :class:`~pina.problem.inverse_problem.InverseProblem` settings. + If the equation is not related to an inverse problem, this should be + set to ``None``. Default is ``None``. + :return: The aggregated residuals of the system of equations. + :rtype: LabelTensor + """ + # Move the equation to the input_ device + self.to(input_.device) + + # Compute the residual for each equation + residual = torch.cat( + [ + equation.residual(input_, output_, params_) + for equation in self.equations + ], + dim=-1, + ) + + # Skip reduction if not specified + if self.reduction is None: + return residual + + return self.reduction(residual, dim=-1) diff --git a/pina/_src/equation/zoo/__init__.py b/pina/_src/equation/zoo/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/equation/zoo/acoustic_wave_equation.py b/pina/_src/equation/zoo/acoustic_wave_equation.py new file mode 100644 index 000000000..8a6d2bf07 --- /dev/null +++ b/pina/_src/equation/zoo/acoustic_wave_equation.py @@ -0,0 +1,62 @@ +"""Module for defining the acoustic wave equation.""" + +from pina._src.equation.equation import Equation +from pina._src.core.operator import laplacian +from pina._src.core.utils import check_consistency + + +class AcousticWaveEquation(Equation): + r""" + Implementation of the N-dimensional isotropic acoustic wave equation. + The equation is defined as follows: + + .. math:: + + \frac{\partial^2 u}{\partial t^2} - c^2 \Delta u = 0 + + or alternatively: + + .. math:: + + \Box u = 0 + + Here, :math:`c` is the wave propagation speed, and :math:`\Box` is the + d'Alembert operator. + """ + + def __init__(self, c): + """ + Initialization of the :class:`AcousticWaveEquation` class. + + :param c: The wave propagation speed. + :type c: float | int + """ + check_consistency(c, (float, int)) + self.c = c + + def equation(input_, output_): + """ + Implementation of the acoustic wave equation. + + :param LabelTensor input_: The input data of the problem. + :param LabelTensor output_: The output data of the problem. + :return: The residual of the acoustic wave equation. + :rtype: LabelTensor + :raises ValueError: If the ``input_`` labels do not contain the time + variable 't'. + """ + # Ensure time is passed as input + if "t" not in input_.labels: + raise ValueError( + "The ``input_`` labels must contain the time 't' variable." + ) + + # Compute the time second derivative and the spatial laplacian + u_tt = laplacian(output_, input_, d=["t"]) + u_xx = laplacian( + output_, input_, d=[di for di in input_.labels if di != "t"] + ) + + return u_tt - self.c**2 * u_xx + + super().__init__(equation) diff --git a/pina/_src/equation/zoo/advection_equation.py b/pina/_src/equation/zoo/advection_equation.py new file mode 100644 index 000000000..81e476bd5 --- /dev/null +++ b/pina/_src/equation/zoo/advection_equation.py @@ -0,0 +1,93 @@ +"""Module for defining the advection equation.""" + +import torch +from pina._src.equation.equation import Equation +from pina._src.core.operator import grad +from pina._src.core.utils import check_consistency + + +class AdvectionEquation(Equation): + r""" + Implementation of the N-dimensional advection equation with constant + velocity parameter. The equation is defined as follows: + + .. math:: + + \frac{\partial u}{\partial t} + c \cdot \nabla u = 0 + + Here, :math:`c` is the advection velocity parameter. + """ + + def __init__(self, c): + """ + Initialization of the :class:`AdvectionEquation` class. + + :param c: The advection velocity. If a scalar is provided, the same + velocity is applied to all spatial dimensions. If a list is + provided, it must contain one value per spatial dimension. + :type c: float | int | List[float] | List[int] + :raises ValueError: If ``c`` is an empty list. + """ + # Check consistency + check_consistency(c, (float, int)) + if isinstance(c, list): + if len(c) < 1: + raise ValueError("'c' cannot be an empty list.") + else: + c = [c] + + # Store advection velocity parameter + self.c = torch.tensor(c).unsqueeze(0) + + def equation(input_, output_): + """ + Implementation of the advection equation. + + :param LabelTensor input_: The input data of the problem. + :param LabelTensor output_: The output data of the problem. + :return: The residual of the advection equation. + :rtype: LabelTensor + :raises ValueError: If the ``input_`` labels do not contain the time + variable 't'. + :raises ValueError: If ``c`` is a list and its length is not + consistent with the number of spatial dimensions. + """ + # Store labels + input_lbl = input_.labels + spatial_d = [di for di in input_lbl if di != "t"] + + # Ensure time is passed as input + if "t" not in input_lbl: + raise ValueError( + "The ``input_`` labels must contain the time 't' variable." + ) + + # Ensure consistency of c length + if self.c.shape[-1] != len(input_lbl) - 1 and self.c.shape[-1] > 1: + raise ValueError( + "If 'c' is passed as a list, its length must be equal to " + "the number of spatial dimensions." + ) + + # Repeat c to ensure consistent shape for advection + c = self.c.repeat(output_.shape[0], 1) + if c.shape[1] != (len(input_lbl) - 1): + c = c.repeat(1, len(input_lbl) - 1) + + # Add a dimension to c for the following operations + c = c.unsqueeze(-1) + + # Compute the time derivative and the spatial gradient + time_der = grad(output_, input_, components=None, d="t") + grads = grad(output_=output_, input_=input_, d=spatial_d) + + # Reshape and transpose + tmp = grads.reshape(*output_.shape, len(spatial_d)) + tmp = tmp.transpose(-1, -2) + + # Compute advection term + adv = (tmp * c).sum(dim=tmp.tensor.ndim - 2) + + return time_der + adv + + super().__init__(equation) diff --git a/pina/_src/equation/zoo/allen_cahn_equation.py b/pina/_src/equation/zoo/allen_cahn_equation.py new file mode 100644 index 000000000..e7091add2 --- /dev/null +++ b/pina/_src/equation/zoo/allen_cahn_equation.py @@ -0,0 +1,58 @@ +"""Module for defining the Allen-Cahn equation.""" + +from pina._src.equation.equation import Equation +from pina._src.core.operator import grad, laplacian +from pina._src.core.utils import check_consistency + + +class AllenCahnEquation(Equation): + r""" + Implementation of the N-dimensional Allen-Cahn equation, defined as follows: + + .. math:: + + \frac{\partial u}{\partial t} - \alpha \Delta u + \beta(u^3 - u) = 0 + + Here, :math:`\alpha` and :math:`\beta` are parameters of the equation. + """ + + def __init__(self, alpha, beta): + """ + Initialization of the :class:`AllenCahnEquation` class. + + :param alpha: The diffusion coefficient. + :type alpha: float | int + :param beta: The reaction coefficient. + :type beta: float | int + """ + check_consistency(alpha, (float, int)) + check_consistency(beta, (float, int)) + self.alpha = alpha + self.beta = beta + + def equation(input_, output_): + """ + Implementation of the Allen-Cahn equation. + + :param LabelTensor input_: The input data of the problem. + :param LabelTensor output_: The output data of the problem. + :return: The residual of the Allen-Cahn equation. + :rtype: LabelTensor + :raises ValueError: If the ``input_`` labels do not contain the time + variable 't'. + """ + # Ensure time is passed as input + if "t" not in input_.labels: + raise ValueError( + "The ``input_`` labels must contain the time 't' variable." + ) + + # Compute the time derivative and the spatial laplacian + u_t = grad(output_, input_, d=["t"]) + u_xx = laplacian( + output_, input_, d=[di for di in input_.labels if di != "t"] + ) + + return u_t - self.alpha * u_xx + self.beta * (output_**3 - output_) + + super().__init__(equation) diff --git a/pina/_src/equation/zoo/burgers_equation.py b/pina/_src/equation/zoo/burgers_equation.py new file mode 100644 index 000000000..07c8eed22 --- /dev/null +++ b/pina/_src/equation/zoo/burgers_equation.py @@ -0,0 +1,84 @@ +"""Module for defining the Burgers equation.""" + +from pina._src.core.operator import laplacian, grad +from pina._src.core.utils import check_consistency +from pina._src.equation.equation import Equation +import torch + + +class BurgersEquation(Equation): + r""" + Implementation of the N-dimensional Burgers' equation, defined as follows: + + .. math:: + + \frac{\partial u}{\partial t} + u \cdot \nabla u = \nu \Delta u + + Here, :math:`\nu` is the viscosity coefficient. + """ + + def __init__(self, nu): + """ + Initialization of the :class:`BurgersEquation` class. + + :param nu: The viscosity coefficient. + :type nu: float | int + :raises ValueError: If ``nu`` is not a float or an int. + :raises ValueError: If ``nu`` is negative. + """ + # Check consistency + check_consistency(nu, (float, int)) + if nu < 0: + raise ValueError( + "The viscosity ``nu`` must be a non-negative float or int." + ) + + # Store viscosity coefficient + self.nu = nu + + def equation(input_, output_): + """ + Implementation of the Burgers' equation. + + :param LabelTensor input_: The input data of the problem. + :param LabelTensor output_: The output data of the problem. + :raises ValueError: If the number of output components does not + match the number of spatial dimensions. + :raises ValueError: If the ``input_`` labels do not contain the time + variable 't'. + :return: The residual of the Burgers' equation. + :rtype: LabelTensor + """ + # Store labels + spatial_d = [di for di in input_.labels if di != "t"] + + # Ensure consistency between output and spatial dimensions + if len(output_.labels) != len(spatial_d): + raise ValueError( + f"The number of output components must match the number of " + f"spatial dimensions. Got {len(output_.labels)} and " + f"{len(spatial_d)}." + ) + + # Ensure time is passed as input + if "t" not in input_.labels: + raise ValueError( + "The ``input_`` labels must contain the time 't' variable." + ) + + # Compute the differential terms + u_t = grad(output_, input_, d=["t"]) + u_x = grad(output_, input_, d=spatial_d) + u_xx = laplacian(output_, input_, d=spatial_d) + + # Compute the convective term componentwise + convection = torch.zeros_like(output_) + for i, c in enumerate(output_.labels): + convection[:, i] = sum( + output_[output_.labels[j]] * u_x[f"d{c}d{spatial_d[j]}"] + for j in range(len(spatial_d)) + ).reshape(-1) + + return u_t + convection - self.nu * u_xx + + super().__init__(equation) diff --git a/pina/_src/equation/zoo/diffusion_reaction_equation.py b/pina/_src/equation/zoo/diffusion_reaction_equation.py new file mode 100644 index 000000000..4f276dd54 --- /dev/null +++ b/pina/_src/equation/zoo/diffusion_reaction_equation.py @@ -0,0 +1,61 @@ +"""Module for defining the Diffusion-Reaction equation.""" + +from typing import Callable +from pina._src.equation.equation import Equation +from pina._src.core.operator import grad, laplacian +from pina._src.core.utils import check_consistency + + +class DiffusionReactionEquation(Equation): + r""" + Implementation of the N-dimensional Diffusion-Reaction equation, + defined as follows: + + .. math:: + + \frac{\partial u}{\partial t} - \alpha \Delta u - f = 0 + + Here, :math:`\alpha` is a parameter of the equation, while :math:`f` is the + reaction term. + """ + + def __init__(self, alpha, forcing_term): + """ + Initialization of the :class:`DiffusionReactionEquation` class. + + :param alpha: The diffusion coefficient. + :type alpha: float | int + :param Callable forcing_term: The forcing field function, taking as + input the points on which evaluation is required. + """ + check_consistency(alpha, (float, int)) + check_consistency(forcing_term, (Callable)) + self.alpha = alpha + self.forcing_term = forcing_term + + def equation(input_, output_): + """ + Implementation of the Diffusion-Reaction equation. + + :param LabelTensor input_: The input data of the problem. + :param LabelTensor output_: The output data of the problem. + :return: The residual of the Diffusion-Reaction equation. + :rtype: LabelTensor + :raises ValueError: If the ``input_`` labels do not contain the time + variable 't'. + """ + # Ensure time is passed as input + if "t" not in input_.labels: + raise ValueError( + "The ``input_`` labels must contain the time 't' variable." + ) + + # Compute the time derivative and the spatial laplacian + u_t = grad(output_, input_, d=["t"]) + u_xx = laplacian( + output_, input_, d=[di for di in input_.labels if di != "t"] + ) + + return u_t - self.alpha * u_xx - self.forcing_term(input_) + + super().__init__(equation) diff --git a/pina/_src/equation/zoo/fixed_flux.py b/pina/_src/equation/zoo/fixed_flux.py new file mode 100644 index 000000000..858f3bdd1 --- /dev/null +++ b/pina/_src/equation/zoo/fixed_flux.py @@ -0,0 +1,54 @@ +"""Module for defining the fixed flux equation.""" + +from pina._src.equation.equation import Equation +from pina._src.core.operator import div +from pina._src.core.utils import check_consistency + + +class FixedFlux(Equation): + """ + Equation to enforce a fixed flux, or divergence, for a specific condition. + """ + + def __init__(self, value, components=None, d=None): + """ + Initialization of the :class:`FixedFlux` class. + + :param value: The fixed value to be enforced to the flux. + :type value: float | int + :param components: The name of the output variables for which the fixed + flux condition is applied. It should be a subset of the output + labels. If ``None``, all output variables are considered. Default is + ``None``. + :type components: str | list[str] + :param d: The name of the input variables on which the flux is computed. + It should be a subset of the input labels. If ``None``, all the + input variables are considered. Default is ``None``. + :type d: str | list[str] + :raises ValueError: If ``value`` is neither a float nor an integer. + :raises ValueError: If, when provided, ``components`` is neither a + string nor a list of strings. + :raises ValueError: If, when provided, ``d`` is neither a string nor a + list of strings. + """ + # Check consistency + check_consistency(value, (float, int)) + if components is not None: + check_consistency(components, str) + if d is not None: + check_consistency(d, str) + + def equation(input_, output_): + """ + Definition of the equation to enforce a fixed flux. + + :param LabelTensor input_: The input points where the residual is + computed. + :param LabelTensor output_: The output tensor, potentially produced + by a :class:`torch.nn.Module` instance. + :return: The residual values of the equation. + :rtype: LabelTensor + """ + return div(output_, input_, components=components, d=d) - value + + super().__init__(equation) diff --git a/pina/_src/equation/zoo/fixed_gradient.py b/pina/_src/equation/zoo/fixed_gradient.py new file mode 100644 index 000000000..2c60c007f --- /dev/null +++ b/pina/_src/equation/zoo/fixed_gradient.py @@ -0,0 +1,53 @@ +"""Module for defining the fixed gradient equation.""" + +from pina._src.equation.equation import Equation +from pina._src.core.operator import grad +from pina._src.core.utils import check_consistency + + +class FixedGradient(Equation): + """ + Equation to enforce a fixed gradient for a specific condition. + """ + + def __init__(self, value, components=None, d=None): + """ + Initialization of the :class:`FixedGradient` class. + + :param float value: The fixed value to be enforced to the gradient. + :param components: The name of the output variables for which the fixed + gradient condition is applied. It should be a subset of the output + labels. If ``None``, all output variables are considered. Default is + ``None``. + :type components: str | list[str] + :param d: The name of the input variables on which the gradient is + computed. It should be a subset of the input labels. If ``None``, + all the input variables are considered. Default is ``None``. + :type d: str | list[str] + :raises ValueError: If ``value`` is neither a float nor an integer. + :raises ValueError: If, when provided, ``components`` is neither a + string nor a list of strings. + :raises ValueError: If, when provided, ``d`` is neither a string nor a + list of strings. + """ + # Check consistency + check_consistency(value, (float, int)) + if components is not None: + check_consistency(components, str) + if d is not None: + check_consistency(d, str) + + def equation(input_, output_): + """ + Definition of the equation to enforce a fixed gradient. + + :param LabelTensor input_: The input points where the residual is + computed. + :param LabelTensor output_: The output tensor, potentially produced + by a :class:`torch.nn.Module` instance. + :return: The residual values of the equation. + :rtype: LabelTensor + """ + return grad(output_, input_, components=components, d=d) - value + + super().__init__(equation) diff --git a/pina/_src/equation/zoo/fixed_laplacian.py b/pina/_src/equation/zoo/fixed_laplacian.py new file mode 100644 index 000000000..8d0fa7cf4 --- /dev/null +++ b/pina/_src/equation/zoo/fixed_laplacian.py @@ -0,0 +1,68 @@ +"""Module for defining the fixed laplacian equation.""" + +import warnings +from pina._src.equation.equation import Equation +from pina._src.core.operator import laplacian +from pina._src.core.utils import check_consistency + + +class FixedLaplacian(Equation): + """ + Equation to enforce a fixed laplacian for a specific condition. + """ + + def __init__(self, value, components=None, d=None): + """ + Initialization of the :class:`FixedLaplacian` class. + + :param value: The fixed value to be enforced to the laplacian. + :type value: float | int + :param components: The name of the output variables for which the fixed + laplace condition is applied. It should be a subset of the output + labels. If ``None``, all output variables are considered. Default is + ``None``. + :type components: str | list[str] + :param d: The name of the input variables on which the laplacian is + computed. It should be a subset of the input labels. If ``None``, + all the input variables are considered. Default is ``None``. + :type d: str | list[str] + :raises ValueError: If ``value`` is neither a float nor an integer. + :raises ValueError: If, when provided, ``components`` is neither a + string nor a list of strings. + :raises ValueError: If, when provided, ``d`` is neither a string nor a + list of strings. + """ + # Check consistency + check_consistency(value, (float, int)) + if components is not None: + check_consistency(components, str) + if d is not None: + check_consistency(d, str) + + def equation(input_, output_): + """ + Definition of the equation to enforce a fixed laplacian. + + :param LabelTensor input_: The input points where the residual is + computed. + :param LabelTensor output_: The output tensor, potentially produced + by a :class:`torch.nn.Module` instance. + :return: The residual values of the equation. + :rtype: LabelTensor + """ + return ( + laplacian(output_, input_, components=components, d=d) - value + ) + + super().__init__(equation) + + +# Back-compatibility with version 0.2, to be removed soon +class Laplace(FixedLaplacian): + def __init__(self, components=None, d=None): + warnings.warn( + "Laplace is deprecated, use FixedLaplacian with value=0.0 instead.", + DeprecationWarning, + stacklevel=2, + ) + super().__init__(0.0, components=components, d=d) diff --git a/pina/_src/equation/zoo/fixed_value.py b/pina/_src/equation/zoo/fixed_value.py new file mode 100644 index 000000000..25c81c8b8 --- /dev/null +++ b/pina/_src/equation/zoo/fixed_value.py @@ -0,0 +1,48 @@ +"""Module for defining the fixed value equation.""" + +from pina._src.equation.equation import Equation +from pina._src.core.utils import check_consistency + + +class FixedValue(Equation): + """ + Equation to enforce a fixed value. Can be used to enforce Dirichlet Boundary + conditions. + """ + + def __init__(self, value, components=None): + """ + Initialization of the :class:`FixedValue` class. + + :param value: The fixed value to be enforced. + :type value: float | int + :param components: The name of the output variables for which the fixed + value condition is applied. It should be a subset of the output + labels. If ``None``, all output variables are considered. Default is + ``None``. + :type components: str | list[str] + :raises ValueError: If ``value`` is neither a float nor an integer. + :raises ValueError: If, when provided, ``components`` is neither a + string nor a list of strings. + """ + # Check consistency + check_consistency(value, (float, int)) + if components is not None: + check_consistency(components, str) + + def equation(_, output_): + """ + Definition of the equation to enforce a fixed value. + + :param LabelTensor input_: The input points where the residual is + computed. + :param LabelTensor output_: The output tensor, potentially produced + by a :class:`torch.nn.Module` instance. + :return: The residual values of the equation. + :rtype: LabelTensor + """ + if components is None: + return output_ - value + return output_.extract(components) - value + + super().__init__(equation) diff --git a/pina/_src/equation/zoo/helmholtz_equation.py b/pina/_src/equation/zoo/helmholtz_equation.py new file mode 100644 index 000000000..57b353bf0 --- /dev/null +++ b/pina/_src/equation/zoo/helmholtz_equation.py @@ -0,0 +1,47 @@ +"""Module for defining the Helmholtz equation.""" + +from typing import Callable +from pina._src.equation.equation import Equation +from pina._src.core.operator import laplacian +from pina._src.core.utils import check_consistency + + +class HelmholtzEquation(Equation): + r""" + Implementation of the Helmholtz equation, defined as follows: + + .. math:: + + \Delta u + k u - f = 0 + + Here, :math:`k` is the squared wavenumber, while :math:`f` is the + forcing term. + """ + + def __init__(self, k, forcing_term): + """ + Initialization of the :class:`HelmholtzEquation` class. + + :param k: The squared wavenumber. + :type k: float | int + :param Callable forcing_term: The forcing field function, taking as + input the points on which evaluation is required. + """ + check_consistency(k, (int, float)) + check_consistency(forcing_term, (Callable)) + self.k = k + self.forcing_term = forcing_term + + def equation(input_, output_): + """ + Implementation of the Helmholtz equation. + + :param LabelTensor input_: The input data of the problem. + :param LabelTensor output_: The output data of the problem. + :return: The residual of the Helmholtz equation. + :rtype: LabelTensor + """ + lap = laplacian(output_, input_) + return lap + self.k * output_ - self.forcing_term(input_) + + super().__init__(equation) diff --git a/pina/_src/equation/zoo/poisson_equation.py b/pina/_src/equation/zoo/poisson_equation.py new file mode 100644 index 000000000..2ab80ff33 --- /dev/null +++ b/pina/_src/equation/zoo/poisson_equation.py @@ -0,0 +1,42 @@ +"""Module for defining the Poisson equation.""" + +from typing import Callable +from pina._src.equation.equation import Equation +from pina._src.core.operator import laplacian +from pina._src.core.utils import check_consistency + + +class PoissonEquation(Equation): + r""" + Implementation of the Poisson equation, defined as follows: + + .. math:: + + \Delta u - f = 0 + + Here, :math:`f` is the forcing term. + """ + + def __init__(self, forcing_term): + """ + Initialization of the :class:`PoissonEquation` class. + + :param Callable forcing_term: The forcing field function, taking as + input the points on which evaluation is required. + """ + check_consistency(forcing_term, (Callable)) + self.forcing_term = forcing_term + + def equation(input_, output_): + """ + Implementation of the Poisson equation. + + :param LabelTensor input_: The input data of the problem. + :param LabelTensor output_: The output data of the problem. + :return: The residual of the Poisson equation. + :rtype: LabelTensor + """ + lap = laplacian(output_, input_) + return lap - self.forcing_term(input_) + + super().__init__(equation) diff --git a/pina/_src/loss/__init__.py b/pina/_src/loss/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/loss/base_dual_loss.py b/pina/_src/loss/base_dual_loss.py new file mode 100644 index 000000000..9287142bc --- /dev/null +++ b/pina/_src/loss/base_dual_loss.py @@ -0,0 +1,54 @@ +"""Module for the BaseDualLoss class.""" + +import torch +from pina._src.loss.dual_loss_interface import DualLossInterface + + +class BaseDualLoss(DualLossInterface): + """ + Base class for all losses requiring both an input and a target tensor, + implementing common functionality. + + All specific loss types should inherit from this class and implement its + abstract methods. + + This class is not meant to be instantiated directly. + """ + + # Define available reduction methods + _REDUCTION_METHOD = { + "sum": lambda x: torch.sum(x, keepdim=True, dim=-1), + "mean": lambda x: torch.mean(x, keepdim=True, dim=-1), + "none": lambda x: x, + } + + def __init__(self, reduction="mean"): + """ + Initialization of the :class:`BaseDualLoss` class. + + :param str reduction: The reduction method to aggregate pointwise loss + values. Available options include: ``"none"`` for unreduced loss, + ``"mean"`` for the average of the loss values, and ``"sum"`` for + their total sum. Default is ``"mean"``. + :raises ValueError: If the specified reduction method is not among the + available options. + """ + # Check that the reduction method is available + if reduction not in self._REDUCTION_METHOD: + raise ValueError( + f"Invalid reduction method. Available options: " + f"{list(self._REDUCTION_METHOD.keys())}. Got {reduction}." + ) + + # Initialization + super().__init__(reduction=reduction, size_average=None, reduce=None) + + def _reduction(self, loss): + """ + Apply the configured reduction operation to pointwise loss values. + + :param torch.Tensor loss: The tensor of pointwise losses. + :return: The reduced loss tensor. + :rtype: torch.Tensor + """ + return self._REDUCTION_METHOD[self.reduction](loss) diff --git a/pina/_src/loss/dual_loss_interface.py b/pina/_src/loss/dual_loss_interface.py new file mode 100644 index 000000000..6db6bc44f --- /dev/null +++ b/pina/_src/loss/dual_loss_interface.py @@ -0,0 +1,32 @@ +"""Module for the Loss Interface.""" + +from abc import ABCMeta, abstractmethod +from torch.nn.modules.loss import _Loss + + +class DualLossInterface(_Loss, metaclass=ABCMeta): + """ + Abstract interface for all losses requiring both an input and a target + tensor. + """ + + @abstractmethod + def forward(self, input, target): + """ + Forward method of the loss function. + + :param torch.Tensor input: The input tensor. + :param torch.Tensor target: The target tensor. + :return: The computed loss. + :rtype: torch.Tensor + """ + + @abstractmethod + def _reduction(self, loss): + """ + Apply the configured reduction operation to pointwise loss values. + + :param torch.Tensor loss: The tensor of pointwise losses. + :return: The reduced loss tensor. + :rtype: torch.Tensor + """ diff --git a/pina/_src/loss/lp_loss.py b/pina/_src/loss/lp_loss.py new file mode 100644 index 000000000..c2d25ea4e --- /dev/null +++ b/pina/_src/loss/lp_loss.py @@ -0,0 +1,91 @@ +"""Module for the Lp Loss class.""" + +import torch +from pina._src.loss.base_dual_loss import BaseDualLoss +from pina._src.core.utils import check_consistency + + +class LpLoss(BaseDualLoss): + r""" + Implementation of the :math:`L^p` loss measuring the pointwise :math:`L^p` + distance between an input tensor :math:`x` and a target tensor :math:`y`. + + Given a batch of size :math:`N` and feature dimension :math:`D`, the + unreduced loss (``reduction="none"``) is defined as: + + .. math:: + L = \{l_1, \dots, l_N\}^\top, \quad + l_n = \left( \sum_{i=1}^{D} \left| x_n^i - y_n^i \right|^p \right)^{1/p} + + If ``relative=True``, each term is normalized by the :math:`L^p` norm of the + input tensor :math:`x`: + + .. math:: + l_n = \frac{\left( \sum_{i=1}^{D} |x_n^i - y_n^i|^p \right)^{1/p}} + {\left( \sum_{i=1}^{D} |x_n^i|^p \right)^{1/p}} + + If ``reduction`` is set to ``"mean"`` or ``"sum"``, the vector :math:`L` + is aggregated accordingly: + + .. math:: + \ell(x, y) = + \begin{cases} + \operatorname{mean}(L), & \text{if reduction} = \text{``mean''} \\ + \operatorname{sum}(L), & \text{if reduction} = \text{``sum''} + \end{cases} + + where :math:`N` is the batch size. + """ + + def __init__(self, p=2, reduction="mean", relative=False): + """ + Initialization of the :class:`LpLoss` class. + + :param p: The order of the norm. It can be a numeric value for standard + p-norms or one of the following strings: ``"inf"`` for maximum + absolute value, ``"-inf"`` for minimum absolute value. The values + ``"inf"`` and ``"-inf"`` are internally converted to their floating + counterparts. Default is ``2``. + :type p: int | float | str + :param str reduction: The reduction method to aggregate pointwise loss + values. Available options include: ``"none"`` for unreduced loss, + ``"mean"`` for the average of the loss values, and ``"sum"`` for + their total sum. Default is ``"mean"``. + :param bool relative: If ``True``, computes the relative error. + Default is ``False``. + :raises ValueError: If ``relative`` is not a boolean. + :raises ValueError: If ``p`` is not a valid norm order. + """ + super().__init__(reduction=reduction) + + # Convert to float if inf or -inf + if p == "inf": + p = float("inf") + elif p == "-inf": + p = float("-inf") + + # Check consistency + check_consistency(relative, bool) + check_consistency(p, (int, float)) + + # Initialize attributes + self.p = p + self.relative = relative + + def forward(self, input, target): + """ + Forward method of the loss function. + + :param torch.Tensor input: The input tensor. + :param torch.Tensor target: The target tensor. + :return: The computed loss. + :rtype: torch.Tensor + """ + # Compute the standard loss + loss = torch.linalg.norm((input - target), ord=self.p, dim=-1) + + # Compute the input norm for relative error + if self.relative: + loss = loss / torch.linalg.norm(input, ord=self.p, dim=-1) + + return self._reduction(loss) diff --git a/pina/_src/loss/power_loss.py b/pina/_src/loss/power_loss.py new file mode 100644 index 000000000..b8a0821bb --- /dev/null +++ b/pina/_src/loss/power_loss.py @@ -0,0 +1,81 @@ +"""Module for the Power Loss class.""" + +import torch +from pina._src.loss.base_dual_loss import BaseDualLoss +from pina._src.core.utils import check_consistency, check_positive_integer + + +class PowerLoss(BaseDualLoss): + r""" + Implementation of the Power loss, measuring the pointwise averaged + :math:`p`-power error between an input tensor :math:`x` and a target tensor + :math:`y`. + + Given a batch of size :math:`N` and feature dimension :math:`D`, the + unreduced loss (``reduction="none"``) is defined as: + + .. math:: + L = \{l_1, \dots, l_N\}^\top, \quad + l_n = \frac{1}{D} \sum_{i=1}^{D} \left| x_n^i - y_n^i \right|^p + + If ``relative=True``, each term is normalized by the averaged + :math:`p`-power magnitude of the input tensor :math:`x`: + + .. math:: + l_n = \frac{\frac{1}{D} \sum_{i=1}^{D} |x_n^i - y_n^i|^p} + {\frac{1}{D} \sum_{i=1}^{D} |x_n^i|^p} + + If ``reduction`` is set to ``"mean"`` or ``"sum"``, the vector :math:`L` + is aggregated accordingly: + + .. math:: + \ell(x, y) = + \begin{cases} + \operatorname{mean}(L), & \text{if reduction} = \text{``mean''} \\ + \operatorname{sum}(L), & \text{if reduction} = \text{``sum''} + \end{cases} + + where :math:`N` is the batch size. + """ + + def __init__(self, p=2, reduction="mean", relative=False): + """ + Initialization of the :class:`PowerLoss` class. + + :param int p: The order of the p-norm. Default is ``2``. + :param str reduction: The reduction method to aggregate pointwise loss + values. Available options include: ``"none"`` for unreduced loss, + ``"mean"`` for the average of the loss values, and ``"sum"`` for + their total sum. Default is ``"mean"``. + :param bool relative: If ``True``, computes the relative error. + Default is ``False``. + :raises ValueError: If ``relative`` is not a boolean. + :raises ValueError: If ``p`` is not a positive integer. + """ + super().__init__(reduction=reduction) + + # Check consistency + check_consistency(relative, bool) + check_positive_integer(p, strict=True) + + # Initialize attributes + self.p = p + self.relative = relative + + def forward(self, input, target): + """ + Forward method of the loss function. + + :param torch.Tensor input: The input tensor. + :param torch.Tensor target: The target tensor. + :return: The computed loss. + :rtype: torch.Tensor + """ + # Compute the standard loss + loss = torch.abs((input - target)).pow(self.p).mean(-1) + + # Compute the input norm for relative error + if self.relative: + loss = loss / torch.abs(input).pow(self.p).mean(-1) + + return self._reduction(loss) diff --git a/pina/_src/loss/sinkhorn_loss.py b/pina/_src/loss/sinkhorn_loss.py new file mode 100644 index 000000000..9feddc458 --- /dev/null +++ b/pina/_src/loss/sinkhorn_loss.py @@ -0,0 +1,138 @@ +"""Module for the SinkhornLoss class.""" + +import torch +from pina._src.loss.base_dual_loss import BaseDualLoss +from pina._src.core.utils import check_consistency, check_positive_integer + + +class SinkhornLoss(BaseDualLoss): + r""" + Implementation of the Sinkhorn loss measuring the entropy-regularized + optimal transport distance between two empirical distributions. + + Given an input tensor :math:`x` with :math:`N` samples and a target tensor + :math:`y` with :math:`M` samples, both in :math:`\mathbb{R}^D`, the loss is + defined through the entropy-regularized optimal transport problem: + + .. math:: + + W_\varepsilon(\mu, \nu) = \min_{\pi \in \Pi(\mu, \nu)} + \langle C, \pi \rangle - \varepsilon H(\pi) + + where :math:`\mu` and :math:`\nu` are the empirical distributions associated + with :math:`x` and :math:`y`, :math:`\pi` is a transport plan, and + :math:`\Pi(\mu, \nu)` is the set of admissible transport plans with + marginals :math:`\mu` and :math:`\nu`. + + The cost matrix is defined as: + + .. math:: + + C_{ij} = \left\| x_i - y_j \right\|_2^p + + and the entropy term is: + + .. math:: + + H(\pi) = - \sum_{i,j} \pi_{ij} \log \pi_{ij} + + where :math:`\varepsilon > 0` controls the strength of the entropic + regularization. + + The Sinkhorn iterations compute the optimal dual potentials :math:`f^\ast` + and :math:`g^\ast` in log space. The regularized optimal transport cost is + then recovered from the dual formulation as: + + .. math:: + + W_\varepsilon = \langle a, f^\ast \rangle + \langle b, g^\ast \rangle + + where :math:`a` and :math:`b` are uniform probability weights over the + :math:`N` input samples and :math:`M` target samples, respectively. + + Unlike pointwise losses, the Sinkhorn loss compares whole empirical + distributions. Therefore, the output is always a scalar value. + + Smaller values of ``eps`` provide a closer approximation to the true + Wasserstein distance, but may require more Sinkhorn iterations to converge. + + .. seealso:: + + **Original reference:** Patrini, G., Carioni, M., Forr'e, P., Bhargav, + S., Welling, M., Van den Berg, R., Genewein, T., and Nielsen, F. (2019). + *Sinkhorn AutoEncoders*. + In Proceedings of the 35th Conference on Uncertainty in Artificial + Intelligence. + URL: ``_. + """ + + def __init__(self, p=2, eps=0.1, iterations=100): + """ + Initialization of the :class:`SinkhornLoss` class. + + :param int p: The exponent of the cost function. Default is ``2``. + :param eps: The entropy regularization strength. Smaller values provide + a closer approximation to the unregularized Wasserstein distance, + but may require more iterations for convergence. Default is ``0.1``. + :type eps: int | float + :param int iterations: The number of Sinkhorn iterations. + Default is ``100``. + :raises AssertionError: If ``iterations`` is not a positive integer. + :raises AssertionError: If ``p`` is not a positive integer. + :raises ValueError: If ``eps`` is not a positive numeric value. + """ + # Initialize the base class with mean reduction + super().__init__(reduction="mean") + + # Check consistency + check_positive_integer(iterations, strict=True) + check_positive_integer(p, strict=True) + check_consistency(eps, (int, float)) + if eps <= 0: + raise ValueError( + f"Expected 'eps' to be strictly positive, but got {eps}." + ) + + # Initialize parameters + self.iterations = iterations + self.eps = eps + self.p = p + + def forward(self, input, target): + """ + Forward method of the loss function. + + :param torch.Tensor input: The input tensor. + :param torch.Tensor target: The target tensor. + :return: The computed Sinkhorn loss value. + :rtype: torch.Tensor + """ + # Extract the number of samples in input and target + n, m = input.shape[0], target.shape[0] + + # Initialize log-uniform weights for the empirical distributions + log_a = -input.new_tensor(n).log().expand(n) + log_b = -target.new_tensor(m).log().expand(m) + + # Initialize dual potentials f and g + f = torch.zeros(n, dtype=input.dtype, device=input.device) + g = torch.zeros(m, dtype=target.dtype, device=target.device) + + # Define the cost matrix, shape (n, m) + C = torch.cdist(input, target, p=self.p) ** self.p + + # Perform Sinkhorn iterations in log space for numerical stability + for _ in range(self.iterations): + + # Update dual potential f with the softmin operation in log space + softmin_f = torch.logsumexp((g.unsqueeze(0) - C) / self.eps, dim=1) + f = self.eps * (log_a - softmin_f) + + # Update dual potential g with the softmin operation in log space + softmin_g = torch.logsumexp((f.unsqueeze(1) - C) / self.eps, dim=0) + g = self.eps * (log_b - softmin_g) + + # Compute the Sinkhorn loss as the sum of the means of f and g + loss = f.mean() + g.mean() + + return self._reduction(loss.unsqueeze(0)) diff --git a/pina/_src/model/__init__.py b/pina/_src/model/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/model/average_neural_operator.py b/pina/_src/model/average_neural_operator.py similarity index 96% rename from pina/model/average_neural_operator.py rename to pina/_src/model/average_neural_operator.py index 6019b96c6..e16e3430f 100644 --- a/pina/model/average_neural_operator.py +++ b/pina/_src/model/average_neural_operator.py @@ -2,9 +2,9 @@ import torch from torch import nn -from .block.average_neural_operator_block import AVNOBlock -from .kernel_neural_operator import KernelNeuralOperator -from ..utils import check_consistency +from pina._src.model.block.average_neural_operator_block import AVNOBlock +from pina._src.model.kernel_neural_operator import KernelNeuralOperator +from pina._src.core.utils import check_consistency class AveragingNeuralOperator(KernelNeuralOperator): diff --git a/pina/_src/model/block/__init__.py b/pina/_src/model/block/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/model/block/average_neural_operator_block.py b/pina/_src/model/block/average_neural_operator_block.py similarity index 97% rename from pina/model/block/average_neural_operator_block.py rename to pina/_src/model/block/average_neural_operator_block.py index 91379abeb..4b5af8081 100644 --- a/pina/model/block/average_neural_operator_block.py +++ b/pina/_src/model/block/average_neural_operator_block.py @@ -2,7 +2,7 @@ import torch from torch import nn -from ...utils import check_consistency +from pina._src.core.utils import check_consistency class AVNOBlock(nn.Module): diff --git a/pina/model/block/convolution.py b/pina/_src/model/block/convolution.py similarity index 98% rename from pina/model/block/convolution.py rename to pina/_src/model/block/convolution.py index 666f66a66..bfe7054af 100644 --- a/pina/model/block/convolution.py +++ b/pina/_src/model/block/convolution.py @@ -2,8 +2,8 @@ from abc import ABCMeta, abstractmethod import torch -from .stride import Stride -from .utils_convolution import optimizing +from pina._src.model.block.stride import Stride +from pina._src.model.block.utils_convolution import optimizing class BaseContinuousConv(torch.nn.Module, metaclass=ABCMeta): diff --git a/pina/model/block/convolution_2d.py b/pina/_src/model/block/convolution_2d.py similarity index 98% rename from pina/model/block/convolution_2d.py rename to pina/_src/model/block/convolution_2d.py index 825ae613b..935bb0afa 100644 --- a/pina/model/block/convolution_2d.py +++ b/pina/_src/model/block/convolution_2d.py @@ -1,9 +1,9 @@ -"""Module for the Continuous Convolution class.""" +"""Module for the Continuous 2D Convolution class.""" import torch -from .convolution import BaseContinuousConv -from .utils_convolution import check_point, map_points_ -from .integral import Integral +from pina._src.model.block.convolution import BaseContinuousConv +from pina._src.model.block.utils_convolution import check_point, map_points_ +from pina._src.model.block.integral import Integral class ContinuousConvBlock(BaseContinuousConv): diff --git a/pina/model/block/embedding.py b/pina/_src/model/block/embedding.py similarity index 99% rename from pina/model/block/embedding.py rename to pina/_src/model/block/embedding.py index 1e44ec143..f9f05c119 100644 --- a/pina/model/block/embedding.py +++ b/pina/_src/model/block/embedding.py @@ -1,7 +1,7 @@ """Modules for the the Embedding blocks.""" import torch -from pina.utils import check_consistency +from pina._src.core.utils import check_consistency class PeriodicBoundaryEmbedding(torch.nn.Module): diff --git a/pina/model/block/fourier_block.py b/pina/_src/model/block/fourier_block.py similarity index 98% rename from pina/model/block/fourier_block.py rename to pina/_src/model/block/fourier_block.py index 2983c840a..2510320ec 100644 --- a/pina/model/block/fourier_block.py +++ b/pina/_src/model/block/fourier_block.py @@ -2,9 +2,9 @@ import torch from torch import nn -from ...utils import check_consistency +from pina._src.core.utils import check_consistency -from .spectral import ( +from pina._src.model.block.spectral import ( SpectralConvBlock1D, SpectralConvBlock2D, SpectralConvBlock3D, diff --git a/pina/model/block/gno_block.py b/pina/_src/model/block/gno_block.py similarity index 100% rename from pina/model/block/gno_block.py rename to pina/_src/model/block/gno_block.py diff --git a/pina/model/block/integral.py b/pina/_src/model/block/integral.py similarity index 100% rename from pina/model/block/integral.py rename to pina/_src/model/block/integral.py diff --git a/pina/_src/model/block/kan_block.py b/pina/_src/model/block/kan_block.py new file mode 100644 index 000000000..77597d310 --- /dev/null +++ b/pina/_src/model/block/kan_block.py @@ -0,0 +1,158 @@ +"""Module for the Kolmogorov-Arnold Network block.""" + +import torch +from pina._src.model.vectorized_spline import VectorizedSpline +from pina._src.core.utils import check_consistency, check_positive_integer + + +class KANBlock(torch.nn.Module): + """ + The inner block of the Kolmogorov-Arnold Network (KAN). + + The block applies a spline transformation to the input, optionally combined + with a linear transformation of a base activation function. The output is + aggregated across input dimensions to produce the final output. + + .. seealso:: + + **Original reference**: + Liu Z., Wang Y., Vaidya S., Ruehle F., Halverson J., Soljacic M., + Hou T., Tegmark M. (2025). + *KAN: Kolmogorov-Arnold Networks*. + DOI: `arXiv preprint arXiv:2404.19756. + `_ + """ + + def __init__( + self, + input_dimensions, + output_dimensions, + spline_order=3, + n_knots=10, + grid_range=[0, 1], + base_function=torch.nn.SiLU, + use_base_linear=True, + use_bias=True, + init_scale_spline=1e-2, + init_scale_base=1.0, + ): + """ + Initialization of the :class:`KANBlock` class. + + :param int input_dimensions: The number of input features. + :param int output_dimensions: The number of output features. + :param int spline_order: The order of each spline basis function. + Default is 3 (cubic splines). + :param int n_knots: The number of knots for each spline basis function. + Default is 10. + :param grid_range: The range for the spline knots. It must be either a + list or a tuple of the form [min, max]. Default is [0, 1]. + :type grid_range: list | tuple. + :param torch.nn.Module base_function: The base activation function to be + applied to the input before the linear transformation. Default is + :class:`torch.nn.SiLU`. + :param bool use_base_linear: Whether to include a linear transformation + of the base function output. Default is True. + :param bool use_bias: Whether to include a bias term in the output. + Default is True. + :param init_scale_spline: The scale for initializing each spline + control points. Default is 1e-2. + :type init_scale_spline: float | int. + :param init_scale_base: The scale for initializing the base linear + weights. Default is 1.0. + :type init_scale_base: float | int. + :raises ValueError: If ``grid_range`` is not of length 2. + """ + super().__init__() + + # Check consistency + check_consistency(base_function, torch.nn.Module, subclass=True) + check_positive_integer(input_dimensions, strict=True) + check_positive_integer(output_dimensions, strict=True) + check_positive_integer(spline_order, strict=True) + check_positive_integer(n_knots, strict=True) + check_consistency(use_base_linear, bool) + check_consistency(use_bias, bool) + check_consistency(init_scale_spline, (int, float)) + check_consistency(init_scale_base, (int, float)) + check_consistency(grid_range, (int, float)) + + # Raise error if grid_range is not valid + if len(grid_range) != 2: + raise ValueError("Grid must be a list or tuple with two elements.") + + # Knots for the spline basis functions + initial_knots = torch.ones(spline_order) * grid_range[0] + final_knots = torch.ones(spline_order) * grid_range[1] + + # Number of internal knots + n_internal = max(0, n_knots - 2 * spline_order) + + # Internal knots are uniformly spaced in the grid range + internal_knots = torch.linspace( + grid_range[0], grid_range[1], n_internal + 2 + )[1:-1] + + # Define the knots + knots = torch.cat((initial_knots, internal_knots, final_knots)) + knots = knots.unsqueeze(0).repeat(input_dimensions, 1) + + # Define the control points for the spline basis functions + control_points = ( + torch.randn( + input_dimensions, + output_dimensions, + knots.shape[-1] - spline_order, + ) + * init_scale_spline + ) + + # Define the vectorized spline module + self.spline = VectorizedSpline( + order=spline_order, knots=knots, control_points=control_points + ) + + # Initialize the base function + self.base_function = base_function() + + # Initialize the base linear weights if needed + if use_base_linear: + self.base_weight = torch.nn.Parameter( + torch.randn(output_dimensions, input_dimensions) + * (init_scale_base / (input_dimensions**0.5)) + ) + else: + self.register_parameter("base_weight", None) + + # Initialize the bias term if needed + if use_bias: + self.bias = torch.nn.Parameter(torch.zeros(output_dimensions)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + """ + Forward pass of the Kolmogorov-Arnold block. The input is passed through + the spline transformation, optionally combined with a linear + transformation of the base function output, and then aggregated across + input dimensions to produce the final output. + + :param x: The input tensor for the model. + :type x: torch.Tensor | LabelTensor + :return: The output tensor of the model. + :rtype: torch.Tensor | LabelTensor + """ + y = self.spline(x) + + if self.base_weight is not None: + base_x = self.base_function(x) + base_out = torch.einsum("bi,oi->bio", base_x, self.base_weight) + y = y + base_out + + # aggregate contributions from all input dimensions + y = y.sum(dim=1) + + if self.bias is not None: + y = y + self.bias + + return y diff --git a/pina/model/block/low_rank_block.py b/pina/_src/model/block/low_rank_block.py similarity index 98% rename from pina/model/block/low_rank_block.py rename to pina/_src/model/block/low_rank_block.py index 1e8925d95..ad67b4dca 100644 --- a/pina/model/block/low_rank_block.py +++ b/pina/_src/model/block/low_rank_block.py @@ -2,7 +2,7 @@ import torch -from ...utils import check_consistency +from pina._src.core.utils import check_consistency class LowRankBlock(torch.nn.Module): diff --git a/pina/_src/model/block/message_passing/__init__.py b/pina/_src/model/block/message_passing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/model/block/message_passing/deep_tensor_network_block.py b/pina/_src/model/block/message_passing/deep_tensor_network_block.py similarity index 98% rename from pina/model/block/message_passing/deep_tensor_network_block.py rename to pina/_src/model/block/message_passing/deep_tensor_network_block.py index a2de3097a..ed19578b7 100644 --- a/pina/model/block/message_passing/deep_tensor_network_block.py +++ b/pina/_src/model/block/message_passing/deep_tensor_network_block.py @@ -2,7 +2,7 @@ import torch from torch_geometric.nn import MessagePassing -from ....utils import check_positive_integer +from pina._src.core.utils import check_positive_integer class DeepTensorNetworkBlock(MessagePassing): diff --git a/pina/model/block/message_passing/en_equivariant_network_block.py b/pina/_src/model/block/message_passing/en_equivariant_network_block.py similarity index 98% rename from pina/model/block/message_passing/en_equivariant_network_block.py rename to pina/_src/model/block/message_passing/en_equivariant_network_block.py index b8057b0f1..28a197230 100644 --- a/pina/model/block/message_passing/en_equivariant_network_block.py +++ b/pina/_src/model/block/message_passing/en_equivariant_network_block.py @@ -3,8 +3,8 @@ import torch from torch_geometric.nn import MessagePassing from torch_geometric.utils import degree -from ....utils import check_positive_integer, check_consistency -from ....model import FeedForward +from pina._src.core.utils import check_positive_integer, check_consistency +from pina._src.model.feed_forward import FeedForward class EnEquivariantNetworkBlock(MessagePassing): diff --git a/pina/model/block/message_passing/equivariant_graph_neural_operator_block.py b/pina/_src/model/block/message_passing/equivariant_graph_neural_operator_block.py similarity index 97% rename from pina/model/block/message_passing/equivariant_graph_neural_operator_block.py rename to pina/_src/model/block/message_passing/equivariant_graph_neural_operator_block.py index f6c739203..8a0f30aed 100644 --- a/pina/model/block/message_passing/equivariant_graph_neural_operator_block.py +++ b/pina/_src/model/block/message_passing/equivariant_graph_neural_operator_block.py @@ -1,8 +1,10 @@ """Module for the Equivariant Graph Neural Operator block.""" import torch -from ....utils import check_positive_integer -from .en_equivariant_network_block import EnEquivariantNetworkBlock +from pina._src.core.utils import check_positive_integer +from pina._src.model.block.message_passing.en_equivariant_network_block import ( + EnEquivariantNetworkBlock, +) class EquivariantGraphNeuralOperatorBlock(torch.nn.Module): diff --git a/pina/model/block/message_passing/interaction_network_block.py b/pina/_src/model/block/message_passing/interaction_network_block.py similarity index 98% rename from pina/model/block/message_passing/interaction_network_block.py rename to pina/_src/model/block/message_passing/interaction_network_block.py index 7c6eb03f6..06fb39406 100644 --- a/pina/model/block/message_passing/interaction_network_block.py +++ b/pina/_src/model/block/message_passing/interaction_network_block.py @@ -2,8 +2,8 @@ import torch from torch_geometric.nn import MessagePassing -from ....utils import check_positive_integer -from ....model import FeedForward +from pina._src.core.utils import check_positive_integer +from pina._src.model.feed_forward import FeedForward class InteractionNetworkBlock(MessagePassing): diff --git a/pina/model/block/message_passing/radial_field_network_block.py b/pina/_src/model/block/message_passing/radial_field_network_block.py similarity index 97% rename from pina/model/block/message_passing/radial_field_network_block.py rename to pina/_src/model/block/message_passing/radial_field_network_block.py index ef621b10e..ede0fb645 100644 --- a/pina/model/block/message_passing/radial_field_network_block.py +++ b/pina/_src/model/block/message_passing/radial_field_network_block.py @@ -3,8 +3,8 @@ import torch from torch_geometric.nn import MessagePassing from torch_geometric.utils import remove_self_loops -from ....utils import check_positive_integer -from ....model import FeedForward +from pina._src.core.utils import check_positive_integer +from pina._src.model.feed_forward import FeedForward class RadialFieldNetworkBlock(MessagePassing): diff --git a/pina/model/block/orthogonal.py b/pina/_src/model/block/orthogonal.py similarity index 98% rename from pina/model/block/orthogonal.py rename to pina/_src/model/block/orthogonal.py index cd45b3c72..24021ada6 100644 --- a/pina/model/block/orthogonal.py +++ b/pina/_src/model/block/orthogonal.py @@ -1,7 +1,7 @@ """Module for the Orthogonal Block class.""" import torch -from ...utils import check_consistency +from pina._src.core.utils import check_consistency class OrthogonalBlock(torch.nn.Module): diff --git a/pina/model/block/pirate_network_block.py b/pina/_src/model/block/pirate_network_block.py similarity index 97% rename from pina/model/block/pirate_network_block.py rename to pina/_src/model/block/pirate_network_block.py index cfeb8410e..752f81901 100644 --- a/pina/model/block/pirate_network_block.py +++ b/pina/_src/model/block/pirate_network_block.py @@ -1,7 +1,7 @@ """Module for the PirateNet block class.""" import torch -from ...utils import check_consistency, check_positive_integer +from pina._src.core.utils import check_consistency, check_positive_integer class PirateNetBlock(torch.nn.Module): diff --git a/pina/model/block/pod_block.py b/pina/_src/model/block/pod_block.py similarity index 100% rename from pina/model/block/pod_block.py rename to pina/_src/model/block/pod_block.py diff --git a/pina/model/block/rbf_block.py b/pina/_src/model/block/rbf_block.py similarity index 99% rename from pina/model/block/rbf_block.py rename to pina/_src/model/block/rbf_block.py index 8001381bc..061e43109 100644 --- a/pina/model/block/rbf_block.py +++ b/pina/_src/model/block/rbf_block.py @@ -4,7 +4,7 @@ import warnings from itertools import combinations_with_replacement import torch -from ...utils import check_consistency +from pina._src.core.utils import check_consistency def linear(r): diff --git a/pina/model/block/residual.py b/pina/_src/model/block/residual.py similarity index 98% rename from pina/model/block/residual.py rename to pina/_src/model/block/residual.py index f109ce03d..d1e8134cc 100644 --- a/pina/model/block/residual.py +++ b/pina/_src/model/block/residual.py @@ -2,7 +2,7 @@ import torch from torch import nn -from ...utils import check_consistency +from pina._src.core.utils import check_consistency class ResidualBlock(nn.Module): diff --git a/pina/model/block/spectral.py b/pina/_src/model/block/spectral.py similarity index 99% rename from pina/model/block/spectral.py rename to pina/_src/model/block/spectral.py index aae915a42..fd5f48f6a 100644 --- a/pina/model/block/spectral.py +++ b/pina/_src/model/block/spectral.py @@ -2,7 +2,7 @@ import torch from torch import nn -from ...utils import check_consistency +from pina._src.core.utils import check_consistency ######## 1D Spectral Convolution ########### diff --git a/pina/model/block/stride.py b/pina/_src/model/block/stride.py similarity index 98% rename from pina/model/block/stride.py rename to pina/_src/model/block/stride.py index 2a26faf07..e802cddc0 100644 --- a/pina/model/block/stride.py +++ b/pina/_src/model/block/stride.py @@ -5,7 +5,7 @@ class Stride: """ - Stride class for continous convolution. + Stride class for continuous convolution. """ def __init__(self, dict_): diff --git a/pina/model/block/utils_convolution.py b/pina/_src/model/block/utils_convolution.py similarity index 100% rename from pina/model/block/utils_convolution.py rename to pina/_src/model/block/utils_convolution.py diff --git a/pina/model/deeponet.py b/pina/_src/model/deeponet.py similarity index 99% rename from pina/model/deeponet.py rename to pina/_src/model/deeponet.py index c65f6b316..800f2acc3 100644 --- a/pina/model/deeponet.py +++ b/pina/_src/model/deeponet.py @@ -3,7 +3,7 @@ from functools import partial import torch from torch import nn -from ..utils import check_consistency, is_function +from pina._src.core.utils import check_consistency, is_function class MIONet(torch.nn.Module): diff --git a/pina/model/equivariant_graph_neural_operator.py b/pina/_src/model/equivariant_graph_neural_operator.py similarity index 97% rename from pina/model/equivariant_graph_neural_operator.py rename to pina/_src/model/equivariant_graph_neural_operator.py index 6b33df6db..3aa7dde69 100644 --- a/pina/model/equivariant_graph_neural_operator.py +++ b/pina/_src/model/equivariant_graph_neural_operator.py @@ -1,8 +1,10 @@ """Module for the Equivariant Graph Neural Operator model.""" import torch -from ..utils import check_positive_integer -from .block.message_passing import EquivariantGraphNeuralOperatorBlock +from pina._src.core.utils import check_positive_integer +from pina._src.model.block.message_passing.equivariant_graph_neural_operator_block import ( + EquivariantGraphNeuralOperatorBlock, +) class EquivariantGraphNeuralOperator(torch.nn.Module): diff --git a/pina/model/feed_forward.py b/pina/_src/model/feed_forward.py similarity index 99% rename from pina/model/feed_forward.py rename to pina/_src/model/feed_forward.py index a1651b38b..fdf6bc91e 100644 --- a/pina/model/feed_forward.py +++ b/pina/_src/model/feed_forward.py @@ -2,8 +2,8 @@ import torch from torch import nn -from ..utils import check_consistency -from .block.residual import EnhancedLinear +from pina._src.core.utils import check_consistency +from pina._src.model.block.residual import EnhancedLinear class FeedForward(torch.nn.Module): diff --git a/pina/model/fourier_neural_operator.py b/pina/_src/model/fourier_neural_operator.py similarity index 97% rename from pina/model/fourier_neural_operator.py rename to pina/_src/model/fourier_neural_operator.py index e1336c999..7517b39b4 100644 --- a/pina/model/fourier_neural_operator.py +++ b/pina/_src/model/fourier_neural_operator.py @@ -3,10 +3,14 @@ import warnings import torch from torch import nn -from ..label_tensor import LabelTensor -from ..utils import check_consistency -from .block.fourier_block import FourierBlock1D, FourierBlock2D, FourierBlock3D -from .kernel_neural_operator import KernelNeuralOperator +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency +from pina._src.model.block.fourier_block import ( + FourierBlock1D, + FourierBlock2D, + FourierBlock3D, +) +from pina._src.model.kernel_neural_operator import KernelNeuralOperator class FourierIntegralKernel(torch.nn.Module): diff --git a/pina/model/graph_neural_operator.py b/pina/_src/model/graph_neural_operator.py similarity index 98% rename from pina/model/graph_neural_operator.py rename to pina/_src/model/graph_neural_operator.py index 3cb5cdd31..e4d844fcb 100644 --- a/pina/model/graph_neural_operator.py +++ b/pina/_src/model/graph_neural_operator.py @@ -2,8 +2,8 @@ import torch from torch.nn import Tanh -from .block.gno_block import GNOBlock -from .kernel_neural_operator import KernelNeuralOperator +from pina._src.model.block.gno_block import GNOBlock +from pina._src.model.kernel_neural_operator import KernelNeuralOperator class GraphNeuralKernel(torch.nn.Module): diff --git a/pina/model/kernel_neural_operator.py b/pina/_src/model/kernel_neural_operator.py similarity index 99% rename from pina/model/kernel_neural_operator.py rename to pina/_src/model/kernel_neural_operator.py index e3cb790e5..81d1be45d 100644 --- a/pina/model/kernel_neural_operator.py +++ b/pina/_src/model/kernel_neural_operator.py @@ -1,7 +1,7 @@ """Module for the Kernel Neural Operator model class.""" import torch -from ..utils import check_consistency +from pina._src.core.utils import check_consistency class KernelNeuralOperator(torch.nn.Module): diff --git a/pina/_src/model/kolmogorov_arnold_network.py b/pina/_src/model/kolmogorov_arnold_network.py new file mode 100644 index 000000000..1782aab4b --- /dev/null +++ b/pina/_src/model/kolmogorov_arnold_network.py @@ -0,0 +1,105 @@ +import torch +from pina._src.model.block.kan_block import KANBlock +from pina._src.core.utils import check_consistency + + +class KolmogorovArnoldNetwork(torch.nn.Module): + """ + Implementation of Kolmogorov-Arnold Network (KAN). + + The model consists of a sequence of KAN blocks, where each block applies a + spline transformation to the input, optionally combined with a linear + transformation of a base activation function. + + .. seealso:: + + **Original reference**: + Liu Z., Wang Y., Vaidya S., Ruehle F., Halverson J., Soljacic M., + Hou T., Tegmark M. (2025). + *KAN: Kolmogorov-Arnold Networks*. + DOI: `arXiv preprint arXiv:2404.19756. + `_ + """ + + def __init__( + self, + layers, + spline_order=3, + n_knots=10, + grid_range=[-1, 1], + base_function=torch.nn.SiLU, + use_base_linear=True, + use_bias=True, + init_scale_spline=1e-2, + init_scale_base=1.0, + ): + """ + Initialization of the :class:`KolmogorovArnoldNetwork` class. + + :param layers: A list of integers specifying the sizes of each layer, + including input and output dimensions. + :type layers: list | tuple. + :param int spline_order: The order of each spline basis function. + Default is 3 (cubic splines). + :param int n_knots: The number of knots for each spline basis function. + Default is 3. + :param grid_range: The range for the spline knots. It must be either a + list or a tuple of the form [min, max]. Default is [0, 1]. + :type grid_range: list | tuple. + :param torch.nn.Module base_function: The base activation function to be + applied to the input before the linear transformation. Default is + :class:`torch.nn.SiLU`. + :param bool use_base_linear: Whether to include a linear transformation + of the base function output. Default is True. + :param bool use_bias: Whether to include a bias term in the output. + Default is True. + :param init_scale_spline: The scale for initializing each spline + control points. Default is 1e-2. + :type init_scale_spline: float | int. + :param init_scale_base: The scale for initializing the base linear + weights. Default is 1.0. + :type init_scale_base: float | int. + :raises ValueError: If ``grid_range`` is not of length 2. + """ + super().__init__() + + # Check consistency -- all other checks are performed in KANBlock + check_consistency(layers, int) + if len(layers) < 2: + raise ValueError( + "`Provide at least two elements for layers (input and output)." + ) + + # Initialize KAN blocks + self.kan_layers = torch.nn.ModuleList( + [ + KANBlock( + input_dimensions=layers[i], + output_dimensions=layers[i + 1], + spline_order=spline_order, + n_knots=n_knots, + grid_range=grid_range, + base_function=base_function, + use_base_linear=use_base_linear, + use_bias=use_bias, + init_scale_spline=init_scale_spline, + init_scale_base=init_scale_base, + ) + for i in range(len(layers) - 1) + ] + ) + + def forward(self, x): + """ + Forward pass of the KolmogorovArnoldNetwork model. It passes the input + through each KAN block in the network and returns the final output. + + :param x: The input tensor for the model. + :type x: torch.Tensor | LabelTensor + :return: The output tensor of the model. + :rtype: torch.Tensor | LabelTensor + """ + for layer in self.kan_layers: + x = layer(x) + + return x diff --git a/pina/model/low_rank_neural_operator.py b/pina/_src/model/low_rank_neural_operator.py similarity index 97% rename from pina/model/low_rank_neural_operator.py rename to pina/_src/model/low_rank_neural_operator.py index 1a7082dff..049894001 100644 --- a/pina/model/low_rank_neural_operator.py +++ b/pina/_src/model/low_rank_neural_operator.py @@ -3,10 +3,10 @@ import torch from torch import nn -from ..utils import check_consistency +from pina._src.core.utils import check_consistency -from .kernel_neural_operator import KernelNeuralOperator -from .block.low_rank_block import LowRankBlock +from pina._src.model.kernel_neural_operator import KernelNeuralOperator +from pina._src.model.block.low_rank_block import LowRankBlock class LowRankNeuralOperator(KernelNeuralOperator): diff --git a/pina/model/multi_feed_forward.py b/pina/_src/model/multi_feed_forward.py similarity index 95% rename from pina/model/multi_feed_forward.py rename to pina/_src/model/multi_feed_forward.py index f2f149ca6..df8fb19e2 100644 --- a/pina/model/multi_feed_forward.py +++ b/pina/_src/model/multi_feed_forward.py @@ -2,7 +2,7 @@ from abc import ABC, abstractmethod import torch -from .feed_forward import FeedForward +from pina._src.model.feed_forward import FeedForward class MultiFeedForward(torch.nn.Module, ABC): diff --git a/pina/model/pirate_network.py b/pina/_src/model/pirate_network.py similarity index 95% rename from pina/model/pirate_network.py rename to pina/_src/model/pirate_network.py index 96102b41f..09aad269d 100644 --- a/pina/model/pirate_network.py +++ b/pina/_src/model/pirate_network.py @@ -1,8 +1,9 @@ """Module for the PirateNet model class.""" import torch -from .block import FourierFeatureEmbedding, PirateNetBlock -from ..utils import check_consistency, check_positive_integer +from pina._src.model.block.embedding import FourierFeatureEmbedding +from pina._src.model.block.pirate_network_block import PirateNetBlock +from pina._src.core.utils import check_consistency, check_positive_integer class PirateNet(torch.nn.Module): diff --git a/pina/model/sindy.py b/pina/_src/model/sindy.py similarity index 97% rename from pina/model/sindy.py rename to pina/_src/model/sindy.py index a40fa37b4..f69842a54 100644 --- a/pina/model/sindy.py +++ b/pina/_src/model/sindy.py @@ -2,7 +2,7 @@ from typing import Callable import torch -from ..utils import check_consistency, check_positive_integer +from pina._src.core.utils import check_consistency, check_positive_integer class SINDy(torch.nn.Module): diff --git a/pina/model/spline.py b/pina/_src/model/spline.py similarity index 98% rename from pina/model/spline.py rename to pina/_src/model/spline.py index d9141fe8c..ed7f74678 100644 --- a/pina/model/spline.py +++ b/pina/_src/model/spline.py @@ -2,7 +2,7 @@ import warnings import torch -from ..utils import check_positive_integer, check_consistency +from pina._src.core.utils import check_consistency, check_positive_integer class Spline(torch.nn.Module): @@ -202,7 +202,7 @@ def basis(self, x, collection=False): :param torch.Tensor x: The points to be evaluated. :param bool collection: If True, returns a list of basis functions for all orders up to the spline order. Default is False. - :raise ValueError: If ``collection`` is not a boolean. + :raises ValueError: If ``collection`` is not a boolean. :return: The basis functions evaluated at x. :rtype: torch.Tensor | list[torch.Tensor] """ @@ -290,7 +290,7 @@ def derivative(self, x, degree): :param x: The input tensor. :type x: torch.Tensor | LabelTensor :param int degree: The derivative degree to compute. - :raise ValueError: If ``degree`` is not an integer. + :raises ValueError: If ``degree`` is not an integer. :return: The derivative tensor. :rtype: torch.Tensor """ diff --git a/pina/model/spline_surface.py b/pina/_src/model/spline_surface.py similarity index 97% rename from pina/model/spline_surface.py rename to pina/_src/model/spline_surface.py index 767e5b0dc..5550d761d 100644 --- a/pina/model/spline_surface.py +++ b/pina/_src/model/spline_surface.py @@ -2,8 +2,8 @@ import torch from .spline import Spline -from ..label_tensor import LabelTensor -from ..utils import check_consistency, check_positive_integer +from pina._src.core.label_tensor import LabelTensor +from pina._src.core.utils import check_consistency, check_positive_integer class SplineSurface(torch.nn.Module): @@ -134,8 +134,8 @@ def derivative(self, x, degree_u, degree_v): parameter direction. :param int degree_v: The degree of the derivative along the second parameter direction. - :raise ValueError: If ``degree_u`` is not an integer. - :raise ValueError: If ``degree_v`` is not an integer. + :raises ValueError: If ``degree_u`` is not an integer. + :raises ValueError: If ``degree_v`` is not an integer. :return: The derivative tensor. :rtype: torch.Tensor """ diff --git a/pina/_src/model/vectorized_spline.py b/pina/_src/model/vectorized_spline.py new file mode 100644 index 000000000..1dfe323e6 --- /dev/null +++ b/pina/_src/model/vectorized_spline.py @@ -0,0 +1,652 @@ +"""Vectorized univariate B-spline model with per-spline knots.""" + +import warnings +import torch +from pina._src.core.utils import check_consistency, check_positive_integer + + +class VectorizedSpline(torch.nn.Module): + r""" + The vectorized B-spline model class. + + A :class:`VectorizedSpline` represents a vector spline, i.e., a collection + of independent univariate B-splines evaluated in parallel. Each univariate + spline has its own knot vector and its own control points, and acts on one + input feature. + + Given ``s`` univariate splines, the vector spline maps an input + :math:`x = (x^{(1)}, \dots, x^{(s)}) \in \mathbb{R}^s` to an output obtained + by evaluating each univariate spline on its corresponding scalar input + :math:`x^{(j)}`. + + For the :math:`j`-th univariate spline of order :math:`k`, the output is + defined as + + .. math:: + + S^{(j)}(x^{(j)}) = \sum_{i=1}^{n_j} B_{i,k}^{(j)}(x^{(j)}) C_i^{(j)}, + + where: + + - :math:`C^{(j)}` are the control points of the :math:`j`-th univariate + spline. In the scalar-output case, :math:`C^{(j)} \in \mathbb{R}^{n_j}`. + More generally, each univariate spline may have output dimension + :math:`o`, so :math:`C^{(j)} \in \mathbb{R}^{o \times n_j}`. + - :math:`B_{i,k}^{(j)}(x)` are the B-spline basis functions of order + :math:`k`, i.e., piecewise polynomials of degree :math:`k-1`, associated + with the knot vector of the :math:`j`-th univariate spline. + - :math:`X^{(j)} = \{x_1^{(j)}, x_2^{(j)}, \dots, x_{m_j}^{(j)}\}` is the + non-decreasing knot vector of the :math:`j`-th univariate spline. + + If the first and last knots of a given univariate spline are repeated + :math:`k` times, then that univariate spline interpolates its first and last + control points. + + The full vector spline evaluates all univariate splines in parallel. If each + univariate spline has output dimension :math:`o`, then before optional + aggregation the output has shape ``[batch, s, o]``. + + .. note:: + + Each univariate spline is forced to be zero outside the interval defined + by the first and last knots of its own knot vector. + + .. note:: + + This class does not represent a single multivariate spline + :math:`\mathbb{R}^s \to \mathbb{R}^o` with a genuinely multivariate + basis. Instead, it represents a vector of splines built from ``s`` + independent univariate splines, one for each input feature. + + .. note:: + + When using the :meth:`derivative` method of this class, derivatives are + computed directly in vectorized form and returned with the correct + shape. In contrast, when relying on ``autograd``, derivatives must be + computed separately for each output dimension of each univariate spline + and then combined, since autograd does not natively handle this + vectorized structure. + + :Example: + + >>> from pina.model import VectorizedSpline + >>> import torch + + >>> knt1 = torch.tensor([ + ... [0.0, 0.0, 0.0, 1.0, 2.0, 2.0, 2.0], + ... [0.0, 0.0, 0.0, 0.5, 1.0, 1.0, 1.0], + ... ]) + >>> spline1 = VectorizedSpline(order=3, knots=knt1, control_points=None) + + >>> knt2 = {"n": 7, "min": 0.0, "max": 2.0, "mode": "auto", "n_splines": 2} + >>> spline2 = VectorizedSpline(order=3, knots=knt2, control_points=None) + + >>> knt3 = torch.tensor([ + ... [0.0, 0.0, 0.0, 1.0, 2.0, 2.0, 2.0], + ... [0.0, 0.0, 0.0, 1.0, 2.0, 2.0, 2.0], + ... ]) + >>> ctrl3 = torch.tensor([ + ... [0.0, 1.0, 3.0, 2.0], + ... [1.0, 0.0, 2.0, 1.0], + ... ]) + >>> spline3 = VectorizedSpline(order=3, knots=knt3, control_points=ctrl3) + """ + + def __init__( + self, + order=4, + knots=None, + control_points=None, + aggregate_output=None, + ): + """ + Initialization of the :class:`VectorizedSpline` class. + + :param int order: The order of each univariate spline. The corresponding + basis functions are polynomials of degree ``order - 1``. + Default is 4. + :param knots: The knots of the spline. If a tensor is provided, it must + have shape ``[s, n]``, where ``s`` is the number of univariate + splines and ``n`` is the number of knots per univariate spline. If a + dictionary is provided, it must contain the keys ``"n"``, ``"min"``, + ``"max"``, ``"mode"``, and ``"n_splines"``. Here, ``"n"`` specifies + the number of knots for each univariate spline, ``"min"`` and + ``"max"`` define the interval, ``"mode"`` selects the sampling + strategy, and ``"n_splines"`` specifies the number of univariate + splines. The supported modes are ``"uniform"``, where the knots are + evenly spaced over :math:`[min, max]`, and ``"auto"``, where knots + are constructed to ensure that each univariate spline interpolates + the first and last control points. In this case, the number of knots + is adjusted if :math:`n < 2 * order`. If None is given, knots are + initialized automatically over :math:`[0, 1]` ensuring interpolation + of the first and last control points. Default is None. + :type knots: torch.Tensor | dict + :param torch.Tensor control_points: The control points tensor. The + tensor must be either of shape ``[s, o, c]`` or ``[s, c]``, where + each univariate spline has ``c`` control points and output dimension + ``o``. In the latter case, the control points are expanded to shape + ``[s, 1, c]``. If None, control points are initialized to learnable + parameters with zero initial value. Default is None. + :param str aggregate_output: If None, the output of each univariate + spline is returned separately, resulting in an output of shape + ``[batch, s, o]``, where ``s`` is the number of univariate splines + and ``o`` is the output dimension of each univariate spline. If set + to ``"mean"`` or ``"sum"``, the output is aggregated accordingly + across the last dimension, resulting in an output of shape + ``[batch, s]``. Default is None. + :raises AssertionError: If ``order`` is not a positive integer. + :raises ValueError: If ``knots`` is neither a torch.Tensor nor a + dictionary, when provided. + :raises ValueError: If ``aggregate_output`` is not None, "mean", or + "sum". + :raises ValueError: If ``control_points`` is not a torch.Tensor, + when provided. + :raises ValueError: If both ``knots`` and ``control_points`` are None. + :raises ValueError: If ``knots`` is not two-dimensional, after + processing. + :raises ValueError: If ``control_points``, after expansion when + two-dimensional, is not three-dimensional. + :raises ValueError: If, for each univariate spline, the number of + ``knots`` is not equal to the sum of ``order`` and the number of + ``control_points.`` + :raises UserWarning: If, for each univariate spline, the number of + ``control_points`` is lower than the ``order``, resulting in a + degenerate spline. + :raises ValueError: If the number of univariate splines in ``knots`` and + ``control_points`` do not match. + """ + + super().__init__() + + # Check consistency + check_positive_integer(value=order, strict=True) + check_consistency(knots, (type(None), torch.Tensor, dict)) + check_consistency(control_points, (type(None), torch.Tensor)) + + # Raise error if neither knots nor control points are provided + if knots is None and control_points is None: + raise ValueError("knots and control_points cannot both be None.") + + # Raise error if aggregate_output is not None, "mean", or "sum" + if aggregate_output not in (None, "mean", "sum"): + raise ValueError( + f"aggregate_output must be None, 'mean', or 'sum'." + f" Got {aggregate_output}." + ) + + # Initialize knots if not provided + if knots is None and control_points is not None: + knots = { + "n": control_points.shape[-1] + order, + "min": 0, + "max": 1, + "n_splines": control_points.shape[0], + "mode": "auto", + } + + # Initialization - knots and control points managed by their setters + self.order = order + self.knots = knots + self.control_points = control_points + self.aggregate_output = aggregate_output + + # Check dimensionality of control points + if self.control_points.ndim != 3: + raise ValueError("control_points must be three-dimensional.") + + # Raise error if #knots != order + #control_points + if self.knots.shape[-1] != self.order + self.control_points.shape[-1]: + raise ValueError( + f" The number of knots per spline must be equal to order + the" + f" number of control points. Got {self.knots.shape[-1]} knots" + f" per spline, {self.control_points.shape[-1]} control points," + f" and {self.order} order." + ) + + # Raise warning if spline is degenerate + if self.control_points.shape[-1] < self.order: + warnings.warn( + "The number of control points per spline is smaller than the" + " spline order. This creates a degenerate spline with limited" + " flexibility.", + UserWarning, + ) + + # Raise error if knots and control points have different # of splines + if self.knots.shape[0] != self.control_points.shape[0]: + raise ValueError( + f"The number of splines must be the same for knots and" + f" control points. Got {self.knots.shape[0]} splines for knots" + f" and {self.control_points.shape[0]} splines for control" + f" points." + ) + + # Precompute boundary interval index + self.register_buffer( + "_boundary_interval_idx", self._compute_boundary_interval() + ) + + # Precompute denominators used in derivative formulas + self._compute_derivative_denominators() + + def _compute_boundary_interval(self): + """ + Precompute the index of the rightmost non-degenerate interval to improve + performance, eliminating the need to perform a search loop in the basis + function on each call. + + :return: The index of the rightmost non-degenerate interval for each + univariate spline. + :rtype: torch.Tensor + """ + # Compute the differences between consecutive knots for each spline + diffs = self._knots[:, 1:] - self._knots[:, :-1] + valid = diffs > 0 + + # Initialize idx tensor to store the last valid interval for each spline + idx = torch.zeros( + self._knots.shape[0], dtype=torch.long, device=self._knots.device + ) + + # For each spline, find the last idx where interval is non-degenerate + for s in range(self._knots.shape[0]): + valid_s = torch.nonzero(valid[s], as_tuple=False) + idx[s] = valid_s[-1, 0] if valid_s.numel() > 0 else 0 + + return idx + + def _compute_derivative_denominators(self): + """ + Precompute the denominators used in the derivatives for all orders up to + the spline order to avoid redundant calculations. + """ + # Precompute for order 2 to k + for i in range(2, self.order + 1): + + # Denominators for the derivative recurrence relations + left_den = self.knots[:, i - 1 : -1] - self.knots[:, :-i] + right_den = self.knots[:, i:] - self.knots[:, 1 : -i + 1] + + # If consecutive knots are equal, set left and right factors to zero + left_fac = torch.where( + torch.abs(left_den) > 1e-10, + (i - 1) / left_den, + torch.zeros_like(left_den), + ) + right_fac = torch.where( + torch.abs(right_den) > 1e-10, + (i - 1) / right_den, + torch.zeros_like(right_den), + ) + + # Register buffers + self.register_buffer(f"_left_factor_order_{i}", left_fac) + self.register_buffer(f"_right_factor_order_{i}", right_fac) + + def basis(self, x, collection=False): + """ + Evaluate the B-spline basis functions for each univariate spline. + + This method applies the Cox-de Boor recursion in vectorized form across + all univariate splines of the vector spline. + + :param torch.Tensor x: The points to be evaluated. + :param bool collection: If True, returns a list of basis functions for + all orders up to the spline order. Default is False. + :raises ValueError: If ``collection`` is not a boolean. + :raises ValueError: If ``x`` is not two-dimensional. + :raises ValueError: If the number of input features does not match + the number of univariate splines. + :return: The basis functions evaluated at x. + :rtype: torch.Tensor + """ + # Check consistency + check_consistency(collection, bool) + + # Ensure x is a tensor of the same dtype as knots + x = x.as_subclass(torch.Tensor).to(dtype=self.knots.dtype) + + # Raise error if x does not have shape (batch, s) + if x.ndim != 2: + raise ValueError( + f"The input must have shape (batch, s). Got {x.shape}." + ) + + # Raise error if x has different number of splines than knots + if x.shape[1] != self.knots.shape[0]: + raise ValueError( + f"The number of input features must be the same as the number" + f" of univariate splines. Got {x.shape[1]} input features," + f" but {self.knots.shape[0]} univariate splines." + ) + + # Add a final dimension to x for broadcasting + x = x.unsqueeze(-1) + + # Add an initial dimension to knots for broadcasting + knots = self.knots.unsqueeze(0) + + # Base case of recursion: indicator functions for the intervals + basis = (x >= knots[..., :-1]) & (x < knots[..., 1:]) + basis = basis.to(x.dtype) + + # Extract left and right knots of the boundary interval for each spline + range_tensor = torch.arange(self.knots.shape[0], device=x.device) + knot_left = self.knots[range_tensor, self._boundary_interval_idx] + knot_right = self.knots[range_tensor, self._boundary_interval_idx + 1] + + # Identify points at the rightmost boundary + at_rightmost_boundary = ( + x.squeeze(-1) >= knot_left.unsqueeze(0) + ) & torch.isclose( + x.squeeze(-1), knot_right.unsqueeze(0), rtol=1e-8, atol=1e-10 + ) + + # Ensure the correct value is set at the rightmost boundary + if torch.any(at_rightmost_boundary): + b_idx, s_idx = torch.nonzero(at_rightmost_boundary, as_tuple=True) + basis[b_idx, s_idx, self._boundary_interval_idx[s_idx]] = 1.0 + + # If returning the whole collection, initialize list + if collection: + basis_collection = [None, basis] + + # Cox-de Boor recursion -- iterative case + for i in range(1, self.order): + + # Compute the denominators for both terms of the recursion + denom1 = knots[..., i:-1] - knots[..., : -(i + 1)] + denom2 = knots[..., i + 1 :] - knots[..., 1:-i] + + # Ensure no division by zero + denom1 = torch.where( + denom1.abs() < 1e-8, torch.ones_like(denom1), denom1 + ) + denom2 = torch.where( + denom2.abs() < 1e-8, torch.ones_like(denom2), denom2 + ) + + # Compute the two terms of the recursion + term1 = ((x - knots[..., : -(i + 1)]) / denom1) * basis[..., :-1] + term2 = ((knots[..., i + 1 :] - x) / denom2) * basis[..., 1:] + + # Combine terms to get the new basis + basis = term1 + term2 + + if collection: + basis_collection.append(basis) + + return basis_collection if collection else basis + + def forward(self, x): + """ + Forward pass for the :class:`VectorizedSpline` model. Each univariate + spline is evaluated independently on its corresponding input feature. + + The input is expected to have shape ``[batch, s]``, where ``s`` is the + number of univariate splines. The output has shape ``[batch, s, o]``, + where ``o`` is the output dimension of each univariate spline, unless an + aggregation method is specified. If both ``s`` and ``o`` are 1, the + output is aggregated across the last dimension, resulting in an output + of shape ``[batch, s]``. If ``aggregate_output`` is set to ``"mean"`` or + ``"sum"``, the output is aggregated across the last dimension, resulting + in an output of shape ``[batch, s]``. + + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :return: The output tensor. + :rtype: torch.Tensor + """ + # Compute the basis functions at x + basis = self.basis(x) + + # Compute the output for each spline + out = torch.einsum("bsc,soc->bso", basis, self.control_points) + + # Aggregate output if needed + if self.aggregate_output == "mean": + out = out.mean(dim=-1) + elif self.aggregate_output == "sum": + out = out.sum(dim=-1) + elif out.shape[1] == 1 and out.shape[2] == 1: + out = out.squeeze(-1) + + return out + + def derivative(self, x, degree): + """ + Compute the ``degree``-th derivative of each univariate spline at the + given input points. + + The output has shape ``[batch, s, o]``, where ``o`` is the output + dimension of each univariate spline, unless an aggregation method is + specified. If both ``s`` and ``o`` are 1, the output is aggregated + across the last dimension, resulting in an output of shape + ``[batch, s]``. If ``aggregate_output`` is set to ``"mean"`` or + ``"sum"``, the output is aggregated across the last dimension, resulting + in an output of shape ``[batch, s]``. + + :param x: The input tensor. + :type x: torch.Tensor | LabelTensor + :param int degree: The derivative degree to compute. + :return: The derivative tensor. + :rtype: torch.Tensor + """ + # Check consistency + check_positive_integer(degree, strict=False) + + # Compute basis derivative + der = self._basis_derivative(x.as_subclass(torch.Tensor), degree=degree) + + # Compute the output for each spline + out = torch.einsum("bsc,soc->bso", der, self.control_points) + + # Aggregate output if needed + if self.aggregate_output == "mean": + out = out.mean(dim=-1) + elif self.aggregate_output == "sum": + out = out.sum(dim=-1) + elif out.shape[1] == 1 and out.shape[2] == 1: + out = out.squeeze(-1) + + return out + + def _basis_derivative(self, x, degree): + """ + Compute the ``degree``-th derivative of the vectorized spline basis + functions at the given input points using an iterative approach. + + :param torch.Tensor x: The points to be evaluated. + :param int degree: The derivative degree to compute. + :return: The derivative of the basis functions of order ``self.order``. + :rtype: torch.Tensor + """ + # Compute the whole basis collection + basis = self.basis(x, collection=True) + + # Derivatives initialization (dummy at index 0 for convenience) + derivatives = [None] + [basis[o] for o in range(1, self.order + 1)] + + # Iterate over derivative degrees + for _ in range(1, degree + 1): + + # Current degree derivatives (with dummy at index 0 for convenience) + current_der = [None] * (self.order + 1) + current_der[1] = torch.zeros_like(derivatives[1]) + + # Iterate over basis orders + for o in range(2, self.order + 1): + + # Retrieve precomputed factors + left_fac = getattr(self, f"_left_factor_order_{o}") + right_fac = getattr(self, f"_right_factor_order_{o}") + + # derivatives[o - 1] has shape [b, s, m] + # Slice previous derivatives to align + left_part = derivatives[o - 1][..., :-1] + right_part = derivatives[o - 1][..., 1:] + + # Broadcast factors over batch dims + left_fac = left_fac.unsqueeze(0) + right_fac = right_fac.unsqueeze(0) + + # Compute current derivatives + current_der[o] = left_fac * left_part - right_fac * right_part + + # Update derivatives for next degree + derivatives = current_der + + return derivatives[self.order] + + @property + def control_points(self): + """ + The control points of the spline. + + :return: The control points. + :rtype: torch.Tensor + """ + return self._control_points + + @control_points.setter + def control_points(self, control_points): + """ + Set the control points of the spline. + + :param torch.Tensor control_points: The control points tensor. The + tensor must be either of shape ``[s, o, c]`` or ``[s, c]``, where + each univariate spline has ``c`` control points and output dimension + ``o``. In the latter case, the control points are expanded to shape + ``[s, 1, c]``. + :raises ValueError: If there are not enough knots to define the control + points, due to the relation: #knots = order + #control_points. + """ + # If control points are not provided, initialize them + if control_points is None: + + # Check that there are enough knots to define control points + if self.knots.shape[-1] < self.order + 1: + raise ValueError( + f"Not enough knots to define control points. Got" + f" {self.knots.shape[-1]} knots for each univariate spline," + f" but need at least {self.order + 1}." + ) + + # Initialize control points to zero + control_points = torch.zeros( + self.knots.shape[0], 1, self.knots.shape[-1] - self.order + ) + + # If a the control points are 2D, add an output dimension of size 1 + if control_points.ndim == 2: + control_points = control_points.unsqueeze(1) + + # Set control points + self._control_points = torch.nn.Parameter( + control_points, requires_grad=True + ) + + @property + def knots(self): + """ + The knots of the spline. + + :return: The knots. + :rtype: torch.Tensor + """ + return self._knots + + @knots.setter + def knots(self, value): + """ + Set the knots of the spline. + :param value: The knots of the spline. If a tensor is provided, it must + have shape ``[s, n]``, where ``s`` is the number of univariate + splines and ``n`` is the number of knots per univariate spline. If a + dictionary is provided, it must contain the keys ``"n"``, ``"min"``, + ``"max"``, ``"mode"``, and ``"n_splines"``. Here, ``"n"`` specifies + the number of knots for each univariate spline, ``"min"`` and + ``"max"`` define the interval, ``"mode"`` selects the sampling + strategy, and ``"n_splines"`` specifies the number of univariate + splines. The supported modes are ``"uniform"``, where the knots are + evenly spaced over :math:`[min, max]`, and ``"auto"``, where knots + are constructed to ensure that each univariate spline interpolates + the first and last control points. In this case, the number of knots + is adjusted if :math:`n < 2 * order`. If None is given, knots are + initialized automatically over :math:`[0, 1]` ensuring interpolation + of the first and last control points. + :type value: torch.Tensor | dict + :raises ValueError: If a dictionary is provided but does not contain + the required keys. + :raises ValueError: If the mode specified in the dictionary is invalid. + :raises ValueError: If knots is not two-dimensional after processing. + """ + # If a dictionary is provided, initialize knots accordingly + if isinstance(value, dict): + + # Check that required keys are present + required_keys = {"n", "min", "max", "mode", "n_splines"} + if not required_keys.issubset(value.keys()): + raise ValueError( + f"When providing knots as a dictionary, the following " + f"keys must be present: {required_keys}. Got " + f"{value.keys()}." + ) + + # Save number of splines for later use + n_splines = value["n_splines"] + + # Uniform sampling of knots + if value["mode"] == "uniform": + value = torch.linspace(value["min"], value["max"], value["n"]) + + # Automatic sampling of interpolating knots + elif value["mode"] == "auto": + + # Repeat the first and last knots 'order' times + initial_knots = torch.ones(self.order) * value["min"] + final_knots = torch.ones(self.order) * value["max"] + + # Number of internal knots + n_internal = value["n"] - 2 * self.order + + # If no internal knots are needed, just concatenate boundaries + if n_internal <= 0: + value = torch.cat((initial_knots, final_knots)) + + # Else, sample internal knots uniformly and exclude boundaries + # Recover the correct number of internal knots when slicing by + # adding 2 to n_internal + else: + internal_knots = torch.linspace( + value["min"], value["max"], n_internal + 2 + )[1:-1] + value = torch.cat( + (initial_knots, internal_knots, final_knots) + ) + + # Raise error if mode is invalid + else: + raise ValueError( + f"Invalid mode for knots initialization. Got " + f"{value['mode']}, but expected 'uniform' or 'auto'." + ) + + # Repeat the knot vector for each spline + value = value.unsqueeze(0).repeat(n_splines, 1) + + # Set knots + self.register_buffer("_knots", value.sort(dim=-1).values) + + # Check dimensionality of knots + if self.knots.ndim != 2: + raise ValueError("knots must be two-dimensional.") + + # Recompute boundary interval when knots change + if hasattr(self, "_boundary_interval_idx"): + self.register_buffer( + "_boundary_interval_idx", self._compute_boundary_interval() + ) + + # Recompute derivative denominators when knots change + self._compute_derivative_denominators() diff --git a/pina/_src/optim/__init__.py b/pina/_src/optim/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/optim/optimizer_interface.py b/pina/_src/optim/optimizer_interface.py new file mode 100644 index 000000000..b60e23624 --- /dev/null +++ b/pina/_src/optim/optimizer_interface.py @@ -0,0 +1,30 @@ +"""Module for the Optimizer Interface.""" + +from abc import ABCMeta, abstractmethod + + +class OptimizerInterface(metaclass=ABCMeta): + """ + Abstract interface for all optimizers. + """ + + @abstractmethod + def hook(self, parameters): + """ + Execute custom logic associated with the optimizer instance. + + This method is intended to encapsulate any additional behavior that + should be triggered during the optimization process. + + :param dict parameters: The parameters of the model to be optimized. + """ + + @property + @abstractmethod + def instance(self): + """ + The underlying optimizer object. + + :return: The optimizer instance. + :rtype: object + """ diff --git a/pina/_src/optim/scheduler_interface.py b/pina/_src/optim/scheduler_interface.py new file mode 100644 index 000000000..55951ee0e --- /dev/null +++ b/pina/_src/optim/scheduler_interface.py @@ -0,0 +1,31 @@ +"""Module for the Scheduler Interface.""" + +from abc import ABCMeta, abstractmethod + + +class SchedulerInterface(metaclass=ABCMeta): + """ + Abstract interface for all schedulers. + """ + + @abstractmethod + def hook(self, optimizer): + """ + Execute custom logic associated with the scheduler instance. + + This method is intended to encapsulate any additional behavior that + should be triggered during the optimization process. + + :param OptimizerInterface optimizer: The optimizer instance associated + with the scheduler. + """ + + @property + @abstractmethod + def instance(self): + """ + The underlying scheduler object. + + :return: The scheduler instance. + :rtype: object + """ diff --git a/pina/_src/optim/torch_optimizer.py b/pina/_src/optim/torch_optimizer.py new file mode 100644 index 000000000..a37bfbfec --- /dev/null +++ b/pina/_src/optim/torch_optimizer.py @@ -0,0 +1,59 @@ +"""Module for wrapping PyTorch optimizers.""" + +import torch +from pina._src.core.utils import check_consistency +from pina._src.optim.optimizer_interface import OptimizerInterface + + +class TorchOptimizer(OptimizerInterface): + """ + The wrapper class for PyTorch optimizers. + + This class wraps a ``torch.optim.Optimizer`` class and defers its + instantiation until runtime. It enables a consistent interface across + different optimizer backends while leveraging PyTorch’s optimization + algorithms. + """ + + def __init__(self, optimizer_class, **kwargs): + """ + Initialization of the :class:`TorchOptimizer` class. + + :param torch.optim.Optimizer optimizer_class: The subclass of + ``torch.optim.Optimizer`` to be instantiated. + :param dict kwargs: Additional keyword arguments forwarded to the + optimizer constructor. See more + `here `_. + :raises ValueError: If ``optimizer_class`` is not a subclass of + ``torch.optim.Optimizer``. + """ + # Check consistency + check_consistency(optimizer_class, torch.optim.Optimizer, subclass=True) + + # Initialize attributes + self.optimizer_class = optimizer_class + self.kwargs = kwargs + self._optimizer_instance = None + + def hook(self, parameters): + """ + Execute custom logic associated with the optimizer instance. + + This method is intended to encapsulate any additional behavior that + should be triggered during the optimization process. + + :param dict parameters: The parameters of the model to be optimized. + """ + self._optimizer_instance = self.optimizer_class( + parameters, **self.kwargs + ) + + @property + def instance(self): + """ + The underlying optimizer object. + + :return: The optimizer instance. + :rtype: torch.optim.Optimizer + """ + return self._optimizer_instance diff --git a/pina/_src/optim/torch_scheduler.py b/pina/_src/optim/torch_scheduler.py new file mode 100644 index 000000000..f33b6020f --- /dev/null +++ b/pina/_src/optim/torch_scheduler.py @@ -0,0 +1,62 @@ +"""Module for wrapping PyTorch schedulers.""" + +from torch.optim.lr_scheduler import LRScheduler +from pina._src.core.utils import check_consistency +from pina._src.optim.optimizer_interface import OptimizerInterface +from pina._src.optim.scheduler_interface import SchedulerInterface + + +class TorchScheduler(SchedulerInterface): + """ + The wrapper class for PyTorch schedulers. + + This class wraps a ``torch.optim.lr_scheduler.LRScheduler`` class and defers + its instantiation until runtime, once the optimizer instance is available. + """ + + def __init__(self, scheduler_class, **kwargs): + """ + Initialization of the :class:`TorchScheduler` class. + + :param torch.optim.LRScheduler scheduler_class: The subclass of + ``torch.optim.lr_scheduler.LRScheduler`` to be instantiated. + :param dict kwargs: Additional keyword arguments forwarded to the + scheduler constructor. See more + `here `_. + :raises ValueError: If ``scheduler_class`` is not a subclass of + ``torch.optim.lr_scheduler.LRScheduler``. + """ + # Check consistency + check_consistency(scheduler_class, LRScheduler, subclass=True) + + # Initialize attributes + self.scheduler_class = scheduler_class + self.kwargs = kwargs + self._scheduler_instance = None + + def hook(self, optimizer): + """ + Initialize the scheduler instance with the given parameters. + + :param OptimizerInterface optimizer: The optimizer instance associated + with the scheduler. + :raises ValueError: If ``optimizer`` is not an instance of + :class:`OptimizerInterface`. + """ + # Check consistency + check_consistency(optimizer, OptimizerInterface) + + # Initialize the scheduler instance + self._scheduler_instance = self.scheduler_class( + optimizer.instance, **self.kwargs + ) + + @property + def instance(self): + """ + The underlying scheduler object. + + :return: The scheduler instance. + :rtype: torch.optim.lr_scheduler.LRScheduler + """ + return self._scheduler_instance diff --git a/pina/_src/problem/__init__.py b/pina/_src/problem/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/problem/base_problem.py b/pina/_src/problem/base_problem.py new file mode 100644 index 000000000..8bccc0d79 --- /dev/null +++ b/pina/_src/problem/base_problem.py @@ -0,0 +1,299 @@ +"""Module for the BaseProblem class.""" + +import warnings +from copy import deepcopy +from pina._src.problem.problem_interface import ProblemInterface +from pina._src.domain.domain_interface import DomainInterface +from pina._src.core.label_tensor import LabelTensor +from pina._src.condition.condition import Condition +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) +from pina._src.core.utils import ( + check_consistency, + check_positive_integer, + merge_tensors, +) + + +class BaseProblem(ProblemInterface): + """ + Base class for all problems, implementing common functionality. + + A problem is defined by core components, including input and output + variables, a set of conditions to be satisfied, and optionally the domains + on which these conditions are defined. + + All problems must inherit from this class and implement abstract methods + defined in :class:`~pina.problem.problem_interface.ProblemInterface`. + + This class is not meant to be instantiated directly. + """ + + def __init__(self): + """ + Initialization of the :class:`BaseProblem` class. + """ + self._discretised_domains = {} + + # Create a correspondence between the problem and the conditions + for condition_name in self.conditions: + self.conditions[condition_name].problem = self + self.conditions[condition_name].name = condition_name + + # Create a dictionary to store the domains of the problem + if not hasattr(self, "domains"): + self.domains = {} + + # Store all the domains object passed to the problem's conditions + for name, cond in self.conditions.items(): + if isinstance(cond, DomainEquationCondition): + if isinstance(cond.domain, DomainInterface): + self.domains[name] = cond.domain + cond.domain = name + + def __deepcopy__(self, memo): + """ + Create a deep copy of the problem instance. + + :param dict memo: The memorization dictionary used by the deepcopy + function. + :return: A deep copy of the problem instance. + :rtype: ProblemInterface + """ + # Create a new instance of the same class and store it in a dictionary + result = self.__class__.__new__(self.__class__) + memo[id(self)] = result + + # Set the attributes of the new instance to deep copies of the original + for k, v in self.__dict__.items(): + setattr(result, k, deepcopy(v, memo)) + + return result + + def discretise_domain( + self, n=None, mode="random", domains=None, sample_rules=None + ): + """ + Discretise the problem's domains by sampling a specified number of + points according to the selected sampling mode. + + :param int n: The number of points to sample. This is ignored if + ``sample_rules`` is provided. Default is ``None``. + :param str mode: The sampling method. Available modes include: + ``"random"`` for random sampling, ``"latin"`` or ``"lh"`` for latin + hypercube sampling, ``"chebyshev"`` for Chebyshev sampling, and + ``"grid"`` for grid sampling. Default is ``"random"``. + :param domains: The domains from which to sample. If ``None``, all + domains are considered for sampling. Default is ``None``. + :type domains: str | list[str] + :param dict sample_rules: The dictionary specifying custom sampling + rules for each input variable. When provided, it overrides the + global ``n`` and ``mode`` arguments. Each key in the dictionary must + match one of the variables defined in :meth:`input_variables`, and + each value must be a dictionary containing two keys: ``n`` for the + number of points to sample for that variable, and ``mode`` for the + sampling method to use. If ``None``, the global ``n`` and ``mode`` + parameters are used for all variables. Default is ``None``. + :raises ValueError: If ``sample_rules`` is provided but it is not a + dictionary. + :raises ValueError: If ``sample_rules`` is provided but its keys do not + match the input variables of the problem. + :raises ValueError: If ``sample_rules`` is provided but any of its rules + is not a dictionary containing both ``n`` and ``mode`` keys, with + ``n`` being a positive integer and ``mode`` being a string. + :raises AssertionError: If ``n`` is not a positive integer. + :raises ValueError: If ``mode`` is not a string + :raises ValueError: If ``domains`` is provided by it is neither a string + nor a list of strings. + + .. warning:: + ``"random"`` is the only supported ``mode`` across all geometries: + :class:`~pina.domain.cartesian_domain.CartesianDomain`, + :class:`~pina.domain.ellipsoid_domain.EllipsoidDomain`, and + :class:`~pina.domain.simplex_domain.SimplexDomain`. + Sampling modes such as ``"latin"``, ``"chebyshev"``, and ``"grid"`` + are only implemented for + :class:`~pina.domain.cartesian_domain.CartesianDomain`. + When custom discretisation is specified via ``sample_rules``, the + domain to be discretised must be an instance of + :class:`~pina.domain.cartesian_domain.CartesianDomain`. + + :Example: + >>> problem.discretise_domain(n=10, mode="random") + >>> problem.discretise_domain(n=10, mode="lh", domains=["boundary"]) + >>> problem.discretise_domain( + ... sample_rules={ + ... 'x': {'n': 10, 'mode': 'grid'}, + ... 'y': {'n': 100, 'mode': 'grid'} + ... }, + ... ) + """ + # Initialize the domains to be discretised + if domains is None: + domains = list(self.domains) + if not isinstance(domains, (list)): + domains = [domains] + + # Check sampling rules + if sample_rules is not None: + check_consistency(sample_rules, dict) + + # Check that the keys of sample_rules match the input variables + if sorted(list(sample_rules.keys())) != sorted( + self.input_variables + ): + raise ValueError( + "The keys of the sample_rules dictionary must match the " + "input variables." + ) + + # Check that the rules for each variable are valid + for var, rules in sample_rules.items(): + check_consistency(rules, dict) + if "n" not in rules or "mode" not in rules: + raise ValueError( + f"Sampling rules for variable {var} must contain 'n' " + "and 'mode' keys." + ) + check_positive_integer(rules["n"], strict=True) + check_consistency(rules["mode"], str) + + # Check n only if sample_rules is not provided + else: + check_positive_integer(n, strict=True) + + # Check consistency + check_consistency(mode, str) + check_consistency(domains, str) + + # If sample_rules is provided, apply custom discretisation + if sample_rules is not None: + for d in domains: + + # Discretise each variable according to its custom rules + discretised_tensor = [ + self.domains[d].sample(rules["n"], rules["mode"], var) + for var, rules in sample_rules.items() + ] + + # Merge the discretised tensors into a single one for the domain + self.discretised_domains[d] = merge_tensors(discretised_tensor) + + # Otherwise, apply the same n and mode to all specified domains + else: + for d in domains: + self.discretised_domains[d] = self.domains[d].sample(n, mode) + + def add_points(self, new_points_dict): + """ + Append additional points to an already discretised domain. + + :param dict new_points_dict: The dictionary mapping each domain to the + corresponding set of new points to be added. Each key in the + dictionary must match one of the domains defined in :attr:`domains`, + and each value must be a :class:`~pina.tensor.LabelTensor` + containing the new points to be added to that domain. The labels of + the points to be added must correspond to those of the domain to + which they are being added. + :raises ValueError: If ``new_points_dict`` is not a dictionary. + :raises ValueError: If any of the values in ``new_points_dict`` is not + a :class:`~pina.tensor.LabelTensor`. + :raises ValueError: If any of the keys in ``new_points_dict`` does not + match any of the domains defined in :attr:`domains`. + :raises ValueError: If any of the domains in ``new_points_dict`` has not + been discretised yet. + + :Example: + >>> additional_points = { + ... "boundary": LabelTensor(torch.rand(5, 2), labels=["x", "y"]) + ... } + >>> problem.add_points(additional_points) + """ + # Check consistency + check_consistency(new_points_dict, dict) + + # Check the keys and values of the dictionary + for key, value in new_points_dict.items(): + check_consistency(value, LabelTensor) + if key not in self.domains: + raise ValueError( + f"Key {key} does not match any domain of the problem." + ) + if key not in self.discretised_domains: + raise ValueError(f"Domain {key} has not been discretised yet.") + + # Append the new points to the corresponding discretised domains + for key, value in new_points_dict.items(): + self.discretised_domains[key] = LabelTensor.vstack( + [self.discretised_domains[key], value] + ) + + def move_discretisation_into_conditions(self): + """ + Move the sampled points from the discretised domains into their + corresponding conditions. This ensures that the conditions are evaluated + on the correct set of points after discretisation. + """ + # Move the discretised domains into their corresponding conditions + for name, cond in self.conditions.items(): + if hasattr(cond, "domain"): + + # Create a new condition with the discretised domain as input + new_condition = Condition( + input=self.discretised_domains[cond.domain], + equation=cond.equation, + ) + + # Set the domain and problem attributes of the new condition + new_condition.domain = cond.domain + new_condition.problem = self + new_condition.name = name + + # Replace the old condition in the conditions dictionary + self.conditions[name] = new_condition + + @property + def input_variables(self): + """ + The input variables of the problem. + + :return: The input variables of the problem. + :rtype: list[str] + """ + # Define a helper function to convert a string to a list if needed + _as_list = lambda x: [x] if isinstance(x, str) else x + + # Collect the spatial, temporal, and parametric variables + variables = [] + if hasattr(self, "spatial_variables"): + variables += _as_list(self.spatial_variables) + if hasattr(self, "temporal_variables"): + variables += _as_list(self.temporal_variables) + if hasattr(self, "parameters"): + variables += _as_list(self.parameters) + + return variables + + @property + def discretised_domains(self): + """ + The dictionary containing the discretised domains of the problem. Each + key corresponds to a domain defined in :attr:`domains`, and each value + is a :class:`~pina.tensor.LabelTensor` containing the sampled points for + that domain. + + :return: The discretised domains. + :rtype: dict + """ + return self._discretised_domains + + @property + def are_all_domains_discretised(self): + """ + Whether all domains of the problem have been discretised. + + :return: ``True`` if all domains are discretised, ``False`` otherwise. + :rtype: bool + """ + return all(d in self.discretised_domains for d in self.domains) diff --git a/pina/problem/inverse_problem.py b/pina/_src/problem/inverse_problem.py similarity index 63% rename from pina/problem/inverse_problem.py rename to pina/_src/problem/inverse_problem.py index 8a2902448..7ee28bb96 100644 --- a/pina/problem/inverse_problem.py +++ b/pina/_src/problem/inverse_problem.py @@ -2,13 +2,18 @@ from abc import abstractmethod import torch -from .abstract_problem import AbstractProblem +from pina._src.problem.base_problem import BaseProblem -class InverseProblem(AbstractProblem): +class InverseProblem(BaseProblem): """ - Class for defining inverse problems, where the objective is to determine - unknown parameters through training, based on given data. + Base class for all inverse problems, extending the standard problem + definition with unknown parameters to be determined through training. + + An inverse problem is defined by a set of unknown parameters that need to be + estimated from observed data. + + This class is not meant to be instantiated directly. """ def __init__(self): @@ -16,15 +21,15 @@ def __init__(self): Initialization of the :class:`InverseProblem` class. """ super().__init__() - # storing unknown_parameters for optimization + + # Set the unknown parameters as trainable parameters self.unknown_parameters = {} for var in self.unknown_variables: - range_var = self.unknown_parameter_domain._range[var] - tensor_var = ( - torch.rand(1, requires_grad=True) * range_var[1] + range_var[0] - ) + low, high = self.unknown_parameter_domain._range[var] + tensor_var = low + (high - low) * torch.rand(1) self.unknown_parameters[var] = torch.nn.Parameter(tensor_var) + @property @abstractmethod def unknown_parameter_domain(self): """ @@ -34,7 +39,7 @@ def unknown_parameter_domain(self): @property def unknown_variables(self): """ - Get the unknown variables of the problem. + The unknown variables of the problem. :return: The unknown variables of the problem. :rtype: list[str] @@ -44,7 +49,7 @@ def unknown_variables(self): @property def unknown_parameters(self): """ - Get the unknown parameters of the problem. + The unknown parameters of the problem. :return: The unknown parameters of the problem. :rtype: torch.nn.Parameter diff --git a/pina/_src/problem/parametric_problem.py b/pina/_src/problem/parametric_problem.py new file mode 100644 index 000000000..12a9cd089 --- /dev/null +++ b/pina/_src/problem/parametric_problem.py @@ -0,0 +1,35 @@ +"""Module for the ParametricProblem class.""" + +from abc import abstractmethod +from pina._src.problem.base_problem import BaseProblem + + +class ParametricProblem(BaseProblem): + """ + Base class for all parametric problems, extending the standard problem + definition with parameter-dependent inputs. + + A parametric problem includes additional input variables, defined over a + dedicated parameter domain, which represent external quantities + (e.g., physical coefficients or control variables) that can vary across + different evaluations and influence the solution. + + This class is not meant to be instantiated directly. + """ + + @property + @abstractmethod + def parameter_domain(self): + """ + The domain of the parameters of the problem. + """ + + @property + def parameters(self): + """ + The parameters of the problem. + + :return: The parameters of the problem. + :rtype: list[str] + """ + return self.parameter_domain.variables diff --git a/pina/_src/problem/problem_interface.py b/pina/_src/problem/problem_interface.py new file mode 100644 index 000000000..d64130d61 --- /dev/null +++ b/pina/_src/problem/problem_interface.py @@ -0,0 +1,150 @@ +"""Module for the Problem Interface.""" + +from abc import ABCMeta, abstractmethod + + +class ProblemInterface(metaclass=ABCMeta): + """ + Abstract interface for all problems. + """ + + @abstractmethod + def __deepcopy__(self, memo): + """ + Create a deep copy of the problem instance. + + :param dict memo: The memorization dictionary used by the deepcopy + function. + :return: A deep copy of the problem instance. + :rtype: ProblemInterface + """ + + @abstractmethod + def discretise_domain( + self, n=None, mode="random", domains=None, sample_rules=None + ): + """ + Discretise the problem's domains by sampling a specified number of + points according to the selected sampling mode. + + :param int n: The number of points to sample. This is ignored if + ``sample_rules`` is provided. Default is ``None``. + :param str mode: The sampling method. Available modes include: + ``"random"`` for random sampling, ``"latin"`` or ``"lh"`` for latin + hypercube sampling, ``"chebyshev"`` for Chebyshev sampling, and + ``"grid"`` for grid sampling. Default is ``"random"``. + :param domains: The domains from which to sample. If ``None``, all + domains are considered for sampling. Default is ``None``. + :type domains: str | list[str] + :param dict sample_rules: The dictionary specifying custom sampling + rules for each input variable. When provided, it overrides the + global ``n`` and ``mode`` arguments. Each key in the dictionary must + match one of the variables defined in :meth:`input_variables`, and + each value must be a dictionary containing two keys: ``n`` for the + number of points to sample for that variable, and ``mode`` for the + sampling method to use. If ``None``, the global ``n`` and ``mode`` + parameters are used for all variables. Default is ``None``. + + .. warning:: + ``"random"`` is the only supported ``mode`` across all geometries: + :class:`~pina.domain.cartesian_domain.CartesianDomain`, + :class:`~pina.domain.ellipsoid_domain.EllipsoidDomain`, and + :class:`~pina.domain.simplex_domain.SimplexDomain`. + Sampling modes such as ``"latin"``, ``"chebyshev"``, and ``"grid"`` + are only implemented for + :class:`~pina.domain.cartesian_domain.CartesianDomain`. + When custom discretisation is specified via ``sample_rules``, the + domain to be discretised must be an instance of + :class:`~pina.domain.cartesian_domain.CartesianDomain`. + + :Example: + >>> problem.discretise_domain(n=10, mode="random") + >>> problem.discretise_domain(n=10, mode="lh", domains=["boundary"]) + >>> problem.discretise_domain( + ... sample_rules={ + ... 'x': {'n': 10, 'mode': 'grid'}, + ... 'y': {'n': 100, 'mode': 'grid'} + ... }, + ... ) + """ + + @abstractmethod + def add_points(self, new_points_dict): + """ + Append additional points to an already discretised domain. + + :param dict new_points_dict: The dictionary mapping each domain to the + corresponding set of new points to be added. Each key in the + dictionary must match one of the domains defined in :attr:`domains`, + and each value must be a :class:`~pina.tensor.LabelTensor` + containing the new points to be added to that domain. The labels of + the points to be added must correspond to those of the domain to + which they are being added. + + :Example: + >>> additional_points = { + ... "boundary": LabelTensor(torch.rand(5, 2), labels=["x", "y"]) + ... } + >>> problem.add_points(additional_points) + """ + + @abstractmethod + def move_discretisation_into_conditions(self): + """ + Move the sampled points from the discretised domains into their + corresponding conditions. This ensures that the conditions are evaluated + on the correct set of points after discretisation. + """ + + @property + @abstractmethod + def input_variables(self): + """ + The input variables of the problem. + + :return: The input variables of the problem. + :rtype: list[str] + """ + + @property + @abstractmethod + def output_variables(self): + """ + The output variables of the problem. + + :return: The output variables of the problem. + :rtype: list[str] + """ + + @property + @abstractmethod + def conditions(self): + """ + The conditions associated with the problem. + + :return: The conditions associated with the problem. + :rtype: dict + """ + + @property + @abstractmethod + def discretised_domains(self): + """ + The dictionary containing the discretised domains of the problem. Each + key corresponds to a domain defined in :attr:`domains`, and each value + is a :class:`~pina.tensor.LabelTensor` containing the sampled points for + that domain. + + :return: The discretised domains. + :rtype: dict + """ + + @property + @abstractmethod + def are_all_domains_discretised(self): + """ + Whether all domains of the problem have been discretised. + + :return: ``True`` if all domains are discretised, ``False`` otherwise. + :rtype: bool + """ diff --git a/pina/_src/problem/spatial_problem.py b/pina/_src/problem/spatial_problem.py new file mode 100644 index 000000000..16ea9365b --- /dev/null +++ b/pina/_src/problem/spatial_problem.py @@ -0,0 +1,34 @@ +"""Module for the SpatialProblem class.""" + +from abc import abstractmethod +from pina._src.problem.base_problem import BaseProblem + + +class SpatialProblem(BaseProblem): + """ + Base class for all spatial problems, extending the standard problem + definition with spatial-dependent inputs. + + A spatial problem is defined over a spatial domain, where input variables + represent the coordinates of the system (e.g., positions in one or more + dimensions) on which the solution is evaluated. + + This class is not meant to be instantiated directly. + """ + + @property + @abstractmethod + def spatial_domain(self): + """ + The domain of spatial variables of the problem. + """ + + @property + def spatial_variables(self): + """ + The spatial input variables of the problem. + + :return: The spatial input variables of the problem. + :rtype: list[str] + """ + return self.spatial_domain.variables diff --git a/pina/_src/problem/time_dependent_problem.py b/pina/_src/problem/time_dependent_problem.py new file mode 100644 index 000000000..b81ab4778 --- /dev/null +++ b/pina/_src/problem/time_dependent_problem.py @@ -0,0 +1,33 @@ +"""Module for the TimeDependentProblem class.""" + +from abc import abstractmethod +from pina._src.problem.base_problem import BaseProblem + + +class TimeDependentProblem(BaseProblem): + """ + Base class for all time-dependent problems, extending the standard problem + definition with time-dependent inputs. + + A time-dependent problem is defined over a temporal domain, where input + variables represent the time at which the solution is evaluated. + + This class is not meant to be instantiated directly. + """ + + @property + @abstractmethod + def temporal_domain(self): + """ + The domain of temporal variables of the problem. + """ + + @property + def temporal_variables(self): + """ + The temporal variables of the problem. + + :return: The temporal variables of the problem. + :rtype: list[str] + """ + return self.temporal_domain.variables diff --git a/pina/_src/problem/zoo/__init__.py b/pina/_src/problem/zoo/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/problem/zoo/acoustic_wave.py b/pina/_src/problem/zoo/acoustic_wave_problem.py similarity index 56% rename from pina/problem/zoo/acoustic_wave.py rename to pina/_src/problem/zoo/acoustic_wave_problem.py index b4b2035a4..f2f7cff8b 100644 --- a/pina/problem/zoo/acoustic_wave.py +++ b/pina/_src/problem/zoo/acoustic_wave_problem.py @@ -1,17 +1,16 @@ """Formulation of the acoustic wave problem.""" import torch -from ... import Condition -from ...problem import SpatialProblem, TimeDependentProblem -from ...utils import check_consistency -from ...domain import CartesianDomain -from ...equation import ( - Equation, - SystemEquation, - FixedValue, - FixedGradient, - AcousticWave, -) +from pina._src.problem.time_dependent_problem import TimeDependentProblem +from pina._src.domain.cartesian_domain import CartesianDomain +from pina._src.equation.system_equation import SystemEquation +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.condition.condition import Condition +from pina._src.core.utils import check_consistency +from pina._src.equation.equation import Equation +from pina._src.equation.zoo.fixed_value import FixedValue +from pina._src.equation.zoo.fixed_gradient import FixedGradient +from pina._src.equation.zoo.acoustic_wave_equation import AcousticWaveEquation def initial_condition(input_, output_): @@ -29,8 +28,50 @@ def initial_condition(input_, output_): class AcousticWaveProblem(TimeDependentProblem, SpatialProblem): r""" - Implementation of the acoustic wave problem in the spatial interval - :math:`[0, 1]` and temporal interval :math:`[0, 1]`. + Implementation of the one-dimensional acoustic wave problem on the + space-time domain :math:`\Omega\times T = [0, 1] \times [0, 1]`. + + The problem is governed by the acoustic wave equation + + .. math:: + + \frac{\partial^2 u}{\partial t^2} + = + c^2 \frac{\partial^2 u}{\partial x^2}, + + where :math:`u = u(x, t)` is the solution field and :math:`c > 0` is the + wave propagation speed. + + Homogeneous Dirichlet boundary conditions are imposed at the spatial + boundaries: + + .. math:: + + u(0, t) = u(1, t) = 0, \qquad t \in [0, 1]. + + The initial displacement is prescribed as + + .. math:: + + u(x, 0) = \sin(\pi x) + \frac{1}{2}\sin(4\pi x), + \qquad x \in [0, 1], + + together with zero initial velocity: + + .. math:: + + \frac{\partial u}{\partial t}(x, 0) = 0, + \qquad x \in [0, 1]. + + The analytical solution is given by + + .. math:: + + u(x, t) + = + \sin(\pi x)\cos(c\pi t) + + + \frac{1}{2}\sin(4\pi x)\cos(4c\pi t). .. seealso:: @@ -69,7 +110,7 @@ def __init__(self, c=2.0): """ Initialization of the :class:`AcousticWaveProblem` class. - :param c: The wave propagation speed. Default is 2.0. + :param c: The wave propagation speed. Default is ``2.0``. :type c: float | int """ super().__init__() @@ -77,7 +118,7 @@ def __init__(self, c=2.0): self.c = c self.conditions["D"] = Condition( - domain="D", equation=AcousticWave(self.c) + domain="D", equation=AcousticWaveEquation(self.c) ) def solution(self, pts): @@ -92,4 +133,7 @@ def solution(self, pts): arg_t = self.c * torch.pi * pts["t"] term1 = torch.sin(arg_x) * torch.cos(arg_t) term2 = 0.5 * torch.sin(4 * arg_x) * torch.cos(4 * arg_t) - return term1 + term2 + + sol = term1 + term2 + sol.labels = self.output_variables + return sol diff --git a/pina/problem/zoo/advection.py b/pina/_src/problem/zoo/advection_problem.py similarity index 57% rename from pina/problem/zoo/advection.py rename to pina/_src/problem/zoo/advection_problem.py index c709b9632..113b36bee 100644 --- a/pina/problem/zoo/advection.py +++ b/pina/_src/problem/zoo/advection_problem.py @@ -1,11 +1,13 @@ """Formulation of the advection problem.""" import torch -from ... import Condition -from ...problem import SpatialProblem, TimeDependentProblem -from ...equation import Equation, Advection -from ...utils import check_consistency -from ...domain import CartesianDomain +from pina._src.problem.time_dependent_problem import TimeDependentProblem +from pina._src.domain.cartesian_domain import CartesianDomain +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.equation.zoo.advection_equation import AdvectionEquation +from pina._src.condition.condition import Condition +from pina._src.core.utils import check_consistency +from pina._src.equation.equation import Equation def initial_condition(input_, output_): @@ -22,8 +24,39 @@ def initial_condition(input_, output_): class AdvectionProblem(SpatialProblem, TimeDependentProblem): r""" - Implementation of the advection problem in the spatial interval - :math:`[0, 2 \pi]` and temporal interval :math:`[0, 1]`. + Implementation of the one-dimensional advection problem on the space-time + domain :math:`\Omega\times T = [0, 2\pi] \times [0, 1]`. + + The problem is governed by the linear advection equation + + .. math:: + + \frac{\partial u}{\partial t} + + + c \frac{\partial u}{\partial x} + = + 0, + + where :math:`u = u(x, t)` is the solution field and :math:`c` is the + advection velocity. + + Periodic boundary conditions are imposed at the spatial boundaries: + + .. math:: + + u(0, t) = u(2\pi, t), \qquad t \in [0, 1]. + + The initial condition is prescribed as + + .. math:: + + u(x, 0) = \sin(x), \qquad x \in [0, 2\pi]. + + The analytical solution is given by + + .. math:: + + u(x, t) = \sin(x - ct). .. seealso:: @@ -54,14 +87,16 @@ def __init__(self, c=1.0): """ Initialization of the :class:`AdvectionProblem`. - :param c: The advection velocity parameter. Default is 1.0. + :param c: The advection velocity parameter. Default is ``1.0``. :type c: float | int """ super().__init__() check_consistency(c, (float, int)) self.c = c - self.conditions["D"] = Condition(domain="D", equation=Advection(self.c)) + self.conditions["D"] = Condition( + domain="D", equation=AdvectionEquation(self.c) + ) def solution(self, pts): """ diff --git a/pina/problem/zoo/allen_cahn.py b/pina/_src/problem/zoo/allen_cahn_problem.py similarity index 57% rename from pina/problem/zoo/allen_cahn.py rename to pina/_src/problem/zoo/allen_cahn_problem.py index 900d5cf33..6a7126e68 100644 --- a/pina/problem/zoo/allen_cahn.py +++ b/pina/_src/problem/zoo/allen_cahn_problem.py @@ -1,11 +1,13 @@ """Formulation of the Allen Cahn problem.""" import torch -from ... import Condition -from ...problem import SpatialProblem, TimeDependentProblem -from ...equation import Equation, AllenCahn -from ...utils import check_consistency -from ...domain import CartesianDomain +from pina._src.condition.condition import Condition +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.problem.time_dependent_problem import TimeDependentProblem +from pina._src.equation.equation import Equation +from pina._src.equation.zoo.allen_cahn_equation import AllenCahnEquation +from pina._src.core.utils import check_consistency +from pina._src.domain.cartesian_domain import CartesianDomain def initial_condition(input_, output_): @@ -24,8 +26,35 @@ def initial_condition(input_, output_): class AllenCahnProblem(TimeDependentProblem, SpatialProblem): r""" - Implementation of the Allen Cahn problem in the spatial interval - :math:`[-1, 1]` and temporal interval :math:`[0, 1]`. + Implementation of the one-dimensional Allen-Cahn problem on the space-time + domain :math:`\Omega\times T = [-1, 1] \times [0, 1]`. + + The problem is governed by the Allen-Cahn equation + + .. math:: + + \frac{\partial u}{\partial t} + - + \alpha \frac{\partial^2 u}{\partial x^2} + + + \beta \left(u^3 - u\right) + = + 0, + + where :math:`u = u(x, t)` is the solution field, :math:`\alpha` is the + diffusion coefficient, and :math:`\beta` is the reaction coefficient. + + Periodic boundary conditions are imposed at the spatial boundaries: + + .. math:: + + u(-1, t) = u(1, t), \qquad t \in [0, 1]. + + The initial condition is prescribed as + + .. math:: + + u(x, 0) = x^2 \cos(\pi x), \qquad x \in [-1, 1]. .. seealso:: @@ -59,9 +88,9 @@ def __init__(self, alpha=1e-4, beta=5): """ Initialization of the :class:`AllenCahnProblem`. - :param alpha: The diffusion coefficient. Default is 1e-4. + :param alpha: The diffusion coefficient. Default is ``1e-4``. :type alpha: float | int - :param beta: The reaction coefficient. Default is 5.0. + :param beta: The reaction coefficient. Default is ``5.0``. :type beta: float | int """ super().__init__() @@ -72,5 +101,5 @@ def __init__(self, alpha=1e-4, beta=5): self.conditions["D"] = Condition( domain="D", - equation=AllenCahn(alpha=self.alpha, beta=self.beta), + equation=AllenCahnEquation(alpha=self.alpha, beta=self.beta), ) diff --git a/pina/_src/problem/zoo/burgers_problem.py b/pina/_src/problem/zoo/burgers_problem.py new file mode 100644 index 000000000..0ba779a22 --- /dev/null +++ b/pina/_src/problem/zoo/burgers_problem.py @@ -0,0 +1,102 @@ +"""Formulation of the Burgers' problem.""" + +import torch +from pina._src.problem.time_dependent_problem import TimeDependentProblem +from pina._src.domain.cartesian_domain import CartesianDomain +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.condition.condition import Condition +from pina._src.core.utils import check_consistency +from pina._src.equation.equation import Equation +from pina._src.equation.zoo.fixed_value import FixedValue +from pina._src.equation.zoo.burgers_equation import BurgersEquation + + +def initial_condition(input_, output_): + """ + Definition of the initial condition of the Burgers' problem. + + :param LabelTensor input_: The input data of the problem. + :param LabelTensor output_: The output data of the problem. + :return: The residual of the initial condition. + :rtype: LabelTensor + """ + return output_ + torch.sin(torch.pi * input_["x"]) + + +class BurgersProblem(TimeDependentProblem, SpatialProblem): + r""" + Implementation of the one-dimensional Burgers' problem on the space-time + domain :math:`\Omega\times T = [-1, 1] \times [0, 1]`. + + The problem is governed by the Burgers' equation + + .. math:: + + \frac{\partial u}{\partial t} + u \frac{\partial u}{\partial x} = + \nu \frac{\partial^2 u}{\partial x^2}, + + where :math:`u = u(x, t)` is the solution field and :math:`\nu \geq 0` + is the viscosity coefficient. For :math:`\nu = 0`, the equation reduces + to the inviscid Burgers' equation. + + Homogeneous Dirichlet boundary conditions are imposed at the spatial + boundaries: + + .. math:: + u(-1, t) = u(1, t) = 0, \qquad t \in [0, 1]. + + The initial condition is prescribed as + + .. math:: + u(x, 0) = -\sin(\pi x), \qquad x \in [-1, 1]. + + + .. seealso:: + + **Original reference**: Raissi M., Perdikaris P., Karniadakis G. E. + (2017). + *Physics Informed Deep Learning (Part I): Data-driven Solutions of + Nonlinear Partial Differential Equations*. + DOI: `10.48550 `_. + + :Example: + + >>> problem = BurgersProblem() + """ + + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [-1, 1]}) + temporal_domain = CartesianDomain({"t": [0, 1]}) + + domains = { + "D": spatial_domain.update(temporal_domain), + "t0": spatial_domain.update(CartesianDomain({"t": 0})), + "boundary": spatial_domain.partial().update(temporal_domain), + } + + conditions = { + "boundary": Condition(domain="boundary", equation=FixedValue(0.0)), + "t0": Condition(domain="t0", equation=Equation(initial_condition)), + } + + def __init__(self, nu=0): + """ + Initialization of the :class:`BurgersProblem` class. + + :param nu: The viscosity coefficient. + :type nu: float | int + :raises ValueError: If ``nu`` is not a float or an int. + :raises ValueError: If ``nu`` is negative. + """ + super().__init__() + + # Check consistency + check_consistency(nu, (float, int)) + if nu < 0: + raise ValueError( + "The viscosity ``nu`` must be a non-negative float or int." + ) + + self.conditions["D"] = Condition( + domain="D", equation=BurgersEquation(nu) + ) diff --git a/pina/problem/zoo/diffusion_reaction.py b/pina/_src/problem/zoo/diffusion_reaction_problem.py similarity index 57% rename from pina/problem/zoo/diffusion_reaction.py rename to pina/_src/problem/zoo/diffusion_reaction_problem.py index fd02b8368..7a5584ca5 100644 --- a/pina/problem/zoo/diffusion_reaction.py +++ b/pina/_src/problem/zoo/diffusion_reaction_problem.py @@ -1,11 +1,16 @@ """Formulation of the diffusion-reaction problem.""" import torch -from ... import Condition -from ...equation import Equation, FixedValue, DiffusionReaction -from ...problem import SpatialProblem, TimeDependentProblem -from ...utils import check_consistency -from ...domain import CartesianDomain +from pina._src.condition.condition import Condition +from pina._src.equation.equation import Equation +from pina._src.equation.zoo.fixed_value import FixedValue +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.problem.time_dependent_problem import TimeDependentProblem +from pina._src.core.utils import check_consistency +from pina._src.domain.cartesian_domain import CartesianDomain +from pina._src.equation.zoo.diffusion_reaction_equation import ( + DiffusionReactionEquation, +) def initial_condition(input_, output_): @@ -30,8 +35,63 @@ def initial_condition(input_, output_): class DiffusionReactionProblem(TimeDependentProblem, SpatialProblem): r""" - Implementation of the diffusion-reaction problem in the spatial interval - :math:`[-\pi, \pi]` and temporal interval :math:`[0, 1]`. + Implementation of the one-dimensional diffusion-reaction problem on the + space-time domain :math:`\Omega\times T = [-\pi, \pi] \times [0, 1]`. + + The problem is governed by the forced diffusion-reaction equation + + .. math:: + + \frac{\partial u}{\partial t} + - + \alpha \frac{\partial^2 u}{\partial x^2} + = + f(x, t), + + where :math:`u = u(x, t)` is the solution field, :math:`\alpha` is the + diffusion coefficient, and :math:`f(x, t)` is a forcing term. + + Homogeneous Dirichlet boundary conditions are imposed at the spatial + boundaries: + + .. math:: + + u(-\pi, t) = u(\pi, t) = 0, \qquad t \in [0, 1]. + + The initial condition is prescribed as + + .. math:: + + u(x, 0) + = + \sin(x) + + + \frac{1}{2}\sin(2x) + + + \frac{1}{3}\sin(3x) + + + \frac{1}{4}\sin(4x) + + + \frac{1}{8}\sin(8x). + + The analytical solution is given by + + .. math:: + + u(x, t) + = + e^{-t} + \left( + \sin(x) + + + \frac{1}{2}\sin(2x) + + + \frac{1}{3}\sin(3x) + + + \frac{1}{4}\sin(4x) + + + \frac{1}{8}\sin(8x) + \right). .. seealso:: @@ -63,7 +123,7 @@ def __init__(self, alpha=1e-4): """ Initialization of the :class:`DiffusionReactionProblem`. - :param alpha: The diffusion coefficient. Default is 1e-4. + :param alpha: The diffusion coefficient. Default is ``1e-4``. :type alpha: float | int """ super().__init__() @@ -80,15 +140,16 @@ def forcing_term(input_): t = input_.extract("t") return torch.exp(-t) * ( - 1.5 * torch.sin(2 * x) - + (8 / 3) * torch.sin(3 * x) - + (15 / 4) * torch.sin(4 * x) - + (63 / 8) * torch.sin(8 * x) + (self.alpha - 1) * torch.sin(x) + + ((4 * self.alpha - 1) / 2) * torch.sin(2 * x) + + ((9 * self.alpha - 1) / 3) * torch.sin(3 * x) + + ((16 * self.alpha - 1) / 4) * torch.sin(4 * x) + + ((64 * self.alpha - 1) / 8) * torch.sin(8 * x) ) self.conditions["D"] = Condition( domain="D", - equation=DiffusionReaction(self.alpha, forcing_term), + equation=DiffusionReactionEquation(self.alpha, forcing_term), ) def solution(self, pts): diff --git a/pina/_src/problem/zoo/helmholtz_problem.py b/pina/_src/problem/zoo/helmholtz_problem.py new file mode 100644 index 000000000..9b11519aa --- /dev/null +++ b/pina/_src/problem/zoo/helmholtz_problem.py @@ -0,0 +1,124 @@ +"""Formulation of the Helmholtz problem.""" + +import torch +from pina._src.condition.condition import Condition +from pina._src.equation.zoo.fixed_value import FixedValue +from pina._src.equation.zoo.helmholtz_equation import HelmholtzEquation +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.core.utils import check_consistency +from pina._src.domain.cartesian_domain import CartesianDomain + + +class HelmholtzProblem(SpatialProblem): + r""" + Implementation of the two-dimensional Helmholtz problem on the square domain + :math:`\Omega = [-1, 1] \times [-1, 1]`. + + The problem is governed by the forced Helmholtz equation + + .. math:: + + \Delta u + k u = f(x, y), + + where :math:`u = u(x, y)` is the solution field, :math:`k` is the squared + wavenumber, and :math:`f(x, y)` is a forcing term. + + Homogeneous Dirichlet boundary conditions are imposed on the boundary of + the domain: + + .. math:: + + u(x, y) = 0, \qquad (x, y) \in \partial \Omega. + + The analytical solution is given by + + .. math:: + + u(x, y) + = + \sin(\alpha_x \pi x) + \sin(\alpha_y \pi y), + + with forcing term + + .. math:: + + f(x, y) + = + \left[ + k - (\alpha_x^2 + \alpha_y^2)\pi^2 + \right] + \sin(\alpha_x \pi x) + \sin(\alpha_y \pi y). + + .. seealso:: + + **Original reference**: Si, Chenhao, et al. *Complex Physics-Informed + Neural Network.* arXiv preprint arXiv:2502.04917 (2025). + DOI: `arXiv:2502.04917 `_. + + :Example: + + >>> problem = HelmholtzProblem() + """ + + output_variables = ["u"] + spatial_domain = CartesianDomain({"x": [-1, 1], "y": [-1, 1]}) + + domains = { + "D": spatial_domain, + "boundary": spatial_domain.partial(), + } + + conditions = { + "boundary": Condition(domain="boundary", equation=FixedValue(0.0)), + } + + def __init__(self, k=1.0, alpha_x=1, alpha_y=4): + """ + Initialization of the :class:`HelmholtzProblem` class. + + :param k: The squared wavenumber. Default is ``1.0``. + :type k: float | int + :param int alpha_x: The frequency in the x-direction. Default is ``1``. + :param int alpha_y: The frequency in the y-direction. Default is ``4``. + """ + super().__init__() + check_consistency(k, (int, float)) + check_consistency(alpha_x, int) + check_consistency(alpha_y, int) + self.k = k + self.alpha_x = alpha_x + self.alpha_y = alpha_y + + def forcing_term(input_): + """ + Implementation of the forcing term. + """ + x, y, pi = input_["x"], input_["y"], torch.pi + factor = (self.alpha_x**2 + self.alpha_y**2) * pi**2 + return ( + (self.k - factor) + * torch.sin(self.alpha_x * pi * x) + * torch.sin(self.alpha_y * pi * y) + ) + + self.conditions["D"] = Condition( + domain="D", + equation=HelmholtzEquation(self.k, forcing_term), + ) + + def solution(self, pts): + """ + Implementation of the analytical solution of the Helmholtz problem. + + :param LabelTensor pts: Points where the solution is evaluated. + :return: The analytical solution of the Helmholtz problem. + :rtype: LabelTensor + """ + x, y, pi = pts["x"], pts["y"], torch.pi + sol = torch.sin(self.alpha_x * pi * x) * torch.sin( + self.alpha_y * pi * y + ) + sol.labels = self.output_variables + return sol diff --git a/pina/problem/zoo/inverse_poisson_2d_square.py b/pina/_src/problem/zoo/inverse_poisson_problem.py similarity index 75% rename from pina/problem/zoo/inverse_poisson_2d_square.py rename to pina/_src/problem/zoo/inverse_poisson_problem.py index 17f30ae14..9a5a8d908 100644 --- a/pina/problem/zoo/inverse_poisson_2d_square.py +++ b/pina/_src/problem/zoo/inverse_poisson_problem.py @@ -4,13 +4,16 @@ import requests import torch from io import BytesIO -from ... import Condition -from ... import LabelTensor -from ...operator import laplacian -from ...domain import CartesianDomain -from ...equation import Equation, FixedValue -from ...problem import SpatialProblem, InverseProblem -from ...utils import custom_warning_format, check_consistency + +from pina._src.core.utils import custom_warning_format, check_consistency +from pina._src.domain.cartesian_domain import CartesianDomain +from pina._src.problem.inverse_problem import InverseProblem +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.equation.zoo.fixed_value import FixedValue +from pina._src.condition.condition import Condition +from pina._src.core.label_tensor import LabelTensor +from pina._src.equation.equation import Equation +from pina._src.core.operator import laplacian warnings.formatwarning = custom_warning_format warnings.filterwarnings("always", category=ResourceWarning) @@ -28,7 +31,7 @@ def _load_tensor_from_url(url, labels, timeout=10): :param str url: URL to the remote `.pth` tensor file. :param labels: Labels for the resulting LabelTensor. :type labels: list[str] | tuple[str] - :param int timeout: Timeout for the request in seconds. Default is 10s. + :param int timeout: Timeout for the request in seconds. Default is ``10`` s. :return: A LabelTensor object if successful, otherwise None. :rtype: LabelTensor | None """ @@ -71,9 +74,33 @@ def laplace_equation(input_, output_, params_): class InversePoisson2DSquareProblem(SpatialProblem, InverseProblem): r""" - Implementation of the inverse 2-dimensional Poisson problem in the square - domain :math:`[0, 1] \times [0, 1]`, with unknown parameter domain - :math:`[-1, 1] \times [-1, 1]`. + Implementation of the inverse two-dimensional Poisson problem on the square + domain :math:`\Omega = [-2, 2] \times [-2, 2]`, with unknown parameter + domain :math:`\Theta = [-1, 1] \times [-1, 1]`. + + The problem is governed by the parameterized Poisson equation + + .. math:: + + \Delta u + = + \exp\left( + -2(x - \mu_1)^2 + -2(y - \mu_2)^2 + \right), + + where :math:`u = u(x, y)` is the solution field and :math:`\mu_1, \mu_2` are + unknown parameters controlling the forcing term. + + Homogeneous Dirichlet boundary conditions are imposed on the boundary of the + domain: + + .. math:: + + u(x, y) = 0, \qquad (x, y) \in \partial \Omega. + + The inverse problem aims to infer the unknown parameters :math:`\mu_1` and + :math:`\mu_2` from solution data. The `"data"` condition is added only if the required files are downloaded successfully. @@ -105,10 +132,10 @@ def __init__(self, load=True, data_size=1.0): :param bool load: If True, it attempts to load data from remote URLs. Set to False to skip data loading (e.g., if no internet connection). - Default is True. + Default is ``True``. :param float data_size: The fraction of the total data to use for the "data" condition. If set to 1.0, all available data is used. - If set to 0.0, no data is used. Default is 1.0. + If set to 0.0, no data is used. Default is ``1.0``. :raises ValueError: If `data_size` is not in the range [0.0, 1.0]. :raises ValueError: If `data_size` is not a float. """ @@ -147,3 +174,5 @@ def __init__(self, load=True, data_size=1.0): self.conditions["data"] = Condition( input=input_data[:n_data], target=output_data[:n_data] ) + self.conditions["data"].problem = self + self.conditions["data"].name = "data" diff --git a/pina/problem/zoo/poisson_2d_square.py b/pina/_src/problem/zoo/poisson_problem.py similarity index 55% rename from pina/problem/zoo/poisson_2d_square.py rename to pina/_src/problem/zoo/poisson_problem.py index 5de38b301..34d86c6fb 100644 --- a/pina/problem/zoo/poisson_2d_square.py +++ b/pina/_src/problem/zoo/poisson_problem.py @@ -1,10 +1,12 @@ """Formulation of the Poisson problem in a square domain.""" import torch -from ...equation import FixedValue, Poisson -from ...problem import SpatialProblem -from ...domain import CartesianDomain -from ... import Condition + +from pina._src.equation.zoo.fixed_value import FixedValue +from pina._src.domain.cartesian_domain import CartesianDomain +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.condition.condition import Condition +from pina._src.equation.zoo.poisson_equation import PoissonEquation def forcing_term(input_): @@ -24,8 +26,40 @@ def forcing_term(input_): class Poisson2DSquareProblem(SpatialProblem): r""" - Implementation of the 2-dimensional Poisson problem in the square domain - :math:`[0, 1] \times [0, 1]`. + Implementation of the two-dimensional Poisson problem on the square domain + :math:`\Omega = [0, 1] \times [0, 1]`. + + The problem is governed by the Poisson equation + + .. math:: + + \Delta u = f(x, y), + + where :math:`u = u(x, y)` is the solution field and :math:`f(x, y)` is the + forcing term. + + Homogeneous Dirichlet boundary conditions are imposed on the boundary of the + domain: + + .. math:: + + u(x, y) = 0, \qquad (x, y) \in \partial \Omega. + + The forcing term is given by + + .. math:: + + f(x, y) + = + 2\pi^2 \sin(\pi x)\sin(\pi y). + + The analytical solution is given by + + .. math:: + + u(x, y) + = + -\sin(\pi x)\sin(\pi y). :Example: @@ -42,7 +76,9 @@ class Poisson2DSquareProblem(SpatialProblem): conditions = { "boundary": Condition(domain="boundary", equation=FixedValue(0.0)), - "D": Condition(domain="D", equation=Poisson(forcing_term=forcing_term)), + "D": Condition( + domain="D", equation=PoissonEquation(forcing_term=forcing_term) + ), } def solution(self, pts): diff --git a/pina/problem/zoo/supervised_problem.py b/pina/_src/problem/zoo/supervised_problem.py similarity index 77% rename from pina/problem/zoo/supervised_problem.py rename to pina/_src/problem/zoo/supervised_problem.py index 61a49c0cb..fea7f80a3 100644 --- a/pina/problem/zoo/supervised_problem.py +++ b/pina/_src/problem/zoo/supervised_problem.py @@ -1,15 +1,14 @@ """Formulation of a Supervised Problem in PINA.""" -from ..abstract_problem import AbstractProblem -from ... import Condition +from pina._src.problem.base_problem import BaseProblem +from pina._src.condition.condition import Condition -class SupervisedProblem(AbstractProblem): +class SupervisedProblem(BaseProblem): """ Definition of a supervised-learning problem. - This class provides a simple way to define a supervised problem - using a single condition of type + This class provides a simple way to define a supervised problem using the :class:`~pina.condition.input_target_condition.InputTargetCondition`. :Example: @@ -20,6 +19,9 @@ class SupervisedProblem(AbstractProblem): >>> problem = SupervisedProblem(input_data, output_data) """ + # TODO: This is necessary to override the abstract properties of + # BaseProblem, but it is not an ideal solution. We should consider + # a different desgin to manage input and output variables. conditions = {} output_variables = None input_variables = None @@ -36,10 +38,10 @@ def __init__( :type output_: torch.Tensor | LabelTensor | Graph | Data :param list[str] input_variables: List of names of the input variables. If None, the input variables are inferred from `input_`. - Default is None. + Default is ``None``. :param list[str] output_variables: List of names of the output variables. If None, the output variables are inferred from - `output_`. Default is None. + `output_`. Default is ``None``. """ # Set input and output variables self.input_variables = input_variables diff --git a/pina/_src/solver/autoregressive_ensemble_solver.py b/pina/_src/solver/autoregressive_ensemble_solver.py new file mode 100644 index 000000000..27e00947a --- /dev/null +++ b/pina/_src/solver/autoregressive_ensemble_solver.py @@ -0,0 +1,117 @@ +"""Module for the autoregressive ensemble solver class.""" + +from pina._src.solver.mixin.autoregressive_mixin import AutoregressiveMixin +from pina._src.condition.time_series_condition import TimeSeriesCondition +from pina._src.solver.ensemble_solver import EnsembleSolver + + +class AutoregressiveEnsembleSolver(AutoregressiveMixin, EnsembleSolver): + r""" + Ensemble-model solver for autoregressive learning problems. + + This solver learns the time evolution of dynamical systems using an + ensemble of models. It is intended for problems defined by time-series data + and accepts only + :class:`~pina._src.condition.time_series_condition.TimeSeriesCondition`. + + Given a sequence of states :math:`\{\mathbf{u}_t\}_{t=0}^{T}`, the solver + trains an ensemble of models :math:`\{\mathcal{M}_j\}_{j=1}^{M}` to predict + the next state from the current one. The prediction of each model is + + .. math:: + + \hat{\mathbf{u}}_{t+1}^{(j)} = \mathcal{M}_j(\mathbf{u}_t), + \qquad j = 1, \ldots, M. + + The autoregressive training objective minimizes the discrepancy between + the predicted states :math:`\hat{\mathbf{u}}_{t+1}^{(j)}` and the target + states :math:`\mathbf{u}_{t+1}` over the sequence and across the ensemble: + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{M} \sum_{j=1}^{M} + \frac{1}{T} \sum_{t=0}^{T-1} \mathcal{L} + \left( \mathbf{u}_{t+1} - \hat{\mathbf{u}}_{t+1}^{(j)} \right), + + where :math:`\mathcal{L}` is the selected loss function, typically the + mean squared error. + + The solver supports adaptive weighting of autoregressive steps through the + ``eps`` parameter. During training, each autoregressive step can contribute + differently to the total loss depending on its accumulated difficulty. Steps + with larger running losses are assigned larger weights, so that the solver + focuses more on parts of the rollout where prediction errors tend to + accumulate. The parameter ``eps`` controls the strength of this effect: + ``eps = 0`` disables adaptive weighting, while larger values increase the + influence of high-loss steps on the final training objective. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = (TimeSeriesCondition,) + + def __init__( + self, + problem, + models, + optimizers=None, + schedulers=None, + weighting=None, + loss=None, + use_lt=False, + eps=0.0, + reset_weights_at_epoch_start=True, + kwargs=None, + ): + """ + Initialization of the :class:`AutoregressiveEnsembleSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param models: The model or list of models used by the solver. + :type models: torch.nn.Module | list[torch.nn.Module] + :param optimizers: The optimizer or list of optimizers used by the + solver. If ``None``, the ``torch.optim.Adam`` optimizer with a + learning rate of ``0.001`` is used for each model. + Default is ``None``. + :type optimizers: TorchOptimizer | list[TorchOptimizer] + :param schedulers: The scheduler or list of schedulers used by the + solver. If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` + scheduler with a factor of ``1.0`` is used for each model. + Default is ``None``. + :type schedulers: TorchScheduler | list[TorchScheduler] + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``False``. + :param eps: The hyperparameter controlling the influence of the + cumulative loss on the adaptive weights. Higher values of eps will + lead to more aggressive weighting of steps with higher cumulative + loss. Default is ``0.0``. + :type eps: float | int + :param bool reset_weights_at_epoch_start: Whether to reset the running + average and step count at the start of each epoch. If ``True``, the + adaptive weights will be recalibrated at the beginning of each epoch + based on the new training dynamics. Default is ``True``. + :param dict kwargs: Additional keyword arguments for preprocessing and + postprocessing steps. + """ + # Initialize the parent class + EnsembleSolver.__init__( + self, + problem=problem, + models=models, + optimizers=optimizers, + schedulers=schedulers, + weighting=weighting, + loss=loss, + use_lt=use_lt, + ) + + # Initialize the autoregressive components + self._init_autoregressive_components( + eps=eps, + reset_weights_at_epoch_start=reset_weights_at_epoch_start, + kwargs=kwargs, + ) diff --git a/pina/_src/solver/autoregressive_single_model_solver.py b/pina/_src/solver/autoregressive_single_model_solver.py new file mode 100644 index 000000000..8d2f0b9ca --- /dev/null +++ b/pina/_src/solver/autoregressive_single_model_solver.py @@ -0,0 +1,111 @@ +"""Module for the autoregressive single model solver class.""" + +from pina._src.solver.mixin.autoregressive_mixin import AutoregressiveMixin +from pina._src.condition.time_series_condition import TimeSeriesCondition +from pina._src.solver.single_model_solver import SingleModelSolver + + +class AutoregressiveSingleModelSolver(AutoregressiveMixin, SingleModelSolver): + r""" + Single-model solver for autoregressive learning problems. + + This solver learns the time evolution of dynamical systems using a single + model. It is intended for problems defined by time-series data and accepts + only + :class:`~pina._src.condition.time_series_condition.TimeSeriesCondition`. + + Given a sequence of states :math:`\{\mathbf{u}_t\}_{t=0}^{T}`, the solver + trains a model :math:`\mathcal{M}` to predict the next state from the + current one: + + .. math:: + + \hat{\mathbf{u}}_{t+1} = \mathcal{M}(\mathbf{u}_t). + + The autoregressive training objective minimizes the discrepancy between + the predicted states :math:`\hat{\mathbf{u}}_{t+1}` and the target states + :math:`\mathbf{u}_{t+1}` over the sequence: + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{T} \sum_{t=0}^{T-1} + \mathcal{L} \left( \mathbf{u}_{t+1} - \hat{\mathbf{u}}_{t+1} \right), + + where :math:`\mathcal{L}` is the selected loss function, typically the mean + squared error. + + The solver supports adaptive weighting of autoregressive steps through the + ``eps`` parameter. During training, each autoregressive step can contribute + differently to the total loss depending on its accumulated difficulty. Steps + with larger running losses are assigned larger weights, so that the solver + focuses more on parts of the rollout where prediction errors tend to + accumulate. The parameter ``eps`` controls the strength of this effect: + ``eps = 0`` disables adaptive weighting, while larger values increase the + influence of high-loss steps on the final training objective. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = (TimeSeriesCondition,) + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + use_lt=False, + eps=0.0, + reset_weights_at_epoch_start=True, + kwargs=None, + ): + """ + Initialization of the :class:`AutoregressiveSingleModelSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param torch.nn.Module model: The model used by the solver. + :param TorchOptimizer optimizer: The optimizer used by the solver. + If ``None``, the ``torch.optim.Adam`` optimizer with a learning rate + of ``0.001`` is used. Default is ``None``. + :param TorchScheduler scheduler: The scheduler used by the solver. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``False``. + :param eps: The hyperparameter controlling the influence of the + cumulative loss on the adaptive weights. Higher values of eps will + lead to more aggressive weighting of steps with higher cumulative + loss. Default is ``0.0``. + :type eps: float | int + :param bool reset_weights_at_epoch_start: Whether to reset the running + average and step count at the start of each epoch. If ``True``, the + adaptive weights will be recalibrated at the beginning of each epoch + based on the new training dynamics. Default is ``True``. + :param dict kwargs: Additional keyword arguments for preprocessing and + postprocessing steps. + """ + + # Initialize the parent class + SingleModelSolver.__init__( + self, + problem=problem, + model=model, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + loss=loss, + use_lt=use_lt, + ) + + # Initialize the autoregressive components + self._init_autoregressive_components( + eps=eps, + reset_weights_at_epoch_start=reset_weights_at_epoch_start, + kwargs=kwargs, + ) diff --git a/pina/_src/solver/base_solver.py b/pina/_src/solver/base_solver.py new file mode 100644 index 000000000..da6f5a60a --- /dev/null +++ b/pina/_src/solver/base_solver.py @@ -0,0 +1,435 @@ +"""Module for the base solver class.""" + +from abc import ABCMeta +import lightning +import torch +from pina._src.core.utils import labelize_forward, check_consistency +from pina._src.solver.solver_interface import SolverInterface +from pina._src.weighting.base_weighting import BaseWeighting +from pina._src.problem.inverse_problem import InverseProblem +from pina._src.optim.torch_optimizer import TorchOptimizer +from pina._src.optim.torch_scheduler import TorchScheduler +from pina._src.weighting.no_weighting import _NoWeighting +from pina._src.problem.base_problem import BaseProblem +from pina._src.loss.base_dual_loss import BaseDualLoss + + +class BaseSolver(SolverInterface, metaclass=ABCMeta): + """ + Base class for all solvers, implementing common functionality. + + All solvers must inherit from this class and implement abstract methods + defined in :class:`~pina.solver.solver_interface.SolverInterface`. + + This class is not meant to be instantiated directly. + """ + + # Define the available reductions for loss computation + _AVAILABLE_REDUCTIONS = { + "none": lambda x: x, + "mean": lambda x: x.mean(), + "sum": lambda x: x.sum(), + } + + def __init__(self, problem, use_lt=True): + """ + Initialization of the :class:`BaseSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``True``. + :raises ValueError: If ``use_lt`` is not a boolean. + :raises ValueError: If ``problem`` is not an instance of + :class:`~pina.problem.base_problem.BaseProblem`. + :raises ValueError: If one or more problem conditions are not supported + by the solver. + """ + # Reset the solver state + self.reset() + + # Call the parent class initializer + lightning.pytorch.LightningModule.__init__(self) + + # Check consistency + check_consistency(use_lt, bool) + check_consistency(problem, BaseProblem) + for condition in problem.conditions.values(): + check_consistency(condition, self.accepted_conditions_types) + + # Initialize the solver components + self._pina_problem = problem + self._use_lt = use_lt + + # Manage InverseProblem parameters if needed + if isinstance(self.problem, InverseProblem): + self._params = self.problem.unknown_parameters + self._clamp_params = self._clamp_inverse_problem_params + else: + self._params = None + self._clamp_params = lambda: None + + # Labelize the forward method if using LabelTensors + if self.use_lt: + self.forward = labelize_forward( + forward=self.forward, + input_variables=problem.input_variables, + output_variables=problem.output_variables, + ) + + def reset(self): + """ + Reset the internal solver state, clearing the stored problem, models, + optimizers and schedulers. + """ + self._pina_problem = None + self._pina_models = None + self._pina_optimizers = None + self._pina_schedulers = None + + def _clamp_inverse_problem_params(self): + """ + Clamp the unknown parameters of an inverse problem. Each unknown + parameter is constrained to lie within the corresponding bounds defined + by the inverse problem parameter domain. + """ + for v in self._params: + self._params[v].data.clamp_( + self.problem.unknown_parameter_domain.range[v][0], + self.problem.unknown_parameter_domain.range[v][1], + ) + + def _init_weighting_and_loss(self, weighting=None, loss=None): + """ + Initialize the weighting strategy and loss function. + + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :type loss: torch.nn.Module | BaseDualLoss + :raises ValueError: If ``weighting`` is not an instance of + :class:`~pina.weighting.base_weighting.BaseWeighting`. + :raises ValueError: If ``loss`` is not a valid PyTorch loss or + :class:`~pina.loss.base_dual_loss.BaseDualLoss`. + """ + # If no weighting schema is provided, use a default no-weighting schema + if weighting is None: + weighting = _NoWeighting() + + # Set default loss function to MSE if not provided + if loss is None: + loss = torch.nn.MSELoss() + + # Check consistency + check_consistency(weighting, BaseWeighting) + check_consistency(loss, (BaseDualLoss, torch.nn.modules.loss._Loss)) + + # Store the weighting and loss function for use in the solver + self._pina_weighting = weighting + weighting._solver = self + self._loss_fn = loss + self._reduction = getattr(loss, "reduction", "mean") + if hasattr(self._loss_fn, "reduction"): + self._loss_fn.reduction = "none" + + def _init_solver_components( + self, + models, + optimizers=None, + schedulers=None, + ): + """ + Initialize the solver models, optimizers and schedulers. + + :param models: The model or list of models used by the solver. + :type models: torch.nn.Module | list[torch.nn.Module] + :param optimizers: The optimizer or list of optimizers used by the + solver. If ``None``, the ``torch.optim.Adam`` optimizer with a + learning rate of ``0.001`` is used for each model. + Default is ``None``. + :type optimizers: TorchOptimizer | list[TorchOptimizer] + :param schedulers: The scheduler or list of schedulers used by the + solver. If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` + scheduler with a factor of ``1.0`` is used for each model. + Default is ``None``. + :type schedulers: TorchScheduler | list[TorchScheduler] + :raises ValueError: If ``models`` are not instances of + :class:`torch.nn.Module`. + :raises ValueError: If ``optimizers`` are not instances of + :class:`~pina.optim.torch_optimizer.TorchOptimizer`. + :raises ValueError: If ``schedulers`` are not instances of + :class:`~pina.optim.torch_scheduler.TorchScheduler`. + :raises ValueError: If the number of optimizers does not match that of + models. + :raises ValueError: If the number of schedulers does not match that of + models. + """ + + # Helper function to map single items to lists if needed + _to_list = lambda x: [x] if not isinstance(x, (list, tuple)) else x + + # Map models to list if a single model is provided + models = _to_list(models) + + # Set default optimizers to Adam if not provided + if optimizers is None: + optimizers = [ + TorchOptimizer(torch.optim.Adam, lr=0.001) + for _ in range(len(models)) + ] + + # Set default schedulers to ConstantLR if not provided + if schedulers is None: + schedulers = [ + TorchScheduler(torch.optim.lr_scheduler.ConstantLR, factor=1.0) + for _ in range(len(models)) + ] + + # Map optimizers and schedulers to lists if single items are provided + optimizers = _to_list(optimizers) + schedulers = _to_list(schedulers) + + # Check consistency + check_consistency(optimizers, TorchOptimizer) + check_consistency(schedulers, TorchScheduler) + check_consistency(models, torch.nn.Module) + + # Check that the number of optimizers matches the number of models + if len(optimizers) != len(models): + raise ValueError( + "You must define one optimizer for each model." + f"Got {len(models)} models, and {len(optimizers)} optimizers." + ) + + # Check that the number of schedulers matches the number of models + if len(schedulers) != len(models): + raise ValueError( + "You must define one scheduler for each model." + f"Got {len(models)} models, and {len(schedulers)} schedulers." + ) + + # Initialize the solver components + self._pina_models = torch.nn.ModuleList(models) + self._pina_optimizers = optimizers + self._pina_schedulers = schedulers + + def training_step(self, batch, batch_idx): + """ + Solver training step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + loss = self.batch_evaluation_step(batch=batch, batch_idx=batch_idx) + self.log( + name="train_loss", + value=loss.item(), + batch_size=self.get_batch_size(batch), + **self.trainer.logging_kwargs, + ) + return loss + + def validation_step(self, batch, batch_idx): + """ + Solver validation step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + loss = self.batch_evaluation_step(batch=batch, batch_idx=batch_idx) + self.log( + name="val_loss", + value=loss.item(), + batch_size=self.get_batch_size(batch), + **self.trainer.logging_kwargs, + ) + return loss + + def test_step(self, batch, batch_idx): + """ + Solver test step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + loss = self.batch_evaluation_step(batch=batch, batch_idx=batch_idx) + self.log( + name="test_loss", + value=loss.item(), + batch_size=self.get_batch_size(batch), + **self.trainer.logging_kwargs, + ) + return loss + + def _compute_condition_loss(self, condition, data, batch_idx): + """ + Compute the scalar loss for a given condition and its data. + + :param BaseCondition condition: The condition for which to compute the + loss. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The scalar loss for the condition. + :rtype: torch.Tensor + """ + # Clone the input tensor if it exists to avoid in-place modifications + if "input" in data and hasattr(data["input"], "clone"): + data = dict(data) + data["input"] = data["input"].clone() + + # Prepare condition data, e.g. by enabling gradient for regularizations + data = self._prepare_condition_data(data=data) + + # Compute and store the residual tensor for the condition + self.residual_tensor = condition.evaluate(data, self) + + # Retrieve condition name for more complex weighting schemes + condition_name = condition.name if hasattr(condition, "name") else None + + # Compute the tensor loss from the residual tensor + condition_tensor_loss = self._loss_from_residual(condition_name) + + # Optional regularization hook, e.g gradient-enhanced or residual-based + condition_tensor_loss = self._regularize_condition_loss( + condition_tensor_loss=condition_tensor_loss, + condition_name=condition_name, + data=data, + batch_idx=batch_idx, + ) + + # Compute the scalar loss from the tensor loss and return it + condition_scalar_loss = self._apply_reduction(condition_tensor_loss) + + return condition_scalar_loss + + def _prepare_condition_data(self, data): + """ + Prepare the condition data for loss computation. This method can be + overridden by mixins to implement specific data preparation steps, such + as enabling gradient tracking for inputs in gradient-enhanced solvers. + + :param dict data: The original condition data. + :return: The prepared condition data. + :rtype: dict + """ + return data + + def _regularize_condition_loss( + self, + condition_tensor_loss, + condition_name, + data, + batch_idx, + ): + """ + Regularize the condition loss if needed. This method can be overridden + by mixins to implement specific regularization strategies, such as + adding a gradient penalty in gradient-enhanced solvers or applying + residual-based attention. + + :param condition_tensor_loss: The original tensor loss for the + condition. + :type condition_tensor_loss: torch.Tensor | LabelTensor + :param str condition_name: The name of the condition. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The regularized tensor loss for the condition. + :rtype: torch.Tensor | LabelTensor + """ + return condition_tensor_loss + + def _loss_from_residual(self, condition_name=None): + """ + Compute the tensor loss from the residual tensor. + + :param str condition_name: The name of the condition. + :return: The tensor loss computed from the residual tensor. + :rtype: torch.Tensor | LabelTensor + """ + # Compute the loss tensor and appply reduction + return self._loss_fn( + self.residual_tensor, torch.zeros_like(self.residual_tensor) + ) + + def _apply_reduction(self, value): + """ + Apply the specified reduction to the loss tensor. + + :param value: The loss tensor to reduce. + :type value: torch.Tensor | LabelTensor + :return: The reduced loss. + :rtype: torch.Tensor | LabelTensor + """ + # Get the reduction function based on the specified reduction type + reduction_fn = self._AVAILABLE_REDUCTIONS.get(self._reduction) + + # If the reduction type is not supported, raise an error + if reduction_fn is None: + raise ValueError( + f"Unsupported reduction '{self._reduction}'. " + f"Available options include {self._AVAILABLE_REDUCTIONS.keys()}" + ) + + return reduction_fn(value) + + @staticmethod + def get_batch_size(batch): + """ + Get the batch size. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :return: The size of the batch. + :rtype: int + """ + return sum(len(data[1]["input"]) for data in batch) + + @property + def problem(self): + """ + The problem instance. + + :return: The problem instance. + :rtype: :class:`~pina.problem.base_problem.BaseProblem` + """ + return self._pina_problem + + @property + def use_lt(self): + """ + Using LabelTensors as input during training. + + :return: The use_lt attribute. + :rtype: bool + """ + return self._use_lt + + @property + def weighting(self): + """ + The weighting schema used by the solver. + + :return: The weighting schema used by the solver. + :rtype: :class:`~pina.weighting.base_weighting.BaseWeighting` + """ + return self._pina_weighting + + @property + def loss(self): + """ + The element-wise loss module used by the solver. + + :return: The element-wise loss module used by the solver. + :rtype: torch.nn.Module + """ + return self._loss_fn diff --git a/pina/_src/solver/causal_physics_informed_single_model_solver.py b/pina/_src/solver/causal_physics_informed_single_model_solver.py new file mode 100644 index 000000000..db243e020 --- /dev/null +++ b/pina/_src/solver/causal_physics_informed_single_model_solver.py @@ -0,0 +1,336 @@ +"""Module for the causal physics-informed single-model solver class.""" + +import torch +from pina._src.condition.input_equation_condition import InputEquationCondition +from pina._src.solver.mixin.physics_informed_mixin import PhysicsInformedMixin +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.core.utils import check_consistency, check_positive_integer +from pina._src.problem.time_dependent_problem import TimeDependentProblem +from pina._src.solver.single_model_solver import SingleModelSolver +from pina._src.core.label_tensor import LabelTensor +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) + + +class CausalPhysicsInformedSingleModelSolver( + PhysicsInformedMixin, SingleModelSolver +): + r""" + Single-model solver for causal physics-informed learning problems. + + This solver approximates the solution of a time-dependent differential + problem using a single model and a causality-aware training objective. It is + intended for problems whose conditions include equation residuals and + boundary residuals evaluated across ordered time snapshots. It can be used + only for forward problems, due to the causal nature of the training + objective. + + Given a model :math:`\mathcal{M}`, the predicted solution is + + .. math:: + + \hat{\mathbf{u}}(\mathbf{x}, t) = \mathcal{M}(\mathbf{x}, t). + + The solver minimizes a causal residual loss that weights each time snapshot + according to the residuals accumulated at previous times. For a time + dependent problem with governing equation operator :math:`\mathcal{A}` in + the domain :math:`\Omega` and boundary operator :math:`\mathcal{B}` on the + boundary :math:`\partial\Omega`, the objective can be written as + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{N_t} \sum_{i=1}^{N_t} + \omega_i \mathcal{L}_r(t_i), + + where the residual loss at time :math:`t` is + + .. math:: + + \mathcal{L}_r(t) = \frac{1}{N_{\Omega}} \sum_{j=1}^{N_{\Omega}} + \mathcal{L}\left( \mathcal{A}[\hat{\mathbf{u}}](\mathbf{x}_j, t) \right) + + \frac{1}{N_{\partial\Omega}} \sum_{j=1}^{N_{\partial\Omega}} + \mathcal{L} \left( \mathcal{B}[\hat{\mathbf{u}}](\mathbf{x}_j, t) + \right). + + The causal weights are defined as + + .. math:: + + \omega_i = \exp \left( -\epsilon \sum_{k=1}^{i-1} \mathcal{L}_r(t_k) + \right), + + where :math:`\epsilon` is a hyperparameter controlling the strength of the + causal weighting, and :math:`\mathcal{L}` is the selected loss function, + typically the mean squared error. + + .. seealso:: + + **Original reference**: Wang, S., Sankaran, S., & Perdikaris, P. (2024). + *Respecting causality for training physics-informed neural networks.* + Computer Methods in Applied Mechanics and Engineering, 421, 116813. + DOI: `10.1016/j.cma.2024.116813 + `_. + + .. note:: + + This solver is compatible only with problems inheriting from + :class:`~pina.problem.time_dependent_problem.TimeDependentProblem`. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + eps=100, + n_steps=10, + regularized_conditions=None, + ): + """ + Initialization of the :class:`CausalPhysicsInformedSingleModelSolver` + class. + + :param BaseProblem problem: The problem to be solved. + :param torch.nn.Module model: The model used by the solver. + :param TorchOptimizer optimizer: The optimizer used by the solver. + If ``None``, the ``torch.optim.Adam`` optimizer with a learning rate + of ``0.001`` is used. Default is ``None``. + :param TorchScheduler scheduler: The scheduler used by the solver. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param eps: The exponential decay parameter. Default is ``100``. + :type eps: float | int + :param int n_steps: The number of equispaced temporal steps used to + compute the causal loss. Default is ``10``. + :param regularized_conditions: The names of the conditions that should + receive causal regularization. Default is ``None``. + :raises ValueError: If the problem is not time-dependent. + :raises ValueError: If the user does not specify any regularized + conditions. + :raises ValueError: If any of the specified ``regularized_conditions`` + are not present in the ``problem``'s conditions. + :raises ValueError: If ``eps`` is not a float or int. + :raises ValueError: If ``n_steps`` is not a positive integer. + """ + # Ensure the problem is time-dependent + if not isinstance(problem, TimeDependentProblem): + raise ValueError( + "Causal physics-informed solvers require the problem to be an " + f"instance of TimeDependentProblem. Got {type(problem)}." + ) + + # Ensure the user specified valid regularized conditions + if regularized_conditions is None: + raise ValueError( + "Causal physics-informed solvers require the user to specify " + "the conditions that should receive causal regularization. " + "Apply causal regularization only to time-dependent conditions." + ) + + # Check consistency + check_consistency(eps, (int, float)) + check_consistency(regularized_conditions, str) + check_positive_integer(n_steps, strict=True) + + # Map conditions to list if a single condition is provided + if not isinstance(regularized_conditions, (list, tuple)): + regularized_conditions = [regularized_conditions] + + # Ensure that all regularized conditions are present in the problem + problem_conditions = set(problem.conditions.keys()) + for condition in regularized_conditions: + if condition not in problem_conditions: + raise ValueError( + f"Condition '{condition}' is not present in the problem." + ) + + # Initialize the parent class + SingleModelSolver.__init__( + self, + problem=problem, + model=model, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + loss=loss, + use_lt=True, + ) + + # Initialize the causal regularization parameters + self.eps = eps + self.n_steps = n_steps + self.regularized_conditions = regularized_conditions + + def _compute_condition_loss(self, condition, data, batch_idx): + """ + Compute the scalar loss for a given condition and its data. + + :param BaseCondition condition: The condition for which to compute the + loss. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The scalar loss for the condition. + :rtype: torch.Tensor + """ + # If the condition is not regularized, or is a supervised (target) + # condition, use the standard loss computation + if condition.name not in self.regularized_conditions or isinstance( + condition, InputTargetCondition + ): + return super()._compute_condition_loss(condition, data, batch_idx) + + # Clone the input tensor if it exists to avoid in-place modifications + if "input" in data and hasattr(data["input"], "clone"): + data = dict(data) + data["input"] = data["input"].clone() + + # Prepare condition data, e.g. by enabling gradient for regularizations + data = self._prepare_condition_data(data=data) + + # Extract the temporal domain + time_domain = self.problem.temporal_domain + + # Define the time steps for the causal loss computation + if time_domain.range: + time_steps = torch.linspace( + time_domain.range[self.temporal_variable][0], + time_domain.range[self.temporal_variable][1], + self.n_steps, + device=data["input"].device, + dtype=data["input"].dtype, + ) + + # If no range is defined, use the unique temporal value + else: + time_steps = torch.tensor( + [time_domain.fixed[self.temporal_variable]], + device=data["input"].device, + dtype=data["input"].dtype, + ) + + # Initialize the list to store the loss for each time step + time_loss = [] + + # Iterate over the time steps + for step in time_steps: + + # Append the temporal variable to the spatial input tensor + spatial_pts = data["input"].extract(self.spatial_variables) + time_pts = LabelTensor( + torch.ones(spatial_pts.shape[0], 1, device=spatial_pts.device) + * step, + labels=[self.temporal_variable], + ) + pts = { + "input": LabelTensor.cat( + [spatial_pts, time_pts], dim=1 + ).requires_grad_(True) + } + + # Compute and store the residual tensor for the condition + self.residual_tensor = condition.evaluate(pts, self) + + # Retrieve condition name for more complex weighting schemes + condition_name = ( + condition.name if hasattr(condition, "name") else None + ) + + # Compute the tensor loss from the residual tensor + condition_tensor_loss = self._loss_from_residual(condition_name) + + # Optional regularization hook + condition_tensor_loss = self._regularize_condition_loss( + condition_tensor_loss=condition_tensor_loss, + condition_name=condition_name, + data=data, + batch_idx=batch_idx, + ) + + # Append the loss for the current time step to the list + time_loss.append(condition_tensor_loss) + + # Compute the time-adaptive weights for the causal loss + time_loss = torch.stack(time_loss) + with torch.no_grad(): + weights = self._compute_weights(time_loss) + + # Compute the scalar loss from the tensor loss and return it + condition_scalar_loss = self._apply_reduction(weights * time_loss) + + return condition_scalar_loss + + def _compute_weights(self, loss): + """ + Compute the temporal adaptive weights for the causal loss based on the + cumulative loss. + + :param LabelTensor loss: The physics loss values. + :return: The computed weights for the physics loss. + :rtype: LabelTensor + """ + # Compute the cumulative loss and apply exponential decay + cumulative_loss = torch.cumsum(loss, dim=0) + return torch.exp(-self.eps * cumulative_loss) + + @property + def temporal_variable(self): + """ + The temporal variable of the problem. + + :return: The temporal variable name. + :rtype: str + :raises ValueError: If the problem does not have exactly one temporal + variable. + """ + # Extract the temporal variable from the problem + temporal_variables = self.problem.temporal_variables + + # Raise error if there is not exactly one temporal variable + if len(temporal_variables) != 1: + raise ValueError( + "Causal physics-informed solvers require exactly one temporal " + f"variable. Got {temporal_variables}." + ) + + return temporal_variables[0] + + @property + def spatial_variables(self): + """ + The spatial variables of the problem. + + :return: The spatial variable names. + :rtype: list[str] + :raises ValueError: If the problem does not have any spatial variables. + """ + # Determine the spatial variables by excluding the temporal variable + spatial_variables = [ + v + for v in self.problem.input_variables + if v != self.temporal_variable + ] + + # Raise error if there are no spatial variables left + if not spatial_variables: + raise ValueError( + "Causal physics-informed solvers require at least one spatial " + "variable in addition to time." + ) + + return spatial_variables diff --git a/pina/_src/solver/competitive_physics_informed_solver.py b/pina/_src/solver/competitive_physics_informed_solver.py new file mode 100644 index 000000000..70ed77030 --- /dev/null +++ b/pina/_src/solver/competitive_physics_informed_solver.py @@ -0,0 +1,306 @@ +"""Module for the competitive physics-informed multi-model solver.""" + +import copy +from pina._src.solver.mixin.physics_informed_mixin import PhysicsInformedMixin +from pina._src.condition.input_equation_condition import InputEquationCondition +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.solver.multi_model_solver import MultiModelSolver +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) + + +class CompetitivePhysicsInformedSolver(PhysicsInformedMixin, MultiModelSolver): + r""" + Multi-model solver for competitive physics-informed learning problems. + + This solver approximates the solution of a differential problem using a + trainable model together with a discriminator network. It is intended for + problems whose conditions may include supervised data, equation residuals + evaluated on input points, and equation residuals sampled from domains. + + Given a model :math:`\mathcal{M}`, the predicted solution is + + .. math:: + + \hat{\mathbf{u}}(\mathbf{x}) = \mathcal{M}(\mathbf{x}). + + The discriminator :math:`D` assigns pointwise weights to the residuals, + encouraging the model to focus on regions where the approximation performs + poorly. The model parameters are optimized by minimizing the loss, while the + discriminator parameters are optimized by maximizing it. + + For a problem with governing equation operator :math:`\mathcal{A}` in the + domain :math:`\Omega` and boundary operator :math:`\mathcal{B}` on the + boundary :math:`\partial\Omega`, the competitive objective can be written as + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{N_{\Omega}} + \sum_{i=1}^{N_{\Omega}} \mathcal{L} + \left(D(\mathbf{x}_i)\mathcal{A}[\hat{\mathbf{u}}](\mathbf{x}_i)\right) + +\frac{1}{N_{\partial\Omega}} \sum_{i=1}^{N_{\partial\Omega}} + \mathcal{L} + \left(D(\mathbf{x}_i)\mathcal{B}[\hat{\mathbf{u}}](\mathbf{x}_i)\right), + + where :math:`D` is the discriminator network and :math:`\mathcal{L}` is the + selected loss function, typically the mean squared error. + + The model and discriminator are trained through a min-max problem: + + .. math:: + + \min_{\theta} \max_{\phi} \mathcal{L}_{\mathrm{problem}}, + + where :math:`\theta` denotes the model parameters and :math:`\phi` denotes + the discriminator parameters. + + .. seealso:: + + **Original reference**: Zeng, Q., Kothari, P., Chou, E., & Masi, G. + (2022). + *Competitive physics informed networks.* + International Conference on Learning Representations, ICLR 2022. + `OpenReview Preprint `_. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + def __init__( + self, + problem, + model, + discriminator=None, + optimizer_model=None, + optimizer_discriminator=None, + scheduler_model=None, + scheduler_discriminator=None, + weighting=None, + loss=None, + ): + """ + Initialization of the :class:`CompetitivePhysicsInformedSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param torch.nn.Module model: The model used by the solver. + :param torch.nn.Module discriminator: The discriminator used by the + solver. If ``None``, a deep copy of the model is used as + discriminator. Default is ``None``. + :param TorchOptimizer optimizer_model: The optimizer of the main model. + If ``None``, the ``torch.optim.Adam`` optimizer with a learning rate + of ``0.001`` is used. Default is ``None``. + :param TorchOptimizer optimizer_discriminator: The optimizer of the + discriminator. If ``None``, the ``torch.optim.Adam`` optimizer with + a learning rate of ``0.001`` is used. Default is ``None``. + :param TorchScheduler scheduler_model: The scheduler of the main model. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param TorchScheduler scheduler_discriminator: The scheduler of the + discriminator. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :raises ValueError: If ``weight_function`` is not a ``torch.nn.Module``. + :raises ValueError: If not all domains have been discretised. + """ + # Initialize the discriminator if not provided + if discriminator is None: + discriminator = copy.deepcopy(model) + + # Prepare optimizers + optimizers = ( + [optimizer_model, optimizer_discriminator] + if any( + o is not None + for o in (optimizer_model, optimizer_discriminator) + ) + else None + ) + + # Prepare schedulers + schedulers = ( + [scheduler_model, scheduler_discriminator] + if any( + s is not None + for s in (scheduler_model, scheduler_discriminator) + ) + else None + ) + + # Initialize the base solver + MultiModelSolver.__init__( + self, + problem=problem, + models=[model, discriminator], + optimizers=optimizers, + schedulers=schedulers, + weighting=weighting, + loss=loss, + use_lt=True, + ) + + def training_step(self, batch, batch_idx): + """ + Solver training step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + # Zero the gradients of the model optimizer and compute the loss + self.optimizer_model.instance.zero_grad() + loss = self.batch_evaluation_step(batch, batch_idx) + + # Perform the backward pass and complete a step for the model + self.manual_backward(loss) + self.optimizer_model.instance.step() + self.scheduler_model.instance.step() + + # Zero the gradients of the discriminator optimizer and compute the loss + self.optimizer_discriminator.instance.zero_grad() + loss = self.batch_evaluation_step(batch, batch_idx) + + # Perform the backward pass and complete a step for the discriminator + self.manual_backward(-loss) + self.optimizer_discriminator.instance.step() + self.scheduler_discriminator.instance.step() + + # Log the training loss + self.log( + name="train_loss", + value=loss.item(), + batch_size=self.get_batch_size(batch), + **self.trainer.logging_kwargs, + ) + + return loss + + def forward(self, x): + """ + Forward pass through the model. + + :param x: The input data. + :type x: torch.Tensor | LabelTensor | Data | Graph + :return: The output of the model. + :rtype: torch.Tensor | LabelTensor | Data | Graph + """ + return self.model(x) + + def _compute_condition_loss(self, condition, data, batch_idx): + """ + Compute the scalar loss for a given condition and its data. + + :param BaseCondition condition: The condition for which to compute the + loss. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The scalar loss for the condition. + :rtype: torch.Tensor + """ + # Clone the input tensor if it exists to avoid in-place modifications + if "input" in data and hasattr(data["input"], "clone"): + data = dict(data) + data["input"] = data["input"].clone() + + # Prepare condition data, e.g. by enabling gradient for regularizations + data = self._prepare_condition_data(data=data) + + # Compute and store the residual tensor for the condition + self.residual_tensor = condition.evaluate(data, self) + + # Compute the discriminator bets for the current condition + discriminator_input = data["input"][self.problem.input_variables] + discriminator_bets = self.discriminator(discriminator_input) + + # Weight the residual tensor using the discriminator bets + self.residual_tensor = self.residual_tensor * discriminator_bets + + # Retrieve condition name for more complex weighting schemes + condition_name = condition.name if hasattr(condition, "name") else None + + # Compute the tensor loss from the residual tensor + condition_tensor_loss = self._loss_from_residual(condition_name) + + # Optional regularization hook, e.g gradient-enhanced or residual-based + condition_tensor_loss = self._regularize_condition_loss( + condition_tensor_loss=condition_tensor_loss, + condition_name=condition_name, + data=data, + batch_idx=batch_idx, + ) + + # Compute the scalar loss from the tensor loss and return it + condition_scalar_loss = self._apply_reduction(condition_tensor_loss) + + return condition_scalar_loss + + @property + def model(self): + """ + The single model used by the solver. + + :return: The single model used by the solver. + :rtype: torch.nn.Module + """ + return self._pina_models[0] + + @property + def discriminator(self): + """ + The discriminator used by the solver. + + :return: The discriminator used by the solver. + :rtype: torch.nn.Module + """ + return self._pina_models[1] + + @property + def optimizer_model(self): + """ + The optimizer for the model used by the solver. + + :return: The optimizer for the model used by the solver. + :rtype: TorchOptimizer + """ + return self.optimizers[0] + + @property + def optimizer_discriminator(self): + """ + The optimizer for the discriminator used by the solver. + + :return: The optimizer for the discriminator used by the solver. + :rtype: TorchOptimizer + """ + return self.optimizers[1] + + @property + def scheduler_model(self): + """ + The scheduler for the model used by the solver. + + :return: The scheduler for the model used by the solver. + :rtype: TorchScheduler + """ + return self.schedulers[0] + + @property + def scheduler_discriminator(self): + """ + The scheduler for the discriminator used by the solver. + + :return: The scheduler for the discriminator used by the solver. + :rtype: TorchScheduler + """ + return self.schedulers[1] diff --git a/pina/_src/solver/ensemble_solver.py b/pina/_src/solver/ensemble_solver.py new file mode 100644 index 000000000..d1a78a870 --- /dev/null +++ b/pina/_src/solver/ensemble_solver.py @@ -0,0 +1,82 @@ +"""Module for the ensemble solver class.""" + +from pina._src.solver.mixin.ensemble_mixin import EnsembleMixin +from pina._src.solver.base_solver import BaseSolver +from pina._src.solver.mixin.manual_optimization_mixin import ( + ManualOptimizationMixin, +) +from pina._src.solver.mixin.condition_aggregator_mixin import ( + ConditionAggregatorMixin, +) + + +class EnsembleSolver( + ManualOptimizationMixin, + EnsembleMixin, + ConditionAggregatorMixin, + BaseSolver, +): + """ + Base class for implementing ensemble-model solvers. + + This class provides the standard starting point for solvers based on an + ensemble of models. It combines the shared solver machinery from + :class:`~pina._src.solver.base_solver.BaseSolver` with ensemble-model + handling, manual optimization, and condition-wise loss aggregation. + + Subclasses can inherit from this class to implement solver-specific + behavior while reusing the common logic for model registration, optimizer + and scheduler setup, manual optimization, loss evaluation, weighting, and + aggregation across problem conditions. + """ + + def __init__( + self, + problem, + models, + optimizers=None, + schedulers=None, + weighting=None, + loss=None, + use_lt=True, + ): + """ + Initialization of the :class:`EnsembleSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param models: The model or list of models used by the solver. + :type models: torch.nn.Module | list[torch.nn.Module] + :param optimizers: The optimizer or list of optimizers used by the + solver. If ``None``, the ``torch.optim.Adam`` optimizer with a + learning rate of ``0.001`` is used for each model. + Default is ``None``. + :type optimizers: TorchOptimizer | list[TorchOptimizer] + :param schedulers: The scheduler or list of schedulers used by the + solver. If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` + scheduler with a factor of ``1.0`` is used for each model. + Default is ``None``. + :type schedulers: TorchScheduler | list[TorchScheduler] + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``True``. + """ + + # Initialize the base solver + BaseSolver.__init__(self, problem=problem, use_lt=use_lt) + + # Initialize the components of the solver + self._init_solver_components( + models=models, + optimizers=optimizers, + schedulers=schedulers, + ) + + # Initialize the weighting scheme for the conditions and the loss + self._init_weighting_and_loss(weighting=weighting, loss=loss) + + # Activate manual optimization + self._init_manual_optimization() diff --git a/pina/_src/solver/gradient_physics_informed_single_model_solver.py b/pina/_src/solver/gradient_physics_informed_single_model_solver.py new file mode 100644 index 000000000..8991420ce --- /dev/null +++ b/pina/_src/solver/gradient_physics_informed_single_model_solver.py @@ -0,0 +1,124 @@ +"""Module for the gradient physics-informed single-model solver class.""" + +from pina._src.solver.mixin.physics_informed_mixin import PhysicsInformedMixin +from pina._src.condition.input_equation_condition import InputEquationCondition +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.solver.single_model_solver import SingleModelSolver +from pina._src.solver.mixin.gradient_enhanced_mixin import ( + GradientEnhancedMixin, +) +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) + + +class GradientPhysicsInformedSingleModelSolver( + PhysicsInformedMixin, GradientEnhancedMixin, SingleModelSolver +): + r""" + Single-model solver for gradient-enhanced physics-informed learning + problems. + + This solver approximates the solution of a differential problem using a + single model and augments the standard physics-informed objective with + gradient-enhanced residual terms. It can be used for both forward and + inverse problems. + + Given a model :math:`\mathcal{M}`, the predicted solution is + + .. math:: + + \hat{\mathbf{u}}(\mathbf{x}) = \mathcal{M}(\mathbf{x}). + + The solver minimizes both the residuals of the differential operators + defining the problem and the gradients of those residuals with respect to + the input variables. For a problem with governing equation operator + :math:`\mathcal{A}` in the domain :math:`\Omega` and boundary operator + :math:`\mathcal{B}` on the boundary :math:`\partial\Omega`, the objective + can be written as + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{N_{\Omega}} + \sum_{i=1}^{N_{\Omega}} \mathcal{L} + \left( \mathcal{A}[\hat{\mathbf{u}}](\mathbf{x}_i) \right) + + \frac{1}{N_{\partial\Omega}} \sum_{i=1}^{N_{\partial\Omega}} + \mathcal{L} \left( \mathcal{B}[\hat{\mathbf{u}}](\mathbf{x}_i) \right) + + \frac{1}{N_{\Omega}} \sum_{i=1}^{N_{\Omega}} \mathcal{L} + \left( \nabla_{\mathbf{x}} \mathcal{A}[\hat{\mathbf{u}}](\mathbf{x}_i) + \right) + \frac{1}{N_{\partial\Omega}} \sum_{i=1}^{N_{\partial\Omega}} + \mathcal{L} \left( \nabla_{\mathbf{x}} \mathcal{B}[\hat{\mathbf{u}}] + (\mathbf{x}_i) \right), + + where :math:`\mathcal{L}` is the selected loss function, typically the mean + squared error. + + .. seealso:: + + **Original reference**: Yu, J., Lu, L., Meng, X., & Karniadakis, G. E. + (2022). *Gradient-enhanced physics-informed neural networks for forward + and inverse PDE problems.* Computer Methods in Applied Mechanics and + Engineering, 393, 114823. + DOI: `10.1016/j.cma.2022.114823 + `_. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + regularization_weight=1.0, + regularized_conditions=None, + ): + """ + Initialization of the :class:`GradientPhysicsInformedSingleModelSolver` + class. + + :param BaseProblem problem: The problem to be solved. + :param torch.nn.Module model: The model used by the solver. + :param TorchOptimizer optimizer: The optimizer used by the solver. + If ``None``, the ``torch.optim.Adam`` optimizer with a learning rate + of ``0.001`` is used. Default is ``None``. + :param TorchScheduler scheduler: The scheduler used by the solver. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param regularization_weight: The weight of the gradient regularization + term. Default is ``1.0``. + :type regularization_weight: float | int + :param regularized_conditions: The names of the conditions that should + receive gradient regularization. If ``None``, all conditions are + regularized. Default is ``None``. + """ + # Initialize the parent class + SingleModelSolver.__init__( + self, + problem=problem, + model=model, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + loss=loss, + use_lt=True, + ) + + # Initialize the gradient-enhanced components + self._init_gradient_enhanced_components( + regularization_weight=regularization_weight, + regularized_conditions=regularized_conditions, + ) diff --git a/pina/_src/solver/mixin/autoregressive_mixin.py b/pina/_src/solver/mixin/autoregressive_mixin.py new file mode 100644 index 000000000..33259ca94 --- /dev/null +++ b/pina/_src/solver/mixin/autoregressive_mixin.py @@ -0,0 +1,186 @@ +"""Module for the autoregressive mixin class.""" + +import torch +from pina._src.core.utils import check_consistency + + +class AutoregressiveMixin: + """ + Mixin that enables the autoregressive rollout loss logic by maintaining a + running average of step losses and computing adaptive weights for each step + based on the cumulative loss. This allows the solver to focus more on steps + that are currently underperforming, which can help improve training + stability and convergence. + + Designed to be used in combination with any solver inheriting from + :class:`~pina._src.solver.base_solver.BaseSolver`. + """ + + def _init_autoregressive_components( + self, eps, reset_weights_at_epoch_start, kwargs + ): + """ + Initialize the components related to the autoregressive rollout loss. + + :param eps: The hyperparameter controlling the influence of the + cumulative loss on the adaptive weights. Higher values of eps will + lead to more aggressive weighting of steps with higher cumulative + loss. + :type eps: float | int + :param bool reset_weights_at_epoch_start: Whether to reset the running + average and step count at the start of each epoch. If ``True``, the + adaptive weights will be recalibrated at the beginning of each epoch + based on the new training dynamics. + :param dict kwargs: Additional keyword arguments for preprocessing and + postprocessing steps. + :raises ValueError: If ``eps`` is not a float or int. + :raises ValueError: If ``reset_weights_at_epoch_start`` is not a bool. + """ + # Check consistency + check_consistency(eps, (float, int)) + check_consistency(reset_weights_at_epoch_start, bool) + + # Initialize the components for autoregressive rollout loss + self.reset_weights_at_epoch_start = reset_weights_at_epoch_start + self.eps = eps + self._running_avg = {} + self._step_count = {} + self._kwargs = kwargs or {} + + def _loss_from_residual(self, condition_name=None): + """ + Compute the tensor loss from the residual tensor. + + :param str condition_name: The name of the condition. + :return: The tensor loss computed from the residual tensor. + :rtype: torch.Tensor | LabelTensor + """ + # Compute the step losses from the residual tensor + step_loss = self._loss_fn( + self.residual_tensor, torch.zeros_like(self.residual_tensor) + ) + + # Retrieve the temporal adaptive weights for the current step losses + with torch.no_grad(): + weights = self._get_weights(condition_name or "default", step_loss) + + return step_loss * weights + + def _get_weights(self, condition_name, step_loss): + """ + Get temporal adaptive weights for each step based on the running average + of step losses. + + :param str condition_name: The name of the condition. + :param step_loss: The tensor of step losses for the current condition. + :type step_loss: torch.Tensor | LabelTensor + :return: The tensor of adaptive weights for each step. + :rtype: torch.Tensor | LabelTensor + """ + # Use the condition name for tracking the running average and step count + key = condition_name or "default" + reduce_dims = tuple(range(1, step_loss.dim())) + step_loss = step_loss.detach().mean(dim=reduce_dims, keepdim=True) + + # Update the running average and step count for the current condition + if key not in self._running_avg: + self._running_avg[key] = step_loss.detach().clone() + self._step_count[key] = 1 + else: + self._step_count[key] += 1 + value = step_loss.detach() - self._running_avg[key] + self._running_avg[key] += value / self._step_count[key] + + return self._compute_adaptive_weights(self._running_avg[key]) + + def _compute_adaptive_weights(self, step_loss): + """ + Compute the adaptive weights for each step based on the cumulative loss. + + :param step_loss: The tensor of step losses for the current condition. + :type step_loss: torch.Tensor | LabelTensor + :return: The tensor of adaptive weights for each step. + :rtype: torch.Tensor | LabelTensor + """ + cumulative_loss = -self.eps * torch.cumsum(step_loss, dim=0) + return torch.exp(cumulative_loss) + + def on_train_epoch_start(self): + """ + Clear the running average and step count at the start of each epoch if + ``reset_weights_at_epoch_start`` is ``True``. + """ + if self.reset_weights_at_epoch_start: + self._running_avg.clear() + self._step_count.clear() + + def preprocess_step(self, current_state, **kwargs): + """ + Preprocess the current state before each step. + + :param current_state: The current state tensor. + :type current_state: torch.Tensor | LabelTensor + :param dict kwargs: Additional keyword arguments for preprocessing. + :return: The preprocessed state tensor. + :rtype: torch.Tensor | LabelTensor + """ + return current_state + + def postprocess_step(self, predicted_state, **kwargs): + """ + Postprocess the predicted state after each step. If multiple models are + used, average the predictions across the model dimension. + + :param predicted_state: The predicted state tensor. + :type predicted_state: torch.Tensor | LabelTensor + :param dict kwargs: Additional keyword arguments for postprocessing. + :return: The postprocessed state tensor. + :rtype: torch.Tensor | LabelTensor + """ + return predicted_state + + def predict(self, initial_state, n_steps, **kwargs): + """ + Generate predictions by recursively calling the model's forward. + + :param initial_state: The initial state from which to start prediction. + The initial state must be of shape ``[trajectories, 1, *features]``. + :type initial_state: torch.Tensor | LabelTensor + :param int n_steps: The number of autoregressive steps to predict. + :param dict kwargs: Additional keyword arguments. + :raises ValueError: If the provided initial_state tensor has less than 3 + dimensions. + :return: The predicted trajectory, including the initial state. It has + shape ``[trajectories, n_steps + 1, *features]``, where the first + step corresponds to the initial state. + :rtype: torch.Tensor | LabelTensor + """ + # Set model to evaluation mode for prediction + self.eval() + + # Raise error if the initial_state does not have at least 3 dimensions + if initial_state.dim() < 3: + raise ValueError( + "The provided initial_state tensor must have at least 3" + "dimensions: [trajectories, 1, *features]." + f" Got shape {initial_state.shape}." + ) + + # Initialize the list of predictions with the initial state + predictions = [initial_state] + + # Disable gradient computation for autoregressive prediction + with torch.no_grad(): + + # Unroll the autoregressive prediction for n_steps + for _ in range(n_steps): + + # Preprocess the current state before the forward pass + current_state = self.preprocess_step(predictions[-1], **kwargs) + output = self.forward(current_state) + + # Postprocess the predicted state after the forward pass + next_state = self.postprocess_step(output, **kwargs) + predictions.append(next_state) + + return torch.cat(predictions, dim=1) diff --git a/pina/_src/solver/mixin/condition_aggregator_mixin.py b/pina/_src/solver/mixin/condition_aggregator_mixin.py new file mode 100644 index 000000000..ad5b023ac --- /dev/null +++ b/pina/_src/solver/mixin/condition_aggregator_mixin.py @@ -0,0 +1,58 @@ +"""Module for the condition aggregator mixin class.""" + +import torch + + +class ConditionAggregatorMixin: + """ + Mixin that logs per-condition scalar losses, weights them following the + provided weighting scheme, and aggregates them into the total loss. + + Designed to be used in combination with any solver inheriting from + :class:`~pina._src.solver.base_solver.BaseSolver`. + """ + + def batch_evaluation_step(self, batch, batch_idx): + """ + Evaluate and aggregate the losses for all conditions in a batch. + + For each condition in the batch, this method computes the corresponding + scalar loss, logs it using the condition name, and combines all + condition losses into a single scalar loss through the configured + weighting scheme. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The aggregated scalar loss for the batch. + :rtype: torch.Tensor + """ + # Initialize a dictionary to hold the scalar losses for each condition + condition_losses = {} + + # Loop through each condition in the batch and compute its scalar loss + for condition_name, data in batch: + + # Compute the scalar loss for the current condition + condition_losses[condition_name] = self._compute_condition_loss( + condition=self.problem.conditions[condition_name], + data=dict(data), + batch_idx=batch_idx, + ) + + # Clamp parameters - null operation if problem is not InverseProblem + self._clamp_params() + + # Log the individual condition losses + for name, value in condition_losses.items(): + self.log( + name=f"{name}_loss", + value=value.item(), + batch_size=self.get_batch_size(batch), + **self.trainer.logging_kwargs, + ) + + # Aggregate into the total loss using the weighting scheme + aggregated_loss = self.weighting.aggregate(condition_losses) + + return aggregated_loss.as_subclass(torch.Tensor) diff --git a/pina/_src/solver/mixin/ensemble_mixin.py b/pina/_src/solver/mixin/ensemble_mixin.py new file mode 100644 index 000000000..17757fc96 --- /dev/null +++ b/pina/_src/solver/mixin/ensemble_mixin.py @@ -0,0 +1,81 @@ +"""Module for the ensemble mixin class.""" + +import torch +from pina._src.solver.base_solver import BaseSolver +from pina._src.solver.mixin.multi_model_mixin import MultiModelMixin + + +class EnsembleMixin(MultiModelMixin): + """ + Mixin that defines the forward pass and optimizer configuration for solvers + backed by an ensemble of models. Provides properties to access the models, + optimizers, and schedulers. + + Designed to be used in combination with any solver inheriting from + :class:`~pina._src.solver.base_solver.BaseSolver`. + """ + + def forward(self, x): + """ + Forward pass for ensemble solvers. If an active model index is set, only + that model is evaluated. Otherwise, all models are evaluated and their + outputs are stacked together. + + :param x: The input data. + :type x: torch.Tensor | LabelTensor | Data | Graph + :return: The output of all models stacked together. + :rtype: torch.Tensor | LabelTensor | Data | Graph + """ + # Retrieve the index of the active model if set + active_idx = getattr(self, "_active_model_idx", None) + + # If an active model index is set, evaluate only that model + if active_idx is not None: + return self.models[active_idx](x) + + # Otherwise, evaluate all models and stack outputs + return torch.stack( + [self.models[idx](x) for idx in range(self.num_models)] + ) + + def _compute_condition_loss(self, condition, data, batch_idx): + """ + Compute the scalar loss for a given condition and its data. + + :param BaseCondition condition: The condition for which to compute the + loss. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The scalar loss for the condition. + :rtype: torch.Tensor + """ + # Initialize model losses for the current condition + model_losses = [] + + # Restore the active model index if it was set, else set it to None + previous_active_model_idx = getattr(self, "_active_model_idx", None) + + # Try - finally to ensure active model index is always restored + try: + + # Iterate over all ensemble models to compute individual losses + for model_idx in range(self.num_models): + + # Set the active model index for the current iteration + self._active_model_idx = model_idx + + # Compute the scalar loss for the current model and condition + condition_scalar_loss = BaseSolver._compute_condition_loss( + self, condition, data, batch_idx + ) + + # Store the computed loss for the current model + model_losses.append(condition_scalar_loss) + + # Ensure that the active model index is always restored + finally: + + # Restore the previous active model index after computation + self._active_model_idx = previous_active_model_idx + + return torch.stack(model_losses).mean() diff --git a/pina/_src/solver/mixin/gradient_enhanced_mixin.py b/pina/_src/solver/mixin/gradient_enhanced_mixin.py new file mode 100644 index 000000000..8a492dc1e --- /dev/null +++ b/pina/_src/solver/mixin/gradient_enhanced_mixin.py @@ -0,0 +1,148 @@ +"""Module for the gradient-enhanced mixin class.""" + +import torch +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.core.utils import check_consistency +from pina._src.core.operator import grad + + +class GradientEnhancedMixin: + """ + Mixin that augments residual losses with a gradient-based regularization + term. + + The additional penalty is the norm of the residual gradient with respect + to the spatial input variables. It is only applied to the conditions whose + names are listed in ``regularized_conditions``. + + Designed to be used in combination with any solver inheriting from + :class:`~pina._src.solver.base_solver.BaseSolver` and using + :class:`~pina._src.core.tensor.label_tensor.LabelTensor` as input. + """ + + def _init_gradient_enhanced_components( + self, regularization_weight=1.0, regularized_conditions=None + ): + """ + Initialize the gradient-enhancement parameters. + + :param regularization_weight: The weight of the gradient regularization + term. Default is ``1.0``. + :type regularization_weight: float | int + :param regularized_conditions: The names of the conditions that should + receive gradient regularization. If ``None``, all conditions are + regularized. Default is ``None``. + :type regularized_conditions: str | list[str] + :raises ValueError: If ``regularization_weight`` is not a float or int. + :raises ValueError: If ``regularized_conditions`` is not a string or a + list of strings. + :raises ValueError: If ``problem`` is not an instance of + :class:`~pina._src.problem.spatial_problem.SpatialProblem`. + :raises ValueError: If the solver's input data are not instances of + :class:`~pina._src.core.tensor.label_tensor.LabelTensor`. + :raises ValueError: If any of the specified ``regularized_conditions`` + are not present in the ``problem``'s conditions. + """ + # Use all conditions if regularized_conditions is None + if regularized_conditions is None: + regularized_conditions = list(self.problem.conditions.keys()) + + # Check consistency + check_consistency(regularization_weight, (float, int)) + check_consistency(regularized_conditions, str) + + # Map conditions to list if a single condition is provided + if not isinstance(regularized_conditions, (list, tuple)): + regularized_conditions = [regularized_conditions] + + # Assert the problem is instance of SpatialProblem + if not isinstance(self.problem, SpatialProblem): + raise ValueError( + "Gradient-enhanced regularization requires the problem to be " + f"an instance of SpatialProblem. Got {type(self.problem)}." + ) + + # Ensure that the solver is using LabelTensors as input + if not self.use_lt: + raise ValueError( + "Gradient-enhanced regularization requires the solver to use " + f"LabelTensors as input. Got use_lt={self.use_lt}." + ) + + # Ensure that all regularized conditions are present in the problem + problem_conditions = set(self.problem.conditions.keys()) + for condition in regularized_conditions: + if condition not in problem_conditions: + raise ValueError( + f"Condition '{condition}' is not present in the problem." + ) + + # Initialize the gradient-enhancement parameters + self.regularization_weight = regularization_weight + self.regularized_conditions = regularized_conditions + + def _prepare_condition_data(self, data): + """ + Prepare the condition data for loss computation. This method can be + overridden by mixins to implement specific data preparation steps, such + as enabling gradient tracking for inputs in gradient-enhanced solvers. + + :param dict data: The original condition data. + :return: The prepared condition data. + :rtype: dict + """ + # If data does not require grad, force requires_grad to True + if "input" in data and not data["input"].requires_grad: + data["input"].requires_grad_(True) + + return data + + def _regularize_condition_loss( + self, + condition_tensor_loss, + condition_name, + data, + batch_idx, + ): + """ + Regularize the condition loss if needed. This method can be overridden + by mixins to implement specific regularization strategies, such as + adding a gradient penalty in gradient-enhanced solvers or applying + residual-based attention. + + :param condition_tensor_loss: The original tensor loss for the + condition. + :type condition_tensor_loss: torch.Tensor | LabelTensor + :param str condition_name: The name of the condition. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The regularized tensor loss for the condition. + :rtype: torch.Tensor | LabelTensor + """ + # Regularize the loss with the gradient penalty if needed + if condition_name in self.regularized_conditions: + + # Apply labels to the residual tensor for gradient computation + self.residual_tensor.labels = [ + f"res_{i}" for i in range(self.residual_tensor.shape[1]) + ] + + # Compute the gradient of the residual with respect to spatial input + residual_gradient = grad( + output_=self.residual_tensor, + input_=data["input"], + d=self.problem.spatial_variables, + ) + + # Compute the norm of the residual gradient + residual_gradient_norm = self._loss_fn( + residual_gradient, torch.zeros_like(residual_gradient) + ) + + # Compute the gradient penalty term + penalty = self.regularization_weight * residual_gradient_norm + + # Add the gradient penalty to the original condition tensor loss + condition_tensor_loss = condition_tensor_loss + penalty + + return condition_tensor_loss diff --git a/pina/_src/solver/mixin/manual_optimization_mixin.py b/pina/_src/solver/mixin/manual_optimization_mixin.py new file mode 100644 index 000000000..bef6380a2 --- /dev/null +++ b/pina/_src/solver/mixin/manual_optimization_mixin.py @@ -0,0 +1,66 @@ +"""Module for the manual optimization mixin class.""" + + +class ManualOptimizationMixin: + """ + Mixin that handles Lightning manual optimization loops, useful for solvers + that require explicit control over optimization steps, such as those with + multiple optimizers or custom training loops. + + Designed to be used in combination with any solver inheriting from + :class:`~pina._src.solver.base_solver.BaseSolver`. + """ + + def _init_manual_optimization(self): + """ + Disable Lightning's automatic optimization. + """ + self.automatic_optimization = False + + def training_step(self, batch, batch_idx): + """ + Solver training step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + # Zero the gradients of all optimizers + for opt in self.optimizers: + opt.instance.zero_grad() + + # Perform the forward pass and compute the loss + loss = super().training_step(batch, batch_idx) + + # Perform the backward pass + self.manual_backward(loss) + + # Step the optimizers and schedulers + for opt, sched in zip(self.optimizers, self.schedulers): + opt.instance.step() + sched.instance.step() + + return loss + + def on_train_batch_end(self, outputs, batch, batch_idx): + """ + Keep Lightning's manual optimization progress counters in sync. + + This hook increments the completed optimization-step counter used by + Lightning's manual optimization loop, then delegates to the parent + implementation. + + :param torch.Tensor outputs: The loss of the training step. + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The result returned by the parent class implementation. + :rtype: Any + """ + # Sync the manual optimization progress counters in Lightning's loop + epoch_loop = self.trainer.fit_loop.epoch_loop + epoch_loop.manual_optimization.optim_step_progress.total.completed += 1 + + return super().on_train_batch_end(outputs, batch, batch_idx) diff --git a/pina/_src/solver/mixin/multi_model_mixin.py b/pina/_src/solver/mixin/multi_model_mixin.py new file mode 100644 index 000000000..723020fbb --- /dev/null +++ b/pina/_src/solver/mixin/multi_model_mixin.py @@ -0,0 +1,103 @@ +"""Module for the multi-model mixin class.""" + +import torch +from pina._src.problem.inverse_problem import InverseProblem + + +class MultiModelMixin: + """ + Mixin that defines the forward pass and optimizer configuration for solvers + backed by multiple models. Provides properties to access the models, + optimizers, and schedulers. + + Designed to be used in combination with any solver inheriting from + :class:`~pina._src.solver.base_solver.BaseSolver`. + """ + + def forward(self, x): + """ + The forward pass implementation that evaluates all models and returns a + stacked tensor of their outputs. + + :param x: The input data. + :type x: torch.Tensor | LabelTensor | Data | Graph + :return: The output of all models stacked together. + :rtype: torch.Tensor | LabelTensor | Data | Graph + """ + return torch.stack( + [self.models[idx](x) for idx in range(self.num_models)] + ) + + def configure_optimizers(self): + """ + Configure the optimizers and schedulers for all models. + + :return: The optimizer and the scheduler + :rtype: tuple[list[TorchOptimizer], list[TorchScheduler]] + """ + # Iterate over models, optimizers, and schedulers to hook them together + for optimizer, scheduler, model in zip( + self.optimizers, self.schedulers, self.models + ): + + # Hook the optimizer to the model parameters + optimizer.hook(model.parameters()) + + # Add parameter group for inverse problems if needed + if isinstance(self.problem, InverseProblem): + optimizer.instance.add_param_group( + { + "params": [ + self._params[var] + for var in self.problem.unknown_variables + ] + } + ) + + # Hook the scheduler to the optimizer + scheduler.hook(optimizer) + + return ( + [optimizer.instance for optimizer in self.optimizers], + [scheduler.instance for scheduler in self.schedulers], + ) + + @property + def models(self): + """ + The models used by the solver. + + :return: The models used by the solver. + :rtype: list[torch.nn.Module] + """ + return self._pina_models + + @property + def optimizers(self): + """ + The optimizers used by the solver. + + :return: The optimizers used by the solver. + :rtype: list[TorchOptimizer] + """ + return self._pina_optimizers + + @property + def schedulers(self): + """ + The schedulers used by the solver. + + :return: The schedulers used by the solver. + :rtype: list[TorchScheduler] + """ + return self._pina_schedulers + + @property + def num_models(self): + """ + The number of models used by the solver. + + :return: The number of models used by the solver. + :rtype: int + """ + return len(self.models) diff --git a/pina/_src/solver/mixin/physics_informed_mixin.py b/pina/_src/solver/mixin/physics_informed_mixin.py new file mode 100644 index 000000000..04229ff65 --- /dev/null +++ b/pina/_src/solver/mixin/physics_informed_mixin.py @@ -0,0 +1,40 @@ +"""Module for the physics-informed mixin class.""" + +import torch + + +class PhysicsInformedMixin: + """ + Mixin that enables physics-informed training by ensuring gradients are + enabled during validation and testing, which is necessary for computing + physics residuals. + + Designed to be used in combination with any solver inheriting from + :class:`~pina._src.solver.base_solver.BaseSolver`. + """ + + @torch.enable_grad() + def validation_step(self, batch, batch_idx): + """ + Solver validation step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + return super().validation_step(batch, batch_idx) + + @torch.enable_grad() + def test_step(self, batch, batch_idx): + """ + Solver test step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + return super().test_step(batch, batch_idx) diff --git a/pina/_src/solver/mixin/residual_based_attention_mixin.py b/pina/_src/solver/mixin/residual_based_attention_mixin.py new file mode 100644 index 000000000..04b72fa4e --- /dev/null +++ b/pina/_src/solver/mixin/residual_based_attention_mixin.py @@ -0,0 +1,150 @@ +"""Module for the residual-based attention mixin class.""" + +import torch +from pina._src.core.utils import check_consistency +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) + + +class ResidualBasedAttentionMixin: + """ + Mixin that augments the residual loss with an attention mechanism based on + the residual values. + + The attention weights are computed as a function of the residuals, and they + are used to weight the contribution of each condition to the overall loss. + This allows the solver to focus more on conditions with larger residuals, + potentially improving convergence and accuracy. + + Designed to be used in combination with any solver inheriting from + :class:`~pina._src.solver.base_solver.BaseSolver`. + """ + + def _init_residual_attention_components( + self, eta=0.001, gamma=0.999, regularized_conditions=None + ): + """ + Initialize the residual-based attention parameters. + + :param eta: The learning rate for the residual-based attention weights + update. Default is ``0.001``. + :type eta: float | int + :param float gamma: The decay factor for the residual-based attention + mechanism. Default is ``0.999``. + :param regularized_conditions: The names of the conditions that should + receive attention regularization. If ``None``, all conditions are + regularized. Default is ``None``. + :type regularized_conditions: str | list[str] + :raises ValueError: If ``eta`` is not a positive float or int. + :raises ValueError: If ``gamma`` is not a float in the range (0, 1). + :raises ValueError: If ``regularized_conditions`` is not a string or a + list of strings. + :raises ValueError: If any of the specified ``regularized_conditions`` + are not present in the ``problem``'s conditions. + """ + # Use all conditions if regularized_conditions is None + if regularized_conditions is None: + regularized_conditions = list(self.problem.conditions.keys()) + + # Check consistency + check_consistency(eta, (float, int)) + check_consistency(gamma, float) + check_consistency(regularized_conditions, str) + + # Assert gamma is in range (0, 1) + if not 0 < gamma < 1: + raise ValueError("gamma must be in range (0, 1)") + + # Assert eta is positive + if eta <= 0: + raise ValueError("eta must be positive") + + # Map conditions to list if a single condition is provided + if not isinstance(regularized_conditions, (list, tuple)): + regularized_conditions = [regularized_conditions] + + # Ensure that all regularized conditions are present in the problem + problem_conditions = set(self.problem.conditions.keys()) + for condition in regularized_conditions: + if condition not in problem_conditions: + raise ValueError( + f"Condition '{condition}' is not present in the problem." + ) + + # Initialize residual-based attention parameters + self.regularized_conditions = regularized_conditions + self.gamma = gamma + self.eta = eta + self.weight_buffers = {} + + # Iterate over all conditions to initialize the attention weights + for cond in self.regularized_conditions: + + # Get the condition object + condition = self.problem.conditions[cond] + + # Determine the number of points in the condition + if isinstance(condition, DomainEquationCondition): + n_pts = self.problem._discretised_domains[cond].shape[0] + else: + n_pts = condition.data.input.shape[0] + + # Register the attention weights as a buffer in the module + self.register_buffer(f"weight_{cond}", torch.zeros((n_pts, 1))) + self.weight_buffers[cond] = f"weight_{cond}" + + def _regularize_condition_loss( + self, + condition_tensor_loss, + condition_name, + data, + batch_idx, + ): + """ + Regularize the condition loss if needed. This method can be overridden + by mixins to implement specific regularization strategies, such as + adding a gradient penalty in gradient-enhanced solvers or applying + residual-based attention. + + :param condition_tensor_loss: The original tensor loss for the + condition. + :type condition_tensor_loss: torch.Tensor | LabelTensor + :param str condition_name: The name of the condition. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The regularized tensor loss for the condition. + :rtype: torch.Tensor | LabelTensor + """ + # Apply residual-based attention mechanism if needed + if condition_name in self.regularized_conditions: + + # Compute the normalized residuals norm for the current condition + res_abs = torch.linalg.vector_norm( + self.residual_tensor, ord=2, dim=1, keepdim=True + ) + res_norm = res_abs / (res_abs.max() + 1e-12) + + # Get the correct indices to retrieve the weights for the batch + len_residuals = self.residual_tensor.shape[0] + + # Get the weights buffer for the current condition + weights = getattr(self, self.weight_buffers[condition_name]) + + # Get the total number of points, together with the start / end idx + total_points = weights.shape[0] + start = (batch_idx * len_residuals) % total_points + end = start + len_residuals + + # Retrieve the weights for the current batch using modular indexing + idx = torch.arange(start, end, device=weights.device) + idx = idx % total_points + + # Update weights + with torch.no_grad(): + weights[idx] = self.gamma * weights[idx] + self.eta * res_norm + + # Weight the condition tensor loss with attention weights + condition_tensor_loss = condition_tensor_loss * weights[idx] + + return condition_tensor_loss diff --git a/pina/_src/solver/mixin/single_model_mixin.py b/pina/_src/solver/mixin/single_model_mixin.py new file mode 100644 index 000000000..74af1ab1a --- /dev/null +++ b/pina/_src/solver/mixin/single_model_mixin.py @@ -0,0 +1,82 @@ +"""Module for the single-model mixin class.""" + +from pina._src.problem.inverse_problem import InverseProblem + + +class SingleModelMixin: + """ + Mixin that defines the forward pass and optimizer configuration for solvers + backed by exactly one model. Provides properties to access the single model, + optimizer, and scheduler. + + Designed to be used in combination with any solver inheriting from + :class:`~pina._src.solver.base_solver.BaseSolver`. + """ + + def forward(self, x): + """ + The forward pass implementation for the single model, which simply + evaluates the model on the input. + + :param x: The input data. + :type x: torch.Tensor | LabelTensor | Data | Graph + :return: The output of the single model. + :rtype: torch.Tensor | LabelTensor | Data | Graph + """ + return self.model(x) + + def configure_optimizers(self): + """ + Configure the optimizer and scheduler for the single model. + + :return: The optimizer and the scheduler + :rtype: tuple[list[TorchOptimizer], list[TorchScheduler]] + """ + # Hook the optimizer to the model parameters + self.optimizer.hook(self.model.parameters()) + + # Add parameter group for inverse problems if needed + if isinstance(self.problem, InverseProblem): + self.optimizer.instance.add_param_group( + { + "params": [ + self._params[var] + for var in self.problem.unknown_variables + ] + } + ) + + # Hook the scheduler to the optimizer + self.scheduler.hook(self.optimizer) + + return ([self.optimizer.instance], [self.scheduler.instance]) + + @property + def model(self): + """ + The single model used by the solver. + + :return: The single model used by the solver. + :rtype: torch.nn.Module + """ + return self._pina_models[0] + + @property + def optimizer(self): + """ + The optimizer used by the solver. + + :return: The optimizer used by the solver. + :rtype: TorchOptimizer + """ + return self._pina_optimizers[0] + + @property + def scheduler(self): + """ + The scheduler used by the solver. + + :return: The scheduler used by the solver. + :rtype: TorchScheduler + """ + return self._pina_schedulers[0] diff --git a/pina/_src/solver/multi_model_solver.py b/pina/_src/solver/multi_model_solver.py new file mode 100644 index 000000000..3fdec7d9c --- /dev/null +++ b/pina/_src/solver/multi_model_solver.py @@ -0,0 +1,82 @@ +"""Module for the multi-model solver class.""" + +from pina._src.solver.mixin.multi_model_mixin import MultiModelMixin +from pina._src.solver.base_solver import BaseSolver +from pina._src.solver.mixin.manual_optimization_mixin import ( + ManualOptimizationMixin, +) +from pina._src.solver.mixin.condition_aggregator_mixin import ( + ConditionAggregatorMixin, +) + + +class MultiModelSolver( + ManualOptimizationMixin, + MultiModelMixin, + ConditionAggregatorMixin, + BaseSolver, +): + """ + Base class for implementing multi-model solvers. + + This class provides the standard starting point for solvers based on + multiple models. It combines the shared solver machinery from + :class:`~pina._src.solver.base_solver.BaseSolver` with multi-model handling, + manual optimization, and condition-wise loss aggregation. + + Subclasses can inherit from this class to implement solver-specific behavior + while reusing the common logic for model registration, optimizer and + scheduler setup, manual optimization, loss evaluation, weighting, and + aggregation across problem conditions. + """ + + def __init__( + self, + problem, + models, + optimizers=None, + schedulers=None, + weighting=None, + loss=None, + use_lt=True, + ): + """ + Initialization of the :class:`MultiModelSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param models: The model or list of models used by the solver. + :type models: torch.nn.Module | list[torch.nn.Module] + :param optimizers: The optimizer or list of optimizers used by the + solver. If ``None``, the ``torch.optim.Adam`` optimizer with a + learning rate of ``0.001`` is used for each model. + Default is ``None``. + :type optimizers: TorchOptimizer | list[TorchOptimizer] + :param schedulers: The scheduler or list of schedulers used by the + solver. If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` + scheduler with a factor of ``1.0`` is used for each model. + Default is ``None``. + :type schedulers: TorchScheduler | list[TorchScheduler] + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``True``. + """ + + # Initialize the base solver + BaseSolver.__init__(self, problem=problem, use_lt=use_lt) + + # Initialize the components of the solver + self._init_solver_components( + models=models, + optimizers=optimizers, + schedulers=schedulers, + ) + + # Initialize the weighting scheme for the conditions and the loss + self._init_weighting_and_loss(weighting=weighting, loss=loss) + + # Activate manual optimization + self._init_manual_optimization() diff --git a/pina/_src/solver/physics_informed_ensemble_solver.py b/pina/_src/solver/physics_informed_ensemble_solver.py new file mode 100644 index 000000000..15cc7e1e6 --- /dev/null +++ b/pina/_src/solver/physics_informed_ensemble_solver.py @@ -0,0 +1,96 @@ +"""Module for the physics-informed ensemble solver class.""" + +from pina._src.solver.mixin.physics_informed_mixin import PhysicsInformedMixin +from pina._src.condition.input_equation_condition import InputEquationCondition +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.solver.ensemble_solver import EnsembleSolver +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) + + +class PhysicsInformedEnsembleSolver(PhysicsInformedMixin, EnsembleSolver): + r""" + Ensemble-model solver for physics-informed learning problems. + + This solver approximates the solution of a differential problem using an + ensemble of models. It is intended for problems whose conditions may include + supervised data, equation residuals evaluated on input points, and equation + residuals sampled from domains. + + Given an ensemble of models :math:`\{\mathcal{M}_j\}_{j=1}^{M}`, the + predicted solution of each model is + + .. math:: + + \hat{\mathbf{u}}^{(j)}(\mathbf{x}) = \mathcal{M}_j(\mathbf{x}), + \qquad j = 1, \ldots, M. + + The solver minimizes the residuals of the differential operators defining + the problem for each model in the ensemble. For a problem with governing + equation operator :math:`\mathcal{A}` in the domain :math:`\Omega` and + boundary operator :math:`\mathcal{B}` on the boundary + :math:`\partial\Omega`, the objective can be written as + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{M} \sum_{j=1}^{M} + \left[ \frac{1}{N_{\Omega}} \sum_{i=1}^{N_{\Omega}} \mathcal{L} + \left( \mathcal{A}[\hat{\mathbf{u}}^{(j)}](\mathbf{x}_i) \right) + + \frac{1}{N_{\partial\Omega}} \sum_{i=1}^{N_{\partial\Omega}} + \mathcal{L} + \left( \mathcal{B}[\hat{\mathbf{u}}^{(j)}](\mathbf{x}_i) \right) + \right], + + where :math:`\mathcal{L}` is the selected loss function, typically the + mean squared error. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + def __init__( + self, + problem, + models, + optimizers=None, + schedulers=None, + weighting=None, + loss=None, + ): + """ + Initialization of the :class:`PhysicsInformedEnsembleSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param models: The model or list of models used by the solver. + :type models: torch.nn.Module | list[torch.nn.Module] + :param optimizers: The optimizer or list of optimizers used by the + solver. If ``None``, the ``torch.optim.Adam`` optimizer with a + learning rate of ``0.001`` is used for each model. + Default is ``None``. + :type optimizers: TorchOptimizer | list[TorchOptimizer] + :param schedulers: The scheduler or list of schedulers used by the + solver. If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` + scheduler with a factor of ``1.0`` is used for each model. + Default is ``None``. + :type schedulers: TorchScheduler | list[TorchScheduler] + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + """ + EnsembleSolver.__init__( + self, + problem=problem, + models=models, + optimizers=optimizers, + schedulers=schedulers, + weighting=weighting, + loss=loss, + use_lt=True, + ) diff --git a/pina/_src/solver/physics_informed_single_model_solver.py b/pina/_src/solver/physics_informed_single_model_solver.py new file mode 100644 index 000000000..1a5f783a2 --- /dev/null +++ b/pina/_src/solver/physics_informed_single_model_solver.py @@ -0,0 +1,96 @@ +"""Module for the physics-informed single-model solver class.""" + +from pina._src.solver.mixin.physics_informed_mixin import PhysicsInformedMixin +from pina._src.condition.input_equation_condition import InputEquationCondition +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.solver.single_model_solver import SingleModelSolver +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) + + +class PhysicsInformedSingleModelSolver(PhysicsInformedMixin, SingleModelSolver): + r""" + Single-model solver for physics-informed learning problems. + + This solver approximates the solution of a differential problem using a + single model. It is intended for problems whose conditions may include + supervised data, equation residuals evaluated on input points, and equation + residuals sampled from domains. + + Given a model :math:`\mathcal{M}`, the predicted solution is + + .. math:: + + \hat{\mathbf{u}}(\mathbf{x}) = \mathcal{M}(\mathbf{x}). + + The solver minimizes the residuals of the differential operators defining + the problem. For a problem with governing equation operator + :math:`\mathcal{A}` in the domain :math:`\Omega` and boundary operator + :math:`\mathcal{B}` on the boundary :math:`\partial\Omega`, the objective + can be written as + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{N_{\Omega}} + \sum_{i=1}^{N_{\Omega}} \mathcal{L} + \left( \mathcal{A}[\hat{\mathbf{u}}](\mathbf{x}_i) \right) + + \frac{1}{N_{\partial\Omega}} \sum_{i=1}^{N_{\partial\Omega}} + \mathcal{L} \left( \mathcal{B}[\hat{\mathbf{u}}](\mathbf{x}_i) \right), + + where :math:`\mathcal{L}` is the selected loss function, typically the + mean squared error. + + .. seealso:: + + **Original reference**: Karniadakis, G. E., Kevrekidis, I. G., Lu, L., + Perdikaris, P., Wang, S., & Yang, L. (2021). + *Physics-informed machine learning.* + Nature Reviews Physics, 3, 422-440. + DOI: `10.1038/s42254-021-00314-5 + `_. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + ): + """ + Initialization of the :class:`PhysicsInformedSingleModelSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param torch.nn.Module model: The model used by the solver. + :param TorchOptimizer optimizer: The optimizer used by the solver. + If ``None``, the ``torch.optim.Adam`` optimizer with a learning rate + of ``0.001`` is used. Default is ``None``. + :param TorchScheduler scheduler: The scheduler used by the solver. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + """ + SingleModelSolver.__init__( + self, + problem=problem, + model=model, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + loss=loss, + use_lt=True, + ) diff --git a/pina/_src/solver/rba_physics_informed_single_model_solver.py b/pina/_src/solver/rba_physics_informed_single_model_solver.py new file mode 100644 index 000000000..d19a8f229 --- /dev/null +++ b/pina/_src/solver/rba_physics_informed_single_model_solver.py @@ -0,0 +1,140 @@ +""" +Module for the residual-based attention physics-informed single-model solver +class. +""" + +from pina._src.solver.mixin.physics_informed_mixin import PhysicsInformedMixin +from pina._src.condition.input_equation_condition import InputEquationCondition +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.solver.single_model_solver import SingleModelSolver +from pina._src.solver.mixin.residual_based_attention_mixin import ( + ResidualBasedAttentionMixin, +) +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) + + +class RBAPhysicsInformedSingleModelSolver( + PhysicsInformedMixin, ResidualBasedAttentionMixin, SingleModelSolver +): + r""" + Residual-based attention solver for physics-informed learning problems. + + This solver approximates the solution of a differential problem using a + single model equipped with residual-based attention weights. It can be used + for both forward and inverse problems. + + Given a model :math:`\mathcal{M}`, the predicted solution is + + .. math:: + + \hat{\mathbf{u}}(\mathbf{x}) = \mathcal{M}(\mathbf{x}). + + The solver minimizes a weighted objective in which each residual + contribution is scaled by an attention weight. For a problem with governing + equation operator :math:`\mathcal{A}` in the domain :math:`\Omega` and + boundary operator :math:`\mathcal{B}` on the boundary + :math:`\partial\Omega`, the objective can be written as + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = + \frac{1}{N_{\Omega}} \sum_{i=1}^{N_{\Omega}} + \lambda_{\Omega}^{i} \mathcal{L} + \left( \mathcal{A}[\hat{\mathbf{u}}](\mathbf{x}_i) \right) + + \frac{1}{N_{\partial\Omega}} \sum_{i=1}^{N_{\partial\Omega}} + \lambda_{\partial\Omega}^{i} \mathcal{L} + \left( \mathcal{B}[\hat{\mathbf{u}}](\mathbf{x}_i) \right), + + where :math:`\mathcal{L}` is the selected loss function, typically the + mean squared error, and :math:`\lambda_{\Omega}^{i}` and + :math:`\lambda_{\partial\Omega}^{i}` are the attention weights associated + with the domain and boundary residuals, respectively. + + At each epoch, the attention weights are updated according to the magnitude + of the corresponding residuals: + + .. math:: + + \lambda_i^{k+1} = \gamma \lambda_i^k + \eta \frac{|r_i|}{\max_j |r_j|}, + + where :math:`r_i` is the residual at point :math:`i`, :math:`\gamma` is the + decay rate, and :math:`\eta` is the learning rate used for the attention + weight update. + + .. seealso:: + + **Original reference**: Anagnostopoulos, S. J., Toscano, J. D., + Stergiopulos, N., & Karniadakis, G. E. (2024). + *Residual-based attention and connection to information bottleneck theory + in PINNs.* + Computer Methods in Applied Mechanics and Engineering, 421, 116805. + DOI: `10.1016/j.cma.2024.116805 + `_. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + eta=0.001, + gamma=0.999, + regularized_conditions=None, + ): + """ + Initialization of the :class:`RBAPhysicsInformedSingleModelSolver` + class. + + :param BaseProblem problem: The problem to be solved. + :param torch.nn.Module model: The model used by the solver. + :param TorchOptimizer optimizer: The optimizer used by the solver. + If ``None``, the ``torch.optim.Adam`` optimizer with a learning rate + of ``0.001`` is used. Default is ``None``. + :param TorchScheduler scheduler: The scheduler used by the solver. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param eta: The learning rate for the residual-based attention weights + update. Default is ``0.001``. + :type eta: float | int + :param float gamma: The decay factor for the residual-based attention + mechanism. Default is ``0.999``. + :param regularized_conditions: The names of the conditions that should + receive attention regularization. If ``None``, all conditions are + regularized. Default is ``None``. + :type regularized_conditions: str | list[str] + """ + # Initialize the parent class + SingleModelSolver.__init__( + self, + problem=problem, + model=model, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + loss=loss, + use_lt=True, + ) + + # Initialize the residual-based attention components + self._init_residual_attention_components( + eta=eta, + gamma=gamma, + regularized_conditions=regularized_conditions, + ) diff --git a/pina/_src/solver/self_adaptive_physics_informed_solver.py b/pina/_src/solver/self_adaptive_physics_informed_solver.py new file mode 100644 index 000000000..7f2b4032a --- /dev/null +++ b/pina/_src/solver/self_adaptive_physics_informed_solver.py @@ -0,0 +1,344 @@ +"""Module for the self-adaptive physics-informed multi-model solver.""" + +import torch +from pina._src.solver.mixin.physics_informed_mixin import PhysicsInformedMixin +from pina._src.condition.input_equation_condition import InputEquationCondition +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.solver.multi_model_solver import MultiModelSolver +from pina._src.core.utils import check_consistency +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, +) + + +class SelfAdaptivePhysicsInformedSolver(PhysicsInformedMixin, MultiModelSolver): + r""" + Multi-model solver for self-adaptive physics-informed learning problems. + + This solver approximates the solution of a differential problem using a + trainable model together with condition-wise self-adaptive weights. It is + intended for problems whose conditions may include supervised data, equation + residuals evaluated on input points, and equation residuals sampled from + domains. + + Given a model :math:`\mathcal{M}`, the predicted solution is + + .. math:: + + \hat{\mathbf{u}}(\mathbf{x}) = \mathcal{M}(\mathbf{x}). + + For each condition, the solver introduces trainable pointwise weights. These + weights are passed through a user-defined weight function :math:`m`, + typically chosen to keep the effective weights bounded or positive. The + resulting weighted objective encourages the model to focus on regions where + the residual is larger. + + For a problem with governing equation operator :math:`\mathcal{A}` in the + domain :math:`\Omega` and boundary operator :math:`\mathcal{B}` on the + boundary :math:`\partial\Omega`, the objective can be written as + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{N_{\Omega}} + \sum_{i=1}^{N_{\Omega}} m(\lambda_{\Omega}^{i}) \mathcal{L} + \left( \mathcal{A}[\hat{\mathbf{u}}](\mathbf{x}_i) \right) + + \frac{1}{N_{\partial\Omega}} \sum_{i=1}^{N_{\partial\Omega}} + m(\lambda_{\partial\Omega}^{i}) + \mathcal{L} \left( \mathcal{B}[\hat{\mathbf{u}}](\mathbf{x}_i) \right), + + where :math:`\lambda_{\Omega}^{i}` and :math:`\lambda_{\partial\Omega}^{i}` + are the self-adaptive weights associated with points in :math:`\Omega` and + :math:`\partial\Omega`, respectively, and :math:`\mathcal{L}` is the + selected loss function, typically the mean squared error. + + The model parameters and the self-adaptive weights are optimized through a + min-max problem: + + .. math:: + + \min_{\theta} \max_{\lambda} \mathcal{L}_{\mathrm{problem}}, + + where :math:`\theta` denotes the model parameters and :math:`\lambda` + denotes the collection of self-adaptive weights. + + .. seealso:: + + **Original reference**: McClenny, L. D., & Braga-Neto, U. M. (2023). + *Self-adaptive physics-informed neural networks.* + Journal of Computational Physics, 474, 111722. + DOI: `10.1016/j.jcp.2022.111722 + `_. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + def __init__( + self, + problem, + model, + weight_function=torch.nn.Sigmoid(), + optimizer_model=None, + optimizer_weights=None, + scheduler_model=None, + scheduler_weights=None, + weighting=None, + loss=None, + ): + """ + Initialization of the :class:`SelfAdaptivePhysicsInformedSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param torch.nn.Module model: The model used by the solver. + :param torch.nn.Module weight_function: The weight function used to + compute self-adaptive weights. Default is ``torch.nn.Sigmoid()``. + :param TorchOptimizer optimizer_model: The optimizer of the main model. + If ``None``, the ``torch.optim.Adam`` optimizer with a learning rate + of ``0.001`` is used. Default is ``None``. + :param TorchOptimizer optimizer_weights: The optimizer of the + self-adaptive weights. If ``None``, the ``torch.optim.Adam`` + optimizer with a learning rate of ``0.001`` is used. + Default is ``None``. + :param TorchScheduler scheduler_model: The scheduler of the main model. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param TorchScheduler scheduler_weights: The scheduler of the + self-adaptive weights. If ``None``, the + ``torch.optim.lr_scheduler.ConstantLR`` scheduler with a factor of + ``1.0`` is used. Default is ``None``. + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :raises ValueError: If ``weight_function`` is not a ``torch.nn.Module``. + :raises ValueError: If not all domains have been discretised. + """ + # Check consistency + check_consistency(weight_function, torch.nn.Module) + + # Check that all domains have been discretised + if not problem.are_all_domains_discretised: + raise ValueError( + "All domains must be discretised before initializing the " + "solver." + ) + + # Compute the number of points for each condition + num_points = { + cond: ( + problem._discretised_domains[cond].shape[0] + if isinstance(problem.conditions[cond], DomainEquationCondition) + else problem.conditions[cond].data.input.shape[0] + ) + for cond in problem.conditions + } + + # Initialize weights container and per-condition parameters + weights = torch.nn.Module() + + # Attach the weight function as a submodule + weights.func = weight_function + + # Register a torch.nn.Parameter for each condition to store the weights + for cond in problem.conditions: + p = torch.nn.Parameter(torch.zeros(num_points[cond], 1)) + setattr(weights, cond, p) + + # Prepare optimizers + optimizers = ( + [optimizer_model, optimizer_weights] + if any(o is not None for o in (optimizer_model, optimizer_weights)) + else None + ) + + # Prepare schedulers + schedulers = ( + [scheduler_model, scheduler_weights] + if any(s is not None for s in (scheduler_model, scheduler_weights)) + else None + ) + + # Initialize the base solver + MultiModelSolver.__init__( + self, + problem=problem, + models=[model, weights], + optimizers=optimizers, + schedulers=schedulers, + weighting=weighting, + loss=loss, + use_lt=True, + ) + + def training_step(self, batch, batch_idx): + """ + Solver training step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + # Zero the gradients of weights optimizer and compute the loss + self.optimizer_weights.instance.zero_grad() + loss = self.batch_evaluation_step(batch, batch_idx) + + # Perform the backward pass and complete a step for the weights + self.manual_backward(-loss) + self.optimizer_weights.instance.step() + self.scheduler_weights.instance.step() + + # Zero the gradients of model optimizer and compute the loss again + self.optimizer_model.instance.zero_grad() + loss = self.batch_evaluation_step(batch, batch_idx) + + # Perform the backward pass and complete a step for the model + self.manual_backward(loss) + self.optimizer_model.instance.step() + self.scheduler_model.instance.step() + + # Log the training loss + self.log( + name="train_loss", + value=loss.item(), + batch_size=self.get_batch_size(batch), + **self.trainer.logging_kwargs, + ) + + return loss + + def forward(self, x): + """ + Forward pass through the model. + + :param x: The input data. + :type x: torch.Tensor | LabelTensor | Data | Graph + :return: The output of the model. + :rtype: torch.Tensor | LabelTensor | Data | Graph + """ + return self.model(x) + + def _compute_condition_loss(self, condition, data, batch_idx): + """ + Compute the scalar loss for a given condition and its data. + + :param BaseCondition condition: The condition for which to compute the + loss. + :param dict data: The data corresponding to the condition. + :param int batch_idx: The index of the current batch. + :return: The scalar loss for the condition. + :rtype: torch.Tensor + """ + # Clone the input tensor if it exists to avoid in-place modifications + if "input" in data and hasattr(data["input"], "clone"): + data = dict(data) + data["input"] = data["input"].clone() + + # Prepare condition data, e.g. by enabling gradient for regularizations + data = self._prepare_condition_data(data=data) + + # Compute and store the residual tensor for the condition + self.residual_tensor = condition.evaluate(data, self) + + # Retrieve condition name for more complex weighting schemes + condition_name = condition.name + + # Apply the activation function to the condition-specific weights + weight_param = getattr(self.weights, condition_name) + weight_tensor = self.weights.func(weight_param) + + # Compute the tensor loss from the residual tensor + condition_tensor_loss = self._loss_from_residual(condition_name) + + # Optional regularization hook, e.g gradient-enhanced or residual-based + condition_tensor_loss = self._regularize_condition_loss( + condition_tensor_loss=condition_tensor_loss, + condition_name=condition_name, + data=data, + batch_idx=batch_idx, + ) + + # Get the correct indices to retrieve the weights for the current batch + len_residuals = self.residual_tensor.shape[0] + + # Get the total number of points, together with the start / end indices + total_points = weight_param.shape[0] + start = (batch_idx * len_residuals) % total_points + end = start + len_residuals + + # Retrieve the weights for the current batch using modular indexing + idx = torch.arange(start, end, device=self.residual_tensor.device) + idx = idx % total_points + + # Compute the scalar loss from the tensor loss and return it + condition_scalar_loss = self._apply_reduction( + condition_tensor_loss * weight_tensor[idx] + ) + + return condition_scalar_loss + + @property + def model(self): + """ + The single model used by the solver. + + :return: The single model used by the solver. + :rtype: torch.nn.Module + """ + return self._pina_models[0] + + @property + def weights(self): + """ + The self-adaptive weights used by the solver. + + :return: The self-adaptive weights used by the solver. + :rtype: torch.nn.Module + """ + return self._pina_models[1] + + @property + def optimizer_model(self): + """ + The optimizer for the model used by the solver. + + :return: The optimizer for the model used by the solver. + :rtype: TorchOptimizer + """ + return self.optimizers[0] + + @property + def optimizer_weights(self): + """ + The optimizer for the weights used by the solver. + + :return: The optimizer for the weights used by the solver. + :rtype: TorchOptimizer + """ + return self.optimizers[1] + + @property + def scheduler_model(self): + """ + The scheduler for the model used by the solver. + + :return: The scheduler for the model used by the solver. + :rtype: TorchScheduler + """ + return self.schedulers[0] + + @property + def scheduler_weights(self): + """ + The scheduler for the weights used by the solver. + + :return: The scheduler for the weights used by the solver. + :rtype: TorchScheduler + """ + return self.schedulers[1] diff --git a/pina/_src/solver/single_model_solver.py b/pina/_src/solver/single_model_solver.py new file mode 100644 index 000000000..265c431c9 --- /dev/null +++ b/pina/_src/solver/single_model_solver.py @@ -0,0 +1,65 @@ +"""Module for the single-model solver class.""" + +from pina._src.solver.mixin.single_model_mixin import SingleModelMixin +from pina._src.solver.base_solver import BaseSolver +from pina._src.solver.mixin.condition_aggregator_mixin import ( + ConditionAggregatorMixin, +) + + +class SingleModelSolver(SingleModelMixin, ConditionAggregatorMixin, BaseSolver): + """ + Base class for implementing single-model solvers. + + This class provides the standard starting point for solvers based on a + single model. It combines the shared solver machinery from + :class:`~pina._src.solver.base_solver.BaseSolver` with single-model handling + and condition-wise loss aggregation. + + Subclasses can inherit from this class to implement solver-specific behavior + while reusing the common logic for model registration, optimizer and + scheduler setup, loss evaluation, weighting, and aggregation across problem + conditions. + """ + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + use_lt=True, + ): + """ + Initialization of the :class:`SingleModelSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param torch.nn.Module model: The model used by the solver. + :param TorchOptimizer optimizer: The optimizer used by the solver. + If ``None``, the ``torch.optim.Adam`` optimizer with a learning rate + of ``0.001`` is used. Default is ``None``. + :param TorchScheduler scheduler: The scheduler used by the solver. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``True``. + """ + # Initialize the base solver + BaseSolver.__init__(self, problem=problem, use_lt=use_lt) + + # Initialize the components of the solver + self._init_solver_components( + models=model, + optimizers=optimizer, + schedulers=scheduler, + ) + + # Initialize the weighting scheme for the conditions and the loss + self._init_weighting_and_loss(weighting=weighting, loss=loss) diff --git a/pina/_src/solver/solver_interface.py b/pina/_src/solver/solver_interface.py new file mode 100644 index 000000000..c6cab1b18 --- /dev/null +++ b/pina/_src/solver/solver_interface.py @@ -0,0 +1,92 @@ +"""Module for the solver interface.""" + +from abc import ABCMeta, abstractmethod +import lightning + + +class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta): + """ + Abstract base class for PINA solvers. All specific solvers must inherit + from this interface. This class extends + :class:`~lightning.pytorch.core.LightningModule`, providing additional + functionalities for defining and optimizing Deep Learning models. + + By inheriting from this base class, solvers gain access to built-in training + loops, logging utilities, and optimization techniques. + """ + + @abstractmethod + def training_step(self, batch, batch_idx): + """ + Solver training step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + + @abstractmethod + def validation_step(self, batch, batch_idx): + """ + Solver validation step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + + @abstractmethod + def test_step(self, batch, batch_idx): + """ + Solver test step. + + :param list[tuple[str, dict]] batch: A batch of data. Each element is a + tuple containing a condition name and a dictionary of points. + :param int batch_idx: The index of the current batch. + :return: The loss of the training step. + :rtype: torch.Tensor + """ + + @property + @abstractmethod + def problem(self): + """ + The problem instance. + + :return: The problem instance. + :rtype: :class:`~pina.problem.base_problem.BaseProblem` + """ + + @property + @abstractmethod + def use_lt(self): + """ + Using LabelTensors as input during training. + + :return: The use_lt attribute. + :rtype: bool + """ + + @property + @abstractmethod + def weighting(self): + """ + The weighting schema used by the solver. + + :return: The weighting schema used by the solver. + :rtype: :class:`~pina.weighting.base_weighting.BaseWeighting` + """ + + @property + @abstractmethod + def loss(self): + """ + The element-wise loss module used by the solver. + + :return: The element-wise loss module used by the solver. + :rtype: torch.nn.Module + """ diff --git a/pina/_src/solver/supervised_ensemble_solver.py b/pina/_src/solver/supervised_ensemble_solver.py new file mode 100644 index 000000000..d602f3fe0 --- /dev/null +++ b/pina/_src/solver/supervised_ensemble_solver.py @@ -0,0 +1,84 @@ +"""Module for the supervised ensemble-model solver class.""" + +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.solver.ensemble_solver import EnsembleSolver + + +class SupervisedEnsembleSolver(EnsembleSolver): + r""" + Ensemble-model solver for supervised learning problems. + + This solver approximates the mapping between input data and target data + using an ensemble of models. It is intended for problems whose conditions + are defined by input-target pairs and accepts only + :class:`~pina._src.condition.input_target_condition.InputTargetCondition`. + + Given input samples :math:`\mathbf{s}_i`, target values + :math:`\mathbf{u}_i`, and an ensemble of models + :math:`\{\mathcal{M}_j\}_{j=1}^{M}`, the prediction of each model is + + .. math:: + + \hat{\mathbf{u}}_{i}^{(j)} = \mathcal{M}_j(\mathbf{s}_i), + \qquad j = 1, \ldots, M. + + The supervised training objective minimizes the discrepancy between the + target values and the ensemble predictions: + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{M} \sum_{j=1}^{M} + \frac{1}{N} \sum_{i=1}^{N} \mathcal{L} + \left( \mathbf{u}_i - \hat{\mathbf{u}}_{i}^{(j)} \right), + + where :math:`\mathcal{L}` is the selected loss function, typically the + mean squared error. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = (InputTargetCondition,) + + def __init__( + self, + problem, + models, + optimizers=None, + schedulers=None, + weighting=None, + loss=None, + use_lt=True, + ): + """ + Initialization of the :class:`SupervisedEnsembleSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param models: The model or list of models used by the solver. + :type models: torch.nn.Module | list[torch.nn.Module] + :param optimizers: The optimizer or list of optimizers used by the + solver. If ``None``, the ``torch.optim.Adam`` optimizer with a + learning rate of ``0.001`` is used for each model. + Default is ``None``. + :type optimizers: TorchOptimizer | list[TorchOptimizer] + :param schedulers: The scheduler or list of schedulers used by the + solver. If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` + scheduler with a factor of ``1.0`` is used for each model. + Default is ``None``. + :type schedulers: TorchScheduler | list[TorchScheduler] + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``True``. + """ + EnsembleSolver.__init__( + self, + problem=problem, + models=models, + optimizers=optimizers, + schedulers=schedulers, + weighting=weighting, + loss=loss, + use_lt=use_lt, + ) diff --git a/pina/_src/solver/supervised_single_model_solver.py b/pina/_src/solver/supervised_single_model_solver.py new file mode 100644 index 000000000..428de8db4 --- /dev/null +++ b/pina/_src/solver/supervised_single_model_solver.py @@ -0,0 +1,74 @@ +"""Module for the supervised single-model solver class.""" + +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.solver.single_model_solver import SingleModelSolver + + +class SupervisedSingleModelSolver(SingleModelSolver): + r""" + Single-model solver for supervised learning problems. + + This solver is designed for problems defined by input-target pairs and uses + a single model to approximate the mapping from input variables to target + variables. It supports only + :class:`~pina._src.condition.input_target_condition.InputTargetCondition` + conditions. + + Given a model :math:`\mathcal{M}`, the solver minimizes the discrepancy + between the target values :math:`\mathbf{u}_i` and the model predictions + :math:`\mathcal{M}(\mathbf{s}_i)` evaluated at the input data + :math:`\mathbf{s}_i`. + + The supervised loss minimized during training is + + .. math:: + + \mathcal{L}_{\mathrm{problem}} = \frac{1}{N} \sum_{i=1}^{N} + \mathcal{L} \left( \mathbf{u}_i - \mathcal{M}(\mathbf{s}_i) \right), + + where :math:`\mathcal{L}` is the selected loss function, typically the mean + squared error. + """ + + # Accepted conditions types for this solver + accepted_conditions_types = (InputTargetCondition,) + + def __init__( + self, + problem, + model, + optimizer=None, + scheduler=None, + weighting=None, + loss=None, + use_lt=True, + ): + """ + Initialization of the :class:`SupervisedSingleModelSolver` class. + + :param BaseProblem problem: The problem to be solved. + :param torch.nn.Module model: The model used by the solver. + :param TorchOptimizer optimizer: The optimizer used by the solver. + If ``None``, the ``torch.optim.Adam`` optimizer with a learning rate + of ``0.001`` is used. Default is ``None``. + :param TorchScheduler scheduler: The scheduler used by the solver. + If ``None``, the ``torch.optim.lr_scheduler.ConstantLR`` scheduler + with a factor of ``1.0`` is used. Default is ``None``. + :param BaseWeighting weighting: The weighting strategy used to combine + condition losses. If ``None``, no weighting is applied. Default is + ``None``. + :param loss: The loss function used to compute residual losses. + If ``None``, :class:`torch.nn.MSELoss` is used. Default is ``None``. + :param bool use_lt: If ``True``, the solver uses LabelTensors as input. + Default is ``True``. + """ + SingleModelSolver.__init__( + self, + problem=problem, + model=model, + optimizer=optimizer, + scheduler=scheduler, + weighting=weighting, + loss=loss, + use_lt=use_lt, + ) diff --git a/pina/_src/weighting/__init__.py b/pina/_src/weighting/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pina/_src/weighting/base_weighting.py b/pina/_src/weighting/base_weighting.py new file mode 100644 index 000000000..2208009cb --- /dev/null +++ b/pina/_src/weighting/base_weighting.py @@ -0,0 +1,109 @@ +"""Module for the Base Weighting class.""" + +from typing import final, Callable +import torch +from pina._src.weighting.weighting_interface import WeightingInterface +from pina._src.core.utils import check_positive_integer, check_consistency + + +class BaseWeighting(WeightingInterface): + """ + Base class for all weighting schemas, implementing common functionality. + + A weighting schema defines how scalar loss terms coming from different + conditions are aggregated into a single scalar loss. + + All weighting schemas must inherit from this class and implement the methods + defined in :class:`~pina.weighting.weighting_interface.WeightingInterface`. + + This class is not meant to be instantiated directly. + """ + + # Supported aggregation methods + _AGGREGATE_METHODS = {"sum": torch.sum, "mean": torch.mean} + + def __init__(self, update_every_n_epochs=1, aggregator="sum"): + """ + Initialization of the :class:`BaseWeighting` class. + + :param int update_every_n_epochs: The number of training epochs between + weight updates. If set to 1, the weights are updated at every epoch. + This parameter is ignored by static weighting schemes. + Default is ``1``. + :param aggregator: The aggregation method. Available options include: + ``"sum"`` which sums the weighted losses, ``"mean"`` which averages + the weighted losses, or a custom callable that takes an iterable of + weighted losses and returns a single scalar. Default is ``"sum"``. + :type aggregator: str | Callable + :raises ValueError: If ``update_every_n_epochs`` is not a positive + integer. + :raises ValueError: If ``aggregator`` is invalid. + """ + # Check consistency + check_positive_integer(value=update_every_n_epochs, strict=True) + check_consistency(aggregator, (str, Callable)) + + # Validate aggregator + if isinstance(aggregator, str): + if aggregator not in self._AGGREGATE_METHODS: + raise ValueError( + f"Invalid aggregator '{aggregator}'. Available options: " + f"{list(self._AGGREGATE_METHODS.keys())}. Got {aggregator}." + ) + aggregator = self._AGGREGATE_METHODS[aggregator] + + # Initialization + self.update_every_n_epochs = update_every_n_epochs + self.aggregator_fn = aggregator + self._solver = None + self._saved_weights = {} + + @final + def aggregate(self, losses): + """ + Aggregate a collection of loss terms into a single scalar. + + This method applies the current weighting scheme to the provided losses + and returns the aggregated result. Implementations may internally update + the weights (e.g., based on training state) before performing the + aggregation. + + :param dict losses: The mapping from loss names to loss tensors. + :return: The aggregated loss value. + :rtype: torch.Tensor + """ + # Update weights when required + if self.solver.trainer.current_epoch % self.update_every_n_epochs == 0: + self._saved_weights = self.update_weights(losses) + + # Apply weights to the corresponding losses + weighted_losses = torch.stack( + [ + (self._saved_weights[condition] * loss).reshape(-1) + for condition, loss in losses.items() + ] + ) + + return self.aggregator_fn(weighted_losses) + + def last_saved_weights(self): + """ + Get the most recently computed weights. + + :return: The mapping from loss names to their corresponding weights. + :rtype: dict + """ + return self._saved_weights + + @property + def solver(self): + """ + Solver associated with this weighting strategy. + + Provides access to the solver instance that uses this weighting scheme, + enabling strategies that depend on training state or model information. + + :return: The solver instance. + :rtype: :class:`~pina.solver.base_solver.BaseSolver` + """ + return self._solver diff --git a/pina/_src/weighting/linear_weighting.py b/pina/_src/weighting/linear_weighting.py new file mode 100644 index 000000000..e57962c81 --- /dev/null +++ b/pina/_src/weighting/linear_weighting.py @@ -0,0 +1,79 @@ +"""Module for the Linear Weighting class.""" + +from pina._src.weighting.base_weighting import BaseWeighting +from pina._src.core.utils import check_consistency, check_positive_integer + + +class LinearWeighting(BaseWeighting): + """ + Weighting strategy based on linear interpolation over training epochs. + + This scheme progressively adjusts the weights assigned to each loss term, + transitioning from a set of initial values to corresponding final values. + The update follows a linear schedule and is applied at each epoch until the + specified target epoch is reached. + """ + + def __init__(self, initial_weights, final_weights, target_epoch): + """ + Initialization of the :class:`LinearWeighting` class. + + :param dict initial_weights: The mapping of loss identifiers to their + initial weights at the start of training. Keys represent loss terms + (e.g., conditions) and values are the corresponding weights. Loss + terms not explicitly specified default to a weight of ``1``. + :param dict final_weights: The mapping of loss identifiers to their + target weights at the specified ``target_epoch``. Keys must match + those of ``initial_weights``. Loss terms not explicitly specified + default to a weight of ``1``. + :param int target_epoch: The epoch at which the weights reach their + final values. The interpolation progresses linearly from epoch ``0`` + to ``target_epoch``. After ``target_epoch``, the weights remain + constant at their final values. + :raises ValueError: If ``initial_weights`` is not a dictionary. + :raises ValueError: If ``final_weights`` is not a dictionary. + :raises ValueError: If ``target_epoch`` is not a positive integer. + :raises ValueError: If the keys of the two dictionaries are not + consistent. + """ + super().__init__(update_every_n_epochs=1, aggregator="sum") + + # Check consistency + check_consistency([initial_weights, final_weights], dict) + check_positive_integer(value=target_epoch, strict=True) + + # Check that the keys of the two dictionaries match + if initial_weights.keys() != final_weights.keys(): + raise ValueError( + "The keys of the provided dictionaries for initial and final " + f"weights must match. Got {initial_weights.keys()} as initial " + f"weight keys and {final_weights.keys()} as final weight keys." + ) + + # Initialization + self.initial_weights = initial_weights + self.final_weights = final_weights + self.target_epoch = target_epoch + + def update_weights(self, losses): + """ + Update the weights based on the current losses. + + This method defines how the weighting strategy adapts over time. It is + responsible for computing and storing updated weights that will be used + during aggregation. + + :param dict losses: The mapping from loss names to loss tensors. + :return: The updated weights. + :rtype: dict + """ + # Determine the progress towards the target epoch + epoch = min(self.solver.trainer.current_epoch, self.target_epoch) + progress = epoch / self.target_epoch + + return { + condition: self.initial_weights[condition] + + (self.final_weights[condition] - self.initial_weights[condition]) + * progress + for condition in losses.keys() + } diff --git a/pina/_src/weighting/no_weighting.py b/pina/_src/weighting/no_weighting.py new file mode 100644 index 000000000..89507409e --- /dev/null +++ b/pina/_src/weighting/no_weighting.py @@ -0,0 +1,16 @@ +from pina._src.weighting.scalar_weighting import ScalarWeighting + + +class _NoWeighting(ScalarWeighting): + """ + Weighting strategy that leaves all loss terms unchanged. + + This is a special case of scalar weighting where a unit weight is assigned + to every loss term, resulting in no reweighting. + """ + + def __init__(self): + """ + Initialization of the :class:`_NoWeighting` class. + """ + super().__init__(weights=1) diff --git a/pina/_src/weighting/ntk_weighting.py b/pina/_src/weighting/ntk_weighting.py new file mode 100644 index 000000000..702d0655c --- /dev/null +++ b/pina/_src/weighting/ntk_weighting.py @@ -0,0 +1,96 @@ +"""Module for Neural Tangent Kernel Class""" + +import torch +from pina._src.weighting.base_weighting import BaseWeighting +from pina._src.core.utils import check_consistency, in_range + + +class NeuralTangentKernelWeighting(BaseWeighting): + """ + The Neural Tangent Kernel (NTK) weighting strategy. + + This weighting scheme dynamically adjusts the contribution of each loss term + during training by leveraging gradient information with respect to the model + parameters. For each loss component, the norm of its gradient is computed + and used to derive relative importance weights. The resulting weights are + smoothed over time using an exponential moving average controlled by the + parameter ``alpha``. + + .. seealso:: + + **Original reference**: Wang, Sifan, Xinling Yu, and + Paris Perdikaris. *When and why PINNs fail to train: + A neural tangent kernel perspective*. Journal of + Computational Physics 449 (2022): 110768. + DOI: `10.1016 `_. + """ + + def __init__(self, update_every_n_epochs=1, alpha=0.5): + """ + Initialization of the :class:`NeuralTangentKernelWeighting` class. + + :param int update_every_n_epochs: The number of training epochs between + weight updates. If set to 1, the weights are updated at every epoch. + This parameter is ignored by static weighting schemes. + Default is ``1``. + :param float alpha: The parameter controlling the exponential moving + average of the weights. It must be in the range [0, 1], where a + value of ``0.0`` means that only the current gradient norms are used + to compute the weights, and a value of ``1.0`` means that only the + last saved weights are used. Default is ``0.5``. + :raises ValueError: If ``alpha`` is not a float. + :raises ValueError: If ``alpha`` is not between 0.0 and 1.0 (inclusive). + """ + super().__init__( + update_every_n_epochs=update_every_n_epochs, aggregator="sum" + ) + + # Check consistency + check_consistency(alpha, float) + if not in_range(alpha, [0.0, 1.0], strict=False): + raise ValueError( + "The alpha parameter must be between 0.0 and 1.0 (inclusive)." + f" Got {alpha}." + ) + + # Initialization + self.alpha = alpha + self.weights = {} + + def update_weights(self, losses): + """ + Update the weights based on the current losses. + + This method defines how the weighting strategy adapts over time. It is + responsible for computing and storing updated weights that will be used + during aggregation. + + :param dict losses: The mapping from loss names to loss tensors. + :return: The updated weights. + :rtype: dict + """ + # Get model parameters and define a dictionary to store the norms + params = [p for p in self.solver.model.parameters() if p.requires_grad] + norms = {} + + # Iterate over conditions + for condition, loss in losses.items(): + + # Compute gradients + grads = torch.autograd.grad( + loss, + params, + retain_graph=True, + allow_unused=True, + ) + + # Compute norms + norms[condition] = torch.cat( + [g.flatten() for g in grads if g is not None] + ).norm() + + return { + condition: self.alpha * self.last_saved_weights().get(condition, 1) + + (1 - self.alpha) * norms[condition] / sum(norms.values()) + for condition in losses + } diff --git a/pina/_src/weighting/scalar_weighting.py b/pina/_src/weighting/scalar_weighting.py new file mode 100644 index 000000000..d977abf67 --- /dev/null +++ b/pina/_src/weighting/scalar_weighting.py @@ -0,0 +1,59 @@ +"""Module for the Scalar Weighting.""" + +from pina._src.weighting.base_weighting import BaseWeighting +from pina._src.core.utils import check_consistency + + +class ScalarWeighting(BaseWeighting): + """ + Weighting strategy based on fixed scalar coefficients. + + This scheme assigns a constant multiplicative weight to each loss term, + without adapting over time. The same weight can be applied to all terms, + or distinct weights can be specified for individual conditions. + """ + + def __init__(self, weights): + """ + Initialization of the :class:`ScalarWeighting` class. + + :param weights: The scalar weights associated with each loss term. It + can be provided either as a single numeric value or as a dictionary. + If a scalar is given, the same weight is applied to all loss terms. + If a dictionary is provided, its keys represent the loss identifiers + (e.g., conditions) and its values specify the corresponding weights. + Loss terms not explicitly defined in the dictionary are assigned a + default weight of ``1``. + :type weights: float | int | dict + :raises ValueError: If the input weights are neither numeric nor a + dictionary. + """ + super().__init__(update_every_n_epochs=1, aggregator="sum") + + # Check consistency + check_consistency([weights], (float, dict, int)) + + # Initialization + if isinstance(weights, dict): + self.values = weights + self.default_value_weights = 1 + else: + self.values = {} + self.default_value_weights = weights + + def update_weights(self, losses): + """ + Update the weights based on the current losses. + + This method defines how the weighting strategy adapts over time. It is + responsible for computing and storing updated weights that will be used + during aggregation. + + :param dict losses: The mapping from loss names to loss tensors. + :return: The updated weights. + :rtype: dict + """ + return { + condition: self.values.get(condition, self.default_value_weights) + for condition in losses.keys() + } diff --git a/pina/loss/self_adaptive_weighting.py b/pina/_src/weighting/self_adaptive_weighting.py similarity index 54% rename from pina/loss/self_adaptive_weighting.py rename to pina/_src/weighting/self_adaptive_weighting.py index c796d359f..d954fe635 100644 --- a/pina/loss/self_adaptive_weighting.py +++ b/pina/_src/weighting/self_adaptive_weighting.py @@ -1,14 +1,22 @@ """Module for Self-Adaptive Weighting class.""" import torch -from .weighting_interface import WeightingInterface +from pina._src.weighting.base_weighting import BaseWeighting -class SelfAdaptiveWeighting(WeightingInterface): +class SelfAdaptiveWeighting(BaseWeighting): """ - A self-adaptive weighting scheme to tackle the imbalance among the loss - components. This formulation equalizes the gradient norms of the losses, - preventing bias toward any particular term during training. + The self-adaptive weighting strategy based on gradient norm balancing. + + This scheme dynamically adjusts the weights assigned to each loss term by + computing the norm of their gradients with respect to the model parameters. + The resulting weights are chosen to counterbalance disparities in gradient + magnitudes, promoting a more uniform contribution of all loss components + during optimization. + + In practice, loss terms with smaller gradient norms are assigned larger + weights, while those with larger gradients are down-weighted. This helps + mitigate training imbalance and prevents dominance of specific loss terms. .. seealso:: @@ -18,7 +26,6 @@ class SelfAdaptiveWeighting(WeightingInterface): Networks*. DOI: `arXiv preprint arXiv:2507.08972. `_ - """ def __init__(self, update_every_n_epochs=1): @@ -27,15 +34,22 @@ def __init__(self, update_every_n_epochs=1): :param int update_every_n_epochs: The number of training epochs between weight updates. If set to 1, the weights are updated at every epoch. - Default is 1. + This parameter is ignored by static weighting schemes. + Default is ``1``. """ - super().__init__(update_every_n_epochs=update_every_n_epochs) + super().__init__( + update_every_n_epochs=update_every_n_epochs, aggregator="sum" + ) - def weights_update(self, losses): + def update_weights(self, losses): """ - Update the weighting scheme based on the given losses. + Update the weights based on the current losses. + + This method defines how the weighting strategy adapts over time. It is + responsible for computing and storing updated weights that will be used + during aggregation. - :param dict losses: The dictionary of losses. + :param dict losses: The mapping from loss names to loss tensors. :return: The updated weights. :rtype: dict """ diff --git a/pina/_src/weighting/weighting_interface.py b/pina/_src/weighting/weighting_interface.py new file mode 100644 index 000000000..7871a5f55 --- /dev/null +++ b/pina/_src/weighting/weighting_interface.py @@ -0,0 +1,60 @@ +"""Module for the Weighting Interface.""" + +from abc import ABCMeta, abstractmethod + + +class WeightingInterface(metaclass=ABCMeta): + """ + Abstract interface for all weighting schemas. + """ + + @abstractmethod + def aggregate(self, losses): + """ + Aggregate a collection of loss terms into a single scalar. + + This method applies the current weighting scheme to the provided losses + and returns the aggregated result. Implementations may internally update + the weights (e.g., based on training state) before performing the + aggregation. + + :param dict losses: The mapping from loss names to loss tensors. + :return: The aggregated loss value. + :rtype: torch.Tensor + """ + + @abstractmethod + def update_weights(self, losses): + """ + Update the weights based on the current losses. + + This method defines how the weighting strategy adapts over time. It is + responsible for computing and storing updated weights that will be used + during aggregation. + + :param dict losses: The mapping from loss names to loss tensors. + :return: The updated weights. + :rtype: dict + """ + + @abstractmethod + def last_saved_weights(self): + """ + Get the most recently computed weights. + + :return: The mapping from loss names to their corresponding weights. + :rtype: dict + """ + + @property + @abstractmethod + def solver(self): + """ + Solver associated with this weighting strategy. + + Provides access to the solver instance that uses this weighting scheme, + enabling strategies that depend on training state or model information. + + :return: The solver instance. + :rtype: :class:`~pina.solver.base_solver.BaseSolver` + """ diff --git a/pina/adaptive_function/__init__.py b/pina/adaptive_function/__init__.py index d53c5f368..d41f25ccd 100644 --- a/pina/adaptive_function/__init__.py +++ b/pina/adaptive_function/__init__.py @@ -1,33 +1,43 @@ -"""Adaptive Activation Functions Module.""" +"""Adaptive activation functions with learnable parameters. + +This module provides implementations of standard activation functions (ReLU, +SiLU, Tanh, etc.) augmented with trainable weights, as well as specialized +functions like SIREN, designed to improve convergence in PINNs and Neural +Operators. +""" __all__ = [ - "AdaptiveActivationFunctionInterface", + "AdaptiveFunctionInterface", + "BaseAdaptiveFunction", + "AdaptiveCELU", + "AdaptiveELU", + "AdaptiveExp", + "AdaptiveGELU", + "AdaptiveMish", "AdaptiveReLU", "AdaptiveSigmoid", - "AdaptiveTanh", "AdaptiveSiLU", - "AdaptiveMish", - "AdaptiveELU", - "AdaptiveCELU", - "AdaptiveGELU", - "AdaptiveSoftmin", - "AdaptiveSoftmax", "AdaptiveSIREN", - "AdaptiveExp", + "AdaptiveSoftmax", + "AdaptiveSoftmin", + "AdaptiveTanh", ] -from .adaptive_function import ( - AdaptiveReLU, - AdaptiveSigmoid, - AdaptiveTanh, - AdaptiveSiLU, - AdaptiveMish, - AdaptiveELU, - AdaptiveCELU, - AdaptiveGELU, - AdaptiveSoftmin, - AdaptiveSoftmax, - AdaptiveSIREN, - AdaptiveExp, +from pina._src.adaptive_function.adaptive_function_interface import ( + AdaptiveFunctionInterface, +) +from pina._src.adaptive_function.base_adaptive_function import ( + BaseAdaptiveFunction, ) -from .adaptive_function_interface import AdaptiveActivationFunctionInterface +from pina._src.adaptive_function.adaptive_celu import AdaptiveCELU +from pina._src.adaptive_function.adaptive_elu import AdaptiveELU +from pina._src.adaptive_function.adaptive_exp import AdaptiveExp +from pina._src.adaptive_function.adaptive_gelu import AdaptiveGELU +from pina._src.adaptive_function.adaptive_mish import AdaptiveMish +from pina._src.adaptive_function.adaptive_relu import AdaptiveReLU +from pina._src.adaptive_function.adaptive_sigmoid import AdaptiveSigmoid +from pina._src.adaptive_function.adaptive_silu import AdaptiveSiLU +from pina._src.adaptive_function.adaptive_siren import AdaptiveSIREN +from pina._src.adaptive_function.adaptive_softmax import AdaptiveSoftmax +from pina._src.adaptive_function.adaptive_softmin import AdaptiveSoftmin +from pina._src.adaptive_function.adaptive_tanh import AdaptiveTanh diff --git a/pina/adaptive_function/adaptive_function.py b/pina/adaptive_function/adaptive_function.py deleted file mode 100644 index e6f86a549..000000000 --- a/pina/adaptive_function/adaptive_function.py +++ /dev/null @@ -1,509 +0,0 @@ -"""Module for the Adaptive Functions.""" - -import torch -from ..utils import check_consistency -from .adaptive_function_interface import AdaptiveActivationFunctionInterface - - -class AdaptiveReLU(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.ReLU` activation function. - - Given the function :math:`\text{ReLU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{ReLU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{ReLU}_{\text{adaptive}}({x})=\alpha\,\text{ReLU}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - ReLU function is defined as: - - .. math:: - \text{ReLU}(x) = \max(0, x) - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.ReLU() - - -class AdaptiveSigmoid(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.Sigmoid` activation function. - - Given the function - :math:`\text{Sigmoid}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{Sigmoid}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{Sigmoid}_{\text{adaptive}}({x})= - \alpha\,\text{Sigmoid}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - Sigmoid function is defined as: - - .. math:: - \text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)} - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.Sigmoid() - - -class AdaptiveTanh(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.Tanh` activation function. - - Given the function :math:`\text{Tanh}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{Tanh}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{Tanh}_{\text{adaptive}}({x})=\alpha\,\text{Tanh}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - Tanh function is defined as: - - .. math:: - \text{Tanh}(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)} - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.Tanh() - - -class AdaptiveSiLU(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.SiLU` activation function. - - Given the function :math:`\text{SiLU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{SiLU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{SiLU}_{\text{adaptive}}({x})=\alpha\,\text{SiLU}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - SiLU function is defined as: - - .. math:: - \text{SiLU}(x) = x * \sigma(x), \text{where }\sigma(x) - \text{ is the logistic sigmoid.} - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.SiLU() - - -class AdaptiveMish(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.Mish` activation function. - - Given the function :math:`\text{Mish}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{Mish}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{Mish}_{\text{adaptive}}({x})=\alpha\,\text{Mish}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - Mish function is defined as: - - .. math:: - \text{Mish}(x) = x * \text{Tanh}(x) - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.Mish() - - -class AdaptiveELU(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.ELU` activation function. - - Given the function :math:`\text{ELU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{ELU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{ELU}_{\text{adaptive}}({x}) = \alpha\,\text{ELU}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - ELU function is defined as: - - .. math:: - \text{ELU}(x) = \begin{cases} - x, & \text{ if }x > 0\\ - \exp(x) - 1, & \text{ if }x \leq 0 - \end{cases} - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.ELU() - - -class AdaptiveCELU(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.CELU` activation function. - - Given the function :math:`\text{CELU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{CELU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{CELU}_{\text{adaptive}}({x})=\alpha\,\text{CELU}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - CELU function is defined as: - - .. math:: - \text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1)) - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.CELU() - - -class AdaptiveGELU(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.GELU` activation function. - - Given the function :math:`\text{GELU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{GELU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{GELU}_{\text{adaptive}}({x})=\alpha\,\text{GELU}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - GELU function is defined as: - - .. math:: - \text{GELU}(x)=0.5*x*(1+\text{Tanh}(\sqrt{2 / \pi}*(x+0.044715*x^3))) - - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.GELU() - - -class AdaptiveSoftmin(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.Softmin` activation function. - - Given the function - :math:`\text{Softmin}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{Softmin}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{Softmin}_{\text{adaptive}}({x})=\alpha\, - \text{Softmin}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - Softmin function is defined as: - - .. math:: - \text{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)} - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.Softmin() - - -class AdaptiveSoftmax(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :class:`~torch.nn.Softmax` activation function. - - Given the function - :math:`\text{Softmax}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{Softmax}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{Softmax}_{\text{adaptive}}({x})=\alpha\, - \text{Softmax}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the - Softmax function is defined as: - - .. math:: - \text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)} - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.nn.Softmax() - - -class AdaptiveSIREN(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :obj:`~torch.sin` function. - - Given the function :math:`\text{sin}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{sin}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{sin}_{\text{adaptive}}({x}) = \alpha\,\text{sin}(\beta{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters. - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - super().__init__(alpha, beta, gamma, fixed) - self._func = torch.sin - - -class AdaptiveExp(AdaptiveActivationFunctionInterface): - r""" - Adaptive trainable :obj:`~torch.exp` function. - - Given the function :math:`\text{exp}:\mathbb{R}^n\rightarrow\mathbb{R}^n`, - the adaptive function - :math:`\text{exp}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` - is defined as: - - .. math:: - \text{exp}_{\text{adaptive}}({x}) = \alpha\,\text{exp}(\beta{x}), - - where :math:`\alpha,\,\beta` are trainable parameters. - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, fixed=None): - - # only alpha, and beta parameters (gamma=0 fixed) - if fixed is None: - fixed = ["gamma"] - else: - check_consistency(fixed, str) - fixed = list(fixed) + ["gamma"] - - # calling super - super().__init__(alpha, beta, 0.0, fixed) - self._func = torch.exp diff --git a/pina/adaptive_function/adaptive_function_interface.py b/pina/adaptive_function/adaptive_function_interface.py deleted file mode 100644 index a655fdbd7..000000000 --- a/pina/adaptive_function/adaptive_function_interface.py +++ /dev/null @@ -1,151 +0,0 @@ -"""Module for the Adaptive Function interface.""" - -from abc import ABCMeta -import torch -from ..utils import check_consistency, is_function - - -class AdaptiveActivationFunctionInterface(torch.nn.Module, metaclass=ABCMeta): - r""" - The :class:`AdaptiveActivationFunctionInterface` - class makes a :class:`torch.nn.Module` activation function into an adaptive - trainable activation function. If one wants to create an adpative activation - function, this class must be use as base class. - - Given a function :math:`f:\mathbb{R}^n\rightarrow\mathbb{R}^m`, the adaptive - function :math:`f_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^m` - is defined as: - - .. math:: - f_{\text{adaptive}}(\mathbf{x}) = \alpha\,f(\beta\mathbf{x}+\gamma), - - where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters. - - .. seealso:: - - **Original reference**: Godfrey, Luke B., and Michael S. Gashler. - *A continuum among logarithmic, linear, and exponential functions, - and its potential to improve generalization in neural networks.* - 2015 7th international joint conference on knowledge discovery, - knowledge engineering and knowledge management (IC3K). - Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321. - `_. - - Jagtap, Ameya D., Kenji Kawaguchi, and George Em Karniadakis. *Adaptive - activation functions accelerate convergence in deep and - physics-informed neural networks*. Journal of - Computational Physics 404 (2020): 109136. - DOI: `JCP 10.1016 - `_. - """ - - def __init__(self, alpha=None, beta=None, gamma=None, fixed=None): - """ - Initializes the Adaptive Function. - - :param float | complex alpha: Scaling parameter alpha. - Defaults to ``None``. When ``None`` is passed, - the variable is initialized to 1. - :param float | complex beta: Scaling parameter beta. - Defaults to ``None``. When ``None`` is passed, - the variable is initialized to 1. - :param float | complex gamma: Shifting parameter gamma. - Defaults to ``None``. When ``None`` is passed, - the variable is initialized to 1. - :param list fixed: List of parameters to fix during training, - i.e. not optimized (``requires_grad`` set to ``False``). - Options are ``alpha``, ``beta``, ``gamma``. Defaults to None. - """ - super().__init__() - - # see if there are fixed variables - if fixed is not None: - check_consistency(fixed, str) - if not all(key in ["alpha", "beta", "gamma"] for key in fixed): - raise TypeError( - "Fixed keys must be in [`alpha`, `beta`, `gamma`]." - ) - - # initialize alpha, beta, gamma if they are None - if alpha is None: - alpha = 1.0 - if beta is None: - beta = 1.0 - if gamma is None: - gamma = 0.0 - - # checking consistency - check_consistency(alpha, (float, complex)) - check_consistency(beta, (float, complex)) - check_consistency(gamma, (float, complex)) - - # registering as tensors - alpha = torch.tensor(alpha, requires_grad=False) - beta = torch.tensor(beta, requires_grad=False) - gamma = torch.tensor(gamma, requires_grad=False) - - # setting not fixed variables as torch.nn.Parameter with gradient - # registering the buffer for the one which are fixed, buffers by - # default are saved alongside trainable parameters - if "alpha" not in (fixed or []): - self._alpha = torch.nn.Parameter(alpha, requires_grad=True) - else: - self.register_buffer("alpha", alpha) - - if "beta" not in (fixed or []): - self._beta = torch.nn.Parameter(beta, requires_grad=True) - else: - self.register_buffer("beta", beta) - - if "gamma" not in (fixed or []): - self._gamma = torch.nn.Parameter(gamma, requires_grad=True) - else: - self.register_buffer("gamma", gamma) - - def forward(self, x): - """ - Define the computation performed at every call. - The function to the input elementwise. - - :param x: The input tensor to evaluate the activation function. - :type x: torch.Tensor | LabelTensor - """ - return self.alpha * (self._func(self.beta * x + self.gamma)) - - @property - def alpha(self): - """ - The alpha variable. - """ - return self._alpha - - @property - def beta(self): - """ - The beta variable. - """ - return self._beta - - @property - def gamma(self): - """ - The gamma variable. - """ - return self._gamma - - @property - def func(self): - """ - The callable activation function. - """ - return self._func - - @func.setter - def func(self, value): - """ - Set the activation function. - """ - if not is_function(value): - raise TypeError("The function must be callable.") - self._func = value - return self._func diff --git a/pina/callback/__init__.py b/pina/callback/__init__.py index 92da661cb..a6b2e3973 100644 --- a/pina/callback/__init__.py +++ b/pina/callback/__init__.py @@ -1,17 +1,46 @@ -"""Module for the Pina Callbacks.""" +"""Training callbacks for PINA lifecycle management. + +This module provides specialized callbacks for training Scientific Machine +Learning models, including adaptive sample refinement (R3), optimizer +switching logic, and data normalization utilities. +""" __all__ = [ "SwitchOptimizer", "SwitchScheduler", - "NormalizerDataCallback", + "DataNormalizer", "PINAProgressBar", "MetricTracker", + "RefinementInterface", + "BaseRefinement", "R3Refinement", ] -from .optim.switch_optimizer import SwitchOptimizer -from .optim.switch_scheduler import SwitchScheduler -from .processing.normalizer_data_callback import NormalizerDataCallback -from .processing.pina_progress_bar import PINAProgressBar -from .processing.metric_tracker import MetricTracker -from .refinement import R3Refinement +from pina._src.callback.processing.pina_progress_bar import PINAProgressBar +from pina._src.callback.processing.metric_tracker import MetricTracker +from pina._src.callback.processing.data_normalizer import DataNormalizer +from pina._src.callback.optim.switch_optimizer import SwitchOptimizer +from pina._src.callback.optim.switch_scheduler import SwitchScheduler +from pina._src.callback.refinement.base_refinement import BaseRefinement +from pina._src.callback.refinement.r3_refinement import R3Refinement +from pina._src.callback.refinement.refinement_interface import ( + RefinementInterface, +) + +# Back-compatibility with version 0.2, to be removed soon +import warnings + +_DEPRECATED_IMPORTS = {"NormalizerDataCallback": "DataNormalizer"} + + +def __getattr__(name): + if name in _DEPRECATED_IMPORTS: + + warnings.warn( + f"Importing '{name}' from 'pina.callback' is deprecated; use " + f"pina.callback.{_DEPRECATED_IMPORTS[name]} instead.", + DeprecationWarning, + stacklevel=2, + ) + + return globals()[_DEPRECATED_IMPORTS[name]] diff --git a/pina/callback/processing/metric_tracker.py b/pina/callback/processing/metric_tracker.py deleted file mode 100644 index 9b1dc9d4a..000000000 --- a/pina/callback/processing/metric_tracker.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Module for the Metric Tracker.""" - -import copy -import torch -from lightning.pytorch.callbacks import Callback - - -class MetricTracker(Callback): - """ - Lightning Callback for Metric Tracking. - """ - - def __init__(self, metrics_to_track=None): - """ - Tracks specified metrics during training. - - :param metrics_to_track: List of metrics to track. - Defaults to train loss. - :type metrics_to_track: list[str], optional - """ - super().__init__() - self._collection = [] - # Default to tracking 'train_loss' if not specified - self.metrics_to_track = metrics_to_track - - def setup(self, trainer, pl_module, stage): - """ - Called when fit, validate, test, predict, or tune begins. - - :param Trainer trainer: A :class:`~pina.trainer.Trainer` instance. - :param SolverInterface pl_module: A - :class:`~pina.solver.solver.SolverInterface` instance. - :param str stage: Either 'fit', 'test' or 'predict'. - """ - if self.metrics_to_track is None and trainer.batch_size is None: - self.metrics_to_track = ["train_loss"] - elif self.metrics_to_track is None: - self.metrics_to_track = ["train_loss_epoch"] - return super().setup(trainer, pl_module, stage) - - def on_train_epoch_end(self, trainer, pl_module): - """ - Collect and track metrics at the end of each training epoch. - - :param trainer: The trainer object managing the training process. - :type trainer: pytorch_lightning.Trainer - :param pl_module: The model being trained (not used here). - """ - # Track metrics after the first epoch onwards - if trainer.current_epoch > 0: - # Append only the tracked metrics to avoid unnecessary data - tracked_metrics = { - k: v - for k, v in trainer.logged_metrics.items() - if k in self.metrics_to_track - } - self._collection.append(copy.deepcopy(tracked_metrics)) - - @property - def metrics(self): - """ - Aggregate collected metrics over all epochs. - - :return: A dictionary containing aggregated metric values. - :rtype: dict - """ - if not self._collection: - return {} - - # Get intersection of keys across all collected dictionaries - common_keys = set(self._collection[0]).intersection( - *self._collection[1:] - ) - - # Stack the metric values for common keys and return - return { - k: torch.stack([dic[k] for dic in self._collection]) - for k in common_keys - if k in self.metrics_to_track - } diff --git a/pina/callback/processing/normalizer_data_callback.py b/pina/callback/processing/normalizer_data_callback.py deleted file mode 100644 index 4d85a7d9a..000000000 --- a/pina/callback/processing/normalizer_data_callback.py +++ /dev/null @@ -1,228 +0,0 @@ -"""Module for the Normalizer callback.""" - -import torch -from lightning.pytorch import Callback -from ...label_tensor import LabelTensor -from ...utils import check_consistency, is_function -from ...condition import InputTargetCondition -from ...data.dataset import PinaGraphDataset - - -class NormalizerDataCallback(Callback): - r""" - A Callback used to normalize the dataset inputs or targets according to - user-provided scale and shift functions. - - The transformation is applied as: - - .. math:: - - x_{\text{new}} = \frac{x - \text{shift}}{\text{scale}} - - :Example: - - >>> NormalizerDataCallback() - >>> NormalizerDataCallback( - ... scale_fn: torch.std, - ... shift_fn: torch.mean, - ... stage: "all", - ... apply_to: "input", - ... ) - """ - - def __init__( - self, - scale_fn=torch.std, - shift_fn=torch.mean, - stage="all", - apply_to="input", - ): - """ - Initialization of the :class:`NormalizerDataCallback` class. - - :param Callable scale_fn: The function to compute the scaling factor. - Default is ``torch.std``. - :param Callable shift_fn: The function to compute the shifting factor. - Default is ``torch.mean``. - :param str stage: The stage in which normalization is applied. - Accepted values are "train", "validate", "test", or "all". - Default is ``"all"``. - :param str apply_to: Whether to normalize "input" or "target" data. - Default is ``"input"``. - :raises ValueError: If ``scale_fn`` is not callable. - :raises ValueError: If ``shift_fn`` is not callable. - """ - super().__init__() - - # Validate parameters - self.apply_to = self._validate_apply_to(apply_to) - self.stage = self._validate_stage(stage) - - # Validate functions - if not is_function(scale_fn): - raise ValueError(f"scale_fn must be Callable, got {scale_fn}") - if not is_function(shift_fn): - raise ValueError(f"shift_fn must be Callable, got {shift_fn}") - self.scale_fn = scale_fn - self.shift_fn = shift_fn - - # Initialize normalizer dictionary - self._normalizer = {} - - def _validate_apply_to(self, apply_to): - """ - Validate the ``apply_to`` parameter. - - :param str apply_to: The candidate value for the ``apply_to`` parameter. - :raises ValueError: If ``apply_to`` is neither "input" nor "target". - :return: The validated ``apply_to`` value. - :rtype: str - """ - check_consistency(apply_to, str) - if apply_to not in {"input", "target"}: - raise ValueError( - f"apply_to must be either 'input' or 'target', got {apply_to}" - ) - - return apply_to - - def _validate_stage(self, stage): - """ - Validate the ``stage`` parameter. - - :param str stage: The candidate value for the ``stage`` parameter. - :raises ValueError: If ``stage`` is not one of "train", "validate", - "test", or "all". - :return: The validated ``stage`` value. - :rtype: str - """ - check_consistency(stage, str) - if stage not in {"train", "validate", "test", "all"}: - raise ValueError( - "stage must be one of 'train', 'validate', 'test', or 'all'," - f" got {stage}" - ) - - return stage - - def setup(self, trainer, pl_module, stage): - """ - Apply normalization during setup. - - :param Trainer trainer: A :class:`~pina.trainer.Trainer` instance. - :param SolverInterface pl_module: A - :class:`~pina.solver.solver.SolverInterface` instance. - :param str stage: The current stage. - :raises RuntimeError: If the training dataset is not available when - computing normalization parameters. - :return: The result of the parent setup. - :rtype: Any - - :raises NotImplementedError: If the dataset is graph-based. - """ - - # Ensure datsets are not graph-based - if isinstance(trainer.datamodule.train_dataset, PinaGraphDataset): - raise NotImplementedError( - "NormalizerDataCallback is not compatible with " - "graph-based datasets." - ) - - # Extract conditions - conditions_to_normalize = [ - name - for name, cond in pl_module.problem.conditions.items() - if isinstance(cond, InputTargetCondition) - ] - - # Compute scale and shift parameters - if not self.normalizer: - if not trainer.datamodule.train_dataset: - raise RuntimeError( - "Training dataset is not available. Cannot compute " - "normalization parameters." - ) - self._compute_scale_shift( - conditions_to_normalize, trainer.datamodule.train_dataset - ) - - # Apply normalization based on the specified stage - if stage == "fit" and self.stage in ["train", "all"]: - self.normalize_dataset(trainer.datamodule.train_dataset) - if stage == "fit" and self.stage in ["validate", "all"]: - self.normalize_dataset(trainer.datamodule.val_dataset) - if stage == "test" and self.stage in ["test", "all"]: - self.normalize_dataset(trainer.datamodule.test_dataset) - - return super().setup(trainer, pl_module, stage) - - def _compute_scale_shift(self, conditions, dataset): - """ - Compute scale and shift parameters for each condition in the dataset. - - :param list conditions: The list of condition names. - :param dataset: The `~pina.data.dataset.PinaDataset` dataset. - """ - for cond in conditions: - if cond in dataset.conditions_dict: - data = dataset.conditions_dict[cond][self.apply_to] - shift = self.shift_fn(data) - scale = self.scale_fn(data) - self._normalizer[cond] = { - "shift": shift, - "scale": scale, - } - - @staticmethod - def _norm_fn(value, scale, shift): - """ - Normalize a value according to the scale and shift parameters. - - :param value: The input tensor to normalize. - :type value: torch.Tensor | LabelTensor - :param float scale: The scaling factor. - :param float shift: The shifting factor. - :return: The normalized tensor. - :rtype: torch.Tensor | LabelTensor - """ - scaled_value = (value - shift) / scale - if isinstance(value, LabelTensor): - scaled_value = LabelTensor(scaled_value, value.labels) - - return scaled_value - - def normalize_dataset(self, dataset): - """ - Apply in-place normalization to the dataset. - - :param PinaDataset dataset: The dataset to be normalized. - """ - # Initialize update dictionary - update_dataset_dict = {} - - # Iterate over conditions and apply normalization - for cond, norm_params in self.normalizer.items(): - points = dataset.conditions_dict[cond][self.apply_to] - scale = norm_params["scale"] - shift = norm_params["shift"] - normalized_points = self._norm_fn(points, scale, shift) - update_dataset_dict[cond] = { - self.apply_to: ( - LabelTensor(normalized_points, points.labels) - if isinstance(points, LabelTensor) - else normalized_points - ) - } - - # Update the dataset in-place - dataset.update_data(update_dataset_dict) - - @property - def normalizer(self): - """ - Get the dictionary of normalization parameters. - - :return: The dictionary of normalization parameters. - :rtype: dict - """ - return self._normalizer diff --git a/pina/callback/processing/pina_progress_bar.py b/pina/callback/processing/pina_progress_bar.py deleted file mode 100644 index 4c322a5e8..000000000 --- a/pina/callback/processing/pina_progress_bar.py +++ /dev/null @@ -1,99 +0,0 @@ -"""Module for the Processing Callbacks.""" - -from lightning.pytorch.callbacks import TQDMProgressBar -from lightning.pytorch.callbacks.progress.progress_bar import ( - get_standard_metrics, -) -from pina.utils import check_consistency - - -class PINAProgressBar(TQDMProgressBar): - """ - PINA Implementation of a Lightning Callback for enriching the progress bar. - """ - - BAR_FORMAT = ( - "{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, " - "{rate_noinv_fmt}{postfix}]" - ) - - def __init__(self, metrics="val", **kwargs): - """ - This class enables the display of only relevant metrics during training. - - :param metrics: Logged metrics to be shown during the training. - Must be a subset of the conditions keys defined in - :obj:`pina.condition.Condition`. - :type metrics: str | list(str) | tuple(str) - - :Keyword Arguments: - The additional keyword arguments specify the progress bar and can be - choosen from the `pytorch-lightning TQDMProgressBar API - `_ - - Example: - >>> pbar = PINAProgressBar(['mean']) - >>> # ... Perform training ... - >>> trainer = Trainer(solver, callbacks=[pbar]) - """ - super().__init__(**kwargs) - # check consistency - if not isinstance(metrics, (list, tuple)): - metrics = [metrics] - check_consistency(metrics, str) - self._sorted_metrics = metrics - - def get_metrics(self, trainer, pl_module): - r"""Combine progress bar metrics collected from the trainer with - standard metrics from get_standard_metrics. - Override this method to customize the items shown in the progress bar. - The progress bar metrics are sorted according to ``metrics``. - - Here is an example of how to override the defaults: - - .. code-block:: python - - def get_metrics(self, trainer, model): - # don't show the version number - items = super().get_metrics(trainer, model) - items.pop("v_num", None) - return items - - :return: Dictionary with the items to be displayed in the progress bar. - :rtype: tuple(dict) - """ - standard_metrics = get_standard_metrics(trainer) - pbar_metrics = trainer.progress_bar_metrics - if pbar_metrics: - pbar_metrics = { - key: pbar_metrics[key] - for key in pbar_metrics - if key in self._sorted_metrics - } - return {**standard_metrics, **pbar_metrics} - - def setup(self, trainer, pl_module, stage): - """ - Check that the initialized metrics are available and correctly logged. - - :param trainer: The trainer object managing the training process. - :type trainer: pytorch_lightning.Trainer - :param pl_module: Placeholder argument. - """ - # Check if all keys in sort_keys are present in the dictionary - for key in self._sorted_metrics: - if ( - key not in trainer.solver.problem.conditions.keys() - and key != "train" - and key != "val" - ): - raise KeyError(f"Key '{key}' is not present in the dictionary") - # add the loss pedix - if trainer.batch_size is not None: - pedix = "_loss_epoch" - else: - pedix = "_loss" - self._sorted_metrics = [ - metric + pedix for metric in self._sorted_metrics - ] - return super().setup(trainer, pl_module, stage) diff --git a/pina/callback/refinement/__init__.py b/pina/callback/refinement/__init__.py deleted file mode 100644 index 396fcabaa..000000000 --- a/pina/callback/refinement/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -""" -Module for Pina Refinement callbacks. -""" - -__all__ = [ - "RefinementInterface", - "R3Refinement", -] - -from .refinement_interface import RefinementInterface -from .r3_refinement import R3Refinement diff --git a/pina/callback/refinement/r3_refinement.py b/pina/callback/refinement/r3_refinement.py deleted file mode 100644 index 863dedfc1..000000000 --- a/pina/callback/refinement/r3_refinement.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Module for the R3Refinement callback.""" - -import torch -from .refinement_interface import RefinementInterface -from ...label_tensor import LabelTensor -from ...utils import check_consistency -from ...loss import LossInterface - - -class R3Refinement(RefinementInterface): - """ - PINA Implementation of the R3 Refinement Callback. - - This callback implements the R3 (Retain-Resample-Release) routine for - sampling new points based on adaptive search. - The algorithm incrementally accumulates collocation points in regions - of high PDE residuals, and releases those with low residuals. - Points are sampled uniformly in all regions where sampling is needed. - - .. seealso:: - - Original Reference: Daw, Arka, et al. *Mitigating Propagation - Failures in Physics-informed Neural Networks - using Retain-Resample-Release (R3) Sampling. (2023)*. - DOI: `10.48550/arXiv.2207.02338 - `_ - - :Example: - - >>> r3_callback = R3Refinement(sample_every=5) - """ - - def __init__( - self, - sample_every, - residual_loss=torch.nn.L1Loss, - condition_to_update=None, - ): - """ - Initialization of the :class:`R3Refinement` callback. - - :param int sample_every: The sampling frequency. - :param loss: The loss function to compute the residuals. - Default is :class:`~torch.nn.L1Loss`. - :type loss: LossInterface | :class:`~torch.nn.modules.loss._Loss` - :param condition_to_update: The conditions to update during the - refinement process. If None, all conditions will be updated. - Default is None. - :type condition_to_update: list(str) | tuple(str) | str - :raises ValueError: If the condition_to_update is neither a string nor - an iterable of strings. - :raises TypeError: If the residual_loss is not a subclass of - :class:`~torch.nn.Module`. - """ - super().__init__(sample_every, condition_to_update) - - # Check consistency - check_consistency( - residual_loss, - (LossInterface, torch.nn.modules.loss._Loss), - subclass=True, - ) - - # Save loss function - self.loss_fn = residual_loss(reduction="none") - - def sample(self, current_points, condition_name, solver): - """ - Sample new points based on the R3 refinement strategy. - - :param current_points: The current points in the domain. - :type current_points: LabelTensor | torch.Tensor - :param str condition_name: The name of the condition to update. - :param PINNInterface solver: The solver using this callback. - :return: The new samples generated by the R3 strategy. - :rtype: LabelTensor - """ - # Retrieve condition and current points - device = solver.trainer.strategy.root_device - condition = solver.problem.conditions[condition_name] - current_points = current_points.to(device).requires_grad_(True) - - # Compute residuals for the given condition (averaged over all fields) - target = solver.compute_residual(current_points, condition.equation) - residuals = self.loss_fn(target, torch.zeros_like(target)).mean( - dim=tuple(range(1, target.ndim)) - ) - - # Retrieve domain and initial population size - domain_name = solver.problem.conditions[condition_name].domain - domain = solver.problem.domains[domain_name] - num_old_points = self.initial_population_size[condition_name] - - # Select points with residual above the mean - mask = (residuals > residuals.mean()).flatten() - if mask.any(): - high_residual_pts = current_points[mask] - high_residual_pts.labels = current_points.labels - samples = domain.sample(num_old_points - len(high_residual_pts)) - return LabelTensor.cat([high_residual_pts, samples.to(device)]) - - return domain.sample(num_old_points, "random") diff --git a/pina/callback/refinement/refinement_interface.py b/pina/callback/refinement/refinement_interface.py deleted file mode 100644 index adc6e4e7c..000000000 --- a/pina/callback/refinement/refinement_interface.py +++ /dev/null @@ -1,155 +0,0 @@ -""" -RefinementInterface class for handling the refinement of points in a neural -network training process. -""" - -from abc import ABCMeta, abstractmethod -from lightning.pytorch import Callback -from ...utils import check_consistency -from ...solver.physics_informed_solver import PINNInterface - - -class RefinementInterface(Callback, metaclass=ABCMeta): - """ - Interface class of Refinement approaches. - """ - - def __init__(self, sample_every, condition_to_update=None): - """ - Initializes the RefinementInterface. - - :param int sample_every: The number of epochs between each refinement. - :param condition_to_update: The conditions to update during the - refinement process. If None, all conditions with a domain will be - updated. Default is None. - :type condition_to_update: list(str) | tuple(str) | str - - """ - # check consistency of the input - check_consistency(sample_every, int) - if condition_to_update is not None: - if isinstance(condition_to_update, str): - condition_to_update = [condition_to_update] - if not isinstance(condition_to_update, (list, tuple)): - raise ValueError( - "'condition_to_update' must be iter of strings." - ) - check_consistency(condition_to_update, str) - # store - self.sample_every = sample_every - self._condition_to_update = condition_to_update - self._dataset = None - self._initial_population_size = None - - def on_train_start(self, trainer, solver): - """ - Called when the training begins. It initializes the conditions and - dataset. - - :param ~lightning.pytorch.trainer.trainer.Trainer trainer: The trainer - object. - :param ~pina.solver.solver.SolverInterface solver: The solver - object associated with the trainer. - :raises RuntimeError: If the solver is not a PINNInterface. - :raises RuntimeError: If the conditions do not have a domain to sample - from. - """ - # check we have valid conditions names - if self._condition_to_update is None: - self._condition_to_update = [ - name - for name, cond in solver.problem.conditions.items() - if hasattr(cond, "domain") - ] - - for cond in self._condition_to_update: - if cond not in solver.problem.conditions: - raise RuntimeError( - f"Condition '{cond}' not found in " - f"{list(solver.problem.conditions.keys())}." - ) - if not hasattr(solver.problem.conditions[cond], "domain"): - raise RuntimeError( - f"Condition '{cond}' does not contain a domain to " - "sample from." - ) - # check solver - if not isinstance(solver, PINNInterface): - raise RuntimeError( - "Refinment strategies are currently implemented only " - "for physics informed based solvers. Please use a Solver " - "inheriting from 'PINNInterface'." - ) - # store dataset - self._dataset = trainer.datamodule.train_dataset - # compute initial population size - self._initial_population_size = self._compute_population_size( - self._condition_to_update - ) - return super().on_train_epoch_start(trainer, solver) - - def on_train_epoch_end(self, trainer, solver): - """ - Performs the refinement at the end of each training epoch (if needed). - - :param ~lightning.pytorch.trainer.trainer.Trainer: The trainer object. - :param PINNInterface solver: The solver object. - """ - if (trainer.current_epoch % self.sample_every == 0) and ( - trainer.current_epoch != 0 - ): - self._update_points(solver) - return super().on_train_epoch_end(trainer, solver) - - @abstractmethod - def sample(self, current_points, condition_name, solver): - """ - Samples new points based on the condition. - - :param current_points: Current points in the domain. - :param condition_name: Name of the condition to update. - :param PINNInterface solver: The solver object. - :return: New points sampled based on the R3 strategy. - :rtype: LabelTensor - """ - - @property - def dataset(self): - """ - Returns the dataset for training. - """ - return self._dataset - - @property - def initial_population_size(self): - """ - Returns the dataset for training size. - """ - return self._initial_population_size - - def _update_points(self, solver): - """ - Performs the refinement of the points. - - :param PINNInterface solver: The solver object. - """ - new_points = {} - for name in self._condition_to_update: - current_points = self.dataset.conditions_dict[name]["input"] - new_points[name] = { - "input": self.sample(current_points, name, solver) - } - self.dataset.update_data(new_points) - - def _compute_population_size(self, conditions): - """ - Computes the number of points in the dataset for each condition. - - :param conditions: List of conditions to compute the number of points. - :return: Dictionary with the population size for each condition. - :rtype: dict - """ - return { - cond: len(self.dataset.conditions_dict[cond]["input"]) - for cond in conditions - } diff --git a/pina/condition/__init__.py b/pina/condition/__init__.py index 4e57811fb..5ae1cc177 100644 --- a/pina/condition/__init__.py +++ b/pina/condition/__init__.py @@ -1,39 +1,33 @@ -"""Module for PINA Conditions classes.""" +"""Conditions for defining physics and data constraints. + +This module provides the interface and implementations for binding mathematical +equations, experimental data, and neural network targets to specific spatial +domains or graph structures. It supports various input-target mappings including +tensor-based, graph-based, and equation-based constraints. +""" __all__ = [ - "Condition", "ConditionInterface", + "BaseCondition", + "Condition", "DomainEquationCondition", "InputTargetCondition", - "TensorInputTensorTargetCondition", - "TensorInputGraphTargetCondition", - "GraphInputTensorTargetCondition", - "GraphInputGraphTargetCondition", "InputEquationCondition", - "InputTensorEquationCondition", - "InputGraphEquationCondition", "DataCondition", - "GraphDataCondition", - "TensorDataCondition", + "TimeSeriesCondition", + "GraphTimeSeriesCondition", ] -from .condition_interface import ConditionInterface -from .condition import Condition -from .domain_equation_condition import DomainEquationCondition -from .input_target_condition import ( - InputTargetCondition, - TensorInputTensorTargetCondition, - TensorInputGraphTargetCondition, - GraphInputTensorTargetCondition, - GraphInputGraphTargetCondition, -) -from .input_equation_condition import ( - InputEquationCondition, - InputTensorEquationCondition, - InputGraphEquationCondition, +from pina._src.condition.condition_interface import ConditionInterface +from pina._src.condition.base_condition import BaseCondition +from pina._src.condition.condition import Condition +from pina._src.condition.domain_equation_condition import ( + DomainEquationCondition, ) -from .data_condition import ( - DataCondition, - GraphDataCondition, - TensorDataCondition, +from pina._src.condition.input_target_condition import InputTargetCondition +from pina._src.condition.input_equation_condition import InputEquationCondition +from pina._src.condition.data_condition import DataCondition +from pina._src.condition.time_series_condition import TimeSeriesCondition +from pina._src.condition.graph_time_series_condition import ( + GraphTimeSeriesCondition, ) diff --git a/pina/condition/condition.py b/pina/condition/condition.py deleted file mode 100644 index ad8764c9f..000000000 --- a/pina/condition/condition.py +++ /dev/null @@ -1,141 +0,0 @@ -"""Module for the Condition class.""" - -from .data_condition import DataCondition -from .domain_equation_condition import DomainEquationCondition -from .input_equation_condition import InputEquationCondition -from .input_target_condition import InputTargetCondition - - -class Condition: - """ - The :class:`Condition` class is a core component of the PINA framework that - provides a unified interface to define heterogeneous constraints that must - be satisfied by a :class:`~pina.problem.abstract_problem.AbstractProblem`. - - It encapsulates all types of constraints - physical, boundary, initial, or - data-driven - that the solver must satisfy during training. The specific - behavior is inferred from the arguments passed to the constructor. - - Multiple types of conditions can be used within the same problem, allowing - for a high degree of flexibility in defining complex problems. - - The :class:`Condition` class behavior specializes internally based on the - arguments provided during instantiation. Depending on the specified keyword - arguments, the class automatically selects the appropriate internal - implementation. - - - Available `Condition` types: - - - :class:`~pina.condition.input_target_condition.InputTargetCondition`: - represents a supervised condition defined by both ``input`` and ``target`` - data. The model is trained to reproduce the ``target`` values given the - ``input``. Supported data types include :class:`torch.Tensor`, - :class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`, or - :class:`~torch_geometric.data.Data`. - The class automatically selects the appropriate implementation based on - the types of ``input`` and ``target``. - - - :class:`~pina.condition.domain_equation_condition.DomainEquationCondition` - : represents a general physics-informed condition defined by a ``domain`` - and an ``equation``. The model learns to minimize the equation residual - through evaluations performed at points sampled from the specified domain. - - - :class:`~pina.condition.input_equation_condition.InputEquationCondition`: - represents a general physics-informed condition defined by ``input`` - points and an ``equation``. The model learns to minimize the equation - residual through evaluations performed at the provided ``input``. - Supported data types for the ``input`` include - :class:`~pina.label_tensor.LabelTensor` or :class:`~pina.graph.Graph`. - The class automatically selects the appropriate implementation based on - the types of the ``input``. - - - :class:`~pina.condition.data_condition.DataCondition`: represents an - unsupervised, data-driven condition defined by the ``input`` only. - The model is trained using a custom unsupervised loss determined by the - chosen :class:`~pina.solver.solver.SolverInterface`, while leveraging the - provided data during training. Optional ``conditional_variables`` can be - specified when the model depends on additional parameters. - Supported data types include :class:`torch.Tensor`, - :class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`, or - :class:`~torch_geometric.data.Data`. - The class automatically selects the appropriate implementation based on - the type of the ``input``. - - .. note:: - - The user should always instantiate :class:`Condition` directly, without - manually creating subclass instances. Please refer to the specific - :class:`Condition` classes for implementation details. - - :Example: - - >>> from pina import Condition - - >>> # Example of InputTargetCondition signature - >>> condition = Condition(input=input, target=target) - - >>> # Example of DomainEquationCondition signature - >>> condition = Condition(domain=domain, equation=equation) - - >>> # Example of InputEquationCondition signature - >>> condition = Condition(input=input, equation=equation) - - >>> # Example of DataCondition signature - >>> condition = Condition(input=data, conditional_variables=cond_vars) - """ - - # Combine all possible keyword arguments from the different Condition types - __slots__ = list( - set( - InputTargetCondition.__slots__ - + InputEquationCondition.__slots__ - + DomainEquationCondition.__slots__ - + DataCondition.__slots__ - ) - ) - - def __new__(cls, *args, **kwargs): - """ - Instantiate the appropriate :class:`Condition` object based on the - keyword arguments passed. - - :param tuple args: The positional arguments (should be empty). - :param dict kwargs: The keyword arguments corresponding to the - parameters of the specific :class:`Condition` type to instantiate. - :raises ValueError: If unexpected positional arguments are provided. - :raises ValueError: If the keyword arguments are invalid. - :return: The appropriate :class:`Condition` object. - :rtype: ConditionInterface - """ - # Check keyword arguments - if len(args) != 0: - raise ValueError( - "Condition takes only the following keyword " - f"arguments: {Condition.__slots__}." - ) - - # Class specialization based on keyword arguments - sorted_keys = sorted(kwargs.keys()) - - # Input - Target Condition - if sorted_keys == sorted(InputTargetCondition.__slots__): - return InputTargetCondition(**kwargs) - - # Input - Equation Condition - if sorted_keys == sorted(InputEquationCondition.__slots__): - return InputEquationCondition(**kwargs) - - # Domain - Equation Condition - if sorted_keys == sorted(DomainEquationCondition.__slots__): - return DomainEquationCondition(**kwargs) - - # Data Condition - if ( - sorted_keys == sorted(DataCondition.__slots__) - or sorted_keys[0] == DataCondition.__slots__[0] - ): - return DataCondition(**kwargs) - - # Invalid keyword arguments - raise ValueError(f"Invalid keyword arguments {kwargs.keys()}.") diff --git a/pina/condition/condition_interface.py b/pina/condition/condition_interface.py deleted file mode 100644 index b0264517c..000000000 --- a/pina/condition/condition_interface.py +++ /dev/null @@ -1,126 +0,0 @@ -"""Module for the Condition interface.""" - -from abc import ABCMeta -from torch_geometric.data import Data -from ..label_tensor import LabelTensor -from ..graph import Graph - - -class ConditionInterface(metaclass=ABCMeta): - """ - Abstract base class for PINA conditions. All specific conditions must - inherit from this interface. - - Refer to :class:`pina.condition.condition.Condition` for a thorough - description of all available conditions and how to instantiate them. - """ - - def __init__(self): - """ - Initialization of the :class:`ConditionInterface` class. - """ - self._problem = None - - @property - def problem(self): - """ - Return the problem associated with this condition. - - :return: Problem associated with this condition. - :rtype: ~pina.problem.abstract_problem.AbstractProblem - """ - return self._problem - - @problem.setter - def problem(self, value): - """ - Set the problem associated with this condition. - - :param pina.problem.abstract_problem.AbstractProblem value: The problem - to associate with this condition - """ - self._problem = value - - @staticmethod - def _check_graph_list_consistency(data_list): - """ - Check the consistency of the list of Data | Graph objects. - The following checks are performed: - - - All elements in the list must be of the same type (either - :class:`~torch_geometric.data.Data` or :class:`~pina.graph.Graph`). - - - All elements in the list must have the same keys. - - - The data type of each tensor must be consistent across all elements. - - - If a tensor is a :class:`~pina.label_tensor.LabelTensor`, its labels - must also be consistent across all elements. - - :param data_list: The list of Data | Graph objects to check. - :type data_list: list[Data] | list[Graph] | tuple[Data] | tuple[Graph] - :raises ValueError: If the input types are invalid. - :raises ValueError: If all elements in the list do not have the same - keys. - :raises ValueError: If the type of each tensor is not consistent across - all elements in the list. - :raises ValueError: If the labels of the LabelTensors are not consistent - across all elements in the list. - """ - # If the data is a Graph or Data object, perform no checks - if isinstance(data_list, (Graph, Data)): - return - - # Check all elements in the list are of the same type - if not all(isinstance(i, (Graph, Data)) for i in data_list): - raise ValueError( - "Invalid input. Please, provide either Data or Graph objects." - ) - - # Store the keys, data types and labels of the first element - data = data_list[0] - keys = sorted(list(data.keys())) - data_types = {name: tensor.__class__ for name, tensor in data.items()} - labels = { - name: tensor.labels - for name, tensor in data.items() - if isinstance(tensor, LabelTensor) - } - - # Iterate over the list of Data | Graph objects - for data in data_list[1:]: - - # Check that all elements in the list have the same keys - if sorted(list(data.keys())) != keys: - raise ValueError( - "All elements in the list must have the same keys." - ) - - # Iterate over the tensors in the current element - for name, tensor in data.items(): - # Check that the type of each tensor is consistent - if tensor.__class__ is not data_types[name]: - raise ValueError( - f"Data {name} must be a {data_types[name]}, got " - f"{tensor.__class__}" - ) - - # Check that the labels of each LabelTensor are consistent - if isinstance(tensor, LabelTensor): - if tensor.labels != labels[name]: - raise ValueError( - "LabelTensor must have the same labels" - ) - - def __getattribute__(self, name): - """ - Get an attribute from the object. - - :param str name: The name of the attribute to get. - :return: The requested attribute. - :rtype: Any - """ - to_return = super().__getattribute__(name) - if isinstance(to_return, (Graph, Data)): - to_return = [to_return] - return to_return diff --git a/pina/condition/data_condition.py b/pina/condition/data_condition.py deleted file mode 100644 index 5f5e7d36b..000000000 --- a/pina/condition/data_condition.py +++ /dev/null @@ -1,120 +0,0 @@ -"""Module for the DataCondition class.""" - -import torch -from torch_geometric.data import Data -from .condition_interface import ConditionInterface -from ..label_tensor import LabelTensor -from ..graph import Graph - - -class DataCondition(ConditionInterface): - """ - The class :class:`DataCondition` defines an unsupervised condition based on - ``input`` data. This condition is typically used in data-driven problems, - where the model is trained using a custom unsupervised loss determined by - the chosen :class:`~pina.solver.solver.SolverInterface`, while leveraging - the provided data during training. Optional ``conditional_variables`` can be - specified when the model depends on additional parameters. - - The class automatically selects the appropriate implementation based on the - type of the ``input`` data. Depending on whether the ``input`` is a tensor - or graph-based data, one of the following specialized subclasses is - instantiated: - - - :class:`TensorDataCondition`: For cases where the ``input`` is either a - :class:`torch.Tensor` or a :class:`~pina.label_tensor.LabelTensor` object. - - - :class:`GraphDataCondition`: For cases where the ``input`` is either a - :class:`~pina.graph.Graph` or :class:`~torch_geometric.data.Data` object. - - :Example: - - >>> from pina import Condition, LabelTensor - >>> import torch - - >>> pts = LabelTensor(torch.randn(100, 2), labels=["x", "y"]) - >>> cond_vars = LabelTensor(torch.randn(100, 1), labels=["w"]) - >>> condition = Condition(input=pts, conditional_variables=cond_vars) - """ - - # Available input data types - __slots__ = ["input", "conditional_variables"] - _avail_input_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple) - _avail_conditional_variables_cls = (torch.Tensor, LabelTensor) - - def __new__(cls, input, conditional_variables=None): - """ - Instantiate the appropriate subclass of :class:`DataCondition` based on - the type of the ``input``. - - :param input: The input data for the condition. - :type input: torch.Tensor | LabelTensor | Graph | - Data | list[Graph] | list[Data] | tuple[Graph] | tuple[Data] - :param conditional_variables: The conditional variables for the - condition. Default is ``None``. - :type conditional_variables: torch.Tensor | LabelTensor - :return: The subclass of DataCondition. - :rtype: pina.condition.data_condition.TensorDataCondition | - pina.condition.data_condition.GraphDataCondition - :raises ValueError: If ``input`` is not of type :class:`torch.Tensor`, - :class:`~pina.label_tensor.LabelTensor`, :class:`~pina.graph.Graph`, - or :class:`~torch_geometric.data.Data`. - """ - if cls != DataCondition: - return super().__new__(cls) - - # If the input is a tensor - if isinstance(input, (torch.Tensor, LabelTensor)): - subclass = TensorDataCondition - return subclass.__new__(subclass, input, conditional_variables) - - # If the input is a graph - if isinstance(input, (Graph, Data, list, tuple)): - cls._check_graph_list_consistency(input) - subclass = GraphDataCondition - return subclass.__new__(subclass, input, conditional_variables) - - # If the input is not of the correct type raise an error - raise ValueError( - "Invalid input type. Expected one of the following: " - "torch.Tensor, LabelTensor, Graph, Data or " - "an iterable of the previous types." - ) - - def __init__(self, input, conditional_variables=None): - """ - Initialization of the :class:`DataCondition` class. - - :param input: The input data for the condition. - :type input: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | - list[Data] | tuple[Graph] | tuple[Data] - :param conditional_variables: The conditional variables for the - condition. Default is ``None``. - :type conditional_variables: torch.Tensor | LabelTensor - - .. note:: - - If ``input`` is a list of :class:`~pina.graph.Graph` or - :class:`~torch_geometric.data.Data`, all elements in - the list must share the same structure, with matching keys and - consistent data types. - """ - super().__init__() - self.input = input - self.conditional_variables = conditional_variables - - -class TensorDataCondition(DataCondition): - """ - Specialization of the :class:`DataCondition` class for the case where - ``input`` is either a :class:`~pina.label_tensor.LabelTensor` object or a - :class:`torch.Tensor` object. - """ - - -class GraphDataCondition(DataCondition): - """ - Specialization of the :class:`DataCondition` class for the case where - ``input`` is either a :class:`~pina.graph.Graph` object or a - :class:`~torch_geometric.data.Data` object. - """ diff --git a/pina/condition/domain_equation_condition.py b/pina/condition/domain_equation_condition.py deleted file mode 100644 index 3565c0b41..000000000 --- a/pina/condition/domain_equation_condition.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Module for the DomainEquationCondition class.""" - -from .condition_interface import ConditionInterface -from ..utils import check_consistency -from ..domain import DomainInterface -from ..equation.equation_interface import EquationInterface - - -class DomainEquationCondition(ConditionInterface): - """ - The class :class:`DomainEquationCondition` defines a condition based on a - ``domain`` and an ``equation``. This condition is typically used in - physics-informed problems, where the model is trained to satisfy a given - ``equation`` over a specified ``domain``. The ``domain`` is used to sample - points where the ``equation`` residual is evaluated and minimized during - training. - - :Example: - - >>> from pina.domain import CartesianDomain - >>> from pina.equation import Equation - >>> from pina import Condition - - >>> # Equation to be satisfied over the domain: # x^2 + y^2 - 1 = 0 - >>> def dummy_equation(pts): - ... return pts["x"]**2 + pts["y"]**2 - 1 - - >>> domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) - >>> condition = Condition(domain=domain, equation=Equation(dummy_equation)) - """ - - # Available slots - __slots__ = ["domain", "equation"] - - def __init__(self, domain, equation): - """ - Initialization of the :class:`DomainEquationCondition` class. - - :param DomainInterface domain: The domain over which the equation is - defined. - :param EquationInterface equation: The equation to be satisfied over the - specified domain. - """ - super().__init__() - self.domain = domain - self.equation = equation - - def __setattr__(self, key, value): - """ - Set the attribute value with type checking. - - :param str key: The attribute name. - :param any value: The value to set for the attribute. - """ - if key == "domain": - check_consistency(value, (DomainInterface, str)) - DomainEquationCondition.__dict__[key].__set__(self, value) - - elif key == "equation": - check_consistency(value, (EquationInterface)) - DomainEquationCondition.__dict__[key].__set__(self, value) - - elif key in ("_problem"): - super().__setattr__(key, value) diff --git a/pina/condition/input_equation_condition.py b/pina/condition/input_equation_condition.py deleted file mode 100644 index d32597894..000000000 --- a/pina/condition/input_equation_condition.py +++ /dev/null @@ -1,157 +0,0 @@ -"""Module for the InputEquationCondition class and its subclasses.""" - -from .condition_interface import ConditionInterface -from ..label_tensor import LabelTensor -from ..graph import Graph -from ..utils import check_consistency -from ..equation.equation_interface import EquationInterface - - -class InputEquationCondition(ConditionInterface): - """ - The class :class:`InputEquationCondition` defines a condition based on - ``input`` data and an ``equation``. This condition is typically used in - physics-informed problems, where the model is trained to satisfy a given - ``equation`` through the evaluation of the residual performed at the - provided ``input``. - - The class automatically selects the appropriate implementation based on - the type of the ``input`` data. Depending on whether the ``input`` is a - tensor or graph-based data, one of the following specialized subclasses is - instantiated: - - - :class:`InputTensorEquationCondition`: For cases where the ``input`` - data is a :class:`~pina.label_tensor.LabelTensor` object. - - - :class:`InputGraphEquationCondition`: For cases where the ``input`` data - is a :class:`~pina.graph.Graph` object. - - :Example: - - >>> from pina import Condition, LabelTensor - >>> from pina.equation import Equation - >>> import torch - - >>> # Equation to be satisfied over the input points: # x^2 + y^2 - 1 = 0 - >>> def dummy_equation(pts): - ... return pts["x"]**2 + pts["y"]**2 - 1 - - >>> pts = LabelTensor(torch.randn(100, 2), labels=["x", "y"]) - >>> condition = Condition(input=pts, equation=Equation(dummy_equation)) - """ - - # Available input data types - __slots__ = ["input", "equation"] - _avail_input_cls = (LabelTensor, Graph, list, tuple) - _avail_equation_cls = EquationInterface - - def __new__(cls, input, equation): - """ - Instantiate the appropriate subclass of :class:`InputEquationCondition` - based on the type of ``input`` data. - - :param input: The input data for the condition. - :type input: LabelTensor | Graph | list[Graph] | tuple[Graph] - :param EquationInterface equation: The equation to be satisfied over the - specified ``input`` data. - :return: The subclass of InputEquationCondition. - :rtype: pina.condition.input_equation_condition. - InputTensorEquationCondition | - pina.condition.input_equation_condition.InputGraphEquationCondition - - :raises ValueError: If input is not of type :class:`~pina.graph.Graph` - or :class:`~pina.label_tensor.LabelTensor`. - """ - if cls != InputEquationCondition: - return super().__new__(cls) - - # If the input is a Graph object - if isinstance(input, (Graph, list, tuple)): - subclass = InputGraphEquationCondition - cls._check_graph_list_consistency(input) - subclass._check_label_tensor(input) - return subclass.__new__(subclass, input, equation) - - # If the input is a LabelTensor - if isinstance(input, LabelTensor): - subclass = InputTensorEquationCondition - return subclass.__new__(subclass, input, equation) - - # If the input is not a LabelTensor or a Graph object raise an error - raise ValueError( - "The input data object must be a LabelTensor or a Graph object." - ) - - def __init__(self, input, equation): - """ - Initialization of the :class:`InputEquationCondition` class. - - :param input: The input data for the condition. - :type input: LabelTensor | Graph | list[Graph] | tuple[Graph] - :param EquationInterface equation: The equation to be satisfied over the - specified input points. - - .. note:: - - If ``input`` is a list of :class:`~pina.graph.Graph` all elements in - the list must share the same structure, with matching keys and - consistent data types. - """ - super().__init__() - self.input = input - self.equation = equation - - def __setattr__(self, key, value): - """ - Set the attribute value with type checking. - - :param str key: The attribute name. - :param any value: The value to set for the attribute. - """ - if key == "input": - check_consistency(value, self._avail_input_cls) - InputEquationCondition.__dict__[key].__set__(self, value) - - elif key == "equation": - check_consistency(value, self._avail_equation_cls) - InputEquationCondition.__dict__[key].__set__(self, value) - - elif key in ("_problem"): - super().__setattr__(key, value) - - -class InputTensorEquationCondition(InputEquationCondition): - """ - Specialization of the :class:`InputEquationCondition` class for the case - where ``input`` is a :class:`~pina.label_tensor.LabelTensor` object. - """ - - -class InputGraphEquationCondition(InputEquationCondition): - """ - Specialization of the :class:`InputEquationCondition` class for the case - where ``input`` is a :class:`~pina.graph.Graph` object. - """ - - @staticmethod - def _check_label_tensor(input): - """ - Check if at least one :class:`~pina.label_tensor.LabelTensor` is present - in the ``input`` object. - - :param input: The input data. - :type input: torch.Tensor | Graph | list[Graph] | tuple[Graph] - :raises ValueError: If the input data object does not contain at least - one LabelTensor. - """ - - # Store the first element: it is sufficient to check this since all - # elements must have the same type and structure (already checked). - data = input[0] if isinstance(input, (list, tuple)) else input - - # Check if the input data contains at least one LabelTensor - for v in data.values(): - if isinstance(v, LabelTensor): - return - - raise ValueError("The input must contain at least one LabelTensor.") diff --git a/pina/condition/input_target_condition.py b/pina/condition/input_target_condition.py deleted file mode 100644 index 07b07bb7b..000000000 --- a/pina/condition/input_target_condition.py +++ /dev/null @@ -1,208 +0,0 @@ -""" -This module contains condition classes for supervised learning tasks. -""" - -import torch -from torch_geometric.data import Data -from ..label_tensor import LabelTensor -from ..graph import Graph -from .condition_interface import ConditionInterface - - -class InputTargetCondition(ConditionInterface): - """ - The :class:`InputTargetCondition` class represents a supervised condition - defined by both ``input`` and ``target`` data. The model is trained to - reproduce the ``target`` values given the ``input``. Supported data types - include :class:`torch.Tensor`, :class:`~pina.label_tensor.LabelTensor`, - :class:`~pina.graph.Graph`, or :class:`~torch_geometric.data.Data`. - - The class automatically selects the appropriate implementation based on - the types of ``input`` and ``target``. Depending on whether the ``input`` - and ``target`` are tensors or graph-based data, one of the following - specialized subclasses is instantiated: - - - :class:`TensorInputTensorTargetCondition`: For cases where both ``input`` - and ``target`` data are either :class:`torch.Tensor` or - :class:`~pina.label_tensor.LabelTensor`. - - - :class:`TensorInputGraphTargetCondition`: For cases where ``input`` is - either a :class:`torch.Tensor` or :class:`~pina.label_tensor.LabelTensor` - and ``target`` is either a :class:`~pina.graph.Graph` or a - :class:`torch_geometric.data.Data`. - - - :class:`GraphInputTensorTargetCondition`: For cases where ``input`` is - either a :class:`~pina.graph.Graph` or :class:`torch_geometric.data.Data` - and ``target`` is either a :class:`torch.Tensor` or a - :class:`~pina.label_tensor.LabelTensor`. - - - :class:`GraphInputGraphTargetCondition`: For cases where both ``input`` - and ``target`` are either :class:`~pina.graph.Graph` or - :class:`torch_geometric.data.Data`. - - :Example: - - >>> from pina import Condition, LabelTensor - >>> from pina.graph import Graph - >>> import torch - - >>> pos = LabelTensor(torch.randn(100, 2), labels=["x", "y"]) - >>> edge_index = torch.randint(0, 100, (2, 300)) - >>> graph = Graph(pos=pos, edge_index=edge_index) - - >>> input = LabelTensor(torch.randn(100, 2), labels=["x", "y"]) - >>> condition = Condition(input=input, target=graph) - """ - - # Available input and target data types - __slots__ = ["input", "target"] - _avail_input_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple) - _avail_output_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple) - - def __new__(cls, input, target): - """ - Instantiate the appropriate subclass of :class:`InputTargetCondition` - based on the types of both ``input`` and ``target`` data. - - :param input: The input data for the condition. - :type input: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | - list[Data] | tuple[Graph] | tuple[Data] - :param target: The target data for the condition. - :type target: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | - list[Data] | tuple[Graph] | tuple[Data] - :return: The subclass of InputTargetCondition. - :rtype: pina.condition.input_target_condition. - TensorInputTensorTargetCondition | - pina.condition.input_target_condition. - TensorInputGraphTargetCondition | - pina.condition.input_target_condition. - GraphInputTensorTargetCondition | - pina.condition.input_target_condition.GraphInputGraphTargetCondition - - :raises ValueError: If ``input`` and/or ``target`` are not of type - :class:`torch.Tensor`, :class:`~pina.label_tensor.LabelTensor`, - :class:`~pina.graph.Graph`, or :class:`~torch_geometric.data.Data`. - """ - if cls != InputTargetCondition: - return super().__new__(cls) - - # Tensor - Tensor - if isinstance(input, (torch.Tensor, LabelTensor)) and isinstance( - target, (torch.Tensor, LabelTensor) - ): - subclass = TensorInputTensorTargetCondition - return subclass.__new__(subclass, input, target) - - # Tensor - Graph - if isinstance(input, (torch.Tensor, LabelTensor)) and isinstance( - target, (Graph, Data, list, tuple) - ): - cls._check_graph_list_consistency(target) - subclass = TensorInputGraphTargetCondition - return subclass.__new__(subclass, input, target) - - # Graph - Tensor - if isinstance(input, (Graph, Data, list, tuple)) and isinstance( - target, (torch.Tensor, LabelTensor) - ): - cls._check_graph_list_consistency(input) - subclass = GraphInputTensorTargetCondition - return subclass.__new__(subclass, input, target) - - # Graph - Graph - if isinstance(input, (Graph, Data, list, tuple)) and isinstance( - target, (Graph, Data, list, tuple) - ): - cls._check_graph_list_consistency(input) - cls._check_graph_list_consistency(target) - subclass = GraphInputGraphTargetCondition - return subclass.__new__(subclass, input, target) - - # If the input and/or target are not of the correct type raise an error - raise ValueError( - "Invalid input | target types." - "Please provide either torch_geometric.data.Data, Graph, " - "LabelTensor or torch.Tensor objects." - ) - - def __init__(self, input, target): - """ - Initialization of the :class:`InputTargetCondition` class. - - :param input: The input data for the condition. - :type input: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | - list[Data] | tuple[Graph] | tuple[Data] - :param target: The target data for the condition. - :type target: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | - list[Data] | tuple[Graph] | tuple[Data] - - .. note:: - - If either ``input`` or ``target`` is a list of - :class:`~pina.graph.Graph` or :class:`~torch_geometric.data.Data` - objects, all elements in the list must share the same structure, - with matching keys and consistent data types. - """ - super().__init__() - self._check_input_target_len(input, target) - self.input = input - self.target = target - - @staticmethod - def _check_input_target_len(input, target): - """ - Check that the length of the input and target lists are the same. - - :param input: The input data. - :type input: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | - list[Data] | tuple[Graph] | tuple[Data] - :param target: The target data. - :type target: torch.Tensor | LabelTensor | Graph | Data | list[Graph] | - list[Data] | tuple[Graph] | tuple[Data] - :raises ValueError: If the lengths of the input and target lists do not - match. - """ - if isinstance(input, (Graph, Data)) or isinstance( - target, (Graph, Data) - ): - return - - # Raise an error if the lengths of the input and target do not match - if len(input) != len(target): - raise ValueError( - "The input and target lists must have the same length." - ) - - -class TensorInputTensorTargetCondition(InputTargetCondition): - """ - Specialization of the :class:`InputTargetCondition` class for the case where - both ``input`` and ``target`` are :class:`torch.Tensor` or - :class:`~pina.label_tensor.LabelTensor` objects. - """ - - -class TensorInputGraphTargetCondition(InputTargetCondition): - """ - Specialization of the :class:`InputTargetCondition` class for the case where - ``input`` is either a :class:`torch.Tensor` or a - :class:`~pina.label_tensor.LabelTensor` object and ``target`` is either a - :class:`~pina.graph.Graph` or a :class:`torch_geometric.data.Data` object. - """ - - -class GraphInputTensorTargetCondition(InputTargetCondition): - """ - Specialization of the :class:`InputTargetCondition` class for the case where - ``input`` is either a :class:`~pina.graph.Graph` or - :class:`torch_geometric.data.Data` object and ``target`` is either a - :class:`torch.Tensor` or a :class:`~pina.label_tensor.LabelTensor` object. - """ - - -class GraphInputGraphTargetCondition(InputTargetCondition): - """ - Specialization of the :class:`InputTargetCondition` class for the case where - both ``input`` and ``target`` are either :class:`~pina.graph.Graph` or - :class:`torch_geometric.data.Data` objects. - """ diff --git a/pina/data/__init__.py b/pina/data/__init__.py index 70e100011..1ebcf2b9f 100644 --- a/pina/data/__init__.py +++ b/pina/data/__init__.py @@ -1,7 +1,33 @@ -"""Module for data, data module, and dataset.""" +"""Module containing utilities for dataset and data loader management.""" -__all__ = ["PinaDataModule", "PinaDataset"] +__all__ = [ + "DataModule", + "_SingleBatchDataLoader", + "_Aggregator", + "_Creator", + "_ConditionSubset", +] +from pina._src.data.data_module import DataModule +from pina._src.data.single_batch_data_loader import _SingleBatchDataLoader +from pina._src.data.aggregator import _Aggregator +from pina._src.data.creator import _Creator +from pina._src.data.condition_subset import _ConditionSubset -from .data_module import PinaDataModule -from .dataset import PinaDataset +# Back-compatibility with version 0.2, to be removed soon +import warnings + +_DEPRECATED_IMPORTS = {"PinaDataModule": "DataModule"} + + +def __getattr__(name): + if name in _DEPRECATED_IMPORTS: + + warnings.warn( + f"Importing '{name}' from 'pina.data' is deprecated; use " + f"pina.data.{_DEPRECATED_IMPORTS[name]} instead.", + DeprecationWarning, + stacklevel=2, + ) + + return globals()[_DEPRECATED_IMPORTS[name]] diff --git a/pina/data/data_module.py b/pina/data/data_module.py deleted file mode 100644 index 52b52a3fa..000000000 --- a/pina/data/data_module.py +++ /dev/null @@ -1,658 +0,0 @@ -""" -This module contains the PinaDataModule class, which extends the -LightningDataModule class to allow proper creation and management of -different types of Datasets defined in PINA. -""" - -import warnings -from lightning.pytorch import LightningDataModule -import torch -from torch_geometric.data import Data -from torch.utils.data import DataLoader, SequentialSampler, RandomSampler -from torch.utils.data.distributed import DistributedSampler -from ..label_tensor import LabelTensor -from .dataset import PinaDatasetFactory, PinaTensorDataset - - -class DummyDataloader: - - def __init__(self, dataset): - """ - Prepare a dataloader object that returns the entire dataset in a single - batch. Depending on the number of GPUs, the dataset is managed - as follows: - - - **Distributed Environment** (multiple GPUs): Divides dataset across - processes using the rank and world size. Fetches only portion of - data corresponding to the current process. - - **Non-Distributed Environment** (single GPU): Fetches the entire - dataset. - - :param PinaDataset dataset: The dataset object to be processed. - - .. note:: - This dataloader is used when the batch size is ``None``. - """ - - if ( - torch.distributed.is_available() - and torch.distributed.is_initialized() - ): - rank = torch.distributed.get_rank() - world_size = torch.distributed.get_world_size() - if len(dataset) < world_size: - raise RuntimeError( - "Dimension of the dataset smaller than world size." - " Increase the size of the partition or use a single GPU" - ) - idx, i = [], rank - while i < len(dataset): - idx.append(i) - i += world_size - self.dataset = dataset.fetch_from_idx_list(idx) - else: - self.dataset = dataset.get_all_data() - - def __iter__(self): - return self - - def __len__(self): - return 1 - - def __next__(self): - return self.dataset - - -class Collator: - """ - This callable class is used to collate the data points fetched from the - dataset. The collation is performed based on the type of dataset used and - on the batching strategy. - """ - - def __init__( - self, max_conditions_lengths, automatic_batching, dataset=None - ): - """ - Initialize the object, setting the collate function based on whether - automatic batching is enabled or not. - - :param dict max_conditions_lengths: ``dict`` containing the maximum - number of data points to consider in a single batch for - each condition. - :param bool automatic_batching: Whether automatic PyTorch batching is - enabled or not. For more information, see the - :class:`~pina.data.data_module.PinaDataModule` class. - :param PinaDataset dataset: The dataset where the data is stored. - """ - - self.max_conditions_lengths = max_conditions_lengths - # Set the collate function based on the batching strategy - # collate_pina_dataloader is used when automatic batching is disabled - # collate_torch_dataloader is used when automatic batching is enabled - self.callable_function = ( - self._collate_torch_dataloader - if automatic_batching - else (self._collate_pina_dataloader) - ) - self.dataset = dataset - - # Set the function which performs the actual collation - if isinstance(self.dataset, PinaTensorDataset): - # If the dataset is a PinaTensorDataset, use this collate function - self._collate = self._collate_tensor_dataset - else: - # If the dataset is a PinaDataset, use this collate function - self._collate = self._collate_graph_dataset - - def _collate_pina_dataloader(self, batch): - """ - Function used to create a batch when automatic batching is disabled. - - :param list[int] batch: List of integers representing the indices of - the data points to be fetched. - :return: Dictionary containing the data points fetched from the dataset. - :rtype: dict - """ - # Call the fetch_from_idx_list method of the dataset - return self.dataset.fetch_from_idx_list(batch) - - def _collate_torch_dataloader(self, batch): - """ - Function used to collate the batch - - :param list[dict] batch: List of retrieved data. - :return: Dictionary containing the data points fetched from the dataset, - collated. - :rtype: dict - """ - - batch_dict = {} - if isinstance(batch, dict): - return batch - conditions_names = batch[0].keys() - # Condition names - for condition_name in conditions_names: - single_cond_dict = {} - condition_args = batch[0][condition_name].keys() - for arg in condition_args: - data_list = [ - batch[idx][condition_name][arg] - for idx in range( - min( - len(batch), - self.max_conditions_lengths[condition_name], - ) - ) - ] - single_cond_dict[arg] = self._collate(data_list) - - batch_dict[condition_name] = single_cond_dict - return batch_dict - - @staticmethod - def _collate_tensor_dataset(data_list): - """ - Function used to collate the data when the dataset is a - :class:`~pina.data.dataset.PinaTensorDataset`. - - :param data_list: Elements to be collated. - :type data_list: list[torch.Tensor] | list[LabelTensor] - :return: Batch of data. - :rtype: dict - - :raises RuntimeError: If the data is not a :class:`torch.Tensor` or a - :class:`~pina.label_tensor.LabelTensor`. - """ - - if isinstance(data_list[0], LabelTensor): - return LabelTensor.stack(data_list) - if isinstance(data_list[0], torch.Tensor): - return torch.stack(data_list) - raise RuntimeError("Data must be Tensors or LabelTensor ") - - def _collate_graph_dataset(self, data_list): - """ - Function used to collate data when the dataset is a - :class:`~pina.data.dataset.PinaGraphDataset`. - - :param data_list: Elememts to be collated. - :type data_list: list[Data] | list[Graph] - :return: Batch of data. - :rtype: dict - - :raises RuntimeError: If the data is not a - :class:`~torch_geometric.data.Data` or a :class:`~pina.graph.Graph`. - """ - if isinstance(data_list[0], LabelTensor): - return LabelTensor.cat(data_list) - if isinstance(data_list[0], torch.Tensor): - return torch.cat(data_list) - if isinstance(data_list[0], Data): - return self.dataset.create_batch(data_list) - raise RuntimeError( - "Data must be Tensors or LabelTensor or pyG " - "torch_geometric.data.Data" - ) - - def __call__(self, batch): - """ - Perform the collation of data fetched from the dataset. The behavoior - of the function is set based on the batching strategy during class - initialization. - - :param batch: List of retrieved data or sampled indices. - :type batch: list[int] | list[dict] - :return: Dictionary containing colleted data fetched from the dataset. - :rtype: dict - """ - - return self.callable_function(batch) - - -class PinaSampler: - """ - This class is used to create the sampler instance based on the shuffle - parameter and the environment in which the code is running. - """ - - def __new__(cls, dataset): - """ - Instantiate and initialize the sampler. - - :param PinaDataset dataset: The dataset from which to sample. - :return: The sampler instance. - :rtype: :class:`torch.utils.data.Sampler` - """ - - if ( - torch.distributed.is_available() - and torch.distributed.is_initialized() - ): - sampler = DistributedSampler(dataset) - else: - sampler = SequentialSampler(dataset) - return sampler - - -class PinaDataModule(LightningDataModule): - """ - This class extends :class:`~lightning.pytorch.core.LightningDataModule`, - allowing proper creation and management of different types of datasets - defined in PINA. - """ - - def __init__( - self, - problem, - train_size=0.7, - test_size=0.2, - val_size=0.1, - batch_size=None, - shuffle=True, - repeat=False, - automatic_batching=None, - num_workers=0, - pin_memory=False, - ): - """ - Initialize the object and creating datasets based on the input problem. - - :param AbstractProblem problem: The problem containing the data on which - to create the datasets and dataloaders. - :param float train_size: Fraction of elements in the training split. It - must be in the range [0, 1]. - :param float test_size: Fraction of elements in the test split. It must - be in the range [0, 1]. - :param float val_size: Fraction of elements in the validation split. It - must be in the range [0, 1]. - :param int batch_size: The batch size used for training. If ``None``, - the entire dataset is returned in a single batch. - Default is ``None``. - :param bool shuffle: Whether to shuffle the dataset before splitting. - Default ``True``. - :param bool repeat: If ``True``, in case of batch size larger than the - number of elements in a specific condition, the elements are - repeated until the batch size is reached. If ``False``, the number - of elements in the batch is the minimum between the batch size and - the number of elements in the condition. Default is ``False``. - :param automatic_batching: If ``True``, automatic PyTorch batching - is performed, which consists of extracting one element at a time - from the dataset and collating them into a batch. This is useful - when the dataset is too large to fit into memory. On the other hand, - if ``False``, the items are retrieved from the dataset all at once - avoind the overhead of collating them into a batch and reducing the - ``__getitem__`` calls to the dataset. This is useful when the - dataset fits into memory. Avoid using automatic batching when - ``batch_size`` is large. Default is ``False``. - :param int num_workers: Number of worker threads for data loading. - Default ``0`` (serial loading). - :param bool pin_memory: Whether to use pinned memory for faster data - transfer to GPU. Default ``False``. - - :raises ValueError: If at least one of the splits is negative. - :raises ValueError: If the sum of the splits is different from 1. - - .. seealso:: - For more information on multi-process data loading, see: - https://pytorch.org/docs/stable/data.html#multi-process-data-loading - - For details on memory pinning, see: - https://pytorch.org/docs/stable/data.html#memory-pinning - """ - super().__init__() - - # Store fixed attributes - self.batch_size = batch_size - self.shuffle = shuffle - self.repeat = repeat - self.automatic_batching = automatic_batching - - # If batch size is None, num_workers has no effect - if batch_size is None and num_workers != 0: - warnings.warn( - "Setting num_workers when batch_size is None has no effect on " - "the DataLoading process." - ) - self.num_workers = 0 - else: - self.num_workers = num_workers - - # If batch size is None, pin_memory has no effect - if batch_size is None and pin_memory: - warnings.warn( - "Setting pin_memory to True has no effect when " - "batch_size is None." - ) - self.pin_memory = False - else: - self.pin_memory = pin_memory - - # Collect data - problem.collect_data() - - # Check if the splits are correct - self._check_slit_sizes(train_size, test_size, val_size) - - # Split input data into subsets - splits_dict = {} - if train_size > 0: - splits_dict["train"] = train_size - self.train_dataset = None - else: - # Use the super method to create the train dataloader which - # raises NotImplementedError - self.train_dataloader = super().train_dataloader - if test_size > 0: - splits_dict["test"] = test_size - self.test_dataset = None - else: - # Use the super method to create the train dataloader which - # raises NotImplementedError - self.test_dataloader = super().test_dataloader - if val_size > 0: - splits_dict["val"] = val_size - self.val_dataset = None - else: - # Use the super method to create the train dataloader which - # raises NotImplementedError - self.val_dataloader = super().val_dataloader - - self.data_splits = self._create_splits( - problem.collected_data, splits_dict - ) - self.transfer_batch_to_device = self._transfer_batch_to_device - - def setup(self, stage=None): - """ - Create the dataset objects for the given stage. - If the stage is "fit", the training and validation datasets are created. - If the stage is "test", the testing dataset is created. - - :param str stage: The stage for which to perform the dataset setup. - - :raises ValueError: If the stage is neither "fit" nor "test". - """ - if stage == "fit" or stage is None: - self.train_dataset = PinaDatasetFactory( - self.data_splits["train"], - max_conditions_lengths=self.find_max_conditions_lengths( - "train" - ), - automatic_batching=self.automatic_batching, - ) - if "val" in self.data_splits.keys(): - self.val_dataset = PinaDatasetFactory( - self.data_splits["val"], - max_conditions_lengths=self.find_max_conditions_lengths( - "val" - ), - automatic_batching=self.automatic_batching, - ) - elif stage == "test": - self.test_dataset = PinaDatasetFactory( - self.data_splits["test"], - max_conditions_lengths=self.find_max_conditions_lengths("test"), - automatic_batching=self.automatic_batching, - ) - else: - raise ValueError("stage must be either 'fit' or 'test'.") - - @staticmethod - def _split_condition(single_condition_dict, splits_dict): - """ - Split the condition into different stages. - - :param dict single_condition_dict: The condition to be split. - :param dict splits_dict: The dictionary containing the number of - elements in each stage. - :return: A dictionary containing the split condition. - :rtype: dict - """ - - len_condition = len(single_condition_dict["input"]) - - lengths = [ - int(len_condition * length) for length in splits_dict.values() - ] - - remainder = len_condition - sum(lengths) - for i in range(remainder): - lengths[i % len(lengths)] += 1 - - splits_dict = { - k: max(1, v) for k, v in zip(splits_dict.keys(), lengths) - } - to_return_dict = {} - offset = 0 - - for stage, stage_len in splits_dict.items(): - to_return_dict[stage] = { - k: v[offset : offset + stage_len] - for k, v in single_condition_dict.items() - if k != "equation" - # Equations are NEVER dataloaded - } - if offset + stage_len >= len_condition: - offset = len_condition - 1 - continue - offset += stage_len - return to_return_dict - - def _create_splits(self, collector, splits_dict): - """ - Create the dataset objects putting data in the correct splits. - - :param Collector collector: The collector object containing the data. - :param dict splits_dict: The dictionary containing the number of - elements in each stage. - :return: The dictionary containing the dataset objects. - :rtype: dict - """ - - # ----------- Auxiliary function ------------ - def _apply_shuffle(condition_dict, len_data): - idx = torch.randperm(len_data) - for k, v in condition_dict.items(): - if k == "equation": - continue - if isinstance(v, list): - condition_dict[k] = [v[i] for i in idx] - elif isinstance(v, LabelTensor): - condition_dict[k] = LabelTensor(v.tensor[idx], v.labels) - elif isinstance(v, torch.Tensor): - condition_dict[k] = v[idx] - else: - raise ValueError(f"Data type {type(v)} not supported") - - # ----------- End auxiliary function ------------ - - split_names = list(splits_dict.keys()) - dataset_dict = {name: {} for name in split_names} - for ( - condition_name, - condition_dict, - ) in collector.items(): - len_data = len(condition_dict["input"]) - if self.shuffle: - _apply_shuffle(condition_dict, len_data) - for key, data in self._split_condition( - condition_dict, splits_dict - ).items(): - dataset_dict[key].update({condition_name: data}) - return dataset_dict - - def _create_dataloader(self, split, dataset): - """ " - Create the dataloader for the given split. - - :param str split: The split on which to create the dataloader. - :param str dataset: The dataset to be used for the dataloader. - :return: The dataloader for the given split. - :rtype: torch.utils.data.DataLoader - """ - # Suppress the warning about num_workers. - # In many cases, especially for PINNs, - # serial data loading can outperform parallel data loading. - warnings.filterwarnings( - "ignore", - message=( - "The '(train|val|test)_dataloader' does not have many workers " - "which may be a bottleneck." - ), - module="lightning.pytorch.trainer.connectors.data_connector", - ) - # Use custom batching (good if batch size is large) - if self.batch_size is not None: - sampler = PinaSampler(dataset) - if self.automatic_batching: - collate = Collator( - self.find_max_conditions_lengths(split), - self.automatic_batching, - dataset=dataset, - ) - else: - collate = Collator( - None, self.automatic_batching, dataset=dataset - ) - return DataLoader( - dataset, - self.batch_size, - collate_fn=collate, - sampler=sampler, - num_workers=self.num_workers, - pin_memory=self.pin_memory, - ) - dataloader = DummyDataloader(dataset) - dataloader.dataset = self._transfer_batch_to_device( - dataloader.dataset, self.trainer.strategy.root_device, 0 - ) - self.transfer_batch_to_device = self._transfer_batch_to_device_dummy - return dataloader - - def find_max_conditions_lengths(self, split): - """ - Define the maximum length for each conditions. - - :param dict split: The split of the dataset. - :return: The maximum length per condition. - :rtype: dict - """ - - max_conditions_lengths = {} - for k, v in self.data_splits[split].items(): - if self.batch_size is None: - max_conditions_lengths[k] = len(v["input"]) - elif self.repeat: - max_conditions_lengths[k] = self.batch_size - else: - max_conditions_lengths[k] = min( - len(v["input"]), self.batch_size - ) - return max_conditions_lengths - - def val_dataloader(self): - """ - Create the validation dataloader. - - :return: The validation dataloader - :rtype: torch.utils.data.DataLoader - """ - return self._create_dataloader("val", self.val_dataset) - - def train_dataloader(self): - """ - Create the training dataloader - - :return: The training dataloader - :rtype: torch.utils.data.DataLoader - """ - return self._create_dataloader("train", self.train_dataset) - - def test_dataloader(self): - """ - Create the testing dataloader - - :return: The testing dataloader - :rtype: torch.utils.data.DataLoader - """ - return self._create_dataloader("test", self.test_dataset) - - @staticmethod - def _transfer_batch_to_device_dummy(batch, device, dataloader_idx): - """ - Transfer the batch to the device. This method is used when the batch - size is None: batch has already been transferred to the device. - - :param list[tuple] batch: List of tuple where the first element of the - tuple is the condition name and the second element is the data. - :param torch.device device: Device to which the batch is transferred. - :param int dataloader_idx: Index of the dataloader. - :return: The batch transferred to the device. - :rtype: list[tuple] - """ - - return batch - - def _transfer_batch_to_device(self, batch, device, dataloader_idx): - """ - Transfer the batch to the device. This method is called in the - training loop and is used to transfer the batch to the device. - - :param dict batch: The batch to be transferred to the device. - :param torch.device device: The device to which the batch is - transferred. - :param int dataloader_idx: The index of the dataloader. - :return: The batch transferred to the device. - :rtype: list[tuple] - """ - - batch = [ - ( - k, - super(LightningDataModule, self).transfer_batch_to_device( - v, device, dataloader_idx - ), - ) - for k, v in batch.items() - ] - - return batch - - @staticmethod - def _check_slit_sizes(train_size, test_size, val_size): - """ - Check if the splits are correct. The splits sizes must be positive and - the sum of the splits must be 1. - - :param float train_size: The size of the training split. - :param float test_size: The size of the testing split. - :param float val_size: The size of the validation split. - - :raises ValueError: If at least one of the splits is negative. - :raises ValueError: If the sum of the splits is different - from 1. - """ - - if train_size < 0 or test_size < 0 or val_size < 0: - raise ValueError("The splits must be positive") - if abs(train_size + test_size + val_size - 1) > 1e-6: - raise ValueError("The sum of the splits must be 1") - - @property - def input(self): - """ - Return all the input points coming from all the datasets. - - :return: The input points for training. - :rtype: dict - """ - - to_return = {} - if hasattr(self, "train_dataset") and self.train_dataset is not None: - to_return["train"] = self.train_dataset.input - if hasattr(self, "val_dataset") and self.val_dataset is not None: - to_return["val"] = self.val_dataset.input - if hasattr(self, "test_dataset") and self.test_dataset is not None: - to_return["test"] = self.test_dataset.input - return to_return diff --git a/pina/data/dataset.py b/pina/data/dataset.py deleted file mode 100644 index 62e3913d8..000000000 --- a/pina/data/dataset.py +++ /dev/null @@ -1,326 +0,0 @@ -"""Module for the PINA dataset classes.""" - -from abc import abstractmethod, ABC -from torch.utils.data import Dataset -from torch_geometric.data import Data -from ..graph import Graph, LabelBatch - - -class PinaDatasetFactory: - """ - Factory class for the PINA dataset. - - Depending on the data type inside the conditions, it instanciate an object - belonging to the appropriate subclass of - :class:`~pina.data.dataset.PinaDataset`. The possible subclasses are: - - - :class:`~pina.data.dataset.PinaTensorDataset`, for handling \ - :class:`torch.Tensor` and :class:`~pina.label_tensor.LabelTensor` data. - - :class:`~pina.data.dataset.PinaGraphDataset`, for handling \ - :class:`~pina.graph.Graph` and :class:`~torch_geometric.data.Data` data. - """ - - def __new__(cls, conditions_dict, **kwargs): - """ - Instantiate the appropriate subclass of - :class:`~pina.data.dataset.PinaDataset`. - - If a graph is present in the conditions, returns a - :class:`~pina.data.dataset.PinaGraphDataset`, otherwise returns a - :class:`~pina.data.dataset.PinaTensorDataset`. - - :param dict conditions_dict: Dictionary containing all the conditions - to be included in the dataset instance. - :return: A subclass of :class:`~pina.data.dataset.PinaDataset`. - :rtype: PinaTensorDataset | PinaGraphDataset - - :raises ValueError: If an empty dictionary is provided. - """ - - # Check if conditions_dict is empty - if len(conditions_dict) == 0: - raise ValueError("No conditions provided") - - # Check is a Graph is present in the conditions - is_graph = cls._is_graph_dataset(conditions_dict) - if is_graph: - # If a Graph is present, return a PinaGraphDataset - return PinaGraphDataset(conditions_dict, **kwargs) - # If no Graph is present, return a PinaTensorDataset - return PinaTensorDataset(conditions_dict, **kwargs) - - @staticmethod - def _is_graph_dataset(conditions_dict): - """ - Check if a graph is present in the conditions (at least one time). - - :param conditions_dict: Dictionary containing the conditions. - :type conditions_dict: dict - :return: True if a graph is present in the conditions, False otherwise. - :rtype: bool - """ - - # Iterate over the conditions dictionary - for v in conditions_dict.values(): - # Iterate over the values of the current condition - for cond in v.values(): - # Check if the current value is a list of Data objects - if isinstance(cond, (Data, Graph, list, tuple)): - return True - return False - - -class PinaDataset(Dataset, ABC): - """ - Abstract class for the PINA dataset which extends the PyTorch - :class:`~torch.utils.data.Dataset` class. It defines the common interface - for :class:`~pina.data.dataset.PinaTensorDataset` and - :class:`~pina.data.dataset.PinaGraphDataset` classes. - """ - - def __init__( - self, conditions_dict, max_conditions_lengths, automatic_batching - ): - """ - Initialize the instance by storing the conditions dictionary, the - maximum number of items per conditions to consider, and the automatic - batching flag. - - :param dict conditions_dict: A dictionary mapping condition names to - their respective data. Each key represents a condition name, and the - corresponding value is a dictionary containing the associated data. - :param dict max_conditions_lengths: Maximum number of data points that - can be included in a single batch per condition. - :param bool automatic_batching: Indicates whether PyTorch automatic - batching is enabled in - :class:`~pina.data.data_module.PinaDataModule`. - """ - - # Store the conditions dictionary - self.conditions_dict = conditions_dict - # Store the maximum number of conditions to consider - self.max_conditions_lengths = max_conditions_lengths - # Store length of each condition - self.conditions_length = { - k: len(v["input"]) for k, v in self.conditions_dict.items() - } - # Store the maximum length of the dataset - self.length = max(self.conditions_length.values()) - # Dynamically set the getitem function based on automatic batching - if automatic_batching: - self._getitem_func = self._getitem_int - else: - self._getitem_func = self._getitem_dummy - - def _get_max_len(self): - """ - Returns the length of the longest condition in the dataset. - - :return: Length of the longest condition in the dataset. - :rtype: int - """ - - max_len = 0 - for condition in self.conditions_dict.values(): - max_len = max(max_len, len(condition["input"])) - return max_len - - def __len__(self): - return self.length - - def __getitem__(self, idx): - return self._getitem_func(idx) - - def _getitem_dummy(self, idx): - """ - Return the index itself. This is used when automatic batching is - disabled to postpone the data retrieval to the dataloader. - - :param int idx: Index. - :return: Index. - :rtype: int - """ - - # If automatic batching is disabled, return the data at the given index - return idx - - def _getitem_int(self, idx): - """ - Return the data at the given index in the dataset. This is used when - automatic batching is enabled. - - :param int idx: Index. - :return: A dictionary containing the data at the given index. - :rtype: dict - """ - - # If automatic batching is enabled, return the data at the given index - return { - k: {k_data: v[k_data][idx % len(v["input"])] for k_data in v.keys()} - for k, v in self.conditions_dict.items() - } - - def get_all_data(self): - """ - Return all data in the dataset. - - :return: A dictionary containing all the data in the dataset. - :rtype: dict - """ - to_return_dict = {} - for condition, data in self.conditions_dict.items(): - len_condition = len( - data["input"] - ) # Length of the current condition - to_return_dict[condition] = self._retrive_data( - data, list(range(len_condition)) - ) # Retrieve the data from the current condition - return to_return_dict - - def fetch_from_idx_list(self, idx): - """ - Return data from the dataset given a list of indices. - - :param list[int] idx: List of indices. - :return: A dictionary containing the data at the given indices. - :rtype: dict - """ - - to_return_dict = {} - for condition, data in self.conditions_dict.items(): - # Get the indices for the current condition - cond_idx = idx[: self.max_conditions_lengths[condition]] - # Get the length of the current condition - condition_len = self.conditions_length[condition] - # If the length of the dataset is greater than the length of the - # current condition, repeat the indices - if self.length > condition_len: - cond_idx = [idx % condition_len for idx in cond_idx] - # Retrieve the data from the current condition - to_return_dict[condition] = self._retrive_data(data, cond_idx) - return to_return_dict - - @abstractmethod - def _retrive_data(self, data, idx_list): - """ - Abstract method to retrieve data from the dataset given a list of - indices. - """ - - -class PinaTensorDataset(PinaDataset): - """ - Dataset class for the PINA dataset with :class:`torch.Tensor` and - :class:`~pina.label_tensor.LabelTensor` data. - """ - - # Override _retrive_data method for torch.Tensor data - def _retrive_data(self, data, idx_list): - """ - Retrieve data from the dataset given a list of indices. - - :param dict data: Dictionary containing the data - (only :class:`torch.Tensor` or - :class:`~pina.label_tensor.LabelTensor`). - :param list[int] idx_list: indices to retrieve. - :return: Dictionary containing the data at the given indices. - :rtype: dict - """ - - return {k: v[idx_list] for k, v in data.items()} - - @property - def input(self): - """ - Return the input data for the dataset. - - :return: Dictionary containing the input points. - :rtype: dict - """ - return {k: v["input"] for k, v in self.conditions_dict.items()} - - def update_data(self, new_conditions_dict): - """ - Update the dataset with new data. - This method is used to update the dataset with new data. It replaces - the current data with the new data provided in the new_conditions_dict - parameter. - - :param dict new_conditions_dict: Dictionary containing the new data. - :return: None - """ - for condition, data in new_conditions_dict.items(): - if condition in self.conditions_dict: - self.conditions_dict[condition].update(data) - else: - self.conditions_dict[condition] = data - - -class PinaGraphDataset(PinaDataset): - """ - Dataset class for the PINA dataset with :class:`~torch_geometric.data.Data` - and :class:`~pina.graph.Graph` data. - """ - - def _create_graph_batch(self, data): - """ - Create a LabelBatch object from a list of - :class:`~torch_geometric.data.Data` objects. - - :param data: List of items to collate in a single batch. - :type data: list[Data] | list[Graph] - :return: LabelBatch object all the graph collated in a single batch - disconnected graphs. - :rtype: LabelBatch - """ - batch = LabelBatch.from_data_list(data) - return batch - - def create_batch(self, data): - """ - Create a Batch object from a list of :class:`~torch_geometric.data.Data` - objects. - - :param data: List of items to collate in a single batch. - :type data: list[Data] | list[Graph] - :return: Batch object. - :rtype: :class:`~torch_geometric.data.Batch` - | :class:`~pina.graph.LabelBatch` - """ - - if isinstance(data[0], Data): - return self._create_graph_batch(data) - return self._create_tensor_batch(data) - - # Override _retrive_data method for graph handling - def _retrive_data(self, data, idx_list): - """ - Retrieve data from the dataset given a list of indices. - - :param dict data: Dictionary containing the data. - :param list[int] idx_list: List of indices to retrieve. - :return: Dictionary containing the data at the given indices. - :rtype: dict - """ - - # Return the data from the current condition - # If the data is a list of Data objects, create a Batch object - # If the data is a list of torch.Tensor objects, create a torch.Tensor - return { - k: ( - self._create_graph_batch([v[i] for i in idx_list]) - if isinstance(v, list) - else v[idx_list] - ) - for k, v in data.items() - } - - @property - def input(self): - """ - Return the input data for the dataset. - - :return: Dictionary containing the input points. - :rtype: dict - """ - return {k: v["input"] for k, v in self.conditions_dict.items()} diff --git a/pina/data/manager.py b/pina/data/manager.py new file mode 100644 index 000000000..1441cee12 --- /dev/null +++ b/pina/data/manager.py @@ -0,0 +1,15 @@ +"""Module for condition data management.""" + +__all__ = [ + "_BatchManager", + "_DataManagerInterface", + "_DataManager", + "_TensorDataManager", + "_GraphDataManager", +] + +from pina._src.data.manager.batch_manager import _BatchManager +from pina._src.data.manager.data_manager import _DataManager +from pina._src.data.manager.tensor_data_manager import _TensorDataManager +from pina._src.data.manager.graph_data_manager import _GraphDataManager +from pina._src.data.manager.data_manager_interface import _DataManagerInterface diff --git a/pina/domain/__init__.py b/pina/domain/__init__.py index 57999f4d8..6782563db 100644 --- a/pina/domain/__init__.py +++ b/pina/domain/__init__.py @@ -1,4 +1,9 @@ -"""Module to create and handle domains.""" +"""Geometry and domain definitions for spatial sampling. + +This module provides tools for defining the physical space of a problem, +including primitive shapes (Cartesian, Ellipsoid, Simplex) and set-theoretic +operations (Union, Intersection, etc.) for building complex geometries. +""" __all__ = [ "DomainInterface", @@ -13,13 +18,13 @@ "Exclusion", ] -from .domain_interface import DomainInterface -from .base_domain import BaseDomain -from .cartesian_domain import CartesianDomain -from .ellipsoid_domain import EllipsoidDomain -from .simplex_domain import SimplexDomain -from .operation_interface import OperationInterface -from .union import Union -from .intersection import Intersection -from .difference import Difference -from .exclusion import Exclusion +from pina._src.domain.domain_interface import DomainInterface +from pina._src.domain.base_domain import BaseDomain +from pina._src.domain.cartesian_domain import CartesianDomain +from pina._src.domain.ellipsoid_domain import EllipsoidDomain +from pina._src.domain.simplex_domain import SimplexDomain +from pina._src.domain.operation_interface import OperationInterface +from pina._src.domain.union import Union +from pina._src.domain.intersection import Intersection +from pina._src.domain.difference import Difference +from pina._src.domain.exclusion import Exclusion diff --git a/pina/equation/__init__.py b/pina/equation/__init__.py index 87a33554b..ae9a385bd 100644 --- a/pina/equation/__init__.py +++ b/pina/equation/__init__.py @@ -1,33 +1,54 @@ -"""Module to define equations and systems of equations.""" +""" +Mathematical equations and physical laws. + +This module provides a framework for defining differential equations, +boundary conditions, and complex systems of equations. It includes +pre-defined physical models such as Poisson, Helmholtz, and Wave equations, +along with equations for common derivative-based constraints, such as +FixedValue, FixedGradient, FixedFlux, and FixedLaplacian. +""" __all__ = [ - "SystemEquation", + "EquationInterface", + "BaseEquation", "Equation", - "FixedValue", - "FixedGradient", - "FixedFlux", - "FixedLaplacian", - "Laplace", - "Advection", - "AllenCahn", - "DiffusionReaction", - "Helmholtz", - "Poisson", - "AcousticWave", + "SystemEquation", ] -from .equation import Equation -from .equation_factory import ( - FixedFlux, - FixedGradient, - FixedLaplacian, - FixedValue, - Laplace, - Advection, - AllenCahn, - DiffusionReaction, - Helmholtz, - Poisson, - AcousticWave, -) -from .system_equation import SystemEquation +from pina._src.equation.equation_interface import EquationInterface +from pina._src.equation.base_equation import BaseEquation +from pina._src.equation.equation import Equation +from pina._src.equation.system_equation import SystemEquation + +# Back-compatibility with version 0.2, to be removed soon +import warnings +import importlib + +_DEPRECATED_IMPORTS = { + "FixedValue": ".zoo", + "FixedGradient": ".zoo", + "FixedFlux": ".zoo", + "FixedLaplacian": ".zoo", + "Laplace": ".zoo", + "HelmholtzEquation": ".zoo", + "PoissonEquation": ".zoo", + "AcousticWaveEquation": ".zoo", + "AdvectionEquation": ".zoo", + "AllenCahnEquation": ".zoo", + "DiffusionReactionEquation": ".zoo", + "BurgersEquation": ".zoo", +} + + +def __getattr__(name): + if name in _DEPRECATED_IMPORTS: + + warnings.warn( + f"Importing '{name}' from 'pina.equation' is deprecated; " + f"import it from 'pina.equation.zoo' instead.", + DeprecationWarning, + stacklevel=2, + ) + + module = importlib.import_module(_DEPRECATED_IMPORTS[name], __name__) + return getattr(module, name) diff --git a/pina/equation/equation.py b/pina/equation/equation.py deleted file mode 100644 index 057c6bcf5..000000000 --- a/pina/equation/equation.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Module for the Equation.""" - -import inspect -from .equation_interface import EquationInterface - - -class Equation(EquationInterface): - """ - Implementation of the Equation class. Every ``equation`` passed to a - :class:`~pina.condition.condition.Condition` object must be either an - instance of :class:`Equation` or - :class:`~pina.equation.system_equation.SystemEquation`. - """ - - def __init__(self, equation): - """ - Initialization of the :class:`Equation` class. - - :param Callable equation: A ``torch`` callable function used to compute - the residual of a mathematical equation. - :raises ValueError: If the equation is not a callable function. - """ - if not callable(equation): - raise ValueError( - "equation must be a callable function." - "Expected a callable function, got " - f"{equation}" - ) - # compute the signature - sig = inspect.signature(equation) - self.__len_sig = len(sig.parameters) - self.__equation = equation - - def residual(self, input_, output_, params_=None): - """ - Compute the residual of the equation. - - :param LabelTensor input_: Input points where the equation is evaluated. - :param LabelTensor output_: Output tensor, eventually produced by a - :class:`torch.nn.Module` instance. - :param dict params_: Dictionary of unknown parameters, associated with a - :class:`~pina.problem.inverse_problem.InverseProblem` instance. - If the equation is not related to a - :class:`~pina.problem.inverse_problem.InverseProblem` instance, the - parameters must be initialized to ``None``. Default is ``None``. - :return: The computed residual of the equation. - :rtype: LabelTensor - :raises RuntimeError: If the underlying equation signature length is not - 2 (direct problem) or 3 (inverse problem). - """ - # Move the equation to the input_ device - self.to(input_.device) - - # Call the underlying equation based on its signature length - if self.__len_sig == 2: - return self.__equation(input_, output_) - if self.__len_sig == 3: - return self.__equation(input_, output_, params_) - raise RuntimeError( - f"Unexpected number of arguments in equation: {self.__len_sig}. " - "Expected either 2 (direct problem) or 3 (inverse problem)." - ) diff --git a/pina/equation/equation_factory.py b/pina/equation/equation_factory.py deleted file mode 100644 index 01560d6c1..000000000 --- a/pina/equation/equation_factory.py +++ /dev/null @@ -1,508 +0,0 @@ -"""Module for defining various general equations.""" - -from typing import Callable -import torch -from .equation import Equation -from ..operator import grad, div, laplacian -from ..utils import check_consistency - - -class FixedValue(Equation): # pylint: disable=R0903 - """ - Equation to enforce a fixed value. Can be used to enforce Dirichlet Boundary - conditions. - """ - - def __init__(self, value, components=None): - """ - Initialization of the :class:`FixedValue` class. - - :param float value: The fixed value to be enforced. - :param list[str] components: The name of the output variables for which - the fixed value condition is applied. It should be a subset of the - output labels. If ``None``, all output variables are considered. - Default is ``None``. - """ - - def equation(_, output_): - """ - Definition of the equation to enforce a fixed value. - - :param LabelTensor input_: Input points where the equation is - evaluated. - :param LabelTensor output_: Output tensor, eventually produced by a - :class:`torch.nn.Module` instance. - :return: The computed residual of the equation. - :rtype: LabelTensor - """ - if components is None: - return output_ - value - return output_.extract(components) - value - - super().__init__(equation) - - -class FixedGradient(Equation): # pylint: disable=R0903 - """ - Equation to enforce a fixed gradient for a specific condition. - """ - - def __init__(self, value, components=None, d=None): - """ - Initialization of the :class:`FixedGradient` class. - - :param float value: The fixed value to be enforced to the gradient. - :param list[str] components: The name of the output variables for which - the fixed gradient condition is applied. It should be a subset of - the output labels. If ``None``, all output variables are considered. - Default is ``None``. - :param list[str] d: The name of the input variables on which the - gradient is computed. It should be a subset of the input labels. - If ``None``, all the input variables are considered. - Default is ``None``. - """ - - def equation(input_, output_): - """ - Definition of the equation to enforce a fixed gradient. - - :param LabelTensor input_: Input points where the equation is - evaluated. - :param LabelTensor output_: Output tensor, eventually produced by a - :class:`torch.nn.Module` instance. - :return: The computed residual of the equation. - :rtype: LabelTensor - """ - return grad(output_, input_, components=components, d=d) - value - - super().__init__(equation) - - -class FixedFlux(Equation): # pylint: disable=R0903 - """ - Equation to enforce a fixed flux, or divergence, for a specific condition. - """ - - def __init__(self, value, components=None, d=None): - """ - Initialization of the :class:`FixedFlux` class. - - :param float value: The fixed value to be enforced to the flux. - :param list[str] components: The name of the output variables for which - the fixed flux condition is applied. It should be a subset of the - output labels. If ``None``, all output variables are considered. - Default is ``None``. - :param list[str] d: The name of the input variables on which the flux - is computed. It should be a subset of the input labels. If ``None``, - all the input variables are considered. Default is ``None``. - """ - - def equation(input_, output_): - """ - Definition of the equation to enforce a fixed flux. - - :param LabelTensor input_: Input points where the equation is - evaluated. - :param LabelTensor output_: Output tensor, eventually produced by a - :class:`torch.nn.Module` instance. - :return: The computed residual of the equation. - :rtype: LabelTensor - """ - return div(output_, input_, components=components, d=d) - value - - super().__init__(equation) - - -class FixedLaplacian(Equation): # pylint: disable=R0903 - """ - Equation to enforce a fixed laplacian for a specific condition. - """ - - def __init__(self, value, components=None, d=None): - """ - Initialization of the :class:`FixedLaplacian` class. - - :param float value: The fixed value to be enforced to the laplacian. - :param list[str] components: The name of the output variables for which - the fixed laplace condition is applied. It should be a subset of the - output labels. If ``None``, all output variables are considered. - Default is ``None``. - :param list[str] d: The name of the input variables on which the - laplacian is computed. It should be a subset of the input labels. - If ``None``, all the input variables are considered. - Default is ``None``. - """ - - def equation(input_, output_): - """ - Definition of the equation to enforce a fixed laplacian. - - :param LabelTensor input_: Input points where the equation is - evaluated. - :param LabelTensor output_: Output tensor, eventually produced by a - :class:`torch.nn.Module` instance. - :return: The computed residual of the equation. - :rtype: LabelTensor - """ - return ( - laplacian(output_, input_, components=components, d=d) - value - ) - - super().__init__(equation) - - -class Laplace(FixedLaplacian): # pylint: disable=R0903 - r""" - Equation to enforce a null laplacian for a specific condition. - The equation is defined as follows: - - .. math:: - - \delta u = 0 - - """ - - def __init__(self, components=None, d=None): - """ - Initialization of the :class:`Laplace` class. - - :param list[str] components: The name of the output variables for which - the null laplace condition is applied. It should be a subset of the - output labels. If ``None``, all output variables are considered. - Default is ``None``. - :param list[str] d: The name of the input variables on which the - laplacian is computed. It should be a subset of the input labels. - If ``None``, all the input variables are considered. - Default is ``None``. - """ - super().__init__(0.0, components=components, d=d) - - -class Advection(Equation): # pylint: disable=R0903 - r""" - Implementation of the N-dimensional advection equation with constant - velocity parameter. The equation is defined as follows: - - .. math:: - - \frac{\partial u}{\partial t} + c \cdot \nabla u = 0 - - Here, :math:`c` is the advection velocity parameter. - """ - - def __init__(self, c): - """ - Initialization of the :class:`Advection` class. - - :param c: The advection velocity. If a scalar is provided, the same - velocity is applied to all spatial dimensions. If a list is - provided, it must contain one value per spatial dimension. - :type c: float | int | List[float] | List[int] - :raises ValueError: If ``c`` is an empty list. - """ - # Check consistency - check_consistency(c, (float, int, list)) - if isinstance(c, list): - all(check_consistency(ci, (float, int)) for ci in c) - if len(c) < 1: - raise ValueError("'c' cannot be an empty list.") - else: - c = [c] - - # Store advection velocity parameter - self.c = torch.tensor(c).unsqueeze(0) - - def equation(input_, output_): - """ - Implementation of the advection equation. - - :param LabelTensor input_: The input data of the problem. - :param LabelTensor output_: The output data of the problem. - :return: The residual of the advection equation. - :rtype: LabelTensor - :raises ValueError: If the ``input_`` labels do not contain the time - variable 't'. - :raises ValueError: If ``c`` is a list and its length is not - consistent with the number of spatial dimensions. - """ - # Store labels - input_lbl = input_.labels - spatial_d = [di for di in input_lbl if di != "t"] - - # Ensure time is passed as input - if "t" not in input_lbl: - raise ValueError( - "The ``input_`` labels must contain the time 't' variable." - ) - - # Ensure consistency of c length - if self.c.shape[-1] != len(input_lbl) - 1 and self.c.shape[-1] > 1: - raise ValueError( - "If 'c' is passed as a list, its length must be equal to " - "the number of spatial dimensions." - ) - - # Repeat c to ensure consistent shape for advection - c = self.c.repeat(output_.shape[0], 1) - if c.shape[1] != (len(input_lbl) - 1): - c = c.repeat(1, len(input_lbl) - 1) - - # Add a dimension to c for the following operations - c = c.unsqueeze(-1) - - # Compute the time derivative and the spatial gradient - time_der = grad(output_, input_, components=None, d="t") - grads = grad(output_=output_, input_=input_, d=spatial_d) - - # Reshape and transpose - tmp = grads.reshape(*output_.shape, len(spatial_d)) - tmp = tmp.transpose(-1, -2) - - # Compute advection term - adv = (tmp * c).sum(dim=tmp.tensor.ndim - 2) - - return time_der + adv - - super().__init__(equation) - - -class AllenCahn(Equation): # pylint: disable=R0903 - r""" - Implementation of the N-dimensional Allen-Cahn equation, defined as follows: - - .. math:: - - \frac{\partial u}{\partial t} - \alpha \Delta u + \beta(u^3 - u) = 0 - - Here, :math:`\alpha` and :math:`\beta` are parameters of the equation. - """ - - def __init__(self, alpha, beta): - """ - Initialization of the :class:`AllenCahn` class. - - :param alpha: The diffusion coefficient. - :type alpha: float | int - :param beta: The reaction coefficient. - :type beta: float | int - """ - check_consistency(alpha, (float, int)) - check_consistency(beta, (float, int)) - self.alpha = alpha - self.beta = beta - - def equation(input_, output_): - """ - Implementation of the Allen-Cahn equation. - - :param LabelTensor input_: The input data of the problem. - :param LabelTensor output_: The output data of the problem. - :return: The residual of the Allen-Cahn equation. - :rtype: LabelTensor - :raises ValueError: If the ``input_`` labels do not contain the time - variable 't'. - """ - # Ensure time is passed as input - if "t" not in input_.labels: - raise ValueError( - "The ``input_`` labels must contain the time 't' variable." - ) - - # Compute the time derivative and the spatial laplacian - u_t = grad(output_, input_, d=["t"]) - u_xx = laplacian( - output_, input_, d=[di for di in input_.labels if di != "t"] - ) - - return u_t - self.alpha * u_xx + self.beta * (output_**3 - output_) - - super().__init__(equation) - - -class DiffusionReaction(Equation): # pylint: disable=R0903 - r""" - Implementation of the N-dimensional Diffusion-Reaction equation, - defined as follows: - - .. math:: - - \frac{\partial u}{\partial t} - \alpha \Delta u - f = 0 - - Here, :math:`\alpha` is a parameter of the equation, while :math:`f` is the - reaction term. - """ - - def __init__(self, alpha, forcing_term): - """ - Initialization of the :class:`DiffusionReaction` class. - - :param alpha: The diffusion coefficient. - :type alpha: float | int - :param Callable forcing_term: The forcing field function, taking as - input the points on which evaluation is required. - """ - check_consistency(alpha, (float, int)) - check_consistency(forcing_term, (Callable)) - self.alpha = alpha - self.forcing_term = forcing_term - - def equation(input_, output_): - """ - Implementation of the Diffusion-Reaction equation. - - :param LabelTensor input_: The input data of the problem. - :param LabelTensor output_: The output data of the problem. - :return: The residual of the Diffusion-Reaction equation. - :rtype: LabelTensor - :raises ValueError: If the ``input_`` labels do not contain the time - variable 't'. - """ - # Ensure time is passed as input - if "t" not in input_.labels: - raise ValueError( - "The ``input_`` labels must contain the time 't' variable." - ) - - # Compute the time derivative and the spatial laplacian - u_t = grad(output_, input_, d=["t"]) - u_xx = laplacian( - output_, input_, d=[di for di in input_.labels if di != "t"] - ) - - return u_t - self.alpha * u_xx - self.forcing_term(input_) - - super().__init__(equation) - - -class Helmholtz(Equation): # pylint: disable=R0903 - r""" - Implementation of the Helmholtz equation, defined as follows: - - .. math:: - - \Delta u + k u - f = 0 - - Here, :math:`k` is a parameter of the equation, while :math:`f` is the - forcing term. - """ - - def __init__(self, k, forcing_term): - """ - Initialization of the :class:`Helmholtz` class. - - :param k: The parameter of the equation. - :type k: float | int - :param Callable forcing_term: The forcing field function, taking as - input the points on which evaluation is required. - """ - check_consistency(k, (int, float)) - check_consistency(forcing_term, (Callable)) - self.k = k - self.forcing_term = forcing_term - - def equation(input_, output_): - """ - Implementation of the Helmholtz equation. - - :param LabelTensor input_: The input data of the problem. - :param LabelTensor output_: The output data of the problem. - :return: The residual of the Helmholtz equation. - :rtype: LabelTensor - """ - lap = laplacian(output_, input_) - return lap + self.k * output_ - self.forcing_term(input_) - - super().__init__(equation) - - -class Poisson(Equation): # pylint: disable=R0903 - r""" - Implementation of the Poisson equation, defined as follows: - - .. math:: - - \Delta u - f = 0 - - Here, :math:`f` is the forcing term. - """ - - def __init__(self, forcing_term): - """ - Initialization of the :class:`Poisson` class. - - :param Callable forcing_term: The forcing field function, taking as - input the points on which evaluation is required. - """ - check_consistency(forcing_term, (Callable)) - self.forcing_term = forcing_term - - def equation(input_, output_): - """ - Implementation of the Poisson equation. - - :param LabelTensor input_: The input data of the problem. - :param LabelTensor output_: The output data of the problem. - :return: The residual of the Poisson equation. - :rtype: LabelTensor - """ - lap = laplacian(output_, input_) - return lap - self.forcing_term(input_) - - super().__init__(equation) - - -class AcousticWave(Equation): # pylint: disable=R0903 - r""" - Implementation of the N-dimensional isotropic acoustic wave equation. - The equation is defined as follows: - - .. math:: - - \frac{\partial^2 u}{\partial t^2} - c^2 \Delta u = 0 - - or alternatively: - - .. math:: - - \Box u = 0 - - Here, :math:`c` is the wave propagation speed, and :math:`\Box` is the - d'Alembert operator. - """ - - def __init__(self, c): - """ - Initialization of the :class:`AcousticWaveEquation` class. - - :param c: The wave propagation speed. - :type c: float | int - """ - check_consistency(c, (float, int)) - self.c = c - - def equation(input_, output_): - """ - Implementation of the acoustic wave equation. - - :param LabelTensor input_: The input data of the problem. - :param LabelTensor output_: The output data of the problem. - :return: The residual of the acoustic wave equation. - :rtype: LabelTensor - :raises ValueError: If the ``input_`` labels do not contain the time - variable 't'. - """ - # Ensure time is passed as input - if "t" not in input_.labels: - raise ValueError( - "The ``input_`` labels must contain the time 't' variable." - ) - - # Compute the time second derivative and the spatial laplacian - u_tt = laplacian(output_, input_, d=["t"]) - u_xx = laplacian( - output_, input_, d=[di for di in input_.labels if di != "t"] - ) - - return u_tt - self.c**2 * u_xx - - super().__init__(equation) diff --git a/pina/equation/equation_interface.py b/pina/equation/equation_interface.py deleted file mode 100644 index 82b86dbd0..000000000 --- a/pina/equation/equation_interface.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Module for the Equation Interface.""" - -from abc import ABCMeta, abstractmethod -import torch - - -class EquationInterface(metaclass=ABCMeta): - """ - Abstract base class for equations. - - Equations in PINA simplify the training process. When defining a problem, - each equation passed to a :class:`~pina.condition.condition.Condition` - object must be either an :class:`~pina.equation.equation.Equation` or a - :class:`~pina.equation.system_equation.SystemEquation` instance. - - An :class:`~pina.equation.equation.Equation` is a wrapper for a callable - function, while :class:`~pina.equation.system_equation.SystemEquation` - wraps a list of callable functions. To streamline code writing, PINA - provides a diverse set of pre-implemented equations, such as - :class:`~pina.equation.equation_factory.FixedValue`, - :class:`~pina.equation.equation_factory.FixedGradient`, and many others. - """ - - @abstractmethod - def residual(self, input_, output_, params_): - """ - Abstract method to compute the residual of an equation. - - :param LabelTensor input_: Input points where the equation is evaluated. - :param LabelTensor output_: Output tensor, eventually produced by a - :class:`torch.nn.Module` instance. - :param dict params_: Dictionary of unknown parameters, associated with a - :class:`~pina.problem.inverse_problem.InverseProblem` instance. - :return: The computed residual of the equation. - :rtype: LabelTensor - """ - - def to(self, device): - """ - Move all tensor attributes to the specified device. - - :param torch.device device: The target device to move the tensors to. - :return: The instance moved to the specified device. - :rtype: EquationInterface - """ - # Iterate over all attributes of the Equation - for key, val in self.__dict__.items(): - - # Move tensors in dictionaries to the specified device - if isinstance(val, dict): - self.__dict__[key] = { - k: v.to(device) if torch.is_tensor(v) else v - for k, v in val.items() - } - - # Move tensors in lists to the specified device - elif isinstance(val, list): - self.__dict__[key] = [ - v.to(device) if torch.is_tensor(v) else v for v in val - ] - - # Move tensor attributes to the specified device - elif torch.is_tensor(val): - self.__dict__[key] = val.to(device) - - return self diff --git a/pina/equation/system_equation.py b/pina/equation/system_equation.py deleted file mode 100644 index 3e8550d9b..000000000 --- a/pina/equation/system_equation.py +++ /dev/null @@ -1,119 +0,0 @@ -"""Module for the System of Equation.""" - -import torch -from .equation_interface import EquationInterface -from .equation import Equation -from ..utils import check_consistency - - -class SystemEquation(EquationInterface): - """ - Implementation of the System of Equations, to be passed to a - :class:`~pina.condition.condition.Condition` object. - - Unlike the :class:`~pina.equation.equation.Equation` class, which represents - a single equation, the :class:`SystemEquation` class allows multiple - equations to be grouped together into a system. This is particularly useful - when dealing with multi-component outputs or coupled physical models, where - the residual must be computed collectively across several constraints. - - Each equation in the system must be either: - - An instance of :class:`~pina.equation.equation.Equation`; - - A callable function. - - The residuals from each equation are computed independently and then - aggregated using an optional reduction strategy (e.g., ``mean``, ``sum``). - The resulting residual is returned as a single :class:`~pina.LabelTensor`. - - :Example: - - >>> from pina.equation import SystemEquation, FixedValue, FixedGradient - >>> from pina import LabelTensor - >>> import torch - >>> pts = LabelTensor(torch.rand(10, 2), labels=["x", "y"]) - >>> pts.requires_grad = True - >>> output_ = torch.pow(pts, 2) - >>> output_.labels = ["u", "v"] - >>> system_equation = SystemEquation( - ... [ - ... FixedValue(value=1.0, components=["u"]), - ... FixedGradient(value=0.0, components=["v"],d=["y"]), - ... ], - ... reduction="mean", - ... ) - >>> residual = system_equation.residual(pts, output_) - - """ - - def __init__(self, list_equation, reduction=None): - """ - Initialization of the :class:`SystemEquation` class. - - :param list_equation: A list containing either callable functions or - instances of :class:`~pina.equation.equation.Equation`, used to - compute the residuals of mathematical equations. - :type list_equation: list[Callable] | list[Equation] - :param str reduction: The reduction method to aggregate the residuals of - each equation. Available options are: ``None``, ``mean``, ``sum``, - ``callable``. - If ``None``, no reduction is applied. If ``mean``, the output sum is - divided by the number of elements in the output. If ``sum``, the - output is summed. ``callable`` is a user-defined callable function - to perform reduction, no checks guaranteed. Default is ``None``. - :raises NotImplementedError: If the reduction is not implemented. - """ - check_consistency([list_equation], list) - - # equations definition - self.equations = [ - equation if isinstance(equation, Equation) else Equation(equation) - for equation in list_equation - ] - - # possible reduction - if reduction == "mean": - self.reduction = torch.mean - elif reduction == "sum": - self.reduction = torch.sum - elif (reduction is None) or callable(reduction): - self.reduction = reduction - else: - raise NotImplementedError( - "Only mean and sum reductions are currenly supported." - ) - - def residual(self, input_, output_, params_=None): - """ - Compute the residual for each equation in the system of equations and - aggregate it according to the ``reduction`` specified in the - ``__init__`` method. - - :param LabelTensor input_: Input points where each equation of the - system is evaluated. - :param LabelTensor output_: Output tensor, eventually produced by a - :class:`torch.nn.Module` instance. - :param dict params_: Dictionary of unknown parameters, associated with a - :class:`~pina.problem.inverse_problem.InverseProblem` instance. - If the equation is not related to a - :class:`~pina.problem.inverse_problem.InverseProblem` instance, the - parameters must be initialized to ``None``. Default is ``None``. - - :return: The aggregated residuals of the system of equations. - :rtype: LabelTensor - """ - # Move the equation to the input_ device - self.to(input_.device) - - # Compute the residual for each equation - residual = torch.hstack( - [ - equation.residual(input_, output_, params_) - for equation in self.equations - ] - ) - - # Skip reduction if not specified - if self.reduction is None: - return residual - - return self.reduction(residual, dim=-1) diff --git a/pina/equation/zoo.py b/pina/equation/zoo.py new file mode 100644 index 000000000..4febb63e6 --- /dev/null +++ b/pina/equation/zoo.py @@ -0,0 +1,30 @@ +"""Module for implemented equations.""" + +__all__ = [ + "AdvectionEquation", + "AllenCahnEquation", + "DiffusionReactionEquation", + "FixedFlux", + "FixedGradient", + "FixedLaplacian", + "FixedValue", + "HelmholtzEquation", + "Laplace", + "PoissonEquation", + "AcousticWaveEquation", + "BurgersEquation", +] + +from pina._src.equation.zoo.acoustic_wave_equation import AcousticWaveEquation +from pina._src.equation.zoo.advection_equation import AdvectionEquation +from pina._src.equation.zoo.allen_cahn_equation import AllenCahnEquation +from pina._src.equation.zoo.diffusion_reaction_equation import ( + DiffusionReactionEquation, +) +from pina._src.equation.zoo.helmholtz_equation import HelmholtzEquation +from pina._src.equation.zoo.poisson_equation import PoissonEquation +from pina._src.equation.zoo.burgers_equation import BurgersEquation +from pina._src.equation.zoo.fixed_value import FixedValue +from pina._src.equation.zoo.fixed_gradient import FixedGradient +from pina._src.equation.zoo.fixed_flux import FixedFlux +from pina._src.equation.zoo.fixed_laplacian import FixedLaplacian, Laplace diff --git a/pina/graph.py b/pina/graph.py index 201f37a24..04c6374f5 100644 --- a/pina/graph.py +++ b/pina/graph.py @@ -1,421 +1,13 @@ -"""Module to build Graph objects and perform operations on them.""" +"""Public API for Graph connectivity and neighborhood logic. -import torch -from torch_geometric.data import Data, Batch -from torch_geometric.utils import to_undirected -from torch_geometric.utils.loop import remove_self_loops -from .label_tensor import LabelTensor -from .utils import check_consistency, is_function +This module exposes core graph types used to define spatial relationships +between points, such as fixed-radius and k-nearest neighbor (KNN) structures. +""" +from pina._src.core.graph import Graph, RadiusGraph, KNNGraph -class Graph(Data): - """ - Extends :class:`~torch_geometric.data.Data` class to include additional - checks and functionlities. - """ - - def __new__( - cls, - **kwargs, - ): - """ - Create a new instance of the :class:`~pina.graph.Graph` class by - checking the consistency of the input data and storing the attributes. - - :param dict kwargs: Parameters used to initialize the - :class:`~pina.graph.Graph` object. - :return: A new instance of the :class:`~pina.graph.Graph` class. - :rtype: Graph - """ - # create class instance - instance = Data.__new__(cls) - - # check the consistency of types defined in __init__, the others are not - # checked (as in pyg Data object) - instance._check_type_consistency(**kwargs) - - return instance - - def __init__( - self, - x=None, - edge_index=None, - pos=None, - edge_attr=None, - undirected=False, - **kwargs, - ): - """ - Initialize the object by setting the node features, edge index, - edge attributes, and positions. The edge index is preprocessed to make - the graph undirected if required. For more details, see the - :meth:`torch_geometric.data.Data` - - :param x: Optional tensor of node features ``(N, F)`` where ``F`` is the - number of features per node. - :type x: torch.Tensor, LabelTensor - :param torch.Tensor edge_index: A tensor of shape ``(2, E)`` - representing the indices of the graph's edges. - :param pos: A tensor of shape ``(N, D)`` representing the positions of - ``N`` points in ``D``-dimensional space. - :type pos: torch.Tensor | LabelTensor - :param edge_attr: Optional tensor of edge_featured ``(E, F')`` where - ``F'`` is the number of edge features - :type edge_attr: torch.Tensor | LabelTensor - :param bool undirected: Whether to make the graph undirected - :param dict kwargs: Additional keyword arguments passed to the - :class:`~torch_geometric.data.Data` class constructor. - """ - # preprocessing - self._preprocess_edge_index(edge_index, undirected) - - # calling init - super().__init__( - x=x, edge_index=edge_index, edge_attr=edge_attr, pos=pos, **kwargs - ) - - def _check_type_consistency(self, **kwargs): - """ - Check the consistency of the types of the input data. - - :param dict kwargs: Attributes to be checked for consistency. - """ - # default types, specified in cls.__new__, by default they are Nont - # if specified in **kwargs they get override - x, pos, edge_index, edge_attr = None, None, None, None - if "pos" in kwargs: - pos = kwargs["pos"] - self._check_pos_consistency(pos) - if "edge_index" in kwargs: - edge_index = kwargs["edge_index"] - self._check_edge_index_consistency(edge_index) - if "x" in kwargs: - x = kwargs["x"] - self._check_x_consistency(x, pos) - if "edge_attr" in kwargs: - edge_attr = kwargs["edge_attr"] - self._check_edge_attr_consistency(edge_attr, edge_index) - if "undirected" in kwargs: - undirected = kwargs["undirected"] - check_consistency(undirected, bool) - - @staticmethod - def _check_pos_consistency(pos): - """ - Check if the position tensor is consistent. - :param torch.Tensor pos: The position tensor. - :raises ValueError: If the position tensor is not consistent. - """ - if pos is not None: - check_consistency(pos, (torch.Tensor, LabelTensor)) - if pos.ndim != 2: - raise ValueError("pos must be a 2D tensor.") - - @staticmethod - def _check_edge_index_consistency(edge_index): - """ - Check if the edge index is consistent. - - :param torch.Tensor edge_index: The edge index tensor. - :raises ValueError: If the edge index tensor is not consistent. - """ - check_consistency(edge_index, (torch.Tensor, LabelTensor)) - if edge_index.ndim != 2: - raise ValueError("edge_index must be a 2D tensor.") - if edge_index.size(0) != 2: - raise ValueError("edge_index must have shape [2, num_edges].") - - @staticmethod - def _check_edge_attr_consistency(edge_attr, edge_index): - """ - Check if the edge attribute tensor is consistent in type and shape - with the edge index. - - :param edge_attr: The edge attribute tensor. - :type edge_attr: torch.Tensor | LabelTensor - :param torch.Tensor edge_index: The edge index tensor. - :raises ValueError: If the edge attribute tensor is not consistent. - """ - if edge_attr is not None: - check_consistency(edge_attr, (torch.Tensor, LabelTensor)) - if edge_attr.ndim != 2: - raise ValueError("edge_attr must be a 2D tensor.") - if edge_attr.size(0) != edge_index.size(1): - raise ValueError( - "edge_attr must have shape " - "[num_edges, num_edge_features], expected " - f"num_edges {edge_index.size(1)} " - f"got {edge_attr.size(0)}." - ) - - @staticmethod - def _check_x_consistency(x, pos=None): - """ - Check if the input tensor x is consistent with the position tensor - `pos`. - - :param x: The input tensor. - :type x: torch.Tensor | LabelTensor - :param pos: The position tensor. - :type pos: torch.Tensor | LabelTensor - :raises ValueError: If the input tensor is not consistent. - """ - if x is not None: - check_consistency(x, (torch.Tensor, LabelTensor)) - if x.ndim != 2: - raise ValueError("x must be a 2D tensor.") - if pos is not None: - if x.size(0) != pos.size(0): - raise ValueError("Inconsistent number of nodes.") - - @staticmethod - def _preprocess_edge_index(edge_index, undirected): - """ - Preprocess the edge index to make the graph undirected (if required). - - :param torch.Tensor edge_index: The edge index. - :param bool undirected: Whether the graph is undirected. - :return: The preprocessed edge index. - :rtype: torch.Tensor - """ - if undirected: - edge_index = to_undirected(edge_index) - return edge_index - - def extract(self, labels, attr="x"): - """ - Perform extraction of labels from the attribute specified by `attr`. - - :param labels: Labels to extract - :type labels: list[str] | tuple[str] | str | dict - :return: Batch object with extraction performed on x - :rtype: PinaBatch - """ - # Extract labels from LabelTensor object - tensor = getattr(self, attr).extract(labels) - # Set the extracted tensor as the new attribute - setattr(self, attr, tensor) - return self - - -class GraphBuilder: - """ - A class that allows an easy definition of :class:`Graph` instances. - """ - - def __new__( - cls, - pos, - edge_index, - x=None, - edge_attr=False, - custom_edge_func=None, - loop=True, - **kwargs, - ): - """ - Compute the edge attributes and create a new instance of the - :class:`~pina.graph.Graph` class. - - :param pos: A tensor of shape ``(N, D)`` representing the positions of - ``N`` points in ``D``-dimensional space. - :type pos: torch.Tensor or LabelTensor - :param edge_index: A tensor of shape ``(2, E)`` representing the indices - of the graph's edges. - :type edge_index: torch.Tensor - :param x: Optional tensor of node features of shape ``(N, F)``, where - ``F`` is the number of features per node. - :type x: torch.Tensor | LabelTensor, optional - :param bool edge_attr: Whether to compute the edge attributes. - :param custom_edge_func: A custom function to compute edge attributes. - If provided, overrides ``edge_attr``. - :type custom_edge_func: Callable, optional - :param bool loop: Whether to include self-loops. - :param kwargs: Additional keyword arguments passed to the - :class:`~pina.graph.Graph` class constructor. - :return: A :class:`~pina.graph.Graph` instance constructed using the - provided information. - :rtype: Graph - """ - if not loop: - edge_index = remove_self_loops(edge_index)[0] - edge_attr = cls._create_edge_attr( - pos, edge_index, edge_attr, custom_edge_func or cls._build_edge_attr - ) - return Graph( - x=x, - edge_index=edge_index, - edge_attr=edge_attr, - pos=pos, - **kwargs, - ) - - @staticmethod - def _create_edge_attr(pos, edge_index, edge_attr, func): - """ - Create the edge attributes based on the input parameters. - - :param pos: Positions of the points. - :type pos: torch.Tensor | LabelTensor - :param torch.Tensor edge_index: Edge indices. - :param bool edge_attr: Whether to compute the edge attributes. - :param Callable func: Function to compute the edge attributes. - :raises ValueError: If ``func`` is not a function. - :return: The edge attributes. - :rtype: torch.Tensor | LabelTensor | None - """ - check_consistency(edge_attr, bool) - if edge_attr: - if is_function(func): - return func(pos, edge_index) - raise ValueError("custom_edge_func must be a function.") - return None - - @staticmethod - def _build_edge_attr(pos, edge_index): - """ - Default function to compute the edge attributes. - - :param pos: Positions of the points. - :type pos: torch.Tensor | LabelTensor - :param torch.Tensor edge_index: Edge indices. - :return: The edge attributes. - :rtype: torch.Tensor - """ - return ( - (pos[edge_index[0]] - pos[edge_index[1]]) - .abs() - .as_subclass(torch.Tensor) - ) - - -class RadiusGraph(GraphBuilder): - """ - Extends the :class:`~pina.graph.GraphBuilder` class to compute - ``edge_index`` based on a radius. Each point is connected to all the points - within the radius. - """ - - def __new__(cls, pos, radius, **kwargs): - """ - Instantiate the :class:`~pina.graph.Graph` class by computing the - ``edge_index`` based on the radius provided. - - :param pos: A tensor of shape ``(N, D)`` representing the positions of - ``N`` points in ``D``-dimensional space. - :type pos: torch.Tensor | LabelTensor - :param float radius: The radius within which points are connected. - :param dict kwargs: The additional keyword arguments to be passed to - :class:`GraphBuilder` and :class:`Graph` classes. - :return: A :class:`~pina.graph.Graph` instance with the computed - ``edge_index``. - :rtype: Graph - """ - edge_index = cls.compute_radius_graph(pos, radius) - return super().__new__(cls, pos=pos, edge_index=edge_index, **kwargs) - - @staticmethod - def compute_radius_graph(points, radius): - """ - Computes the ``edge_index`` based on the radius. Each point is connected - to all the points within the radius. - - :param points: A tensor of shape ``(N, D)`` representing the positions - of ``N`` points in ``D``-dimensional space. - :type points: torch.Tensor | LabelTensor - :param float radius: The radius within which points are connected. - :return: A tensor of shape ``(2, E)``, with ``E`` number of edges, - representing the edge indices of the graph. - :rtype: torch.Tensor - """ - dist = torch.cdist(points, points, p=2) - return ( - torch.nonzero(dist <= radius, as_tuple=False) - .t() - .as_subclass(torch.Tensor) - ) - - -class KNNGraph(GraphBuilder): - """ - Extends the :class:`~pina.graph.GraphBuilder` class to compute - ``edge_index`` based on a K-nearest neighbors algorithm. - """ - - def __new__(cls, pos, neighbours, **kwargs): - """ - Instantiate the :class:`~pina.graph.Graph` class by computing the - ``edge_index`` based on the K-nearest neighbors algorithm. - - :param pos: A tensor of shape ``(N, D)`` representing the positions of - ``N`` points in ``D``-dimensional space. - :type pos: torch.Tensor | LabelTensor - :param int neighbours: The number of nearest neighbors to consider when - building the graph. - :param dict kwargs: The additional keyword arguments to be passed to - :class:`GraphBuilder` and :class:`Graph` classes. - - :return: A :class:`~pina.graph.Graph` instance with the computed - ``edge_index``. - :rtype: Graph - """ - - edge_index = cls.compute_knn_graph(pos, neighbours) - return super().__new__(cls, pos=pos, edge_index=edge_index, **kwargs) - - @staticmethod - def compute_knn_graph(points, neighbours): - """ - Computes the ``edge_index`` based on the K-nearest neighbors algorithm. - - :param points: A tensor of shape ``(N, D)`` representing the positions - of ``N`` points in ``D``-dimensional space. - :type points: torch.Tensor | LabelTensor - :param int neighbours: The number of nearest neighbors to consider when - building the graph. - :return: A tensor of shape ``(2, E)``, with ``E`` number of edges, - representing the edge indices of the graph. - :rtype: torch.Tensor - """ - dist = torch.cdist(points, points, p=2) - knn_indices = torch.topk(dist, k=neighbours, largest=False).indices - row = torch.arange(points.size(0)).repeat_interleave(neighbours) - col = knn_indices.flatten() - return torch.stack([row, col], dim=0).as_subclass(torch.Tensor) - - -class LabelBatch(Batch): - """ - Extends the :class:`~torch_geometric.data.Batch` class to include - :class:`~pina.label_tensor.LabelTensor` objects. - """ - - @classmethod - def from_data_list(cls, data_list): - """ - Create a Batch object from a list of :class:`~torch_geometric.data.Data` - or :class:`~pina.graph.Graph` objects. - - :param data_list: List of :class:`~torch_geometric.data.Data` or - :class:`~pina.graph.Graph` objects. - :type data_list: list[Data] | list[Graph] - :return: A :class:`~torch_geometric.data.Batch` object containing - the input data. - :rtype: :class:`~torch_geometric.data.Batch` - """ - # Store the labels of Data/Graph objects (all data have the same labels) - # If the data do not contain labels, labels is an empty dictionary, - # therefore the labels are not stored - labels = { - k: v.labels - for k, v in data_list[0].items() - if isinstance(v, LabelTensor) - } - - # Create a Batch object from the list of Data objects - batch = super().from_data_list(data_list) - - # Put the labels back in the Batch object - for k, v in labels.items(): - batch[k].labels = v - return batch +__all__ = [ + "Graph", + "RadiusGraph", + "KNNGraph", +] diff --git a/pina/label_tensor.py b/pina/label_tensor.py index 535954d23..78b4fafde 100644 --- a/pina/label_tensor.py +++ b/pina/label_tensor.py @@ -1,753 +1,5 @@ -"""Module for LabelTensor""" +"""Public API for LabeledTensor.""" -from copy import copy, deepcopy -import torch -from torch import Tensor +from pina._src.core.label_tensor import LabelTensor - -class LabelTensor(torch.Tensor): - """ - Extension of the :class:`torch.Tensor` class that includes labels for - each dimension. - """ - - @staticmethod - def __new__(cls, x, labels, *args, **kwargs): - """ - Create a new instance of the :class:`~pina.label_tensor.LabelTensor` - class. - - :param torch.Tensor x: :class:`torch.tensor` instance to be casted as a - :class:`~pina.label_tensor.LabelTensor`. - :param labels: Labels to assign to the tensor. - :type labels: str | list[str] | dict - :return: The instance of the :class:`~pina.label_tensor.LabelTensor` - class. - :rtype: LabelTensor - """ - - if isinstance(x, LabelTensor): - return x - return super().__new__(cls, x, *args, **kwargs) - - @property - def tensor(self): - """ - Returns the tensor part of the :class:`~pina.label_tensor.LabelTensor` - object. - - :return: Tensor part of the :class:`~pina.label_tensor.LabelTensor`. - :rtype: torch.Tensor - """ - - return self.as_subclass(Tensor) - - def __init__(self, x, labels): - """ - Initialize the :class:`~pina.label_tensor.LabelTensor` instance, by - checking the consistency of the labels and the tensor. Specifically, the - labels must match the following conditions: - - - At each dimension, the number of labels must match the size of the \ - dimension. - - At each dimension, the labels must be unique. - - The labels can be passed in the following formats: - - :Example: - >>> from pina import LabelTensor - >>> tensor = LabelTensor( - >>> torch.rand((2000, 3)), - ... {1: {"name": "space", "dof": ['a', 'b', 'c']}}) - >>> tensor = LabelTensor( - >>> torch.rand((2000, 3)), - ... ["a", "b", "c"]) - - The keys of the dictionary are the dimension indices, and the values are - dictionaries containing the labels and the name of the dimension. If - the labels are passed as a list, these are assigned to the last - dimension. - - :param torch.Tensor x: The tensor to be casted as a - :class:`~pina.label_tensor.LabelTensor`. - :param labels: Labels to assign to the tensor. - :type labels: str | list[str] | dict - :raises ValueError: If the labels are not consistent with the tensor. - """ - super().__init__() - if labels is not None: - self.labels = labels - else: - self._labels = {} - - @property - def full_labels(self): - """ - Returns the full labels of the tensor, even for the dimensions that are - not labeled. - - :return: The full labels of the tensor - :rtype: dict - """ - to_return_dict = {} - shape_tensor = self.shape - for i, value in enumerate(shape_tensor): - if i in self._labels: - to_return_dict[i] = self._labels[i] - else: - to_return_dict[i] = {"dof": range(value), "name": i} - return to_return_dict - - @property - def stored_labels(self): - """ - Returns the labels stored inside the instance. - - :return: The labels stored inside the instance. - :rtype: dict - """ - return self._labels - - @property - def labels(self): - """ - Returns the labels of the last dimension of the instance. - - :return: labels of last dimension - :rtype: list - """ - if self.ndim - 1 in self._labels: - return self._labels[self.ndim - 1]["dof"] - return None - - @labels.setter - def labels(self, labels): - """ - Set labels stored insider the instance by checking the type of the - input labels and handling it accordingly. The following types are - accepted: - - - **list**: The list of labels is assigned to the last dimension. - - **dict**: The dictionary of labels is assigned to the tensor. - - **str**: The string is assigned to the last dimension. - - :param labels: Labels to assign to the class variable _labels. - :type labels: str | list[str] | dict - """ - - if not hasattr(self, "_labels"): - self._labels = {} - if isinstance(labels, dict): - self._init_labels_from_dict(labels) - elif isinstance(labels, (list, range)): - self._init_labels_from_list(labels) - elif isinstance(labels, str): - labels = [labels] - self._init_labels_from_list(labels) - else: - raise ValueError("labels must be list, dict or string.") - - def _init_labels_from_dict(self, labels): - """ - Store the internal label representation according to the values - passed as input. - - :param dict labels: The label(s) to update. - :raises ValueError: If the dof list contains duplicates or the number of - dof does not match the tensor shape. - """ - - tensor_shape = self.shape - - def validate_dof(dof_list, dim_size): - """Validate the 'dof' list for uniqueness and size.""" - if len(dof_list) != len(set(dof_list)): - raise ValueError("dof must be unique") - if len(dof_list) != dim_size: - raise ValueError( - f"Number of dof ({len(dof_list)}) does not match " - f"tensor shape ({dim_size})" - ) - - for dim, label in labels.items(): - if isinstance(label, dict): - if "name" not in label: - label["name"] = dim - if "dof" not in label: - label["dof"] = range(tensor_shape[dim]) - if "dof" in label and "name" in label: - dof = label["dof"] - dof_list = dof if isinstance(dof, (list, range)) else [dof] - if not isinstance(dof_list, (list, range)): - raise ValueError( - f"'dof' should be a list or range, not" - f" {type(dof_list)}" - ) - validate_dof(dof_list, tensor_shape[dim]) - else: - raise ValueError( - "Labels dictionary must contain either " - " both 'name' and 'dof' keys" - ) - else: - raise ValueError( - f"Invalid label format for {dim}: Expected " - f"list or dictionary, got {type(label)}" - ) - - # Assign validated label data to internal labels - self._labels[dim] = label - - def _init_labels_from_list(self, labels): - """ - Given a list of dof, this method update the internal label - representation by assigning the dof to the last dimension. - - :param labels: The label(s) to update. - :type labels: list - """ - - # Create a dict with labels - last_dim_labels = { - self.ndim - 1: {"dof": labels, "name": self.ndim - 1} - } - self._init_labels_from_dict(last_dim_labels) - - def extract(self, labels_to_extract): - """ - Extract the subset of the original tensor by returning all the positions - corresponding to the passed ``label_to_extract``. If - ``label_to_extract`` is a dictionary, the keys are the dimension names - and the values are the labels to extract. If a single label or a list - of labels is passed, the last dimension is considered. - - :Example: - >>> from pina import LabelTensor - >>> labels = {1: {'dof': ["a", "b", "c"], 'name': 'space'}} - >>> tensor = LabelTensor(torch.rand((2000, 3)), labels) - >>> tensor.extract("a") - >>> tensor.extract(["a", "b"]) - >>> tensor.extract({"space": ["a", "b"]}) - - :param labels_to_extract: The label(s) to extract. - :type labels_to_extract: str | list[str] | tuple[str] | dict - :return: The extracted tensor with the updated labels. - :rtype: LabelTensor - - :raises TypeError: Labels are not ``str``, ``list[str]`` or ``dict`` - properly setted. - :raises ValueError: Label to extract is not in the labels ``list``. - """ - - def get_label_indices(dim_labels, labels_te): - if isinstance(labels_te, (int, str)): - labels_te = [labels_te] - return ( - [dim_labels.index(label) for label in labels_te] - if len(labels_te) > 1 - else slice( - dim_labels.index(labels_te[0]), - dim_labels.index(labels_te[0]) + 1, - ) - ) - - # Ensure labels_to_extract is a list or dict - if isinstance(labels_to_extract, (str, int)): - labels_to_extract = [labels_to_extract] - - labels = copy(self._labels) - - # Get the dimension names and the respective dimension index - dim_names = {labels[dim]["name"]: dim for dim in labels} - ndim = super().ndim - tensor = self.tensor.as_subclass(torch.Tensor) - - # Convert list/tuple to a dict for the last dimension if applicable - if isinstance(labels_to_extract, (list, tuple)): - last_dim = ndim - 1 - dim_name = labels[last_dim]["name"] - labels_to_extract = {dim_name: list(labels_to_extract)} - - # Validate the labels_to_extract type - if not isinstance(labels_to_extract, dict): - raise ValueError( - "labels_to_extract must be a string, list, or dictionary." - ) - - # Perform the extraction for each specified dimension - for dim_name, labels_te in labels_to_extract.items(): - if dim_name not in dim_names: - raise ValueError( - f"Cannot extract labels for dimension '{dim_name}' as it is" - f" not present in the original labels." - ) - - idx_dim = dim_names[dim_name] - dim_labels = labels[idx_dim]["dof"] - indices = get_label_indices(dim_labels, labels_te) - - extractor = [slice(None)] * ndim - extractor[idx_dim] = indices - tensor = tensor[tuple(extractor)] - - labels[idx_dim] = {"dof": labels_te, "name": dim_name} - - return LabelTensor(tensor, labels) - - def __str__(self): - """ - The string representation of the - :class:`~pina.label_tensor.LabelTensor`. - - :return: String representation of the - :class:`~pina.label_tensor.LabelTensor` instance. - :rtype: str - """ - - s = "" - for key, value in self._labels.items(): - s += f"{key}: {value}\n" - s += "\n" - s += self.tensor.__str__() - return s - - @staticmethod - def cat(tensors, dim=0): - """ - Concatenate a list of tensors along a specified dimension. For more - details, see :meth:`torch.cat`. - - :param list[LabelTensor] tensors: - :class:`~pina.label_tensor.LabelTensor` instances to concatenate - :param int dim: Dimensions on which you want to perform the operation - (default is 0) - :return: A new :class:`LabelTensor` instance obtained by concatenating - the input instances. - - :rtype: LabelTensor - :raises ValueError: either number dof or dimensions names differ. - """ - - if not tensors: - return [] # Handle empty list - if len(tensors) == 1: - return tensors[0] # Return single tensor as-is - - # Perform concatenation - cat_tensor = torch.cat(tensors, dim=dim) - tensors_labels = [tensor.stored_labels for tensor in tensors] - - # Check label consistency across tensors, excluding the - # concatenation dimension - for key in tensors_labels[0]: - if key != dim: - if any( - tensors_labels[i][key] != tensors_labels[0][key] - for i in range(len(tensors_labels)) - ): - raise RuntimeError( - f"Tensors must have the same labels along all " - f"dimensions except {dim}." - ) - - # Copy and update the 'dof' for the concatenation dimension - cat_labels = {k: copy(v) for k, v in tensors_labels[0].items()} - - # Update labels if the concatenation dimension has labels - if dim in tensors[0].stored_labels: - if dim in cat_labels: - cat_dofs = [label[dim]["dof"] for label in tensors_labels] - cat_labels[dim]["dof"] = sum(cat_dofs, []) - else: - cat_labels = tensors[0].stored_labels - - # Assign updated labels to the concatenated tensor - cat_tensor._labels = cat_labels - return cat_tensor - - @staticmethod - def stack(tensors): - """ - Stacks a list of tensors along a new dimension. For more details, see - :meth:`torch.stack`. - - :param list[LabelTensor] tensors: A list of tensors to stack. - All tensors must have the same shape. - :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained - by stacking the input tensors. - :rtype: LabelTensor - """ - - # Perform stacking in torch - new_tensor = torch.stack(tensors) - - # Increase labels keys by 1 - labels = tensors[0]._labels - labels = {key + 1: value for key, value in labels.items()} - new_tensor._labels = labels - return new_tensor - - def requires_grad_(self, mode=True): - """ - Override the :meth:`~torch.Tensor.requires_grad_` method to handle - the labels in the new tensor. - For more details, see :meth:`~torch.Tensor.requires_grad_`. - - :param bool mode: A boolean value indicating whether the tensor should - track gradients.If `True`, the tensor will track gradients; - if `False`, it will not. - :return: The :class:`~pina.label_tensor.LabelTensor` itself with the - updated ``requires_grad`` state and retained labels. - :rtype: LabelTensor - """ - - lt = super().requires_grad_(mode) - lt._labels = self._labels - return lt - - @property - def dtype(self): - """ - Give the ``dtype`` of the tensor. For more details, see - :meth:`torch.dtype`. - - :return: The data type of the tensor. - :rtype: torch.dtype - """ - - return super().dtype - - def to(self, *args, **kwargs): - """ - Performs Tensor dtype and/or device conversion. For more details, see - :meth:`torch.Tensor.to`. - - :return: A new :class:`~pina.label_tensor.LabelTensor` instance with the - updated dtype and/or device and retained labels. - :rtype: LabelTensor - """ - - lt = super().to(*args, **kwargs) - lt._labels = self._labels - return lt - - def clone(self, *args, **kwargs): - """ - Clone the :class:`~pina.label_tensor.LabelTensor`. For more details, see - :meth:`torch.Tensor.clone`. - - :return: A new :class:`~pina.label_tensor.LabelTensor` instance with the - same data and labels but allocated in a different memory location. - :rtype: LabelTensor - """ - - out = LabelTensor( - super().clone(*args, **kwargs), deepcopy(self._labels) - ) - return out - - def append(self, tensor, mode="std"): - """ - Appends a given tensor to the current tensor along the last dimension. - This method supports two types of appending operations: - - 1. **Standard append** ("std"): Concatenates the input tensor with the \ - current tensor along the last dimension. - 2. **Cross append** ("cross"): Creates a cross-product of the current \ - tensor and the input tensor. - - :param tensor: The tensor to append to the current tensor. - :type tensor: LabelTensor - :param mode: The append mode to use. Defaults to ``st``. - :type mode: str, optional - :return: A new :class:`LabelTensor` instance obtained by appending the - input tensor. - :rtype: LabelTensor - - :raises ValueError: If the mode is not "std" or "cross". - """ - - if mode == "std": - # Call cat on last dimension - new_label_tensor = LabelTensor.cat( - [self, tensor], dim=self.ndim - 1 - ) - return new_label_tensor - if mode == "cross": - # Crete tensor and call cat on last dimension - tensor1 = self - tensor2 = tensor - n1 = tensor1.shape[0] - n2 = tensor2.shape[0] - tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels) - tensor2 = LabelTensor( - tensor2.repeat_interleave(n1, dim=0), labels=tensor2.labels - ) - new_label_tensor = LabelTensor.cat( - [tensor1, tensor2], dim=self.ndim - 1 - ) - return new_label_tensor - raise ValueError('mode must be either "std" or "cross"') - - @staticmethod - def vstack(tensors): - """ - Stack tensors vertically. For more details, see :meth:`torch.vstack`. - - :param list of LabelTensor label_tensors: The - :class:`~pina.label_tensor.LabelTensor` instances to stack. They - need to have equal labels. - :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained - by stacking the input tensors vertically. - :rtype: LabelTensor - """ - - return LabelTensor.cat(tensors, dim=0) - - # This method is used to update labels - def _update_single_label(self, index, dim): - """ - Update the labels of the tensor based on the index (or list of indices). - - :param index: Index of dof to retain. - :type index: int | slice | list[int] | tuple[int] | torch.Tensor - :param int dim: Dimension of the indexes in the original tensor. - :return: The updated labels for the specified dimension. - :rtype: list[int] - :raises: ValueError: If the index type is not supported. - """ - old_dof = self._labels[dim]["dof"] - # Handle slicing - if isinstance(index, slice): - new_dof = old_dof[index] - # Handle single integer index - elif isinstance(index, int): - new_dof = [old_dof[index]] - # Handle lists or tensors - elif isinstance(index, (list, torch.Tensor)): - # Handle list of bools - if isinstance(index, torch.Tensor) and index.dtype == torch.bool: - index = index.nonzero().squeeze() - new_dof = ( - [old_dof[i] for i in index] - if isinstance(old_dof, list) - else index - ) - else: - raise NotImplementedError( - f"Unsupported index type: {type(index)}. Expected slice, int, " - f"list, or torch.Tensor." - ) - return new_dof - - def __getitem__(self, index): - """ " - Override the __getitem__ method to handle the labels of the - :class:`~pina.label_tensor.LabelTensor` instance. It first performs - __getitem__ operation on the :class:`torch.Tensor` part of the instance, - then updates the labels based on the index. - - :param index: The index used to access the item - :type index: int | str | tuple of int | list ot int | torch.Tensor - :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained - `__getitem__` operation on :class:`torch.Tensor` part of the - instance, with the updated labels. - :rtype: LabelTensor - - :raises KeyError: If an invalid label index is provided. - :raises IndexError: If an invalid index is accessed in the tensor. - """ - - # Handle string index - if isinstance(index, str) or ( - isinstance(index, (tuple, list)) - and all(isinstance(i, str) for i in index) - ): - return self.extract(index) - - # Retrieve selected tensor and labels - selected_tensor = super().__getitem__(index) - if not hasattr(self, "_labels"): - return selected_tensor - - original_labels = self._labels - updated_labels = copy(original_labels) - - # Ensure the index is iterable - if not isinstance(index, tuple): - index = [index] - - # Update labels based on the index - offset = 0 - removed = 0 - for dim, idx in enumerate(index): - if dim in original_labels: - if isinstance(idx, int): - # Compute the working dimension considering the removed - # dimensions due to int index on a non labled dimension - dim_ = dim - removed - selected_tensor = selected_tensor.unsqueeze(dim_) - if idx != slice(None): - # Update the labels for the selected dimension - updated_labels[offset] = { - "dof": self._update_single_label(idx, dim), - "name": original_labels[dim]["name"], - } - else: - # Adjust label keys if dimension is reduced (case of integer - # index on a non-labeled dimension) - if isinstance(idx, int): - updated_labels = { - key - 1 if key > dim else key: value - for key, value in updated_labels.items() - } - removed += 1 - continue - offset += 1 - - # Update the selected tensor's labels - selected_tensor._labels = updated_labels - return selected_tensor - - def sort_labels(self, dim=None): - """ - Sort the labels along the specified dimension and apply. It applies the - same sorting to the tensor part of the instance. - - :param int dim: The dimension along which to sort the labels. - If ``None``, the last dimension is used. - :return: A new tensor with sorted labels along the specified dimension. - :rtype: LabelTensor - """ - - def arg_sort(lst): - return sorted(range(len(lst)), key=lambda x: lst[x]) - - if dim is None: - dim = self.ndim - 1 - if self.shape[dim] == 1: - return self - labels = self.stored_labels[dim]["dof"] - sorted_index = arg_sort(labels) - # Define an indexer to sort the tensor along the specified dimension - indexer = [slice(None)] * self.ndim - # Assigned the sorted index to the specified dimension - indexer[dim] = sorted_index - return self[tuple(indexer)] - - def __deepcopy__(self, memo): - """ - Creates a deep copy of the object. For more details, see - :meth:`copy.deepcopy`. - - :param memo: LabelTensor object to be copied. - :type memo: LabelTensor - :return: A deep copy of the original LabelTensor object. - :rtype: LabelTensor - """ - - cls = self.__class__ - result = cls(deepcopy(self.tensor), deepcopy(self.stored_labels)) - return result - - def permute(self, *dims): - """ - Permutes the dimensions of the tensor and the associated labels - accordingly. For more details, see :meth:`torch.Tensor.permute`. - - :param dims: The dimensions to permute the tensor to. - :type dims: tuple[int] | list[int] - :return: A new object with permuted dimensions and reordered labels. - :rtype: LabelTensor - """ - # Call the base class permute method - tensor = super().permute(*dims) - - # Update lables - labels = self._labels - keys_list = list(*dims) - labels = {keys_list.index(k): v for k, v in labels.items()} - - # Assign labels to the new tensor - tensor._labels = labels - return tensor - - def detach(self): - """ - Detaches the tensor from the computation graph and retains the stored - labels. For more details, see :meth:`torch.Tensor.detach`. - - :return: A new tensor detached from the computation graph. - :rtype: LabelTensor - """ - - lt = super().detach() - - # Copy the labels to the new tensor only if present - if hasattr(self, "_labels"): - lt._labels = self.stored_labels - return lt - - @staticmethod - def summation(tensors): - """ - Computes the summation of a list of - :class:`~pina.label_tensor.LabelTensor` instances. - - - :param list[LabelTensor] tensors: A list of tensors to sum. All - tensors must have the same shape and labels. - :return: A new `LabelTensor` containing the element-wise sum of the - input tensors. - :rtype: LabelTensor - - :raises ValueError: If the input `tensors` list is empty. - :raises RuntimeError: If the tensors have different shapes and/or - mismatched labels. - """ - - if not tensors: - raise ValueError("The tensors list must not be empty.") - - if len(tensors) == 1: - return tensors[0] - - # Initialize result tensor and labels - data = torch.zeros_like(tensors[0].tensor).to(tensors[0].device) - last_dim_labels = [] - - # Accumulate tensors - for tensor in tensors: - data += tensor.tensor - last_dim_labels.append(tensor.labels) - - # Construct last dimension labels - last_dim_labels = ["+".join(items) for items in zip(*last_dim_labels)] - - # Update the labels for the resulting tensor - labels = {k: copy(v) for k, v in tensors[0].stored_labels.items()} - labels[tensors[0].ndim - 1] = { - "dof": last_dim_labels, - "name": tensors[0].name, - } - - return LabelTensor(data, labels) - - def reshape(self, *shape): - """ - Override the reshape method to update the labels of the tensor. - For more details, see :meth:`torch.Tensor.reshape`. - - :param tuple of int shape: The new shape of the tensor. - :return: A new :class:`~pina.label_tensor.LabelTensor` instance with the - updated shape and labels. - :rtype: LabelTensor - """ - - # As for now the reshape method is used only in the context of the - # dataset, the labels are not - tensor = super().reshape(*shape) - if not hasattr(self, "_labels") or shape != (-1, *self.shape[2:]): - return tensor - tensor.labels = self.labels - return tensor +__all__ = ["LabelTensor"] diff --git a/pina/loss/__init__.py b/pina/loss/__init__.py index d91cf7ab0..280cbf76a 100644 --- a/pina/loss/__init__.py +++ b/pina/loss/__init__.py @@ -1,21 +1,41 @@ -"""Module for loss functions and weighting functions.""" +"""Module for loss functions.""" __all__ = [ - "LossInterface", + "DualLossInterface", + "BaseDualLoss", "LpLoss", "PowerLoss", - "WeightingInterface", - "ScalarWeighting", - "NeuralTangentKernelWeighting", - "SelfAdaptiveWeighting", - "LinearWeighting", + "SinkhornLoss", ] -from .loss_interface import LossInterface -from .power_loss import PowerLoss -from .lp_loss import LpLoss -from .weighting_interface import WeightingInterface -from .scalar_weighting import ScalarWeighting -from .ntk_weighting import NeuralTangentKernelWeighting -from .self_adaptive_weighting import SelfAdaptiveWeighting -from .linear_weighting import LinearWeighting +from pina._src.loss.dual_loss_interface import DualLossInterface +from pina._src.loss.base_dual_loss import BaseDualLoss +from pina._src.loss.power_loss import PowerLoss +from pina._src.loss.lp_loss import LpLoss +from pina._src.loss.sinkhorn_loss import SinkhornLoss + +# Back-compatibility with version 0.2, to be removed soon +import warnings +import importlib + +_DEPRECATED_IMPORTS = { + "WeightingInterface": "pina.weighting", + "ScalarWeighting": "pina.weighting", + "NeuralTangentKernelWeighting": "pina.weighting", + "SelfAdaptiveWeighting": "pina.weighting", + "LinearWeighting": "pina.weighting", +} + + +def __getattr__(name): + if name in _DEPRECATED_IMPORTS: + + warnings.warn( + f"Importing '{name}' from 'pina.loss' is deprecated; " + f"use 'pina.weighting' instead.", + DeprecationWarning, + stacklevel=2, + ) + + module = importlib.import_module(_DEPRECATED_IMPORTS[name], __name__) + return getattr(module, name) diff --git a/pina/loss/linear_weighting.py b/pina/loss/linear_weighting.py deleted file mode 100644 index 9049b52fa..000000000 --- a/pina/loss/linear_weighting.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Module for the LinearWeighting class.""" - -from ..loss import WeightingInterface -from ..utils import check_consistency, check_positive_integer - - -class LinearWeighting(WeightingInterface): - """ - A weighting scheme that linearly scales weights from initial values to final - values over a specified number of epochs. - """ - - def __init__(self, initial_weights, final_weights, target_epoch): - """ - :param dict initial_weights: The weights to be assigned to each loss - term at the beginning of training. The keys are the conditions and - the values are the corresponding weights. If a condition is not - present in the dictionary, the default value (1) is used. - :param dict final_weights: The weights to be assigned to each loss term - once the target epoch is reached. The keys are the conditions and - the values are the corresponding weights. If a condition is not - present in the dictionary, the default value (1) is used. - :param int target_epoch: The epoch at which the weights reach their - final values. - :raises ValueError: If the keys of the two dictionaries are not - consistent. - """ - super().__init__(update_every_n_epochs=1, aggregator="sum") - - # Check consistency - check_consistency([initial_weights, final_weights], dict) - check_positive_integer(value=target_epoch, strict=True) - - # Check that the keys of the two dictionaries are the same - if initial_weights.keys() != final_weights.keys(): - raise ValueError( - "The keys of the initial_weights and final_weights " - "dictionaries must be the same." - ) - - # Initialization - self.initial_weights = initial_weights - self.final_weights = final_weights - self.target_epoch = target_epoch - - def weights_update(self, losses): - """ - Update the weighting scheme based on the given losses. - - :param dict losses: The dictionary of losses. - :return: The updated weights. - :rtype: dict - """ - return { - condition: self.last_saved_weights().get( - condition, self.initial_weights.get(condition, 1) - ) - + ( - self.final_weights.get(condition, 1) - - self.initial_weights.get(condition, 1) - ) - / (self.target_epoch) - for condition in losses.keys() - } diff --git a/pina/loss/loss_interface.py b/pina/loss/loss_interface.py deleted file mode 100644 index 728c9f77e..000000000 --- a/pina/loss/loss_interface.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Module for the Loss Interface.""" - -from abc import ABCMeta, abstractmethod -from torch.nn.modules.loss import _Loss -import torch - - -class LossInterface(_Loss, metaclass=ABCMeta): - """ - Abstract base class for all losses. All classes defining a loss function - should inherit from this interface. - """ - - def __init__(self, reduction="mean"): - """ - Initialization of the :class:`LossInterface` class. - - :param str reduction: The reduction method for the loss. - Available options: ``none``, ``mean``, ``sum``. - If ``none``, no reduction is applied. If ``mean``, the sum of the - loss values is divided by the number of values. If ``sum``, the loss - values are summed. Default is ``mean``. - """ - super().__init__(reduction=reduction, size_average=None, reduce=None) - - @abstractmethod - def forward(self, input, target): - """ - Forward method of the loss function. - - :param torch.Tensor input: Input tensor from real data. - :param torch.Tensor target: Model tensor output. - """ - - def _reduction(self, loss): - """ - Apply the reduction to the loss. - - :param torch.Tensor loss: The tensor containing the pointwise losses. - :raises ValueError: If the reduction method is not valid. - :return: Reduced loss. - :rtype: torch.Tensor - """ - if self.reduction == "none": - ret = loss - elif self.reduction == "mean": - ret = torch.mean(loss, keepdim=True, dim=-1) - elif self.reduction == "sum": - ret = torch.sum(loss, keepdim=True, dim=-1) - else: - raise ValueError(self.reduction + " is not valid") - return ret diff --git a/pina/loss/lp_loss.py b/pina/loss/lp_loss.py deleted file mode 100644 index f535a5b6f..000000000 --- a/pina/loss/lp_loss.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Module for the LpLoss class.""" - -import torch - -from ..utils import check_consistency -from .loss_interface import LossInterface - - -class LpLoss(LossInterface): - r""" - Implementation of the Lp Loss. It defines a criterion to measures the - pointwise Lp error between values in the input :math:`x` and values in the - target :math:`y`. - - If ``reduction`` is set to ``none``, the loss can be written as: - - .. math:: - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad - l_n = \left[\sum_{i=1}^{D} \left| x_n^i - y_n^i \right|^p \right], - - If ``relative`` is set to ``True``, the relative Lp error is computed: - - .. math:: - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad - l_n = \frac{ [\sum_{i=1}^{D} | x_n^i - y_n^i|^p] } - {[\sum_{i=1}^{D}|y_n^i|^p]}, - - where :math:`N` is the batch size. - - If ``reduction`` is not ``none``, then: - - .. math:: - \ell(x, y) = - \begin{cases} - \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ - \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} - \end{cases} - """ - - def __init__(self, p=2, reduction="mean", relative=False): - """ - Initialization of the :class:`LpLoss` class. - - :param int p: Degree of the Lp norm. It specifies the norm to be - computed. Default is ``2`` (euclidean norm). - :param str reduction: The reduction method for the loss. - Available options: ``none``, ``mean``, ``sum``. - If ``none``, no reduction is applied. If ``mean``, the sum of the - loss values is divided by the number of values. If ``sum``, the loss - values are summed. Default is ``mean``. - :param bool relative: If ``True``, the relative error is computed. - Default is ``False``. - """ - super().__init__(reduction=reduction) - - # check consistency - check_consistency(p, (str, int, float)) - check_consistency(relative, bool) - - self.p = p - self.relative = relative - - def forward(self, input, target): - """ - Forward method of the loss function. - - :param torch.Tensor input: Input tensor from real data. - :param torch.Tensor target: Model tensor output. - :return: Loss evaluation. - :rtype: torch.Tensor - """ - loss = torch.linalg.norm((input - target), ord=self.p, dim=-1) - if self.relative: - loss = loss / torch.linalg.norm(input, ord=self.p, dim=-1) - return self._reduction(loss) diff --git a/pina/loss/ntk_weighting.py b/pina/loss/ntk_weighting.py deleted file mode 100644 index fe1c4fc6a..000000000 --- a/pina/loss/ntk_weighting.py +++ /dev/null @@ -1,76 +0,0 @@ -"""Module for Neural Tangent Kernel Class""" - -import torch -from .weighting_interface import WeightingInterface -from ..utils import check_consistency, in_range - - -class NeuralTangentKernelWeighting(WeightingInterface): - """ - A neural tangent kernel scheme for weighting different losses to - boost the convergence. - - .. seealso:: - - **Original reference**: Wang, Sifan, Xinling Yu, and - Paris Perdikaris. *When and why PINNs fail to train: - A neural tangent kernel perspective*. Journal of - Computational Physics 449 (2022): 110768. - DOI: `10.1016 `_. - - """ - - def __init__(self, update_every_n_epochs=1, alpha=0.5): - """ - Initialization of the :class:`NeuralTangentKernelWeighting` class. - - :param int update_every_n_epochs: The number of training epochs between - weight updates. If set to 1, the weights are updated at every epoch. - Default is 1. - :param float alpha: The alpha parameter. Default is 0.5. - :raises ValueError: If ``alpha`` is not between 0 and 1 (inclusive). - """ - super().__init__(update_every_n_epochs=update_every_n_epochs) - - # Check consistency - check_consistency(alpha, float) - if not in_range(alpha, [0, 1], strict=False): - raise ValueError("alpha must be in range (0, 1).") - - # Initialize parameters - self.alpha = alpha - self.weights = {} - - def weights_update(self, losses): - """ - Update the weighting scheme based on the given losses. - - :param dict losses: The dictionary of losses. - :return: The updated weights. - :rtype: dict - """ - # Get model parameters and define a dictionary to store the norms - params = [p for p in self.solver.model.parameters() if p.requires_grad] - norms = {} - - # Iterate over conditions - for condition, loss in losses.items(): - - # Compute gradients - grads = torch.autograd.grad( - loss, - params, - retain_graph=True, - allow_unused=True, - ) - - # Compute norms - norms[condition] = torch.cat( - [g.flatten() for g in grads if g is not None] - ).norm() - - return { - condition: self.alpha * self.last_saved_weights().get(condition, 1) - + (1 - self.alpha) * norms[condition] / sum(norms.values()) - for condition in losses - } diff --git a/pina/loss/power_loss.py b/pina/loss/power_loss.py deleted file mode 100644 index 1edbf4f86..000000000 --- a/pina/loss/power_loss.py +++ /dev/null @@ -1,76 +0,0 @@ -"""Module for the PowerLoss class.""" - -import torch - -from ..utils import check_consistency -from .loss_interface import LossInterface - - -class PowerLoss(LossInterface): - r""" - Implementation of the Power Loss. It defines a criterion to measures the - pointwise error between values in the input :math:`x` and values in the - target :math:`y`. - - If ``reduction`` is set to ``none``, the loss can be written as: - - .. math:: - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad - l_n = \frac{1}{D}\left[\sum_{i=1}^{D} - \left| x_n^i - y_n^i \right|^p\right], - - If ``relative`` is set to ``True``, the relative error is computed: - - .. math:: - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad - l_n = \frac{ \sum_{i=1}^{D} | x_n^i - y_n^i|^p } - {\sum_{i=1}^{D}|y_n^i|^p}, - - where :math:`N` is the batch size. - - If ``reduction`` is not ``none``, then: - - .. math:: - \ell(x, y) = - \begin{cases} - \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ - \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} - \end{cases} - """ - - def __init__(self, p=2, reduction="mean", relative=False): - """ - Initialization of the :class:`PowerLoss` class. - - :param int p: Degree of the Lp norm. It specifies the norm to be - computed. Default is ``2`` (euclidean norm). - :param str reduction: The reduction method for the loss. - Available options: ``none``, ``mean``, ``sum``. - If ``none``, no reduction is applied. If ``mean``, the sum of the - loss values is divided by the number of values. If ``sum``, the loss - values are summed. Default is ``mean``. - :param bool relative: If ``True``, the relative error is computed. - Default is ``False``. - """ - super().__init__(reduction=reduction) - - # check consistency - check_consistency(p, (str, int, float)) - check_consistency(relative, bool) - - self.p = p - self.relative = relative - - def forward(self, input, target): - """ - Forward method of the loss function. - - :param torch.Tensor input: Input tensor from real data. - :param torch.Tensor target: Model tensor output. - :return: Loss evaluation. - :rtype: torch.Tensor - """ - loss = torch.abs((input - target)).pow(self.p).mean(-1) - if self.relative: - loss = loss / torch.abs(input).pow(self.p).mean(-1) - return self._reduction(loss) diff --git a/pina/loss/scalar_weighting.py b/pina/loss/scalar_weighting.py deleted file mode 100644 index 692c4937b..000000000 --- a/pina/loss/scalar_weighting.py +++ /dev/null @@ -1,59 +0,0 @@ -"""Module for the Scalar Weighting.""" - -from .weighting_interface import WeightingInterface -from ..utils import check_consistency - - -class ScalarWeighting(WeightingInterface): - """ - Weighting scheme that assigns a scalar weight to each loss term. - """ - - def __init__(self, weights): - """ - Initialization of the :class:`ScalarWeighting` class. - - :param weights: The weights to be assigned to each loss term. - If a single scalar value is provided, it is assigned to all loss - terms. If a dictionary is provided, the keys are the conditions and - the values are the weights. If a condition is not present in the - dictionary, the default value (1) is used. - :type weights: float | int | dict - """ - super().__init__(update_every_n_epochs=1, aggregator="sum") - - # Check consistency - check_consistency([weights], (float, dict, int)) - - # Initialization - if isinstance(weights, dict): - self.values = weights - self.default_value_weights = 1 - else: - self.values = {} - self.default_value_weights = weights - - def weights_update(self, losses): - """ - Update the weighting scheme based on the given losses. - - :param dict losses: The dictionary of losses. - :return: The updated weights. - :rtype: dict - """ - return { - condition: self.values.get(condition, self.default_value_weights) - for condition in losses.keys() - } - - -class _NoWeighting(ScalarWeighting): - """ - Weighting scheme that does not apply any weighting to the losses. - """ - - def __init__(self): - """ - Initialization of the :class:`_NoWeighting` class. - """ - super().__init__(weights=1) diff --git a/pina/loss/weighting_interface.py b/pina/loss/weighting_interface.py deleted file mode 100644 index bc34c3181..000000000 --- a/pina/loss/weighting_interface.py +++ /dev/null @@ -1,111 +0,0 @@ -"""Module for the Weighting Interface.""" - -from abc import ABCMeta, abstractmethod -from typing import final -from ..utils import check_positive_integer, is_function - -_AGGREGATE_METHODS = {"sum": sum, "mean": lambda x: sum(x) / len(x)} - - -class WeightingInterface(metaclass=ABCMeta): - """ - Abstract base class for all loss weighting schemas. All weighting schemas - should inherit from this class. - """ - - def __init__(self, update_every_n_epochs=1, aggregator="sum"): - """ - Initialization of the :class:`WeightingInterface` class. - - :param int update_every_n_epochs: The number of training epochs between - weight updates. If set to 1, the weights are updated at every epoch. - This parameter is ignored by static weighting schemes. Default is 1. - :param aggregator: The aggregation method. Either: - - 'sum' → torch.sum - - 'mean' → torch.mean - - callable → custom aggregation function - :type aggregator: str | Callable - """ - # Check consistency - check_positive_integer(value=update_every_n_epochs, strict=True) - - # Aggregation - if isinstance(aggregator, str): - if aggregator not in _AGGREGATE_METHODS: - raise ValueError( - f"Invalid aggregator '{aggregator}'. Must be one of " - f"{list(_AGGREGATE_METHODS.keys())}." - ) - aggregator = _AGGREGATE_METHODS[aggregator] - - elif not is_function(aggregator): - raise TypeError( - f"Aggregator must be either a string or a callable, " - f"got {type(aggregator).__name__}." - ) - - # Initialization - self._solver = None - self.update_every_n_epochs = update_every_n_epochs - self.aggregator_fn = aggregator - self._saved_weights = {} - - @abstractmethod - def weights_update(self, losses): - """ - Update the weighting scheme based on the given losses. - - This method must be implemented by subclasses. Its role is to update the - values of the weights. The updated weights will then be used by - :meth:`aggregate` to compute the final aggregated loss. - - :param dict losses: The dictionary of losses. - :return: The updated weights. - :rtype: dict - """ - - @final - def aggregate(self, losses): - """ - Update the weights (if needed) and aggregate the given losses. - - This method first checks whether the loss weights need to be updated - based on the current epoch and the ``update_every_n_epochs`` setting. - If an update is required, it calls :meth:`weights_update` to refresh the - weights. Afterwards, it aggregates the (weighted) losses into a single - scalar tensor using the configured aggregator function. This method must - not be overridden. - - :param dict losses: The dictionary of losses. - :return: The aggregated loss tensor. - :rtype: torch.Tensor - """ - # Update weights - if self.solver.trainer.current_epoch % self.update_every_n_epochs == 0: - self._saved_weights = self.weights_update(losses) - - # Aggregate. Using direct indexing instead of .get() ensures that a - # KeyError is raised if the expected condition is missing from the dict. - return self.aggregator_fn( - self._saved_weights[condition] * loss - for condition, loss in losses.items() - ) - - def last_saved_weights(self): - """ - Get the last saved weights. - - :return: The last saved weights. - :rtype: dict - """ - return self._saved_weights - - @property - def solver(self): - """ - The solver employing this weighting schema. - - :return: The solver. - :rtype: :class:`~pina.solver.SolverInterface` - """ - return self._solver diff --git a/pina/model/__init__.py b/pina/model/__init__.py index 05ccc6c8c..ee221c17e 100644 --- a/pina/model/__init__.py +++ b/pina/model/__init__.py @@ -16,18 +16,25 @@ "PirateNet", "EquivariantGraphNeuralOperator", "SINDy", + "SplineSurface", + "VectorizedSpline", + "KolmogorovArnoldNetwork", ] -from .feed_forward import FeedForward, ResidualFeedForward -from .multi_feed_forward import MultiFeedForward -from .deeponet import DeepONet, MIONet -from .fourier_neural_operator import FNO, FourierIntegralKernel -from .kernel_neural_operator import KernelNeuralOperator -from .average_neural_operator import AveragingNeuralOperator -from .low_rank_neural_operator import LowRankNeuralOperator -from .spline import Spline -from .spline_surface import SplineSurface -from .graph_neural_operator import GraphNeuralOperator -from .pirate_network import PirateNet -from .equivariant_graph_neural_operator import EquivariantGraphNeuralOperator -from .sindy import SINDy +from pina._src.model.feed_forward import FeedForward, ResidualFeedForward +from pina._src.model.multi_feed_forward import MultiFeedForward +from pina._src.model.deeponet import DeepONet, MIONet +from pina._src.model.fourier_neural_operator import FNO, FourierIntegralKernel +from pina._src.model.kernel_neural_operator import KernelNeuralOperator +from pina._src.model.average_neural_operator import AveragingNeuralOperator +from pina._src.model.low_rank_neural_operator import LowRankNeuralOperator +from pina._src.model.spline import Spline +from pina._src.model.spline_surface import SplineSurface +from pina._src.model.graph_neural_operator import GraphNeuralOperator +from pina._src.model.pirate_network import PirateNet +from pina._src.model.equivariant_graph_neural_operator import ( + EquivariantGraphNeuralOperator, +) +from pina._src.model.sindy import SINDy +from pina._src.model.vectorized_spline import VectorizedSpline +from pina._src.model.kolmogorov_arnold_network import KolmogorovArnoldNetwork diff --git a/pina/model/block/__init__.py b/pina/model/block/__init__.py index 08b313387..e9e8e793d 100644 --- a/pina/model/block/__init__.py +++ b/pina/model/block/__init__.py @@ -1,4 +1,10 @@ -"""Module for the building blocks of the neural models.""" +"""Architectural primitives and building blocks. + +This module provides a comprehensive collection of neural network components, +ranging from standard units (Residual, Enhanced Linear) to specialized layers +for Scientific Machine Learning, including Neural Operator blocks (FNO, GNO, +AVNO), spectral convolutions, and coordinate embeddings (Fourier Features). +""" __all__ = [ "ContinuousConvBlock", @@ -19,21 +25,30 @@ "RBFBlock", "GNOBlock", "PirateNetBlock", + "KANBlock", ] -from .convolution_2d import ContinuousConvBlock -from .residual import ResidualBlock, EnhancedLinear -from .spectral import ( +from pina._src.model.block.convolution_2d import ContinuousConvBlock +from pina._src.model.block.residual import ResidualBlock, EnhancedLinear +from pina._src.model.block.spectral import ( SpectralConvBlock1D, SpectralConvBlock2D, SpectralConvBlock3D, ) -from .fourier_block import FourierBlock1D, FourierBlock2D, FourierBlock3D -from .pod_block import PODBlock -from .orthogonal import OrthogonalBlock -from .embedding import PeriodicBoundaryEmbedding, FourierFeatureEmbedding -from .average_neural_operator_block import AVNOBlock -from .low_rank_block import LowRankBlock -from .rbf_block import RBFBlock -from .gno_block import GNOBlock -from .pirate_network_block import PirateNetBlock +from pina._src.model.block.fourier_block import ( + FourierBlock1D, + FourierBlock2D, + FourierBlock3D, +) +from pina._src.model.block.pod_block import PODBlock +from pina._src.model.block.orthogonal import OrthogonalBlock +from pina._src.model.block.embedding import ( + PeriodicBoundaryEmbedding, + FourierFeatureEmbedding, +) +from pina._src.model.block.average_neural_operator_block import AVNOBlock +from pina._src.model.block.low_rank_block import LowRankBlock +from pina._src.model.block.rbf_block import RBFBlock +from pina._src.model.block.gno_block import GNOBlock +from pina._src.model.block.pirate_network_block import PirateNetBlock +from pina._src.model.block.kan_block import KANBlock diff --git a/pina/model/block/message_passing.py b/pina/model/block/message_passing.py new file mode 100644 index 000000000..652e9dbde --- /dev/null +++ b/pina/model/block/message_passing.py @@ -0,0 +1,25 @@ +"""Module for the message passing blocks of the graph neural models.""" + +__all__ = [ + "InteractionNetworkBlock", + "DeepTensorNetworkBlock", + "EnEquivariantNetworkBlock", + "RadialFieldNetworkBlock", + "EquivariantGraphNeuralOperatorBlock", +] + +from pina._src.model.block.message_passing.interaction_network_block import ( + InteractionNetworkBlock, +) +from pina._src.model.block.message_passing.deep_tensor_network_block import ( + DeepTensorNetworkBlock, +) +from pina._src.model.block.message_passing.en_equivariant_network_block import ( + EnEquivariantNetworkBlock, +) +from pina._src.model.block.message_passing.radial_field_network_block import ( + RadialFieldNetworkBlock, +) +from pina._src.model.block.message_passing.equivariant_graph_neural_operator_block import ( + EquivariantGraphNeuralOperatorBlock, +) diff --git a/pina/model/block/message_passing/__init__.py b/pina/model/block/message_passing/__init__.py deleted file mode 100644 index 202e1fde4..000000000 --- a/pina/model/block/message_passing/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Module for the message passing blocks of the graph neural models.""" - -__all__ = [ - "InteractionNetworkBlock", - "DeepTensorNetworkBlock", - "EnEquivariantNetworkBlock", - "RadialFieldNetworkBlock", - "EquivariantGraphNeuralOperatorBlock", -] - -from .interaction_network_block import InteractionNetworkBlock -from .deep_tensor_network_block import DeepTensorNetworkBlock -from .en_equivariant_network_block import EnEquivariantNetworkBlock -from .radial_field_network_block import RadialFieldNetworkBlock -from .equivariant_graph_neural_operator_block import ( - EquivariantGraphNeuralOperatorBlock, -) diff --git a/pina/operator.py b/pina/operator.py index bf2351bce..fcd214804 100644 --- a/pina/operator.py +++ b/pina/operator.py @@ -1,483 +1,29 @@ -""" -Module for vectorized differential operators implementation. - -Differential operators are used to define differential problems and are -implemented to run efficiently on various accelerators, including CPU, GPU, TPU, -and MPS. - -Each differential operator takes the following inputs: -- A tensor on which the operator is applied. -- A tensor with respect to which the operator is computed. -- The names of the output variables for which the operator is evaluated. -- The names of the variables with respect to which the operator is computed. +"""A public API for differential operators and automatic differentiation utilities. -Each differential operator has its fast version, which performs no internal -checks on input and output tensors. For these methods, the user is always -required to specify both ``components`` and ``d`` as lists of strings. +This module provides standard vector calculus operators (gradient, divergence, +laplacian, advection) implemented using automatic differentiation. It includes +both high-level general operators and optimized 'fast' variants for improved +computational efficiency during training. """ -import torch -from .label_tensor import LabelTensor - - -def _check_values(output_, input_, components, d): - """ - Perform checks on arguments of differential operators. - - :param LabelTensor output_: The output tensor on which the operator is - computed. - :param LabelTensor input_: The input tensor with respect to which the - operator is computed. - :param components: The names of the output variables for which to compute - the operator. It must be a subset of the output labels. - If ``None``, all output variables are considered. Default is ``None``. - :type components: str | list[str] - :param d: The names of the input variables with respect to which the - operator is computed. It must be a subset of the input labels. - If ``None``, all input variables are considered. Default is ``None``. - :type d: str | list[str] - :raises TypeError: If the input tensor is not a LabelTensor. - :raises TypeError: If the output tensor is not a LabelTensor. - :raises RuntimeError: If derivative labels are missing from the ``input_``. - :raises RuntimeError: If component labels are missing from the ``output_``. - :return: The components and d lists. - :rtype: tuple[list[str], list[str]] - """ - # Check if the input is a LabelTensor - if not isinstance(input_, LabelTensor): - raise TypeError("Input must be a LabelTensor.") - - # Check if the output is a LabelTensor - if not isinstance(output_, LabelTensor): - raise TypeError("Output must be a LabelTensor.") - - # If no labels are provided, use all labels - d = d or input_.labels - components = components or output_.labels - - # Convert to list if not already - d = d if isinstance(d, list) else [d] - components = components if isinstance(components, list) else [components] - - # Check if all labels are present in the input tensor - if not all(di in input_.labels for di in d): - raise RuntimeError("Derivative labels missing from input tensor.") - - # Check if all labels are present in the output tensor - if not all(c in output_.labels for c in components): - raise RuntimeError("Component label missing from output tensor.") - - return components, d - - -def _scalar_grad(output_, input_, d): - """ - Compute the gradient of a scalar-valued ``output_``. - - :param LabelTensor output_: The output tensor on which the gradient is - computed. It must be a column tensor. - :param LabelTensor input_: The input tensor with respect to which the - gradient is computed. - :param list[str] d: The names of the input variables with respect to - which the gradient is computed. It must be a subset of the input - labels. If ``None``, all input variables are considered. - :return: The computed gradient tensor. - :rtype: LabelTensor - """ - grad_out = torch.autograd.grad( - outputs=output_, - inputs=input_, - grad_outputs=torch.ones_like(output_), - create_graph=True, - retain_graph=True, - allow_unused=True, - )[0] - - return grad_out[..., [input_.labels.index(i) for i in d]] - - -def _scalar_laplacian(output_, input_, d): - """ - Compute the laplacian of a scalar-valued ``output_``. - - :param LabelTensor output_: The output tensor on which the laplacian is - computed. It must be a column tensor. - :param LabelTensor input_: The input tensor with respect to which the - laplacian is computed. - :param list[str] d: The names of the input variables with respect to - which the laplacian is computed. It must be a subset of the input - labels. If ``None``, all input variables are considered. - :return: The computed laplacian tensor. - :rtype: LabelTensor - """ - first_grad = fast_grad( - output_=output_, input_=input_, components=output_.labels, d=d - ) - second_grad = fast_grad( - output_=first_grad, input_=input_, components=first_grad.labels, d=d - ) - labels_to_extract = [f"d{c}d{d_}" for c, d_ in zip(first_grad.labels, d)] - return torch.sum( - second_grad.extract(labels_to_extract), dim=-1, keepdim=True - ) - - -def fast_grad(output_, input_, components, d): - """ - Compute the gradient of the ``output_`` with respect to the ``input``. - - Unlike ``grad``, this function performs no internal checks on input and - output tensors. The user is required to specify both ``components`` and - ``d`` as lists of strings. It is designed to enhance computation speed. - - This operator supports both vector-valued and scalar-valued functions with - one or multiple input coordinates. - - :param LabelTensor output_: The output tensor on which the gradient is - computed. - :param LabelTensor input_: The input tensor with respect to which the - gradient is computed. - :param list[str] components: The names of the output variables for which to - compute the gradient. It must be a subset of the output labels. - :param list[str] d: The names of the input variables with respect to which - the gradient is computed. It must be a subset of the input labels. - :return: The computed gradient tensor. - :rtype: LabelTensor - """ - # Scalar gradient - if output_.shape[-1] == 1: - return LabelTensor( - _scalar_grad(output_=output_, input_=input_, d=d), - labels=[f"d{output_.labels[0]}d{i}" for i in d], - ) - - # Vector gradient - grads = torch.cat( - [ - _scalar_grad(output_=output_.extract(c), input_=input_, d=d) - for c in components - ], - dim=-1, - ) - - return LabelTensor( - grads, labels=[f"d{c}d{i}" for c in components for i in d] - ) - - -def fast_div(output_, input_, components, d): - """ - Compute the divergence of the ``output_`` with respect to ``input``. - - Unlike ``div``, this function performs no internal checks on input and - output tensors. The user is required to specify both ``components`` and - ``d`` as lists of strings. It is designed to enhance computation speed. - - This operator supports vector-valued functions with multiple input - coordinates. - - :param LabelTensor output_: The output tensor on which the divergence is - computed. - :param LabelTensor input_: The input tensor with respect to which the - divergence is computed. - :param list[str] components: The names of the output variables for which to - compute the divergence. It must be a subset of the output labels. - :param list[str] d: The names of the input variables with respect to which - the divergence is computed. It must be a subset of the input labels. - :rtype: LabelTensor - """ - grad_out = fast_grad( - output_=output_, input_=input_, components=components, d=d - ) - tensors_to_sum = [ - grad_out.extract(f"d{c}d{d_}") for c, d_ in zip(components, d) - ] - - return LabelTensor.summation(tensors_to_sum) - - -def fast_laplacian(output_, input_, components, d, method="std"): - """ - Compute the laplacian of the ``output_`` with respect to ``input``. - - Unlike ``laplacian``, this function performs no internal checks on input and - output tensors. The user is required to specify both ``components`` and - ``d`` as lists of strings. It is designed to enhance computation speed. - - This operator supports both vector-valued and scalar-valued functions with - one or multiple input coordinates. - - :param LabelTensor output_: The output tensor on which the laplacian is - computed. - :param LabelTensor input_: The input tensor with respect to which the - laplacian is computed. - :param list[str] components: The names of the output variables for which to - compute the laplacian. It must be a subset of the output labels. - :param list[str] d: The names of the input variables with respect to which - the laplacian is computed. It must be a subset of the input labels. - :param str method: The method used to compute the Laplacian. Available - methods are ``std`` and ``divgrad``. The ``std`` method computes the - trace of the Hessian matrix, while the ``divgrad`` method computes the - divergence of the gradient. Default is ``std``. - :return: The computed laplacian tensor. - :rtype: LabelTensor - :raises ValueError: If the passed method is neither ``std`` nor ``divgrad``. - """ - # Scalar laplacian - if output_.shape[-1] == 1: - return LabelTensor( - _scalar_laplacian(output_=output_, input_=input_, d=d), - labels=[f"dd{c}" for c in components], - ) - - # Initialize the result tensor and its labels - labels = [f"dd{c}" for c in components] - result = torch.empty( - input_.shape[0], len(components), device=output_.device - ) - - # Vector laplacian - if method == "std": - result = torch.cat( - [ - _scalar_laplacian( - output_=output_.extract(c), input_=input_, d=d - ) - for c in components - ], - dim=-1, - ) - - elif method == "divgrad": - grads = fast_grad( - output_=output_, input_=input_, components=components, d=d - ) - result = torch.cat( - [ - fast_div( - output_=grads, - input_=input_, - components=[f"d{c}d{i}" for i in d], - d=d, - ) - for c in components - ], - dim=-1, - ) - - else: - raise ValueError( - "Invalid method. Available methods are ``std`` and ``divgrad``." - ) - - return LabelTensor(result, labels=labels) - - -def fast_advection(output_, input_, velocity_field, components, d): - """ - Perform the advection operation on the ``output_`` with respect to the - ``input``. This operator supports vector-valued functions with multiple - input coordinates. - - Unlike ``advection``, this function performs no internal checks on input and - output tensors. The user is required to specify both ``components`` and - ``d`` as lists of strings. It is designed to enhance computation speed. - - :param LabelTensor output_: The output tensor on which the advection is - computed. It includes both the velocity and the quantity to be advected. - :param LabelTensor input_: the input tensor with respect to which advection - is computed. - :param list[str] velocity_field: The name of the output variables used as - velocity field. It must be chosen among the output labels. - :param list[str] components: The names of the output variables for which to - compute the advection. It must be a subset of the output labels. - :param list[str] d: The names of the input variables with respect to which - the advection is computed. It must be a subset of the input labels. - :return: The computed advection tensor. - :rtype: LabelTensor - """ - # Add a dimension to the velocity field for following operations - velocity = output_.extract(velocity_field).unsqueeze(-1) - - # Compute the gradient - grads = fast_grad( - output_=output_, input_=input_, components=components, d=d - ) - - # Reshape into [..., len(filter_components), len(d)] - tmp = grads.reshape(*output_.shape[:-1], len(components), len(d)) - - # Transpose to [..., len(d), len(filter_components)] - tmp = tmp.transpose(-1, -2) - - adv = (tmp * velocity).sum(dim=tmp.tensor.ndim - 2) - return LabelTensor(adv, labels=[f"adv_{c}" for c in components]) - - -def grad(output_, input_, components=None, d=None): - """ - Compute the gradient of the ``output_`` with respect to the ``input``. - - This operator supports both vector-valued and scalar-valued functions with - one or multiple input coordinates. - - :param LabelTensor output_: The output tensor on which the gradient is - computed. - :param LabelTensor input_: The input tensor with respect to which the - gradient is computed. - :param components: The names of the output variables for which to compute - the gradient. It must be a subset of the output labels. - If ``None``, all output variables are considered. Default is ``None``. - :type components: str | list[str] - :param d: The names of the input variables with respect to which the - gradient is computed. It must be a subset of the input labels. - If ``None``, all input variables are considered. Default is ``None``. - :type d: str | list[str] - :raises TypeError: If the input tensor is not a LabelTensor. - :raises TypeError: If the output tensor is not a LabelTensor. - :raises RuntimeError: If derivative labels are missing from the ``input_``. - :raises RuntimeError: If component labels are missing from the ``output_``. - :return: The computed gradient tensor. - :rtype: LabelTensor - """ - components, d = _check_values( - output_=output_, input_=input_, components=components, d=d - ) - return fast_grad(output_=output_, input_=input_, components=components, d=d) - - -def div(output_, input_, components=None, d=None): - """ - Compute the divergence of the ``output_`` with respect to ``input``. - - This operator supports vector-valued functions with multiple input - coordinates. - - :param LabelTensor output_: The output tensor on which the divergence is - computed. - :param LabelTensor input_: The input tensor with respect to which the - divergence is computed. - :param components: The names of the output variables for which to compute - the divergence. It must be a subset of the output labels. - If ``None``, all output variables are considered. Default is ``None``. - :type components: str | list[str] - :param d: The names of the input variables with respect to which the - divergence is computed. It must be a subset of the input labels. - If ``None``, all input variables are considered. Default is ``None``. - :type components: str | list[str] - :raises TypeError: If the input tensor is not a LabelTensor. - :raises TypeError: If the output tensor is not a LabelTensor. - :raises ValueError: If the length of ``components`` and ``d`` do not match. - :return: The computed divergence tensor. - :rtype: LabelTensor - """ - components, d = _check_values( - output_=output_, input_=input_, components=components, d=d - ) - - # Components and d must be of the same length - if len(components) != len(d): - raise ValueError( - "Divergence requires components and d to be of the same length." - ) - - return fast_div(output_=output_, input_=input_, components=components, d=d) - - -def laplacian(output_, input_, components=None, d=None, method="std"): - """ - Compute the laplacian of the ``output_`` with respect to ``input``. - - This operator supports both vector-valued and scalar-valued functions with - one or multiple input coordinates. - - :param LabelTensor output_: The output tensor on which the laplacian is - computed. - :param LabelTensor input_: The input tensor with respect to which the - laplacian is computed. - :param components: The names of the output variables for which to - compute the laplacian. It must be a subset of the output labels. - If ``None``, all output variables are considered. Default is ``None``. - :type components: str | list[str] - :param d: The names of the input variables with respect to which - the laplacian is computed. It must be a subset of the input labels. - If ``None``, all input variables are considered. Default is ``None``. - :type d: str | list[str] - :param str method: The method used to compute the Laplacian. Available - methods are ``std`` and ``divgrad``. The ``std`` method computes the - trace of the Hessian matrix, while the ``divgrad`` method computes the - divergence of the gradient. Default is ``std``. - :raises TypeError: If the input tensor is not a LabelTensor. - :raises TypeError: If the output tensor is not a LabelTensor. - :raises ValueError: If the passed method is neither ``std`` nor ``divgrad``. - :return: The computed laplacian tensor. - :rtype: LabelTensor - """ - components, d = _check_values( - output_=output_, input_=input_, components=components, d=d - ) - - return fast_laplacian( - output_=output_, - input_=input_, - components=components, - d=d, - method=method, - ) - - -def advection(output_, input_, velocity_field, components=None, d=None): - """ - Perform the advection operation on the ``output_`` with respect to the - ``input``. This operator supports vector-valued functions with multiple - input coordinates. - - :param LabelTensor output_: The output tensor on which the advection is - computed. It includes both the velocity and the quantity to be advected. - :param LabelTensor input_: the input tensor with respect to which advection - is computed. - :param velocity_field: The name of the output variables used as velocity - field. It must be chosen among the output labels. - :type velocity_field: str | list[str] - :param components: The names of the output variables for which to compute - the advection. It must be a subset of the output labels. - If ``None``, all output variables are considered. Default is ``None``. - :type components: str | list[str] - :param d: The names of the input variables with respect to which the - advection is computed. It must be a subset of the input labels. - If ``None``, all input variables are considered. Default is ``None``. - :type d: str | list[str] - :raises TypeError: If the input tensor is not a LabelTensor. - :raises TypeError: If the output tensor is not a LabelTensor. - :raises RuntimeError: If the velocity field is not a subset of the output - labels. - :raises RuntimeError: If the dimensionality of the velocity field does not - match that of the input tensor. - :return: The computed advection tensor. - :rtype: LabelTensor - """ - components, d = _check_values( - output_=output_, input_=input_, components=components, d=d - ) - - # Map velocity_field to a list if it is a string - if isinstance(velocity_field, str): - velocity_field = [velocity_field] - - # Check if all the velocity_field labels are present in the output labels - if not all(vi in output_.labels for vi in velocity_field): - raise RuntimeError("Velocity labels missing from output tensor.") - - # Check if the velocity has the same dimensionality as the input tensor - if len(velocity_field) != len(d): - raise RuntimeError( - "Velocity dimensionality does not match input dimensionality." - ) - - return fast_advection( - output_=output_, - input_=input_, - velocity_field=velocity_field, - components=components, - d=d, - ) +from pina._src.core.operator import ( + grad, + fast_grad, + fast_div, + fast_laplacian, + fast_advection, + div, + laplacian, + advection, +) + +__all__ = [ + "grad", + "fast_grad", + "fast_div", + "fast_laplacian", + "fast_advection", + "div", + "laplacian", + "advection", +] diff --git a/pina/optim/__init__.py b/pina/optim/__init__.py index 8266c8ca1..f88b85e7a 100644 --- a/pina/optim/__init__.py +++ b/pina/optim/__init__.py @@ -1,13 +1,34 @@ """Module for the Optimizers and Schedulers.""" __all__ = [ - "Optimizer", + "OptimizerInterface", + "SchedulerInterface", "TorchOptimizer", - "Scheduler", "TorchScheduler", ] -from .optimizer_interface import Optimizer -from .torch_optimizer import TorchOptimizer -from .scheduler_interface import Scheduler -from .torch_scheduler import TorchScheduler +from pina._src.optim.optimizer_interface import OptimizerInterface +from pina._src.optim.scheduler_interface import SchedulerInterface +from pina._src.optim.torch_optimizer import TorchOptimizer +from pina._src.optim.torch_scheduler import TorchScheduler + +# Back-compatibility with version 0.2, to be removed soon +import warnings + +_DEPRECATED_IMPORTS = { + "Optimizer": "OptimizerInterface", + "Scheduler": "SchedulerInterface", +} + + +def __getattr__(name): + if name in _DEPRECATED_IMPORTS: + + warnings.warn( + f"Importing '{name}' from 'pina.optim' is deprecated; use " + f"pina.optim.{_DEPRECATED_IMPORTS[name]} instead.", + DeprecationWarning, + stacklevel=2, + ) + + return globals()[_DEPRECATED_IMPORTS[name]] diff --git a/pina/optim/optimizer_interface.py b/pina/optim/optimizer_interface.py deleted file mode 100644 index 5f2fbe66a..000000000 --- a/pina/optim/optimizer_interface.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Module for the PINA Optimizer.""" - -from abc import ABCMeta, abstractmethod - - -class Optimizer(metaclass=ABCMeta): - """ - Abstract base class for defining an optimizer. All specific optimizers - should inherit form this class and implement the required methods. - """ - - @property - @abstractmethod - def instance(self): - """ - Abstract property to retrieve the optimizer instance. - """ - - @abstractmethod - def hook(self): - """ - Abstract method to define the hook logic for the optimizer. - """ diff --git a/pina/optim/scheduler_interface.py b/pina/optim/scheduler_interface.py deleted file mode 100644 index 5ae5d8b99..000000000 --- a/pina/optim/scheduler_interface.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Module for the PINA Scheduler.""" - -from abc import ABCMeta, abstractmethod - - -class Scheduler(metaclass=ABCMeta): - """ - Abstract base class for defining a scheduler. All specific schedulers should - inherit form this class and implement the required methods. - """ - - @property - @abstractmethod - def instance(self): - """ - Abstract property to retrieve the scheduler instance. - """ - - @abstractmethod - def hook(self): - """ - Abstract method to define the hook logic for the scheduler. - """ diff --git a/pina/optim/torch_optimizer.py b/pina/optim/torch_optimizer.py deleted file mode 100644 index 7163c295e..000000000 --- a/pina/optim/torch_optimizer.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Module for the PINA Torch Optimizer""" - -import torch - -from ..utils import check_consistency -from .optimizer_interface import Optimizer - - -class TorchOptimizer(Optimizer): - """ - A wrapper class for using PyTorch optimizers. - """ - - def __init__(self, optimizer_class, **kwargs): - """ - Initialization of the :class:`TorchOptimizer` class. - - :param torch.optim.Optimizer optimizer_class: A - :class:`torch.optim.Optimizer` class. - :param dict kwargs: Additional parameters passed to ``optimizer_class``, - see more - `here `_. - """ - check_consistency(optimizer_class, torch.optim.Optimizer, subclass=True) - - self.optimizer_class = optimizer_class - self.kwargs = kwargs - self._optimizer_instance = None - - def hook(self, parameters): - """ - Initialize the optimizer instance with the given parameters. - - :param dict parameters: The parameters of the model to be optimized. - """ - self._optimizer_instance = self.optimizer_class( - parameters, **self.kwargs - ) - - @property - def instance(self): - """ - Get the optimizer instance. - - :return: The optimizer instance. - :rtype: torch.optim.Optimizer - """ - return self._optimizer_instance diff --git a/pina/optim/torch_scheduler.py b/pina/optim/torch_scheduler.py deleted file mode 100644 index ff12300a1..000000000 --- a/pina/optim/torch_scheduler.py +++ /dev/null @@ -1,55 +0,0 @@ -"""Module for the PINA Torch Optimizer""" - -try: - from torch.optim.lr_scheduler import LRScheduler # torch >= 2.0 -except ImportError: - from torch.optim.lr_scheduler import ( - _LRScheduler as LRScheduler, - ) # torch < 2.0 - -from ..utils import check_consistency -from .optimizer_interface import Optimizer -from .scheduler_interface import Scheduler - - -class TorchScheduler(Scheduler): - """ - A wrapper class for using PyTorch schedulers. - """ - - def __init__(self, scheduler_class, **kwargs): - """ - Initialization of the :class:`TorchScheduler` class. - - :param torch.optim.LRScheduler scheduler_class: A - :class:`torch.optim.LRScheduler` class. - :param dict kwargs: Additional parameters passed to ``scheduler_class``, - see more - `here _`. - """ - check_consistency(scheduler_class, LRScheduler, subclass=True) - - self.scheduler_class = scheduler_class - self.kwargs = kwargs - self._scheduler_instance = None - - def hook(self, optimizer): - """ - Initialize the scheduler instance with the given parameters. - - :param dict parameters: The parameters of the optimizer. - """ - check_consistency(optimizer, Optimizer) - self._scheduler_instance = self.scheduler_class( - optimizer.instance, **self.kwargs - ) - - @property - def instance(self): - """ - Get the scheduler instance. - - :return: The scheduelr instance. - :rtype: torch.optim.LRScheduler - """ - return self._scheduler_instance diff --git a/pina/problem/__init__.py b/pina/problem/__init__.py index e95f99703..3248c22e5 100644 --- a/pina/problem/__init__.py +++ b/pina/problem/__init__.py @@ -1,15 +1,35 @@ """Module for the Problems.""" __all__ = [ - "AbstractProblem", + "ProblemInterface", + "BaseProblem", "SpatialProblem", "TimeDependentProblem", "ParametricProblem", "InverseProblem", ] -from .abstract_problem import AbstractProblem -from .spatial_problem import SpatialProblem -from .time_dependent_problem import TimeDependentProblem -from .parametric_problem import ParametricProblem -from .inverse_problem import InverseProblem +from pina._src.problem.problem_interface import ProblemInterface +from pina._src.problem.base_problem import BaseProblem +from pina._src.problem.spatial_problem import SpatialProblem +from pina._src.problem.time_dependent_problem import TimeDependentProblem +from pina._src.problem.parametric_problem import ParametricProblem +from pina._src.problem.inverse_problem import InverseProblem + +# Back-compatibility with version 0.2, to be removed soon +import warnings + +_DEPRECATED_IMPORTS = {"AbstractProblem": "BaseProblem"} + + +def __getattr__(name): + if name in _DEPRECATED_IMPORTS: + + warnings.warn( + f"Importing '{name}' from 'pina.problem' is deprecated; use " + f"pina.problem.{_DEPRECATED_IMPORTS[name]} instead.", + DeprecationWarning, + stacklevel=2, + ) + + return globals()[_DEPRECATED_IMPORTS[name]] diff --git a/pina/problem/abstract_problem.py b/pina/problem/abstract_problem.py deleted file mode 100644 index 441356def..000000000 --- a/pina/problem/abstract_problem.py +++ /dev/null @@ -1,348 +0,0 @@ -"""Module for the AbstractProblem class.""" - -from abc import ABCMeta, abstractmethod -import warnings -from copy import deepcopy -from ..utils import check_consistency -from ..domain import DomainInterface, CartesianDomain -from ..condition.domain_equation_condition import DomainEquationCondition -from ..label_tensor import LabelTensor -from ..utils import merge_tensors, custom_warning_format - - -class AbstractProblem(metaclass=ABCMeta): - """ - Abstract base class for PINA problems. All specific problem types should - inherit from this class. - - A PINA problem is defined by key components, which typically include output - variables, conditions, and domains over which the conditions are applied. - """ - - def __init__(self): - """ - Initialization of the :class:`AbstractProblem` class. - """ - self._discretised_domains = {} - - # create hook conditions <-> problems - for condition_name in self.conditions: - self.conditions[condition_name].problem = self - - # Store in domains dict all the domains object directly passed to - # ConditionInterface. Done for back compatibility with PINA <0.2 - if not hasattr(self, "domains"): - self.domains = {} - for cond_name, cond in self.conditions.items(): - if isinstance(cond, DomainEquationCondition): - if isinstance(cond.domain, DomainInterface): - self.domains[cond_name] = cond.domain - cond.domain = cond_name - - self._collected_data = {} - - @property - def collected_data(self): - """ - Return the collected data from the problem's conditions. If some domains - are not sampled, they will not be returned by collected data. - - :return: The collected data. Keys are condition names, and values are - dictionaries containing the input points and the corresponding - equations or target points. - :rtype: dict - """ - # collect data so far - self.collect_data() - # raise warning if some sample data are missing - if not self.are_all_domains_discretised: - warnings.formatwarning = custom_warning_format - warnings.filterwarnings("always", category=RuntimeWarning) - warning_message = "\n".join( - [ - f"""{" " * 13} ---> Domain {key} { - "sampled" if key in self.discretised_domains - else - "not sampled"}""" - for key in self.domains - ] - ) - warnings.warn( - "Some of the domains are still not sampled. Consider calling " - "problem.discretise_domain function for all domains before " - "accessing the collected data:\n" - f"{warning_message}", - RuntimeWarning, - ) - return self._collected_data - - # back compatibility 0.1 - @property - def input_pts(self): - """ - Return a dictionary mapping condition names to their corresponding - input points. If some domains are not sampled, they will not be returned - and the corresponding condition will be empty. - - :return: The input points of the problem. - :rtype: dict - """ - to_return = {} - for cond_name, data in self.collected_data.items(): - to_return[cond_name] = data["input"] - return to_return - - @property - def discretised_domains(self): - """ - Return a dictionary mapping domains to their corresponding sampled - points. - - :return: The discretised domains. - :rtype: dict - """ - return self._discretised_domains - - def __deepcopy__(self, memo): - """ - Perform a deep copy of the :class:`AbstractProblem` instance. - - :param dict memo: A dictionary used to track objects already copied - during the deep copy process to prevent redundant copies. - :return: A deep copy of the :class:`AbstractProblem` instance. - :rtype: AbstractProblem - """ - cls = self.__class__ - result = cls.__new__(cls) - memo[id(self)] = result - for k, v in self.__dict__.items(): - setattr(result, k, deepcopy(v, memo)) - return result - - @property - def are_all_domains_discretised(self): - """ - Check if all the domains are discretised. - - :return: ``True`` if all domains are discretised, ``False`` otherwise. - :rtype: bool - """ - return all( - domain in self.discretised_domains for domain in self.domains - ) - - @property - def input_variables(self): - """ - Get the input variables of the problem. - - :return: The input variables of the problem. - :rtype: list[str] - """ - variables = [] - - if hasattr(self, "spatial_variables"): - variables += self.spatial_variables - if hasattr(self, "temporal_variable"): - variables += self.temporal_variable - if hasattr(self, "parameters"): - variables += self.parameters - - return variables - - @input_variables.setter - def input_variables(self, variables): - """ - Set the input variables of the AbstractProblem. - - :param list[str] variables: The input variables of the problem. - :raises RuntimeError: Not implemented. - """ - raise RuntimeError - - @property - @abstractmethod - def output_variables(self): - """ - Get the output variables of the problem. - """ - - @property - @abstractmethod - def conditions(self): - """ - Get the conditions of the problem. - - :return: The conditions of the problem. - :rtype: dict - """ - return self.conditions - - def discretise_domain( - self, n=None, mode="random", domains="all", sample_rules=None - ): - """ - Discretize the problem's domains by sampling a specified number of - points according to the selected sampling mode. - - :param int n: The number of points to sample. - :param mode: The sampling method. Default is ``random``. - Available modes include: random sampling, ``random``; - latin hypercube sampling, ``latin`` or ``lh``; - chebyshev sampling, ``chebyshev``; grid sampling ``grid``. - :param domains: The domains from which to sample. Default is ``all``. - :type domains: str | list[str] - :param dict sample_rules: A dictionary defining custom sampling rules - for input variables. If provided, it must contain a dictionary - specifying the sampling rule for each variable, overriding the - ``n`` and ``mode`` arguments. Each key must correspond to the - input variables from - :meth:~pina.problem.AbstractProblem.input_variables, and its value - should be another dictionary with - two keys: ``n`` (number of points to sample) and ``mode`` - (sampling method). Defaults to None. - :raises RuntimeError: If both ``n`` and ``sample_rules`` are specified. - :raises RuntimeError: If neither ``n`` nor ``sample_rules`` are set. - - :Example: - >>> problem.discretise_domain(n=10, mode='grid') - >>> problem.discretise_domain(n=10, mode='grid', domains=['gamma1']) - >>> problem.discretise_domain( - ... sample_rules={ - ... 'x': {'n': 10, 'mode': 'grid'}, - ... 'y': {'n': 100, 'mode': 'grid'} - ... }, - ... domains=['D'] - ... ) - - .. warning:: - ``random`` is currently the only implemented ``mode`` for all - geometries, i.e. :class:`~pina.domain.ellipsoid.EllipsoidDomain`, - :class:`~pina.domain.cartesian.CartesianDomain`, - :class:`~pina.domain.simplex.SimplexDomain`, and geometry - compositions :class:`~pina.domain.union_domain.Union`, - :class:`~pina.domain.difference_domain.Difference`, - :class:`~pina.domain.exclusion_domain.Exclusion`, and - :class:`~pina.domain.intersection_domain.Intersection`. - The modes ``latin`` or ``lh``, ``chebyshev``, ``grid`` are only - implemented for :class:`~pina.domain.cartesian.CartesianDomain`. - - .. warning:: - If custom discretisation is applied by setting ``sample_rules`` not - to ``None``, then the discretised domain must be of class - :class:`~pina.domain.cartesian.CartesianDomain` - """ - - # check consistecy n, mode, variables, locations - if sample_rules is not None: - check_consistency(sample_rules, dict) - if mode is not None: - check_consistency(mode, str) - check_consistency(domains, (list, str)) - - # check correct location - if domains == "all": - domains = self.domains.keys() - elif not isinstance(domains, (list)): - domains = [domains] - if n is not None and sample_rules is None: - self._apply_default_discretization(n, mode, domains) - if n is None and sample_rules is not None: - self._apply_custom_discretization(sample_rules, domains) - elif n is not None and sample_rules is not None: - raise RuntimeError( - "You can't specify both n and sample_rules at the same time." - ) - elif n is None and sample_rules is None: - raise RuntimeError("You have to specify either n or sample_rules.") - - def _apply_default_discretization(self, n, mode, domains): - """ - Apply default discretization to the problem's domains. - - :param int n: The number of points to sample. - :param mode: The sampling method. - :param domains: The domains from which to sample. - :type domains: str | list[str] - """ - for domain in domains: - self.discretised_domains[domain] = ( - self.domains[domain].sample(n, mode).sort_labels() - ) - - def _apply_custom_discretization(self, sample_rules, domains): - """ - Apply custom discretization to the problem's domains. - - :param dict sample_rules: A dictionary of custom sampling rules. - :param domains: The domains from which to sample. - :type domains: str | list[str] - :raises RuntimeError: If the keys of the sample_rules dictionary are not - the same as the input variables. - :raises RuntimeError: If custom discretisation is applied on a domain - that is not a CartesianDomain. - """ - if sorted(list(sample_rules.keys())) != sorted(self.input_variables): - raise RuntimeError( - "The keys of the sample_rules dictionary must be the same as " - "the input variables." - ) - for domain in domains: - if not isinstance(self.domains[domain], CartesianDomain): - raise RuntimeError( - "Custom discretisation can be applied only on Cartesian " - "domains" - ) - discretised_tensor = [] - for var, rules in sample_rules.items(): - n, mode = rules["n"], rules["mode"] - points = self.domains[domain].sample(n, mode, var) - discretised_tensor.append(points) - - self.discretised_domains[domain] = merge_tensors( - discretised_tensor - ).sort_labels() - - def add_points(self, new_points_dict): - """ - Add new points to an already sampled domain. - - :param dict new_points_dict: The dictionary mapping new points to their - corresponding domain. - """ - for k, v in new_points_dict.items(): - self.discretised_domains[k] = LabelTensor.vstack( - [self.discretised_domains[k], v] - ) - - def collect_data(self): - """ - Aggregate data from the problem's conditions into a single dictionary. - """ - data = {} - # Iterate over the conditions and collect data - for condition_name in self.conditions: - condition = self.conditions[condition_name] - # Check if the condition has an domain attribute - if hasattr(condition, "domain"): - # Only store the discretisation points if the domain is - # in the dictionary - if condition.domain in self.discretised_domains: - samples = self.discretised_domains[condition.domain][ - self.input_variables - ] - data[condition_name] = { - "input": samples, - "equation": condition.equation, - } - else: - # If the condition does not have a domain attribute, store - # the input and target points - keys = condition.__slots__ - values = [ - getattr(condition, name) - for name in keys - if getattr(condition, name) is not None - ] - data[condition_name] = dict(zip(keys, values)) - self._collected_data = data diff --git a/pina/problem/parametric_problem.py b/pina/problem/parametric_problem.py deleted file mode 100644 index e361074b3..000000000 --- a/pina/problem/parametric_problem.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Module for the ParametricProblem class.""" - -from abc import abstractmethod - -from .abstract_problem import AbstractProblem - - -class ParametricProblem(AbstractProblem): - """ - Class for defining parametric problems, where certain input variables are - treated as parameters that can vary, allowing the model to adapt to - different scenarios based on the chosen parameters. - """ - - @abstractmethod - def parameter_domain(self): - """ - The domain of the parameters of the problem. - """ - - @property - def parameters(self): - """ - Get the parameters of the problem. - - :return: The parameters of the problem. - :rtype: list[str] - """ - return self.parameter_domain.variables diff --git a/pina/problem/spatial_problem.py b/pina/problem/spatial_problem.py deleted file mode 100644 index 608e31691..000000000 --- a/pina/problem/spatial_problem.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Module for the SpatialProblem class.""" - -from abc import abstractmethod - -from .abstract_problem import AbstractProblem - - -class SpatialProblem(AbstractProblem): - """ - Class for defining spatial problems, where the problem domain is defined in - terms of spatial variables. - """ - - @abstractmethod - def spatial_domain(self): - """ - The spatial domain of the problem. - """ - - @property - def spatial_variables(self): - """ - Get the spatial input variables of the problem. - - :return: The spatial input variables of the problem. - :rtype: list[str] - """ - return self.spatial_domain.variables diff --git a/pina/problem/time_dependent_problem.py b/pina/problem/time_dependent_problem.py deleted file mode 100644 index ea2ad7d54..000000000 --- a/pina/problem/time_dependent_problem.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Module for the TimeDependentProblem class.""" - -from abc import abstractmethod - -from .abstract_problem import AbstractProblem - - -class TimeDependentProblem(AbstractProblem): - """ - Class for defining time-dependent problems, where the system's behavior - changes with respect to time. - """ - - @abstractmethod - def temporal_domain(self): - """ - The temporal domain of the problem. - """ - - @property - def temporal_variable(self): - """ - Get the time variable of the problem. - - :return: The time variable of the problem. - :rtype: list[str] - """ - return self.temporal_domain.variables diff --git a/pina/problem/zoo.py b/pina/problem/zoo.py new file mode 100644 index 000000000..47b204425 --- /dev/null +++ b/pina/problem/zoo.py @@ -0,0 +1,27 @@ +"""Module for implemented problems.""" + +__all__ = [ + "SupervisedProblem", + "HelmholtzProblem", + "AllenCahnProblem", + "AdvectionProblem", + "Poisson2DSquareProblem", + "DiffusionReactionProblem", + "InversePoisson2DSquareProblem", + "AcousticWaveProblem", + "BurgersProblem", +] + +from pina._src.problem.zoo.acoustic_wave_problem import AcousticWaveProblem +from pina._src.problem.zoo.supervised_problem import SupervisedProblem +from pina._src.problem.zoo.allen_cahn_problem import AllenCahnProblem +from pina._src.problem.zoo.advection_problem import AdvectionProblem +from pina._src.problem.zoo.helmholtz_problem import HelmholtzProblem +from pina._src.problem.zoo.poisson_problem import Poisson2DSquareProblem +from pina._src.problem.zoo.burgers_problem import BurgersProblem +from pina._src.problem.zoo.diffusion_reaction_problem import ( + DiffusionReactionProblem, +) +from pina._src.problem.zoo.inverse_poisson_problem import ( + InversePoisson2DSquareProblem, +) diff --git a/pina/problem/zoo/__init__.py b/pina/problem/zoo/__init__.py deleted file mode 100644 index 73e3ad9b6..000000000 --- a/pina/problem/zoo/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Module for implemented problems.""" - -__all__ = [ - "SupervisedProblem", - "HelmholtzProblem", - "AllenCahnProblem", - "AdvectionProblem", - "Poisson2DSquareProblem", - "DiffusionReactionProblem", - "InversePoisson2DSquareProblem", - "AcousticWaveProblem", -] - -from .supervised_problem import SupervisedProblem -from .helmholtz import HelmholtzProblem -from .allen_cahn import AllenCahnProblem -from .advection import AdvectionProblem -from .poisson_2d_square import Poisson2DSquareProblem -from .diffusion_reaction import DiffusionReactionProblem -from .inverse_poisson_2d_square import InversePoisson2DSquareProblem -from .acoustic_wave import AcousticWaveProblem diff --git a/pina/problem/zoo/helmholtz.py b/pina/problem/zoo/helmholtz.py deleted file mode 100644 index f7f288627..000000000 --- a/pina/problem/zoo/helmholtz.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Formulation of the Helmholtz problem.""" - -import torch -from ... import Condition -from ...equation import FixedValue, Helmholtz -from ...utils import check_consistency -from ...domain import CartesianDomain -from ...problem import SpatialProblem - - -class HelmholtzProblem(SpatialProblem): - r""" - Implementation of the Helmholtz problem in the square domain - :math:`[-1, 1] \times [-1, 1]`. - - .. seealso:: - - **Original reference**: Si, Chenhao, et al. *Complex Physics-Informed - Neural Network.* arXiv preprint arXiv:2502.04917 (2025). - DOI: `arXiv:2502.04917 `_. - - :Example: - - >>> problem = HelmholtzProblem() - """ - - output_variables = ["u"] - spatial_domain = CartesianDomain({"x": [-1, 1], "y": [-1, 1]}) - - domains = { - "D": spatial_domain, - "boundary": spatial_domain.partial(), - } - - conditions = { - "boundary": Condition(domain="boundary", equation=FixedValue(0.0)), - } - - def __init__(self, alpha=3.0): - """ - Initialization of the :class:`HelmholtzProblem` class. - - :param alpha: Parameter of the forcing term. Default is 3.0. - :type alpha: float | int - """ - super().__init__() - check_consistency(alpha, (int, float)) - self.alpha = alpha - - def forcing_term(input_): - """ - Implementation of the forcing term. - """ - return ( - (1 - 2 * (self.alpha * torch.pi) ** 2) - * torch.sin(self.alpha * torch.pi * input_.extract("x")) - * torch.sin(self.alpha * torch.pi * input_.extract("y")) - ) - - self.conditions["D"] = Condition( - domain="D", - equation=Helmholtz(self.alpha, forcing_term), - ) - - def solution(self, pts): - """ - Implementation of the analytical solution of the Helmholtz problem. - - :param LabelTensor pts: Points where the solution is evaluated. - :return: The analytical solution of the Poisson problem. - :rtype: LabelTensor - """ - sol = torch.sin(self.alpha * torch.pi * pts.extract("x")) * torch.sin( - self.alpha * torch.pi * pts.extract("y") - ) - sol.labels = self.output_variables - return sol diff --git a/pina/solver/__init__.py b/pina/solver/__init__.py index 43f18078f..44b6aa690 100644 --- a/pina/solver/__init__.py +++ b/pina/solver/__init__.py @@ -1,43 +1,109 @@ -"""Module for the solver classes.""" +"""Module for all solvers in PINA.""" __all__ = [ "SolverInterface", - "SingleSolverInterface", - "MultiSolverInterface", - "PINNInterface", - "PINN", - "GradientPINN", - "CausalPINN", - "CompetitivePINN", - "SelfAdaptivePINN", - "RBAPINN", - "SupervisedSolverInterface", - "SupervisedSolver", - "ReducedOrderModelSolver", - "DeepEnsembleSolverInterface", - "DeepEnsembleSupervisedSolver", - "DeepEnsemblePINN", - "GAROM", + "BaseSolver", + "SingleModelSolver", + "MultiModelSolver", + "EnsembleSolver", + "SupervisedSingleModelSolver", + "PhysicsInformedSingleModelSolver", + "SupervisedEnsembleSolver", + "PhysicsInformedEnsembleSolver", + "AutoregressiveSingleModelSolver", + "AutoregressiveEnsembleSolver", + "SelfAdaptivePhysicsInformedSolver", + "CompetitivePhysicsInformedSolver", + "GradientPhysicsInformedSingleModelSolver", + "RBAPhysicsInformedSingleModelSolver", + "CausalPhysicsInformedSingleModelSolver", ] -from .solver import SolverInterface, SingleSolverInterface, MultiSolverInterface -from .physics_informed_solver import ( - PINNInterface, - PINN, - GradientPINN, - CausalPINN, - CompetitivePINN, - SelfAdaptivePINN, - RBAPINN, -) -from .supervised_solver import ( - SupervisedSolverInterface, - SupervisedSolver, - ReducedOrderModelSolver, -) -from .ensemble_solver import ( - DeepEnsembleSolverInterface, - DeepEnsembleSupervisedSolver, - DeepEnsemblePINN, -) -from .garom import GAROM + +from pina._src.solver.solver_interface import SolverInterface +from pina._src.solver.base_solver import BaseSolver +from pina._src.solver.single_model_solver import SingleModelSolver +from pina._src.solver.multi_model_solver import MultiModelSolver +from pina._src.solver.ensemble_solver import EnsembleSolver +from pina._src.solver.supervised_single_model_solver import ( + SupervisedSingleModelSolver, +) +from pina._src.solver.physics_informed_single_model_solver import ( + PhysicsInformedSingleModelSolver, +) +from pina._src.solver.supervised_ensemble_solver import SupervisedEnsembleSolver +from pina._src.solver.physics_informed_ensemble_solver import ( + PhysicsInformedEnsembleSolver, +) +from pina._src.solver.autoregressive_single_model_solver import ( + AutoregressiveSingleModelSolver, +) +from pina._src.solver.autoregressive_ensemble_solver import ( + AutoregressiveEnsembleSolver, +) +from pina._src.solver.self_adaptive_physics_informed_solver import ( + SelfAdaptivePhysicsInformedSolver, +) +from pina._src.solver.competitive_physics_informed_solver import ( + CompetitivePhysicsInformedSolver, +) +from pina._src.solver.gradient_physics_informed_single_model_solver import ( + GradientPhysicsInformedSingleModelSolver, +) +from pina._src.solver.rba_physics_informed_single_model_solver import ( + RBAPhysicsInformedSingleModelSolver, +) +from pina._src.solver.causal_physics_informed_single_model_solver import ( + CausalPhysicsInformedSingleModelSolver, +) + +# Back-compatibility with version 0.2, to be removed soon +import warnings + +_DEPRECATED_IMPORTS = { + "SingleSolverInterface": "SingleModelSolver", + "MultiSolverInterface": "MultiModelSolver", + "DeepEnsembleSolverInterface": "EnsembleSolver", + "SupervisedSolver": "SupervisedSingleModelSolver", + "DeepEnsembleSupervisedSolver": "SupervisedEnsembleSolver", + "PINN": "PhysicsInformedSingleModelSolver", + "DeepEnsemblePINN": "PhysicsInformedEnsembleSolver", + "GradientPINN": "GradientPhysicsInformedSingleModelSolver", + "RBAPINN": "RBAPhysicsInformedSingleModelSolver", + "CausalPINN": "CausalPhysicsInformedSingleModelSolver", + "CompetitivePINN": "CompetitivePhysicsInformedSolver", + "SelfAdaptivePINN": "SelfAdaptivePhysicsInformedSolver", +} + +_REMOVED_IMPORTS = { + "SupervisedSolverInterface": ( + "`SupervisedSolverInterface` has been removed. Its logic is now managed" + " by `pina.solver.BaseSolver`, from which every solver inherits." + ), + "PINNInterface": ( + "`PINNInterface` has been removed. The underlying physics-informed " + "logic is now handled by `pina.solver.mixin.PhysicsInformedMixin`." + ), + "ReducedOrderModelSolver": ( + "`ReducedOrderModelSolver` is no longer supported." + ), + "GAROM": ("`GAROM` is no longer supported."), +} + + +def __getattr__(name): + if name in _DEPRECATED_IMPORTS: + + warnings.warn( + f"Importing '{name}' from 'pina.solver' is deprecated; use " + f"pina.solver.{_DEPRECATED_IMPORTS[name]} instead.", + DeprecationWarning, + stacklevel=2, + ) + + return globals()[_DEPRECATED_IMPORTS[name]] + + if name in _REMOVED_IMPORTS: + raise ImportError(_REMOVED_IMPORTS[name]) + + raise AttributeError(f"module 'pina.solver' has no attribute '{name}'") diff --git a/pina/solver/ensemble_solver/__init__.py b/pina/solver/ensemble_solver/__init__.py deleted file mode 100644 index 0e4eab54b..000000000 --- a/pina/solver/ensemble_solver/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Module for the Ensemble solver classes.""" - -__all__ = [ - "DeepEnsembleSolverInterface", - "DeepEnsembleSupervisedSolver", - "DeepEnsemblePINN", -] - -from .ensemble_solver_interface import DeepEnsembleSolverInterface -from .ensemble_supervised import DeepEnsembleSupervisedSolver -from .ensemble_pinn import DeepEnsemblePINN diff --git a/pina/solver/ensemble_solver/ensemble_pinn.py b/pina/solver/ensemble_solver/ensemble_pinn.py deleted file mode 100644 index 33d929ad2..000000000 --- a/pina/solver/ensemble_solver/ensemble_pinn.py +++ /dev/null @@ -1,170 +0,0 @@ -"""Module for the DeepEnsemble physics solver.""" - -import torch - -from .ensemble_solver_interface import DeepEnsembleSolverInterface -from ..physics_informed_solver import PINNInterface -from ...problem import InverseProblem - - -class DeepEnsemblePINN(PINNInterface, DeepEnsembleSolverInterface): - r""" - Deep Ensemble Physics Informed Solver class. This class implements a - Deep Ensemble for Physics Informed Neural Networks using user - specified ``model``s to solve a specific ``problem``. - - An ensemble model is constructed by combining multiple models that solve - the same type of problem. Mathematically, this creates an implicit - distribution :math:`p(\mathbf{u} \mid \mathbf{s})` over the possible - outputs :math:`\mathbf{u}`, given the original input :math:`\mathbf{s}`. - The models :math:`\mathcal{M}_{i\in (1,\dots,r)}` in - the ensemble work collaboratively to capture different - aspects of the data or task, with each model contributing a distinct - prediction :math:`\mathbf{y}_{i}=\mathcal{M}_i(\mathbf{u} \mid \mathbf{s})`. - By aggregating these predictions, the ensemble - model can achieve greater robustness and accuracy compared to individual - models, leveraging the diversity of the models to reduce overfitting and - improve generalization. Furthemore, statistical metrics can - be computed, e.g. the ensemble mean and variance: - - .. math:: - \mathbf{\mu} = \frac{1}{N}\sum_{i=1}^r \mathbf{y}_{i} - - .. math:: - \mathbf{\sigma^2} = \frac{1}{N}\sum_{i=1}^r - (\mathbf{y}_{i} - \mathbf{\mu})^2 - - During training the PINN loss is minimized by each ensemble model: - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^4 - \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + - \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)), - - for the differential system: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - :math:`\mathcal{L}` indicates a specific loss function, typically the MSE: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - - **Original reference**: Zou, Z., Wang, Z., & Karniadakis, G. E. (2025). - *Learning and discovering multiple solutions using physics-informed - neural networks with random initialization and deep ensemble*. - DOI: `arXiv:2503.06320 `_. - - .. warning:: - This solver does not work with inverse problem. Hence in the ``problem`` - definition must not inherit from - :class:`~pina.problem.inverse_problem.InverseProblem`. - """ - - def __init__( - self, - problem, - models, - loss=None, - optimizers=None, - schedulers=None, - weighting=None, - ensemble_dim=0, - ): - """ - Initialization of the :class:`DeepEnsemblePINN` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module models: The neural network models to be used. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is ``None``. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Scheduler scheduler: Learning rate scheduler. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param int ensemble_dim: The dimension along which the ensemble - outputs are stacked. Default is 0. - :raises NotImplementedError: If an inverse problem is passed. - """ - if isinstance(problem, InverseProblem): - raise NotImplementedError( - "DeepEnsemblePINN can not be used to solve inverse problems." - ) - super().__init__( - problem=problem, - models=models, - loss=loss, - optimizers=optimizers, - schedulers=schedulers, - weighting=weighting, - ensemble_dim=ensemble_dim, - ) - - def loss_data(self, input, target): - """ - Compute the data loss for the ensemble PINN solver by evaluating - the loss between the network's output and the true solution for each - model. This method should not be overridden, if not intentionally. - - :param input: The input to the neural network. - :type input: LabelTensor | torch.Tensor | Graph | Data - :param target: The target to compare with the network's output. - :type target: LabelTensor | torch.Tensor | Graph | Data - :return: The supervised loss, averaged over the number of observations. - :rtype: torch.Tensor - """ - predictions = self.forward(input) - loss = sum( - self._loss_fn(predictions[idx], target) - for idx in range(self.num_ensemble) - ) - return loss / self.num_ensemble - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the ensemble PINN solver by evaluating - the loss between the network's output and the true solution for each - model. This method should not be overridden, if not intentionally. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation. - :return: The computed physics loss. - :rtype: LabelTensor - """ - return self._residual_loss(samples, equation) - - def _residual_loss(self, samples, equation): - """ - Computes the physics loss for the physics-informed solver based on the - provided samples and equation. This method should never be overridden - by the user, if not intentionally, - since it is used internally to compute validation loss. It overrides the - :obj:`~pina.solver.physics_informed_solver.PINNInterface._residual_loss` - method. - - :param LabelTensor samples: The samples to evaluate the loss. - :param EquationInterface equation: The governing equation. - :return: The residual loss. - :rtype: torch.Tensor - """ - loss = 0 - predictions = self.forward(samples) - for idx in range(self.num_ensemble): - residuals = equation.residual(samples, predictions[idx]) - target = torch.zeros_like(residuals, requires_grad=True) - loss = loss + self._loss_fn(residuals, target) - return loss / self.num_ensemble diff --git a/pina/solver/ensemble_solver/ensemble_solver_interface.py b/pina/solver/ensemble_solver/ensemble_solver_interface.py deleted file mode 100644 index 6d874e1bf..000000000 --- a/pina/solver/ensemble_solver/ensemble_solver_interface.py +++ /dev/null @@ -1,152 +0,0 @@ -"""Module for the DeepEnsemble solver interface.""" - -import torch -from ..solver import MultiSolverInterface -from ...utils import check_consistency - - -class DeepEnsembleSolverInterface(MultiSolverInterface): - r""" - A class for handling ensemble models in a multi-solver training framework. - It allows for manual optimization, as well as the ability to train, - validate, and test multiple models as part of an ensemble. - The ensemble dimension can be customized to control how outputs are stacked. - - By default, it is compatible with problems defined by - :class:`~pina.problem.abstract_problem.AbstractProblem`, - and users can choose the problem type the solver is meant to address. - - An ensemble model is constructed by combining multiple models that solve - the same type of problem. Mathematically, this creates an implicit - distribution :math:`p(\mathbf{u} \mid \mathbf{s})` over the possible - outputs :math:`\mathbf{u}`, given the original input :math:`\mathbf{s}`. - The models :math:`\mathcal{M}_{i\in (1,\dots,r)}` in - the ensemble work collaboratively to capture different - aspects of the data or task, with each model contributing a distinct - prediction :math:`\mathbf{y}_{i}=\mathcal{M}_i(\mathbf{u} \mid \mathbf{s})`. - By aggregating these predictions, the ensemble - model can achieve greater robustness and accuracy compared to individual - models, leveraging the diversity of the models to reduce overfitting and - improve generalization. Furthemore, statistical metrics can - be computed, e.g. the ensemble mean and variance: - - .. math:: - \mathbf{\mu} = \frac{1}{N}\sum_{i=1}^r \mathbf{y}_{i} - - .. math:: - \mathbf{\sigma^2} = \frac{1}{N}\sum_{i=1}^r - (\mathbf{y}_{i} - \mathbf{\mu})^2 - - .. seealso:: - - **Original reference**: Lakshminarayanan, B., Pritzel, A., & Blundell, - C. (2017). *Simple and scalable predictive uncertainty estimation - using deep ensembles*. Advances in neural information - processing systems, 30. - DOI: `arXiv:1612.01474 `_. - """ - - def __init__( - self, - problem, - models, - optimizers=None, - schedulers=None, - weighting=None, - use_lt=True, - ensemble_dim=0, - ): - """ - Initialization of the :class:`DeepEnsembleSolverInterface` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module models: The neural network models to be used. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Scheduler scheduler: Learning rate scheduler. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param bool use_lt: If ``True``, the solver uses LabelTensors as input. - Default is ``True``. - :param int ensemble_dim: The dimension along which the ensemble - outputs are stacked. Default is 0. - """ - super().__init__( - problem, models, optimizers, schedulers, weighting, use_lt - ) - # check consistency - check_consistency(ensemble_dim, int) - self._ensemble_dim = ensemble_dim - - def forward(self, x, ensemble_idx=None): - """ - Forward pass through the ensemble models. If an `ensemble_idx` is - provided, it returns the output of the specific model - corresponding to that index. If no index is given, it stacks the outputs - of all models along the ensemble dimension. - - :param LabelTensor x: The input tensor to the models. - :param int ensemble_idx: Optional index to select a specific - model from the ensemble. If ``None`` results for all models are - stacked in ``ensemble_dim`` dimension. Default is ``None``. - :return: The output of the selected model or the stacked - outputs from all models. - :rtype: LabelTensor - """ - # if an index is passed, return the specific model output for that index - if ensemble_idx is not None: - return self.models[ensemble_idx].forward(x) - # otherwise return the stacked output - return torch.stack( - [self.forward(x, idx) for idx in range(self.num_ensemble)], - dim=self.ensemble_dim, - ) - - def training_step(self, batch): - """ - Training step for the solver, overridden for manual optimization. - This method performs a forward pass, calculates the loss, and applies - manual backward propagation and optimization steps for each model in - the ensemble. - - :param list[tuple[str, dict]] batch: A batch of training data. - Each element is a tuple containing a condition name and a - dictionary of points. - :return: The aggregated loss after the training step. - :rtype: torch.Tensor - """ - # zero grad for optimizer - for opt in self.optimizers: - opt.instance.zero_grad() - # perform forward passes and aggregate losses - loss = super().training_step(batch) - # perform backpropagation - self.manual_backward(loss) - # optimize - for opt, sched in zip(self.optimizers, self.schedulers): - opt.instance.step() - sched.instance.step() - return loss - - @property - def ensemble_dim(self): - """ - The dimension along which the ensemble outputs are stacked. - - :return: The ensemble dimension. - :rtype: int - """ - return self._ensemble_dim - - @property - def num_ensemble(self): - """ - The number of models in the ensemble. - - :return: The number of models in the ensemble. - :rtype: int - """ - return len(self.models) diff --git a/pina/solver/ensemble_solver/ensemble_supervised.py b/pina/solver/ensemble_solver/ensemble_supervised.py deleted file mode 100644 index e4837ccdb..000000000 --- a/pina/solver/ensemble_solver/ensemble_supervised.py +++ /dev/null @@ -1,122 +0,0 @@ -"""Module for the DeepEnsemble supervised solver.""" - -from .ensemble_solver_interface import DeepEnsembleSolverInterface -from ..supervised_solver import SupervisedSolverInterface - - -class DeepEnsembleSupervisedSolver( - SupervisedSolverInterface, DeepEnsembleSolverInterface -): - r""" - Deep Ensemble Supervised Solver class. This class implements a - Deep Ensemble Supervised Solver using user specified ``model``s to solve - a specific ``problem``. - - An ensemble model is constructed by combining multiple models that solve - the same type of problem. Mathematically, this creates an implicit - distribution :math:`p(\mathbf{u} \mid \mathbf{s})` over the possible - outputs :math:`\mathbf{u}`, given the original input :math:`\mathbf{s}`. - The models :math:`\mathcal{M}_{i\in (1,\dots,r)}` in - the ensemble work collaboratively to capture different - aspects of the data or task, with each model contributing a distinct - prediction :math:`\mathbf{y}_{i}=\mathcal{M}_i(\mathbf{u} \mid \mathbf{s})`. - By aggregating these predictions, the ensemble - model can achieve greater robustness and accuracy compared to individual - models, leveraging the diversity of the models to reduce overfitting and - improve generalization. Furthemore, statistical metrics can - be computed, e.g. the ensemble mean and variance: - - .. math:: - \mathbf{\mu} = \frac{1}{N}\sum_{i=1}^r \mathbf{y}_{i} - - .. math:: - \mathbf{\sigma^2} = \frac{1}{N}\sum_{i=1}^r - (\mathbf{y}_{i} - \mathbf{\mu})^2 - - During training the supervised loss is minimized by each ensemble model: - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathbf{u}_i - \mathcal{M}_{j}(\mathbf{s}_i)), - \quad j \in (1,\dots,N_{ensemble}) - - where :math:`\mathcal{L}` is a specific loss function, typically the MSE: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - In this context, :math:`\mathbf{u}_i` and :math:`\mathbf{s}_i` indicates - the will to approximate multiple (discretised) functions given multiple - (discretised) input functions. - - .. seealso:: - - **Original reference**: Lakshminarayanan, B., Pritzel, A., & Blundell, - C. (2017). *Simple and scalable predictive uncertainty estimation - using deep ensembles*. Advances in neural information - processing systems, 30. - DOI: `arXiv:1612.01474 `_. - """ - - def __init__( - self, - problem, - models, - loss=None, - optimizers=None, - schedulers=None, - weighting=None, - use_lt=False, - ensemble_dim=0, - ): - """ - Initialization of the :class:`DeepEnsembleSupervisedSolver` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module models: The neural network models to be used. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is ``None``. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Scheduler scheduler: Learning rate scheduler. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param bool use_lt: If ``True``, the solver uses LabelTensors as input. - Default is ``True``. - :param int ensemble_dim: The dimension along which the ensemble - outputs are stacked. Default is 0. - """ - super().__init__( - problem=problem, - models=models, - loss=loss, - optimizers=optimizers, - schedulers=schedulers, - weighting=weighting, - use_lt=use_lt, - ensemble_dim=ensemble_dim, - ) - - def loss_data(self, input, target): - """ - Compute the data loss for the EnsembleSupervisedSolver by evaluating - the loss between the network's output and the true solution for each - model. This method should not be overridden, if not intentionally. - - :param input: The input to the neural network. - :type input: LabelTensor | torch.Tensor | Graph | Data - :param target: The target to compare with the network's output. - :type target: LabelTensor | torch.Tensor | Graph | Data - :return: The supervised loss, averaged over the number of observations. - :rtype: torch.Tensor - """ - predictions = self.forward(input) - loss = sum( - self._loss_fn(predictions[idx], target) - for idx in range(self.num_ensemble) - ) - return loss / self.num_ensemble diff --git a/pina/solver/garom.py b/pina/solver/garom.py deleted file mode 100644 index 372eeddfa..000000000 --- a/pina/solver/garom.py +++ /dev/null @@ -1,362 +0,0 @@ -"""Module for the GAROM solver.""" - -import torch -from torch.nn.modules.loss import _Loss -from .solver import MultiSolverInterface -from ..condition import InputTargetCondition -from ..utils import check_consistency -from ..loss import LossInterface, PowerLoss - - -class GAROM(MultiSolverInterface): - """ - GAROM solver class. This class implements Generative Adversarial Reduced - Order Model solver, using user specified ``models`` to solve a specific - order reduction ``problem``. - - .. seealso:: - - **Original reference**: Coscia, D., Demo, N., & Rozza, G. (2023). - *Generative Adversarial Reduced Order Modelling*. - DOI: `arXiv preprint arXiv:2305.15881. - `_. - """ - - accepted_conditions_types = InputTargetCondition - - def __init__( - self, - problem, - generator, - discriminator, - loss=None, - optimizer_generator=None, - optimizer_discriminator=None, - scheduler_generator=None, - scheduler_discriminator=None, - gamma=0.3, - lambda_k=0.001, - regularizer=False, - ): - """ - Initialization of the :class:`GAROM` class. - - :param AbstractProblem problem: The formulation of the problem. - :param torch.nn.Module generator: The generator model. - :param torch.nn.Module discriminator: The discriminator model. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, :class:`~pina.loss.power_loss.PowerLoss` with ``p=1`` - is used. Default is ``None``. - :param Optimizer optimizer_generator: The optimizer for the generator. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Optimizer optimizer_discriminator: The optimizer for the - discriminator. If ``None``, the :class:`torch.optim.Adam` - optimizer is used. Default is ``None``. - :param Scheduler scheduler_generator: The learning rate scheduler for - the generator. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param Scheduler scheduler_discriminator: The learning rate scheduler - for the discriminator. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param float gamma: Ratio of expected loss for generator and - discriminator. Default is ``0.3``. - :param float lambda_k: Learning rate for control theory optimization. - Default is ``0.001``. - :param bool regularizer: If ``True``, uses a regularization term in the - GAROM loss. Default is ``False``. - """ - - # set loss - if loss is None: - loss = PowerLoss(p=1) - - super().__init__( - models=[generator, discriminator], - problem=problem, - optimizers=[optimizer_generator, optimizer_discriminator], - schedulers=[ - scheduler_generator, - scheduler_discriminator, - ], - use_lt=False, - ) - - # check consistency - check_consistency( - loss, (LossInterface, _Loss, torch.nn.Module), subclass=False - ) - self._loss_fn = loss - - # set automatic optimization for GANs - self.automatic_optimization = False - - # check consistency - check_consistency(gamma, float) - check_consistency(lambda_k, float) - check_consistency(regularizer, bool) - - # began hyperparameters - self.k = 0 - self.gamma = gamma - self.lambda_k = lambda_k - self.regularizer = float(regularizer) - - def forward(self, x, mc_steps=20, variance=False): - """ - Forward pass implementation. - - :param torch.Tensor x: The input tensor. - :param int mc_steps: Number of Montecarlo samples to approximate the - expected value. Default is ``20``. - :param bool variance: If ``True``, the method returns also the variance - of the solution. Default is ``False``. - :return: The expected value of the generator distribution. If - ``variance=True``, the method returns also the variance. - :rtype: torch.Tensor | tuple[torch.Tensor, torch.Tensor] - """ - - # sampling - field_sample = [self.sample(x) for _ in range(mc_steps)] - field_sample = torch.stack(field_sample) - - # extract mean - mean = field_sample.mean(dim=0) - - if variance: - var = field_sample.var(dim=0) - return mean, var - - return mean - - def sample(self, x): - """ - Sample from the generator distribution. - - :param torch.Tensor x: The input tensor. - :return: The generated sample. - :rtype: torch.Tensor - """ - # sampling - return self.generator(x) - - def _train_generator(self, parameters, snapshots): - """ - Train the generator model. - - :param torch.Tensor parameters: The input tensor. - :param torch.Tensor snapshots: The target tensor. - :return: The residual loss and the generator loss. - :rtype: tuple[torch.Tensor, torch.Tensor] - """ - self.optimizer_generator.instance.zero_grad() - - # Generate a batch of images - generated_snapshots = self.sample(parameters) - - # generator loss - r_loss = self._loss_fn(snapshots, generated_snapshots) - d_fake = self.discriminator([generated_snapshots, parameters]) - g_loss = ( - self._loss_fn(d_fake, generated_snapshots) - + self.regularizer * r_loss - ) - - # backward step - g_loss.backward() - self.optimizer_generator.instance.step() - self.scheduler_generator.instance.step() - - return r_loss, g_loss - - def _train_discriminator(self, parameters, snapshots): - """ - Train the discriminator model. - - :param torch.Tensor parameters: The input tensor. - :param torch.Tensor snapshots: The target tensor. - :return: The residual loss and the generator loss. - :rtype: tuple[torch.Tensor, torch.Tensor] - """ - self.optimizer_discriminator.instance.zero_grad() - - # Generate a batch of images - generated_snapshots = self.sample(parameters) - - # Discriminator pass - d_real = self.discriminator([snapshots, parameters]) - d_fake = self.discriminator([generated_snapshots, parameters]) - - # evaluate loss - d_loss_real = self._loss_fn(d_real, snapshots) - d_loss_fake = self._loss_fn(d_fake, generated_snapshots.detach()) - d_loss = d_loss_real - self.k * d_loss_fake - - # backward step - d_loss.backward() - self.optimizer_discriminator.instance.step() - self.scheduler_discriminator.instance.step() - - return d_loss_real, d_loss_fake, d_loss - - def _update_weights(self, d_loss_real, d_loss_fake): - """ - Update the weights of the generator and discriminator models. - - :param torch.Tensor d_loss_real: The discriminator loss computed on - dataset samples. - :param torch.Tensor d_loss_fake: The discriminator loss computed on - generated samples. - :return: The difference between the loss computed on the dataset samples - and the loss computed on the generated samples. - :rtype: torch.Tensor - """ - - diff = torch.mean(self.gamma * d_loss_real - d_loss_fake) - - # Update weight term for fake samples - self.k += self.lambda_k * diff.item() - self.k = min(max(self.k, 0), 1) # Constraint to interval [0, 1] - return diff - - def optimization_cycle(self, batch): - """ - The optimization cycle for the GAROM solver. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The losses computed for all conditions in the batch, casted - to a subclass of :class:`torch.Tensor`. It should return a dict - containing the condition name and the associated scalar loss. - :rtype: dict - """ - condition_loss = {} - for condition_name, points in batch: - parameters, snapshots = ( - points["input"], - points["target"], - ) - d_loss_real, d_loss_fake, d_loss = self._train_discriminator( - parameters, snapshots - ) - r_loss, g_loss = self._train_generator(parameters, snapshots) - diff = self._update_weights(d_loss_real, d_loss_fake) - condition_loss[condition_name] = r_loss - - # some extra logging - self.store_log("d_loss", float(d_loss), self.get_batch_size(batch)) - self.store_log("g_loss", float(g_loss), self.get_batch_size(batch)) - self.store_log( - "stability_metric", - float(d_loss_real + torch.abs(diff)), - self.get_batch_size(batch), - ) - return condition_loss - - def validation_step(self, batch): - """ - The validation step for the PINN solver. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The loss of the validation step. - :rtype: torch.Tensor - """ - condition_loss = {} - for condition_name, points in batch: - parameters, snapshots = ( - points["input"], - points["target"], - ) - snapshots_gen = self.generator(parameters) - condition_loss[condition_name] = self._loss_fn( - snapshots, snapshots_gen - ) - loss = self.weighting.aggregate(condition_loss) - self.store_log("val_loss", loss, self.get_batch_size(batch)) - return loss - - def test_step(self, batch): - """ - The test step for the PINN solver. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The loss of the test step. - :rtype: torch.Tensor - """ - condition_loss = {} - for condition_name, points in batch: - parameters, snapshots = ( - points["input"], - points["target"], - ) - snapshots_gen = self.generator(parameters) - condition_loss[condition_name] = self._loss_fn( - snapshots, snapshots_gen - ) - loss = self.weighting.aggregate(condition_loss) - self.store_log("test_loss", loss, self.get_batch_size(batch)) - return loss - - @property - def generator(self): - """ - The generator model. - - :return: The generator model. - :rtype: torch.nn.Module - """ - return self.models[0] - - @property - def discriminator(self): - """ - The discriminator model. - - :return: The discriminator model. - :rtype: torch.nn.Module - """ - return self.models[1] - - @property - def optimizer_generator(self): - """ - The optimizer for the generator. - - :return: The optimizer for the generator. - :rtype: Optimizer - """ - return self.optimizers[0] - - @property - def optimizer_discriminator(self): - """ - The optimizer for the discriminator. - - :return: The optimizer for the discriminator. - :rtype: Optimizer - """ - return self.optimizers[1] - - @property - def scheduler_generator(self): - """ - The scheduler for the generator. - - :return: The scheduler for the generator. - :rtype: Scheduler - """ - return self.schedulers[0] - - @property - def scheduler_discriminator(self): - """ - The scheduler for the discriminator. - - :return: The scheduler for the discriminator. - :rtype: Scheduler - """ - return self.schedulers[1] diff --git a/pina/solver/mixin.py b/pina/solver/mixin.py new file mode 100644 index 000000000..53ab1acc4 --- /dev/null +++ b/pina/solver/mixin.py @@ -0,0 +1,29 @@ +"""Module for solver mixins.""" + +__all__ = [ + "SingleModelMixin", + "MultiModelMixin", + "EnsembleMixin", + "ManualOptimizationMixin", + "ConditionAggregatorMixin", + "AutoregressiveMixin", + "PhysicsInformedMixin", + "ResidualBasedAttentionMixin", + "GradientEnhancedMixin", +] + +from pina._src.solver.mixin.single_model_mixin import SingleModelMixin +from pina._src.solver.mixin.multi_model_mixin import MultiModelMixin +from pina._src.solver.mixin.ensemble_mixin import EnsembleMixin +from pina._src.solver.mixin.manual_optimization_mixin import ( + ManualOptimizationMixin, +) +from pina._src.solver.mixin.condition_aggregator_mixin import ( + ConditionAggregatorMixin, +) +from pina._src.solver.mixin.autoregressive_mixin import AutoregressiveMixin +from pina._src.solver.mixin.physics_informed_mixin import PhysicsInformedMixin +from pina._src.solver.mixin.residual_based_attention_mixin import ( + ResidualBasedAttentionMixin, +) +from pina._src.solver.mixin.gradient_enhanced_mixin import GradientEnhancedMixin diff --git a/pina/solver/physics_informed_solver/__init__.py b/pina/solver/physics_informed_solver/__init__.py deleted file mode 100644 index f0fb8ebcd..000000000 --- a/pina/solver/physics_informed_solver/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Module for the Physics-Informed solvers.""" - -__all__ = [ - "PINNInterface", - "PINN", - "GradientPINN", - "CausalPINN", - "CompetitivePINN", - "SelfAdaptivePINN", - "RBAPINN", -] - -from .pinn_interface import PINNInterface -from .pinn import PINN -from .rba_pinn import RBAPINN -from .causal_pinn import CausalPINN -from .gradient_pinn import GradientPINN -from .competitive_pinn import CompetitivePINN -from .self_adaptive_pinn import SelfAdaptivePINN diff --git a/pina/solver/physics_informed_solver/causal_pinn.py b/pina/solver/physics_informed_solver/causal_pinn.py deleted file mode 100644 index ab085be2d..000000000 --- a/pina/solver/physics_informed_solver/causal_pinn.py +++ /dev/null @@ -1,219 +0,0 @@ -"""Module for the Causal PINN solver.""" - -import torch - -from ...problem import TimeDependentProblem -from .pinn import PINN -from ...utils import check_consistency - - -class CausalPINN(PINN): - r""" - Causal Physics-Informed Neural Network (CausalPINN) solver class. - This class implements the Causal Physics-Informed Neural Network solver, - using a user specified ``model`` to solve a specific ``problem``. - It can be used to solve both forward and inverse problems. - - The Causal Physics-Informed Neural Network solver aims to find the solution - :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - minimizing the loss function: - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N_t}\sum_{i=1}^{N_t} - \omega_{i}\mathcal{L}_r(t_i), - - where: - - .. math:: - \mathcal{L}_r(t) = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i, t)) + - \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i, t)) - - and, - - .. math:: - \omega_i = \exp\left(\epsilon \sum_{k=1}^{i-1}\mathcal{L}_r(t_k)\right). - - :math:`\epsilon` is an hyperparameter, set by default to :math:`100`, while - :math:`\mathcal{L}` is a specific loss function, typically the MSE: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - - **Original reference**: Wang, Sifan, Shyam Sankaran, and Paris - Perdikaris. - *Respecting causality for training physics-informed - neural networks.* - Computer Methods in Applied Mechanics and Engineering 421 (2024):116813. - DOI: `10.1016 `_. - - .. note:: - This class is only compatible with problems that inherit from the - :class:`~pina.problem.time_dependent_problem.TimeDependentProblem` - class. - """ - - def __init__( - self, - problem, - model, - optimizer=None, - scheduler=None, - weighting=None, - loss=None, - eps=100, - ): - """ - Initialization of the :class:`CausalPINN` class. - - :param AbstractProblem problem: The problem to be solved. It must - inherit from at least - :class:`~pina.problem.time_dependent_problem.TimeDependentProblem`. - :param torch.nn.Module model: The neural network model to be used. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param torch.optim.LRScheduler scheduler: Learning rate scheduler. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - :param float eps: The exponential decay parameter. Default is ``100``. - :raises ValueError: If the problem is not a TimeDependentProblem. - """ - super().__init__( - model=model, - problem=problem, - optimizer=optimizer, - scheduler=scheduler, - weighting=weighting, - loss=loss, - ) - - # checking consistency - check_consistency(eps, (int, float)) - self._eps = eps - if not isinstance(self.problem, TimeDependentProblem): - raise ValueError( - "Casual PINN works only for problems" - "inheriting from TimeDependentProblem." - ) - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the physics-informed solver based on the - provided samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation. - :return: The computed physics loss. - :rtype: LabelTensor - """ - # split sequentially ordered time tensors into chunks - chunks, labels = self._split_tensor_into_chunks(samples) - # compute residuals - this correspond to ordered loss functions - # values for each time step. Apply `flatten` to ensure obtaining - # a tensor of shape #chunks after concatenating the residuals - time_loss = [] - for chunk in chunks: - chunk.labels = labels - # classical PINN loss - residual = self.compute_residual(samples=chunk, equation=equation) - loss_val = self._loss_fn( - torch.zeros_like(residual, requires_grad=True), residual - ) - time_loss.append(loss_val) - - # concatenate residuals - time_loss = torch.stack(time_loss) - # compute weights without storing the gradient - with torch.no_grad(): - weights = self._compute_weights(time_loss) - return (weights * time_loss).mean() - - @property - def eps(self): - """ - The exponential decay parameter. - - :return: The exponential decay parameter. - :rtype: float - """ - return self._eps - - @eps.setter - def eps(self, value): - """ - Set the exponential decay parameter. - - :param float value: The exponential decay parameter. - """ - check_consistency(value, float) - self._eps = value - - def _sort_label_tensor(self, tensor): - """ - Sort the tensor with respect to the temporal variables. - - :param LabelTensor tensor: The tensor to be sorted. - :return: The tensor sorted with respect to the temporal variables. - :rtype: LabelTensor - """ - # labels input tensors - labels = tensor.labels - # extract time tensor - time_tensor = tensor.extract(self.problem.temporal_domain.variables) - # sort the time tensors (this is very bad for GPU) - _, idx = torch.sort(time_tensor.tensor.flatten()) - tensor = tensor[idx] - tensor.labels = labels - return tensor - - def _split_tensor_into_chunks(self, tensor): - """ - Split the tensor into chunks based on time. - - :param LabelTensor tensor: The tensor to be split. - :return: A tuple containing the list of tensor chunks and the - corresponding labels. - :rtype: tuple[list[LabelTensor], list[str]] - """ - # extract labels - labels = tensor.labels - # sort input tensor based on time - tensor = self._sort_label_tensor(tensor) - # extract time tensor - time_tensor = tensor.extract(self.problem.temporal_domain.variables) - # count unique tensors in time - _, idx_split = time_tensor.unique(return_counts=True) - # split the tensor based on time - chunks = torch.split(tensor, tuple(idx_split)) - return chunks, labels - - def _compute_weights(self, loss): - """ - Compute the weights for the physics loss based on the cumulative loss. - - :param LabelTensor loss: The physics loss values. - :return: The computed weights for the physics loss. - :rtype: LabelTensor - """ - # compute comulative loss and multiply by epsilon - cumulative_loss = self._eps * torch.cumsum(loss, dim=0) - # return the exponential of the negative weighted cumulative sum - return torch.exp(-cumulative_loss) diff --git a/pina/solver/physics_informed_solver/competitive_pinn.py b/pina/solver/physics_informed_solver/competitive_pinn.py deleted file mode 100644 index 5375efba1..000000000 --- a/pina/solver/physics_informed_solver/competitive_pinn.py +++ /dev/null @@ -1,271 +0,0 @@ -"""Module for the Competitive PINN solver.""" - -import copy -import torch - -from ...problem import InverseProblem -from .pinn_interface import PINNInterface -from ..solver import MultiSolverInterface - - -class CompetitivePINN(PINNInterface, MultiSolverInterface): - r""" - Competitive Physics-Informed Neural Network (CompetitivePINN) solver class. - This class implements the Competitive Physics-Informed Neural Network - solver, using a user specified ``model`` to solve a specific ``problem``. - It can be used to solve both forward and inverse problems. - - The Competitive Physics-Informed Neural Network solver aims to find the - solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential - problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - minimizing the loss function with respect to the model parameters, while - maximizing it with respect to the discriminator parameters: - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(D(\mathbf{x}_i)\mathcal{A}[\mathbf{u}](\mathbf{x}_i))+ - \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(D(\mathbf{x}_i)\mathcal{B}[\mathbf{u}](\mathbf{x}_i)), - - where :math:D is the discriminator network, which identifies the points - where the model performs worst, and :math:\mathcal{L} is a specific loss - function, typically the MSE: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - - **Original reference**: Zeng, Qi, et al. - *Competitive physics informed networks.* - International Conference on Learning Representations, ICLR 2022 - `OpenReview Preprint `_. - """ - - def __init__( - self, - problem, - model, - discriminator=None, - optimizer_model=None, - optimizer_discriminator=None, - scheduler_model=None, - scheduler_discriminator=None, - weighting=None, - loss=None, - ): - """ - Initialization of the :class:`CompetitivePINN` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module model: The neural network model to be used. - :param torch.nn.Module discriminator: The discriminator to be used. - If ``None``, the discriminator is a deepcopy of the ``model``. - Default is ``None``. - :param torch.optim.Optimizer optimizer_model: The optimizer of the - ``model``. If ``None``, the :class:`torch.optim.Adam` optimizer is - used. Default is ``None``. - :param torch.optim.Optimizer optimizer_discriminator: The optimizer of - the ``discriminator``. If ``None``, the :class:`torch.optim.Adam` - optimizer is used. Default is ``None``. - :param Scheduler scheduler_model: Learning rate scheduler for the - ``model``. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param Scheduler scheduler_discriminator: Learning rate scheduler for - the ``discriminator``. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - """ - if discriminator is None: - discriminator = copy.deepcopy(model) - - super().__init__( - models=[model, discriminator], - problem=problem, - optimizers=[optimizer_model, optimizer_discriminator], - schedulers=[scheduler_model, scheduler_discriminator], - weighting=weighting, - loss=loss, - ) - - def forward(self, x): - """ - Forward pass. - - :param LabelTensor x: Input tensor. - :return: The output of the neural network. - :rtype: LabelTensor - """ - return self.neural_net(x) - - def training_step(self, batch): - """ - Solver training step, overridden to perform manual optimization. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The aggregated loss. - :rtype: LabelTensor - """ - # train model - self.optimizer_model.instance.zero_grad() - loss = super().training_step(batch) - self.manual_backward(loss) - self.optimizer_model.instance.step() - self.scheduler_model.instance.step() - - # train discriminator - self.optimizer_discriminator.instance.zero_grad() - loss = super().training_step(batch) - self.manual_backward(-loss) - self.optimizer_discriminator.instance.step() - self.scheduler_discriminator.instance.step() - - return loss - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the physics-informed solver based on the - provided samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation. - :return: The computed physics loss. - :rtype: LabelTensor - """ - # Compute discriminator bets - discriminator_bets = self.discriminator(samples) - - # Compute residual and multiply discriminator_bets - residual = self.compute_residual(samples=samples, equation=equation) - residual = residual * discriminator_bets - - # Compute competitive residual. - loss_val = self._loss_fn( - torch.zeros_like(residual, requires_grad=True), - residual, - ) - return loss_val - - def loss_data(self, input, target): - """ - Compute the data loss for the PINN solver by evaluating the loss - between the network's output and the true solution. This method should - not be overridden, if not intentionally. - - :param input: The input to the neural network. - :type input: LabelTensor - :param target: The target to compare with the network's output. - :type target: LabelTensor - :return: The supervised loss, averaged over the number of observations. - :rtype: LabelTensor - """ - return self._loss_fn(self.forward(input), target) - - def configure_optimizers(self): - """ - Optimizer configuration. - - :return: The optimizers and the schedulers - :rtype: tuple[list[Optimizer], list[Scheduler]] - """ - # If the problem is an InverseProblem, add the unknown parameters - # to the parameters to be optimized - self.optimizer_model.hook(self.neural_net.parameters()) - self.optimizer_discriminator.hook(self.discriminator.parameters()) - if isinstance(self.problem, InverseProblem): - self.optimizer_model.instance.add_param_group( - { - "params": [ - self._params[var] - for var in self.problem.unknown_variables - ] - } - ) - self.scheduler_model.hook(self.optimizer_model) - self.scheduler_discriminator.hook(self.optimizer_discriminator) - return ( - [ - self.optimizer_model.instance, - self.optimizer_discriminator.instance, - ], - [ - self.scheduler_model.instance, - self.scheduler_discriminator.instance, - ], - ) - - @property - def neural_net(self): - """ - The model. - - :return: The model. - :rtype: torch.nn.Module - """ - return self.models[0] - - @property - def discriminator(self): - """ - The discriminator. - - :return: The discriminator. - :rtype: torch.nn.Module - """ - return self.models[1] - - @property - def optimizer_model(self): - """ - The optimizer associated to the model. - - :return: The optimizer for the model. - :rtype: Optimizer - """ - return self.optimizers[0] - - @property - def optimizer_discriminator(self): - """ - The optimizer associated to the discriminator. - - :return: The optimizer for the discriminator. - :rtype: Optimizer - """ - return self.optimizers[1] - - @property - def scheduler_model(self): - """ - The scheduler associated to the model. - - :return: The scheduler for the model. - :rtype: Scheduler - """ - return self.schedulers[0] - - @property - def scheduler_discriminator(self): - """ - The scheduler associated to the discriminator. - - :return: The scheduler for the discriminator. - :rtype: Scheduler - """ - return self.schedulers[1] diff --git a/pina/solver/physics_informed_solver/gradient_pinn.py b/pina/solver/physics_informed_solver/gradient_pinn.py deleted file mode 100644 index 0de431c41..000000000 --- a/pina/solver/physics_informed_solver/gradient_pinn.py +++ /dev/null @@ -1,130 +0,0 @@ -"""Module for the Gradient PINN solver.""" - -import torch - -from .pinn import PINN -from ...operator import grad -from ...problem import SpatialProblem - - -class GradientPINN(PINN): - r""" - Gradient Physics-Informed Neural Network (GradientPINN) solver class. - This class implements the Gradient Physics-Informed Neural Network solver, - using a user specified ``model`` to solve a specific ``problem``. - It can be used to solve both forward and inverse problems. - - The Gradient Physics-Informed Neural Network solver aims to find the - solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential - problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - minimizing the loss function; - - .. math:: - \mathcal{L}_{\rm{problem}} =& \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + - \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)) + - &\frac{1}{N}\sum_{i=1}^N - \nabla_{\mathbf{x}}\mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + - \frac{1}{N}\sum_{i=1}^N - \nabla_{\mathbf{x}}\mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)) - - - where :math:`\mathcal{L}` is a specific loss function, typically the MSE: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - - **Original reference**: Yu, Jeremy, et al. - *Gradient-enhanced physics-informed neural networks for forward and - inverse PDE problems.* - Computer Methods in Applied Mechanics and Engineering 393 (2022):114823. - DOI: `10.1016 `_. - - .. note:: - This class is only compatible with problems that inherit from the - :class:`~pina.problem.spatial_problem.SpatialProblem` class. - """ - - def __init__( - self, - problem, - model, - optimizer=None, - scheduler=None, - weighting=None, - loss=None, - ): - """ - Initialization of the :class:`GradientPINN` class. - - :param AbstractProblem problem: The problem to be solved. - It must inherit from at least - :class:`~pina.problem.spatial_problem.SpatialProblem` to compute the - gradient of the loss. - :param torch.nn.Module model: The neural network model to be used. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Scheduler scheduler: Learning rate scheduler. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - :raises ValueError: If the problem is not a SpatialProblem. - """ - super().__init__( - model=model, - problem=problem, - optimizer=optimizer, - scheduler=scheduler, - weighting=weighting, - loss=loss, - ) - - if not isinstance(self.problem, SpatialProblem): - raise ValueError( - "Gradient PINN computes the gradient of the " - "PINN loss with respect to the spatial " - "coordinates, thus the PINA problem must be " - "a SpatialProblem." - ) - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the physics-informed solver based on the - provided samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation. - :return: The computed physics loss. - :rtype: LabelTensor - """ - # classical PINN loss - residual = self.compute_residual(samples=samples, equation=equation) - loss_value = self._loss_fn( - torch.zeros_like(residual, requires_grad=True), residual - ) - - # gradient PINN loss - loss_value = loss_value.reshape(-1, 1) - loss_value.labels = ["__loss"] - loss_grad = grad(loss_value, samples, d=self.problem.spatial_variables) - g_loss_phys = self._loss_fn( - torch.zeros_like(loss_grad, requires_grad=True), loss_grad - ) - return loss_value + g_loss_phys diff --git a/pina/solver/physics_informed_solver/pinn.py b/pina/solver/physics_informed_solver/pinn.py deleted file mode 100644 index 914d01451..000000000 --- a/pina/solver/physics_informed_solver/pinn.py +++ /dev/null @@ -1,133 +0,0 @@ -"""Module for the Physics-Informed Neural Network solver.""" - -import torch - -from .pinn_interface import PINNInterface -from ..solver import SingleSolverInterface -from ...problem import InverseProblem - - -class PINN(PINNInterface, SingleSolverInterface): - r""" - Physics-Informed Neural Network (PINN) solver class. - This class implements Physics-Informed Neural Network solver, using a user - specified ``model`` to solve a specific ``problem``. - It can be used to solve both forward and inverse problems. - - The Physics Informed Neural Network solver aims to find the solution - :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - minimizing the loss function: - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) + - \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i)), - - where :math:`\mathcal{L}` is a specific loss function, typically the MSE: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - - **Original reference**: Karniadakis, G. E., Kevrekidis, I. G., Lu, L., - Perdikaris, P., Wang, S., & Yang, L. (2021). - *Physics-informed machine learning.* - Nature Reviews Physics, 3, 422-440. - DOI: `10.1038 `_. - """ - - def __init__( - self, - problem, - model, - optimizer=None, - scheduler=None, - weighting=None, - loss=None, - ): - """ - Initialization of the :class:`PINN` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module model: The neural network model to be used. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Scheduler scheduler: Learning rate scheduler. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - """ - super().__init__( - model=model, - problem=problem, - optimizer=optimizer, - scheduler=scheduler, - weighting=weighting, - loss=loss, - ) - - def loss_data(self, input, target): - """ - Compute the data loss for the PINN solver by evaluating the loss - between the network's output and the true solution. This method should - not be overridden, if not intentionally. - - :param input: The input to the neural network. - :type input: LabelTensor - :param target: The target to compare with the network's output. - :type target: LabelTensor - :return: The supervised loss, averaged over the number of observations. - :rtype: LabelTensor - """ - return self._loss_fn(self.forward(input), target) - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the physics-informed solver based on the - provided samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation. - :return: The computed physics loss. - :rtype: LabelTensor - """ - residuals = self.compute_residual(samples, equation) - return self._loss_fn(residuals, torch.zeros_like(residuals)) - - def configure_optimizers(self): - """ - Optimizer configuration for the PINN solver. - - :return: The optimizers and the schedulers - :rtype: tuple[list[Optimizer], list[Scheduler]] - """ - # If the problem is an InverseProblem, add the unknown parameters - # to the parameters to be optimized. - self.optimizer.hook(self.model.parameters()) - if isinstance(self.problem, InverseProblem): - self.optimizer.instance.add_param_group( - { - "params": [ - self._params[var] - for var in self.problem.unknown_variables - ] - } - ) - self.scheduler.hook(self.optimizer) - return ([self.optimizer.instance], [self.scheduler.instance]) diff --git a/pina/solver/physics_informed_solver/pinn_interface.py b/pina/solver/physics_informed_solver/pinn_interface.py deleted file mode 100644 index 65a0dd78f..000000000 --- a/pina/solver/physics_informed_solver/pinn_interface.py +++ /dev/null @@ -1,220 +0,0 @@ -"""Module for the Physics-Informed Neural Network Interface.""" - -from abc import ABCMeta, abstractmethod -import warnings -import torch - -from ...utils import custom_warning_format -from ..supervised_solver import SupervisedSolverInterface -from ...condition import ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, -) - -# set the warning for torch >= 2.8 compile -warnings.formatwarning = custom_warning_format -warnings.filterwarnings("always", category=UserWarning) - - -class PINNInterface(SupervisedSolverInterface, metaclass=ABCMeta): - """ - Base class for Physics-Informed Neural Network (PINN) solvers, implementing - the :class:`~pina.solver.solver.SolverInterface` class. - - The `PINNInterface` class can be used to define PINNs that work with one or - multiple optimizers and/or models. By default, it is compatible with - problems defined by :class:`~pina.problem.abstract_problem.AbstractProblem`, - and users can choose the problem type the solver is meant to address. - """ - - accepted_conditions_types = ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, - ) - - def __init__(self, **kwargs): - """ - Initialization of the :class:`PINNInterface` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - :param kwargs: Additional keyword arguments to be passed to the - :class:`~pina.solver.supervised_solver.SupervisedSolverInterface` - class. - """ - kwargs["use_lt"] = True - super().__init__(**kwargs) - - # current condition name - self.__metric = None - - def setup(self, stage): - """ - Setup method executed at the beginning of training and testing. - - This method compiles the model only if the installed torch version - is earlier than 2.8, due to known issues with later versions - (see https://github.com/mathLab/PINA/issues/621). - - .. warning:: - For torch >= 2.8, compilation is disabled. Forcing compilation - on these versions may cause runtime errors or unstable behavior. - - :param str stage: The current stage of the training process - (e.g., ``fit``, ``validate``, ``test``, ``predict``). - :return: The result of the parent class ``setup`` method. - :rtype: Any - """ - # Override the compilation, compiling only for torch < 2.8, see - # related issue at https://github.com/mathLab/PINA/issues/621 - if torch.__version__ >= "2.8": - self.trainer.compile = False - warnings.warn( - "Compilation is disabled for torch >= 2.8. " - "Forcing compilation may cause runtime errors or instability.", - UserWarning, - ) - return super().setup(stage) - - def optimization_cycle(self, batch, loss_residuals=None): - """ - The optimization cycle for the PINN solver. - - This method allows to call `_run_optimization_cycle` with the physics - loss as argument, thus distinguishing the training step from the - validation and test steps. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The losses computed for all conditions in the batch, casted - to a subclass of :class:`torch.Tensor`. It should return a dict - containing the condition name and the associated scalar loss. - :rtype: dict - """ - # which losses to use - if loss_residuals is None: - loss_residuals = self.loss_phys - # compute optimization cycle - condition_loss = {} - for condition_name, points in batch: - self.__metric = condition_name - # if equations are passed - if "target" not in points: - input_pts = points["input"] - condition = self.problem.conditions[condition_name] - loss = loss_residuals( - input_pts.requires_grad_(), condition.equation - ) - # if data are passed - else: - input_pts = points["input"] - output_pts = points["target"] - loss = self.loss_data( - input=input_pts.requires_grad_(), target=output_pts - ) - # append loss - condition_loss[condition_name] = loss - return condition_loss - - @torch.set_grad_enabled(True) - def validation_step(self, batch): - """ - The validation step for the PINN solver. It returns the average residual - computed with the ``loss`` function not aggregated. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The loss of the validation step. - :rtype: torch.Tensor - """ - return super().validation_step( - batch, loss_residuals=self._residual_loss - ) - - @torch.set_grad_enabled(True) - def test_step(self, batch): - """ - The test step for the PINN solver. It returns the average residual - computed with the ``loss`` function not aggregated. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The loss of the test step. - :rtype: torch.Tensor - """ - return super().test_step(batch, loss_residuals=self._residual_loss) - - def loss_data(self, input, target): - """ - Compute the data loss for the PINN solver by evaluating the loss - between the network's output and the true solution. This method should - be overridden by the derived class. - - :param LabelTensor input: The input to the neural network. - :param LabelTensor target: The target to compare with the - network's output. - :return: The supervised loss, averaged over the number of observations. - :rtype: LabelTensor - :raises NotImplementedError: If the method is not implemented. - """ - raise NotImplementedError( - "PINN is being used in a supervised learning context, but the " - "'loss_data' method has not been implemented. " - ) - - @abstractmethod - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the physics-informed solver based on the - provided samples and equation. This method must be overridden in - subclasses. It distinguishes different types of PINN solvers. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation. - :return: The computed physics loss. - :rtype: LabelTensor - """ - - def compute_residual(self, samples, equation): - """ - Compute the residuals of the equation. - - :param LabelTensor samples: The samples to evaluate the loss. - :param EquationInterface equation: The governing equation. - :return: The residual of the solution of the model. - :rtype: LabelTensor - """ - residual = equation.residual( - samples, self.forward(samples), self._params - ) - return residual - - def _residual_loss(self, samples, equation): - """ - Computes the physics loss for the physics-informed solver based on the - provided samples and equation. This method should never be overridden - by the user, if not intentionally, - since it is used internally to compute validation loss. - - - :param LabelTensor samples: The samples to evaluate the loss. - :param EquationInterface equation: The governing equation. - :return: The residual loss. - :rtype: torch.Tensor - """ - residuals = self.compute_residual(samples, equation) - return self._loss_fn(residuals, torch.zeros_like(residuals)) - - @property - def current_condition_name(self): - """ - The current condition name. - - :return: The current condition name. - :rtype: str - """ - return self.__metric diff --git a/pina/solver/physics_informed_solver/rba_pinn.py b/pina/solver/physics_informed_solver/rba_pinn.py deleted file mode 100644 index 5c8d50fed..000000000 --- a/pina/solver/physics_informed_solver/rba_pinn.py +++ /dev/null @@ -1,327 +0,0 @@ -"""Module for the Residual-Based Attention PINN solver.""" - -import torch - -from .pinn import PINN -from ...utils import check_consistency - - -class RBAPINN(PINN): - r""" - Residual-based Attention Physics-Informed Neural Network (RBAPINN) solver - class. This class implements the Residual-based Attention Physics-Informed - Neural Network solver, using a user specified ``model`` to solve a specific - ``problem``. It can be used to solve both forward and inverse problems. - - The Residual-based Attention Physics-Informed Neural Network solver aims to - find the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a - differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - minimizing the loss function: - - .. math:: - - \mathcal{L}_{\rm{problem}} = \frac{1}{N} \sum_{i=1}^{N_\Omega} - \lambda_{\Omega}^{i} \mathcal{L} \left( \mathcal{A} - [\mathbf{u}](\mathbf{x}) \right) + \frac{1}{N} - \sum_{i=1}^{N_{\partial\Omega}} - \lambda_{\partial\Omega}^{i} \mathcal{L} - \left( \mathcal{B}[\mathbf{u}](\mathbf{x}) - \right), - - denoting the weights as: - :math:`\lambda_{\Omega}^1, \dots, \lambda_{\Omega}^{N_\Omega}` and - :math:`\lambda_{\partial \Omega}^1, \dots, - \lambda_{\Omega}^{N_\partial \Omega}` - for :math:`\Omega` and :math:`\partial \Omega`, respectively. - - Residual-based Attention Physics-Informed Neural Network updates the weights - of the residuals at every epoch as follows: - - .. math:: - - \lambda_i^{k+1} \leftarrow \gamma\lambda_i^{k} + - \eta\frac{\lvert r_i\rvert}{\max_j \lvert r_j\rvert}, - - where :math:`r_i` denotes the residual at point :math:`i`, :math:`\gamma` - denotes the decay rate, and :math:`\eta` is the learning rate for the - weights' update. - - .. seealso:: - **Original reference**: Sokratis J. Anagnostopoulos, Juan D. Toscano, - Nikolaos Stergiopulos, and George E. Karniadakis. - *Residual-based attention and connection to information - bottleneck theory in PINNs.* - Computer Methods in Applied Mechanics and Engineering 421 (2024): 116805 - DOI: `10.1016/j.cma.2024.116805 - `_. - """ - - def __init__( - self, - problem, - model, - optimizer=None, - scheduler=None, - weighting=None, - loss=None, - eta=0.001, - gamma=0.999, - ): - """ - Initialization of the :class:`RBAPINN` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module model: The neural network model to be used. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Scheduler scheduler: Learning rate scheduler. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - :param float | int eta: The learning rate for the weights of the - residuals. Default is ``0.001``. - :param float gamma: The decay parameter in the update of the weights - of the residuals. Must be between ``0`` and ``1``. - Default is ``0.999``. - :raises: ValueError if `gamma` is not in the range (0, 1). - :raises: ValueError if `eta` is not greater than 0. - """ - super().__init__( - model=model, - problem=problem, - optimizer=optimizer, - scheduler=scheduler, - weighting=weighting, - loss=loss, - ) - - # check consistency - check_consistency(eta, (float, int)) - check_consistency(gamma, float) - - # Validate range for gamma - if not 0 < gamma < 1: - raise ValueError( - f"Invalid range: expected 0 < gamma < 1, but got {gamma}" - ) - - # Validate range for eta - if eta <= 0: - raise ValueError(f"Invalid range: expected eta > 0, but got {eta}") - - # Initialize parameters - self.eta = eta - self.gamma = gamma - - # Initialize the weight of each point to 0 - self.weights = {} - for cond, data in self.problem.input_pts.items(): - buffer_tensor = torch.zeros((len(data), 1), device=self.device) - self.register_buffer(f"weight_{cond}", buffer_tensor) - self.weights[cond] = getattr(self, f"weight_{cond}") - - # Extract the reduction method from the loss function - self._reduction = self._loss_fn.reduction - - # Set the loss function to return non-aggregated losses - self._loss_fn = type(self._loss_fn)(reduction="none") - - def on_train_start(self): - """ - Ensure that all residual weight buffers registered during initialization - are moved to the correct computation device. - """ - # Move all weight buffers to the correct device - for cond in self.problem.input_pts: - - # Get the buffer for the current condition - weight_buf = getattr(self, f"weight_{cond}") - - # Move the buffer to the correct device - weight_buf.data = weight_buf.data.to(self.device) - self.weights[cond] = weight_buf - - def training_step(self, batch, batch_idx, **kwargs): - """ - Solver training step. It computes the optimization cycle and aggregates - the losses using the ``weighting`` attribute. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param int batch_idx: The index of the current batch. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The loss of the training step. - :rtype: torch.Tensor - """ - loss = self._optimization_cycle( - batch=batch, batch_idx=batch_idx, **kwargs - ) - self.store_log("train_loss", loss, self.get_batch_size(batch)) - return loss - - @torch.set_grad_enabled(True) - def validation_step(self, batch, **kwargs): - """ - The validation step for the PINN solver. It returns the average residual - computed with the ``loss`` function not aggregated. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The loss of the validation step. - :rtype: torch.Tensor - """ - losses = self.optimization_cycle(batch=batch, **kwargs) - - # Aggregate losses for each condition - for cond, loss in losses.items(): - losses[cond] = self._apply_reduction(loss=losses[cond]) - - loss = (sum(losses.values()) / len(losses)).as_subclass(torch.Tensor) - self.store_log("val_loss", loss, self.get_batch_size(batch)) - return loss - - @torch.set_grad_enabled(True) - def test_step(self, batch, **kwargs): - """ - The test step for the PINN solver. It returns the average residual - computed with the ``loss`` function not aggregated. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The loss of the test step. - :rtype: torch.Tensor - """ - losses = self.optimization_cycle(batch=batch, **kwargs) - - # Aggregate losses for each condition - for cond, loss in losses.items(): - losses[cond] = self._apply_reduction(loss=losses[cond]) - - loss = (sum(losses.values()) / len(losses)).as_subclass(torch.Tensor) - self.store_log("test_loss", loss, self.get_batch_size(batch)) - return loss - - def _optimization_cycle(self, batch, batch_idx, **kwargs): - """ - Aggregate the loss for each condition in the batch. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param int batch_idx: The index of the current batch. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The losses computed for all conditions in the batch, casted - to a subclass of :class:`torch.Tensor`. It should return a dict - containing the condition name and the associated scalar loss. - :rtype: dict - """ - # compute non-aggregated residuals - residuals = self.optimization_cycle(batch) - - # update weights based on residuals - self._update_weights(batch, batch_idx, residuals) - - # compute losses - losses = {} - for cond, res in residuals.items(): - - # Get the correct indices for the weights. Modulus is used according - # to the number of points in the condition, as in the PinaDataset. - len_res = len(res) - idx = torch.arange( - batch_idx * len_res, - (batch_idx + 1) * len_res, - device=self.weights[cond].device, - ) % len(self.problem.input_pts[cond]) - - losses[cond] = self._apply_reduction( - loss=(res * self.weights[cond][idx]) - ) - - # store log - self.store_log( - f"{cond}_loss", losses[cond].item(), self.get_batch_size(batch) - ) - - # clamp unknown parameters in InverseProblem (if needed) - self._clamp_params() - - # aggregate - loss = self.weighting.aggregate(losses).as_subclass(torch.Tensor) - - return loss - - def _update_weights(self, batch, batch_idx, residuals): - """ - Update weights based on residuals. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param int batch_idx: The index of the current batch. - :param dict residuals: A dictionary containing the residuals for each - condition. The keys are the condition names and the values are the - residuals as tensors. - """ - # Iterate over each condition in the batch - for cond, data in batch: - - # Compute normalized residuals - res = residuals[cond] - res_abs = torch.linalg.vector_norm(res, ord=2, dim=1, keepdim=True) - r_norm = (self.eta * res_abs) / (res_abs.max() + 1e-12) - - # Get the correct indices for the weights. Modulus is used according - # to the number of points in the condition, as in the PinaDataset. - len_pts = len(data["input"]) - idx = torch.arange( - batch_idx * len_pts, - (batch_idx + 1) * len_pts, - device=self.weights[cond].device, - ) % len(self.problem.input_pts[cond]) - - # Update weights - weights = self.weights[cond] - update = self.gamma * weights[idx] + r_norm - weights[idx] = update.detach() - - def _apply_reduction(self, loss): - """ - Apply the specified reduction to the loss. The reduction is deferred - until the end of the optimization cycle to allow residual-based weights - to be applied to each point beforehand. - - :param torch.Tensor loss: The loss tensor to be reduced. - :return: The reduced loss tensor. - :rtype: torch.Tensor - :raises ValueError: If the reduction method is neither "mean" nor "sum". - """ - # Apply the specified reduction method - if self._reduction == "mean": - return loss.mean() - if self._reduction == "sum": - return loss.sum() - - # Raise an error if the reduction method is not recognized - raise ValueError( - f"Unknown reduction: {self._reduction}." - " Supported reductions are 'mean' and 'sum'." - ) diff --git a/pina/solver/physics_informed_solver/self_adaptive_pinn.py b/pina/solver/physics_informed_solver/self_adaptive_pinn.py deleted file mode 100644 index b1d2a2cb4..000000000 --- a/pina/solver/physics_informed_solver/self_adaptive_pinn.py +++ /dev/null @@ -1,455 +0,0 @@ -"""Module for the Self-Adaptive PINN solver.""" - -from copy import deepcopy -import torch - -from ...utils import check_consistency -from ...problem import InverseProblem -from ..solver import MultiSolverInterface -from .pinn_interface import PINNInterface - - -class Weights(torch.nn.Module): - """ - Implementation of the mask model for the self-adaptive weights of the - :class:`SelfAdaptivePINN` solver. - """ - - def __init__(self, func, num_points): - """ - Initialization of the :class:`Weights` class. - - :param torch.nn.Module func: the mask model. - :param int num_points: the number of input points. - """ - super().__init__() - - # Check consistency - check_consistency(func, torch.nn.Module) - - # Initialize the weights as a learnable parameter - self.sa_weights = torch.nn.Parameter(torch.zeros(num_points, 1)) - self.func = func - - def forward(self): - """ - Forward pass implementation for the mask module. - - :return: evaluation of self adaptive weights through the mask. - :rtype: torch.Tensor - """ - return self.func(self.sa_weights) - - -class SelfAdaptivePINN(PINNInterface, MultiSolverInterface): - r""" - Self-Adaptive Physics-Informed Neural Network (SelfAdaptivePINN) solver - class. This class implements the Self-Adaptive Physics-Informed Neural - Network solver, using a user specified ``model`` to solve a specific - ``problem``. It can be used to solve both forward and inverse problems. - - The Self-Adapive Physics-Informed Neural Network solver aims to find the - solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential - problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - integrating pointwise loss evaluation using a mask :math:m and self-adaptive - weights, which allow the model to focus on regions of the domain where the - residual is higher. - - The loss function to solve the problem is - - .. math:: - - \mathcal{L}_{\rm{problem}} = \frac{1}{N} \sum_{i=1}^{N_\Omega} m - \left( \lambda_{\Omega}^{i} \right) \mathcal{L} \left( \mathcal{A} - [\mathbf{u}](\mathbf{x}) \right) + \frac{1}{N} - \sum_{i=1}^{N_{\partial\Omega}} - m \left( \lambda_{\partial\Omega}^{i} \right) \mathcal{L} - \left( \mathcal{B}[\mathbf{u}](\mathbf{x}) - \right), - - denoting the self adaptive weights as - :math:`\lambda_{\Omega}^1, \dots, \lambda_{\Omega}^{N_\Omega}` and - :math:`\lambda_{\partial \Omega}^1, \dots, - \lambda_{\Omega}^{N_\partial \Omega}` - for :math:`\Omega` and :math:`\partial \Omega`, respectively. - - The Self-Adaptive Physics-Informed Neural Network solver identifies the - solution and appropriate self adaptive weights by solving the following - optimization problem: - - .. math:: - - \min_{w} \max_{\lambda_{\Omega}^k, \lambda_{\partial \Omega}^s} - \mathcal{L} , - - where :math:`w` denotes the network parameters, and :math:`\mathcal{L}` is a - specific loss function, , typically the MSE: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - **Original reference**: McClenny, Levi D., and Ulisses M. Braga-Neto. - *Self-adaptive physics-informed neural networks.* - Journal of Computational Physics 474 (2023): 111722. - DOI: `10.1016/j.jcp.2022.111722 - `_. - """ - - def __init__( - self, - problem, - model, - weight_function=torch.nn.Sigmoid(), - optimizer_model=None, - optimizer_weights=None, - scheduler_model=None, - scheduler_weights=None, - weighting=None, - loss=None, - ): - """ - Initialization of the :class:`SelfAdaptivePINN` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module model: The model to be used. - :param torch.nn.Module weight_function: The Self-Adaptive mask model. - Default is ``torch.nn.Sigmoid()``. - :param Optimizer optimizer_model: The optimizer of the ``model``. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Optimizer optimizer_weights: The optimizer of the - ``weight_function``. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Scheduler scheduler_model: Learning rate scheduler for the - ``model``. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param Scheduler scheduler_weights: Learning rate scheduler for the - ``weight_function``. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - """ - # Check consistency - check_consistency(weight_function, torch.nn.Module) - - # Define a ModuleDict for the weights - weights = {} - for cond, data in problem.input_pts.items(): - weights[cond] = Weights(func=weight_function, num_points=len(data)) - weights = torch.nn.ModuleDict(weights) - - super().__init__( - models=[model, weights], - problem=problem, - optimizers=[optimizer_model, optimizer_weights], - schedulers=[scheduler_model, scheduler_weights], - weighting=weighting, - loss=loss, - ) - - # Extract the reduction method from the loss function - self._reduction = self._loss_fn.reduction - - # Set the loss function to return non-aggregated losses - self._loss_fn = type(self._loss_fn)(reduction="none") - - def training_step(self, batch, batch_idx, **kwargs): - """ - Solver training step. It computes the optimization cycle and aggregates - the losses using the ``weighting`` attribute. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param int batch_idx: The index of the current batch. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The loss of the training step. - :rtype: torch.Tensor - """ - # Weights optimization - self.optimizer_weights.instance.zero_grad() - loss = self._optimization_cycle( - batch=batch, batch_idx=batch_idx, **kwargs - ) - self.manual_backward(-loss) - self.optimizer_weights.instance.step() - self.scheduler_weights.instance.step() - - # Model optimization - self.optimizer_model.instance.zero_grad() - loss = self._optimization_cycle( - batch=batch, batch_idx=batch_idx, **kwargs - ) - self.manual_backward(loss) - self.optimizer_model.instance.step() - self.scheduler_model.instance.step() - - # Log the loss - self.store_log("train_loss", loss, self.get_batch_size(batch)) - - return loss - - @torch.set_grad_enabled(True) - def validation_step(self, batch, **kwargs): - """ - The validation step for the Self-Adaptive PINN solver. It returns the - average residual computed with the ``loss`` function not aggregated. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The loss of the validation step. - :rtype: torch.Tensor - """ - losses = self.optimization_cycle(batch=batch, **kwargs) - - # Aggregate losses for each condition - for cond, loss in losses.items(): - losses[cond] = self._apply_reduction(loss=losses[cond]) - - loss = (sum(losses.values()) / len(losses)).as_subclass(torch.Tensor) - self.store_log("val_loss", loss, self.get_batch_size(batch)) - return loss - - @torch.set_grad_enabled(True) - def test_step(self, batch, **kwargs): - """ - The test step for the Self-Adaptive PINN solver. It returns the average - residual computed with the ``loss`` function not aggregated. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The loss of the test step. - :rtype: torch.Tensor - """ - losses = self.optimization_cycle(batch=batch, **kwargs) - - # Aggregate losses for each condition - for cond, loss in losses.items(): - losses[cond] = self._apply_reduction(loss=losses[cond]) - - loss = (sum(losses.values()) / len(losses)).as_subclass(torch.Tensor) - self.store_log("test_loss", loss, self.get_batch_size(batch)) - return loss - - def loss_phys(self, samples, equation): - """ - Computes the physics loss for the physics-informed solver based on the - provided samples and equation. - - :param LabelTensor samples: The samples to evaluate the physics loss. - :param EquationInterface equation: The governing equation. - :return: The computed physics loss. - :rtype: LabelTensor - """ - residuals = self.compute_residual(samples, equation) - return self._loss_fn(residuals, torch.zeros_like(residuals)) - - def loss_data(self, input, target): - """ - Compute the data loss for the Self-Adaptive PINN solver by evaluating - the loss between the network's output and the true solution. This method - should not be overridden, if not intentionally. - - :param input: The input to the neural network. - :type input: LabelTensor | torch.Tensor - :param target: The target to compare with the network's output. - :type target: LabelTensor | torch.Tensor - :return: The supervised loss, averaged over the number of observations. - :rtype: LabelTensor | torch.Tensor - """ - return self._loss_fn(self.forward(input), target) - - def forward(self, x): - """ - Forward pass. - - :param x: Input tensor. - :type x: torch.Tensor | LabelTensor - :return: The output of the neural network. - :rtype: torch.Tensor | LabelTensor - """ - return self.model(x) - - def configure_optimizers(self): - """ - Optimizer configuration. - - :return: The optimizers and the schedulers - :rtype: tuple[list[Optimizer], list[Scheduler]] - """ - # Hook the optimizers to the models - self.optimizer_model.hook(self.model.parameters()) - self.optimizer_weights.hook(self.weights.parameters()) - - # Add unknown parameters to optimization list in case of InverseProblem - if isinstance(self.problem, InverseProblem): - self.optimizer_model.instance.add_param_group( - { - "params": [ - self._params[var] - for var in self.problem.unknown_variables - ] - } - ) - - # Hook the schedulers to the optimizers - self.scheduler_model.hook(self.optimizer_model) - self.scheduler_weights.hook(self.optimizer_weights) - - return ( - [self.optimizer_model.instance, self.optimizer_weights.instance], - [self.scheduler_model.instance, self.scheduler_weights.instance], - ) - - def _optimization_cycle(self, batch, batch_idx, **kwargs): - """ - Aggregate the loss for each condition in the batch. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param int batch_idx: The index of the current batch. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The losses computed for all conditions in the batch, casted - to a subclass of :class:`torch.Tensor`. It should return a dict - containing the condition name and the associated scalar loss. - :rtype: dict - """ - # Compute non-aggregated residuals - residuals = self.optimization_cycle(batch) - - # Compute losses - losses = {} - for cond, res in residuals.items(): - - weight_tensor = self.weights[cond]() - - # Get the correct indices for the weights. Modulus is used according - # to the number of points in the condition, as in the PinaDataset. - len_res = len(res) - idx = torch.arange( - batch_idx * len_res, - (batch_idx + 1) * len_res, - device=res.device, - ) % len(self.problem.input_pts[cond]) - - # Apply the weights to the residuals - losses[cond] = self._apply_reduction( - loss=(res * weight_tensor[idx]) - ) - - # Store log - self.store_log( - f"{cond}_loss", losses[cond].item(), self.get_batch_size(batch) - ) - - # Clamp unknown parameters in InverseProblem (if needed) - self._clamp_params() - - # Aggregate - loss = self.weighting.aggregate(losses).as_subclass(torch.Tensor) - - return loss - - def _apply_reduction(self, loss): - """ - Apply the specified reduction to the loss. The reduction is deferred - until the end of the optimization cycle to allow self-adaptive weights - to be applied to each point beforehand. - - :param torch.Tensor loss: The loss tensor to be reduced. - :return: The reduced loss tensor. - :rtype: torch.Tensor - :raises ValueError: If the reduction method is neither "mean" nor "sum". - """ - # Apply the specified reduction method - if self._reduction == "mean": - return loss.mean() - if self._reduction == "sum": - return loss.sum() - - # Raise an error if the reduction method is not recognized - raise ValueError( - f"Unknown reduction: {self._reduction}." - " Supported reductions are 'mean' and 'sum'." - ) - - @property - def model(self): - """ - The model. - - :return: The model. - :rtype: torch.nn.Module - """ - return self.models[0] - - @property - def weights(self): - """ - The self-adaptive weights. - - :return: The self-adaptive weights. - :rtype: torch.nn.Module - """ - return self.models[1] - - @property - def scheduler_model(self): - """ - The scheduler associated to the model. - - :return: The scheduler for the model. - :rtype: Scheduler - """ - return self.schedulers[0] - - @property - def scheduler_weights(self): - """ - The scheduler associated to the mask model. - - :return: The scheduler for the mask model. - :rtype: Scheduler - """ - return self.schedulers[1] - - @property - def optimizer_model(self): - """ - Returns the optimizer associated to the model. - - :return: The optimizer for the model. - :rtype: Optimizer - """ - return self.optimizers[0] - - @property - def optimizer_weights(self): - """ - The optimizer associated to the mask model. - - :return: The optimizer for the mask model. - :rtype: Optimizer - """ - return self.optimizers[1] diff --git a/pina/solver/solver.py b/pina/solver/solver.py deleted file mode 100644 index 57a28a8a7..000000000 --- a/pina/solver/solver.py +++ /dev/null @@ -1,638 +0,0 @@ -"""Solver module.""" - -from abc import ABCMeta, abstractmethod -import lightning -import torch - -from torch._dynamo import OptimizedModule -from ..problem import AbstractProblem, InverseProblem -from ..optim import Optimizer, Scheduler, TorchOptimizer, TorchScheduler -from ..loss import WeightingInterface -from ..loss.scalar_weighting import _NoWeighting -from ..utils import check_consistency, labelize_forward - - -class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta): - """ - Abstract base class for PINA solvers. All specific solvers must inherit - from this interface. This class extends - :class:`~lightning.pytorch.core.LightningModule`, providing additional - functionalities for defining and optimizing Deep Learning models. - - By inheriting from this base class, solvers gain access to built-in training - loops, logging utilities, and optimization techniques. - """ - - def __init__(self, problem, weighting, use_lt): - """ - Initialization of the :class:`SolverInterface` class. - - :param AbstractProblem problem: The problem to be solved. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param bool use_lt: If ``True``, the solver uses LabelTensors as input. - """ - super().__init__() - - # check consistency of the problem - check_consistency(problem, AbstractProblem) - self._check_solver_consistency(problem) - self._pina_problem = problem - - # check consistency of the weighting and hook the condition names - if weighting is None: - weighting = _NoWeighting() - check_consistency(weighting, WeightingInterface) - self._pina_weighting = weighting - weighting._solver = self - - # check consistency use_lt - check_consistency(use_lt, bool) - self._use_lt = use_lt - - # if use_lt is true add extract operation in input - if use_lt is True: - self.forward = labelize_forward( - forward=self.forward, - input_variables=problem.input_variables, - output_variables=problem.output_variables, - ) - - # PINA private attributes (some are overridden by derived classes) - self._pina_problem = problem - self._pina_models = None - self._pina_optimizers = None - self._pina_schedulers = None - - # inverse problem handling - if isinstance(self.problem, InverseProblem): - self._params = self.problem.unknown_parameters - self._clamp_params = self._clamp_inverse_problem_params - else: - self._params = None - self._clamp_params = lambda: None - - @abstractmethod - def forward(self, *args, **kwargs): - """ - Abstract method for the forward pass implementation. - - :param args: The input tensor. - :type args: torch.Tensor | LabelTensor | Data | Graph - :param dict kwargs: Additional keyword arguments. - """ - - @abstractmethod - def optimization_cycle(self, batch): - """ - The optimization cycle for the solvers. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The losses computed for all conditions in the batch, casted - to a subclass of :class:`torch.Tensor`. It should return a dict - containing the condition name and the associated scalar loss. - :rtype: dict - """ - - def training_step(self, batch, **kwargs): - """ - Solver training step. It computes the optimization cycle and aggregates - the losses using the ``weighting`` attribute. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The loss of the training step. - :rtype: torch.Tensor - """ - loss = self._optimization_cycle(batch=batch, **kwargs) - self.store_log("train_loss", loss, self.get_batch_size(batch)) - return loss - - def validation_step(self, batch, **kwargs): - """ - Solver validation step. It computes the optimization cycle and - averages the losses. No aggregation using the ``weighting`` attribute is - performed. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The loss of the training step. - :rtype: torch.Tensor - """ - losses = self.optimization_cycle(batch=batch, **kwargs) - loss = (sum(losses.values()) / len(losses)).as_subclass(torch.Tensor) - self.store_log("val_loss", loss, self.get_batch_size(batch)) - return loss - - def test_step(self, batch, **kwargs): - """ - Solver test step. It computes the optimization cycle and - averages the losses. No aggregation using the ``weighting`` attribute is - performed. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The loss of the training step. - :rtype: torch.Tensor - """ - losses = self.optimization_cycle(batch=batch, **kwargs) - loss = (sum(losses.values()) / len(losses)).as_subclass(torch.Tensor) - self.store_log("test_loss", loss, self.get_batch_size(batch)) - return loss - - def store_log(self, name, value, batch_size): - """ - Store the log of the solver. - - :param str name: The name of the log. - :param torch.Tensor value: The value of the log. - :param int batch_size: The size of the batch. - """ - - self.log( - name=name, - value=value, - batch_size=batch_size, - **self.trainer.logging_kwargs, - ) - - def setup(self, stage): - """ - This method is called at the start of the train and test process to - compile the model if the :class:`~pina.trainer.Trainer` - ``compile`` is ``True``. - - :param str stage: The current stage of the training process - (e.g., ``fit``, ``validate``, ``test``, ``predict``). - :return: The result of the parent class ``setup`` method. - :rtype: Any - """ - if self.trainer.compile and not self._is_compiled(): - self._setup_compile() - return super().setup(stage) - - def _is_compiled(self): - """ - Check if the model is compiled. - - :return: ``True`` if the model is compiled, ``False`` otherwise. - :rtype: bool - """ - for model in self._pina_models: - if not isinstance(model, OptimizedModule): - return False - return True - - def _setup_compile(self): - """ - Compile all models in the solver using ``torch.compile``. - - This method iterates through each model stored in the solver - list and attempts to compile them for optimized execution. It supports - models of type `torch.nn.Module` and `torch.nn.ModuleDict`. For models - stored in a `ModuleDict`, each submodule is compiled individually. - Models on Apple Silicon (MPS) use the 'eager' backend, - while others use 'inductor'. - - :raises RuntimeError: If a model is neither `torch.nn.Module` - nor `torch.nn.ModuleDict`. - """ - for i, model in enumerate(self._pina_models): - if isinstance(model, torch.nn.ModuleDict): - for name, module in model.items(): - self._pina_models[i][name] = self._compile_modules(module) - elif isinstance(model, torch.nn.Module): - self._pina_models[i] = self._compile_modules(model) - else: - raise RuntimeError( - "Compilation available only for " - "torch.nn.Module or torch.nn.ModuleDict." - ) - - def _check_solver_consistency(self, problem): - """ - Check the consistency of the solver with the problem formulation. - - :param AbstractProblem problem: The problem to be solved. - """ - for condition in problem.conditions.values(): - check_consistency(condition, self.accepted_conditions_types) - - def _optimization_cycle(self, batch, **kwargs): - """ - Aggregate the loss for each condition in the batch. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param dict kwargs: Additional keyword arguments passed to - ``optimization_cycle``. - :return: The losses computed for all conditions in the batch, casted - to a subclass of :class:`torch.Tensor`. It should return a dict - containing the condition name and the associated scalar loss. - :rtype: dict - """ - # compute losses - losses = self.optimization_cycle(batch) - # clamp unknown parameters in InverseProblem (if needed) - self._clamp_params() - # store log - for name, value in losses.items(): - self.store_log( - f"{name}_loss", value.item(), self.get_batch_size(batch) - ) - # aggregate - loss = self.weighting.aggregate(losses).as_subclass(torch.Tensor) - return loss - - def _clamp_inverse_problem_params(self): - """ - Clamps the parameters of the inverse problem solver to specified ranges. - """ - for v in self._params: - self._params[v].data.clamp_( - self.problem.unknown_parameter_domain.range[v][0], - self.problem.unknown_parameter_domain.range[v][1], - ) - - @staticmethod - def _compile_modules(model): - """ - Perform the compilation of the model. - - This method attempts to compile the given PyTorch model - using ``torch.compile`` to improve execution performance. The - backend is selected based on the device on which the model resides: - ``eager`` is used for MPS devices (Apple Silicon), and ``inductor`` - is used for all others. - - If compilation fails, the method prints the error and returns the - original, uncompiled model. - - :param torch.nn.Module model: The model to compile. - :raises Exception: If the compilation fails. - :return: The compiled model. - :rtype: torch.nn.Module - """ - model_device = next(model.parameters()).device - try: - if model_device == torch.device("mps:0"): - model = torch.compile(model, backend="eager") - else: - model = torch.compile(model, backend="inductor") - except Exception as e: - print("Compilation failed, running in normal mode.:\n", e) - return model - - @staticmethod - def get_batch_size(batch): - """ - Get the batch size. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The size of the batch. - :rtype: int - """ - - batch_size = 0 - for data in batch: - batch_size += len(data[1]["input"]) - return batch_size - - @staticmethod - def default_torch_optimizer(): - """ - Set the default optimizer to :class:`torch.optim.Adam`. - - :return: The default optimizer. - :rtype: Optimizer - """ - return TorchOptimizer(torch.optim.Adam, lr=0.001) - - @staticmethod - def default_torch_scheduler(): - """ - Set the default scheduler to - :class:`torch.optim.lr_scheduler.ConstantLR`. - - :return: The default scheduler. - :rtype: Scheduler - """ - return TorchScheduler(torch.optim.lr_scheduler.ConstantLR, factor=1.0) - - @property - def problem(self): - """ - The problem instance. - - :return: The problem instance. - :rtype: :class:`~pina.problem.abstract_problem.AbstractProblem` - """ - return self._pina_problem - - @property - def use_lt(self): - """ - Using LabelTensors as input during training. - - :return: The use_lt attribute. - :rtype: bool - """ - return self._use_lt - - @property - def weighting(self): - """ - The weighting schema. - - :return: The weighting schema. - :rtype: :class:`~pina.loss.weighting_interface.WeightingInterface` - """ - return self._pina_weighting - - -class SingleSolverInterface(SolverInterface, metaclass=ABCMeta): - """ - Base class for PINA solvers using a single :class:`torch.nn.Module`. - """ - - def __init__( - self, - problem, - model, - optimizer=None, - scheduler=None, - weighting=None, - use_lt=True, - ): - """ - Initialization of the :class:`SingleSolverInterface` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module model: The neural network model to be used. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is - used. Default is ``None``. - :param Scheduler scheduler: The scheduler to be used. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param bool use_lt: If ``True``, the solver uses LabelTensors as input. - """ - if optimizer is None: - optimizer = self.default_torch_optimizer() - - if scheduler is None: - scheduler = self.default_torch_scheduler() - - super().__init__(problem=problem, use_lt=use_lt, weighting=weighting) - - # check consistency of models argument and encapsulate in list - check_consistency(model, torch.nn.Module) - # check scheduler consistency and encapsulate in list - check_consistency(scheduler, Scheduler) - # check optimizer consistency and encapsulate in list - check_consistency(optimizer, Optimizer) - - # initialize the model (needed by Lightining to go to different devices) - self._pina_models = torch.nn.ModuleList([model]) - self._pina_optimizers = [optimizer] - self._pina_schedulers = [scheduler] - - def forward(self, x): - """ - Forward pass implementation. - - :param x: Input tensor. - :type x: torch.Tensor | LabelTensor | Graph | Data - :return: Solver solution. - :rtype: torch.Tensor | LabelTensor | Graph | Data - """ - return self.model(x) - - def configure_optimizers(self): - """ - Optimizer configuration for the solver. - - :return: The optimizer and the scheduler - :rtype: tuple[list[Optimizer], list[Scheduler]] - """ - self.optimizer.hook(self.model.parameters()) - if isinstance(self.problem, InverseProblem): - self.optimizer.instance.add_param_group( - { - "params": [ - self._params[var] - for var in self.problem.unknown_variables - ] - } - ) - self.scheduler.hook(self.optimizer) - return ([self.optimizer.instance], [self.scheduler.instance]) - - @property - def model(self): - """ - The model used for training. - - :return: The model used for training. - :rtype: torch.nn.Module - """ - return self._pina_models[0] - - @property - def scheduler(self): - """ - The scheduler used for training. - - :return: The scheduler used for training. - :rtype: Scheduler - """ - return self._pina_schedulers[0] - - @property - def optimizer(self): - """ - The optimizer used for training. - - :return: The optimizer used for training. - :rtype: Optimizer - """ - return self._pina_optimizers[0] - - -class MultiSolverInterface(SolverInterface, metaclass=ABCMeta): - """ - Base class for PINA solvers using multiple :class:`torch.nn.Module`. - """ - - def __init__( - self, - problem, - models, - optimizers=None, - schedulers=None, - weighting=None, - use_lt=True, - ): - """ - Initialization of the :class:`MultiSolverInterface` class. - - :param AbstractProblem problem: The problem to be solved. - :param models: The neural network models to be used. - :type model: list[torch.nn.Module] | tuple[torch.nn.Module] - :param list[Optimizer] optimizers: The optimizers to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used for all - models. Default is ``None``. - :param list[Scheduler] schedulers: The schedulers to be used. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used for all the models. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param bool use_lt: If ``True``, the solver uses LabelTensors as input. - :raises ValueError: If the models are not a list or tuple with length - greater than one. - - .. warning:: - :class:`MultiSolverInterface` uses manual optimization by setting - ``automatic_optimization=False`` in - :class:`~lightning.pytorch.core.LightningModule`. For more - information on manual optimization please - see `here `_. - """ - if not isinstance(models, (list, tuple)) or len(models) < 2: - raise ValueError( - "models should be list[torch.nn.Module] or " - "tuple[torch.nn.Module] with len greater than " - "one." - ) - - if optimizers is None: - optimizers = [ - self.default_torch_optimizer() for _ in range(len(models)) - ] - - if schedulers is None: - schedulers = [ - self.default_torch_scheduler() for _ in range(len(models)) - ] - - if any(opt is None for opt in optimizers): - optimizers = [ - self.default_torch_optimizer() if opt is None else opt - for opt in optimizers - ] - - if any(sched is None for sched in schedulers): - schedulers = [ - self.default_torch_scheduler() if sched is None else sched - for sched in schedulers - ] - - super().__init__(problem=problem, use_lt=use_lt, weighting=weighting) - - # check consistency of models argument and encapsulate in list - check_consistency(models, torch.nn.Module) - - # check scheduler consistency and encapsulate in list - check_consistency(schedulers, Scheduler) - - # check optimizer consistency and encapsulate in list - check_consistency(optimizers, Optimizer) - - # check length consistency optimizers - if len(models) != len(optimizers): - raise ValueError( - "You must define one optimizer for each model." - f"Got {len(models)} models, and {len(optimizers)}" - " optimizers." - ) - if len(schedulers) != len(optimizers): - raise ValueError( - "You must define one scheduler for each optimizer." - f"Got {len(schedulers)} schedulers, and {len(optimizers)}" - " optimizers." - ) - - # initialize the model - self._pina_models = torch.nn.ModuleList(models) - self._pina_optimizers = optimizers - self._pina_schedulers = schedulers - - # Set automatic optimization to False. - # For more information on manual optimization see: - # http://lightning.ai/docs/pytorch/stable/model/manual_optimization.html - self.automatic_optimization = False - - def on_train_batch_end(self, outputs, batch, batch_idx): - """ - This method is called at the end of each training batch and overrides - the PyTorch Lightning implementation to log checkpoints. - - :param torch.Tensor outputs: The ``model``'s output for the current - batch. - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :param int batch_idx: The index of the current batch. - """ - # increase by one the counter of optimization to save loggers - epoch_loop = self.trainer.fit_loop.epoch_loop - epoch_loop.manual_optimization.optim_step_progress.total.completed += 1 - return super().on_train_batch_end(outputs, batch, batch_idx) - - def configure_optimizers(self): - """ - Optimizer configuration for the solver. - - :return: The optimizer and the scheduler - :rtype: tuple[list[Optimizer], list[Scheduler]] - """ - for optimizer, scheduler, model in zip( - self.optimizers, self.schedulers, self.models - ): - optimizer.hook(model.parameters()) - scheduler.hook(optimizer) - - return ( - [optimizer.instance for optimizer in self.optimizers], - [scheduler.instance for scheduler in self.schedulers], - ) - - @property - def models(self): - """ - The models used for training. - - :return: The models used for training. - :rtype: torch.nn.ModuleList - """ - return self._pina_models - - @property - def optimizers(self): - """ - The optimizers used for training. - - :return: The optimizers used for training. - :rtype: list[Optimizer] - """ - return self._pina_optimizers - - @property - def schedulers(self): - """ - The schedulers used for training. - - :return: The schedulers used for training. - :rtype: list[Scheduler] - """ - return self._pina_schedulers diff --git a/pina/solver/supervised_solver/__init__.py b/pina/solver/supervised_solver/__init__.py deleted file mode 100644 index f681d2dd3..000000000 --- a/pina/solver/supervised_solver/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Module for the Supervised solvers.""" - -__all__ = [ - "SupervisedSolverInterface", - "SupervisedSolver", - "ReducedOrderModelSolver", -] - -from .supervised_solver_interface import SupervisedSolverInterface -from .supervised import SupervisedSolver -from .reduced_order_model import ReducedOrderModelSolver diff --git a/pina/solver/supervised_solver/reduced_order_model.py b/pina/solver/supervised_solver/reduced_order_model.py deleted file mode 100644 index 727f438e2..000000000 --- a/pina/solver/supervised_solver/reduced_order_model.py +++ /dev/null @@ -1,190 +0,0 @@ -"""Module for the Reduced Order Model solver""" - -import torch -from .supervised_solver_interface import SupervisedSolverInterface -from ..solver import SingleSolverInterface - - -class ReducedOrderModelSolver(SupervisedSolverInterface, SingleSolverInterface): - r""" - Reduced Order Model solver class. This class implements the Reduced Order - Model solver, using user specified ``reduction_network`` and - ``interpolation_network`` to solve a specific ``problem``. - - The Reduced Order Model solver aims to find the solution - :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m` of a differential problem: - - .. math:: - - \begin{cases} - \mathcal{A}[\mathbf{u}(\mu)](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\ - \mathcal{B}[\mathbf{u}(\mu)](\mathbf{x})=0\quad, - \mathbf{x}\in\partial\Omega - \end{cases} - - This is done by means of two neural networks: the ``reduction_network``, - which defines an encoder :math:`\mathcal{E}_{\rm{net}}`, and a decoder - :math:`\mathcal{D}_{\rm{net}}`; and the ``interpolation_network`` - :math:`\mathcal{I}_{\rm{net}}`. The input is assumed to be discretised in - the spatial dimensions. - - The following loss function is minimized during training: - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathcal{E}_{\rm{net}}[\mathbf{u}(\mu_i)] - - \mathcal{I}_{\rm{net}}[\mu_i]) + - \mathcal{L}( - \mathcal{D}_{\rm{net}}[\mathcal{E}_{\rm{net}}[\mathbf{u}(\mu_i)]] - - \mathbf{u}(\mu_i)) - - where :math:`\mathcal{L}` is a specific loss function, typically the MSE: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - .. seealso:: - - **Original reference**: Hesthaven, Jan S., and Stefano Ubbiali. - *Non-intrusive reduced order modeling of nonlinear problems using - neural networks.* - Journal of Computational Physics 363 (2018): 55-78. - DOI `10.1016/j.jcp.2018.02.037 - `_. - - Pichi, Federico, Beatriz Moya, and Jan S. - Hesthaven. - *A graph convolutional autoencoder approach to model order reduction - for parametrized PDEs.* - Journal of Computational Physics 501 (2024): 112762. - DOI `10.1016/j.jcp.2024.112762 - `_. - - .. note:: - The specified ``reduction_network`` must contain two methods, namely - ``encode`` for input encoding, and ``decode`` for decoding the former - result. The ``interpolation_network`` network ``forward`` output - represents the interpolation of the latent space obtained with - ``reduction_network.encode``. - - .. note:: - This solver uses the end-to-end training strategy, i.e. the - ``reduction_network`` and ``interpolation_network`` are trained - simultaneously. For reference on this trainig strategy look at the - following: - - .. warning:: - This solver works only for data-driven model. Hence in the ``problem`` - definition the codition must only contain ``input`` - (e.g. coefficient parameters, time parameters), and ``target``. - """ - - def __init__( - self, - problem, - reduction_network, - interpolation_network, - loss=None, - optimizer=None, - scheduler=None, - weighting=None, - use_lt=True, - ): - """ - Initialization of the :class:`ReducedOrderModelSolver` class. - - :param AbstractProblem problem: The formualation of the problem. - :param torch.nn.Module reduction_network: The reduction network used - for reducing the input space. It must contain two methods, namely - ``encode`` for input encoding, and ``decode`` for decoding the - former result. - :param torch.nn.Module interpolation_network: The interpolation network - for interpolating the control parameters to latent space obtained by - the ``reduction_network`` encoding. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Scheduler scheduler: Learning rate scheduler. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param bool use_lt: If ``True``, the solver uses LabelTensors as input. - Default is ``True``. - """ - model = torch.nn.ModuleDict( - { - "reduction_network": reduction_network, - "interpolation_network": interpolation_network, - } - ) - - super().__init__( - model=model, - problem=problem, - loss=loss, - optimizer=optimizer, - scheduler=scheduler, - weighting=weighting, - use_lt=use_lt, - ) - - # assert reduction object contains encode/ decode - if not hasattr(self.model["reduction_network"], "encode"): - raise SyntaxError( - "reduction_network must have encode method. " - "The encode method should return a lower " - "dimensional representation of the input." - ) - if not hasattr(self.model["reduction_network"], "decode"): - raise SyntaxError( - "reduction_network must have decode method. " - "The decode method should return a high " - "dimensional representation of the encoding." - ) - - def forward(self, x): - """ - Forward pass implementation. - It computes the encoder representation by calling the forward method - of the ``interpolation_network`` on the input, and maps it to output - space by calling the decode methode of the ``reduction_network``. - - :param x: The input to the neural network. - :type x: LabelTensor | torch.Tensor | Graph | Data - :return: The solver solution. - :rtype: LabelTensor | torch.Tensor | Graph | Data - """ - reduction_network = self.model["reduction_network"] - interpolation_network = self.model["interpolation_network"] - return reduction_network.decode(interpolation_network(x)) - - def loss_data(self, input, target): - """ - Compute the data loss by evaluating the loss between the network's - output and the true solution. This method should not be overridden, if - not intentionally. - - :param input: The input to the neural network. - :type input: LabelTensor | torch.Tensor | Graph | Data - :param target: The target to compare with the network's output. - :type target: LabelTensor | torch.Tensor | Graph | Data - :return: The supervised loss, averaged over the number of observations. - :rtype: LabelTensor | torch.Tensor | Graph | Data - """ - # extract networks - reduction_network = self.model["reduction_network"] - interpolation_network = self.model["interpolation_network"] - # encoded representations loss - encode_repr_inter_net = interpolation_network(input) - encode_repr_reduction_network = reduction_network.encode(target) - loss_encode = self._loss_fn( - encode_repr_inter_net, encode_repr_reduction_network - ) - # reconstruction loss - decode = reduction_network.decode(encode_repr_reduction_network) - loss_reconstruction = self._loss_fn(decode, target) - return loss_encode + loss_reconstruction diff --git a/pina/solver/supervised_solver/supervised.py b/pina/solver/supervised_solver/supervised.py deleted file mode 100644 index 70cd8fe4b..000000000 --- a/pina/solver/supervised_solver/supervised.py +++ /dev/null @@ -1,85 +0,0 @@ -"""Module for the Supervised solver.""" - -from .supervised_solver_interface import SupervisedSolverInterface -from ..solver import SingleSolverInterface - - -class SupervisedSolver(SupervisedSolverInterface, SingleSolverInterface): - r""" - Supervised Solver solver class. This class implements a Supervised Solver, - using a user specified ``model`` to solve a specific ``problem``. - - The Supervised Solver class aims to find a map between the input - :math:`\mathbf{s}:\Omega\rightarrow\mathbb{R}^m` and the output - :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m`. - - Given a model :math:`\mathcal{M}`, the following loss function is - minimized during training: - - .. math:: - \mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N - \mathcal{L}(\mathbf{u}_i - \mathcal{M}(\mathbf{s}_i)), - - where :math:`\mathcal{L}` is a specific loss function, typically the MSE: - - .. math:: - \mathcal{L}(v) = \| v \|^2_2. - - In this context, :math:`\mathbf{u}_i` and :math:`\mathbf{s}_i` indicates - the will to approximate multiple (discretised) functions given multiple - (discretised) input functions. - """ - - def __init__( - self, - problem, - model, - loss=None, - optimizer=None, - scheduler=None, - weighting=None, - use_lt=True, - ): - """ - Initialization of the :class:`SupervisedSolver` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module model: The neural network model to be used. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - :param Optimizer optimizer: The optimizer to be used. - If ``None``, the :class:`torch.optim.Adam` optimizer is used. - Default is ``None``. - :param Scheduler scheduler: Learning rate scheduler. - If ``None``, the :class:`torch.optim.lr_scheduler.ConstantLR` - scheduler is used. Default is ``None``. - :param WeightingInterface weighting: The weighting schema to be used. - If ``None``, no weighting schema is used. Default is ``None``. - :param bool use_lt: If ``True``, the solver uses LabelTensors as input. - Default is ``True``. - """ - super().__init__( - model=model, - problem=problem, - loss=loss, - optimizer=optimizer, - scheduler=scheduler, - weighting=weighting, - use_lt=use_lt, - ) - - def loss_data(self, input, target): - """ - Compute the data loss for the Supervised solver by evaluating the loss - between the network's output and the true solution. This method should - not be overridden, if not intentionally. - - :param input: The input to the neural network. - :type input: LabelTensor | torch.Tensor | Graph | Data - :param target: The target to compare with the network's output. - :type target: LabelTensor | torch.Tensor | Graph | Data - :return: The supervised loss, averaged over the number of observations. - :rtype: LabelTensor | torch.Tensor | Graph | Data - """ - return self._loss_fn(self.forward(input), target) diff --git a/pina/solver/supervised_solver/supervised_solver_interface.py b/pina/solver/supervised_solver/supervised_solver_interface.py deleted file mode 100644 index 97070ce8f..000000000 --- a/pina/solver/supervised_solver/supervised_solver_interface.py +++ /dev/null @@ -1,90 +0,0 @@ -"""Module for the Supervised solver interface.""" - -from abc import abstractmethod - -import torch - -from torch.nn.modules.loss import _Loss -from ..solver import SolverInterface -from ...utils import check_consistency -from ...loss.loss_interface import LossInterface -from ...condition import InputTargetCondition - - -class SupervisedSolverInterface(SolverInterface): - r""" - Base class for Supervised solvers. This class implements a Supervised Solver - , using a user specified ``model`` to solve a specific ``problem``. - - The ``SupervisedSolverInterface`` class can be used to define - Supervised solvers that work with one or multiple optimizers and/or models. - By default, it is compatible with problems defined by - :class:`~pina.problem.abstract_problem.AbstractProblem`, - and users can choose the problem type the solver is meant to address. - """ - - accepted_conditions_types = InputTargetCondition - - def __init__(self, loss=None, **kwargs): - """ - Initialization of the :class:`SupervisedSolver` class. - - :param AbstractProblem problem: The problem to be solved. - :param torch.nn.Module loss: The loss function to be minimized. - If ``None``, the :class:`torch.nn.MSELoss` loss is used. - Default is `None`. - :param kwargs: Additional keyword arguments to be passed to the - :class:`~pina.solver.solver.SolverInterface` class. - """ - if loss is None: - loss = torch.nn.MSELoss() - - super().__init__(**kwargs) - - # check consistency - check_consistency(loss, (LossInterface, _Loss), subclass=False) - - # assign variables - self._loss_fn = loss - - def optimization_cycle(self, batch): - """ - The optimization cycle for the solvers. - - :param list[tuple[str, dict]] batch: A batch of data. Each element is a - tuple containing a condition name and a dictionary of points. - :return: The losses computed for all conditions in the batch, casted - to a subclass of :class:`torch.Tensor`. It should return a dict - containing the condition name and the associated scalar loss. - :rtype: dict - """ - condition_loss = {} - for condition_name, points in batch: - condition_loss[condition_name] = self.loss_data( - input=points["input"], target=points["target"] - ) - return condition_loss - - @abstractmethod - def loss_data(self, input, target): - """ - Compute the data loss for the Supervised. This method is abstract and - should be override by derived classes. - - :param input: The input to the neural network. - :type input: LabelTensor | torch.Tensor | Graph | Data - :param target: The target to compare with the network's output. - :type target: LabelTensor | torch.Tensor | Graph | Data - :return: The supervised loss, averaged over the number of observations. - :rtype: LabelTensor | torch.Tensor | Graph | Data - """ - - @property - def loss(self): - """ - The loss function to be minimized. - - :return: The loss function to be minimized. - :rtype: torch.nn.Module - """ - return self._loss_fn diff --git a/pina/trainer.py b/pina/trainer.py index e92928d1e..5cd598b4c 100644 --- a/pina/trainer.py +++ b/pina/trainer.py @@ -1,362 +1,5 @@ -"""Module for the Trainer.""" +"""Public API for Trainer.""" -import sys -import warnings -import torch -import lightning -from .utils import check_consistency, custom_warning_format -from .data import PinaDataModule -from .solver import SolverInterface, PINNInterface +from pina._src.core.trainer import Trainer -# set the warning for compile options -warnings.formatwarning = custom_warning_format -warnings.filterwarnings("always", category=UserWarning) - - -class Trainer(lightning.pytorch.Trainer): - """ - PINA custom Trainer class to extend the standard Lightning functionality. - - This class enables specific features or behaviors required by the PINA - framework. It modifies the standard - :class:`lightning.pytorch.Trainer ` - class to better support the training process in PINA. - """ - - def __init__( - self, - solver, - batch_size=None, - train_size=1.0, - test_size=0.0, - val_size=0.0, - compile=None, - repeat=None, - automatic_batching=None, - num_workers=None, - pin_memory=None, - shuffle=None, - **kwargs, - ): - """ - Initialization of the :class:`Trainer` class. - - :param SolverInterface solver: A - :class:`~pina.solver.solver.SolverInterface` solver used to solve a - :class:`~pina.problem.abstract_problem.AbstractProblem`. - :param int batch_size: The number of samples per batch to load. - If ``None``, all samples are loaded and data is not batched. - Default is ``None``. - :param float train_size: The percentage of elements to include in the - training dataset. Default is ``1.0``. - :param float test_size: The percentage of elements to include in the - test dataset. Default is ``0.0``. - :param float val_size: The percentage of elements to include in the - validation dataset. Default is ``0.0``. - :param bool compile: If ``True``, the model is compiled before training. - Default is ``False``. For Windows users, it is always disabled. Not - supported for python version greater or equal than 3.14. - :param bool repeat: Whether to repeat the dataset data in each - condition during training. For further details, see the - :class:`~pina.data.data_module.PinaDataModule` class. Default is - ``False``. - :param bool automatic_batching: If ``True``, automatic PyTorch batching - is performed, otherwise the items are retrieved from the dataset - all at once. For further details, see the - :class:`~pina.data.data_module.PinaDataModule` class. Default is - ``False``. - :param int num_workers: The number of worker threads for data loading. - Default is ``0`` (serial loading). - :param bool pin_memory: Whether to use pinned memory for faster data - transfer to GPU. Default is ``False``. - :param bool shuffle: Whether to shuffle the data during training. - Default is ``True``. - :param dict kwargs: Additional keyword arguments that specify the - training setup. These can be selected from the `pytorch-lightning - Trainer API - `_. - """ - # check consistency for init types - self._check_input_consistency( - solver=solver, - train_size=train_size, - test_size=test_size, - val_size=val_size, - repeat=repeat, - automatic_batching=automatic_batching, - compile=compile, - ) - pin_memory, num_workers, shuffle, batch_size = ( - self._check_consistency_and_set_defaults( - pin_memory, num_workers, shuffle, batch_size - ) - ) - - # inference mode set to false when validating/testing PINNs otherwise - # gradient is not tracked and optimization_cycle fails - if isinstance(solver, PINNInterface): - kwargs["inference_mode"] = False - - # Logging depends on the batch size, when batch_size is None then - # log_every_n_steps should be zero - if batch_size is None: - kwargs["log_every_n_steps"] = 0 - else: - kwargs.setdefault("log_every_n_steps", 50) # default for lightning - - # Setting default kwargs, overriding lightning defaults - kwargs.setdefault("enable_progress_bar", True) - - super().__init__(**kwargs) - - # checking compilation and automatic batching - # compilation disabled for Windows and for Python 3.14+ - if ( - compile is None - or sys.platform == "win32" - or sys.version_info >= (3, 14) - ): - compile = False - warnings.warn( - "Compilation is disabled for Python 3.14+ and for Windows.", - UserWarning, - ) - - repeat = repeat if repeat is not None else False - - automatic_batching = ( - automatic_batching if automatic_batching is not None else False - ) - - # set attributes - self.compile = compile - self.solver = solver - self.batch_size = batch_size - self._move_to_device() - self.data_module = None - self._create_datamodule( - train_size=train_size, - test_size=test_size, - val_size=val_size, - batch_size=batch_size, - repeat=repeat, - automatic_batching=automatic_batching, - pin_memory=pin_memory, - num_workers=num_workers, - shuffle=shuffle, - ) - - # logging - self.logging_kwargs = { - "sync_dist": bool( - len(self._accelerator_connector._parallel_devices) > 1 - ), - "on_step": bool(kwargs["log_every_n_steps"] > 0), - "prog_bar": bool(kwargs["enable_progress_bar"]), - "on_epoch": True, - } - - def _move_to_device(self): - """ - Moves the ``unknown_parameters`` of an instance of - :class:`~pina.problem.abstract_problem.AbstractProblem` to the - :class:`Trainer` device. - """ - device = self._accelerator_connector._parallel_devices[0] - # move parameters to device - pb = self.solver.problem - if hasattr(pb, "unknown_parameters"): - for key in pb.unknown_parameters: - pb.unknown_parameters[key] = torch.nn.Parameter( - pb.unknown_parameters[key].data.to(device) - ) - - def _create_datamodule( - self, - train_size, - test_size, - val_size, - batch_size, - repeat, - automatic_batching, - pin_memory, - num_workers, - shuffle, - ): - """ - This method is designed to handle the creation of a data module when - resampling is needed during training. Instead of manually defining and - modifying the trainer's dataloaders, this method is called to - automatically configure the data module. - - :param float train_size: The percentage of elements to include in the - training dataset. - :param float test_size: The percentage of elements to include in the - test dataset. - :param float val_size: The percentage of elements to include in the - validation dataset. - :param int batch_size: The number of samples per batch to load. - :param bool repeat: Whether to repeat the dataset data in each - condition during training. - :param bool automatic_batching: Whether to perform automatic batching - with PyTorch. - :param bool pin_memory: Whether to use pinned memory for faster data - transfer to GPU. - :param int num_workers: The number of worker threads for data loading. - :param bool shuffle: Whether to shuffle the data during training. - :raises RuntimeError: If not all conditions are sampled. - """ - if not self.solver.problem.are_all_domains_discretised: - error_message = "\n".join( - [ - f"""{" " * 13} ---> Domain {key} { - "sampled" if key in self.solver.problem.discretised_domains - else - "not sampled"}""" - for key in self.solver.problem.domains.keys() - ] - ) - raise RuntimeError( - "Cannot create Trainer if not all conditions " - "are sampled. The Trainer got the following:\n" - f"{error_message}" - ) - self.data_module = PinaDataModule( - self.solver.problem, - train_size=train_size, - test_size=test_size, - val_size=val_size, - batch_size=batch_size, - repeat=repeat, - automatic_batching=automatic_batching, - num_workers=num_workers, - pin_memory=pin_memory, - shuffle=shuffle, - ) - - def train(self, **kwargs): - """ - Manage the training process of the solver. - - :param dict kwargs: Additional keyword arguments. See `pytorch-lightning - Trainer API `_ - for details. - """ - return super().fit(self.solver, datamodule=self.data_module, **kwargs) - - def test(self, **kwargs): - """ - Manage the test process of the solver. - - :param dict kwargs: Additional keyword arguments. See `pytorch-lightning - Trainer API `_ - for details. - """ - return super().test(self.solver, datamodule=self.data_module, **kwargs) - - @property - def solver(self): - """ - Get the solver. - - :return: The solver. - :rtype: SolverInterface - """ - return self._solver - - @solver.setter - def solver(self, solver): - """ - Set the solver. - - :param SolverInterface solver: The solver to set. - """ - self._solver = solver - - @staticmethod - def _check_input_consistency( - solver, - train_size, - test_size, - val_size, - repeat, - automatic_batching, - compile, - ): - """ - Verifies the consistency of the parameters for the solver configuration. - - :param SolverInterface solver: The solver. - :param float train_size: The percentage of elements to include in the - training dataset. - :param float test_size: The percentage of elements to include in the - test dataset. - :param float val_size: The percentage of elements to include in the - validation dataset. - :param bool repeat: Whether to repeat the dataset data in each - condition during training. - :param bool automatic_batching: Whether to perform automatic batching - with PyTorch. - :param bool compile: If ``True``, the model is compiled before training. - """ - - check_consistency(solver, SolverInterface) - check_consistency(train_size, float) - check_consistency(test_size, float) - check_consistency(val_size, float) - if repeat is not None: - check_consistency(repeat, bool) - if automatic_batching is not None: - check_consistency(automatic_batching, bool) - if compile is not None: - check_consistency(compile, bool) - - @staticmethod - def _check_consistency_and_set_defaults( - pin_memory, num_workers, shuffle, batch_size - ): - """ - Checks the consistency of input parameters and sets default values - for missing or invalid parameters. - - :param bool pin_memory: Whether to use pinned memory for faster data - transfer to GPU. - :param int num_workers: The number of worker threads for data loading. - :param bool shuffle: Whether to shuffle the data during training. - :param int batch_size: The number of samples per batch to load. - """ - if pin_memory is not None: - check_consistency(pin_memory, bool) - else: - pin_memory = False - if num_workers is not None: - check_consistency(num_workers, int) - else: - num_workers = 0 - if shuffle is not None: - check_consistency(shuffle, bool) - else: - shuffle = True - if batch_size is not None: - check_consistency(batch_size, int) - return pin_memory, num_workers, shuffle, batch_size - - @property - def compile(self): - """ - Whether compilation is required or not. - - :return: ``True`` if compilation is required, ``False`` otherwise. - :rtype: bool - """ - return self._compile - - @compile.setter - def compile(self, value): - """ - Setting the value of compile. - - :param bool value: Whether compilation is required or not. - """ - check_consistency(value, bool) - self._compile = value +__all__ = ["Trainer"] diff --git a/pina/type_checker.py b/pina/type_checker.py index e8c908ac9..cfa886da3 100644 --- a/pina/type_checker.py +++ b/pina/type_checker.py @@ -1,93 +1,11 @@ -"""Module for enforcing type hints in Python functions.""" +"""Runtime type enforcement and validation utilities. -import inspect -import typing -import logging +This module provides decorators to validate function arguments against type +annotations at runtime. This ensures that PINA components receive inputs +adhering to expected specifications, improving the robustness of the +scientific computing pipeline. +""" +from pina._src.core.type_checker import enforce_types -def enforce_types(func): - """ - Function decorator to enforce type hints at runtime. - - This decorator checks the types of the arguments and of the return value of - the decorated function against the type hints specified in the function - signature. If the types do not match, a TypeError is raised. - Type checking is only performed when the logging level is set to `DEBUG`. - - :param Callable func: The function to be decorated. - :return: The decorated function with enforced type hints. - :rtype: Callable - - :Example: - - >>> @enforce_types - def dummy_function(a: int, b: float) -> float: - ... return a+b - - # This always works. - dummy_function(1, 2.0) - - # This raises a TypeError for the second argument, if logging is set to - # `DEBUG`. - dummy_function(1, "Hello, world!") - - - >>> @enforce_types - def dummy_function2(a: int, right: bool) -> float: - ... if right: - ... return float(a) - ... else: - ... return "Hello, world!" - - # This always works. - dummy_function2(1, right=True) - - # This raises a TypeError for the return value if logging is set to - # `DEBUG`. - dummy_function2(1, right=False) - """ - - def wrapper(*args, **kwargs): - """ - Wrapper function to enforce type hints. - - :param tuple args: Positional arguments passed to the function. - :param dict kwargs: Keyword arguments passed to the function. - :raises TypeError: If the argument or return type does not match the - specified type hints. - :return: The result of the decorated function. - :rtype: Any - """ - level = logging.getLevelName(logging.getLogger().getEffectiveLevel()) - - # Enforce type hints only in debug mode - if level != "DEBUG": - return func(*args, **kwargs) - - # Get the type hints for the function arguments - hints = typing.get_type_hints(func) - sig = inspect.signature(func) - bound = sig.bind(*args, **kwargs) - bound.apply_defaults() - - for arg_name, arg_value in bound.arguments.items(): - expected_type = hints.get(arg_name) - if expected_type and not isinstance(arg_value, expected_type): - raise TypeError( - f"Argument '{arg_name}' must be {expected_type.__name__}, " - f"but got {type(arg_value).__name__}!" - ) - - # Get the type hints for the return values - return_type = hints.get("return") - result = func(*args, **kwargs) - - if return_type and not isinstance(result, return_type): - raise TypeError( - f"Return value must be {return_type.__name__}, " - f"but got {type(result).__name__}!" - ) - - return result - - return wrapper +__all__ = ["enforce_types"] diff --git a/pina/utils.py b/pina/utils.py index efc48424e..8e40ae9cd 100644 --- a/pina/utils.py +++ b/pina/utils.py @@ -1,270 +1,19 @@ -"""Module for utility functions.""" - -import types -from functools import reduce -import torch - -from .label_tensor import LabelTensor - - -# Codacy error unused parameters -def custom_warning_format( - message, category, filename, lineno, file=None, line=None -): - """ - Custom warning formatting function. - - :param str message: The warning message. - :param Warning category: The warning category. - :param str filename: The filename where the warning is raised. - :param int lineno: The line number where the warning is raised. - :param str file: The file object where the warning is raised. - Default is None. - :param int line: The line where the warning is raised. - :return: The formatted warning message. - :rtype: str - """ - return f"{filename}: {category.__name__}: {message}\n" - - -def check_consistency(object_, object_instance, subclass=False): - """ - Check if an object maintains inheritance consistency. - - This function checks whether a given object is an instance of a specified - class or, if ``subclass=True``, whether it is a subclass of the specified - class. - - :param object: The object to check. - :type object: Iterable | Object - :param Object object_instance: The expected parent class. - :param bool subclass: If True, checks whether ``object_`` is a subclass - of ``object_instance`` instead of an instance. Default is ``False``. - :raises ValueError: If ``object_`` does not inherit from ``object_instance`` - as expected. - """ - if not isinstance(object_, (list, set, tuple)): - object_ = [object_] - - for obj in object_: - is_class = isinstance(obj, type) - expected_type_name = ( - object_instance.__name__ - if isinstance(object_instance, type) - else str(object_instance) - ) - - if subclass: - if not is_class: - raise ValueError( - f"You passed {repr(obj)} " - f"(an instance of {type(obj).__name__}), " - f"but a {expected_type_name} class was expected. " - f"Please pass a {expected_type_name} class or a " - "derived one." - ) - if not issubclass(obj, object_instance): - raise ValueError( - f"You passed {obj.__name__} class, but a " - f"{expected_type_name} class was expected. " - f"Please pass a {expected_type_name} class or a " - "derived one." - ) - else: - if is_class: - raise ValueError( - f"You passed {obj.__name__} class, but a " - f"{expected_type_name} instance was expected. " - f"Please pass a {expected_type_name} instance." - ) - if not isinstance(obj, object_instance): - raise ValueError( - f"You passed {repr(obj)} " - f"(an instance of {type(obj).__name__}), " - f"but a {expected_type_name} instance was expected. " - f"Please pass a {expected_type_name} instance." - ) - - -def labelize_forward(forward, input_variables, output_variables): - """ - Decorator to enable or disable the use of - :class:`~pina.label_tensor.LabelTensor` during the forward pass. - - :param Callable forward: The forward function of a :class:`torch.nn.Module`. - :param list[str] input_variables: The names of the input variables of a - :class:`~pina.problem.abstract_problem.AbstractProblem`. - :param list[str] output_variables: The names of the output variables of a - :class:`~pina.problem.abstract_problem.AbstractProblem`. - :return: The decorated forward function. - :rtype: Callable - """ - - def wrapper(x, *args, **kwargs): - """ - Decorated forward function. - - :param LabelTensor x: The labelized input of the forward pass of an - instance of :class:`torch.nn.Module`. - :param Iterable args: Additional positional arguments passed to - ``forward`` method. - :param dict kwargs: Additional keyword arguments passed to - ``forward`` method. - :return: The labelized output of the forward pass of an instance of - :class:`torch.nn.Module`. - :rtype: LabelTensor - """ - x = x.extract(input_variables) - output = forward(x, *args, **kwargs) - # keep it like this, directly using LabelTensor(...) raises errors - # when compiling the code - output = output.as_subclass(LabelTensor) - output.labels = output_variables - return output - - return wrapper - - -def merge_tensors(tensors): - """ - Merge a list of :class:`~pina.label_tensor.LabelTensor` instances into a - single :class:`~pina.label_tensor.LabelTensor` tensor, by applying - iteratively the cartesian product. - - :param list[LabelTensor] tensors: The list of tensors to merge. - :raises ValueError: If the list of tensors is empty. - :return: The merged tensor. - :rtype: LabelTensor - """ - if tensors: - return reduce(merge_two_tensors, tensors[1:], tensors[0]) - raise ValueError("Expected at least one tensor") - - -def merge_two_tensors(tensor1, tensor2): - """ - Merge two :class:`~pina.label_tensor.LabelTensor` instances into a single - :class:`~pina.label_tensor.LabelTensor` tensor, by applying the cartesian - product. - - :param LabelTensor tensor1: The first tensor to merge. - :param LabelTensor tensor2: The second tensor to merge. - :return: The merged tensor. - :rtype: LabelTensor - """ - n1 = tensor1.shape[0] - n2 = tensor2.shape[0] - - tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels) - tensor2 = LabelTensor( - tensor2.repeat_interleave(n1, dim=0), labels=tensor2.labels - ) - return tensor1.append(tensor2) - - -def torch_lhs(n, dim): - """ - The Latin Hypercube Sampling torch routine, sampling in :math:`[0, 1)`$. - - :param int n: The number of points to sample. - :param int dim: The number of dimensions of the sampling space. - :raises TypeError: If `n` or `dim` are not integers. - :raises ValueError: If `dim` is less than 1. - :return: The sampled points. - :rtype: torch.tensor - """ - - if not isinstance(n, int): - raise TypeError("number of point n must be int") - - if not isinstance(dim, int): - raise TypeError("dim must be int") - - if dim < 1: - raise ValueError("dim must be greater than one") - - samples = torch.rand(size=(n, dim)) - - perms = torch.tile(torch.arange(1, n + 1), (dim, 1)) - - for row in range(dim): - idx_perm = torch.randperm(perms.shape[-1]) - perms[row, :] = perms[row, idx_perm] - - perms = perms.T - - samples = (perms - samples) / n - - return samples - - -def is_function(f): - """ - Check if the given object is a function or a lambda. - - :param Object f: The object to be checked. - :return: ``True`` if ``f`` is a function, ``False`` otherwise. - :rtype: bool - """ - return callable(f) - - -def chebyshev_roots(n): - """ - Compute the roots of the Chebyshev polynomial of degree ``n``. - - :param int n: The number of roots to return. - :return: The roots of the Chebyshev polynomials. - :rtype: torch.Tensor - """ - pi = torch.acos(torch.zeros(1)).item() * 2 - k = torch.arange(n) - nodes = torch.sort(torch.cos(pi * (k + 0.5) / n))[0] - return nodes - - -def check_positive_integer(value, strict=True): - """ - Check if the value is a positive integer. - - :param int value: The value to check. - :param bool strict: If True, the value must be strictly positive. - Default is True. - :raises AssertionError: If the value is not a positive integer. - """ - if strict: - assert ( - isinstance(value, int) and value > 0 - ), f"Expected a strictly positive integer, got {value}." - else: - assert ( - isinstance(value, int) and value >= 0 - ), f"Expected a non-negative integer, got {value}." - - -def in_range(value, range_vals, strict=True): - """ - Check if a value is within a specified range. - - :param int value: The integer value to check. - :param list[int] range_vals: A list of two integers representing the range - limits. The first element specifies the lower bound, and the second - specifies the upper bound. - :param bool strict: If True, the value must be strictly positive. - Default is True. - :return: True if the value satisfies the range condition, False otherwise. - :rtype: bool - """ - # Validate inputs - check_consistency(value, (float, int)) - check_consistency(range_vals, (float, int)) - assert ( - isinstance(range_vals, list) and len(range_vals) == 2 - ), "range_vals must be a list of two integers [lower, upper]" - lower, upper = range_vals - - # Check the range - if strict: - return lower < value < upper - - return lower <= value <= upper +""" +Utility functions for tensor manipulation and input validation. + +This module provides helper functions to manage tensor operations and ensure +data consistency across the PINA framework, supporting robust input checking +and seamless data merging. +""" + +from pina._src.core.utils import ( + merge_tensors, + check_consistency, + check_positive_integer, +) + +__all__ = [ + "merge_tensors", + "check_consistency", + "check_positive_integer", +] diff --git a/pina/weighting/__init__.py b/pina/weighting/__init__.py new file mode 100644 index 000000000..d4eb46716 --- /dev/null +++ b/pina/weighting/__init__.py @@ -0,0 +1,19 @@ +"""Module for weighting strategies in multi-objective optimization.""" + +__all__ = [ + "WeightingInterface", + "BaseWeighting", + "LinearWeighting", + "NeuralTangentKernelWeighting", + "_NoWeighting", + "ScalarWeighting", + "SelfAdaptiveWeighting", +] + +from pina._src.weighting.weighting_interface import WeightingInterface +from pina._src.weighting.base_weighting import BaseWeighting +from pina._src.weighting.linear_weighting import LinearWeighting +from pina._src.weighting.ntk_weighting import NeuralTangentKernelWeighting +from pina._src.weighting.no_weighting import _NoWeighting +from pina._src.weighting.scalar_weighting import ScalarWeighting +from pina._src.weighting.self_adaptive_weighting import SelfAdaptiveWeighting diff --git a/pyproject.toml b/pyproject.toml index ea08dc243..1321a0212 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "pina-mathlab" -version = "0.2.6" +version = "0.3.0" description = "Physic Informed Neural networks for Advance modeling." readme = "README.md" authors = [ diff --git a/readme/applications.gif b/readme/applications.gif new file mode 100644 index 000000000..52d8b1d49 Binary files /dev/null and b/readme/applications.gif differ diff --git a/readme/pina.svg b/readme/pina.svg new file mode 100644 index 000000000..73da49573 --- /dev/null +++ b/readme/pina.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 000000000..5bff82941 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,17 @@ +import shutil +from pathlib import Path +import pytest + + +@pytest.fixture +def clean_tmp_dir(tmp_path): + path = Path(tmp_path) + + if path.exists(): + shutil.rmtree(path) + + path.mkdir(parents=True, exist_ok=True) + yield path + + if path.exists(): + shutil.rmtree(path) diff --git a/tests/test_adaptive_function.py b/tests/test_adaptive_function.py deleted file mode 100644 index bce5059d7..000000000 --- a/tests/test_adaptive_function.py +++ /dev/null @@ -1,85 +0,0 @@ -import torch -import pytest - -from pina.adaptive_function import ( - AdaptiveReLU, - AdaptiveSigmoid, - AdaptiveTanh, - AdaptiveSiLU, - AdaptiveMish, - AdaptiveELU, - AdaptiveCELU, - AdaptiveGELU, - AdaptiveSoftmin, - AdaptiveSoftmax, - AdaptiveSIREN, - AdaptiveExp, -) - - -adaptive_function = ( - AdaptiveReLU, - AdaptiveSigmoid, - AdaptiveTanh, - AdaptiveSiLU, - AdaptiveMish, - AdaptiveELU, - AdaptiveCELU, - AdaptiveGELU, - AdaptiveSoftmin, - AdaptiveSoftmax, - AdaptiveSIREN, - AdaptiveExp, -) -x = torch.rand(10, requires_grad=True) - - -@pytest.mark.parametrize("Func", adaptive_function) -def test_constructor(Func): - if Func.__name__ == "AdaptiveExp": - # simple - Func() - # setting values - af = Func(alpha=1.0, beta=2.0) - assert af.alpha.requires_grad - assert af.beta.requires_grad - assert af.alpha == 1.0 - assert af.beta == 2.0 - else: - # simple - Func() - # setting values - af = Func(alpha=1.0, beta=2.0, gamma=3.0) - assert af.alpha.requires_grad - assert af.beta.requires_grad - assert af.gamma.requires_grad - assert af.alpha == 1.0 - assert af.beta == 2.0 - assert af.gamma == 3.0 - - # fixed variables - af = Func(alpha=1.0, beta=2.0, fixed=["alpha"]) - assert af.alpha.requires_grad is False - assert af.beta.requires_grad - assert af.alpha == 1.0 - assert af.beta == 2.0 - - with pytest.raises(TypeError): - Func(alpha=1.0, beta=2.0, fixed=["delta"]) - - with pytest.raises(ValueError): - Func(alpha="s") - Func(alpha=1) - - -@pytest.mark.parametrize("Func", adaptive_function) -def test_forward(Func): - af = Func() - af(x) - - -@pytest.mark.parametrize("Func", adaptive_function) -def test_backward(Func): - af = Func() - y = af(x) - y.mean().backward() diff --git a/tests/test_adaptive_function/test_adaptive_celu.py b/tests/test_adaptive_function/test_adaptive_celu.py new file mode 100644 index 000000000..171845d61 --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_celu.py @@ -0,0 +1,89 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveCELU + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveCELU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveCELU(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveCELU(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveCELU(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveCELU(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveCELU(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveCELU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveCELU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_elu.py b/tests/test_adaptive_function/test_adaptive_elu.py new file mode 100644 index 000000000..7c231d8da --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_elu.py @@ -0,0 +1,89 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveELU + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveELU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveELU(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveELU(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveELU(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveELU(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveELU(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveELU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveELU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_exp.py b/tests/test_adaptive_function/test_adaptive_exp.py new file mode 100644 index 000000000..5bad49b00 --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_exp.py @@ -0,0 +1,89 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveExp + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveExp(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveExp(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveExp(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveExp(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveExp(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveExp(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveExp(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveExp(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_gelu.py b/tests/test_adaptive_function/test_adaptive_gelu.py new file mode 100644 index 000000000..53c4af8d1 --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_gelu.py @@ -0,0 +1,89 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveGELU + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveGELU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveGELU(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveGELU(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveGELU(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveGELU(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveGELU(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveGELU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveGELU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_mish.py b/tests/test_adaptive_function/test_adaptive_mish.py new file mode 100644 index 000000000..218438337 --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_mish.py @@ -0,0 +1,89 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveMish + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveMish(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveMish(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveMish(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveMish(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveMish(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveMish(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveMish(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveMish(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_relu.py b/tests/test_adaptive_function/test_adaptive_relu.py new file mode 100644 index 000000000..02f8c7ff5 --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_relu.py @@ -0,0 +1,89 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveReLU + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveReLU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveReLU(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveReLU(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveReLU(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveReLU(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveReLU(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveReLU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveReLU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_sigmoid.py b/tests/test_adaptive_function/test_adaptive_sigmoid.py new file mode 100644 index 000000000..0d5bb7c08 --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_sigmoid.py @@ -0,0 +1,95 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveSigmoid + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveSigmoid( + alpha=alpha, beta=beta, gamma=gamma, fixed=fixed + ) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveSigmoid(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveSigmoid(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveSigmoid(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveSigmoid(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveSigmoid(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveSigmoid( + alpha=alpha, beta=beta, gamma=gamma, fixed=fixed + ) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveSigmoid( + alpha=alpha, beta=beta, gamma=gamma, fixed=fixed + ) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_silu.py b/tests/test_adaptive_function/test_adaptive_silu.py new file mode 100644 index 000000000..56ed77cbb --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_silu.py @@ -0,0 +1,89 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveSiLU + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveSiLU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveSiLU(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveSiLU(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveSiLU(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveSiLU(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveSiLU(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveSiLU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveSiLU(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_siren.py b/tests/test_adaptive_function/test_adaptive_siren.py new file mode 100644 index 000000000..5211ed290 --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_siren.py @@ -0,0 +1,89 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveSIREN + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveSIREN(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveSIREN(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveSIREN(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveSIREN(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveSIREN(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveSIREN(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveSIREN(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveSIREN(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_softmax.py b/tests/test_adaptive_function/test_adaptive_softmax.py new file mode 100644 index 000000000..af53fcca9 --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_softmax.py @@ -0,0 +1,95 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveSoftmax + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveSoftmax( + alpha=alpha, beta=beta, gamma=gamma, fixed=fixed + ) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveSoftmax(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveSoftmax(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveSoftmax(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveSoftmax(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveSoftmax(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveSoftmax( + alpha=alpha, beta=beta, gamma=gamma, fixed=fixed + ) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveSoftmax( + alpha=alpha, beta=beta, gamma=gamma, fixed=fixed + ) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_softmin.py b/tests/test_adaptive_function/test_adaptive_softmin.py new file mode 100644 index 000000000..ef1968930 --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_softmin.py @@ -0,0 +1,95 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveSoftmin + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveSoftmin( + alpha=alpha, beta=beta, gamma=gamma, fixed=fixed + ) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveSoftmin(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveSoftmin(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveSoftmin(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveSoftmin(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveSoftmin(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveSoftmin( + alpha=alpha, beta=beta, gamma=gamma, fixed=fixed + ) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveSoftmin( + alpha=alpha, beta=beta, gamma=gamma, fixed=fixed + ) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_adaptive_function/test_adaptive_tanh.py b/tests/test_adaptive_function/test_adaptive_tanh.py new file mode 100644 index 000000000..4ece0b8eb --- /dev/null +++ b/tests/test_adaptive_function/test_adaptive_tanh.py @@ -0,0 +1,89 @@ +import torch +import pytest +from pina import LabelTensor +from pina.adaptive_function import AdaptiveTanh + + +x = LabelTensor(torch.rand(10, 1), labels=["x"]) +fixed_variables = [None, "alpha", ["beta", "gamma"], ["alpha", "beta", "gamma"]] +param_names = ["alpha", "beta", "gamma"] + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_constructor(alpha, beta, gamma, fixed): + + # Construct the adaptive activation function with the specified parameters + activation = AdaptiveTanh(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + + # Build expected values for parameters + expected = {"alpha": alpha, "beta": beta, "gamma": gamma} + if fixed is None: + fixed_set = set() + else: + fixed_set = {fixed} if isinstance(fixed, str) else set(fixed) + + # Verify fixed parameters consistency + for v in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is False + assert param.item() == expected[v] + + # Verify trainable parameters consistency + for v in param_names: + if v not in fixed_set: + param = getattr(activation, v) + assert param.requires_grad is True + assert param.item() == expected[v] + + # Should fail if alpha is not a number + with pytest.raises(ValueError): + AdaptiveTanh(alpha="s") + + # Should fail if beta is not a number + with pytest.raises(ValueError): + AdaptiveTanh(beta="s") + + # Should fail if gamma is not a number + with pytest.raises(ValueError): + AdaptiveTanh(gamma="s") + + # Should fail if fixed is not a string or list of strings + with pytest.raises(ValueError): + AdaptiveTanh(fixed=123) + + # Should fail if fixed contains invalid parameter names + with pytest.raises(ValueError): + AdaptiveTanh(fixed="delta") + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_forward(alpha, beta, gamma, fixed): + + # Compute the output + activation = AdaptiveTanh(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x) + + # Verify the output shape is the same as the input shape + assert output_.shape == x.shape + + +@pytest.mark.parametrize("fixed", fixed_variables) +@pytest.mark.parametrize("alpha", [1, 2.5]) +@pytest.mark.parametrize("beta", [1, 2.5]) +@pytest.mark.parametrize("gamma", [1, 2.5]) +def test_backward(alpha, beta, gamma, fixed): + + # Compute the output and perform backpropagation + activation = AdaptiveTanh(alpha=alpha, beta=beta, gamma=gamma, fixed=fixed) + output_ = activation(x.requires_grad_()) + loss = torch.mean(output_) + loss.backward() + + # Verify that the gradients shape is the same as the input shape + assert x.grad.shape == x.shape diff --git a/tests/test_block/test_kan_block.py b/tests/test_block/test_kan_block.py new file mode 100644 index 000000000..08f549f40 --- /dev/null +++ b/tests/test_block/test_kan_block.py @@ -0,0 +1,146 @@ +import torch +import pytest +from pina.model.block import KANBlock + +# Data +input_dim = 3 +data = torch.rand((10, input_dim)) + + +@pytest.mark.parametrize("output_dimensions", [1, 5]) +@pytest.mark.parametrize("spline_order", [3, 4]) +@pytest.mark.parametrize("n_knots", [10, 20]) +@pytest.mark.parametrize("init_scale_spline", [1e-2, 1e-1]) +@pytest.mark.parametrize("init_scale_base", [1.0, 0.1]) +def test_constructor( + output_dimensions, spline_order, n_knots, init_scale_spline, init_scale_base +): + + KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + spline_order=spline_order, + n_knots=n_knots, + init_scale_spline=init_scale_spline, + init_scale_base=init_scale_base, + ) + + # Should fail if input_dimensions is not a positive integer + with pytest.raises(AssertionError): + KANBlock(input_dimensions=-1, output_dimensions=output_dimensions) + + # Should fail if output_dimensions is not a positive integer + with pytest.raises(AssertionError): + KANBlock(input_dimensions=data.shape[1], output_dimensions=-1) + + # Should fail if spline_order is not a positive integer + with pytest.raises(AssertionError): + KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + spline_order=-1, + ) + + # Should fail if n_knots is not a positive integer + with pytest.raises(AssertionError): + KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + n_knots=-1, + ) + + # Should fail if grid_range is not of length 2 + with pytest.raises(ValueError): + KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + grid_range=[-1, 0, 1], + ) + + # Should fail if base_function is not a torch.nn.Module subclass + with pytest.raises(ValueError): + KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + base_function="not_a_module", + ) + + # Should fail if use_base_linear is not a boolean + with pytest.raises(ValueError): + KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + use_base_linear="not_a_bool", + ) + + # Should fail if use_bias is not a boolean + with pytest.raises(ValueError): + KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + use_bias="not_a_bool", + ) + + # Should fail if init_scale_spline is not a float or int + with pytest.raises(ValueError): + KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + init_scale_spline="not_a_number", + ) + + # Should fail if init_scale_base is not a float or int + with pytest.raises(ValueError): + KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + init_scale_base="not_a_number", + ) + + +@pytest.mark.parametrize("output_dimensions", [1, 5]) +@pytest.mark.parametrize("spline_order", [3, 4]) +@pytest.mark.parametrize("n_knots", [10, 20]) +@pytest.mark.parametrize("init_scale_spline", [1e-2, 1e-1]) +@pytest.mark.parametrize("init_scale_base", [1.0, 0.1]) +def test_forward( + output_dimensions, spline_order, n_knots, init_scale_spline, init_scale_base +): + + model = KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + spline_order=spline_order, + n_knots=n_knots, + init_scale_spline=init_scale_spline, + init_scale_base=init_scale_base, + ) + + output_ = model(data) + assert output_.shape == (data.shape[0], output_dimensions) + + +@pytest.mark.parametrize("output_dimensions", [1, 5]) +@pytest.mark.parametrize("spline_order", [3, 4]) +@pytest.mark.parametrize("n_knots", [10, 20]) +@pytest.mark.parametrize("init_scale_spline", [1e-2, 1e-1]) +@pytest.mark.parametrize("init_scale_base", [1.0, 0.1]) +def test_backward( + output_dimensions, spline_order, n_knots, init_scale_spline, init_scale_base +): + + model = KANBlock( + input_dimensions=data.shape[1], + output_dimensions=output_dimensions, + spline_order=spline_order, + n_knots=n_knots, + init_scale_spline=init_scale_spline, + init_scale_base=init_scale_base, + ) + + data.requires_grad_() + output_ = model(data) + + loss = torch.mean(output_) + loss.backward() + assert data.grad.shape == data.shape diff --git a/tests/test_block/test_low_rank_block.py b/tests/test_block/test_low_rank_block.py index 0e6ddcb89..17f0dabd6 100644 --- a/tests/test_block/test_low_rank_block.py +++ b/tests/test_block/test_low_rank_block.py @@ -4,7 +4,6 @@ from pina.model.block import LowRankBlock from pina import LabelTensor - input_dimensions = 2 embedding_dimenion = 1 rank = 4 diff --git a/tests/test_block/test_pod.py b/tests/test_block/test_pod.py index d10625fc3..ffe9a8a81 100644 --- a/tests/test_block/test_pod.py +++ b/tests/test_block/test_pod.py @@ -1,7 +1,7 @@ import torch import pytest -from pina.model.block.pod_block import PODBlock +from pina.model.block import PODBlock x = torch.linspace(-1, 1, 100) toy_snapshots = torch.vstack( diff --git a/tests/test_block/test_rbf.py b/tests/test_block/test_rbf.py index 65912fb76..c36836743 100644 --- a/tests/test_block/test_rbf.py +++ b/tests/test_block/test_rbf.py @@ -2,7 +2,7 @@ import pytest import math -from pina.model.block.rbf_block import RBFBlock +from pina.model.block import RBFBlock x = torch.linspace(-1, 1, 100) toy_params = torch.linspace(0, 1, 10).unsqueeze(1) diff --git a/tests/test_callback/test_data_normalizer.py b/tests/test_callback/test_data_normalizer.py new file mode 100644 index 000000000..ac2d3969c --- /dev/null +++ b/tests/test_callback/test_data_normalizer.py @@ -0,0 +1,196 @@ +import torch +import pytest +from pina import Trainer, LabelTensor, Condition +from pina.solver import SupervisedSingleModelSolver +from pina.callback import DataNormalizer +from pina.problem import BaseProblem +from pina.model import FeedForward +from pina.graph import RadiusGraph + + +# Tensor-based problem +class TensorProblem(BaseProblem): + input_variables = ["x", "y"] + output_variables = ["u"] + conditions = { + "data1": Condition(input=torch.rand(20, 2), target=torch.rand(20, 1)), + "data2": Condition(input=torch.rand(20, 2), target=torch.rand(20, 1)), + } + + +# LabelTensor-based problem +class LabelTensorProblem(BaseProblem): + input_variables = ["x", "y"] + output_variables = ["u"] + conditions = { + "data1": Condition( + input=LabelTensor(torch.rand(20, 2), ["x", "y"]), + target=LabelTensor(torch.rand(20, 1), ["u"]), + ), + "data2": Condition( + input=LabelTensor(torch.rand(20, 2), ["x", "y"]), + target=LabelTensor(torch.rand(20, 1), ["u"]), + ), + } + + +# Graph-based problem for testing unsupported dataset case +input_graph = [RadiusGraph(radius=0.5, pos=torch.rand(10, 2)) for _ in range(5)] +target_tensor = torch.rand(5, 1) + + +class GraphProblem(BaseProblem): + + input_variables = ["x", "y"] + output_variables = ["u"] + conditions = {"data1": Condition(input=input_graph, target=target_tensor)} + + +# Mapping from stage to dataset names +stage_map = { + "train": ["train_datasets"], + "validate": ["val_datasets"], + "test": ["test_datasets"], + "all": ["train_datasets", "val_datasets", "test_datasets"], +} + + +@pytest.mark.parametrize("scale_fn", [torch.std, torch.var]) +@pytest.mark.parametrize("shift_fn", [torch.mean, torch.median]) +@pytest.mark.parametrize("apply_to", ["input", "target"]) +@pytest.mark.parametrize("stage", ["train", "validate", "test", "all"]) +def test_constructor(scale_fn, shift_fn, apply_to, stage): + DataNormalizer( + scale_fn=scale_fn, shift_fn=shift_fn, stage=stage, apply_to=apply_to + ) + + # Should fail if scale_fn is not Callable + with pytest.raises(ValueError): + DataNormalizer(scale_fn=1) + + # Should fail if shift_fn is not Callable + with pytest.raises(ValueError): + DataNormalizer(shift_fn=1) + + # Should fail if apply_to is invalid + with pytest.raises(ValueError): + DataNormalizer(apply_to="invalid") + + # Should fail if stage is invalid + with pytest.raises(ValueError): + DataNormalizer(stage="invalid") + + +@pytest.mark.parametrize("apply_to", ["input", "target"]) +@pytest.mark.parametrize("stage", ["train", "validate", "test", "all"]) +@pytest.mark.parametrize("scale_fn", [torch.std, torch.var]) +@pytest.mark.parametrize("shift_fn", [torch.mean, torch.median]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_routine(apply_to, stage, scale_fn, shift_fn, use_lt): + + # Initialize problem, model and solver + problem = LabelTensorProblem() if use_lt else TensorProblem() + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + solver = SupervisedSingleModelSolver( + problem=problem, model=model, use_lt=use_lt + ) + + # Initialize the callback + callback = DataNormalizer( + scale_fn=scale_fn, + shift_fn=shift_fn, + stage=stage, + apply_to=apply_to, + ) + + # Initialize the trainer + trainer = Trainer( + solver=solver, + callbacks=callback, + accelerator="cpu", + max_epochs=3, + train_size=0.6, + val_size=0.2, + test_size=0.2, + ) + + # Run the training and testing routines + trainer.train() + trainer.test() + + # Store datasets to check normalization + datasets = { + "train_datasets": trainer.datamodule.train_datasets, + "val_datasets": trainer.datamodule.val_datasets, + "test_datasets": trainer.datamodule.test_datasets, + } + + # Save the expected normalized datasets for each stage + expected_normalized_datasets = stage_map[stage] + + # Check computed normalizer exists for all input-target conditions + for name in solver.problem.conditions.keys(): + assert name in callback.normalizer + assert "scale" in callback.normalizer[name] + assert "shift" in callback.normalizer[name] + + # Check normalized datasets + for ds_name, dataset in datasets.items(): + for c_name in callback.normalizer.keys(): + + # Extract the data and container for the current condition + points = getattr(dataset[c_name].condition, apply_to) + + # Check normalization parameters are correct for normalized datasets + if ds_name in expected_normalized_datasets: + expected_shift = shift_fn(points) + + # The expected shift should be close to zero after normalization + assert torch.isclose( + expected_shift, + torch.zeros_like(expected_shift), + atol=1e-5, + ) + + # The expected scale should be close to one after normalization + if scale_fn is torch.std: + expected_scale = scale_fn(points) + + assert torch.isclose( + expected_scale, + torch.ones_like(expected_scale), + atol=1e-5, + ) + + # Should fail if the dataset is graph-based and therefore unsupported + with pytest.raises(NotImplementedError): + + # Initialize problem, model and solver with graph-based problem + model = FeedForward( + len(GraphProblem.input_variables), + len(GraphProblem.output_variables), + ) + solver = SupervisedSingleModelSolver( + problem=GraphProblem(), model=model + ) + + # Initialize the callback + callback = DataNormalizer( + scale_fn=scale_fn, + shift_fn=shift_fn, + stage=stage, + apply_to=apply_to, + ) + + # Initialize the trainer + trainer = Trainer( + solver=solver, + callbacks=callback, + accelerator="cpu", + max_epochs=3, + ) + + # Run the training routine to trigger the error + trainer.train() diff --git a/tests/test_callback/test_metric_tracker.py b/tests/test_callback/test_metric_tracker.py index 062664b79..0574db1a2 100644 --- a/tests/test_callback/test_metric_tracker.py +++ b/tests/test_callback/test_metric_tracker.py @@ -1,39 +1,71 @@ -from pina.solver import PINN -from pina.trainer import Trainer +import torch +import pytest +from pina.solver import PhysicsInformedSingleModelSolver from pina.model import FeedForward from pina.callback import MetricTracker -from pina.problem.zoo import Poisson2DSquareProblem as Poisson +from pina import Trainer, Condition, LabelTensor +from pina.problem.zoo import Poisson2DSquareProblem + +# Initialize the problem +problem = Poisson2DSquareProblem() +problem.discretise_domain(10, "random") +problem.conditions["data"] = Condition( + input=LabelTensor(torch.randn(10, 2), labels=["x", "y"]), + target=LabelTensor(torch.randn(10, 1), labels=["u"]), +) + +# Initialize the model and solver +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) +solver = PhysicsInformedSingleModelSolver(problem=problem, model=model) -# make the problem -poisson_problem = Poisson() -n = 10 -poisson_problem.discretise_domain(n, "grid", domains="boundary") -poisson_problem.discretise_domain(n, "grid", domains="D") -model = FeedForward( - len(poisson_problem.input_variables), len(poisson_problem.output_variables) +@pytest.mark.parametrize( + "metrics_to_track", [["D_loss", "train_loss"], "data_loss", None] ) +def test_constructor(metrics_to_track): + MetricTracker(metrics_to_track=metrics_to_track) -# make the solver -solver = PINN(problem=poisson_problem, model=model) + # Should fail if metrics_to_track is not a string or list of strings + with pytest.raises(ValueError): + MetricTracker(metrics_to_track=123) -def test_metric_tracker_constructor(): - MetricTracker() +@pytest.mark.parametrize( + "metrics_to_track", [["D_loss", "train_loss"], "data_loss", None] +) +@pytest.mark.parametrize("batch_size", [None, 8]) +def test_routine(metrics_to_track, batch_size): + + # Initialize the callback + callback = MetricTracker(metrics_to_track=metrics_to_track) + # Convert to list if a single string is provided + if isinstance(metrics_to_track, str): + metrics_to_track = [metrics_to_track] -def test_metric_tracker_routine(): - # make the trainer + # Initialize the trainer with the callback and train the model trainer = Trainer( solver=solver, - callbacks=[MetricTracker()], + callbacks=[callback], accelerator="cpu", max_epochs=5, + batch_size=batch_size, log_every_n_steps=1, ) trainer.train() - # get the tracked metrics - metrics = trainer.callbacks[0].metrics - # assert the logged metrics are correct - logged_metrics = sorted(list(metrics.keys())) - assert logged_metrics == ["train_loss"] + + # Get the logged metrics from the callback + logged_metrics = sorted(list(trainer.callbacks[0].metrics.keys())) + + # Define the expected metrics + expected_metrics = metrics_to_track or ["train_loss"] + + # If a batch size is provided, expand metric names to match convention + if batch_size is not None: + expected_metrics = [ + f"{metric}_{suffix}" + for metric in expected_metrics + for suffix in ("step", "epoch") + ] + + assert sorted(logged_metrics) == sorted(expected_metrics) diff --git a/tests/test_callback/test_normalizer_data_callback.py b/tests/test_callback/test_normalizer_data_callback.py deleted file mode 100644 index 7cdcc9510..000000000 --- a/tests/test_callback/test_normalizer_data_callback.py +++ /dev/null @@ -1,244 +0,0 @@ -import torch -import pytest -from copy import deepcopy - -from pina import Trainer, LabelTensor, Condition -from pina.solver import SupervisedSolver -from pina.model import FeedForward -from pina.callback import NormalizerDataCallback -from pina.problem import AbstractProblem -from pina.problem.zoo import Poisson2DSquareProblem as Poisson -from pina.solver import PINN -from pina.graph import RadiusGraph - -# for checking normalization -stage_map = { - "train": ["train_dataset"], - "validate": ["val_dataset"], - "test": ["test_dataset"], - "all": ["train_dataset", "val_dataset", "test_dataset"], -} - -input_1 = torch.rand(20, 2) * 10 -target_1 = torch.rand(20, 1) * 10 -input_2 = torch.rand(20, 2) * 5 -target_2 = torch.rand(20, 1) * 5 - - -class LabelTensorProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data1": Condition( - input=LabelTensor(input_1, ["u_0", "u_1"]), - target=LabelTensor(target_1, ["u"]), - ), - "data2": Condition( - input=LabelTensor(input_2, ["u_0", "u_1"]), - target=LabelTensor(target_2, ["u"]), - ), - } - - -class TensorProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data1": Condition(input=input_1, target=target_1), - "data2": Condition(input=input_2, target=target_2), - } - - -input_graph = [RadiusGraph(radius=0.5, pos=torch.rand(10, 2)) for _ in range(5)] -output_graph = torch.rand(5, 1) - - -class GraphProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data": Condition(input=input_graph, target=output_graph), - } - - -supervised_solver_no_lt = SupervisedSolver( - problem=TensorProblem(), model=FeedForward(2, 1), use_lt=False -) -supervised_solver_lt = SupervisedSolver( - problem=LabelTensorProblem(), model=FeedForward(2, 1), use_lt=True -) - -poisson_problem = Poisson() -poisson_problem.conditions["data"] = Condition( - input=LabelTensor(torch.rand(20, 2) * 10, ["x", "y"]), - target=LabelTensor(torch.rand(20, 1) * 10, ["u"]), -) - - -@pytest.mark.parametrize("scale_fn", [torch.std, torch.var]) -@pytest.mark.parametrize("shift_fn", [torch.mean, torch.median]) -@pytest.mark.parametrize("apply_to", ["input", "target"]) -@pytest.mark.parametrize("stage", ["train", "validate", "test", "all"]) -def test_init(scale_fn, shift_fn, apply_to, stage): - normalizer = NormalizerDataCallback( - scale_fn=scale_fn, shift_fn=shift_fn, apply_to=apply_to, stage=stage - ) - assert normalizer.scale_fn == scale_fn - assert normalizer.shift_fn == shift_fn - assert normalizer.apply_to == apply_to - assert normalizer.stage == stage - - -def test_init_invalid_scale(): - with pytest.raises(ValueError): - NormalizerDataCallback(scale_fn=1) - - -def test_init_invalid_shift(): - with pytest.raises(ValueError): - NormalizerDataCallback(shift_fn=1) - - -@pytest.mark.parametrize("invalid_apply_to", ["inputt", "targett", 1]) -def test_init_invalid_apply_to(invalid_apply_to): - with pytest.raises(ValueError): - NormalizerDataCallback(apply_to=invalid_apply_to) - - -@pytest.mark.parametrize("invalid_stage", ["trainn", "validatee", 1]) -def test_init_invalid_stage(invalid_stage): - with pytest.raises(ValueError): - NormalizerDataCallback(stage=invalid_stage) - - -@pytest.mark.parametrize( - "solver", [supervised_solver_lt, supervised_solver_no_lt] -) -@pytest.mark.parametrize( - "fn", [[torch.std, torch.mean], [torch.var, torch.median]] -) -@pytest.mark.parametrize("apply_to", ["input", "target"]) -@pytest.mark.parametrize("stage", ["all", "train", "validate", "test"]) -def test_setup(solver, fn, stage, apply_to): - scale_fn, shift_fn = fn - trainer = Trainer( - solver=solver, - callbacks=NormalizerDataCallback( - scale_fn=scale_fn, shift_fn=shift_fn, stage=stage, apply_to=apply_to - ), - max_epochs=1, - train_size=0.4, - val_size=0.3, - test_size=0.3, - shuffle=False, - ) - trainer_copy = deepcopy(trainer) - trainer_copy.data_module.setup("fit") - trainer_copy.data_module.setup("test") - trainer.train() - trainer.test() - - normalizer = trainer.callbacks[0].normalizer - - for cond in ["data1", "data2"]: - scale = scale_fn( - trainer_copy.data_module.train_dataset.conditions_dict[cond][ - apply_to - ] - ) - shift = shift_fn( - trainer_copy.data_module.train_dataset.conditions_dict[cond][ - apply_to - ] - ) - assert "scale" in normalizer[cond] - assert "shift" in normalizer[cond] - assert normalizer[cond]["scale"] - scale < 1e-5 - assert normalizer[cond]["shift"] - shift < 1e-5 - for ds_name in stage_map[stage]: - dataset = getattr(trainer.data_module, ds_name, None) - old_dataset = getattr(trainer_copy.data_module, ds_name, None) - current_points = dataset.conditions_dict[cond][apply_to] - old_points = old_dataset.conditions_dict[cond][apply_to] - expected = (old_points - shift) / scale - assert torch.allclose(current_points, expected) - - -@pytest.mark.parametrize( - "fn", [[torch.std, torch.mean], [torch.var, torch.median]] -) -@pytest.mark.parametrize("apply_to", ["input"]) -@pytest.mark.parametrize("stage", ["all", "train", "validate", "test"]) -def test_setup_pinn(fn, stage, apply_to): - scale_fn, shift_fn = fn - pinn = PINN( - problem=poisson_problem, - model=FeedForward(2, 1), - ) - poisson_problem.discretise_domain(n=10) - trainer = Trainer( - solver=pinn, - callbacks=NormalizerDataCallback( - scale_fn=scale_fn, - shift_fn=shift_fn, - stage=stage, - apply_to=apply_to, - ), - max_epochs=1, - train_size=0.4, - val_size=0.3, - test_size=0.3, - shuffle=False, - ) - - trainer_copy = deepcopy(trainer) - trainer_copy.data_module.setup("fit") - trainer_copy.data_module.setup("test") - trainer.train() - trainer.test() - - conditions = trainer.callbacks[0].normalizer.keys() - assert "data" in conditions - assert len(conditions) == 1 - normalizer = trainer.callbacks[0].normalizer - cond = "data" - - scale = scale_fn( - trainer_copy.data_module.train_dataset.conditions_dict[cond][apply_to] - ) - shift = shift_fn( - trainer_copy.data_module.train_dataset.conditions_dict[cond][apply_to] - ) - assert "scale" in normalizer[cond] - assert "shift" in normalizer[cond] - assert normalizer[cond]["scale"] - scale < 1e-5 - assert normalizer[cond]["shift"] - shift < 1e-5 - for ds_name in stage_map[stage]: - dataset = getattr(trainer.data_module, ds_name, None) - old_dataset = getattr(trainer_copy.data_module, ds_name, None) - current_points = dataset.conditions_dict[cond][apply_to] - old_points = old_dataset.conditions_dict[cond][apply_to] - expected = (old_points - shift) / scale - assert torch.allclose(current_points, expected) - - -def test_setup_graph_dataset(): - solver = SupervisedSolver( - problem=GraphProblem(), model=FeedForward(2, 1), use_lt=False - ) - trainer = Trainer( - solver=solver, - callbacks=NormalizerDataCallback( - scale_fn=torch.std, - shift_fn=torch.mean, - stage="all", - apply_to="input", - ), - max_epochs=1, - train_size=0.4, - val_size=0.3, - test_size=0.3, - shuffle=False, - ) - with pytest.raises(NotImplementedError): - trainer.train() diff --git a/tests/test_callback/test_pina_progress_bar.py b/tests/test_callback/test_pina_progress_bar.py index ec7129852..cc1c3560d 100644 --- a/tests/test_callback/test_pina_progress_bar.py +++ b/tests/test_callback/test_pina_progress_bar.py @@ -1,35 +1,84 @@ -from pina.solver import PINN -from pina.trainer import Trainer +import torch +import pytest +from pina.solver import PhysicsInformedSingleModelSolver from pina.model import FeedForward from pina.callback import PINAProgressBar -from pina.problem.zoo import Poisson2DSquareProblem as Poisson +from pina import Trainer, Condition, LabelTensor +from pina.problem.zoo import Poisson2DSquareProblem +# Initialize the problem +problem = Poisson2DSquareProblem() +problem.discretise_domain(10, "random") +problem.conditions["data"] = Condition( + input=LabelTensor(torch.randn(10, 2), labels=["x", "y"]), + target=LabelTensor(torch.randn(10, 1), labels=["u"]), +) + +# Initialize the model and solver +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) +solver = PhysicsInformedSingleModelSolver(problem=problem, model=model) + +# Define metrics to be used in the progress bar +metrics_list = ["train", "val", "test", ["test", "data"], ["train", "val"]] -# make the problem -poisson_problem = Poisson() -n = 10 -condition_names = list(poisson_problem.conditions.keys()) -poisson_problem.discretise_domain(n, "grid", domains="boundary") -poisson_problem.discretise_domain(n, "grid", domains="D") -model = FeedForward( - len(poisson_problem.input_variables), len(poisson_problem.output_variables) + +@pytest.mark.parametrize( + "metrics", ["train", "val", "test", ["test", "data"], ["train", "val"]] ) +def test_constructor(metrics): + PINAProgressBar(metrics=metrics) + + # Should fail if metrics is not a string or list of strings + with pytest.raises(ValueError): + PINAProgressBar(metrics=123) -# make the solver -solver = PINN(problem=poisson_problem, model=model) +@pytest.mark.parametrize( + "metrics", + [ + "train", + "val", + "test", + ["test", "data"], + ["train", "val"], + ], +) +@pytest.mark.parametrize("batch_size", [None, 8]) +def test_routine(metrics, batch_size): + + # Initialize the callback + callback = PINAProgressBar(metrics=metrics) -def test_progress_bar_constructor(): - PINAProgressBar() + # Convert to list if a single string is provided + if isinstance(metrics, str): + metrics = [metrics] + # Convert to list if a single string is provided + if isinstance(metrics, str): + metrics = [metrics] -def test_progress_bar_routine(): - # make the trainer + # Initialize the trainer with the callback and train the model trainer = Trainer( solver=solver, - callbacks=[PINAProgressBar(["val", condition_names[0]])], + callbacks=[callback], accelerator="cpu", max_epochs=5, + batch_size=batch_size, + log_every_n_steps=1, ) trainer.train() - # TODO there should be a check that the correct metrics are displayed + + # Get the expected metrics based on the input and batch size + suffix = "_loss_epoch" if batch_size is not None else "_loss" + expected_metrics = sorted([metric + suffix for metric in metrics]) + + # Check that the progress bar metrics are the expected ones + assert callback._sorted_metrics == expected_metrics + + # Assert that metrics in the progress bar are subset of expected metrics + displayed_metrics = { + key + for key in trainer.progress_bar_metrics + if key in callback._sorted_metrics + } + assert displayed_metrics.issubset(set(expected_metrics)) diff --git a/tests/test_callback/test_r3_refinement.py b/tests/test_callback/test_r3_refinement.py index 191266ee1..5a9a49a1e 100644 --- a/tests/test_callback/test_r3_refinement.py +++ b/tests/test_callback/test_r3_refinement.py @@ -1,54 +1,104 @@ +import torch import pytest -from torch.nn import MSELoss -from pina.solver import PINN +from pina.solver import PhysicsInformedSingleModelSolver from pina.trainer import Trainer from pina.model import FeedForward -from pina.problem.zoo import Poisson2DSquareProblem as Poisson from pina.callback import R3Refinement +from pina.problem.zoo import Poisson2DSquareProblem -# make the problem -poisson_problem = Poisson() -poisson_problem.discretise_domain(10, "grid", domains="boundary") -poisson_problem.discretise_domain(10, "grid", domains="D") -model = FeedForward( - len(poisson_problem.input_variables), len(poisson_problem.output_variables) -) -solver = PINN(problem=poisson_problem, model=model) +@pytest.mark.parametrize("sample_every", [1, 3]) +@pytest.mark.parametrize("residual_loss", [torch.nn.MSELoss, torch.nn.L1Loss]) +@pytest.mark.parametrize("condition_to_update", [None, ["D"]]) +def test_constructor(sample_every, residual_loss, condition_to_update): + # Initialize the callback + R3Refinement( + sample_every=sample_every, + residual_loss=residual_loss, + condition_to_update=condition_to_update, + ) + + # Should fail if sample_every is not a positive integer + with pytest.raises(AssertionError): + R3Refinement(sample_every=0) -def test_constructor(): - # good constructor - R3Refinement(sample_every=10) - R3Refinement(sample_every=10, residual_loss=MSELoss) - R3Refinement(sample_every=10, condition_to_update=["D"]) - # wrong constructor + # Should fail if residual_loss is not a valid loss class with pytest.raises(ValueError): - R3Refinement(sample_every="str") + R3Refinement(sample_every=10, residual_loss="not_a_loss") + + # Should fail if condition_to_update is not a string or iterable of strings with pytest.raises(ValueError): - R3Refinement(sample_every=10, condition_to_update=3) + R3Refinement(sample_every=10, condition_to_update=123) + + +@pytest.mark.parametrize("sample_every", [1, 3]) +@pytest.mark.parametrize("residual_loss", [torch.nn.MSELoss, torch.nn.L1Loss]) +@pytest.mark.parametrize("condition_to_update", [None, ["D"], ["boundary"]]) +def test_sample(sample_every, residual_loss, condition_to_update): + + # Define the problem, model, and solver for testing + problem = Poisson2DSquareProblem() + problem.discretise_domain(10, "grid", domains="boundary") + problem.discretise_domain(10, "grid", domains="D") + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + solver = PhysicsInformedSingleModelSolver(problem=problem, model=model) + # Initialize the callback + callback = R3Refinement( + sample_every=sample_every, + residual_loss=residual_loss, + condition_to_update=condition_to_update, + ) -@pytest.mark.parametrize("condition_to_update", [["D"], ["boundary", "D"]]) -def test_sample(condition_to_update): + # Initialize the trainer trainer = Trainer( solver=solver, - callbacks=[ - R3Refinement( - sample_every=1, condition_to_update=condition_to_update - ) - ], + callbacks=callback, accelerator="cpu", max_epochs=5, ) - before_n_points = { - loc: len(trainer.solver.problem.input_pts[loc]) - for loc in condition_to_update + + # Initialize the conditions to update if None + if callback._condition_to_update is None: + callback._condition_to_update = [ + name + for name, cond in solver.problem.conditions.items() + if hasattr(cond, "domain") + ] + + # Check initial population size and dataset before training + n_points_before_train = { + cond: len(trainer.solver.problem.conditions[cond].data.input) + for cond in callback._condition_to_update } + + # Train the model to trigger refinement trainer.train() - after_n_points = { - loc: len(trainer.data_module.train_dataset.input[loc]) - for loc in condition_to_update + + # Check population size after training to ensure it has been updated + n_points_after_train = { + cond: len(trainer.solver.problem.conditions[cond].data.input) + for cond in callback._condition_to_update } - assert before_n_points == trainer.callbacks[0].initial_population_size - assert before_n_points == after_n_points + + # Assert population size has been updated according to the refinement + assert n_points_before_train == trainer.callbacks[0].initial_population_size + assert n_points_before_train == n_points_after_train + + # Should fail if the specified condition does not exist in the problem + with pytest.raises(RuntimeError): + callback = R3Refinement( + sample_every=sample_every, + residual_loss=residual_loss, + condition_to_update="non_existent_condition", + ) + trainer = Trainer( + solver=solver, + callbacks=callback, + accelerator="cpu", + max_epochs=5, + ) + callback.on_train_start(trainer, solver=solver) diff --git a/tests/test_callback/test_switch_optimizer.py b/tests/test_callback/test_switch_optimizer.py index 3383c792c..8f29c359f 100644 --- a/tests/test_callback/test_switch_optimizer.py +++ b/tests/test_callback/test_switch_optimizer.py @@ -1,16 +1,14 @@ import torch import pytest - -from pina.solver import PINN +from pina.solver import PhysicsInformedSingleModelSolver from pina.trainer import Trainer from pina.model import FeedForward from pina.optim import TorchOptimizer from pina.callback import SwitchOptimizer -from pina.problem.zoo import Poisson2DSquareProblem as Poisson - +from pina.problem.zoo import Poisson2DSquareProblem # Define the problem -problem = Poisson() +problem = Poisson2DSquareProblem() problem.discretise_domain(10) model = FeedForward(len(problem.input_variables), len(problem.output_variables)) @@ -18,7 +16,9 @@ optimizer = TorchOptimizer(torch.optim.Adam) # Initialize the solver -solver = PINN(problem=problem, model=model, optimizer=optimizer) +solver = PhysicsInformedSingleModelSolver( + problem=problem, model=model, optimizer=optimizer +) # Define new optimizers for testing lbfgs = TorchOptimizer(torch.optim.LBFGS, lr=1.0) @@ -27,27 +27,35 @@ @pytest.mark.parametrize("epoch_switch", [5, 10]) @pytest.mark.parametrize("new_opt", [lbfgs, adamW]) -def test_switch_optimizer_constructor(new_opt, epoch_switch): +def test_constructor(new_opt, epoch_switch): # Constructor SwitchOptimizer(new_optimizers=new_opt, epoch_switch=epoch_switch) - # Should fail if epoch_switch is less than 1 - with pytest.raises(ValueError): + # Should fail if epoch_switch is not a positive integer + with pytest.raises(AssertionError): SwitchOptimizer(new_optimizers=new_opt, epoch_switch=0) + # Should fail if new_optimizers is not an instance of OptimizerInterface + with pytest.raises(ValueError): + SwitchOptimizer( + new_optimizers="not_an_optimizer", epoch_switch=epoch_switch + ) + @pytest.mark.parametrize("epoch_switch", [5, 10]) @pytest.mark.parametrize("new_opt", [lbfgs, adamW]) -def test_switch_optimizer_routine(new_opt, epoch_switch): +def test_routine(new_opt, epoch_switch): # Check if the optimizer is initialized correctly solver.configure_optimizers() - # Initialize the trainer + # Initialize the callback switch_opt_callback = SwitchOptimizer( new_optimizers=new_opt, epoch_switch=epoch_switch ) + + # Initialize the trainer trainer = Trainer( solver=solver, callbacks=switch_opt_callback, diff --git a/tests/test_callback/test_switch_scheduler.py b/tests/test_callback/test_switch_scheduler.py index df91f0c59..7a110db22 100644 --- a/tests/test_callback/test_switch_scheduler.py +++ b/tests/test_callback/test_switch_scheduler.py @@ -1,16 +1,14 @@ import torch import pytest - -from pina.solver import PINN +from pina.solver import PhysicsInformedSingleModelSolver from pina.trainer import Trainer from pina.model import FeedForward from pina.optim import TorchScheduler from pina.callback import SwitchScheduler -from pina.problem.zoo import Poisson2DSquareProblem as Poisson - +from pina.problem.zoo import Poisson2DSquareProblem # Define the problem -problem = Poisson() +problem = Poisson2DSquareProblem() problem.discretise_domain(10) model = FeedForward(len(problem.input_variables), len(problem.output_variables)) @@ -18,7 +16,9 @@ scheduler = TorchScheduler(torch.optim.lr_scheduler.ConstantLR, factor=0.1) # Initialize the solver -solver = PINN(problem=problem, model=model, scheduler=scheduler) +solver = PhysicsInformedSingleModelSolver( + problem=problem, model=model, scheduler=scheduler +) # Define new schedulers for testing step = TorchScheduler(torch.optim.lr_scheduler.StepLR, step_size=10, gamma=0.1) @@ -32,19 +32,27 @@ def test_switch_scheduler_constructor(new_sched, epoch_switch): # Constructor SwitchScheduler(new_schedulers=new_sched, epoch_switch=epoch_switch) - # Should fail if epoch_switch is less than 1 + # Should fail if epoch_switch is not a positive integer with pytest.raises(AssertionError): SwitchScheduler(new_schedulers=new_sched, epoch_switch=0) + # Should fail if new_schedulers is not an instance of SchedulerInterface + with pytest.raises(ValueError): + SwitchScheduler( + new_schedulers="not_a_scheduler", epoch_switch=epoch_switch + ) + @pytest.mark.parametrize("epoch_switch", [5, 10]) @pytest.mark.parametrize("new_sched", [step, exp]) def test_switch_scheduler_routine(new_sched, epoch_switch): - # Initialize the trainer + # Initialize the callback switch_sched_callback = SwitchScheduler( new_schedulers=new_sched, epoch_switch=epoch_switch ) + + # Initialize the trainer trainer = Trainer( solver=solver, callbacks=switch_sched_callback, diff --git a/tests/test_condition.py b/tests/test_condition.py deleted file mode 100644 index 9199f2bd9..000000000 --- a/tests/test_condition.py +++ /dev/null @@ -1,154 +0,0 @@ -import torch -import pytest - -from pina import LabelTensor, Condition -from pina.condition import ( - TensorInputGraphTargetCondition, - TensorInputTensorTargetCondition, - GraphInputGraphTargetCondition, - GraphInputTensorTargetCondition, -) -from pina.condition import ( - InputTensorEquationCondition, - InputGraphEquationCondition, - DomainEquationCondition, -) -from pina.condition import ( - TensorDataCondition, - GraphDataCondition, -) -from pina.domain import CartesianDomain -from pina.equation.equation_factory import FixedValue -from pina.graph import RadiusGraph - -example_domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) - -input_tensor = torch.rand((10, 3)) -target_tensor = torch.rand((10, 2)) -input_lt = LabelTensor(torch.rand((10, 3)), ["x", "y", "z"]) -target_lt = LabelTensor(torch.rand((10, 2)), ["a", "b"]) - -x = torch.rand(10, 20, 2) -pos = torch.rand(10, 20, 2) -radius = 0.1 -input_graph = [ - RadiusGraph( - x=x_, - pos=pos_, - radius=radius, - ) - for x_, pos_ in zip(x, pos) -] -target_graph = [ - RadiusGraph( - x=x_, - pos=pos_, - radius=radius, - ) - for x_, pos_ in zip(x, pos) -] - -x = LabelTensor(torch.rand(10, 20, 2), ["u", "v"]) -pos = LabelTensor(torch.rand(10, 20, 2), ["x", "y"]) -radius = 0.1 -input_graph_lt = [ - RadiusGraph( - x=x[i], - pos=pos[i], - radius=radius, - ) - for i in range(len(x)) -] -target_graph_lt = [ - RadiusGraph( - x=x[i], - pos=pos[i], - radius=radius, - ) - for i in range(len(x)) -] - -input_single_graph = input_graph[0] -target_single_graph = target_graph[0] - - -def test_init_input_target(): - cond = Condition(input=input_tensor, target=target_tensor) - assert isinstance(cond, TensorInputTensorTargetCondition) - cond = Condition(input=input_tensor, target=target_tensor) - assert isinstance(cond, TensorInputTensorTargetCondition) - cond = Condition(input=input_tensor, target=target_graph) - assert isinstance(cond, TensorInputGraphTargetCondition) - cond = Condition(input=input_graph, target=target_tensor) - assert isinstance(cond, GraphInputTensorTargetCondition) - cond = Condition(input=input_graph, target=target_graph) - assert isinstance(cond, GraphInputGraphTargetCondition) - - cond = Condition(input=input_lt, target=input_single_graph) - assert isinstance(cond, TensorInputGraphTargetCondition) - cond = Condition(input=input_single_graph, target=target_lt) - assert isinstance(cond, GraphInputTensorTargetCondition) - cond = Condition(input=input_graph, target=target_graph) - assert isinstance(cond, GraphInputGraphTargetCondition) - cond = Condition(input=input_single_graph, target=target_single_graph) - assert isinstance(cond, GraphInputGraphTargetCondition) - - with pytest.raises(ValueError): - Condition(input_tensor, input_tensor) - with pytest.raises(ValueError): - Condition(input=3.0, target="example") - with pytest.raises(ValueError): - Condition(input=example_domain, target=example_domain) - - # Test wrong graph condition initialisation - input = [input_graph[0], input_graph_lt[0]] - target = [target_graph[0], target_graph_lt[0]] - with pytest.raises(ValueError): - Condition(input=input, target=target) - - input_graph_lt[0].x.labels = ["a", "b"] - with pytest.raises(ValueError): - Condition(input=input_graph_lt, target=target_graph_lt) - input_graph_lt[0].x.labels = ["u", "v"] - - -def test_init_domain_equation(): - cond = Condition(domain=example_domain, equation=FixedValue(0.0)) - assert isinstance(cond, DomainEquationCondition) - with pytest.raises(ValueError): - Condition(example_domain, FixedValue(0.0)) - with pytest.raises(ValueError): - Condition(domain=3.0, equation="example") - with pytest.raises(ValueError): - Condition(domain=input_tensor, equation=input_graph) - - -def test_init_input_equation(): - cond = Condition(input=input_lt, equation=FixedValue(0.0)) - assert isinstance(cond, InputTensorEquationCondition) - cond = Condition(input=input_graph_lt, equation=FixedValue(0.0)) - assert isinstance(cond, InputGraphEquationCondition) - with pytest.raises(ValueError): - cond = Condition(input=input_tensor, equation=FixedValue(0.0)) - with pytest.raises(ValueError): - Condition(example_domain, FixedValue(0.0)) - with pytest.raises(ValueError): - Condition(input=3.0, equation="example") - with pytest.raises(ValueError): - Condition(input=example_domain, equation=input_graph) - - -test_init_input_equation() - - -def test_init_data_condition(): - cond = Condition(input=input_lt) - assert isinstance(cond, TensorDataCondition) - cond = Condition(input=input_tensor) - assert isinstance(cond, TensorDataCondition) - cond = Condition(input=input_tensor, conditional_variables=torch.tensor(1)) - assert isinstance(cond, TensorDataCondition) - cond = Condition(input=input_graph) - assert isinstance(cond, GraphDataCondition) - cond = Condition(input=input_graph, conditional_variables=torch.tensor(1)) - assert isinstance(cond, GraphDataCondition) diff --git a/tests/test_condition/test_data_condition.py b/tests/test_condition/test_data_condition.py new file mode 100644 index 000000000..ff26af335 --- /dev/null +++ b/tests/test_condition/test_data_condition.py @@ -0,0 +1,389 @@ +import torch +import pytest +from pina._src.core.utils import labelize_forward +from pina._src.core.graph import LabelBatch +from pina.graph import RadiusGraph, Graph +from pina.condition import DataCondition +from pina import LabelTensor, Condition +from pina.data.manager import ( + _TensorDataManager, + _GraphDataManager, + _BatchManager, +) + + +# Number of graphs and tensor samples for testing +n_samples = 10 +n_graphs = 10 +n_nodes = 20 + + +# Helper function to create tensor data +def _create_tensor_data(use_lt, conditional_variables): + + # If LabelTensor is used, create tensors with labels + if use_lt: + input_tensor = LabelTensor(torch.rand((n_samples, 3)), ["x", "y", "z"]) + cond_vars = LabelTensor(torch.rand((n_samples, 1)), ["a"]) + cond_vars = cond_vars if conditional_variables else None + + return input_tensor, cond_vars + + # Standard torch.Tensor without labels + input_tensor = torch.rand((n_samples, 3)) + cond_vars = torch.rand((n_samples, 1)) + cond_vars = cond_vars if conditional_variables else None + + return input_tensor, cond_vars + + +# Helper function to create graph data +def _create_graph_data(use_lt, conditional_variables): + + # If LabelTensor is used, create graph data with LabelTensors + if use_lt: + x = LabelTensor(torch.rand(n_graphs, n_nodes, 2), ["u", "v"]) + pos = LabelTensor(torch.rand(n_graphs, n_nodes, 2), ["x", "y"]) + cond_vars = LabelTensor(torch.rand(n_graphs, n_nodes, 1), ["f"]) + + # Standard torch.Tensor without labels + else: + x = torch.rand(n_graphs, n_nodes, 2) + pos = torch.rand(n_graphs, n_nodes, 2) + cond_vars = torch.rand(n_graphs, n_nodes, 1) + + # Create a list of Graphs + graph = [ + RadiusGraph(pos=pos[i], radius=0.1, x=x[i], cond_vars=cond_vars[i]) + for i in range(len(x)) + ] + + # Create conditional variables if needed + cond_vars = cond_vars if conditional_variables else None + + return graph, cond_vars + + +# Helper function to check tensor types +def _assert_tensor_type(t, use_lt): + if use_lt: + assert isinstance(t, LabelTensor) + else: + assert isinstance(t, torch.Tensor) and not isinstance(t, LabelTensor) + + +# Helper function to check input graph +def _assert_graph_type(graph_list, use_lt): + assert isinstance(graph_list, list) + for graph in graph_list: + _assert_tensor_type(graph.x, use_lt) + + +# Define a dummy solver for testing +class DummySolver: + + def __init__(self, use_lt, input_vars, cond_vars): + if use_lt: + self.forward = labelize_forward( + forward=self.forward, + input_variables=input_vars, + output_variables="z", + ) + + self.cond_vars = cond_vars + self._params = None + + def forward(self, pts): + + # Tensor case + if isinstance(pts, torch.Tensor): + factor = self.cond_vars if self.cond_vars is not None else 1.0 + return pts.mean(dim=-1, keepdim=True) * factor + + # Graph case + else: + factor = pts.cond_vars if pts.cond_vars is not None else 1.0 + output_ = pts.x.mean(dim=-1, keepdim=True) * factor + return output_.reshape(n_graphs, n_nodes, 1) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("conditional_variables", [False, True]) +@pytest.mark.parametrize("case", ["tensor", "graph"]) +def test_constructor(case, use_lt, conditional_variables): + + # Tensor input case + if case == "tensor": + + # Define the condition + input_tensor, cond_vars = _create_tensor_data( + use_lt, conditional_variables + ) + condition = Condition( + input=input_tensor, conditional_variables=cond_vars + ) + + # Assert correct types + assert isinstance(condition, DataCondition) + _assert_tensor_type(condition.input, use_lt) + if cond_vars is not None: + _assert_tensor_type(condition.conditional_variables, use_lt) + + # Assert numerical parity + assert torch.allclose(condition.input, input_tensor) + if cond_vars is not None: + assert torch.allclose(condition.conditional_variables, cond_vars) + + # Assert labels if LabelTensor is used + if use_lt: + assert condition.input.labels == ["x", "y", "z"] + if cond_vars is not None: + assert condition.conditional_variables.labels == ["a"] + + # Graph input case + elif case == "graph": + + # Define the condition + input_graph, cond_vars = _create_graph_data( + use_lt, conditional_variables + ) + condition = Condition( + input=input_graph, conditional_variables=cond_vars + ) + + # Assert correct types + assert isinstance(condition, DataCondition) + _assert_graph_type(condition.input, use_lt) + if cond_vars is not None: + _assert_tensor_type(condition.conditional_variables, use_lt) + + # Assert numerical parity for graph inputs + for i in range(len(input_graph)): + assert torch.allclose(condition.input[i].x, input_graph[i].x) + assert torch.allclose(condition.input[i].pos, input_graph[i].pos) + + # Assert numerical parity for conditional variables + if cond_vars is not None: + assert torch.allclose(condition.conditional_variables, cond_vars) + + # Assert labels if LabelTensor is used + if use_lt: + for graph in condition.input: + assert graph.x.labels == ["u", "v"] + assert graph.pos.labels == ["x", "y"] + if cond_vars is not None: + assert condition.conditional_variables.labels == ["f"] + + # Prepare for invalid input tests + input_ = input_tensor if case == "tensor" else input_graph + + # Should fail if the input is neither a tensor nor a graph + with pytest.raises(ValueError): + Condition(input="invalid_input", conditional_variables=cond_vars) + + # Should fail if the conditional_variables is neither a tensor nor a graph + with pytest.raises(ValueError): + Condition(input=input_, conditional_variables="invalid_cond_vars") + + # Should fail if the input is a list of tensors + if case == "tensor": + with pytest.raises(ValueError): + Condition(input=[input_], conditional_variables=cond_vars) + + # Should fail if the conditional_variables is a list of tensors + if case == "tensor": + with pytest.raises(ValueError): + Condition(input=input_, conditional_variables=[cond_vars]) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("conditional_variables", [False, True]) +@pytest.mark.parametrize("case", ["tensor", "graph"]) +def test_get_item(case, use_lt, conditional_variables): + + # Tensor input case + if case == "tensor": + + # Define the condition + input_tensor, cond_vars = _create_tensor_data( + use_lt, conditional_variables + ) + condition = Condition( + input=input_tensor, conditional_variables=cond_vars + ) + + # Extract item using __getitem__ + index = 0 + item = condition[index] + + # Assert correct types + assert isinstance(item, _TensorDataManager) + _assert_tensor_type(item.input, use_lt) + if cond_vars is not None: + _assert_tensor_type(item.conditional_variables, use_lt) + + # Assert numerical parity + assert torch.allclose(item.input, input_tensor[index]) + if cond_vars is not None: + assert torch.allclose(item.conditional_variables, cond_vars[index]) + + # Graph input case + elif case == "graph": + + # Define the condition + input_graph, cond_vars = _create_graph_data( + use_lt, conditional_variables + ) + condition = Condition( + input=input_graph, conditional_variables=cond_vars + ) + + # Extract item using __getitem__ + index = 0 + item = condition[index] + + # Assert correct types + assert isinstance(item, _GraphDataManager) + assert isinstance(item.input, Graph) + _assert_tensor_type(item.input.x, use_lt) + if cond_vars is not None: + _assert_tensor_type(item.conditional_variables, use_lt) + + # Assert numerical parity + assert torch.allclose(item.input.x, input_graph[index].x) + assert torch.allclose(item.input.pos, input_graph[index].pos) + if cond_vars is not None: + assert torch.allclose(item.conditional_variables, cond_vars[index]) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("conditional_variables", [False, True]) +@pytest.mark.parametrize("case", ["tensor", "graph"]) +def test_create_batch(case, use_lt, conditional_variables): + + # Tensor case + if case == "tensor": + input_, cond_vars = _create_tensor_data(use_lt, conditional_variables) + + # Graph case + elif case == "graph": + input_, cond_vars = _create_graph_data(use_lt, conditional_variables) + + # Define the condition + condition = Condition(input=input_, conditional_variables=cond_vars) + + # Create batches using automatic batching or condition's collate_fn + idx = [0, 2] + data_to_collate = [condition.data[i] for i in idx] + batch_auto = condition.automatic_batching_collate_fn(data_to_collate) + batch_collate = condition.collate_fn(idx, condition) + + # Check that the automatic batch has been properly created + assert isinstance(batch_auto, _BatchManager) + assert hasattr(batch_auto, "input") + if cond_vars is not None: + assert hasattr(batch_auto, "conditional_variables") + + # Check that the collate_fn batch has been properly created + assert isinstance(batch_collate, dict) + assert hasattr(batch_collate, "input") + if cond_vars is not None: + assert hasattr(batch_collate, "conditional_variables") + + # Retrieve tensor class for expected batch creation + cls = LabelTensor if use_lt else torch + + # Validate batch contents for tensor case + if case == "tensor": + + # Create expected input batch + expected_input = cls.stack([input_[i] for i in idx]) + if cond_vars is not None: + exp_cond = cls.stack([cond_vars[i] for i in idx]) + + # Assert that the automatic batch input is correct + assert torch.allclose(batch_auto.input, expected_input) + assert batch_auto.input.shape == expected_input.shape + if cond_vars is not None: + assert torch.allclose(batch_auto.conditional_variables, exp_cond) + assert batch_auto.conditional_variables.shape == exp_cond.shape + + # Assert that the collate_fn batch input is correct + assert torch.allclose(batch_collate.input, expected_input) + assert batch_collate.input.shape == expected_input.shape + if cond_vars is not None: + assert torch.allclose(batch_collate.conditional_variables, exp_cond) + assert batch_collate.conditional_variables.shape == exp_cond.shape + + # Validate batch contents for graph case + elif case == "graph": + + # Create expected input batch + expected_input = [condition.data[i].input for i in idx] + if cond_vars is not None: + exp_cond = cls.cat([cond_vars[i] for i in idx]) + + # Assert that the automatic batch input is correct + for i, graph in enumerate(expected_input): + assert torch.allclose(batch_auto.input[i].x, graph.x) + assert batch_auto.input.num_graphs == len(idx) + if cond_vars is not None: + assert torch.allclose(batch_auto.conditional_variables, exp_cond) + assert batch_auto.conditional_variables.shape == exp_cond.shape + + # Assert that the collate_fn batch input is correct + for i, graph in enumerate(expected_input): + assert torch.allclose(batch_collate.input[i].x, graph.x) + assert batch_collate.input.num_graphs == len(idx) + if cond_vars is not None: + assert torch.allclose(batch_collate.conditional_variables, exp_cond) + assert batch_collate.conditional_variables.shape == exp_cond.shape + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("conditional_variables", [False, True]) +@pytest.mark.parametrize("case", ["tensor", "graph"]) +def test_evaluate(case, use_lt, conditional_variables): + + # Tensor case + if case == "tensor": + + # Define the input and the target + input_, cond_vars = _create_tensor_data(use_lt, conditional_variables) + input_vars = input_.labels if use_lt else None + + # Define the condition and the solver + condition = Condition(input=input_, conditional_variables=cond_vars) + solver = DummySolver(use_lt, input_vars, cond_vars) + loss_fn = torch.nn.MSELoss(reduction="none") + + # Extract the batch + batch = { + "input": condition.input, + "conditional_variables": condition.conditional_variables, + } + + # Graph case + elif case == "graph": + + # Define the input and the target + input_, cond_vars = _create_graph_data(use_lt, conditional_variables) + input_vars = input_[0].x.labels if use_lt else None + + # Define the condition and the solver + condition = Condition(input=input_, conditional_variables=cond_vars) + solver = DummySolver(use_lt, input_vars, cond_vars) + loss_fn = torch.nn.MSELoss(reduction="none") + + # Extract the batch + batch = { + "input": LabelBatch.from_data_list(condition.input), + "conditional_variables": condition.conditional_variables, + } + + # Evaluate the condition and compute the expected value + loss = condition.evaluate(batch, solver) + expected = solver.forward(batch["input"]) + + # Assert that the evaluated loss is correct + assert torch.allclose(loss, expected) diff --git a/tests/test_condition/test_domain_equation_condition.py b/tests/test_condition/test_domain_equation_condition.py new file mode 100644 index 000000000..22e533a15 --- /dev/null +++ b/tests/test_condition/test_domain_equation_condition.py @@ -0,0 +1,65 @@ +import pytest +from pina import Condition +from pina.domain import CartesianDomain +from pina.equation.zoo import FixedValue +from pina.condition import DomainEquationCondition + + +# Define a simple domain and equation for testing +domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) +equation = FixedValue(0.0) + + +def test_constructor(): + + # Define the condition + condition = Condition(domain=domain, equation=equation) + + # Assert correct types + assert isinstance(condition, DomainEquationCondition) + + # Assert that the domain and equation are stored correctly + assert condition.domain is domain + assert condition.equation is equation + + # Assert that the data attribute is set to None + assert hasattr(condition, "data") + assert condition.data is None + + # Should fail if domain is not an instance of DomainInterface or a string + with pytest.raises(ValueError): + Condition(domain=123, equation=equation) + + # Should fail if equation is not an instance of BaseEquation + with pytest.raises(ValueError): + Condition(domain=domain, equation=123) + + +def test_get_item(): + + # Define the condition + condition = Condition(domain=domain, equation=equation) + + # Should raise NotImplementedError when trying to access by index + with pytest.raises(NotImplementedError): + condition[0] + + +def test_create_batch(): + + # Define the condition + condition = Condition(domain=domain, equation=equation) + + # Should raise TypeError when trying to access condition.data since None + with pytest.raises(TypeError): + _ = [condition.data[i] for i in [0, 2, 4, 6]] + + +def test_evaluate(): + + # Define the condition + condition = Condition(domain=domain, equation=equation) + + # Should raise NotImplementedError when trying to evaluate the condition + with pytest.raises(NotImplementedError): + condition.evaluate(None, None) diff --git a/tests/test_condition/test_graph_time_series_condition.py b/tests/test_condition/test_graph_time_series_condition.py new file mode 100644 index 000000000..01edc639d --- /dev/null +++ b/tests/test_condition/test_graph_time_series_condition.py @@ -0,0 +1,330 @@ +import pytest +import torch +from pina.data.manager import ( + _TensorDataManager, + _BatchManager, + _GraphDataManager, +) +from pina._src.core.utils import labelize_forward +from pina.condition import TimeSeriesCondition +from pina import LabelTensor, Condition +from pina._src.condition.graph_time_series_condition import ( + GraphTimeSeriesCondition, +) +from pina.graph import RadiusGraph + +# Number of samples and time steps for testing +n_samples = 5 +n_nodes = 20 +time_steps = 10 + + +# Helper function to check tensor types +def _assert_tensor_type(t, use_lt): + if use_lt: + assert isinstance(t.x, LabelTensor) + else: + assert isinstance(t.x, torch.Tensor) and not isinstance( + t.x, LabelTensor + ) + + +# Helper function to compute expected unroll windows +def _expected_unroll(data, n_windows, unroll_length, randomize): + + # Compute valid starting indices + last_idx = data.shape[1] - unroll_length + start_indices = torch.arange(last_idx + 1) + + # Randomize indices if required + if randomize: + start_indices = start_indices[torch.randperm(len(start_indices))] + + # Limit the number of windows + if n_windows is not None and n_windows < len(start_indices): + start_indices = start_indices[:n_windows] + + # Build expected windows + windows = [data[:, s : s + unroll_length] for s in start_indices] + + return torch.stack(windows, dim=1) + + +# Helper function to create graph data +def _create_graph_data(use_lt): + + # If LabelTensor is used, create graph data with LabelTensors + if use_lt: + x = LabelTensor(torch.rand(n_nodes, time_steps, 2), ["u", "v"]) + pos = LabelTensor(torch.rand(n_nodes, 2), ["x", "y"]) + + # Standard torch.Tensor without labels + else: + x = torch.rand(n_nodes, time_steps, 2) + pos = torch.rand(n_nodes, 2) + + # Create a list of Graphs + graph = RadiusGraph( + pos=pos, + radius=0.1, + x=x, + ) + + return graph + + +# Define a dummy solver for testing +class DummySolver: + + def __init__(self, use_lt, input_vars): + if use_lt: + self.forward = labelize_forward( + forward=self.forward, + input_variables=input_vars, + output_variables=input_vars, + ) + + self._params = None + self._kwargs = {} + self.aggregation_strategy = torch.mean + + def forward(self, samples): + return samples + + def preprocess_step(self, current_state, **kwargs): + return current_state + + def postprocess_step(self, predicted_state, **kwargs): + return predicted_state + + def _get_weights(self, condition_name, step_losses): + return 1.0 + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("n_windows", [4, 6]) +@pytest.mark.parametrize("unroll_length", [3, 5]) +@pytest.mark.parametrize("randomize", [True, False]) +def test_constructor(use_lt, n_windows, unroll_length, randomize): + + # Define the condition + graph = _create_graph_data(use_lt=use_lt) + original_timeseries = ( + graph.x.clone() + ) # Store original time series for later comparison + condition = Condition( + input=graph, + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + key="x", + ) + + # Assert correct types + assert isinstance(condition, GraphTimeSeriesCondition) + + # Assert numerical parity + if not randomize: + expected_tensor = _expected_unroll( + original_timeseries, n_windows, unroll_length, randomize + ) + assert torch.allclose(condition.input.x, expected_tensor) + + # Assert labels if LabelTensor is used + if use_lt: + assert condition.input["x"].labels == ["u", "v"] + + # Should fail if unroll_length is not a positive integer + with pytest.raises(AssertionError): + GraphTimeSeriesCondition( + input=graph, + n_windows=n_windows, + unroll_length=0, + randomize=randomize, + ) + + # Should fail if n_windows is not a positive integer + with pytest.raises(AssertionError): + GraphTimeSeriesCondition( + input=graph, + n_windows=0, + unroll_length=unroll_length, + randomize=randomize, + ) + + # Should fail if randomize is not a boolean value + with pytest.raises(ValueError): + Condition( + input=graph, + n_windows=n_windows, + unroll_length=unroll_length, + randomize="not_a_boolean", + ) + + # Should fail if the input tensor has less than 3 dimensions + with pytest.raises(ValueError): + Condition( + input=torch.rand(n_samples, 2), + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + + # Should fail if unroll_length is not greater than 1 + with pytest.raises(ValueError): + Condition( + input=graph, + n_windows=n_windows, + unroll_length=1, + randomize=randomize, + ) + + # Should fail if unroll_length is greater than the number of time steps + with pytest.raises(ValueError): + Condition( + input=graph, + n_windows=n_windows, + unroll_length=time_steps + 1, + randomize=randomize, + ) + + # Should fail if n_windows is greater than the number of valid windows + with pytest.raises(ValueError): + Condition( + input=graph, + n_windows=10, + unroll_length=unroll_length, + randomize=randomize, + ) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("n_windows", [4, 6]) +@pytest.mark.parametrize("unroll_length", [3, 5]) +@pytest.mark.parametrize("randomize", [True, False]) +def test_get_item(use_lt, n_windows, unroll_length, randomize): + + # Define the condition + graph = _create_graph_data(use_lt=use_lt) + condition = GraphTimeSeriesCondition( + input=graph, + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + + # Extract item using __getitem__ + index = 0 + item = condition[index] + + # Assert correct types + assert isinstance(item, _GraphDataManager) + _assert_tensor_type(item.input, use_lt) + + # Assert correct shapes + expected_shape = torch.Size([n_nodes, n_windows, unroll_length, 2]) + assert item.input.x.shape == expected_shape + + # TODO: Why this test? + ################################## + # if not randomize: + # expected_tensor = _expected_unroll( + # graph.x, n_windows, unroll_length, randomize + # ) + # print(item.input.x.shape) + # print(expected_tensor[index].shape) + # assert torch.allclose(item.input.x, expected_tensor[index]) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("n_windows", [4, 6]) +@pytest.mark.parametrize("unroll_length", [3, 5]) +@pytest.mark.parametrize("randomize", [True, False]) +def test_create_batch(use_lt, n_windows, unroll_length, randomize): + + # Define the condition + graph = _create_graph_data(use_lt=use_lt) + condition = GraphTimeSeriesCondition( + input=graph, + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + + """ CHECK + # Create batches using automatic batching or condition's collate_fn + idx = [0, 2] + print(condition.data[0]) + print(condition.data[0].__dict__) + data_to_collate = [condition.data[i] for i in idx] + batch_auto = condition.automatic_batching_collate_fn(data_to_collate) + batch_collate = condition.collate_fn(idx, condition) + + # Check that the automatic batch has been properly created + assert isinstance(batch_auto, _BatchManager) + assert hasattr(batch_auto, "input") + + # Check that the collate_fn batch has been properly created + assert isinstance(batch_collate, dict) + assert hasattr(batch_collate, "input") + + # Assert that the automatic batch input is correct + expected_shape = torch.Size([len(idx), n_windows, unroll_length, 2]) + assert batch_auto.input.shape == expected_shape + + # Assert that the collate_fn batch input is correct + expected_shape = torch.Size([len(idx), n_windows, unroll_length, 2]) + assert batch_collate.input.shape == expected_shape + + # Create input values + if not randomize: + expected_tensor = _expected_unroll( + graph.x, n_windows, unroll_length, randomize + ) + assert torch.allclose(batch_collate.input, expected_tensor[idx]) + assert torch.allclose(batch_auto.input, expected_tensor[idx]) + """ + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("n_windows", [4, 6]) +@pytest.mark.parametrize("unroll_length", [3, 5]) +@pytest.mark.parametrize("randomize", [True, False]) +def test_evaluate(use_lt, n_windows, unroll_length, randomize): + + # Define the input tensor + graph = _create_graph_data(use_lt=use_lt) + input_vars = graph.x.labels if use_lt else None + + # Define the condition and the solver + condition = GraphTimeSeriesCondition( + input=graph, + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + solver = DummySolver(use_lt, input_vars) + + # Extract the batch + batch = {"input": condition.input} + + # Evaluate the condition and compute the expected residuals + residuals = condition.evaluate(batch, solver) + + # Compute expected autoregressive step residuals + step_residuals = [] + current_state = batch["input"].x[:, :, 0, :] + + for step in range(1, batch["input"].x.shape[2]): + predicted_state = current_state + target_state = batch["input"].x[:, :, step, :] + + step_residual = predicted_state - target_state + step_residuals.append(step_residual) + + current_state = predicted_state + + expected = torch.stack(step_residuals).as_subclass(torch.Tensor) + + # Assert that the evaluated residuals are correct + assert torch.allclose(residuals, expected) diff --git a/tests/test_condition/test_input_equation_condition.py b/tests/test_condition/test_input_equation_condition.py new file mode 100644 index 000000000..1cdc6b271 --- /dev/null +++ b/tests/test_condition/test_input_equation_condition.py @@ -0,0 +1,229 @@ +import torch +import pytest +from pina._src.core.utils import labelize_forward +from pina.condition import InputEquationCondition +from pina._src.core.graph import LabelBatch +from pina.graph import RadiusGraph, Graph +from pina.equation.zoo import FixedValue +from pina import LabelTensor, Condition +from pina.data.manager import ( + _TensorDataManager, + _GraphDataManager, + _BatchManager, +) + +# Number of graphs and tensor samples for testing +n_samples = 10 +n_graphs = 10 +n_nodes = 20 + +# Generate input data for testing - tensor case +input_tensor = LabelTensor(torch.rand((n_samples, 2)), ["x", "y"]) + +# Generate input and equation data for testing - graph case +input_graph_list = [ + RadiusGraph( + x=LabelTensor(torch.rand(n_nodes, 2), labels=["u", "v"]), + pos=LabelTensor(torch.rand(n_nodes, 2), labels=["x", "y"]), + radius=0.1, + edge_attr=True, + ) + for _ in range(n_graphs) +] + +# Initialize the testing equation +test_equation = FixedValue(0.0) + + +# Define a dummy solver for testing +class DummySolver: + + def __init__(self, input_vars, output_vars): + + self.forward = labelize_forward( + forward=self.forward, + input_variables=input_vars, + output_variables=output_vars, + ) + + self._params = None + + def forward(self, samples): + return samples + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +def test_constructor(case): + + # Tensor case + if case == "tensor": + input_, equation = input_tensor, test_equation + + # Graph case + elif case == "graph": + input_, equation = input_graph_list, test_equation + + # Define the condition + condition = Condition(input=input_, equation=equation) + + # Assert correct types + assert isinstance(condition, InputEquationCondition) + + # Assert that the equation is stored correctly + assert condition.equation is equation + + # Assert correct input type + if case == "tensor": + assert isinstance(condition.input, LabelTensor) + + elif case == "graph": + assert isinstance(condition.input, list) + for graph in condition.input: + assert isinstance(graph, Graph) + + # Should fail if input is not an instance of LabelTensor or Graph + with pytest.raises(ValueError): + Condition(input=torch.rand(n_samples, 2), equation=equation) + + # Should fail if equation is not an instance of BaseEquation + with pytest.raises(ValueError): + Condition(input=input_, equation="not_an_equation") + + # Should fail if input is a list with wrong elements + with pytest.raises(ValueError): + Condition( + input=[LabelTensor(torch.rand(n_samples, 2), ["x", "y"])], + equation=equation, + ) + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +def test_get_item(case): + + # Tensor case + if case == "tensor": + input_, equation = input_tensor, test_equation + + # Graph case + elif case == "graph": + input_, equation = input_graph_list, test_equation + + # Define the condition + condition = Condition(input=input_, equation=equation) + + # Extract item using __getitem__ + index = 0 + item = condition[index] + + # Assert correct types and numerical parity + if case == "tensor": + assert isinstance(item, _TensorDataManager) + assert isinstance(item.input, LabelTensor) + assert torch.allclose(item.input, input_[index]) + + elif case == "graph": + assert isinstance(item, _GraphDataManager) + assert isinstance(item.input, Graph) + assert torch.allclose(item.input.x, input_[index].x) + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +def test_create_batch(case): + + # Tensor case + if case == "tensor": + input_, equation = input_tensor, test_equation + + # Graph case + elif case == "graph": + input_, equation = input_graph_list, test_equation + + # Define the condition + condition = Condition(input=input_, equation=equation) + + # Create batches using automatic batching or condition's collate_fn + idx = [0, 2] + data_to_collate = [condition.data[i] for i in idx] + batch_auto = condition.automatic_batching_collate_fn(data_to_collate) + batch_collate = condition.collate_fn(idx, condition) + + # Check that the automatic batch has been properly created + assert isinstance(batch_auto, (_BatchManager)) + assert hasattr(batch_auto, "input") + + # Check that the collate_fn batch has been properly created + assert isinstance(batch_collate, dict) + assert hasattr(batch_collate, "input") + + # Validate batch contents for tensor case + if case == "tensor": + + # Create expected input batch + expected_input = LabelTensor.stack([input_[i] for i in idx]) + + # Assert that the automatic batch input is correct + assert torch.allclose(batch_auto.input, expected_input) + assert batch_auto.input.shape == expected_input.shape + + # Assert that the collate_fn batch input is correct + assert torch.allclose(batch_collate.input, expected_input) + assert batch_collate.input.shape == expected_input.shape + + # Validate batch contents for graph case + elif case == "graph": + + # Create expected input batch + expected_input = [condition.data[i].input for i in idx] + + # Assert that the automatic batch input is correct + for i, graph in enumerate(expected_input): + assert torch.allclose(batch_auto.input[i].x, graph.x) + assert batch_auto.input.num_graphs == len(idx) + + # Assert that the collate_fn batch input is correct + for i, graph in enumerate(expected_input): + assert torch.allclose(batch_collate.input[i].x, graph.x) + assert batch_collate.input.num_graphs == len(idx) + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +def test_evaluate(case): + + # Tensor case + if case == "tensor": + + # Define the input and the target + input_, equation = input_tensor, test_equation + input_vars = input_.labels + output_vars = ["z", "t"] + + # Define the condition and the solver + condition = Condition(input=input_, equation=equation) + solver = DummySolver(input_vars=input_vars, output_vars=output_vars) + loss_fn = torch.nn.MSELoss(reduction="none") + + # Extract the batch + batch = {"input": condition.input} + + # Graph case + elif case == "graph": + + # Define the input and the target + input_, equation = input_graph_list, test_equation + input_vars = input_[0].x.labels + output_vars = ["z", "t"] + + # Define the condition and the solver + condition = Condition(input=input_, equation=equation) + solver = DummySolver(input_vars=input_vars, output_vars=output_vars) + loss_fn = torch.nn.MSELoss(reduction="none") + + # Extract the batch + batch = {"input": LabelBatch.from_data_list(condition.input).x} + + # Evaluate the condition and compute the expected residuals + residual = condition.evaluate(batch, solver) + expected = solver.forward(batch["input"]) + + # Assert that the evaluated residual is correct + assert torch.allclose(residual, expected) diff --git a/tests/test_condition/test_input_target_condition.py b/tests/test_condition/test_input_target_condition.py new file mode 100644 index 000000000..f0f6a35b5 --- /dev/null +++ b/tests/test_condition/test_input_target_condition.py @@ -0,0 +1,488 @@ +import torch +import pytest +from pina._src.core.utils import labelize_forward +from pina.condition import InputTargetCondition +from pina._src.core.graph import LabelBatch +from pina.graph import RadiusGraph, Graph +from pina import LabelTensor, Condition +from pina.data.manager import ( + _TensorDataManager, + _GraphDataManager, + _BatchManager, +) + + +# Number of graphs and tensor samples for testing +n_samples = 10 +n_graphs = 10 +n_nodes = 20 + + +# Helper function to create tensor data +def _create_tensor_data(use_lt=False): + + # If LabelTensor is used, create tensor data with labels + if use_lt: + input_tensor = LabelTensor(torch.rand((n_samples, 3)), ["x", "y", "z"]) + target_tensor = LabelTensor(torch.rand((n_samples, 2)), ["a", "b"]) + return input_tensor, target_tensor + + # Standard torch.Tensor without labels + input_tensor = torch.rand((n_samples, 3)) + target_tensor = torch.rand((n_samples, 2)) + + return input_tensor, target_tensor + + +# Helper function to create graph data +def _create_graph_data(is_input, use_lt): + + # If LabelTensor is used, create graph data with LabelTensors + if use_lt: + x = LabelTensor(torch.rand(n_graphs, n_nodes, 2), ["u", "v"]) + pos = LabelTensor(torch.rand(n_graphs, n_nodes, 2), ["x", "y"]) + tensor = LabelTensor(torch.rand(n_graphs, n_nodes, 2), ["f", "g"]) + + # Standard torch.Tensor without labels + else: + x = torch.rand(n_graphs, n_nodes, 2) + pos = torch.rand(n_graphs, n_nodes, 2) + tensor = torch.rand(n_graphs, n_nodes, 2) + + # Create a list of Graphs + graph = [ + RadiusGraph( + pos=pos[i], + radius=0.1, + x=x[i] if is_input else None, + y=x[i] if not is_input else None, + ) + for i in range(len(x)) + ] + + return graph, tensor + + +# Helper function to check tensor types +def _assert_tensor_type(t, use_lt): + if use_lt: + assert isinstance(t, LabelTensor) + else: + assert isinstance(t, torch.Tensor) and not isinstance(t, LabelTensor) + + +# Helper function to check input graph +def _assert_graph_type(graph_list, use_lt, is_input): + + assert isinstance(graph_list, list) + for graph in graph_list: + value = graph.x if is_input else graph.y + _assert_tensor_type(value, use_lt) + + +# Define a dummy solver for testing +class DummySolver: + + def __init__(self, use_lt, input_vars, output_vars): + if use_lt is True: + self.forward = labelize_forward( + forward=self.forward, + input_variables=input_vars, + output_variables=output_vars, + ) + + def forward(self, pts): + + # Tensor case + if isinstance(pts, torch.Tensor): + return torch.cat( + [pts.mean(dim=-1, keepdim=True), pts.sum(dim=-1, keepdim=True)], + dim=-1, + ) + + # Graph case + else: + return torch.cat( + [ + pts.x.mean(dim=-1, keepdim=True), + pts.x.sum(dim=-1, keepdim=True), + ], + dim=-1, + ).reshape(n_graphs, n_nodes, -1) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize( + "case", [["tensor", "tensor"], ["tensor", "graph"], ["graph", "tensor"]] +) +def test_constructor(use_lt, case): + + # Tensor - tensor + if case == ["tensor", "tensor"]: + + # Define the condition + input_tensor, target_tensor = _create_tensor_data(use_lt=use_lt) + condition = Condition(input=input_tensor, target=target_tensor) + + # Assert correct types + assert isinstance(condition, InputTargetCondition) + _assert_tensor_type(condition.input, use_lt) + _assert_tensor_type(condition.target, use_lt) + + # Assert numerical parity + assert torch.allclose(condition.input, input_tensor) + assert torch.allclose(condition.target, target_tensor) + + # Assert labels if LabelTensor is used + if use_lt: + assert condition.input.labels == ["x", "y", "z"] + assert condition.target.labels == ["a", "b"] + + # Tensor - graph + elif case == ["tensor", "graph"]: + + # Define the condition + target_graph, input_tensor = _create_graph_data( + is_input=False, use_lt=use_lt + ) + condition = Condition(input=input_tensor, target=target_graph) + + # Assert correct types + assert isinstance(condition, InputTargetCondition) + _assert_tensor_type(condition.input, use_lt) + _assert_graph_type(condition.target, use_lt, is_input=False) + + # Assert numerical parity + assert torch.allclose(condition.input, input_tensor) + for i, graph in enumerate(target_graph): + assert torch.allclose(condition.target[i].y, graph.y) + + # Assert labels if LabelTensor is used + if use_lt: + assert condition.input.labels == ["f", "g"] + for i in range(len(target_graph)): + assert condition.target[i].y.labels == ["u", "v"] + assert condition.target[i].pos.labels == ["x", "y"] + + # Graph - tensor + elif case == ["graph", "tensor"]: + + # Define the condition + input_graph, target_tensor = _create_graph_data( + is_input=True, use_lt=use_lt + ) + condition = Condition(input=input_graph, target=target_tensor) + + # Assert correct types + assert isinstance(condition, InputTargetCondition) + _assert_graph_type(condition.input, use_lt, is_input=True) + _assert_tensor_type(condition.target, use_lt) + + # Assert numerical parity + assert torch.allclose(condition.target, target_tensor) + for i, graph in enumerate(input_graph): + assert torch.allclose(condition.input[i].x, graph.x) + + # Assert labels if LabelTensor is used + if use_lt: + assert condition.target.labels == ["f", "g"] + for i in range(len(input_graph)): + assert condition.input[i].x.labels == ["u", "v"] + assert condition.input[i].pos.labels == ["x", "y"] + + # Prepare for invalid input tests + input_ = input_tensor if case[0] == "tensor" else input_graph + target_ = target_tensor if case[1] == "tensor" else target_graph + + # Should fail if the input is neither a tensor nor a graph + with pytest.raises(ValueError): + Condition(input="invalid_input", target=target_) + + # Should fail if the target is neither a tensor nor a graph + with pytest.raises(ValueError): + Condition(input=input_, target="invalid_target") + + # Should fail if the input is a list of tensors + if case[0] == "tensor": + with pytest.raises(ValueError): + Condition(input=[input_], target=target_) + + # Should fail if the target is a list of tensors + if case[1] == "tensor": + with pytest.raises(ValueError): + Condition(input=input_, target=[target_]) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize( + "case", [["tensor", "tensor"], ["tensor", "graph"], ["graph", "tensor"]] +) +def test_get_item(use_lt, case): + + # Tensor - tensor + if case == ["tensor", "tensor"]: + + # Define the condition + input_tensor, target_tensor = _create_tensor_data(use_lt=use_lt) + condition = Condition(input=input_tensor, target=target_tensor) + + # Extract item using __getitem__ + index = 0 + item = condition[index] + + # Assert correct types + assert isinstance(item, _TensorDataManager) + _assert_tensor_type(item.input, use_lt) + _assert_tensor_type(item.target, use_lt) + + # Assert numerical parity + assert torch.allclose(item.input, input_tensor[index]) + assert torch.allclose(item.target, target_tensor[index]) + + # Tensor - graph + elif case == ["tensor", "graph"]: + + # Define the condition + target_graph, input_tensor = _create_graph_data( + is_input=False, use_lt=use_lt + ) + condition = Condition(input=input_tensor, target=target_graph) + + # Extract item using __getitem__ + index = 0 + item = condition[index] + + # Assert correct types + assert isinstance(item, _GraphDataManager) + _assert_tensor_type(item.input, use_lt) + assert isinstance(item.target, Graph) + _assert_tensor_type(item.target.y, use_lt) + + # Assert numerical parity + assert torch.allclose(item.input, input_tensor[index]) + assert torch.allclose(item.target.y, target_graph[index].y) + + # Graph - tensor + elif case == ["graph", "tensor"]: + + # Define the condition + input_graph, target_tensor = _create_graph_data( + is_input=True, use_lt=use_lt + ) + condition = Condition(input=input_graph, target=target_tensor) + + # Extract item using __getitem__ + index = 0 + item = condition[index] + + # Assert correct types + assert isinstance(item, _GraphDataManager) + assert isinstance(item.input, Graph) + _assert_tensor_type(item.input.x, use_lt) + _assert_tensor_type(item.target, use_lt) + + # Assert numerical parity + assert torch.allclose(item.target, target_tensor[index]) + assert torch.allclose(item.input.x, input_graph[index].x) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize( + "case", [["tensor", "tensor"], ["tensor", "graph"], ["graph", "tensor"]] +) +def test_create_batch(use_lt, case): + + # Tensor - tensor + if case == ["tensor", "tensor"]: + + # Define the condition + input_tensor, target_tensor = _create_tensor_data(use_lt=use_lt) + condition = Condition(input=input_tensor, target=target_tensor) + + # Create batches using automatic batching or condition's collate_fn + idx = [0, 2] + data_to_collate = [condition.data[i] for i in idx] + batch_auto = condition.automatic_batching_collate_fn(data_to_collate) + batch_collate = condition.collate_fn(idx, condition) + + # Check that the automatic batch has been properly created + assert isinstance(batch_auto, _BatchManager) + assert hasattr(batch_auto, "input") + assert hasattr(batch_auto, "target") + + # Check that the collate_fn batch has been properly created + assert isinstance(batch_collate, dict) + assert hasattr(batch_collate, "input") + assert hasattr(batch_collate, "target") + + # Create expected input and target batches + expected_input = torch.stack([input_tensor[i] for i in idx]) + expected_target = torch.stack([target_tensor[i] for i in idx]) + + # Assert that the automatic batch input and target are correct + assert torch.allclose(batch_auto.input, expected_input) + assert torch.allclose(batch_auto.target, expected_target) + assert batch_auto.input.shape == expected_input.shape + assert batch_auto.target.shape == expected_target.shape + + # Assert that the collate_fn batch input and target are correct + assert torch.allclose(batch_collate.input, expected_input) + assert torch.allclose(batch_collate.target, expected_target) + assert batch_collate.input.shape == expected_input.shape + assert batch_collate.target.shape == expected_target.shape + + # Tensor - graph + elif case == ["tensor", "graph"]: + + # Define the condition + target_graph, input_tensor = _create_graph_data( + is_input=False, use_lt=use_lt + ) + condition = Condition(input=input_tensor, target=target_graph) + + # Create batches using automatic batching or condition's collate_fn + idx = [0, 2] + data_to_collate = [condition.data[i] for i in idx] + batch_auto = condition.automatic_batching_collate_fn(data_to_collate) + batch_collate = condition.collate_fn(idx, condition) + + # Check that the automatic batch has been properly created + assert isinstance(batch_auto, _BatchManager) + assert hasattr(batch_auto, "input") + assert hasattr(batch_auto, "target") + + # Check that the collate_fn batch has been properly created + assert isinstance(batch_collate, dict) + assert hasattr(batch_collate, "input") + assert hasattr(batch_collate, "target") + + # Create expected input and target batches + expected_input = torch.cat([input_tensor[i] for i in idx]) + expected_target = [target_graph[i] for i in idx] + + # Assert that the automatic batch input and target are correct + assert torch.allclose(batch_auto.input, expected_input) + for i, graph in enumerate(expected_target): + assert torch.allclose(batch_auto.target[i].y, graph.y) + assert batch_auto.input.shape == expected_input.shape + assert batch_auto.target.num_graphs == len(idx) + + # Assert that the collate_fn batch input and target are correct + assert torch.allclose(batch_collate.input, expected_input) + for i, graph in enumerate(expected_target): + assert torch.allclose(batch_collate.target[i].y, graph.y) + assert batch_collate.input.shape == expected_input.shape + assert batch_collate.target.num_graphs == len(idx) + + # Graph - tensor + elif case == ["graph", "tensor"]: + + # Define the condition + input_graph, target_tensor = _create_graph_data( + is_input=True, use_lt=use_lt + ) + condition = Condition(input=input_graph, target=target_tensor) + + # Create batches using automatic batching or condition's collate_fn + idx = [0, 2] + data_to_collate = [condition.data[i] for i in idx] + batch_auto = condition.automatic_batching_collate_fn(data_to_collate) + batch_collate = condition.collate_fn(idx, condition) + + # Check that the automatic batch has been properly created + assert isinstance(batch_auto, _BatchManager) + assert hasattr(batch_auto, "input") + assert hasattr(batch_auto, "target") + + # Check that the collate_fn batch has been properly created + assert isinstance(batch_collate, dict) + assert hasattr(batch_collate, "input") + assert hasattr(batch_collate, "target") + + # Create expected input and target batches + expected_input = [input_graph[i] for i in idx] + expected_target = torch.cat([target_tensor[i] for i in idx]) + + # Assert that the automatic batch input and target are correct + for i, graph in enumerate(expected_input): + assert torch.allclose(batch_auto.input[i].x, graph.x) + assert torch.allclose(batch_auto.target, expected_target) + assert batch_auto.input.num_graphs == len(idx) + assert batch_auto.target.shape == expected_target.shape + + # Assert that the collate_fn batch input and target are correct + for i, graph in enumerate(expected_input): + assert torch.allclose(batch_collate.input[i].x, graph.x) + assert torch.allclose(batch_collate.target, expected_target) + assert batch_collate.input.num_graphs == len(idx) + assert batch_collate.target.shape == expected_target.shape + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize( + "case", [["tensor", "tensor"], ["tensor", "graph"], ["graph", "tensor"]] +) +def test_evaluate(case, use_lt): + + # Tensor - tensor + if case == ["tensor", "tensor"]: + + # Define the input and the target + input_, target_ = _create_tensor_data(use_lt=use_lt) + input_vars = input_.labels if use_lt else None + output_vars = target_.labels if use_lt else None + + # Define the condition and the solver + condition = Condition(input=input_, target=target_) + solver = DummySolver(use_lt, input_vars, output_vars) + loss_fn = torch.nn.MSELoss(reduction="none") + + # Extract the batch + batch = {"input": condition.input, "target": condition.target} + + # Tensor - graph + elif case == ["tensor", "graph"]: + + # Define the input and the target + target_, input_ = _create_graph_data(is_input=False, use_lt=use_lt) + input_vars = input_.labels if use_lt else None + output_vars = target_[0].y.labels if use_lt else None + + # Define the condition and the solver + condition = Condition(input=input_, target=target_) + solver = DummySolver(use_lt, input_vars, output_vars) + loss_fn = torch.nn.MSELoss(reduction="none") + + # Extract the batch + batch = { + "input": condition.input, + "target": LabelBatch.from_data_list(condition.target).y.reshape( + n_graphs, n_nodes, -1 + ), + } + + # Graph - tensor + elif case == ["graph", "tensor"]: + + # Define the input and the target + input_, target_ = _create_graph_data(is_input=True, use_lt=use_lt) + input_vars = input_[0].x.labels if use_lt else None + output_vars = target_.labels if use_lt else None + + # Define the condition and the solver + condition = Condition(input=input_, target=target_) + solver = DummySolver(use_lt, input_vars, output_vars) + loss_fn = torch.nn.MSELoss(reduction="none") + + # Extract the batch + batch = { + "input": LabelBatch.from_data_list(condition.input), + "target": condition.target, + } + + # Evaluate the condition and compute the expected residual + residual = condition.evaluate(batch, solver) + expected = solver.forward(batch["input"]) - batch["target"] + + # Assert that the evaluated residual is correct + assert torch.allclose(residual, expected) diff --git a/tests/test_condition/test_time_series_condition.py b/tests/test_condition/test_time_series_condition.py new file mode 100644 index 000000000..120433343 --- /dev/null +++ b/tests/test_condition/test_time_series_condition.py @@ -0,0 +1,300 @@ +import pytest +import torch +from pina.data.manager import _TensorDataManager, _BatchManager +from pina._src.core.utils import labelize_forward +from pina.condition import TimeSeriesCondition +from pina import LabelTensor, Condition + +# Number of samples and time steps for testing +n_samples = 5 +time_steps = 10 + + +# Helper function to create tensor data +def _create_tensor_data(use_lt=False): + + # Input tensor + input_tensor = torch.rand((n_samples, time_steps, 2)) + + # If LabelTensor is used, create tensor data with labels + if use_lt: + return LabelTensor(input_tensor, labels=["u", "v"]) + + return input_tensor + + +# Helper function to check tensor types +def _assert_tensor_type(t, use_lt): + if use_lt: + assert isinstance(t, LabelTensor) + else: + assert isinstance(t, torch.Tensor) and not isinstance(t, LabelTensor) + + +# Helper function to compute expected unroll windows +def _expected_unroll(data, n_windows, unroll_length, randomize): + + # Compute valid starting indices + last_idx = data.shape[1] - unroll_length + start_indices = torch.arange(last_idx + 1) + + # Randomize indices if required + if randomize: + start_indices = start_indices[torch.randperm(len(start_indices))] + + # Limit the number of windows + if n_windows is not None and n_windows < len(start_indices): + start_indices = start_indices[:n_windows] + + # Build expected windows + windows = [data[:, s : s + unroll_length] for s in start_indices] + + return torch.stack(windows, dim=1) + + +# Define a dummy solver for testing +class DummySolver: + + def __init__(self, use_lt, input_vars): + if use_lt: + self.forward = labelize_forward( + forward=self.forward, + input_variables=input_vars, + output_variables=input_vars, + ) + + self._params = None + self._kwargs = {} + self.aggregation_strategy = torch.mean + + def forward(self, samples): + return samples + + def preprocess_step(self, current_state, **kwargs): + return current_state + + def postprocess_step(self, predicted_state, **kwargs): + return predicted_state + + def _get_weights(self, condition_name, step_losses): + return 1.0 + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("n_windows", [4, 6]) +@pytest.mark.parametrize("unroll_length", [3, 5]) +@pytest.mark.parametrize("randomize", [True, False]) +def test_constructor(use_lt, n_windows, unroll_length, randomize): + + # Define the condition + input_tensor = _create_tensor_data(use_lt) + condition = Condition( + input=input_tensor, + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + + # Assert correct types + assert isinstance(condition, TimeSeriesCondition) + _assert_tensor_type(condition.input, use_lt) + + # Assert numerical parity + if not randomize: + expected_tensor = _expected_unroll( + input_tensor, n_windows, unroll_length, randomize + ) + assert torch.allclose(condition.input, expected_tensor) + + # Assert labels if LabelTensor is used + if use_lt: + assert condition.input.labels == ["u", "v"] + + # Should fail if unroll_length is not a positive integer + with pytest.raises(AssertionError): + Condition( + input=input_tensor, + n_windows=n_windows, + unroll_length=0, + randomize=randomize, + ) + + # Should fail if n_windows is not a positive integer + with pytest.raises(AssertionError): + Condition( + input=input_tensor, + n_windows=0, + unroll_length=unroll_length, + randomize=randomize, + ) + + # Should fail if randomize is not a boolean value + with pytest.raises(ValueError): + Condition( + input=input_tensor, + n_windows=n_windows, + unroll_length=unroll_length, + randomize="not_a_boolean", + ) + + # Should fail if the input tensor has less than 3 dimensions + with pytest.raises(ValueError): + Condition( + input=torch.rand(n_samples, 2), + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + + # Should fail if unroll_length is not greater than 1 + with pytest.raises(ValueError): + Condition( + input=input_tensor, + n_windows=n_windows, + unroll_length=1, + randomize=randomize, + ) + + # Should fail if unroll_length is greater than the number of time steps + with pytest.raises(ValueError): + Condition( + input=input_tensor, + n_windows=n_windows, + unroll_length=time_steps + 1, + randomize=randomize, + ) + + # Should fail if n_windows is greater than the number of valid windows + with pytest.raises(ValueError): + Condition( + input=input_tensor, + n_windows=10, + unroll_length=unroll_length, + randomize=randomize, + ) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("n_windows", [4, 6]) +@pytest.mark.parametrize("unroll_length", [3, 5]) +@pytest.mark.parametrize("randomize", [True, False]) +def test_get_item(use_lt, n_windows, unroll_length, randomize): + + # Define the condition + input_tensor = _create_tensor_data(use_lt) + condition = Condition( + input=input_tensor, + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + + # Extract item using __getitem__ + index = 0 + item = condition[index] + + # Assert correct types + assert isinstance(item, _TensorDataManager) + _assert_tensor_type(item.input, use_lt) + + # Assert correct shapes + expected_shape = torch.Size([n_windows, unroll_length, 2]) + assert item.input.shape == expected_shape + + # Assert numerical parity + if not randomize: + expected_tensor = _expected_unroll( + input_tensor, n_windows, unroll_length, randomize + ) + assert torch.allclose(item.input, expected_tensor[index]) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("n_windows", [4, 6]) +@pytest.mark.parametrize("unroll_length", [3, 5]) +@pytest.mark.parametrize("randomize", [True, False]) +def test_create_batch(use_lt, n_windows, unroll_length, randomize): + + # Define the condition + input_tensor = _create_tensor_data(use_lt) + condition = Condition( + input=input_tensor, + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + + # Create batches using automatic batching or condition's collate_fn + idx = [0, 2] + data_to_collate = [condition.data[i] for i in idx] + batch_auto = condition.automatic_batching_collate_fn(data_to_collate) + batch_collate = condition.collate_fn(idx, condition) + + # Check that the automatic batch has been properly created + assert isinstance(batch_auto, _BatchManager) + assert hasattr(batch_auto, "input") + + # Check that the collate_fn batch has been properly created + assert isinstance(batch_collate, dict) + assert hasattr(batch_collate, "input") + + # Assert that the automatic batch input is correct + expected_shape = torch.Size([len(idx), n_windows, unroll_length, 2]) + assert batch_auto.input.shape == expected_shape + + # Assert that the collate_fn batch input is correct + expected_shape = torch.Size([len(idx), n_windows, unroll_length, 2]) + assert batch_collate.input.shape == expected_shape + + # Create input values + if not randomize: + expected_tensor = _expected_unroll( + input_tensor, n_windows, unroll_length, randomize + ) + assert torch.allclose(batch_collate.input, expected_tensor[idx]) + assert torch.allclose(batch_auto.input, expected_tensor[idx]) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("n_windows", [4, 6]) +@pytest.mark.parametrize("unroll_length", [3, 5]) +@pytest.mark.parametrize("randomize", [True, False]) +def test_evaluate(use_lt, n_windows, unroll_length, randomize): + + # Define the input tensor + input_tensor = _create_tensor_data(use_lt) + input_vars = input_tensor.labels if use_lt else None + + # Define the condition and the solver + condition = Condition( + input=input_tensor, + n_windows=n_windows, + unroll_length=unroll_length, + randomize=randomize, + ) + solver = DummySolver(use_lt, input_vars) + loss_fn = torch.nn.MSELoss(reduction="none") + + # Extract the batch + batch = {"input": condition.input} + + # Evaluate the condition and compute the expected residuals + residuals = condition.evaluate(batch, solver) + + # Compute expected autoregressive step residuals + step_residuals = [] + current_state = batch["input"][:, :, 0] + + for step in range(1, batch["input"].shape[2]): + predicted_state = current_state + target_state = batch["input"][:, :, step] + + step_residual = predicted_state - target_state + step_residuals.append(step_residual) + + current_state = predicted_state + + expected = torch.stack(step_residuals).as_subclass(torch.Tensor) + + # Assert that the evaluated residuals are correct + assert torch.allclose(residuals, expected) diff --git a/tests/test_data/test_aggregator.py b/tests/test_data/test_aggregator.py new file mode 100644 index 000000000..2f79f213e --- /dev/null +++ b/tests/test_data/test_aggregator.py @@ -0,0 +1,112 @@ +import pytest +from pina.data import _Aggregator + + +""" +Note: this test intentionally avoids relying on the actual DataLoader +implementation in order to keep the test focused on the aggregator logic itself +and independent from the behavior of external classes. The full pipeline is +tested in the DataLoader tests, which ensures that the aggregator works +correctly when used in the intended context. +""" + + +# Define a dummy dataloader for testing purposes +class DummyDataloader: + def __init__(self, data): + self.data = data + + def __iter__(self): + return iter(self.data) + + def __len__(self): + return len(self.data) + + +# Create dataloaders for testing +data_loaders1 = { + "condition_1": DummyDataloader([1, 2, 3]), + "condition_2": DummyDataloader([10, 20, 30]), +} +data_loaders2 = { + "condition_1": DummyDataloader([1, 2]), + "condition_2": DummyDataloader([10, 20, 30, 40, 50]), +} +data_loaders3 = { + "condition_1": DummyDataloader([1]), + "condition_2": DummyDataloader([10, 20, 30]), +} + +# Create expected batches for testing +expected_batches1 = [ + {"condition_1": 1, "condition_2": 10}, + {"condition_1": 2, "condition_2": 20}, + {"condition_1": 3, "condition_2": 30}, +] +expected_batches2 = [ + {"condition_1": 1, "condition_2": 10}, + {"condition_1": 2, "condition_2": 20}, + {"condition_1": 1, "condition_2": 30}, + {"condition_1": 2, "condition_2": 40}, + {"condition_1": 1, "condition_2": 50}, +] +expected_batches3 = [ + {"condition_1": 1, "condition_2": 10}, + {"condition_1": 1, "condition_2": 20}, + {"condition_1": 1, "condition_2": 30}, +] + + +@pytest.mark.parametrize("batching_mode", ["common_batch_size", "proportional"]) +def test_constructor(batching_mode): + + # Create dummy dataloaders + dataloaders = { + "condition_1": DummyDataloader([1, 2, 3]), + "condition_2": DummyDataloader([10, 20]), + } + + # Initialize the aggregator + _Aggregator(dataloaders, batching_mode=batching_mode) + + # Should raise NotImplementedError for separate_conditions mode + with pytest.raises(NotImplementedError): + _Aggregator(dataloaders, batching_mode="separate_conditions") + + +@pytest.mark.parametrize("batching_mode", ["common_batch_size", "proportional"]) +def test_len(batching_mode): + + # Create dummy dataloaders + dataloaders = { + "condition_1": DummyDataloader([1, 2]), + "condition_2": DummyDataloader([10, 20, 30]), + } + + # Initialize the aggregator and check its length + aggregator = _Aggregator(dataloaders, batching_mode=batching_mode) + assert len(aggregator) == 3 + + +@pytest.mark.parametrize("batching_mode", ["common_batch_size", "proportional"]) +@pytest.mark.parametrize( + "dataloaders, expected", + [ + (data_loaders1, expected_batches1), + (data_loaders2, expected_batches2), + (data_loaders3, expected_batches3), + ], +) +def test_iter(batching_mode, dataloaders, expected): + + # Initialize the aggregator + aggregator = _Aggregator(dataloaders, batching_mode=batching_mode) + + # Check yielded batches + assert list(aggregator) == expected + + # Check that the number of yielded batches matches len(aggregator) + assert len(expected) == len(aggregator) + + # Check that the aggregator can be iterated multiple times + assert list(aggregator) == expected diff --git a/tests/test_data/test_condition_subset.py b/tests/test_data/test_condition_subset.py new file mode 100644 index 000000000..f7e54afb0 --- /dev/null +++ b/tests/test_data/test_condition_subset.py @@ -0,0 +1,149 @@ +import torch +import pytest +from pina.equation.zoo import FixedValue +from pina import Condition, LabelTensor +from pina.domain import CartesianDomain +from pina.data import _ConditionSubset + +# Define an equation and a domain for testing purposes +equation = FixedValue(value=0.0) +domain = CartesianDomain({"x": [0, 1], "y": [0, 1]}) + +# Define input and target tensors for testing purposes +n_val, n_dim = 5, 2 +input_tensor = torch.rand(n_val, n_dim) +input_label_tensor = LabelTensor(torch.rand(n_val, n_dim), labels=["x", "y"]) +target_tensor = torch.rand(n_val, n_dim) +cond_vars = torch.rand(n_val, 1) + +# Define conditions for testing purposes +# Domain - equation condition is not tested as __get_item__ is not implemented +input_target_cond = Condition(input=input_tensor, target=target_tensor) +input_equation_cond = Condition(input=input_label_tensor, equation=equation) +data_cond = Condition(input=input_tensor, conditional_variables=cond_vars) + +# Define indexes for testing purposes +indices = torch.randperm(n_val).tolist() + + +@pytest.mark.parametrize("automatic_batching", [True, False]) +@pytest.mark.parametrize("indices", [indices[:3], indices[:2]]) +@pytest.mark.parametrize( + "condition", [input_target_cond, input_equation_cond, data_cond] +) +def test_constructor(condition, automatic_batching, indices): + + # Initialize the condition subset + subset = _ConditionSubset( + condition=condition, + indices=indices, + automatic_batching=automatic_batching, + ) + + # Verify that the attributes are correctly assigned + assert subset.condition is condition + assert subset.indices == indices + assert subset.automatic_batching is automatic_batching + assert subset.dataset_length == len(indices) + assert subset.iterable_length == len(indices) + + +@pytest.mark.parametrize("automatic_batching", [True, False]) +@pytest.mark.parametrize("indices", [indices[:3], indices[:2]]) +@pytest.mark.parametrize( + "condition", [input_target_cond, input_equation_cond, data_cond] +) +def test_len(condition, automatic_batching, indices): + + # Initialize the condition subset + subset = _ConditionSubset( + condition=condition, + indices=indices, + automatic_batching=automatic_batching, + ) + + # Verify that the length of the subset is correctly computed + assert len(subset) == len(indices) + + +@pytest.mark.parametrize("automatic_batching", [True, False]) +@pytest.mark.parametrize("indices", [indices[:3], indices[:2]]) +@pytest.mark.parametrize( + "condition", [input_target_cond, input_equation_cond, data_cond] +) +def test_get_item(condition, automatic_batching, indices): + + # Initialize the condition subset + subset = _ConditionSubset( + condition=condition, + indices=indices, + automatic_batching=automatic_batching, + ) + + # Verify that the correct data is returned for each index in the subset + for local_idx in range(len(indices)): + + # Retrieve the true dataset index + true_idx = indices[local_idx] + + # If automatic batching, check data equivalence + if automatic_batching: + + # Save actual and expected data for debugging purposes + actual_data = subset[local_idx].data + expected_data = condition[true_idx].data + + # Check that the keys of the returned data match + assert actual_data.keys() == expected_data.keys() + + # Check that the values of the returned data are equal + for key in actual_data: + assert torch.equal(actual_data[key], expected_data[key]) + + # Otherwise, check that the raw dataset index is returned + else: + assert subset[local_idx] == true_idx + + # Check cyclic indexing + cyclic_idx = len(indices) + true_idx = indices[0] + + # If automatic batching, check data equivalence for cyclic index + if automatic_batching: + + # Check that the keys of the returned data match + assert subset[cyclic_idx].data.keys() == condition[true_idx].data.keys() + + # Check that the values of the returned data are equal + for key in actual_data: + assert torch.equal(actual_data[key], expected_data[key]) + + # Otherwise, check that the raw dataset index is returned for cyclic index + else: + assert subset[cyclic_idx] == true_idx + + +@pytest.mark.parametrize("automatic_batching", [True, False]) +@pytest.mark.parametrize( + "condition", + [input_target_cond, input_equation_cond, data_cond], +) +def test_get_all_data(condition, automatic_batching): + + # Initialize the condition subset + subset = _ConditionSubset( + condition=condition, + indices=indices, + automatic_batching=automatic_batching, + ) + + # Retrieve all data from the subset and check that it matches expected data + data = subset.get_all_data() + expected = condition[indices] + + # Check that the keys of the returned data match + assert data.keys == expected.keys + + # Check that the values of the returned data are equal + for key in data.keys: + assert torch.equal(data.data[key], expected.data[key]) diff --git a/tests/test_data/test_creator.py b/tests/test_data/test_creator.py new file mode 100644 index 000000000..c173e7f29 --- /dev/null +++ b/tests/test_data/test_creator.py @@ -0,0 +1,159 @@ +import torch +import pytest +from pina.data import _Creator + + +""" +Note: this test intentionally avoids relying on the actual Condition and +DataLoader implementations in order to keep the test focused on the creator +logic itself and independent from the behavior of external classes. The full +pipeline is tested in the DataLoader tests, which ensures that the creator works +correctly when used in the intended context. +""" + + +# Define a dummy dataset for testing purposes +class DummyDataset: + def __init__(self, data, length=None): + self.data = data + self.dataset_length = len(data) if length is None else length + self.iterable_length = None + + def __len__(self): + return len(self.data) + + +# Define a dummy dataloader for testing purposes +class DummyDataloader: + def create_dataloader( + self, + dataset, + batch_size, + automatic_batching, + sampler, + num_workers, + pin_memory, + ): + return { + "dataset": dataset, + "batch_size": batch_size, + "automatic_batching": automatic_batching, + "sampler": sampler, + "num_workers": num_workers, + "pin_memory": pin_memory, + } + + +# Create dataloaders for testing +dataloaders = { + "dataset_1": DummyDataloader(), + "dataset_2": DummyDataloader(), +} + + +@pytest.mark.parametrize( + "batching_mode", + ["common_batch_size", "separate_conditions", "proportional"], +) +def test_constructor(batching_mode): + + _Creator( + batching_mode=batching_mode, + batch_size=4, + shuffle=False, + automatic_batching=True, + num_workers=0, + pin_memory=False, + conditions=dataloaders, + ) + + +@pytest.mark.parametrize( + "batching_mode, batch_size, expected_batch_sizes, expected_max_len", + [ + ( + "common_batch_size", + 2, + {"dataset_1": 2, "dataset_2": 2}, + {"dataset_1": None, "dataset_2": 4}, + ), + ( + "common_batch_size", + None, + {"dataset_1": 3, "dataset_2": 4}, + {"dataset_1": 4, "dataset_2": None}, + ), + ( + "separate_conditions", + 2, + {"dataset_1": 2, "dataset_2": 2}, + {"dataset_1": None, "dataset_2": None}, + ), + ( + "separate_conditions", + None, + {"dataset_1": 3, "dataset_2": 4}, + {"dataset_1": None, "dataset_2": None}, + ), + ( + "proportional", + 4, + {"dataset_1": 1, "dataset_2": 3}, + {"dataset_1": None, "dataset_2": None}, + ), + ], +) +@pytest.mark.parametrize("shuffle", [True, False]) +def test_call( + batching_mode, + batch_size, + expected_batch_sizes, + expected_max_len, + shuffle, +): + + # Create dummy datasets + datasets = { + "dataset_1": DummyDataset([1, 2, 3], length=2), + "dataset_2": DummyDataset([10, 20, 30, 40], length=4), + } + + # Initialize the creator + creator = _Creator( + batching_mode=batching_mode, + batch_size=batch_size, + shuffle=shuffle, + automatic_batching=True, + num_workers=0, + pin_memory=False, + conditions=dataloaders, + ) + + # Call the creator to create dataloaders + created_loaders = creator(datasets) + + # Check that dataloaders are created for all conditions + assert set(created_loaders.keys()) == set(datasets.keys()) + + # Iterate over datasets + for name in datasets: + + # Assert that the dataloader is created with the correct parameters + assert created_loaders[name]["dataset"] is datasets[name] + assert created_loaders[name]["batch_size"] == expected_batch_sizes[name] + assert created_loaders[name]["automatic_batching"] is True + assert created_loaders[name]["num_workers"] == 0 + assert created_loaders[name]["pin_memory"] is False + assert datasets[name].iterable_length == expected_max_len[name] + + # Check that the correct sampler is used based on the shuffle parameter + if shuffle: + assert isinstance( + created_loaders[name]["sampler"], + torch.utils.data.RandomSampler, + ) + else: + assert isinstance( + created_loaders[name]["sampler"], + torch.utils.data.SequentialSampler, + ) diff --git a/tests/test_data/test_data_module.py b/tests/test_data/test_data_module.py index 53e7334ec..9bb81fbad 100644 --- a/tests/test_data/test_data_module.py +++ b/tests/test_data/test_data_module.py @@ -1,331 +1,170 @@ import torch import pytest -from pina.data import PinaDataModule -from pina.data.dataset import PinaTensorDataset, PinaGraphDataset -from pina.problem.zoo import SupervisedProblem +from copy import copy +from pina.problem.zoo import SupervisedProblem, Poisson2DSquareProblem +from pina.data import DataModule, _ConditionSubset from pina.graph import RadiusGraph -from pina.data.data_module import DummyDataloader -from pina import Trainer -from pina.solver import SupervisedSolver -from torch_geometric.data import Batch -from torch.utils.data import DataLoader -input_tensor = torch.rand((100, 10)) -output_tensor = torch.rand((100, 2)) +# Number of samples in the synthetic datasets +n_samples = 100 -x = torch.rand((100, 50, 10)) -pos = torch.rand((100, 50, 2)) -input_graph = [ - RadiusGraph(x=x_, pos=pos_, radius=0.2) for x_, pos_, in zip(x, pos) -] -output_graph = torch.rand((100, 50, 10)) +# Define helper functions to create synthetic tensor data +def _create_tensor_data(n=n_samples): + return (torch.rand((n, 4)), torch.rand((n, 2))) -@pytest.mark.parametrize( - "input_, output_", - [(input_tensor, output_tensor), (input_graph, output_graph)], -) -def test_constructor(input_, output_): - problem = SupervisedProblem(input_=input_, output_=output_) - PinaDataModule(problem) +# Define helper function to create synthetic graph data +def _create_graph_data(n=n_samples): -@pytest.mark.parametrize( - "input_, output_", - [(input_tensor, output_tensor), (input_graph, output_graph)], -) -@pytest.mark.parametrize( - "train_size, val_size, test_size", [(0.7, 0.2, 0.1), (0.7, 0.3, 0)] -) -def test_setup_train(input_, output_, train_size, val_size, test_size): - problem = SupervisedProblem(input_=input_, output_=output_) - dm = PinaDataModule( - problem, train_size=train_size, val_size=val_size, test_size=test_size - ) - dm.setup() - assert hasattr(dm, "train_dataset") - if isinstance(input_, torch.Tensor): - assert isinstance(dm.train_dataset, PinaTensorDataset) - else: - assert isinstance(dm.train_dataset, PinaGraphDataset) - # assert len(dm.train_dataset) == int(len(input_) * train_size) - if test_size > 0: - assert hasattr(dm, "test_dataset") - assert dm.test_dataset is None - else: - assert not hasattr(dm, "test_dataset") - assert hasattr(dm, "val_dataset") - if isinstance(input_, torch.Tensor): - assert isinstance(dm.val_dataset, PinaTensorDataset) - else: - assert isinstance(dm.val_dataset, PinaGraphDataset) - # assert len(dm.val_dataset) == int(len(input_) * val_size) + # Define input graphs and output tensor + input_graphs = [ + RadiusGraph(x=torch.rand((20, 4)), pos=torch.rand((20, 2)), radius=0.2) + for _ in range(n) + ] + output_tensor = torch.rand((n, 50, 2)) + return input_graphs, output_tensor -@pytest.mark.parametrize( - "input_, output_", - [(input_tensor, output_tensor), (input_graph, output_graph)], -) -@pytest.mark.parametrize( - "train_size, val_size, test_size", [(0.7, 0.2, 0.1), (0.0, 0.0, 1.0)] -) -def test_setup_test(input_, output_, train_size, val_size, test_size): - problem = SupervisedProblem(input_=input_, output_=output_) - dm = PinaDataModule( - problem, train_size=train_size, val_size=val_size, test_size=test_size - ) - dm.setup(stage="test") - if train_size > 0: - assert hasattr(dm, "train_dataset") - assert dm.train_dataset is None - else: - assert not hasattr(dm, "train_dataset") - if val_size > 0: - assert hasattr(dm, "val_dataset") - assert dm.val_dataset is None - else: - assert not hasattr(dm, "val_dataset") - - assert hasattr(dm, "test_dataset") - if isinstance(input_, torch.Tensor): - assert isinstance(dm.test_dataset, PinaTensorDataset) - else: - assert isinstance(dm.test_dataset, PinaGraphDataset) - # assert len(dm.test_dataset) == int(len(input_) * test_size) +# Fixture remove data condition from pinns, caused by external tests in suite +@pytest.fixture(autouse=True) +def remove_data_from_pinn_conditions(): + yield + + # Remove the data condition + Poisson2DSquareProblem.conditions.pop("data", None) + +@pytest.mark.parametrize("problem_type", ["tensor", "graph", "pinn"]) +@pytest.mark.parametrize("batch_size", [None, 5]) @pytest.mark.parametrize( - "input_, output_", - [(input_tensor, output_tensor), (input_graph, output_graph)], + "train_size, val_size, test_size", + [(0.7, 0.2, 0.1), (0.8, 0.2, 0.0), (0.0, 0.8, 0.2)], ) -def test_dummy_dataloader(input_, output_): - problem = SupervisedProblem(input_=input_, output_=output_) - solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10)) - trainer = Trainer( - solver, batch_size=None, train_size=0.7, val_size=0.3, test_size=0.0 +def test_constructor(problem_type, batch_size, train_size, val_size, test_size): + + # Build a tensor problem + if problem_type == "tensor": + input_tensor, output_tensor = _create_tensor_data() + problem = SupervisedProblem(input_=input_tensor, output_=output_tensor) + + # Build a graph problem + elif problem_type == "graph": + input_graph, output_graph = _create_graph_data() + problem = SupervisedProblem(input_=input_graph, output_=output_graph) + + # Build a pinn problem + elif problem_type == "pinn": + problem = Poisson2DSquareProblem() + problem.discretise_domain(n=n_samples, mode="random") + + # Initialize the data module + dm = DataModule( + problem=problem, + train_size=train_size, + val_size=val_size, + test_size=test_size, + batch_size=batch_size, + batching_mode="proportional", + automatic_batching=True, + shuffle=True, + num_workers=0, + pin_memory=False, ) - dm = trainer.data_module - dm.setup() - dm.trainer = trainer - dataloader = dm.train_dataloader() - assert isinstance(dataloader, DummyDataloader) - assert len(dataloader) == 1 - data = next(dataloader) - assert isinstance(data, list) - assert isinstance(data[0], tuple) - if isinstance(input_, list): - assert isinstance(data[0][1]["input"], Batch) - else: - assert isinstance(data[0][1]["input"], torch.Tensor) - assert isinstance(data[0][1]["target"], torch.Tensor) - - dataloader = dm.val_dataloader() - assert isinstance(dataloader, DummyDataloader) - assert len(dataloader) == 1 - data = next(dataloader) - assert isinstance(data, list) - assert isinstance(data[0], tuple) - if isinstance(input_, list): - assert isinstance(data[0][1]["input"], Batch) - else: - assert isinstance(data[0][1]["input"], torch.Tensor) - assert isinstance(data[0][1]["target"], torch.Tensor) + # Check that the data module has been initialized correctly + assert dm.problem == problem + assert dm.trainer is None -@pytest.mark.parametrize( - "input_, output_", - [(input_tensor, output_tensor), (input_graph, output_graph)], -) -@pytest.mark.parametrize("automatic_batching", [True, False]) -def test_dataloader(input_, output_, automatic_batching): - problem = SupervisedProblem(input_=input_, output_=output_) - solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10)) - trainer = Trainer( - solver, - batch_size=10, - train_size=0.7, - val_size=0.3, - test_size=0.0, - automatic_batching=automatic_batching, + # Expected keys in the split_idxs dictionary + expected_keys = ( + {"data"} if problem_type in ["tensor", "graph"] else {"D", "boundary"} ) - dm = trainer.data_module - dm.setup() - dm.trainer = trainer - dataloader = dm.train_dataloader() - assert isinstance(dataloader, DataLoader) - assert len(dataloader) == 7 - data = next(iter(dataloader)) - assert isinstance(data, dict) - if isinstance(input_, list): - assert isinstance(data["data"]["input"], Batch) - else: - assert isinstance(data["data"]["input"], torch.Tensor) - assert isinstance(data["data"]["target"], torch.Tensor) - - dataloader = dm.val_dataloader() - assert isinstance(dataloader, DataLoader) - assert len(dataloader) == 3 - data = next(iter(dataloader)) - assert isinstance(data, dict) - if isinstance(input_, list): - assert isinstance(data["data"]["input"], Batch) - else: - assert isinstance(data["data"]["input"], torch.Tensor) - assert isinstance(data["data"]["target"], torch.Tensor) - - -from pina import LabelTensor - -input_tensor = LabelTensor(torch.rand((100, 3)), ["u", "v", "w"]) -output_tensor = LabelTensor(torch.rand((100, 3)), ["u", "v", "w"]) - -x = LabelTensor(torch.rand((100, 50, 3)), ["u", "v", "w"]) -pos = LabelTensor(torch.rand((100, 50, 2)), ["x", "y"]) -input_graph = [ - RadiusGraph(x=x[i], pos=pos[i], radius=0.1) for i in range(len(x)) -] -output_graph = LabelTensor(torch.rand((100, 50, 3)), ["u", "v", "w"]) + # Check that the split_idxs attribute has been created correctly + assert hasattr(dm, "split_idxs") + assert isinstance(dm.split_idxs, dict) + assert set(dm.split_idxs.keys()) == expected_keys + # Iterate over keys in split_idxs + for k in dm.split_idxs.keys(): + + # Assert that the value corresponding to each key is a dictionary + assert isinstance(dm.split_idxs[k], dict) + assert set(dm.split_idxs[k].keys()) == {"train", "val", "test"} + + # Expected lengths of splits + expected_lengths = { + "train": int(train_size * n_samples), + "val": int(val_size * n_samples), + "test": int(test_size * n_samples), + } + + # Iterate over splits + for split in ["train", "val", "test"]: + assert isinstance(dm.split_idxs[k][split], list) + assert len(dm.split_idxs[k][split]) == expected_lengths[split] + + +@pytest.mark.parametrize("problem_type", ["tensor", "graph", "pinn"]) +@pytest.mark.parametrize("batch_size", [None, 5]) @pytest.mark.parametrize( - "input_, output_", - [(input_tensor, output_tensor), (input_graph, output_graph)], + "train_size, val_size, test_size", + [(0.7, 0.2, 0.1), (0.8, 0.2, 0.0), (0.0, 0.8, 0.2)], ) -@pytest.mark.parametrize("automatic_batching", [True, False]) -def test_dataloader_labels(input_, output_, automatic_batching): - problem = SupervisedProblem(input_=input_, output_=output_) - solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10)) - trainer = Trainer( - solver, - batch_size=10, - train_size=0.7, - val_size=0.3, - test_size=0.0, - automatic_batching=automatic_batching, - ) - dm = trainer.data_module - dm.setup() - dm.trainer = trainer - dataloader = dm.train_dataloader() - assert isinstance(dataloader, DataLoader) - assert len(dataloader) == 7 - data = next(iter(dataloader)) - assert isinstance(data, dict) - if isinstance(input_, list): - assert isinstance(data["data"]["input"], Batch) - assert isinstance(data["data"]["input"].x, LabelTensor) - assert data["data"]["input"].x.labels == ["u", "v", "w"] - assert data["data"]["input"].pos.labels == ["x", "y"] - else: - assert isinstance(data["data"]["input"], LabelTensor) - assert data["data"]["input"].labels == ["u", "v", "w"] - assert isinstance(data["data"]["target"], LabelTensor) - assert data["data"]["target"].labels == ["u", "v", "w"] - - dataloader = dm.val_dataloader() - assert isinstance(dataloader, DataLoader) - assert len(dataloader) == 3 - data = next(iter(dataloader)) - assert isinstance(data, dict) - if isinstance(input_, list): - assert isinstance(data["data"]["input"], Batch) - assert isinstance(data["data"]["input"].x, LabelTensor) - assert data["data"]["input"].x.labels == ["u", "v", "w"] - assert data["data"]["input"].pos.labels == ["x", "y"] - else: - assert isinstance(data["data"]["input"], torch.Tensor) - assert isinstance(data["data"]["input"], LabelTensor) - assert data["data"]["input"].labels == ["u", "v", "w"] - assert isinstance(data["data"]["target"], torch.Tensor) - assert data["data"]["target"].labels == ["u", "v", "w"] - - -def test_get_all_data(): - input = torch.stack([torch.zeros((1,)) + i for i in range(1000)]) - target = input - - problem = SupervisedProblem(input, target) - datamodule = PinaDataModule( - problem, - train_size=0.7, - test_size=0.2, - val_size=0.1, - batch_size=64, - shuffle=False, - repeat=False, - automatic_batching=None, - num_workers=0, - pin_memory=False, - ) - datamodule.setup("fit") - datamodule.setup("test") - assert len(datamodule.train_dataset.get_all_data()["data"]["input"]) == 700 - assert torch.isclose( - datamodule.train_dataset.get_all_data()["data"]["input"], input[:700] - ).all() - assert len(datamodule.val_dataset.get_all_data()["data"]["input"]) == 100 - assert torch.isclose( - datamodule.val_dataset.get_all_data()["data"]["input"], input[900:] - ).all() - assert len(datamodule.test_dataset.get_all_data()["data"]["input"]) == 200 - assert torch.isclose( - datamodule.test_dataset.get_all_data()["data"]["input"], input[700:900] - ).all() - - -def test_input_propery_tensor(): - input = torch.stack([torch.zeros((1,)) + i for i in range(1000)]) - target = input - - problem = SupervisedProblem(input, target) - datamodule = PinaDataModule( - problem, - train_size=0.7, - test_size=0.2, - val_size=0.1, - batch_size=64, - shuffle=False, - repeat=False, - automatic_batching=None, +def test_setup(problem_type, batch_size, train_size, val_size, test_size): + + # Build a tensor problem + if problem_type == "tensor": + input_tensor, output_tensor = _create_tensor_data() + problem = SupervisedProblem(input_=input_tensor, output_=output_tensor) + + # Build a graph problem + elif problem_type == "graph": + input_graph, output_graph = _create_graph_data() + problem = SupervisedProblem(input_=input_graph, output_=output_graph) + + # Build a pinn problem + elif problem_type == "pinn": + problem = Poisson2DSquareProblem() + problem.discretise_domain(n=n_samples, mode="random") + + # Initialize the data module + dm = DataModule( + problem=problem, + train_size=train_size, + val_size=val_size, + test_size=test_size, + batch_size=batch_size, + batching_mode="proportional", + automatic_batching=True, + shuffle=True, num_workers=0, pin_memory=False, ) - datamodule.setup("fit") - datamodule.setup("test") - input_ = datamodule.input - assert isinstance(input_, dict) - assert isinstance(input_["train"], dict) - assert isinstance(input_["val"], dict) - assert isinstance(input_["test"], dict) - assert torch.isclose(input_["train"]["data"], input[:700]).all() - assert torch.isclose(input_["val"]["data"], input[900:]).all() - assert torch.isclose(input_["test"]["data"], input[700:900]).all() - - -def test_input_propery_graph(): - problem = SupervisedProblem(input_graph, output_graph) - datamodule = PinaDataModule( - problem, - train_size=0.7, - test_size=0.2, - val_size=0.1, - batch_size=64, - shuffle=False, - repeat=False, - automatic_batching=None, - num_workers=0, - pin_memory=False, + + # Call setup + dm.setup() + + # Expected keys in the split_idxs dictionary + expected_keys = ( + {"data"} if problem_type in ["tensor", "graph"] else {"D", "boundary"} ) - datamodule.setup("fit") - datamodule.setup("test") - input_ = datamodule.input - assert isinstance(input_, dict) - assert isinstance(input_["train"], dict) - assert isinstance(input_["val"], dict) - assert isinstance(input_["test"], dict) - assert isinstance(input_["train"]["data"], list) - assert isinstance(input_["val"]["data"], list) - assert isinstance(input_["test"]["data"], list) - assert len(input_["train"]["data"]) == 70 - assert len(input_["val"]["data"]) == 10 - assert len(input_["test"]["data"]) == 20 + + # Iterate over datsets + for dataset in ["train_datasets", "val_datasets", "test_datasets"]: + + # Assert that each dataset has been created correctly + assert hasattr(dm, dataset) + assert isinstance(getattr(dm, dataset), dict) + + # Assert that the keys in each dataset are correct, if not empty + if getattr(dm, dataset): + assert set(getattr(dm, dataset).keys()) == expected_keys + + # Iterate over keys in each dataset + for key in expected_keys: + + # Assert that the corresponding value is a _ConditionSubset + assert isinstance(getattr(dm, dataset)[key], _ConditionSubset) diff --git a/tests/test_data/test_graph_data_manager.py b/tests/test_data/test_graph_data_manager.py new file mode 100644 index 000000000..dd0bb47d8 --- /dev/null +++ b/tests/test_data/test_graph_data_manager.py @@ -0,0 +1,115 @@ +import torch +import pytest +from pina import LabelTensor +from pina.graph import Graph +from pina.data.manager import _DataManager, _GraphDataManager, _BatchManager + + +# Define data for testing +standard_graph = [ + Graph( + x=torch.rand((10, 3)), + pos=torch.rand((10, 2)), + edge_index=torch.randint(0, 10, (2, 20)), + ) + for _ in range(3) +] +label_graph = [ + Graph( + x=LabelTensor(torch.rand((10, 3)), labels=["a", "b", "c"]), + pos=LabelTensor(torch.rand((10, 2)), labels=["x", "y"]), + edge_index=torch.randint(0, 10, (2, 20)), + ) + for _ in range(3) +] +target_ = torch.rand((3, 10, 1)) +label_target = LabelTensor(target_, labels=["target"]) + + +@pytest.mark.parametrize("case", ["standard", "labeled"]) +def test_constructor(case): + + # Define data for testing + if case == "standard": + graph = standard_graph + target = target_ + exp_type = torch.Tensor + else: + graph = label_graph + target = label_target + exp_type = LabelTensor + + # Create data manager + data_manager = _DataManager(graph=graph, target=target) + + # Check that the data manager is an instance of _GraphDataManager + assert isinstance(data_manager, _GraphDataManager) + + # Check that the attributes are set correctly + assert hasattr(data_manager, "graph_key") + assert hasattr(data_manager, "graph") + assert hasattr(data_manager, "target") + assert data_manager.graph_key == "graph" + + # Check that the graph length is correct + assert len(data_manager.graph) == len(graph) + + # Check that the attributes have the correct types + assert isinstance(data_manager.target, exp_type) + assert isinstance(data_manager.graph, list) + for g in data_manager.graph: + assert isinstance(g, Graph) + + # Check that the values of the attributes are correct + assert torch.equal(data_manager.target, target) + for i in range(len(graph)): + assert torch.equal(data_manager.graph[i].x, graph[i].x) + assert torch.equal(data_manager.graph[i].pos, graph[i].pos) + assert torch.equal( + data_manager.graph[i].edge_index, graph[i].edge_index + ) + assert torch.equal(data_manager.graph[i].target, graph[i].target) + + +@pytest.mark.parametrize("case", ["standard", "labeled"]) +def test_create_batch(case): + + # Define data for testing + if case == "standard": + graph = standard_graph + target = target_ + exp_type = torch.Tensor + else: + graph = label_graph + target = label_target + exp_type = LabelTensor + + # Create data manager + data_manager = _DataManager(graph=graph, target=target) + + # Batch over indices + idx = [0, 2] + batch = _GraphDataManager.create_batch([data_manager[idx] for idx in idx]) + + # Check that the batch is an instance of _BatchManager + assert isinstance(batch, _BatchManager) + + # Check that the attributes are set correctly + assert hasattr(batch, "graph") + assert hasattr(batch, "target") + + # Check that the graph length is correct + assert batch.graph.num_graphs == len(idx) + + # Check that the attributes have the correct types + assert isinstance(batch.target, exp_type) + assert isinstance(batch.graph, Graph) + + # Check that the values of the attributes are correct + assert torch.equal(batch.target, torch.cat([target[i] for i in idx], dim=0)) + assert torch.equal( + batch.graph.x, torch.cat([graph[i].x for i in idx], dim=0) + ) + assert torch.equal( + batch.graph.pos, torch.cat([graph[i].pos for i in idx], dim=0) + ) diff --git a/tests/test_data/test_graph_dataset.py b/tests/test_data/test_graph_dataset.py deleted file mode 100644 index 81d6a2c5d..000000000 --- a/tests/test_data/test_graph_dataset.py +++ /dev/null @@ -1,138 +0,0 @@ -import torch -import pytest -from pina.data.dataset import PinaDatasetFactory, PinaGraphDataset -from pina.graph import KNNGraph -from torch_geometric.data import Data - -x = torch.rand((100, 20, 10)) -pos = torch.rand((100, 20, 2)) -input_ = [ - KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True) - for x_, pos_ in zip(x, pos) -] -output_ = torch.rand((100, 20, 10)) - -x_2 = torch.rand((50, 20, 10)) -pos_2 = torch.rand((50, 20, 2)) -input_2_ = [ - KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True) - for x_, pos_ in zip(x_2, pos_2) -] -output_2_ = torch.rand((50, 20, 10)) - - -# Problem with a single condition -conditions_dict_single = { - "data": { - "input": input_, - "target": output_, - } -} -max_conditions_lengths_single = {"data": 100} - -# Problem with multiple conditions -conditions_dict_multi = { - "data_1": { - "input": input_, - "target": output_, - }, - "data_2": { - "input": input_2_, - "target": output_2_, - }, -} - -max_conditions_lengths_multi = {"data_1": 100, "data_2": 50} - - -@pytest.mark.parametrize( - "conditions_dict, max_conditions_lengths", - [ - (conditions_dict_single, max_conditions_lengths_single), - (conditions_dict_multi, max_conditions_lengths_multi), - ], -) -def test_constructor(conditions_dict, max_conditions_lengths): - dataset = PinaDatasetFactory( - conditions_dict, - max_conditions_lengths=max_conditions_lengths, - automatic_batching=True, - ) - assert isinstance(dataset, PinaGraphDataset) - assert len(dataset) == 100 - - -@pytest.mark.parametrize( - "conditions_dict, max_conditions_lengths", - [ - (conditions_dict_single, max_conditions_lengths_single), - (conditions_dict_multi, max_conditions_lengths_multi), - ], -) -def test_getitem(conditions_dict, max_conditions_lengths): - dataset = PinaDatasetFactory( - conditions_dict, - max_conditions_lengths=max_conditions_lengths, - automatic_batching=True, - ) - data = dataset[50] - assert isinstance(data, dict) - assert all([isinstance(d["input"], Data) for d in data.values()]) - assert all([isinstance(d["target"], torch.Tensor) for d in data.values()]) - assert all( - [d["input"].x.shape == torch.Size((20, 10)) for d in data.values()] - ) - assert all( - [d["target"].shape == torch.Size((20, 10)) for d in data.values()] - ) - assert all( - [ - d["input"].edge_index.shape == torch.Size((2, 60)) - for d in data.values() - ] - ) - assert all([d["input"].edge_attr.shape[0] == 60 for d in data.values()]) - - data = dataset.fetch_from_idx_list([i for i in range(20)]) - assert isinstance(data, dict) - assert all([isinstance(d["input"], Data) for d in data.values()]) - assert all([isinstance(d["target"], torch.Tensor) for d in data.values()]) - assert all( - [d["input"].x.shape == torch.Size((400, 10)) for d in data.values()] - ) - assert all( - [d["target"].shape == torch.Size((20, 20, 10)) for d in data.values()] - ) - assert all( - [ - d["input"].edge_index.shape == torch.Size((2, 1200)) - for d in data.values() - ] - ) - assert all([d["input"].edge_attr.shape[0] == 1200 for d in data.values()]) - - -def test_input_single_condition(): - dataset = PinaDatasetFactory( - conditions_dict_single, - max_conditions_lengths=max_conditions_lengths_single, - automatic_batching=True, - ) - input_ = dataset.input - assert isinstance(input_, dict) - assert isinstance(input_["data"], list) - assert all([isinstance(d, Data) for d in input_["data"]]) - - -def test_input_multi_condition(): - dataset = PinaDatasetFactory( - conditions_dict_multi, - max_conditions_lengths=max_conditions_lengths_multi, - automatic_batching=True, - ) - input_ = dataset.input - assert isinstance(input_, dict) - assert isinstance(input_["data_1"], list) - assert all([isinstance(d, Data) for d in input_["data_1"]]) - assert isinstance(input_["data_2"], list) - assert all([isinstance(d, Data) for d in input_["data_2"]]) diff --git a/tests/test_data/test_single_batch_data_loader.py b/tests/test_data/test_single_batch_data_loader.py new file mode 100644 index 000000000..14a1aeed2 --- /dev/null +++ b/tests/test_data/test_single_batch_data_loader.py @@ -0,0 +1,111 @@ +import torch +import pytest +from pina.data import _SingleBatchDataLoader + + +# Initialize the test environment +size = 8 +distributed_rank = 1 +distributed_world_size = 3 +full_data_value = "all" + + +# Helper functions for testing +def _distributed_idx(size): + return list(range(distributed_rank, size, distributed_world_size)) + + +# Helper function to set up the distributed environment for testing +def _setup_distributed_environment(monkeypatch, distribute): + monkeypatch.setattr(torch.distributed, "is_available", lambda: True) + monkeypatch.setattr(torch.distributed, "is_initialized", lambda: distribute) + + if distribute: + monkeypatch.setattr( + torch.distributed, "get_rank", lambda: distributed_rank + ) + monkeypatch.setattr( + torch.distributed, + "get_world_size", + lambda: distributed_world_size, + ) + + +# Create a dummy data class for testing purposes +class DummyData: + def __init__(self, value): + self.value = value + + def to_batch(self): + return self + + +# Create a dummy dataset class for testing purposes +class DummyDataset: + def __init__(self, size=size): + self.size = size + self.fetched_indices = None + self.get_all_data_called = False + + def __len__(self): + return self.size + + def get_all_data(self): + self.get_all_data_called = True + return DummyData(full_data_value) + + def fetch_from_idx_list(self, idx): + self.fetched_indices = idx + return DummyData(idx) + + +@pytest.mark.parametrize("distribute", [True, False]) +def test_constructor(monkeypatch, distribute): + + # Set up distributed mock environment + _setup_distributed_environment(monkeypatch, distribute) + + # Create dataset and data loader + dataset = DummyDataset() + data_loader = _SingleBatchDataLoader(dataset) + + # Distributed case + if distribute: + expected_value = _distributed_idx(size) + assert data_loader.dataset.value == expected_value + assert dataset.fetched_indices == expected_value + + # Non-distributed case + else: + assert data_loader.dataset.value == full_data_value + assert dataset.get_all_data_called is True + + # Verify that the data loader yields exactly one batch per iteration + assert len(data_loader) == 1 + + # Should fail if dataset is smaller than world size in distributed case + if distribute: + small_dataset = DummyDataset(size=distributed_world_size - 1) + with pytest.raises(RuntimeError): + _SingleBatchDataLoader(small_dataset) + + +@pytest.mark.parametrize("distribute", [True, False]) +def test_iter(monkeypatch, distribute): + + # Set up distributed mock environment + _setup_distributed_environment(monkeypatch, distribute) + + # Create dataset and data loader + dataset = DummyDataset() + data_loader = _SingleBatchDataLoader(dataset) + + # Iterate through the data loader + batches = list(data_loader) + + # Expected value based on the distributed setting + expected_value = _distributed_idx(size) if distribute else full_data_value + + # Verify iteration behavior + assert len(batches) == 1 + assert batches[0].value == expected_value diff --git a/tests/test_data/test_tensor_data_manager.py b/tests/test_data/test_tensor_data_manager.py new file mode 100644 index 000000000..7624e5971 --- /dev/null +++ b/tests/test_data/test_tensor_data_manager.py @@ -0,0 +1,54 @@ +import torch +from pina import LabelTensor +from pina.data.manager import _DataManager, _TensorDataManager, _BatchManager + + +# Define data for testing +standard_tensor = torch.rand((10, 3)) +label_tensor = LabelTensor(standard_tensor, labels=["a", "b", "c"]) + + +def test_constructor(): + + # Create data manager + data_manager = _DataManager(standard=standard_tensor, labeled=label_tensor) + + # Check that the data manager is an instance of _TensorDataManager + assert isinstance(data_manager, _TensorDataManager) + + # Check that the attributes are set correctly + assert hasattr(data_manager, "standard") + assert hasattr(data_manager, "labeled") + + # Check that the attributes have the correct types + assert isinstance(data_manager.standard, torch.Tensor) + assert isinstance(data_manager.labeled, LabelTensor) + + # Check that the values of the attributes are correct + assert torch.equal(data_manager.standard, standard_tensor) + assert torch.equal(data_manager.labeled, label_tensor) + + +def test_create_batch(): + + # Create data manager + data_manager = _DataManager(standard=standard_tensor, labeled=label_tensor) + + # Batch over indices + idx = [0, 2] + batch = _TensorDataManager.create_batch([data_manager[idx] for idx in idx]) + + # Check that the batch is an instance of _BatchManager + assert isinstance(batch, _BatchManager) + + # Check that the attributes are set correctly + assert hasattr(batch, "standard") + assert hasattr(batch, "labeled") + + # Check that the attributes have the correct types + assert isinstance(batch.standard, torch.Tensor) + assert isinstance(batch.labeled, LabelTensor) + + # Check that the values of the attributes are correct + assert torch.equal(batch.standard, standard_tensor[idx]) + assert torch.equal(batch.labeled, label_tensor[idx]) diff --git a/tests/test_data/test_tensor_dataset.py b/tests/test_data/test_tensor_dataset.py deleted file mode 100644 index 81a122f2f..000000000 --- a/tests/test_data/test_tensor_dataset.py +++ /dev/null @@ -1,86 +0,0 @@ -import torch -import pytest -from pina.data.dataset import PinaDatasetFactory, PinaTensorDataset - -input_tensor = torch.rand((100, 10)) -output_tensor = torch.rand((100, 2)) - -input_tensor_2 = torch.rand((50, 10)) -output_tensor_2 = torch.rand((50, 2)) - -conditions_dict_single = { - "data": { - "input": input_tensor, - "target": output_tensor, - } -} - -conditions_dict_single_multi = { - "data_1": { - "input": input_tensor, - "target": output_tensor, - }, - "data_2": { - "input": input_tensor_2, - "target": output_tensor_2, - }, -} - -max_conditions_lengths_single = {"data": 100} - -max_conditions_lengths_multi = {"data_1": 100, "data_2": 50} - - -@pytest.mark.parametrize( - "conditions_dict, max_conditions_lengths", - [ - (conditions_dict_single, max_conditions_lengths_single), - (conditions_dict_single_multi, max_conditions_lengths_multi), - ], -) -def test_constructor_tensor(conditions_dict, max_conditions_lengths): - dataset = PinaDatasetFactory( - conditions_dict, - max_conditions_lengths=max_conditions_lengths, - automatic_batching=True, - ) - assert isinstance(dataset, PinaTensorDataset) - - -def test_getitem_single(): - dataset = PinaDatasetFactory( - conditions_dict_single, - max_conditions_lengths=max_conditions_lengths_single, - automatic_batching=False, - ) - - tensors = dataset.fetch_from_idx_list([i for i in range(70)]) - assert isinstance(tensors, dict) - assert list(tensors.keys()) == ["data"] - assert sorted(list(tensors["data"].keys())) == ["input", "target"] - assert isinstance(tensors["data"]["input"], torch.Tensor) - assert tensors["data"]["input"].shape == torch.Size((70, 10)) - assert isinstance(tensors["data"]["target"], torch.Tensor) - assert tensors["data"]["target"].shape == torch.Size((70, 2)) - - -def test_getitem_multi(): - dataset = PinaDatasetFactory( - conditions_dict_single_multi, - max_conditions_lengths=max_conditions_lengths_multi, - automatic_batching=False, - ) - tensors = dataset.fetch_from_idx_list([i for i in range(70)]) - assert isinstance(tensors, dict) - assert list(tensors.keys()) == ["data_1", "data_2"] - assert sorted(list(tensors["data_1"].keys())) == ["input", "target"] - assert isinstance(tensors["data_1"]["input"], torch.Tensor) - assert tensors["data_1"]["input"].shape == torch.Size((70, 10)) - assert isinstance(tensors["data_1"]["target"], torch.Tensor) - assert tensors["data_1"]["target"].shape == torch.Size((70, 2)) - - assert sorted(list(tensors["data_2"].keys())) == ["input", "target"] - assert isinstance(tensors["data_2"]["input"], torch.Tensor) - assert tensors["data_2"]["input"].shape == torch.Size((50, 10)) - assert isinstance(tensors["data_2"]["target"], torch.Tensor) - assert tensors["data_2"]["target"].shape == torch.Size((50, 2)) diff --git a/tests/test_domain/test_cartesian_domain.py b/tests/test_domain/test_cartesian_domain.py index db9297ced..e5b80a5ad 100644 --- a/tests/test_domain/test_cartesian_domain.py +++ b/tests/test_domain/test_cartesian_domain.py @@ -76,8 +76,8 @@ def test_update(dict): # Define the domains domain_1 = CartesianDomain(dict) - domain_2 = CartesianDomain({"new_var": [0, 1]}) - domain_3 = CartesianDomain(dict | {"new_var": [0, 1]}) + domain_2 = CartesianDomain({"new_var": [0, 1], "x": 1}) + domain_3 = CartesianDomain(dict | {"new_var": [0, 1], "x": 1}) # Update domain_1 with domain_2 updated_domain = domain_1.update(domain_2) diff --git a/tests/test_domain/test_ellipsoid_domain.py b/tests/test_domain/test_ellipsoid_domain.py index ced0f9dd0..6e1920b7f 100644 --- a/tests/test_domain/test_ellipsoid_domain.py +++ b/tests/test_domain/test_ellipsoid_domain.py @@ -91,10 +91,12 @@ def test_update(dict, sample_surface): ellipsoid_dict=dict, sample_surface=sample_surface ) domain_2 = EllipsoidDomain( - ellipsoid_dict={"new_var": [0, 1]}, sample_surface=sample_surface + ellipsoid_dict={"new_var": [0, 1], "x": 1}, + sample_surface=sample_surface, ) domain_3 = EllipsoidDomain( - ellipsoid_dict=dict | {"new_var": [0, 1]}, sample_surface=sample_surface + ellipsoid_dict=dict | {"new_var": [0, 1], "x": 1}, + sample_surface=sample_surface, ) # Update domain_1 with domain_2 diff --git a/tests/test_equation/test_equation.py b/tests/test_equation/test_equation.py index 096b2d5e7..0569d2f49 100644 --- a/tests/test_equation/test_equation.py +++ b/tests/test_equation/test_equation.py @@ -1,10 +1,11 @@ -from pina.equation import Equation -from pina.operator import grad, laplacian -from pina import LabelTensor import torch import pytest +from pina.operator import grad, laplacian +from pina.equation import Equation +from pina import LabelTensor +# Define equations for testing def eq1(input_, output_): u_grad = grad(output_, input_) u1_xx = grad(u_grad, input_, components=["du1dx"], d=["x"]) @@ -24,26 +25,38 @@ def foo(): pass -def test_constructor(): - Equation(eq1) - Equation(eq2) +@pytest.mark.parametrize("equation", [eq1, eq2]) +def test_constructor(equation): + Equation(equation) + + # Should fail if the equation is not a callable function with pytest.raises(ValueError): Equation([1, 2, 4]) + + # Should fail if the equation is not a callable function with pytest.raises(ValueError): Equation(foo()) -def test_residual(): - eq_1 = Equation(eq1) - eq_2 = Equation(eq2) +@pytest.mark.parametrize("equation, last_dim", [(eq1, 2), (eq2, 1)]) +def test_residual(equation, last_dim): - pts = LabelTensor(torch.rand(10, 2), labels=["x", "y"]) - pts.requires_grad = True - u = torch.pow(pts, 2) - u.labels = ["u1", "u2"] + # Define the equation + eq = Equation(equation) - eq_1_res = eq_1.residual(pts, u) - eq_2_res = eq_2.residual(pts, u) + # Manage number of points and variables + n_pts = 10 + input_vars = ["x", "y"] + output_vars = ["u1", "u2"] + + # Define the input and output tensors + pts = LabelTensor( + torch.rand(n_pts, len(input_vars), requires_grad=True), + labels=input_vars, + ) + u = torch.pow(pts, 2) + u.labels = output_vars - assert eq_1_res.shape == torch.Size([10, 2]) - assert eq_2_res.shape == torch.Size([10, 1]) + # Compute the residuals and check the shape + eq_res = eq.residual(pts, u) + assert eq_res.shape == torch.Size([n_pts, last_dim]) diff --git a/tests/test_equation/test_equation_factory.py b/tests/test_equation/test_equation_factory.py deleted file mode 100644 index 578d9ba30..000000000 --- a/tests/test_equation/test_equation_factory.py +++ /dev/null @@ -1,217 +0,0 @@ -from pina.equation import ( - FixedValue, - FixedGradient, - FixedFlux, - FixedLaplacian, - Advection, - AllenCahn, - DiffusionReaction, - Helmholtz, - Poisson, - AcousticWave, -) -from pina import LabelTensor -import torch -import pytest - -# Define input and output values -pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) -u = torch.pow(pts, 2) -u.labels = ["u", "v", "w"] - - -@pytest.mark.parametrize("value", [0, 10, -7.5]) -@pytest.mark.parametrize("components", [None, "u", ["u", "w"]]) -def test_fixed_value(value, components): - - # Constructor - equation = FixedValue(value=value, components=components) - - # Residual - residual = equation.residual(pts, u) - len_c = len(components) if components is not None else u.shape[1] - assert residual.shape == (pts.shape[0], len_c) - - -@pytest.mark.parametrize("value", [0, 10, -7.5]) -@pytest.mark.parametrize("components", [None, "u", ["u", "w"]]) -@pytest.mark.parametrize("d", [None, "x", ["x", "y"]]) -def test_fixed_gradient(value, components, d): - - # Constructor - equation = FixedGradient(value=value, components=components, d=d) - - # Residual - residual = equation.residual(pts, u) - len_c = len(components) if components is not None else u.shape[1] - len_d = len(d) if d is not None else pts.shape[1] - assert residual.shape == (pts.shape[0], len_c * len_d) - - -@pytest.mark.parametrize("value", [0, 10, -7.5]) -@pytest.mark.parametrize("components", [None, "u", ["u", "w"]]) -@pytest.mark.parametrize("d", [None, "x", ["x", "y"]]) -def test_fixed_flux(value, components, d): - - # Divergence requires components and d to be of the same length - len_c = len(components) if components is not None else u.shape[1] - len_d = len(d) if d is not None else pts.shape[1] - if len_c != len_d: - return - - # Constructor - equation = FixedFlux(value=value, components=components, d=d) - - # Residual - residual = equation.residual(pts, u) - assert residual.shape == (pts.shape[0], 1) - - -@pytest.mark.parametrize("value", [0, 10, -7.5]) -@pytest.mark.parametrize("components", [None, "u", ["u", "w"]]) -@pytest.mark.parametrize("d", [None, "x", ["x", "y"]]) -def test_fixed_laplacian(value, components, d): - - # Constructor - equation = FixedLaplacian(value=value, components=components, d=d) - - # Residual - residual = equation.residual(pts, u) - len_c = len(components) if components is not None else u.shape[1] - assert residual.shape == (pts.shape[0], len_c) - - -@pytest.mark.parametrize("c", [1.0, 10, [1, 2.5]]) -def test_advection_equation(c): - - # Constructor - equation = Advection(c) - - # Should fail if c is an empty list - with pytest.raises(ValueError): - Advection([]) - - # Should fail if c is not a float, int, or list - with pytest.raises(ValueError): - Advection("invalid") - - # Residual - residual = equation.residual(pts, u) - assert residual.shape == u.shape - - # Should fail if the input has no 't' label - with pytest.raises(ValueError): - residual = equation.residual(pts["x", "y"], u) - - # Should fail if c is a list and its length != spatial dimension - with pytest.raises(ValueError): - equation = Advection([1, 2, 3]) - residual = equation.residual(pts, u) - - -@pytest.mark.parametrize("alpha", [1.0, 10, -7.5]) -@pytest.mark.parametrize("beta", [1.0, 10, -7.5]) -def test_allen_cahn_equation(alpha, beta): - - # Constructor - equation = AllenCahn(alpha=alpha, beta=beta) - - # Should fail if alpha is not a float or int - with pytest.raises(ValueError): - AllenCahn(alpha="invalid", beta=beta) - - # Should fail if beta is not a float or int - with pytest.raises(ValueError): - AllenCahn(alpha=alpha, beta="invalid") - - # Residual - residual = equation.residual(pts, u) - assert residual.shape == u.shape - - # Should fail if the input has no 't' label - with pytest.raises(ValueError): - residual = equation.residual(pts["x", "y"], u) - - -@pytest.mark.parametrize("alpha", [1.0, 10, -7.5]) -@pytest.mark.parametrize( - "forcing_term", [lambda x: torch.sin(x), lambda x: torch.exp(x)] -) -def test_diffusion_reaction_equation(alpha, forcing_term): - - # Constructor - equation = DiffusionReaction(alpha=alpha, forcing_term=forcing_term) - - # Should fail if alpha is not a float or int - with pytest.raises(ValueError): - DiffusionReaction(alpha="invalid", forcing_term=forcing_term) - - # Should fail if forcing_term is not a callable - with pytest.raises(ValueError): - DiffusionReaction(alpha=alpha, forcing_term="invalid") - - # Residual - residual = equation.residual(pts, u) - assert residual.shape == u.shape - - # Should fail if the input has no 't' label - with pytest.raises(ValueError): - residual = equation.residual(pts["x", "y"], u) - - -@pytest.mark.parametrize("k", [1.0, 10, -7.5]) -@pytest.mark.parametrize( - "forcing_term", [lambda x: torch.sin(x), lambda x: torch.exp(x)] -) -def test_helmholtz_equation(k, forcing_term): - - # Constructor - equation = Helmholtz(k=k, forcing_term=forcing_term) - - # Should fail if k is not a float or int - with pytest.raises(ValueError): - Helmholtz(k="invalid", forcing_term=forcing_term) - - # Should fail if forcing_term is not a callable - with pytest.raises(ValueError): - Helmholtz(k=k, forcing_term="invalid") - - # Residual - residual = equation.residual(pts, u) - assert residual.shape == u.shape - - -@pytest.mark.parametrize( - "forcing_term", [lambda x: torch.sin(x), lambda x: torch.exp(x)] -) -def test_poisson_equation(forcing_term): - - # Constructor - equation = Poisson(forcing_term=forcing_term) - - # Should fail if forcing_term is not a callable - with pytest.raises(ValueError): - Poisson(forcing_term="invalid") - - # Residual - residual = equation.residual(pts, u) - assert residual.shape == u.shape - - -@pytest.mark.parametrize("c", [1.0, 10, -7.5]) -def test_acoustic_wave_equation(c): - - # Constructor - equation = AcousticWave(c=c) - - # Should fail if c is not a float or int - with pytest.raises(ValueError): - AcousticWave(c="invalid") - - # Residual - residual = equation.residual(pts, u) - assert residual.shape == u.shape - - # Should fail if the input has no 't' label - with pytest.raises(ValueError): - residual = equation.residual(pts["x", "y"], u) diff --git a/tests/test_equation/test_system_equation.py b/tests/test_equation/test_system_equation.py index bf6268148..7fbc7baae 100644 --- a/tests/test_equation/test_system_equation.py +++ b/tests/test_equation/test_system_equation.py @@ -1,10 +1,12 @@ -from pina.equation import SystemEquation, FixedValue, FixedGradient +from pina.equation import SystemEquation +from pina.equation.zoo import FixedValue, FixedGradient from pina.operator import grad, laplacian from pina import LabelTensor import torch import pytest +# Define equations for testing def eq1(input_, output_): u_grad = grad(output_, input_) u1_xx = grad(u_grad, input_, components=["du1dx"], d=["x"]) @@ -20,82 +22,63 @@ def eq2(input_, output_): return delta_u - force_term -def foo(): - pass +def reduction_fn(residuals, dim): + return torch.sum(residuals, dim=dim) / residuals.shape[dim] -@pytest.mark.parametrize("reduction", [None, "mean", "sum"]) -def test_constructor(reduction): +# Test cases for the SystemEquation class +eq_list1 = [eq1, eq2] +eq_list2 = [FixedValue(value=0.0), FixedGradient(value=0.0, components=["u2"])] +eq_list3 = [FixedValue(value=0.0, components=["u1"]), eq1] - # Constructor with callable functions - SystemEquation([eq1, eq2], reduction=reduction) - # Constructor with Equation instances - SystemEquation( - [ - FixedValue(value=0.0, components=["u1"]), - FixedGradient(value=0.0, components=["u2"]), - ], - reduction=reduction, - ) +@pytest.mark.parametrize("eq_list", [eq_list1, eq_list2, eq_list3]) +@pytest.mark.parametrize("reduction", [None, "mean", "sum", reduction_fn]) +def test_constructor(eq_list, reduction): - # Constructor with mixed types - SystemEquation( - [ - FixedValue(value=0.0, components=["u1"]), - eq1, - ], - reduction=reduction, - ) + SystemEquation(list_equation=eq_list, reduction=reduction) - # Non-standard reduction not implemented - with pytest.raises(NotImplementedError): - SystemEquation([eq1, eq2], reduction="foo") + # Should fail if the list of equations is not a list + with pytest.raises(ValueError): + SystemEquation(list_equation=eq1, reduction=reduction) - # Invalid input type + # Should fail if any element of the list is neither callable nor Equation with pytest.raises(ValueError): - SystemEquation(foo) + SystemEquation(list_equation=[eq1, "equation"], reduction=reduction) + # Should fail if the reduction is not available + with pytest.raises(ValueError): + SystemEquation(list_equation=[eq1, eq2], reduction="foo") -@pytest.mark.parametrize("reduction", [None, "mean", "sum"]) -def test_residual(reduction): - # Generate random points and output - pts = LabelTensor(torch.rand(10, 2), labels=["x", "y"]) - pts.requires_grad = True - u = torch.pow(pts, 2) - u.labels = ["u1", "u2"] +@pytest.mark.parametrize("reduction", [None, "mean", "sum", reduction_fn]) +@pytest.mark.parametrize( + "eq_list, last_dim", + [(eq_list1, 3), (eq_list2, 4), (eq_list3, 3)], +) +def test_residual(eq_list, last_dim, reduction): - # System with callable functions - system_eq = SystemEquation([eq1, eq2], reduction=reduction) - res = system_eq.residual(pts, u) + # Define the system of equations + system_eq = SystemEquation(list_equation=eq_list, reduction=reduction) - # Checks on the shape of the residual - shape = torch.Size([10, 3]) if reduction is None else torch.Size([10]) - assert res.shape == shape + # Manage number of points and variables + n_pts = 10 + input_vars = ["x", "y"] + output_vars = ["u1", "u2"] - # System with Equation instances - system_eq = SystemEquation( - [ - FixedValue(value=0.0, components=["u1"]), - FixedGradient(value=0.0, components=["u2"]), - ], - reduction=reduction, + # Define the input and output tensors + pts = LabelTensor( + torch.rand(n_pts, len(input_vars), requires_grad=True), + labels=input_vars, ) + u = torch.pow(pts, 2) + u.labels = output_vars - # Checks on the shape of the residual - shape = torch.Size([10, 3]) if reduction is None else torch.Size([10]) - assert res.shape == shape - - # System with mixed types - system_eq = SystemEquation( - [ - FixedValue(value=0.0, components=["u1"]), - eq1, - ], - reduction=reduction, + # Compute the residuals and check the shape + res = system_eq.residual(pts, u) + shape = ( + torch.Size([n_pts, last_dim]) + if reduction is None + else torch.Size([n_pts]) ) - - # Checks on the shape of the residual - shape = torch.Size([10, 3]) if reduction is None else torch.Size([10]) assert res.shape == shape diff --git a/tests/test_equation_zoo/test_acoustic_wave_equation.py b/tests/test_equation_zoo/test_acoustic_wave_equation.py new file mode 100644 index 000000000..7d4c3b3a8 --- /dev/null +++ b/tests/test_equation_zoo/test_acoustic_wave_equation.py @@ -0,0 +1,29 @@ +import pytest +import torch +from pina import LabelTensor +from pina.equation.zoo import AcousticWaveEquation + + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize("c", [1.0, 10, -7.5]) +def test_acoustic_wave_equation(c): + + # Constructor + equation = AcousticWaveEquation(c=c) + + # Should fail if c is not a float or int + with pytest.raises(ValueError): + AcousticWaveEquation(c="invalid") + + # Residual + residual = equation.residual(pts, u) + assert residual.shape == u.shape + + # Should fail if the input has no 't' label + with pytest.raises(ValueError): + residual = equation.residual(pts["x", "y"], u) diff --git a/tests/test_equation_zoo/test_advection_equation.py b/tests/test_equation_zoo/test_advection_equation.py new file mode 100644 index 000000000..d78aef106 --- /dev/null +++ b/tests/test_equation_zoo/test_advection_equation.py @@ -0,0 +1,38 @@ +import pytest +import torch +from pina import LabelTensor +from pina.equation.zoo import AdvectionEquation + + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize("c", [1.0, 10, [1, 2.5]]) +def test_advection_equation(c): + + # Constructor + equation = AdvectionEquation(c) + + # Should fail if c is an empty list + with pytest.raises(ValueError): + AdvectionEquation([]) + + # Should fail if c is not a float, int, or list + with pytest.raises(ValueError): + AdvectionEquation("invalid") + + # Residual + residual = equation.residual(pts, u) + assert residual.shape == u.shape + + # Should fail if the input has no 't' label + with pytest.raises(ValueError): + residual = equation.residual(pts["x", "y"], u) + + # Should fail if c is a list and its length != spatial dimension + with pytest.raises(ValueError): + equation = AdvectionEquation([1, 2, 3]) + residual = equation.residual(pts, u) diff --git a/tests/test_equation_zoo/test_allen_cahn_equation.py b/tests/test_equation_zoo/test_allen_cahn_equation.py new file mode 100644 index 000000000..7f9bf23f2 --- /dev/null +++ b/tests/test_equation_zoo/test_allen_cahn_equation.py @@ -0,0 +1,34 @@ +import pytest +import torch +from pina import LabelTensor +from pina.equation.zoo import AllenCahnEquation + + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize("alpha", [1.0, 10, -7.5]) +@pytest.mark.parametrize("beta", [1.0, 10, -7.5]) +def test_allen_cahn_equation(alpha, beta): + + # Constructor + equation = AllenCahnEquation(alpha=alpha, beta=beta) + + # Should fail if alpha is not a float or int + with pytest.raises(ValueError): + AllenCahnEquation(alpha="invalid", beta=beta) + + # Should fail if beta is not a float or int + with pytest.raises(ValueError): + AllenCahnEquation(alpha=alpha, beta="invalid") + + # Residual + residual = equation.residual(pts, u) + assert residual.shape == u.shape + + # Should fail if the input has no 't' label + with pytest.raises(ValueError): + residual = equation.residual(pts["x", "y"], u) diff --git a/tests/test_equation_zoo/test_burgers_equation.py b/tests/test_equation_zoo/test_burgers_equation.py new file mode 100644 index 000000000..89825aa9b --- /dev/null +++ b/tests/test_equation_zoo/test_burgers_equation.py @@ -0,0 +1,37 @@ +import pytest +import torch +from pina import LabelTensor +from pina.equation.zoo import BurgersEquation + + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.sin(pts["x", "y"]) * torch.cos(pts["y", "t"]) +u.labels = ["u", "v"] + + +@pytest.mark.parametrize("nu", [0, 1, 2.5]) +def test_burgers_equation(nu): + + # Constructor + equation = BurgersEquation(nu=nu) + + # Should fail if nu is not a float or int + with pytest.raises(ValueError): + BurgersEquation(nu="invalid") + + # Should fail if nu is negative + with pytest.raises(ValueError): + BurgersEquation(nu=-1) + + # Residual + residual = equation.residual(pts, u) + assert residual.shape == u.shape + + # Should fail if the input has no 't' label + with pytest.raises(ValueError): + residual = equation.residual(pts["x", "y"], u) + + # Should fail if output and spatial dimensions do not match + with pytest.raises(ValueError): + residual = equation.residual(pts, u["u"]) diff --git a/tests/test_equation_zoo/test_diffusion_reaction_equation.py b/tests/test_equation_zoo/test_diffusion_reaction_equation.py new file mode 100644 index 000000000..ae2d7fc51 --- /dev/null +++ b/tests/test_equation_zoo/test_diffusion_reaction_equation.py @@ -0,0 +1,36 @@ +import pytest +import torch +from pina import LabelTensor +from pina.equation.zoo import DiffusionReactionEquation + + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize("alpha", [1.0, 10, -7.5]) +@pytest.mark.parametrize( + "forcing_term", [lambda x: torch.sin(x), lambda x: torch.exp(x)] +) +def test_diffusion_reaction_equation(alpha, forcing_term): + + # Constructor + equation = DiffusionReactionEquation(alpha=alpha, forcing_term=forcing_term) + + # Should fail if alpha is not a float or int + with pytest.raises(ValueError): + DiffusionReactionEquation(alpha="invalid", forcing_term=forcing_term) + + # Should fail if forcing_term is not a callable + with pytest.raises(ValueError): + DiffusionReactionEquation(alpha=alpha, forcing_term="invalid") + + # Residual + residual = equation.residual(pts, u) + assert residual.shape == u.shape + + # Should fail if the input has no 't' label + with pytest.raises(ValueError): + residual = equation.residual(pts["x", "y"], u) diff --git a/tests/test_equation_zoo/test_fixed_flux.py b/tests/test_equation_zoo/test_fixed_flux.py new file mode 100644 index 000000000..95abc1d17 --- /dev/null +++ b/tests/test_equation_zoo/test_fixed_flux.py @@ -0,0 +1,40 @@ +from pina.equation.zoo import FixedFlux +from pina import LabelTensor +import torch +import pytest + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize("value", [0, 10, -7.5]) +@pytest.mark.parametrize("components", [None, "u", ["u", "w"]]) +@pytest.mark.parametrize("d", [None, "x", ["x", "y"]]) +def test_fixed_flux(value, components, d): + + # Divergence requires components and d to be of the same length + len_c = len(components) if components is not None else u.shape[1] + len_d = len(d) if d is not None else pts.shape[1] + if len_c != len_d: + return + + # Constructor + equation = FixedFlux(value=value, components=components, d=d) + + # Residual + residual = equation.residual(pts, u) + assert residual.shape == (pts.shape[0], 1) + + # Should fail if value is neither a float nor an integer + with pytest.raises(ValueError): + FixedFlux(value="invalid", components=components, d=d) + + # Should fail if components is neither a string nor a list of strings + with pytest.raises(ValueError): + FixedFlux(value=value, components=123, d=d) + + # Should fail if d is neither a string nor a list of strings + with pytest.raises(ValueError): + FixedFlux(value=value, components=components, d=123) diff --git a/tests/test_equation_zoo/test_fixed_gradient.py b/tests/test_equation_zoo/test_fixed_gradient.py new file mode 100644 index 000000000..37cdf7c04 --- /dev/null +++ b/tests/test_equation_zoo/test_fixed_gradient.py @@ -0,0 +1,36 @@ +from pina.equation.zoo import FixedGradient +from pina import LabelTensor +import torch +import pytest + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize("value", [0, 10, -7.5]) +@pytest.mark.parametrize("components", [None, "u", ["u", "w"]]) +@pytest.mark.parametrize("d", [None, "x", ["x", "y"]]) +def test_fixed_gradient(value, components, d): + + # Constructor + equation = FixedGradient(value=value, components=components, d=d) + + # Residual + residual = equation.residual(pts, u) + len_c = len(components) if components is not None else u.shape[1] + len_d = len(d) if d is not None else pts.shape[1] + assert residual.shape == (pts.shape[0], len_c * len_d) + + # Should fail if value is neither a float nor an integer + with pytest.raises(ValueError): + FixedGradient(value="invalid", components=components, d=d) + + # Should fail if components is neither a string nor a list of strings + with pytest.raises(ValueError): + FixedGradient(value=value, components=123, d=d) + + # Should fail if d is neither a string nor a list of strings + with pytest.raises(ValueError): + FixedGradient(value=value, components=components, d=123) diff --git a/tests/test_equation_zoo/test_fixed_laplacian.py b/tests/test_equation_zoo/test_fixed_laplacian.py new file mode 100644 index 000000000..0fe6ef022 --- /dev/null +++ b/tests/test_equation_zoo/test_fixed_laplacian.py @@ -0,0 +1,35 @@ +from pina.equation.zoo import FixedLaplacian +from pina import LabelTensor +import torch +import pytest + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize("value", [0, 10, -7.5]) +@pytest.mark.parametrize("components", [None, "u", ["u", "w"]]) +@pytest.mark.parametrize("d", [None, "x", ["x", "y"]]) +def test_fixed_laplacian(value, components, d): + + # Constructor + equation = FixedLaplacian(value=value, components=components, d=d) + + # Residual + residual = equation.residual(pts, u) + len_c = len(components) if components is not None else u.shape[1] + assert residual.shape == (pts.shape[0], len_c) + + # Should fail if value is neither a float nor an integer + with pytest.raises(ValueError): + FixedLaplacian(value="invalid", components=components, d=d) + + # Should fail if components is neither a string nor a list of strings + with pytest.raises(ValueError): + FixedLaplacian(value=value, components=123, d=d) + + # Should fail if d is neither a string nor a list of strings + with pytest.raises(ValueError): + FixedLaplacian(value=value, components=components, d=123) diff --git a/tests/test_equation_zoo/test_fixed_value.py b/tests/test_equation_zoo/test_fixed_value.py new file mode 100644 index 000000000..b53168ffa --- /dev/null +++ b/tests/test_equation_zoo/test_fixed_value.py @@ -0,0 +1,30 @@ +from pina.equation.zoo import FixedValue +from pina import LabelTensor +import torch +import pytest + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize("value", [0, 10, -7.5]) +@pytest.mark.parametrize("components", [None, "u", ["u", "w"]]) +def test_fixed_value(value, components): + + # Constructor + equation = FixedValue(value=value, components=components) + + # Residual + residual = equation.residual(pts, u) + len_c = len(components) if components is not None else u.shape[1] + assert residual.shape == (pts.shape[0], len_c) + + # Should fail if value is neither a float nor an integer + with pytest.raises(ValueError): + FixedValue(value="not a number", components=components) + + # Should fail if components is neither a string nor a list of strings + with pytest.raises(ValueError): + FixedValue(value=value, components=123) diff --git a/tests/test_equation_zoo/test_helmholtz_equation.py b/tests/test_equation_zoo/test_helmholtz_equation.py new file mode 100644 index 000000000..34b1a8a9f --- /dev/null +++ b/tests/test_equation_zoo/test_helmholtz_equation.py @@ -0,0 +1,32 @@ +import pytest +import torch +from pina import LabelTensor +from pina.equation.zoo import HelmholtzEquation + + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize("k", [1.0, 10, -7.5]) +@pytest.mark.parametrize( + "forcing_term", [lambda x: torch.sin(x), lambda x: torch.exp(x)] +) +def test_helmholtz_equation(k, forcing_term): + + # Constructor + equation = HelmholtzEquation(k=k, forcing_term=forcing_term) + + # Should fail if k is not a float or int + with pytest.raises(ValueError): + HelmholtzEquation(k="invalid", forcing_term=forcing_term) + + # Should fail if forcing_term is not a callable + with pytest.raises(ValueError): + HelmholtzEquation(k=k, forcing_term="invalid") + + # Residual + residual = equation.residual(pts, u) + assert residual.shape == u.shape diff --git a/tests/test_equation_zoo/test_poisson_equation.py b/tests/test_equation_zoo/test_poisson_equation.py new file mode 100644 index 000000000..b56a69073 --- /dev/null +++ b/tests/test_equation_zoo/test_poisson_equation.py @@ -0,0 +1,27 @@ +import pytest +import torch +from pina import LabelTensor +from pina.equation.zoo import PoissonEquation + + +# Define input and output values +pts = LabelTensor(torch.rand(10, 3, requires_grad=True), labels=["x", "y", "t"]) +u = torch.pow(pts, 2) +u.labels = ["u", "v", "w"] + + +@pytest.mark.parametrize( + "forcing_term", [lambda x: torch.sin(x), lambda x: torch.exp(x)] +) +def test_poisson_equation(forcing_term): + + # Constructor + equation = PoissonEquation(forcing_term=forcing_term) + + # Should fail if forcing_term is not a callable + with pytest.raises(ValueError): + PoissonEquation(forcing_term="invalid") + + # Residual + residual = equation.residual(pts, u) + assert residual.shape == u.shape diff --git a/tests/test_label_tensor/test_label_tensor.py b/tests/test_label_tensor/test_label_tensor.py index 973864d0e..ca4ae2f1a 100644 --- a/tests/test_label_tensor/test_label_tensor.py +++ b/tests/test_label_tensor/test_label_tensor.py @@ -1,7 +1,7 @@ import torch import pytest -from pina.label_tensor import LabelTensor +from pina import LabelTensor data = torch.rand((20, 3)) labels_column = {1: {"name": "space", "dof": ["x", "y", "z"]}} diff --git a/tests/test_loss/test_lp_loss.py b/tests/test_loss/test_lp_loss.py index 8f1f48d58..3f56dcabc 100644 --- a/tests/test_loss/test_lp_loss.py +++ b/tests/test_loss/test_lp_loss.py @@ -1,47 +1,46 @@ import torch - +import pytest from pina.loss import LpLoss -input = torch.tensor([[3.0], [1.0], [-8.0]]) -target = torch.tensor([[6.0], [4.0], [2.0]]) -available_reductions = ["str", "mean", "none"] - - -def test_LpLoss_constructor(): - # test reduction - for reduction in available_reductions: - LpLoss(reduction=reduction) - # test p - for p in [float("inf"), -float("inf"), 1, 10, -8]: - LpLoss(p=p) - - -def test_LpLoss_forward(): - # l2 loss - loss = LpLoss(p=2, reduction="mean") - l2_loss = torch.mean(torch.sqrt((input - target).pow(2))) - assert loss(input, target) == l2_loss - # l1 loss - loss = LpLoss(p=1, reduction="sum") - l1_loss = torch.sum(torch.abs(input - target)) - assert loss(input, target) == l1_loss - - -def test_LpRelativeLoss_constructor(): - # test reduction - for reduction in available_reductions: - LpLoss(reduction=reduction, relative=True) - # test p - for p in [float("inf"), -float("inf"), 1, 10, -8]: - LpLoss(p=p, relative=True) - - -def test_LpRelativeLoss_forward(): - # l2 relative loss - loss = LpLoss(p=2, reduction="mean", relative=True) - l2_loss = torch.sqrt((input - target).pow(2)) / torch.sqrt(input.pow(2)) - assert loss(input, target) == torch.mean(l2_loss) - # l1 relative loss - loss = LpLoss(p=1, reduction="sum", relative=True) - l1_loss = torch.abs(input - target) / torch.abs(input) - assert loss(input, target) == torch.sum(l1_loss) +# Define input and target for tests +input = torch.rand(10, 2) +target = torch.rand(10, 2) + + +@pytest.mark.parametrize("p", [2, 0.5, "inf", "-inf"]) +@pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) +@pytest.mark.parametrize("relative", [True, False]) +def test_constructor(p, reduction, relative): + + # Define the loss + LpLoss(p=p, reduction=reduction, relative=relative) + + # Should fail if p is invalid + with pytest.raises(ValueError): + LpLoss(p="invalid", reduction=reduction, relative=relative) + + # Should fail if reduction is invalid + with pytest.raises(ValueError): + LpLoss(p=p, reduction="invalid", relative=relative) + + # Should fail if relative is not a boolean + with pytest.raises(ValueError): + LpLoss(p=p, reduction=reduction, relative="invalid") + + +@pytest.mark.parametrize("p", [2, 0.5, "inf", "-inf"]) +@pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) +@pytest.mark.parametrize("relative", [True, False]) +def test_forward(p, reduction, relative): + + # Define the loss + loss = LpLoss(p=p, reduction=reduction, relative=relative) + + # Forward pass + value = loss(input, target) + + # Check shape + if loss.reduction != "none": + assert value.shape == torch.Size([1]) + else: + assert value.shape == torch.Size([target.shape[0]]) diff --git a/tests/test_loss/test_power_loss.py b/tests/test_loss/test_power_loss.py index 4ea90282b..55fc1da7a 100644 --- a/tests/test_loss/test_power_loss.py +++ b/tests/test_loss/test_power_loss.py @@ -1,48 +1,46 @@ import torch import pytest - from pina.loss import PowerLoss -input = torch.tensor([[3.0], [1.0], [-8.0]]) -target = torch.tensor([[6.0], [4.0], [2.0]]) -available_reductions = ["str", "mean", "none"] - - -def test_PowerLoss_constructor(): - # test reduction - for reduction in available_reductions: - PowerLoss(reduction=reduction) - # test p - for p in [float("inf"), -float("inf"), 1, 10, -8]: - PowerLoss(p=p) - - -def test_PowerLoss_forward(): - # l2 loss - loss = PowerLoss(p=2, reduction="mean") - l2_loss = torch.mean((input - target).pow(2)) - assert loss(input, target) == l2_loss - # l1 loss - loss = PowerLoss(p=1, reduction="sum") - l1_loss = torch.sum(torch.abs(input - target)) - assert loss(input, target) == l1_loss - - -def test_LpRelativeLoss_constructor(): - # test reduction - for reduction in available_reductions: - PowerLoss(reduction=reduction, relative=True) - # test p - for p in [float("inf"), -float("inf"), 1, 10, -8]: - PowerLoss(p=p, relative=True) - - -def test_LpRelativeLoss_forward(): - # l2 relative loss - loss = PowerLoss(p=2, reduction="mean", relative=True) - l2_loss = (input - target).pow(2) / input.pow(2) - assert loss(input, target) == torch.mean(l2_loss) - # l1 relative loss - loss = PowerLoss(p=1, reduction="sum", relative=True) - l1_loss = torch.abs(input - target) / torch.abs(input) - assert loss(input, target) == torch.sum(l1_loss) +# Define input and target for tests +input = torch.rand(10, 2) +target = torch.rand(10, 2) + + +@pytest.mark.parametrize("p", [1, 2]) +@pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) +@pytest.mark.parametrize("relative", [True, False]) +def test_constructor(p, reduction, relative): + + # Define the loss + PowerLoss(p=p, reduction=reduction, relative=relative) + + # Should fail if p is not a positive integer + with pytest.raises(AssertionError): + PowerLoss(p=-2, reduction=reduction, relative=relative) + + # Should fail if reduction is invalid + with pytest.raises(ValueError): + PowerLoss(p=p, reduction="invalid", relative=relative) + + # Should fail if relative is not a boolean + with pytest.raises(ValueError): + PowerLoss(p=p, reduction=reduction, relative="invalid") + + +@pytest.mark.parametrize("p", [1, 2]) +@pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) +@pytest.mark.parametrize("relative", [True, False]) +def test_forward(p, reduction, relative): + + # Define the loss + loss = PowerLoss(p=p, reduction=reduction, relative=relative) + + # Forward pass + value = loss(input, target) + + # Check shape + if loss.reduction != "none": + assert value.shape == torch.Size([1]) + else: + assert value.shape == torch.Size([target.shape[0]]) diff --git a/tests/test_loss/test_sinkhorn_loss.py b/tests/test_loss/test_sinkhorn_loss.py new file mode 100644 index 000000000..86eb4de62 --- /dev/null +++ b/tests/test_loss/test_sinkhorn_loss.py @@ -0,0 +1,54 @@ +import torch +import pytest +from pina.loss import SinkhornLoss + + +@pytest.mark.parametrize("p", [1, 2]) +@pytest.mark.parametrize("eps", [0.01, 1]) +@pytest.mark.parametrize("iterations", [2, 5]) +def test_constructor(p, eps, iterations): + + # Define the loss + SinkhornLoss(p=p, eps=eps, iterations=iterations) + + # Should fail if iterations is not a positive integer + with pytest.raises(AssertionError): + SinkhornLoss(p=p, eps=eps, iterations=0) + + # Should fail if p is not a positive integer + with pytest.raises(AssertionError): + SinkhornLoss(p=0, eps=eps, iterations=iterations) + + # Should fail if eps is not numeric + with pytest.raises(ValueError): + SinkhornLoss(p=p, eps="invalid", iterations=iterations) + + # Should fail if eps is not positive + with pytest.raises(ValueError): + SinkhornLoss(p=p, eps=-0.1, iterations=iterations) + + +@pytest.mark.parametrize("p", [2, 3]) +@pytest.mark.parametrize("eps", [0.1, 1]) +@pytest.mark.parametrize("iterations", [2, 5]) +@pytest.mark.parametrize( + "input, target", + [ + (torch.rand(10, 2), torch.rand(8, 2)), + (torch.rand(5, 3), torch.rand(5, 3)), + (torch.rand(1, 4), torch.rand(7, 4)), + (torch.rand(6, 4), torch.rand(1, 4)), + (torch.rand(3, 1), torch.rand(4, 1)), + ], +) +def test_forward(p, eps, iterations, input, target): + + # Define the loss + loss = SinkhornLoss(p=p, eps=eps, iterations=iterations) + + # Forward pass + value = loss(input, target) + + # Check shape + assert value.shape == torch.Size([1]) + assert torch.isfinite(value).all() diff --git a/tests/test_model/test_average_neural_operator.py b/tests/test_model/test_average_neural_operator.py index ded81c43d..4a7ecb44b 100644 --- a/tests/test_model/test_average_neural_operator.py +++ b/tests/test_model/test_average_neural_operator.py @@ -3,7 +3,6 @@ from pina import LabelTensor import pytest - batch_size = 15 n_layers = 4 embedding_dim = 24 diff --git a/tests/test_model/test_kolmogorov_arnold_network.py b/tests/test_model/test_kolmogorov_arnold_network.py new file mode 100644 index 000000000..ec26f81d2 --- /dev/null +++ b/tests/test_model/test_kolmogorov_arnold_network.py @@ -0,0 +1,83 @@ +import torch +import pytest +from pina.model import KolmogorovArnoldNetwork + +# Data +input_dim = 3 +data = torch.rand((10, input_dim)) + + +@pytest.mark.parametrize("use_base_linear", [True, False]) +@pytest.mark.parametrize("use_bias", [True, False]) +@pytest.mark.parametrize("grid_range", [[-1, 1], [0, 2]]) +@pytest.mark.parametrize("layers", [[input_dim, 5, 1], [input_dim, 2]]) +def test_constructor(use_base_linear, use_bias, grid_range, layers): + + # Constructor + KolmogorovArnoldNetwork( + layers=layers, + spline_order=3, + n_knots=10, + grid_range=grid_range, + base_function=torch.nn.SiLU, + use_base_linear=use_base_linear, + use_bias=use_bias, + init_scale_spline=1e-2, + init_scale_base=1.0, + ) + + # Should fail if grid_range is not of length 2 + with pytest.raises(ValueError): + KolmogorovArnoldNetwork(layers=layers, grid_range=[-1, 0, 1]) + + # Should fail if layers has less than 2 elements + with pytest.raises(ValueError): + KolmogorovArnoldNetwork(layers=[input_dim]) + + +@pytest.mark.parametrize("use_base_linear", [True, False]) +@pytest.mark.parametrize("use_bias", [True, False]) +@pytest.mark.parametrize("grid_range", [[-1, 1], [0, 2]]) +@pytest.mark.parametrize("layers", [[input_dim, 5, 1], [input_dim, 2]]) +def test_forward(use_base_linear, use_bias, grid_range, layers): + + model = KolmogorovArnoldNetwork( + layers=layers, + spline_order=3, + n_knots=10, + grid_range=grid_range, + base_function=torch.nn.SiLU, + use_base_linear=use_base_linear, + use_bias=use_bias, + init_scale_spline=1e-2, + init_scale_base=1.0, + ) + + output_ = model(data) + assert output_.shape == (data.shape[0], layers[-1]) + + +@pytest.mark.parametrize("use_base_linear", [True, False]) +@pytest.mark.parametrize("use_bias", [True, False]) +@pytest.mark.parametrize("grid_range", [[-1, 1], [0, 2]]) +@pytest.mark.parametrize("layers", [[input_dim, 5, 1], [input_dim, 2]]) +def test_backward(use_base_linear, use_bias, grid_range, layers): + + model = KolmogorovArnoldNetwork( + layers=layers, + spline_order=3, + n_knots=10, + grid_range=grid_range, + base_function=torch.nn.SiLU, + use_base_linear=use_base_linear, + use_bias=use_bias, + init_scale_spline=1e-2, + init_scale_base=1.0, + ) + + data.requires_grad_() + output_ = model(data) + + loss = torch.mean(output_) + loss.backward() + assert data.grad.shape == data.shape diff --git a/tests/test_model/test_low_rank_neural_operator.py b/tests/test_model/test_low_rank_neural_operator.py index 3702df91b..ba4b2fffe 100644 --- a/tests/test_model/test_low_rank_neural_operator.py +++ b/tests/test_model/test_low_rank_neural_operator.py @@ -3,7 +3,6 @@ from pina import LabelTensor import pytest - batch_size = 15 n_layers = 4 embedding_dim = 24 diff --git a/tests/test_model/test_sindy.py b/tests/test_model/test_sindy.py index 223c4eba2..5133a1729 100644 --- a/tests/test_model/test_sindy.py +++ b/tests/test_model/test_sindy.py @@ -37,8 +37,6 @@ def test_forward(data): output_ = model(data) vals = data.pow(2) + torch.sin(data) - print(data.shape, output_.shape, vals.shape) - assert output_.shape == data.shape assert torch.allclose(output_, vals, atol=1e-6, rtol=1e-6) diff --git a/tests/test_model/test_spline.py b/tests/test_model/test_spline.py index b47ea8d30..baff81940 100644 --- a/tests/test_model/test_spline.py +++ b/tests/test_model/test_spline.py @@ -5,7 +5,6 @@ from pina.model import Spline from pina import LabelTensor - # Utility quantities for testing order = torch.randint(3, 6, (1,)).item() n_ctrl_pts = torch.randint(order, order + 5, (1,)).item() diff --git a/tests/test_model/test_spline_surface.py b/tests/test_model/test_spline_surface.py index dee57173c..4cd6dc3aa 100644 --- a/tests/test_model/test_spline_surface.py +++ b/tests/test_model/test_spline_surface.py @@ -5,7 +5,6 @@ from pina.operator import grad from pina import LabelTensor - # Utility quantities for testing orders = [random.randint(3, 6) for _ in range(2)] n_ctrl_pts = random.randint(max(orders), max(orders) + 5) diff --git a/tests/test_model/test_vectorized_spline.py b/tests/test_model/test_vectorized_spline.py new file mode 100644 index 000000000..cc0107073 --- /dev/null +++ b/tests/test_model/test_vectorized_spline.py @@ -0,0 +1,287 @@ +import torch +import pytest +from pina.model import VectorizedSpline, Spline +from pina.operator import grad +from pina import LabelTensor + + +# Utility quantities for testing +order = torch.randint(3, 6, (1,)).item() +n_ctrl_pts = torch.randint(order, order + 5, (1,)).item() +n_knots = order + n_ctrl_pts +n_splines = torch.randint(2, 5, (1,)).item() +output_dim = torch.randint(1, 4, (1,)).item() + +# Input points +labels = [f"x{i}" for i in range(n_splines)] +pts = torch.rand(10, n_splines).requires_grad_(True) +pts = LabelTensor(pts, labels) + + +# Define all possible combinations of valid arguments for VectorizedSpline class +valid_args = [ + { + "order": order, + "control_points": torch.rand(n_splines, output_dim, n_ctrl_pts), + "knots": torch.linspace(0, 1, n_knots) + .unsqueeze(0) + .repeat(n_splines, 1), + }, + { + "order": order, + "control_points": torch.rand(n_splines, output_dim, n_ctrl_pts), + "knots": { + "n": n_knots, + "min": 0, + "max": 1, + "mode": "auto", + "n_splines": n_splines, + }, + }, + { + "order": order, + "control_points": torch.rand(n_splines, output_dim, n_ctrl_pts), + "knots": { + "n": n_knots, + "min": 0, + "max": 1, + "mode": "uniform", + "n_splines": n_splines, + }, + }, + { + "order": order, + "control_points": None, + "knots": torch.linspace(0, 1, n_knots) + .unsqueeze(0) + .repeat(n_splines, 1), + }, + { + "order": order, + "control_points": None, + "knots": { + "n": n_knots, + "min": 0, + "max": 1, + "mode": "auto", + "n_splines": n_splines, + }, + }, + { + "order": order, + "control_points": None, + "knots": { + "n": n_knots, + "min": 0, + "max": 1, + "mode": "uniform", + "n_splines": n_splines, + }, + }, + { + "order": order, + "control_points": torch.rand(n_splines, output_dim, n_ctrl_pts), + "knots": None, + }, +] + + +@pytest.mark.parametrize("args", valid_args) +@pytest.mark.parametrize("aggregate_output", ["mean", "sum", None]) +def test_constructor(args, aggregate_output): + VectorizedSpline(**args, aggregate_output=aggregate_output) + + # Should fail if order is not a positive integer + with pytest.raises(AssertionError): + VectorizedSpline( + order=-1, + control_points=args["control_points"], + knots=args["knots"], + aggregate_output=aggregate_output, + ) + + # Should fail if control_points is not None or a torch.Tensor + with pytest.raises(ValueError): + VectorizedSpline( + order=args["order"], + control_points=[1, 2, 3], + knots=args["knots"], + aggregate_output=aggregate_output, + ) + + # Should fail if knots is not None, a torch.Tensor, or a dict + with pytest.raises(ValueError): + VectorizedSpline( + order=args["order"], + control_points=args["control_points"], + knots=5, + aggregate_output=aggregate_output, + ) + + # Should fail if aggregate_output is not None, "mean", or "sum" + with pytest.raises(ValueError): + VectorizedSpline( + order=args["order"], + control_points=args["control_points"], + knots=args["knots"], + aggregate_output="invalid", + ) + + # Should fail if both knots and control_points are None + with pytest.raises(ValueError): + VectorizedSpline( + order=args["order"], + control_points=None, + knots=None, + aggregate_output=aggregate_output, + ) + + # Should fail if knots is not two-dimensional + with pytest.raises(ValueError): + VectorizedSpline( + order=args["order"], + control_points=args["control_points"], + knots=torch.rand(n_knots), + aggregate_output=aggregate_output, + ) + + # Should fail if control_points is not three-dimensional + with pytest.raises(ValueError): + VectorizedSpline( + order=args["order"], + control_points=torch.rand(n_ctrl_pts), + knots=args["knots"], + aggregate_output=aggregate_output, + ) + + # Should fail if the number of knots != order + number of control points + # If control points are None, they are initialized to fulfill this condition + if args["control_points"] is not None: + with pytest.raises(ValueError): + VectorizedSpline( + order=args["order"], + control_points=args["control_points"], + knots=torch.linspace(0, 1, n_knots + 1) + .unsqueeze(0) + .repeat(n_splines, 1), + aggregate_output=aggregate_output, + ) + + # Should fail if the knot dict is missing required keys + with pytest.raises(ValueError): + VectorizedSpline( + order=args["order"], + control_points=args["control_points"], + knots={"n": n_knots, "min": 0, "max": 1}, + aggregate_output=aggregate_output, + ) + + # Should fail if the knot dict has invalid 'mode' key + with pytest.raises(ValueError): + VectorizedSpline( + order=args["order"], + control_points=args["control_points"], + knots={"n": n_knots, "min": 0, "max": 1, "mode": "invalid"}, + aggregate_output=aggregate_output, + ) + + # Should fail if knots and control points have different number of splines + with pytest.raises(ValueError): + VectorizedSpline( + order=3, + control_points=torch.rand(5, 4, 5), + knots=torch.linspace(0, 1, 8).unsqueeze(0).repeat(3, 1), + aggregate_output=aggregate_output, + ) + + +@pytest.mark.parametrize("args", valid_args) +def test_forward(args): + + # Define the model + model = VectorizedSpline(**args) + + # Evaluate the model + output_ = model(pts) + + # Check output shape + if model.aggregate_output is None: + assert output_.shape == ( + pts.shape[0], + pts.shape[1], + model.control_points.shape[1], + ) + else: + assert output_.shape == pts.shape + + +@pytest.mark.parametrize("args", valid_args) +def test_backward(args): + + # Define the model + model = VectorizedSpline(**args) + + # Evaluate the model + output_ = model(pts) + loss = torch.mean(output_) + loss.backward() + assert model.control_points.grad.shape == model.control_points.shape + + +@pytest.mark.parametrize("args", valid_args) +def test_derivative(args): + + # Define and evaluate the model + model = VectorizedSpline(**args) + pts.requires_grad_(True) + output_ = model(pts) + + # Compute analytical derivatives + first_der = model.derivative(x=pts, degree=1) + + # Compute autograd derivatives -- we need to loop over output dimensions + # since autograd doesn't support vectorized outputs + gradients = [] + for j in range(output_.shape[2]): + out = output_[:, :, j].squeeze(-1) + out = LabelTensor(out, [f"u{j}" for j in range(out.shape[1])]) + gradients.append( + grad(out, pts)[[f"du{j}dx{j}" for j in range(pts.shape[1])]] + ) + first_der_auto = torch.stack(gradients, dim=-1) + + # Check shape and value + assert first_der.shape == first_der_auto.shape + assert torch.allclose(first_der, first_der_auto, atol=1e-4, rtol=1e-4) + + +def test_1d_vs_vectorized(): + + control_points = torch.rand(1, 1, n_ctrl_pts) + knots = torch.linspace(0, 1, n_knots).unsqueeze(0) + + # Classical 1D spline + + spline = Spline( + order=order, + control_points=control_points.squeeze(), + knots=knots.squeeze(), + ) + + # Create a VectorizedSpline instance with the same control pts and knots + vectorized_spline = VectorizedSpline( + order=order, + knots=knots, + control_points=control_points, + aggregate_output=None, + ) + + # Input points + x = LabelTensor(torch.rand(10, 1), labels=["x"]) + + # Evaluate both models on the same input + out_spline = spline(x) + out_vectorized = vectorized_spline(x) + + assert out_vectorized.shape == out_spline.shape + assert torch.allclose(out_vectorized, out_spline, atol=1e-5, rtol=1e-5) diff --git a/tests/test_optim/test_torch_optimizer.py b/tests/test_optim/test_torch_optimizer.py new file mode 100644 index 000000000..dffc04c67 --- /dev/null +++ b/tests/test_optim/test_torch_optimizer.py @@ -0,0 +1,27 @@ +import torch +import pytest +from pina.optim import TorchOptimizer + +opt_list = [torch.optim.Adam, torch.optim.AdamW, torch.optim.SGD] +kwargs_list = [{"lr": 1e-3}, {"lr": 1e-3, "weight_decay": 1e-4}] + + +@pytest.mark.parametrize("optimizer_class", opt_list) +@pytest.mark.parametrize("kwargs", kwargs_list) +def test_constructor(optimizer_class, kwargs): + TorchOptimizer(optimizer_class, **kwargs) + + # Should fail if the optimizer is not subclass of torch.optim.Optimizer + with pytest.raises(ValueError): + TorchOptimizer(object, **kwargs) + + +@pytest.mark.parametrize("optimizer_class", opt_list) +@pytest.mark.parametrize("kwargs", kwargs_list) +def test_hook(optimizer_class, kwargs): + + # Create the optimizer instance + optimizer = TorchOptimizer(optimizer_class, **kwargs) + + # Hook the optimizer with model parameters + optimizer.hook(torch.nn.Linear(10, 10).parameters()) diff --git a/tests/test_optim/test_torch_scheduler.py b/tests/test_optim/test_torch_scheduler.py new file mode 100644 index 000000000..bc7dd96c9 --- /dev/null +++ b/tests/test_optim/test_torch_scheduler.py @@ -0,0 +1,37 @@ +import torch +import pytest +from pina.optim import TorchOptimizer, TorchScheduler + +opt_list = [torch.optim.Adam, torch.optim.AdamW, torch.optim.SGD] +sch_list = [ + torch.optim.lr_scheduler.ConstantLR, + torch.optim.lr_scheduler.ReduceLROnPlateau, +] + + +@pytest.mark.parametrize("scheduler_class", sch_list) +def test_constructor(scheduler_class): + TorchScheduler(scheduler_class) + + # Should fail if the scheduler is not subclass of torch LRScheduler + with pytest.raises(ValueError): + TorchScheduler(object) + + +@pytest.mark.parametrize("optimizer_class", opt_list) +@pytest.mark.parametrize("scheduler_class", sch_list) +def test_hook(optimizer_class, scheduler_class): + + # Create the optimizer instance + optimizer = TorchOptimizer(optimizer_class) + optimizer.hook(torch.nn.Linear(10, 10).parameters()) + + # Create the scheduler instance + scheduler = TorchScheduler(scheduler_class) + + # Hook the scheduler with the optimizer instance + scheduler.hook(optimizer) + + # Should fail if the optimizer is not an instance of OptimizerInterface + with pytest.raises(ValueError): + scheduler.hook(object) diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py deleted file mode 100644 index 037de9929..000000000 --- a/tests/test_optimizer.py +++ /dev/null @@ -1,21 +0,0 @@ -import torch -import pytest -from pina.optim import TorchOptimizer - -opt_list = [ - torch.optim.Adam, - torch.optim.AdamW, - torch.optim.SGD, - torch.optim.RMSprop, -] - - -@pytest.mark.parametrize("optimizer_class", opt_list) -def test_constructor(optimizer_class): - TorchOptimizer(optimizer_class, lr=1e-3) - - -@pytest.mark.parametrize("optimizer_class", opt_list) -def test_hook(optimizer_class): - opt = TorchOptimizer(optimizer_class, lr=1e-3) - opt.hook(torch.nn.Linear(10, 10).parameters()) diff --git a/tests/test_problem.py b/tests/test_problem.py deleted file mode 100644 index bdd6a1d4d..000000000 --- a/tests/test_problem.py +++ /dev/null @@ -1,132 +0,0 @@ -import torch -import pytest -from pina.problem.zoo import Poisson2DSquareProblem as Poisson -from pina import LabelTensor -from pina.domain import Union, CartesianDomain, EllipsoidDomain -from pina.condition import ( - Condition, - InputTargetCondition, - DomainEquationCondition, -) - - -def test_discretise_domain(): - n = 10 - poisson_problem = Poisson() - - poisson_problem.discretise_domain(n, "grid", domains="boundary") - assert poisson_problem.discretised_domains["boundary"].shape[0] == n - - poisson_problem.discretise_domain(n, "random", domains="boundary") - assert poisson_problem.discretised_domains["boundary"].shape[0] == n - - poisson_problem.discretise_domain(n, "grid", domains=["D"]) - assert poisson_problem.discretised_domains["D"].shape[0] == n**2 - - poisson_problem.discretise_domain(n, "random", domains=["D"]) - assert poisson_problem.discretised_domains["D"].shape[0] == n - - poisson_problem.discretise_domain(n, "latin", domains=["D"]) - assert poisson_problem.discretised_domains["D"].shape[0] == n - - poisson_problem.discretise_domain(n, "lh", domains=["D"]) - assert poisson_problem.discretised_domains["D"].shape[0] == n - - poisson_problem.discretise_domain(n) - - -def test_variables_correct_order_sampling(): - n = 10 - poisson_problem = Poisson() - poisson_problem.discretise_domain(n, "grid", domains=["D"]) - assert poisson_problem.discretised_domains["D"].labels == sorted( - poisson_problem.input_variables - ) - - poisson_problem.discretise_domain(n, "grid", domains=["D"]) - assert poisson_problem.discretised_domains["D"].labels == sorted( - poisson_problem.input_variables - ) - - -def test_input_pts(): - n = 10 - poisson_problem = Poisson() - poisson_problem.discretise_domain(n, "grid") - assert sorted(list(poisson_problem.input_pts.keys())) == sorted( - list(poisson_problem.conditions.keys()) - ) - - -def test_collected_data(): - n = 10 - poisson_problem = Poisson() - poisson_problem.discretise_domain(n, "grid") - assert sorted(list(poisson_problem.collected_data.keys())) == sorted( - list(poisson_problem.conditions.keys()) - ) - - -def test_add_points(): - poisson_problem = Poisson() - poisson_problem.discretise_domain(1, "random", domains=["D"]) - new_pts = LabelTensor(torch.tensor([[0.5, -0.5]]), labels=["x", "y"]) - poisson_problem.add_points({"D": new_pts}) - assert torch.allclose( - poisson_problem.discretised_domains["D"]["x"][-1], - new_pts["x"], - ) - assert torch.allclose( - poisson_problem.discretised_domains["D"]["y"][-1], - new_pts["y"], - ) - - -@pytest.mark.parametrize("mode", ["random", "grid"]) -def test_custom_sampling_logic(mode): - poisson_problem = Poisson() - sampling_rules = { - "x": {"n": 100, "mode": mode}, - "y": {"n": 50, "mode": mode}, - } - poisson_problem.discretise_domain(sample_rules=sampling_rules, domains="D") - assert poisson_problem.discretised_domains["D"].shape[0] == 100 * 50 - assert poisson_problem.discretised_domains["D"].labels == ["x", "y"] - - -@pytest.mark.parametrize("mode", ["random", "grid"]) -def test_wrong_custom_sampling_logic(mode): - d2 = CartesianDomain({"x": [1, 2], "y": [0, 1]}) - poisson_problem = Poisson() - poisson_problem.domains["D"] = Union([poisson_problem.domains["D"], d2]) - sampling_rules = { - "x": {"n": 100, "mode": mode}, - "y": {"n": 50, "mode": mode}, - } - with pytest.raises(RuntimeError): - poisson_problem.domains["new"] = EllipsoidDomain({"x": [0, 1]}) - poisson_problem.discretise_domain(sample_rules=sampling_rules) - - # Necessary cleanup - if "new" in poisson_problem.domains: - del poisson_problem.domains["new"] - - -def test_aggregate_data(): - poisson_problem = Poisson() - poisson_problem.conditions["data"] = Condition( - input=LabelTensor(torch.tensor([[0.0, 1.0]]), labels=["x", "y"]), - target=LabelTensor(torch.tensor([[0.0]]), labels=["u"]), - ) - poisson_problem.discretise_domain(1, "random", domains="all") - poisson_problem.collect_data() - assert isinstance(poisson_problem.collected_data, dict) - for name, conditions in poisson_problem.conditions.items(): - assert name in poisson_problem.collected_data.keys() - if isinstance(conditions, InputTargetCondition): - assert "input" in poisson_problem.collected_data[name].keys() - assert "target" in poisson_problem.collected_data[name].keys() - elif isinstance(conditions, DomainEquationCondition): - assert "input" in poisson_problem.collected_data[name].keys() - assert "target" not in poisson_problem.collected_data[name].keys() - assert "equation" in poisson_problem.collected_data[name].keys() diff --git a/tests/test_problem/test_base_problem.py b/tests/test_problem/test_base_problem.py new file mode 100644 index 000000000..25acfcadd --- /dev/null +++ b/tests/test_problem/test_base_problem.py @@ -0,0 +1,158 @@ +import torch +import pytest +from pina import LabelTensor +from pina.problem.zoo import Poisson2DSquareProblem as Poisson + + +# Define sampling rules +rule1 = { + "x": {"n": 10, "mode": "random"}, + "y": {"n": 5, "mode": "grid"}, +} +rule2 = { + "x": {"n": 5, "mode": "lh"}, + "y": {"n": 10, "mode": "chebyshev"}, +} + + +@pytest.mark.parametrize("n", [2, 5]) +@pytest.mark.parametrize("mode", ["grid", "random", "latin", "chebyshev", "lh"]) +@pytest.mark.parametrize("domains", ["boundary", "D", ["boundary", "D"], None]) +@pytest.mark.parametrize("sample_rules", [None, rule1, rule2]) +def test_discretise_domain(n, mode, domains, sample_rules): + + # Define the problem + poisson_problem = Poisson() + + # Discretise domains + poisson_problem.discretise_domain( + n=n, mode=mode, domains=domains, sample_rules=sample_rules + ) + + # Transform domains to list for consistent processing + _as_list = lambda x: [x] if isinstance(x, str) else x + d_list = domains if domains is not None else ["boundary", "D"] + d_list = _as_list(d_list) + + # Check that the discretised domains have the expected number of points + for d in d_list: + + # Compute expected number of points if sample rules are provided + if sample_rules is not None: + n_tot = sample_rules["x"]["n"] * sample_rules["y"]["n"] + + # Otherwise, expect n pts or n^2 based on domain and mode + else: + n_tot = n**2 if mode in ["grid", "chebyshev"] and d == "D" else n + + # Check that the number of samples matches the expected number + assert poisson_problem.discretised_domains[d].shape[0] == n_tot + + # Check labels of the discretised domains + assert poisson_problem.discretised_domains[d].labels == sorted( + poisson_problem.input_variables + ) + + # Should fail if n is not a positive integer when sample rules not provided + if sample_rules is None: + with pytest.raises(AssertionError): + poisson_problem.discretise_domain( + n=-1, mode=mode, domains=domains, sample_rules=sample_rules + ) + + # Should fail if mode is not a string + with pytest.raises(ValueError): + poisson_problem.discretise_domain( + n=n, mode=123, domains=domains, sample_rules=sample_rules + ) + + # Should fail if domains is not a string or a list of strings + with pytest.raises(ValueError): + poisson_problem.discretise_domain( + n=n, mode=mode, domains=123, sample_rules=sample_rules + ) + + # Should fail if sample rules is not a dictionary + with pytest.raises(ValueError): + poisson_problem.discretise_domain( + n=n, mode=mode, domains=domains, sample_rules="not_a_dict" + ) + + # Should fail if the keys of sample rules do not match the input variables + with pytest.raises(ValueError): + wrong_sample_rules = {"wrong_var": {"n": 10, "mode": "random"}} + poisson_problem.discretise_domain( + n=n, mode=mode, domains=domains, sample_rules=wrong_sample_rules + ) + + # Should fail if the rules do not contain both 'n' and 'mode' keys + with pytest.raises(ValueError): + incomplete_sample_rules = {"x": {"n": 10}, "y": {"mode": "random"}} + poisson_problem.discretise_domain( + n=n, + mode=mode, + domains=domains, + sample_rules=incomplete_sample_rules, + ) + + +@pytest.mark.parametrize("domains", ["boundary", "D", ["boundary", "D"], None]) +def test_add_points(domains): + + # Store initial number of points in the domains and point to add + n_init, n_add = 5, 3 + n_tot = n_init + n_add + + # Define the problem and discretise the domain + poisson_problem = Poisson() + poisson_problem.discretise_domain(n=n_init, mode="random", domains=domains) + vars = poisson_problem.input_variables + + # Transform domains to list for consistent processing + _as_list = lambda x: [x] if isinstance(x, str) else x + d_list = domains if domains is not None else ["boundary", "D"] + d_list = _as_list(d_list) + + # Iterate over the domains and add points to each + for d in d_list: + + # Add new points to the domain + new_pts = LabelTensor(torch.rand(n_add, len(vars)), labels=vars) + poisson_problem.add_points({d: new_pts}) + + # Assert that the number of points in the domain is correct + assert poisson_problem.discretised_domains[d].shape[0] == n_tot + + # Assert that the new points are in the domain + assert torch.allclose( + poisson_problem.discretised_domains[d]["x"][-n_add:], new_pts["x"] + ) + assert torch.allclose( + poisson_problem.discretised_domains[d]["y"][-n_add:], new_pts["y"] + ) + + # Should fail if new points is not a dictionary + with pytest.raises(ValueError): + poisson_problem.add_points("not_a_dict") + + # Should fail if any of the values in new points is not a LabelTensor + with pytest.raises(ValueError): + poisson_problem.add_points({d_list[0]: torch.rand(n_add, len(vars))}) + + # Should fail if any of the keys does not match any of the existing domains + with pytest.raises(ValueError): + poisson_problem.add_points( + { + "not_a_domain": LabelTensor( + torch.rand(n_add, len(vars)), labels=vars + ) + } + ) + + # Should fail if any of the domains has not been discretised yet + with pytest.raises(ValueError): + poisson_problem = Poisson() + poisson_problem.discretise_domain(n=n_init, mode="random", domains="D") + poisson_problem.add_points( + {"boundary": LabelTensor(torch.rand(n_add, len(vars)), labels=vars)} + ) diff --git a/tests/test_problem/test_inverse_problem.py b/tests/test_problem/test_inverse_problem.py new file mode 100644 index 000000000..8a91cbac0 --- /dev/null +++ b/tests/test_problem/test_inverse_problem.py @@ -0,0 +1,27 @@ +import torch +from pina.problem import InverseProblem +from pina.domain import CartesianDomain + + +# Dummy inverse problem for testing +class DummyInverseProblem(InverseProblem): + + output_variables = ["u"] + conditions = {} + + # Define the unknown parameter domain + unknown_parameter_domain = CartesianDomain({"mu": [-1, 1]}) + + +def test_inverse_problem_initialization(): + + # Initialize the dummy inverse problem + problem = DummyInverseProblem() + + # Check that the inverse problem is initialized correctly + assert problem.unknown_variables == ["mu"] + assert isinstance(problem.unknown_parameters, dict) + for k, v in problem.unknown_parameters.items(): + assert isinstance(v, torch.nn.Parameter) + range_low, range_high = problem.unknown_parameter_domain._range[k] + assert range_low <= v.item() <= range_high diff --git a/tests/test_problem/test_parametric_problem.py b/tests/test_problem/test_parametric_problem.py new file mode 100644 index 000000000..00c4568e8 --- /dev/null +++ b/tests/test_problem/test_parametric_problem.py @@ -0,0 +1,22 @@ +from pina.problem import ParametricProblem +from pina.domain import CartesianDomain + + +# Dummy parametric problem for testing +class DummyParametricProblem(ParametricProblem): + + output_variables = ["u"] + conditions = {} + + # Define the parameter domain + parameter_domain = CartesianDomain({"mu": [-1, 1]}) + + +def test_parametric_problem_initialization(): + + # Initialize the dummy parametric problem + problem = DummyParametricProblem() + + # Check that the parametric problem is initialized correctly + assert problem.parameters == ["mu"] + assert problem.input_variables == problem.parameters diff --git a/tests/test_problem/test_spatial_problem.py b/tests/test_problem/test_spatial_problem.py new file mode 100644 index 000000000..4848db018 --- /dev/null +++ b/tests/test_problem/test_spatial_problem.py @@ -0,0 +1,22 @@ +from pina.problem import SpatialProblem +from pina.domain import CartesianDomain + + +# Dummy spatial problem for testing +class DummySpatialProblem(SpatialProblem): + + output_variables = ["u"] + conditions = {} + + # Define the spatial domain + spatial_domain = CartesianDomain({"x": [-1, 1]}) + + +def test_spatial_problem_initialization(): + + # Initialize the dummy spatial problem + problem = DummySpatialProblem() + + # Check that the spatial problem is initialized correctly + assert problem.spatial_variables == ["x"] + assert problem.input_variables == problem.spatial_variables diff --git a/tests/test_problem/test_time_dependent_problem.py b/tests/test_problem/test_time_dependent_problem.py new file mode 100644 index 000000000..e041f507b --- /dev/null +++ b/tests/test_problem/test_time_dependent_problem.py @@ -0,0 +1,22 @@ +from pina.problem import TimeDependentProblem +from pina.domain import CartesianDomain + + +# Dummy time-dependent problem for testing +class DummyTimeDependentProblem(TimeDependentProblem): + + output_variables = ["u"] + conditions = {} + + # Define the temporal domain + temporal_domain = CartesianDomain({"t": [0, 1]}) + + +def test_time_dependent_problem_initialization(): + + # Initialize the dummy time-dependent problem + problem = DummyTimeDependentProblem() + + # Check that the time-dependent problem is initialized correctly + assert problem.temporal_variables == ["t"] + assert problem.input_variables == problem.temporal_variables diff --git a/tests/test_problem_zoo/test_acoustic_wave.py b/tests/test_problem_zoo/test_acoustic_wave.py deleted file mode 100644 index 0cf794d18..000000000 --- a/tests/test_problem_zoo/test_acoustic_wave.py +++ /dev/null @@ -1,19 +0,0 @@ -import pytest -from pina.problem.zoo import AcousticWaveProblem -from pina.problem import SpatialProblem, TimeDependentProblem - - -@pytest.mark.parametrize("c", [0.1, 1]) -def test_constructor(c): - - problem = AcousticWaveProblem(c=c) - problem.discretise_domain(n=10, mode="random", domains="all") - assert problem.are_all_domains_discretised - assert isinstance(problem, SpatialProblem) - assert isinstance(problem, TimeDependentProblem) - assert hasattr(problem, "conditions") - assert isinstance(problem.conditions, dict) - - # Should fail if c is not a float or int - with pytest.raises(ValueError): - AcousticWaveProblem(c="invalid") diff --git a/tests/test_problem_zoo/test_acoustic_wave_problem.py b/tests/test_problem_zoo/test_acoustic_wave_problem.py new file mode 100644 index 000000000..a5102efae --- /dev/null +++ b/tests/test_problem_zoo/test_acoustic_wave_problem.py @@ -0,0 +1,36 @@ +import pytest +import torch +from pina.problem.zoo import AcousticWaveProblem +from pina.problem import SpatialProblem, TimeDependentProblem + + +@pytest.mark.parametrize("c", [0.1, 1]) +def test_constructor(c): + + problem = AcousticWaveProblem(c=c) + problem.discretise_domain(n=10, mode="random", domains=None) + assert problem.are_all_domains_discretised + assert isinstance(problem, SpatialProblem) + assert isinstance(problem, TimeDependentProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) + + # Should fail if c is not a float or int + with pytest.raises(ValueError): + AcousticWaveProblem(c="invalid") + + +@pytest.mark.parametrize("c", [0.1, 1]) +def test_solution(c): + + # Find the solution to the problem + problem = AcousticWaveProblem(c=c) + problem.discretise_domain(n=10, mode="grid", domains=None) + pts = problem.discretised_domains["D"] + solution = problem.solution(pts.requires_grad_()) + + # Compute the residual + residual = problem.conditions["D"].equation.residual(pts, solution).tensor + + # Assert the residual of the PDE is close to zero + assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5) diff --git a/tests/test_problem_zoo/test_advection.py b/tests/test_problem_zoo/test_advection.py deleted file mode 100644 index e1a656a74..000000000 --- a/tests/test_problem_zoo/test_advection.py +++ /dev/null @@ -1,19 +0,0 @@ -import pytest -from pina.problem.zoo import AdvectionProblem -from pina.problem import SpatialProblem, TimeDependentProblem - - -@pytest.mark.parametrize("c", [1.5, 3]) -def test_constructor(c): - - problem = AdvectionProblem(c=c) - problem.discretise_domain(n=10, mode="random", domains="all") - assert problem.are_all_domains_discretised - assert isinstance(problem, SpatialProblem) - assert isinstance(problem, TimeDependentProblem) - assert hasattr(problem, "conditions") - assert isinstance(problem.conditions, dict) - - # Should fail if c is not a float or int - with pytest.raises(ValueError): - AdvectionProblem(c="invalid") diff --git a/tests/test_problem_zoo/test_advection_problem.py b/tests/test_problem_zoo/test_advection_problem.py new file mode 100644 index 000000000..0d7114771 --- /dev/null +++ b/tests/test_problem_zoo/test_advection_problem.py @@ -0,0 +1,36 @@ +import pytest +import torch +from pina.problem.zoo import AdvectionProblem +from pina.problem import SpatialProblem, TimeDependentProblem + + +@pytest.mark.parametrize("c", [1.5, 3]) +def test_constructor(c): + + problem = AdvectionProblem(c=c) + problem.discretise_domain(n=10, mode="random", domains=None) + assert problem.are_all_domains_discretised + assert isinstance(problem, SpatialProblem) + assert isinstance(problem, TimeDependentProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) + + # Should fail if c is not a float or int + with pytest.raises(ValueError): + AdvectionProblem(c="invalid") + + +@pytest.mark.parametrize("c", [1.5, 3]) +def test_solution(c): + + # Find the solution to the problem + problem = AdvectionProblem(c=c) + problem.discretise_domain(n=10, mode="grid", domains=None) + pts = problem.discretised_domains["D"] + solution = problem.solution(pts.requires_grad_()) + + # Compute the residual + residual = problem.conditions["D"].equation.residual(pts, solution).tensor + + # Assert the residual of the PDE is close to zero + assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5) diff --git a/tests/test_problem_zoo/test_allen_cahn.py b/tests/test_problem_zoo/test_allen_cahn_problem.py similarity index 92% rename from tests/test_problem_zoo/test_allen_cahn.py rename to tests/test_problem_zoo/test_allen_cahn_problem.py index 80c11ce5c..2406e1f75 100644 --- a/tests/test_problem_zoo/test_allen_cahn.py +++ b/tests/test_problem_zoo/test_allen_cahn_problem.py @@ -8,7 +8,7 @@ def test_constructor(alpha, beta): problem = AllenCahnProblem(alpha=alpha, beta=beta) - problem.discretise_domain(n=10, mode="random", domains="all") + problem.discretise_domain(n=10, mode="random", domains=None) assert problem.are_all_domains_discretised assert isinstance(problem, SpatialProblem) assert isinstance(problem, TimeDependentProblem) diff --git a/tests/test_problem_zoo/test_burgers_problem.py b/tests/test_problem_zoo/test_burgers_problem.py new file mode 100644 index 000000000..d7f9d2128 --- /dev/null +++ b/tests/test_problem_zoo/test_burgers_problem.py @@ -0,0 +1,23 @@ +import pytest +from pina.problem.zoo import BurgersProblem +from pina.problem import SpatialProblem, TimeDependentProblem + + +@pytest.mark.parametrize("nu", [0.1, 1]) +def test_constructor(nu): + + problem = BurgersProblem(nu=nu) + problem.discretise_domain(n=10, mode="random", domains=None) + assert problem.are_all_domains_discretised + assert isinstance(problem, SpatialProblem) + assert isinstance(problem, TimeDependentProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) + + # Should fail if nu is not a float or int + with pytest.raises(ValueError): + BurgersProblem(nu="invalid") + + # Should fail if nu is negative + with pytest.raises(ValueError): + BurgersProblem(nu=-0.1) diff --git a/tests/test_problem_zoo/test_diffusion_reaction.py b/tests/test_problem_zoo/test_diffusion_reaction_problem.py similarity index 50% rename from tests/test_problem_zoo/test_diffusion_reaction.py rename to tests/test_problem_zoo/test_diffusion_reaction_problem.py index 163d30f55..d8decf697 100644 --- a/tests/test_problem_zoo/test_diffusion_reaction.py +++ b/tests/test_problem_zoo/test_diffusion_reaction_problem.py @@ -1,4 +1,5 @@ import pytest +import torch from pina.problem.zoo import DiffusionReactionProblem from pina.problem import TimeDependentProblem, SpatialProblem @@ -7,7 +8,7 @@ def test_constructor(alpha): problem = DiffusionReactionProblem(alpha=alpha) - problem.discretise_domain(n=10, mode="random", domains="all") + problem.discretise_domain(n=10, mode="random", domains=None) assert problem.are_all_domains_discretised assert isinstance(problem, TimeDependentProblem) assert isinstance(problem, SpatialProblem) @@ -17,3 +18,19 @@ def test_constructor(alpha): # Should fail if alpha is not a float or int with pytest.raises(ValueError): problem = DiffusionReactionProblem(alpha="invalid") + + +@pytest.mark.parametrize("alpha", [0.1, 1]) +def test_solution(alpha): + + # Find the solution to the problem + problem = DiffusionReactionProblem(alpha=alpha) + problem.discretise_domain(n=10, mode="grid", domains=None) + pts = problem.discretised_domains["D"] + solution = problem.solution(pts.requires_grad_()) + + # Compute the residual + residual = problem.conditions["D"].equation.residual(pts, solution).tensor + + # Assert the residual of the PDE is close to zero + assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5) diff --git a/tests/test_problem_zoo/test_helmholtz.py b/tests/test_problem_zoo/test_helmholtz.py deleted file mode 100644 index 5e78e4d68..000000000 --- a/tests/test_problem_zoo/test_helmholtz.py +++ /dev/null @@ -1,17 +0,0 @@ -import pytest -from pina.problem.zoo import HelmholtzProblem -from pina.problem import SpatialProblem - - -@pytest.mark.parametrize("alpha", [1.5, 3]) -def test_constructor(alpha): - - problem = HelmholtzProblem(alpha=alpha) - problem.discretise_domain(n=10, mode="random", domains="all") - assert problem.are_all_domains_discretised - assert isinstance(problem, SpatialProblem) - assert hasattr(problem, "conditions") - assert isinstance(problem.conditions, dict) - - with pytest.raises(ValueError): - HelmholtzProblem(alpha="invalid") diff --git a/tests/test_problem_zoo/test_helmholtz_problem.py b/tests/test_problem_zoo/test_helmholtz_problem.py new file mode 100644 index 000000000..408e32a33 --- /dev/null +++ b/tests/test_problem_zoo/test_helmholtz_problem.py @@ -0,0 +1,38 @@ +import pytest +import torch +from pina.problem.zoo import HelmholtzProblem +from pina.problem import SpatialProblem + + +@pytest.mark.parametrize("k", [1.5, 3]) +@pytest.mark.parametrize("alpha_x", [1, 3]) +@pytest.mark.parametrize("alpha_y", [1, 3]) +def test_constructor(k, alpha_x, alpha_y): + + problem = HelmholtzProblem(k=k, alpha_x=alpha_x, alpha_y=alpha_y) + problem.discretise_domain(n=10, mode="random", domains=None) + assert problem.are_all_domains_discretised + assert isinstance(problem, SpatialProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) + + with pytest.raises(ValueError): + HelmholtzProblem(k=1, alpha_x=1.5, alpha_y=1) + + +@pytest.mark.parametrize("k", [1.5, 3]) +@pytest.mark.parametrize("alpha_x", [1, 3]) +@pytest.mark.parametrize("alpha_y", [1, 3]) +def test_solution(k, alpha_x, alpha_y): + + # Find the solution to the problem + problem = HelmholtzProblem(k=k, alpha_x=alpha_x, alpha_y=alpha_y) + problem.discretise_domain(n=10, mode="grid", domains=None) + pts = problem.discretised_domains["D"] + solution = problem.solution(pts.requires_grad_()) + + # Compute the residual + residual = problem.conditions["D"].equation.residual(pts, solution).tensor + + # Assert the residual of the PDE is close to zero + assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5) diff --git a/tests/test_problem_zoo/test_inverse_poisson_2d_square.py b/tests/test_problem_zoo/test_inverse_poisson_problem.py similarity index 93% rename from tests/test_problem_zoo/test_inverse_poisson_2d_square.py rename to tests/test_problem_zoo/test_inverse_poisson_problem.py index 423d15d74..25af3ae9e 100644 --- a/tests/test_problem_zoo/test_inverse_poisson_2d_square.py +++ b/tests/test_problem_zoo/test_inverse_poisson_problem.py @@ -11,7 +11,7 @@ def test_constructor(load, data_size): problem = InversePoisson2DSquareProblem(load=load, data_size=data_size) # Discretise the domain - problem.discretise_domain(n=10, mode="random", domains="all") + problem.discretise_domain(n=10, mode="random", domains=None) # Check if the problem is correctly set up assert problem.are_all_domains_discretised diff --git a/tests/test_problem_zoo/test_poisson_2d_square.py b/tests/test_problem_zoo/test_poisson_2d_square.py deleted file mode 100644 index a9e6fa973..000000000 --- a/tests/test_problem_zoo/test_poisson_2d_square.py +++ /dev/null @@ -1,12 +0,0 @@ -from pina.problem.zoo import Poisson2DSquareProblem -from pina.problem import SpatialProblem - - -def test_constructor(): - - problem = Poisson2DSquareProblem() - problem.discretise_domain(n=10, mode="random", domains="all") - assert problem.are_all_domains_discretised - assert isinstance(problem, SpatialProblem) - assert hasattr(problem, "conditions") - assert isinstance(problem.conditions, dict) diff --git a/tests/test_problem_zoo/test_poisson_problem.py b/tests/test_problem_zoo/test_poisson_problem.py new file mode 100644 index 000000000..b093329bd --- /dev/null +++ b/tests/test_problem_zoo/test_poisson_problem.py @@ -0,0 +1,28 @@ +import torch +from pina.problem.zoo import Poisson2DSquareProblem +from pina.problem import SpatialProblem + + +def test_constructor(): + + problem = Poisson2DSquareProblem() + problem.discretise_domain(n=10, mode="random", domains=None) + assert problem.are_all_domains_discretised + assert isinstance(problem, SpatialProblem) + assert hasattr(problem, "conditions") + assert isinstance(problem.conditions, dict) + + +def test_solution(): + + # Find the solution to the problem + problem = Poisson2DSquareProblem() + problem.discretise_domain(n=10, mode="grid", domains=None) + pts = problem.discretised_domains["D"] + solution = problem.solution(pts.requires_grad_()) + + # Compute the residual + residual = problem.conditions["D"].equation.residual(pts, solution).tensor + + # Assert the residual of the PDE is close to zero + assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5) diff --git a/tests/test_problem_zoo/test_supervised_problem.py b/tests/test_problem_zoo/test_supervised_problem.py index 19b3920ce..e638623f3 100644 --- a/tests/test_problem_zoo/test_supervised_problem.py +++ b/tests/test_problem_zoo/test_supervised_problem.py @@ -1,7 +1,7 @@ import torch -from pina.problem import AbstractProblem +from pina.problem import BaseProblem from pina.condition import InputTargetCondition -from pina.problem.zoo.supervised_problem import SupervisedProblem +from pina.problem.zoo import SupervisedProblem from pina.graph import RadiusGraph @@ -9,7 +9,7 @@ def test_constructor(): input_ = torch.rand((100, 10)) output_ = torch.rand((100, 10)) problem = SupervisedProblem(input_=input_, output_=output_) - assert isinstance(problem, AbstractProblem) + assert isinstance(problem, BaseProblem) assert hasattr(problem, "conditions") assert isinstance(problem.conditions, dict) assert list(problem.conditions.keys()) == ["data"] @@ -25,7 +25,7 @@ def test_constructor_graph(): ] output_ = torch.rand((20, 100, 10)) problem = SupervisedProblem(input_=input_, output_=output_) - assert isinstance(problem, AbstractProblem) + assert isinstance(problem, BaseProblem) assert hasattr(problem, "conditions") assert isinstance(problem.conditions, dict) assert list(problem.conditions.keys()) == ["data"] diff --git a/tests/test_scheduler.py b/tests/test_scheduler.py deleted file mode 100644 index 157a818d2..000000000 --- a/tests/test_scheduler.py +++ /dev/null @@ -1,26 +0,0 @@ -import torch -import pytest -from pina.optim import TorchOptimizer, TorchScheduler - -opt_list = [ - torch.optim.Adam, - torch.optim.AdamW, - torch.optim.SGD, - torch.optim.RMSprop, -] - -sch_list = [torch.optim.lr_scheduler.ConstantLR] - - -@pytest.mark.parametrize("scheduler_class", sch_list) -def test_constructor(scheduler_class): - TorchScheduler(scheduler_class) - - -@pytest.mark.parametrize("optimizer_class", opt_list) -@pytest.mark.parametrize("scheduler_class", sch_list) -def test_hook(optimizer_class, scheduler_class): - opt = TorchOptimizer(optimizer_class, lr=1e-3) - opt.hook(torch.nn.Linear(10, 10).parameters()) - sch = TorchScheduler(scheduler_class) - sch.hook(opt) diff --git a/tests/test_solver/test_autoregressive_ensemble_solver.py b/tests/test_solver/test_autoregressive_ensemble_solver.py new file mode 100644 index 000000000..0ca9a86bd --- /dev/null +++ b/tests/test_solver/test_autoregressive_ensemble_solver.py @@ -0,0 +1,246 @@ +import pytest +import torch +from pina.solver import AutoregressiveEnsembleSolver +from pina import Trainer, LabelTensor, Condition +from pina.condition import TimeSeriesCondition +from pina.problem import BaseProblem +from pina.model import FeedForward + + +# Settings for test purposes +n_traj = 5 +t_steps = 10 +n_feats = 2 +n_windows = 3 +unroll_length = 5 +n_models = 4 + + +# Helper function to create tensor data +def create_data(n_traj, t_steps, n_feats, use_lt): + + # Define the data tensor + data = torch.rand(n_traj, t_steps, n_feats) + + # Add labels if use_lt is True + if use_lt: + labels = [f"feat_{i}" for i in range(n_feats)] + return LabelTensor(data, labels=labels) + else: + return data + + +# Define a dummy problem for testing +class DummyProblem(BaseProblem): + + # Input and output variables + input_variables = [f"feat_{i}" for i in range(n_feats)] + output_variables = [f"feat_{i}" for i in range(n_feats)] + + # Conditions + conditions = {} + + def __init__(self, data): + super().__init__() + + # Initialize the time series condition with the provided data + self.conditions["time"] = Condition( + input=data, n_windows=n_windows, unroll_length=unroll_length + ) + + +def create_models(): + return [FeedForward(n_feats, n_feats, 32, 2) for _ in range(n_models)] + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("bool_value", [True, False]) +@pytest.mark.parametrize("eps", [0.0, 1.0]) +def test_constructor(use_lt, bool_value, eps): + + # Define the problem and models + data = create_data(n_traj, t_steps, n_feats, use_lt) + problem = DummyProblem(data) + models = create_models() + + # Define the solver + solver = AutoregressiveEnsembleSolver( + problem=problem, + models=models, + reset_weights_at_epoch_start=bool_value, + use_lt=use_lt, + eps=eps, + ) + + # Assert accepted condition types + assert solver.accepted_conditions_types == (TimeSeriesCondition,) + + # Should fail if eps is not a float or int + with pytest.raises(ValueError): + AutoregressiveEnsembleSolver( + problem=problem, + models=models, + reset_weights_at_epoch_start=bool_value, + use_lt=use_lt, + eps="not_a_number", + ) + + # Should fail if reset_weights_at_epoch_start is not a boolean + with pytest.raises(ValueError): + AutoregressiveEnsembleSolver( + problem=problem, + models=models, + reset_weights_at_epoch_start="not_a_boolean", + use_lt=use_lt, + eps=eps, + ) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("batch_size", [None, 1, 2, 5]) +def test_solver_train(use_lt, batch_size): + + # Define the problem and models + data = create_data(n_traj, t_steps, n_feats, use_lt) + problem = DummyProblem(data) + models = create_models() + + # Define the solver + solver = AutoregressiveEnsembleSolver( + problem=problem, + models=models, + use_lt=use_lt, + ) + + # Trainer + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("batch_size", [None, 1, 2, 5]) +def test_solver_validation(use_lt, batch_size): + + # Define the problem and models + data = create_data(n_traj, t_steps, n_feats, use_lt) + problem = DummyProblem(data) + models = create_models() + + # Define the solver + solver = AutoregressiveEnsembleSolver( + problem=problem, + models=models, + use_lt=use_lt, + ) + + # Trainer + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("batch_size", [None, 1, 2, 5]) +def test_solver_test(use_lt, batch_size): + + # Define the problem and models + data = create_data(n_traj, t_steps, n_feats, use_lt) + problem = DummyProblem(data) + models = create_models() + + # Define the solver + solver = AutoregressiveEnsembleSolver( + problem=problem, + models=models, + use_lt=use_lt, + ) + + # Trainer + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.6, + val_size=0.2, + test_size=0.2, + ) + trainer.train() + + +@pytest.mark.parametrize("use_lt", [True, False]) +def test_train_load_restore(clean_tmp_dir, use_lt): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Define the problem and models + data = create_data(n_traj, t_steps, n_feats, use_lt) + problem = DummyProblem(data) + models = create_models() + + # Define the solver + solver = AutoregressiveEnsembleSolver( + problem=problem, + models=models, + use_lt=use_lt, + ) + + # Train + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + default_root_dir=dir, + ) + trainer.train() + + # Restore from checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the restored solver + new_solver = AutoregressiveEnsembleSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + models=models, + ) + + # Test points + test_pts = LabelTensor( + torch.rand(n_traj, t_steps, n_feats), problem.input_variables + ) + + # Assert that the predictions from the loaded solver match original ones + assert new_solver.forward(test_pts).shape == ( + n_models, + n_traj, + t_steps, + n_feats, + ) + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_autoregressive_single_model_solver.py b/tests/test_solver/test_autoregressive_single_model_solver.py new file mode 100644 index 000000000..f7b12463e --- /dev/null +++ b/tests/test_solver/test_autoregressive_single_model_solver.py @@ -0,0 +1,265 @@ +import pytest +import torch +from pina.solver import AutoregressiveSingleModelSolver +from pina import Trainer, LabelTensor, Condition +from pina.condition import TimeSeriesCondition +from pina.problem import BaseProblem +from pina.model import FeedForward + + +# Settings for test purposes +n_traj = 5 +t_steps = 10 +n_dofs = 40 +n_feats = 2 +n_windows = 3 +unroll_length = 5 + + +# Helper function to create tensor data +def create_scalar_data(use_lt): + + # Define the data tensor + data = torch.rand(n_traj, t_steps, n_feats) + + # Add labels if use_lt is True + if use_lt: + labels = [f"feat_{i}" for i in range(n_feats)] + return LabelTensor(data, labels=labels) + else: + return data + + +def create_vector_data(use_lt): + data = torch.rand(n_traj, t_steps, n_dofs, n_feats) + if use_lt: + labels = [f"feat_{i}" for i in range(n_feats)] + return LabelTensor(data, labels=labels) + else: + return data + + +# Define a dummy problem for testing +class DummyProblem(BaseProblem): + + # Input and output variables + input_variables = [f"feat_{i}" for i in range(n_feats)] + output_variables = [f"feat_{i}" for i in range(n_feats)] + + # Conditions + conditions = {} + + def __init__(self, data): + super().__init__() + + # Initialize the time series condition with the provided data + self.conditions["time"] = TimeSeriesCondition( + input=data, n_windows=n_windows, unroll_length=unroll_length + ) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("bool_value", [True, False]) +@pytest.mark.parametrize("eps", [0.0, 1.0]) +@pytest.mark.parametrize( + "create_data", [create_scalar_data, create_vector_data] +) +@pytest.mark.parametrize("aggregation_strategy", [torch.mean, torch.sum]) +def test_constructor( + use_lt, bool_value, eps, create_data, aggregation_strategy +): + + # Define the problem + data = create_data(use_lt) + problem = DummyProblem(data) + model = FeedForward(n_feats, n_feats, 10, 2) + + # Define the solver + solver = AutoregressiveSingleModelSolver( + problem=problem, + model=model, + reset_weights_at_epoch_start=bool_value, + use_lt=use_lt, + eps=eps, + ) + + # Assert accepted condition types + assert solver.accepted_conditions_types == (TimeSeriesCondition,) + + # Should fail if eps is not a float or int + with pytest.raises(ValueError): + AutoregressiveSingleModelSolver( + problem=problem, + model=model, + reset_weights_at_epoch_start=bool_value, + use_lt=use_lt, + eps="not_a_number", + ) + + # Should fail if reset_weights_at_epoch_start is not a boolean + with pytest.raises(ValueError): + AutoregressiveSingleModelSolver( + problem=problem, + model=model, + reset_weights_at_epoch_start="not_a_boolean", + use_lt=use_lt, + eps=eps, + ) + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("batch_size", [None, 1, 2, 5]) +@pytest.mark.parametrize("compile", [True, False]) +@pytest.mark.parametrize( + "create_data", [create_scalar_data, create_vector_data] +) +def test_solver_train(use_lt, batch_size, compile, create_data): + + # Define the problem + data = create_data(use_lt) + problem = DummyProblem(data) + model = FeedForward(n_feats, n_feats, 10, 2) + + # Define the solver + solver = AutoregressiveSingleModelSolver( + problem=problem, + model=model, + use_lt=use_lt, + ) + + # Trainer + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("batch_size", [None, 1, 2, 5]) +@pytest.mark.parametrize("compile", [True, False]) +@pytest.mark.parametrize( + "create_data", [create_scalar_data, create_vector_data] +) +def test_solver_validation(use_lt, batch_size, compile, create_data): + + # Define the problem + data = create_data(use_lt) + problem = DummyProblem(data) + model = FeedForward(n_feats, n_feats, 10, 2) + + # Define the solver + solver = AutoregressiveSingleModelSolver( + problem=problem, + model=model, + use_lt=use_lt, + ) + + # Trainer + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize("batch_size", [None, 1, 2, 5]) +@pytest.mark.parametrize("compile", [True, False]) +@pytest.mark.parametrize( + "create_data", [create_scalar_data, create_vector_data] +) +def test_solver_test(use_lt, batch_size, compile, create_data): + + # Define the problem + data = create_data(use_lt) + problem = DummyProblem(data) + model = FeedForward(n_feats, n_feats, 10, 2) + + # Define the solver + solver = AutoregressiveSingleModelSolver( + problem=problem, + model=model, + use_lt=use_lt, + ) + + # Trainer + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.6, + val_size=0.2, + test_size=0.2, + ) + trainer.test() + + +@pytest.mark.parametrize("use_lt", [True, False]) +@pytest.mark.parametrize( + "create_data", [create_scalar_data, create_vector_data] +) +def test_train_load_restore(clean_tmp_dir, use_lt, create_data): + + # Define the problem + data = create_data(use_lt) + problem = DummyProblem(data) + model = FeedForward(n_feats, n_feats, 10, 2) + dir = clean_tmp_dir + + # Define the solver + solver = AutoregressiveSingleModelSolver( + problem=problem, + model=model, + use_lt=use_lt, + ) + + # Train + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.2, + test_size=0.1, + default_root_dir=dir, + ) + trainer.train() + + # Restore from checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the restored solver + new_solver = AutoregressiveSingleModelSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + # Test points + test_pts = LabelTensor( + torch.rand(n_traj, t_steps, n_feats), problem.input_variables + ) + + # Assert that the predictions from the loaded solver match original ones + assert new_solver.forward(test_pts).shape == (n_traj, t_steps, n_feats) + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_causal_physics_informed_single_model_solver.py b/tests/test_solver/test_causal_physics_informed_single_model_solver.py new file mode 100644 index 000000000..401895b11 --- /dev/null +++ b/tests/test_solver/test_causal_physics_informed_single_model_solver.py @@ -0,0 +1,248 @@ +import torch +import pytest +from pina.problem.zoo import AllenCahnProblem, SupervisedProblem +from pina.solver import CausalPhysicsInformedSingleModelSolver +from pina import LabelTensor, Condition, Trainer +from pina.model import FeedForward +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) + + +# Helper function for direct problem definition +def define_direct_problem_model(n_pts=10): + + # Initialize direct problem + problem = AllenCahnProblem() + problem.discretise_domain(n_pts) + + # Add input-output condition to test supervised learning + input_pts = torch.rand(10, len(problem.input_variables)) + input_pts = LabelTensor(input_pts, problem.input_variables) + output_pts = torch.rand(10, len(problem.output_variables)) + output_pts = LabelTensor(output_pts, problem.output_variables) + problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + problem.conditions["data"].name = "data" + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +# Helper for a dummy supervised problem definition +def define_dummy_problem(): + + # Initialize a dummy supervised problem + input_pts = torch.rand(10, 2) + input_pts = LabelTensor(input_pts, ["x", "y"]) + output_pts = torch.rand(10, 1) + output_pts = LabelTensor(output_pts, ["u"]) + problem = SupervisedProblem(input_=input_pts, output_=output_pts) + + return problem + + +@pytest.mark.parametrize("regularized_conditions", [["D", "t0"], "D"]) +def test_constructor(regularized_conditions): + + # Initialize problem and model + problem, model = define_direct_problem_model() + + # Define the solver + solver = CausalPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + eps=100, + n_steps=10, + regularized_conditions=regularized_conditions, + ) + + # Assert accepted conditions types + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + # Should fail if the problem is not time-dependent + with pytest.raises(ValueError): + problem = define_dummy_problem() + CausalPhysicsInformedSingleModelSolver(problem=problem, model=model) + + # Should fail if no regularized conditions are specified + with pytest.raises(ValueError): + CausalPhysicsInformedSingleModelSolver( + problem=problem, model=model, regularized_conditions=None + ) + + # Should fail if eps is not a float or int + with pytest.raises(ValueError): + CausalPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + eps="invalid", + regularized_conditions="D", + ) + + # Should fail if n_steps is not a positive integer + with pytest.raises(ValueError): + CausalPhysicsInformedSingleModelSolver( + problem=problem, model=model, n_steps=-1, regularized_conditions="D" + ) + + # Should fail if regularized_conditions is not a string or a list of strings + with pytest.raises(ValueError): + CausalPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularized_conditions=123, + eps=100, + n_steps=10, + ) + + # Should fail if the provided conditions are not present in the problem + with pytest.raises(ValueError): + CausalPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularized_conditions=["D", "nonexistent_condition"], + eps=100, + n_steps=10, + ) + + +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("regularized_conditions", [["D", "t0"], "D"]) +def test_solver_train(batch_size, regularized_conditions): + + # Initialize problem and model + problem, model = define_direct_problem_model() + + # Define the solver + solver = CausalPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularized_conditions=regularized_conditions, + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("regularized_conditions", [["D", "t0"], "D"]) +def test_solver_validation(batch_size, regularized_conditions): + + # Initialize problem and model + problem, model = define_direct_problem_model() + + # Define the solver + solver = CausalPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularized_conditions=regularized_conditions, + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("regularized_conditions", [["D", "t0"], "D"]) +def test_solver_test(batch_size, regularized_conditions): + + # Initialize problem and model + problem, model = define_direct_problem_model() + + # Define the solver + solver = CausalPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularized_conditions=regularized_conditions, + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + ) + trainer.test() + + +def test_train_load_restore(clean_tmp_dir): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Initialize problem and model + problem, model = define_direct_problem_model() + + # Define the solver + solver = CausalPhysicsInformedSingleModelSolver( + problem=problem, model=model, regularized_conditions="D" + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.1, + test_size=0.2, + default_root_dir=dir, + ) + trainer.train() + + # Restore the training from a checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the solver from a checkpoint + new_solver = CausalPhysicsInformedSingleModelSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + regularized_conditions="D", + ) + + # Create input data for testing the forward pass + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + + # Assert the loaded solver behaves as the original one + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_causal_pinn.py b/tests/test_solver/test_causal_pinn.py deleted file mode 100644 index 82e61ed3f..000000000 --- a/tests/test_solver/test_causal_pinn.py +++ /dev/null @@ -1,160 +0,0 @@ -import torch -import pytest - -from pina import LabelTensor, Condition -from pina.problem import SpatialProblem -from pina.solver import CausalPINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.problem.zoo import DiffusionReactionProblem -from pina.condition import ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, -) -from torch._dynamo.eval_frame import OptimizedModule - - -class DummySpatialProblem(SpatialProblem): - """ - A mock spatial problem for testing purposes. - """ - - output_variables = ["u"] - conditions = {} - spatial_domain = None - - -# define problems -problem = DiffusionReactionProblem() -problem.discretise_domain(10) - -# add input-output condition to test supervised learning -input_pts = torch.rand(10, len(problem.input_variables)) -input_pts = LabelTensor(input_pts, problem.input_variables) -output_pts = torch.rand(10, len(problem.output_variables)) -output_pts = LabelTensor(output_pts, problem.output_variables) -problem.conditions["data"] = Condition(input=input_pts, target=output_pts) - -# define model -model = FeedForward(len(problem.input_variables), len(problem.output_variables)) - - -@pytest.mark.parametrize("problem", [problem]) -@pytest.mark.parametrize("eps", [100, 100.1]) -def test_constructor(problem, eps): - with pytest.raises(ValueError): - CausalPINN(model=model, problem=DummySpatialProblem()) - solver = CausalPINN(model=model, problem=problem, eps=eps) - - assert solver.accepted_conditions_types == ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, - ) - - -@pytest.mark.parametrize("problem", [problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_train(problem, batch_size, compile): - solver = CausalPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - val_size=0.0, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_validation(problem, batch_size, compile): - solver = CausalPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_test(problem, batch_size, compile): - solver = CausalPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.7, - val_size=0.2, - test_size=0.1, - compile=compile, - ) - trainer.test() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem]) -def test_train_load_restore(problem): - dir = "tests/test_solver/tmp" - problem = problem - solver = CausalPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.7, - val_size=0.2, - test_size=0.1, - default_root_dir=dir, - ) - trainer.train() - - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = CausalPINN.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - model=model, - ) - - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 1) - assert new_solver.forward(test_pts).shape == ( - solver.forward(test_pts).shape - ) - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_competitive_physics_informed_solver.py b/tests/test_solver/test_competitive_physics_informed_solver.py new file mode 100644 index 000000000..a471b7237 --- /dev/null +++ b/tests/test_solver/test_competitive_physics_informed_solver.py @@ -0,0 +1,199 @@ +import torch +import pytest +from pina.problem.zoo import InversePoisson2DSquareProblem +from pina.solver import CompetitivePhysicsInformedSolver +from pina.problem.zoo import Poisson2DSquareProblem +from pina import LabelTensor, Condition, Trainer +from pina.model import FeedForward +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) + + +# Helper function for direct problem definition +def define_direct_problem_model(n_pts=10): + + # Initialize direct problem + problem = Poisson2DSquareProblem() + problem.discretise_domain(n_pts) + + # Add input-output condition to test supervised learning + input_pts = torch.rand(10, len(problem.input_variables)) + input_pts = LabelTensor(input_pts, problem.input_variables) + output_pts = torch.rand(10, len(problem.output_variables)) + output_pts = LabelTensor(output_pts, problem.output_variables) + problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +# Helper function for inverse problem definition +def define_inverse_problem_model(n_pts=10): + + # Initialize inverse problem + problem = InversePoisson2DSquareProblem(load=True, data_size=0.01) + problem.discretise_domain(n_pts) + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +def test_constructor(case): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = CompetitivePhysicsInformedSolver(problem=problem, model=model) + + # Assert accepted conditions types + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +def test_solver_train(case, batch_size): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = CompetitivePhysicsInformedSolver(problem=problem, model=model) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +def test_solver_validation(case, batch_size): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = CompetitivePhysicsInformedSolver(problem=problem, model=model) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +def test_solver_test(case, batch_size): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = CompetitivePhysicsInformedSolver(problem=problem, model=model) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + ) + trainer.test() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +def test_train_load_restore(clean_tmp_dir, case): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = CompetitivePhysicsInformedSolver(problem=problem, model=model) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.1, + test_size=0.2, + default_root_dir=dir, + ) + trainer.train() + + # Restore the training from a checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the solver from a checkpoint + new_solver = CompetitivePhysicsInformedSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + # Create input data for testing the forward pass + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + + # Assert the loaded solver behaves as the original one + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_competitive_pinn.py b/tests/test_solver/test_competitive_pinn.py deleted file mode 100644 index 8f585f029..000000000 --- a/tests/test_solver/test_competitive_pinn.py +++ /dev/null @@ -1,159 +0,0 @@ -import torch -import pytest - -from pina import LabelTensor, Condition -from pina.solver import CompetitivePINN as CompPINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.problem.zoo import ( - Poisson2DSquareProblem as Poisson, - InversePoisson2DSquareProblem as InversePoisson, -) -from pina.condition import ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, -) -from torch._dynamo.eval_frame import OptimizedModule - - -# define problems -problem = Poisson() -problem.discretise_domain(10) -inverse_problem = InversePoisson(load=True, data_size=0.01) -inverse_problem.discretise_domain(10) - -# add input-output condition to test supervised learning -input_pts = torch.rand(10, len(problem.input_variables)) -input_pts = LabelTensor(input_pts, problem.input_variables) -output_pts = torch.rand(10, len(problem.output_variables)) -output_pts = LabelTensor(output_pts, problem.output_variables) -problem.conditions["data"] = Condition(input=input_pts, target=output_pts) - -# define model -model = FeedForward(len(problem.input_variables), len(problem.output_variables)) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("discr", [None, model]) -def test_constructor(problem, discr): - solver = CompPINN(problem=problem, model=model) - solver = CompPINN(problem=problem, model=model, discriminator=discr) - - assert solver.accepted_conditions_types == ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_train(problem, batch_size, compile): - solver = CompPINN(problem=problem, model=model) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - val_size=0.0, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_validation(problem, batch_size, compile): - solver = CompPINN(problem=problem, model=model) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_test(problem, batch_size, compile): - solver = CompPINN(problem=problem, model=model) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.7, - val_size=0.2, - test_size=0.1, - compile=compile, - ) - trainer.test() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -def test_train_load_restore(problem): - dir = "tests/test_solver/tmp" - problem = problem - solver = CompPINN(problem=problem, model=model) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.7, - val_size=0.2, - test_size=0.1, - default_root_dir=dir, - ) - trainer.train() - - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = CompPINN.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - model=model, - ) - - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 1) - assert new_solver.forward(test_pts).shape == ( - solver.forward(test_pts).shape - ) - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_ensemble_pinn.py b/tests/test_solver/test_ensemble_pinn.py deleted file mode 100644 index 50669f00e..000000000 --- a/tests/test_solver/test_ensemble_pinn.py +++ /dev/null @@ -1,149 +0,0 @@ -import pytest -import torch - -from pina import LabelTensor, Condition -from pina.model import FeedForward -from pina.trainer import Trainer -from pina.solver import DeepEnsemblePINN -from pina.condition import ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, -) -from pina.problem.zoo import Poisson2DSquareProblem as Poisson -from torch._dynamo.eval_frame import OptimizedModule - - -# define problems -problem = Poisson() -problem.discretise_domain(10) - -# add input-output condition to test supervised learning -input_pts = torch.rand(10, len(problem.input_variables)) -input_pts = LabelTensor(input_pts, problem.input_variables) -output_pts = torch.rand(10, len(problem.output_variables)) -output_pts = LabelTensor(output_pts, problem.output_variables) -problem.conditions["data"] = Condition(input=input_pts, target=output_pts) - -# define models -models = [ - FeedForward( - len(problem.input_variables), len(problem.output_variables), n_layers=1 - ) - for _ in range(5) -] - - -def test_constructor(): - solver = DeepEnsemblePINN(problem=problem, models=models) - - assert solver.accepted_conditions_types == ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, - ) - assert solver.num_ensemble == 5 - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_train(batch_size, compile): - solver = DeepEnsemblePINN(models=models, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - val_size=0.0, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_validation(batch_size, compile): - solver = DeepEnsemblePINN(models=models, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_test(batch_size, compile): - solver = DeepEnsemblePINN(models=models, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.7, - val_size=0.2, - test_size=0.1, - compile=compile, - ) - trainer.test() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -def test_train_load_restore(): - dir = "tests/test_solver/tmp" - solver = DeepEnsemblePINN(models=models, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.7, - val_size=0.2, - test_size=0.1, - default_root_dir=dir, - ) - trainer.train() - - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = DeepEnsemblePINN.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - models=models, - ) - - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_ensemble_supervised_solver.py b/tests/test_solver/test_ensemble_supervised_solver.py deleted file mode 100644 index c5f0b9e52..000000000 --- a/tests/test_solver/test_ensemble_supervised_solver.py +++ /dev/null @@ -1,276 +0,0 @@ -import torch -import pytest -from torch._dynamo.eval_frame import OptimizedModule -from torch_geometric.nn import GCNConv -from torch_geometric.utils import to_dense_batch -from pina import Condition, LabelTensor -from pina.condition import InputTargetCondition -from pina.problem import AbstractProblem -from pina.solver import DeepEnsembleSupervisedSolver -from pina.model import FeedForward -from pina.trainer import Trainer -from pina.graph import KNNGraph - - -class LabelTensorProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data": Condition( - input=LabelTensor(torch.randn(20, 2), ["u_0", "u_1"]), - target=LabelTensor(torch.randn(20, 1), ["u"]), - ), - } - - -class TensorProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data": Condition(input=torch.randn(20, 2), target=torch.randn(20, 1)) - } - - -x = torch.rand((15, 20, 5)) -pos = torch.rand((15, 20, 2)) -output_ = torch.rand((15, 20, 1)) -input_ = [ - KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True) - for x_, pos_ in zip(x, pos) -] - - -class GraphProblem(AbstractProblem): - output_variables = None - conditions = {"data": Condition(input=input_, target=output_)} - - -x = LabelTensor(torch.rand((15, 20, 5)), ["a", "b", "c", "d", "e"]) -pos = LabelTensor(torch.rand((15, 20, 2)), ["x", "y"]) -output_ = LabelTensor(torch.rand((15, 20, 1)), ["u"]) -input_ = [ - KNNGraph(x=x[i], pos=pos[i], neighbours=3, edge_attr=True) - for i in range(len(x)) -] - - -class GraphProblemLT(AbstractProblem): - output_variables = ["u"] - input_variables = ["a", "b", "c", "d", "e"] - conditions = {"data": Condition(input=input_, target=output_)} - - -models = [FeedForward(2, 1) for i in range(10)] - - -class Models(torch.nn.Module): - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.lift = torch.nn.Linear(5, 10) - self.activation = torch.nn.Tanh() - self.output = torch.nn.Linear(10, 1) - - self.conv = GCNConv(10, 10) - - def forward(self, batch): - - x = batch.x - edge_index = batch.edge_index - for _ in range(1): - y = self.lift(x) - y = self.activation(y) - y = self.conv(y, edge_index) - y = self.activation(y) - y = self.output(y) - return to_dense_batch(y, batch.batch)[0] - - -graph_models = [Models() for i in range(10)] - - -def test_constructor(): - solver = DeepEnsembleSupervisedSolver( - problem=TensorProblem(), models=models - ) - DeepEnsembleSupervisedSolver(problem=LabelTensorProblem(), models=models) - assert DeepEnsembleSupervisedSolver.accepted_conditions_types == ( - InputTargetCondition - ) - assert solver.num_ensemble == 10 - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("use_lt", [True, False]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_train(use_lt, batch_size, compile): - problem = LabelTensorProblem() if use_lt else TensorProblem() - solver = DeepEnsembleSupervisedSolver( - problem=problem, models=models, use_lt=use_lt - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - test_size=0.0, - val_size=0.0, - compile=compile, - ) - - trainer.train() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("use_lt", [True, False]) -def test_solver_train_graph(batch_size, use_lt): - problem = GraphProblemLT() if use_lt else GraphProblem() - solver = DeepEnsembleSupervisedSolver( - problem=problem, models=graph_models, use_lt=use_lt - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - test_size=0.0, - val_size=0.0, - ) - - trainer.train() - - -@pytest.mark.parametrize("use_lt", [True, False]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_validation(use_lt, compile): - problem = LabelTensorProblem() if use_lt else TensorProblem() - solver = DeepEnsembleSupervisedSolver( - problem=problem, models=models, use_lt=use_lt - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=None, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("use_lt", [True, False]) -def test_solver_validation_graph(batch_size, use_lt): - problem = GraphProblemLT() if use_lt else GraphProblem() - solver = DeepEnsembleSupervisedSolver( - problem=problem, models=graph_models, use_lt=use_lt - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.9, - val_size=0.1, - test_size=0.0, - ) - - trainer.train() - - -@pytest.mark.parametrize("use_lt", [True, False]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_test(use_lt, compile): - problem = LabelTensorProblem() if use_lt else TensorProblem() - solver = DeepEnsembleSupervisedSolver( - problem=problem, models=models, use_lt=use_lt - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=None, - train_size=0.8, - val_size=0.1, - test_size=0.1, - compile=compile, - ) - trainer.test() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("use_lt", [True, False]) -def test_solver_test_graph(batch_size, use_lt): - problem = GraphProblemLT() if use_lt else GraphProblem() - solver = DeepEnsembleSupervisedSolver( - problem=problem, models=graph_models, use_lt=use_lt - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.8, - val_size=0.1, - test_size=0.1, - ) - - trainer.test() - - -def test_train_load_restore(): - dir = "tests/test_solver/tmp/" - problem = LabelTensorProblem() - solver = DeepEnsembleSupervisedSolver(problem=problem, models=models) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.9, - test_size=0.1, - val_size=0.0, - default_root_dir=dir, - ) - trainer.train() - - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = DeepEnsembleSupervisedSolver.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - models=models, - ) - - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_garom.py b/tests/test_solver/test_garom.py deleted file mode 100644 index 62575825c..000000000 --- a/tests/test_solver/test_garom.py +++ /dev/null @@ -1,208 +0,0 @@ -import torch -import torch.nn as nn - -import pytest -from pina import Condition -from pina.solver import GAROM -from pina.condition import InputTargetCondition -from pina.problem import AbstractProblem -from pina.model import FeedForward -from pina.trainer import Trainer -from torch._dynamo.eval_frame import OptimizedModule - - -class TensorProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data": Condition(target=torch.randn(10, 2), input=torch.randn(10, 1)) - } - - -# simple Generator Network -class Generator(nn.Module): - - def __init__( - self, - input_dimension=2, - parameters_dimension=1, - noise_dimension=2, - activation=torch.nn.SiLU, - ): - super().__init__() - - self._noise_dimension = noise_dimension - self._activation = activation - self.model = FeedForward(6 * noise_dimension, input_dimension) - self.condition = FeedForward(parameters_dimension, 5 * noise_dimension) - - def forward(self, param): - # uniform sampling in [-1, 1] - z = ( - 2 - * torch.rand( - size=(param.shape[0], self._noise_dimension), - device=param.device, - dtype=param.dtype, - requires_grad=True, - ) - - 1 - ) - return self.model(torch.cat((z, self.condition(param)), dim=-1)) - - -# Simple Discriminator Network - - -class Discriminator(nn.Module): - - def __init__( - self, - input_dimension=2, - parameter_dimension=1, - hidden_dimension=2, - activation=torch.nn.ReLU, - ): - super().__init__() - - self._activation = activation - self.encoding = FeedForward(input_dimension, hidden_dimension) - self.decoding = FeedForward(2 * hidden_dimension, input_dimension) - self.condition = FeedForward(parameter_dimension, hidden_dimension) - - def forward(self, data): - x, condition = data - encoding = self.encoding(x) - conditioning = torch.cat((encoding, self.condition(condition)), dim=-1) - decoding = self.decoding(conditioning) - return decoding - - -def test_constructor(): - GAROM( - problem=TensorProblem(), - generator=Generator(), - discriminator=Discriminator(), - ) - assert GAROM.accepted_conditions_types == (InputTargetCondition) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_train(batch_size, compile): - solver = GAROM( - problem=TensorProblem(), - generator=Generator(), - discriminator=Discriminator(), - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - test_size=0.0, - val_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_validation(batch_size, compile): - solver = GAROM( - problem=TensorProblem(), - generator=Generator(), - discriminator=Discriminator(), - ) - - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_test(batch_size, compile): - solver = GAROM( - problem=TensorProblem(), - generator=Generator(), - discriminator=Discriminator(), - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.8, - val_size=0.1, - test_size=0.1, - compile=compile, - ) - trainer.test() - if trainer.compile: - assert all( - [isinstance(model, OptimizedModule) for model in solver.models] - ) - - -def test_train_load_restore(): - dir = "tests/test_solver/tmp/" - problem = TensorProblem() - solver = GAROM( - problem=TensorProblem(), - generator=Generator(), - discriminator=Discriminator(), - ) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.9, - test_size=0.1, - val_size=0.0, - default_root_dir=dir, - ) - trainer.train() - - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = GAROM.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=TensorProblem(), - generator=Generator(), - discriminator=Discriminator(), - ) - - test_pts = torch.rand(20, 1) - assert new_solver.forward(test_pts).shape == (20, 2) - assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_gradient_physics_informed_single_model_solver.py b/tests/test_solver/test_gradient_physics_informed_single_model_solver.py new file mode 100644 index 000000000..7a394bdf1 --- /dev/null +++ b/tests/test_solver/test_gradient_physics_informed_single_model_solver.py @@ -0,0 +1,270 @@ +import torch +import pytest +from pina.problem.zoo import Poisson2DSquareProblem, SupervisedProblem +from pina.solver import GradientPhysicsInformedSingleModelSolver +from pina.problem.zoo import InversePoisson2DSquareProblem +from pina import LabelTensor, Condition, Trainer +from pina.model import FeedForward +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) + + +# Helper function for direct problem definition +def define_direct_problem_model(n_pts=10): + + # Initialize direct problem + problem = Poisson2DSquareProblem() + problem.discretise_domain(n_pts) + + # Add input-output condition to test supervised learning + input_pts = torch.rand(10, len(problem.input_variables)) + input_pts = LabelTensor(input_pts, problem.input_variables) + output_pts = torch.rand(10, len(problem.output_variables)) + output_pts = LabelTensor(output_pts, problem.output_variables) + problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +# Helper function for inverse problem definition +def define_inverse_problem_model(n_pts=10): + + # Initialize inverse problem + problem = InversePoisson2DSquareProblem(load=True, data_size=0.01) + problem.discretise_domain(n_pts) + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +# Helper for a dummy supervised problem definition +def define_dummy_problem(): + + # Initialize a dummy supervised problem + input_pts = torch.rand(10, 2) + input_pts = LabelTensor(input_pts, ["x", "y"]) + output_pts = torch.rand(10, 1) + output_pts = LabelTensor(output_pts, ["u"]) + problem = SupervisedProblem(input_=input_pts, output_=output_pts) + + return problem + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("regularization_weight", [0.5, 1]) +@pytest.mark.parametrize("regularized_conditions", [None, "D"]) +def test_constructor(case, regularization_weight, regularized_conditions): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = GradientPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularization_weight=regularization_weight, + regularized_conditions=regularized_conditions, + ) + + # Assert accepted conditions types + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + # Should fail if regularization_weight is not a float or int + with pytest.raises(ValueError): + GradientPhysicsInformedSingleModelSolver( + problem=problem, model=model, regularization_weight="invalid" + ) + + # Should fail if regularized_conditions is not a string or a list of strings + with pytest.raises(ValueError): + GradientPhysicsInformedSingleModelSolver( + problem=problem, model=model, regularized_conditions=123 + ) + + # Should fail if problem is not an instance of SpatialProblem + with pytest.raises(ValueError): + dummy_problem = define_dummy_problem() + GradientPhysicsInformedSingleModelSolver( + problem=dummy_problem, + model=model, + regularization_weight=regularization_weight, + regularized_conditions=regularized_conditions, + ) + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("regularization_weight", [0.5, 1]) +@pytest.mark.parametrize("regularized_conditions", [None, "D"]) +def test_solver_train( + case, batch_size, regularization_weight, regularized_conditions +): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = GradientPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularization_weight=regularization_weight, + regularized_conditions=regularized_conditions, + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("regularization_weight", [0.5, 1]) +@pytest.mark.parametrize("regularized_conditions", [None, "D"]) +def test_solver_validation( + case, batch_size, regularization_weight, regularized_conditions +): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = GradientPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularization_weight=regularization_weight, + regularized_conditions=regularized_conditions, + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("regularization_weight", [0.5, 1]) +@pytest.mark.parametrize("regularized_conditions", [None, "D"]) +def test_solver_test( + case, batch_size, regularization_weight, regularized_conditions +): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = GradientPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularization_weight=regularization_weight, + regularized_conditions=regularized_conditions, + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + ) + trainer.test() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +def test_train_load_restore(clean_tmp_dir, case): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = GradientPhysicsInformedSingleModelSolver( + problem=problem, model=model + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.1, + test_size=0.2, + default_root_dir=dir, + ) + trainer.train() + + # Restore the training from a checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the solver from a checkpoint + new_solver = GradientPhysicsInformedSingleModelSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + # Create input data for testing the forward pass + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + + # Assert the loaded solver behaves as the original one + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_gradient_pinn.py b/tests/test_solver/test_gradient_pinn.py deleted file mode 100644 index c28fc347e..000000000 --- a/tests/test_solver/test_gradient_pinn.py +++ /dev/null @@ -1,164 +0,0 @@ -import pytest -import torch - -from pina import LabelTensor, Condition -from pina.problem import TimeDependentProblem -from pina.solver import GradientPINN -from pina.model import FeedForward -from pina.trainer import Trainer -from pina.problem.zoo import ( - Poisson2DSquareProblem as Poisson, - InversePoisson2DSquareProblem as InversePoisson, -) -from pina.condition import ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, -) -from torch._dynamo.eval_frame import OptimizedModule - - -class DummyTimeProblem(TimeDependentProblem): - """ - A mock time-dependent problem for testing purposes. - """ - - output_variables = ["u"] - temporal_domain = None - conditions = {} - - -# define problems -problem = Poisson() -problem.discretise_domain(10) -inverse_problem = InversePoisson(load=True, data_size=0.01) -inverse_problem.discretise_domain(10) - -# add input-output condition to test supervised learning -input_pts = torch.rand(10, len(problem.input_variables)) -input_pts = LabelTensor(input_pts, problem.input_variables) -output_pts = torch.rand(10, len(problem.output_variables)) -output_pts = LabelTensor(output_pts, problem.output_variables) -problem.conditions["data"] = Condition(input=input_pts, target=output_pts) - -# define model -model = FeedForward(len(problem.input_variables), len(problem.output_variables)) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -def test_constructor(problem): - with pytest.raises(ValueError): - GradientPINN(model=model, problem=DummyTimeProblem()) - solver = GradientPINN(model=model, problem=problem) - - assert solver.accepted_conditions_types == ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_train(problem, batch_size, compile): - solver = GradientPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - val_size=0.0, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_validation(problem, batch_size, compile): - solver = GradientPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_test(problem, batch_size, compile): - solver = GradientPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.7, - val_size=0.2, - test_size=0.1, - compile=compile, - ) - trainer.test() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -def test_train_load_restore(problem): - dir = "tests/test_solver/tmp" - problem = problem - solver = GradientPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.7, - val_size=0.2, - test_size=0.1, - default_root_dir=dir, - ) - trainer.train() - - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = GradientPINN.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - model=model, - ) - - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 1) - assert new_solver.forward(test_pts).shape == ( - solver.forward(test_pts).shape - ) - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_physics_informed_ensemble_solver.py b/tests/test_solver/test_physics_informed_ensemble_solver.py new file mode 100644 index 000000000..b1644655a --- /dev/null +++ b/tests/test_solver/test_physics_informed_ensemble_solver.py @@ -0,0 +1,202 @@ +import pytest +import torch +from pina.problem.zoo import InversePoisson2DSquareProblem +from pina.solver import PhysicsInformedEnsembleSolver +from pina.problem.zoo import Poisson2DSquareProblem +from pina import LabelTensor, Trainer, Condition +from pina.model import FeedForward +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) + + +# Helper function for direct problem definition +def define_direct_problem_model(n_pts=10, n_models=3): + + # Initialize direct problem + problem = Poisson2DSquareProblem() + problem.discretise_domain(n_pts) + + # Add input-output condition to test supervised learning + input_pts = torch.rand(10, len(problem.input_variables)) + input_pts = LabelTensor(input_pts, problem.input_variables) + output_pts = torch.rand(10, len(problem.output_variables)) + output_pts = LabelTensor(output_pts, problem.output_variables) + problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + + # Initialize the models + models = [ + FeedForward(len(problem.input_variables), len(problem.output_variables)) + for _ in range(n_models) + ] + + return problem, models + + +# Helper function for inverse problem definition +def define_inverse_problem_model(n_pts=10, n_models=5): + + # Initialize inverse problem + problem = InversePoisson2DSquareProblem(load=True, data_size=0.01) + problem.discretise_domain(n_pts) + + # Initialize the models + models = [ + FeedForward(len(problem.input_variables), len(problem.output_variables)) + for _ in range(n_models) + ] + + return problem, models + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +def test_constructor(case): + + # Initialize problems and model based on the case + if case == "direct": + problem, models = define_direct_problem_model() + else: + problem, models = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedEnsembleSolver(problem=problem, models=models) + + # Assert accepted conditions types and number of ensemble members + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + assert solver.num_models == len(models) + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +def test_solver_train(batch_size, case): + + # Initialize problems and model based on the case + if case == "direct": + problem, models = define_direct_problem_model() + else: + problem, models = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedEnsembleSolver(problem=problem, models=models) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +def test_solver_validation(batch_size, case): + + # Initialize problems and model based on the case + if case == "direct": + problem, models = define_direct_problem_model() + else: + problem, models = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedEnsembleSolver(problem=problem, models=models) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +def test_solver_test(batch_size, case): + + # Initialize problems and model based on the case + if case == "direct": + problem, models = define_direct_problem_model() + else: + problem, models = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedEnsembleSolver(problem=problem, models=models) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + ) + trainer.test() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +def test_train_load_restore(clean_tmp_dir, case): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Initialize problems and model based on the case + if case == "direct": + problem, models = define_direct_problem_model() + else: + problem, models = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedEnsembleSolver(problem=problem, models=models) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.1, + test_size=0.2, + default_root_dir=dir, + ) + trainer.train() + + # Restore the training from a checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the solver from a checkpoint + new_solver = PhysicsInformedEnsembleSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + models=models, + ) + + # Create input data for testing the forward pass + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + + # Assert the loaded solver behaves as the original one + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_physics_informed_single_model_solver.py b/tests/test_solver/test_physics_informed_single_model_solver.py new file mode 100644 index 000000000..d73fc84e1 --- /dev/null +++ b/tests/test_solver/test_physics_informed_single_model_solver.py @@ -0,0 +1,199 @@ +import torch +import pytest +from pina.problem.zoo import InversePoisson2DSquareProblem +from pina.solver import PhysicsInformedSingleModelSolver +from pina.problem.zoo import Poisson2DSquareProblem +from pina import LabelTensor, Condition, Trainer +from pina.model import FeedForward +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) + + +# Helper function for direct problem definition +def define_direct_problem_model(n_pts=10): + + # Initialize direct problem + problem = Poisson2DSquareProblem() + problem.discretise_domain(n_pts) + + # Add input-output condition to test supervised learning + input_pts = torch.rand(10, len(problem.input_variables)) + input_pts = LabelTensor(input_pts, problem.input_variables) + output_pts = torch.rand(10, len(problem.output_variables)) + output_pts = LabelTensor(output_pts, problem.output_variables) + problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +# Helper function for inverse problem definition +def define_inverse_problem_model(n_pts=10): + + # Initialize inverse problem + problem = InversePoisson2DSquareProblem(load=True, data_size=0.01) + problem.discretise_domain(n_pts) + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +def test_constructor(case): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedSingleModelSolver(problem=problem, model=model) + + # Assert accepted conditions types + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +def test_solver_train(case, batch_size): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedSingleModelSolver(problem=problem, model=model) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +def test_solver_validation(case, batch_size): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedSingleModelSolver(problem=problem, model=model) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +def test_solver_test(case, batch_size): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedSingleModelSolver(problem=problem, model=model) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + ) + trainer.test() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +def test_train_load_restore(clean_tmp_dir, case): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = PhysicsInformedSingleModelSolver(problem=problem, model=model) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.1, + test_size=0.2, + default_root_dir=dir, + ) + trainer.train() + + # Restore the training from a checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the solver from a checkpoint + new_solver = PhysicsInformedSingleModelSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + # Create input data for testing the forward pass + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + + # Assert the loaded solver behaves as the original one + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_pinn.py b/tests/test_solver/test_pinn.py deleted file mode 100644 index d726047ef..000000000 --- a/tests/test_solver/test_pinn.py +++ /dev/null @@ -1,145 +0,0 @@ -import pytest -import torch - -from pina import LabelTensor, Condition -from pina.model import FeedForward -from pina.trainer import Trainer -from pina.solver import PINN -from pina.condition import ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, -) -from pina.problem.zoo import ( - Poisson2DSquareProblem as Poisson, - InversePoisson2DSquareProblem as InversePoisson, -) -from torch._dynamo.eval_frame import OptimizedModule - - -# define problems -problem = Poisson() -problem.discretise_domain(10) -inverse_problem = InversePoisson(load=True, data_size=0.01) -inverse_problem.discretise_domain(10) - -# add input-output condition to test supervised learning -input_pts = torch.rand(10, len(problem.input_variables)) -input_pts = LabelTensor(input_pts, problem.input_variables) -output_pts = torch.rand(10, len(problem.output_variables)) -output_pts = LabelTensor(output_pts, problem.output_variables) -problem.conditions["data"] = Condition(input=input_pts, target=output_pts) - -# define model -model = FeedForward(len(problem.input_variables), len(problem.output_variables)) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -def test_constructor(problem): - solver = PINN(problem=problem, model=model) - - assert solver.accepted_conditions_types == ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_train(problem, batch_size, compile): - solver = PINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - val_size=0.0, - test_size=0.0, - compile=compile, - ) - trainer.train() - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_validation(problem, batch_size, compile): - solver = PINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_test(problem, batch_size, compile): - solver = PINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.7, - val_size=0.2, - test_size=0.1, - compile=compile, - ) - trainer.test() - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -def test_train_load_restore(problem): - dir = "tests/test_solver/tmp" - problem = problem - solver = PINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.7, - val_size=0.2, - test_size=0.1, - default_root_dir=dir, - ) - trainer.train() - - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = PINN.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - model=model, - ) - - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 1) - assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_rba_physics_informed_single_model_solver.py b/tests/test_solver/test_rba_physics_informed_single_model_solver.py new file mode 100644 index 000000000..8515708ba --- /dev/null +++ b/tests/test_solver/test_rba_physics_informed_single_model_solver.py @@ -0,0 +1,252 @@ +import torch +import pytest +from pina.problem.zoo import InversePoisson2DSquareProblem +from pina.solver import RBAPhysicsInformedSingleModelSolver +from pina.problem.zoo import Poisson2DSquareProblem +from pina import LabelTensor, Condition, Trainer +from pina.model import FeedForward +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) + + +# Helper function for direct problem definition +def define_direct_problem_model(n_pts=10): + + # Initialize direct problem + problem = Poisson2DSquareProblem() + problem.discretise_domain(n_pts) + + # Add input-output condition to test supervised learning + input_pts = torch.rand(10, len(problem.input_variables)) + input_pts = LabelTensor(input_pts, problem.input_variables) + output_pts = torch.rand(10, len(problem.output_variables)) + output_pts = LabelTensor(output_pts, problem.output_variables) + problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + problem.conditions["data"].name = "data" + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +# Helper function for inverse problem definition +def define_inverse_problem_model(n_pts=10): + + # Initialize inverse problem + problem = InversePoisson2DSquareProblem(load=True, data_size=0.01) + problem.discretise_domain(n_pts) + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("regularized_conditions", [None, "D"]) +def test_constructor(case, regularized_conditions): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = RBAPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + eta=0.001, + gamma=0.999, + regularized_conditions=regularized_conditions, + ) + + # Assert accepted conditions types + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + # Should fail if eta is not a positive number + with pytest.raises(ValueError): + RBAPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + eta=-0.001, + gamma=0.999, + regularized_conditions=regularized_conditions, + ) + + # Should fail if gamma is not in the range (0, 1) + with pytest.raises(ValueError): + RBAPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + eta=0.001, + gamma=1.0, + regularized_conditions=regularized_conditions, + ) + + # Should fail if regularized_conditions is not among the problem conditions + with pytest.raises(ValueError): + RBAPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + eta=0.001, + gamma=0.999, + regularized_conditions="invalid_condition", + ) + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("regularized_conditions", [None, "D"]) +def test_solver_train(case, batch_size, regularized_conditions): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = RBAPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularized_conditions=regularized_conditions, + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("regularized_conditions", [None, "D"]) +def test_solver_validation(case, batch_size, regularized_conditions): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = RBAPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularized_conditions=regularized_conditions, + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("regularized_conditions", [None, "D"]) +def test_solver_test(case, batch_size, regularized_conditions): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = RBAPhysicsInformedSingleModelSolver( + problem=problem, + model=model, + regularized_conditions=regularized_conditions, + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + ) + trainer.test() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +def test_train_load_restore(clean_tmp_dir, case): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = RBAPhysicsInformedSingleModelSolver(problem=problem, model=model) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.1, + test_size=0.2, + default_root_dir=dir, + ) + trainer.train() + + # Restore the training from a checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the solver from a checkpoint + new_solver = RBAPhysicsInformedSingleModelSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + # Create input data for testing the forward pass + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + + # Assert the loaded solver behaves as the original one + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_rba_pinn.py b/tests/test_solver/test_rba_pinn.py deleted file mode 100644 index b464f3a7c..000000000 --- a/tests/test_solver/test_rba_pinn.py +++ /dev/null @@ -1,167 +0,0 @@ -import pytest -import torch - -from pina import LabelTensor, Condition -from pina.model import FeedForward -from pina.trainer import Trainer -from pina.solver import RBAPINN -from pina.condition import ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, -) -from pina.problem.zoo import ( - Poisson2DSquareProblem as Poisson, - InversePoisson2DSquareProblem as InversePoisson, -) -from torch._dynamo.eval_frame import OptimizedModule - -# define problems -problem = Poisson() -problem.discretise_domain(10) -inverse_problem = InversePoisson(load=True, data_size=0.01) -inverse_problem.discretise_domain(10) - -# add input-output condition to test supervised learning -input_pts = torch.rand(10, len(problem.input_variables)) -input_pts = LabelTensor(input_pts, problem.input_variables) -output_pts = torch.rand(10, len(problem.output_variables)) -output_pts = LabelTensor(output_pts, problem.output_variables) -problem.conditions["data"] = Condition(input=input_pts, target=output_pts) - -# define model -model = FeedForward(len(problem.input_variables), len(problem.output_variables)) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("eta", [1, 0.001]) -@pytest.mark.parametrize("gamma", [0.5, 0.9]) -def test_constructor(problem, eta, gamma): - solver = RBAPINN(model=model, problem=problem, eta=eta, gamma=gamma) - - with pytest.raises(ValueError): - solver = RBAPINN(model=model, problem=problem, gamma=1.5) - - with pytest.raises(ValueError): - solver = RBAPINN(model=model, problem=problem, eta=-0.1) - - assert solver.accepted_conditions_types == ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -@pytest.mark.parametrize( - "loss", [torch.nn.L1Loss(reduction="sum"), torch.nn.MSELoss()] -) -def test_solver_train(problem, batch_size, loss, compile): - solver = RBAPINN(model=model, problem=problem, loss=loss) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - val_size=0.0, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -@pytest.mark.parametrize( - "loss", [torch.nn.L1Loss(reduction="sum"), torch.nn.MSELoss()] -) -def test_solver_validation(problem, batch_size, loss, compile): - solver = RBAPINN(model=model, problem=problem, loss=loss) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("compile", [True, False]) -@pytest.mark.parametrize( - "loss", [torch.nn.L1Loss(reduction="sum"), torch.nn.MSELoss()] -) -def test_solver_test(problem, batch_size, loss, compile): - solver = RBAPINN(model=model, problem=problem, loss=loss) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.7, - val_size=0.2, - test_size=0.1, - compile=compile, - ) - trainer.test() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -def test_train_load_restore(problem): - dir = "tests/test_solver/tmp" - problem = problem - solver = RBAPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.7, - val_size=0.2, - test_size=0.1, - default_root_dir=dir, - ) - trainer.train() - - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = RBAPINN.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - model=model, - ) - - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 1) - assert new_solver.forward(test_pts).shape == ( - solver.forward(test_pts).shape - ) - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_reduced_order_model_solver.py b/tests/test_solver/test_reduced_order_model_solver.py deleted file mode 100644 index 5427ec7a2..000000000 --- a/tests/test_solver/test_reduced_order_model_solver.py +++ /dev/null @@ -1,228 +0,0 @@ -import torch -import pytest - -from pina import Condition, LabelTensor -from pina.problem import AbstractProblem -from pina.condition import InputTargetCondition -from pina.solver import ReducedOrderModelSolver -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.problem.zoo import Poisson2DSquareProblem -from torch._dynamo.eval_frame import OptimizedModule - - -class LabelTensorProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data": Condition( - input=LabelTensor(torch.randn(20, 2), ["u_0", "u_1"]), - target=LabelTensor(torch.randn(20, 1), ["u"]), - ), - } - - -class TensorProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data": Condition(input=torch.randn(20, 2), target=torch.randn(20, 1)) - } - - -class AE(torch.nn.Module): - def __init__(self, input_dimensions, rank): - super().__init__() - self.encode = FeedForward( - input_dimensions, rank, layers=[input_dimensions // 4] - ) - self.decode = FeedForward( - rank, input_dimensions, layers=[input_dimensions // 4] - ) - - -class AE_missing_encode(torch.nn.Module): - def __init__(self, input_dimensions, rank): - super().__init__() - self.encode = FeedForward( - input_dimensions, rank, layers=[input_dimensions // 4] - ) - - -class AE_missing_decode(torch.nn.Module): - def __init__(self, input_dimensions, rank): - super().__init__() - self.decode = FeedForward( - rank, input_dimensions, layers=[input_dimensions // 4] - ) - - -rank = 10 -model = AE(2, 1) -interpolation_net = FeedForward(2, rank) -reduction_net = AE(1, rank) - - -def test_constructor(): - problem = TensorProblem() - ReducedOrderModelSolver( - problem=problem, - interpolation_network=interpolation_net, - reduction_network=reduction_net, - ) - ReducedOrderModelSolver( - problem=LabelTensorProblem(), - reduction_network=reduction_net, - interpolation_network=interpolation_net, - ) - assert ( - ReducedOrderModelSolver.accepted_conditions_types - == InputTargetCondition - ) - with pytest.raises(SyntaxError): - ReducedOrderModelSolver( - problem=problem, - reduction_network=AE_missing_encode( - len(problem.output_variables), rank - ), - interpolation_network=interpolation_net, - ) - ReducedOrderModelSolver( - problem=problem, - reduction_network=AE_missing_decode( - len(problem.output_variables), rank - ), - interpolation_network=interpolation_net, - ) - with pytest.raises(ValueError): - ReducedOrderModelSolver( - problem=Poisson2DSquareProblem(), - reduction_network=reduction_net, - interpolation_network=interpolation_net, - ) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("use_lt", [True, False]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_train(use_lt, batch_size, compile): - problem = LabelTensorProblem() if use_lt else TensorProblem() - solver = ReducedOrderModelSolver( - problem=problem, - reduction_network=reduction_net, - interpolation_network=interpolation_net, - use_lt=use_lt, - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - test_size=0.0, - val_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - for v in solver.model.values(): - assert isinstance(v, OptimizedModule) - - -@pytest.mark.parametrize("use_lt", [True, False]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_validation(use_lt, compile): - problem = LabelTensorProblem() if use_lt else TensorProblem() - solver = ReducedOrderModelSolver( - problem=problem, - reduction_network=reduction_net, - interpolation_network=interpolation_net, - use_lt=use_lt, - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=None, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - for v in solver.model.values(): - assert isinstance(v, OptimizedModule) - - -@pytest.mark.parametrize("use_lt", [True, False]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_test(use_lt, compile): - problem = LabelTensorProblem() if use_lt else TensorProblem() - solver = ReducedOrderModelSolver( - problem=problem, - reduction_network=reduction_net, - interpolation_network=interpolation_net, - use_lt=use_lt, - ) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=None, - train_size=0.8, - val_size=0.1, - test_size=0.1, - compile=compile, - ) - trainer.train() - if trainer.compile: - for v in solver.model.values(): - assert isinstance(v, OptimizedModule) - - -def test_train_load_restore(): - dir = "tests/test_solver/tmp/" - problem = LabelTensorProblem() - solver = ReducedOrderModelSolver( - problem=problem, - reduction_network=reduction_net, - interpolation_network=interpolation_net, - ) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.9, - test_size=0.1, - val_size=0.0, - default_root_dir=dir, - ) - trainer.train() - # restore - ntrainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - ) - ntrainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt" - ) - # loading - new_solver = ReducedOrderModelSolver.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - reduction_network=reduction_net, - interpolation_network=interpolation_net, - ) - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 1) - assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_self_adaptive_physics_informed_solver.py b/tests/test_solver/test_self_adaptive_physics_informed_solver.py new file mode 100644 index 000000000..805506c0b --- /dev/null +++ b/tests/test_solver/test_self_adaptive_physics_informed_solver.py @@ -0,0 +1,225 @@ +import torch +import pytest +from pina.problem.zoo import InversePoisson2DSquareProblem +from pina.solver import SelfAdaptivePhysicsInformedSolver +from pina.problem.zoo import Poisson2DSquareProblem +from pina import LabelTensor, Condition, Trainer +from pina.model import FeedForward +from pina.condition import ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, +) + + +# Helper function for direct problem definition +def define_direct_problem_model(n_pts=10): + + # Initialize direct problem + problem = Poisson2DSquareProblem() + problem.discretise_domain(n_pts) + + # Add input-output condition to test supervised learning + input_pts = torch.rand(10, len(problem.input_variables)) + input_pts = LabelTensor(input_pts, problem.input_variables) + output_pts = torch.rand(10, len(problem.output_variables)) + output_pts = LabelTensor(output_pts, problem.output_variables) + problem.conditions["data"] = Condition(input=input_pts, target=output_pts) + problem.conditions["data"].name = "data" + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +# Helper function for inverse problem definition +def define_inverse_problem_model(n_pts=10): + + # Initialize inverse problem + problem = InversePoisson2DSquareProblem(load=True, data_size=0.01) + problem.discretise_domain(n_pts) + + # Initialize the model + model = FeedForward( + len(problem.input_variables), len(problem.output_variables) + ) + + return problem, model + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("weight_fn", [torch.nn.Sigmoid(), torch.nn.Tanh()]) +def test_constructor(case, weight_fn): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = SelfAdaptivePhysicsInformedSolver( + problem=problem, + model=model, + weight_function=weight_fn, + ) + + # Assert accepted conditions types + assert solver.accepted_conditions_types == ( + InputTargetCondition, + InputEquationCondition, + DomainEquationCondition, + ) + + # Should fail if the weight function is not a torch.nn.Module + with pytest.raises(ValueError): + SelfAdaptivePhysicsInformedSolver( + problem=problem, + model=model, + weight_function=lambda x: x, + ) + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("weight_fn", [torch.nn.Sigmoid(), torch.nn.Tanh()]) +def test_solver_train(case, batch_size, weight_fn): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = SelfAdaptivePhysicsInformedSolver( + problem=problem, model=model, weight_function=weight_fn + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("weight_fn", [torch.nn.Sigmoid(), torch.nn.Tanh()]) +def test_solver_validation(case, batch_size, weight_fn): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = SelfAdaptivePhysicsInformedSolver( + problem=problem, model=model, weight_function=weight_fn + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("weight_fn", [torch.nn.Sigmoid(), torch.nn.Tanh()]) +def test_solver_test(case, batch_size, weight_fn): + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = SelfAdaptivePhysicsInformedSolver( + problem=problem, model=model, weight_function=weight_fn + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.2, + test_size=0.1, + ) + trainer.test() + + +@pytest.mark.parametrize("case", ["direct", "inverse"]) +@pytest.mark.parametrize("weight_fn", [torch.nn.Sigmoid(), torch.nn.Tanh()]) +def test_train_load_restore(clean_tmp_dir, case, weight_fn): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Initialize problems and model based on the case + if case == "direct": + problem, model = define_direct_problem_model() + else: + problem, model = define_inverse_problem_model() + + # Define the solver + solver = SelfAdaptivePhysicsInformedSolver( + problem=problem, model=model, weight_function=weight_fn + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.1, + test_size=0.2, + default_root_dir=dir, + ) + trainer.train() + + # Restore the training from a checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the solver from a checkpoint + new_solver = SelfAdaptivePhysicsInformedSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + # Create input data for testing the forward pass + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + + # Assert the loaded solver behaves as the original one + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_self_adaptive_pinn.py b/tests/test_solver/test_self_adaptive_pinn.py deleted file mode 100644 index b2d1361ca..000000000 --- a/tests/test_solver/test_self_adaptive_pinn.py +++ /dev/null @@ -1,176 +0,0 @@ -import torch -import pytest - -from pina import LabelTensor, Condition -from pina.solver import SelfAdaptivePINN as SAPINN -from pina.trainer import Trainer -from pina.model import FeedForward -from pina.problem.zoo import ( - Poisson2DSquareProblem as Poisson, - InversePoisson2DSquareProblem as InversePoisson, -) -from pina.condition import ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, -) -from torch._dynamo.eval_frame import OptimizedModule - - -# define problems -problem = Poisson() -problem.discretise_domain(10) -inverse_problem = InversePoisson(load=True, data_size=0.01) -inverse_problem.discretise_domain(10) - -# add input-output condition to test supervised learning -input_pts = torch.rand(10, len(problem.input_variables)) -input_pts = LabelTensor(input_pts, problem.input_variables) -output_pts = torch.rand(10, len(problem.output_variables)) -output_pts = LabelTensor(output_pts, problem.output_variables) -problem.conditions["data"] = Condition(input=input_pts, target=output_pts) - -# define model -model = FeedForward(len(problem.input_variables), len(problem.output_variables)) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("weight_fn", [torch.nn.Sigmoid(), torch.nn.Tanh()]) -def test_constructor(problem, weight_fn): - - solver = SAPINN(problem=problem, model=model, weight_function=weight_fn) - - with pytest.raises(ValueError): - SAPINN(model=model, problem=problem, weight_function=1) - - assert solver.accepted_conditions_types == ( - InputTargetCondition, - InputEquationCondition, - DomainEquationCondition, - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("compile", [True, False]) -@pytest.mark.parametrize( - "loss", [torch.nn.L1Loss(reduction="sum"), torch.nn.MSELoss()] -) -def test_solver_train(problem, compile, loss): - solver = SAPINN(model=model, problem=problem, loss=loss) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=None, - train_size=1.0, - val_size=0.0, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert all( - [ - isinstance(model, (OptimizedModule, torch.nn.ModuleDict)) - for model in solver.models - ] - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("compile", [True, False]) -@pytest.mark.parametrize( - "loss", [torch.nn.L1Loss(reduction="sum"), torch.nn.MSELoss()] -) -def test_solver_validation(problem, compile, loss): - solver = SAPINN(model=model, problem=problem, loss=loss) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=None, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert all( - [ - isinstance(model, (OptimizedModule, torch.nn.ModuleDict)) - for model in solver.models - ] - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -@pytest.mark.parametrize("compile", [True, False]) -@pytest.mark.parametrize( - "loss", [torch.nn.L1Loss(reduction="sum"), torch.nn.MSELoss()] -) -def test_solver_test(problem, compile, loss): - solver = SAPINN(model=model, problem=problem, loss=loss) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=None, - train_size=0.7, - val_size=0.2, - test_size=0.1, - compile=compile, - ) - trainer.test() - if trainer.compile: - assert all( - [ - isinstance(model, (OptimizedModule, torch.nn.ModuleDict)) - for model in solver.models - ] - ) - - -@pytest.mark.parametrize("problem", [problem, inverse_problem]) -def test_train_load_restore(problem): - dir = "tests/test_solver/tmp" - problem = problem - solver = SAPINN(model=model, problem=problem) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.7, - val_size=0.2, - test_size=0.1, - default_root_dir=dir, - ) - trainer.train() - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = SAPINN.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - model=model, - ) - - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 1) - assert new_solver.forward(test_pts).shape == ( - solver.forward(test_pts).shape - ) - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_solver/test_supervised_ensemble_solver.py b/tests/test_solver/test_supervised_ensemble_solver.py new file mode 100644 index 000000000..469baac7f --- /dev/null +++ b/tests/test_solver/test_supervised_ensemble_solver.py @@ -0,0 +1,263 @@ +import torch +import pytest +from pina import Condition, LabelTensor, Trainer +from pina.solver import SupervisedEnsembleSolver +from pina.condition import InputTargetCondition +from pina.problem import BaseProblem +from pina.graph import KNNGraph + + +# Helper class for Tensor problems +class TensorProblem(BaseProblem): + + # Input and output variables + input_variables = ["u_0", "u_1"] + output_variables = ["u"] + + # Input and target + input_ = torch.rand(20, 2) + target_ = torch.rand(20, 1) + + # Condition + conditions = {} + + def __init__(self, use_lt): + super().__init__() + + # Add labels if use_lt is True + if use_lt: + self.input_ = LabelTensor(self.input_, self.input_variables) + self.target_ = LabelTensor(self.target_, self.output_variables) + + # Initialize conditions + self.conditions["data"] = Condition( + input=self.input_, target=self.target_ + ) + + +# Helper class for Graph problems +class GraphProblem(BaseProblem): + + # Input and output variables + input_variables = ["a", "b", "c"] + output_variables = ["u"] + + # Graph attributes and target + x = torch.rand(10, 20, 3) + pos = torch.rand(10, 20, 2) + target_ = torch.rand(10, 20, 1) + + # Condition + conditions = {} + + def __init__(self, use_lt): + super().__init__() + + # Add labels if use_lt is True + if use_lt: + self.x = LabelTensor(self.x, self.input_variables) + self.pos = LabelTensor(self.pos, ["x", "y"]) + self.target_ = LabelTensor(self.target_, self.output_variables) + + # Initialize the input graphs + input_ = [ + KNNGraph(x=self.x[i], pos=self.pos[i], neighbours=3, edge_attr=True) + for i in range(len(self.x)) + ] + + # Initialize conditions + self.conditions["data"] = Condition(input=input_, target=self.target_) + + +# Helper class for Graph-consistent architecture +class DummyGraphModel(torch.nn.Module): + + def __init__(self): + super().__init__() + self.layer = torch.nn.Linear(3, 1) + + def forward(self, batch): + return self.layer(batch.x) + + +# Define the models for the tests +tensor_models = [torch.nn.Linear(2, 1) for _ in range(10)] +graph_models = [DummyGraphModel() for _ in range(10)] + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_constructor(case, use_lt): + + # Initialize problems and models based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + models = tensor_models + else: + problem = GraphProblem(use_lt=use_lt) + models = graph_models + + # Define the solver + solver = SupervisedEnsembleSolver(problem=problem, models=models) + + # Assert accepted conditions types and number of ensemble members + assert solver.accepted_conditions_types == (InputTargetCondition,) + assert solver.num_models == 10 + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_solver_train(batch_size, case, use_lt): + + # Initialize problems and models based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + models = tensor_models + else: + problem = GraphProblem(use_lt=use_lt) + models = graph_models + + # Define the solver + solver = SupervisedEnsembleSolver( + problem=problem, models=models, use_lt=use_lt + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_solver_validation(batch_size, case, use_lt): + + # Initialize problems and models based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + models = tensor_models + else: + problem = GraphProblem(use_lt=use_lt) + models = graph_models + + # Define the solver + solver = SupervisedEnsembleSolver( + problem=problem, models=models, use_lt=use_lt + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_solver_test(batch_size, case, use_lt): + + # Initialize problems and models based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + models = tensor_models + else: + problem = GraphProblem(use_lt=use_lt) + models = graph_models + + # Define the solver + solver = SupervisedEnsembleSolver( + problem=problem, models=models, use_lt=use_lt + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.1, + test_size=0.2, + ) + trainer.test() + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_train_load_restore(clean_tmp_dir, case, use_lt): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Initialize problems and models based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + models = tensor_models + else: + problem = GraphProblem(use_lt=use_lt) + models = graph_models + + # Define the solver + solver = SupervisedEnsembleSolver( + problem=problem, models=models, use_lt=use_lt + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.1, + test_size=0.2, + default_root_dir=dir, + ) + trainer.train() + + # Restore the training from a checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the solver from a checkpoint + new_solver = SupervisedEnsembleSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + models=models, + ) + + # Create input data for testing the forward pass + if case == "tensor": + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + else: + test_pts = KNNGraph( + x=LabelTensor(torch.rand(20, 3), ["a", "b", "c"]), + pos=LabelTensor(torch.rand(20, 2), ["x", "y"]), + neighbours=3, + edge_attr=True, + ) + + # Assert the loaded solver behaves as the original one + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_supervised_single_model_solver.py b/tests/test_solver/test_supervised_single_model_solver.py new file mode 100644 index 000000000..43e95d619 --- /dev/null +++ b/tests/test_solver/test_supervised_single_model_solver.py @@ -0,0 +1,262 @@ +import torch +import pytest +from pina.solver import SupervisedSingleModelSolver +from pina import Condition, LabelTensor, Trainer +from pina.condition import InputTargetCondition +from pina.problem import BaseProblem +from pina.graph import KNNGraph + + +# Helper class for Tensor problems +class TensorProblem(BaseProblem): + + # Input and output variables + input_variables = ["u_0", "u_1"] + output_variables = ["u"] + + # Input and target + input_ = torch.rand(20, 2) + target_ = torch.rand(20, 1) + + # Condition + conditions = {} + + def __init__(self, use_lt): + super().__init__() + + # Add labels if use_lt is True + if use_lt: + self.input_ = LabelTensor(self.input_, self.input_variables) + self.target_ = LabelTensor(self.target_, self.output_variables) + + # Initialize conditions + self.conditions["data"] = Condition( + input=self.input_, target=self.target_ + ) + + +# Helper class for Graph problems +class GraphProblem(BaseProblem): + + # Input and output variables + input_variables = ["a", "b", "c"] + output_variables = ["u"] + + # Graph attributes and target + x = torch.rand(10, 20, 3) + pos = torch.rand(10, 20, 2) + target_ = torch.rand(10, 20, 1) + + # Condition + conditions = {} + + def __init__(self, use_lt): + super().__init__() + + # Add labels if use_lt is True + if use_lt: + self.x = LabelTensor(self.x, self.input_variables) + self.pos = LabelTensor(self.pos, ["x", "y"]) + self.target_ = LabelTensor(self.target_, self.output_variables) + + # Initialize the input graphs + input_ = [ + KNNGraph(x=self.x[i], pos=self.pos[i], neighbours=3, edge_attr=True) + for i in range(len(self.x)) + ] + + # Initialize conditions + self.conditions["data"] = Condition(input=input_, target=self.target_) + + +# Helper class for Graph-consistent architecture +class DummyGraphModel(torch.nn.Module): + + def __init__(self): + super().__init__() + self.layer = torch.nn.Linear(3, 1) + + def forward(self, batch): + return self.layer(batch.x) + + +# Define the model for the tests +tensor_model = torch.nn.Linear(2, 1) +graph_model = DummyGraphModel() + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_constructor(case, use_lt): + + # Initialize problems and model based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + model = tensor_model + else: + problem = GraphProblem(use_lt=use_lt) + model = graph_model + + # Define the solver + solver = SupervisedSingleModelSolver(problem=problem, model=model) + + # Assert accepted conditions types + assert solver.accepted_conditions_types == (InputTargetCondition,) + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_solver_train(batch_size, case, use_lt): + + # Initialize problems and model based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + model = tensor_model + else: + problem = GraphProblem(use_lt=use_lt) + model = graph_model + + # Define the solver + solver = SupervisedSingleModelSolver( + problem=problem, model=model, use_lt=use_lt + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=1.0, + val_size=0.0, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_solver_validation(batch_size, case, use_lt): + + # Initialize problems and model based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + model = tensor_model + else: + problem = GraphProblem(use_lt=use_lt) + model = graph_model + + # Define the solver + solver = SupervisedSingleModelSolver( + problem=problem, model=model, use_lt=use_lt + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.9, + val_size=0.1, + test_size=0.0, + ) + trainer.train() + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_solver_test(batch_size, case, use_lt): + + # Initialize problems and model based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + model = tensor_model + else: + problem = GraphProblem(use_lt=use_lt) + model = graph_model + + # Define the solver + solver = SupervisedSingleModelSolver( + problem=problem, model=model, use_lt=use_lt + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=2, + accelerator="cpu", + batch_size=batch_size, + train_size=0.7, + val_size=0.1, + test_size=0.2, + ) + trainer.test() + + +@pytest.mark.parametrize("case", ["tensor", "graph"]) +@pytest.mark.parametrize("use_lt", [True, False]) +def test_train_load_restore(clean_tmp_dir, case, use_lt): + + # Initialize the directory to store the checkpoints + dir = clean_tmp_dir + + # Initialize problems and model based on the case + if case == "tensor": + problem = TensorProblem(use_lt=use_lt) + model = tensor_model + else: + problem = GraphProblem(use_lt=use_lt) + model = graph_model + + # Define the solver + solver = SupervisedSingleModelSolver( + problem=problem, model=model, use_lt=use_lt + ) + + # Training procedure + trainer = Trainer( + solver=solver, + max_epochs=5, + accelerator="cpu", + batch_size=None, + train_size=0.7, + val_size=0.1, + test_size=0.2, + default_root_dir=dir, + ) + trainer.train() + + # Restore the training from a checkpoint + new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + new_trainer.train( + ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" + + "epoch=4-step=5.ckpt" + ) + + # Load the solver from a checkpoint + new_solver = SupervisedSingleModelSolver.load_from_checkpoint( + f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", + problem=problem, + model=model, + ) + + # Create input data for testing the forward pass + if case == "tensor": + test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) + else: + test_pts = KNNGraph( + x=LabelTensor(torch.rand(20, 3), ["a", "b", "c"]), + pos=LabelTensor(torch.rand(20, 2), ["x", "y"]), + neighbours=3, + edge_attr=True, + ) + + # Assert the loaded solver behaves as the original one + assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape + torch.testing.assert_close( + new_solver.forward(test_pts), solver.forward(test_pts) + ) diff --git a/tests/test_solver/test_supervised_solver.py b/tests/test_solver/test_supervised_solver.py deleted file mode 100644 index 6f7d1ab4d..000000000 --- a/tests/test_solver/test_supervised_solver.py +++ /dev/null @@ -1,254 +0,0 @@ -import torch -import pytest -from torch._dynamo.eval_frame import OptimizedModule -from torch_geometric.nn import GCNConv -from torch_geometric.utils import to_dense_batch -from pina import Condition, LabelTensor -from pina.condition import InputTargetCondition -from pina.problem import AbstractProblem -from pina.solver import SupervisedSolver -from pina.model import FeedForward -from pina.trainer import Trainer -from pina.graph import KNNGraph - - -class LabelTensorProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data": Condition( - input=LabelTensor(torch.randn(20, 2), ["u_0", "u_1"]), - target=LabelTensor(torch.randn(20, 1), ["u"]), - ), - } - - -class TensorProblem(AbstractProblem): - input_variables = ["u_0", "u_1"] - output_variables = ["u"] - conditions = { - "data": Condition(input=torch.randn(20, 2), target=torch.randn(20, 1)) - } - - -x = torch.rand((15, 20, 5)) -pos = torch.rand((15, 20, 2)) -output_ = torch.rand((15, 20, 1)) -input_ = [ - KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True) - for x_, pos_ in zip(x, pos) -] - - -class GraphProblem(AbstractProblem): - output_variables = None - conditions = {"data": Condition(input=input_, target=output_)} - - -x = LabelTensor(torch.rand((15, 20, 5)), ["a", "b", "c", "d", "e"]) -pos = LabelTensor(torch.rand((15, 20, 2)), ["x", "y"]) -output_ = LabelTensor(torch.rand((15, 20, 1)), ["u"]) -input_ = [ - KNNGraph(x=x[i], pos=pos[i], neighbours=3, edge_attr=True) - for i in range(len(x)) -] - - -class GraphProblemLT(AbstractProblem): - output_variables = ["u"] - input_variables = ["a", "b", "c", "d", "e"] - conditions = {"data": Condition(input=input_, target=output_)} - - -model = FeedForward(2, 1) - - -class Model(torch.nn.Module): - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.lift = torch.nn.Linear(5, 10) - self.activation = torch.nn.Tanh() - self.output = torch.nn.Linear(10, 1) - - self.conv = GCNConv(10, 10) - - def forward(self, batch): - - x = batch.x - edge_index = batch.edge_index - for _ in range(1): - y = self.lift(x) - y = self.activation(y) - y = self.conv(y, edge_index) - y = self.activation(y) - y = self.output(y) - return to_dense_batch(y, batch.batch)[0] - - -graph_model = Model() - - -def test_constructor(): - SupervisedSolver(problem=TensorProblem(), model=model) - SupervisedSolver(problem=LabelTensorProblem(), model=model) - assert SupervisedSolver.accepted_conditions_types == (InputTargetCondition) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("use_lt", [True, False]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_train(use_lt, batch_size, compile): - problem = LabelTensorProblem() if use_lt else TensorProblem() - solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - test_size=0.0, - val_size=0.0, - compile=compile, - ) - - trainer.train() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("use_lt", [True, False]) -def test_solver_train_graph(batch_size, use_lt): - problem = GraphProblemLT() if use_lt else GraphProblem() - solver = SupervisedSolver(problem=problem, model=graph_model, use_lt=use_lt) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=1.0, - test_size=0.0, - val_size=0.0, - ) - - trainer.train() - - -@pytest.mark.parametrize("use_lt", [True, False]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_validation(use_lt, compile): - problem = LabelTensorProblem() if use_lt else TensorProblem() - solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=None, - train_size=0.9, - val_size=0.1, - test_size=0.0, - compile=compile, - ) - trainer.train() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("use_lt", [True, False]) -def test_solver_validation_graph(batch_size, use_lt): - problem = GraphProblemLT() if use_lt else GraphProblem() - solver = SupervisedSolver(problem=problem, model=graph_model, use_lt=use_lt) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.9, - val_size=0.1, - test_size=0.0, - ) - - trainer.train() - - -@pytest.mark.parametrize("use_lt", [True, False]) -@pytest.mark.parametrize("compile", [True, False]) -def test_solver_test(use_lt, compile): - problem = LabelTensorProblem() if use_lt else TensorProblem() - solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=None, - train_size=0.8, - val_size=0.1, - test_size=0.1, - compile=compile, - ) - trainer.test() - if trainer.compile: - assert isinstance(solver.model, OptimizedModule) - - -@pytest.mark.parametrize("batch_size", [None, 1, 5, 20]) -@pytest.mark.parametrize("use_lt", [True, False]) -def test_solver_test_graph(batch_size, use_lt): - problem = GraphProblemLT() if use_lt else GraphProblem() - solver = SupervisedSolver(problem=problem, model=graph_model, use_lt=use_lt) - trainer = Trainer( - solver=solver, - max_epochs=2, - accelerator="cpu", - batch_size=batch_size, - train_size=0.8, - val_size=0.1, - test_size=0.1, - ) - - trainer.test() - - -def test_train_load_restore(): - dir = "tests/test_solver/tmp/" - problem = LabelTensorProblem() - solver = SupervisedSolver(problem=problem, model=model) - trainer = Trainer( - solver=solver, - max_epochs=5, - accelerator="cpu", - batch_size=None, - train_size=0.9, - test_size=0.1, - val_size=0.0, - default_root_dir=dir, - ) - trainer.train() - - # restore - new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") - new_trainer.train( - ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/" - + "epoch=4-step=5.ckpt" - ) - - # loading - new_solver = SupervisedSolver.load_from_checkpoint( - f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt", - problem=problem, - model=model, - ) - - test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables) - assert new_solver.forward(test_pts).shape == (20, 1) - assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape - torch.testing.assert_close( - new_solver.forward(test_pts), solver.forward(test_pts) - ) - - # rm directories - import shutil - - shutil.rmtree("tests/test_solver/tmp") diff --git a/tests/test_trainer.py b/tests/test_trainer.py new file mode 100644 index 000000000..27ccdf7bf --- /dev/null +++ b/tests/test_trainer.py @@ -0,0 +1,187 @@ +import pytest +from pina import Trainer +from pina.solver import PhysicsInformedSingleModelSolver +from pina.model import FeedForward +from pina.problem.zoo import Poisson2DSquareProblem + + +# Define the problem, the model and the solver for testing purposes +problem = Poisson2DSquareProblem() +problem.discretise_domain(n=10, mode="random") +model = FeedForward(len(problem.input_variables), len(problem.output_variables)) +solver = PhysicsInformedSingleModelSolver(model=model, problem=problem) + + +@pytest.mark.parametrize("batching_mode", Trainer._AVAIL_BATCHING_MODES) +@pytest.mark.parametrize("automatic_batching", [True, False]) +@pytest.mark.parametrize("pin_memory", [True, False]) +@pytest.mark.parametrize("shuffle", [True, False]) +@pytest.mark.parametrize("batch_size", [None, 5]) +@pytest.mark.parametrize( + "train_size, test_size, val_size", [(0.8, 0.1, 0.1), (0.7, 0.2, 0.1)] +) +def test_constructor( + batch_size, + train_size, + test_size, + val_size, + batching_mode, + automatic_batching, + pin_memory, + shuffle, +): + + Trainer( + solver=solver, + batch_size=batch_size, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batching_mode=batching_mode if batch_size else "common_batch_size", + automatic_batching=automatic_batching, + num_workers=0, + pin_memory=pin_memory if batch_size else False, + shuffle=shuffle, + ) + + # Should raise ValueError if solver is not an instance of SolverInterface + with pytest.raises(ValueError): + Trainer( + solver="not_a_solver", + batch_size=batch_size, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batching_mode=batching_mode if batch_size else "common_batch_size", + automatic_batching=automatic_batching, + num_workers=0, + pin_memory=pin_memory if batch_size else False, + shuffle=shuffle, + ) + + # Should raise ValueError if train_size + test_size + val_size != 1.0 + with pytest.raises(ValueError): + Trainer( + solver=solver, + batch_size=batch_size, + train_size=0.5, + test_size=0.3, + val_size=0.3, + batching_mode=batching_mode if batch_size else "common_batch_size", + automatic_batching=automatic_batching, + num_workers=0, + pin_memory=pin_memory if batch_size else False, + shuffle=shuffle, + ) + + # Should raise ValueError if automatic_batching is not a boolean + with pytest.raises(ValueError): + Trainer( + solver=solver, + batch_size=batch_size, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batching_mode=batching_mode if batch_size else "common_batch_size", + automatic_batching="not_a_boolean", + num_workers=0, + pin_memory=pin_memory if batch_size else False, + shuffle=shuffle, + ) + + # Should raise ValueError if shuffle is not a boolean + with pytest.raises(ValueError): + Trainer( + solver=solver, + batch_size=batch_size, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batching_mode=batching_mode if batch_size else "common_batch_size", + automatic_batching=automatic_batching, + num_workers=0, + pin_memory=pin_memory if batch_size else False, + shuffle="not_a_boolean", + ) + + # Should raise ValueError if pin_memory is not a boolean + with pytest.raises(ValueError): + Trainer( + solver=solver, + batch_size=batch_size, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batching_mode=batching_mode if batch_size else "common_batch_size", + automatic_batching=automatic_batching, + num_workers=0, + pin_memory="not_a_boolean", + shuffle=shuffle, + ) + + # Should raise ValueError if num_workers is negative + with pytest.raises(AssertionError): + Trainer( + solver=solver, + batch_size=batch_size, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batching_mode=batching_mode if batch_size else "common_batch_size", + automatic_batching=automatic_batching, + num_workers=-1, + pin_memory=pin_memory if batch_size else False, + shuffle=shuffle, + ) + + # Should raise ValueError if batch_size is not a positive integer + with pytest.raises(AssertionError): + Trainer( + solver=solver, + batch_size=-1, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batching_mode=batching_mode if batch_size else "common_batch_size", + automatic_batching=automatic_batching, + num_workers=0, + pin_memory=pin_memory if batch_size else False, + shuffle=shuffle, + ) + + # Should raise ValueError if an invalid batching mode is provided + with pytest.raises(ValueError): + Trainer( + solver=solver, + batch_size=batch_size, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batching_mode="invalid_mode", + automatic_batching=automatic_batching, + num_workers=0, + pin_memory=pin_memory if batch_size else False, + shuffle=shuffle, + ) + + # Should raise RuntimeError if any domain has not been discretised + with pytest.raises(RuntimeError): + + # Create a new problem without discretising the domain + new_problem = Poisson2DSquareProblem() + new_solver = PhysicsInformedSingleModelSolver( + model=model, problem=new_problem + ) + + Trainer( + solver=new_solver, + batch_size=batch_size, + train_size=train_size, + test_size=test_size, + val_size=val_size, + batching_mode=batching_mode if batch_size else "common_batch_size", + automatic_batching=automatic_batching, + num_workers=0, + pin_memory=pin_memory if batch_size else False, + shuffle=shuffle, + ) diff --git a/tests/test_weighting/test_linear_weighting.py b/tests/test_weighting/test_linear_weighting.py index a11952073..939cfacb2 100644 --- a/tests/test_weighting/test_linear_weighting.py +++ b/tests/test_weighting/test_linear_weighting.py @@ -1,26 +1,27 @@ import math +import torch import pytest from pina import Trainer -from pina.solver import PINN +from pina.solver import PhysicsInformedSingleModelSolver from pina.model import FeedForward -from pina.loss import LinearWeighting +from pina.weighting import LinearWeighting from pina.problem.zoo import Poisson2DSquareProblem - # Initialize problem and model problem = Poisson2DSquareProblem() problem.discretise_domain(10) model = FeedForward(len(problem.input_variables), len(problem.output_variables)) +condition_names = problem.conditions.keys() -# Weights for testing -init_weight_1 = {cond: 3 for cond in problem.conditions.keys()} -init_weight_2 = {cond: 4 for cond in problem.conditions.keys()} -final_weight_1 = {cond: 1 for cond in problem.conditions.keys()} -final_weight_2 = {cond: 5 for cond in problem.conditions.keys()} +# Weight dictionaries for testing +init_dict_1 = {cond: torch.rand(1).item() * 10 for cond in condition_names} +init_dict_2 = {cond: torch.rand(1).item() * 10 for cond in condition_names} +final_dict_1 = {cond: torch.rand(1).item() * 1 for cond in condition_names} +final_dict_2 = {cond: torch.rand(1).item() * 100 for cond in condition_names} -@pytest.mark.parametrize("initial_weights", [init_weight_1, init_weight_2]) -@pytest.mark.parametrize("final_weights", [final_weight_1, final_weight_2]) +@pytest.mark.parametrize("initial_weights", [init_dict_1, init_dict_2]) +@pytest.mark.parametrize("final_weights", [final_dict_1, final_dict_2]) @pytest.mark.parametrize("target_epoch", [5, 10]) def test_constructor(initial_weights, final_weights, target_epoch): LinearWeighting( @@ -64,32 +65,56 @@ def test_constructor(initial_weights, final_weights, target_epoch): # Should fail if dictionary keys do not match with pytest.raises(ValueError): LinearWeighting( - initial_weights={list(initial_weights.keys())[0]: 1}, + initial_weights={"invalid": 1}, final_weights=final_weights, target_epoch=target_epoch, ) -@pytest.mark.parametrize("initial_weights", [init_weight_1, init_weight_2]) -@pytest.mark.parametrize("final_weights", [final_weight_1, final_weight_2]) +@pytest.mark.parametrize("initial_weights", [init_dict_1, init_dict_2]) +@pytest.mark.parametrize("final_weights", [final_dict_1, final_dict_2]) @pytest.mark.parametrize("target_epoch", [5, 10]) def test_train_aggregation(initial_weights, final_weights, target_epoch): + + # Initialize weighting, solver, and trainer weighting = LinearWeighting( initial_weights=initial_weights, final_weights=final_weights, target_epoch=target_epoch, ) - solver = PINN(problem=problem, model=model, weighting=weighting) - trainer = Trainer(solver=solver, max_epochs=target_epoch, accelerator="cpu") + solver = PhysicsInformedSingleModelSolver( + problem=problem, model=model, weighting=weighting + ) + trainer = Trainer( + solver=solver, + max_epochs=target_epoch + torch.randint(1, 5, (1,)).item(), + accelerator="cpu", + ) + + # Train trainer.train() - # Check that weights are updated correctly - assert all( - math.isclose( - weighting.last_saved_weights()[cond], - final_weights[cond], + # Check that weights keys are the same as loss keys + assert weighting.last_saved_weights().keys() == problem.conditions.keys() + + # Check that the weights have been updated correctly at each epoch + epoch = min(solver.trainer.current_epoch, target_epoch) + progress = epoch / target_epoch + + # Check that the weights are updated according to linear interpolation + for condition in problem.conditions.keys(): + + # Initial and final weights for the condition + initial_weight = initial_weights[condition] + final_weight = final_weights[condition] + + # Compute the expected weight based on linear interpolation + expected_weight = ( + initial_weight + (final_weight - initial_weight) * progress + ) + + assert math.isclose( + weighting.last_saved_weights()[condition], + expected_weight, rel_tol=1e-5, - abs_tol=1e-8, ) - for cond in final_weights.keys() - ) diff --git a/tests/test_weighting/test_ntk_weighting.py b/tests/test_weighting/test_ntk_weighting.py index 49442b9fb..0acabdb87 100644 --- a/tests/test_weighting/test_ntk_weighting.py +++ b/tests/test_weighting/test_ntk_weighting.py @@ -1,24 +1,29 @@ import pytest from pina import Trainer -from pina.solver import PINN +from pina.solver import PhysicsInformedSingleModelSolver from pina.model import FeedForward -from pina.loss import NeuralTangentKernelWeighting +from pina.weighting import NeuralTangentKernelWeighting from pina.problem.zoo import Poisson2DSquareProblem - # Initialize problem and model problem = Poisson2DSquareProblem() problem.discretise_domain(10) model = FeedForward(len(problem.input_variables), len(problem.output_variables)) -@pytest.mark.parametrize("update_every_n_epochs", [1, 10, 100, 1000]) +@pytest.mark.parametrize("update_every_n_epochs", [1, 3]) @pytest.mark.parametrize("alpha", [0.0, 0.5, 1.0]) def test_constructor(update_every_n_epochs, alpha): NeuralTangentKernelWeighting( update_every_n_epochs=update_every_n_epochs, alpha=alpha ) + # Should fail if alpha is not a float + with pytest.raises(ValueError): + NeuralTangentKernelWeighting( + update_every_n_epochs=update_every_n_epochs, alpha="0.5" + ) + # Should fail if alpha is not >= 0 with pytest.raises(ValueError): NeuralTangentKernelWeighting( @@ -37,17 +42,22 @@ def test_constructor(update_every_n_epochs, alpha): with pytest.raises(AssertionError): NeuralTangentKernelWeighting(update_every_n_epochs=0) - # Should fail if update_every_n_epochs is not > 0 - with pytest.raises(AssertionError): - NeuralTangentKernelWeighting(update_every_n_epochs=-3) - @pytest.mark.parametrize("update_every_n_epochs", [1, 3]) @pytest.mark.parametrize("alpha", [0.0, 0.5, 1.0]) -def test_train_aggregation(update_every_n_epochs, alpha): +def test_aggregate(update_every_n_epochs, alpha): + + # Initialize weighting, solver, and trainer weighting = NeuralTangentKernelWeighting( update_every_n_epochs=update_every_n_epochs, alpha=alpha ) - solver = PINN(problem=problem, model=model, weighting=weighting) + solver = PhysicsInformedSingleModelSolver( + problem=problem, model=model, weighting=weighting + ) trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + + # Train trainer.train() + + # Check that weights keys are the same as loss keys + assert weighting.last_saved_weights().keys() == problem.conditions.keys() diff --git a/tests/test_weighting/test_scalar_weighting.py b/tests/test_weighting/test_scalar_weighting.py index bbf71afde..90773f9d3 100644 --- a/tests/test_weighting/test_scalar_weighting.py +++ b/tests/test_weighting/test_scalar_weighting.py @@ -1,22 +1,23 @@ -import pytest import torch +import pytest from pina import Trainer -from pina.solver import PINN +from pina.solver import PhysicsInformedSingleModelSolver from pina.model import FeedForward -from pina.loss import ScalarWeighting +from pina.weighting import ScalarWeighting from pina.problem.zoo import Poisson2DSquareProblem - # Initialize problem and model problem = Poisson2DSquareProblem() -problem.discretise_domain(50) +problem.discretise_domain(10) model = FeedForward(len(problem.input_variables), len(problem.output_variables)) condition_names = problem.conditions.keys() +# Weight dictionaries for testing +weights_dict_1 = dict(zip(condition_names, [1] * len(condition_names))) +weights_dict_2 = {cond: torch.rand(1).item() * 10 for cond in condition_names} + -@pytest.mark.parametrize( - "weights", [1, 1.0, dict(zip(condition_names, [1] * len(condition_names)))] -) +@pytest.mark.parametrize("weights", [1, 3.0, weights_dict_1, weights_dict_2]) def test_constructor(weights): ScalarWeighting(weights=weights) @@ -29,11 +30,25 @@ def test_constructor(weights): ScalarWeighting(weights=[1, 2, 3]) -@pytest.mark.parametrize( - "weights", [1, 1.0, dict(zip(condition_names, [1] * len(condition_names)))] -) -def test_train_aggregation(weights): +@pytest.mark.parametrize("weights", [1, 3.0, weights_dict_1, weights_dict_2]) +def test_aggregate(weights): + + # Initialize weighting, solver, and trainer weighting = ScalarWeighting(weights=weights) - solver = PINN(problem=problem, model=model, weighting=weighting) + solver = PhysicsInformedSingleModelSolver( + problem=problem, model=model, weighting=weighting + ) trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + + # Train trainer.train() + + # Check that weights keys are the same as loss keys + assert weighting.last_saved_weights().keys() == problem.conditions.keys() + + # Check that weights values are correct + for condition in problem.conditions.keys(): + expected_weight = ( + weights[condition] if isinstance(weights, dict) else weights + ) + assert weighting.last_saved_weights()[condition] == expected_weight diff --git a/tests/test_weighting/test_self_adaptive_weighting.py b/tests/test_weighting/test_self_adaptive_weighting.py index 066e8855e..593eb04a0 100644 --- a/tests/test_weighting/test_self_adaptive_weighting.py +++ b/tests/test_weighting/test_self_adaptive_weighting.py @@ -1,18 +1,17 @@ import pytest from pina import Trainer -from pina.solver import PINN +from pina.solver import PhysicsInformedSingleModelSolver from pina.model import FeedForward -from pina.loss import SelfAdaptiveWeighting +from pina.weighting import SelfAdaptiveWeighting from pina.problem.zoo import Poisson2DSquareProblem - # Initialize problem and model problem = Poisson2DSquareProblem() problem.discretise_domain(10) model = FeedForward(len(problem.input_variables), len(problem.output_variables)) -@pytest.mark.parametrize("update_every_n_epochs", [10, 100, 1000]) +@pytest.mark.parametrize("update_every_n_epochs", [1, 10]) def test_constructor(update_every_n_epochs): SelfAdaptiveWeighting(update_every_n_epochs=update_every_n_epochs) @@ -24,16 +23,21 @@ def test_constructor(update_every_n_epochs): with pytest.raises(AssertionError): SelfAdaptiveWeighting(update_every_n_epochs=0) - # Should fail if update_every_n_epochs is not > 0 - with pytest.raises(AssertionError): - SelfAdaptiveWeighting(update_every_n_epochs=-3) - @pytest.mark.parametrize("update_every_n_epochs", [1, 3]) -def test_train_aggregation(update_every_n_epochs): +def test_aggregate(update_every_n_epochs): + + # Initialize weighting, solver, and trainer weighting = SelfAdaptiveWeighting( update_every_n_epochs=update_every_n_epochs ) - solver = PINN(problem=problem, model=model, weighting=weighting) + solver = PhysicsInformedSingleModelSolver( + problem=problem, model=model, weighting=weighting + ) trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu") + + # Train trainer.train() + + # Check that weights keys are the same as loss keys + assert weighting.last_saved_weights().keys() == problem.conditions.keys() diff --git a/tutorials/tutorial1/tutorial.ipynb b/tutorials/tutorial1/tutorial.ipynb index abb72bd03..d48c486b0 100644 --- a/tutorials/tutorial1/tutorial.ipynb +++ b/tutorials/tutorial1/tutorial.ipynb @@ -19,14 +19,14 @@ "id": "ef4949c9", "metadata": {}, "source": [ - "In this tutorial, we will demonstrate a typical use case of **PINA** for Physics Informed Neural Network (PINN) training. We will cover the basics of training a PINN with PINA, if you want to go further into PINNs look at our dedicated [tutorials](https://mathlab.github.io/PINA/_tutorial.html#physics-informed-neural-networks) on the topic.\n", + "In this tutorial, we will demonstrate a typical use case of **PINA** for Physics-Informed training. We will cover the basics of training a `PhysicsInformedSingleModelSolver` with PINA, if you want to go further into Physics-Informed solvers look at our dedicated [tutorials](https://mathlab.github.io/PINA/_tutorial.html#physics-informed-neural-networks) on the topic.\n", "\n", "Let's start by importing the useful modules:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "86478a84", "metadata": {}, "outputs": [], @@ -48,12 +48,13 @@ "from pina import Trainer, Condition\n", "from pina.problem import SpatialProblem\n", "from pina.operator import grad\n", - "from pina.solver import PINN\n", + "from pina.solver import PhysicsInformedSingleModelSolver\n", "from pina.model import FeedForward\n", "from pina.optim import TorchOptimizer\n", "from pina.domain import CartesianDomain\n", "from pina.callback import MetricTracker\n", - "from pina.equation import Equation, FixedValue\n", + "from pina.equation import Equation\n", + "from pina.equation.zoo import FixedValue\n", "\n", "warnings.filterwarnings(\"ignore\")" ] @@ -84,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "f2608e2e", "metadata": {}, "outputs": [], @@ -128,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "622f705c", "metadata": {}, "outputs": [], @@ -146,18 +147,18 @@ "source": [ "## Generate data \n", "\n", - "Data for training can come in form of direct numerical simulation results, or points in the domains. In case we perform unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy, here we show three examples using the `.discretise_domain` method of the `AbstractProblem` class." + "Data for training can come in form of direct numerical simulation results, or points in the domains. In case we perform unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy, here we show three examples using the `.discretise_domain` method of the `BaseProblem` class." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "09ce5c3a", "metadata": {}, "outputs": [], "source": [ "# sampling 20 points in [0, 1] through discretization in all locations\n", - "problem.discretise_domain(n=20, mode=\"grid\", domains=\"all\")\n", + "problem.discretise_domain(n=20, mode=\"grid\")\n", "\n", "# sampling 20 points in (0, 1) through latin hypercube sampling in D, and 1 point in x0\n", "problem.discretise_domain(n=20, mode=\"latin\", domains=[\"D\"])\n", @@ -177,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "329962b6", "metadata": {}, "outputs": [], @@ -197,28 +198,18 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "3802e22a", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "for location in problem.input_pts:\n", + "for location in problem.discretised_domains:\n", " coords = (\n", - " problem.input_pts[location].extract(problem.spatial_variables).flatten()\n", + " problem.discretised_domains[location].extract(problem.spatial_variables).flatten()\n", " )\n", " plt.scatter(coords, torch.zeros_like(coords), s=10, label=location)\n", - "_ = plt.legend()" + "_ = plt.legend()\n", + "plt.show()" ] }, { @@ -241,8 +232,8 @@ "**Choosing a Model**\n", "- Select a neural network architecture. You can use the model we provide in the `pina.model` module (see [here](https://mathlab.github.io/PINA/_rst/_code.html#models) for a full list), or define a custom PyTorch module (more on this [here](https://pytorch.org/docs/stable/notes/modules.html)).\n", "\n", - "**Choosing a PINN Solver & Defining the Trainer**\n", - "* Use a Physics Informed solver from `pina.solver` module to solve the problem using the specified model. We have already implemented most State-Of-The-Arte solvers for you, [have a look](https://mathlab.github.io/PINA/_rst/_code.html#solvers) if interested. Today we will use the standard `PINN` solver.\n", + "**Choosing a Physics-Informed Solver & Defining the Trainer**\n", + "* Use a Physics-Informed solver from `pina.solver` module to solve the problem using the specified model. We have already implemented most State-Of-The-Arte solvers for you, [have a look](https://mathlab.github.io/PINA/_rst/_code.html#solvers) if interested. Today we will use the standard `PhysicsInformedSingleModelSolver` solver.\n", "\n", "**Training**\n", "* Train the model with the [`Trainer`](https://mathlab.github.io/PINA/_rst/trainer.html) class. The Trainer class provides powerful features to enhance model accuracy, optimize training time and memory, and simplify logging and visualization, thanks to PyTorch Lightning's excellent work, see [our dedicated tutorial](https://mathlab.github.io/PINA/tutorial11/tutorial.html) for further details. By default, training metrics (e.g., MSE error) are logged using a lightning logger (CSVLogger). If you prefer manual tracking, use `pina.callback.MetricTracker`.\n", @@ -254,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "3bb4dc9b", "metadata": {}, "outputs": [], @@ -273,7 +264,7 @@ "id": "c3b92328", "metadata": {}, "source": [ - "Then we build the solver. The Physics-Informed Neural Network (`PINN`) solver class needs to be initialised with a `model` and a specific `problem` to be solved. They also take extra arguments, as the optimizer, scheduler, loss type and weighting for the different conditions which are all set to their defualt values.\n", + "Then we build the solver. The `PhysicsInformedSingleModelSolver` class needs to be initialized with a `model` and a specific `problem` to be solved. They also take extra arguments, as the optimizer, scheduler, loss type and weighting for the different conditions which are all set to their default values.\n", "\n", ">##### 💡***Bonus tip:***\n", "> All physics solvers in PINA can handle both forward and inverse problems without requiring any changes to the model or solver structure! See [our tutorial](https://mathlab.github.io/PINA/tutorial7/tutorial.html) of inverse problems for more infos." @@ -281,14 +272,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "f5127744", "metadata": {}, "outputs": [], "source": [ - "# create the PINN object with RAdam Optimizer, notice that Optimizer need to\n", + "# create the solver object with RAdam Optimizer, notice that Optimizer needs to\n", "# be wrapped with the pina.optim.TorchOptimizer class\n", - "pinn = PINN(problem, model, TorchOptimizer(torch.optim.RAdam, lr=0.005))" + "solver = PhysicsInformedSingleModelSolver(problem, model, TorchOptimizer(torch.optim.RAdam, lr=0.005))" ] }, { @@ -311,7 +302,7 @@ "source": [ "# create the trainer\n", "trainer = Trainer(\n", - " solver=pinn, # The PINN solver to be used for training\n", + " solver=solver, # The physics-informed solver to be used for training\n", " max_epochs=1500, # Maximum number of training epochs\n", " logger=True, # Enables logging (default logger is CSVLogger)\n", " callbacks=[MetricTracker()], # Tracks training metrics using MetricTracker\n", @@ -336,23 +327,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "f5fbf362", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'bound_cond_loss': tensor(4.2729e-08),\n", - " 'phys_cond_loss': tensor(1.6728e-05),\n", - " 'train_loss': tensor(1.6770e-05)}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# inspecting final loss\n", "trainer.logged_metrics" @@ -368,29 +346,19 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "ffbf0d5e", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "pts = pinn.problem.spatial_domain.sample(256, \"grid\", variables=\"x\")\n", - "predicted_output = pinn.forward(pts).extract(\"u\").tensor.detach()\n", - "true_output = pinn.problem.solution(pts).detach()\n", + "pts = solver.problem.spatial_domain.sample(256, \"grid\", variables=\"x\")\n", + "predicted_output = solver.forward(pts).extract(\"u\").tensor.detach()\n", + "true_output = solver.problem.solution(pts).detach()\n", "fig, ax = plt.subplots(nrows=1, ncols=1)\n", "ax.plot(pts.extract([\"x\"]), predicted_output, label=\"Neural Network solution\")\n", "ax.plot(pts.extract([\"x\"]), true_output, label=\"True solution\")\n", - "_ = plt.legend()" + "_ = plt.legend()\n", + "plt.show()" ] }, { @@ -403,21 +371,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "03398692", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot loss\n", "trainer_metrics = trainer.callbacks[0].metrics\n", @@ -427,7 +384,8 @@ "# plotting\n", "plt.xlabel(\"epoch\")\n", "plt.ylabel(\"loss\")\n", - "plt.yscale(\"log\")" + "plt.yscale(\"log\")\n", + "plt.show()" ] }, { diff --git a/tutorials/tutorial10/tutorial.ipynb b/tutorials/tutorial10/tutorial.ipynb index 4bb87f623..f94f336ec 100644 --- a/tutorials/tutorial10/tutorial.ipynb +++ b/tutorials/tutorial10/tutorial.ipynb @@ -9,14 +9,14 @@ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial10/tutorial.ipynb)\n", "\n", "\n", - "In this tutorial, we will build a Neural Operator using the **`AveragingNeuralOperator`** model and the **`SupervisedSolver`**. By the end of this tutorial, you will be able to train a Neural Operator to learn the operator for time-dependent PDEs.\n", + "In this tutorial, we will build a Neural Operator using the **`AveragingNeuralOperator`** model and the **`SupervisedSingleModelSolver`**. By the end of this tutorial, you will be able to train a Neural Operator to learn the operator for time-dependent PDEs.\n", "\n", "Let's start by importing the necessary modules." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ "from scipy import io\n", "from pina import Trainer, LabelTensor\n", "from pina.model import AveragingNeuralOperator\n", - "from pina.solver import SupervisedSolver\n", + "from pina.solver import SupervisedSingleModelSolver\n", "from pina.problem.zoo import SupervisedProblem\n", "\n", "warnings.filterwarnings(\"ignore\")" @@ -88,19 +88,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data Loaded\n", - " shape initial condition: torch.Size([100, 12800, 3])\n", - " shape solution: torch.Size([100, 12800, 1])\n" - ] - } - ], + "outputs": [], "source": [ "# load data\n", "data = io.loadmat(\"data/Data_KS.mat\")\n", @@ -137,20 +127,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# helper function\n", "def plot_trajectory(coords, real, no_sol=None):\n", @@ -249,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -281,7 +260,7 @@ "\n", "## Solving the KS problem\n", "\n", - "We will now focus on solving the KS equation using the `SupervisedSolver` class and the `AveragingNeuralOperator` model. As done in the [FNO tutorial](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb), we now create the Neural Operator problem class with `SupervisedProblem`." + "We will now focus on solving the KS equation using the `SupervisedSingleModelSolver` class and the `AveragingNeuralOperator` model. As done in the [FNO tutorial](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial5/tutorial.ipynb), we now create the Neural Operator problem class with `SupervisedProblem`." ] }, { @@ -298,7 +277,7 @@ " output_variables=sol_train.labels,\n", ")\n", "# initialize solver\n", - "solver = SupervisedSolver(problem=problem, model=model)\n", + "solver = SupervisedSingleModelSolver(problem=problem, model=model)\n", "# train, only CPU and avoid model summary at beginning of training (optional)\n", "trainer = Trainer(\n", " solver=solver,\n", @@ -322,20 +301,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sample_number = 2\n", "no_sol = solver(initial_cond_test)\n", @@ -356,18 +324,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training error: 0.118\n", - "Testing error: 0.109\n" - ] - } - ], + "outputs": [], "source": [ "from pina.loss import PowerLoss\n", "\n", diff --git a/tutorials/tutorial11/tutorial.ipynb b/tutorials/tutorial11/tutorial.ipynb index ef5f9c8ba..d62a273f1 100644 --- a/tutorials/tutorial11/tutorial.ipynb +++ b/tutorials/tutorial11/tutorial.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ "import warnings\n", "\n", "from pina import Trainer\n", - "from pina.solver import SupervisedSolver\n", + "from pina.solver import SupervisedSingleModelSolver\n", "from pina.model import FeedForward\n", "from pina.problem.zoo import SupervisedProblem\n", "\n", @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -71,16 +71,16 @@ " input_dimensions=1,\n", ")\n", "\n", - "# create the SupervisedSolver object\n", - "solver = SupervisedSolver(problem, model, use_lt=False)" + "# create the SupervisedSingleModelSolver object\n", + "solver = SupervisedSingleModelSolver(problem, model, use_lt=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Till now we just followed the extact step of the previous tutorials. The `Trainer` object\n", - "can be initialized by simiply passing the `SupervisedSolver` solver" + "Until now we just followed the exact step of the previous tutorials. The `Trainer` object\n", + "can be initialized by simply passing the `SupervisedSingleModelSolver` solver:" ] }, { @@ -132,7 +132,7 @@ "source": [ "## Trainer Logging\n", "\n", - "In **PINA** you can log metrics in different ways. The simplest approach is to use the `MetricTracker` class from `pina.callbacks`, as seen in the [*Introduction to Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb) tutorial.\n", + "In **PINA** you can log metrics in different ways. The simplest approach is to use the `MetricTracker` class from `pina.callback`, as seen in the [*Introduction to Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb) tutorial.\n", "\n", "However, especially when we need to train multiple times to get an average of the loss across multiple runs, `lightning.pytorch.loggers` might be useful. Here we will use `TensorBoardLogger` (more on [logging](https://lightning.ai/docs/pytorch/stable/extensions/logging.html) here), but you can choose the one you prefer (or make your own one).\n", "\n", @@ -156,7 +156,7 @@ " output_dimensions=1,\n", " input_dimensions=1,\n", " )\n", - " solver = SupervisedSolver(problem, model, use_lt=False)\n", + " solver = SupervisedSingleModelSolver(problem, model, use_lt=False)\n", " trainer = Trainer(\n", " solver=solver,\n", " accelerator=\"cpu\",\n", @@ -194,7 +194,7 @@ "\n", "## Trainer Callbacks\n", "\n", - "Whenever we need to access certain steps of the training for logging, perform static modifications (i.e. not changing the `Solver`), or update `Problem` hyperparameters (static variables), we can use **Callbacks**. Notice that **Callbacks** allow you to add arbitrary self-contained programs to your training. At specific points during the flow of execution (hooks), the Callback interface allows you to design programs that encapsulate a full set of functionality. It de-couples functionality that does not need to be in **PINA** `Solver`s.\n", + "Whenever we need to access certain steps of the training for logging, perform static modifications (i.e. not changing the `Solver`), or update `Problem` hyperparameters (static variables), we can use **Callbacks**. Notice that **Callbacks** allow you to add arbitrary self-contained programs to your training. At specific points during the flow of execution (hooks), the Callback interface allows you to design programs that encapsulate a full set of functionality. It de-couples functionality that does not need to be in **PINA** Solvers.\n", "\n", "Lightning has a callback system to execute them when needed. **Callbacks** should capture NON-ESSENTIAL logic that is NOT required for your lightning module to run.\n", "\n", @@ -206,12 +206,12 @@ "* Directly calling methods (e.g., on_validation_end) is strongly discouraged.\n", "* Whenever possible, your callbacks should not depend on the order in which they are executed.\n", "\n", - "We will try now to implement a naive version of `MetricTraker` to show how callbacks work. Notice that this is a very easy application of callbacks, fortunately in **PINA** we already provide more advanced callbacks in `pina.callbacks`." + "We will try now to implement a naive version of `MetricTracker` to show how callbacks work. Notice that this is a very easy application of callbacks, fortunately in **PINA** we already provide more advanced callbacks in `pina.callback`." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -227,7 +227,10 @@ "\n", " def on_train_epoch_end(\n", " self, trainer, __\n", - " ): # function called at the end of each epoch\n", + " ): \n", + " \"\"\"\n", + " Function called at the end of each epoch.\n", + " \"\"\"\n", " self.saved_metrics.append(\n", " {key: value for key, value in trainer.logged_metrics.items()}\n", " )" @@ -252,7 +255,7 @@ " output_dimensions=1,\n", " input_dimensions=1,\n", ")\n", - "solver = SupervisedSolver(problem, model, use_lt=False)\n", + "solver = SupervisedSingleModelSolver(problem, model, use_lt=False)\n", "trainer = Trainer(\n", " solver=solver,\n", " accelerator=\"cpu\",\n", @@ -276,22 +279,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'data_loss': tensor(104.4973), 'train_loss': tensor(104.4973)},\n", - " {'data_loss': tensor(104.3082), 'train_loss': tensor(104.3082)},\n", - " {'data_loss': tensor(104.1189), 'train_loss': tensor(104.1189)}]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "trainer.callbacks[0].saved_metrics[:3] # only the first three epochs" ] @@ -312,12 +302,12 @@ "outputs": [], "source": [ "model = FeedForward(\n", - " layers=[10, 10],\n", + " layers=[64, 64],\n", " func=torch.nn.Tanh,\n", " output_dimensions=1,\n", " input_dimensions=1,\n", ")\n", - "solver = SupervisedSolver(problem, model, use_lt=False)\n", + "solver = SupervisedSingleModelSolver(problem, model, use_lt=False)\n", "trainer = Trainer(\n", " solver=solver,\n", " accelerator=\"cpu\",\n", @@ -378,7 +368,7 @@ " input_dimensions=1,\n", ")\n", "\n", - "solver = SupervisedSolver(problem, model, use_lt=False)\n", + "solver = SupervisedSingleModelSolver(problem, model, use_lt=False)\n", "trainer = Trainer(\n", " solver=solver,\n", " accelerator=\"cpu\",\n", @@ -415,7 +405,7 @@ " output_dimensions=1,\n", " input_dimensions=1,\n", ")\n", - "solver = SupervisedSolver(problem, model, use_lt=False)\n", + "solver = SupervisedSingleModelSolver(problem, model, use_lt=False)\n", "trainer = Trainer(\n", " solver=solver,\n", " accelerator=\"cpu\",\n", @@ -454,7 +444,7 @@ " output_dimensions=1,\n", " input_dimensions=1,\n", ")\n", - "solver = SupervisedSolver(problem, model, use_lt=False)\n", + "solver = SupervisedSingleModelSolver(problem, model, use_lt=False)\n", "trainer = Trainer(\n", " solver=solver,\n", " accelerator=\"cpu\",\n", diff --git a/tutorials/tutorial12/tutorial.ipynb b/tutorials/tutorial12/tutorial.ipynb index 238e80f9c..c3d957c2c 100644 --- a/tutorials/tutorial12/tutorial.ipynb +++ b/tutorials/tutorial12/tutorial.ipynb @@ -9,7 +9,7 @@ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial12/tutorial.ipynb)\n", "\n", "\n", - "In this tutorial, we will explore how to use the `Equation` class in **PINA**. We will focus on how to leverage this class, along with its inherited subclasses, to enforce residual minimization in **Physics-Informed Neural Networks (PINNs)**.\n", + "In this tutorial, we will explore how to use the `Equation` class in **PINA**. We will focus on how to leverage this class, along with its inherited subclasses, to enforce residual minimization in **Physics-Informed Solvers**.\n", "\n", "By the end of this guide, you'll understand how to integrate physical laws and constraints directly into your model training, ensuring that the solution adheres to the underlying differential equations.\n", "\n", @@ -53,16 +53,17 @@ "# useful imports\n", "from pina import Condition\n", "from pina.problem import SpatialProblem, TimeDependentProblem\n", - "from pina.equation import Equation, FixedValue\n", + "from pina.equation import Equation\n", + "from pina.equation.zoo import FixedValue\n", "from pina.domain import CartesianDomain\n", - "from pina.operator import grad, fast_grad, laplacian" + "from pina.operator import grad, laplacian" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's begin by defining the Burgers equation and its initial condition as Python functions. These functions will take the model's `input` (spatial and temporal coordinates) and `output` (predicted solution) as arguments. The goal is to compute the residuals for the Burgers equation, which we will minimize during training." + "Let's begin by defining the Burgers equation and its initial condition as Python functions. These functions will take the model's `input_` (spatial and temporal coordinates) and `output_` (predicted solution) as arguments. The goal is to compute the residuals for the Burgers equation, which we will minimize during training." ] }, { @@ -135,16 +136,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `Equation` class takes as input a function (in this case it happens twice, with `initial_condition` and `burgers_equation`) which computes a residual of an equation, such as a PDE. In a problem class such as the one above, the `Equation` class with such a given input is passed as a parameter in the specified `Condition`. \n", "\n", - "The `FixedValue` class takes as input a value of the same dimensions as the output functions. This class can be used to enforce a fixed value for a specific condition, such as Dirichlet boundary conditions, as demonstrated in our example.\n", "\n", - "Once the equations are set as above in the problem conditions, the PINN solver will aim to minimize the residuals described in each equation during the training phase. \n", "\n", - "### Available classes of equations:\n", - "- `FixedGradient` and `FixedFlux`: These work analogously to the `FixedValue` class, where we can enforce a constant value on the gradient or the divergence of the solution, respectively.\n", - "- `Laplace`: This class can be used to enforce that the Laplacian of the solution is zero.\n", - "- `SystemEquation`: This class allows you to enforce multiple conditions on the same subdomain by passing a list of residual equations defined in the problem.\n", + "The `Equation` class takes as input a function that computes the residual of an equation, such as a PDE. In the example above, this is done twice, using `initial_condition` and `burgers_equation`. Once defined, each `Equation` object is passed to a specific `Condition` inside the problem class. When multiple residual equations need to be enforced on the same subdomain, they can be grouped using `SystemEquation`, which takes a list of equations defined in the problem.\n", + "\n", + "For common use cases, PINA also provides several predefined equation classes. The `FixedValue`, `FixedGradient`, `FixedFlux`, and `FixedLaplacian` classes can be used to enforce fixed constraints on the solution or on its derivatives. In particular, `FixedValue` enforces a constant value with the same dimensions as the output function, making it suitable for conditions such as Dirichlet boundary conditions. Similarly, `FixedGradient` and `FixedFlux` enforce fixed values on the gradient or flux of the solution, respectively, while `FixedLaplacian` can be used to impose a fixed value on the Laplacian of the solution. Many ready-to-use equations are also available in the `pina.equation.zoo` module.\n", + "\n", + "Once the equations are assigned to the problem conditions, the physics-informed solver aims to minimize the residuals defined by each equation during the training phase.\n", "\n", "## Defining a new Equation class\n", "`Equation` classes can also be inherited to define a new class. For example, we can define a new class `Burgers1D` to represent the Burgers equation. During the class call, we can pass the viscosity parameter $\\nu$:\n", @@ -240,7 +239,7 @@ "From here, you can:\n", "\n", "- **Define Additional Complex Equation Classes**: Create your own equation classes, such as `SchrodingerEquation`, `NavierStokesEquation`, etc.\n", - "- **Define More `FixedOperator` Classes**: Implement operators like `FixedCurl`, `FixedDivergence`, and others for more advanced simulations.\n", + "- **Define More `FixedOperator` Classes**: Implement operators like `FixedCurl` for more advanced simulations.\n", "- **Integrate Custom Equations and Operators**: Combine your custom equations and operators into larger systems for more complex simulations.\n", "- **and many more!**: Explore for example different residual minimization techniques to improve the performance and accuracy of your models.\n", "\n", diff --git a/tutorials/tutorial13/tutorial.ipynb b/tutorials/tutorial13/tutorial.ipynb index a865fc6d8..609ea3946 100644 --- a/tutorials/tutorial13/tutorial.ipynb +++ b/tutorials/tutorial13/tutorial.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -35,10 +35,12 @@ "\n", "from pina import Condition, Trainer\n", "from pina.problem import SpatialProblem\n", - "from pina.solver import PINN, SelfAdaptivePINN as SAPINN\n", + "from pina.solver import (\n", + " PhysicsInformedSingleModelSolver, SelfAdaptivePhysicsInformedSolver\n", + ")\n", "from pina.loss import LpLoss\n", "from pina.domain import CartesianDomain\n", - "from pina.equation import FixedValue, Poisson\n", + "from pina.equation.zoo import FixedValue, PoissonEquation\n", "from pina.model import FeedForward\n", "from pina.model.block import FourierFeatureEmbedding\n", "\n", @@ -80,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +93,7 @@ " )\n", "\n", "\n", - "poisson_equation = Poisson(forcing_term=forcing_term)\n", + "poisson_equation = PoissonEquation(forcing_term=forcing_term)\n", "\n", "\n", "class Poisson(SpatialProblem):\n", @@ -124,11 +126,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A standard PINN approach would involve fitting the model using a Feed Forward (fully connected) Neural Network. For a conventional fully-connected neural network, it is relatively easy to approximate a function $u$, given sufficient data inside the computational domain. \n", + "A standard Physics-Informed approach would involve fitting the model using a Feed Forward (fully connected) Neural Network. For a conventional fully-connected neural network, it is relatively easy to approximate a function $u$, given sufficient data inside the computational domain. \n", "\n", "However, solving high-frequency or multi-scale problems presents significant challenges to PINNs, especially when the number of data points is insufficient to capture the different scales effectively.\n", "\n", - "Below, we run a simulation using both the `PINN` solver and the self-adaptive `SAPINN` solver, employing a [`FeedForward`](https://mathlab.github.io/PINA/_modules/pina/model/feed_forward.html#FeedForward) model.\n" + "Below, we run a simulation using both the `PhysicsInformedSingleModelSolver` solver and the `SelfAdaptivePhysicsInformedSolver` solver, employing a [`FeedForward`](https://mathlab.github.io/PINA/_modules/pina/model/feed_forward.html#FeedForward) model.\n" ] }, { @@ -137,8 +139,8 @@ "metadata": {}, "outputs": [], "source": [ - "# training with PINN and visualize results\n", - "pinn = PINN(\n", + "# training with PhysicsInformedSingleModelSolver and visualize results\n", + "pinn = PhysicsInformedSingleModelSolver(\n", " problem=problem,\n", " model=FeedForward(\n", " input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]\n", @@ -156,8 +158,8 @@ ")\n", "trainer.train()\n", "\n", - "# training with PINN and visualize results\n", - "sapinn = SAPINN(\n", + "# training with SelfAdaptivePhysicsInformedSolver and visualize results\n", + "sapinn = SelfAdaptivePhysicsInformedSolver(\n", " problem=problem,\n", " model=FeedForward(\n", " input_dimensions=1, output_dimensions=1, layers=[100, 100, 100]\n", @@ -177,30 +179,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# define the function to plot the solution obtained using matplotlib\n", "def plot_solution(pinn_to_use, title):\n", @@ -213,6 +194,7 @@ " plt.plot(pts.extract([\"x\"]), true_output, label=\"True solution\")\n", " plt.title(title)\n", " plt.legend()\n", + " plt.show()\n", "\n", "\n", "# plot the solution of the two PINNs\n", @@ -225,27 +207,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can clearly observe that neither of the two solvers has successfully learned the solution. \n", - "The issue is not with the optimization strategy (i.e., the solver), but rather with the model used to solve the problem. \n", - "A simple `FeedForward` network struggles to handle multiscale problems, especially when there are not enough collocation points to capture the different scales effectively.\n", + "We can clearly observe that neither of the two solvers has successfully learned the solution. The issue is not with the optimization strategy (i.e., the solver), but rather with the model used to solve the problem. A simple `FeedForward` network struggles to handle multiscale problems, especially when there are not enough collocation points to capture the different scales effectively.\n", "\n", - "Next, let's compute the $l_2$ relative error for both the `PINN` and `SAPINN` solutions:" + "Next, let's compute the $l_2$ relative error for both the `PhysicsInformedSingleModelSolver` and `SelfAdaptivePhysicsInformedSolver` solutions:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Relative l2 error PINN 3143.01%\n", - "Relative l2 error SAPINN 3091.39%\n" - ] - } - ], + "outputs": [], "source": [ "# l2 loss from PINA losses\n", "l2_loss = LpLoss(p=2, relative=False)\n", @@ -282,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -310,8 +281,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will train the `MultiscaleFourierNet` using the `PINN` solver. \n", - "Feel free to experiment with other PINN variants as well, such as `SAPINN`, `GPINN`, `CompetitivePINN`, and others, to see how they perform on this multiscale problem." + "We will train the `MultiscaleFourierNet` using the `PhysicsInformedSingleModelSolver` solver. \n", + "Feel free to experiment with other PINN variants as well, such as `SelfAdaptivePhysicsInformedSolver`, `GradientPhysicsInformedSingleModelSolver`, `CompetitivePhysicsInformedSolver`, and others, to see how they perform on this multiscale problem." ] }, { @@ -320,7 +291,9 @@ "metadata": {}, "outputs": [], "source": [ - "multiscale_pinn = PINN(problem=problem, model=MultiscaleFourierNet())\n", + "multiscale_pinn = PhysicsInformedSingleModelSolver(\n", + " problem=problem, model=MultiscaleFourierNet()\n", + ")\n", "trainer = Trainer(\n", " multiscale_pinn,\n", " max_epochs=1500,\n", @@ -342,27 +315,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Relative l2 error PINN with MultiscaleFourierNet: 2.47%\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot solution obtained\n", "plot_solution(multiscale_pinn, \"Multiscale PINN solution\")\n", diff --git a/tutorials/tutorial14/tutorial.ipynb b/tutorials/tutorial14/tutorial.ipynb index 3b5f88ec7..291ac8e24 100644 --- a/tutorials/tutorial14/tutorial.ipynb +++ b/tutorials/tutorial14/tutorial.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ "from lightning.pytorch.callbacks import Callback\n", "\n", "from pina import Trainer, Condition, LabelTensor\n", - "from pina.solver import DeepEnsemblePINN\n", + "from pina.solver import PhysicsInformedEnsembleSolver\n", "from pina.model import FeedForward\n", "from pina.operator import laplacian\n", "from pina.problem import TimeDependentProblem\n", @@ -51,9 +51,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Deep Ensemble\n", + "## Ensemble Methods\n", "\n", - "Deep Ensemble methods improve model performance by leveraging the diversity of predictions generated by multiple neural networks trained on the same problem. Each network in the ensemble is trained independently—typically with different weight initializations or even slight variations in the architecture or data sampling. By combining their outputs (e.g., via averaging or majority voting), ensembles reduce overfitting, increase robustness, and improve generalization.\n", + "Ensemble methods improve model performance by leveraging the diversity of predictions generated by multiple neural networks trained on the same problem. Each network in the ensemble is trained independently — typically with different weight initializations or even slight variations in the architecture or data sampling. By combining their outputs (e.g., via averaging or majority voting), ensembles reduce overfitting, increase robustness, and improve generalization.\n", "\n", "This approach allows the ensemble to capture different perspectives of the problem, leading to more accurate and reliable predictions.\n", "\n", @@ -61,19 +61,19 @@ " \"Deep\n", "

\n", "\n", - "The image above illustrates a Deep Ensemble setup, where multiple models attempt to predict the text from an image. While individual models may make errors (e.g., predicting \"PONY\" instead of \"PINA\"), combining their outputs—such as taking the majority vote—often leads to the correct result. This ensemble effect improves reliability by mitigating the impact of individual model biases.\n", + "The image above illustrates an Ensemble setup, where multiple models attempt to predict the text from an image. While individual models may make errors (e.g., predicting \"PONY\" instead of \"PINA\"), combining their outputs—such as taking the majority vote—often leads to the correct result. This ensemble effect improves reliability by mitigating the impact of individual model biases.\n", "\n", "\n", "## Deep Ensemble Physics-Informed Networks\n", "\n", - "In the context of Physics-Informed Neural Networks (PINNs), Deep Ensembles help the network discover different branches or multiple solutions of a PDE that exhibits bifurcating behavior.\n", + "In the context of Physics-Informed Neural Networks (PINNs), Ensemble models help the network discover different branches or multiple solutions of a PDE that exhibits bifurcating behavior.\n", "\n", - "By training a diverse set of models with different initializations, Deep Ensemble methods overcome the limitations of single-initialization models, which may converge to only one of the possible solutions. This approach is particularly useful when the solution space of the problem contains multiple valid physical states or behaviors.\n", + "By training a diverse set of models with different initializations, Ensemble methods overcome the limitations of single-initialization models, which may converge to only one of the possible solutions. This approach is particularly useful when the solution space of the problem contains multiple valid physical states or behaviors.\n", "\n", "\n", "## The Bratu Problem\n", "\n", - "In this tutorial, we'll train a `DeepEnsemblePINN` solver to solve a bifurcating ODE known as the **Bratu problem**. The ODE is given by:\n", + "In this tutorial, we'll train a `PhysicsInformedEnsembleSolver` solver to solve a bifurcating ODE known as the **Bratu problem**. The ODE is given by:\n", "\n", "$$\n", "\\frac{d^2u}{dt^2} + \\lambda e^u = 0, \\quad t \\in (0, 1)\n", @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -151,18 +151,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Defining the Deep Ensemble Models\n", + "## Defining the Ensemble Models\n", "\n", "Now that the problem setup is complete, we move on to creating an **ensemble of models**. Each ensemble member will be a standard `FeedForward` neural network, wrapped inside a custom `Model` class.\n", "\n", "Each model's weights are initialized using a **normal distribution** with mean 0 and standard deviation 2. This random initialization is crucial to promote diversity across the ensemble members, allowing the models to converge to potentially different solutions of the PDE.\n", "\n", - "The final ensemble is simply a **list of PyTorch models**, which we will later pass to the `DeepEnsemblePINN`" + "The final ensemble is simply a **list of PyTorch models**, which we will later pass to the `PhysicsInformedEnsembleSolver`." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -190,32 +190,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's visualize the networks output before strated training" + "Let's visualize the networks output before training" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot solution\n", "with torch.no_grad():\n", - " pts = problem.input_pts[\"interior\"]\n", + " pts = problem.discretised_domains[\"interior\"]\n", " for model in models:\n", " plt.plot(pts, model(pts), \"--\")\n", - " plt.plot()" + " plt.plot()\n", + " plt.show()" ] }, { @@ -224,15 +214,15 @@ "source": [ "As you can see we get different output since the neural networks are initialized differently.\n", "\n", - "## Training with `DeepEnsemblePINN`\n", + "## Training with `PhysicsInformedEnsembleSolver`\n", "\n", - "Now that everything is ready, we can train the models using the `DeepEnsemblePINN` solver! 🎯\n", + "Now that everything is ready, we can train the models using the `PhysicsInformedEnsembleSolver` solver! 🎯\n", "\n", "This solver is constructed by combining multiple neural network models that all aim to solve the same PDE. Each model $\\mathcal{M}_{i \\in \\{1, \\dots, 10\\}}$ in the ensemble contributes a unique perspective due to different random initializations.\n", "\n", "This diversity allows the ensemble to **capture multiple branches or bifurcating solutions** of the problem, making it especially powerful for PDEs like the Bratu problem.\n", "\n", - "Once the `DeepEnsemblePINN` solver is defined with all the models, we train them using the `Trainer` class, as with any other solver in **PINA**. We also build a callback to store the value of `u(0.5)` during training iterations." + "Once the `PhysicsInformedEnsembleSolver` solver is defined with all the models, we train them using the `Trainer` class, as with any other solver in **PINA**. We also build a callback to store the value of `u(0.5)` during training iterations." ] }, { @@ -245,7 +235,7 @@ "optimizers = [TorchOptimizer(torch.optim.Adam, lr=0.006) for _ in range(10)]\n", "\n", "# define solver\n", - "solver = DeepEnsemblePINN(\n", + "solver = PhysicsInformedEnsembleSolver(\n", " problem,\n", " models,\n", " optimizers=optimizers,\n", @@ -254,19 +244,26 @@ "\n", "# callback\n", "class StoreValue(Callback):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.store = []\n", + "\n", " def on_train_epoch_start(self, trainer, pl_module):\n", - " input = LabelTensor(torch.tensor([[0.5]]), \"t\")\n", - " output = pl_module(input).tensor.flatten()\n", - " if trainer.current_epoch == 0:\n", - " self.store = [output]\n", - " else:\n", - " self.store.append(output)\n", + " input_ = LabelTensor(torch.tensor([[0.5]], device=pl_module.device), \"t\")\n", + "\n", + " epoch_outputs = []\n", + " for model in pl_module.models:\n", + " output = model(input_).tensor.flatten().detach().cpu()\n", + " epoch_outputs.append(output)\n", + "\n", + " epoch_outputs = torch.stack(epoch_outputs, dim=0)\n", + " self.store.append(epoch_outputs)\n", "\n", "\n", "# define trainer\n", "trainer = Trainer(\n", " solver,\n", - " max_epochs=500,\n", + " max_epochs=1000,\n", " accelerator=\"cpu\",\n", " enable_model_summary=False,\n", " callbacks=[StoreValue()],\n", @@ -285,28 +282,18 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "with torch.no_grad():\n", " metrics = torch.stack(trainer.callbacks[0].store, dim=0)\n", - " plt.plot(range(metrics.shape[0]), metrics)\n", + " plt.plot(range(metrics.shape[0]), metrics.squeeze(-1))\n", " plt.title(\"Ensemble Convergence\")\n", " plt.ylabel(r\"$u(0.5)$\")\n", " plt.xlabel(\"epochs\")\n", - " plt.plot()" + " plt.plot()\n", + " plt.show()" ] }, { @@ -322,25 +309,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot solution\n", "with torch.no_grad():\n", - " pts = problem.input_pts[\"interior\"]\n", - " u_ensemble = solver(pts)\n", + " pts = problem.discretised_domains[\"interior\"]\n", + " u_ensemble = [solver.models[idx](pts) for idx in range(len(solver.models))]\n", " u1, u2 = true_solution(pts)\n", " plt.plot(pts, u1, label=\"Reference solution u1\")\n", " plt.plot(pts, u2, label=\"Reference solution u2\")\n", diff --git a/tutorials/tutorial15/tutorial.ipynb b/tutorials/tutorial15/tutorial.ipynb index 631dde14c..1de5cab3e 100644 --- a/tutorials/tutorial15/tutorial.ipynb +++ b/tutorials/tutorial15/tutorial.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -37,9 +37,10 @@ "import warnings\n", "\n", "from pina import Trainer\n", - "from pina.solver import SupervisedSolver\n", + "from pina.solver import SupervisedSingleModelSolver\n", "from pina.problem.zoo import SupervisedProblem\n", "\n", + "from torch_geometric.data import Batch\n", "from torch_geometric.datasets import QM9\n", "from torch_geometric.nn import GCNConv, global_mean_pool\n", "\n", @@ -84,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +94,7 @@ "\n", "# save the dataset\n", "input_ = [data for data in dataset]\n", - "target_ = torch.cat([data.y for data in dataset])\n", + "target_ = torch.stack([data.y for data in dataset], dim=0)\n", "\n", "# normalize the target\n", "mean = target_.mean(dim=0, keepdim=True)\n", @@ -110,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +159,7 @@ "source": [ "## Train the Model\n", "\n", - "Now that the problem is created and the model is built, we can train the model using the [`SupervisedSolver`](https://mathlab.github.io/PINA/_rst/solver/supervised.html), which is the solver for standard supervised learning task. We will optimize the Maximum Absolute Error and test on the same metric. In the [`Trainer`](https://mathlab.github.io/PINA/_rst/trainer.html) class we specify the optimization hyperparameters." + "Now that the problem is created and the model is built, we can train the model using the [`SupervisedSingleModelSolver`](https://mathlab.github.io/PINA/_rst/solver/supervised.html), which is the solver for standard supervised learning task. We will optimize the Maximum Absolute Error and test on the same metric. In the [`Trainer`](https://mathlab.github.io/PINA/_rst/trainer.html) class we specify the optimization hyperparameters." ] }, { @@ -168,7 +169,7 @@ "outputs": [], "source": [ "# define the solver\n", - "solver = SupervisedSolver(\n", + "solver = SupervisedSingleModelSolver(\n", " problem=problem,\n", " model=GNN(in_features=11, out_features=19),\n", " use_lt=False,\n", @@ -196,37 +197,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e7a06580230642638d95afa18a31a798", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", diff --git a/tutorials/tutorial16/tutorial.ipynb b/tutorials/tutorial16/tutorial.ipynb index 367f8c337..4408be4b8 100644 --- a/tutorials/tutorial16/tutorial.ipynb +++ b/tutorials/tutorial16/tutorial.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "014bbd86", "metadata": {}, "outputs": [], @@ -76,12 +76,12 @@ "- Image reconstruction (perturbed image as input, clear image as target)\n", "- Classification (e.g., input: molecule, target: chemical properties)\n", "\n", - "To build a data-driven problem in **PINA**, you can inherit from the `AbstractProblem` class. Below is an example of a regression problem where the input is a scalar value `x` and the target is a scalar value `y`.\n", + "To build a data-driven problem in **PINA**, you can inherit from the `BaseProblem` class. Below is an example of a regression problem where the input is a scalar value `x` and the target is a scalar value `y`.\n", "\n", "```python\n", - "from pina.problem import AbstractProblem\n", + "from pina.problem import BaseProblem\n", "\n", - "class SupervisedProblem(AbstractProblem):\n", + "class SupervisedProblem(BaseProblem):\n", " \n", " input_variables = ['x']\n", " output_variables = ['y']\n", @@ -95,13 +95,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "464d4ab2", "metadata": {}, "outputs": [], "source": [ "from pina import Condition, LabelTensor\n", - "from pina.problem import AbstractProblem\n", + "from pina.problem import BaseProblem\n", "\n", "# creating some fictitious data\n", "input_1 = LabelTensor(torch.randn(10, 1), \"x\") # <== input_variables\n", @@ -110,7 +110,7 @@ "target_2 = LabelTensor(torch.randn(10, 1), \"y\") # <== output_variables\n", "\n", "\n", - "class SupervisedProblem(AbstractProblem):\n", + "class SupervisedProblem(BaseProblem):\n", "\n", " input_variables = [\"x\"]\n", " output_variables = [\"y\"]\n", @@ -129,31 +129,17 @@ "id": "d27c1341", "metadata": {}, "source": [ - "You can define as many conditions as needed, and the model will attempt to minimize all of them simultaneously! You can access the data in various ways:\n", - "\n", - "- `problem.conditions[''].input`, `problem.conditions[''].target` – Access the input and output data for the specified condition ``.\n", - "- `problem.input_pts` – Access the input points for all conditions.\n", + "You can define as many conditions as needed, and the model will attempt to minimize all of them simultaneously! You can access the input and target data for a specified condition as follows: `problem.conditions[''].input`, `problem.conditions[''].target`.\n", "\n", "To ensure that the problem is ready, you can check if all domains have been discretized, meaning all conditions have input points available to pass to the model:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "5bd8397e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# check if all domains are discretised\n", "problem.are_all_domains_discretised" @@ -236,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "f2608e2e", "metadata": {}, "outputs": [], @@ -244,7 +230,8 @@ "from pina.problem import SpatialProblem\n", "from pina.operator import grad\n", "from pina.domain import CartesianDomain\n", - "from pina.equation import Equation, FixedValue\n", + "from pina.equation import Equation\n", + "from pina.equation.zoo import FixedValue\n", "\n", "\n", "# defining the ode equation\n", @@ -301,7 +288,7 @@ "\n", "Once we have defined the function, we need to tell the neural network where these methods are to be applied. To do so, we use again the `Condition` class. In the `Condition` class, we pass the location points and the equation we want minimized on those points.\n", "\n", - "Finally, it's possible to define a `solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `solution` function is a method of the `Problem` class, but it is not mandatory for problem definition.\n" + "Finally, it's possible to define a `solution` function, which can be useful if we want to plot the results and see how the real solution compares to the expected (true) solution. Notice that the `solution` function is a method of the problem class, but it is not mandatory for problem definition.\n" ] }, { @@ -312,26 +299,15 @@ "source": [ "## Generate data for Physical Problems\n", "\n", - "When training physics based models, data can come in form of direct numerical simulation results (tensors, graph), or points in the domains which need to be sampled. In case we perform unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy. But first, let's check if the domains are dicsretized by using the `are_all_domains_discretised` method." + "When training physics-based models, data can come in form of direct numerical simulation results (tensors, graph), or points in the domains which need to be sampled. In case we perform unsupervised learning, we just need the collocation points for training, i.e. points where we want to evaluate the neural network. Sampling point in **PINA** is very easy. But first, let's check if the domains are discretized by using the `are_all_domains_discretised` method." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a561b984", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "problem = SimpleODE()\n", "problem.are_all_domains_discretised" @@ -339,28 +315,20 @@ }, { "cell_type": "markdown", - "id": "ff0852f9", + "id": "ec331ead", "metadata": {}, "source": [ - "This is false becase the input points are not available (we need to discretize!). If you call `problem.input_points` at this stage you will get an error due to point missing in the condition.\n", - "\n", - "```bash\n", - ">>> problem.input_pts\n", - "```\n", - "```python\n", - "---------------------------------------------------------------------------\n", - "KeyError Traceback (most recent call last)\n", - "Cell In[32], line 1\n", - "----> 1 problem.input_pts\n", - "\n", - "File ~/GitHub/PINA/pina/problem/abstract_problem.py:78, in AbstractProblem.input_pts(self)\n", - " 76 to_return[cond_name] = cond.input\n", - " 77 elif hasattr(cond, \"domain\"):\n", - "---> 78 to_return[cond_name] = self._discretised_domains[cond.domain]\n", - " 79 return to_return\n", - "\n", - "KeyError: 'x0'\n", - "```" + "This is `False` since no domain has been discretized. One can check which domains have been discretized calling `problem.discretised_domains`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb7ff84f", + "metadata": {}, + "outputs": [], + "source": [ + "problem.discretised_domains" ] }, { @@ -368,18 +336,18 @@ "id": "db601e90", "metadata": {}, "source": [ - "To discretise the problem you can use the `discretise_domain` method:" + "To discretise the domains in the problem you can use the `discretise_domain` method:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "09ce5c3a", "metadata": {}, "outputs": [], "source": [ "# sampling 20 points in [0, 1] through discretization in all locations\n", - "problem.discretise_domain(n=20, mode=\"grid\", domains=\"all\")\n", + "problem.discretise_domain(n=20, mode=\"grid\")\n", "\n", "# sampling 20 points in (0, 1) through latin hypercube sampling in D, and 1 point in x0\n", "problem.discretise_domain(n=20, mode=\"latin\", domains=[\"D\"])\n", @@ -399,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "329962b6", "metadata": {}, "outputs": [], @@ -414,34 +382,16 @@ "id": "ca2ac5c2", "metadata": {}, "source": [ - "The points are saved in a python `dict`, and can be accessed by calling the attributes `input_pts` or `discretised_domains` of the problem." + "The points are saved in a python `dict`, and can be accessed by calling the `discretised_domains` of the problem." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "d6ed9aaf", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input points: {'bound_cond': LabelTensor([[0.]]), 'phys_cond': LabelTensor([[0.3963],\n", - " [0.4620],\n", - " [0.8240],\n", - " [0.7956],\n", - " [0.1866]])}\n", - "Input points labels: {'x0': LabelTensor([[0.]]), 'D': LabelTensor([[0.3963],\n", - " [0.4620],\n", - " [0.8240],\n", - " [0.7956],\n", - " [0.1866]])}\n" - ] - } - ], + "outputs": [], "source": [ - "print(\"Input points:\", problem.input_pts)\n", "print(\"Input points labels:\", problem.discretised_domains)" ] }, @@ -455,38 +405,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "3802e22a", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "for location in problem.input_pts:\n", + "for location in problem.discretised_domains:\n", " coords = (\n", - " problem.input_pts[location].extract(problem.spatial_variables).flatten()\n", + " problem.discretised_domains[location].extract(problem.spatial_variables).flatten()\n", " )\n", " plt.scatter(coords, torch.zeros_like(coords), s=10, label=location)\n", - "plt.legend()" + "plt.legend()\n", + "plt.show()" ] }, { @@ -503,20 +433,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "c70dfd4b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The AdvectionProblem has 2 conditions with names ['t0', 'D'] \n", - "The problem inherits from ['SpatialProblem', 'TimeDependentProblem'] \n", - "and the domains are of type CartesianDomain\n" - ] - } - ], + "outputs": [], "source": [ "from pina.problem.zoo import AdvectionProblem\n", "\n", diff --git a/tutorials/tutorial17/tutorial.ipynb b/tutorials/tutorial17/tutorial.ipynb index 646517929..8195ca0c1 100644 --- a/tutorials/tutorial17/tutorial.ipynb +++ b/tutorials/tutorial17/tutorial.ipynb @@ -21,7 +21,6 @@ "\n", "- **Physics-Informed Neural Networks (PINNs)**\n", "- **Neural Operators (NOs)**\n", - "- **Reduced Order Models (ROMs)**\n", "- **Graph Neural Networks (GNNs)**\n", "- ...\n", "\n", @@ -47,12 +46,12 @@ "\n", "1. ***Problem & Data***\n", " Define the mathematical problem and its physical constraints using PINA’s base classes: \n", - " - `AbstractProblem`\n", + " - `BaseProblem`\n", " - `SpatialProblem`\n", " - `InverseProblem` \n", " - ...\n", "\n", - " Then prepare inputs by discretizing the domain or importing numerical data. PINA provides essential tools like the `Conditions` class and the `pina.domain` module to facilitate domain sampling and ensure that the input data aligns with the problem's requirements.\n", + " Then prepare inputs by discretizing the domain or importing numerical data. PINA provides essential tools like the `Condition` class and the `pina.domain` module to facilitate domain sampling and ensure that the input data aligns with the problem's requirements.\n", "\n", "> **👉 We have a dedicated [tutorial](https://mathlab.github.io/PINA/tutorial16/tutorial.html) to teach how to build a Problem from scratch — have a look if you're interested!**\n", "\n", @@ -61,9 +60,9 @@ "\n", "3. ***Solver Selection*** \n", " Choose and configure a solver to optimize your model. Options include:\n", - " - **Supervised solvers**: `SupervisedSolver`, `ReducedOrderModelSolver`\n", - " - **Physics-informed solvers**: `PINN` and (many) variants\n", - " - **Generative solvers**: `GAROM` \n", + " - **Supervised solvers**: `SupervisedSingleModelSolver`, `SupervisedEnsembleSolver`\n", + " - **Physics-informed solvers**: `PhysicsInformedSingleModelSolver` and many variants\n", + "\n", " Solvers can be used out-of-the-box, extended, or fully customized.\n", "\n", "4. ***Training*** \n", @@ -75,7 +74,11 @@ "\n", "## A Simple Regression Problem in PINA\n", "We'll start with a simple regression problem [2] of approximating the following function with a Neural Net model $\\mathcal{M}_{\\theta}$:\n", - "$$y = x^3 + \\epsilon, \\quad \\epsilon \\sim \\mathcal{N}(0, 9)$$ \n", + "\n", + "$$\n", + "y = x^3 + \\epsilon, \\quad \\epsilon \\sim \\mathcal{N}(0, 9)\n", + "$$ \n", + "\n", "using only 20 samples: \n", "\n", "$$x_i \\sim \\mathcal{U}[-3, 3], \\; \\forall i \\in \\{1, \\dots, 20\\}$$\n", @@ -91,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "0981f1e9", "metadata": {}, "outputs": [], @@ -113,7 +116,7 @@ "warnings.filterwarnings(\"ignore\")\n", "\n", "from pina import Condition, LabelTensor\n", - "from pina.problem import AbstractProblem\n", + "from pina.problem import BaseProblem\n", "from pina.domain import EllipsoidDomain, Difference, CartesianDomain, Union" ] }, @@ -124,7 +127,7 @@ "source": [ "#### ***Problem & Data***\n", "\n", - "We'll start by defining a `BayesianProblem` inheriting from `AbstractProblem` to handle input/output data. This is suitable when data is available. For other cases like PDEs without data, use:\n", + "We'll start by defining a `BayesianProblem` inheriting from `BaseProblem` to handle input/output data. This is suitable when data is available. For other cases like PDEs without data, use:\n", "\n", "- `SpatialProblem` – for spatial variables\n", "- `TimeDependentProblem` – for temporal variables\n", @@ -136,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "014bbd86", "metadata": {}, "outputs": [], @@ -148,7 +151,7 @@ "\n", "\n", "# (b) PINA Problem formulation\n", - "class BayesianProblem(AbstractProblem):\n", + "class BayesianProblem(BaseProblem):\n", "\n", " output_variables = [\"y\"]\n", " input_variables = [\"x\"]\n", @@ -183,37 +186,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "6f25d3a6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The Label Tensor object, a very short introduction... \n", - "\n", - "1: {'dof': ['a', 'b', 'c', 'd'], 'name': 1}\n", - "\n", - "tensor([[0.0906, 0.7385, 0.9804, 0.2950],\n", - " [0.7645, 0.2285, 0.0513, 0.3863],\n", - " [0.8320, 0.8914, 0.9107, 0.4953]]) \n", - "\n", - "Torch methods can be used, label_tensor.shape=torch.Size([3, 4])\n", - "also label_tensor.requires_grad=False \n", - "\n", - "But we have labels as well, e.g. label_tensor.labels=['a', 'b', 'c', 'd']\n", - "And we can slice with labels: \n", - " label_tensor[\"a\"]=LabelTensor([[0.0906],\n", - " [0.7645],\n", - " [0.8320]])\n", - "Similarly to: \n", - " label_tensor[:, 0]=LabelTensor([[0.0906],\n", - " [0.7645],\n", - " [0.8320]])\n" - ] - } - ], + "outputs": [], "source": [ "# EXTRA - on the use of LabelTensor\n", "\n", @@ -241,9 +217,9 @@ "\n", "#### ***Solver Selection***\n", "\n", - "For this task, we will use a straightforward **supervised learning** approach by importing the `SupervisedSolver` from `pina.solvers`. The solver is responsible for defining the training strategy. \n", + "For this task, we will use a straightforward **supervised learning** approach by importing the `SupervisedSingleModelSolver` from `pina.solver`. The solver is responsible for defining the training strategy. \n", "\n", - "The `SupervisedSolver` is designed to handle typical regression tasks effectively by minimizing the following loss function:\n", + "The `SupervisedSingleModelSolver` is designed to handle typical regression tasks effectively by minimizing the following loss function:\n", "$$\n", "\\mathcal{L}_{\\rm{problem}} = \\frac{1}{N}\\sum_{i=1}^N\n", "\\mathcal{L}(y_i - \\mathcal{M}_{\\theta}(x_i))\n", @@ -270,8 +246,8 @@ "metadata": {}, "outputs": [], "source": [ - "from pina.solver import SupervisedSolver\n", - "from pina.trainer import Trainer\n", + "from pina.solver import SupervisedSingleModelSolver\n", + "from pina import Trainer\n", "\n", "\n", "# define problem & data (step 1)\n", @@ -295,7 +271,7 @@ "model = BayesianModel()\n", "\n", "# solver selection (step 3)\n", - "solver = SupervisedSolver(problem, model)\n", + "solver = SupervisedSingleModelSolver(problem, model)\n", "\n", "# training (step 4)\n", "trainer = Trainer(solver=solver, max_epochs=2000, accelerator=\"cpu\")\n", @@ -322,21 +298,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "f2555911", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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paWls2bKl1bENGzZgNPpHfY8cORKz2UxRUZF08wghRG+x5nWo3gmxqXD6PeGuJqT6VlCJIgMGDGDVqlUUFhYSHx+PqqptnnfWWWfxzDPP8I9//IOpU6fyz3/+ky1btgS6dRISErjnnnu48847UVWV6dOnY7fbWb58OVarleuuu64nb0sIIcSJaqmDxU/4n595P1hs4a0nxGR6coS655570Ov1jBw5krS0NIqKito8b9asWTz44IP87ne/4+STT6ahoYFrr7221TmPPvooDz74IPPnz2fEiBGcd955fPrpp+Tn996mQiGE6LWWPOMPK2kj4KTe/z+binb0AIco43A4sNls2O12rFZrq9ecTif79u0jPz8fi8USpgpFsMnfqxCiz6raCa9MBdULP/83DD473BV1W0ff30eSFhUhhBAiGmgafPZbf0gZdkGPhBRN03B6jj9eMpQkqAghhBDRYPt/YO9i0Jth1hMh/7hyu5OVe2qoanCF/LM6IoNphRBCiEjnboYv7vc/P/U3IZ2OXNngZG9VE43OyNhPToKKEEIIEemWPQ/2A2DLhel3huQjNE1jW5mDsnpnSK7fXT3W9fPkk0+iKAp33HFH4JjT6WTOnDmkpKQQHx/P5ZdfTkVFRU+VJIQQQkS+2r2w/EX/81lPgCk4C4geSdM0tpZGXkiBHgoqq1ev5rXXXmPs2LGtjt9555385z//4V//+hdLliyhtLSUyy67rCdKEkIIIaLD5/eDzwUDZ8CIi4J+eVXV2Fxip9weeSEFeiCoNDY2cvXVV/PXv/611W6/drudv/3tbzz33HOcddZZTJw4kddff50VK1bw/fffh7osIYQQIvLt/BJ2fgY6A5z/dNB3R1ZVjU0ldiod4R0w25GQB5U5c+bwox/9iJkzZ7Y6vnbtWjweT6vjw4cPJy8vj5UrV7Z7PZfLhcPhaPUQQggheh13M/z3bv/zU26BtGFBvXyjy8v6A/VUh3lWz/GEdDDtO++8w7p161i9evUxr5WXl2MymUhMTGx1PCMjg/Ly8navOX/+fObNmxfsUoUQQojIsvRpqC8Caw6ccV/QLtvg9LCvuimiW1GOFLIWlQMHDvCb3/yGt956K6irh86dOxe73R54HDhwIGjX7q0GDBjACy+8EDXXFUKIPq9iG6x4yf/8gqfBHH/Cl2xweth4oJ5Ve2ujJqRACIPK2rVrqays5KSTTsJgMGAwGFiyZAkLFizAYDCQkZGB2+2mvr6+1fsqKirIzMxs97pmsxmr1drq0dvMmDGj1eyoE7V69WpuuummoF2vu954441jWtCEEEIcRVXhkzv9K9AOvxCG/+iELuf2qmwvc/DDvtqwL97WHSHr+jn77LPZvHlzq2PXX389w4cP59577yU3Nxej0ciiRYu4/PLLASgoKKCoqIipU6eGqqzuUX2wfwU0VkB8BvSfBjp9WEvSNA2fz4fBcPy/wrS0tB6oSAghRFCsfxMOfA+meDj/qW5fRtM0DtS2sLe6Ea8verf1C1mLSkJCAqNHj271iIuLIyUlhdGjR2Oz2bjhhhu46667+Pbbb1m7di3XX389U6dO5ZRTTglVWV237WN4YTT8/UL44Ab/zxdG+4+HwOzZs1myZAkvvvgiiqKgKAqFhYUsXrwYRVH47LPPmDhxImazmWXLlrFnzx4uueQSMjIyiI+P5+STT+brr79udc2ju2gUReF///d/+fGPf0xsbCxDhgzh4487vp/KykouuugiYmJiyM/P56233jrmnOeee44xY8YQFxdHbm4uv/71r2lsbARg8eLFXH/99djt9sB9/eEPfwDgzTffZNKkSSQkJJCZmclVV11FZWXlif1BCiFENGqsgq8e8j8/836w5XTrMrVNblburWFnRUNUhxQI814/zz//PBdeeCGXX345p59+OpmZmfz73/8OZ0mtbfsY3rsWHKWtjzvK/MdDEFZefPFFpk6dyo033khZWRllZWXk5uYGXr/vvvt48skn2b59O2PHjqWxsZELLriARYsWsX79es477zwuuugiioqKOvycefPmceWVV7Jp0yYuuOACrr76ampra9s9f/bs2Rw4cIBvv/2W999/nz//+c/HhAmdTseCBQvYunUrf//73/nmm2/43e9+B8C0adN44YUXsFqtgfu65557APB4PDz66KNs3LiRDz/8kMLCQmbPnt3NP0EhhIhiXz4AznrIHAOTf9Xltzs9PjYX21m3v45mV3g3EwyWHl1Cf/Hixa1+t1gsvPzyy7z88ss9WUbnqD74/F6grSSqAQp8fp+/7zCI3UA2mw2TyURsbGybY3UeeeQRzjnnnMDvycnJjBs3LvD7o48+ysKFC/n444+59dZb2/2c2bNn8z//8z8APPHEEyxYsIAffviB884775hzd+7cyWeffcYPP/zAySefDMDf/vY3RowY0eq8I8fVDBgwgMcee4ybb76ZP//5z5hMJmw2G4qiHHNfv/jFLwLPBw4cyIIFCzj55JNpbGwkPv7EB5AJIURU2P01bHoXUODCF0Hf+a9oVdUoqm1mX3UTPjW6W1COJrsnt2f/imNbUlrRwFHiP68HTZo0qdXvjY2N3HPPPYwYMYLExETi4+PZvn37cVtUjlwlOC4uDqvV2m53y/bt2zEYDEycODFwbPjw4ccMjP366685++yz6devHwkJCVxzzTXU1NTQ3NzcYS1r167loosuIi8vj4SEBM444wyA496DEEL0Gq4G+M8d/udTfgU5Ezs8/UiqqrH+QD27Kxt7XUgBCSrta+zknkOdPS9I4uLiWv1+zz33sHDhQp544gm+++47NmzYwJgxY3C73R1ex2g0tvpdURRUVe12XYWFhVx44YWMHTuWDz74gLVr1wZayjqqpampiVmzZmG1WnnrrbdYvXo1CxcuPO77hBCiV/l6nn/TwcQ8OOvBTr9N0zS2lNqpa+q9/72U3ZPbE58R3PO6wGQy4fN1rm9x+fLlzJ49mx//+MeAv4WlsLAwqPUMHz4cr9fL2rVrA10/BQUFraaWr127FlVV+eMf/4hO58+/7733XqvrtHVfO3bsoKamhieffDIwFmfNmjVBrV8IISLa/hWw+q/+5xct6NKaKTvKG6JqTZTukBaV9vSfBtZsoL19FRSw9vOfF2QDBgxg1apVFBYWUl1d3WFLx5AhQ/j3v//Nhg0b2LhxI1ddddUJtYy0ZdiwYZx33nn86le/YtWqVaxdu5Zf/vKXxMTEBM4ZPHgwHo+Hl156ib179/Lmm2/y6quvHnNfjY2NLFq0iOrqapqbm8nLy8NkMgXe9/HHH/Poo48GtX4hhIhYnhb46OB4wgnXwKAzO/3WPVWNlNS1hKiwyCFBpT06PZx3aP760WHl4O/nPRmS9VTuuece9Ho9I0eOJC0trcOxGs899xxJSUlMmzaNiy66iFmzZnHSSScFvabXX3+d7OxszjjjDC677DJuuukm0tPTA6+PGzeO5557jqeeeorRo0fz1ltvMX/+/FbXmDZtGjfffDM//elPSUtL4+mnnyYtLY033niDf/3rX4wcOZInn3ySZ599Nuj1CyFERFr8JNTugYQsOPexTr/tQG0z+6qaQlhY5FA0TYvqkTcOhwObzYbdbj9mlVqn08m+ffvIz8/v/jL+2z72z/45cmCttZ8/pIy8+AQqF90VlL9XIYQIt9L18NezQfPBz/4fDL+gzdNUVaPB5cXR4sHe4sHh9PTo1ONhmQnkJscG/bodfX8fScaoHM/Ii/1TkCNsZVohhBBRzOOEhbf4Q8roy9sNKXVNbraU2nF5gtulH00kqHSGTg/5p4W7CiGEEL3Ft49B1XaIS4Pznz7mZU3T2FvdRGF1E9Hd73HiJKgIIYQQPWn/CljxJ//zixZAXGqrl50eH1tK7NQ3e8JQXOSRoCKEEEL0FFcDLLwZ0GD8z4/p8imzt1BQHv378wRTnwgqUT5eWBxF/j6FEFHry99D/X6w5cJ5h2dGNru9bC9r6NULt3VXrw4qh1ZfbW5ubrXmh4huh1as1etlQLMQIors+grWvuF/fumfwWJFVTUKa5oorGkiyEtg9Rq9Oqjo9XoSExMDe9jExsaiKO0t4CaigaqqVFVVERsbi8HQq//xFUL0Jk01hxd2m3IL5J9Oi9vHxuJ6Gp3e8NYW4Xr9f+kP7dTb3oZ7IvrodDry8vIkdAohooOmwce3QWM5pAyBmQ9jb/awsbget1eaUY6n1wcVRVHIysoiPT0dj0dGUPcGJpMpsJ+QEEJEvDX/BwWfgs4IP/kbFS0KW0trpaunk3p9UDlEr9fLmAYhhBA9q3I7fHG///nMP7DPOJg9xfbw1hRl+kxQEUIIIXqUxwnv3wBeJ978s9iY/TPqKhvDXVXUkaAihBBChMJXD0HlVrwxqawY/Sju5p7bn6c3kY5+IYQQIsjUHZ/DD68BsHnSfNwxaWGuKHpJUBFCCCGCyFWzH3XhrwAoGjKbmqwzwlxRdJOgIoQQQgSJvaEJ1/+7FoOrHkfSaHaNvSfcJUU9CSpCCCFEEJTUt2D/5H6s1RvwGBPYNPVFNL0p3GVFPRlMK4QQQpwAr09lZ0Ujni0fMa7gDQC2TX4KZ3xueAvrJSSoCCGEEN1U3+xma6kDavcxefVcAPYP/QVV/WaGubLeQ4KKEEII0UWqqrG3upH9Nc0oXheTVtyO0dNAfcpJ7B57d7jL61UkqAghhBBdUN3oYndlo38zQU1jxNqHsdZvw21KZPPUF9B0xnCX2KtIUBFCCCE6oarBxb7qJhwth/eNy9n9T7IL/42m6Ngy9XlcsZlhrLB3kqAihBBCdMDe7GFHuYMGp7fV8cSq1QzdMB+AXWN+S23GqeEor9eToCKEEEK0w+H0sO5AHT6f1uq4ubmMsStuQ6d5Kc+7kKJhvwhThb2frKMihBBCtKHF7WNDUf0xIUXndTJ2+RxMrloaEoezbdLjoChhqrL3k6AihBBCHMXl9bG+qA63V239gqYxfO1D2Oq24DYlsvHUP6MaYsJTZIg1Or0s2VnF7f9vPeuK6sJWh3T9CCGEEEfw+lQ2FNXT7D52t+MBO14je/+HqIqezVNfwBmXE4YKQ6fF7WPDgXpWFdawvbQBn+ZvTfp4Qykn5SWFpSYJKkIIIcRBbq/K5hL7MQNnAdKLv2Dw5ucA2Dnh99RlTOvp8oJO0zRK6lvYUuJgS6mdXZWN+NTDXV15ybFcMCaTn5/SP2w1SlARQgjR5/lUjaLaZgprmo4ZkwKQULuZUat+C0DR4GsoHnx1T5cYVB6fyhdby1mys4q6Zk+r1zKtFibnJzN5QDKZNgvDMhPISYoNU6USVIQQQvRhmqZRaneyt6oRl0dt8xxzcxnjl92M3uekOvN0do2f28NVBo+maWw4UM+7aw5Q3egGwKhXGJaZwJhsG6P72UhPMKNE0OBgCSpCCCH6HFXVKHM42V/TRLPr2LEoh+g9TYxfdjNmZxWNtqEHV56Nzq/OcruT/7e6yL83EZAUa+Syk3KYmJeEyRC5c2ui809bCCGE6AavT6WkvoWi2uZ2W1AOUVQPY1beTkL9dlzmFDZMfxWfMb6HKg2efdVNfLWtgjX7a1E1MOgUzh2ZwQVjsrAY9eEu77gkqAghhOj1NM0/BmVfdRPeNsagtPEGRqx+gNTy7/DpY9g4/dWomuGjqhobi+v5clsFuyobA8fH5tj46aRcMqyWMFbXNRJUhBBC9Gq1TW4Kyhtoch07k6c9gzY/F5iGvGnqCzhSxoWwwhPn8akUVjexq7KRXZWN7K5spMXj79LS6xQmD0jmnJEZ5CWHb1Bsd0lQEUII0Ss5PT52VTRS4XB26X05u94kf8drAOyY+Cg12WeGorygaHJ5+XxrOd/sqMR11OJ0cSY9ZwxN46zh6STGmsJU4YmToCKEEKLXsbd42HCgHs/RK8seR1rxFwxb/xgAe0b9htKBPwlFeSfM5fXxzY5KPttSHliYLsFiYEh6PEPSExiSEU9uUix6XeTM3ukuCSpCCCF6lZpGF5tK7G2uh9KR5PLljPn+LhQ0igf+jH0jfx2iCrtHPbjWy9YyB9/sqMTe4l//pF9iDD+e0I9xObaImlYcLCENKvPnz+ff//43O3bsICYmhmnTpvHUU08xbNiwwDlOp5O7776bd955B5fLxaxZs/jzn/9MRkZGKEsTQgjRC1U4nGwttaN2rSEFW/Vaxi3/NTrVQ2W/c9lx0sMRsdGgvcXDuqI6tpc52FHe0GpZ/9R4E5eM68eU/GR0vaDlpD0hDSpLlixhzpw5nHzyyXi9Xu6//37OPfdctm3bRlxcHAB33nknn376Kf/617+w2WzceuutXHbZZSxfvjyUpQkhhOhliuuaKShvQOtaQwoJdVuZ8N2N6H0tVGeexuZTngNd+Kbten3+ZfyX765hc4k9sN8OQIxRz7CMBMbm2pg6MAWjPnLXPwkWRdO6+lfafVVVVaSnp7NkyRJOP/107HY7aWlpvP322/zkJ/5+wB07djBixAhWrlzJKaecctxrOhwObDYbdrsdq9Ua6lsQQggRYZweH3uqGimr79qgWYA4+24mfnsVJnc9dWkns/60/w3bbsi1TW4Wba9g+Z4aGo+YoTQgJZbxuYmMyLIyICWux8edDMtMIDcEs4U6+/3do2NU7HY7AMnJyQCsXbsWj8fDzJkzA+cMHz6cvLy8doOKy+XC5XIFfnc4HCGuWgghRCTy+lQKa5o5UNvcaiO9zoppLOKkJbMxueuxJ49hw/TXwhJSyu1OPt9azsq9NYH7sMUYmTowhWmDUshODE9wihQ9FlRUVeWOO+7g1FNPZfTo0QCUl5djMplITExsdW5GRgbl5eVtXmf+/PnMmzcv1OUKIYSIYMV1zeytasLdxVk9AKg+Mos+ZeiGxzC562m0DmHDaf/bY6vOelWVMruTotpmNh2ws66ojkMxa2hGPOeOzGRMP1uvmLETDD0WVObMmcOWLVtYtmzZCV1n7ty53HXXXYHfHQ4Hubm5J1qeEEKIKKCqGtvLHd3q5gH/9OPha+dhdlUHjhnc9SRW/UBVzqxglXmM+mY3X2yrYGdFAyV1LXiPagEan5PI+WMyGZQWfUv0h1qPBJVbb72VTz75hKVLl5KTc3gJ4szMTNxuN/X19a1aVSoqKsjMzGzzWmazGbPZHOqShRBCRBiPT2VTcT11TZ5uvT+t+AvGrrjtmONmZzVjV9zOpmkLgh5WWtw+vthazpfbK1q1/sQY9eQmx9A/OY7pg1Ppl9S3u3c6EtKgomkat912GwsXLmTx4sXk5+e3en3ixIkYjUYWLVrE5ZdfDkBBQQFFRUVMnTo1lKUJIYSIIs1uLxuK6ltNz+0S1cfwtf5hA0d3qChoaCgMW/8EVdkzgzLjx+tTWbqrmv9sKqXB6R8YOzA1jpkjMhiQGktqvBldBEx/jgYhDSpz5szh7bff5qOPPiIhISEw7sRmsxETE4PNZuOGG27grrvuIjk5GavVym233cbUqVM7NeNHCCFE71fd6GJLib1zmwm2I6twYavunqMpaFhaykiqXkNd+pRuf46qany/r4aPN5ZS3egGIMNq5rIJOZyUl9grF2QLtZAGlVdeeQWAGTNmtDr++uuvM3v2bACef/55dDodl19+easF34QQQvRtHp/KzoqGbo9HOcRas5Fh6x/t1LkmZ1W3PkPVNNbtr+PDjaWU2/31Wi0GLh6XzfQhqRh0vX+9k1AJedfP8VgsFl5++WVefvnlUJYihBAiilQ2OCkob8Dl6casniMkVaxg3PJfY/C1dOp8tyWtS9dvdHr5obCW73ZVcaDO/xmxJj3nj87krGHpmI3hWziut5C9foQQQkQMl9e/4/GhVokTkVb8JWO+vxOd6qEmfRpxjt2YnVUoHPs/0RoKrphM6lInHfe6Hp/KlhI7K/bU+PcUOjiDx2zQce7IDM4ZmUGsSb5eg0X+JIUQQoSdqmoU17Wwp7qxy5sJtiV77/sMX/sgOs1HZb9z2XzKc6SWfcvYFbejobQKK9rB4bUFE+5vdyBtg9PDpmI7G4vr2VrqwHXEDJ685FimDkxh6sAU4i3ytRps8icqhBAirGoaXRRUNNDs6uaMniNpGgO3LmDgNv9wgtIBl7N90qNoOgNVObPYNG0Bw9Y/jqXl8KKirphMCibcf8zUZFXVWFtUxzc7Ktld2diqHSYxxsiUgclMHZhCTlLwl5cXh0lQEUIIERZur0pBeQMVjhPv5gFQfG5GrH2Q7MKFAOwbcQt7Rt/RahfkqpxZVGXPJKl6DSZnFW5Lmr+754iWFI9PZeWeGj7fWk5lw+EtW/KSYxmXY2NcTiJ5KbEyvbiHSFARQgjR4yobnOwoa+jeEvht0HsaGbviVlIqVqAqegpOepiSQT9r+2Sdvs0pyHXNblbsqeGbHZXYW/yLysWZ9Jw9IoPpg1NJjjMFpVbRNRJUhBBC9BiPz9+KEozBsodYmkoYt+xmEuwFeA2xbJ76AjVZMzpdz8biepbtrmZrqYNDk1WTYo2cOzKT04akYpGZO2ElQUUIIUSPKLO3sLuy8YSnHB8psWo1Y1fchslVi8uSyobpf6EheXSH71E1jd2VjazaV8va/XU0uryB14akxzN9SCpTBiRj0MvaJ5FAgooQQoiQqm1ys6uiIbCUfLBk732P4evmoVM9OBJHsvHUP+OKy273/AO1zXy/t4YfCmupaz68X1BijJFpg1M4dVAqGVZLUGsUJ06CihBCiJBocnnZWdFAzcGl5INFUb0M2TCfvN1vAlCeez7bTn4S1XDsxn5Oj4/VhbUs2VlFYU1z4HiMUc+EvESm5CczItOKTicDYyOVBBUhhBBBV253sr3MEVgMLVhMLVWM/v4ukqtWAbB79J0Ujri51cwegAN1zSwpqOL7fTU4D3Y16XUK43MSmTIwmTH9bBilaycqSFARQggRNJqmsauykaIjWi+CJbFqNWNW3onZWYnXEMfWKU9T1e+cwOsen8q6/XV8W1DF7qrGwPG0BDOnD0nl1EGpWGOMQa9LhJYEFSGEEEHh8vrYUmKnrslz/JO7QtPoX/A3Bm3+IzrNR6N1CJumLaDZOgjwj4FZXFDJd7urA+Ng9IrC+LxEZgxNY1hmgqx5EsUkqAghhDhh9c1uNpfYgzqjB8Dgqmfk6rmkly4CoKz/xWyf+AiqIZa9VY18vb2SNftrUY+YVnz6kDROG5JKYqyse9IbSFARQgjRbZqmsa+6iX3VTYE1SIIlqfJ7Rq36LZaWClSdkYIJD1LY/wrWFtXzzY797K1uCpw7NCOes4dnMD43Eb0MjO1VJKgIIYToFqfHx9bS4Hf1KD43A7cuYMCOv6Kg0ZSQz+JRT/BhRTorVm+iye3fE8igU5icn8zM4Rnkpch+O72VBBUhhBBdVtngZFupA28Qdjo+UkxDIaO/vwtb3RYA1iRfxDzPNWxe7AUqAEiONXHakFROH5qGTQbH9noSVIQQQnSaqmrsrGyguLYluBfWVHJ3/YPBm59D73PSoMRzn/uXfFo6GfCiKDC2n40zhqYxOtsm6570IRJUhBBCdEqTy8vmEjuNQV5hNqahkMEr7yWjfj0Ay3yjuMdzM+Wk0C8xhlMGJjMlP0U2BeyjJKgIIYQ4rtL6FgrKGw4v4Kb6SKpeg8lZhduSRl3qJNB1bfM+l8eN7ofXOK3kNcy4adQsPOG9ms9N5zFlWApT8pPJSZKxJ32dBBUhhBDtcnv9ux1XOA7vdpxW/AXD1j+OpaU8cMwZk0nBhAeoypl13GvWN7vZvXEZFxY9zWhlLwDL1NH8M/0ehg8bxZNZsqS9OEyCihBCiDZVNjjZUdaA23t4bZS04i8Yu+J2oPUgWnNLBWNX3M6maQvaDCuaplFU28yKbfs4o/hVfqv7Cr2i0UAsX2T9Gt3E2fxE1j0RbZCgIoQQohWPz9+KUm53tn5B9TFs/eOAxtHtHQoaGgrD1j9BVfbMQDdQg9PDyr01rNhdzUmOb5hv/Cfp+noAtqbMomrqgyTGpof8nkT0kqAihBAioMLhpKC8dSvKIUnVa1p19xxNQcPSUoatag3LvcNZsrOKTcV2RrGbF4xvMsm0E4D6mDz2Tp5HbcapIbsP0XtIUBFCCIHT42NHeQPVDa52zzE5qzq8hl2L41++0/nbUo2yll1kUMvTxne5XP8dAF59DPuH38T+4b9E1ZuDWr/ouuzEGHKTY6hwOCmpd+JpI5xGAgkqQgjRh2maxoHaFvZUN+I7zuJtbktam8e3q7n8wzeLD33TaMGC1dvEfaaFXK//DLPm7z4q7X8pe8bchSs2M+j3ILrGZNAxIstKWoI/LCZYjAxMjaeywUVJfTM+FeLMehLMRuLM+rDvOC1BRQgh+iinx7/bcX1z55bAr0uZgNuUhNFdh1fT86U6ib97z+UHbQQAFlz83vhPrjJ9R6yvATSoTzmJnRPux5E8NpS3Ijop3WpmeKYVk0HX6rhOp5Bps5Bps4SpsvZJUBFCiD6o0uFkW1nnl8A/NCW5yqXjXe/lvOebQTkpAMTQwsOGf3CZfhkmxQc+aLQOZs/oO6jqdw4oMtU4mBTF3yriUzVUTUM9To+NxagnLcFMeoKZpChcNE+CihBC9CE+VWNnRQMldZ1fAj+p6AtKl/+D53zXslQdi4b//8ZzKecx4+tM121Br/gDT0tcDntH3UZZ3sVdXgCutzMbdXhV7bhdbG3R6SA5zh82UuPNrVpENE3Dp2q4vCpOjw+nV6XF7UNRIC3BjNUS3fshSVARQggA1Qf7V0BjBcRnQP9pve6LtqbRRUFFA80uX6fOr2t2s7Sgku+3GKnS7gwcP0e3mrsM7zNcORBoLHHGZFI4/CZKBl6Jpo++/2sPJYNeYUBKHLnJsaiaRnFdC0W1zccMXrUY9aRbzcSZDSiATlFQFNDrFJJiTejbWQRPURQMegWDXkecufd9rfe+OxJCiK7a9jF8fi84Sg8fs2bDeU/ByIvDV1eQNDg97KpspLbRfdxzNU1jZ0Uj3xZUsq6oDv+K+VZSqecm/Sf8zPAtVuXY1phtJ8+nNlOmGx9Jp4PcpFgGpMZh1PtbQPQo5KfGkZccS2l9CxUOJ4mxJtKt0d/yESoSVIQQfdu2j+G9azl6pVUcZf7jV/4jasOK0+Njb1UTZfYWtOP0NrS4fazcW8PinZWU1h9e6G2U1cXvW55mim47Ha1qb3TXBanq6BZr1pMYYyIpzkhynAmzoe1WOb1OITc5ltxk2cvoeCSoCCH6LtXnb0k5OqTAwWMKfH4fDP9R1HUDlda3sLOi4biDZQ/UNrN4ZxXf763BdbArwmTQMTNPz/VxKxhT/P+w6Ntf5O2Q9qYuRwO9XsFqMWCLMWKNMWLS62h0eWly+Wh0eWl2e9HrFCxGPWaDDrPB/1OnU9ArCjodGHQ64s2GY2bTiBMnQUUI0XftX9G6u+cYGjhK/Ofln9ZjZZ0Ip8fH9jIHNR1083hVlXX76/m2oJJdlY2B4/2sRq7vV8x5nq/JLv0SneqftuxfMP/YZfMPveaKyfTvnhwlYkx6bDFGbDFGEmONxJsNKEfNTEqUfYcihgQVIUTf1VgR3PPC7HitKPXNbr7bVc3inVXYW/whRK8oXJRt55rYlYyq/hzLvsP36kgaTfHAn+IzxDB61W8PtjEdvvah6FIw4f6wtzjFmvQYDTrcXhWX1xeYsmvQK1hjjFgtBqwWf4uJxRhdrWN9nQQVIUTfFZ8RnPPCPGOotsnN7spGHC3HLtzmVVU2F9v5bnc1m0vsgbEqoy3V3JS6mdPcy0iq2Q41/uMeo5WK3AsoGXglDYkjSKpeg8lZxZ5Rt5Gz510szsNBxhWTScGE+9vcLbknxFsMpCeYSbdaiD9qtovHp+JTNQklvYAEFSFE39V/mn92j6OMtsepKP7X+09r/xphnDHkcHrY3c5snqoGF4t3VrJyTw0OpxfQGKyUcLVtE+frfyCzeSdU+89VFQPVWWdQPuBSqrLORNObSCv+gnHLf91qE0JnTCZ7Rt1Oc8IA3JY0f3dPEAKZQa+QYDGQYDFitRiJMekx6BT0OiXwU9PAd3C9EJ+qoVMUYkztf7ZRr0MySu+gaNrxxoJHNofDgc1mw263Y7Vaw12OECJYeqqVIjDrB1qHlYNjFjqa9dPejKHOvPcE1Da5Ka5rptLRegNBVdPYXuZg0Y5KNhfbMeFmim4755k2co5hI2nessPnKnrq0k+hMmcWlTnn4jEnB15LK/6CsStu5+hxKYd+2zRtQbdbUXQ6AoHEP3jVQKxJ/p+5L+rs97cEFSFE5OnpVoo2P68fnPdk+5+n+uCF0R0Mxj3YGnPH5qAELI9PpazeSXFdM83u1gu2OVo8rNpXy5KCcpIbdzFNt5Xpui2cot+BhcNhxqczBcJJVb+zW4WTI+9r+qdnYm4p73Dw7LIffXPc+1IUiDP7x4YkWAxYY4wkmA3oOprnLPqMzn5/S4wVQkSWcKxrMvJi/xTkrrTg9MCMoRa3j+pGF7VNbmqb3PjUw38mXp/KluIaygpWk1i9ljOUAn6n206KuaHVNZwx6VRnzaA660xqM6aiGjpetyOpek2r7p6jKWhYWsrIbdyAJ/dULEY9FqMOi1GPyaBDr7Tusjl6No0QXSVBRQgROcK5rolO37VAEaQZQ5rm36PF5VVxeXw4PSotHh81Ta7WS91rGqamUpr2/YCvZD2p9i3cwi7iFFer/5J79bHUpU+mLn0qtRlTabQN69KmgDGuqk6dNyyuGfrZOn1dIbpLgooQInJEybomHp+K15xKTCfOLWiKxVFYi6pq+DTNPyj04HNV1dpcMVbxuYlt2EemvQBz7XaM1dtItm/HqtYfcZL/R5Mugbrk8bRkTaY+bRKO5LFouuMvxW4y+FtBYox6Ys16f9eMxYglZiis7MSNdXbGlBAnKCKCyssvv8wzzzxDeXk548aN46WXXmLy5MnhLksI0ZFQDHYN8romh3aV1SDQbaJqWmAHW4+qoqr+4OH2qXh8Kh6vhtunAq1DhE/VcHs8xJX/gLGlCrc5hVExmZhbKlqtLRL47INjOQ7Ej4fmY6cNo2mYnFXENBVjcezDVL8bi30vCY37sDqLMWjeY97i0fTsIpfy+BGoWROw5E/DmTgYlPZXQz00ePXQAmcJFgMWg779cSLBmAklRBCFPai8++673HXXXbz66qtMmTKFF154gVmzZlFQUEB6enq4yxNCtCXIg11V1R8eNEsa5k6cX+hKoLHEHgghqqahaRqqBl6fhldVA2EkWNKKv2Dc+sextJTj1XQ0YaHCmIFLzaYJC82axf8TC02aGTcGauNPh+WfkuCqxOqpJNFbRbK3knRfOZlqBRbaXz3WocWwQ8tjh5pHsWkgrpQRpA6ayJCcNAw6fzBxHvUeg14h3mwg/uBU33izoeuDV3V6/9/je9fCwRVpDzt4nfOeDPsCb6LvCPusnylTpnDyySfzpz/9CQBVVcnNzeW2227jvvvuO+77ZdaPiGphXiisWzoxJdc77EK8quZvofBpgdVC3QfHYrh9Kh6v/7VDC3MBR8w46biVor0ZJ5qm4fFptHh8OA8+3Aev71MPBZnDNbl9/rpcHpVmt49mj5cWt48Wjw+vV0WvubCoLVjcNcQ5y9FrKnrFhwUPVqUZG00kKQ0kKY0k0kiy0kAqdtKUeixKG60oR/FpCmWkUKhmsE/LptiQQ4Uxj/rY/sSk9mdQegL5qXHYYo7tytHpIM5kwBZ7uLUkqNN8uzMTSoguiIrpyW63m9jYWN5//30uvfTSwPHrrruO+vp6Pvroo2Pe43K5cLkOT7dzOBzk5uZKUBHRJ4wLhXWKz4vH2YCnpQFPSxM+VyOqu4mkj2ejc9a1M3UVvIYECoffiA4VRfWCpqKg+n9qGqCiaCqapuH1+fD4/D99qn8VVX1zFZb6XXgw4NX0eNHjQY8PHV4MOM2peHQWVE1DVdXDYz1UFVXTUDQVBQ09KjpFQ4eKAd/Bnyp6fJjwYsCHUfFiwv8w48aseLDgxoKbGFzolRP7z2OLEoPdkIrdmIbDmEGDOZ1mcwZNcTk44/LwxGdhMluI62TLR7zFQEqciaQ4E0mxJvShnuYbjUFaRI2omJ5cXV2Nz+cjI6P1oKyMjAx27NjR5nvmz5/PvHnzeqI8IUKnJ6fgqj68DZV4HBX4HBWoDZWozTXQXIvSUovSUofOVY/iakDvdqBzN6D3NKD3uTACxx+WeZgCGL0NDNny3InV3NEGtMcO3Tj+e06QT1NQ0bXanE+PjyNzQuHQG7CnnYTLkobbnILbkopqOHa4reXgoz2K4l9VNdakJ85sIM5kINasx2ox9vzOvF2dCSVECIR9jEpXzZ07l7vuuivw+6EWFSGiRhCm4B7ZjeJqrEWtLUSrLURnL8LYWIqhqQxTUxmm5nJMzmoMmtrtf9lVRY9XH4sPHZqnBTdGnJqJFkw0Yz44NiOGJsx4MOBDj1fzt4D42zH8X/LqwecaCioKh7a30+t0GPQ69Dqdf/0Nvc6/BocCZq0FI170egOa2Yru4Dl6RUGnU9DrdIePHXxuMBjQ6/UHB5gqaDo9mnLoYUBTdGh6I6rOhKYzour8z1W9GVVvxqe3oOrM+Awx+AyxpJZ+w5hV96DH1+GfU0vqaBrzz0NRwKAoGAGdTkGnHF5TxKBXMKASX74ak7MKJSEDLW8aRqMB48H7Nuh7OIwIEeHCGlRSU1PR6/VUVLQewV9RUUFmZmab7zGbzZjNnRluJ0SE6uQU3JKNi2jIOsU/ONTtxFC/F33tbkz1e4h17CHWsY/Ehr0YvE3H/UgNBbc5GbclFbclBY8pCY85EafBhkNnpV6No8YXQ6XbRIXbTLnTSLnLQFmzgZoW7aiF1NtnwEsKDuKsScQm2AIrkiaYjcRbDP6BngcHe8aZ9MSaDJ3qvji4ES6+g4+26I/aG8agPxheFFBQAkuJKAr+8KAo/tcO/jTodOh0h3/qFH/IMJkHw6rj3/uIIUMgP7XjkyK9u0+ICBTWoGIymZg4cSKLFi0KjFFRVZVFixZx6623hrM0IYJOVTUcTg+u0v10ZgUK3bo3sClvEm8vIK5hLzq148GZqmLEnjIWe+pJOGOyaInJoEaXSonPxgF3HFWNPqoaXVQ3urDXeXC0eGnxdNxK4KegUzTStDoylTrSlDoylHoylDrSqSNDOfxIpBGf2cbSi1e22xqkKP5QYdTrDv5UMBxsFdEp/oBxZCuELtCCgv/nwdaUQ+HCoPefG7IVUIeeHpzpuuFYcVeIXiDsXT933XUX1113HZMmTWLy5Mm88MILNDU1cf3114e7NCFOmNPjo6rBRU2Tm7pmNz6fRpLH2qmgknXg01a/e43xNCUMwqc3k1T1A6oG9SRQpKVTpKVTrKVTXJbK9uZTKfMmUNPoxqv6gNqDj7YZdAq2GCMp8SaS4w4+Yv0DNhNjjCTGmhhU+QXjV93V7jUO0QBNUchLicViMmEx6vzLqh8MJlHZtRGM6brhXHFXiCgX9qDy05/+lKqqKh566CHKy8sZP348n3/++TEDbIWIFm6vSoXDSWWDk/pmT6tFw/SeRnSeRryGOPTepnY7VFRFT2W/c6lLGEaRMZ8dWh67nTZqmtz4ilZR7vsZZVoK7raGutYCBzeiUxRIjjWRlmAmNd5MaryJtHgzibGmwM61MUb9cVsjfDFpnbp3BTC46hjq3AxZvWgQ5siL/S0ebXbbdGK6bmdX3F31Kky5WcKKEEcI+zoqJ0rWURGRwKdqVDW4KLO3UNvkDoQTRfVgq9lAcsUKkitWYq3dhO6oFUdVTaGOeIrVNHZq/VinDmFjwhmUuGOxt3Tc3aNDJYsa+inV5ChV5Bz82TLhl5hzx5MYawwsDtYZer1CgtlAnNkfYCxGPWaDDrNeI+bP41E6/LI9wuV/gzE/6fTnRo3uTtfd/D58cEPnPkPGrIg+IiqmJwsRzTRNo7bJTZndSVWjK7AKqtFZQ2r5ElJLl5BS8R0GT2Or99VbcthhHst6Vxbf2HNYrQ2Fo9tW7AD+kGK1GMi0WchIsDDAt4+Tiv9BtlJDNtVkKnUYlWPHmWyOu4SK+LYHnRv0CrEmQ2DH20OBJMFiwGLs4Ev3vKfgvWs694fTW/eB6e503a78eciYFSFakaAiRBd4fCrVjS6qG9zUNLnwHgwnlqZi+h34gvSSL7DVbGy1qqrblMR+22S+843i3dqBFNQnH3PdrBgvGXF6bEmppMRbSIn3d9dkWi2tVhtNqqxgYtny49bptqSh00G82T/rJv5gK0mcWY/Z0M1uhZEXwxV/h/evB01t5yTZB6ZNx90/50gyZkWII0lQEaIDTo8PR4uH+hYP9hYPjpbDY0784eQz0g98hq1uS6v3ORJHUpp+Bp+0jObt4lRqDxxu9Yg3GxiRlcCAlDgGpMSRlxxLjKlzX0Z1qZNwHmcjPF98FoMnnUtCrLlre7x0xqhL/d+j71/XxouyD0y7OhyQ25bI2CVaiEggQUWII6iqRm2zm0qHi5omFy5P65YDg9tB+oHPyNr/EUnVawLHNUVHXdpkKnNmsTf5DD7eB99uq8TlVQEfMUY9E/ISmTwgmeFZCV0aN3KIxagnxmSiavoj5Hz1KzSUo8KK4h/MesFT2OI7Wvv0BI2+FHRvdn9gaV/V3oDcjnR2N2khejEJKqLP8/hU6prdVDW4qGo43J0ToKkkV6yg395/kVq6CL3q3/FWQ6EufQoVuRdQ1W8m5T4rX24rZ+mqStw+f8DJSYrhgtFZTMhLxNjFablxZgNJcUYSY0wkxhoPjx/p/1NIiglvUBh5sb9bQvaB6ZpDf26rXoUv7j/++b11rI8QXSCzfkSfoqoaTW4vDqcXe7OH+hY3za62Fz0zOavJ2vcB/fa+R2zTgcDxRusQygZcSnneRbhiMym3O/l8azkr99YEdgEekBLLhWOzGZdj69RCZHFmA9YYQ2Al13iz4fjrjciGcdFL9cELo4+/iNwdm+XvVPRaMutHCPxjTErqW2h0emly+VdibRXNVR9J1WswOatwW9KoS52EtW4LeTvfIL3ky8BqsB5jAuX9L6E0/yc0JI4ARWF/TRP//WEP64rqAl81wzISuGBMJiOzrB0GlBiTnqRYEynx/l1wu7XZnGwYF72CsYicEH2EBBXRK7m8PvbXNFNS1xJo5ThaWvEXDFv/OJaW8sAxVWdstVS9PXkcxYN+RkXuBYGdcAtrmvjPxlI2FtsD543PSeT8MZkMSotvtya9XiHTaiEnKYYES1f2JBa90okuIidEHyFBRUSX43R3NLu9lNS1UNxBQAF/SBm74naObnbXqR40oC5tCrvGz6UhaWTgtcLqJj7eVMqmgwFFUWDygGQuGJ1Fv6SYdj8rzmwgJymGLJsl+paPF6ElY32EOC4JKiJ6tLHzrDc+i/Jp8yjPPocGlzew6FqHVB/D1j0KHewJHNtYRINtGABl9hb+vb6E9UX1gD+gnJKfwo/GZpFpbX92jUGvMCgtntzk2E7eoOiTpAtPiA5JUBHR4eDOs9pR4ULfWE6/L39FzbQF+HJmHfcyek8jQ9c/jsVZ2e45CmBpKUMtXsM/SzL4bnc1mtb5gAKQYbUwJCO+45VehRBCHJcEFRHxmp0uTP/9Hfo2WkCUg9Fl2PonqMqe2W6Tuc7rJGfP2wzY/iomd32Hn1enxfOa90L+b6kOt1oNwPjcRC6b0I/sxPa7eMA/SHZoRgJpCW0vXy+EEKJrJKiIiNTs9lLd4Kbc4US/fxkTG8vaPVdBw9JSRlL1GurSp7R+TfWSte8DBm77E5YW/+JZblMSJnfdMddxaDH8zXsBf/OdTyP+7poh6fFcdlI/hqQndFivXqfQPyWWASlxwV8NVggh+jAJKqLHeX0qLR4fOkVBr1NQFP+aqvUtbmqb3NQ0umlxH17bJMNZ1anrmo46L6VsKUM2zifesQcAZ2wWVVlnkrPn7UO7qQDQpJn5u+9cXvNehB3/rJ3h+hJmnnYa43KTjrsOSqbNwuB06eYRQohQkKAieoTT46O60b/ya12zG7W9Pe3a4Lakdem8WMcehm6YT2r5Uv9xUyL7Rv6akvwrmfb5eYA/pDg1I2/5zuYV7yVUYwNgsFLMnYb3yZ5+DTW5x24eeKRYk56R2VYSY02dvxkhhBBdIkFFBNWhTfycHhWn14fT46PZ7aPR6e32NTuzEZ8rJhOHbRhD1z9Gzu630Gk+VJ2RA4OvYd/IX+M1WUmqXIWlpRy3pudfvjN4yftjykkBoL9Szp2GD7hIt4J9o2+jMLfjgbn9kmIYmpGAXrp5hBAipCSoiBPm8vqodLiocDipb/Yc/w1dpdNTMOEBxq64/ZiN+LSDq3pW5M5i2hc/wnyw+6cy+2x2jbuXloQBgXOV5ire8p7Fq76LOaClA5BNNbcb/s3l+u8wKv7upiPfczSTQceILKsMlhVCiB4iQUV0mtPjw+H04PKouLwqbq9Ki8dLfbOHUO8YVZUzi03TFhyzkqzbnIrbkkL/nW8A0JSQT8FJD1GbcWrgHJfHx9Jd1Xy1OYNa7y8BSKWeWw0f8j/6bzArrVt72utqSok3MSrb1r3l7oUQQnSLBBXRIVXVqGp0UVLfQl2TO+SBpCNVObOoyp7p35unuZzkqlVk7v8Ys6sKn85E4chfUzjsl2h6/5gRr6qyuKCKTzaV0ejyAgoZSj2/0n/M/+i/IUZxt7r+oS6kutRJx3x2XkosQ9LjO7XBoBBCiOCRoCKOoaoa9S0eqhtdlNmdeLxdGPkaajo9HpONIRvmY63fBkBNxnR2nPQwLQn9A6dtLbXzzuoDlNmdAKTGm7hgdBYXW2qY+P0Xx1z20AotBRPub7UWi6LA0IwEWV1WCCHCRIKKQFU1Gt1e6ps81DS5qG/2dLhPTrgoqocB218jf/sr6FQPHpONggm/pzzvYn+iACocTt5bcyCwYWC82cCPJ/Rj+uBU9DqFemaxSXdsF5IrJpOCCfdTdcTqtnq9wph+NlLjZTyKEEKEiwSVPkLTNFxeFdfB2Tgtbh+NLi8NTi/Nbm9Yu3Q6I86+m1Gr7gm0olRmn82OiY/gjvGPJ7G3ePhkUylLd1XjUzX0isKZw9O4eFw2sabW/5i36kJyVuG2pPm7e45oSYk16RmTY5NdjoUQIswkqPRSqqpR2+ymutFFTaMbp8cX8WGkTZpGzp63GbLxSfQ+F25TIgUnPURF7o9AUWhyefl8azmLtlfi9vm7qEZnW7lyUm7Hy93r9MesYnuITD0WQojIIUGlF9E0jaoG/7iS2iZ3RHbfdIXRWcvINfeTVvoNANWZp7Ht5Cdxx6TR4vaxaEcFX26roPngKrYDU+O47KR+DM+0duvzZOqxEEJEHgkqvYDL66OkroWS+hZcngga+HoCksuXM+qH32F2VqHqjOwa+1sODLmWFo/GN5vL+HJrOU0HA0q/xBh+PKEf43Js3Z6Vk5pgZkRWAmaDLIMvhBCRRIJKFPL6VBqcXhxOD/aDs3O6siR9RFN95G//MwO3/gkFjUbrYLac8kcqY4fwzeZyvtpWEQgoGVYzF43NZvKA5BPaCDA/LY5BafHBugMhhBBBJEElStQ3u6lwuKhpdAW6Onobo6uW0d/fQ0rFMgCKB/6UH4b9li8K7CzZtQnnwdaiYAUUvV5hVLaV9ARLUOoXQggRfBJUIpBP1fCq/tVfDy1N39JLw8kh1poNjF1xO5aWcnx6C6tGPsif605m5Ue7AmNtshMtXDA664QDCvhn9YzNTSTeLP8KCCFEJJP/Svcwz8FumyaXF6fHh9Oj4vL6/EvS+1RUVYvO2TknoN+edxi2/lF0qofGuP48ZX2At9ck4NOqARiSHs95ozMZ08+GLggrwybHmxjTz4ZRL0vhCyFEpJOgEmJOjy+wWV+D0x9OhJ+iehi6/nFy97wNwMaEM/hF/XXU1FgAjdH9rFw4JpvB6cEbPzIgNZZBabIUvhBCRAsJKiHg8alUNrgotzupbw7v/jiRyuiqY8zK35Bc+T0aCn9S/oc/Vv0IUMhLjuXKSTndnmbcFr1OYWS2lQyrjEcRQohoIkGli3yq1uZCYKqqUd3ootzh7F2zcEIgzr6Lcct+RWxTMc1YuN09h6/ViSTHmbhsQj8m5ycHpYvnkBiTnrGyyqwQQkQlCSqd1OD0UFDegL3FQ4xRT7zFQLzZQKzJQG2Tm8oGJ16fNJ0cT3L5ckYvvxWTr4n9ajq/9NxDsaE/l4/PZOaIjKCPG8lOjGFIRryMRxFCiCglQeU4PD6VPVWNlNS1BLpwmt0+mt0+KnGFt7goE7f9XcZu/gMGfHyvjuDXnjsYN3Qgvx6XHfTWjhiTnhFZVpLjTEG9rhBCiJ4lQaUDNY0utpQ68HilH+dE1DQ4MSx/lpmOfwDwoW8a/8y4lzsnDiDL1sF+PN2gKJCTFMvg9HjZq0cIIXoBCSodcDi9ElJOQFWDiy83H+CC/U9zuX4xAO9ZrsA+9T5uTksI+ufpdQqj+skCbkII0ZtIUBFBV1rfwn+3lLFpXxkvGRZwtn49PnQsG3ofyeNnkxyCzzQZdIzLTcQWIwNmhRCiN5GgIoKmsKaJzzaXs66ojniaed30LFN0O/DozGydtgBP9pkh+dw4s4EJeYlYjLKhoBBC9DYSVMQJ0TSNnRWN/HdzGVvLHAAk4+Bf8c8yyLsbrzGeDdP/gj1tUkg+PynOxNgcWWVWCCF6KwkqoltUVWNjcT2fby1nT1UTADoFzs/z8XjjUyQ27cNtTmb96f9HQ9LIoH++Tgf5qfEMSImVVWaFEKIXk6AiusTp8bFiTw1fba+gqsE/PdugUzhtSCo/HuDlnNW/JKa5BGdsFutOf51m68Cg1xBvMTAq2yoLuAkhRB8gQUV0Sovbx2dbyli8s4rmgzs5x5r0nDE0jZkjMsj0lTFx8S+wNJfRFD+AdWe8gSsuO6g1KAr0T4llYGr8Ce+eLIQQIjqErGO/sLCQG264gfz8fGJiYhg0aBAPP/wwbre71XmbNm3itNNOw2KxkJuby9NPPx2qkkQ3aJrGqn01/P6jLfx3SznNbh/pCWaunpzHM5eP5fKTcg6GlGv8ISUhn7Vn/jPoISUx1sjJ+ckMTk+QkCKEEH1IyFpUduzYgaqqvPbaawwePJgtW7Zw44030tTUxLPPPguAw+Hg3HPPZebMmbz66qts3ryZX/ziFyQmJnLTTTeFqjTRSaX1Lbz9QxE7yhsASE8w85OJOYzPTQzsxRPTsN8fUlrKaUoYyNoZ/8Adkx60GsxGHUPSE8i0ydooQgjRFyma1nN7+z7zzDO88sor7N27F4BXXnmFBx54gPLyckwm/1Ln9913Hx9++CE7duzo1DUdDgc2mw273Y7VGrzddgH2VTexp7IxqNeMBk0uL59uLmPR9kp8moZRr/CTgT5+klkJcWnUpU4Cnf5gSPk5lpYKGq2DWHfGP3DHpAWlhkPdPANS4jDIjB4hhOh1Ovv93aNjVOx2O8nJh5f7WrlyJaeffnogpADMmjWLp556irq6OpKSko65hsvlwuU6vMeOw+EIbdF9iMen8m1BJZ9sKguMQzk51c1899MMPrANDvjPc8ZksnfkLQzc/urBkDKYdTP+gduSGpQ6DHqF0f1spMabg3I9IYQQ0avHgsru3bt56aWXAt0+AOXl5eTn57c6LyMjI/BaW0Fl/vz5zJs3L7TF9jFeVWXt/joWri+hutE/hqhfYgw39K/g+oJbgNaNbuaWckasfRgFaErID2pIiTXpGZebSJxZxnkLIYToxmDa++67D0VROnwc3W1TUlLCeeedxxVXXMGNN954QgXPnTsXu90eeBw4cOCErteXlda38K+1B/jd+5v463f7qG50Y4sxct3U/jx8wTCuKvoDoHH00FXl4ENT9Kw77f+CFlKS402cnJ8sIUUIIURAl78R7r77bmbPnt3hOQMHHl47o7S0lDPPPJNp06bxl7/8pdV5mZmZVFRUtDp26PfMzMw2r202mzGbpUugu7yqyup9dSzeWRlYqA0gwWLgrOHpnDsiA7NRT1LlKiwt5R1eS9F8xDYX44rvd8J1ZSfGMCIrQRZvE0II0UqXg0paWhppaZ0bMFlSUsKZZ57JxIkTef3119HpWjfgTJ06lQceeACPx4PR6F+866uvvmLYsGFtdvuI7nN5fSzbVc0X2yqobfJ37+gUGNsvkVMHpzAmx4bhiL8fk7OqU9ft7HkdSU0wS0gRQgjRppC1sZeUlDBjxgz69+/Ps88+S1XV4S+0Q60lV111FfPmzeOGG27g3nvvZcuWLbz44os8//zzoSqrz6ludLFyTw2LdlTS6PICYLUYOHtEBtMHp7a727Db0rkw2tnz2mONMTKmn01CihBCiDaFLKh89dVX7N69m927d5OTk9PqtUMzom02G19++SVz5sxh4sSJpKam8tBDD8kaKieo3OFk3f461uyvo6i2OXA8Nd7EeaMymTYoFZOh4+FJdamTcMZkYm4pP2aMyiEaOgyu2m7XGWPSMy7Xhl4WcBNCCNGOHl1HJRRkHZXD6prd/G3ZvsACbeBfj2RoegKnD0ll0oDkLoWCtOIvGLvitg6CCoDCpmkLqMqZ1aVaDXqFkwfIwFkhhOirInIdFRE6BeUNvLp0Dw1OL3pFYXhWAhPzkhifm4i1ne6d4zG6G9oNKXBw5g8wbP0TVGXPBJ2+U9fV6xXG5cgUZCGEEMcn3xRRTtM0vtxWwQfrilE1//onv54xiAzriS05n1K2mOFrHzzueQoalpYykqrXUJc+5bjnW2OMjO5nJdYk/+gJIYQ4Pvm2iGLNbi//WLmfNfvrADhlYDLXnNIfs6FzLRvtia/fzpiVd6DTfNSlTiKpes1x33O82T/+JfHjGJQWJwNnhRBCdJoElSikahor99Tw/rpif1ePTuGnk3I5c1jaCYcAU0sl45fdjMHbTG36KRQO/xVJS68/7vs6mv1jMeoZlW0lKc7U7jlCCCFEWySoRJnC6ibe/qGIvdX+xdoyrRauP3UAg9LiT/jaOq+TcctvwdJcRlNCPpumvYTXEH9w9k8FCseOu9ZQcMVk+jcqbENirJGxOYnHnWUkhBBCtEWCSpSoaXTxn01lLN9djQaYDTouGpvNzBHpwdldWFMZ9cO92Go34zYlsmH6X/CabAAUTHiAsStuR0NpFVYOLa5fMOH+NgfSZtosjMyyopPpx0IIIbpJgkqEq2pw8d/NZazYU4Pv4EzyKfnJXDExh8TY4HWlDNyygIziz1B1RjZN+xMtCf0P15Azi03TFjBs/eOtltV3xWRSMOH+NqcmD0qPJz81Lmj1CSGE6JskqEQgVdXYW93Est3VrDwioIzITODi8dkMSU8I6udlFH3CwO1/BmD7xEepT598zDlVObOoyp5JUvUaTM4q3JY0f3fPUS0pep3CyGzrCc86EkIIIUCCSsRocHrYUuJgc4mdraV2mty+wGsjs6xcNC4r6AEFIL5uGyNX3w9A4bAbKcu/rP2TdfoOpyCnW80MzUjAYjyxWUdCCCHEIRJUwkxVNb7aXsGHG0rw+A6P/4g1+WfKzByREZSBsm0xuuoYt+JW9D4n1ZmnsXvMXd26TqxJz9DMBFLjZVdrIYQQwSVBJYzK7C28vrwwMIOnX2IM43JtjOlnY2BqfEj3wFFUL6NX3kFMUzHNcblsOeW5Tq8sG7iGAgNS48hPiZMBs0IIIUJCgkoYqKrGF9vK+WhDKV5VI8ao58pJOUwfnNpji6EN3vQsKZUr8Rpi2XjqnwMzfDrLoFcY089GirSiCCGECCEJKj1I0zQ2l9j5YF0JJfUtAIzOtnLt1AEk9+BiaBlFn9B/5/8BsO3k+TQlDuvS++MtBsblJBJjkrEoQgghQkuCSg/ZU9XIB+uK2Vnh34051qTnykm5nDoopUeXlI+z72Lk6gcA2Df8Jipzz+/S+zNtFkZkWUPaLSWEEEIcIkElhFRVY3u5g8UFVaw/UA+AQadw9oh0zh+dRXwP7x6s9zQxZuXt6H0t1KRPZc/oOzv9XkWBIekJ5KXEhrBCIYQQojUJKiFQWt/Cij01fL+3hvoWD+D/op82MIVLxvfr0W6eAE1j+NqHiXfswWVJ79LgWRmPIoQQIlwkqARRg9PD68sL2VRiDxyLM+mZnJ/MjGHp9EuMCVtt/fa+S1bRx6iKns1Tn8djSenU+2LNesbnJhJrkn9UhBBC9Dz59gmSnRUN/GXpXupbPOgVfwvE1EEpjM2xYQzGXjwnIKFuG0PXPwbAnjF3UZ92cqfelxJvYkw/W3D2EhJCCCG6QYLKCVI1jf9uLuOjjaVoGmTZLNx8xqCwtp4cSe9u8I9LUd1UZZ3J/mE3dOp9/VNiGZwe36MDfYUQQoijSVDpJk3z78fz0YZStpU5AJg2KIWrJ+dhjpQl5DWNEWsfJLaxiJbYfmyd/BQoHbeO6HQwPNNKdoQELSGEEH2bBJUuqmpw8f3eGlburaGywQWASa/j6il5nDo4NczVtZa1fyGZB/4bGJfiNSd2eL7RoGNcji2ouzILIYQQJ0KCSidomkZBRQOfbipje3lD4LjJoOOkvEQuGJ0VcS0QMQ2FDFv3CAB7R92OI2V8h+cnWAyMlUXchBBCRBgJKh3QNI0tJXY+2VTG7ir/Qm0KMDwrgWkDU5mQlxiROwUrPjejv78Lg7eZ2rQpFA6/qd1zjQYdg9Li6JcYI+NRhBBCRBwJKu34ZkcFT31WQEGFvwXFoFM4bUgq543KjPj1RAZteRFb3RY8JhtbpzzT5nopigK5ybHkp8aFfVaSEEII0R4JKu1YUlBFQUUDJoOOM4amMWtkRlSM3UiuWM6Agr8CsG3S47hiM485J9asZ2xOYo+vjCuEEEJ0lXxTteNXZwzCo2qc3D+JBIsx3OV0isFVz8gf7gWgeODPqMo595hzkg+ujSKtKEIIIaKBfFu1IzsxhhtPGxg1IQVg+Lp5WFoqaUrIZ+f4uce8npcSy4TcRAkpQgghooa0qPQS6Qf+S+aBT1EVPVunPINqODwLSaeDoRkJ5CTJhoJCCCGiiwSVXsDUUsnwtX8AoHDEzTiSxwJgMerJtJnJssUQJ+NRhBBCRCH59op2msbINQ9gctfjSBpF4chb6JcUQ5bNEhWDf4UQQoiOSFCJctn7/kVq2RJ8OhNbJz/N0H4p0sUjhBCi15BRlVHM0niAoRvmA7BnzJ2kDRonIUUIIUSvIkElWh3s8jF4m6hLnYR70s0MTk8Id1VCCCFEUElQiVLZe98jufJ7fHoLxWc8y8h+SeEuSQghhAg6CSpRyNxcztBNTwFwYPzdDB85Dp1O9ukRQgjR+0hQiTaaxvC1D2PwNNKQOp7Mc++QBdyEEEL0WjLrJ8pkFH1CWtm3qDoj5stfxmSWKchCCCF6L/lf8ShidNYwbP2jAKin3YMpa3SYKxJCCCFCS4JKFBm2/lFM7nrU9FEYTr873OUIIYQQISdBJUqklH5L5oH/oil6dJe+DPro2SxRCCGE6C4JKlHAjJMxm/xdPsopt0D2hDBXJIQQQvSMHgkqLpeL8ePHoygKGzZsaPXapk2bOO2007BYLOTm5vL000/3RElRI9NmYdqB/8XgKAZbLsyYG+6ShBBCiB7TI0Hld7/7HdnZ2cccdzgcnHvuufTv35+1a9fyzDPP8Ic//IG//OUvPVFWRIu3GJiQl8hoQzH6VX/2H7zgGTDHh7cwIYQQogeFfHryZ599xpdffskHH3zAZ5991uq1t956C7fbzf/93/9hMpkYNWoUGzZs4LnnnuOmm24KdWkRKdakZ1B6PBlWC6gqvHMHqF4YfiEMOz/c5QkhhBA9KqQtKhUVFdx44428+eabxMYeu1neypUrOf300zGZDq8FMmvWLAoKCqirq2vzmi6XC4fD0erRGxgNOkZkW5k6KMUfUgDW/R2KfwBTPJwvXWJCCCH6npAFFU3TmD17NjfffDOTJk1q85zy8nIyMjJaHTv0e3l5eZvvmT9/PjabLfDIzc0NbuFhoNPBuBwb/RJjUJSDS+E3VsLXD/ufn/kA2PqFr0AhhBAiTLocVO677z4URenwsWPHDl566SUaGhqYOze4gz/nzp2L3W4PPA4cOBDU64fDsEwribFHrTD71UPgtEPmWJjcN7vBhBBCiC6PUbn77ruZPXt2h+cMHDiQb775hpUrV2I2m1u9NmnSJK6++mr+/ve/k5mZSUVFRavXD/2emZnZ5rXNZvMx14xmucmx9EuMaX2w6HvY+P8ABS58AfSy04EQQoi+qcvfgGlpaaSlpR33vAULFvDYY48Ffi8tLWXWrFm8++67TJkyBYCpU6fywAMP4PF4MBr9C5h99dVXDBs2jKSkpK6WFnWS400MzThqFo/qg//e439+0jWQM7HnCxNCCCEiRMj+Vz0vL6/V7/Hx/i/kQYMGkZOTA8BVV13FvHnzuOGGG7j33nvZsmULL774Is8//3yoyooYsSY9Y/rZDo9JOWTt61C+GSw2OPvh8BQnhBBCRIiw9inYbDa+/PJL5syZw8SJE0lNTeWhhx7q9VOTLUY94/MSMeqPGiLUXAvfHGyFOvP3EJfa88UJIYQQEaTHgsqAAQPQNO2Y42PHjuW7777rqTLCLsFiYFxuIhaj/tgXFz0CLXWQMRom/aLnixNCCCEijIzS7EHJ8SbG9rNhOLolBaB0Pax9w//8gmdkAK0QQgiBBJUek5VoYWSW9dgxKeBfgfa/vwU0GHMF9J/W4/UJIYQQkUiCSojZYo30S4wh++gpyEfa/C8oXu1fgfacR3uuOCGEECLCSVAJAZNBR5bNQnZiDHHm4/wRu5th0Tz/89PuAmtW6AsUQgghooQElSBLijMyITcJna6NLp62rHgJHCVgy4NT5oS2OCGEECLKhHRTwr4m1qxnbE5i50OKoxSWv+B/fs4fwGgJVWlCCCFEVJKgEiQmg44JuUnHro3SkUWPgqcZcqfAqMtCV5wQQggRpSSoBIFepzA+L5EYUxtro7SndD1sfNv/fNZ8aGs2kBBCCNHHSVA5QYoCo/vZsFqMnX+TpsEXD/ifj7lS9vMRQggh2iFB5QQoCozMtpKW0MXdnLf/B/YvB4MFZsp+PkIIIUR7ZNZPN+l1CqP72boeUnwe+PpgOJl2G9hygl+cEEII0UtIUOkGg15hQm4SttgudPccsvYNqN0LcWlw6m+CXpsQQgjRm0hQ6SKzUceEvCTij7eQW1tcDbDkKf/zM+4Fc0JwixNCCCF6GQkqnaTXK2QkWBiYFtf2zsedseJP0FQFyQNh4uyg1ieEEEL0RhJUjiMpzkiWLYYMqwV9Zxdya0tjpX8VWoCzHwJ9N7qNhBBCiD5GgkoH+ifHkp8aF5yLLXkKPE3QbyKMvDQ41xRCCCF6OZme3IFOL4V/PDV7/INoAc55RBZ3E0IIITpJgkpPWPQIqF4Yci4MmB7uaoQQQoioIUEl1ErWwrYPAQVm/iHMxQghhBDRRYJKqH3zmP/nuJ9Bxqjw1iKEEEJEGQkqoVS4HPZ8AzoDzLgv3NUIIYQQUUeCSqho2uHWlJOuhaQBYS1HCCGEiEYSVEJlzyIoWgF6M5z+23BXI4QQQkQlCSqhcGRrysm/BGt2eOsRQgghopQElVDY8SmUrgdjHEy/M9zVCCGEEFFLgkqwqT749nH/81Nugfi08NYjhBBCRDEJKsG2dSFUbgOzDabdGu5qhBBCiKgmQSWYfF5YPN///NTbICYpvPUIIYQQUU6CSjBt+QBqdkNMMky5OdzVCCGEEFFPgkqwqD5Y+oz/+bRbwZwQ3nqEEEKIXkCCSrBs+TfU7PJ390y+KdzVCCGEEL2CBJVgUH2w9Gn/86lzpDVFCCGECBIJKsGwdSFU7wRLIkz+VbirEUIIIXoNCSonSlUPj02ZOgcs1vDWI4QQQvQiElRO1LYPoWoHWGwwRVpThBBCiGCSoHIiVBWWHBybcsqv/WFFCCGEEEEjQeVE7PgPVG33r0Ir66YIIYQQQSdBpbs0Db77o//5lF9BTGJYyxFCCCF6Iwkq3bVnEZRtBGOsf/NBIYQQQgSdBJXu+u45/8+J10NscnhrEUIIIXopCSrdUfQ97F8OOqPskCyEEEKEkASV7jjUmjL+KrBmh7cWIYQQohcLaVD59NNPmTJlCjExMSQlJXHppZe2er2oqIgf/ehHxMbGkp6ezm9/+1u8Xm8oSzpxZZtg1xeg6ODU34S7GiGEEKJXM4Tqwh988AE33ngjTzzxBGeddRZer5ctW7YEXvf5fPzoRz8iMzOTFStWUFZWxrXXXovRaOSJJ54IVVknbtnz/p+jfgwpg8JbixBCCNHLKZqmacG+qNfrZcCAAcybN48bbrihzXM+++wzLrzwQkpLS8nIyADg1Vdf5d5776WqqgqTydSpz3I4HNhsNux2O1ZriJevr94Nf5oEaHDzcsgcHdrPE0IIIXqpzn5/h6TrZ926dZSUlKDT6ZgwYQJZWVmcf/75rVpUVq5cyZgxYwIhBWDWrFk4HA62bt3a7rVdLhcOh6PVo8csfwHQYOh5ElKEEEKIHhCSoLJ3714A/vCHP/D73/+eTz75hKSkJGbMmEFtbS0A5eXlrUIKEPi9vLy83WvPnz8fm80WeOTm5obiFo7lKION7/ifT7+rZz5TCCGE6OO6FFTuu+8+FEXp8LFjxw5UVQXggQce4PLLL2fixIm8/vrrKIrCv/71rxMqeO7cudjt9sDjwIEDJ3S9Tlv1KqgeyJsKeVN65jOFEEKIPq5Lg2nvvvtuZs+e3eE5AwcOpKysDICRI0cGjpvNZgYOHEhRUREAmZmZ/PDDD63eW1FREXitPWazGbPZ3JWyT5yrAda87n8+7fae/WwhhBCiD+tSUElLSyMtLe24502cOBGz2UxBQQHTp08HwOPxUFhYSP/+/QGYOnUqjz/+OJWVlaSnpwPw1VdfYbVaWwWciLD27+CyQ+pQ//gUIYQQQvSIkExPtlqt3HzzzTz88MPk5ubSv39/nnnmGQCuuOIKAM4991xGjhzJNddcw9NPP015eTm///3vmTNnTs+3mHTE54HvX/E/n3or6GSNPCGEEKKnhGwdlWeeeQaDwcA111xDS0sLU6ZM4ZtvviEpKQkAvV7PJ598wi233MLUqVOJi4vjuuuu45FHHglVSd2z5d/gKIa4dBj703BXI4QQQvQpIVlHpSeFdB0VTYNXp0PFFjjrQTj9nuBeXwghhOijwrqOSq+x5xt/SDHGwaRfhLsaIYQQos+RoNKRFQv8P0+6BmKTw1uLEEII0QdJUGlP2UbYuxgUPZzy63BXI4QQQvRJElTas+JP/p+jLoWk/mEtRQghhOirQjbrJ+qddjfoTTD5xnBXIoQQQvRZElTakz4cLn053FUIIYQQfZp0/QghhBAiYklQEUIIIUTEkqAihBBCiIglQUUIIYQQEUuCihBCCCEilgQVIYQQQkQsCSpCCCGEiFgSVIQQQggRsSSoCCGEECJiSVARQgghRMSSoCKEEEKIiCVBRQghhBARS4KKEEIIISJW1O+erGkaAA6HI8yVCCGEEKKzDn1vH/oeb0/UB5WGhgYAcnNzw1yJEEIIIbqqoaEBm83W7uuKdrwoE+FUVaW0tJSEhAQURQnqtR0OB7m5uRw4cACr1RrUa0cCub/o19vvUe4v+vX2e5T76z5N02hoaCA7Oxudrv2RKFHfoqLT6cjJyQnpZ1it1l75D+Ahcn/Rr7ffo9xf9Ovt9yj31z0dtaQcIoNphRBCCBGxJKgIIYQQImJJUOmA2Wzm4Ycfxmw2h7uUkJD7i369/R7l/qJfb79Hub/Qi/rBtEIIIYTovaRFRQghhBARS4KKEEIIISKWBBUhhBBCRCwJKkIIIYSIWBJUusjlcjF+/HgURWHDhg3hLidoLr74YvLy8rBYLGRlZXHNNddQWloa7rKCprCwkBtuuIH8/HxiYmIYNGgQDz/8MG63O9ylBc3jjz/OtGnTiI2NJTExMdzlnLCXX36ZAQMGYLFYmDJlCj/88EO4SwqapUuXctFFF5GdnY2iKHz44YfhLimo5s+fz8knn0xCQgLp6elceumlFBQUhLusoHrllVcYO3ZsYCG0qVOn8tlnn4W7rJB58sknURSFO+64o8c/W4JKF/3ud78jOzs73GUE3Zlnnsl7771HQUEBH3zwAXv27OEnP/lJuMsKmh07dqCqKq+99hpbt27l+eef59VXX+X+++8Pd2lB43a7ueKKK7jlllvCXcoJe/fdd7nrrrt4+OGHWbduHePGjWPWrFlUVlaGu7SgaGpqYty4cbz88svhLiUklixZwpw5c/j+++/56quv8Hg8nHvuuTQ1NYW7tKDJycnhySefZO3ataxZs4azzjqLSy65hK1bt4a7tKBbvXo1r732GmPHjg1PAZrotP/+97/a8OHDta1bt2qAtn79+nCXFDIfffSRpiiK5na7w11KyDz99NNafn5+uMsIutdff12z2WzhLuOETJ48WZszZ07gd5/Pp2VnZ2vz588PY1WhAWgLFy4MdxkhVVlZqQHakiVLwl1KSCUlJWn/+7//G+4ygqqhoUEbMmSI9tVXX2lnnHGG9pvf/KbHa5AWlU6qqKjgxhtv5M033yQ2Njbc5YRUbW0tb731FtOmTcNoNIa7nJCx2+0kJyeHuwxxFLfbzdq1a5k5c2bgmE6nY+bMmaxcuTKMlYnustvtAL323zefz8c777xDU1MTU6dODXc5QTVnzhx+9KMftfr3sadJUOkETdOYPXs2N998M5MmTQp3OSFz7733EhcXR0pKCkVFRXz00UfhLilkdu/ezUsvvcSvfvWrcJcijlJdXY3P5yMjI6PV8YyMDMrLy8NUleguVVW54447OPXUUxk9enS4ywmqzZs3Ex8fj9ls5uabb2bhwoWMHDky3GUFzTvvvMO6deuYP39+WOvo00HlvvvuQ1GUDh87duzgpZdeoqGhgblz54a75C7p7P0d8tvf/pb169fz5Zdfotfrufbaa9EifOHirt4jQElJCeeddx5XXHEFN954Y5gq75zu3J8QkWTOnDls2bKFd955J9ylBN2wYcPYsGEDq1at4pZbbuG6665j27Zt4S4rKA4cOMBvfvMb3nrrLSwWS1hr6dNL6FdVVVFTU9PhOQMHDuTKK6/kP//5D4qiBI77fD70ej1XX301f//730Ndard09v5MJtMxx4uLi8nNzWXFihUR3ZTZ1XssLS1lxowZnHLKKbzxxhvodJGd1bvzd/jGG29wxx13UF9fH+LqQsPtdhMbG8v777/PpZdeGjh+3XXXUV9f3+ta+hRFYeHCha3utbe49dZb+eijj1i6dCn5+fnhLifkZs6cyaBBg3jttdfCXcoJ+/DDD/nxj3+MXq8PHPP5fCiKgk6nw+VytXotlAw98ikRKi0tjbS0tOOet2DBAh577LHA76WlpcyaNYt3332XKVOmhLLEE9LZ+2uLqqqAfzp2JOvKPZaUlHDmmWcyceJEXn/99YgPKXBif4fRymQyMXHiRBYtWhT48lZVlUWLFnHrrbeGtzjRKZqmcdttt7Fw4UIWL17cJ0IK+P85jfT/ZnbW2WefzebNm1sdu/766xk+fDj33ntvj4UU6ONBpbPy8vJa/R4fHw/AoEGDyMnJCUdJQbVq1SpWr17N9OnTSUpKYs+ePTz44IMMGjQooltTuqKkpIQZM2bQv39/nn32WaqqqgKvZWZmhrGy4CkqKqK2tpaioiJ8Pl9gnZ/BgwcH/pmNFnfddRfXXXcdkyZNYvLkybzwwgs0NTVx/fXXh7u0oGhsbGT37t2B3/ft28eGDRtITk4+5r830WjOnDm8/fbbfPTRRyQkJATGFtlsNmJiYsJcXXDMnTuX888/n7y8PBoaGnj77bdZvHgxX3zxRbhLC4qEhIRjxhQdGsPY42ONenyeUS+wb9++XjU9edOmTdqZZ56pJScna2azWRswYIB28803a8XFxeEuLWhef/11DWjz0Vtcd911bd7ft99+G+7SuuWll17S8vLyNJPJpE2ePFn7/vvvw11S0Hz77bdt/l1dd9114S4tKNr7d+31118Pd2lB84tf/ELr37+/ZjKZtLS0NO3ss8/Wvvzyy3CXFVLhmp7cp8eoCCGEECKyRX4nvRBCCCH6LAkqQgghhIhYElSEEEIIEbEkqAghhBAiYklQEUIIIUTEkqAihBBCiIglQUUIIYQQEUuCihBCCCEilgQVIYQQQkQsCSpCCCGEiFgSVIQQQggRsSSoCCGEECJi/X9ry1PYIh/tRgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x_test = LabelTensor(torch.linspace(-4, 4, 100).reshape(-1, 1), \"x\")\n", "y_test = torch.stack([solver(x_test) for _ in range(1000)], dim=0)\n", @@ -374,7 +339,7 @@ "\n", "### Solving a 2D Poisson Problem\n", "\n", - "In this section, we will solve a **2D Poisson problem** with **Dirichlet boundary conditions** on an **hourglass-shaped domain** using a simple PINN [4]. You can explore other PINN variants, e.g. [5] or [6] in PINA by visiting the [PINA solvers documentation](https://mathlab.github.io/PINA/_rst/_code.html#solvers). We aim to solve the following 2D Poisson problem:\n", + "In this section, we will solve a **2D Poisson problem** with **Dirichlet boundary conditions** on an **hourglass-shaped domain** using a simple `PhysicsInformedSingleModelSolver` [4]. You can explore other PINN variants, e.g. [5] or [6] in PINA by visiting the [PINA solvers documentation](https://mathlab.github.io/PINA/_rst/_code.html#solvers). We aim to solve the following 2D Poisson problem:\n", "\n", "$$\n", "\\begin{cases}\n", @@ -394,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "02518706", "metadata": {}, "outputs": [], @@ -440,21 +405,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "47459922", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(8, 4))\n", "plt.subplot(1, 2, 1)\n", @@ -483,7 +437,7 @@ "source": [ "#### Writing the Poisson Problem Class\n", "\n", - "Very good! Now we will implement the problem class for the 2D Poisson problem. Unlike the previous examples, where we inherited from `AbstractProblem`, for this problem, we will inherit from the `SpatialProblem` class. \n", + "Very good! Now we will implement the problem class for the 2D Poisson problem. Unlike the previous examples, where we inherited from `BaseProblem`, for this problem, we will inherit from the `SpatialProblem` class. \n", "\n", "The reason for this is that the Poisson problem involves **spatial variables** as input, so we use `SpatialProblem` to handle such cases.\n", "\n", @@ -492,14 +446,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "e1eb5a09", "metadata": {}, "outputs": [], "source": [ "from pina.problem import SpatialProblem\n", "from pina.operator import laplacian\n", - "from pina.equation import FixedValue, Equation\n", + "from pina.equation import Equation\n", + "from pina.equation.zoo import FixedValue\n", "\n", "\n", "def poisson_equation(input_, output_):\n", @@ -535,7 +490,7 @@ "source": [ "As you can see, writing the problem class for a differential equation in PINA is straightforward! The main differences are:\n", "\n", - "- We inherit from **`SpatialProblem`** instead of `AbstractProblem` to account for spatial variables.\n", + "- We inherit from **`SpatialProblem`** instead of `BaseProblem` to account for spatial variables.\n", "- We use **`domain`** and **`equation`** inside the `Condition` to define the problem.\n", "\n", "The `Equation` class can be very useful for creating modular problem classes. If you're interested, check out [this tutorial](https://mathlab.github.io/PINA/_rst/tutorial12/tutorial.html) for more details. There's also a dedicated [tutorial](https://mathlab.github.io/PINA/_rst/tutorial16/tutorial.html) for building custom problems!\n", @@ -545,22 +500,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "a95bb250", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Points are not automatically sampled, you can see this by:\n", - " poisson_problem.are_all_domains_discretised=False\n", - "\n", - "But you can easily sample by running .discretise_domain:\n", - " poisson_problem.are_all_domains_discretised=True\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Points are not automatically sampled, you can see this by:\")\n", "print(f\" {poisson_problem.are_all_domains_discretised=}\\n\")\n", @@ -588,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "b893232b", "metadata": {}, "outputs": [], @@ -612,11 +555,11 @@ "\n", "The thir part of the PINA pipeline involves using a **Solver**.\n", "\n", - "In this tutorial, we will use the **classical PINN** solver. However, many other variants are also available and we invite to try them!\n", + "In this tutorial, we will use the classical `PhysicsInformedSingleModelSolver` solver. However, many other variants are also available and we invite to try them!\n", "\n", "#### Loss Function in PINA\n", "\n", - "The loss function in the **classical PINN** is defined as follows:\n", + "The loss function in the **classical physics-informed solver** is defined as follows:\n", "\n", "$$\\theta_{\\rm{best}}=\\min_{\\theta}\\mathcal{L}_{\\rm{problem}}(\\theta), \\quad \\mathcal{L}_{\\rm{problem}}(\\theta)= \\frac{1}{N_{D}}\\sum_{i=1}^N\n", "\\mathcal{L}(\\Delta\\mathcal{M}_{\\theta}(\\mathbf{x}_i, \\mathbf{y}_i) - \\sin(\\pi x_i)\\sin(\\pi y_i)) +\n", @@ -629,11 +572,11 @@ "\n", "### Training\n", "\n", - "For the last part of the pipeline we need a `Trainer`. We will train the model for **1000 epochs** using the default optimizer parameters. These parameters can be adjusted as needed. For more details, check the solvers documentation [here](https://mathlab.github.io/PINA/_rst/_code.html#solvers).\n", + "For the last part of the pipeline we need a `Trainer`. We will train the model for **1500 epochs** using the default optimizer parameters. These parameters can be adjusted as needed. For more details, check the solvers documentation [here](https://mathlab.github.io/PINA/_rst/_code.html#solvers).\n", "\n", "To track metrics during training, we use the **`MetricTracker`** class.\n", "\n", - "> **👉 Want to know more about `Trainer` and how to boost PINA performance, check out [this tutorial](https://mathlab.github.io/PINA/_rst/tutorials/tutorial11/tutorial.html).**" + "> **👉 Want to know more about `Trainer` and how to boost PINA performance? Check out [this tutorial](https://mathlab.github.io/PINA/_rst/tutorials/tutorial11/tutorial.html).**" ] }, { @@ -643,11 +586,11 @@ "metadata": {}, "outputs": [], "source": [ - "from pina.solver import PINN\n", + "from pina.solver import PhysicsInformedSingleModelSolver\n", "from pina.callback import MetricTracker\n", "\n", "# define the solver\n", - "solver = PINN(poisson_problem, model)\n", + "solver = PhysicsInformedSingleModelSolver(poisson_problem, model)\n", "\n", "# define trainer\n", "trainer = Trainer(\n", @@ -672,21 +615,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "dea7acf4", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# sample points in the domain. remember to set requires_grad!\n", "pts = poisson_problem.spatial_domain.sample(1000).requires_grad_(True)\n", @@ -694,7 +626,8 @@ "solution = solver(pts)\n", "# compute the residual in the interior\n", "equation = poisson_problem.conditions[\"interior\"].equation\n", - "residual = solver.compute_residual(pts, equation)\n", + "residual = equation.residual(pts, solution)\n", + "\n", "# simple plot\n", "with torch.no_grad():\n", " plt.subplot(1, 2, 1)\n", @@ -725,7 +658,7 @@ "\n", "Congratulations on completing the introductory tutorial of **PINA**! Now that you have a solid foundation, here are a few directions you can explore:\n", "\n", - "1. **Explore Advanced Solvers**: Dive into more advanced solvers like **SAPINN** or **RBAPINN** and experiment with different variations of Physics-Informed Neural Networks.\n", + "1. **Explore Advanced Solvers**: Dive into more advanced solvers like **RBAPhysicsInformedSingleModelSolver** and experiment with different variants of Physics-Informed solvers.\n", "2. **Apply PINA to New Problems**: Try solving other types of differential equations or explore inverse problems and parametric problems using the PINA framework.\n", "3. **Optimize Model Performance**: Use the `Trainer` class to enhance model performance by exploring features like dynamic learning rates, early stopping, and model checkpoints.\n", "\n", diff --git a/tutorials/tutorial18/tutorial.ipynb b/tutorials/tutorial18/tutorial.ipynb index bebb8b825..ab95e0acb 100644 --- a/tutorials/tutorial18/tutorial.ipynb +++ b/tutorials/tutorial18/tutorial.ipynb @@ -10,7 +10,7 @@ "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial18/tutorial.ipynb)\n", "\n", - "In this tutorial, we will explore the Solver classes in PINA, that are the core components for optimizing models. Solvers are designed to manage and execute the optimization process, providing the flexibility to work with various types of neural networks and loss functions. We will show how to use this class to select and implement different solvers, such as Supervised Learning, Physics-Informed Neural Networks (PINNs), and Generative Learning solvers. By the end of this tutorial, you'll be equipped to easily choose and customize solvers for your own tasks, streamlining the model training process.\n", + "In this tutorial, we will explore the Solver classes in PINA, that are the core components for optimizing models. Solvers are designed to manage and execute the optimization process, providing the flexibility to work with various types of neural networks and loss functions. We will show how to use this class to select and implement different solvers, such as Supervised or Physics-Informed solvers. By the end of this tutorial, you'll be equipped to easily choose and customize solvers for your own tasks, streamlining the model training process.\n", "\n", "## Introduction to Solvers\n", "\n", @@ -22,126 +22,83 @@ "\n", "PINA solvers are built on top of the [PyTorch Lightning `LightningModule`](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html), which provides a structured and scalable training framework. This allows solvers to leverage advanced features such as distributed training, early stopping, and logging — all with minimal setup.\n", "\n", - "## Solvers Hierarchy: Single and MultiSolver\n", + "## Solvers Hierarchy: Single-Model, Multi-Model, and Ensemble\n", "\n", - "PINA provides two main abstract interfaces for solvers, depending on whether the training involves a single model or multiple models. These interfaces define the base functionality that all specific solver implementations inherit from.\n", + "PINA provides three main base solver classes, designed respectively for training a single model, multiple distinct models, or an ensemble of models. These interfaces define the core functionality inherited by all specific solver implementations.\n", "\n", - "### 1. [`SingleSolverInterface`](https://mathlab.github.io/PINA/_rst/solver/solver_interface.html)\n", + "### 1. [`SingleModelSolver`](https://mathlab.github.io/PINA/_rst/solver/single_model_solver.html)\n", "\n", - "This is the abstract base class for solvers that train **a single model**, such as in standard supervised learning or physics-informed training. All specific solvers (e.g., `SupervisedSolver`, `PINN`) inherit from this interface.\n", + "This is the base class for solvers that train **a single model**, such as in standard supervised learning or physics-informed training.\n", "\n", "**Arguments:**\n", "- `problem` – The problem to be solved.\n", "- `model` – The neural network model.\n", "- `optimizer` – Defaults to `torch.optim.Adam` if not provided.\n", "- `scheduler` – Defaults to `torch.optim.lr_scheduler.ConstantLR`.\n", - "- `weighting` – Optional loss weighting schema., see [here](https://mathlab.github.io/PINA/_rst/_code.html#losses-and-weightings). We weight already for you!\n", + "- `weighting` – Optional loss weighting schema., see [here](https://mathlab.github.io/PINA/_rst/_code.html#losses-and-weightings).\n", + "- `loss` - Defaults to `torch.nn.MSELoss`.\n", "- `use_lt` – Whether to use LabelTensors as input.\n", "\n", "---\n", "\n", - "### 2. [`MultiSolverInterface`](https://mathlab.github.io/PINA/_rst/solver/multi_solver_interface.html)\n", + "### 2. [`MultiModelSolver`](https://mathlab.github.io/PINA/_rst/solver/multi_model_solver.html)\n", "\n", - "This is the abstract base class for solvers involving **multiple models**, such as in GAN architectures or ensemble training strategies. All multi-model solvers (e.g., `DeepEnsemblePINN`, `GAROM`) inherit from this interface.\n", + "This is the base class for solvers involving **multiple models** with distinct behaviours, such as in `SelfAdaptivePhysicsInformedSolver`.\n", "\n", "**Arguments:**\n", "- `problem` – The problem to be solved.\n", - "- `models` – The model or models used for training.\n", + "- `models` – The models used for training.\n", "- `optimizers` – Defaults to `torch.optim.Adam`.\n", "- `schedulers` – Defaults to `torch.optim.lr_scheduler.ConstantLR`.\n", - "- `weightings` – Optional loss weighting schema, see [here](https://mathlab.github.io/PINA/_rst/_code.html#losses-and-weightings). We weight already for you!\n", + "- `weighting` – Optional loss weighting schema, see [here](https://mathlab.github.io/PINA/_rst/_code.html#losses-and-weightings).\n", + "- `loss` - Defaults to `torch.nn.MSELoss`.\n", "- `use_lt` – Whether to use LabelTensors as input.\n", "\n", "---\n", "\n", - "These base classes define the structure and behavior of solvers in PINA, allowing you to create customized training strategies while leveraging PyTorch Lightning's features under the hood. \n", + "### 3. [`EnsembleSolver`](https://mathlab.github.io/PINA/_rst/solver/ensemble_solver.html)\n", "\n", - "These classes are used to define the backbone, i.e. setting the problem, the model(s), the optimizer(s) and scheduler(s), but miss a key component the `optimization_cycle` method.\n", + "This is the base class for solvers that train **an ensemble of models** with the same behaviour, such as in `SupervisedEnsembleSolver`.\n", "\n", - "\n", - "## Optimization Cycle\n", - "The `optimization_cycle` method is the core function responsible for computing losses for **all conditions** in a given training batch. Each condition (e.g. initial condition, boundary condition, PDE residual) contributes its own loss, which is tracked and returned in a dictionary. This method should return a dictionary mapping **condition names** to their respective **scalar loss values**.\n", - "\n", - "For supervised learning tasks, where each condition consists of an input-target pair, for example, the `optimization_cycle` may look like this:\n", - "\n", - "```python\n", - "def optimization_cycle(self, batch):\n", - " \"\"\"\n", - " The optimization cycle for Supervised solvers.\n", - " Computes loss for each condition in the batch.\n", - " \"\"\"\n", - " condition_loss = {}\n", - " for condition_name, data in batch:\n", - " condition_loss[condition_name] = self.loss_data(\n", - " input=data[\"input\"], target=data[\"target\"]\n", - " )\n", - " return condition_loss\n", - "```\n", - "In PINA, a **batch** is structured as a list of tuples, where each tuple corresponds to a specific training condition. Each tuple contains:\n", - "\n", - "- The **name of the condition**\n", - "- A **dictionary of data** associated with that condition\n", - "\n", - "for example:\n", - "\n", - "```python\n", - "batch = [\n", - " (\"condition1\", {\"input\": ..., \"target\": ...}),\n", - " (\"condition2\", {\"input\": ..., \"equation\": ...}),\n", - " (\"condition3\", {\"input\": ..., \"target\": ...}),\n", - "]\n", - "```\n", - "\n", - "Fortunately, you don't need to implement the `optimization_cycle` yourself in most cases — PINA already provides default implementations tailored to common solver types. These implementations are available through the solver interfaces and cover various training strategies.\n", - "\n", - "1. [`PINNInterface`](https://mathlab.github.io/PINA/_rst/solver/physics_informed_solver/pinn_interface.html) \n", - " Implements the optimization cycle for **physics-based solvers** (e.g., PDE residual minimization) as well as other useful methods to compute PDE residuals. \n", - " ➤ [View method](https://mathlab.github.io/PINA/_rst/solver/physics_informed_solver/pinn_interface.html#pina.solver.physics_informed_solver.pinn_interface.PINNInterface.optimization_cycle)\n", - "\n", - "2. [`SupervisedSolverInterface`](https://mathlab.github.io/PINA/_rst/solver/supervised_solver/supervised_solver_interface.html) \n", - " Defines the optimization cycle for **supervised learning tasks**, including traditional regression and classification. \n", - " ➤ [View method](https://mathlab.github.io/PINA/_rst/solver/supervised_solver/supervised_solver_interface.html#pina.solver.supervised_solver.supervised_solver_interface.SupervisedSolverInterface.optimization_cycle)\n", - "\n", - "3. [`DeepEnsembleSolverInterface`](https://mathlab.github.io/PINA/_rst/solver/ensemble_solver/ensemble_solver_interface.html) \n", - " Provides the optimization logic for **deep ensemble methods**, commonly used for uncertainty quantification or robustness. \n", - " ➤ [View method](https://mathlab.github.io/PINA/_rst/solver/ensemble_solver/ensemble_solver_interface.html#pina.solver.ensemble_solver.ensemble_solver_interface.DeepEnsembleSolverInterface.optimization_cycle)\n", - "\n", - "These ready-to-use implementations ensure that your solvers are properly structured and compatible with PINA’s training workflow. You can also inherit and override them to fit more specialized needs. They only require, the following arguments:\n", "**Arguments:**\n", "- `problem` – The problem to be solved.\n", - "- `loss` - The loss to be minimized\n", - "- `weightings` – Optional loss weighting schema.\n", + "- `models` – The models used for training.\n", + "- `optimizers` – Defaults to `torch.optim.Adam`.\n", + "- `schedulers` – Defaults to `torch.optim.lr_scheduler.ConstantLR`.\n", + "- `weighting` – Optional loss weighting schema, see [here](https://mathlab.github.io/PINA/_rst/_code.html#losses-and-weightings).\n", + "- `loss` - Defaults to `torch.nn.MSELoss`.\n", "- `use_lt` – Whether to use LabelTensors as input.\n", "\n", + "---\n", + "\n", + "These base classes define the structure and behavior of solvers in PINA, enabling customized training strategies while leveraging PyTorch Lightning under the hood. \n", + "\n", "## Structure a Solver with Multiple Inheritance:\n", "\n", - "Thanks to PINA’s modular design, creating a custom solver is straightforward using **multiple inheritance**. You can combine different interfaces to define both the **optimization logic** and the **model structure**.\n", + "Thanks to PINA’s modular design, custom solvers can be easily built through **multiple inheritance**. By combining a chosen base solver class with one or more [`Mixins`](https://mathlab.github.io/PINA/_rst/_code.html#mixins), users can define flexible and specialized optimization logic. Each mixin encapsulates an isolated component of the solver behavior, making it possible to assemble reusable building blocks into a fully functional custom solver. Examples include:\n", "\n", - "- **`PINN` Solver**\n", - " - Inherits from: \n", - " - [`PINNInterface`](https://mathlab.github.io/PINA/_rst/solver/physics_informed_solver/pinn_interface.html) → physics-based optimization loop \n", - " - [`SingleSolverInterface`](https://mathlab.github.io/PINA/_rst/solver/solver_interface.html) → training a single model\n", + "- **`PhysicsInformedSingleModelSolver`**\n", + " - Inherits from:\n", "\n", - "- **`SupervisedSolver`**\n", - " - Inherits from: \n", - " - [`SupervisedSolverInterface`](https://mathlab.github.io/PINA/_rst/solver/supervised_solver/supervised_solver_interface.html) → data-driven optimization loop \n", - " - [`SingleSolverInterface`](https://mathlab.github.io/PINA/_rst/solver/solver_interface.html) → training a single model\n", + " - [`PhysicsInformedMixin`](https://mathlab.github.io/PINA/_rst/solver/mixin/physics_informed_mixin.html) → enables gradient computation for validation and test phases\n", + " - [`SingleModelSolver`](https://mathlab.github.io/PINA/_rst/solver/single_model_solver.html) → manages training of a single model\n", "\n", - "- **`GAROM`** (a variant of GAN)\n", + "- **`AutoregressiveEnsembleSolver`**\n", " - Inherits from: \n", - " - [`SupervisedSolverInterface`](https://mathlab.github.io/PINA/_rst/solver/supervised_solver/supervised_solver_interface.html) → data-driven optimization loop \n", - " - [`MultiSolverInterface`](https://mathlab.github.io/PINA/_rst/solver/multi_solver_interface.html) → training multiple models (e.g., generator and discriminator)\n", + "\n", + " - [`AutoregressiveMixin`](https://mathlab.github.io/PINA/_rst/solver/mixin/autoregressive_mixin.html) → implements autoregressive logic\n", + " - [`EnsembleSolver`](https://mathlab.github.io/PINA/_rst/solver/ensemble_solver.html) → manages training of an ensemble model\n", "\n", "This structure promotes **code reuse** and **extensibility**, allowing you to quickly prototype new solver strategies by reusing core training and optimization logic.\n", "\n", "## Let's try to build some solvers!\n", "\n", - "We will now start building a simple supervised solver in PINA. Let's first import useful modules! " + "We will now start building a supervised single-model solver in PINA. Let's first import useful modules! " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "0981f1e9", "metadata": {}, "outputs": [], @@ -158,12 +115,13 @@ "\n", "import warnings\n", "import torch\n", - "import matplotlib.pyplot as plt\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "from pina import Trainer\n", - "from pina.solver import SingleSolverInterface, SupervisedSolverInterface\n", + "from pina.solver import BaseSolver\n", + "from pina.solver.mixin import SingleModelMixin, ConditionAggregatorMixin\n", + "from pina.condition import InputTargetCondition\n", "from pina.model import FeedForward\n", "from pina.problem.zoo import SupervisedProblem" ] @@ -173,39 +131,47 @@ "id": "7b91de38", "metadata": {}, "source": [ - "Since we are using only one model for this task, we will inherit from two base classes:\n", + "We inherit from three classes:\n", "\n", - "- `SingleSolverInterface`: This ensures we are working with a single model.\n", - "- `SupervisedSolverInterface`: This allows us to use supervised learning strategies for training the model." + "- `SingleModelMixin`: manages the forward pass logic for a single model\n", + "- `ConditionAggregationMixin`: combines the losses from one or more conditions into a single quantity\n", + "- `BaseSolver`: initializes the solver components" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "014bbd86", "metadata": {}, "outputs": [], "source": [ - "class MyFirstSolver(SupervisedSolverInterface, SingleSolverInterface):\n", + "class MyFirstSolver(SingleModelMixin, ConditionAggregatorMixin, BaseSolver):\n", + "\n", + " # Accepted condition types for this solver\n", + " accepted_conditions_types = (InputTargetCondition,)\n", + "\n", " def __init__(\n", " self,\n", " problem,\n", " model,\n", - " loss=None,\n", " optimizer=None,\n", " scheduler=None,\n", " weighting=None,\n", + " loss=None,\n", " use_lt=True,\n", " ):\n", - " super().__init__(\n", - " model=model,\n", - " problem=problem,\n", - " loss=loss,\n", - " optimizer=optimizer,\n", - " scheduler=scheduler,\n", - " weighting=weighting,\n", - " use_lt=use_lt,\n", - " )" + " # Initialize the base solver\n", + " BaseSolver.__init__(self, problem=problem, use_lt=use_lt)\n", + "\n", + " # Initialize the components of the solver\n", + " self._init_solver_components(\n", + " models=model,\n", + " optimizers=optimizer,\n", + " schedulers=scheduler,\n", + " )\n", + "\n", + " # Initialize the weighting scheme for the conditions and the loss\n", + " self._init_weighting_and_loss(weighting=weighting, loss=loss)" ] }, { @@ -213,16 +179,20 @@ "id": "b1b1e4c4", "metadata": {}, "source": [ - "By default, Python follows a specific method resolution order (MRO) when a class inherits from multiple parent classes. This means that the initialization (`__init__`) method is called based on the order of inheritance.\n", - "\n", - "Since we inherit from `SupervisedSolverInterface` first, Python will call the `__init__` method from `SupervisedSolverInterface` (initialize `problem`, `loss`, `weighting` and `use_lt`) before calling the `__init__` method from `SingleSolverInterface` (initialize `model`, `optimizer`, `scheduler`). This allows us to customize the initialization process for our custom solver. \n", - "\n", - "We will learn a very simple problem, try to learn $y=\\sin(x)$." + "When a class inherits from multiple parent classes, Python determines the order in which methods are resolved using the method resolution order (MRO). In this case, because `SingleModelMixin` and `ConditionAggregationMixin` appear before `BaseSolver` in the inheritance list, their initialization logic is handled first, before delegating to the `__init__` method of `BaseSolver`. This allows the custom solver to extend or modify the initialization process while still relying on the base functionality provided by `BaseSolver`." + ] + }, + { + "cell_type": "markdown", + "id": "03ee809a", + "metadata": {}, + "source": [ + "Let's use `MyFirstSolver` to solve a very simple problem: $y=\\sin(x)$." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "6f25d3a6", "metadata": {}, "outputs": [], @@ -236,32 +206,6 @@ "model = FeedForward(1, 1)" ] }, - { - "cell_type": "markdown", - "id": "9f7551bf", - "metadata": {}, - "source": [ - "If we now try to initialize the solver `MyFirstSolver` we will get the following error:\n", - "\n", - "```python\n", - "---------------------------------------------------------------------------\n", - "TypeError Traceback (most recent call last)\n", - "Cell In[41], line 1\n", - "----> 1 MyFirstSolver(problem, model)\n", - "\n", - "TypeError: Can't instantiate abstract class MyFirstSolver with abstract method loss_data\n", - "```\n", - "\n", - "### Data and Physics Loss\n", - "The error above is because in PINA, all solvers must specify how to compute the loss during training. There are two main types of losses that can be computed, depending on the nature of the problem:\n", - "\n", - "1. **`loss_data`**: Computes the **data loss** between the model's output and the true solution. This is typically used in **supervised learning** setups, where we have ground truth data to compare the model's predictions. It expects some `input` (tensor, graph, ...) and a `target` (tensor, graph, ...)\n", - " \n", - "2. **`loss_phys`**: Computes the **physics loss** for **physics-informed solvers** (PINNs). This loss is based on the residuals of the governing equations that model physical systems, enforcing the equations during training. It expects some `samples` (`LabelTensor`) and an `equation` (`Equation`)\n", - "\n", - "Therefore our implementation becomes:" - ] - }, { "cell_type": "code", "execution_count": null, @@ -269,42 +213,15 @@ "metadata": {}, "outputs": [], "source": [ - "class MyFirstSolver(SupervisedSolverInterface, SingleSolverInterface):\n", - " def __init__(\n", - " self,\n", - " problem,\n", - " model,\n", - " loss=None,\n", - " optimizer=None,\n", - " scheduler=None,\n", - " weighting=None,\n", - " use_lt=True,\n", - " ):\n", - " super().__init__(\n", - " model=model,\n", - " problem=problem,\n", - " loss=loss,\n", - " optimizer=optimizer,\n", - " scheduler=scheduler,\n", - " weighting=weighting,\n", - " use_lt=use_lt,\n", - " )\n", - "\n", - " def loss_data(self, input, target):\n", - " # self.loss stores the loss passed in the init\n", - " network_output = self.forward(input)\n", - " return self.loss(network_output, target)\n", - "\n", - "\n", "# initialize (we use plain tensors!)\n", - "solver = MyFirstSolver(problem, model, use_lt=False)\n", + "solver = MyFirstSolver(problem=problem, model=model, use_lt=False)\n", "\n", "# simple training\n", "trainer = Trainer(\n", " solver, max_epochs=500, train_size=0.8, test_size=0.2, accelerator=\"cpu\"\n", ")\n", "trainer.train()\n", - "_ = trainer.test()" + "trainer.test()" ] }, { @@ -316,20 +233,14 @@ "\n", "Solvers in PINA play a critical role in training and optimizing machine learning models, especially when working with complex problems like physics-informed neural networks (PINNs) or standard supervised learning. Here’s a quick recap of the key concepts we've covered:\n", "\n", - "1. **Solver Interfaces**:\n", - " - **`SingleSolverInterface`**: For solvers using one model (e.g., a standard supervised solver or a single physics-informed model).\n", - " - **`MultiSolverInterface`**: For solvers using multiple models (e.g., Generative Adversarial Networks (GANs)).\n", - "\n", - "2. **Loss Functions**:\n", - " - **`loss_data`**: Computes the loss for supervised solvers, typically comparing the model's predictions to the true targets.\n", - " - **`loss_phys`**: Computes the physics loss for PINNs, typically using the residuals of a physical equation to enforce consistency with the physics of the system.\n", + "1. **Solver base classes**:\n", + " - **`SingleModelSolver`**: For solvers using a single model.\n", + " - **`MultiModelSolver`**: For solvers using multiple models.\n", + " - **`EnsembleSolver`**: For solvers using an ensemble of models.\n", "\n", "3. **Custom Solver Implementation**:\n", - " - You can create custom solvers by inheriting from base classes such as `SingleSolverInterface`. The **`optimization_cycle`** method must be implemented to define how to compute the loss for each batch.\n", - " - `SupervisedSolverInterface`, `PINNInterface` already implement the `optimization_cycle` for you!\n", - "\n", - "\n", - "By understanding and implementing solvers in PINA, you can build flexible, scalable models that can be optimized both with traditional supervised learning techniques and more specialized, physics-based methods." + " - Custom solvers can be created by inheriting from one of PINA’s base solver classes and combining it with one or more mixins.\n", + " - Several solver implementations are already readily available in PINA and can be used directly or extended to define more specialized training strategies." ] }, { @@ -342,9 +253,9 @@ "Congratulations on completing the tutorial on solver classes! Now that you have a solid foundation, here are a few directions you can explore:\n", "\n", "\n", - "1. **Physics Solvers**: Try to implement your own physics-based solver. Can you do it? This will involve creating a custom loss function that enforces the physics of a given problem insied `loss_phys`.\n", + "1. **Physics-Informed Solvers**: Try to implement your own physics-based solver. Can you do it?\n", "\n", - "2. **Multi-Model Solvers**: Take it to the next level by exploring multi-model solvers, such as GANs or ensemble-based solvers. You could implement and train models that combine the strengths of multiple neural networks.\n", + "2. **Multi-Model Solvers**: Take it to the next level by exploring multi-model solvers. You could implement and train models that combine the strengths of multiple neural networks.\n", "\n", "3. **...and many more!**: There are countless directions to further explore, try to look at our `solver` for example!\n", "\n", diff --git a/tutorials/tutorial19/tutorial.ipynb b/tutorials/tutorial19/tutorial.ipynb index efd0debc4..5ab20f900 100644 --- a/tutorials/tutorial19/tutorial.ipynb +++ b/tutorials/tutorial19/tutorial.ipynb @@ -57,24 +57,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "6558c37a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([1, 2, 3, 4])\n", - "tensor([[0., 0., 0.],\n", - " [0., 0., 0.]])\n", - "tensor([[1., 1., 1.],\n", - " [1., 1., 1.]])\n", - "tensor([[-0.4420, 0.9948, 0.3727],\n", - " [-0.2328, 0.0719, -0.1929]])\n" - ] - } - ], + "outputs": [], "source": [ "# Creating a tensor from a list\n", "tensor_1 = torch.tensor([1, 2, 3, 4])\n", @@ -104,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "d5369bf3", "metadata": {}, "outputs": [], @@ -130,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "6b82839b", "metadata": {}, "outputs": [], @@ -155,9 +141,9 @@ "source": [ "## Label Tensors\n", "\n", - "In scientific machine learning, especially when working with **Physics-Informed Neural Networks (PINNs)**, handling tensors effectively is crucial. Often, we deal with many indices that represent physical quantities such as spatial and temporal coordinates, making it vital to ensure we use the correct indexing.\n", + "In scientific machine learning, especially when working with **Physics-Informed Solvers**, handling tensors effectively is crucial. Often, we deal with many indices that represent physical quantities such as spatial and temporal coordinates, making it vital to ensure we use the correct indexing.\n", "\n", - "For instance, in PINNs, if the wrong index is used to represent the coordinates of a physical domain, it could lead to incorrect calculations of derivatives, integrals, or residuals. This can significantly affect the accuracy and correctness of the model.\n", + "For instance, if the wrong index is used to represent the coordinates of a physical domain, it could lead to incorrect calculations of derivatives, integrals, or residuals. This can significantly affect the accuracy and correctness of the model.\n", "\n", "### What are Label Tensors?\n", "\n", @@ -166,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "25e8353e", "metadata": {}, "outputs": [], @@ -193,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "0e9dc23e", "metadata": {}, "outputs": [], @@ -216,19 +202,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "235b92d4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor.labels=['a', 'b', 'c']\n", - "tensor.full_labels={0: {'dof': range(0, 2000), 'name': 0}, 1: {'dof': ['a', 'b', 'c'], 'name': 1}}\n" - ] - } - ], + "outputs": [], "source": [ "print(f\"{tensor.labels=}\")\n", "print(f\"{tensor.full_labels=}\")" @@ -248,35 +225,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "45365ea8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tensor:\n", - " tensor([[0.0000, 0.0000],\n", - " [1.0000, 0.5000],\n", - " [2.0000, 1.0000],\n", - " [3.0000, 1.5000]])\n", - "Torch methods can be used, label_tensor.shape=torch.Size([4, 2])\n", - "also label_tensor.requires_grad=False \n", - "\n", - "We can slice with labels: \n", - " label_tensor[\"x\"]=LabelTensor([[0.],\n", - " [1.],\n", - " [2.],\n", - " [3.]])\n", - "Similarly to: \n", - " label_tensor[:, 0]=LabelTensor([[0.],\n", - " [1.],\n", - " [2.],\n", - " [3.]])\n" - ] - } - ], + "outputs": [], "source": [ "# Create a label tensor containing spatial and temporal coordinates\n", "x = torch.tensor([0.0, 1.0, 2.0, 3.0]) # Spatial coordinates\n", @@ -305,21 +257,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "caec2d14", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extract labels: label_tensor.extract({\"points\" : [0, 2]})=LabelTensor([[[0., 0.]],\n", - " [[2., 1.]]])\n", - "Similar to: label_tensor[slice(0, 4, 2), :]=LabelTensor([[[0., 0.]],\n", - " [[2., 1.]]])\n" - ] - } - ], + "outputs": [], "source": [ "label_tensor = LabelTensor(\n", " tensor,\n", @@ -355,18 +296,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "9427b274", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data(x=[2, 3], edge_index=[2, 2])\n" - ] - } - ], + "outputs": [], "source": [ "# Node features: [2 nodes, 3 features]\n", "x = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float)\n", @@ -390,23 +323,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "bdebb42e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[1., 2., 3.],\n", - " [4., 5., 6.]])\n", - "tensor([[0, 1],\n", - " [1, 0]])\n", - "tensor([[ 7.4528, -3.2700],\n", - " [ 7.4528, -3.2700]], grad_fn=)\n" - ] - } - ], + "outputs": [], "source": [ "# Accessing node features\n", "print(data.x) # Node features\n", @@ -435,24 +355,10 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "27f5c9ac", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Graph(x=[2, 3], edge_index=[2, 2])\n", - "tensor([[1., 2., 3.],\n", - " [4., 5., 6.]])\n", - "tensor([[0, 1],\n", - " [1, 0]])\n", - "tensor([[-0.0606, 5.7191],\n", - " [-0.0606, 5.7191]], grad_fn=)\n" - ] - } - ], + "outputs": [], "source": [ "# Node features: [2 nodes, 3 features]\n", "x = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float)\n", @@ -490,19 +396,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "3866a8ae", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Graph(x=[2, 3], edge_index=[2, 2])\n", - "Graph(x=[2, 1], edge_index=[2, 2])\n" - ] - } - ], + "outputs": [], "source": [ "# Node features: [2 nodes, 3 features]\n", "x = LabelTensor(\n", @@ -532,27 +429,7 @@ "execution_count": null, "id": "c8edb68f", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm7b.mat\n", - "Processing...\n", - "Done!\n" - ] - }, - { - "data": { - "text/plain": [ - "Data(edge_index=[2, 324], edge_attr=[324], y=[1, 14], num_nodes=18)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from torch_geometric.datasets import QM7b\n", "\n", @@ -576,7 +453,7 @@ "\n", "2. **Working with Graphs in PINA**: In PINA we implement many graph structures, e.g. `KNNGraph`, `RadiusGraph`, .... see [here](https://mathlab.github.io/PINA/_rst/_code.html#graphs-structures) for further details.\n", "\n", - "3. **...and many more!**: Consider exploring `LabelTensor` for PINNs!\n", + "3. **...and many more!**: Consider exploring `LabelTensor` for Physics-Informed Solvers!\n", "\n", "For more resources and tutorials, check out the [PINA Documentation](https://mathlab.github.io/PINA/)." ] @@ -584,7 +461,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pina", + "display_name": "deep", "language": "python", "name": "python3" }, @@ -598,7 +475,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.21" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/tutorials/tutorial2/tutorial.ipynb b/tutorials/tutorial2/tutorial.ipynb index 61e625920..3c37d845c 100644 --- a/tutorials/tutorial2/tutorial.ipynb +++ b/tutorials/tutorial2/tutorial.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ad0b8dd7", "metadata": {}, "outputs": [], @@ -37,7 +37,7 @@ "\n", "from pina import LabelTensor, Trainer\n", "from pina.model import FeedForward\n", - "from pina.solver import PINN\n", + "from pina.solver import PhysicsInformedSingleModelSolver\n", "from torch.nn import Softplus\n", "\n", "warnings.filterwarnings(\"ignore\")" @@ -73,19 +73,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "82c24040", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The problem is made of 2 conditions: \n", - "They are: ['boundary', 'D']\n" - ] - } - ], + "outputs": [], "source": [ "from pina.problem.zoo import Poisson2DSquareProblem as Poisson\n", "\n", @@ -139,7 +130,7 @@ " output_dimensions=len(problem.output_variables),\n", " input_dimensions=len(problem.input_variables),\n", ")\n", - "pinn = PINN(\n", + "pinn = PhysicsInformedSingleModelSolver(\n", " problem,\n", " model,\n", " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8),\n", @@ -170,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "1ab83c03", "metadata": {}, "outputs": [], @@ -198,7 +189,8 @@ " spatial_samples.extract(\"y\").tensor.flatten(),\n", " field.tensor.flatten(),\n", " )\n", - " plt.colorbar(), plt.tight_layout()" + " plt.colorbar(), plt.tight_layout()\n", + " plt.show()" ] }, { @@ -211,21 +203,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "7db10610", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12, 6))\n", "plot_solution(solver=pinn)" @@ -305,7 +286,7 @@ " extra_features=SinSin(),\n", ")\n", "\n", - "pinn_feat = PINN(\n", + "pinn_feat = PhysicsInformedSingleModelSolver(\n", " problem,\n", " model_feat,\n", " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8),\n", @@ -335,21 +316,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "2be6b145", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12, 6))\n", "plot_solution(solver=pinn_feat)" @@ -415,7 +385,7 @@ " extra_features=SinSinAB(),\n", ")\n", "\n", - "pinn_learn = PINN(\n", + "pinn_learn = PhysicsInformedSingleModelSolver(\n", " problem,\n", " model_learn,\n", " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8),\n", @@ -457,7 +427,7 @@ " input_dimensions=len(problem.input_variables) + 1,\n", " extra_features=SinSinAB(),\n", ")\n", - "pinn_learn = PINN(\n", + "pinn_learn = PhysicsInformedSingleModelSolver(\n", " problem,\n", " model_learn,\n", " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.006, weight_decay=1e-8),\n", @@ -495,95 +465,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "a04e8a5d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PINN\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6fd1b7f849df400b96ea7e2b3da5dad1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# --- Generate Data ---\n", "coords, sdf = generate_sdf_data()\n", @@ -167,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "a883f43d", "metadata": {}, "outputs": [], @@ -180,19 +169,16 @@ "id": "085b412b", "metadata": {}, "source": [ - "## Solving the Problem with Supervised Solver\n", + "## Solving the Problem with Supervised Single-Model Solver\n", "\n", - "We will use the `SupervisedSolver` to solve the task. A Supervised Solver in PINA aims to find a mapping between an input \\( x \\) and an output \\( y \\).\n", + "We will use the `SupervisedSingleModelSolver` to solve the task. A supervised solver in PINA aims to find a mapping between an input \\( x \\) and an output \\( y \\).\n", "Given a PINA `model` $\\mathcal{M}$, the following loss function is minimized during training:\n", "\n", "$$\n", "\\mathcal{L}_{\\rm{supervised}} = \\frac{1}{N}\\sum_{i=1}^N \\mathcal{l}(y_i, \\mathcal{M}(x_i)),\n", "$$\n", "\n", - "where $l$ is a specific loss function, typically the MSE (Mean Squared Error).\n", - "\n", - "### Specify the Loss Function\n", - "By default, the loss function applies a forward pass of the `model` on the input and compares it to the target using the `loss` attribute of `SupervisedSolver`. The [`loss_data`](https://mathlab.github.io/PINA/_rst/solver/supervised.html#pina.solver.supervised.SupervisedSolver.loss_data) function computes the loss for supervised solvers, and it can be overridden by the user to match specific needs (e.g., performing pre-process operations on the input, post-process operations on the output, etc.)." + "where $l$ is a specific loss function, typically the MSE (Mean Squared Error)." ] }, { @@ -206,7 +192,7 @@ "model = FeedForward(input_dimensions=3, output_dimensions=1, func=AdaptiveSIREN)\n", "\n", "# Define the solver\n", - "solver = SupervisedSolver(problem, model, use_lt=False)\n", + "solver = SupervisedSingleModelSolver(problem, model, use_lt=False)\n", "\n", "# Simple training\n", "trainer = Trainer(\n", @@ -219,7 +205,7 @@ " enable_model_summary=False,\n", ")\n", "trainer.train()\n", - "_ = trainer.test()" + "trainer.test()" ] }, { @@ -236,21 +222,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "1a725f92", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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5aTSaPtn2/wqyA0xGRkZGRkZGRkZGRkZGRkaml86vHG0cmuDbTCIWZGVlsb6g7ASLHtkBJiMjIyMjIyMjIyMjIyMjI9MLKPOLnF/PC8XQIbYaiZ2w4aqaI/wZsgMsevpGufIkQUKe1CmNRB5JnNHZsSUQ1AmF1vNeqIWxjIyMjIyMjIyMjIyMjIyMTG/QSwL0KjG2iySc5L34YXJaO8CohTN1THviiSciet++ffu4A45zoQ4mMjIyMjIyMjIyMjIyMjIyMjI/TE7rEshFixbxEink8EpKSuqTMcnIyMjIyMjIyMjIyMjIyPxvohAVUCoUsd2mPbbb+1/ltHaARcvIkSNhNBpRVlaG++67D1OmTAm4Lq1HixObzYbGxkakpqZy+aSMjIyMjMz/Gna7HW1tbSxBoFSe1snkpxSyzSEjIyMjI+OJbHPIxJL/KQdYdnY2nnrqKYwdO5YNzGeeeQYzZ87EunXrMHr0aL/v+cMf/oD777//pI9VRkZGRkbmVOfYsWPIy8v7vofxg0G2OWRkZGRkZE5/m0MhKaFQxNZZp7DbY7q9/1UUdnKp/gCgyOg777yDc845J6L3zZgxAwUFBXjppZfCisa2tLTw+l/e/isMWnYRtAV5/NnkjaZIbfW776PmrXcx5JGHoM7McL3ufpjpNXqP8/WWjRtQ8bdHUPzzXyJx1GhYrZ5tU52eblo/nNdbV3yGEy/+E+nX3ILEabNdx4fWp89zX59et3e0AWotIDiE9Wid9tVfQFs0AFJBscfYaX1zxWEok1OhjEsIuE8E7ceut15E1r6NSPnxfRCT0z3Wb337GRj3bkXClXdAyi1yjP0/f4O9uQEJN90HRfd4CEEQPMZut1qhFEWPfbKbTGh/8Y8QcgoQt/hKGLZ9B8PqD6E940oI6TlQanQQVGqf78P9+wv2PXm/7v09KWCH9ehe2CQVhPQ8KFTqoN+Tc5+MezfCrolDuVGBkpISSJIEy4ljsEsaKBNSgo4x3NftZhNMnz0PZWI6NDPOh6WuCuZtqyCOmA5lSpbffbJ1tkGpAJT6xLDPPe/vKdS5F2zs1s522KxmKHUJHq+bzWYcOnQI/fr148/z9z2Z96yF/egOCPmDIJRNC2vskewT7DYIouR3n9BcC3PtMdhFEfbyvZAmnQmlNi6878lkAh10Ok8jOfcCjZ3eS8equLiY98O07hPY25ugHr8ACn0i7Eoh6PdkN3TA3t4MKbPAYyyGT16CraUO6lEzYW1thjhiKgxvPQ5laja0Cy517ZPN2AUIEl/L9FrXh/+GufIQdBfdBmViWlT7FOx7sjTWofmfD0KZmgVbQw3U42cjfs45nvtkMqHhkTshpWQi6cbfeBxfWodaS9PxojG1/vd5KJNSkTD3bI/vqenVf6Jr+yZk/fz3kNIyg35PrSs/Q9N/X0T6dbcjbsQ4v2Nv/fpz1L/yNLJu+z/oykYGPfcM5UdR+dD9sNmsMDe3IGHqdCSMnwhNfhEqHvgNUpacjdTFZ/dse/MmHP7V3UhdtBj5t/0UB379KxgrjyH9rHMgxccjdf58v2PvOFqBpnUbkHnOma57rfv31NzczNcg/UxMTPTYH5noCWRzrLrnNyi94kqoujPBnOdjzZuvo3n1Nyh58PeQEhLCszlWf42a//wbhXffC33JgF5fe80fvoWGd15D1o/vhn7k2NA2R3sroNMD3RMDep63rfoc+iHDIWbl+twjTUf2QczIhYLeE2CfCNqPHS8/jazD25H+iz9B0Hqu3/TcIzAe3Y+U2x6AmOKwR5qfvJ/HQfcCd3xtDguUouSxT7b2FrQ+9xCk0pGIX3QROlZ/AuO6L6G/6GYo45MhxCVAkKS+sznoHnBkF+wqLcSsQigkVdDvifaJXjNu/xZIzkR5l63H5qg6BLs+Ccr4pNjYHF1tMC5/DkJOP2imncP3ffPO1ZDGL4IyMdW/zdHeDEEUodAlfD82R3sL6FWFNi5im8O05Sugaj/E/qOhHDQ+9jaHzcbnkt/rqb4S1qYTsFktsB8/AmnK2VCqNWHaHEa2/QVJ1Tc2x+r32F5Sj5wJxCfBDkXQ78nW2QqFsQtiWo7HWLrefxqwmKEaPBY2kwlC6Sh0vf0ExPxSaGYu7bE5DJ2ApIZCqeTXOt9+Epb6auiv+DkUGl1U+xTsezJVHUHrfx6FMiMHtppKaGcugX7SPI99srY0oemJ30CVX4LEK+/wuF+RvdG/f3/HsTeb0fLWs5DySxA3dZ7H99Tw9J9hKj+I7F89wveVYN9T8/K30PzpO8j+yT3QDhjsd+zNH76NhvdeR+7//Q6a4gFBz72OvbtQ9ZeHYDUYeElddBZ0Q8qgyshE+e/uQ9Zly5A0a45r/cavvkL5Q39A5iWXIOfqa7Drllv4Hp86fSa0eblInjHd79hb9x9E69ZdyDp3sevcOF1tjtbWVh7j28kDoVfEVrS+w27FeU372DZISOiZh8tExv+8A+xnP/sZVq9ejTVr1kR0Um956C/ov3QJ4kr741SDnSpHD0FdVMLH5fjffgtrSzNSL7gCYlIKVDn5J2UcdNPas34N0j54BprSYUi66k6P/1tOVMF0eA9UA4ZBTM10jN1qAWx2KCQpqs+0W8z8MHd63G1tzS6D7mRgra2AcfmzEEpHQz21ZxIaDHtnOwyr38ORhAIMHDcZSrsVhjf/AkVCKjSLr+/VeMgIhVrPxgA5M9gZ0W0kh8Lw0bOAzQLNmTcEXMdSdQD2rnZI/UfhZEHn1YEDBzBgwAB+WPvDbjbC+PmLtDI0S27EycS09kPYjh+CsmAwbJX7oJpzOU+Gvg+8j5Xh85dhbzrB54UwYDTUk88M+n7DZ/+B7dh+aC74KTtjrRX7YGuugzh0EmCzepxLfO0qlHyu8d9dHeh46Q8QyEBdcDnMW1ZCodWzwS2NmNpn5Vxt7zwLa101NKOnQzV0DJRuk2AnXRtXQZmYAvWAMv7b2lQPw7Y1UE2Yg0PlFX7PLWtbC4z7tkM7ajJgtcDa3uqaRIeCHHPB1u3YthH1rz6HrJvugrqwX1jbbPx0OToP7oeUmoaa559G5uXX4MRbryLj/IuRdfnVrvXsFgvqPnwfiRMmQp2dg0O/fQCG8goMeeqfHkGGSGlqakJKSopshPUxTptj56N/Q8n5F0CTk4NTDZrcGI8dhaaohP+u+v3dUGq1SJyzGFJ2HqT0rJN2v9v9zQqkfPg89GOmIukSz3u/+dhhmCqPQDNoBITktB6bAQooxOgKIii4BFFyOCLsNnbunUybw3JkNwwfvwhp1EyoJy0M6z221gYYVr6Lo5mDMXDMBCgM7TC89giUWUXQLL6mV+OxdbRCoYt3HI/ONkClgUIMbc/ZyTH3zqPsfFKfEdjusRzdyeebVDICp5LNYevqgOmT5wCNHppFvTuGkWJc+Qbs9VVQ5pbCVnME6oVXQ6Hxfe59LzbH8mcBk4EDb2LZZEijHEH5QBg+eBq2+uPQXvozKNQ6WA7vhN3YCbF0NE1uPM4lH3u/pQGdr/4F4qAxUE9dwjYH9InsgFMNndAn+0vXfPub/+TzXj1iMtRl413Bb3c6134BMasAqqJS/tty4ji69mxGZXo/lA4c6Pe8sjTVw3z0ADQjJ8JOx7CrE0JSaugx2WywtjRC7L7H+aP1u6/R+P6byLn911BlZoe1r3XvvAFTXZ3j97deQ/Y1N+L4i88h74ZbkH72ua71bAYDTnzwAVLnzIaUkoo9d9zJ39WQv/8dveF0sjmcz+130gZB7xZojgUdNiuW1u89LY7Dqcz/VAmkP7Zu3cqlkZFS8dS/oWluw6A/3NOrz6fJye4fXQ1tcQn6/ea+sN9nrq+DkJAIpcrXmUGGh6a4xzFn6+zgyVv1w7+CurAEeQ88ipOFKjkN2gmzoS5xRCHcoYiu+cBOtDz2K8Rf8mOoBo6AQhBhbWtA28t/hWbcbGjGOx6WlspDMO3ZxI4E+l1/9jWuyTc5/JyTaW9D62Qaovx5KZkQB42FUDws4DqmDZ/xOKVRs/hvckbY6o5BIXVne4kqSKPmQKHv3Y3N1nwCps//A2HAKEgjZ0VsEAn9hsN2bA8sBzZBHDDGd/ttjbBs/JR/F/NKT6rBFar+XyGpoZp2PjtpTha2+iqYt34JcfAkCIVDIWQXwz56DhQxfvj15lhp5l3GWVnmNR9CyAvtvKdz2ZaQCkWcI9pm3vI1G6di6Sif75uuXQ8oEzKrCEJGPmyNNTCt/RhC8VBoz1iGvoTuC9YTlZCKB7Lzy9pcz/cB9/Fpx073eE/X2i/RuWo5FMlpUGr9G5jtn7+Ljm8+gVKjhaZsbNjOLyLUuvoRY3mJhJQFi3kx1dbA2tqCuGEjYDh6GEkzeiKxThLGjmPnF1Hy6+ifWRShVkYZnJDpHTWvvIwElQr5N97Uq+1YO9px4LYfIX7sBOTe9JOw32euq4WYnOrXUUSOVKfzi7BR9q6xCzV/uQ/astHIvusBnCxUGTnQjp8B7XDPLBxCyu8H464NaPjjHUi+8TeQCgfws9hSW4mWF/8G/bxzoRk5mdc1HdwF8+Hd/CyxNlQj7tzre5z77jaHWxCAJuKKk2xzCFkFEAePg1jicOb7m6CbVr0LRWIaVCMd9z17WxNsJyqgSHAEQynjSho7F8rU3jlXrTVHYfrqVYjDp0Mqm8KOsHDhjJ2SkbAfPwTLoW0Q/Ti46Blr2dBtcxQN9X3mfI82Bz1rVLMvBpQnb0zW44dh2bkaYtlUYNAECJmFsFNg6hSyOdRnXMOOUPOGT6HMKg75XnHIBNiaTgAqLf9t3vgF7J2tEAeO8fm+ve19hVoLZVYBhLQcWGvKYdrwBV8bmlnnoa9g55tSAWvtMahKR7Dzy9pUx5nj7qVvuolzPd7X+c1H6Ny0GlhybcBtt334Gro2f4u0pFSoiksdlTrhjEmpDOr8IhImz+QlEtKXXsg/DRVH+ad2QCmSp0xF4iQvHW0FkDx1Cju/iMF/eQT/qzaHQlJAQaU0sdymTdYCxf+6A6y9vR0HDx50/U2ppOTQIg8xlQz88pe/RFVVFV588UX+/9/+9jdOyx06dCgMBgNrgH311Vf47LPPIv7swb+/BwVzZvR6HzgBj9I7g0zUa156Fq1rvkHJHx+HEBcHU201Dt6yDAlTZiLv9v8L+Rl5//cH/tn88TuQsnN7NdbOb7/gG7GUWxhyfYpolA4aBNASADG3CFLxYAhpbhFiqwW29lZHxlI3xh1rYdr6LcTCUliry2E3GtjwpJ9tz/0OYuFA6M64POx9oSyWrveegWrMLEhlExEr2PEyeUnQdazlDqPa6QCjsjHd0lsxSKNzGdjiIEepVK+QNFCk5UZt1IrFZTAe2AhbQzXQkyHtQqHSQpGUAWVKdsycX3ZDJ+ymLigTUoOfV6WOKFow3LOubE01MG//GlLZ9F4b+YGgKCWVC9LTn5xfxPdtiPo7Vkq1FuqZF4T1frFgEEBLN6qZ58FOEX6v75uyvyz7N7OzicoiqdSAjFPtWde51tEsvALK9OjvP+GiP+NS6GadDaU+AZbj5Wh55vfQTJgN/YKLAr5HN20RhLRMaIaORWmAbAX91PlQ6uOg6s4aOxl07t2F9o3fIe3CK6H0E1UmVJlZyPnRraj/4B0ubYsfMx5iUhLEeIcDvfKf/8CJt17HwMf/ibih0Y+9ff8BbFp6KQpuvBbFP74xYCaETN/Q72e/QPa8eTHZFmXQUElVIKr/9XcYDu5F4e/+BqWkguHwARy9+1YkLzwbmdfcHHL7+b99nLMQmj98E5ruEpzoxmlB+zefQTNkFKSM0IFKOicHDhkC0BIAqbAUqgHlULpNECmLy9bRAruhy/WacePXMO3dAiG3GLbGE1x+BZJQaGtG678ehGr4ROjmhXcfJaw1Feh87xloZp4DaaB/zdlooIypoBN8ujcf2QllcibQ7QATcvtDd/GdGNSdqUVIIzyDAlFB9/20PLYJokHsPwqmg1thb6wB/GV4aeOgSEyHMm9AzJxfFICkQHSwLO2wbY7EnkCH9UQ5LLu+gzR6rsfrscRuaHfYHJIEIT3/lLQ56PyiYG7YNkfJcI+/1XMv4Yx+7/2i1yz7t3AZsmr0LJ4PkN2hO+cGl+NXPe8SCDmhnW69Je6cazmzXamLg/nQbrS9+ii0M86CdtrigO/RzzkHUtFAZI6a4rL7fbY7+0yIlEFbEF5WeCygbPTO3TvY5giUIa4pKELuj27hMvrmlV8hacZsKHU6iHEOh3f5nx9GwxefoeyFV6ApCD1XDETz+s3YdME1KH3gbuQvu1i2OWRiymntANu4cSNmzXI4EYg77riDfy5btgzPP/88qqurUVFR4fq/yWTCnXfeyU4xnU6H4cOH44svvvDYRrhknDEParcaZKpJr33nXVS98AKSJk1C/9/8OqztkGd76AuvBF3H2toKS3OTo8SIvrSEJMSPmYC44ZEZUUmLlgb8X+fWdRASkqDuNxCmqnLU//1+JJx1KeKm9EQtaDLZ/OYz0JSNQer1Pw/LYdbR0QG9Xh+w3IkeALS4I6RmIvnnj3q8RztzKVSDx0DMK2Gnl1LfHVmkh6suzlXbHy5knJOjhcsXujGu/oAjSKpxnpGaWKM583qX9okLrd7jWFlryUDPhEKlifpzLDtWwd5SB2VadE4HOhbqBVcDAQxN/v8MR0TIUrGbDRQhbyCXV5CDJJqxmzZ/BnvzCajnXun3/aZtXwGiBqbC4UHPK2vNETY6nVoe7Fhra2KDMRjmfethq6uEauKSsMo23BFyB3CE82RGpWNxDUYC63Z1a3fx9i1m2JpqYasuh2ndx/yaNbMAYj9fR4tYEjgrMhLMpLNHjraRU2FtqIGQ6lleRechGdyUhWKhTLD+Q3iybyAHemYexGxfg4z0DNVDxqBr02pYB45EXGKSz/ESM7IRvyC6SLLpeAVaP30HSWddCjE1/MlQyxfL0frNl4gfPxXa0uCOBNL90pUOQuuWjdh10Vko+eOjiCsbgYTRY2E8XgV1Tu+cj4JOB01eLtQZjvH/QNQTThtS5s6F6FbuYO3qRP0H76Hm9VeQtvAM5F4fXmaYoI/D4OdfD7oO6fpZmppcTjI6Z0nbSzsk/GuYJnXJZwV2Onds+IbLI1V5xTAe3I26Jx9CyuU3QTdqkmsd48E9aHn7eZirjiLlsptjcr9TDx7FiztSXj+k3fsvj/foz7wcmknzIOYUwm4295Q10f0lGpuD9JmMXd0llw4bxPDFGxz8U42OLBMjEiijXHvh7b7ZMrp417EibNWHoUzPD1siwR+WjV8AZHNEGehQxiVBveQGDt75/b8+Eer5Vzo+6+AWKLTx7MyjbHeFPimqsZu+e49lHNRn/MjHEUHfkXnTZyxHYcwdHPC8Ys2k6kNQpuayXcSvdbbD3tbIgbFgmMlOa2uENOmsiMWyxX7DIRQO+WHbHCmZnts3Gfl4kVPXsu0bfs1WUOoKOjqhYykNiE2ZrPnAdi5BlIaMha2pDoLXmOjaooWc49a2RojFg1nLzLhzHYT0XLY7vBGS0iCVDEHj+pVIHjuNdTa9IW1kpz5ypBgP70fbqk+RfN4yCN3BsHBoXP4OOrZsQOLM+VCFSJjIvPgKJIyfhKavv0T57+9D6RPPQlvcDwkTJsFmNEJKC56FFgohTg9Nfi5UqSmnrc2hFEnLLLYZW0o5AywmnDp3zSigDo7BLghygrnz85//nJdY4C0oWP/Flzj0+4ccBmqQmz5FXo+/+G/EDxuOhLG+Kfr+yL3lduTc/FPXw4S0NfJ/+WAv96B7PBYzCzS3f7UcUnY+su99lOvs6cFPP92hGzFpaqiK/KQDBThGlZWVQXUTAuH94FRqdVAWOTJR3A05Mkrjl/0CkUIPsLjre8oy6DyyHNzGDpNQDjBrXSWMn74I1YRFEAcE174iw8r42X8glAzjkgAes59sKfdjhYYqmFe+CaFkBKQxkUf8yalnJYdUSjbYnOqFEy1cJxZFOslRpkzJgembt6DMyIdq3BkRf55YMAS2pAwWMfU+jjZTF2zUHEClQ6WQ4nNekRFMpXaK5EyYN38OZXY/qEY5vkshux+UC64NaSBTdpO9o7k7IzPytOtghihFLMnYFfJKIWQU8mTIevwgC/VH6myj97LzN8T3430N2hqOc8SYjOZYYN70JSy71kCaeg5rdNha6mHesRpC0RDOPKBxKrvLJ2OFYdUHsJ2o4u/I8OVb0C68FKphk/ys9yGMm1Yi7uJb0fbWU7BvXQ2YzUi49peQcn2jwp3ffYb2lctR12nGkOlzYhpt7Nq+ER1rVkAzeATiUoNPdju2rEfTR/9F5o9uR/qVP0LC1NkhnV8ETd50AwfD3FAPbUkppGSH0Zg4aTIvvcFUX4/Ow4cx5q2XIMbH+30GyvQt3se74aPlqHzycc5K9AmouL/PZETtKy8iYcJk6AcPDeuz8u66x6PMT0xMQv7//a6Xe9A9HkMXmt54Fh3ffQX1gCHIvPO3sNvsDnvDy+ZQDxiKpIuuZ82uk25zxCW67l2Use16XR+PxJsi79Ip5vZDwm1/6nnBYoLlwDbYW+pDOsAsx/bD8Ol/oJlzEcTi4N8hBaCMH78Acfg0SIMdmeRKP6WIHjZH5T6YVv0X4rCpkEisPEL4WVa+C0oqwaPPEqIvWSLdp5CfZzHDsms1FHHJHOwwff0alPmDoYrCXhKKh/P4vZ1fnBFINkfdMXZ+VNrjfc4ra0MVB9bIEUc2h1BYBmnYNFeJJgXFQtoc7c2wtZGz2Q5E8cgJanMYOmHetRpi8TDOyiM7ylp7FELB4IgzxcLVkPWxOU5UsO1DxyIWUKDNemgbVNPPY3kPW2M1N3RiyQmTgTM1Iym9DQfD1//lZmFUmWL85gPozrkOUn/fYEDnl2/DvGcT4i67HW0vPQLjtjWUeYHEmx+AkJLhs37HyuU4WlUNXWIytIN7mt/Egs7N36Hjuy+hHzuFy9CD0frtCrR98yWybvkZsm74KUzVVSGdXwSVw1PQrevoYWgHDHQFaFLnzuelNxiOV7Pe2MTP3uTgGyHbHDKx5LR2gJ0KNK5chYYVX6Pglpt5yViyGJogmmLG6uOofv5ZdIyf6OEAI9HAmleeR9LUmXxD8aa3kRRLcyOExGSf7Zhrj7PzS1XUH0lLr+DXVHlFyP3T8+wE8xZv1k8MP1vO2ngCxv07YNaKEApPvWYB7tBx0V34UxIvCGNdJSCqOBIcEso0I8PBaAh7LMrkLBYoF9xKzyLBWrWfs79Ii0qaeKZ/R8yGj6HMLmHDKBaoJp3lcPxSp82CwVxmSMYW6VFQ1DZc2DkE31ID09YvYW+th2raBbAJKuCIQ4PAHcvBzWxsCSPnODS4vAyucKLDpL1G35kz9dtaud9R5hnXe10XNnSP7XNkymUUwnpkByz71vN5JEbokDJ+/gKXoKrnL3NFw8mIl4YGd3SYN37O5SUU0aaIP51n7pO7SBEKB7ORLRaUQlE6ikXz7c31tLfoeucfPLnTnHUDhMz8yMqzqFdUAMNet+hSNkRpIioWDQq4bdWoqVCoNRDzSxB/2U85g5XKpynD1B/a8bNhl1RoSouufMcfHetXQohLRMLcJVAXl/KE34nx6AGYa6oQN9Ex4Wx69xUuO5MKStC1ezss9Se4fMxUUwWpJgeqrPBKdxMnT+OF6DywD9XPPsklktp+0d+D9/7il2hetxHxw4dh1CsOSQGZ74fGLz9H+47tyLp8Get5pS05B1Jy4PKtrsMHUffGyzDXnfBwgFnb21D31stInrMQ6vyiPrA5GiD6EWymTmZO51fSuY5sHk3pUOT95SUuRbM0NbDWGI9BqeRuaOFirq7gRhXmeA2EKDMnThYUvNAvuzu8rCWyNcK1OaxWh81h6innDLn5zEIIpWOiDoxYD22FZcc3EMfMhzTMSw+oO4BlWrfckbFUEH1JrBMKGKmmnufo9kdZYAVDoKRMsI4WtkOcnaPDgcbkD/O6D/nZRk1sbGTzHT7is45l73rYm2shjp4PZWEZhKKhkdscZKORs1lJ3fdssB7bByEtLyZOHOrUbKvcDyt1oU7JZj1XsjsoCCuEocfl7nA0fvYCl5+qZ1zg2neyl8XS4NqVpnUfAdSMaf7VsFcf4vOsNxlr7ACmTtn5pZxpTqL5HBCke93bjwFd7Y6mPRE0HnI08CGtJv/Xl+7Mq2G3mDjQYC0aBGUAG0EzdhY7usTcYsRfdjssNRXcjVqZ4H8s2snzoNq8EVK/6Gx9f7Sv/hxSVh4Sl1wC7Yhx0AzoOScN+3fB2tYK/RhHwLD+5X/B0ljPjv7OXVu50kjKyIKp4jBUOXmQUsLL4EqdfwYvRNvWzah5+UUU3P6zXmWdb7v2ZnQePIzUOTMx/KmTp1sda2QNsFMX2QHWS+o++RR1H32MnMsuRcGPevRuCGtnJyzNzWj47BPUvPYqhjz7b2jy8lH6yKNQ5xd4rNu5fy/q3ngF1pYWvw4wvw6thCSPqJWVxO5bmqDK9ky3pZtezZ9+haSzLkHSEs+SBFVuITJuv5+zv2h77rQufw3tX7yHtJ/cD7XbDZpSkMnQVBUGj+iYdqyHlVoEb/oMwrW/gFQQ2QTMsHo5lMnpUA31nyln2rkW5t3roT3zar8RzkihsoZwUKblQHfxXT6vW47sgJ065JHgvFMgV58AzYV3RDSZYC0pitRV7ufyMtLF8IZT/qnEz8922fFjMXFmkV/MRkemlDZ2UTIlZW11Iw1zaIkYV77OEVT13GUh95+y1izlOx1lhH7GJRYOha3dEWlVBIgCkcPPllUMy85VHKmUhvoa4uHgdH5RJJ10w5QZhVCNXRDyfRzFNRsC6p9QSatqxoVQdDvT2FgWRAg5kV0XZIwKOQNYj8YJRURJKJaMXaH/KL8CwoQ0Zi5HrOncsu5ew+LH3kZ7JAhZhbw40czv0eFTJmfAShMSTXjCrU663n6cy5z1V/jP7KSSAloI/QW3BNwOlzt2lx5IeSW8BN2XxGTopp0BxYEDiAV0Tje99ATEtExk3fsYNAM9y0IbX30axkP7oC4tg6CPh/HIAf4748afObrnpaahY8dm1L3wJIzlh5B1g6PEPxK6Dh1A+9bN7ATpjQMs+6ILYWpoQvqC2GhQyURPwycfoXXzJmRddiWyr+zp9ul0anFp5H/fYG2W0n88B/2goSi6/yFo+3sGFjp2bEHje2/ypC/zytDdhi1N1FUsxfPz2lpg6+r00efq2LwGdf94CCmX3YiEWYs8/qcZOAzpt90DdWF/R/aaG81vPIOOdV8j85d/5kmcE/oMS30NVPnBtXCMm1bDUlmF5o2fIu2230JMD9+ZTcGarhXvssyCaqD/bAzDui9gObwL+vNu9NvpLVLCzY4Vc0sQd5WvrIZ5z3rA0OHSE+VtJmdAe+WvI7M5DB28WA5t5QCctw4TZQOSNAHriPmBstX5WVbo37nFpZ+N1bClZMfEAUa4a3mSzhZh+ORZdhRq5jqCucGgMVnKd3HwyV/WmVAykrsXkmamIyjjizR8BqzNJ2DZ+iWgS4SyO/srUpyOF8rQtuxYCXveIEgjQmfiUcY1dTh0t7889iGzEIrpF3DAy7FPo/iZT6Wu4cLfPX1Gdgl3gnZCQTfSO7Ue2Qlx6GQOXvpDGr+Ij6Pt4BZYD2/jgKJ3uWIkCHkDeHGiWXK9h0SDjUqMI8ioZ23jV/7MtqXufP/2hOAmmyCeH7jUXMzrx0sgeRef7aZkuBpxxAJrcyOaX/sXV+lk3PV7D+cXUf/8Y7DU1UD7+KvszDMe3seBtvw/PoOUcy7mwAN1h6x76V/sDEu/NLBAfyA69uxG2+aNMByr6JUDLG/ZZah84WWkz41cokhGJhxkB1iUOI2Lkv+7G7lXXgH9AN/JxeEH70PLd98i87IrodRpXRNrf6WPccNHovj+h6AbGDoC13VwLyp+fTtSzrkQ6Rf3GMAn/vlndG7biII/PwspreeBSO2+1f0HQxVASJEMUn+oigdCPXA4RK/U3fZP30Tn6k+RfP3dUPUPPHnWTp4PndEGKUHjN/03GJTKbFz3OZTpOQEdYLb6athOVAIU/YlRyjNHTkV12C3RyTCi1H/SoSARcMquEYdM8uzWYrM6RDyDCMXT+aRSqRytw6mUr/qQQ3tLVHk4wGwdzRwhtO5dB7Fsml/nGGX0iP19U56tVQccDrUhk6Gef5UjotyHUBYW73cYhritrgLWQ1s4Eqr003GSnESC17Gyd7bAuGMlxJJRUKYXcKaZrfaowyBM89VcCCcKaPzmTTaiVKPmQRGfwsdKmeKpMRUI85YvuHxSPe+qgAaNu6FK54M4YLSPdplCnwhlvOdE04ll/0ZY9qyFavr5PFFxopp2nqP8kKLWxg6/5xV/PunBUWMEKvmgz8kfGPKYmDd9ASGnX0ADNxCaeZciGuh4cylDFBjWfModpLSLLuNMTXP5ftYII42vUFDnJuq4pFQnoPHdp5B44Y1Q9SIyS9H/1Bvv7tEr9CLloutgrqlEywevoWP9N8i57+8Q4uK53Jt+ErohI5BxzY+hH+F5TZCTo+qP9yFu7EQOXNS99gLyf/kA1Hme+mapC8/kZ4s6J/LrwZ30+fN48di/GOi7yISP83gX//pemBsboUr31ZI7fM/dMBw6gJQzz2GpBGdGc8I430Yv8eOnIO/u+6EfFryMn+jYugHH/3Qv0i6/HsluWqI1jz4I09FDKHzsZT5vnZBDTF0yCKpcz0CfE+0Q/w4m1YAhHMTzCca98zzr86Xd+QdIOYFFlfVzz4Huq0+gSo6D0msbobC3t8C49ktY+1UFdIBRpzcLNeExGWPiACNsnW2sGxVuVgxLHBzZBaF4CJefU2mWOGKGZwkflZ9TKVgQnTL3ZwPpXtqqDvCk2JaQ4uEAo+eK9eguWPdtgDThDL9ZYuRAEgf6Nu4hnSZ6JopDp0C9+IZeyTGEg0hOqzCPI8kPWA9udthLfjLhKegGb5ujpQ7G3as52KZMzubnNGXcU/aQU4Q+EihAaPzmLXZUcYOelGzHtjPCEw43bfgYsJqhWXBNwHXcnZbUGEbpZTPScVAkZvD//GHZ/R2sh7dDNetiKON6Mpnob2omZVn/sSsDy5/NQfvGn52aw5lTlG0YDLq2LFu+hJKy+N2Ca+GgOcMzIBAONE6yOZx6sZFiWPkuZ65q55zPf5uP7OHOrNQVNBTWumooKvbD2F4Lw1fvIuGK2yFm+79nhoOQlILU6+6CkO7fZk298hbOnq9/5q8wHNiNnPsfh1Ktdi1E3NjJSL/qZn4+uEMOsco/P4Ck2Qu5jL3xo/dQeN8fIaV5PocyL7oUyTNm9VpzNPfi83k53W0OpaDgJabbtJ5+x+FURHaA9bLNr0MPxY6Ogweh7+/pBEuaMg1KjQa5y65C/o8cnUmCQRod7rRt+A6WlhYkz/WMoJKXXkiIh2H/Ho/X48ZP43IbMdEz3VZKz0T2LxydICNBWzaGF2/UZeM4IhvqRi1otBgURHjfHcvRvTAf3AHNjLMd3VxUGsRdfhesjbVoe/ZBaBdcypFZj3HMOBvqSQtdoqPeWCnLSSlCmRReGi+lu3e99SiUWUXQzA1v8k7OKPPa5bAPmQT1tHMdWhIcMbSwUUkPe/O65bBVHYR68Y8CprXT+dSvX7eDMq+UtS3snJLtWZJpXvcR7G0NUGQWRSxubz22l9uIk9MsVl0bgyEWhd9xjsTjJXJ+pYd++CvsNu7mSo5CS0cLt17nDo+j57HWF6WqR/LZPRtWOMT7u6PB9LB1bsddDycQVApAmXvRRvPoveaNn0CRkgX15KWuLlLiqLkQkhxGBkVvFZTa7yUSTOeVoIuH8qxbPM4Zj/PKfX21lks/bO3NMFPpyuAJfqPIdjq+XF7ZGbEDLBoo0qyZe5Hf183bv+MsOyptUI2Y6vf9lsO7+Z6hJcd0Uy06Xvs7pNIR0C8NneFi3Lwaxm+WI2fCXBio3MEeWG/C3/lgM3Q6JrLuzTuGBtbeUPcr5YUEx6WcfCj1eg8nAkFBk6R5vp2kbJ2d6Nq/h58Fmn4DYK4/wa/x/8xmtK3/lhulKDXaqJ1fjStWoOqF5zHgt7+DJi8vaKt7mZNocyQmwVh5DMaqY1Dnek66k6bOgCG/ADnX3gjF9cFF4+ncSpjgeR21fvs1t7BPmOyZfSKmZXAHauOB3YDbMz1u0iyYC0q41NgdklHI/uXDEe+jftx0XrzRjJrs6HQXoHzZNU59PAYvCa/bHHV4tFQegnb2Us7AoeyW+Kt+Dkt9FVqeupfvGd7i1bozrwTMpsA2x4lKdjq5Z8oEw9baiM7X/gKx/whoZoc3bsuBLTCv/QiSsRPqhcscmkdKJQebqGOzkFUE04rXOZtIc95PApa4uz8bFP1HsT3B9zUvJ5Vp1VuOTKjc/qwrGgnkOCHBcnHQhIDHLJaIfoJngeAMeUkFISt0hz2FzcrHip7JJGVAGdQkjq+acCY3/mEZg35RCK4rBIctpuq2Ody2E47NIQ2awJmL0WJrrXfopWYWQzVuISxVB9gpKI1d6JKucNoc1EzBHfo/LcLZN3uUDga0OchGKSpzBOoogDt0il9pCXIyWg/v4GMaqQMsGsi20C6+yvd1qxXm7d/C3t7EVR9OPT1vLId2cpMMzDkflmMH0fn2U5CGT4Ju/sUhP9u06WukbVoF2/jZji6zEWpc2bo6fBxt2pETAq6vHeRw9JrKD8Ha0cGdG6nLrztKlQrJC87yeS+VTpLNQfqi5Pw319XC1i3tQg6xto3rED9hCjd2i9b5Vfvue6j97zsY9Jc/Q+VHQF+2OWRiiewAixJ38f2d110PMTERYz9azn9TeULbtq3IufIqpM5fwDeUaKh57kmYT9QgacZcKKSeSbWUms6dnKztrR7rx0+ZzUtfQ5lhtIRzjFpaWpCYmBjyQW7auQ7mPRth2rHWITBZPBhCZh4/LO2tjY7MLH9aXAGMKvrsrv/+g40L/eVhiuSTMZRTwgLu4SLklsA+bBqE4jJ+wDsdXNa962HZuZr1HTjdnDQGgkSMvY+Vstvh4dfJ0tHiN9oacvdGzeXsIG/nl+XIdhZ6lUbPZ+cNOZConJOyrjirirs6xgWNeHPTBJs1aucP62KFIZJK54Hh23fQkZyP1FHToZ51GWxNNawPRgYapbFLpeOiHoN6ou+D33x4GzuByEB0z7ryhjQ16Hs07/7OkT0WqAQ10Odr4yAOnQpld7kCTXRo8mAr3w0haUaPRloQR5RPF6sQ16C16iDrktmSszwcYGRUWzZ9zpl06oVXc7ZYX2NrrEXXO09CGjXTRxTa1lAL46r3OKOFO7UGcIDpL7iJo7F0HipT0qGeMI91wghrXRW6Vn4A7YwlrhJKdzST50OZmomu/IFIXXiRz/GizAtyzjc9+zBsTfVIueNh1/G21Fah/q//B93UBdBPWQBlQmLYGR2JC8/lJRKoPLL/P1+BpbUFTR+8iX5//RfUOY77VsuKz3D8iUeQccV1SD8/uiw8oqv8KDr27YO5ocGvA+x07Mh0OuM83jRxOXDnj6EpKsagp/7Nr7WuXwND+VFkXHAJd6T2vg+ES+0zf+dAgLcDjPTnlCoJ1o42j9epVPdkoBk8kpdY2hyGzavYYU4/Ey67nXV7xNwiWGsrYGtu8MhqccIT/UA2h9GAztcf5cmy/pLbw9ovcpaRDSFkh69XJhaXAcZOdpq5Z62Yt65ku0M15xIO4rHDKYgYvfexouYxfj9vyGTOMhJLw3cuOVFNPstx3/SyC8x71sDe0gBp/BkO5x2VIx7ZzllXtE8s8RCX5ON08Rg/OX7ISRSlnhSNSSTnVQhIesH43fvoyCpF6rCJLOlgO3GUbQ4qtVPqk6CM1uaQVFBP8b33k4OIApbUjTpQNjhBtgAdB/PObzjDSsguiezzyU4ZPJG7VxKUhc82x/GDrkx8yo4LphXrrZsV7Bqk/7G0By0UxHVzgFHGonnzF1xNoZp3BRSJvvqBscZaUw7D8uegmrwY0mDPShMKrJrWfMS/K6hjYwAHmP7SO1zNO0iPVDVmJqRBjiw7S9URGNd9Bu3s8/wG4rXTz0RnVjF0oyYibqGvw8xpczQ+fg9nNqbcdI/rf6Yj+9H45IOIW3QhNCMmQkhKDfu+n3yeQ3sxEtT5hRjw7Bsw19eh+dP30f/x5yGlO2zG+g/eQe0LTyP31juRstBXdzhcOg8eQvuePbC0tPp1gJ2ONodCUPAS021SlEqm18gOsCg5+NDDGPOH3/PvhT+5zdWlgqh59WW0rPkO+tJSHL7/HmRdejlyroq8ljrvrt+wyK2788tJ4cNPcaT2VIY6dtTU1CA+Pj5kRybNnPOhzMiFacNXHjdxqXQkxJJhrvLRcOE07LFzIypTIENKPTtw23a/75HUkIb5TsYVmYVQdjSzM421trxK3aI9Vr3JwuHsFD/GOzm4SCyVjFwy6ChLzHp4KzvtzG0NrN2hzCjgssBAmLd9xeWVatKb6MvSSjJ29Yk4YVIg2WaDIEpcehBN+UG4kFg8ZXmyCGooLCY2XBWJaSEdYI7IrWeWn7uhSbompK8hDBwb/di7z6s4tQq2TZ9yYwWnBou1fDes276GQNlfRUNhXPE6hPyBEPuP5LJi+mx7ZxtU3aK3AT+j6QSXngqlY6OeeDPUSpycyH40w4S0bGjmX8KTT6eD0B+UvaBQ9VzP2plnu/5HIvgUrbUMGO7XAUaRVGnYBBw9cAAJSUke16Gl5hiannwAuhmLHSWNZhLE7bkBm2uOcZklnfsnfncbO8ISz4ncyIwEKpOk7k0tX30CdVGJywEWP34yUquOIXFa74IhuVddjcxzz3N1dvJG7sh0cql6/hkk/uROCHo9sq+5Hmq3pgjU5bHrwF5o8gtQ/vt7kH39rUhbEl72tTu5v6DOyAq/zgKSVuiNePWpZnPEnXUVDFtWw7RppUcXTfXo6VCNmBKxzUEBLuogHSh4Feh+pV0cWdmWgu5TbppfTsiRRs8fdoTklMTsWHnrgUU21jgo/PgLSSuVnFzcbVmphLXmMGceUdaPrbYc9tY6ztCShgfWwTKtX85Z0+qZFzuCoX0E2zNxSThhsPXYHDkDHDqcfQR1aqQMZA4shoCys0negmQDQjnAWOydgkjdx4t+koSEExLzp1JYwY+ERsQ2h6SAbetXEPqNdGl+UbMEctIKZVM5MGz8+g0+v+i7Zg3T8t3sTFJNCN5B3FZ/3CHnEaILe0io2oRsDpUfmyOnGOpZ5wO6OFcGvj/cy4xpvqGd1XPftVQeYJvDOmyi/0oUXTwa9alI9ePXMR/dh5bn/wz9oksc5ale16i5poLLvMlJVv+H2xG34HzEzT0HfYkYn4DmT95Dy5cfc+m80wGWNGM2Z4jFe1UxRUrRHT9F/g3XuzpN/xBsjj4pgTzVJ/+nCae2NXMKI7hdoNkXXujxv8I7fw7D0SMcoVXn5PjV6ggHSjX1BwndVt5zG7e3TbvUU3g/VtBN1Xz8KGC19UoHJ1xo8qkZP5cXbyI2RLtRjfQtpSCoKws9TCIxVCPBZjHBTGUDCakxFZoPBBlJ5HQRMgqi0jGQhs1gZ5ezYxHpT5DhSWKl1u/eBdQ6CNn9Q0YSlWxcRfddReJwVI07A8oYCZWHA0VIKdqrTMsPb3xTz+NjydHOqgOOjotVB6BMTHdNTOh/xlVvQKHWQ00dNP2gpM5NQ3y1e6KBjGTK8qOmDE4HGDnpFBn5jsw7q9khUsw6ICP5PFJR5lcYpbKWHathrdjL2WKKtPA6FXpnfpGWCHV00l1yZ8D1pIHhG+W2jlbW65H69ei4qIZPgphTyBkakUKOYyEtE8r4JKjiEqEqHe4R3e5cSaXJzZBIN7F0GGfImo+XQ8zKj8op2LVzM2ey6Ub61z90kjTnDG5XTlphTsSkZGRdfWPIz6DJEDk0uo4c4oxiVYZvBkgg55fMyYcaJTjJvPAyj/8V/OxXHJmn8kg6H6SU8ErwvNEN8l86TiW2xx+8k7uZJi/xtHdiBZXRWKqPsWOZRJz7GhKg101bzEssbA66H6gn+m+WQvciLo3so6wWW2crTF+/BUVWwUkpNaRsHWvFbn52+BOQD4U0YbFHxjiVESoEibPQWAtUG8+i68FQJqTBzlIAfTsZpGcglTqeTJtDGjKZnXtCYmj5DmoapJqylL8HtgWrqHN1JmzH9rCTyRkcpAAe2RyU6a0aPd/vtoS4JAhRVBf4g8ZPwVQqm3U6wOizeUxkB5kNsDfVwNaUxQ4wCmyp5i/jkstQWLat5I7fyuziqDp0W+ureHxi/kDoLvFtaOV0DkqDww8+2lookNwIsaDn3qUeNxdSvzII6VHYHFo9hJRMbs6lHjoa6iGeY+n65mPWLSR7w3xkP4vvk80RTCMxGNS4hCQTAukzOkk560JoBw6FrqxnPVVGFrKvCd/m6Ni3l+fFUorn/ZBspUDOLxmZWCM7wKIkaXQQbZesLF6Ioc+/3KvPsZlN6Ni8DvqR46Ds1tlo/ui/3LmDtGP6irblr6JrzRf8e9L1d0PdfyiMe7dyVEI/77w+jQSbD2zj1GT1lDP9Th5ZeL7yIIS8kqjGYfj4OY74aC8Mr0whUuzN9Q6HEtX0nwRs9ZWw7FoNe9sQSGVTA4+LNI3svmVy/LdS5fG3M5KonnERO7UCtYd26lRIvYgYxgprUw3M+9ZDGjwJQmJ4zk06lwJNdrj7ETm+kjIj0j5xamfY2hph2f0tv9feeBz29iyXA4zLXONTI5o8WOl7PrCJnZPOzwgFR3xpTPHJEBdeC7hp9ZAxqp55kUfWIpU+UCSZMhOCZVq5I46a6dCHSY1MH8aJac1y2GqOQpmeG1Hr8mB0vPYod7vE2VROPcR1zP1lfoUDTV7FtCxYm+vRtXI5NONnIf6sngwvITUDluNHYO9sR8qP7mbB7oa//grxZ18B/VTfSXH9Uw/xRD/tBv/l2XXP/g12QxcKn3wz6LgoQ9jW3sZt4SPB3NiAvddfgcQp09H0xadQFxS5yulkTk10pYGdQurcPF6Igf98sVefQ7ounds3QT9qgqsZTNO71KG6CTavEshY0vLm0zBsXeu4Lu74PU/kDNvWwnKiCvq55/apALJpxxqevGqmLg7s8Kk8BKGgNGKHNol6G977F5QpmdCefyv6AsqIAWwOHaGTgO34IVh2fMNln9LgwJpDnMGk6JarcIPtCTebguw4Z3a7eu6VXLoZ6Di7bA4qzfyeIU0w8+GtUJGAvZtAfPQ2hw321gZHMCmC89352dbGalj2roUiLR/2ugrWhXNlx5POXXwK68uGCzXlsR7bA2nYzKANFXrGb++xOaibKDVbcguikc6cavp5PW8gh+exPRDLpjh0+MJw+BEidbNuqYvK+UWYVr/HWYjCZXcH1MiLlI7X/gYYOqG79E6Xw4tt6SicX84AqJCaDsvxozB8+ylsbS3Qz+nJLlMmp8HWVAe71YyUG36Jjm8+QfOzf0TipTdDS5qJXtdh3aP3QUzPQsrlvtqQ/P+n/sidsPP/9FzQcZH0DmmPRXofNFRWYN9N1yB14RLUvv0m4keNQekjj+KHjkJJVR4xLoG0yxlgsUB2gEXJ/l/9CunFRdAPDK0h0BtaV36OE88+hrTLrkPKmY6OGFJmFqTsPKScd3mffa56yGhY6qohJCRDynHoUxjWr4Bp/3Zox83iCV8o6AGu1+tZXL7tnaehGjwG6jEOLaNgkNi1teoQbI0nWL/HW+/HsncjjCvfgWrKYqhGRN52Who9J2Zth/2hTM2COHIWlw2Gi/NYRWPkk1HBnYOCdNeh7pHcMYh0rqZd4Oj81NbE3RepW2MgR2KwckaKOhs3LIeYPxhSv9D6LLEi0LEix429qw0wG8PaDhmLpm0rWEhW9BNtpjIEM3V8KhkNya0jVNjjjEt2dJHsdsZ5p9mrxviPwjqxtTWwnphUOp47OZHBR1lapAFHZaChsOzbANP+jdCWzmCj2q5UBu1MJKTl0hQq7C5a/Bm713CWY2/KZKSxc2FrrAnp/KJuacbV70MaOhEilfsEQew/HJZDOyBk5MXk3KJrxbx/G9RaHfSLLoa6W+PD9XkZOTDRe7qF86WCEr6HBtJKtLY0epRQepN+3R2uiUQwGt99FS2ffYBsjRZxowNPRL2hDEXK+FJlZiHjwsugyg7fedmxfz/qP/4I+vM8OzTJ9C1HH/g1Ml/5r99MvVjS8um7aHzrJaRf/1MkTHOUvaty8lnYPumMvvvOScfG1tkBMTUTYrrjfOxc/QlPAHVTF3JGRLjXr621Ce3vPA3NuNlQDwt9XRg3fc06g9bqCkhDxvh0njZtXgnTmo+5DDuSrBAek0oNaeycsIXxo4F0k+yjZkPILz05NkdOP4imKUElGSir17TuA87oUU9znDdU9mhrrIZQPCxg2WIwhwRpwpo2fARxwFiIfrpRnnybo4ttjrDkEVgrkmyKb9lh5k+2wVq5D+YDGyENomzl4Fn3/qBgnTiINL1ygBLSiOvJpiEHE2XPB4PKCs0UZCNdsPgU2FpOcDMFh3ZsaAcYBfzMFXuhHTAd9pYTsKu0UAY5v4T0PNipaiHMElZysFl2fcvBO3/dSMNFNX4hl1yGcn5RkwrTuk8gjZwRMngmFg+FtfooZ9bH4hq0Np2A+cAOaFKzoJt/AdTDPe9jdI+0HN4DWBx2AtkaZHNIeY5sOw+oGqG5IaAkDDmzMm6+26/8hDf1Lz+DtjUrIT7wV2gHOKoJwoESOChTjKqiMi+4GPoh4dvULZu3oGnVN0i4JDKJGhmZYMgOsCjJvexSaEsiE5yMhrgxE2GurkL8xJ5yvoQZC3iJNcZ922HY8h1nLVBaLS3uxC+9BtamurCcX86OHfn5+bC2NHAJgLXbqA2FZuFlsNVXo2v58w7HjJcDTCgYCHHoBIhF0T0ApSHhTxSjgXUVQmh+BTpWUX0edQ4KIlJK2E4c405O5JRxPmyphMF6/ACXOgphZjjZDB3sGBLJ8KXMMJU2ZhG03h4rMbMIQlp+2OUr3G2UNEoCtGanaKSQURj2sfHZPnWRDENkNxDk6LI31XImGTnAWEsjp3/4ZbUUQYxLQn5+Hkwr32CHi3rB1bBTd1Rq++1mdJIYvt3QDmnSkrCzKslBQ10qSayYmkBEi5BZwEsoSGvMengnTyRDOcC0M84GaInVuZWRi6Tbfs8lkP6c5/qZZ0LKL4Gq+55Jxmny1XcE/JyMnz/MnXQtjXUQU3yzFdUlA3Hir/fAVH4QSUsu9sge4IyK7uyJpLmLWQvMvQQyHKjMYOCT0WV81X3wAWreeB3ZERiwMr0n6+IrfVrO9wVxE2dwZqFuRE8pVNIZ5/ESa7q2rYVp3w4kLF0G7fDxvLiTeNmtnHUWzHHv7/olzT5rTQWs9dVhvU9/7g2wnqhC53vPQZGQ5OMAE/sP43IjygCLBu+mHrGG7tnBMrFibnOIKu4mHQzS9SJNMtJ2dGI5vA22E+VQpudBER9ehrGto4Wz3LlroyB22xx9qDMayXMhbxCE7AHh2xykUUnP70A2R3IW2xz+OjKHtX0qIYuw+Y634L+9uRb2jlaABPJLx0EsLAu7azjtG9scuTkwrXiFnZ+q6Rdwx25FcpaHo8dSsYcdM9wIIVwnLOmd7VkPW1pOr/RwhZzQnT8JW10VrEd3s5ZvKAeYNoyuj5Fcg1JhKZJ+/DvWDvN3fsUtvNARZBvgsDnI8RXI5qD3Z937GGduWZsbIST5OuPVxaWo/dt9ME+di4Q5S1yvkxQDScY4v6PkM8+HKr8ImuLIytRV6RkY/Ox/EA01b76Fuo8+RvHEvp279QUKQclLTLdJpTwyvUZhPx3bKnyPtLa2cneT4zu2I7ssuNOBqHrq7zxpybulb8rtCEtDHZRaLZS6yPWf3Gl9+zl0rV+BlFvv48lcbyHBwsbGRqSkpEBBWTkqddhipdSxjbIuxIKBfZqtdargfqz6otUvCa5TdE/p5vigzDxbcw23wA7XADEf3AxLxS5IQ6ZCzPITafoBHKuAn9vZCsuRbRCLhodVgkgZacZ170PIKII0MLiWU8BtdLVHpevmfawS2qpdkVjL9pUQh033cJqa1n4AW80RqOZdyV2twt5+Uy0LyYZb/hEJ5t3reP9VY+a4XrM2VLN2X29LsKksqeO//4LUbwjEwlJ0ff0utDOXQpGe0+tzq+39F2HavwPJtz7AmhqBqH/8QRiP7EPWA0966DsR1rYWHL/3VuiGj0PqVbc5xmy3o/LuGyAkJiHn//6I7wtLayvatm8Hhg7l40QdvxJkrbA+tzlqDx5ARknorJCqx/4IKSUNGZdd02djstRRxmZi0PM7HJpeeBTGXRuR9vM/Q0zLjOmzgQM+1PglzGebpfoo7F0dEAtJj+qHHxvuc5vDYubmOgJlIzk/s7ONZQWoY3K4mHd9y4E66lLtvq3/CZuDZBQq9kAsGQllGE4oW1cbjBs/YSeYVBR6fuIPCoRR1+9e2xwtlRA0es60suzfwF3IKYDnxPjN2+xsUy+4JmAQ0u/2G6sdXcm7O67HEvP2b7hJgFQ2xfWatf64w27u5fdO537X+89BGjqebRjDmk+hnnsBWmzKXp9XLa8/xQ785JvvC+qMrX34Z7A21CH7D8/43OPMdTWovv8niJuxACkXXOMqhyebQ108AJm3/RrfF6aGBnTs3QfF0CFITk4+LWwO53P7s5GjoI9SxzoQHVYr5m/dcloch1OZk3cn/4HRtn1HWOu1rvsOrWu/ZSeY6URtzMdhPHoQVf/3I9T86Ve93lb8kkuR8pPfxsT55Zyw1dfXOzQb2BBVekQVuj75D2tv+MPwyUswfvqfiEXVLYe2wbxtJU42lvLdXGIYTtlSqGPVF1DGCBmP7t8Bt2DP6ueaIJDBatq3jksDvY0qS+V+1gkQCwZDGjgBQnrojB06Fsa9pOFSEdN9sXa0oGbPFlg7W9FXUEkDRZ7dofIN64kK2JpqwtsIZepQG/ooJlNcKtJS3yvnl/t5JRQMgVhUxtoiytxSFqL1WI/OAYquC5FF1ml8lKXWF+etZc96WHau6e6W6UBIzQ46OSXxe7uhI+S2SRvFWlvBjnbriUpYK/bz7+Feh13ffYb295536Op5b9tq5WuJBfeCoBk5gaOu1sZ6n/8J8YnI++NzSFn2Y9drrHuTmQ3JK5O2c9dWnHj6r7B2+u533Wsv4Phjf/TZn45d23Hop9ejc98eRAoJ4ydPDaw1KNM3dB3cH3IdOvfa13+H9s3rYDebYW6oi/k4OndswvHf3Iy6fzi6YPeGxAuvR+odf4iJ84twv36VGp2H88tu7ELHe8/CtGeT3/d2/vefMHz+esTOL/OudTBv/xYnG8v+TTCv/zisToHfi81B3RK9HFZKXbyH84uCROZdqx0dIb0ykSxV+3lsYr8RDimBFIeubqjmTabd38LaQJposcPS2uCwOcJ4tkQLHQuytdyxNtbAWlcBW6vvM8IvCoXj/I2iGRFpqNJx743zy8PmKBrGTRKoqZKSfiZ7fX/k8KHqgUg7vGv0sDXHfi5FmHevhWX3Oo/XhLScoM4vW3sza+GFgu4/1rpKbsJlrS532BwNtWFfg50r3kPHx6/6/6fVEpbNoRs9hRuMkBPMGyk9C/l//Y/L+UWQM410w7zvz+0bv0Pts4/BZvIt/a159gnUPPO437nw/h9fD0NFOSJFlZqK5CmT++xedTK6QMZ6kek9P/wwVx+RtiC8EsTSx56mLF/U/uc51L31KnJvuwt1L/8bmVfdgKSZvh0PI8Xa2sRpxJEYkOTVt7W3QnQrZTQd2s2ZCwnnX4+TAXWlI00dEnpWDZsEW3sLPyCE1CzY2pohlo7iLk2RRl3MO7+DvaUe4tDJJzVzjLJnaDEZOrk0QMgOL8U6FvCEX1L3PiumqxXW4wcphOdR9kcRSGv1IUe7aF08bPQ92Rxp0f6wNtXC2tYIW2cLbNTO22qFGIEeWiisLfUcVbQ210HyI4JKRrBhy+cQUnOhKolOm8y0dx0b0ZoJi6HsNgipMQBlR1FXx3BQSBqoJ/eIlhJkYJKDkUo6JNLq6O54RNcDTWRI+JQ/f/NngFKEZmbkafXe0HVl2vQp1APHc1mnaoxD18cdmqjYqLzO65qxHNnO+lzSqDl+GyFQCaT18HZI0zUQMotchjxIO46M8Qj0ZWicXe/+g3Vs1JOXQL3wShZ15nIMUWJnGzkl2ZnuR5uOnFGdr/2Fj6nuksDlhwS1FU+46UEeJwvV5vWHkJwOKx0Df2Oz2TzuRaZ9W2CpqYRu4YVo/tfvICSlIeEKR5ZvwtKrw9pfbdkYtL79b7S+8wLSbrvP5//+7l9Zd9zv81rH+tVo++YLJMxcCMFLk6Nj6waYaqodOiFSz/bMdbUwHDnE3f0wMHwdD5nvj4Txk0KuQxOWkidegEKSUPPs42hZ8Smyb7wdda/+G1k33YG4UdFlorpDYvgENYUIFyq9oWe9kNyjj2PYuQHtn72NpMt6nLx9CdkYlgPbHPp3g8dw1zZyrgvJGSwmrRo5le2PSDFvXsEBE2l4T9bIyYA6DNsbqvmeKA6dAiEjunLGaLNZKIAWqDlO2NtpbXDYHJLao+zPTKWS9ce40yM5c+j5SHYERP/2ID2rnTaHnQJuoiqm2WLkcCGbg6oS4CfzyGbshHHrlxCz+0Pq7rIcKcbd3/L2tZPOcj3fKJOLzk8+DmFAtorGy+awtjZwpjbtg2rQBFfjHZKzoOczd4+0WmDa8gWXLGom+u9KHalNatryOdSkTUs2x8g5vmNNy4WdOpZ6nUOWg5th72zjLHV/9oN5xzewkT0682LXOUO2g7OsNBKbg64d4/JnIQ4YBWnULGgWX8d2C52XbGeodezgYqkMP/Y1Ob66Xv8bN/DRnhV87kTfY/yPHuBKGIqOiqUjANI9DdBd1NvmMO7eyE12tLPPQcvj90AsKkX8eY7PTLw0vOYamiEj0fbR62j94BWkXOvbcZueGx5/ixJy/u9hn/XaVq9gJ1jyGUuhyvbUWW3fuJbnpLjOc0zG6ioYDh+EpbEBKIiuU6WMTCyRHWBREq5jhvRZCN2goYgbMZrblJO+BpXgxAIqkcl96GkPwzIULa/9E8bdm5B+919c7yOj1lpXzZ1GnJD4rJCWHVA4sTfQBDTuql+6hDUNHz3PkRH9tfexwL21Yh90l/pvTxwM9eyLHaUPJ7lsUhozn7ODLBs+hq0xO6QDjJwKdrPB5TSIFipjNK5+2yGEP2AMFNqEqFO1qSuhevQCKPSeKbViwRAucSOBVcuxPbxQJFfM8r+Pxh2rXJEoqf8Y1uZyx1J9mA0uKQwNBzIKSfND5dZlkkovhYb2ICWYdnbiOQXJo0Eghx05Fd2E6y2V+/hvMQKRU29M1K2TM8vsbFCj2wFm3PI5G3Ga6Rc6NOTIUFVEP7ngCQF9TwMnOJxrFHnMLArYZUmk4+unkyd1+yIRXFB3UT8dK4X+I7nLE51/BO2b8ePngO7rWr3o2vAdswolG5xOTTkqM6WMlq5XH2btOvW8y2B4428OB9ncS/y3LS8dFb5eiVvZBTm/AmHYuAJdX/4X8ZffATHXcc7FX/JjvofT+UBOy3AEur0RklKReMG1kLLDcw4bDuziCae6xFPjJfWSaxE/Yz40/RzXU9PH76Jz20Zk//TXyL/3j5wJ5G3YJs2ch/ixkyDE9S7aL3PqISY4yrN1Q4bB0ljPZYokLm/3E62Phvipc6EdMsKvdl0gmp57BKaKQ8i870kotY57g7Wp3mFzdLa71jNXHoaYVeDqPhlLyLkVd+1vOLBGdLz1D77nJtz8e3R9+jJstZVQ3/BAxNvVnHltr5410aKaspS1Gy0bP4GdspJDOMCspAMKu+PZ1gsoiGNa/V8IeQMgFA5jDchoO3RS4xXFuEU+emBSyUjY6H9xyTAf3MTi8OQEEtJ8m5pQ5otx56qe9w6aCNErw9lMz25B9NvsxmNbNhuMu75hW0hV1KNrSVqeYosZYvdzzveNjsyn3pwHZCfZSEqAssadgu/H9vDx7U2pjnHHSg4kKajDpKHT5QAzbvqUnyeaSWc7NORKx0XUldobsn9JH1ZZOo4dYPbmKtjbGgM27AkkC2E9fgj2TnLqTvUbZCU715aUAUW3U5CbLax4le0Teqar54bfHIxsLLYDnDZHfDIfI8Pb5NTKhzRuAQwf/BNC/1FQTz7Tz06oIJYMgzLMDtjkVHNCQbNAAbeub5bDuOFLxC/7BYRUR3JD4tW/YLuWvjOFTh9WUwJvxOwC1lv0tiEC0bVrC5RxCVAXel43GdffhuRzLnY5v+rfegnG8iPI+emvUPznJ/1uK/2cC5Ayd6FrTvy/AjuZY90F0iZngMUC2QEWJZE+8BMmTOaFGPLmx0EfwNWP/o5vLGkXh5dJUP/UQ5zun3HHg2Gtrxk6CgpR4BubE+3oKdCUjXU5u8zlB9Dy7EPQTJiNuMWXIdpjRDXQgY6Ve1ckqWwyl3zRA0UaOQ3KjDwo3P7PERkytEJEHEN1kusryOEm5pRAmL/Mo+1zIMzbv2YHiJL0D6j0LMSxCgjpL5FBq9HBtPZ97uhITqdo8ecgoQk+pbBzhLWjhbdPQq2BIHFSa9V+jibSMfHWfTMf281ZWmLugKD7SwYgaWB4G2WklZCckRVQM4EcKNqJPSKe0SBmFPLiGovNCvPRHVweIAbptukNRQ9Nh7dB1W84OxGpWyZFiyk7zZntRQjZ/QGzwXWsxJzIBEZ9PrfuGGckKnIHIjm/H1SDBkGIj7wLGQnUUlfNQIYxdbh0drl0vEENBRmD7IC0B+106A3de7Tn3OT5GqXgDxzL5xIZj0LBIAhBBPDV0yIXvvf4vO7rkIxgu9aR4cDnf0KyRyCA7rdOJ1/idb+M+vP0k8PPAq57/HecvZL35+c9XicHh9P5RRgO7kXXvl0ssi8mpwABZJp66/yKdtIrc3KOd+L0ubwQ8ZN6muh4YzMZUfv330I7aDiSzrwg5HbJoVr39/uhyi1C6vXhBak0w8fzRE6h7nE666ctgm7CLJcj2rh7M1pfeQy62WdDP/scREOo56i7o1s1egZgdjgFVePmcpMN90klZYUpklJD6pYqgzjP+xK6H1HnObaVugMpwTBv+pQdNMIZP3K8P0qbg54FJAoOQQXTmnchlo6FGKXeFEGBNZ/X9Em8UDY5latLA6mzYW7gUsvMYlhJXD8hFZKfwJj56E6HcH8IBxhIK7W9yScrSakUkJThKeLu8X+NDrpeZk75jM1shLliN++TGGDfA2XIm4/tgapkFD+7KHhI2dFCUqaHjhh3mXQ7t3vTsIegjuK2msNQFgxBcuEAqIaUQYii86mKbDerJaCWFZXCupfDcqYWlVfarBEH6xW6OGjOudlrABoO7FEnTap4oDJOIdt/sJWCzeqZ0TcHcb8GbaT3ymXbStZz5oY7boErd43npJvui+7zlErETV8Y1ro2oxEn/v4AxMxc5D7gWdJImqXuuqWGfbtgOHKQ7XpBH9iu6K3z63S0OSiOHeuSRcXpVwl6SiI7wKKkr4QwOeNh7y5Y28LXN6IJUDjta51ox07nxfWZFgtHXN0fHkJ6NtQjJkI1KHinH9c2KLLkFZGgY5SdHV5kRBrS03FKzOvPixNrTbkjTXnYVKjGz8epTLgd+sSyaY5Mte7sGDpWGfYOoLUO8GMQBvw8QYRq+ExHSWlXB5TJ2bA2VMG8bz2koVMhuDknHKVpUtRlCyQ+aqVuTOkFQbehKhgCUiMgB5K/yYO6bAad6CEfZvR/zdgzfJwokZxXsYIMaBZH9ZMlFQxy4NmaKLMyjx1g1Prc35GTCmPTUY9LRSQ1T0qE3P4cyfY+UqS3oohPCSszi7OxIujyyRHYWdGVbdJEh7PAvLI3VeN6rnn1HM822Bwl374ayuQM7g7rFM9n52tR5KUodG5lalVo+9e9fE/SLbgEqqHjeHGn6+v3uVRbv+RKnCxSLrsxrO8s66a7YCw/jOq//RZKfRwyrrkVUlp0XcUIS2uLK6vInZMpBi3ThzaH0cBOU3fnVFAUjgm/QhO+cLV+muekq8fm6NmGmFcM9bAJUJUOD2ub5OB1ZpNF82xQj+qxgaQSzy62liO7YPj4JagmLYJq1AycyoTTkIUgEXJ3jSA6VunmZqBNAiLIaqZngmr0fG4KQ01KlElZ3PHRfGgLVCNmezRE4dI06rYcpc1Btoy17hgHhILZC6p+I2Cm514AB5eGSvDCuH7o2aMdf6bPut+HzWEq3wk7BYT7RWhztNazTikFK8kB5p2B70QqDu86C8f2h1rDWetCYRmUcUnITvR8Rttb6jhjK5zqhEgE8Xl9bRzUMy+MZugOm1iQPJxtNEbVhDNcf6vnXOyTpGDZvgrKrCIIWUUOG2Tnd1CmZkHIiUw/2XleWWuPoeOVv0I1dhY005ZAPWYGL+50fvY6O2Z1c8/HyUCpViPl8psgJoWW/Mj52QMwHNqLYw/+AmJKGjKvvw1iQvjNlLyxtLRATJRtjlixatUq/OlPf8KmTZtQXV2Nd955B+ecEzzI9PXXX+OOO+7Arl27uFPpr3/9a1x11VUe6zzxxBO83ZqaGowYMQKPPfYYxo/vvczCyUI+m3rR6cQdc30dKv70O3SGIVTrdHSZT/SIaVPEvuGtl/ghX/TX55B79++Cvt9cU8k34rYVy6Eq7I/0m/8v7LGbKw7B2uwQ1exY8T7qHrgBhl2ewrAUbaD6clXJEI/XrU11aHnyNzBu7kk5t5TvQ+tjd8O46WufY0QXm/exihTKFFPml0LI7l25oDtsmPl73W6D8bMXYPr23d5t32aDtZ5EtW0BU//dH5ZWQyeqtq2Fcc/aqD6PorKqEbNY94JKAuymLk57d43HbETXug9g3BW9WC/pUahHzoGYnhd8LKLEBikZQt6Ya4/CXH0grIg1b4vaL3s99GJ1XkUCGf0KtR5CmJMNJ2J2P2jGLgodeY4BrGfxzZuw7FnryFyKT/U5VjSZoExB0tn4PqD7HmXF+Xvd8P6TMH0VQOQ1EIYOmDd+AfPWlT2NHFZ/ANO6T8LehLXxhEtclY5TbWsbFDnFEDIDlxSZD+6A+cB2D4H+vkY3ZjJU/Qb6v9e0tzkmGt2OBUtLEzs1qBSSRPKjpWXtt9h5/pmo//A9n/+dzOtPxvd4m45XourRh/hnOFDmFnWMdhezb3r/dSj18Sh45N/IvOkXIW0Oou3zd6EpG42UK8LTneGxHtkHa5vjum/74D+oe+BGGA/v9VhHSEhGwkU3QsrzLK231FSg+e93w7hrQ8/29mxCy9/ugnHn+r6xOVIyudRayIqdVg0HJ/y9TpqVHzwF07qPerd9i5kDVIGEommy7i5AT8Lux7evZ73LaCCtRdXoudzRjvYBZFO5lXSRoLthzbscjIsWctJoqPujmz5YIKcJ6X2SNIM35uMHYa6rcOlthsKRkX8K2BwqDWt6KXWRlddTJpdmzILA5ZoxhKo2DGvegeXwNm74Qzaf97GirDDTho9grdiN7wPuRko6ot6vG7tg/PCfMK95P7LttTawvWHe6WjgRXpl5vWfshZgWO+32zjblHAeK7tGDyG7EEJ6YN060/5tMB/YhpNJ3MSZUBX082uvWTvbHTaH1QqlSgVzbQ2Mh/ejY+N3MBzwvLdHQsMnH2HbOWegaeWKH4TNoRAUfbJEQkdHBzuoyGEVDkeOHMHixYsxa9YsbN26FT/96U9x3XXX4dNPP3Wt8/rrr7OD7N5778XmzZt5+wsWLMCJE54NTU5lZAdYlPh01dq9E01ffoq2dWtgM5thMwTvClL36nM4/ONl7Pgimj9+F43vvApTZTkEnY5vKIHo2rYeNQ/8BG1fvIfOtSvQ8e0XDgMkDEj8vumpB9D6mqNOW0F6GNQ56sOXe7a/7ks0/oXa5frptGK1wNbRBhtFfbohzShlRq5PKQAdI2rTGk7nDsrycnQx8YXSkDXzL2eDNBZYj+2F8b0nYD3qOPbeY7aT8dNLDTFr+U6Y1y+H9dg+/ttSsRuWo0E6h0pqtKeXQBw0ETGJyMWlepYzUuaISuvQg/LzfZCR2PndO470/2CdJMMUgA+Epf4YLCfKXaUn0RDsvLK2N6Nj3Ycw1xyJbtvUrZVE/zs8nTRS/iBoxy6MODoZSYQ+JgZzSjYUyRkBjxWVUyizS3qtAxMtlm1fw/TRM6yB54FS6egWFcK56jf6u2gZVDPOczlfNYuvhnpueFlo5r2b0Pnyn2DetbbneHUaoD//ZqhHBu50GH/FHUi48b5ei0BHQvPbz6PmnptgqvLsotT01gs4ducyLmOruGMZO8PiRk9A4R+fQs7dv0PCNF8BYmNlBWxhXIOq9Exo+5dCnes7mTodOzL9oGyO7VvQuupL/knlKv46crlT9/zjKL/zGpiqHF15mz98E03/fQnWpgYuWwmmu9Xx3Zeo/e3t/JPsDVrCxVJXjaanfou2//6b/yb9Grr/ty/vsTk6vnoPjY/8zEOD1LXfZE91tHl0WqOgmJCZxzp60dgcNAm1Hj8S0IFNz07tkmtiFnSjjo2Gtx9lzS7fsVg5s6O3TWwsBzbB/N17sHY/+yyHtrDdEQiFPgntGf0hDJqAmDj3EtOhcJOg4OxhlYYF8/1hLt+Jzm//y6VfAccoiFBGUbrvDtkbltrA33U4BDuvLC11aF+/HJYoO0+yhlnFHp/joCosg3bkHEcn6QigIHq4zr7eQlUfVMLqLtLvfawUSRlQkv5oDJsSRIJ58xcwffa8T1dvsvHJ3iCbKRLI4aueexlUE89w6Rmr518O1dTwyrbN21aj85U/w3Joh+tY0TxMf/FPIA0KLF+ScO2vEX919HIL0dDw4uM4/psbWUvSnfoXHkflXVej+uH/Q8Vd1/A9OnHmfBQ8/CTyfvMw9KN97ymGiiNsX4dClZMD7YBSqDJ9G5LINkd0LFq0CL/97W+xdKlng4xAPPXUUyguLsYjjzyCwYMH49Zbb8X555+Pv/71r651/vKXv+D666/H1VdfjSFDhvB7dDodnnvuOZwuyCWQMSJx2kyU/Pkx6AYOwZFf3g5TbTUGPf+G3zp2Y1UFCxVLmTncepZIv/IGJMycD3WRZ6ZI8wev8UMmcV6PvoCUU8A6XuqSwdBPms2GIWnDhINCHw/drLNYDJHQjZsBWEwe2gAcxSNnlJ/sJRLFT7rzrx7p6PQaiTVGi+XoHhg//Q+kUTOhGu/boS7WKHSJUCRnsqaYN9a964G2RggjZ/bqM1jPjLNLqiEWDIbl0FbAag6qlcFC82F2+3FiLt/FhibrOXRjp1baXa3dXZMcr/FE3dhF0uuslcAOMTf4/0rRQxPCHcP+DbC1N0I7cm6vJv2a0gmwW4xROZKCYTy6kzXK1EVljmsujFRpdrgqlR77Q/tortrnaFBQ4r/811x7BNaWOqj7jzmpDpBwS1PCyRT0hjT2KEsw0s5ZVEJgWvM+hJz+EAeELtVQZhaw9p13owW6n6inBX44UydI7/c4EXI8I5TBNMJ8xpOeC6GgNGAHNeuJSnR9+ALUM872KJNi3RE/ne4oM1Y9cgobxbFG3W8wLHU1EBI9dQ5VuYVQF/XnTsDUzMTpyFDlFvDiTdf+Paj4zU+RtOgcZF7lqbnmjbakPwb+49kY74lMLEiadwbU+YXQDhqKw7dfx5my/f7m/7syHNiDttVfQZWbDyHJ4VBIv/ansNTXQEx1K5O329H835cgZeUgbkqPPp1U2B/qISP5Z8Zdv2d5v3ARUtKhm3EGVCWOMu+4OUsdmplpbhNPq8WRuezH5pDyS5B89+MeNgc1o0i4Jvysd28sezfB+NWbUE1dAtWIaehruOQ8JcvvPcy641ugs9XRUKQXUGk7/2yohj2rGJb9G7khCDWxCQQHRNxKFkPB5V6Ht7LzzL0JDXdf7GxxaD86n4n002RwlJn5/XBy+gkBdSINu77hZxLZHL1BM2QKO79i/aw2HCLHcxdUuaWObYehT+SwOTyz2i0tJ2A+Tp0A7VAH+K5M1YfYQaYuHn5K6SCRPaHm0trAKLXxLNPhja2ljnVC3bt/hgOV3Zo2fsJ6t2Rbh0IgzVa7zUfHlOVDpgXW76LMLgq++92mm0QL/x1BcJ6ySkmugUom/d1GLZUHYfjiDWjmXughBeNd7k1QwMC0Yy3Uo6dHJIMTLprSMk6uICkFd9QF/WCpPwExNcOh9dp9PmsK/GultW1ci8o//AZpFy9D+gXBmxTEDx+JIf9yBEt+CNC1Hm1jsmDbJFpbPTMb1Wo1L71lzZo1mDvX87qm7C7KBCNMJhOXU/7yl7/0KE+l99B7TxdkB1iMoIdSXNkI/l07YCDEpOSAk3ClSg0pPRPJi5ZCTHFEMKlumhZvWj59l4UD3R1gQmoG37y7tq1D0rnLgPieDBNrazOU8YGFTXmc8zxv+rpJnk4n7eT5vAQUJq+r4oljsAcxlTlRVDocSMRVHDASQlF4nUksR3bCVlvBHVoCCWUG/bzUbKjnXRHgfzmwZxQ4OtBRm+gwO8p5w4a8IEDoFutUjTvD8aCIIfQZFnKAqbUeDjDqriNRR0gvJ5dEBjZ1kfETaSZjNnBXxW7xyV50JXRtR1LxuOk8Cnb+mI4fdHSaDFMPjYxsKvukyKdu7KLQ61st6NjyBYS4RGgHO5pTEOSAVJeOg+DVlcodcmpaqS160TCPLpHhQI4zui+4b9/aWg/j7jWQSkZBijADKlZwUwbSDJl7ZWSZCDYr7O3NjslPGLCgbG5kAv+Wwzth/u59SJPOhFgSG90S13hSs6A723/7csPKd2GtO87ObHLAhcK0awO6VrzLEyHt5PCEZj0+b8cG1mEyV5VDN3G2j8GrGzuFl64dm9C1+VskXXgdrxM3ZTYv4ULODf24ydCPiL5Zhsz3Dz37dEMd14N2wOCgE3CaHIlp6Ug66yKXSLGUkcWLOxRMa1vxITtVPRxg6dkcUKFzNGGhpwaNtbWJyxcDj1NE/BmeXVv1Mzw7qunnncdLYDmB4xAzgt8bbS0NYZfIkNOcbA4xzImree8G2FvqIY1fGJUDgkS0Awlpk/1D917KZrV3dUTVUZZhm0PkiTWNUTXlXJ9AV6+xmGCp2MVdG93tBdWwGYDN7qWlJEAkh43of0Im5Q/mJSCkBxmiAUE4ULAtoBOuG7JHyOagDHcxzIwzsjdooffox4a+39tMBnRs/5ptGm3/nuCaSBlIA8ZATAzsCLI0UrfUVqhJKzTC79RCmp8qNQRdzzzB0lQDE2m2UZfuCB1QscK0bQVgs0Az89KI3me3mvl64UBvGAiFQ3iJBMu+jbBs/QrStPMi1vUKOZ6sQmiXXMu/e3eB7Pr8NbY1bM0NfC8IhXHrahhWfchi/hR4ixSyI8gpb62ugG7KPJ9Eirhp83np2PANjPt2IPmi61mYP2HuEl7CRVPYD/qxE6Ev652TX8YT0uZyh8oR77svugYJ7pCmV2am59yL/iaHW1dXF5qamvjc9bfO3r3Rl7+ebGQHWJSQgWGqrUHj5x8jfekFHp0vcm74cdD3kvOr39/CSxPMvON+tH36Xxj274KYnoXmV/8J/fSFMJUf9ChDJIyH9qDxyd+yZoB6QBmSrrjN9T+b0YDmF/4CKTMX8Wcvc6y/exO3HKcIbbiYt3+Hrs9eg3b+xVCNCHzD7Xr3aVjampB67k9CGozk6FDPDt19yomVHGDHD0EcNgWKGJeXUbSIFipdNG/5goVjhfzIu+OoSHSVoo5OkXs/2Wbu0DFKS0uLyLgm41BNGT/e2Vy0DT9GktSLzoLqAWMRC6xtTejavRpSTn+o3YzfroNbYO1ogn7YDHZOmY7t4ci0PweYv2OlKR0b0qnmATmhSFvOzSh0bjtUxyV16XhHVCxC5xeNz9GuXQEdTU6c0KTNYuR277YTR6Gm9t8xItzzSqTW5cYOH+cXfRfm7SugTMtnDTif7Wv0UC+8xqdrVixRJqY6ShW6S3ppMmP86FnuTOoukB/r42WpOgx7ewvibn0IyjAaAaiHT2QnhLdgfjhQiVfLf/7O3SatzY18D6cOef6gwEfnhm8QN3sJVPmBndaB6Nq7A51b1yNpduROOndOpUyE/wXoeFPpatvab5Cy5HwWKXaS8+OfB32vOr8IhY88F1ZDHdITbV/5CUwVh9kh2/z2vxE/60yYKg75OGXJIdb0/F+h1OqhGT0Zief2dK62trWg5aW/cuZX3IILHE6GneshFpYGdZh5Y1j7GQwr34f+3B9BNTDwBIpEpG10/Z4fhs2RmArN/PAn3tb9W2BrqoU0enZETUHCQcgfxIvl4BYW15Ymn816XZGimnSWhxOKsrtibnNIaqjHLoJC8szgZkeVENvmLpS5FQsszbUw7FvPzXlUblqcnXvXcYaZrmwabIZ2mKr2Q+jI8OsA82tzDJrk+l840LNVoEY4XjYrHTsphM2hHTgBdht1RoxsymYzdcGwdy0/n+PGL3b7h41tGOP+9bCm5EAdYXOfWJxXUuk4v1Um9Hw371oNgaQa/ARlyS5Uz1vW65LhYCiS0qFIy3NViVA2mPHzlyCWjIBYNqXPjpW16hB3K42/9SGfZkD+UI+dydnm0dgclhPH0fr6U1AmpcLaWM+JFZph/rfTuXE1DDs3I2HR+Y6srwjp2L4Rhp1bgKXRNUg6nW0OhVLBS6y3SRw7dgwJCT1ZxbHI/vpfQnaARQml+zV88iFOvPoi1Nm5SJ7TR90JTSZ0bdvAzi/t8HEw7N4CqaAfsu573Cf7iUobxIL+sLe3+qTDtn/yOizl+zl9mLCcqELba/+AVDoCiZf3OMpCIeQUQ+w/DEJuP9jamtgB5S+9Uxo+GWJHKxK9PMS9haLB4sDxwKjZLm0ly+51sFbug2rmhTErreMHHxmQ6ujSigM5oYKdTwkn9sF0sB7qcYthrT3KHSWF5ODHL5RjzRsy+MxV+yFm9fMse40CzgakrCE6B8I1AkmnKj4Fgj7JT5pwt7NQUkMzaGJABxMdKzIafLYdofOQjMqooBKGCNttOz5T4Shx9XIWkcCvdvK5MG5fwZopsSTQsfLGXROMnF7chpzGYjLAduKYQy7SjwOMcBqiVGZiazgORVwKTCtf5y6U0rCeTmvuWMt3Q5GYznoaIfchNRvqBW7dFimTksbYBwL07sdLf+GtjozJABNedg4e2A6p3xA+VrRovDo3hf25+ngknH89FHHxsDXVQzPKMbnyR9IF1yB+1mJIudEJdFMHPzExGcoIuvj53Y7ckemkQse7cfk7aP7sQ2hKShE3KvJJTzhQ5gE5ttTFpRAzs2Hcsw2aIaOQ/eBTPhkCQmomxPxi2NvboPR6Vra9/wIsVUd5QkdYju5H25v/gnrUFMQvvSbs8UgFpbAOGM5NKWwtjVAkJPu916v42lMgMYz7XSSQbo04ehaU8cmue4F581ew1VVCNffSmE3EFWRvkJM/ymdAICdUUJvj2BaYDxkgjVsMW9V+DjKEkmGg5iqRYDN2wnL8ICQqFezl840FxDtaIURg9yjVOoe8hHewSynATk12FAoI2nhoS8dCqU0I+zka6WSczhNdtHprJMwfRZCJHJW039QN0h0xNQfCuDO4zDTQ863PbQ435xaXhlKZoqRmcXpbfRUH1/w5wDxsDtIkbq4FNHEwf/sOxOJhEAeM8Wurks2hTMsJq+TXGQh32wBnwNL8oy+Plf7SuziIFsj5RU5LyoinTHiSOqDAg3p0dCXcQno24s9ZBiE5HdameqgH+5f8IFKX3cbyCtE4vwgK6AmJSR5Bm6i2I9scHpDzy90BFiuysrJQW+upAU5/02dptVoIVN0kCH7XofeeLijssqpcRFAKYGJiIqcAxglKtK75Fkkz5wQVrQ8ETRgb33oJmv6DoB8dWPzcVH4IUk4+G5/mE9V8E4q09M90eA8Mm1dDv+AiCPEJfCPv+vYTSIWlkAr6Bx8ndUvZuAKqsolQJjqiY5Qd0fn6Y1BNmAvNlMUe9etCWg7rllE5QlVVFXJzc2N247Ls3QDzxs8hTVwMsb+j5NS8/mNYy/dCvfi6gDX7kUKp7cbPXwR08fzAlEiMNEydtWigY1W+4WtkogOaEXNgXPseFHHJ0IRIrbdbTLCbjH67H/mDujCaDm2GRNHQvPBKTgNhqquA8cgOTstXBWi13Rf0xXkVCYaDm7msQD9ytmtyFwxa11JfyY4/9+NkbWvgLk+RGKBULmltrIFUMDgsgziaY2Xa9CnryainX+gwSA0dnPUQaqJnObIDForcDhjLjSaUqbmOkkcvfS0qjzZ99gJndQXT4DiZ2NpbuDOTMrfE43iZtq2Ged9m6M66ju9p7ph2rkXXxy9DPXUxNJN6l011umCqOwFVusMIbm5uRnJyMov49oURJuNrc+jtVnRu34KEqbOiKv+nxgfNb78I7Yhx0A52PD/9YTx6AKr8fvwZ5loqP8yOeMJv3LsVxt2bEXfGJZxZRpPcrtWfQDVoJMSs/ODj7GiDccsqqEdNc+npUefVznefgWbWUqjHzOyZ2FbsZ7F6Ckr0xbPBtPVrWLatgmrWRRALHNngxq/fhO1EBTTn3BpVMMQffF/84kUoEtJYM0waM69PNSbZ5ljzObIkC2cdG9e9z4Lg6hCaWyw3YLX4OFUCYa7cx4L3XOKf5dtRLhKoTJGywyljKVTW1A/J5ugkDdauds6QD0dPyNxUAzNlLCamQ3LT9aTOn4IuIazsIifW5hOwtjdCyh0Y1j0gmmNl2PAxZ6BrJp/L+8eBetLZDHH+mw9uhvXQFofNcXRXd6fTflB2S4+4xkQln9+8DWVOCVRhlKueDGytjVwRUWNTeRwr04YvOBNMc+Y1Pt+TceNXMK56H5o5FwStwPkhYTpRC1VG5mlnczif29/Mmoi4IA1moqHdYsG0FWujOg50Db/zzjs455zATRt+8Ytf4KOPPsKOHT2N2y699FI0Njbik08cHdYnTJiA8ePH47HHHnNd9wUFBSyYf/fdd+N0QHanRgkZXmJ8AlLmL4rK+UVYmxrR8vHbaHj1GTYyA6EqLHE5XyQyRKMwfFX9BnOGATm/CHrI6KadEdL5RZgP7YLxu49h2v6tZxemggEebcKt1UfR9c4/Yfz2Q9cxovarsfSxKukBVzgYSrdJtThuIdRLb/VxfrGorh/tBzLerFUHHJkugZDULLJJGk+2umPsEOtLuDwuMQfqsWdwhoaqbBpUlOkWAuO+dTBs/dxjP8kpZjq2l9Pf3bdPGhRiegHUpRMgZYf+3kMhxqWwfkUwvaxwieQcCXZeWdoaeT97PR6bDaYTFa5t8bnUfb6QmCob/2HqkxjLd7IDzHRku2vMpOdh2PkNjIcja2tNkXTLsT2+HY3COFYO7bXQEUxlSg47r9DdfYoiseFkOShJOy+zCNaDmyHklcLeUgfz2vc569BjvbgkiKPnQizre/HpcDGufh+G5c/B0ljrcW7ZGmpgO1HFQQBvxH5lUI2fC9WQvsnEOdWofe0l7F12IWc+E3Ls7ORCx1tKTkXijLlR2QCEuboSrV98gMZXn4GlybOzlzvqogGuz6BmPdGUnqgHjUTCudew84ugyZxu5pKQzi8e555NMH77MUy7N7peUyZnQMjvD8FNC8xyZDc6yeZY91mf2RyUySoUl3H2iBPV9POgOe8nPs4vutd53++cr1OWerAMEtIAI5uDnis26uTYi07JYdscqYVQjVnIWdzS0GmQBoS+l9Fzy7DpUw/7iewj87E9bHu4XqMyO7I5skugGjgBYkZ0GavuCImklZvDpYSnjM3R2gCb235HPR7qQE22Znc3dNqms3OlUq13ZOyHeR1yU6CGKhjc7AsaZ9fetTBWRqbRYzp+AOZjex2dPqOyOUIfZyE1mxvwOJ17dC2E4/wVqLtkWh6sBzZC7DcM1hPlMG34yOczFUmZEIfPcFSPnCIYv34Lho9eQHtzo8d4rQ3VsNZVsd6eN9LAUVCNnQ2xf2y1UE9VKp96DLsvPx/N3646bW0OZwlkrJdIaG9vx9atW3khjhw5wr9XVDg6QpOY/ZVX9lRZ3HjjjTh8+DB+/vOfs6bXP/7xD7zxxhu4/fbbXevccccdePrpp/HCCy9gz549uOmmm/i6p66QpwtyCeT3CHVfSj5/Gbexb3r/NUgpqYiffSbE5FRHdth/noSUnc+dOKT8Yr+RH3pYNr/8D6j6DYR+WujIhrWhlkVWlbrwoneERLobNgvEkp4Ohsr4JOjPv9ljPTIQpeFT+vTmTCVTKq9ucWyc+4lomT5/kYUypTlXQEhM9WiLbCPB06FTILmJkXpuUwmpbKqjba+pix/IrvfvW8dlIqqRsz072tisUCb7T//kG7fFFHa2j5DmKfjbtflzdkJovDr4kUPLSt1t3DKRLPVVMB/b7dAjynNErClqaj5+kHUrxLTII6dkyJjrKiFSiUR3Nx1yAmljoB1h7WxDx961UOeUQN2LCLHV0MGRUjExHbog4+qq2A1LUy30Q6dAGSCDi7LbTBW7YTMUQVMwBB07V3E7cv2w6VAHKAUMhKZkNIyUEaXWuSaS5FSic8VG3c+o3FAQw9IwU/UbAVtGIYQIWsOzpgZ1mao5zCVCmnHBmwRQGYE71prDMO/fCNWoOUHLX3gSVTYNpi1fOLqLJWexOL6/c14sil4bpi+Qhk3m69f4yUuwl81xva6edS7UU86Eedc6KNOyIRb0iGbTPVQ74+zvacT+O4r1FeamRpx4+d8Q4vTQFvUui0Pm+4O6dyUsWIrWz97h4Jug1SF+/jkQ9PGcSdz4n39AXVoGVUEJVAX+v2dbVydaXnkC6uEToBvnv8TZHWtdNRTxSS5HWDiohk8CVGqoBo32KLeMu8hTX5Uyv0hugSaGfYWQnseLO45rzve6M374NGA1Qb34Rx42g2nth7CfOAZxnC1g5zp6Bkij5jiua7IV3EpKzdtX8vPcvaTc1ljNnZsDlZGzs410o8LIUiZEt+xksj8pK4fu6WqvQAWtZze0e5TyW2rLYa7YzSV3TnF845FtsFKW3LAZEL3smXCgMZjrKyEmZ7k0GEk6QfBT4hYppE/beWATNAWDoYrCHnJtp70ZnQc3Q0rJhraop0uwN52UHd3RgvghkwI6doyUnV9zmL9/VWYh2nd9C0EbB33pOGgiFHHX9B8NI0lduElNCPoEdiBSFiidG+E+N9T9RnIWeCSSGRR8NZETt/oA2zqasuD3Camfp7afpWo/LBW7WUuXukgGgrLqxUETYN5h5IoJadBEdsJ621Gs7Rrk+/k+kEZMh23nWlj2b4atiGw6x3dF2oR2kwnmnWu4o7WQ3eM4pjJszfSehmgnGxoXJOmkaHEZyo+i4d03ISYmQZMXOmgiE5iNGzdi1qxZHs4rYtmyZXj++edRXV3tcoYRxcXFWL58OTu8Hn30UeTl5eGZZ57hTpBOLrroItTV1eGee+5h0fyRI0dydpi3MP6pjOwA+56JnzYPze/+B8YDu9DV3MAt7hPmnsU3mq7N38GUmILW9/7DHb/ipvecfE5s7W1cakCaOKEcYLbOdjT/416Ief2QeHVw0VyfdsFlE0OvJ6mgmRE4rTIaSGfM+NmLUKblQjX5rMjKEEkjrKsNCptXphdHaBU+TiZ/cBTcK9WfxHDJseZuRHBHG5MB6jlX+H04WA5thqVyP9TjznBpl0UCp0L7696YUegTWRXT82n3PMTc7fQCjaOhEmIYuku8n8ZOds5Qyry1rZEzmWxpedAUx9bByfpfVGIXpqEeDBUdjxAaJqQ1Rue0ovuYeEOlA+SwIgF+VabDmCenWrQdtUivRDfY8/ohI5i6Y1nrj8Fm6ICpci9snW3QjZgd1DCljEQhJfLOk5bGBt6HYNcPnc/k3CXnlZjb4+jhCRllwnl1LPI7Pm0c1JNjew84GYi5JbDVH4e56jCs9dXoePEj6M+6nrtEUiSeslqV2YUeDrC+pu2jV2GpPIyka37uUwrRvuJDtLz/MtQDhyPtxp5W1O6Yjlegc+s6xE2azUGV3kBGaPp5l0A7oBS6QZFNxmROLeKnzEHbF+/BsHsbrPU1kHIKoJ8wA9aOdm6wYD5eAUttFVKu/il0o3s65DqxtjTAuH8HlHEJQAgHmLWpDs3/uAeqQaMQf5FnwCwYlFmlJidYCEgDRzvbsytlb7E1VMP4xcvc/U0imyOCbDvKQre31juCGu7/MBnZYUTZKiG3Qde61/Vuazzuk3FMpeqUqa6Z6dlh04l5z3ewnSiHetJSn/LtMPbEMQY/gUXJTxdfMbsfP8MFsj264Qwg2GFpPB62ZheV+TmDaxSkMpbv4uzbSINOoaDvlM6xXuu3KQVI6fmQAgQ+3ddzfJZ/m8NUXwljzWFISZmQyCGnUEKMT4UQZUdQEvIXB3nZHJTRrRTZHqCMxE7KDrNZoA/RdIcdXxHqxVqb62BpPQyJdPOCvJcbHh3YCCElB6K7DilLexgcDYJCoNQnQT3x+3MKRYtYOAjmmnLYj5TDsn01TIe2Q7v0JnZyUYa/af1nEIoGQ5vtaFp2Mmh9+xnYWpuRdPVdPv8je6P9qw+gHTsNKZff4vf9xqMH0bV7C+KnzYcQ37sGZarsHKQtvQDxYyZAUxh5s59TBUqmiHWQMtLuuDNnzgyaPff888/7fc+WLVuCbpfKHWk5XZEdYFESKx0AKklMXLCUuzOSQKB2hCNFl6KlmXf/Ee2rv4C1vhbqAZ6Tjs4NK2GpPApreyv/rR42FlZyoCUFnuiQEUStcsXsngdNuNgNXWwMkfBiJMeIBPHCPVZcpldfBWV3KjQZouY96wASxaw6yJ0ZKc2ZIAFaRXJGUKeJevr5HEX0jrhJ4xfxw1Wpi66GXDXuDIdgp9t+SZRaTUZvgMiIQp/E0apA4/V3rChji4wPISUbmuEOzZNw4Ehyt+OGMFbsgaX2CETq5JdRBGtXG4u+hqKLdCeMXYgbPR9CXAp3beyLltm0j/EBxNLDPVYU1ezYs4ZLIzQhjGVNXilAS6DtUylKYjp3jHIKOwdz+nGZArdsjywqpi4eBim7H3eFovOCz40YBNbYeLSY+bhSGWJmcgJUyUXceTPoGC0mzvai6L67A0zMGwiBBIyDvNdac4Qdw1RiEMnD3tbeBPOWr/h93lphoeBW6FSq+tWrUJChrRAgzTg/amNDNWIad5bN2vIdFJUK13dBGkTas67jDo0nE3JOUKcmEn3uWPEBtGOmQcp3dDGzNNazMK8QxLHV+OITMB09AOPubci867e9Ggsd06yrrvd4TRakPbnE6niTsH3CnDMhFZVyKYVulGOiTE7S9Dt/j871X0PMzIGq0LNMvuObT2BraYLl+FF2HqgGDIWtvdXhCAs05rhEdmRJ/SPP+KQsaxKEj8QBFbHNYbM5bI70PL6/WWvLYdmznh3+rClUXAYhb4DDmVN3DIrU3KDjUS9Y5tfmYK1DykyJUqNUNdXXySdRl8QgDhyyN1i/MYDN5n2saB8tlfscTWqoMcsY34BrIOjZ5cz8IgyHt8LaVMP2BgXiyLEVjmZYJ3UsBBA3ai4HoMjxRTILsYaCevERlOD7O6+shk6079sAKSkDYggHny5EBheNhwJ36ux+rmw3Xb8gNgcFo5SR2xza4uGwmQ1s1yglFezW2GjM2SiobLPxdkmUPjMtBZq0gZBClL1S6ayVmuyQpIybA0wqLINYMDTo/pEcBAWixX4jIzoOVK1h3v0dpMGToIzAnmUHAgW+jV0wrX6HG2WRHAYF5qNFM34+8kuboTy8BXZ2ajj2gwJvmkVXQpl6cgXFKVuXxO4trY3o/OI96CbPg5jlcNpbGuscYwsyx6z/159gqa+F6dhRZNzws16NhaSFcm/wzPiVbQ6ZWCI7wKIkVimgtJ2U8/17+I37dqJj5UdIPPtyLoV0p2vjNzCXH4B+7lLYWpvQ9vYzMA0dg6Qrfhr4s5RKxC25IuIx2o0GtD33IKfi6pfeENG+JSX5Nwys1UegTM70iExaj+yE+bsPII6eA2nIBFj2bYCtYo/D252QAuvRnRCKhnBXOvOqtyCUjoE00rMk0LzpC9bsUs25jLNd/KWbu5wNAbAc3Ql7ezPEoVN62hNTOjd1C8os8hs1pNeDQToYtIQ6VqyZQNofCiXMR7Zxl01tLw1AjnSqtFAVDIK5thym6kPcAZHKGYNBgrV2s8HlUFC5d+0hY4Ay4LonA6SVZW1v4nKFvkyP7qo+DFNdFRIGjfP4HHLMUhmCd4tx13htVli7OiB2CyoHQ9DooQ+zzIL2u33Xao4AByuBCNgWvXu8mpLYlfAYKfLf3gTtuDO5lEDfXAuxJLgxyeNRaaCecJbfksVQ7yV9G+oAyWUGYYojE/aOVtgbq2FvrQMicIBZDu+AZeOnEIZPd2RXdLSyE5Q7Q/bCSOKuTGOmArS4IRY6Son7gq61n0PMzIdU7Om4Tbz8J7w/psN70bXmS74nOB1giecuQ8KSS6AMIsCddN6VaP34Le4Y2Recji3JT2diZnMIIpIvvNbv/4y7NqHzm0+RdMmNPh2/utZ9DWtjHXQzFnHmRusb/4R23EwknHdt4M+SVIiLoOOjE1tbM9qf+x2kAcOhPeOKXtscHFw7fgjK9HwP3S7LnnUwb/gMqqlnQ+w/Epad38J2nErFAUViGiyHtrEDzFa+B+Y1H0AcPh3iUM/MNNO377K9oJp/ZXe034/NQZ8Z5Fq1HNjEWTnSkJ6MO2vVfoAyfmnMfjKxqMFIMMSCIbyEtDlIq6s7aGI+uoOdAuQA6w3cfVCt4wY5RpJfqDsG3dCpAZ/PTijo5NqGIEDlJonANgcHHrttDmMnyydIITpl95bOyv0wt9QjYaCnzUGOKvpsKYAdRU4qq6kLYhjPQ3KA6QPIcfiTemjbuwHq9DxocyPTcqXzSOg+l2IhX+Gka/d3bCvqxyxkuQVdRwvE0tCVAlTeqBm7wKPcN9z7naVqn8NOLyzzm6kYCHqPvamWbaRIHGDW/Ztg2fUtxGHTuIqEAnDodCQgRAvtI4m6Y8xsx+KGWNw3mdZ0vRu++xRi0UBIeZ5l7kk/+hVfY9QFuGvdV1AmJCGu2wGWcuVtwKU3QxFE8zr54uvR9tWHSJgZXGbjf8nmiEazK5xtyvQe2QEWJdTxIBwMh/dDTMuAmOBrlJnralD/9CNIXHgudKN90/1146fzxF0/fobv/6bMQ3vjCaj7D0HcrCXoWPEeVCWxvWFSy/GON/7u0PXK7cdtyCN6v82Go0ePoqioyDNyVlcJ46cvQigZAfW0nnIpErYnB5eQ7XC0kHNLyB/oMES3fMUZJhxlSs7sjsz6yeKhGwN9Vi/uD5wF09oAcdB47szEhuFuagDgcID1Bc5jlWNtgq2uHJqR86AeOq3XbcMJMiKdhiSLyBrCi8aq3MoZvDFSN6YT5dAPmsg6FcbqQzA3HIdOUnP6fV9BEwybQoEj5RUoLunvOq/odS0JCLuPsb4KxhMV0JeMgKn+uOt3idrNx2o8SoH3nxoXhIKuZcqoo/X7EirtVVCGnyBCLBmF8iOHUdyt2xYKZzdROucpwipmFnKUMxCk8UI6X9KwGdzFyV33hrdj7Az6fmo1rpx/FZdYmLethK2tAarJ54TM4lImpkFB2jyZRZAGjXdk4dFErrONy6ZJOyMaTPu34PDG71AycxGknMBaV7aWBpg2fsWCtEo3fcFIsbY2ofOzNyHm90eilwOMj4FSCXXpMCRdf7eHsUqGYKjuc5oBQ3n5vp+BMifZ5ji4hwNmgt73PmOqPMo6X0lLr4BmoKfWH6GfOt9RVjjKjz0yZT46vn4P2hETum2O96EuG4tYQuLPHW88AfWkhRByi6HMKoiJzWE7th/Gz1+GWDYZqgk9UhFU5mgjgfvMQld2uK2lnrOgTGveh735BL+uzMiDktbz53Agh0wvMxPI2UWZJeLgSdyxjjJizDu+4Ywx97LCWOI8VtmGGtibaqAddwZ3g+RnRy/hksXuTGxypnFGchjPIPcgmzddFXtYnoB0tJQqDboqD8DSUoc4tQ5CjLp/+4NsCzuUOHL0KIr7lfTYHIIAvZeWZdeJYzA21SKhZDi6qo/A1FiD+AGjIcZwfBw40+hc2enBIEc1ZWdRUK8vobJNp/aWqt8olJeXo5ikJsJ4r7MSgwK/XKmQWRxUqsFSe5SzKdXDZ7HT2N05zE5SCtqqAh8bchxTt1OyVUxbvuRtqULoohJUcUIlzMrsftAMGO3IwoMdtuY6tnPIlonmGjy05mtkH1kH3RnXQJkS2JlrbaiBefu3UI2f5+qMGw3WE1Xo/Ood1naWvMrSndqGmuET+Dmg6jfI838hGr7pho/lpa+QbQ6ZWCI7wKIknG4U5OCquv8O6IaPQfad97N4LN00hTjHw9Da2gxT+SGYqo9BB1+DU0kCtQGi9wpK3W9rhr2rkx/EcXPP9VnHsHEl3+gSlv0MYmb4Qp+0XUv5XsckktL5FQpol1yDzrce59a92vn+NScIKi10Om3oGJlMJp9jRQ4scfAE7ubo8XpcElRTexxibPzpHJkXEpUQUPSPsq9ECdI4/3pnJCIbKea962Crr4Rq4hLODFONns8GG/1u2r6SJ/bS6Pl9KjbtPFaKxGQoyZEgqVzOCI/1SMDUbmMDMBLBVxs5Kbr1sUJpZIUDp9Br4lzZcKS9paRotZvoal+gySpizY3aAwdCXoP0HdooO8hm4ygtG4IxMO7dIeOLnIDhYKBIcl0FdJR9F6BkgsZMmXRUghlttMu9wyc5n8yCOuLuOdb6SlgObwUsZkhBSjEsR7bB3trIJZJKr/PKUr4Llr1rIQ6cAGvtURaoJceVyxlIpUfsrHNM1O2drY6oKneqDOEAS82GetbFPfvJk1ABpg2fsNi08qyboAjTSLQbOnnSK2QWwK4QYBJV3eUIgbGU74N593q+H4pFg6Aa5RukCAchIRn6xZdxuaPjnuM/kq12KyEzVx1F0/N/RfyZl0A7IrxzLxiG/bvQvvE7pJx3BZTq8O8rp2NHptOZcI43abBU//4XiJs8C+nX3c66nxS4UeocE2BrUz3MFYdgoa7TfhxgpNsSN/OMAAOwwtbaApuhC6JKjbgFF/is0rlqOQzrv0Lidf8XtFTGG2tzPaxVR6BMy3KItwsCdGdfi47XHuX7i2bmUv9D4gmvMbTNkVkAcfB4iCWe9zLqLKmecV7P3/HJvBDq+VeyI4pQ6BKgmujfFlNNWoJIMW9bwRkoqkkOZz/9pONL93zz5s/ZoSaNWRDSyR0TmyMuBQoKIJBjxU+2OWU5s8M9zAY+BDmm6H0URKPs6JAaWWEgqHWwUUfi7gwwdVYRRH1iWMG83kBZVuSYqwnD5rCRzdEtMq+iZx2VqYfhqIoEyjyLHxReN8POir0wN59A/OCJ7DTzO2aziWUxehMYVLlJJpDml1kpRfx8sNSVw1y+03Hu5wTObrRQN20KJOYM8HGq0v8sR7dDHDCO9e+4zNHpYKPvpvmEQ2LFzebw123RH0JGAS+u/eyufjCtW873KCV1og9Tx9be1e6QcEjOgomOE+mzhcqyP7wTFrI52hoh9R/OVTLRQF14tfPOB2gMAZoh0GukL+rEeGg3Wl59CokXXA+1n+dGpHRs24iu3duResGVEcnqnI42h1JQ8BLTbdrkDLBYIDvA+hBbVxfEjCzYjEa0rvgYLZ+9B1tHO1IvvQ4NLz6BzNvuQd4jz0Opj3xSrhk5Ceph43zK8Yy7N6Pj41cRf/HNjokkGZM8oQwfailu3v4dtEt/hPgbHnRNWGmiSEsgzDu+hWndJ9CccTWEINkTNMlzj8KGvOFRFglHsPrIAUXpzCS46TR2SXek26AWSbzcbOIWzX0NGYxQJQbtmNNF5W2mLk41D9dBQm2sScSe9LuCZeNEgiotjxcnlNXU15lN0TjL1JTB1H2cnE4niopaKdoew6gsOdcsHa1QBWkwQE44ipIGyxYzUsvx2qPQDhjbJ3pr4cJZB9QAwU/2gbXhOEw7V0FFXVTLZgCmTv+TIzrX1DrWoaEMA3t7E9DtALMc2gordewsmw6xWx9FmngmX4Os/9feBGvFXogDRkc08RLLpsDeWAtE8N2a1n3M5deas26A2G8oVFaVS4/M1lzP9zRpzGwIaT33AKlsAhQJKTB8/BJnnQVygJGz2nrsIN8PAxl61tpKGDevgtS/DKqS0Blb1CDF1tbi0GWMAa2rPmetybjxU6Hp779Dnczpgc1ogJCWAUtbK1q/Wo6Wj95gLUDS/Gp57z/IuPO3yPkjdfOMPIOA9GC0E2b52ByGzd+gc+WHSLjidsczlG2OyCYqhlUfwLJ/K/SX3YHEHz/kkl5gm8MY+DwnoWjztlXQnnsrhLTAUgFUYqWafGZYY2EHHHV+7sOsGbY3eL+6bQ43Jwl10mW5Ba/Ok30BZSQrSe+tILBuZteOlRxc0I+aG/Z2jVQS39XOsgS9FprvhhxetDgh5xctpxL6nH7QZRezzSGoNC6nEjvGLCaIMTynrCQ7YeiEKiFwxr1E9ohC4dIV84ehaj/MjTXQDxz3vR5P0otj8f9036xPy4kKmA5sgrpsKlRkH1N3Uz/2L19HZHOQVldzrcPB1e0AozJj67E9kEbMclVyULa58xqkIBhJnYj9R0V0zlJJJH1eJE2cTN++B1v1EYjn3AIhLRfaSTOhdMqJ1FfDvGUFpPELPLLLpVEzoEjNgvHTl2G2WgI6wLj0tuoQhLz+AYP2FGww790M1eAxEN26TAbCbjQ6bA66b8WAls8/RMeW9UiYOR+q7L6/z8nI+EN2gPUhnVvWwVJbDWtdDXd5FJPToNRo0Pb1x1Bo9Wj98gPET18A7bDo2jr7u0lTKi7fqIxGaMbN4iVSVGNm8Y1XzO+JwlDULe7Ku4OPJyEVypQsnzIob6jbGmlvkNZXqFRe6771sOxYDSV1ZBo9Jybp+d5Qa3GRJt5+Hqh9VX7gjqWpBhYyZhqrYWo/CtX4xQEzQcTUHEfadwTZQeqCIdxpMJwShJONpbMNnTVHocvp52McdtaUw9hwHAkDRrExGQ3O42SmaJvVysZiZ+UBmJpPIKF0TECnHemFUZZbIAPCmf7uvAa7qg5yeYaydExAI5K7MKp1MJTvhjq7xG/ZhtTtVCQx/+8T2i8xdwBshk7fMgZ6gY+rwnH9BrqGrRYWk2ZtsRkXeVy7lP3F73eLOvN35dTcq9gL68HNUCalQ8gJX+eEyxAiLEUQB4xiLUK6f3lP26nZhvXoHnZguTvA6H4oFQ2CcOmdAUWmCTIyu5a/BPXUxdBMnO93He20MyHml0AqHhwwCGDY8DXrf1EDE1VxKTJ//1zUGammqnJ0rP0aiWdcwM7g5HMuQdykmSGdX80rPoXh4D6knncZGv77KjAhePcwmZNP55a1sDXWwUSO2z1bIKSkcxZ358bVgEaH1o/eRPycsyD0T4iZzUGZ7WRzUKBIN+NMXiKFrg1LTpGHzIJCrUH89fcFfR/dHygbVKEJnmVDmqOWA1ugGr8wZEdEy/aVsO7fCGX+IEij5/rVJ+otVHJlD2RzBCkDjBWW+kpY7XZYGyph7CiHNOnsgDaFSM+kCLuOUdMYtlNi5PyKJeb2ZhjqKqHL7e9jV3QcPwxjawOS+o+EMgJdKXecx9HU3szPOFVcItorD/DnJg0cAyGAM8rS1cFZWoG+B1t3kyVnBlxn5UGY25uQOHBswCwzVXIm2zEdFXugzSnxux4FMqkj9fcdwCSbVyTdWbIZvI+9yzZQcKVIIOxOm0OfCPWUc6HQ9OwT2df0fkW8l83RbeFYy3exlil1nI8k4E1l1JEilo6BjTWQaXy1Hv+z1hxl+0coHurhAKNjIvUrg3DJHUED2aZtq2H8+h1o5l4I1XDfLr6Ebs55sAweHdD5RfatYf0KSAPKIKZlQTNkFDL/8O+obQ7j4X3o3LYBSWdeBFtXB9KuvBHJi88L6fxq+OBtmBvqkHLGUtS9/SrE2eElTpxKyBpgpy6n3tPpNMG7G4Xx2FEICUncLt5J4qJzuZStbc1X0I+dCk1RCeqe+B0bp8kXXYfmN5/jMsdoHWD+0IyaCvWIyb0q1xOS0yGMm8M3KtP6zyENpdKl0LpOVN6kWXy16+ZMxygvL8/nWFmrD8NavsdRAhnCAaZIzGBRbRKwtVD5W8lIxArSFDPvWQOJNCWSHLX3/oxSMuQM65ezthJ1xLNU7IZYNCxmqfemI9u5rKSgdATUSltA55d3qnm49GV2Fmsv0TGLwtCl6GXroe2coWjtavdxgNE57DD2PL8P9/OKPr/t6G5IcUnQBhFR76jYx1laycOmQkrO4DR7Ktn0h6mlHh1Hd0GdlstGsnOsZFyRiD6VDLTuXst6c0kkiEpOxvR8NjRDlVhaO1q4PERMSPXrACMxXCGIcHGkBLoGw8FMkeFDW6EaMgUitWZ3jjElB9rpF/msT1lblsq9kIpH8D1AyC7hc1lJndO8zmkSy7eRjldieo/hSiWA3QY6ZX7x5DY7cCZprGDnVnfGqtJu5+Nl3bYSlo4WLsnWkPZHgG5Moe6LYl5/SMMmQioJ3CBBGZ8I9bCJQTU72t9/EarS4Ui88nbO8un8+gNoRkxydWiKBMr2avvyA3akNTz/OBQaDRumoWhd+QU69+6Eurg/mj5+D1JfZeTKhGdzlB+CmJbpofWVfPalUOrj0LV1HeuKkhZYw9N/4kzGpAuvRctb/2Zxe3UMM/10UxZAO2le72yOjFxeSH6BJnDSqOlh6dwI6bkQl1znytgOaHMc289ZnrbS0RDcsoj8QRINVo0etmP7YM0bCDE/8mduIKx1x2DZvwHS8JmsMxbQ5jC0w0g2R24pB+GoO6NUMjpmzjjjoc0UvkFB6VBoVELQgBoJ2UcKPcf6CkfwqacELRLMnW1oPbqHy03VpNvkHVhz2hxex8P9vKJsrtaKfVAnpkEbpEtfW8U+dhymDh4HNT3LVOqATrWuphPoqDoEXUYedN12DDnECFGr52yv5v1b+DmaMsihsaTJyIMYlxBSDsPS3gJzawNUyRl+HWCUGR+qi+VJszmO7YW5ah80Q6dBcAuOien5vHhjbW1wyCsUD3N0IiUpBtJkJZvDqxkF2RyUEeYqfySbgyReuh2S4sBxUGYUsD5YXyMUDOKFrn3nsTKt/5SDqtK4+RCyi9ix7w+yi4JB4vm2uuNBG/cIyWm8BIIarLUvfxnqkZORcP71sHW0oXPVR5wBLKREXpnQ+tVHHHQjzeoTT/wBQlIKks+5NOT7mr/8GKaaaoip6Whc/h70SX2nL9xXOOYxsbWV+lKO538J2QEWJe4Gg6WlGeV33wxVfhFy77oPUloGzPUn+IGXdMa5vDjJuOu3MFdXIn7aPKj7D4KUEfubbaQXh2HV+7Ac2QPVmJmcnaWefhY/PCjzwbRpBWtuqcfPC/h+KvOxHNwO8zfvQpldBM2iqxz/sJgQF+freKEuSgK1e3bLqAgECeLbB0+AZesKwGxGb7Ac2QFr9SGoxi5kg5lLs9qbHK3CeaJZDvPOVZCGz2JnVw8KdvCQwWVrrOYHLnWPCccBRplX5AwIamAOGOsoN+tFyRt1Q+KyiSBjou+J9yVKbSluBW+zQunm7Go7tI1LNxOHTIp8u5SaL4pQJWVySWLLwa1I6DfMZbiQQ8vbqUVjaCvfzSn9irj+sFkdzrNQDjg9dfKymh0irQmpvPjD1NqA9qO7uXOT5Cbm3350J+9nUtlUh+FEOmxiTxSOHGPBOkxyFk/1YUeHSdLCOknRVtpff9dgOCj1yazr5RyruWIPt7ZXl03ze7ytdRWwHj8IZXI2ROqWqlQGbBpB1z8tTsxbv+RsK/WsS3mSR0apd+YXtx9f9SaUOf0hDfUf2YzV8TJUHmB9L0y2emR+RYoyPgm6Bf41E+2kT8faOkEEf2uOwdpUh/hzr4WY68gMMR/Zxw4wKhGLPyvyrr6JZ14IDU18h4+DdvhYdKxfheYP30D8dP8Zak5y7/wNLK0tUGXnQkpOhSW/CLj+log/XyY63O+vpuPHUHXvT/h7zLjlboiJyTCfqGY5heQlF/PiRHnLr2Fta4F+4kyoCvtDyin43m2Ozk9fg62uip3DpJ+jnnom75/lwFa2Oai8OFD2AkHPIfPuDTCv+RBCv2HQzLkoqM0hjZ7N+l+BJpXuCIVDuJGGdfcalkfoDeb9G7g7Lmd1C6JD57Ct0VHWGQ92bFn2rGWbhBxvPShZk4vtsLpj3JhHSC8IKyOdSg9DPV80gyY57IVe6IFSMEchSEFL+rmcVNE7m4OdFG7Pm5aDW/hn0sDIxbY5g0oUoU0tgIkakNQdR2JxT4dkfVYRL55jsKH5yB5Iujgo4uJgs1lhoYBdiIz6uDx6fjm2Sw4wWvxhaKpDe9VhdnRRIM+1n0d3s0MkdfB4drrR74Lbs4LWdV/fGxp3Z/VRtkviKTP9dLA54pLY5nA6ek3lu2DraoV64ES/5xDZ7JbqQ2yvC5RRRTp2fkooCcpod8e0+TPuCKmefqHDvqegnVf2pa2zDaa170MsHAoxhsF3f8fKemwf25WcpRrGfSpYAoM2kM1B9x3a1yBBdnPlYQ76x529zNVYzbhvG+s7kiahfk6PTnO4JF9wFfRjp0A7eBi0Q0agY+t6NH/8X8RNDK6dWnjvH2EzGCClZ0CVlQNbv+Cdb2VkIkF2gEWJtTsKRQjx8UiYOgdtq79E7dN/Q+5d96PyVzezcyvvwcc83qctLeOFUBf2h6Wpnm8qJztVnLK7yLCgKCvrbHS2wbhpBezNdVCNnQUFRYVKR0JDpVBFgfUhCGvFPphX/ReKpHSXyCxFXLu+eh2VpdMxYPxUCG7ROjLqFBFMKqlrCwlGivm+EQ1yXpk2fsIPKKG761Ag7F0kst0MWMmY0HQ74fJ6JqAkRElZQV7fBf1fM/EsxzZINDI+BcruzJVgWBqrYdy7BlLBUEiUDRPgOxbiU/h82r9/P0pKSjyOVbh07FvPk5D4EYFLXtv2rOMxxA8cF942K/ZwVE3XneLdefwQjI01SKTSQWemjj4RNim68kTaRtJgh45B+7F97GDiaHiQ99DxN7Y1o6q5C8Oyirmld9KgcY5OXEGQuoWNO2rKuRQhsbjMbwSZoqm0T9qsQg/jUptZxBoe7NTR6JA4bGrIz/QYt9UCY20FZ33FR2G4B912gFIags6rQ4cOeZxXtL6towVCICH+7u0JVH44crbrNWtzDWyt9Y7SRj/nslhQBmViBpR+BI+poyQLApMmmx+tDI660mQnRNmJM2rbVziPV795V0BwK3HtC1qffhAKrQ4J1/xfwHU6PnwJlsrDSL7zz+xMI1Slw5Bw2Y+h8uoaGS6CPh66UY6MMxJKT1ywFAop9H4K8Qm8EHFjJqCpqSmqz5fpvc1BmV+6MZPQtWUdGl99BqmX/QjH7/0xNAOGIPOOBzzepy0b7fpdXVwKS2MdP9NOdhTZ1tHKGTFKXRyLQFNWgXH9F0BHC9RUHiypIQ2bxOVLYpCMScKyfws7vxQpmRD7OdYl55nhm3dRWTYPA8ZM9LQ5RCmiSaVA3f0sZr9appTpat74GWeMuDvx/cFC11QK1y0yz3ZKzgCXzcH3QrUfm0Ojg2bKea6MJ8poCSdAZjlRDtOBjZBKRkFMLwyYJUVNVnpjc5ADsmPfBnZ+xQ2ZHHCd1l1r+JkXVzIi9DbtNrSX72VHjTbT4cToOLYPprYmdnY5s6ekXuhU0bZTyF7odjBRVjg/d4I46KwmI0ydbSivrsUw6jys0nAWlneGkTfq7iBb2/EjsBi7kFQ02H/Jq1rDeqRx2UWQtD1Z8PruY0BIungkDx7vEXwMBWWqk4QEacbGe3WrPOk2BzXtMrQHzAx0bk9MyebF+R7KlidZF9YV9HOOSkXDHM4vr2uDg/LVhx1dutPz/T7HyeawkxMz6Pdod9g7Eeooh4v7sdKceT36Emqg1fav+6DMyEXcRbcFXK/9v8/A1lSHlF/9w+Uo04yY6NBudhPGjwQK0IgjHY0bMm/7NQyH97tsiaDvS+4JQieMm3Ra2hxyCeSpi+wAiwF0k8284aeQMjKhGTAYCklC/PR5XGoQDPPxCtQ+9DPop8yDkJQMMTUTurEnR1el49VH2ekVf8vvoZ1/MSz11eh6+c8QcopdNfb00JBKQ0c9qKSAjEt70wko0/M4amLevRaKuETY/Ex2be3NMH/7HndlEgpCl2KQGK00PIDINDlNWurZKHU+xihDi5xZJC7pjjh4Mnekcz4MyfFnqdgFsWAw1+JT3b8w9fzgYyEHSPfDlh0JLXVcyuD3AauNZ0eZpeE4zNWHoKNOkkEMmGAtfkms1txUA1VGgcvwMrfUoevwNmiLh0NDpXohdDpIAyzcCT3tm7mtCUqxR6+CspfE7hI/U2sjO5WczrFIIUcUtQtP7D+CNTHi8gcGNapc47JaoKFSVFuj67VInBQkQk9GLxlICtcZ0wOVYSb09zXWqXzAnWCfaaivgrGxFvHFZVB2T3TIeI9zM+JjhaW1AV0HNkJdMBSqAKLJ3ueVueYwR1bV/Ue7NMecGA9vhaXuGLSj5nKJKE2+aBJFUVYSiZX6jXSVG3lD6/ntItbWCMue73iiYW9vhDTQV7xVLB4O0BIEigprFl7j8zoL3aq0QaOakUDHyxGUiNwRHQ52Cj5otBALB4QsadLNPR/W+mqX88t5D9KUhefEDgfKXJY5vVCqVMi88edoeu8V6IaPY6dS3KRZXNYaDOPB3ah/7H7EzT8XCtihKhwATVnsJBiC0f78ww5dr+t+A/0518F87AC63n6SG084y5DopzRwVMhtCQUDoUxOd3Rfy8jjbE3L/s1QxCfBRoEsL2xNJ2Ba+yGkETODNujxyEQZOdP/Pylg2FLvcGx1Q5mvVFrl7fyXRs7xyGCydbbCemwv3+/oPkoZJ6E0v/i+6rI5bLA117HchD8HjFKXyDaHmZqpVO6DbuyioNsOZnNYDe2wtNQ7bI5u28LUcByGit3Q9R8NdU5J8PI7yvDW6AJKDfgZDAenyHGmdbc5LGZ+zdTeAVVcEmdzRwM5okiXK7lkGDuSErq7kIfOTlNAm5oN0dYz+Y7IEcU2hymgo42dW/18HVSaFPeMwOCf2XGikvctsXCQaz1y1CWUjOBKlFhiaq5D59Fd0BUNDdj0x/u8MlYfhKnmCHQDxvh0Ie86uAXWtgboh890ZEnS9aIU+Bym64XslEDPYnrm+3MMU7dH84GN/Ltk7ITkp4yXnMShoA6SmnnLfLdPpZQR2NPBcB6rvtAadH1GVzvbSEJeCYT04MkH+kWXwt7R6mFP0X5qhkfXddIfmn6xKyuXkYkW2QEWK6w2pJ53uevPtMtuCPkWZWIy1INGQCroh5Y3nmG9jlg5wMxH98K0bQ208y5g49gbMjIpe8r58BdSs6CeuRRCVmQC0s4btzR0EiwHNnPk0tZYC1vNUSjLpnAZlQ/GTtibatl47S0koq2at8xlQLPjhtqIUzrz7Ms8x0n76vbAstZXcFcYcrCJUegusWj97m8h5g2Cqti3NTCVIWiHTnOkcXe2sL5EtJgosnuinHWmnC3FOZOOItskyJ0R+nsLJwrrfqwSBo2ngklHJK6rHZrUbF5IKNbQcBy6rGJo0z2djP6g9H1vx49T34u27/6ZoWijiHBnOwTJM3uJIqxdjbXQpeV4lAl4E5c3ICxHWyCnHRmz8SEMcJvJyCWdrI/mRrhdJykTztRQzQ6tUA4zTt2XND7OH0tbI0zVhyDl+WYJUdkLOar8Ce3TdfT/7L0HmGTnVSb8Vt1QOXd1dU7TEzVJ0ijnbFmWZcm2HHDGEViCwSzws4tNWC/LAmZhwbA2GC/GOHtxUrQlWcmKM5oZTZ7pnCvnW+l/zrld1ZVTV480pl899bSmwq17b937fec75z3vyy2IGgGp2VNQpo5Av/sGZgxkiYVQ0qZTCqLN5zIpaPVmdm2igF8kxpfZAXH8Uk6gCT3tJUxrfmc8jOTDX4a2ZwTyFa2Lb59PUOIr8i//kxfO8v7rYHpTZWBdDmlkGz82sYlqcL5t7Rpyva9xO6pA+l/b9jCDLPi1f4C89aKOJcCUEweROvkKjHe8gxPI5ZB2XVri6Cr2jbHcgjjSuh6Z1miBuPMKFoum8S+zeBLZ5WkIB26DVqxkFrArHMUcxABfJ0gkW3fnLxYKATTupV5+hE00dNe9vW7MkSX906lXobW42hLPzixOQiENsbF9kAa2V03ckYs0a3xx21z7UObPIeWbY9aOuCoHoMYcMrNmdI0SdxotLFvX2IeNQHMZsbkpNqC5M52IFWQQwjOnkAiswNy/BYayYlRVqQZizZWZk5RrijYbAwSmTiKtpCBIpS2PqUQMcdond1/d5JRtqLniXjWE5yd5fy299duWMymKOdaczFuNOSheSQZXYOjqa8hsU2OOyq6VVHAFyZVZ6Kro1FLSK5uMcVG4YntUbKIkqUaD5PRxKIsTMF10LcccQiwEoUi0vmoBPJfjuDhNhW9JhujsYya6NLyb4+5SOZP1gwp6yhNf544T0vJ7PYNauSP/+jkgHobuqjthuvejDT8jb63Pvt1Ea9hkgL1+sZkAWwdSy4vwfeer0G/dgZUv/W90f/Q3YLlGbRmqRUEt1nuhVhT3L6ntL3L/MOt3dGzfTr4C5dXnIV9yHbTGShc1/TVqZTA9d46rifTd8r72k29UkaWqC/WYC/1boL/7Y8hS29mZsxXv1br6oLvv16oGya2C2h+pLUt3wzvV3naNBtLFt7EzWzawCOWVxzkokC+9HdqyBT+3IsgGaF3tTZA0MVP1ll2S6kBuQ0SWkImFkJw7A93ANsg9o2xnL5IpwCooKLXsqd9DXwtp0j2jBEUdXYh8UBdbmkZ8aZpZWjqbCzpHNyfEYouUkNMXqP7VEF+Z48SRsWeYA1pzzzAndUjoNS/2yt+xMsdsKb2tviaJgVh6xigQiJU8nwz5kPAtcgsBte5lqV0oR5V3qSLwbFePhKrTKnusMpglkVqtqLYVkaOlgViRRQlPOt9Mxy+rmBOjjSqqsr27UOWktoUkJZBlPeQGbTuCyQZzFXZkJhJggVhhVd+u/DOGbdUZRPLgTn4QiK1BzEgWsjdYoKvR6pJH8vATyEYDMFz1Fta+KSTAqLWhyj2Qnj6ObHAJ0q5rGgbdNUH3LzERzoNb63qRo/+ojZaYoU0kjjcKJKLPi5g67pWETCTELam5RAKi/bV1Jd3EmvZX8IHvQB4agf9r/4TuX/19GOuY6OQUBRp5LeYgJ+quX/59/n+pu6+uEHLL+3bsRaSOvgj9lbdBcFeOW4abVC3U9PQpFq+nYpnukvYXkOSSxi2GShzi2B6OY7IkTXD6dOV7+7ZA/7bfqMlebQXJp7/D2mAcc+RZa/tVh2rS60odfZLZ2DrS/SqbX6m1UmNxQNvV3nhF2qOkq1iNaVsM3ZbmE08VhZOlSegHdzLDS7A4SgolksPDj7a2TVqdGlVCoBbyBZ/I3DkkSG5h9CJIJiv0Dg/SiTg/T3OoXEdvM7o8i9jKAkyeAU7smHuGoCUX86LiLs3hURIKN5igL2LXVoOpqxfpVAoBX6jk+UTQi7h/GTpyHLTY2KWRtkuyDOVoN+ZIRoIlRcJipJUkF/to2xZiNfbmSmKOFDlZE0OqrCBIBUklHOBYLv/+uHee5S3ofMhF2qfVQMx/20VXVe5PJMCMdKk7UfEaxar5JGo59EXFZyq+UZxLsTzp2Roa7Ev86E95XWW49A1QTqsFeEqA0XFJI5WJHNIxJYMJaeuBtn8TKphrPSMXRMzBraMZhdvKtS7Pa7cbibjadt+AVZ8JBQFZx/qmxYZym9jERmAzAdYmyLUjdvQQQo8/BK1eD9Ht4R51qrpVa8VRJk9j8X/+f7Dd/U5Yb7+34nV5qLPMCMNNb4G8l1zC1EGaXKAo8CoegKgtIP6Nv+EWBMPdla1FrYBtxtkZT4B86S3QOj3cEjkyPFzVDaYTyS91O/pC9SiPvMsTuU2CEwAa1vOo+CwlJFaZUywQn07XFaSudgwsYL/6+Yx/katW7dCi6RyNjo6WnKs0ueoFlyA6PJBdfZA7OOGGzx1BLp2Gc881Dd8rWxyc4CGqPoGSZsbeUWaBFSfQqApJzkU6q7NE54uCKkp+JYNe6O1utgWv0BNZmmUtjEYJMJ29C5LViV7NPCejBEENpo3UoqA3cosEwXfmFb4XDU5PSeDbLiihZiUdDXbjLg2c6NhIwF/n8HCFmlAciFJQHDp9SH2OmHVjewsaaknfPOJzZ5llp19NirCjpExMv8YBC2vSVbm/KGFKnyea/iiJ2bfBPhS7BvnRLEjfSxt1sOYIB7A1BPDzIMYGmUqAknEN2mQoyE29+CC0nmG1XXIVdK/Jl9Vv82kFdJ76/eeQfuYEhGvvaWsb6dmzvDCXtl1cwQi1fuJPmt5OecGkEyB24sKnfxlS/wjcv/Jf6nx3CjO/+3FOgqZXltDzqT+G8aLSdvh2rqlNrDPmOPQcu3ha33AvxK5uvv/zbcrlSBx/BSt/+ydwvOtjMF1VWZhr1C7ZKqh1Jnv1GwrJrwxJIti7Cu1z+YJb7Ft/B+miK2C4rbHzaD0Q+4tiDo3RAu3uq1ngPudbxMjISI2YY/3JL3U7FG9oK3RKCenJo2rMQSymTLoidUEFmULMQYtTavdqoXWbkgO67ZcX5s1MYBGC3dNWAaFqzBH2Ih1YQpZkBmxuyA2E3psFz4FnXuGEDGt2NoDO5uTjyyfLKAlm6hlGIrAMsahVjJJAFJvoimIKEqenmCMVi3Cix+j0VAj1UxIo6l1gYftGCTCDw83C955sjj+X17UydfVxIo4e9Fsunn6VtaLMXT2wdNV2h2wl5nCOUXKoMlFDmmT+yZMwunpg6e5XY5KiuISScYFzR9UOAWi4xTKfXIx5F7gwSS7W+XjL6B7g+E6qwgpvNubQ941B7urnwt3oqLGt+UHuHuZHsxBJ8J5aZBNhtcWvp357c2ZlmtsXpfFL6Gas+1423Tn0E763iwX0KeFNphWdQP4eTD/7A2Spu+NAbbOxekiT42g6zWu5ku3bXLD+0mdf05iDDFgWPv0rzDR2ffDX675v8pMfhGBzQFlcxOAffQ760fELPubYZIC9frGZAFsHrNffwo6Phh27kX7jvZj53U8gcfgluD/yGxXvJbFjsbsHgr02W6aToEEsn/xKz08g+pW/ZJdHw01ryTdyWpIuuQHi4PqdNcjSV77p/oKeRWbmFJJPfBPaPdcBuxsnWZoF997rTYWArx4FWRW572c9sHqCvzTRJY88oTrCXH5XUw59meAKlKmj0I3th9ZkQ4Z0n06/yG0JeQZNqxDLGBmyewii2dFxS3HSqEJRwqXhfhnMFW1/HCwZS1swogtTzMSyj+0uiLlS8oweFDSRZbhU5dzSb2kf3VnSRkCVz+D0aVj7x6C3OioSTgkK4JQ47EPqBEm/LzGwIoszMLn7IButUEjovQOLHmotoIQatV6QUG05iLlGVVGxxu9EwSm1cVAFPE1C0EUgB0xawBZrjFGgqmtCsDmTiCF8/Dno3AMwkAZc2XfSfUILj/LrqlPIBJZ4AUYmD1xxXW19ID0+agtqlAjme5fe14xGTCbFDFOcBycrYWUGGSWOxL9/HsL4xZB2taZ9oTz578j6FyGO7CpJ9JPrY2bmNAQSQ24QyCWefRjxx/8fLO/5zYL7YyfACcOhcYh9DRKb5JRGzCLS5bG7ILkam35sYuNhu+3NbJ6j37EHqStvwPyf/BYst7wJzvsrC1hakxmiuwda6/mppBN7Q+hW55TU2aOIffsfoLv2LuhJ4H4V5Kgq778W4tb1O6qJWy9hhpWwOvalTx+C8uwPIBy4A9jeHgOqGsgwRGO0Fgof9Ra/qsj9eMOYgzR5lMOPsQyF/qq3lLSG1jPVSc2dhG78ACfCqOVLmTgMeWQvpAZi/DX3t2xu0PVuYYkFoUqr2npADHJum3Q1Z35EQvflYvdUOCsvnlGLICWDurbshrA61uptTn5QAomKclQYKwexohzD20vYUfGQH4H5KTgHxqAr68ZIRiOI+ZagyWVgX21H1FIiTCsgvLIAs7MbspEKfXGInehsiMfgnTxZM5lGuqmUeCsWzS+PqYxdvUgl4siUscApjqFrUy5KdlEMU8ulshjpWBjBc0dg7B6qkL9gdp/OsKExB+ngkpmG3L9VdfRebQPOkFg+sauraA4XQ7fnRuSy6aYSxizpEFpBrkFRdr2ghGpqeZrbN5Pf+3sIu64qmIk1C+Wxb3M7qDj2mZLnSec4szAJYXhHQ8Zb7CffQfKFx2D90O+yJE6nQDEQETyk/vqJTUpSG/dcwu/X2pybrPNNbDg2E2DrEUqmSWb3/kI7I9m76rZWT35Qu0Hvf/kr/v/YS08hfuhncLzrE9Dq1xZ/2UgIGoOpopqbmjgJobuvqpZXM9BanRDHdkEcLFskk7DhdW+uf5xEwSb750a6AFotxKE1vSFyhNT0bcVZRcb2bLYtZ8OKffEvQnn6OxBG90Jq0I5V2I9mAsuFs2xNTsF0s9VY0hbIhr3IJqKcACMhTrF3S0uMmZLtZbM4deoUtm7dWjhX7MTX4eRXwVpco2HGUidBCS5mfBVd03lwgqRO8qL8NU7gkN5IlUmbKozLKS22lbXZkEYItSTIZiusA+tjVFIFNZtK8bHQ/UhBdJ61VQ6qVGdzFIyrbk/UclEOSoBVAwWe1C7ZDmi/SPuCrvFMPAphNRhmp8z5MxAtLqa+l19XrV4r8aNP8oJLT1XTIpAwfsY3B7F7mBeIhWMyO2C49q2Nxwy616rcb5TkpoQX6eSUGGFceTcyp19iLTFqd6LFY/rUixBG91S0N7cLug8nt1yNLT1dyDz0f1cZpKU6XlQlJUdZYrzxc6uivXnobryPDUbKWa7KS49Bef4R6G9/d0Ohb63VwSxaaunoJOia6frE7zZ+n0aD7o/+ZtsC2pvYoJhDlDjOIAh2J/Tbd0NXQ1BYHhxDz2rMEX36UShnj8P+zo+VtL5mw0FoTJaKZA29VyRZhjaFmQVnD7tHi/2liRm6J/Q33lf/OMN+NtApZo5VA4/Lq0LmBC3pF/WN42wc2NahmIM0t1LP/xDijisglo1/64o5Zk+qQtOU5GmSvUXt5dmQl1s+oTdxG6SYiDZsh2wt5tB2PPlViDm02qaSLK3A2NUDOW4tmM0UgxJU2lWGeDXIZfE0jXnaGjEHJZq8yRy6HKX7Hw14kQgHobfY4BpcX8yRoZgjk4FECQC6timWqpFMozk+kwMCsxOwuHthKmtvo2Mw1Yg5KFFoarMFn9YMVFSkcYhinTwbj4p4JIlBDH2NzrS+mCOTZldz0eLgVtxipBbOIRP2QSJjhqLiJnVeGK+4u2FhidvwUMWYKxrkvxTLF+vpSZfcypp99Dq9xiYWE0dUE4sOFOPoHjx9+jTG3/AhaCI+pB79Khd2i0GxBOv5JeMF6YRyFp7u1ndwR0c5lJ89iNSRZ6C/+8MQh+ozfgWbCwLFHB1iyuZBMinuX/t0w/eReVzPr6nt+T9PMYfKAOssc22TAdYZXHh8wtcptAYjen7jD2C9Ua0OplcW4fvK3yG9slDx3sSrLyNx6Dlkgmsi8OmlOSz/ya8i/L3/W/Le1NwEAl/4LCI/+Er7+0b08fs+BmlcFWpPz5xB7Jt/i4xvse7nlIOPI/7V/wnlmR+2/p1mO+Tr7uXvbhaZ2VMsaE2aXtXArQ7uIdYQ6yTEvq2sCaA7cGdTwStB6t0Cw6V3QlzdF/qcbmRPU+yx9YICBGJx0d92YHAPwLHrypoJnXZBrDBT90DDhUszoOqme/t+6Kq0J7DOm8EEsWyiNnX3wzY4XlcfpFkEps/Ae/YoW6ATI8sxtpvbKauBqr4pWsxoBWglNaGjRILwnznMLaEbBa2kg3XnFXw+wsd/BsWnjjUkNptcnESSqorrRk6thJJdfBnkLRdDdxFpDFae77Y1vWjMe/kRKD/7gdoiVIxYCNmVGdb2I2RXZjk4zZw7jKx/odCKTJXQwt5TewQ5cLUAbo22dUF//ychXbLWOkbnIP7tv0bih19A8kdfYjc25YVHEP+Xz/LCNA/BMwRxtIrr1PaLIe27FsLQ1sbtnsdfhP6S6yAULWySh55G/KkHsJEIP/EgZn77Q0jNdeLa2cRGQrDa4fnkH8K0apyTmp+G/6t/j0yo0io+ceQFxA8+o7qBrSI1eQorn/1VRB/9bsl7ldNHEPrSnyFW9nwr0NpdML3tE4WiW+rMEY45KLlVL0mSfOYBxP/1fyL14k/a+E435BveykXEZpE+dxjJR77MrJJqYL0u9yA09s4Wi6TBnZC2Xw7dgTc0LZlAhiaGA3eyiUne1Vk3urftJGUrIPMXkjwoN3ZpFtS+6NxxWdVE1Xqgo9i2q6dtPadiUCukZ+vuisQYgdlG1MVRtv+27j44B0Yhd6BQsTJ9DosTpzgJRokv9+h2GMrY73koiRhSpKlGSb7VhHY8FMDyuRPcFrpRoJjRvvVijosCpw5yiykhnYio2qUN1hTNgmOOKrIl+rF9MOy8qqr76HqSDMmXH0Hy4KOV+xENIkvXfdhXkG3IzJxAmpJiq3M+t6EXxUfERGvVgIISc+Rar3v7J9nco7CtWBiJ7/4tkg/8M5IPfgk5Yo4+833Ev/Y/VTfpVZCzbbUEl7jrMkj7r4dQpShbjGw8ivTpw9BfcUuJ43Ti+R8j8fxj2EgEfvANTH/qQ0j7qq/7LnRQskordPaxmQDrDDYZYBuE+NGXEX3qEaZ9Wm58Y8lr9vs/Ausdb+P2hDxIhF4a3QaprGIquvtguPJWyDsbW/Y2i+zKHGt25YJeoGhRn3j828guzcBw7yeYXZF6/mGArborGSqpo88gM3cWupvu52oQOaNoTPZ1TULs6EIBeo3Jg/SM5MtLz2UjZONh1lBIvvSQmuiq4tbI7aJFPf7N4nwEntVAYqXxuTMsMq53ty7gT0mCTiSp1otqWhKJSAiS3lBVSLYRqIWyWsKsVVBlk4JLqpQ2cmEk0HeStToFh/lAnHRJqFUzk05t+CArmKwQrS4IqxptLB47ugei2Y5ST6j6IE21xLlXILmHCvpjdJ0YSeC5ygKDKoWdaDEtBzG6QELtq9cGswdID6ZvHBpita3qvGn7xyHJeqQOPorM7Eno3/BhpI/8lLWB5BvewUl45aff5BZn3R0fbHmRRN+vvPgIL1Cl/TdyW5MwuI21Hll70OJUGbtUNRakQsAKeq7K/aV1dEN/fWNdMRKATU+eUFuGLl1r8U48/SCyQR+LjDcSk20bJOSsKKom4iYuKMQPPssxh27rbhgPlMoOON73q8iSe27R+Egu1NLwVkh9pYsjarnVX3YjdHs6Z3ufXZpGhrTxIkGAzHHy+/zQV5CLhmG89+PIeueRfuWngN4IbRX2ivLSj5Ejw5Ab3873ZjboZRmHdSU/yJGa7tlM9ZiDkvutussSS4QSRcrLD0Ma3QexilsjtYuK1CrZApgV3Uy7+AaAJAcSS9NcHGiHOf56jTlobklEw9AZTGpLY4ugOKWdWKUcyVgUKSXJ7K9mYmijzQmJriEyqlm9/tMUcyTjzF4HOqOxWwukyZZO2AtFVGpXNQ5uZw3WVmIOMimIT5+AzjPMMhIEmm/Ne2+szv7XGfnRaUgje0qk1nj+IxbpwA5oHb0cSxDI+RFGK9IvP8LsUP31b0fq0E+QXZmG7vr7eY2SfPxr3Iqpu6ENjUONBqnnH+AYR9x5Ja+/tP1beLs0ZhDzk2Qg1JhDvV6zxDan56qcL8E9wI9GoHVXevoka4Zh99q4n/jpD3mfaD7YKBSKlJsxxybOMzYTYB2GMnkG0sAwzNfeysL4+u2VCRctuVyIIpb/x6dgvPYOmK6+lRNgzo+qjpDlyRnzm36h7f3J+EiIltxe1iZ3ef91EMepbagsWaAkebFIiRWVwfUWZIlyfO4whLGLSgKY7PIMsktTrOFD+lfKj78K8aKrIe1rz5WQII7ugTC8qy57hAbqPPWYXOSIeZXXHas49uASlBcfhNBHlRGalluZml+/kCkAzWXVvy0i7l1AZGGKNbfyovadhEKteFWch8qRjITgmzkLW88gjKu6ePRZ/8xZ1vxy9DUWQg0tzrAVedfINm5d6BRUN0ondObKhC4Fl1RhlctYBuU6I8QWI5elevbonQJpj5m3qG1RhFTIi9jkMdYFE1toj6F7n5wjyfGxWIC/E9X1VkCJ6mKkjz2NzMI56K6+F9qixTO363QPARddWwieNGRC4QgUaPzargFOrLd7DJnJV1m4mhJgdC3ori9t35Iuuoof/N7lWSS/9w8Q91wD+bI13aNWoTVbYfnoZ1iAvhjmd/4K64h1Ivnl++KfcwLE/rZS7SjLTXfxw/e1LyAT9MP90U/x8+mAD95//QdYb3kTDNs3bdJfL6AFfGrqLKTBUVhueTPkkW3QVYs59AZkAl4s/+lvwnzH22HYfyUEexccH6tsOdEaTDDf/d629ynjXVDbd4vuOfnKOyDtuRraMg0nauuhlh86Dq2rB/I1dyM7f5ZjjnJGAyXRssRyJfH36RNQfvodSAduhbhDFYVvB9TaKGy7rGbSgZPv5OCbjzkmDvPiUyDx7WrHvjIN5ZXHIFDrFn2WHj8H0Lt6odGKkFfZZ62A3BajK/Nwju6qKo+wXiRjEWaDCw10p2KhAHwLM3D1DcFgVlnLiWgE3rkpmO0u2Lsbz5XehRlmaHkGRzs6L4o6HQwWG0w2R6XDdDrNhTS57NxRobAYpBVmcnSdl5ijXI+NzAkiM6dhHhiH1MC5sRjEVif3SGp3zCfAXpOYY6B0rFGOPsWao/or31yyTqK1iUhmVORcvao3RsxTpBUg/++ufmiENpmONLZNHefEPiXAiFygu+FtJW+RqRhHD+6aOYPkw//C4vnS7uZkYaqBWh8tH/vDirZOy3t/s5oHQ8ug9kzv3/93yCPjsN6luufm4bjn3fxY/sJfchtk1/t/hZ9PLc7D+29fhOOed0E30lmTuPOJTRH81y82E2BtIvL0j+G4660lz8UOPoeVz/93dnq03XU/DLsuruvGlfYuIVulXaFTIBZB7Dt/D/mK26G/qlS4lQZ1dg7J5lirg6C/7V0l75F2HEDizCGutLKdrrAWJMq0ECTHELItzmWhJcF5WowWf4dWyzoAILFsJc6i9I1QL/mVJurxkZ+yeDZ9X/r4z7gyUisBptWboXX2MbVY3tG5avZ6UKsSmj9XeZcTEhuNzZyEoW8LpPJEJdHxnb1NsZMqvkeUoJVlZijFVuZg7hkpiMc2t/85+KZOcZLL3j/KwSAlhYiyT0kt/8wZbk90DW+r+tmwd5GTRxSkkZBr8TFQRdPk7Ia+fJFU41xRAmwjFhiUTLP1jTIbjR56s5UD0PDSrGohHovANbqD97fudgSRA3/6jKUBBZ3A7QT02zY4/ob7T4sBahcmJkXZdVUPgtkO054bGjq0suZZLMzv53/nch0JWElnIz2p6mtQEi4PjdGmsr5qBPZ5BzaCOLSLH3lIZMLRAsrPl/6Nv1jVhatmizY5YXatv0U7dewFZLyLMNx+f2G8EMr0Z9bDglCmz0AI1zZkUc6e5IRJnjGhzEwi9sLTkDx9hQTYhejIdCEjfvA5OG66o+S52DM/hv9fPw/7/R+G+fo7oN+5lggvR44EsX0rrOu5UaDW3fgP/y/0N90H+eLrC8/TNUy6XumzRwBirqy65Rru/nDRpzWQdl+J+PHn1HijDLpbf4F68VRnO3s3tL2j0Lr6q8cc3jlkctmCTl891GPcZM69gvTJ5yFdege0NjfSJ56DxtpVMwFGY5WWROTdg5BXHaIvlJgjFfYjtjAB08DWEndnhkYLnaunreQ7zfE016cSUUSWZ2HpHW6JNUVj0PLUaW4ztPcMqDFHNgtRkhAN+hFYnIHeZIGrf6TqZ4O+ZRhMFm4VpBbG4gSRzmCE2eGCsYFRRP5c+RZm1aRohyEIIly9g4iFQ0jEotCToH5KQXBlCSlqiU8m0De6teF5o2MLrizwjGVtwokyHg5y4rC8oNfy/st6vmYE2dBSzCFZXbDsvKJqS2MxSGMsm4wWNHE7FXNkwn6k509DHtlTon3Fsg7ESqrBWixOmLErdZEztbyv0nG3FsrPFbHfdBRzrDLKG4HiImLLajug56u88jT11kJ/45pRWt7RtxMsL2XmbFXN1zySp49BI68lDhNnTiD64jPQb7uokADbjDk20UlsJsDaRPLMyYrn5KExGC+5GvpdjR2OJE8/ev7kiyWCtJ0CMR6Szz3KWjPi6C4IvcPILE5D8JS2FSQe/hoHlOaP/GFV16Nc2A/59vfyZFquU8GJqtUEAL1Xd9M7KvYhffJFZIj+/8JDQMQH7T2/0pLldzm01HLk6OFBnwI6dmOqsz1Kjun234LXC1LBZcTOHIJhZDdkZ2lwQkmSeNAPg83B7KmskkAmHuG/5QidPczvd+y4rOUggFhJ9KDkF+lUpZOxkgQYJXoI9QItSnjlA+rA3CSS0TC6x3dxJZZCQ4lo2quVy1jAC6PdydujbUe8S6yx0TU4hu4tawKnqWQcy9NnYXV52E2pEdLpNLsxbWSlMDA3wX97tu1lna9EyA+dxQajvQtik1pxiaCPWwvNHqKhq/taa5+D06d4Mda149J17bdWNsAwsA2i0cKBIp0ruSiwKAdpydEiiVw0m2nrVeZO88Ow7QC36SWOP8taNILJgcSZlyEP7oBoX3O1pH3ILE1Aa/fUbV/I+uaRWVx9X1ECTBzZzQ/aTi0L9k6i+HyR+H6zIL1D/V2VbnzVwMfiX+KWBioiVBifnDyIzNIcDDe9hdsgOgHlzKus7yF296H79/6ybrHB81t/zIuO/Lk27r4YfZ/+HORG7pGb2FB2eTnksR0w7L8Cuq1rSd9akEe2wrNBMQe7Gr7wE2ZtCSM7mAVBjEihSGybEueJB7+itve89z+Xfj4SZHYYGVzo7vl49ZZr3m9136lVR3dLacGONHHSZw4iM3QRcs98D5pMCsJ9v7au49LYutSYg1wgZT0k0gmtM4bRwll38W14vUDxziM2dQymLfs42VCu6xUL+mGiDgGan/MxB+kZlSXA/KcOcYugY1vrUhwGh5sf4cUZdnc2KsmS+CKTSq3q5FS/Lpm7n8kUNCFXZieQSibRu2UHkvGYGnOsJnDSqRSioQAsdifvL7UVhv1e/g5X7wB6RtcSF4l4DMuzU3B290JuYt6jeaGrb3DDYg6aE1YWZjmx1zcyzscWi4RgtFg5Udcss4t+Uwo1KAGmtrJrqu4zJRH9c5NcvOwerWzTbQXEgDf1b4GkNzYVc6htmmrCrhkt2sTsSSgrszBtv4zXFvFzh2EY28sdMompYyyWT5IPxWNNamUGoqOHtVJrgUx8MstTyLj6C3q+BGls35qm6Aa375afK00LJhQ0Durf2GTMQVplpFtG8g1VZBrSx19i6RvdDW/p2DWePH6IDdxEpxs9n/7buuu/vs/8dUmt0XL1jZB7+yEPt+dw+3oBxVCdF8HfTAR2ApsJsDbhfFdx5VKF6OxC10d/q+ltbEQgSkhPn4by3MOQU0kY7/kwkk/9AImXHoPhno+UtBXob3tnRd91ZvY0kg98GcKOA8iceBHi3mshH6gd0FFgkjn5Ivepa4sCrMzcaaSOPYsJXwzje6+HoMQKg5/ywoPc+iBf85aWjosqsLoiPY5OVD1qLajJbYb0NsSyRNV6tk+tVLTN8kmA3hM49jNM+aLYMjwA6+huyHY3RPPVVVlestW5moRqf5IyuHohWxwFF588vBPHOWDqHq/e5kTf6R5bW2yR+xELsWoFpuCTfXheRDYeDjDji47f7HRz9dU1OFa1PTJLNoo0+zVxTBS4nTt3jitn9G4/JQrMVhgtrTGnqMJKlddak4mjb62irDNb4Rzexqyvam2RUb8Xeou1ghXmGN62Wq3UwnvuGN8vXTXOrXVgvKnjb4T40hTrtZhHdkFrtBXOVS1Hpuipl7gVwbLn+qYmVtHWjWwixtvm9kJJz9d2VomrTmWk42fvRpIcGxMRSP3boJx+CUL3CHRbayf3hP5trKvFC550quI+oXZm0uyRr753wxYhxddWJ1zkaoGcmZQnvsu/Nzk76e98f0l7GJmWcLtjh5JfpP/k/z//HWLfCFy/+ofcht/QLats6NENjTFrmfZTq9NdkI5MFzKsd99f8ZzU0w/Xh18HMceZo1Cef5QlCUz3fZyLa6lXn4PxXb8BoVvVoKGEq/6OXygUztY+exjKY9+AsPNyZI4/D+my2yHtKdUwKwbpxWROvaTKJRQZnlC7curVZzERVDB+ya0QiiQPlKe/yy1KrWqICq5+fqz9u29DYg5uZZ0/w05zos3duZiDGd/q+FwMKqD5X30O04EYxsdGYRncxm2Osq2rasyhsznXZWxCMHf3wUDFo6LWbtrXpYkTXFByj1TXYSXWh6eoWGYw2yDpkjwH2N09MFpt0K/GHNGQHyHfCieRTFY7tw129Q1BLmsnV787w+NvM3NJ8bxArHPv8hIsNhsMTRTrikFsLpEYjNUSvBoNunr7C4kuimcoZqJjKH8/JU0ioQBMZiukskRT9/B44ZpamDjFMU730Jaq59XRO1QQ0V8PwgvT7MJtH9kOQWdsOIcGTx/i1KZje3MsSSqo0e9F2ya3TGKpU1E+l4ypmnvJGGC2I376JT5uydmD5MQRVf+TdLtqQBrcwQU3DZn9lLk5ExIvPMBFPsOlpczbCy3eIKRe+jG7UCOngbZvBIY3vKekwGd816+RFWnHYisygPN/8c8gb9sD50f+M7tB1gPFFOXQjW5FamkBosPF7ZGbMccmOonNBFibaDaQjDzxAMI//Dq6fvXTFWKzGwHS/CJHEsObPgBhUA0ohJGdECNBGuKhvPQYpP3Xqb3sq8mw5JP/zokvw72/rLoe9QxD2zPCNt2Cp5JWXgxyRUkf/AkE0g664s7C8yQWKWRz0MRpgTcITWyt7YKqDKSr0YjGXPw6V7JIGLsDbjuFfU9EED/4KKSeLZBHdpcwYpKk9WEwN0yA0aQZP3uIRcjl7tJ2C3Lli08cgXFsHyS7m9k1xpGLSipVDHI1pPMeTjMzjL4/nYiysGg1GJto62gEOq/lyS+CweosJGHI3jsW9MHeO1iz+kj6XXkNLwIlwAqv2ZwcZJGm1tL0WdjdvdBVcVgixCNBntxaFZQlVhnR+Ol4WkmAUWWYHJeo9cHZU10klJJexajVJhBamkM86EM86EX3llImRvFiggJ/SoDVgq6G41OroGuJxGUj06dYEL/RME/XbjZpbJh84wSXkmC9DkNer0PWw3TxrYX3mC69AxmyCo8GkUslkFPi0Fq6WGRWaKBHxos2Wgge+jGy/VsrWoi0xGgjAfrzrA+yESA2rnZgC3LhABuPKC8/Dt21bypUZWmc6+RYpzWaYX7ju0qMV1pF2ruMmd/7OIz7r0D3J0oZPJvYeDSbgAg/8E1En3oIXb/xJ1x532hklmYBWQfDXe9jxjlBHN+rsidiYSgHn4C8X22HFMfUeTbx8FeRC6xA/9ZfgtbeBa1niKUMyG2tmgB+MbJzp5E+9Dgn39mcYhXCln3caqdJChy/aBJrjpc5MsMRG7N2S2IOWmylEnUZX60iE/EjcfgxSEMXQe5fK0bSsShTxNB0NkyAkctc7OwrkChhVdYCmlyeQXzmFMxbL+Y4g1ypjaO7WSeyGBz/0evRDCR7NyfEiAFWSxfU3Ld+FgaNbcXJr9UnYbDYC0UxSmCRLpfD01+z3cniXNMho+JbPvnFr9ldHENotSIWZqbg6u6BwVQ95ohEoshQMqDFmIOSWLFohJNsrSTAEvE4FmanYbU74Oyq/hsbi2IOug6J+VUN3sV5ZtwnImH0DJVKgBTHUGQSU0/c39Cg9bNZUOxCsU1g8hTkJoyIqPjaTCMpxTEUDxN7Mc9gpHjFsntN2kCwdCETC651TORyEO0eZqJLzr7GYyqZ/5z8GTuzyqTdVwR2ddf+fCyTuWW8fwtyAS+yc+egHH4GusvWYrdOO9gLzm6Y3/B2NlppF8mps5j9g1+D5cY3wP3+X8aFiE0NsNcvfj7u7Nd5oow1dc4TZTHx+HeRmTwO03t/m8VsCWL/GD/iD/wLMmePcGIs35pAbpDZSKCgpUQsLv1dpHsDSFvW+tprgXS/xMvvrHCKpOqMSBXaU6eQOXMI6WNPQbrsTtbrob+plx5GdvYU64KQpkZ5cJ8NLEN57vsQt1/B20mfegmZicPsyERMsI5AK6juKmUsC9p3/c6rm2rX5GQV0YpJu6ksAZZvKctvJzZ1AumwjzUPimnfFOjoScdsKcCTLelwkNOjZXQPpHXqQbUK0p/KB1DJaIhbFjIpT1vCqhTAUhKMdDqUeEwVj68hgmuk5E+OyAGtVVQlnR6ekcbaGOWgqijphhQHz7FwEGHfMifEMpkMB5/NJFuoJTKTUkoSgdVAumLNIJ1MIDR7FiZ3X0NXy2RwhSuvFmJgrp4DSpyKBgvCZw8zkwdK/aqZvq++wChrnuSy3HZACzhyaKrZViCI3BJJlUXTvpsLC0pt0WKvHrRmB4SesaoaO9Ku2qyQVkFJ+PTEUYjURlF0/6ePP4f09DxyoyMFl6WNgNA9COM9H1O1MZ57CKmDT/BvpbvyDbX3OZtF7Nt/zwLjhptLxfibgen6tQJFu+L8lPwikXX/t74MpYPOxJvoHKhSzgyJ8xRzxB/9OrKL0zCTccPqvSSO7uRH7DufR3buLMQtewoGFunpU2xmk3ca1bp6oX+TyqgXxy5q+H1aau++7A4IA1sr3ZxHLuKYg+5jnHoR0nX3sR6XdOU9nFhPL5zjNmzSEKsQG1+eQeqlByHtuRFC3xbVfGPuNDNOiZnVCVBsodGZmClXckw6A/Tbr4C2iZZral3MUKKQYoiyBBgJcxPTIi9ZEZ14FZl4GLbd15TM4XRtGHpHgJUQx6YRWhCHvLCP76swdNlIcJLHYmf3QwIldOKREGxdHmi1rQuJU7LHbHMg6PcimYhzS2Q5QyoPi9XGrBu5CvOkHvQGI/qGhiE1MPopRz5hpjesxUChUBDhUAjdnh5mden1a66O9WC1OxHMZWFz1I85uvqbK5YqiTj8i7OrRcr612AssMKyF/be4cIYoyNWnsGMzOSJ1ThEqbsNU4OEqhpz5BA5d4QLb/bd19Yezyg2OXMQAjlR7rxq7brqbU44XUuC8+6hqgU63e41HcP1IhsNITt3CsLo3pK1RWZpEorvJPRXrRXANgKku0gPTrY/+yBSzz/C47W879q6bNvot/4O4tB26K+uHZtUA3d+3HLP+vbZ6YZx/+XMBPN+7R+RubRzMeAmNrGZAFsH1HYQawW1M/rUw4g99wScH/kUOzzSoxFSEycR/PKfs/uS/uLaA1IjkNh9mjQ4nGvtgUQDJp0Z3dVvRHZ8b4kuR/LRr/GAaPrQp9v6PhrkxDqJMmYAOXuR84ywQ5v6pTHkVmaQ0WiQ9c5C3HYZxPLWKFp8UqC6GmRobV3IkRaHbn1inSX7JhtgrKHXIdqaczuiRICZWsdWWwzS0SBXYdmBz+qE5aK1AVvX1Q/BYGKNjYR3DkY6J6uLbHLCod8pPHEU5t4R6Jw9/N7zCdLs8k6fZXch9/BWWLv7WZSeNCLyr0eDPphspOnV/NBBzkYUVBGdvxaoRUFjd7IALGleNEJxdTgfPLcaKJcHh+lVsdkoBaUBL7dXkA5HNp2Ge2C4ZmAqG4xwDbVmaV8PVI1PJ2JckS8HLRoj85MsOEu6KqlIiMWLWc9leRaZZAzW4Z3MTCTWYSoWbko4NJOIIXr2FbYj17lKA8E4CUGHfdAPbIdgcRTcj6qBEtmkB5Zf4NE5oyRP/NjTrMchkyZgHdD9vtGGFaQzlDr4Y+SCy8yyIn2xwmuhFWiTEXZjImTmzrAOUKcWwOVIH3+RWx2JdSs0Es8nh6jFaeQyqk5fSStPWm0L2UhQiwIxv8jsZelf/x654MaJqW+iOtIrixDIKa5sLA0/+u+st+L88Kd40dHMwiN5/CDCX/s7WN7xCeh2NNYtrQX9dXcj619mhmYeuUSM7y39jfdxMSuf/KKFbfKhf+HXjL/QHouQi2vj++vHHF39QMTPCXVGIoLcyiwygoj08jTEPddDHL6oktVPbK/Vc8tC++QA2aE2ZN43gwXGS6o7xIpNSjoIejPMe28oLKJTIR8U/yKMA1shO7r5kYfePcBzApnqUIxh8KwlLVIhPzN0Q2ePwNI3qorVd/BYm2Vir8xNcSHK3T8ER88AbOl0IVagBFYkHILVbm/J6ZkSRNQeSEmnWqBEFJ2LeDwOk6lxrFU8j8ptjLWiKMLTV5qwTCkpKMkkgsEgQsEguru7EQ6Hed7s6a3NmDaYTPzoFDKrsQ/FQDqUbjebzSCwqMpMkFMlJb8S4RCy3WmEFxd4/nH0j6qth8Q+TylNxRypeASh6VMw9wxDR50HRQhTsS2ZgNEzxEmYesl8avHVUWxiMBdiNIqxYydfgOQegK6sMF35eQN0G2xYQe61ypGf8pikoSJfkSGYltZDUT8bkuW0OWTnznBRoBX90VZArekgzT3PUMk6sRoodssszpSM7fw8dTJQy2iLSeBWIZgt6Pm1/4LwTx9G8EffhjZ74TGfNhlgr19sJsDaRC4UxNz/+G0Ydl8C9yd+p+S11Pw0lOmzyMWiQBGlOTV1GlpKHtiq2ARLElNQ1xtsCT3USrDGnsgsTCL+nc9Dvuw2yAdu5mpHMeTr3kKZjbrbpDYGot0Xa3zxthenWISx1kBN1bVt21aZH11vLjxPOj+wOpEjZkjvFm6RLAe1AuhvWBPWp1bMhu2YYR9S08dZwLKZSup6kVw4VxDmLASVtLj2L0J2eKC1lQZJks3Fj+j0SSTpPbauQpujoasP23fkoIR93K6gkOgr/S7nwdI6D0pqkY4XsaoKyc2iQC8WDiDkXeIkh6VB5bEc9ZJfefiXFzkB1qvT1U1qlVxXLbYuEPOrVnBmdbphtpG+WoZ1JsiJiarR9P/+pXlm+ZFg7kZDNlnQtX1/VdYdXRPE+JKozdnhhql3BAZ3PxsZZBKTnDhL+pcRmTvDixkKBMd3HChpgyAmUXxpGrLNVXD7YmfHVFK95qh+G1hGfPoETGN7+F4S0gokh6epNiypLOCkpB1phmWTcbwWYBH+mROciKdEVnZpErnQCoSR3dyqXQz9lXdhJ+lgiBKyIS9ST32XHWfla9eckTqJ1NGfcYLA9It/UJVxSuM3tWLzOCtKsHz008xcLUb0u/+I9MRxWD/xhwXWb7ugwDZ57GXIW3fX1Oww7D0A9yf+M5Re+p1/dV3ft4nmkV2aw8LffAbGK2+C8xc+UfJamoTBKeZQkkDR76acOwHR3cuFunLQ4kVrNK17ESOSdiE98vsycQyJH/wTdDfcx66O2qKEDDMzbnxbIclU81hXWen5xFkexFjnxWGNOKlkbuhdY9xy8Y3mWhr/KeaoouVF7o36G9eE9UVirTZgrmYCi0jPn4U8fkkFq2sjEJ85yYxzM8Ucq2wRMtehmCPT1Q+xSBONkE+GhUkfLeSDzuGBsMrwMnUPYKsGSEVDbJhCBS5VB+n8CSxTXEDzbr7Vj5OXRYytcCiAkN8HiTS9miiMlUg81El+5bG8tARFUTA8MlJXg6ndmENRUhBF0kitfk6dLhccTidSqRTHHZSUCwQC/Nri4iIfg8tZZb3QYVBiq8+ounNXl5mgfcrx+4j5RckvaltNJeJ8zUR9SywFQUx8+vfWrXtKCoakkxpbWYDe7ipIb6gxR6ogC5HwLyG2OA3ryE4IxJLUCBxHN8OI05U50NOahnRNqbj/WoBirAyxvboGeH1E5j6IBiDQ2qRoHUPX1Y5b3syut1QMz67MIvX8D6GldsxLN8ZMI33kGU5smT7w+9Vfnz3L8QZrfRpMsH7ijyvMxiL/+lfsUm37T39SYZDWKnKpFBLHDkK/6+Ka0kLmq25kEke6hk7gJjbRDjYTYG1CazLBePEV0O+oZD/Z7vsArHe9E9qi/v1M0Aff3/4hpLEdcH709yo+I/WPwvmpPy95Lvwvf8mLHcu7219k8OKpqw9aR/W2QXG4tkAkgRJfiX//Ox6g9ff/ZmGRlvUvIfX416Ed3A75qrurfzaXQzQa5epa+STGtuiCCPmS20q+i1ojKPnVDjLkIrc8xS0P5yMBxu55xMYoMhKgVkbJ5q4IRIth6B1lkftyja+M0Q6rux9JSj4sz6q6Vg30UNre91wOoZUFDmLMRa17FtfaYqUak4sCOQqCNgI2p5srwY2SZfWuq1qgSvLc5DnWA+nuLa3CFoMSRfRw9ajv6R4Y4e+bnzi94dpTVFml79ITq7RGUEFttQ5qJ1oNFGixknfxpMCRrkVKgol6E4w9Q1wdjcUTJecqHQ8jsTyNbDoJ86qdNy2AbHuvXzvGLCVgUxyk6vuqs9vSoRWkfAvQD26vEFmmpJcyewqC2QnR7mZtsE4I/LeDXGAJ6cNP8MJXvvgWCFv28wKY3d0q9kmDaCIJk0lkPURx97UsUr9R0L/pg6xBUi35RYzQ2Nf/Blp3H0zv+g1176rcG0J3P7d0rsdhN4/4y08j8JW/heUNb4PljrdWfQ9dc6ZLr0Z6kwF2XqG1OqDfcwD6nZXsJ/sv/DIzJbRFLebphWkE/uFPoNtzOWzv/pWKz8hbdsH5qb8o/Jvu9fA//im0Lg/M96oyCO2AZA0o5tDYqhdJqB2yHuhaTv6/vyOqFwzv/u3C85mFCaSe+AaE8Ysh1Vgc1o85NMzYlvbdtPZ+Svon49CW63I2iQyxypYmke0dg2BfvylPI7AbIjk1r0pWEAx9WyC7+ngMrwVT3zgyrngh+cUgdq7JCVv3IOLeBcS98xwPGBqwQtre91yOi1w6vQEmq63wm9hc7rpMLkoCGc2d1SfKw+VyIZVONxQgbyfmSCYVTM/Mwmoxo7u7+jHStjgpTOxaj3reBwYH+fsmJichpesXp9cL0jKjmI7aOmvFHKQj1j28FcJqQpFjjtX21C5KSOSoqSPCDpCW7n5+X/m5UqJhxLwLnEQzr7rRyyYbunatOZlzQowKcLkcO0pWAxXmUpEAjH1bKo0kyGV2/hwkWxfr21n23/KaueVRZ0v62DPIkQ7ZjishbjvArC9tkTt24bqKrTEQNZSg3n55CUOs09Df89GS8aNkv4NexL/5vyEMbYPx3o+p+1SlGC30DHZM2ify5EMIfvufYX/7L8J8fXWzAYptzJdfy0zJCw2bLpCvX2wmwNpEThDR9eHfrH3Bl4lXkv288Ya7IA1uaf47EvGqAwxVeSlLL45sb9gzTpl849v/U9PfWfllOa6aaszmkgWWxuqEsP0yFlasBRI1n5mZqXA44Qn/+kpHq9SxZ1jgljU32kiCCX3buG1hvS5KzULfv5UfxWDWVFnyK748w62R5qGd/Dq1GpRXtovPlWx1wUyaYmXv6SRo4o0EvMy0Kk6A1QO1IFD740ZBZzDwox68KyvMyPL5Ay0559D7jGZLS6K1ipKEd3EBdpcbPUNjJRbNjZBOp1SHyRaSPoG5SW436N2uWnDXQoWQ8Cr4uzQCCxnbx9XEfDIexdnjR7Fz737+rTOpJMLTJzlJSy24FZ9fBZkx0IOQIoMLWQ+hLKmcoiS4bx6ye5D1N/i46b20ECNtq7nTECgZbHe/phO2hhxVd10N7apeDrHYNDUE+fP34SjC0ISWIR24oynWG+tqPPEtCEM7IG6r7XRZDm2dRDlVPOUrb4fWXn88M1xLznatudvVgm77XpiuewMMF1/d8L2bjkznGeRo99G1hFAxiD2gEUrHTqGrB4Zr7oBuR/3xpBiUDKJHxfOJGNILU5BG6hfM1O/tg/Edv462QY7AdM+VLRaJ+SVsuxTC0K7WYw5Rgu7mX6h4f+qVx3ixqrv+HW21HIlDu6DtHoZQxo7fKBiHSaR7Z2VLaFnyK7YwyS305sFtq4k/mR+1zpXe4YZWkqBrs/jYDGjejgT93GaXT4A1Av2Glg4JtVeDwWhE/YgDWFzycu3G511uKeYg5pfJZITR2LymWjyRwPKKH91dTgwNDrYUP1AiT2zBJIZiwOX5WWb+D4zUX5fkuwLKwesPDbhoRw8CsfjPnDiGXRRzSJKqabowDb3dDWNX6bxbvK/kTE4PQjLkYy268pZcKg5TPEKtvaSjS8dA8gyiycYyDsmlKWayUwLstYw5iG1O0i6kkVzQ/isbz8rvweyp5+nkQdp/c1PfwQYjT/87xK0XsyNu0/tWZ11BRT/5slsg1FnXEYy3vR2dgmHf5cwmo+LOz2PMsdkC+frFZgLsPIEGY8uday19zYDaADRVBDyTzz2C5LMPwXj3ByFtb1+7oxkQrV/csg+ZM6+wK2TedpwGdGnfDR35Dha8TCuFFkeNoXYlsxjUepD2zQGkoePqRXruNLRGG8S9ndmvTiEdDSFFrnhNtBfQxB+dPweTZ5gp48TuoVY3oqFTha5TLCSq+lFVj7ZH7CYKgojt9HpHPBZlsdg8IuEwvCvL8PT2sYBsLdB5d/e0ZmEfDvihJBMI+lfQ0z9UqJgSm4ycnGKRMCRq1yxbWNBnFqfOshivo4WWSVvvUAmbsBao7SAWWIa5q7eh+L8SWGEdMGpzkWQ3V2spqU3BY/mCqBqyJIJ69jAEowWWssSOfmAbZApEVx3G0hE/YqdegOTqh2FkNww7roT2PAoq13UfK9L5KgcxT9MnnmeDDljUhWx2ZQYa/4LaHt5kAozaFzRtMklqQXd5daZL9JvEkNHC9NaPIT1zmoNaKnasF4LFBtt971/3djbx2oNbZt9UmfSpiUwG2XgYmnhlIijx0+9BOfQ0TPf/CsShjW1D0RjNEIZ3IDNPLZ1xaGRDIRaRLr6lI9/BDo+ZNITeLWriq0npidTMCWRCXmapid3DSE29CsHhgWhVxbdfL0iREy8lMim2ahAzxP3LiK7MMTMnvDwHyWCCweroeMwhihIXkmgunp44C51ej+4W5+TzDZX5FSs5B4FgGF5fEAP9Hujk2vMvJcp6e1pj0/n9IWaO+QMh9PaohY9wJMoLf2KSRaIxGPQ61hMrBumYzc3Pw+FwwOlornBKx9TV09eUXhdpoUbDIS4G1nOVJMQCXtX0iJI5ko3PIf0n6A1NmSnR50gbTDbbYRsu1Qw1DW5DVkkWTKQoGRadOAqdexDGvjGYyAm2yfXDRkI1Aatt6EHjWvr0y0CRJmp2aZo7YMRctikx/By1ePrmkA30t5QAq7vfWi1rRVd8VzaL6Fc/x+3sxje+B2kyO3D1cJvkekFC9/a3fWjd29nEJlrFZgLsdQzd/muqakBJ2y9mdpgw2Dnh7UZtlJz42iBXtPSJnyEzfQLy1W+BvKd28oraFSDKhWAk451FlhJgWhEao4UFb0lktupniV4dDyND7pLzZ2Ck1ib9xtDqy2Ee2sHf34zdNrNTBBGRxSnkQJUDLZ/30Ow5GB3dsPQ0bokkLQmuOjSYRPM6W/m2vwsBff0DSGcyOHv2bOE5lczdjKl2a6Bgj8qbNsdaVTzgXWHhXp1eh+WFOWaUdfcNlHyOkomkH1bLwrwW8hXURkiE/YgHvNAZrRAaVMf1rh7oFr0FNiElVJ07L296nyhJRi6RVFUlMWWBRKJXQddpPvnF2zZY2Ak1L+ZMduUXAqidO3PuFdYGE65R2/7YsRbZpnV9qIVKf99/InoezgeysQiPFdlICNGv/i8I/aMwv1ttk9zEJtoCsaT2XlWVcShddAUgSNz6cj6gMVqhIVapZmPmpdThx5FdnoLuurdDqNHinY85iseADLWOh32cfM4lo6wpSEW3qp8lHSJym12Z4Yehnntuh2Ed2aU68DaR3GD2oFZAeGGadVk1QR/PpoH5aVi6PLC4PE3GHNqGyTJyXKRkjqAlXazXf8xBxzM81IdMNouzZyL8HBsUqv/X8e/rdruY3e50rM3rKyt+dqWWRBELiyuwmE3o8ZSaNFFCjAqA1ErZCkzm5pJF0XAQkWCAWfQU29SD2eWBftnHiVQCmSp5tjXPQqXklrF7gO8fYqwLRfcMJdC0hrU1ETG/dK6+gtaddKHEHN45ZKZeRc6/iJxTZdXKV9+jaoE16QSpdXigu+dXmk7erwuUxIxHkdNHkVmZR/TrfwNxfA9M935047/7AsdmC+TrF5sJsDax0XpABMON1Z2cBHcfDLe+reJ55YVHkQ37ob+p8rX1QNx5BT/aOUeyvJawyiM9dQzp0y9BvvyNnLTSmJ3stFZvsUmC1MmXHmabc2lE1RCRd165FqBSEqzOb5Kk71w8B7F7aLWlqb0BhCtO547AOLyLdbw6NQDmzxVZgsvG3YgsTnOQnQj5WPdDNlogNiFwTYHowrnj0BlMcPU3x+jyDNanO7+WSCoK5ubmuKpps9k4UScWXVdmi4UfGwGq4Lq6S4P/Lk8vt3LIOgPsri7oqjCcqPXR3aT9eDVkSBA3l61pBGB29UA2mvmaaHgMogSjlRy02p8wKcgMn36Z2WDmkdqVRkqI6ctE5S8ECO4BpO3dqitkKsnXFiX+Wg0y8kyVRiD2CAWUVFhoF5b3fUrdVi4H3TVvZB2wjUQuncbCn/wG5P5huD78W+dtDtzEGjb6fLPm5O3VWepi3wg/ypF86vv8V3fNmzq6L9L+GyHhxs7FHGcOIj19DLor7lZ1Ua1dgBLnpF4tZLxzUA4/DmnrpaogPh3nnutZg4udcBuwoxLknuudheQeUt+L9kCGObHpEzCP7YXUJMOUC2lNniuDzQWdyYrI8hwndxKRII9PssEEaZVpUw/EiJ6fPAOj2QpXE4wumov6htqfHzcasXgSMwsr6OlywGoxchxQfF057BZ+bASobZKSYMXo7XUjl81x0c3ltMFUpaWSWg37+9pn05EIf3471WB3dfPv2yj5xcewaljQbszBBgZ6E0LTJ3l+s/TWjmOpqEzupxcatD2j0Jx5GdloEDqPOo5ohNbNSEgmodl2SR6Dmnx/xfdQK/KH/4u6rUwGuitvhzjUuilEq0W++T/6dRj2HIDz3R9X92Mz5thEB7GZAGsTtQZ3coCMPf8Y5JEd0F90CQ8cnUbq9CscuEmjpVoQ6TOHkaWEyfVvWbczh9p+qV+XKyWdo7GxKmKO1OaVVcUuCeLANn7UhaRj98jiaisJb5eLb9dCNuzlhJLctw3a0earURVg8X6pKV2gts+VVoZtQNVkMLv7mLVjrGFiULF7FKjpjVx1+3mAppXragNBbZfkGGW12QqCpbYWnDCJQh4M+GA0mSHX0NPIY3l2gq3E+8Z3VZ3wmaZeRzuq4+dKENgB0rBBhgyvB8hX3o1cMsGaXGNjpeeW2JvURtRsZbYREt//Ale3je+oriFZC9l4BMpzj0Dee3XBWY+uD/3Vb8CGQwNuZ9UUXbvrSapuonXUOt/K5GkkXvkZdOO7IO/Y3/FFAi1CUydehtZkgThYuthMUyyCXEcSYBS7MNt8Ha6UtcY7vofZ4XY15hjdA9CjDjQ6o1qcK2KVU+KLk19NIBPxc5ymG75oXfGYahgkdWz8qXautLIO9v5R/q2zpF8pyTA2qfdJ25H1Bsh1JAguKORvH81rG3OQg+TCcgBdTiuMBvXcFjPDGoFYa4FACBaLCXIDR8zZmRmV7TZSPdlEhcdmkl+dOldskEU6pauaYD9voHtZvuJu1kodq1IIY2fMJhiVzYBdv7/7t8yoNdyjJpKaRTYcgPLy45AvvoF1rHnf6Vq4rrrxWUeh0bKpS7EI/wUZc7A2b4cTd5uJwI7gAryaXh9g3aoqiD//OOI/fRDB//tXiL/409qfTylIHv6ZalveAhJP/QDx738J8f/3f1Sr8CIY7vkojO/+rfXb0iZjUH7wBaSe/G7D92b8C0g8/M9QDv64cju5HFs6l58r6o3X3/r+loTutQYz9JfeDqHc7rhJCNYuCMQyWyddWLI4YNt9NSRrZ8Via52rvMNfs6DkSNfAKKx12haoYns+QFXFyakpBNbh3EJV19GREWZ/NTpXteD1+jA7N9/0+2sdC+lshEKhtj6fTCQQ9HkRWrU4L9l2MomV+VmkFIX/bbY5YXa4OhL8RJbncfbQz5CqImzNjktNIL4wCSW4sq7zV/HdiShSKzMtbTNx7GkkTz6PTiKzNAXluR8A5KBnslZcWxSIJh/6Z6Se+V7HvlMY3Q1xbE/r+zp1EqmDP0Xq+Es1K6bVBMwbOrN98U8R+ub/qfs+mlM8//lP4XzvmptgJ6+HTawj5njmEcSffACBL/0Fkq9WvzYIWRKJppijyfs+j8SPv4nEA/+C2Lc/rzoQFsF4/6/CeP+vtbS9qvsW9kH54ReQeu5HDd+bWZxE4sF/ROroUxWv1ZobiMWlv+W9TeuLFtqaD9wBoYZhRjMxh2j3rDseI6a5/aKr6jpLt4Nq54rmHEp+tQJKjngGhmGx146JinU7NxKJZAqnJ+cRDEfb3oZRr8O20X5Yzca2Y475pQCm5n1YD+JKCrFEEoE2jyUWi8PnDyIUUls3i5FIJLGwuFT4XWx2e0mMtR74lhdw8uihqnEmdSg0g8jSDHc/dHKhn6ECkn+p6fdTq27s+LNITB7t2D7wfiychfLyw2oyXm+quK5ITzT56JeROvhoR76PjbnG9kCsw96vhfTZI0gdepL/VgNJMLS6hqW1r+9vP4Pw979S931agxG9f/DXcLztg2uf3Yw5NtFBbDLA2kQtNwrzbfey+1J6dhK6bbUXOcmDTyH6o6/CeNvbYLjq9oYDRvRr/wtC7whSh59RJwXqySaXyCJKvIYq9NU+n80g9eyPoPUMQdyiusPVhaSHMLILGldjSnX61MtALIxcyFv1HC0sLMBisTTtnFPYZxpUiXWxjmpwMagK+1qCtAy0Rfpltc5VNhrgCqxraHxD6L7RSBgrC3NwurthstjY6VDfJFuMJp/ZuTnWovB4PAiGQpy0IStx2tdEMkmqZchkM1CSCowmI2tXdNK5JRZP8OXfynVFbZQkLMuaKG2eU9LYYP34dThcUvtkNV2wRDyKWCTEFVZJljn51SnQ/vojCfSX/QRKNITA5EkWPjY20HgxeIa49YZcIElYOb40Db2rt8J5rOG+0HWwavuenDmBtH8BRr0ZQhNtPan508j45qFtksLPOj3ZLLS2rvrvCy4j65tn8Vlin1SMWVoNJ+rXK26fPvYcssEVSFfcCfnSW1h3THnpx5D2XNv0GCdu3Q+9KFewcAi5XBaRf/wTaMw2WD7wO83vWC6HjH+Fz1WruBAdmX4uY4673gVxcBSZpXnII7WZ1ImfPYr449+DKZuBfl99l88sjUkUc4zuWos5oGEdGM0qE4BQq42XjCWUZ37IYvbiUOO2aEpMaYd2Quhv7JJNhhWIR5AL+zsbc1DymNqEmtDqbAaGsXUwzTsAYhHXS2blz1Uq4ue4kQpnGwGKE1ZWVuDp7obBYORikl7fXGGP9nFyZpEZUB63A15/CNlsDm6XmqyJJ5IQtFokUxnWBtXL0mrM0bmFcjiagCRqW7quKBGXSmfWFXPopNWYo81DMZuM8HR3wWSsLPpGYzFEIlHWE6PYhsTzO4YcEAipwv3FiIWD8M1Pw9EzAFMD7VJz9wDSyThfv6lEDFHvIkyunpY7GyjmyEsZxGdPIx0JsKFPXkS/HpS5U6rhRVnSvxYyYR8zuyhxXg/ZwBKy/gVmnOe0UuV1Re3LJjs0NbQFm0X68JMsESNdcgvkK9+I7PIsUq88AXH3NU13sEi7r+TYR6zi/kvjZfgLn4HQOwzzO361pd8k619B1ur4DxFzcHtrp10gNxlgHcFmAqzD0BpMMF19a8P3yTsvZXqpbncTgtSU7IqGWTtGf8e7WAhe7O6v6vpFyTISsy25QcjC/MSL0JK1cBMJMJowpCsqnUCqHsfua5Dp26I6qHUQied/wK0G+isqWyuogp2ePwPBPQhtkSj3RoACmMTCORb7losW06SHlM2kWKugEZRIEMGpEzC6B2By108qUvKLtq2mLjo/yJFjoU5vYH0pPzGSgkH09PaxI1PA74fJbK4rpEoTUHY1IguHw1AUBU6nk6+32dl5pihLksgVxhGzidlbnRqs0+kMZuaWINdwXqoVbJITE722Hvo0aWMMDg5WBL+hcBjhUIgrqH6/H+6urqpulLRfpItRDeQWSW2RyWQSCzNTcPf2l3wPuU3S9UAaHMuzkxxkdfU1145odHbD5ApCLHOTJe0M1WZc11TAQkEjHUMqEoASWOIH6YHJTbI4s0oCkVefhuTshWFoJ+S+cQhkWmGyQVmc4PfIqy6wxUgtT0GZfBVi1wA0Fhfksf1NscuSrzwGpJPQX//OutefOH4JxMEdqhNc4XgznEAT7G61VeGat2C9yEwdR9a3COnSW7idmxbx6eMvMKNVGGjOzITH5S27aztdbt3HbWqtgLbp+uSfblLqL2CQc6fpmjsavk+37ypuA5SbEaSmcT4ahpBOQX/z2zjpJZDrV1Hyq5ZQPD8XDiBDxbFMurkEmChBvrq5thpa0JEmqFhHvL5VUEyReO770Jrs0F1yW9W4KrV4DpJnpGljjLb3hZgnCxOQrc4Sza+MkuSxicbtRoiH/AjOTcDaMwijvauh5uRG3v46WeY5kebQlRUvwpEIBgf62BXSFwjCZjHXnNMJFG+QJiYhGIpwax8lwOjv5OwyOzFSvktJpbF9tI/ZW52KOSiRNTXvh8kg1dw3bZXvGhnoWjXgbH8/9DoZowMeyFJpzOENhBGJJmC3meD1R9DvcVR1o6Tvtlqqx6fUSmkyGljvzB9cQl9PV0l8FA5HoBW0MBoMXPQkJn63uzkZDpvLDasvwGZA5Xqkkk7PGmHN3AN5Ef1kOMDu6PRwjGyH3KR2ZiYRRfDUy9C7B2DsGYG+d5RZYJT8SixO8vpCV6XFMrk0CWVhApKTxjsn9KON10zZRATJw49zMctw2V113ytuuxzi6F5usaZkLR8vrSfiSU6e0Viou+ZerBeZyWPczSNefDNfC+ljP0Nm8lUI/eNNkRsIxF6tFXNAkiGN74HQXWoC1QhanR6u3/kct3huYhOvJTYTYK8RtGYrjDc3N8hRD7T5o59uOJlmg17Ev/G/IO66HLqr1wZhckjUv+UT0FQRUeds/MxJaHtGCu2BqSNPsQtTM206/L4iJ7j1gPUnvHNccRDcQzVbB4gJkpo8wlVmeVUQvxYyZAce8qqBa9n5I9ZEOrDMbnW1vouC+8TSNESzvSQBFp4+iXQsBMeOyxq6O1KSQTJaChN6PTgGxjgQ2YgMfz551TOQbyPV8HPkzETJF6Ji04Ts7lb1hQjBYIg/47DbeJ+GBtcSLzqdAdns2n66XE4OBqm6m0qlK6y61wtB0KLLaePtzkVL2yqp5WFhyYf+ni6YTasW2ek0s9Vov7nVKxAuiNpStVgoO8/hSIyP326rnkSgILAcSjLJzDdjKsXJwGZaPeh9iXick415gV1ihkXCZIGeQDabKUmA+RapfTML47iVt18t4G4Vos4A51hzlPjgxDGuGLp2HoDO2cOMgVTIh1TY33QCjD5PyWrt6hhDCeW8g6Qyd5p/n+IEGP1bmT6GbDIGpBJsYGEizYz869kslLMvq21G3aWCyskXHuC/0tYDDe+jzMQRFoYtdoIjO/LUuacgXPdWaDvkKiXfdD8HufnFszC+HxrPMLSrjJdsJIjE9/4B4vYDkC+5qa3vMN7xzrY+t+ko9B8Dgr2r6ZiDEqmWX/qTxvfP8gwS3/17SJfeXHLdkkad/p6PQ7PqPlvhjjh7Ctq+cV7s0b2cPvwEtI4eCE0ky9jt2dwZxgrNfznvLDQkkUBJ9hqxTHplGqmpo3w+pFVB/HraX5lYGHIVuQZm4xMTlOQYarAwyG036Z3j+KY4ARaaPIaMkoBr5+UN71lR1kMymHmcb4SuwbGW2XLNIpNRWb95gXazxcTJNkqGRWMJnpMpDnG71o6TWF4019utZo6FtgyvLdb1Bj0UJc3zA83fbqcNkiRw0Y2+S9thpoVOFuF2mGHQi5gNr5S8thKIYmEljLEBJ4x6NTYg1pckqnM6Jeh8gTgcVj3vK70mCqUxhz8U4+N3WKv/TnqdVDUpF0+mYFTSSCopLgxWS4CVfkZBIqFw/JOPLykp6QuEmUVX0oKXy2FxaZkF+YeHBjkmynSghZViHM9w46Q1xT++mbN8DbvHdjDzS4mFkYpFoETDTSfAiEklyIaC8yqZ+dCDYqnEwgTHIsUJMBqHErMnkU3GkUslkPLNw7L3xtIk+cQRiI4eTo4VPx8/+Aivn6QGnSZ0btOTRzjRRWucPFJHfwqNdw666962LnOcYsi3/cKqq6R6vQnbD0AYuQja1eRXNrCMxI/+CdL+GyC1Y3KmFWB80wfa2reN0MZ+vWLTBfL1i80EWJsonsTih55F6Fv/BMcHfxPy6MY4Y1QLRNMzp5E+8RJ0197NizhKlFHgiU5uGAABAABJREFUWW3RlhdNLgclv5QnvwNx55WQLr5JtR4+/pxKe21Dp6Z8n0kwvJlkjvLqU5zYIlFIbdcAdLuvq/legWyPt1wMwdnXFI057V+EYHFAMJVWr1P+JcQnjkDXMwp9X/XWC5okLeMXs0hsMfROD9Ik2NuEvocg62EfKTUsqHeuGp0v0lbwLc2z5obB1PxkWWB89fVxK4LeYOAHH49eD09PD/Rl7C+y5KaAgRJgFfuRzhRo/gS7zVo3WbRe0HlxOmy8aCm/riiwpGQXBc7FCbEetxM2qwmhcAxL3gAn8ygIPDc1zy0CfZ61e2XF6+fjsVnNTScgqf2T2gdoAWG1WpmtOTM7D6vFDKu1eiJtkdpOUilm0PX29RUqr063B3aXu2QxQufW6ektvKdnqDVxWQ50dTLrfbW7yDH3jxUEmPk67d+KhDwNXQvaOLTQNe+8suprhh1Vgq+0gtT8WWgtDsjbLq9kKKSTyCxNMg2/PAHGzBBBhNinslKzsRA0enNF0MAL79MvccCZT4Cxs2iXB1q7zIWDasgsTPCiTvAMFxyWMvNnObisNR4UG4pQZVZ54luQDty2dp2xTmrje//1hAtpX38eUHy+Y88+ishD34Lzo78LsWfw/MUcZ48gPXkCuuvezPc0sRi09u6qep7aGgLWmbOHkXr+AYj7b4K060ogGUPmxPPIdg00lQDrVMyRPPQTlhyAkoDQuwXyzqtqvpfco+meF7san+vkzElOgok2d0XLtuKdQ3zmFAz9W6GrYSoiGsywjO2BUMZu17t6mB3ezOKHWsVcw1ubOlfNFNyoaONdWYbD6arKcK6FZWZ8xTA00AudTmYHw7yLIRWqej1dBYH3/Hy34g9BFAROgJUjlcpASa/pSLkcG+PGmAedl26XpWbMQeys/NxMCbH5lQiGe+2wmnTwBWNY9EWpix5GvYRT0z64bAb0udfipPnlMM9ttRJg1dDX7eAWT4p1nDYzJ9rOzSzDZTfDaq6+nem5FX5fOBbHQE9X4Tjo/Oe3VQxPt5sLjfmiZytjPRf0dDo+Z+3EHFqtAFvPYKGwTNe7rW8UMd8iDA0kDcrjbtv2S6vsnxbmrZUGZVkljtTyNERbN/TDuytifnqdXF0JxQkwSrSJnlFo9WaIq0ktjjkMKmu+BKkk0hNHeI3GRf7Ve1C0jLIeGGokrDPzZ9hlupC8igSQXZ5RpWpqmGPQ2FwsVUNjrnRVGct2tbX9QsGFGHNQ+2PHWyA7vL3/qNhMgLWJknYqbtTPqo8WQC2QqdNHIO+9si2h1PSZI5wAk3ZdAY3VwQs5w1t/uTVNHqqK7LwSwtjeNfeVW99ToktDA2367CvcCy/0jTJ9t9lzRG1jTUHUAbKBF61auwepqWPcHqSt0mJIlQexp7lEgG5gB0R7T4l7ZOErrS7I3cPcllV316qIz+rsbn40C0oipeIxZoFVG8RbOVfplIJELMrOS60kwMiBkBlfso6resXBSX4iLkd/X09N3aseT1fN1kKi1lNVsjywKgdVML3+MDsdyVLte4Cqu9TiYNDLVc8V0fnHhteCB50kQa+XIcvqNi0WYyH5RQwqo17P2ypGX4+bz0+rwV7+PFKVl9ldiQS3fdQCtYz6fD6Vdef3w+lyVWwrD793BaGAH32Dw20FAPRuXSoK/+wkuttMzuvLrnO2Hu+t4rRGldDFKQgGM5tFFDMjFd8CtyToe8cqjkGoIkxNTCnD7uv4b541VvK6bID+4ttK2pGSx57hwJDMMvJi8JnAMpSDj3Cbo7TlYm4jV449A3FwJ4SufkiX3ob0q88iffYgxLH9fG0N77647vlIvfAg0/eFN36E/50+9RIyZw6uJtIaaxhpKGFANuhFbQjUCmp452/hQsIF6cj0cxNz5Li636o+EOm9padOqjFHG66C6VOHOO6Q91+PnCQzw8vw9v/U9OdpjKDrX9x5BYRhtShE9418KwnUr80/lFDOTJ9gJoY4vAvCwPamtt/KPEpjh0ZngrZnDIKjl2MOke7LauMNuW43G3MM7eKWqGp6hZK9G1klyX/rQTJVxiuG4kV3E6C5LJ1MQK6iO9nquSKtUGItE0O5lQQYaUzRdUYMLWJdU2IrD5oHLEVC8/nnhvs9NZlcQ33VWwspDonGFWZiNWKBxRIpTk71uCwQxdr3QDqdRSqTgUEnVT1XdouBH3mQ/hgluvIti06bkffTZtHzPpsMMgz6UqbWSL+DdVNbgRonrLJ6BC2SShrxRIr/1oLbZeV2yUgsCV8wApfdUrGtPBaXfZy0HBnqbSvm4IJoNoOVpSX09vejHRjtpUV80gKzeAarSoZEVxY4rpYtNk6e5RH3LbCJjdFduQ/V9EtJ49S0/XImE5Beb8XrBguMu68vua/jrz7N6yLDnuu53ZCQ9s5BOf4MpOHdkAa2czJMOf0Ss8MEmxvS7uuQPvMS0rMnIfZvW7uuasjI0JiZeulhdnHU3fgu9TtOPKd27lidTbUzaqmd0zNSQoTQ2t0wvvNTuJCwGXNsopPYTIC1iWIxPsP+K/nRKhLPPoTkC49BYzJD3tZY26YcuqvfCGnnAQ6G4//yPyDtvw7yFWtaIJmZ01BefAS66+6F1umpKpKYefUZSDe/s0RPjAbGYmQWznHLAi36stS/32QCjM4RLfRpwV88cGVJcJosxouek7cdWNuv5WmkJg6rLY7rFJKlBFq1JBq/JkowDHRWu6wWIt4lRFYWYO8bgaGKAGitc1UNJJbeO7yloKVAAUcwEIDBaIBOVzs4NRgM/CDdquXlZRalNZvrJ9DqMblqBUaU/JqaW4bNYkJvd/1WFWqDCEViMBp0dRNgCysBhCJxjA64+X3l52phOYgccuh1q+eWkl8USBOjK9+W4FwN+ggDfZXJS6pQrxeqa+VQxW+YW2WGERvL7e5izTWf1wtjlaRj6fZ0/F5y2qqHeDSCoG8FLk8v67sVvpcSQxoZTlsHRW5rIBUNIbEyW3AuMxexOaiNmO57XfdQQ5HpbCrJCaxG4vja8sQZBb/U3krjx4nnIJG+l6sfWnrY1fGPFqZZ/zyyVIHt6mfNn5wSQy4Ra/o+lC67o0QzS9x6MTQmK7SeYU50E8OLGGfZuVMQL7q6pBLLu2lzQUftCRc4LkRB2p+X82286lZ+tIrEE9+D8urzEJzdEAdb19DS3XgfpEtvYo285I++DPnKOyHtu7bUNezo09Dd+HZmb5Yj/eIjyJw5BPkNH+Skb/ECrRiZ2dO8wKP7LGuyN50Aqxtz6EuLT/KuNSOA1NwZllWg75MG18dCY5HtGuxRascy9HdOu6weQisLiAa8LG6vM5rXFXOYzRboZF0h5qDPkruz2WSqGyOYTEZ++PwhLPuCGOxzlzC+mm37y0P9/SqzvuFoElMLAXQ5TJzYqodQNAF/OAGrWQ8rFV5rYHoxhHBMwY4RF0RBU3GuphZCHFf0dqlzuNkow2x0QklRzJFl4Xy3Y21+H+uvvB/yrZPrgdEgY9uohwtw5UXDiVkvzCY9PC4Ln/dlXwimBuefincpHUkt1L8mSESfroEeT3eF3AVpsjWKbTqBZCSIuG8RVO4yOLth6RkqxFuxpVlOHhm6+hrLxygJTqZRi2Q9VNzXdM4FgfUBlYkj0G89wG7SWoen0ImTjYVV4XtnHyfAtEYrxzf0aOYeZB2uS24vScyL2y5D1tkLjbNH7dqZPsEmaNnFKYgXXcX6ZiW76eqF7hY1eXYh40KMOTZbIF+/uKATYE888QT+7M/+DC+++CLm5+fxne98B295S33B4sceewyf/OQncfToUc68//7v/z4+8IHW+5ibtWOlCkTi6QchjmyHNKiyAxLPPoLEMw/CeO8vQmt1QBprz6GQWFokQJiNBqHtG4W2TIwwG/Ej519khzOgMgGm7R5ELrBUtX2hGOKuqyAMbFN701sQgGU6+8pKwWEmPX0MmeVpZAOLEId2QRpX6cnplRlkvHOQx4mWLKotjlsP8N/XC8iRJh0LM+urHRqu3mxFWkmyHhgxuMQyd6byc9UI5BZYXJ2l9sZk0gRPT+O2NGoVJIo66XCsB4qSwrmZJTjtZm5n8AUicDus0Okk2K2mEhvxWnDYzJwUKmdjlcPG2hUaTn5VO1fhWIKf7y3La52bXuZweftoa9XzZpAkp6dMBuYyl6VqtH/WfkinkV59jQLGbo+n4WRvNJthptbKBkgpSSiJOLfHliTAyMQhk4PJ5ixhfvrnp3hRZHI031LQCMT6Mg3tYH0wSoDlQaww0rUxDu1oqJdHSE4eRTqwBNPu62omr6tBt101FKHxMGPvVp2UdAbo9lxfeI9g90B31b0cLBIoOaW78d2FgKLatUWVXeXlR5lFRmYfQplYP+sgjl9caI9Mv/gQNDY3j72UFOu0QcjrBZuW5OcXTcccKQXxpx+EvONiiB41Joj/5LtIvvIsTPf+IoQ+uibbc/xjvTzSfgyssG6otqt0vsmFvcgR84LYEFUSYNreUX6NEsb1IO27ETlKLNOirwWjm/L7N3XuFY43aAFKDM+8Rk96cYKF9KXxi1UDCc+wyoZxtybovJFIk3lRMgZ9C21fxTCYbaw5RjFVmhb3ZWPvemKOeCIBny/AMgjd7sb7RyL3NM8XM8DaAbG3zs140dNFCTkJvnAcvS4zM6+ojdBmasxO63aYYTHqmJFVD6TdRWwuSmTRXFx8rrjoGFX49V4UGajkcjg+6YcsabFjuDl9zFaPn1oWKdlWjGrnlUYLKv4R845AsVO/p/4+UZskSUA4ioqFtUAMdjI7omugPAFG+qtmy9o2qONgeWmRdU8tNcyA2oF+tXBP2mC6ovEmujjNv5l1cFtT8Xp08lVkoiFYd19dlf1VC4YdVxYcIEmPlNZI1Aqp37VWFBCp0HbZGwtGO5T41193P8ccdF6q3YOUNEsdeYK7bcjsSyhj3FNxIV9gyMydRPrgo6xjmAssqyL3Xe0x717v2Iw5NtFJXNAJsGg0in379uFDH/oQ7rvvvobvP3fuHO666y58/OMfx1e+8hU8+uij+PCHP4ze3l7ccUdjF6V2kFmeQ+zRb0Nrd8Fy/ycg9o2ofUkUbFmdkKvYy1ZsY2GSOMAQajgIcvvM3b9Y8hz1fNMCTP/OT0FbwzVI6B3lRznSE0eRXTjHtG35kltUfTFH/cV6MyCBewpGyc1Na1uj4pKWDyXGcgPbmebLLY5lAz4NfCxIf/J5SO5BFrVfL8gRJrE8AwMF8lXaHooRW5yCEvJB0BtZXLZVSHojHH3DWJw4xfbkrv5hSES17oAYJLG+3B5PTfYXnTuvL8D6XmazkVlgA21S08v70EkolSqP4UicGVrxhMJtCi6HlaujDbdB7YiGMgexXA5nZ1Y4wB3wqCwgSjLR9RiKJJjpVY6xgertqHZray6hy74w73czn5tf9rEg7fhIb8PAnqp7I8OtaWnMzszwuRgaLtW4qgarwwWTxVbhvFRLZDYRJfFhtJUAU6IhTuQKZclw1v6o0hpMC2ZqiyQjiWYgkRg1axrW10WhlkqNVqzQ6qDxULB1QznyBLQX3wat0cILweTRJ9UWhKFdqoj+6ec5IVYsRlsNbFfuX0CWhLIbJLO07gGIu6+FhjSDkjFuO8gjffRp3g9pT219w3bALRKHnoQ4thvaBo5vm/j5R3r6DOKPfw/JI8/B+o5fhtDVsyoyp4Xg9EAaapyQTc+e4fumlnYoXWeGe9QW4DxSrz6LnJKA4V3/mfVIq0EY3M6Piu87/TKyxB7VCpAuuZWLe5REXi+ouIaIX405irRR0wtnWQRaGt7F0guUJKoWc5AJR5Lal3q3QOpAQY5Yskn/Aow9ow2LAZGFSaRiYUgGS1NuveXQGU38mDt7kmdMV+8Auw13oo2I3AFJJ8pQg01E527ZG4TJqOcHFcjy5jTrgXY15qBjCITjCIQSSCQzGO6xottp4WRVM9soT34RW+rEdBBWk4QBtxrf2al1ERr4wwrZBVXMdTtHyP0aFc+7bPqm9iN/nua9MRj1Iuzmxr/x5HyQ2WV7xhsXYkmjbPuop+mYg/blzNQiJEHA6GD9Fl2C0+mA3W5rSueLEj3xWAyCILaVAKN4hRwkyUmyGHRsBhqLyuY9gdwmKVY3NldA03X1I0MFM6H+PZmJR6GRpIokmWBx8ngZP/oUjPtu4vUEdbAkjv+ME2DUPk3ztHL2EJttCI3WU4kokxNyYR/grt+mTHIK4s6r+C8VF7RFya/Uoce52CduvwydBI3zyuFnIG2/BFpzfdbcf3QQkbLzGmAd3dx/WFzQCbA777yTH83i85//PEZHR/Hnf/7n/O+dO3fiySefxF/+5V9uWAKMBGr1174Riad+hOSrL3ICTH/FrfxoBkzl/dbfQWOywPyB/6/ktWzYj/Tx5yHtuaZQXcgjffYwsguTkPa37ihGejZUSeDv336AF6MdgcUF+BeQiwa4LYm3T5XJgR28KC1uiShH/NCP+S85tFBLRCdATBXFv8jMFblBAszYPci6HGILjJSq27HZmamzPDMBg8UGV297AsZUfYvH49zCSNU3alGoBQru/IEQuzPSQEwVxHLtjXZATLItQyqzirZJzkRUET07vczaFFuH20ua5la3R4/CPRBXMDnvZ0HZbCYLDblL5R2LvGHW6bAV6XHk0e1qPtiiiiElwMj5qZkEGDlQkS5Zs1XtZgJR2gclleJkJRkUsE5fk2gm+aW+T4JnbEeJXgZ9D+u5NTiWjJJEcPI4JKMV9iaS9wRigxUzwhpBJI2gBho5qvPSoxwJmK94U5WNSCp7JL/YI+e54HJB94gCxcz0cRawb5QAI50N3c3vAZpYhPJCukawmT57iPej0wmwzPRpJJ/6AbsAUwGBRL1111Y5J5v4DwFxdDt0l96A5EuPQzl1CIauHhhuuocfzYDagePf/nto3X0wvfPXS17L+peQPn0I0v7rS/T3COlTB3nBJl1yS8v7nD71IvVxc6Iut+sqNuHpBGhhSoL0uUQYgqOnwJCTRvezCHatJDuNh7FDj0Ij6ZlRmourBYP1IhXyQvEvQUetUFUYcsUweQaZBdZO8qtkOzY7m64szk7DYrOz2Uo7iMUTq06CJk5CWSy1C4E0L5LWlJJOq/M46X01wc5qBL0sYvuIOpdYTDKSSpYdGo9P+WHQidg22F6rP0UaGY45UMLwOjcf4SQXxRyG1XiEjmdmJQabSYLNVMkW6l9NoDWDdCaLRX8cZoPUVAKsz23heK7ZpFYz76PtEVOMWk9NBl1D3dbibTcrck9tsoPDIyXvz7ezNUrIUry8MjsJg9kKV1/9uToPg6ObH81CpoRUg0snk4wj+uqTbLBj3V85xlHbIesK5mOMdArZsBfZVQICaYGl589w7NIoAUYt4bob3gk0WJfw90q6qjEHyzGcPcRi/J1OgKXPHoXy9A9Z1B/kEEpM+8taH/c3sYnXEhd0AqxVPPPMM7j11tLEEyW+fv3XS4O88kQDPfIIhUKFwZuqGvmJgAZxei76k+8hPT8By9s/Di1VCrRa6G+6B8LYToi9I/yZvOsOW3AXUTrzz+e3m4dMjks6fcXz6ZMvI3XwCWTNTkjb1oSbaZKRr38rMySyNIBmMoV95Kpm0aK6eN/z+yJceTeLRZOLConkF39v8fuzmQyyixM8+GmC1GKwjytm+e0wBdlqLRxTzmBGVjZCSwtOjfq+xInnkVmZge7SO7hdND8Zlvd6a3VGtgDP0gTjHuLtFR8T2TTn25iK95HYVinvHAvdUyBZfKyisxcGEq5eTbwVn4OEdwG5bBrmnmFVGF3S8SKe/r/W71Rz34ueN1qdMJjpdS1MVlvh96CHxWIpfH+934ke01NTHLR5fT4MDgxwEqz4Wip+P8VAfb3dHNjMzi9zwGM0lDLA1nNMeQx4VDdErSZYsq1mrj16LPljHMTazHpsHe4u3GP+UBzzyyHYrQYYdTLSmTSZAPLrFLgt+6MwG3WwmHRN3U/1jmlkoIuu4JLP0DFV23eqbBvp2qtxf9T6Peo9v7ziQzAUweBAL9xu9Xor3n6rx0Sge7D8eUqC8X2zup2V6bNIKQr6x3equju1xgitAL2zh53K+NoqJCyzSAZW1CSxrvQ+K9/3/POKb14VkO0Zafl3ymm0yNHiVSsUPlf8O5GwtUyi1fkFgChDvuyuwvu59fHAnbyN4u+lz9ts6n1Z8ruSGL+m8TFV+50yc2eQ9c1BuPGdzJYsv5bqHWuta6/kfuofg3jTWyEObUPi259HJhGDeJVaGMrGo0g99wh0+6+Bxu5ueE3mUikknvg+dDv2QRjYUvd+uhD1OC4EtBpzRH70NU7SWN76kcLzhjveAXHHftb5ajnmoLnu6jcyy6v8nkwdew7po88i19UPcWh7yXVKunY03/Ko0GrMce1beTHFMYfRWjvmoEXl0qTK2or4uEWI2eG1Yg7S26GYo2+88J74saeRDa5Af+CumjEHvZfGiFwmiyy1YLoGqsQcKWarl+9jJplEKrAA2dXHLK/iY5WpHcpMjtRqYab4HMRW5rgoYXL3qWOYbIC8Oj6tZ342213quYAGRrOlrZiD2tymZhb4ea8/yALp+bGs2vuJfTTQ42I29ZnpFX7v1pHudc/Pxc/TyD7SZ+XnMzkyvlFbyor3pd4xUbvfvDcBq1GE1SRjz5izcI8t+BOYX4mjyyZzcS2XyyCTUOdRSuot+RPMxLIYxHUdE52nrQNWUM6pmZjDZlZjnE7GHDOLAYQjCYwPd3OL5EbFHPkYNb+d2ekpNiYaGR2r+ztpBZElHPQmS2nMQayykB86s5UlRZqJOeLeBbXroKu35WuPtEtzgsz3ZbWYQ+gd50eO3Jzz8goX30EHro4dJjt0+24mPZSSOZTuwWpjsxpzaNqLOaZPIBsJQrz1fdCunvdOxhya0Ysg3ngvtKO7kfi/f8prO/GSG9Xthf1QXnwchstuBqgNu8E1mU0mkHjsezBcfDW0noGfu5hj0wXy9Yv/UAmwhYUFeMp0d+jfFGASm4Zaw8rx2c9+Fp/5zGeqtlPmBcRpwURtlIuLi1hc8SMbz8Bw6iTcPb3o6urC3NwcoooGmJzk9/f09MBut2NiYoJd4/IYGBjgbZ45c6Zwo1O1clDSQT+2B6dOnSrZh/FdVyFtsuNcVg/N6ms0YGzbtg2xDE1sPoAeeXHugT74zx7HYo6qFOoNRM5/pIVGQozUi54HHVPX3BEsxtOIWHvVQSmVhLtvgI9pdnYW4cUZbpWkKkV3JoQuVz8m/NGKY6J9OnnypHpMrp0gNvmoovCEeC4hICs4IUzNcgJr69atrJVE5zcPPqadV8F/7lVMnz0N8ew5phjTMY2NjcG/NI/pV19m0WzR4io5poXJM0iHfBAty3D1DxV+p2AwWNh+V1e2cEzUVkuIe+fhMEgweYaa+p0IxC6kYyr/nWodE7UkRCIRzMzMFJ6nhQ8dE+0fXa88acdisNpsGB8fL/xO5DRI75VX7aYrj6mr4pgIdoeT2WKTk5Mbckx07Zn0Ah+TTyURFn6n/DHlUfw7LS4tYSUQ44B5dLCn5HeiKmksloTL0geXw4Hp6Wk+pnBYrcp3O7vgdNoKvxO5QdHlumPbWMvHJGhyJcdE98nYlnEkE/GS34mOyWxzI5OKIxjwVRyT1+vF/MKi2q5BgWvRGFHvdyL2l5JMIeEi/ZYcZmdnuGWx2d/pxIkTJRVVOiaXy8XvL/+d6Pvyx5SMRSBoNOjXaEp+J0os0/cNj4yUjhGBGPRLizCmokganTyGJgIrzJDsH9ta9dorH/eSyzNcDd1i7uIgkPYxGVhmer3k6sXY2Ja6196scZU9eepU4ZiCU6cwdfo425EXjxG1rj06npWV6cLz+d+JNCXr/U4kAk4Bbu/QSMOxPHXuCHLRIAStA2PbdkDMZhveT5Q0I7epndfcXPidSMuREoZ6l6fymEQ7TMEo+t/+K1iha291+9SCbzh5FP0GI3wj+6oe07nHH0Q06Ie0fT8yKwuwvPwMnGE/ltMC4kvz0OgN3EZffu3R2LWJzqPlmMMfRjaZhOHkSU6ac8wxP49oSgDOnms95lASGDLboR/ZWRlz7L8BGdcAziY0lTFHVoOZeRr0l9dijj4P/JOnsZiVm4s5Jl/EUlaHsKmreswxN4Hs8jRpCqA7G0KXZxgTi746MYdWjTmSazHHhKJDVuuEMDHB+1RzLtt1DXynXsH09BTkM2d5AZwfT3zzM5g5cRiC1cXC2cXHNH/uFDLRICSbF86e/hrjfrpijKQEmMti4gTYRsUc7p6+hjEHMZxi8Tgcdju2bBkr/E7EACPXZpJayGV7sLjcZMxhc3L8slHHRNeew6jBzMw0fIvNxxzzC0tYDiahl7Tc8lf8OynpLJR4CrK9Bx6ntSLm6HO40d1lKhxTOJbilNzObSMtH5OkLY05qGizZctWJBOxkt9JkmTorR5ocwkE88FVWcwxO7/EiUBNCzEH/aZJJYNY1AglZcDC/AyymfSGxxyxaBTiqvFR8e9EvzHFA8PDw6VjhD8ESZODTpsF9Bb4fT4kwkF2Oe0fHm0q5ogtz/H53WawFo4pEVjmZBpJN9A1U/vay2Be31sRcwQmjmN64hwk9wCzwJqLOUrHPdpWvZiD7tkctXLrzegdHGo4liunjwJKHILGirHx8YYxBye25s5wYn7HldcXfqdslIovOegd7spjkpww+YPo/4XfwkowhLnV7afnJmA4+SoGnN3w9YxXPaazD38PsUQC8vZ9SM2eg/ngs3DnMljaeRXiCzNsJECkj82YYxMbCU3u50RVjgb8RiL4NFh98IMfxO/+7u8WnvvhD3/IumCxWKxqAqxaNTY/iNEgVFGhTKWQy6R48VKzAkOVQw2QE+WG1SPlqe8jffgp6O98P7TDO1rO4GejAWj0Fq4CZA7/FOlXn4Vw1ZsgrDod0fdpUgmkpk5AO7Sz4NBGzysPfQk5rQj5ll9gXbDMq09BuvgWSAPbVo81ifTEEdVlLZWAQD3oZdVYchukSaj8MqN9Ty+cQ3LiMHRkLWy0NnVM+Sps8Xknge3oqZdYG0wm/aDV50l0npAKLLHteHk1tvB7VPmdqJKtRY51BJpl6rXClsofE1mLU7uFKMtYWlrihGy+UsbVRkVhLSiaPHv7+ir2hSvVGi1iMUrgriU2a117tapHVA2dmvNyG6HTZl7XMTVkrNQ47/GkAkErcAtivd+JJm0K6rq7u/nf5cd0csqLTDqLXVvU11v5neg80Gfz9ujnZn1IprLYPkLMsDWQ5Ti9RmK0eZ2y4n0MhWOYXvDCYTPB47K1XKWl/z83OcttpoMDawL+9D5KrJH7ls221t5J2wiHQiw02+3pKXFgonOVZ5MRC5Cul1q/Ex1jMh6DyIucDBbPnIBs0KN7ZFvFPirhAGIr87CQAYcsI+FfhmS2s1ZHM9ceMTrJLl0yqhVQ1gk5dxTpqB/mnVey9Xmja4/2MbU4yWxOyWiGQmPK5FHodl7NzM7iay/lX0Tq9IuQth6ASC1INc47jVl0vorbR0qYAxE/Uj/9FjTuAegvuxOZ6WPIKilmWKVfeZw1wEQKhvMsFNLziIWgdfY2dT9Rkivxjb+ExuKE6Z6PFX6nxLf+F3LhIAzv/R1myjCLbvI4a5RJ43uROfcqC5AT61foGYbg7uexJTN5HPLwNuQElfVXfkyBf/kLDlptv/JZhL7435AL+uD4tc/yXOb73O9CGtkO2/s/WTHuBQIBDmYpwKWK/yY6g5ZjDiXJ7S6U8K05zqSS3J6bo0eDuSz56NeROfUyDPf9EjTdg63HHGE/t+JSO3XmhYeQPnMIwg33Q/AMrY0ziSg7L2qHdxVYVDQWpB76J+RMTsjX3YfUqReRJf2tK+7iuZ23TWY0k0dZrF6TViB6RkrZBA1ijtTMCaRmT0HeeyOzytuNOcgQJ3b2EGTSBiPDjfzzKYXvuXRwBZKjm9krTcccigKtoIEo6zc05qBiL4HijGoxBwmbz8wtshh6j8ddZYxU/8YTyRL9zlZjDiWVxcR8EG6HAQ6L/jWJOSLxFHSSwNIN64k5Dp2mAgewd9zR8u9EBb48a45wbDKEdBbYO1bKnonE0zgzF4HTImOw21ixj95ADJOLEfS4jPA46qw/ajxPjLbT014YdSKG+xwlz08v+OGyGUtkJvg7/SEseYMY6nOXGBmVxhxCoV2y2u9BSFIyRKdDKqVgbmoSBop3+wcr9jEWCiAa9MHZO8jjRjzoY4Mpsay7o3gfi3+nNMUclCDVGwu/U2iC5u8EbFv3F1o0687PpOW1MgPJ2QdJb0By7gySC2dZEJ/GlOJrT/HOQZk+Bt34pZwor2A/UefD0hInKgm1fqeMfwGpFx6EpncM+j3XIzN5BFmtxN03GdIZ23sjxFWDLo454mGOC7R2Nc5peEyJKJLf+Rtou4dguP09hd8p/q9/BmTTML7399aO6YzqliuO7GStacQizMjTDu2A4HAjl04jM3UC8tgulalf5Zh8f/9HPE/Yf+1P4fsfv87MV+evfxYp7xL8f/sZ6HYfgPUdn7igYw6atym5efo33wVLBxzmixFOKhj/869eEOfh9Yz/UAwwCopoYC4G/ZsuoGrJLwK55dGjHHRjlve/88TI7y0TZS7rcQ//03/j9jrrR9Uqb+r0K8gRA2OvastdvF3dbhpU9RAHxlmzohqq9eFzkBlYQvrBL0HYdoCTclkScd9xBaS+LSXbogA1e+xZbtkURnavffdN71K1c2kCc3p4wUci0qwXtHCW3UnKe8vLNQdoEKAAiye/VBLJY89w0KohcWjSHMplIWioM6lIj0hJqK0QVY6p3G2Gj19nhHX3musKH1PEj8ipl6HvG4dhNfCu9nvwApqqtWZ7yfOCUDrRV0Mt/YNWnl9ZmOWJpn9kC1cX6RrNHyu9nwXrBwcLjo3V9oXa5gLBMLc5moyl13GtfS9/nvQvqDWSfvBq+9nKMeX3vdnn8/tibnLfaTv5c1W8vfz7xwddTK3P/7vevgcjSaz4YxjssUIQNJiYCyKZSmPnCAUOGnauUtIJpFKZkgDPaBAw3OuETify/tB2korCIv1jA13cHulyWNi9sto+1joHxXB3OSFJYoVuRiqVLgSWBA5KFIVdukg3TNbJhdc4wAuF4HI6MT8zxU5gPQODJcEIsdt0ejVgDnmX+OHsGWCNOourixNa1fbRYHfxIw9zmUlHo2MVDKV6erTP5nG1pSk/jrBehlB9mqL3p0LLSM+dhCajsMOb3DPKj3LwsWZSvPCmv2owl4WGEgdF26fzRUEFLXQ0SpwGCLUoQEGcjpxItZyY0lx0NbSrC9/0yReYqSLtv4XbsrRKrLD//DtQu1OZ613d+0kQYLz7oywEnt93XkTEwqrz7+rYyN/95L8jFw1Bt3UfkgcfR2Zxij3ocyM7Id/9Id6WOL5HfX/VbwRs9/8SckoSWqMJpqtuZw0owWKHltqnrr8L4sBoyf7m/7/W77uJ9aHlmENfGbuUznFZhL/4R6xlZ37vp/i51PEX+a+w49LKmGP/dcg4uthhutWYA4uTSD/2NYh7rmMnV5DG50VXQSzbVurki8iSILTRCqFvVXye2udveQ9r7PB7u/qQCS2zeQaNA7nFCQjdwxB3XF7zWMtjjmwiCuXE81y007j6+H7X5DLMdi2OOaiYV27qUS/mEI2WiphDCa4gOnEUxsHtMJQ5chfvI7FrKIEml8ccho2POWhsJXYgPU/SCdViDpPJiKGBXp5Hau3L7KKPTW9G+ksTH/X2vfz5XCrLyZ/XMuawmSvvp3ZijotG1WJLybhfY99XggpWQgrG+9RkybGpKMcr+7eoUhx6ncjJrlQmx8m5PGhfx/o0MOlFTjgEoylmntF7dw7bYDXr4VaycFr1bcUc9O8elwUGvVTyeUpUUvxDbabFMQcVAXU6mZOg5DBZHnOQs+H03DI7gA72qXpc6jnSIqkkYdCr8xgl9kPBIHp6SZJED5vdAb2xNDmdh8Xh4kceUpdn3TGHfUx1hl2LOTJ1fz+FklEL55jZL/WOQde3hR81Y45kHNqs2sZMYzFJbBSPPcXXFemEkUwD7wtpiclqMVHj6IFm2wFou9RxhWMO2QBxbC+yFHOkEqXXHmkomlu4n0xWaN/0kYKWNO877XMqAZAZSpG8TOqxb3Jbp27LbiReeBTZoI8CU4jeBci3vVONObbuVd9f9RsB+/t/k9vkac1pvuFu1VjAYILcMwDzdW9gZtjPS8xRPC50cpvt4H//7/+NP/uzP2MW3759+/DXf/3XuPzy0vk0jxtvvBGPP/54xfNvfOMb8YMf/ID//wMf+AD++Z//uUJW6oEHHsCFgP9QCbCrrrqKGV/FePjhh/n59YKy7eS6JO++jNlf9UBaLZR4ySP5xL/zICJddDlXaYuhdXqgu6I9gX6uwvZtgeAZRvrE81wVkPddX9DKykMY28saOUL/ttLPFx2H1uGBfOXd/P+Z+TNIvfIYhLF9kLY1L65Iia2sfxFZclH0jHAijh7FSHvnkDz5HGSyLF+H0yNRkcl5jhzo6iG2PIv48iwsg1uha9NufD1wdJXqYlQDDf4zs3Ow22wlrJ88LBZ10iLh9HZBAcy2UVUXoVOIJdOYXQrDaTPAZV2/AG6zaMZ9Mg8S7o8mUlzlpCowCevqqHUIOUzMhVa1QtQEYTnMJvV8rwSiWFgJw2Qgho2qT0HV3J6u9Qk5222VxgaUCB0bHS75nQKBILw+PzweN/pXk1vloICLPktiyPFYFBarGmyHAz4EVpbg9PTCbLVzNZVYDDqDSW2j6G7f+YyCPWolli0OZlI2i/yxEXMzdvYVGIZ3sZ5ONYi2buhG9jQUzef9yaTUxfXqmKAcfRKZ4AoMV9xdYL4WQ3npQSCtsDMUtx7e+C5kI37WPRRG9hTGavmKu2nj0Fq7oL3zI4XEVSNQG6XyyFcg7rwc4o4rSl7Trtq7F0O69GZ2Ay6G/tZ38rhKY7r+9ndzMozE8LVlzJ16ILMANgyg7R24Ye15rRamW+9tejubOL/IxiJQjr8M3d4rq16/xb+jOLy9RFg+/pNv819pNQFWDEp80aMdaGwuaHtH+fpLTx3nxZy89/rK79h6CTRmG7RlxaliMx92S8sv+CZfRfr4MxC3Xw5xRE3oNgMyu8gGl5CxdUFw9bHZDj2KkVqaRPLcK9ATM9S52uLUBrSSDoLe1NBROro0g4R/CbbhHZDLEuMbDRpb3V1dDReSdM1MTC/C7aLESuXY7bCa2PyFnJrbhVEvYfeWxm6GrSAcS2N2JY5uh46ZUucLOrn5mCOSSCMSSyOVzoFCFbtZZeZS6+npuSjHEER4SXMCrPSzdrN6TLMrMSz4EjCTBtnqa+Q8OdjdukN5HvQ7dDkqTZ4oIbZjSw8nfPJY8obhDUQw3OfCyICnZsxBEhCKkmK2oNmkXkc+f4BNmXp73PwctbhRrEBJf0qOubrbd5wnuYiIfwVGq4N1wZo97jwS/kVEZ8/AQvemxVn1/ZKTEsY5SPbG+0lxAxnx5J2qE0efRE6JwXDJHRXXPScVieUl65gsQHOy/oq7Wa8w65+HMHJRQWBfvvLNPLZqzXbWHWs65vAtQHnimxD33QBxtHQcJbZYyXmRZIj7b6gw69C/4b0FG0LDXR9kDa+cb5E1Q5sFSSrkYbz2DWvfKUow3/nOpreziebxta99DZ/85CfZDPCKK67A5z73OU5WUQszFXzL8e1vf7ukxZZarClp9va3v73kfW94wxvwT//0T4V/VyvevV5xQSfAqB/49OnThX9TL/PBgwfhdDoxNDTErY7UP/3lL3+ZX//4xz+Ov/mbv8Fv//Zv40Mf+hB+/OMf4+tf/3ohm7keJF5+EtEffhWmZAKGq2+v+17D7e8o/febPoBcMl6R/GoWWRI+oLajMhdFsr/V3Xj/6peYkCGb8liowmGJgk5xW2UgXAvarkEIW/ZXJMwafs5kg/7KN1e4R5XvM4nEailJtg5QIGopqxRXAyW9qOJD4t2EVCyCyNIMzD1DkFpYtJeDEifRYIBbGw2m2sdiWrWELqfMl2yLqNvpDLfnVQM7BnZg0Ol0lSKeSCPKjzBkQcvJJcL0UoQn+yFPbefK8wWP0wSXzVBImnmc6m9FwShpiVE1dteou64zEon2E3uOWgQo4CPQ8Z2d8XJicbCnM45mtX4nYgmaTMm61wAteHoHh5GIx2AosgY3UNugJQG9Qb3WJVkHPdlad0BkMxUL82KPnCMt/assjxbAIrANFpS8uKd7mCqVDRaeNL5yskezKlxtskNLWic1FoPi4E52beRixaomSmbyKDIzJ6Cxd0NYdbItDhCbDURX361+d5Oe1tKeMpbr2SPITB2HfO2b1f2wOgGrExqrA8nHvwtp37UQ+5sPSjdxYSHx7COIP/F9vq51++oX8Yxv+kDpv+/5SO3yfBPIeuegMVqhKZunaY7X3bS6gLn4ZmTOHGLtusr3WaEdXzPuaQSWV1BiEHpbu56Jsa6/4k013R55X/RmaE121vRbD5gVtv1Aw/fp7V0qs2w12ZeMhhBZWYStd4hbudYTc1AbPC1AiAlcC/mWmboxRzbHBjMqK7wSxPopbn98vcQclFwKxzMIx2PQDWuZLUU4M0f6ohoMdXfI0XwdGO42oN+l54QVIb9PVISjBJ7VJGL/uJ0Z6bXQZdPx7etxENtL3Q4V6ah90mKUMOxZv8t3MYqTXwQyHSKnT5KrqAVKkI4M9q62yq7NzZT0ong2H6/Q9Zo2qgW39SIRDSPsXWL9VJt7TTqipSQ2Mb3F2vM4tV4KZqfKGq1TeGBwzKErzPE0PuYkqeqx8pgwtIOL99k4sb3V85OmdsflKZaaIfY5b8fqai/mYGbZ6qMJyJfcVPLv1IkXkV2ahnyNSobQUps3jRfUCvrT/wf5stvaLp78vIJi1HLSSSe22Sr+4i/+Ah/5yEdYBorw+c9/nnMf//iP/4jf+Z3fqXg/5VGK8W//9m8wGo0VCTC6f/Ms4gsNF3QC7IUXXsBNN63doJTdJLz//e/Hl770JRYVnJqaKhFtpB/8N37jN/BXf/VXLLD3hS98gbOgraJ8ANPtuQI5aiVqEIhWw3oGDKoUJH7wf6Cxe2C4+yO137cyy4L1yhPfUDVqhna2/Z004JKOTlMVpS412MujETtOMNlh2HMDLzpjJ5+HaHGyzkYt0CTEC9u8jkiLIGaKuW+tZSqdjHMSLEO6OjUSYOT+5JuZgNHuhKmoBawYvsU5xCJhroINjFVPFFKrFdGfe3t6eEArP1d56EhUs4z1Uw4KKFRtio11B6Ek3ORCmO2/KXFUD2TtbdAJnFwiq3JCPJnGSpDo2uBqZTtBT7Xrql3QNqoxxug87hhxrbYJ1v8e+rzHZSlZPLB2DNuqb7zEol6vQ2+Pp+G5ouvDWJaMlXQ6dPWsOYKS/tfK/AzMNjuc3aVsCDom0t+gbZErUzVQJTeyMge9xQHJZIWlfwv/bQekmWHZ1Xg8jZ9+mVsHzBffWpcJQ8L49MhDHlVp+rXOl1DGFOFtjF+iank51xhpyqHHkPPPQ77u7S0VMSgpoLvjA8iF10wUWgHrMp45Amn/Daw/lkdm7hwy544ye7idBFg2HETi2Yegu/wWCDV+541YvG6ixZjj0ut5gUXmBa1C7GufXU2Ok4nvfwHavjHob3tP7fctTyM7fwbJn/ggXXIzJ7HaBbE224856icDSMjeuPs6ZmtwzOHogeyuzaAkzTUaZ9pd1EgGMz/ySCViSMUjyKSSNRNgKSUJ7/wMrM4uGC2lhc48Fufn2ByHmL4DQ8NV3+P1BVjMvr+3u+48Ssmt7WP9de9xSpBR8qU8OdJpUGLo3HwUbrsOjgasLqtBhFEnsLC9fpWVFYymsRJKQdS2nwDrfMxRuR1KiO3bQq6WmobnlFoj+7rWrut8rHG+Yg6TUcePZmKOclkOild69GufjUSiWFxahsNhh8vpqIg5goEARGqVWzUAKQcJ2JMpgNnqgMFsRc4zAL25veIqtSXLWxuPp9EzB5FNKbDtu6HuNVEuyaAb21f3upKqsFvFrQeQ6xmFtoiRprz0MOt2yVfd09I1SQk03W3v46JEO0ifeQXZuXOQD9wKFI1h6amTyEwcQ2ZgvK31bMa3jMTzP4Hh2juhNdX+7TZjjlLkHaIbyScQk+vFF18s0T/XarW49dZb8cwzzzT1XV/84hfxzne+k/Woi/HYY48xg4xanm+++Wb88R//MZtgXAi4oBNg1KNar32MkmDVPvPyyy+v+7sr+udNFhhvVLPi5xWiDJH0aKj6XwfC9suY/UUV2czx5zkBRgFftQUjLWKJ4ttqUikTWOLtEYMrf45ocG8GyaljnMzTb1EnH9L8yIS8YFnuGl0J2bSC8JGnIFocMDVZTSb9jdjiFDu+kPh2OQwON2SztaoeyNo2MkgpiYLIfjUYTBZ20LMW6RWUg9rRUorCDC+pwbmqm/zKZHB6ahEmgw6DvRs78BAtPxQlsXpNwwRYJJFCLJlFv8VQEHilIM9iFGE3rwn2E6aXYxzojvU2Zv61cl0RMpkc5rwxuKw6GFcTcc2gHuurHk5P+zgQHR90ttSOuRFo9VzJegMsDidMZmsFu2Dh3EnV4GPVlrwaUokoYt5FZNNp2PpGmO2w0aAAM5uIcDJgo88XsUQ01i5kFych9KhJBLqOc6ussmqg9i1ingm9ayy49LkjSL/wIEBjZdgH+Y4PQNtiC7bu2rcgd/FNJa0L5BqcfOzb0Hb1czW2HSgnDyLxs0egsdhhuLL2Ni5EPY4LGeXnm5KTxhvedN73Q2OwQNx1BQsm14O451q1JXf2JNLHn1dZXDVjjgyzEvItPs0i41tQWeNFJjpNxxznDjMDUzesagDRvmXCPpVxWiMBlknGET72HAvcm4abKyKSoU50eQ5GSkhX0WszOT0wWJ0F04/aMUcS6RS5DVaHaXXRb3fWjzloMURxM0krtBtzkP4Tmc2QeP2Ap3pCrlMgR8ZQLA29TmiYAAvFMogmsvA4dByjEPSyFmajgB576WdPz8WZ6DzWa+j4PEqxzMyKgh6HDIOu+Ws6Hye1AvotD58LcYJv57AV8iqz7LVCq+fKaDTAbrPCYi5dWJPxwNT0NOtLset2jQRYIh5FJKAW5uxdHphspUm0jYDOM8JrlPUmZJo5V9QNQ+uWzMpMoSW8EXOczMlIAod0mot1FzNHngRorIwGoHvjRyqYuY2gv/kdrBFa/DlyilSe/iG0VOzcew3aQfLwz5B45iEInn7o91/zcxVzaLQafnR6mwQyxynGH/zBH+DTn/50xftJZ48Yv6SNWQyPx4Pjx483/L7nnnsOR44c4SRYefvjfffdx+Qicur8vd/7Pdx5552cVKulOfd6wgWdAHstUe6o0Qg0aGTjUXbJ6CRoAJYvubmp94kX3wzIRgijFyF95iDSh56AfP1biQbCbZF59oLy5Le45YdF8JsECUorBx+BRmeC/qp7CueIWlD7+/sbDlyZwCI7seQFsImKbNp3c00RXj4mrcjtTySSWw/pRBSCbOCKbToeQYJaRqkKVyUBRqiX/CKQKHjPOPXj1x7UTFYbP+qBsuREM807vTR7rsqh1Wg5+dWJloRGoMrjrlEnxCb20WnRQS8JJUknCvDG+yt/r0gsxYFusQB6LbR6rigRtxRIslbGUJUEGAWrFCx3ij1nWBWxffWcFyO9NtjMr11PfKvnit7jKBOWJdCZIbq/aDDC6qqttyUbLbAPjENabak8H5Bq6INt1PlKn3gO2ZUZaMz3sgaHtHdNN6vauJg+9BigN5UkwJBPApAg97ZLK9rSmwG1Vmjkst9CFCGO7ebWBBo702ePcuFD2l3JpFOOPAet3QWxTLtDt+9qaI1mSKvi+Z2aAzexPrR6vkkjDORmXIfF1w5oHpUva8yapwKaePkbkDlmh7DtEqSOPYvMqRcg3/BO1VzC2sXbYu2bx74Kjd4M3dW1XbzLQdp3yqFHobG4oD/whpbHu7R/gXV08gkw0qUx7b2RNf9qgZykRauzuZhj1RGOGOWJwAonuKolwJht2qCNSWcwon/LjrrHZLXZ+FEPnu4uPt/rjTloHicHZKNh43W2qI1xz5gNchXWVDl6nDIsRgEmfZGpg6TFRUOVC31KqjWbwGj1XAWiGcz7UxAFDQbdlfM/OUsTC6wT7DlmZOsERJNZvHw6jF1DJi4yvlZo9VypiVhXTQMKapV0rLrf1io2d/UO8j1yvqDr6juv50o5/ixyYS/019zH7dzy/ltqvjenxJE+8lNobO6SBFjByZZICtsv55ikVVCxgR4lz+kMEEcvgtA/xgWM1ImXVXfvnZWM3eTBJyH0DEHsKS2eGK68FYK7l8Xv62Ez5ijF9PR0iQvkRulvffGLX8SePXsqBPOJEZYHvb53715s2bKFWWG33FL7Gn29YDMB1iYaCZeXI/a9LzFN1PKxP4S2jF3RDDLzE5yUEgbG2Xo2M3MK8nX31m35IZFlqtbm30ODk7RbdZrMUkLIbOfsffrgo9AObIV8qRrU8mKsjj5ENfC2tx4o0ffilqkoiXrmkAkuQzl7EPL4pRCqiEsadl2jBsXFrQs1gkJiqCn+RUjUujC6B5loUHV0q1IRSUWCCE0chb6rD6aeEaY4W0d2QWxQ+SC75FjAC7PLU+ISV9i3KpNV0O9DPBpBd99AUxN/sTtI8blqFZS4aYf5FQgnML8SwUifnZM2zUJuktXEgZmhOZHc7YNWFn9tJiBt9VxZjRLG+8wwGSqPkZJuh88GYTdJ2NK/Pt25PGhR4A0rfJ5IWL8VTC8EOBnX191+VZ1cIiOxOOxW87quq2LQQtUzvIUDkEa/ka5Gi86FgGbOlzi2D1lnL1dYmxoXr3pzxTgtDm5X9ciM1gqR2ZL9IcfdeLhC37He9+lvuX8t4Hzmh8gFVyDuOFCyD1SMiT/wFWi7B2B536dKtyFKkHc21oRc7zW1iQ2OOb75t8j4FmH95c+2qE2ngmIMMl0QekeQOvoMsuTyde09dVv/ssTapmLaKnucEkbaPdepr+kMrLtHQsxpMtEZ3Qtp7/Vqwcvs4OJZK6D7R9xyCeuJVbt/0745pKaOQbftsgJDrBhGTlyXjmVUeKsGYqgp/iXIdjdMo7uRjoVqFmuSQS9CM6dh8gzC2NUHndUBu7ANkrH+/KIk4oiFg7C6yIG4tnNhMXxeL5LJJDvoNTN3dirmIHb0aF/rTJslfxKL/gS2DZhbEo6nJFYzoGMjYfhmsG+s+fm+1XPVZRUhaA2wmSqPkVjxL5+JweMQMd7bGXMgo0GEN5KEQdK0xACj4zkzF+F20QF3+wmkhJJmzVSn1dCxmIMSY0ODgw1jDnrN0GbL42uNZs+VtGU/ctFgXS3DPOg90uVv5IJCMUQ2HnGwwQ7FHTX3idZ+yRg0NQgCFd8nStDf8e7Cv5NPfFfVVS5LgGV8S0g8/DWIwzsg3v/LpdvQ6aHb9XMac9C6tNPMtdW1LiW/ihNgtUAsQ7qfFhcXS55fXFxsqN9F1yfpf/3hH/5hw+8ZGxvj7yJt9gshAXbh8QkvUEjbL4a06zJoymx4m4Xy2DeQfPgr/P+Z2dPITJ3gHvBiXQ4euPL/DixD+eEXkH7pkarby0wcZlFZrXsAMNmQnT2NLFVEWfjwNsiXvaGU4vr8D5HxztXdR7F/G9uUVwOJ/BMTgaoTuTRR8Esz+aypUYd5lYmFED3xHNLRAFLBFcSnjiG5NIWUfxGR0weRXJqp+jlBb4BsdXLiKw+Z3Kca0DPjQT+ivmUko2E0gnd5Ed6lBW5RUJKJjlQpqC1yxRdEIrnmwlGMYDiGmQUff1cwEsfxc4uIxau/t15rILGfKHh5rUFJPBJ9jSUySCjNnz9iWp2bV9sna4HdDM1y1fYCSjZRgqyTFVOrSQe33YDxATWxSJocxyd9mFoo7dcvB+tdRBLwheI1DQ+agS8QwvKKH7FYoqXPUXtMLBar+Tpda/PnTmFlbqp0n1cWEAv5caGCkvPZZPXjVsfV0vtDa++GOLK7afaA4B5gF92K5z3DJcmv5CNfgfJT1Z0vj/QrTyD5//4WmeXq41sj6O94D/Rv/khFAo4sx413fxCG29aSZZv4+YK06wDk3VessQ1bRPLhf+W4g5CZPonM5KvMKMsjGwmsyiWs/ntpCsqPvoj04Z9W3V7m7CsscyCQ86PezDEIbYMgX34XpH03rm0r5IXy/I+QDa3UP8ahnQUziqqs+1iQv1ONOUoXTyQ2Xa+AmA77Eaa2oXgEim8RsZmTSJJLtXcO4bOHuQhXDaLBxM630uoik1n6ZmtDzbBo0M8Odko8jkZYWlxkVy5KftGjEwtDmnMWvWFub6wGbyCG6QU18bcSjOPIWS8SydZihzQVL1NZJFKvPZOD5n56kGg+MbKaBbG7Ts0lWQ6iFug3d1ooCVZN60sDu0mA1dC5FiGXRUavU8auYUosannfDp4JY3Kp/rVEcmGBSBqLvvVdQ4veCOaWw6zx2gqSSgqxeLKutu25iSnWCMuD9tO7ssIL8wsVaYo5UsnaMQe1hBdBcPRAHNje9PZpHVYslF94vne0kPyiuCb58JehPP9A6b69+DCS//537D7ZDgx3/yKM93y48rud3TC88X3Q37TpLH2+QS3El156KR599NGSeP7RRx/FVVfV19n9xje+wXPMe95TW+8zj5mZGZ6Xenvbd1M+n9hkgJ0nyHuv4kfbn7/mzay/w/9P2i8HYtCSWxtdyMEVJH/wBbUiesktEMf2qMyCgW1qe8FPvgb52ntLqsBEjc3qzdzLTS08mZmTXB2ohlwsiKxvHlrqP2+z3UjsHoLg7OVBN/bigxCdfdBtbd55MpuIMdOLmAukeyaYbJBdvVxplhweSDU00MjNxTK0o+X9NTm7uV2BHfFWEY+EOYjVF7noERIxtYLTPzwGp7ubq7fUbx0Jh2GxWBom2/ITPQUC5LpEmfpEIgmvP4RUOo3e7rWJjF6fnPOqv6uSRreT2gZXhdebPLZAJAklleEkzcxKBPMrUdbyyIM0vqhtsR09inogtlVSydZMNlHS7+hkhKu9e8dKq08kZEvtBMWtDQR/OI3lYAp2iwSHufX9pQB160DrzK9ARMHMcgwjPSYW++f9z+Y48KQAl9oe8y5P+WNrpE3Lgqh2ExJKal2tEU67le3pjUZ9RTI2qShYXFyB02kv2JLnsby8jEQ8jsGhIRZSrrZ/sl7PLcAlove+ZUh6A1uPv95AifNMxA/JPVQ1YZVNRJE88gS0dg/0F5W6LGYjfiRfeIC1i+QdVzb1ffSZ1MEfQ9x6KQRPi0LjFPSWBb6k5aXtGWmaAVYOoWstEEmfexWJR74O/Z3v5bbHYvH0bNCL6Df/DroDN0He156OxyZeX9AdaCyNUA/yDW8tJIh0t7yT9eWoUk/ILE1DeeRfWBNPvOKNEAe2ssCytm+cBZaVJ74F6dq3lOiIaj3DAGmAGa2Q9lzHCbNaGjS5iJ8LcsRi11rb0xGU+sYhdg9zq2T0pYcgeUYL7Y7NIJOIqjGHEucKPrU9SnYP36OyPVKzDVKQ9bANteaQTbB2dUNvMkNXFF9EI2EIglji6khzPxUqSBicWqfyLY2kmRSJxGC1mptioJOLHxXNaN6iECUSU7Dsj/Jc1du1xiqgWGFiLsDfQ3N4r5uYxeqc1mzMsRJUv6fbrsPcShKzKwnYTOq1RdsNRNOwGqsnjNYDSrSl0jlYaiSb6LVD5xKwGrTYO1rKrvGG0zDIWhjLNLyWAmksBNLw2EVYja0nsSRBg4uGWxfjXwqkWFdsx4AextU4iLRGqZCpkzRwcJEvf/5yyFDM0SCvR+e7yybzdtajZ+VxmWExyqsFv9IvjSUUzC2F0Ou2wlTWMju36OMk2NbRPghVrlkqjLKwt7z2ObrOyUBKSaUqBLlfD0hHAlxQk2uslTKRAOInnoPo6oM8srdCRzl59KcQ+7ZWNempBiI7pI4+CXHnlbzGagkUb5SREbTuQeRi4Zb1wfKgFsc8UsdeQPKn34PhLR9hcXz5ossKr2WWZxH9zhegv/7NkHc07wh8wWEDNMDacWknk0AyCDxw4AC3Mn7uc5/jJHLeFfJ973sfzyef/exnK9of3/KWt1QI20ciEXzmM5/BW9/6VmaRkQbYb//2b2N8fLwtY8HXApsJsDZxvsX4hKHtpWyposQMscrYHjfiR+bkC2oCTNZBvuYtUJ77EXLE7EorQFECTNxxBf8l1hi5mglVBG2JpZWZepUdz+Tr7m95QKRzRDdG/lxxMK0Btzvkk3fNQnL2QDDbWaA2cvYwMjFiZmn437XEaCmwinsXOJFVzABrat8FAQZL6WdWFmY4udVf5urYO0gL3RwzaEj43mA0sh25z+djrTFbA10OQjgahyDpEY0nIMsSDHod+jwu/pvHkjeEQCjGgaTVrMdgrxOyJEKWRdgtjQMqYiFR6wJ9nip1LqsePU5TSdAZjqVwejbMNttDnsa/d955qDjZUwsT83EOdPeOmQsOTcWgpE+/qzRxVLD3no5yMLpvzFJyXfV16WAziWwdfj5BFWN6TC7GcNGI+vtOLEbhDysYdBs4OdbrMqDXZeQgbtdIcy2qPV3rp/JLkgi7Td0OnSMSulxY9kOvowBV4oCT2iTLYbfbkTQYWHejGihAdvcPV9wn7qFxXqR1AtHFKU6Sm/uK9LLWgeTMSWSCSxDMDghVaP+8gB/YwU5whFxgEc7gFJDs4xYCqqSS8CwnnF98gNsLdHvXmCoVIB1DYsbEI8hSlTe4DLHITZKNSEjMdnBnpavf7e+r2JwwsJUfnQCN9cSEKU6yJZ7+EbL+ZciX3cJJsGwk2PT2LkRB2gsZ5/t8k5ZcHsTOLmZok/YdMwnoOj/5IkAJMIMZ8nX3MYuR2hxZRqEoASbtuZ7/spGGZwhCX6VDKZvfTB+DtnsY8rVvg7bJNpx6MQfFMVqTvWobZD3o3AOQ7G5uiwyePsgxBxXAtLIO5sHqbAwqCJAJiGQysx5iK6AxlFzs8shmM1hemIcky+gfWkum07hBRQr6qygpTjgYDHoEgmH4AyEIolAhKF4N/lACOdGIWCIFm0RzqI4F7c1F7n4zS2EuiFFBj0xvuuxGLoxR8YwejWKDwxNh1qeKJNJcHOqySehx6tgdOg9fOI1TczGe+wfdjVsCKR6gBNxasqc2TswkEY5nccU2Y1XnRVJzGHBJMOlL7y2a249OJWE1arF/1FByXY31yNy+2E7yaz0gja+4ksPZRQW7VxNox2fiCMcyGOiSMbWsYKxHx8L79BtdOt7c9U5FvPVCL4v8INA56u72YHoxBItRB1HUcpcBuYaWo8tp5VikWvIrv62B/tKkDhXn+vr7IdWIU1pFcGGG72trd2d0vRKzpzhxLlpdVVuqaUyTesYgOjyqCL5ei8Sxp2FYbdUmRisxvmi+Trz8MDO5qI27FnLJGHLRAFXiuWhAySuhf2tJUS4XWIYwULpuIc1n3R1q8qMYpBFNj06AxnMyFynOxMZ/rGpMU4skxRwUE/08xxwkS9GquUsz22wV73jHO7jI/V//63/FwsIC9u/fjwceeKAgjD81NVVxfk+cOIEnn3wSDz30UMX2iKjxyiuv4J//+Z8RCATQ19eH22+/HX/0R3+0YVpknYYmd0E21b62tqOU0AisrMDgnUXs0W/BfO+HIfasiQ1ypezQU1xlL67AbyRY8+L4cxCGd5UEjdxqSFXXGu2Fyae+w73lulvfywml9LGnobV182CZDSxB+dn3oPWMQt7ffjWZGBip+TOQh3erDktFoEoJU3Gnj0FHC9EmmA5EHc4m4w2FaJVwAMGpE/z/zm0XNxSabYRYJMSC81SlrYbZmWkoySSGRlTb41YYYBRghiNxWC2UMKk+uC2uhBAIxzAy0MX6Uq1W7E5NBzho7O0ycTCaZy6V7Ec2h/mVGLstNaPfdWo2ghAntawNk2CBSIqFZwfc+pYZTov+JGRJC4e5vXaeToO1M2YjrL3R51KD0eVAAsFoCgNdBsz74vA4DHVdJ6lt0xtMoMuu7zjbjvbPH4xCr5eglyWcmZiDTidjqL+bWYS0aFr2+uCw20qSrBuFqH+F24ntPQMIzJxlDT6rp7J1yX/qILLpFJykWdUBgWBqX8pGA5Dy7kk1Fq3pxXNcPc1QW/XEYej23ADBviYwzwmwF36kJsD23VT3OynJRO1VynM/ZLF8+fr7OWFAUF5+FNnp45CuvmfN0alDIM2mrH8J0p7mGVzRr36OdaJIm5Jd+AQRqbOvIv7ot2C8+/0VYrXV5kKqxjejQ7GJdcYcfj/0U8cRf/pBWN7xSyWGOrRYUg49CXHsIgjnwXk170yYoZhj68Ul8zq372QzfA9UQ/InX6UbCrqb381zP8cc7iFmTGaWp5F6+REIAzsg7WqfMZ8OriC9Ms2Mr+L9oPuYZBiyShLJ+TMwDO2Etgkx6AwltlMKxCK9sWpIBLwIzZ3je6lr2/6m5v56iIRDqhB4DYHvc5MzzB4fHxtSWectMMAoIRGOJmG3GmrOx1OLYYRjCrYN2lt2NCaG2LHJCLOVyJWREmLV9LlUx8QkPA4ZxqLEWC0cOhdHXMni8q1qcakeloNp1twackstzSd0ncx60zAbtNyu+HoAxWbHp+PcWkntjoRZbxKReBaDXTJmvAoGuyi5WPu3p8QeMeZ7HTJLTnQSdM6W/HFYTSoT7cTkCifAhnvtHHOkMmqLrcdphl638XFc0O/leNzR5cbK3AyMZguszsqxcfHUETaM6R5rztW1KdZ5IgbZWVtbicbr1Mo0RGcvUnOnkZo/C8Oe60vWP5wAe+khNQFGovV1kHfXTT7zXeRCXuhu+gXuCCKQfA2xbeXr3l61LXI9SJ87yq3m0s7aCbpyhL/4x7y/1o99RnXSFEQoR59H/Kkfwfy2j3Or5M9DzJHf14nf/xCs+s6ahYQSCkb++B8viPPwesYmA6xNKIvT0MWCyKwsIMtspDVkFmcQf/DfIG3dC9N9H0WOdKHC/paSYcy+OnMY2t7REpHXWqDJXdp5RZXntezyWAukTZMjYUV6n5JAZuoYcs4AJ8DIRUTafT1ygsRV21ruSLUE6GmRPTExgT4hwayHrKuP2WR5pFZmkDh7CGLXADIhL7KxUFMJMKqq1BKrLYZkskIy25GKBBCePQP7SGsTHFVgY6EgjBZVM8zYwLzA4XAilVIK9q+2Os415aDfz+ddgtVS2TYVisS52uXpsvKjXWwdrL4/8WQG00tR9HUZOCk20N18RZAD2pxKpS9GNJHBvI/YULqCgK3dLPGjHVAAXX5djYyMvGYVIfq9xgcsFRbswVgGwyQQ3FtZ/Y8m0jg5HWKxWbddDx+ZEPhiCESTXE0nRl6nQO0ti94gV2VzqSiGBwchrl6XxAKMxxOIRuPssHQ+EmBKLIwkGW6kklDiUSiJGCzdfRWLEhuxTsrEpWlBHZ48Bslkg6G71Pa5GlLBZU5CyVRJNZj5UQ/EEFPOHYJIbomj+zCd0GKkrO2K9kd/2RubOtb8gptaEnKhlULyi58bvwRZqgy32qrQBFIv/QTZ5VmI4/uaZusa3/YJ5NLpUoH8cABZ3xJy5CJYB5uOTOcXaf8KMgEvMivzyMVjQFHHMRnsxB/+BuS9szDe+W6WCiAWouCs1J6rBWZfnT0MgQwaypy+qoHmRO1FlUkqbnusIuJe+Jyzp6A3RPtI8gu0IKIEmNbVD3HXNdxqSfd9cQtlKzFHby7MyTRqfc6zOwmpxQkkZ05AdPUjE/Zym2MzCTBqa0RZ8a4aZIsNotEMJRZFaGEK9n61GNYsKIkVi4Rhslh5bjNb6s/3XS4HshlVJJwSZXZ78/EBzdkB7wLsFHOUjcO+UILb2YY87TOSKal20Uj1z5Pu1tRSAiMePcsajPY03xJICSlq+SvPZ9Hcu+BPY8wjF9heblt7Sxw6nwNd0usq5qDf66Lh0vvSH8kimshivE+Lbf2V55D0vYglNt6nR5dVwoJfwZxXgS+cYsady9q5RFQknsK8N4pIPAlN0o+tg4MFlhbFHKRvSglXSoqdjwRYPBplTV6Lzc5/SaO3WgLMPbqj3AsD2Uwagekz0NucMBYVGmpBCSxzO7DO7mameTW2eTHS/nkkp15FRkliPmPA8MW3QtCV/n6UlDNcdmdTx5qfv6Vd1/CYmk9+EcRtlyFLEhBVzMfWi9QLDyMXDkDcfknNsboc5vf8VqHtkpJfhGzQh6x3UZ3Xft5iDlobvQ5aIDdRic0EWJsg+r9+137o9l7J7YbFEDwDMNzxLogDahtP/OF/Q/rkIZg++LsQHLWz28XIzp+D8vi32D5cd13z1uCtQtqlukIydAbI172t4DTCjkFGK1Iv/Ai5wR2QqujgpCaOIDV9DPpLbq/QqWEmnKJA3DIOyemBYHFxG0HaOwu5bxxag4UdIWXPCHT07zZsecuhRIKI+xZg7h1lxhdpcUQWJiCWOaI0g2goiMDyAlcyrY7alRM6zsWFBQ5Au9zVJ8voqiC5yVg9iM6fq3JCZjqTZWdAvU7ElsHGE3E7SCgZhONpFqCvxgqrh16nHnAWsRCzqsYFBV5UaaT2REmkVsEkB2HlGl6t7WeWmWuUWEvGOyP820lQWwdVs2tVpSlgpyA2X23vsum5lWPeG+M2k04lwCj5FY6q15tOJ/G2qcWAAndqN5iZX+YWyaGBXm63LcfyiheRSBRDg/2FZG4t1HJCK4e9d5gTyoIoweJRk1jVPlfuuJoM+dhxjR7NBljU9kjMUslBGoFN2LDbulmLgzUK6T7MZJu+tjJLk8gsT0HafmWl0DwFnGVBJ4ne13N9rMtSO/xTaElLsbf6olq+/j5uhWilVb28tY2g23c15F3k6Fu/avl6u/9+7iGKMF5/FwxX3Vrxm4lD22C47e3MACPEv/8lZGbPwPzRP4S2gftgHpnJY1Ce+A7EvddCvux2bBSkIgYlJYfla+6FRq8mSuh+peRXKu8SObamU5eHcvolpBcnoT9wB7Q6Y9V5VBrbAZnaLM0O1twh0Wm5dwvHKBxz9IxC1zvWkZgjEfKz86Olb4THL8fQNgQXpqFrw+07FPCzmzS7GVprFwOJTTU7vwSDQYcuZ/XCFs0BNN8YDbqWYg5qV5tcjMBC7sn9G+PoS7EGFYyIndVqTDDcXcroy2TVdsiVUAYLgQzctix3304up3DRILX9t5+wov1TKObwppB5HcYcJj42iimqv0454uKYo88p83tJS4x0VTuVAKMYkuIMOjuUOA2FFWa1UyxERgkT80F4nEZsGXQVWiWLMbPg51bc8SFyQK0dT+TPfzMxB7mx07wpiCJsLjfHHtWgLWuljAV9SCcSSCWiEIv0TushOntaZbXam4vRRUcPdMQUt/dCmZhkXeNmkV44y8Y98tbLKuIbrc0N0KPsOX6+RVBRIn3kSSZDkBZpNehuJm3IRNOxGSGvI1kM/dV3QHfZTZsxxybOKzYTYG0i+r0vo+s/fboi+ZUfnHX719pQpG37+cYuZgI0AgnGSpfeAqGGvlU5cvEoUgd/ApHaEWoMVoX3knskLXyq6PaQPlfJsZDuFgWONdwdqSpKOjqsoloDNDhS8ouQXprgKqxgcUC0e2DcWbvNIbk0zQFqLYH7alDIsTIcQMZFmloy/xaWGgvGRjBarDyBmuoEooV9TSbrVifmF1UXm/HRxgyWYlAQ0ddtgyxtHA2f2h13623QrfM7plncVsGeERN6XTIsRoHFZ0m8lfQrkqkcTE3EE4FIhityxa0HNPG9fDYGgXRPUhnoWnCKPF/oc+n5UQtGnYi9W9buL0pK9TiNcFh0kFpogWyUdDo74+VgzGk3w2rSI+Rbe42SudT6QkwDaomsBk58r26frulwOMxis+XaYMuLC4hHI+gfHm2YKKNALa/zYXI2H4zFl2eRjkfgKGuzqgfDyG6VWt9kpZ7eR4LZBDovrYCSX9mFc8iN7IFGbDy+kxtuZvIoBBLjJn2ieKRiXuDxmX7jokQWiYJnXn0W2aVpdtOTLrmlItGlpda3DrW/1QtEo49+G8mDTyN3/6905Ls20RyiD34drg//dlU5A2IL6C65ofBvaeel0Drc0OibZ9bQQke65CYI45VJp2ogB0dyKRV3XcnOqPVA1zkV2KotlLSrsUHh39Yull2o1SJMBTqKOeotuii2oeQXQVk8h7RvHqLNDdHihFijnYjbnJenIZps/Gil6JYMB2BKKZwAo/HE3lcjXmoAYqvQ56ldqx5UE5wUtDXmDXp9et4HSRKwdbh5FiCB4oABt6lu+/56Qe2OVBzTy+tjU52eT2E+kMZl43qMdEvosgqwGQVOfkUSVMzIoZk7YDmUgSxoYDOt7U8qk8OzpxToRHDLpa2O6+NrhdGe+uxtm1HEZdvW5gmSqSCdNbdNhlxFE62dmIOSsa9OBiGLWnTbjXBaZBSbt1LrJsVs9Jc0SKuBkl75xBe1S5LLuc1iZOZYMeYWlpFMpjAy1New/ZWZeqsxgK0K86sWIiuLyKQUdI/vhrZG10s5/n/2/gPKkSvNDoQvwsDb9N5XZXlHFln0ZDe7m23Y3TM9Umt6nEbjNJL2lzSr3f3PkWaklXZ/aSXtHq202pXXjFba0dgd0zPth21INj1ZLJZ36b1BAgkfiPjPfZHIBJABIIBEFqu6854TpyozA4FAIOK9+z5zr39oR+fTDjg+ObtG6uYbhLY0BT26CFAkf6tYoRpEle3cTZNzwCFawMv1EI1UnJFAOIqq0Iz1ReSvvylE9h0uH9SHnt+13pVaK7d5NpNzxP/wN5C7fRnGl/4mHjSIpE6Tq0abfbwfVhwEwBqE+/yOEHLqu18WwoPeF37ccl8GwLjVO0CqZ3YIba0BTrtzSWjL5H2hqgEw7pt56b+YIrMPv1D7PFQXnCeeqvh3LhwLi0c7cPYeFsEwVl3U0vlKzd4UpcT1BMB8HX1wh9uglJUTNwKK0paXTMc2NpBMJtDRWSS0WyRKWwldRU6O9SIS9AoCsryeRMDntMyg7RV7DX4RXqcMv1sW7QfMONLVqUB2mWm0I1pLXJ1Ji4r0C0d2svO8tl1hlUUQ8KgKlud+cCaAeq79cjSFueVNjPWH4XNbk7PWEKsiHGhv8e8iWHSHPDTcW/VebWttQUskjKnpWSGon06noFEEuaX0OWRAjE5kdrVVkvEoYiuLaO0ZLHGSrIZA/yHobCsue543p69Dz6YRGDm16/0rtVHr6U1RDcL2p8JrsgxeZdNwDVgnGrTlaWhzt4QLpGQxprDyi8EvimwzY5qfvQ65c6RiC1l+9ga0G2+KjC+1Omgy4nzmi5BCbaUaSfm8EBQX2f1Qm9DuUM6/AO3yq9CXpyENHYcUbDGrzJoEjjF6dKVEW8oKDDww4GI0QaPtAPbhOmeKyBPJb/wOHKoKz7PW1eHOExcAbnWAixv1bHV9u+LgF9sldVZbUtqgSgCMTo7Z7/w25IGjwnG65nm4vNU5B49T4Xm1gqv/qNDaoYlONbBqNDV/V/AN//BJ28cPdA3A29YFxWaAvhpEG2Ok9JleW99AJpNFV2fb9rjFwMDI4O4W8gL4+97OcM3ERKXXsiWfgY2FtZRIkDWDH5Rjr8Evwud2IOCRoEgOUenE4Bcx2K6ip0UR1ei1wMDM+5M5eJwOPD6+s8CnpGlPRIbP5YBL0bE6/4Mz3tVz7aeXKdWQwckhf4l5QQEMRHWE3WYALOLexTmoJXtyrL0qT+jpCIvE3M3JJbicinBEZ7FPa8S/6/ngfWl36iFf59bV3S24ih209A2LxJ9cVpUVnbxhJmYtHF4pz2AFLRkXDvbOolb0zMJd8a+ryzoxn1ucFF0y7sPnd1WVi9cdexxGNiMSAUYuI/iJ0jNWUedZ47rwzntweALIz90SemCu5760nUBjO3nmW/9F/Kyeex5QXGalensflNPPQrv+FozkXehjZ+DweHcVSewFbHPXY+s1dSvJN8CCjR+cR/AA9wEOAmANwn10x7Y1e/1dGJsb8Hzii013e6g1eDADq6/OwVidh/Lop2s7hjndZjWX24fMS/8P1LMfhURr72YtoJIbkH3hHQeXvr4SzQQO0rT/rQVqfPlGTkOyuVDePr7DISq/4vOTcAUicDbQhlANDAakknRizJd8LitdiNX1DXE+LeEg/L7qmipW16oYCeorrGwinXGjvyvYcFl4s0Fb9NWYJpyWRrtdwuXJCnaDX8ThHpflRFfIdvLzxja7MbGkYaSLDisfzqyYzOSFjToz2XbA874ymRABwsN9jbXekPwxi1vNRKC9Zad6wPIZrON6UcPD19ICv393G1WktU1sts9d00Rmlc+OXVB7R+jvlIHBJorl2wGrOFMTH4gAGB2HvGy93iKs2tIk9EwCzr5x0+Gt7HrRQUmPrYisKStYSo5LHQuHJIJfYt/lKWi33hHnpo5a23ozCMDKL7nnkCCion2gzC1Opmuknkf2pd8WfbPuz/0V8w+6JgxLlBNPAvF1ZF76bahPfR7KYO3sc/adl6BdfQvuz//irlb1nX2+g8x3/gCeF38W6qHTFY8lBcNwjp9Grslj6wGqwz26E/TJXn1bzKWVAmD7BWrrkXPkl2eAjRWoT/yI0CmtFdCSqN3nkJD5zm9BfegTdVXDVz0fXRfVC4V72mq8Y+Wo5KxdqSC7ffANHRf/1gMxbsgKovNT8IZb4fTsva2yGMlUGuk0K8yNEuHycq4gRMhX46LqqyXkExU0e+EcNHSZXWHywxC6lcVgJTHP5MOYe9M5A0tRitsbONzLQJc157AT/NrW1upXUZ5X5Gc70qsWmcr0YGJZx2jXhyeKT56lG6zssrfOYHDvndsp4Vg53tuY1ic/O4Nb1Squiu+PvXIO6oNRJywc3H3/drbXl/DJ02SDXKGO1rlKbY/UBbMLVqAnpq4JjUFWfcu+4HYSL7s0tR0As7pWNA3Lx9e2zHTU3ZxDkreNzvILd6AxuCXJUPqPWH+eoROCY0hdw+K8RCCpiFNxzSp4idON7Eu/Jbp+XB//GfP96BqdikM591Ho87ehffAKnB/5cchdu7WKy5F59U+gTV6D90f/qmXbo9jnla8g8/o34PvifwOlv3IhhRxuhevoWWg2tCnvNzgkh9iafcwD7B0HAbAGUTygB37yV0zXIytRVpbHv/R7UE8/KXQ6COF6+O3fh9w7CvXIQw2fAxdDLFF1tPVCPnoBcv/hXW0BYsCkKOJWyasovX34BeRnriM3eUVkEup6z1wW2RtvCC0aBt7YvqBuWe5q87eRnXgfzrGHoHYMmjoWFgvnatASMbHoU2gVHGqsakrLpJFaXxbk2G4AjJP8xuoyXB4vPBVcHon2jk60tLbtagezwlo0LlwjGQCrhcK1ml2KiUwYnXOK7zFm0fo6AvB7S7NSnNgv343C7ZRxqO/eL0gvTaSQyRpC+4FORB7X3gfm1uDua8sWhMvTGfS3qugMK1hPu5DMaBjqdKLJZka2cWM2LTRCHj3st+2oJFzIjMZOmNosc6tJhHy8zvaG7krP4N2ZFRFEG+y1fsZIyIYG62vXrQV/pA2+UEtTyrcDwyds72vomtAAkr0BOOn6VlT+7z7yqKi2KpxT+fVSh05C7T0sWsbTH3wPktsP55gZ3Mpe+h70+CrcFz4rxlUmFpS8BrlSu/iWQL7CABdJXc+o2MqhHjd1GZmxLU51y4fOQWrrg9TSKcTupa6hmq1n29A0kS0Wq6cKkLsHIQ+OQ6ph1pK99Dq0+UlID33U3nsfoCkong+CP/e3dwmXF6CvLyP9vT+C65GPQd5y8aQRT+Y7vw9l7BQUGk00CCO6jPzNd+DoHIJ84glIPSO7FrfCBTKT2q4woCCz85FPQ7vzPoyZ6wAXVXWAlVm5W+9A7hpFnm6tFMrvNJ+x3Mw1ZGdvwH30MSGp0AjnoEmOxKC02wdnUSVmPcimkkhtrEGSZNsBMFaZRNfW4PX54PZUrljv6WoX83x5O1g5uMZf3UjApSoiAFYLhWt1Zy4ubqVy45aQT8VAhxdhfynnYEDsvTtxkfg51HPvF6Rv3kyDahMcyUa6dgeuGkFXWLYMNl2a0nCoW0Z7UMZiwo18nAEwfGh4fyIDLQ88c8J+hwOvU6PSSXTtnl3NoSOsbpsZ1YLVM0jec20qCo9TwZCFQRBBl9Hx4eYk4wuItLSKispmBGpbRuy3OTI5l4utQQmE4aRuZ1HyzFfUgm11rVwjp8yKdElB6vL3IAfb4Ow3kx+Ziy+JinUa8ohkf/eYCIjJnZWTEBx/lb5x8X8RJLMIlBUqc7mGLZZWUI49Brn3MKRIB/Lzd0QQzWFTw5SJQJBziDvQGnLfKJTFGVO+oQrS774iikzkRz5m670PcAA7OAiANYjiMl/JgvDQ9VG7exWOUCu0O5eF89F2ACy5Ce3Gu8gvTkLuG4Pk3y0en3vz6yJSrxw5LwgndTnKs6Zsf3F+5EsiYl9J+Fi7+pp4vfOpL5Q4j8l945BYNluHeKH4XMxO0NExERWZDbZCFAJgBo/lkLZdMXmNbt++jdHRUdul+Ju33oFDUhA6+SQaherxITJ0BHId1WN5LYfY+ircmXTVAFjBbckOBvu6LCt2k6msCGa0iFY1lFwrh7tFZO3M3+m4ORMVttI9bX60hHaTHh6fxMQuOdkLtp27ANyezwonpo6QKkTDaau9F7FZK/A68C1ZPUbSx4xvKmuIa+XJTmF8eGSX++R+wUoDo7/dKXTNGPzioiCd00ULaCXw9SeGd8jfZjovvj+7IsCC9BsUubevf8ZrdfXaDYTbutHdHtzW4RKBuA8hcPhhaBdIihP+408IF1tRSl/8tzINjfIxS3znTjrS6UJgPk8Xy4kPoPaPm+68urYdiGAQrEA0i/W8+NpCxrYeKGVaTEKbbauVQmrvhev5L9k+lvORj4mt6vv1DEP5wi/XPJb/z/8VMfbHDujDh8c5fLvvp/z6EvKzd0SwNn/3CvJ9Y9sBMMFHbl0S+9BEwVHGWZiUy77xNcEjeN/lb70LZfz8rv0or6A++0UReC3WiymGqBCbvgrnsz9eYvigjJyCPHS8fs4RXUJ+dQ75TBJIxoQ8QiEAJjR+Cpwj3Fk35zDyeWzevgjJ7UVo/DwahdsfRMvAGNQ6NNdy2Sw2NqLQ8lrVABiTEXZGTVbo0CjHqlInnsgil9fRUmS0UrhWWblVOHoWKrqvT8XRHnahq8Ut3IrLweFOcA72CN4jzkEucG1WQ9AroSfCVigD3RGlLh0rO+A8TpBzZDU6NxtIZ81r1eqYxOCoaWz1YXGO0S51O4fB6vOMZsDvrvw9kB89cshb4pTJyjivTa5GgwG+XaYOzVVeq8tXbyDU1o/+Tt92tTq/SjNVem/xYVQpMuAVOvqISHjtEqovGjetxitRSKG6RPWVzoIASMhOXxPrLIc3ZOo3FziHokJhgq4IOvUWHY6GDD445u+SO9gybpO7R8RmF+6nPw9wqwJ1+KjYaiH0s/+dcKuOPYgi+Pyumt0ZdiA/0RQcMNh9QuaNbyF36VV4Pvfz8P7kfwepyB1ECoShPvI8cm9+E9rN9+A8W6aLkUlCu/q6mDFI/oy52+aAdnq3fgb7tKuBQS+jvd+sJihDPURUT8VF+xDdRBxunzhufnFCVL4VQJHZfKAFcpHjSL22tZ5eVrFJlkQ1l9iAGojYmtBUm85XBSiqEx19g1AqOMU0Aqdq/XgtrMaQSucQ8LlE1qv4Wo31hAUZ5WckWSBpLZAyK3C/w/3749RUjkuTaWRzBiSHgWSWou4OnBvdvwzwu3cyIsD0+BG30Pl44ohHEDqSBp6DpwkaInawvKHh6kwGxwdcaA3sfKetgZ175c5CRux3ZsRjO6B1eXJTkMPzh3dX7kU3c/C6ZDiLAptul4yOsEe4c1lhaT0lKgUorF+MZDoDYyMptOR8HjObP7JPjqIFrCwuQNM0dPZU1xu7VygX0M9zIZ1NQbVo/7YaszgmuR/+FLJ334c2dUXoYFRqcyxG9q2vwsgk4frIT94X16EZEK6C3NbXP+xTOUARMi//CbSbF+H78b8J70/8LUitO/e23NYN9fTjgpNok1ehHnm45LUGNb2uv2X+f20e+uwtMc8zAVcOuRB8qgCptQdGJmGpg1cX50hsiGoHSjSwjVKOdAtDjOJyFv5Oii4LZ8dGOAcD4p6eUUuDDbY85ZKbcPpDtp5dV52cw+V2o6u7B06nfQe4msesUA41s7yJTC6PsN9VEiDjtRofCUHZWnyL5KvgHJWvIedg6kHdC7x+kw7GEDwgozFAB5wbad71Kser13MipvrkESdaAxKeO+EUwTByDtmhC52we4HplTyuz2p4eExFuEiYv4vBvy1cnslhNa7jiXEX3DbOi0FEcirue+FweTu/gdV4XrRKFgcV+XMXNVyLeE8xZlbSQn+tq6W0vXIzmUNmPYOOiEfohvH5OTYUadhF2g5m55fFsXq6mmMEs1eUjykax7O8BjVYWnVfabxi8Mz70CeQufOeqHTlusp1uHTctkLmzT8RyTj3E1/ADwq2i0QeQM5x0AJ5/+IgALZPcD30rHBhUtiWWFYxxKi+dvVtyCMnoJ7Y7YJI4un6+E8hd/M9qEfOQx86JioIWElQHhzS1xehs5Js/GHLFky5a1hse0X2xlvCecT9yKfhefRF8727x0SvurY2B6WlB5LHD8+JHaHeRuBq69ndTuGQkFqZRWp5Bv6+Q7ashkle16Zvwx0Iw1+0EKgGt+felPN3tASQzWklwa8COIEXMmZ0gDwx3GqbIKQyeZGZreWOs71/VsfkUgZ9rSpuzmUFuTvSXzkTzYovHpntlk5Vx5G+yhV20UReECm7mUYrRPyyIL7bwr8NDvqbqTzevp1FxC/h1FD9OhhcG/Czk+hVQltQ2bo29j+vKkkig8sAZ7FGGi3ib84mEPHThn4ni8f7oq/D/DmZ1sT3UPxdL0VTQiemPADm97jQ1xPZDn5ZgUT0zvSSuOcGe/ceHMvlckJ7o15kUwkhPivbFKy1CxJPIaa/tShPTV6GnoxBPvWs0Bu0u1h2Dh5DPtgKubV6m2Bxpa3Q+doDyacIrRDLf+rHKorrF0NkjfOaEMk/wA8PXI+9AIUV5V39u7gAnbxyty5BHn8IyqHdhjw0WnB+5C9Am7wiXB31/iNwBK1bXfTlGZGYUw6ds/y73D8utr0ic+11UXXpeezz8Dz6GfE7pb0f+c015KNLkMMdQsvPW0Uw3w7cZUlEJtsYBUkszSC9voTQwLgIgtWClstidXYSvnAr/GF7z57He284R0+bTwRArLgB55XC+ESx+7NjpfIL1ZBI58Ucb3d/thXSoXGoXcEHUzkxJ4/3Vp6X3CrdAbe4hwM4uqXJZQUGg5iU20uQikGv4ktUj3ZpMVbiOt6+o6EnIuHkQP3LLOZO3U5ynuptm7wudltAyZ/4eTK53YGnaELHlemMCLAJDdai14x0ubd5lM+9813zGHOrGaFJWh4AC/lV9PX7LEXzS90j1+FzKxju3rt8Ry6nNVQYk04loTqdwvSqmWAbJNcvBQ3T5ORVYdwTOv20ba1oIVkzcBx6S7dtwxul78i2+2WjyH3witAodT79BRGIqwVWGIsijSZpOx7gAPcCBwGwfQJ7mottyUvADGY+JyL6laxf2abATeweWxbihOrDH4dyuFQzTLv2hnBioiaXo2X/xAnUwePQI11wuHcyfxSeZY86e7OUC59t+nsyiLVx/S1hSe7tGhSTiWpzgGWwUMtmhOj2/Yb5lTg0LS8qcmoRR9vEMqnh6lRCOC4Odtprw4gn81iLawh67E2W4732WkpZmv/e3SwCHgceGjVfw8ok3vZ2g1hsdeS+h3v2PkQlMoZoG2AwbT2hI1KUUbWDFr+CRw9XP4+WgCK2ejDQ6RZBy3KS63ZJ6Gl1IVih0ovf9fXpODoiLqHRUsChvpCl1gcXPdWCX9utvbJUojPDzCQrASpVMlZDV2/f9nF5nFwuC1eNlmSxgJy8CacvgNb+3dpY5Yje+UCMPeGxUxUJqGlBLSMxeQXaxiqCxy+Itkd33zj0dEI4NGnRRTjbzVaxWhAmHh329i0I0NYCq1y0a69B7j8CKbJ7DBdtlEkzqGUHma//32J/z4//D+JnVvNQqLwgqKvdvYLMS78L98e/JFrw6wGDa9SBrOXadIB7D1Z5cbMEqwzyWcgtnWJRZQVlYFxsYveFu8h+6ytQn6DJQmmLyrYTKSunKhgqNAPO4ZOinaeYIzl8QWQvfVtUhHnPfbzp78kW57Wb78MdboU7Qk0xCUoFeQkrzsFxjnIK9xsmFxNiPi1ugdwr5yB3uDqdxkC7U8gB2AETYysxHS1+GojU7uQ5M+y0Pccz4NQWcODciLodYCHsJgQ306wqd2Cofe+V5fGUIR458phGOAeDW1a6ZPXuU46RTlUk3Mq/Y7aW9repaAtaH49GR9dmSr9rHoOVgFbXl/ea31M7iSU4R7EAfF4XgVpnA66jg/07c6fJOTS4XNXvn0w6jcXZaXj9AbR3VTfnYsBv5e41YbLVUoGfmJxDFrwjPnVNVKxGjpwXJhmevkOijU+LR0V7o9Pmeo0tk5KrF3ahjlQ2sNn+LOkEtBtvQaaDtUWizNgyDBI3sQ2k/+TfCekaz5//m2ayce4OpN7R7Yrf3LW3hCi+5zN/CTINUeoAXSIJqUJC5r4H7+9mS398CFIiP4g4CIA1iErOObZe6w/B9zN/2/7+DG7ROdETEK2R2pVX4Xz+p0TWVjn1NIy+Q0JjrJYgoXbtdSG6XKwFxjYDitKytbEaRLbVQnDZffgRZCl+zwqFLXHn7fOWJAwPm04n9YLVGsnFSVFGzKoN2eWFr4bbVMn5qk50jp24L3ulgz6XmOSLSUila5XPG9hIZBEOOKs6/7HyKBJQbDsSEu0hBV5eV7eEZDaLWDIvzmuvulp0ORzp5LF3Psu7d3NIpHX0tshoDcglZf1WmF3VMLmSF4S0s4zk1XtfUTSfn3FiKYd37mRxftQpCF8tFGdJm1mqX0BbkARxN0nk99zb5qnxXbM1ovS1Vlb19Vyr8sqvuaUo4okURgc66w6ClWSXV1cQ24iKoFi1KktZUeFr6agoIp1h8MXQ4d4KggtyVeE7IQnbuPo6FG8QgdFTcFKzSFa2s5kKs6mBFiSuvY58Igol1GG6xe1hzLKD/MJds428aFGtbywjP3VVaD5aBcDU089AOfmUbf005cjDMBg4kCRoty4i+93fh3ruI1DPbCVkeM2EdpKjutvfxDUoQ8dKKpg3/+u/gL6xitBf/yf7do0OsA+co6UT/r/09+zvT7Hj6Rui4jB38dvI33kfrk/8rHAToyOYEV+rGfwSgd3rbwhXU3KVYh1RRuprVTTILd0oH9EYkHIefgTa8iSyc7fg7CkN4O7l+c1n00gszQrtUNnpEVqi3OxCdbnRO3r0Q9E5rIVIoLT1sdq1ymm60Ipi21u1OY/ze0tARshnP1jR16og7JOFpMFq3MBG0mjK3EpvoJFOqSTQ9Mp1TazfuyMSusISgt7q73F3MY+ZNUPsx0DaXu6r4Q7yHEm0Mb52I49njrEa3vGhc46eFut5nJxvuLNysMjvkbZ4W+l3bVXhZfda8X48MlCa0L47H0MincPx4VYRHKsHxddqcXkN8c2kCIq5qrQYs/IrEIrAW0H3N7UZF+1m7q32ZjO4JVcJnl+EK9iCYN8YXKE2sXYp7F9ofYxdfR16hvIL7fvON8iX2CHE9aGjSO9UX5sXRmjCHdIiAOZ8+BOWHUeVoB59dDsoo119A7k3vgb18c+IDqZSzlH5eDRq0aZvCJOW4veN/Yd/JF4f/uv/6IBzHKCpOAiAPQjg4LG2AP3mO5DYWsCBbGtQFSTURhbWiK+KNhpqdhUHwHJ0FUluwPXcT1TMDFcDF3P6zbeEzk15AIywKxhfcq6GgfjEZWEh7OkYgKfTuuJCpxZUFaHb/SSizC5txBOIhAI13ZmKsR5LweN2iiCYnWu1FE1jbjWFXk1HR5gLdGtCxDL0Q0XtcuWgW2Eqo5c4LAoHGo95/Rj4qiL7UTcG2kuDM363A4buwMRyXmRrw77qmbmBdgbQHGgPWl/beu8rn8shBOolh/n+bqcONnQyWGcFtgS8czeLIz0KYkkdK7E8Hj3srrj/vQS/69Ee+xosjTyDRMDnFkNPvUS0HDSVoIaK6nRtV3rF1tcQjLRCKWp15P0Y7KichY1O3RT/uo6cE/uGhqqIp0qS0AuUtwJNzLZaZVzdg8dEFVixXkej16uA3MQH0KYuw/XwJ0scJ0Ub2vvfFsEF56lnt38vU6Pxsc/BUSUJUc9YJsho4dg9I0LHSR4+vv07ZegolL/4d6oeI3vxVWS+90dwP/cFOE8/sf1756nHYMSju8wEDvCDBQfdo9cXkb91UVSz00mssMCRuJAr07Gxgr6+gPz0NSHaXBwAy779NcFD3M/9REPnpoQ7hC6OcHkuC4A1zDn0PKKT16BnM/B3D8GzZTZxP3GOTFbDxmYabWGf7aomYjmaFk6OVtqRVtdqZiWLhfUs8l0UwVcrJt6YiDlaRTKB7Y5ZzRCBkwI4brMynNB0c2sGeNyxrtLPEvQ4RAXW7UWeB3BioPqYNdYtI+I30Op3NIlzACGvBI/TQDTJxKLZgkoReissbuh4904eZ4dlzKzpiCUNPH1MuWdGP9VAaY1q8hjlaHQOpUadU5H2/JkDfu8WvzPPIyMMJzbR0rKjd0cwoNLSbu2kLHTR5ibF//sOnxD3WNtQqeB8MSRJgdMXhLpleOOuIJPgHTwqChIYGON77JVvENnb70FbnIDnoU+UmJPoq3PI0Umybxzq+A4vkLpHobq8Ql+xKZzj9I70jTxwBEZsDXLfzrVSxx8SWzVk3vgGsm9+C57P/EWoh3fa9F3n9tbi/mFDmBc1OZD9g6In+2HjIADWIOoVd2/4feLrMBIxKI+8IKoDmMlVDtUWXy4HX+u88CIcZVlXOjOxHLaR4JeAocM5eAJymS4XB3a2H966cxeHDh2y7QK59eItvR4f3O3Wpb+pjVXE5iYQ6h2Bu0ZpLM8lurIosjjVHB7rAYNfq+sxMcGGgvayxDyP6cW4mOCDw65d99PNmzfFtZpeTgnSOdDpQ0vQKZyZppfTooJqtKf6ey1Fc2L/vrbS49+eT4tAzkNjXkudKrvtjXYws5oXAaRjfco2UR/vMck3A0nMJtYC9cMqZSuLr1X5fcXy/rVNA+3B0klnfl3Hzfk8In4H5qa1bd2Mp4+5dgUK2QpBoVinTP0vM0jG86k156RzhggyUrvsfkG1a1ULoYBXbHsFdW6KtW6Sm5vY3FgXAbFA2H5ZuysQFvo8diZ/Vor4h3aCPpUgewJia8b12n5vZavSrOw8HcFWKGPn4Ih0WgqH1wMS6Ox3fw9SRx/UE5Udc5nhdT5u6ifVA6mlA+rxR6CMlrZwus9/5J7PgQe4x5wjuiwCPcrDH4PUOSgCXsrxx+s+jtQxCPWRTwvHyGIoo+cEb2gcBpxDJ6BEundVO+RzuYY4h3B51TQo3gDcEetA9ObaMmLL82jtH6kpeM/van11FT6/v6rDYz1Y3UhibSMJj0sRyQk7YDXX1FJSGKocHSxNlBbGurGxMWHiQpOVgXY3OiOqSIjdXkgjkdG3NaAqYXbVbPnsbS0NsF2dzopk01PH3JZaWmdttjfawZ1FJraAE/07GlWnhxTBuZY2mHCrPWew2ryv1Xq/avMCdTyjCQOdoVJeM7ls4MaCgRY/MLGig6aZVCJ45lgpr4ml6DTJABmNk0zdL5figEs1Xb6rgc7YlHagltr9gr3MoW1hPit7f178Pq/YCtjcTGIjFofH40LAb4+v8z5yeRlstvcZmBQKDR6puR+r0pvJNwRka84hRTqhDJ82O4iKz9XhgFwv58ikkP3e7wmpBmW8shg/K8oa4hwdvVBPPgal/1DJ7z1P7xzrgHMcoJk4CIDd58i9/hUYy9Nwfe6viMXMXlBc+VWA3LU3W+fc4gSyk5fhYi95kdh+euIDZNfmYbh3BlmSkdTEZWE57q5ip8vMQ5guVSJyvntiz6USiC1MQVJU0epYC6w42YyuCXLcjADYZiKFVCaHjtYwAjUCBJvJLPK6jpCf1TQODPeERVClGmJJbTsDxra2vnYvchpbDmRcndxET5u7Yqvj7GoW6ZyOntbSlsmBdpcQrC0Ev9jueHM+i95WRWRHQx4JV2dyONSjlmRsG8HyRh5rCQNj3QZY9V8csKikMWF5nJguKrfstA4UcHcpj7tLOk4NKmgPmlocbLdsCUjoa5VERdm61xCtpVbemrRaZwDtsXEVTx41iX9nSBb247VwYzaLtU0djxxy7Un83w6yOQbq8mgpcqJ8UMCgl+pUt9sK7CLcV1sX7H4ARWiFEK3FuMaMa/bNr8IYPw9l8HjFpEfu9S9DOXxeZFOtd8rD2FiGYUMUv15Q4yv95V+HFOnYcV86wA8Nsq/8AYzYKtx/7r/d1o1rBGKR1bY7gaUUVQY0dH5zt4XsgkNWSzhN6vZFZDfXYTi7SgJbiamrQke0XOy+GOQSrYfPiio3qwB7OhFHbGVRtGlTz6fmOWYyiMU2BOdpRgAsGk8hl8ujpz0Iv7e6aUd0Myvm/qBP3a4WLnYTLgd1I8kHcltu0+QZgx0u8TNF1t+fSIn2uMBWtXg5JpatA2Cj3aoIzhSCXxSGvz2vYaBNFsdmwOn6nIYTA6VOh41gbt1AIm3gSA+1LHc4B//tDNvnD/NRHRGvw5arYgHXZnVMrRh47DAQ9joQTwMRn/m+mxkHukIOrPkMpHKGEPUvx8WJPDZSwMdPyfjICfMattnUhL84qYk2Ur6u2L1xP0DTJAbc9soPPwxEwkG4XS54vfUletv79m4gdi/AhAAs9EY5RjoCEWTf/irUY09AqbDeo5la9t1vQD3+JOTOIes30bIwNlZgRJrvxKhvRpH+yn+B3DcKRx1t5w8EuIZtdmWwTROFA1THQQCsQVCcVafj3j4sEEiatA9eMSsGjl+AsTYM2BBi1SY+QP7Wu1Af++y+itMWQ2npFpkBuay9yKysSJBa7vzS0JGLrUDO+oAqAbBadukMXXCq97f32tLoYKVJR98QFBvBMjtIpDJIJNNoiwSq6nIRM0sbIgt7YtRllv/XaP0jjg+FhIDrxTsx0bbHqi+6AW4kcphezohAVqUAGMvUhdtT2XlRp6NYq4PVSqwom1/TRAaS4qj8HQXs94qTg6pofXjrVk6QskcO1X/dC6K2rQEHzo/aX4R1R2RB0pg9ZVaYwbAzQwo6QhKO9tUml9Quaw3o8DbgJNXXpiDg0esiz41icimN1XgOJwZ98HsejGGc2TtRWaKq8Pj2Fsy/l+AiOjNxCbI/ArUOAXxLON1w+MMlGmDbbr5r85BHTgmxeyF8n0tXPAxb0lwv/mWR+W02KDrufPRjQpMj/n/9bXg+8RNQRnba23PX30X6ta8j/8wPjs36gwAaEOhOBdI+PDvCNObidyB19EOlrmgiZiv4pd18W7Q5Oh//EVsOpc2A0tYnqr2UsjZMOmXL7OPXHSVagLnYmhnlqRIAI2q19fKooc5eofVVCwx6dfX0wLnV9r1XxBMZxJMZdLUFalbA3pnfFAm006NmdS3bH6uBVdpnRgMiKfTGjYRwNB7pcuFovxdLG5pIyJErVAqAnRmyvh7lgZJUxkA8bWByWRNBokPdMlJZbAfe9oJHxmRkcwZeuqKLIBR/rhfrmwbeuk2dUgfODdt//WCbJMxiXApwedrA5IqBJ49IaPE7cGqr7bKjCh0/0iuJ6rVGAlgjHTI2M8a+B7+IG3NZUdn/yCE3PHW4XX+YoKg+DZjouO7zNacS816A41Z66opo91YtdEHrAbW/HL6wMA4pRn51DsbmukjEGfkckE4CuUzl4/hCcH3ur+4P5/AF4WR1eV5D7F/+f+H9/C9C6d1ZI2beexmZ916F/vwXm/7eB/jhxYOxcroPkfz9fw1FzyL43/wvgpDkF6dF5lxpRpVCLg3tg5dFq4z7hZ+tGSzaeV1GaHHZdQtrBiSXFy6LzIOza8jUtrl5czuop2fSCBx7fM86GU6PHx3j9bWBusrEt1OplHCfcbrqJ6jtrSGEgz64WK9eA30dIVEBVk/PNskr92bitFjrI+RTcXo0UJHscKJfWMsh6JPhr8HRO0IKIj5ZBMs2kjo6QrKoBqsV0LMDZnxZxs8qKLsW3VaitiTIJLP1gC0GC1EDsZQm7MfTOepw2D8GM9HVstEL63k4VQdaLFoOWvyy2KzAgCAz0824vkR3ixMelwSv+8HJxi4uLCCdTmNgcHBv5f73GCSH2so09ExyzwEw2oS7Hv/8rt9rt98VtuM0PJHC7XB9+pdqO8TuoTqnFlyPfAza5HXkPnht19/y68vQl+eE0P4B7h0Sv/m/wxkKIfCLf1f8rM3eEVqgSvfgno9tJDagXf4+pPVFuD7yF+y/LpuGwYWTkce9AnX9KKxfDnfPGFS2SBc4B9sa8xpCRx/ds2ad2xdA9+Hajq7F8JRxjkQyJSQTnDZ4Qzl6O0LoaPXDZcOMZKTbX/c8wznbKHAORylPoEu0lWwCQf4wu6ahPSijViqyv42uhZKYCykzQHObgXa5KXMiOZHsMATvsdkdugshL3C427GrlbEW3E5gds10fBzvkYTjdKCOc2BVejULqqkVBh8doqqsHJ1hCZVUnFh9R/7VLL0g6rpGfLplFdv9iqm5ZaHXOzbc0zTudS+g5zLIsYOGwbs9BsCo0+x+9MVdv9duvQ0jugS5axhyWx+kT/7Ch8Y52OnjfuLTyF1/B7hkwTlWF5FfmhHzzYMGmihwa/YxD7B3OAw+YQewjVgshlAohIWv/S6oJ+569GNiUEj8538KIxmH/6/9o5K2vczLfySy/eq555B7/2VTw6v/8HbZZ37qmuinLtfg0lfmALdXLJgqQU9EkXv7G2arTNfQvjjH8BwzN9+Ec+gUZAvtmmoQQS9dF0KTmYUJpBfuwjdyCmqRIO6HAZ7T5N07UFUVfQODptjl6ho8Hg98RboBe0U6S60p2ZZgbTyZExoRbld156Xq76fjrVspoUF1fKB5ml714oNpDYtRHU8eUUULRbNRfF9ZXSu2QbICrCfS2KLn0pQmtMQYQGNLRHdYEgsEkv0/u5QVLZlPHKmcVScR5lYIklFX7I0bGXS3yBjvde5o5OnmwmM/Ueta7dd7Wr1XbGNDBMDaOzruWxHPwvXKx1dFe7XsM8ff9N33kVuZhe/UMyLoX/dxtey2A6UV9GRMtJ1RCJ/tj/LAUSg27MwbBStokE3vygoXkF+dh8Ptr1htxKrfWDqLcDiMjY0NBIM2e3YO0DDnmP+j/4xwJCxMCRxuD+L/+tdEJWDgF//HEjH3zLd/H1JbD9QTF5B79zvCCEHuNvmBHl0RTqTK4Yd2JaLyS9OCb1STWhBGDhdfErpzBd26ZnOO/MYyMncuwjV2DnINp8hq411q7jYyK7MIjJ2F4vtw709N03B3chZutwv9vV3iHJdWNxDwe+DzNG+uTmXycDklW4v96GZOzJNuZ+Ocg7qib9/OiGDWsf7maXrVizdv57GeMPCR4/K+zKnV5lH+7ca8IRJtXXW0WxbjjVt5oft1tM+B+XVDaJExEUoe8c1LeUR8wJNHKgc/GVCkTAY1Ton1hC7cJ0c7JRzukX9oOQd1enNaHp1t4fuScxRfq9zGCmSXZ9u4J3nrXWjxNfhPPgWpCndomHOwXTwVBzwBaG9/HcrYWci9e2tPr3o+1O/KZUpE+ovBAJcUiFi2QIowRTb9QHGOwrw9+0//BoIeV3OPncqg92/9swfiOtzPeDDqWO9DuB99Hu6nP4fN//j/Q+J3/0+4nvkc3B/9MWRf+xqyb31r+4HP3XgX2q33Rbac1rC5d17aPgYzrvxdfvb2ruOTwFYLfonjJ+MwmCWIrWz/zu4gz6qG7I03kF9frL5fLg2DizNWljVI/AglEIESbNse3OtBLp1EbH4S+tax9gpONq1tbYi0tm6fY3Qjho1YDM1CKqPh2uQ6ZpZrV0mwRfLq1CZuze68P4Mt/H09YJb21JAbh3qcWE/k8fLVJJY37l01YAFsBWCWcD+TFIX7qpIFeaPBL4K6YWwLnV3TcXk6j+lV83sgIaW22PH+6ln4y9M5vHMnJ1pKCbo+hf2SyKQXcHdRw8tX09hMm8dmG8ibt1K4u5jFvbxW5cjk8rg7v4EkS+caQCqdwY07M1iLxnf9LRgKoaOz874kosXIZTNI3XoHqbuXtn8nuX0mcWtAe0GPrSL98u8iV3S8ctAtkplY7dobMBJRkdzYT+Te/CZS/+UfQ19dED8brB7Omfce2y9Tv/2/I/21/7vi6yuR2APsHygG7HzoGWz+h/8ZqT/+dbg/+ufgevZHkP72/4vsxZfNnXJZaDcvQrvzAYzoCnJvfQu5i9/bPkbu4neQe+1Poa/M7jq+3NFfU2eU96axvgA9vlY/58hlkL3+BvT4avX9sikYqVjD2f5izsFkm9TAvZpJJrCxOAtdb05lGyteW1vCaImEth0do7EENmKN8SorxJI5XJ6IYW4lVXPfZCaPy1Mp3J6lTIUJJn3qbUcMeiWcHXHiULeKxWgef3Ypg7X4vReqdqtm9fd+Ti2V5lHe/6z8ajT4xcX9RhJCy+vOooGLkwbm1szvgTppZ4ekmg6Wb93J49UbecEbCfIvtmAGi6rfL8/k8dLlnAiqERTe/97VLCaW8h8q5+C9eHM2gVS2sfNga/AHt5eEw3o5WiNBdLVH7mvOwWtF99nkxGUkZ25s/17y+CC5qB1cP+fIry0g9eofIDd3q+I+kj8CuX0A2rXXRCBMT+7mbM1E9pU/Quo3/wn0uMltKLFAM59CYib5W/8c6T/7ncpOigec4wBNxkELZIMQbhSyDPXQKWHxrY6dEr/f/Hd/Dw6XG86HPyoyrN4v/k1Rfu+go+Gn/qLQfylAOfG40PmSe3dbedsBWwylF34OaKQigdVnM9c5+lat7JIjXfA8+tmGSl95je7eNR2ZFH8Y/hoBvUpIx9aRiq7A6Q/BHWjsGOUIhnaOIyrBersbtiNOpLKYnI+ityMoxO4JOj3S0jloQ/OLlVtBr4zU+hx0nSL5Mq5NJwQxODcWrMsSmsch+FrO+R/GvH+oW8EhawfopqD4vtqPVroLhxTRDsJsqZZnJdkOAWGmu4D3JzWxYHhopPTZGGqXEfMbIhBI0EXyTJnrJ4kt2xOml3M40ufEZkoXLQu5Mg6Yyekis2vHObPea8UgLXXlOiLu7YoBBr42NrPwuVV4uaqwgUQyg7yeR9DvFcFlVVWgsAfWSttQ08Tzdr+C12ticgqDfUeQX5lBZmkKro4BOLtHxdYQVBccvogtExO6NunJjRLL8v0AK5ElOuxu6TalfudfiPmM8xVcHqgnn4DUXt0l6sCR6d6C19vh9EEZOwGlZxjOow+LoOXmt34bUksXnKefFIsE75f+lqgMIw9xvfBT4m8FqGeeFdXiUg09rEqQew8J4flyDTtb57+xgvzcTVHtLgVK9buKobQPQI5075lzOIOtYmsEqdg6khtrgm/QCW6v4AKuEPwiPG4nBnrabckoVBK6n1hICIH7gNc8hscpI+xXEdz6uRrYhhjyStBiM9D1gJgb3r2TEnPehfH6+GR4S1uUU4igKh8C5zhZI0B0P3MO3hsfOSGJy5bRzGvYFdm5iDTvKeD1m3nBJ84Nl86vh7slpLNmkq7ALx49VMpnaSikyhABr6N9igi4ZXJm4LMY1FBlHM3vdjT9WsVTecSTeXS3qNtBqXhSw3pcQ9iXF/ewHZCjECG/05QyUWTIFpyDWrrUAVP5we9DFF8rV9cQcmuLyLANnfI3rMZqsCKLYz81u5i4qwWu8TRqs+5jxTkhtXbDiK3B4XSJ1vTEb/4T4RDs/fwvwREIQz15AfJWd9QPFOfgM9nsaoCDFsim4CAAtgdwAPd84kvi//rmhiBs3r/wN6imur2PVFR6Xx7oolB9PhlD5uv/Ca6P/WRDhI+BtUb7wp1nP76ryoxZ18zV70PpGoay5Qayn1ozJe9tGIjP3IKkOuHv2tE18bd1wekNwLmPwtlud+NtCKzOLWwFcDIe6rZXmjq9nEY8kYdPkUTVF6uA6PDnc1Efo7FzIil94oh3z98H7b1DPlZz3ZsBl6SMlVP16nAUXrsSNzDSQffQxs+30LJKPjXaJe9qNaAmB1sLKNRvZRpAp8tiof2FaF5olBTrhnW3KEI7ZWlDx3CngYsTWfjdbFcoDZhemc4iltLx+LinxN2qGVhYT2MtxmCXsr2IigTcwnnUU4je2cD88rpoMwj4PGJBNzJgHf1cWVkTVZb9fT1wNaC9dy8hBVqQm7uBzOwN0Q7pHT3b+LE8frjPf9LWvsrQcbEVdMGYIVXHH2n4vSu+z9hpsRUg9wwLPSmCGWfXk7s1Qw7w4YPBI++nf0b8X+diwuWF70t/CygSW5eKkkTKYKmLqBRqRe7yq9C+fhmuj/9UQ+NJI8Ev8d6tPXCe/iiksqCUnk4ge+NNKL2HoWy1Vd47zqEjOnNHmOn423bGrWB7FzzB5gS/KsG7h7YYk28YJZyDjo9jvfa+mzvzGSTTOlpUh0iyTM6bFcvhrQRaI6COKLe9gJ9pPUEXxebOddVAzsB1dUfI0dBrWb012rm3c90JXAGHe0qPRQ42vWrgSK/pMOlUdnOO4iQdwar1gNuBcJFuGCvjJ5d1zKwZGOow8M7dPNoCEsa6S+d6s3od+OhJ8xls5vdAE6doIo+wXxZuo0RH2AmfWxabXUwumtVKp/ytIlE3PmQtqzK3FEVsM4WxwQ44bejnfZhQ/BEhFZOavo58PArvwHjDx5J8YXge+oS99x09KzYid+MtMfbuRzBMPfao2ArjLlvzOR8V5jX3sz/a9Pc8wAGq4f4eEe5j6BtrQMR02RHR7P/MaHYbfAyAWYCBJWZldx1ncwNGfJ2hbdxrWFV+MaPMFgWdgbFKdrh7hM5WGwbFLfrTs4kY5DLnJDpCuvxBaJm0+FulCTmbTiGfz8Pja4ygW4HZI2ZHqmVq/V4njo922Does23lGgyDnR4kUjLWFh1Y39SwvMGyYAfaw84PtXSb+lfvT+ZxuFvGSGfzM2gZzcB3ruTRHnDg7Jbr0rsTecRTwAtnmK20/9lvLeiYW9cFGW31ywjv07plfl0XJJLi/IVKsWpgS8KlSc1SN+zUkEvcD9RJ629VxD7l6GlREMrowo3r9mIOZ4Zc21V+tYOXGvwWxyygt9UjqgXKXSTtVn5tn2NHxJbRg8fjRk7LNVxpWS9IslJrS8I3VnH74LSpA8SgU+rGm3CxPSC+VlVHYz+Rn7wi2sYaCYBRCyp/813kZ29BOfkk5BoVP65nDxwd73foyc1tzsH/J/7TP4LcOwrvj/yS5f5GNiOy7bt+H1+DwTYURk/u4fzC8aHcLVqcTzYlOIeR3AC2AmDNRp7aMw4JUllgTchUsPWnTApXkhW4vH7RDk0X6UqgpiFfS9fHZoGOguQdrioOMpGAExEb+miVdJ/GetxCo3RtwSHmluUNfTsx82FiYtnAB9M6Tg1KGGxr/r3JpNW3PjAw2AacHjSDRq/dNJDVgB99xP778bpenQWm1wzBV7rCZoXVfoCukneXmBR04CPHqatVfX+2OL43oVvqhpGzsLqL2m+D7ZKlsP5guwx2I9JBm3qqFw6p8NmoBiPXoUtk0FOZFQ13upHI5LeDX4VxoV4n66Eue8lwn9clviurivT9AMeTRNSUpHF6fGKzAy2dQOLuB3B3DiEXXYTjHnGkcuSnLsOhuhsKgJE3aTfehr44AfXsR4WZTyVwLPZ8/CfwwwB+1kbaWGsd8wB7x0EArEHoFA8sQJKhjp+FI2hNSHJX30Du1T8R7QjlVWDOp3+Uo6YI8twPYMWah44hTVr0sSWqHBu33hOTXvjI+d1tAodOWz7c6fgG1mfvItDeDX+rdcvmysIctFwW/aPjNZ0mhe5CbBM+r0e0bFXC7FIUm4l0UzJIi+sZTC2lMN7nQ9C3Q8TplrgcNUlYW1CFotBNUIW8z2KltRDxSeiOGGgL1Hce1JZwO228xmCLH0RG8uyw+avjfRKSWVM3y/LYOQM3Z3VIulRS5n5tloEkCL0Mr4uVU3lhZx6q4uhopf2VzFavPhvukISuRkfQrDKr9SkLumFWzp38XeH3o93WQafOsHnPLUZpqOCwZahALEVzuLOQRn+bavkMivdXZbQ2oTWA7Ty29vN4IMlsVbg3Yx0XtomFSfF/2ekWY4sd8BwluCBRYPzY4005F2ot6rFlKAPHKwYKmUjJT12F1DkEiRWvj3/OFI5tAPrkVeTe/qZ4yHThyNtYy9sB7h8U63Cy1VE5fBZyUaV0MbLvvCT0Rt2f+yXIbHUtguvjPy2CNnt1Y24W5GAbPOc/LVqFm4Hy8Y7PUPTmRfE8R7akKrb3lRW0jZ20vBabG1GsL80h0tENf8gMPJZjYd50ahseqe3UzfadaCyJoN8j5vhKmJjbQCqTw7HhNsu2rnowvZLF7EoWJ4e98BdV2LDaeCmqI5t3YDiiwuMy0B5UbM8v+wVyDc7bbVti7nbA608eYYdzkCuQZ00sMwBm/u7ckAPV5FYTaQM353U4jZ3vgse4Mmsg6AEujNGBEnh/SsdIh3lt7YLC/ZQ4qFZ9drSXzpQOtAftVWMVdMOsWhiLr9GxPrmiWycxtcLKdT5P9j7LzKomtE0PdUsVOQedq7ntFaxYt3MtAl6X4O2VzqfZSG/GEF+aE/9XPV60DdZuYTTNAszgvOxyw3u0OfIH2iqdmuNQ+8arCuVTCkfuHhXVxM7Hf7TheSF/6z1o733b5BwD81UDYAc4wP2AgwBYo5i/Cxw9I/7LCi59YQJqBTty0aYYarFsHRCDeFHL5P0AR5OIqJHexNjIsFhQFsPFgbHC5EVCmtdyoq+/GAr1THwBaFoOG8vzCLXvbrOKtHcgr2m2BvBkMoXllTVkg350tFfWCaGmlyI5qhLWSmA7Izev23zMXKo5+bNVoRjUfppf1xAO9otgXPuOVEhdmFzOCQHaU0PURdg7kSWROj1Y3xDBrCEzuOdHKQpb/Xtg5ROzsMXJd1qCVwMdkm4vSzjeN7a9OCBpf/IInRpN3YqJZR03FoA7SwY+85D9c784SScp4GOnHLusvkmyb84b8Ht2txvUQrFuWKNgIKwQDLODsJ/7q2gNudDXvn/OPsStqWXxXYz0VXd3XV5dRyyeQH9PJzxNdsWxguoNwt/NyKoO2aZOIoNz40ePNeZcd/s907nOQncoN30V+voC5I5BODzW2Wt9dRbajTcgpzchHX1MuDA2+hRLPaNQjl2Ao3NAiOtbvt/KnKgUo/h5I7hXgcwDmDDmJ4ERczGjry1CX5ze1h4tB/XmHOE2S+Fgk3PcXxoiVtXxjSYmD42OCt3V7WNLElzhNkgVeM025yibl51uN9xeHzKsLNc0hFp3L+ja2tt3VY9VAluxFlc3RLVse0vlalS2obMFvZFgFNsZWYlTqLDxOCV43dKuhBLNV2bXDXSEhkSlWWeD+c7rc5qQBTg7rDRFJiHgob5VfePK1VkD1+aAZ44CrTWSdQGPhDODZrKsgJ6W6q+ZWgVuLsp4eISaVo5t7vLsMbNlkZVfV2d0XJ8jBwNerINzvH7TQCID/Mj5HemF4mAdg2yskK9XYL9YN6xRDLTJYrOL9qAsuCyTuL2t+8c5eF0+mNgQ9/hYb/VKsNnlGOKJDA4NtFWtqGwW3P4ggp1MNhlQ7ehvyTIOHzavlfNofZXeubV5ZKavw3PoHGTv7vFEm7oinB6VzmE4VOsHPL84Ae3m22x1gTJ6RiTeGoU8cAR6atN0He60XgvnF6dMDci2nh8eznGgAXbf4iAA1ihcO4Mb3SzoYmFsbljuql1/m+lEOCwGqWaArl2QlT0Hrthu06zgl5aMI37jHWi+FrQdOlWSrfGKRak10vEoNmZuI9A1AG9kh3AqThda+kexOHED+VwOwbauXRkgTx0aYV6vB+2tEfh81RfGoYBXbBubaaQzGtrCXmwkMgj5XDWzs3fmE0Lg89RoaEsUXxWblXvj0X4PdC1ty1I+kdGhUcR2S3y2gM0Uy/FJgE39qg8DAY+p32G3HWCgrb4THWgzhVwDagKG4d++VsVaF30tDuGitOVHUBEXJ3Whr/HUEWYsTScntjOUB78ItgRcnzfbCuoNgH0YYLB1pNsj7qfNzU34fL6S+yqr6bg1mxT6G22hHXKkM5ue1eEpalGoBbWCAG05QgGfuM6u4tWHjaqJWHQNvkAQagUSV1WjscVea3IBvF6JRGLX9ar5OrooZhIio2oF5+Hzwk1XqhD8IqS2fijHnhDmJuXnJOzD6wgSMPBBwfNqyPzZf4WRScH7U3+7YkUaAyWsTs6+/wpy196B58W/JNypts/rAPcMxcEsVoNRVF5PWDsXa1fpJhqDw2bbb72gqzVU557ag8X9o+UqLs7qRS6+jtidS9ADbWgbPlry/Pp7KldoJaJr2FicQaRnAJ4i/TSny4323kHM3LkhjmUVAPP77cstsPKLwakQsyhV0Bo2/74ay0DTdLSFXIhu5kTbY62g2LXpJFJZHecPB0QSrD2kim3XuXhlnBhwQtJTMIzaUgvkFWydo3B+MWJJ0zFZDAUf0pqMc39rwBDBKDsY66rvRA91AX4XEHZtlnCO4sr40S4HFjYMdNR43L5/QxfVY88cM6/jqQEH0hS+t/heGRi7MQ9xfTsbdJi8l2AnAzVMK3EO3pfXZjIYaFfRGlBKAloMnNmtDOMhWQ1fnki2QkvQKyrd6xHBp+TJxkYcoaC/brkGBtt9keqJwGbwjW3OkU2a87QFnOOPivbyauOr3DUi5HfkMrmbhjgHq9bPPlf5fMktv/obcLi98Hzxv7Xeh66QkiyuY+aNbyA/dQOez/389rr0gHMcoJm4/1dy9ymUYzvte3J7D3y/8PeF86MV6GwhDx4tEaptFpjBz3znt5B97Y/3dJzc9DWkXvsjUcnQDMhuL9TWbiyRONXRxiOrTpE5UZxuaNkMculSm/C2vmF0DB0Sk0U6mcD85G1kUrVtv8shWjDDXFTbm+BWokksrSewFkthZjGO1Q3r90xn81hcNwNZJK4dYZctLStmJpcWZkuuFcXwX7ueRDZXev0uT2Xx7t3stuV1AUf7VTx+xG3ZbmcHdP95/aaGlT3YmFNUlboTzOTuB0jqu8MG5udKr1UxqHfy+LiMU4NyTa2MVFZ0Ym5Xn410mkPiSswocUdyKQ48dlhCXnfg4kRz9Prm1vNYiO6v9h+v0cwMnb5K34cB1M1UHsl0qe3k3EoalydiiCVMe2orZLW8IK0FDPW2or/LukWoXAOso61F6NtwqwTq+CU2udgwkEpsYmNtFZsbpcmFtYUZrC/OohFUI1G8TtPT09DSqZIWqs3LryB552LF1yltffA++lkoLdYGAHRjkiv8jQmM3I03gUwSSv+RXcYm+VvvIPON34A28YGZ7GgS1Ic/Duej1uL8+fg6Er/+D5D63f9D/KyvLYmKMWRTD7Yj0wMMefTE9v+VvjH4//L/DOfJx6z3HTwKZeiYyLY3G8Io59u/idzbX9/TcXITl5B688vQE9aJw3ohe/xwRjqxvJmr695kck11e6CoLuQyabEVo6t/GJ395gKRi1WOD9msdaC7VqtTa5iLanuL8YXVJOZWk1iMpjGxmMBa3Po9E+k8FtazYlzrijjR3WKvApw6lovzpfPo1ZkcXrmW2eUM+PYdDW/d3r3QfmhEwTPH1IYrzmlc88p1Vl43vrDtiTjwzFFKH+wP5yCf6IkYmJ2tzDnIuZ47LuF4f/UlFflGMrMzB7H6jKY9/Hlpg3ptO9eBHOqxQ6zWAy5NNWespX4p3+fD4BwMcjGQmkiX/v7OQgbv3E4K46dqlY1MzhW4+5GBEAY7a1dYBXwudLcFhUkPKy8rQdPy2EyYc9vmZhJr6xuIx3fmWn4/CwsLWF1dRTM5R+FakfNQp3B7/7yG6JXXkJi5UfGYzs4h+B96AUoFLUBWc8lh6wSgnowhKyq/NJNzlAW6tGuvI/PS/wNt5roY75sBfm/OC5+Cet5anD+/PIfEv/8fkfrjf2ee4+qC+J2Ryz3QnIPBvP3YDrB3HFSANQnVXIvU4xcAbvvxvpIMuWfMsr1SF7ozK5AHjtXMLrA6TfJHbEf8dQ6KhgHJosWicF6e3kOQkzdRD1S3Fy3DpnPV0p2ryOey6Dp8clsXTC66zmxLyGWzyOc5QDZPhNYK/Z0hMYmyjDqfNxAJWl+n+dU0VmNZUZ7dGnSKba8onzqHOlRBCMpJJ1sQiqXkiqvJ3pvQBAF59NDO9WPrwjQdDbtoI+0QWhercUNkU9tsFtORKLNMvzfCLOzeCOitBUOIyj5x2DzOzBqEWG0z2jmtcOGQbFlxtxg18OoNXWSKTw44RKDs/Ukdw+0Okb2lDlkz8MFUXrRtdoXvvci61y3j7Fhglzhy0KsgnVXhrmBHns3lcXViDSG/S7icMpC1uJYQz4PHRmUX9787vQC3y4mBXmtytr62hlgshq7ubnj9AbQaxi5ji0wysU0CWBGaiK3BzzanGiXy8bm7yMTW0DJ2apcg9vY5JmOIX3sdgdFTUNnOyEvE96ohPNqwdsbKNPLTV0XgSxnYab9ksEubvibGdkegBdrll6EvTcL5yKfRDCgjOwGVcugzt4BcFsbWZ3I98zm4Hv9k01rVDrC/nMN5rnImfs9QVEjdo8LCvhz5Ld0ZpYruTLELtuAcNivA9ExKPINShUQin2dv7xikOjkHnR7bBw+J/8/duir+7Rk7uv13Rd25zlout8U5Ki/Wm4XR3qBINFDAm+tnq+pxYmqJznqaqOrqjOzPXHKoWxYVYOVg5ZJUtsgvzKev3cyLSvTzoztjMiuuZ9cMjPc4xNzDCrLlGLCRMCwF2a1AHnN5xnR7Lq78bgQfTJvv//RRU9drbh0YbreuyGoGnjtuHrecc0ytAK/fMgTfONpr6pFenDRwuIt8w9z2CnI1HjPgZuvrvW8lozP5hXGvqOAvRsSviHNzq1LFAO+liU0R3B3q9AhZkbnVNDojroo8pZyz3JhcRdDnwmB3qet9AUur64hvpjDY24kgK9UpzO8v7Q5Jp1LQt557jgGJzTiC4UhNfTFqF2dTSXSMHK3IEVJLU8iuLSA4chLqVhuiHfH0hjnH4iTyM9eE27VSJJHAwJg+f0dUDjv8YWjvfwd6zxicZz6CZkAZr9wfrE1eEwG5As9yf+InSO6a1pX0oWE/JAfuMwmDBxUHAbAfAKgnn7b8vXbnPehr85Da+2u2X9J+vGBBXl5hlrr8siCr7hFT84xIXH9dZCmCZ6yr3poBit3rbJGoMAn4giF4/IG6BS6XVqNCY6w1Urs9hK2Pm8kc2iNe+DxO3J3bQDKjoaPFOvvU0+oWjjb1utpYYbjTKbZymOTFeuJncIZVReubuiCXTx41s7MkjXRdLA74zK3R5UdHyOsQmcj2kIRnjztstxIQdF28s2hQQmDPATBmg9c3gVze1NJgYI0y88P1dbHVBavAMFsOqCXWEzGJ6EuXzfaPoBd4/iRJSXMmH2bP70UiJ5c3cGsuA0mWMNzp2g56WbUQ0Jyh2KChHGx1DPicQoSWSKRzWImamVNPe+0AGBcWQb+3ahtkIBgUz7Tb7TYdooK7RfE6h8bEojCXySC1GUV8dUkEx32h6s5o1PsRga8q3yHbumS3uh3c5/jjP2pW2mSXJpFdmoL38MOQnM0Juss9h4QArdRSKlaen7uF/O13RVuk88kvQLv+BqQKVWT7UW3keO2rkLbmDTEGHwS/DrCV3Kq0INJuvQ1jc1201NRauCgdg2KzaoNJXH0VSrgD7n4zEMV5K371NUiqE4HjT2C/EGyrPtmEwuHt8ckueO7zyzGRPGutYU/MfddiGeGU193iEQv8q1NxESDorSAXMNjpQluKLsJ7D2oc7bMel3tb5KrOiqwqogszg0jPHpPNCv2sgfJ8xOSygbvLFNx3oCNEwXUJEb8539rFShy4vcjKLGPPATBqfq5tMjgEXJkxWw5dCjW0sC+oxB1iKSDgZjUbRBLy25cNsAiP4vgvnrMnfl8LnPcvHJJK9M/2C9mcgaszWVHteKh7p0LQqjuhNaiIrRL4mpCPXQXmPhuJHJaiWcFfelpr3/MMIFPLN+CtTGwjoQCcqip4Ca91MFiacOPvBgYHxfPJ6s/4RlRsTpcL3hqu85KsQq7RSql4AjB8KchbYyYrd0NbmmDphQlko0vwj52tmLSrF8rAUUj+MKTWMs4xfR35yQ+gnnoOzgufNTlHWXvkfoFGcjRu2eYcHGOlBzz4dYD7GgcBsAZR74SUu/QyHP4IlOHje3pfbeIytMuvQD3/AuSOgar7KkcuwEhEtweURiF6zMv6zJ3t/TBqZEFFyauztr5EOZgtSW2sCsdHLlgr7pfJIJvNwB+w//kK7o8UU6wVAEums5ha2BALbUWR0NniE5OpyrRmhY/kcspot5GVata1Ksf0Sl4EtVr8DuH4UzgaK7/Kq53Y7iccDbcciBgooZtSPa0EDHo9flhCDV5vC+eGHTg1wMAMg17mufa17N+1qoT5KLCZMQX1SehZpTXQBpG1buZ7tgb2P/olFiI5GdGYBskhoSOk7tKOqwcksiM9OwEpksqhnhB87t3EjPfb7ekVoRE22NOyfT5dHdWDVC6XS2zVIEky4uuriK4sCpe2cGcvvEX6PZXg6+wXW1XdsEAIgaEhy0UuA/I6tTeaWIovqnjbd4/l8gDb5j2Qu0cEGVSP7q4iZoJDu/kO1JNPNVVj0iE7IbV1Q4q0Q99YFU7F5a5O+/X8HcAadWnS6TpyF78Dua1XSDDsBbkbbyF/613hSmpV9VUM9diTQpdmb1l7w+QbRfyCn93FRF6NBWCjcwPlFFKbcYTaOqoGt9KZjKgCq0f7i61b0XgSbqdaMwDGxf3Eoin74CGXCLvFQt/hqFx1zMBXI8GvZs2jtxZ0TK8aaPWbxjmF4z2zFQgrxtE+BzrCpqMhwcQcE171gEGip444bFepV8MT4xCaqZSpONxt6oh1he8952C1eyLjEEGwaJKJJuBot6k/1sz3rOY22SzwfONZBcmoAUnS0ddaX4CzHLz/j/bvPDfsqmAijtXq5WDF5NXJKHweFUNd/u2k20BXdWcpj9sltmrguLC2uopoNIqOjg64XG54vLWJb6irsgNz4b5yhVrhKdI8LvlMWhZGjlodTeQcsrJLa5RQhk9CCkQgdQyYnMPCBTu/NIn85FWop56xNFlp+JxcHkitXZDCrdDXl4SutRRsefA5hxDBbzLXPxDBbwoOAmANoq4MYC6L3LsvwRHu2HMAzIitiLJQYV1bIwDGqi1ws2GXm739DlxHLghL8vLFmc+iystF8cQayG2soEPVamqjZpObyG5uwNfWLQbddGwdyegqXP6QcFWphLWVZaRTSTERqU57ZUscQIf6u0RlUS2QsLaEPFAkCW1bwrT9nU1gXRXupxEbVuq1QNceOhT1tki7WgfLJw9mBIvdhS5PG5haMUXhI3XYkNslVaysYpvjsV4zOFcOnl9Bko1Emq0A9V4rBvES6VJR/Hrx6JgZ+OJ1ZLb0U2cfDOeZVEbHzfkcBtuV7SAXr9XpE4ewGssJkdnC7+fWsphczOLkkAd+z87nm11JI5nRRcaV4sk1W6eZLfVVJo77aTjn9Hjh9vrh8nihNklfsfzeSk5fRy62Bm//ONRgC9w9Y3DRMvweEDFWoil91YMX+vIM9IW70AeOWTpBNfzesgzPiz8v/p/4v/+hmMP8f+nvluxzr6zlD9AA50jFoV38LvTu4T0HwIzoMqBlkZ+9WTMAxsUTUFsPMLc0idzUZbiPPmHylLL73m/BOdy9YzWPm1lfQoer9kIpk4iLRJu/tUPsm4hFkYxvwBsMweWuvKhbXl4WFSAej8e2IxmrzccG2m19f6wcp3aoU3WgNWiOaWM9Tcgu7SPnONQlIew10N/mKHGCtPoOWM1T3IX21h1DJJxeOG0mnOxAJFFq5zoEWE11dwk4NWhdfVQsG0HNrWN99V8rBvHYoshK+kbx3DFA081gTYsf+Pz5B2OBG0uRc+RxuFsWOraFa/XQyTEsx/Limvrd5u/vLuYwvaLh/JirRPD+9kJWVDiyRdKO0zW//0iFduDtfbA/cHs88Gaz4t96BfLt3lfxqWvQ0gn4ew9B9QXh7TsMo9fUPd5vUOKAkjrVoC9NQZ+/DWP0dHMDYE43vF/4qzAMHYl/86sioef7qf+hZJ8DznGAZuIgANYg6nGjoMaF6/kvIXfxu9BuvQdlbKeVsF4oLP/nINU9iqZBzws3JrqBNBOZjWVEV5bh7uiHUmWgTK0vIx1bQy6XRaCtG/72Lrj8QbhquDpGWtuQyaR3Bb82YnFk0hm0t7daThqqzYmLZKS33dThuDMfg9+joqulumtkJUwuZYSY/ViP2dZlWZm2sYFQKLSniY5Bm3qsq8vFZAtOjsU6YXSPblRYn6DmBwNrPrfZusDMLSuqCtnxG3Nm1tVO0Mrc34BT34Amh4T7UnGg763bwOQK8MkzRsOEtBD42i+QMLOIsFx/q9pnvjydF5/H6ruNJnQsb+gIekj4GbziJm/fV8nNDXRFSu8rXjO+f3kiaSWeE4G01XgOPrcsMuMUy28J1n9B+H6j/dZZzXqgaRpSyST8gUDJZ+BCtb23ehKgXpQ/h9rmhhCBza7NiwAYUXwO3D9996Joh3TV0D0SjkxFbZO0DM+vL0BhhVcNrY9KkA89BKlrGFKoDfmpq2JTz3+yqcRUPf20SLqU48CR6d6inuvNoJLzmT+H3OVXhbaKMmjqajYC9exHod0MmxWJzUJeE62OXOw0E+noMjaiUXg6+0v0QsuxubaMTHJTcI5gWyfC7V3whcJVg19EW2srcpq2K/i1Fo0LjdDONuvIjNOm2Q4rzCnureV13JhJIBJQhdZRI7gxZ4rm05nPCs3iHKwYH2xv7PVD7Q54naUOjpRu8LjMqqxGcXeJUhCmG/atBbZwAt1bcVkGW67PmRzEjlkPK8SuzhjwYAOaEsKh7tJ58+XrwMI68COPGvBaJPbswCoh2ExQx5Q8zq6eKq/RexN0nwT6W3fPTeQba5v83qihZqAzZGzzRjEnJjfQ11rOOczvtPxWW9rQxPstRrVtl1E6nRc7RdoFOfvx4doB+FrI5TQk0xkh11DiYO/1iq1ZsHoGc8m4WTQRWxUBsF2cQ9eRnLgExReCq0p7Io9Np0ipSLqArsF6fNVsUW/wmVeOPg558LjQD9Nuvyd0SdXzn6pZnWsX5ELUsKRb5A8E5zjQALtvcRBObRD1ulE4fGEYq/PQV+cq7sPgWOZP/i3ysTXk5+5UtLfN372E3Pvf3vV7IZpMp7AKr6sEpb0f3sd/pKJjSKPw9oxh091a0yrd39kHT0sn0vENpOJR0d5UK/hFuNxuBEO7CedmfBOxeLxhx5BMrtTljgGIRFoTW6OgSO16QtslaL/9HrouXGbsnrNJUHKYXWueGC8ruc4MSduBGeqJfeOSgTdu1T/pFE9Uc+sGplfN1oUnxx0iqPbOXfPvq3HgvUngnbsMjpm/K3egKsYa95/Q8cGtBbxzVxeitcXobyWpBqoUJX2o4Gf71ge6sEO3/xoK89O9yfo1M6t5TK7k4XZJeOSQCwNtSs37qjOs4vxhnxDDj25qiKfM++jYgA9H+7wY7nQLW/Pp5TRuzSURTzZ+7+8V6+vrovJidWVl39+r/HoFxh+Gb+yMqACzhGFAiy5D26h+brm5W0i9+afIr83v/G7qMnI334IeXbJ1bnRkSn/7N6Fv7tz0rJhl8Euc+/oC9JUZ5K69hmbCeeoJOM8+u+v3D6Ij0w8X5wjAWJ0TbbKVoF15DZmv/AfhxJifuy00P3chryE/cUmYMOw6p8QGtMnLdbcEq92j8F34HGT/3herxfD3HULSHalpWMG2JE+4RVR9pRNxEdBye2pXWrHyKxjYzU024klEY4mGF2h0jy5+LYMum2kuwhuf39fiutjQJM7BxM3rt/KYX2/ec9/b4sDZ4Z1qdVaJf/Wigbdu741zTK8AE0sQIvLPHgcWNyAE4AlWnL07Abx5m4GynTm2Esgx3r2r44ObC3jjJpNNpX+naP5oF+C+T8sJyOO+8h6/O/vXNKMB8+vUdrP++8SyjtuLOloCEp4YV9BXpMVV6b4aaFfx2BE33E4JK7E8NrfcIB8a8eB4v0sEavk3VoRdnc6ISvQPC0srUSwsrWF1Pbav72N1rSKHzyEwdByezgrJPT0PLbYGLV5GfsuQnbmO5DtfRz6+tv273MT7yN58C4ZN513t7iVkvvvbJe7TrA5n8KtgeqIvTkG79Q6aCef556Ge3N1+ecA5DtBM3KdD9g8epFAr3F/460C1zLyWgZFJQZ+9gfzV16GceALK4YfLDiQLi3OHZ3e7izZ9FfmJD4SbmFzk7LGfKIjUc1C06jO3I9rITK2/rRuqxw+Xf+8thl1dHcJtzm6LQjHSWQ03JtcQCbjR3xXczsoeH6Lbi6NmcIOVM23B3bbgJwa9Qki9uEWg/LWprP3BnS16FJ5djefRE2meMHsxKGVGF0bqidWLr7zHqjEDz5904OwQ9TV2WgTem2CGDzgzZLpNPjoGXJ4B3rjFfQx86xJwpNfA6UELwdQA8PAosDwDzGtAriwu09fqaIqALa/t964aOD/GqqvmXVtmQTtDptaHXbD67skjqtAis8LhHgXdEVa8SQ0tGi5PpUXV28OHfELfLuSXUGhK6mpxwaVKohrsw4LP50M8FrO1uExsrCEZ20BrT39V/UArZOPrwiGy+H2EDoZo67IG/+4/9UzNBTd1GKVAqxibC1AHjpkueMFWZG+9AznSBdnCiKQAoQNC912LIAWTHtLgCeRnbsBYmrLxaQ/wgw65ox+eP/8rQNE9Vw4jl4aRSSJ/5xLyN96E8tDHoAyVuYM6XZD7x+EId+56PflGfu6m0Detdu82E9TEcUiKpQMa+Yad515RnQi2dsHlDQgjnb2iv7tNJMoamYc3Uzlcn46jM+JGX7tZ9cAx98xosGbFDqvKVzfzonWsnFs8PFY9C5TVyDnsB0VSWXNepGNjd2R/cufUKSfnoClPPSB/+qO3gVa/gWeOOfDYYSCVA4IeB5giffW6Iaq2Tg8CvRE6QDOoxSCPmSz7+kXg3AgsWyC5/yNjwPKs6caYLRt+RzodGNn9aNQNVsm/fM0M2BWq8ZsBui6y7ZTGA3bhcznwkZOSMASwwskBWZgfFNob6wHvu4uTOVF998iY2e7bUlQh2deqIuiVhAzGhwW/34PNZMoW59iIriOVSqGjs6vuFr1MPIpMopTbCJORKnqmrLSiEYjVmqsYsi+EfKClxLlZHTphGum4vCbnaO+HHKpcpS/0TjlH6PldbaWGloU8dg7a4gT0xUngyKO2PvMPGzhPNerWWe2YB9g7DgJge0D2rW8hvzAJ9yd/2lyYON0i6FMJjhoZRuXIo5DHHwEYbc+mIfUestZJOlbqgpS79S7yszfgPPtRONx+wBsUiyU79uLM3GZuvwM50AK1TNdLi69B9vgrVnBxUIxffV20/zjbuuGMdDX0YOa1HJbvXIXbH4InaFPcoQoY+CoPfmWyOcQ2U2gN0zWy8sTKAAA1jfy+0s/MIFgtLKxnMLOSERNFR7j09SSy1aaru0s5LKzn4ZnPIpWTcXLADBxSIJZEgARPaO9vkVz+P+LfHQBqJnidKExfQFbT8cG0mfGM+KtfD2qiF9oIWVEWKqpmfu44hMNRgbDT5ZGBLxJsEmCW0leq4OLnH253YGMRyKTN67Mf4C1CTlbpa780pWMlBjx11GwltAue/0Mj5v5cMG2mTZJeDlbJMQNLcs17p1qLBANkVg6ced3ARlKveT6HenacIcvBwFch+JXK5EU7ZJsNbTAikcoKy/KwRbRvfikqvv/O9toahWw5GKwgSl+ObDqFTCqJfD5fdwAss76I9MYqDIc9LS22b+VWZqEEWyG5qrdFsLq2vMJW8gQg9QZEWwI1HZllrRRE0JNx4QgpD52EVKbTSNAYhZXBysOfhNxm3ymS4/8BmXpwkHnly9Dj63B/4idhpDZFmwgXTJXg8FQXa1dPPwvl1DNmRUA+J1pjdh1DkqGefKbkd7mrr0FfmYZy5qNwcAGlukRLo502GAZr03Q3jXRBLRNi1mKrYuFWiUcx+BW9+iYUXxCucDuckc6Ggk65bAaLU3fgC0XgDdQeg2qBbnflSKZz2ExmhYN0tXN0qTJCPnXb5a4ezjG9wipwTbSWtZU56dWal27M5YUTtHNWQ1rTcXZIEZVn3NxbnKP4GJzTOY834PFjG3y/Rw/tvCedJC9N0YCGLsxVnHsdgKeIc9BIp7jr9FNnd4ShyGvGujjvmp+Vr6HDcyXOweGR+5NzZJP7x7n4PmxTrERP37xt8oVnj9UnBk7+8NhhxzYnSGasWz+jCUNUyh3qNrkZg2CVwPvDipNQgzVeg3OQr4z30LXU+viUbyhIOLAyPZUx0GFDG6xgIsFYUrhMI4wBpqnFTThVCd2tNoTrAz74PG4htl8L6XQa6VTKrIKtcy5Nri8hl9yEzq4dG9IsXHNlV+ehhturjvuE0tIttmJIvrDY8hvL0OZuiXOuFADTY6vQp65AGTtnaaSWe+8l6HO3oDz22apBNKvPUOvcD3CAe4GDAFiD4ASUX5oRATC6ZKX/4P+CPHgE7o/+hT0fFx6/IKb2YYhWHOrLSG29yHz/D8W/rlPP1X4pBfXZlqllSwJg+cQGUtdeg9LSA89oBc0yhwQl0IJ8No3kzE04ZBXOIqcwfhZWb5Rr5iTXlqB6fHB6TXLOCjLZ6RZ2wfuFtY1NRGNJeNxO+L2Vy2844dHVrhEw6MVP2hKo/3OwJS0e8SOnObCe0AWRuDaTx+qmjnMjKt6+o2GwTRLVPsygfeeKhha/hMfHlXtWRv8n75iEcW4N+MxD1ff/yInq5KkYr980BAk9M+TYIatVwPupo8WPU8dJcvcnS9gWdOCzD1c+djwFbKQYxGr8Pa7PUYvEEC6a5UYCd5d1YRnPTCgrxhrBnUUNk8s59Lo9VQlzR9je/cp2SLZLsm3STkXYzGJctBMn0jn0tgdKytjXY6bTWWtLwNZCz241Z7ijB8HWzpq241bw9YzC2dKF9Hrc1gIjvxlFevoa1NYeeAZ3zE2yixOiSiu/sQJn7yGRXKgGyReE8+zzIiBWNYvo8lRMajj8ZuJAX56E0mtPH5Lta5k//reQx07D+fDHbL2m5D0PdCjuPedYmISxsQp9dQGp3/s/oB57BK6nPrfn4/L+kc7Y4AvbMMRCU3L7xZyeeetPIXePwXn0sdqvzKaRX1swA2tFATC2EaduvgW1YxDuCnpjrPxSA2HouSwSs7eEvo26de9X5hw6NtdW4PYFoG5pfFFmQVadkPdxIba8nkAskRFOuR4Ll9xil7ux3saq0HpaVTEHRvz1f46eFhnpuE8ERKIpQ4iwv3Urj3jawEMjMl6/peNIr4TRTgmJtIE/u6yjr0USLYv3AkwC/fHbgtpiJQ588mz1IE+1vxcHWzhnv3odiPiA41u332fLGi3Kwfupu92Hs6dKA2vNRF+LAz+22+h3GxsJIJ42r0ejQy/bQKnD+vwJJjFLD3Jj3hBulKwUoxB/I7g6k8fMqo7RYPWgb1+rvYt4ez4rhPZDPklURdbCnfmkCNz2tbnR3brD8zWN7cAZ8X/q+NqZu6yC2lZg5Rc5TSMdJ6GeYbTpqqhItYPcxipSs7dEZZanZ2eezyzcFf/mY6tCi1T2Vh9PGLBynXoWUtHYuXsnWbhQo0IBhGPLvMRYmYXDwlHSCnR3zHzlP0I5+STUk6WFHD+wnIPdAQ1qvFY95gH2jIMAWINgNYLr4z8O7Z1vQ48uQ+oeBjIp5GduQu7bXbm1V4j2Fy0Dh8UiSR07JzaBfF4I5IsyVxugXbnn7PO7qrwkBuE6h6BU0QUTZHPomAiA5aLL2yLR28eQJPT3lw6M+WwGm0szcHLRN2BeJ0mWYUgSNmNr8Ld3CnLaCDYTKTERBQO7MzztkSC8bhd8Hpdoj1yJJhAJemyL09qBU5HQ21ZHb1sRIn4F508Ni6og6lEwS9YWZAbWzLzSIalA4hgvaAuQ9FafDChSyqwm29vsYCVOEddSslgAs5I8FgNgJ6rMdcmMAbezWqsns7w7P3PxRNF6iuCeqaznWfO+ajbm1kxh3krX+MJhh/CMqKf6qxz8DjfCO+Kx5c5azM7dXdRxa17H+TG5biOCjqAsKuRGuwZqtu/aAUll2KcJbTA76G7zY3oxtutemF+OCS28gNeF1egmohtJjPS3QW3Cs8gxqZHgV6F9yuUPo78aKdxC4s5FEfB3941DKarI4liYmbwsWtUdTC4EW0UATIjazt+C0jEIyaIlrTiDypYCElNmasUxl6dFZtn11J+reD5y72HhzkQ9MH11HlJrtzlnKJWr9Vhl4/AFLecUOzhwZLq34PV2f+YvIffWn8GIr0PqGhLJt/z8BORum4NnHWCgioHc4rbdAtSjj0EtxKgcAchdIzVdqYu5hZeVY2XOraz8UtsHoFbhLgwEUx8nn04it7kuKsFqzQ3ZVArxlQVo2TQi3eY5cozg3BOLriPY2rhRB6vKiaB/9yDe3RYQ1a8MfrESdjmaQnvYIwJezYLHKWGg3d7iuRztIQXtp4aE3imDX5xfOsMO+DNmVZTfbc7LBCuTmMOgBEE1UBA97DUrsOxgMcrWfWsR+ALnINi6WAmsiuJ+ldbG5ZyDP08tQ7hFFwJg9wPnmFoxK+yKq+WL8ZGTZvBrL3N5V8ghkpk0JCrH8X5Wmxu4MmvAAQMXDpUaDNkBW2P5ktHe/qZwjpEup9ACsxP8Ivra3ZhaMp/JYkwtmRpWkYATM8ubiG1mMT4YsZV8s8U5Ggh+EarLjaHR2u62DOLH716G7PbBzURd0bosn4wjM3fLrJrNZaAnu0QATOe6bHECKk1y1N3ljYWKdI6D+cW7kELt20m4/OKESLi5n67MOZTBY8IhmByCOqZ0CKboPteUlV+kisCZ1ZzyA8s5ODA14VnYdcwD7BkHAbAGIcT40ink3v0OpJ5hkYVN/96/gKa6RABMu7ulxcXAWBOQe/trQuTY9dGfKunpLgf7wu1kYYth1b7D7Kx74Jit17N6i3ojVtdobW0NLS0t2wOX4nIj1DsCpcxtyRsII+fKNOyGRiytrEHT8giUObeI91VkhALm54wn0yI7S3RaMLpoPANVZZVL7ceD4uDTKxkMCdHwxrPJxdeKgTSiv00WG/HY4Z3rQlJybqT6ucVTBr57VRfOR4+OVT8vZndfu2lgLcGgCYSGRjlIjl84U33QXYkZ+Pr7po6GVTCLmUdmXhlAiyaAx8YZ3HPgsw9ROwW2YXVfEWyVuDkPvHCGQTw0DFbYffsKBJH/1FZcuZp9eqNgprU9KO8Sl31/0sCTRyQhCMzblJeGbRdchNQDZk0ZXFtbW911razAzGksmRfVBFZBE1Z+cbOLoN+F4/7SxeXM0obQ2WMgurczgrWNhEmU75MJvfzeIvFMzt6G4g3C1dK5QxgTMUEqnYNlY2QuLf5he5erZ9RsSee1XV9AbvaGyNw5+ys78unJGHJXXoHE15/5qHlIGp7IKuRnK1cXk6w6L7yIzHd/B9r6d6Gc+xi0t78BeeQkVGqUWb3G7YPrM79Q/0UqnOuBIO09Ba83A1+5i9+DvhmF69GPI/2H/wZaICwCYNqNd+AIt1vOxY0g+/0/gpGKw/Xxv1i9zVJxwlkmy1ALkoXDF9sn3eXPUwXIbq/Y7MwNTo9XBL74bzF8gdCe7+HZRbOV2yoA5lRlsRHrmxksrqfEgrsjsnvf1VgWHpdsi0OsbeYxs5LD4V4n3DYDA3Y5x2jXzvs/c2zn2AxoXTgs10ygfeeqISQNHiqSTrAC2+2+fwOIJoGBVuAJiyGRibhalVkzq8CffUAhdetg1uVp0xn6xADdJYEnj5rz6OcfMbWx9so53rwFYfDDivh65+fyivJvXzbdsD9+2nofkUja4zRJbbVyfbVrs2bQi3qtrLRjRSDfioHCeuND7UEJrX77nCOnGYLntAas9wt6ZbHZfv+QS2zFuD0XFx0VQa8TAx0BzK+anON+YBxW95WQlpmbgCsYgatQVEDOwZZ3Fh30lGo0GppZ2cZ2R2dbn5CtIbTVOWTnb4v1orOKWyRbHbPX34DcPgDXscdFK3vug+/C4Q3B9Vjl6mKHNwjnYy8i863/DC0Zh3LmI9De+TqUIxegjJ+3fI0UiMD12V+q/0IVzvWAcxygiTgIgDUILoKYpXe/+HNwBCLmg/3pnxNi99S4yH7394W4sedH/mpT3o9W9/D4oM3eFI5Mzkc+LazO9wK6g3DBxUqv/bpGKysriERKRaTdwd2i0v7Ibl2betHd2SYGyFplsiGmNrcqUMpB+/E785uCjB4d3F0JkkiTfKYx0O4W+2ym89hIUKdAr0peTZF7VvtIdV2rRsGPNtLh2NVad2fJEEKux3p3fp/MQgS/mOGlxfde3rMzVDlLzGwydTdiKYhSexItVrfVsgAX5PAKcLIfGOqwvlYkt+9Pmv8vtCUyGHZ1xsycbn3ltsBg3/lRo67XNAtKkQ4I21BEttblqLv6q4DiazW/nsfUioZTg6qoHLDSk+F2rN+1S0+mWUilNVFFOEa7ThLWloDY7hfwei0vLsKZjsLb3iuqUTNri6KyqxAA4/gSOE6Hot3fiewLw3vyGaFxlJm/A8+IuZpR2vrMTHGkq+r7U2tDPXx+22WJUE89a1tbRDn0ELSb70B75xsAM63+6u2X2W/+ZxipBFwv/lLd7QUPpCX5Awxeb7mtR1SBSa1donXW/Zmfg9TWLXTksi//IaSOfsif+fmmvJ/cM2o6S99+D/nZm3A99lk4amjd1QLbHCmqrLZZqI03AVZzA+9rK23R0B4qvwoY6G6xFZRoC7qhSA6E/S5LB8jbcykEvQqODOyujGBSYnY1J6phWAmzkcwLmQTqY1XprBTBBc4f94pzhDzkHHRiLguwzLGiiDpeO79nKx+DX+QL1NdqFOQTgnNUaNljZRj1vdY2TTfHdBZQPbUTZNz/e1doJECnSutrRQ3QKzNmkKgwFDIJd2cR+MQZk9vYBbkGzX1qVdjtB1gd59riHE8ecYj7xtskzjGxzHvXwMOjsmUnws15DXPrOh4aVRHxNb+6h+dC93Zq+472mtWive1+9O790W8KCtcq6PMhvbEMX2uXWD9mhKu0sR0AYwIifPQRy9Y3VqD7xh9FcuKSyTH6TMdqtWNAVGMpLTU4R7AF6uhZyJEtfqOoUE88LZJqdqAcPg/t9sUtzhHebo2shMyf/FuhGen+xM+gXjyInEOYxDW5ZbHZx/thxUEAbI+Qe3Z0s+SunTpt53N/XgjUNgsFZybt7vvClpwzLqsT9vIgZCcvQ4+vilLYWiLO9SITX4eW3ilFzrMdhwTe6UJifQXx5Tm0DoxB3bpGtCKnJXm4vVu0RDYCj9te2Q8nibBV39mW8Oxgp287c1uOeErD+qaGiD8vAmBdEacQ3LQKKBTj9kJOBCDOjbiErtN+g1VipyxcFG/OG8KB8QiFTrfKclmJ9JlzJmHbS489SdNHT1b+e08E+Nx5s43SFGK1d1yK46/FgY2tINfdRWC0bJ3AaZGnfmpgJ9jFz8lgWyNC+QwErm/S+n1HWNcOKDL70mU6Z6LEQMAu+lqlEhfLZjohMQBL5yZhrFChJYbnH6oj41ovRvu5mtj7cXK5nBCf9fv9TdeFyOfSSK9vQHG64e3oQ+jwuW1xb6F3tDID2e0vcYfk73WK2Hv8kFwekcUtdmtk65ZiUydD6T1c8jOdmopBp2AjFYNk4cond48I7Y78xBVhqlJrDmIWF3TUu08q8A5QG8rAzv0h947sVAA+82MiAde09zlkCj3mrr9B9fmmcI7MxPtCvJ8VktUMgxpBOraGvLajUK5ls2JOoNZXbHUJ8fVVdA6MQnGafX2JzThSyQRa2joabq3x2Sw15lzbErTOqLidMgY73RV1FaOJvHB77gybrWBD7Sq6wkpNznFlRsPSho4njjgrCo43E6wSs5rzGCTi90Ah+wIYJPvcw2agZS9gcIvBpkpgwowbq5mY+LOb1GJ75OqmmXxjlRfdIscsOAcpFB0iCxyB7ZjkHHw/1MEbeH1YOc8KLDp811NNxv2/8T65Q/VW0UoY66IpQNH30MR7hTyPWm7kfJV06CgjYWUE1AxwXjsxtHdjrYKRFregv7lrJXHsRBSp9WUoLg+8kXZERk9uty2SW6RptsMW8SJtL47FeiYl2iLJOThGCyH+LXB8Vdt6a743x3N1K2hWQLkZChMhRjZlacBDh2DBOeZuQxm3wTkou1CtTfIAB7hHOAiA7RGM1htshynTv1IGKre57AXK8CmxUZ8j8+3/KnQ31ONP1nxdfm0euZlrcB46LzQ4CCePk4ojv7kuBJHLHUP2gsTiNHLpJAzJzLqsT16Hns+jY/yMKborhAF3Jr3UZgzJWBT+cAuccuMTDPUsRKV4lQXdynoSK9EkRvoiloGu1lBlltQZdgq3pgJZFdllG0EKtrqxMuleENFqeHLcIfQ+yvUZ7qXlNDOmdoNfREcI+AtPmKTwtRsmKdXLbhHqZnyp7DFgG+bJAfP9FqN0NLRPgElkKfrf00JBf/vnygQVX1tuHz67ZgrMnhtm60XzrzVJUjRJ/RU+W9bHH+lUMNguV9T1iCXNKka7+i2TiymkczoO99oTlbXbxpGnoGyNBenq2hoSiQRUVYXb3dxSPdnlRaBnCO4tK/LiVitaf6fm7gjtoeIAWHZpCpnZG/AMn4Ia6YSfVVtNQn7+tqg2E8EtBiQuvwx9eQrOJ3/MsgpY7hgUW/pP/40wRnE9/1MVj+184nMlcxk1RBrV5zjAvQFbVFi1J23dnwUoo1WyD3uAOv6I2OhEmv2z/wK5/4ioUqwFbWkK2uIduLgoopgygz0jZ4ROjLY2L1pzlDrcw2ohvjANjQEwyWdWck7eEE6wnSNHxPgkglxFY08iHkcysYlQhO1HjeloEYWq82pj4NxKEmvxLI4MBC11hzojlReE/W0qWgPkHFJdnKMrLIk2v3oSOPuB5yvclveSC7HKqR6fgP424CeeMq8dWywp9WaUDYvtQeCnyrrLHztsBsT4FdMsKOyzL8ewGgf++C2zIu4paw8ISzC4RM7BoF0x7i4ZWNowK8vq1fKyy7VZxddSRYv2aK+E8R6p4vuzFZaOlHbP7/qs2e433ms/gGJHi4y6wLUcHxdXokimMnC7nE3VDiY8oTY4XV44/eZ6SSniHNQ7TC5OQQ20QB3cWVem5yeQWZqCb/S04CKBMqfevUCbvSHG7ELyLXvxJRixFbie+QuWEjxyzxikjgFkvvof4Qi1wfXUFyoe2/XcjpSD0CmlzqTNarMHErz/mv387cPz/MOIgzq6BlEgO7nXv4r07/zv0FfmKu5L23Lt+lvIvfdnSP/xv4KRSVbeNxETga389PUaJyCZC5UtYlkLbHXU42siil8sOsvWnMzt95CZuCSINaHF15Ca/MAk2rqOfDqBzVvvIXbtDdiFv3sYsjcIn8sUYfZE2uFtMUUXveFWdB46AbVo0Au1d6FjYBTOPVTNkYjenpzHzDzLhyuDVS5sSWyknFb04Nehg1RAS0DG0T5nRdF0HjcUCtkOJLw/qeOD6fr74X1uB0IV7MTv5/JiElFeGuqGnR1x4MpCCOuJ2teKnIZZyD99F/juFfvvx3ZN2oGP7C6yqQp+v9QtebIs/k2dkLtLu0lqszC9auD719niqFe8r0TbtqN6leKdxRzWN+2VzMXTeWym8s0o6CoRlb5+ZwHReOUxkoiEw2htaYHL1dxMoqgODYfhCkQsdY+YlfUPn4C3LGOq+MOiFaGQXKgEVokl3/uWEKe1C1bg5G6+WZJxldr7RVKjGiiMLxU5RdUC2/bT/+//WXV+KsZB1di9ReF6Z779e0j91/8Vemyt4r4UJaYmWPa1P0H6y/9me2633De2gsxLv4n8QvV7ks8DOUchmFULeiIqzpEBrwJoCEHDh/Td95GZumIGXYW72TJSU1dF5SS3fCaJ2I23Eb99EXbBoLXi8QnOwWCXL9IGX9isiAu0tKN7ZLzEba21oxM9/YNQbTqwWYGao9fuLmNmcaP6ftucAw1yjvqpemdYxrF+tWJwoR7Owbnjzds6rszU/wFY3VOpwuc+phzbgcPnjjPg4sD7cyFspu1xDlaPk3O8UoPGl7dzknNQQ60ekKv8uQvA46VTkuAbtxbMyrf9wM0FupHrIrlXkXNsdSNU4uE35/O4tZDH+qY9Lst2YG7NxOpGCpfurArH1mpobwmisy3c1OBX4Vqx68UVsH4WOab5B8bhKzM6UQLkHK2WWojFyCdj2Hz/28jV4AsFGPk8cjffQu7OezvvNXgMjtZe5Fcrr3OpU0q+IVOuxyYy3/wvSP/B/yne0w4OOMcBmomDCrAGUSiZl3pGICc24PCHRLCIrS7l0N7/LvKTV+Bgj7VhDvTcN/vd34EUbIV67vmdnXNpGCSNyeqEyqE6qwoUWrXV0IHMKnrvHn8UubVZbL77TXiPPoYcBZtXZqG29CK3sYLs0qTQO6un9UFSFOjpJMLhNpOMsrfdMJCKRYUYLdsSSvaXZDjLhPHrBQdHl0uFy1k95dnZ6kOHTSvkewVeo+5u+xV4C1FDyAJVc2SshFjSwJt3TE2tgkYYhdevzQGfPGNm5BoFK7CZkazFESiGz6qsFx8udWiyOt4fvWXuQ3F7/svqoLyru0QWifvx/NkGQCJZjILDZD36GiSxjzZo5mrlgPnQsNkCEvbtzz3HLGxn0AGuw9xOHZ0hqeS+Itn83pUcgl4Hzg5bPx9nhl24vZDFxYksHh5zwV+26OIxlqI5tAYVIZp8rN+31QbSvM9EswqXU4Faw4Kcga9mB7/sPIei1ZFOu2Vt2kwmeMfO1n4DLvC32gnswnn2+ZJqWYrV5i6/AqzOQa0iqK+erq8KTeocMDVGKtie/0A4Mj3AKFxvuf+wGPAcHr8IFlkFanNvfRP6wgQckY7tVlxm27Pf/i3hHqmefGp7XyOdghFbFdXg1cDWFtcTP2r7fNWhk6K1przdha05nsPnkVudw+Z7fwbf8SeQW52HFl2Es70fmeUZZNcWzGByPZxDkpHPpBBuCYhrFWwzOQcry10+P+Sylks+w426t21/FskBt0sRY1Y1DHT40N/+4HIOVq9TQyvgNnCsr/7PQIH8d+4AD1Hnaqti6I1bwMSSyQEKbpONgHyDgbQaUwa+dcmslGKCqtrXwJbCP3zTrFL/+ClTfpHXKu8s5Rxsc7w2aybJyqu82Jp5ahDoqy7BWAJypvLEmV1YDcVPjJvyEdQQ3Q+QO9JpfHJZh0uR0BZ0lNxXmRzlIPLoCjtwZmj3l8PA2PkxBVdnNbx5W8Mzx9RdOmEUr19cz6MrYrZKnhttfqUQuQbbkGs5QlJixa7MSjOfQa4VzbVl6TUUFWFFWqFVu5TYjVOUiKgGYaR2+qNijbl9np3DMC59TxRHKFuV6Lte53DAWbyWtQG5awgGXbdtcokHknOIbqcmn/eBBlhTcBAA26MbhTJ0TGys3Er/5v8CZewMnI9+smRfeewc8tM3hJC98+xzO4NSMgajjBzSStb1yZ8vGXws7clVV11kSuxbwT1SCbWJ1h62OPB93b2HhRW54o9s9Zkn4Rk4YmmlWwnsZY+MHMfK2jr8ui4GrlwqgejcBDzBCMI9DYgV2PiMAz32Wir2i4iScE8saQh5JVH1Vf436i9ZiYvyflpcXERnZ6etQf7Z4xImlgzcWtAx1lV9fwYu3rljCIIy3OEQArTLMQiHQbYXEuTv1ACzM7/QkZCW3YPtu0nnb3/f1M1iO2K1IBiPQaJZKwPMP1NTg/sywMWA3+FuHSFpEUF3JwyDApPAfNQMqh3uBp4uMxLj3882x4y1YbCtMGJztJ1YNoSGWD26GH63A6M9Ml6+lhffI4WBRdb+Vg56ehkPH+0SZgN03awEiiUPtqsIeHR4LVpsVmMa7ixkhUvmYIerKTbn5fC6nRgdqDMFXgFCOyOVhNuze+GZS6cQX1sSi2TF6bL1HIrjLU0jtTgJV0sXfH31R0gpcu995NNVXfVEVUxe205W0J7cMijWZEck5eijcBy7YHv/A0eme4vC9VbHz4lNX19C+g//FdTTT0Hd4hUFyIfPQV+cgjR4HM4Tpis0g66Cc6Q2S/ft6If0qV+syTmquU9bQTxzFTiDEu6Ank1DZyBYVuEZOAq9c1Bo6CnBFlER4B0Yr0snzOn1o2XoCFbXo/BtcQ5qi64tzMAfbkW4o3kSDwUwGTNSLNr4IXAOtqLdXsyjLSAh4i8ds3SDlWd75xwMVnziNKuEDWGkQ4OdamDggtpZvDQDbQ5sJIClmKnjWRCsJ+dglZWdaYScgo6PrI4qPlXyh//ne2aA7qefqR7YIvcij6gFHpMyC3SqvrNktiSeHtTR5V6EW93hHJPLEE6WLBwqT5YxlsJWyA8TDCbZaX/lPUJdVTpQskPALiI+B0Y6Jbx6XUfIa/JLcs1Xr2tw5ZdxerxTBDbdVYKbYZ+E0U4FsZRuqXs2v6bh9oL5pfW1KfvSyhn0ORH01RGprAKR5E+n4XG7dz3v5CKb0XVE2jshK4qtZ5Drr8TSDFKr8/C0dcPPJFWdUAIt8D/8ieqcgxXC1HfcGq8Lgvglwa2HXxBaX82EcubZusbFA85xgGbiIADWIMrbxUjUHHTA8OwuM5E7++H+4t8qedC5v+uTP2fpJFaNiLKNMfvaH0EePC50OLSpK6Jf23X2Y3vSblFbe8RWAINf4vesUAs2JqwrqU5sxGLo6DQHU9Xjhb+tC+6tPvcPCxuJrCCNkUDzK0joujS5rAlHm/IA2LVZDbOrOh4bV3e1NPB+2tjYQEeHvcU/Ce2tRV2sgQsuShQ5f+eugUNdpe6PJH0TK0AqR4tyB3pbHHjxIdNdsIAjvQ4cqa2XKXB93rQWz+ap8VD6NxJR0fJeI5D2zHF770Ui+YVHgSvMtHYU6V1FN/DtWx0iS/uZh4HusKm/sWUu+MCCmhhv3jaEYcAT4/WRPep/PX1UFs5XBWymdeixDTgcXXj0UGU2fHM+J0jo2SEnIhXEUlj5xYVN+z45RDYbm7ENrC0vwuvzw+V2Q3U64fGZ43MmuYl0fANuX7AkAFbtOczFVpFamhJitK62IkXnCuCxaEMueQJCF6yAakRUnNsH3xPW5B667lWoxrIKiu0F+mYUmT/991AOPwT1zLPQ1xaQn7wK5eST2wYAD1LL9A8idl1vRYWD2XOLtltqkMpf+h9KOQf14F78Zcsq9WqcQ+iHvvVVKIcehjJ8UrTG5Jen4Dr3QtXX1YKzY0BsBciKmY1xhjvE1ghYWV78/Lq8PtH+6LVwgbyXoP4XK2VpmNNsxNNMuuXF/F8eALt4N4/FDQPPnlB2JT/q5RzUtWQSyuvaCYDFUgbevWsmploDRc7SGSZyzP8PtAGjXQ50hY2SAAursrnZAd0VaYDD27lYlsDY4hzkCbXW0awgt/U5VeALF4Abczt8gtdqbX0DX7/aARp/fvyMmQC8cLh+mYT7DdQJe+O2ya/qrXrvDDnw3HFpu+re1EA1kE1FoUideOZYZa7w/qQmuOnDowo6tirWy9G9lTXsjOyfMU8zEY1Gsba+Lsx5XKoqeIfHY16cdGITyc0YfMEQPIrf1jOYjq4gtbYA1ReCh9W8NcCAWYYapYEWkUiwyznS778kzHU8Fz5bMSAllQXF9gp9fRGZr/0GlNPPQD36KPKLU9Dn70I59ZTlHPXAcg5TlLr5xzzAnnFQR9cksD3A8/lfhnrKWpDealAx7VEd1gsn6sRce333a1Q3HKF2SFulryxrFZnd+yAynomtYf3OB8izQs0C/LyBtq5t58d7ic1kFnfmNpDN5TG9tInJhXhDgylfM72SRbSCThKdmU4POTHeu5vo0v2RLXB2RcZr4akjEp4+tvMIx5Jmm8JSrPRzUTD3hdMOXDi08757KYsnUaLb4pDFGvyLjwNfesokpMWYXTOzqo2A4vXnR63bDLgRnC+P9+/8XMj43prfcmTaZzAbTOv0WpnWr7xr4JXrle87ivmfHXLgeANtJgSrxgpZUo4tTx1RRMCzFpIZ0yGy2hPB4/a2UgD2wZg2PF4f3F6fIJ3RlSWsLS1s/436QG0DY/DUsTAWga/WHnh7R6HYSDawqjY7d6suvS9CjnRBZjKiyS55NfWdPD5gS5eRmpXaB69AX5m9Z+dwgPogBSLw/Nj/B+qR8/Y5R6WFhZ5H9q2vQrv97u7XuLxwBNvMYNtWNRgXSwU5hw8TyfVlrE1cQ76CxhnbIkNtnVCLgtz3CtHNLG7PbkLL67gzn8TdBXvaeuVgsu7uYg4bSevrzWpztrWP9+weL8g32B6vNGHI5v300ROcU3buK8555BzLZd2z1Bn99DngfFEVVD3VReVgZTeDZeXFdpzqfvLp3QY4xNSyGYhrBJyHee7FFVSOrd8XOAar32myU5xwYqUaOUcl18Nm4vYCk7nV92Gl/R+8YbabVkJHkJwDONrX2HnwHitUg7NN8aMnJPREan/X1ENlsKwaDydXHmhX98U4aD/g8/mEKc9mPI61tTWsruzoEYda2tDZPwSPr7pOaDGc/jA8LZ0I9AxDtlGBS9mZzOIEMsvTdZ233NItOMc9bdFm0YgnsK0pqV3+PnIXvwNjYyty/oMCs4e6+VsD+Jf/8l9iaGhI3KOPPvoo3nijsq73r//6r2+buxS2csMpPru/9mu/Jtp4Geh9/vnncfPmTTwoeDBS+fchhM39/h0c+tKUaVGPR3frcDzy6e2f1dGzUEZMZ8Vy5GOrwhpXDteO3BfszcXnKrg01gktk4aWTkLXNMh70HTYD8RTOcQSWaRDeQx1BbbcIusf7KlrMLmUQ8ibR9jvwcRSFovRPEa6VER8pk5BpQqa3hZZbKtxutwZovx7LyjX6uoMm20KVk6Hxfvys1dqX2PZPwNGbVWK9BiIOmctA7Ar8IWtFsY/3XJUJCm2C5JXnqZVCT2/OmZ0q1VkX50Bvn8deOIIcKKocpwZ42szQG+rSWgJitYy0FaP/Xjxeb50GWgLAJ95qMqOBsWQ6Ti08ytm7c02kJ2gVaGirxng92znPj81qIrs7X60NX5YEOXyFJltaYOiKNuL4Pj6itADqrcqRFJU+HpGxBjHib/WdWXLuGf8UUh1to4x00qdjUbGYLtgm6U+fV2I1rK1zeENwP3iX97+u3rmOci9hyB1Nr9V/QCNYV+z35oGnYsmDk6jpVp2dBp1XXhx+2fnkQsw6OxoxTmiS0KfRA611ck5rJOBtcBkm3Cb5nlXqFT8sLCR0LC+mUNXTsehXl/DWolMTFBWgUmK0IAT12ZpVGJgtEtGa8B02GsLWo8Vw50yhjtZ5aOLeaaSAY5dlGtYDrU7EPYa2/NoMYrfi0NxpTUb3Q95adj2XwkMOtXDOZY3gK+8C4x2As+fhm1QI4wcwIoH8PxfPF+dc1ycgNA6++gpU9C+RC9sht+HyTMKn5vXrZZ2mRXIV75zxaxQ+1iVz8cRg5yjOAnIlk2vc6eIhHO+3ep/O+D9aOdZfvSwLE7wftLF2yvoJulwKAiHI1BVReiUiiqv6LrgHwyQ1VvR6u8aFJzD1v4eP3yHzkGqsxuIwS/Js7+dOUwI5mduQKYupKIK/Wv35355++/qoy9AOXy26ZVmBzDxW7/1W/iVX/kV/Kt/9a9E8Ouf/bN/hk984hO4fv16xQrEYDAo/l5A+bP6j//xP8Y//+f/HL/xG7+B4eFh/Oqv/qo45pUrV5ruzr4feDBS+fch8jd2Z0qrQdi91pGNdz3743Be+Ky9/StMINnrryNz5ZWaxJmi95tvf10Iz25efhnJm2+jEfjae9BKfRKvf/u82tra6p7gkskkcln718sOulq8GB+IiH5/v0cV/zYCt1PC8QEXDvWYi2lm+tgWdnkqKxz07OCtOxrevVsqRtHotbKqAKpGtN+bMPBb36cWh1FRKPbL7wBfbuwWsETAbVqGL6yb5MsOGKT6ze8Bf2iRoLB7rUh+mTUub1GYXwe+d9UkqgSzqL/9KvCdy2gIdFkiYa4leEui+eJDDjx9zLHd7sjs7Ntb57EfsHut+PfKQdE8rs6Y2l/3G6IbcaxHY5Z/o+ZGKpEQzm/+UAQuj1csujeWFxFbXWroemVja9i49R7SFSqjklNXsXHpZehbFSlKIAKpDotvPbWJzHvfQvbGW9gvUAMq9+5LyL35VWg3rB90VoPJ1GCqct/8IC1cHgTkJ+oboOyKHhMOpwuuj/wk1Ic/YW//Ct995uoryFx/rebrs8vTiL/zTeSiy4hd+h6Sd99HI/B39KH90GkoLvee5tHNRBI5lss0Ef3tHpwYDsLnVhDyqQh4G8s3U5vx5KATY93qNufgWHxxQhOtj7VAfvL6LR3vTuT3hXMwKFbtGK/dAH7r1crVWAxUUXT+G/YNP2uiJUBJANN92Y7uV6F66ze/C3zlnd1/s3utaHRDvVG2fZZXiL98zWzjJGgA9LvfNzXEGuUcqsykavX9yEt+rMghku/7e68BF7fOYz9g91pJVTjHckzH5WlNOKfeb1hZj2O9QuldMpVGIpkSi38GDxgAo2nO+toaNtbXG7pWqfVl0VmTWrfmLPGJK4hee3O7C4jtj/XoNefja0hf+g6yE42NwXYlFnJvfxPam19D/u4ly32YaJH7Dlc9zgPJOQoi+M3e6sT/9r/9b/iFX/gF/OzP/iyOHTsmAmFerxf/4T/8h8qn7nCgq6tre6NWXQGMKzCI9nf+zt/B5z73OZw6dQr/6T/9J8zNzeEP/uAP8CDgIADWINQ++wqX+socMn/wL6Bd3d3SWAkiK7/HjKZz9Cych87XXvzKqhCrza3PQ3L5IFvomNmFVNS2Q1FHDu7CQUezx0I0TcPC/DyWl5tbBstr4KGIBcluSkOaAlY2EU/lsbyxE9yK+BURCCNGu5y4cNgjtAo6Q9VTeWubOu4s5nGiX8axvlIyXHyt9hMUJfW7KmcdGTCinkMzaQdvP7YusDLNbiU7edFYN6+vxd+qXCsGt964aS4SmGl99PDu1knqhbEq7NyWML7XbWZrG9XyKIjrVhN7tQL37wyblWP7hWbcVyuxvKhyTKQ//JanclCXZW09avm3QCiM7r4B+AI7mU1WnHT0D6O1Z6Ch60XLcdUfhiKqc3dDaEFSm6nCmMvKq/ym9fmK17u8UHrGhDvSfkG79jr0iUuQBo4ITadG8UA6Mj3AUOu4J/Kzt5H53X8G7bb9qILD5alLdN4K5BvO0dplvoLbqC5k1xdEtYLstt8WVHIcLqKLSnIa4RyZTBbzC8tYXl1DM8HFPd3liFhSQyZnf/xk0oHjbgFtQXnbIe94v4oLh53obZEqaicVsBTTMbNq4NSAhGO90ofCOTj/irm/wts8THkDp5n0ahb4Xqzw5vvaXTMzoDTSZQrtl6PatZpeAd6+bepfhXwm5ygXn6deGINQJ7cKarkfuQ1/fy85BzkRDXLud86xENUxu6aLCvn7CVz0L6/FsRK11rtoiYQw0NcNn2+nJJKC9909vWgvCh7Uc61Ujw9OfwiqhdbjNufguO2oUnmVtE4SinPwBqB0DkFpa8BS3ia0D16GPn0N0vBJyFWcq2vhgHOUIhaLlWyZjHWWIZvN4u233xYtisXX8vnnn8f3v//9isff3NzE4OAg+vv7RZDr8uWdJNzdu3exsLBQcsxQKCSqy6od837CQQtkowiYIvG2QGIZ2tHQuFdgX7cdKMFWOLuGkZm5DvfAUTjb63caqdSCNDs7i5ZQANGFaYQ6eoQbUzlyuayo1OCClbbkLS0tcLp2ohZ5XUc2m2uKBTFdaq5Px+BxyTg2uGWBWAN35jNCTDzkk+G0ENNg2+N4b20mcntBF3bgh7okxNO6cM0p6DUVrlVvb+++DvLjPQ6RpawEkjKroNNeQZLLrR6UC+UnKK6qsW3AwNWbM5ja7MUTxyjA6kA+bwh3y4t3Hbi9aLYasOrMCry8xS2RJL4faTwOIAjlTz9df1s+RYk/csL+/syGXqcwb5t9h8ji+4orgfl1itg7LF3BKmG4U0VHSBFOk1ems0Jzpre1OVPHejyDzVRO6OQMdAbqdnnq6+lEJeUyLo4pQlsOp8dr73rpOhIr8/BE2rcrTKjDERw6tosUZ5ZnoFCvo/cQPL07wa7U5GUooQ44W82xODN1FbmVaXiPPg7ZYj6gRhMTF3omJSp4Cq5M1cD3p3aTyKB2137I6ErMlkfx7x4CHgeOTPcYXvurVlbwUSvUlFG4d1Da7AkJqZEu5JObQq/GO3yyxCRiLyg8v6GAD9HlRbR29ZYEwIuDXul0GsFgAE6nKhauXs/OWKHl88jl8vDUG2GwAANfV6YSCHplHBuwF+i7NpMThjpPH3dbVnQzGHasv3aC9NqsLpwXD3VLWIlzTtxp3b5XnOPUoLlVAtvvmtmCV8CTR+vbn5eArYvl8g0MbAXcOj64Pov5dC+eOiaJoB6DUKmMaQg0twYc7mG1nvWxmXA8VRS/plPz82XvVQ9YYfazz9XPORgQ/IRNMwCClYbXZs1qersabsX3lQEH5tYMdEccgifbxdFeGYNtktCvfe9uDh1h6oo1Rwh/eSOLRCovNFmHujx1tSbz2Rnua6/4GsE5XLvHDPeWEH45yp/BfC6HzfVl+CNtov2RUNxehAa2Svi2wGqv9OocnMFW+Pt3qqZ0LYvk1HW4WruhbrWhk4No0SX4jz9p2RrJAgjX6DnomaQIllUy3il/f+3W25CoV9peO3CmHHkEOnXGDp3dk7TDA8k5hCtYkyvXto7HwFQx/u7f/bv4e3/v7+3afWVlBfl8vqSCi+DP165dgxXGx8dFdRgru2jU8E//6T/F448/LoJgfX19IvhVOEYx+HPhb/c7DgJg90CPg2K1ro//DO4FtIW7IpNLIeV64Gzvh+T0QGmiuxivUSKRQHtbK1SXp6IQ7cb6GjZjMdOlzetDOFIaXFxeWUcsnkB/b2dJECydySG2mUJbJFBVu+ja1LpYWB/qC5tC3m2euoS8h7tcSGdp07w3knisX8ZmysDMqo6lDUOQPlZkFV8rkmXdcFQlG2ydYyvjqUFT4Lb49yQ49RCNamCbIFsI6MJYnNGk8O3sKnB8oPnjumXGbQP48lsm2UtlgZ/9iI7F1QRurxsY6zEr1niuH0wBLz5sYLzXUTH4tRdQH+SVa7REB7rK4t/NXD+kswY+mDYJdXGgizbyl2cM0apxbsTehS/cV/yXbk/vTujCwetYnyyyrB9M5XF2WBaBRbp4WQXG+MwEvQ5BhpdjeRGIa0YALBrP4O48M6lclAHdrXnIFUTY+JxnshraIv7tBRyD4gyYy5VKC4Qmh1lFwf3qvV65xAZSa4siKOXvqLxCy6c2kZq/Iwinf+h4Sdu7Fl0W5eqFAJgikhIGJArOVzmH9DtfE+KwnodstKQxw3vnPeiBVlsBMM5H0vgj9lsXXv8KlOOP7apKeyAdmX5YOEdLF1yf+ku4F8jN3TKDr3VyB1fXEGS6sO4D52hricDpckNRrYNEa2vrSCSTcLtdok2ptaU0GD2/HMVmIo2R/g64nDvHSKRywlCno8VbscqTGpuXJ+LwuCSM9frFmNrb6oLfY38BTxMdBlga1Q0r4OyQLKpors8ZiCYZiHKIhE8J58jqMBwOeKsY4yzHDNG+xyRWsbYXuQB1rKoMwXWBbZA85o89VnpMzl0rMXIo7Dt4XWja89V3zaIaBnF+9jkDC6sJ3IoZgvcwAPat94E7i8DnzpsV55WCX3sBtb5ev2kK8rcG9o9zkNtcmQGO9+1olBEzqxBchKgWxKw0h06tmrIbx/rM5Ovkso6rswYeHXMgmTVdJK34Kn9HzkH9O957smw6Y+8Vi+sZ3F1Mi9Ynvmtfu7tiMnAtlhaJuY7ITsIsndWgqjJkqQbncDC5LdV1rYjU5gYS1CilbEOkso4iuUlycQr5XAb+np05X8+mocVWhe5oIQCmtvTAobi2xeYtz0PLIfXuNyD5I/CceLr2eacTyE98AGNz3VYATAp3iM0O9PUlZN/6OpznPgKJZkDF73vAOUowPT0tWm0L4FzWLDz22GNiK4DBr6NHj+Jf/+t/jX/wD/4BfhBwEAD7AQIrDrI33xLOGp6HX6jrtdQdazQLG52+hXw2g5aRY5akkIGvjsHKLaOhCCu+3HBXqMwIBnxismKmthhrGwlEY0l4PS74y/vciiCsmZN0f0ygv8OHrpb6mAozt9z2CjovcmOggXIjzG6V481bOtaTDrxwRtpueSgHM7oMhqwndoRjKYr7lffMNr4LdVpZVwKJMrfyr5TaWXQg6giZLXz7CQa+2GJQsD/3eyAcrbojwNEjQPeWwyGroqgv1hHeCSo2GxStnVgyiWh5AGwvoDsmCT4/Hz/nfBS4MW8GHekyVUBXGII4Fl/z71yh0DvwzNHaZItB1xafAwNt0vZzwbYT3js3F3QMtUs42rf7Po8ldUFEuyMyHhp1icq1ZoDvzc9Lfb5IwAl3FQeC5fWECHi3hHyQZQc0LY8704vi2R/oKV1AFwvUz87MiJ8Hh+pvKXQFWxBk8MpXPZqqeAPwDRyBXFZtoyVjkEMdcHUN7+wbahNbMZh5ZbWZtNXiwHNXOgZtVX+J/VUXnI++KNrmmw0jtgp97jY0t3df2zIP8GCC926O1YfBNsinn6vrtaw+VG0uisqxOmmKJ7UOWmvGON0edA3sPHflaGmNwOvzwum0nizCAS9UWRZC1sVYXk8ilsgg6HfCU97nVgRWl6zF85hdSaO3zY3+9vqezUpGOvWC5jfcIn5DCKFbuel997ohBPY/93BlExTOfYsbptN0QfCecgPfvGQGTSoJ1NcLVkrxkpefBWUNGJQqNq7ZL1Cbay1uCvePdgORrVwFNT5PHwc6t+Z+umDzPMk5CkHFZoPXnJyDEhLlAbC9gNyRwbWC7MPUCqsFzWRicTUeWzQ5lRa0xnhfM0hJHvbEeHUewH2XY0xGOtDfau5rbM37CxsGbs6b73W422EpF8JEX1eY7uUKvHtwEC0G35+fZ7DDJXT5qiW1GfDM5XW0hz1iTmbw6+bUKkJ+Nwa6Qpacg//enZqHqsgY7LfXgVMMX6hFtEy6a3AOyjD4eseES2QxtNSmSCi4ioJSXNeVr+30dEJcCMm19TDJiqjelbz2umLYNul85FNirdls6OuL0OfuIEfuVBYAeyDBG67ZhkZb/JbBr+IAWCWwzZZJ4MXFxZLfLy4uCm0vO1BVFWfPnsWtW6albOF1PAZdIIuPeeZMHWWmHyIOAmB7AKPgjjrdNkpeT6FktrlU6O3e3o924xxgJbkmoXQdf4oRJ9xLCPHFOiPzmXQKK4sLaGnvEFVfaqj0nBOJFBRFFuXEbE0obk8oZGiZJerrisBXI+JxqC+EG9OxplVGVcPkMrWSDBFIqJQh5nlU0uDqbXXA73UIElgJg+0MorGc3VFSqk8djWZoO1CwldnMC4eBCxZ/ZzaShIwBMDtgZRoJ7MNjDNhV/g5ymiGCXUOdO5lvWmUzu/rCWeBQjwML6wa+fRnoUvj+O8ca7XLsS+tmMUgGmZkuEGIrMAjH9oZ6MuLv3gUmlk2nK1au8X1IqBnwKgavSblmCB2e7IBkdHbdYWrAbd03JJfdEVVUdJGU9rRYn/T1OU2YJqSyOu4u5XFmSEVrYO+MvyXoEpsd9HeFoWn6drUX/2WbU3mLUiy2iaXlVfT2dMLjcQvXpUZzhsL2ORixHo9RKvzstEgesPWA42LW6YKnv7SFoWS/q9+HkU3Dd/5T28dkG2Qx8hvLMDIpKB0VtMtqVNFoU1egXXsDzgsvCvclu5C6RwR50+fv2n7NAfYHXFztmXNQGD+v1TxGQVCZ1Y/VwAWU8+jj28HbewWeXyPmOiurq+hobxci1a6y4Fd8MwWXUxGJtoDPI7bt9zMMzCyyqktGSzBUNfglXPUGArg2lai7pbsRXJ/LizmSVb2VwMBXpSANgxMZrboDMGUTGAQpdpNmMIx5x2qO0XbBhBqP81wFSYDHxneqzexgJWaIRB1fV+6WXQy2mc6vAUMdOwLbrDJn9d0XHmOizYHpFQPfu2KgXTZKPuuRPnPbT/C6k9NVC36xeovcr56KMFaVMYDJY5N38H3YPVAuqs/7t1wTrdxVshK4z9w60OrnfWJe28E2h0i00U2dz1TfVgKzHKxMJ5digG5iycCFw8qeHUyJrohLbHYw0hsqcYt3KjLCATeCvtLXr0bjQhdsqLcdbpcKv88j1i6NgOOtJxCuyTkEN4mUfjF0wk3N3BT/l71+eKo4OCcuvwyHpMB/9qPbx3ONlVqY59cWYBh5KK3W1e9SuHrBhHbrXWh334friR8Vkgt2IfeZGXx9fh/doX7IwETPQw89hG9961v4/Oc/v91O+q1vfQt/7a/9NVvHYGXjpUuX8KlPfUr8TNdHBsF4jELAizpkr7/+On75l3fcPe9nHATAGkT+5rtIXfkeXM9/afuBrRe5t78OfWkSro/9TFVCmnvnG9ATG3A9+YWaQTC5ZScKYORzZuDMRk/3XpCnXo2e30VI2dPOB8RKX4IPk5bLWgrV8m9zC0si+EVByXIIW+FYCk5VRkdr7SiMIks4NnRv9NfYVhZPGzjcI9u2t2ZZ+OSygSMdnWhvVUQwQsQTq8z3xcEvgq4+JAzMGI41EAhiAI0FOFz3fP2iGdxiaX+lbPCNWdOCu7hcvhKYub0xZ1aLVbM6p5Ds6zfMANvVGUPYl3/mPDC1vKNLdn0WuDwpofdE6X1FnYl3bgPPnKhOePeKakSUlXm/9bJJ2oqt15ldpbsm9b6KA1gk6KxkY5UXiXXh2Ax0McBoBx89sfvZYjZ/IWpmqAvPoCLLeO74TmBubdPAq9fzODEgCULK+7USDvcoogKMHhLUQqlUmbifcKoKigtAOdZ0d+wOTjlowS5J21oRrW2V2wisUG3MKow9azffh6SqiAyXidSVwTtyClp8Ha4qRJRQu0ZMva8qC3q6QhrJDaHr2JA5CtsyGPwQRNpsMzCyKcg1zo3n5Hz2zwEW73kgSHtvkX//ZSRvvQn3i78IubOxXrDs9/8IRmwFrhd+vup9lH39jwE9LxYvtaC09ZYm9RyOPYvpV4NoT85mIFe4Jys9v6auV05oipYjk81hdnEVPo8L/WUVpQXt0OhmGj63ik6u6GvApUo4PbqPSuNFmF03REU528zs4sa8IQIgJ7o60RKRRQCNn7FSwI7jQHmL37sTphA7JQrKXQ/tgPMlEzKbKeCb75vH+GQF/wS+x60Fc58qsceSJB45BwNb1VoTX79uOjOePwRcnTbE+3/uEdOxupCAujoDXJ+R8OkzpfcVOdvVaZNzWFXzNwOcEqoFGHld6OpITdNizTNWV1EW4pNnTY5BkFdSr4wBrYeGgRVqiRYKgCR73yG5yactvqN4itVe5rELzyANID5+aqc6jvqjr9/UcX5MQm8LZRgqX7MTA7KoAGPNVtBL+ZF7zznKK9IZIO6n4GsZ+Hn5twLn6OpobRrfIHQ9j9VbHwgx/HB/5U4ahyzDO3AE+VSiZuWUs3u0phZX5vobQl5BeerH0BC4/sxlRfCO356+Oi/4h1Q0X1iBlezOZ34MDosquAeSc4gKsCbfvw0c71d+5VfwMz/zM3j44YfxyCOPCAdHtt/SFZL46Z/+aaFF9w//4T8UP//9v//3ceHCBYyNjSEajeKf/JN/gsnJSfz8z//81ik48Df+xt/A//Q//U84dOiQCIj96q/+Knp6eraDbPc7DgJgDUIOtUJq64HDZ7MMxgISs/kMaNVodXF4Q6ZdZ503ferS9wAtA89DL+yrfayvrXsrQ1EKvmc4bB148vr86B85ZDmgsVSzva1FLHqtwOOODnbcl5a454YVkSGrp9osmgDWEg54fGHxmb75vi6qVl44bf8YTx4xCd/RBjKSJLEs+2c28LPnTbeialVOS1EzqMXMIyuDSGIjVSq7SMxbfDutA5Uw3mtakdPCnJbpPC8G+op1Px47YlZ79bWVHoxBslvzwNH+3YT3T940RPb4J56pnuXeK3gdGBCkCP/DCdrD75BLcqniqrA3bgGXp01S/+cf5/Vrzjm8c5fuSaZNutvpQE9k5xksLpTkufDnKl2H26DofSHzzvtzNa7D726ciFBzho9uuQ5fNpcX7qxBX+MBe1aKkoiuRWMlTkxWWJ6bQS6bRvfgaElm1eNUsTY7iWB7F9Qt8fsC+HfZ5YZclFSgFgcrUnzdpS2CaqhdbEIwNrYKOdBiOWbRgKQWFNqDS3LDzsB0eyx2fMy99TUY8TXop58FElEoJ582WzhSm7sqkisleO7H8fcHGXJLJ6T2Pji8jQ8WctcwdHKWGgEqwWsaEBxOXvwzkXDz1tkOWQ9EMKatu8T90Q7nCAYC8Pt8lpyDXIPJNHcF0Xsm0cYHW+9JRVe9eOKwXG8BvkiArG464PWHBdf48jvm3PWxOsxgmFChBhbn7XpB7vA73zeDLh87ZVabFwI1VqAsAHVHWZ1FuQDOR4XgjRXODLOieXcVdTnILXg70BmbnIOVX11eR0ml2dPHGJBxoKel9AQnFs1A0+khwFNWOfX73zerqn/s8f29X/idsQqPQTq2oRaUQDi/u8o4x3cuA3eXTO72I48A7Y0vXUrwynVTX5RVhPxO2gI7z2CxMgkDYeQcdtpFW/w7J85nbjmmY6Ct8arzVIbV47s1Tqntm9V0BL2NL4V9bhfmNQOr0YSQZKiGudlZwU/6tsTLC+NVOrGJ+MY6Iu1du3QLHXAI8x25SEM5Pj8BSVHhay99+FysRI+Y1WC5+DrUCmZtrp7KgbRCkkEdOCr0pBuFMv6I2EqSKvk8lCOPiop39fjj5ntZcA6lzGiogAPO0Ti++MUvYnl5Gb/2a78mROrPnDmDr371q9si9lNTUyVz4/r6On7hF35B7BuJREQF2auvvopjx3a+m//+v//vRRDtF3/xF0WQ7MknnxTHZIX1gwCHcaAqVxdY4kerT94clYjW/YLMxCVAy8E1tjtlk7xz0Sy3HWrc/k5Lp8QxigfmYnCgn5iYwNDQ0IMZua8TbCV7f1JDW1Cqa7IWotdZHfOzk+JavX3X1Cp4ZLQ514zrGIq3M5NoFSDjCPDyNbM667iNwgISO5LFW/MGXr5qkqxffqH2xFSszXR30RAE8qnj1sHC4n3t3FfMXrMyrbgt0m4AjG0QJGXNWNxcmTa1NJh5LeZCi1Ez401he+IP3jBbOh49ZO+aF3Bt1sDMGjW/qJWy+3y/d00X+h6Hu4FDXaRORlOfwbduZ0VL65NHqNll/3qRZM6vZdEZVnF5YlN856dHSzN8t2dj2EjkcHSQLUaNEVLeNyurUbhcKoKB6oGC1YU5ZLMZdPUPlTijvf/m99EW8iPS2QdfpHo2V9dyiN66KB6ilqPWJZN0ukvP3oJn8BicdWpa5NfmkZu6Cj22AqX30K7WyEaRX5gQxDM/cQnG+hJcL/5l0eaYe+3LUM6/AGW4tj0pCQ+JER2C7OhQHOAHn3Okb79nSjEMndj1XCZvvwvZ7YO7r3I7cC3k0klIsgq5grj9DxvnoDEJkx5sY+yt0MJuBbZ2cUyem5kUNvev3pTAzq5zw81ZYDKQxAokBrisKtLZHkfOwZa7Qzakklihls4Cb98BLk2aVWA//3zt1xXziBuzhpiHnzhmbS5QL+cg32Pykm7e5fh/XzPEOVcKgNHohhXNezU5IN69Y1aZf+qcqZ9WAAOGDEoVdL5+82WzS+D5k2ZlnF1cnIBwEH22qIK8GF+7aBrzUM9LfNdGcznHd6/mhBTGx04pdSWWyann1jR0RxS8cycFn0vCmZHSgM7lyU3EU3mcGws0bHJFeYnFlQ34PS4EKI5WBQvz8+Je6qHLdNF9JWusAAfaunrhtXCtLUY+l8XarffFuqtl1Hr9lpq7jczSNHzDJ7bF8O1CW5lBbu6mSJCpA8eh9h+p6/UVz3v2pliMaJdfgZFJwvXZv4o82yTf+SbUp34Uco2g3IPGOQrz9uJv/a8IepvrkBFLptD5xf/2gbgO9zMOKsAaRDPjhtT1yF39PqTWXigDpd7NenQJuSuvQj36GKQ6RepdVYJb+cSGKJlt+JxZZXH3ihiEW0dPVLxG2SxLYH9wY6zRhC6yqUMdkiB1y3EDkmTU1RIg2owUbF+r86PNVVRlJRWrkjYz1gEwTrxP1WEZzjgR9SIKXKRWlpX4+ruGEHP96Y8YQkT9ytRW9nTYrPjafU6ViY7VfcXgVSVNsk+fd2yTzot3DZwY3LH0Zrvgf/wmRVHNdsu9ghllK6cqBiDZIsDWRmZFnzlmZsEp3l8PGDRjoO/9KYryGiJQSi2vAp4cd2Bu3STfDFJ6nc19Bo/2KkhmjbqCX+K84xrmVrOCwHZGXJbBRuqCsK2ZrRONgvdNe1l1YCW0du0ORonrJKtChNYbLkvrW2Bj4ir0fB6RsZ2e13w2LbKzhXZ1itKq6SSUQO3jlSMfXRLBL2E33to8wZmCoL3cPSyysRTSd/jDcLR0Qwq2QF+egbEZhVwlEPaDPK7fj2jm9aakgnb9DcidQ5B7Syv88ssz0G6+CfXkM5DqvGfdoxXEbw0dejJet1ZoyXlpOaxOXIfq8aN18NAPLedYiemIpQwMd0ii0pcueZxTyvWbqoGJICZ9eK2Ip440t7KCc89d6i0b1gEwykMwoGIXDLyw2qnQ/khB+lr4ozcMrMZMzsH5hoEzBoXOjVpLN9TLOTiXVWpP/JELBQ1TA5enTJ5TkA7YSBj4z981K+Ofazz/vI2zI+ZWju9cMdtLf+558/qx0o4BsXruE4LBL3IXymz0cmp10L1x5+88Lt0iiZTQQG3uM3hmUBGB3no1fBc38phe1eBxUetUEa6s5YErcg6PU7Y0h7ALBjG72+0lJrqKxMKL76vWIJ3spZrBL2J94pqoBg8PHinjHM5tzUY13C5M0eiyWy/y6wvQ42uQyTkizRPWLcwzdHYsSOY4gq1wtLKTKow8dUZzGcgDlQNuDxP2HEAAAQAASURBVOS4fp+0QB5gNw4CYE0AdS8yf/rvILX3w/nYZ3b9PT99Dfm521Af+pilHpe+MiOctij6J3EBUuTswUi5kYjCyCTqqyi69G3RS+0+smNjWgz/8Seqi0zVAAdatj5yobefWFyJCkeVlvC90dOoFxPLeeGuxyxg0CPhmWNqQ45A2Zwh2gkbGeAZ7OB4WKmAiV0dX7hgTzvDLkjiiL/ySXtZTArDs/y9cI4fOQ08RFH8wL0byKkfxkAUT/eRLfMwBh67Wxp3s4wlDXztXZOAjpW5GfF7+co7Zmsp9TkKosEEWx4baXtkq0jHAvDaLdMdk1binztvCHfRwnu+ct3c1+cy8EKRFlk94H1IbbqQzyECuyTyQx0yfG7Jlu5bOTpCqiCwEb9SRWeG7RHSh1LmXpz9d/uDiPQM2DoPd7jNJJ9bVbB6NoON629BDbQgsFXGz6oX76B1SX8tqEMnoXSNCNel/QC1Jwv6k1JLF1zP/4T4f+arvw5jfRFSz6hog9Amr8CIR6GeMNsWDvDhgkmz9B//G8hDx+E8//Fdf9duX4S+Mgv1/CcsdUP1hbvQ526JTbRVOosE39ObIvhJjTjb56PnkXz/26LN121Rpchz8J94ck/EXZIV+Fo6oLi9+zoOzC3H4HWriFTrsfsQQcdeOvd2RyQEPQ48f1JuaG5PZRvnHJwTGFSp9HWyhZCGMVXMuevGatxAwA381U/au4fIe1iFXdj7hXNmBVQh+XUvwODX96+b14GJN4LfFZOGNLxpBEyAfet9U+C/PIHGZCc5BxOwDDAy4FWo2mrUrIDHYVX7O3dNswJWe33xiZ1WRralFjgHpTPqaaMtD0jdWTSEayQ1R6lrN9guISjE7+v/zvpaFKHN1haQK0pfCPF39cPjHAW0dHYL6Rc7cIdaRSKhsPbS0glEb70PV6QDgd5R8TvFGxRbI3COnIHad2TfjE0oiF+42tQgLeiQpr/9O0AqDql/XHwf2q33RAeTcqQJmekDHMACBwGwZoGR9wrlvoxs6zM3YRx7HHDmAaerRIBQ6j0EOZMy3ZnKIvbM0ErP/YQt7ReSUG3xLqRIj9lDV4XXVBPTz6wtIrU4Cf/wcShVxPl97fbaeXLZLPKSA84yPR07ZfrRWELoc9wvATBO0ryuhQl1vEdBT8QQwS+C1U1TKzpWN3WcGpBst9VdmzOENkNPtL6qILY3UgSVXP0Fi+Q7iQtb7x4ZM50GSVAK2lR2wAop6nwc7aU2285noVg92/1Ymv7IuFEzCPb0CUfJxL+4blqaF0Ai/sevG3j4kANHqoij7gWszOJpHi6STWBQ5ketY8S2M93Uv2BlltV3Q5Fa4sK4fdfMwgLDykSB13m4k46wACW04lsOUMWf5+FRVlwZW7psRsOf6+qsLvQ8cnlDVBzQJbKSGC1bDagLFvRaj4F8XtqC1cew8f4gFtfTQgdsL1Vg9SIR3xDtkO09/XC66ytV97T1lLZ5Lc9A9vhFAKwe5FNxOByyMC5JX3sNzoFjUNv7RaKhloMSq3ly778EZfi00Hiqum8yBn32FuSRU1WFyhk4MVglvKUBon3wKozoMpTxhwBJ2VP18AGagS1r9Qqcg3xDX54GTj9rjgAuT8kiTx46aQrWy2pJ8ItQ+o9ApkiyHc6h5aAtTUIutPY2yDlSK3NIr84jOHyioqSC0P7qsCc4xaoKLijpflUP8nkd67GU0CO8XwJg5EH8ugtz7MkBGakMK1fMn/nvrQXTfOfMYKk7bTWwinhuFRjarC8BxAAIOQc1u6xcGykqz/mLmlTU5eSxqwnRl4NzzcyK6bBYzCtevUZHQLPtk7pbtT7nx8/s/J0SCaxiKuZWdIpk8uqJo2wJ3B/OcYr6YK5SnTRWT9NhslHEU2ZFFtsvy7kir/tKzKzQt6oKa4RzMIBG6QYG7khn+f0XJ3mZfHto2MBaorQyrF7w81yZMUSF2npCF9WNA23W9zPn2ulVHREfzRmsx0Dy1Y5Q5TmO99bJIR8W1rLI5Gjuc+9aptm6trqysqsizA78RWMgO3GSKwtQPH44iwon7CCfjImCDJ3JlNvvwj18Ckq4Q/CCck2ucrAqPXv5ZaiHz0Ou4BK5vS+1RpemIA+fquoqzOKRYjOg3MXvAJm0CIBR0+yB5Rycp2sYDjR0zAPsGQcBsAZR3NtOouj+zC+V/N3IZoTQLB9a9dzzMI49JlyVMl/992LxoZ58euf1DglqpdaBreNXJaDLU8Kly8ikkZv6AEomBc8eRGgNXRPaNpVEcAuZi/KJSViTF10XXqO+vj6szE+Lv/WPmaWtwvmRYtI1BjQumof7O5uik9AsvH07JzSjqIFkCmabWzEmV3QRdDraA8g2+fdolwzk+9AVrj6wiTbLmJlBLFTWMqBViWBenTWJBXU26LTUEwE+Xep2XFNfgm6FrJQq6FcRdE0iEXvthplZHOuprxLry28YQtD+0XEIsqsqpj7HUtQQn6W31fo7p45HV8Qh7qt69SWomUVC2izwOSC5Z4uBVQaeehw/KXTHimzB10zSWu2WJoH98tvA2WFTYJegqyMDbXSMZFB1tEo39EiHQ2zmOUoNXStmyc8MyduOmrznKwW/Ulkd12dzCPsknB1pPOUfS2qYXUmLBc5gZ+niM0dnCXFNmz/xS5IsHOVMN6fK14tjGL+45OoCspsbCA3QxEMuGYuza/NQfCG4W+23DojAGW3JVTc8o2dE60JdAuRaVuh52anYyd95H3nqP/pCkHvMbLEVpNZugNsWnE//KJBOIT9xBdnvfxmuj/0EJK99t6sDNJlzuL3w/NhfL/k7W1mx1QajPv6i+NnYXEf2u78L5dhjUI7sCBILXjJ+vjHOkU0jvzoHQ5JFZXpu5hpUPQ/fmY82/Nl4z+vCtbo+zkENneLrwv/TyWphbhay8v9n7z+gJLmuK1F0Z6T35b337X03uhum0fCOAOidKBpRJCUONdL8NxLnP2k0GpkZjZ5G7+lzxCdxJJIaDkkRFC1IAoR3DaABtLfV5b036U1E/LVvVFZlZqWvakfUXitWd2VlZUbciLj3xDn77G1AQ6M2gUaj0eX7OxMMBj3aG8qgTyV2dJ3w8sWoSELc2qWdExrDxLtAc2z6JlXBxNlWr8vZfbqzRoJVqkOpM/OxklVE1lFMpJ774ranF6E/O6hpgLHdn+7H1KC6Jw8m8vFuTdSdcUB8goeMJyZFXj6vsafiC2jZcHYAeP4McGQbWw81l2ReUixQcb1l2x7jimRwbGkuVF9WWMzBZNe2zEa7eYH7Qyfpz9ylEwy3ZJBp9okjTP6snDvGDdlcpVl8/QWTgV2alhfBtkbGsdQoZcIrk7t4R0389VhYzMHiLDXoShwsuumFmVS6JCeTpBdHZKH5urOp8Ht11hPB0HRI5O3ryxNjl3CEc4tmgLHe0EsSDAaDeAbKNFac3zgGnulxREJBlNSu6JUSZJ+HFqZhdpXATGZYjmCs4j9/DJLdBXN1m/iZBIqc/z4SWoo5glnfG+1+G8rwZUhMrmXQQJUqErVDzHd9VDwzR868isiJ52F56DOQDDdGUWIDvxq46RNgX/nKV4Q9J50KduzYgb/9278VFp+p8PWvf33Z8jMGs9mMYDD7TZyXXkA4hNCTfy9cIk23vU8Ek9RXUYN+6EqqILlX22wXiuh4LyJ9pzXrcUcxTM3boS8pwJZHLFwKPH3nRDWheOuhtMc413ceqqKitG2l/OdfnMP82BCKaxphdWplIP69w+EA5HKtirm0gA8PDUKvN6CuoSHrPqVzglwrKHw5vRBBQ7k5gdmUDQw8qReQdmH2s61AC9Dy0Ukic2ZXe3aW25lB4CyttzdrNHfuxr0Zgkv+jlRytiQc7MifBs+EEQMt2lvHg1qfzZWak1B9npczg1pqYnTU6kSw+/I5FQ/t1eG3HwJeu6Difz2v4gO3Ai1VieM3MqPiB8dUbG4AHtl/dejZ6cDvZqIxvn3iWy9qFdGP3pG4n2Rfcbx5XcVfvif7gONXgLu3A+0ZEoZMNvLhIr7VkOedQSqDX342q7CMmeIrsUxSTS0Fu/GuhuIezBGslPPBgEmo8QUmI3WrHriSYTVJ2FxvFCKzawHHkvpglSWrmaKXBufEtb6lOf+kC+ecqalpmC1muFMIhlrtDtQ2rwiwphov6g9N956H2eGGjkKuAb+WpIpLgElGE1xtO4UWRz4QGoA1baIaq3cUw7H/IVHxDA2ch6GsFvosTsNcT8xHPiIYNtHB84JJbGjenvK9hrZd0DmLIS1pgGVD5K1fimSLqpOgTI1AauwS6wzb6zccma4tMsYcvkWEnvwHSPVdMB18SNN0M5rBdBJ13XSuPMUGMyAychnR4Uti8ZGKKmFq2g5DeR5OHvH7LUfh6T8v3MpKNh1Iy/aY6jkvhO9LGztW1vD5WcxPjaOirhFmq32FJeZ0QpGZ8NIvP0QODA6LWK+uNjvjwpyLNW4BmPPKmPXKaK5gsj33e4dzcKYQhRpNbO2jLlY+OklMfJU6s8ccb/UAl8eAB3ZqbC4m2B5a7au0jEf2asklspBo8pIt+ZIMMsdoyEMXx3gwIcZkGj8vX8mCpkpg66IWx7x2CXinhzpdwOfvB148C7x0jiY5LGgljl/vOPDT49wnHe7cfm1jjuFpSmusxJGMof/5Ba3g+eiB1e2hTCAJbbe4y/fNbi0hyaJnps4CSlQw5ohvWT3Rp11bLLYxBmTykfWn+AQrrzuy3+vjznG+MQfNBAhqi04sqCIB5hZtj+nB1t8t9XpRdFsLDAYdqktMYosHx/ps/wIsRj02N+XfSsg5Z3xqHg67BS7H6qSNg460S/ce56VUiIRDmBi4Aoe7BHIogEgosMqoge3gRS1boDfmqUuhNwj3abY5GorK4dz3gEiCBQfPw1TRAMmS+fyR9WWJxRx8/jSYBHM4FQydB6CU1Yl1KBtYZIy8+XNIxRVQ/D7Rii9VNojnZxhNwhHzpkMGpvaaPnMD7+4E2He/+1383u/9Hr761a/iwIED+Ju/+Rvcd999uHTpEioqUtuc0DGBv4+h0CBeljNky/UGIfRHPa948EHCfNv7E16LnHsF8kQ/zLd9QLQ1yBN90Fe15Gx3b6ho1B7E+J2OIuhTJNc4aSoBD0LDl4X9rT4dVVZVIdOSlsm0DONCS95k3Qi9cGYyiapr/Bj19PSgtbV1me3Fz7U7HDn3uyeD+hysyFQwQirQDpnuR9OLUUzMR1DiNMBtz/022FSX+bwwMdRWqUO5K78JKtVYpQIDIrK4ctWPoLMTltbXrdnzjSmD7z0piCKn+rSg8e4dK+KuuYJJpHt2aX8j6VQs+ICGCi3QY3sitUmqUuiYM+g9tIlBsILLl69kHatYQvKFMyoOdFB8XZdXIohC/a3VWrLpOy8zIacFzfEBefJtMj6n4nuvacyt25NEfvn3DBizVa4ZiL43KcC9pV1j8jH5xdudLpK81uIfRNh6cmVcs6ePVetzva5ieO2SIvbRYVEx72dgrGBTbfa/qyzS7qHu0TCmPQr2tJhEApDBba6tBX3jfpF4qytfTWcscRXOLGMwuujxwhqNpkyAxSPdeJGlazBbYTBZYC+tFIGalGI8WTwoBOaaRFHv6MwwoqOXoSxMwrr9SNa/j7WXRQfOCUZYugQY9b4MjbmpT3OOl/vPiRZMNRKG6pkDLHZY3/tF8Xt5bi6nz9nA+iBjzGEyL8UcidRQye6G+cgHE14Lv/MM1IUpmG7/oLhW5KlB4cCVqUUxHobqNtE6yaCe8QZ1v5LB+0P2LyA01gNLXZdoC04Fvo8aNtTQyxRzGEzmVc6PjDUMfCiK2+90MYfDbofJnF9iWuyfqmJwwitcaSuKC3Py8gUVRBVVtIlPe2RUFRny0qHa3Zw5PmHio61Kh7qS/NbhXNcGtrXRgTHXkCuejb4zc0d2SrB1P5U+JotHb18BHtyTX6KPYEKFsQrRVasKNjZjDMYcnXVMKmjjmAwmjQ50kC0n4/Ll3pzWURbAXr8EwXDXZAhyQySqCnY8i2NMKn3/mCbbcM9Scwj3kUWxZA3O/kkVP31LSzaSmR8PMv8ZqmeTXyh1Ah9IasukMRKlEJj8Ygz0xOvatUDR+/jkqHCg3KV9RiExx3PntKKbQVKFpEORXUVrZeZx4z1dW6J99pmBKBYDCm5pN0DlM5SSe0x6eTgokoZNlYmDyrEudZqEKU8hiMoKFr1+MX+kSoDFkGmsmMA3miwwmExwl1eJz0rFFKMpSL4Q2md1K8UEIjzRj+hYDxTfImybbsn+GWQAqyqiA2dFQSxdAowsM245gesR9Ua981DmJgG6/rqKN2KODVwV3NQJsL/+67/GZz/72WVWFxNhTz75JP7xH/8Rf/AHf5D2xq+qWj9ni1RQ5yehUtOlPBfxPua0lwSshy4g2v2WeMJNdoNM+9cmC4xZ3hu4/CZk36Kgk8rusrQJME5oRZv2Z80uu+tWZ0TMdgcqW7pSPnwmo6w8Dw/mOHCynfcEhCh+oQmw7tEAvEEFu1psKM0z+ZUL2K5ZV0qtpPz/NtVYxcAWBDKAxubobqOJnV9PsB2SLoZM6qwFpS4d7t+zEqzUlWkBzN/+WMGD+6jRsHItMui9bYsOL5yW4ZuR0Zq+g2sZl4YVnO1n8K6isjj3YKZ7FHjqHRX7OoBbN+uEYH+ys+ejB3T4zsuqcJt6z37tGNga0loFNKRgxfGSvSMP56vkh4rYgwWDMwr3s1uIbQqxCjuDXbZiJidHY9fV2JyKQERrkUzGvE/FuWFFBNcMeKmtQe2v5vL8HjSmFmURLFOPZmwuIhgPB9otOSXB2PbIxGOqB+GassKr7wwsGxvStxlQo3Bxfg7ukhKR6Ep1HzLZVdLQvsKsu8p6FJKzFDCaoc+hahoP057707aREcr8hGCtSTkwggQzTYjia86Y1OQwtKR3hdzA9YMyPSoMcyhon48rFav3cv8ZUXBj4S0XSBYbTFliDv+FY1DYJiNHoAQW0ybAyJoszhJz8DosaVhhaMZgc7jElozk+5d/X1lZGOue89GcNySKZoUmwM4NBhGKqtjfbkVtKZNf61u957rYUKppTa1nzEFJAsot0K2ZrPbrDbomcmrL1zk5GWx1jC+wUf+Lyaf/8XPgPfvVBOa5yajDrZuBZ06oULy5xRznhrQWTha68jG6oaYq2zTJtGIBjVs8U5zX8WMHVHz3FeDZUyru2qHtJxNPZLalKqyxaFiowU9RnF4sWV/VJOHoWeTTkoexc8L3JCcPY9fV0LQKNn80pogj6F5K7VsW9cgso5bp2DzQmOf5nfIoIqFJQyBKMTD2uH2zKSf93faa1ExmvtZYlYdYborOFcq38FklFUKhEBYXPXC7XWnvQSb4KxpaEtj8VxP6onJExntgjJM+yClG2PdQRoaTMjsGmCyQHMU5Pc+a7v64YC9TAD9y9lXo0yTWbhpsuEDesLhpE2AUOX377bfx5S9/efk1PuDcfffdOHbsWNq/83q9aGxsFJPO7t278ed//ufYsiX9UyknKm4xLC4uLmfuYxVZTgL8bn6mqJr7FyEvzkDye8DpL/Z6/H7yb4R+RdctYuMUKNW0wcAVvrxJfDZZW5FTz8PYtAWGmvZVE2XsgW7V66qC0PBFSKW10NvcgMkOnarC2rgFOrM9oZKcvO8CqpLwejQUFIFqjBm26v1JxySqApEI5memEKWWWIrqddp9T/N6rDrSXF0ESS+Jz+P3seLDdS7+/en2ka/XlZnhC0ZEgoqaRrHPyeWYkl9PdUy0VX7ubBSldjr26bMek2DnsVouywhHZbx4XkZnrSRYXvHvPzekQ/+UhD3NCqrcKiwGjmnmY833mN7uIUNOxqE4S/R0+07hUSZztH1PfUzJ+5LqdSaAXzgjC+HTtiUdCZ5POkYa9GrKa/XigAxL3P2X6ZjGZgWvEtXFPF+Zjym2j5dGFFS6VRzu0qGrnnoNBtyxJXaeVvaFWyisQq/j69q+mw06PLhHG3cKKmc6H9QWUVQdtjTkf+0d7pDxnVe1BNiHD2uvF9kAV532nbH95N+KOUmWcaJfEbbotUU60eYTfz5mPQom59nqoRdjb9IrS8kv7T25XmO0LPeGGJRTqDkKqDpI4FioKY9J7JvC/aHemHZe4s9ruvOU6vVM1x71NmL3WfK+ez2LWJifE5VWm90BJe7aisfMwGVRJCipb13THJHTMZmssOy6N+/7iW6R8ceZfKzB134i2ELmOz+adY4IvviEJqROA5NHfhumw48mHFNGRtIGCkYhMUfUuwB5YQZSwAteSZnmE/0So1DEHPWbxMOGWlyjxRyeWUTOvgRT+x7oK7Q4KZfrV6dEER6+JP5GtM+Y7ULTztbQCamgmCME/ZKAfa5rGRPZc3NzmsboOsUcfIjuqHUK1nmhMUdTpQnhsCwYLnQxXO+YwxNQ8cwZWcQM+1qlvGKOYFjBc2cVbG/So8KVOJ+c7NdhbF7C/lZFtITxmNc75qCZjl4nY3dr9pij2CGJZNRaYw6uuS+dlUXxjtpeBOUtLUZpaS1f+ezYOn9xSEGptDKvZjomzfiGTD+Oly7rtcexvTyqiMQeXZ47a6h1acAdW1fHHIxmGHNQtD322RShf2hvbjEHk2xkR3XU5neeeB/c1iXjf7+saYo9vl97nQ7XJXa+P3XMcfwK13e252qav/HngwkwJlkPdTJOVuEwq+iozj/m2N+qQyCkxaQsMpoNqtCzkhl7pIs5VEkU5WItlFcj5mASLF3MsbDowfz8AowmozZ3RSLa3BJ3rPz/5FCvYJ2XVtVe9ZhDZ3HCuvOe/GMOuzttzCGHggi99mPoXCUwHX5vxjmCCD7zLags0pmsMD/8OZhue3wj5tjAVcNNmwCbnp4WN0NlZSLlnz9fvHgx5d90dnYKdtj27duFC8df/dVf4dChQzh37pwQIkyFv/iLv8B/+k//adXrvb29op2ScLvdqK6uxsTEhPhcQt1yP8odlSge78fAqeMIVbUtO2qRgVZUVIT+/n6RyBPvVxXUSEE469twZWhImyBCAUQ9UTQHApAUBd3d3Qn70N7eLsRd+/r6EJkc0KxxJT3aO9qxONKHyfE54erB02wyVaDF6sT8/LzQS4vBbrejvr4es7OzYkxjiB3TSH8vJof7xYOftbQK5eXlKCsrw8jICHw+3/L7k48pHArC71kUiTMmwQYGBhMmzqLiEphNJkxMjKc9pvjJsaOjAxPTc7jU3QuX3Qyr2SgWsbChBKV2Bf7F2ZyPKeSdQnf3wgojrawsp2OKgdcKNQ5IXY4/pubmZrF4uKI9MASA7m5d1mPi9w0PD4vPmZ6exXRkEOMLrdhaNQ9TdGL5/TUWOxo218OOWYyMTePnL2ttbrWVq6+9Qo/p3KAdkr8XZZIWoMeOaTGox0tvdIsAjUEwW/HuuKUdXl8UQ0N9y+LoyccUA524WlpaxP4lX3uukjqcvjyDWecMVJ+0fJ6+9Gg1xsbGUp6nuzaPY3RkDleuXBHfmXxMQhR4Aigtr8PhLQ441SsILyjoXlw5JiZEUt1PfeMRvHa8DxVFrA7qMDUmodiZ/pge3aMdU+yjsl178efpZC8Q1JVhS0Nh1167Q7v2+N3pjon7yHmSY1Wt10G2AQP9+pTnqavIhJqSZjz5+hzM8jiaK6SMx6Q3uzCvlKPcNI2gL/E8AWVQA5MwBHzo6Ul/TBS+D+nLsL2tDBMjvavup3TnKXY/BUIREZxbzKa8r73YMU1NTYmEfWhsHFazWWgLDfb1IhrX5V1aWgpJb8D0ggeT588h5F0QgSnPKc/t8MRUznNEtmOKoZD7Kdu1N1fSoTHAuruzzhGDAQVhYxl0zhLoe3pWHVMsMbOB9UVhMYcF6vaHUG4uQvHQRQycP41QTadWSU8XcyiyiDlcTV3oXlqfKWwse2Q0BwOwZIg5uC/RiX7tWpIktLc2Y3G0H1NTHuhdpKJYtOvU4sg75hjq7cbU2IhgXVqLK3KOOYLBAPw+PwxGo0iC9Q8MLN97ohWpqAw2mwUTYyM533sjE3Po7ulHsdMsnGnDsoSQoQLVrii8C9M5H9PY2NWLOcwGPYqUK5B8BcQcM7OYiQzilwut2NuwAARXzlOzw45NdfWwYRaDI9P4WS9Eoaq6Yv1ijjODdjjlXjiTjmnGq8fr73SjqUIzj2Fb/u37OzDviWB0uE8Y2qQ6plzmSJOjDhd6ZuGdnkGwdom57Xbj8w/wPE2mPE/3bh7D2Ohs2piD7uD9E0BtbR1u3exAmdSL2XEZcxPZY45LQxG8capPaJ6RKTU2LMGZ4Zjeu6+wmIP5htM9ZHCWoaM2/2uvv68HXW5FmCDkGnM00kRHBXp6VsccLMpvKTbDbWvGz4/Pw6FOoGmJKZbumFSjC4tqJaotk/B7V8ccxsgEjEEfrlxJf0yzXsYcldi3qRSjg/mvz75gVOgAW8zGgtfnmekpRKJRjI6MiPcNDw2J+S5+3EtLSkTMMTu/gImZOYT8XpjMVlRVVojnp+Gx8Rs+5picncNcaadgs2eLOfg3QyEdwqZK6Nip1N39qxFzbLhA3rDQqcliTjcJRkdHhdvPa6+9hoMHV5rX//2///d48cUX8cYbb2T9DGbdN23ahI985CP4z//5P+dcjeUNPzMzI25YIlNFTOk9hfDpl2A4+B7oKxrSZvDl6WHIJ54RfdRkhMXAxBgF47Nl8IPnXhbOjWyDsLTt0YT3be5lLbHkDH404BEJM6PdnXbf2b4wP3AJ0aDmLmYtq4ajQmslylY94hbweTE+NgaL2YyahhUrHP6uf2BIJIsa6lcqG+FwBBMzCygrdsGaZHHD9/oCIQyOL6Cm3AmH1SQWouEpP+or7LCZNbYVX1v0y6gtsy7vR/wxrVflck3sjrhjip2PWBVowmPEW7167G5W0ViWeh8HplQ8f5bVXmBLw/odE/UeIlF52fkv9jqdF3/6loIjWzRhVlqbf+xOPX54jFU+BZ9/cGUyzrt6JEkYmpLhsqpwLH1vtvMk2HLhMIxG4/Jr8cdEHan/50cK3DYJn31Qv6olLNN5YtvfsQuyEOinw1ChxxS/78+eVjEyrcPH7pQECyD2Ot2nyCaoKk5/np45pcAXVIWILXOSuV57/CjqaVD7hGPFACieQp987cWzO6+My7CZVFS6M5+P/kkF3RMqdjZKKHfpCrr2zvT5EIio6Ky1wZ0kaJvtfopGZVzomxI6He0NpXmfJ44L/yb2eeIzI2FMj42iqKwcFpvWAuGZnYQSjaKkul58hm92Ep6JYdG4zoQw5+eS9u1XfY7I5ZjymQvWOu8xGGZikP/GEjMbWDvWGnPI519D5OKbMB75MKTiyvQxx+gVyGdfhqF1J6TWXXnFHEwwBc++KBgpStAL6ya6XCuaQcLS366KOZgkp66NzZk+XoqEMDd0BdFgiB8NZ2UdbCUVOcccfp9PPKDZbDZU16z0j3Gd6h1kLGJEfc2KBEMwFMH4jBdVZS6R4Eq+9xa8IQxNeEU7lM1ixIIvipGZEFqqbMvi5Ezic46uK7dcs3tvPWOOoVkTTg3qcbBDRXVR6vnk8qgqxONv7QLaqtfvmCijoKqyYDHFv366X8XzpxXcu4stpDShAT55l4T/9QKZPgo+fiT1Wpa8L+leH5iUUepUlx281xpzeIMqvvY010xJrPO5ng++TvOa45cVbGlgO+H6xBw/P6Fi0afDh2+XRDAQe52MKxYsy9zpz9PP3lGENmtM6yvXa4+tjt4CYg5ul0ZVFNmU5Tgi3fm4OKqgb0qHg2265fbMfK+9490B4VK6rdG6yj092/3E+eJ8/wLsVgPa61x5n6dgiNeQYdnZXlw7Xq9IBpUUF8O0JIjP7hlOgKUVlRpjbHoc3tlpcS71UGA0W1Ha1LERc9zA4LrN9XriB/8DLrt1fT/bF0Dl4791U4zDjYyblgHGLDJvVFY34sGfc9X44mK2a9cuUalIBzp0pHLpEPR4ThQBH3RLDkTxD1IxSG27oK/vhM68Wggx/v1SeT2kLYehp1tGgr7Myv/TCUrydXusrYGigZb0ooucTPh+b98ZIUBr3nFH2n2PhKJQQgHYSivEe63u0uX3pXp/8usOlxslSw+ZyfvOxFeM0h17sJeVsFhgQpEoHEsTRv/orLAMrq8qgt1qxqY4O0KXXY/NQuV9BRPzQcx7KW5vgo19gln2cS2vZzofub4eOx+xgKCxXBKaFCL6T7MvdF+kGD5dlmLxRT77zu/yhXTLLWcxaOKqq8ess04TkKdWQ1sNRFsgdTS2NCqQFZ5bKeUxpTvWZNSX63Pa95iTKF9nRSq2OCa/36oH7twhCw2vV8+ruH1r7ueDweEd21aPWb7HlHBvS2Rmamc0/vXKOEmEdOcvGJHgp+OjXku2ZNp3ChWfH9aLFoqhGeB4D3C4k4lU46qxiu372306jM/rcP9OHUxLwsJtVanHa3yB50DTuCNaqiRUlWgtGKmQyzVpMhngj8jonQhhb7sjr/uJgWRTbYloT4oXvM7lPDFAHxkdFRXMqjgWsSRZUNXQlDBeYb9XJMaYGODrjtJKGM2adghZNNQNuxZzRLzLMBm/hqpm6FOYpazX/JbtmAo1MtlAZqw15tBtOQxD+55lxnnaeammFZIqQ1/VnHfMQZaVY9fdWuIpHICUIr5JfugJ9FFrzATzloNp951MTF7fjvIqUdCzOIvyizmcThSFQoJdHr/v/H9TfZVYq7iKSOKe1SEcDYtW7XAkCrvVJI7nyvA8LGYD6iuccDvMYouhxKVfZcjBhNhiQEZ5kSmt1uGNHHO010jCfEZboVKvZV11QE0p3fcK23eu3f4w3YQTf6clIFbHHDuadWisYJKDBSIVcz6gyKHD5gYVdgvvA92a1ufGihxjDjW3mIPanwc6FLx2UWN272rN/XwwCXf71vWNOfSMOfSrX+c5THesMQTDvDe4r9n3nQmvy6N6IdZ/cUQz4rlrm4oqd/qY45VLktCUo4FPzA21SzDxVu/P0Cx1xygJor1vUx3j47XFHAajHiFZRc9EGHtarXndTyzMt9RqyfJ8Yw5/IIih0SkUueyoLNeMQ3gP0rWWiZL4sQr4Y+yoSrHvReXVsFjtgiUmR8Ji3b+WMYcS8iMyMwJDVUvWa+9qvL4Rc2zgauCm5dFxMdqzZw+effbZ5deYPebP8YywTGBW+cyZM4KqmS9ERbP3NII/+H8QHTgPxTuP0HPfgTy2QieNITn5Jc+MaMKA8e+hTk19F5S5cYRefgLhMy8h0nMi7/1KTn4xcRX//8jCtHiQs9a2w1rXgfDcJKJ+z6q/8Y70QAkHUdq5G46qJrhqW2FcCrrzGaOpmVmYrTZBtU0+f6wQ9Q+OYmhES2Iy6dXaWIUS98qDcDgiiy0e/mAUFwfn4aWidxIaKixorbGlTX6tBf7Q1SNLcqxIUU6uxlD8PhVHM5UDYa546wrwj88AA5Ppj4ei6N95SUHfuPYeBqJcFN12uiZpX3zbFglHUiSLrgYuDSn4029HcWFQSTtWMYzPqphdVLGpTqtWx4MabecGmLhL1Fp4+ayCC0OKqMQOZhiXXEAXqB8eUzC1oH3O3Tt0+PW7aE+e/wl7eO+SxlcOfzowDZweBHonV9xCi+2Zx4r259T8Sv58PrAkk4PPDik4M5RY/UsXiOaKzQ02tFZb0FxZmMsj26G5n5cGpuEhhTFHcO6hI6RryYo8hlTXVlldCyqbOkSiK3bcbH8MeheEA5PZWaDKcAqERq/A887TUIIr7QHJiI73ItJ3CtGpQVxPpLumNnD1xjty9jUE/uX/gjzaC2VuAqFf/jPkqZVWFUKwKpKSX/LkAJT5ycT36Q0wNGyGPNaL0CvfR+jU84j2n81rn0Ryy5wh5pCjIuYgrHXtsNa2IjQ7LpwfE45NjmJxpFfo/5R17BDML3dtMwzmJMu7LOBcwHYco9m8ignBNmkmvnoGJjA8PiNeK3ZZ0dFYjmJa7y4xZ8MRBZFI4rW96I/gXL9HxB7JaKk2o6vOmrPb7Y0cc/D4GXOkQnzyK1+QPfaN56gflf54uF7+yyt0y1yJOYgSpw6tS8L0R7frhKPztQBZaP/Xv6oiHsgWc5AZT/H6ztrVBjg8h5dGEtdTrq8vnVXQM67gzcuqcI9eCybmVfz0OB21tc95YLcOH76VWnX5j9XjB4DH9uf23p4JLeagEyQdItki67JkHivGGzbz6vg1VuCMgeN1sl/FmcGVz5HWIebY22pFW5URzZX5u8ISbIfmHHGub050nOQKJuXdLjucjpXnqHTXVXV9k9gSElJGEwKeBVgcLpjtiXHLWhAcugjPqecF+zYdoqPdiNAwZTZRsuZa46aMOZjUuxrbBtaMm3oUf+/3fg//8A//gG984xu4cOECvvCFLwgqacwV8hOf+ESCSP6f/Mmf4Omnnxb6Fe+88w4+/vGPY2BgAL/xG79R0PdTG0VXWg3JWQzVtwh1ehjqXCIjLRmKdw6RN55E+K1fpPy9SueksB8Kk2STa3vAkQMeeE88Ix6qiPDUEPw9JxGZGYWppApGVyl8gxfgH0nsDedEGJybRGhhGhJbKdfgOMEJa3R4CJNJWl8EP5eaHNxioPhn/Pe1N5ShpS7RZp1i8azahsKrBRHNRr1gf603xucVvHg+iv5J7Tsvjshiu5pgcPHjt8jqWd/PpSsQnQNZtUyHBR+DOo0yfyOA7oZ8RuG/2fDmJRlvXFKxp11CTWnitfv6BRXff0XBuX41gTn14lkFz55U8PQ7Kl44s7ZFdnQWok2U47ceyPX2a63UrMu7aiDEae/alv2hZXujhHu2Swm28mwh/dkJBW/3Jgak+1r12N+2ugJHB63k4DUfVBYZUerKfGIjUQV9Y96UASeTmUySU2g/V7DSSBYxW6XiWWEUxE9O/LHqmsy0YvIrMD+DkC/N02KBYJJNJxkynnRDdQuMLTthKNda6jfw7oFUXAGpvBY6Cg8vzkKZGoa6wHaZ9JBnRhF5/aeIvP3LlL9Xw0EhncD4RV5jUjXqmYX3nV8ivBS7hMb7RcwRnZ+CubQGksUO33A3AmP9iftIseSFaYQWZ0XMsRYw8TU8PIrJydUTMBkndpsZtjjLRLZQx/9+c3MJmmsS20ooFu8P0eF29RxD+YUS5/oX3AamFDx7VsbwrLYenRpQcHns6j4AXhjRYo6edX7OrVqKOTSWeWrMebW1c+YGkflhcchhZUya/b1MYr19BTi0SSecrePx2gUVP3pd0yWNgQyo1y8BL50BXjqrinhlLaAZDs/ZesRr+YT7m2uB2zdpsQc1aRlzMLmVCfvbdLh7W2Jyji3EP3yLCa/EAtuhDpowrH5MZYwSY+cVgtpSI0ocmdlEwbCM7lG/MIZKRlRWlowIcr8fyT6tKi9JmHtC4TBCoXCK967umgl45uFbmEUosM6WrDG6X6aYo6YDxpYd0Jes0fZ9Axu4gXDTtkASH/rQh4SA8R/90R8J3YedO3fiF7/4xbIw/uDgYAKlkg5Bn/3sZ8V7i4uLBYOMGmKbN28u6Pv15XXQ3/fJ5Z+lhz8HxNl9sxIaPf0CdEWVMDRpTpPy0EWhSaSvT20jbmjcAn3DZiASzEnoLkIdD+8szG17BYssHhSul8zWZSFcY1G5aGk0uDSfYQrU2+o7oU+q4OrNVhS1bodkKoyVkbAPOh2sNpuoLMYo9wQnfZPJiOrKzJ7HqZJvRQ4ztrUYReuTNxDF5SGP0Pxi8FrqIk298IQdH6YX/apwHIqHw6JDiUMHl0377OFZVbRSdK1ImK07mMSg8Hw6G2sS41gQySUpRHD8h6YhxFbfdyjzGLH94HP3awHgtQSTKaEIvzdx/5qqJPzbxxMde9Lh6E492mtVNFWuPsbtzTpEZB3alsRvCatZh53NOrxzRcWhLWy/WFtdgFoe5W5qiBV2/V0aoTU7A8n8rmM6bjMQXSt4+7C9NjmQLV6qyCcHoi+ci6LUqcOeltyXk4vDISz4Zexts+ZkV87E16wnLFgW9iR2J1lgW1orCqp2x2NudgZejycnpyF7cbnQ4TCtYyWWMFW3iC0TOJ8ba9txNaFGwpB7T0HfsAm6uDVtA9cX+to2sRGSuxR6JsNsK8kaldbxJ56FvroF+roO8ZoyeEH728bUcY6xfQ8MbbuBUIDUyKz7EB48LxJmJmqNJrc46Y2CfaYzakUoY0kVVCUK/RJLkrGGrbYNBlvifWO0OVDUvBmGFG2b+YIxn8ViFXNpLObgv6FwBGaTEXVVcX1gOcYcFUVmFDuMMBokIbFwacSPpgpqEEiocCcW7fIFdSepIVWcpIHIWKPUATgt1Mbh2q2KObkj/4aFnEEWD2MOMohTIbxUf6CLdi7gOaB+V0vlajZ2Mqi7WV2iJmiQXguEIyqiyur1lvvDjci2JNy9U4ftTdq6n6qd02RQURcX6hY7dOioofsjcHgTsKVxbce8q0U7bzHd0nwQiQLdY0BrVe6xZAxMDvLcrhXMvzDmiGmyxRCvLRrPqHv+nCycJXc05d4Sd7IvLPRGb2nXHO2zgVrCM4sROCx6WJc0hmModVtQ7DKvOeaYnp4RpI1IJJq1vc9ZWgGzzSG29YSltkNsmcDnSKlaW3euFrimyP3noG/eBp0pP+bvjQyV688ar5NUn7mBd3kCjPjiF78otlR44YUXEn7+7//9v4vtaiHadxrKWB9Md3xQCwAjQcgD5yF554GlBBgDTamoAlJVc9rPEZOzKbdAUF6YhLw4Q1VaQEpkPrE1wb71toSfrQ1dCe8xp8noG/Jsd0wHTWw6IphgFHtkv7vXF8Do+BRKil0oKymsfYjJL8REqCUdPIEo5rxRUcQocxeeuOsZlzEwJWNnswHlLn1CAuxAuwEXRmTM+xXc1qUXCbCrCWpu3b0t/e9/+pbmyPiJIxyD1O8JhlWRKGNQycTKL94BDnUB+zOvd5jxMBBN/TCQ8e8W2f4nCx2ttpr8E0nfeSGK/gkV/+59RtgshU3yTJ5takj9t8VOHe7etTrQ2N0mCVr7/g5pOclZKDhm6ZKW2cAq8XNngF3NWmB8PUA22JEtuQWWvO6YGCvKc8z4d+mu2dGZEOZ9UXTW2ZaTY0UOEzrrnbCZ0+j6ZblOKT7L88IH4GSQ+cXiCPWDzGYLxuJcjtKBxQazI3/xUSUSFgUQ6ToFeMriDMLHfgRDxz4YmtNPLspIN6KnX4IaDcO45fA13ccN5I7ohTdFK6Tp6IdFwYsujiLhpcjLCTDDpltEvMGkWDqIeT6Ddmg85Llx0aJrYlUr6b7T25xwbLt95WerA9b6roTvsZSmzuCwnXjdYg5qialAIBiEzWrFgsePsal5VJa5EyQW8gGTX7HPN0g64SY351Vg1FvXxAC7MCxjZFbFwQ62/a1MipxXD3bocXqQAuFk10hp58z1Qqkzc8zxr68zqQV8bOUUr0IgRHYO2V46nBkAXj4P3LkN2JqFsDq9qCYIm+cKtg/+8oSKozt0qE1ifOeC//2iIthnX3qEWpKFrf2MGZa6aFeBSbFUibHdbZRFULG7VSeKcGsB179CCm7EpVHtHLHwyETa9YDFqMO923MbAxI2eW9QjiMf8N5ZuoVXYWAyDF9QwaZ683LMW+42wmKS4KSobAExhz8YFs8pphQUQsYjc/OLKHIXicIb9UyzQZL0sBRQcBMxxxLh4XqAcj/seDJuuRX6DIU7uf+8iDkoXmdo331N93ED707c9Amw64WU4uI+D1QmuxSWyUxC+8t018cTstms3uurW/P+PmppyFND0JfXL7ssEeaO/eJ3sYrrjTZGtNr1+/3wLC6KQJQwm41C78u+9HM2yIqCkSkfih1mOO2Jx0mtrx1tRQhHFdgsYRQ7TIIaPeeJoshhyIldEg862FQVScJBMBnC6nqKzkEqWipyu3XiWW+5jFU6EchUoN4Cq2aZDvFHbwDTHuA379UqkAxCW5eeP/omqEOh4sG9iQEaA9H/+bQqmEwP789v/Bb8dDwEJuc1wfx0oEaXy64lW+LRUi3BqFeQIk+xaqzmvDqYjOqqhBUrutThqirJfSzZKvneW6+/wGZjOXBLB9CRYezyQSHXVTrQrYpaJs0VK0xI3l/72vJfRtpr0iepvUEZHr8s2AOx+9cflEV7c77sTibeef/1D0+KfztbVlM2g8EgfF4vLBYL3EVFcLpc6zJeqeDpOSVazIu2Hl4Ta6Rg8LjICM7SZibVdcAQjUBfnz5TfrXGaAN5xBze+aWYQxEPDpRjMN3zCeisKw9KOos9Y/IrHSjHwPZJulczuRaDZcttIombzDi/ERCb7xY9Pvj8AeFAHROvdtgtsFlyK46x5Xpk2o8ytwUOa+L85rYbsKfdhUBYgcsWQZFdL+aqOa+MEqc+b1YImeWxIlsqxhVjjhIH1wbp+sccpdqllglPHNOSKb9xj8ZkpqZYwxL7iesHWwUf2stxXNnHkRkV330F2N0KHNmKvMAi4NgctTe1/ctU1Cu2rwivx9BcqUOJM7OsTmysZr062MwU4tetKjR6AqkZYOnQUK4T2/VGW5V2vqhfdqPFHGyNvDSmorNatzzm1FI91Jl/rLatMf0zEk0sfAFFJG5jIak3qMBq0ue9Tsf0U3uHaeClw6YUFDm/PwCP1w+7zYKtW7de1bV0/spJMVcXd+7FdQHXDsYccc+tqaBv3iqogOmYyuKjbsA1Jyt4/eTQzZX3Z25gzdhIgK0jjPvuA9R7EoJFyZEbFUQksTJMENGxXkR6T8IoR2CMo6vybzL93fUGxe8dDodgfsXrfNVUJSmFZgC1vmYXg+L/yQmwGEwG6j1picaJuTB6x4OoKzOJ4JUB6pyP/fxhdNaaxc+pwMTJmcGoqC7RDTAZDGxv36TPuQr7xhUF4/PAAzs1d8FcxormALli/1Ix5WQf0DepueokJ47aqQfl1drjGDjcvTNR52tqQXPyKY+rHpL5tameCazs++wNqOKzY5bwdAb8vffqVrUwxmN8VsHf/SSKPR0S3nMw8do9TOZRDuwjtrN85UcMoiX82/cljtmTb8p4u1vBFx42oLZMO1mjMwoW/XSwLHwhosAsC3X5tibmA56jvevMNM/3uooP5M4OqShz6YQLEx8gBqZVWExaxZvB6alBGe1VEspdmcf14khEXCcUn82GthqreMiKJUdDERnnBjyiEtvVsLoCOrMQEP+WuhMT6vOLfoxOzqO+umSVU2k8ODdxfGLOe4WOVy4wF1dCiWpstOsByVEMy9GPZX2fzmCEoS1ustjADQnj7e9bSkbFxRyuzC1+hNC5o4NphtghMnwJ0cHzMJHxVBknyLxGja6rDd6/RW4niotWGJpkftZnaX2Mhz8UxcyixuBIToDFYDVJqC3V5ozBqTD6JyNorTIKTbBihx4T81H0jkfFg7cjyXE5BrY+UuS7uli3qhgk9tuow9EtUs4thy+eV5cc9laKB5mQ71xHjUnizW4K2lNsfTWTl8WbmG8Rhezv2r7yu3kvMLUIBMKJGqTFDqC9WkuYZYMnoIpWvVhM1VWnE8W9VAnEGMgq//aLitDoumNb4vtSuT6n/F5fBP/zaQNqSnT4xF2JMcpP31TQPQp84UFJuFXGhPHZXti8JOBfCGhGRNF4XgdXC1zP9+Rfk8+IQtfQqKwKxmNtiQ6Vbp1wnu6f0rohWi3aeJweVLC1XlpOHKeb384MyuKaaKnMHk9uqbdoya+le8YXkHGm3y+YnTS4SMbEbFAwQktcicc4NefH2LRXaAgy0UoH+1Tg3GS1WoQxBxnoVyveiMUc1yveINjxZLnzo1nfR6KIoW3XNdmnDWyAuAnTqTcGUrlRCPeluEA0V0SunEDouW9B8cymfY+hokE4Nl0r4WP/zBh8UyM5vz8aCa8Sj+YY9fX1rdm5w2Yxor2+CLVlufHjix0GVJeYEI4A5weDmPVQIFtzAIx3/0sG5U+qiyRUFqU/h3SgodkctaqygVJFXE+HZrK/t5CxYuWTGxleZFyl8AQQ9Pq7dmgOjsnY2aLDFx/WiQpoPBhsvecArc8zL5ocy6/8JIpv/DJRmDxT8otw0UmyXidEZv/o6yFcGJRFsoXJtFzAMRoc6MeBTTrs71o9hW1ulLCjRUoQo/3XV2T88zMRHL8YzVmwnfsTc2diYPZ3P5Xxv569usYH6w2KtL50vA+n4sRlUzG7ku9dgiarVyaYXNV+xyTYwQ4JbUsBvT+sYtarCfpmAj97clHG1GJu1zYTzfEPg0OTQZrDiTbIVBid9oktGUaDXghcz8x5RftARWnRqrEZGBrFzOy8YH/xHlmvOSsdLJUNQgMpE6KLM4h65nC9QQ1LtvCraRwpb0pHppsY6xlzRC8cQ+j5b0ENeDKaLQhN0tJ1oqNmgXdqFP7ZzCZC8YhcxZjDbddarmtKc2Opl7sNqC01YDGg4vQAW7hlRBlzRDPHHFwDmfyqdGdI0Ft1WAxoa1A28PMIislnQyFjxfmeTCcmsSi4nkoDnC6Nt25KHQMc6NTh8/cDVcWJv2dR6ZH9OjRVZI4dAmEVf/8LFT94LXEsMiW/CDK8yHynUcpfPiFjaEo7L7m6bHKMxkb6sKtFFZpeyaDkwvYmFv5WXvvBa4pw0j4f52KYDdSfpYwEwX0jE58C+jcTolEFL7zZh/NxjtHJIGMuVczBa4vmTwNT2u+ay4HDnTrxbyxhzGuQ91km8LpkvDHtyTHmkBJjjn7GHKIl2JCyE2R4KoCRaa3wFg+TkS2PekzO+WHQ61FVnliwi0Rl9A6OY3bBC6vFfNXjDcJe1QhbZebnxujCNGT/9XefIBEk2ndWGLP8ysQcZH9djW0Da8aNSx16F0FntUPnKILOYMqYHdeX1iLw9i9gbN4OY1VuLQ2BwfOCs25tyo9XHpidgCrLsJdn50WHggFMDPXDWVSC4vJ1UMRMAXsssssBXISaKi3wBWVBFXXZ9IJZU+bKTGfmg/eWhszfM+dV8cpFBQ1lOuxs0qUVtR2ZZbuhDt3jwOmB9REnT6XHwRjiU0e1ymxMwPT1SyqGpoDHbtEYRQw03ulh0MkWgcR9ZjLviVcVHN6ky7tSKejd9flrMTDY/ehRI870yTCbqIcA/ORYFMcvKfjiY8aUrYvTCwrKlh4SyMQanlJw9BYmOFZPYZ11ktji8cA+PV44peKHr8lw2KgRpse8V8WFQQX7OhNdEGP43kuysED/d+83CPcqCujHqrs3C/jsxSRpYFpz4jo1ABxoZ2Ve+/3YHAVltSp+V62KHXFivEz2Htm8IobPe6c8TvaKrK+7tmqJzEzg3x1sp+95YcfgshtFQFvuTj0/ttamFj+h21tZsROjk3OCMR5iOT4BKqJRJsdXgqrFhQWEQit24LEgPXneCPo8CHkX4SqvzqsVTA4HIQe8MLnTm38ErrwjkhqOnUdxPaGM9yHy1lPQt+6Eceed13VfNrDOsDmhsxcB+vQ3r2RxQO+uQOCNn8DUcQCG8rqcPtrfe1oY6FjqOnPeHd5n/pkJ4QBpK8m+WPp9PkyOjaCopFRsVwMOa+4xB9lgrVVmYexh1OvgtEiC/VRTkjnm4LqzsylzGD42r+LYZVWI32+tT/1ZLO6xDZDakd9/XZvnKYq+nuA0+cRrGkP8g4e1n2PMtJfPU3YAeGS/FkcxuXSiV2vpT24L5Hr03GlVtDpWl+S3KPD7yGonCysfkLH8wdv0eOeKDMpIsrj149dVXB4BvvDQat1PXo/UBStxaq9TUmFiDrhrvw6GFEJSWxokIRkRj/v2SDh2QcEPj6lw21TUlulEcmtgUsWu1tRFye+9ogqW3O88qsVzZOLne6zXG1xmeY4xCzhtmrPo4U5NroNgQe3VS1rMsbNJxaY4QyKaA9y5ZcW9mokpGjPEUFcqodRJVlzmMeF9dWuXsWDNPE0+RUmZAOP13dngTMmwdDssQkt2bNojYg7+P/m6io85+HMgEBTt2i6nI2PM4fcsIhQKoKi0Ii9GlxwKiM3kKkn5ez7n+XtOCIdex+ZDuJ6QBy8i+tZTtLyEYfNB/CpgQwT/xsVGAuw6Q/EtIHrhdeibtmV129IJxXdjQsVXVWREZ8dhIM01RTuD7JmHquafNS9qJNc9t8qTwWCExWaHwWTGxOgwXEXFsNrWR0Q/BrJ2Bid9cNmMKHHlpuNBAcsyl0EkgYhcFw0K277dK2NLvR7VxYkrKIO/xnKtJSwdrowDZwZZYaSYeHqNLq6Br1zUEhMt5RTDBUbCSGgZINhqQHF00tTZRqb9rYpdLTpxivj8bY7bTQairNCyAs38EIORF85qwej7l/Ssmfzpn+TxaC5N/I7mAhyOk1sY88GTr0eFKOkPXo2KFojGytStkyeuyPju8xE8fqsBBzYZ8NKpKIaHFNTUK9i80pmTEa01Elx2A073KiKIfvkMBYxVvHFBEcEOE2JMsjHBFUuG7WmXUFWiOW/x2nlwvz6nCu63nleEwO2BzpWT8uxJRZyL27fmF5Hx8/j9qRJ0uUBLbAFtbcAzZ4E5n+b4tK9txT6d4HXFcbk4ooqk7dGtWgIsU5sBkS0QjSF2D+aDwakgdNChvtyMiiJTRoZoOridVsEEM5r0MMa5LLGSODE1i5KSIhS7ncuB58zMDIKBlcruxOgQopEoahubE+YP/8Isgp4F2IpKYTTnLmjvH+1FZHEGrvZdMKSZ7y0sVqwhwImOdguxe2PH/jXpNElkHW85BCnJISryxs+h+heh7rin4M/ewPWDMj8J+fJbMHTuz+62xUozk2Rx1xGdJqPzkzCUrE7+8h6SvXNQ8jR54L1V0rIJuhwr20aTCRarDZLegLHRURSXlAgW53qCjKuBCb9wli5y5JYMs5nZDk4pm/xijol5BSf6FexpWd1OXmLX1u5MMcf5YeDiKHBrlzZ3p3Pzo6bYyxeA5gqy3YFzQ8C0AtyaJLtDBhm33S2J2lhMsHE9SjYxmV7U2GFLUnSCkf7qBS0OYZuk9h5VsMb4eUKzy8sEGPICkw7vOaAr+Hw+e1JrvfzBMVWIxjdUpB6r45c1Yf33HZbQVa/Dy2dVzIyraJ0D6nNU76CDpMsqoWdcFUzAt7pVDE+zK0FFRZHWtskxob5bTJeMjH22eMbWe+qzZgOTat9/TRXtnVuXCli8D585RX05YE+bLu+YgwW/fPVzY2BRc1Md0NEO/OQdwBcio4tthtrvh2ZWxOwZc5waUMVr927X2lrLlpKOa405Cmkb7RkLwWySUMfW5gx59XRt0URZkRUWswEWox5GHuQSorKM8akFVJQXo4iZwaU4xB8IYm5uYTkBNjw8LGqFdfVLA7YEz8IcQgG/IBrwmStXeEd6EPUvoqhjD/Sm1c9OOr0elobNomhRKCKDF6CGfDC1r01nTDgcBzzQJxE2Qi99X3vu3XLHmj5/AxuIx0YC7DqDNzVFahmIRocuIjp8EaZd94hsfDIkexFs+x9KeC06NYRQ/xmoDZthSiGub990YDmPxQev0PQo9HYXjM6SVVbk8Ug1UaZ9r8GAitoGBPx+sZnMluUE2HqJFkZkBbOLIVE5yTUBNjgVwchMBJvrzXm5NHG40nUtMDCJZ8nMeFW806diZyPZMdrrjWVa8qmuJPMiTOFRBgZ8LxNgfNhIFS+fH2JiQqvo1pQAF4dVPHsaeHifKpJxybh/j2bbHUs6sKr2nv2qcHiK4dULKs70a8HXJ+/SoTJDcH21sL2F1XEVgRCEVteBTakTTKwS01Eypud1eJseJ1WDSGrlg3K3hLt2SfjG0xFcGlLwqfsN4rX2WglDUwq++pMIDm7W4+FbtGtle4uE7XFEy3e6ZcF2y/S9HHdvgE5Yia9zrBkY3p4HEZN6F998XmNrFdlVPLIvf1dOggEdteEort8zrj3EsNWACViKE7O9hg5kZOYxccuHpBTdCesKf0gRunGZBKMn5yLifmACrFBwvMgESwZbEehGy406HGxH4HtramsRia4wxRhoMgkXQyjA0rYO7opaOIrLMia/lGgEc73nYCkqg71CY89YK+rFvKtPMb/HYCxJ7ZIne2ahRoIwlGRuSZMnB0WSw9hC8UFLwbqTQges60DiZ4/1Qh7vE2Nw1S+SDVwd8JpmzGG0INpzEvJEP0z7HhAGPcnQF5XDdst7El6LjPciPHwJIDMwSZKB95CDrqFL93V4fhKh+SkYrE6Y3KUZr3tDHkkzo9GIqto64SwdCARgC4WWE2DrFXNQe3DWExHJiVwTYFfGIhifk7Gn1QxXCiOdbDFHqjuKMcSeuLa78Xm2WarY16YTeqUxIXMWMarcmYsNZHwPTUOwoGqK0sccZK0zNmmp0pIop/rJLAceO4CUDseP7tf2P5a4Idv8/t1s71x5D50Ge8chEk+fPLrCrrpWYMJuc4Pm1sgiIVsitzZJaU1xmitVlC2xjw5v0eG8ngXR/L6zqkQntq//UhYJxY8fZUGHbpXAlVEV//Kygtu26nDbFm0syAyLp0pTy5SxWV1Z+rFi/OgLavFk/GssqJa5mADLfX8n5lV87zXtnPNv7y1QBpLFZxb8yPwantUSYpRK4LXG5CtZYkzW8tp+q0cVciU5KlMUDLo8MsZJF0OxyD42F4XVzARY4TqH/HynbXXBLhyOwusPwhcIwmo2Cl1CvV6PIrcLFRWlCRrJ8TdlwO+DpNejrKpGaKtlSn5Fw0HM91+CrbQKtlKNSWurqEc06Mv4TGcqS93pE12cFlltQ1FmcT6uIWrQC7V1V8aW/KwxB3XAkphfZIUpU8NZCSI3LK5Gy+JGC+S6QKemasTeQFosLi7C7XZjYWEBLldcP1Ac5LEeRK+cgGnPfdBlSDIlI9J7CtHBczDvfxiSLfVnJ4OOYpGxXhirmiCZMutV+Ie7EZoeEZMrE2COlgx+13HwTI0hEvSjuI4sCCmrLod4aIxnSwSCgkXhcuU+gXExujIyL1ofa8u1vwuGtfaCTILWCfsdkDE+F0VThSlnBkrPhCacmUmTIx7Dsype71axr1UnhF1NeqAlSVMrGeNzmugrK48MXJicCIa16mx8kioGJlMmFlYEYkmhf+m8FpxUFuV2XKw0XhhShQYHq2yk9P/ibUVUJD9zb6Je1q86WDEdm1GwtVmPJ1+PiLG/f78BP3glitZqHVprJVQUSataTP78f0dQXqTDFx815u3CxZYLFlRtWbRK4hGOqnj6hBasByPAp+/OjQlG163XL2smCfEti/H4wRvU8AL2tKh4qxc41Am0ZNFfWS/QLe1kXxiN5Qa0ZBDFDy21D5iNV2exn5v3wOP1obqybJUNeSQSwfjoqHCENBj0sDuc4pwOX7kgWC+1Ldnbu+RIGHM9Z2ApKoejKrt2I5m6aiQMKU0iwH/qeSgBD+z7HswYRPIz1GgIUpwT4Kp9mxhA5O2nRLBpaNqW1r5cHr4s3hNrzw+/9ASU0R4YH/gMfDpT1rVwA9co5ug/J/TajLc8DJ05N90qInLhdcjjvTAfeky4VucCJehHZLIfpuq2rO7TvoELCM1PioDdVFwBR31ubZELEyOQoxGU1Gan+FJEmgmx+DmXiW1q9bgcuR0TEZUVXB7yoNhpQvWS9lcgJAtJhVzZMJzbJhdkIYafy1zNteLyGFutyHzJbZ7rmVDxdq+KWztZgCPLSGOlp/8OjXHFtYDTHIsfFFYnM4eMLcYiyaAxDhlaMffGy6PAW1c0Nleq96cC9TNpzsN1iOM3uaDimZMaW+zT92TX7fpVwtSCKkyHaCz05Jss/gB723V46h0F7dU6NFXpRKEyuQD2d08qaCgHPnanPu+Yg0wyY5xBUS6g7hiLqzz3LE792pHc/o5mT+8sxRExeYVkfPsVjfXFFlYmWMlUpJP5tcDEvIzTA1F01OhF3JEOdHbltZqLaVUhmJpdhC8QQl1VidAIiwelF8bGJ1FU5BK/o3mY0LvtvSIYr7UN2efCaCiAub6LsJVVwV6WupCWrPOpyhFIKYofhPfEM4Lx69z3QObPCYegKtGUxI0Y5JHLiJx+EYYdd8JQ05a+eDcxIJjnsURa6OlvQpkZg/nxL8Iblm+amCO2bo/97J/gstvW97N9flQ/+KmbYhxuZGwwwApEpryhMj8FdWZUCMwq/kVRZZVsDkjFVdBn0MUwtuyAoXm7WMjkhWmo3lkYktpPksGJy9ywZM2TBdaaVpjLaiAHfNDbck9GRYIBhAN+qIoKnT6z1bYxLiDme3w+H2ZmFxCORGG3W0XFIxeoYFVIFk4rMViYXcoDdI3jliuY5Lg8qsBtyz0BVleiw3v2aAv7v76hOeRlSoCx1fDp01rFlsECqebET06pWPT68NEj9lWLr9Wc6I7UWKHDr+XglhSPk72aDhgZZKx8svp67y6dqAbfTMkvTcBehd/vh91uL4gRxeMtdWnXBZlgDPred7sRH7/biD/+ZgiW0zL+4COJAQErlR89akgQuU2H+H2iU+LglCoMBfLdV14HD+/T2l3jXRGzgcHr6Jz2oFHm1O7B5LHaXK9VjOn+WVOstdmsF97pjYqHrj2tadzTzBJKnNTISX+Pce64MBQQ476p3pbw+thsCE6rAU6bIeNclOkaml3wwGm3orgoMUkUm7NYhZXlKHxeDyKhoJi3yGotrqjJ+bv0RhPKuvbkvF+hkW6EJwdh7zoAfYoCiLl5G5RwMKvrL5MS2RITWpJEJwo16RJgcv95yP1noa9uhW5J/8m4736onjlI7jKoCws5H9sGrnLMMTsmquRqyC/+H+0/J7RF9ZVN0Fen1ws1brpFsPxEzDE7LtpYDCmY5PGQLDaYG9Jb1cfDVtcBc2UDZL8XBkdqrb5UCAf9kCORhHs73X0e76AWu3/ZaiQrirjHc71fObzhqCK2GKzm/GIOuj9yyxVMQnWPa86NZTnWSlsrdahfIoy8dEFLSLFFMh3YXvbcWeqHAXtbNRkH4sm3VchhHz5+dPU6Sr2mmGZTzNmRWz54uwe4NAI0lWutjhVuGvIoYl1yWG4eBgPXCw5PqnU0V1AHjWstr8/uUU3W4K6dEh67RcJf/0BBmUvFb9yfeN1QQ+7xgyxOZv/8+H3y+FURZ7KYly+EGcE+jUGWD3hOGXNQYoF6Z6nGaluDxsLj9crEV9E65QQ4pq93KyKpu7Mp9b3HZCuTzEUZWJmMs073h4SzeFetOeH8D8+EUeJg/Kd9PpPrmdjrqz5bVjCz4EOR047yEteq+Ypjxe8hw8vj8SIcDomkPp2pS8srRJdNLjCYrSjvyt1J0T90CZH5STi79kOfonBiad4ukmTZoDOZoUNmpr7KJBtb5LvfTp8A6zkFZfQK9I2boHNp2XfT4UehBnwiuaaGbsKYg9fJemt2bWiArQs2EmAFIpMbBQNKQ/M20WYQvXwc6tQgZEmCsjibMQFGxBaMaN8p0cYilVRnrOTnAzIX2IKQqQ0hFcj84iRIGi7B/48N9IhWx7KalT51Tt58UIwdA8eI/ewNDY1ics81+UXQPnhLc+k1te/lw/b+Nn1eFTMilrC6a1uiLgYXSWpsVLhWmFpc/KnJlBywbqlXMDI4DL2unUeP9Qb1Iag5wZaGGChE2z3CpJiatQWSwvNjMyq6Gq5f4EoNjz/+egDhsIwPHBjG7h0deV1TqfBbj5oE9Z6unt9+LiwqtO11qT+zsz7/Y3/xjIK3r6j46BFJ6J395E0FD++nSUPm8V70KUIrxWGVRDDL3G8ugTBBO3kKx7LVg4EX78H29na8eUWPGS/wwC6gc/lhhiLMWmtMqsotDR24p/lokDGhmKmdwWLUYUeTOce4IUl8PqxgZDoIt50JMIdgbZzrm4fbbkRTdW7zJBmp07OLQoy2qjyxpyU2Z3G8GptbBKPV7/XCbNGCQ7sr94f4fKG3F0Hv9KZsRRO/d5VlnRmi432Ijl2BoW5TRtFyWpPr29iuEK/tFEb4he9CKq+HcccRGLYehlTbBinuc9iGEGtFuCkdmX5VY46dRzWmnsWOSN8ZqBN9UFlBDwUyJsCI2D0WufI2VN889OX1GQ158gH1ZQx6Owx5xhxl9a2iHTC2b7IsY3SwDza7A6UVK4uY1hJkWHX/1tU3JhxbLmCxbUdr0TWNOfhgfqBNypsNFYs57tmuJmhYsa39zCBEgizGJmfLIed7tp7FY3uDgpmxYShK+5rX0VS4fYuWNIsXuqcGF1sBG8pV4W6ZCWSpU7+0JU9znvVEIKTib34gQ1VlPLhlCNu2rC3m4LX1uQcksbbTCfGHxxS01yKt4zb1x/LF82c0cf9fu1OTN3jxrGZOkK1bgIwztsZazTpMLKhwmKmXltv3U9+LbMHkmOOVi3qEosC9O2gKtfJ+xslkGcYKwMnMd01jLvdj94XoKpn+93aLhN0t2ec0fmXyt3qDMoanw4KR3m61IhRWcKbPg/IiExorc2PbenxBTLEyqQIVcW0e8fGG1WpBa0uTYLT6A4HlxL7TnaLneJ3AooRggKVpq8zW+khEhi9Dnh6CsXEr9MXpDUz05Q1Qm7ZCF1fco8N06KXvQV/fJYoxxl1HobTthLSU/CJ0drfYiI2YYwPriY0E2FWACKCWAj59+x5IzHYrSnbB2TgY2/dC9S0sJ78iYz2IDF3U2hcbt8BY0YjI1BDCI92wdu0Xjk1X83gYyJIJMTPaD2dJpUiGxRJiBLW/xscoRluKouJiTE5NIRIOi9+ZzaaCgoZrFYi+3RsRDK7tjUaU5tiGkAqs2MWDrQZ0YyLDJqabwe/ZH1f84KLNoJWMMDW9I/2awcpeV9LzMAXZqXNRUbQ60fT3T0ZRXarD44e1KeLnx6M4P6DiCw8bUF16fZJgvBzsZkBONvNbA2LJTlZNyQbrqJNwoKvwAPfSsII5j4pblvTM9rRJsJoVoftB/ZNFv9bumi0Q/dpTWiD26XtUfPdlzU3pM3fnvh9sdXmzW0u2xuANafpkyYEijRjIQPjwYe36jE/gPvkOQDkLBrC54nDX2pcV3vs7mlc/NJOR0V5rX2Zm8H3mJetxIhiKIhiRUcToPQ3sNguqK4pht2aej/nZJpMZppLCNciS4RvtEW3rjgYKfifOF8biSrGtBdHxXigL04hET2d17TN2pBOsVVechyu1RMIGbmyIROZSzGHYdptw7kQ0kpduimnTLVBDgeXkV2TgvND74lOhqXWX0J4LMw6ZGIB186G0rbrrdTy8Oyj6PDM+AndZhYgh4vW9PF4vJicnUV5WJtpARsenlllyVsuNG3NwH9/olkXr4uZ6wyrR+3xAVkty+zsTYJRWONylvUaPEGo8rny/prdEXabuFb+PdQcTXDG2WQx3bNMJtlBy8ovJoG8+p6KtGji6QxuPn7+lYHAK+O2HV7s0XiswEUNWPzWq1gtMMMVkKRgT0F16S2Ph1wClLSifsWNJJ25vGx3PIfTLKJ3BJFi2/Z+cV/Htl7UE0MePqPjXY1rMSrfPXMDbhrfm8SvAlriYw5OkTRbDC+dYXAM+emvi6yxE/vgtjQ1JA6ncvluHo1v1hZpML4MJt33tqxNaLpsBnXWW5W4SYTpFXdWl7hRfMCr2O5NOoGvpRnCmyvglHQtZX9zWC4vDPSKYdNWuZvaaS2vEthZEx3qEKU548BysGeIXHpsxlcukmLOXYo4CCBo3PHjBrJMuZcJnbmDN2EiAXWVQM0vnyFM5Uwjeu4GlrLcAn/plrSWAgS2hRkJQwwFhY3stQOaXHIkK58mqhhZ45mfh8yzA7nTDYDQIEVrT0sTN5Fd4qYXhRgYrClOLGstle6OW/LkwqqCmWEJpFge8bGAr5Z1baIGtUcrpvsdWRlK1Y3j1IjU9gEf3ae2RVQFaMBf+nRzvNy9rrY5MuqQDF+yqYp3YVv1OVkVrBoVKYzi0WY/KIhUVSe/3s8VvUhHsqKv98MAA5csftwk2QHd37t81Mi3jO8+F8cghEzrSsLucNh3+4CMWEeyuBc+dkIVd+o5WSbgVkVlXWax9JxOQv/+BRD0Z6nRQHywU0S2zwphwYpGQDylsf2UlnRcoWykbMpxTJrfe6gF2NmtV9hN9AOWzYiHd3du0WCN57dzTojHA4pNfMfBaTafpkQ5X8zoYmgqKhBcfiHitcyy7GlcyuMNTXngDEWxqKoE51QEt7Z/buT5BFvch6F2EyWqDPgdnpqjfCyUSLPi75IVJ6B3FaRk65i23Ijo9Ithi+YKfab7719J/vyIDkVDOWlEbuI7GOo78WQOSsxSII1FS04UxB9gSGIs5wkERc4DXwjVan6kFxuejmoZmLMzNwu/zCiaYyWjUYo4lpkSIT/k3eLxBBMIqpr3AnD/Wiq7i4qiKpnKdiBnWArK9jmzWBMyZeOidAFqrNLe9GJ49o2k2PbZPS5DUhck0Lvw72Tr2VjfQVJmZZURmT22pJgCfDI4BNVTjW+8ObZbQMkumWOJ7uWZOzQMtBbT45QuTUYfffS/b4XV5xRwDEyqeelvGwwf0QlA/FcpcOnzxEUkU9dYCMvmpE7u9SVvb+H01S2PM17Y0aOtkvMsj13ve1nVL+2a3Ml7VCmdsfb2lE7AYGEeoqIlj8CWDDD3qedEtlPHtyX6tSBkLMR7andrYgUVgFn6TwfeyPTTfmCOfdsR819z+iTCcNr0YQ/5Mpui25pWJsn/MJ7TDdrUXpWWtcf+KSI9bp30KeD2w2OwJJIR0iAZYWdetLeZwlqaVXrDsOAJ5ZkywyvMFE16W+z6V/vv53MtCTh66lhvYQK7YSIAViGtJkyeMdZ1ii9fBMNW0wVjdkrN1+FrBh7ya9i3i+7kfc1MT4qGPCTBqf1XXaowD6jMFgiHBnjAao9d8rPIBK8qb6/TLD/8XRhQMTDMhIaPUsfbbo3YpeKBN+ZtXKFqrud/EQJ1vfvfUvA4DMyYo/TocSeEUeGVMFSK22SjpDEhePKuKRFu6BBirfV97SsbBTTrcuX31Avr2ZVUwouLdDhsqJGEZHg9eA0+8GMG5ARm/8ZAJ7bVLLJwwtdCu3jnXmDmmnK+rBa+K0WlqMDDiSx8wMAnGBCiD8UzunQz4mehqqpTQXpd4773vNoNIaMWsuvl/tnJw/IjkAOm7LymYXtA07z58Bxl5OhF0f+qelfftbgWeeE3FC2eAz9zDhOrqfWMF+IdvakwuxllM5vKBgroroUUTphZ1QguGDpDJs0U6IVo+JJG1X3yDFOQ47qMzIXFtDU0GRVBaXWISwaV9yZa8qtSOQCiaNvm13tdWOODD3OgAbO5iFFUl2pbHI7Q4i8DUCBx17cKNqZA5UV6cRvDSmzBWNsGcRreL7ZNcE1IJ7FMbSiqqXBXIKr5F6Cy2rNpikeNPQRm+DPMDnxZtDDfyvP6riGs93qbm7WKLjznMjVtgEuzFaxNzWO0O1LVpbEkWPuZmpkWRjQkwsiRqazT2gsfrRyQSFY6uyYL4NxpsZgmbatXltsdTA4qYa3VQsb1x7a2IdIsmKEp+mmywkKb9FQMTD3SMHJ7WYWjWBOuQDvuTpGaZR7wyprk4ZkuOTc4Dxy5pa8UDaSQPh6fJZFZxZBtZ0avPzTtXtGIa19QYuBZyiwelNH70miIcKj9zn7RcwMu2Zq8V+a4Lc15VmB1ROiJdAoxgIYfFSCXKxErmguXL51S012gukvF43yHGLSvzA2M3thfGWk+TY47vvKwVvFhgI9uLMYLdrMMn71p5z7424Nsvq4JR+Nl7Uo8tP+Mnx7Xri4lXtkHyM8ucOngjJozN6TC5qBXkkpFOs25kVoth6KB+I4BJ29HZCJwBGVdGZZS5jChzGcS5slu0e7WuwoZwRBPPv9rXFcHk1/T4CJzFpSguS590CsxPIzg/DXfjprQtjtkQnR1DsPekeNY0p9Gj1pmsMKSKORQZyuy4kPGJl1ogFO+8FkNkYTJFXv0hlOlRmB/+nKYzdgPP6+mg8nl5nfd7vT/v3YqNBFiBWC+r7XyRPAFcq0A0+fv5b2VdY8IExmrt9NQUDEsBKHvZKyurchoravl0D87C7TCjpnylusKHWS7+LnvqCZwJi4n5CCrcXJQKG4uGspWgk4GAUa+iLS4QWw/EWF/Jz+VsiWMgwbbCXVtbll2X4sEEys/f0YKG9+zP/D10EnrvodRukjFQM4QV4uSWzRg2NUgigGuPS4DFwIch7jMTcad6ZJH8aqpickx772tno/jRKxG8/4h2vvZ2rmjCrRd4PbW0ZNa1icfmJgP+86f1OTkw/t2PQpj1KPg/f82KRZ+K4xdl3LbdsNy2QMx7gedPyuioU1clwCqKdGKL4Sevy7g4pOLfPKpDmXv19+9u1YmHAwaYmSzW97ZprD62zWSyQmcya0cjwMLg6X7gNCR86q4WPH8WuDKu/f6Nbq3F4LYsOtbUEdvZyCoxEkT9+bDWXJlYdSVrkPIM6/EQws+aXoyKe5pBJVsx5zxRFDkM2NbkEA7Q/eMBWE16XB72wiDpsKNNY7w4rEaxreXaam5uRjAYFOySVW6eC3NYmJ1BRW2DcGYyWWxwlFbA4sgs0CYH/YgGvJoNuFRYJZjML2NVCwylqS3LM0EZ70fk/CswtOwURivLr3vmEH75e9DXdgjdr0yQ2CoRCgBGy3VdA9+teLfHHGxprKypS9D8YlJscnoWRvGAp0MgFEFddU1OY8Ui16XBBVQUWVBVunJPegOaiUfMZCPVQ/HkfBRVxYa8tBHj0Vq5Egg4LcCsQRO3X09Y0sQcC0sxR0uVhKjagpYUXUt0jXz6FNBZC9y9Ml2kBBMtD+0lozxz/FPiXBHgT8aWRp1oiYsl75KTXkyysG2SeppMflHLNOZwTJ3NV84pePQW7ZxvbVr/6zPfmGNnq4TOOl1C3JAOf/8LWbQQ/tZDBtEWeX5QxcEuXUJCjEnG1y+SLb66sJnM4v/xmywUsXVUk75Ixq4WrVWWcWBJhm4DJsF4raRb01l0Y5GNxdaOao29Rdb5lXEJHzrUgl+cBIZntHP64nlNIyxe/iMVmPhi8a45LkG24NcScdSwi5+LeB8S6+HYyATqrFdGZZFexDUstvHnUoceO5qtgjV/ZYzscx0uDPlFEntHizZ41B9d63XFa5zapFbL6iTP/NwcvJ5FVNXUirmPzC9XcWlWPdIo3Xr9PtG5ky3RlA56V6mQ22H7e76Qhy8h2v0WDJ0HYIjTvmYhLvz6T6Bv3i50vzJBKqsDJAOwNOfflDEH18v1XjOv8Rr8q4qNBFiBYCIg/ObPoc5NwHT3x7NWz9cDoZ6TUHzzsGy9bdki9nrCbE18kKMgrdfrhd3hQEtzE/x+CpaH0ro3BUMR9I7MorLEAXeayKhP0Itl7Gwthj5FsDm9GMHApPYddWUrXHIuYCMzfIimqH3uk0VLpSS29QIrYtR84uJNandpUsBBK2gGo3azijrXApxWLmqJx0nhfOp50EkpF7RlaQ1g8uo3HzBkdEl85JbUv3/hlIynjkfxGw8a0VSlx+52BbdvNywHScVOHSpLdDhxJYrLQwoqi81oiAv214qXToXxy+NBfOreKJrri8V1da4vgvoKPVwZXAVzSX4R9RWSYIKxmMfk1zNvR8V47OlcGQ/+/JkHjCkTWsnY2yGh2El9iNS/390mYXeWoJBoqtAlOIEmg9fWp+/SKvuxW+1glzZPzc8vYH+bW7iTslLL1gc5h04hJp+oExOPC6OatT3ZCzFdO+KVCwr8YQrsSwW3I2htMAoWfDIGpiLiLqgqNmJiNozeiTAaK0wiUT7riaDcZURduVk8YEgFVF7TgeM1MjKCYCiE8vJyuJMsphVZhiLzAVnr32Bg6SqLc5ZIA2t5LSzUTiywEiu+S28QDJxCIJVUiSSXVNGwqgVBqmqGVJGevRaDoW0nwG0JN3p7+68aON6hF/5F6HRZ7vv1a/KdwYuvC3ME69bbcSPAakuMOSiz4PX64XY50dZcK5jnEcYc1tXJa8IXCKNvZAG1lU7YKI61rD6zgu4Rvyis7etM/YA5NhsR8xOLDNXFxoTE/ciMjJoSfV4P5Zvq9Kvm2bWADC8mJzbVavFGLFEUA/UcmWwyGrSYw2paHXNwbj/Qnug+nQlkJmUC18xP3Z3+PSwa3bs79e+fO0VZBxWfvlcSgvjbmoDbtuiW40Em1qhj+uYlBcPTEG2WjEPWCy+clnHiiowPHfKhemnRuzRMVrcuZYIphlySXwT3N7aEHb+s4K1uFdUlUsKY1pbp8OHbJVTmoKZChl19GVnoqX+/r123LnEk2xQ/dTQx5rilnYlXLeY41OHGYkDTgaNKSy765Uz6bUm6F84OqhiZ04q78Wz0Z8+oIjF13w7dmtqRmUibmI+Ke9eop36aHkNTYQxMR9FRY8KiT8ZiQEF1sUE8Y/A95nXqcOCcvrCwIO7H2XkPaipL4XTYVsUcfLaKrbdseyzKwPyKwVFZD3tZ9ZpiDslohqXQmKO8Hnr/IvRliQU7CtpLFY2Qkl5PBcPmxATZRsyxASJWoF4rNhJgBUK4UVAHYw2uFKSBqgGPcF3KBQxEqfuVsqn+BgCpvHX19aJKweDTYjFjaGhQCNSmE6Tl+8jympybQVNNEaxmI0amfaIiUl/hQG2ZFaGokjL5RfBBmHNimTtxkp/xKOidiIpAsK06v4SWJ6gKx75YG1u+YLsjtRBYFWMC7MIw8PAezfb5FyeYZNO0PwgGB1qAoGB8fBxOp3PVWHGM2AaXDq9eUHGqV8WvHWWwcXWpsQzWmqu0ALPIocOH7kyMsjY16sU2NqPgSoOMuvLUY88HjEKq52QD0oVnZnocjbVuDE8BX/1RQCSoPvlAbsyanpEoXj0TwXtvN8ORZIv92K3a8bxxIYLnT4Zx124jdrStvnbj20Mzob1WEi5Pq/ZhTMFLZxS85xa9eDhYD1C/Ix4UwOd11d09jvZ2JxrLteP4wMHCv6Orhjo1qx+qgktCt7kkv2JBTPID6qWRMGa9CnY1m8TzGFsNCAaffMFm1mPeK2sPmrNhVBSbUeZeP7HY2LzOgLS0tBQ2awpB3JIy0XqQL6tRGImsIRBdK6jbZexaXW3VGU0w7b4n7d9xvQk9+y3oK5uEQ1M8NhyZrlfMUbj+lrI4DTUcWvVQkjXmuEFhtVjQUFcNo9Eg2AFsgRwaHEi5jmrQiYd1jy+MkWk/OuvdMJv0GBj3Qa+XUFduRWOFVRTQ0qGmxCjWroql+SmGiXkFV8a1c9NUkV9oTZYLGTl0yC0EZN4w7tjeAMz7IYoUj+/XXPZ++jawuY5xkPbemL5SppiD4u9kHafDC2dU9E0AH71jfVi/mUA9K7LfqU3lsOrwnlsSv29bkyS2kWm2Happk1+yTCfyQmIOIBJRMDk5jspyF3rHdfj28zIOdEl4cH9uxb3LwwouDCq4f59+1Xg9dlD7jNcu0F1QxV07dWhN4XyZq+YZHSVTuUoyaXeiV8XD+3RiHK9GzEGnx/iYw2XTju1DOYrpp8LWBq3Yxvg5PoYQSdwcDyNdzHFmICI05fa1GWA26FDs0OK6hYDwoIXDwm4IWSTJ+icjQnKhqmR9HHJjczrvwYbGRkRlWTDAklFSVobi0psv5qCBm5Qq5jDbYNp7X9q/UwNehJ77NvRNW2DccuimjzlUnSS29f7MdxsURcGf/dmf4atf/SomJiZw+fJlwZ78wz/8QzQ1NeEzn/lM3p/57hvFdYTploc1PZQC2V+Ri8cQOf081KA/63vDg+eheKZhJvtrnS2rI34PPGP9omfbPzclesdTiiEuCeFmS4LFaKrzCx54fYG0WXuL2YhNzRWiEisryvL7FrxhzNOyjmwlh0m0KKQDAxouSqzKxKPMKaGjxoj6svzODQPfl87LwqWpUFB/gVXYCyOaE2RVkcbQYTKOGmBzPqwrOG4iXr8KidFXz0bxtz8IYcGnLTwd9Xp87hEzSl2SGKvuYVkElslgS+dt240p2TmXh6L4P77ixbGzWewQU+CuPWb8xeeccC4lrmrKJNyzz4Tbd+QelJzpjeLtS1EMT8l46o0QTl1ZfV2zrY7CwZub9AW3uWQChfKHpoCTvQrCkcTxe/mcgr//uSws2AvBjEfF//uUFuyuN8j8aq9azbriQ1WuWmHHr0TxysXVBhm1pQY0lBnEuW0sNy2PO5PbLpuEK6NBlLlN2NHsxKZ6uxDDvxrgwyDZX9QSikcoFBStCDelDoWqItp3GvLkQMrfJZ8LZX4SyvTISsvbuzDguhFhPvoRWB74dMF/Hzn9AiJvP6WJC2dBuPckEPLDui1za2whCHkX4BkfEkxK38wEgotzaWKO7PtJl+lYzDEz54HPn95kwm41YktrOUwmCptTeVG79ue8Ecx7tfWoxGVEeZEpI0ulttS4KplSVSShq9aA2hJ93szX588qOH6l8Ic7xhmMAd7p09rh2U7PhBqF8BlzsJVtPRFzkrwatdiXzir4xjOyKHQRmxskfOyoXiRtWDjrG1+JFeNBltSe9tTz1PkBBX/2nahIQuWLe3br8e/eb1zWNSXz6/AWMrdznxPPDyo42atiZkHFsydlUQBLBpOOXHK66qR1ZTXHMD6vCobc2QFVjGM8nj+j4lsvMqlU2Blly+w//BK4qC0Z6wpqnrZVMXGdOCZuOxCnlpIRL1+I4tjl1TE9i4LNlXo4rRIaK4zLOl5VRQbxvWcHQmgoN2FXixXbGq0F6XzlArPJhOqKUhhIp0tiurCr5qaMORQFkZ4TkGdGV/9uiUEfD2VmTGiGCXA+34g5NhCHP/3TP8XXv/51/OVf/uWy8Q2xdetWfO1rX0Mh2GCAXSeokbDgDUvVbUKAOBny/CRC51+FqWMfDGV1UAIeqExAcVtf0oMQSgzOT8HiLoVnYlhQbK1FiWIM3rlpLExPoKy2CRZ7bjaFi14/QuFwVtpqidsmthg66jP3tieDApQz7N93swqsLRT8t6Yk8+XdNyELwfHakpWJlgscW8Xi2V9koz17ToXLwraA7JMydQ46qoAXL2iBKKuvMdD2OWmNWzNu3Szh1ix6ToVicl7ByLSCbzwdxpce1xKRA+MyzvbLQmz9R69G8ditRty6LfdKE1sDSly6Veyrl06GcGEgik89aBPnJRfwYeQ9h1cSpEwaZWs9ePCgGbvajSh16/DVHwXRUClhR1vi/pNRFt/2uN6gAQErjM+cUGE2qLh168o+U8uLWh+pXJJyAW8BPvwUqAO/DGpvUXuDemHZArCDHbl/Gc8Pg/3kzyx16sWWjFKnQcwh3aNBQbhlS3M+bc1rgc/nQzAQQElpKeamp8T/Q34/isvLIeXYhh5cmIZ3fAjuhg4YrdfJUSAaQrT3FHSuUugrGhMC0fBL34PO5oRp34PLr0eO/xxqwAfzI5+H+b5PXp993sC6Qg36AIMJUuOWlEW76PQwwhffEE6i+uJKYY4gXB+pW7fOTILA3BTCnnmY3cXwTI3CYLbA4krs75qfmYRnbhZV9U0wWXJzAVvw+IS0QjZUldjEFsOWJpo65LH/YQXzXgVVxSsal0zY15WmnxPEHDauCh1HJstiYKGFMUe88x2Zzk+fUVHlpktv9rnutk3AJg/w0nlNs7F9ie1FLdCP3bb2tSAZd27X4U5cHUzMaYmaH7ym4sNLuVcmvQYmNbfuF8+oeO9hCVubcj9hdFPm+PLfeLx0hq7NKt53qz7npBMZXPfuWRnQXEx/Htinxy1dGmPqlbP8Th1akzoTDnRKYrtaYNuopFPxynmtILs9Tpw+FnOIvEQB1wrXc8Yca41tWVRlgZhC+pnAe+4OEUPlBraqppIIpuZXKlQWGcS+9I5rzy82Ct6v83NXOix6fKK1u7TYjampKfH/UCiE4uLinDWwfLNT8M1OoKShDQbT2lvFCoEa9CI6cBaSdw566ofGXo+GEXrpe0Ic37RzhVUePvZjIV5reeA3YHkgfzbPDQuuD+udwLwJE6JrxTe/+U38/d//Pe666y58/vOfX359x44duHjxYkGfuZEAKxCcgBXfghAX1jdvy1tkkJODOj8JyZZOPDnxpqG7l2bxvTqZJHvnoDOYIaVIpMUQ8czBN9oDR30nDLbEsom9sh5mdymMNidKGjtS3lxGs0UIPuuTWBGZUFddDp0STtv+uOgNYs4TQG2FGwauoEuI/38uGJmJYGQ2utS/b8j54f7SmCJ0EuITYERblYS3ehTodAoay/ikvqSQkcOcw1NE5xvm8yjm2ViWmTIefz3Z7fYbrtLz6CED5hYVlMUF7MfOR/HWJRm/dq8J+7v06GrIL+qhXtcffnJ1ErV7KIoL/VHhBsUEGKuU3/5lAG11Bhzcalo1Vj9+OYgStwF37DLjZ8eCeOGdELx+FR+/34pD27Ro5UxPBI1Vifpg1Gfha8TvvN8GVxZnzfUArcfHZ1V0LInm8xh2tEjwhxRsa078/vv3SLh3F/UtCtsvamV8cimuUJTM1xVdl/hwRG2wZLB190Q/cLhT07BbL2xryH/Z0dyXrj6dP/k+XFxYQCAQgMvtRnFpOeZmpuD1LMBqt8PmyK38rCqqYNdq8/d1ggoYd94lEl2J0AEWu9ACi4dhy62i9S2T4PmNNle9K2KOhWnBzNO3bM97/BmvqAtT4sEjJxgoL6AsPRUnIro4I+INyZQ+MRVemEZgYhCOhi7ok2ITV3UjoqWVMFkdKGloh5QiIWcyW4TOqBQnep8NjbUVMOrSO0/PLgax4AuhodKVwObI10CnfzKKiXlZJD6KHbmtf2SAXx5TBFM2PgHGfaVcwlu9KvSSuuwcLUokOZxiFgWmPZqY/p7W1TEHE2w3VcxxUIfvvqSipmxlv964xAKIig/eKmEXj7Eiv31urpLwpcdWn+OeURVjs5oOFM8l2Xg/fV0WRkCbGxPPkdVqw8+Py6gvB3a06vHz41Gc6VNE4ugjdxrE3zCuvDioCpmI+EIck2ZVSxqun7h7/WQPMmHeqwpX8KYlcwXKE+xo1mLf1qQpgG2RfL1Q/U7GD584klvMMTStteamEuA/NaC18h7dqqImSdx/Ldjbmn/MwVZnblcTqe7BuQUPQqEwit0ulJWVYXZ2Fgvz87DbbLCkkGVICVVZijlw3aDTG2HcfhR6Z5JwnU4S8UYy8UOY8GS5/m60uWoD1w7U5m1ra0vZGhmJZC96pcJGAqxAKN3vIBrxQhnrgc5RBH2lVlVnxVSZHIBU05ZRqF5ylsB08DHozKkZAfqictgOPrb8c+wBhQK4sK+oT5MV5r/4hkik2TcfSr+/0TCUUED8u2pf9AaY7FoiLh1DwWJ3ii0fsIWIvbnp4A2E4fGFEInIaZNe/pAshK9dtvQLUXWJUTCBYoEoRbRtFmlVW2Q8uNCTtZKK0swCMpNYDBwZTPK9d2/LbeKl096xy1rb4+gccPsmTfMrG1jZqa/PTQsuF1AL4zsvyrhrp6aPQVfH774QxeEtemxrzj3Y5359+sHECtJDt5iwo1URWl87CggsUsHrV3DbDhPef6cV//Bjn0iCfemDDrx+ju2wSkICjPtUVV2H559YQEWxLBJg8x5FVM0pwF/ikpa1vr76A5/QKvs3H3CgKq46z5bOnuEodnVojqVXG0++EcW5ARW//YhBtIcSdosOd+9KPUesVwtEpuuK+Rg6NdnNwIdTaHTUFAOzvtV6X2sBHzCujCtoLJdEO+WNhth4serLxFdpWZlYYEU7pNGI8qoaBAN+WHNkwRLW4nKxpUsSBGfGRGFCMq6ftkg8GAgHX/8RdFYnLHEsL4LXvvnAw6v+Rl+jiQ6KVrloWOh2JOOmdGS6iSFfeAORwDzUiX5IxZXQlWjmC2TqKTMjkGrbM85lrMLrDr9P2M+nApnmhltX6MoiWTo3BjUcEEnSGJSgH4FLb0DvKoOtM70tsRwOQQ75hWlE8ixHYWbTEqvMZEt9L9mdbrHlA5OJMccKwzEZi76wkFiIlinQp4nPhAuk0OdMv7Y1lBuEPpB7qbAy51WE/mamdnkmQQ516LGkvZ8AGogw5nDZtAQY45kHd+U2PzJhwNZHuvaOzQP3bNfcfq91zDE0peKnx1Xct1snxOqnFlT89E0Vd2zTfs4VJoOEX0uUG8SD+yThkEjnys512mWPX8WdO+jOrMc//CwijHIeOaDH6d6l1su4BBjHqrSiHm+9GMHUgoJtLRI8fo2lXe7medPed2lIxXdeiAph/k/dZxSGQ/EJKcZlXfXXZt588riCwSm6QUpw2bT94L93xLHN41Fo8iuf64paob88rTETH92X2gWSplAl60iU9odU9EwoorBdqK7v1UL8WImYIxhCdWWp6DqhJqHVakVlZaVggOWc/OLcWVoptlQIzk0itDADZ33qwsN6gMUzxhxSUQUMO46uNvM5tPJsG4O+XnOKFJ1OShS6FMWVmzHmUHEVNMDehepVmzdvxssvv4zGxsT1/YknnsCuXbsK+syNBFiBCJ8/BrRthWHHEUjlK0Fj9MoJyFfegVEnQV/bnvEz0rO/UoA2KjodVP4bB7YmmGrbIZntmhZUwCPEB5MDYXNxJUzusmvqHskHR1YvSkpKRFuC1x9EWbFr+eG+qsyJ0iIbzOloUQB6R70IhhXsai9K23/Pdqi6Mu3hcXw+iovDEZS7JWypz8xZdie14MXAB/N7t0tpK6eZwMQX2TLcWOHKJRAlukdl9AzO4s49JVBUCT9/m+5QwKb6whZs6oDQopoCrgQTStSdYhC5VtAlkcmv9cS3nwngnUsR/OGnNH0vvZ7OiZL4ORa8JQiVz8/iy59ww2rW49VTIbx0IoRPPmTDLVtXzjndJzc1GXCuL4re0ahIgP3373hht2ruTdQg+50P2tHRcPWZRQc361HmVlCxjlVNgrbwFDx+dD/w8nnN3vx9B1cSaPH3YHLwwCmCCdp01zmZZLd1revuYmxOwcCUIr6zvfr6ONmGI1HMzPtRVmyHMalnIzZeJpMZk1PTKC4uQmnJSgWTbFZ7jswvIp0DbgxRvwdR7zyUSOiqJMCYwAqdeEYUWvSlqYXPBdPLaBZt+RTFjwdbIZWpIZjv/eSqJNjNKEh7MyN87jXoNu+Ffu+90BWvPNxEzx+DPHAOxlut0Gdx85Qccdat2UD9LQr4Jp1nndkKU3WbqOyTIaYEvNCniGWE82lp1XWLOXyBCPzBMCpKHMv3YEOlE9Vl1A9Mv0+Xhv2i/Wl/GhdIwm6WxBZjg1H8vq6UGmCZ15IShy7t6/fvYCyDvMEYg21jjDlYrEjF5k2F80MKRsdncWRXCQIRCc+cBnY05VawSxdz0PGa/xJkRk0uMPGDNYMxQHIcsFY88XIU/RMq/j/vl0QxiqykimIJv/0eA9xJ54nXVdA3i88/XASnTY9Xzyo426/gY3cZ0LnE6ibI/Kqv0GFoUmOW8TO/9vMoqkt0os2QQvS/9YgOlescB6TCLV0SmipVYbK0nnjjsoqpReDB3cAzp3WC2fjw3pXfZ4o5mPw91AnRBpwKFW7NqXo9MTSjYGCa46Ciqfz6JMCC4ShmFoKoLLElFPvjx4qSMRNTc6goK0KxeyXGoKkYt/WKOSK+RUS8C1qi6SokwGiyEjz1vCi06NOwjTPFHOHXfgjVMwvz/Z9Z1aq/EXO8e/FHf/RH+PVf/3XBBON18K//+q+4dOmSaI386U9/WtBnbiTACoRh9z0wtnatqqbqGzcLAT/avK4nJFGB1UEyr+7nNldr1frQ5CBCw5eEba0pruc6Bgai1N3gg5YhQ7vkeoET8fQ0HyKLMbfow6I3AKfdCutSlMeKU6bkF0EXyAhdIHNkxCz6tejLsRScFor4ShHp8ePzWnUqnpkjxGvPAQ1l1PnSXmewQT0OglocIzOa9gSrtBQIffyA5maUjCvjKqampuELag/bw0t/t6nAamddmQ5f/uDK2NaWSfj9D3G8sS44dSWKk1ei+NBRs2gd4Lk+fjGClhoDytz5j/2hbSZRLS0vkvC5x1bKf1UpBIVj11V7e7GoktVX6tHZaEBdReJ7WUX/rffZMTIpL//O46eArg4PHLSiyKlDc83q64+aHtQj27/JhCKnVJBxwLk+GZ+4z7SsDdJUyWB0/as2FJ/lNRaK8tqHaHmgOLGU4h5MhZhWzLUC2V989mxZGgveW3RzsudoGb8eYOv17IJPuL+VUEk3DrHxam5uhtPpgN1ug8fjEQEoq7H5gDocnskRlDS2w5SGWWutaoK5tAZ6U+4CI8uaioqc3YBFkTWn4ZJqkfyIjnTDEFeYodh9+I2fQqLO5EQ/jPseSHAl5uuiGcuw+sl8w5L82sKw9z4Yu3auah3Rt+0UbC2pdH1vZsnqEAkwifILyazBug7x/+DoFYTH+2Bt3Qmjuzx1zLE4K1og9ddAiyZ+vpue98EXCKPYZYVpKc7g+m3OkpBrrLBopjI5YtGvPZS51uisx6JM/BpERlh9aWLbjy9EwXygsxbCjIQodiTGHIPTWiLs7R7t/++7RdNnSgZbCr1T0whFioVxz+isxvotNAHG5M/vPqZL+Pl33sOk3vrM7Sd7FAxMKHj4gF4YEDBJeapHRVttYcmx/Z161JQqIkn16ftXBohJsMwxhw515Trhylie1MrIY/30fQZMzquoLpGEqLwvqCIQAm7dqkdtmSoYY8nge97pVoSIP5lo+eKVcwqGp1V84FYWD7W/b62mztj6r6u8TqYWgKiimSswARaPbDEH3amvFcik6p1UhdRJQ6k2FsGIKtqG4++3qw22Xk/NB2C3GFHkNKccK5vVApfDBrvVgvlFLywmIywpXCEzYXF6XMQdZY3tQrYmFRw1LbBVNkCfR8Ftea1XlawFDVWJAoFFSFUtIm6ITvTBULkiOCdPDSHyztOQyupFt5Tp4KOCKRaDVF4P1epka1L6/biZsKEBti549NFH8ZOf/AR/8id/ItqGmRDbvXu3eO2ee9I7mWfCRgKsQOjr2lO2Ekh2N6SuA+v+fYbyerGlQmRmDKHxXpiqW2EoqoDenrqEokQj8Aych8HqgLt1O64lKkvdcDtty8mvXFHkyK9FraXShDKXgpIcdTlyweUxVTg6qm26BH0NeSkAuDzGhJmK5iRdCiYjnjqlJchI+WYMnu5QjmwBLho0xxsGMJ+5R4V1DcmqVJbf6xWIEmf7ojjZHcW9+4yoLqWNvIx/fiqIXe0GfPqh/JOrW5qNYisEDVUG/O6HU7NymGStr1yZ5v7o0yv3bP2SVf3MgoK3LoZxZJcZZpNOuEL++JUQwhHg4TiB/VzBsegdU7Dgyy6OG9+O8eSbmrU69UpyxQN7tACUCdtHD2huoKf6tIebRw+sv/jxWsHWRyKW0D7ZFxWW40e2GDO2LKeCNyhjeCooHljJBMwVTHrxgdjB3s80YMKrsqJcVJpYcTIZjXm3C0kGvaaZGBezRfxe6E0mkbiKeOZhZPt8Hskv2bcA//nXxByveOdg234HJEv6dkxWWS2H3iuKMqFjPxAt9Hq258cmIpNFCOPrnMXQ+T2rWF6Gtl0Atw1cdxgaN6U0zJHcZWJbbxhr2sSWCqGJQURmhldiDj6wpEA06Idn8KK4zl1NV8mpJQ2oLRqJ0ugmvzC31JVfzNFVZ0RdUEWJY/0KHOeG2bal6VWSVR4DGd2MOc4PARYDUJ902lmoI5OLhQ3O/ZliDrZKXu7WWGd2K/DJOzVx9EJBzU7DVYw56KRIza6jO8lC1/S7fnRMxr5OSSTF8sWWJklshYBrdLp1mmsbGV8E17Tfe9/K9dSw9JzP9tDLwwpu2SSJ95/qVfDMCUWcr1s25X8sg5Oq0NYKhFXhlpkLKIvxwhkVB7t0qMqDkfbIPi325bVJtjnXtzcua8U4MsFupEdzJrwby3TiOowVr9/sjopWzHu2G/KWv1jwRYXmcGu1OS8X6spim0h+uezpn384T7H9MRKJYnJqFlaLGfW1+WWj9XqDaC+PDzrCfg8MZqs41rDPA5PDnVfyK7o4jUD326JjiR1G9m13rCqKJJM1LIcZc+gRfPlfGAglJMCE/pezFDpHMXQBr4hB4mHcdAt+pSASYOtc/H4XJsCI2267Db/85S+xXthIgP0KIDw1ACXoheydhd5eBCXogz5J1JjgxGiraoLevM6c6BxAe19HnhYxw1MBTMyHsLXRKdgaOX2PXreuyS9Co02rqE7qHmEFqcShYmJBE+9MFgonzXtnk+YEyW1XnOsOwaogmTtkejGYYOz4T8+qaKtWcSSD5hgDl/4JCEHTVFpRTKb8/34SFdpfhQSGueD9R8y4a48JVUsGAmw3fPx2Mzrrr8+UEo6oOHk5jB0dJqG1kg9eOBHCM8dDQuh/T6dJOERST2xnu3E5QfZPP/Pjrj1moRmWDTVlEk50y+geVlCZopqcCqwYXxhUUeaigG7q9/SOq/j+qwoeOSCha4lxyOCZ1U2CAQ6voVmP1qJAZtWNlgBLbnusKZHgDKgpHZriwaDTbpFQZNcnaPXM+6IoC8p5JcB4z7hS0TCXICsKJqfnUFrihtFggMvlgs1qRcDvF4FHrkwwq6sEwfkZzA91o6xtm2hHnO8/D6PdDYvDDd9Yr5iPrWWr2bohWoergDn5d5IEnckMiYkq6nPps1+PuiUTEtO2I5qjX1zwxKDWfPBR7YeOFVGWaN8ZkQyLaYFtYAPxiEwOQAn7RfsjryE56IOUguHFWINsA0M+cg/rBJNRL7Z80DMWwKwnip0tbI/Obe7m2p2utbFQtFWRra0KB954FNl1cFpULAR0ODu0OgHG929rAJqW2iGTHyXZhierWmxiNmmPyd94XsWWBhUHOtIfA6UTmODY1pj6PTOLKr7xrIp9HSpu23J19Gnee1gv9p8SDERztQ5375awueH66OGQpdc9ogitsEwdCqmSLHSCPNmjoqZUJ5hyu9uoWQtsX3L8ZDzww9c0DdfWmuzHV1FMx0Kgf0KHrelldxPA89k9ym4BoCo1WQuXRlT88iREMa1+yZSAMXYslBeHrQNmvLxGtMRYnn4SVx2b6xLnALYqs2iYKflFptHQdFTEG644qZQFv4I5ryzMi/JJgLFTwe1InzQKh6NY8HpQWuwUz0oulwNOhw0+n1/ILuTKBLMXl8G/OIeZoT5Utm4SBYi5gcuwuEpgMJngnxqBs7YFlhQFk+DUiKbNWJz4IKOTDKKQJmIOtlfm0NIeY6abdhxdZaRD/etlDbDOfcvjLfechM5VkuBSvYENxHD8+HFRkD5wIJFg9MYbb4h7ZO/euD7sHHGDTVU3D/KpHCieWYRe/zFkPtRcBVhbd8FU0w5zVQuCI90Ijvamf29ZDUzJrhzrCE5kwWAIs/OL4v9ut7tgkXEymIx6Cbp1EgQvFGYD0FmtE4FuMva0AFvqgKNbEl8nU3dwBthcryW/UuHJd4DvvKKJ7gs3GGcRfEEdzgxk3p/XLqh4+oRW8UsFBifUH0vq7ioII9Myvv9iCL5AIvVYczWKF4nV4ehuM2rLUy+OE7My/u/vetAzsuLWQdehp98IoGc4vYMHryFu/aMR8X6CY5V8Xb1yMoi//6EHL74TzPsY79lnxkfvtWJH25Ios1GHW7eb4bBKy22Tg5MKxmZz0x841cMkA4RbVK5gkPuFRww4siP9lEzpCGpnZTNJvWsH8Jv3sR1Xl3KsbiTUleqxqc4gEsEXhiNCsDYZwYiCnvEw+icTDTwqi0zY3uxAmTs/VikTXKGYOF4SeImZzRZhRe71BRAMBrG4uAifz4fx8XFMjI9n/OxwwIfxK+cR8Cwsm4oYbQ4xh6lQhTMSq7FGZzHMJVUwuTSRQDkUgH+8X7B0xTGP9iAwemXV55Np49hxFJbWnbBtuz1jJTYZDDz1ca0GmUTzo+dfQ/TSGxnfd6NeU7+qyCvmmBtH6NV/FS2uVwPWjr0w17TDWFKF0GgPQuP9Kd8n3PPK62BcMtm5GuC6EAiEMDPvEffvWuY7MnbYOn+9r20WNbpqUovq39Khw7b6lbbHGNjWNTSjFd3SGZf86DjwvVe1eU6cG5sb/pAOp1OfvmW8ch4iETKVRkOUzCXGG7myj7KJ6T/1liyKWvEgiyfePZHn6rat6R0VR2cUfP3pCCbmlASW2ounZYxnWMvFw7iiYmR65T2p1tFXz8n47osyTvflr0t05w49HjukR+OSQyOPbV+nfrl4R/Y4NcRmPGpObX6XhzWSSW1p7vuyuV6HX79Lh72pSZ4rMYd+KdGVAffvAn79qNZqe6PHHC2VenTV6kU78flhssFWj7E3qKJnPIqBqcTYtL7MiF2tNhQ78iv00sgrzIpkEjhGLpcbHn9QyMT4/EERdyws+uD1BjA6PoXxyTSB/hICPg9Gei8h6PeJnym3YLKtOEsyGaWno66rBJbiCpiWOoSiQR/8E4NivaeWY2CsB4HxvlWfr3cUwbH9CKwtO4TRWlbZhfi/dZdDcpVmf2M4gOjF1xHtfjvj227UayoTVGp3X4Xt3Ybf/u3fxtDQ0KrX2aHB3xWCDQZYgcjHjUINeoX9uOqdB1Joc615X9jmUt0i/m9v3wNVlbFw7hgslY2rGQRrQDgURCgYgMNVlHYimpldEMkvgla+1dXpdUkWvSEMT3rQUO2CI0ZjiUN1iUVsuWLGI8MXUtBQlsiK4ELH6lqqBFYuePqMKmjdya5MT52ibTlSOkSOzALPnQE21QIHNXOTlFoIs24tqcEqSWtTNe43qKJCmwmHNlEolJbVfOPq76b99uceWh+xr3cuR/HKmQg66vXY1pJ6umD74Hd+GcBvvdeOllrDclAWCKmwWyXBpvrK9z2YmFGwudmA1iWh4IkZGd971o+trUb8zodW7y+TWv/8Mw/uPmDFL14L4GMPOHB0r1Xce8nX1e4uM2Y9CvZtMq1OyIZVWDNowrnsEm7bkT6R0FRtwF98zpmyPYQC+0w4djZoxz01r2BwQgtC/+mpMEqdUfyb9+Z2DWdrQ6D1++88mkP1Tcdkrfb/VGN1I2JmUcHwjAKrSUbTUmtqDBajhM31ZlhNySL+NDPIPh7UEByd9qLMbYXdasTQ+AI8/jC6msogywomphdQUeoS4rS9QxNwu4rhsJkF64vTXAn1Oex22Gy2hHkvGolgemwYzqIS2F3ulYTtUkBJOMpX5l8amKhyBDpJEm2P1OKIITQ3geDkEPRmG8zFFbC37sT1Aiu8poPvSan7dbM7Mr1rYg7fAtT5Saj+RSCHpGe+ILNLvxRz2Np2CaF8xhzWmtZVDIK1IBgIIBIJw7l0f6XC5PQ85hd9gs0UleWM893MYgijUz601rlgY2UrCQ0VluU2tVwwMS8Lpi0T+fHwBFQh+M1kWr7g2vnTdwCnRWtTXH5d1YpmbptmXpKM3kngpfNaAmz3ytSSgM11sYKbFnN0tVVDNqlZtUEPb6bzpebFlArU4PrMveun9XWiR8WmhpWWwWS8eVHG86cUfOo+AyqKdMvJLZ4LygEw9vjWc1Es+JgIU1G5VISkTtYv35ExtSDh/betvp9ePhPFs+9obZWvnVfw4SMGbGvRp1xHd7XpBeMpXgQ/lpBlfSUTE53O1Lva0v++vVbC//EBzawnVSsoi1v1S4Lu1ACl+Q3xj08DdeUyPnR7bszHyqWxS4e2ah3acggfmCCLEYNulphjgrHatCK0+5LvX6eVJlpGYci0OubIfp2HwjJGpgOoLrUKdnrP6CICoSi2tZQKQ7DJWR9qyp0iXpj3A6UmHeqqSuCwWcRrpcUuuJx2WC1GwW6JIRwOY2piHMUlpSImWYk5qG2rPTi4K1ZiDoWxiBwV54SFN2f1Cj0wODuO0OwEDDanIEU4W3cus8WvNcg2N97yCHQZ5ByIjZjj3Yvz588Lza9k0AGSvysEGwmwApGPG4W+vAHSkY8K96SrDYOzGHLAJx60hIX9OmJ+ZhoBvw9miw0mc+qEgdVqhtlvFCwLCjqOjY0JG99UExcDBb7PF4gKsVC72ZB3y0I8+icjIvCsKjIsJ7v4ua9diohF7sBSS1u+qHKnbrnmepNOk5GW5GxFaE3TzkZsbVhpbTNKCgzyJNprUo9Vsjsf2yB/eULFx+5URWKkEPSPy+gfV3D7DkNaC+xbNhlQ7tZha3Pm88Lg8+s/8wvReQrXf/tpv3BZ/I+fccFhk+Dzq8KR8e59K8mg6jIDPv9eB+qSEh40F3jqNT+OnwvCYtahudqA3V2mZbfGF97y4Y2Tw9i5pQa1FSbB1GqqMeL9R1dT3n70oh8/e9WP/++ni0RwzKry5ub8GEOvnQnhJ68E8cX3O1Yx3P7xyYBIOP7pZ7WFmyL+jx42YmpexeS8In6XcGxhVeiDddZLKdtXr8Y8NTExkfYevFFAwWBqYKTT0SlzFb5U+UMRzHlCMBn0IgHG9ke2JHDz+cluDIuglK+z7WlxYQ4VpU3L5ycm5ms2JV43ihwVRQE+oMdgtjlQ3bEt5X6QDVbauTtlC4GlvE4kv0xFWmsCg9KrBcUzB2VuDPr6rlXtCTFIadybEj5nw5Hpxo056jqFmHCyptvVgNFViqhwFQuL5O96Ym56CqFQEFabPa0Tms1qRiAYFqwdp82aOeZQNHaPxx9BOEIBdEPOrY6pcGVc0xOqLZGWk+PUYXrlYlQIpO9tzX/e4sdQ9yuVRGGmmIMC+Iwp2jLEHDuXZBjY+mY3K9CFJ7CpLvvawGN587KK588An7iTQu6FrV29YxqraV9H+u/b36lDYyWTXxla1ERhQ8W/vBjFJ+42wGXX4YmXorgyquJ332cUiTpKTGxpYqJpZb5lrPSB28m8Svx+FslePiPjbJ8sNNGaqiShtVa7pFn54qkwLl6ZwN5tVbBZJJQ46eQo4d49q8/vk2/IeLtbwZceM4o4jSFtQ0V+1xjZZa9fUPCZ+w0iWRYfGz3xsiKE9L/wsPbdbGGkZAaF9Fl4paNlPMiqHpvVRPGvBW6WmIN6pHR8L0vDIKwoKjzm8ASimPVEYLMYRAKs2MECHuNsXpcReBlzhKOijTIYWIShzCkMwgjOI2UlWsLfZExMCMnRqEiCRSIrzDSbwwVbW2rKp9nuQnn7Dkgp5k5bRb2QY6A+I2G4mizdhSnRCWWo60y/ZgnDnV/BmIMx1rprgN2499XVgtlsFvNKS0tidYfrfT4uqfHYSIAViHzdKJKTX2QDhE/+EpK7HMa2PXl/f3RhGoHekzBVNUMJB2Gp61ympuqtdri33ZaVLsrqwFzfBZjsLjirGhL3T1UxOzYknERcpVoZrqisHLagE3qDAXOzM3A6XTBQ5DkOdptVbIQsyxgaWkBFReoyXpHTIvriT3TPCCYTk1abmopzdnxMxqY6kwgQ4plejG3rS7WFrlDsWdJlSMb9O9N/JlsC9mWglseP88/eAewmFQfr0o9VMjrrdKKSm8pRKFc8dyKKS0MKNjfpl6uoyfjHn4cwvaBgb5dxWfchGXu7TJhbVPCDl4Lw+lWgRBOmZ5sBHY2YBPovv10EFpeSr8k9Xauj/DfPBvHDFzQ69wfutmPvZovYYpiek4GoB9992gsVEiqK9XA7JFSW6vGZRxMHpLJELxJsZKL9xdfn4fUr+OqXy/Da6RC+95wPv/thNxqrV0+DrCB/7Ude7Oo0ico7E1d8eIphwauI5NwnH7DAkFTlv31H+gTb8yejYvvY3SZsb7n61TZeXwsLuV9X1wtMwJa7rs54uO1mtNcVwbrE+ChxWcVGFLlsMJuMUFRFVFobayvQ3d2d0/xuslhR19IhGF25QkrTPkA7cFNReco5m8WMwMB5GIurYCwu0KItDtH+08J9iWuPLoVzX664KR2Z3lUxh22V9Xz47aehr2yEoTl/E5zI7BiC/Wdhqm4Rn2Wu61q+9g0ON9zbb88ac8jRCOb6L8LiLk1gR8YebmbGhmGxOwSrkigpr1hOMM/OzorWqnhGBMFCGzfx+bKccb4rL7Kg2GnCySvzIuYgq3RTo6vgYsT2Rhb7Etc1sm/rS6kLVthDCj/rkGaymQDu4nsyyJyQcbY/h5gjGAaePkW9MBU7ynJfG7rqNE1JapYWihfOUEoA2NygrkrUxPC/npNFO+f2DEW3A1160SZ47LwiEo5MgDFZFY4qYvyNFh2+/OHVMQvHdkeKdZeMshdOacnb995qwJYmvdhimF1UIIcX8MNXyqBAEi6QPO+t1RIe2J84p5e5NVF5no9//mUUVjPw+x8yiaTWy2dlfPYBY8q2TRZvmcSjO2WUxbqoxvqLYdGnwmahFpq0rIMWOya2gqbDC6dVnOxlsVQquFj6qxhz8DmjosBEbjaUuugArofdop2XiuKV579Stw02i1Ekso0GPWxGFUW0j88BVpsNDU3Nq+bATEiV/OI5UqJRmNOYpyiREAKDF2Fi+3ouLYxZEOk9KdryWVijZmSh2Ig53r2499578eUvfxk/+tGPRBxAzM/P4z/8h/+w4QJ500FVoLA9YY3W4FHPHGTPDIyltTAsZfKJbIFoNBRA2LuoteWkqNqSUhv0e0WSLAaTySw2z+IC5ufmxGuk4q4F3M/GKgdmF0Pw+KMYnPCiubow9oPNLK1qU+Pndy215d2I4P49sFsTAQ/M5v53DGRyCWaeeCkiAqdP3b/a2YpMpfFZFRVF6QP1g1sMmPOsdndKxj37Lbh1h3mZ8dRYrcelAa1iycJWPq0gezaZsehTBOOrrX41a+/xo3Ycc1pxp8ONYFgHh02Hb/3cI7S7Vu3/dovYiI8/4BCJLI4DA0tWieMXVF9AwV98YwG7O024bZcFFweicNolfPIhO27fuXJheQMK/vgfPWivM4i2z2RcGoziu8+H8LF7LGitSQxUdrXrEY5qFfTXz0exr0tfcMJ3LTjZp22P36K11Kw3hmdVoZ2XbBN/vUDmVzosev2YmfehvrpE6BeynYrM1FyCTGkdWgZCc5PwDV+GraYVltLVzCslHEJ0flIUONYjAWZo2w21rA46V37OgdErJxE9fwymOz5wVVwHN3CVwXYY/yJUOm8VAjFXqoiymu+dh6miUTh65RxzBP3CkUy05aSo5CuyjGDAp7FGlhJgZotFsM3nFxYwNz8v7slY8Fso2OpcX2HFnCcKT0DG0FQAjZWFTYJslUoG5/OtSy3xNyLIcLpnB0BN7sWp3P+utUqXkdEew/delkXi6fFDq+dGGrgs+lezlOKxv1MSCbBsuGe3HndsX9HNYkvgxJxOtB+yAJlPzLG7XS8u75YaCfVLrK+E/T5owBvvSDhSycQbGT3Av77C62f1A/nBTXqxEY/fql8uyPKYuMX/xbxPxdd+FsGBTXrB0OqfUFFdouD+fQZxbDHQ2OYrP45ia5MOjx9efW2dG1Tw3EkFH7hNv0pKYWeLJolgMWqJsB3N10fj7s1u4MoY8PgBynSs/+cPTKlgHmm9DSkKAcfXYTWk/d3sQgBzniCaa9zw+kMYGJ1Fc11ZTucln+RXOgRnxuAbH4CzvgNm9+pnOCXoF/O8ZLGtSwLM2LFPrBn5Jr8i549B7j0N89GPQHcdjFTWAxTQ0UR01vcz3234q7/6K9x+++1obGwUbY/EyZMnBdP0n//5nwv6zBt3lf4Vh85ghOXw+wqmMhrcZXDuuluwA+SANyH5lQt8k8MIe+ZQ1LwZRqsj5YNdVXMHpKT9m54ch8/jRUlZGRyO9WnTKXVZBEV4YMK7bBM8PhvEnCeC9jq7CFivFRb8pJGr6KzRrUpMDM2oGJ4B9rZqAqypwODLE9RE6PMJLGVZh+48EmCpsOjXKqIHN0tCk4MgBX/BS/nt1WphpS4JpVnWlExspmTEt/uduRLGG+fC2NFuQsnm3Bfs8ekoXj0VwIO32jPqdpUV6dHebl4OBnZ2mLNW8Xd1rkRdt+20iC0eTIr5g5pmWHmRHn/+BTfsKUR9KVi7rcWI5qTkVgyBMBliKoIpBN3ZNvGeQyb86NUIjp2XReV6c+O1111g9TrKYDxpF+d9mpBtCkm+BLx5hUxL6s2sHh+2GPH3fMC6YzP18FQ0lKUWdL4R4HRYEQhGMLfggw6K0BFS40vv6wDx4B+NwuJa7YhhsDpgdBSnbXvUWx1CfDaVy17a7+s/A53RAmNt+6rfSVYnwC1fMDFBBtu7kH7/qwAywsxHPlzw+TOW1ohNiYShhgPC7j4feMaHEPF7UNq2TWjgJYNs8pqm9lVJZRpP0IyirLQUTuf6xByVxVaUuhT0jQfgsmvJ8cGpIDx+GZvqbdekPT0GtgROLgKdNRoTNh69E5qj777W1I7PBJngXHPyYWZ11GgFoHwSYKnA+IJJlVs6NaZ3bA1JxxZn4Scbaz0TmykeTBbEa5edH1CEftiuVjon5n7+RqcVXBhUcMcOfdo1it/FeKm9Tr8cc/zBh7PLGMSzzW7bphdbPJgQIyOP0gw1pXr8u/cbkcqg2G6hLphOtGamQjAELAa0+DMZdJrk9sNjCs4Lp2mdaJu8bjFH0utzXhavtbgjHViofL1bK9ZtXnLAjgdZgMd7VRF339IOjM8DTWXp75nrDXa/UBR/et6PaFQWEgyUhNGvY2Iy6POIAM/iWB3kM9Zg66PBYksrpePYfBBSjrI9PD+MOSS2VKZwcZTsRWLLF0IuQrDmb8zzeLPhK1/5Cv7bf/tvwtBpx44d+Nu//Vvs378/5Xv/4R/+Ad/85jdx9uxZ8fOePXvw53/+5wnv/+QnP4lvfOMbCX9333334Re/+MW673ttbS1Onz6Nb33rWzh16pRwY//Upz6Fj3zkIzAmdaLlio0EWIFIl6lXgz6Ej/8c+obNMDRuzvwZOdjJrvp8MrJ0K3oTwlXMkb+ro728VrQ+GjIEsfoU7Tp8jReb0+FMyX4IhSPw+4Mocju0Xvay3KoaXKjimV+BsAJ/SKPC4xrmB3omVPRPaXbiFUmBGl0X+TsK26cLNo9dpi4I8Nj+/JJgsbGiaOvP3mFAqQmQxkOWVbx1RUVbjQ6lTh3O9CnCFvyWTdJyAEjhVpddq0ISv/GARrWOD6ypc0Gh9vv3G65agHDfLVZ0NhjRWpfbFMMAkAyu59/y4xev+dFYbUxoe4xHquuK+hfneoLYv8VS0DFRw+y54wF86YNOoSdGsK0yFRgkf/phLXA40xPBhf4IHr/Dulxx3tlmwPYWe8b9YCBc4tKhY0lAl0k3vj2ZxUa9MOqNxOuA5IN09+CeVm2LBwPx77+uXfeP7M0c7AxMaQErBZeTwcTxgTbt93yAuziqmUU0Lon2XmsIur+iCs2vVLBZTEJ/bN7jR2NNCUz6RoxOzAhtocpyjYmyViyODUAOh2B27FrVMhmYHUM0EoLelD7Y1OeRbKAAf3SkWxOWTZEAS4fIpTehzE/AtPeBlE5PhpbtYruZHZluZqQbb1bWI2/+DIbOvdDXdqx/zCFHE64HyWgCuOUJR2WdYIGlSn7FQHmFZFDfgzGHy+VKOQbBUFgksNnOnE/MwcJae+3KfeUPcj2VRSHkWqZ4L42qGJkDqot0q+IKzrN8mKeeaCoTFoLC98OzwAcPaomSXBEbq+lF4LmzKm7bTB2xxHGjzhaZwp21mtD9qV4yqNmeqL3v7ICKNy9TLwnYsqSi8Zl7GZ8mftepPgXzXhV3JCWA1hN379ZjWxPZfbmdPR4b12y2Jp64ogj2V3NV+gRY8nXlDQKDkzK2xrVK5gOy49/ulvGbDxmXJShihctkkOVGQX7ibD/bSFXcvWvlOWBPu4RdrbqMMcfhzTrUlgI1JStJI7a0Jif9qElbSQ26AmVD0t2DNINKNoQiG/AHbzI2AO5KLZ0pwMQZ42+vQzNySAaND/a3QiQPL4+qwhDCatSldWC/2mC8wURfOnY/TXamFwJY9AbR2FArjHb6hqaFDikNedYDc6ODIgFW3bF11e8CM+OiHT1TUS2fmAPRMKLjPZAcJSkTYOkQOfuyeGY27rkv5Zxt6Novtps55lB1ktjW+zPzxXe/+1383u/9Hr761a/iwIED+Ju/+RuRrLp06VLKduUXXnhBJJcOHToEi8WC//pf/6toQzx37pxIRsVw//3345/+6Z8StLquFux2O37zN39z3T5vIwFWINKJO6rRCFTvHFT/wrp9Fz+TEQVFZn1nXoKxrA6WptWTWj5g5j9d9j/j3xkMcBUXp239mZ1bFC1FZrNJPEByIcwXozNBOCwSGirc17w9rKMKqHBRj2j171iF5eJbZI8J7GuLMhfv2LN1U7lW4aKDU77XE8eKLkWT88Bsii6VwSng2ZMqpheAh/br8OIZRVRb93VqbLU9HZJwB+qoT9SHiF8z/u/vBzE9ryIcAQ5vNcCdR5IuHzCoStW+mApTc1F8+W+ncWSvFY8dcaK51ihcHbONVTx+/IIHP3/Vh3/7sWLs3Zy/2UT/WBQ/fsmPyVkZn30892oChf5PdUdwxy6z0Btb2cfM122JS8Jt26TlBM1fPxEW5+7fPG5KaHv4xi9l0RrxiXsKm6pjY8UEG1tfMsFk1JxJmfzNBF5Tj+zRHoTSobZE+yVZYGQD1Cz9fD3QO+oRorRbm4vSskmryt0oLXbAbDLAajGhb3BcOESuF1zVjYIBllIvTHSWMWDOTmzntSIvzkDvLE6bzKCwvWX3PSu2XHFQ6EjsXYC+rBbhU88J2qNp113aZ/sXxO/Ynr+87igKdGkSFjeywPGvItKOdzgA1TMD1ae5L68HqEnH60cJ+RA4+zKMNW0wZxAwzgU0geCWL8gMo/ZNuoefyZlF+AIh2KwmoeeXb8zBe2p4OiSMNtprrdc85thSrxUHYnFFPA53aeyumPMc2V6jc1rMEdvNlkqNsZttfk+3NvSMqyIJxmQEkrqdesaBl85p+3D7Fv5fK9TEEmD7OzRGFxllK5+rS0gEfPVnimgVZBLj4KZEjdb1BD+3PkeNq9EZBX/34wju3KnHfXsN6GpQ0FSpyyvmeOp4VCTOfvOh9MysTOgbV/HiaUXEYg8dyH19P35ZwdCUikObpYSEZ7aYQ2Pfxa4jFX/3pILqEuAjd6ysE0ysPfGqik31wCP7CztPHKvS0jKEooAly7AwqdtRDZGYywR2XDy8R0vYpQNZ5gQLbxyX5AL2tcSlYS+CYQU7Wl1pDabqK12Qyxyi+EYWGOON9Yw5imsa0jpm5MNvZ7u67J3TYo40iRed0QzrjrugS+EcTakfNeiHVFyJyDtPA1YHTJsPa7+jW3GAFqZaj4pYd8TnmX51Yo4bRAT/r//6r/HZz35WsKaIr371q3jyySfxj//4j/iDP/iDVe8n0yoeX/va1/D9738fzz77LD7xiU8kJLyqqnLojV8HUJ/3+eefx+Tk5CpDhD/6oz/K+/M2EmAFIp0bheQogvmeTwraZqT7baG7YexMTTFUwyGEL7wGfVUzDJVNaYMz/6nnxIRg2XRYtB1IV9nZKeT3CRaBxZ74JEztjpnpKeglPRzO1FWK0hK3cILkQyTHaGRkRGSLc524GDAxAUZ3FArWrgXCHnhJYDtX9tepAeC2rtSVBlYL441hLo0A7/RpwSFdm0qdmlU4t3wRP1a/9aBOtNklo7ECeHCvbrlK+eE79CLIiAXsDBLoepQJrOpZyoEHDpjgXmIVcczpBskgLhZEUacqFqgOTUbxi2MhPHq7RQjOrzdY3awq1aOsyCAcI2/ZljmBleq6unOfTbCntrQWVn1oqzPit97vQkueenEfvceKe/YnJr/yBa+1thoGs9p4P/NOFKd6tMrwke1MBOsEQy1bCyEdT5kgJXOQ1yRzPV11Kl47MYy3hmvx3kMSGpZYWGz1ZTAZb+nNU88HrlyQ6vpM9772a+Q+lQ50ektmQSaD1z2TX7Frq6m+Zl20NmIwZXB1dNS2Cmavf6wPUd8i7A2daZm5UQqR952GqaYd5pokCl9ym2MKRC6/BWVmBOb9D2taUHEBsnHn3SL5FUushV//sUiqmO/5RMpk203pyPSrGHOUVMP80OfEw0fk7CvQsbjVttounFCDXkTOvQp945a0rltkfPlPPC1aVsxtu5dijqvrYB3weYTcgtmWeN3T7Wx2ZgYmk0lUf1OhsswtnNWEmUUBMQfnzZGZEJxWPcrc+TPb1hJznBtScGEUuDtNPZNrMFvNYzgzCJwfJmNZK1RQi4q6XLlocyUjNlbNtbX47D2pY462auDuHdq/xIduSyyoce2mOH62JAe1IFnwicUUZLOPzFC3ayXWijGyiIEJBa9fVPDAPn1aVtRaYDGypVFjVlOGYFsWl+tU19WhLXoUO3VCEL8QdNTq8KE79Gipzu9h9v236gX7rFCGFsG1n/Fk5RLz7JcnFFFg/cgdwIGl7gNKGWRLBgv3zPM0oAKmPdTEA5orVLz09gjOT9fiA4ek5ZZXFmtpDBCfqGVx7NZNue1zfKySCXazDu3X5nk8LRwWAwx6JWNBSy9J4vdDQ0PiuuporlxXhlPy81s83PXtUKIReEd6EA354arvTMvMDU8PITR8GZb6TUIUPx3S6XuFL74OdXEG5oOPCrZXgmHI/gdFDBJLrIVf/p5IhpmPfjzlZ23EHIlYXEwsejEZlYqBRefQt99+W4jIxyBJEu6++24cO3Ysp+/y+/1iPS4pKVnFFCODjG7pR48exZ/+6Z+itHTtunGpWjK/8IUviEIEE27x1xH/v5EAu4bI5Eah+uahUpx+pDtLAiwAZW4COj6spEmA8cTq3RVCM4ytB/att+FqQo5EMD3cK/5f07414SKbmZoUk1VJWfoMD6sZMeteTlY+H5Npudcb+CC6ucGBYETB8csLaKiworKosKD0zKCMGY+CWzcZ02p2xYNVVJrDxQecmdBcqTn1jM5SEwl43wFS2DP/DZNNqSp1HKPYWFlMqQMi/t3OVl2C21C++I2HVk+Or52L4okXQnj/ETNu3WbEub4I/t8fBfDReyy4ZYsJ3UNRnOyOYEe7MWsCjFWvJ57xYd9mMzqbTHjuuB8VJXpszZCYcjn0+NPfzj1rGBurv39iFj3DUfzZv6lETbkRH7insD7wePH9fMGE3Y9fCeJfng3gwYNmXB6URaIwX72rDx5Z2XdWhIPC5VqHO3dKotX1T/93FB+5U4/OpZbJVGD77Kk+rbrbNwEhQt9Zq0Kv+lDhpuuW9j4m0779ktbG++GrO53khVmvgjmfipaKldYOtkJTKLiqaOW1fFFVasv72rpa8E4MI7gwg5KWzZAMced8fgqhmbFl0XukSYDpnSWCAWwoUAzf0LAZiqsUOpsTpgOPJPxOjK8ujsVYVguVQW2aauOGI9MNFHMsTEMO+CAPX4LOak+fAAv4RJurrrgyve28JImYgw80LLbZth/B1UQkFMTMyIC4/mrbtyT8bnp6WvxbmoHVxcQXt+R1NFeYDBK2NtpFS9jrlzxor7ag1FXYWnL8igxfSMUdWww5JcGYxHBZmfjJ7fPJlmFh49Io8KoH+PCh7MwvPjOmJJ7mEHNwHdvRvPJzOrfodGDM8sl7VscMr55X8cJpBe+/VcLmRrZWKvjBKzI+clRb466Mqjg/oGJ3m5o1AUbdzqffjmJfp15oXb12TkZduYTGyvRrJeUH/u37Mg8cxyW25sTG6ptPh+ANSPji42bUlkliKxQcm2yJt1SgA+TPjkcRCJH9Lwkjo6M781sf+d3vO7zy3YxjyS5kwuuOrTq8c0XFt19URUKsNqktNh4zHuD8kGhQwcVhoMTJLggVRvhQ7lphnXNf/+VViJbER/bhhsHkgiLu1+aKlbHwBhV4g4w5Ci+A0WQjF8Tfg1eL3bQwPoSQz4Oy5k5IcYWs4NwkQvOaCKASDadNgBld5VBKfTAUKIZvbNwqmF5cS0yHHkvQ8xKJr7jLS8qQYLtZYw6V3Vvr3LoZ+7z6+vqE1//jf/yP+OM//uNV7+c6SodkCsbHo7KyEhcvXszpO3//938fNTU1ImkW3/743ve+F83Nzejp6RGOjA888IBIqq1nAZlgYu3P/uzPxH6sFzYSYOsICtJTk4vOFercOEyHH4POnL7HjGwxy8H30LM54+da2jTHg3Tw958Tk4itMTF4LAiSDnqDEUazBeODvbDa7Cgq18opdocTOlqHp6nEpkIkKiMcicKax81gI2MDsta+h8LBahOrRrl2NNQU68SWK+xmYHeLptNB7YFs+hsvnFVxeRT46O0qHGuo3q03Our02NNpEP8SDquE8mIJbru2IB/ZbUZbrQENVYnTxdd/6hXJls89vlJpGp6QhZYWNb3qKg345pMe1FUY8CefN61Jb+yP/25SaLT85e+ulPaEk+M6F4SYoCTy2Ve6bC54Fbx6OowzPVEc3m4SjLZC8eABg9himJzn9ZJZmP67L8miheUTR3UoduiEsKx2CKr4ef8uHfRLSTk+1Gxp4IMFbij0TsiY9qiocq+0dlwZj4og1WY2wb1GJgAr2hSetTIzeJ2RHMaZ3GVQ5Kgmhp+hTYyaHWtpf9cXVYgtFxi7bin4ezZwbcA2VRbHIiefF9ILprs+JswP0oFtKGaa72RgkYsW2jRFO/GdqopA7ynBCrOssS1SfJ8kQdIbBPtrtP8K7K4iuEu0hJfT4RCyC9QgyRW8xxl35BOA07FNVqLanLmGaUbT6co9bmkq14ktV3DO3tWsMW1mPZmFw4mnTwJjc8BHbsu9sHctQEH3mcUV9hTXtxLXis7ZHduZGJNQndQ6/7+fi8Bh1eE9B1cOZmBSwfFLimCwWUx6/OxNGU1VitA/LbRoEpMl4H597hFzQjJRXudncIqfIw/WIOENAP6Qijcvae2QB7okONZA0nxon7TKmIFrcKZr5tsvadqeH7pVS3yRBRaLUdgNQTH62C1oXpJXoFbcjYQr44pgw9eWrLATL41EMetV4bLSVX5tMUdUpqkOE4HX3ugoW9LIUlwh4hCzqwSGDAxfOkFas+hZZ4K+tEZsRLoWyhiM2+4o+HvejSB7kNqYV1t/67/8l/+C73znO4LtFb8Wf/jDH17+/7Zt27B9+3a0traK9911lyatsV6Ym5vDBz7wgXX9zBtoSby5EOtVjkGZHUf42A9h2HQQxs0HBQNMcmd/0KBQccH7oMgITw0h6p3LOrGkAlsavZMjsLhK4J2ZEBNlaUMbqlq6hCX5WH83ohFBRRGw2R1iy2fyn1/0Y2RiHm2N+TEW7BY9drU4MTYbFqyidBXKTOioWf/Lm6LzFLqnFlhMTJR6HNyygcEUA1fDDdbGPjghi2ppRbG2Y41Vevzhr6+cZwZmyckvYmA8KijwBJNTNoskdL9+/9eLRPLLbpXw6w878Y2fLuB//WwRn3i4cFEGCtIns6o+9/7i5YecZ173oKXOhJa6tS0Af/g/ZjA2HcUff64UDdW5sQA+/7hdBLE+v4qpeSVt8ottH7EkVAwj0wq+8YswHrzFIMTzk8G2kBdPy6JlgddOOmi27xAW6Az6qe2hfWfq99+2+cZJwMawuc4gKq/xrR1N5Qa4rQpcKdw488XwpBeznhC6GoqWk2ALngCm5nyoryoSLZBrhRyNCn3EdA9eFALnlgyKjFuXqp+sxvr6z8PEZEXpUu/RBt71YGtiPOSxXkSO/QTGPffCuPNO0V4i5WCIo8vTvTFxHyIITQ5BJsudGnF5gjp4nukxWN2lWJgcFcW2ktpG1LR2iVhjbKBHsNBjcDidYssV4XAU854ARqe4HuTHWHDbDdjV4sDYXAROqyKYYflia8P6xxxsHTveoxmOxLRJ21gHqso95rjRzPBGZ1Qh5RBjd7XWSPjSYyvjzbU+tobFwDWWGqnOpZBZ07XUoatewqfvN6K+XCdkEO7do8fTb8t49oSMu3cXdj44fzOhlFyo/PX7TCLmYKHs9QuykC6IxU2F4r99Nwx/CIKRxmJVLvjM/WzrV+EJAB6/KpKCqZCqjXFgUsWPX1fw0H4JLSlE//l5J3q1ArLdkso/XANjEpKWGHMQMR2vVDEH30cNuRsN2xv1CEYSdelaqwyoCKhrTn4RfWM+LPii2N7ihsmoXScUv59ZCKKlxr0u92U0GhXXZLqYo6h6yZ0iCWSg0wyNkMNBeAcvwUpzNPd1sAj9FcbVFMFn8is+AZYObBvkNTIxMZHw+sTERFb9rr/6q78SCbBnnnlGJLgyoaWlRXzXlStX1j0BxuTX008/jc9//vPr9pkbCbACofScACruX3nBaIbOXgSdxQHJXQ5wWwNkzwxCl96EqXkHDEvZ8+RA1Ec9l4AXxvI6WLK4P6VCNBhAYG5KtDWyXZFihzFwMuVrEbbjFAijQY+6OjqcFGZdPueLYmg6BFlV0VhhQe94CJMLUexutaUVUj03FBELflcKLaezQ9rKvLU+92rM4LQqqlrUsCBoNT02rwWisQRYrtjbpsPettS/I/2ZE9H1EHn82bGQaDM7uMWAF05E0NVoQH0cJTwd/sMntYTWqycD+J8/8uCLH3IL8Xq2Psawd5MFr50KCFfHteB3f60s7ViNTkXwD0/MYlu7Bf/n5wprDYvBbKL2BROd+VHLmCR02rmlPn+/eCOEp94M40vvs6G5ZmVsIxFVBJx0YIyBgS1bQhorJKExUlkMTMxRG0zBB+5I/fnvPaTP+brigwQDXD4fxjRA0oH7dXEUKLEDL5wHDncWpjeTCxhwJgedTiuNHdbnnnA7TKLiyUDUF4ggFIlCpgV5OAp5ae5by32oPcBfgc3uRGl1FmGcDFCjUUT9i/m5MOXz+aqKKC3LncXQl2kUenlmVOOlUWR/9AqMmw4KYdtMuCkFaW9iqL1ngLKjCcUzHRNeVjukFDFCvpBnxxC68jbMHftTsgSVcBB+xhwhH0xVrTBXt+T9HeGAF4H5acH6YgFPVaSEmIPX5lpiDrPZiNqaGpSls2nOgunFCIamwoLZUltqwoXhEBZ8Cva1WVYVL5bn6gFZsMHbq1fHHCf6FBE/dNXmfq/0Tapw2+giHdNr1Jwg2W6WypwnEw5l0HS8njHH0+9oRZ0tjTrhXL2lSULZUoyVaY393feS1QW8ciaKX7wl4zMPGNFcJaG1ZuVvt7focWFQQdUajVc+97A57ViRdfXj16LY0SrhI0fXphvHwpXsp4N6+mRTKvB+ISMwXYsox4etjJ99QL8sfh9rd2Qhl1ILMTChd3ZQRUulTrRYumyqYJS/fhE4uiP197//sC7n64oxFeUZKBtC3bpM4L71TGjJ29cuAXduyS6SXyiY4ExOchbZpbQu7/mixGmC0SDBYNDB448IUkAovBJzGIz6Nd2D4VAIo0MDcLqLUFqeG7M7FZRIGHLAAznoB66CeQCfLaN9pyGVVEFfrAWQ8tSQ0K1UQ34oU0MwbD6U0n06HhsxR2GghuaePXuEgP1jjz22LFH07LPP4otf/GLav/vLv/xL0Xb41FNPYe/eDNbwSxgeHsbMzAyqq9e/cNvW1oY//MM/xOuvvy7YZnSGjseXvvSlvD9zIwFWIAxJ1H8+TJiPrNABGdzJgxegr2zUNL7yBRNSwoVLTjthKX4PJLsb5qqWguzNTTYHihs6YLBYRQtCPLgUu0rKYTQVvrhzsmpuKDwwL3Ea0FptQbFD2zcmtlgZzBQiUPPLkKasMrGgirY5h0VBY5nGlMkEUpdfvaTpgj20JKlCDYPH96e3JM8HrDyRncy4+rsvq2iqcOG2PDU2UuHKiIzBSRVHdupzotX/xiNWERSNTCn40ctBDE0a8emHsjMTY4K1pUX/f/beA8yN67waPphB71hs75W7XC57J8UmURSp3mVLluUWJ26xI8dO7OT3F9vJ589OsVNcYsc1tuQqWbKs3gspib2X5fbeC3qZmf957yx2ASzKAIulJIvnefCQi8UCM4M79773fc97Ds8YXw7L/MWJEkJ/92FnDAvq5SNerGjQwRntKJAB6Huz2+2zzLODJ7344M0Oxjj77D/34cO3OtFcl52Bwpc+6mSBKCXCcoUJl4gn3wiyQFcXdztVl/D4pz/Tx3xPPcMSfv0iVQ05vGeXBnftVOOFYwL2ruNiNl4X+iRW9U5VqYy+VhGQ49erZ+RE1vVp9DguDgKH2ihpLLdD5Lit/5LCbtaxB2FgdBJefxhLa5zIsxuZIG2y65XQiZGC17iLQcwvvcEErWFhvaW83ghb8+aYYDA4PsiKHSR8v2Ch3KAP4Y7jjKEcSYCFTrzI1hoyZBH7L0KqbIYqTavkO9GS/J0Mvjy2ekKbCd2eOTcmiheE7jPgyxqyYpaT8Q0o5phxAU2oWep3g7c4oSuqTOxomgY6sw2OijrmBmmNL7KoAKvDCZ3BsKCYo646+5ij0K5hc7HTKt97FG+QUHeyoIPiCYo5AqH5L6CkwuCkxNq/DFoRVQXprxfpEb3eKie6di+XnyN2+c3m1AxgpSCdMwqPKCHx8OvUtmbDmhzEHK19Emsb29iobEzct5s00mQnxOePiayl77qNvOKYgzS8iHmUqO2PikZ/cYM2hkV9pFXAsmo+KVMqHaLXBbdPQnt/GDdsJr1b4Fu/CzANz1Jndpvz+2/XxRgA5AJDE9QeKTEWV7xzYkOpCn9zR6xmGLl9/vFNCWvrgT1rVLh5E3CoFdjaHDue6XU0HolplwyJ1lDSyX2jVS4g70zTwU8FtxNdwPLKmZjjHZzzcNp07EHoHiLJEHKGdKDYaZpl5qWLNyJFS0oixbd182qKOQzQZdAinggakxX2pRtjYo7AWD/rdNIXJ9anzgSSdxrh7tPgqLDnKGbnEjr6HNM65Sx5EIc6wNeuhMpk+9OLOZi2ao6PO4v3u//++3HfffexRNaGDRvwrW99i+nPRVwhydmRzBi+9rWvsZ+//vWvM2H5Bx54ANXV1RgcHGTPm81m9nC73fjyl7+M2267jSVxSQPs85//PEtUXXPNNbk9XwDf//732ee+9NJL7BE/Li4nwC4lLKlbDcTRXoTJ/cI7DU3zloSvkcJBBE6+DL6gApq4hBpvK4Bp041J35/YAebl26BS69IGooGpMQSnx2Eum58o0844hQR9HgT9PpjsTjaYRvu6EQz6UVqTObMsAsowd3Z2sptnbMoHjy+IymK7Yn0lCkQLowTwqwq17JEKmxu1LFalVipyMswzz12b7U08q9ae7BZh0fNMpyAVKPjd2igzwKJBlalc4Jcvy2Ln911JOlIiJke7IIo1s1UOovj3jAD1pbETP+lrdQ5JqC8jfbP51/Kl4wJa+2ih5ZjTUTIMT4iMIl+aPzcmPnCtATUJKtmRhOAT+31YXq9FddRrmqq1+MpfKKPDnWoL4Ae/m8CVG0z4yK3p23XSjavXj3vwiz9O4M69djisPHoGQxidjG0VyhSJkl8/e2waI5MC7r/HnvEiTC19K+rUWFrFx1zrCOK/w7ICFfau59EwwxrIt3K4Y3vsPX6qS8RDr4rMFOGmKD2UVNcqMq4KbSrsWyMp0uMge3vaANYWAZsSTAWUwE11OVw+CS+ckdBUqsKSt9gNMhqlBRYEQgJjqaa7XvEYGR2Fy+VCRXk5m2sG+npgd+TBanegoCxxu0GmiBbIJ1Cru+CZhpYSDwmsxjMBJUe0a/ZApZPL3OLkMHMFJE1KLq8UfEUTOGv6NojLjkyXGElctiIQBtoQPrOfbVo0jYkz21RtD5x4kRXw1CWxLqJqZxnUW25J+v682QHz8u1QafVpJRf840MI+dwwl9bOc2vSmeTzCHjdCAeDMNnltWOotwuSIMKevwAmQ9T9OzTuY/d4VbFF8ZxNm9Iix9y911CiTfv6bUs1bJM+7aUCm8QYJASKc65s4XCwTcTRTopFJFjSJGDIwW5Tg8wAi0Yukl+UuH/gZVmn6fp11OomYmyoC2LN3FxHulJDk8TUiT1OikUGxjHrQB2Pl0+J7O9W1FC8lCLmmCQ3YzCxegKtQbds5WMYXNEgjdFXTgqM1RUtwN9cxbOHEpzqJIH9MCbdEq5Zr1nwuDp0XsCTBwXcfAXHNMGGxmUWNxbAUkqU/PrVS3Icc9eOzLdpxAwjl8kVNbLTZTzi74fqQmB7iwpLK+Tnix0cro+TASTW+IungPUNqdsZE62hxOC6crkyJteycjnGrisC1tWlNidIBErEvnJOwsqqzPT1FhtVRWaEqSU1KqOnJN4gDA6NwOv1o7qyDGEhjIGBAeQ7nbBYLCguixVCz1XMQa3uxPplxY4FtvBRbKFdeSUjbBDIiIWvX83iDOqYkuj/aZJf7O8uxxxZ46677sLIyAhLalEya9WqVXjyySdnhfG7u7tjxuB3v/td5h55++23JxTap2TsiRMn8NOf/hSTk5NMIH/Pnj346le/uihaZB0dHTl/z8sJsAUKC4YvHmHOWeq6lbM6HUJ/G7jCSkbp5AuSb4ioYiuSfleawDYZOG3qqCjs94DXGhCYHkNwagyGwvKkYoeu0SEWkJJ1rlqrY4K0nFq9oIw7XSO6gehfrz8Ety/IAkTZ/Hc+Oga9cHnDWFZtSWu/nAwRt8cTF0OY9kkotXNYXiX3x1OQ0VRGiS8KRpW9X2X+4i2gtcUyHZ0Cxo/uBdraQjGClfvPSnj9nITbtnJojOqoeuWUiJdPirhjO4+W6vnHd8sVaoxMSXBaUy9a337IC19Awjc+PpcJXNeUPODvGgjjkZe9GBwT8Gc3Z9fW2lyrwz3X2rCmeX4Wkc79P34+goI8Ne6+Lk/RuNqyysQSc5tXmmAx8fjJPxpgzFHLXDT6RsIYHhdYwEtFOArMf/aYCwV5PG7ekZovT9XSD12nfAdDY3/7itRT88SM+3EiMwWq1LYPApWFxC6cu1a0QaPhZTOpWHCsBFSBJfHaRCBdmvYh4MZ1sQL9MkNKTiDTrc4KYMgt6DvvHw+hxKFmLQaZwqjXsEeqsZUMep1uVneDtBLpoiZ6vWdybGYuVa6bmPR4a5azggmn1iI0PgCotVARY8uanxUTh7fPtQoHj78AhAPQXCWziVQpkl9SKMDWN758yTvSkemdjMj1Dp1+jcUM6prlMd8JV1wD9bKt4Esbkr9HKMDE8iXPVFbHQE5eqRD2eRiDkYpuIe80TCxhmzjpMDUywBwgDRYrY6AbjGamS7oQRN+/FG/4g0LKJP35Xg9jZSyvNmcd60TkGA61UVs1MWRENFfI87dOo0JLJcckBtIlvyKoKVycmIPOr7ZIZqRR29yHd0toa4ud6145A5zuJmFzKcb97+VTEo51AHdto0Lk/OO7aTPHWFypkl/0Of/zRBhGPfCZWzQJna3jQQwxcouk63pdlDFMJiAx/b3r1VhdzydcRx54LojKQg47V2kUjasNS9Xse13TwLN/V9bxTIss1yAWVzRIluGRAwKTRti6LHXyz6BT4T07ldO1KTbemkYXlLToCInMnqiQSmZQTAs36lqRwDzleyhOaUwSR8SDnCMbk5A4Xz0nYWgKuHFtLGMuJuaIkG6QW1BBfWAijLI8HuosaGlmY3bxBsGg17N5jJEHwrJUQfxfME24yXHo9MYFsWgjMNWuYKxwSn4FR/uY6Ql1Nqkp5shiruTz5LY41uZ+7DlAq4emVt43qzQp4n1qkRzqAlfe+M6MORZBAyyZM3c6ULtjspbHF198MeZnSsymgsFgYK2R72RcToAtEOELh6n3ZTYBJvReQPj0q1A3boC6blXKv+X0Jhg237wofUUh9yRcHaegseXDXFILsSB58otgLSxFKODF9Pgw9EYLbM6FaZgRXJ4AJqa9zJGpotjGNuaJFo6eYW/OGaJ1xTzO9ggYnJLQzBbG5D3/lxoPvCQHobdtmTuOaFbcwLiE378uYn2DChuWqFgiIxrLazgWECarxuZZOeaolA5Xr9ey90mEExeDePZNPz5wnQl5Nvni1ZVr8LHbLKhdgLkABS3XbU+cPGM6Eed9KHRSAkzZ++l1HPZsmTvZxUh+ET7/fgdrd4nowPzwkWkcPhtEcX76BNhigFyfSHOspWr+GDjXK+GR1yXmzPT+XXMBwy9foXYQ4BMKr206kLER5ZDi792jnbKGx95VEix6FW5Yk/v7rWs4iL7xMMZdAlbVzp/XKFDqGvLDqCOR4txWo6KFRykJVlk7X9hPEMKYHO6HRmdAYVUS4b8MIAZ94DR6VmDxdZyQWWChAHSVzdAWLox1plm6KblbQhyE9uMIn31dpv7ZFq47dRmZgbQ/hXMHobI4ZhNgQvsJhM+9AfWqK6GuSS1SSyL5+m13APzCNBkTITg1Cnf3OWbeYKZ2TVGYxyqIhr2onOl9TQwPwGixw1GwMP1GwqTLj4lpH9PaqSuzywW3uGIazQ2dgz7osjDWSYXGMh5negX0jUtojiJl2I0q9nirQLf2g68CBTbgmlWJY47uEQnPHAPWNcjFjMI4MsbyanIRllCchLRN+l35aWIO2jSTu2My58rj7QKOXaTCnhrGmRiNCjV37eBRW5L9d0VJqp2rEscspIPV2ktOfcDO1OH6LEhygPRSI1iM5Bfh4zfEHvMDzwtMwN7jF9ImwBYDm5uIGQY0JZC3PNUls8NoDNyxRZq9zyjmILmQe3fm5hho7OgSxBzUNtw/QVIlVNxW4ca1uf9OWvuDGJkWMe0VsbxqfkxB+5uOQT8z08i35XZ+ddit7EEgF76ampp5rwmHgpgcpf2bCYU5YKJTzEGdRmLAB1/3WVkTNOiFoXYlNDM6XtmAkRGoK0rhGhQ+fwhC+zFoNFpAn6Hw8tsAElTskev3fDeit7cXjz76KGOrUeI4Gv/2b/+W8ftdToAtENptt8fMxjwJw9LEoVCUPll1NAKafEiTg5Jl834XDiEw0AYt2czGsch4nRG80cKYX5xGB1PJ/AkzGhodtTWoMDHYx/qzjVY7E3WmSZUm1GjQwkZU1HQ2475AkCW/wmEBep0WXAIR2e5hD4YnSPRWhdUNdsXML6o4newKwmHiUFU4/xoWWlUwN8gaWPHugW81KAeYqoBESakpj6zdsHv1/BdSK8C1G1Jf+//4nZ8Ny0/dmrxf84oVydlenQNhXOgOY2xanE2AEdY2ZZZM+MUfJ5kO2I0702fk6Hv6jy+Wv+2+LwIlvqKvOLWBhsPA+69bOLsngkx0QCY8cptKotfTuKHxNemWW2wjWFUDhIS5e/ixg2AaKruWqxK6RaXDymr5EQ9qXyDdvBnTI8WgY/IF5Q1GOhTnaTDuFlCUREeOkqkjUwEYdHzOE2BKwPNq5JVWQR0n1JkNxFAAnguHwZvtMC9ZxwJQgjA9DnUanS5Fx1pYpfy1lc0s+cWX1gEe34I/+zIyg4rXQLv7HiZ9EAFfuVT+ThSK0qdroRX9HhbTJGJ7UUtMYLCDJV3jTRp4g5k9F5gYBq8zzLqaJoNWT4lrCRODU6z1wmC2IERizBQvxDEYmEkPnWOamIOY5sGwAFEQwWlVCdnmbQNejE/L7WUbmmwZMUCOdxLrlEd5AqffEgcluvis2euLBtVMzKFKbXZCouck5r8yAau8JI9cGZO/AW3+v/e4yIou79mR/DtKlbgh7UtifJHGViQBRkm65TXKkz10HI/sD7Nj3dScfntDn/O3d+sZ0/nthvg4iJhsZoOIm5MY3mQDYsApjbdofBDDK9H4pvFFz467yJVQfo72Eyuq5SJZZE1+/DAZ7wAbGuSfMyVSrU/CFqQWYVsg8/ejBDnF26mYixGUO8mlOoRie+LrHxIkDE8RC1TMeQJMCTRaHfKLy6DJQfuZ4HPD03aCEShMNS0wVC1jVp6iaxy8ZeFJKL5YuYEKX7uCtd1z1E3lci/4sy/jnYnnnnsON954I3OaPHfuHFpaWhhLjeKCNWtmRLozxNtw2n9nINIrS+L30aAbVd2wNmef4zt3AFLQD9O6ffNop2RFHhzpZXRIQ1wCjNNoYalqhne4Gzq7MjYXtesUVjeAn8nMjw8PwO/1oLiyBlrdXCJlcGgIPp8PVZWVKQPSIqcVBm0jTMR5T3Z+/jBbOMsL9BkFjrR4unziPIHPiF7F4fYQXH5yraMh/vYJSCnguH3r/CCCxlN5eTn7t7pIhc/dysW4Th04K+JYm4jVdSqc6ZJw547Ugq6URMiWVef2ibh2qx7bVurhSNNGmQwj49QyGMbzb3pgt/KKEmAEszF9cBd9rbKFxycyAf0CR3ZT4LZVBvbIFV4+HsIfDgTx8Zv0qCnh027Evv/HMKus3r6dR3VR7HUIhOX7Y+9a0v/goJm5VsQmjIBqtNRKQAknspZ/5CCwq4WYC8kHDVm1pzN/oNZQeoerWuYH8OnQPiTiwoCIdXU8CtKMO5OOw/qG5O1Y9NnFDi0GJ4KYdIdgNysLSNONrUBATtbr4t0MEsBglsc8c9P1eZjpSDatA5SwIMc9fsZMJVJ9zaQKSy6PKhKcTaKzIU4MIdR6mLHBSJQ26bEYzFA3bWT/53zZu/VdxgJijjiHafk7iRPsWQB8J1+EilPDuHa+kG3YNY7QaC84rZ4ZJkSD1+phqV4G73APtFZlmySt3oiiqnrwVN0ng47BAQQDfpRV1cYkj/sHBhEMhlBTXZnyHiortMJiWAI99VElO7+AHHPUlGQm5kksIdI0JBMdxJRDqD1NwqG2MJsjd7a8vcJqatu/6wrSmkw+1xHTqq4ktgjy6mkRHUPEwgI6h4GbN6kYmyoZiDmmJJGQCHT99m3gsX0Fn9TVMB1GJkWm83X4goCyfGUJsIhG5yWJOfwSY+NZTdmdHyUPc8n8otZSktj48DVcjEtkIlBS8hcvSLCagBs2EBMs9vUk5UGk4H0kh6DnZ6/V5kZVbFFqWk5SkUTDsyeAq1dRyy8WFHOQsQMl2cgtMtPk8/k+ER3DIrY08rPafclgM/HYuCT59ddp6DpqMDIVgssnwGLITSzr8weYSY+WHBfSwGiZiTkEAcGAD/os5Rc4aqMkGZ+Z/a3WOePq51DO0g2P9Mq6oobExyCM9DI3ak3LFeBSGMWx95hZ396JLpDSIrRA5ryl8h2AL3zhC/jrv/5rJrxP2ne/+93vUFhYiHvuuQd79+7N6j3fXiv1OwiXyo1CU1jNtF/iP0+k5zR6GOtXQ51kU0PtB2aq1Gfyedq5oNBiz2Mb5YGeLhSVVc5WZYmCS4g/ppHxKVaBK8q3z05WdlvqxEd9uZX9TaY6PqS7sblRT0WJeRhxiSz5RYtiRJ/j7ZL8+tEzgNNK7Y+xv6NrSQ4XEcRbrpPIKmkwDE6QML6EIxcFlvgg7YpE+EiUhbdS/P5lH14/GcCUW8TuDXrctit7N7vv/mYc5zuD+MrHC5EfVzE7cMyDQ6e9+LPbnayFMVNEXyvK/n/p33tRkKfBX75feULgGz8cQmt3APffVwCnTY2a8kvPEopOaHUMCEyslhKXFGw+8GwQ65t4rG6YP0XTmF7fqMLrZ2UHrQ9dE3sNW6o41JdIs60Z0eMqAmJGfuBKEpMlFzOZsRVv9hABVUh7xoAXTwPblibX5yC0DgInu+XNFrlMdo2QKDRgU7CpIW0y58w1WAjcfgG+gACbScN0U/QZtDrR2DIaTWxOir8HCX39A+w1NdXKWVPusSG4x0fgKK2CwRI7V7v6O5gxibk4ecsCfZ5eIbsnmXtf4PSrLLGlX7074WtIi1KaHGKmLVBY4X1HOjK9g3HJYg4Sx1fxCdlfxDQ31K2e3RjFgwpv5rIMY46o4prNkYfpyUn0dneitKISWq1uVgNHHedUTRgcmWKi0gVEPVIYcyyttLJ1I9OYg+al7c3ahCyTwUkRnoDMoIrz1XhLQQWOn78EVBZQ+2OamCMueUCOfVNeijmAvlHgWLuEmiKZgR4PYmp9cE/mJ/6HA2Fc6BMZ4/3qtTy2tWR/8f73mRBGpyR8+lYtzHHrzcFzYXQNi7jlCjIsyKIIEXWtiKn9348FUF3E4/rNypk+338siCmPhLt2qeG0UGv+W7eJpSIxaYzZTIBOTWxxCX94U8TmJg71CQwJqIa9tBI40w28cV7CTZtiX7OmToVllSTtkTzmIJbdvTvkBNjApMzaos9OFnOQjAI5R161nLR4k5/L2T7gwoAc75flSegYoQ6QxPqo8XCYVHBZVFknbiOY8giMAUaO9cGwCJ3CfUdkXJGrNGPxxm1oqHjW3TcMrUaNmsoS5cczMgjP9AQKKmqgM8wxdZkWbH8n1Do9TPnJ3490vwyl2cccomcKwXMHwOWVQL/sioSvkVxjcszhcwMpEmCxx3U55ni34uzZs3jwwQfZ/9VqNSPh0L3zla98BTfddBM+9rGPZfyelxNgWUJIo5lCTkzB1x8Bl18ha6xkANb2SPoZBjO0STY+vo6TEFwTshNkgsAwFzCYzIwiTC2M0c4ljiS2vdMub0wCjK4RWaPW1dUlZYpRMJJty0AyhklZHscWIKcl+/deDNDaRjocidwn012rPWt5XLVaZtdsXSbh3x8Oo9Qp4eM3ZB5EHb8YYtW4NUtig7dwWGLBXWUxj7KCzAPRg6d9+M6vxnH/vU7csMOClroQaso08zRY3jjpwf6jHty624ayoswd7aKvFbEfh8bIPCCz99i4wsjYX//842EU5mnwn1+ca9f57dOTeO2oF1/+RBGs5uwD8qde90GvU2HH6tRMg7Y+0j4JY0uLGkV5HAbGRLT3iyjOo7bgxH+zb70aNUViUpfPSPIr1biK3D+k97J9mYSuYaAiP7YlwuUDfnMAqHDKQSUls1KBnJvozylgJbbEm20Siuj9l6a/D/MtHHssFF1DPlaBXV1nQVNlZhVQul6HjpyExVGAptrSeWPXmZeXsQW1wWJnFVltVCAaQWB6nAmAI0UCbKFQaQ3QkMuSUU6+EaOYdCuj2+/VFU3gC8qh0qe+XkL/RabdwRdVpV0DL+MSxxx+DwKvPgR15VKol6zL6L1Z2+OMLqm2vCnha3yth9nrzKuumucmnSsYzRZ2ngLFP1FVbqdzflKWNnOTLi/UPD+bAFMSc8jzW25jjupCjonckxZWImfmtwqU48u3AM4Et3W6a3XtOnI5lMW2KVnyvy+Q9qKEO7dlfn4nOkSWoFhSHju/U0s+7f3JFZLciTPFwfNhPHYgjI9cp8VVa9QswURraDyOtwlo6xdxzToNLFnU9aKvlShymHBRYSczRzpyshx3ifjpU2FUFKrwiZvmim6PHgihc0DCX9yoybpoS/fDC8flmGBlbep1tLVfwsUBMOF7YqTR90qO4zVFUsIEGI3pa9epUFucXAcukvxKNa40M1uVsjy5DbJ7VNYVi15mqSD36CGZGVZgBSxpiJpLSgCjllqQgTE3cLhdYvHKpgYFUgoOjj0WirYBP2OibWoywWlVnhSNXCtRY2dxRVN1LLuXEvoFeTa24c8ERpv8ftHFBQZJRMA9CSFkTJkAWyjIpEVTs2LWTVqiPa1aE7NX5WtWgC+tZ8z0VBB6zkFlsIDLL3tnxhxsucnxmvD2WWIuGUwm06zuV0lJCbtvli2T7WhHR0ezes/LCbBFg8QcMyRxpiE+A/jP7mfOF8b11yV1+CLdr6CKg7f9BHMIo5aExYDJbGEPJagqL5R7q3JkW+sPiZhwhVFkn59ESQVarLMJpjLF8BRY1TcVhTv+uG7ZnPz36a5VJJlHdHVygMyzZHeOv3zOj2BImpcAu/1KI3uwY5EkPH/IhyWVGpQXytMEBcNjU9Q2mCyZKYvc02GubjKwRyL8+R35uH2PPWnyyx8Q8dsnx7BtnRVVZbqU14quyXe/XDOvvSMRvvvgEBxWNd5znRPXbpeTAU21eljNsfcYOWNSGyglCbMF09h6zQeLgUubAGus5HHvHh0ayuXrWuLk8Hf36hO6LUWjqVJZ4DY4LoAzU2Cb/DVneoC2QaClEkzHJbpqS0FoaR6wLIXb9oELsgvTFU0qLC2fC3bX1lCVNfa6kFgtbchyJR487RUw4RZQUaBh91hVkYExwKglIdtNrsmgSxiz2NKwSxJBozfAXjyXYBWFMKZ626G3OpBXvyJnwVForA+Bi0ehb9oIdVybnGbGGVASBPjfeIwFqPq1e2Jeky75xT7j8NN0QuD3fignx3wZOQRVAcRwdjEHuXLRxmnTjUlfoymoQGhqBN62ozDUrmCOpIsBi83OHkrYAHWVhfNun4XEHN4AiVzL2oKZsA1oHboUMcfAhNyKWZGCERMNmoNvSVF/TXetInEXJaj2rU2e/EgFmvN/v1+A1Tg/AXbrFeoYhvz+0wIaK7jZwg49R+6SxI5OeHxM41VOoJAbYzLcvVsLr1+CJQkTmX730vEwNi7lmYlQqmul06rwxXv0aWMO0tX8zYshlOVz2LZCzRJ0hJK8MIriWggDQRp7tGdA1iDW1Csn5cJZugQYkzqQgIYZN2hy9vzUjRzT70yFpnKV4pjD4JBQUZA65qDxvKIqts2RupcpaUtJLGKRJ8MrZ4nlLuuCRZyqnWYJq6pVrOgWAcWy/eMSuz9z1RFC+qMUI1bky/NEXYmeMcCyKbjTuDJTBi9JViPPkXnMQayvaOaXEAphcqALRns+8uta5Gp8DhAc6kKg5yyMSzeDj+pEYkL35Y2zBTffm4+BsxdBv3x7zGuQJvlFrsWhI89CZXVCt+u9OTnmy3hnYtOmTXj11VexdOlSXHvttfjsZz+LkydP4qGHHmK/ywaXE2A5QPDQk5Dck9Buv3M2YUWOGbottyDw6u8QlF6BtmWboveiCqu6qJo5fKWyt9fklUDwe5kgLbUmpEqABT3TEAI+GPKK4B0fZlblltIqBL1uhHxemJxFWVFLKSEyPDoOo0EPq8XEKrEReLwBuH2BBdnWtvX7MeUVMOkRsLQiNpkyMiWgdyyM5gptSl2KxQC1Bjx2SN63UotB6TbZmSYexzslHGkHbtlIzJmFH+PzxwQcPC/iz69TY2UtzwK2i30idiVxOEqGD19vYFXXVOgZFPDAUx6sbtTiE7fLC/C/PziJ461B/N2HHGionL/5WbPUgO9/Kb0ulkHPoTyFRsvpVi9+8+Q40+n68/ek1xxQEnRQMLr/qBtOu5wAY88JEnZtNM/7+3tvcLDHQkD30xfeb4VaQcBFQXx8MjJZoJ4p2gZEtPYDp8eSJ8BePk3MP+D2zZT8iv1cGtc3rJP31md6gRI74EiQJ6GqbURgPxq1RbHvN+oC3rwooipfhTW1uTnHvrEQxlwCnFYeZv3cI1uYjDqUFzsWjW5PCbCQx8V0jwyOhbvtRqBSyawu0TWBwFg/tNUt85k6HAc+v4xVVLOBZsN1i8Y4vgzlCLz0a1bN1+18T4wWmG7b7Qi88lu2uVXKPBd9LqipEp9mvJPwveD3IDTaBykcItHQpK8NuiYghsPQOwrgGRlgkg2Wkir4XMQ+CMKcl515gyCIGBqdgNVshNlkgCaq33DaE4A/ma2xQpzr9bNEBOmLNpTGFl/6xwUMTgpYXqlRbFaSK9C8+eRROeagBNj7dyTOmx+6CJzvl2OOdNpJSvDMURGtA8AHd6uwokbFdKPIMXJdQ2Zt5fdcyacVmicB/McPChieknDzFjWLHX/0RAjdwxL+8lYNCu3zP3PtEp49lLSvpmqtP9st4IWjYVbE27Oey0nMQQ6TpzoETHsllgCLxCFbWmRzpmjcsWPhoulUUProtWoYFHzv1N6/vCb2GIjBmAuc6ZaYZlx/EHhPkuXthZNgx0nJ2fhxSm3EFHNQQY1iDkqEUbtkNGhs0D0RL9tAY60hLmlGSbaDbRIaSoCWFBqnmaB7JIQpr4giO8/2H6QPthCU5FvmseXoHHMVgwjhENvvafRGGKzpiwtKQfEFxQPhyWGER3uhrWyef8xqDfi8knnalYreX6ODZv0+tra9UyGBY49cv+e7Df/2b/8Gt1s2QSAdMPr/r371KzQ0NGTlAEm4HMnmAkTVp2QVFVXCIYRbDzOBWJUlj9E7VTplYtnC1Aj8p19hmXNtpUztSwUmjEwCgfrEnG5ycxSFEFy9F5ljpM7iQMA1gZDXDXNRBTxjQ2xSNNjyZkVoMwHRUaddHta/TgmwaIxMuOH2+KESs0+AlTm18AcD8IeInh1ERcHcMU77REx6RPa7S50Ao0+j+KfQLi/SpM1FzjbxoN9RYiFVDpAWuTfOUxta+utEwRnF+5H1Zf8Zgb13pgmw+jL1vGN49XgQNaX8LNuL2iA/cL0Z9eXyz2OTAkt+kWtSvKZXIri9Av71JyPYusaE3Zsy22yvbjbh8x8pRcuS3InMU8D6n39fFbNx+dQ/dTP21/+7PwW1aQEoLXjrp9ehSflfajeIB7W7PnNMZjJS8opaIZOBxGv3n5fZjqTJEY99q1OP8wjyzEBTqYo5pkWD7mPa2CnR7YhHbbEWRXZxQUmvdOjqHWQnWFWx8LYBtVaP/CUroErjaJfx++YVs4f/3BsQJgaYllN84EjBqXZpChrqDGgdE/ouyC0KZH8+A2p9vIy3HqTRErFCpwp7+Pyb4KuWsVhDpTMxMx6lQsXBs/uhqV0FTXl652p9RSNjF5KzdNKYIxSAq7eVsQ11NicCrnEIwSDMxRVwjw4iHPDDaHeCy6KNMhQOw+X2sgQCJcCiMTTmhs8fhHoBMUdFvgadwyEWX1Bivcw5l5iY9IqYcEuM6XGpE2AUb9Cj3CnP1ePuxFIKNIemizkoCXOwlRILCmOOGYc/+e9kTcd1SVrzk6GuJHbDRu91+IKIutI5tldtiQo3beZRXya/tn+M2vIl5lSsJDkz6Rbx4HMhlmBaWZfZ2ruqjtw7iVWduznZoFPh8+/RzxZHSevzGw/6UV7A4QN7F0dztMT51vdGjbjk8bc6gXoLFYxfOi3HJXRdiF2eDMQUJw0w0ofbLJOJYtaxG9bKGqbpQNINjaUqVObHvtgblFihIBvN0cYy2pvQ3mNxEhEUk7f1DEGrVqOyVCHdMwW0BiMK65fJcgs5hKagnD28Z15jOqLa0noSdZyXJNMl0QGLBq1jwmA7+NKGGHkG5jh9Ge961NbWxrRDfu9731vwe771O7R3KKLdKLRRrSTi9DCErlOQgh5oV14F3eabFL8nJct4exE4i8xQUSJu7L14FGqyqq2LVTgNTI7C3XsBWls+C0j1ecVMoNZWXs9aJDi1GtbiSpYAI32aGePHjKDRqNmGMJr5FUFpoQ3+gBE6TX7Wzh12s8x0OtjqZdWW6DRFbZGa2RIrTX71T4gYnZawrIJbsC4YVaPK84EL/USvlqnnpJW0tlZ2w2mukINVYrjkWyU8+iawe5WEsjgbcaL3U0D58inSTFLh1s01Ka/VjhU8e0TwFzdo57WcZoP+URG/eMqLllo1PnmHZTbAuGLl3CbKaefxZzdbUVaohsOqJAEm4nynHyWUBMowAUZtF/VVOjzx0gRu2u1kjLHY33OoqZl/rfoGA3jq1UncsS8flgQVObs1drorLSQ9kMwD3kmXwPLd1gVW/XKBC70i2gcl7F7NJdSn2dLMo7GsBgX2+VO9209aIGDMMHJjorH85GEap8DKuOowtSxuXiJrdySC0nuKXrc0QRvFwYsCM67Ys4LP2D2SgtBcBaLJxpaa55JuKrOp1JJBSTbwtB5mrWeGmgRZyBno6lZDDDQmdV9SAqGvFeHTrwHhENRxa0sE70RHpj+ZmGP77bP/F8f6IXScZONTu2I7dDvuVP6eJitrTUnl/BkN0euCr+0oNM5SGKpii3T+sX54+9tZgoySX4aCMsZit1c1ssQYJe3spdUI+b2sJYfTZT5/6nVaVJUXMVHoeFQU2xAKCdCq87Iem+TiZjXyeKPVz5JgM51VDE1laqZxqDTm6BoRGVucWCcLZXJQ4YCKbO3DsmYWJbpIoJ5iDkqIRVrANjXKOqO/fwPYu2Z+koxceoklRo/RYg5Xr0gdc1y5ksOVK+d+/vAeLqHzdqYgVtdjbwhYVSfNtkHS2rC+ce7Nqe3y5q1q1p5HyaR0cHlJo0xEzRiHlRnumYkRVZLH4cDpMGNrxa9BSWOOUREn2gTW3qhNMC6iHR9picy3cchLotuZCqRtRgw6JddhsXGuV8LIlIQrmhOP6+3LOExW1sBpmz9Qpr1gzqLLyoHNS+XY+bmTsnZoY1mcPpcN2FAPVCUhDimNEyhZ3Zwg5th/XmD30d4MC8gEg5ZLahqUCZKNKwLtq6K1lxcac/BZxBw0b7svHmHmJ8YUBRJ9/VrZrC1JYUQJhJ6zjDxChBLSJP1TiTkklYo9cv2e78YE2MGDB+F0xuZHJicnsWbNGrS3t2f8nu+80fQ2B5dXDJWzDOJgJ3PCiJ6whOEuWQww2d/qjNA3b4Vaob09uUBqC8qZHti891KrZatygxnm0lqYi+XKPUeT6swkpdbq4BodxEjXBbjGhrM4W9JD0MybpIOhMPqHp1hA7vKGWKtktqBFbl2DEc0VcVUFlQqdw2EcvBiIef+RaRFu//zP66Vq4qgEf5IOCfp+jnaIuDCg7FgpEUBC32S9XFskB19ne4FXz8quSdHaDpQcIzp8PM73Aq+cBpZWANesVaUUunzllIAfPhlmAWwEDrMKY9Mi069YCEjw/t69RtyyM7U67NZVBlSXKltEi/M1+O//U4GP3KYsmUsYGQvinvvP49s/H8BPHxrGzx8ZwcGTroSvTXStnjswhYeeGsPRMzJNNh7BkIgDR10IheT+z7//i1LGAPuf344gE3zxP0fwD98bZTpqX/vROH73XOJjTAeqgj/0kg9f/4UHHf3z+wdfPRnGUwdTt/QcuiDiwBliQyb+PTElkomy0vi570pgzyrZWZKSt90jdK/Mf+2zJ4FDbblpq0mEynwO1QVkWgFMeER0jbx1YqeJxlZZSSHKS+e3bYWCQXR1tGN8LDsRznhQq5hvpJe1SbI1Y0acnEA/i0Efa3lPBZVGy5jBmSLcdRpCf9ts1VXduAH8jI7HZbx9wRHb3F4IsfsM0w6N3rwIA+1MRyXp35IW3Iod4G3KWAYkkq9xlkGTIEahxCzFHBqTFabSOhgL5ZIVsQ4iDHMSZZ4c6sdI5wV4JhNMNAqTYPEbIV8ghIGRaUiSCJdXvneyBSXTNy8xMJZHzPmpVLjQH8bhNlmIN4KhSZG1TcaD4o32YWKMJf4cilvIJKR9SNmxUitYVT6wYxlQ6gArGJDj7itngQl3LMuGihs0n8fjdLec/CI9x+3LEs91Ebx4UsSvXyEtzOiYQ2YM09q3EFBS6/qNsQW9eFCMRwkxpW6JFYUc/v59euxZrzyhMTQu4Es/8rF19tH9ITzxRhgdA4n1IRJdq9dPh1nrZNdQ4r8JBCWc7hTYd02x7F/cqGOx4NOHlLfqEnPsW78L4idPhZg+6ff+EMSrpzLX+Yuwvinx+IMnwxienP8dvnZGxOvnUutjHLko4c3z8jhLBEpkOiyJYw5K4r7nChLfB0uk+oMkYSC7QsbjqePy+F6smKO6gEPNjIv66LSIntEFCLAtAInGFY396rIClBXNL0z4/QG0tXdiYnJuf7kQCMEAvCP9smY1xRzRe1SKOQJ+2TgnBdhe05iZVhl9Vrj9ONsXEyjWUC9ZD744e/fJtyMkYmsvwuPdhs7OzoQmCIFAAH19fVm952UGWJaICGKy9oOzr4OvbAI3ExRSr7Pgc8VoZIiTQwieehl8Sb2iFpREEDxTELzTrOeaNMCoumqIy5RTq6MkhKEx22EorIC79yKMRZVJ9cR0Zhu8U+OsqziCqfFReKYnUVReDT5D9xFCIBiGxxdkrZF9PV1Y1rwUznT2cZHjp8pGnN0t+1+ChHcgLLGEUOTI6f+H28OwGVXYHKeptKqaw8i0hJPdIprLOVjjKPUU41F11aKXsKQkfXadWsGixe8bS8HEWvvGZVebCBpKZeecCEOGEnE2o0zpJ+bNjhYVmivJxUZEa2sr62dO5Mg0MCahZ1jE80cl7Nsgn1vnoIj/eTyEdY08bt+uiWF0tfUL2LpcrdiRauvK3EcZ8Sysb/5kEOc7fPj3v6uCThs7Hrv7A/j0V9uZa6LJwOHEORfbzKxqMiW89xJdq9v3OlFXqcemVYkZZ0++PIkf/nYEH72rENftlL+k14+7QXPqR25Xrk9AiUCq9hKDr2coBJNBhWmPyDTSrlilx651ymymugZJdyTEviMSjaUW1GjsPxVmld+r16qTmkDcuJnH8KQYI1yv5FpFQAwFSgrTmKVWgPfvkivN8SAmAbX0JilIKgJpu9CGb12tCsX22POpKph74wv9AsbdEgqsXFbtCQtB9PVyewMIBEMoyLMmrbbSvKomjYss5slECEwMwzvUDRWnZvN9cGIQhvIG6J2lsmV689bZ1/q7zrBWAV1Zhv1IMwiefpXpP+nWXsOm//CFQzILubSOVXKTMb9yITZ+GQuIOXxuhE/vB9+wBpwtn40Lla2QsfUkXjO7VIrDXQideIG5bWmWrM/qMwVqX/S5ZC0WRzGLPQxVzbHHRQk2SWJsc9L38g50wFRWl/Se0Zss8LtjN28TI0Pwez0oqqjKqjXSH5iJOQQ55li5ohn2eOGgFDFH/DpJPyY6emrVpgRGhIFBTrdHOgQUWFVYF9d2t6GeY0yZI+0iVlTNn8soQUVaSR6/XERLhyWl8iMCYpoTC4za06N1GcnIhIxIInN1z6jE5m/6fHqe5vGWKkDLS6ljjnGgdxSMpb6pUT72C33A714TsblJhV0r586nd1RiCZU19coWCFrPNjTlnkFtjovr/ufxAFxeCZ+5jUxNYn/XOSDgu48GGZOH2vGIzUXLbN1MC6aSdfSaDRrWNhlp24zHi8fDeP5oGHdfpWFtmZRMPNUpsOLTnnXKiokke7GyTm4VJcbU4ISEIofEnCgffDGMbS0cltcou5btAxJLYNH+mdbYwrh1+I3zEktMbUpMwGG4YYMKU14pKQMqXcxBY5bibRqTNhNw9zZZ+D4eJIRPn7GQho2+cQlHOyVsbpBd4aNRXzz3nZ3tlRnoxQ4VNBky0BeC+Gs1NuVlc1ghUT5T3DsajSZh50028I0PwTc2AF6rQ2BiCGHXOMxVS6G1OplMg7VlC9uA0Zzn7zoNzmCBLksphMCJF9laoVu5i+lch9uOMYF7vrAKKp0R6tooumkCXI453n149NFHZ///1FNPwWab02qhhNhzzz2H6urqrN77cgJsgRAnhxl1k6qs2nVyAkyi5Jd3GvC5ILgnIHSeAt+4AerqFeALstcb8rUfh0jZeRVk7S/t/ADP1Xkags8NjTUPeqrUmm3wDPeyPmyDM1HVVj2rSzN7TqLABla2VVSLSY/6ygL2PpOjWlhMyrjCYUHEsTY3bGY1GkrlJAIFDIdavTAbOLRUxZ7viiptDBWYWhNoUSMGGFkSG6Jc5mSGi4TBSaDYLs1LgFGC6pqVcrAhXwMJZ/vlSqtDgYA9BZu0mNNj/u/kvycx1F+/KlO6b9siH+/GGYJFOnffves5XOynauyM7otEwQuwtYWfF/w8fSiIk+0Cqos5VBSmXyTPdIRYZXBlg7Lv6c1TPhTm8aguzYwDTkkjSnxFx6FTrjBjZa1sNKKsWIvrd+Vh73YHNq+2YHwqDKtF+RRlNPDYujZ5FYrec3g8jE2r5oKLf/2bSkXaVQdPeTE4GsYNO62459q5Cfibny1kTlvDEwK6BkKoLlXDFxDx6MtebGrRoapEDnKpYh7fIlFTwuOuK/UYdwlY0zg/GP7oDVqm5xKd/KLvvWtIQnkBuV+pWCD54AsiWqol3LQl9lrR5kyvTn5ydG89fADMneuDu+XnkiWc1uVAhkGQ5E1fOnfN5nKe3cPRxzLqEnGuN4SWSg3spktT/RqfcjNRbaedBGpVSau35ZWVOftMHTGIeTV0tgJ4XDJDJlp0PjLX0TgIjQ8wLaboBFh4Yoi5MtFzfJ6cNEsGKRxgQSglvyiRp91wLZL1wpMeGLMiz1s86/TLUAZhuBtC12nqaQG3+kr5Se8UJPcEEPBCGBmD0Hse6uYt4GtXMk2VbOG9cJBcG9j/1Wv2JDRAcF08zpiLJMVA41dttMLd185MGXT2/BQxhy4u5iDxquyO02E1wKjXMDmHqTENTHqNYqfpY20e5jRdU6yfnavfaPUh38pjaXlsYWhdnfy+kfuKXPNqC1WMDUMFuOj2SL1GxXSGiN1SWTCfyUKi5ftW0d/IP1NB5Vy/zPJSonlFMQftkxPtlSPJLyo6PPImmAD4NavlFrr1DcpiDmqL/+lzsqZYZM4pdUpYW09Fu9jje+6YwNjDpOOlxPDnbLfICi2kAaYExy4KKM1XJRTCTwX6DFqfo+fBSbeE1l5yDab3U+HqdRqsqOMZg4yuidKiYSThtixF8mlNA8+uX0MZPxsL/vWdeijJXRy9KLCE16alPG7eOjeev/heLegWIo20wXFqR6Tiq4SXTwrY0Mgjf8aNlGK6eK26+jIVrlmjgi9EovDzz/ODV8fGZ5EEcd8oUJYvX5ugAPzmVWBtvYTtLbEvpuPQa5LfxN4A8MQRWW/0thkeQDK3603pJQnTIizOxBxp5pUV1WqW3I5Ofg1MCLg4KGBVtZq5eV8KjE54EBYEFDhMSddurVaLqso5V+mFwphfAl6rh9biYG3sdKlUUXEAta4TiFgRmhwCH/TFJMDIFIXM2PTlDVDbU2fyGRt55rsgnUrtur1kWZnwteHus+Cs+eDsuTMKeitAlI6IXmcu3/Pdgptvvpn9S/fDfffdF/M7SgRT8utf//Vfs3rvywmwLBE69QpCBUVQ16xgP0uU8JqBpmkjxNJ6cPZCluEWx/vBB7zQpMlup4OuvBGCZ5K1IUSSX8SSiUxQ7DX2QgSgQmh6nFVkTcXVCHefjxFcdo8OMNqrtaQKlrxC6IwW6IxzmRtHfhHsTrIXz+4mowWTFno1r4bJoAWvsG+bPo8SJPooPR86BNJpMsfpQEX/TTQoAL04KMJiEFFXFBtlUItVnlkFa5LCcLQANznMHO2QWwuuSFENywQk5rpxCVCShcGg1cjhC+/RzJ7vr18K40KPiD+/QYOCOPv16zZpsaxaYGKrSvDjx9ysjfLbn0utA/PYK248/6YHQ6Nh1JZr8NVPZObk9Yl7imLaEbUaDr9/Zgy/eWIMn/1wGf7r/8xlWRprk7OoAsHsqkAFeRp85I7YYzYr1AD73bNT6B4I4cqNZsZQi4As0QnFTjX+43MFbFNzuj2Ep1/3sWCaEmBkHvDd37nxoRtN2NCsY+KpFJxajBzbND1zMAS7mcf2lbEJRbt5/vd3ulPEA8+HmX056Y4U2FXsEe/eODAu4cfPiNiwREJ5kvFO9yhp01G7zLPHJdYSE28TTmLLtJfMRRtCiV2FWzfEJvOIgUb3Y/R9TJu/+A1gmLE95U3ipUJ5sZMVAqJbvKddblaFtNsytyZXAmoXI71GgqlyKWPX8AkCRLpeppYr5s1/ot8NyTOFwIWD0NavgaYgeXJOt/Kq2M9O4tLErMiPPgeVxZmRvtRl5BbBw88gXFELVXEN00mJlljQrNwFkWIDkw2hzlMQR3oYI0zTsG5Bn6mvaoHgdzMphUjyKz7m0OYVIzg9jtDUKPSOIsY4d/VciHEgdQ31sr8j3VFrQQmMVgcTZo7AWVS6INczKljRfUpFAYo5kjFm40Ev02s5thZFQLc7rbeJYo7446Of6TNJYzTPIs0T2qYkA7FdiZWeCNHzHLnVHeuU28JyUXAgUHJsdQ1QnYXpJulV3X/L3Hfyq5dFxja7d5cKdnPs+exdyzONVSXJL/qef/VimMVDf31H6iLaUwfDjDE1Ng0sKefwgWsyS0S8f8/cwhVJCD17OIQ3zwr48HVafPauuexLXRwDOxrZtnwW2Dlcv5lLyVJLhqcPhRk7cGMTJaWiirkzSdbyfBWLCYk9daJdxOtnRZbg3LWSx9GLIh45IOB9V8rsNIo5qNBHvyem0/4zJEAvYVl17LEQMy0eJzqAp45IqC8Bti2TYwHG3oob02RY8PDrwNZGIJnqK/3ttqWALwS8egbY3DSfVU6O0pQUU5jDTglym6ZHfMwRf+z0sy0uqUCty8SOT1ewyyVqyhysqBn9fU9MuVni0WZV1kWTKUiP1DDjymutbmbuvcQGiwfN/8RAjy+CEItc8kzCf/5N6InokSIJpie2efRnOxK/lta28IkXwTnLoN18Y5Zndhl/ChBnWH+klUcaYPn5CzeFiOByAixLMI2vsAeqpo3QrNsbYytPVE6+QA7uqALLldSBM1qU90WPdIM3O5g+RzQ0jiL2iLwuNNYPX/c5mBpWQz0jYqvPL4XOWcJYYOqZz3QuWR3zPkHXFMJBH1Ast0ZGJ7+oDcE9NQ5HQUnWbT1d/ePw+oIoJg5zBqAN+fLq2HImTfzLKpU5WhHKHBxzLaL2qXjQQvZGq4h8qwpr4kS+40HuNFuWpHbGyxR0LluXZv/30YsiJTwogPzmb4LYs06NXavVMUEXPaKRqBoYwQevNytKLLg8AqbcItY167B3a/YC2wPDQXzyy23Yt8MxI3LPY2MUK+vEOTf+5fs9+Nj7SrF5TewX8Nxr4/iXH3Ti/vs4NMxUskfGgzh6yoWrrshbsMFBMnz6nnxMuQXmgvn0ATdqy7Wor4gN3o0zG6aWOg3+8i4r6is0eON0AL9/yYs8m4ptqgj//ms3BscENFWpsW2VDtdu0mHNEmX3WmUhh6WVKpztpo1JGB+4Rsusz+NBY4NYB8xtMU6+4fnjInNhuuMKurdUeOqohLM9wPKqWDdT2oj9dr98D+xdDTx/SmYS0CMTEJvruVMSc38kN6ZojZxjnRJWVatYcjoVih08q/6nqs4HQuJM4j03Y0Cj5tkjGmPjk6xFwWa15MyiPBlobk6U/IogkROftqQOvNGGELkpmfNycxzU/rZmD2MciVMjWdmZX8bCIQ20Q1QD2sZ1wPq9UEVVxlVGC/iZ9V69dDPUtStiYpK0McdgB3h74TzjBE1+GTRRrwsOdSEwcBEm0oebiU8MRZXQF5RDCHihnvn7vKbYxFvANckSYBRzkH5XdPLL63bB65pGXiGxH7Nr62nvHYM/GEZFcWaJaa2aw6ra2I0lsT1XViuPOah9m9jmRXHtZARin7x2XkSlU4WWSi6trujmBqA0N7ctA82FuYo5HBa55fM7f5Swb52I1XVz51PkULFHNCimSDQX03u+Z6caGgUJDmIUkbRES7UKO1Zm3/LVOyLi2w8HcPU6NbavVLMYMJp9dqojjN+/GsJ7r9LOS4S9ciKEx/YHcPPauUzI6JSI7iFR8bqdDe7bo2FJGEqIkDh/fSmH4rzYMRQRxW+poSSuCnUlKrx5TsBrpwXm1mmYWSK+/zjpxwJVRRzWNahYoYvYYEpQVSgnUC8OUMFPwvXrObz/qgSC/0Z5DOfbyIQr9nfPHJcY++umDSrWfvv4YTKJIOaV/HfR7ZEPvyl3SZDpDunbkV4d6e1mAnJrfemshJVVKtQUzh1r64CEM30Sa08ujRuv8ajM51HuTB1z+IMiY40lY4hnikTmHsNjUyyuWawEWDSoaMFr+ZRaX/HQljeCt+QhPNIDzpibDRMVcjSrrgR0RojTY+CsyrWE325YDM2ud6MGWEdHx7znSADfbs9c7zaCd99VzBH0O+6A9opb2f/54hqmxZEItNgrTX5FmGTBtqMIUotDElDri+vYc4wdQBNSJCPvH+3DdNtxJmYYSX4lgr2yAc66loS6YH6vGz63C6FgcuHcdNDNTOIU6FJv+6V07qDKb4kjuZMcHYqSHAl9b7VFqhhWWIQRc6RddmHKJdJdK5mGLs62pZIu1Pv3aFBZpEprfU3teX/zXRceeTWxkGVzjQYr6tO3M753rw1rGrU4dCaguMKeCHodh9JCLYqcWsbKuvPafBj1c4uu1ydiZDyEb/x31zy2V55dg5JCHUrL6mav1c8fHsS//KAbx05nLkbv84s4dMrNGGmpUFKgQVONHoNjYfzkkUn86qmplGNn5RIdY4qNT4uYmBbxZzeZ0VQtR/wttRrWmnriYhi9QwL2bdKxFl8lIFepe6/W4p6r1Lhhc/IdBDELqPrbXMXPG1fkxETMxoi28Y4W4K4rKPkVz6YEls3oyVCLD9mSD2Wpu0oxJI3dIx0i+ifkD84zETtC/lcJUgWitNk6ctGLM92+pO2e055Q2rbuKbcfxaXyJj0eoXAYZpMJpcXZs2OzBQmaiwrnZBI0p0rsQlwg571naR2EjhMIvvTrWbbzO9GR6Z0M/dX3QrNVbgfgKxqTOjfSuq40+UWgpGao/ShCveeSviY43AXX0WdZbMHY5zPsLt9gJ1xtJ9j/I8mvRMiraYKzJlY7LAKfxwWvexrhsHJh8HhoZzZtnOrSxxwUa5TmJXaXpmcoFFEyXdCaWlesipFuIJDo/PEu2f3xksYcosT0piK4Zg2HGzdyKHOCJZBSgWKVr/0yjP1nEgdKTZUc6krSf0e3bdegogA41UkFPGQNYklRayAx16iQsnM1aSjNnYMvILdG/uyp+XMs/Y3TzqGqZu5aPXYghAeeC6JvJPMvhdj2F3qFGHOBRKBkV0UBh74RCY+/IeCFY0Jqd+VKOQlGyR8yXrp3twZl+fLxNlZwrFPgXI+EKQ+wfTk5Jytbw4gVduc2FW7cqML2ZaqUr6OiWnVR4piDkluR9XfXcuD2zbHJr0hLMTma1hfL2ngkWTI611yjHDP3HF3jw+3EXJQ/l8YtxRykw6sE6Qpub7b6cbYvmFTSZdqb2qyArpGjsAIeX+LXkQap3WpCWXHuWC9KQXO9GEridBDPgrUXQt+wNmGCLFuQKD7pa7OYY+Y4Lscc7158/etfx69+9avZn++44w7k5eWhrKwMx48fz+o9LzPAskToyLPANe+LeY5VOGf0VLKFymiFtnYVy6ST4H1CZw2Oh0qtZawvfcmcY0bY50HY62a92lStj0eQLMzHhmEurph1ZYqHzVkIo8UGrS79REZMiO7eQZhMBhTmz/X1mU06pp1j0GkQDodZz3o8aDNKSQ91hqra1DJGgZlBl/jvSCtocJLYJnI7RDTo593LE1c3SB+M4td07JEzvSTSLTNlqNqVDahV4DnWckbW23Ofl+xaEQ5fEPGH1wXs28Bj81L5HJxWDh+7MX1vGgU6BQ4ODsvCF49br7KisVqHhsrM9L8o8HF7RSaM77Cp8Z//pw6T02HG9lrRFLtx2rTaitv3OZkwvjqKtTY1HcK3f9KN5noD6qvmPv/2awtRVqTD8rj3SQevT8B//nwQ+4+4WWL0X/6mionop0JJvgaffG8eKouV8fP3bTbgynX6mGDzui169ugeCqO8ILuq9rLq9H/XNyZhYEzE8qrYcXXTJhUT0KXYjir6FGDWFCWq1ANbo9p/774iu7YESiLfuFaFaZ+Ep09I7F6j6qvVqMKmhtwIudI0km9Tw5hkXugb9WNoIoCGMhPsZk3SMdo3PA0OImwJBLQnp1yYmHJBn0ixN4cgFyZWxDCY2TEFRvsQHBuA6HPD0rwRvG4ueg8OdVIPA3MDXmzwlUuhMtlIxXzRP+sy5iN07EWo9t4zLzEqb/ayn9upeKehmMPqZO0sJHI8/0VqFnMQA11fOtefR0zzsFd2X1QlqKcGXFPwT43CUlI1q/0VD0dBMawOJzQJ2m4SJaG7ekdgt5mQ75iLjSxGPWM567TqhOsoa33yUtudOmOWMLWPEai9PREGJ0WMuyQ0lc9ni9C8f80qPmksQwTTdMdzqgfoHgXK8+TWs2xAiawD52UHyeh2+VQxx+vnyaWaGD8SllXJ3y2xvO7bnX7OpvPOsyrTMksHMv2htSxT/S8qGhKLmbQkqZ2T2h0pydU5SPqoseewvknNTGkCcXmI8WkBf3w9xApJTstcsuvqtRrUlnAoiWt5TQePX8KvXgjifI/IdMA+e4eOxXGpUFGowu3b1aguUnb+16xT48pVsYz/a9by7B4gY4OSLOJWSnKQW3k6kOECGfc0FMeOq1s3ycVjyvmRYzrJLpQ7E6/j0ZIjd22RhfAzBen23rROxRJfx7oo1iFTHVkCJVcxBzG/Cmw87HFmTxF0DvkxOh3CsioTLAZ10iRZ79AkTEYDrOb589/YpBtTLi/MRmWGHtki7Jfdg9V6I1tTSAssMD7EHKftS9czh98ISO+LiiCaGamGxQRf3QLJNQGoc9AP+xZBUqnYI9fv+W7D9773PfziF79g/3/mmWfw7LPP4sknn8Svf/1rfO5zn8PTTz+d8XteToBlCdFLvtNMLnD2ueDrf2AitNod78maIUB/pymqRqDrNEIDbTCQkK01NvuvsReyRzxM5Q0wltYyHZlECLqnEXBNQG93Jk2A0edHJ79o0fR6PDAYjQmz7/T7eFZFIBBmtuS+QBBnz15AY+OSGBdIlzeE1j4X8m06VBVltpk60RlgAvdXLNUnZCH1j4vM+aXCmVx3IxF75PEjEqtGXRkn6hmPNTWyMD49sgUlHHpGyD59LhihPmeieCZzziHqem2xiMdfJ7crCbvXyN8xVRJfPh7GbTs0cCTQjCLYzBy+8L7csEGICUWPTPHrJ8bwwKMj+H+fq8LSOnkD/91f9OOVg1P4r3+oR21l7AL/4bvK5r3HuTYPOnt9GJsM4PodwdlrVVGiR8X1mVWeHn1uDGfbfHj1sAu1FXomXE/MtHSg5OtDz0wi36HG335YmbBKskprZdHiTr8vHBfRPSyA97djxbIls+OKAtGfPAssrZQQElRoGwRzf0xX1V+oDhgZT1y5TK7y5ho0bzWUJn9jp1XDvjuzgU/5HlUldnR1tkMUi+fdhw6bFRq1GiajwvJxlnB1nWGBp6N5M0sw+PrbGP2D2iGjA1FCoK+VsiAQpkdgqIttdc81+NJ69ojgsiPTpYXomU+FCD7/AKDRLkibjZJnmpI6BC4eRni4C4aVV7EWlGho88vYIx6m6mY2NhOJ47Pjc08iMD0BY14ROGPiNYjiCi5GEF+E1+uFMYOYg9ofvf4Qk144dfY8li1tYsL4EUy4Q2jv96AkT4+ygsw2kwfbAizS27408fzSMyozTKoLyelReVLtD0dIFzG9xuiGeqCuKPvkV4RF1jsGjLkpAaYs5qguok088NhB2f2SxO8JZ3sknOgQccPG5C69xLb6xA252bSWODmUZNEB9dj+EA6cFvCZO3Qocsjj6NcvBNHWJ+IL79PP0zG7fef8L+9Cr4jRKQkn28JY4uyZvVZlBRx7KAWtPS+fCKNjQGTJLyp8hsTkshTRIBH9Zw+HmanRnTuVXdNE70vrW+kid5K9dJLiWxGob8fSprmYg5hcvzsArKqR4176+QO7kgvg5yrmoKTXzmbljK9MQPuPeJOMaBTataygbtSlaCmkontoAhWFiRle+Q4LDHotjNlkATPAZOdZ+fOa1iLknmTMXhKNY9qP3NzcTuSKAMUjnIq5RcY7Auca6srY/u3LMce7F4ODg6iokLPwjz32GO68807s2bOHieBv3Lgxq/e8nADLEpqdd0JyT0F0jbP2kAh7i1y0Zp26fG4Ejz8PdWVzzMZBCUiPQ/R7oNIrS1xQ1n76wmHmwGSqnLEWjIOpoAQ6qwMavfLVwON2Y2R4CHaHA4682NWTRGdrq+cHxfkOExw2A4LBMEKCwILTaBj1auRZNDDrMx9+JXlqVjlN1oK3tIxHhVOCbUZvSQkoxiadIyWu6bQgK7EsT4WmcpmGHR+ApQK5Fd20VY3vPxZEW7+IXatko4GuQRGtfSLGpqQYK/QIPD6DvLGFAAEAAElEQVQR//WQF2uWaHD1enmxJlr466eCrPUxF6ywCEIhCcPjIZQVzV+sK4q1qKvSw2Gd+8737cxjLZAVJcqinI2r7fiH++tQW6nD9ER31sc5NhnCfz84wBJfN16Vh/delw9zkipePLoGgmzsRSfLyB3yV09P4+ZdllnXRyXwBSQ8/JIP65dq0VChZkHyE68HUVPKo7lavk6nO8JMu25F3fx7hQLzw60C1jeSyC1Q6oz9Lves4TA8IUIdiB1nlICqL6VxLOvI0dhP4bqdM9D5USC6WDptEbQP+uHxi1hWZZhlZJj0apiK0883JKAdr/sVgVrNw25bwC5UIfTOUoRmBM5J10lfUgPR74WhuHpeosHYtBG+i0chRAmi5wqB/b9nyQ3dTKv/Zby10Oy6i7lOU1zBzzC/VWZ7THWc4hEyLFA3rgdflJk1OO8oZhsc0jBVAjEcgqv1CDS2fBijWGHRMBdVwOAohFqvPOnkmp7G2NgYnPn5MZbnBEpAN9SUzvubYqeZOad5/QGEwyL8ZFUXBWJgOCwaGJMwMVKh3KlO6RS8oopnRblkyaBEoCmm0AookUml+XqhRYMV1SpUFdL8q/wYS/NUuHo18OBLIi72UwJMnhe7hyW0DchMnkQJCmIA/eL5MDY28VjbIK9JIUHC8TaJ6Vea4mQlFgISpycWcyIWFSXOygtiv5ety9WoLBLntd4lw4alaqazRddusC/74xyakFjxsqZEhe0r1EyLLN5wJhn6xmS2mDYqtOgfE/HicYGxvZxpClfxmmqkAbp+CceScFT8ffGEiCVlHCpntLJOd4vMwZT0xOadx6SEC30S0+4KhokBFfua3avkcSG44kXmgZpCoMIJVOSDmRrkwlgnHSjWzTPFOmkvBs73+pnjZLResdWoZo90INaqnuxKk2iCJdIFyzWMzhKEiXkuSdCY7dAXVjLzGxZzRBUhKP4wLlkHX9tRiL5selOTg2lMvvJbpmfJ3CH/RHDZBTI3cDgc6OnpYUkwYn794z/+4+y4IbOqbHA5AbaAqmn45MsQR3uhuvIecGY7tCt3xbxGCgchuSchel1QeaZYMkupyCu1PobVGkgBL6Cor1piTAD2SHHM/ulxuAZ74KhsSNqqGQoGMdjbBbVGi8LScthsdpgtyjd+sjMSD5WWBNlNKKIVKPrcOBXcPgGTbg8LSjPp66ZgNF3lK5GTTSrQJnlL46WdUDJJfkVA50WV1Z5hCcGQLHBKToBrl/DIs3I430NW8tJs8oRAuUcSbJ1wzY2Lsx1h/O8TXmxbpcU91+Suneknvx/FE69M4eufLUdDVeyY3bLGyh7RWLXUzB6Z4Ir1DjbZTU8oD4Ae+P0gWhrNWL1sxhTCrsGXPlmJkkItKlOwhhLhy98dYjob37h/Tgm+tSeIg6f9aKzSZpQA6xsR8NqJIGs1pAQYtSU+fTCIpsq5BNivXwyy7zBRAuzIRQGvnBLRNSShd1TCp27WsM0HJcwayzmWNHVaOLS2zmekURtkBOWXSF6ChPBpX3rd6sW910ibwx8ivbwYgu6igbVXTU/BaDQxW+aFv6HIHPUCRiszNTEUVqZcJ0wt22bYyMpBLkuS382SHaGOE9DUrWamK+L0KISBDqjrVjHx+1TryWVcWtC6Gjr4JKTJEXA3fUK2kd90fcxrpKCfxRyUJBPdE6woF+3GmAqc2QHV+CCkoA8qhS0nrAUzRXaIYgzvxDCEUBD2ivqkzPiA34fBvl7oDQY4C4tZa57JpHxtirgxmvRaFDqMKHTEJtw0ag6T7jBcPhIHt2bE0K8pTB1z0HyqVE8pAjrWHYtLnpiHTJJfEVCCw6SjJIxcwKBEwu7VKmxqUsFmUuF8rwjau9cUz8VwJLY+7iLdp7lxcaZTYhIOUx4OV63OTQsa4TcvhXCyQ8Dn7prfSkhtjfSIlw5QIh8QHRuurFOzmGNQ4d9QK+5zh8NoruZROdO2SMm4912tRVm+Ck6b8niX5AJ++HiYxX03b527J9sHROYI3VwpwmlVfj4UJxxrk2DWEwuNx/AkcOAcJRFFVBby7Dt+9HUJFoOET94w/32Ptkk40UlFJlnK4y/2UdeFnJyrKlCh2KFirKvWODlWSkJeF+WLQSL3l2JdfvKYxLRMdy9f3ECAGJKU5L1UICbU9LQLZrOZFeYWCpJcCEyNQmuxQ29zMiffZFCb7TAv355xcEXFGdoTE6Ms3HsWmrq1UOmNECcGIYz1g69ZAVDMEcdyv4zLINx66624++67GQuXCmT79u1jzx89ehT19ZkRjCK4nABbANRNGyFOVsu6KAlAIrW6ne+FMNqLwOuPsGBUv1kWsU0HquiHKbmm0TGHjXSgINe2ND0NUAgGmAMksxxP8pqA3wtRECByAqMw52VpOzow6oLLOyeiGAwJuNg3hXybAYV2PfrGfGgf8KK+LHP6yZRHRFCgvv75kz+d28VBkfX7UwIgEcZcZNmdno0SnrFCXkgFlpIwSlgvShKB91ylYQFmxP2HAtK8mQrgA8/6GRPo638+d1sTw+uf/swMQZz7/MYqNW7ZacCqhtz21a9qMmJiOozi/Ln3bev2o7pMlzOXnAiUJk0HhgL434cHsX6FdTYBRti4Krlb2EsHpzE4EsJd187vF3jvPger3kfjilUGlBWoUV2q7HruPxGANyBh93o9mmt49I+EmV4JBcafut2A/KhA/t49ulmx+nhsa+EZ64tTSaxCT+P5J08LrGXii+/l0l6riwMSzvYCV69Mrm8TjcEJMAv1TF0gI6DjI1erxUZTuYGlg1IJ2KZCpkKrPq8X46OjCFlDyC9IHdlTIsA9Osi0NgxWB7gEBRGtLZ+Jz9K/qRCaGEJ4vB/6qpaECQsKaqHiEm72Q+ffZMkudXULxJFuiM4ylgATei9A6D0Pjlre1ssBxmW8faBesQMSFdOSFMV4Zym4Pfch3HsewdcegspWAN0mZTby5LYVHu2RdUgTaY/GgVNrYF+2Oe3rwgE/hFBqAwe/18t0xEhXVK1WM/ZXNugZmobbNyem7wtQzOFCqdOAIocOA+MBdA76UFOSeU/UuEtk80qimILW+NYBEUV2jukPJYpJWPuhAjYKzd80Ty6EIUN5SSXTmJK57u5dHKjAHjlu5khnks/pt6+KjEX86Zvm3ocKL5+/g+a1ufNsqlThKi+HlbW5FbFuntEmi2iNMR3HUYklmnJtVKJ0XSADgOePhjHpkVAZxYZfUZc8UfHG2TAram5bEbslI5bYnnU8SzZGY0szz1oiy2dE7tPhjXMCS7quqVexghfp5BJKnSrcs4ubNcCh7/jObRxLaibCtmUq1BbLsiEUD5Ar5B8OklkU8NFr0l+r833UMgzsbJFZkOnQNy7H4NkkzOj7d5ikGObcYmFFBq6x8chG2N3t9mB0bByCKMKZl1qPJRT0wz0+ypx3jRZ7QuKD3l4ACRK05tQujsGxfoSnx1jrY6L3oZgjWcElcPYAK8yoKxohDHeDL6oBrzci3H0W4nAXYyzrNitbq95JuOwCmRt885vfZO2OxAL7xje+wZK/hIGBAXz84x/P6j1VUjpbrMuIwfT0NKPlT01NwWpVZrntP/gEJNcYu8G1y65I+jomXq9SzU4ggmucaXEoreAqAfu6KfmVYtJlVNSAn2mBLSSIaO8bRyAooLE6n21G6f/nuidR4NCj2GHEhV430+QpL8g8GD1wQU4i7Fymm5dccvslvHwmzCpR6+vnr+S0+O+/QEKdwPI01uQvnwG6RoFbNgA6NfDSGdmSecn8LoyEeOUM6WUA9+yUA9ruEbAAItvNebyg6iOvhbChiUd9GY/z3WGZhh3FACP8/CkvjpwP4f/7oGXBLY8kZE9sE7Mx/Zjcf8SFr/+gH/felI/b9751NsaHTk6jskSPwnxllaXP/N8u9PQH8PN/qYdBn/uF5u++N4Upt4j/+Kwd//UbD2PoffE+M/TahX9W24DINj8NZenf6+mjEk52Ae/bOd8BMhF+e4ASx7J2B1VViWk26gKKbHPadofbgfX1yVsqqUq6/7yEYrsKjaV/GjRumi9dMwwwdQoGmJ80GD3T8E2MsJ9NziJYChROJInej3QiR3pgJJ3IuIQFtS94jzwNdV4JdA1RpfcZCBODCPecYywwTf0acI4SNtcTg4hcAbn88rRzfzZr4WVkjkyvMzNOePV3gG8afGUzNE2bkr+WnBfJNZKTRbJF1xg4c96CjHwSHQ9r2lClizkC0OkX1ut3oWuMFQyaquX1xuMP43zPNMryjXBadTjfI7POS52ZZ5dePBUAkTyuWj7/b8fdFFMIKM9TYVUChlHXqISDbRKWV6Sf9545AYxMA7dtlMl1r56Tiw5KkwDPnwTah4B7tlE4KTG9JfrbXCSEiNX17DEJmxpJT0qFc70ii4uiGWCEB18Io3tEwqdvpvYu1YLjHF4lKdLofP1MGA+9HGLukRub37r6/pkugTk4WhSy7v7pFz74A8BXP7Q4Yudf+2WIJYI+e5sG//14mKUmP3RNcrf0TECFNBoDFQXp3+uJI0DHEPC+HfMdIBPhl6+RXh5w307ZlIcSw+RimT8zDQ5MSDjZA2xuSG64QOys185LqCmQnd3/FKCUAeYlExKPC76pcfadWwtKYM7Lnn7naT+B8OQILMu2gNPFjlWS7PGeeJFpWOuqls37W2GsD6HeC0A4CE39avA2WUNXIsKFewJ8Ao3Jd3LMETnWU0fehGUmWZMruNxutKzZ8I64Dm9nXGaAZYlM8oY0cRNLLGXyS5LgO/o0VBo9DCuvZM8pYX5lChYEzQRCIul9kKNkXGBEP+sy0OxIhuoSOzwez2wdUKflsaIub/bzmiotyTeUPhEWQ2L2AqGxVM1awxIxq8h1bn09z/5NlPyiam2lk4Ts0y+GpXmycw1t+Gkh7hmTK1dKE2DUPmAxkmMMcLBVdlYiKnhT+fxzpmtFbR9KA1XSdzreJrAKLCXAGisT386l+TxGJsWYQPTgmQDy7TxqSjObAj7/b/2YdInYud6Ej96ePKk1Mh7C6YterF9uwoqm3CqQZnqt1i1XvkBQC0C+XY2GSl3Gya8XD3mh06qweUXqe+dTd5hnXEdV+NQdJrx8LIDPf9uFv7zDhPryhU3JNcWqmORqqmu1awWwtp4YDcrG27alMgOM7gXCiS7gWCcZR8gbrEkvMDwNTHqSJ8CISUB26CbdJepPjAJthkNhEXZz8kTo+JQXg8NjqK8ugYp87XgV+DTJALquVpt97j1GhhAOhVBQUhZzzSf6u9nUay2pQtjvgcGax3SUqI0s6JmCe6AT9uql0BiVtZvrKpqgLaoGl8iVUcWBIwHbuN8xh6eDj4OjNjBqP5gaBmdyzB4nMYv4goqcr4GXcYljDko4OYpTJ7+EEHxv/hGcxQH98h1sDMQb7uQC8thSEnMs3B2jrsLBTHsYw11FLZFqrK6fG9/Lqs1J533SDbSkKOwsq0yuBUZs8/W1HOwJ2F9kyqPm5ZiDtD/TgdweSfOapH/IsKR3XNYnVZoAYzGHQXbU238OONkN3LxR1l5aaMxBLoLneiSmG0kJMHK+TIRi0pcSY1k+Ry8K7G8igvRKQEyjr/8ywFhxO1bw2LsheYGBYpzBcZGxwqpLcsy4yPBakWskMmiZJAOabByhXzsdZq6eSytT/+0Hr1Ejkuv66D4eL50U8fVfC/jIXp45ey4EdcWxydVU1+qqFYA3oCz5Rdi+FGwcRd7mSDtwvh+4ZqXsxE73Bxk8TPuSa/jS2KGYw+W/9DGHy0vsfsBmShzX0bXqHhhDIMyhtszO5iE1z6VliRJrzG6fY2sND8kNuoVFxTFJsrHBPvBqDRzF5Qj6fdBbbIyFTteB3Hm9IwNw1C6FOi6ZlQzGqmaIZcF5yS/5oHgWi6jifkdtj/5DT4JzFDMTH8E9yWKOCCgOISaYElyOOd5dePTRR1mrI0mL0P9T4cYbM2cPXk6AZYlM3Ch069K3ktBCwVmcsu7KzKRBtuNKwJw5xgegZROMJuGEEb8QhYMBjHScg9GWB1tx8g0P6YGRHge5QMbD5w9gYnIaBU4HNBo1xqfcTKTWMmPtwujofX2sZ5cmbDoGJcHDwHgIHUMBVp1aWWOMYcYc6wiwxXBldeoKbkECUVQ6ntdb5YrlPoU6FPXF8oPYLhRY3rEZ0MfFYIcuyi2S8Uktwpo6FdbM6AM3lcvaXZUFicdTb29vUkemRKgp4fHpW3UoSMPeuXKtjj2iWVz/86gHlcU8/u4DtqTisqR1FY9VTQYcPOVLuVEgPHdgCo+9MIm/fH8xllQrW1z/6svn4PYI+ME35lePFnqt4vHvP+5FR48f//LFOqijxGgpGCV3yMrSzHUI/vePUzAbubQJsIExAQ+/6MNHbzahvFANm5k03CjRMpfYLMxgkxDB8XYRv39NwPuu4lFXysVcK1thPfrGOObkFbkHycY7E2cxCjij/R9I0JYCz4KZ/GIkAKVEWDJQEvba1fLm7FKjc9DLWqJW1WuSVr79gRBGR4ZQUZKP3qEJGPU6VJUpYy/SXDk2Msw2+dRCHg9HaSWL5PUmC2CTixtjrSdZe5jBWSS792ZAbWeJhETJL/qdWgPDil0JfgGoyAFYrYV2yQZIDetSMnNS4bIj06WF0uvNkknb7lDwQh6c1QnOZJfjBEpOKdT+ohbd4MQgdM7SecYMyWKOoM+Lsa4LMDPmY/I+amKCUWuPwTB/HvV4/Zic9qAo385MeMYmPTDoNDBF+gVnYg7SBKG1QWnM0T0aQvdIiLWcra0zzLro0bkcbAvCqFWhpTL1mkDtj/POJSzhjYuy+cdVLcrus6UzcQS1flEhgZhgFHtEQJf34EWZBZPIkGfjEvlBaK6Uk1A0d+diHW0sV+GDV5O+ZOrX7VoZ+34jUxIefk1AfakK7786ifZseL4rIq0TS8pUaBuQZmUfkmH/6TAOnBHwgWu0ipJs9N3+86/8jDn0sZv0ix5z/O/TAcby+rMbYmNXkrQgXVe9NrP5lJxEn3hTYAmsdAmwgTEJ+88KuHuXmmnJkh6c3UTSHOSoSi6p5A6aeXLoSJuEl08D790uzbLII9fKkl+PCQ+Plqq596XWymTtlckK0NGoypfvi4ipfCTmoARXWRK+AH2/N6wF1G9BzHGh38uSuBuWJNYdpGs12N8HnbUY/kAYHf0TsJn1qCxO3YoYQSAQwNj4ONvPxXeV0J7LWVwOXs1DZzDBOPOWQxdOsEKYKa8AKjXFHMq/d5rr+SSOv5xWD+OKnQn+iKwutWxt0TZuYK3u76aYQ8IitEDi3dECefPNNzP3x8LCQvb/ZKB7Kxsh/MsJsEUAayugBFaSzUkEgTP72eSgWyrraOga1iPYfgz+828gPDEEQ/NW8Nb0m6/AxBB8/e0sMiL3jmhMtJ1gGyVHbUvM86Q9o9Ub02b+R0aGEfD7UVFVzbQ5ouHz+eH2+BirgjK0U24fczSJJMAi6Bt2YdobQmNVXlKHtWjYTDyMOnK2kxfnaBB7i5gZ2YBukvV1XMYLIVVwfvcGYNYB166JPx65KkUBbqIEGAVuZ3pkdxxi2hDrJlNQ8EiJikRVoVKFGhDRoCTNB683oSBBwE548bAXP3l0Gn99rwMt9bHBGrG+Pnp7+s+4fpcDhU4Ntq1VnmGxmNWL7tYTwcRUCOOTIaa9FV0V1Gk5fO8rNQmTf+nwtx90zgbwbb0hnO8KYu9m47xzmvZIGJ0UmQskgQTvf/G0hIdf9mPjMi1+/XwA77tGj/VNmYlXUGKWAkIar08cFLCmnpttFXjttIjWARUq8qkCj5yA3jtayNmiJ1al7G6WCunaLoYmRVatrStKzgDNBkV2LWvBTPX5hXkmTNpNMOg1sJoNMOiUJUL9fh+GBwcZo8vhzGeMsPhj15vnXxiN0QRhyg+1Vg9LY9zkkiVYxfX8m5CIZbZyV0whhQJP/Zo9MT8rfl/SaOo8BS6/grGGLuPtA2p5JXolVdNTthkef1522VqygbU5UuwRbD2EwNn9ECeHoF+1m2nBpUNwtA/+wQ62IaIk2OxniCImLhxlFX1bdayFPa9WMwdqtS51smFoaAihUAjVNTXz9HHcXj9cHh/TE9XpNJiY9jPn1tkE2Ay6htzwBgQ0VzvSMjgJeWYeI1PEEKWYY25NoP9RXE3rfDZgCbVawJRhPYXY7Q+/ITv07ooN3eALyuxbSmolSoCRzlLniMzMLbSp2HtkChI4p5gj0fxL7oHZiOnfuJln2lyJ8NLxMJ47KuCj12tidK3o8993tbJ21StXa1BZyKGxQtmcxhiCOhWMOXSlTIVpj5y0iofZoMLf3q2HPsMxQsWkD+0lN/U5+YORSQmblvIJW1cnXFTYlH9uKFPhiUPAc8cklOaDOUG+dweH+pnCmVIQG5zCfUriPXecnELlOIDw2llggNpvC0lUPzfXuNghPyJwmIASO8XWqf+Oin2p0DcusnuupjC3iYXSPG3aJLzVpEVdTR5LrFpNOliMygaCx+vFyMgoBCGMgvx8WBIYlRktCWIOgxFBjwsagxmWwvRth0ogBv3wXzjIWMVGYhNHyfaoeA0MUa6OGcUcQhhC12nwxbVs3bqMdxfEqITnYiQ/LyfAFgGhY88xZwvdjrug0hpSOnHFKJUKIVmE1mCRtb9m2GDpoHMUseSXlv6NA6fRgUugIUZsA2dVQ+zxUADNNMjmjslmt2N0eBgjw0MoKY2dLB12K4xGA3oHSXcjhMoSZ8LqmEbDQ8MLGBr3othpYhTfVDDpeayuM822MURjbd3C2iTKooI30gYgu+dASGKLX7JFmp4l8dpEorR0KrdtltsVEqFrBGgbBDZ45bYGAiVdyCo6Xtg0EZ4+HMarp0RsX85h95rc3a6bWpKPLYeFR7GTZ4mybEEaYVdusqG924/fPDGK+24tRHFB6oX9K5/NzskjFR5/fgSvHpzAZz9aDadj7vP/4TM1s65W8bCYsqvwNkQxBB592Y3jF4JYUa9DeVHs97ZjtQ5blmtnk2WUV15Wo2FV64pCHksqaKOg/No/cyTM2F8fu16D+2/X4Gy3iIMXJLYBpKpytVnCjuUcllRQ8mvxgn26P9x+4FQPubVm9rfDUyJL3BXbOWZgMeWV2P1Slpf8u6BkVtdwCMUONcxx7aq0kaVpLNIiPe4KomPIx+5l2oKQKHZSJi6nQjAUhsvjhz7S75kGtGGnCpTZYoPVPtdylQ7WslqYi8pZa0CuEBrqhDA9qphBrBTS+CDCJ18BV9YA7dq5JNplvPUIvv4HJo6vu/oDyZ2mJZHFHFxUVUkK+CCM9DDDHmKCKWWAafPLWPJLa5d1XGZB949WBz5B7MJrtMivnqEmzYA2bxSfRN8vZHc+OjqKkZERFBXFxjQFThusZiN6BsYYA6yyxAFtgsWXXB/VYQkDYz6UOI1pTWisRh7rG4zzYg5iVWxuXIAaPWOtzI85KBFCrV2JZBoicQWxXKiwlmievWkDkGyf3DkMtA0Bm5YANvVc4dDjp/NMPy89fkjEqS65XW1tQ+7Wi3VLks/lNjIssqpgWIBeGOltrW5Qo2NAwIHTAm7YokmrwfWJWxbedhuPF4+F0DUo4M6d2hgZhU/cokvawkVJsGxQG9Xq+ewRAT0jEpZVzdceo/bRrcu42eIPJdvqSlSoLlIxNl9tsXz9leKZoyITs793lwrLKlU40SnheKes0aVTSyjWSNjRIjOzcpX8SgSKEYiFfq5vTotUKUg/jOrxpBV8vl9kiWWzTkKBLfk4pb1C10gYFfk8DNr5MQfdt5GYcmgigK5h2fyDgwqFUfFnNFjrOccxBpjbG4SVKu0KEAxQd44Au92ekQ5UXkU9hHCItUbmCsGBdgieSXCa3N5P4kgPwmf2Q/K6oGlJLiH0dgZRNpJbzmX/npexcLzjeXTf/va3mTOAXq/Hxo0b8eabb6Z8/W9+8xs0NTWx1y9fvhyPP/54Vp+baoPDFVSCK6xKa+eqX3sN9GuuZv8P9V1AeKQHhlW7YVh2Bcuik4aLomPh1dAX0CZq/oRmr2qCtSI20ZUIoihgoOM8Rvu75J8FgW3oSNhZo9GCT5BEo2ug12lRW1GMqvJCmIz6mA0j/V6r1aIozwSrRY/RKarezrlCpj2vNJtICiY6hsKYcGeeGe4Zk/DYEVrMZEH8p45LbHFLfBzA7hXAlsbE70XVJ2uSPOf2ZcB9V8aKjL92RsJ/PymxYCX+WsWf84RLfg2tqV/9eQCHLyy+jd7KJTr8v78sUOxqmApHz7rxyqFpnG/3IVdIdq0S4fDJKbx5bAp/+aVz836XK7bZS4c8+Mfvj8Dlmftu7tlrwSfutM1LfkUQ3epBm6wPXGvEvs16VBbx+MStRqbbphTUEhAOy60xhKYKFd67k2OL5NE2ICzJFvErqhdnuqfgk4JhArXqRLfrkOaey5det+Fop4jD7fJ9TCLSFNie6BLw8tkgJj2J7296vndMwNBk7D1Bm70jbW6c7Zkbc3otD72GGGVA/6ifvSbd2KLvRWkiS6fVsQQDPTJlreUq+RXZXKkLKqGtXAbj6qtzmgRT5ZVAvXIn1I0b5J9z7LJ2GamR6nrzxTXgS2pTWv9RVV6/+SZoV8kao8HOkxCnR6Bfuxe65TugX3llyoJdNDiNFrrCinntj3SM9toWmMtq075HOBzCQEcrxgf72M8Ub1DcYTSZGNs8kTsa3ZMGvRb1VUWoKHEytjmxzuPv3/ICM0x6DYYn/PBEuUKmQ7oxTUWTi4NhTDEzmMxwcVDCHw5LbOP9whngyWPyXJUItJG+ZhWwJsllpLbzOKL9LK5cDrxve2zy7MVTwE+ek5ld6dZREhknUDLjm78X0dq/+Lo7q+pIzkGbURImGc52iTh2UcDAmPiWxBznusI43SniB3+cH+vmas587VQYP34yyKQqIrh5C4/3XaVOmvSLZj7T/+/awWNjE8cMDO7ZxbPWyIxiDkEuJhGWVco6cy4fyZSoAE6HAhvH2ldzDVrmzvRSAkuOiyneiC5O909Q8U9Ku1YeahNxrFMeI+vrqEgvsRjklbMhZqSVCKMuAT1jAivYRSMYFnGw1YvzfXNut0YdD51G7lfpHPKlHVfygwruCmMOPZ00mZplPs5zlfyKxBza4mroKpthXHVlTk3buMJKqFu2QV23iv18OeZ4d0IURfzoRz/C9ddfj5aWFpa7Ic2vn/3sZwvShcuaUvLGG2+whNNbiV/96le4//778b3vfY8dy7e+9S1cc801OH/+POsZjcf+/fvx3ve+F1/72tfYhXzggQdYX+mRI0fYRc0E0ng/Aof+AM3qq8BFtQAQ1FXNAD3SIJppFeo5y6iimtLcs2CUgGip1CeunRG/H+zrZgFpeXUdSssT9PYpSCRQAFtbK0dwhXYDDFrSO8puQ+YPiqydIPqzaJFrGxLgtEhwmDPb3NOiSfRpoo9ThdZqIP0J5BwUaDji8pileSpUF0qzOgbx1yoat25T4+q1ZOcuYf8Z2ugrv9lPdYTYUkrsorcKN+92omWJCY01cxurKVcYL74+gT3b8mDQZ75YJrtWifA3H6uFRt0JkwLXymSgyp4/ICZlhnX2h3ChKwiXZ+41hXlq9rgU2LdejX3r536mIIFaHMoLJGxu4lCWr+xaKQGxGQNhoDlKp+aNVlnUlkSWKVEcfX++cFrerO1envp9l1dwjBFBMOlVWF7JY2BSnG1NSIR8C8d0eeym2Hufpgi7Sc3aqKOD0eW1Nnh8YYQEMSkjZGTCA5OtAAa9Dktq5gRl00Gr06G0vAJq0tdSANroB91T0FmpVZJjiziJ4Kv1RhjyEvQ1pQETHj7xIjitAcalm6AtS1/0yBQ0rtRR7k7Z2Lf/KeJSxULSQDsCB45Bs+UGcLZYIUl1w1pF7xHZnFBrCdnPq4xWGNblbn7IBMT80hmM0M5offV1d7JCW1lVNcorUhsxJBt70WtDab4RViOxgLJb/3xBkW1go7V1pnwS2odktkg6B+l4UHt6hEleU0hremITn4WC4pj4WIYYuW6fHO+kW0dv20oMNWLN0k+xLaHpcKZLhFEPVBe9dXPD1evUWFFH7uJzxzDplnCmU8CGpdk5H2YSc3xgnw6/ej6EojxuQTEHra3JdM96RyR0DUnwh8DcHQnEIM+VvEE67FsXe240jsndvNhBBS8ORfbczSkX+uW8PmnxErxB2W2apBauWaXCnpVzr530SHj5rGwkcUVT6rVsZZVq1qSBWGorq3gMToosQU3XP9GYL3Hw0PIq5Fnmnz9JtxCTNAKLUY3VdVZMe8NJN+k0rrSWQkYOKHQY0Vw7f9+aDEaDAWVlpdBplcUcQjiMoM8NvdkmOz6LIlwDXdCardDbMndppzXEc+oV8GYHDHWroC2ZETvOIWi9Ule3vKNjDomuda41wN5FiUBJkliyi8hKK1euZMkveu7s2bP4wAc+gIceegi///3vs3pvlZRl+owGIgmN3nvvvbjnnnsULw65BAWd69evx3/913/NZgkrKirwqU99Cn/7t3877/V33XUXcyd57LHHZp/btGkTVq1axZJomVibjp05DGPr69Bs2MeqrwuF6J2W2wcMb48+54nRYXY9nYVzm8Dx8XFMTU+jorx8nh5YItDQIptWul4Lydx7AyIOXvQzJ8XGMh2clrlFZnRagEnPLYg6/3aAkmv1s2dCzOXo/tu1ioK4z31nmlWUvvGxt9Yml5JHX/9+D9a2mHH9Lid+8cggfvbQID7zwQrs25nFwpujcaUU//yjARw548V3v1QFu1Wd2EGMXEuzbJ3MJfpGRdbqsmTGnSvX1+rBV2UXpw9dOaedSpVYIn7GOz/SZ5/pkxPN6RxXXzoThicA7F3JxyS5iSGxGJvEZDjbPoSAz4OVzTWLOrbcIwPwjPYzR0iDPZ8J54+dPwK13gRHbWoTiKQuwudeZy6OhrrVc88LISZ2Hl1syRVoXFH7xbvdinuxY6FIzDF65CWY2g5Du/MOcPmpi1JKQNbzxFJP6CL6FmB0eIhdy7z8ueTe8MgovB4vKirKFImP52q+I4bXkbYAE+xexpLsc59N7A8SEafk2J98zPG8yHSjPnJN+vmDxL7/7y8F5FmAT9741qqrePwSHnw2iLWNPGuLfOTVIF47JeD912jRUsO/7WOO7z8WxMC4iC/crWOF33jQuki6W8Y0xgCXAj2jslZvZYFqUa7V/74sC9i/N6oDrm9cTubGtwhTLHa6V9bHK0zDJnz6uMDKydfEGTZcypiDzD6OnO6G2WLB0urMY+FMMDXUB8/kGPLKqpkeqRAMYOziSWjNdtgrMydeSKIA77k3wJvt0Fc2x2pgMwOSd3fMEVm3jx89klCfbSFwuVxYuXrNO+I6LBQ//vGP8elPfxqPPPIIdu2KNXZ6/vnnGYmJckDvf//7M37vrFepn//85/jFL36Br371q/iHf/gHlkiiAPDOO+9EXl4SO44cIhgM4vDhw/jCF74w+xwFT7t378aBAwcS/g09T4yxaBBjLFX2kFw26BE9qAliQSXU9S3M+pUSRfTZ9G90PlHWk5n/fMQRMeZ5nWk2ux3vZhB5Pl4ELtnzFCjS+0Y/HzmW6OepIjA11AuzwwmdyRpzjLa8/NljJxfIqclJtlhElgUl50TnMTAwkPDmT3VOk27aDIdRMtMzz6skWA0cprwCXN4Q7DOLHn1evlU+1+hrluy6Z/p8wu8p6vlMvqdj7RLebBVx+xZip8hX8fkTVF2WsHO5fK36+/uZdTQZCkR/TyRgGrGO9vnpeQHCzDeR6pw+eK2R0aMjx0naYx6fCnYLn9E59Q6F8Ltnp3DbbgtKCzQZjT1ie/3X/w7gyCkPS5Ls227Hvh12pkGxbb08cUcfSzgsQq3m2Pv88IFOnG934yt/3QgNa1+Tz5XGI10ro9HIXpfL7ynR843VepbE02nmxln0/XSmzY9pr4CNLYa3fOw9/FqQVe4/f4eatVnS39I9GLlWyb6n+HNK9Dw9t3u5XJmmX0eeL7LJxxg5pOj3aSqJzAWpz6ncycHrp0opndvc8xSHznd3UeF4Z5AFwHXFmnnn5PGHcLbbj/J80lXTJTwnYoINjPtRXmBg7ZERVJXY0NExxsZYdLUx198TBZ1Brxsi5NcQG8ZRt5y1rUb/TSbfk75xY8wxUoXWd/QpVqE1LtuW9dhjvwsFmLB59DmR7tll5D4WShZzSBXNULdsgpSrmMNgveQxRzgUxPRQHyz5RdAaZJ3PCCjxFTn2YCjEzlsg9Xm21KkWNeYYnSYBfAFFdnk+0XIS0+dy+US4feFZYW/6vEIb946IOQ62SjjXJ+KWTaQXJq8Fz53gYTNKWN8gpYw5iEkjiCrGjAmpKYZQcOwqFe7czsWsk3KihjSpuIzOqWdExCsnQti3QT2rlap07E24RDz8ShgX+wCnFVhRq8IVy1VwWDg0zMjYxo4ZicUc9P5kROPySrjnajn2fKtijupiWQ9NJZEUSWysR593vjvE4vGmCv4tH3uPviGwmOCT180lwHIZc+xZQbp8cgwReb7EnjjmIMZic5mymKMyn56n6xt7ToliDlFS4UhHCPlmoLpQPe+cpjwhnOsJorZEg3yrJuE5TXnCGJ4MoqpID+2MExd9jkHlQU1J2aJ/TzqrA8FgYHbuojZ2VmzjuKxjDkPTpphjFIMB+E48D3VeMYwN6xYQc4gUYIDXG97xMcdlDbCF4cEHH8QXv/jFeckvwpVXXsnIThR/XdIE2N13380eJFb6y1/+krUTfvzjH8dnPvMZ7N27F+973/sYbY36mxcD9Ll0U8SLpNLP587N1/shkJ1motfT88lA7ZJf/vKX5z3f3t4+m3mlLG9JSQlzL6KMbAT5+fnsQbbcxDyLoLi4mGWxOzs7WSIvgvLycpjNZrS1tcVMMjU1NYxx1draGnMMZMlMC3NHR0fMRLJkyRL2eWRFHAF9D1SZpuOLnC8JIQreaVTq9CzhRNc0AjonOk5ayOh5r8cDvcHAGHZ0LD09PWnPKZI8I2RyTp2DbowOdmPKSdpj8oS5eskSjE+6MDTQjdYJVdJzIlBAR8dJjLX4c3qrvqdAMAR9sANdHcCITj6nC3310HFelOn72PvQ8XZ3d6Ouri7mnA5dEDDmMeLPb6uGe3oK7W1z52Sx2nCw3YkC4wicRtdsFcxsdaK5upB9T62t8jmd7wph/3k7/vz2EsDfrficznUG4Z3qwKnTenjKtBmNvaNn3HBPBVFbWYTb9hhmr82yGmBiPASzae57CoZEPPTEIGw2O/7qz9dgeHgYOm4Kra0cS4BFvicKROlvLl68yD4z1/fT7584xRJeG1da2PvfeOUSXLlBg66uttnXRo+95165yBhgNo0FVosZenMJHn+xF8uqvLNGAonG3qsnAoA6Dx+5tSJnY29NaQdrGewkHQ4Sya2tZfNk5Fol+56U3k9jM/fT1HBu76e6IjMuXOhEa2vi74n0+cbdIpwWDk2NDcwNbnq4FwM9VAXnYDOpZ8+pvbMbU5MhwKuGb8qQ8JwElRYBlQ1dvUNQhd2zlWraOJMgLY09qrIt5JxSfU9tnZ3wjA5AhR7o7floal4GAVzWc3n890T3Ewt0g3rYRB2oZpDN9+Qf6YMU8EKaHEXVuitgKa2aPadIYubdjlzHQn/KMUc4GIDod0NnMsPtCySNOUgA3+vzsU10eRmxv7hFjTna+z2YGu3GVL5mdpO2fibmGB7sRuvMYb6TYo5gMAS1twMdF6lNjsSFOLQNNsBp9MCONDFHqwRv2Ig/u7kSY2OTaI1cABJst9hwor8IxcYhGPmp2USXjeaxioLYmKNXxPmhQnzoeiemx5SfU2svMDlyEafOcCiccatWOvZOtYfhmtCgoaIKa+s9aG0dZs8X6oDhodjvicwI9p8Ko7jQjlt3VzDXcyk8hQsX1HKB9RLFHE+8eJG+HjRWyMm1PeuWwO12o61t7lyjx96rb/ayBBjvV7OxpzWX4bVjI6h1jjN3yERjj+LCw60iLLZ83LyzKGdjb3VhK2OAtbYuTswxMXM/jef4fmoqo5jjYtKYwxeUmM5ovpVDfV0D3J4A/GM96O6UWyatxrmYo7O9G66pMHoCarit+oTnFBS1CPB56OwZBkLTswkfcpAm047FniM6OjrhHh8GurphcuSjsWkpRBWHjvYcxhw0/wa1sAe57GKOjg74R/sh+dyQXBOo2rgLlsKSd3TMIbdA5jgB9i5qgTxx4gS+8Y1vJP39vn378B//8R+XtgUyEWiQUvBH2TiaQGiyuv3221lm7oorcuvgQAtSWVkZ0/XavHnz7POf//zn8dJLLzFdjnjQTfzTn/6U6YBF8J3vfIcFm3SjKq3G0g1PARq5FaWrtIidJyEEfFAvmRPpWezqUSYZfEqCabRatjhGg14/MTqC6alJFJdXIBwKswRYpPqltBpLY4Im1HikOidyr/MHBabjk805kStM/wRQX0QaXHhbVGMJb5wXcLYXuGMrudeoEBQ42b1TLc0GDBQQxVdjT7YLGHUBV62Wn48+ltY+AT98PIz6UuAj18lKoP/7VAAnO0K4YrkOezdoZjUijrcG8cLRED54nRl2MxSfE42Njr4AE8WPJAqSnavbK7+neUaIdWQsiH/7UR94XoXTFzz43j82oCg/troaub7BoIi//qdzWN5kxV+8rwqBgFyVN+jVMa+nKhDNL9R2tBjV2Pu/1oHxyTB+8E+1LAEbGXujE0F85buD2L3Rgo2rzPj//msY+7aasbROB7dXxJqlevZ5Lx/x4yePTuG9e83YvdGYdIx94+dumE0qfOp2a07G3h/fENAxGMZH9qrlDc9MNZauFW1wclGNfavYDSQ8e6FfwLIKHqV5GjYmfcEwDrWFmBbY0nJtRuc0OhXA4LgPYUGA3axBaZ4JWq3MrKE5i67XYjLA6PWuwR74J8dgr14CjY7GDhkXxCLROVH9j+fVi/I9CaO9UNsLwekMCI8NMHdBGC2yRuSGfeCM1tlzmpiYQEFBwbuChn8pY6GFxhzC+TchknZK/Zq3Z8wRCrLxHv8e9HpynPZ43CguLWPzPCXAGCtjkWMOt09g2oC2KA2fTM5p3A2MuICGYlmD8O0Sc7x2hkS7ZYFyaqULhDnwnAQ1lzrmON4uIRBWYfPS+Wzxsz0SHnldheYKCTdukk/2F88LrC1tUxOPK5rnvBiOt4k40QHcuVMNvUZSfE6UUBsYE1Dq5NKeqydA7oYScx8kDE4I+P0rMpt4YFzCF+7WzmppxV930qn8wWMBrKxT46q1OgSCAltbIuvnpYo5vvaAl30/f3W7LmbsDU2E8bOnw9i5UnaG/p8nBOxZy8NpldhaUV8qf94rpyQ8cySE27ZyWFHLJzxXalH9wRNhlDk53HKFNidj77E3RUy6Rbxn+5w+759KzNE2FEbHsIjV1WoU2NRsTBIb9FB7GOV5HBpKNBmdU/+YH6NTIYQEahPWoCzfwLpbIvMV/d1in9PkQDcC7mk4qxqg1miYxlZ8CuBSxhzs/cb6GGOM0+gQGuxA6OhzjJ1Mx6bbeC2LRd6JMUekBfLosWOL0gK5etWqd8R1WCgob9PV1cUS38lyQZTkjY6ZlCKnjfoGg4EFLOSwSAObBjv1bf7whz/EmjVrWPKpuTm9OLwSUBaZbtT4xBX9TNnkRKDnM3k9QafTsUc8KKMer0kRvWGKIDTYzqzGuaaNs8mDVK8nJNO6UPq8GA5CCPqhMc6/MegY4hclv8eFsb5OOEoqYLTYZ3+nMxhgCIWg1epgMBgVHXv8ppEqEnP0ZGXnZNKr2SPdsUdAji2kAxYRqx2YFNA1IqLAyrN2hVTHeKSdNtckpkn25OnPKd2xp3reH+Lh9hOdml6jgoG9bC7QYuyTmb+NPtf6cg5L1fJz0WOIApqfPhlkrJi7d+vYexLqytXoHhLx4tEQ6st4LK+TM2BrmgzskQqJjp3et77SoOj1n/6nNuh1HL73ZVkXr7jQgG/8bT2efmUcRr0aDpucqEh0fQ0GHt/+xzm1dJ1OzR7x8PoknG8Lo7ISMJv5nH9P/3R/NWuL0JLo3AxYcAIOU26JibASI4GCZGqdWForX5uTrX4UOdXYvsaAwjweS6q06B0O499/OY3brzJhywp9zDF+4T5bRvcT4fUzYRw4E8aedZTYJLH7ud9Tt1AoTHpP/OxYoECD7sFoV7WRKQkPvCTiimYV1tbzeO44CdcCN21MfZ8lO8aeMY61QNTFTaNKzymCRJ/JXNJUHKoKOORb1TDP2MrT+ZkNWmxu1DBtwEjwnezY458nkVt6azLloLbqcW0QZQXyXEVVzcgmR+mxDw8NspZyEsKPvkdTjT17WTUCVjuEgB8T3eehNpqZay9BDAWhUmsghgJwd5yGLr8UurxiBMcG4Os6DWP9amiszoy+p3TPixODEI8/D6G0AVzLFeDziqBp3gLh7Gtyi/7MehL5TCU6kO9WLCQWWmjMEey7wAx1+MYoZ4wUr89JzBEKsDGrNloUxBwGeKcnMT7Yi3zSozHNiQfqDXqIksjOn67hpYo5SLA6EZLNJy6/BIte3nASeicoASSh1MHPShwkO8YDF0SMuYC9qySo+fTnlO7YUz3vDfFMW5HYRTRnGhXGHA3lEpMpiD8WirX+8KaIYgewd/3cOlNTomJu3JSIqSvhUVkoP79mCY81s7lIleJjJ43TisL014Dm8X/5FQmIq/DJm+U+1bJ8Hp+4RYtXToThGBZh0M0dZwSRc7IYgfvvnPvuddok1zHAYXDKiHoVvVfuY46/ut3ILk/0ccoJEfr+RIQEkoXgoFEL7NosqZCP+Ww3JQlV2LpMhYoCLWudJHH8X78Uxs1b1GismDtG+uhP3qTOeJ3Yf1ZkTL6tS1Uw6lUoz4/W6FQhJMjXN7IGJ4o5+scl/OGQhJ3LSMeXxzPH5CTpVSuyizk6RyjpCVTmK3t9Jt8TxdWcihJcWpTlyaY8BLrudrMWVyzVMG3AyH5DccxB0hESSaBoWDukSR9mEg3p5qtkz/f1D7JrXlZaonjsOctr4JueRNjvxWRXN/QWB2xlNbOFCU6tYYQNV88FGArLobM64R/ugX+gHeaG1VAbrTmNOYThboinXoJQuQxcw1qoCyuBhrUQLhyEyupkya93esxBiXh65Po93y0QBCHl905jI8L6zhTqXGQif/vb37JKJzGvaGATJe1LX/oSbrjhBvbzww8/jM9+9rP44Ac/mJCZlW1WcO3atXjuueeYCFpk4qWfP/nJTyb8G2KK0e+pNSGCZ555JoZBphTJbux5x7luHxPMiU9+KUGQsuF9F2Bo3grOEKcwnQLuvnaEXOOwN6wGPzOBpAJV9zm2WMVObGaLlT2iMTY2zipP+c48RdeIKte5AGksGbQUAMRex9FpEUc7Qqgp5FFfIg/nhhIOBVYV8i3przndV/JitvBjpMIICZMa5u9dGHYuV2F7Cy2u8z+MNAZe7yxFuRvYvHTuebK5/veHQyhyqPCRfbFuVrQgk+OR1aSKsb7etkKL1fVqnOsW0FyT+BaXKzmxAVcusGapEdoEhgTk+EiPXOCPzw7hOz+egKgawW3Xz4h65BDGmSRLPArzNPjZ16pnf/723819dntfEF//yTiW1WnxhQ/lo7lWN5uU8lEAy1yFssfYtIgJl8TcrEanJPz+1RD8IRW+8oE5a/aty3jUltDGRZXyHqSX074rMuZHpun+kv1FyW2UxoXZoHxckAskuVHVFs0J4+cKr50LsSDviiYN0+SJR7ZC1GUFRuYSR8c7Ph2A1aRd0JwlyxQpO5agz8O+M43eCNdgN9PN0Jot0MzM8YGJYXh6L0BrL4ShoAyC3w3B70VocoQVN1RaA1R8Zku3GPSzxKjomQJntif8e5U1H3x1C/jC6tl1QVPTAs5kgUqtzXoNfLdgsWMhpddbt+12xa598Qj2nEV4uBv65duZo6hSuLvPI+ydhn3pRraJSgcyZeDV6nnmDFabnT2iMTwyDrWaR55jrmBwKWKOKY9ssBNvONM/LuBEt4CmMh7VBXLMtKycQ4WTnJ3TX3dK2FPMkYu5khU9BECf5JLvWSWzShLFnyFBhYO95ZgQgBVzyxrcPgnff1Jirn63bFbNc8/evFSFYrusURXBthYOK2pUzJ2wItagdBakQUpkkFwKjNO+mIpA+bb577ltRe42y6+cFPDisUI48iWszr3BblK3xxInhy/fNxdQfvHuuf+f7xXw82fDWF7D4T27NKgtkd+DincUh0ZclbPF6LTE3ECnPLIj6KNvyEnRT94wd6xbmlXs99HGNUljDlVszBG59akoTM+TO6pSHLggG+/EJ8AWCmL/kSEPJb02NVAhfv5r9FnGHDXFRlTPKPCMuwIsmbaQ+Uq+pZUdS8DrBserGfN2arCbBXs6gxmaGWKDd2wAnsFuGPJLoDPbIfg9EAN+BCaHmdg9sbMiDsJKIQZ8ALHGPJPgLHkJ/55zFIGvbAZfIhvH0Gs09avBmWxQJTCEuxxzvPsgSRJze0xUFCRkw/yKIOsVgqqZFOiRo6Lf72dujN/61rfwnve8B05nrJsFUf+JuviJT3wCuQQJ2t93331Yt24dNmzYwD6f+oopuCRQuwG1SZKmBoGcBHbs2IF//dd/xXXXXcf0Og4dOoTvf//7GX924NybCPIiNGuuhuQaZ3biVLGXgn5AQ5tS+UYlV65MwBw0SOKONhyz7W6ZbZ71eUXgtTo2aSUDiT96J8ZgsDqgM5pQUhuVdUkBl9vNghglCbCIxgQJAS9k4vL4KckVYIyu5orYc6KNOrG8iAUVgYZXsQSYEqyo5LCiEjnBS2eAc73Ae7dRlSfxaxIlvwiBoIjBYdK1y8PmpXPnQlWuxgrSwUj8d1euSRz5kvbUuqbk1/z7v3fjQncY//jnNhiiEj6UrPnZH11Y36zDmqbMxi7h8GkvC2z+Yq7LmOGXjw2zJNC9N8dq8EXw/77TwZbyv/l4ekfVfbsKoZKmcdX2HEc/STA4GsL//GYUt15th4pToaFSB3VcIvaBx6dYQHLVhlimZG2ZBt/9W3lHcKYjiDMdIdy8w5jWxZO0Sb79sA/N1Tz2bdTh1y+E0DEg4u/u1eOqNWq09YssyI1sbOj1D78WRu8o2aGrZsdLonsw36rCJ6+f+3xqyaV7msbm7w7Ihgsf3ZN40xQNYo1dHJTtxumliyFLYDPJorTxCIYlHLroZ+5sS8s1GHMJzChDm4TFGQ+ZTSn/32mbG+fx18vrC8AXCCLPJldpk6GopDTh8+Qo6Rofhb2wBGqNnEQa724Fx/MoqG1m87TOmgdLUXkMm2Y2SaA3wb5sCxM9nT7xEquIWlu2IhME+y8i3HYUoLWI2vFLG6CtXz3/mlCAHNWqHwFfWJXwfePbed6tuFSxUODoCwiZjdCsvRri5AhUFgf7zohhTt9tZHyqdHHWaGkghYLs5qX4RY45xExDDuicJVCbrCkTs+R06p0cg9HuhMFsZY+0xyZJLOagCrCSBFiuYo5Jj4BjHUGUEpO3NDb5azNyKLSKyItKdlH7mpKCG2FdXe42cc+dIBc+4O5tyQtvyeYtr1/E8PA4DNo8rKie25xSkoPYvFUzLK7496JCXiKQWH1EsD4RfvJ0mBVxPnOrJiYJ5vFJ+MPrYWxoogJOZteGpqDzPSJGp1TYN8NgjuCxAyFYDMCOVZqE4+onTwaRZ1HhpivSa/NtXcZBh2k0VSbJ7uUY/WMinngjjD3rid2gQlXRHMMqgiffDLO1cWNcnFdfxuFL75PP6Wy3iMFxCTtXzrEVk4ESnw+8KGJVrQobGjk8tF9kia/7b1bh6tUcWvtjJUWoffTxQxLGXCr8xV7qwkgec5RQAffqub+9K6oL/Lf7AY0auGd7+usy6QG6R4Fdy+S/yTXoEpHDqylBQtIXJDf6IIrsPJaUqDEyLcBh5tl+I9OYI38m5kh0rVzeAILBMJzJNhEzKC9LHHP4PB64pyeRV1jE2hZFQcBobwfUWj0Kymug1hmgM1thcc7F4uQIGTlGjckKx9IN7OaaPPsGeKMFtuZNyASB7jMI956DihyGfW5oKpuhqZi/x1RpdNA0rJv3fCQh9qcRc5C0Ra4Td++eROB9992X9jXZCOATsp5CbrnlFpa5/qu/+iv24Y2NjSlfv3LlSmYRnkvcddddTBeDKqwkzLdq1So8+eSTs0L3JO4ZHQRt2bKF6XL8/d//PXMVIO0DcoBsaWnJ+LPF0T6ICEKcGkHo1d+Br2gCX7cKwdceBl+5FJqlmbHKRJ8LYdqoTI1SDyOM6/ZBW1LLHplCa3GwRyr4XVNwjfSz7L6lIHFv7fTkBNM+IHemyOJJgrRKwXSTRkdndUsSfoYnhOGpIKoKDUwIMhGI0VLqUCPPwiesxqysTl9xzhVIX4wquIYEMVORTa6WJavGpgLHSdhSN46W5tgNEwU9t2/L/Uqfb+eZW1I8A2x0UsCBE34WWGWTALtulz1hIuTJlydYki9ZAuzcxTkh8nSwWtVYvYyHJUojLgI/6cf5Rdht87+Ej//9Oeh0HL56fy1LlGi1yhaRnoEgjp3z4Wy7nwnMv/c6B27dHctS2LHOhMqSENY1J2dNvHDYj2MXQti6Qo+S/NTVNGopHJ8WMemSj3HnKjWWlIvMCZSuU1Nl7N8/8aaAnhEJW5o5FNgyuwejNyNN5ZRMS75pisaFAeBEF7C8kjZ1UARyNKVNidLvemWVOuU1Gp4irUAVLg5QYKpGY1kGZeQEiL9eoxMueHwBWIwGaLVqxmJwudwwmSiJmb4iGvB6WIt5KOCYTYBZiypYcksIhRDyuMDxsWNVX1AOjdkOfoYRRkkFulrG6mVykiJDsEIItcXaClnZnS8oT37+oojQ6VfB2fKhjrI2T/ja3MmHvqNxqWIhcbgbgksHbrgHoVcfAt+wFnxZHYKvPAR10waoE7Q8pnw/9wTCg50Ij/RARTIHa6+BtrKZPTKFzk6JgdTJAd/UOFwjA3KbcV5hwtdMTowzFkbeTOKQXltVUap4vlAy3427QhhzhVBbRJqmid+XmF8lDpJQSCTRoMKa2ksXc0x55ZiCGC/xKLLLTJ9skgGkybWhehQtzbHXioozN8cxv3IBSjbRUhP/VQ5NSjjZQWubKuMEGB3rlhb1POYdjSGSC6DnEyXAaB/dPSzA41f2ecSyr3BMQqueP8ap+ETvR+2B0SAm1r/8KoDiPBXu2KllWqzpCl8R9I2KaO0T0TEosu6Am7aqsbEpdixuaFJj2iOhpiT5OrT/tIieUQnrGzmY0xA6iUk46ZYLW4RtyzjmiBkpKi2NIyo9eZjY6MAVy2QJksxijrn/N5XFJtZS4VyfHHesrQWWKSBOsQS6jxzUlSep1tWljjl6xwSWILs4EEJFvhTjRJ0pEl2roTEX/IEwbBYDa5GmpM+U2wer2cAMetLB53XD63HDGswDb1CzYputoAS8RotwKICQzwONPnYwmIsrobcXQGOQk27EFgMPmMobwGVYUJklfvBq8LYiwOIEn1eS/BoIIYROvwa+oAJ8SepA8nLM8e7Dj3/840V776xF8F988UXs3LkT7zZEhO3GhofgsFmZuELo1MvsxuUcxQgeexZcST3UFY2zLDAlCPWeQ6jrNDh7MVSUoa9fAykUgER2tfrMJ6B0oI2Od2oceosNfJJNVX9PF0LBICpqYgWhM+ndJTFMSjQm60vvHvZhaCKAxgoTrMZLF1RmA1r8fvGKzO66KbO9Rlo8+GIQ3vGLuHZHA870cNjIApbEi/bQhMz+qUyikbFQ9A6FkW/nmJZXrjA6EWKLV0He/Mxhd78fZy64ceWWPEVJqVTj6q+/cganL7jwm/9eC3Ncguxz/7eVtdCe7/ShrlLWJlOKtp4A/vXHQ+z4PvP+QlTHsQKi8aNHpnDyYgD/9xP5Mew6EosdGhPQWKVsnFMArbRFlRhhhy6ImPCIuGqVelYbLNm1Ipv7185I2NioYoywbEBj8MHXAJJNIQZCOnSPSnjjooRVVSo0zLRrLATjbgEjUwJqCtXonwjDaeFhkYX1skb89aJKbCAUgsUkB4wul4cxNR12K/KdqYsMBBrzIWpx1CVOJocDfvAzYrSJEJgYQnBiCKbKpbOtZcQSziYRRuxk0TUO3ilXjsO956EyO8DbC2NeE3jpl+DsRdCu35fy/YjJRJXrd4MQ61sZC83GHIODcDjsrEAWOv4S+NoVjHkeOvwU+JoV4MtlIWWlCLYdQ7jvAvuuqS1WW7uStcpCErPa9KQDMRF80+MwWPPYpiwRero62Yavqibzwp/SmONivxej0yGsqDbDqF/YfLHYIDbub14HSuzAnpW5fe9fvBiGON2Ka7Y34EI/j3X1crExEQYniIMqs4sXi/FUYFPNk7hYCKiARO8XLQ8x+3mjxIwSsaKOV5SUSjWuvvkbH5Mm+D/3GWJYWqRf+V8PB1ji71y3iKWVPN63R5kTLKFjQMAvX5Tb8e65SgNninX6gRdCGJ6Q8KmbY9l1dFwuH7WlKovl6JiVtqi29kk420tOicBVK1Uoc6pSXqsxl4TDbcCmJcoTUokK0L9/E7AagJs3pH/9xUEJx7okbKhToTJKuyxbUMGNtO5qinj0jgoodvDMhTpbJLpW/kAIobAIC/Ub0ziecmNodAqFTiuc9vRi6sTgDc1oNydCKOBnxbj49vMI/GR+456EpWLJbFwihkOKWtvjIZKDtM8F3l7Ejivce4HFG9QSOfsa7zQjjnBFVdCu2PknE3NE1u3DR0/BnGMRfLfLhbWrW94R1+HtjKypJe/G5Fc0aAMSaW/Urt49+7x27TUIvPxrSKO90K6J4vymgbqkAZzRxnqiI4kzH7k5eadhWntNxpovaY+f45gVbioUlZax/tpwOPlkqhQTLj8TgHRaYzeC5fl65Fu1b/tANFK1aiyVF99co7ZIhf6gCm0DEvaflWAzSVjXkHjBJt0HFnC9X6u4opgMVClldtpRQU950SIwzhzJF8/v/6IXbxybQmOtCTVJxPaVYs1yG4xGPmHy7p+/2MACvH/8z05Ul89PSEy5qOqW+NzrKnT4zpeU9cpS0M0C+bivxm7m2EMpMtFnqyvl2Of9/DlqSZCQTqaEWiXP9sgMgvwE62fnMHC8E7hqObUZJ34PYiRQK0ISHed5oMRxsR1w5igWyDPz7EGoLky8sZh0h9A74kddqZGJIWcKYn3RIwKTyQBnnp1Zl/f29TMB2lRJB/pdsuQXQZ3id4SwZwph1wSkcBBhvxvB8UGERvtgrFkOjSMxmzIZgu3HIQx3QbfqSqYjFmo9BM5WAH7Nnrnj1eqhu+I2IEX7/GW8NbGQSqtlTC1AB+3Ga2efp0Rl4PlfQBrrg2bVlYrfT1O1DLyjiBXuZlupT73Cxpppw3U5P35KepkcqVliJWXlCAYCbANHzoQLweiUn61rDkvsWK4pNqDUqYMxi/ngUoOYX/XFMrs816gpBMYleR041AbGHG5MQvB/8CW59eivbl74NaM1mEZbdLIo2u0xV8izJn/Ph14OontYQkM5z4TwF4LGCh5THmleiyKNvU/fpmc6rj97OoiKIlXCtsNkhU5idn3hvcqut5pTJWRR2c0k3K48jshEn62hTAVBAv54SNYQLYttXpiHrhHgQr+s20VM9niQnMLFAWBnS/IuCoq9ty0FjArziHlmoNiWXJIkUxAjNMIKrS1OPL5GpkLoHw+iqdwAnUJJhmjodRroo6YsYn5RUcDt8cLn86O8JPUcSvvHVPs10gFLhZBnGiH3JCNf0P+pABceH4SpbiU0aTqL4hFsOwJxYgh6ijHCIYTaj0HML4du2VwPLJnraLfewogfl3EZlxLvPEuFtwmSbno4Xhb8y3CiUPH8PJqoOr8cks/N3jNXoLYbSQyzPvB0oP7x0aEupgtSWaucLRN9jSgDTv/2jbhZ4BOfAKOgIdvkF1GRCeXOSxPI0ldO1avFwPpGHkN2OxxOngUsTRXJA5Hda3gWOC00+UX4fz+bhscn4p8+ltl4zRbUBvnxL7WiqdaIz31U5rDfd3sp1q2woroidmy43GF89qtnsWNTHu65pSzhuBoY9uPff9iFu28uwYqlVtwd9bpkAd7/+fR8nbE3T7jw1W/3YkWTCV/5y4qMkk+Pv+Ji4vG37ZYzSfdeZ81J9fqVEyHsXK1hOlhKUFfC4fN3qGIEdaOvVTSWVVJQqEJpEim/wUm6v+SKa6IE2LFO0qoArliqXPvLalBhW1P2Y3ZoUmAMBdLgUQp/UIQ3IDLNsGQaOdFIdr0iICYsaRH19nkRJkroAhHReEzGFjaW1cNQVMXaGKfPnoLo94DTmzLWliSoS+tZkMmZHayyq23ZDlUC1z7ozQi+9hDTkuKcpUwjSpOgvS4bY5fLyB5JrzcV46z5jM2X0fupNfNjDnLhErJzVEoGchej6j9p0KQDsSDIUZUE8isqE2vPKbl/CT1DHubSG58AozUg2+RX53CYGW9Qe+SlAJE0tqbuqM0am5o4DDnssDlUKHKQeVDy1+5aocrSVmE+/vP3IRh1Knz0ukvD+Pf4JXzz134sr+Vn9b6u26zB8KQ0jx1GLO3/+WOAtVVuWaZJHHOMiXjsQBDXbtKiLJ9j/6YCuTV/5Lr5i8+h82H85qUQmqs5vP/qOTMbJXjuKI1D4IoWeQt3546Fb+XIXOd4B8koxBocpEJTuQpVhVLM65OtoSur5QJYSZJpamAC6B0nHdrECbCDbXLr6sYMTAjyzGSgk/3IJcMLi0EFi0F5zEE6uqSvR5q3idqWM4k3CCS1kO+wMva5RCyCBSLS9JXsM83lDZCEMDiNFp7WI5DCYWbQQz9nCk3pEojmPKj0soaqdunmGPZXBBRrBPf/HipbAVQGMyN8qGtX/knEHJTul1P+uX3Py1g4LifAskSylkC2sVg/V51dCITJYYgBD7SMo5ObAT/V24aQ34uCJSuTtiFEw+7Iy1QPN+YalZTIUVVtqY0JbecSFwfD7KpcqgTYYsITUOH5c4VYW8+hpTr1d72yLvPzHZsS8P1HvLhqnQ4bmucWskIHB08Gjn8LBa1fxI6KFpFvqDGyR6JkWd+gHwPDgaTjqrPHhzeOTqKl0cwSYNmi0KmBycjjdKsPz+yfgsOmxsYVypxXnz7ggcsr4tarLDlboI9dDOPFYyEU2DlsaeGydpOKvlYRUOB/olPC1ub5wroRbGgAmiuSsx1JiJb07jY3KtfvWAgogXWyOwyLXoWNS5QHYsV5OuTblDMlE12vRCBWbHwCjJgrtHGPtDKEQ0HW/mgwJae8TXScZa1h+Q3LZ1vTyU1PTS5ITDSXY0KxBGNlk+wY6Uisn5QOvNXJHrM/FyQWUWFjmGmAqCF0n4UU9EG9ZB17njQjxb5WOQCTLocPb4uYQ6ODbtttOfkMYXwAucZE1wXWQlPQuCrt/Ei/tzscSdsXM7l/GyqsSU1nsgEV8C4OCjBqL10CbDEx5VXhlbZibFwiJzJSYVVt5kyWoQkJD70Wxu7V/GxLPoFa+ZK5Hi4G6JNo/o8uahG7qibBNB8MyYmgSZeUdFz1joSZ8P7yWpElwLIFOVeSdhu1R75xNgynjUNDmbJx9dopgbkmRhJgucCJDgmvn5NYK2NTBsaE8cmyRGto/7iEtkG5gJxsDtjSCKyuAcxJ8uTdI7LeXSYJsIXAE5BwqkeA06zC2gyMKyoKtCh1Kos5lMYbhLAgID4NTR06xJSNrA0kWRMOh2EwJqc1jrSfZRpf+dVyNZ/WdNIF05qss91BKk6OscyVTSwm0Vqzc3CndsdoiQV1EkMd+cU8I4IIXaeh0mhnE2Ak+SD2t0GiU1dlXvh7q3E5Afb2xeUINktcCjcK1mYZphJC7ga7wZEPTcAf0/9NIs1COASTbf4kZ7U7FnSNhoaGmCmBMRtl+DRVjLoiPmstgbcbpjwixkcH0WsvQlNF7m9LclrqHxUwOhk7bj9yk7J+tM7+EFweAcsbFrYAkaDq9/4xOY3u2BkXigu0KC7QIT9Pi4f/Z+08XZDocbV5rQM//eYKlJcs7Liqy/T43pdrMT4Vxhf+rZdpdyVLgI1MhHHgmBd7tpqh13L4+4/mIxzObXXqiuWk+cFhWc3CNlrR1yoSJJ3tkXCsHagpItZY4r+jvFiqVt+9q2hDmDr5NUI26iGgYkYbZCEgl7XmCjXbfGYKJYGoyxPA2JQXJflmjI+NxlyvRNeUkl/0+8h3Hg6FmGaiwWhireOEieFB+L1uFFfVQZOkJYGYuOSOF4F/rB/eoW5WhZWFxaNea7IBOWrlSAfd5pvYv5Kfqs4ChNbDEAY7wFctQ/jY84wVHODeecHoOxmXJubIvMqfDsa8QjbGo+dHn2sK1HxvtMSaiUSKbrmY78yG3MYcdPQNxXyM4/Q7GcR2Gh8bxuBkIaoK+UVhXg1PyjpU0bh3t7LvpXtIZC12NUlazZSCxOn/9p7kc9X5bgElTo4J3VNB8KsfMqSMOdY3qVFZxKHIsbDjqi7m8bm79Jj2iPjOI1TsEnH/HYm/h5FJkSXdNi/jGYPxEzdpFUsPKAUxv0rzVViS2FxwYTFHr/yoL0nOAKPzSZb8Ily/Nr057eAkudjKrpMLBQndLy3nYcuwQEzznJKi4IQrgPEpH/QqL0pKilPqLFO8QXIlJIofnfzq7euDxWxGYaGcZBoZGkQw4EdFTS3r4Ekmu8BFdRV5hvvgGx+ErXIJtObY+ZjMeC4FiPGl23IL+z9fs5LtT0NnXoM4MQyuuBbC6VdZzBHUX5pOlct4d+ByAixLXAo3Cn39mgX9PQWd051nWQulsaAMWrMNBvt83a+p4QHmDkLBaMDnZQuY0WLNyTUikb7I5JwrDE+G0TEchjtA2llq2Iy5Y5mQTkF1ITk9LmwBHZmS0DkCrKlNraswOi3hzfMStiyVsL1pGs1NRThyUUShXYXyGdHO3lGRJQDouWxRWazGP3/SllTkNh2+8+sJDIyG8YMvFbOkz2JgeCyIz3/tIlYuNTO9LkLEgSjVuKosM+Skuv/AY2NM6+vv/qI0ZdLk6ddceOxlFwqdamxaYUS+Pbtp9HR7CD96zIMPXm9CS5yrGLVNrKxXL8o9uLmJ2hbI5j7x3/zugMT0+u7YkvwaRMliYcINjLrmt9C80Qq4/MAdm3LTrlsaxbroHA4hLEioL8nNht3jD8LlDcIZCqedsyhQLch3go+Kcun/ZqsVesPcZGRxOKHVG2bdHxPBWhbbjqux5EEX9EM9U429lKCxEm47xgTR+WL5uMjGnL45we+G5J0G5ywDT+2QKtqIXdYJ+5OLOZq3LOjvhWAA093nWfHOVFgOjdHCEmDxmBzqY+dDMYfX7WKbRoNJGeP2rYg5+sYEdI0IjBXSxKnSOuophS8ooW9c1uPKRH8pWQvZ8BSwoip1WzoJ2p/qBjYvkbC5dhLNNQU40iahPJ80juQ/JOdASkY4MtCPigc5Ov7tnSrosow5/vdZEgMH/uG+xZtnyG3xh48HsaJ2TqA+kRB//LhaaPKLQC1yTx0MMQ2x9+/RwJyimPvySQFHWkWU5nOoLVGlFMRPBXLbfPygiLt38agoiH0Pig3JBXox7sGtTbJ+bqLEFE1rv9kP5g69L8WWJ7qdcGQacPvl+yYa+y/IbZK3b0JOUBHVXXKhP8wSW7TnyAUo3pjyBOENTaK4OLWep1rNo8Bph46sRGdAzC+z2QyTeW7etOflMRZYsuQXIa881mBEZ8tjRS71jOv0pQRjvLcdBZdXDN4pFw65GVkGyeuG5JsGX1IDhPxyzKG99Me4UFxmgGWPRx99VPFrb7zxxozf/3ICbIEgh4vAqw9BXd3CJn5pehSaNXuSOmxcSgTdUwj73Kxq4qVNq9nG2h89owMwF5ZDPcNKcJSUszYcOubx4X4IRKE1L33b9ltPekUWiBYxQcrcXGeysj7TC5yf6QBJpYeRCFTdooWc2sYIR9plwc9yp1zx6h2V4AmQyKxqnpMO6S4U2USYNCq2qP/xoIjKAuC+3WqWmPnRkwJsJuDTt2gWFGx985dulBfxuPeazDOG9+yzYnyaNJgWb1yTUP77bytBY23q4xubCOLRZ4bR0GHDLfsy/KLi8NVv97B2zE++rwTPvz6NviE9/umvUvP/r9thRXG+Buuas9sFkT4E6cjQ7cXa3OIqva+eDOHqddqk4rgLBSXXqlPEW1Rk5DLYax9uB/ongAJrrNjs+nq6r5Rbv2eqB0biwsUOAeYcGGgU5ZnhsBqYmLAS2MgBOArUqphfWBzznN5oYo9EQV+y9UGtN8JclrneYk4QCkBoPw7Rlj+bAItAs2In1C3bWIs/17SRPcdPTLw1x/kuhzg1iuArv4O6eTMkzySkgA/addfg7YDg9DgEYg3SPMfxsFVaEPS64Z0YhqWwgjmeEvLKquQFk+bzwT52P5TXLpLAZg4w7hbhDUoocXAoSCGungm8AQknuoGOYbmgQOLgmeBkN0AkN2LVEMhhj3QbqwrkeZiKeZQMIKZvNFr7gVNdtLmnpIKKFS9eOCm/z00b5eN68CUJJXnAvbtUC4qpfvBEGEvKOVyzNvM5+sYtFP9gUUGulleuVqOxMvV3Ojol4rVTYbgRxrqm7Ncb2iN8/7Eg8qzy5x69SPqUwPt2py7k0Guri0RUF2f3fZBGKSWQ5JgjNkFKCdHTXRKuWKZi42ExQMk1SrCmijkyYbQdvAiMuWV3VH3UpdvUMDut5BwDE8T6llBs5xbk/BhBeYEZBTYdujrHFb0+L84BkgpxRXGJfiMVERKwxFPFHKTvRY+3ApLfDaH7NCTv1GwCbPa41u5hXyYddyTm4C7HHO8q3HzzzTE/MxmOqBs8OkdBjqqZ4nICLCeQvxCJNLsmh5lNOWZ6qHOBQN8FCJ5pGOrXZJRY01nzIBZXs9WOkl+EoMeFgGsSOot9NgGm1c8lHPKKyuTJ8m2a/CLUl2hQma/OaTLmtQtyVWlZuRxAZoKQABw4L/8/kgDb0kSVormK17PHKYim56SYCuOGJSq8eUHCmxeAXfWyPfStW1TIn6nwUVX46rUco2MvFIGQrHGRCqfbg3jkRQ8+eKMFJflz08PKRpmbLggSnn3Dg5VL9CiO+n0uQHpU45MhPPjoENYssyQVog8EJbg9AkYn0pyMArx5ws3a/b7w5+X4z7+vgtmkQBfPwuOqTdkFDNTu8KUfTDPG10duNOFfPiXflxMuEc8fDrJr8NLxECoKOaxrvDQiwfG4eWNmY41YjlWu+U5LRTNsgsXA6lodDl7042h7ENuyTERGg+Y7nUad1SKaCSgZMN7dCktReUJHvJDXBf/4EIyULMjQeZeExkGFjCzsygkkrK/deB1ziEz4+xyasVxGDiBJEMcG5DZV2ijkcM0OdJ5k5gf6hrUZ/Z3eWcRaG6HioJsxAqJCXGB6AgabE7xGnu90hrnJIr+k/G0dbxCo/Zpaw3KZIHjxjMxiWVFJ7NbM/pYKC8SwtejnEmBkSEJs3Mg8TDEHyRT+eVxudH0DcKILONIGrC6SCxd714AlvAgGLbBtGcUgCzs/2qdQciedV8jpThEHzgq4Y7saNtPc9V0+0/5Pxbs3zwloqeFjfp8LUHGGWPh9R8L48HV8ytjJH5Dg8i0su+L1S2gfENE9DNyxQ4u/ul0LmwIZD2LirV2S3fw7Ni3hu4+Fsbqew3UbeLRUy3EznffhVhJrB453ANVFqqSSCIsJuvVv25zZ31BxzeWLTX4RSnPQ+pgM/z977wHeyHleC58Z9A6w916Xu9xetastWnVZXZar3EsS37jd2M69duzEdtpN8qfe6ziJEju2Y8dNLuq9S9v7cht77wSIjpn5n/cbgkQZAAMQlFYSzz7zLAkCg8Fg5vvO95Zztjfr8OrFEE70hrGrdflVicT19LqVn1P9825MD/fBVVYNsz25pTHkdSMwN8WKIvgsuQOtFSGJrIUxFzAHyM03gjMlDzZsTrjK5wU1kCQK2uS5AizP+3sryD489dRT+PKXv4w//dM/xc6d8oDx6quv4qtf/Sp7LBesBsByRJSwcSYbjNd/mP1MtrEQhbzraAjzsxC8s2ygAZaCPlS2Sha1Okfx4sBFRDg4NQKt2Qqt2Q5zUfyMRu0IevpbTNArFkoVC8s5R0VFRXkntyRsm2srXyo0lAAuC7C2Rt6/Eqgt7NHjYC2Xu9uWHn/9okz09nUsPSY7xyz9fv0GEtVMLq+nIA9zfJT4xXPVURsf2NvZrsmpnZMul2j7Ab3vNz4Wn0GioAtVmBUt2DoTBsciuDwQxuSsEBcAi6KrJ4jv/XoW124248BWC9tvfVVu1/vkdBgFTm2cEPvEdBhjkyF2XKkCYJVlRvzeRzpQXLyUUvzpI+MoLtRh33YXJqZD+NyfXMIt+wvx/jviq3IS8fkPl0O30GZZXpJ//ZtE0AKqvkKLmrL47/R0dwQvnAzj9t16fPQW47K1v7K9B1+7IGHKI7cg0HOpepEqF5XsytliICzfB1Grcdpo7KGKhkIbUFWQ+/1J1y4tmsgpLBWoJbi2WAcxz+nelRqz4tx+9QZoFIgmzR/unjNs5UitY5oCuXRDDIfguXgUelcpTBXx7QuxCF45gcjMKMzrD4DPwSWSwDuSg3JSKLDoOskEaakdcvAiUJTsprqKlUP0muQdRTDe9in2s6aug5X55Pt6FTwzkMKBpMAauYOFiXO4yti1zB6TJAQmh6CzuqA1WWAuihcRshSXs4SbLiboFYt8tD6u9P1Liah8ay41lcmBrI40js+UXCPOQQGyLY3xrV6EWFdqCnzFJiFuWNBpTARVAjWWya68dK6okqSjJt7Fb2cMv8lm3Ka9RDkOCd1/8Z74cW5mXmLnMVa7dWhKRN+4BI9PUgxwne8X8fDrAtw+sGoyaj/NtQVxZl6Ey8on8aBgKP3rqoo1uPdgGYqLZY5A887TRyOoLOaxplaDsRkR//zrIA5u1jEHyVSwmHjceY0WLpv8OUucK98pQgHNyiIOZQnBobN9Iku+3rKVY+e1IT1Vyvs9+HKXzCUOrJMrFS+NylWJJO6fCH9IDqRGOTUFbWkjnkiVkMRVlpNwC4Zl2Yd07peUcK8v0ebV9Gel+QaBzM5IhoEMepQceuf6utjPBquDFU2wx4N+uLtPwVhUCVNx6r5Y/+WjrDjD2rk39yCYszQj5yD5BXG0Z5VzvIPxuc99Dt/5znewe/fuxcduvPFGmM1mfPKTn8T58+ez3ueb36f3FoWSYCERQnKvyDdMzVtgXb8/aYCh4Je/vwuhicHFx8RQAL6RbvhG+xT3RQMtEdGVGnCZjswC66JzFCVYVzvqSzhsqufSukZRmyNViRERiwVphlHLJP0fBREkagGIgpx1WiqU933jJmoR0Ob1XP3Dz/340x/40urG/PUPPfj2v3vij2WnGX/9hUKsa1LOcLXVG/DRO52464AN3/ruGP7qexM5Hd+pLi8+/OVL+It/Xrp2CX/y+Xp8/2/WMO2v3kE/HvjiWTzz6kxy6XdpyeK5IrfIf//ZMH740Cj7nT4yZY2pWi0TDux0Ys+WldVbooq1//jtPI5fCLGA5GffbcUN2+IDFLvW6vDx24zYu16PzkZtSj0YEhb+v78K4aUzS8Lp6aB0Dw5PSTh8kcRil87P0NSSyxJpyTx1CnhtYZFF8AXlADDh2TPAL16XSWksfCG5LYfuk+XgWLeAF89HEM7w/VUValFTlN8quUxjliCImJicRiDTiikFdAYTihvWwKgg/g2eh9ZkYxVcmsRgAfuu0p8P3uKAxurMmYgSwpePIfjCT1hbHUGYGEDwyf9ApOc0RO8cgo/9GyJHHkek63Vgcijn91lFvjiHdmU4R8c1LJCayBNCk4MI9J9DeHp48THB54F/pAeB8X7FfVF7cKrg1zuZc7SUc+isTc/DTvfJbrukpRgLqi5vKQcqY/wC6BRQQCGK6qJ4ThIFfac3beKwq12T13P1nYcF/NsTQtrv6f/9JoIHH4ufuw5u0uBL9+lQVax8HO01PN61U4stLTwefDSM/35O3dyXiFPdAv78R0H87IX4sfv37jLgC/fLfKd3RMCf/iCArn5BgXMUL54rjw946kgEz5+ILA7PLP+tIh+zs0OHtpqVrT2gSrOfvRBh1WZkAvCRG7TY3JyYWOXx7j08NjbwaKkkSQbla3HWK+I/nhJxulddP6rSPUiacuQ8HQviHIOT8rkjV+kXzgEnemM+Q3CpnfHJU8BDh5OrCYmLk3TJpWWa175yUcDz54WMOot1JVrGO/IFNeMVCeCPTswglKl9IwWo2ra0vhUGs1UxOKYxmsFr9dAYEiq/VSQXNWaZcxB3yRXhrtcQfOWXLNBFEIavIPjMDyAMX4Y4N4Hg4w8ifPxpxjm4udzWG1eDBli+t1zwT//0T6irq4PRaMT27dtx6NChtM//6U9/ira2Nvb8devW4ZFHHon/bJKEP/qjP2JOpiaTCQcPHsSlS5ewErhy5QqczmTe7HA40NsbM3BkgaufJbyDHZmiYLa0muSFHlV+GcvroS9cyrjSIEbWtZaKmHRhDqALO5fPODHtwcWeUfgDIfb6gYGBN/RcZUL3mIhTfWJOgsJELikuQYKesSCdr+s648uxXzgLPPi0nPFUg9hzRZoMl4dFRcHWv/l5CF0DoiqHocZKTRypIR2Lh18JMD0Iwp6NBuzflBzoctk0aVv4Du6worRQhw/fUYAP3JqbS0xJoQ42C4+Xj3nQO7jE7ul4o8GfQFDE9GwE8954wpt4XRn0PP7mfzfjj35fzg6VFOrx3/+4Fg/c/SbU8ytgyi3itTMhHLsQStuKsa5Rx/TI6No82xthJDYRl4cEpif34il1rXpK9+CrXRKePyNhOib2SdovH7lOrqwqdcqW5JsXhhAK+v7geeDQwrxW5gSKbECiPwG16ZIAM7XhjM3mXplFbcOVBTy0b+DsNOPx42LfJBu30o1ZgUAQs3MeeDzzWb+HZ3IUU/1X5LaBVG2YzmJGBElHKQpep4dj7S6YMozp+vJGmNp3LSsABiECiSxNWbXxggi+oxgcBda0enC2AmgqmqDbdD1QUpP7+6ziKuccGsXrSFdQDkN5A7Supay9xmyDpboVJhIrXgaYhmoOn3Fkwo2LPWMIhiJXJee4MCzirIo5WwlUWU4VJ6RtFIu6YuDaNfEuvE+fAv7zeTlwoAax52p4WsLARPKY3Tsm4Z8fFdmckwk1JRxqEoTVx2clPH9KYIYlNL5tb+NZICsWlHS0pWkDpETLzjUaFDt53LJdg+tz0BMjFDup5Qw43EXVZFLc+0cTn8Tx3N745CUh8bqiSrVP3a7H/ftlXl5WwOOPP2rC7nVXR1MNmTCd7pXQNZD6e6O5vrWKZxX45DB4YVBiupqJ6BqQzRVeX5D5yASle/C1C3KAi/TlorhrO/Dua+QuN+LQWxuBtQsSImQM8eOXwcwaCFThVWKX+Xdia2hbBTAySxIjuXOO6kIeNYWpg4ArgYlZP871TKG3ty/teOX1BzDr9sLtlZNS2WB2YgSTQ30p1zs0zhtJKicSghAKxK0jXWt2pK3+IhiqWmBu3sySHDlDEBaCX/IxcmYbOHsh666CzgDO5oKmuo1xDkmhWmwV6vCTn/wEX/jCF/D1r38dx44dw/r161kF1fj4uOLzX3nlFbz3ve/Fxz72MRw/fpzpcdF25syZxef85V/+Jf7+7/+eVWa9/vrrsFgsbJ+BQELGJg/YunUrO35ymI2Cfv6DP/gDbNu2Lad9Xh2j9VsQqQaU0LlXIM3PQr/15pWrsloQNKS2R2OCWDFB71CnphoJBhAO+mGyJwtQzExOYN49i/KaOuiyyDCTS4nJoGN2vXSOvF5ZnyTu+CUJA+NeRop8QQEtVba898Kf7JWDJuQUSYvpuoXsYv+UxLJGa6rou5FY1Qq1AqjVOmqvAs4OyBVf6dwnix1A2XyyRkEqxJ6rX70qMsLxpXvjRUmJCFJwgaqAMuGevcmBrVdOh/DEoSBKC3hsadPjxu3q2qT+7MEpphNxww4z/vEnM/jEXU7s3WLGge25t65QAGzTWhvLY9RWysf6349M4OlXZvEXX6qH065FW6MFv/6XzrgWSYLSdUXPXSmcuuhnbR3tDbm1lVUUafC1j9lRaFd3jV8cEPDgI0Fcs1aLu6+N/x7XN2pZ9rmhQh3hUDpXB9ZzmJhDnJMUBeB+8pIEo17CPTs5bIzptDPrgTIXFjVh5vxyUIzEk+nxWLjMC8nAhV1fHJHgoJaVLBxMa4uXztPZgQgm3CKuadUpunPlC5GIiFBYgCCKimNWFGazERVlJTAa47+XSCQCDVUApxnzwwE/wkyvSQSXIvdkLCiFzmJPzsa+QdC1bmNbFDy5Uu6+Z/F3w953Lz15VZD26uAcR5+CJEZg2HrTyr13lHPoDDCUNyYHbl3qXBfpHhDCQcUKyKmxEfh9XlTU1qd1MUuE0aCDyaiHJg3noAV+35iPtUwFQiLaaqyMo+QL9H5HusmtGZjzSWgs5RnvIPROSGz+7KiWK5ODEcBqVDeWUYsjJRWIc7RXApY0U1CJQw6Y6VSeuthz9etDHGvH/FyCkRZxDqpAS6z2VcKdO5Pnt9e7BBy7LKGqmPSlOFy3MfMcSMfzDw+F4bRyaKnk8ZvXIvjAQS3aazRsTswVxQ4OTRU8C7ZFWzB/+2oYlwcF/M6dBsa1Wms0+NNPGpM6AZSuq4aKldNvogo0MsFJVRWXCbWlPD59m3odt/MDEn71miyEf+3a+M++vZUqTiTVDpGKnKNT1uyKlTWgU/yzV+VWxhs3AhtiljLkQErXs2uBYs565SCX25+sNUoGUaSWQfuj97wwLHOVooU2UzVoKls6z8d7wnD7Jexp0yVxz3wiFBYRDAsQw6n5BsFuNUPD8zCb4jlHOBKBTqGtMe49An5EQhQRp/0rfxZTYTn0Nhe0bxbn6LgGseUdvLMEhl13Lf5uuPatzTmuFhfIv/mbv8EnPvEJfOQjH2G/f+c738HDDz+MBx98EF/5yleSnv93f/d3uOmmm1iAifDNb34TTz75JP7xH/+RvZau2b/9279lGlx33HEHe873v/99lJaW4qGHHsJ73vMe5BN0nHfddRdqampQXS1HyinQ3tzczN4vF6wGwPIMcfACQBn80T5oy+uS/i47RU6xTHouTpGCfx7e869CX1YPY0WTshAyc87IPDm7x4cQ9HqgNRhZa04stDoddHo9+Cwj+w6biW3sWBMEpb3+MMZnfShzmTHlDjKSQS1X1OoU4+7L4PEL6BsPor7UwCah491+VBboUKug00T9+5MekTk0RYnLjJcyjqS7RWRnaXLZ2cyzMmpa8B++IqJvErh+HQWz1A0oY7NymxftkTJWqUC6HkR4c8HBjTwjnXHBr6DEtDA+cbMGNSW5Ea/rNutZpnJjc3ZtY1SRJZL7TZEWzTU6VJRkN2x4vAK+9Z1h7N5kw7v2ywsfn1/EK8fcaK4zLQYN3J4IpufI+nwpG7aSBEQt/vLBCdZG8N1vqP9CqXrt8df92LnOiBKXBpXF6s9ZbZkG+zbosLFZ+XtOzDJTtvqVcyI2NPIoUEH6KGsaJZaJWiHRgC0FiUkrhjZadN2+del5G+tIb4P0S5L3UV3Esdab6HGd6JWYPlg2AbA5n4iRGYmRUqpwoNi4UlzJFxSZZkc+Eg3FLgsKHWZ5/EwDei+LJX6s9Pt8GB0ZhqugEE5XajVrV2UdCyRQ20EiAjMT8I0PwF7bllKfcRWrUII4fJH1XolTw+BjqsGjoGtampuUK/lyuFcinmn4L7wOQ3U79KUKnGahekENn5kbHWBO1CVNHUk6eKRTo9WFs64mKHCY2abEOdzeEOMaJS4TptwhNu/T/B8RpSQtn9l5AQOTITRVGNhzTvYGUF+qY7wjEf6QxPSkiHPQOaXWN+IcRp08ds7HJKmubafAnPzz61ckjM0Bt2xIrzcUC2oPOzMAtm/SKE0FCiDEBhGyAVWwJ7aXkeB7/4SED1OxZ476Svs6NagrldCQpXshcyLmZJdGqixTM6/FYnZewn8+GcKuDu2ieDxVdp3vE9Eeo7Hq9VNClLodll6bTgbjjQDx2f98MsIq1j53j/rkMyVGX+8SWYUdBfjKs9DhrC/jsK0F6FBoyZU14bik9zreLV9vatyqC20c0waN36/MOYhjEOZ8crKNArh0O98W47+xpUGuekwMfhEaSbx/oTCIgs+n+iXmDrmnXf3nn/KIzBSguZxnfJfl4xVe7g2IsBjzEzivLLagxGnAlSvpXSDp/FsTOAdVoI+NTzAdXIc94cTGoJg4B4VLFMZU39QY/DPjcNa2vmnBr3cCVjIA5na74x43GAxsS0QoFMLRo0fxh3/4h4uP8TzPWhZJSF4J9DhVXMWCqruiwaaenh6Mjo6yfcS2I1JrJb023wGwpqYmnDp1igXhurpk3br29nb2/rmuAVYDYHkGX9UKsfcMIhdeUwyACeN9CJ97GdqGDdDVxqimqwSzoTeYWCZWCZ7u00zA0Nm+LSORtBaVw2CxQxQE+OZmYHYsLd60Wi0TSAyHQ4riibnA4wthbj6EApsR7bVODE/6MDMfZrey2xeGTsvDpJfJii9AZeoCW+TaTBo2KaXi173jAvomBVwZ5dBWqUWJg8eedvmYiZjGtlJRUCk64ZJjDGl1KYlupkJNsSx2n+jaRAQ3X7ypsTz5g1J7wuELInQaHjXqku2KAqzb1ujjgmpTcyKq0gTUhsYj+IMPuRYHmK9/KjuLzAs9Adbq2DccQn1VCFcGAvjW/x3Gh+8uwt9/rQG2GNfFj99fjo/eV3ZVBL1i8XvvKYQuS/evM91h/OZFP3PdvO+67KrTyODhXbv0i0GkabeEiqLU9/LlYQkvnhHZtX5QRYY9Fe7eudB6GgJ+/BIFrYA7FCqLSRhfSRxf6XNsrJOylocgvZCBKRHFdg6tFVq0Jq/pMemO4OxACA2lOlSn0AEbnw2xRVSRQ90igq67XEwgtTot00jQKxCPWNA9FCscPn3lLHidDq7aViYuTm0IFKygVoTgzBgToOWX0864incE+PJGiEOXED77MgzX3pf0d2HgAiIXXoe2fRe0VTGq6SpB8gvkDMql4Bzui0cZJXe0ZW5DsJVUsOs7HAwg5PfBZJNdIaNJNyESRiQcgt6QW7VtIua8Ycx4Qih2GtFRZ8fAhA9zXgEcOMzOh2HUaxbdpD0BgQXfqUKMuIgchFEe9y+PChieFnFpVMC6Gi0KrDwOdFAFqCzoHRtciw10kTmIQSvBkMVtTdXmxFlIIHylOEezgj4pORWSOyAlB6kaJxdQtdW6+vhE3rxfbkdUAvGxyTng9+5YGrM/dZv6IBC9vn+MnLYljE7TvkRcHgJ+9HQI796nx2fvNcQlO+8/oGevebODXrEgjnr3nnhHTDU41yfi+VNyFeLutdnxAKrMOrhRfj9qU/QG5Iq5VCCTHDJ/oqDsloT2XLWg+yvq/khB45++ImvWHexMfi4l7JSSdomg73ZdjQRLliaNfRMiRmclFtDurFW+OYemI+gaiqC9UouKAuXnjEyH2PdXYFOXZM6V6+r1OlaFTh03GeVzYhIVE93nmR4jJeNE4hzhMOMckYAPQfc0MzDJpTBjFW8OopVQUVB74ze+8Y2k501OTrLkEFVnxaK0tHQxmJQICm4pPZ8ej/49+liq5+QbxKFvuOEGXHvttSzQt9zk9yq7zhGpRAv1a3ZBKK5OSRbJwUlTUgtNQW76RBT8snYsuSAkQmO0LlR/Zb4w9CYz28Z7L7J2SKPVtrTgWtDjyEWTY/FYeR5lZWWL56q0wAy7RQ8zzZqs0sWK8rDAyObZy26YDRp01MnZjFKXHg6LdpGcbm+RV9wz8wJGZyNoKtMvtkRVF2lYO+XQzJJ2Q1RHKh0NqCwgrSEuZRaO2gnIzTHuM3EkmL/0O7WCDU8Bhy4Dt2wisfv8nKtElLqA3R089qzL3+T04yd9OHQ+jK9+2IbKmLazKF464ceDv/LggVtt2Lclc4boiVc8OHzGh89/sBhmE4/Dp734ywfHsb7FiMZqI95/WwFGJiLwB8l6W0J1uSEnQpDpXOUbDdUG2BOcozJhQ4seH7vdinWNyxOofuilMI5fFvD5ew2sek8J7TUc7r5Gg6aERQwFxfrGJNy0oTSrc0Ui9hUFygLKsQ5O9FXtbE2/r8ujVA0B1BZRxYW6yaqlgmcVB4W21MdMWVhy87Kbk59DwSW6vnrH/GwciA2AUZsjjRVmo/LUR+fJWVC44EKq6nBZi3h5pcr+kAXQxE1jLc/Lx2EqKoexsIw97hvtRWBiEBqDmWmCRRGeHYd/4ALM9eugJeHZNxlvBaHxdwTn2HIjhIomcBblfie+oBx8aR14V242b6TxZe3cl/Lv5DitVlKTCTGbrRjt7mLBrgrr2kUSG9UdXY6xa+LcUFlkRpHDANNCxKmh3MoqjOm9ugZ9jGO0V8sJiqpCHYrtS5xjZ6vMOSbcAibdAloqdIu8giQV6DiHZ0QEFnSTouNbus7KumKObUqgqjJKYpCWYixof7Gcg6rQSSPpVB9w2xZZjzEf5yoR5QUStrfkHuBQws9fEtA9KuHzd2kVdb9eOSvisSMC3rtPi466zOPLcyci6B+X8L7ryJ2Pw9ELAn7+YgSt1TzqSjns36BBz6jEtL3IJKBcYQ5VE/x6ozlHbSmXdQCMKsCZVEOMo2cu+PXrInrHgN+7VW4XVcLaWjkoS8HZWJC26Mw8h72t6jkHXfPEExjnSJFfpXvthfOAzShLkaQDtUDSsqVGnRIMw5pqDaqLpDh30kTYTTwLdNtMyZ+Lgqi0ZugZC8Kkp0r8pcBUIETi+uSKqkl5XfmDERgNnOq2bFr8V1UqZAYzgHQdoxXo1pJKWIor2PjrGe5mVejkPq23LkW7g9Oj8I/2wtawDhrjysmMvJ05B6sAk1amAoxaAO32pXlfqfrr7QJRFPHtb3+btV+S9tfFixfR0NCAr33ta0zYn7TKssVqACxHpIs8ilND4HRG1sucCN5ohX4hgCW4p9j/GnuOURMFWCrlvjxJECCKAhNQzgRnaRWESCiu2sBss7Ntueco1rWBfo8GvwhEJqPEtLrYyLKxsYgS0VhMeQSMzwkod4lwLqRZKXvVUaNDa6X6RXYm0GQ75QHu3i4xsVAlkC7Grw+TmKgcbxydA45coQW8rBW2nHOVCMrsHb0koaEcaKpEXrCpjXRTKEAQf54pQBAISqiv0KGzWY+GKnXDxJWBEC70BtEzFERJgQ5NNQZcu8UK93wEl/qCGBgNY02jCT/+m+TW3Xyeq3xiek7A//zrUaxvNeJ/fig9o6JFVXRcoOuQ2h8T8eKJIC4ORPDAzWZVmladjVT9iJQtII8cEjDvl/DuvVpFIdyxOQ4WEjBXQfKp7fDcgBwAozZJuo5TYXgmWZBWCRvqOHafZHNf0v2WqeWGSGZn7dL5DS20zeq1PHrHghidCaGp3ASTIf7a7h72MN3B9Y0uphmUiHBEwLQ7jGB4DrW5RrMVtJPof9/cNIw2x2Lrl6s+3k0j+h0Zi6tY8EvviH9/SSSx2BAi7klIQR90Cu1ui59jtJvNL4amzSuW0X0jBYNXkfp8UwZfnBgAFywE70heQfJWJ/QLASxhZoy5jJK2W75gqWln/1NFAa30SJs0E1xlVfK9EfOZbA4n2/I5N/AxHINA45BWo2FjdVWRATaTNu61VLWaiIlZAWNzAmqKtLAsaHfRIrizlseaquw4R+wckYhnzshOuvduT/0cahN75LhceUOBg/FZ4JUuuTWSnCHzOY++eBa4NAy0ViVrPeaKznoeTislIJDEOcICUFtCwuwcylS27/WNS+gdFZlBECVESBtzQxOPWY8s6j87D7RWa/Dtj5neMpxjbEbEP/wyjI3NPO7Zo1N9PRGf2NCYfN5ePiu3992+Q27XzYS1tRxrNUz8jqJ49KjcPnz9huR9TbpJjJ6D3aGOcwxOydVkFAAjLbBUSTe61smd2qei8nxzAwUckBWMOo5t6UD3/Mb6pfVUMCyy4Cmd90tDQUx6BLRUGmFOWLd0DXgRiUjY3GJPOif0u95oweWBGThtBtSU5VhqGQOmMcrxbP3nc8/BbHOwoBfxgOL6+Ixl9HgsxVXQWRxMfzRuX8wUJ4zQ7AQ0Rh/0MQm5RITItTHghaG+c8W4wSrniAcFv2IDYKlATqOkURsrIE+g3ykAqwR6PN3zo//TY+QCGfucDRs2IN/41re+he9973tMeJ+0zKJYu3Yt0yLLJQD21gunXiVgmcqQn1m2xgoY0s/C4AUIw5mtQIOnn0Pw9PMrcnzu3jOYuXAE80OXMz6XqsBMCqK0+ThH3d3dcQ4ndH56RtwYnfbFPbeswAinNTNxJk2OTQ1GOGNa5yhzShUb2RLRx0+KeOmCcoUbZaKIUCbqhFAmkSrDCERCO+tIUH/JpYaysyQwno9zFYtdazQ4uIlHXZZaGumwvknHAjGGBNL/b7+ex+f/dgaPverH597nRE2ZunLuT9xbgH/4XxX4y3+fwB//v1G4HFp86t2F2LXBwgjM6YvkQCnis3/aj3/9We52xpnOVT5B7Zs715uxZU16Av1vv3Lji387Da9fgC+Q+rjO9oRx6nJY0eFRCWtqNbhvnx76GHJGLQo/fDqMM70C+sZEXBmhasXk9yy003UjYWSoB9NugWUpCVMeCXNe+WfSP3n1gsQI8q8OycGvpjJZ+PhUGmfhO7cCt28hsgs8ehw43ktafsqOjg2lK09aTnb72UaglmmHWQOnVQurScsysP1jPlb5UewyoazAlLLSkILyYf8snJRqXibG+i5j+PJZeOdmEJifg3tsEN7pzNc9JSJIVDyxhV1fUA77hgMITwzA33c27T6E6REIU0NARIV6dY64mpz23jGcwz/POEccSHN0oAvCUHrOQQHU0IknETrzwoocn/vyScyefx2+0T5VlWBG6/ISbGrnBhLAvzzkwfhMvNtwVZGRVYBlQkulDlubDHHaPzQGi1lyDtI6ffSExLRHlUBVXk2lyYs8akmPjq0kDk7aotQWTpyD9BnJMIeSdfmeR3e0cdi3jlPUeswVnQ08btsuS1rE4vtPRvBn/xXGuX4BHzyoizNoSYf3X6fF79+lw78+EsEPngqj0C4HjUjniyq+Lg2JLEH0lz8O4smj5DR39XMOqvyi6jdyaEyH/3wqjL/9RRgRgdp2U/OJC4MizvfLAUY1WFvL46bNshZWFB6fhJ+9LKJnjHThgCuj8n2VyKlJfH5Hs4i+3h5Me4S4wBhVghPIyOnIZTl4++gx4NIIVYjLpjoUDFMC5aru3g5c3yk7TD92XMLpfpn3J6K6kENN0cpyDvrshy8HcKpP/lB24hsWDQqsFCTXwBsQWAU6BXbLCwyoKFJu16LraWRoAC67AQX25etwDfZ2o//KJfi88/C65zAzMYp5d2bheCqUMDoKk46RknGOtdcgONYHf4a1ZGR6BBHiHBl0VN9pnIMsj1ZiywZ6vR6bN2/G008/vXRcosh+37lzoQ85AfR47PMJpL8VfX59fT0LgsU+hzTJyA0y1T6XAxLY/+53v4v3v//9LJgXBblZpmrjzITVCrAcQYN9pOswhJ5T0G2/DZqyuiVHpGvuWbBCSw990yZVrYrK7y+yyHzEMwMNcw2LT43orC7W102VYG/mOSLxvak5WnxKKC+yskAI6X3RpF1WkL3YM03KsW2JFPx6pSuEYgdlZLMTd2ci3ynugOby5O+F4ge/PCQHvu7cJutvJArhP7APSYL+2ZyrVG4wRQ4Oexwr5zqUKMJ+4iJ9vuzSaLQYcFi1eNdeO0xGHs8fmcf/+8kUvvihYnz2gyXYtMbM5kYKElEgLB2IOBD/oiM4dHIe61rNsJqX9JOGRr148tAYPnhnKcx5EiVVAmX3fvd+5WoJuqb/4zcetNXpmL4atQs89moAj7/mx//+iBO15ckX10dutbDFkyPLlspYEBm9OCTBZpbQVs3jhTMizrIAlIAnj0vY2sJjd4eEV7uoIkrErpogfn5cxK52HrvaJfzgOXkh9bHr5YDXoYtyFSOd79ZKWTyWMsDpKsCiTmPkzkTElTYSsI3V6yIhaNLfo2qxM4MSrqX9qjSbyBbFjqVzXeTQsS0K0gAanw3CbNSwdqhMMOh4WGPs1gRBRP/gKHOALHQ5oE2MiqeEbE1Fk7XebIGtuFzRcTcWwdlx+IauwFrXkZSNZXvkOJgaN2YkmYbW7axajNMvP5AnkT26RsvaJ+IeX06v2iqyBmvtPfk8xIEucAfeB75QzrqS3IJ+//vAZaj2JmkEXfNWcDkKHjNJBMY5pqC1FYBPuLZ0NieC0wHmSPlmITqPjs/4WJtIeaGFLZKJc9B8XeLK/n6geS1W6NvtE/H6pTCqCnm0V6mf7GkPxDdYxbgCZGfqeFDQgjgHGYncuEEOBGxLKKB+/7UyJ8k35yDjEtKBfCNQWcSxii0ph+/GaQV2dWiYrthLpyN47LCAj9ykxf37eayt41kFMgWISI8zE+eg80uBM9LTaqvhF6v/6RwNTwRwZjSIW7Yb89ZpoASqQnzvAeUvlD7Hr14RsLaeZ50PpI3+6GERJy5L+L07tIqV4u8/oGFJ21SdDGpA8zy1RZY5JTSWcTjZC3SPUfBVwpFLwOZGoKMGONEDWA0S2lxBPHxOwt61EloqOabvRRVe9+6SHU3P9Mv3AnGONdXA+jrANZBediF638wQ55iXN/Lbim2bJPdG2m/3OHBpVMJ1Hdxi1WY+QQm0Eod2sWqsvEDHtigm50KsCt1h1qLUZUg/podDqCuyxi3qqRK9Z2ACTrsZBU4Lq1pVAxrjmLwCr4HB5mBjtiVDcYN/ehTeiSE4a9sVDXio7dDS0JnRWM3UtoMlWdQYsGWCFPQDOkNS9foq58gdJGj/oQ99CFu2bMG2bdtY1RS5tUZdIR944AFUVlbiz/7sz9jvn/3sZ7F371789V//NW699Vb8+Mc/xpEjR1gQikDX2ec+9zlWmUVOjBQQo3bEiooK3HnnnXk//qGhISaEnwgK5IXDuSU3VgNgy4CmroPU4pOclzhTarVGGpCCh34LzmSjNCi4HB2/Av1dCE8NM4cPraMElsb1cX83l9bAVFJ9VZSMzrj9rEKltNDC7Hw76pZE1bOxDKbgWWJbJE2KFPwqSqMXRDodtPCOt2DmsG9N+mMgbY/haXmCpQmPDpmEaKMi+kqIuuhlgyOXJBy9JGJ7lfrBfcot4fEjEexbr0FFYX4DQDftNLMtV9x7gzzhnrroZ86RLrsGTTXy/v7pvyZQXmzA7743NdMJhkR86o960FxrxHU7HfiLfxnBnQdduHG3A+ULLqC9Q0E89sIsdm92oKM5/655v3rWjXmfiPffmpo80N8PnQnAHxDx+++Vn/f6mQBqyrSwpgj0ULVdYsVdtih18bh5K+neyS0lxBEqCoF/fVx2Pj18kZwXqaRfdmCkxVFtsfwcuu63NUvMfYlALk6UeGyuIBK7dFw7Mmh7RUFVkuTYRJo09B5R0KLz8ZMS0+wgwsywTO5ycTgEg5ZDbUnyDVhXmppklrqMTH/DYUm/SqQKOV9AeUFIj/l8fua+VFdbyQjpzMwMfD4fK/+O1aaYn5thxiGltfHRcWuhmh6lBT/3NFCj/8UpBKyiEGbHEOk5DV3rNvBm+6I5S+TyMejW7QNvWwrSSaEggk//gAVb9NtuVXH8q1hJaJs3QaCWZlf8+Mmn0P8iUCA0+MpD4AvklgXOXpRWGzMVAt0nEZmbYLexrqgKpto1cX+3VDTCXN5wVXCOyVk/y5JTAIw0RtfVO5OqjjKBxPDpo1BAPDFAUWznU2oUsgTNNFXDxLdVUdDkunXpj4GSI+QSSZyDziMFZCoLAFca+Z3oWJ4NXu2SkyibytQPyuOzEl6moMa67F0ZM+HGLVrcuCW319J5unmbPNad6hZQ6OBYMqqxQv5+fvRMCDUlPG7doUubVPqr/w5gbZ0GtWU8fv5CGDdt1aCzUcuqygjDkyKODYnY3i4xd8p84/HDZATF4cDG1Msyqtym4BwlWe/ZIz/vUJeA8sLUQdBY46dcUV3MYc8aCQY9OUWS8yNp1QGvXQAoFnj4MlggknJDVAVGybCqQnIPlQNdxDOIixA2N8iPN5UD62J8wrap1Jprq6TrT2LdFnRvxAYHiXNQJ0aurqWJfOD8YIRpf5HOcCJaKlLfeJVFRtgtWjgzVJgSTyJd0iSQBjMFHt1ezLjn0VxLHIPD5NQMAsEQKsupQnzpM87NzbIqn8raeIE0e4EaITQuI+/QxXCClHvR6sBB+UKLTA0jMnQJ+tat4BeKNYSRK4j0n4O+c3/cmln0eRB64SfM3EW/fj/e6lhJF8hscP/992NiYgJ/9Ed/xETqN2zYgMcee2xRxL6/vz+Ox+7atQs/+tGP8NWvfhX/63/9LxbkIgdIajmM4ktf+hILon3yk5/E7Owsdu/ezfZJplD5xpo1a/Diiy+itrY27vGf/exn2LhxY077XA2ALQO8vRD8ml2qnivOTSB04ilom7cCWj1ZHrEWFZ6EBevWZf3eGqsTYsgPjdUFnUN5kLsaiCiB+tqpnYeCXwS1Qo+xON0XYCKTO9vMcZ+LSG26yq9Zr4RjPSLKnRw2N2RH+6k65vyQPKlHBTWviZftyQsoO+cPy+2VJ7sldDZkbq0YnBRxrl9CTYnEAhvZ4B9+7kMwJOF/vjc/opahsITRqUhSq2Rniwl/++V4wbKZOQFTc7IoaCrQRF9erEdpkQ7rWk2463oXq7D6nW/04jMfKMX+7VasbzOjpaUCf/3vo9jcYcHvvT91cGFmLoKnXvXgpj32ONfJdPjZk26WCb55tw0FKSrvKLD37c8UxgnFbl9rZNtyQZWNTx+NMAv3coUA5+GLIqbdwNp6Dns7New7aCyn4JB836+t47CeaWFI6L7C4Z5reKb3Roi1NCdy3B5vJJM1yN480eKc1poUHCNBZ9mmnFs2GR2dEdiCUikAlg50PTmtmVeJk9PzGJ9yIxiIzyaRVlhDXSVmZt3wBwKLJCEcCiEUDLIMVPQxSnDMjI8wnS9jrSVr/S0SvY8Vvl8JiO5piLNjkHxuYCEAJgW8kObnIIWX2sQYKMFDbpQ5CqivIr+gQGS08isThMlBhE88A23HbnBaPThei8h4L/hIGKiJD16pgcbmYhl+CpDqnKVXNedorJR1b6KgIFi2ONYdYJ9nZ2t8xRxV0myoTz0GTbglHO8VUVvMobMmO85BwvZUvUIBBKq+onH02uy/KlWcg1oraSMesbYute5YFAOTcotaUwVpUqp/L5qDvvuIwKroqBopHyAuODcvocQV/712NmjYFgu3l9pV0++PjM6p7dJlIxF5DfZ0yqLm1Dr5oRv0aK0G2mo0aG/V4Tu/CWFbqwY3b099DUy5qSpLxJ51mjj5gnTVZy+clg+SHBxTvYaSX//jTh3sMdRtW5sG2/LAS70BCa+el7CpSTnASW2LxIk+cRMlyDgWrKWkV0OZ3B1B1ePRqvErlzncvpZb4hwxCTWTIX11uRqU2DkW6EpMhtcXExfhUF/Cob1yeWMRNc+MzojMuVQpAJYOxN9dKiRdRqa8mHYHMO0JoJjK7heg02nRWl+GiWk3QuHIYmwqyDiHnKSL3q+RSATTk5PMhbqi0pT1GGwqKGXbSkJ0T8qcI+BjhR/sMb8HkndOrlSPeS5VNPMFFeBTzDGryB2f+cxn2KaE5557Lumx++67j22pQNfan/zJn7BtpUGBO6pgo0ow4ty/+MUvcOHCBdYa+dvf/janfa4GwN4oNwpWm0oilDyMW25iD0nhIHssFrSAikwOQOsqT9vSoC+sYNsbCVrsBUNB2NKI48cOzHSOqqqqmFXvcolxmUuHUCQzSUuE3Qy0VfAsG5styOGGgl+ktZEIStr0T8raBao7omJAAQsiXSSiuaudw842LZ46WoVjJzlYzDKZSIf1DRpmU12WQyZSpwGEPJbw//eTbjzxqg//++OFaK1NH2j46qfLmH5COrdHyoL+2ReXojIfuqsY3QMBnLzgQ32VgV1XDfU14DUmWMyzrN0yHZ47NI+fPDoDh02DG65Rpzvz/lsdGBwLw5mmspBQ4lqZtlQS9n3xdITdT+/aFX9Oyd6d1nM2M/BPvxZQVSTiAwc0eM9eTdz1RQuWuhL5HlwJ95zT/WAVkgfWyddULOg+3VjHJS9afLllZ4emBFQUapkQ9UrBQAya42Ch9i6F8+Vy2uHC0vVTXFKCIhL9jnkuE5qtqMH87BSGu7tQVtfCqsGWA7oGgiPd0Jjt0OUhOKatboOmuBr8QtZVGOuFODMG/bX3yQmZGFA7g37rzW8bR6a3MrI/33JWn9NqYdgltyRomzYCGl2S0LEwMcCuiVRVgwR9aR3b3kgEAgG2wLNarWmD43wC5zAals85Kgq0Oe2DFuCtFTxLumWLtkq5rYuqaBJBCbKhxYr0rHfNWgCpdZ0+0t61HK7t0OCxw1W4eJpHgT2zg/WmRnpObm2RFIvMhSelwq9eieDUFRG/f7cOJc70J+Mzd2ZOfpj0HH7/7qXE1W07dOgZEVhbJrVWEl9pqK+GyGlh0oczBrVeOyfgxdMCSlwc1tVn/uCsOnCjhiW+Mu2b5DBWAldGJRy+JLEA1TUJ3RGDkxKTTfAEgX99gqq7JNy1k8M9u+IrsEiPjoJFK8U5jvVIrB1zX0eyeyd9R1sSTADofHqD5JSa/TkbmBJQV6JBjYJDer5g0mthcRbB6wujWOG+KqYbMwYVZSWMD8SeW61Wi5KycrhnpzHQ242q2vpln3tah/rH+ljlly4PjtO6urXQljcu8ovI8CWmZ6nfcy94XXzCmNa9+m23vG04BzlA5t0FMs/7eyvgjjvuwG9+8xsWbLNYLCwgtmnTJvbY9ddfn9M+VwNgOSJbYsTbi2C89v74feiS23bCo1cQ7j0NqaoV+pqOtPukbKzg90BrWTmhhtiA1vTUJPx+PwwGIyu3TTp26lvvH4XdZkZZsdzmmI64poPbF8GMJ4LqYgp4cKgszK2GmybJJpXC8T3jEqbnJWys59jrrEYO62qW9Iyo1SDaRnFhCDh8hQQ/1Ts+UpaPJm/aBekwNZYDty6U/dO52r7GCoddzqhlakUgkhzb+tg9IjJL8Duv0aEgQ7Dv03fmt2VwQ4sRcx4RFSqDE+mCX6nQUG3ENz+7FBSLXlf/8NX4clgl3LDbDquFZ86Wo5NhlBVlvpZuuiZ9intiRsCjr/hwyzVmFDnTE6SxaQE/fMKPm3ca0K5Cp44WdK+ela3cD25Ofj6J2I/OAC1VHDNcIIFZQUJcW9PL5yW81iWxyq+WytzuQQK5M5F47QaFdS8J4E/Ny5nSaACMSvoHpuTWBN1CkPVUv8g0wmxGibk+7V1DduLZXQN9kxHW8tmk0pAhFzhsJjbemYx6VeM7PUfpeUaLFaGAD0KEMrfLJypSOITgaA+r+s1HAIwdd2zLweQgxPE+oJGce9RXhV4t1T7vFGR7vjVFldBc98H4feiT9b/CA12IDJyHTghDV9mSdp+SEIYY8EFjWb5bmRrOMTE5yXSqTCZTnEZOFMFQGN0DEyh0WlFSaF8W55j2RODxC6gplu//uoV2+2xBHKFFQUNUCReGJfhCEjbWyXO50yLrLxI8fomJ3EcX+mcG5KQD/UqC+WoQjgCegBz8+sVrJHC+VFHGOEe7FeVFykm+RM5BxxWbcKMWyuNXRNy2jU+rs0Tv8/Gb8rvU6KjlWdUICcav1DhVX67BJ25duuai19WX3pNZR3Lvei0LVM14RMx5eVXHma71MeoOeahLxP4NmjhNOiUMT0l47IiAGzZrUKVCEJ70TE91S2irAra2KLs7TriJm3IYd8uan4mOpi+dB872yzpflYW5c44Lw6Tro8yrpz2yBhj9PSo1Rcc+NCPLk0T5+ZFuEb6grJlHLcU3dJLuaXYmWT3jAmvfbCxbuWVyscsMraYEVir5VIFUnIMCAkG/j8lM5GNeFoJ+BCYGIYT8eQmAUeEHF5Nco+pkcWoIOqaDjbc156Bml/y3QL4zsWfPHibEny+sBsByhLAC4vKiz41wzylmW64tqc/4/ODwZYTG+2Fu2gStPcs+OBWgAXVssA+u4jLYnC64CotgDgYUg18EmnuodFen1SAYisA978PM5CgaGxsVyWs6kIDklDuCQrsOZgOP6XkBBVYNC57Ik1OY9eYX2eMv4dN9stjt2prsF8r9kxImPMDa6nidr2mPhEdOkAAocHChW7W+lDTCZNFOKv8mfbJMnZ0vn5dFQuk80fOd5vjraXTwCjYnnCuqmvntIRHt1RzW1PBMA+L//iaC5gqOOSAtHvu4iMvDEibmRBTYl5ex+tETPkzOivjMfZakLJsS1jYZ2PZGgc7VlStXVF9X1D5J7ZTf+ucxDI6H8Tv3q9FFSI+Tl4J4/lgA1aVa7N+SXlSa2iG6hwUMT4hozxyvY9fTyJSIQgcPU4xuXSz5/+K9HOxmjlXT0bWU2DK7poZji56KAgEXL3anPFeXRuRqsvoULYp0vc765HsiMYO/p30h+BVzC/aME/kE1tcC7QtVjJSB9QaAjkrSxJE1x9Jh0iPCbuLiRHs31euztjZPKToriNCnKEewWQxZXVupYC8sYVs+wOsNsLRsTsqU5gvath3Q1Hcu6oG9mXPgKt7Y8y3MTTAdFtIF0xYvZHsyaY9OD8PSvpNVJOYb5F42MTKEorIKWKw2Zt8eCYdT3os0P+kXOIc/GMa8N4CZyZGc7t/h6TDmvAJKnTo21s14BRTZNGzRRYH97lFyGSSnN03ceHKsJ8L0Rdsrs6fTvRMSGxs7a0iAfWm8G5mW8PRZucKcxllCc5ms1kPJBWZ+J2WuBHv+rOyyF911IueYHLmCdY2NTDQ7thXuqZOywzXNCxT8+v4zEhM5v3nL0jFSdVT3qCyQHuMZkhP++/kIW9Tdv1fdOeyo07DtauUcFKAirTgSrPcFBNy0oFG2HJzpFZn0QX05iftzGTViByfl/9UEwKi1kQJcZoOyWP6GBo4lbMmAijiHpBCMIDF7SoQVWNNzDkoeUzVZqorD4z2ULFYOgB1YiyS+c3EEOD0gB8VIdoFA9xTxjk31skYpfa5UoHuYXLBdVm5xvyw43KxTxX/VcQ4JeoU2bGrlmhwbgKMxwU0rBxQU5U86QWuywFa/FpqEivB8Qb9mN5Nb4BLM2zJhlXOsIkQmN+PjSY6gNTWZ+UsiVgNgeQKVc4aOPw1tQyc0ZfXZv16SZE0wWyF0rFQ088CgdZYwVybenLpaRQyHWAsEReCzQSQckh3MtLpFLQ2DwcC2VKDJrr5anoGGx2YwPeeFGAwhF1QW6lnFFGF0Noye0RDqS/WoKNQjGKH2rggLONlnBHTWLh3TnI/Kg3N6S+xs4VibAekiRUGTPbUdEKI2zgQSAN2yMGc9dkLOTL17V/oy/7pSoG+cnGrkFsedbZktfj0+oGtAJhZramSiSRoNdaXxH/LaTg0rtVeyD3f7JFwaFLCxSQ4gZsLYtIiRqQh++Zwft+wyKQZh1GB0MoLSQnkB8UbaISu1WHY0GvHxewqwIUHPJVfs22xCaYEW7Wm0YKJYU6fDtz9pU+38SAuhL73HmPY6puAXgT4nn8LB68bNHARBYqSBsqRKvP2JE4BJJwd0lXD9ern1JvG6Jg0ZFvBOmEGqCmTiSQu3KHY0UdBaPtbY9hkihdEqsShmvCKOXImgsoDHupqlnVMQXAk0RgSCAjwBEYV2rSLJjMXYtB8j0z40VdphS5F1vRqttrXWzCK0uYIJ5ke1wIK+BfelN25xuYrctdzCxDnW7GDti9mCVZBPj7Lgl766TZVjqJaE9GnMSbNwEcNBWXMsy3GfOAe1uFDLcLTVxURiumkEdSnh1lgjB5r7hmfgnvfL5U45oHqhMpgW2INTYfRPhNFWaUCJU8t0gAanBIzMCCwI1lEtjx00rhHnoAV7LtjbziEiLlWuRMc0qmghUPVWrJzDpgWN698ckcfge3ak3z+N6yQW7gvJ7ZUU1Mo01lFA68ooVaPJr6f/iXuQm18s9nVy2NxMFWvJ3/PsPBkISSxppwYTcxKrVnv6uIC9nXxOLovEoSngU+RYmTapbDnHmloe79qpRcdCdd9yQXpitSU8Gioyn5t19TzqSmUzADWgYN1nbiOd3tTPibqvp+KQFQUcKgooSEG8Q5BdNROmEeISz56RBfHffY3y+9y4XrnCReYhFKSKf5xagimAFyuIT47TUc5BiesoiAeRxEYsxt0SjnULqC/h0VYZU/GXQlqDeAu5mHv8IhsbEjlMIgbG/cyFek2tDWaj9i3BN9QK3+cKJpiv1cVwDmPWeqlvFay2QOYHly5dwkc/+lG88sorcY9HK1FzCY6uBsDyGACTpochFpbnFAAT52cQ7j0DTWEltCrJLC2K0i2MqIx19tIJGFzFsFYm24emgt87j8nhfjgKi1FZr/51sSgqsDFHNbIDzgWkJ0DZ2BkTOePIkxLLetJkreNZK9TgFGkkxb9uZys5keQGynwlWpSPzMrZJVrQE/mkuYpKwEsdS6YpVNFCBDhTbIlEcR84kFw6ng5URv87t/Ks5ZFABOD2ncm3LWWqClMk5J86EsYrZylLTS14mRe2n323BT99xo8nDwXRVKXF+ubsWkFePeXHz592Y3RSwPtutuGW3Vko5i4DpHv13Z9N4oUjXnz2A0W4ZqMVPr+IJ171YN8WK27Ypa5i4YePutE7HMaXP1QAbQrrcPoe1jVlPi+jUwIjgcUZ2iQTkUjSloMrI8ATFyV87AYpqXXiti3pg7aUPU0E3YcPHZKzuLdsSibSVP2VXLYf/9iEW8Rrl0SsreYZ8YyCKr9qi3mUqVzEXBkJYtItV32SRiAFzi8MelFgI1vy5GC9yaiF1aSFPlG0TAX8/gDGJyZRUlwEk2llqrFSITI/wyrAeEN+ArhKkPweBF/4Kfiy+reF+9LbHZJ3FhI5Qc9NAjkEwMTZcUT6zzJtFo1KPVGdo5htqRDxeeC+chLG4iqYy9TrhXndc5geG4KrpBxVdblVQ5QV2RAIhTHrzi0A5l/gHLPeyCKHEBYIhtXEo6FEg4FpMY5z0AJ77xrSG8vpLdl4mQjSFqWqlqZSYH2dzC2o1Zw4R2wwjAJGmdBYJm90zGqPkXS+PnxAgmOh+IM0qW6JqfyKgoJ2MbrdcXjmpITzA9Q2SSY9md/407dp8dDLAhOCb67kUFOS3Ql9/byAl85EWCLynj0abGp+Y5Y2VKH/38+Fcb5fwkdv0qKpUsNaV49eFLC9TYOda9TNMz9/MQJvUMIDB3Vp+WmTCmH30RlZrkNN2+VKcQ6qOnx5AHhgf7ymGXVW3LghfcWgS6F7koK9vzwku0dSFVgsqP22UwXnGJoWcbRbwuYGjiXYoiiwyJyjIsFQIRXODwQx65UX23RflTg1OD8QQJlTh1JX8vdnMWlhCwk5GXF4vH5MTM2horQARrLgfAMR9sxAYzSDV5DqyRdEzxRCr/0Wmpo26Fq3r9j7rOKtjw9/+MNM744E78l5PR+FFasBsDyBLMYNBx9YdLjI+vVWF/QtW5mzZL5AEXad1QFtmgoxJYjkErUwuOcKakugy1Ok1EwOoNbHDi3pcJEwqAibiYJAmkXScXk0DLuZx/q6+ME5W6vzTKDsEZVRk6UzOdqdG5BbvHa1kh6R/Bz6ORtke+Mu13J89zotywQ2VSZPwKPTAv7jkQBu3qHH+ibdIqm/7Roj6is0sobFvKi6eolA4vGkkVVaoMF/PebB06/7cMNOC27clbsuRBTzPgG/eHIGbZXJ0f4fPTzDgl8EqnwivHrKi/96ZJb9fs/16rQMxqYirHqNLt3lDJDkdvkXP5xn1Vp//PGl4Bu101wcENBcpUmZ6T7dI7DX1SZU+uUCErUtVqjWigZlswW3cF+kayvIBKqypMBx4j7o/s2mlUhugSatHB4lDh3LxHsDAgw65fPmsOjZlgtYJV04/IaX4VNFjffiUaa7ZG3dumLvI3io7IT6S+JJvOiZhkgi6XXr3rZZ2rciNOUN4G/+WJyeWzbgXaUy58ijwyev00NrcUCb5TFRm2M+TCxYJX2OnKPUSQLnPOMVbp/IZBeIdxDmAyK6xwUU2XmsrdGvKOcgLaPggrwCibNTSxjpf+3vkHkIYW+WzpDZrhWWyzl2tpNBD2mGJf+NBNV/85qAm7bwqC/jF88h6VW1VIms/bPYSUEc9ccwMk1aW3Lw5OcvCnj2hIADG7TY2Lz8SlZykXzhVASV5mRC/NDLERb8isWxiwKeOCKw4OaOdnXvTy14834pq+SoEkanRfzLYyLTdPv4zUvzKFVjUfcBVfSlquA60ycx84VcjJUSQQG4QrOyLEiqavN0oP1Q8Kt4GV3X5CBtM0ns/8TA35oq9ddJiYMqzSnwxrMxgyrCvH4RPpPyuFNo17MtF0QiAnOBzHVMyxVCwIv57lPQ2QthrU+IOOYRontK/iHBmEWcm2AJGk3Nmrek7lcsSP8r/xpgb+1zkgtOnDiBo0ePoq0tD5a3C1gNgOUIJTeKWJE/unmprURNWwF7LcdBm2eHJV6jhb1uTVqnj3DQB30CWWUtCBygT9F64PV62ULQ6UwfUKivLkZlqYN9Np8/BJNRdmYaGPfA7Q0x0tNaI4vlK8Gol8+xxahBZ/1SYJEmn7piHcvKpsI0BW3MHHsPyshRm0JVjGi8WtDrqXUgCirxbvDKAYB8X0/19dm5t1AgUDauSz8YklX49VuU9+v1S4w0zXikJN0s2u9Pnvazv921d6nyhCb701fC2NKuVwzg3HvQhpuvsTK9rX/75RwmZwWMTKpIV6vA2csBPPziPLT7i7A14VxtWmPC9FwEt+2zo6VWvnZ3b7Swqr2d6+Xr51JfEM8e9uG9N9thsyiTns+9z8Vek6r6Sy1I+J6y9DvXxpOfI10R/PiZIHN3PLBJr1jJ9uNnIihycvj8PcvL+tH1tHtLA/T63BzNlEC7iQop5wqq9Nq7ZvnTD7U90haLTU32jHp8udyHVqsFDebaN9yJiNrJDOUN0JhWtpKStSRo9dAUxYuvRC4ehTh0EbyjGFxMpdBb0ZHpbcc5Ys0MpkbAOQrZ9aIGJItA1V95PUadAfaGBaFMBYiCACEchC5B4kGn17HPp9MrR9U98/OsVchhT78CbqktRqicSqU4+AJhmEl0kLQJRzyY95N7nwat1coC/jQ+UtCCZkKHRYONDUtzHml81RZr4UqRCKLABRnouCyyY+CsV4IvKKEiptJELWhBHqt/VF1E1WnkLom3DOcoL+DYlopzkHmLxx//OCXpSLj8kUMiq3q7pmNpfvb4JPSOiaydUEmXiarib9wC9pxHXo9glgxaEjhNrjg/IOKVcxJu3FyTdK7oeCjpsn+DFmUL3/W2Ng27jjY2yb9fGhJxtlfAzdu0cfIasYgGq5Y7R//yFblCcWNT/H4OX5RYVd5t2zisb1CWyXj4MOmFAe/ft7xjoHN07VbiHPmTv6D5PLHyKxd31v0x11SuKHNp2RZ7v25rteTEOTLdgy6HFQ6b+Q2fa6nS3FhSA20eBPDTgRnBaXXQFMRXIEcuHGJJN76wAlxMl9Mq53jnYs2aNZicnMzrPjmJZu5VqIbb7YbD4cDs7Cz7P1U7YyjaSrJpyZ4zfO4VSKSPYXEgMtoN47ZbFV2ZYgNUwvQwNM4SVaQ2NDPGnqe2d9szNgjf9Bic1U0wWNW7Og0ODDAhusqqKkZKyaFJ8fglSRZ5nPVhYsaL6lIHc1q70D+DQCjCyGhbigBYKCzieLcHLqsWLZXZCTFSe9Xxngjqinm0VGiZptCEW8LuNu2ijkG2oAza6KwcAMtFGDO80CLZPUaW7dSWwCmeKxrg1ZAGykr+468jTAxVqSUySlZfPStrfznSuO4FQxIMC9lWOo4XToZQU6JBZbEGL5wIYkubHk7b0sTz82e9eOzVAD55pxVb12QuAwqEREb80n2uf/nFDGbmBHzpI0WydgSvTAYDQRHHzvvQ2WJgpeXZEqwfPjKHR16cx5c/UojOlpVtYzvdHUbfiIBbdhnirpnZeRFPHA5h3wY9C04mOkAOjEuYYQFcngneLgdK1xUju0eBjfVAW9XbL5NEosPdIz5Ul5jgsGQXZMv2PrwawER2R7uhsRdl7c6npuJA9M5BnByCpqY97rlzc3MsCUL/2zMEJlaxwpxjahihl38JTW0HdOv3LT4eOvG0zB04DuL0CPQ771zUXlGCRAEq4hwF5UwbLhOCU8PQGK3QWtR9/3MjffDPTaOwrjUpCJYOvX197L6srKhk16zRaEh7/45OeTE1F0B9pZNp/Z3unmFVFEaDFm01yufQFxRwotvHRPAby7ObG4amBZzuF9BaoUF9iQYvdkUw5wOuX6dJGfTIBGrpJmHyCldugRFKvtA8Su2UpFlKgbTljHWk0/Wfz0jY0gxcu1Z5XqKg38luiQVZKGiY8rNRIG3hvFAA6fULEnMYpNbOY5dFbGzkYY5x7XvolQiOXRLxoRu0aFQxJxL3yXTef/h0mLX/379PxzhHqmpsX1DEpUERrVVUZZh9UOdnL4Rx/LKI371dh8qilV3Ak1A+abjt7oh/H9JGo3O8u0M2z4kFVaSPTMtmT8WO1IFLtVC6rqY8wFOnZNf02vx4w1xVINf6ntEgGsqNi50qb1e+EdWPDI31QecqBZ+lUL4qzuGZZq39mqp4V+K3EueIzttPH+6H1ZrfY52fd+O6rTVvifOQLzzzzDP46le/ij/90z/FunXroNPF85hczsNqODVHpBMu5Ew2aGrXQJNgKU7lnqJ7csFBhYckiQhdOITI8GXF/RARDV46gnCKvycOSL7es/APXFD9GQw2J4yOgqyIKKGktBTlFRWYnJrC8MgIIpFIynNEwnVWsw4OqxEmoxzEa65yYl1DEdprC1IOhBoN6UtoYTdnXyVClV/U41/qlC/vlnISreWZZlGuOD8EvHAe6JvI/rXUMfWfzwG/OQw8fQp49nTqc6VWEFOvA8oKOBQ7OfSOiviTH4Rw8kp8a9b5PhGPHY7g1fPpq6+iwS8CuT/+9JkAfvNygBHUg1uNccEvwrUbjHjXbhPWNqpz2qRKvkwT3tBYGAOjYcx5Ividbw3jn3+6oAKcgK/90zh+9tQcBvuvLJ4rqkhTC6pO+6NPFakOfhGRfuF4IKv3iGJdg461kiYGTJ1WHu/eb0wKfhGI5P/zb8Pw+smGffnDs9J1Re01pJVCZgxXA84PRvDUqTCmPQLG5yiDvbycTChCFugigqHsWxWzvQ9XGnQuQtOjzOwkFUTvLEJ9ZxEa6GJzilpEek4h+Px/sQBX/HuKS60JRBKopa02uRXhajlH7xSk5Rz2wgXO0Rz/mtkJiKQRRiY4xDmECEJnX4Qw2qO4n8hYD0IXD7H/Mx5PKAB/fxf8Q5dUfwajzQWTowCaLHVlysvKUFFejtGxSQwMjbKgSbr7127RwWkzwmSQ+UNHnROdjQUpg18EnYaH06KB3Zx9lUiBVdYQKrbLY3ZHlQbra/mcg1+EU33A8+dkHdJsEQgBP3gBePIU8NwZ4OWu5Y911NZW4qQ2Qw6XhiX89S9FXBmJ/x7O9kl47rSEM73px/BYXajhaeDJYyJePCMywx2q/IoNfhF2tvM4sEGjWhtMzXkfm5HYNu2W8O0fhvDooWSeRNfZ3/8ijFfOhNHbc5mdK0pSkUatWty2Q4tP36Y++EX7PnKRWt+ynwfX1vFJwS8CmSPdspXae5PPy5FLwA+fI8H65Qe/Ul1XVMFIVXmxpg5vJo73CnjiVIRpeVHCfLkIhCV4iXOExbc034gWXgRnxtmaMhUE9zRCQxcRHL6SFV8LXTqCwEs/k4Xv495TYIUjUfC2gqTgF+FqOk+reGNx8OBBvPbaa7juuutQUlICl8vFNgqI0v+5YLUFcgVAmVNdx+6kx/XbbmHCWiyz2rSJVYMRESXxYW1FvNg8Bb1C/eegpTLUkgSFRwUEhq9AayuAMQvhWb3ZyrZsodfLgSwnDZQmU0ZbaKNeh+qypYCDGidCWms1lZvZpJz18Wk5dFQvXdo06edCamNRWywLcZK2QjqMzsgueCQ8GwW3oF9AgrFbmpbE8inzdviSrIlQlGV7A5XYf/RG+TMOTIigTo/Elj0qz79rN7kQqf/sxS4NPnqrCeVFmrTPuf3a3LTuUuFrnyxmpfuU9S4p0KIwhWh8YzW5gy5NzL982s0E97/x6WI01WReVBn0PFoTdOPS4fUzQXz/kXm4vWbctju/nzkWv3gxjHN9Aj58kx6dDbyiXlu+QFneT9xArSwJAQ1yTcpDEnJ0VsIrFyVsb6JAdOYdTs2TTThweYyy1yRSq2XtCrnCadVhU7MNWpU9CaGwgJ7BSRQ4zGwjDAxPMIe5yrKEsokFCJEIpqenYLc7YEjjUrdcUFIjMjOKSEE5zHUdis/hLU7m4hfqP49Qz2kYGtar2zk59emMNCDHPSz0nEak63XoNl4HTZ5b5Faxcq0kOgXjAsOee9lkGnX1FH0eiKO9zM0l0awn1HsGkZHL0JQ2QFMY3warhMBIN9OIofZctTBY7WzLFlH36QKXAxEhkpFDmI162CymrDgHDRetVaacNL1Ir6qzdolzFFg5ti0HDaTXxAHFGbgBuVSTRFBNzFBFlIw4BWkmtVcSX5AfJw5zrJuSgsoGJ+lABiof2C9/pu5RqrBKNlDprCfZCXKNVP/ZKwuBO3fyqE6jR1nq4lGaZ1O6379LNi/wBQCXjYNdoZCFrpuqYh62GP2vR18X8HqXgP9xlw7FKsxaiKtVZyHqf+iCiKeOCYwPbW1dOTdeapckh9A7dgBrqpf05VYCtO+PXCfLl6wE5+iflHCkW8KeNg7FCk7oiaB2ZboXzg9JmPWK2NXKscR5riD9UepWyeQIGYU/GMHloVmUOuUxioKqvQOTrEK1kqLMCgiHI5ianmVjoJ4y4CsET88ZRLxzEAJVMJcrG7ppHIXQldUjPHIFIYMRhoRij1TgtAZZFogWRTGIXD4GoecUdFtuVm3I8lYBjRz5Dtu9E9v2nn322bzvczUA9gYi0VqeSKuB2iCV2hupJJYCaeVNqkpMw7MTFEZf8Z7tWFgsFratBC4P+eDxRbC+0Z6TJXa+QZpFmxN4Pk2gpFkxPQ+c6Qf2rwVevgDMeOV2g+hkTxM8ue3JWPosRD5euyBnxq7fkPuxVRfz+NL9erx0RsDhC8IiaSJtgu3t2d/im1rfWLcZAhH4F475sGWNEd/+H6lVUj99XwETIb90Sa5QKS/WorZcl1LPKxVm3ALsFj5jgJV0zij4tXv9yrZL0gKFWkaK7Bzes189uZkhu/kpCR21PLwBub2WsuiZkBj8Oj8IvHhOvk6pzTdb9E5ITNNlbbUcvKZFUDpiS1lXCno5zRx2NGtY0JiePjora/ctF2qDX7GEgja6tnx+0igS0upNBINBzHs80Gi0KxoA01jsiLinoCtILVROlVm6kloIcxPgs7Au11a3sS0RfEE5+NJacPbUbn+reGsgsY2RN9ug33mHovQC8RNOo4Ouui2j2yhVCUZI51RvzNpkZzmw2VaGbxDO9ftYu/7mZutV0Y4kB9GSK7uCEWBkBrg8ChxcB7x4XuYiD+xdeh6Z3L5LwS9jaAo4ekVuj8zWvCcW1K74mds4vHROxHwA6FgIeFHl1aYE/alMoHPdqaBLtdIgnTFqTdzQyOP370rNeT5wUAdB4HFpodCxxMUxZ8tsRPoJlNyhFs9MEhqkHUbBL0pgriTIVJAMckqcHN61Xf1noXZK4rzNFRzmAxK71tRU3CUGv072Asd6gNu35KZvd3lUTpy1V3KMa5DJYrpTS9wiyuWvbePhC8m8kwwIbHmYwtUGvxYhLXAOUcT0rI9xD0FIzWN9/gA8814YDPoVDYBpzXYmgq9P4/ZLHUz64hpWtZWN7IKufh3bEsEXVkLyusG/gevXNwqSxLEt3/t8p2Hv3pgJLk9YDYC9yeBTuCXpyhrYlgm0OKJh1Fy3FqHJAeYYtpK2tRmPJyIwQqPJVYV6ASR8T9Wu0UX0vF+A2agsgLpckItL1pMX5LaCgSmgtQKY9sqtZbvb5AqwxMleCRUFHG7bKi0K6lMA41evS9jTIWsxZIvnTgqM0KTLGpKrIwXtlMrx/UEJPSMC2mvzJ16qFkfPB/D937qZc+L9N6ivENjRaWZbOlBg7aXjfnzmPS4W9OobCeMb353GwW1mvP/m9MyLrrlo5Vc4IrGWyHTmC7nilu063JKDC/QzJ0VcGKS2FBE/fVl2VfzEjdl/d9QpRK9VcoqMBS20iDRSO0wsLgwDsz6grQIodXC4Y0v6YzjRRy0e8s8b6jSLtuTkwPZGg2kRLlhTTUzPsQBYTa0VRQVLxM7vDzDiGQ2KmS0WlFdUQm8wIBQMQqfXr8g9Q0K0tGUCJVNMa65J3UZ55kU21+iaNmXcF+8sgX7zjTkd7yqufvBm5fFVV9POtkygwBc0Wpjq1iI8O87ac9Ppiq00wpEINDy/bIFk4hzRZBvdM+T8aDWujDZPrpyDWiInPbJDJAUhaDy+tl0ek9WA9Jdu3CBXXRHIIOjhoxL2raXgTHbHQ3Pha11AkUNaDIApgbSl6PiUOA1xHmpDbMhDu3+2oODXb1+LICJosGed+qUQ8atMlVkvnxVwcVDE+w9oWbvnlRER//54BNdt1GD/+vSvJZ3avZ3yc6ginngwVZHlG7ekMEbKhGdOy4HUD+6T8N+vyFWG9+7Kfj9UQUjadIlVhErXGQWKEt0bzw3KweC2CjK44tiWDke7qX1V/nlH81K7cmKQ+Y0AtWavayxiQa+B3jDGp+dRVeaEiyKkC+OPP0DGYUu8wm6zQK/TMv1DSsBRJ85KjE3m8jq2ZQIlScxtO1JqSQbPvQSNoxi6msyOSVT19Xar/FpFfnHq1KnU5jVGI2pqahYrxdViVQMsR7xZbhSkuyEGlvqn/d0n4btyAhHPFMLTo4gwK3sg7JmGt+88xDTaMQQhFMTEpVPwTo0uPkaD71jvJQxf6cJI9wWIaXrB445NlNDbP4zBkfHFc9Tc3JzTuaooNKCtxsJK0Gfmwzjd68XgZBDLBZU7x/b8D0yJePiYiLMD2Rep1pcATWVglWE3b5Qn9FKn3LqQDtT6SGL4giChqZxjrQV0jrT2ZvSM86wyjEB24F2D6otdP3GLFh+6Pv1C5PtPhPCPvwwy4ftEPHYohO/+JoDzfQJ6RwX8nx950T2cvXsjBYqeOULukfJ1MzgWwdh0+mtoU5sR773Jhut3pM/we7z0PXFZXVd9IxFcHgjjoWfn4Q+KLIDVXqdDc3V2i7Z//uU8/vd35uANiOwzPvZaAKNTyZ/r1JUIvvMrPzy+1NfU5SEBX33Qj1Pd2etURXF+gJzGgGs7ZOvyjhoStY9/zqSbw7PdTTg3kEyUaFFydoCqyCijD3xgL1BgAR4/CbySQkrw6dPArw7L2XMaJ4am5aDg7lYSfJarDqMl/RdHqL0g/jqL6kXUFPKs4suiB2wJei9vJlx2K9rb2+B0LLHiea8XQ8MjmJmJF+MxmkwIBvwYGejF7FR+3Wli4es9A//gxdx3wDS9JpmwrBLCl48h8MwPWCt+Nlh1ZHqHcI6gn23Ryi8/XS89p1jyLTw1jIjPzf4Wmp2At78rrXYMIRzwY/zSafhmp+J0Z0Z6LmCo+wJG+tTrylDCrbt/FENj08vmHHWlBrRVmxmhHp8N41SPHyPT6fmTGkx6pDhHwksjIn57VMTlkew5B3GL5nJZTPyGTrnVkSp2EwXuE0EVX70yLUNTuZzAYOfI0oz+CZ6JlBOGpiT0jKk791T1Qy2Rd+1IP37/6DkR//GU8md98riIHz4nsvelINF3H4mwgFi2YMG48wJ8Afm1w1MiZufT74cqv27aqsHm5vQRGFZZneV1NTgpoW+MnBcFJrBvM9H1BVQVZTfXff9JAf/wqwjjjPQZnz+t/LlOdov4yQsCe04qEJf8q58L6FX5/SrhTJ/E5m3iHC6bfC0Sd4jF8AyPl/qb0TuefK5ILpg4B1WLk7v6u3cBZj3w6HG5NVcJj50Afns0xiRoSmL8a3+HzDmiQSA6R9TSSEHdWETHkuqFAJnVRIG3q4Nz0PW0rqMd5SUOOOjAFjDn8aF/eAIzc/OLj9HnNJmM8Hq9GBwaYgLoKwEa4+dJT3qsL/edkKaXZxrivLKAYfjC6wi8+FNI4cDbnnPQHbMS2zsNGzZswMaNG5M2erytrY0ZDnzoQx9CIKD+mlqtALtKQaWlRCTJ2YtA4rWBi4ch+OfZ49ZNN0ASwjBSOakkQUsiuGY7+58QnptCaHYchqIK8FpH+tYfMVl4muM5cCJph9CAo+5mo3nIajWzLEUUJJAf1QwjFybaFWVrYxEICbgy5EZZgQmFDiPm/RF0DcyjqtiEMpcBc/NyEMaoW/7gd7xHYG0EN26ksmluMXM658ueFFAWljbax+MnwET2705RxfPkSZl07m4HzvYDL5wD9qwB1sckWtoqBVQW8ihayJQ+flzC+KysP6amrY10MjJhT6cWk3NLro+x2Nomf2/15Rp09UcwOCGycvcGhcTM4LiAh17w485rTagqiSeQpy6H8F9P+Jig/r0HzPjWgzNMSP/PP6MsMvHKST+qSrW4+Zr0qbie4TD+5LtTuPUaM9rqODx3LIIP3GKH06ZJcrOMBVV50eVNQbnWOh1+/LiXaZxtW5td3XtTtY4FZIn4XxqI4DcvBdA/JuCTd8QH7a4MC7jQL2DGI8GWojiNbgGqElxOoSQR2YEJ4MZNVP3A4eAGhWuE7jcuAo5LbvF49KhcwUiLJ2p9pLaah4/J7TPhhcI4Ip6011s3L2l5kHYMHff4nGwM0VgKbGviEHsWyInqzICEygKgtRw40i2ipZzDuUGRZWo7qnhWKUbtjldDu1EUVLlqNRvixiijwQibzQqLNTk4Sy2QJosFRnP8Fz010M3GzoJK9ZqMShACPjaW8/rcqnrFUBDBE0+BL2uAXqH1YLE1X6OFFA4j0vMqNDVt4GOsx1fxzoBAYvk8D41N7oGWwiH4Lx5iumGcRgMLcY5IGMaGDay1UmN1QusoYjpg0QBYeG4CxtIaaAzpqnIl2awhwbCB2moYtc9iPKD7lbS+TDGukLGcIyKIbJ5P1ADzBiLoHvYyp1inVY9ZbxhdAz40lZtQ5KDfxUUDl+Xi8GWqigdu2iDPr5Q8IMwmLNTVgAIOBOIwJHBfZJeTb4mgqiH6O7WWbWmU282OXAGuW0eta0vPW1sTQUO5flFz8YkTcgX7792s7nugxEsm7GznWABOCZuayJxIZInDE1dkDVVyKS5V2G//uIiXzoi4eauGaXbF4lS3iEcOCawibksLj+8+HEF5IYdP3Zqc5CKue/SSiNpSPmPl1+VhEd97IoIbt/BwmsM4N6BlrtvRiqxYN8tY3LtHg59JEjveulIOv3xJQEM5h+YstT3ryji45uVOCKooe/6UBI9XFtaPBQUtLw9L8AbkRKwSqOBQq12e5lbfuGxacOMmuZXzoILcJO2eT8E5fnVUNuAhd9N9HbKpFFWUURUY8SGq0PrNEaqCAw6slV9D2naRhaFicEqWGVlTBayvjf8g427g7KDEBPerC4ETvSLaKjmc6pPQVMZhXQ3P9uW0XD18Q4aEQocceI/CbDLAbjXDYk7mqFqtFmazmVW9xBUtDPaxSvTC0uVVUgl+L0LuaeiECFCaWX86EaJ/HgGq/qpphz6VNhhrt9dCCvoRuXIS2toOcCm6oVaxCsIvf/lLfPnLX8Yf/MEfYNu2beyxQ4cO4a//+q/x9a9/nc37X/nKV5hT5F/91V9BDVYDYDlCCIdWdP/B86+y6Lhp511sYKSgl0AuGQYzNKYChGfGEOw5CUPtWuiL5bIPnWMpDWiqaIC+sBzaDIOKVm9ASWu8ABW9n6u0Cp7pcdgLy1RH3el1ZSWFcY4dPT09LHNGQvlXBiYZEWxO8EGmyjFybqNMGYFEaPVaHtqFmdpi0sDiE+CwLP9y7ajWMJIUbaWsKyYCJsGeXvIkLSgYQIGsxLawKCj4QsEFajEjULcVBQgWuq6SzlUUB9dzmPOqC34t7UduN4hW4iRiU3Pqc1hVrGEbYWOzDi2f0sKSojqndzSCsz0RbGyJoKKIZ4GgunJ5351Netx/0IxNbdQyxuH2ay2wmOT9UFXUr573Yu9GE6rLtBifEfCvD3nQVqfDlz6UftHtsPBoqNShslSDCxcv4+i5YlaB9YX3u3B5MIz/99M5fOJuOwtQrW8xsHaW/tEwE+2/a78VzTU6bGoz4OWTQZS60md9H3nFj4v9YfzuPbZFgnvTjiXCUezkWZCYyHoibt+lx971OhQslNgngrKXDeUa/NEDy7joAFy/kcPONg7ONGLLRTYJ19b3orkq3h2OQIuOOZ+8QCIQASUtDKpojFYx0rUduw5aF9ONR4srEliOFV+O1a/Z2ii3FxApJ50Yaueg96AAG40VVx8RTR6zCFqtBqUlyXoY1PpI1V82pwsmsyVJJF9OHuQOwefBPInRF5bDVJWs1aUG4twEq+wSJ/rBNSoLDWob1rNNGLkCofc0KyvhW7dl3veqI9MbCiG8/GqkdAiefh7Q6GDeeTv7XRJCLIPPmW0suRYc62XGPMamTSzwRYgGvwiW6haIZbUZgl9grtOlLfErZ7pXXKWVmJ+dgrO4THUQjJ5XUarMOSigdqF3Cga9Bk3V8cKGVH1Njm3xnIOkG+T3tZl4BMI8bKbli5CTG2RsDrG9ikeRXYJrGXJmFOToqE7dvkUcYHR2qTWSqr4C4XihczpXfb29cZzjuk65rTKbICRV3hDHSaXVurkp9ThYU8yhZoFzbGnh0FHLpeQ7/eMSc5/cNCsnlig5SFpchHX1PDtuquoiHrZnHc+0rQhzXgkvnBKwq0PD3BCHJiU89IqAtXUS3rNPm9lRvIi0OSV0dfXg9HA9q5b70PVanOkR8ZvXBbxvPx0/h9ZqOak6NCmy9755qxZrakWml0a6YdHEZio8fkRgCcf37Ncs8tODG5euP3LfpGCJkpPibdt4HFgvGz4pga7z5koOn6tc3vV8w0b5OqI2zVQod4nYVd2D2mK6ruLfr9whu6JHE7+sDdIoVzRWFS1xjtjE4Mb6pfcqdcgyC/UK8lRlDmBrA8cMp6jikjgHVZwZ4jgHrnq+QaAigorSZDFWfyCA4eFhFLhccQEwugGJc/AZDMkyITw/C0/3aRiKKmHOwlAtFpHpUSDghTgxCC4Fb9E1bwGatyDSfx5C/zkW/KIg2NuRc6xqgOUH3/72t/F3f/d3uPHGJXmOdevWoaqqCl/72tdYMIw0yb/4xS+uBsBWGuFnfwzc9z9WbP+6+k4WHZeCXnBGK9P1smy8HqHxfoQGu6Ax2Zg9fSqBfMrqpwt+Bd3T8I4PwF7VDK0xmbAGffPwe+ZgMNugXcimZgOvz58kSEmVFUq8ymzUYkNTwSLpMhk06GxY0ikpdujZRugbDzKiWl6gZ4EEyhypJWtEAq6MiUkuMUXLcJyLYn1CooQyXD3jNHnLk+97dy8FEog8UAl5KhDJIwJAxClKCtTiR88KGJ2R8Nk7tSmDYIkYmxHh9VOlV/zkmSr4RdjdaUBjhZZVUj1xKIBfPh/AR28zY2u7nr3vwW1LwZ1brlm6vi71h/HskYDsjFRmRYlLgw+/y8aCYZlQ4NDga58oZLoJF7R6zEZMeOlECP/wk1nUV+pQVqTBxb4wnjvqx0feZUNDlR7f+OcZ7Ow04BN3ObCzUz6mL7w/WWiTrqUXTwSZAQBVq/WPRtAzHJGDNgoZ3iKnBh+40YQKBbdMWkQVpHAiOtQVYY6PH71Zj5aq5ZEVWnA4l5E029osbwTKxFLWdXOjrOkRjd0oVRdEcWVMJqqp3M6iLQek8/GuzTxbZGp4kZFTWjTl4ramBMp+0r2tIxXcNxBEWEkAX683IOj3YX5uBs7iUlYVVlxHC/DlfT4mMM4qbIpYBU4mUJJECgXBx4zn2uIqSM1boXFl6MumpGxZPXSbbwC/qsVxVSL0/E+Be35nxfavJ2dqal0JeBmv4I1WWDbfgNDwZeb2pSMeYnGCTxHgYlVhCaL7sfDPjMM/NQpHTSs0ChWNAa+HcQ6zzQmTNXtNMY/Xv5g0Y8fDATYzCUYn3zt2iw6bWpyLgQabSYtNTUuco6JQzzYaW7pHg7AYeZQ6dWw+iC6m1YBa0ohzVBXGj01qnOrSgd5+U4JELFVPDc0AG+tkLUfiHNFAAi38qfo8FS4MSSwQQQGpbPGj5+Sqm0/epP611OZIwbloACuKdMm+azp4tFbzTEvsiaMCXj4r4oGDGjRW8IxPXNOx9D1ft3HpOrw8JOLwRZFVje1eq0FFEYfbtmtU6Y4VOzl8+jYSwacENAe/lsP5QQn/8XgYNaUUyATO9Yk4fkXC+/ZTZZiE/3gigp3tPG7epsW6evmYPnZT8nsFQhLTIVvfwMNs5FgbKFXnM+1bheGeAnG3buNRo+AoSVxgQT4qCa91iXj+jIQHDvAoV+HKnA7E7TLphKbDrph4CPFjl1XmyAW2pcq02xaqzZVwaVRONCsF+uiepG4JAlWZl7tkjhEWRGZMRfdyvqrNWaJZykH4fpnQEucwGFiFq8/nhW/ei4KiIlakUFHXuOzPp9GboLO5oLe5VCXwqDOJqoJjTVN0FY0sOawpyMwjNFUt4Axm8EWVyzruVbz9cfr0adTWJlck0mP0NwK1Q46MjKje52oALFfEVFutBLRFVfCfeg6h/rMwk1OkRseEZnWuUhYU0xVXw1CV2Xo2NDcJ32gvrLXt0MYEy0gbjPS/RErHKMDiLITOaIJeITimJko/ODKZ5MRWQamZFFAzcFP//8h0CAYdD7tZi+PdflQUaNFQpq49iAjXvF9imgMrja4hWRi8qkDW6MiGNDy60Ib2Ozdn/75E2Cgbm01r3Q+fDDFC+scfMWYltkrBL0J7rRZ9rTrWOpkJG1v1+Pz7HKydMLpAaKzSobIku6GIKsc2NBtww3Yrvv3gDMoKtfizzxRhYkZgASuqADMaOOzeYMSmdkPG6+pYVwg/ecqHabfctvmJO6wsoxwl5BRg+csfzrN2zwduku+JHR3ZX0hE3CgLnS64mA1Ia4QCprs7uCQyNj4nQcOlbrMhTRi6TqiS64mTcjUBEUWq6LpBobUhERdH5FactdWZiWU02EV6OBNuClgtr/0zFt2jfky6w1hfb4VRYbG7UtBotSirkifkmYlR+DxzsNgc0Fjy4yLHa3WwpKjaUkKo5yQiY70wbTgYJ3ROhFQNqGJGU7q8ls1VrBw458q6cmrL6uE//CjCvWdgIqdIjmMO1Vpy6BIi0JfUwFCZXEmaiOD0KAKTQ7DWdUBDlvexnCMcTKkRZi8ohtFig8FkzkkIf3B0GoYY9xk6/hoqOUkBNYY6NE6NTEdgM/OMd5zsDaK+RIeaYnUBOmrdooodn4LmZr5xdhDon5S1SWksV2PEE03+kEQDBSPef23271vskA2AssFPXhBZJdGX7lU/XtP3Se9FaKmi6nhOsVUyERuaeNjMHGtBJJD5CgXNsjUaouosatnc1aHFg49HUFMKfPYuPUsgmo0i6ss41sJHAa22Gj5jAOXEFRGPHRHZNbJnrYYF84ijRivpKED2r48JrHLrxs2yMdHm5uznFZKWou82VWtktuibIOdpYHsLJcrij4f0a6niKhW6x+QAllEHPHtGTraRqUNzWfoAbSyvJi5N+mGZEOUc424Jbj/xfy6j4L5aXBgKYHZewJZmi+pEcz6g0+lQVSl/+ImxMczPe2Bz2GEwGPPDOfQG2OoXek9VIHDlOITZcZg3HAC/4CzMXKnLVXIOXgNNDm2WbyWshGbXO1EDrK2tDX/+53+O7373u4sSB+FwmD1GfyMMDQ2htDRzsjeK1QBYjpCmR5kGFwWmUiHSewaSzw1t+86cBidtSQ1En5NWQouPkZOXsb5T9T7IFZIEbInA+ieG2ABncBTBVFAKo6uYLXrYsYYC0OgMi8dJ/xtMudULUzaitNjFJqCRYW9O+1DcL8dhfT0J48sZUHKMsxrVz2jUGndgnXZZGghqQe1flI2i4JdaRFtNb98Wr9NAZOnxYxLT29jYmP7giShlixu26jDtlnJ2Gqou1eITt6ceSvpGI6x9kSqr6Lpa27gUOPrJk168cDyAr37EgboK9Qzt6Lkwuqc8+KevlOJvvlC0qP1F7Y4WE4+fPu3Dx+6w4qN3ZHaU/JeH5tHVG8Zde03YvtawWMUV2/1CQUXSMaGS+ihIc+znLwTR2ajF2vr0QylVPNHr22o0bMsXTvZITFOutSr+WqOg3k9elKuvdlMLjgKeOilrb3z4ALCrFXCagRmf3EqgBgc6ZM2ObMa2TfU8sy5XY5uuFlaTlrUzJQbcc6okiwhpW76Zo2IoyAhnJBxmLQf0fEdhCcxWe9rFu7z4F6GNyZbmE+S4JIUCrHJsuRCnKYvGgS9IUDhexZsGaWIg43MiF4+wuV7XruzOpSYIxlwdY+5psrnXZME5KLEmkHB+JILgTD+0Ziv0tgJYiithLipnnIPuI7oftDHXKlUc5BL8Iui0WpQWOaDXajDsz584NC1uNzSY2P/UNmk38awaTC1IbPvG9dTShhXHzhY5MJCNqx2NXfTZbtksi5HHBsXI8ISCaa2V6Q/+ps3Zj7sH1lNlDnJGXSnPtlQYmBBRYJMTTcRDKWAWxUMvR5iBzGfv0rHnqAHN32f6OBwdEvD5e/T48v06FsSJaq+Spt1TxwS8a6cW9+zJvKz63pNyu+MNm3lsbJQ/BwW+tAqcIzZH7QtKePyoyHhgus/PjlmQq+zW1vJYm8cYw8keuWK8vVquLIyCkmG/fE0Oaq1TcBmn7/uZM7KG6L07gK1N8nOpW4I0u9TgxvXZa5iR42NsYDEfoNbobBPNSiCelkljmZ4TDoWZCzUt+KnynO7bwqIi2B0OxkVSIRIi4zAupy4eNdA6KCkjJ0qWC2FqiLlZ8wu616tYRSz+6Z/+CbfffjtreezslPkIVX5Rde5vf/tb9nt3dzd+93d/F2qxGgDLERqrgxhb2ucIA12QvHPQkp5KmtaAVNCVJdS4LwjWhycHoXWWgFex0DEWVcKwsIhx95yDxmBiATBCNPgVcE9jbqgHttJqmAvi9bmyRSgUxsysGwUuB3Q6LWwtmavUMoEtOsMkcM7DZFg655112S8k89V2lQlGvbI2UirQpNaycK5IWD8WlF09NyCLw2YKgFGl0mvnJbTXyJVGarB2oUx/JeD2ivjz73vQVKXFF9+3oKwegw0tepbpLCnQ4MyVENMRs5r5jOfq5us6FsgDtdDGP/9cTxgjUwIjjjGx45Sg93Z7JVy31Zgym0ePf/2j8cc/5RZx6HyEtSxkCoD9+JkwLgyK+NL9Bub6mS/s7+SwpoaCX1xSsHhXGwU1NWipUb4Hr9+wRCZJT4ZQqZKIEnKpYpPbINU9d2w2gqFpAR3V+rQBs1Knnm3LxdjkLGbdXtTX1cfpccRibnYWM9NTjHzOTIzBZLGipLySEdJUi/e5kT6I4RDEoI9VwRS1bc4qaChFQhACfrmtIKayKxEaZyk0jtK8kNHQoUdYlsF440dTv98y9UZWkR00tszGBJR0o4Ev1wCYrjpZs4UCahHiHIUVqq4tc1ktTCVVEFnSbRBai50FwGI5h392Eu6xQTgqamGyZ5ElUkAgGMKsex5FBQ7WIhSdR5cDtuiMSKzqazHgpeWwscF41XIO0hiN6oxmyzkSnavJXfjysOyS1Jqh2oaquI93g7kQp9OGisXaupVrV5+YlfCvjwqMAylpfHXU8dDrqOVTwoUBiQnNZ0rIkCbTgV2tizqv5oRWzUtD5AwNvEvlMZIeGAW7dq2Rk4JKoOrzz98df/zjsxSIk9tVyVUyHX7yosiMaj5zG5/X4M+BTlnDNlHDkyrMNjdRAFaDxrLke5C6Gq5bS59LTmBH9UTLkhUpUkLt9RWLxMBiOgxMChifE9BZp0vb3lhVlJ+A0sjkPASdgyUSUx3i1NQsZufcKC0uwMTkBGw2G0qKi1nyzaAwB9N6aXp0gEVQg/Me8BoeZY1rsjou4itCyA+e10KTphBCW1DBKoSZkc4yQHNM+PBj4Mx2GK69723FOei7pS3f+3ynYdeuXUwv74c//CEuXpRd0e+77z68733vY/cE4YMf/GBW+1wNgOUI/Z570t70os8NyTMNvrSOaWPkC5G5cQR7T0MsrYMxhWggq2RwT7HMLbXRRI/T3rAWvMKxUEWC3mKDLmGgi4RDcE+NsyxCcXVD3ERNUVelwYi0v9yeeRiNeti1VmbZS8J0yynPHZoMYngqiLYaC2t9TMTEXIQNCKXO/J1natManJKwtia5tWwlQN9ZqnNFROjjN8hBtUzoGSXrbRFuH4dbtqmbLCi4+J3fBNFUocEtO/JUJ78Aq5nDDdsMrEpMCSSYT9uFvjD+7iceXLvBgA/eYsWxC5S5Aja1GhTPVU1JiJ0rJXzhfQ62eFFbmn7X3vRVBzMekVXHxeqTjM+IOHklgo/eYmCEOxAS4fHJ4vhKIKMAfxCYmycnKwl7OzVZmRukAhFxMlOgz0suSBNzJLIsV3ZRMIuE7ufn5xWvq6hexpsNCoBSW0PiQnE+IMHtE9E/IRuOmPQcC/DSuW6tyk/JfyzISS4YCsPv84LjLNDrku8Fk8mEILk+Gk0wW20wZqhYoWs1MDvJfrYUlrJcb7bH7e16HVLAxxLF1g0HU+qBBU49y6p3zNtuw3KhW7cnY4In0Tl4FSsL/e670v5dnB0H/F7wddktdjIhMjWEYM8p5gqpTyG7QBUw5FaqsxWw65O1tvAa2Os7mH5pIkh3VE/6ognVkMQ5qJ2YFm/FlbVx1xpJKyhxDs+8D3NuLywmI6wWU144R89oEGOz1FZthkWhynxkJsIW1sX2/C3Ihmck1rbeWZPsWvlGcw6HhcMH9kmsgjgTLo8AL5+XJSauUdHGRvAGJPzwWRFr6zjsas9vMMxlA3a082iqUD6HpMu1rh442yviJ89HcO06Hgc3aZmTJAUQmxScGulcVbl8KTkHuU1mMxq+a0f662Z2XgIZAJLebRSk7Uquz3fv5DAf4BAKi/AGuQVx/GSUu4i7ShiblR0id63JD5e1GjnmeE6JWTIEIFfHtirZUb6umJxJJczPK19XscZPbyb85BiuS26D9vhF5gjfPUpVsFQVycMfEllAvLUyNyfmdDAbdQj4fJBEufqcDHcSQU6Q5HBnNBqY+6PZlD7xL4oCAh65CtbqKkqry6gEutY9XYcYlyBDHGfntanHj1PPsvHd3LkPywGtkbUd14DLUB3/VuQcqy2Q+QMFuj796U/nbX+rAbAckcmNgpVyFpaDL5IdGmMRuXKCuV5oKprY78L0KHirU1XrCpWcGqrboC1Y8MNWAAW/vN2nmDukOYaw6sw25X0aTHAlVImQGO3McJ+8CCLv5Bi4Z2cwPTmB0orKJPczp8MGvV4Hs8koa4ENDiY5nGQDGvD8wQizIzfqlInS5ZEgI1/5DID1T0mszJuCBNm0FOSKTOeKCGkmkL5ZYzkJvHKYmQeGp6QkkVkl0LkjwqXkaNg7KmBgXMTuddqcFhREMO7al7mtpbZci4NbjTjXG8b3H/HiaFeQlZgrBcACQQFnuvqxdWOr4rmidshoS6RaEMF5/LUgass0WFO/FPjwByV883vzqC3V4LP3LV3rL58J4/kTYXTUa3CuV2AW7USof+cOMgdIPqb9G7TYvwF47HAEL54WUF3MMRt2Eswl5yoS510OSE/jTN/CZxHldoPXLgA3bxIgepZ3D64Uzg9FWPXA5JyIUiePdbXxAaeGUi2qi7Q4etnHdFK4BfJN7Y50beQ5/gWHzcyMOo4eOw6HqxhN9VVJ54xE70vL5LG3uCyzyCtznqpuYu1gJmcW5XUx0LpKIczPQrsQXEgFjauchJZyeo+kfS3Yl4eOPUVRCei33fy2cGR6W3MOgxlcYTk0CZyDglPUGsk7S5jGGwsmTQ+DtxeD02XOqlCWn4Jf2uKFMlEFhKfH4Os/D2N5PYxl9YuP6yzK/dR6kwUFNTL/iWJ+dhqz48Psxo7VDiPMzkxjdmYGFVVVSS0/hS474xtmkyEvnIMkBwIhARYDVQopB0MuDoWZ1mQ+A2C9pK00AzSWyW68K41M50qNSy8FsqhCjKqfPH5ZJ0tN5TlxDrcP8PqVk3gz87LeVi6gwOTNWzN/L6TZtbWVx9k+0uGKsMp5aun73N3J7+sPCOi6NICNnS2K5yoX+QhqUXz+tMgCdTUlS+9JrpX/+GsBrVUc7rtWEydmf7pX5nh0jnrHiPtK+MRNS46Xsdi/Xt7nI0fodWSqRBVUEn76soRtLSQnsrwJlFpke8bknymBRcm30/3Au7aImJ+8OjnHqT45WT4yI6G2mMeaBCOitiotGkslvHZRTsCKksCq7Vaq4sZlM2JydAB9w9SmKaG1PtkB12w2sY1QXpZZkoACXq7yGhaRNNtV6lnEgGl4OUsghnyMc6R7ntZVlpeKc4J2ofo4dOQxthbWKQTVVjnHOwu//vWvcfPNNzPtO/o5Hag9MlusBsCWAXJpDB97ChoSey1vSAqA6bcnF0STblik5xQ40tWoaILomUbo5NPQlNRC37E743tSZlWfQWBQa3XCUFINvSt3/Rat3sCqxQQhgpLKOpZVIJczq90FrU4HvcGgmFmgQdGyMFirIZqjUx7YLQa28FQCTQpz3ggsJo0iGSV01BjZgjiK3vEw5gMi1lTrVQndKqGzmkNdUWqHu6sBxy5LLFu1v5PHuX4Rv3xFwm3byCGTx29fF5igeyyBSgWqRPrqB4yKWefHD4dxaVBkbWvkVLShSd2QMTgu4LevBHDHHiPKCzMfAxHIu/ebcfK7c/D4RPzO3crBWsKvX/RjoC8IZ1EYbXXJ+z5+MYQXjgfx4VstcFjVkeg5j4TfvhxAS7U2LgCm1wEbm3WoLObZcRHBryvT4PotekZa68p5tNcIKLDzmJgL41cvhfHp22VXJyVQ5RcFv9preLYAoMCjxy9fvBQMous4FzLdVEaaZJR9lbU55nxyZpYqwEY8eNPROyFn10vsS9/HpFtkgboiO49CG8+qu+jzRyv3SO/EGxSZBg8FvUiQ2mWVtTfoWqXH+scDqCg0KFZp5AqabKmSJJ0WWDYwULt8AlgQgnQXFzKewalh+Ef7YGtcD02C8YhxIRiVCfoYnSYKegSOPg7eZIeh45qcj11yTwJhuQIvFjRviXNyZdsq3jhIPg/CR5+EtnUr+JL4gBQl1Qy7705+UdAPgSq4CipYAIz03cInnoamqhW61u0Z35PMd/QZxO+19kIYiquZSU+u0JGQM69hIvnFFTWIRMLwut2wOZzQ6fQy51Couqf7lKok1CAiiBiZ8qHATmOGcrUzOT26fQJcVq1ixQxxnM46fVzF6oXhCBu/OhbMXXLB5noObRUU/Lp6OcfhSxILBmxv4XCiR8Izp4A7toO5ET99CkzYnlrr1ZjBfOEuXpFzPH1CZGLqgMha3kgEXg36xiW8ek7EzVt5VQlDmqMPbtTgH/olBILA+/ZTVbbycx89LGBiRERljYRyhTwGuTlSIO3ePRrV8ze1J754hjhFfACM5sm2ag6NFRxLSnoDYO6NB9bzaK6UWMtg37jccjg9L+HhwyI+sF/WclPC3rUcWirAeMfEHF3bcotrtPqaprnYSrNsOAd9fUU2ubKLAkXBCFBgAebf5KmB5tcrYxJcFg6FCzpv9BhVWFIrJrmwErcPk5Uja8/kFqvOiYdtbTaw/yne4rSSxpu8X19QrkivLdbHybEsF3aria118lXVbrYpc45Y3UXfxBAC02NwNHQw/ee411er4xzGhvVxa1v/yWegcZTA0JjGPjwNGC9yT7JkTiKIb4geN95qkCQyJstzBVie93e14s4778To6ChKSkrYz6lA9w11pWWL1QDYMkD6XuLwZXBGS1IALBVINF+//VaKMMm/mx3QVreDL8yfDSyVk5oWCGvE62Y93Nn2aBMZLW1oYy6RGp0OntlpuKcm2IVmdxXBbFl+WVQwFMHUnJ9lwhIDYBQcuzTkgdWoRUedLa2Ggd289Nm6BoOY9Ahs4kplJ60GRCbIHe9qBhHQGQ+wp0NiZfAVBXL2lcjSvbt5VdVfBCJZ5Bil1GJ51249RqdF/OLFECt5VxMAe+pIEGPTAs50R7ChWVAVAIue82//jiMjCaCAHT2FqrOU0D0UQVdvhAUA1QbAXHYev/9uCwpjAjQEWuR88EY5SPHd3/hZtddXHzCjyMFjc6u82NndKX++rn4Rxy4JzD3SnObYOxaCdrRo+MP36hYXUqRbQgGhz92V/bDcUMaxDO/i++iBmzdTlnnpXNK+6Tuk9oVM2flHj8vukCSsvFxQe+aZARF2U3wAbGeLLi7gdfhykLml7WjW49JImLU6eoMStjUb4bQknxOPP4IpTxhmoybnAFhEEBAKR2A2GuJaIctKClNeh9Ey/OWQVXLK8470wFrVAoOziGk7Mnc8Kd8ZztSpaykcBMhdOE2gT096HAq7CL/4C4QnJ/J1kKtQCXFugnEOwVWSFABLBeIn+h23s/8JvKMImup2aGIqtZYLXqeHqapZll/wzkFjti3qfakFaeiVN7Sy+0Cj1WF2ehJz01PMbdVqd8C6oPOxHPgCEUzOBdj8kRgAowDWhQEvCmw6dNZTC3Tq46dAfBQne0OY8Yos2UafP9dxgXSo8uXUt1I42SuLspMDIFXGl7lkUXOaK0hIv1plWz1xDpqjlIaed23nWWLv5y9JsJokNFemH9vpnFMl1bQbuDgkYVOzpCoAFp2P/+C+zNXtVO1HT6GgiBJ6x0RcGZaDVWqkKgjE0yjoRuZGsaD5kAJphAefiGB4Gvji3RoWNOyokZ9buCAFeWFQYJ0KNGfr0mh1Ni0UK5Pz5efvkBNIdN4efEp2iXxgP7IGBdVoi6KqUN5i16HEhShxlUmbjp732AlZDH9jHoYlXwg4NyihyCZhV6t8Luk73ruGvuslQfwXz4fY9LatUYeuoQi8IZEFBfeuMcCsEOCa9QqYdAtwWoScA2DhiIBIRIQpZvwpclnTVsvlg3P4JkfgnRiGs6YFequdjbMSVYzns7pNUsE5tPqUn4OZr+19r6IvQOj5/0bYn5yMW8XbF2JMxd9KVP+tBsByBN2ovKsM+us/BM6S2WkuFrxtKYVELS26pk3s53DPKYgzY9Cv358X3bAwa4U8zarBTBXJVWNh3zwbrFK1RtLCSLPAUCx2J/vM5HRGmJ2aZIOyqyg146Hnk11pSpFPow4Nla446/IoaAj1BwUWHDAZ4icGypTQptfyitlbmtw2NRlyFv6kfT99RoReA1zTxudNA2w+ALzcJYt/JrpDZjpXsTjbL6FrQGKZV1aGrKGgF/CRG5bO05oUzn+JIEL3//0sxMjYJ29LZm4lLp5tJNxOJCsT6Pw//GoIJU4ef/hBKyqLswtMqPn8VSVaXLysT+nGdedeE/ZvNrKqrGzQWpN+9bGrQ8c0vlJpbty1R4fbr9Fldd1R7Ov7T5FLppyh7RmTcGVERGN5frKLsdfVI0dkm/IP7E1PSIlrUSY/k7U9BanpmiZing7UvrilgU8SDk48T5TtJwkM1hbpFlllGFUzmvTK56LQpoOhNjsn2ESMTszC4w2goboEBr1O1X04MTKEUDCAitqGtFViEaoQ9vsUWx81JitrD4tWexmLq9imBBLPDwxegKGiCRqTuiAABR9MW5LbFqOQ/PMIvvwLFgTRrd2Tej8pMgia9h3QTo6rOpZV5Ad0TVKijbvxI+Ds2bXTUvvj4n60euhatrKfQxdeZ07V+g3XZR2wUkJ4hlohu2CsaICxZEHlOgYhr5tdU4l6o4vHSffTwj1lc7hY8Iu09ghTkxNskeh0pW/LSXf/2i16NFXaYTZqFcczf0iAP8SjvDB+gEysTo0FjVekT7i5Qd38rQTaxzNnRBasuKZVdi7MB0is/NBlct+Vq4Nz5RwneyUMTgL37GCyQAw1xRzeF0P/qPJYDaji+buPSSxhc/eu5PcudXFsu/9aiVVgZ8K8H3jhtISaYuDTt2oU2wHTQc3nLy/kMTykV+ScUU2v6zZmngsT0aygNxaLrS1UWS4tOk4m4q5dPEv0kmu1WlAF34+flxgHpW3SDQxP0+/5ueZir6vfHJZbYz9wraxLmu6YiHNQICwTN6fqNTVi+FsauKTvI/H+dZgoEAiWaJv0iChzcqgv1qTkcOUuLaxGHjZT7mPlwOgcvAHqXihi97mae3BoeJgVI1RXV6d9bijgZzqKShVgOpMVeosdGr08tllKa9imhAjxg9EeVkihxmwtWtxh3nxjyr+L8zMIHn6YFXzomjan2Y/yhaJdsxNajxdvNdD1lW/psregFNpViZWzYnmbI7rw4e0FqoNVwsQAK+9M195A4vlsRlP6eziEwKUjiJDYrQrQYknnKoFuwfUxEXP9FzDX16X681odLuY8Qpj3zLEt02saGtIvEi0mPbQKtnA0MXQ2ONFYkVxp1jXgw/Er86xyLBGddUZsbzamzd6qAWlazPpkYpAvEBkdmAJGWHl/9ucqisEJCb3jMtFQa+OdCqTdQK5IbTXp37ehQoPmBL2EWAxPChiaFFgp/effbcY1nToc6oowwhJFV18EX/5/bpy6nCGykgGb2oz4nx/tQEutPJFTtdkX/34WT7weWLx20gW/qDLs6AX1x0DP/9q/eTEyLeKuPdT6y+HSoMA0wGJFOaPByHSgtr2eUWrnk19HL5/xyJV86xt5uP2UTc7fNRe9ruaDPEZmwIi0UpUBBbJeuyj/T0SVbMr3KXtsLOLsoITHT0kYm0t9vK9fjuDpMwIGp6kCLP25mfeLmJ0nwwEe25oNWFOlR2VB6hUQnW+bKTdtuiicNgtcDgtzrFV7H1ILuFaXmbR6xgbhHulFJJAsdENJBxIIJzHwTAj0n4cwM4bg4KW0z5NCfgROPIPIWG/GfbIsrKMYXEwyJhto69dC37olp9euYpmcw1mctmovFsJIN1t4pAIFv0TvXEpGzYKvFw5B8Eyrej8y3iH9GBLDT3ovScJcbxfcA5fU7Uujgc3uZJ+bCTN7PJj3UNIuNdTcvzYztS8m/504w8YmO+rLkiUcTvf6ceyKV1GEeWuTHtuaiMcsby6mCt1JDzCWnlZlBRIpH5yS2+2WwzkGJsEqjQz67IM8iaDKL6pIoqrldGgo51BVlPo5pHE66ZZgM3P48PUatFRzONMnxn1H1Jb4f34aQd/Y8qoHtrRo8al7m1FRJHOgwUkR3/6vMI5ekvdL332680LH2TWg/hjGZiT8zS8ExkMPrNewuYaq26LvFxcUT3Pd0bkIhkgqgNr55PNCywuSSCCuQQ7S9D9VmeUL0etqep7HjFd2fVRyfqZjeP0SCdLLvOS+HcD29J3WONEr4bGTEma8qTnH8+cEPH1axIRH1gxNB7efnieypCZVpK+p1qGiIPV6jnXAmOXvI1cUOMwocprZukftPUiyDMQ7Mr3vzPgwpkcGIJCAfQKo6stZ27IYAEsHf38XwrMTCIym5xKifx6+0y8gMj2acZ9MF9teBM6S2c1YCdqmjdA3Lsk8rOKdgVdffRW//e1v4x77/ve/j/r6etYa+clPfhLB4EJPd5ZYrQDLEdm6UVDwKnzsCXDWAhiuUXZz0q3ZBZ0kpsy6i4F5RKaGwWkN0MZkdNO1JZirWxX3Nz/Sy9ptyJ6cfraU1WY1qJdX1WasnGWEd24ODkfmtjbF41fIglJAhdqhKJOTas5Ybh89BVCu7+QxNC2hMk9ZMQKVh1NggVrBCEQ66DOQpgOdq2MX5zA458BNG5daIejzPnyENAuAnW3ysRzcSM5JROSXf2zUtnHvtbqk9gQqW88muPZ/H/KzLN6ffsKKmlINqwK7OChg/0YdnAsVU9SySNm7h18LorMp916PxOuKziG1MugyWJlH8auXAjh2McLaRevKk4fAS4MRPHYohPv3G1n1G3vPBaH8KJ46GkHPiMhaQh0qu4GfPBrBy2dF5uByYAPP9MDoGv8fd8hl+fRZfvc2DVwJxRGPHZW1UK5Zw+V8rqw2BzqqOVQWKJNRCsx2DdPiBlijXIjEQCT60BWJ6WeUOji4/dLi9awE4uaUNCfNjXRVg6f6QrCbePY8CqLmokmSC6wWco4zZjVmFRSr0zmyllQi7HdCkyDaHfteYtCfpPkVRcQ7C4gC018KgYOxRhaJTTfHiPPTEOZs0BSUM8LJ9jN0kTk66WJcg0n83LA1dYWYGrwVHZneysj2fFMyjTgHySvodyTrkRKo8ouCXyk5h3cOAgnmm+3QpBFEjoI07cw1bYoBuvmhy+A1PHQWG+bHBmAtVVkytDCnV1XXZJzbl8s5lCqvaA6mlseIwCvuMx+6PdT+SJxjdBYoy163OiVIUL/ABjjNS+O8WS+3LNK5OnRuDtNBB27YQFVn8nOoCufRY7IB0IaFdrSbNsoVwYlVvLmAgkV37ojfT9SAR21wjb6Tf39KYJ/r996lRU0Jh8ePkcYTsHuNrN0ZNQaiAAu1SD5QmntSNPG6ouuEWh0X8iYZ8ehhSnxJ+MztyklLCo4dvijhjp28fA44uSoq9p5/9pTIdD0769U7XD9xXMLZfnnePriBw4YGuZ3zkzdKrPqcPsuHD9D1HX9unzwh89INDblzDrvFgdYKDvUlyoY1lMS9MCxz24bS1KY2pNNFgbIKFx0Th2AkvTspXcdM5ysNfyV90VP9ERTZNex+IB5M1ZdvBJw2I9uyGa9KS0rU7bu4HJFQkLWQp+Qc4WCSyUgU4flZVglsrGhCcKIfxgzSPtTSKHpnIbgnobEXMr1Itp+BLkCjhW7B6I1Aul6GzTep+hxvJ84hgmNbvvf5TsGf/MmfYN++fbjtNtnd/PTp0/jYxz6GD3/4w2hvb8f/+T//BxUVFfjGN76R9b5XA2A5IlM/Kjk9CmO90G+9hS02aNOuuQZcmhYWNgByaVy+bAUwde4Dv6DlkQmB8X4Ehq7A2rIJWgU3Jso6COEAgnPzMBVVQKPCEWrxWBacIec9bng9HhSVliX1sNM5IgE7si7NhxvMjCeMS0M+NJSbUFmkvGikyXt0JoJih3ZZi2h6bX1J/gcZchmSiQ3wyDHAoAU+uE8+V2Ojoxj22RAIa2ICYHKgLLb9kMiXLebjH70ssoAEuRrlAw8+FsbcvISvfiBe6DcdbtlhiCsi+MANBibwTgKiUXQ2abGxWYvqEuVrYWhCYK5SJESfDonXVbFTg299Sv2q4eAWA2vNrC5VPo7BCRHdwyLGZwQWACNHqkCIRBaXzsW9e3WYdkusmuvEFQHv3qfLuDhw2TgUOjim+dFWzSsGeqOCrbHXc9cguYBKOQXAoueq2WbDnjWp78GWcsBqSG7NTcTwrIT+SbmNpaWTR4kj/TFtadSq0giTA2k8akvSB0Zn5sOMrDoUNMHygVzGrIB3HkIkBIsj/uTpjGa2pQIJ3/uGu2GpaYPBmdxK7r9yAlIkBNvG62FultvkU0HwTCE8cJ4ZqYTOvIiAzwMuEmKOgMLIZUZUtTVr8rJYj+ocrToyvbHIdL7D516FNDsG3Y53sYAWcQ3iHLwjnUwBr6i3EgW5TXPEOUzqZB78w1cQHB+ArW0rNEk8hZIVGtYWLIanYSmuVF3Jxo5lgXPQgtHv97Psb2LlRL45x9hsCFdGgmivNjFR/FQL9LEZAaUuzbLkEqiNkoIF+YZrgXNQNdHjJ+TfqZWRztX4+CjGBRvCEQ00C/SPnjc6I1dqxQatoi1sdP8fvULtYEClSp3RTPje07IY++/dqm5/xEuuW09t9UuPvXuPhrXRUYAnio1NPLpHRTRXKO93aFIW9Sf5gXRIvK5IMuKL96hP4u1dx6O5Ij7QFHccU1TVLzHORAEwqgingGM4hnPctZNnwTxyguwdF/GubamF76OgyqYCq4RiB1AXk7eJ5XWJATn6/i+PyEL5FDDLmXM027CnPfU92FEtB2IzcQ4S/B+clhAROexbQ0m89J95d1vm+142upCYJmlNBpmOSXeErQditYbzhVzHK9/8PLsPLQm6iKSjSFvK100MwTc5DEdtG2uHTMR89ynWyujs2AmdzZn2GKgTKTzaA2PLVgQvvA7R7wEC89CW1iPcf05utY8JgC0Hq5zjnYsTJ07gm9/85uLvP/7xj7F9+3b8y7/8C/ud2oK//vWvrwbAriaInilIcxNAJAQsBJaiNq/LgcasXm+Mp5JTg2mxRTPi97KWBr2zGNbyOqC8DmIkzDY1wS8lIcaA3we/z8scGGIH8OhAFQ5HMDE5A4vFzJzVll+dFV+JozRZXRkNsQmO+vUpuPBGVZOoAZ2W/35VFo7d2xFfjVNXAlxbJ4uSRkHHfsc2qpCRs6SJGdInj4s43k36HhILgFFr3YtnJTSWkbNQbp97SwvPdDWy0SHZtXYh8xORWOvjugYKdMnWzpSFdVhIT47DR29NPTn/+yN+jM+K+KvftcWR2EStlIeeD6DSJiJDtXxKUAAuVRCOsG+DjlWHfe+JEG7fBXQ2atBey6OxYuk1pAVW7AR+/kIYFwZEeHxSxgDYlhYN27IBnbNP3KhctRWLS8MSy6YeXE/tBLlUPgDVyp3ScRBF2reE1hjx2yjIYYkys9lmUknLbU8bafZlHn8uDPih1XLY0nz1OFTMTQyzrKvJ5mALfDUQIyFozTborC5oUyQ0jNXtTKhWzZgZGetj+pEoqYW2rIE5AkZ6TkMKeKFfT1U+6vajBqHH/x3Qm4CNy8vmriK/IL7BrgFSoaYAGLVk169b9n41lvQLoViQXgzjHAv3QYRkHSJh6O0FsFXJiyEhHJKvaxXBLyXO4ff5WACMOEZsACzKOUKhMMan5pgbtc1izAvnSFd5MDoj4PJImFUJk24hBYvypRuaD1BF189fkytodrfFm6A0lgIH6uOF26kglgTtL43I7WmxgTACVaRfGZUr2u/ZJc/Jr3YBbVUkip/b597YoFzxnw472uTvnt7/VI+EtXWUGJE5UCBEPErmfu/dl3pM/q/nBFbt/pX7tWmrhZ46JqDMkHv1CXGxdHyMnLyJw/3gWQl37BBZgK2hHHEtoFFtsyOXRFwZkTWzMlWgbWvh2JYNyMWSqsIy6a+dG5Dbaw+sS6/vlQr0GrqGMoEM7+ibJofURIzMUEeIHDzOBk4Lj30dOlYplg50LZ0bCMJi4LC5KbNcwRuFyYkxZpyTGABLBxqHSXpBb3WkbINkHUMqtSDD430QKOFSWgdtcQ14ix3h3jEmxWCkyuI8uWhLQgTBJ74H3lkKtOXuaP1mYdUFcnmYmZlBaelS9P7555/HzTcvdS9s3boVAwMDOe17NQC2QtCt3w907GGVX28W9K5StkXhG7qMiM8NB7UzLAyAvFbHNjUY7+9mxLWsfskarrC4FM6CQmhj9jE358bE5BTKSksQCIYw657H3LwPpcUFcNpzd4+kwBdtkUg8EfH4BOYGV11sQJFdyzJYRETJVY5aC8hFTsmdiYI1J/oiKHPyqFbpVLhc0CEQAaVAQXN5/N+I/yWSTcLQNLlbAjVFcotaLKg1zmyQsGehu2nKDRy6KLsSUdZzdwePvnERv3pVYq2TazJofRF2r819WDjTI+CXL4ZZJvPWHXr86uUQXj0TwRfebWJCsulwx265aixV8IswNi3i5TNh7G5KbXm7HCcuAr2WqsooM0oBRwrefewW5ZLxO67R4rpN2sU2z5WAmoASabT0jEF2oVohJzGyFad2gXu2J4s00yLhlYsiy+juUZGBTYSalg76XporTfniVXmDq6wKQiSiOvgVnJ2Ad+ACLFUtsDWsTfk8XczYnQmiZ4ZF1zX2ImiL5NYybUVzXJBBCgXYczgVumPpwNwEVQrjruKNg47cpSmwtOAw/WbAUFTJtijmBy6wNl/X2l2LQTG1leY0jo/0XYFGo0Vpdd3i4yWlpSzYFa0II0zNzGFqeg5VFcXwB0OYmZvHrMeHqrJC2CxperQzQHaTllhCLRYz8wJzhKOK1TKXhh0rOQi/ejEEh5kE8Q2K8xA5zJ3sFVBTzKN8ob1+pUFDNQW1SIupLcFsnIYHJddCmk8uDMktlNQKGQsybKHKq52t8u9ULXaiRzZYoSqnTY0cLo9IeOYUcMNGSuxlHtuJp+QKCn49cYyMkSgoxuHXr4noGpTwe7dpMrpB3rSFknTp909aYye7JWjKpRXjHBT8I2dHp0VOpBH3eO9ejeK+b9/OswCfGiH4XEHBQ7W6cBSIizFFzRvoc18ciVYsJnOOWa+E1y6JLLC7vTn7A1CTGKdgdmulHoZlagrnGyVlFVm1A/qmxzA/OgBHdRMcNQs3rgIMWXAOgbQjaTx3FEG78DptaUM85wj6yEkH3HL4AscxzsEZch/H30ysiuAvDxT86unpYZVeoVAIx44dwx//8R8v/p20QUkjLxesBsByRHRCEoYuQ5wahnbt7rgbn0XR8xD8YoNcOBg3gERmxhAaOAdD4yYmOqsWpvI6CAGfKhFEJWj1ehYAiwXLMicE0IiY6nU6aDQ8iosL4XAWIBSKwGqOH8BYCf70POxWI8wqvKPJnlyrsTDh61iMzYYxPhdGgU0Lu1mLqiL5eIiYGrQcsy0+0xdCR40exQ5NnOjs9Dw57EjMfnmlQCT6N0epHB24th24TcEAhc6jxWJRJFGUHSU9hsqYY6RA4MkembwGIxxzaGqukB2U3nMt8PBhyspS25yE509JzIlnIo/iukp44nAYJy8LuHGrFtva5O+orlSDiRlRlV7ZukYdWyD8+uUgNrdoFR0kSV/ss/eaIYWUNRN+8EQAFwYE/K8PmJkuWK6gYN0fvj85UPCdXwdZa8AX7jMsEiRnDjHdF86IeL1LxMdv0qR0lcwGVE24uYkqCznV11U6UCsqVQ5Ej43afI52y3pfN21YGuf8IQmvXhRRV0zaYRycedClSzcWTnlCMGjJiTO7CY/GGn8wwkw30iGX86XPMqBE+hvkAplKGywXGFq2QgrMg6PKrAUkVtiEDj/CgmCGfe9V/HxMQ2y8F3xZY0onJoJ+3/3sf9+sgpvHKlaec/SeZQFP3brd8X+nAJPKIGw6UGUBVa7HcY7JQYSHLsHQuk21BAPBUtHIqh1TaYylA+MWOv2i8U4UVPWV2PqoI86h17EgdGlxEexOF0IRAWZjopsjcQ4vCuwmGEl/IANKXTrmQuuwxB/D8HQYUx4BpU4tzAZqo9Kx8ancqWGBieFpAecGI9jcoIMrRgYgEJYwNS/BYaYKdawYaOx++BhQVUAi/cDtW7Ib67Y2yxXpse1pZDp0ul8OflHQgxJzFYVAbQmHu3bIWqXuK8CmRuDFc7KhituHFcUTx0T0jUvY10naWPLnoEor+vwU8MuEtXU8S948eVzA5iZeUZ+rqYLHBw5ogaBV8VxRFRmJ3JN+53LcO6nai/aRiH95XGTn/AP7NYvJIrXaY7F49rTEgpoP7M+PlhtVfrHgV8I0livnIBkEWu5EOQzxrOO9skbYdWuX9kXyC69flphsQ1slh5I0+qLLBQW/af1gM1GlWXZjGI01wZAAiym9kU8u58poyi4YpNWbWKW5ZkEXNB8wtWxh2qKxY3si5wgcfpQlZIwpNCipWozWz3xZfcrKM9q/Yf975J9XOcc7Drfccgu+8pWv4C/+4i/w0EMPwWw2Y8+eJefyU6dOobGxMad9rwbAckSUgAlXTkAc6wNnskDbnNraNVeEe08jMngBxg0Hwdtci1F10edhui7pQIQsNDkEMRyGJEZgqmyCLouAWSIKy9WJ1lK7I22EutralM/zB8KYmvWyxamaABhNEg7L0mQSioiYmY+gukTPKr8o+BWL5nJ5n9Pz5E64ZN8dBZGAfWt0OZGJVKCWP5rLSKcoCgrWU5aROlPSXU8U4VYCkaqqhPY0yro+e1quItveEm9BToT0fXt5VtpP52znGh5loyJ2p9GQYi5b/uU5PJHI/XxAwo41usWA16YWLdvUomdEwDPHwgiFgXtTtC40VtH3WpMyc0kLkJWqErJksW9ydyRXKmrVSKxwovJ/ajHIU1cac4Ii8vjQ6/KCJxosTXddpcPz5+XFyz07JNbOQxsFbxMdJCmITNcNuZcVWpFRE2y5oGrPkC779BcF2mmsqS0vgDWFgm4kEsHM7BzKyspVOaPlCmp9dDRtyOs+qf0ACpoeUQgjVxjZ5ItS26gLPacQuXgY2vWiqnb9lTxHq0h9viNdhyHNjIOzuaCty2DVmgNCl49CGOuDcctN4E3WRQMe0e9mmnSAJW3wLDg5yNptOHAwVeQgIhSDkkrlcT4RdpuFbYS6utScY94XwuSsn90D5YbMmQuax50x2l+BkIg5r4DGMh0qC0n3MTbpyaG9Sh4gx2ZlzpF4i1Dr1YF1HNP+zCfniAqaxz8erx2aiHRzA7WGJWozkfbjS+eBNdXA5kagtTKec7znWmnRVp6qw0iQfl3qr4Il8XwBddVGqUBBEmrT3NLMs2p/Av28JQt9hMvDEl7rkqDXiti7Tplz1JfT48rXopkq+kNppfSWBZquYrXO0mFiTkLvGLCpKVnCgjgHcd18cQ7aFzmlP3tG/r4pULUczvH0aZlPvHvnUuX7njYJC3rxi6DgJnEOur6o4jxRNzWfIH04ty8NcU+DoQkvpj1BtFY7YTam0A+MCNCbbOxeWMnZlBwgC6zq5XPy0RofISF8SQRfWJH6OZePQ+g/B51GB01JzduSc5DpFW353uc7Bd/85jdx9913Y+/evbBarfje974HvX4pXvDggw/ihhtuyGnfnPRWtFV4E+F2u5ljB/WlOp1OFogKPfYgYLLAePPHU75OkkSEXvkVOIsD+g0HVL9fZGIAkeHL0LftYNoai/sjgpmhdZHaHecvHKGeAzYQOdZes6gHlgtCAR90pO+RZgadnZ1lAa2CggL2//T0NPs51cDl8QZgMpKFePYDW++YH6MzITRXmFFoX6G+ryyJ6K+PyEL3+zuyG6DUnKtY0G17pl/WUMhHBdFLZwQ8fULEB6/ToKF8eW5Jy2kFoNef7haY3hYFmxIxOSfiO7/yYl21h3rEYNTLgb5bd8YzxIlZkVWC7ezQLisrqxYXB+n9RNy4VbNYWv/YEYFVed2/VxMner/ca+yFs7IAcVsVl+Sq9PBRYFcbsLE+t+sqiu4xCmYCnbWcqmN6uUtkZPjG9UsLkXxeF4vvJcgBZnrPibkQih166MhmMgN8gRBm3D6UFtpTjjWz1Lo9Mcn2V1NT85YkW6lATpCRS0eg33QDsyJXguTzINJ/DtqG9apaFmisd7lcsuOXPb/kehVpOMfsOEKP/gc4VwkMN3045euII4Re/gVzgdStja8WS4fIyBXGOwxrdsW1U0pCmAkkp0PYPQXvlZOkrcBW2o51u3O+72nMCAf9GTnH1PQMu1ddTkfG8Y4lerwhWMw6aHK4vy8OBTAxF8HaWlNSVdibAdK/oupyagPb1bqynIPG3PMDcmXYcoJWUTx3WsTRS1TZxLHK9Vyx3LmFqnwuDEloKCP37eT9jM5I+O8XwmgvmwFvoKggz3SyrlkTf87GZiUmqr+xkcub3mI6UJsniefvX0dJOfn9Hj4ssu/oPddS0jQ/x0CSIhT4JBH9RJMGqih77iywr2MpIJor57g4TEL3ciV5JpA219OnRfjDwB1blN1Z88U56L3oY5BkysRcGGUuWq9k3q/bG8LsfAiVxZaUHHR8ahYDgyNoqKuGS62d+FsE4b6ziPSfh2HzjeDNylpl4vwMhJFuaOs7M65n32qcIzpv/+KlCVjyHHz0zrtx9+7it8R5yBeYm73VmmQWQWMNPR4bFFOL1QqwHCG4pwGnk93Y+v33M8vXtKAwI1VsZajaSoS2uJptiYgdLKIDfXD4MmtxMdbJujIakw2mmjbwRjN4rT4p+CWGQyxFyasIivnn3Zge7oPVWQST3ZGy7cc9NwdhIQBGxzU5OckGrFQgkVq16B31sgxsa7Vcil5eYICetUO9sZcxlcyPzAL1xfHCrTTXkrg9tYllCzXnKhb0+dNlV7NFWYEs0rpcLat0hINaJOe8Eu7bp0/7+s40zoFEQkhwfnp6CqdHrLBZNOyxxADY08fCONwVQVkBj6bKlV+onOwWcbpHxLY2fpHMk64J/dxcmT8yzFyg+oDZeVl0OBa0MPngXtIGSX9dPXZCJrXvUmiLiaKhVP0xE7nbWM+zdgilBQS1Sb5yIYzqQh4tFdplkVOqdCOMz4UwNBlglQ9lBZnHEKowzVRl6rDbWMXH0NCgKn0NSmrMjY9Ab7LAbFcvFJ5PREZ72MCjLV3SSFKCtrKFbenAkSh/23bV772aO3tjIZDOG3EOZwn0B98HZGpFlCRIoSCkSJaco7yRbYmIDX5F799A/3lW+kKGDey1NhdM1a2MexBHSapKCgfB89q0LbZR+NyzmBkbgr2wFEarDXqFlmE6jtnZOUaKKQCWaR6l47GT3a1KXBzyssra1ir5XFcV6WE18rCb39jgOLWHTbrlAETsOaVYPnGO2DF/pTgHjfNr88k5XByqiiWmT7YcpJtHnjwmMl52cGPq74s4XHt16n3QXEmV7ePjU+iZd7JqbouR5CXi9/ncKRGXh+VWxpI3YDog7TNySNzWsvT97+7gWKAqVi5juaBqq64hsOBUYgCMgl6UjIvVpk28ruj+oSAtqQ9c35n6fVpSOHUqgQJQO1qWuhwSQYZRr1yIoLmcR2OZspZaNu9FGJsJYXAqxPTAih2ZgzV2i55t6eCyWzDCRVSvg1hwcWIMZqsNZsubEzALjVwBTy2VaSq7CLraDralA291gc+ic+qtyDmoipC2fO/znQaHQ7l7jWINuWI1AJYjws/8F6QHvgJxtJe1QGrX7037fOqNNuyT+5jVQPTOIjx4Ebq6dXGVX0pZXu+p56BxFEMMeCGG/DCQ3T0vZ0UMKQYptni7eJS5NjmaNyb/XRQR8rqZYwjtR28wwWR1IBwKwtPfjZKaRuiNycdVUVHBYn3ZYNbjx9ikGzXlBTDFKHgLgsh0xKIIhUWEwgLbP01JNBFVFOavp10tLo5I6B6XWyjLY4gOLZ4PpNa0vqowMk3CsSIObuBRWcQxnQvaVhLnegXMzEu4+1op56qs8kINvvVxM86f12LbJgOqinWKbR67OrQYmhQRCGZ3NU57JEbUEvXDKEt8/IqIhjIeLoWS+9u2KQKDBQABAABJREFUa7CznQJesfpYwNk+ibk35YuQWo0c3r+XhHKV/55okqAEuV0mP8dDwS0qwKK208RFGGVOT/aFWdukXisx/RvSeKNWjFcvBuGy8Fhbs0QQu0dDCIRFtFcZMhJVqvwiXlpg12ckTG5vABYTuUymv75lPQ6zapJMwvfeuWlEwqE3LQAW6j3FdJ8yBcBW8dZH6JkfAx/6Q0R6z7IWSO2GfWmfTwY8xusfUL1/cW4SkdEr0DVsAJdGK4ZE7X2nn4OuuBqRmXF5Ml4IgJGOS6wIftzrImHMXjzOnMjs9ckLI1EQEPbPQ2+xy5zDZIbJakfA58Xc9ATK65qYJljcZ+Q41FRXZj2gTc76MDHjQ0OlEwbqVVwYK2hhETs3BUNi3GKDWh7NhjfeZODcoITBaRKg51ireeziPF1Q4WpC/4SEl88D128gkyKOVTAnVjGvRJUUVe8cTKa4qlFdzOFL92hw9jyHHYU8Ch0aNvckYmszB7dXYvN+tpyDdLQSzX9o/jzTRxpk8ryfiFu3cqxKO1YMn1pKyZ2xzCW3B+YDBTbgvl2puUUmziFLguSPc3iDEmshTtQ7JQTDIo73iqAYN93W9FwysKB7mJJwFS4ebZVLy97zQ2FWnBBtXU6H8gISw+dQaEu/bCauOOcNseBXJp5LFXIGfXKiIBWIa3g9bvbzmxEAIw3oUP95JkqfKQC2ilVc7VgNgOUIvnkTE+cTes5AHL4MTdMGcLbcI5GJECaHIIx2Q+MqBV9Sm94hQ29kZNdc28ECW2rsxYmo6h1F4FMQXd/MOObHh2Arq4XZVQSNToeCihoEvB5otDomiK8EbQ5uDIx4ihLoXxTjMz4MT3rRVOWEdUFEsrlKHvDzXV5ONuZzfhLVlK3jM6GlXF7sl9qVieqFEeD6dcqkJR1GZoBgP7B+oX1tJUGka2gKmPJILAC2XND31zUgsvZJag9QwqduN7Bg1XJbEinQSGSxuUbLAqTHLkZYdrLEGX/dj0xJuDwsYm2DeuH3/+9nIdSXcfjozfHXd++YhF++LGBjo4R79iQPmxQwqyrmkloiesYktE5JqCzM3zW7XM2LG9bHZ7epGCPbr2R8joJbIqv6KrAA1yg4P47MipiQuRoaS3n0jovQcBG0VWmZOUWiCxNpbfhDIssYZ7oN6Roqdi6NXZNzASY6W1YQz8Y93iAGR2dR6LSgrCj7UvF5jxuRcJg53SaCFuMltc3QxLjRvdEwrr12IR2QP1DyQ7h4GHxxdVr9jlW8sdC0yiWbwqVjECeHoG3fDizT0TMWkfFeCMRlimugKUiwKI4BS67pjKw90tyhvrWSqr709gJoF3TFEuGdGoV3ehzOynoYbU7o9AYUVtTA53FD5zcw3qGEXBygSHOHWvpi0yNDk36MzwbQXmOHaUGka23dyiwyByg5E5bQzLSlMoPawkhjicbaRBzvkZg+100blCtw02F4GpCG4zVEVwpTHpnjkLZkDkNxEihAdGVUdqpM1ZL28Rvzk9RjyWQdVVeRAYNcfUWVVrEuk6LEsXmxf1ximmhqedh3HxNZIPDOnfGvuTwCPH5MwrQHOLA+eX+kkZVYODQ6S4FG2fAoXwEwwnK/r1jjJ9LwIo25bGn80LSEs4MS42mpnB97JySmRzrpkdBSxuPKmMi03RpKeRY0S9RinZnPYAEaA3ptqWuJF45OB9h1UeqKX0OR7tfA+DzKC81JfEQN5uY8bHxyOZNPOlXBllfX5rTOygdozWtirfH5fX9qr49cOgZNeSN4h7JEw1sWEunz5jnQn+/9vUOxGgDLEboWeUTXbbke0vw28ArBL9EzjdBLv2QjvX733eCt6qsESISYtFqo3SEdqK3RsnbJEYGLydKywFiaWcZSlVop1GBzQQiHYEjoXTZabGxTA3pvo9GE0dFxlJYWx9mWx8JlN7MtGI6gq2ccRU4L9DotjHpNHLHJNvDlC4oYmQmjukiftNimrBARYHJ4GpgSMeOVUFesSRL5VgJVfjWmcQvOZWiizzbidWB0kHtDAmAdtTxqS6RFLQ+qBjvfL2H/euDQRQl37dJkFWg52yvix89FsLdTgxu2KH/Py3FlTDJDcMiViSNTIn78TBjttTw+crMhzi3yy+81KlZrpQJ1yG1o5FFVkkyaa0s53LxVg4pCjul9tVRlJtZra8nqnizNkVdQYI0CV2o0PqLnish53xhQXSSTz6iA8EOHwRwcd6Vwxk7VNkCLN29QFr4tcyofR1UBj0m3yALb1YUaaCloZedZ8Gpbc3LgfV2dkQW/YtuK1WJ8xs9c30pd8XpBFrMeRS4LnDZlIkpVpvR+0dfEXlsE98wMQqEgc5VT0jPRKbRlUaVLJOiH1mhmGdN8Oi8ptRDQd6RGE1ItJM8UIl2vg5+bhF4hABa9Jt4InZtVLEG3EADTX3MnpIAXnELwS5weQejlh+jCZBXnSs9Juf/69dAUVbOkWzpQdZhl/f7k9w76wBvMaZNuturUbbhGuyyboDfHB53MNjvb1ECuVjdiZGwS5aXFcRXksShxWdjmC4RxpnsSFUVWxjdMenLyixe2zwYev4ix2QjqSsixmkvS66LxjRJEvRMCG39pYa4mIWQ3cTlJK6SDIMqcwz1MLYBYcWxs4NBSQS2P8ud95KiEkWnZcZL0TG/fll3S8FQv8ORxCQc6OWxNcVmtBOegtkNy2e6so8rvpf03VXD41M3kJKl+v1YTBR+pAj/5bxTY27eOQ0UhtTqqC6ptqJelEPKhC5sYKGVyAy715yoicOifAmqK5FZdwqxXdidtqwQ2N2THOeh+8QaAIhvHXNGV0FTGYc4nQa/hUFNEcxSPygKe8f9r2pKT9tub9eyezAXDU0EWxEsMgDksegRdJhTYDBk5RyLfIEzPyDIySgEwglIruBAJs/WaVm9g5+//Z++/oxzJ7uxg8CIQ8CaBRHpvK8ubruqqau/Zjk0OzZCc4cxoOBrvvtVK30p/SJ/0SUfS6siszJnVnJUdjZU0lhyy6cnuZnezXVV1l7fpfSItPBARe+4LIDOAhAkgkdVdZN45mGYhgUDEC/Pu+5l7rXUOUBlh9enPaTOakGahrsxDuXUOoFHP0dKVzXucYw/1xF4ArEZI2VJ8i8MtXkVB27+c/oaSqTrSXomIlkJmYxmxG+/A3j4IZ8dQTdvgg9Tftt2VQ2UJbDwGh7u4JbQRXDB6fT6h0ZFKp4sGwJjpiEQT8NJKR9MnP/5fwOsQr51gaT2D2eUMfC4rWhryf/vCWEpkkh496MDRXlmQ02qzpznMLOvinZxwmak9WKDNZAYcqxcfbK+6EqccLk9ouHAH+MSZ/DL5HIxCtiQodPVhtpEOk9F4dRnEwU4JDx2y4sRQ8QXHN97JiGqsX3rBtq3Uv5axam/XKxTaGjV88iEbetskIZD/lTczeOo+GT0tEpoLKsIqgQuRzzxqK1rd9gffyYi2DYrcsq3xV1+0oN1EVReDVbdngdMGgeIrE5o4Nx8/bYGrRLUcs9sknQxYFd5nXz+vE8EvPqrhK+9YBBHnq9xYUcPjzRvAqUHgSPa2Zisi22lo2lAM1D351kUNw20aDheI+Pc0SegIsp2x9Bhwv0/0b41nf2v587GTysDBTr84T/xNtjfRXam9yScWsxS+L0VExyam4HI60dHesu3aIprbO0RrViyyLtq/A6GWis+9yPwEkmthWB0uKMkEQiMniuosppbnEJu8DntTJ9ydtT2nifTYJd0p+MTTVSVZSkFqaIbt7Et5YvnUklLGLkOhs1MyCvvzv/gjZRJwLyAn/kqtNr6KQctkgHSa/YTQlExVyRgGUGvlHOmVOcTvvA9n937Yy1Wsl4HN6YLNuX3yVJUMUskEnAWBsWLgNen2+LC+ERWurlbr9kUvF5fRWAq+rBusmuUdTQ1ONDXsjHPMrWQwvayg0WtFoy+/QuWtW2lBCZ84bMepQVnoF9X6zJtYoowA0NnIZyxf1W/DbtM5Rz1dsC+M6lVZHz9VvBotF/wiuP+cg8Lr1FeiwyYDYOZ/a7iDFTzUZyv+95ffVUWFOx2xa0mqGGGcF7qbNTx1zILBDgvmljUh5v/UcepCWdBUpQsyAzOfPLv9O9Q1/d8/0NDdDLx+RW8f/dXnK7t0c26iMD5fhw0GNh+MaaIy7NkT26ugjJVZrBwrJtfwtXP6uaJu6Dcu6E7TdCAvN1bvjwHnx4CHRrY+y7bEoFfXrSsGSmR895Im9G3ZaWHEcLtFaJCV4xyc708bOGipfdz6fO3XxYEe72a2ezYcF/9tD7mEiU5nU3FSlU5ncGtiAX6vC52twW18g+jsaBXPo7WcnrIJjb7VuSkkoxuQZZt4XrYOHy7KU+JLM4jNj8Pd0g1Xcw0LlSxSo+8LoxTX8afKJj3MQhi1UCy/sW3zPS0Rg0LznjsfiGpjxxNfuCc5BwOs9ZYuuwel0D6S2AuA1Qgz158UaIbj478mrlYzbYn1ACsOmKFllN7qKS4atxNshBcQWVlCY0ev0OeoJNjIRWNXZzuczuLMZnU9ivmlNbSEGhAKeHFgoDYCXgy0KadgbdC7vVS6PWAVYvac/0jUag1+EefGqDOgB8AKSSqDSB4HtRo0vHMb2N8BtDRsn3g5VrG1ebS28vilurUc0CqaxLKSSC4J3FPH9f99ZkQrmjklKeNxGkmssSruhTOlHyerUQ0rG6y6w47BsZqf18eKE+JDR/Tf/eCOIpwYhzpVEQAzCx7XX7+liAzukf7t1woXSAsrbNMFnjphRcivodlknOHVi5rIet43tBUsmlzSMDoHROK6MGwxvHsL+OF14IWTwL4CSZ1HDrKaQG+5YAsircMrjVV3qBUbPVKeiC3J6LPZc14M4t6QdZJeDGackO4WHLmyNmZQ12NCK7A15BXtsqVgkVih6oDDoOlTeG2J9iqbDSuLs0glEmgINlUU8HYGmkXAiwEFhZW4UonPc980DanFqR0FwGjEIvFZb3DsM0KNbwiHR2sV7YzWtq0VtbIwCXX6pmi9s7A9weER5JpjtYe7BzPjbW3phvSp37q7nIOBNrtbcA7JXX9HqtXFOSGI39w9AIfLXXGM1EwSPZ2tefe1EeHVGBaWI+hsaUDQ78LRwfq13PS12kTgK+jdPvZdjVZRdU6USnyYxTu3NfH8ZgDMmExkKySrY5w2i5hzz40Ch7v1FrbCZyHHKr4+D38dOQcTaAyikCdUqqZ/9oS+P1zsP7Bfr4wrRCqtQdGKjxeTegxElQINd2gWU4/FonFeYJDlzH79d9+7peLOHHBoRRMBMLNgwpXVawd7LMJ9shDkSTQ9YIvjmRELeprNGx28dkX/r9GwYHJJd4kmbygV8PzhDd1g55OndXdxIx47KPyyxH6Z5Rx9za1IKxK6DduidukLZfTYGBTlbWuYzj+ynMPl2NpJtk7nAmDlQE5B13tX9tlUyDcIu12/cebm56AoCoKBQMWkmycQEkUL/JymKKU/n+Uc8fDsjgJgkssnnvUWuv0WgRpdh5ZOwFqhg2lrtyywtm+VBCrzY1CmbkEdvwyLLwhLVgd7j3PsoZ6waPeircJHwNqU1ptm3XPuJiLvf29bW2Q9kUrEEVtbhr+ptaJ7JB/eN2/eREtrG+KxONraWre1JLBSY3l1A40NXtjqmYq8i6DeAIlBq4EA8b1vXwR6m4AH9lnw3h1NVOFw7Rz0AM8WELfcWA0PD2+zea0VvLV1Iroz0sAAEZ8Sf/KKipmwhr/1aWtN2yT5ryXjNrGg4uq4iidPWEX2MjdWVu8gIgkJJ4YMRGRFRXOgdHvW/IoqMu+dTVKeDsf/58/S2N9jwacekgX5KsySsiKLm8zt/7wgvJXb9ZbWNSRT/D1L3jiwgot+DzdmdCelwt/j987fAR7cXzzgaBZmrisK2b91U8+WGhdUdwtrUUWQa1Zq1gN8prAaLCduXY/xymTSohKsWPtByf2IbUChK2+gufTvxTZEMkUuUdGTg5qIITVzE/aOIUiV3P8KkHr361BX5uB4+LOwlNBfKofEX/wH8V/54AOw9h3aJKMrKyvCgefHyYr7w8BHmXMw4RZ9/3siCOseMe8gWg2SsahwoRaco0JQL3f/hppbkUyl0dHWvC3wk0xlRJC8OeitaIzxUcX8ml4BxnYwo0bSq1dZOcP2Mgtev65hdEEPKlAz6bGDu885GIRjEeJOq7wpUcEt/PGrrEQGfu356tufRDdBjS311Pxk1dXDBy3i+7mxkrxD0GDFSFa8XzgertOUpfRvzK3o58r4GZoQ/Y/vajjaDzx2mHqY24M7aUU3mBFBDU0TepqUHKg0DtQhI1oMv0f+wsAV94MtnKyeK+Ri1Ge7Pg08MFI5eFkOZq6rjbiGd+8Ah7ry9/NuIbyhCA7LBHk9kEwr4nq1l4rc1TBW6XRabwkvobdcDKnYhuAp1FAsx0uYlKNEQ9l9i28gvTChc44qZRwS73wNWnQdzoc/U7U0AxMqya/8R8DlhTxwDNb+w6IC7F7jHLl5+3++Eoa7QrFItWBHwucfC90T4/BRxr0ZcbgHoCVjIusulWkp0NJJqOvhqjLzlWD1BkQArBiURAyRm+fgbO2Fo6V782GjplOwlngYLo9fh2S1IdClR+fp/Gh3Fnd5KoVkMolEMinaJwsDYDbZitamrYc1F66pdAbOGmfg6aUkUhkV/W11FswoAyMJzYFBriPdukU0MRnWM28kMNWU+e9cuHXn2/m976iiJfK+IV1To8o5fhNmgl9vXlHgclCLa+tH3rqq4PwtFQd6JPS0bm3jq29lsLAq4WAPnXT091sMLozF8PvfzojKq//rZ22bC6NGnwW/+UlZBKQYCKPO15eezR84Izml2P//elXFk8ckPHy4/DGxbbLYONA56Z2bGl69THIKHB/Y/j26ZZUDg2gpRb/WdgJqec2u6m0JnSV8PEjGmXzbaTC1ECR4H4wlxILpzD533kJqZSODgFeuOmjKZ0q9wdYCZIlcMhZBQizI28pW2URnx5Chq523AVKJ6ixrhcAXEb/xNtRYBFoiIrKudkN1lhlY+49Aoqh5lYGzHGz3PyuUDa1lNCP38OGDfENLJUTlecnPJOPQomt5rSY7gsUiqg+truLXcSa2jujtD+DqHII9+5ts0aFmHVuEt+2fqiI8cQM2pwcNbTpHcbg94lUNEokUkskUNGHrmP/8YGC83aDqzTajdEaFs4aAOTE2nxKtUH0td88d0phs23pPr/Zi0o3P1akw4LbrLn2l2tzrDd2gZufb+e/f0YMw/a269lMt2j+6xlLlz/3wmgZ2yQ93bH34ret6ZRfbCI2aXi+/pyKVseD/7Mq2I1ssIhFWDn/0fU2MyW9+fGv77Y0W/PzTrHTS8Ltf0zDYjm2tkDbDdcvKrG9d0B00S8kd5FAsoET+wkaIN65pOHdHT8QWtgeSq+b4ailsxPXul53q0a3HdcF+XrPsiCgVBGUAs96cgwndC2NpUUF4esiRl5hcjigIea1VB02NFej1gtHcIxaNIJmII9DYVPZeWJudEFpgrSPHhP5Z0e2a4BzR629Dy6SgxSN6ZW8ZU5Siv9F3BGoiUpMuKdevtvueBhwuWH8E3K33WiA/utgLgO0S0ue+BXV+HI6P/TwsJdoCMjfehTJ1DZb7X6j6AVMKrsFyfs+8E+mytnX3RKdvIbW2hIZ9J7cRUuHOmEmb+l1qdCxOT6Ih1AxvQ2Db3xwO2+YDnfocqVQaocZ88UdiPryO8FpMlJn3dzaWDYRNLsSwHstgfw+1fvTtLK2nhRVyb6uzbPtTvUAdMf4MWwCN4P4cMgjLPn5Qb6VrrLM46d1Ad5NFkFCKrrcEasuomgGDny+/nRGE3RgAe+60jKMDKnoKNKRYrRVNbAW/zOCp+6yiJbTw2mgNSoIAUdejrUIQrS1oERngvoLWhUhcw3/6hoojvRY8faJyZpHCwxSzL2xxNIuXz+uisn/jidItA2YDuC+d1ER7Qin84JoqAm7Pn6gs2rwcUXHuTkbohO3r0KeYSIKLTOp/5I8L7/+hDrsg6ImUXt7utEtYXEtjdC6OnmYnOkK7JyJfC2Kry4hvrMLlD8BeJovqae8TFWClgl9mwWABS+Sc+8/CatDlMguL0wsp0FqzgKy1q7Rw+R4+Oki98WVoqwtwfPLXhQxCMaQv/wDq3Cjsj3y2qHFPteAiy73v/tIfyOl6GjjHxsR1ERgLjpyCVGSBpGYyIkhWCYl4HAvzcwg1N8Pj2aps5G8lUyk4XY7NhNvKWlQEupqC2xd/0wsbWNlIis/u6w6WreK4PRtDPKniUK/eBkwsrOn7ercCYKyeYYy/sC2Qz9CcviPxzFG98qihgm7URxFswWPQiAmZOhWnFUU8qeHVSxpag/kBsOdPSUJCgskxIz5xRoJWZbvoE0ctRY+hNWDRpTNatIqGNh2Nuig+/2vEckTDH7+iGwmc3lf5PB/q0bW8ekvHyMvir9/TE3Y/+xh2BFaaf/w+rWwymFpg5MwvZFtly2F2WcX74wr2dUgYaNUHey2mO0oHPIVJdwuG22UhTRJNqOK+YZBtOpzG2EIa+zrsaAt+OC6LpbCxuoJEPAafP1DWAZK6zXoLZO2VbbqpTkpUXTmHT9bGOdx+yKEaiS05R8+Bmr+7hz2YxV4ArEbkyI+6OIXMrfOwHX8ir73E2nsYFk9QZN3FA2VtEZaG5rxFiJUC9bQU9xdRnawRajqJ2KXXIDd1CUFaI6zMqh59FEoiirVLr4tKMBt/m1ncrKh/4TGGBg6ZWzgxWEbhXW2rR5vfa2pqEtVfxtaF1bUNJJIpBAO+bWW/Po8T0XhaaPhUCnKzrJuZW51c6/t4sMctJs27Efwivn+FEyhbGsuztIDBLrsYcmNldpFKckYdhiafrt1AJ7+uXXIPfvakfu7+/V8pIgjy9z4n1byYLteWycDaL71oE+0ARrAyq79N2jZWjY3M1BWf6KkHdmlMxacfppvo1m+dMATWCsHAzhcer0x8Al4LPv+YVWQSGQDNtSgyLkRNr8KYLa/T6TDFc/OvSzpePbiDeZ5mC8yksk3CzHXF/Z3MujIVtltUct9qD1hEpZiZ2OfsiiLuQbZc5HBpIo1oUsPjhxxinMPrGdyYSeJAtxPtWbL51vUN8bdTw14EvTISQTsafXVyGRLPJ96r1h3fh/6WDrgbGssGv3KZVjPZ1kqtj7bWfsj+UMmgRtnv04n4zb+CtXMf5H2nkLl9HtbOEUi+rVQ/2yMz196G9cAD0BYnYe0cLimybsSeI9PdRW68lZnbUEYvw3b/x4TTcw7y0HFxvqkFx9ZEbT28zUXa2n1ABEQt7vrpgzLTH73yJuztA3C0D+b9Tfb4ETj6iAh4rV56XWjdORqaYKVDdZFKdVZUNg+a4xwiQaeqepVXwf1LDmF0cwyvbSCTUYXOaOG2GzwOxJMKkuQSFX5T18HUP5fbyvGBu1TSnZ1LvnNJE3pQT1WoPq6UbKuWc7AFj5/kb799E9jXwSAOdgUvnNL36d99heZEwK8+V/u2eJ2QKxXTsmRF+089pmttFTpCG/nUFucoLah//jY12DS8eMqSN7+eGCw9vtynzz5UefxZEf4TZ/WqKEoW5IKf/Bnua6F6CD+3sLrdKZrB0NM7KORlUreSjqvxuqJb9MwK0Ne0PXFazJjJCAb7zFa6TGc5B7XVcjg/mhH7+tQRfW0zs5LB7bkMjvfZ0dMki4TnD64mRPL6/iEnmhtkUene6JPrltAVjoxl2qzN3oOh1jZkaCRWJvhFODw7txxXk1E4Ooche4ObrYfVQFmZQ+rCdyD3HITcNYLM+GVYu/cL3bDNzyxOQRm7CHnkDNSFcVh79sNid/1Icg5NYwtzvTsn7r1x+ChiLwBWI3ILb2X2NtSJq1CpjZJK6MEkfwjWjkHxIjKjF5G5+Crk40+Ih8LmNoKtZVskcwLGdMGQew+Zd/iSrJUzANmsLPVpHGVaJko9cNhnrippyFkCztbI7qH928aID/dCtLc1CQc2Y/CLbY90gww2eDDEFboJ9LfrwUXjPtKB5W5isNWyafG8E5Qaq1LIOQF+6oxeHh8XFVr63xiUoegpK4vqGQj8xFlJBK9KXRN0SPzLHyoiezpgCFgZ8d0LKn5wScWvvCijrYjeVHfz9u/9p5cziCU0/J2ftJseq9szGq5PaaJknwSxHiC54jgzAEb8+RsqJhaA33xJEkTa7bTgV1/YHmB57xbw2mUNz5+04FBtBmlFcdCEdb1xrK5Oa8KViY6l1ImpBvs7zV/kzK42elW0Glw4B1tlQYZz1WMkqySnRoLbGbIjkVbxwWgEg+0u9LbWr415Zn4Z0VgCgz1tkEu0SNI1bmlxAf6GQFmtIasswypXr6VFJMOzyKwvw03CZ6K0ITlxWRDKJIWgjzxatbGJxemB1NILqakT6vIslLFLjDJAGjmdZ0GuLk7C4g1AuX0BWioO26GHKm77XnRk+pHgHBPXoYxegrz/fmh8FtudghtQny13RWWuvYPM1beEm6e1Y6u/2trcJV7loEZWxKJFHjgOyZRmnEUErspyjizfEJxDtEKW5j2l5heFrZOKCjmrieNyu9E3MLhtjJqbm1HIaHram7ZxhUQyjVgiLYTwG3zmglj7u7dzDvtd5BzkGoOtO9OFrJVzfPU9/fefOAxcmdL1tHMBMCZi5lcru+5Vi5fu13+znFbXdy/qZjGszC6Gb1+g6zLwpacZ7CzGOba/91++ySAT8PNPW0yP1Z05TQjNM/FTxHepJiRSesIw5/74p6/rVd+/8pw+l/J4/uYz27/39g0GgDh+Gvpa6scBj5rgL8axujKuiWuFgbpqq86O9pi/rw53y1gOamg3XAMjHVbBM3KghIPgHNl/c/y6QnRjpd5qHAe6HBhsq1+1+djMCuKpDA70NZcMmmYyiqh8p16hy+koK8EgZBhqQHxpFplEBN7OIVMBpMT4ZSiRVfG/vYcfhVSkVb0cKNMgNXVBCrZBWZqCMnlV6IZKvYfzkm7kHBmXD+rYRfEwkQcraH7scY491Bl7V1ONyLlRyIcegv3pnxUOFqnv/jFSr/7pts9KoQ5IbQM1tTmqa4tQFifEA8MMKFboPfYELDY7UgvjRT8jKsGOPAxnVgesFqzMTmBh9AaUrFBjMXcOvjc5ObntbzZZhrPAoSm8soH58BqijDBUgWIPdFaFXRqLYHIxgXO3IphfSW3+bWIxhXO34yLTUw8wMDDcvvPbqNRYEfGU7uZoxJlhXSCd+hN07Xl4K66Kd28D3/6Ax1r692iTPTpf3Rj0tVqwr7P0BMoKH7pAUWMrB5KLi2Mq3r6m4J//SUaInbPdoFJQammN1X3aJkHtNbg6lhurHF56wIq/9WlbVc5MlfCXb6r4j19VRQsK0dtiQX+bftz//ssq3rpefDyH2oEjfRBOTncbxrHicFEvrNDhqd5gVrs9aM0LvjY3WNHdtJVvYbb14YOePIfWJr8NqRRbLamzsnO3HzrMLoTXxPPJ7bTD7XKUJVDpdArRSATj4+M7dhuKL89j9fYHUDNbzx4is7GM9NrStvdLgQK01sZ2EZwS2734faTnR/O3OXcHqWtviaqfQrBqzH78SaGlwUCY7cQzIrBhhLX/KOxPfhHywQchH3sCctk2+i3sOTLdXeTG23byaThe+AVYGkJIffP3kX7tL7Z9VgQ9KV5cJrlVCsryHJSFccE9zICmDN7jT4nrL700XfQzsqcBwSMPw7ED7bGl6XHMjd/MVppXxznsNhmOAnGq+eUIZhbXkUhWbresxDm4iL1wJ4bJxSTevhHDYrYtkrg1m8Z7t5ObDpA7AX/7SI+EgToENcrNo6z2XdrIf+/sPp13cP74+EnkVRK9dQP4zgd6EKwUKCo/Fa5uDOiQyHm2FLifKxGdIxmdI69OanjjqirmZXINaq9Wknij8UzuHHWGgA7DPGmKc5yx4JefpbB6/TjHX/wQ+O/f1SsPid4W8jDdafN3v87AXvHxpMg92x13q0KvGs7R6NVNGHYTrPLvaMzvTCAH6Wzc4hcMdj1+yAm/a4sDtAWsiKcUbMTJOXZ+fy6txjAfjor/7XHZ4XPby+rQxRNJzM7MIBYzkOYaEQnPIzx+QxQnGJHaWEZybbkoPygGR/sQ5ECrqABjK2T08g+QXp7N+0x69jaSdy7ktbbnYHG44TjymOAt1vZB2I48DmtXfnGEPHwf7I99Hrb9pyEffsR0y+O9yDn4SNmN125heXkZX/ziF4W4fiAQwN/8m38TkUik7Od/67d+CyMjI3C5XOjp6cFv//ZvC4H+7ZqM+a8/+ZM/wYeJvQqwGpG78VnGbwnpgS350IMsD9j2WVaE2U8/r38vGUPq7a+JklC5bysiXgrW1n5ILj8s/ur0OpLTN8UFZm8pn7KhRk1k/CqcTZ1wBPV2icjsmBDH93UNlfyeyxcQlRCSbMXq/AxiG6to7du3WaLLxSQzwtFotOhDshChoE8ExTxUQN8hmOWJJhXYbaxYYvn71u+zCiWe4sS8XRz3wwTHqNRYvX4Nwt78M2d1ofJcUCWHQl0IZuk8jvKBjq+f092Vfvvj+dnsnWBfp4S/95OWPEfDD0Y1fPUdVTgdsvDmQLeEJwraRXnMX31bFVbtZw9YsbCq4T/+dQbHByz45IMyXjormx6rHNiCEPDq1XCvXlRxclhCS8CySZAv3NFwqNdSNJNOLa8b03Roym9lGOm0iHZMZoaJs/uz+jIRDZJF14IrhpDfgmfv+3CuNeNYLaxbxDm/y0WSZTG7nMJ6TMVwpwNLa2msxRT0tToQ9FbpHERr72QaLodt83peWY+KKg8+WxoD+qscXC43Oru6MTo2ZuqZVQ5KMo5MIibIqNEpnJVfanuqqAB4MVi9QbiGT4n/rdLhKbIC1Zu/mlAWJkV1l63/CBXDy1uNt26fDwQZyQbYZG6jiFalhULnnfm9M3sG0h8S57DZYckGkuTDD26eOyOkUDvsoRfF/1aja0i//bJYdJjRc2PbipVyDVVIM3BxRZdSyeGBram89ksmEUV08gZcrb2wZ3nNxvQdURHpbSvNVzy+ANLOpOAVS7NTSMZj6OgbgpStpEynUqISzSznaA354PM44HTsnAYz0RNLqHA7JFEpnMc5UqrQDfuo3S7l5tFXLgML68AXHtxKVu03nFYGiIw40c8Ehh5oKoWvvcdKFuAXn67bIeBQD5NyWp5gPCuffnAFGGrTOceRPgseOWTZdr6+eV4T+qb8++SShv/5qoZTw8DjRyxC66tazsHED4Ns6zENb9/gtiyb0hdsW7w6qQelisk/rMXoOqn/3Zg4YhU/xzVXtPzQgazu3KruklkK1Bf7MIJfhWM1t2pBLLW7Om7VYnwhjXhaw0iHDbMracE/9nc64HdXt5M8vlgiA7dT3uQcSysxcW21NLrRGqpcPev1uOB22dDgr62i3IgMOQdNToQMzdax+HpGxPNZKmGOVgjKLfBFKBsrYv5Q4/kBkAyLMmJrsLOqq8x26TZpbenZ/r5FEpxC/F7BOpjjqpBzBFu3fXePc+w+vvjFL2J2dhbf+ta3hBPpl770JfzyL/8y/uiP/qjo52dmZsTrX/2rf4WDBw+KBPKv/uqvivf+9E/zC4L+23/7b3juua1+dgbYPkzcswGwXNTxK1/5isjqf+Yzn8G/+3f/Dl5v6QfJ448/jldeeSXvvV/5lV/B7/7u79Zln+R9J00JGmuRFWixdVPbFIuThurFndz7z26JVFTYHyUeFQu2HFJ86KWTcIXaIJdogXA3BMWLkB0O2FLOzeqKZCKBualxeHzm7VmZobU31OdypIj2qSG/qDYijAGe4XY7htrsuybkvhsY6dCdI/licK9Sy2XQa8GpIrHLt2+y6ktvm3zqGANB5nrqZ5YZxNLwzHGLqAIrB2PwS+x7lwWrUepPAYmMJoheYesj//b+HVW0MJw9oAf5GJwa7tpZpObCbRXfPq8KAX+nXUVLQCcFzJp+/V0V8ZSEx45sPx5WctEZisT/YM/W348PSjie33GzOd6/+ZK56+kPv6+BxY+PHtLHv5L4bSm8ekVfmJwuHaPehkcPIE8LhWTi/XHqyVhEK++HgeUNRQS9PGFmtdMi+NUarL5ndS2SwNT8miCdzUH9mdXVFtJbrctUfW1robYzY7vzsfC09cLd0r25OM8jhFW2FOQguX1w3/8iYM0PDtqp3ZVOiMxrvUGn4syF78Hib9oWANvDhw/5cOVWVaQS1XEOtjNWyTl4XbsPPFjSgdoILZ0SOqQqJSNyovUbK6JN0hlsgVzi/vAGt6IuNodDVAPk7tVYLIb52RnRvmwWdH2s1flx2765rDg94hFzM6tvjM+QQz12Efy6lzgHg11056P+lhnOwSCNwVhzE69f1bXD2MpIB0OzQzC+oOH7l4BnT5RubczBGPwiDnRRA4vJLwviaV0qotAFkxVj1yb1FkMGwFilxPO203bBd2/qwS9yDk5DJ7PzM2UqXrusH3+h4zPxw2vA5Umd+xgrxU+W0A9jMu9XTOiisRjoj36gByaP9elLgrYaqrFYePPKFb2ai9sxi2eO6tUquaAeK+zOjer701ek9fRuYGlDQSypwSFrWIlSON8uKtSrxeJqAlOLMfS0etDUoLdQD3QFhd5oOQ5RyDkoBVMPztHQ3iuCX5KUfywi8GUy+FUIqy8I730f2/Z954EHRZGEmed91aDsz+U3ILFdv0jw7F7DveQCefXqVXz961/HO++8g1On9MTrf/gP/wEvvPCCCHB1dHRs+87hw4fxZ3/2Z5v/HhwcxD/9p/8UP/MzPyNkRWRmPQwBr7a2OjlQ/zgHwKqNUubwS7/0S/jH//gfb/7b7a7/oiH1/f8FON2wn/34tr/RMpzOkHllAbsAq0nxZdntQ+DQWUFgc3A3dyFCd8iN1ZIBMCN8wSbx2tymTYbL7YHT5ab9Ej4MWEtUd5m1xi6GybCCmWUVJ/rlooKqu4XuJv1FXS+2Nj56UHcEqhZrMV0/gt1lZone7LKGWzOaaDMgkawWrLB68phVtFzGkio6Qtt/126z4Dc/IYsqKlbm0dXxs4+Yvz9WI9w/LU8on6AWBx0fnzvFwNXW7zKolUxJONJffAyYuaUjotEVqhRI6DgZFYrKlwLFamky9vJ7egXerz3P6rHqriX+HisCuY/VBMAYnDSK5fI6uD0PBNzahxYA29/tFBlTtimzWjPgtVUcDwa11mMpBLyOTeLodtrQ4HXCa7CytMlW8SqF+YUFxONxdHd3Fw2SKZk0VuZnxMLb6a4uQyueM7uQ9s6J0qqxdSTOfxu27gOw9RwQVUG1goGR1MXXIA8cFfOT0bWYLZS2xz+/K8G1PdQHXIhQfkFqbIPt5HZBIOqMOp7/xZos6auBWX06my+IwMGzmxUJvFfcTR2Izk8gHd0oGQAzIhDKF/enwzQrOJ3OuydIXwh5FzjH7XkVi+saTg1IpueYemCgVX/dngNeuwo8fbS21vkc52AQZLDANbkU2CY5vqh/z9jaaBbUy3rkIDC+yJUnECpChZn0+YVneM50XTrylE+eNT++dF8UztgFfCan2fWxE0zibb1/WFR26c7PxUAXx6aG7ZV1xaCwutCia1hVhEXnG3x99V09kPmlp1A1yBWmwjqnqiYAVljtxu/fmYcYOxo3fRg42sfgOa+PlEhCUvS+UgCKsiob8YyoTM991ue2IeC1w+vaeq6Wc5AlJmcWhd7xQE9bcfmWdBrhpSUEgsGqn2X6c2Y3OIdtsxosceUHsPcdga21ryZTnhxo1pK+/o6QW6DGmNF0h/zD9uAni1Y27yEf6+v5SS2HwyFeteLNN98UQapc8It4+umnRXHLW2+9hU996lOmtsP2R7ZQGoNfxG/8xm/gF3/xFzEwMCCqxBi3+TCNDe7JAFgtUUpjwKseEcjU1/870vuPw3biibz3hdAry0UNboiFsBRk8D9sFJbGOoPNsDqckF1baTNNVZGKrsPubah4wVqtMlo6usRYtLVpebo76xsbop+4taUFiqII4nqvOHtQ+2k1pokWOuo+0VGHi3Uu3GnB3OK3oLfZ3ATEkvjXr+uisb1NFjFGvC7LaRQxG8sqMKPL4Lk7wM1Z4KVTekAkh6V1PVBCjbAcSGKTB3SNsKE2rWJmlfjWBWZwLfj1FzR4K7j2FMNaVEMyTWJnwaceLD02dGGiXgdL97/4RPkxLByrv35bFXpmv/US2x4tePldBbdnNfzSc1Yk03r1mTFgSVJ2NttKUAwUln3A8PdyzpV//IpOhH/pWXPVdJ972LIpmEsyaDb49fJ5Xn/ATz6giw9/9gFz2fRy1xWdRANu4Hg/PjSQxPPV3GATOh4u+/b9jCczmFmKob3JDbdDxsJqHPPLMWitQKM/a8Jhk9HdVh1hYuZVZF9LjFcyHkciugGb3VF1AKwaUBcsfvM9OPsOw2ZWJzIrOK6szosA2E5AEXxt9jbSM7fFQ0M+8WReK2SpLOyeIO3dRfIv/yPSpx6F7cjD+X8QnGMDWrz0Nbrbwa+dcg5Wm9vc3ryEG7W+0rEo7B5fxWcreURbR0eWc+Q/71ZW1xGLJ9He2oR0JiOeFfcK51iNskpFE0kCPv9ZycR9J394f0xFd8iCzpC5+3A9ruGHNykYTk2mKjmH4XTRdXpmWeccxoQKdUqZb2CVUA7P3wfRAkdRdlZmNZrIy37zvB74+vXnt1eUm02IMeDWS+3QMkEWSsL912/pOp4vbq33THIOnWP9xot0qbTgr9/RxL9/5nEL0hRbp8KGYYKm02CxqvwcGn2WvLFhFVGmhHPlH7yqn48vPIKKYMDrC9nHxY0Z8xV4xF+9o3+e55mFkuQcFeI7AuWuq+UIncs10TJrqkVlFyAqBq3UCJMRcEtFxzgSz2A6nERvixNOuxUz4QQWVlMY7vSIJB3hcsgY6KjOdZEGPDy3pcYqmUyKalany7WrwfzU6iJiE1fhIecwK6+TvXiUtQURANsJKNmgzd1Bem6UjmqwnXpu0zSOsLb1/chwDjYrl29Yrm2bBJO3RvzDf/gP8Y/+0T+qebtzc3NoaclPLjGI1djYKP5mBktLS/gn/+SfiIIkI1h49OSTT4oYzDe/+U38+q//uogFUC/sw8I9GQDbSZTyD//wD/EHf/AH4qHz0ksv4R/8g39QtgqMDyS+CiOubBHMpJOQFEUQEv62sOSm7e0zPyfe47+N76evvQ1tdQGOsy8KAph7Pwd+lt9jYKjYTV8oAFjqfS7qCkVic/tY6v3CfaFQPvu0c+9T1DkyPw1fRy88jS0l973w/YaGhrxj4sOd+gAbGxEsLoURDDYgGAiIyp9YPAGvxy1aBYodEx1TjLy12mOq9v3CY9rXBvQ3S4KQvj+uYqhVw0CrVWhGza4omF2RhAAqK2oqnSdqU4U3rIIMdAX1930+32Z5dLFjammQ8Jmz+r7kLpG0YkFKkURVjMgMZvHVd0nEJPzCk/nHFN6w4MIoK6DUPL0O7iMDORZNERVYOTxxxCIcnlx2Le+6NHvt/elrCsIRC/7WT8iCHBceEwNU/+nrGQy2a2ht0NsoVLX8eeJ7HKvcts7uB3paJPhcHAN94aSJyiwJ790k4cng7H5rVfeTsBFf0zO8379kEZphP/cELbItefvS5Kf2FDXlpKqusRwxL3WshdceiSdJL4+J79sk/VzkTkm5Y+I9WOw8MYu9HFGQSvP+1OpyP/G/fK/Y57kAiyRUdAQLSvQlCeMLCaxspOBmEMxhzTumSCyF1Y0kvC4ZDtmCoE9vf/I4reKa5HNhaTWKpqBXLG7NPveaQqHNfcxd23yfcwvftzlcaOoegGyzb3uWlzpPtTzLhfAy73lxPpWi91NqcRLq+jKQjEFu64clFRdJicxaOG/fzZynzM1z4n3bvpP6frYPAgPHoIZnYIEEi08vQ6h0TPeiIO29gJKcI5OCkk5t5xwWCfILv1ycc3zwKrREBM6zH9/xdXp3OMfWPkYWZxFdXkCgs1/ILdTKOSKxOGKxBFbXNrAQXkVLKIAGvweKqiIWT8HvdW1up/CY0hklL2hwtznH0R49EDKzouHylIrD3Rp6QpIIjM2tqphb5bxHzc/KnIM8hZyD3231m+McXSEJnz6TzzlSGSb9JDF+UpZz8BC+8g6dkCV8/sH8Y5pfkXBxgser4vRQ/rFGEhbIFuq1bu37k0csolWfFeHGy9Lstfe/X6eBjgW//mLxa5K/+fvfU3G4VxPVYUE3tylVxzlGLFiLWWCzch+zmrIatcUkfDBmQaNHwdFeS1X3UzKlYSVGYXYLvv2+JJJ4P/uEIoJnxn0hZ7TL5DqWqq4xuofqv2Xu2rNb+d4W57Bbd845ltZVLG+oSJNz2Hefc5DjMNHY2aj/hnEfb81SfzQDGsBSL9h4TKuRNJbXU2j0WgX3amqwi+cAzwWfK6m0goXVBNpDHuE8b/a5197SmDUH2HqflTK5caeIeHtHhwjo7ybn0INw5TlHcmFCaH9piRjsHQPQIsvQyLfWl6rmHOkb7whHalvvIX0/u/ZDW5kH1lf0OGi2evhHkXNwj+stWp8bBZpN8PrJoVT119/7e38P/+Jf/IuKhUU7BbnKiy++KLTACgNxjLXkcOLECREH+Jf/8l/uBcDuVpTyp3/6p9Hb2ysqxD744AP83b/7d3H9+nX8+Z//ecnv/PN//s/xf//f//e296cOPIU1nw+4eVM87Nvb2zE/P5/nfEArYL6mp6fFyc5MzYkFTOfKCoJNzRgbG0MqtVXj3dXVJTTMbt++nXej9/f3i+O7efOm+DcfShQm3H//g1AgYXR0NO+BsW/fPvF7U1NTm+/b7XZRdsj9M46Rx+MRUWRqqjFym0PumMauXEAkkRKZ2XRcgSWlgHVhuWPKBRs6u7rEwjF3THyIbayuiIfpiVP3bx5TzgqdmRCXyykcUJYWF4UbSjSWwIED++F1O7cdU1tnD26PzbFcQghd7+SYKp2nHBgkdXkacPPWKGRLevP95tZOtAdciK+M4ua6fp6a2b7n6oFLduDmzVt518rw8LDohS48pk/eP4x0IoqbN6fFmKyurorrmj3U5Y5pYWFJTBqckHsaGnDm0XbMzs5j2nBMR9qb4PAWP6aX7m9AYnUcN29uXXudnZ34gx94EZLu4Ghv/rXX0bh17VU6psJrb6hBQ7fPDps8KI6v8JiaW7vgtS7DElvGyWzL4fx8+fPEhz4FFnm9scXx9korHjoaxPi4fu0N+iFemVQX/uazHkyM3cbNm/nHtB63IjxX+jyxQmt2BTjca0VTcBht/ihmp6YQzmYLc9fe2cENcUy54anntWe8n7rY4WZnANncMyIHXks8HhKIXNVD7jx1N0ShrE5hbd6Ctfmd308Uf55ZUdDT0YxDQ63bjimiBZHQvIivTEJV0nnPve5mF6LL45gc191hjMcUnp+APaNieX4JKwsWcZ6a/HaM3rktPkeh+41YCraRfSKAtpPnHslEIpEQ2VdjebnZ82Q8pmrO0/DRJ8S1Z3zfeD+NXb0iDFTIWJ0xBV2IYCOlYDFthfWD85BcXtPnKXOb42ZBW1P/1jHZW4F2fYXUJrvBWrpKx7SxUWATt4e6oBTnmD7+ItbdVXKOmUVASaNzdQXBYGPtnCO6JgJpI6cfEQHb3eIcbW2tGL18AbG0AsnuRCaRhpxIwd1g4BxiMaSho3M751hbXYXNJuPEfSeLcI6QcIKdmZ7EwrwV0VgSsUQKhw8dEC3UhccUau3B7alF2JW1Ta2wu8E5HO4G3Lk9CquBcwSbO9ERdCO6dAc3l/Xz1ETjFU8vbFa7ac7xE/cPQ0nFcPPmVFWcY35hSfANPpqHgw149KDOOYzHdLSjCYHg9mNqbW3D8ycCSK2P5XGO5rYu/O+3vGiz3cb+DhPPSLOcw6/BInEhWPw8BZp0zkE33hOt1XMOJgqnIm145NjWtbeP6/cGnXP83BMeTE/cyeMcfX19iKZkLM6UPk+3ZjWE14Gj/VY0+YexEY1iYmxqs5Isd+09NLR+VzhHf7YuIBqtH+cYaopBjk5hccaCxTrcT9TymllRMdjThAMD2znHUqoRSYsfibUZkTwwPvcG2ly4dXMM42OWbce0sTQGj6ohPCchPKefp5YGK+7c1s9fNJHGRlyB2zkCl6zU/NwTgaZkEkePHq35PNXMOQ49WJZz3Ll2VbhAWjQNrriKTmUZG2kNi9EkbJcvwmJ3mjpPq6srUEbHAdmONl+7OCbeS1FnB8AXj0mT9zhHDSBfNQbASuFv/+2/jZ//+Z8v+5mBgQFxbS0sLOS9z2uE57dS5xzPDQXumSj4i7/4CxHELYczZ86ISjFe/ztp29wJLNpHyFbBbJSSAavf+73fE8ErIziRkzj+2q/9mqnf++53v4unnnoKt27dEg9ts9lY3vCLi4sIBoNVRcGTX/+vIlXmevGXNiP4JHOZG+9ACrRAbu5G5urrQGs/rM1bbSdaeEpYkkt9x4RAberme1Dmx+A88RQkbxCJK29Ci63CfuBBSG5/zdnYTDKB9Oo8HKEOSLLemrh09R1IsgOBwcNFj4m2u+lkEm3Dh4UrpHEMZsfvYG4pjBMn7982roVZCbYmrK5F0Rj0wZbdjjh2BtL4wLXJmF1cFyLXtBc2e0x65lITmmC1ZGMvTmQwv5rB6SEZXqdUMdNyaVKBBSoOdFauODKeJ26L1yEnWj44Sh0Ts41/9kMNTgfw4on8fWcA4p1btA7fanGoJnv0vYuAx64IDawPsxKg0vvUSeCkPTQ0hBvTFnz5bQseOijhkUP5LjHxlAWzKxYMtObvIzO0dIB64aSWJ3JvPCYK/18a1/DoIQs8Lrlux5RKq7g5q2GgRW/BMFsJUGvFBr/LseLzjeeG4AKWlVLGfWd11tu3NBzqpnW4pabzRI2398fS6G+V0Rmyb/t8kkYIKQ0Bj1TXKhRuKxpPwedxbqvkqPY88d8kWxyv3O+U+nwmlYTd6borlTV0ztNSCVGVSzF8C6uBVuaRWV+Gre+waHGj0Lly5XVI1NUIdZbcdy2hk2kGzXZy7a2srKC5uXlT72EP9UG9OIdKbbCv/C4tm+F67ue3OEcmjcz1tyC19EL2NyFz7U2ga7+wrc9BnR8VbZXWvqPi36krr0MJz8B1+kVYHC4krrwONRmH4+DDQsOlZs6RiCG9HtY5B59PmoqlG+chOz1o6BkpeqxzozdE4q1r+FDedcr/Tk9OYmk5jBMn7ts2roX3WCqVwepGDE2NfsjWrSoOVvNQY1C2WjG9FEVPi0e0O5k9Jv2FmjnHO7czCG8oeGy/vFmNXep5wvfOj2nw2FUMt0u7wjlYLf7nb2kI+YGnDufvOwMQ524D+7tYvV3dXAZI+N5lC5q9Cg4b9LI+6pzj0oSE71+WhFvk8T49uJoDq8to+tMVyt/Hd29Z8OY14KXTap7+qvGYJhY1oY/10H4IZ9J6HRMr1McW2K2gt6h+GJyDLbwOe/4xhSMa3r0DnOy3CsOFWs7TWkzF++MZHOi0oS24vauG7besJgy4LTs6psL3WUEViaXR4HOKAqZazxN/n3yDQadcFWapz4u1TDoNu9N5dzhHPApNzYhnstXthyWVQGZtEUp0XdcdlaxiXZq59R7k4VOQ/E0l912NbwjBfMnh/rHhHJy3GQj8799egdtT332NRdfx808H6z4OV69eFdVb7777Lk6e1E392K7IwBaDvKXkpXiszz77rAhkfe1rXzOlrU6h/H/9r/+1CK59WPhIVYDdjShlYQSSKBcAKyUql9OPMcK4YCr2vv3wg3qtuJJB8u2vwtrSK5y1MuOXgEgn1eShzo3CarXB2rYlzJOcvgF1ZU4IHltsHjiHT0DrHIbkC4pFEdbmxENYUtKb+8SHRuH+lXuf+5hZW0BycUqQTzmoV9iFRu5DJraB1OoCXKH2bcfkbWxBJpUQ5DU8Mw6rbEOgRb9J2nsHEEnpD65iv2l8n/9ta3Fse39tI4bZxTXR3jRcQtCh3DFNLyUws5TEwT4vPIYAVjEUe78rZIXLboHPZd3m4lTsN1k1ZLVKONyz/W+VzkeujLvcMXHb1PWiA6Dxz/zeWhy4MUd9lS3B2mqO9cmjpR8Jlc5fqWMy8341+5h7ny9u63C/Fd+/pAj3pUcPb40f8b0PVFwaV/GzT0p5bou6TbQFkST3Z/tvcLvdzRCveh/TjVlJWMyzLP/koLljpfYLNciMYr7G31za0PVH6BZauC+5LGzueTWxpOHt2xoeGNZEoCv3eRK6REYVZLHW8+RzW/HwQVvJz9Nl3KhTVwhjgI66X3RWKzxWI0iqw6tRNDd6RRvT7OIqXA47gg2eqs9TOpUU7km5f+eur1Kfj68sYmN2HP6OfjgDoZJjU819U+p97ruN2khGQxLZBql9ALb2LUsxRUkLQXu2R5Y9fwVkrNr7z/jM3kP9US/OIUl24OgjImCFVBzJt74Ka88BSAx0TVxhBBcak03zY7C6G2Bt7tr8fnrqGrToGmy9enDVuf+sqEBk8JUGDNr6kr7IUTM74hzplTkkl+dg8/gh+xjck9A0fBypaASp9WW4gs3bjtUfahbtv8Ti9LgIQjeEWvTndm8vEtkKgkr3mMvFKvTtnGNlI4aZxQjam7w42FdcH6fcMY0tpLCwmsbxAY9wpK50ngrR12xF0GOBy7ndHW7b811lpTKdfCXs79odzsEWLwa/mnzbOUc4AtxaADxuOhOWPqZS71OXVAgy3UOc48SghDevAxfuAPcN5H/+exc1jC0AP/ekhEbv1rkTzoAS533ew9v1gLhdapHxVe9jujoticQoFwnUYTNzrDQgYBgiaJhKjb85t6prklLDthLnYMLvwpiGxw9paPZv7bvCooC0tiPO0eiz4onDpTkH24PLwRigS6TUTZ3bUtce9ze8nkR7yCVMdybnN+B32xHMapEaUemYksmUqKg0VsmVO9aN8ALWw/MIdfbB6fHtPuco1D11eWEvMEXTyDkY3Mqkyh6rtUDQfo9zfDRx4MABEeyiWeDv/u7visD/b/7mb+ILX/jCZvCLFYksHPof/+N/4PTp0yL49bGPfUzIG1Feiv/OdU8wWMlz9pWvfEVUA549e1Z0V9C88J/9s3+Gv/N3/s6HerwfqQAYB4uvSnjggQdE6fZ77723GaVkNRejx7mglhlcuHBB/JelmtVCWZwEgkHhwJT82n+BFGqH/cFPlP2O3HtI/Jc91draElQXRV9PwP7Qp0R/NJ227A99GhZXvrCi/eBDgnzyMzkRfYsgixBlqM7jTwtRZJLTncDZ1Amr3Qm7wQKdWmWxxWkRBLP7G2EtcP5wB/RIC6P2qXhMBMDqCbZDhoJeBHzbI8r8zYm5ddG+0EwhhyJw2CS4HLU7KPldFgQ8VtNi5Y8dyhfVrgXza7pV9OlBBj22b+3UYHFbcmqAU7DUSFp+HHByyCICSoWLBb5PAlRo+90atKDBraGl4e6LsA616Q5ItJk3i+9c1N20vviInsEtxPtjwOwqSRydu7RNzZBicNogNC+MRgoETR0eHKYY/YcrMhpNZDC/nMBKJIXhLh/87tLPk/VIAivrMXH/ez0OrG/EhGZPsQBYMcSiEUQjEQQbQ6JalQvp5o4SNl0FkJ1u2Dw+yM7yDFtNJRAdvwJHczfsgfy5TU3EhHtjvazEWcEjPf5Tu2NNbgDb4SzO3TMF2ENxqGtLOudIxJD4yv8P1t79sJ9+rux35KET+ncjK9DWFqFttEHqOwz7gz8Biycg+IOFnKPAwdF+5HERIMuJ54vrNOs0yipz59EnhAuYlOUktcLV0g2Zrl+GBRKrz6OL01BSCTgbGvMcqomc4zSr55OJeN3FtBu8DuH6FvBtD0KK9s/ZiHB/a2ooHs2nkYfbIRWdo80g6LWg2b89+FUMbI97+mjtv2V0Xrw4ATw0orsoGsE16ZlhXQi9EMxJvnCfHhz7cQHPy/EBXZezECeHdL5BswIj2hvJJXlu79puboKJMWoQ9Wc1wMzg6xd0PvEzjxb/+3t3dEH7lKJhsIUJ2dLXKhs2yMMKr594Enj0QHGOezexEVMwtZTAalTB0X6P0BYtheWNJBZXE4KXUBx/ZSMpahqKBcCKYX0jKuRegg1eTE5Nw1WF0L3d5YaDRiH28u1irKqNTN+Gu7VHmJYZoSRiorWcnUT1ALuWrA9/dvc5R2Q1z536XgKvj3r32e1m394f/uEfiqAXg1wMSH7mM5/Bv//3/37z7wyKsfuOAS/i3LlzQnudYIWsEWyFZvs3K4x/53d+B3/rb/0tsW7n5/7Nv/k3ItD2YeIj1QJZDZ5//nkRUcxFKWmnSVH8P/qjPyoapWSZKf9Gp8hQKCQ0wHgy2GP8yiuvVF3WuLI4j0BTix4Ae/m/6gGwB14q+T1NVaDOT0Bq6RYPC/ZWM5OafvMrkPedynPduptgZVd8+hY8/Ucglwig8YFKMuqo4BYiSmk1VRDWXDkve8nZJ26GzFWLjKLi6uiSaIkc6KyPZe7liYRo5To55BJin29eTyDolXCk1yHI79WpDJp8EtoKhLx3CuNYjS4Ab90CHtm/3WabROZ/vq5X0nxye2fpXcP1aQ0/uMKSfuwokMR2k4lFvdrKlK13wViJNt11DT+8puHhQwxW3hvuXtXg4jhwaVJfhLCNoRBrMQ13FoCrU3qA7fRQvoNlpXswrWh4+bwqSOoThz7cDNv7t1fFfR3y29HV7BatSeWunWg8KSoUcmX7VpGlN0fuFudnEd3YQHtXD2Ibq7BR08LfIMaLJdwcr2JjxorX+PIiPE1tYrFeDpnoOiI3z8HR2gtX+1ZVr5qMIXrxNUgev1i+O9m2aN8916diEJpidr2F0yzU1UUkvvy7sA4dR+LQo0KL5F5oR7iXsck5wksINIagJeNI/PV/grVnBPb7ny35PXITdWECEi3r2ZKTTkFdDyP99ldhO/IorF378GEgGZ5Bcn4CnsGjsDqKJ67ScWp9ZeAoWMAVQmiQalutMrvNOZIpBZfG1tDos6O/vT7RjPfuJKGofG47hLvjG9dSaA9KONhtE2YsV6YUdAQlYYJTTxjH6to0cGEMeOrI9vmcLs7/+029Avl5PZ76oeDSOHB+FPjEaaChcodNSbDahy6W3U3mnJuJwutqboUunMDDB2lA8KPHOegUTnfxRw5sdRMYweDX7XkN12d0V9HjfdVxDibcvvG+JgKnjx748JJu3NcfXo+A+fGQX0Zfq7MsD6UECZN0TKjb6OioqkLP2Cx3nZpZEGZffT3tWF1dEwEwDhHHKodiY5ZOxhFbX4Uv1CIq1cshtbGC9fFr8LT35XXtZGLriNw4B6unQXAOd//hivyl3hCFHCWe+aWgLE4h9a3fh3zwAcT6T9wznCM3b/+3b+1OC+SXnql/C+SPGz5SFWC7GaWkMOG3v/1t/Nt/+2/Fw5maGvzO3//7f7+m388tVhjMcn483+6zGJTxq8ic/w7kI49AHr4PFtku9Fw+bGiZDLRUkhZTZasd+Kq8MRXzd66LLEVjZ694kFPA0PS+cOKMxeF2OUuWwubtl1XC/r6QWPQyY8sWRbMTUTGkM3STIZm26C2lErOhdFnKliynWfrNHnVm+azCyXFqWcFAi7WiXTcX68Zy5xwiCTr66HbXubFikKMrxPe3b5OHN9IJuMrMW4xDMlBWWC3ENjpqTAy2mbOzLoXvvK/h+rSurTC5SMJc+7YujgHfuqDhiaMWkT01g8Lr6s4ccGUS6GvdnnnVW0ToAlScWPC8sLqKWXfitcuasDB//HD111F4Q8NfvgWcHgaOGBygdgpmlM+NkTQW/3uD2yLcwpwy0FlAVovdg3RGOjeq4livJFoSeD3zmmsPfvhEnkEvnpPmQGVRTN6nHpcD10dnhabZYE++MUolhJpb0RBohJ0tZ86t3hOS0fmxG7BaZTT3bG+NT64tC1dcPhNd2QrYUpA9fvgPPbhZSZODRXZAbmwVz1+FlTnpJFAhAMbPMIBhDRXXYagGdHxMvfankPefhbz/tOnvWagb1jkkbMp3I8Cwh9LILVbY0uj6TGXnJOXmOWQuvw7bqY/B2ntIr+L6CJwzXvMqBanLOHrZXOYqy1RFxdz4Lbh9ATS2tlfNOYSWTzQhKs0LJQ6KwWG34kh/g+Ae5BzkG2a+VwoMcInqcknPQ8uCc1jgzXIOamyRc5AjMADGZMfsioqhtspV7aU4x3pME1U55Cy5sWJr3GBbcc7B397XUb6ynAE8rQjniKeAiSU9MbOTKrWXzwGTSzrnmFvZWQDs/B3ghzeAjx3XuZQZFF5Xt+eAa1P6uBRWVjHAtrheej7l3zmXc94mvn+JVdvkDdVfRxyLl88DjxzUx7heaA/qSTeev2Jo9OpVXbyO+gum3WL34Nyqhg8mNNw/aBHtvbx0WTlWQtXkroH7OtDqEPdxU0PlYBCTa9QDvDwahtdlw1BXdYn39tYmZBS6ntrQ0rzVaSP0C8dHhb5Xa/v2izK6toLoalisrdj+WA52XxCN+09CkvWK3Rwkuwu2hiZo1ICMrooECaUUykGl23RsA9ZAddyqGKhbnX7365APP7LZDWUGkqcBUls/pObOj8T8VS1UzSJe9d7mHn6MA2B0fMxVexUDy+6MxW0MeFVT6VUJhUKDlWBt7YU2cFTotuQg+RrhfPZLeZ/jPhe7yZm9pZtTrg2yXnA0dcAe0onjTsGAns3hhJzVL8kJPBrFMMshEo1hbn4JwYAfTaFgkQCVgsXwOhoDXjizPVzMwjArwwmJk9iRwa1JpRpQs+PmTBIDbXa0BfRsMifp4/1bC3G2lj0wYoMjSxLpeDe+qKLBLQnr6lLgOf3OJVWQhYf3W/P0BL51UUOTF3hwn5o3VsWIaA73bRWSFMU33ofQcPjcg/mkkxVCb9/S1x0HzXV6FYVN1lvoSEYNEipFid6lCZ2Y5bQVCsGg1ZE+XbifVWXTYeD+IepJWUQwaiWiV5kZr8/C6+rUENDRaNkW/CEo0PvqZeD5+4CDW74Sm3jjGvD2TeDsPp20Lqzp4/O47vlQEQwmfeN8tkXEpluE17umttkPfPFhnktNiLrS6bAQvPaN+h45rEQyWJy5k3cPsl2U+h2sNmD70O15FaMLmiCmRvDvDJTW2j5cC1j5lQMrweaWY2j0O+HOigJPL6yJhaff60SogYKqFvE8sBfrDa0ABtkZ/DIid20FnDKsJVxsXKFWWB0uOHzmyK+UbR3LIUpLcNkO98BxXdw2k4KUbS1XIitIjV2Cvf8IrJ787adG34cyeweOY0/AGijez0JSm7l9HtbWfkiGVvZtYMt9YzssDeUDeIVg8MX51E/p+7qyUtV393B3OYfUOQxrPCLE7jffa2yD8/lfNMc5qC/Kiu4qM/aV4GztgYOV8PXgHJLOOWx2e02cY3U9ivmlNbSEGhAKbA+cJVMZLCzH0NLoFiLehN1mFWYmF0dXYZclHBmorfp8cimD23Np7O/a4hQMSt0/tPW8oGHIw/ttgjsQE4sKJsO6llK51jHOvd94XxVVW2eHt8ZhI67hGx9o6GqkxII5zsHTdLpCcurL7+pBsM+ezX//g3GI1krOI8Wql6vhHNQ9TceLt2IaA4pXs4EparcWAxOAnP/IF65MaoIrHesDPE6L0PDicTx9LP+7hdfVmX3ZpFHj9t946wZw7g7w0v1anuB9Dq9e0RN2pwY1UUk1FdaDSUycmcH8KvD9y+SS+ly+G5yDCcOfeURPAjMIlrv+jLBZLaL6q/BZshJRsDSbzzm4jfW4vr/E1WkNY4uaqHQ0ggFfcsudJLKrRWtw6+BSGRWz4QRagw7R4sgE6thcRASTG/0OhPwOwYeaAi54mHGsEgygGSvUc9cVta1lmw1yiYCUP9Qigl98mYEx+EW9xI3bFyC7fPD0HxYauOQJuc9k1sNIzd6Gs/fQtpb21OgHUJZn4Tr2pGh9L5WYy4xfhrV9UASrSoHSPpZACywFemCVYHH74HjyC+J/73GOPdQT92wA7KMAIcTKks4CYcBtnyPBdPtgO/5E2c9R6D711l9DPvgQ5J4DeX9Lvf9daBthOB75SbF4qgVKdA2p+TE4ukagJqK6C5jNURciynaF2MoSgh09eTpghQ4j5cDKr0CDD35f8fGMxVNY3YgLR8hcAIzIVX5pZch8OQinyXhG6BgsbyhYiaZxtM8CP1W7C+BxbE1e/S1WEfwiGS0HkRFzUn/Jso3U9TWR5PL9fMcYI0haSfpIIJY2gP0d5Uv3Q149k1z4EQqWktwxu/f6NT0IZlYv7OYMMBkGHjsI4Yz46CG9uqocURmdB75/kRlnfqf4Zxo8Fjx7n76N73ygYXxBz9A+c1zD9DKrqvR9Znzj5oyGJr+upWEcK57/rhJr/b4WYHEtvzLqzpwmCCozwPweA1+sAhubBz77UHU6HSR2yxv6ublyg6KnrP4q/tmVKPCVd4AT/aU/Uwo8fupuXJ0Gnj2mL4ByVWdza8Chru3nYXyJrqAaOmQFxjomOoS+dHKrgqC3SYJkUfNaXxicffVKRiyyTg3Wd5rg4nFqMS4CUH1tLnH/zYbj8HtsIquaQzSextJqXC/Xb/aK6211IyH+Jtoks2UArVRoziKX9NjJM43XVqiz9AKaLQhOfxCJ1UWhmWirQntRBLyygYXcflJLafO3Y+tQN8Ii64qCAJjcNgiL1Q7JVzpoRVcmZfQDIUxrP/Zkyc+RqDoe/UnT+72HjwZE1j6VhKVChRSvM5rkSCdKXwOEsjCB9HvfgO34U7AaknNE6r1vQEvF4Xj08zXfT1xcpcMzcHbvhxJbE2L7rIasB+dQMmnE1lbQ3NGjO0jWwDl8HhfS6Qz8nuLVlxuxFFY2EnA56ci49RykkDmnPk2wjuqhcw5FJDNmwhlR2XVmn0NohxXC69waq5FOK1oCWp7AejFweKn1WNiex0qj3qZcdVJpzsH9YdAjnJ3bRjrK/x5b2ThPF+Jgl54sI88g52CgiVzIDBgk4u8/fCAnlq//RrlKshvTwGv0d1DyTWaMaPRZsoY/dITWBKd67zaEKzQTcBnV4GY4q/Mltz3/uuLcSV2vYhjI6nwaK+OZ3GObKTlHTxPdkHXeQU712QdY9QfT4LaZGGQVGRN4PKfkdsXAhB4rxB4YAfa1V8853rgOIa/w0smtqjtWczFouL9z+zVxcw64MKqh15kfrO9vsaA7tJVMG2y1iOvLaOzDbojvXVbQ1WjBif76SjHEUwrG5lNwO6zobbELLjG1lEKTX87T/FqPprGwmhKyCh0hKzIZFauRlOAgvB4YAOOzq6t5iyTWsuYwgtcVv08phlKgFrPL60dsNQwb9UcraI/mQ4PK6vHsupGFCsY1JNeFysaykGUoDIDZ2gYgMXBVRvNTpRv15FUxN9lHSleTS/4QHA99Gj9uuNc0wH6csBcA2wHSb38dmRvn4Pzkr5bMtiszt5F+469gO/vxypobFBK0u4sGuOgYqbGP2ITYYKkHcmZ1HpnwtIjkp6auQw62wjVYH1GHxPoqIkuzQo/WG8qvxY6sLiMWWUdLZy+scun954Kzuam0zhirPmRrCK6CdBSP9fBASDwUqpmIRIXSeFQEuuZXM8LpsbvZDrdTgTfrGFkOnMzN6l89sK+IQ4qFE73+/VLJfQYiXr6gEwVyB4qddwbLE6ZSmUQOzdFeYGxRD6RwGIP5a56SuDELEZyiAH/utytl6dgawMDXsMmOrRdO6r/B4FTArQeJqNPBcV6JaCJ4xJL5nzD4XEwu0R4a6Gkuvi/NDRa8cGp7BnVqSc8CM0PLIBkJ0UMHtgsAVwKdFH/pY3rrCPeDbbKlLsFcW20tXOnmnCbOGUko12G8djkur17Vs6puuwo27nK8cueFi462BsCVzboaYazqYmXjSEf+9cmMfWtAb1cwC+5TJKEK04hywa/372xsGlQQ8aSC2eWEIKnGAJjfY8dAhx8uu4zRmRX43A7s621GJqPAVqKHd3xyFpJVQld7C5aWwqIVw+2uhiyag5pJIzJ1C7LLg8DgUaQjq0Lw3tFYvg+FzyfPoYdL/t1GZ2B/U1Fxcas/JF7lIAXbRHUXtZ+oO2kUEFc3lkVgjHPJHu5NpF75cyiT1+H6yf+HSKoVgzJ6Cel3vg7bYz8pKs/LQYgXs8KrGOdo7RPZfTNzaknOwcVReBopt1+4WduaOuHqOYh6gG1BG+F5SLIMT0M+b1hdDguB/LbOrrKaOawgb20qXZEQanDBaZfhKdAc4DP22FCwavn9JCvHxqkrasXiuoKQz4qOoIzVGCvEK2+N80yrCc7B/Xv0YBFXSMmyqRFZinMwEMGgCZNG0YSu99TbrG1L4BlBraji+wEc6dH5Bl9MLjEoZgbUl2Lyinwml++s1EZJrsHK9P0mf+Pj9+syDqxSYyDpxMBWSyOdDr95Qa8mozZaDuMLepVSW4kWRwYXGTQzgjIMrPRi8Gqo3YKh9qzzYDo/wGkG3L9feErnAQxEVZKz4JjVUlDF6n1KZvCcsfpcDz7qnIMBRhoJJTN6QDV37zMQ2h4ACuJf2zgHeVYh16JBDzspKiWUC2VLWDXW4Cl9YVDT99zthLhX05kt4fvpcEqcg8F2a14FOtubaZx1fXIdTX4HDvcFkFZUOIoMNCvDbk4sioR8Z7Mf8+xQafDAxXLFOiOdTGBtbhIOjw+N3YNIbqyIIgxnhSpucoCGA2dLPsftbf2w0UCHbsEFsDY0i1c5SHQPtspQFsaBggAYjVtY4V4P2YZ7FXsBsI8u9gJgO4DU1AlpPVy+RUBVAKcXmtWK9KUfwMpe5qbiwgOSvwnOJ396u+OWbN9WEVYKLG+Nn/umiNi7ChZa9vZBsbiy2F3CFcrWXDrjwAerygdXEV0aJRnXnUQMD1RnQ1Cs8J1Zd8q8z2fSUNIpsW87gVg8kqWU+Fu1gQUtO4HyYTLY5oDdpld9Fav8+rDAKrGBFp2EtPiB1Vj1QRoikRWypVMks6kOWc9smgW/QzKcC34xePSdD/SMbil3IRKe+0pkYYuBgZgD3RCva1MavnVBJ78PHtC1vR47pLspGfHlt/XJ4DdfNP87Z0eAw716IIrZysM9+r5Wk4UtXJSML5LY6q0MbMEgmSscFx5DKVelSvA49ODXgyPAK1f07OwLJ4CDncDksl5ld31WX4TmdDXoYPrAPgk3b1Z/vbCq7nhf+elhZjmDO3NpdIZk9LXIGJ1PYXYlg6N9zpJBMI5z0MtKLyuaAzpJdDtlDHV4hbaGETwWv4eObAqi8ZSu0xFwixakUpBlWex7MpXC+kZEhB13IwBGPSZv19Dm8zE2e0c4LNFBt5IjUqWAQiVnvcz8qGiFZCBLmbwK2+FHN1sPuG2597AuNluw8E9f+C60lXlIz36p7m1te7g7kFp7xIICZZzAGPiEqBCzIP3+K7D2HoBUQsNFCnXA+eQXtztuOVyQB46Z2idqysTPfxNSQzOc+/IXQI6ufbBx8SPJUBqaYQ91lt1v0ZpT4DRNZJIJWO351ereQEgk1FxFWpEzmQwy6fSOFwqiepvlP0Vg1hnaCO4POYfVYsFgmw0+p4QGjxWNvo8O52BijEkhBjI45zBoUy74VQrkCH/2ls5fHtqvV26ztc4snjuhJ5RywS9WPbF9kBIJHSW247BZRBDLLBh8ynGO98c0UT3GANrxfr2C6+w+oNdw6zBR9tfv6vP8z5Vv6MgDNboo1aCoFmFqc6hbnwu9NZ52ToE0SyJ3ISVmAI8Jr0LBeh5DzZzDyQp9cg4N37igXwvkgexAyFWvsTrMyCVZmfjgSG2cg+Nx/6C1bJCdkiN35jMYbLOip8mKazNpLKypODNsL5m0Jj9r9Or3WHODPjfznhvpcsLvsm673wNem0jKReIZuBxWhBoccKD4fvERIFutImgWT6axthEXovi7EQBjq3dDW7eoACM2ZidE502lAFjuuMr9jc/7ckjP3oayMgdrQwsy7CI69BCkLIdgRZncXXx9mmbnUnwD0jNfqpvz5B72UC/sBcBqBNt35KFj4lUK6sYK0m/+Nax9ByE5PEhdfwdabB32EgGwwkwqiWXqzb+CxRuE42xph8l8WESAq5ijGBdEsq8R8TsXkFmZg711S0xKiUeQXluEo6VXPKhi0zeRWp6Df+QUrIYFWSqyhvWxK3A1d8LTuhVAY5bVHWhCOhGDmpEgO3Qh+/7+fmFAEGypj85YJTAjI9zgTCiuUsPg1LBX7Nfb19fFwvnU8N11RckhN1aF4v8k2ay6yoHEq1bCRHLEIBqzgawUqgYkoYauU0Fu2aJI4lPJXptmAWyjqEZkndpg9w8Dh7KXGM9RTiQ/mrAgnOlDV8oiWgqqBbfFcXz9qt4KSQLJLG8tYAvkD6/r2WoGnrg/r13V20+qsR3ndtjiSHLJNs9CsG2x46T+v5v9bD3V//dIp0UI+TI4aZE03FnUkFKA4TZL2etqp3j3VkJUCvBaoovk61djaAvKaA/KZasneY8NdW4PvjR4dcJIfS+FWU1DuxGrNPb3NZvSBenq2FqtdHW2i2dPOfBZkUklBbmsdrycga3MqKdrnxD23m07cEJdC0NdnRdBLy26CqTZFrrVbyP3FRewk0fOQIssI/XGX4kAmePZX8huUKlpv+t9Te2h8njbDp0Vr3LmBpm3vw7rgTPivCo33hX/LdcKmcc5ElGk3vhzSKFO2E+WdpjMAxdQdBO1FeEcVhlWbwDRm+9BWV+Co3M4z5UsE1mFo1nXA4tMXEc6soLAyP15unmJ9WWsT92Bt7Ub7tDWQ5Vtj6z8SsVjogpMttnzOIe+a3eLc+htkZXgtEs4O+IWJjVvXI2JpM/JoQ8nGF3qWcfn7FlDFXmhsYxZkC8w4MWkG+crtv5VG4gzFvsz+cdKKrZFlgqA5SD0plLVGfQwgcVgX66dkOPAoBWxFpOwrPajV7Pi6WO6Hlk14LbY+vm9ixCtkExoFnNXNINIAnjrpl6dxbZGJgkp6k+t0Gq2SQ5HJ0sG0YqdY4rb88VrldvOJQiPZg1+2CbL4NeVKU0EKnN6Z7vBOWhW8ca1NGIpBo95bjV891IKPU0SukLWstWTDKwd7Ml/NvG5EPLZNqvSuX1qfuXAwNfRgUBF/VNuZ5C9nVn0dYbgdFTmHDRp4zOq2rFyG0x3GroGdRmeuwCa76irC7DYndCia0CGkemtv9v6sz3FBZBHTgvJh+QrfyLWps4nfkqXqWDbpwmNxh8FziEOd68C7COJvQDYLoJRdam1F5amLiE+a3voU5AMi6ZCKFPXkf7gFdjPfFxkZllWyrZJS1YLRlMUKPOjsDb3CEenor9pscB15LG89/jAodOY1RsU+hv2lj7hCCIZrFmT1AIJz0B2+yH7Q7B5g6KiIb2xLAJgrPpiAM1KwVlvALYigoj8nfD4DaEB1jx4aLMiI7dfdwNjs2uIJdI4QHdIE0Gw3H51N9dHC20nyI3VboBD8aRJYXczIAH9G4/rWcJK+P1X9JL5T53R0Bmy5LUSkFTlHBiNYMaZLYml9MjevSPD6QROZts5agGzvCSlOxHnpTMVg18cD26P4/zxk8WFeslVaELABQEz7EYsreuElpVexQJgRjywb/sxU8B3uA24/b4Gj10Xty91XbGtdn6NQsi1O5jxXmdm9VCPTQSSKbbPwFdbcGcB5NHZNcSTGRzu337/Lq5EEPS7RUDMDJy8QCoguraM1YVZBNu64PEHTN2HybUlpDZW4e3oF89EURFrtUGuoAVZLZTlOaTuvA/HyGmh55SDbeg+2Ojqxyqd/mPbHCZLwdrSDbR0C2dAKBlBnpUL34UycRWO574kRGr3cG+D55Ccg/xBauuD7aGfKFlxTmTuvI/Mtbdhf/hTogKdTqTWzn16K202CcfWFrZDlgqS8n2KJG/nHAt6xblkhaOtD4o3sFk1QCTnJ5BeW4LsDUKmRqovKCrAUhvLcDa2IZNgYMsG2eGG3eMv6gzJCojFyduwOVxo6R36UDgHW6Uons0Fs5nfzBns9LbYNtvAfxQ5B+fAZ4qvi2sCgzFffLRyEpBc449f1Redn3soX2eKuqScX4ttg3pp1MoqBjo+nh+TEfSxWqz264q6ZM0NTGjVvAlxDOQKDHaxkoxBQlbLeYsUhbKajSY/fc3bHaLZ4skKrkZf+SAnk7CPFulapotlX4uGG3NAY0xDXxnOwTbFpQ0N3SF2atTIObJdAkd7aYgDLKzrBlQtDTurnrw6ERUtjieH/Xn7xmfY/HICTQFKr5gMUJVzhcpidWUFKysraGtrg8vtNnUPUvuLxQX+1i6xj7mK2JwzcL2QDk8jPX0TzuFTQiM6B/vwKYBBLlbn9h+FxWqSc7T2if9mLtKATh/b9Dsvi0SN4+mfK7mO3cMe7gb2AmA1wozQKqPlFn8j1Okb0PoOCet4EsqSool8qLAUNUs0+Rnb/q1sL4NfmatvAImo6fYE8b3VBSSuvyUEDR19h0VGli8jnO0DkH1BWH16as0ebEFicRLxmTsisxuduiECYQ1Dx9HQtxWZSEXXkVwLi2owZmR9Te2bRJljdPPmTQwPD5tyZCp7DKqKSCQGn5fOb6UnI4/TJhZ21YrTtjduTVwUp2VWd2IxhYE2R55IZrVgdczkkoqORqlsG0E9x6ocMaS2B7OidC2sFtSA+Op7W60BXhfw7ff1oM8TBo2MQjALTK0Ko7h8JK7hK+/qFWSfecD8PjDreGtWxf7gTRzs5kHUvoBg0OhYBUfNSqDDJQ0HWFHHdlXCmJ1mxppBLQb66OBIHRRmpwsDYPz+c8fzyTqvQXKGcq02DGYxI0yxemZBXzjO6kfjNlS8+/4NRGwD2NduQ3eTJFwf7yxQQ0wSlWW14P7h/BXEmX3mqhiouXF7JiLaIFuC2wNUIb8TiZSyLTC3HoljcTkixqKpjEvB5NSM+ExnpznFX7vTLZyV7NkKsML7kK3bXMAbRbb5vEutL8Pd3AWrw4r1m+dFECqw/37UE6zS0mJrSE9chv3Ag7ojn1XWWwmyLQtmg19GOD728yKZwmNSPQ2w8JlvktAaUY3Y+B7uEudw+2DxNEDhfN29D9aOQb1lshR4/fBayrbL8lq3GaQTlOkbyNx6j6J3kLv2m97XzNIUkncuwN61H/bOYci+kHgZ4ewYhC3QIoJf4t+hdiQWpxCbviX2hxp7Nm8DGvoOItC7pZ+a2FhFKroBX0un2F9fqAW2bMV7PefRjKIgEk2iwecqu2j3umTRMqVrkJrffk+z3SCIr4rWyIklRbhCuuy1z2ucE+gU2dNkKesmfTc4B6uwvnZeD17R/KVasLKInIXz5fGsgP7L53KtecW/Qz7SGtC/awx0Mcn09ey+PHef+X3g96aXVIw03MRgKzlH7WNFHsCKq52Abs9MPLLiPBeXMRQhCSF/jg/HitII1G8ldysMgJG7sC2VATljiyen3nLXO92hWbHOdkcGwV66TzdpMl5Xb52/gah9EIe6ZMF9r83w2qaxAjW+qj9mzumPHMgPljxyoHKwKVfhdW0qIZJzLYHt81xL0A5F2b4mC68nhQu1zSYJXlIMvHdvCw0wGV1t5vp7nS4XXMmkcK4tdg+ydZv/29gyGF9bRioRg6+5HRosWL5zGbLDhcaB+ugpbh4PO3holjZ1HY6h+zarw8W+5LqKauAK9ie+uPlwZEGHJRnXBXF/DDiHplEfuL7JmHpv78cVewGwXYa6PAttfVk8SMSD5Tt/COvQCdiOPAJlbky4OVE7g6ALU6ETkxHW5m4gcQzWjgqe1IXf8wVha+2HTLHCEg9xNb4BW0Nz3iTg7h4RLQrR8Ssic+sI6qUyydVFxJdm4OsZEYtBuqGxHUhye+ExtCjUE6trESwtrwmSGWgovQD2um1YWIlgLhxFF/v9qsRKJI1rk3GhA7Yeo8CmuqMA2PyqihuzishGDrVZS47/xQkVscju1bWSiLJSiVpgOSHQakESRR0Oo1TTwnppYVq2F/LzL57SHbOMIIGjLghFVOdWKChrbh/o1sigUrBR13aoFTwWVm8xi2qyoGgTdEbi9zmObCP4xP3FFz4kiSTvJHwvndIJ6SdO6QGxYihs1/jqeX2sP1Ym1n1+TMPUMvDMET0j+/YtXrPAM8dIZLMZtwwQVYEL46pw3exrlmCTNbRUITZrFqyEYEVYKQJNgk1tDTujhiVEp4vB63agvdkvFqPlIH62isOyO11o7tKzlIVQFQXLty5BdrkR7Nta/Hs7B6G2dItqWMLR1FGyOkYloUzFILO6pkrI7QPILE5CCc9AWZxA+uoPYRu+T1TnKGMXYe0/KhyaqgX1v3JDJO8/LV57+NGBujSta4+K/z2D1Pf+GPKRRyHvvx/K9E1YaLKQrSiUew6KVylY2wagKWlYW4rfIyW/19AMuaUXcmPxQDT1QOlEbSswD/L2jCAd3UBk6iasdhcc2Yr5+PI8EmtLaOjeJxaDycga3I3NkO1O+HeJcyytRBFejcIiWdDAbE8J+Nw2LK4mMBuOobO5+n7BhbUMrk+nRDUL5zcGGAp8fqoCg1/XZlSR4OgrYQ7D5Mq5OyqU6O5xDrblb3KOEoL7lcB5lskzBnII8ijqT5Xa3ls3OAflG+Xk0OjV2wW5TQa1KlVa58Dfn18DmpknsO4sAUnZCAarql37f/+yLtXA9k/OcUyWldrXb36g/wb1uphQ+7jBwdEIboeBwq390/CX7+jfeaKEazfx1i1NmBO8eJ9ekfXqNf39jxmq/XgOIor+2eePaxhqswhZCDqU1xsM+DIAV4pzMAkdiauIutWKyW8jgl47bDI1S8sH2vSfNU86XAyAufTniVLgRJFJpzA3ehMurw+hjq1IabCrX/ARukFyveAKtgiZmWLIJKKi0IKdOtWCyQolPA0lPIVMQxPSN9+Ffd9pUQ2Wnr4Fe/9h0e5eLYzVZLZDD1b9/T3sYTewFwCrIzRGtRktN2Tl7Y99bktjxeYQ2XaLNyD6otM//DIsDU1wPJEvfF8KFpujYuWXGl0VgTaSz83vyXY4+kuX6NAdMn7nfdjbBuA0aHSwpcfqcAt3M7Yn2LIOZGz5YXsk2xVY+cXgl81dv5mNAracToyOkQ0+j3jwO6kuXgCKZM8srKGxwQ23y45GvwsNZr22s4t2ag95nLolMq3I12MK9nU4sbCaFhNoR2NtjLQ9KAnS1hYozXj498mwCinCzJomKquKYXGdVTsUO7WYmviNuD0PfDAB3D9I++qaDkX8FlsQSN7eva3rXH3uwdJZbzo6krCxfF6S9b51WoxTj4z7fLAb+M/fBsYWgC+UNsbLA7PaFJ+dmcCOcH0aeOUyzQ/0jGo1mmgUfyVvYSFSIqOX5vP/FY4DL9WTA/mVb8YKr1LnmGR/sM0isrPFWimN4ALHYdM2reXZxmGxMEe4hSa/BUOtElbjFpHppPDvcFv9W29iSQUX7sTQGrBhsL34/ceWn2ODDab0vHJIpTO4Pb4Av88lHJbKoavTnJhbKhHHyvw0Gprb9EpboVlYIIgrSbD7A7AVCMaThPKVg7utdHAgPnEZSmQV3sOPFNVlzIE25Mkbb8PefywvaODYf0bYk1N/A063CF6pJKizt/VARleJMohsYB1Kuqir8B5+NMCMPWz2vACs/dm/sfUBUYUegsXjF+L26be+BqmlG/aHzdnRs83WNlBeaJHXLrmPbGi15LXuLKELQ6SWZpCYvAZn1z44WrYWepRgYLJNSSfgaGiGLSvTkEnGRUskhfIb2ntEZSaDX/VCOpMRCQNjJRT5BCtRHbnSXgOSKQWTCxtoC7kFbwj5HQj4zFWkEIm0Kp7F5BtMtrEyZi2m4mC3HeOLaaTSmtBUrAW9TRbYrJJwKC4FBiimljXY4xqSKQ2lfELmVjXQAyBnvFMN57g2A1ye1B0iWXVVCxj0oYg7f466V0wm/dQjpV0NySUYcKPofI5bsWiEvIm8hTpff/ya3v7HxJUZ8Dv83dlJ7AgfjAPv3NI1xsh9KIVgBjwGOkgyaSjyO2WGnpyBlXLGbVequJpd0TW8qNHGCjXyunIYbLUg4KZmlv5vJvQK96m1ARhstiCaZOW7Jjirv73+nGMtquD8aAq9zTL6W4tXJvEeu3+fp6pEZyyZwfWJVTQHyAvKC8gP9pi7uOPxOMJLS2hqbhbV3LmqcyPIKxj8YlV6Kc4hNGzbukv+TnT8GpRkDMHDD24zwjEis7aIxNglOAeOCW3oHBzUCs2kRCCMDsHsZMosTYsOJKWxDTILMUpAGJ0pSk2V6T+q2HOB/OhiLwBWJzAYlPjL3xEaXw4DARXENPvgovYK/y5ng1HysSdEu0LJbZZqlcz9PRUXlVt05sghdesC1LUFSL5QXtS9HKzeRhFcy2yEaSS9bRHo7syvOHO1dMPZ1LH5QGblVz0xPTUljrundyuIR2cVWg1PTM2jORREMODbHJ9oLIlILCFEaH0eJzqrrPy6MxvDRiyDY4N+UZnS2eTAzFISDrsk2iELK40SKYp050/mdMMjd+5t3m6XTreaHEYXFEEo2QbHEvKQTxKfOdRlwcUrJHAqkooqAiWs1GHFzPcuUeuAhNUCSgw8d8yCjbiGb3ygCYel+/q39i+V4ZjoAv8Ex4i/NdxOUlydMHsxkEQwk0nxVOpGUICdIMnMZTVJ2M7fgXCAJPni52mpTRHb23PA5x/SWxMoksvqpjLJ9aJgACd3X+jW3NUfR0+zbsd+ZUqvUvu8yQAc8alswUwuOPW9S8ziA58+m29Jzl2kS2YpcDGxEtWtv41Vc+EI2yg1PHZw+71P4XkWTzmzoq/8rvH79w0UH4xmPwOxFrx5Q8H+TgkDrbWnsnlPrEYVeB0SwhEF/a12cQ1z0UV3JV+Bi2pGUbEey4i2R+FSWOUJoxYYtTXq5ay0Fl5AJpUStuKJaATRlUUhph1o3VrA875ZGb0qqrzcTboeUi1wtA1AiW2I52tZUJMrGReZWyNIPiVYkLp9HhZvI6xNXaLFm20EUrYitxQyl16DMnkN9se/AKmIZqNZcCy0tTCkQPVVbHvYPVCwPv6n/060OTqe/Pzm+7nFhzB4uPia0ASzdo+If8tHHy173VTkHKwuSCUgZZNhROrGu6JV1/rAT5he+Mh+ve02s76cFwAjyCu8Hfn2wd62XnhaujdbkY0B6J2C1VBjE7Ow223o7dq61+02WTzT7kwuorM1gAafe3N81iIJ4Uy7HpXR0WRHb1t1HIhV5vGUitP7vKLdsStkw9yqXhm7EUvD59yqVuFvJtKsCpPy3rsxo4gKnG4DvyBYtdKbrfzi527NUWtTw3KEzo4SAh6LmD/2dVhw8wYwusQ2eso0WIRGEznEty9qIohBjSi6Cz59xILwBt/X580jPfnzGB/pOcFwjudGgp/Tq36qFb8vxjlY1UwOwUrxnGankXOQY1wYAx4+qFd6sTKbSb/5VWB2WQ+akW+QRz1+uHIiqhC5BJPIKdTIOQZbGbDRWxK5f580WXjL+MtPPqD/NxeLZWU5x+TTZ/KDTxyPcq2m/A4lE4zV5m/d0qv1mAT82NHt9z65JrkOHTYJXiN85fDQyPbB4D3SEZQwtmTBq1c1nOgjN6694nwlomIjoQoZkdWoiqF2WQSsuU+snPS5pG1tj2wrDvnlzXuiGvC+97pscBc4U9eKpaUlKJkMUqkUopEINlaX0RBshD+w1fqgqgoWxm/D6fHCG6jCLrUArrY+qOlk2eBXbs3KZzm5hxGSwwVVSSMzeRVSoFVU80q+RlgDLZvdSqWQfv/7UMNTcDzyOcFdaoVwBI6sQeI8sYc97BL2AmA1YpsOFQXi2weE5lcpqItTef/OBcKKIX3lDaHj4Xjks7CUCGSlrv0Q6tIUHGc/ubnAsfUfgbK2BDW2DovTUzFTl16eFf/lAs2sOKWwzTVBQDlG7G2v1rnD69WdGQths9ngcjrgcNgwv7SKtY0oBrp1wspPu4w2hVWgJWCHx2HdDBo1+W3iRdAl0kh25lbSuDWbwr5OB1qylsrEdDgjCGBhAGxTEFjVA0PXZxRRpaOTED0ARjCDZT08jIBXwmtXNdG2RuHSTVgsON5HTQ/9n/wvA2lsszP+DvU2OAzPH9/KwpIYPjSiCcJbC5jFI3k26kxQ3JbiqcTMik7ITg/pxJgVTKwQI/F9/j7gwqgu/s7jIZk2BomGa3BfzF1Xr1+TcGMG+MIjWwTVLPj5xw7rbpPVtpsUVmXlHDIthgoxZnuZ+WZGthTevaNbmT93XEOTT/82Ld55bRRzNeLC4psfaCLj+uwxc+fSeA8yYMlgWe6aqxUTi2mENxRhLc5AWFtAhtdlFSTzUM/2XouZcBLzK0kMdbgR9JUe7OW1GFY3Euhpp/vS1j5aJQm9hQImO0BkbUX81+50iooPtz8Ily+QN158/AgStkPNCRqK8FUJJJfuBz4pLMVzYMUOM6rUZbSNnBYC53RjYgWxtURrmRF0D2aV2E4rwDKX3kD6vW/D/uTnYWWVDyvRqIt2Dzoy3cvYNt6yA1JbPySDG3Me2Ga4OAlLXL+2RfB56ETJ7acufEdwFMfjXygZsE1dfg3q2hKcD392c4FjHzyhV56vh2FtrBwsToWnBX8QAs5VcQ7rrnAO7oLf54GtiBg1k25upx0Ouw2Tc6si6DXc26Tvj3AqrC2R0N5oRzKtblbCtjfaxIt4YMSVV60ysZjBnfkMjvbZEfLpfyCfmAorIhlUGAAj6GrHRxeroa7P6gk0tscxMBDw6J/f187Ks2EEPBK+f4XPWV2kPAc+go/36o7JYixsPF69xT4327Ft7ivv6fP6U1k6+/44cHmKQbPaOQcDRHzl9DTJGZ45BjRlOQfnze9e1KvLWVG1tAGcH9XnzscP6dVinIN7m/QKbCOHq6UCPnddffeShJllvWK9WrrJ4mVqpfa2AIEqjT8Lf4vnwjg1MdhHLvTYQb1qrhTevKHztZ+4f0sf7cF9ekVgsapsJnu/fkEPGD55uHrO0eyj4L8mNEp3gtvzGaxGWUnGRKqG7iZZ8DYmok8MbD/gsYUUFtcyONLnEhWWpTC3nMB6NI3BTvL8rX20yRKGu6qwEC0DkYTe2BBi93yOyDYZXp8fbq83b6yEdrGmQtuhZaC9wRxXsjW2Qw625nEOJbIi/m1x+2EbOAaLpxHKeli0zJOjVAI/x8KMXNFHrci8/woyV9+C/emfgeRtAFw+8cy9FzmHqEKtc8VWvbf344q9AFidwJvT/mj5tgL7Mz9nfntsXWDrjSGKLzL/2QeAFo/A2tgpFkUMdOVg9YeEXkxq9H04Dj0MK90ky4AlsExr+U5+DLsBtjPmLMnNItRUPGXIycNmk7G+EYVMdygSYgsQ8LsFUXVU6hcrgsXVJCYXYhjp1h+whSi0QfY4JZFx8jjyH8Snhh0lxcovT2awsKbiwf123D8oi6yVaHV0bn2eZdZtDSrsdlm4NeYOhZPykV5JBFKMQRGRjbPQWWfrd7j/bQHdmjoHVmDRcYjEoRB05nn9OjDSobcWlgLJ5Pgi8Mn79ewqv3NnTie8DG5NLOnZSZb3s9WBgZbnT2y1/rHFkMdDolqkm6QqUCNlJgz0N2fgcdpFyX4JOak8MKDI/WbFHKu/cqCQ7E5RKMbLijLqlDCrWi4ANtSqa3wZyTCrAjnGxcBqMU581WqW5e5BVgvcN1D8BIQ3VCysKRhulytafw+02dHSoCLotQqNPAa/yqG5wS6uD7+7/IqB7o90cFUUXb9mt9DaPQAlk8LS1Khwj6MDZLHxCg2VcXbYBRiJKJG8+IpoRXA/8pOQ2wehLE0j+cE3IPcehm2wdCDDmGApl2QpBWVhAqlv/zFsZ56HPHhUtMxJrAK2SIj/yb+GfOB+2B94sert7qG+sMgynE//VOm/033xxV/K4xBlt2d3w+J0i/NcjHMwqSY194qqAOEGloU10IzM/B2k71yA49iTgoOUAhd45BzUkfEdeRQfBc7BebOVAk9FQF7BCtTV9Zh4JvHF0FdTwCX0RmtJuk0vJTEdTuJIX/F27sJqFZ9bQtAjied3DnxGnx2xwVaiPevcHVbpanj8kIzTg5LgGutxvRXNuI3OgM45qN+USwSx4p1tdJyHjMY9DKxQwoCyBjnw0mC7nXEO4785T3NuLgSTYwzCHO3VDXBK4QfX9DbFzz2gt/5977Ku/fn0ET25RqF3/vYb1/Q5nL/JpFyusomJOM69+2imvsOphMk/aob1huh0bBfbNbMOZ3Lyhzf0gJvxWHfiOp3DEwWO3ssbehU6A57lAmDkeuSCRvduJmJLgeeaa+1aOQdbZ08PFf/y3KqK5Q1NVKRXcqM+0CkjmtREsDYhNPLKf57BZHJkOlOXQzSREZqkTC5WI8tQDfh86e7qQjqdxvzsDJSMgqbWtq0q3exYsbq1fcC80Uh99i2/qjRx6TWR/HCffBa29iFk5seQvP62aIO3dW0ZkZSCPHhCvKoFtSnTb3wZtgc/AWvnsO5kvBYWhm+Jb/yeqFy2Hdud+WK3sdcC+dHFXgCsRpRyoyBhVGdHIbX1bquSMtMeQJLJklR56D7xykGIIJ/7NmxHHxeVZqkL3xbtCI7HfirPLYSQW/tFWlMqEJgtBtfwyV27mzhGo6OjGBoc3OZqUi0oFsmJJJ5Iihm5vzeEpkZ/HlGtBYxZcfI1697kc1lFVqkQ5VybSD7jKYsI1OSqb4zBL+NYMRvkdmwRBpLWt26qoh3hzHA+kXj8gGWb1+XZ4fztsrKIWcFioCshxV2ZVS3XpnCoSye4OZLLSjBqYN2c0wNjxMFOvcUxF7gzBpl4aqh5UQ+8ewvCBTLaOooTR4ZxctAcMyMhp+4XJQqM+2YkehTWNavLQULMYF4xcVmSbwb7KlWWtQYseSK0pXBlSkNG1XCw0yLaKLrKaLsUwnhdlXP6mltVMLuiorOR7TLlt89rPXe9l8uubn7eYRVBsutT6+hodAp9jdZG9zbCyUUl39rN4Bch25iFtaF94MC2ZxLH69bNG2i2ZuBr74brLrT9sdKMKGxZsPUfFW0KOYgsbEsfJIPW0m7AQsE+h2tzvrK29sD6zBfFfCO190HKmqnci45MP5KcI5OBOj8GiRXoBddzxdbbbKUhYTuYb8erzNxE+tJrsN33rKj8S73zNRFMcz7yk9u2wQAtg1pSBeFlLrjcwydNVZDvmHPIsumq9mLIZMg5gFgiBbtsRX93/sRRa8U5t8lnn9l9Y6UtX4UoTMIZ4XdZRLKEz9OWBv1zxgBa4dzgNRj9UIfyhzc1UT11cmDrO9xfBsqMj20m/R4uWLO3By0lg1tsjaRxDpM55QJgnEOZuMsFcxjkYtUTdUwZGLNkgzmsRsslwIzOyvweE2/1AINY02EV97eO4vQh846ZDErdmtN5QLFjZZKMx2FWjJ9cjVX/xXxgTg7qY1ZJJYCmP3xVwoUxVQRCqfc10KphsKX+nGMqrIprra+Fwd3y22TiORe0M2N+RJ5ON+lL41F0NTkQTShC2qQwSU0BfIds2bXgl7F7ha+evv68KqbcWPV0dWB1fhrB1k64vLXLFZgFXaD5IDLOF6KIo+9wXvWW5G8SsgvWYI0ifmZBZ0lWFGcdJtnWzxfnJlY4SyG94n2Pc+yhntgLgNURXCAkvvn70FYXYTv1DGwHz1a9jfSbXxbbsT/7C0BkBVK27FS0sbDSy6bPcNbuA1RyLhpUIgm1e8sL1+bKXa1skzRBkneC8Ph1WCUrmgYOlP2cyIak08IeuPD98YkJMYH0dm2vaEskUyKDQ52gatHU4BCv3cDcCsu2VfS3yehrqe1WY6veSIdls0VufFHFpUlN6C5Qy2Mn4DY/fXpLzLQUGKQxBmqYweSLQSUGw2inzcuQxPRPf8h2y/pUVhXDfQMQZfW2hLnPs7Sf+8X9pEZXsYAV8e0PgEgS+NLjlbO7bD9hqymJaE4PzAhyrFpdvChIy99vNWRlx5c0EZw72mPBib78c74c0fD2LRVHeizobKwcOKKmHHXoWvwS/O6tz490yOgKMfi1O8Entt6wwmsmHEUyrSCVVtDdyraDrd9j+2N4LQaX04ZABbfHeiCnJ7QN1Lwi0aoxMZCcGxXC9y62EJgI+icufEf8luvks3nvy235Yi6sCLYffgS7DampA67P/Pa291lp7Hz+53f99/dgHurGCpLf+H1oG8uwP/IpyEPlTXKKIfXan4rAluOpn4EWXdtKnMkOXUYhGwiVuTgqqFLMwcqFkgmn08zGMqzuhl0VSSZfmBu7KVqcW7oHKn6WFRjkFkawCvX2xCxcDgeG2bNW4PIWi6fEgpHPqmrREXKI125gYklBNKFhsE0SQbtawMqvkXa2H+rHfH1Gw41ZDU8cYkXNzjhHZ6OuW8VATjWBGga7+GI1FiusyT84z7L6/E/e0F0LywXUdgLKEjAYaM06UVYCq7CYIOP+56rmi+Hr5/XqtJ828UjPOUqzwu2FIgU2jN/UKpFJ11Bexs1ZV2jeE2wxddo0HOyScMoQBM0ZI7x7W8P9g0zgVb4eaCJ1e05FV0ivRMzhaK9VHFdhYLZeoMZeLKFgbCEuTCVodNXX6sqrNltcS2FpLQmfx4YGz+6bxZQMCLJKSLRA1sY5YrOjwqzM07O/YmCdVbjRi98XfMJzID/pYWvLf15SR9pxcPddG61tfbB+8jeKrmUdHzPfOfVRxF4F2EcXewGwGqH3am+1B2xqtqwswBJogdVEuWgxWOm4lIpDuXVOvGz3vwAry0GDbXA+tiV0Kxe4f7H9QFmeg+P4U+YqzeIbiF/+AazBdrj2nappX0tuO5PW9T2ysDnpplSZjK2thLG2HEZLRxdc7q32AApHCiFmVS3aAz41GxYVYvsGOrc9/ElUbTbqE5X+/dvT62KS3t9Tn57/HNhSxtay6RUFx/rsaMrqd1SLkQ5DmTLqi52QDwaTjAGlnEDsbvans62SLQg3b5r7PPXPqAXC1gljhrgQh7qB9+7o+iXlRGQJktb7+lkBgLrjzZu6yP0nTm2dFy48Sk14fN8Yq6H2S3hdE+N0blSFU1ZgpHVrMQ135lUkUsDhHimvHaahQuVXKdBAYn41hd4Wp9DOKIaWoFO4Md2YWhO/tRpJIpFMY7g7KFqMiKaAR7Qx+w2CbnR4FW1HO6jkqBZWmx1Nw4dMZ/oL54X02iLUeES4/xqjqaKaS+Kx5I+R5N2lldsefqSQ04jJa1tZW4JGjZamTkgd5YM9pSAqzSUJGeqOTlyB/aFPC6F8a0uPeG1+rje/7yp1/W0hfm8/9pSpQC+DX/Grb8LW3ANnDa25paBzAyVPGN/udBV1WStEOBzG2vo6ujo74XA48pJq3K6qFeccozPLIni/v4i94UYsBaddLvksJC6PR0TVyf7u8q621YJVvOsxTbhKnx6SRctYNRAi/2I+3L0qXGpY1goGk4wBJTXLOXZzQUgZiUaPec5BvVPqcX38Pl20vxRYFU9h/6tTlavVGKDKtaXWE+S9lIYgp8oF1jjXPnusdCJQy467ZkiqUYoj5NXw5g0NQbeat7BcWtdwa16/lw52bc2prOSqQbVEYDmSwdKagsE2uzC/KobuJoeoBLs+FYPdasHSWloExA72eTcrwTpCLvjccp48A5NzvHfvJudwuD3oHD5U87yQWl8Wcgn6WbEUcA5rwbFYYPU0iIrdPezhxxl7AbAakXz59xBXU3B++tchZa262Sbg/PRvCQFBM4KtxSD36yRTXZ6DRs0NE22Mm+5McTbsmysRZfTf1tov9DvqiXh4DpG5cTT0jsDq8gny2NDea2ox6XC64HR7YMtWueXA/viGhga4XcUf2C1NDaICrHDCSqUzGJ0Ow+Oyo6+MiLZula3lOWAtb6SEML6jRpFb4kC3HQurCqaXFRHUuDSRQrPfitZA8W0WEu0Lo9QO0/DIQQp+6vtFZ8g8cfwdIEdcaJqZO25m95iV66+i5D0HZmB/too2fTpJkth1NQEc5mr0wcyKYbI6jdXe5YgowTGlI6RRoL8cDpfQnc5VZVGPgxnraknU2SEKfea/Z9RhMYICxBSXfeG+rZ2eDmu4OKFgf4ckKgFUG+A0bLDBpQctmd2tF5YjaSytp9HUYEOgzKLP47JhsNMvAmAzCxFREWZcuDAQZqz8SqXSuDM5L9zX2lvunhuQmWtLOChx0V1QPZtamIAaWYGj+0BeIoKfj174NqzeIFwjZ/K+49h3/9bnVBWZ6RtCTFzymOzHrTOU+XERANmJi9Me6o/4n/9/4XA74f7Mb+haXbxnuobh/Mxvw+IL1iwxIA/ft6n9pilpYbJgBloiIhJpZtMyVrcPtpYeyI01OJ+UQXRpVrwa+/ZDsuvGFqGOHlOcw+lyIZ3JCIFqI+g4G/R74fMWLxnuaPYXfbbHEhncml5H0OtAX7uv7NxrBLnH0npGtJQ7bLUHn070yyIIxhf37vxoGl0hq3ABrvSs4z788KYq2hOfOSJtapGxAp2veoBzFqu4jLpTU2EGR8rrkJabt83yIc4150Z1DSxWUpF7VFMkZ5Zz7O/QtT1DFTrZqEfKFkkz+8BLjQGwUiBPpMlBtaYDNH56YJ8Gl81cYpTnrz1gyUvQ3Z7XhEzDsV6LMExyyBoChrGiWRJ5R61V8cWwuKZgfjWD9kZZBLmKgfcnnadHutwi2DY6G0MqwyTCVoyIga5G39YcTg3SW5MraAq40dFc52jjDq8rlcEsTcsrLiDi8xNQkzF4ekbyTXTSSUQuvw5boAUuVu9mISpXh0/lcZP03B3IoU5IBj3puwWRaFiYEG2OOzXs+ShiTwT/o4u9AFiNkGQ7tNUw0u9+B47HPrX1vq8+2XypsQ12E45KOdgOPAgbAzgmH6ZCINfwUKwXrA4nZKdHjA8J6L595ivhWPVlrPwyPrCbQqUDWP4Ckjq3uCrKnJsb/WgKeoSLUw6RWEoEtYwVYcNd/m0k9s5MFAGvDUOdtU+CJBedIVm8okkV82t6cLJYAKxwrCaXVMys6KXplSSRaFuuaydVR36uz+iaGmeHt0gkBWbX42wZ1KreHsFWPVqP073JKKtAdyZmRZn1FA5Gml6dRVJEG25qb33MZPdONdcVM6YPFIjUF4PXBXyuykrvOwua0EBjy6dRl+LiBDC9rLeNFjMfKIdc20klUED2ndsqTvRJorUghyY/XcEktAYk9LVSsJn3z9ZYKRodSDWREa0XupqcaPTZ4HNVnk782d6Xwa6AIKLlxG95nj1c8Dt3VmqXayswE4wsdm0piRjSkVU4SNCy24iOXkImto6GQw/m6RnJ/iaojeuwFSYuLJJo/ZJc5VdF6kZYVPOqkV44CtoT7gaUhUmkvvn7sA4dh/2BjyMzfhVIJiDv2953U0uF3B5qBxc+2loYqfOvwvHAc1vvm3T9qoTCiq9KsB99QizITHMOqw3OvvobS8iCc7ghyXLVnMPr8YhXsUVpa3NpLhc0KLzz+TK1sC6qV5sa3GgOOPPaqdajKbidNBfZGqej/fnBsbWYgpszCbQ02DDUUXvgmfNQb7NVvFai5BzkEGrRAFjhWN2a04TOFAtwK53SRFoTyaJqtZM4N9KZ+rGDGjqC+ncpik9x/XI6pOWQZMvhht5eaQQlGu7MA4e79eQa5RD4+5yTGeRj8V4pfdRCVHNdMbiWE+MvB1Zpf75KznFtWtcCo+6akReeH9M1xShjUG1Vf6/JwCPlN86NanhgnyTcpHNgQIxabHyP2xIugkbOoeicgwYI9QIrvzrKBL+MYBCMONTnq8g5GBCjuQWTdXeDc5S6rtLkHPEo3MGt6O7q6FWomRRCI/flbdfe0Ag1k4StIGHGNZ4oQnCWX8MoawtIT1yBlkrA0X8UdxvqzC2kf/AXsB44A9vRx5AZvSQeQHLv9ptzj3PsoZ7YC4DVCMfTX4Bt7API/bWVrdYb4oFYQ8mumoyL0lmWxCrRNaTD03B0DNes0WEX+mOBzUlgZWEWiG+gobNftBaZQTIeE2KHLk9twaf1aFwQs5ZQA1oNabhEMoPRmRX4PQ70tgfKinZ3NbtMLejNgoK1D+yz6+6NRcCxikaj8Hg84lxSiJzVPQc6rWVFP1m59q0PVEFaHztY3eRAwshgF18kKNRnYDCMGha1BL+IyxN6UO2RA/r2aaFN/S0Sar7PQNO+dj049tIp/ZL9YIxOgeZ/o3Cs6o2cY2R3E4TofCkw0EdLdFbMGUv52R5JDTSj41a9oTsc6cFEI1gpeKTH4ByraYhEIptjxWvpkf0MTu98H+JJBRMLCXQ1O8veK7xGr46vwOWQMdCh34/cl0qnjkTVYbNutkjWisXJO1CUDNr69pnQx9h+bcUZFFpbhNXlhS1b7Sv7Q3q2smClyM+4ipBIBglcFQJa6sYyFOpH7ju9+6KzJSAFW2DddxLWPp18pt/6OrToOqxD2/XMatUr2UNtcH38S7CNfgDbyJY5zj3JORIxaJoiFmeZyAoyqwtwdAxtM4EwC6e/Ubxy12R4bgaWdALBzt68tshyiMfjIlnAirBqwdtgPZKE06GgJehBl6FyZCOWxq3pDaEz2tNams/4XVb0NNvR6Ksf56Bz5MP7bSX1tgqfdZyvWhs0HO2Vyga2kmkN33hfE+Y5D41Ud/45p+Y0sgJuPVhDrrCTigYGf5hcowsk45LfuKBXX7MaiVXdoazJDz0LPn5SF82n3EFhwOzD5BzkEG9c181zygUCZ7Ocg8E8Y0XV6SEgEt89PS3CabfA58x3GSd8LrqGWkpyDu7Tk4dl09X15bAeUzCxmBYBsHLBr7Si4uKdCII+G/rbXKY5B9uamSDfqSj+5OSk2FZnV1dN11VkYRqp6Drsbp8I8BMOfxBqWtceNEJ2++F1b0+sMTHnGdmqLi8GZX0JajwqXB7loPmCi3pCCnXCOnAM1qysT/qtl4UQf7EA2L3IOfY0wD662AuA1QhNssJ+8snd/Q0lA3VlHlKoAyptYi+9BvvpF0V1WL2QuPG2cJ70nHwO6aUppOfHRBWDnBXf3wkYxBofvYN2rwOZRMx0AGxpbhpKJoPuocqCjsUw0N2CZCqNmYUVUQWWq/ay262itNmXZYRLqzHhFNPZ7M37Hf7vtsb6t/+4y7g2caympqY2nXOox3RmuPLtyd2mYHolF51CxJKa+I3eJg2vXaNAqO74xAqinYA6WymFWUFdmyqe1AnvyQE9s2xsV8jpWTxWJIbM77JdgZnUQkJYOFb1Bvd3Kqy3JpQLgJF00s7dU0A6SQh3S8M9ndHww1uKIMAPDFsrEt5iY2UvEYStFpGEgrUo2xjjGOp0l3ZvpOuZ1WI6qBqNJbC4vCZcXlfWIkgm0/B6ah9Q2e6ApBTqYJgfL1drD2RvA2T3VtWGs6VOtqYGpCevQlmchOPEMxWztjW7TSqZsqYn/Jv9zPOb/7Y/+TmApLtIScieI9PdhSbb4Tj9zO7+RjoFbX1JcI7MnQ+QufUe7A98EpKvfi3IsWs/FEk378lnkV6cRHp5FjZqjtWh5VdwjrFRtDV4kEmlYDeZxJqbnRVVX719ZfrMygTq9/WGRIJtamEN7SHfZtCelV8tASeCPp1zzIbjov2xq9m9bRt0q6s3yonWFz7rOPc3+SvPp5wXOS83ec3PI1y4MshD452NOFst9fc5fxqriWrBUCvAqYUBOc7ddLdmVdjRHv09o5g+/008VaQQMaPogTEG6ehAeTc5RzShV41Ti6tcAOyR/duDXwSDl9VWm5tFIqXhB9f11sFHD0glk7jlxqrSd8xiPaZiOaLAMp/C/i5HyUCVBTrfKKPIkAca8HA90Bx0Y3ktjkxGhbeSU0MZ2G02U62Npa4rb0uXWDPlgl+Ep6VOtqYGpCavQV1fgoucYxckD4QGGXUry7Q2sp3fdv+WAZD9yS+UTKzscY491BN7AbC7BJaXUteLbhdmkbnxLpSb78F2//NCV0aUbdSo8yG2Nz+G1K334Dj8GKzZFh26fgjBZqsMR+c+EfyyNtRPFyyXgTU+yCsh2NymGwxkH4IMhvF/l3RtK4BstWIlHsXaRgxetxM2lh1lLbvbcwwoO+lRh6i9yQNrHbJ6t2YSiCZUHOl3bbNbNmooXJ1KIeSzihLunWA9ruG9O7reU3vQ/HVBzYbzY5qo9iLRo8sPWxDrgYBHDwzlwLbC3FCUCyYVgiT20iTQEdEryOi6SPHcxw/tHsnj7zEDy8DgT5zeXl1VCJKvQkFfbmd6RQ/05cgZyf97oxq8Dgv2d9Z2nbHNdWFdEwSZrR3U3WFLwgGDqOzdRnODXVQ4UFx2PZZBY3aRVwjeC/t7zLeGM3hNgXzS2GDAh5XVdaxvROH3eYRj28LiIhr8fpE1NYPGtuLEcWN+CulEFMHu4bJtXFaHS7x2E2oiAi2dhNx7BFZ/fVraCpF+48tQGWB78ZdN63tR13IP9ya0REwsbqppa8xceR3KuC6ET1MfCiiXcn40g/TMTaQmrsJ15DFIHr3M194+IBZGnNMdXSOwNbbXJfiVxzksEmSTCTeiuaUlz+eRzxkuYM3q8zDJtrgSxcp6AgGva3PxzDmgq2XrORVeTwoOUBgAqxWXJ5JCduB4v6Ps3HFpIoOORivaAjurpg1vaMJc5XiftOkaaAZXpoHLkxoeP6jP5xSu53/rgSa//iLY6vjFh7c4B90izYLz6tVpPXhGXdSvvMfkIvCgCQmFWoNerFA72KXvPx2lKxkEMPhYqBlGrc+5Nb2lM8c7GWSlGD8F/IfaauMc8ZQmzjd50EZcf298UataZ6yeoJzIelzB8oaCSFxFg6c4/2Hw6+hAaQ2+QiRSGSF9wvs46HNiZSOOSCwJr9sh+AhlVZiQ85h0Pmprby/6/vL8jFjPNHeWfybbnC7x2k2w8IE80t57eNe0v1Kv/6UwSnF87OdNV/hadyG5+GGCMbt6x+324oD1we5ZvewhD+n3X0Hqu38MZW5sMyDGIE85WNsHYe05ICq+lLGLQmtW2kllFh9A1vy2HVtzNxw9B/TyYNkGmeLHdSzxtnsb4O/sr6q9we31weNv2AweTE+MYn5msqrfDQV96O1ogs+jL/SSqQzWWSNuQF9HACO9IVGqXC9HHZLNnCYwW79iSXXbZ8LrKlYiO3+CMWNJ7QVmPasBRdDZFuCj1ofFgoFWvUy9FHgchaK9ZlHrpcRqsRfv07UuCArZkzTvFthaQG0SaqCR+PH3a3GRvz7LoKQmsrk5cOgml4Cp5drrlm/Oqbgwporz/eQhCU5Z1+Sod0k4r9c3r0UxNp/E2HwCyXT5Qe9tcWO4072ps1Hz78aTQvA+lkiiMeDDUG+7CF77PC6hAeaw2zYXpmxXSiQS2CmUVAKZZAJaERFvtj2u3npfF569C2CwQV1dqPl+2drOCjKjF4VbYCHoJAzZjuQ3f09U+uzhRxvpd76B1Df/AOrKgvi3RrmDCs8La+c+WNl64vFDnbwiMve0oq8ZdCCjnIJhjrW39omWR/FnmwNyHRNuhNPXgACD3lXcTF6vFx6vXnVJR+nxiSnMzunjZhatIS8GOoObwS8m19bZh2fASLcfB3vr5zadzOgBrhyUIpyDSZmlDYqk73wCZfURD4ltkNWALooMxjDAw+AEgzLlqoKUD4FzkBM9fxw4PaxTOPKN3eQcE2Gdc7xzWw+6sWq8Fip6cVJ3nqTxTg6s6J8M61VltYIBy3dua2JAnzlqEbIV1D2tN9ZiKr53KYHRhTRuzabzrudCcG0y3O7AwW5HyeCXWfDevDa+LO7T1kYPDg40we20ocHnhNdlhz3bs0lXyFgiJZxhd4p0KilexZ7D63OTCI/fKDp37waUjWWo62FR/LATMMmSmbxW9G9SY6u4fpLf++O7dlwf1RbIer/2sHPsVYDViGqDRDl3RwazqKmSfPk/w9q9H7YzL5T+jazTE4mr7cTTVWdiWdnFwJMlW70gN3eL190cIxkaNmZuQ2lsha+9+vYCwuP1wVrg0lQJDOy4DdmaucU1RONJDPXYYM8KNomWLSsQjacxOb+GzmY/fAbx2mpxoNuV5yQ5tpDCzHIG+zrsaAnoC3iXXcLZ/c5teghCn8luN9empWl497YCv8uC544X1ytgqfCNWZ1UdTbmXzdsQ3ji4Pbv5MjNQIED5PeukFTrAalSlW2l2iypL2ZscViJ6sElGgRUAglzDp+4v7axMgtqdoU3dGOAm7N6a0Y5kJwXG3dmjDlWxhYKfu6545XNDMruX5MEWVLFueMC4vSQRVwHxjHgv0fnFTT5JDR4pJrGSpgpSBYk0iqWN5gRldAZKn1PzK0k6mIZns4oIkidYVSX92Y2+uhyOtDduZXGdzqd6O0x5/BWCQ1dg9tEvHPjRa0NuijdLaYhN3VBOvWccBAuBIlj6o2/gsXtg53zQBlkrr8DdeoGJGqUhfLd9uRDDwhnYXWeSRhtx9VF6Utv7mgbe6gO1d5jNDKAywuLv1EEwVLf+D1YR07BduKJ0l9yebOrf0VwDrpFV1tVYJEdsNgdmxXmfN0tiEpxTcXa3LgQhvY1V+84yaovj8cNp6O6lkQm0ug4ncPk/Lpoi+TCOtcezmclsRZJYXIxhv42Lzw70Bq9b8CRt5i+Pp3G/KqCo702hPz6dv1uCY8csAkNLCOqmRtYtUaHSMotvHSyuEaYoqqigirktaA9K3CfA/9d+J7Y3xlNtPMVOkC+fF4PBr2w3XujLKhlyvnX6DIZjmgIuM0J9tO4JofPnt1dzkEt1JUIK/KB1gXgQGcFpzyt+DF0BPRpilVrRr2uF+/T8rRJqwWDlORqDF7ynnj0gLYtQMfr4s68iragJPhozZzDSg0zOqEq8Dh5rRTfcWE4sZSCx7XzpDWTe5RAySjsNqFJhb6/PrdDvHJgEn2op3WTk+wELV36Gig3NsaxUjJpKExMGVwqdxNyS69wpS7KOTJpJN99WRRc2PcbboQiyFx7G+rSlHCOlrz5lf62w4+I9a7GDqYdHpca20Dmytu1b2APeyjAXgCsRpgtjd/8fFMn7Nl2Eg0W3fK1gpaXujQt2hEsLi/kkdNlP5sZvwyLy7fZ7sDqsuQ7XxUE1nnmpar2dXObFGXeWIa9fVD8l5lca5GHZbkxGhoZQXRxGo4a23o4MYRadq551tzohStmQ3gtArtNFmSiKaALT5K4pdIqMnWoK+X2SAp4eQS9VqxGFdyY0YUrmxtk8b8p5HliIL8FiddETO4Srk3tFbrFuJtsfyRxKEaImJ397iUVacUiiJ9ZsdeLEwyobA+AcRskUtXOXcxszqwAL57QhKj/SkTDyxd0+++zFQyVJpc0sHiv0Zsf4CHBJgkfGBioGKAaXQC6GnUyWAkM7B3v00TAsLeCG9XFCdUgupu/bQbQFtbZ7qnh3JiGkXYL+lssQpy+GHhMb1xX4XNacKxPKqvlMtKxRcD0383fJgnknXlFtEQczQbAeA9WGiteryTQtL132iXcv88tqv6WIxkEvfoUQaLI3+PfN/dd1TC3nIDDJqEl4Ki58stmk9Hgc4uKLzOC93KZYPj68iISsSiaOnoqPqOLiXgbx8sYzC4Hfo6aRqxm2Qmkcm1gPJaCKlqS1Mzl1yG1D2w+9zlPqKGOknOLUWtjJ0h8538hc/V8Xba1h93hHNb2fvEScLphCbVXrCBXF8ahTFyBpaEJcv+Rstc8q9ItDc2wNurtPmzhTb7zsgi+OioEakshs74EJR6Bo7UP6bUlSGw/rqI9h2M0PDKCyNIcXFlh/GohNEBbd66B2h7yiuTafDgqhLXJOZoDLp0jqJpYgNeLc7CynLE1Jj824io+GE/jWL8FjV6r+N90bbx/MD8CpqgWrFm6hXugMfBTDHRppIsf4wLFOAcDT9+5qAmXYQrpFwt2FZujP5igNMD2ABgdEmvRIf/BNd0R8rNnNDHvz65o+M4lPbhEHdJyIF+gLikr5Lf2UW9TpNNypXmUVVfcBnVQzQSeONWdGtR/s1KrJivTx5Z0LlVYrc/20tUYnRE1vHNLw7FeC7pClGcoLcPx6lVN6Lgd7i79TAl4LOKVQ7CI5hsTmrfmVFEZeDhrvmOGc7DKi1MrE6E+FwO0TrFfyxFq0en7RDkRxpzIL3LgdT6znBZBXTqm1oKNWAouuyzuxUafwxTnIEcphaVwGOlUCm1tbRX5QuHfjWMV7BqojnMoaUhltLUqQXT9ZNvSi/xRL7iwFHAOVs3feAfWrn2QAvpFK+8/A3VtcFvwKwf7mRdr3sfN39U0JL/9J8jcuY57DXsi+B9d7AXAasROWo+YHbU/8VMVPyd1DsNmd4lgmRHpG+9A21iB7b6nheUw21mY+bf4gpsLIVY1yHRz3IF2TWr2NpS1RaELFrv5rugT9x56uKoxWlvfgL+5C/GlGSjJGJyBZsRXFqEqCjxNd891hNVg8WQaSyuRbO2DBW6nXWRs6Qp5eKi5quqmUkhnVJy7FRGBg31dblFNM76YhscpbZI+ksliQavZhVUoagPag+UzTcxUPXFILlnqz/eddqDZBRzsNL9oeuxA8Q2eHKgt20YnJmqCZSXYNkXwKTJbDiTrr10jOWIFmyYCTSTd1Og4Pwp0BjWc6FpDQ0ODmMQZuCPhHDFoU7D8n20BkS7guMnCQ44r97kSSEC9Tq1oi+SRHouwWGe2nRVwa7HyaS8+RpixlqWdz2gkhcf6ZPgN2VFxD65tjVUxfDAWF4T0zD735mcoytzk3yKYl8ci4r0TQ34R9OJ+t4ecONjr31wQsVUgmsgg6DMXCEqlMxifWRK6Gj0dTTt2eySS8ThSCbZ68SarfntmxqvYczI1cwuukTOQfaWj1wwQ8HlqDXVWVUnAZ7zjgU9u3150FcrkVd1RL/vcZ7tasZY1BssYRKvVaS9vW6oCZeI6tGyF8h7uAc7h8sLxzM9U/Jy19xAsnqBI2BmRvvy6uIbsxx7X9yURQebWOUjU78oGwMAEWXvphZAZJGduCTdqq68RsdsXYPUG4N13qqoxWt+IwN/cgcjSLOwuD5y+ACLLi+I+8gR3R1+vGFhNHk2kEF6LQ5/yLfB77HDaZYT8DiGMXw/OEU+qeOd2Au0BGcMddsiyBZOLGbizSRdF0YRcQiHYKknOIVsDaK2gDea0WfCxY6z8Kv53vs9KLkqsHugyd0ycN548pOt2FaJad8kcDnUBKzF9/iUoDM/qbibCyoHcgsEzh6wh4AU+dlT/fbYVXhhj8E/DoTZ9Xsg5TzJQNti6tZ+s5GLSj5psh0w2WpC37Dchs8hkICUrio0/XafZlkrh91hK5xwMgJUCA7HUWE3UoQueFekn+q15iUozc+gPbyYFN35wxJHHv1oarJs8+dyduDCOOjnowsRiSvDB9kY7jg+4RLKOiKcUcf03+swFw2KJNG5PrwmO0tvmrwvnoBxDOp02HbwyonCszH4/PnMbyaUZ+EdOlk0QqKkElMgqbFUap9FB0nnm49u3tx6GMnMLsDk2A2BMePBVlHNYaT5UB4kZ6lnOjQOlAnZ72EMN2AuA1Yha3SgYQWeEopwTVw58GFIQUItvIDM3DmvvAbGAoZi+trGslwJZJVhsdthOPQuLPT/YZRvS7dIzbEFcGIP90MOmfjcHZ+8hKLENQUKdPQer+m5ujObm5uBxuxFbnIHsdIkAWHRpFmomDXfIvN5YJp3GyuIcfMEQnK7aFl5Bv0fsUyyeymoLbU2a9SCiYjuSBT63FZ6sRTP/9+HerUXnga7iY0ji1eYMY1+Huax1uXJ+u2wRQu5zqxoOVqFhbcz21QNsfTSK65O0lBKUZQaZ9tr8jN2qE2lmcklMc+s+Oh09sl8T5/DO+CyOHfaJDNrVKZ1EN3q1TWFeVr0x8BXyAqtRra7HRtJrJL5GkAjm3C3bG1SRER5u10pmY3kenzlaPynGHIEsvAd9PjqTFQ9+hHyyyKyWuxfbQ47Ne2R+JSEePQyAuRxb25xeimI1koLdJsFTxkGAga/p+WWEAl7x4nbXIzH4vTsPqGhqRgRohL5iDbEeM+NVCMnlE9VbUrbtq5zLY2b2NhwHHtwKGuwAUkMz7A9/pnQWNwtNUZD82n8WCRLHE1/Y9nd1fVkESDiPmAHnINdnfgPJWBz4rf93zfu/h7vEOZIxwGrTtbhMnFtyDmrSKeFpWDnvWyxQwzOiytF4zYsWSUNFOBc69mywKj15DcrKPByHHhKLKbNw9h4WHMnq8sHZOQyrwXm1mvuXuoGx5QVkPD4RANsIz4ukYDUBsFQqhfDyMhqDQTiqbIfMga5yepKD1bQOEfyqN+dg4IBJj1ySjVVffOVwYqD4fc2EVJdnCSOVSs4Nv1MKrHBmkGZ6RcPhHvPHFfLVl3N0N1lgjD2xCozOicVAN0oG7XhcbrsmgmVMXrEtMRfMIH+hDimTDKMTszh6yAdFk3BtWh+/gFvbPAaK0DP41ezTpR9yLYH1wMEuixDLLwZjuyc5EHXFyDnIA4uB77O1tB7XH8eosNrPzBza6rcKT69SEOYRIZs4f0IHeIm8wiICYB7n1hdH5+Kio+L4gC+vOr0QFLifXIigvcktKr8YTKMGGJPfOwXnVyb0a0EtfIOwsivI44fFWv6Znpy8hszyDCT7A6LdcaegKY7l9IuwVHAF5jM8+a3fg9TcBfvpF4sG0shbzM4NFiYyPvFLSKQ4B/5fuJfAPa5RzrDsNvewc+wFwO4i+CBPfPl3YXF64Pz4L5n+Xubmeb3dwOWBta0f9vufB1Ql7+GhbYShJhOQimRLVYodri2JSrFqgliSwy1ehL0G7bB0dE0ITdO9MThwaHN/Az37RIVGNdmSdCqBeDQCu8NZNgCmZ1TW4XQ6hF5Q3vFIFjQ3mm/hrAWcuA/21OaowuCPkWQyQzu/qqKn2SraAaoZL+pwMOu7Ew2IuwWK9NLlkfpZjx3U3R8Z/CLBvH8wn3j3Nlvwv98A5BhwLEvAnjuh4RvvA69c2dLtYGk9CeOX32Orid4SUU/9jmJgFRWzqrm2SJJxh72ym+Ru75exZfH2XAoBj1W04+bQ21I58NER2rqX9nf7iipItQbdcDlkuBnJrKD3RZdHVoy1hBpEMDOdyYig9E7Hwh9qERVgVhOL/WqhJGJITN2Ao70fsiHoZAu2ilclyNRCstpE4MqI5MVXoSWjcJx8rurjN2WKQje7QAssRSrDGPxK/OXvCOFzx2OfMf271pYuSCsrVe3rHu4+OOcn/uJ3hPao42M/a/p7mWtvQZ25BckXEu209gd/YptunEZOsb4MaeBoUWFkdW2BFs5ViSxzYSd0yJgUaq0gxFgEqcgalHQSVpsdjb37xH+Jph6DNbFJJJJJxGIxuF2usgEwco7wakS0cDsLhLaoC9belM2I7BLIG473m3fZLgyGGJNp6zEVixsa+lskUbdczfOoxW+Bzaq3Yn7UwYqur57Tq8Me2EdXbL2NkFVdD+wzajTRrVLD/3wd8GaAo9kxe/aYLufAqrFPZvVJeeqP9AD/6w2dd9FJ+m5wJwbdfNlgW3+zBWsebZvGbCHqFXytBLY1Xp/JoC1gRci3dWGMdFaen/tbt3jJsQF30ZbYriYnogmlrKECkcooIgidTqvobPbi8h3d4OIQ9Th2iMZQSCToq21RN4NMPILYwiTcrb2QDRXXDj7PTVR12dv6ROeO5M5PksUvfl+0N7oOP1L1PpniHFYZFn8TLL7tCQc1PIvUN/8HrMP3wXbqGdO/a23r3eMce6gr7oHl8Y8OREVXx1DVbYkUs7V4GyA1Z9sbSSgLSKUycRVaIgp56L48UWfCNnwStv6jm8K0RqgMUrHNsX2w7gvx6MR1pFYj4n/Lrq2gkOyonqy5PD609fTDVqnKIp3GUnhZkNaOjrvXYlmvAMpb15MY7LCjNSBjMqxgKqyAWv43ZjMIeSUc6i4frEhn6NyjiOqr00PbWRADasxOUuB0p+ebARVdRmln22H1Ftsim3162yA1Ldg6+cQhPbNMEnV5SkNno0WU3N83ACxOb32fmdbTg8VTLKyAI0G8G0Gmd2+rmF/Tbc3bg5LQDekqUnDA42F1HjOnZoR564VkRsPsSkZcZ8YAWLWgnk0xuJ2yeJUDF4vU/epsbYQ/2xvb1tIoMqH1OEdOt1e8dgNKbF1oFLHN0RgA4zFlwtNILU6KdgRn//aAACG5/bD3HtL1O3i8OyDMmpLRtTha+0QbWsV5Z+g4lLHLIjNrsTuhjF9F+v1XYXvwJRH8snZXEOXbw70Jqyw04qRgdQs9eeR+XUsuG9gtVj2Wmbgi2vrkIgEw+4EHRPCrWMJNuI/F1mFrrc0UpxSYVFufvIkkJzjug4Fz2GrgHH6fDw67XYhUl0M8kcLi8jqSqbR4rt1LoOPxD66mcLDHjma/FaOLKmZXVKHLdXFCQWdIwoFOuWIQ5q1bCvqapaKcYyqsCqfqobadBwjIOZjI3ClY+UXOwYL71Shwa06vOifnYDCFcyQTcYyPsHr85CCwPr/1fVZ9nR3Wiia3GASrg166Kbx+g+6e1PLS+REdvYtJLlBig/qybI28W8EvIpok51DFHhkDYNWCrZDF4HfL4lUOwo09kcZgh2+z4qunrX6tdB5PbQlvM8jENpDeWIHiD+UFwIQxztIsUsszkL0BuDqHi37f6gmIl+AcouhgB5yDUju3z4s1bLGWRyO4RqWGpDI3JlohOX9k7nwA5eY5YfwmUUMsp0/5Iw597OtbAlbv7f24Yi8AViOSb34dqa5e2I8+aD4T+7X/Amv7AGwnn6rqt7hwkgZY71IGXJDF1oUujLE8dXOhVSJwlJ68AiU8Aytdw+pQIpu3S51DCNiovVGfCZfVXxU/Y7cLAdtipJUElePB6rDdBDU3Lo9HxMTc12Yu2MkxcjrdWE5s6XX0t+h6TqzmcrA1sEhJOy2kWe2UE0ZVNL2dMJoo/ju35lVR5t/TZN3mCmX2wTu/qutkfOci0OQHzgzt7PwyCPSAYf5+eER3iswJx5PgUVyeJI4BMGan7Wlv3nXFyrBiYKCvmIg9Cfn9g5I4nu9e1lshHtxXnBzMrmpCYJ4Es1CA1ggK+VolTYjmrsZUnCmyGCDGFtmmoEJRpZL7XS9wjEjQ+F86kN436KqYLd1NsP1xaWVDVEvkAmBGt9bdBNuuGbGVssmD5PoyonPj8Hfv2wzQG8erEKLKS1UgF2RemURI3HlfF4010aaWvPSqcEWiOQlbzhxHHq36WDRqcdy+IJIedhPtlOr0LajjV6GNnBLukGyr19volaoqvz6MysU96Ei88pdIjRyB/YA5TSwtHkHi5f8GefAYbEery/RLvkbxKge2P2pri8IRNOdYvcU5rNtMG3JIjV+GuhGGNdCyWWFeD3Bx5+vsR9CxUrdr00zro8tpR0dLUGiKFoKVrkRhZVi9kcqoeH80gZYG2VRFL8ExcjjdSMV1sXdiX7tVVHJRQoG7XIxzLEdUMTfn5mcmmCIJPdhRDFenNaE5NdBSW/CKyREmllid9Y0PaFKj4b5+acdVc2xtzOHBEU3IJeTmRmp/kXPwXyf6OS4SptV8zlGMWxDFNNDevaOBhewn+vWE3rcuMiAEnB4svo3JMKvJ9YBVKQOdXMXXrI1mAkAkqeFkf/HP3pzVcHNOd642a4pUK4xzaIObQVFbWd6024gl01hYiSOTUTcDYEaXx90EnR35LMxVh8VWw4guLwjBe9nuKMs3CDurregUGcivGs+sryA+dT3LZyo/W+KXXhOi+Z7j+trTdUTXcqwG6uoclPFLQpOkUgCMUKZvQp2+DW3/aVF9LtwgN1YET7I/8inUgj3OsYd6Yi8AViOUq+8gvbFoOgAGijIn47oG2C5A7j4IxSJBXVvcJK50hmTE3n7qOUj+4srjtt7DsDZ2wFLOgcwACirGb52Do+cAbIaFF0kvXZts/sbNVkdnsBn9wfwH992A11s8IzMzt4h0OoPhge7NBylbrzaiCaEPVuzhWo0rS3g9Ba9Lhs0qiaxTNVF6TpD7hnoxbPg9ks+cIP6Z4fwJmw45JKLXpzNC++OBEfumWO1TR+SSDkrH+6yiTa/WIMh7d1QhMN8d0sXg3bWb0JREoYBrk1/X8aAzVG6sururb8nNYXaVme+tLAoDjsVEgnM4P6oJYf478xqePV46tcuKr85GDeNLJNNlNNqsGnpDujV8DjyXt+YUHOySheNjvVA4Vt6sTsyHBYfdhu72EBx3uTeX2c/wrUuw2h1oHDi4GRCjSKzKNi1UvrbS4WkkJq7CoWbgaNlqz5I8DXD0HBTi32ZcciWnN/ubtZ9nWo7bTr8IqaGCo0QW8oknRcuBlCXS8v7TsA6dKKsLRadHJk8cT3w27301HkHyu38GtedQzfu/h+qhXD+HDNLmA2CqCo2cI53clf2RqRE6NwpldR5ym57NT98+h8zkNThOf1wk7orB0X8UanzDdPArvR5GfOwSXH2HYTMsvKj1l1pfhp3Ju+wC0x1sQX+V1W47hVjo+4ofy+h0WIiT7zfY/CVTGUTiaTT6nTvmHPOr6U2nXp1zmN9vPusO7uvFAcPv6YEtfY575ED+5M72yHBEw9UpVVRAnRnWf5ftd88es5YUyD87LIkgWa2VWz+4rjsrD7cCjF+UCwjVir6CRBSDRI8eAJr99eEc08u6gD2DaUS6Aud466YmdIMmwhqeOlz6eFnxRWOh0UWgVFETrxMGM1nNRufHHObXVMFpjvdJdR3TwrGiOc+HCa/Ljv52f57m790AdcHmRm+IStTmrr7NgFgmnRLPLjPXVTI8g/j8uG6EY3iusQrd2TEI2d8Iq7NyxTvbIFk1vhNITd2wnXhGcA8zoEmbdvCBTVMe25GHIR84U5JziKq2b/8JLP5G2M88l/c3dWMVyR98Gdqgrmt9L2HPBfKji70AWI1wfupX4Go3rzDOthPX5/6fu7IvLDHN3D4nMr60MWfbgdTcLUTxqTdG3ZmyAs6u4kKzConn1TfhGDgGW1YDTLffzWyrdEitzCM+cRXO9n44s2Q4FV3HWiSOUHPzrvTHV4tQsAGKouSRy/BKBCvrUdhkq9AgMmIuHEF4NYbhnpDQLQqvJdDR7IVchOnFEgrG5mIIem0Y7PTi+JC/6iznMgV3GxtNkd/b8xksrqvoDFkR8OTvT7m2Ooqx70T3k8EoltJ3NFrQEbx759QYLDKOVS3X1RMHLYJc5sb52WPlx5sVbpSMaDRR6c5tFpJpI9jieWlSFWL5xuw6NUmWIxTPVeE1iLyahSpm2e2LDDNjxe+uRTJo8Mh1aS+pBFZ/3W0IAukPQjKQL1djqzDl4OI5vjAlMqTO1t6S4yX7QrCFOoQrbv62LbBnn3lmYB8+mffvzPwo1MVJ2PY/sEkO0zffFVVi8uFHRUKhmHtjzvnR1PHLNlgKssiVRNHVhSmhH1YIbWURmVsfIIW7u6D4cYfr878NV5t5ziF5/HD/1P9rV/aFgbXM6PvQ4lGdc8h24VZtsbthcXrLihszYMxXMaRX5pG4fR6uofsg57RmNBVaJqMnEQ1IhGcRnx2F1jkEZ9YpW3COaAKhpqaPBOdoZZlPQaB7LhzFWiQpBPE9rvx7aGI+IoxEDvYFEIkr4n93t7iLzumrUQV35pJoDagYbHfizIhnVznH1WkFK1ENPU0Smv2SaYH8nD5VrWDQhvpcDEod67s755TjYayU2inneO6YrieWG6tP5E8B28DqNBroUMqiEjhnD5aJSbCCnhISHUFdGzUH8o2lDU10DNQSAGPAlZd2YUulmbGiy+NKREGjj/q2u885GtjTe5dBXuH0+mF3bq0rfE1t8Da2iL9FFqahWixIWewlx4oFBdRRtnkbtm+7Co1EZyHnmL0DZX0J9uFTm8mD1I13gEwKtn2nRfVuoUSDbspWBeewOba1wJflHKoKZWkaUpEiEXVpBsroFaSd954LJKetGv1rym5zDzvHXgCsRlBTQ8qKtVYDig5L/tI1yJnr74rSUfvDnxJBs8zYZd3avsCWPH+jGWiRVZHdt7h7kb7wHcjDpyAPHBMtl3kfXVuCxUGSaiL7apH0hVPe4kvTfy+Tn1VmZlZt6YEt2LYp3rhx8xxmkzIam8xVKVSDyPoqVhcX0NLZnTfBlAODXHKBOANd6Ow2GZ4ii3KSTlmWBHEhYV3dYKWYE75s2ZMxU+t2WtHT4oLXXduikNuanFnAYsyN4U5XWUJJDLTKaPSq6Gy03tWyYOp8MOO4u/3y5bPF/MzS0hKCQfMtu3Q5fPWqJrKkh7qrI7DMdtfLrYpVc/x96paRQDKI2dwgie3zFxbXVNHuSr0MkkRqtrU0SBWvhwujSaFbcmY4v6rAzFgtraUxNhdHd7NTuD0akc6oWIumEPLr5fr3Mvwd2zWHciQvuTIHNZ0SZh+lxktyuODqNV/1xLFP3T6nO+Z1ltbYUpemoSxMQO4/BousEzxtZUEIiade+1NYXD44HvjE1ufDM8jcfl9kVPm3asB2zcwHr0E+9EDFNgbXT/5W0fetHf1w/8z/ibR6l4Ru9iDAaj9T83bh83RjpSznSF/6AbSlGdge+bRY+CijH0AKtpXN9DNYLDgHE2OyHelz34R88CHI3fvFywhldUFUg5HPVALvRxE8My6+OM+CQTC9nTAHe0OzkJZgBZg4jsgq1kYvYzZpFcLU9cbq6irWVlfR0dkJm83cPE9uURi8ag15ROCrmF6i1cpnvX7sKxspUVXeEnBsftbIORrcVvS1ONDoq43Gc1vjUwuYj3mwv8tZUY9yf6dVOCd2he7ufb+/Q8L+jt3bvgjkZANJ9eQc1Ff7/hUNAy0WjHRUN3e2BaQ8B+2dgHqqh7stwiGb7ZdL6ypaA5Jodb01R40utmeqaPSxUk+XuDCjT/rG9ZTgJWf32aseq+lwGmMLaezrsKMtmH8vJdPkHBk0N9juac7BfQ+1d5fkHLHVJcF1VzRHybGSnR54u4rrexUDK8sSoxdh9TXCXiZYlQlPQV1dBKhXmn0uq6vzolpYeePPIDW0wHHsyc3PK4uTYl1qO/igqed4oeOjcu1tyIcfhqWMqy+f+67P/h9Fk25y/0G4Pvd/IGXZS7rtoX748FNkP0bIXH8PiT//D2LxUgra+hK0lXkgkxb25Zn3von0+98vu11Wejme+GnYTj4nFlHWzmFIRR5+zNomz30TqSs/MLW/Vl8QnpPPQg5tsQ+R6XX5RBDNCMlmh6tzCNaswL/V7hT969YqCbtpiIKX0nWg6+vrmJyYEKL4OczOLWJmVnd/ycFmk9EY8BbNQjUHPRjpbRKBs7aQF0Pdwc3g1/jcOi6PhqFkxTM42bUEnXA7zJPDxdUU3r8TyVr7AvGkiqV1Rfy3EtjK1hWS71mCwGANs8mFIvJffk/F19/XNkmpWTCbOb1c+jszy5rQKKH2VyGuz6g4N6pua1k9P6riG+8rwlignuhttiLolXB9RsGFMQXvj2VEBrbz/8/ef4BJcl7XwfDprtA5T85pc0ZOBAEQBMAoZjEoUbKSJdmW/cmWH0uyLUvWL332Z1myZdmyrExZkaBIURSjQCLHBRabd2cn5+kcq6q7/+fe6u7pUB2nN4Ccg6efxfTUVFfq973vveee4zezftyrsyq2YzmshXI4t6Rhcau5xTYx++iZ6OR58DpF9Ptk+Fy1wcV6MIWF9QQzEYogQdmNYKKrQpwUMCeSyYbbZDUNq3NXENmu/A5Xg44rEwtXtDY2g3vqODz7b2v5+lGg2fT8c1k9cNwqc2wwgHTwHljueT8zeLVrb/B+pdsfg/zgd+uFlirmVm5rCbm1WQ4s+VjyeXZWauV+5FZmkb1yGtmly/rfqhlknvpraAsXa7bl4kediq0QGIBJ3Kuf3erQzjzNMUfxfhshH95ELkyOjVnWhdNe+zq0s8803C8ltCyPfA+kE49AnDyuCyMXWFjVCdfM61+Hcvnllo5X9PTCeerRCpalmRjtVifMVQsvame2D01x7ME/Wx2cDKPY47qgKGZc53sWDEUxt7gKrdDbRtsurGxjaa3SuYyYXz1eu+FYM9xjZ/YXJcHG+uw4PO4qJb8uLsbxxrWYzvYtJGyGAjKscuth/NK2ileupnn+LUopUJGF9DWbgfScbnTyq5ugcwxXxRzfupDDky+TJlf7c9lGRE8e1QPpeCXTYPmEaryxkMPp+do475mLOfzd6Vzb8U8j0HNGBgSk63Z6PouXZnUWOjHzSNeU4qLnL2eZhb6wmWPzg+Vg8xjUZTN3LNdAJjxDfhE+Z+3ztLCZwdXVNGKpnbgnltSwFkx3LeYgltrqZgjJdOP2cFo/zM0vIhyONDf3iUd5v60iMHkIvvHWzWeKbZNNu4GCK9BoDdkAloP3wnrHE8wCU0lLjN67/QlY7nwPy+WQ+29NzLF+DflktHQsuSafUfrb5SvIXjuD3Pq8/rfpBJRvPYnsmv5zOUySXJc9LPQMGjLhb3UUp4xuv/awe+xFsDcQpsAgO19QdbUexDseh3jyER4ICNLd74HJ0bydjhgE2fmzEEcOQDpqLKqcI3vwscNcHegUZFNuP3w/a3g0Ag1i9tEDENL1A+/dwOnx8ssImxsbSMTj+sKwbELq7fVXO7m3DAo2yzUEKEClxFiJ194BKDCiZBcxfci22usUMDJugcuuD/JrIZXb5Dqt8FaDkipUAaRky250GcjViTQsSIy+XuWTklmTfSbWxTLCG/N5LAXzePiIHpgRKOajoqPLmsfX3sxzq+Z9B1qb8ChZReZf772N7kvlPaHr++o1fX+3T5mNRe7TwIlxEwvVFkH3RDa4xavhHCfUjo+bK1oK2oHuypTj8/U7dYbX0TERkUQO9lAWbruJWWJpVSjpwDXCgeHOKf6yaMZ4/w6LcnU7hc1IGofGPOj1WpkF6XHsVHk3Q0lmRDrtcku6GpQkXtsIwuWyw+2sTYjHYjFEolHEEwk4xhvR+vOsq9EsyFTiEYSXrsLu74OrvzXdluIimlqkm4F0wxJnvgkpMAjrxDF+T91ahjJ3BqJ/kFvGi2Og9bbHGraDFbcjQfHM2ae5+CGQI6/Vwe/Lp2ptwoV9d8A8MMWMIEqaKa98Bfn1OZ43SPCcj+fMt3TevaZCPP5gaT4Rpk/ARC1ohfaJ3MYysldfR96Uhzh2oKVrtYe3Dsy9IzAPTcHUQCtOuu+7mNVNxS1yipbueBymFnS0sqtXmRUgThyDdLRWZL/odkoxidAg5mkGweGBg1wpM7q7Yz1QezOxJYTU9Yk5vD4fv4ywsraBVCpTof1JiYehPi+zujqBQOY2ZWMHjdOU/NpNyYuE1ZOFmEMQSNJAwOiEXHLZW9zSOKlhlJjoBJRco3md9rmbVsjZjRzPzeO9xteSmGkkNH9o2FS3bfDFqzTnAu8+qTOxCTRE0hxMpjtfeDXHml93z7R2v164koeiAR++22QYA70+T3M7xRVmQ00wqp2eHK91xCZ5zOqYY34zh82oLqLfqXM0Jbq2YzrBpijBcNukwIywjWgeDisxFukZ0xlozXBionM2DhnyzAzuxCxz62kEYxqOTzowHJDhsJjhtO08gyvbaUSTGrxOCdY6LtTlIMmSpfUYerw2uAw0P4KROMLRJJIpBdNjDZiu+TzHBMWkcz0kYxEE11fgCfTC7W+tRUKgOdncPN4gZNMJxC68BEvfKGxD0/xeZnMRmZXLkHtGYS24QFKRwHH8IR7LG6FY3MqceQr5ZAwixRwUhwgiLCcfrdle3H8nhNGDrHOai4ehvvEPyAdXId32GIShadbvomILG5/ksxCP3F9KVgn7b2c34SIpI0tFuPlzgM0JYaD1Ns497KHb2EuAdYpMEplX34B85C6YCqynZhB6hiA8/n0Nt+GqYGGxwn8z0rxCQNl47dUvs9ZE1uqo+JsiZT67Ngv17NMQD9zVtI87l0myZk49qquychnK6lXY9t/JFVv6jBS1TVgdsA7NVJyLx+O54SwlSlZRq+Pg8AgEivIKcNURx+8Ew73tt79WY7TXwhVcCmhokvV5vXAWqr0UoF5ZVQqitGbYymygSTthK5bFVL/UcjDESaBZVQ+eTcAjx+SOtRcurlLFWBeONbq3FBSS7kTBjb6u0D3lDyk2IWF9wuERE+tRTPbptuT1kktGzxUFmUklX5P8ItA1unOaXK2M93f/fjO7Z1Zfy6Nj5vqV33Ae+wYAqSyfE07k8NLVLA4NC3UTf+WgkGqsl5wgd55Rj4OSTTt/Oz2wuyG62XeQA7xcpYYLCTSTY1I4lkGvz4YBf+X4NtjjZE2NVkVlNU1DPJnihaBRAsxut8NqtcLtatzOJ4gShqdrkzTE9Co6OxIku4uTX1ZP+21QrYxZ7OpktTPztgh14RygpmvatNpxuZOOPMAJLdZtNABXXwu6GsWERnbpEvIbCwA56vWO6NtpKrcc8AOW1SCM7oepMOZT+4UwtNMWT39PEAtBdTt4q7JP36ogF63MtTcgH7u3lNBsBmFwkl+NwC055rKYY/xw82MhdvobT/FKPUesqzI9mlLMsXQR6pVX+LkWmjiV5tIJmASp7nmlaV9bS3AcvIcTYhTzJObOQnR6YS2LZ25azGGiwouIkeH+Ci0fr7t7DPjJwd3va3pAwmQhbuCYw+eFo6A5SUz0K6tqwQGSHI93zmMzout/zQy2rtlEybZXZvUFPiV1Hj3eecLk7KJeJByvk1sgU5tQIeao1zpI8QoV1yxiHs9c1Atix8dNeOlqnmUdEhm96GUEo+eKYgqtTi2G4rY7p6mYZ/z7R44YX8O7po3jhpUQtSbqMVK5gSHFIlRsvG2KWiYb35eCTCgODJJZz87n9LjN7OZNoFztvkHhhscc0WS21PoYcJNrZOUxjPXbkM5kW0p+ETJqFrGkAqtFNEyAkWlFPJlhCZRGIDf56ala6QT67pSvLax2J5xeP+xVWl3N0Op4xTGHxVbBgs0sX+L5nZjmncYclsP3Mwu8XpEul4zqWtKUMHPqyf/s4gVOfsHbzwx1/WBSyF5+jW4qF91oDjEVWLy0b2Fg5xrm1ub04yyLQ76dYw5KKHeR0Fna5x52j70EWIfQLryKzOlvcPAon6ysfmrLs8h860lY3/4hCIMTpfZD0CDShA3QCYhSSiCrWenYDvuLWmSUF/8W4sxtMPeMwOwfgtltHEFQBj+7Pg+ztw/p01/jRZbt9scLv8sjG9mA4O7hgVjw9ELMJHfE8yn7H90CIps8GFtH9EUqBYKDg3rgy0my1WsQ7S629GU6L2lolk0i3UKg58Y7T3YKCkRJ5+PySgoHRnaEe+n9I2MWhONZvHo1iQMjVvS49WdnPaxhPZLFgFeAq6xK1uxzyGEwns5xcLsb4dG7pgUOYKono61YDqfncpyMeuyEmYPeeqBgrRiwhRJ6FKloJsRSFMya8NCR+gmk8ueqXJy/nqtesfI5M7jDvrq2kcNEn5ldMylp1k5ofnTUzKKzRvR/PooWLi3dg4ePXAcLzSbXip3DQhkOMN0OCfPrSWxHFFhlE8YHnOxiOhiwccK0WqC5CFkS+NUqLBYZk2ODNfp7RVAgOTzUmchLJhFDcPEq3H1DcPj1YMwsCHWZX2oiiuTaHBzkoGRztPRsVYOCQceRByres+y/E8iqPDZ2CnbNq+Ocx62KX/8MTN4+WB74UOl9YfIYt1nmqb2gUHGl45Me+m6oX/0TwOkzbIcvQjx8N0w2B4SJw+0f7y0gMv6dBO3Mc8icfx5mhwvSgUo3LG32LDLPfwnWxz/F7amEfDoJyJbr0jZCiVqCydMH8cDdpfezwVXWA5MO36+30vgHmTVguA9qEd5Y4Bbf1Omvw+zwwlaIX/h30SAE0j0zmXRB/Fy2tLjLZ7PQokFosTCv7IuC0BUxRy6HxPo8ZKcPssvL7FHaV7W4czcw0N99ndPrARayNpEkgIqrawpOTPSVvsfUSnlsXMZ2NIsXLmVwclIuMcGWgjlmkI/2CLC1OG3RnHJ4xMzGL07b7q752w6aDZ2tiYl9ZiGHu2bMePcpE8/n9TAaMPGL2DyUzFOyQL+qJ80o9njseP2/NZoXGiWcqJ0wFCfNUVMpBpnfzGNmQGdetevCfce0mYuOReZaERRrtBrKUbzyxAnxpsQcS1sqPA4BbruAi0tpLuLaLMD+YRvHIlQMXtqiuMT4OSFn0qI7aavOjwfG/XXjFEpWTwx3NlcnYlFsra8i0D8Ap0tPeAmiCF+vMcs1HY8gsb0Oz+A4RNnSdrzB28lWuA/tjLME+4w+B4h1xtdWUM+MhMDs8qf/EubeMcindlhh4swp5EJr3DpfijmsDkj3vAfq85/X2V5VRkHlIP1RimM6YX/txRx76Cb2EmAdQjx4GyxuNyQDS/J8KoY8CRwWElN5JY3Un/1/MPePwtqEAdYJSAhZ3H8HTN7+ygQbUVqJnUYaGk5vxSBWjVxog11AhIFJtjSnamwR2uYClKuvQRo7Anl4H0SXn1+lzzcLcB66D/Hzz3F7UGmfuRzW19fR39/Pi0Oi7GYdHk6ARa6cZv659+CduFURjiYQjCQwOhjQ2x2vEyg5JQl5bG2sw2UbhFhICpK9uWg2cctCeWAw0S+1lfwqghwjgd2fB7UykIYItR2UtzVQ9xgFaZQcKw/wKPij96ilzwjUBlm8Du+5rbnge/G5sjj7mKnoc5pY2+O5SzkcGTWxS1URl1dzzNYiRhoV46ktczmUx+W1PGQxzzbi7YKOk1omqkFunI8ev7Um6PLvIAUPpGe2uJnm9gJKgFHwGZfMSClZKNTXahPhtEk4ONYdt51UOoPNrRB6e3wsCL0blAtAF0HMLwoqSSeo3t+osTBEu5NbpLLEXEnF2VnJKAFWfb1ahVCw+r5uECVm9pqqhOsp2SX0jiIbD1eM/QKxwd72QZjsje+jyeaEeXCqacuEEdrRO9nD7iEeuxeW/kGIU0drfpdLRJGPbAOU9KKf42Gk/vzXIUwegfXhj3b9WEyuAITpk8wmL08ocdxASSpR4hbdciHlamS3l9kkQhzeD7FvvMKNWlmfg7J4AdbJ4+xALXl6+VXesuzcfzvil19jaQej7y/FIult3dyCXNRCV9/gMcBncP1uFVB7OZntTA7rOmDXCzTHUsyxsb4Gx9hAic1CLZGUaCJ5hnLW+YEhAYpmbtstcKKvO3GT10FyALpjYXnhScvlkdb0mKM8+UWxCU0VRkUqKv6RKyOdJ3ULfOCOxi6W5c+VYO/jVlQ6HmJfPX8ljzumyBF75+9JX4vaFYmRRsyq0QC1MOZxYUVvtRzrIFdKbHijXA4J27/r1K2li1Q9hyYyOcxvqvCnczhsF5hxSDEt6c9RMZIYbRTr0qsbIGYXOa2O9Lt3zRgyijko2UUmGEIdMgP9TToehcXhhNksQEunoKYSyKpKTQKs03hjt4mvliBbWWpBCFQasBE5gsgUOSVTSoARhOEZ4K53N2y357+3uzj51Ulh5q0Yc1wPza49DbDuYC8B1iksdlhue7vhr6SZExAnDu8ICBMFdHCisZPjLsAV0n13GGb3LW/7KFdCc0oK2WtvMhXVbMBSoCqsRGK2gSGdiVAGgaq8fROsb1MeoGaWLsF+6B4IdjfTc10nHuKERPlEEIlE0NfXB0GywLXvVGmhKnEbw609mCmqBkVR+RUMx+H3OFg0vxG0rC6m3k7CjHQNDo3Y8ewrc4DFi33DO7Rs0gM7Mr7TapVWc3j1Sgp9XpHFy6tBQcVaWMN4r9Q0qNsNXp+jNsw8HjoiclBMAWevx4R3n6p1pXzhcpbF5991svJ3xMKigLDcUr2VYy4+V3PrfuTyAt59qv49IZ2teAo4NqYHrH4n2bibsBIkkVpige2ODdcNUDCo66R0f8FTvFa9vb28SCCNj33DDlgKwslk3EAvem4bLbiiiTS2wykOKo2ebaNAkaCqGtIZhf+1WdvTKSvfZ2h9hXU2Bib2cQBKyGoqtucv6QxUKjYUqrEVnx+PILpwAVZ/P5xDU7D6ByC7/CXNr4rPy2WRWV9AeDvOY9atgLySQV5JcQFDOmmcTBD33cavarTSPq++/FVoZ5+D5V0/UNEa2dKx7UVhNxSkBVov5qC2SCrGFWMOalsxD4xD6Bu9PsciCJAO3FXzPiW9rBxzZLmtkXRJheH9/PxWQ/APQRo/AqF3rKZtR/L2M4ONGGBFZFauQNlc5DZIijeITe45/kDdmEO02uGZOsqi+DSOUGs0JcBuZWQUDRkly/9uxBX0+eyswdgI5NRLkJpsV44+jwiLKOOFV7dhsvoxPbgTY/hdAr+KiKdyePlqBmM9IryO2rE/ksxhI5LDVL/QsT5VK3jxSpYZ4o+fEDhOIJZVkdVVPfc8dT4HuhxPnBQqng1yPST9q3JX51ZjDnIBvbgYYAbc44X9Gv0lCeMrKnBizITFrRx8DjMzv1ZDOcxtUvxx8xNWsVSOk39knnN9Yw4qFgo4NGKBoxDfjPbK/KLnttEzux3JIBhTMDnoMIxN6sUcaYWMpFSoKrVMtrfELd/n+toa0uk0RsfGSskpVVGwsbzAGQg1k4bNXltES8XCCK8uwhnog7tnAI5AP2zeAEs4VCOnadhaXUSP38tj2q0AImyQyy8VJOTjDxluIx28C6BXFYTR5jqiyvN/h+zsG7C+70dh9ve3d2x7Mcceuoi9BNh1Qrl7FgsLvvNTN+1Y1Je/hFxsW+8Xp4HNIAFG2Xipyr68CBqYLdMnkUvFkSEm2DAtrIpBx84EVB6IGkEsS6w5qFqwCyipBCfQLI7GukG7QV/Agx6fC5FYikUzaRIO+Bp/3uxSiPUHpoa9rJHUagWK9JEskrnE6iLXG6qUVVtzk0sgscFIF8wI1B65tK2xY1OxZbIeqI1yYVPD0TGp7v7qgTQk7JY8LBJYRJW0r8hR6NhYbXA3PUDMo8qWSWKQnV3KwWOvTIC1A2qvKD5zVJE1qoTes09v19yM5rEZy6E/kedrQww1qibfCnhtNsPH+OCR6xcALW9nsBFW2FWMEq7VaMY2SKZUxFMKs8SqE2AkODu7uM66GgM9lQtdt8sBu42E9NsP+tcWZjnw7B+d5LZGSnyVP0N071lIFvm64wAtkm2BQVi85a5yxmwnaqlKr1yBpu3+PihXXmUWjoUEaZtUdtmd99m/ZvF7coQsB7WT5bZXYHn0e2ucd7sBYfwgM5WrA9HUk/+TK7XWxz7Z9c/cww2IOWQLbO/+9E07FuWFz3PyFlqGC4DmQrtOOSgukkbqxBw2J6zU3puIIL18GTLJKhT7vcqmxGYxByW9inCP7C7mSCcT/PlW23VytqZ5tc+FoV4XNsNJbIWTrHnk9zQej87NRfmazAw54bC1HtLLgt6KR25+tLCMpsgZkFoNa01kqPXOVidZsrSdxUowhx6XzsZuhMVt3WHwtkmRWwHbATG7E+k861TRPsi5mRjdR0dr55YDQ5WGNgQyujm7lEefJ4/7yhJ8rYLmHiqkFV03+zwmvO/22nN4+2G6nroLJInuEzudNEEpF1MwDb+poHv9wmUNsgQ8eOj6yTCQhi21zd4+Y0fAIBZtlrCNpTTWBCPjpurwIZ3RcHExhH6fHQOByiQUOaySRmm7XRvs2rqwANliweCAzooUq5yOWUNTkmBG/XHAYnfB4euB3e3daTuuk3hXYttcpMuENiDtUhA+ffkVTlzZqmIII+SSMWRe+TuIo4chTVQyYpVX/p71Ji2PfPK6tM8LE4d4HDW5dhhs+ayG9Od/B+beUVjufy++nZAnY5Qui3Z1e3/fqdhLgN0AUCWTM+oN+q0JZCtLwoHd1sUyuf28gDSTA2RZ8ouqtO0McNnwGrSNedb0kPsnIJcJ395ohJfnmAEycOAET1yJ0BasLg+kKorxbkELcI/bzgkqZ5n6aCKVYQeZHp+zYlHucVoRTyq4uhTmYDbQJHgtr0L2emR+heMqLiwlMeiXMd5nqwhEz8wlOQE2HDCeUMd6JXZ4DLQQ4FEwGU3pTkat6noUMeQ3Y6jQBeu0UVukHpRSIqq6wjle1pJYBAW/pNtha0MLg6rMVFE9XCBSDvvMTR226LpSHESi+9SCScdJuHO6O98xevbOLWlc+aY2kU4o9xN90nUXtSRWAR2ZoubY7ald9Aec8HvshpoadMrUtlts3a0GJb/ITjwYCsHr9cIityjgTQvnwv319PTzqxw0pvVOHWq4D9rGUdBhbAbRHWD9QnFLt/quRl5TWm4VJPaL3gLfwo2lYJBaxwxaKsxDM9ziTm3s1wNGIunsnkstlfk861lmFy5CvovcLG8+c2EPrYEWOARzWQLIcLvgGuuxdFsXizRgaFEjDM3A7O3vOObQQmtQNxeZhW4ZnObXzcLmyiJfp5Gp/SyCHYuE4XS5IUrdY5WxRpmJFvE2WCSBF/JFRBMZZrcQK6wcfreMRDqL8wsxTA854HO1NkYRG7jfKyHgElnk/tJKhueikZ6dv6cWtdNzCrOYBn3Gy4V9AyL6PDn4nM2fIWKKR5J5Loi1Sc7BRJkDJBXO6NJc28jD78hxPFJ5TLXHQnIN5PDotrXneL0aAo6P5UvHQO6cjVBM7JFOKLUoFl2u33aoO+MnOY2+MU8aWnrM0ckzNj0g1BX87xYUVb9majaPDkIOjPXbMRSw8XNaDZOZtNQo5jB+5ij5lVFUbIXi6PW7WpZfoCJbqR24t5YoQAmx4fHppvvw9LWmaSq7A5CdXlh8/cZspyy580qtxxzZShOepjGHwVhMYzbJ+FyP5BdBHD3Arwrkcpx0yztj0OYvIre5DOn2h9+SovfV2BPBv3WxlwDrFMSmahHpz/8ucrEQ7N//b+ouIrKrs1C++hmIR+6DdFt93YxOUGQVlNNHyd2DqrRkbSvN3N7SfsT+KZhtbm5zaAU0ePX06CK23Ya7fwQ5CqZNJhbCjm6vc0LM29/9NlOqiLopy1OG7VCctQbofUtZJNcfcHAyJJHWKditovxaEfOrxy3B76qc+IgM1ushG2hzw4QPMb8o6CCxUWp1oHZJI0z2CRgNCIbOie2AtDdOToi4spaFv4UguIi+NplfW1GqqJJLZGfPlbsQiLaDejT7IiiYp+o3odOgcjjQ/WGYbe7NOkuTr5UkQ8sqNe5KS5txXlRNDzXWy6Df1ROUpcRXIytxArUSxONxdlUqT4ApmTSi4RC8gd5StTURiyCTTKB3eMzwmDQlDbMgcXKrE5BmUHp9AZbeEQiFtgN2bzSB3+sVgqXPpfufWTzPyaxsaA22w/dDKNM/rAcLieTTs9NCYoHcdi0Pfszwd+LYIYBezc5pexUmX19XglY6d/unfobZvem//QNoV89AnDkBobcyqP92CE7fSmjneqf+8r/xIsfxvT9bdxvt2lmoT38W4u2PQjrcnDXQDsj5sXr8JFOezKtfhjh1AtJYa6YL8uAMBHKZdAVueszh7x8k3jv/fyoRRzi4zefnC3RfAF8wm+GtshBc204imdHgd1srFv6jfXYsbyURT2mldsh2rxXNjb0eoUaHiYb8XrfA+pb1IEsm9EoCa3SdX1I5piAtMSOQQ/K+gd3HHKTtdWpSwNX1HLxNWGflGCzT6moFa2GwZigxytp9rtjhrwOyYLOYg9js5AppQh77B4kF3/61pNivm+CiScFJu/hcqWYR+Ui2RvD/6mqSOxn2jziaxt30bBmBksOHJhrPwxSfU/eGw2apSIAlU2lEYwn0BnylAmo0GoWiKGzGY+hsrmQgkq5hh4UC0hxNbq/B3jNUYK1T+yO5spswMDZZeo9AnS2p5cvIZZLIxoJwkvOtgV5pNWxHyYyttcwIM2zv+6Dh78TxIy3d7zwRNnz9XRlrKT61ffJnmNGbfPJ/IrdyDeLB22GqctXcizn20E3sJcA6hNAG00iYOQ5zItKwgk7ui+axgyVrWPXM08htLkJ++0e75hypPP85Hh8t932A90nMApOl/sCao2O2Okqfz/RfcmMyQDG5Vj5A0WRBE2E3waLWyRgszp0FO7U/+QZGYLE3tjTuJgZ6PazTUZ78KsJpl2G3ivxvqyi/VjTHzgzZjSt3g1aE4iquraUxHJBxaTnN9HJii1VX34LxLFcj6yXAWBelSyMABaSUBCNQ8o2o732e2naK3eDAsBljvWZd2NZ1/V23qPr9zAVFtw4fEjHCJgKVoOouaZ+Q0zYJ1d4KWN1OY3krjYNjuqtj8bkKeKx19DI0btm4nrGF0+nkBJfVqh9DKplAVstCUxV2VbI5nBCdOlslGY0gnYzD7e+tYVdomTS2rp2HZHPAPzrTEXNFjQahbC2zHqHQrzskxs89yxVR55H7KsesrAZ1fZ6TVMTgJQHYZsil48iFNyD0VzKrrheyS5egPvMkxKMPcAGlGyi2llke+iCko/fUJL8Ie45MNxbtXG9x/6mmX2hzzyDMowcgFJjcyitfRT4ehvzgh7uy0KCFnPLsZznOkG9/THfBtjlZn6xhzEFxSeFcKWYS6ziKkXN1dQvk9Yg5SHhZTSdhL4s57E4XKCVnd9y4mGNswMUJLiPWi8chI5rQeLxvFeXXyirT/Fo7P5AEw+FRGZsRDbNrCob8Is4uKhj2ixioYoSlFYo5qI2SinDCdY85iG1++5RQmqvJdbHfW6sHthucnDDh4DDpWJkA+/WPOUiW4ZvnszCb8jgxLmDQV3uvyfGazHsCzs6SX9cDcxsqVoMqTk3Z2Dyh+FwNGuSoiK14I1pBSbPXapFgp4ebrm0iyXFOMQFWlGcgRGMxZDIZ+P3+mmuaTqWwvrwAq92OvsGRjq55JhJEOrjOuoQ2Xx8zYSMXX4ZgdaBn+njFtnkto8cnVjvrK5uq2jDrMX5ziTDE3uuj+ViN7NxZaK99DeLJhyFOVR5/pyiO5daHP8KEEXNV8uutGnPsieDfuthLgHWIdtwo5FPGwrXloMDQ8vaP7Ow/tM5VfdLsKm+NIaZCq5TYms8gDZlioopE/O95f91taQBKv/r3EAanYNlfK3ZYDhrMk69/nUUTy/vP6RotLy9jeHiYBy46diW4Ctk/2PE5JIMbiG8swT04DltB14cZC+7r7IhSBaoo1aNVWy0iZkYrZ/7tSAqalmOGmBGqr1UjbIRVBGMatxDE0yT0Wbs9CY7etc/Wts5GNzC3keXXsTEKkrtXaSy6L7ZzrXYDYtzRNSYBf2JU1W8nuLWGUWoZoGeC2IDNrhUxv3hIMKFuQrcboOtks+0sfLc3N6CpKkYnpjiwtFh3fhcYGGY2p1FrEVmKswBtMobE1iqcfe0zPmV/P4thi2UaFKK3FyazWHO9aJyyH32Ak2NkRd4o4V+EOvcmsmvXIGyvQt5/J1c3a1riU1GYfbptOpuBcOtTZ99Vs38A5tGDMA9M7q6w8MYznBAUBsZ29u30GAaib1VHprcy2rnelnueaLqN2eWH5aEdh8h8cJXnfTKVgEnYfcxBzzMxLAssSzLXsd79vrqbZ8PryJx9GuLIAcjjjZ0aqR05/ubTED09sE3uLMCqv7/kAKlENmHxD3TMjoxRzBHchH9oHDanrmNK+3a5u+OU2ypI0Luwlq8BJb4OjVeaF60FdUfuAb9xA1o78+hyUGOxe445Unl2dq4GscTuOyDXMH5uBC6tZFlz664ZYp917/NpDnUKNy7moNwmXWMS0a/3dac46ODwrRVzUBcAJb6EFq7V0QknF+IppqKELuncXQ9wN4Vtp2C1thHkeW56YhhupwO2st8NDAwgl80aHm8qHuWYI52IIxYOwe1rzgCvhi0wAMFqg1ycS01mSC4/azUuLi5WXCuzbIPrwJ160a1s3G3EDFTm32SGejayAXnyZA3ZgvSboaa5Nb2TVvRqkKGbeXjfrozd6BjUM89CHNkHc2BgZ9+eAL+MsBdz7KGbeOulU28RXE83CuXZzyMfDcLyvh9FnoTnv/xHyK7Nc1Is8+RvQjv7bEf7lW97DPLtj7e0rcnmgNA7CsHfwgBHizeLvUakmbW5EonStVK2V5BeusRJsE5hcXpg9QQgN9E2udWwFUpiI7RzLapRfa0aYWrACrOZWu8U3LHPienBnYk8o+aQUvTWS0qCVIvol4O3zXR3Qklm8ogmc1y5DHQobt8IuXweGxENsVi84bWi352+puLyauutytWgNo0HDlnw6HELxnsrA86Uolt434oIuGUcnXRzYNnsuaKAip6Rta0YLi1sI5lWdv35FKQ0e457+wfQNzjEehkkJlvBHCWHOYvxos3q8sLq8fO/Flets1yrlUbJ01MRANrGDsM6st/wegk2V03yK/Xql5E++62K/Waj29xaLo0egtnTh9zmAnKRjZrPV889DeWlv+OqLTkuZb72R1DfeKqjc+Hzsbsh3/d+mAM7Lr3tIh/egvLMF6C89NXW/2avDHlDcT2vd+Ybf458Ngvr+38M+eAaMl/+Qy7AZTcWkPnsb0K7crrtfdJ32nLnuyFza05zUIKMXKiFMs2w+js3w2Sx8SKxHNXf3/TWCpIrs1Ai2+gUNqeHC2yy9fqJ318PUAKsmATbbcxxYEg3G1kNabjvkBVT/WLFXJhW9DiCzHQasb6JJUbbdxOkY5rIAKM9pqYi/J2AEjWroSxi8cYxB233wpUst2V2CnLUfvSYiHffJmE4UBk/UdLxVo05Bv0Ss79k0dz0uTIXYo75tSTevBZlFvqNiDmGBnowPNDLiaby5FdRxoHkGYzgcHvgcLmY9WlzNG9FNAIxWi0uX4nlRGOjc+wArL2jhtdKqHKsJT3FxOtfR+rKK7UxRzqpxxxOL2s0ExOsGsq5Z5B59avIqwpyqRgyT/0Z1Msvo1OYXT7Id78bZndrrelGyG0sQX3x76G8URlHfbvFHKTZdz1e1wvBYBCf+tSn4Ha7WbP3h37oh1i+pBEeeuihgn7lzuvHfuzHKrYhg4n3vOc9sBOTsq8PP/MzPwNN63x91g3cWmWEtyjUS68j/bU/h+39PwRxuD0r+br6YprG7TYUhOa3V5CPbsE0NA0TVQ0aiOnngqswObwcHO4GJPZsOXy/8WcoKWTOPwdxYApS/wQP6vY625ZDDuhtNMQAKyKbTnCVlqoBotMLc5N2T9FihWdoAqngBifebL5aocpbEZPDXh60ukFZJ/ecfUOUNKi08KYA7PXZOGsx3H3A1fSz3lzIcALsvoP2homyIuj4m20XT+cQSuQxM2BuuSUwkc7h4orGQXUjrRHCejiHM/NZOLXGEwBdg2AijzZk2EpY3NKwEc3hxLhkaJFOk/DLl0kTwoR7D7Qu70r359xCGj6nUBIapiQkuX9eL2yG0whG03zvGklmkcEDVWNbFYttFIjOLyxwqyM5KdVDOeOrHYiyBd6hCWOb+qtnOGj0kMvQdYae8C9LiuWyyJz+Krdv2e58NyxH38bJL3OgtoAgjB4GrA4gl6Ud6S1fthvXSmUEs6+XHR+rk2ic7Pv878Hs9sH6kLFmyB5uPJQ3nkX66b+F46M/Ydie2jbYIZoEl2XkokHkKe6IhWAiZiQt3BoUnHJbyzB5emuYju2CWiMtB+81/oxUHOkrr0Ae3gfRP8SyDM5DxtuWw9ozBLMkQS5jFGipOLMuc1mNGRnNmBAyMTcGRhALbrIDnL3DxPuNxsGx7hUJid1zaMTCjPLyeZ3kDl66kgYZ+lEc0QwvX83w3PzgYWvLBa9mMgrkskgxx3BAYHZUKyDtzosrWdYko5bNRpjfyuPsYg7NGiC1LBCM5/latAvST6XzIF0zo3MgJ8RnLqh8rPfub52NSW7br8+pGPQJJQkH0mojxtb1wuJWBttRFTNNkhUep4Q88k3dIJuBXKivzm+wHu9Qf/3vZrHdsV3IFit6BmrncRpDNucucmLcO3SdDcEoqSBbYZJ24qackkbq3DPM5LUfeQDWg/ciGw+xZmI1KEGWDW/oRj5mkr9xXhdX6XZg7h+D5eGPwFxlpkbJvtQXfg/C4AQsd73zph3fdyo+9alPYXV1FV/5ylfYvOrTn/40fuRHfgSf+cxnGv7dD//wD+MXf/EXSz9ToqsIMo2h5BcxLZ999lne//d93/dBkiT8x//4H3GzsJcA6wbyWV1EuUv0TPltOwsNYXgG5g/8RGmBZHn8B+r+HYnMKt/6K/LhhTB2CNLh5gFiM3Db4oXnIPSMQiy22GgqMx3IKaQdUBuFpWqwS8yd091LOEE2CEe1O0gdUBskVYFvhQSYqmocqFnIV7oO6gmId4pqgXwCJbNIW4GC1VYSbYNekYOh6qQWJaRWgirG++RSC+XsuoqlLQ23z1jgsNQPWPo8Au7Zb4KjjmX6tQ2Nq6m3T+20SpDuxXYsj4CLXCQbB0MBlxmjPWaoYVNFgEwL9ZVQjjW5nFYzB5FvOyRxG2OroPOe38qyQCux2CigNXLSpms7FBANf1cPdHyvXk1xBbcoALwZUXFpOYOZQQv6fbX3k0SNg3ENIwFLSwlKI8RTKhSNnos8GoXNboeFX0bHrWlZSC0mxlgsX5b5RX+bTCZgs1GCtfK+koEFwdxtp6Eb0H3D7ZxVrBZaREuTJ0qFB0oGCD0jhn8v9I5APfMUlLVrsD7yPbA88CHcChBn9FayXGgDZl9B6zGXQ3ZlFvnE9de/2UPrILZWnqqn1K7YBVge/WTp/4WpYzAPTu7EHE98umHyS3n2SU7okri9OHNq18fCrMjLL3O8IRYSyLRwo5iD2A7twCzJsFa16UTnz/Mii8YnW88IHGUtv40S+9GtNU7A3woJMGpXJzRqWe92axkZ6lSD5knKc1DM0QpGAqKhgxm1V1JcMDNA86o+iJ9bUrEeogJd47bK0YAJPodYN5F1fimLrVgO9+7f2Xc4kWNTnagv3zQBNug1IZk2IV8Id4sxB/1LrtS9bjPsFhMf4zuOkjNhS5eidN60j5SiJ/JoCWFkbEjvjQbMcJAWWYugpNnzlzJcBNTPUeAY7vKqiqPjMpsbVCOUyCKSyDHjvdNibSyRZUZgM5IKMdXpZdiOz8W41i4k3Qv6HsiywPckkUzBYbfVJE5pEa4z3t96jU8UXziqSAZEkJBHDrA2Kf8sWyGWkQvKQa2KyrmnkQuuwHrP+xvK39wo0L0QZ06ylmMuvAmzt7CW01Rk1xYMXbHfingraYCdP38eX/rSl/DSSy/hjjvu4Pd+8zd/E+9+97vxn/7Tf8LQUP1iGyW8KMFlhC9/+cs4d+4cvvrVr6K/vx8nT57Ef/gP/wH/6l/9K/y7f/fv6rIvrzfeeiPBLYLyQVQ6cBvcP/Vr0ObOI/X3f8rv8Zc62Zg22CooEM0rKRZWbridwwNh4ijrhuUjm6X3sytXoM2+rju1RLfa+uy8mkaWWiHC6xWtCva73gOpiU4HXSP6QjSacGyDk8wMo6mK6Lmtwjt+EL7x/bgVsLi6hbmldZ58k6kMEsn6bQdF8L3I5TmQnV0OI5XJNr1WzeC0CTg8ZseRsdYqO0MBCVMDlQOPHtQp3OoQTuzQpyyiCVbZhFZIXZSAosltK5pFPFW5QKPkElWNy8fvfq+A+w5IGOtpHvBQQu7gsIyxkUGEk8A3zihY3s4iliYHKg1X17KlwJw+xKiaSlVRI1CQTEG436m3PtL51sP0gITx3p2U0uKmwknDZscecAk4MGwptYu4bBQ8G9/ztZCCle0MLi0lcHaucfsFgX5/cSGC2ZWdxPTkoBvHDk50rO0VisRxdWEN8USqpe3pvpOTUsDvRyIex8baGiLhUM12a/OzWF+YbbgvEp5ObK+3RHunz/XNHIdnfHfsr1bGrGx0ixfQ1ZBGD0Lsa60SLE4chThxDLcalDefR+IPfxXquRf5Z9IScf7gz8P+0Z+s2fatuJB4K6P8eltOvQ2ef/KrUF5/BqlvfLbEQsylEt2LOYidHW3cOmjy9MA8chBQM8jFdrbVFi9AWzivM61iwbY+m9zPcuH1is8m51XH7U9AHprZ9ffXMTABmVqgC4m1VkD76x2dZh2wWwGzyyFcXdKvayypIJFqPPeUxxyJtIZLizEuxuw25qD57Pi4BcfGWjOFmugTK9onCVScmdvQsLSdY2ZWha6UbGpaxKKxv5jEWgvnavTJKBGkUq647O2xHjMePCzWtBkagY7hyKiE0eFBbESBvz+t8ecEY3mcW8rh6roec4QSOT7W6sQLXfd6MQftZymY4/bNh46Kdd0x6RwPjYilGIn2eXVNw1oo21iCTzJh2C+wiQ+BEmgeuxn2OrHN0paK+U0Vb8yl8cZc8zk/V+g6uLq6s+2hcQdOHh6D1E4msAyr2wmcmwu29EwTyMlxcrQXPT4XItE4Vta2EYlUrr3oes0vLGF5Za3hvjKpFLtSt9rW2Dd1aFfsr1bGKy5CRrd1rdDqYuPIAYgFLdHGxypAHD3E+oq3GtTXvonkZ/4zr58JVER0fvrfwPau76vZdi/mqAS5l5a/yMhhN3juuee47bGY/CI8+uijfN1feOGFhn/7J3/yJ2x+cfToUfzrf/2vkUwmK/Z77NgxTn4V8fjjj/Mxnz17FjcL3x4p1psAo+qIdu0c8okYJ78yT38B6mvfgv0TP92VFgXl+S8it3QJlvf/GMxOY8F3aguQTjykO4GVMSu0Sy8jn4hwAJC9+AKkEw9DGN7X0ueSsL317vfWaG2QKHQr14i+TI1AWjwkRs3HXkf40PDvbK3Td8lpjlorWxk8E/EY8rk8nO5KQdlG8HudTMOmwGdpPYhcNoeD042105Y3oojEMxjscSKeVOCyy+j1eZFWiE0G2C2dfTU9jt19pVe2VU5cUaW2vEI4HKBgUdwJKLN5TuBUBrEq/w0xuigQJEt0yruQixQleUi7iwLgmUHJULS/VRSfK0pykSgwkZNcVl0YlpyhkpkcXrqqcGX2+Hhlgm8lqOHisopj43KNUxXZt1Mg7XO0567EwdUmnasJQ37j7wXt78SkrSZhOdEvYyOiwGYha/vKzxzrsyLgkrAZUbhVMpbSsB7MYKzfxjbgRqD7YC4rvVJwSMmoTmGxSLBZ5boMsEgkytUbm0F7gc1uh9vjgaPg7lj+fSTtL6ngpMttdtEwLHZHhR14fHsdmVgYst3Jro9FZGIhiBYbhKoxqRxKZAvK9irsoweYBdIqyscsCjjVhXMwe3oh+vSJm4RmM+eegTi0D3KZ+Ha7ILZMM9A8kqfKaOGzO4V29Q1AVSAe3Alq6kHoH4Uwth/m3h32Ggn1GuFWcSD7ToGh4cKVN/W5+eEPIv2VP4d66TU4v/9nYXZ3/p0vQnn6SZZfsHzgJ+tKKjAL4bZ3IK+9raJir118kVt885kEsrOvQ7r98bqMyGpQsst6+xM1n9mKEH8rMYfF2wvJ5eN2afr/ViG3EXOo9H0jp+0WYo5oLA6zyQyns/X993jtpbFzbjnM4/zhqcYszasrcU5+DfhsiCY1JDNZ9Hi9SKaJGUMsrs4SFtTSvxtcW9e4+ESJMWJ4F0E/TxWGPlXLQyN2e1niht6bXc9i2G9m90kqdp1dysIqAScmRJ6PNyI5HBkVahjUrMHURkdc8bnKxnKwUcwh0HmbcHjEjB63mZNfL1wmtrYZR0cr58rZjRwur2Zx94wIn7PyeZgZENDjMlecdyuga0FGQxSv1DMaoutx9z5Lzb2i8GAlpGG6X6q5LvsGZSSVPBY2FP6MUIySfSrrzJK+VzXyhZij3CSIXEoD/s5NqRxWCU6bBslAGoKe92Akwc6OFJdUw+mwcUcG/VsORVE4hinGKbSfeCwGu8MBoUwbIhTc5CQYaX1JZXFDKhbh77/QYAxKh7c4NnENTbGOaSuoHq+osJZZucxJLbGwztNIO3n2NCe7LE0KAI0gTZ9qjVkcD8Hs2R3jW7v0KiDJECcbkyQIZLojjO7fYZ0X2GzfLjHH9WSAjY5Wun7+23/7b5lR1SnW1tZYn6sc5N5O7qj0u3r45Cc/ifHxcWaIvfHGG8zsunjxIv76r/+6tN/y5Beh+HOj/V5v7CXAOoSRG4Xju/8pB32kiSX0DiM3OAGTvTvaLuLEYWRJd8bWXNeBdDzKId3xOFdoYRaRD43D5K18wJvBTHo1BtC2lnhBKE+dqnEdKV6jubk5TExMNEw+UXXCNlSrnaZEtxFfuAjn+CHILh/iy1ehxkLw7DtZ0gpLbq0iq2bgHBg3HBzJTW5j9iIsDicCI81d0oKbG8jmck0TYNFoDKIoMO3T6965x4O9utZXM5DWkkXW4HFaYbfJrKsxOzuLZN6FbB44Pt1zUwZ7v0vkAIjYYfU+/+xCmqn7JyYspYRbLJXD0rbG1PW1MJ0/tTQK2C6wqmxyDvObGid5Bqvs09tF+XN1/8GdAK+ocUHXn6qeRkElBdAUNBY1MLgqXmCKUTDo7yCYp+tEArCddCluRVR29ez1SHDbK68LBZyyywxfod11dTuFSEJFKkMtIYLhcRye8Lb9HUymFCTTGQS8zpp77rBZ4Rg2DkZUTcPm1jasVgtGhocMEzg2q7WG3hwNbkFJp+AJ6ItPJZVAeG0RNrcPvsGdCd3VOwSr08PW4UVomRSiC5dYu8czflA/jngYieUrcIzsg1RoB9DiYaixIHLkfNRGAqz8eiGThLp6BUI6XkqAUfFB6BuHYKDttVvkMylo556DMHGEk17Zy69Be/NpSHc+AaHF1vBqZL7xF8jO65XVlhJgvcOwf/BH+f+VM8/zMVnueNhw2z1HphsLo+vt+vS/1qkevJAYRS4e7pq2izB5DHl67hskmuvFHPJd79GjdGKjx8N1i3btxhzqxgLr3Fgmj5UEpTuJOSh+ICZYNdLhTcRX53lskexOhJeu8pgTmDpc+rzoJgXsebh7jduNFCWD1YU5OFxu9PQ3Nqag+WdjY5sXzM0SYMFwgh2m7TYLen0712dkwA2hhViBWiIpUdHro3Z3idlBFHMEVS/HMienb6yrZblsArXpFedvI7wyqyCeBjPFi4xpSnotbGWZtU7sMULAacZ2nAyC9J8Xt3Pcnti7S2fI8ufqoSM7SZDxXv2YqV454jdj0Fv7zDktxLrSWySrYw56BVztHxvFi3fOSB25fK+FNS5yDvnFGqkKMk6ivJLfqSeQrq1lEIxlMRzIczGzGnT8t8242v4ORhMK0moWfd7axLrHaeGXEahAvL4VhcthxehgbZKfri1pfVUX7IKhEH8v+3r1xE4ykcDW5gY8qhf+wE6yx9/bD5WTZTvjWSaVQHB1ATaXF/5CfJKOhRHbWIF3eBJSQc9USUSgxELIZ1WqPKIVVF+rbCoGZWOBE1HFBBgVBKgVXGxz3dYKyGRNu/IKhMnjPEZrl15E9uppSHe/j6UaOkHma59BbvUauYe0lgAbmiqt/5TXvgmIEuRj937bxBw0PtGr2/skkIMoidUXYbEYf29+9md/Fr/6q7/atP2xU5BGWBHE9BocHMQ73vEOXL16FdPT07hVsZcA6xBGbTnlosjSwdv51S2Qphe9OkF58ClTMqxLyFICbHsZ+ZEDhok5pn4rSula0aCeWTyvD+SijPTcGVjHDkOs6yRScJQo/kSTadWEmoluQ8uk4ewfKy0EykEaQzaXmxlmyUgIdk/jQJyc6Zq1XNEgvLG5xRPW5MRYRdLAVag8RWJJBMMxDA8EDIXF+/wOfhGoekv6BHSt+vvtyBfO+3pgcTONcFzFoTEn1GwOipqvYI2F4hokgURS6y8eiiK4pB9WDJm9DoHbIKilz2XTcG1D5crkaI9YYncRvZ+CXWKCLQdVHBmxYC2sV59He1oXdq1+rqpBiayDw8b7owroHTM7wQlpmxEr7NCIzMdOgvQ0udB7lAyr157YKYMtktCThKshBeN9VmZ59Rgkv4xAdvZep9y0Ur8ZSiKWzGB80MOL0EbXirAViiGezHBQ2UjHrhqSKGKgv4+FLI0Q2tpEMh7DwMhYhei929fDLo+WAqtLlGVYnW44fJVVR9LboVc5iPVl7xuBVCbKnSNNQiWtayIVQAGVpWcYQptmIOXPlmBzwnrkbTCVLcaJ8WLZVz+RRAkjGtuMCgL1Pi976SWYqI0hn0N24Ryp7nICzNw7CvPQDEz+5i0OdfcfDXI7m/zwR9v+2/Rzf498Mgb5dt3hx+jY93CTY46y5LB84gF+dQvidOcMx3J3sHLTm92Ci26xbVhGDwKSpXnMoalILV+C7Kd5PYfU8hXYxw9BtNcrcJn0OKLwuFPSS0987Tz/qWiI918vAUbML5vDyfEBMcqrGbA1epJD/U2F3onVsrYVYcHwmfH+iu+jz6XHndvhJEKxFCaGfMzCqcZI786zQnNIMeYY6rVBMhKe6hKurGbYHOf4hBWpTJ4Z5B7Hzvi4HdNZW42SOXrMobtNFufkPo8ZJyd0VpVFymJ+M8sJqfE+cMKJNDydVj3BtLSd5XZB2n5uk5jjpoYJt3ZjDirsHR0znsP7vWZ+FXFuUcN6JIfj4yLcdjOfNyUm6Rj7PUJD6YVy0N+2gmA8y3EexTT7h2XsH5QxGsg11HItYrxfZn3SZnHQ8laKGYUzQ46m14qwup1EMqPB57S0JYJvs0gY7vfCZjEuam1shpBMpTExNgi5LC4hBovDbi8V4yRZhsPhgNNVOQ7IsoVfFe9ZbHD5+2B1VsYcVHintvMiXIOTcPSNQDAYl+qh+lpR0su+7w52gSzCbLHBNn2y7j5ymRTHJa2wTfkzc1loV16D0DuKfDKK7PJlNk4rFvdozu+UQUznwTGHpxfy/e3rjGWe+xIzzuslwPZijkpQ8qs8AVYP/+Jf/Av8wA/U1w8nTE1NcTvuxkal1BI5NZIzZD19LyPcfffd/O+VK1c4AUZ/++KLuqxGEevruqxSO/vtNvYSYNcJvBgTisFTJdSLr0G7+Aqs7/wETGWtPbcyaIGpvPoVXpTJhcFYnrkd+bHD3CbZ2j6SULeWkM9p7OREWh95badnWY1s6r3qLn3wld1+yEd2BkIHTTCDkxWUY7q+3snDNYM/TVCJ7TUWyfcNjWN99gKS4W3Y3B7De9KOOx0FtlaLBelMBhlF4f83Clgzitp2xcLvtlZQsrsNEkSnxBVrSKykEE9nuYJXpLeTMDsFmeN9lrpB+cFRC2t7ue2Vx+l36T8PB8jCuzYpQm2VhI2whmgyj2uberslfY5RAiySzOLausq0/HZaJNsBMcKopeH8YoY/47ZpG0LxLGbXFMTcAg6NduYcZATSPzu/mOCAlzRB6DpT4quV5FdJfL2FNpVUWmX9DHbtbCGWHuj1MAuMtOsEs5nZAK3C6aw/frm9fg40yUWpHJTwcsk7AVYiSK0DYa6wosn3j66Bo7eSfUVtTLI7UDEGMAu3QfIrSy3hmgqxjOqvbswjNfcmctad/Vc7KmUjm8huLUOaOMpJ9XLkM0mkn/sszIERWI69veF5lP4mGYN28SWYe0Yg3/ddMN3/wRJDl1oC5Hveg93A8r4fZrZKM6c7Izg+8mMsSPtWbDv4TgQb8VDbncH9Us48i+z8RVgf/9Su3RpvFHKpGDJvPAVxeB+kgnaNdd8dupNZi4tMYlSo26v8/JvtHl4s8nUqQKEWY8kC0aEvIqzeHn4V4RmuZI0nQpuc8HcPVLaeFFu746FtOH096BscxsLsZSRi0YYJsFbd6YjRQjqOFFPUMyXJqBozZIjBbpQAq4d+r3xdY460QnMdxRzA2cU0Mkoe9x/acZ4mvVHCZH/94svJSYm1vdy28jHehF6PUNYuWXlNiCQ9WtDMWtnOIpKimCOLhU2dFWaUANuK6kz1w6NSRbtlN0GfbZeB03MaAk5ij8tYD2dxeZXuXx4H6hTvOgHFGGfmM3wuKboPag5OK2mNtS4wT8fbDPFUFok0sfFaO66xfic/q+FYuhDztv68elz1mZJ+n1tngImVz4JFlvlVRDQU5OKcy+2hrFfDz6O4wt1T2b5l9/XC5qmKOcxkvlR/XNISES5Iktt9Ecr6HDJr15AbG4FQ6BaqJgRo4Q1ko9uQRw/UrF1yiSjSr36ZDUPkfa0RLvKxILLXXmcTM+n4Q5CsjpLMgjkwxJrMnYK+k5bv+sf6/3eg18Vao9dxLLoZIJ+aLnnVVOyzHfT29vKrGe69916Ew2G88soruP12/Xn6+te/zuvYYlKrFZw+fZr/JSZYcb+//Mu/zMm1YosluUxS8u7w4cO4WdhLgF0HEAsg/ru/BGF0BuaeQV4oWe56tPT73PoCssuzbO0tdCkBRkFddukihJH9Ne0IzZCLbMFkdzcMjLXlyzxglovokyZHK7ocRQg2F+yH7oWZWjlFCeKpR0sLM3aMu/oGH4P7WKXDWj2oyRi/jKbnTCKKZHCD9+/sHYJvcIyd5xolv9rBwEAfMhnj5Behx++Gz+vkhEInYFfDzRjsVgk+d3sslkYgxlE6k0QwpmK4x8LOkeXaDofHbOxE1KgiTb+rTn61g+lBGdkVBX1uEWOUKDPVd2WkVstEJnfdEmCk2UUtmaRfVgx4qUJNsXk9QdpmWNhIl651OQTBxKwvYpm5bMKu7b/rYXTAjaFcnhdBVOVvBmIoknnDxnaEF1d9Pd1xObNYrfxqBrs3oAdODn2hmFUy/PyLVYmzRmg32CJNDXKfdZx6J4/PnDQgtmmTRJG2dg3Z9TmuntbYjYsyzP4hCC2I0hZhdrgh3fv+kpMTuTUReJGupHhcbgYWPw+uQ+ipDVxbrgqTQ1ZV4Cn4d6c9tocbh1wsjPjv/wrEg7fBbLHC7AlUsMFyq3PILl/l2KRbCTBya8ytXoWZGOBtOnaR65epoP1ZD+rSRU4ql4vh07G3c/xUTHPsv4NjD3q+ZV9f6Tuey2qIL1yAYHXAs/+2lvanJuNQUnHDJGMqHkU8vM0Jfqc3wEmwboJavqiwVk+PcbDHhf5A5zEHFUwWNhLwOGT4XN1LkpLm54UlhZlek30y64eWa0/dNm1tat5LrXbuJm6NjXBwVMSlFY0Z6IM+AWKdylA0lUMwnmem2vVKgE32ixjvE1gXjDRLCXRN6JFq0Wy5BldWFS6sjZUZ8xCo7XKyT2J2O5kTdRrTNMO+YQcz5+k+tRBywGYRmaW+tpXgub7YDbFbUPKrlYSy2+uDKEmwFrZVlYzuStiOXmib37PE7Bk+V8/R+/VYoxBzkJu9UfdKEer6NWTDG5D6RmGyVkrqkFYWFcra0ewiTVPptseYpUvHIBQSXmxCRsWFgvtvI1B8Qok0I33SXcUcXdDL3kPnOHToEJ544gn88A//MH77t38bqqriJ3/yJ/Hxj3+85AC5vLzM7Y1/+Id/iLvuuovbHD/zmc+wU2QgEGANsJ/+6Z/Ggw8+iOPHdRb5Y489xomu7/3e78Wv/dqvse7Xz/3cz+EnfuIn6rZt3gjsJcA6RENBdao2+vtg9vVCff0Z7mkuT4DJD7wX0h2PwFxGc90tstfOQDv9DUDJQNzfeuslBZfKc09yq418/KG624l9RJeNQJqqT8U1ukYjIyMV10ooLPQIpeQX0Yip0jR1rEK8v4jE4iVOkjhGKl0fnYPjsPcOVwhnF2F1+znZJTvddQVsc9ksW5tTW6Rc1kpSnBAFEs6vU40gYUB61QNlzIvV+6sAAQAASURBVClBZrNaWmJQVF8rLZtDMJpCRs12NQFGwQlR1VMZAf0+C3xVc52R0Gm3QeL5JyasLSWnqA2RdCmaPVe7Ad2fyX6Z2xDCCY21y9Q+Cb0Gtu+tgFh0VO2uToARBv3Xf7DnQK7Qpmp0rVJpcg2r1Pyi9sf1TbIwT7adAMuk09hcX4W/p49FZRuBktCbc5dgsTvhLbAoqM3RVdZOFJ6/xNp9vQdPGX53cqqiJ993wUyyjB5CTkkheeYp3dX2wF2Qescg9oxiLJGo+2zJUyeQIw0wg7ZtLnQcN9bLagRKplVDfelLyK3OwvLED8JUmCdonMwH12Ai19yyc9fOPAP19D9AfuijEDtwwdRmzyL1hd+D9YlPQdq/I5SrrS+xE6A0bqw/tufIdGPRUEdTlPSYw+WH8tJXYPb3VyTALO/4GCxKuqXFTavQLr+K7IUXIMEEYbz1Km52ewXqK38PYfwIpAN31d1O7J9kJn2Rcd4KjMa7csZFKeagxZdZgINMMqpanmiRGlu6zK1MjoFKhzfP4DhcuayhGLbT44coSrAWEvlWg5iD2knCwW24vd6aVitikxNzpe7YI4mGcgpFUAIik9FYU7STa0Xs8O2owgWwbibAqBWRillUbBvvM4jVCtpY1xPEHLtjuvk5TfbpCbLq5Fe3Yw4qIpIzIxXbiHE+5BORz5OJTvuFRXpeSdeLrmN1Aozmier3rgcooWkupDGNrhU5laYzGnp9O98Jj8OC9a04ool02wmwZDKN9a0gBvoCHGM3An3nVpYW4XS5SnpfssXCryLWF6/x8Q5NGjvLUzzSSAC/FdhG9rM+WPTNp5l57hg/DOvAJKZdfRU6p9WwTJ7gedhclfwiUDHAevTBrsQcyot/i3x4A5bHPl0qMpAofz6yBXOVDIP2+lPIXnoF8js+CXNf7b6aQb3wCjLf+CtY3/tpiKM7hmzayhwz1sWhyW+bmIM877vdukn7vF74kz/5E056UZKLrveHP/xh/MZv/Ebp95QUI4H7ossjtRV/9atfxa//+q8jkUiwMD/9DSW4iiCG8Re+8AX8+I//OLPBqP34+7//+/GLv/iLuJnYS4B1iEaLLxo8HB//p/z/8vH7arL7FHgVFzVFqBdehtnphTDSmcuHMHqQK7LCmC4M3SqIYUCsMXN/Y4F4s8MLy5FKfZFsaB3K5ZchH7gLgqeWXslOO87GATcNsPEz3+TEmL0OhVeLky1x7fXmNqc6FRv6bKu7sd6Xkk4iEQny/SlPgFErw+r8LGwOF3qHRqBkiJGSa6k9sohgKIpgOIqhgR44HWX7zmYxt7gBl9OG/rJEQ/W1Ior6vrFAW60MNMg2SwpQMunUjJu6cyuQVrJ8hS3yrUU/rk5+tfpcdYLlLQWLWyoOjFgx0tP5AuDYpEO3R2oRsaTKAbHDZjwcU/BI1fl6wrCNYHStdM2vNJx2K6wWqTRBkZtpO60I5YlkTVWRzWpQMmn+f3vDth960uo/p7ZAP48Lhq53iQhis2dg6xuDjXT/Wjm+dIKFwcv3RzqE5PJIDkvlgtvl14sExXlMLQv+qPVK6NCVkT4vt3KF28jrueoVYR6YLPjY72yXvfASB57Sve+DMLGTcDAPTUMIbXD7Qkew2HQ2TpXwePJvfg+50AY8P/2fDVk3e62Rt1DMYXPA+al/wf8vHzxZo4/FTKuq5Jf65vPMUBeqkjytggSO6Zjo+WsHlKQzD05D6Gv8/aUkc3WiWdtehjp/FpYDd5dYk+3ODTk1g+j5F9mB2mGUMM7nocYjyBmI/+ttTsZjJP3O5mosJJ9OJRGPRrh4JpcVQ0hOgSrrbpeLW1XoZ56P26iOkzh4KJrC5HCgIgmmqFlcWdxm58g+v7PutSKR/MPj7paLYMVFXbNxgNyO7z5gr4k5yH2SWNHEiL5VwDIDBlP/9Yo5ZtdULAezODkps0N2J6Bju3PG2tZ4HCatV9EEh9U43iOdWNpfJ67iRtdqbTvBDqReF2l+6Z8pSwICXhusRur6TUBxNLEhieGeTqe57Zc0vgyPRz+ohjGH20dsKOPnkATvw8vX4O4f4dbHVpDNpGokGIh9SnGNQjFHYWypvlZk8EFxQnkbJG/bghGJEejzsuvXWNurWWeQMDCJHMnZlBX2tbPPInv5FUgPfAhC/848YR6eYXauyVNPv7kxSLvS5PLWuD2mPvc7XPRw/5SxYPtezHH94ff7mdFVD2TYUJ7Qo4TXU0891XS/5BL5xS9+EbcS9hJgHaKV1iJCPTHBcvpnXslAeepJmHy9sH/sn9Zuqyq8EKN2mUYDinTkvpaPv/R3ogSpSQWBWn5y4XWI++6soLfm1TS3RebJYdLo77LZkgtEXY0Jk5kZGOYGlWn3/tt5UkgsXOBqrmUXotDloEotOUPKVYktYn45XB5YC2yWjdVlZDUNY9P7Wh6AyYaZJunqFkmehgvM52bXqpXAYD2YYEdASqKE4hkcGvc3ba0rsoPKcXY+xm0BJ9pwgiLGlKLmWrZPp6BLFMl+fHdJNkXVcPXKVQiuUQwGSHi/cn+rQV3HbKJfbmvCDLhFZLR8y+Ky9dAui+7ysn7tj08bJ2wX1mMsnH9ipn1nUKPnqr/HA0/GXkp+FdEbaL/1kRhdZBk+NjXD1aLla5f5uzI8uQ+CAUOSTCn6pxon6e3++m5HrNdDSfsWW8fV0BpSs6/DOnoQcl/lQp/GMmrJrne9lEsvIp+IwHbvB9pq9a6H3MY81Ne/zo5L0uHGYzW7J1U5KJkHp2AOb8BcpYFGzo3CQx/htjJt4SLEseaOkZmXv85JOBKbFYen4Pz0v6nZxvbwB5CLR+q2nLU6B+7hBscc3t6mMUcuFoLyzOdhHpyE7f3/qHZbJa3rvzVgqVPxTDxsLFbcCLTokY81jjm0lSss0CxOV7JA6bjIuYx0wDqNOagASeOHUId1QeOCb/8pZlyS46zFG4ClrlFPeyA9MDqu6mIaMb9oIVxk0C4vr5ZMdlqFh537TLCQJWEVSu1WTa4VtaY1Ai18lrfScNpEbIYV1n46Pu3i1rd2Yg5iPp2eTcFuNePkVOuupfR3qpaHrUVzGtIYJTbXbiUUMoqGK1euwuQcY33U6v3Nb5LmZmMdMyP0eQWulVGL4m7QThKRruG5xRQL25+cMp5HLy8ndYfHfc1b8Kth9FyN9ru4m6Fce4wNIHrd7YuskwGFy8GFZXqkr80tcMfF9NSEYXxEccjYeK3jaznc/vqJLWKCSjYHxBYNddLbK0guX4Vj7AAsVc6NVIhwHbiz7rVKXX6F2wtdd74L3UB2dRba+WfJyhLiZGNTE9GAaSsMTSOfSdTMKcLABL9ywTVdfqFMm7mu2D8xk729zDIXJw7xqxrWhz/Ma7265/MWjDlIr6vb5pXd1hT7TsVeAuwmQDn9LWSe+VvYP/ITEPpH2fXC8sT31A02M0/9JXIrs7B99J91tYWhVWRXryC3vQpx/ChljUrvi9QKFBhuqOPRTASegk3S6Gi4jSAin01DCa1zRaNbCTCCtSA8WX1MgYEdNoUv0MMDbzvJB6KHUmKqWlCcJrnpcWP3qHYF87PZHNa3dWt0t8PCiZdOCySDfmuFLkcrmFtPcbvf8QlyvAKWtzMYDli4klyTKNNyHHSRTsUd+zp/hjNqDq9eTkCNKjBnNa4iVifA1sMqkpkcxvpkGOT66oKqofuGdvZFSbTFrTSG/RbYd5m0a4TxfkdDzbWxPheyBXZfLJFhRthAj7Opc1i958qolYbaBFKpNIvaN3vOybWIGAzEZAhtbaBvaAQ2u4MDF2oVIN/WTgRQWwG5QLrbcKejtgGyEScB7FZB14uKDvLkcZ091kbyi1leW0us5VWtJ0ZC9+L+O2Ae0in/lLBSz3wT0pH7a1oMDM+FRPHvq++sRAmN3Po8zB/75zA7PU2C0a/yfFPPbYkgzRxrekx7eGuA3LWU09+E4+M/zdIMZpcPlnd+om6yLP2VP0E+vAnbd/+LriR/24W2chn5eBjixDGWkChCGpyG2DfR0GW1acxBi9B9jTW/zIIITc1AiW7zWNatBBgzjOy1SQeKC/oL4sCEnh4/j6Otgr7TxOq1WoQahhrNkYcme7sSc5B5y2owA7c9y/O8rJFndfsgNthQgITm25snLixluGXwrv02qFmKOVRM9Ek1CSBK8pDo+5sLChwWE+7c17mZDbVvvng5A1NCRU7LwWHL1iTAlrezbHLTbgKM3LPpVf5ZC1v6ObV7bVoFJSOnBiwNk2YzQ3pyiRCMKczWG+5pnWVW/VxRMbe6oEuFTDLs8biaJ5ZIToS076irgrorxkYGYLXIHLfQZ11PZpBktSEwbtwaaQTR5oLo9LC2YMvxBiX2NBWW8SNEqW/r+GhNlAutwxwYrGGxEWuLEljCoM7SzVH7+aWXIR17G7s+NgPFMXJBl9QIyjOfQz6yDevH/5/GDDMlDfX0U8xSL5dZqIZ0sDUtxj3soRvYS4DdACT/5v9wK4nje39Gb3+0OZj+ibLKujhey4ogp0j1zWd1sUHKsHdIg90tpMMP6HTXwoCuXnudtcPkY29vW/y2UxAN2H3wLpjLgmEa+IlBdr0W20U4qqySW0E4EuUEhc9XyaqhRBqxR9tx2qsOGorJC2pXmxn18b+UbBsItK6jQIwiyncVW96GAu0/Wz6nxJUNWTJjbi2FrZgKu0XAYJWOxeXlFGtrUbxFATQlxJpVjOuB/o4qlxnZjH3jFrgdtZPu4VErtJy+7W4QiqvYjqpwWISOEmD1WlLXQymsbaewf9TNFfeAu3Gbi6vsHEkXLprIwO/prHWgHoLBEKKxOC/E7Pb6AamqKlhZmIPd6WQLcUm2MGOSwAtFq51bi7mKV/heqpkUO7C6egZ4YXkjIdiccOzfqbi2AnJcSr94Brbjb+cFd1t/u3wJ2rlnIB64S1+8l4GNP/btJPvz8SDywVXkaHzvQlJfuu0R5IKrDZNffBykt/ixfwJTAw3DPby1kfjz3+Syc1GKgZKdlPSCtDN/ilOVDEOCeuYZaJdfQz4egjB6gF0lbwYsxx7S3R4L871y5VXklRQsh+9vmPzqJkSrA959JytkFkg4n8a5bpnp1IOnBWv7clBMEYrGIUsS/J7KAhMZm1CWSuzgunGyXMtxfEGgpMnBUSf/S/N+OyC2OLU80rxMY9BEf/st/T0eMo8xQRJMuLSiJ8N8DjP6vJXHcmYhw8kkCgEoEbYb0DE7SBNMNWFqgnRJa78Tt03JXVHl2Yho/PI5SIesvevbqCWVjHmoMHh80sH3bsDXuB3O59oZJzZCGcRTGmvGdlNEf20rygU9cjetZqOXI53OYGF5HR63k/W+LBa5lOTlVmJZgkai7GXxViadQiIeh9cfuOG6UaLdBfdU60U6grp8CamNOdgP31+hk9wKtMUL0K69DunQvRAHpip+R5IN0vROwolijXxojZntaCEB1gzSbe9gJ+tm7ZXENrd96Ce4U+k7DUXWYrf3uYfdYy8CvgEgt8dyzqJ04DZ+NUN2fZ6z61ypv/OxG5ZsMmpZKO/VJifIXGRTr1Q0cU3rJsp76qk9IXL+BV7guqZP4GaBNI9IKJ/Eb2nC3dxYR//AIEZHiEFWGywsLq8xc2tqYqTtqlUomsTKRgTDfR543fpEQi6R7YLEcs/NhfVgdrxzxz+/S+IXQc3mQGGG31X7jHod5LJH9t9mrHEwlWVtCRrEQ3GN/7/VZBVVL49O2HH5sgC33fjvKDDvhoTvRkgBxXsDvtav8VowVdJUIx2No5Oeko6bPhGWt8G2H0wO97nRo2ic/MooKmKJNGt3tcoGqweP18OuSLYmDkp6e7CLWQx2B7XsVC62+obJbTVX0f6YioaQDG1CSyXhH5u57gnr3YKCOZPTU6NP0aq4bH70EMxV7ZaG244c4IqoiXQ3CtCuvg4TaUGW6W20/NmDE/xqadtA91i0e7j1kE8nKwps8on7+dUM2bU5LriZHB5Idzx20zRXaMFUrpPHMQcl1vPE9rhx40d521NOU7F9+TQsTi/cZcLNNxqUECA2DBUrwpE4NrbDGB3qxdRIvyGL++rCJidxZsbb1y7cCKewspXE1JCb3SEJLnv7cSgV3F6bjfGcfWi0c8e/Aa+EAW9lEc/vqo1BA04BNokSZXmsh7OIpbLMFCctzWA8y+Y6rTLeSVz+9hkLxxyUmDL6OyOt0k5A8g1EoO/3th5XL2xkIEsmjqUo6XfbtKN0jBTrUWaOtfoo5ujgmKYGHXytSVojmdYQS6no87anOWaEXp8TdqvMCbBGkCQJLqcdDruNpUWo/bEcI8NDHFtViO5Ho4hHw1CVNPoG24+1bzSos4ekYJolkoxAWopUHBB8xp0l5RAnjkLonyh1EtF1y86+zm6OZv9g+5893LpmtZFL9XcCcnn91e197mH32EuAdYhWqwrZ7XVkF69CnD5a0xLTDPL97wfueGdDHY6bAfnYQ5z8ajZYs4bF5OT1qcCYzBAdrdOMrweIzbW6OA/ZYsXg6LgeZNC5mkw8aRvB5XSwYCdtS7TucCSGgN/LTKxm18oqSyxua5F315JCoYDXKXOiKBzPYGE9jslBF1z2xveTJsuV7TSzvKpdosgGm5wri5Xicgz4ZX4FY6o+GRRG762oiqurKYz2WJhe3wrIpYr+Znpg9LpX9vq8MrRcZWBVD6mMhpSSxXoozT/3ePQWg/LA6+pKlLc7MuFHn6+WZTW7HObE2ORQ/aQkJdPEgkrvdjiBcDTJrQBOu2VX30GLTM9V8+CL9tPbXz9QMhKJdgb6oSZjUFMJZlAI5s7SkzeC8UnnN33kODvbdBI0E0u2mb5XOYhtw+PFsbdxEKu9+lWYvH0Q3vm9uNng4HhtAcLAqGHS4a3oyPRWRqvXW1u6iuzGCqSjd7f9GZaHP0YWyMxSv5VgOfEI05yaJb+uZ8xB445kc0K8ideGHLiWlleYpTs0OAATue8VGFVynUQCtZgVEyKJlIJILIX+HldL18puEeG0UYvh7gqdlICjYpnLKmAjrDAj6dCYo64IexGUwKEEDxXRvFXMq2PjNmaTGxkFFZ0P10IqsvlsSYNnJahhdl3BvkEZg/7W4qiVoIq59QwODY1f9zGP2kJJtqGVuYeSeiQJsRJUOAFHCT6NTqnsT9+ci/M1OjXtxmhvrdvp+YU4bLKAycH6zBy5jO23up1EJKHCZZNgt4q7+g7arDK/moFi48F+3b2x3udVg2RLiImeSaWYjd4pa/R6Mz6L14rjjSamIHX3YXdDbpHlTvdcu/QyrynFfbczE0x782k25pHv+y7cbBCxgeYuseAQXo29mGMP3cReAuw6IBvaZPF7GnTNvh5Y7nkM4thOxTCXjCGfiLJ4cSMw4+smJL+0pUvQLr3EWjXywR2bcupR1668ym4h5ipxx3oginKrYtXa5hIsk8dhriO6XA52T6kSid4NlGQcoaVZuAdGYWviHlk+GLu9fkgW/XhJwHbM0VgMkpJdRcQTSW47o8qWw2GruVbpjIKV9SC7RTrsVtisEjs87RZ07cYH9AoQJcCqiwmUpLm8FMVgwI5er7VCV2N1O81Ba3UCjJhZzVosKAC++8AOvZuYX0TFD7il0uRMAe/sSoqF9Ud6a5Ni+rHmDQXWW0WRidWsAkwC+61iYSPJbQL7RpywiAK7aQ5XxWy2wgKlXmyraZQYbfkj0et3cQXV0cT2vtXv4PUCCd/7RmeQ07S6rq3NsBvGZzYeRvriC5CGZiC30NLYyfUiIxBixQo9I+0d2/JlXrFQAswk2yA98MEbpvNIbBrtyhmIY/sNXSmV088g+cU/gf2JT8Bye/tW63u4ccgGNzjWYGfk/lHId74D0v6dNhx2NM2kmzL/uOXwZmh+zb0Jbe4MhPGjkCaPVYjek+SCOLy/Jc2adr6/5MamRTZhGz/SErueCpheA+HmTpGKx7C9voyegRFYq5i0jc7N43GXmLoel4NfjTDQuzPnUvKLmOQel5XnjuprFU8qWNmKYqTPw+xyKoo1K4y1AmIo7xvSkyyUAMvzfzuIJTVcWEpicsCKHrdcocNJCShqY6xOgHErZBNOE7G3yxncPW4BaVUsscY4yZ/L4+KSAo9TwEjA+NmnYxWrdDPbATOxCtehESb6Wr/WV1bTSGVyOD5uhyQZu2lSrEbnZ4R80VQg27oO3HCvAz6XVjf5davEHMSOHCA2ej7HHRqdQGd8vg6Lywv3SOtMJ4IaDSF17XVYR/ZDbuLO3Om1ooJZLhGB4GuDzZ3PIbt6FSaHmxNgZqcX0p1PwOTqjsZh04/PatBmz0GcOGhorpN58WtIP/U3sH/XD0E+WF8r7K2EfC7Pr27vcw+7x146tUPUEw/VlmcR/a1fQOrrny0FTdYH3sOLjCJSf/fHSHzm/+OgtBzKy1+Deu6F63rc+VQCyot/x2KI9ZBbnQXSCdanqXg/uoXs4nlkV660fI0unXsT2bTeFtZMd0cLr7PW2G4G19T6PLJKGlo6gdT6Ql1HEVr8KfHIrnqpKZHk6+mFs4H1OVVsF+bnEQlX3muCz+vG8GAfJ7/oWl2+fLniuSLtDtL8UknDowPowriZUvBVDmKh0ftepwUnpgM1QS4z56v+jijwB8dcmBrqzgKdRPvHeq2IJjTWB7m8nMAbszFEkxpXGRc2Uky9LwcFx7fPuLC2dK1tAd8iLizG8ca1aFf76Id77Bjrs8NtJyaVUDd4nBn21K3u7hvzYd+YsWtsNejZoMo3tcIa7Y/YhZRgNXquWkUkuM0i991Kgoly+5ov1YxP0tdoB9lEBKmzT+vssQZQly4i9eqXkU2nO7pe2uWXob78JWS3l+sfy+YictFgxXuWRz4JyyOfKP0skNNji8UFw8/YWuGxraVjvnIGyc/9H6Sf/7Lh78XRaUgHTkIcMw7+O/3+7aEz1Lve6uUziP6Pf4v0s/p9pIWF7e3vh1jWDpv83P9G4jP/ucKxmZ6TzPNfgnr59et73IkIlJe+hFxkq+42tChDJolceK3yb8PrrKtHTtQtxxxnz3AM0AwaxxybdR0lW/o8TUViY4n/VVNxJLZW684rFItkErFdxxy9PQE4C26RRqCx/+r8CiKxRM3v+gMuTAz74bBZDOcGijUySpYdrDsBxRTkSG10jiQST78nZvWd+z0GbtB6u145iG1+ZNyG6cFdzB1lIKbUZL+M7RjFVXm8cS3FbpSRZBahmMZMr+qE0ZBfwj37bVheuNrxmHf6ahKvzzaPg9vBVL8FM4NWOO1CXUH76UE79g8bPyuUjDs26cL+kdbiOXo26HP8dTRLSdCe481dxByb2yFsByPoBorSJLtifNqdrAXYDrRYEMkrrzR0MyRkFi8g8ea3cOnixY6ulXrpJSivfYV1veohu7GAXCJa+pnWo5aHPgH5nh0zHWFoRteH7ABs+rO92voxn38Z6S/+AZTXnzb8vTh+ANL+kxAGjdlwezHHHrqJvQRYl2H29ECaOQqxjmsICeJn1xchHbuvorWRLMopAaa89lTpPVosZb71NzWJsmqob3wLytNPthRYEUsht3SJtT7qQTr5CKT7PgD57vdWvC/4ByHd9hhXDloBBZbaylUoF55tuq1l9BAcxx+C4DRu/8omo8gVglpig2TW55HLVAYUSjTISa9McA2ZrRWkNhagJXcG/3KkttcRmb+AdEhf4Mt2J/r3H2+Z/dUqSJyTWiWLAze5Q2Y1rcQgo1aGegkR0jvYNzkEr7uzlotwLIW5lRC2wglsBuMsnh6Jp7kF8eLcFuaWQ4Z/R8LsM8MupsYTEikV8+sxbnEk+/N2LLebgRJd8xsprAbTvF8Sfj0+5WJ22Hoow8mwboPOiwLrboKuSzlbrhO02nJHSdEr86tY3TC+f4T1zSBW1rZ0AWQD0PO4tLiI4PZ23X2Qy2M0QjoanS8Q6yEd2YaWab5IrWZ82gYrRV6bgTWDbE5Ypk40ZH9R4j0fjyBz5hs142guHoJy7hl2hKwHYWgfhPEjMHuM3dYo8aA++zmor35F/zmr6a0ZVVpH/Hmh9YZBbT2ol15D+q/+O7SzlUUUmluMkmLi6D7IdzwC+cjdpXb99HNfZqYvn1PPIJwf+VEIvUNQ5y9j+5d+HOnTz7R9XHu4vjD3DkKcOgxxxPi7kfiz3+BnWz71EIsil5BOQX3tm1DP7MzPZGmfeeYLyDd41gnqK1+D8uKXWjo+NnpYucLuqPUg3/445Hu+C/KJd1Sd2xjkE4+wdk0ryGWSUNevsWh+M9jGD8NJDqwW4/Yvih1yheQYF9c2FpFTlZpxLLm5jHRkC8mtNSQ2lqFVxSVFRLfWsL04i3RcX+DbnC6MTB9smf3VKmh+KMYcXARLpFh3tNhORsmvevC5bTg81Qu3o7O5bDOUZvb4ViRTaJdTEIkrXMh6fTaMayvGzxXpih0YtsNKYvMcF2i4tpbiZJTHLnKxrFvYDKu4tqZgLaS3DpI26Z377bDJJnaVjCU7S/41AjGx6HO6CYqR+rzSDYk5kmkVZ68FsbJVf1xYXA9zvGlUcC0m0K4urGM7HKu7j0g0jlAkys9vN0Hfg2QkhGzV97cp43P8IOw97WljsRi/xQH71LGG7C+KOXKJMLStRUNCQObSSxUFC6OYQxw9zHqNRqDEFxXltLPf2ok56Nis9sp5gMf9NeTrrJUaQTv3PNKf+21os2cqz63wWdUQJw9DOvV2TnLxeW4sIfPyN0rJQnFwHI4P/iMIngCUS68j+Cs/wf++laGTCbr/2sPusdcC2WWYnW44v/sn6v7eZHdyksz69u+q0ASjdknbR3+qQlcrO3ce2rkXYA70w3y4vp5HbuUqcuFNSMRyaFLxEAYmYHr44zA1sPWmCrJQh4UgNKHzVm4scWVB6GveFsR99gaVFmZqbSxytYSSY46Dd3PlNr18mRNiNnKqKkD29PDIILn9/K/o9DJrxAiZAhMjub0Oq7e3K7pC5DyTjEXhCfRycovYX+vLS3A4nKwJNjd7lTWwrFYrBoeGKoIPRVG4HTKjKLDbdhbE1XpK7YACXa/LBtFsxspmlBlDlMSaGSVnHBP/vxEoQXZ5SU+6HpsKIBxXEIxm4HMauy7SREfOhg5umWgvICu2NBLG+3cWIoMBKweNXmd32nGinGhLYHLAgYmBt7YTTXERQ3pw9dAT8EJR1Lpuo6wFoekLJV4sZbOQqjTAXB4PM8CS8Sg8/voaHO1CSycRW7oC2emFZ3zn+1uN9MYi1Og2J746MQDJqRmkzz0LwdMLqUlrojx9ihNcWTIsqQKxurJrsywSa65yWSqCWFuNmFsUcIpH38bBKjNB/v73+P8tb/9YxXaU3Fe+/EfcomB5749wIEnnni8sqovJsnwqDvXiK9BOPwXLE98PYWCcx1lh4hDMZcwfSmYl/uBXYO4dhv39P1R5TFY7bA/t6H6kX6ICzNMQ+kcgTR2u3FYU9cC5A5HePVxfCN4euD7xU3V/T8+SucBEr3jf5oDtw/+4ohCnXX2DFzVkpmDkEllEdumyvji764mmx2ceOQDZ6YPJa5wcNjLaKb1vMkEINJaKqNhessDs8LIZRdNtyZHboPWXC2wbC0ivzkH09MA5dQxKZAspKhrm87D177ATbF6a6wVY3H5YXBosngCkOk5nmXgEJuQR396AjVzAu4BUKoVkKgW/z8fXKp3JYG19E163nlS7cm2JYw6SUBge6KmIOVIZ0gNL8jxhswldiTncDglpxcLzy+p2iucqarMjMxiS6lLrJDYoQXZxSU+u3LHfg+2IwhqhxBYz0gmjJMvKtq4NRsWnTmOOAyM7z9x4n4VNfDyO7hTH6PhJv+zAiK3ic96KkESBteAatT4O9rg4pqzX5qnHHBRv5Phf+pnMHMpBTo+hcJTZ6x539+RflFQC4bVFLnD7BuuPDcTmJCanh2QBOtD9yqYTSF45DSkwAKlOMawIy/Qp7lRBwTipYj9bS8x6zfWMQqjjEE2EBHrVA+t8HbiLNUVprM589Y9g7hmGfOe7Kraj2EL5xv+FyT8Ay8Mf34k5yEhFEErJMpLtUS+8CO3CS7C++wdZOJ/Wk7mxg7zf0v5SCST+9D9BnDgE6yOV8Y3Z4Yb1wR32GRXctMuvQxyZYa3RiuMXpULMceNb8vfwnYG9BFgXkXrx68i88Tzcn/wpmOu06tgerRwQyiEEKgcz8cjdMHkCEEYaOw7J7/gkoKkNB4q8qiAf3YY5MMgD1/WAtn4NJpsbQiG5xsGrf7DGmrcZayx59mmI/kFmhWUjW1AWz0MgJ8xCUEtBqW3sIER35QRDSSyLf+fcLHUC7tT2GrPH6Hrlc8Qw0m1q42sLTHm2ejrrh49HwkhEw7A5nLDaHaxDYHM4+JWIRzmwlmUr0ukUotEIPJ6dIJgSX5qqIpPOVCTAitjaDiMaS2BsZICDypX1bU5u9PfUZ6zJkoCRfk/JHpocExWiscvknqgnw8qto0vX0USMJv1ZokTZQMAOj1MuvVcNal+kai8lv1z29iycKXjV3SR3FtbxlMr7rKbak8MitWFS0FsEHT8l6OizjcRwi6BzpX1SMP5WwWaQkpYCfJ7KxDDdO3L9agS7zcqvepVUejbHJyb43q8tLyKdSmF0cprfL8Ll8bHro83ZPYYC3a9MZAsW+g6XfVfrBZNaKsbfm0YJMGZSVS3alKWLyKZinJQS3M2Td7QPSvoLE8eQP38e6sJ5CAWNQZFcHd29DRNcxLJSX/l7ZtiKM7dBLNMxKkIsaJexY5W7hxMTRoty8dgDgNUO7dIrUF/6MuRHPwnlqb9GnpxUP/rTvF3ma/8Xuc0lgNgrhWtj9vbC+vj3VO8QZl8vzC2Mabb73w1xaJL1OWqOfXgS/v/nPzfdxx5uLJLf/ALU2fNwf/KfwFSnxdj+3h+o+/fVxSnp5IMw949BaOJyaHnXDzQtQ5NGDS2aKN4wXYeYg/Wb1udgcvkhFApd/P2hmKONpFlOSSFx8SXIfWOw9E9ADa0js3IVgt0DSyEmk729XIyTqxa1VLi0+vT3aOwU6tyD+NYaLyzNtLAssCPoFd1YgdXp5lcniEQiSCSTcDqdbGJCekLEKqcYgpg0fMyShEQyhVg8WeGgl0orzBbLqFkYhBzsOJ1IZTA1qifOFlZDsFkkbqOsB2KPk74oJago3tALbXkW0ae4ot78SyL5lOgiXS/6rLF+G/p8xskvQiKdxdJWBiklh31tJsBISyzgzlY4VhPjjI6tqEdKoPuzuJXhVk2PfWd+YSfJmAafq7F7NbVYZlRdY+ytAD1pmWCHab+7MmFHcdfMSOOkravAGqwXc8iSiP2Tg3x/5xZXoaoaZiYrHRr9Pg+bPDVq8W0X3Oodj3Dyy+lvHDdRQopepLFkapAHNYo5UksXkdc0iJ4ARGcLUha5LERvP0RTBurGPHLIQyqw1KXxI5z4F6ioX+8YVAXK619jdq+0/04IQ5UMd7qu4rTOtOKkljsAk5GOomyFcPAuJmZoZ5+Fdu45SI98EiolxWgMfPcP8maZr/4xcrEwJ6VgLsQcPUOwPLoj4cAQRN4XjcvNYH37+5Hdd7wm+cXXYOowfD/9/+KtDhovisZf3dznHnaPvQRYhzByo8iR4+PGMovN5iULctEQhEDngR8t+sTx2sUItSfkUnFkr52FdOwBXUywibi0dvobyF47w8kyczssrgKLQHnms5zll44YW6nT8ahnqa/bBIGEH/ffxddo3759NddK25iHyWJnZkbth+X1FpyCZg8l0/hFLI7CcVNlRi4wOogFZjaoHFccW6Glx1xIENLPNCFYvf1wECPOZGJqdCq0iayS6TgB5u3phd3p4uQXf57ZjP5BPRDPpNOcuOjt60M8FoO9sE0RHrcbp06dZHZY3fPI57EVDKEn4EOKrNBbFM9krTJ3ZYQ7NeIr2WMbbV8e7JArUb3kF4E0r8gm3VpH+6oRqLVhukpTbHEjgVQmy+0JFFBTYBqMZbCyleLP8rvcpeeK2iuurcbR57NipLd+0ETJNBLuvxF22FThpI/ZjWMNBaPUKkD3uDoB1iqIgRiJRjE1NWV4LMVr4XC5IUpyzTYU4Dnc7SU0m4G+26nNZdbyomRzI9hHD8A+PNMw+aVuryB97Q3YZm6HWJbwJnHrXCoGx22PteS+mwtvQL3yKsz9k5jIbiI3N4v82EH+bPp7odkCXlOQI/0vajtq0j7Gjm0PfLDu78XD9/C/2cWLMLl8LI6fz6rszldMWIv7b0OOxuO7H29YqaYFuv1DP45WYHZ5YTl+b8OAO/H1J2E5dBuksZk9R6YbDKPrnd1cZUkFXccqj1w8BqHJIq8RmKk4VsvKJMYhSTHkVmYhHrvfkK1VDUreUuuj/MQPtq0xw6yEF/6GjXikmduMt6HW5It6u69E4vkTR+vGHOr6nM4MM5JYKMQcRZ1A0dPL28k9Q5AKC1DWcg0M6UYtagbmqhYiw5iDks+FggL/nNXgCAzA0TPA32FNySAZCfJiutMEWE9PD9yKUnLwpYIJ6YoSovE4JEHAYF8P/z8VRMoR8Lpwx6njsFrrnUuedbvWt2Lo9Tu5Dc7URHS+CGIBBTyVn3dk3FPXAYa2PzzuqjDVacTsctlE7B+2G+iINQfJLBwcqYyHrqykoGh5nLSYOe6g/ycG1/KWArddgM9pLz1XG2EVs2tpjPWSe3X952DQL6PfJzUVvu8GisyrZsY+jUCJug1m8tcmwFpFOqMiGk9ienqmYcxBxg3EAquOxyhGdru62xJM37NEcBNWpweSpfF5uUf3s1B8o5ghs7mE1PJVOPedrOgw0SLbXKxzH3tbS8eVJeOvhXOYGJxBdvEc6/IWE2AUdzRKfhUNQvLhDX38aqKdTPuz3G8cc9A9kI7cV7pWnCSTrZzMQ2JHfkfcfzuzwqRTDzf+LNkC+4frd0FVs5fpVQ+5VBLJpz4P66n7IfaP7MUce+gq9hJgXULm3Cusw+F/xwc5OEx84Y+QOf0M3D/8byD2N6fjtwrq1U4/+dsw+XqRJ/0qUYZ8/IGmf2ce0xNpjVof64IGWFqANRCSNpM9+Mzt0JYu8oKtCGqzIovfiqrFxRe5/cd222OGAbjz9sfLPjvHLDCi8FJ1ttrFKT1/lm2AycmJHOKMEL38Gv/rPaQ7Wjr6R6FQG2VwHQ5qZzAJ7E7nnzpcSpI1QyoWRppaw8oGZWbL1NHz6OnrR763j1lidge59tR+TnE/dM3o/8sHe2ppy5ETXjTOAcLU2GB9O8EWYNTmQEEU6YZQpa5deAxaIzvFWJ8D0aSKi4sR9PlsHHQT+2sgYIPfZal4roj5NeC3VbDFqKK9GU6h32dj6n4RNyL5RQukSwtbesV0tPO2QTrWyZG+jo85Go1ie2uLgyraB7XI1GwTCvJ19Pf2wVUn0ZWkih9MsDUwemgVZDpB3zPP5JGmC0gCn3uT1kdqyaNkejX71XbwHk5GtZL8Iph9fZD23cFsFSUzDRlUAW7jeyBKrFdkoqRUi3pFzSCMHuAXgQodFOQWnwdKgN1oaCvzSH7zi8jFIpwA28PNRfr15yCfvB/O938fz5uxv/gfUC+9Ac9P/jIET2tmGq0gu3oNmS/+Pkz+fuQjmzx3izPNnViFicPcZlneYtk6KObQGsYctFATx49AW7sGlDGvqmMOKs4ps6dZn892uLaARxpg7lNl2mNZjXX/VEpU+4dqF7+rs7zwtY8dhGCQCKSEVvDK6xAkK3zT+lhA7tIku5AMrsHZq7PKaDHeMz4DsYWxkECxA8ks+Pv0BBqBGF/1XOSGBvp4Po9EY3A57TXt8KayhAmxcej35fPNUJ8X2WyIXSP9HjsOTPTuKplDzHWj1keaM4mt3i6IOd4tzAzZEElqeP1anI150mqO3SrH+iwlV8ric0XMsbQqo8ez8/nJDMUcCifEypno5htUcDszG+Yi5b6RztsG6bj3j/kgUcWzA2yFYvyiZ45up9dTm9TdDoZr3NCrEYtGmU1Jruq7BcXoosUK/+g0/9sM/Pw3on4VYg6zxVoTHzgP3tWWOJPgG4Q8cQxZVw+sve25R/NxyFaY/UMwD05BbMHduhWIU8f4xf9Psjtl5ygeqi/Dc72gzl8qmbs4n/huvBVRZPt2e5972D3esunUX/7lX8Z9990Hu90Or9fb8kPzC7/wCxgcHITNZsOjjz7KbiWdoNqNIvaX/wvxz/1+qTJKQvjSoVMttZ4YQbt2DtqVN2reJ50sc98IzCO6yH5uo1ZA0QhC/zikOx+vET9sBcQwsz7yKUjH395wO2nsMGz3fRDSxLHSNbp2rdKtj/YlTZ2E0DtuuA+qoJQLP5oECfaD98A2fcp48SvbkEtGkY3XFwQXXb4a9zj3+EF4p45WLJBFqx1KPIztS6+xTlEjpGNRpKJhdnVqFZTACm5tILxd64ZVvFZKJsOukZsbte57Ab8Pw0P9XMnlBFlZcEXB2W4HxfmVIK4sbLZFr11aj2B5o33xzCLSiobFjRg2gsmSJpnDJqHHY+HkltMqosdrZZdFSmgRy6z8uaKgeqiHxHN37mM4nsFmOM0C+zcaLNZuo3bR3btWUStAJ8lIQlFU1OfzYWN9veI7qCgZbK6tIBYNIx4NN3xuwuvLCK3XdzZsFcSAiM6dY+0vyeGu2yrULlij59iDNcwOZm41YcVWbG8yQxqaITcMzK9tAS3Q9yugpJFbv4Y8tSV2GazFkcuWqrQ3C9L4Pni+75+XAtE9R6Ybi/LrnUunEP+r/43k3/1paU6X95+EfOQOmB2dLYLVy6ehzZ031hDrHYYwrC+ychsLLe1PGN4H6bZHO9Lvo6S29eFPQTpwV/1tKEE1cQz2e94PaWhf3ZiDi3OjhyBWJbMqTCLKxLHNFhvs+26HbaxSB49/J1n4emuJCBvz1DkwyJQktFUu4H3j++GvYvTLVjuS4W1sXDnbVKCbtBhJYoGSYK2C4oLt7SBChcRDOYrXKpVOY3ZhFRtbtdsM9rkxMRyA1SJx0aw8QUai5ruNOS4thnFhobHJUzno866uxLC02blbeCKtYW4tifXgjuMjCcr3eiRme1Hb5ZBfxnifFYM+C5vzlD9XVNyi35WbAZFm2XpIQfQ6COg3AyUx3RQrtdkKagS7RawoGrYDnZ0M9AXcWFtdqfgOptMZrK5vIhyJcRG3HuhvNjc3sd3AnKdVkMlOaGkW0bUlWBwuCF3SkpJ9fXAfuhtClWYxxxxtfAaxs4X+CcwvrwI2V10DsHrIp+NsLJIPVjrndgPUuk5tjsT6upmQ9x+H65M/Bfvb38c/78Uce+gm3rIMMBIN/+hHP4p7770Xv/u7v9vS3/zar/0afuM3fgN/8Ad/gMnJSfz8z/88Hn/8cZw7d65h61krcH/iJysE6OWDp/hVjWxoi1sWyGmJtDmEOuywzDf+AnklA+fM8Yr3qZpqfe8/0vdF7XtNdHSqQQ5j6gtfhHjyIQgDk/we0/o3F7k1kgZx7eLL7Aginnx4V6wZznwXBiym62ZVmG0uZLeWkYtsQOwZ5uC04rwvv8LUYGKHUWtjNhaEtr0CebQycKSWhfTV12B2eGCdPMYssLrQFKiRLWRJKLrg+GRUuS3Z+mZrA7tUJIj45jK8IzOQrDZ4BoahpOIIr8zBuv94S9eJGGJ9g8OGEzE9z+lUktvOqPJls9cK6VKyp7qNoRhcLCyvw+d1oTfQuYslCeYrqtQSsYySZBlVY72uXbDuEY5lsB0puHsij36/HlRQEDYz7OHPWdmMwe2wNNTbKEevx8aaI91kpbWD0YH2BY5pYqdzrSda3y48Xi+/SI+DAspyZIhWHo/BG+iFw+lq+OwGhihRbfx7JRlHlowovD38vcmqGYgFUenIwiV+lt0jOlOImJX2/jFOMteDlowhr6YbiseqW8tIL5yFbd+dnNi+VUBOk/JDn2BR7Vxki1kk5jritQQaY7ML5yE/+BFDIe6KbWffgPrq17kaKx2p36J4I2A5UDkf7eHmwGy1wfXxn+C21SIsJ+7lVzWywXVkl2eReenrsH/wRyAUNKtqdHK+SpovLogT/6bys9wBWN//I7p2HZsttMdoJ6069bWvQbr9MdYg5c/LZZHbWoa5Z4THCfXcs7qBTR2JhVZB51GMOdglmo7Zakd2a5FdHUX6vKr5N3XpRU5mOU++Q49/IlvQoluwDFc6eZPcQnL+LLdIWicOQ6wTc9B4Sm2S1BLloGJl4fOKY6PRMVNxrDrmiIe3EQ9toWdkklvUewaGsXztCrZWlzAyZewyXg1iLA3090G21M6FmYzCyS9KbJFIvs2gaEMtlWKZQH7p2BIZzK8GWQ+sx9d5y1rAba3rGlgNSlZlFIo5NNbX6hRbEQWbET3ZSN3jfV5LyR368JiTC3GLG2kEPFLLLYVDASsnoEhf7EaDnrfp4faT3sQOp0vfSDu1HfT63fyimGNrvfK6kQZdPJ5EX68fDnv9+Y4KuwODg3WNGNKJOLK5LBwuig2z7KguybrpwvbSNf5/T78uO0JFNmfPADu814NKMUc2C7mBKUV6cxGptXm4952qSXrdTJhdfsj3fxAmq5MlHGAWeKyuB/Xcczzmyvd/oGlRQrv8GrQ3n9HngwIj7GaAdZ0P6jpmb1WQAbeBCfeu97mH7+AE2L//9/+e//393//9lranAfLXf/3X8XM/93P4ru/Sna/+8A//EP39/XjyySfx8Y9/vK3PT3zzS/B91474n7yv/iCR3VqFQi2S4/sR/9P/yrby+RBp1CRAoQVb1FO7TtmgZH3ie3lgJqgXXtGZUcE1SMcfgJlcDrlFprFQreF1IIHHyCbyCd2Km5BbvAD1pS9BPHI/xIN3Ibt6lX8vEuOrTBS7XagXnoe2tIb85Dgy557mfVrv/xCk8cPIxQYMXR9ZQD+/I3qtbi2xZhg5oYjlYtYkdktCi7INZquz4SJeJhFealGSmic5SZjbSJyb2jFyqop8Xr8n5Pzk9PchR+KSbSQJ67VIxiIhFiJXFQUDA/UXzuVIp9O6HpAowGa1wGppsZUikeIkV3UgEvC2PrlvBGPYCicxNuCF0945m6fXZ+f2B0XNwl+lU0ag94PRNAe/bmdrn0NBq9dp4b+9vBhEj9deSqwR4yyVVms00W42yNQgmcpgenywQoi+U1BCrd5z6XR7IVus/Gr27FoaBI/x9SWo6QRkpwfJzWWkQxvwks21zYlsJsXfjfDsm/BQKxQldnsbC1OnFi6wcL372AMwF9wGadwjPS9adDJbk1gIZgH5bOt25jcKlIwnZF74AmUZYXnvj9W9vvnYNo/DlJxHkwSYuO8UJ7+MhPX38J2D1MvfhO+dO66dlsO3NWxXVa+dh7lnAMnP/g7by+dCmyU3UU4SEWOjMNYQA9L6nk+X2GTKm8/zM5ffXoF06iF27+K5piCl0A6omJYnGYMyh9Xs7BvQ3vwWxJOPQJw4iuzqLEf1u02AKeeegbYeRn5mGsrrX+eim/3eD0AeO6K3EBsUn8gkg8+7wAZXKVkWWofUMwzB5qrUZCXBfYsNgrVx0sfq64dmc7bEfnP1DPCrGjR+sl5pYbVDemKeQPtt9U6n8bweDEWQTKZ5rhgZbE03juYoKsRJkgC7VWZmWCsIx9IQRTOzo8tBBjutgrRByY1636gbDkvnS5fhHt1hOkNGOwZtlKlMDltRhaYaZla1AhLx97kkJNMaLi7GMdxDIv6WEuMso5Cpz63loHttOcQx0sHJ3bW2thJzkLg9xZv19eZ2QF099RDaXGWzKLvTjfDaMlLxCPon9nNRmRhfxZd/dIqPxeh7VY7Y4mVOVgeO3FM6dor11VgIkifA46LJJHD8ktM0Xq/dSiBdw1LMIcqwvqPKBKcM+WiQx2FuK28yLokH72TmrzB+qOvH/J0GSvC3muRvZ597+A5OgLULoi+vra1x22MRHo8Hd999N5577rm2E2Bmmw3ZWBixv/pd2O55R8MsdfqlbyDzylMQJw5w0ClOHYHtgZ8ptegk/+p/Ibu5DNcP/0IpQBOGpkpMp8zX/kzXe6LFH4mznnyww6sACINTMH/wn1Y4Rpl7RyGQgOzQNAfIVCGgQbIYHHcKchARLGE+bnFgErlkjINMdlur46hGIpBFIUiCZfSg7upU5eRGE5I8OI0kVY5J0L6B7o7s6eHXbmD398Hm662Y4B2+3e2zHB6fH07XNixWK7ctbK2vwuX2wuGqX9lbWV3litnE+DhGh/vbSrZQwDMz2bpTFrlFbWxH0eNzcdBLSS/S77BbW6+SGoFYXY0EV60WETOjPmZ0FRPZ1HKZVlprNSD9qvKjW9uKIZZU2M2K9t0O2MY7Sy0Q3Q+DmF3BB9qNQDSLpblrsNhs6OkbqNCSUzMZmAUzLNbOE4C0KKMklGtwjE0jKPiUnV4OHIvMSt/MMUQXLkFLxvXWiAasr8z6PKxDU7AOzyCXSZaSX3y8W0tIL16AZeQAawCKvgGkr51BZuE8JO/1cbPdrdCqOH2K3WUbJRfF2x/TGbZl51qO7NJlqKf/gcXyzSQUS2Ozmqnr9LeHb39QwSgb3EDsc38I+0PvgzxZK1ZfRPr5L0M9/yqEoQmdWXX4DlhOFgxzaFH+p/+FnFng/MEdtldR/D6XiEL55pM8bxMvl8xvzAc7b4URxg9zbFEuv2AemIQQ22bdPIo5LG/7CH/WbsExh5zieEkcnCoVESmGqAfLcGUh0TJ2GFLfeEXyi/ctiLD2jiJx5VWY8nker+pBd4bs3IyA4A70w+Wv1IF0+zqT1DBCIOBFKORklpiiqljf2C4lKuoxhuZXtthFemq0H5MjrR0LMZsX1yM8h+8fbz1mSmU0rAeTGAw42PzG65T5WaY2vd3EHMR4IomFenDZRRwac8JmKRgY5PK4tpZgXbCmMJGLZaU868J6kl0rab/UPtl+zJFv++9aBSe+aDG9ywSYqmmYW1hlrbnegLdiDs1kMqxT10ryqx6ymsoJYH//MHdo0HeCjSNIx06UeD3QP30Q2/NXmIneCEoiquv/DozDOTRZU8Qmk570xgIco/th8fVzyyM5PKaXL0M6cAe6jW4IuwvTJ5vK20h3v4sE4+q2aWpzZ5G99ArkBz4Ak93NbsCga9lBC/se9vBWwHfMk03JLwIxvspBPxd/ZwQavOlVLi5NsN35dqhb60hfOgNT7xAnwKgCUk5j10VGzbDc/26YR6Zh7htC9toFWO+ghY+4sz25YGRV5GnyLLMRzjz7t9AuvwFx/DDyJjPEQ7fBPDxT+n1x4KzuiyYGCbc1lmtvFY6F36cBrbAPft/mhHDqHdDmz0F79SsQ73gC4ugBPpZ651T9Pr1Hv1NXZqFdeQXi8YeY6XVo8qjusjgwzdWT4jHVO/aa96n6UrAUrj4niBZeEJNDJF0TqpQmL7/Kk6Fz5hRfs1aOnd6nF4t0U9W2MBkWz6neuVbbPbd8Tgb3SRQlHDp8WL+G5EiZTHLbg7VQDTM6dtJ3Klbuiu8nkxT8A/F4Aqqior+/t0Ikl/Yx1B/QLeTLjr/ZOcWTaYSjSVgkPYhz2GR+sWZDMIpQJImxIT+sFrnxs2fwfrNnTNfa0P+WJDsi8TScBVc+Ot5694mO8/BUD29TPK9enw1OuwxZMrd8/4r3iRKAm8EYxocDcNgsHZ2ToqjcemK3Wyve18+veJx5w3Miur+WzXKFtNGx0/dAlCR+poquaPQ+sQtX5i4z82twYqaj+0Q6NdtXzkJ2OOEd2wfJatefX6LK2128hKWfaXvnyD69JanBfVKiQSjhTW57JNcjamUuH98ETx/EdBJmd1/pfVqc0vhZfQ2ajnst3qf9+/fr51q2/2bjHm8f2YR25mnkw5swWayQ9t3e+Bkjsd06Y7ka3oS2tQopGUXO7UPys/+DBcWtH/rJhudkQh7pZ/8O5qHJkoNwq98zo3NqZdzbQ3dRL+awHr0dyso1pC+/CWFiPyfA6sYcD38AwoHbYPb6kV25Bvn2h3QWQynm6APUdMlVtHhP01//S95eGNkHE+nSTB+GaXT/7mMOc1XMQXo3Jx6BeuU0sueehnjP+yH2jXUUcyiLl6AtnoN0/GHIk8dxiBaDdE5Deptgs2Oved8sciLNMOaw2CBSAc/l02OOrIb41TMwSzJcU0d5rGs55uA28hjEMhmIGxlzyJKEw4cO6Y6UqoZUKo0UM8nlusdO7Y5F9+ni+7EEtVGaEIwkmak90u+tELynfYwNeLjA007MQfN8KJri1kJRsMDjkPhFn0saYOQAvW/YDVmWuh5z2GRKDFFrqglqNo9wXIHXN9I05iCHyZMzuvRA8bxIGD+j5tnZst2YY2E9xhIRB8Y8XLTr5JzSGYWZXhSvlb+vn1+29HdG50SJLUoAkvRGw5gjR0k6gVtmy2MO6lJYXFqC3WbD8PBwR/eJinbr81dgd3vgHxgpvU+6XvQq/i297xudrtiX0TlloiGkI9ucpBbsbl6XlI8RsreXk2Jmu6ewrgDkwDDMVIC4DjHHzMxMx/NzNriqS9ZENmB297ApSP37RIlOc92YQwttILu9CimdRF6QkPzc/2QZB8sT39845shqSD//JQiTRyAM6kZlezHHDvZE8G9d3FIJsJ/92Z/Fr/7qrzbc5vz58zh4sH0afqf4lV/5lVK7ZTkunXkDls1liO/5NAITM6BwaX19HZFIpMKiml6rkRgSkgcIJQDvKAbicRbun5ubY+0njBzl10gqDafTiatXr/JAoKVNyDn6se+B98Pi8uiC/dfmSvufGR9F4h8+iyX3MA9q5GQmWKy8gEskElha2hFkpirf1NQUH195ws/hcGB0dBTBYBCboRSythEIwQS88jqbBdQ7p+XlZf6MIqhtj85pfm0D6VgWwrV5wLqFQCCA3t7e0jkRcukEpvYfgmSx8DnxAJFJwGRx8LETA4oYe+WDYN1zmj6JcDiMNd5PDsraNkiyYty7hJjgxNbWVgXjr945OZHBtUsXkLV5SgFp8ZxK96mAkZGRivtEoHPwWwVYbXashitFPikYaHZO9PeqqvJ+6T65/T3Y2NjAVjBUc5+K50SLJNKuc7vdpXMKhsi1D3A4nbDbHZibny9popSf0+zsbNNzIpBWHiXQ1lcWkc/msLURw/amqeKc4skMUmkFWjrEAXW9Z29pZR3ra2tw2C08qRmdU7P7RC+HmEEsuIFL8U3eTzv3qXhOdL2rDTCa3ae1lUU+zwU1CpvNyudEzx4dZxHNzunK1VmEQmF43E5uXS2eU17LIKskcf78Od5+fHy8dE50n+m4KJh0OhyYnp7m+2p0nyrOKZFiZiEtoIvHmEmmIWayID5ER2PE5iYy8QyETB4ZS/0xIhDwY2lxAan0zkLe6D7RfRgaPQzZ349Lly4ZnJOMhaQJmF+ouU/l59pwjKD7FApidf4as2iaPXsulwuxWKzpuEffq4GeAHx9/XxO6c0V5IJx6qnCYDoOq6bi6mylIPd4jxfm4Cpms5XtpzXPnuiH+a4PY//QFOLxOOaHT7DGpHD5cs19ym6vcXuZe/9xjLjt2Dz7KiJbUUiK0NL3qd5YXnGfNA2ub/wVPBPTWD1wF58TXaM93MCY4/XXYNleg/TeH0J+YhrET6p3T9fiaSQEBxDLAK4hDESjlfd0+k7efiSRqIw5NAk5Wx/2P/JRSDa7/h27Olva//TwAJJPfx5L3nFu3yXXVNLa6TTm2IgqyFmHIKyH4M1bOos51jeQjqoQZ2cByVoTc+jxRRJTBw9DkuSymCPJYvttxxxTx3dijlwWmY1t2EQTrP4+RHNiy3OZLZfG3OwV5CnmKLRBtxtzuK0iHC431rZCu445Aj4XNjbWsLWpzxVGYySN53a7E173NJ8TXQdy/qMEmMXmgs3hxtz8AnLkGr7LmCO0sQghm8PmahBba5UxRySeYRa4Oe3AoUMH6j57c0sbfD4uu5786TTm8EtxxLYjuBSTO4456Od2Y47g+gIz4eaubbPMRScxx4Ur84hGIgh47JyYLJ6TDJLcSOCNNze4kDoxPloRc5DJQTKVht3pxeH9k5idNb5P5eeUTkbh9TgrYo5kKoVMOs0JsI5ijo0NpOIJiGkNqkmqH3P4KeagGC3dPOYYnGbWumHMIVmwFFOB2NzuYo7gNlYX5zlxVu8+0bFQDD8xMdHSuEfjzUBfL3w9fXrMsb6I3DZ11jgxGI/CQiYRVc/euN8FczyEa4rY8NnLWwcgnHgv9vsHeG5fGDwGWB3GMcfmMqCpcM8cwZCUx9bFNxCJKRDjavdiDiUD1zf+Er6jt2N59PBezLGHrsOUv4VSia24f9CXsNzimjTA/tk/+2c8KTQCTb60cHzttddw8uROu+Lb3/52/vm//tf/2nI1lgaxxa98DuYv/yVc7/0EHA88fl2z3fWqRAhtIPVX/x2mwQnk1uchnngbixZKbj/MozqLoVsVsSKys6/rLKkhnUFSRH57WRe0d3i5qkS983QeNBjvm5qARn/XP4E8CdK/+S12jJSmddactjYL9fIrEKdOcMsjfw5dA2q1KrRstFppoZYsbWsJ1oFx/n8St5UK7Q+NzjWnpJBcW4S1f6zkUNfOfdKUDEILlziQ9Y3OtF2NpX1duXKFJyVJql/RLD+WleVlniwmJidLx0nJFUtBD4zYRpSoqT6W61FhjiVSWF4PYXSwBy6H1fDYry1uIpHKYGzIx1XmYrXQ6H6QThe1Xbqd1pr9UNBOgQhVzuga3siqefmxRGJJrG/p59yoal7+fiKRYkFYn8/NQWf19tfm9ETP9NRE6Zxo0qcg0Oly8TUL9PS0fOzFRB+NfeXaYt1iS5W/ryda88imElDC68hEtuCeOaU7pxW27cZ9ylx6CblMAtajb69IIjU6p8y1N6DOn4N06B6IPaN1z4l+pjGLrheNabntVXZCMgtizfbKi38LbK/C+tj3Iy8WmI/kSHvlNWB7DZaHP4ZcVfOn9vwXkFu4APGRT3BrWek4kee20tI4Rm6SpP9DbQmby5Df/YOl1oXq+5T8q99CbmMZzh/8OQh2J5SlqzDRHOBwd60am43HEP7Nn4Nl3zE4P6KbsIRCIU40UIBLSfg9dAf1Yo75Jz8D8Zm/g+djPwzbbffflJgjvz6P9N/+H5iHp5FbnYN45zv1FksSex+Y6HrMwX87+xqzvE3ULlkec2wtshA0SAS6OuaYGIc69wbE4X3IxcMcX8hTxyGN6Kw5dfkStIVzkGZuh9w/XmLYsvRDuzFHJgk1uA7bwDi7R5MxSFFyodG5ZtNJJLZXC0L5ctv3ScmkWfibdBr9g6M3JOa4triGbDaH/VMjJRb5djgOu83CjsWKmmMX5+pjuR7zczCSwspmHNOjfv5Mo2M/Nxdk4fzpIRczqCgBVO9+xFIat9e5y8xzbrWYg4rUG6EEGwTJstjS9ykST3E8RVqozNgpZ3Tncrg4u8Y6rPsmBkrnFAzHsLEdgdft4JijN+C5ZWMO+ldLJ5AKb7Ere8/UEZgLLMVu3afkpZdYTMO2v7IFstE5pa+ehro+B8uhe3WdQYNzKo5XlEij39P2+XgY4uRx3rYm5iCtr2QU1kc+VWKb5lIxaBdfhCmVgOX+D9Qcu/bMk8htLkB89Pt05m29mGN9niVq8hdeRC4Rg/zE99e9T8k/+y/sFOn49C9AEEUoC5fY4MRUMDnqRsyhhTYR+e//DrY7HoS94Dz9Voo5aN6mROCP/9oyLLbuHmsmFcX/+JfDb4nrcCvjlmKA0YNNr+sByuxTlvlrX/taKQFGD+gLL7yAH//xH6/7dxaLhV/VsJ+6H47xKchTB7m1oHzwrEa779cTwS6+ry1dgfLCl2F9+COwf+Kfk5c2spdehXnsADJ/9d+gkF7V2AHD/dAgY/R+8ViowqC+/BWYAwMwT5+sOEYWh7zwAvIODywjOy5E5PCYeflLMLl7YHngQxV/Q5+Xj4eAlUvcmidNnQAGJyH26YtQhNdZWBYDEyw6W1zQ5ubOQFu7Ciu5QRbcluodO0/qOQ25RASytw/yqH5sqbkzyEa3ITm9nLTLJiLIiTLbnNecv9UB98TBju9TbGOJqcDegbGm96/62IvvFyc8o3NNJxM6Nb9MRH9waIgnjOJ2lCiJRSOQpQAnicsTxZ2cUzvvUwBtkfUAs959Gh30cwKMEmUelx1Dfb66x7K6GWbaPm1XrfdRvE70GeWf0+1zKofROVECSyRhZBKLzuvBCIkDNzoWh8PGr2oUtx8ZGSp9XvF9qhwqHg+7OhbvKR1LJp3C9sYa/D39pVbZ6mOkAKt47NW/K76fCG7wc1QUjG02RtR7P3LtDC/+aBFJ3zeL04vM6iwzPt0H76o4p3buh7p6Fer2MuSeEW5ZMEsSkJV5+/IEWKNjl3pJzyIN2TdQoW3Y6JyyNGZtLMA8cbgkcF++vdQzojtpkW5ZQfuMWqOkU4/snFPVfk3HHkCub5Sd9IrHnk/GkHrytyBOHoV873v4PeWpv0SeTFLGDsKkpPXvVZ3753jX9yCfjHPyiyCPTNc9p2rQAkJ5/TlIU4cgePyG2ye+8TeIf+Wz8P3jX4A8Mrlzbl0wa9hD6zGH8753wHH4GGRqS7zRMcfsm1DeeAbWR78b9u/+aUCyIHvlNMyD08j8zW9DHZyE9V2TncUcmgr11a9CGJwERg9WxhzpBLTLryDvH2QNwCJorldf/wY7V8u36wXI8s8jl2msXQWoFXlwBiAH2kIhLB9eh+TthSkzDsm3o7GlXTutO1CfehRm2dY85sgq7DQpkxubXdcLyyxfRjYVh+XofZzU0shsSLZyEaDm/IlJZd/XecyxucIFB2oLux4xRzKpu1LbyhzSx0f6C5JReiKFEiWRaLwgiE8vw8No+ZzaeZ9aH0mPjNhn9e4TJYqiSQWzq3H0+WwY6nHWPZb5tRgnFU4YGOTcMjGH2QRJMHPSgTraCPRzo2PxOG38qndOU6N9vN/y+dnttDMDrMfnhiTpy0SOFZIpbG2F0N8fKJkudRJzhINbbCTl9vp2FXNszl3gBDwJ2Us2B2xON2LLV7i90z99rOP7lFm5Ai0aZAMt0eXVnVwbjAWGMUdhjpdc/oYxR0VctHIJ+egW6yaaRGvN9mLPEHIRmcdFc2HMEZxedtitd06m429DLrQB0bOj2ZeLbiPzd/+HTc+kE2/n95R/+HMe102UvFczpaSU0bk63vMDPG7Tc0iQx/a3HnNoKpQzL0Def7xUpKvePvbFP0X6pX+A/yf/A8TAjl70Xsyxh2/bBFg7WFhYYCop/UsD7unTp/l9qtAQBZlArZLUTvDBD36Qv7jEFPulX/olrnhRQuznf/7nMTQ0hA984AOd2bPuqy+8fj2R215Dbn0BuViwpPNiPv4A/2t51/fDVKYp0TbUDLtCIhUDpk/WiMCSKDMNkhXvy1aIh++DqeBIUg7d+ckN6dRjXK2l6qp86D79PCJbUN74BjPDLIcr3Z+oUmF2BWASWxPOzCycg7a9Avvh+3lCIFiH9iHr6ePkFg26iYsv8aThPHh325elWJWonhCKsPn7IFrt3IJ6PbC1tsyL1dGZnSRdUduLNMNI74kSJVSptDu6b9WsqlqpemoE0sSaGW/suEMtf06HFV6XHS6DgKwcQ31e1ha7trSByRE9QLtRIJdMutsug0RVOTxuB78IJABLehkzk7pOCP0/aay0CyMnT7rPvX21phHEWCBdL03baTfpBIngJjMomjkm1bMRp+8EtQ2TBhiJupNLGlUlJYeb3SDpu5dHDslr5yDYXbD2j/N79cRYy0GBrbpKAS2gLF3gBJj9mB6wtQMaTyz772rrb6Qjb0N+MlpKflVDmDkF7cLzUJ/7G1ge+4GK32lXX9cFZR/8MExlf0825TVW5YLI5ibE3i19NiXClAzEmRPNz83lA+jVAdRrF5D43O9BPnEvxP5RyEfvhOCu3JfZ5YXg74W5UN3dw80BfV9uVsyR3VphljlV/QUSR6bn4lgh5njnp2AqW1y1C1rM5Zb01iKhwAAvglqW5Xu/i1txKt63uyHuuwNmAzMdGnvQ74N84hGYiTkmiLAcvEc/j+0VKGefZmaYZV8Vm8PpY4a6SWht3E7Nn0c2tg3nkfthtujfDRtptGZSnPyif2NXTnOBzzXVvoNrUZ+tHpy+XmgqmZB0P4ynz15dW+MFJ5nsFFHU/yLRfPp/SpRQEYA0oroNYoBTzFDPodDjtPCrEYjZ5HVYkHCp8Dgabzsx4ML6dhyXF4JsvNPo2ncbpHdGMY6riaM2uWbTi3Dh2gb/e3CyT29n1bLMwmsX9ipnzmJycbCvsiBCoJY5uvdZLQd0qGlPxxoNBSEIYikB1g6UZJwTSpLFxkkvXTPVzYxKi8MNJRFh8wsq5pPLo+TysbM763q1IOhO+1E3FznhlVm7imzCD8e+9k1ABFeAX+1ApphDSfOaygjEDMt85Q+gvvplWB74cMXvtEsvI7t0CfL9H4SprMhPY2TNOClIHJeUrxWle9/LYyUV3ZrBvAtTMeXiaST/9o+Q3XiYYxfLyfu5GFBxeB4/zL5emFt0tr+VoRfHu7/PPXwHJ8B+4Rd+AX/wB39Q+vnUqVP87ze+8Q089NBD/P8XL16s6D/+l//yX3Lf8Y/8yI9wy+QDDzyAL33pS9yD3S6aTY7q4iy01XnY7nq4pQlBOf00hMFxiAN6cGkEtjHP5yCfeADiTG32vNw9shMwvXX1GqQHP2wYWBLMPuNFMlmZV4OrBeuzULfOwvaO7y1VrUu/d/ogjh2GuWekdn8Dk/xqFXLfOFdti1URguDw8IvPjZJ35DxVFUi3Ako8hS++DNHmgGviiOE2FpeXJ9zo0hW4hiZbmmiLSCdivL0pn0M8tAVPT627nb9v0HDUozYI0hlwur0I9PVzf/31SH7NLSyzOxRpVzkcnS+EBbMZgwXmVyNQSwWJqqoG4pf0XBET6noFqGubQRYMdU3VPpf14HY7kNX0ymcwGGIttqHBAdjrOGp1A3anC6NTJKBaWxVTMynEQ9tw+nubXiv/GLXsdjajRufOMo3ef/BOOIZ05hEFn+Fzz1EfAVwzJ3fYo9FtvfonSkjPn4Vt6gSkOuNMEbRvweXnFgL6Lpuk3S206Di4zalOgFn+bDH75MILEA/fywtpo23FA3eyGUcN0gnkExFkvvFnnBSU3/6RuoUJClZt7//RivfEFoLQbkAa3w/7Oz+KPPJIfOnPuPXB8c7KwJpaEOhVjRu5QNxD8+utzJ5HNhKE7VRlMckIrF31yjchTR6C0CDxTfpylHiT73wU0tF7K+bXIoQC47oT0Nya21yC9OBHYPYas//NBg6O/N2bNEgqkcv22lVo0VlID36s5prRwk0c3g+xjE1WhDQ0w69WYekfh0ZjUoEtRhCdXn7xZ8lWWHpHITmNE+jNnHaDV9+A7PTBPWwc09ndXiRCWwgtz8E7OFYTXzVCKh6DIMl8/eORMDz+yoU6Xbf+vj7DfZLRzsrqGnw+LwJ+H/p7Oku+Nzy+tIrZpS14nFZ43WRc0/m4L4pmjA80bxOi1sftsJndrW9kzMFi92sRNu05ONF6B4zPbSsdz9oWGRElMDnSA5u1PvN/t/C4XXA6HRzHVSOdSiEei8Hj8zW8VvT+4Kgu8dAuqFAXXLgMUbaiZ+oQvORyS+OUqiB45XUoggDPqM6qJAkUJa670JPJWGptDq7JY02/j2SwQ7EGSaeYZQsEy+4KynnSw8vlDF0aq5+rPLUzXj0N8dC9xoU3KjBOnYDZ6TMkG+QTYWS+9idcIJDve3/dOIfWjtb3/kjlridvTHFF3ncMuYc/iJyqIPnlv+D3bPc9XrGN/f7H+VWNvZhjD9+2GmBvBRT7epv13oZ+91ehzp6H/5//WgWFs16QGfvfvwRx8hCcH/uJutvFf+ffceDq+tH/sKsFoPIPfwFz/zikozoTq4jc1gqUr/0xzGMHId/7/rb2S2w0pu4OVwbDyit/j9zWEqRTj0LoGy9NCPlM2jCYvhVBScfY7Jvs9ESTqY0s0gs6YeWIrcwiHd6Cb/pYSdS2FXvn9avn2U2PFqFqJo3B6cNs+dzS35MGx/oqHG4PHM6d65lMxBEJhdDTP8DtibsBtceRRTqxmsjBcHR4oGNL62ZV7RsFEnel4yBh2Wokkro7mrMJA6zuvpMphMMR9Pb2lNoHbjSi2+uIbW/ANzgKu6uWmdktkJ04s2G9vVBjIU5wkW04BTf0PlUUi9DfE5BNRpBeugjb2OFSgvpGQTn9NeRC69yq3SyZll26yG1W5GgrVjFTWkGOquV//3tANAiB9IfIhrwAcm7KXnsT8kMfK+lm3EzkVQXpV78Fy+E7YHZ5ujoX7mF3aPU6b//GzyG7voSen/+tpmw9bekq4n/yXyAdvhOO9+1ovZSDYo34b/8cTC4fnN/7Lzs+/nwmBeVbfw1h7BDE/bdV/C67chXqM09CmDkJ6dQ72tpvLrLJiz6hqlCWeemLHIvItz9RKuQRs4vGpqIg9a0O0qKKzJ+HYNF1m3SdsNp5PLg0y7pHfdOHIbTAqCUomQzWFmZZToHmc0oqjE23nsSkOGBzYwter6eiwBONxRGNxjEw0Mu6UbuBls1hZSPMxTfS8JoZ7+uI3XQrxRxkFkSJI5u19j5FExlmupFDdSeIxlPs0j3U7931te8U21ubLLY/MDgEW0GS4XqAJBsoeWt1eZGJ6eYPFnJlVRW9kFyWnOOYQxChxkNIrS/ASdIwBbbmjUL6ta8il4rDds/7OP5pBG3+LLRLL0E68QiEvvpkiEaMeeUrf8QdPML+OyAde9vOvs8+i9zaPCQqxrU4VlxP5NJJZE4/C8uJ+2C22b/tYo7isf7o/28JFmuXNcDSUfzPnx15S1yHWxlvWQbYzUazvKHrPZ+Etr7cNPlFEAIDsL/3+1gXRlu8wi0xYqGyUQ75zndwJWFX0BTkNhZY7LAaJv8AxBMPsZhtdnWWLXOFgdrjqAbrcbz891x9AFmnF7U28nnEh47A5emFOTBc2paourntJcj3fwhmW/eTYJ0EPCTcnU3FIPtrq+EkruuePo7U5hJXkSRilsk6Syu5sYQM6Yb0j8PeM8wvo+RYPVDQ6u4d5L+hpAz9f3nyK6uqSCeisHv8xudETlAeL6xl2mCEdDLJGlFENSdm2G5AbY+DA73sAEXJHUtB8L1daFoWc4srcDkd6O+tpde3c39p4KfJpdPAdnlVd5AstiyWw7GLajM59iWSSQwMUNtm6xX5bsPl64XF5uDEKrFdd3OtykEaXwrp6rn8nOS1+neereTSZTaTkDw9erK4KvGevPwytwNZRw/Ceejetj6XAkg22dglmMFK96VOm1P5syWMHICJqsAFDcLSNmQTnlVgNmj5rvgsUYTliU8je/4FmAuaiSTITc5zufAmC+zn1fSuE2C5aIjbNWiBn3nuS7Dc8zgEf2Hhn8/x+NUM1Jpuu7txAiIbiyD+9c/Dcf87IfaQFtBe7exGotn1dn/oB5GLhFpqVRWGJ2F716cgju2Deu08MwKEvh1TBgIt1oj5tStJhYJGaI40Mg2+L2bS5jr2AH8/skuXYbLaDBnhRgU35ZUvM9PS9OBHS4wIukaJ4aNw9YRZk5S3jYegknFGLAjrfR80ZGLcDGipOHJlgvnloBjAN3UUcXIB3F6DTK1ChUJGbH0J6VgIzr5hePpJ/D7fcvKLIMkyPIFeyFYb9fuz6205qK0+nU7B6XIbzhn0js/ngc1WWSAifSiKESKRKDPDdgNRMGNs0F9ymCY2eCeg5NnVxS30eB3oC7huWsxBjPK5lRC3ZO4fr2V5uZu0ZzZCOJpAMq2wxurNTPT5/AE4HE5YrNauxhxaJo1MIsoJL0p8OQpzGyG2TA61eVgO3sG/Kwclv6JXX4fF1w9b/xjkaumBBii6xHYjYU5OuSark9dTzZ4rgTpi+sZq1kW6lEwepibrJdIatLzze6FdeoV1xAi5WIjlFTjmCK4xG44cpXeDXHiLpXDyySiUV5+C5b53s1RCOzEHzVO2ex5tuE02tIXEt74Ex0PvYWmGt2LMwZ1VXT7ut+J1uBVx81Zob3HUODJWQRwYhfWErjvRCuQjd0HoHUT8z/4bEp/9HeNtTr4N8m3t69+UgxZf1g/9E9amqfmd2cyiiFQ1VZ/7PNTnv1CzDS3atAsvFhzfdGSvvIp8LAhTzyjUZ59EljTECtdoIxKHefIEV2EoEFaefRK5RBjmvomK1oF2kbn0ItLnnuEqdTlSF19E8rWv1rzfDOnlS0jNvckCtvVgDQzBRULV5W1b+RxXa+NLV5Ak2+42kl9FcJuazYHN7SDrGpQjFtxAeGOF2ySNEN5ax+bKAie7KvbJEyol2LpXDSS2VMDv7TiooT8jPStKDFGyr1PQc0VWzEUnq1YmA5qAyrejBBwJ7Kd2cRxGoBZrSoKRVsbNBLOy7E4+5+K16gZSGwtIrV5D9NKrNd8xx9gBOCaO1G3FoSSY0feSFoHJ889BJX2e5cv8HS4fX7TNRaROfxXq2o5VfKcQx49APv5w3Ups+bNFqE5+EZTnPgfl65/R2ymbgMY98ej93N5Fyf/Mk78F5dm/gXTXE7B+9J/rLna7ROKP/xMS//fXoV58Deq5l6GceV4/l2Qckf/6s0h88Y/RDWTOn0biH/4WqVef0fffpWdqD62h2fWWRqZgOdKaVg0tUCzH74XZ5kDiz38LiS/8oeF2ljvfAflo+5qZ5SCdF+uHf6qCAVk6DkGCePBuTrKRlp760pdrtsluLkKjGKNs/NYuvsQsB1PvKJRv/oXuYFaMOaJJmMf0cSiXjEF54fPMcGQGeov6XtWgz05deAFpEuSvdoi78AISbz7d9j4TS5cRnz+PnKbU3cbROwzP+MFS8ouPhYw3sllEl68hGVqvWfg3A83fHn8PZIsV28EgJ8TKEQpuY3tjHZmM8dxIjsSrKyusN1oOr8fFznLdFKp22i3o9bs6jjmIVUWthfTnyVT969wM5fMCsdNa+5udmIP0vYZ63XDbZU7odRPE/iJHanLnvJmguM5qs9XMobsFGUzF1xexNXu25nfukRm4R4yNJChhRPGGUcxBOl+xSy9DjWwjvXwZyWtvVIwv6sY8Em9+CyoljHYJeeIYLIfuMXyGq68VuyQaJLkyzz6JzNN/1dLnUYJfOnIf656S+L3yhf8F7ZWvQrr3fbB84Cd4Dbgb0Fia+OP/F6nP/Q6Usy9CPf8y1Muv6+cT2Ub0N/4V0k99Dt1A6vTzSDz1RaTffEXf/17MsYcuYo8BdovB/vjHgQ6SKO2gXCCxHqR73qtnLKpAjky5laswD0zCVNDsEPbdDpPTj1w6Digpw0qvvlMZwsh+mFwBiGUukp2AqrpUFVEuvwzLgbsrmAx6hbe9gMkyOA3R3cMVHw5ISTCzaqKggJocJcth7x+DrXcYifVFWBoIAWeiQWZrNdqmXnJMlC2wVolEln7v8bEIrlz1zNDP4220NdwIsKDu6CDWN7exvLKOkaF+2HYhnptRFCyvbMDndaMn0LjiPL+wzJ8/NqKzE6m9cX1ji6vWE2OVzIfdgFxsfQUNjHrgql9wG1abveTe2A5Y8DadgkQVfLoOyTizvdrRgKmH5NYKmznIVc95EbaeEeQ1jXWtqiuaYoN2RkoEuY7V6kgR8moG2ViIGVX5VJSdW4kZRdpaJHZLWhikwVVu4X0zQVXafDLCjCsSndXOfIsX8WJB76weKPAkgVnz0JQeDHdpnJdOPqC3mlLrYj63s998Hmanu0Zgthx5JcPjMrWkqwtXIO87Wt/o4/b7maFjOXC8K8e9h5sP1p9750dh3oWIfWuf03ico0QYxRxGbEhKduW3V3R5hQIbjQp1ucAQJ5Xh7qnLjODv3NAMzP4hNtvZDahwR2MVjX2WMv0xfSxsP0FjH5pCNq0L5hNbhZgTQpVmD7PwqrRe3YNjcPYOIb61Aqu7fgI9GQkyk8zapgaZ1+9nXVxLHUMfciMmlk/RhKcIcoucmd7dNe42yJV533gfFleD2AzGMDXaC6ulc/ZLIqVgcS2M/h4Xerz1x1VaqF+e3+DPGh/Sv1skcH95M4xURmWtrm5huN/PRgRkGNDoeIKhCJwOe0fyFRRzZDIZfi44GZxMcpvjbhle3CWyvQ6Lw8VFYCM4e4ZAZWmj4rLc4NkmNpTvsDEJIaemkU1EkU3HoZF8i0JmPYouDk9un9Tl4Qp0hXXeDYhjh3h8IFZadm0O2vnnIR19gNdTjWByuGEe2Qfz4KQeH5q7oA8nSpCO38dzBsWC3JVUYJRRDpHYxKY695K3yaRhsli5QKetLUKeOlR3W8cDj0EI9MF6WNf4fisiT4nwomVrF/e5h91jLwF2i0E+ZjxgJ7/4x8hursD5Pf+8Qlenm+D2HOqhtzkhFAStqyEdfxC5sUMVgrXEksiJEnIk3njwbpgN2giLVWepzdanerAce5iTX+Solnrpi8xaI1cn61TjRWg9iA43vwgJcnBLxuA+/mBL15qCVOdg48CP9ME6SYCJkgynr36wRG0M7bLdiiDmGus03WDavNvp4PQksa9ILH6ww3ZB+htqxyTHItoXsd00VUMkGkVvT6AiQKfthDLWD/3tQH9P1/UyaL+Nkl8ETVUQCW2zgUEnCbD/P3vfAebWXWV/Rq+pTe+9edx7txPbiZ04vZEGCYRQQu99WdqylGVZWHbhv0DosEAgQEgPSRyn2XHvvc7Ynt5nNOpP8//ufSONypP0pNE4cXbOflpijcrT09N793fuuec4B/tYFZhXUsG/qYGO85zgmG1g3DoRyEPD2dHCaY7xCDDBbEV2nSatJ7jaTiPgccFKyq8UjyM2hzVphrO2RRu0hSQVeWRYu38zh1pY5l/B5xdLVEpsJkEeRb5Dr2gJdGOj2okgNmrJjDT2TT5eNLJOhHm4lyIpaMUlV0WcR9lMfO1bMr795tXXhRYTQm4RhLJaqAO9GPr5NzhhyXKFfsqx2teNvh9+CebFlzOZ6dryD+Te/2koM2KTJ0e9Xji2PAvLwpUwTVLS7RReHyiL9Ylp5yMPsmrbdvfHJu29A0N9TMLRTaieofsYaeGVnD4ZPopJI4+BjmYEWg5BnLsGpjhEEKfFjaVOTwTsFzl3Ldxn9rF3qWPXM5CKq6HUzoFlWnqLM7JSoBth6MwBVlbkzVll6DxK49U5CQKTuMnSfk7zSkqRAKPmWXRDLRwcGpXmCA4ptk2vQ82Rn2PlMcohhxPdfX5UlaU3LigJJia1FElkr1DyJSNCi163vDifk7IJ9NpmWYJZFiPIOEq3VsLuywToPYPvGw/k9dY/MAS/qqIsDQJscKAf/X19KCkt49Tp/p5uFBaXIDs3b8LjjY6eDvjcThRU6Yc9SBYr8qvHwymGW0/z8ZddZTywIghqWtH1miwccuZeppHZxVWaqolqAFserNOXciKslQJuJgnUvPcd24asGv1QrWiIYymU3u1PIkBKeJMQUfczKbbneUjLr4OpaLyGIRN8eU1maw4+ttfeon2OQABCWQ2Eijr4Oy/A8bv/gHn1tVCW6482UjDcwE+/CcvaG6D2dbEHWN4Hvszq5WgEXE6MvLYJ1uXr3hCeZVN482GKAEsTF/sCzidunyfdsLYY+I7thKmwAkKxdrIkSb3niZ8hy54H840PxH0epYsIOmNB1GWFzw2haua4TH+wGzabLem+UntaERjohNi40NDsOIE8hsyzV/MYkr/9TEZJQamgDAIV20kMK1NBTpKkLDa6NbCv4pEhucXlCYmyaPi8HrSfO8vpkQUl8VPAJgOk+qJbR2cX3G43dydTIcCC+0qWJFZ0UVF9trmFi8BsezZGRpzIzc2JIMAqymLJIfIiez0gyQpKyqtiRk+MQrbaYcnOhWy1cQIk+cOZ7TkTPq5osZRTO5PTy4zCPzLEZqa8IErh2A14nBg58BKkokqY6+dzt1bbYEFLfiRVRwrbMRFQ8lKgs1kj/ouqQvuLC0yPM666RKidw+cIcd5aVp6GXs/n5gRIeCLHkjMFGjOg9w16bkQk440lxtH2CPlFMOVEKiPpfOne/TLkpvnchRVLqzgFUKIkUFXV/jcKdC7v+fE34D1zHIGhAeTe8vbQ+03hTVxzkCrJl5lxLTqGyDpBKK9nTxy+j0yQn/oZTEQkbbgn7nPZ30sn9YwJaxrzH0tu5BHr4T5jNUdXCwLOIUh1OkmS8bZDscA6axUbTWe1ncpofUA+RVTjZeo7ptcpqG5IaLqdbs0xNNCPgb5eDtghnzCjoGt9a1sbK6QL8jOfHJkIdpuZby1tPXB7vCn7xAb3laJIaKwugpe9xTrZq4w8Q4dH3CjKVyMIsNrK2GZnXs7rE3hC9Rb5uJqV9FTHpPbyer1QzArkgAxfjhcWq23CxxWp2PMr6yGOqdmNwO8cjhlDNgJqajuO7eD0VnPltIiag/ysiPS6WKE8JDagcDBTdhFstsLxFEhqpNH5JY73mECNRmsOxDmrI3/bpGAbGWKF6qRsb383oJhjgsvYNidYc9Df84qQFVWXkMrcvfdVKHOWsjJMKK2EUFAMqX4Gq9PFKO9J7Tle9Pzo6/BdOKuliV91yyVbc0wpwN64mEqBTBHJUii6v/t5mKw2FH74K/C1NmPk+Udgv+EeNg1OBH9bM4Sy6qQpIZmA//hueF97ggtP8w3vHk9j2vMCx+yaiiogL716Yu9xcrcm0116TahAjQcysw30tkK+/I6kyZBUkPt7zkHtaYMyY/lFWyQng7u7Fc6OZuRMWwBxArLpwQuaqWdulb4CT09N5Ojrhi2vEF7XCBMhRgxxVb8fPe0XOD2SxigzDfIHobQpm92e2BySOkgZUGGdO3+e37OmupqN+smvTE6z2Ms0vG4X3C4nsvMya1LrcQxylz+3ohZK1JhMpsGFWZYpZsySC1EyPU2RgKbFnuvETogF5ZCjUtz8vW3wtZ2A0rQsoQktJ9oe2UpSRl7IBkb6eWErz12rqclSVb9asyPOv769L0A9fxTylfewj1FKny9A6sqwMIuOFvj2vQh52UaYCrUx3HQx9D9f5FGx7Hd/KeXnek8exNDvfgDz0nWw33yfoee4D+9F74P/xuOUJZ/7dwhj54tLKZHpUkai/UzXw+5//yykyjrk3/9JeE8fhXPLs8i+9T42DY4HGqdR285BqKi9KIsK776X4N//Equ8lPV3833+liPwHdyikbXl9awunwh8R1+DevYgpBU3QiisSPhY984nMTrcD/OauyLIaz3QAs7fc55TJ+WmpW8YNQLZLrj6Ojl1Oh3v0eA1uK+1mWuGvLLk4QMEIkKGB/qRnZcPj8sJqz3b0DWcrs+dXV3Iy82FPUFdkC68Xh+8Pn/C9OagL1cyxVQy0Gucaung0cNpNWUYHnHBZlW4KfdGgMvt5vqLzhuZxMjQIPp7OlFcUQ0lBdIqHQRUP19Do89PQeVTqmsl9ho9cwBycTXkqGuwr+cC38yNi8aJMR0QwUT+w0SaybVzoPZ3IDDYA5k8vlLdHqo5aFwwrOnv3fUMAt3noVx5b8rhONE1h//8CajHd0Fads2ERtzp/Of4+ddgKiyD7e6Pp/x8z/7X4HjkF7CsuQHWDbcZes7Iay9g4M8/g5BfjJLPfCtk43Ap1RzBbX3vv7ZAznAKpNc9hJ9/ufaS2A9vZEyZ4KeJeGZ8JpsdWWM/Vu+Zo/Ac2QMfkxrx4Tm0A4M//xbcrz2X0jZ4dm/GyF/+R/NxiQItTD3b/wF/y7HYbXcMMKsuTB+X7tMoz2h/J0apM3H+BCYKItdQ1oDegJTcvHfOakhLr01KfhG8h1+Fj1Ik+9o4Ge71gG+wB4OHXoWf9mMQJhOPJRhVsEWD9lFPTw9Lwf1Rhvbx4Pd6MNLbycov1efBYGcrHL1dhp5LvmGl1XWTQn4R+np70NXZwb4R8cCpN2mQX8F9FX5cVVVWMvlFSjKK4+7q6tItWl8Pc3ry+xro6YJvzFSYFg00fjpRUOuCisRE4yh6+yrl9wmoGDqyDSNnDsT8jUdo01Bf0iLSOnt1DPnF2+wc5MQ2Gr9KuF0+j3Ye6GuHv+0EL1ADdA7TOR8mA/mLUfEYvr9olIASmYisowSllD5fVDHs3f40Ap0t8B3fBbX9LBMQ6UJetAby/PTGuqT6WbBdfw8sl2tjk8OP/gZDf9EPXYm4pmXnIfe2+0LkF2HKkPbiQnd/EylttYU8V7wnD8FzeBf8HRcSvpZ754sY/MW34NmnBRoYhWfr03A++nPdsXsthfQpqG2xYRWUFkYQpi8O3aeePggMdGN0qIcbcBOFiUzuy+rR59EaK4kgz1kLecnGpOQXwX3oJfjO7ofa15b0nDRZ8PR3YuDYTvjdI6H7mBygc2+aBGbwXEcNGqoljICuYY6BXuQVFPLz+ro7MTzYb+i5kiTxdXoyyC9CZ3cf2jq64UtwjScz+nTIr+jrKNUuDdWlaKwp44Cdrp5+dHbH7gc2zfdP/FqfKvr6+tDT2xsKKnC5XBk5X4/S/1HtMsk1R8DvQ++JvRi+cCrmb3zcpyEUIGW7febyGPKLoI4MQiVfYZq0SZZoO9CFQH87/KQiHeyGOtAZYYFgeHvsebwbw/cVJeBSzRHwjHANlAqi94lv5z94TNx/9lAoJCQtSDKkeasgzV6e1tPlWYthveYumJdfyf8e/PNPMPzkHxI+R8gvRJYtG3l3vCvCw/RSrDlIrDUZtylMHFMjkGkinnCu8ENfDv239bJrIDfOhlRRm/C1xKoGPklICcwA9aC2NUNtPQ3fiX0xSU1EZPl2b0aguglirTaWGIS06EqIM5ZEmEpTl0CcvhRZCUxVUwF5cklLNqLv5EkUJhEZUkSwQDHBBiCU1ABmG8TqmSnJld3Ht7NhrmXhhgmr7DhZxu+LkGGbC8v5lgg0OuHp64RSVAFTFGlAxxNdCKdNm2GYFKJoaOdAL0TFDGtuIXKKy+OOwlHhOtzXjZyiUvYVm2zk5uVDUchLJPF7jTiGOXY9N78A3ZQ85fagcozIiofgvqJRCsfwMLxeD0dwU4FNKCwqghRl0Eug1x9xOFBVUxt67MVAXlEJrNnZ7NfmdTnRff4MrDR6arDjHg0iz5z9GsGXlYRICd9XevA5BuDubYOtopHHGWlBJUWrnYioJFPYDESCG4FUNRNSaT2rOz1HX2ODbHn60pjHkTrMvOJmqEPdECiZVVQgNS6aULps+P4itQrdPM/+BqPOYSg3fzCtcweNeIszl2F0sIdM++B56lcwVU6D+drECiw6v+gFG5hXxabpJXwdipE/sA3yvOVj0eMbQn+jJg39PRHkuiZUfOOnsa87JR6/qNDb31miiOJPfiv0b9vVt0FZsBJSeXXC15LqZmg1R028BDV9UL2htrfA33IcUv24FyAh0NcJ3/5XeBRHqIgktuWV12N0wTo2SA7dt/pG/l3RSA8yoAJm1VdeKXpPnkRBaeKxfq3ZlrzhRhBLajngh85LiRSp0aBkW6oTbHHCP1KtOYgUYMPpMViLK/iWCH6PiwN4rIVlMecuOp56e3sxbVoTBINNDKdjGCOD/RzgYrXboapFsFH4hg4omXp4cAD5hcXccJtsFObncjCO3rU/HINDDl5IU3jOhbYutlCoqSpLqIQMvy4MDDn4OfR+wtjfigtyYTbH1jptHT1wuT1oqK3IaDpmMhQXFcHr83Gd4xwZ4aRBCi8oLExPCaSqfjgG+sdqXrI7SL/mcA8PwD3Yi5zyWniGByDKZkhRQS0cOGWxQ0xgpp5JKDWzIVc08fmU0l7p/ED3RYMCeZTF1yIw0gcxr1Qbg6ZQoASqsUSI3ldi3Vygbi7cT/+c6y7zte9J63XVrnM8JklWDKN+DzxP/xpC/Vwo625PueZgz681N6f0/mRw7z26m5OGs2QFllUbQ6/vO30kqaLePHMBKr7xYOz2TdUcU8ggpgiwSQSfxJOQXwQhrwjZd34g5deXl1wB/+mDXHhGg0xhLbe+D1k5hfqqjSiTa1psZo35gb0eICNqGmUKN7rVg1gxjW+pghbRlPDiJ6+xPCoG0xc/yvmlkGjBbRB80h4NMPnl6jrHEmulQH8klhRkRlVk1rwiCPRaNi0mnBIj48E51A/XUD+TX0SCJcJAdwdL3ctqGwyNU8YzymWz3CQY7O9jqT6ZqdJnSNmPZGgQHrebCbdggRlP9m+xWCc0cjnY1wPn8BBKqmrYe8s1MszpnMn8y8jrK+j3JSoKbLn57OE1kdHHke42KNn5EBULTBMYySECzDfUB39BORznjvGxmTdL6/S5Wk/B29eJ7JlLYW8w7pWTLrzNB/l3ItcvYKKIEBjqDSUM6YHGBERz2Dk2DvnF47aUXltQlvQcEw1hxjKMUoordb7TIMA8mx4izTqs7/hnjiX3H9zCnVka4YynnvMe2gbPS4/ActsHIEaRCfGg9nfDs+slmFdt5HHF0Pvv34qRp/7A3WvLZdfyfQFScjzxe9g33glpWmyhHw1f+3nNMyzBOWYKrz+YwE5CfhHIdyWdmkNcfAX8j/8SgZ52IIoAE0qqYL7xPTwqo6vaiBrTJp+beF43FwOjpKaia06ShEqpSt+c30jNwarjgS4IucUTGjU1F1ZAKTA+Oh1szrn7u3hMkpJ96XoRv+Ywtm3Z+YXcyDGT6jArC7n58QkVIr9Ghocgm83ISaI0p+YUJQtScyqdQByC1WrmWzJQ8I6qBpCXq9VNfAyk8N2wmbxfRUEeja9pzy3I12880vaQ6izdz9TT0weX24XKCu27d7po1DJ5+iI1HoPNR0rstGdnsy9XuqC6Z6ivG9bsXEiK2TBhqgevY5CJL19eMYbbmvnYLGjQziWk+PI5BpHXtBB5dakJAtKB++xBJmiUyumsBiWimZRgtBaJB4F8rMKJOVmI+xsMdJxhVVeqVi3ijOVjyZS+lEeu6Zzjff6PXBuZ3/JRqN2tUI/uZOV5Inh3b4Z31yZY7vgIBJ1zuB7UrlZ4Dm2HZfV1nBAdhHvnZrheepwDB8wLtQCjwMgQHE//Cdl3vA9ybfLGi/fCWQjZeRGq80sRUx5gb1xMEWCXMGhhZH/3l5EVR/UjVOinquiRT+SHQd5fhsgcWrilueimeXkynBZIqRFGRHi2PwG4hiGtvoXjh6Ph58XxKMT6+Wm9rzJtMXztZ+A9vgOomw+pPPm+oTQ6Js7G/IZo5NF97ggsdfPgG+iEr68d9tmrQ54B8YxVh88chOpxIqdpMUyUsJSbmUUkd2bifPfRkIjgNGgiqY21UcGWFaE6cjsdsNi1oi8It3MEjsF+5BeXpdXlLS6r4DE+IqWKSxITc3qgVCLqThohtbJzcvgWBJFhzuFBWO05HBdvxDeNEpDoe6bn9XW2IaegGLmFxr9PIs7yS+MTzTQCS68fLxKcYCaT0fJaKPY8HrtNBTR65+lt5xQkQbHAUloDOa8YotkGe+3sSEKGR2yEpCM2bHA92A0xt2hC6kq154KmcCACbAzmpdemPeITsY197fDtfwGmyumQF1wxfr97BJ4X/8QLcTr3ePdvRiArsjkglNbBs+tZHhGX12odVFKu0FhXeOJSPMjLr6WITf5vU34JLHd8TCtsEywimGBAFjc4hIJSQ34g3iO74NmxCUJJBZQF4yOS8tzlrPKiRMgg1J4OuHe9xIW+MkdLmAqHv7cLrj1bYVt3HZ8POr/zeYhFJSj70g+SbscU3ryQG+dCfNc/x1WKiwaT2UZdI5yEZorTCIp4LJ0TqDmWbs1BPj19HRAaFkRcuzxbH9UI99W3wKQTdOE7vZfTKcWxYJ9UYZm+FJ4LJ9jrkLyFpCS+ZIRwUpz+m5oT7vYzsNXP5eaZ3zGI3JnLxh8Tp+YYOH2Qz/V5DXP53B4v1TdVEJFjsdkNh73wcww09OgxdG2Mvt6Scb41ylB9ZGSEldxFxcVpEUuV5aU8tkivWVmeei1WVV7CtYORWopIsnCQcswxMoJse/LGGcFPdafPz6Nyw45hVuwVFRam5O1FtVFJSfyGLVky0DleTpDua8vRGpTW7JyY7ykZiFRy9XVDyS3goJ3s0mpYC0p5ciGnsiHCv06rPcn3K/lr0m9DyiUD+fTIRfrt+PraOG2aCDDt/QXYFl3Fo+UTRaD7HHyHX+WwHGn6sggLGu9rjyKLarD5V/D4d6AoG0JYLWEqqYb3+f/lSR556TXa8yhYx+sOhYjEA31P0vJrQ+PdFHRGRFjS7e2lmgPwnTrAAgoj4+GeA6/Bs/MFiNXTOFgnCPPiNXyOIpVxEL62c3Dv3cKjjUrT3JjX8nVcgPvIXtjXXYeAcwRd//4FyPUzUPLJr+NSBh1nmVauTSnhMoMpAixNXKw0CipiEp3ggylf3iM7tThaAwVlNLxbH4d69jDMt32YF2ih93aP8P3CtIWhk6HvtScQaD0F5ab3xe3eBnrb2Mgxq2kJX6jD95X/7H4ELpzgBMqssIKQRibZ++fCCQizVsW8pv/8saQEGHdbO87wCJSoU2wKBWUQXcP8v8lAXVvXyV1Q6uZBzC3miGSTNUfryqg+Vt2YRFnzDJAUePs64Go5DFvjQohRqjvqFtGFnZ5DaU964G5q1L7KJKy5+dy5o6IjYiyypxPZRaX8tyByC0v4Fo7Bnk6MDPbBmpOPgrLxC7V7xAGXY4i9xNIhwHgUMcVxxPB9RcVdeNojxXPT6EVpZVXSwohJrK4OLkpzC5InaFJaZn5xqdb1tdlhzyvggjCT6D9/mhd8pTPGSaBo0LFkyTOW+Bl9XNGYo7P9LBS3E7aqJt5HtEAiyFFGqZbyer4lg6+3FZ6Ww5Arp0MxQCzHg3nB+tB/q0M9bGRPv7lMICu/FOL0ZZpPUBj8p/dx0iP5iY2SB0jrSeSUzOT9pV44jkBPG4S5l8FUPYPPWUH4tj3Jnl48FpkkgECsj4w6JwVa0l85eYr4ffDteYkX4cry5KEk5uUbIBSWQ2qKVOuRh4ZlzfUR97Fp+oe/BqFIX1Uy8so/4Nj8BIe3WBatQvbVt7KqrO8PP4V5xjxYl6y+JBOZLmW8YWqO3EI+R3kPb4dYMyMmadQIPC//lVWQljs/FfJMDaoEuAaYNt4g873yV/b2U258f1wSjEZ+aMTY1LgwtuY4tZfrEVNpLbLC0iSz8oox2nMBgfbTMDUsjCX1Lxxne4ZEBBjtK0qhNllzIeTFkiliYTlG/V4IOkr8aHh72+BuOQxLwwIetXQc3QbBXoCA18O+S1Rv0IiW1jAS4e5pg7P9DHIaF0CM8k8VLVZWCVDNYY5zrZjsmiMnL59HJUmBFDEW2d+LPLZiGP8uC4tj911PTzeP7+Xl57PFQTgB5nA4eKSPbBZShSynTqSG7ytJiiSAurp72Fu0vEyrDRJhcGgYfX2aV1iuAfPqstKSEMlpt9ng9/kmpOSKBr12Z+s5rqMq6+OrcoisM+oXG31ceYYH4ei6wMetvbRKSw4cqzfNuZFEut1g097TfQHu9rOwUKhGmqEynFY5b12oaacO9vC1NpUx50QwFVZCJFP96JCfU3s46XF0qJeV5dkjXXx+QlElB4MQ6SVMXwJTRSM334LwbX0Uo4O9UG75cFJySpwWWT+Gj54nDDpSffDteI5HQOV5seuwaJgvvx5iTROkaZGEFiVUB31GgyDSK//9X4JYXqP7Wo5Nj8G54yXI1fVsHWRffyMrlXt/+yOuNyxzFk/VHFPIKKYIsDSRrHsz8Oefw3uhGXJNA+xrr4M60IPBP/wYuW/7AJQZsSSO7/xpiJTIFKYKcO/YBOemvyHn/s9BLI8/Sql2XYD7md9DbJwL6y3vDd0fcI0g0HkeYl3iDqbYuIBJo6woibz/xB5ObqIRJLFp0XjxS52IMWWUHvzHd3FRKZc3oLw88uIkzViBQEktsqLk/NK8tQiU1MAUdT+lXHn3PAuhtJ59vxIh4OiH78RO/u+sRVdByIks/EyKFUpDfGIhHDTTb7LYeWxIHRniDjERYdYZy7TFQV4JvJ0tcJ7eg2xKnSO1DCXP6XTHyF/JyPEUva/C4aOI40AAclTEcCqgyOlwUGqk2zEIxWaPIMDCMTLQB+dwPyxUqAzGHvekfrLm5CbsHmYa0fuKFGDUlaSLo4+OF48brhEHK7sSgf5O5Jc9x/g+DV6AaQFCqrdMI7ukMq5BOhvnU3qb1w3JQGCE3r4SbbmwVk6DNEFFQLhSgX4X9JuWDBDLiRD00aDX9uzbhCxLNizLIombtF+bjg9rNnz7NrE3IXl58P2iopm8rr6NF7Dm9fei0prDRbq3+TACnefYG1FeHlnMUYGaRZ93AomviaBccRukhZfDf/owpDnGzGepKJZnRi7kE0Gqir/YsK+/CVkWa+h8lnvDXfD3dKL/oZ9BHejlgjTdsZ4ppIdk+7vvV/+JwMgwj0DaN9wMb8tJDP/9t8h716cg10yLJXhaz0KsrI+wA3C++Cjc2zch94EvQSiIrxpRL5yC+/k/Q5qzApartVTHoEoh0NcFsTrxiAvVEwGyEYhSNvqPbOPkMvIFE6rHVBm5RTDROTGB8sR/bDurPJWq6THXUXH2ZRgd7oMpjPwiyAvXI9DVAlNR5MjoqMcJ7/4XINbMTmq3QIbYZJBPMC/eGKMkEyzZEMjXx6BJNy2+A65hVqDQc+X8YtimkXKN/m2H93AbnM2HkU3nH0FgUowCeKKRXdU04ZqDPEbpeicbvNZEg54bTn4FFePkI0bX3nACLNoWgR5nsWfD6XDELHqLioo4SfJipjzH1hwq30fb5vV44fV5Wa1msST2n8zJ1q4XRGYZRfDzU6OPPnsmQa+dX1QKU5xgACKtiEhVVR9kg6mE0ftKyc6DvawG5jSI8gjFC6lAx2oOsiAJ+LyQJuhZTL+5YEKk+8irMGUXwjJ3DTIBJuslGb79myEv3sjkGt9PNg2yGfKqW5mYqlp7U2jtRSm2o8O9YzVHZO1D93Fy5CR5+JqvfhvU/i6ozccgNRlbJ5GnqDzd2GMJUtR1KBzZ190JU2EJAn5KszQh79Z3wNtyGn2//RH/nQiwS7HmYAFzhkcWL8EsgDckpgiwNJEsjYJGSNTudjjPn4aQkwepup5PssyyR8FzaCeGHvofWK96C2xX3MTF6cgTv2N1kcmem1D6TwTRyCM/h6m4Esoqzd8lCO/Wp+A7sgOWt3wAYuU4CeM7tgv+k/th3ngPn5SFmhl8i4bYtJhJHaFu3OuDjBXplgjSgisQqJsDZBeivb0dpaWloRMXqR8ES1QxTvvF44KgkwiHgJ+VGbDlJvXuoQJXLG9kw1q9kYZUQKbf1jmXw7Hzab5g2Reuj/2cJFkeK8qlvGK+pXJBpy6WaM/j7i0dT52dnRH7KhzD50+yCW7hnBUZ64JYcwsgKZYYYiwcPo+L057yS62onDY71iAziXQ+EyBCi8YhgqMX4ftK9fvQ0XKau5P5JeUoKC5F29kTGOrriUuABTuqNPZoRPkVxDD9pv0+5MUZYXQPD/LfbfnpF6lMNMbZ5r5T+9l/lhSIuXWzIBvo6gX3VZ40yp+XFIjmMaLK3XkOvqFe9vdKJcnR034a3gsnYWlaAjGvmItIc018vw51sBve1hMw2Qt4ESdSQmwyH6P6BUm9eVIFG8IO9/IYAZ1PCOKMpXwLwZ6PjrFjS1p2LY9pEXEWDaGqiW96UC+chL/lKI8+plOskhoNJpHVXHR7PUDXLPfR/Rh6/CGUff1/2PuL1GBl//w9Tmi6VBOZ3vQ1x0AvvKePQiyrgslswSil8OokztL4q+Ox38J+832wLL8Soz4fHI/9mqfek9Uc1FhzPv4bmMpqIS/Rkr2C8Lz0d1aNW976qQgfGd+BV6G2noFy9dv4tUXyFNTxFRRnrWCDZFOYEkRaGPkeepAWXYVR5yBGFSs6omoOVj9EnSuJ5KL6SiiLJYE54IYSnvPLkp6DTLnFEIprNLuECYRvEMTsAliblsJxYDMrX+3RSni61uaXciOPQOfyeIpyPVBd6epth5xTwN6RyWqOgfOnuHFQksICNxly8gvZP0xJUHPQ9Z6UYkWl5bDzyF3kttG/J5v8Il9RGpMMElrh+8rr9aKttZWVaQUFBSgpLsT5Cxcw0N8flwAL1hxEYhXkG28+9fVo6cMFRfq1JXms0Wvbc9L3FM3Oy4/bcOs8c4zJVyLCiuumx22URjxvbF9li6P8eCWnANYxMn2kowV+txM5tZrK2ijcbae5XrY3LeImHtk3WBOQvL6+Dvi6z0Gw50Gw5kJMNjYoKZCqZ8Jky8yocETNwemS3hABJs2mRMVV4/vK6UNpdharwuUVN/CaTq9uEGpnc+iCHvzUrOs6D2np1WnZUHByNp2Xiyv59nqAagzXri0YfvLPqPiP38CkWCDXNqLsn74LsVRT30/VHFPIJKYIsEmawS38wD9xUUmx5HLTHPZ+KvvWL+OmQCrzlkMOjq74vPAc2M5S0byPjic86YIMTM0WmEqq2ISWDA/pZCZWNnB3FkRwlWgLzsBgL9z/+D3fRz5c5MMRPCnrvrTFBnF2ZLqkEdBIkGDT1DWDg4MJ/Qf44+55DoHeVigb3hE7Vklz5Jffbqi4ZHl64yL4Tu5ir7HwmXqCv6cVo54RSGPz/kGow7087ikWR0lzAwFIZfUQcvSLD3MSc1xPRzO8/R2wkfcXdWrD39PlgKv9DKTcItjr5vDxlGhf2SsbeAzCaNEw0t0Kv8uBHBpzi9M14e4uqTwSILekgk3zg6mVVAiplC6UoIDNNMhri8Y1KxpmsGqNzFj7h928r5jUsVhDhRkVmMUV1XHN+6mo62g+AcVqR6EBs+hwuByD8Pu8vE/0voeh7jaoXg8TixMJWYj7XdlytEKaRggMdmODx5Xk64Vg0giwIFS3A6prWEv+EcA+df7BXsjFlXFHoNg3o7OZk6BcJ3cySSxEjSmqTlIOmFhBSfCc3ssLzsBgF/yinJQAI1AxmmmITUsg1M/TlJphID8v79ZHIdTMhK+7Ff1ysXZsKda0SDjfwa0ItJ+BWkUJvKkZ+VKh7PjlN7ihYXtL6ibl6YDHuOk6EuWDl3P9XfCdPwshjNCVq8ZHMqZ8KC4uku3v4k99k79L74lDUGYtYCLZsuRy3cdK9TOhzF3G/0sI0G/g8C4en8378L8mfB8eAScj7PI6Von525t50SWUVkOafxn7epnGmkFqTxs8m/4MyApGKayHyN0E5BqRb6aZ4345RsGpYtn5hmsO786nmeRSrn5nDPlPhtXy5XcYSnajzy3Xz4f3zH4EhvtjxiD93efZr0gKG2Xi+we7ecxZjFK802VFKq7RXbCzB1N14prD2XaGvUqzSTUWtRD2jQzC2XmORyq5nkhSc+RS+EYKJMVQZytUvxd5FXVx6xRNFZa4diDii65JQV9Oan4FaB+GeUVNNjo7O/h96xoa2W9saHAQA0NDvK9oXJCUbcqYyTyRcUSMxUu8pvHIC+da2H+0MA6RFQ8jw6QGjE+A9XV3TZgAi4csE9WHNv4eWIFosJlD2zMw0A+TOsx1IhFgQfjdI/C7RljJTkUH/5sSlvPjj4/S74fGg6nmcJzcg5zZK9m3KxzqyCAT60Fy2E2ewaqPrRRMNMZswDdLTtPrLxHEGSsgTlsS01AIDPfBR+egmjnoO9uCQmER5JIazR4hjdLad3ALRtnncF7MusdIYuPIb/8NYt0sWK69FxcDRPLR2i66Ts695e08LUXkVxBEgl3KNceUB9gbF1ME2CSBi0RZhnnOuAlgPAh5hci5+4P834HhQZbN5n3kX7nIjD7oXZv/DiG/GMoirbAlT4jsd34+9Bjno7/AqGMQ2R/7ruYJVlYDz85NTKqJjXMQ6O2AtHQ9zFe/dSwK/PWHUDVdi0LXSUrxvPpXLlCVNXcaeq1R1zDU9tMsl46+EPhaDnFEu1haH3FB4uJ1ZABCXmlE0ettOwV/26mYBb5RBHxuBDjdMrZrQaovW83M0GvzSTJBd0NOEhscDSoyfE6H5ucC08SO47EFAhm0D3V3wDsyhOKGWRAvUkFKfmREPFHhSWo0j9MZkhTTKGJJ2KKcYLFpx7Xf60VP61lk5xfBljfmIUK+YaRqTMNQuaiqXtd02O/1YKjjHGx5RextMVHyi5R+jo5zsOSXQBr7LIQcgwbT4fD2d8I70A377LkQoz6zlVRbYWMFRNj6etsgULJiAs8aIoWyJO1zRpNJBNehV/h+qbCcHyc3LII60KH9vsK2IeBxaglkcRKSfGPBF1KY7x+pRb17nmO1KEeGpwi97SX1Bi2GA0N9GO04g9FCncf4PFCbD3MXNlmik6m8jgmwdKLR1d4u9mEk49qLgVG/H53f+DjE4nIUffjLEX8zT5/LtylcGuDfo6zAPDc21CAa9H3nvPVD/N/qYB9MFhvXHNFeMWxg/dxfuEknz9GIKQpkyH7v+LHi/MuP+Xed/cFvsAk+3Txbn9Kab0UVCPR2Ql5zEyQiZRKEe1xMCFUzOcQieqySx69f+Qt7hSlRI0jxQGOfamczB+ZEE2CUbEvnl2gCzHNmP5/LbMtviDTmbz0Jf1cLN8bSrjlIUU/Xqai/0di7vbIRkj3XUM2R6sgaNadUWthOEFrNoX0vdL0nn05ShVU2NE0ofTAVFBYWhYzu3W4XXC4tnCboW1pZGXl+ttvHmj0eN7ra25iwstm1azcrvyQprW2vqNb3S6L9MdDVgfzCQkOqrGSg722oux32gpJQc5OuzYVVxhKIw0Gpo97hQeTMnAUpSqlHyi8EqImnfb+ujhb4hvsgkjI8wYQHXXNNWcKYSb4U85t1HtvGRJdgz4dgzYa5fj6nOZL3XjCkKlRzEPGiUwcQvGcP8HuFN8kD7hF4D74EsWoGT5ikAv5t69WaPi+vRWg0m5WrvR1ASeR3TVMxKvkhUs2RpF4VSmvg7+9MKxCAkqlN+cUwxfEDzTSIcOv85ifYE6zg/k9E/M0yL/m1awpTyBSmCLAMIeB2MeE1kRQ0Um71/eALEEoqYbvyFnhPHYRt453jJz9Shu14AUJxeYgAC72/YxDeQ9uhrLyGxwbDF+G+Q9v4ZKqsvg62d38lhljjxxzZAf/x3TBf844IU9pU4T+1jyohiGMS35jP6PfBf2gLhMppMBVX8X1C1Qy+6UEorOROgVHQGKSy9Fr2Dwq9p8fFPh1S9Sy+uEVfTKS6efwYWrDyBZL80EhNVlSldWnTTG00V0xjPw9KjFSizLcJNM4QhKfjLDxd56DWVkMIIz3SRU71tLEuavx95+rvhqOrFfm105Mqioj86m0+DslihzWvkAkk6sqmmggUkSrpGIQlO4+LXFKWWbL1u5jhqVM5hSWcfOlsbkn6HkT+EZnE3llh4xMl1dTZTn2b4+1LKh69Tgd7pSg6352rrwvO3k7k1U6PSDyKl+BFIwIeMnQWxQgCLB1QUpLqdkKQFDZGDge/d9hnMpfVMSErJCJbRwOwzlgRcX7x97bB132ek87Yh4rUW1km+JoPMFlmXXQ1j0qOf25adGXBvesfrEC1UNKjDsiEOpoAo98jF4zkk5MhmLILoNz0Aa0wnr4UptaOmMeoZw7Ct/s57lxKs1cmfD154TpIM5amtdh3/f2n/Lu1XHNvWhHoMa+380UtDn1eHB8xkwlSWTWESfCzm8LkgpKySP09EcKdGm59//l5yNPmwrxwNfxtzbBedXvoNUcdw/DsfglqX2eIAAs9d6AHvuP7YF57MwfBhJ/XvAdfYzWDct/nYXvPV3VrDu++l6GePw7zNfcxcZcu/Md2UieELRv0QME1/qPbeSFJYTsEkSwa9GAStICesRFpIxDySqAs3BDyFSQEXA74zh3hc1dQlRIOCtchcpHOwQG3k8+DoZqD1DZRfmVGYa1oxMjZQ/ANdMeYg3NqdFjIEanFXD1tUBvqIQgTV3QX1kyLe00LtxEYGehBUc00iEkURZQ63d16DorVBntePtcaREql6wNEii6X0wFbdi7cLifbCVjieHHZxggt/lxFxcjOycXZs2eTvgf7ZZFiLazmoMZdBYfypF50xAsWouaex+3kEB49RR3tZ+fwAIqqGiJeI973w36wQ/1Mpk1U3e8d6oPf44RotsQ0SZmgCSsZrWV18OUUQEhiPJ9N19Mwcsfbc4FVlJb6+VraYHkj1x4UxhOw5cE6axWksPo6mF7v2vs8rxH0fL40X8STWiJ0+JQIjURT6rN7BJmCqaAMyvUPgNrTgnAAgk4qov/kHqhHXuNrtFgfOy4eDmnZNZDmrzWUFh0Omipx/e0n7EEmTZuvTZkYSERPBOeWf0AoKoMSL8hJlCCVVkEsSZ6K+2YAnRPolunXnMLEMUWApYnwi4g62I/WL30Q1oUrUPSeT/J9rgM7mRSzLV8behwnJ509Abl+un7RKkqQZy+BWFQO955X4D2yG+ZlV0As1k4UVCTmvPeLumOLXoqj3fIkLNfcA3n+eNw9QdlwBwJdbWPqDXNoEep+8tccd6usvRWBgW4Eetq5WJwIAaYe38G+OcKslbyPCvPzoJ7exylqVBCPDvVAPbOfruAhAiwRKEI40ciEHsjIMhwBes+Os7wYF0vC1CQBlbfVd3wHQIlzZhs8B1+EWDkdct08HuFSEqhMWHZNZvlxFCHUmWIDfQPjm/TeBbnZTHqkCzIFHTx9AFJ2HpTc4lCnNx64KKAusIHXJgLFnFMAc04eFGs23MMDGGxvQV5lA8xxiKtEGBnoxXBPB2+Do6+bSSRz09ykizleJMgKm8HGKyiJTBvp72GirnzanIjH0ft1nT7ChVlhbXKjYCMg0qu4cQ4nbsXbHhoNIcIwWFrQ76/v5AH2YsmtjSR/ZXsu8upnGx5zJJJPdQ7rGsLaqmegwpwPwUCXmEMiihO/58jBl7gDawsrIP30+xroRMDr4jEJKkYJTByHFVN0bnEffJl/c+Yl10Asq9NM6ONAWXodH5/h4EX1urfGNcQOOIfg3fI3TosVp8VbELvh2/UspyzRuYkUqOIMbXFPniHFxbER9wKNKft9EBvip9BGbmf8gj7oA6nnu6asvh4BxxCGf/6vkGYvhfW69EcS6Hoz/PjvOJEpHgFGvzca108HU4lMFxfh+9vX0Yq2r38c2euuRcHdWvCNc9cWmgHnOiT8WPO2nOI4eb3vixZMysxFPArp2rEZ/pYTsKzeiKyxa4cpJw/Z7/kijyZGw7P7RXj3vATrrQ9AapofSbRcdbcWlhNec/h9cD3xSx6dVFZcw8mOgd52zR9nIgTY0W1jQT1aSlhhXo5Wc9TN4YZXoK8DavMhfkyQAIsHVuvUzU159Dk65ZHM8VUy2CdFCpFaYd8HNcU8p3YzYUa/P/fxHZBrZkOumMbeo3SLB5VMsBUrTHHII1J0qk4HArbki3VKpy7IywmNGqYDv8eN/uZjnOZH9YGUtHbUag4jRYckm5ngseXmQ1EscAz0YrC7A8XV9VDSaC4M9fdieKCPCbTerk7ehJqG5DWAlvwoJaw5/H4fRgYHkJ1XgNrGpojHERnW2nwKZosNxRXJa14joP0im5viBglQMBD5kYYr/KgO6T5zjOuVvIrIhqw5Ow+FNRIkg/uV6jXV4+JaJRo5NdNRZSswpLIntTnd4oFqdMehV7kWtzUtiag5iADTzh0WKGNhFZSaGqEyd4/AffQ1VnBbFm5g+4V4ydL0WzQv2cj+m+Ew2fNgJhuWOA3QwFAvvDuf4pTpeEFdvM7Yt5l9QymVVqD6hM5PgQBbdug1kjmcTBC5PkkGPt4S7cc4Ngf0b+WyG7g2I+sF8nQ0r7sFE1F3OZ5+iO174hFgZAdU9LGvpfX6UzXHFDKJKQIsTYR3oajAk2saIVWOX1T6//RzJsasSy8PLeqd2zdj4KEHkXv7/ZwMGQ16XM7tWjFLaU7q6o0h8iuIeLH18qI1MNmyIc2KlZB6d70I9dxxiA2zIRRoXZFRtxNqewtGPW7+t7hwLaSFayc8Fimtu4s7LSxjz8pCgXcQvv0vwu92QJq/jlMe5TW3c7JTMvDs/5a/sZpLueKt+moykj6X1kFI4CtEf5OpQxtFjPmaD7HKhIxsefaeCsu8kpjH6W6bz8OjXuQ9Yp0dSTgGQcRY9vy1MRfUcHh72+HpaoFSWo+aRfqvkwpGxzpwnv4u5DYugJOi5kuqIekYppMxadCcNBHI14rIHSqaSOnUdXwvLHlFrGYiwo47/iNDrIBKRGDRqCCpyGwFxbDmFTEJRKovIqMC/kjVYrLfXqI0JCLnHD0dWnR4Yaz3g0RFVxy/jnSRyB/DVlwBr2MAwxdOIr9xPm8XdXB4/C8O4Zko5ZGITvJzkXO1gpy85Lx9HbA3LohJdxQkCaUVxsbpAj4PK7nIgybeAosMZaMVmUrtXMjl0zgRKBxBD7AgKGacF8Vjaalyo5YsG4T//DEelZRnr9ESzuJ0hZMa9nORFL9QIvVYoOMsq1bYoHZsoZ7o2KKmgzQneSy4ETj/8D3uVlvfqjVLwkHR43ROVim9tzZ54ZsInKT07s9PiFxIhEsxkelSRvj+NlltWs0R5mPY97sfsSIsnABzbH4Sw08+hLx3fATWxbHXlyxJCo1CyrMWsX1CNNklluov2pUVV0MoroDYEKum8u54DoGuCxCbFoTOA4HhAU6lDo4Qk2GzvPJ6Hr+cCOQN9/DiLriP8ke64T/4MtTRABPbVB9Iq2+ByYBhPC0Eva89hixSda28SefvbvhO7oRYMT3h69GItmLJ5tTscJDdgr+rGaacYpisudrYVk6RIfNtWszTqJeYVwJLHHKfSK2cuasTniPd3a3sTWopq0PdfGMJs8ng7u+Be6AHebUz4Oi8gOyyat0GTnZhKd+SgWoFuqYWllXB5RhC26nD3NCiWoHIAqodPCMOJnMSLYppVLDr/FnkFpVyUA4dH2by/yxN7dyVrOYYGRrEYF8Pq63ofWL8pUhZlWHj/njkFyGvpAIexxD628+huFYjhwJkh2Ey6SrZtbRPe8IakPy6gn5ejvaz8A4PIL9xXsz3TMq+0jJj6h4i0ciwXimp1j9mKayIg7Mit81CCkpqokc1l6MfR425UbdDm/wgD9SomsNHijHXMGRStfN7ZU+g5ogPTsftPkfMJdstBBXsiY4rWpNQKMhEwSOiv/sOTIVlsNysrS/DIS9ep603289BqEptxDMaJqsdefd/hhsnk4FLseaYUoC9cTFFgKWJ8DQKSluSyqrg7+4M3Vf4nk9xsRS+qFea5rAprTIjuYqAFi2JImOjQUWkvCC2wKWRHVYVNM0PkV8E9cxhNqbOyi/m0UvXH/8DQnEVLDc/YPg9dbcjbAFO+6g1oKBswRUQq6fDu/Uxvhgp6+8x9Fp0wWIVR5xuLPt9tZ5kz4tEBFhwTCHmvvwy3h552uKQyb55jsEIZPI4Km/gIjbhZ4jyK4gGjVzSuMTI2QNo66lGzbTpaZ/kibQomLmUCTAiM1Sfm4kSyZWvS4DpdfUGzhyCOa8YtlJtf6peN/pOHYCSW4icykbuFgtkEmzLQU65Rvi6Bvsw1HYWtqJy2KMI23AwKcrR4SbuDuYUaWNXZEhvBOGKtdbWVvbh0NtXFi7SsrirqbcNBdWxyV/pIthh1SPvOM218xxkSiKSZATGSGGCq/sCAp4RmMuqYwpCAqUcxYOrswWevg6Y6udCys6HUlDOBZqoU7zxbzDBvgoHjTKS7x11UGWdkV0CJT9Gg79TA2o1ivAm0sm86KoIk9Mg1P52BPra6aCjSlbbpgsn4D++HeKcyyHqpMR6dz7D5wF5zR1aAWvNgTT7cqjk5UXdYR2vDyK85Wvepam0Fm+IUJOlsr90PyONnDYf4fCQeOOL7O+VYPFCDRVp+gJ492+BWD87KUFAXVf37ldgXrKGC9BwyHWpkWiqY5hT/iQDI5FTiUyvY81BjQMyoA+rOYo+9MWYhZp51kL4Oy6wAiwZ6DjLSsH3iYgyeX5sIjTVE8q6WzE6RN5i9ogxYlJ+Z+WXsGUD1xxkvLxxYsbLpjD1Fe2jNiEbZfPW8sij9+WHqQsA+bJbjb2YZIbQMD9uE0wd7mN/P3+WlsgYD3QuivYDIwiUwKv6tZpj7PxgmZ04VTv0mrIFUkltQn9GQjwlcnjNQSE8Q2cOwpFTheq6hrRrDvK8LJ6xEO7BXr4+0/i+zzkMv9tlSMHs97rR23KSG1W2sWac1+VEz7lTnKZMgTPknUWkisWWg/yxsSlHfw8Gu9v57/agv6cO6GpLhJmmHJeRW6h9J9YwW4VM1BzZufm8naTMitkGkwmlVfrX00zXHKQ2G+5ugyUnn/dZuCXLSHcH14Wk4g8HfVdZApncxyfoHB0t8A73I79xLkSzDZaCMgiKVVdZnso1lMztPZ0t3DzT+z1RrRiu/ArdT9+pgckKIp6I/DLPX697PVb7OxAYGWIv1GBjj0gxf8shyPPW8RohGp7tTzDhHvQIpPMPqb9G+zsxWqk/3UN+xFkb3q4lSs+9PHSenmi9wa/R3wW19bRWc+i9BgUSFVXAlKDZTeIJoW4mq3rFmulJ06vVoX4OaqME4egGmzwtznh5vNcapJRMH8TCkjdlzRHAKCfKZvo1pzBxTBFgGUphcB3ZF/FvpSG24BSLylBw30eTvrbn6F4M/u4HyLnzfTBPQBVECxkepamdAevN74r4m9C0ANj8V6gtx4ErRIi1M2GK8ozIxD5yutwQmhZq0ltKgCLzxyQ+EdHJbXHB5NKo9rppQMgv5Vs64DSjmtmYKJTyBh678vZ1wuUMZCTdg2LOg6CxEJIcG0FWnGKaDPjlsbEMGtkraoy8wMm2bFjyiuOa5nqGB1nNR2MSJdPSN9UeaGuGd2QYBXUzMDIyEndfURFA3eKLgYHmowj4vSigYzzqmCbykAxhaRQht7op5jsiJRcVk+EYOr2fC5b8WfE780phBZOdFAdOEG05fNMD7SPaVzwm6RqCmBN/jEMqruYiUcyfJDPUgF+7jSkoaVxRCPPXk4l8Jt+rsMKWu6bk+zXUB+gQYDxiGTU+oLafQaD1BEabliArO3YslBBt9h29v+IdW/4Te+Dd/Tz7FpGPRzR8h7bCf3gbsnILtWAPnwdCRSThar727UgG7+Ed8J87Bc+uzbCsuTHhYz17t8D53F9YzWNZeRXfp/Z1w7njRW64kOG5UfT8+NvwtJxG1b//klVGiTCVRHRxEbG/ybvlyF6IxWUInnXNM2J9YkiVnv/2Dyd9bdfuVzD8158j975PQZkZx7vFAChpevhX34Y8ZzksV98V8TexcT68255B4NxxZC3dAKFmBsTKiSkOdGsOtzdUcxAZB8n4ccpEyfT4KZSc5kzfA71uGhALK/mWDnhEi4JLJghL5TSIVHMM96PL4cvI79gcpnQj30oxSVBIOKKvRtQsImUXJRASKKm6NIrAJR9QUomZ43hkuoYHtVFcew4qGtJX0vZcaIbP60FJbWPC6wI1Bu25k6N6iUZX8wkmhkrqYz+Xz+OCc6CX/7swqtFHDUHa/vDvhsi0vrNHuKlZGFXXhcNaVM7jkUR6EWj0UW/8MfwaymOSXiekONdgglJSw68ppemzmwxUdzK5laWpOymUSgjbHmXeWj6XhjcO1O7zmu8XKcN1CDAmFaPM5gNtpznJXpy+NG7DPuQRKJgM1xuhmuLYDijXvFNLu43++76XuLlAydHMi5iyIJRUR5w3LDe9O/5OCr7OwW2sPKfaQ1monx4chHv7C3C98hQrvczzNU9Uf2crXPteg3X5FRHJ0cnQ+f2vcBBL1fd/l3QKZKrmmEImMUWAZQgVX/3P0H+rIw4thTA7ly8wvtYWyGS8bRC0OCMTQerypgLNTNUcMoukxSyNKJD0NeY9qCty9dug9nZAPX8S5qveFvt6HC0s63qOxTx2sJffL8uWA9/ezWymKCy6MuIx8jpjSY5GQeOa4qxVER3gRKBtoitEUjlzGhg59Aov1G0LIj+zoYKbkmpsecg6eTKt96ZxgJGWoxCtOTCHXfgIRryfgiBSpXDG4piLfTR5Ew1Wc5XXxBjsU1FIkvnBC6d40UDSe9meXqImgQo3InLSSbqZLIgWGwI+SUv6jPJXoA44eXlRcUmgcwGZ2xOhSCOOemOO5uLEZrmU6OjuaIatbnZK5tfezmYevbE0LIAUVdTRdmnpSqJmwjxJoFEflcYcO5t59JA8cpRl17NPDoF/l1G/TWnuGqgU711Sw+ow3+EtkOatDXn5yIuvjnkfacE6jBLxm6DwnhASfD/SgrUwFZRCqJ7OYwdknmt7/zdTNio3r7kJrpFhSHXJo9mVJWvY30hZoI1oBjxujLz8JFyvbYJz82PIf/8/Q24wFvFuXbGOSZMs88STxaYweaDR6cpv/SSkMFYpPdokQLDZeeTF134BclQ6biKQTxzXHCmGblCTjdSfoXOWbObkRz21gSmvCMqVtyMw2Ae16zws174j9vWG+7XXM5CgSsoHfqzFDu+OZ1jRaYpSUylXJSebU0FWTgGrPfTI77h+f6x8npixdMzr0sL54Mu8n2yzEody6I675RUzEZB2zUFKowsn+FpmjtoXqZBf9NiSpkjilsYIk6UPkroprzSSSBzp6+YRSWrI9bdpITlFtdMgp2gMHvE+NLaYZKT+YkMmk3q2UdCu2+EgNX1BzTRIYzU7EUBkbq/k5EOx5/AtpllYWJbQwsHd3wVXbwdyavV9BOM+r6MZ/oFOXWsGTicnZRI1WDPceA+HmF0Ib1cLAmSa33UegYFOmFfcGAqm4PNn1JQGkWKk9BSLqpgM85/eC2nu2tB0i7ws1r5GWrRBC9JK0TvQEILHX7zG5ZINEGg6o6QKzl98netQ27siE52NgFS77hf/DrE8+XXDsuoqmHILoIxZ7tB1wPHCY/Ds24qRFx5D4Se+GTGenwj21euhOoYmnJ7+RsXUCOQbF2/OI+51AEl4gx447d/+HFq/oim9HC89g85vfwYj214MPVYdoTETZ+jf3gtn0fFP78LAn3/G/5ZqGlH4qe9wMlMQJNMd+NV34d6/Tff91b4uDP7g83D940+h+0jGan/rx2C+LPaEze8zYxF8e1+C+/k/645Ouh76AdxP/cZQMeR58mfwbPqDti0tR6G2HNG8dga6J5e193r4PQw9dNeT8G57TPdvJMH1HHgR/rbTKW8CG0w6+jj98vUAk6zDffAN978u76/6PHBSgMKYPJmN7dub4eg8F+oeUuc8nt+VUWQXl6OwNk6AxOuE7PI6VjP1ndwLZ3cbBluORRjPEskVHEmh0dSR1lOcvBVE9G/DUlwFc1F8hQCN+5IXDB9zKUAsKINcXBPR/ST4+trh2PU0+3BMNkiNZSqqgpBXCqGsAQKNC8Tx3Ag9h9IrS2v5t+U/tUeLDU+SxkSkvRFSnIxp1XPHUjo/idMXw/rWz8ZdAFPzQpq5TBvRoOsBjc8MaR35VCCW1SD7/i9w82Hk6T9oYxpxQNcdy4r1PIpPGH7k10x+iTVNEKsbYYrypEmE7LXXoOCe92d8wT6FzEOw2mFSFD5+2776UXR85wt8/9DTf0Hntz4N16HdoccSQUbEaBDu4wfR/oX7MfTkQ/xvZfo8rjmkMNWI2t+DgV98B57j+3XfnxIjB//zc3C//ETEGKX97Z+CsuQK3eeI0xbAu+sFeF76e8zfAs5huB76T3ieH69h4oHOAZ4nfw7vy3/j8y3XHOeP85h1YLAHk4csXuiS8XXSbRwdhXv7Y/DseVb/71433Adegp8UJyli1Odm+4ZR73gdeTERUH3wOQbgS3BemkyQ+f5IX1fo3E0G70NdrRjuaWeShpOqJTntlOogaOSyuKruDWW+nV9RC79zGD3Nx+DobsPAhTMR1zAiwYLBBi4KG2prhrt/vEaOvt7ZSyphyY+vwFLJk9LjDIW3GIVMSYdFlTHWDJTgOLz3BfiH+zDZoDpAKCiHKacIYnkjxKrpIQ/CeOCwrKIqPh/5zx5k9TmpxxI+h8Kwokg+PZCogGxbUgF5j1ru+Hjc16cgM6pLuDFssfL5KZ3ESql2OrLf+Tm2jnD+40/8OonG33n8cczKYfAP/wPP/m0Q62dCrGlMqZGSc81tyL/9nSlv7xQmB319fbj33nuRk5ODvLw8vOc974HD4Yj7+Obm5pDnd/Tt4YcfDj1O7+8PPaTVH68XphRgaSLRvLZt8SpmxAlK02xY5i+DXN8Uuvh0fuMT3HEt/thXofZ0Qh0eYKm+r7U5YVy578xRzRR/QWzHjxZbYkUthJIq+DsvwLP9OVjW3cIsvdrdxv8mVQH9OxyWG+7XT1kURYjT5iHLgDSZRg3IrJHn2+kzX3s/K628uzeh8PxpoL4WKI1UCOmBSDffa4/DVF4fN9I8Gv4z+6hNx8lNyUAXwXgXcR7J6u9ElmQGKlIcyyBjUXtBTBJUai9hQllZWVo+AKSsyp25IibhxT8yxEWGUlozqaopV28nXL3t3CkktROlGtItuIgmX7BE3mAXc19NBkQiuXwyfM4h+BzauKceSUfpnBRIoIx5w5Cx7NCJ3RxXby03phAlhR893qiKMbivJGsOsnTSxYIKz3h+VZkEmdorYemRQmHsMUFjkWrrCYh183gMITDYxWQZdWIDPa0Qpi1ig+lMwH/wFahnD0JS7oZAJNsEjy3fib1cLMtLN/C/lTW3QO1o4XFIPahdF2AqKk9INnmP7oF3zys8xk4JwUagzF0Cz7F98DWfQMnXfpyyqice/EODEOz20Pa+UX5//1cQb3/zgn/J6pD/m3nmAvhaz0Gq1Dr5lEbd8S8fZXKr8J0fg+oYhL+7je0IEtUcRID5zh7jhEi9RK8s8o0pr2GVuf/8KXj2bYFlw+28Hf7Ws/DufxXmdbdGHH/sx3PdfbqpknQeEhrmckpkUsgWCDOWsm0Djwbe+AArH7xbnkBRZyswcyZgJGjH44R3+5MQamZzKltSqD6ozQeRlZ3PadGJwJ6ENHJONYXue7v4/BaghW0SD9OY1zaJPFIlFaSvnpnIuY6u8/nTl8Rch+j656eU36KKSSWNHL2d7Dvm6uvi5Mncijr29gw2m6LVYW+2mkMzrM9iSwg/ERVEaunsb7KlINU8ebgSyKOt/8xh2EoqYS0yVpNZS6thKa40nBYa3Fc0Ipml58NKNQeZ0k/CJEbMtlhzoMzSVKGc/qiTPB8YU6OLtXM5HGx0ZEBTnHc1Y3SgE+KM5UyiZQL+fZt5XDLr6vtgyiue8HHlO7KDfRWl+ZeHag5qAGTpBAjR2jPQ3cqjkgmnDA5sh2fPy5BmLDCkQCeYF6yA9+xxvl6Ufvs3GWtS+wf6IeTmhbb3jfL7SwW03zMtAplMUcm9996L9vZ2PPfcc/D5fHjXu96F973vffjDHzSBSzSqq6v58eF48MEH8d3vfhfXXRcpvvnVr36Fa6+9NvRvItheT2SNTg3VpoShoSHk5uZicHCQGVI9DDzxZww+/zgqvvQfkIpiPab6//hTLgqJqHIf2o3SL/0XkyhCXkHERYGSOahIEsbGCYgoo7G/aKIjGq6tz8D90mOw3fwuyHOWwbP9ebhffhSW6+/jf18sEKGktp6COGtl0m0mjLpG4Hn21zBVNEJeNv4j0QN1UbQkySyNgNKJNvbufZ4vtNKcxPPsofenQiLKOHSyQBJwGpubzCJgpPkwfAPdsE9fzOORkwXyeiA/EfK7IjKOxv7+L4KUCER+JTMhDoI8wMjzSykohaVknCCmREdS9ST7zujU7Wk9yYbycgoLKHWoh5VkmRx3DAz3wXtqD6TGRRMign0nd0M9sw/SvHVQu84h0HEG8sqbkZVTxH5gpCDT+83wuDaRfCksugIDXQhcOAmByPs0foek4BgdGYIpXzs/Ox/+bz7n2e7/StLkRd+pg3D+/edQLr+B1TL+5mOcphd9nqSmAHmBiQ0zUyKxvedOsyrYsjAzyZWe8804988fRd7VN6L4He83fC2cwsRhZD/3PvQLjOzcgsqv/QBClBE3+/z89oeQKmrhObwb3nOnUPaNB/l6S7VF+GKFRynJviBPa5SRNws165ItaJyb/grPjk2wv/WjkOpnwfXi3+HduQnWOz4I6SJeDwIULkJE+czlhs4FfN564Q8Q6udDosTmOODFY+sJmAoqMKr6WPERvchkJfa+51npKjUtNba9NDZFZMBFUBixMnl0cmuOwbOHuPGWN31xaPR/MkD+X56RITh7OzjVOW+C6XWXdM0xGtBNdoynnBtsOcrklyWYCE8p3v2dXG8IScZF6bGu1lMQbDlQUvDP9VMCdECFlKbnrh7UwW54zx2B0rh4Qun1vmPbuOkmLdwAtfkQWy3Il72FlWDk62UqqtYPHBgZHPf1MohAbxsCXechkEo8jaY0Efa0Tgkq3J3/++/8b9t7vpr0ud6D2+B+7iGYr7oT0ozF8F84DbFhdsx2kJUOJUJS4yMVeE7T1I8L5rnGGnXJ4Dx2CBe+/lkU3vEOFL7lnkuu5ghu692fPQ5ZyUwTMgivZxh/+u6MjO+Ho0ePYvbs2di5cyeWLtWuYc888wyuv/56XLhwARUVxkjzRYsWYfHixfjFL34Ruo+ucY888ghuvdVgIM1FwKVHp75BkDCNguV98Xdt/tvej9yb74F11QbY1l3HhoGUgBFdmAz8+vvo+d4XuEgiCFSIGiCSzCuuQvY7Pg1ptnYAy0uvhO1tnwz9O/QZHINw/O93OW2MQH5gIw//kLu5/O/O8/C8+rhmJJsOcotx3lqOUYPFHXl4iAuvhKl8fAyDLprRUlzyCfHtfR7+w1tZFqxHfnHBSgv9JKMKTEQF35+UMJNEfvE4xNmD8Fw4rhEXp/ZiZM9zrDwLHk9nzpzJaMqJpaIR1ro5k0p+EWjUgLp9+Q1zmfyikUjqOiaCe6AbvUe2pzVCMRn7KhPgiHED5Jfj7CEMHt3Bqqu8mcsiyC8aN3C2HMHI2UPJ33A0AG/3eXh7WlPaV+5Te+E5vTd0XskEAs5B/r2NOic2EkNKTmneFTCVNUCsnw+xaSmTX7RvhdI63cUbeYp5n/sN1BO7Unov8hET514W2XRI4djyvPQIXER6DWrnGPM174Dltg/Bf/Yw1O7430kwCVJsms9jipS8NPK3B+E9tD3iMYHhAbhefRpCWXXE9cS1Y3NitbBzBHJNY8bIL97enDxYmmZBqRtPJn6j/f7+r9ccZH6sZ1VEv53C+z+OnI23wrbmGtjX38SjihTKE72w6//xv6L3B/8U6jALuQWGuvmWtTch+77PMvlFMF92PWz3fCqG/CJS1vGbf4Pv2B7t3x0tGPnzD7nWIPhbT8Oz9amUx61CKKzAOaXYcIecfALFeWuRFXYOpvcmcjsco/0d8O3fDN+JnfwcPYUFRlWuN2hMOxHCt42aF5NFflHt5Dp7EN6OM/xv18ld7BvGxuCTdB21VzQiu3bmpJJfBPL6UqzZKGqcy+QXqalpDDIRnH1d6Dq+Dz63681Vcxggvyjde+D0AU7tLJy+KER+EfyuYbZmcJw/nvR12CqE0qITWCbo7Svn2f1wndkfUW9PFIGRAQSGexFwxx/PMgKxfoHm8VVQAbFhoVZzWEhoIEIoqdU9/6kXTsC7+Q/wtxxJ6b1MhRXatMzY9TzV48qz6SG4//qj0Iij+cZ3wXzr+1kJRp6IiSBU1EFsnMuhPO6tz8D5t5/CfyqyzlT7u+He9jyEitoIktX52nPwd41bd+glUSuNszNGfhGkgiKYqeaoGVf8v9F+f0b9ugIZvgU9wIhkC795PGmu1cfw2muvsSorSH4RrrrqKlbebd8eWZ/Gw+7du7Fv3z4enYzGhz/8YRQVFWH58uX45S9/+bqHGkyNQKaJRF9c3g138k33eaofvT//Hi9Qcq67A5YEJwzzotUQK2oMGcJGIMsEoaI+VFjxiGJVQ5yxv24mlPjfQ/0IdJxDoK8Do5X1cL/wMEZprK1uFsSq8YWPUXBnyaulPhqFf8/ztME8q0/w7XmeVRrKxvu0VDX6PPY8iHMugykvcQy5cvkdXMj6Dr0KoWZWjC8QdVM8Wx+BUN4IaWZqRrKpgka7/B2ntUhi2cKkneD3hBLs0tlXyUBBB3KGC1HavuEzB/k4tlU18fif3zWCoVN7oRSUwVrRiMGT+9hQP69pUdzX4WOTioCo4p8utn73iK5BfPg2BPcVk4n9XZAoOCJBWAONZ5KBvL16uiGSit9jsIdf15Ti74/j4B0DnNIYHIcMJ1bZHyqMyPYNdvN4IKUhmdgnLSthl57JXbcDgiWbDZCTPTb6uDJZ7FB9bibQjEAd6tWUp5SAFgdiaT1MuSX8OdIBF8aUxkTKCkooe/lhiDOWQWxcmPS5WfZ8mCjB0qAxtfZ++km0qfwOxfo5oeAPgonGTYYAz/MPwVRRD+ttH9Rek8hgaoqEfU80im67RSsQyMQ74BiCFJXC5Tm4A65XnoTJngOpcS57eVG6o+Ox3/Jj8971mZht8pw6jP4H/w3ZN94D21p970d/bxf6fvsjZG+8FZY5xkbNxdw8VH3532P21RQuHhLt78K73803PdAIJNUc5nlLkL1O/5gIwrx0DUZdztRJGUGIWDSR/6hYqTOqTElsgz2snuBto/qj8xzfR+ERnk0PY5TOndMXspl+qkj1OkrXMf/eTVpNUaYlZft2PMlKDWXju0NKziwiy2euhCmMKIsGGWora+5EwDPCYR1C/byY5hx9bs/OJyHWzoFUn37aphGQ/YG/+xyIFjIpNpisuVq9Mbb4noyag67Bia7D6YA8Ziltmc6h2ZUNnETtHRliv01WMhWWoe/0AUgWO/LqEqRkhrxnol4/oLIyilIOjdUcAbgHernpl8g83tnbyduZW9VgqLFKr+sZ7OPXNaoiH/8MAbZgkKjmUP1cX4Q3TZjEGR3/4FTbUP0gyEooSCNxHRHQEhTNNuTMWJqwFtCtOUhN5RrWti3Bc4NQh6nmyE24TVJFE4TCypChfdqKSCKhZQu8r/4V4uzVhuxUsii4qqgSJgNj1olqjlR/g2LDfAToPcfqehqjJCsFz4t/g9AwJxQsQspxRNWZQmEZrLe8l/9bnruCx2bF6sh1nXv3K0yOCcXl7ENqyi+G//xpjDz5B/jnr0DOXR+I2SbX/u0Y+O1/IfdtH4R16bjFRTi8rS3o/+ODyLv17VCmGUuylUrKUPMv34+4b6rmiB0/DMdXv/pVfO1rX0v79To6OlBSEhlgI4oiCgoK+G9GQKqvWbNmYfXqyECar3/961i/fj2sViueffZZfOhDH2JvsY997GN4vTBFgF1kkJqKDGgDHg9yEteisK6OTTgzgsEH/4WLnLz3J5bFCvklyP7gN0P+P2L9LNje/WU2ciaDaCK/yOw5HfIrXYhLr4F6cg/U9rMQyuthosQ6txMIIyHoIkLqkGSgi2dgsBvqhWOAYo41xs4yIUux8cVvsjFGRWo+J2QGbrEDlU1QHf1s3JtVlJoHyOuG0VGoZMKu+uFh2Xw2TLLCUdeU9MPEY15x0iJHyS3iWzRc3Rf4ll0zE3JObIofdXm9Az2hCyH5jYy0nYacW4js6sio9HCQaSypzWh8RdfzLvrxI4MYOX8ccn4J7GNkLIGCBgSLPW6ByiMCbafh6WmFubQWnq7zTITZG8ePV1vteAGgup0YOXMQYnYB7NMW8jGbM2dVwsLP19UCz/ljMNPYjo6PVjKYpy/TxgfO7IdcN1dXQRnuj+HZ/wJ3QpUZyxO+LpFfVHhRVzYVzwy17TR8h19l30Bl7V3saTHqGkpqPBt6X1su5MtuM/x+/lN74dv1HOQN90DQ8QQxCnHafL5FbAulba2/MyJ51/Hb7/Dv3n7vp3VfR8gvhvWqO2LuNy9dy6PyWTn56P/BF2BZvRG2a+9G9h0PQIxKXQ29VnYeN00o0S8eiADznDwMZcY8wwTYFC5dUOCO+8QhmKw2IAkBZl9/S8qvTwQCHZ9CYSly3xlLyoZDKKtF9oe+Hao5pFlLIdRM55qDA3OG+5k8Tof8Sgd0nhUXXwW1+TCPTvKils6pRB6EhbYQgWGIjBdEjPa280gVkWqm2ihfMVqUKpRyOfkpq0ECxGS28znSPDZ+5ieVmsfJipdLAUS+aD5XRBD1QiypYoUZEUXUKKPvxpxbCDFJ88VK51kds3dH5wVOrc6rmwFZp/FGanYP+fSO1RxexxCG21vYOD67fJz0jYbP6WACjFQrggECzDs8gOG2M0zo2cc8c+k9yVdNoutAnNfQQofOciPQWloDV9c5yLlFyK4aT+/OrRtXYlId5Dh3DHJuMew1M5hQzCU/vQR1ESU6eih9mhId07A4sE1fBv9gF9xn9sNcO5eVj/HgH+iC+9g2SGUNUJKQUUR+UZObGszCWDK0EfjPH4P/5C7+nStr79RCJVwOgBqDBkBrCbJmMPx+R7bBf2QrZPL+SoE0i4Y4M3a02lRcAWXtrXzeJJCJveNX34CpqAK2t8QSVgTybbSsf0vM/ZbLroFYUgGYRPT/1xdhvep2WC6/FvZb3wU5zkikkF/I/tNiQXy/aH9nGzynjjIRZpQAe7NgMlMgz58/HzECqVBqrQ6+8IUv4Dvf+U7S8ceJwuVysVfYl78cm0Iafh+NSI6MjLBP2BQB9n8INHpQ/q8/ZtIgUyCFweAvvsMnIduNb4fa2x2TuOd84W9Q25phveXdcP7tQUizlsC8fEOM+TUVokFVgvWeT0dI/Vl2SwqNdPxyAgFOiqTOibIudrEXen+LHf6eVqin98F/6FVIK66H2Jh+p9RUWgdp6XWsdvPu/gdLnWkxSiDvDWV15uaRmSw4tgNywwIIUWoUKkBtS68l7X5EF8h7/hiTEUp2+r5JFxPUScyduZwToEgd5abuvd+L7LECyzvYC09vO+yJOrE6ICKIlFZSdj5UrxtCnG6sp6cNI50tUFXtOxTpOC2v585nIthIkVNSzco0I6CxURpNlMIMzOkzO84cgFxQDlu1vgGyb7AHnu4LTJJJuUVQncMIeF0YPr4LdjINjuoAUiFormhkkix0X5Rqj7vOPjcrBwmCPR9CbjH7cBhBwOOEf6gHwtgCiBdpXhdUMmAmHwtrDny0YJMtEKO8xGgUQKTExgTJlBGf/8xeXvzJi66GQOS1ATDRRZ3h7HxAEGEqKIdyzbsTds1JtRroOgehYV7qXhpj/j2T5YUjzYr0WRRKqyM8wdyvPsnnIGXZ+oSvw+enBas4AEVqmgexTiN4zQsjO2vhEEsrUfSJbyZ8XfP0uSj/5oMQUkiHnMKlCzG/EBXf/ClMlvSUEvFItcFf/BvkmQthWXMtRkeGoSKyyB956n95jNd23T1w/OWnUJZeAWX+qrg1B6kZrG/7FLLCDPJ5UZqmNQFd8z1P/gKmsjrIK6+P+zj6LY72tMJ/cg9GD7wEeeVNEKen75UaTLeleol8SCUasRxrCFF9Y16VuZqDagfPmf1Qpi2OSfgVcwphW7hBU9WGXXc8548yYWDJSX8hfjFBxEzhjMXwOgag2PPg7GhhNVduzYyQutsz0A1zXfLApnCQITwRaUp2PgIBlYkgPYx0t7KaS1W1c7hsy4G9tIqflwg5lfUpeYJSDUOKNnPe+PfiHe7D8LkTbEJvixMkRY1IbkZasjloh9TnVHcMnz2E7PpYAolqJktpDTctg4geWSXSkdYVQfW7aM+HmutgBZjRmkN1DEAYq5+4Ge0a4eOV1Ot0bfO1neTaQ4yqEwRbHsTiGogGm3veU7s5JMe85FpDSYz8+YhQJcU5eR1mCdzgM131jsQ1B237QBerN1MGKe3MdlbKZhpU/0hzwyZYTAIb3QuFWhOSFYub/85NMXl+/NqBn2qxQZm/kkfVSWUu1TRyzW9ZGt8fUa6ZhuJPfzvh61oXr0LFt36akCR7s2IyTfCJ/DLiAfbpT38a999P4XTx0dDQwIEMXV2Ro7R+v5+TIelvyfCXv/wFTqcT9913X9LHrlixAv/6r//KY5vxiLvJxhQBliYmkkYhZCiVKwQaiRoeQMCRrylweNHUj77vfwY593wcYlk1An1d7PEFijTu7YAQFoscD1SQht7CNYKR334bQnUTLNe/0/A+qqqq0vYV/2CTj1VQB1a+9l1Q284gQH4VRLpNwFSb9gct3n1HX0Og+zxHhwcJsEyDTbGdgxj16EcQh4+yMilBBt+V0xGgi701e3xfvcFBaUDBVCEqvAI+D/uNaWONWdrIn853TSSXd6ALZhpXCysEVI8Lgyd2Q7TnIadhXsLxR1JkUUe11prH+4re0zJ2oU9WJNBYTsxFhCTwOkUPe2tICvzD/aHOMhWOSmEFb0M8EJFFKY0U/00kmL1hHhyn97OxKCmcon8DtP2kFEsE8nDxtp6EZfpSiDlFEKhQbjLmtUD7qNjdCe/xVigrbxrfztJ6JtFMtFALqPC1HGJSKIYAE0TIBt+LX7esgY8B8skx/Jy6uZzCFu61kWzB6z+2HYHzx7mbmmWQnAu9X+1svsXbXxXyKEa7zgEGkzmTwXqDdr5kddzIEDy7NrOyKxkBFtqm7FzkvuMTab//yGubMbLlORQ+8NkQ6UWkyERxKZyr3kyYUM2RnZpZc1IEVPYQpaAeUjPJc5Zi1O1C/w8+j5x3foZVjWpvp2ar4HYi0NeJwEBiP04CjUGG3mKwF84/fh/ijEUwXxm/aRa35uAJb+2alAhCxTRkbXwn/OeOYbT9DBPywUTrdMB+hUWV8B54kYM7Rj1uQ2Nf6YC2NZFaNryZEnA54Os5D6VmNqtEiPS4lGoOUnkR3JTWbTKx2onBY4008hf7PZM9AwX0WIopldIUoc4iXyyqY3KqmyDb4y8iyS+LrkdWa26o5rCGKXzjQbM+MOnUHKO6vlL0GQVBhG9kMERIUXqzuaAUSoIamJT35vwSWKjmUCyQ6udg8NS+uD6sTGgkGOUluMmLr/s87GRDYCViLZ9vRkD7p9DVCc+ZPsiLrgrdL1dMg1RYzopECuHxXjgOkz0/hgCjOs1sQG0ZhFjeqKkqU/jNStMWQWxYkFrNcXgrryFIOWkyuC9C20jvN21R3PPVaOspqFa74UZjItDnsN2mBdWwOm6oH959r0AoqUpKgAVB4Si57/xU2tswvOlxHo0s+uAXQ42XTJBfl8K56o2I4uJiviXDqlWrMDAwwD5eS5Zodf8LL7zAay4irIyMP958882G3ot8wvLz81838oswRYCliXSNS537d/IogrkpcTISGw/uegXKtDkQCxJ36uiCkf+pfw+dzO03vgPuPa/A13wyZCZru/19WsdDlJD70X8zNAYWAUmGUNnAIwzUUVDPn+K48mhSQe06D+9Lf4W86gYIVU2w2+2hIsV80/vif96wwoDkxXQTSd2RIQ8rccYKCLVzEo57TRQ09mVZfVtEh9vbfJC7Rsq8dRGKE3/3eU11Y7ZBKtUW2qF9lQZINk6JgGZSn9F45UVCNqnziEQa+z3Q2KI8R998m8xTaTSQto8k+tGgoigZqDC0lddlYMsB54WT8A50InfGcl1Fpruzmck9IrPYO0QQYQ0bK9ADKcysUaOY9gkoGAmCNZdVX+l4XdB25zctDBkfh99PKgX+b5MAZd4VIS+QZODgBnq+zqLOlFvMoxW6zxs7F+kpr8ILUd+Bl7nQlNfdGXfhSEaygcJKZBnsElM3m85LJup8z41fBPJ49St/hVeSIN7zeb6PmgujtCgpS0xUJoPrH39k82/rnR/i8fOLBV/HeXjPn2Gj2kyqvi5Gct0UJr6/R3ZtgVhUGhFgoIdRvx/OnS+zkXEywowI3ILP/Wfod5v9lvdyYAMH6IwZFWff+8lQ6mDux/89plZIhiyzlb1MhdIa9oGiRGmhclpMEJB64RS8rz0Bed3tEEqqx6+jkgLzrR80VnPkFkGaexlA/kYZqjk4fZoNtSfvekxKGSG/PKLm4IATlwPmOZdHHDM0Ou9rP80KG2lsRH0iNYevrx3ervOwNMyPUS1PJnKnLYhosJnzivmmB1dPGzzk5Tk2Lhnhx4RRVpsnAynD7KWZsagYPn+CRxDzpy/SNa8f6WzWRjrH1NqkHrNXxPr3RtdE9srI33buNOMEku5r2nIhuh0p+58GSYqCJiKXhNiag1RQY8SsZdZqw81oDqUgXzOdGoWU5vHU5mx5Qc3POE3OILx7N2F0ZAAy1e9xSBbyHaZa3ij5xf7DrzwCU81MiHH8cGmf2BQZrhce4lR7y20fDpH/RGpPxKKB4Hz0F3xOtt39UZjClIWTDRp19J47w75xmVQeX4o1h5YOn1nz/ky/XhDk3XXttdfigQcewE9+8hP4fD585CMfwVvf+tZQAmRrays2bNiA3/72t2xmH8SpU6fw8ssv46mnnkI0Hn/8cXR2dmLlypUwm8147rnn8K1vfQuf+Uxiy4TJxhSdmiZUNXJBaQRUYHb96Fvo+cUPkj7We+oIBn7/Pxh++s+GXjv6pG1evAaF//RDSGNGtKyAGSuSqBBNdCJRezpiJJv0XMuN74a8+Ar4Tx+C+4lfwrv3pdgne1zs4zFKUbqqihMnTvD/+k/uhXfv5vif99VH4P77D0OpiPyeBosq8rMIkC9V1CI/YvupyJ3kNER+nyhikRRsVIwGFwVBSOWNUJqWcQFLCN9XeiByyHVsG/z9nfp/dzs4iY9G5S4miPAxWvyaS2pgq5kZ4yFBXUsyzydTfSNItq+CoA6oNqLpi2/YS+quOAWPvX4usqctmvSLbjJ5tJhbBOvMFWkRYLSPzvQMw5SfWCXHvnQGO6ju7Y/Ds+vplLfFu+UReLc+kvyBoypGA35NMBe8y+9lrzwqKtWuc1rnmMcfk383TLz5fQh0X2Bz62T769zMdRBWj6vlPJv/AvejP2P11kRAHouc/Fhex15hE4GvrYWvJ0aQe8vbUfHtX0Aqr37dr4FTuLj7Wx0aQPdP/h29//uTpI91HdyJgYd+CscLj6dVc1guvw4Fn/8v9gIL/j1Idiciv+j8RzVHzOsrFlhufi+k2cvhP7oD7id+Bd/hbbHPp2s/Bfl4XBHXBh+lDB/aGvd9vZv+AM/jPwmdf3mBbrTmcDu1902wEKHPPpnkV7yag2shSsaLuq7IlU1Qpi0JkQVJaw73CJzHd7CZvh5UlwOqc4jPzRcTgqQYtjIglVh2dROrt8MhWe2wltXCbrCZZrjm8HnZw5QIW91tl7WQgHhj+7n1c5ET5tf1etUc7H06bZHh/RwO2kdnex2c3pwIAjW6DdQ0dP127XgSngM6641ktiuv/pWtT5KC1g5R3xmtRSjVmn7rVDcQSR5POa63zay+6mnlBNlE++rk2WYIq2+GvGLcn9H93O/hfvTBCf+2KPVRmrGIm3fBUfN04btw1jDxUvD2D6Li2z+HmJdZa5epmmPy8fvf/x4zZ85kkuv666/H5ZdfjgcffDD0dyLFjh8/zqOO4aBUR1Izbty4MeY1JUnC//t//48VZgsXLsRPf/pTfP/732fT/tcTUwqwi4CR/TuhVNVBLCxG8QOf4u5pMsgNM5Fz870wX2STYu+RXXA99TuYN9wBZZF+ogd1ZcU5KyA2zov9W/V0WN75ZS7+6GQVjK31HdmG0cFeSPMujynYgh5Zo87hUCpiIpAvCKlEWFIsCPBufojvzyquwuhgN6TFV0Moj981Cwz3wfvaYxBppMxA4stEIM/Q0lbCFwvUFfGdOwyJvELCPm9oX3W1jBnlh3Us3SNQqfvEngmx6ZdyWQOkoipWzGjFDSXOJOa3vV3nWBmjVDQa+izBi1+8DlkyUDElj207kVJUWEtkmJ9lgjlF0+N4ccicwDjUyx1MX38n3O1nNPVhSezin+7Tuz8Io34XE4HnwnH+HmyUappmmlEyGI2Opt+Fr+UwpIaFITKMCWmTGJkmRMeZQbVYOLK4S59lyEOHfXTCim/frmcRaD8DU0UjAhdOIOuKuzWz6iTwNx+G79VHIa+7A+a3fMSY8jW3GGK1RsbS70OcvgijpHa16i9mvfu38LlLWXVtwpeV5yznWzpwbtuEwGAf7NfcCc+JQ+j/2b/BdsWNyL7hrUmfy0QEGaBP4f8E6Bzo3LMNStMsiDl5KLz/o5BKk4/WmGcvQs6Nb4Vl8WW4mPDueQnuzY/AcuM7Ic/Ur3cEMuuevQKijrekOG0BBEpkjao5/Adf5YVoPMUne40Z9A8k8p1MrMWZy9k/yLdFI/JNJbWsCpGXX8/2DXGf398B785nIM29nEcuJxPm2ZfF1hyuYVacS9WzIpoGtK/YaJ1qDhqJD7sGqa5hqEM9CBCRoTPSrlRMg1Jax/Ucp/iSLitJQ4KM1KnhpCQZwQuCm5o84phezUEph3QLklN+SkrMKdTGGDNYc/iolrHlwd3XDnfXeVZuKTp1mq0s8eeOJuomA86WI/APdsM+exVMkzSaa7TmIE8wf9spreYYU4Ox2ou8coPHr0mAUFhhuEEXAlkx5BYbIqGF6pmaV164Kmz7kxilcdvCCox2tUDe8A5D6i//iT2cZi+vfxuUWz8cEeAVb19RY0wYq7Go5iKFOxnyx1PAe3Zu4m2Vl1yZ8LWVxeuQLkZefILPI7Yrb4Jr71YM/O+PkH3j22C/crw5GA+cdJ5B5deljEBglG+Zfs3JQkFBARvZx0NdXZ0ugU6KLrrpgVRldHujYYoAmyR0/PT7fPLIv+F2dHzva7AuXonyT3wZtuWRpNLAk3+Gr7UFRe/9VAQZQib22RuMp4wQfOdPc7qHyZy+x5VQWqUpFRJ0xuhCZF4XP3VNb8RJ2fA2wOeJIL8CAz3IonQbMj9fvAFGl9QB8p06dxQqybQbFyCLOpoBMtEuxOhAZ0znczLgbz0B/4mdkBdv5ItsPAR9sYIEktp7AaM+H9SOs1o6U5QqTXUOwndqN481mGetiuiWWReuZ6+DuO81drF0HtnKC3f7gisSfgZv51m+2MrlDYaUNCPHt3NBmk2jHRMALUrcHWfh7b4Aa/1cyCmk98R9vc5zkAtKEfB6MEJG9UWV7EtGRTnd/0YFfWfsDZegwOeY9r4OJj4ny7g9WIyqva0Qiqv5N06eVe7X/g5TbgmUBeOFljwzzHA1BcjzEx+P4YUnk5bXPxC6jwltKoRr52h+HwaTlPixVLRy1HnqYzruTX+C2nIM1ns+G3cR5tv/Co9JUlNAvXAa0qzFaS/Y4sG17XmovV2wbbgVYlkVzPOXQ56ZPAl3Cv83QAVp+w++AbGoBNlLVqLjv76B7HUbUfKejyP78nEfHkL/w7+C6nSg6J0fjbjfpJiRfbXxNFWCr+Ukh++kOt4Y01Crnw2hJD6BZMopgPmKFGuO6+6PqQUC/V18PqA6RF55o+FtJCUHeQ4G8stgouslXbMCAWTZ84G+DoxGBQBMBsin0d9yGAqZfScIfYmoOWh0tK8NAc8I1M5mCPYCmMoivQ3V4V74zuxnNXq495JEo2UUGhSnEcTvM1bPjRx6lfepLaxm0QNd9+m7MkKA0TE9dGQbj+Flz4hNv0sFlB7tbD/LHqTZ5DNq0Cw9obq8pxVKQRknTFNiNDfUiKgRZV2LhzcKaPu47khUc6h+ttUgf650G55GQE1dOj7ZO1SxsOrQvfNprkGUmZrnEPsaJzmu9EDPkxduSPo49kDd8RSyrDlQ1t8Tul+oaMSoLRdZ5Q0YJdLUoIIqy2LTzguyOS2/YddTv+Hzje0dX4z7GN+eF7keIhGC2nmeVV6ZhvOVZ5jYJgKMzPDN85axJc8UpvBmwRQBNklwHT/MxVfpA59A/m33wjJbf7HiPnqAZ6VHvV5kTYC48p0/g/7/+ReYl6xBzh3jC8dUIRSWwXa7fnRuOiAzfufvvgXLmlsjFGOBoT64H/0fCLWzoVxxR2rbSIbr69/GRSgXU1fcNf7HBck7HmTQbd6YOBEjGbjgpqI/hXQqtasF3uPbIdbMgTJ3jS5xZrLkQK5fwB5o0cUgEWYsy45Kkox5DbOVCbBksE5fxqSc0RE/gQ3TJzZ7ThfU4cNbuQBTSmsjkojipSpyVH2CgtVP6ZudzTw2Zylv4OJaIo8lSsfJLcwoaUQJSxRgIBv0nkoGuayebwnfs/s8PGcPYLRmFuQKY2OiqYCSIKn4E0l5lVfC44UMQYAprzQmtpuDKajbb56cDp+0KNYcns4TdOP/jpOGpQehvB7CLfF9gILgcSZXbOCGWKWpNeizqt0XWHUWM/pFhrN+Hzzbn4Xv4Gus8BVrI33gJoq8d312zExbgpCTh7x3aNHR6mAfhNwC/l0OP/s3Vg5TymM6cJ89BZM9G84De5B7xcYYr6UpvIERCMB1/Aik/l4U3X0/8m66C/Zl+kou1+G9UB0TG+cleI4fwOCv/wPWtdfDfl1yJWI8iOW1EG/XTJszgcBAN5y/fRiWq98KMWysXu1uheepX0CcsTRhKqQe6NxD6o+sgjImDpQrxj+vND9+QloQpvyyDNQcktbkSoGQ8Lefhu/sfjb8VmZfBpNOs4lIsazaORDzSmNrDoudfTCj06ujQcoxPWV/NOwzlycNJYgIMLJmT1ilRGTO4JFtrOgmGwZKeE4E72APk25k/h4PvqFeVnrxsVBYzuE4CqmNSJVDZEkaqaVx32u4jz/DRBuFQZgrp/EtEbydzfC2noC5bh6kqGCcjKTiOYd5JI8UiWJRVYjQpeOb6uLo+pdsROh6REnVmQZ9V9LCK5mwCgf9ZkJIUqOFQ6iewbdkCFBCuCfWskSsmY6ALQejgsC2DaQsjf7tWe/+OP+O3C89Cv+pAzDlF7PJfSZR8OGvhPoHYmEp8u//ZGTN4fNi6Om/wDx/GZS61OtSIuc955phUhQ4TxxF7rqrLkmPr9czBXIKE8MUATZJaRRV//wd+Lo7+aRdcNt4VyEaJR/9MpMaE1FtEYSScpiXrYMyN/URG/+FM3Bu+iusG++GWG58cWlkH9WVFSFwVIgp2iixRaifx6mSab12YQUCfR3wnzkAcd6aSUt2jAehjAIBGlJ7TmEFxKqZEMvqubCM3lf19fUsg84qjxxJ9Bx7DWp/F+TZq+E58irEkjoojfE7PpYEf4t4TwMjd+Q9RoSPmF0Ai8Fx0XBPlWhQwUhkFqlxNIVW4tdxnt6PLFlBztxx1VnWaABF7i54LgRgpUS/nCJY6+ZApAWKIMIyVty5aPytrx322auZFMwE3GMR8uTLpVeY09gILUZpBDNTIM8Wye2EWJhaQhB3cc8dQXW2Ne75yt/ZzAmp0rTFEKtmaJ3LMbA5vo5qy7P9cSYXzWvDiOcMYrJHhPTgfv4hBDpaYLnnc/w7DO4vac5KvvlO7IXnuT9CXn0D5EWRJHtwLEJevI6VKhQWkmlQwRkN177t6PvV95F39wOQp83B0JN/hjJrYVoE2OBLz6HzJ9+DefpsuI4d5uAV+6JlCZ8zlch0cZFof1OdUf0v/4mAY4gDPQrvjJ/UXPaF78T1KEoFUmUdzIsvhzLbeEpsEL7TR+B65UnYbrov5BmWqX1UW5SPUYHS+iL3F/826+ZwOE+qYH+wwgr2HwycPwFx/toJqd7SAZ2f6ZYKyESbzLSJxIgmD0I1hyhykl44XIdeQcA1xD6l7mPb2K5BqZ4Z932s040ptIzYCpBFBKdU2nJhb5g/4ZqDG2G2XAi2HFiSJC6Tsmuk+TAH9eRMDzuuVR8K3T3wdErsV8pkVBaRXQVcc9jGjOodZw5ycnTOnJUZGy90Ujqp1wNpIdlF6KRrO4d4WzIZfiRRg0/1c0MsFdD35jt/HNV5WmKmHmjk0XdmH9uDiCU1yAqrlYhENc9fF0uUbH+Mm3Tm5cZVm6lASPF3lQm4n/41CwHq7v5MxL6SF2k1l/fAFnhfeRTKhrsgzYz8fQUN7ZXlV3EYmSnFcV6jSZDRcLz6HAb+9DMUvufTMOXkYeiZv8Lf1w3l/o+n/Pp9Tz2C7t8+CKVxBtwnj0GpqoFlWuLvYarmmEImMUWATRK6f/sTDG97BXXf/xmUykhSydNyGmJhCQR7NrPfMBgDGnA54yZq0Px8zlveE/e5al83hv/2cwiFJbDffH9Ed1/t64TadpbHA5BBAoygVDTA9I5/irlw04VOWRs50kDJJ0SUUUHh3f40xJnLWMER9zOdPwb1zAFkVTRCKKtL2nULDPWymaVQNze0Pd49z7GnmLz61pht9Dcf4hQrgYpD8tSYYIFBo25ygnhnURT1DYLJuJv8DGQzTNmFrNAhRVBgsBvmqHTJTMPdfJBH42zz1sFk0BOLjHOpELJFpVAFYTVY1NJzLXWat0s0JNEU6iSz1F3Hb0PIzuPtyJJS96uKB0vNLC3ZJk5x6zq+k5OHspdck7H3pO9dMWi+Gg5/xxn4qXim46i0NqZD72s+yMe41nEtivmt0FgxKcCiwf5cGR7x4/fsbYNv/0vcjTUVjKc6UZFIqZBCw/xJ6xCSz1eAVIOKJeaiSJ1OGtc21c6EUNWYUD1Lt0yCkhsdT/0J5mVrIddGLtzF4lJIdU0QS6sglZSj6ENfhDjm9UTnjZHXNkNpnAmpNHlxbGmaCdvS1az88i49D+vciSWXTuHio/On34fz6EE0/vgPEPMivWrcZ05ALq/i+iGVZlvCmsOeg5w74yc7+zsvYPjvv4JU2QDbDfdE/HZJjaW2nmFfu0wSYARz/SyYGufE1hyKBcq62yPu45qHlB+qH75dz0GavybhYjLQfBhq82GYamfBVFCedDyM7RqGeiPMs73bn9DIk+WxKjT/6b2AbIGJEm7JR2eCjRtqcsnhShYjNYfPg8BQN6vbiTQjxTyRR55Tu3lEzTxnzaQqNZxn9nOTKXvBFXE9kKLhOPIa1wmsMIsCfUf2hli/Wj2Qd5e1anrIjyocItUcYx6W9JqKTpIvN/eILDVlriaz1szi41Nvn/N5/vhO3k/Z8/Q9e9M9bhIRnvHgazsJf9tJjFJ9oJOc6T29F/72s1rNEaXqpzqT6opo9RetVYSKJsP1ZypQO5rhP7Yd0pKNEf5e5Os3OtDNhPlkQZqxlL0FRSXWmoGN90eGYKqbnTB9Wiir4VsmQQqv4acfhm3ddZCi1oMUpCPXNUEoLoNcWYuiD3wBcu20EFHp2LIJlrmLIBbEt4UJwjZ7PlzLViN3/XXwdbTC3JD56YY3AkYDo3zL9GtOYeKYolMnyeQxZ80G5G64HlJxZHHn62rHha98HF0/+8+U3s+5eyvaPnc/nLu38L/V4UF0fO1DGHo8vlldOPytZzgO17v/NS46w6HMX4Xcj34b8uyJ+Szo7aOTJ08aNsR0P/kzeP7xWwQGe9joOtBxNuHjxTmrIV1xF9QTu+F5/Kdscuo79Cr8p/bqPt5/9DX4929mo/wQyHDT69J//Km9fPNsexweA+l1RCZ4921KmEaZ6r6iokdqXAR5+nI2xbfMXQuptA6jfo+W+DjJUli5pJYN9lNR2JG6iwrnYMFG+2Pk8KtwE6GY6vsXlEEaG8GjYo/GMai4aheLoFROT/zcwkpYKcUxDcP2eAiPkNeDQklbGe4mklTee3oPE2+pgDxdsopr0GKt0grSKLDpq9kKaebKmKLTe/BFjRzWOb5YLZaAyCXQCK7v8KsI9LUb3t5RSnmkwpM62mHwH94C/+7nMJrCa0U8v/kInL/9BtQLJ+M+Rpq+GMplNzFZGP079B3fA+/2ZyGU1k44ljwe3Ae2Q+3t1E1ecu18EZ4D22O3ubIOJZ/8BpNcBPOshVx4+jrb0Pkf/4zeX/83Bh75naH3lyuqUfbBT8Pb1QFvW2tMIpYejJ7Xp5AZJNvfuVdei7xrboaQkxNDfrV+7ZPo+f14kpMROF7+B9cc7qP7+d/+nk50fOl9GN70mKHn+5qPQ21thnvnZl7QhcO88mrkfvzfIDXEmtpfrJqDiB734z+F94WH+DwVaD3JCq9EEBdeCfnKt7LJtffpn/P7+Pa9CLXliO7juR7Z9wJGSRkcfF9SOOnUHDTG7D+9D+rZg/Bu+Ru8O55M+hl8p/fCe+iVtEZi4tYc1KirmwdlxgoeQbTOuRxiQYVmv0D10iT7nZE1AvmSIoXrNhFWpjCFW8DvhYNqjtaTqb9/YXnII4wsG6iupG3pFAt42xKBfMBs9XMz6pslZedzWJAe2B+Lao4xBVqmoI4MaLYLKaYQ0nSCqaAS55Ry3d9gsOZQqOaIGkX17n8BnoMv6r6uPH0ZxJrETUA6NrnmoOadQYzSeOlAp5aaGgb//pfg2/kMAo5+pAPf8V1w/u6bULsuxH2MNHcVxGUbdX+D3oOvwbfrBbZSMMX57icCOte49rzKZFc0vKePwbXzJbgP74n5G9UaJZ/+JpNfBMv8ZRBy81nU0fatz6Pn1z/CwBMPG9oGc+N0lH3gk/Ccb4aHag4D5+xLseYIEmCZvk1h4phSgE0S7EtX8S0aYn4Rcq68DpY5iReQ0RDyCyBV1EDIH1+osgrDYCdOmbdCS+UQFQg67LyJEpEuMviCtfdFiE0LWfFBHVWYraz6Um56P7JyEvtDUddLKKmBenQbe/j4j+9A4MRuzc+IEiKjIM5ejUBpPbLCvLfklTdpxePoKHdms7ILIM25LPQ3QoDM4qMjklU/vLue4e2WmpaGkp5oLJP8gCDHqtGYsCIfjzhKNU7E6ZJjEiwlnVFLc9My3u7JnpknM3TqvKWi+LFGd5xHx2KhUyQGaZTAfWY/zDTemFvMiYm+zmYunlJ6naFeTj2iQpFJuEAAFp3jIx34e9t4BEGm8UEiKzPsl8Hv0dUMf8dZyvVMOPqqR0QqM5ZBOKm/CCDPL7rpQZq2RFPPpXl8jQ718EKOfgv8uzbqs0NKzqjRXCK6AzTCY/B1ouE/vI1jztP1r5OaFvCiVZqV2QZBEP72cxj+808gz1qE3Hs1b68glOnzkP+BL7HRuFGQp6Tn1FHIDTPgG+iHr7sDUnFiZZqvrwedP/8RHDu38r/zrr7hTduRfbMiZ816vkWDlF/Za6+GfUVyr6pwCIXFWs2RE6bUIIW2wcW9edmVMOWSYjkHJovtjVFzUIL0/lcgzl4OkFqHTL7zSyHWzdES45IsNrm5wyR4FkZHhhA4uRvqqb0IFJaHPArDIc1bg9HhfmSFJTora+/UtsXvg3fHU0yqi01LNP+oVbcAlGZ5/pgWjhK+7V43vLv/wdYLYr2maCLijhsGlMKYpVNzEGEl6Rues7p8oAv+XivXURHbreM1aZ61+qLUHDT6SI2mVN7HFn0955pD5fN+KvANdLPNgaV+Pqu5XC1H4evvgDXFmsNHgTIjg1DKG+E8vZc9xSy1mVET+XouMCmnjNWJRhM1U3qPjrPsPcpkaAoNPaoVlelL49YcUs1svun+jeroCXinUe3NRLQgxTT04kFsWgyhZlZMg5dGnEkFFvJDTRH+w9u1JlKavxWJ0qJNJkhNqa0TjcJ35iiGH34Q5qXrkHN75OSQeeFKFOTkQq5L3GAOB9Ub3rMnuObwtJ6Df6APYl7i9Zu3sx2tP/wunAf38b8Lb7kTcmlmFfRTmEIiTBFgFxk0jlV8/4dTfp7SMBOl//QfoX8L2bko/eqPUnoNeZoxGfjFAnnu+A9tZfJJWr6RDR+zxjpvqXQ9pGXXwbf/RYiVTUDt7JDih8xwKaUoKLUmKT/dgvAdfBmBzhbI6+7iCy893hSMUD+1F2r7acgrboKoRzrQgnpkAKNhi3WJ/JJIqq6TNjfqcbKKzFRcDXlu7EKEyCEax1BHB4AoAiweLoZhpIvIRa8LtiXXpt3VJAm73WACYAQCfk3ppvpDhXEgmzw35HGPK0cfhJyiiH3hOrWHSSkbJViZTPB1n4e/v4N9LchMlRcLEwB5oqnDfezJ5e04w11CNrJPEnedLiiFEa0neGE0GQb4tKiiBZVYPa7GEErrYo08h3tjRiXjbnNBOaSVNxl+fBB6Kad0H6kzyHMnfJFJCxzvpj/ARB37JVfF3xYaxaBR5jS75Dw6lSRufCKg5F3bdW+FFKfglOuNLUC8befQ/4efIveWe1D+lR+g43tfRmBoEM6925G78ZaEz2377r/A3Xwa2auvQN7V102RX28i0AhjyXs/kfLzLHMW8y0IsagUZV//ieHn85jYzMlZwKULUoH6D7/GKhRx5lKM9rRhdOy6ZtIZaYsHaeWN8B/ZChP5QZXVhxbQtAin1ETyOOXXJA+lMB8l7+5nMTrUq9Uc1BQa6kZgbMzRd3QbAv3tXHNIM3S8XOnxVHO4xhWy8uKrtTRKHeKAzu2eHY/zyDopyGPAo91dUC94AIMkysWoOSjFmogDu4EEv3gwSTKyo7ykDNccpPgaI86o5iDlW9CGgUhL/8gAxCiCZeTELq5H7LM0oszbdY5rBKmwksdGjShqE0F1ORBwDbP63NNOoVluyKVk+zE5QzxUU/m7WhKmU6eLwFAPPPs3M+FFfrhBRPvQUcOKjvfwmj0RTBSqtOw6DpxIBXrTDVxzUNK82RbRkKb97tn0R/YRlObFT0MXSqsRsNhhKipPu/E8mTUH1Rq2a+6EMmv8/B5x3jaY9ug+fQx9f/oVCu59Hyq//t+48C+f5rAe97FDsK9M3HBp/tKnmSjLvXIjCq676U1LfgXo/ya45tB7zSlMHFME2JsYgZFh9P3wy1DmLUf2DZoRP41RuV55BsrCVRCLUjvhkBeP87FfQV56BeSZsca3/gunuPNiyjHWNTFVN0G++h4eLaICznzT+zhtKFVQsSmv0tRa4YSS9/n/5ehi5br3cKGXFZ3O4/dpEu9RLdVRueodIbN+6qrSSBaZnwKxxAZ1xpR1b43oWHGRFI+kICKOEvVy4kjZKdK4vB5iTRU8e57ljiwZ5l8MEKHjO3eEu77BNJ7gSIWQnQ9/n5uLr2hjdz+Njfi98LYcgblpcUyK1ERBqi/74o2hopuKP7qpY8WkjzyuOs/C3LiYTemdR7fx/7J3Sdj3otTMYmWWYM2BdfYqjPpVJq5orIP8tVKF9/xRVn6ZFBvMpJTyuWO69ZmE79RuQLJArk0v3S85NAVkIqgth+EnM+T569iPwwiEoqqMqTYC7WdZ2RmhshhVtXEHKfF3KC+9Gm9kUMFpvSx9zzjnXiKpPchSzNyJ9V1oQfaV16Pwvo/AfewAcjYkNw7Ou/ZmeFvPo+ht74pZ5KpOJwTr5CR+TuHNBfIa7ft//wLrZRthW39zqA5xbn0WluXreVwmpdfraYfzyd/BvOZGSA2xyhF/y3FeZFKinBEIjfMhS2YIlY1MqJtveT8TVqmCDPXllTfGNLm8m37PSlVlwz3s/8VJ1eG/J1KHj42VMbF+1Ts5bZefT+QW1RzUhNMRwpAHqXLlvVE1h6T7WP6bpHDzhHxDdf8uKxDKtJrDvftZbiCGExKTCWpEUcogqYuC3k6hmsOWB9XRx8RRuO8T/Z1qDqrZPK0nYG1axvVJJkH1hUiqwLHvTCmu4luw5vC0nYLa18oJ2rRtzhO7uLHG9UbYNZTUXgGvC4JigX3OZRj1++HtOAuJEv3SqHE95w5DHerVapimJVzfThb5RcSTt/kAjyjK1FTO+BsE/1/imsN/Zh/UM/shLb7aUC3BqaEZUuDT7zDQ0cy/5YiJDCat+7TfaQLIl2nnvjcqyA/WdkXkmikVOF57kc8fdG6nmsPf0coK4+J3fQTe882wLY9PDgZReMsdCDidKL7r7TF/U0dGINgy7/k2hSmEY4oASxOXTBpF1GLGd+ownC8+xl2tVKPLyZBZ7TiHQE9H7N+G+uH8y/9wqqPt9g+G9lFTU1PcfcWJgDXjJE8mZ92ZUJq9ikcq1Y6z8L3yNwizVkAKSxOUFm1AhMuEIIYKH3HeWohzLtOMuTf/EeLcyyGSGWn4e0SRXeRdRkWqrgLM7eSuUvSoQRC0j6aT8bTXCe9wHy/2LxaIRPB3n+MxDXmM5HIfepm7nVLFNPi7zo8RgeMghZX7xE6YbHljx5ixznBwDM1o8abXcQ4eV+TbkDWqQqAO4WgAo14np1ZaovypaPyAbtrrmeAfbIfn3FHu9KZj9CqVT4PJmssjPDxaMskJpEEVFZGypGSjhYrRkdRkv0EiPM1r7x7/bkYDuqEDpOgyldQii9RokwzqpHP8NxHjFP6QVwzlpg8AUQtVWkiYb/uIobEJ3/5X2XBWuezGCe2vyYLn6D649m5Bzq33w2RNrfDrf+jnUEeGUPOjP6Pi2w/ymD3BtnA534wgd904Sai6XDDJMis3Rw7tx5kvfgpl73o/im+769K8Br5JcKnub8/hXXC++DgvmGzrUktxCwwPQO04j0BfFxBFgJG/jvOvP4Y4fSGsN95vrOaguqBu/DoeHrYxYcgWCDOW8/mKRrF8O56GuOAKiGFpgvKKGyKfE1ZzSBScEggg0H4avgMvQVp8FRNUCWsOauzRKKcOqUK2ECZLTlzygPbRjHkLAecgE06jzkFcLPhp9LLnAgQK9Rk7rzv3v8CfQ8wvhUq+TGPK7yBIUeU+vVe73lO9YVCMxtYLZEyfNfGag8z5VVFgIorqIyK5qJYmr9GIx1Ng0VgdSO/r62tjCwcCq8VTBPmdqrkDXENOugqPiKTsQk0F5ehn4o3UWUbfN2nNkVsEyxrtWhK0xdBTMFJqOhFN6Y4hpgIOuulr52Y8b2NJNeQb3x8xuszbabHDfMfHDdUc3p3PM8knL7v6DVdvEFx7tsJzfD9y73hPyom2Pb/5IQRbNqq/9+sI0/vs1cZVa4U3viWC8DKZzVxzDLz6Ms589Yuo/fw/o3DjdZf8NZDEX5k3wc/oy/2fxRQB9iaGyZaNgo9/KyL1SZ65CNl3vg9yU+rjkGJZDbI//C3dZKKs7FzIKzbGJJL4/X7IsvGTK6VD+bY/DXnl9RFpTP4Tu7lYFBuNp5OJs1eGFCSUFEnjh/HgP3sI/v0vQl7zFpioo0cXe0GE/8Qu7thGG3PrjpJt+Rsv2mWdBMBAVwvUC8fZf4xHNfW2gfaV2Q7zZbeHusKTBZb5k6uUpHCBbB5LmAyCip9RT482/rj8+pgChQz55epZEHIKxwpSDd6Wwwi4HVCmL9ctmJwHNvNrWQ2MRFLH13X4VZio6IgaQ+V9RV5vYaoo26KrDBW5YkEZFP9siGl6Sqm9rfD3dUAiMjNBN5eUdd7j2yGWT4M4gc6kMjYO4zm2DWpnMxeEqcSTG/0Nerf8lb2u5PVvjyk0SNnJ4zYXAeqxHfAfeAXSqhtDKUyk5NSD0QRU/4m9rGCVV16b9DlG91eqC6vQ80ZH4Tm4HVJNE4Q87TfnObIHnv3boK69DiZraqOaRR/4HEa9mqokSH6lC9UxjBPvuwe2uQtR88V/ZcWOWFCI/heeQ/6GayDmXHzfpilcOiB/0cLPfjei5jAvuox9qJTZseM2ySDVz0LOR78NkzU2gZnIK3nZBoj1sydWc7Sehnf3C1DW3hrRhPMd2srkklhnLIGXPSDna0l8AfL9Kq9HVpGWyqoH//Gd8B/bwab6dH7Vag4BvhM7tZojSegJkV/ebY9BqJoOScdWQe04A/XCMZiKKuMqY3hf2fNhXv0WVqlPJogoopE6Up9IZXWsMKL6IQiTbEXAPcz1gW3ptTE1h2DPg0y+lfmlTEAF4T67n4nD6BqB3zMQwMj+zVxDGPHxItLNeXQrjwCao7yqtH2Vx6b0BLqOZC9cb+j8T6OQ4f+bKsjGgWpMrjl0vN7CA3MorVOqnslKtnRAx6F5bF+5D73C3rYC+eNFTQAkgtHfoOflP/P/mq94W8zfaJRRTvMzpAr/oVc4TAtr7wwlzwenIdKuOY7t4v9NRICler5iXzvyYUyRBKXnufdvgzJ9bsh7kYJ33Id2wb7xLRBTTOIt/cTXQqSZkcTHRPB2deLYA29H/pVXo/oTn4NUVASxsAg9TzyGvDVXQLBMboN5Cv93cenRqW8Q6KVRdP/tz2j55lcQ8EWqZV4vOHe+hK6vvA/uo+OpiMSwmxes0i0ojYDMbPVOvlQEmFddCymsGKV9dPbs2ZSSOygFLtB5TosnD4N3xz/g3UUdlTQgShjtbOGFdTxQUUYd3OgRRu7Akv9RVIpVzPPNdgg1syFU6vv10N9Iyk2dJf+5o5ovRFjR5Tm6HacPH+B9Rduia1rrdcPffjptM+9wuPc9D/eeZ7Vtp44fFThhpIcybTFvl9rfoesDQc8heXyQ/KLPQwb1ZDhPHcPAyAB3eGP2AymnEqjbiFhyk58GjYrQfqeFAP13GOIdV4Y7vCaBu7Dh449k+ErknZE0LU7D8rmSJ3BSnPxwPwI6nXUe5+i+APfeTTxeqvs+owGo/Z2h71uqnaulgYaFOCSDkd8gB0icO6KNA/t9yKKFyusIU1UTjyqZMmjua77x3bDc9TEuXklVQqNTejB6ziLya/DHX4Hjjz9MeVv8505h6E8/gePZ8bSk7JvuRf5HvgapKnWfMqV+OswzEo/HBrwetP3XtzC4+R8JH0cqHcu0mTA3aNHm5uoaZC9dCdfpk/D1dF/yiUyXMvT2d8fvfolz//GttFIAJwOOzY+j88sPwNt8InQfLZQsiy7jpL50EK9WyRJFmNfcBLGqcUI1R6C3nb1A6Vwdfo737XwOvr2b09pmOs+MdjRDpQZaPFDzhPZJNNFTqtUcRHgkfA97HgQaISxr1H/5hoVcc2SRn9O5I6xWCv98nsNbceboIa3mYKP82LqOruscwDLB44ubWXuehfvgS+Pq/9xI706yUaBUblKH6dYcJoGDbILkF103yRBeHeqDOtwL1THAI5KRT8riZh3d4oFeg5Rl3NDgmsOHUTXDNYcoaZ5dYSQj1Us0BmoEoQTOJN8DNSyp9qKmb8zfRkfh62qG++CLuimk/JiAysEIwe9bblgAmVSNKZBfRn6DpKTzkyKOCB2vJ63k9EyCjPCFxgUZVYSa3/IhvhECAz1sERONVM5Xo24XBv/7Cxj5+y9S3hZSenHN8cJ4gm/O3e9D4Se+kTL5RbDMnAdz44ykzbTz//ZVDL32SsLHkb2CpXE6zHUa8WibORv22fMwcvQI1KHBS77mmEqBfONiSgGWQTj27Ybj4H4EXE6YpNQ65bTIzfRMv5BbCLGkEkJ2WIpTmqBZb7W9BdI0baHlv3CGFUThxedE4HntaahtZ2C5+QGYyxtiEqKUa9+JrASdr4QwidwFzUpwcROqZ/At5v7a2TwiKFRqC8J4IGIxfLwy9u8iK60o5txPnk4nd8G84R38Nxo9CFw4hlEl8cXXf+4wF7L8WiXGU+H0IFAnMuxi4j17gAtMqXYOp1FSAWVddDXcx7bBvecfMC++JmHXyUe+WN3noMy+jEdB6XlEAI1mmdgjg8DE2FAP5KgRxYjPONAJf18bJCp0bbmwTcAINxlUkveTOX7lDPYTo8KR3jeZDx0lMcoNC5N24UipZVl+o25n3Xt0K1RKDKUwBer06xToakczfKQga1wIqXoWd7Hplmn4zx6E2nIIApFr9vyQKaza1QLfnuchLboqNBpwMUAEn2l5pPTdiKKRR37ieMJkEXEPbdTG8/LfoTYfgeWtn4JQmF7BS+9H3kGmvNQVV2JVPWwb74hQ4Y688gwcz/4NhR/5CqTqzJxTw6EOD8Gxcwv/5nOvjO83RqOPdV//bsR9Fe//KEruvOdNa1J7KWN49w5421rZY4gCdl73mqOgBGJpFavPJwp1qJ/tFqQGbWTR13ycz01ieWaIcffmvyAw2Avzze+FpWFeRM3BHp/X3a+rdjcESWYleSJDblKz6yna6T4a7xeThOHQuKA0d02Cv0t8nfcdfQ3quSNA8yGY12qjZ1TTBNpOImBN7K3kP3uAlWS034UJqHG4yUbK+jG/RiZjTu/lETO65tJ1mIgny+KNcB16hUkay/zE41SelsNM1ljmruEGluvoa2zVYJmzJuQNxqOUpJyqiF+/EWlGjSb2I1OsEzLfTwaqgWjclGwUvNTM9LnZezVZLWGZbiz1m5Th1qXX6XqSeg69zDUO+1iRsT81fKPgbz0J39kD3GgTx1R6iRqW6YLqYBr1FWauZHItqPZT6f0Pvwp56XWZHU9OAv6tjtWpRkGEJHlvxkuzDfcldG/6EwId52C9/0tpnxsD9J1RDZhrLOEyHMq02bBdfTvMC1aE7ht+5q8Y2fIsSj/3XYgl6U1DJAI1zBy7XoOYn4+cVfHPU4I9G03f/38R99X905fgHxqEXDz5lhtT+L+LKQIsTVDBGY3af/46VOdIymMivX/7A/oe/RNqvvnfUKoyt9AkuavymX/j/3Yf2AF/VxtsG25JQz7rh/P5v8J3aDuy7/sMxOpGOB7+H+5G5X3qexnZVjaApQ5sIKB7QRESjBIEt5FiycmcVo+cktfekdZ2cQz04vgJc6lCqJgGtescssisfQw0eigtvRam9h7d5wQ8bviPbYWpqApiPXWpUrtQ64E6e+Hwt5/RRhNq58B3Zj97P1hW3MQFIZFYyY4ZqXI6FzKkJPOe2c/kFw+qh3caiezxe7gAiwelbi5kIkATdGwnAjbuJ6KraiZ8bSehkg9JXgmPbNK2EflH3qxikuPNyG+IFFw00qKrmDTbOZ1UpgIwjgmziUY9yuohFKQ3NqEH//ljvOgI/z2JdXOZIKIxYNXvhXD1O8c20qSFQqRwvuDfoaM/JZVaquBu8ZhSMgjvS3+Feu44zHd9ks8fpJ5wPfxDSDOWQF4VSabJC9fAX0yL0/S30f3qUwh0nIe8LnWzWyKwo72QTPlFTGCMbN2EvLszT4BJhcWo/89fcnpwqjBJpF6YIr/eiGj49vd5/JW+o1TQ9esfY/DFZ1H3vQf52MgULAtW8I3g3PESAs4R2K+4PuXXGfX54Hjif+E/dRB5H/o6/1Ydf/hvmPIKkfuhr2dkW0eHtZqDHSz1ao6yxLUYK4VcRLrn6dcNV6bmsRp6rtkGeclGZAqm6lk8xhZuAUFklLR4I0xd46q3cJC6iuwfsoqrIRIJkoHzuTItLDgpoMJP4/zWHCbAvKTA8jhhWXUbBCIdDXgsyVUzoVKwkC0P/qNbAVJFkBeoxwmMEWA0+sVJ0gkURjQ6SUQU1TqTAX9PK/uTEtFFii+qMcT8clhmruL9QDUINd1otHPCNQcHKOifC5g8MZkgNy2LG/4jFJSzOitTfrxE2hH5yk2tMNsGsWEB1JxCqCd2IiCbIYwRs1xrZKVYc9DvkLY5Kpkzkwj60YWPP7qf+R0CPW2wvv3z7Psb6OuE82//A3nJlZAXRVp8yEvWI9Dbgaw0J28Irs1/h9rTBttN96X8XNo++1W3RtxHdglEPDq2bUbezVpIWiZhrmtA449+zeOMqcKkKG8a8osT1DOs0H6jKL4vdUwRYGlC9Xh0f7R0SxW0KCGfFZOsxCVA6ESVaoc3ejTB39oM6+qrkZWiyfLAT/6FF5zKyqshlGlFlHXj3YZ+hEZNC81XvW0s2UaAv/U0Rvu7IM1dZXgbfTufhf/4Lsgrb4Bv93OQVt9s2LvjYoKTnFbFLpqpQBB69H3GfPuex+hQDxdJ8pxYlRl1c30nd3KsdNAwPVWYl4ynLcpNS3j0gS6a5D9FBZtz698hU2x1cRxD3bBOoWXuWu3YUH0RJqLcnVxxc8JCjo9zA+SXkeOKtsF7fAfvc7lW85Ii0otUaGJJHacqBgpJITlWOCkWOA+9zMRukACjzrQ60M3EYLRagrrP3mPbIU9fyoVjNDwHXsSocxjmFTfG+JlIVdOBrJkJ0yOJGCOCbKIw0Shl+2lezPgPv4qs7EIIRbeF/k77R6ybB5CKKmz0g3xjhI3vSum9/Ie2QD21B/LaO2GKc6xMFJ5nfsNJTER2BferUDNDM5QeU2yQWpR87KBzTqUEOLpN5NiSZy3BqNsNoaIOmYBl/goMP/o7qGEBI44tz7PRrGXheNd2IpCKM5vSOoWLC73Rd8FiBeiWIoTcPF6YxKspSMXOY3Fi+iXi8D/+CnVoALa116akNKPmYv9/fYEVFJbLr4epgMbzBVivexsnPmes5rjpPXyup23zNx9lmwNJJ906HryvPspm99Lya+Hb/TyU9XdDqMg8eT1RkHeWsHr8fB/RYOmLHZUjeMkewT3Cz5V0vLXoukgpfdLMVSmNxwVBRIJ5yTUhQoEUR+R7Rt+FefZlUHvb4HztUSizVsX1u+TPZdfIR8v89Rpp4vfBFHaMUOqiGPR0TbAtWYI9MzVHQIXnxA4IOcUh1ZmX1P2k6Cuth1I/n8nFoJKbftOe/Zv4WiXma6Snv7eVVWti9ayY7ab0ad+ZfZBnrhivW8Lg3vs8k4CWpdfG/I1U5PxZEyjc6bsMeo5OBCYi9jrOALnF8B/fwU1beek1Ee/Dx41rOKIGogYx3VKBb98LCJw7BnnDvWywPxlwP/oT/l/LWz4Suo+8BwOkxgruT2p2Uv2hU9Nx8EZY+EY65ytl/io+JwvFE2+AE6zL1mLwyT/B39kWum/o+ccglVfDMif2N58O5LLMbOulDBrbzPTo5qU4CvpGxBQBliaUnMzJgvOuvoFveqAO1vnPvpclqpVf+m767/H2jyIwPMgJY9SV9Z0/DWXGfEPPFUur+eRu3TCe2iHPWZb0eYIgYPr06UkfRzJi3/6XIU5fjKzcQni3P4NA1wUIDXMjiplEMJU3QHAOawWypBg2qnyjIHpfkUeD/+RuCFUzeYwiQJ4ZVCTqgDymAoPdrHpJlQAj5RepfMRSbSGv9rTC33YS8oywRTeRWWMJgUZAxJlWtlm4INTGc4IpjBNPMDJyXFHiHyc2DXbCRH5dYV1o6hAH/Wiiu74KdWXDiF0/x5638/6JWXzRZ6MxzziKNkqKVAOj8B7ZysWsMFacETHn3v4E7yfzivSjqI3uq3rTCNRD+zG64Eru+pPCQA9ik/HFnx5oMZiVUwhT1XRkhQUjZBq0cAtQ0Rl2LNG5g26BgW549r3MHVfr2z414WOLlF7UMFAWr4t4nFhRx7dMgYiG4i/+gNWqwfP+wJ9/ASG/UJcA8w/0wvHqJmRfeT0EW+bHYo3uqylcPIgTaIBFo/C2t/FND+qIA+c+/W72lSv/5FfSfo+C932e1WlEaqjku9dxAUpTYq86Bl2Pyqp5nNK6fly1oCyKbzGQas0RcDng278F0rxVHLDhefVxVoSJTQtDv8Gk71VFC/VR7XxK1zfTJV5zuBzwU8pi/Twed6QmGCnO9UAqX6o5Rsm/MkUCzN96AlmyFcJYg4SUYGr3uYhmDzfQ2JPLWM0R8peTzdr1mNKMx4iJi1VzUErlqGcE6iAp+U0hAsw8g9TlvjGiTYxQfNNvg2qOcDUz7Z/AcB8nL8aQKTy+6Na8s3RApDGN53sOvQKpjsZ6NYKQ3t+940m2ODAvytxEQ9yaI9AH9dAJCEuvhbTgSvar04M0gQYfe6S2HOWEaiayJzE5nccxoxr+0rzLxoO7Dm2DvOIa2N7++Yn9BkcDcL3wdya5lPmR+0aqm8G3TMFks6P86z9G1pgBvzo8iP6HfwW5dpouAebtbIdj28vIu/YWmBR9BeFkY6rmmEImcWldsd9AuGgSRLpATpsJsWhi3XuxsASgGykLnnn5WsL9AABgUElEQVQYzteeR/4DX+AxyWTIvuN9uvcH3C4uUuKZ1NI+GhkZgc2mb5wfBI0ueXc8x0SYctmNUNbdzqMJRskv/ny1M/lGsFQnL4CNgFOWXn0E0ry1EGr1uzfJ4Du8RUvWSzJGGb2vaCFPqZFUAEnTlyVM/YFzGMrKm5lwSfgeZOx7dj/EiqZQtLSP/MgoGn6MAGMijcYE3Q4IY0UlK7cuHyc/U4Hn8BZWXAmFVaw2kHW6yanCyHHlPbmLxzjleesiUiq5+IxDABFohDMc0rQlEKlja83m4sR/4QSrvXjcs6Aclstvj7sNpKSj8QYvJSlRcmNukUZstp+BUFwT8toyAs/OpzXz/qjRmOBCIR7hy2RbYTUU2kbJjNHBbkPKLPIfC/RcgDh7Fb8vFdXUyWXTZZ3xCPqt+HY8DVNFI+TLIqX28RBwDPKorCk/Nam7vGq8WUDpbUSEyYs1vxh/yzFOXxJKa2CatYx/R6mMc4QfW1Twunc8zyOV0QRYuvAcPwiphkZ8Y49BKiqDKh9ahBd/9MtxjxEivwb+/nsIOXnIXpu5calUMCXDf3Pub1KiKw3TodSlpsSIhlQ6PmY9+LffwL1vG4o/821IlYmJYyIFct7+Cd2/BVwjmrF5HG8uozWH/+QBeLc/y/6MytL1MF/9Nvb0MUp+EcRpC/nG/12vKYwnigCFsWx7EtLSjRCSeIAlUsTQGJw0L77vjt6+UvvaoBI5ZcuDNPuy+Nvo6GciRll1W1JPSmo2+ZoPcgOIFOJETvlO7uFmUpAAI58utbdd2/9jpBUpsMWiNGuOAy8ywSnklzIpElR/TwRGjitSftHnNS9YH2HhkMzOIXr0kdRwvC8khfcXNSSFomre12JJDSuz422DMnMlByV5T+xk0oYIMNoX5M9KHrTBui/p5w0E4N35JCc/ylEJo2ztwMpJIX7NUdwAs6JdQ4lYFcbqy0Qg/y8m/mZoCeK0Lyn9nQjZIJEXjkD3Bfh3P8tJ0fKyWMWbHigJmsjRVJViyhXjFiqe157ikWl5jADznz4I/9GdEOtmIqtuFkYH+wx7g0YfV2R079mxCUJ5bQwBlg5YYXh0P+Rps3RJK5PFGrqu0CRSyce+ArFAf9sHn30MA888CrmyGvalq/F64FKsOSbDtH7KBD8zmEqBfINLEOmkWPaxL6Hongcy9pqWZWtgXXMtL8QmguHf/DsGf/JV7YIYZx9duHAh6b4S6udA2XB3aBFLxtQsGU4B/hN74d36eMbSZKgAoQssPC4tHW8CBW2g61zSE3f0viK/DomMQEvr4X75z9yZ1XsNlczn6W+OAf3PEfYcIimIVFM7zobuU+ZfCWXe+OJerJ8PZcVNMURQuqDXIQJNHejQTN8zACPHldSwgMcKvIdendB7UWc5OILBo6Zn9sFHhsJjiFeIBlVhVHQqS66FWDObn+ve9TT8p/fAlF0Aqd6YAnPsBXWPbd/Bl+B58Y9xU51oH7X2DEBoXAS1+RD8x7bx+GAyqM0HoZ7eB7X5MH+WAC2OTu2B2nJY9/FZucUQF17JhJlReF98GJ6nf4XAyBDfKOmViLdE8J8/wR3XIHyHt8F/eHvo39Lc1TDf+B6IM5bw35y//y58x3eH/k7nKt+Zwxj1uOPur/PNZ+F67R/s7yNU1MNUmBmDWM/JQ+j/+XfgeHo8/TEcfb/9Idq/9H4ePwsa18rVWjJSNLLXX4/C+z4M28rMEHPxQN/9yPFjUJ1OuM5S8Mk4pmT4b9KaQ5JQ8blvoOC2zPnC2C67GrYrb4QYRoqlg4EffxWDP/9W3L8brTmk2ctgvvpuyPO0hZxQWg2xuimlbfEd2grPtqeRKVCIh9rTDpCHVVTycSogj1GqO5Ihel/R6Bl5kWbllcD90p/gj3Ou99N15ORuHpHU/Rxh9aDa28qkmjq2PdSokReuhxxGsJG1AtkEZMpsnbymqKlCxvaBgc6MvKaR44qDcQQJngMvxSWHjICadMKYnxV9Bt/Zg0yCGa45yho4tEgko/0TO+DZ9zxfy8WiKkjVWpPYEOiz6nxeCsbxvPxwXOU71xz9wzA1LID/5B74j2zVTaWMhv/Mfqgnd0OlkVEaG6Pk9tN7EaBGsA4o2EqctwZC01LDH8nz3P9qFgruEU6D9m57mn21Em7X2SOhVHomcKnmODqe7kpqc/MtD0Comw3vzhcw8ptv80h1EKTm9p06GJNmHn5c+YcG4NryDPvPCuU1MBVlxnPTtW8beh/8Nzg2jac/hqPrh/+Kti99MPRdkvKLRiD1kH/TnSh53ydgWzjxEdlECHg8GDlxAv7hYTibI+vBqZpjCpnElAIsAxjetwc9Tz2B6g9/HGJu6n4IFxuUMpaJpDGpaT5GnQ7DUdAJExRT8N4IQpO6j7J83H96PwIdLXxBJKWTqSC5Yo5SJ2n8Qa8b5N//EtQz+yFd8VZWk6QLmZRTYek9NJrnP7ET4vSlCbtxwdQkUmSRwosLTskMsSaSGCTvpix7PkxF1br7x7Plb2wOKi/cwIVplmKDKSxBMvqzU/edPKEyBWlsezUSceKjCKkQbzRCwAq5CRSj4aDxUhoNTWYITCOT3n2bIM1YDpHCAcYMeTmBye+D2LgYQllq43MKpUnqbZMtVxtDSTJ+Q95e4vQlCND7RkW860FauB7+U3vhP/Aid/uFaYsg0SIlTlISHa8ixdjHgW/fZgQoaWvdXSE/IHHWciZuPc/8WisOaQHYeQ5CcSWkFddpqoSOZu7iU7gFj3Y88ztk5RXDeufH+DUst30o4rCic4nIo0lEIldBqGqCqWh8m/2nD8H1xK8hk+rjcv19qna3wb3zSZhMAseXk+9GJiBVN8B6+UaYF42NTgz0wXVwJ2yr1vM5zJRNIRL5WvBAEghW+0VRfnX88fdo+8WDsM1fAMe+vZj9y9/B2jgxZdAUJo6Bra9i4NWXUf3RT0KwGFeSvl4gMpduE4U8c1FCDyOj4MCX2csnZIbtP76HmyLSwrUAmeEbULKq50/w+UsvrZZ8TAMXTkC69l0QUlTFhkO58m0R4+FUQ/hP7YNEat4E13aq4yg1kk3ZyQT/6GvcRKIglnCQJ1iAfLV0vLmocejZ+ggrnKU5dJ7L4sAX+ncQ0Z5e7PuZQJWdKuSxxhLVgZm69huBSGOjpfWGrq9GQSpzIgj1PEbDQYnNXkpPpATMkhpkBUMAqDEaULV6M4WUQx7NXHWL/t/Iv4tIziR1P9V84qxVnHJOhFOyupI8wih4wb/7OQ40EKpnQhJlmEr0CRk6buhzxYN321PcGFTW3h66T5qzii05XI/8P7agoP0T6GmFWlYHeak2paG2nuaQKg7Tofrk2d/DVF7HCfX0u7fe+XFOeQ1thyRDHPMUFcrrINRMjzgXUHiY67k/wbzuFijL1utv6/6t8L7yBHuIqQPk95uZcXelaQ5sl2+EZbFG9Pu7O+A+fhC21Rv4OyYFOTfcDIwJi3kFyF03+TVHy4/+H9r/+EfYZs+B48gRLH3ycSill65/Ka274olEJvKaU5g4pgiwDKD/5ZfQ+4+nUXjdjchZFLsIHHj1JXhamlFyz30Z8SMwAvex/XAd2oO8W+7N2AIuGtb1scaqkwnfsd3cqQ1eXNxP/JLVLJZ7Pwvlyrs0yfThrfAf2Q7lhvckJK7ogux94SFkFVbAfN39MX8X6ueyqbYRIi0Root1GiujIjeQX2ZIjk5ki7T6VlbfkPdRzOdQfRDKG/VNhrOo+LRpZuAE6thSITJWoKmdY+TCJPk1UeeLx/PYTHlyjsFE0Ot2cipRx1ktlShsxJbuD3SfgymvlAujuCRP1GJA93HkQWfNHt/vY+BRAkovzKA/ndi4iG/J4HvtMWR5XRAqp8O753lIy29ISMLRcSM2LtS2154H365/QJp7Oe8bWkz5yfx/3V2hhZz/8BYenaFxBD1QQhIlkbGvS5AAaxxbqHjdPJYgVM9gFZj/5F5IS6/WCK8nfwVTcSXMN7+PjyN5zS1cgAehl8AWhFBSBcstkcpZsaqRUyDJNN+zfwvkuStivg+hpBKWq+9io3tl6RUpJVIlAo1t5dwynuA0/MLjGHnpKS5CKTkv77bU051SgX+gDxe+903kb7wRuWs0tW0y2OctgH3JUhSsWw+5uBRKeebj0qeQOvo2PYe+559Fye136RKSfc89A//gAEruSC+JMB04926D5+wJ5N1674QUMIlgv/5eXCyQCsVHo03V02HK0c5zzof/m88Htrs/CeW6d3JDw7v1SainD8Jy58cSjlsHBnvgffHPMFVOg7I+9nsRpi3k0Sq9JOtUEB2sQo1BUtFQ80OoTK5yI9UyVtwI9exBZOl4ihIJaCpr0K9lqYFG172x0W1K5+O6I6ARhxzGklOUlnF+qk3RRAEzkwW5bq7ucUSG8EJBRSikRbtfhdp9gRud4R5g4aC6TjQyCku+qzRqGVW7yAuv0vZHBr2TpJnGQlm8Lz8MQZbZw9Z77ijky27j/44HTqemWoYCbCQZ/j3PQ5y/lmtH/5FtrGCX17+NaxPap/5Dr7C5PjW59BCgiQNSU4ZBnKnZiZAtBxFytEbwvvIo/Md3MwFGlgnup37F9YH5mndoSd2XUeNv/LpnIuP7OBCrp/Et4r6G2ZAXreW1i2f/VsjzV8aIBuQlayHacyDPWwFlwSpDTTAjoLHGvDvfE/o3md47d7wMqaySGxKF7/woJhOetla0/Pu3UHrPO5C73NhIZ86ypXA1NyP/8sthbWyAVDB5frJT+L+NKQIsTYRf/Cvf90EUXL0R9ln6i7/uh/8A58njKLzpVog5F0ch5tiyCa7dW2BbeSXkqsyZNae6j2RZjthXgZFh9sQig1vP3lf4YiI1JO8Mk0zZ88LDEOpmwXL9O/k+U2EZRqmLQ34FLz8CU2kNhKrpWjR5khl/uohKS67iTk88eTXdMg2hdjYTTllkqplkX4Wek1MIYfHV+h5l2x6DUDc3xkyUCi7fwZfZ9JwKLAKN4dGYAxUURAD6DryIrNwSyDT2EHXBJUKIUp44raeoOm6BlgjeA5vZL8S8+raMkj6J9lUyjA73wnd8O/tohCdq0ogfdVCJIJITeK4ZAX2/5pWx3VMueoTMTZ37T+/TOvSUKJlkX1EBl+XzQCiv5+8+PJI8HNQ1p46oqayeC0Rp/lr4j25D4NxRqPllGCUSi7w9fN6QQTF1ev1HXtPSJeMQYPIVd2pkqM5xJC8b7ypS0UnEFz1uVBAhLVmPrLBuqjTT+LiD7v4w22C+4ja4Nv8N3t0vsX+h1LQAI4/8jNUClpvuh2KxQpk123A6UzjI7Nuzfxssy69EVpxU3yCUaXN43Iw6sI5Xn4P98tjfuBHwiEVHG3tzJIKvrw8jh/bDXNdomADre+E5DO/cgcp3PQC5tBwH334vGr7yVeQsXHTRmjlT0BC+v2s++VmU3n1PXDVex//+Cr6ebhTfdmdGF7+JMLz5KVYXUDCDmD85aWxp1RxD/Uw2UK3h2fkCxPLamEWqHkgJ4nnhLwjQuOSGu/g+gdSk5NVDSqfND0Osn6ulTVOzKQmpQwEh4qIrYSrRb8zRuZlumYbQtIhVsFlRKqKENUd+Gd/0xit9u56BOH0ZxGmRjRf/uaOsbpeXXx8K4+EGTd1crjkCdO0l0oLsHeZfGVtzEKlxeg/XJKR4Sqfm8Oz+B0b9HlhWp+cfNhk1R6C/Hb4TOxGomBZRW/C+JEuC2rmQ6udNaPuEvFIIK2PTxVNJX00GrglP7uaJgmhVoG7NUVQBk6hwLcEkXJyGL6WvUi1LDTkinEzz17JinOwWsgrLMUojioKoKfqCI3CkFj+6HaPkiRaHAFOuuS/GuD70t9Xj4UPmmx4IKWqycgogLboi4neYShq9HqhJZ9lwO0ae+A18h3fy2keobMDwH/6LaxuZkk4tNkiLE3v2xYO/rxvuQ7thW31V0tReZdZC9pP2nG+Br6cL9pVXpPWeAZ8Xvu4uKBWJ/WS9nR1wHNyPnJMrDBNg3Y8/ib5XtqD+05+GmJePnTfchjk//D7sM6ZfkjXHlAfYGxdTBFiaCF8cDW7bipbvfgfTvvUdZC9YGHF/55//iPL3fggmwXTRyK+Ax428t9yH7HXXxpBfPBZ38ghLY/UKY8dzj8C5bRMKP/ovEPLidzqM7qOGhsjulfORn0LtakX2A1+Be9NfYSouN0SAEdklr7mZJcZBKJffHLqAUrFKF0d5wZqxdKbkoBGseOBZ/72bIVQ2pm1GqwciQUh1FuoAnj3EBQKl90Tvq6SvZbFzMckpODF/NLGMOroACiUjkcJn1io2HiUPKWnZDRDC1TR+H9SWQ1Cpo3hkC2TyI0sxYZJHBamITVCEUQKUKUUVmt5xpWv4f2Yfj0FGbLclh8cDosc3SPkl1s6FUDo+Hvp6gpVqrScxOjLAi43oCz+RHv6Tu5BlyUlIgI3vq/H9JSfowhKRpZ7aC2n1LaHjXqDxieJqqH2dfLyKizfAfNtHI44p+ap3RHSfA4O98O1+HtL8NdriixQhRlQhpJ4bex36zNLCyfG4UhZfwcV20GtQ7e3kpFsjx1YiuLZtgvOFR5FltcOyOH5qHZF8vb/4HsTiMja+VR1DsF12VVoFXu8jf0DfX3+Pyn/6Jmzz44+SWxqmYfov/gQxN75qLhp5l62BOjQMc3U1BnfugqezE+qw5ueSDkE4hfQRvr/7Nj2P8z/9CWb9949gnTZ+vSNVWM8zT6L281/i3+XFIr+IxC2478MIDA3EkF8Brwfesyc4cEfv+B585De8gCv+9LfiBuoYhd7vd/j3/8lG4Nnv+ie4Nz8CsXY67G/VRqgTQahogHL5TRF+pOar3hoy01Zbz7Bqxbz+TogNycOE+Hw2N765PJFqXHM0zAsZxGcCPGIYrDnoun72IKttyWsq5ZqDCAquOXSuIXR+p0ZXmLqFv+9gzWEvYINz//nj8Lz8Zygrb4oYfeQERfLXVJrhp+CgVbek7AvGjZ0Enq2aAvzMmAI8J7M1Bxn+nz0AsXomTLbxcywpo3m/5UbWHNSYHKWmpAFV+cUA7xva/z5vDLnJ8Lq4NgjklybcZr2aI9HxTD6mgXPHkHXlW0O1rDj3cj5GAx3NfLxKq26EtHhD6DmksJeventEMnegtx2+fS+yepwa4IabroolpMjioKGxUchMw7zqGohlNRAqNUIw0NvJJtwTqTcIjhceg3PL80xsmWfHnwZQB/vR+8sfQGmcCU/zKT7XpkuAdf7yx+h76lE0fO8nsEyLX39mL1qCuQ89AqnI+Nqh6OqrYFIUyKUl8G3bAU97B1SH5jc4VXNMIZOYIsDSRLi5OC1GA15fKMErCOeJY3Ac2McnO9vseRdtu9q+/GGWvpZ/+fsxfx959TkM/OVXyLv7vbBfrjPPzalyOmbrA71sHJ2KiS1ty+DgIHJzc0OFrzR3JYS+Ti4IrHd8gIkfI6ALVNCwNuZvZiusb/t0qNDKBEaH+6Ce2MWdpkwRYGr7WYy6HRDHun1kVEsybsHRj6z562L2VTKQ+kdeco3u3yh1J1nyjlg1g32ZKBnQf2I7hCXXjPmGPcKFpLz0egQGu6C2nYqN4zYAMqJPBIpZ9x0mFWAJlDifw+hxFfPaQ73adptEyOEEmNvBn3eUxgUqxy/cVCwl296I1ycvt5O7uFA0ZRcy4eY/tYdHTIhw40j5c0c0j7YU0h6D8B98GSoZ39LYJXmaRI020MJWXnVrUq8II/sq9J6n9vHiRZi2OEL9yPHtRZWsICAVBS2AohE9nkvHVaDtNAJkIh/mwZUMrr/+iN/PcvtHMJnwnz8F1ytPIuDzwbx4LbLf/U8p7y+C79yp/9/eeYBHVWZv/M30TCopJKGThBYg9I6CgDTFhhV1Fbsr67rq2nbFvoq6q2vZVf/2hm1RLBSliRTpvQdCqAHS6/T5P+cMk8wkM5M7aZDh/HyuJDczd+58c+fec893zvuicu1SRE66FqqIKDjKyznBquscWGyYWjxiLpnGml+a5LZwWq31nt009siEuX8WdG3r1irUxgU3qUGztu6Z2/ixY9Fq1CioTs8yt0RHplCKOeiYqakFUrZzO0o3beJ22/DUhut8Kp1wO/zQ7dB1SkPKA0/X+nvpz9+iZN7XiL/tQRj7+6imoPfl41iy5ZGAuROaIAShfX1/9f3O46pzTatEREy9E6o4ZfpafI7t5+2A54baFI03PoIwXePpr1HLln2vy7CjsRJg9sN7eGLQ7WLtOJoF27bf2HFbkzE06JiDkka6QZN8/k3Trisv/mAJgQ4ZLmOgskJY961nl+Yq3bDkzjzRRi7UVB1VH9mEuqq3nWUFsO5axVXt+kzlN/9KrgskWM8tnoZIrwQYtYFSPEnV5/CYYKMWzaBiDorTDmyFptsgl6OmqQK27C1Qd8jgSn2KSShm0HTuUy/ZE+umRWzYRBOW6rQ+tdr0ON4cdkmdem1BxRy717g04roOYCOdqteiydvTOnNh5F7pQwespqYZVa5zzJHfS7HLI8W7lZ+/zLp84VOqWwWbAmvWdlT+9iMcdhvHHDEzniPNexQVFQX1HSQn6cqtvyP6khvZ2dFRVgZVXCK0HQO3N6uiYxEz5Vo21qH7w4bojEX0HQhrfh60reuWiNElBmeolTD+Ql6INtdeheSpl0Gl1bbcmKMJKsB83aMLwSMJsHri6UYRP/ZCXmqSfMPNrAumS6y/oGmw0ElUn94dqkjfiSWaITAOy4Ghe3WlmieR46/gpSZFH/6TqyQSZ/7X78XVsocc8rJYG4yCRxqj3NxcREVFQX16Jlrf11UVYd6wDLacvTBOqa2/5cZRVgxH3nG2F67zfTeiiCrBCaBxN3C5d2Nh274CztJCFvdk0evE9tBkjuKqI19j1Rxoe50He0SsV0scBx+kfxYdzwtZmDcECsws6+dxYKbxsCSnGWhtt6E+La4Dbk/BWNHsKgn/U3LKE6o00w+azJVTDcFZkufSc6PgLCre5bB5eDfPglMCjGaZuYQ/IoYTjcHCM5tkR9/TpbvlCyVBXjDHFQWj1KZouOSPvveJAmOFFXLUFkRafdT2EwysudccwsU0kxgWBtOvc7kl1DjGdc6z2+1+x8t26jjsp45Dn1Gt82jevg6mDb9BT9od3TLhKC2EvbgA+e++hNZ/nRVwF6LGVrdhBIMpOwumPTsQM34KfybGnn14UYrl5Ank//gdEq+4BprY4L577uQXIY5MzYvneCddMZWXmrS/516kXP8HaOObrwWRrhX6tG5+HUvD+w7l6gN9uu/rSMwVN/NSk4L/PM2JvqSn3vL72qYtv8OelwvjmEs59vF1vjMMdcVmlb/Ngz3/OCIune53e1S56igugKZD3VpZqkY0iyGoBUx34Q2+q6vqiXXTYsBmq0qAkf6YxmbmVv8zFnP0G8eOf57C+NzuSDEHVWaRgU0wDsk+YHmIDQuh6TEUmjbVnyVVoWm7DPIrAdCgmCOpE1+ra26b2jlpgo8qwRoCTyrlHYazTRpgjHY5Mx/Zw7GvqnMmV4xT1T5NKNankp30YBEdzzGhP2MrXzq0tfZT4XHFLa87VnOso590q99zi1ITKkqiqaiNUmHyy/UCKoTFJQXU9Wo01GpO4FQs/Bqw2RE+YiIcAeINwnosB47SYo4t3FRuWonKdcthHDoWuvapXJxAkwV5//cSku57yu/L0/kx9uJr6rXrFTu3wXwkh/VDieghI3hRCml65S1cgDY33Ah1RHD3au7kV0uNORxOBy+NvU2h4UgCrAmhm5PmTH65Sbzjr7XWkTht5Za1iLnoasRNuyvobRqHX8gn2kAzS6a1S2HdvxOGgaOhbpXgZZNcPv9z2A7uQfStj7qEtHP2wpq9i6vK/G3TtOxb2LK2wnDBFaz9E6b3nQhQ7MRRUaa44oxQ+3GfqS/aQRPZlcatbcEuNm7tBxKMbwKozZJK12mWze+sbGqml7uVfvhljZ9soNlcHyXpGgWivIGwZm1kq3Ndvwtri5n7afWrmRSrD6ylFhXPlYy8TbIY7z++KvilWVlqj/HUe6Ggn2a36QagrvJ8TXp/XpoT3air/Gpm1IegAtHTGMZdh6aEzjn2YwehHz4B0akZMK1aAH2mMo2Psh8+hfXATsTdP4t1PIiIcVdAlzEAuk6uyofoaTNgeuwWdpKsL9SmVL5hNYyZA6EKN/J3+Njzj0Kb0g6tb7kXBXM+Q/nGNQjPyIS+Q/DtM4VLfsbJrz6Drk1bRPYbiKy/3ofEK65Cq1EXoGLPHsQMbZjmiXDm4ErWZkx+8WtqNEi+b2at9ZW7tsC0dydip1yNuOvvDnq7EaMuqlVVX+s1Vi6ELfcwwkeM50pwcnhzU/rVW9y2HXPH4xyL2bJ3wnbyKCeEPF3cPKmY/znsR7JgmHgddN36N8hAiL63VKXjabZSF8G6A9eFbsRlVVqNBMUebG7SlDEHbddq9hJ894TjHhI7p9iEftcZoB95ZePuBEtA6BBWwx2Z4x0f5jjBYN25ip2X6XrvWbXDzt1+3BqDlY/whbpjT5c27WktLVVKKrSUcDsd51AcRyYMntVS1j3r2IVR23dsnRVGJG7fON6DQdwfjb2+0RwP6f350/QNtA/hF/lPiDcGVBhgLziJ8GHjoeven89Zup7KtExLvnobttwjSHziLY4FCDLSMQ4bx8kvIu6uR3H8rzfDdjSn3vvoMJlQtuF3RA4eDpVWB4epEjkzH0BE7/5ofeNtOPHxO6jcsxORA4dCGxf8sXxy7nfI/WI2Inr0gL5NW+z+y/3ocM8fEd2vHyoP5iB2SPCOvILQUKShtp6cWvgz8pf96rXOVlyMrdOuxZF3/w9nG6WLf0DJgjk49vgfUbltfdDPJ1HnyPGBg5TIy6Yj+tZHOPllO5aDwpf+AuvhLNcfKShihx7Xr8ZLpiP6rqf8VqoR5Nam6dIHpqVzWLSaRTAVWsFyK9+qH2E7vJd/t25cisrPX4I99yDOFBSckEW130RVEK0Sph/fgi1nR52Pte1cBfPiz7gl0B/WXath/uUjOMqLeZbRtPRz2E/U/2JaEyrXN4y8sl6VUHVBgSg7Tfk4FmxH9roCwCYqmyYHRHdQycEvtQm6k5s0c0lC8h7VTDQrTePL34NmwpX4LfU7BvSZW1Z+x/oZVB3nOHEI5iWfu5wZlWy/mWfklNo/s0ba0exa79u8ZhFMK37iKg9qE6PKL3WCMmfDiDGXImLStezURgEiQUL37uQXYT9xlBO9ht6ugI7a1Aq/eAcVG1Ypfo9lq5fh1Nsvo2TRj64VNFN+6iRsBXn8a8J1tyLp7gdrJb8sJ3Nx9N8vwHw48Dku4ZKp6PDwTLQaMx5Oi4VbGewlxTjyn9ex7+H7UbZ9m9/nFq5chWOff6H4vQiNx4m536NozRqvdebcXGyeOhXHv/wSZxvF8/+H4h++wJGH74Bp386gnx8xajIiL3BVHPgj+rp7EHvn3/m7bMnagfzn7uGqiaprKsUdp88Bkdf+CTF/fCZgUks/eCw06b1hmv8ZKn/9jpPRimMOauVb/h3sx13fP8vKH1HxyQuchDtTUJsYTc74TVQphByjTd+/6ZIVqAPrliUwL/mEZQD8P2YpzIs+5tZUanc0Lf6EJ4cac/LFMOqaJtHXoniDrps+23ZzdsC2fxOaAooxPJ3DqUqLHDvdk2nUUslVaB7VW46iXNe4NmPFCMXeFHMEdENdMYf/pZjYkbMLlqVf1vqunT0xh7L4kXQ9bUdrX3srl/+IyiVzOGZQRcYgYsLVUMcqSyKRtELUZTfxJDK1mhOUCNN1rNZ9tB3JBjQaGAe6umvodfI++HdQ93mFC7/H8deeR8myX1zvxWZjkXvr6Zgj5c770O6vM2slv0w52ch58TkWuw9Em5unI/2Z59BqxEhOtlny8mArKUHW089i+x13oTLnkN/nnpy3CEc/n4OWilsEv7EXoeFIBVg9OTDrJRQnJyF+dLVIs8NmgyUvH9bCwgZvn052dDNdsmIpdO07Ijy1/pUyZauXonztbzAOPh+VG1bBXlqCxoAuRKaNv0GXmgF1XCK7ndBCULWWKjYBEUYjX7gjLr7R67n2k8c4QFKl+XaNIzTt0th1qTJMBdvu9TCpVQi/0CVCa9myEublc2G88h6oU2qXeztJhHvrSm6jZBtzmjlL7ujTqcm6+Vduh9L2qdb7sO5cw2KqJOLd1JDLnmX3WoRnKBTBVlXPbrKQftYmrjTyVdZPs4PO8uJas7H2vCMcpGg69OBKJapkouONdLlo9rY5kzSsQ2a3cQJJCTRGxnADtyBqMkbSR+eVaHJD7QGkvaFJ69Oo+nC+Ah/Sz6AZ2UCzy9q+Y3hcA9mzUwBNn4u2hzLHnLpwHNgK/cHNcMSGAz6Emp3UVnE0Cw7S9yLHpaITcOYf55s5f62XVdsuPAnz/PehyTwvoLhzfSDXV9v+bbBu/x3aHoOgHz4ZlfM/ZR29iBseqtNh0bx+MczLf0D4xTdB16N6tjV80jQ4Ck/VCkArV/0M06bfEHXDXxAREeHze6jt1JWXwref40qShEderX0zfTqRrUs7La5fWoTyVYu5LdI4wLeGYU2MfYcgZvIxRAxzadTQ97LDv96rEpfWtWnHS00qtm9G8bJfYOiUCn17/5UkaqMRrS5wCf0a2ndAnx9+5plwSnxpExJh7OL/WpPzxpso27UbiZMmtEhHppbMwVdehSkjA31nf+YtMJ+fx5NvDYWTuhotipb8DGOPXnxs1JeiBd+iYttmRA4fjYq1v8FR7j8ZEgx0Y1a57lfoew6AOjqWJ9toIcLCja6Y4/T3N/rae7yeazt+iL+fqgD6fGTIo27dDpUOO6ybfuNzdfgol6OvefUCmNctRsT1D0AdX7u62JF3DNZtq/iaTGY97EpddMpnq6RlzUJOpGt7DvWOQ3QGaDOGoKmxbloCa/Y2GDMnKYw51C4dUHLjIyH9fRuhatfFp3kNazNRMqPGdc6em83rybSFNKucVD1NbV8Ua1gtXh0DTY09/xif89zVU4piDoPeFXP0vxBh9J8PQW4SkSd9NXVq3yY9P1JlISUaqdI8kEYtaarRBIqv+MjTTZoeo+nSOBXn9j1roc/eBUfrWJ8OjSwdQTEHGRTFJPCELn13+DiooxqMJmbNCz+BbuhkaLo2boU8Ja6t2Tth3boKuoFjoB84BuVfv8kuspG3POa3LdRN5a8/8ARb5HX3Qpta3e4decVtcJSXQmXw1gss+/kbVO7ewjGKv2PF3fp48oUH+bhKmvm670S201nVYm47eRzlq5fyPU14b2WVZtEjRvM+Rg52xXHqyCh0effLKhMVQ+c0XmpSsvZ3FC5agKi+/RE33rc2IKGNiUH8OFfMEdWrF4auXOGqvuvQEZEZGTC09a8Tu2fmizAfP4k217ja3AWhsZAEWD3J+OeLaFWjvVEXH48BCxbWy3qYkknu51Hya+9t10HXriNMu7fBmNEbnZ57teqxlMDK/3Y2YsZOgl6B8LGGBBLbdUL02IuRcPOf6mUv7Qvrwb0o/d97MPQbgeir7/T6mzo+CfH3PA1/zWbl338AR/5JxD7k4ybSgzBDOMLHXoVKKl1P6+21ngJIf60MpCUUfukdLG5JaDp258Xn+9jxe60EGGkTULticyTAQCKgETFo366tIpcTSnQZJt7CP1PwQAk0VUk+dANrC8mTKDstvtz+SJSVgieyKaeFH683Qj2+7pJwqsazrP6eg3xttyFeM+S2vRtYK8JftVtNLFuXstlA2HlX19kuQt8Tx8FtSLGYYN+zE2E9R0BzWsyeAmgSoeVZUBKY7TPaJepaR+ASCE5IURKt5wgOgBw0+3ssC2oStz/9PXJaTXDkHwP0RiBAAqwuJ0QW1t+xigNBTdeBAdskSbfKvPRL1nbRZvhvWdMkd0S7whPQtPZdBUAaXao26XCCHKB2QzNgPDR9RteZ/HJtXMP6HfUR+a8JfXa2rG3suEbJc+uGpbBlbXFpoZ0OwshZMYxamBV8R7SdSGz5GDRtvG8O1DHxvNTEUVEGR2kRwhwOxB/ahaI5/4Vx3FQYetZ2VaQkGAne+2rp1bbrjOR/vOd17k165CWoY5W7nKqjohF35U1e6wLdwLiJGT0e2tYpMHb3P6ngC/d1J7JXb178QYYBXZ6cyZM82latoCppnIkUQRndX3geiWnexzPdQAxasjTomIOrGpzOqufZigqx+9brEZHRC2Wb1iF62Hno+LdqQXuqEsyf+xXip1wJrQJZB21SG+jad0LsRVchcXrjxRzmnRtRMucDGPNyET3leu/XbNsZife/AH+Sy6VfvAmn3Yr4x94M+BpUlR4+4TqYFn/j5VDNrsuRMX7fC7lGGi69A+pE10SOtms/Xnw6TG9bhbCY+KoEGH0e1i2/8URVcyTA6L2oI2LRvl07RceOOrE91JNu459pEoJiDrW5Eqq+tYXkWefTQ+vTjXXrryzHoGrbBZouA3jhbaekKjIaoglT8+rvePvcQul57dizDqqkjqz9qQTrxp/5OkKtlyTuHvB1bVY4crYjubIM9j17oeo3DqrWHasmv+wnsqFOSXO1H/cdx9pcXB1Wz5t1W9YmV+yZ4ZowcZQWwHEiB+rUzKrrADlmUtV2WHRCwLHjGCLApYOqsCgWpGtsXQkw0re0LP+GRfg16b41hAltm3S0o8nVeN+V1WExrRHWtgs7s9qP7Yd2xCV1TgxWPZcmfikGaIyYw2KG7cB2aNIz+TtNCW774X383XDHXiyZwhVndX+Wum594CwvgTrJO9aiZLnPhHlZCVBaiPZJSSj/ZQ5M29Yh+rI/QJ9e+7tDE2p0rPnC0K032r36efVjO6SyAZomUVllO6FNaI3Ead46bEochBMvvwrGbt0R0Uu5Bilv+/Q5J3boEF78YTeZkfl//4K9spL3pyW6QHIXRiNXLSrthBACE+ZskbYKZ46SkhJ27CgsLERskALC/jgw82GYsvej+3ufQ6XTIX/e9zj1xUeIGTUWho6dYejQGeFdq2+sS1b/iuOv/gNxl1+HxGv9i8g3NeygsnoRdF17Q9Pau3rHsn8nSr//GNYLr0XrjMxaJy7r/h1cnaVOas8XH3UQTk+NDV3Y3S1tXutotrgeOkb12geHAwUFBYiLiwv6JE/ipzST6WkJ7QtuIaUAgsSCi/M4iaJULNV2cDtse9ezCxDN+nK7x4r/sWaJNqO6+sdRUQrL8i+him/r1y2q1raP7IFtxwqo4tr4fQ45KlJlFLVgOCwmFMe0RVxMNHSpfaoMENzb0aQPYJ0T2+Fd3AKq6Tq4WmstSMxrfmTXSP0F13Oi1rr7d9gPbGUdL8/AkyzQoTXUK/ld/R53wbZ5CdTp/aqC34A3A0s+Z12QQNVXvo4rDno3L4Wm93lsM26jGxMK8MKjYLj07vrv/94N7NpUUzvPdmgPbPu3Qj9iis/EmmXDUtjJAW3fFugGjePFfuoo7Lk50PYa1myzfhyoOIGcd19CxOG9LGwfc/UdTfJaZWt+hWnnZsTfcHeDdIaaCwrg1k++GLqkJLSbPh2Fq1Yj7rbpiE9KYsev6OiGmUoIzRtz7J5xBxwVFch4/1P+PXf2x8j/fg7iL7mCZ+sjMjJh6FTdPlaw4Huc+L/XkTT9bsRdXNskp7mgm9aKVb/A0GdoVeWXG9P2dShb+DXsU25GIhkB1TgXm3ducF3TYxM4ga1uFZw7WWPiKDrFk3dUCeW1joTgPeKQszHmoPOk4/Be1pqq0xHQYq6q1qVWPK709uEi7Avb3nWw5eyEfuRUTkrQ5JPlt2948knbbZCXO6KFnKvbpENHVdZKtn1gC29flZzq9zlUGcUJriO74bBaUNyqI+KiI6BLH1D1ntzb0WSM4Gp6an+07dsATa/zAzpiBsK8/Cuu8NJf6KoMonZRqmbXDpniVSVPj4HeUGdlUiBs+zZyjKTuOQLa9NrJWk8oEWdZ9hU03QcHTJb5jDnyjsK6bQW0A8bBnrUZtt1reX1Yq2QYJtVPg4tuXW0713Bnh7pGss2atYW1/PTnXVprIpGeZ/l9ARxFeawxrB91OXSZw2E7eoDX6Xo2nx6V3WZDYVER7P99ig2yjOdPRvTF05rktYp++RHmQ9loPf2PiibUzjS2snIs7z8BsYP6IvnyySjZvBNJ99+CuPj4FhFzuK/bo69aBo22cQ1TbNYyLPt6dIsYh7MZqQCrJ42ZNyTRWntZKcLUrotF2cZ13B+dcOlV3JJSk6ghIxH24BOI6OV/FqYmZWt/g72oADHjXeX8vrCXFnMZrDZZmQU3XViMIyf6/BslQmyFecgrLESij7HSpvXkMSyc9SeeZYn90z9wpvAVcDZXEOqGxiIvLw+tWgX/ur4qvGpCwbVl+ddQdxkAbY8hrsReA5J7lMgwjPGegSeogks35GJuq6x67fxjHDy5q8x87T+3wwZwZrIf3M4JO6qgIlvsIn0KWnfr6TVLRZVfMFdUjQe5TDk7FisSFrZuW843+bo+3jPauv4XchDsTlKwxXhkK4TVCOLrmkVWArmDksZHWKu6KyzohsBwUXVyhioBrRt+Ztcr64ZFUHfuBW3m+T6PK257zM2Go2061N0GsgsSwtS1EjGs5VGQC1Vi9fmANFs4iVpjdpBvTlZ8zzdF6otdlQJu7Nk7YM/aAkfvEdzSzBUQW1ZwtRdValo2LuMbCu2AMdB0d5XsVy74FDCboOutrG2wMeAKP4cdpqETkHz+RBhSvav5HJXlKHzvZRgyByPifGXJXa7sOLQf2vapXslR0gSr3LIOsVOuhSahbmetxoba5o789w20nnoVInvUXTHGbpPp6Wxpnvu/75C3aAmMk2pXnAotI+bQJbSGg26gT1O2fi0clZVIvPRKbpGtSezYidC2ikdEv+rEQ10UL1vIdu0xY3zHCIStMJ/Pr1S5qAQ6T0SM9q0LRm5pFHPk5xcgIa32WOkzBsBRWYGCWX+GJrk9Yu+qLdzfXJD2kZJ1Z2PMwdpTHeoWkidtL+uKb9npmiqGVH4E4oPSEp1wS+31MYnQDrnYSx/LfjKHE0SUlPKFulMvnhAMlIyj5BZXj1OlZHInFGmT0LpbhnfMQQ6T1PJ6WmtMlZIGdR3bdWPZ+DMnED0nEAndkCm8TffEj6brIJ5QDKvhwOjPZCAY1Gl9uWW15rZ9QROfhil3ebUj2rb8CnXGUNjWzocmYyg03Yf4PK4oAeY4fgDOopPQ9B4JVZs0jrdqmlLRuYAm6ChOqFpH5ymdvlbSxllwApZVP/KxqJ5wg9ffqHrcnrMbuv5jEBbdihOxli2/QUvGFoYIWDb+yt833SDS/Mvkfa6c+y5XlzVnAoyqBGmsUu95Ao6TR6FPr644JUg6Ie+dlxB53nhEDL1A0SapBduScwD6zt73BaUrl8J0YC8Srp0OdUTjJmSUUHnoEA6+8V90uPM2RKTVbqf05f4Y2T0dEV06I+ftz1H0+ya0uunMTb7Ul6bQ7BINsMZBEmBNREV2NoydfQtwUs927pezEd2vPyJ6ZKD9n71dG9s/+DcUr16BrAdnoP0DjyGyt3d5KV0IogYFd2NY9N3nsJ7KReTIcbDk7IehW69a1Sp577wIy8EstHnhXVgO7EHRV+8i7pb7oe8c/EyWofcQFrAvPOhfkNl+4gjfTGvSfSdGmhp73nFYfp8P3dCJ3hdcmzVgywYJoNJsjWdixVFSAGdJvk/Ng7MBSliRQ47qtGthsHi2Sdb5WlFxsG78BaqkTtyuYN27Hs7CXFdrYngkrDtWcsUUJZcIKhunsQujBJYfqDKMNULsdjgiYhGWVVuM17rtNw4c3e0RHFx2V6alRQkkt36T13vR6r1L88l1KaY1LAvfh6pdV2gzRzdahRK7GMUl11uY11l0ihOJfOw6na6SeR8zfepOPTngpfZHFu+vIRRMwSCtt21fCdvW39gdUt2hGycgK7/5N9Rt06G/4OpaNye60Vf5tBPXDb8I2l7DoTrdBkBaGzwDW5wHwwVXwnjlDG5H8XyuNq2335L/5viu6Lp08brRMe3Y4LIbP3YIttbV54rCT9+EJXsvEh9+ESofumQVa35FwcevI/bK6YgaU33jnjD9z7AXFzZp8suSdwp77rwZ8RMvQpvb/+i9X7t3oeCXhdCntFGUACN6vvEa/0sal+1vvRn2jo3rkivUH7qZpBsMYyff51CH2Yzc2Z+h1fmjEZ6aitQnn/X6e+enZ6Fw2SI+Xjo/+Q+Ep1WLLBPkDBYVhO09kTf7A24fiho+CubsLIT3qF2Fm/vPx2EvzEeH1z9H+ZrlKP5hNlrPeBzaNsEfW+yM1m8Eig76N3Cx5R5ibSptl/pVBDcU27FsWNYtgmHU5VyJpjjmKM7nRABVzFetKzjhquKu0eZ9tkDX37CYRJ+6q0qgxA8tSqC4hiaA1B0zeAKMqpqc1GHQNh1hai0sm5dw3KzNdGn2kqyBs6wQaOc/kacjN2y6z3RY4YxohbB9+7z+7mpbXcwO2+7KZroOqhTEHFxFV3Dcpzt3LUkBGkdjNCw/vcMJK23PxpsUonuAesccZUWc0AJPimn5voaF7H1UpKm7DWKNVHdHRc1JSXfMYd3wC+v96idNhzqpA5s3mf73BjRd+kE38hKv51A8oRs1lTX7amK44CqOK1XRriSc7eAuWH5fCFgs0I+YDOO197l0ij3iYU23fnVWNDYV6phW0NUQmK9YvwL2shJYjx6E7US1q/TJ15/hc2by46/4jD2LF36P/NnvovVdDyJqeHXSrM2DT8JeXtakya/K7Gxsu2U62t1yC9rc+Afv/Vq/EafmLUB0Zm9lCTC9DoO//5B/Nh07AfOJPDgSmrcwQQhtWl5DbQvg5Pc/YNMVVyL3f76dKyoP7MfRt9/E8U8/8vl3lcGAMK0G1rxTcFSUN8o+tZ7xGFIeeR4li39E7kt/R/m6FbUeEzFsDM80kEuHacdG2EqKeOakvoRpA+t+UEDnrKzwq0/U1NCslP3IPjhOHa1aZz95GBUfPA3L5uV+n2f5bS7M8z6oap3kdat/gHnxbNZxCvia+bmwrPoBzorGEQVWCrVH6kddDXX7up0Y7Yd3w7TwQ9iO7q/fi1nMcOQfdSWVKJnR6zxoB4zn5BfhKHQ5E7krGvj33ANVLlCUYHQnyar2PzyKgy3Lim9gP7TLfxKIHCEDQAkiamP03DahG3kFdOcFdjm17vod5nn/x/oc1L7iyNnJY3U2QMLC+kvv4dYE3cRbYD+wBZZl/p3hKBD1FTzZ9m+B6fPneXaX9eM6kfX66YSPRsc6K6rTGjc10aT2YiH9mlAC0Z384n1tmwr92KuhG+RKgKpaJdZKnJHovWHUZfyzeeOvMK2chzNJybcfovSHz5Dw+OuIvvJWr3McL340QnSpXRHedwj0Xb2Tx+TkpE2uPY6m7H2K3LDI7THrj3+ocobyBSXw1JGRrO+Wt+AnL5Hp2BHnoft//g8pNwbfQk/6X9F9XeK8wtnB0U8+w8ZLr0DeosU+/166dQuOvvsOcr/27eCpjojgm31L3knYK73PjfWl3aPPoe3fnkf+1x/jyLMPo2J7bXe8qFETWZuUXE5Nu7bCUVrSoMR3Xe3EzspyV2VQ8plJ3jpyc2A/tJdbvj1bxMvf/jusO9f5fZ550WyY5r4Dp8f33bzsa5gXfFRnjEat5OaV37uqd5sRVXQ89GOnQZ3SWZHulWn+exx/1Qf6TLnKqPB0zNFvHLRDLuLkV1UVuofLJAuxc0WSy6GTJmOs21d4OY1Te6rj5EGunPfnfklJFqp+DgTFhBQ7eB7XVEWnHz0NumH+OzII65ZlMC94z+VoqtXBnrXRZSZwFkDmQvrLZrj+nXAzbNtXwbLiW/8Oln46Dqw7VqPy039wvMgJzLQ+VdWQlAxUJbWvjkFqQBp7vion+XlU2V61r71gGHs1tP1dCVDS5Ko5GUxaw4YRk/nnyhXzYFrjckQ8UxR8+h+U/vwt2jz/HqIvmeZ1jgt0ngvPyETEoBEwpHknd0nYXpfkHZ9x5du+3Yo0qvY98iB23nZTwErkMI0Gmqgo2E0WnPxpntdjky67BH0++QBtrrsGwWJok4SYfsFpm54tuKQ1Gn8RGo5UgNWTQFUfERk9EDt8GCJ7epezujF26YrUp55DRDf/s0+xI0cjZu55ioQIfVGZtRvFi+YhYdot0ETHQnd6RlWlN8B26gQMXXvCln8KJUvncVukKjIKlVvXswZF6fyvUL5qCRL+NJMFFn1hzt4DTUIy1FExAceIeqD9jZW2UzfEPfZGQLHvpoJmTu1HD0A/5movoVq6cLoqparfl2Xtz7DuXo/wqTO4ZJuEa+2Jbb1mNkksn1rK6prttB/ZC/uBbVwp5tlGUNdYNSYUkPHMn9F37zjpbqCylMVdNW3rnqnxmWwbc0OVCxS3k3q0lHLQ53RUvVd1u25cXk+zxbx/x/fDfnAbVyh5uiqGxSZxVRkJ7MeYqlsEqrY74nL6ZOvUSyPXTEqokUg//UsBLDkfUkVXnVV0BqOr2mnIFNj2rFXsIuWGAmzbzt/5s6f30ZhU6WtRNVVKKh+L/o4rSvqSQUTNQIqr3TR6diTTn09Jwcur/6bRwnDh9Q3fzzAVtyIoxbqZXORKoB8+0UvvhBJFppULoO2aCY0PJ1g35p3rUbHkO0RddRc0NQRqa++b7/GKveFeV8tMuHfbSew1gTXCtK3bIOGOh6CEso1rcPyfTyHu8mmIv/KGwPvJhgo0Fv7PF9pWccj46EscfuNV5Mz6BzSR0YgdWW3qEdkzuMrbrFmvwHToMHq+8U9XtaI4MjUrgcabEpIUc0T4cfCMHjAQqTOfQlR//9+7hIsvRfyki+sdc5Rv3YiSlb+i9c138vdE3ymtuq3JbIa+czosxw6jdOUStLr4KhbertiyHpq4BBR+8wEqNq1BysxXWMTZF6a926Fr1xkqo/8qjbquo9QGmfDEO3yj1tyQyQa5xxom3wSthyMvV0q1okqpatkA09L/wX5oD4zX3s+VKtq+o7gKjH52Q3pK7PBchyOu/eBO1l1ypGV6Vd40a8xRnOe6fvpwxCRsB7YBpPF56nAtDUklUHJFP/4mQBde1RbpiX6U9023OrUPVPHtqpInNJFFk0aqhHZeY0S6pBxzxLZGjMbkNVb0s270dXWK3dtpomz/Jo5n1HEpLj2z0kI4S/Lqlq+gMdMbOZmoG3IRJwqD1aWlWJdjDmpDjfZnS9XAmIMmx1I6s1yCv+OKkps0QVYz3ueYg6r01i+CfvSV0HskTOm9Gybf0vD9JIOCDOXt2+T2SvGrYYhrks7TKZeSY/rMYdB4VILXpHLtUlSsWIDY6X+tpVXotV8BvoMJdz/K8RlNlnmSeNfDAfdd3zENyX96DEooWrIQx157Ecm3z0B8HdqOdF2oSzvM0L49+n//A/Y8+jfkvPEfGNq0QXQ/l1QP3XPEnP5ZKVvvepydf/u8+7xrGxJzCI2IJMDqSSDR0Ij0dPR8842Az48b7Vt4015Rgb333oGoAYPR7u57fT6GSo3Ld2yBsUdv7pP2Rdm6VSj59WdEDhmJyH7VPe26dp2QeItru0XzvkHJgjmsvxEx5HxUbtvAbiyx19yGmEuvh6FGP7ob64mjyHt1JgyZgxB/64N+32PlivnQb18PTH8QCPcdtDZF8su6bytMS+fAeMktUCf7diK0nzrGpdYcUHokwCi4MF7959pud5QocCds2nflxROlTkYaagUji/QaGhF0PKWkpNQZSJH+lK8qm7pwl5gTliWzWVPB7SRZax879YI17wi3vdSXQJpYtQIgEuU3V8D2+w/Q9hsLdefervdZw0WSZux0A116Mr5GwPX+Al8gSfuDbjioAsz8y8fQDprkEtY/ug+6iGiEBdApIWF9Wvi1wiOhG+yaLfSHo6wQjsN7oO7Sn92LeB2JvZPtuNPZ6Akwr8B8aHW7Xc3jypZ7ENZfPmX9L91gb30e1tM4dYRnc0k3Tu1xw0LVE5R0seXs4u+OYdw0dmZsSqhqKfxySjJRwsX7nGvPPQTTqvncfqyZcpP/bZSXsritZ/WEP/x9D3Wdmr612dApjc/XFbu2oezxv6DD0//yG/C1+ZN323wgYkePRcW+fQhX0HYQiOKNm1GZnQOn1YYwnbZFOjK1ZAKNd3SfPuj1X//uhnTzET/et2abNT8fu2fcifiJk9Hmplv8OoCW79iKyMx+fo0+ilcsRfHiBYgdNwnhXaonLsK79OCFKPz+SxTP/5b1aQzp3VG5g86FDiTe+mfWv6H4xBfmrF049eoTiBgxDnHX+zfqKJv/FbTZexF25yOAyvf1qymSX+Ytq2BatQCRV9/DDti+sB/Lhm33BqjjWgNp1QkwauGKmOYdR3E7JCUG3DpQHg7Ybvy5WtdEO2As1Km9aiWWlMQcVBFMGlH10SdzxxyUALX88glPLOovvNHnY8mkxrZ1Wb2Tr0Sg9rWa7aWs90ii7rtWc3U6ORuqEtpyhbMnLp0ol95jig+PISWmN1SVTXElJQHN6+ZDN3IqGwo5Th0+Pdnqv61L22MoQAu9VlwKdIMDf16OopNc2UYth+5kBTlhU8zBVVGN2D5ZM6bTnz+16veax5U1ezusy76Btt9oaGs4h2q69of9xCHY927ktkrPlkya+KIEGU1A23N2wTDhxjoTvg2FJtYib3zQZ5WV7eBumFbMdzl1T/BfyeQoKYK9MI/NogIR6Dvor/igMTH26IXoYeejYPEvKFm3Fp2fesHvY9Ofm6V4u4mTJsJ8Kg+GjspMtvxR8NtaOG3V0iQtMeag4jpHI2t2NbKp5DlLyzuaTvPcc89h+PDhMBqNip2Rbr755qqZa/cycaJ/gdZAkNNJk0DaAOXl7NDkj6KlPyPn8QdR8KPvFksi/oppMHTvzVVgvkpWbUUFKF211NX2OOwCrgxr/ZenEDXuUv49esIVflsYNfFJiBh9ESKGj+PfqQ3H4aOlz5qXi1MmK+ym5i29p4sOOQZ6lrPXRNO+C8Kn3gPdkLo/fxLSNF73IAu801g2RIyYAgWuPKpZkeNw4Pjx436PK27dW/UDayQ4Ck8GZatr3bQE5rn/4W24AzJ3IscXLGR+WkujuSBLbwoIzat/cGmdJHf2O9tU11gFgh1H23XlGV0KKKlKii3ZM4ZzhZnf/asohcOjhawuzIs+heW3ObDtWgPHiUNV68l1UzviMmgyhqE58DVWDpptp5YED3F7T7R9L4Dhkju9Wleo5Lryf6/B9ON73MbrOHnEZUrQxJTPfhXlX73hpZfjRtMuDRFT70T4Ba5WSX8YBl2AuEffgLaDt65RYx9bnpC+lzX/pNc6y9FDKPzhKzgsvlu8qBIm5d7H+LOxlxShiM7d1rrbIX0lDY+9/w5KN23g38t3bkfp5o3Y/8TfUbxuDa/LW7gApVu3eu9zZSUOvvYG9v59JmylpbW22/fjdzB08Y9Q6U63FUkU1qw01XjThJqtrJRF8P2R9903OPDIX1C4aKHfx7S+6U7o07qgcMEPPq+RluNHUbr+d0SNuxgR/YdAExuH5PufQMzFVyNy6GjETLjMb0JB264TIkdNRMSQ0VWGFCRoX+s1ThzFyUoL7H6+Y00FtRdyzBGgfVnTtS+MV82AbkDdYtb68y5BxLQH+Gbf1fLSgJhDq/NZVVXXuY7iDIo5LBsW8cRNMPtAMg8cc7DztJYngai1zR+k1cUxR0rDkvTBwG2QJw7CsnIuJzQ45vAz4dCgmENncMUccckIIzF7YxSLxWt6nYewCP/3LuSSrvT16JxvnvcuvxfS7qSKdjeq9t2gHXk5J8WaA19jRSY4HHPUmNR0oxs2GYapf+Ix8ozjK2b/E5XzP4Lj1BFOktWVUGoMit96EuXfvge1Dz1Tbdc+iLz6boSfd1HAbUSMuxyJT75dZ8V5Y8UbhDX/FKwFeV7rKrP2IP+7r/gc7wt9m3Zo/8iTHGdYCwuQv/CnoO4tPGOH7FdeRem27fx78aYtKPp9LXbccx+KN7nijKNffIuS7d7yJdaiEux+7EVsv3cm7Kba92vnrfsO5238vur3lhhz0Hg2xSKcwxVgFosFV111FYYNG4b33ntP8fMo4fXBBx9U/a7X68+4I5MnefN/gj61K9r96S9+HxPRpz+7MkUN8n8TTQkt0g+zFRXiwB+nwdizL1JmuEpnK3Zs4eSW7cRxqHoPqEp0GdJ78FIXNIMae3m1wGH+60/BVngKyU+/7TWDFznlDzi+d0+VEGV9Ydvi7WuhaZsKdVzdM5Hk4kLlznWVy6qTlc9OmNf9ArDOxHEO6mpViTUQeo9kadu6dWvW9aBKG/346tlSqjjSDpnMSQfLgg+g6X0eV5MpwdW6F366ZQrQ9BhS/brmylqiq2G6cNbQaE4oIKRpDWoVoBsJBGgl9RwrpbD+Eb1Xo2sal2a19SOvYL0xqv6ilgh/N1/2whOwLv0CiCAXKoWW3Vy6Hsn24tSOyPtACUiHg5NgTYmnoLKvsaIgk52W/Ignc6l7TcepMBU0nXuyIK+2/wXQ9RvdKC5U/rAXnoJl3RKX42RkrN/vsq6r/5uq+lSa1ufY8rWNo4//kRMKcdPuRPQFk1A0738o/PYzroag6hdjL9+W86S/1Pqmu1C0eAFOvPNvqKOiET20um2RKPrVpfMUO2qsz22YDh9G7mcfozL7AKL6DUDilMtgLy3D0ffexZ7770ePN/+DrJkzYezSBZmfflb1vBNzvsOR996vumnu8sTfvbarpmulx/Wyqa6Bgm+aarxP/TAX0f0Ho+0d/iuroocMg+lwDiL7+m+hVBvCYS8qgjnsEPbedAViRo1D8q338N/KNq9nl2vriePQxCZUTW4Yew/gpS5UhnC0uub2qt9zn72fb6jbPPuW1+Nib7wXp/budV3vGgDdYJg3r4I2vSfUCuIXw+Cx0A8aEzDmoL9RDKMU07Jv+TpiO7Cd2+6NU/1/PvXBfa5LTEiAee5/XW2EY6qrWqhFXjtkElf+mn96l3/WdK37s6qSkqAq8DAVv29tn1HVx7DVXN065368MQragc3rKqvpM9olRn9wF0+6Baogq1fMYbMA5CB9uoqaEmy0OEryOPmmTu/n93ixH82CddVchMW29ls1V/sN6VhuQt37PIQltK3SKKPviZLuhIbg6ZTta6zUNNlmtUDlR++XKuTpePOCDLI6Z3DHgzZzBPQjL6ltENCI2E4cgXnjcv68/FW2U4xIbdSNFXM0RrxB0GRA9r3TOfZLue8xRA8bhZOfvoeCn+bAabEgIrM/DKm+JwBpsqvDIzNx4vOPcfifL7iMcTK945OT389l7c9W553vcxtlO3fi6Icfw1ZUjKjevdDu5j/AYTLj0LsfY/ONtyHz3f9g14MzETdyCPp/8W7V8w6+8SEOvuG6H9cntUaXv83w2q7a6P15S8whNCYtNgH21FNP8b8ffuhyiVAKJbySk+vnetIclGzaiJI1q1GRlYXIDN+if7rEJLS9t25dmY7Pv87VWUeefqhKq8t85BCO/uNRRAwcjo5vfgGVrv5tbp598ZRsM2ftgKFbtTgy60w1oMWxfP4XsB7cjYjJ16N87vvQdu+HqCurbZgD0di94rb92ziBoWmThjCbd/DW6FTp+3ijTu3tEl0lvaogkneansN5qYlt91rYti6HbvQ1XPUVLJa181l4Vj/+D1UtfvWFjhXez7Q+fnVCLGvnuRJIp1sSgoFcKR3H9kM37g9e1tuk+8HtARGxPAtdExpr+8EdPItNGiXWrcuhzfQOAsy/fs3Bn254taAtGQ7U2v9lX3KgTcKxTaVlQGK/5vkfQtNnFLS9fbu2aboP4iVYdCM8XJiaMPlF2LK2wbJlBZwqDesSku4GfSfCh9evYrc5qNiyFroOadC0ioe6VSIc1mNQt3LPIjsBnQ5xU29EeIb/pN2hJ/8K84F9SLrzz0i84TZE9qv9OR39z6sBE2DhnTojfdYrMHR0tZOpw8PR9tbbYTdbkDdvHjQxsUh/9jnoa1wHEyZciLKsLJyavxC6xOBbnoSWSfHaNSjfuxdtjx6Bob3vCg1Dh07ocP8jAbdDifP0/34CW2kJDs18gJO3RMXOrTjy3GOIGTsJ6e/P8emWGix0o2cvL4U5Jwv6juk1Yo76t9EVf/kW7PknEDH2MpTOeQ+GgaMQddnNZybm2LsZIKe66FZVEzdNG3Ooayfsug7gyRKqAKsp2xAI0ifzhW3zMtj3rIdu4s31aqu0LP+GYyDdxOkNHm92hew7FugxHGE+JDrY5XHFHNYJVfXzfa4NhHXV9yy8r598u1fCz7ZnPRyHdrF+aJiPKmwyoKEEGF33nMWnuIrca9LSYYdl8eecsNQOnlTdgjje23WPMC/4gFsI9Zcoi5vrg/3ofph/+RS64Rf7TZCShh0twUDfZcNYjzbDJkx+EZYd62Bav4zlFqjivHzhV1DFxiN8SPCffXNAx2fZupUI796LBe7VsXGwkbO0O+ZwOlg/LPGWP/pNfhF7Z9wGy/Fj6Pj3pxGemo6Inr1rtb9nvzgL+pQUvwmwmAEDkPHGa4jq6bpn1cZEI+3hB2Atq0DB8pUwdu6AjH89g6he3gUWbW+4ApWHjuHYdwuhjaufQ/3ZjtPh5KWxtymcwwmw+rJs2TLOtrdq1QpjxozBs88+i/h4/8KQZrOZFzeUrSeKioq8HU5UKi7P9MxQ+1tP61j3yMd6w7gJOPLbr8j68nN0vO9Br77nmuWf/tar1Wrernt99EPP8uuVlJTAER4B1diLgR69UVxejrCKiqD23dd6a98RKJ3/DcremoWkJ96sWm+321FaWsqvWxMl76ksPw+2gnzYtEY4hk+Gs106CgsLa+2L53utz+eh5HNyjL6WAw9reCSvt5SU8PsL9j35+5xoWzROdFxph17C6yt8vFfex16nNRQKCxv0nmwODezaaGjMNmiKi3l9MO/JarLAbnZAW1RcleiseezV63MyF/lcbzlxDCrSzioq4rGiY4FeL9D3yf2ebLpWcES2hrbCBLXttPskPT4xHXaHDmpDLNSnv9ue+2jd9jtweDc0gybCunMtVCYbNB7jTq9nos+JEmB1fB6OxM6wW8yoVHDuUPKefH1OtnIzrBoj1HYV7yc9l76D7rHydezV+3NqxPNezffk7NQLjguNXG3pCFOjaPE3FOUjpscQxee9+rwnPu+UlfH30FNvoq73VHlwP078+xkYMwcj/qYZKLXaYXeGIbptJ1jo+Bg2FrHDxnJQX1Ja6vccYU5uh9KDBxAbnwz9gFSUmS1wmsxej4994DGowlR8LfL7ntLSQQ2q5R7HZPS06xF13TRQE4l64EDY3dcE93tSq5F47wwk3XcvP4fGINDn5L4Gyqxs49LcMUf4hEk4vmUr9s6ejfa339EoMUfcUy6jBDq+7HGtoRozCRg4HMXlFQirqGx4zNFvJMp+mYvKN19A0mMvN1rMUZyfD0dBPpyxSbCPvAiWrplnLOawX3wbV96y8Qpdb5so5iguKYH2gmn+Yw6nBs6+FzZOzBFmgF0XA22lGeqw4GMOi9kGh9kO7en9qPme6v05ma211lN1GFUsqoxl9Ys5jAlw2JzQlpZBrXF9n/nxbTLg0MdCpTb6jDksm1ci7PgBaIZPYTMaVaXFK+Zw2Kww0+fk0NQZc9iTu8Kp1viPJRsj5jBbYdFGQGsF1C055ug5HI5WKSx3AZ0RBXM/gDo2ETFd+zdZzOF5vmK9vCDeU+m2jTj+7+cQff54JF5/G0qohVAXDnNSWz4+tBdficSLr+TH14wVPD8/S9sOKM/LhyUpBbrefVFaXlFr35OfeQ666OiAMUdYr55gkRWPYzL5gT8h6f4ZoGb18AtH1Y45YiLQftbD6PjSoxJzCM1OmLOFH0lUAXbfffd5BYf++OKLL1gzrHPnzti/fz8ee+wxREZGYvXq1V4nak+efPLJqmozQRAEQRCqoWtpamrTtticS0jMIQiCIAgtN+agRB85fA67aAE0Wv/t1fXBZi3H6p8mckIyOrq6o0VowQmwRx55BLNmBXaa2LVrF7p3716vBFhNDhw4gLS0NCxatAhjx45VNBtLr9OxY0ccOnSID24h8Amgffv2OHz4sHxJ60DGSjkyVsqRsQoOGS/lUPDVoUMHnulXakQj1I3EHPVHvr/KkbFSjoyVcmSslCNjFboxhyTAzn7OqhbIBx54gJ0aA9GYWV/aVkJCArKysvwmwEgzzJdQPh3YcuApg8ZJxkoZMlbKkbFSjoxVcMh4KaclWpOfzUjM0XDk+6scGSvlyFgpR8ZKOTJWoRtziAbY2ctZlQBLTEzkpbk4cuQI8vPzkZKS0myvKQiCIAiCIAiCIAiCIJzDCbBgoHaAgoIC/pcE8jZv3szr09PTWdeLoFbJ559/HpdffjkLG5OuxtSpU9kFknqIH3roIX78hAnNa78sCIIgCIIgCIIgCELoYbOUwlnDgKCh2G1kcSScswmwmTNn4qOPPqr6vV+/fvzv0qVLMXq0yyVvz549VQ5KJHK/detWfg5parRp0wbjx4/HM88847PdwB/02CeeeCKo55yryFgpR8ZKOTJWypGxCg4ZL+XIWDUPMs7KkbFSjoyVcmSslCNjpRwZq9AdL51Ox8U26xdf3STbp23TawghIoIvCIIgCIIgCIIgCILQEjGZTLBYLE2ybUp+GQyGJtn2uYIkwARBEARBEARBEARBEISQpuVYKQiCIAiCIAiCIAiCIAhCPZAEmCAIgiAIgiAIgiAIghDSSAJMEARBEARBEARBEARBCGkkAaaA5557DsOHD4fRaERsbKyi59x8880ICwvzWiZOnIhQpz5jRTJ05OqZkpKC8PBwjBs3Dvv27UOoU1BQgOuvvx7R0dE8VrfeeivKysoCPoccTmseV3fddRdCjTfffBOdOnVikcchQ4Zg7dq1AR//9ddfo3v37vz43r17Y968eThXCGasPvzww1rHz7kipLl8+XJMmTKFHYDpfX/33Xd1PmfZsmXo378/uw6lp6fz+J0LBDtWNE41jytacnNzm22fQwWJN4JDYg7lSMzhH4k5lCMxhzIk5lCOxBxCcyMJMAWQi8NVV12Fu+++O6jnUQB6/PjxqmX27NkIdeozVi+++CJee+01vPXWW1izZg0iIiIwYcIEdtAIZSgQ3bFjB3755Rf8+OOPfAG444476nze7bff7nVc0fiFEl9++SXuv/9+tjveuHEj+vTpw8fDyZMnfT5+1apVuO666ziY37RpEy677DJetm/fjlAn2LEi6ObH8/jJycnBuUB5eTmPDwXvSsjOzsZFF12ECy64AJs3b8Z9992H2267DQsXLkSoE+xYudmzZ4/XsdW6desm28dQReKN4JCYQzkSc/hGYg7lSMyhHIk5lCMxh9DskAukoIwPPvjAGRMTo+ixN910k/PSSy91nqsoHSuHw+FMTk52vvTSS1XrioqKnHq93jl79mxnqLJz505yX3WuW7euat38+fOdYWFhzqNHj/p93qhRo5x//vOfnaHM4MGDnffcc0/V73a73dmmTRvn888/7/PxV199tfOiiy7yWjdkyBDnnXfe6Qx1gh2rYM5hoQx997799tuAj3nooYecPXv29Fp3zTXXOCdMmOA8l1AyVkuXLuXHFRYWNtt+hToSbwSHxByBkZjDPxJzKEdijvohMYdyJOYQmgOpAGtCqESTstHdunXj2cn8/PwzvUtnHTTjQSWr1ILgJiYmhsuqV69ejVCF3hu1IAwcOLBqHY2BSqXiGelAfPbZZ0hISECvXr3w6KOPoqKiAqE0m79hwwav44HGhH73dzzQes/HEzQjGcrHT33HiqCWl44dO6J9+/a49NJLuSJAqM25elw1hL59+3Jb2YUXXoiVK1ee6d05p5B4QxkSc0jM4YnEHMqRmKNpOVePq4YgMYdQXzT1fqZQZzvCFVdcgc6dO2P//v147LHHMGnSJD6RqdXqM717Zw3ufu2kpCSv9fR7KPdy03urWaqr0WgQFxcX8H1PmzaNAwnqk9+6dSsefvhhLgGeM2cOQoG8vDzY7Xafx8Pu3bt9PofG61w7fuo7VnRz/P777yMzMxPFxcV4+eWXWT+HAtJ27do10563DPwdVyUlJaisrGTtIMEFBaDUTkY312azGe+++y5rB9GNNemZCE2LxBvKkZijGok5JOYIBok5mhaJOZQjMYfQUM7ZBNgjjzyCWbNmBXzMrl27WOSyPlx77bVVP5NAJp3809LSeJZ27NixaEk09ViFEkrHqr546nXQcUUXATqe6KaHji9BCMSwYcN4cUOBaI8ePfD222/jmWeeOaP7JrRc6CaHFs/jis5Jr7zyCj755BOc60i8ERwScyhHYg7hbEZiDqEpkJhDaCjnbALsgQceYOekQKSmpjba69G2qIQ8KyurxQWkTTlWycnJ/O+JEyc4sHJDv1Npa0tD6VjR+64pGmqz2dilyT0mSqC2DYKOq1AIRuk7QhUL9Pl7Qr/7GxdaH8zjQ4X6jFVNtFot+vXrx8ePoOy4IkFfmYmtm8GDB2PFihVnejfOCiTeCA6JOZQjMUfDkJhDORJzNC0SczQMiTmEYDhnE2CJiYm8NBdHjhxhTQ7PgKul0JRjRS0bdNJfvHhxVfBJ5b5UxhqsC1ZLGiuaESsqKmI9hQEDBvC6JUuWwOFwVAWYSiCnGKIlHle+0Ol0PB50PJCrEkFjQr/PmDHD71jS38kxxw25XHnOOoYi9RmrmlA7w7Zt2zB58uQm3tuWBx0/Na3tz4XjqrGgc1OonJcaisQbwSExh3Ik5mgYEnMoR2KOpkVijoYhMYcQFM0itd/CycnJcW7atMn51FNPOSMjI/lnWkpLS6se061bN+ecOXP4Z1r/4IMPOlevXu3Mzs52Llq0yNm/f39nly5dnCaTyRnKBDtWxAsvvOCMjY11zp0717l161Z2s+rcubOzsrLSGcpMnDjR2a9fP+eaNWucK1as4OPjuuuuq/r7kSNHeKzo70RWVpbz6aefdq5fv56PKxqv1NRU5/nnn+8MJb744gt25Prwww/ZueqOO+7g4yM3N5f/fuONNzofeeSRqsevXLnSqdFonC+//LJz165dzieeeMKp1Wqd27Ztc4Y6wY4VfS8XLlzo3L9/v3PDhg3Oa6+91mkwGJw7duxwhjp0DnKfj+jS969//Yt/pnMWQeNE4+XmwIEDTqPR6PzrX//Kx9Wbb77pVKvVzgULFjhDnWDH6pVXXnF+9913zn379vH3jlzjVCoVX/uE4JB4Izgk5lCOxBy+kZhDORJzKEdiDuVIzCE0N5IAU2gxTl/ImgvZsLqh38nul6ioqHCOHz/emZiYyBfFjh07Om+//faqC0QoE+xYuW3JH3/8cWdSUhJfWMeOHevcs2ePM9TJz8/n4JOC9ujoaOf06dO9gnYKOD3H7tChQxx4xsXF8Tilp6fzhbK4uNgZarz++uvODh06OHU6Hdtu//7771627HScefLVV185u3btyo8nG+mffvrJea4QzFjdd999VY+l79vkyZOdGzdudJ4LuG2zay7u8aF/abxqPqdv3748XnTj53neCmWCHatZs2Y509LS+MaGzk+jR492Llmy5Ay+g5aLxBvBITGHciTm8I/EHMqRmEMZEnMoR2IOobkJo/8FVzMmCIIgCIIgCIIgCIIgCC0H1ZneAUEQBEEQBEEQBEEQBEFoSiQBJgiCIAiCIAiCIAiCIIQ0kgATBEEQBEEQBEEQBEEQQhpJgAmCIAiCIAiCIAiCIAghjSTABEEQBEEQBEEQBEEQhJBGEmCCIAiCIAiCIAiCIAhCSCMJMEEQBEEQBEEQBEEQBCGkkQSYIAiCIAiCIAiCIAiCENJIAkwQBEEQBEEQBEEQBEEIaSQBJgiCIAiCIAiCIAiCIIQ0kgATBEEQBEEQBEEQBEEQQhpJgAmCcNZTWVmJ7t2780I/uykoKEBKSgqGDx8Ou91+RvdREARBEISWj8QcgiAIoYskwARBOOsJDw/HRx99hKysLPztb3+rWn/PPfeguLgYH374IdRq9RndR0EQBEEQWj4ScwiCIIQumjO9A4IgCEoYMmQIHnroIcyaNQuXX345Tpw4gS+++AKvvvoqunbteqZ3TxAEQRCEEEFiDkEQhNAkzOl0Os/0TgiCICjBYrFg4MCBKCsr4yUjIwNLly5FWFjYmd41QRAEQRBCCIk5BEEQQg9JgAmC0KJYv349Bg0aBIPBgJ07d6Jz585nepcEQRAEQQhBJOYQBEEILUQDTBCEFsXChQv5X5PJhH379p3p3REEQRAEIUSRmEMQBCG0kAowQRBaDFu3buWZ2Ouvvx6bN29GXl4etm3bhpiYmDO9a4IgCIIghBAScwiCIIQekgATBKFFYLVaWZS2sLCQg9Ls7OyqwPT9998/07snCIIgCEKIIDGHIAhCaCItkIIgtAieffZZnoGlwDMqKgqZmZmYOXMmPvjgA8ybN+9M754gCIIgCCGCxByCIAihiVSACYJw1rNx40aeib377rvx2muvVa232+0YNmwYjh49ih07diA2NvaM7qcgCIIgCC0biTkEQRBCF0mACYIgCIIgCIIgCIIgCCGNtEAKgiAIgiAIgiAIgiAIIY0kwARBEARBEARBEARBEISQRhJggiAIgiAIgiAIgiAIQkgjCTBBEARBEARBEARBEAQhpJEEmCAIgiAIgiAIgiAIghDSSAJMEARBEARBEARBEARBCGkkASYIgiAIgiAIgiAIgiCENJIAEwRBEARBEARBEARBEEIaSYAJgiAIgiAIgiAIgiAIIY0kwARBEARBEARBEARBEISQRhJggiAIgiAIgiAIgiAIQkgjCTBBEARBEARBEARBEAQhpJEEmCAIgiAIgiAIgiAIgoBQ5v8BEnuIs4eEr2AAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", @@ -317,21 +292,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "0f200270", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# --- Generate new Data ---\n", "coords, sdf = generate_sdf_data()\n", @@ -371,7 +335,7 @@ "source": [ "## What's Next?\n", "\n", - "Congratulations on completing the introductiory tutorial on supervised solver! Now that you have a solid foundation, here are a few directions you can explore:\n", + "Congratulations on completing the introductiory tutorial on supervised solvers! Now that you have a solid foundation, here are a few directions you can explore:\n", "\n", "\n", "1. **Experiment with Training Duration & Network Architecture**: Try different training durations and tweak the network architecture to optimize performance.\n", diff --git a/tutorials/tutorial21/tutorial.ipynb b/tutorials/tutorial21/tutorial.ipynb index 056e5cbc0..ee5832878 100644 --- a/tutorials/tutorial21/tutorial.ipynb +++ b/tutorials/tutorial21/tutorial.ipynb @@ -43,7 +43,7 @@ "warnings.filterwarnings(\"ignore\")\n", "\n", "from pina import Trainer\n", - "from pina.solver import SupervisedSolver\n", + "from pina.solver import SupervisedSingleModelSolver\n", "from pina.model import KernelNeuralOperator\n", "from pina.model.block import FourierBlock1D\n", "from pina.problem.zoo import SupervisedProblem" @@ -109,21 +109,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "d331c971", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def generate_data(n_samples, x, c=1, t=0.5):\n", " x = x.T.repeat(n_samples, 1)\n", @@ -150,7 +139,8 @@ "plt.title(\"Generated 1D Advection Data\")\n", "plt.xlabel(\"x\")\n", "plt.legend()\n", - "plt.grid(True)" + "plt.grid(True)\n", + "plt.show()" ] }, { @@ -215,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "ee9b1b1a", "metadata": {}, "outputs": [], @@ -280,7 +270,7 @@ "id": "4aa44dd1", "metadata": {}, "source": [ - "Done! Let's now solve the Neural Operator problem. The problem we will define is a basic `SupervisedProblem`, and we will use the `SupervisedSolver` to train the Neural Operator.\n", + "Done! Let's now solve the Neural Operator problem. The problem we will define is a basic `SupervisedProblem`, and we will use the `SupervisedSingleModelSolver` to train the Neural Operator.\n", "\n", "> **👉 We have a dedicated [tutorial](https://mathlab.github.io/PINA/tutorial16/tutorial.html) to teach how to build a Problem from scratch — have a look if you're interested!**\n", "\n", @@ -298,7 +288,7 @@ "problem = SupervisedProblem(input, target)\n", "\n", "# making the solver\n", - "solver = SupervisedSolver(problem, model, use_lt=False)\n", + "solver = SupervisedSingleModelSolver(problem, model, use_lt=False)\n", "\n", "# simple training\n", "trainer = Trainer(\n", @@ -311,7 +301,7 @@ " enable_model_summary=False,\n", ")\n", "trainer.train()\n", - "_ = trainer.test()" + "trainer.test()" ] }, { @@ -326,21 +316,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "1a725f92", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# generate new data\n", "input, target = generate_data(100, x_train)\n", @@ -355,7 +334,8 @@ "plt.title(\"Generated 1D Advection Data\")\n", "plt.xlabel(\"x\")\n", "plt.legend()\n", - "plt.grid(True)" + "plt.grid(True)\n", + "plt.show()" ] }, { diff --git a/tutorials/tutorial22/tutorial.ipynb b/tutorials/tutorial22/tutorial.ipynb index 7d5b575e0..6828b96e8 100644 --- a/tutorials/tutorial22/tutorial.ipynb +++ b/tutorials/tutorial22/tutorial.ipynb @@ -14,14 +14,21 @@ "> ##### ⚠️ ***Before starting:***\n", "> We assume you are already familiar with the concepts covered in the [Data Structure for SciML](https://mathlab.github.io/PINA/tutorial19/tutorial.html) tutorial. If not, we strongly recommend reviewing them before exploring this advanced topic.\n", "\n", - "In this tutorial, we will demonstrate a typical use case of **PINA** for Reduced Order Modelling using Graph Convolutional Neural Network. The tutorial is largely inspired by the paper [A graph convolutional autoencoder approach to model order reduction for parametrized PDEs](https://www.sciencedirect.com/science/article/pii/S0021999124000111).\n", + "In this tutorial, we use **PINA** to construct a graph-based reduced-order model for a parametrized partial differential equation. The workflow is largely inspired by [*A graph convolutional autoencoder approach to model order reduction for parametrized PDEs*](https://www.sciencedirect.com/science/article/pii/S0021999124000111).\n", "\n", - "Let's start by importing the useful modules:" + "We will proceed in four stages:\n", + "\n", + "1. load finite-element solution snapshots;\n", + "2. represent each unstructured mesh as a graph;\n", + "3. train a graph autoencoder to compress and reconstruct the solution fields;\n", + "4. train a parameter-to-latent network and use it to predict solutions for new parameter values.\n", + "\n", + "Let us begin by importing the required modules." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "0981f1e9", "metadata": {}, "outputs": [], @@ -38,24 +45,20 @@ " !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial22/holed_poisson.pt\" -O \"holed_poisson.pt\"\n", "\n", "import torch\n", - "from torch import nn\n", "from torch_geometric.nn import GMMConv\n", - "from torch_geometric.data import (\n", - " Data,\n", - " Batch,\n", - ") # alternatively, from pina.graph import Graph, LabelBatch\n", + "from torch_geometric.data import Data, Batch\n", "from torch_geometric.utils import to_dense_batch\n", "\n", "import matplotlib.pyplot as plt\n", - "import warnings\n", "\n", + "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "from pina import Trainer\n", "from pina.model import FeedForward\n", "from pina.optim import TorchOptimizer\n", - "from pina.solver import ReducedOrderModelSolver\n", - "from pina.problem.zoo import SupervisedProblem" + "from pina.problem.zoo import SupervisedProblem\n", + "from pina.solver import SupervisedSingleModelSolver" ] }, { @@ -63,9 +66,9 @@ "id": "c04276af", "metadata": {}, "source": [ - "## Data Generation\n", + "## Problem Setup and Data Loading\n", "\n", - "In this tutorial, we will focus on solving the parametric **Poisson** equation, a linear PDE. The equation is given by:\n", + "In this tutorial, we consider the following parametrized **Poisson problem**:\n", "\n", "$$\n", "\\begin{cases}\n", @@ -74,33 +77,19 @@ "\\end{cases}\n", "$$\n", "\n", - "In this equation, $\\Omega(\\boldsymbol{\\mu}) = [0, 1]\\times[0,1] \\setminus [\\mu_1, \\mu_2]\\times[\\mu_1+0.3, \\mu_2+0.3]$ represents the spatial domain characterized by a parametrized hole defined via $\\boldsymbol{\\mu} = (\\mu_1, \\mu_2) \\in \\mathbb{P} = [0.1, 0.6]\\times[0.1, 0.6]$. Thus, the geometrical parameters define the left bottom corner of a square obstacle of dimension $0.3$. The problem is coupled with homogenous Dirichlet conditions on both internal and external boundaries. In this setting, $u(\\mathbf{x}, \\boldsymbol{\\mu})\\in \\mathbb{R}$ is the value of the function $u$ at each point in space for a specific parameter $\\boldsymbol{\\mu}$. \n", - "\n", - "We have already generated data for different parameters. The dataset is obtained via $\\mathbb{P}^1$ FE method, and an equispaced sampling with 11 points in each direction of the parametric space. \n", + "Here, $\\Omega(\\boldsymbol{\\mu}) = [0, 1]\\times[0,1] \\setminus [\\mu_1, \\mu_2]\\times[\\mu_1+0.3, \\mu_2+0.3]$ represents the spatial domain characterized by a parametrized hole defined via $\\boldsymbol{\\mu} = (\\mu_1, \\mu_2) \\in \\mathbb{P} = [0.1, 0.6]\\times[0.1, 0.6]$. The two parameters specify the lower-left corner of a square hole with side length $0.3$. Homogeneous Dirichlet boundary conditions are imposed on both the outer boundary and the boundary of the hole. \n", "\n", - "The goal is to build a Reduced Order Model that given a new parameter $\\boldsymbol{\\mu}^*$, is able to get the solution $u$ *for any discretization* $\\mathbf{x}$. To this end, we will train a Graph Convolutional Autoencoder Reduced Order Model (GCA-ROM), as presented in [A graph convolutional autoencoder approach to model order reduction for parametrized PDEs](https://www.sciencedirect.com/science/article/pii/S0021999124000111). We will cover the architecture details later, but for now, let’s start by importing the data.\n", + "For each parameter value $\\boldsymbol{\\mu}$, the scalar field $u(\\mathbf{x},\\boldsymbol{\\mu})$ is evaluated on an unstructured finite-element mesh. The supplied dataset was generated with first-order ($\\mathbb{P}^1$) finite elements using [RBniCS](https://www.rbnicsproject.org/), with an $11\\times11$ uniform sampling of the parameter space. Our objective is to learn a reduced-order surrogate that predicts the full solution field from the geometric parameters. In the implementation below, the decoder is built for the fixed mesh size contained in this dataset.\n", "\n", - "**Note:**\n", - "The numerical integration is obtained using a finite element method with the [RBniCS library](https://www.rbnicsproject.org/)." + "The next cell loads the coordinates, mesh connectivity, solution snapshots, triangulation, and parameter values, and displays one representative snapshot." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "9cbfd29d", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# === load the data ===\n", "# x, y -> spatial discretization\n", @@ -125,67 +114,151 @@ }, { "cell_type": "markdown", - "id": "f3619e4f", + "id": "3702daa4", "metadata": {}, "source": [ - "## Graph-Based Reduced Order Modeling\n", + "The dataset contains a solution field $u(\\mathbf{x},\\boldsymbol{\\mu}_i)$ and a corresponding mesh for each parameter realization $\\boldsymbol{\\mu}_i$. Because the mesh is unstructured, we represent each snapshot as a graph:\n", + "\n", + "- **Nodes** are the finite-element mesh points.\n", + "- **Node features** are the scalar solution values $u$ at those points.\n", + "- **Edges** encode mesh connectivity.\n", + "- **Node positions** store the physical coordinates $(x,y)$.\n", + "- **Edge attributes** store the absolute coordinate differences between connected nodes.\n", + "- **Edge weights** store the Euclidean distances between connected nodes.\n", "\n", - "In this problem, the geometry of the spatial domain is **unstructured**, meaning that classical grid-based methods (e.g., CNNs) are not well suited. Instead, we represent the mesh as a **graph**, where nodes correspond to spatial degrees of freedom and edges represent connectivity. This makes **Graph Neural Networks (GNNs)**, and in particular **Graph Convolutional Networks (GCNs)**, a natural choice to process the data.\n", + "For every parameter realization, we construct a PyTorch Geometric [`Data`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Data.html) object. The resulting list, `graphs`, contains one graph per solution snapshot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5e9e683", + "metadata": {}, + "outputs": [], + "source": [ + "# number of nodes and number of graphs (parameter realizations)\n", + "num_nodes, num_graphs = u.shape\n", + "\n", + "graphs = []\n", + "for g in range(num_graphs):\n", + " # node positions\n", + " pos = torch.stack([x[:, g], y[:, g]], dim=1) # shape [num_nodes, 2]\n", + " # edge attributes and weights\n", + " ei, ej = pos[edge_index[0]], pos[edge_index[1]] # [num_edges, 2]\n", + " edge_attr = torch.abs(ej - ei) # relative offsets\n", + " edge_weight = edge_attr.norm(p=2, dim=1, keepdim=True) # Euclidean distance\n", + " # node features (solution values)\n", + " node_features = u[:, g].unsqueeze(-1) # [num_nodes, 1]\n", + " # build PyG graph\n", + " graphs.append(\n", + " Data(\n", + " x=node_features,\n", + " edge_index=edge_index,\n", + " edge_weight=edge_weight,\n", + " edge_attr=edge_attr,\n", + " pos=pos,\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "41e21e69", + "metadata": {}, + "source": [ + "## Graph-based Reduced-Order Model\n", + "\n", + "Convolutional neural networks are naturally suited to regular grids, but the domains in this dataset are represented by unstructured meshes. A graph representation removes the need for a regular grid: mesh points become nodes, and mesh connectivity becomes the edge structure. This makes a **graph neural network (GNN)** a suitable choice for processing the solution fields.\n", "\n", "

\n", - " \"GCA-ROM\"\n", + " \"GCA-ROM\n", "

\n", "\n", - "To reduce computational complexity while preserving accuracy, we employ a **Reduced Order Modeling (ROM)** strategy (see picture above). The idea is to map high-dimensional simulation data $u(\\mathbf{x}, \\boldsymbol{\\mu})$ to a compact **latent space** using a **graph convolutional encoder**, and then reconstruct it back via a **decoder** (offline phase). The latent representation captures the essential features of the solution manifold. Moreover, we can learn a **parametric map** $\\mathcal{M}$ from the parameter space $\\boldsymbol{\\mu}$ directly into the latent space, enabling predictions for new unseen parameters.\n", + "The reduced-order model has three components:\n", + "\n", + "- an **encoder** $\\mathcal{E}$, which compresses a high-dimensional solution snapshot into a low-dimensional latent vector $z$;\n", + "- a **decoder** $\\mathcal{D}$, which reconstructs the solution field $\\hat{u}$ from that latent vector;\n", + "- a **parameter-to-latent map** $\\mathcal{M}$, which predicts the latent vector $\\hat{z}$ directly from the physical parameters.\n", + "\n", + "Their roles can be summarized as\n", "\n", - "Formally, the autoencoder consists of an **encoder** $\\mathcal{E}$, a **decoder** $\\mathcal{D}$, and a **parametric mapping** $\\mathcal{M}$:\n", "$$\n", - "z = \\mathcal{E}(u(\\mathbf{x}, \\boldsymbol{\\mu})), \n", - "\\quad\n", - "\\hat{u}(\\mathbf{x}, \\boldsymbol{\\mu}) = \\mathcal{D}(z),\n", - "\\quad\n", - "\\hat{z} = \\mathcal{M}(\\boldsymbol{\\mu}),\n", + "z = \\mathcal{E}\\!\\left(u(\\mathbf{x},\\boldsymbol{\\mu})\\right),\n", + "\\qquad\n", + "\\widehat{u}(\\mathbf{x},\\boldsymbol{\\mu}) = \\mathcal{D}(z),\n", + "\\qquad\n", + "\\widehat{z}=\\mathcal{M}(\\boldsymbol{\\mu}),\n", "$$\n", - "where $z \\in \\mathbb{R}^r$ is the latent representation with $r \\ll N$ (the number of degrees of freedom) and the **hat notation** ($\\hat{u}, \\hat{z}$) indicates *learned or approximated quantities*.\n", "\n", - "The training objective balances two terms:\n", - "1. **Reconstruction loss**: ensuring the autoencoder can faithfully reconstruct $u$ from $z$.\n", - "2. **Latent consistency loss**: enforcing that the parametric map $\\mathcal{M}(\\boldsymbol{\\mu})$ approximates the encoder’s latent space.\n", + "where $z\\in\\mathbb{R}^r$ and $r$ is much smaller than the number of mesh degrees of freedom.\n", + "\n", + "### Stage 1: train the autoencoder\n", + "\n", + "The encoder and decoder are trained jointly to reconstruct each solution snapshot. For a dataset of $N_s$ snapshots, the reconstruction loss is\n", "\n", - "The combined loss function is:\n", "$$\n", - "\\mathcal{L}(\\theta) = \\frac{1}{N} \\sum_{i=1}^N \n", - "\\big\\| u(\\mathbf{x}, \\boldsymbol{\\mu}_i) - \n", - "\\mathcal{D}\\!\\big(\\mathcal{E}(u(\\mathbf{x}, \\boldsymbol{\\mu}_i))\\big) \n", - "\\big\\|_2^2\n", - "\\;+\\; \\frac{1}{N} \\sum_{i=1}^N\n", - "\\big\\| \\mathcal{E}(u(\\mathbf{x}, \\boldsymbol{\\mu}_i)) - \\mathcal{M}(\\boldsymbol{\\mu}_i) \\big\\|_2^2.\n", + "\\mathcal{L}_{\\mathrm{AE}}\n", + "=\n", + "\\frac{1}{N_s}\\sum_{i=1}^{N_s}\n", + "\\left\\|\n", + "u_i-\n", + "\\mathcal{D}\\!\\left(\\mathcal{E}(u_i)\\right)\n", + "\\right\\|_2^2,\n", "$$\n", - "This framework leverages the expressive power of GNNs for unstructured geometries and the efficiency of ROMs for handling parametric PDEs.\n", "\n", - "We will now build the autoencoder network, which is a `nn.Module` with two methods: `encode` and `decode`.\n" + "with $u_i=u(\\mathbf{x},\\boldsymbol{\\mu}_i)$.\n", + "\n", + "After training, the encoder produces a latent target for each snapshot:\n", + "\n", + "$$\n", + "z_i=\\mathcal{E}(u_i).\n", + "$$\n", + "\n", + "### Stage 2: learn the parameter-to-latent map\n", + "\n", + "The autoencoder is then kept fixed while $\\mathcal{M}$ is trained to reproduce the encoder's latent vectors:\n", + "\n", + "$$\n", + "\\mathcal{L}_{\\mathrm{map}}\n", + "=\n", + "\\frac{1}{N_s}\\sum_{i=1}^{N_s}\n", + "\\left\\|\n", + "z_i-\\mathcal{M}(\\boldsymbol{\\mu}_i)\n", + "\\right\\|_2^2.\n", + "$$\n", + "\n", + "At inference time, no full-order solution is required. For a new parameter value $\\boldsymbol{\\mu}^*$, the reduced model evaluates\n", + "\n", + "$$\n", + "\\widehat{u}(\\mathbf{x},\\boldsymbol{\\mu}^*)\n", + "=\n", + "\\mathcal{D}\\!\\left(\\mathcal{M}(\\boldsymbol{\\mu}^*)\\right).\n", + "$$\n", + "\n", + "The following cell implements the graph autoencoder as a `torch.nn.Module` with explicit `encode` and `decode` methods. Graph convolutional layers process the mesh data, while fully connected layers compress the graph representation into an eight-dimensional bottleneck and expand it again during decoding." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "3197831b", "metadata": {}, "outputs": [], "source": [ - "class GraphConvolutionalAutoencoder(nn.Module):\n", + "# Convolutional Autoencoder Model\n", + "class GraphConvolutionalAutoencoder(torch.nn.Module):\n", " def __init__(\n", - " self, hidden_channels, bottleneck, input_size, ffn, act=nn.ELU\n", + " self, hidden_channels, bottleneck, input_size, ffn, act=torch.nn.ELU\n", " ):\n", " super().__init__()\n", " self.hidden_channels, self.input_size = hidden_channels, input_size\n", " self.act = act()\n", " self.current_graph = None\n", "\n", - " # Encoder GMM layers\n", - " self.fc_enc1 = nn.Linear(input_size * hidden_channels[-1], ffn)\n", - " self.fc_enc2 = nn.Linear(ffn, bottleneck)\n", - " self.encoder_convs = nn.ModuleList(\n", + " # Encoder graph layers\n", + " self.fc_enc1 = torch.nn.Linear(input_size * hidden_channels[-1], ffn)\n", + " self.fc_enc2 = torch.nn.Linear(ffn, bottleneck)\n", + " self.encoder_convs = torch.nn.ModuleList(\n", " [\n", " GMMConv(\n", " hidden_channels[i],\n", @@ -196,10 +269,10 @@ " for i in range(len(hidden_channels) - 1)\n", " ]\n", " )\n", - " # Decoder GMM layers\n", - " self.fc_dec1 = nn.Linear(bottleneck, ffn)\n", - " self.fc_dec2 = nn.Linear(ffn, input_size * hidden_channels[-1])\n", - " self.decoder_convs = nn.ModuleList(\n", + " # Decoder graph layers\n", + " self.fc_dec1 = torch.nn.Linear(bottleneck, ffn)\n", + " self.fc_dec2 = torch.nn.Linear(ffn, input_size * hidden_channels[-1])\n", + " self.decoder_convs = torch.nn.ModuleList(\n", " [\n", " GMMConv(\n", " hidden_channels[-i - 1],\n", @@ -214,166 +287,235 @@ " def encode(self, data):\n", " self.current_graph = data\n", " x = data.x\n", - " h = x\n", " for conv in self.encoder_convs:\n", - " x = self.act(conv(x, data.edge_index, data.edge_weight) + h)\n", - " x = x.reshape(\n", - " data.num_graphs, self.input_size * self.hidden_channels[-1]\n", - " )\n", + " x = self.act(conv(x, data.edge_index, data.edge_weight) + x)\n", + " x = x.reshape(-1, self.input_size * self.hidden_channels[-1])\n", " return self.fc_enc2(self.act(self.fc_enc1(x)))\n", "\n", " def decode(self, z, decoding_graph=None):\n", " data = decoding_graph or self.current_graph\n", " x = self.act(self.fc_dec2(self.act(self.fc_dec1(z)))).reshape(\n", - " data.num_graphs * self.input_size, self.hidden_channels[-1]\n", + " -1, self.hidden_channels[-1]\n", " )\n", - " h = x\n", " for i, conv in enumerate(self.decoder_convs):\n", - " x = conv(x, data.edge_index, data.edge_weight) + h\n", + " x = conv(x, data.edge_index, data.edge_weight) + x\n", " if i != len(self.decoder_convs) - 1:\n", " x = self.act(x)\n", - " return x" + " return x\n", + " \n", + " def forward(self, data):\n", + " z = self.encode(data)\n", + " return self.decode(z, decoding_graph=data)" ] }, { "cell_type": "markdown", - "id": "4d14d91d", + "id": "e38ad2d8", "metadata": {}, "source": [ - "Great! We now need to build the graph structure (a PyTorch Geometric `Data` object) from the numerical solver outputs.\n", + "## Train the Graph Autoencoder\n", + "\n", + "We now train the autoencoder with PINA's supervised-learning workflow.\n", "\n", - "The solver provides the solution values $u(\\mathbf{x}, \\boldsymbol{\\mu})$ for each parameter instance $\\boldsymbol{\\mu}$, along with the node coordinates $(x, y)$ of the unstructured mesh. Because the geometry is not defined on a regular grid, we naturally represent the mesh as a graph:\n", + "First, we define a [`SupervisedProblem`](https://mathlab.github.io/PINA/_rst/problem/zoo/supervised_problem.html#module-pina.problem.zoo.supervised_problem):\n", "\n", - "- **Nodes** correspond to spatial points in the mesh. Each node stores the **solution value** $u$ at that point as a feature. \n", - "- **Edges** represent mesh connectivity. For each edge, we compute:\n", - " - **Edge attributes**: the relative displacement vector between the two nodes. \n", - " - **Edge weights**: the Euclidean distance between the connected nodes. \n", - "- **Positions** store the physical $(x, y)$ coordinates of the nodes.\n", + "- **input:** graph objects containing the solution fields and mesh information;\n", + "- **target:** the original node-wise solution values that the autoencoder must reconstruct.\n", "\n", - "For each parameter realization $\\boldsymbol{\\mu}_i$, we therefore construct a PyTorch Geometric `Data` object:\n" + "In other words, the model receives each graph and is trained to reproduce its node features." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "8f098b6d", + "execution_count": null, + "id": "bbb3f90f", "metadata": {}, "outputs": [], "source": [ - "# number of nodes and number of graphs (parameter realizations)\n", - "num_nodes, num_graphs = u.shape\n", - "\n", - "graphs = []\n", - "for g in range(num_graphs):\n", - " # node positions\n", - " pos = torch.stack([x[:, g], y[:, g]], dim=1) # shape [num_nodes, 2]\n", - " # edge attributes and weights\n", - " ei, ej = pos[edge_index[0]], pos[edge_index[1]] # [num_edges, 2]\n", - " edge_attr = torch.abs(ej - ei) # relative offsets\n", - " edge_weight = edge_attr.norm(p=2, dim=1, keepdim=True) # Euclidean distance\n", - " # node features (solution values)\n", - " node_features = u[:, g].unsqueeze(-1) # [num_nodes, 1]\n", - " # build PyG graph\n", - " graphs.append(\n", - " Data(\n", - " x=node_features,\n", - " edge_index=edge_index,\n", - " edge_weight=edge_weight,\n", - " edge_attr=edge_attr,\n", - " pos=pos,\n", - " )\n", - " )" + "autoencoder_target = torch.stack([g.x for g in graphs], dim=0)\n", + "autoencoder_problem = SupervisedProblem(graphs, autoencoder_target)" ] }, { "cell_type": "markdown", - "id": "e38ad2d8", + "id": "79875c61", + "metadata": {}, + "source": [ + "Next, instantiate the graph autoencoder. The bottleneck dimension is set to $8$, so each solution snapshot is compressed from 1,352 nodal values to an eight-dimensional latent vector." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "601b8b11", + "metadata": {}, + "outputs": [], + "source": [ + "autoencoder = GraphConvolutionalAutoencoder(\n", + " hidden_channels=[1, 1],\n", + " bottleneck=8,\n", + " input_size=1352,\n", + " ffn=200,\n", + " act=torch.nn.ELU\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "45f2d8b9", + "metadata": {}, + "source": [ + "We then create a custom mean-squared-error loss that accepts either standard tensors or PyTorch Geometric `Data` objects. Finally, we pass the problem, model, loss, optimizer, and learning rate to `SupervisedSingleModelSolver`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47a02df1", "metadata": {}, + "outputs": [], "source": [ - "## Training with PINA\n", + "# This loss handles both Data and Torch.Tensors\n", + "class CustomMSELoss(torch.nn.MSELoss):\n", + " def forward(self, output, target):\n", + " if isinstance(target, Data):\n", + " target = target.x\n", + " return torch.nn.functional.mse_loss(\n", + " output, target, reduction=self.reduction\n", + " )\n", "\n", - "Everything is now ready! We can use **PINA** to train the model, following the workflow from previous tutorials. First, we need to define the problem. In this case, we will use the [`SupervisedProblem`](https://mathlab.github.io/PINA/_rst/problem/zoo/supervised_problem.html#module-pina.problem.zoo.supervised_problem), which expects: \n", "\n", - "- **Input**: the parameter tensor $\\boldsymbol{\\mu}$ describing each scenario. \n", - "- **Output**: the corresponding graph structure (PyTorch Geometric `Data` object) that we aim to reconstruct. " + "# Define the solver\n", + "autoencoder_solver = SupervisedSingleModelSolver(\n", + " problem=autoencoder_problem,\n", + " model=autoencoder,\n", + " use_lt=False,\n", + " loss=CustomMSELoss(),\n", + " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.001, weight_decay=1e-05),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "063b118a", + "metadata": {}, + "source": [ + "The `Trainer` manages the training loop and the train-validation split. To keep the runtime suitable for a tutorial, we train for 300 epochs using 30% of the snapshots for training and 70% for validation. These settings are intended to demonstrate the workflow rather than maximize predictive accuracy." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "bbb3f90f", + "execution_count": null, + "id": "7081ca73", "metadata": {}, "outputs": [], "source": [ - "problem = SupervisedProblem(params, graphs)" + "autoencoder_trainer = Trainer(\n", + " solver=autoencoder_solver,\n", + " accelerator=\"cpu\",\n", + " max_epochs=300,\n", + " train_size=0.3,\n", + " val_size=0.7,\n", + " test_size=0.0,\n", + " shuffle=True,\n", + ")\n", + "autoencoder_trainer.train()" ] }, { "cell_type": "markdown", - "id": "79875c61", + "id": "ac536052", "metadata": {}, "source": [ - "Next, we build the **autoencoder network** and the **interpolation network**. \n", + "## Train the Parameter-to-Latent Network" + ] + }, + { + "cell_type": "markdown", + "id": "2659eba4", + "metadata": {}, + "source": [ + "After training the autoencoder, we encode every solution snapshot to obtain its latent representation. These latent vectors become the targets for the second model. This step is the key transition from compression to reduced-order prediction: instead of requiring a full solution field as input, the new network will learn to predict the corresponding latent vector directly from $\\boldsymbol{\\mu}$.\n", "\n", - "- The **Graph Convolutional Autoencoder (GCA)** encodes the high-dimensional graph data into a compact latent space and reconstructs the graphs from this latent representation. \n", - "- The **interpolation network** (or parametric map) learns to map a new parameter $\\boldsymbol{\\mu}^*$ directly into the latent space, enabling the model to predict solutions for unseen parameter instances without running the full encoder." + "The call to `.detach()` removes the latent targets from the autoencoder's computational graph, so the autoencoder parameters are not updated during the next training stage." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "601b8b11", + "execution_count": null, + "id": "ca01450e", "metadata": {}, "outputs": [], "source": [ - "reduction_network = GraphConvolutionalAutoencoder(\n", - " hidden_channels=[1, 1], bottleneck=8, input_size=1352, ffn=200, act=nn.ELU\n", - ")\n", + "latent_representations = torch.stack(\n", + " [autoencoder.encode(g) for g in graphs], dim=0\n", + ").squeeze().detach()" + ] + }, + { + "cell_type": "markdown", + "id": "18b3c798", + "metadata": {}, + "source": [ + "As before, we formulate the task as a `SupervisedProblem`:\n", + "\n", + "- **input:** the two-dimensional parameter vectors $\\boldsymbol{\\mu}_i$;\n", + "- **target:** the corresponding eight-dimensional latent vectors $z_i$ produced by the trained encoder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78e2066f", + "metadata": {}, + "outputs": [], + "source": [ + "interpolation_problem = SupervisedProblem(params, latent_representations)" + ] + }, + { + "cell_type": "markdown", + "id": "0ca8e00e", + "metadata": {}, + "source": [ + "For the parameter-to-latent map, we use a small fully connected `FeedForward` network. It takes the two geometric parameters as input and returns an eight-dimensional vector matching the autoencoder bottleneck." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b710be9b", + "metadata": {}, + "outputs": [], + "source": [ + "# Interpolation network\n", "interpolation_network = FeedForward(\n", " input_dimensions=2,\n", " output_dimensions=8,\n", " n_layers=2,\n", " inner_size=200,\n", - " func=nn.Tanh,\n", + " func=torch.nn.Tanh,\n", ")" ] }, { "cell_type": "markdown", - "id": "45f2d8b9", + "id": "e4032ac8", "metadata": {}, "source": [ - "Finally, we will use the [`ReducedOrderModelSolver`](https://mathlab.github.io/PINA/_rst/solver/supervised_solver/reduced_order_model.html#pina.solver.supervised_solver.reduced_order_model.ReducedOrderModelSolver) to perform the training, as discussed earlier. \n", - "\n", - "This solver requires two components: \n", - "- an **interpolation network**, which maps parameters $\\boldsymbol{\\mu}$ to the latent space, and \n", - "- a **reduction network**, which in our case is the **autoencoder** that compresses and reconstructs the graph data. " + "Then, we pass the problem, model, loss, optimizer, and learning rate to `SupervisedSingleModelSolver`." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "47a02df1", + "execution_count": null, + "id": "cf7185fe", "metadata": {}, "outputs": [], "source": [ - "# This loss handles both Data and Torch.Tensors\n", - "class CustomMSELoss(nn.MSELoss):\n", - " def forward(self, output, target):\n", - " if isinstance(output, Data):\n", - " output = output.x\n", - " if isinstance(target, Data):\n", - " target = target.x\n", - " return torch.nn.functional.mse_loss(\n", - " output, target, reduction=self.reduction\n", - " )\n", - "\n", - "\n", - "# Define the solver\n", - "solver = ReducedOrderModelSolver(\n", - " problem=problem,\n", - " reduction_network=reduction_network,\n", - " interpolation_network=interpolation_network,\n", + "interpolation_solver = SupervisedSingleModelSolver(\n", + " problem=interpolation_problem,\n", + " model=interpolation_network,\n", " use_lt=False,\n", " loss=CustomMSELoss(),\n", " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.001, weight_decay=1e-05),\n", @@ -382,21 +524,21 @@ }, { "cell_type": "markdown", - "id": "063b118a", + "id": "52d1158a", "metadata": {}, "source": [ - "Training is performed as usual using the **`Trainer`** API. In this tutorial, we will use only **30% of the data** for training, and only $300$ epochs of training to illustrate the workflow." + "We train the interpolation network with the same optimizer and data split used for the autoencoder. Once training is complete, the two models can be combined into the full graph convolutional autoencoder reduced-order model." ] }, { "cell_type": "code", "execution_count": null, - "id": "7081ca73", + "id": "d59eac3c", "metadata": {}, "outputs": [], "source": [ - "trainer = Trainer(\n", - " solver=solver,\n", + "interpolation_trainer = Trainer(\n", + " solver=interpolation_solver,\n", " accelerator=\"cpu\",\n", " max_epochs=300,\n", " train_size=0.3,\n", @@ -404,7 +546,15 @@ " test_size=0.0,\n", " shuffle=True,\n", ")\n", - "trainer.train()" + "interpolation_trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "6603d52a", + "metadata": {}, + "source": [ + "## Evaluate the complete GCA-ROM" ] }, { @@ -412,25 +562,27 @@ "id": "b1d11289", "metadata": {}, "source": [ - "Once the model is trained, we can test the reconstruction by following two steps:\n", + "The complete prediction pipeline has two operations:\n", + "\n", + "1. **Map to the latent space:** evaluate the trained `interpolation_network` at a parameter value $\\boldsymbol{\\mu}$.\n", + "2. **Decode the latent vector:** pass the predicted latent representation to the autoencoder decoder to reconstruct the nodal solution field.\n", "\n", - "1. **Interpolate**: Use the `interpolation_network` to map a new parameter $\\boldsymbol{\\mu}^*$ to the latent space. \n", - "2. **Decode**: Pass the interpolated latent vector through the autoencoder (`reduction_network`) to reconstruct the corresponding graph data." + "The next cell performs this procedure for all parameter samples. The graphs are batched so that the decoder can process them together, and the output is then converted back to a dense tensor for comparison with the reference solutions." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "8dd5c0d4", "metadata": {}, "outputs": [], "source": [ "# interpolate\n", - "z = interpolation_network(params)\n", + "z = interpolation_solver(params)\n", "\n", "# decode\n", "batch = Batch.from_data_list(graphs)\n", - "out = reduction_network.decode(z, decoding_graph=batch)\n", + "out = autoencoder.decode(z, decoding_graph=batch)\n", "out, _ = to_dense_batch(out, batch.batch)\n", "out = out.squeeze(-1).T.detach()" ] @@ -440,40 +592,30 @@ "id": "91685b70", "metadata": {}, "source": [ - "Let's compute the total error, and plot a sample solution:" + "Finally, we compute the mean relative $L^2$ error across the dataset and inspect one representative prediction.\n", + "\n", + "The three panels compare:\n", + "\n", + "1. the GCA-ROM prediction;\n", + "2. the finite-element reference solution;\n", + "3. the pointwise squared error.\n", + "\n", + "The predicted and reference fields use the same color scale, making their amplitudes directly comparable." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "29d3dbac", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L2 relative error 10.06%\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# compute error\n", "l2_error = (torch.norm(out - u, dim=0) / torch.norm(u, dim=0)).mean()\n", "print(f\"L2 relative error {l2_error:.2%}\")\n", "\n", "# plot solution\n", - "idx_to_plot = 42\n", + "idx_to_plot = 100\n", "# Determine min and max values for color scaling\n", "vmin = min(out[:, idx_to_plot].min(), u[:, idx_to_plot].min())\n", "vmax = max(out[:, idx_to_plot].max(), u[:, idx_to_plot].max())\n", @@ -524,9 +666,9 @@ "id": "c152bfd1", "metadata": {}, "source": [ - "Nice! We can see that the network is correctly learning the solution operator, and the workflow was very straightforward. \n", + "The model has now completed the full reduced-order workflow: graph-based compression, latent-space interpolation, and solution reconstruction.\n", "\n", - "You may notice that the network outputs are not as smooth as the actual solution. Don’t worry — training for longer (e.g., ~5000 epochs) will produce a smoother, more accurate reconstruction.\n", + "With only 300 training epochs and 30% of the data used for fitting, the result is primarily a demonstration of the method. The reconstructed field may appear less smooth than the finite-element solution. Longer training, a larger training split, and systematic hyperparameter tuning can substantially improve the reconstruction.\n", "\n", "## What's Next?\n", "\n", diff --git a/tutorials/tutorial23/tutorial.ipynb b/tutorials/tutorial23/tutorial.ipynb index e7ec98805..3f37e94cb 100644 --- a/tutorials/tutorial23/tutorial.ipynb +++ b/tutorials/tutorial23/tutorial.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "3f1f226d", "metadata": {}, "outputs": [], @@ -46,7 +46,7 @@ "from scipy.integrate import odeint\n", "from pina import Trainer, LabelTensor\n", "from pina.problem.zoo import SupervisedProblem\n", - "from pina.solver import SupervisedSolver\n", + "from pina.solver import SupervisedSingleModelSolver\n", "from pina.optim import TorchOptimizer\n", "from pina.model import SINDy" ] @@ -90,21 +90,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "3e7c600b", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sigma, rho, beta = 10.0, 28.0, 8 / 3\n", "\n", @@ -186,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "0480bd46", "metadata": {}, "outputs": [], @@ -208,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "af16aa54", "metadata": {}, "outputs": [], @@ -230,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "805a5aee", "metadata": {}, "outputs": [], @@ -268,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "2e94b470", "metadata": {}, "outputs": [], @@ -284,7 +273,7 @@ "id": "849b4a33", "metadata": {}, "source": [ - "Finally, we will use the `SupervisedSolver` to perform the training as we're dealing with a supervised problem.\n", + "Finally, we will use the `SupervisedSingleModelSolver` to perform the training as we're dealing with a supervised problem.\n", "\n", "Recall that we should use $L^1$-regularization on the model's weights to ensure sparsity. For the ease of implementation, we adopt $L^2$ regularization, which is less common in SINDy literature but will suffice in our case.\n", "Additionally, more refined strategies could be used, for instance pruning coefficients below a certain **threshold** at every fixed number of epochs, but here we avoid further complications." @@ -292,12 +281,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "f19a48b3", "metadata": {}, "outputs": [], "source": [ - "solver = SupervisedSolver(\n", + "solver = SupervisedSingleModelSolver(\n", " problem,\n", " model=model,\n", " optimizer=TorchOptimizer(torch.optim.Adam, lr=1e-3, weight_decay=_lambda),\n", @@ -351,20 +340,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "786ad778", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dx/dt = -9.99 * x +10.00 * y \n", - "dy/dt = +27.99 * x -0.99 * y -1.00 * xz \n", - "dz/dt = -2.67 * z +1.00 * xy \n" - ] - } - ], + "outputs": [], "source": [ "def print_coefficients(model, function_names, tau, vars=None):\n", " with torch.no_grad():\n", @@ -422,21 +401,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "b9b8f972", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def SINDy_equations(x, t): # we need a numpy array for odeint\n", " with torch.no_grad():\n", diff --git a/tutorials/tutorial24/tutorial.ipynb b/tutorials/tutorial24/tutorial.ipynb index 4227d9ede..f172998aa 100644 --- a/tutorials/tutorial24/tutorial.ipynb +++ b/tutorials/tutorial24/tutorial.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ "\n", "from pina import Trainer, LabelTensor\n", "from pina.model import FeedForward, DeepONet\n", - "from pina.solver import SupervisedSolver\n", + "from pina.solver import SupervisedSingleModelSolver\n", "from pina.problem.zoo import SupervisedProblem\n", "from pina.loss import LpLoss\n", "\n", @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -123,20 +123,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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WCH9+o3U0QtRYktgJobENBy8Ql5iKh9HAP/s01Tqcm3qwY33Cg2qRllPAvC3HtQ7HeZkKYOM0db3nC1DLzufeMnqokxaD2t8uL1PbeISooSSxE0JDZrPCrKutdaNuaUSgt/13gjbodbw6QJ1/6uvfE0hOy9U4IicV+z1cPg6eAdBjjNbRlE3Hf0DtxpB9CXbO1ToaIWokSeyE0ND//jrH4fMZeLu78Ozt9t9aV+TOloF0aVSbvEIzn2w4onU4zic/G6LfU9d7vQpu9jeYpkQGI/R5Q13f/hlkX9Y2HiFqIEnshNBIgcnMR+vVpOi5Xk3x9TRqHFHZ6XQ6Xhuottot3XOGUylZGkfkZHZ9DpnJ4NcQOo3SOhrbRD4IQZGQlw6/f6x1NELUOJLYCaGRpX+e4VRKNgG1XHmiZyOtw7FZ50b+9Aqvi8ms8NnGY1qH4zxy02Hbp+p6n0nqo7sciV4Pd7ylru/6AtLPaRuPEDWMJHZCaCC3wMRnG48CMKZPM7zc7OPRYbYqGiG7PPYMJy5KZ/lK8cc8yLkCAeHQ5mGtoymf8AEQ1g0Kc+G3WVpHI0SNIomdEBpYuPMUyem5hPq682i3BlqHU27tw/zo2zIQswKfXk1URQXkpML2/6jrvSc67hMcdLprrXYx30HqaW3jEaIGkcROiGqWnV9omSbkxTub4+bioH+8rxrfV221+7+95zhyPkPjaBzcjjmQlwaBraDV/VpHUzGNb4PGt4O5QFrthKhGktgJUc2+23GKS5n5NPD35EEneN5qZD1fBrYORlHg0w3Saldu2ZevTRHS+3W1r5qjKxohG/cDXE7QNhYhaggn+OYQwnFk5hXy+dXWunF3NsdocI5/gi/1C0eng1X7kjhwLl3rcBzT9s8gPwOC20CLu7WOpnI06A5N7wRzIWx5X+tohKgRnOOvihAO4pvfE7iSXUCTAC+GtA/VOpxKExHszeA2IQB8LPPa2S7zIvzxhbre5w3naK0rUtRq99diuCSjp0XZpaSkEBgYyMmTJ7UOpcKGDRvGhx9+WC3ncqJvDyHsW1pOAV9uPQHAuL7NcXGS1roi4/uGo9fB+gPn2XcmTetwHMu2T6AgC0I7QvhAraOpXPU7qZ9JMcOWd7WORjiQ6dOnM2TIEBo1alRpx3zppZd44IEHKu1410tOTubRRx8lODgYV1dXQkNDmTVL7V/65ptvMn36dNLSqv670bn+sghhx+b/nkB6biHhQbW4u63ztNYVaRZYiyHt6wHw0frDGkfjQDKSYfdX6nqfN9QRpc6m9+vqz30/w4VD2sYiHEJ2djbz589n9OjRlXrcXbt20blz50o9ZpFnn32W1NRUNmzYQEJCAitXrqRjx44AREZG0rRpUxYuXFgl576eJHZCVIMrWfl8/bvaeXx833AMeif84406yteg17H58EX2nJLHSZXJ7x+r873V7wrN7tQ6mqoR2v5qv0EFomdqHY2wA1FRUXh5eWE2my3b4uPj0el0XLp0idWrV+Pm5kb37t2t9lu0aBEeHh4kJSVZto0aNYq2bduW2hqWn5+P0Whk+/btvPHGG+h0umLHrqi8vDwSEhLYsWMH+fn5dOzYkTvuuMPy/j333MPixYsr9ZwlkcROiGrw5dYTZOYV0jLEh4Gtg7UOp8o0DvDioY7qSN/3ow6jKIrGEdm51ET482t1vc8k52ytK9JnkvrzwApIjtc0FKelKJCfpc1i47/12NhYIiMj0V/XnzQuLo7Q0FACAgLYunUrnTp1KrbfsGHDCA8PZ8aMGQBMnjyZDRs2sGbNGnx9fW94PhcXF7Zt22Y5T1JSElFRUVZlZsyYQa1atUpdTp8ueU7GwsJCBg4cyOLFi+nXrx9z5szh3nvvJTPz2sTtXbt2ZdeuXeTl5ZW9osrBpunuZ86cyS+//MKhQ4fw8PCgZ8+evPfee0RERFRVfEI4vJTMPBZsPwnAy/3C0Ttpa12RcX2bszz2LH8kXGbr0UvcHl5X65Ds15Z3wZQPjW6DJr21jqZqBbWG1vfD/uWweQYM/1HriJxPQTbM0Kibx6Rz4OpV5uJxcXG0a9fOatvevXst206dOkVoaPHPotPpmD59Og899BDBwcHMnj2brVu3Uq9evVLPp9frOXfuHHXq1Cl23iLPPfccQ4cOLfU4JcUEMG7cOO644w7LsWfNmkWjRo2YO3cuEyZMsOybn59PcnIyDRs2LPU8FWFTi92WLVsYM2YMO3fuZP369RQUFNC/f3+ysuQB4ELcyLwtx8nON9G2vi99WwZqHU6VC/XzYER39Uvrg7XSandDF49A3NXk5s7Jzt1aV6T366DTw+FVkLhb62iEhmJjY2nbtq3VtuuTvZycHNzd3Uvc9+6776ZVq1ZMnTqV5cuX07p16zKf80ZJHYC/vz/NmjUrdXFxKd4eFhcXx8KFC7n33nuttvv6+lrdMvbw8ADU/oNVyaYWu783Wy5YsIDAwED27NnD7bffXqmBCeEMzqXm8O2OU0DRXG814I83MKZPU37afZp9Z9OIik9m0NWpUMR1Nr+jjhSNGAxhXbSOpnrUjYB2j0LcQtjwb3hiZc1IaKuL0VNtOdPq3GWUlZXF8ePHrZIss9lMbGysZbBEQEAAV65cKXH/qKgoDh06hMlkIigoqMznLamV8HozZsyw3OK9kQMHDtCggfVjIJctW0Z4eDhGo9GyLSsriyNHjvDiiy9atl2+rPY7rlu3au9iVOjJ40UdFf39/Ut8Py8vz+pecnq6TFwqapaP1x8hv9BM9yb+9K5BtyTr1HJj9K2N+WzTMWatO0z/1sFOO2CkXM7GwIFfAR3c8abW0VSv3hNh31I49Tsc2wjN+2odkfPQ6Wy6HaqVhIQEzGYzLVq0sGxbu3YtKSkplsSrQ4cOJY4gjYmJYejQocyfP58FCxbw1ltvsXTp0jKdd9++fTz44IM3fL+8t2KvXLlS7M7lF1+o81JeP7VKfHw89evXJyAgoEzxlle5B0+YzWbGjx/PLbfcQmRkZIllZs6cia+vr2UJCwsrd6BCOJrDyRksizkDwMRBLWtMa12Rp25vgp+nkeMXs1gee1brcOzLxqnqz7aPQFArbWOpbn5h0PVpdX3Dv+G6UZGiZqhTpw46nY7du9Xb8Tt37mTs2LG4u7sTHq4+e3rAgAHs37/fqtXu5MmTDB48mEmTJjF8+HCmTp3KsmXLiImJKdN5zWYzhw8f5ty5cyWOoC3vrdhu3bpx8OBBPv74Y44ePcrs2bN5/fXXmTNnDrVr17aU27p1K/3797eprsqj3IndmDFjiI+PL3Xo7uuvv05aWpplSUxMLO/phHA4H6w9hFmBu9oE0z7MT+twqp2Pu5HnezUF1JbLvEKTxhHZiYTf4MRm0Buhz+taR6ON214BNx84vw/il2kdjahmISEhTJs2jREjRtCwYUPmzZvHww8/TGRkJAaDAYA2bdrQsWNHlixZAqi3MQcOHMiQIUOYOHEioCZUgwYNYtKkSZZjL1iw4Ib/iX7nnXdYsGAB9erV45133qm0zzNixAjeeecdPvvsMzp16sTixYv55ZdfePLJJy1lcnNzWbFiBU8//XSlnfdGdEo5ejaPHTuWX3/9ld9++43GjRuXeb/09HR8fX1JS0vDx8fH1tMK4TB2JVxm6Oc7MOh1rH/pdprUraV1SJrIyTfR64PNXMjIY8q9rRnZs5HWIWlLUeCrvnD2T+jyNAyepXVE2vntA9j0DtRuBGN2g4ur1hE5lNzcXBISEmjcuPENBxk4ulWrVjFhwgTi4+OtpkUpzeTJk9myZQvR0dFVG5yN5s6dy/Lly1m3bt0Ny5T2O7Ulf7KpxU5RFMaOHcvy5cvZtGmTTUmdEDWFoii8u+YgAI90CauxSR2Ah6uBF+9sDsDsTcfIzi/UOCKNHV6tJnVGT7h9gtbRaKv7P6FWEFw5CXsWaB2NsEODBw/mmWee4ezZsnflWLNmDe+//34VRlU+RqOR2bNnV8u5bErsxowZw8KFC/nxxx/x9vYmOTmZ5ORkcnJyqio+IRzOugPniTmdiofRwPirSU1NNrRzGA38PbmUmcf8rQlah6Mdswk2TlPXuz0H3mUfzeeUXL2g16vq+m/vQ15m6eVFjTR+/Hib+ufv2rWLrl27VmFE5fPUU09V25y/NiV2c+fOJS0tjd69exMSEmJZfvrpp6qKTwiHUmgy836U+izM0bc2JtDHOW+R2MLVRc8r/dUO0f+NPk5yWq7GEWkk7ke4eBDc/eCWcVpHYx86jgT/JpB1EXbM0ToaIZyCzbdiS1qeeOKJKgpPCMfy854zHL+YRW1PI8/0aqJ1OHbj3nahdGpYm5wCE+9F1cCHwOemwcYp6vrt/wIPP03DsRsG47XpXrZ/BlmXtI1HCCcgz4oVopJk5xfy8YYjAIy9ozk+7sab7FFz6HQ6/n1Pa3Q6WB57lj2nSp541GlFv6e2StVpDl2f1Toa+9LqfghpD/mZ15JfIUS5SWInRCWZvekY59PzqF/bgxHdG9x8hxqmTX1fHu5UH4Ap/9uP2VxDHjV24RDs+lxdH/iujP78O71erReAmO/hzJ/axiOEg5PETohKcOxCBl9tPQHA5Hta4+Zi0Dgi+zRhQAtqubnw15k0fr46ebNTUxSIeg3MhRBxlzxl4UYa9lAfNYYCq15RB5qIMjHLBM9Oo7J+lxV6pJgQQu17+taK/RSYFO5sEUi/VjV8tGMp6nq78eKdzZix+hDvRx1mUGQw3s58y/rQKjgRDQY3GFD6MyhrvH5T1PpKilOnP+kyWuuI7Jqrqyt6vZ5z585Rt25dXF1da9zTbZyFoijk5+dz8eJF9Ho9rq4Va9WXxE6ICvq/vefYcSIFNxc9/763tdbh2L0nejZm0a5EEi5l8Z/Nx3h9UEutQ6oaBTmw9uqTJXq+AP4y72epagXCHW/AmlfVR661ug+86mgdld3S6/U0btyYpKQkzp07p3U4ohJ4enrSoEGDMk/GfCOS2AlRAem5BbyzSp2M+IU7mhHm76lxRPbP1UXPW3e35MkFf/L17wkM69KAxgH2/+Bym22fDamnwTsUbntZ62gcQ+fRaj+78/tg47/h3uqZ0NVRubq60qBBAwoLCzGZ5Pa1IzMYDLi4uFRKq6skdkJUwEfrjnAxI48mAV48fbtMb1JWfSIC6RVely1HLjJt5QHmj+zsXLeRUhNh60fqev9p6mS84uYMLupj1r4eADHfqfPc1e+sdVR2TafTYTQaMRqduEuDsIkMnhCinOLPpvHdjpMATB0SKQMmbKDT6Xjr7la46HVsOnSBX+Oc6FaSoqi3YAtzoOEtEPmg1hE5lgbdrw6kQAZSCFEOktgJUQ5ms8Jbv8ZjVuDutiHc2jxA65AcTrPAWoy7+si1t3+Nd54nUvy1BA7+D/QuMOg9cKaWyOrSbwq4+V4dSPGN1tEI4VAksROiHBbtPk3s6VRqubnw1t2ttA7HYT3fuynt6vuSnlvIq8v+QlEcfG671ERYPUFd7zURgttoG4+jqhV47YkU6ydDynFt4xHCgUhiJ4SNjl3IYNrKAwC81C+cIHkebLm5GPR8OLQ9bi56fjtykR93ndY6pPIzm2HF85CXBvW7wK0vaR2RY+syGhreqj6R4penwVSgdURCOARJ7ISwQW6BibE/xpJbYObWZgGM6tlI65AcXrPAWkwYEAHA9FUHOZ2SrXFE5fTHPDi5FYyecP/n6kAAUX56AzzwObj7wtk9EP2u1hEJ4RAksRPCBjNXH+RQcgZ1vFz5aGg79HrpP1UZnrylMd0a+5Odb+JfS/dicrTHjV04BBv+ra73fwfqNNU0HKfhWx/u+VRd3/ohnPxd23iEcACS2AlRRuv2J/PtjlMAfDi0HYFyC7bS6PU6Zj3cDk9XA7tOXuabbQlah1R2hflXbxXmQbN+0PlJrSNyLq3vh/YjAAV+eQZyrmgdkRB2TRI7IcogKS2HV5f9BcDTtzWmd0SgxhE5nzB/T94crA5EeX/tYQ4lp2scURlteQ+S/wKP2jDkPzIKtioMeg/8m0L6WfjfeHVKGSFEiSSxE+ImTGaFcYvjSM0uoE09XyYMaKF1SE5reNcwekfUJb/QzOgFf3IxI0/rkEp3bAP8fnUi4rs/Ae9gTcNxWm614MEv1SlkDqyAuB+0jkgIuyWJnRA38Z9Nx9iVcBkvVwOzh3fA1UX+2VQVnU7Hx0Pb06iOJ2dTc3jm+z/JLbDTCWqT42HJE6CYocMIaH2f1hE5t3qdoM8b6vrqVyHpL23jEcJOyV8oIUrxa9xZPtl4BIB37o+kkTM+09TO1PZyZf4TXfBxdyH2dCoTfrbD+e3Sz8GPQyE/AxrdBoM/1jqimuGWcdCkNxRkwQ8PwZWTWkckhN2RxE6IG9h8+AKvLNmLosDIHg25v0N9rUOqMZrWrcW8xzvhotfxv73n+HjDUa1DuiYvQ03q0s9CQDg88j24uGodVc2gN8DD30JQJGSeh+8fgKxLWkclhF2RxE6IEuw5dZnnF+6h0Kxwb7tQJt/TWuuQapyeTQOYfn8kAJ9tPMqK2LMaRwSYCmHpKEjeB1514bGl6qAJUX08/OCxn8G3AVw+rrbc5WVqHZUQdkMSOyH+5lByOqO+2U1ugZneEXX5UOar08wjXRrw7O1NAHj157/48+Rl7YJRFFgzAY6tBxcPGP4T1G6kXTw1mU8IPP4LePjDuVhY8rg67YwQQhI7Ia53OiWbx+fvIj23kM4NazP3sU4YDfLPREuvDWxB/1ZB5JvMPPHNbrYd0+DWm6LA5hnw59eADh78Cup3qv44xDUBzdUWU6MnHN8Ev45RH+smRA0nf7GEuCopLYcR8//gYkYeLYK9mT+yCx6uBq3DqvH0eh2fDGtP9yb+ZOYV8sQ3u/i/veeqLwBTAfzfWPjtffX1wJnQ8u7qO7+4sfqdYej36jQo+5bA/70gLXeixpPETgjgz5OXuWf2Nk5fzqaBvyffPdkVX0+j1mGJqzxdXfj2ya4MbhNCgUnhxUWxzP+9Gp5OkZuuDpSIXQg6Pdz9MXR/vurPK8queV8Y8l/19xO3EL6/D7JStI5KCM1IYidqvEW7TjP8y51cylRb6n58ups8LswOubkY+Gx4B0b2aAjAtJUHmLn6IOaqeq5s+jn4ZpB6m8/oCcMXy+PC7FW7R9Q+j67ecGobfHUHXDiodVRCaEISO1Fj5ReaeXPFPl7/ZR8FJoXBbUL45Z89qV/bU+vQxA0Y9Dr+fW9rXh0YAcDnv53g5SVx5ORX8iTG5/fDV33hfDx4BcITqyB8QOWeQ1Su8P7w1Hrwa6jOb/dVPziyTuuohKh2ktiJGulSZh4jvvqDhTtPo9PBhAER/OfRDni6umgdmrgJnU7HP3s344OH2mLQ61gRd44Bn/zG9uOVMKjCVAjb/6MmBUXz1D21Aep1rPixRdULbAlPb4aGt6iTRy96BH7/BMx2+vQSIaqATqnGKd3T09Px9fUlLS0NHx+f6jqtEBYms8KiXaf5cN1hrmQXUMvNhU+HtefOlkFahybKYduxS/xr6V6S0nIB9Vmzr9/VEh/3cvSPPBcL/xsHSXvV1417wdBvZZ46R1SYD6tfgZjv1NfBbeCuWdCgu7ZxCVFOtuRPktiJGmP78UtM/d8BDiVnABAeVIv/PtaRZoHeGkcmKiIjt4D3og6xcOdpAIJ83Jh+Xxv6tipjsp6XqU5l8sdc9bmv7r7Qbxp0eBz0clPDYSkK7FkAGyZDbpq6rd1w6DsFvOU/csKxSGInxHVOp2QzY/VBovYnA+DrYeSlvs15rHtDmaPOifxxIoWJv+wj4VIWALc1D+Dp25pwW/MAdLoSJpjOz4a/foKtH0Jaorot8kEYMFP+8DuTrEuwcQrEfA8o4OYDvV9XB8IYZZCUcAyS2Ikaz2xW+P3YJX7anci6A8kUmBQMeh2PdWvAS33Dqe0lz/Z0RrkFJj7ecISvtiZgujpatkWwN6Nvbcy97UNxczFAaiLs/hL2fAu5qeqOfg1g8Mfq1BnCOZ3ZA6v/Bedi1NcetaH9Y9BpFAQ00zY2IW5CEjtRY51LzWHpn2dY8mciZ1NzLNtvbRbAW3e3IiJYbrvWBKdTsvl6WwJL/kwkO9+EG/n09zrGC347aH45Gp1ytTO9X0Po+gx0HgWuXtoGLaqe2Qyx38NvH1xrpQVofLua4LW4G1zkP33C/lR5Yjdnzhw++OADkpOTadeuHbNnz6Zr166VGpgQZZGdX8ifJ6+w40QKO0+kEJeYStEV7ePuwgMd6zO0cxitQuV6q3FSjpNzcB0XYlYSdHkX7lx7IsFuXRsONhhOUJf7uLV5EF5uMhq6RjGb4NgG9RFxR9epfSsBXGtBw57qwJkmvSCwtfSzFHahShO7n376iX/84x/MmzePbt268cknn7B06VIOHz5MYGBgpQUmxN+l5RRw4mImxy9mcfRCBn+evMLexFQK/zZBbfcm/gzr0oCBkcG4G+WRYE7PbIYrCerccxcOqD+T9kLqKatiOe6B7DJ25aO0XuzNr2fZbjToCA/ypmWIDy1DfGh1dZEnj9QQqYnq6NnY7yEjyfo9zwBodAsERapT39RtAf5NpFVPVLsqTey6detGly5d+M9//gOA2WwmLCyMF154gYkTJ1ZaYML5KYpCdr6JzLxCMvMKycor5Ep2ARcz8riUmWf5mZyWy4lLWVzMyCvxOPX8POjepA49mqpLPT+Pav4kotKZCiEvXV1y09RHe+WmQeZ59Y9vehJknFN/pp6Cguzix9C7QIMe0KyvugS1Bp2OvEITf5y4zKZDF9h06AKnL5ewL1DHy5UQP3eCfTwI8XUnxM+dIG93/DyN+HgY8XE34uPhgo+7EU9XQ8kDNITjMJvhwn44EQ0ntsCp7VCQVbyc3kVN7nzDwDsYagVd/RkIXnXVwRlu3ld/1gIXt2r/KML5VFlil5+fj6enJz///DP33XefZfvIkSNJTU3l119/tSqfl5dHXt61P8bp6emEhYVVS2K384epuJzfW6XncFg3+I0rNymkXLe56F1FUSzbleteK8q1dbOiYDZf/akomK6+LjSbbQ7dw2iglpsLtdxd8PNwJcDbFU9XF+RPaiW64VeCct37f1u3+ol6a+vvi9l0dSm8uhSorwvzri651xaTjQ9yd3GHuhFqy0pgKwhqBfW7qH9gS/2oCmeu5LD/XDoHk9TlQFI6Z67klLpfSdxc9Li56HE3GnA3GnBz0eNi0OOi1+Fi0GHU6zHodRj0OvR6HXod6HU69DodOh3odaBDXdddXb/+wi5avT6BvP66l7yychmUAhrmHKRRzn6C808RlHeK4PyTuJttuzYKdUbyda6YdK4U6Fwp1Bkp1LtSqHPBjAGzzoDp6k+zzoCCHgUdZp36U0EP6FCu/n4VdOrrv33rWV7f4EL4e3lReRoO+5C6oY2q9By2JHY2dSy5dOkSJpOJoCDrqQCCgoI4dOhQsfIzZ85kypQptpyi0rid3UmH7G2anFuUgQ4oz11SM5BzdblSqREJe2T0VOeVc/MBdx/18V4+IeAdAj6h6k/fMPBvDHrbLyidTkeYvydh/p4MjAy2bE/PLeDM5RyS0nJISsslOS2XpLRcLmTkkp5TQFpOAem5haTnFFi6AuQVmskrNJOeW1hpH19oLQDodd1rhWAu00x/jhBdCnVJo64ulbq6VAJ1qdQhHS9dLrXIoZZOnTTbRSnARSkASmj9E07hdGaq1iFYqdIew6+//jovv/yy5XVRi1110HUYwc4LMsv4jdzw/25laSG4+lp3dVV/XcuCXn9dKwQ6DDrQ6XWWVguDTm3BMBh0uLrocHMxYNTr5DZWtbChjm/4+7i+VeBvLQRF24p+6g2g01svepdri8GoljG4qS1uRnf1p4s7GD3U1jaDNv3cfNyNtAo13nTQTVF3gpwCE7kFJnILzOQWmMgrNJFXaKbQpGAyKxSYzOpPs4LZrFxtvcZqXUH9yXWt3kXnAKy2FYvDhs9WjRMh1AgmIPnqcj2dYsJYmI3RlInBnP+3JQ+9uQC9YkanmNArJnRKofoTMygKOszoFAWdYkJ3/W9YUa6+vrZN9/dbKcUUf0Nn01UjStOyTojWIVixKbELCAjAYDBw/vx5q+3nz58nODi4WHk3Nzfc3LTpX9C+36OanFcIUXPodDq83FxkVK0Qwm7YNI7b1dWVTp06sXHjRss2s9nMxo0b6dGjR6UHJ4QQQgghys7m/2a+/PLLjBw5ks6dO9O1a1c++eQTsrKyGDVq1E33LboFkJ6ebnukQgghhBA1UFHeVJauFDYndo888ggXL17k7bffJjk5mfbt2xMVFVVsQEVJMjLUh69XVz87IYQQQghnkZGRga+vb6llqvWRYmazmXPnzuHt7V3lneWLBmokJibKnHlIfVxP6uIaqYtrpC6ukbqwJvVxjdTFNdVZF4qikJGRQWhoKPqbPA2lWnv86vV66tevX52nxMfHp8ZffNeT+rhG6uIaqYtrpC6ukbqwJvVxjdTFNdVVFzdrqSsiD8ETQgghhHASktgJIYQQQjgJp03s3NzcmDx5smbz6NkbqY9rpC6ukbq4RuriGqkLa1If10hdXGOvdVGtgyeEEEIIIUTVcdoWOyGEEEKImkYSOyGEEEIIJyGJnRBCCCGEk5DETgghhBDCSThUYjdnzhwaNWqEu7s73bp1Y9euXaWWX7p0KS1atMDd3Z02bdqwevVqq/cVReHtt98mJCQEDw8P+vbty9GjR6vyI1QaW+riyy+/5LbbbqN27drUrl2bvn37Fiv/xBNPoNPprJaBAwdW9ceoFLbUxYIFC4p9Tnd3d6syjnxdgG310bt372L1odPpGDx4sKWMI14bv/32G/fccw+hoaHodDpWrFhx032io6Pp2LEjbm5uNGvWjAULFhQrY+t3kL2wtT5++eUX+vXrR926dfHx8aFHjx6sXbvWqsy///3vYtdFixYtqvBTVA5b6yI6OrrEfyPJyclW5Rzx2rC1Lkr6LtDpdLRu3dpSxlGvi5kzZ9KlSxe8vb0JDAzkvvvu4/Dhwzfdzx7zDIdJ7H766SdefvllJk+eTExMDO3atWPAgAFcuHChxPLbt29n+PDhjB49mtjYWO677z7uu+8+4uPjLWXef/99PvvsM+bNm8cff/yBl5cXAwYMIDc3t7o+VrnYWhfR0dEMHz6czZs3s2PHDsLCwujfvz9nz561Kjdw4ECSkpIsy6JFi6rj41SIrXUB6izh13/OU6dOWb3vqNcF2F4fv/zyi1VdxMfHYzAYePjhh63KOdq1kZWVRbt27ZgzZ06ZyickJDB48GD69OlDXFwc48eP56mnnrJKZspzrdkLW+vjt99+o1+/fqxevZo9e/bQp08f7rnnHmJjY63KtW7d2uq6+P3336si/Epla10UOXz4sNVnDQwMtLznqNeGrXXx6aefWtVBYmIi/v7+xb4vHPG62LJlC2PGjGHnzp2sX7+egoIC+vfvT1ZW1g33sds8Q3EQXbt2VcaMGWN5bTKZlNDQUGXmzJkllh86dKgyePBgq23dunVTnn32WUVRFMVsNivBwcHKBx98YHk/NTVVcXNzUxYtWlQFn6Dy2FoXf1dYWKh4e3sr3377rWXbyJEjlSFDhlR2qFXO1rr45ptvFF9f3xsez5GvC0Wp+LXx8ccfK97e3kpmZqZlm6NeG0UAZfny5aWWefXVV5XWrVtbbXvkkUeUAQMGWF5XtG7tRVnqoyStWrVSpkyZYnk9efJkpV27dpUXmAbKUhebN29WAOXKlSs3LOMM10Z5rovly5crOp1OOXnypGWbM1wXiqIoFy5cUABly5YtNyxjr3mGQ7TY5efns2fPHvr27WvZptfr6du3Lzt27Chxnx07dliVBxgwYIClfEJCAsnJyVZlfH196dat2w2PaQ/KUxd/l52dTUFBAf7+/lbbo6OjCQwMJCIigueff56UlJRKjb2ylbcuMjMzadiwIWFhYQwZMoT9+/db3nPU6wIq59qYP38+w4YNw8vLy2q7o10btrrZ90Vl1K0jM5vNZGRkFPvOOHr0KKGhoTRp0oTHHnuM06dPaxRh1Wvfvj0hISH069ePbdu2WbbX5Gtj/vz59O3bl4YNG1ptd4brIi0tDaDYNX89e80zHCKxu3TpEiaTiaCgIKvtQUFBxfo5FElOTi61fNFPW45pD8pTF3/32muvERoaanWxDRw4kO+++46NGzfy3nvvsWXLFgYNGoTJZKrU+CtTeeoiIiKCr7/+ml9//ZWFCxdiNpvp2bMnZ86cARz3uoCKXxu7du0iPj6ep556ymq7I14btrrR90V6ejo5OTmV8u/Okc2aNYvMzEyGDh1q2datWzcWLFhAVFQUc+fOJSEhgdtuu42MjAwNI618ISEhzJs3j2XLlrFs2TLCwsLo3bs3MTExQOV8Jzuic+fOsWbNmmLfF85wXZjNZsaPH88tt9xCZGTkDcvZa57hUmVHFnbp3XffZfHixURHR1sNGhg2bJhlvU2bNrRt25amTZsSHR3NnXfeqUWoVaJHjx706NHD8rpnz560bNmSzz//nGnTpmkYmfbmz59PmzZt6Nq1q9X2mnJtiJL9+OOPTJkyhV9//dWqX9mgQYMs623btqVbt240bNiQJUuWMHr0aC1CrRIRERFERERYXvfs2ZPjx4/z8ccf8/3332sYmba+/fZb/Pz8uO+++6y2O8N1MWbMGOLj4x2ib2BJHKLFLiAgAIPBwPnz5622nz9/nuDg4BL3CQ4OLrV80U9bjmkPylMXRWbNmsW7777LunXraNu2ballmzRpQkBAAMeOHatwzFWlInVRxGg00qFDB8vndNTrAipWH1lZWSxevLhMX7yOcG3Y6kbfFz4+Pnh4eFTKteaIFi9ezFNPPcWSJUuK3XL6Oz8/P8LDw53quriRrl27Wj5nTbw2FEXh66+/5vHHH8fV1bXUso52XYwdO5aVK1eyefNm6tevX2pZe80zHCKxc3V1pVOnTmzcuNGyzWw2s3HjRqvWl+v16NHDqjzA+vXrLeUbN25McHCwVZn09HT++OOPGx7THpSnLkAdmTNt2jSioqLo3LnzTc9z5swZUlJSCAkJqZS4q0J56+J6JpOJffv2WT6no14XULH6WLp0KXl5eYwYMeKm53GEa8NWN/u+qIxrzdEsWrSIUaNGsWjRIqvpb24kMzOT48ePO9V1cSNxcXGWz1kTr40tW7Zw7NixMv1H0FGuC0VRGDt2LMuXL2fTpk00btz4pvvYbZ5RZcMyKtnixYsVNzc3ZcGCBcqBAweUZ555RvHz81OSk5MVRVGUxx9/XJk4caKl/LZt2xQXFxdl1qxZysGDB5XJkycrRqNR2bdvn6XMu+++q/j5+Sm//vqr8tdffylDhgxRGjdurOTk5FT757OFrXXx7rvvKq6ursrPP/+sJCUlWZaMjAxFURQlIyND+de//qXs2LFDSUhIUDZs2KB07NhRad68uZKbm6vJZywrW+tiypQpytq1a5Xjx48re/bsUYYNG6a4u7sr+/fvt5Rx1OtCUWyvjyK33nqr8sgjjxTb7qjXRkZGhhIbG6vExsYqgPLRRx8psbGxyqlTpxRFUZSJEycqjz/+uKX8iRMnFE9PT2XChAnKwYMHlTlz5igGg0GJioqylLlZ3dozW+vjhx9+UFxcXJQ5c+ZYfWekpqZayrzyyitKdHS0kpCQoGzbtk3p27evEhAQoFy4cKHaP58tbK2Ljz/+WFmxYoVy9OhRZd++fcq4ceMUvV6vbNiwwVLGUa8NW+uiyIgRI5Ru3bqVeExHvS6ef/55xdfXV4mOjra65rOzsy1lHCXPcJjETlEUZfbs2UqDBg0UV1dXpWvXrsrOnTst7/Xq1UsZOXKkVfklS5Yo4eHhiqurq9K6dWtl1apVVu+bzWblrbfeUoKCghQ3NzflzjvvVA4fPlwdH6XCbKmLhg0bKkCxZfLkyYqiKEp2drbSv39/pW7duorRaFQaNmyoPP3003b/pVTElroYP368pWxQUJBy1113KTExMVbHc+TrQlFs/3dy6NAhBVDWrVtX7FiOem0UTVHx96Xos48cOVLp1atXsX3at2+vuLq6Kk2aNFG++eabYsctrW7tma310atXr1LLK4o6HUxISIji6uqq1KtXT3nkkUeUY8eOVe8HKwdb6+K9995TmjZtqri7uyv+/v5K7969lU2bNhU7riNeG+X5d5Kamqp4eHgoX3zxRYnHdNTroqR6AKy+Bxwlz9Bd/UBCCCGEEMLBOUQfOyGEEEIIcXOS2AkhhBBCOAlJ7IQQQgghnIQkdkIIIYQQTkISOyGEEEIIJyGJnRBCCCGEk5DETgghhBDCSUhiJ4QQQgjhJCSxE0IIIYRwEpLYCSGEEEI4CUnshBBCCCGchCR2QogabdGiRXh4eJCUlGTZNmrUKNq2bUtaWpqGkQkhhO10iqIoWgchhBBaURSF9u3bc/vttzN79mwmT57M119/zc6dO6lXr57W4QkhhE1ctA5ACCG0pNPpmD59Og899BDBwcHMnj2brVu3SlInhHBI0mInhBBAx44d2b9/P+vWraNXr15ahyOEEOUifeyEEDVeVFQUhw4dwmQyERQUpHU4QghRbtJiJ4So0WJiYujduzeff/45CxYswMfHh6VLl2odlhBClIv0sRNC1FgnT55k8ODBTJo0ieHDh9OkSRN69OhBTEwMHTt21Do8IYSwmbTYCSFqpMuXL9OzZ0969+7NvHnzLNsHDx6MyWQiKipKw+iEEKJ8JLETQgghhHASMnhCCCGEEMJJSGInhBBCCOEkJLETQgghhHASktgJIYQQQjgJSeyEEEIIIZyEJHZCCCGEEE5CEjshhBBCCCchiZ0QQgghhJOQxE4IIYQQwklIYieEEEII4SQksRNCCCGEcBKS2AkhhBBCOAlJ7IQQQgghnIQkdkIIIYQQTkISOyGEEEIIJyGJnRBCCCGEk3CpzpOZzWbOnTuHt7c3Op2uOk8thBBCCOGQFEUhIyOD0NBQ9PqbtMkpFTBz5kwFUMaNG1em8omJiQogiyyyyCKLLLLIIouNS2Ji4k1zrXK32O3evZvPP/+ctm3blnkfb29vABITE/Hx8SnvqYUQQgghaoz09HTCwsIseVRpypXYZWZm8thjj/Hll1/yzjvvlHm/otuvPj4+ktgJIYQQQtigLN3YypXYjRkzhsGDB9O3b99SE7u8vDzy8vIsr9PT08tzOuHkzGaFPxIu87+/zlFQaObW5gHc1rwu/l6uWocmtJafDcl/wdkYuLAfAsKhw+Pg6a91ZEIIYZdsTuwWL15MTEwMu3fvvmnZmTNnMmXKlHIFJpzf8YuZLI85y/LYs5xNzbFsX7rnDDodtK3vR6/wuvSJqEv7MD8ZcFNTJMfDrs+vJnMHQTFZvx/9LrQbDt2fh4Dm2sQohBB2SqcoilLWwomJiXTu3Jn169db+tb17t2b9u3b88knnxQrX1KLXVhYGGlpaXIrtgY7cC6dN1bsI/Z0qmWbt7sLd7cNwcfdyJYjFzmUnGG1z/CuDZg2pDUuBpmhx6ntXw7Ln4fCa4k+tYIgtCMEtoSj6+B8/LX3mveHni9C49uqP1YhhKgm6enp+Pr6lil/simxW7FiBffffz8Gg8GyzWQyodPp0Ov15OXlWb1XkcCEc9qbmMo/vt5FWk4BBr2OXuF1eaBjPfq2DMLdeO3aOZ+ey5YjF9ly+CJr4pMwK9C3ZSCzh3fEw/XG15hwUGYzbHkXtrynvm56B3QeDfU6gncIFLXWKgok/AY758KRKNSBYsDA96D7c5qELoTWTCYTBQUFWochKsBoNFZa/mRTYpeRkcGpU6esto0aNYoWLVrw2muvERkZWer+ktjVbHtOXeaJr3eTkVdIxwZ+zBvRiUAf95vuFxWfzLjFseQVmunQwI/5I7tI/ztnkp8Fy5+Dg/+nvu4xFvpNBf1NEviU47D1Q4j7QX199yfQeVSVhiqEPVEUheTkZFJTU7UORVQCPz8/goODS+x2ZEv+ZFMfO29v72LJm5eXF3Xq1LlpUidqtp0nUnhywW6y8010bezP1090oZZb2S6/gZHB/PBUN0Z/+yexp1N5aN52vh3VlTB/zyqOWlS5tDOwaBgk7wO9Ee75BDqMKNu+dZrCkDnqQIrts2HlS+DiBu0frdKQhbAXRUldYGAgnp6e0g/ZQSmKQnZ2NhcuXAAgJCSkQser1idPiJrp96OXeOq73eQWmLm1WQBf/qOzzbdTOzfy5+fnejDy612cuJjFA3O3s2BUF1qH+lZR1KLKpSfBl3dCZjJ4BsCwH6BBd9uOodNBv2lQmAe7voBfx6jJXeSDVROzEHbCZDJZkro6depoHY6oIA8PDwAuXLhAYGBgqbdlb6bCPdGjo6NLHDghBMDmwxd48ls1qesTUZevRtqe1BVpHuTNL/+8hRbB3lzMyGPk17u4mJF38x2F/TGb4Jen1aQuIAKe2Wx7UldEp1P72HX8ByhmWPY0HPxf5cYrhJ0p6lPn6Sl3LpxF0e+yov0lZYihqDKJl7MZ+0MM+YVm+rcKYt7jnawGSJRHsK87Pz3bgxbB3lzKzOfVn/diQzdRYS9+mwUnt4LRC4b9CH4NKnY8vV7tY9d2mDo9ytJRcGxDpYQqhD2T26/Oo7J+l5LYiSphNiu8snQvWfkmujbyZ85jHXFzqZzRrL4eRj4d1gFXFz2bD1/kux2nbr6TsB8nf1dHwALc/REENKuc4+oNap+71veDuQB+eRayUirn2EII4SAksRNV4uttCexKuIynq4FZD7fDWMnzz0UEezNpUAsApq8+yJHzGTfZQ9iFrEuw7Cn1lmn7x6DdsMo9vsEF7v8cAltB9iVYM6Fyjy+EEHZOEjtR6Y6cz+D9tYcBeOvuVjSoUzV9QEb2bESfiLrkF5p5cVEsuQWmm+8ktGM2w4rnISNJfTTYXR9UzXlc3NSWO50B4pdJfzshnFBKSgqBgYGcPHmyTOWHDRvGhx9+WLVB2QlJ7ESlKjCZeXlJHPmFZnpH1GVYl7AqO5dOp+P9h9oRUMuVQ8kZvB91uMrOJSrBzjnqkyMMbvDQN+DqVXXnqtcRbhmnrq98GbIvV925hBDVbvr06QwZMoRGjRqVqfybb77J9OnTSUtLq9rA7IAkdqJS/WfTMeLPpuPrYeS9B9tWecfeut5ufPBQO0C9/bvlyMUqPZ8op7N7YMO/1fWBMyG4Gua97D0R6raArAuw5rWqP58QolpkZ2czf/58Ro8eXeZ9IiMjadq0KQsXLqzCyOyDJHai0vx1JpX/bD4GwLT7Igkqw1MlKkOfFoGM7NEQgFeW7OVyVn61nFeUkdmkTh5sLoRWQ6Dzk9VzXhc3GPJf0Olh3xI4tLp6ziuEuKmoqCi8vLwwm82WbfHx8eh0Oi5dulTqvqtXr8bNzY3u3a9NkbRo0SI8PDxISkqybBs1ahRt27a1tNLdc889LF68uJI/if2RxE5UitwCEy/9FIfJrHB32xDubRdared//a6WhAfV4lJmHh+uk1uydiXuB0jaC26+MPija899rQ71O0HPF9T1lePllqwQdiI2NpbIyEj0+mtpSFxcHKGhoQQEBJS679atW+nUqZPVtmHDhhEeHs6MGTMAmDx5Mhs2bGDNmjX4+qoT2Xft2pVdu3aRl+fc859KYicqxZe/neD4xSzqersxbUj1P17O3WjgnfvaALBo12kOJadXewyiBLlpsHGqut77NfAq/Qu7SvSepA7WyDwPaydV//mFqCaKopCdX6jJYut8onFxcbRr185q2969ey3bVq5cSUREBM2bN+err76yKnfq1ClCQ60bD3Q6HdOnT+fLL79k+vTpzJ49m6ioKOrVq2cpExoaSn5+PsnJyTbF6mjkkWKiwi5k5DJ3y3FAHQVb28tVkzi6NvbnrjbBrN6XzDsrD/L96K4yeafWtrwPWRehTnPo8rQ2MRjd1VuyX/eHvYugw+PQ6BZtYhGiCuUUmGj19lpNzn1g6gA8XcueUsTGxvLiiy9abYuLi6Nz584UFhby8ssvs3nzZnx9fenUqRP333+/5dFpOTk5uLsX7+pz991306pVK6ZOncq6deto3bq11ftFj+3Kzs629eM5FGmxExX28fqjZOebaB/mxz1tK/bw4oqaOLAlrgY9vx+7xKZDFzSNpca7dBT+mKeuD5wJLtok/ACEdYGOI9X19W+BPK1ECM1kZWVx/PhxqxY7s9lMbGws7dq1Y9euXbRu3Zp69epRq1YtBg0axLp16yxlAwICuHLlSrHjRkVFcejQIUwmE0FBQcXev3xZ7YpRt27dKvhU9kNa7ESFHE7O4KfdpwF4c3BLzVvIGtTx5MlbGzNvy3GmrzrIbc3r4uoi/3/RxNo31AETzQdA835aRwO9X4e/lqgjdPf/ApEPah2REJXKw2jgwNQBmp27rBISEjCbzbRo0cKybe3ataSkpNCuXTv2799vdQu1Xr16nD171vK6Q4cOxUa3xsTEMHToUObPn8+CBQt46623WLp0qVWZ+Ph46tevf9M+fI5O/uKJCpmx+iBmBQZFBtO5kb/W4QAwpk9TAmq5cuJSFgt3yuPGNHF0PRxdC3oXGDBD62hU3kHX5rbbMAUKnbsDtah5dDodnq4umiy2/Ke+Tp066HQ6du/eDcDOnTsZO3Ys7u7uhIeH33T/AQMGsH//fkur3cmTJxk8eDCTJk1i+PDhTJ06lWXLlhETE2O139atW+nfv78NNeqYJLET5fbbkYtsOXIRo0HHxEEtbr5DNfF2N/JK/wgAPtlwhCsy/Un1KsyHqNfV9W7PVd6zYCtDz7FQKxhST8Hu+VpHI0SNFBISwrRp0xgxYgQNGzZk3rx5PPzww0RGRmIwGAgNDbVqoTt79qzVYIk2bdrQsWNHlixZwuXLlxk4cCBDhgxh4sSJAHTr1o1BgwYxadK1wVK5ubmsWLGCp5/WqK9vNdIptg5lqYD09HR8fX1JS0vDx8enuk4rqoDJrDD4s60cSs5g9K2NeevuVlqHZOX6+Eb2aMgUDUbq1lg75qijT73qwgt7wN1X64is7fkW/vcieNSGF+PAw0/riISwWW5uLgkJCTRu3LjEgQSOrLCwkJYtWxIdHW0ZPLF9+3bL4AmAVatWMWHCBOLj462mTLmRuXPnsnz5cqu+evamtN+pLfmTtNiJcln6ZyKHkjPw9TDywh121CJzlUGv4+2ryebCP05z7EKGxhHVENmXIfo9df3Ot+0vqQNo/5j6RIqcK7C1Zjw7UghH4uLiwocffkifPn1o3749r7zyilVSBzB48GCeeeYZq5a90hiNRmbPnl0V4dodSeyEzbLyCvlw/REAXrijGX6eGo52LEXPZgH0bRmEyawwY/UhrcOpGbZ/BnlpENhaTaDskcEF+l2dW++PzyH1tLbxCCGKuffeezly5AjHjh3jmWeeKbHM+PHjCQsr2/PIn3rqKSIiIiozRLsliZ2w2ee/neBiRh4N63jyjx6NtA6nVJPuaoFBr2PToQvsOVV8eLyoRBnn1UQJ4I43QF/2UXLVrnl/aHQbmPJg0ztaRyOEEJVGEjthk5TMPL7aegKA1wa2sPupRJrUrcVDHesDyKPGqtrvH0FBNtTrBBF3aR1N6XQ66D9NXf/rJzgXp2k4QghRWez7r7KwO/O2HCc730Sber4MigzWOpwyeeHOZhgNOrYfT2H7sdIfLi3KKTUR/vxaXb/jzep9Hmx5hXaANkPV9aLHngkhhIOTxE6U2YX0XL7boc4L93L/cM0nIy6r+rU9Gd61AQAfrj9i8zMNRRn89j6Y8tXbm036aB1N2fWZpM61d3wjnN6pdTRCCFFhktiJMpuz+Rh5hWY6NaxN73DHeiTL2D7NcHPRs+fUFaIPX9Q6HOeSchxif1DX73jLMVrrivg3vjbIQ/raCSGcgCR2okzOpuawaFciAK84UGtdkUAfd0b2bATArHWHpdWuMkXPBMWkDkho0E3raGx3+wQwuMLJrZDwm9bRCCFEhUhiJ8rkP5uOkm8y06NJHXo2dczn7D3Xqylergb2n0tn7f5krcNxDucPwL6f1fU73tQ2lvLyC4OOI9X1TdNBkn4hhAOTxE7c1KmULJb8eQZQW+sclb+XK6NvbQzAR+uPYDLLH/AK2zwdUKDVEAhpp3U05XfbK+DiDok74dhGraMRQohyk8RO3NSnG49iMiv0Cq9L50b+WodTIaNva4KPuwtHzmfyv73ntA7HsZ3dA4dWgk4Pfd7QOpqK8QmBLk+p65vfkVY7IYTDksROlOrYhQxWxKqPbHHk1roivh5Gnu3VFICPNxyhwGTWOCIHtmm6+rPtI1DXCWZ0v2U8GL3gXCwcXqN1NEIIUS6S2IlSfbzhKGYF+rcKom19P63DqRRP9GxEHS9XTqVk80vMGa3DcUyndqhThOhdoNdrWkdTOWrVhW5XH120eQaYJekXQjgeSezEDR1MSmfVX0kAvNTP8Vvrini5ufB8b7XV7rONx8grNGkckQPafLW1rsMIdcoQZ9HzRXD1hvP74OD/aR2NEDVaSkoKgYGBnDx5UutQKmzYsGF8+OGH1XIuSezEDX247ggAd7cNoWWIj8bRVK4R3RsS5OPG2dQcftqdqHU4juXEFnVqEIOrOlWIM/H0hx7/VNc3zwCzJP1CaGX69OkMGTKERo0aVdoxX3rpJR544IFKO971kpOTefTRRwkODsbV1ZXQ0FBmzZoFwJtvvsn06dNJS0urknNfTxI7UaLY01fYcPA8ep1ztdYVcTcaGHtHcwBmbzpGTr78AS8TRbk2kW+nUeBbX9t4qkKPMeDuB5cOw76lWkcjRI2UnZ3N/PnzGT16dKUed9euXXTu3LlSj1nk2WefJTU1lQ0bNpCQkMDKlSvp2LEjAJGRkTRt2pSFCxdWybmvJ4mdKFFRa90DHevTtG4tjaOpGo90DqOenwcXM/JYuPOU1uE4hmMb4MwudWqQ217WOpqq4e4Lt4xT16NnQmG+tvEI4YSioqLw8vLCfF1f1vj4eHQ6HZcuXWL16tW4ubnRvXt3q/0WLVqEh4cHSUlJlm2jRo2ibdu2pbaG5efnYzQa2b59O2+88QY6na7YsSsqLy+PhIQEduzYQX5+Ph07duSOO+6wvH/PPfewePHiSj1nSSSxE8XsOJ7C78cuYTToGHdnc63DqTKuLnrL55u75TiZeYUaR2Tnrm+t6/IUeAdrG09V6vYseAXClZMQ+73W0QhRNooC+VnaLDZOERQbG0tkZCR6/bU0JC4ujtDQUAICAti6dSudOnUqtt+wYcMIDw9nxowZAEyePJkNGzawZs0afH19b3g+FxcXtm3bZjlPUlISUVFRVmVmzJhBrVq1Sl1Onz5d4vELCwsZOHAgixcvpl+/fsyZM4d7772XzMxMS5muXbuya9cu8vLyyl5R5eBiS+GZM2fyyy+/cOjQITw8POjZsyfvvfceERFOMNWBAEBRFD5cdxiAR7qEEebvqXFEVeuBjvX4b/QxTqZks2BbguX2rCjBoVWQFKdOCXLrS1pHU7VcveD2f8GaV+G3D6D9o2D00DoqIUpXkA0zQrU596Rz6r+bMoqLi6NdO+tJzffu3WvZdurUKUJDi38WnU7H9OnTeeihhwgODmb27Nls3bqVevXqlXo+vV7PuXPnqFOnTrHzFnnuuecYOnRoqccpKSaAcePGcccdd1iOPWvWLBo1asTcuXOZMGGCZd/8/HySk5Np2LBhqeepCJta7LZs2cKYMWPYuXMn69evp6CggP79+5OVlVVV8YlqFn3kIn+euoKbi54XakCS42LQW/oQfv7bCdKyCzSOyE6ZzepgAoDuz4GXYz5WziadngDfMMhIgt3ztY5GCKcSGxtL27ZtrbZdn+zl5OTg7u5e4r533303rVq1YurUqSxfvpzWrVuX+Zw3SuoA/P39adasWamLi0vx9rC4uDgWLlzIvffea7Xd19fX6paxh4f6n8Ps7OwyxVteNrXY/b3ZcsGCBQQGBrJnzx5uv/32Sg1MVL/rW+v+0aMhQT4l/6NyNne3DWXO5mMcOZ/JV7+f4JX+0gJdzIHlcGE/uPlCzxe0jqZ6uLipc/T931j4/SPoNBLcvLWOSogbM3qqLWdanbuMsrKyOH78uFWSZTabiY2NtQyWCAgI4MqVKyXuHxUVxaFDhzCZTAQFBZX5vCW1El5vxowZllu8N3LgwAEaNGhgtW3ZsmWEh4djNBot27Kysjhy5AgvvviiZdvly5cBqFu3bpljLg+bEru/K+qo6O9f8mOm8vLyrO4lp6enV+R0ooqt3Z9M/Nl0vFwNPHf16Qw1gUGv4+V+4Ty3MIavf09QJzCu5aZ1WPbDVACbZ6rrPcaAR21t46lO7YbDtk8g5RjsnAu9XtU6IiFuTKez6XaoVhISEjCbzbRo0cKybe3ataSkpFgSrw4dOpQ4gjQmJoahQ4cyf/58FixYwFtvvcXSpWUbvb5v3z4efPDBG75f3luxV65cKXbn8osvvgCwmlolPj6e+vXrExBQtXc8yj14wmw2M378eG655RYiIyNLLDNz5kx8fX0tS1hYWLkDFVXLZFYsI2GfvLVxjUtsBrQOpnWoD1n5JuZGH9c6HPsS8x2kHAXPOtD9ea2jqV4GF+gzSV3fPhuyL2sbjxBOoE6dOuh0Onbv3g3Azp07GTt2LO7u7oSHq11jBgwYwP79+61a7U6ePMngwYOZNGkSw4cPZ+rUqSxbtoyYmJgynddsNnP48GHOnTtX4gja8t6K7datGwcPHuTjjz/m6NGjzJ49m9dff505c+ZQu/a1/whv3bqV/v3721RX5VHuxG7MmDHEx8eXOnT39ddfJy0tzbIkJspEsPbq//ae5eiFTHzcXXjqtiZah1PtdDod/xqg3oL9bscpEi9XbR8Ih5GXoU75AdBrIrg710TVZdLqfghqA3npsO1TraMRwuGFhIQwbdo0RowYQcOGDZk3bx4PP/wwkZGRGAwGANq0aUPHjh1ZsmQJoN7GHDhwIEOGDGHixImAmlANGjSISZMmWY69YMECdDpdied95513WLBgAfXq1eOdd96ptM8zYsQI3nnnHT777DM6derE4sWL+eWXX3jyySctZXJzc1mxYgVPP/10pZ33RnSKYuMYZWDs2LH8+uuv/PbbbzRuXPbHCaWnp+Pr60taWho+PjXwD4Sdyi0wceeHWzibmsOEARGM6dNM65A0oSgKj8/fxe/HLnFPu1BmD++gdUja2zwDtrwH/k1hzB9gMN58H2d0OAoWPQIuHjBuL3iXvV+PEFUhNzeXhIQEGjdufMNBBo5u1apVTJgwgfj4eKtpUUozefJktmzZQnR0dNUGZ6O5c+eyfPly1q1bd8Mypf1ObcmfbGqxUxSFsWPHsnz5cjZt2mRTUifs19fbEjibmkOwjztP3lJzf6c6nY7X72qBTgf/23uOuMRUrUPSVkayevsRoO/kmpvUAYQPgPpdoTAHokvvXC2EqByDBw/mmWee4ezZs2XeZ82aNbz//vtVGFX5GI1GZs+eXS3nsimxGzNmDAsXLuTHH3/E29ub5ORkkpOTycnJqar4RBW7lJnHfzerfcpeHRiBh6tB44i01TrUlwc6qI/JmrHqIOVo0HYe0TPVebHqd4GW9968vDPT6aD/NHU95jtI3qdtPELUEOPHj7epf/6uXbvo2rVrFUZUPk899VS1zflrU2I3d+5c0tLS6N27NyEhIZblp59+qqr4RBX7eP0RMvMKaVPPl/valz7BY03xrwHhuLno2XXyMusOnNc6HG1cPKwmMAD9pqmJTU3XoDu0fgAUM0S9bvNM+0IIUR1svhVb0vLEE09UUXiiKh05n8GiXerjUd4c3BK9Xv54A4T4evD01QEk7645RIHJfJM9nNCGf6sJTIu7oWEPraOxH/2mgMENTm6Fw6u1jkYIIYqRZ8XWYNNXHcSswMDWwXRrUkfrcOzKc72bElDLlYRLWfz4R8nPBnRaJ7epSYvOAH3/rXU09sWvAfQcq66vfQMKq/aZj0IIYStJ7Gqo6MMX2HLkIkaDjomDWtx8hxqmlpsL4/uq8yl9uvEo6bk15FFjigLr31LXOz0BAc7/WDmb3foS1AqCKwmw6wutoxFCCCuS2NVAhSYzM1YfBGBkj0Y0CrD/mcq1MKxLGE3renE5K7/mTFr8109wdg8YvaD3RK2jsU9u3nDn2+r6lvch65K28YgazWyugV1FnFRl/S4r9Egx4Zh++jORI+cz8fM08sId0iJzIy4GPZPuasnob/9k/u8JDO0cRmNnToKzL8PaqxN93v4vqBWobTz2rN2j8MfnkPwXbJ4Od3+sdUSihnF1dUWv13Pu3Dnq1q2Lq6vrDSfmFfZNURTy8/O5ePEier0eV1fXCh1PErsaJjU7n4+uPjps/J3N8fWswXOTlcEdLQK5rXkAW49eYtIv+/jx6W7O++W5/m3IToHAVtDzBa2jsW96PQx8FxbcBXsWQJenIKi11lGJGkSv19O4cWOSkpI4d+6c1uGISuDp6UmDBg3KPBnzjUhiV8O8s+ogKVn5NAusxWPdG2odjt3T6XRMv68N/T/Zwo4TKSzdc4ahnZ3wmcentkPs9+r63Z/U7MmIy6rRLdBqCBz4Fda8BiP/J9PCiGrl6upKgwYNKCwsxGQyaR2OqACDwYCLi0ulNBxIYleDbDlykZ/3nEGng/cebIPRIF0sy6JBHU9e7hfOjNWHmL7qIH0iAqnr7aZ1WJWnMB/+N15d7/QENOimZTSOpd9UOLJOnf5kzwLoPErriEQNo9PpMBqNGI3ynzGhkr/sNURmXiGTflFnyx/ZoxGdGvprHJFjefKWxrQO9SEtp4CpKw9oHU7l2v4pXDoMXnVlehNb1W50bSDFujchtYZNjSOEsDuS2NUQH0Qd4mxqDvVrezBhQPU81sSZuBj0vPtAW/RXnyO7+dAFrUOqHCnHYcsH6vqAmeBRW9t4HFG3ZyGsO+Rnwv+9KE+kEEJoShK7GmD3yct8u+MUAO8+0BYvN7kDXx5t6vsy+tbGALy5Ip6svEKNI6ogRYFVr4ApD5r0gTYPaR2RY9IbYMgccHGHE5uvPYpNCCE0IImdk8stMPHaz38BMLRzfW5tHqBxRI7tpX7h1K/twdnUHD68OrrYYe1bqiYiLu5w90fS8b8iAprBHVcndl77BqQmahuPEKLGksTOyX268SgnLmUR6O3GG4NbaR2Ow/N0dWH6/W0AWLA9gdjTVzSOqJwuJ8Cqf6nrt/8L/JtoG48z6P481O8K+Rnwv3FyS1YIoQlJ7JzYX2dS+eK3EwBMuy8SXw8ZNVUZeoXX5b72oZgVGPtjLKnZ+VqHZJuCXFg6EvLS1L5ht4zXOiLnoDfAff8Fgxsc3wixC7WOSAhRA0li56QuZ+Xz/MIYTGaFwW1DGNA6WOuQnMqUIZE0rOPJ2dQcxv8Uh9nsQK0z696ApL3gWQce+lrmrKtMAc3hjjfV9bWT4MopbeMRQtQ4ktg5oUKTmRcWxXA2NYeGdTyZcV8brUNyOr4eRuY+1gk3Fz3Rhy8ye9MxrUMqm/hlsPsrdf3+L8C3nrbxOKMeY9RbsnnpsPhRyMvUOiIhRA0iiZ0Ten/tYbYdS8HT1cAXj3eWx4ZVkVahPpb+dp9sPMKWIxc1jugmUo7D/41T1297BZr31TYeZ6U3wMPfgFcgnI+H5c+CPKhdCFFNJLFzMv+395ylX90HD7UjIthb44ic20Od6jO8awMUBcYtjuXMlWytQypZQQ4sGal27G94K/SepHVEzs23Pgz7AQyucGglbHlP64iEEDWEJHZO5MC5dF79eS8Az/VqyuC2IRpHVDNMvqcVber5kppdwJgfYsgrtMNnNkZNhPP7wDMAHvwKDDKXYZUL6wp3f6yub3kX9q/QNBwhRM0giZ2TSM3O59mFf5JbYOa25gHydIlq5G408N/HOuLrYWTvmTQm/7ofxZ6muvjtA/U5pujUpM5HEv5q02EEdB+jrq94HpL+0jYeIYTTk8TOCeQWmPjnDzEkXs4hzN+D2cM7YNDLZLPVKczfk0+GtUeng8W7E5m28qB9JHfb/wOb3lHX+78DTftoG09N1G8qNL0DCrLVwRSZdt4XUwjh0CSxc3BZeYWM+mY324+n4GE08PmIzvh5umodVo3UJyKQGVcHU3y9LYF3Vmmc3O36Up3aBKDPm9BzrHax1GQGF3VaGf+mkJYICx+ArEtaRyWEcFKS2DmwjNwCRn69ix0nUqjl5sJ3o7vSKtRH67BqtOFdG1iSu/m/JzBjtUbJXcz3sPrqkyVuewV6Taj+GMQ1HrVh+GK1j2PyX/DNIEg7q3VUQggnJImdg0rLLmDE/F38eeoKPu4ufD+6K10a+WsdlgAe7daA6fdHAvDl1gRmrjlUvcndvp/h/15Q17v/89ozTIW26obDk1HgUx8uHYGvB6pT0AghRCWSxM4BXc7KZ/iXO9mbmEptTyM/Pt2dDg1qax2WuM5j3Rryzn1qcvfFbyd4t7qSu9gf4JdnAAU6PwkDZoBO+lvajYDmanJXpxmknVaTu+R4raMSQjgRSewcTOLlbIZ/sZMDSekE1HJl8TM9iKznq3VYogQjujdk2pDWAHz+2wnG/hhLem5B1ZysIFd98Pyv/wTFBO0ehbs+lKTOHvmFwag1ENQGsi7AgrsgcZfWUQkhnIQkdg5kRexZ7vp0K4fPZxDk48biZ3rIBMR27vEejZhxfxtc9DpW7Uvi7s9+Z29iauWe5MpJ+Lr/tSlNer8OQ/4DevnnbbdqBcITKyGsG+Smwbf3wM558oQKIUSF6ZRq7PyTnp6Or68vaWlp+PhIJ/+ySssp4K0V8fzf3nMAdG5Ym0+Gtad+bU+NIxNlFZeYytgfYzhzJQejQcfEQS158pZG6CraonZkrXrrNTcVPPzhwS+hmTwqzGHkZ8HSUXB0rfq68e0w5L9qq54QQlxlS/4kiZ2d25VwmZd+iuNsag4GvY5xdzbnn72b4mKQ1hhHk5ZTwMRlf7EmPhmAvi2D+OChttT2Ksf0NHkZsOV92P6Z+rpeJ3j4W0kIHJHZDH/Oh/Vvq3PdufnAwHeh/aNyK10IAUhi5xTOp+fy383H+H7nKcwKNKzjycePtKejDJJwaIqisHDnKaatPEi+yYyPuwvP9mrKEz0b4eVWhsd8mQrUW67R70L21bnQujwNA6aDi1uVxi6qWMpx9ekUiX+oryPugkHvgV8DbeMSQmhOEjsHlpSWw7zo4yzanUh+odrf5uFO9Zl8b2tqleUPv3AI8WfT+NfSvRxKzgAgoJYr/+zdjEe7NcDdaCi+g6LAgV9h4xS4fELd5t8U+k+DFoOrMXJRpcwmtRV28www5YPeBdo8DLeMh8AWWkcnhNCIJHYO6FxqDnOjj/PT7kTyTWpC16VRbcb3DeeWZgEaRyeqgsms8L+95/h4wxFOpWQDEOrrzj/7NOPe9qH4uBvVPlgHV8KuL+Dsn+qOXnWh90ToOBIMRg0/gagy5/fD2klwIvrathZ3w60vQ/1OmoUlhNCGJHYO4kJGLmvjk1m9L5k/ElIwX/1NdG3sz/g7m9OjaZ2Kd64Xdq/AZGbpn2f4bONRktNz0WPmNuNBnvPdTZec33ExqUkfRi/o+YL6aDA3GQ1dI5zdA1s/gkMrr20L6wYt71Vbav0baxebEKLaVHliN2fOHD744AOSk5Np164ds2fPpmvXrpUamDMymRWOX8xkx/EUVu1LYvfJy1xf+z2a1OHFqwmdqGHSz5F/4neO/bmBgLMbCVSuPUv0tBLEgbp3Ye40ksjwCML8PSThr2kuHIJtn8BfS9R5CosERaoJXsQgdV48g3TXEMIZVWli99NPP/GPf/yDefPm0a1bNz755BOWLl3K4cOHCQwMrLTAHF1eoYmzV3I4mJTB3jOp7E1MJf5sGln5Jqty7cP8uKtNMIMiQwjzl+lLnJ7ZDOln4fJxuHQUzuyG0zsg9bRVMZOrL3/53cG81K6sTW8AXEvkAmq50qFBbTo2qE1kPR8a1fEixNddRkrXBOnn4OD/1Ba8k9uskzwXDwiOhJD2ENIOQttDneZgdNcqWiFEJanSxK5bt2506dKF//znPwCYzWbCwsJ44YUXmDhxYqUFZm/MZoWcAhM5BSYycgtJzc4nNbuAK9n5XMku4HJWHmev5HDmSg6JV7I5n55X4nE8XQ20re9L35ZBDGoTQj0/j2r+JKJSKQoU5ql94fIz1Z85VyDrorpkp6g/M5LVUY9XEqAwt/hxdHoIbgMNekCj29S56IzumM0KMaevsCY+mT2nrrD/XBoFpuL/ZF30OurV9qCBvycN/D0JqOWGv5crtb1cqePlSm1PV7zdXfB0NeDl5oKbi15a/Rxd9mV1HsNDK+HEFsjPKLmcV6A6DY5vffANA5964FEbPPzUn+5+6rprLTB6gL6EwTtCCE1VWWKXn5+Pp6cnP//8M/fdd59l+8iRI0lNTeXXX3+1Kp+Xl0de3rUEJz09nbCwsGpJ7LZ9PxnX5DiKPtz1n1JRFJSr25Srb5qvbjcrYFYUlKs/TWZ1MZejK6JBr6OWmxE/z6uLhyu13F3kcR83ZEMd3/D3oZRQRrF+ff12xXx1uX7dpLasmQuvrpvAXKBONVKYp/405UFhvprMKdatsDelN0LtRlCnqdqy0qA71O9Spn5zuQUm9p9LI+ZUKjGnr3DkfAaJV3IsI6jLHIIOPIwGPFxdcDXoMLroMRrUxdWgQ6/XYdDpMOivLXqdDp0O9Segu/paXVePq0N3bb2EvFFHiRvLTFLRkukUM3ULzhCWe5QGeUeon3uUsLwjeJqzbD5Wgc5Igc6NfJ07BXpXTBgw61ww6Vww6QyYcMGs06Ogv/YTPYpOB+hQrltAd3X7NdfeK+5G28tM/rMiNNBg+CfUDW1UpeewJbGzqUPGpUuXMJlMBAUFWW0PCgri0KFDxcrPnDmTKVOm2HKKSuOZtJsO2dsq52AVycTyry6plROKsGMuHuDqqbaCeNUFzzrqT68AtdWkThN1ihLfsHL3hXI3GujU0J9ODf0t28xmheT0XE5fzuZ0SjZnrmSTkpXPlex8LmflcyWrgJSsfDLzCsgtUBNAswJZ+aZiXQOEIzMCra4uAAp+ZFJPd8myhOpSCNZdxpcsfHVZ+JKFny4TH7LR69T/8BiVAoxKAZ5kglweQtzU6cxUrUOwUqU9bV9//XVefvlly+uiFrvq4NLlCf68cBsUtSgAXNfSoNdd/1NtSTAYdLjo1JYJg0FtsTAadLgY9Lhebc1wMajvi+pmS7NOSa1CRduu/sKLjqc3XN2mv7roQGdQn7Oqd7m6blBb2VxcwXB1cXEDgxu4el1bNLqFpdfrCPXzINTPg+5NSh94Y7rapSA7v5DsPLVrQYHJTIFJufrTTH6h2dJKXXhdi7XJfH1rd1GrNihXW0QtLeBFL7Bug7Wl0bsaB+vXOClXFyuKGYM5DxdTruVn0bpeKURvLrz6swC9UgiY0V9t4dYpZvSYQFHb4tRf9NV1QGfVcn5tezElbpbrQNi/lgH1tA7Bik2JXUBAAAaDgfPnz1ttP3/+PMHBwcXKu7m54eamzWz4bfoM1eS8QtgztXuAizrZtcyYIoQQTsemm4yurq506tSJjRs3WraZzWY2btxIjx49Kj04IYQQQghRdjbfin355ZcZOXIknTt3pmvXrnzyySdkZWUxatSom+5bdHslPT3d9kiFEEIIIWqgorypLN1UbE7sHnnkES5evMjbb79NcnIy7du3JyoqqtiAipJkZKjD8aurn50QQgghhLPIyMjA19e31DLV+kgxs9nMuXPn8Pb2rvI5tIoGaiQmJjrcnHlVQerjGqmLa6QurpG6uEbqwprUxzVSF9dUZ10oikJGRgahoaHo9aX3oqvW58/o9Xrq169fnafEx8enxl9815P6uEbq4hqpi2ukLq6RurAm9XGN1MU11VUXN2upKyJz5QohhBBCOAlJ7IQQQgghnITTJnZubm5MnjxZs3n07I3UxzVSF9dIXVwjdXGN1IU1qY9rpC6usde6qNbBE0IIIYQQouo4bYudEEIIIURNI4mdEEIIIYSTkMROCCGEEMJJSGInhBBCCOEkHCqxmzNnDo0aNcLd3Z1u3bqxa9euUssvXbqUFi1a4O7uTps2bVi9erXV+4qi8PbbbxMSEoKHhwd9+/bl6NGjVfkRKo0tdfHll19y2223Ubt2bWrXrk3fvn2LlX/iiSfQ6XRWy8CBA6v6Y1QKW+piwYIFxT6nu7u7VRlHvi7Atvro3bt3sfrQ6XQMHjzYUsYRr43ffvuNe+65h9DQUHQ6HStWrLjpPtHR0XTs2BE3NzeaNWvGggULipWx9TvIXthaH7/88gv9+vWjbt26+Pj40KNHD9auXWtV5t///nex66JFixZV+Ckqh611ER0dXeK/keTkZKtyjnht2FoXJX0X6HQ6WrdubSnjqNfFzJkz6dKlC97e3gQGBnLfffdx+PDhm+5nj3mGwyR2P/30Ey+//DKTJ08mJiaGdu3aMWDAAC5cuFBi+e3btzN8+HBGjx5NbGws9913H/fddx/x8fGWMu+//z6fffYZ8+bN448//sDLy4sBAwaQm5tbXR+rXGyti+joaIYPH87mzZvZsWMHYWFh9O/fn7Nnz1qVGzhwIElJSZZl0aJF1fFxKsTWugB1lvDrP+epU6es3nfU6wJsr49ffvnFqi7i4+MxGAw8/PDDVuUc7drIysqiXbt2zJkzp0zlExISGDx4MH369CEuLo7x48fz1FNPWSUz5bnW7IWt9fHbb7/Rr18/Vq9ezZ49e+jTpw/33HMPsbGxVuVat25tdV38/vvvVRF+pbK1LoocPnzY6rMGBgZa3nPUa8PWuvj000+t6iAxMRF/f/9i3xeOeF1s2bKFMWPGsHPnTtavX09BQQH9+/cnKyvrhvvYbZ6hOIiuXbsqY8aMsbw2mUxKaGioMnPmzBLLDx06VBk8eLDVtm7duinPPvusoiiKYjableDgYOWDDz6wvJ+amqq4ubkpixYtqoJPUHlsrYu/KywsVLy9vZVvv/3Wsm3kyJHKkCFDKjvUKmdrXXzzzTeKr6/vDY/nyNeFolT82vj4448Vb29vJTMz07LNUa+NIoCyfPnyUsu8+uqrSuvWra22PfLII8qAAQMsrytat/aiLPVRklatWilTpkyxvJ48ebLSrl27ygtMA2Wpi82bNyuAcuXKlRuWcYZrozzXxfLlyxWdTqecPHnSss0ZrgtFUZQLFy4ogLJly5YblrHXPMMhWuzy8/PZs2cPffv2tWzT6/X07duXHTt2lLjPjh07rMoDDBgwwFI+ISGB5ORkqzK+vr5069bthse0B+Wpi7/Lzs6moKAAf39/q+3R0dEEBgYSERHB888/T0pKSqXGXtnKWxeZmZk0bNiQsLAwhgwZwv79+y3vOep1AZVzbcyfP59hw4bh5eVltd3Rrg1b3ez7ojLq1pGZzWYyMjKKfWccPXqU0NBQmjRpwmOPPcbp06c1irDqtW/fnpCQEPr168e2bdss22vytTF//nz69u1Lw4YNrbY7w3WRlpYGUOyav5695hkOkdhdunQJk8lEUFCQ1fagoKBi/RyKJCcnl1q+6Kctx7QH5amLv3vttdcIDQ21utgGDhzId999x8aNG3nvvffYsmULgwYNwmQyVWr8lak8dREREcHXX3/Nr7/+ysKFCzGbzfTs2ZMzZ84AjntdQMWvjV27dhEfH89TTz1ltd0Rrw1b3ej7Ij09nZycnEr5d+fIZs2aRWZmJkOHDrVs69atGwsWLCAqKoq5c+eSkJDAbbfdRkZGhoaRVr6QkBDmzZvHsmXLWLZsGWFhYfTu3ZuYmBigcr6THdG5c+dYs2ZNse8LZ7guzGYz48eP55ZbbiEyMvKG5ew1z3CpsiMLu/Tuu++yePFioqOjrQYNDBs2zLLepk0b2rZtS9OmTYmOjubOO+/UItQq0aNHD3r06GF53bNnT1q2bMnnn3/OtGnTNIxMe/Pnz6dNmzZ07drVantNuTZEyX788UemTJnCr7/+atWvbNCgQZb1tm3b0q1bNxo2bMiSJUsYPXq0FqFWiYiICCIiIiyve/bsyfHjx/n444/5/vvvNYxMW99++y1+fn7cd999Vtud4boYM2YM8fHxDtE3sCQO0WIXEBCAwWDg/PnzVtvPnz9PcHBwifsEBweXWr7opy3HtAflqYsis2bN4t1332XdunW0bdu21LJNmjQhICCAY8eOVTjmqlKRuihiNBrp0KGD5XM66nUBFauPrKwsFi9eXKYvXke4Nmx1o+8LHx8fPDw8KuVac0SLFy/mqaeeYsmSJcVuOf2dn58f4eHhTnVd3EjXrl0tn7MmXhuKovD111/z+OOP4+rqWmpZR7suxo4dy8qVK9m8eTP169cvtay95hkOkdi5urrSqVMnNm7caNlmNpvZuHGjVevL9Xr06GFVHmD9+vWW8o0bNyY4ONiqTHp6On/88ccNj2kPylMXoI7MmTZtGlFRUXTu3Pmm5zlz5gwpKSmEhIRUStxVobx1cT2TycS+ffssn9NRrwuoWH0sXbqUvLw8RowYcdPzOMK1YaubfV9UxrXmaBYtWsSoUaNYtGiR1fQ3N5KZmcnx48ed6rq4kbi4OMvnrInXxpYtWzh27FiZ/iPoKNeFoiiMHTuW5cuXs2nTJho3bnzTfew2z6iyYRmVbPHixYqbm5uyYMEC5cCBA8ozzzyj+Pn5KcnJyYqiKMrjjz+uTJw40VJ+27ZtiouLizJr1izl4MGDyuTJkxWj0ajs27fPUubdd99V/Pz8lF9//VX566+/lCFDhiiNGzdWcnJyqv3z2cLWunj33XcVV1dX5eeff1aSkpIsS0ZGhqIoipKRkaH861//Unbs2KEkJCQoGzZsUDp27Kg0b95cyc3N1eQzlpWtdTFlyhRl7dq1yvHjx5U9e/Yow4YNU9zd3ZX9+/dbyjjqdaEottdHkVtvvVV55JFHim131GsjIyNDiY2NVWJjYxVA+eijj5TY2Fjl1KlTiqIoysSJE5XHH3/cUv7EiROKp6enMmHCBOXgwYPKnDlzFIPBoERFRVnK3Kxu7Zmt9fHDDz8oLi4uypw5c6y+M1JTUy1lXnnlFSU6OlpJSEhQtm3bpvTt21cJCAhQLly4UO2fzxa21sXHH3+srFixQjl69Kiyb98+Zdy4cYper1c2bNhgKeOo14atdVFkxIgRSrdu3Uo8pqNeF88//7zi6+urREdHW13z2dnZljKOkmc4TGKnKIoye/ZspUGDBoqrq6vStWtXZefOnZb3evXqpYwcOdKq/JIlS5Tw8HDF1dVVad26tbJq1Sqr981ms/LWW28pQUFBipubm3LnnXcqhw8fro6PUmG21EXDhg0VoNgyefJkRVEUJTs7W+nfv79St25dxWg0Kg0bNlSefvppu/9SKmJLXYwfP95SNigoSLnrrruUmJgYq+M58nWhKLb/Ozl06JACKOvWrSt2LEe9NoqmqPj7UvTZR44cqfTq1avYPu3bt1dcXV2VJk2aKN98802x45ZWt/bM1vro1atXqeUVRZ0OJiQkRHF1dVXq1aunPPLII8qxY8eq94OVg6118d577ylNmzZV3N3dFX9/f6V3797Kpk2bih3XEa+N8vw7SU1NVTw8PJQvvviixGM66nVRUj0AVt8DjpJn6K5+ICGEEEII4eAcoo+dEEIIIYS4OUnshBBCCCGchCR2QgghhBBOQhI7IYQQQggnIYmdEEIIIYSTkMROCCGEEMJJSGInhBBCCOEkJLETQgghhHASktgJIYQQQjgJSeyEEEIIIZyEJHZCCCGEEE5CEjshRI22aNEiPDw8SEpKsmwbNWoUbdu2JS0tTcPIhBDCdjpFURStgxBCCK0oikL79u25/fbbmT17NpMnT+brr79m586d1KtXT+vwhBDCJi5aByCEEFrS6XRMnz6dhx56iODgYGbPns3WrVslqRNCOCRpsRNCCKBjx47s37+fdevW0atXL63DEUKIcpE+dkKIGi8qKopDhw5hMpkICgrSOhwhhCg3abETQtRoMTEx9O7dm88//5wFCxbg4+PD0qVLtQ5LCCHKRfrYCSFqrJMnTzJ48GAmTZrE8OHDadKkCT169CAmJoaOHTtqHZ4QQthMWuyEEDXS5cuX6dmzJ71792bevHmW7YMHD8ZkMhEVFaVhdEIIUT6S2AkhhBBCOAkZPCGEEEII4SQksRNCCCGEcBKS2AkhhBBCOAlJ7IQQQgghnIQkdkIIIYQQTkISOyGEEEIIJyGJnRBCCCGEk5DETgghhBDCSUhiJ4QQQgjhJCSxE0IIIYRwEpLYCSGEEEI4CUnshBBCCCGchCR2QgghhBBOQhI7IYQQQggnIYmdEEIIIYSTkMROCCGEEMJJuFTnycxmM+fOncPb2xudTledpxZCCCGEcEiKopCRkUFoaCh6/U3a5JQKmDlzpgIo48aNK1P5xMREBZBFFllkkUUWWWSRxcYlMTHxprlWuVvsdu/ezeeff07btm3LvI+3tzcAiYmJ+Pj4lPfUQgghhBA1Rnp6OmFhYZY8qjTlSuwyMzN57LHH+PLLL3nnnXfKvF/R7VcfHx9J7IQQQgghbFCWbmzlSuzGjBnD4MGD6du3b6mJXV5eHnl5eZbX6enp5TmdqGRms8LFzDxOX87mdEo2py9nk3g5m8y8Qh7sVJ/+rYKkD2RNlZcBMd9DzmUwuIHLdYtvGDS9A+TaEEIIu2VzYrd48WJiYmLYvXv3TcvOnDmTKVOmlCswUTX2nUlj3E+xnLiYVeL76w6cp009X17uF07viLqS4NUUigIHfoWo1yHj3I3LNesL93wGvvWqLzYhhBBlplMURSlr4cTERDp37sz69estfet69+5N+/bt+eSTT4qVL6nFLiwsjLS0NLkVW80UReGHP04z9X8HyDeZ0esg1M+DhnU8aeDvSZi/J2k5BXy/4xTZ+SYAOjTw45V+EdzSrI4keM4s5TiseRWObVBf124EzftDYR6Y8qEwFwpy4PhmMOWBmw8MmAEdRkjrnRBCVIP09HR8fX3LlD/ZlNitWLGC+++/H4PBYNlmMpnQ6XTo9Xry8vKs3qtIYKLyZOUVMmn5Pn6NU1ti+rYM4sOH2+HraSxWNiUzj89/O8F3O06SW2AG4K42wXw6rANGg0x76FQKcmHbJ7D1IzVhM7jCrS+pi9GjePmLh2HFP+Hsn+prab0Twi6YTCYKCgq0DkNUgNForLT8yabELiMjg1OnTlltGzVqFC1atOC1114jMjKy1P0lsat+R89n8PwPMRy7kIlBr+PVARE8c3uTm7bAXUjP5b/Rx/nxj9Pkm8w82LE+sx5uKy13zsJUAAsfhIQt6usmfWDwh1Cnaen7mU2w4z+wafq11rsHvoCIQVUfsxDCiqIoJCcnk5qaqnUoohL4+fkRHBxc4t9ZW/Inm/rYeXt7F0vevLy8qFOnzk2TOlH9th27xFPf/klOgYkgHzdmD+9I18b+Zdo30Medf9/bmtuaB/DM93tYFnOGgFquvH5XyyqOWlQ5RYFVr6hJndELhvwHWt9fttuqegPcMg7CB15rvfv5SXhyLYSUfeojIUTFFSV1gYGBeHp6yn+8HZSiKGRnZ3PhwgUAQkJCKnS8an3yhKg+Z1NzGPtjDDkFJm5pVodPh3UgoJabzce5s2UQ7z3Yln8t3cvnv52gTi1Xnrn9Jq06wr7t+A/EfAs6PTz0NUQMtP0YdSPUZO7HoXB8Iyx+FJ7eDLXqVn68QohiTCaTJamrU6eO1uGICvLwULu/XLhwgcDAwFJvy95MhTtNRUdHlzhwQmgnv9DMmB9iuJJdQGQ9H+aP7FKupK7IQ53q8/qgFgDMWH2In/ecqaxQRXU7tArWvaWu959evqSuiMEFHpoP/k0hLRGW/AMK8ysnTiFEqYr61Hl6emociagsRb/LivaXlN7wTmjG6oPEJabi4+7C3Mc64W4sf+Zf5NleTXn6tsYAvLbsLzYePF/hY4pqlrQXlj0FKND5Sej+fMWP6VEbhi9W+9qd3q6OrhVCVBu5/eo8Kut3KYmdk/nf3nMs2H4SgI8faU+Yf+X9b+71QS15oGM9TGaFMT/GcDBJJpx2GOnn4MdhUJCtDpQY9H7lTVVSNxwenA/oYM83sPuryjmuEEIIm0li50SOXchk4rK/APhn76bc2TKoUo+v1+t478G23B5el9wCM68t+4tCk7lSzyGqQGEeLBquTjwcEAEPLwBD8aluKiS8P/SdrK6veQ1O/l65xxdCCFEmktg5iez8Qp5fuIesfBM9mtTh5X7hVXIeo0HPrIfa4uPuwl9n0vhm28kqOY+oRNs+haQ48KwDj/4EHn5Vc55bxkPkQ2AuVPvbZV6smvMIIWq8lJQUAgMDOXnyZJnKDxs2jA8//LBqg7ITktg5iTdXxHP0QiaB3m58Orw9LlU4mXCgjztvDFanPflw/WFOp2RX2blEBaUch99mqeuD3gf/xlV3Lp0O7p0Nga0hOwU2TK66cwkharTp06czZMgQGjVqVKbyb775JtOnTyctLa1qA7MDktg5gW3HLvFLzFn0Opg9vAOB3u5Vfs6hncPo0aQOuQVmJi3fhw3zXIvqoiiw6mV1IuEmfSDywao/p6sn3POJuh73A5zaUfXnFELUKNnZ2cyfP5/Ro0eXeZ/IyEiaNm3KwoULqzAy+yCJnYMrMJmZ/H/7AXi8e0O6Name+Yx0Oh0zH2iDm4ue349dkilQ7NG+n+FENBjc1KdKVNfoubCu0PEf6vqql9WnXAghxHWioqLw8vLCbL7WTzs+Ph6dTselS5dK3Xf16tW4ubnRvXt3y7ZFixbh4eFBUlKSZduoUaNo27atpZXunnvuYfHixZX8SeyPJHYO7tvtJzl2IRN/L1de7hdRreduFODFS1f78r2z6iAXM/Kq9fyiFDlXYO3r6vrtE27+qLDK1ncKePjDhQPwx+fVe24hhN2LjY0lMjISvf5aGhIXF0doaCgBAQGl7rt161Y6depktW3YsGGEh4czY8YMACZPnsyGDRtYs2YNvr6+AHTt2pVdu3aRl+fcf6sksXNgFzPy+HTDUQAmDIjA17OSRzqWwVO3Niayng9pOQX8+2rLobADG6dC1kWo0xxuebH6z+/pD33/ra5Hz1SnWxFCVClFUcjOL9RksbU7TlxcHO3atbPatnfvXsu2lStXEhERQfPmzfnqK+splE6dOkVoaKjVNp1Ox/Tp0/nyyy+ZPn06s2fPJioqinr16lnKhIaGkp+fT3Jysk2xOhp5pJgDey/qEBl5hbSp58vQzmGaxOBi0PPuA20ZMmcbq/YlMWR/Mv1bB2sSi7gqcTf8+Y26fvfH4FL+p45USIfHIfZ7OLMb1k5Sp1kRQlSZnAITrd5eq8m5D0wdgKdr2VOK2NhYXnzR+j+dcXFxdO7cmcLCQl5++WU2b96Mr68vnTp14v7777c8Oi0nJwd39+J9ye+++25atWrF1KlTWbduHa1bt7Z6v+ixXdnZzj3gT1rsHFTs6SuWfm1ThrTGoNdu9vHIer48fVsTAKauPEBeoUmzWGo8UyGsHA8o0O5RaHybdrHo9TD4I/WZtPuXw/FN2sUihLAbWVlZHD9+3KrFzmw2ExsbS7t27di1axetW7emXr161KpVi0GDBrFu3TpL2YCAAK5cuVLsuFFRURw6dAiTyURQUPF5XC9fvgxA3brO/UxrabFzQGazYrnt+WDH+nRsUFvjiGDcnc35JeYMZ67k8MPO0zx5axVOqyFubNcXcD5efdRX/2laRwMhbaHrM/DHPFj1L/jnDu1aEIVwch5GAwemDtDs3GWVkJCA2WymRYsWlm1r164lJSWFdu3asX//fqtbqPXq1ePs2bOW1x06dCg2ujUmJoahQ4cyf/58FixYwFtvvcXSpUutysTHx1O/fv2b9uFzdNJi54CW7klk75k0arm58Nqg6h0wcSMergbLQIrZm46SnisjIatdbhr89r66fudk8LKTL68+k6BWEFw+DjvmaB2NEE5Lp9Ph6eqiyWLLc07r1KmDTqdj9+7dAOzcuZOxY8fi7u5OePjNJ9cfMGAA+/fvt7TanTx5ksGDBzNp0iSGDx/O1KlTWbZsGTExMVb7bd26lf79+9tQo45JEjsHk5ZTwPtRhwEY37d5tcxZV1YPd6pP07peXMku4PMtx7UOp+bZPlsdDRsQrvZvsxfuvtBvqrq+7RM1RiFEjRUSEsK0adMYMWIEDRs2ZN68eTz88MNERkZiMBgIDQ21aqE7e/as1WCJNm3a0LFjR5YsWcLly5cZOHAgQ4YMYeLEiQB069aNQYMGMWnSJMs+ubm5rFixgqeffrr6PqhGdEo1ziybnp6Or68vaWlp+Pj4VNdpncp7UYeYG32cZoG1WDPuNoxV+ISJ8li7P5lnv9+Du1HPlgl9CPKxn8TTqWWch8/aQ0E2PLIQWt6jdUTWzCaYdxtc2K8+eqzfFK0jEsKh5ebmkpCQQOPGjUscSODICgsLadmyJdHR0ZbBE9u3b7cMngBYtWoVEyZMID4+3mrKlBuZO3cuy5cvt+qrZ29K+53akj/ZV1YgSnUhI5dvtiUAMHFgC7tL6gD6twqiU8Pa5BaY+WTDEa3DqTl++0BN6up1hhZ3ax1NcXoD3PmWuv7H55CeVHp5IUSN5eLiwocffkifPn1o3749r7zyilVSBzB48GCeeeYZq5a90hiNRmbPnl0V4dod+8sMxA3N2XSM3AIz7cP8uLNloNbhlEin0/H6ILVD7E+7Ezl2IUPjiGqAyydgz9XpTfr+u/qeMGGr8IEQ1g0Kc671BRRCiBLce++9HDlyhGPHjvHMM8+UWGb8+PGEhZVtqq+nnnqKiAj76JNe1SSxcxCJl7P5cddpAF4dEGFTR9Xq1rmRP/1aBWFWsPQHFFVo8wwwF0LTO7Wd3uRmdDp1UAdAzHeQIv0whRCiskli5yA+23iUApPCLc3q0LOZnYx2LMVrAyPQ62DdgfP8efKy1uE4r6S/YN/VIf19J2sbS1k0ugWa9VUT0c0ztI5GCCGcjiR2DuDYhUyWxaiTEf+rv2M0JTcL9OaRLmoT+cw1h2x+3Iwoo41XR5tGPggh7Uovay/ufFv9Gf+zmpgKIYSoNJLYOYCP1x/BrEC/VkF0sIPJiMtqfN9w3I169py6wqZDF7QOx/mc/B2OrQe9C/R5Q+toyi6kHbR+QF3fZAeTKAshhBORxM7OxZ9NY9W+JHQ6eKX/zSdutCdBPu6M7NkIgI/WH5FWu8qkKLDh3+p6x5FQp6mm4djsjjdBZ4Cj6+DUDq2jEUIIpyGJnZ37cJ06+ODedqG0CHa8uf+evb0pXq4G9p9LZ+3+81qH4zyOroMzu8HFA3q9qnU0tqvTFDpenUR54xQ1URVCCFFhktjZsd0nL7P58EUMeh0v9XWs1roi/l6ujLpFfW7sJxuOYDbLH/AKUxTYPF1d7/o0eAdrG0959XoNXNzh9A44vlHraIQQwilIYmenFEXhg7Vqa93QzmE0CvDSOKLye/q2Jni7u3AoOYPV8TIxbYUdWgVJe8G1lvoUB0flEwqdn1TXN8+QVjshhKgEktjZqW3HUtiVcBlXFz0v3tlM63AqxNfTyFO3NgHgkw1HMUmrXfmZzRA9U13v9ix41Sm9vL279SUwesLZPXBkrdbRCCGEw5PEzg4pisJH69XWuke7NiDE10PjiCpu1K2N8PUwcuxCJv/be07rcBzXwV/hfDy4+UCPsVpHU3G1AtXbyaDeXpZWOyGEqBBJ7OxQ9JGLxJxOxd2o5599HGy04w34uBt55na11e7TjUcpNJk1jsgBmU0Q/a663v2f4OmvbTyVpec49bZy8l9waKXW0QghhEOTxM7OKIrCx+uPAPCPHo0I9HbXOKLK80TPRvh7uZJwKYtfYsv24GZxnfhf4OIhcPeFHv/UOprK41UHuj2nrm+eod5uFkLUeCkpKQQGBnLy5EmtQ6mwYcOG8eGHH1bLuSSxszPrD5znrzNpeLoaePZqC5ez8HJz4ble6mdSH5Emf8DLzFQIW6621vV8QU3unEnPsert5QsH4MAKraMRQtiB6dOnM2TIEBo1alRpx3zppZd44IEHKu1410tOTubRRx8lODgYV1dXQkNDmTVrFgBvvvkm06dPJy0trUrOfT1J7OyI2azw0dXWuid6NqJOLTeNI6p8j3dvRF1vN85cyWHpn2e0Dsdx7FsCKcfAw/9a65Yz8agNPcao69HvqredhRA1VnZ2NvPnz2f06NGVetxdu3bRuXPnSj1mkWeffZbU1FQ2bNhAQkICK1eupGPHjgBERkbStGlTFi5cWCXnvp4kdnZkTXwyh5Iz8HZzsfRHczYergb+2VvtN/jZxqPkFsgf8JsyFcCW99T1W8aBm7e28VSV7s+Dux9cOgzxy7SORghRhaKiovDy8sJ8XdeL+Ph4dDodly5dYvXq1bi5udG9e3er/RYtWoSHhwdJSdemzho1ahRt27YttTUsPz8fo9HI9u3beeONN9DpdMWOXVF5eXkkJCSwY8cO8vPz6dixI3fccYfl/XvuuYfFixdX6jlLIomdnTCZFT7ZoLbWPXlrY/w8XTWOqOo82q0Bob7uJKfnsnDnKa3DsX9xP8CVk+BV99oIUmfk7gu3vKiuR7+r3n4WQpSdokB+ljaLjSPaY2NjiYyMRK+/lobExcURGhpKQEAAW7dupVOnTsX2GzZsGOHh4cyYMQOAyZMns2HDBtasWYOv7427qLi4uLBt2zbLeZKSkoiKirIqM2PGDGrVqlXqcvr06RKPX1hYyMCBA1m8eDH9+vVjzpw53HvvvWRmZlrKdO3alV27dpGXl1f2iioHF1sKz5w5k19++YVDhw7h4eFBz549ee+994iIiKiq+GqMlX+d4+iFTHw9jIy+rbHW4VQpNxcD4/uG8+qyv/hv9HGGdW1ALTebLsWaoyAHoq+21t36Mrg67kTVZdL1WdgxBy4fh72Lrj12TAhxcwXZMCNUm3NPOmfT91NcXBzt2rWz2rZ3717LtlOnThEaWvyz6HQ6pk+fzkMPPURwcDCzZ89m69at1KtXr9Tz6fV6zp07R506dYqdt8hzzz3H0KFDSz1OSTEBjBs3jjvuuMNy7FmzZtGoUSPmzp3LhAkTLPvm5+eTnJxMw4YNSz1PRdjUYrdlyxbGjBnDzp07Wb9+PQUFBfTv35+srKyqiq9GKDSZ+XTDUQCeub0JPu5GjSOqeg90rEeTAC8uZ+Uzf2uC1uHYr11fQsY58A279pQGZ+ZWS01gQW21K8jVNh4hRJWIjY2lbdu2VtuuT/ZycnJwdy95Voi7776bVq1aMXXqVJYvX07r1q3LfM4bJXUA/v7+NGvWrNTFxaV4I0RcXBwLFy7k3nvvtdru6+trdcvYw0OdkzY7O7tM8ZaXTc0kf2+2XLBgAYGBgezZs4fbb7+9UgOrSX7ec4YTl7Ko7WlkZM9GWodTLVwMel7uH87YH2P5cusJ/tGjIbW9nPf2c7nkpsH/t3fn8VFVZwPHf3cmmSRAEraQhBBCgrLIviWCVUJBFimFurBUFKmo9YW2vCgW6KuIUkGlxYpoXNBoW9lkqwIBBALIagkUwiZLkC1hz0L2zD3vHxcmTkkgE5LczOT5fj7zmZk7Z+Y+9+Rk8uTes3z3V+Nx7CTw9pypb26p2xjY8T5knoHvPzFGzAohbs+7lnHmzKx9l1F2djbHjx93SrJ0XWfPnj2OwRINGzbk6tWrJb4/ISGBw4cPY7fbCQ4OLvN+SzpL+FNvvPGG4xJvaQ4ePEjTpk2dti1ZsoQWLVrg7V18UiY7O5sffviB3//+945tV65cASAoKKjMMZfHHV3/utFRsX79kidKzc/Pd7qWnJmZeSe780i5BXZmX+9bN+7nd9eoS5IPtQ3lntDjHEzNJG7TcSY/1NrskKqXbXMg9yo0bAnth5sdTdXx9oXYyfCvcbBllnE51tOmdxGiMmiaW3TXSElJQdd1WrVq5di2Zs0aLl++7Ei8OnXqVOII0qSkJIYOHcq8efOIj4/n5ZdfZvHixWXa7/79+3nkkUdKfb28l2KvXr1605XLjz76CMBpapXk5GSaNGlCw4YNyxRveZV78ISu64wfP5777ruPtm3bllhmxowZBAYGOm7h4eHlDtRTfbo1hfOZ+YTV9WPkvU1v/wYPYrFoTOxn9M+M33aS85ly2c3h2gXY/r7xuPfLYK05CT8AHUYYCW3uVSPBFUJ4jAYNGqBpGt9//z0AO3bsYNy4cfj6+tKiRQsA+vXrx4EDB5zO2p08eZKBAwcyZcoURowYwWuvvcaSJUtISkoq0351XefIkSOcO3euxBG05b0UGxMTw6FDh5g9ezZHjx5lzpw5TJ48mblz51KvXj1HuS1bttC3b1+X6qo8yp3YjR07luTk5FsO3Z08eTIZGRmO2+nTp8u7O490NbuAuMTjALzYrwU+XlaTI6p6sS2D6BpRj/winXfXHzU7nOpj8ywozIawLtDqF2ZHU/WsXkZCC8Zgiqzz5sYjhKgwoaGhvP7664wcOZKIiAji4uJ47LHHaNu2LVar8XewXbt2dO7cmUWLFgHGZcz+/fszePBgJk2aBBgJ1YABA5gyZYrjs+Pj49E0rcT9Tp8+nfj4eMLCwpg+fXqFHc/IkSOZPn067777Ll26dGHBggUsXbqU3/ymuF90Xl4ey5cv55lnKn9mA00p11fdHjduHCtWrGDz5s1ERpZ9BGdmZiaBgYFkZGQQEBDg6m49zvRvDvLJdym0Dg1g5e9+hsVScmP0dDtPXGbYRzvwsmisf6EnEQ2q/6WESnX1JMzpCnohPPkviOppdkTmUAo+6QNn/w3dnoGBs8yOSIhqIy8vj5SUFCIjI0sdZODuVq5cycSJE0lOTnaaFuVWpk6dyqZNm0hMTKzc4Fz0wQcfsGzZMtauXVtqmVv9TF3Jn1w6Y6eUYty4cSxbtowNGza4lNQJZ6ev5PDFdmMOt0kDWtXYpA4gJqoBD7QIokhXvPOtnLUzVl4ohKjYmpvUgdFfqM+rxuPdn8GVE6aGI4SoWgMHDuTZZ5/l7Nmyry2+evVq3nrrrUqMqny8vb2ZM6dqupW4lNiNHTuWf/zjH3z55Zf4+/uTlpZGWloaubm5lRWfx5q97gcK7Dr33dWAB+6u3I6U7mBiX6Ov3fK9Z0k+W/lr6VVb5w/Cf653b+j9irmxVAeR98NdfUAvgo23Hq0mhPA848ePd6l//q5du4iOjq7EiMpnzJgxVTbnr0uJ3QcffEBGRgaxsbGEhoY6bgsXLqys+DzSwXOZLNtr/AcyqX/rUvsD1CTtmgQyuGNjlIJpXx+gHD0EPMP61wAFrX9p9K8T0Huqcb9/MaTuMzcWIYSo5ly+FFvS7amnnqqk8DzTmwmHUQoGdWhMuyYyjcMNkwa0ws/byvcnr/LNvtTbv8HTHPsWflgNmhV+/rLZ0VQfoe2h7aPG42+nurx0kRBC1CSyVmwV23bsEpt+uIi3VePFvi3MDqdaCQ304/nY5gDMWHWI3AK7yRFVoaICWG2M9CLmOQiStuHk5/8HVhsc3wCHV5odjRBCVFuS2FWhIrvOa98cBODxmAgZ/VmCZx+IIqyuH+cy8vhw83Gzw6k6O+Pg8lGoHWSsMiGc1Y+EHtdncE+YDAWVuySPEEK4K0nsqlD8tpMcTsuibi1vft/7brPDqZZ8va1MfsiYjTxu03HOpteAgTlZabDpTeNxn1dllYXS3D8BAppAxin4brbZ0QhRLei6bnYIooJU1M+yhk1nb57UjFxmrzOWDps8oBX1ZV3UUg1sF8oXzX5k18krzFx9mDkjOpkdUuVaNxUKrkFYV+jwa7Ojqb5staH/G7DoSdj6N+g4AupHmR2VEKaw2WxYLBbOnTtHUFAQNptNBuK5KaUUBQUFXLx4EYvFgs12Z/mBJHZV5PVvDpJdYKdLRD0e6yJLq92Kpmm8MugeBr33HV//5xxPdo+gW7OS1yN2e6d2wr4FgAYPvQVlnISzxmr9S4jqBSc2Gn0SH19kdkRCmMJisRAZGUlqairnzp0zOxxRAWrVqkXTpk3LPBlzaSSxqwKJRy6wan8aVovG9CFta/RkxGXVNiyQ4d3Cmb/rNNO+PsC/xnrgyhy6HVa9aDzuNFKmNykLTYOH3ob3u8PRNXBkNbQcYHZUQpjCZrPRtGlTioqKsNtr0GAzD2S1WvHy8qqQs66S2FWyvEI7r6w4AMDoHs1oHSpLqZXVC31b8s1/Ukk+m8mXu04x8t4Is0OqWEmfQ9o+8AksnqtN3F7Du6H7WNj6Dqz+o7FCh7ef2VEJYQpN0/D29sbb29vsUEQ1Idd9Ktn7icc5dSWHkABfxj8oU1i4omEdH/73ep3NWHWI01c8aCRkzhVY/7rxuNcUqBNkbjzu5oGJEBAG6T8a/e2EEEIAkthVqhMXrxGXaEzZ8cqge6jjIydIXTWqRzO6NatHdoGdiV/9B133gMlplYJv/hdyr0Cje6DbGLMjcj8+daDvdOPxd7Phcg2aGkcIIW5BErtKopTilRUHKLDr9GwRxIC2IWaH5JasFo1Zj3XAz9vKjhNX+GL7SbNDunP7FsLB5WDxgiHvg1US/nJp8yvjMmxRHix9BuyFZkckhBCmk8Sukny+7STfHbuEzcvCtF+2kWHodyCiQW2mXJ/bbmbCYVIuZZsc0R1IPwWrJhqPYydBYw+fyqUyaRoMnmvM+3d2d/FcgEIIUYNJYlcJks9m8MaqwwBMGdCKZg1lhYk79XhMBPfd1YC8Qp0XFu3F7o6XZHU7LPst5GdCk2i473/Njsj9BTaBQdf72G35C/y4zdx4hBDCZJLYVbBr+UX8bv4eCuw6fVoHM6pHM7ND8ggWi8Zbj3agjo8XSafS+WTLCbNDct22OfDjVrDVgYc/lEuwFaXNr6Dj46B0WPos5KabHZEQQphGErsK9sqKZFIuZRMa6Mvbj7aXS7AVKKyuHy//ojUAf1n3A0fPZ5kckQvS9sOG6539+8+QFRMq2oA3oV4zyDgNKycYA1SEEKIGksSuAi3ZfYalSWexaPC34Z2oJ8uGVbihXcPp1TKIgiKdPyzYS05Bkdkh3V5hHix5BvRCaDkQOj1hdkSex8cfHv4ENCskL4F9siKFEKJmksSugpy4eI2XVyQDML5PC6IjPXQJLJNpmsbMR9rToLaNg6mZTFjoBlOgrP0TXDwEtYOM/mByFrdyhHczBqQArHwBrp40NRwhhDCDJHYVIL/Izrgv95BTYOfeqPqM7XWX2SF5tOAAXz58ogs2q4WEA2n8Zd0Rs0Mq3fb34ftPjMeD58pExJXtZxMg/F4oyIIFIyEv0+yIhBCiSklid4d0XfHHr/ZxMDWT+rVt/G14J6yetqZpNdS1WX1mPtIOgLkbj7Nk9xmTIyrBwRWwZorx+MHXoEU/c+OpCaxe8MjHxtnR8/th4Ugoyjc7KiGEqDKS2N0BpRSvfn2A5XvP4WXReGdYR4IDfM0Oq8Z4uHMTxvZqDsDkpfv5/uQVkyP6iVM7jRGaKGNliR6/NzuimqNuU3h8sTH6OGUTLH8edN3sqIQQokpIYncH/rruB77Y/iOaBn8Z2oEHWshltqr2woMtGdA2hAK7znN/31091pO9fBzmDzdWRGgxAPq/Kf3qqlrjTjD0C2N1j+QlsO5lsyMSQogqIYldOX2y5QRzNhwD4PXBbRncMczkiGomi0XjL0M70C4skCvZBfwm/nvScwrMCyj7EvzzUWMd2Mad4NF5Ml+dWe7qbfRrBNj+Hmx7z9x4hBCiCkhiVw6Lvj/N9JWHAJjYryUj740wOaKarZbNi4+f7EpwgA9HL1zjsbjtpGbkVn0guenGmborJ4zLgb9eBDZZdcRUHYZDn2nG47V/gv1fmRuPEEJUMknsXLRqfyqTlu4D4LkHovif2OYmRyQAQgJ9+fvTMYQE+HL0wjUeeX8bxy5U4QTG6afg035w5nvwrQuPL4E6japu/6J09/0BYn5rPF72HOyONzUcIYSoTJLYlZFSig8SjzPuyyR0BSOiw5k0oJWsLFGNtAj2Z8n/9CAqqDbnMvJ4NG47SaeuVv6OzybBx73h4mHwD4VRX0NQi8rfrygbTYN+M6DDCNCL4Os/wJo/GWv3CiGEh5HErgxyCooYN38PbyYcdiR104e0k6SuGgqr68dXv+1Bx/C6pOcU8uuPd7Dx8IXK2+HhlRA/ELIvQHBbGLMeQttX3v5E+VgsMOQD6PUn4/n292DBryHfjZalE0KIMpDE7jZOXc7h4fe3sXJfKt5WjT//qi0zHm4vc9VVY/Vr2/jymRhiWwaRV6gz5ot/8/ftJ1EVvX7ojjhY8DgU5sBdfWD0agiUQTTVlqZBz5fg0c/Ayxd+SIB5/YzL6EII4SE0VeF/7UqXmZlJYGAgGRkZBAQEVNVuy23zDxf53fw9ZOQW0rCOD3EjO9O1mSwV5i4K7TovfbWPZXvOAnDfXQ2Y+XB7wuvXurMPvnYBEiZD8vWO+F2egof+IqNf3cmZ3cZAl+wLxmTGv4ozknMhhKiGXMmfJLErwaVr+fx13Q8s2HUKXUHH8LrEjexCSKBMPuxudF3x6dYUZq09Ql6hTi2blT/2b8UT90ZgcfWsq65DUjx8+yrkZYBmgd5Tjc75clne/aSfhvkjjBUqAFr/EvrPgMAm5sYlhBD/RRK7csovshO/9STvbThGVn4RYPSne/WXbfDxspocnbgTJy9l89KSfexKMVaniG5WnzcfbU9kwzJOR3L+AHw9Hs7sMp6HdoRB7xhz1Qn3lX8NNr4BO+NA2cG7FjwwEbqPAy+b2dEJIQQgiZ3LlFKsOXCeN1Yd4tT1lQvahgXwyi/aEB0pl149ha4r/rnzR2asPkxOgR1vq8bgjmE8c38ULUP8S37T1ZNGX7rvPzZGVNrqwM9fhuhnwCLJvsdIS4ZVL8Kp7cbzBndD3+lwd19j4IUQQphIErsyysgp5F//OcvCf58m+WwmAI38fXipfyse7hTm+qU64RZOX8nhT8uT2fzDRce2ni2CeO6BKLo3b4AGxhqjOz+EI6uB678irQcZy4PJAAnPpBTsWwhr/w+yr7eNepHQdTR0HAm1G5gbnxCixpLE7hZ0XbH9xGUW/fs0Cclp5BcZi4P7eFl49oEoftuzObV9pBN8TbDn1FU+3nKChOQ0dAVBXGV0/WRGaGuol32iuGBUL+gxTjrX1xS56bBlFuz+AvIzjG1WH2gzBLr+BppEy1k8IUSVqvTEbu7cubz99tukpaXRoUMH5syZQ3R0dIUGVlGUUpy5msuOE5fZceIK245fIjUjz/F6qxB/hnYNZ0inMOrXlj41NUpRPpzaTkZyArkH1xGSd8zx0jXly7e2n3O+1ZN07BRNl4h6eFnlj3mNUpANyUvg+3mQurd4u199iOoJUbFG0l9PlhQUQlSuSk3sFi5cyJNPPklcXBwxMTG88847LF68mCNHjtCo0a2XUKrKxG7F3rNsOnKRnSlXOJvuvG6ov68Xgzs2ZmjXcNqFBcpEwzVBXgZcOAwXDsCFQ3D+IJxLMuagu06hcSmgNau0nsy+2JV03c/xmq+3hVYhAdzTOIB7Qo37ViH+1LLJ2d0a4exuI8E7uAIKrjm/Vi8SmnSDoJbQqDUEtYJ6zaQPphCiwlRqYhcTE0O3bt147733ANB1nfDwcH73u98xadKkCgvsTj312S4Sjxj9ZLwsGu2bBHJvVAPujWpAdGR9fL3lS9et6XZj1YD8TMjLLL7PvQJZqZCVBpnnfnJ/ruTPqRMMzX9uXGaNioXaDQHIyC1ky9GLbDh0gY1HLnA1p7DEtzesYyM00I+QQF8aB/oSWtePBrVtBPh5E+jnTYCvN4G1vPH39cLXy4q3VZN/JNyZvdBI8o5vhBOJxtrAqoSlyaw+0KA5+IdAnRDwDy6+96sHPv7gE3D95g/efjJljhCiVJWW2BUUFFCrVi2++uorhgwZ4tg+atQo0tPTWbFihVP5/Px88vPznQILDw+vksTuwOLX8E7dQ/3a3tStZcNLBkKUgQs5/q2ajeM19ZPnqoR7/Sc3ZSRrym6MPtWLwH79Xi+EogIoyjUunxblGdtdFRBmnFFp1BoatYGQdhDc5rZ/UO264uTlbA6ey+RgaiYHz2VyKDWTC1n5t3xfSSwa+Hpb8fW24udtJHpWi4a31YKXVcPLYsHLomHRNCwWsGjG65qmoWGEatwXPwfNcQg3jqSkQ9Io+++A5Bhl46tnc1fufsLyTxBacJLQwpOEFJzCpgpc+hwdjSLNRqFmo0jzdtzbNS90rNg1KzpWdM14rNBQWIx7zYLxG6UB2vXfOg2lade33azk7WX/oZf2uULURE1+/S6NwiIrdR+uJHYuXUe6dOkSdrud4OBgp+3BwcEcPnz4pvIzZsxg2rRpruyiwrSxH4ErG+CKKbsXVcXqA77Xz3z4BoBvXfAPhYBQ4/7GrUGUcaakPLuwaDQPqkPzoDoM6tDYsT09p4Cz6bmkpueRmpHLuYw8UtNzuZpTSEZuIZl5hWTmGo8L7cafW11BToGdnAJZgN5zNLt+M1jQaaJdpJmWRiMtnUakE6Sl00i7SiMtnQByqKPl4k8udcjFohkpmk3lY1Ou/7MghDDXqewMs0NwUqkdhCZPnsyECRMcz2+csasSXUZD815Vsy+PV8p/5yWe1tFKfl2zGK9pWvG9ZvnJ7cZzK1i9weJVfLN6g5ePsb7njXurD/jUMZ6bpG4tG3Vr2WjTOPCW5ZRS5Bfp5Bfq5BXZySu0k1eok1dop9CuU6QriuyKQl2nyK6w6zq6Al0p7LpCKeOsobr+WQqME54YrznOj944QVrCmdfSTrBW2ZB4wRVK+D9T6XjZc/EuysGiF2B13PKx6AVYlB1NLzLulR2LKkRTCg3jTLeGQrt+xltznLf7yVnx65x+U0tsDCW3BE1aiBC31Sqoeq1W41Ji17BhQ6xWK+fPn3fafv78eUJCQm4q7+Pjg4+PSX9475apKUT1oGma4/JrIN5mhyOEEMKDuTR/g81mo0uXLqxfv96xTdd11q9fT/fu3Ss8OCGEEEIIUXYuX4qdMGECo0aNomvXrkRHR/POO++QnZ3N6NGjb/veG+M0MjMzXY9UCCGEEKIGupE3lWW8q8uJ3bBhw7h48SKvvPIKaWlpdOzYkYSEhJsGVJQkKysLoOr62QkhhBBCeIisrCwCA2/dr7tKlxTTdZ1z587h7+9f6XN53Riocfr06WqxLq3ZpD6KSV0Uk7ooJnVRTOrCmdRHMamLYlVZF0opsrKyaNy4MZbbLGlYpdPmWywWmjSp2tEjAQEBNb7x/ZTURzGpi2JSF8WkLopJXTiT+igmdVGsquridmfqbpDFL4UQQgghPIQkdkIIIYQQHsJjEzsfHx+mTp1q3jx61YzURzGpi2JSF8WkLopJXTiT+igmdVGsutZFlQ6eEEIIIYQQlcdjz9gJIYQQQtQ0ktgJIYQQQngISeyEEEIIITyEJHZCCCGEEB7CrRK7uXPn0qxZM3x9fYmJiWHXrl23LL948WJatWqFr68v7dq1Y9WqVU6vK6V45ZVXCA0Nxc/Pjz59+nD06NHKPIQK40pdfPzxx9x///3Uq1ePevXq0adPn5vKP/XUU2ia5nTr379/ZR9GhXClLuLj4286Tl9fX6cy7twuwLX6iI2Nvak+NE1j4MCBjjLu2DY2b97MoEGDaNy4MZqmsXz58tu+JzExkc6dO+Pj48Ndd91FfHz8TWVc/Q6qLlytj6VLl/Lggw8SFBREQEAA3bt3Z82aNU5lXn311ZvaRatWrSrxKCqGq3WRmJhY4u9IWlqaUzl3bBuu1kVJ3wWaptGmTRtHGXdtFzNmzKBbt274+/vTqFEjhgwZwpEjR277vuqYZ7hNYrdw4UImTJjA1KlTSUpKokOHDvTr148LFy6UWH7btm2MGDGCp59+mj179jBkyBCGDBlCcnKyo8xbb73Fu+++S1xcHDt37qR27dr069ePvLy8qjqscnG1LhITExkxYgQbN25k+/bthIeH07dvX86ePetUrn///qSmpjpu8+fPr4rDuSOu1gUYs4T/9Dh//PFHp9fdtV2A6/WxdOlSp7pITk7GarXy2GOPOZVzt7aRnZ1Nhw4dmDt3bpnKp6SkMHDgQHr16sXevXsZP348Y8aMcUpmytPWqgtX62Pz5s08+OCDrFq1it27d9OrVy8GDRrEnj17nMq1adPGqV189913lRF+hXK1Lm44cuSI07E2atTI8Zq7tg1X6+Jvf/ubUx2cPn2a+vXr3/R94Y7tYtOmTYwdO5YdO3awbt06CgsL6du3L9nZ2aW+p9rmGcpNREdHq7Fjxzqe2+121bhxYzVjxowSyw8dOlQNHDjQaVtMTIx67rnnlFJK6bquQkJC1Ntvv+14PT09Xfn4+Kj58+dXwhFUHFfr4r8VFRUpf39/9fnnnzu2jRo1Sg0ePLiiQ610rtbFZ599pgIDA0v9PHduF0rdeduYPXu28vf3V9euXXNsc9e2cQOgli1bdssyL730kmrTpo3TtmHDhql+/fo5nt9p3VYXZamPktxzzz1q2rRpjudTp05VHTp0qLjATFCWuti4caMC1NWrV0st4wltozztYtmyZUrTNHXy5EnHNk9oF0opdeHCBQWoTZs2lVqmuuYZbnHGrqCggN27d9OnTx/HNovFQp8+fdi+fXuJ79m+fbtTeYB+/fo5yqekpJCWluZUJjAwkJiYmFI/szooT138t5ycHAoLC6lfv77T9sTERBo1akTLli15/vnnuXz5coXGXtHKWxfXrl0jIiKC8PBwBg8ezIEDBxyvuWu7gIppG/PmzWP48OHUrl3babu7tQ1X3e77oiLq1p3puk5WVtZN3xlHjx6lcePGREVF8fjjj3Pq1CmTIqx8HTt2JDQ0lAcffJCtW7c6ttfktjFv3jz69OlDRESE03ZPaBcZGRkAN7X5n6queYZbJHaXLl3CbrcTHBzstD04OPimfg43pKWl3bL8jXtXPrM6KE9d/Lc//vGPNG7c2Kmx9e/fny+++IL169fz5ptvsmnTJgYMGIDdbq/Q+CtSeeqiZcuWfPrpp6xYsYJ//OMf6LpOjx49OHPmDOC+7QLuvG3s2rWL5ORkxowZ47TdHduGq0r7vsjMzCQ3N7dCfu/c2axZs7h27RpDhw51bIuJiSE+Pp6EhAQ++OADUlJSuP/++8nKyjIx0ooXGhpKXFwcS5YsYcmSJYSHhxMbG0tSUhJQMd/J7ujcuXOsXr36pu8LT2gXuq4zfvx47rvvPtq2bVtqueqaZ3hV2ieLamnmzJksWLCAxMREp0EDw4cPdzxu164d7du3p3nz5iQmJtK7d28zQq0U3bt3p3v37o7nPXr0oHXr1nz44Ye8/vrrJkZmvnnz5tGuXTuio6OdtteUtiFK9uWXXzJt2jRWrFjh1K9swIABjsft27cnJiaGiIgIFi1axNNPP21GqJWiZcuWtGzZ0vG8R48eHD9+nNmzZ/P3v//dxMjM9fnnn1O3bl2GDBnitN0T2sXYsWNJTk52i76BJXGLM3YNGzbEarVy/vx5p+3nz58nJCSkxPeEhITcsvyNe1c+szooT13cMGvWLGbOnMnatWtp3779LctGRUXRsGFDjh07dscxV5Y7qYsbvL296dSpk+M43bVdwJ3VR3Z2NgsWLCjTF687tA1XlfZ9ERAQgJ+fX4W0NXe0YMECxowZw6JFi2665PTf6tatS4sWLTyqXZQmOjracZw1sW0opfj000954oknsNlstyzrbu1i3LhxfPPNN2zcuJEmTZrcsmx1zTPcIrGz2Wx06dKF9evXO7bpus769eudzr78VPfu3Z3KA6xbt85RPjIykpCQEKcymZmZ7Ny5s9TPrA7KUxdgjMx5/fXXSUhIoGvXrrfdz5kzZ7h8+TKhoaEVEndlKG9d/JTdbmf//v2O43TXdgF3Vh+LFy8mPz+fkSNH3nY/7tA2XHW774uKaGvuZv78+YwePZr58+c7TX9TmmvXrnH8+HGPahel2bt3r+M4a2Lb2LRpE8eOHSvTP4Lu0i6UUowbN45ly5axYcMGIiMjb/ueaptnVNqwjAq2YMEC5ePjo+Lj49XBgwfVs88+q+rWravS0tKUUko98cQTatKkSY7yW7duVV5eXmrWrFnq0KFDaurUqcrb21vt37/fUWbmzJmqbt26asWKFWrfvn1q8ODBKjIyUuXm5lb58bnC1bqYOXOmstls6quvvlKpqamOW1ZWllJKqaysLPXiiy+q7du3q5SUFPXtt9+qzp07q7vvvlvl5eWZcoxl5WpdTJs2Ta1Zs0YdP35c7d69Ww0fPlz5+vqqAwcOOMq4a7tQyvX6uOFnP/uZGjZs2E3b3bVtZGVlqT179qg9e/YoQP31r39Ve/bsUT/++KNSSqlJkyapJ554wlH+xIkTqlatWmrixInq0KFDau7cucpqtaqEhARHmdvVbXXman3885//VF5eXmru3LlO3xnp6emOMi+88IJKTExUKSkpauvWrapPnz6qYcOG6sKFC1V+fK5wtS5mz56tli9fro4ePar279+v/vCHPyiLxaK+/fZbRxl3bRuu1sUNI0eOVDExMSV+pru2i+eff14FBgaqxMREpzafk5PjKOMueYbbJHZKKTVnzhzVtGlTZbPZVHR0tNqxY4fjtZ49e6pRo0Y5lV+0aJFq0aKFstlsqk2bNmrlypVOr+u6rl5++WUVHBysfHx8VO/evdWRI0eq4lDumCt1ERERoYCbblOnTlVKKZWTk6P69u2rgoKClLe3t4qIiFDPPPNMtf9SusGVuhg/fryjbHBwsHrooYdUUlKS0+e5c7tQyvXfk8OHDytArV279qbPcte2cWOKiv++3Tj2UaNGqZ49e970no4dOyqbzaaioqLUZ599dtPn3qpuqzNX66Nnz563LK+UMR1MaGiostlsKiwsTA0bNkwdO3asag+sHFytizfffFM1b95c+fr6qvr166vY2Fi1YcOGmz7XHdtGeX5P0tPTlZ+fn/roo49K/Ex3bRcl1QPg9D3gLnmGdv2AhBBCCCGEm3OLPnZCCCGEEOL2JLETQgghhPAQktgJIYQQQngISeyEEEIIITyEJHZCCCGEEB5CEjshhBBCCA8hiZ0QQgghhIeQxE4IIYQQwkNIYieEEEII4SEksRNCCCGE8BCS2AkhhBBCeAhJ7IQQNdr8+fPx8/MjNTXVsW306NG0b9+ejIwMEyMTQgjXaUopZXYQQghhFqUUHTt25IEHHmDOnDlMnTqVTz/9lB07dhAWFmZ2eEII4RIvswMQQggzaZrGn//8Zx599FFCQkKYM2cOW7ZskaROCOGW5IydEEIAnTt35sCBA6xdu5aePXuaHY4QQpSL9LETQtR4CQkJHD58GLvdTnBwsNnhCCFEuckZOyFEjZaUlERsbCwffvgh8fHxBAQEsHjxYrPDEkKIcpE+dkKIGuvkyZMMHDiQKVOmMGLECKKioujevTtJSUl07tzZ7PCEEMJlcsZOCFEjXblyhR49ehAbG0tcXJxj+8CBA7Hb7SQkJJgYnRBClI8kdkIIIYQQHkIGTwghhBBCeAhJ7IQQQgghPIQkdkIIIYQQHkISOyGEEEIIDyGJnRBCCCGEh5DETgghhBDCQ0hiJ4QQQgjhISSxE0IIIYTwEJLYCSGEEEJ4CEnshBBCCCE8hCR2QgghhBAe4v8Bq1malLkFy/UAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# storing the discretization in space:\n", "Nx = data_0_training.shape[1]\n", @@ -152,7 +141,8 @@ " plt.plot(x, u.flatten(), label=rf\"$u(x, t=\\delta)$\")\n", " plt.xlabel(rf\"$x$\")\n", " plt.tight_layout()\n", - " plt.legend()" + " plt.legend()\n", + "plt.show()" ] }, { @@ -183,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -204,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +269,7 @@ "source": [ "The aggregation and reduction functions combine the outputs of the branch and trunk networks. In this example, their outputs are multiplied element-wise, and no reduction is applied — meaning the final output has the same dimensionality as each network’s output.\n", "\n", - "We train the model using a `SupervisedSolver` with an `MSE` loss. Below, we first define the solver and then the trainer used to run the optimization." + "We train the model using a `SupervisedSingleModelSolver` with an `MSE` loss. Below, we first define the solver and then the trainer used to run the optimization." ] }, { @@ -289,7 +279,7 @@ "outputs": [], "source": [ "# define solver\n", - "solver = SupervisedSolver(problem=problem, model=model)\n", + "solver = SupervisedSingleModelSolver(problem=problem, model=model)\n", "\n", "# define the trainer and train\n", "trainer = Trainer(\n", @@ -307,18 +297,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training error: 0.73%\n", - "Testing error: 1.43%\n" - ] - } - ], + "outputs": [], "source": [ "# the l2 error\n", "l2 = LpLoss()\n", @@ -340,40 +321,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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KK69k8ODBFyyzYMEC5s2b1+TghDBL5zzfdR+O976ZnnrrH+Os6Tpf95pGn/0Lic9bBurhsL+RmhDBoOs6PXv25Pjx4xQUmHR/kFYkMjKSHj161LubaqA0pQK8OXyt++67j1WrVrFhwwa6d+9+wTIX6rFISUmhpKSEmJiYpkUsRIgdzf2UlH9+B5eyUjprJ10Swv/eII1RWHSCmMVDiNRcFHx/Jclprb8nRojGUkrh8XjweuWXURdjsViwWq0X7NEpLS0lNja2UefvJvVYzJ49m/fee4/169dfNKkAcDgcOByOpmxCCNOcXP8CKcCODteQ0UaSCoDEhK580mEC4yqyOLP+eUksRLuiaRo2mw2brfVcPbe1CqivQynF7NmzWblyJevWraNnz56hiksIU7iryuhb5BukbBn5I5OjCT7rKN/PZnsXr8VbccbkaIQQbVFAicWsWbN47bXXeP3114mOjqawsJDCwkKqqqpCFZ8QLeqrda/SgSqOkkj61TeaHU7QjbwykzxScVLDQbkSpxAiBAJKLJ599llKSkoYP348SUlJ/umNN94IVXxCtCjHztcA2Nf9e9isbe9WOg6blQPdfwBA5M7XoGlDrIQQ4qIC/irkQtOdd94ZovCEaDlnDn3B5dW78Cid1GvvNjuckOl53UyqlY1uNfmU7vvM7HCEEG1M6/8dnRBBcizrGQC2OK6gd6/LzQ0mhAb07MEGh++Cdieyl5gcjRCirZHEQgiguuQkfQrfA8DIuNfkaEKvZqhvYGpKwWpUxSmToxFCtCWSWAgB7P1gEU5qyNN6ccW475odTsiNHTeZ3eoynLg4smax2eEIIdoQSSxEu2e4a+j2lW/QZuGAmVitFpMjCr24KAe7U+8AIPbLl8HjauAZQgjROJJYiHZv19pX6aJOUUwsI6a0/huONVbGjXdTqDoSZ5zm6H9eMzscIUQbIYmFaPec254HYHe3W+gQFWVyNC0npUscWxN+CIC28a/y01MhRFBIYiHatX3b1tHHvZcaZaX/d+83O5wW13/KL6hUDrrXHOT4jtVmhyOEaAMksRDtWkn2IgB2xGWSkJRicjQtr89lPdgUcwMAJeueNjkaIURbIImFaLcKDu9naGk2AJ2vm2NqLGZKmDgHQ2n0L9vEyYNfmh2OEKKVk8RCtFsHVz2DVTPY7Uind9oYs8MxzeAhw/nc6av/sVVPmhyNEKK1k8RCtEvFRYWkH/8nAMbotn9BrIZYr5oNwMCiDygpLjA5GiFEayaJhWiX8t7+I9FaFfmWngyaMM3scEw37MrJ5Fn64NDc7HnnCbPDEUK0YpJYiHbn5ImjDDvuuyNv+djfoOlt/4JYDdF0nfKRswAYcvR1Tp84ZnJEQojWShIL0e4cePsPRGouvrL2ZbD0VvgNm/gj9lkuJ0qrZt9bj5kdjhCilZLEQrQrxV/nM6zwLQCqr34YTZddoI5usVA97lEAhp14m4JDeSZHJIRojeSoKtqV/Hd+j0Nzs8s2iCFXTzU7nLAz5JqbyHUMw655+Xrl78wORwjRCkliIdqN4qN5pBe9C4Bn3CPSW3ERzht+D8CIsx9ycNcWk6MRQrQ2cmQV7cbRlY9h17x8YR9G2pXfMTucsHX5sGvY3mEcuqYoee9Rs8MRQrQykliIduFk/k7STq0CQE34HZqmmRxReOvyX7/Ho3SGVW1k9+YPzQ5HCNGKSGIh2jxleDnzxiwsmmKr4wrSr7jW7JDCXkrfoWzvPMX3x0ePoQzD3ICEEK2GJBaizdv570X0rf6CSuUg7vtPSW9FI/X6wXyqlY2B7l18/q//Z3Y4QohWQhIL0aadOX6InjmPA7C518/o02+QyRG1HvHdepLT23e58/47/siZ4/kmRySEaA0ksRBtl1Icfe0+oqlkj6UfV972iNkRtTojb3uUPZZ+RFNJ4Ws/BaXMDkkIEeYksRBtVk7Wy6RVfEaNsmCd+lfsdpvZIbU6Npsdpv4/XMrGgIotfJUlX4kIIS5NEgvRJp0pLqTH5scA+DxlJn2GjDY3oFZswJCRZHe/B4Bum+dTffKwyREJIcKZJBaizVGGwb5XZtOZEg7pKYyYPt/skFq9K6c/ypdaP6Ko4vird8tXIkKIi5LEQrQ5G1+fz+iyNRhKo2bKX3A4I80OqdXrEOGg/Ia/UKXs9CzdwvE1fzE7JCFEmJLEQrQp29f8gyv2PQ3Atv6/pO8IuWZFsIzNuIL3uvwEgC6fPUbx9n+ZHJEQIhxJYiHajANfbqD/hv9B1xRbOk9l1K3/x+yQ2pwJMx4ly3otVgw6/OsuivZ8anZIQogwI4mFaBOKCw4S8/YdRGouvnSOYPg9z4NcCCvo4qOdDP3Z39lsGY6TGuxv3MqJ/FyzwxJChBFJLESrV11RQulLP6ALpzmkp5B67wqsdofZYbVZiZ2iSb13BXv1y4mjFO/fv8+JAvmliBDCRxIL0aoVHjvIsacm0MtzkFPEYp3+T2LjOpsdVpuX2CWeuLtXckxLJFmdoORvUzl67IjZYQkhwoAkFqLV+mJzNvrfruNy7wFOE82J775C9179zQ6r3UhM7oF1xkrOEENf4yDWv01g17b1ZoclhDCZJBai1VFKsfqtv9Hng1tI4DSHLT2onvERA0dOMDu0difxsoF4Z7zP13oySRTT+1/fZ+u7z5odlhDCRJJYiFal6GwZ7y56gOu/fJBIzUVe1Ci6zllPck/pqTBLfM80Os35lJ2RGTg1N6NyHuLz5+7B8LjNDk0IYQJJLESr4DUUa95bTukzVzD19EvomiKvxzT6PrAKZ3RHs8Nr9yJiOjHol6v4rNtMAEYeX86RP43h5JcfmhyZEKKlaUq13LV5S0tLiY2NpaSkhJiYmJbarGjlvtrzBadX/porajYBUKrFUHr17+h+7U9NjkxcyGf/fokhn/+WaK0KgCOdxpL0w8exJaeZHJkQoqkCOX9LYiHCkuE12PnZB1Rt+TvDStfi0Dx4lM5XqbfRb9ofsURJL0U4O3joEHlv/o7MivexaV4MNE73nkrncfeipWTINUaEaGUksRCtklKKgvw8jmS/TI+jK+mmTviX7YkcSZebnyK+Z7qJEYpAKKVYtf4zLB/PZxIb/fNPWhM5lDQZPX0a/YaMooPDamKUQojGCHlisXjxYp544gkKCwtJT09n0aJFjB7d8G2pJbFo37yGoqzaTVm1h9JqN2WV1VQW7KHm4KdEn9hKr6ovSeKUv3w5EeR1zqTTlT+m57AJ8im3lTpdUcM/3nqb7vv/wfXaFjpo1f5lh1VX8p0DqEkcQcd+Y+mXPpaYKLlpnBDhJqSJxRtvvMGPfvQjlixZQkZGBs888wwrVqwgLy+PhISEoAXW2pS7PFg0jQi7xexQLsrtNQCwWYIzZtcwFNUeL9VuA5fH+82dtL1uNHclpSWnOXTkMAUFX3PmZAHVJUUkqSIu0wpJ1U6Qop3EpnnrrdOjdPIcg6ke/N8Muu52nFFt633Snrk8XvYcOcGZ7f+i86F/MaB8MzY89csoK8e1Lpy0JlHq7IarQ3escd3o3CWRxMRuJCYmYYnqBLYosEhPRzDVnQo0SeBDrtrtpcLlIcphxWHVW8VrHtLEIiMjg1GjRvHXv/4VAMMwSElJ4ec//zkPPfRQvbIulwuXy1UvsJSUlKAnFl53DXsXfQ8NDU3jm0cNPF6FxzBqHxWGUlg0DV3XsGgaFh10TfNNuoZe+zxDgVK+k6eBQkfDon8zKaCyxkOly0tFjYcaj++k7bBaiLRbiLBbiLBZUIChlH9dHkNR4zWo8Ri4vb5JARr4YwffTm7gi0EphdWiYbdYsFt17BYNm0X3x6xp+OL+9utiKKo9BtVuL9U1Xmo8XhS++lp1sOg6Vv2b52oa6CiU8k0ow/+oKy8aBhoKXXnRlYEVN3Y82PBg07xEUk0ELuzfShYupRoHX3cYTFXiaCL7XE23IVfhiIxt9ntCtALVJRTnfUbR7v+gf72N5PJcYihv9NNrsOLCQTUOajQbXs2KV7NhaFYMzYoXHY/S/JOhNHRdB03HouvouobVYsFh1bFbLditFqwWDY/hS8I9hsLjNXB5FTUe3z7r8hh4DYXNouG01T3Xtz5D+fb1uv297rhi0cGiaxiK2vV4qald57nlDaVq90/fccB3LNKwWnz7vN2qY7PoWGuPP6r2GKVQtftv3fHPx1t7vKl7VEqd0+nn+4/Ha+D2Kn99NSDCbiHS5juGOW0WDKVw18br9hp4lTovxrpteGrXYyiF1aJj033HKptFB632uGb4HhX4l9mtmv8Dz7nr8RrfnJ602n/qjpHaN9XwvY6Gr86+19F3zNY0DV2vTZbqXmOlfMf32iOvds66fO2lnfPoW78/CkW9djZq1+UrcO6ptO64rPlf8xqPQbXHwOX2+j/kUdtuFh2suq/+dW2qlC8uvTYeXeOc89Y5Meoa1trzUt2jbrWR/JM3g5qwBJJYBJTy19TUsG3bNh5++GH/PF3XyczMZOPGjeeVX7BgAfPmzQtkE03iMbwMKt0Q8u1cUl1HhQJctVNjXKzdtQss99ZOTfXtzhSjdgqUf0+8NDcWyvQ43M5O6B3iiYhNICI+FUt8b+jUCzr1xhmdRG9dfvXcLjljiU+fTHz6ZN/fSlFaeJCSgn1UnDiAcSofvfQIekURVtdZIjwlxFFOhFYDgB0PdjxEU+Hb7xrzEak5+08dDd9+E8h+3tj1nquuTgbQEpcEqdsNPbVTVTPX52m4SLt1oY7tix2Lm3CMdimbqb0gASUWxcXFeL1eunbtWm9+165d2bt373nlH374YR544AH/33U9FsGm61bW9XnEn/n7s1YFdquO0+brPXDadCy65s/Q3V6DGo/Cq3w9Gm7DwOv1ZaCWczNATcOrfBm7y2vg9vh6GTpH2Ynv4KBLtIPOHewYBpyqcHG6oobi8hpKq2p8nzh0DYtFw6b7Pt1E2a10cFqItNuIslv8PSSG8h1JlPL1JtRlqDpQ7fFSXu3rHSl3eah2e/29MHWfFr6dMVs0iI20ExdpJy7SRlyEDavFgsvj++rCVfspzPBn4b73sAZYLVasFh2L1YJV19EtFnTdgmaxousWdKsNu92Bze7E7nBisdrAFgn2KP+jzWKnUyvo4hNhQtOISepNTFLvCy72Goqjpys5cvIMVk8VDlWNg2rshgvNU427pga3pwZPjQuPx41NM7DryvdpX/f1tLlr998ajwe3x0t53Xifajel1W5cbi/OumNGba9jlMNCtNNKtMNGtNPXdV3hclNa7aG0yk1JtRuvV/mOFxYda21PYt0n7xqvgcdroGkaHRxWOjitRDusRDms2Cz6Nz2htZ9u645fvv0aqtzf9IxWuLy43F7/p1zfJ2LfPub/BG34PovbLToOm+57tFqw6Bp1n9Pr+qnrjo11vawer+J0ZY3/OFZS6cFm0Yh0WIiyW4myW7FZtNr4wKMMDAN/D47TpuOwWrHqGtVuL5U13tpHD4r6x1WAqhovFTVeKl0eyl2+rC/C5nv9HbU9Qr7joy9gZdSdZ5W/Dgqw1vbs2Ky+46yu1++x8RoKjdqeAYte22tdv9fHqD13uOt6lGt7uuv6R+p6g/x1qO05tpzTK1GXHPp7u2vXqVBE2a3ERtiIjbARE2HDabNQU3scdnkMXG5fzXT9m94gxTcxeQwDt6fu3OU7X7k9dT3g9ddjaDozm79HNllIv6R0OBw4HKG/y6TNZuPa238d8u00RhTQIwTrjQEuPYKl8WRonGiNLLrGZfFRXBYfZXYoxJsdQAh1BvqYHUQ74ayd2pqA+qDj4+OxWCycOHGi3vwTJ06QmJgY1MCEEEII0foElFjY7XZGjBjB2rVr/fMMw2Dt2rWMGTMm6MEJIYQQonUJ+KuQBx54gBkzZjBy5EhGjx7NM888Q0VFBTNnNvyNTt0PUEpLSwOPVAghhBCmqDtvN+aHpAEnFtOmTePkyZM8+uijFBYWMnToULKyss4b0HkhZWVlACEZwCmEEEKI0CorKyM29tKXBGjRS3obhkFBQQHR0dFB/ylM3S9Ojh492uYuvgVtv37Q9uso9Wv92nod23r9oO3XMVT1U0pRVlZGcnKy73owl9Cil67TdZ3u3buHdBsxMTFt8s1Sp63XD9p+HaV+rV9br2Nbrx+0/TqGon4N9VTUkSsTCSGEECJoJLEQQgghRNC0mcTC4XAwd+7cFrkglxnaev2g7ddR6tf6tfU6tvX6QduvYzjUr0UHbwohhBCibWszPRZCCCGEMJ8kFkIIIYQIGkkshBBCCBE0klgIIYQQImjCOrFYvHgxl112GU6nk4yMDLZs2XLJ8itWrKB///44nU6GDBnCBx98UG+5UopHH32UpKQkIiIiyMzMZN++faGswiUFUr8XXniBq6++mo4dO9KxY0cyMzPPK3/nnXeiaVq96YYbbgh1NS4qkPotXbr0vNidzvo3FA639oPA6jh+/Pjz6qhpGlOmTPGXCac2XL9+PTfeeCPJyclomsY777zT4HOys7MZPnw4DoeDyy+/nKVLl55XJtD9OlQCrd/bb7/N9ddfT5cuXYiJiWHMmDGsXr26XpnHHnvsvPbr379/CGtxcYHWLzs7+4Lvz8LCwnrlwqX9IPA6Xmj/0jSNQYMG+cuEUxsuWLCAUaNGER0dTUJCAlOnTiUvL6/B55l9LgzbxOKNN97ggQceYO7cuWzfvp309HQmTZpEUVHRBct/9tln3Hbbbdx1113k5OQwdepUpk6dSm5urr/Mn/70J/7yl7+wZMkSNm/eTFRUFJMmTaK6urqlquUXaP2ys7O57bbb+Pjjj9m4cSMpKSlMnDiRr7/+ul65G264gePHj/unZcuWtUR1zhNo/cB3pbhzYz98+HC95eHUfhB4Hd9+++169cvNzcVisXDzzTfXKxcubVhRUUF6ejqLFy9uVPn8/HymTJnChAkT2LFjB3PmzOHuu++ud/JtyvsiVAKt3/r167n++uv54IMP2LZtGxMmTODGG28kJyenXrlBgwbVa78NGzaEIvwGBVq/Onl5efXiT0hI8C8Lp/aDwOv45z//uV7djh49SqdOnc7bB8OlDT/55BNmzZrFpk2bWLNmDW63m4kTJ1JRUXHR54TFuVCFqdGjR6tZs2b5//Z6vSo5OVktWLDgguVvueUWNWXKlHrzMjIy1D333KOUUsowDJWYmKieeOIJ//KzZ88qh8Ohli1bFoIaXFqg9fs2j8ejoqOj1SuvvOKfN2PGDHXTTTcFO9QmCbR+L7/8soqNjb3o+sKt/ZRqfhs+/fTTKjo6WpWXl/vnhVMbngtQK1euvGSZX//612rQoEH15k2bNk1NmjTJ/3dzX7NQaUz9LmTgwIFq3rx5/r/nzp2r0tPTgxdYkDSmfh9//LEC1JkzZy5aJlzbT6mmteHKlSuVpmnq0KFD/nnh2oZKKVVUVKQA9cknn1y0TDicC8Oyx6KmpoZt27aRmZnpn6frOpmZmWzcuPGCz9m4cWO98gCTJk3yl8/Pz6ewsLBemdjYWDIyMi66zlBpSv2+rbKyErfbTadOnerNz87OJiEhgX79+nHfffdx6tSpoMbeGE2tX3l5OampqaSkpHDTTTexa9cu/7Jwaj8IThu++OKL3HrrrURFRdWbHw5t2BQN7YPBeM3CiWEYlJWVnbcP7tu3j+TkZHr16sXtt9/OkSNHTIqwaYYOHUpSUhLXX389n376qX9+W2s/8O2DmZmZpKam1psfrm1YUlICcN577lzhcC4My8SiuLgYr9d73q3Yu3btet73fXUKCwsvWb7uMZB1hkpT6vdtv/nNb0hOTq735rjhhhv4+9//ztq1a3n88cf55JNPmDx5Ml6vN6jxN6Qp9evXrx8vvfQS7777Lq+99hqGYTB27FiOHTsGhFf7QfPbcMuWLeTm5nL33XfXmx8ubdgUF9sHS0tLqaqqCsr7Ppw8+eSTlJeXc8stt/jnZWRksHTpUrKysnj22WfJz8/n6quvpqyszMRIGycpKYklS5bw1ltv8dZbb5GSksL48ePZvn07EJzjVjgpKChg1apV5+2D4dqGhmEwZ84crrzySgYPHnzRcuFwLmzRu5uK4Fi4cCHLly8nOzu73gDHW2+91f//IUOGkJaWRu/evcnOzua6664zI9RGGzNmDGPGjPH/PXbsWAYMGMBzzz3H/PnzTYwsNF588UWGDBnC6NGj681vzW3Ynrz++uvMmzePd999t94YhMmTJ/v/n5aWRkZGBqmpqbz55pvcddddZoTaaP369aNfv37+v8eOHcuBAwd4+umnefXVV02MLDReeeUV4uLimDp1ar354dqGs2bNIjc317TxHoEIyx6L+Ph4LBYLJ06cqDf/xIkTJCYmXvA5iYmJlyxf9xjIOkOlKfWr8+STT7Jw4UI+/PBD0tLSLlm2V69exMfHs3///mbHHIjm1K+OzWZj2LBh/tjDqf2geXWsqKhg+fLljTpImdWGTXGxfTAmJoaIiIigvC/CwfLly7n77rt58803z+ty/ra4uDj69u3bKtrvQkaPHu2Pva20H/h+FfHSSy9xxx13YLfbL1k2HNpw9uzZvPfee3z88cd07979kmXD4VwYlomF3W5nxIgRrF271j/PMAzWrl1b71PtucaMGVOvPMCaNWv85Xv27EliYmK9MqWlpWzevPmi6wyVptQPfCN558+fT1ZWFiNHjmxwO8eOHePUqVMkJSUFJe7Gamr9zuX1etm5c6c/9nBqP2heHVesWIHL5WL69OkNbsesNmyKhvbBYLwvzLZs2TJmzpzJsmXL6v1M+GLKy8s5cOBAq2i/C9mxY4c/9rbQfnU++eQT9u/f36jk3sw2VEoxe/ZsVq5cybp16+jZs2eDzwmLc2FQhoCGwPLly5XD4VBLly5Vu3fvVj/96U9VXFycKiwsVEopdccdd6iHHnrIX/7TTz9VVqtVPfnkk2rPnj1q7ty5ymazqZ07d/rLLFy4UMXFxal3331Xffnll+qmm25SPXv2VFVVVWFfv4ULFyq73a7++c9/quPHj/unsrIypZRSZWVl6sEHH1QbN25U+fn56qOPPlLDhw9Xffr0UdXV1WFfv3nz5qnVq1erAwcOqG3btqlbb71VOZ1OtWvXLn+ZcGo/pQKvY52rrrpKTZs27bz54daGZWVlKicnR+Xk5ChAPfXUUyonJ0cdPnxYKaXUQw89pO644w5/+YMHD6rIyEj1q1/9Su3Zs0ctXrxYWSwWlZWV5S/T0GsWzvX7xz/+oaxWq1q8eHG9ffDs2bP+Mr/85S9Vdna2ys/PV59++qnKzMxU8fHxqqioKOzr9/TTT6t33nlH7du3T+3cuVPdf//9Std19dFHH/nLhFP7KRV4HetMnz5dZWRkXHCd4dSG9913n4qNjVXZ2dn13nOVlZX+MuF4LgzbxEIppRYtWqR69Oih7Ha7Gj16tNq0aZN/2bhx49SMGTPqlX/zzTdV3759ld1uV4MGDVLvv/9+veWGYajf/e53qmvXrsrhcKjrrrtO5eXltURVLiiQ+qWmpirgvGnu3LlKKaUqKyvVxIkTVZcuXZTNZlOpqanqJz/5iWk7vFKB1W/OnDn+sl27dlXf+c531Pbt2+utL9zaT6nA36N79+5VgPrwww/PW1e4tWHdzw+/PdXVacaMGWrcuHHnPWfo0KHKbrerXr16qZdffvm89V7qNWtJgdZv3LhxlyyvlO/ntUlJScput6tu3bqpadOmqf3797dsxWoFWr/HH39c9e7dWzmdTtWpUyc1fvx4tW7duvPWGy7tp1TT3qNnz55VERER6vnnn7/gOsOpDS9UN6DefhWO50K5bboQQgghgiYsx1gIIYQQonWSxEIIIYQQQSOJhRBCCCGCRhILIYQQQgSNJBZCCCGECBpJLIQQQggRNJJYCCGEECJoJLEQQgghRNBIYiGEEEKIoJHEQgghhBBBI4mFEEIIIYJGEgshRLMsW7aMiIgIjh8/7p83c+ZM0tLSKCkpMTEyIYQZ5CZkQohmUUoxdOhQrrnmGhYtWsTcuXN56aWX2LRpE926dTM7PCFEC7OaHYAQonXTNI0//vGP/PCHPyQxMZFFixbxn//8R5IKIdop6bEQQgTF8OHD2bVrFx9++CHjxo0zOxwhhElkjIUQotmysrLYu3cvXq+Xrl27mh2OEMJE0mMhhGiW7du3M378eJ577jmWLl1KTEwMK1asMDssIYRJZIyFEKLJDh06xJQpU/jtb3/LbbfdRq9evRgzZgzbt29n+PDhZocnhDCB9FgIIZrk9OnTjB07lvHjx7NkyRL//ClTpuD1esnKyjIxOiGEWSSxEEIIIUTQyOBNIYQQQgSNJBZCCCGECBpJLIQQQggRNJJYCCGEECJoJLEQQgghRNBIYiGEEEKIoJHEQgghhBBBI4mFEEIIIYJGEgshhBBCBI0kFkIIIYQIGkkshBBCCBE0/x8eIROgLJOuEAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for i in [1, 2, 3]:\n", " plt.subplot(3, 1, i)\n", @@ -389,7 +339,7 @@ " )\n", " plt.xlabel(r\"$x$\")\n", " plt.legend(loc=\"upper right\")\n", - " plt.show()" + "plt.show()" ] }, { @@ -401,20 +351,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for i in [1, 2, 3]:\n", " plt.subplot(3, 1, i)\n", @@ -426,7 +365,8 @@ " )\n", " plt.xlabel(r\"$x$\")\n", " plt.tight_layout()\n", - " plt.legend(loc=\"upper right\")" + " plt.legend(loc=\"upper right\")\n", + "plt.show()" ] }, { diff --git a/tutorials/tutorial3/tutorial.ipynb b/tutorials/tutorial3/tutorial.ipynb index c545e1cf3..d57b0741e 100644 --- a/tutorials/tutorial3/tutorial.ipynb +++ b/tutorials/tutorial3/tutorial.ipynb @@ -38,10 +38,10 @@ "from pina import Condition, LabelTensor, Trainer\n", "from pina.problem import SpatialProblem, TimeDependentProblem\n", "from pina.domain import CartesianDomain\n", - "from pina.solver import PINN\n", - "from pina.equation import Equation, FixedValue\n", + "from pina.solver import PhysicsInformedSingleModelSolver\n", + "from pina.equation import Equation\n", + "from pina.equation.zoo import FixedValue, AcousticWaveEquation\n", "from pina.callback import MetricTracker\n", - "from pina.equation import AcousticWave\n", "\n", "warnings.filterwarnings(\"ignore\")" ] @@ -72,12 +72,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "b60176c4", "metadata": {}, "outputs": [], "source": [ - "wave_equation = AcousticWave(c=1.0)\n", + "wave_equation = AcousticWaveEquation(c=1.0)\n", "\n", "\n", "def initial_condition(input_, output_):\n", @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "9fbbb74f", "metadata": {}, "outputs": [], @@ -183,13 +183,13 @@ "outputs": [], "source": [ "# generate the data\n", - "problem.discretise_domain(1000, \"random\", domains=\"all\")\n", + "problem.discretise_domain(1000, \"random\")\n", "\n", "# define model\n", "model = HardMLP(len(problem.input_variables), len(problem.output_variables))\n", "\n", "# crete the solver\n", - "pinn = PINN(problem=problem, model=model)\n", + "pinn = PhysicsInformedSingleModelSolver(problem=problem, model=model)\n", "\n", "# create trainer and train\n", "trainer = Trainer(\n", @@ -215,31 +215,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "77bfcb6e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "trainer_metrics = trainer.callbacks[0].metrics\n", "for metric, loss in trainer_metrics.items():\n", @@ -248,7 +227,8 @@ "plt.xlabel(\"epoch\")\n", "plt.ylabel(\"loss\")\n", "plt.yscale(\"log\")\n", - "plt.legend()" + "plt.legend()\n", + "plt.show()" ] }, { @@ -261,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "c086c05f", "metadata": {}, "outputs": [], @@ -294,7 +274,8 @@ " points.extract(\"y\").tensor.flatten(),\n", " field.tensor.flatten(),\n", " )\n", - " plt.colorbar(), plt.tight_layout()" + " plt.colorbar(), plt.tight_layout()\n", + " plt.show()" ] }, { @@ -307,41 +288,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "0265003f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12, 6))\n", "plot_solution(solver=pinn, time=0)\n", @@ -371,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "33e43412", "metadata": {}, "outputs": [], @@ -426,7 +376,7 @@ "model = HardMLPtime(len(problem.input_variables), len(problem.output_variables))\n", "\n", "# crete the solver\n", - "pinn = PINN(problem=problem, model=model)\n", + "pinn = PhysicsInformedSingleModelSolver(problem=problem, model=model)\n", "\n", "# create trainer and train\n", "trainer = Trainer(\n", @@ -452,41 +402,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "019767e5", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12, 6))\n", "plot_solution(solver=pinn, time=0)\n", diff --git a/tutorials/tutorial4/tutorial.ipynb b/tutorials/tutorial4/tutorial.ipynb index 9e7776f5b..4e7c2fa33 100644 --- a/tutorials/tutorial4/tutorial.ipynb +++ b/tutorials/tutorial4/tutorial.ipynb @@ -43,7 +43,7 @@ "\n", "from pina import Trainer\n", "from pina.problem.zoo import SupervisedProblem\n", - "from pina.solver import SupervisedSolver\n", + "from pina.solver import SupervisedSingleModelSolver\n", "from pina.trainer import Trainer\n", "from pina.model.block import ContinuousConvBlock\n", "from pina.model import FeedForward # for building AE and MNIST classification\n", @@ -134,19 +134,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "447bb133", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Domain has shape: torch.Size([1, 2, 200, 2])\n", - "Filter input data has shape: torch.Size([1, 2, 200, 3])\n" - ] - } - ], + "outputs": [], "source": [ "# batch size fixed to 1\n", "batch_size = 1\n", @@ -208,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "b78c08b8", "metadata": {}, "outputs": [], @@ -245,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "0fbe67dc", "metadata": {}, "outputs": [], @@ -270,19 +261,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "07580a3c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filter input data has shape: torch.Size([1, 2, 200, 3])\n", - "Filter output data has shape: torch.Size([1, 1, 169, 3])\n" - ] - } - ], + "outputs": [], "source": [ "print(f\"Filter input data has shape: {data.shape}\")\n", "\n", @@ -302,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "0e234c69", "metadata": {}, "outputs": [], @@ -346,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "6d816e7a", "metadata": {}, "outputs": [], @@ -385,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "a872fb2d", "metadata": {}, "outputs": [], @@ -418,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "889c1592", "metadata": {}, "outputs": [], @@ -468,7 +450,7 @@ "id": "4374c15c", "metadata": {}, "source": [ - "We now aim to solve a classification problem. For this we will use the `SupervisedSolver` and the `SupervisedProblem`. The input of the supervised problems are the images, while the output the corresponding class. We will train with `CrossEntropyLoss`." + "We now aim to solve a classification problem. For this we will use the `SupervisedSingleModelSolver` and the `SupervisedProblem`. Because the supervised single-model solver works on prediction-target residuals, we encode the MNIST labels as one-hot vectors so their shape matches the 10-class output of the classifier. We then train with `MSELoss`." ] }, { @@ -479,16 +461,19 @@ "outputs": [], "source": [ "# setting the problem\n", + "train_targets = torch.nn.functional.one_hot(\n", + " train_data.train_labels, num_classes=10\n", + ").float()\n", "problem = SupervisedProblem(\n", " input_=train_data.train_data.unsqueeze(1), # adding channel dimension\n", - " output_=train_data.train_labels,\n", + " output_=train_targets,\n", ")\n", "\n", "# setting the solver\n", - "solver = SupervisedSolver(\n", + "solver = SupervisedSingleModelSolver(\n", " problem=problem,\n", " model=ContinuousClassifier(),\n", - " loss=torch.nn.CrossEntropyLoss(),\n", + " loss=torch.nn.MSELoss(),\n", " use_lt=False,\n", ")\n", "\n", @@ -516,18 +501,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "b54638c1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy of the network on the test images: 81.550%\n" - ] - } - ], + "outputs": [], "source": [ "correct = 0\n", "total = 0\n", @@ -536,6 +513,9 @@ " for data in trainer.data_module.test_dataloader():\n", " test_data = data[\"data\"]\n", " images, labels = test_data[\"input\"], test_data[\"target\"]\n", + " # if targets are one-hot vectors, convert back to class indices\n", + " if labels.ndim > 1 and labels.shape[-1] > 1:\n", + " labels = labels.argmax(dim=1)\n", " # calculate outputs by running images through the network\n", " outputs = solver(images)\n", " # the class with the highest energy is what we choose as prediction\n", @@ -543,7 +523,7 @@ " total += labels.size(0)\n", " correct += (predicted == labels).sum().item()\n", "\n", - "print(f\"Accuracy of the network on the test images: {(correct / total):.3%}\")" + "print(f\"Accuracy of the network on the test images: {(correct / total):.2%}\")" ] }, { @@ -560,21 +540,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "6ca0e929", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# create inputs\n", "def circle_grid(N=100):\n", @@ -627,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "13e8ae0e", "metadata": {}, "outputs": [], @@ -704,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "a4db89a7", "metadata": {}, "outputs": [], @@ -731,7 +700,7 @@ "id": "2df482a7", "metadata": {}, "source": [ - "Now, let's proceed with training the autoencoder by minimizing the mean squared error (MSE) loss and optimizing using the Adam optimizer. We'll use the `SupervisedSolver` for the training, and the problem will be defined as a simple problem inherited from `AbstractProblem`." + "Now, let's proceed with training the autoencoder by minimizing the mean squared error (MSE) loss and optimizing using the Adam optimizer. We'll use the `SupervisedSingleModelSolver` for the training, and the problem will be defined as a simple problem inherited from `BaseProblem`." ] }, { @@ -746,7 +715,7 @@ "\n", "\n", "# define the solver\n", - "solver = SupervisedSolver(\n", + "solver = SupervisedSingleModelSolver(\n", " problem=problem,\n", " model=Autoencoder(),\n", " loss=torch.nn.MSELoss(),\n", @@ -776,21 +745,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "0269fedf", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "solver.eval()\n", "\n", @@ -820,18 +778,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "ded8f91b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "l2 error: 4.78%\n" - ] - } - ], + "outputs": [], "source": [ "def l2_error(input_, target):\n", " return torch.linalg.norm(input_ - target, ord=2) / torch.linalg.norm(\n", @@ -856,21 +806,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "fcbbaec6", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# setting the seed\n", "torch.manual_seed(seed)\n", @@ -911,18 +850,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "ab505b75", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "l2 error: 9.72%\n" - ] - } - ], + "outputs": [], "source": [ "print(\n", " f\"l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}\"\n", diff --git a/tutorials/tutorial5/tutorial.ipynb b/tutorials/tutorial5/tutorial.ipynb index 5a2e76389..30a9461fd 100644 --- a/tutorials/tutorial5/tutorial.ipynb +++ b/tutorials/tutorial5/tutorial.ipynb @@ -46,7 +46,7 @@ "from scipy import io\n", "from pina.model import FNO, FeedForward\n", "from pina import Trainer\n", - "from pina.solver import SupervisedSolver\n", + "from pina.solver import SupervisedSingleModelSolver\n", "from pina.problem.zoo import SupervisedProblem\n", "\n", "warnings.filterwarnings(\"ignore\")" @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "2ffb8a4c", "metadata": { "ExecuteTime": { @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "c8501b6f", "metadata": { "ExecuteTime": { @@ -112,18 +112,7 @@ "start_time": "2024-09-19T13:35:29.031076Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.subplot(1, 2, 1)\n", "plt.title(\"permeability\")\n", @@ -144,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "8b27d283", "metadata": { "ExecuteTime": { @@ -167,7 +156,7 @@ "source": [ "## Solving the Problem with a Feedforward Neural Network\n", "\n", - "We begin by solving the Darcy flow problem using a standard Feedforward Neural Network (FNN). Since we are approaching this task with supervised learning, we will use the `SupervisedSolver` provided by **PINA** to train the model." + "We begin by solving the Darcy flow problem using a standard Feedforward Neural Network (FNN). Since we are approaching this task with supervised learning, we will use the `SupervisedSingleModelSolver` provided by **PINA** to train the model." ] }, { @@ -187,7 +176,7 @@ "\n", "\n", "# make solver\n", - "solver = SupervisedSolver(problem=problem, model=model, use_lt=False)\n", + "solver = SupervisedSingleModelSolver(problem=problem, model=model, use_lt=False)\n", "\n", "# make the trainer and train\n", "trainer = Trainer(\n", @@ -213,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "0e2a6aa4", "metadata": { "ExecuteTime": { @@ -221,16 +210,7 @@ "start_time": "2024-09-19T13:35:31.256308Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final error training 28.54%\n", - "Final error testing 28.58%\n" - ] - } - ], + "outputs": [], "source": [ "from pina.loss import LpLoss\n", "\n", @@ -289,7 +269,7 @@ "\n", "\n", "# make solver\n", - "solver = SupervisedSolver(problem=problem, model=model, use_lt=False)\n", + "solver = SupervisedSingleModelSolver(problem=problem, model=model, use_lt=False)\n", "\n", "# make the trainer and train\n", "trainer = Trainer(\n", @@ -317,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "58e2db89", "metadata": { "ExecuteTime": { @@ -325,16 +305,7 @@ "start_time": "2024-09-19T13:35:44.729042Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final error training 3.52%\n", - "Final error testing 3.67%\n" - ] - } - ], + "outputs": [], "source": [ "model = solver.model\n", "err = (\n", diff --git a/tutorials/tutorial6/tutorial.ipynb b/tutorials/tutorial6/tutorial.ipynb index e5fd2b1f4..d14c479d8 100644 --- a/tutorials/tutorial6/tutorial.ipynb +++ b/tutorials/tutorial6/tutorial.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -121,44 +121,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cartesian samples: 1: {'dof': ['x', 'y'], 'name': 1}\n", - "\n", - "tensor([[0.3672, 0.5710],\n", - " [0.5258, 0.3927],\n", - " [0.3316, 0.7359],\n", - " [0.9124, 0.8232]])\n", - "\n", - "Ellipsoid samples: 1: {'dof': ['x', 'y'], 'name': 1}\n", - "\n", - "tensor([[ 0.3378, 0.0636],\n", - " [ 0.2436, 0.1680],\n", - " [ 0.3567, 0.1652],\n", - " [-0.2776, 0.1676]])\n", - "\n", - "Simplex samples: 1: {'dof': ['x', 'y'], 'name': 1}\n", - "\n", - "tensor([[-0.1643, 0.4065],\n", - " [ 0.3280, 0.1269],\n", - " [-0.1841, 0.3838],\n", - " [ 0.2982, 0.0638]])\n", - "\n", - "Fixed variable samples: 1: {'dof': ['x', 'y'], 'name': 1}\n", - "\n", - "tensor([[0.4529, 1.0000],\n", - " [0.5599, 1.0000],\n", - " [1.0384, 1.0000],\n", - " [1.4100, 1.0000]])\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(f\"Cartesian samples: {cartesian_samples[:4]}\\n\")\n", "print(f\"Ellipsoid samples: {ellipsoid_samples[:4]}\\n\")\n", @@ -176,20 +141,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Basic plotting function\n", "def plot_scatter(ax, pts, title):\n", @@ -215,20 +169,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Boundary definitions\n", "cartesian_boundary = cartesian.partial()\n", @@ -291,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -309,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -328,20 +271,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, axs = plt.subplots(1, 3, figsize=(16, 4))\n", "pts_list = [cart_sim_samples, cart_ell_bnd_samples, ell_sim_bnd_samples]\n", @@ -379,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -446,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -483,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -530,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -551,20 +483,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "plot_scatter(ax, heart_samples, \"Heart Domain\")" diff --git a/tutorials/tutorial7/tutorial.ipynb b/tutorials/tutorial7/tutorial.ipynb index 6082f42df..c8ad80c24 100644 --- a/tutorials/tutorial7/tutorial.ipynb +++ b/tutorials/tutorial7/tutorial.ipynb @@ -11,7 +11,7 @@ "\n", "## Introduction to the Inverse Problem\n", "\n", - "This tutorial demonstrates how to solve an inverse Poisson problem using Physics-Informed Neural Networks (PINNs).\n", + "This tutorial demonstrates how to solve an inverse Poisson problem using Physics-Informed Neural Networks.\n", "\n", "The problem is defined as a Poisson equation with homogeneous boundary conditions:\n", "\n", @@ -37,28 +37,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "00d1027d-13f2-4619-9ff7-a740568f13ff", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Seed set to 883\n" - ] - }, - { - "data": { - "text/plain": [ - "883" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "## routine needed to run the notebook on Google Colab\n", "try:\n", @@ -85,8 +67,9 @@ "from pina.problem import SpatialProblem, InverseProblem\n", "from pina.operator import laplacian\n", "from pina.model import FeedForward\n", - "from pina.equation import Equation, FixedValue\n", - "from pina.solver import PINN\n", + "from pina.equation import Equation\n", + "from pina.equation.zoo import FixedValue\n", + "from pina.solver import PhysicsInformedSingleModelSolver\n", "from pina.domain import CartesianDomain\n", "from pina.optim import TorchOptimizer\n", "\n", @@ -112,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "2c55d972-09a9-41de-9400-ba051c28cdcb", "metadata": {}, "outputs": [], @@ -138,21 +121,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "55cef553-7495-401d-9d17-1acff8ec5953", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "points = data_input.extract([\"x\", \"y\"]).detach().numpy()\n", "truth = data_output.detach().numpy()\n", @@ -178,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "8ec0d95d-72c2-40a4-a310-21c3d6fe17d2", "metadata": {}, "outputs": [], @@ -234,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "c4170514-eb73-488e-8942-0129070e4e13", "metadata": {}, "outputs": [], @@ -257,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "e3e0ae40-d8c6-4c08-81e8-85adc60a94e6", "metadata": {}, "outputs": [], @@ -279,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "e1409953-eb1b-443b-923d-c7ec3af0dfb0", "metadata": {}, "outputs": [], @@ -306,7 +278,7 @@ "id": "fc6e0030-f6ae-40cf-a3b3-d21d6538e7f2", "metadata": {}, "source": [ - "Then, we define the `PINN` object and train the solver using the `Trainer`" + "Then, we define the `PhysicsInformedSingleModelSolver` object and train the solver using the `Trainer`" ] }, { @@ -317,12 +289,12 @@ "outputs": [], "source": [ "max_epochs = 1500\n", - "pinn = PINN(\n", + "solver = PhysicsInformedSingleModelSolver(\n", " problem, model, optimizer=TorchOptimizer(torch.optim.Adam, lr=0.005)\n", ")\n", "# define the trainer for the solver\n", "trainer = Trainer(\n", - " solver=pinn,\n", + " solver=solver,\n", " accelerator=\"cpu\",\n", " max_epochs=max_epochs,\n", " default_root_dir=tmp_dir,\n", @@ -345,21 +317,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "dd328887-7c18-4b96-ada4-c9eec630c069", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "epochs_saved = range(99, max_epochs, 100)\n", "parameters = torch.empty((int(max_epochs / 100), 2))\n", @@ -367,7 +328,7 @@ " params_torch = torch.load(\n", " \"{}/parameters_epoch{}\".format(tmp_dir, epoch), weights_only=False\n", " )\n", - " for e, var in enumerate(pinn.problem.unknown_variables):\n", + " for e, var in enumerate(solver.problem.unknown_variables):\n", " parameters[i, e] = params_torch[var].data\n", "\n", "# Plot parameters\n", @@ -391,7 +352,7 @@ "\n", "We have covered the basic usage of PINNs for inverse problem modeling. Here are some possible directions for further exploration:\n", "\n", - "1. **Experiment with different Physics-Informed strategies**: Explore variations in PINN training techniques to improve performance or tackle different types of problems.\n", + "1. **Experiment with different Physics-Informed strategies**: Explore variations in Physics-Informed training techniques to improve performance or tackle different types of problems.\n", "\n", "2. **Apply to more complex problems**: Scale the approach to higher-dimensional or time-dependent inverse problems.\n", "\n", diff --git a/tutorials/tutorial8/tutorial.ipynb b/tutorials/tutorial8/tutorial.ipynb index ad2fc3f29..015d3b288 100644 --- a/tutorials/tutorial8/tutorial.ipynb +++ b/tutorials/tutorial8/tutorial.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "00d1027d-13f2-4619-9ff7-a740568f13ff", "metadata": {}, "outputs": [], @@ -48,7 +48,7 @@ "\n", "from pina import Trainer\n", "from pina.model import FeedForward\n", - "from pina.solver import SupervisedSolver\n", + "from pina.solver import SupervisedSingleModelSolver\n", "from pina.optim import TorchOptimizer\n", "from pina.problem.zoo import SupervisedProblem\n", "from pina.model.block import PODBlock, RBFBlock\n", @@ -70,21 +70,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "2c55d972-09a9-41de-9400-ba051c28cdcb", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from smithers.dataset import NavierStokesDataset\n", "\n", @@ -108,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "bd081bcd-192f-4370-a013-9b73050b5383", "metadata": {}, "outputs": [], @@ -131,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "2edc981a", "metadata": {}, "outputs": [], @@ -165,13 +154,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "2166dc87", "metadata": {}, "outputs": [], "source": [ "pod_nn = PODNN(pod_rank=20, layers=[10, 10, 10], func=torch.nn.Tanh)\n", - "pod_nn_stokes = SupervisedSolver(\n", + "pod_nn_stokes = SupervisedSingleModelSolver(\n", " problem=problem,\n", " model=pod_nn,\n", " optimizer=TorchOptimizer(torch.optim.Adam, lr=0.0001),\n", @@ -206,8 +195,8 @@ "\n", "# fit the pod basis\n", "trainer.data_module.setup(\"fit\") # set up the dataset\n", - "train_data = trainer.data_module.train_dataset.get_all_data()\n", - "x_train = train_data[\"data\"][\"target\"] # extract data for training\n", + "train_data = trainer.data_module.train_datasets[\"data\"].get_all_data()\n", + "x_train = train_data.target # extract data for training\n", "pod_nn.fit_pod(x=x_train)\n", "\n", "# now train\n", @@ -224,27 +213,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "26c91385-5cd8-400a-90db-1c9f2afdf110", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error summary for POD-NN model:\n", - " Train: 4.385251e-01\n", - " Test: 4.857099e-01\n" - ] - } - ], + "outputs": [], "source": [ "# extract train and test data\n", "trainer.data_module.setup(\"test\") # set up the dataset\n", - "p_train = trainer.data_module.train_dataset.conditions_dict[\"data\"][\"input\"]\n", - "u_train = trainer.data_module.train_dataset.conditions_dict[\"data\"][\"target\"]\n", - "p_test = trainer.data_module.test_dataset.conditions_dict[\"data\"][\"input\"]\n", - "u_test = trainer.data_module.test_dataset.conditions_dict[\"data\"][\"target\"]\n", + "train_dataset = trainer.data_module.train_datasets[\"data\"].get_all_data()\n", + "test_dataset = trainer.data_module.test_datasets[\"data\"].get_all_data()\n", + "p_train = train_dataset.input\n", + "u_train = train_dataset.target\n", + "p_test = test_dataset.input\n", + "u_test = test_dataset.target\n", "\n", "# compute statistics\n", "u_test_nn = pod_nn_stokes(p_test)\n", @@ -270,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "0bd2c30c", "metadata": {}, "outputs": [], @@ -300,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "af0a7f9b", "metadata": {}, "outputs": [], @@ -319,20 +300,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "41a27834", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error summary for POD-RBF model:\n", - " Train: 5.860014e-05\n", - " Test: 7.156110e-05\n" - ] - } - ], + "outputs": [], "source": [ "u_test_rbf = pod_rbf(p_test)\n", "u_train_rbf = pod_rbf(p_train)\n", @@ -361,21 +332,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "ed8bf2ce-9208-4395-9a64-42ac96006bc3", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "idx = torch.randint(0, len(u_test), (4,))\n", "u_idx_rbf = pod_rbf(p_test[idx])\n", @@ -445,7 +405,7 @@ "\n", "3. **Evaluate Performance on Larger Datasets**: Work with larger datasets to assess how well these methods scale. You may want to test on datasets from simulations or real-world problems.\n", "\n", - "4. **Hybrid Models with Physics Informed Networks (PINN)**: Integrate **POD** models with PINN frameworks to include physics-based regularization in your model and improve predictions for more complex scenarios, such as turbulent fluid flow.\n", + "4. **Hybrid Models with Physics Informed Neural Networks (PINN)**: Integrate **POD** models with PINN frameworks to include physics-based regularization in your model and improve predictions for more complex scenarios, such as turbulent fluid flow.\n", "\n", "5. **...and many more!**: The potential applications of reduced order models are vast, ranging from material science simulations to real-time predictions in engineering applications.\n", "\n", diff --git a/tutorials/tutorial9/tutorial.ipynb b/tutorials/tutorial9/tutorial.ipynb index 14409639c..845d649ff 100644 --- a/tutorials/tutorial9/tutorial.ipynb +++ b/tutorials/tutorial9/tutorial.ipynb @@ -9,7 +9,7 @@ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb)\n", "\n", "This tutorial demonstrates how to solve a one-dimensional Helmholtz equation with periodic boundary conditions (PBC) using Physics-Informed Neural Networks (PINNs). \n", - "We will use standard PINN training, augmented with a periodic input expansion as introduced in [*An Expert’s Guide to Training Physics-Informed Neural Networks*](https://arxiv.org/abs/2308.08468).\n", + "We will use standard Physics-Informed training, augmented with a periodic input expansion as introduced in [*An Expert’s Guide to Training Physics-Informed Neural Networks*](https://arxiv.org/abs/2308.08468).\n", "\n", "Let's start with some useful imports:\n" ] @@ -38,9 +38,9 @@ "from pina.problem import SpatialProblem\n", "from pina.model import FeedForward\n", "from pina.model.block import PeriodicBoundaryEmbedding # The PBC module\n", - "from pina.solver import PINN\n", + "from pina.solver import PhysicsInformedSingleModelSolver\n", "from pina.domain import CartesianDomain\n", - "from pina.equation import Helmholtz\n", + "from pina.equation.zoo import HelmholtzEquation\n", "from pina.callback import MetricTracker\n", "\n", "warnings.filterwarnings(\"ignore\")" @@ -63,7 +63,7 @@ "\n", "In this case, we seek a solution that is $C^{\\infty}$ (infinitely differentiable) and periodic with period 2, over the infinite domain $x \\in (-\\infty, \\infty)$. \n", "\n", - "A classical PINN approach would require enforcing periodic boundary conditions (PBC) for all derivatives—an infinite set of constraints—which is clearly infeasible.\n", + "A classical Physics-Informed approach would require enforcing periodic boundary conditions (PBC) for all derivatives—an infinite set of constraints—which is clearly infeasible.\n", "\n", "To address this, we adopt a strategy known as *coordinate augmentation*. In this approach, we apply a coordinate transformation $v(x)$ such that the transformed inputs naturally satisfy the periodicity condition:\n", "\n", @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ " return -6.0 * pi**2 * torch.sin(3 * pi * x) * torch.cos(pi * x)\n", "\n", "\n", - "helmholtz_equation = Helmholtz(k=10 * torch.pi**2, forcing_term=forcing_term)\n", + "helmholtz_equation = HelmholtzEquation(k=10 * torch.pi**2, forcing_term=forcing_term)\n", "\n", "\n", "class Helmholtz(SpatialProblem):\n", @@ -122,7 +122,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As usual, the Helmholtz problem is implemented in **PINA** as a class. The governing equations are defined as `conditions`, which must be satisfied within their respective domains. The `solution` represents the exact analytical solution, which will be used to evaluate the accuracy of the predicted solution.\n", + "As usual, the Helmholtz problem is implemented in **PINA** as a class. The governing equations are defined as `Condition`, which must be satisfied within their respective domains. The `solution` represents the exact analytical solution, which will be used to evaluate the accuracy of the predicted solution.\n", "\n", "For selecting collocation points, we use Latin Hypercube Sampling (LHS), a common strategy for efficient space-filling in high-dimensional domains \n", "\n", @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +184,7 @@ "For example, `periods = {'x': 2, 'y': 3, ...}` indicates a periodicity of 2 in the $x$ direction, \n", "3 in the $y$ direction, and so on.\n", "\n", - "We will now solve the problem using the usual `PINN` and `Trainer` classes. After training, we'll examine the losses using the `MetricTracker` callback from `pina.callback`." + "We will now solve the problem using the usual `PhysicsInformedSingleModelSolver` and `Trainer` classes. After training, we'll examine the losses using the `MetricTracker` callback from `pina.callback`." ] }, { @@ -193,7 +193,7 @@ "metadata": {}, "outputs": [], "source": [ - "solver = PINN(problem=problem, model=model)\n", + "solver = PhysicsInformedSingleModelSolver(problem=problem, model=model)\n", "trainer = Trainer(\n", " solver,\n", " max_epochs=2000,\n", @@ -206,20 +206,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot loss\n", "trainer_metrics = trainer.callbacks[0].metrics\n", @@ -229,7 +218,8 @@ "# plotting\n", "plt.xlabel(\"epoch\")\n", "plt.ylabel(\"loss\")\n", - "plt.yscale(\"log\")" + "plt.yscale(\"log\")\n", + "plt.show()" ] }, { @@ -241,37 +231,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pts = solver.problem.spatial_domain.sample(256, \"grid\", variables=\"x\")\n", "predicted_output = solver(pts).extract(\"u\").tensor.detach()\n", "true_output = solver.problem.solution(pts)\n", "plt.plot(pts.extract([\"x\"]), predicted_output, label=\"Neural Network solution\")\n", "plt.plot(pts.extract([\"x\"]), true_output, label=\"True solution\")\n", - "plt.legend()" + "plt.legend()\n", + "plt.show()" ] }, { @@ -283,20 +253,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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oNm3ahC996UtJrx84cABvvPEGLrjggthrcnR5+/btKS8mfvjDH+JnP/sZHn30Udx4442499578bOf/Szlcbdv344pU6aYLj8hhBBnuP322/HrX/8664KYZj8DsL9OhZ7++oYbbsDNN9+M0047DQCwYMECvPvuuzEhPVN/zr6cEOJVnnnmGQwdOhSPPfZYv/eef/55/PGPf8TSpUuTxLTNmzcnuZ63bNmCaDSKMWPGxF5L1+ecffbZps/xWvuddH2OmfvgIUOGoKSkBJs3b+73Xjq3sVGsuF+36p7/yH1WVFTgww8/dPS4evpswLl+uaamBpdffrmBbyTQ+70yXY9QW8gfGO1CiIOMHz8eLS0t2LBhQ+y1/fv3449//GPSdn6/H//+7/+OP/zhDyk7wYMHD2Y8TiQSQUtLS9JrQ4cORW1tLcLhMPx+P8466yz86U9/ik3zA8TK47/5zW9w6qmnoqKiwvT3ABDL9Ep1QZWI2TJpJRKJoLe3NylPrK+vD//xH/+Bvr6+pIyzOXPmAADefffdfvt54YUXcMstt+Cee+7Bddddh6uuugpPP/00tm/fnvK4a9euxSmnnGK6/IQQQpxh/PjxuOSSS/D444+jrq7Ols+wv06P1v66sbERr776Ki655JLYdtFoFKFQKPZ7uv6cfTkhxKt0dXXh+eefxxe+8AWcf/75/X4WL16MtrY2/PnPf0763JGi+yOPPAIAOPvss7P2OYD5c7zWfiddn2PmPtjv92PevHl44YUXsGvXrtjrn3zyCV566aW0nzOC2ft1q/ZxJD6fDwsWLMBf/vKXlPewqqraclw999hA7vTLer5XtusRagv5Ax3phDjIRRddhJtvvhlf+tKX8K1vfQudnZ346U9/iqOPPrrfgh4/+MEP8Nprr2H27Nm48sorMXXqVDQ2NmLt2rV49dVX0djYmPY4bW1tOOqoo3D++edjxowZKC8vx6uvvop33nkHDz74IADge9/7Hl555RWceuqpuPbaa1FUVITHH38c4XAYDzzwgGXfY9asWQCA2267DRdddBECgQC++MUvptyvmTJpJRAIYPr06fjpT38ay0N77rnnYk6OxM5w3LhxmDZtGl599VV8/etfj73+3nvv4atf/Sq++tWv4rbbbgMA3HTTTVi6dGnKkeP33nsPjY2NOO+88yz5DoQQQpzhtttuw69+9Sts2rQJxxxzjOWfYX+dHq399cqVK9Hb25vkwOzq6opNjwZS9+fsywkhXubPf/4z2tracO6556Z8/+STT8aQIUPwzDPP4MILL4y9vn37dpx77rmYP38+3nrrLfz617/GV77yFcyYMQPNzc1Z+xzAmX4nXZ9TVlZm6j74u9/9LlasWIHTTjsN1157Lfr6+vDII4/gmGOOSRL3rcBMOa3cx5F8//vfx8svv4zTTz8dV111FaZMmYL9+/fjueeewxtvvIGBAwdaflw999hA7vTLer5XtusRagt5hEoIMcR1112npmtCd911lwpAPXjwYL/3Xn75ZXXatGlqMBhUJ02apP7617+ObX8k9fX16nXXXaeOHDlSDQQCak1NjXrmmWeqTzzxRMayhcNh9cYbb1RnzJihDhgwQC0rK1NnzJih/u///m/SdmvXrlXnzZunlpeXq6WlpeoZZ5yhvvnmm0nb/PznP1cBqNu3bzf8Pe655x51xIgRqs/ni+0r3X61lCnd3zfdPo9k7dq16qxZs9RQKKQec8wx6hNPPKE+9dRTKgB1586dSds+9NBDanl5udrZ2amqqqru3r1bHT58uPqZz3xG7e7uTtr2mmuuUQOBgLpt27ak12+++WZ11KhRajQazVguQggh5pD9wDvvvKNru0yfu+yyy1QA6jHHHKP5WOk+cyTsr5P3eSRa+usf/vCH6iWXXJL0udGjR6sbNmxIei2xP2dfTgjxOl/84hfVUCikdnR0pN3m8ssvVwOBgNrQ0BA733788cfq+eefrw4YMECtqqpSFy9erHZ1damqqr3PUVVn+p1UfY7E6H2wqqrq66+/rs6aNUsNBoPquHHj1KVLl6Y8fqryv/baayoA9bnnnsv4mt5yZtIHzO4j1ffYuXOneumll6pDhgxRi4uL1XHjxqnXXXedGg6HdR83HaNHj1bvuuuu2O967rFVNXf6Za3fS8v1iJ3awpH1QexDUVWNKxIQQkgB0tLSgnHjxuGBBx7AFVdcofvz4XAYY8aMwS233IJvf/vbNpSQEEIIKVy+973vYffu3Xj88ccBCEfYf/3Xf2H9+vVJ25npz9mXE0IIIcmMGTMGl19+Oe6++25Dn8+3flnL9Yid2oLZ+iDaYUY6IYRkoLKyEjfddBN++MMfxlY418PPf/5zBAIBXH311TaUjhBCCClsZs2ahZUrV+LQoUP45JNPcN1116VcmM9Mf86+nBBCCLGWfOuXtVyPUFvID+hIJ4QQQgghhOQk0WgUX//61/Hcc89hxIgReOCBB7BgwQK3i0UIIYTkNXRAJ+P29Qjrwzm42CghhBBCCCEkJ/H5fPjFL36BX/ziF24XhRBCCCEFCq9HCgc60gkhhBBCCCGEEEIIIYSQDDAjnRBCCCGEEEIIIYQQQgjJAIV0QgghhBBCCCGEEEIIISQDBZmRHo1GsW/fPgwYMACKorhdHEIIISQlqqqira0NtbW18PkKe+ybfTchhJBcgH13HPbdhBBCcgE9fXdBCun79u3DyJEj3S4GIYQQoondu3fjqKOOcrsYrsK+mxBCSC7Bvpt9NyGEkNxCS99dkEL6gAEDAIg/UEVFhculIYQQQlLT2tqKkSNHxvqtQoZ9NyGEkFyAfXcc9t2EEEJyAT19d0EK6XJaWUVFBTt0QgghnofTodl3E0IIyS3Yd7PvJoQQklto6bsLO7SNEEIIIYQQQgghhBBCCMkChXRCCCGEEEIIIYQQQgghJAMU0gkhhBBCCCGEEEIIIYSQDBRkRjohhBD7iEQi6O3tdbsYOUEgEIDf73e7GIQQQgoE9tHmYd9NCCHehH0cSYeVfTeFdEIIIZagqirq6urQ3NzsdlFyioEDB6KmpoaLkhFCCLEN9tHWwr6bEEK8A/s4ogWr+m4K6YQQQixBXrwMHToUpaWlvLnMgqqq6OzsxIEDBwAAw4cPd7lEhBBC8hX20dbAvpsQQrwH+ziSCav7bgrphBBCTBOJRGIXL4MHD3a7ODlDSUkJAODAgQMYOnQop4oTQgixHPbR1sK+mxBCvAP7OKIFK/tuLjZKCCHENDKLrrS01OWS5B7yb8Y8P0IIIXbAPtp62HcTQog3YB9HtGJV300hnRBCiGVwGp1++DcjhBDiBOxvrIN/S0II8RY8L5NsWPU/QiGdEEIIIYQQQgghhBBCCMkAhXRCCCGEEEIIIYQQQgghJAMU0gkhhBBCCCGE5CWHDh3C0KFDsWPHDk3bX3TRRXjwwQftLRQhhBBCchIK6YQQQogG9N6IA7wZJ4QQQtzm3nvvxXnnnYcxY8Zo2v7222/Hvffei5aWFnsLRgghpOBZtWqV5v4pkXy6N7Xquxj9W+qFQjohhBCiAb034gBvxgkhhBA36ezsxFNPPYUrrrhC82emTZuG8ePH49e//rWNJSOEEEKMk0/3prn2XSikE0IIIVkwciMO8GacEEIIMcuKFStQVlaGaDQae+3DDz+EoihoaGjI+NkXX3wRxcXFOPnkk2Ov/fa3v0VJSQn2798fe23RokWYPn167Ib8i1/8IpYvX27xNyGEEELMk0/3prn4XSikE8Ns3Qo8+CDw/vtul6RwaW8HHn0U+NOfAFV1uzSFiaoCv/89sHQp0NXldmm8haoCHR3O/Bw4AGzbBjQ2it/1todsN+mpbsQB3oyT3GNL4xY8+OaD+KD+A7eLUtBsb9qOxq5Gt4tRsKiqiuc+eg5PvPcEuvu63S5OwRJVo+jq7YKapdNet24dpk2bBp8vfuu6fv161NbWorq6OuNn//nPf2LWrFlJr1100UU4+uij8f3vfx8AcNddd+HVV1/F3/72N1RWVgIATjrpJKxZswbhcNjIVyNu8847wEMPAa2tbpeEEPcIh4FHHgFeecXtkjiPkzeiR/6YFGa0DB4bHSQG7Lk3tXrAG/D2fXaR40ckeUFdHXDyyUBDA1BSArz7LjB1qtulKixUFfjyl+P94k9/Clx9tbtlKkR+9CPgv/5LPF+xAnjhBVeL4yk6O4HycneO3d4OlJVp3z7bTXqqG3FA3Iz/4Ac/wPe//3088sgjsZvxt99+O+lm/N5770U4HEZxcbHp70aIUfa17cPJPzsZh7oOoXRVKdZetRaTqie5XayC4/v//D5u+/ttKA2UYsVXV+C00ae5XaSC44F/PYBbVt4CAHhl2yt4buFzLpfIeVRVRWdvp+PHLQ2UQlEU9EX7sLFhI7r7ujEgOABHDz4aiqKk/Mz69esxY8aMpNfef//92Gs9PT247rrr8K9//QuVlZX461//ikGDBgEAdu7cidra2qTPKoqCe++9F+effz5qamrwyCOP4J///CdGjBgR26a2thY9PT2oq6vD6NGjrfwTELvZtg047TQhIr78srhAJ6QQue024XwExE3qeee5WhxHyaUb0SPQMnicbpA4230pYM+9qdUD3lq/j1v32RTSiSEefliI6IBw4S5ZAtBw6Syvv548uHznncCiRQB1Oufo7BT/+5I//Ql4+20xyERyi2w36aluxAHejJPc4sE3H8ShrkMAgM7eTtzzj3vw6y97Z2pnIbCjeQfuWnUXAFEH1/z1Gmy4ZgN8CieJOkV7Tzvu/ee9sd9///Hv8d6+9zCrtv9NXD7T2duJ8vucFxnab21HWbAM+9v2x2YDtPW0oaGzAUPKhqT8zLp16/Ctb30r6bX169fjhBNOAAB897vfxamnnoonn3wSt99+O5YvX45rr70WANDV1YVQKNRvn1/4whcwdepULFmyBC+//DKOOeaYpPdLSkoAiCnnJMdYtkyI6ADw0kvCnX7iie6WiRCn6ekBnnoq/vv99xeWkJ7DZLsvBYwPEgP23JtqKXO6Qe9cvM/mVTvRjaoCv/udeH7rreLxL38RoiJxjt//XjxeeikwfDhw8CDw2mvulqnQ+L//A1pagLFjgUsuEa8984y7ZfISpaViQN7On+Zm4B//ED+bNonHNWvEsfWwbt06TJ8+Pem1xAuCdDfiQPLN+B//+EfejBNPoqoqnvtYuG5v+Yxw4v5p058Ya+Ewyz9cjr5oH44ffjzKg+X46OBHWLN3jdvFKij+sukvaOtpw8RBE3HRtIsAAL/ewAElJ1FVNTaoNyA4AABwsPNgym07OjqwdevWpJvxaDSKdevWYcaMGWhpacE//vEPXHbZZQCAsWPHYtu2bbFtq6ur0dTU1G+/K1aswMaNGxGJRDBs2LB+7zc2iuilIUNSi/vEw6xcmfz700+7Uw5C3GTdOnGjBAA+H/DWW8AHBRTr58SNaLofvTeiR5DtvhTQNkic6r4UyHxvesstt0BRlIw/GzduNFRmOej98ccf48wzz4xFsuTifTYd6UQ3n3wCbN8unM/f+Q7wm98AO3eKAf8vfcnt0hUGqhqPELnwQiAUAp54QgxozJ/vatEKildfFY8LFgCf/Szw618Df/ubq0XyFIpialabJpqaRLxUKASMGwe0tYn20d0tXtdCppt0uehJuhtxgDfjJDf44MAH2N26GyVFJbj9s7fjVxt+hb1te7Fy20qcc/Q5bhevYHhh4wsAgKuOvwort6/Ecx8/h79s+gtOPopTmZzilW1iOt+CyQtw0oiTsPzD5Xhp60sul8p5SgOlaL+13ZXjdvR2oC/aB7/ix9iqsdhQvwGdvZ3ojfQi4A8kbb99+3ZEo1FMnjw59tpLL72EQ4cOYcaMGXj11VexZcsWzJw5EwBQX1+Pb37zm7FtjzvuuH4Lka1duxYXXHABnnrqKfziF7/AHXfcgeeeS473+fDDD3HUUUdlnZJOPEYkAqxfL54/8gjwzW8Cv/2tyEsPBDJ+lJC84t13xePZZwvh5oUXRFs49lhXi+UYTtyI2oCW+1LA+CAxkPne9L/+679w+eWXZyzjuHHjdJdZDnrfe6+YETh27Fh88sknGb+Llu/j1n02HelENy8dvtf43OdE7JScISRFRWI/mzYBe/cKofDMM4EvflG8/uKL7par0JD/82eeKX4CAbEI7+bN7parkJBrSFVUAEVFwABhbENbm/Z9ZLtJB8SN+Mcff9zvs4k342eeeSbuuOOOftvwZpx4gZe2iM77/439fygLluHcSeeK1wtQQHSLhs4GrN67GgDwxUlfxHmTxAXU/23+PzeLVVCoqopXt4nOe+64uZg7bi78ih+fNHyCnc07XS6dsyiKgrJgmeM/iqKgNSw674riCgT9QZQGhHtPvp7I4MGDoSgK3nnnHQDA22+/jcWLFyMUCuHoo4/G+++/jwceeADr16/H+vXrMXPmzKSb+Xnz5uGjjz6K3aTv2LED55xzDr7zne/g4osvxpIlS/CHP/wBa9euTTruP//5T5x11lm2/O2JjWzbJtwUoRBw1VXAkCHAoUOctksKD+kanj4duEjMvsKzz5peCJPYi5b7UiD1vamW+1Ig873pkCFDMHny5Iw/wWBQd5kTB71nzpyJ22+/PbaWSS7eZ1NIJ7o5fB2Lz35WPH7mM+Jx9Wp3ylOIyDo4/ngxwHza4XXKduwADhxwrVgFxZ49YmaG3y/awoABwOzZ4r0333S3bIVE+2EzXUWFeJTGg44O7fvIdpMO9L8RB3gzTnKLd/aJ/+/TRokO49RRpwIAY0Uc5J29og4mDZ6E2gG1+Pz4zwMAPqj/IKWASKxnZ8tO7G7djYAvgFNHnYqBoYE4oVbkbL+x6w2XS1c4tIXFaHdFcUXSY2tP/3YwfPhw3HPPPbjkkkswevRoLF26FAsXLsS0adPg9/vR3NwcW2CstbUV69atwxlnnBH7/LHHHovjjz8ev/vd79DY2Ij58+fjvPPOwy23iIir2bNn4+yzz8Z3vvOd2Ge6u7vxwgsv4Morr7TnD0DsY9Mm8ThpEhAMAv/+7+L35wpvQWFS4OzYIR7HjgXOOUc48LZtA464TyHeQst9KWB8kBiw/t5US5kzDXrn4n02hXSim/feE4/HHy8epXj4/vti4VFiP1JIl+vmVFaK68XE94i9yFmjU6bEXdCH17yKtRFiL9Fo/Jwjo+jk4uztOmarZ7tJB5JvxAHwZpzkHO/tFycmuaDi7BGi815Xtw49kR7XylVIvLtPTLOWwu3QsqEYXTkaKlS8t48dhxOsr1sPAJg6ZGrMBX1irbiYWruf4oITqKqKzl6RZVoWFKPf5UHReXf2pM44ve2229DY2IidO3fiF7/4BX7wgx/EbtgnTJiANWvEgOCSJUvwrW99C6VH5NPeeeed+PGPf4yBAwdi48aNWLp0adL7f/3rX7FixYrY7z//+c9x0kkn4WSuHp977NkjHkeNEo8LF4rH558Xiy8SUihIIX3MGHGD9IUviN+ffdatEhENaLkvBYwNEgP23JtqKXOmQe9cvM9mRjrRRWsr8Omn4vkscS+OUaOAYcOA+nqxpsUpp7hXvkLhSCEdAE46SZgw3n1XDDoTe9mwQTwmLk4t2wSFdGeQInpRUTz2UjrSw2Ggr0+8p4XbbrsNt912W8Zt7rzzTtx444248sorMWjQoJQLrfz1r39N+p0348QLNHU1YVuTWHzv+OFiFHxc1TgMLhmMQ12H8H7d+zhxxImZdkEs4N39QkiXwi0AnDjiROxs2Yl39r2DM8aeke6jxCLer3sfADCzZmbsNTm4JAebiL30RHoQUSNQoCBUJBYXk4MaXX1diKpR+BTtXq+vfOUrmD9/PiZOnIj58+fj5ptv7rfNOeecg82bN2Pv3r0YOXJk1n0GAgE88sgjmstAPIQU0mU9f/azwPDhwP79wO9/D3zlK+6VjRCnUNVkRzogFlZ77jmxqNc994hp7cSTaLkvBZLvTbXclwL23ZtmK7Mc9L7gggtSDnrn2n02HelEF1I8POooETkHiHUcpIBYSAtBu4WqAh9+KJ4fXlcJQNwNzdlazvC+uBdPKaSvWydEXGIvcnHukhJxHgKEcC5j26yeIXPOOefgqquuwt69ezV/hjfjxAu8Xy9OWGMGjsGgEpFHqChKTFTfUL/BtbIVEuv2rwMQF26BuKgu3erEXmRbmDEs3nnLdrB2/1pE1agr5SokpBu9JFASE8wDvgCKfGLku6tXX+ddXV2Nd999F5s3b8YjjzyS5NhL5Prrr9ckogPAN77xDUySUz1JbiGF9KOOEo9FRcA114jnDz3EfGhSGDQ2xqfnytkZX/wiUFsrBpWOWICZ5Ca5dG/6la98BatWrcLEiRMRDof7DXrn0ncBKKQTncjYuWOOSX596lTxmGKNAGIx+/eLftHvByZMiL8u6+Tw4sfEZuSg0vTp8deOPlqsbdTVFTcBEPs4MtZFUlKS/L6V6LkRB3gzTrzBpgbReU8dMjXpdfn7Jw3sOOymo6cDu1t3A0iuh2OHHgsA+PggL6Cc4IMDwvExoyYupE8dMhUBXwBtPW3Y3bLbraIVDF19onMuKSqJvaYoSsyVLoV2QgxxpJAOAFdfLS4W33sP+Mtf3CkXIU4ib0SHDxc3p4BwGt1wg3h+zz1iUV6S8+TKvamWQe9c+S4AhXSiEymkJ6xzAIBCupPIWS7jxsWdt4DI6gaArVtFrAWxj74+8XcG4v/7gBjckG1DthViH/L//MiZifJ6kdeHhAg2HRInpEmDky82paBLEdd+Pj0kcvGqS6tjswIAYMqQKbH3+6KcymQnPZGeWMTR5OrJsdeLfEWYMEg4EzY29J9KTKylu090zjLWRSKFdfk+IYbYfXgwLFGMGTIE+Na3xPM77hCL7BCSz8h2IN3okmuvFYNMO3cCjz7qfLlISsaMGYPrr7/e7WLkBU79LSmkE13IfHQK6e6RuBh9IsOHAxUV4tpw82bny1VI7N4N9PYKAXfEiOT3ZL2kiPUiFiOF9FDyvbitjnRCchEp4h49OLnzppDuHOkGM0ZVjkJpoBS90V5sbdzqRtEKhp3NOxFVoygNlGJ4+fCk96SwLuuJ2Ee4T3TeRwrp8ncK6cQwqprakQ4AN94obpQ2bBA50YTkMwcPisehQ5NfLykRbnQAuPdeoLnZ0WKR1FBItw4K6cSTpBPSpRt63z6gpcXZMhUaUkifPDn5dUWJ1wPjXexlyxbxOH484DviLCrrhY50e1HV7I50zswgRJBNSN/ZshMdPR2Ol6uQkPE6RwrpPsUXE3EZsWMvWxpF5z1h0AQocmGNw8h6kfVE7EMK5cVFyZ03hXRimtbW+AI6tbXJ7w0aBPz3f4vn99/vbLkIcRoppFdX93/va18TN6zNzcAf/uBosQjJFyikE81EIvE4iyOF9IqK+IDntm3OlqvQkH/fxHx0iRRx5YAHsQcppGeqg0J1pEcdmi4bDgsxXVGSI46AuLDe05Mbs3ed+puRwqQ30huLszhSSB9UMghVoSoAwPbm7Y6XrZDY3Cimik2q7p/lGHNDU8S1lUQh/UhkHWw8VKCdt0P0RfoQUSMAgGJ/spAuhfVwJMxFX4kxGhrEY2lp/wV0ALHoqM8HrFsXj74gJB+RQvqQIf3f8/uBr35VPP/rX50rEyF5RJHbBSC5Q12dEKb8/uTYOcmYMcCBA8D27cBxxzlevIJh1y7xOHp0//fGjhWPXOjSXjIJ6RMnisdCG1AKBoPw+XzYt28fhgwZgmAw2M/xZyVtbfK4/Z3nUmBXVbHdkY51r6CqKnp6enDw4EH4fD4EjxwRIMQC9rXtQ0SNIOALoHZAbb/3x1aNRdP+Jmxv2o5pQ6e5UMLCYGfLTgDA2IFj+703buA4ABzMsJuYkF7Vv/OeOHhi0jb5iqqqrh6/OyLc5gFfAH5f8kJjAV8APsWHqBpFuC+MkkBJql14Brf/liQFhw6Jx8GDU79fXQ2ccAKwZg3wz38CX/mKc2UjxEkyCekAcNZZYr2AVaviN06EEM1QSCeakQLuiBFCTD+SsWPFdcl23gfaiqyHI9cOAcRgBkAh3W6kSD5uXP/3ZB3s2ycEXq+KuFbj8/kwduxY7N+/H/v27bP9eG1tQGOjiPpLdc5pbhY59j5fPDPdq5SWlmLUqFHwHZkTRIgF7G4VrruRlSPhU/r/j40dOBZr96+liGszu1pE5z2ysr8TYWyVENd3NO9wskgFh/wfH1fVv/MeM3AMAGBv6170RftQ5MuvW6RAIAAA6OzsRImLnWJPpAdA/1gXAFAUBaGiEDp7OxGOeF9I7zwcISL/tsQDSCE9VZyF5JRTxA3r229TSCf5SzYh/bjjhBupqUncSKW6qc1BOMuXZMOq/5H8ukoktpJqEfREpBuaQrp9tLWJ/g5IXQ+sA2eQ6xilqoPqaiHcdnWJNpPKtZ6vBINBjBo1Cn19fYhEIrYe65FHgMceAy68EPjud/u//8MfAn//O3Dnnd6+T/L7/SgqKrLVvU8Km5iAW5G685YO6e1N7Djsoi/ah72tewGIxUWPRIq4FNLtZU+r6LxTDWbUlNcg6A+iJ9KDva17MXpgiml/OYzf78fAgQNx4MABAGIA141+p6OzA+gT5enu7p+F7o/4gT6xXQihFHtwH1VV0dnZiQMHDmDgwIHwp3IXEXfI5kgHgBkzxCMXlCL5jIw5SiekBwLAtGnA2rUi6ijHhXSnZ0aT3MPqmeAU0olmKKS7j6yDqipgwID+70s39K5dItOe1/b2IIX0o47q/56iiHr45BNg587CEtIB4SgLBAK2O7Q2bhR/3wED4ouLJlJZKd7/5JPU7xNSKOxuiTvSUyHd0HSk28f+tv2xeJ2a8pp+78vBjB3NOxBVoylnDhDzyNkZR1X077x9ig+jKkdhS+MW7GjekXdCOgDU1Ij/PSmmu0FjVyPawm3oKe5BX2NfxvdbS1pdKKF2Bg4cGPubEo+gRUiXC31xQSmSz2RabFQyZYoQ0rfkfqSZ0zOjSe5i1UxwCulEM5kiRQAK6U6QrQ5qa4GiIhFpsX9/aqGXmKOnB6ivF8/T/X1HjxYCLiN27EPrwF6hZdUTciRSPBxVkbrjkG5oCun2kSjgphLJ5evhSBj17fUYPmC400XMe7r7utHQKRx6qYR0QLSFLY1bYnn2+YaiKBg+fDiGDh2K3t5eV8rwo7/9CC9vfRm3f/Z2XDL2kn7vr1q3Cvf/636cPfFs/Gjej1wooTYCgQCd6F5EunAzCemTDi/4vGsX0NmZelFSQnIZVc0e7QLEXeh5crPk5MxokptYOROcQjrRjFbhascOrllhF9mE9KIiUT/bt4t6oJBuPfv3i//vYDD9ID+z6u0n2/koz64NCTFMpmxuIDnaRVVVToW1AVkHqWJdACDgD2BkxUjsbNmJ7c3bKaTbgIzWKSkqQVWoKuU2oyuFCz3fI3b8fr9rIvD7h97Hzo6dqK6oRijFdLFhA4dhZ8dOrDu4LuX7hGREiyN98GBg0CCx0M6WLcD06c6UjRCn6OgAZHSWFiE9j1yQTs2MJoRzR4lmsglXo0YJ8byrK+7YJdaSTUgHODPAbmSsy4gRYiHLVEghfWd+mtpcR1Xj56N0g0V5eG1IiCFijvQ0Iq50pLf1tKGxq9GpYhUU2YR0gDnpdiPz0Y+qOCrtYJGsg53N7LztIhY1lWbNBjmYka+zAojNaFlsFIjHu2zaZG95CHEDuaBaMAiUlaXfjq4jQgxDIZ1oJpuQHgzGRS2KV/agRUinG9peMuWjS0YfjlZlHdhDS4swWwDp60G2kZYWoNXbMauE2Eq2xUZLAiWx3G7Gu9hDtjoAEiJ2uOirLSQK6emIOdJbdjhRpIKjN9KLuvY6AOlnyMhs+rr2OnT39V+MlJCMaHGkA3HXkby5JSSfaG4WjwMHZo4IkEL6zp1AX/81Kwgh6aGQTjQRDsdd5plEXCmy791rf5kKEQrp7qNFSGcd2Iu87xk8OH20ZXk5UFEhnvN8RAqVjp6OmMs8nXAFxJ3SUmwk1qLFkZ644CixHi1COmcF2Mu+tn1QoSLoD6K6NLVjeHDJYJQFhINSthtCNKNVSB9+OD5r/357y0OIGyQK6ZmorRVOyL4+3iwRohMK6UQTUjwMhTJfm4wYIR55LrYH+XeVf+dUSIGXC1bbgx5H+t69HOC3Ay11APB8RIiMdSkPlqOyuDLtdrUDagEIoYtYj6yHTIMZUuDd28YTlh1IIT3TrADpht7dshtRNepIuQqJxMGMVIvuAiLfVtYDI3aIblpaxGNl+v4OQFxI580SyUe0Cuk+HweVCDGI7UL6Y489hjFjxiAUCmH27NlYs2ZN2m0/97nPQVGUfj/nnHNObJvLL7+83/vz58+3+2sUPPI6Y8SIzDOEamuTtyfWUidmxKKmJv02sg4oHtqDFhF36FDA7weiUeDAAWfKVUhki5mSUEg3B/vv3Gd/m7gxGjFgRMZFREcMEI1FLshIrEXGWQwvT7+I6IgKUQcczLAHOZiRyZE+vHw4FCjojfaiobPBqaIVDFrqAIhH7NCRTnTT1iYeBwzIvB3FQ5LPaBXSAWDYMPHIBe4I0YWtQvqzzz6LG264AXfddRfWrl2LGTNmYN68eTiQRll6/vnnsX///tjPhx9+CL/fj4ULFyZtN3/+/KTtfvvb39r5NQji51Z5rk0HhSv76OgA2tvF80z1wMEMe9EipPv98cEO1oP1yPse+b+eDp6PjMP+Oz+o7xCd97DyzJ13TEinG9pyItEIDnSIdiOz6FMhZwWwDuxBuqHlgEUqAv4AhpYNBcABDTuQA3XZhHTZFva3U+QkOtEqpMsLSArpJB+hkE6I7dgqpD/00EO48sorsWjRIkydOhVLly5FaWkpli1blnL7QYMGoaamJvbzyiuvoLS0tN+NeHFxcdJ2VVVVdn4NAgrpXkDWQUlJ5utDWQeHDolse2It8pp7eHpjIQAOaNiJ1vORHOzg+Ug/7L/zg/r2w0J6WebGwmgX+zjUdQhRNQoFCoaUDUm7nayDhs4GhPvYeVuNnBUg/87pYFuwD1kHNWUZplWCdUAM0tsbv/HR6kjnRTrJRyikE2I7tgnpPT09eO+99zB37tz4wXw+zJ07F2+99ZamfTz11FO46KKLUFZWlvT6qlWrMHToUEyaNAnXXHMNDsmFRYhtyHNrpkgRgOKhnSSKh5nidaqqgOJi8Zz1YC2qGo9qySbisi3YBwf27IX9d/4gHemZnNBA3KVLN7T1SPGwurQaRb6itNsNLhmMoD8IgE5cq1FVNTYrQDrO00ER1z4OdIo6yDZDRkYgsQ6ILqQbHdAupLe2Ap2d9pWJEDfQI6RLcYdCOiG6sE1Ib2hoQCQSwbAjlI5hw4ahTgY9Z2DNmjX48MMP8Y1vfCPp9fnz5+Ppp5/GypUrcf/99+P111/H2WefjUgkknZf4XAYra2tST9EH0aEK1W1t0yFhpZ8dECI7LIeKOJaS0cH0NUlnlNIdw+tgxkU0o3hlf6bfbd56Eh3n1gdZBEPFUVhPdhES7gFvdFeABTS3US2Ba11wAElogsppBcXA4FA5m0rKsQUX4DxLiT/MOJI13B9TwiJk94a4zJPPfUUjj32WJx00klJr1900UWx58ceeyymT5+O8ePHY9WqVTjzzDNT7uu+++7Dd7/7XVvLm+9oFdKleNjRIa5nKirsLVchobUOAFEP27ZRxLUaWQelpcARRtt+UEi3D1kPQzPfi1NIdwmr+m/23ebRm5He3N2Mzt5OlAZKbS9boRCrgyyDGYCohx3NO7joq8VIAbeiuAKholDGbSmk24fWtsA6IIbQmo8OCNfR0KHAzp1AQwMwfry9ZSPESRjtQojt2OZIr66uht/vR/0RjbK+vh41WSy1HR0dWL58Oa644oqsxxk3bhyqq6uxZcuWtNvceuutaGlpif3s3r1b25cgMbSKuGVlQGWleE7xylq0OtKBuIjLOrAW6YTOJuACFNLtRO8Mmbo6EZ1JtOGV/pt9t3lkrEg24aqiuAJlATE6SBHXWmK50FnidQAKiHahNdYFYKyInch6yBrtMkDUQV17HaJq1PZykTxBj5AOAIMGiUdGzJF8g0I6IbZjm5AeDAYxa9YsrFy5MvZaNBrFypUrMWfOnIyffe655xAOh3HJJZdkPc6ePXtw6NAhDM+w8l9xcTEqKiqSfog+9Lih6QK1ByN1QBHXWrRGigAU0u2iu1tEWgLZ62HoUKCoSMRMccaidrzSf7PvNo/WjHTGitiH1ngdID4zgHVgLXpmBTBWxB6ialTzgMawsmFQoKAv2oeGzgYnikfyAb1C+uDB4rGx0Z7yEOIWLS3iUYuQLt1h8iaXEKIJ24R0ALjhhhvw5JNP4pe//CU++eQTXHPNNejo6MCiRYsAAJdeeiluvfXWfp976qmnsGDBAgyWHdxh2tvbceONN+Ltt9/Gjh07sHLlSpx33nmYMGEC5s2bZ+dXKWhUVX+sCEAh3WqMONIp4lqL1kgRgHVgF7IOgsH47Jd0+HyM/jMK++/cJ3GBxWwOUIALjtpFXYd+RzrrwFr0ONI5oGQPTV1N6Iv2AcheDwF/ILYN64Foho50QgTSka7FgCLbQUsLkGHNQUJIMrZmpF944YU4ePAg7rzzTtTV1WHmzJlYsWJFbAGzXbt2wedL1vI3bdqEN954Ay+//HK//fn9fmzYsAG//OUv0dzcjNraWpx11lm45557UFxcbOdXKWja2+MLmmsR0qW5kDOErIWDGe5jxJF+8CDQ0yOEX2KexHgdRcm+/bBhoh3QaKEP9t+5T3N3M3oiPQD0RVrsb6MT10q0LjYKcDDDLmIDSjoc6XXtdYhEI/D7/LaWrVCQswKqQlUI+rNfEA0fMBz1HfXY17YPM2tm2lw6khfQkU6IQLYFLUJ6VVX8eUtLXFgnhGTE9sVGFy9ejMWLF6d8b9WqVf1emzRpElRVTbl9SUkJXnrpJSuLRzQgBdyyMqC8PPv2jNqyBz2OdDmYQReutejJSB88GAgERDb3/v3A6NH2lq1Q0DOglLgdz0f6Yf+d20jhqrK4MusCi0BcZJSiI7EGrTn1QHwwQ4rvxBrk31PLgNLQsqHwKb5YFInM6ybm0DMrABADGuvr1nNgj2iHjnRCBO3t4lGLcBMIiO3a28WgEoV0QjRha7QLyQ/0CleM2rIHKYprqQfWgT3oiXZRlPigB0Vc69BTBwCFdFK46HFCA3GB60AnOw4r0ZpTD8TrQH6GWIP8n9bSFvw+f2zQgznp1qH3fMRFX4lu6EgnRMSzyCgBvYNKTU32lImQPIRCOskKHaDu09kJdHWJ50OGZN9eioyNjcIRTaxBT7QLwAENOzA6sMfzESk09CywCMQFLrqhrSMSjeBgx0EA2t3QANDY1YjeCDtvq9DjSE/cjrMzrEOej7TWQWx2BgeViFaMCul0pJN8QrrRAW2OdCAe78JBJUI0QyGdZKWhQTxqEXABiod2IOsgENB2fTh4sFhoMfGzxDx6ol0St2NbsA69gxlyO9YBKTQaOsXJn+KhezR3N0OFiDsaXDo4y9ZiG58iOm9Zf8Q8ejLSAbYFO2AdENsxGu1C8ZDkE1JILyoCtK5BREc6IbqhkE6yIoXY6mpt29ORbj3SLDF4sLYFFn2++MAH68E69MaKUEi3Hvm31Dqwx/MRKVSkEDu4JLuAC8QFLjpArUPWQUVxhaYFFn2KD0NKxcmN9WAdevO5KeJaD2cFENuhI52Q5HagRTQA6EgnxAAU0klWEkVcLUjx8OBBIBq1p0yFht7BDIAirtX09cWvLyiku4c8H2ltC4x2IYXKoU7RWLQ4oYFk4SrdorFEH4e6RB1Ul2rvvCkgWktPpAct4RYAwJAybSOwXHjXehq6xIWs1rbAdpCaxx57DGPGjEEoFMLs2bOxZs2ajNs/99xzmDx5MkKhEI499li8+OKLSe+rqoo777wTw4cPR0lJCebOnYvNmzen3Fc4HMbMmTOhKArWr19v1VeyDjrSCdG30KiEjnRCdEMhnWRFirhahXTpFI1EeG1iFXoHMwCKuFbT3AxIbUnrguaMFbEevW2BdUAKFaPCVaLwSMyhd1YAEM+qp4BoDXJAyaf4MDA0UNNnKOJaj6wHCunGefbZZ3HDDTfgrrvuwtq1azFjxgzMmzcPB9Jc4Lz55pu4+OKLccUVV2DdunVYsGABFixYgA8//DC2zQMPPICf/OQnWLp0KVavXo2ysjLMmzcP3d3d/fZ30003oba21rbvZxq9QnplpXhsbaXzi+QPetsBQEc6IQagkE6yotcBGgzGz8cUr6zBiCOdAqK1yHZQWSli57TAwQzrMSqkNzSIWQWEFAoxR7pGEbckUIIBQXHjRfHKGvSKh0BcQOSir9bQ2CWEgapQVSx/PhsUca1Hzs7Qej6SdXCo6xD6ouy8AeChhx7ClVdeiUWLFmHq1KlYunQpSktLsWzZspTb//jHP8b8+fNx4403YsqUKbjnnntw/PHH49FHHwUg3OgPP/wwbr/9dpx33nmYPn06nn76aezbtw8vvPBC0r7+9re/4eWXX8b//M//2P01jWNUSFfV5AUaCcllZDugI50QW6GQTrJixA3NXGJr0TuYATDSwmo4K8Ab6K0Hua6AqnLhXVJYxNzQGqNdgLgbmiKuNRipg6GlFHGtJCbg6qkDCumWozdqalDJIC68m0BPTw/ee+89zJ07N/aaz+fD3Llz8dZbb6X8zFtvvZW0PQDMmzcvtv327dtRV1eXtE1lZSVmz56dtM/6+npceeWV+NWvfoXS0tKsZQ2Hw2htbU36cQS9QnooBAQC4rlTZSTEbuSgEB3phNgKhXSSFb3RLgDd0FZjpA4o4loLhXT36emJ3ydprYeiovgAFAeVSCHBfG73idVBifY6iEW7dLIOrEDvzAyA7cBqVFXV7Uj3+/yxcxfrAWhoaEAkEsEweYN1mGHDhqGuri7lZ+rq6jJuLx8zbaOqKi6//HJcffXVOOGEEzSV9b777kNlZWXsZ+TIkZo+Zxq9QrqiABUV4nkL48xInmAk2iUx5ogQogkK6SQrdEO7j5k6oIhrDWaEdC68aw3SKKEowMCB2j/HgT1SiBgRELnIorUYcqQz2sVSzDrSufCuedp62mLxLJwZkFs88sgjaGtrw6233qr5M7feeitaWlpiP7t377axhAmYERAppJN8wchioxTSCdENhXSSkb4+scgiQEe6m3BWgPtIIV3rQqNAfOHdxHZEjCProKoK8Pu1f45RU6TQ6In0oK1HiAqG8rk72FisgLMC3MfIgNKQMtF5hyPhWDsixpF1ECoKoTSQPRpEwrYQp7q6Gn6/H/VHXMjU19ejpqYm5Wdqamoybi8fM23z97//HW+99RaKi4tRVFSECRMmAABOOOEEXHbZZSmPW1xcjIqKiqQf21FVY0K6LBsFRJIvsB0Q4ggU0klGmprEtQmgT0CkI91aOCvAfYw40oPBuHOaAxrmMVIHANsCKTykcOVTfKgMVWr+HB3p1hJzpBuIFeFghjXojRQBgNJAKcqDws3HtmAeI3UAUEhPJBgMYtasWVi5cmXstWg0ipUrV2LOnDkpPzNnzpyk7QHglVdeiW0/duxY1NTUJG3T2tqK1atXx7b5yU9+gvfffx/r16/H+vXr8eKLLwIAnn32Wdx7772WfkdTdHcDkYh4Tkc6KWSMLDbKiCNCdFPkdgGIt5HC1cCBImtYK3RDW4t0pBuNdlFVEYdBjCNjRYyIuM3Noh4mT7a8WAWFUSGd5yNSaEgBN3HBPi1QxLUWOaChx5GeOJihqioUdt6m0LvIpWRo2VC097TjQMcBTBg0wY6iFQyG64AL7yZxww034LLLLsMJJ5yAk046CQ8//DA6OjqwaNEiAMCll16KESNG4L777gMAfPvb38bpp5+OBx98EOeccw6WL1+Od999F0888QQAQFEUXH/99fje976HiRMnYuzYsbjjjjtQW1uLBQsWAABGjRqVVIbyw+Lc+PHjcdRRRzn0zTXQljBzxEikBQVEki8YWWyUjnRCdEMhnWSEDlBvYCTaRcaKhMPi+tKJmZX5jJm28OmnFHGtwKyQzvMRKRSMRIoACQtdUriyBCMZ6TJWpCfSg9Zwq64ZBaQ/ZtzQ25q2sS1YAB3p1nDhhRfi4MGDuPPOO1FXV4eZM2dixYoVscVCd+3aBZ8vPnB6yimn4De/+Q1uv/12fOc738HEiRPxwgsvYNq0abFtbrrpJnR0dOCqq65Cc3MzTj31VKxYsQKhUMjx72cKKaSXlQE+HRPuKSCSfMNMtEt3N9DTI6ZUE0IyQiGdZMSIgAvQAWolXV1AZ6d4rseRXlYmfjo6RD1QSDeH2UEltgXzcGCPEG0YyYUGuNCllUTVKBq7xFQmPQMaMlakvacd9R31FNJNIkXcQSU68glBEddKjDrS5aAS6yDO4sWLsXjx4pTvrVq1qt9rCxcuxMKFC9PuT1EULFmyBEuWLNF0/DFjxnhzAV4j4iFARzrJP4wsNprYbtra9N9oEVKAMCOdZMRINjdA4cpKZB0UFekXw+nEtQ4K6e5jtA7k+Ut+npB8RzqhdTvSD8eKMNrFPC3dLYioIrNX74AGs+qtg7Ei7kNHOrEdCumECIy0haIioPTwQtCcnUGIJiikk4wYFa5krEhnp3BUE+PIbO6qKv055xzQsA7ZFvQsugtQSLcSs0K6nGFDSL5jVLiSDtDWcCt6Ij2Wl6uQkHVQHixHcVGxrs9yZoB1UMR1H7MzZFgHJCtGhXRGu5B8g22BEEegkE4yYlS4GjAgvjgpXaDmaGoSj1VV+j8rBzRYB+bhQpfuQ0c6IdqQwpXeOIuBoYGxxUllLAkxRlOX6LyrQvo7bzmgIWcWEGOoqhr7Pzay2ChAEdcKYoMZrANiF3SkEyIwEu0CUEgnRCcU0klGjIq4ikLxyiqam8WjESFdCo504pqjs1OsvwIYF3EppJvHqJAut29tFWvoEJLvNHc3AwCqSvR1HD7FFxPfKeKao6n7sJCusw6AuHNXCpDEGG09beiL9gGgG9pNzM4K6OjtQEdPh+XlInkEhXRCBGbbAoV0QjRBIZ1kRIq4Awfq/yxFXGsw40iXdcDBDHPIeJ2iIv3XJRxQsg7ZFvTG6wwcCPgO93asB1IIxERcA27omIjbycZiBjmYMTA0UPdnZbY968Ac8u9XUlSCkkCJrs9K9zQHM8xjdFbAgOAABP1BAMDBzoOWl4vkEYyzIERARzohjkAhnWTEjIjLXGJrMDOYwTqwBimkDxqkP6eegxnWYXR2ht8fF99ZD6QQsELEpSPdHGaiXeRgRkMX68AMlswK4GCGaWIzZHS2BUVROKhEtEFHOiGAqsaFdKODSmwLhGiCQjrJiBWOdApX5qAj3X2siNc5dEhc3xDjyLbAGTKEZMaUgEgnriWYqQOKh9ZgZjAjcUBJZedtClkPRgb2GHNENGHWkU7xkOQDnZ1ANCqec3YGIbZCIZ1khI5092EduI8VA0p9fbw2MUNPj7g+BDg7g5BsmHKkl9CRbgWxOigeqPuzcjCDdWAOM+1A1kE4EkZnb6eFpSosVFW1pB44qEQywlxoQuJudEUBSkv1fZZCOiG6oJBOMkJHuvuwDtzHjBO6pCR+LcN6ME6iWUje9+iBWfWkkDAVK0LhyhJidWDGkU4XrinMCLhlgbJYPjfrwTgdvR2IqBEAXHiX2IhZIT0cFj+E5DKyHZSX688ipZBOiC4opJO0qCrd0F7AijqgeGgOM4MZAAc0rEC2g4oKkXmuF0a7kEIhqkbRGhY3QqYy0pnPbQorFnylI90csWxuAwKuoijMSbcAWQcBXwAlRfoWfAW4ZgPRiFEhPXF7xruQXMfoQqMAhXRCdEIhnaSlowOICBMJ3dAuYkVGelNTvC6JfqwS0iniGsdsHXBgjxQKLd0tUCEynU1lElM8NIUVC742dzejL9pnYakKCzPxOgBFXCtIzEdX9DokwfMR0YhRId3vB8rKxHMKiCTXMdoOAArphOiEQjpJixRwAwH9MVsAhSurMCMgDhokHhNnFxD9WCXiclDJOGYWfAVYB6RwkOJhSVEJiouKdX+e4qE1mFlsNPEzjV2NlpWp0JB1YGQwA+DCu1ZgZkAJYB0QjZgREOVnpJuXkFyFQjohjkEhnaQlUTw0YCKhI90izDjSA4F4/B8HNIxjVsRlWzCPmZx6gLMCSOFgRsAFKFxZhRkBschXFIuEoRPXOKZFXLqhTWMmXgdgRjrRiBVCutwHIbmKmWgXLrxLiC4opJO0mBWu6Ei3Boq47sNoF/dhtAsh2jArHtKRbg1mFnwF4gMarAfjWCWksw6MY3ZWQGzhXQ5mkExQSCfEGkc61wogRBMU0klarBJw29u5ELpRenqAzk7xnAKie1g1qMTBDOMw2oUQbZgWcA+Lh8znNo6qqqaduDEBkU5cw1g1qMQ6MI5V0S4czCAZoZBOiDlHOtsBIbqgkE7SYtYBWlkp1nABKF4ZRQq4ihKfcaUXOtLNY5UjnXVgHDrSCdGGWeGqqqQKCkSeG/O5jdHe046IKlb4phvaPUzHijDmyDSxOjA5sMc6IGmJRuMCIoV0UshwQIkQx6CQTtJiJpsbAHy++GKXFBCNIcXDykrx9zQCnbjmYbSL+1iVkd7aKmZ6EJKvmI1SKPIVxT5LEdcYsg6C/iBKikoM7YORFuYxvdgoBzNMY5Ujvb2nHT0Rdt4kBR0d8ecUEEkhY9Wiu9GodWUiJE+hkE7SYlY8BOgCNYtZ8RCgiGsFVrmhOZhhHLPRLgMHxgejGmmyJXmMWQcoQBHXLIl1oBhZrR0Uca3AsmgXtgPDmB3MGBgaCJ8iOm/WA0mJFA99PqDEwMAlhXSSL5iJdpEZ6Yn7IYSkhUI6SYtZRzpAEdcsVtQBRVxzRKPxBcwZ7eIeZgcz/P74DBmej0g+IzPSjQpXAHOJzWJFHTCf2xw9kR509opFZsy6oVkHxjE7sOdTfBhUIjpvno9IShJduEYGLimkk3zBjCO9uBgoKkreDyEkLRTSSVqsdKRTQDSGFXXAwQxztLYCqiqeU0h3D87OIEQbzeFmAMZzoQGKuGaRLlwzdcDBDHO0dLfEnlcUV2TYMj2cFWAes7MCAOakkyyYEQ8TP0fxsHBQVeB3vwOefBIIh90ujXWYcaQrCttCIbJ1K/Dww8Crr7pdkpyjyO0CEO9CR7r70JHuPrIOSkrEYL0RZDvo6gI6O4HSUmvKVkiYjXYBRFvYtIltgeQ3ljjSKSCawgrxkIMZ5pB1MCA4AEU+Y7c7R+ZzB/1Bq4pXMFg2Q+YQo11IGiikE73cfz9w663i+erVwM9+5m55rMKKttDUxLZQKHR2AnPnAjt2iIGU998Hjj3W7VLlDHSkk7TQke4+HMxwHyvawYABQCAgnrMtGINrNhCiDWaku48UD83UAQczzGHFrADmc5uHg0rEdiikEz20twPf+17892XLgN273SuPlVjVFmSmKclvHn5YiOiAmKWxdKmbpck5KKSTtDBKwX2sjHahgGsMK+pAUVgPZlBVno8I0YrZxf2ABBG3i43FCBzMcB8rBNzEfG6KuMaItQUzMUccVCKZoJBO9PDcc0BHB3D00cBnPytuMp5/3u1SWYOZaBcgvuAo20L+o6rAL38pnl98sXh87jmgr8+9MuUYFNJJWqyKUgAoHhrFymiXxkaxcCbRhxVCOkAR1wxdXUBvr3jO8xEhmbFCuKKIaw5LBjMOx4o0djUiEo1YUayCwgohHaCIa4aoGkVrWDgbLclI5/mIpIJCOtHD8uXi8bLLgH/7N/H8jTfcK4+VsC0QrWzYAHz6qcitfewxIVQcPAi89ZbbJcsZKKSTtNAB6j5WDGbIOohEgJaWzNuS/lglpFPENY6sA78fKCszvh9Gu5BCwLJMYlA8NIoli40eFg9VqDFRmGjHKiGdg0rGaelugQqxWrsV5yPOCiApoXhItNLTA/zzn+L5uecCc+aI5/kiHpp1pLMtFA4vvSQezzpLCE2nnip+f/dd98qUY1BIJynp64ufi+kAdQ8pIFZWGt9HcXG8P2U96MdqRzrrQD+Jg3qKYnw/g8QMfTQ2mi4SIZ6ku68b4UgYgEWxIhSuDGGFiBvwB1BRLKZZc0BDP5Y50iniGkbWQWmg1NRCrTFHOuuApIJCOtHKmjVimmt1NXDMMcCsWcKls3cvsGeP26UzD9sC0YqchfG5z4nHWbPE43vvuVKcXIRCOklJonPZjIhLR7o55FofZuoAYD2YgdEu7mPFzAyAQjrJf6RwpUDBgGKDN1JgnIVZWrrFRZRlbmgKiLqJzcwoHmhqP2wLxuGsAOIIZsVD6TaieJj//OMf4vH004Uzp6wMmDZNvLZ2rXvlsoJwOJ6DycVGSSZUFVi9WjyXszKkkJ7r7cBBKKSTlEghvbQUCASM70cKV62tXLvACLIfk2t/GIVuaONYkVMPsA7MYNVgBoV0ku9IAbeiuAI+xfglnlxgsbm7GVGVi2voReZCS0e5USjiGseKtQIA5nObwYq1AgBGTZEsWOXCbW/nYlL5zjvviMdTTom/NmWKeNy0yfnyWImMEgCM52BysdHCYNcu4MABoKgIOO448drxx4vHjRuB7m73ypZDUEgnKbFKwE0UH6UYRrQjBzSscqRTQNSP1RnpdKTrx4qII4BCOsl/rBJwpfiYuFgg0Y5V9SAHNKS7mminOdwMgLMC3MTqBV9ZByQlVgnpANDRYb48xLvI/OcTToi/NmmSeMx1IV22g5ISIZAagdEuhcGaNeJxxgwgFBLPhw0T9a+qwI4drhUtl7BdSH/ssccwZswYhEIhzJ49G2tkxaXgF7/4BRRFSfoJyco9jKqquPPOOzF8+HCUlJRg7ty52Lx5s91fo+CwKlKkqCguxlO80o9VAxoUEI1jdbQL60A/Vp2PEtuBqprbVyHA/jv3sErADRWFUBooBQA0dvGkpZeWsBgFryw2d9KSQjrrQD9SxDVbB3RDG8eqiCNZB01dTYhEI2aLRfINs0J6aSng8yXvi+Qf9fUiB11R4i5cADj6aPGY60K62YVGAQrphYIcUDrxxPhrigKMGyeeb9vmfJlyEFuF9GeffRY33HAD7rrrLqxduxYzZszAvHnzcODAgbSfqaiowP79+2M/O3fuTHr/gQcewE9+8hMsXboUq1evRllZGebNm4duTkGwFOmENivgAhRxjdLXB3R2iudWCelNNLXpxqq2wHZgHKsHlCIRXiNmg/13bmKVkA5QxDWKqqqWO9JZB/qRdWBWxGUdGMfqiCMVamyAhJAYZoV0RWFOeiGwbp14PPro5P8V6Uj/9FPny2QlZttB4mfZDvKbjz8Wj9OnJ78+dqx43L7d2fLkKLYK6Q899BCuvPJKLFq0CFOnTsXSpUtRWlqKZcuWpf2MoiioqamJ/QwbNiz2nqqqePjhh3H77bfjvPPOw/Tp0/H0009j3759eOGFF+z8KgWHVcIVQBHXKInrfFgVsUMRVz9WL/jKjHT9WHU+KimJz2Dj+Sgz7L9zEylcVYZMnrBAAdEo3X3d6IuKRWEopLuH1SIu60A/sToImquDgD+AAUEh8LAeSD8oIBItbNwoHuXiohLpSD9wIO6eykXkzZIV7YCLjeY3UkifOjX5dTrSdWGbkN7T04P33nsPc+fOjR/M58PcuXPx1ltvpf1ce3s7Ro8ejZEjR+K8887DRx99FHtv+/btqKurS9pnZWUlZs+enXGf4XAYra2tST8kM1YK6RRxjSHroKTE3IKvAN3QZmC8jvtYtVYAwHrQglf6b/bd+pGRInSku4esAwUKyoIGF/w6TKwOulkHeuGsAPexcoaMjHdhTjrpB4V0ogUZ3SId6JIBA+I3B0fMpMwprGgHXGw0/+nsjDvOKaSbwjYhvaGhAZFIJMmRBgDDhg1DXV1dys9MmjQJy5Ytw5/+9Cf8+te/RjQaxSmnnII9e/YAQOxzevYJAPfddx8qKytjPyNHjjTz1QoCq1y4AIUrozBexxtYLaQ3N4toEaIdDuw5i1f6b/bd+rHKAQpwoUujJIqHPsXcZTbrwDiWOdJL4450lYtr6IJRU8QRKKQTLUhH+uTJ/d8bPVo8FrqQznaQ/2zaJBYKq64GhgxJfo9Cui5sX2xUD3PmzMGll16KmTNn4vTTT8fzzz+PIUOG4PHHHze131tvvRUtLS2xn927d1tU4vyFIq77cDDDG1gtpKtqfAFTog07oqbYFqzFjv6bfbd+rBSuqkJi1InClT5YB+5jZU69rIOIGontk2ijtcdCR/rhiJ1DnXSkkyOQoh8XWSSZSOdIB/JLSDdzs8R2kP/IdpBqQClRSKdxICu2CenV1dXw+/2or69Per2+vh41NTWa9hEIBHDcccdhy5YtABD7nN59FhcXo6KiIumHZIbClfuwDtwnHAZ6esRzs/UQCMSvT5iTrg+2BWfxSv/Nvls/zEh3n5Zuxuu4TUdvB6JqFID5eigJlKCkqAQAY0X0ItsCz0fENvr6ALlgOZ24JB1tbcD+/eJ5vgvpVmWkU0jNT2SsixTNE5HtoL0daGhwrkw5im1CejAYxKxZs7By5crYa9FoFCtXrsScOXM07SMSieCDDz7A8OHDAQBjx45FTU1N0j5bW1uxevVqzfsk2qBw5T52zArgAov6SIxkNnNdIpELjrIt6IPnI2dh/5272JKRznxuXXAww31kHfgUH0oDpab3lxjvQrRjaUZ6CTPSSQoShW8rBMT2dnPlId5kxw7xOGhQ6qneY8aIRwrp4jESiQ9QkfxCCuljx/Z/LxQCamuTtyNpKbJz5zfccAMuu+wynHDCCTjppJPw8MMPo6OjA4sWLQIAXHrppRgxYgTuu+8+AMCSJUtw8sknY8KECWhubsYPf/hD7Ny5E9/4xjcAAIqi4Prrr8f3vvc9TJw4EWPHjsUdd9yB2tpaLFiwwM6vUnAwVsR97MqFjkYBn6dCnbyLrIOyMsDvN7+/QYPEtRzbgj4opDsP++/chJnE7mNHHTR3NyMSjcDvs6AjKgAS60BRFNP7G1QyCHta9zBWRCc8HxHbkeJhIAAUFxvfDx3p+Y0UyKVgfiR0pAsS45Ha2oCSEnNlIt5D5p+nEtIB4KijgH37gL17nStTjmKrkH7hhRfi4MGDuPPOO1FXV4eZM2dixYoVscXGdu3aBV+CotfU1IQrr7wSdXV1qKqqwqxZs/Dmm29iasKKsjfddBM6Ojpw1VVXobm5GaeeeipWrFiBUChk51cpOOwQruiG1oeVgxlSSI9GRb9oxT4LASvrAIg70hntog8K6c7D/js3ibmhi+mGdgsrZwVUlYjOW4WKlnBLrE5IZqwUcIG4G5ptQR+WOtJL6UgnKbBCPEz8PIX0/EQ60qVgfiRHHSUe9+1zpDi2YEVb8PmEmN7eLvY3dKg1ZSPeIZMjHRBtYc0aCukasFVIB4DFixdj8eLFKd9btWpV0u8/+tGP8KMf/Sjj/hRFwZIlS7BkyRKrikhSwMVG3cfKOigpET9dXaIeKKRrw0oBF2BbMApjjtyB/XfuQQeo+1g5mBH0B1EeLEd7TzsauxoppGvEaiFd/t0p4uqD5yNiOxTSiRak0zydkH44hhB1dbk7ddvKtiCFdJJfRCLArl3ieSYhHQD27HGmTDlMDp4liBPYFStCtEMR133sqgM60rXT0xOP6WPUFCGZsWOhy6YujjrpwS4Rl/WgHTrS3UdVVXsy0hmvQxKhkE60kC3aZdgwQFHE4rW5usiivGm1qi0kLhRG8oP9+8X/eFFRPAv9SEaMEI8U0rNCIZ2kxK6MdC4ArR2rY0UoIOrHaiGdi43qx6p1pCQc2CP5jJXCVVVINJbGrkao7Lw1Y+VgBkAnrhGsnBUAsA6M0NXXhYgaAUBHOrERCulEC9miXQIBYMgQ8TxX412sagvyppdtIf+Q/9vDh6df/E060hntkhUK6SQldmQSRyI8J+vByjgLgJEWRqAj3X1kHZSWigF0s3BAieQriQ7QypB1GenhSBhdfV2m91cotPZY64ZOHNAg2rDckc58bt3IASWf4kNZoMz0/lgHJCXyxtLshTqF9PxGCohSJEyFdOju329/eeyAg0okG4lCejoY7aIZCumkH4lRClblc8u15CheacdqEZdOXP3Qke4+jDgiRBtWO0DLg+Uo8onRK4q42qEb2n3sitdhHWgnsQ4URTG9P1kHreFW9EZ6Te+P5AlWXSRSPMxfolGgvl48zyQgyvcK3ZHOtpC/yP/tdLEuQDzaZe9eRklkgUI66UdiJJYVUQoAxSsjMNrFfSjiuo9dddDVJX4IyResdoAqikIB0QCMdnEf2xYbZT63ZqyuAzkzAwCaujm1khyGjnSSjUOHRC40AAwdmn67fHGksy2QdGgR0uWAUlcXc/KzQCGd9EO2mbIya6IUAMaKGMGuaBeKuNqxy5HOaBftWN0OKirisXA8H5F8wmoHKEAR1wh0Q7sPFxt1H6vrwO/zY2BoIADWA0nA6gUWKR7mH3V14rG6WmShpyOXHemqCrS3i+dcbJSkQw4SZRLSS0vjN92y7ZCUUEgn/bBaPAQo4hqBbmj3YR24j9V1oCiMOSL5idXCFUAR1whW5tQDCXXQzTrQim2OdOZza8aO85Ec0ODMABKD0S4kG1I8zBTrAuS2I72zU0TYAFxslKRHS0Y6ANTUiMdcbAsOQiGd9MMOIZ3ClX4Y7eI+dgnpLS3xWYYkM1a3A4AzZEh+YqeQ3tTFxqKVljCjXdzGrsVGm7qaEFWjluwz3+HAHnEEq+Msurp4gZ5vSFetFAfTIcXFXBQPZTvw+YSj2AwcVMpftES7APG2QEd6Riikk35YHaUAUMTVi9ULvgIUD41g14KvAOtBK5whQ4g2rBZwgXguMYUrbaiqapsbmoMZ2rGrDlSoaO5utmSf+Y4tjvTDAxqcGUBiWB3tAsQjMkh+oFVIl+JiLka7SNG7vFxMvTUDhfT8RauQTke6Jiikk37Y6QClcKUNOxZ85awA/Vgt4hYVxdsVc9K1wRkyhGgjFilSbF3nTQeoPjp7O2OOZavqgXWgH6tF3KA/iPJgOQDWg1ZiA3tBOtKJjVh1kVhcHM/PpoCYX0gxUKsjva4uHpOSK8j/WStEAwrp+UlPD9DQIJ7TkW4JFNJJP+gAdR87F3xlHWjHjrYgFxxlPWiD5yNCtMEoBfeR4qFP8aE0YHJ69WE4K0A/bAvuw4x04ghWRbsAFBDzFSkGZsuFHjZMPPb25p7byaqZGYn74GKj+YVsB4FAXIxIBx3pmqCQTvpB4cp9GK/jDeysh1y7RnMLno8I0Yat4iEXutREYh0oZqdXHyZRwFVV1ZJ95jt2xBxRxNWH1YvuAhzMICmwQ0CkkJ5faI12CQaBIUPE81wTEK10pHOx0fxExrrU1GSP/6EjXRMU0kk/KFy5j5110N0t1tIh2bEj5oiOdH1wUIkQbbR0Wy8eUrjSh53xOr3RXnT0dli233wlMaeeMUfuYasjnRnpRGLlDVO5iG+igJhnaI12AeICYq7lpDPahWTjwAHxqKcd5NqAksNQSCf9kMKVHRnpXGBRG3YIuAMGAH6/eE4BMTu9vfEBB4q47sGBPUK0QfHQfewYzCgNlCLoDwJgPWihu68bfdE+APYMKlHE1QbjdYgjMNqFZENrtEviNrkmIFJIJ9mQU+GzxboAcbGdjvSMUEgn/eDifu5jhwtXUTigoYfE6werFnwF4v0Xo120QSGdEG209tgnXDV1sdPQgh3ioaIoFBB1IOtAgYKyYJll+5VuaNaBNmxxpJfSkU4S6OsDOjvFcwrpJBXhcPymeujQ7NvLnHTp3s0VOKBEsiEXGq2uzr6tHFBqaBCLlJKUUEgn/aBw5T521AHAetCDrIOSErEuh1WwDvRhx+wM1gHJR+wQrrjQpT7sqAOATlw9yDoYUDwAPsW625yYI50Z6ZqgI53YjtWOFwqI+Yd0Lfn9wMCB2beXYvvBg7YVyRbscKR3dYnBKpIf6HGkDxoEFBWJ5/X19pUpx6GQTvphp5De1cV8bi3YIR4CnBmgB7sHM+hI14ad5yPOzCD5hIwVsWNxv7aeNvRGei3bb74iF7m0sg4AzgzQg12DGdINzYV3tcEFX4ntSPGwuFgsFGkWCun5h3ThDhoE+DTIXlJIz1VHupVCeuJ+Se6jx5Hu88UX3s21QSUHoZBO+mFHRnpFRTyfm+JVduyIdgHoxNWDXUI6FxvVB2fIEKINOwTEgaGBsedN3ey8sxGrg6C1HQdnBmjH7lkBFHG1YacjvaO3A+G+sGX7JTmK1ReIFNLzD+la0iIeAnHxsJCF9GBQDE4l7pfkPnoc6QCFdA1QSCf9sEO4UhS6ofXAaBf3oSPdfXp7rY2/lMhzUUsLZy2S/MEO4crv88fEdIq42WG0i/vY5khnRrpmVFW1pR4qQ5WxuB7WA4ldqFu1kBGF9PxDunC1iod0pCfvh20hf9DjSAcopGuAQjrpB0Vc92EduA8d6e5j14KvUkgHgOZm6/ZLiJtQxHUfO+J1ANaBHtgO3Ke7rxt9UTFKbWU9+BQfZ2eQOFYusAhQPMxH9DrSmZGevB+2hfzB6OyMXGsLDkIhnfTDbhGX0S7ZsfraUEIhXTsczHAfWQehkDXxl5Kioni9sh5IPpDoAK0spojrFq09hxe6DFo48gfWgR7szkg/1MXpZNmQdaBAQXmw3NJ9xyJ2WA+E0S4kG2Yc6apqT5nswOpMWNkWZBsjuY/etiCFdPk50g8K6SSJnh6gu1s8p4DoHvIartza+w8OZujAbkd6W5tobyQ9dtUBwPMRyS86ezsRUSMAgAHF9oi4XOgyO21h0Xnb5obmQpdZsSunXtZBc3czItGIpfvON2QdDCgeEItisYrYoq8cVCJ2Rbu0t1uzP+I+Rl24PT25JSLLG/vEKbdmkDdeHFTKD6LR+A0vHemWQSGdJGFXlALAjHQ9yGs41oF72CXiVlaKNQMADmhkg0I6Idpo7xGdhgIFZYEyS/fNKAXtyHqwy4XLOshObGaGxfE6sh0AXHg3G3bNCgC46CtJgNEuJBt6XbglJXEXWy7lpFstpLMt5BctLUDksAFAa1uQgjuF9LRQSCdJSAE3FBLxB1ZC4Uo7djvSWQfZsUvE9fuBgQPFcy44mhkK6YRoo61HdBrlwXIocqTOIijiakfWA2cFuIddIm7AH4jtk20hMy1hETNgh5DORV9JDC42SrKh15EO5OaCoxTSSSZkOygvB4qLtX2GjvSsUEgnSVi9VkUiFK60Iwc0KKS7h50iLhcc1YYT5yPOCiD5gIwUsVrABSik6yFWD8xIdw2ZU083tHvY1Q4AZqSTBCgekmzodaQDubfgaHd3PJfX6raQS/E2JD1G2gGF9KxQSCdJUEj3BnbVA+tAO064oelIzwzPR4Row65IEYD53Hqwy5HOeB3txPK5bRBx6YbWhl3tAGAdkAQopJNsFIIjXbYDn8+6GyZmpOcXRtoBhfSsUEgnSdjlhAYoXGlFVe13pLe2An191u4737BTxKUjXRt2no+4XgDJJ2LClY0OUApX2bE7I72jtwPhvrCl+843ZB3YOTuDbujM0JFOHIFCOsmGGSdurgnpAwcKMd0K2BbyCzPtoKkJ6O21vkx5AIV0kgQdoO4TDsdFbqvrQWZzA0Bzs7X7zjc4qOQ+PB8Rog1Gu7hPJBpBZ28nAOsFxMpQJRSI7HsudJkZ2RbsmJ0xuJRuaC3YOUOGdUBi2CWk9/SIH5Lb9PbGpxcXgiPdqnYAUEjPN4w40gcNAuSaS5xCnxIK6SQJZhK7jxRwAaCszNp9FxXFZ2tRQMyME4509kuZoZBOiDaccKRzocvMSPEQsH5Aw6f4UFXCeBct2NoWQhxU0oIT5yPm1JPYBZy8oDNL4sUmBcTcR95kKUqykywbFNIppOcb0pGuR0j3++OCBeNdUkIhnSTBKAX3kXVQUiKEb6uhgKgNOtLdx4k64GAGyQccyUineJgRKR76FT+K/cWW75/1oA0n3NAUcTNjax0wI51IrBYQi4qAUEg8p4CY+8gL/KoqIQpqJdcWG7VTSOdio/mBkWgXgDnpWaCQTpJwwgHa0sJ87kzIOrBDPAQo4mrFibZAETcznCFDiDbszCSWC102dTchqkYt33++kJjNrcjpsBZCIV0bjsQcceHdjDhRB8xIJ3TikozI/w+9MxZyNSPdynbAxUbzCyPRLgCF9CxQSCdJ2ClcJZ7fmc+dHunCtaMOAArpWrHTDc3FRrXBOiBEG7EoBRuEKxkpElWjaA3TnZQOOwczAEbsaEFVVUdmZ9CRnhk7o13krIDuvm509XZZvn+SI4TDQNfh+qeQTlJhVGCWjvT6emvLYxccUCLZMOpIl8I7hfSUUEgnSdjphmY+tzboSHefvj6gu1s8pyPdPZzKSI/SZEtyHDvFw1BRCKWBUgB0Q2fCzsEMgI50LXT3dSOiRgDYJOIyVkQTdp6PBgQHoMgncg/pSi9gpHioKPGbSyuggJg/SNeennx0IC4eNjYCqmplieyBQjrJhhR9jEa7SCGeJEEhnSRBN7T7OFUHjLRIT+KCr3RDu4cTM2SiUUYAktzHTgcoQBFXC9KRbod4CMQjdlgH6ZHtAADKghav1g7GimjFzkElRVF4PiLxm5iBAwGfhXIGBcT8wajALG+UIxGRR+t1KKSTbBj9H2G0S0YopJMk7BSuAArpWrDbkc5FX7Mj6yAYFD9WQ0e6NuyMdgmFgFJhsmVbIDmPnZnEAIV0LcQy0jmY4RqyDsoCZfAp1t/iyFgR1kFm7HSkA4UdsfPYY49hzJgxCIVCmD17NtasWZNx++eeew6TJ09GKBTCscceixdffDHpfVVVceedd2L48OEoKSnB3LlzsXnz5qRtzj33XIwaNQqhUAjDhw/H1772Nezbt8/y76YLeeGmN/86GxQQ8wej4mFJifgBcuMGwW4hPRdc+SQzRmdnUEjPCIV0koRTQjrd0OmxUzwEOJihBbvrQDrSOzvjETKkPxzYI0QbTjnSmc+dHseiXbjQZVqcGlBqDbeiN9JryzHyAbvXCyjUiJ1nn30WN9xwA+666y6sXbsWM2bMwLx583AgzaKIb775Ji6++GJcccUVWLduHRYsWIAFCxbgww8/jG3zwAMP4Cc/+QmWLl2K1atXo6ysDPPmzUN3wsXpGWecgd/97nfYtGkT/vCHP2Dr1q04//zzbf++GbFDPAQopOcTibMW9CJv1HLB8WTnYqOqCnR0WLdf4jy9vfE6pCPdUiikkyScEnFzoV9yC4qH7mN3HVRUxGeiclApPU4NaPB8RHIdxxygjLRIi1OLjRaaeKgHu9uBjNcBgKZudt7pcGpQqdDORw899BCuvPJKLFq0CFOnTsXSpUtRWlqKZcuWpdz+xz/+MebPn48bb7wRU6ZMwT333IPjjz8ejz76KADhRn/44Ydx++2347zzzsP06dPx9NNPY9++fXjhhRdi+/nP//xPnHzyyRg9ejROOeUU3HLLLXj77bfR2+viYBKFdJIN6cI18j+SSzcIdrSFkpL4jSrbQm4j2wEAVFbq+yyF9IxQSCdJUMR1HzrS3cfuOvD54tc7uXCN5gaqyvMRIVqx3YkbooibDacGM1gH6bF7Zobf58fA0EAAhRkrohW720IhRuz09PTgvffew9y5c2Ov+Xw+zJ07F2+99VbKz7z11ltJ2wPAvHnzYttv374ddXV1SdtUVlZi9uzZaffZ2NiIZ555BqeccgoCgYDZr2UcCukkG2b+RwpdSFcUtoV8Qf5/VFQAfr++z1JIzwiFdJKEU8JVLvRLbkHx0H3srgOAC45mIxwG+vrEc7YFQjLjVLQLxcP0cMFX97FbwAUKN1ZEK1E1av96AaHCOx81NDQgEolg2LBhSa8PGzYMdXV1KT9TV1eXcXv5qGWfN998M8rKyjB48GDs2rULf/rTn9KWNRwOo7W1NenHciikk2yYiXbJpRsEM98zE2wL+YHRfHQgLqQfOgREo1aVKG+gkE6ScCpKIRf6JbdwypHOSJH02F0HAAeVsiHrAADKyuw5Ri5dJxOSCcccoMznTotT+dzMqU+P3XUAcEAjG529nbHndKTnDzfeeCPWrVuHl19+GX6/H5deeinUNIsQ3nfffaisrIz9jBw50voCyQs3q4V0eeFP8TD3KYRol3AY6OoSz+0aVLJjIIw4h5lBx+pq8RiJUDhKAYV0kgQd6e4jBUS76kCeRxsbuRB3OuhIdx9ZByUlQFGRPcfIletkQjKhqqpjIm4hOUD14pQjvbm7GZFoxJZj5DqyDux0pBdqPrdW5LnIp/hQGii15RiFWAfV1dXw+/2or69Per2+vh41NTUpP1NTU5Nxe/moZZ/V1dU4+uij8fnPfx7Lly/Hiy++iLfffjvlcW+99Va0tLTEfnbv3q39i2pFijryxtIq6MLNHwoh2kV+R0XRn3+dDbngKNtCbmPGkR4Mxs+JFCz6QSGdxOjrA+Qi7XYJiBQPsyP7K7sd6ZEI+8Z00JHuPk7WAc9HJJfp7utGRBXCql0iLuMssuPUQpcqVLSEW2w5Rq5jd6QIQDd0NhIHMxRFseUYhXg+CgaDmDVrFlauXBl7LRqNYuXKlZgzZ07Kz8yZMydpewB45ZVXYtuPHTsWNTU1Sdu0trZi9erVafcpjwuICJdUFBcXo6KiIunHchjtQrJRSEJ6ZWV8cVCrYFvID8yeK3mznBabfH4kF0k8T9od7eL1fslN7Hakl5QAoZAYNGlsjA84kzh0pLuPE3XAawOSD0jhCgDKgvbkIEnxsJAcoHqJOdJtmhUQ8AdQHixHe087GrsaY65cEic2M8NGIb0Q87n14EROfSE60gHghhtuwGWXXYYTTjgBJ510Eh5++GF0dHRg0aJFAIBLL70UI0aMwH333QcA+Pa3v43TTz8dDz74IM455xwsX74c7777Lp544gkAgKIouP766/G9730PEydOxNixY3HHHXegtrYWCxYsAACsXr0a77zzDk499VRUVVVh69atuOOOOzB+/PiMYrvtUEgnmejtBTo6xHMzGeleFyzsagcA20K+YMaRDoi2sHMnb5ZTYLsj/bHHHsOYMWMQCoUwe/ZsrFmzJu22Tz75JE477TRUVVWhqqoKc+fO7bf95ZdfDkVRkn7mz59v99coCKSAGwyKHzvIlX7JTex2pAOsh2zQDe0+FNLdh/13biCFq7JAGXyKPZd1jHbJjiMiLushI44sNkpHekacaAeFWgcXXngh/ud//gd33nknZs6cifXr12PFihWxxUJ37dqF/fv3x7Y/5ZRT8Jvf/AZPPPEEZsyYgd///vd44YUXMG3atNg2N910E775zW/iqquuwoknnoj29nasWLECoVAIAFBaWornn38eZ555JiZNmoQrrrgC06dPx+uvv47i4mJn/wCJUEgnmZDiIWBMQMwVtxOFdJINOtJtw1ZH+rPPPosbbrgBS5cuxezZs/Hwww9j3rx52LRpE4YOHdpv+1WrVuHiiy/GKaecglAohPvvvx9nnXUWPvroI4wYMSK23fz58/Hzn/889rurHXke4aQLt6VFRMnYlX2cyzgl4u7bx3NiOpwUcTmYkRon2gFnyKSH/Xfu4MQCizJKoam7CVE1aptgn8vY7UgHRD3satlVcAKiVpyog0J1Q2vFUUd65yGoqmpbhIwXWbx4MRYvXpzyvVWrVvV7beHChVi4cGHa/SmKgiVLlmDJkiUp3z/22GPx97//3VBZbYVCOsmE/P+oqAD8fv2fz5UbBCeEdC42mttY4UgHKBqlwNY7oYceeghXXnklFi1ahKlTp2Lp0qUoLS3FsmXLUm7/zDPP4Nprr8XMmTMxefJk/OxnP4vlvyVSXFyMmpqa2E+VHSePAsQJ8TCxqrj4b2qcHNDw+vWBWzgp4rJfSg0d6e7C/jt3sHuRSyAuXEXVKFq6mc+dCifd0BRxU+PEYqOFmM+tB6cGlACgN9qLjt4O245DPIy8cKOQTlJhVjzMlRtlO4V0LjaaH8j/kXxvCy5gm5De09OD9957D3Pnzo0fzOfD3Llz8dZbb2naR2dnJ3p7ezHoiBW5V61ahaFDh2LSpEm45pprcIgVawlOiIdFRfFFpSlepYYirvvQke4+TsfrHF47i4D9d67hhIBbXFSMsoDIX6eAmBpGu7iPE4uN0pGeGSfaQWmgFEG/yKBkWyhAuroAudDpIIvXipAX/u3tgKpau2/iHFbFWbS2irx1r8JoF5INOajEaBfLsU1Ib2hoQCQSieW2SYYNG4a6ujpN+7j55ptRW1ubdDM/f/58PP3001i5ciXuv/9+vP766zj77LMRiUTS7iccDqO1tTXph/THCfEQoICYiWg0vjYKHenu4UROPQczMuPkYEY0yuvERLzSf7Pv1oYT0S4ABcRMRNVozBnrhBOXdZAa2RaYke4eTgzsKYrCmQGFjBQP/X7rLxLl/iIRoLvb2n0T5zArMCd+zss3ahTSSTbMOtIppKfFswnVP/jBD7B8+XKsWrUqtuAJAFx00UWx58ceeyymT5+O8ePHY9WqVTjzzDNT7uu+++7Dd7/7XdvLnOs4JaQPHgxs3872mIqOhBmqXGzUPaQbmo5093DifBQKAaWlQGenqAc5W4aYw6r+m323NpyIdgGEgLi7dTeFqxRI8RBgrIibxBzpDmSksw5S49T5aFDJIOxv389BpUIkURiyOh8/8earrQ0oKbF2/8QZzEa7FBWJzzY3C8HiCGOJZ6CQTrJBR7pt2OZIr66uht/vR319fdLr9fX1qKmpyfjZ//mf/8EPfvADvPzyy5g+fXrGbceNG4fq6mps2bIl7Ta33norWlpaYj+7d+/W/kUKCCeiFAAKiJmQdeDz2XvtRkd6Zpx0pHd3i1mqJBmnz0e8Pojjlf6bfbc2nHKkx9zQjFLohxRwfYoPJUX2dd7MSM+Mk+sFtPe0oyfSY9txchUnHOkAZwYUNHaKhz4fUCZizCgg5jBW/I/kws2yExnpnA2a29CRbhu2CenBYBCzZs1KWmhMLjw2Z86ctJ974IEHcM8992DFihU44YQTsh5nz549OHToEIYPH552m+LiYlRUVCT9kP446UgH2B5TkSjgWm2ySIR1kBknHOkDBgjDA8B6SIXTUVOsgzhe6b/Zd2sjJlwF7BWuGO2SnsRcaMXGzpsZ6ZlxQsQdGBoIBaKOKeL2x/GoKbaFwsNO8RCgEzcfsOJ/JBecf3Skk0yoqnlHOkWjtNgmpAPADTfcgCeffBK//OUv8cknn+Caa65BR0cHFi1aBAC49NJLceutt8a2v//++3HHHXdg2bJlGDNmDOrq6lBXV4f2w6pWe3s7brzxRrz99tvYsWMHVq5cifPOOw8TJkzAvHnz7PwqBYHTQrqX+yW3cELABVgH2XDCka4ouXGN5hZ0pLsL++/cIebCdciRTvGwP07XAQczUuOEiOtTfKgqETekFHH741jUFM9HhYu8YLN6oVEJBcTcx2y0C5AbN8sU0kkm2tvFeg+AeUe6l9uBS9iakX7hhRfi4MGDuPPOO1FXV4eZM2dixYoVsQXMdu3aBZ8vruX/9Kc/RU9PD84///yk/dx11124++674ff7sWHDBvzyl79Ec3MzamtrcdZZZ+Gee+5BcXGxnV+lIGC0i/s4IeACrINMqKpzAxqDBgEHDlDETQUH9tyF/XfukOiGthM6QNPjxCKXQEK0C+ugH72RXoQjYQAO1EPJYDR2NVLETYFT0S6cIVPA0JFOsmFltIuXb9IopJNMyAGlQEAsCmYEKRo1NQHRqIi/IgAcWGx08eLFWLx4ccr3Vq1alfT7jh07Mu6rpKQEL730kkUlI0fCaBf3cWowg+Jhejo7hZgOcEDDTRjt4j7sv3OD9l6HM4m72ViOJLbIJV24ruHUgq8ARdxMcIYMsR0K6SQbVka7ePkGwQkhnRnpuYsVCzPL/y1VBVpa7Dvv5iAcUiAxnBauKB72x+nBjObm+IwfIpCDGYpifPBWKxxUSg+jXQjRBhcbdR+nxEMp4Lb1tHGhyyOQdRD0BxH0B209Fhe6TI9Tg0oczChgKKSTbFgR7SI/K/flNXp6hPsLsHex0d5eIBy2fv/EfszmowNAcXF8AWbeLCdBIZ3EoCPdfZwWDxPXoCAC2Q7KyuyfvcRBpfTQkU6INpzKJKZwlR6n4nW40GV6nBJwAcYcZcLpmCO2gwKEQjrJhhX/I14X0uV3BIDKSuv3nyhGsC3kJomOdDMwyiAlFNJJDGaku49T4mEgED8G6yEZp/LRAQ4qZYIxR4RowzFHOoWrtDiVC+33+bnQZRqcagcAY0Uy4fTsDLaDAkReNFNIJ+mwQkiXn/W6kF5ZCfj91u/f749PzWZbyE2scKQnft6rbcElKKSTGHSku49T4iFAATEdTi34CnBQKRN0pBOiDccX96Nw1Q+nZgUAFHHT4VQ7AOJtgXXQH6fqge2ggJECoryAsxoK6blNNCqynIHCcKTbmVnNtpDbWOVI93pbcAkK6SSG08JVe7uI9yJx6IZ2H9aB+/T1Ad3d4jmFdEIy45SIK4WrlnAL+qJ9th4r13DUDX14ZgAjdpJxcjCDMUepUVXVsZijxMEMVa4QTwoDRruQTLS1CTEdyO+MdArpJBtWrBUAxKODvNoWXIJCOonhlBt64MB49jTFq2ScdEPTkZ4aNxzpbAfJyHMRwMVGCcmGUyKujBQBgKaupgxbFh6uiLicGZCEU9ncAN3Q6QhHwoioYgV7p6JdImoEreFWW49FPAaFdJIJ+f8RCokfo1BIZ1vIdayKdpFtQc70IAAopJMEnHKk+3zx9kwRNxkn3dCMFUkN68B95LkoEBCLhdtJ4qwAmtpILuJUlEKRrwiVxcKV4pSA+LfNf8O1f70Wf/30r44czyhOxopIEZdu6GRii406MCvAaUd6W7gNS15fgltevQX17fWOHNMIcjADAMoCZbYeqyRQgpKiEgBsCwWFqlJIJ5mx6v9DiodNHjUOUEgn2WC0i61QSLeZnTuBM84AxowBnnrK7dKkJxIBOjvF83wTEHt6gP/4D6C2Frj6aqC31/5jGiWfHelPPCHawf/7f8CuXc4c0whODSgBzke7vPMOcNxxwJQpwEsvOXNMIzi5VoC8/oxEgFaa2kgCnb2dqGuvc7sYGQn3hdEbFZ2aI/ncDsaK/HnTn/Fvv/k3/PTdn+ILv/0C/vjJH20/plGcWmARSBDSHXSk72/bj67eLseOZwRZB44MZji48K6qqlj43ELcteou3P+v+/G5X34Onb2dth/XCLIOSgOl8PtsWPzuCLgAcgHS1RXPBaWQTlJhVZyF/Hx3dzxv0ks4IaTLGzG2hdzEKkc6o11SQiHdRqJR4N//HVi1Sgjq3/gG8I9/uF2q1HR0xJ87KeI6ISAuWSJE3P37gccfB+691/5jGsWNfG4nhPRXXxWDGTt3Aq+9Bixc6F33r5MibuKAkt1/j+Zm4EtfAtavBzZuBL78ZVEfXsTJwYySEvEDMN6FxFm5bSVqH6zF8AeH49q/XuvZDF4pXAHOLrJot4gb7gvj+hXXAwCGlg0FANz86s2IRCO2HtcoTuVCA86Kh1E1isteuAy1D9Vi5I9G4s3db9p+TKPEHOl5Fq+z/MPleGnrSwj4AqgorsDGho148r0nbT+uEZycmQEw5qggkRdqfr99F+oU0nMbqwTmykpAUcRzL0Za0JFOskFHuq1QSLeR3/0OeO89cQ6aP1+85lURV54fi4rsj1IAnHOk790L/M//iOdf+pJ4/NGPvHsecMORbrd4qKrAHXeI51/4gvhua9YAr7xi73GN4oYjvacnPiPELh58ULSHMWOAmTPF8X78Y3uPaRQn6wBgTjpJpi3chq8+/1W0hMWN00/f/Sl+88FvXC5VaqSA65gD1KFs6Oc/eR7bm7djePlwbLh6AwaVDMLmxs34y6d/sfW4RnHSke5krMiydcvw9PtPx4538R8uRk/Em6vEu5GR3tXXZbtT/ydrfgIAuO202/CDM38AAHjsnccQVaO2HtcITg4oAcyqL0ikMDRoUFzktBqKh7mNVQKzzwdUVIjnXhQOKKSTbMgBIKuEdC8OKLkIhXQbeewx8fhf/wX87/+K5y+/DGzb5l6Z0pEoXNl1XZKIUyLuL38JhMPAKacAv/89MHWqiG/4jTc1Edfc0HayZg3w9ttigObJJ4ErrhCvP/qovcc1ipN1UFYmcsABe+uhtxf42c/E8wceAL7/ffF82TL7BXwjOFkHABfeJck8suYR1HfUY8KgCbj9tNsBAA++9aDLpUqNaw5Qm0XcpzcI8fYbx38Dw8qH4fIZlwMA/vDJH2w9rlHyMSO9N9KLO14To+BLPrcENeU12NWyC7/76He2HtcoTi74WlFcAb8iBq7sFHE/OvAR3t7zNop8Rbj6hKvxtRlfw4DgAGxu3Iz39r1n23GN4mROPeB8Vj3xABQPSTasinZJ3EehC+nyxozkFvL/VkazGMXL7cBFKKTbxM6dwBtvCFH6iiuAsWOBuXPFe7//vbtlS4VbDlA7hStVBZ4W9+L4xjfEwPJll4nf//Qn+45rBjfc0HaLh787fM/95S8DNTWiLgDhSPeiiOtkHSiKM4NKr7wC1NUBw4YBCxYA8+YBI0eKgeXXX7fvuEahI524haqqeGqdWNDkzs/eietPvh5BfxDr6tZ5UrhyUjwEnHGANnU14ZWtYsrS16Z/DQCwYPICAMBfP/0reiPeW+jEjWgXu+Ms/rr5r6hrr8PQsqG4+dSb8c2TvgkAePy9x209rlGcFHEVRYmJuHa2hRc2vgAAmD9hPoaVD0N5sByfH/95AMCKLStsO65RnMypB+hIL0iYC02yQSHdOjiolNtIB7lZIZ0Z6SmhkG4Tfzy8JtZnPwscdZR4/oUviEcvRlq45QC1U7j69FNg0yYgGATOP1+8dt554vG117y5sKCT9eCEkK6qwB8OGwgXLhSPxxwDjB4t1m35+9/tO7ZRnG4LToi4//d/4vFLXxIOeJ8POPts8drf/mbfcY2Sj3VAcoO397yNbU3bUB4sx79P/XcMLh2M8yaJjkOKWl4iJuA67QC1UcRduX0lImoEU6qnYOLgiQCAU0aegkElg9DU3YS1+9fadmyjuLHYqN3i4TMfPAMAuGzGZQj6g7hk+iUAgDd3v+nJTGqnRVwnhPS/bv4rAOALE78Qe23+eJEV+bct3uu8nY52YUZ6AeK0CzfqvQglkgUK6dZBIT13iUbjYhcz0m2BQrpNSLH8C/FrX5x1lnj85z/FouNeIh8d6S+9JB5POy3+vSZNAsaNE1EXb79t37GN4oYj3U7xcNMmMTujuFi4oAHhwv63fxPPX33VvmMbJd/agqrGhfTE85Fct0G2Ey+Rb3VAcoc/b/ozAODcSeeiNFAKAPi3ieKE9fK2l10rVzqcjnaJuaFtjFJ4eav4O88bPy/2mt/nx2dGfgYA8Naet2w7thGiatTRhS4T68CuRXB7I714aYvoHBZOFaPgoypHYfqw6YiqUU+6oZ2sA8D+WJHm7ma8vUdcqMpzEICYI/2dfe+gu6/blmMbxa3zUWM3R8ELBnnT4oR4CAAdHfYdh9iDVS5cIC4gStHaS1BIJ5loaxMiAGBdtAsz0pOgkG4D4TCwapV4/vnPx1+fPBkYPly8/+67rhQtLU4LV064oV8+rHnMm5f8+mfEvTjefNO+Yxuhp0cI/ICzGekdHeJ/0g5ee008fuYzQGlp/PXTThOPXhzMyLfZGdu2Abt3Cyf6GWfEXz/9dPH46afec2I7fT6qrhaPDQ3OHI94FymWnz3h7Nhrnx8nOvJ3972Lpi5v3UzlY7TLqh2rAABzx81Nev2UkacAEI5oL9HZG88oc0JAlAJuT6QHHb32iDxv7HoDbT1tGFI6BLNqZ8Vel+1C1pGXcHKxUSBBxLWpLazesxoqVIyvGo+RlSNjr4+uHI2hZUPRF+3Duv3rbDm2UZw+H9GRXoA4IR6WloqpmwAFxFzEDiHdi05cxhyRTMh2EAgAoZC5fcl20N4O9PWZ21ceQSHdBtauFdnP1dXAscfGX1cU4KSTxPN33nGnbOnIN/EwGo0L5YniISAWHgW8J6QnruPhRD1UVsavE+0a0JBC+pF1cPLJ4nHtWhHx4iXyzQ0t/89nzUoezBg0CJgoUhOwZo09xzaK0+ejIUPEI4X0wuZgx8FYbEiiiDuiYgSOHnw0omoUq/eudqt4KXEt2sUmF+6hzkPY3LgZADBn5Jyk97wqpMs68Cm+2CwGOykLlCHoDwKwT0B8bYfovM8afxZ8SvxWQdaB12YFAC4udGlTHcj/c/k3lyiKgpNGiJsJr52PnK4DZqQXIFI8lBfPdqAoFBBzGSuFdClSe01I7+2Nz5agI52kQraDgQPFOc0MFRX99+sVHnxQiK433uj4oSmk28Dqw9e1c+bEhUrJiSeKR68J6fkmHm7eLK61QiFgxozk9+YcvjdfvTo+48ULyDooLhaDh3bj89lfD//6l3iU7mfJmDHA0KHiOmCdtwxVsXrIl0ElKaTLmRiJzJ4tHr0mpLvlSD940JnjEW8ixcEp1VNQU16T9N6JtaLzfmevtzrvWC50ID9cuGv2ipPR0YOPjgmVkuNqjgMA7G3bi4ZO74x6JWZzK2ZvVjSgKEpMQLRrQEO2hVNHnZr0+slHiVHwjw9+jJZub91M5dtCl2/uEZ33nKPm9Htv9gjReb+zz2PnI4dnBdg9sEc8iBMuXIACYi5TCI70xKgZK7Lg08F2kLvI/1kr2kEgAJSViedeE9IbGoSQJWMdHIRCug1IIV2KVIlQSBfYLR7KyJBZs/qL0lOnitdaW4Fdu+w5vhGkC9epOgDsrYd9+8SPzwccf3zye4oCnHCCeL5+vfXHNoPT9eCUI/2UU/q/J89HXouaoiOduMHqPaLzTiVcnVArTlheE67yzYUrM6GlUJjIgOIBGFc1DgDwQf0HthzfCE6Lh4C9AxqRaCRWD0e2haFlQzGuahxUqJ5rC64tdGmDiJtYB0c60oH4oJKX2gHgQtSUzQN7xIM8+iiwcSPwta/Ze5zEBUdJbpHoxDWL14X0igrA77fvOBTScxcr20HifrzWFqxcXFgnFNJtQLo7ZYxLIseJa19s3eqt9UucFq6keNjdLWJwrEYOZsgIkUQCASGmA8CGDdYf2yhOO6EBe0VcKc5OnRofxExExh59+KH1xzZDPjnSW1uBDw7fZ8/prw3GZmt4tQ7oSCdO8vbewyLuUf1FXOlIf3eft0adnBYPpQu3o7cD4T7rF9eQdSCdz0cyfdh0AMCGeu903k4vcgnYO6Dx0cGP0N7TjvJgOaYNndbvfSnieqkOomo0lhefD7EiHx/8OGMdHDP0GADAxoaN6I0478JKh9OLjcp20NTVhEg04sgxictUVQGTJsUdEHZBATF3KSRHOmdmkHRY2Q4A77YFWR6720IKKKRbzMGDYnE/IO72TGTIkHjf/8knzpUrG04LVwMGAEVF4rkdAqJ0pKcS0oG4iPv++9Yf2yhOD2YA9i76KmddpGoHADDt8L3hBx4yVPX0iB/AeUe6He3gvfdEfNHo0WKh4yM5RtyLY/t2b5lunD4fJTrSvRT3RJwjEo3EYltSibhSwN3fvt9TC9vFHKAOiYeVocpYZrbVAqKqqrE6SOVIB4DpQ0U9vF/vnc7b6ToAYGu0i8zmPvmok+H39Xe6HTtUXEB9cMA7nXdHT9yZkg+xIuvqRObdcTXHpayDUZWjUB4sR2+0F1sat1h+fKM43RZkHahQ0RL22HRzkttQQMxNVLUwhHSnXLiJMzN4g5RbWBntkrifQm0LKaCQbjHShTt5cvr6lOLVRx85UiRNOC1cKUpcxLXaBRoOx8XZVLMCAGC6uBf3pCPdjWgXO4V0GeFyJHIw44MPvNM3O73gK2DvrAAZmyNnwhxJdTUwbJh47qWBPacHlaQjvbvbWzOFiHNsbNiItp42lAXKcMyQY/q9P6B4AEZVjgIg3KJewWkHqE/xxcQrq3PK97TuQVN3E/yKP6ULF0h24noFp2cFAAlCug2DOrEBpRGpnQjHDjsspHsoVkS2A5/iQ0lRiSPHtDNW5P06MVA0Y9iMlO/7FF/sPPXhAe9MKXN6dkbQH4yd+7w0wEnyAArpuUlHBxA5PDvFSiE9MZPcCzgtpEci4iaJ5A52Rbt4LSOdQnr+IB3OR2ZCJyJjRT72zr24K25o6QK1WkjfuBHo6xP958iRqbeZMkU8fvqptcc2Qz450lU1PqiUzpE+ebLIT29uBurqrD2+UWQdOLXgK2BvtIs8Hx254G4icmaAl+JdnB5UKisTdQ4wJ71QkZEts2pnpXSAAogJVx8d9M4ouNOZxAAwpFR03gc7re28pSA4qXoSiouKU24zYdAEAPCUC9fpwQwAqC4Vo392LLq64YBwGMysmZnyfTnI8fHBjz0Tp+H0gq9A3A1ti5B+eMbFjJr0nffUIeJm4pMG74yCu7JegM2LvpIChUJ6biJFPr8fKC01vz8ZF+E1F67VsR3pSMxmZVvILQot2oVCeu4jxahpqc1UAOKOdC8J6W64oYcOFY9WC+myDo49VjjfUzFxonjcssU7bmg36sCuwYy6OiHO+3zp20JxsYgcAUQ9eAG3c+qt/l+UQvrMmem3mTxZPBbyoJKi2NcWSG4gRdx0DlAgLlwVsiMdAIaUHRbSO6xtLDIqREaHpGLiINF5H+w8iJZub7hinF7wFUioA4sHMyLRSKwtyDijIxlfNR7F/mJ09XVhZ8tOS49vFDdmBSTm1KsWdt6qqsaF9AznIzmotLVpq2XHNosbMUd2RuyQAoZCem6SKB5aMaiaKB56RTAAnBPSfb64mM62kFsUipAuZ4tQSM99tAjpUsTd6p1r35hwlQ8irox1OTb9vTjGjhV9Q0eH99zQ+TArQLaD8eOBkgyzrCeI+0DPCOlutAPpSO/rszanvLc3PliXyZE+frx49NL5yI1BJRnvQkd6YSJF3HSRIkDcke5FB6ijIq5NjnQtdTCgeACGlYk8Kq+40mNu6ICDgxk21cGWxi3o7utGaaAU46rGpdzG7/PH3tva6I2Ow40BJemEDkfC6Orrsmy/+9v3o6GzAT7Fl7EtjK8SnbdX6gBwqR5sjNghBQyF9NzELvGwpwfosu48bxqnhHSAbSFXsdqp7fWMdC42mtv09sZzhjMJ6VK42rYNiEbtL5cW3HDiuimkB4Pec0Pn02CGzP/P1A4A7wnpbrSDkpJ4rIiVETsbN4rrvooKYMyY9Nt5rQ6i0fxqCyQ30OKGjjlAC1y4GlomppMd6Dhg6X6lEzpTHQDxetjcuNnS4xsln2YFbKgXsS7Thk5LG3EEeC9ixw0ndHmwHEW+IgDW5nPLfPSjBx+NkkB6J8L4QYeFdC850l2eGUCIZVA8zE2sFpjLy+PO9tZWa/ZpBRTSSTbsGlTyUkZ6d7dYHBGgIz3X2bJFCFfl5XGRNhUjR4rornAY2L/fufJlIp/c0FqEdCAuIG72xr14Xg1mSCH9mP5r9iXhNRHXDQE3ceFdK3PS5UK606dnnt2Y6Ej3wqzFzs74cyfbAh3phUtjVyP2te0DEF/MMhXShburZRd6I72OlC0bTi/uByS4oS0UcfuiffjkoHAiyMUs0zFxsJjW5xUR15VoF5sc6VJI1zqY4bk6cLAdKIpiSz63nPGSyY0OxB3pde116Ohxf5Xs3kgvwhFxQ+tkW2BGOrEFiofWsWEDcMUVwG9/a/+xrBYPfb74/4IXhXQnxEO2BWuIRoEHHgC++lXhprWbQoh2kWXx+ZwVDQ5DId1CZJzFMceI+kxHIBAX2r0Sp+CGgCgz0g9YaGprbgZ27xbPs4m4iTnpXiCfXLhahXQp4nqlDtwYzACSc9KtYtMm8SgXN07H2LHisbXV+kVnjSDrQFGsWSdIK3SkFy7SCT26cjQqiivSbjd8wHCEikKIqBHsatnlVPEykrjIolPYkc+9pXELwpEwygJlGDNwTMZtJ1TRkZ7oSLcyn1suNJouH10SE9KbvNF5u7HIJWBPPvenh8SCJZMGT8q4XVVJFapCYirztiYHbsqzINsB4Gw9MCOd2ALFQ2vo6gLmzweWLQO+8hVg1Sp7j2eHU1vuy0tOXDrSc49f/Qq4+WbgN78BvvQl+2MprI528bKQXlmZWXy1CQrpFiKd0NniLABg3OHoSScGpLKhqvnjSJcC7lFHZY9KoiM9XgcdHdZFv6mqsWgXL7ih3RjMAOJCupWOdLl46NFHZ96upAQYMUI898LAXuK5yIp1grRCR3rh8kH94ViXLE5on+LD2IFi5MkLcQp90T5093UDcFjEtcENLevgmKHHwKdkvjT1miPdlcGMw3XQG+1Fa9g6l5ysh2xCunRDe6UO3JgVANiTzy2F9KMHZ+m84a14F9kOgv4ggv6gY8elI53YAsVDa/jDH5Kn3z/wgL3Hs0NgrjhssPCSIz1RQLQbtgVr+N//jT/fsAH417/sPZ7VbcGLGekuLjQKUEi3FC0LjUq8tMBfV1d8UCzXhXStsS5A3JHuFSHdDRG3okLMkACsq4c9e8S1RlFRdhFXDii1tnpDwHTLkW5HtItWIR3w1vnIjYVGATrSCxkt+egSKVx5wQGaGOfgpIBoR0a6nBUwbUj2CyivLbLohiO9JFCCskAZAOsGNFrDrdjevB2A9miXrY1bEVXdX+zHjQVfAXvyueVMi4mDJmbd1ksRO260A4COdGITFA+t4S9/EY8XXigeX37Z2pudI7Ej8oSOdPHItmCcQ4eANWvE8/nzxePy5fYesxAy0p2MOEoBhXQL0SOke8mR3h6fjZnzUQpysddskSJAvA527rTu+GZwQ8RVFOvrQbrRJ04Ui7pmoqREzB4AvBHv4rYj3apoFVWlkK4XOtILFymkZ8skBoBxA0XH4QURVwpXAV/AUQeoHQtdftooTliTqydn3XZU5SgAQH1HPcJ9YcvKYBQ38rkB6+thU4PIA6spr4k5rdMxeuBoFPmKEI6Esbd1ryXHN0NskUunHekWu6E7ejqwp3UPAI2OdA8NKrmx0Chgz6wAQigeWsSbb4rHq68GpkwBIhHgH/+w73iF4kh3UkiXwkSiYET0IdvB5MnAt74lnv/5z/ZNx+/tFXEDQH5Hu8g2WZE+ltNOKKRbRE9PXBSfMiX79l4SrhKjFJyMF5IZ6c3N4u9nBVI8nJQ5WhKAWPRVHt8LfaMb8TpAXEi3Kqtea6yLRK4XsMsDkcP54kjfv1/0n35/PAM9E149HzkJHemFiaqq+PjgxwC0CekxR3qz+6PgbkSKAPFYkcauRkSiEUv2ufmQcOFqEQ+rS6sRKgoBAPa2uS/iuuXElfVg1cwAPZEiRb4ijK4UnbcXYkXcdkNbJeJKZ/mgkkFZBzMAxOpgd+tuS45vBrfideyYFUAIhXQLqKsT05QVBZg1C/jc58Trr71m3zHtiDyRIp0Xnbh0pOcGMsblM58BPvtZMW1/zx77xI9EYcsqkTnRkW53vrtWKKTnB9u3i0HW0lKgtjb79lLc2r7d3nJpwS3hqqpKCH2AdS5QGdMyMfuMWAwYED8n7Hb/HiRvIi02bhSPWgaUAGCUMBd6og7yxZEuB5TGjs0+KwDwVh3QkU6cpKGzAc3dzQC0RSmMq/KeI91p8XBw6WAoUKBCtSROQVXVeJzF4Ox1oChKzJXuhUVf3Vro0upFX2NC+iAN05ggXOkAsLvF/Y5DDio57Ya2OlZEz2AGAIysFI4QLwjpbg3sMSOd2ALFQ/O88454nDJF/D3POEP8/vrr9h3TzsVGveC6A4SLWZaFQnpu8Pbb4vEznwHKyoDjjxe/v/GGPceT7aCsLJ7haxb5v5a4uKLbuCUaHIZCukVIAffoo7UtkCfd0AcOAGGXZya75cL1+eJOXCtE3J6e+MCEljgLwFsCYr44cRPbghZkW/BCHbjVFqwWcfXEugDxOtizx5rjm8HtdtDYCPT1OXts4h5SwB1VOQolgZKs28vFRne2uJ8J5pYDtMhXFBMQrXBDH+w8iNZwKxQosYGKbIysOCwgekDEdaseYou+WhTtIuN1NIu4h+vAC4MZrg0qHRZxGzqt6bz15KMD3moHbkW7yHNRS7gFfVF23sQipDDT1cWLQqOsWyceTzhBPM6ZIx4//FD8Xe3AzmgXrzjS29vjjmAnsqEppJtHTtefOVM8nnaaeLRLSLdjZkYoBBQXJ+/fbehIzw+kcKXFCQ0I4Uz+L+7bZ0+ZtOKWcAVYK+Ju3y76lfJyoKZG22ekkO6FWBG33NBWC+l624KXhHS36kDGHFkVr2OmDuyKa9OKW4PLclYAYO86SMRbGHWANnc3x8Q7t3DLCQ1Ym88t62BU5ahYZEs2vORIdzvaxXJHusa2IOvAS25opwczrF54V28dHFUhFplp6m5KWnzYDdyM11EgHExWDWgQknQR6hX3Za4h8yInH177ZMQIIYBEIvGF5aymEBzp8jsWFYnFxuyGQro5Dh4UTjlFiWcPy8Gl9evtOaZd0T9ey0mnkJ4f6HWAKkp8kUW3XaBuiYeAtSJuonioZVYA4B0hPXGWTC470tvbRT43kJtCuluO9GHDxGN9vTX703s+GjFCPHZ1uS8iu9UOioriYjpz0gsHvXEWFcUVMcel24ssuiVcAdaKuHry0SVeEdJ7I70IR8S0wlyOdlFVVf+gUoV3YkXcagvDykXnXd9hTeettw4qQ5Wx85Hb9eDWzAy/z4/qUjGtz6oBDUJQXByPRKCAaAw5TVzm2SoKcNxx4rl0q1tNISw2mug21ip4mIGLjZrjY7EOE8aMERnQADBjhnjcsEEMLFmNbAdWz1jwWltgtEt+oFe4ArwjpLslHgLWOnH15KNLvCLidnbGncC57EjfItbJwuDBIgNfC16M13HLkV5fb40jXO/5KBSK/x+4XQ9uDuwxJ73w0CtcAd7JJXZTSLfSias3zgLwjoibOCvBNUe6BbMC6jvq0d7TDp/i0x6vU8lYkWFlQkh3y5EOxOthT6u7NxOxjPSA8+ej2IBGu0VuBEIAOnHNcqSQDuSmkC735ZVoFycXGgXi4m+Hu7OechYppE+dGn/t6KPFbILOzvjMDSuxI9olcX9eaQt0pOcHenOhAe+IuPkS7WJkMMMrjnR5jaYo8f7KKdyuA9kO6usLd70A6Ujv6TE/yNvXF++TeT7Sh9UxR8T7GBGuZJyC28JVzAHqsHgIWCviyjrQstCoxCuOdFkHQX8QQb+GlZ0txEpHuqyD0ZWjUVxUrOkzXop2cduR3tnbaTrqqbGrMbZo6YRBEzR/zis56W4O7MkBDatmBhACgEK6GcJhYO/hWXtOCel2LcLpNReu00J6WZl47Ox05nj5xqZN4lFGHAGA3w9Mmyaev/++9ce063/Ea21BloOO9NyloyPuKtfjhvaKIz1fhCsjjnSvCOmJdeDELK1E3K6DwYOFIxrwTltw+nxcUhI/ptl4lx07hJgeCsXPMVrwyoKjbs6QkY50CumFQVSNxtzQuhygFR5zgLqZkW5FtIsBR7rXhHRX43UsHMww0g6au5tjjnC3cCtWpCxQhpIikVFr1g0tI45qB9Tq+n+SA3tuD2i4VQcAHenEJuSFKIV0/ezcKYTt0tL4jSYQF9LtiLTo6Ijvk45065BCOh3pxtixQzyOO2K2n4x3sSMnvdCiXehIz11knMWgQUIU1IpXhKt8zEjXSmIdyAWw3cBN8dBtIV1RvOOG9kLMkVkhPbEd+HSc4b1SB26ej2QdUEgvDPa07kF3XzcCvgBGDxyt+XMx4aqAHaAy2sWskK6qKrY0iosoI3EWbT1taOl27+bWzcGMxDpQTWaCGRHSBxQPQGWxuJF3U8RVVdW1elAUxbKcdCN1AHjHke5mW6AjndgCHenGSYx1SXSITZggnD5dXXGB0SoSF+G0cnq318RDCum5hfw/HzMm+XUppNvhSLcr2sVrbYHRLrmPkVgXIO4WdVu48oJ4aDYjvbMzPiChpx5qa0X/3tPjbi6yF2YFtLaaj1YxEu0CeCMnPRqNXyO4IeLKeBezbcFoHXjlfORmW6ipEY91dc4fmziPFK7GDxqPIl+R5s/Fol3avBHt4qYb2mw29L62fejs7YRf8WPMwDGaP1caKEVFsbhw3t++31QZzOBqHRyeFdDd1206VsSwiOuBnPSeSA/6on0A3BVxzbYFvQsfS2IZ6QV8PqKQTmyBQrpxpJB+pAvX74/foGzcaO0xEwVmK6d3S5HOK450p124FNLNkU5IP/ZY8Sgz1K3ErsEWr83OYLRL7mPECQ0w2gWwzg0tZwVUVembFRAIxMuw3717cVcXHa6qEoP3gHkR14gjHQBGjBCPMk7PDRIXfHWjLUgh3SpHul4h3SuOdDcH9iikFxZmHaBeiXZxJSO9zJpYEVkHY6vGIuAP6Prs8PLhAID9be4L6W7UQWKsiGUirs624IWcdDcXfAWsixX5tNFYHYwYIC6g3D4fudkWGO1CbIFCunFSLTQqkVnRdgrpViL319bm7vR1idPCTaKQbnL2W8HR3Bz/vzxSSJ80STzu2AF0d1t7XEa7OAKFdAswK1zV1wtHtFvkg5BudFYAAAwX9+KuCulu1oHPZ42I29QUd/VP0L5OFgBv1IGbC74C7gvpcjDDzToA6EgnzmHUAcpol4R8bpPRLkby0SXDBxwW0gvUka4oiiV/g0g0gq1NYoVqvSJubXmtOL6LgxlyQClUFNI1s8QqhpaKqZVm3dAyI13PortAvB24LSJ7IWqKjnRiKRTSjZNPQroU6VQ1fpPiJk5nYEohXVWtF3zzHdkOhg7tLy4MGybqUFWBrVutPa5sC1YLzF4T0ulIz32MCleDBwtHtKqadwKbwQsZ6U1NQG+v8f0YnRUAeEvEdek8EBMQzfwN5GDG8OH6v4cX6sDNBV8B6zLSjQ4qeUVEdvN8JAcz3P4bEGcwG2fR1N2Ejh73prp6Qbg61HkIkajxBcOkeKi3DgCxKCPgsogbdi8XGgBqysWJu67d+ElrV8su9ER6EPQHY7MtnDy+WdxsB4A1bmhVVQ2fj2QdNHQ2oDdi4kLaJG62hVi0Cx3pxErkhagXxNNcY9s28eikkG5XLnRJSXzqthcERLcc6QDjXfSSLtYFEGKDdKVv2mTtce1yantJSA+H4+IhHem5i1HhKtEJ7KZw42aUwqBB8QURzWSUW+FI37fP+PHN4qYLF4j/Dcz8HxqNdUk8vleEdDewIiO9qwvYtUs8Nyqkt7aKmBu38IojnbMX8x+jwtWA4ACUBoSzxE0HZCxKodj5UafBpYOhQIEKFYe6Dhnej4yzMORIL/eOI92NOgDifwMzQrZsBxMGTYDf59d3fA/NCnAjUgRIyEjvNN5572/fj47eDvgUH8ZVjcv+gQQGlwyGX/FDhWp6hogZ3GwLcjDjQMcBRFUPRC+Q/ICOdONocaRbLR7a5UhXFG8JiE4LN34/UFwsnlNI10cmIR2IC+nSEWoVdi3C6aWM9MS26JJ4Y7uQ/thjj2HMmDEIhUKYPXs21qxZk3H75557DpMnT0YoFMKxxx6LF198Mel9VVVx5513Yvjw4SgpKcHcuXOxWSp4LtDYaDzOAvCGC9RN4crvjztxzYioue5Id9OFC1jrSM/VOnB7VoAV0S5G1woARF8bConnhTqwJ+ugq4v3TUB+9989kR5sbxY3enqFdEVRPOHEddMBWuQrQnVpNQBzfwOjcRaAt4T08oC7jnQzrnyjA0qJx6cj3ZwbOrZWwMCxCPqDuj7r9/ljM0QKtR7k94+oETR2NTp+fJKnUEg3RmurEEiA1EK6dPocPAgcMj4Qn/K4gPVCeuI+vSAguiHccMFRY0ghPVU7AOJtwS5HutWihpcGlGQZysqEoOgCtgrpzz77LG644QbcddddWLt2LWbMmIF58+bhQBrL5ZtvvomLL74YV1xxBdatW4cFCxZgwYIF+PDDD2PbPPDAA/jJT36CpUuXYvXq1SgrK8O8efPQ7VJmk9QAamuNnc8KXUgHrBFRcz0j3U3xELDm/9BoxBGQXAduOYHdbgdWRLsk1oHeeBpFsWZmghl6e8VMLcCdAY3y8nj9m43YyXXyvf/e1rQNUTWK8mB5TAzUAwXEuBt5X5ux6VyJ2dxmMtKNHt8KXK8DCx3petcKSDy+m4MZbsfrWJHPbWZACUiYGeBSzJGqqrGsejfqIegPoipUBcD8wruExKCQbgzpRh88OPXFfFlZfKE4K524drlwE/fpBQHRDfcdhXRjaHWkWy2k29UWvNQOXF5oFLBZSH/ooYdw5ZVXYtGiRZg6dSqWLl2K0tJSLFu2LOX2P/7xjzF//nzceOONmDJlCu655x4cf/zxePTRRwGIC7WHH34Yt99+O8477zxMnz4dTz/9NPbt24cXXnjBzq+SFjMCLuCtaBe3nLi1IubUsJDd0hKPwzDihjZ7fCtw25HulWiXzk73rlfdrgMrol3MDGYA7g/sJV6f5fKgUj6Q7/13ogtXMbAoAoV08xnlu1t3x7K5R1WO0v35mIjrgYUuXc9I7zAhpDda40hXXRoFdztex4p8bjODGYD756Puvu5YpEouzwwgJAkK6caQQvq4DDFV0qErt7UCOwUNOtLFI4V0fcj/byejXVS1sBzpbgk3sFFI7+npwXvvvYe5c+fGD+bzYe7cuXjrrbdSfuatt95K2h4A5s2bF9t++/btqKurS9qmsrISs2fPTrtPAAiHw2htbU36sQozkSKAN0Qbt524ZjPKpYArFz82enw60o3/DVTVnJBeVhavO7fqwe06kEJ6W5uIFjFCrgvp8lwUCABBfbPbLcPs3+C3vwU++1ngxz+2rkxO45X+286+28wil0BcPPOCkO5WNrRZN7IUD8dXjdedzQ14LJ/bJRHXimgXM25oefzuvm60hN0RGNweUJICbku4BeG+sKF9mBnMAICaMneFdFkHAFAWKMuwpX3EBjQMzgzYUL8By9Ytw/t171tZLJLLyBsCiof6yLTQqES+Jx27VmCnkO4lAdGNG1YK6fpR1eyOdDnYdOiQdQN2HR3x6f12OdK9MKBk5wwUjdgmpDc0NCASiWCYVIcOM2zYMNSlUSjq6uoybi8f9ewTAO677z5UVlbGfkbK6UQWkOvCVTQaPye6LaQbFVDN1kGikF+osSJmHekHD4pzqqIA48ebK4NbQrrbdVBREV/LxWisSK6fj9wezADM/w3WrQP++c/4fUQu4pX+29a+O8cdoL2RXoQjQrRzO1bEaLSK2TgL6YhvDbeis9edFZLdFnHlYILR/8NwXxg7mncAMCbilgRKUFlcaaoMZnF7VkBVqApFviIAxmNFzOTUA+6fj2Q7KA2UGhoUswKzjvQ/b/ozrvjzFXh49cMWlorkNKViUXGKhzrJtNCoxA5Hup0OVTrSxSPbgnaamuI3tqNHp96mokIsbAYAO3dac1x5TJ8PKCmxZp8S2Q66ukQeq5vke7SLV7j11lvR0tIS+9m9e7dl+/7Od4Bf/hL4t38z9nm3hauurrh47HasiFEB1YwTGojXQU+POOe5gdvxOomOdCODCbIORo40fs72ipDuVh0oivmc9FwX0t2uA8D838Ds+YjEsbPvnlw9GWeMOQPHDT/O0Oe9IlwBQFnQHQeoWUf45sbDQrqBfHRAOPFLA0LocCvexW0hXf4fHug4gEg0ovvzW5u2QoWKAcEBMUev0TK4XgcuLfiqKIqpnPRINIKtjYfXCjA4qGRFxI8Z3B7MAMw70s2ej0geIsXDTncGanMWt4R0O4U1LznSmZGeG0g3ek1NZnFEutWtmp2R6NQ2EF2ZkcT/Obcjr/I52qW6uhp+vx/1RyhC9fX1qJFKxRHU1NRk3F4+6tknABQXF6OioiLpxyqmTwcuvRSYOtXY590WrmQbUBTrB620Yjaj3Kx4GArFBwPdFnHdjnYJh40NtlshHrotpHvBDW3mb9DYCDQ0iOcTJhg7vtvnI7fbAWCdkG60DryAV/pvO/vu/5zzn/j7ZX/HgskLDH3eK0J60B9E0O9ODpLZjHSzLlxFUeIirkvxLm4LiEPLhkKBgogaQUNng+7Pm10rADDvijeL2/E6gLm8/p0tO9Eb7UWxvxgjK4zNuvHK+citmCnA/N8gNkOGQjqRSEc6hXR9aBHSpXiYKxnpXom0UFU60nOFbLEuEulWt9qRbkc7CATigqHbg0puu1Bho5AeDAYxa9YsrFy5MvZaNBrFypUrMWfOnJSfmTNnTtL2APDKK6/Eth87dixqamqStmltbcXq1avT7tPreEm4snrQSituO9KBeD0YdQKbxe1zQUlJfLaOkf9Fs4MZgPtCuhdE3BEjxOPevfo/K9vBiBHGv4PbdeCFwQwzC0BHo8BWYSzMaUc6++/suC1cSQHXTeHKbEa6FQ5Q6UI92HHQ8D7M4LaAWOQrwpCyIQCM/S+aHcwA4P5gRth9N/SICtF5723T33nLOpgwaILhWBSvDGa4WQdyYM9w1FSjuagpkocw2kU/ibnQWhzpu3YBEf2zqVLiRLSL2+JhTw/Q1yeeU0j3NtkWGpXY6Ui3A68MKskBzjJ3ZuUCNke73HDDDXjyySfxy1/+Ep988gmuueYadHR0YNGiRQCASy+9FLfeemts+29/+9tYsWIFHnzwQWzcuBF333033n33XSxevBiAcB9df/31+N73voc///nP+OCDD3DppZeitrYWCxYssPOr2IYUcDs64kKek3hBPEwU76JRfZ9VVWtEXBmpccBYvKVpvFYPerFyMMPtOnAzVkTOzjCy8K7ZhY8B7wzseSHaxcig2t69QHc3UFSUPg4vV2D/nZlEIV11YXENLwhXUrzb17ZP99+gN9KL7U3iJsOMcCUjNYxmU5vFE/VgYkDD7KK7icd3TcTtdb8OasuNi7hWDma4NrDngcEMKaQbGcxo7m6OzeiYMCiHp5MRa2G0i34OHBB/L0UBRo1Kv11trXC39vUZcw+lohCiXRLjNJwUECmk68ctR7rdQrpXBpXkeVkOeLpAkZ07v/DCC3Hw4EHceeedqKurw8yZM7FixYrYYmO7du2CzxfX8k855RT85je/we23347vfOc7mDhxIl544QVMmzYtts1NN92Ejo4OXHXVVWhubsapp56KFStWIBQK2flVbKO8XJybOjqEeOV0HIAXhCvpAO3rE4sWDxmi/bMNDfEBMaOLXAJxIf2gO6Y2T9RDTQ2wcaN7jnRZ724J6V5wQ5txpFtRB4kicjQq1ilxEi8MKJkZTJADSuPGCTE9l2H/nRnphO6N9qKpuwmDSgY5enwvCbg9kR7df4MdzTsQUSMoKSqJCWBGGFIqOo5CFtJrymvwfv37xhzpjaLjMDMrwG1HutuzAoAER3qr/s7bisEMWQftPe1o72l3/P/RC/E6IwaYr4Ph5cNdbcvEY0iBprtbuKb97iykm1Ns2yYejzoKKC5Ov53fL4T2rVuFczeT6K4VO6d3e2WxUXmjFAo5e6NBIV0/WoV0qx3pdscceGVQKd+FdABYvHhxzJF2JKtWrer32sKFC7Fw4cK0+1MUBUuWLMGSJUusKqLrDBsm+h03hHQviIfBoBBRDx4Ubmg9QroUD0eNMpfx7rYj3Qv1IB3pegXEaBTYskU856wAc1jhSLeiDnp7xcK7gwcb35cRvFAHZgYT8m2hUfbf6SkuKkZVqApN3U2oa69zTUh3U7gqLirGoJJBaOxqxP62/br+BtKFO3HwRPgU4yN2bjvSpRPXzXow40amG9oaYrEi7QYc6RYMZpQHy1EWKENHbwfq2uscd1V7YUBJDmY0dTehq7cLJQHtNwWMdSEpSXT8dnW5e3GaK2jJR5eMHSuE9B07gNNPN3dcVbU32sUr4qFbzjsK6frREnEExIX0XHGke6UtyP9FF4V0h/2GJBVu5nN7QbgCjMeKWCVcuSni9vaKRT4B9x3pgP462LdPDAoWFWUfdM0EZwW470gvLgYGHdbC3Ih38cKA0rBhQjzv69N/PsiHhUaJdtwUEL0gHgJxV7reSAsr8tGBBCG90/nOuyfSg95oLwBvzAzQu9Bla7g19r9rRkA0s9CmFXhCxDXhhrZiMANw93zkhTqoLK5EaUDcUOs9H8UG9rjQKEkkcbYc4120oUdIP+oo8WjEPXQk4XA8O7wQHOlO3yhRSNdH4loBWqNdZCySWZxypLvdFvI9I51ow81cYq8J6Xr70k2bxKMZ8RBwV0hPzMb3giNdr5AuBdxx40TcnVES68CFyGFPiLhSSNfbDqxaKwAwPjPBCrwwmBEIxM/Ju3fr+6ycmZEvjnSSmWHlIt6lUIUrIJ6TrjfWY1OD6LwnDZ5k6vhuOtJlHQBAWcC9C/lYVr1ON7Ssg2FlwzAwNNDw8T0T7eJmrIjBxUa7+7qxs1m40HJZSJeLH5cH3DsfKYoSH9DQWQ9WDeyRPMPni093poCoDT1CuplpuEeSmB1ux42cV1y4bt2sUkjXx6FD8ZvabLFFAwfGb3z13nimolAc6R6IdqGQ7gHcFNLtHrTSilERVwrpk8zdi3tCSA8GxY9bSBFX7zncikUugXikTzicfD3kFF4YVJLXlM3N+gal5awAv1/btWsm5JoFhTywJ00ye/bo+1y+RbuQzBS6AxSIR1rodSNvOiQ6b7PioReE9GJ/MQJ+E6PIJjmqQpywdrfo67xlHUyqNncBJQeUGrsa0RvpNbUvI8REXA9EuzR2NaK7r1vz57Y2boUKFRXFFbH/ZaN4oS24OZgBJCw4qnNmgMxIZ7QL6QcXHNWHESHdisVGpahXVmZPlj0d6eKRQro2pBt9+PDkmS2pUBTjQlgq7Bb3uNhoDArpHsBopIYVeEW4kn1pIQrpXnBCA8DIkeLRqJBu1gldWhrvp90c0HBzUKmiIv430GPQkHUwdqz5wRg5oOFGxI7X2oIeIT0aFVGPAIX0QqGmzH0HqJsLLALGo11klIJZEVeKhwc7nD9heSEfHQBGVogT1u5WnUK6RbMCBpcMhgIFAHCo65CpfRnBC4NKVaEqhIrEzbKetpDohFYUxVQZ5MK7DZ0NpvZjBC/UAWBsZoCqqnSkk/RIkYZCujbkYqNuOdLtjrPo7IxHyLgBM9JzA6356BKjQlgq6Eh3DArpHkAKVw3OX/t6TkjXMygdicSjFHJZSPdKHciZR3v2CFFQK1YJ6YC7OeleEHEVxVhbsGpWAOCukO6FwQwg7kjXM6i0a5eYTREMZp/FR/ID6cSt73B+gROvCFcxB6gO4aq9pz22vVXRLg2dDYhEI6b2pRev1MHISiGk72/br8sRHnOkm6wDv8+PwaViZWqnBzRUVY27oV0cVFIUJb7gqA4hXQ5mmJ2ZAQBDykTn7cqgkgdmBQDGsuoPdR1Cc3czFCgYP2i8XUUjuQoFRO309cUvnLUIiEbzLFMhb+LsFg8BdwVEOtJzA6356BKjGcepKLSMdArphY0XhCuviLi7dmn/zI4dQE+PWCDRrHAlBdzWVqBb+6xcS/BKvE5trRBye3r0/S9aKaTLtuD0gEZfX7ze3W4LRhYc3bhRPE6ebP74PB8Zc6TLOpg40Z5ZpcR7SAeoG8KVV0TcUZWi89XjhpZu9CGlQ1BVUmXq+INLhRtaheq4G9ordTC0bCiC/iBUqPpEXIuiXYCEttDpbFvo6utCVBUj/27XgxERV9bB5GrznXd1aTUA5+sAgCcGMwAYykjf2CA671GVo2KLlRISg4507ezeLVxuwWDcFZSJRBeuHgdXKuy+mQ4G4xEdhSyksx1ow6iQTke6diikEwCoFte+rrpw3RZxpRC+c6f2z8hYFyuEq8rK+EKZTteDV8TDQCB+HtfqxO3tjc/is9KR7rSQnjjA7nZbMOKGppBuLUbqwKqYKZI7xBygLgpXbouHUkiXCyZqQQrpVrhwi3xFMTe009nQXnHh+hRfPCdd44BGVI3GcqHNOtIB99zQSQu+Bt1b8BWA7joA4iKuJXXg0mAG4J3zkZEZMrIOrBjMIHmIFGnoxM2OzEcfM0Ys1JqNYcOEg6uvz/y0fCnq2XkT5wUBkYuN5gaJbUELdmSk2yWkMyM9BoV0D8BoF2D0aPHY0KD9HG2lcKUo7om4XomzAPTnpO/YIcwHpaXazAfZcKsOZJ9TVOTugq9AvC0YGVTKdSHdKwN7RhYb/f/tvXmYHNV97v/2Mrs0Mxptow20ooVNIEAIiMFGRgrERjeOt2DjEAKOYxwTHGPwtSExjkm8YBtMDFwHsBP44di+YBtzhcXugCyBhEASQqANgaTRNqtmn+76/XF0uqpneqnqOqfq1PT7eZ55qmemuru6T586Xe/3Pe9RWcwg0SDMTGJTMtJPbBAnrIPdB10vsqgqm1sS1iKLprhwAUdOussFR9/teBe9Q72oiFdg1jifK1TD7gthtUFdRR3isXAvaWRfcFtUsixLqYgbZrSLKUL6iY3e2gCgkE6KQCeue7wsNAoIB5e88PO74GgQFxAmLDgalmjAgpI3vDrSVa4XoLuoZEJBCbA/i3XhmSgopBuAFK7a2oTDN0hMEdIbG+1+6VbElZEiqhygYYu4YbcBYAvpbiN2nNncbswHxQgrI93ZD3yu9+UbOebKMbgYvb32vlEX0k05H8l+sG+f+9mmKosZJBpQuAKaappQVyG+xO7tcDdwqIwUAcIX0sNuA8DOSXfrhpZtMKdpDpLxpO/nD8sNbVIbzGycCQDY07HH1f5Heo6gra8NMcQwb7z/BU7CdKSbsvCubIP9XfvRP9Tv6j4U0klBGO3iHq9COqAuJ123C9f52GEKiFI8DNqFS0e6eyzL+2KjUYx2YUY6hXQTaGqyxbujwUZ8GiNcAd7jXVRHKdCR7t2RrjIfHQgvI92kNpBCutt+8PbbYsweN85+//zAGTLi+0wsJgqbbj+L0pHOaJfyQQpX3YPd6B3sDfS5TREQY7FYJt7FrZCuMtoFoJAOeHekq54VEFZRSQq4JrSBFHHduqFVZ3PLNjjScwSWZfl+PC+Y0hcm1k5ETbIGFizXRSUK6aQgdOK6R2Z9ehHSVTlxg4h2McGRLsXDoF248vkGB4N3fUaNI0dEO8VitrBSjCguNhpmQSmVAvqPF8sppJc3iYQQ04HgXaCmRCkA3iMtRouQbqIj3a2Q7sypVwHbwO4He/YIgbwYzkgRFW56uWZDa6uILQwSU4T0igr7u72b81Fnp20ioJBePtRX1aMiLhbXCNoFapKA6CVOwbIs25GuKtqlNhwhPePCNSnaxaMjXZmQHrIjPWwnNGD3gz3te1wJ2SoXGgXsxUaH0kPo6A9W6DFFSI/FYvbMgPY9RffvG+rD7nbhoo2KkH733Xdj5syZqK6uxtKlS7F+/fqC+//iF7/AggULUF1djVNPPRVPPPFE1v8ty8Itt9yCKVOmoKamBsuXL8fbb7+d+f+ePXtw9dVXY9asWaipqcGcOXNw6623YmBgQMvrMw5Gu7jHqwsXsL9sRyHaxQQBMSwXrlO4Z1GpMLIfTJ0KVFW5u4/sB11d/t7fdLo8MtJ7HeYlCukkLBeoKcIVYDvS3cSKdHXZRbuoC+kmuaG9tAEAbN0qtosWqXl+E6Jdwka2QU+PuxkqqrO5x48PZ4bMwID4AczoC7Nni6002BRCFpSam+3vF2T0E4vFMuJV0DnpRgmIMhu6o7iQfuDYARwbOIZELIE5TXOUPL904pa1I/14tIubNgDUx+uEtfCuSW0g+0HXQBfa+tqK7q/aCV2drM68D0HODLAsy6h28CKk72jdgbSVRkNVAybXTdZ7YAr4+c9/jhtuuAG33norNm7ciNNPPx0rVqzAoTwXLi+99BI++clP4uqrr8arr76KVatWYdWqVdiyZUtmn29/+9u48847cc8992DdunWoq6vDihUr0Ncn1rx48803kU6nce+992Lr1q34/ve/j3vuuQdf/epXA3nNoUNHunu85kID4oszABw86O+5g4h2McmRHrR4WFkpnJ8A+0IxvC40CogLX9mmfuJdnG2j25He2xve7ARnYbO6OpxjAIV0Ywgrl9gkAdGLI11GikyaJPLVVUA3tH3Ol2NAISwLkN/FTzlFzfMz2kUUr+UMLzc56aqF9LBmyMg2AEJdNySDFyGdC42WL2FFWpgkXMloFzcirox1mTVuFioTalZ2ZrQLMGecKErsatvlzg2tacHXwKNdBsyZmVFTUZMRY93MztARKRLGzICewR5YEJ85E2ZneBHSnW0QC3uBHBfccccduOaaa3DVVVdh0aJFuOeee1BbW4v7778/5/4//OEPsXLlSnz5y1/GwoULcdttt+HMM8/Ej370IwCiCPKDH/wAX/va13D55ZfjtNNOw89+9jPs378fjz32GABg5cqVeOCBB3DJJZdg9uzZ+PCHP4x//Md/xP/9v/83qJcdLnSku6O/3xYA5QW9G1Q5qIKIdilnR3osxpx0t5QyMyMWUxPvIj+byaQ+gdnZx8LqC7If1NSoWaSvRCikG0JYQrpJIq4XIV1HHnHYIq4JbTDnuEHw4EH7s5GPAweA9nZx/lI9K+DwYfeLPKrApDYAvC04qkPEDeN8JNugqkpEq4SN7As7dxbflwuNli9hR1qYICBKJ66bjPRth7cBUCfgAiEK6YPmtMGscbMQQwyd/Z1FZ0d09XdlImCUOdLZDwB4E3FVx+sA4RT2ZBvEEENNRU1gz5uPUoV00xkYGMCGDRuwfPnyzN/i8TiWL1+OtWvX5rzP2rVrs/YHgBUrVmT23717N1paWrL2aWhowNKlS/M+JgB0dHSgSTouRjtcbNQdMhO0psbbgk2qLr6DjHYpR0c6QCHdLaXMzADUzM5w9gNdxeGKCtHPgfCF9BBjXQAK6cYgc4mDFK5SKftzaIITV55v3DhApRP65JPVPX9YQrpJOfWNjfZnsZiAKGNd5s5VV/SUbZBKAW3FZ0Yrw6SCEuB+wdF0Wv1aAUA4QrpJ/QDw5kiXfYFCevkRhnDVP9SPwbSYTmmSA3Rna/Gq0+ZDmwEAp0xSNI0JwPia8QCAo73BrtaeyUg3IF6nOlmN6fXTAYi4ikJsPSxOWM1jmjPRRH6R/eBoz1Gk0iklj+mGTMSRAf0AyM5JL0TfUB92tYnBJeqOdNkGdZV1iMfCv6z0IqS/cfgNANEQ0o8cOYJUKoXJk7MjaCZPnoyWlpac92lpaSm4v9x6ecwdO3bgrrvuwmc/+9m8x9rf34/Ozs6sn8jCaBd3yAuWE0/0JuCpcqQHcRFhQjY0hXTzKVVIV3HxLT+bOiOOnI8fVl+Qn0EK6QQIJyPdWVw3QUA86SSx3bcvO+YhF5vFtThOPVXd80sBOchcaMA8N/TcuWK7o/C1uJZiRmWl/T0lDDe0KSKu26LS7t2iH1dV2Q5qFYTpSDelH3hxpOs4H5FoMKFGDBxhCFeAEK/C5qTxYvB+t/Nd9A72Ftx3yyExcJw6SV1nkWLw0Z5gB2/T3NBzm8TgvbOt8ElLRxvIYoYFC629rcoetxgmLboLADMbZgJAZgHLfLxx+A2krTTG14xH85hmZc8fRmFPxuuYUsyQQnqxNgDswp7KvjCa2bdvH1auXImPfvSjuOaaa/Lud/vtt6OhoSHzM2PGjACPUjGMdnGHU0j3gioXWxACYtjiIUAhPQqEKaQH5UoLu6gk+0HIWbAU0g0hTOEqHg81pz9DU5NY6BBwL+KqFK7kcwctpJvmxJVCultHuqp8dEkY7WCaiDtvntjKtQDy8frrYnvyySIOTRUU0m1H+r59wPE1t3Jy7Jhd8KCQXn5I4SrIxUalgFudrEYyrrDjl8iE2gkYVz0OQGE3tGVZehzptWLQaO1tDcUNbYqIK4X0Yo70zQfVi4cViYrMZyCMopIpbSCLSnItgHxk2mDyqUqzucN0pJvSBnK9gP1d+9E9kF/w6R/qz0S7nDb5tECOzQ8TJkxAIpHAwWHT/g8ePIjm5tzFmObm5oL7y62bx9y/fz/e//7347zzzsN9991X8FhvvvlmdHR0ZH7elbEfUYSOdHeUKqRLR3prKzA0VPrzB+lIL/doFxaV8mNZ4QrpQV1Mhx1zxGgX4iTsKAVT1tiRrvRCAmJnpz1eqxRxpYB77JhYMyUoTBMQ3TrSpZCu0pEO2DMDgpydYVq0i4wIkfnn+ZBCumoBN4yoKdP6wcSJ4juj8ztRLrZuFfs0N3uLhSSjgzCEK5MWWASAWCzmSkDc37Uf7X3tSMQSSqMUmmpEVq8FC+197coetximCYhuhfQth4870ierHThkUSnIrHrTol3k51oKtPl4/aAYvE+bpFbAlbMzyllIH187PvM+yBz6XLx55E0MpYfQWN2YiUUymcrKSixZsgRPP/105m/pdBpPP/00li1blvM+y5Yty9ofANasWZPZf9asWWhubs7ap7OzE+vWrct6zH379uGiiy7CkiVL8MADDyBeZHG3qqoq1NfXZ/1EFoqH7pBflL0K6U1Ntgjh58KPGen64XoBxTl0COjtFS5VrzNxVIiBsuAXlJAetiOdQjoBKFxJ3Ajp0o0+bRowbpy6525stBf+DcMNbZojvZCQbln6hPQwHemmtIHMO3/vvcIxR1JIP02xmSqMqCnTZmbEYu7iXRjrUt6EubifKeIhYDtxCwlX0o1+0viTUJWsUvbclYlK1FeJL/VB5qSbFmkhnbhhONIBR1EphFgRU0RcKaS/0/EOegbziw2vHxKDt/JiRm3wM2RMWitAsnDCQgD24sa5yBQzJp+mdFaATm644Qb8n//zf/DTn/4U27Ztw+c+9zl0d3fjqquuAgBceeWVuPnmmzP7f/GLX8Tq1avxve99D2+++Sb+6Z/+Ca+88gquu+46AKIIev311+Ob3/wmfvOb32Dz5s248sorMXXqVKxatQqALaKfcMIJ+O53v4vDhw+jpaUlb4b6qIPioTtKdaQnEmpEECnojeaMdMuikG46sh9MnSryar2g0pGuO/KEQjoACunGEIZwFVUhXZdwFY+LwjgQrIhrmhvajZC+d6847mTSbjNVMNpFfA7lbMdCfUG3kM7Cnthuy38trq0NSDRglILAjSNdZnOrjHWRyIzuMCJ2TGkHN470g8cO4nDPYcQQw8KJC5U+f6aoVMZ9wemGLtQXZDFDdaRImIU9U9oAsIX0QjMDdM0K0MnHP/5xfPe738Utt9yCxYsXY9OmTVi9enVmsdC9e/fiwIEDmf3PO+88PPzww7jvvvtw+umn45e//CUee+wxnOKYznvjjTfiC1/4Aq699lqcffbZOHbsGFavXo3q45mfa9aswY4dO/D0009j+vTpmDJlSuanLGC0izukgOg1zgLwn5M+OGhPJQ/CkR6WeDgwAKTT4jaFdDORMValrAsRpWiXsItKFNKJE6eQblnBPKfJwlUh8XDTJrHV4QANWsS1LPPc0DKf+7338p8fN2wQ25NP9l5wLUYYi76aVswAise7dHfbTmkK6XqQ5xhZvMuFrngdEg0yUQplLly5EdI3HBADx+mTT1f+/DInPagFRy3LMq4d5o2fh3gsjqO9R3Hw2MGc+8g2mD9hPmor1F6ATKoV1d9QZmcY5IYuFu9yqPsQDnYfRAwxnDxR7ZQ+FvYEsg22HSngSD9kO9KjxHXXXYd33nkH/f39WLduHZYuXZr533PPPYcHH3wwa/+PfvSj2L59O/r7+7FlyxZceumlWf+PxWL4xje+gZaWFvT19eGpp57CSQ6HzF/91V/BsqycP2UBo12Kk0qJi0bAuyMdsJ1DpQrpziLHaM5Id34Gudiomch+EJaQHnS0S9h9gUI6AWzxcGgIaG8P5jlNi1IAbCF9+/b8BYX168X27LPVP3/Q+dy9vXZx2RQBsakJmH48LlKKhMN5+WWx1dEGsphR7rMzZLxLPiH9tddEH5k82f4Oqoqw12wwhWJCeiplF5XOOCOYYyJmIR2gbX1tGEr7WCjLAzJKwSThav54ccLafnR7XoHl5X1i4Dh7mvqBQxY0gop2GUgNZNrbFBG3tqIW85pEJfy1g6/l3Gf9PvEF6pxp5yh//qDbADAv2gUAFowvLKS/sv8VAKLwUVepdvp1pg0CKigB5kUcAcjMtsjXBpZlZc5HZ0zh4E0KQBducfbvFwJGMgmUMlPB70WHvIhLJtW7u5xI8XBgINjF1CTyM5hMAhUVwT8/+0JxpCNdCilecLpqpTjklXKJdpEFAwrpBACqq20RLyjxykTx8KSTRFxae7sYl4fT12eLu+eovw4M3JHuzL/Wfc7zwunHDYOv5b4WxyviOlCrkF7OGemA7UjPFyvyxz+KrcOMpAxnG5TzDBkppL/xhrhGGM6bb4rjrqtTv1YAiQbja8YjBpGvG5R4ZaIL96TxJyEei6O1txUHjh0Y8f/W3lbsbBNTaM6aepby5w862kWKhwBQV2HO4C3dta+15B68X95/vJgxVf3gLRd9DVJIj6Ib+o/vicH73OnnKn9u2Qbdg93oHwpG6DG5Dd46+lbOAufbrW+jra8N1cnqyDnSScDQhVscGesyY4a4iPeKKke67gsI50ViGE7csF24FNKLIx3pfoT0VKp0V225LTYasnhGId0ggo60MFG4qq62BcRXXx35/02bhKA1cSJwwgnqnz/oNpAu3Lo6e6FTEygkpFuWLaSfpV4PCUVINzHaRQqz+WYFSCH9XPXX4pk2GBoqvNipSkw8H82eLb439vfnXnB03TqxPeus0q4dSPRJxBMZ8SqoOAUThauaipqMeLWpZdOI/0v359ymuZn3SyVSSA+6mFGTrEEibk7nl7E5uRzplmVpdaQHHa8DmLnw7qKJiwDYGdzDWbdPDBxLp6mvgjdUNyAeE18mgypomHg+OqHhBNRW1GIwPZgzbkoWM86cciYqExodrCT6SPFwaEhkcZOR7N0rtqVemKtypOsW1RIJ+yIlDAGRQrr5+Il2qaqyizWlTssPqi8wIx0AhXSjkOJVa2swz2eieAjYEQm5hHQZ63LOOUAspv65g44VMTHOAigspO/YIQqlVVV6cqGDjtcBzBRxzzxTbN96K/c4JUVcHUJ6TY1oX6C8C3vxuF3QyBXvIttAx6wAEh2kgNjaG8zgnRGuKgzqLAAWNy8GALx6YOTgrdMJDQQfK2KieAgApzfnF9Lf6XgHR3qOoCJeoSenXhYzgox2MTDmaMnUJQCA7Ue2Z45PkrbSmWKGDkd6PBbPFKoCPx8Z1AbxWBxnNIuLiQ37N4z4/7r3xOB97jQNX6DI6MIp1NCVnhs5hXzatNLuHxVHOhCuEzds8ZAL7xbHT7QLoK6oVC6OdArpRNJ03KQVtHBlmoi7eLHYFhLSdUSKAOFFu5gkHgK2kL55s5hh5EQ6oRcv1hPRxmgXgXPWxfC+sH+/MIDE43pmBcRiwbeDqUUlWSySixw7kUK6jpgpEh0ykRYBOXEzmcQGRbsAyAhXr7aMHLylA1SXkC6LGUFFu5gYrwPYjvQ3j7w5Itpj7btrxT7Np6MqWaX8uYN2pKetNLoHxQW9SSLupLpJmFE/AxasEX3h7aNvo72vHdXJapw6Sc8K1aGdjwyaFQDY5xpZxHPyx3364nXIKKOy0p5ySCdubg4cj3MrJR8d8H/BEZQLFwh3wdGwxUMuvFuYVMouKoUlpMsiR1AZ6VxslJhC0I50U0VcKQz+8Y/Z+cyWBTz7rLh9/vl6njusaBfTxMO5c8W5qbd3ZEa3bIMLL9Tz3M5+EEQ+98CA+AHM6wtLhLEts7irZK3QQ3Dyyfo+OzwfCaTb/KWXsv/e3m671HXMCiDRIWgnrokOUMDOPl/73tqsBUeH0kN44Z0XAADvO/F9Wp476DYw0QkNANPrp2N8zXgMpYdGiLjP7H4GAHDhiXoG76DboGfQvpg3raAh+4KMNJL8Ye8fMv+vSOhZLE62Qzk70gFHGwwT0jv6OjJrCFBIJ0WJxRhpUQwppE+dWtr9/V5wBHkBQUc6+0E+Dh0SEVDxONDcXNpj0JHujrD7wnEopBtEWI5004Src84RBoADB7JzibdvF4W+qip9QjqjXQSJBLBsmbj9/PPZ/5NC+vvfr+e5nfncQZyfnRngpvUFKdD+4Q/Zf//978VWVxsAPB9JLrhAbP/4x+x4zGeeEYuqz59f+mxWMjoILdrFMOFq6bSlqExUYn/X/szCogDwyv5X0DXQhXHV4zLxL6rJRLsEnJFuWhvEYjH8yYl/AgB4fk/24P307qcBAB+Y9QEtzy37QWd/JwZT+rOEZRvEEENNskb783lBCrQv7H0h6++/3ykG7+Wzlmt77szMgDIv7C2dLqrgGw9sRPeAHUXw7J5nkbJSOGn8SZjRUEKOLSk/uOBoYaQLt1RHurzgKFVID8qFC4TrSO/tFVsK6WYiY12mTgWSydIeQ5UjnUJ6IFBIN4iwohRME65qauyohBcc1yBr1ojt+eeLfXTAaBcbKdI+95z9t7feAvbsEeODFBhVU1NjnxeDKGjINqiq0hNV44cPHNc7nntOFBYA4dJ/8klxe8UKfc8dVl8wrai0YIH4jt/bC2xwRK3K89Ell4RzXMQcmqrDiVIwTbiqqajJLGLpFHGf3CFOWO+f9X5tC3OGFe1iWhsAwEUnXgQAeO6d5zJ/235kO3a370YynsSfnPAnWp63sboxc7utr03LczhxzgqI6Vg0xwcXz7oYAPDs7mcxlBaDdyqdwlO7ngIAXDJH38AReLRLv5lRU3PGzcGJDSdiIDWQmRED2OejFXM0foEiowsKiIXxG+3iFNJLmYpMR3owMCO9MHKh0VJjXQC7L7SV+B2Ki40GCoV0gwgrSsE04QoALrpIbH/3O/tvv/yl2F56qb7nZbSLjWyDZ56xnbi/+pXYXnyx3u8rQYq4phaUALHwbmOjGKdkvMvrrwPvvCNmbeiK1wG4+LEkHreLSr/9rdimUsBjj4nbOosZJBqE5QA1LZMYAD4wU1T/Hn/78czffvHGLwAAHz7pw9qe1xkrYgWQCWZqLjQgChaAKGbIz4psg+Wzl2sTPJPxZEZMD0LENbmYsbh5MZpqmtA10JVZH+DZPc+ira8N46rH4expmhb6AaNdJLFYDB+c/UEAwJM7hXg+lB7Co28+CgBYOXdlaMdGIgYFxMJIR3qp0S5SPOzvL61YEeRio2EKiGGLh8xIL4zfghLgf3ZG0NEuvb3Z07WDIshZKAWgkG4QjFKw+fM/F9snnhAC23vv2fEWH/uYvueV4mFbm+0A1onJQvrSpWIh9dZWESViWcD/9/+J//3FX+h97iALGib3g0QC+NM/Fbfle/+zn4nthz6kd/zg+chGno9+9SvRD555BmhpEe/RBz8Y7rGR8KFwZfMXi8Tg8MTbT2SyiLce3oqKeAUuX3C5tueVxYyh9FBG5NaJyW1w6qRTMbdpLnqHevHYm48hbaXx8OaHAQAfXfRRrc8dZE66qQu+AkAinsCl84Tr46HXHwIA/Ofr/wkA+PjJH0cyXuK0bxdkHOllHu0CAJeddBkA4Odbf47B1CCe2vUUDnYfxITaCRmRnZCiMNolP8eO2V/gSxUQx4yxpwSXIiAGudhomIsshi2kc2ZGYQ4dEtvJk0t/jKjEHDmFq3IsKh2HQrpBcHE/m8WLgXnzgL4+4MEHge99TwhY73sfMENjpKE8fwGlz6rxgsmzApJJ4JOfFLd//GMhpm/eLM5ZUljURZBZ9Sa3AQB85jNi+9BDwon+wAPZf9dFkLMCLMvs89Gf/ZmI/tm+HXjqKeC73xV//9jHxMwAUt5QuLI5ZdIpWDRxEQZSA7jnlXvwry/+KwBg1YJVWdEfqqmtqM3kZAcR72JyG8RiMVxx6hUAgLvW34Vfv/lrbDuyDWMrx+LPF+odvDOzMwJwpJsacSS5avFVAICHtzyMl/e9jEe2PAIA+PTpn9b6vGEtfmzi7IxL512KSXWT0HKsBY+9+Ri+9YdvAQA+econtS32SkYhUqyRGdXERrpw6+pKv5CKxfwJiEE60hntQiE9HwcPiq0fIX3cOLEtRYQK8mK6osLOWS7HvnAcCukGEbQD1NQoBUCMqf/wD+L2l78M/OhH4vbXvqb3eZNJEaUBMFYEAD77WfGe/O53tnh+7bXZBQcdMNrFZvlyYO5c8d3ypJPE2HrKKbZTXRdBFvb6++0ZICYWNOrrgb/9W3H7z/9cFJUqKsS5iZAgxUMgOxvaNGKxGG4870YAwE1P35QRD//3n/xv7c8dZDuYLB4CwLVLrsWYyjFYv289/vy/xeD9+bM/r7WYAQQ7O8PkYgYAXDTzIpwy6RR09nfinJ+cg4HUAD44+4NYNn2Z1ucNcvHjtJVG96AQsUxsh8pEJa4981oAwMd++TH8Ye8fUJOswY3n3xjykZFIQQExP35jXSR+hPQgHelhLjYatnjIflAY6UifNKn0x/DTD/r7gXRa3GbMUSBQSDcIZqRnc801wJln2iLb//pfQlTUTZBuaJOjXQBg4UJbLOzpEbMB/rd+PSRQId1kJzQg4l3+z/8RWd0DA6Kw8aMflb4guFuCLOzJNgBCjzvLy1e/CjQ328f6la8As2eHe0zEDMKKdjEx0gIQjlu52CIAfPm8L+P05tO1P++EWpEJFoQT1+RiBgBMHTsV317+7czvJ088GTf/yc3anzfI2RmmFzPisTh+uuqnqE5WAxCLsf5w5Q+1L4wa5GKj3QN21IWpfeErF3wFp0w6JfP77Rffjun1PhaDI+UHBcT8qMiFBuhId0PY4qEzIz2AtWgiR9hCuvNiOojPSDnHHB1HsxRDvCDFw64uIZjpjgwwXUBMJoGnnwZ+8hNx7r76auFU18348cDOncEKiKYK6QDwzW8CCxYA774L/PVf2/nlOgkjI93kNrjoIuCFF4QT+rLLgHPO0f+cYRQzampE4cBEJk0C1q0DfvpTETv18Y+HfUTEFJzioWVZWoUyy7KMd+LGY3E8ccUT+M3236CxujFLVNeJLGgEEu0yaHYbAMDnzv4cTpt8Gva078HlCy4P5FgzsSJBRLsYXswAgDOnnInNn9uMF/e+iOWzl2Na/TTtzxlGTn08Fs8UDExjTOUYrL16LX795q8xa9wsnDfjvLAPiUSNKAvpb78NvPyyWNxKh7CgSkj3c9FBR3owyOe1LJG9K6M9osDQkH0B97736XkOFdEuKoT0oC6mwxLSLSv8vnAcCukG0dAghGLLEv2nuVnfcw0N2VFvpgrpgIhZ+cd/DPY5gxRxTY8VAYQT+sorg33OMGYFmNwGAHD++eInKIKcIROFYgYAnHAC8PWvh30UxDRklMJAagDdg91ahb2+oT6krBQAswXEykRlZuHRoAgj2sXkNgCA8084H+efENzAkWmDAEVc09tgbtNczG2aG9jzOaNddBf2ZE792Mqx2p32fhhTOQZXnHZF2IdBokpUhfS9e8XU7mPHgDVr7EWWVNLSIrZ+RQsVAiId6XpxCuc9PdES0r/yFeCOO4TQ9vTTwPvfr/45VDrSe3tFsaLaQ4E6qIVGJWH1hb4++zajXYgkkbDXGNAtXjkXHjddQAwaRruED6NdwkeO5W1tQCql97miUswgJBd1FXWoTAinl+54FykeyuclNmE4cU2N1wmLUNrA0GiXsJAzZGRhTydRKWYQ4ouoCun//u/2Rc5PfyqmFqtGXixPnOjvcaIS7VLOudDJpD2rIUp94dgx4Mc/FrctC/j+99U/R1+f/Znw40ivr7fd5F4XHA1a0AirLzhFTArpxElQucSyryWTQFWV3ueKGowVCR+2QfjIc5FlAe3tep+LxQwSZWKxWGC5xFK4qq2oRSJuaA5SSGQy0hkrEhpBLnQp3dBsg2zCKOyxDcioJqpC+q9/bd+2LJEPqRp5oSYdUKUSlcVGyz0XWr7H3XqLtEp55hk7hgEAnnwy29WsAulGr6iwBeZSiMVEHAPgvS8EWVACwnOky35QVRV6HiyFdMMIKk7BKVwZPBszFIJ0Q9OJmxtGu4RPZaVdXAjqfMRiBokqQTlxKVzlJ5OR3htARjrbISdBZqSzDXITi8UCawfOzCBlQRSF9LY24M03xe3PfU5sn31W/fPICzW/C2j5EUDCWmw06AU3TRDSo9gX/vAHsf3sZ8XMiYEBYONGtc/hjHXxK6yVWlQKsqAEhLdegAn94DgU0g0jKBGX4mF+GO0SPox2MYOgZsjwfESiTlBOXLpw8xNGRjpjRbJxLryrG4q4+QmqHTgzg5QFUpiKkni4fr3Yzp0LfPSj4vYzz6gXf+UFgl8h3c8FRxiLjaZSwX8e5POFmU0eRSF9wwaxPecce7Gxl15S+xwqFhqV+BXSy8WRTiGdDCfoaBcKVyPhIovhI9ugr0//WM02yE9QBQ2ej0jUCTrahQLuSGQbtPV5zJUsAbqhc+MsZlia3XosKuUnqMIe+wEpC6RgE6U4iz/+UWzPPVf8xOPAgQO24KcKVY70qGSk19WJ9xIoTwExakK6Zdnu8zPP1Cekq1hoVOJcpMwLQS82Skc6hXTTCDraheLhSPyM5V7o7wcGB8VttkM2Y8eKmDGAbugwCSNqipAowmiX8JFCum7x0LIsirh5kP2gP9WPnkG9F9rsC/kJPNqFhT0ymomaeAgAr70mtkuWCAfzvHni982b1T1HOm1fIISVkZ5K2XnXQQiIsVh4OekmCIhRKyrt3i3aqbISWLRI9AcAeP11tc+jQ0inIz03JvSD41BINww60sMnKCFdCrhAcMXDqBCLBdcO7Av5Cfp8xIISiSpSuNIe7cIohbwEJaT3DfUhbaUBsB2GM6ZyDCriogoelBuaIu5IAot2YUGJlANRFNJlPvqiRWJ76qliq1JAbG8XYjrgX0gv1bnjFHQpIOonajFHsnB08slCTD/lFPH7rl1qX8Phw2KrQkgfN05sS+0LdKQHhjYhvbW1FVdccQXq6+vR2NiIq6++GsekWpJn/y984QuYP38+ampqcMIJJ+Dv//7v0TGscWKx2IifRx55RNfLCJygHKB04eZHnr+8zqjxiuwONTVAMqn3uaJI0LEiFHFHwjUbyg+O3aURlHBFF25+ZBt09ndiKD2k7XlkGwBAXSWr4E5isZgd78J87tAIqrDH8xEpC6ImpA8OAjt2iNsLFojtaaeJrUohXca61NcLkdIP0rnjNdNTfj+Nx4GqKn/H4BYpIJajkB61vvDWW2I7f77YTpwofiwL2LZN3fNI4U5+jv1AR3phgi4YFECbkH7FFVdg69atWLNmDR5//HG88MILuPbaa/Puv3//fuzfvx/f/e53sWXLFjz44INYvXo1rr766hH7PvDAAzhw4EDmZ9WqVbpeRuBQPAwf51je26vvebjQaGGCnhlAEXckjHYpPzh2l0ZQC11ygcX8NFY3Zm6397Vrex7ZBnUVdYjHOLFzOEGvF0ARdyQs7BGikKiJh7t2CTG9rg6YPl38TQrpKqNdpJDu140OiAsA6SrzctHhvICIxfwfhxvCiHZJp21RgkK6e7ZvF1sppAO2K33LFnXPU45COh3p0OKD3bZtG1avXo2XX34ZZ511FgDgrrvuwqWXXorvfve7mDp16oj7nHLKKfjVr36V+X3OnDn4l3/5F3zqU5/C0NAQkg7LbmNjI5qbm3Uceugw2iV8xo4FEgkRu9baCkybpud5KOAWhn0hfBjtUl5w7C6dwB2gFTxhDScZT6K+qh6d/Z1o7W3FhFqfi5/lgXEWhQl6vQAWlUYSeGGP8TpkNBM18VDGusyfby+MKaNdtm4FhobUTIWWFwd+FxoF7EzPQ4fExbcsABQjyIVGJWE40mUOPGCGkB6VjHTpSD/pJPtvJ58MPPus6AuqkDEGMtbAD1FZbLScI46Oo8VKs3btWjQ2NmYuxAFg+fLliMfjWLdunevH6ejoQH19fdaFOAB8/vOfx4QJE3DOOefg/vvvh2VZyo49bBjtEj5B5XPTkV6YIPqCZVHELUTQM2R4PgoXjt2lw0xiMwgiJ50u3MIEIeKm0in0Dgl3HtthJIGt2cDzESkHoiaky8gKGesCADNnii/ZAwO2uOgX6UhXIaQDpbl35AVEkDEPYTjSnZ+9mprgnnc4UctIHx7tAtCRrgqnIz3I6zmDhHQtjvSWlhZMGha2n0wm0dTUhJaWFlePceTIEdx2220jppR/4xvfwAc+8AHU1tbi97//Pf7u7/4Ox44dw9///d/nfaz+/n709/dnfu8MunLiAbpwzaCpSawboVPEpYBbmCD6Ql+fmHkAsC/kgoW98oJjd+kE7QClcJWbppom7Gnfo1VAZDZ3YYJwpDtz6tkOI2G0CyEKiZqQLh3pTiE9HhdO3HXrhBNXLkLqB9VCunTzehGow3Ckh+HElbEuVVVi2nxYRKkvdHQABw+K2/Pm2X8/+WSxNdWRHpXFRmU/GBwE+vuB6upgntcgId2TI/2mm27KuWCY8+dNefL2QWdnJy677DIsWrQI//RP/5T1v69//es4//zzccYZZ+ArX/kKbrzxRnznO98p+Hi33347GhoaMj8zZszwfYy6kMKV7nxuiriFCdKRTvEwN0GIuM41FA1Ys8I4WNgbHXDs1o8UD9v62pC20tqehw7QwkgBsa1X32rhsg0YKZKbINzQUsBNxBKoSgS0wFyEkIW9oKKm2BfIqEYKNgMDIhbFdJzRLk5UO3HlxYGKjHQAaGwU2/Z29/cJw5EeRja0KeJhlIR06UZvbrZFX8AW0vfuVVcMKUdHulNELMe+AI9C+pe+9CVs27at4M/s2bPR3NyMQ4cOZd13aGgIra2tRfNRu7q6sHLlSowdOxaPPvooKioqCu6/dOlSvPfee1muteHcfPPN6OjoyPy8++677l90wIwda8eW6RSvKOIWptR4Ki8w2qUwQRQz5JhTWxtugd9UuPjx6IBjt36kgJu20ujo0/eFUrqh66vqi+xZnoyrFk6eIBzpbIPcZGZnBOBIH1s1FrGgFpiLEM5ihtbCHmdnkHLAKdjodLmpwLLsBRadjnRAvZCu2pFeipBeLo50U8TDKArpwwtK48YBcs2nN97w/zxO96vKjPT2dnvavBuCFtLjcfvCvRz7AjxGu0ycOBETJ04sut+yZcvQ3t6ODRs2YMmSJQCAZ555Bul0GkuXLs17v87OTqxYsQJVVVX4zW9+g2oXUwQ2bdqEcePGoaoqvyOlqqqq4P9NwrnWxtGj7tfa8ApF3MKUOqvGCxQPCxOEiEsndGFkGxw7Jow4lZV6noeFPb1w7NZPVbIKdRV16B7sxtHeoxhXo+DLdA7ohi5MEBnpmTbgAos5ycSKaIw54syMwgwv7Ok6HzHahZQFzu80PT1mX7gdPiwEuFgsO84C0OdIV+HCBaLnSC9H8VC+z1FYbFQWlJwLjUpOOQXYv1/0hXPP9fc80nUZj2c730tF9gNAfMbcivNBR7sAoi90dQXrSJevM+y+AE2LjS5cuBArV67ENddcg/Xr1+PFF1/Eddddh0984hOYerwCtG/fPixYsADr168HIC7EL7nkEnR3d+M//uM/0NnZiZaWFrS0tCB1vBrz29/+Fj/5yU+wZcsW7NixAz/+8Y/xrW99C1/4whd0vIzQCCLSQp77VfT30QijXcKHbRA+DQ3iuzjAdigHOHb7I4g4BekApYibm0CEdLZBQYLMSKeAmxtZ2AOCKSqxHcioJhaLjhNXiocnnDByUUoZabFjh3DR+kWKCU7hzw9+hPQwHOnlGGcRlX4A5HekA2pz0qWQ3tgoxHS/VFbafdfLZyzMvhBGUcmATF4ti40CwEMPPYTrrrsOF198MeLxOD7ykY/gzjvvzPx/cHAQ27dvR8/xN2Pjxo1Yt24dAGDu3LlZj7V7927MnDkTFRUVuPvuu/EP//APsCwLc+fOxR133IFrrrlG18sIhSByielIL0yQIi7bIDd0pIdPIiEK4a2toh2KpHuUhGXZ7cDCXvhw7C6dppom7O3YG4gTl4703GSE9L4AHOlsg5wEsfAuI0WKM752PLo7xAyZOZij5TkYc0TKhro6IeCYLiBKIT2XeNjcLC5wW1tFjvrixf6eS4p8qr68RyXapZwd6VES0os50gE1szNU5qNLGhtFXEwpfYHrBQSGNiG9qakJDz/8cN7/z5w5E5ZlZX6/6KKLsn7PxcqVK7Fy5Uplx2gqQTjSKeIWJsh8brZBbpxtYFm2M1ol7AfFGT/eFtJ10N0t2hdgO5gAx+7SCcKJ29kvLtwoXOUmCEe6bAM60nMTxGKjspjBfpAfWdjT1Q6DqUH0p8QaF+wLZNQjRRvTIy0KCemxmBAQX3hBCIh+hXQpJEsxzS9RiXahI918IT2dDt6RriIfXdLQABw44L4vOF1p5eJID7svQFO0C/FHEE5cCoiFoSM9fGQ/GBy0xwbVMOKoOLoX3pVtEI+PnIVKSJRgtEv4yMVG23r1rRROEbcwspjR1tembaFL9oPi6C4qyX4AcHYGKQOiIiAWEtIBtU5c1RdRUXGkl7N4GJWC0r594j1LJoFZs0b+f9EisT1wwL/Yo8uRDrgv1vT32wuThjE7oxyLSqCQbiS6RVzLoohbDN3iIcBc6GLU1ABynUFdfYH9oDi6z0eyDerr9cw6ICQomqr1LrKY5QClcJWTQDPS2QY5cS50Kd37quHMjOLo7guyDWqSNUjGtU1wJsQMRouQLp24o0VID3Ox0XIUD+X7HJV+MGcOUFEx8v9jxwInnihu+3Wl63Cke+0LzsJGGLMzyrGoBArpRqJbuHJGKdCJmxt5LmS0S3jEYvrXC6CQXhzdUVNsAzJa0O1Iz3KA0ombk0CE9AG6oQvhXOhSV1GJbVAcWdjTdj5iQYmUE1EQ0gcHgV27xG3djvTBQZHhDKgTE6RAHRVHeleXiBAJAlPEwyj0A0CsAQDk7weAur6gw5HutS9IUam6WixwFhTlXFQChXQjCUq4iseN+AwaCaNdzEB3X2C0S3F0FzNkG7AfkKije6FL6QCtTlajIpHDYUOyhPRi2f2lkslIp4CYF+2xIhRxixJUYY/FDFIWREFA3LULGBoSxzptWu59pCP9nXfsC9FScDpQy9WR7nx+3ZgiHkahHwDFZ2YA6nLSdTrS3QrUYSw0CtCRHvYBkJEE5cIdM4ZRCvmQbdDZKYruOmC0S3GCihWhiJufIKNdCIkygYmHFK7yItsgZaWyHPwqYTsUJ6hYEUa75CeoNmAxg5QFURAQ5eKK8+YJt1wuxo+3RfbNm0t/Limc1daKHGoVSPGws9O90zsMR3pVlR0XEpQT1xTx0NkPNJkVlCCF9AUL8u+jypEuhXQdGeleHelBi0qyqBSkkB5W0SAHFNINhOJh+MjzF+CtMO4FRrsUR/fCu+wLxeH5iBB3jK8RJyztcRYUrvJSU1GDqoRYXEPXgqNsh+IEtdAlixn5Caqwx2IGKQuiIKS7ceECwOmni+2mTaU/l44pvVKUcy7mVowwHOmxWPACoilCunyfLQvo6wv3WArhxZG+ZYu/ooC8QFbpSC812iUsR3pQBSXLMqcvgEK6kegWrhhnUZxk0j6HUUAMD/aF8KGQTog76Eg3AwqI4SNjRY726i0qsQ3yI/uB7jbg+YiUBaNJSF+8WGz9COlSOFN5AVVdLX4A8524QQuIpoiHNTX2bVP7Qk8PsHevuF2oLyxcKIoiR48Chw6V/nxSTHBG/vil1GiX0e5IHxiwZ6uE3RdAId1InMKVjlkzFK7cIduhTYOpbXAQ6O8Xtxntkh860sMnqGIG24BEnaBcuBQPC6OzHdJWGscGxIU7BcT86F7okrEixWG0CyEKoZCejQ7xEPAeaVEuAqIpQnoyCVRWitum9gUZcdTUBEyYkH+/mhpgzhxx20+8iw5XXlSiXcIqKAHh9wVQSDcSKVz199sLYquE4qE75AwdHQKic20StkN+mM8dPmwDQtwhhav2vnak0inlj0/hyh06BcTugW5YEA4HtkN+ODsjfAKbmVHJwZuUAaNRSN+8WSxOWgq6pvSWKiCO9kgLU4R05zHIIoZpuO0HgJ2T7mfBUZ0xR6YvNhpWQSmZtNcpCBEK6QYyZoy9bocO8YpCujt0CoiyDZzrlZCR6Hak0w1dHEa7EOKOcTWi+mrBQke/+osriofu0CkgylkB8VgcNcmaInuXL0HFinB2Rn6c/SBtuVy4zwNcK4CUFaYL6e3tdjzFSScV3nfOHCG49fUBb79d2vOZIKSnUrbjMCwnbrk50gFbrDW1L3gR0p056aVCR3p5FpRAId1IYjFbQNQp4tIBWpgghHSKh4WhiBs+8lzU1SWiyVTDYgYZLVQmKjMit44FR5lJ7A5Z0GjrU5/L5ixmxGIx5Y8/WpAZ6YwVCY9x1aIfpK105nOrkkwb8HxEyoGoiIdTphS/wI/HgdNOE7dLjXfRkZEOeBMQnW1RLk5cEwRE04tKQTrSLUuvI930mRnOfqAjj3o4JvUDUEg3Fikg6nDiUrhyh04RN6zCYdTQ2Q8AFpXc0NAginuAnvUCWMwgowmtbmgucukKnfncdOG6Q2c/sCyLszNcUFNRk5k1obMv8HxEyoLRFGcB+M9JNyEjXbZFLJa9CGYQlLMTNypC+oIFxfd1OtJLEYJ7esTMCECPI72jw91xhbVWgHzNlpWdW6yLsCJs8kAh3VDohg4ftkH46JyZkU7b53y2Q34SCXs85wwZQgqjU0CkC9cdLGaEj8426E/1YzA9CIDtUIwg+gLPR6QsiIp46FZIl470UiMtTIh2cbpwg54hRke6mX3Bsrz1hfnzRZ5yZyewb5/355PtH4+rFXdlPxgacvc+h+XQrKmx86iD6Asm9QNQSDcWirjhwzYIH2cbqJ4xxAVf3aOzL3CGDBlNBJHPTRduYQIpZrANCjK+RlTBtUQcOWJKxlRyWl8h2BcIUYTJ4iHgXUiX+731VmnPZ5KQHsb07qAd6TIL3gQBUQrGJs7O2L9ffC7icbEWQDEqK4F588TtUopKzn6gsphTWyucbIC32RlBO7VjsWD7AoV04oYgRFw6QAsj20BHnAWjXdwh2yCVUl/olI+XTALV1Wofe7TBohIh7pDZ0DoWWWSsiDsCKWawDQoi26Ctr035QpeyDeoq6pCIJ5Q+9miDfYEQRZgupEtB3KuQvnt3aQsg6cpIl05vN6JcWHEWQHiO9KAjbHJhcl+Qi+fOmiVEcjf4yUnXVVCKxbLjXYoRprAUZF+gkE7cQAdo+IwT6zRRPAyRmhr7O4PqdnC2AdeMKwwLe4S4Q2s+N2NFXBHUYqMkP7IN0lY641xWBSOO3KNz0Veej0hZYbJ4mE4DO3aI2yed5O4+U6YI0S2VAnbt8v6cJmSkh7XAIhC8I92kbGiT+4IsKLntB0B2TrpXdAnpAPtCLiikEzfQARo+bAMzkDnpqhccpYDrHhb2CHEHo13Chy7c8KlOVqO2QlzoqG4HFjPco7Owx2gXUlaYLB4eOCCiP5JJYOZMd/eJxWyxUcbCeMGEaJcwHenydQfhwh0asmcNmCAgmtwXpCNdxrW4wURHOmAXqUzvC3SkE9PQucgiRVx3BCEeUsQtjq52oIDrHl3nI8vi+YiMLrjYaPgEsthoJQfvYujKSZfFDDqhi8OiEiGKiIJ4OHOmvfCfG6TYKO/vBd1Cuptc1TBduEGKhzIfHTBDQJTHYGJGuh9H+tatYnaHF3RFHAHRiXahI52YhhQPVbtwATpx3eIUcL2eV4uha0bcaES3I50CbnF0nY/6+sSsUoDtQEYHQYi4dIAWRrZBz2AP+ob6lD42ixnu0dUX2AbukW2ges2GVDqFnkFxQcuCBikLTBbSZazL3Lne7ifd6+++6/05dWekuxGoTXCkBykexmJAVZX+5yuGLFyY2BdKcaTPnSvy1Ht6gHfe8fZ8pkS7hBn9E2RRyaSII1BINxbGioSPzEhPp+33TBV0pLtHV19gQck9utsA4MK7ZHTAxUbDp76qHvGY+Hrb1qs2J53xOu7RJaSzoOQebW0wYA/ebAdSFjiFdMsK91iGU6qQfsIJYrt3r/fn1HUh6yUypVwc6U4XrgmLeplaVEqlgJ07xW0vjvRkEpg9W9zes8fbc+p0RnqJdqEjPRQopBsKM4nDx7nQpZsZZl6gkO4eXY509gP36I7XGTMGiHM0IqMAXcJV2krj2ID4okwHaGHisTgaqxsBqF9wlMUM9+gqKjHaxT26ixkV8QpUJQ1wSBKiG6dw06d2ppNvShXSZ8wQW6+O9IEB+z1QLSB6EdJNcKT39ACDg3qfyzDx0Fghfe9e8dmsrLQ/226ZPl1s33vP2/2CcKR7iXYpp6KSAVC6MBRdwlV/v32+p4BYHOlK1yUgUkgvjm43NPtBcdgGhLhDl3DVPWBnUdIBWhy6ocNH10KXXOTSPbod6SxmkLJBOquA0gXEI0eA73wH2LhRzTFJgnakO6eTqv4CLy+MBwaEaFGIMMVD5wW86mnrwzFMPFSSkf6rXwHf+AZw6JCaYwLsWJc5c4BEwtt9TRTS3TrSLcuMolIZOtI9rEhBgkQKV7294sc5fvtB59g3GmlqAvbvp5AeJox2CR+2ASHukMJVW28b0lY6EzHiFykeJmIJVCerlTzmaEa3gEhHenF0FzMo4haHOfWEKCKZFE7XgQEh5sjpsm4ZHAQuvhh4/XWRc71uHXD66f6Py7L8C+mHD3sTG6RgVlvrbXFTNziFwM5OYOLE/PuGGWdRUSHer95e8X7ICyUdGCYe+s5If+454C/+Qtz+3e+AtWvVTEuW+eYypsULJgrp8jGLFWoGBoChIXGbjvRAoSPdUOrr7WKaylgR2Rdra70X68oRXQKizkWeRxuMdgkf3dEubAMyWpDClQUL7X3tyh7XKeDGTMjINBzdAiJF3OKwmBE+zjawFOY6c2YGKUv8CIi/+IUQ0QHhtP7mN9Uc0+HDthtVLh7qlsZGW4T2Eu+iUzxMJOxjKibMhenCBYITEOXrNEQ89B3t8r//t317/Xrg+ef9HxNgf4alKO6FUoV0nYKO25gj58yAMGdnlKEjnUK6ocRitnilUkCkcOUN3QIihfTi0A0dPrINOjrsorcKGO1CRhuViUqMqRQXdSoFRLpwvcFol/DRlZHOaBf3yH4wmB5E96CPafjDYDGDlCV+Ii3+v/9PbC+/XGwff9xfNIZk3z6xnTxZON29EIvZAuL+/e7vp3OBRcC9gBhmtAvgLc/dD4aJh76E9L17gZdeEg70yy4Tf3v4YTXHJUVwr/noADBtWvZjuCUIR7rbflBVJWZKBA0d6cREdAiIFK68QSE9fHQ50tkX3CPXCgDcLR7uFrYBGY3oEHEzwhXFQ1fIfO62Xi42GhbM5w6f2opaVCYqAahtB87MIGVJqQLiwADw7LPi9j/9k4ie6OsDnnzS/zFJ4U8KgV6ZPFlsDx50fx/dF7FenbhhC+m6nbjy8xbW6xyOHyH9t78V2/POA770JXH7//5fIJ32f1zSkV6KkC4LSl4X3tVZVIpaQSkIR7phszMopBuMTiGdAq47ZBuojNcZHBSRagDbwQ2MFQmfZNL+jqBjhgz7ARlN6BAQmUnsjXE1ovpHR3p4aJ8VwL5QlFgspqewx35AypFSBcQXXxQC0OTJwGmn2a701av9H5N0pAcppOvOJ/UqIIZ1IReUE9cwF25GsC1lRoUsHn3oQ8AFF4jX1NoKvPGG/+PyE+0ydarYHj7sbeq1CY70cok4AowrKlFINxg60sNHOnF1tAHAdnCDdKS3takpWEtYVPIGz0eEuIPCVfhk2qBPXRv0D/VjMD0IgCKuG8bXHI926WG0S5jIduAMGUJ8UqqQLjOgL75YRFpceKH4fe1a/8ckHemliIdAtB3p5eLENU1IL7UfWJb9mb/wQhFDcu654vc//MHfMVmWP0f6+PEi6gjw5hgzYbFRU/oBo12ISVC4Ch8dbSDPMzU14URZRQ3ZBum02u8q7Ave4PmIEHfoEBAZZ+ENnbMCAGRy8El+ZBu09bUhbamrgrMveENnX2AbkLKiVAHxlVfEVoqGy5aJ7dat/vMSw3Ck685Id+twlQLiaHfiGiYeltwPdu0CjhwBKiuBxYvF3973PrH9n//xd0zt7fbxlFJUSiSACRPE7UOH3N3HsvQK6fLiOCqO9O5utQup5cKwvkAh3WB0irgUrtyhsw3ohHZHZaU9NnDh3fBgXyDEHVod6XRCu0JnTn1tRS2S8aSyxx2tyHidtJXOKkL4hX3BGzwfEaKIUgREywJeflncPvtssZ00CZgzR/xv3Tp/x1TO0S5hC4h0pIvPsFvkZ/2MM+yFcWVxSRabSkW60SdMEE7FUpg4UWzdCum9vbZwrNOR3t8vfvIRdkHJKaQUc8/7xbC+QCHdYGSkBTPSw4PioRlwvYDwoSOdEHdwsdHwybihFS42yngdb1Qnq1FbIS52tKwXwHZwhewLOmbIsA1IWVGKkP7ee0KYSyaB00+3/y5F9Y0b/R2T32iXSZPENsrRLmE7ccvVkW5ZYtFct7z6qtiedZb9tzPPFNu33vL3PvrJR5fIvuBWSJfHG4vpiVVxK1CHHe1SWQlUV4vbZVZUopBuMFK4UunCpXDlDQrpZqC6qGRZ7AteoZBOiDt05HNTPPTGuGr1i41ywVfvqI45GkwNoj8lnFmMFXEHo10IUUQpQvprr4ntwoXZTtkzzhBbKS6WSpjRLmEK6amU3Q5hO9J1C+nSeW+IeJh1HF76wtatYnvKKfbfJk60M8399AU/+egSKaQfPuxuf+fU9rgGSTWRsMXxQp+xsGdmAMEUlQYHxQ9gTF+gkG4wFK7Ch0K6GaguKnV327PR2A7uYNQUIe5gRnr4SPGwva8dqXRKyWNK8bChSlMu7ChEtYgr+wHAgoZbtM6QYRuQcqIUIV2KhyefnP13KaRv2lT68XR12V+kVQjpbmM6dGekuxGonW0QtiO9zFy4SCaFCxnw1he2bBFbp5AOAEuWiO2GDaUf0/79YltqPwBKd6Tr6geAu74Q9swMIJiYo95e+7YhfYFCusHoEK50x5qNNmQb9PVl918/UEj3juq+IAtK8XjpUWrlhpwVoGOGDPsCGU3oEK46+sTg3VBNEdcNMp/bgoWOfjVf7OXjsA3co7ovyGJGTbKGOfUu0TFDhjFHpCxRKaTLxRbffrv0XGHpRh87tvQv0jIXur/fdrYWw4SMdCkexmJ2rETQBOVIN01IB7z3hc5O2zU+vC9IId1PzJF0kUsxvBS8ZqQHIehERUgPwpEuz0/xuJ2xHzIU0g1Gp5Cus3g2mhg7VsysAYA2RVGrFNK9o1rEdTqhYzE1jzna4QwZQtyhRUg/LuLSke6OykQlxlSKiwrVIi7bwD2ZfO5eNYM3F7n0js5oF7YDKStUCukTJ9ruWRn/4pUDB8R26tTS7g+I1yRFKbcXWUFFuxRytzrFw7Au5Mp1sVHAPha3xZc33hDbKVOAceOy/6fCkS7Fbz9CuldHehDuVHmBbLqQHkRfcPYDQ8QbCukGozNKgUK6O2Ix+3yvqh0opHtHlyOdbeAeFvYIcYdWRzpjRVyjesFRtoF3ZMyR6mgXOqHdw4x0QhThVUhPp4Ft28Tt4UI64D8nXbpwpZO2FGIx7/mZJmSkl0suNGCmkC6zu932hXyxLoC9CO/bb3tbvNSJir4g73vkiLv9g3Sku1lsdLT3BQP7AYV0g5HjWne3mHGlAka7eEe1gEgh3Ts6HenEHar7gWVRSCejk4yA29eGtJVW8piZfG7GirhG9YKjmWgXCumuUS3iMuLIOzpnyLAvkLJCioduXbh79ohc0KoqYM6ckf/3K6RLwc+PeAjYF1luv+CbkJFugngYtCNdfv5MwGtRKd/MDEC41JuaxAKyb75Z2vGocKRL56TbCAJGu9gE7Ug3BArpBtPQYC8CrEq8onDlHQrp4aO6DdgPvKO6DXp6xHcmgH2BjC6kcJW20hnhzy8UrryjK5+bLlz3KBfS2Q88o7oNBlOD6BkUF7QsaJCyolTxcMECOyfUiQmOdMC7W8mkjPTR7sIFjBQQlQrpsZjtVN+8ubTjUdEXvEYQmCakh1looSOdmEY8rj5WhAKid3SJuBQP3aPakc5+4B3ZD9rbbQHcD7IN4vFwvwcTopqqZBXqKsQXWtVOXIq47tEm4lI8dM34WjF4q8pIpyPdO7If9A31oXew1/fjyYISwIIGKTO8iofbt4vtggW5/y8jLd54Axga8n48UjycMMH7fZ14ucjq77enyZe7kC6Pc2BAXXRALgwUED1npMtol1xCOgCceqrYliKkDw7aIg0d6eFARzoxEZUibjptRyxRQHSPbAMuNhoedKSHj/x+4Yxk8YOzHxiyZgghylAp4vYP9aM/JS7SKCC6R1esCIsZ7qEjPXzGVo5FMp4EoKYdZBvUJGtQkajw/XiERAavQvrOnWI7d27u/8+cKR5zYADYscP78aiKdvGSke7MataVjykvkHt7hUiaCxNcuM7XX2YCoqeM9M5Oe2HcRYty7+NHSJefW2fefynI+/b3i89eMYJYeJDrBdjI12lQP6CQbjgqBcRjx4QIBlDE9QIXGw0fr/F9xaCQ7p3KSnuMVtEObAMymlEpIDodoFxk0T3OrHoVZHLqKeK6hsWM8InFYlrORyzqkbKjVCE9Vz46IKZkSoeudOx6QXW0i5sv9/Iitq4OSCb9PW8+nAJ1vkUWTXDhxuP2sZaZgOipL+zeLbYTJuS/6JNCein9QOajT5iQO0LJLWPH2vd3454MYsE1OtJtZD8waK0ACumGo1JIl5/tigqgutr/45ULXhczLwaFdO/oihWhiOsNlRE7bAMymlEZaSEdoGMqxyAR93GRUGZI8VBZrAijXTyTaYMexW3AYoYnVArpmXgdtgEpN1QL6YCdDe1HSA8y2iWIfNKKCqCmRtzOJyCa4MIF9AuI6bTtjo6qkL5rl9jOmpV/H1lQeu897xEAqgpKsRjQ2ChuexHSdV7IuinUmCCkB+lIp5BO3KJDSG9oYJSCF7jYaPg4Y0Xa2/0/HkXc0tB1PiJktEHhKnxUi7hcbNQ742uEQNPW14a0lfb9eCxmlIbKohLbIFzuvvtuzJw5E9XV1Vi6dCnWr19fcP9f/OIXWLBgAaqrq3HqqafiiSeeyPq/ZVm45ZZbMGXKFNTU1GD58uV4++23s/b5l3/5F5x33nmora1FoxSayhEv4uHgIPDOO+K2GyFdLsboBVXRLl6E9KAuYos5cU0QDwH9AmJfn3076kL67Nn592loAE44Qdz2WlSSjnQ/+egSLzEEdKTb0JFOTESlcEUBtzRUL3TJdvBORYX9ftENHR46hHT2AzIaaapWKKRTuCoJKeKqjhVhQcM942rERWnaSmdFFJUK26A0WNgbHfz85z/HDTfcgFtvvRUbN27E6aefjhUrVuCQFJKG8dJLL+GTn/wkrr76arz66qtYtWoVVq1ahS0Ooerb3/427rzzTtxzzz1Yt24d6urqsGLFCvQ5BLyBgQF89KMfxec+9zntr9FovIiHe/eKKbTV1cCUKfn3K9WRblnhZKRTSM9Gt4Do/KxJl74JSDHTzWKjMtqlkJAOlB7vosqRDnhbGC/IxUbzRRxZlhl9QRZYKaSrobW1FVdccQXq6+vR2NiIq6++GsdkQ+fhoosuQiwWy/r527/926x99u7di8suuwy1tbWYNGkSvvzlL2OolJWuIwKjFMJHZRukUvb5jgKiN+iGDh+2weiHY7cadGQS0wntDZUuXMuyWNAogepkNWorhPjEolJ4KBXS+5lTHxZ33HEHrrnmGlx11VVYtGgR7rnnHtTW1uL+++/Puf8Pf/hDrFy5El/+8pexcOFC3HbbbTjzzDPxox/9CIA4r/3gBz/A1772NVx++eU47bTT8LOf/Qz79+/HY489lnmcf/7nf8Y//MM/4FQpdJUrXoR0Gesye7bI0s6HFNLfeivbfVyMjg57IU6/0S5eRLAg4iyA6Ajpuh3p8rNWVeUv/1s1qh3pALBggdi+9Za3Y9HhSDdNSM/3+erttRdANEFIVxEbkI9yEtKvuOIKbN26FWvWrMHjjz+OF154Addee23R+11zzTU4cOBA5ufb3/525n+pVAqXXXYZBgYG8NJLL+GnP/0pHnzwQdxyyy26XkboULgKH5ULXTr1KArp3mBRKXx0zJBhG5gFx241KI1SoAO0JDI59QqiXfqG+jCUFoUfCojekDMDVLQD+0JpKJ0hwzYIhYGBAWzYsAHLly/P/C0ej2P58uVYu3ZtzvusXbs2a38AWLFiRWb/3bt3o6WlJWufhoYGLF26NO9juqG/vx+dnZ1ZP6MCKR4ODQEDA4X3lUL63LmF95syRURDpNO2c9cN0o1eV+ffqexFSA9qOmlUhPSgHOkmxboAeoT0efPEdli0VFFUOtK9COnSJR6mkO4UlsL8jDiFdCnsq8aUdREcaBHSt23bhtWrV+MnP/kJli5digsuuAB33XUXHnnkEezfv7/gfWtra9Hc3Jz5qXd8OH//+9/jjTfewH/9139h8eLF+NM//VPcdtttuPvuuzFQbECLKIx2CR+VAskD/ZcAAFY2SURBVK5sg8pKLvjqFRaVwodtMLrh2K0OKeLShRseKvO5ZRvEEMOYSnO+xEcBHW5o9gVv6JghwzYIliNHjiCVSmHy5MlZf588eTJaWlpy3qelpaXg/nLr5THdcPvtt6OhoSHzM2PGjJIfyyicTshikRZuxcNYzBbbd+xwfywqxUMvblLTol3CdqfK9063Iz2qQrqzQFSsL5x0kth6daTLopLfmRlAaRnpJgjptbWFZ77oRvaDdDpb3FeJKX3egZZ3fO3atWhsbMRZZ52V+dvy5csRj8exbt26gvd96KGHMGHCBJxyyim4+eab0ePooGvXrsWpp56aNeCvWLECnZ2d2FpgkY4oV8YpXIWPbIOeHm+z7nLBYkbp0JEePjwfjW44dqtDRyZxfSUHDi/INkhb6cx7WCqZNqiqRzzG5YW8wHzu8NFSzGAbkDzcfPPN6OjoyPy8++67YR+SGiorxQ9QXCySr/nEE4s/bilCuqp8dMD+It7bC/T3F943qAtZuYBjvvfZFHeqfO90RVrI79IGiYcAbCG9WEFp/34xeyOZBKZPL7yvFNJ37bJji9wg3eNSBPeD24z0/n67r+hcbFQ+dleXEKmHY0o/qK4WC9oB+vpCuUS7tLS0YNKwnKJkMommpqaCFe6//Mu/xH/913/h2Wefxc0334z//M//xKc+9amsx81VNZf/y0eUK+MUrsKnocGOJfMr4lJILx32hfBRGXPENjAPjt3qoAM0fKqSVairEF+4/UbsMKe+dFT1Bcuy2BdKhLMCos+ECROQSCRw8ODBrL8fPHgQzc3NOe/T3NxccH+59fKYbqiqqkJ9fX3Wz6hBClbFhPT33hPbadOKP6YU0r1EWkhHugoXrrN9ikWUBJXLKN/nfIssmhLtojsbWoqHpjnSpZhZzJG+d6/YTp8uxPRCTJ0qXmcqBezZ4/5YVArpbqNdnJ9LnUK6s2/mOueY0g9iMf0LjkZdSL/ppptGLCg2/OfNN98s+WCuvfZarFixAqeeeiquuOIK/OxnP8Ojjz6KnTJnrESiXBmneBg+sZi3Bc0LQSG9dFSJuIODwnQBsC94RVU/AHg+ChKO3cFDB6gZqMpJp3hYOpmMdJ/FjO7BbqSsFAD2Ba9wVkD0qaysxJIlS/D0009n/pZOp/H0009j2bJlOe+zbNmyrP0BYM2aNZn9Z82ahebm5qx9Ojs7sW7duryPWfZ4FdKLuXABOxvaiyNdXgzJL+Z+SCTcZ30HlZHudOLmwhQBUbeQHvVol337xNZNP4jF7L7gJd4lTCG9trZ4gcAP1dX24+fqm6b0AyC4opJBQrqnlv/Sl76Ev/qrvyq4z+zZs9Hc3IxDcgXd4wwNDaG1tdVThXvp0qUAgB07dmDOnDlobm7G+vXrs/aRVfRCj1tVVYWqqirXz2sSUjzs6hICoJw1UQoUcUtn/HhR/Pcr4rINSkd1MQNgO3iFazZEE47dwSPFw9beVqSttK84EIq4pTO+Zjz2duz1LSBSPCwdVSKubINELIHaCsNEBcOhI310cMMNN+Azn/kMzjrrLJxzzjn4wQ9+gO7ublx11VUAgCuvvBLTpk3D7bffDgD44he/iAsvvBDf+973cNlll+GRRx7BK6+8gvvuuw8AEIvFcP311+Ob3/wm5s2bh1mzZuHrX/86pk6dilWrVmWed+/evWhtbcXevXuRSqWwadMmAMDcuXMxxgQBJ0jcCOnptDcBsZRoFylWqRAPAeFq6ewsLoIF9eW92PtsioBIIb3wfrIfTJ3q7nFPOgl47TUhpF92mbv7hCGkB9UPYjFRVGprM9uRDlBIL8bEiRMx0UUW17Jly9De3o4NGzZgyZIlAIBnnnkG6XQ6c4HtBjlQT5kyJfO4//Iv/4JDhw5lpp+vWbMG9fX1WLRokZeXEhkaGkQfsiwhXg2bHe8JOkBLR1U+t2wDnbOARiuqHOmyDerq9BaRRyOcIRNNOHYHz7ga8WU8baXR2d+JxurGkh+LsSKlk3GkM9olNJQJ6Q4BNxaL+T6ucoKO9NHBxz/+cRw+fBi33HILWlpasHjxYqxevToTlbZ3717EHQvOnXfeeXj44Yfxta99DV/96lcxb948PPbYYzjllFMy+9x4443o7u7Gtddei/b2dlxwwQVYvXo1qqurM/vccsst+OlPf5r5/YwzzgAAPPvss7jooos0v2rDcCOkHzoEDA2Jxf/cmBCkkP7OOyJPWuawF0KleAgIEezdd80R0ulIF5gupBfLSJdCupuII8B2pLuNOUql7M+kyoz0Yhe68jmDEHQopAsMFNK1ZKQvXLgQK1euxDXXXIP169fjxRdfxHXXXYdPfOITmHq8IrVv3z4sWLAg41LbuXMnbrvtNmzYsAF79uzBb37zG1x55ZV43/veh9NOOw0AcMkll2DRokX49Kc/jddeew1PPvkkvva1r+Hzn/98ZF1rxUgk7M+lKgGRwpV3VLmhZRvINiXuUd0G7Afeca7BkmvNEy+wHcyDY7c6qpPVGdcs3dDhIQVERruER6YNfBYz2A9KRxaUuge70T9UZDHBIsi+wKJSOFx33XV455130N/fj3Xr1mUVuZ977jk8+OCDWft/9KMfxfbt29Hf348tW7bg0ksvzfp/LBbDN77xDbS0tKCvrw9PPfUUTpKL/h3nwQcfhGVZI37KTkQH3AnpMtaludndVPLmZiFMptPA7t3ujkOHkA6Yl5FezJEetqhWrkK624x0r0L6zJliK7PVi+F830ejIx0ovF4AhfRQ0SKkA8BDDz2EBQsW4OKLL8all16KCy64IDOVDAAGBwexfft29BzvgJWVlXjqqadwySWXYMGCBfjSl76Ej3zkI/jtb3+buU8ikcDjjz+ORCKBZcuW4VOf+hSuvPJKfOMb39D1MoxAlQs0qFiz0YgqR7rq7z3lBPtB+MjPbTqdHZFTChTSzYRjtzp0OHGJN1Tlc2cWuaSI6xkp4rIfhEd9VX0mXqqtr4hAUAQu+ErKGi9CuptYF0BMPZ89W9x+5x1395EXlKqcWW5FMBMy0gcGRN4tEL6AGJSQXlOj5/FLxW20y/79YutWSD/hBLF1K6TLflBX5y//WOK1oBSEmCD7QiFHugnisryg1y2kh93nHWgLNmhqasLDDz+c9/8zZ86EZVmZ32fMmIHnn3++6OOeeOKJeOKJJ5QcY1RoagJ27lSXz03hyjuqhHR5bqEj3Tuq43XYD7xTXS2+O/X0iPNRqZ/j/n7xA7AdTINjtzqaaprwXud7/t3QfXSAloozq94PbIPSUZ2RzmKGd+KxOMZVj8PR3qM42nMUzWPcr3nhJG2l0dUvhC22AylLdAjpgBAat2yx71sMHRnpzsfNhwkZ6c44kbAFRKeQblmiKKIS+fpNy4R1CumFXrfXjPRShXTV/aC/H+jrExe+uZAFHjrSbdwWIUrBsswqGhxHmyOdqEO1E5fClXdU5XNTSC8d2Q86O20jQimwH/hDxfnIOcaa9t2QEFWoEnHphi4dZbEi/RRxS4UzM8xARTt09XfBgiiksh1IWaJLSJf7SuGxGGFHu4TpSJfvfWWluzx5ncj3bWiouDu7FEwSSp1IId2yhOCcC8vyHu0ihfSODneCrOp+MHasXRQo9PxhRLuUc0Z6f7+dKUshnXhBhXBlWYy08IOqfG4K6aXjHCOLRZcVgkK6P1TMDJDfP8aMEetAEDIaUSFcpdIpdA0cd4BSuPJMZrFRn7MCGGdROs6CUtoqfXENOtL9oeJ8JIsZlYlKVCfzOPUIGc3odKQ771sMXUJ6IREsyOmkUXHh1tYCyeMBDzoERPn6TXitTpyZ7fkKCO3tQG+vuO3WkV5XZ19ounGlq+4H8bgtlBVqz6AXGwVy9wWT4k50CukmzUJxQCE9AqgQrvr6bBcvBUTvMNolfFQtvEsh3R8qHelsAzKaUeIAHbC/ODNWxDvKol24wGLJjKsRF7hpK50pSJQCZwX4Q4mQzmIGKXekiFNISD9wQGzdioeAN0e6ZanPSHcT7eJcHEm3gOgmF9oE8TAW0ysgmhrtkkzaswHyCenys9zU5C3j3Uu8i47F59zMzqAjfSRBCOmVlXbhygAopEcAFcKV7O+xmHnn4ihAId0MVETsUMT1B4V0QtyhUriiA7Q0Mo50v9EuFBBLpjpZjdoK4V5TIuJyVkBJqHSksw1I2eLGkX7okNhOnuz+cb040nt7bXdckNEuUkyoq9M/ndTpSHesywPAvKxknQKiqY50wHalO93CTuRCo14KSoBdVHr33eL7ql4rAHBXVAoyI91NzJEJn48ghHRT+vxxKKRHAJXC1ZgxYtYK8QaFdDNQEbFDEdcfFNIJcYd0Q/sRcenC9UcmI91ntEtbn3A9NVY3+j2kskRFO3QOcK0AP9CRTogCvAjpkya5f1wvjnTpwk0k1AlobkSwIF24UjxMp0dmcJskHgLl6UgHiveFUmZmAMCUKWJ78GDxfelIz94nTCikExNRIVzpOM+UE04n9PDCuBd0FE7LCTrSw4dCOiHuUCFctfWKwVvGYxBvyGJG10AXBlIDJT9Oe187ALZDqaiI2KEj3R8qzkdcK4CUPVKwyufCTaWAI0fE7VKE9CNH8i/eKHFe1MuFEf3iJdoliC/vTsFsuBPXpFxoIBgh3ZTX6qRQ/A5QWkEJAJqbxbalpfi+OgQuL30hyKKS6UK6m/etVCikk1KhkB4+sg1SqeyIOC8MDtrnATrSS4OO9PChkE6IO5QI6ced0OOqOXiXQmN1I2IQQoMsSnjFsiy7oMF2KAmVsSLMqS+NTBv0sQ0IKZliLtzWVuGiBoAJE9w/7rhxQPXx+DYZiZEP1fnozscq5MKV/wtCPIzH8+fRmyQeAuUb7VJoQVgAOHxYbCdO9Pa4YQvpXmZnBDFTwM3CuyYIzM5ziB/XaS5M6/PHoZAeASikh09Njb1ORqkirvN8HMR3kNEIRdzw4fmIEHfQkR4+iXgiE8dSasROz2APBtMij5btUBoq+oKcFcBYkdJQej5iQYmUK8WEdOnCHT/e26J4sZjtSi+Wkx5WLnSQLlwgfza0aaJauUa7FMruBqIvpBcqKoWRkW66I12+b4ODYh0HldCRTkqFwpUZ+M1Jl+Pr2LFGLTgcKVRk1TNexx88HxHijkwutI+MdDrS/ZNZcLTEfG7ZBsl4EnUVZn2Jjwoq1guQIq7sV8QbnCFDiALcCulexUPA/YKjOuMsurpsR/1wghbS8zlxTRIPgfJ1pI9WId20opIbR7oJnw/nIsSq+wKFdFIqUjzs6ACGhkp7DApX/vGbz82FRv2jQsSV92VfKA0VxQy2ASkHpIDb2tuKtJXnwrQIdID6x6+I62yDmKo82jJDqYjLWQEloSKnnjNkSNnjVkj3mgsNuF9kUad4aFn5hdGwHOn5ol1MEdV0CempFNDTI26b6Egv1hdUCOnFIkLKfbFRk9YLiMX09QUK6aRUnMKrPF94hUK6f/zmc1NI949fEdey2Bf8otKR3kRjIRnFSPE7baXR1Z/nwrQIFA/94yxolALbwD9+hfSh9FBmoUs60ktDRTFD5quzDUjZolNIl/eRAmQ+dFzIVFcDlZXidj4BMehszHJ3pEsRHTDntTrR7Ujv6yu+MF0YjnRnsSmIAke+93loyF6Y2JTPB4V0YhrJpN2nSxWvKB76R1W0C9ugdPyKuD09IrrL+VjEG842KHUtEZ6PSDlQU1GDmqRYXMO3iEtHeslkInZKjXY57sKVWevEO35jjmQ+OsB2KBXZBp39nRhMDZb0GJwhQ8oeKVh1d+eOQPEjpEvBUT5GPnQsNgrYYkM+Id0UR7pJLlxAn3gohdNEwl6I1iSKCemlxhzV1NifxULxLum0/VkN0pHe02P3/TAd6bIfOPcJGzexOKVAIZ34wa+ASBHXP6qEdDrSS8dvvI68X0UFUFur5pjKDXkuSqXyf3cqBoV0Ui74dYEySsE/fqNdpIhL8bB0fM8KON4PxlaORTLORWZKwVmAkAU6r3B2Bil7nIKV0zEsCcKRruuivpiQTkd6bnQJ6c7XaWKsXKHZGb29tvhZSl+QrvQDB/Lv09FhO7qCdKTLglI8HoyY4CwoOR1s8n1PJu3ZJGHjJhanFIJc3NUDFNIjgl8hncKVf/wK6boMBOWE33gdZz8w8TtJFKipsY0RPB8RUpjMQpel5nPTke6bjJDuc7FRioel47ug1MeFRv2SiCcyYrrvwh7PR6RcqamxLyByCYhBOtKDFtKlsBiUkF4sI72chHQTKeRIl8WgiorSxE83C47KflBbq1ZILiYGO2dmBCEmyPZPpewoF8DMQouuviDfc8PWCqCQHhEopIcPFxsNH9kGx44BAwPe789sbjX4OR8xp56UE34FxIwbmiJuyWTc0H0UD8PCbz+Q92M/8AfbgRCfxGKFnbhSQIxaRjpAR3qp6I52MUw8zOBGSJ84sTSR14uQrqsfFHOkB+WOdsaZOM85pi26C+jvC3Skk1JQ6cQlpcHFRsOnoUHMpAJKE3HlfdgP/OFHSO/tBfr7xW22AxntKIt2oYhbMr4z0jkrwDfOfpC2cuQKF4H9QA1++sJQeghdA11Zj0NIWVJISB/NGelBC+lRdKSXunhULkx7ncNxU1Dymo8uCVNIl+3Z1ZV7HYSgCxyJhB0h4yxamPj5oCOdmIhfNzSFdP8wIz184nH7M1xKX2A/UIPsC0eOeL+vbINEwrjxkBDlNFWXLqRblsVYEQX4zUhnG/hHCq9pK43O/k7P92e0ixom1E4AABzp8T54c8FXQo5TSECUX4zlF2UvSPG9qys7wmE45ZKRns/xbJoTV17YDw3lzs0vldHiSC+FCWKsKii66HakW5Yt4DoJ2pEO5C4qlZOQTkc68YMfN3QqZfd5CoilQyHdDPz0BUa7qEF+Lyo2+zQXzKkn5YQfB2jvUC8GUiLDik7c0lG10CXboHSqk9WorRCOqlLaIRMpwjbwxcRaMXgf7vE+eHPBV0KOk09IT6fti71SLjQaGkSmNFD4C3a5R7vIRSxNERBra4U7CFArIJoolDoJQkgv5NjS1Q+qquzFwHL1hTCE9Fx9wbR+ABSPxSkVOtKJH/wIV87PMkXc0lElpLOY4Q8/sSKMdlGDKiGdkNHOxDr/wlUilsCYSoO+KEcM6Ug/0nMEVgnTrqUbmi5cf/gpKmWKGZwV4As/jnTmoxNyHOmEHi6kd3TY0R6lfMmNxYp/wR4YsF3PQQrp6bQtZgUlJkQl2iUW0+PENe11DsfNzIwoCulAYUE4TCGdjnS1j+sTCukRQYVwNWaMXegm3pFt0NlpZzx7gY50NfgpaFDEVQOFdELc4csB6ogUiXH6RsnIYsZAaiCT8ewFirhqkAWNUhzpjHZRg4rzEduAlD35BETp1qmrE67WUiiWky6/RAPqRaVCQvqxY3aRIExHumWVj4AY5WgX2RdKnQLu5mJf5wWlbE9THOm53muT+0G+WS2lQkc68QOFq/BpbLRnbpWSDU0hXQ1+HOmMdlEDz0eEuCPjSO8u3ZHOOAt/1FbUZmJFSmkHmQ3NdvCHjNjx5YZmG/iC5yNCFFBMSPdzkSFz0vN9wZYXkw0N9kWpKgoJ6fJvFRV27IVucjnSe3ttQd9EAbGcHOnO9hk+28/vxZ7JjvQwChzl7Ei3LDrSiT8oXIVPPG6f1722g2XZ368opPvDz8K7jHZRg5/zEduAlBOT6sRF8aHuPO6yAnCRS3WwHcJH1ewMUjpsA0IUkE9IV3HBXewLdhDiYSEhvaEhuAWOcjnSne95bW0wx+EGHU7cqDjSLcvO65b4vdiTgktrq4gVykXYjvQg26WcFxvt6xML+QLG9QUK6RFBjqtHjuQ/n+SDQro6ShUQe3vtOJhSFnInNioWG2Vf8AcLe4S4wylcec3npgNUHaUKiH1Dfegb6gPAdvCLr2JGL2NFVEBHOiEKkMJVPvFQhSO9WLRL0EK60wkfFIXEw9pa4XAzhXJ0pNfW2kWVfEUlv9EuqVT+4kRYjnR5PEE6IwsVleSaDSagM+IIMK4vGHQGIoWQhbl02rsTl8KVOop9v8mHFH0rKow7B0QOP450Rruowc2Mu3ywDUg54Sefmw5QdZQqIErxMB6LY2yVWU6YqJEpZpQi4vZRxFWBH0c643UIOU4uUQtQI6Q7nXO5kF+idYh4bh3pQVFIPDTtYlqHgBhGFrcXYrH8fcGv+FRZaRdSivWFoB3pYWT1RsWRLs8P/f3CSa4C5wwAk4pnoJAeGSor7f7q1QVKIV0dpTpxpZA+fnxwM+JGK34c6YwVUYPsB0ePcoYMIYXwk89NB6g6ShUQpYDbWN2IeIxfmf2QcaT3eHekZ0RcFpV8MaFWVMGPDRzLzLRwCxcbJeQ4+RZZVCGkS7dQPvFQinhhRrsEhXyf+/uBwUFxW84CMEk8BPQI6VFYXC1fX1BxsSddW/ku+MNypIfRLlEpKo0dawtdqvqCwRFHvCqIEKWKuBSu1KFCSCf+KNWRnk7r/f5ZTnCGDCHuKTXSgi5cdZTqhpbFjMbqRtWHVHaU2g8GUgPoGewBwL7gl8bqRiTjSQDeF33lDBlCjiMdwtIpKVEx5bLYlM8gxMPOzpEumTDjLABbNDRRPAT0COlhFC+8kmu9AFUX3MWKSmE70sOOOTKxqBSPFy5ClILBMzMopEcICunh41dIZ5yFf0p1pHd12d8L2Rf8UVHBGTKEuMWvG5rClX8y0S6ltgEFXN/4jdeJIYaGaoMFhQgQi8UyrnTOkCGkRKSgk8+RrsKFG6aQblkjM6/DEHUrK8UPYL/X5SSkR9WR3tEhPkOAvr6QTtufyXLPSDetL0ihRp6r/BLG4q4uoZAeISikh4/fjHQ60v1TqiNd9oOaGqC6Wu0xlSOlno8Yr0PKDb8CIoUr/5Q8K6CXxQxVlNoGMtaF8TpqKLWwx3gdQo6Tz5GuItolzDiL6mrhlAFGOnHDckcPdzybKh7qFNJNdqTnEtLlZ7S2FqiqKv2xC/UF56wJnY5006JdTM9IB+z2KGUxu1wYHCfAb6QRwq+QTje0f/yKhxTS/SM/xz093taxoICrFhb2CHFHqcLV0V5x8SAdpKR0Sm0DGX8h709KR76HXQNdnvK5OTNDLSUX9jg7gxBBEEJ6ayuQSo38v04RLxbLn5MelpA+XKiV4mFdXbDHUQzZHqpcuOm0/fky2ZGeS+BVdaFXKNpF9jW/Yn0+8kW7DA3ZrzWMxUaj5EhXJaTrXGDZJxTSI0SpwpXcfwKvxX3DjPTwqa8HkiLiM+/Mx1ywoKSWUvqCZVFIJ+WHdOJ6Fa6kiEsh3T+liodsA3U487m9tIOcFcBFLtUgCxqeM9I5O4MQgRS1dGSky/s6c6ZzPYeuL9GmCummO9JVx1kcO2Y7rqPmSFflXCsU7RJUPxjeB539Ish2iZIjXXVfoCOdqKBUEVeefyik+0dGu1BID49YzO4LXiJ2KOCqpZTzUVcXMDgobvN8RMoFKVwd6vEWaUERVx1OR7olsztdwDZQRywWs/uCh3iXTKQIndBKyGSke5id0TfUh+7B7qz7E1K2FHOk+7nQqKiwBbpCTlxdriDThPTh2dAmLrAIqHfhyve7stLsPNJC0S5+P6OFol10X9Tnc6RLUbeuznb1BUGuxUZNnZ2hOtqFjnSiglJEXMuikK4SKR62twMDA+7vRyFdLZMni+3Bg+7vI/sB20ANpQjpct/aWvFDSDlQiht6MDWI9r52ABSuVCDbwCkIuuFIL4V0lWRmZ3gQceW+sg2JPzJFJQ/nI1lQSsaTaKgy2B1JSBA4FxuVhVnLUidyF3LilpuQHjVHent77kgerzgjfGIx/4+ni7CjXXT3g+GO9LAWgB1eUHIuCGxqX1AtpBvohKSQHiFKceHSAaqWceOARELc9hIrQiFdLc3NYutFSJci7kReiytBvo9e+oHcl21AyolS8rmlCzcei6OxulHHYZUVdRV1qEnWAChNQKSQroZSFhzNtEEN20AFmcKeh/ORsx/ETBZ1CAkCKaSn02LBJgDo7QX6+8Xt0RBpkc+Jy8VGcyPbw7JGvnelEIWFRoHCjnRV/SBMR/rAQPaCbLJtgxbShxeU+vrs6B9T+4LqaBc60okfSnGAyjG4rg6oqVF/TOVGPG6L4V7aQY4BzOdWg3Skt7S4vw+FdLX4OR+xqEfKiVIy0qVw1VTThEQ8oeW4yolYLJYREEsScSmkK6GU2RlyXzrS1VBKYU+2AfsBIRBTKuPHJRQZ7yLFnkTCFr1KJZ+QPjBgC2lhOdLDcuKaLqRXVNjHpMKJG9b77ZUghPRCBSVd/WDMGHsmgNOVHrYjvadHzHjodsysNC3ahY50YiJOB6jbiE8KV+opJWJHnkvoSFeDn2gX9gU1+Il2YRuQcsIp4LrN56aAq55SBES2g1om1Xp3pGeiXWoppKuglGKG7AdsA0IgBLbhOelS+Kyv9x/FkU9AlIJSLKbPqWxatEs+Id008RBQKyBGxZE+PHIEUBe7IkWTo0dHCl+qFjTNRzyeuy+ELaQDQkSX/aCmxo5KMAXVQjod6UQFUrgaGsq9kHcuKB6qx2vETjptf/ehkK6GUoR0OtLVIgtKpRQz2AaknJDiU3+qH8cGjhXZW0ABVz1eBUTLsnC0R0wnYzuoIVNU8rDwLvuCWibXiS9QB7vdD96ymME2IOQ4+YR0FcJnvkgL56J7usSzXOJhOm0LpUELu1IwN92RDpSnkO5cL0CiOiM9lRopfAXhUs6Vkx6WqFtdbff5ri6z+4HqaBc60okKqqrs85Vb8YpCunq8OnHb2+0YK0a7qIFCevjInPpDh9yvq0NHOilH6irrUFshVtd168SleKger470jv4OpCxxchtfwyq4CkqJOeJio2ppHiMG7/a+dvQO9rq6Dx3phAwjCCF9uCNdtwsXyC2kOxdVDduRLiMtTBQQ3Qrpu3YB995b2BUZlcXVhvcDQJ3YXFVlt3O+opJOYUUefy5HetD9IBaz3wunIz3K/cCygMcfB556qvB+dKQTVUyZIrZus6EppKtHOnHdOtJlG4wdC1RW6jmmckOKuF4y0tkX1DJpkpj5lk67LyrRkU7KFSleHTh2wNX+GQcoF1hURsaJe8xdBVaKh2Mrx6IqWaXtuMoJKcR6inZhPrdSGqsbUZUQn2e3rnS2ASHDkNnQOoR0KZ7mE9J1ioe5hHR5u7JSOGODJCoZ6YA7AfHgQeCss4C//Vvg4ottp91wpHBs+kVrLiE9iNkZQRaVnAWPMLPrnTE6UegHbW2Fs6h/8hPgQx8CPvhB4KGHcu8zNGTPdqAjnfhFCukH3F2L0wGqAa8irhTcpYua+MerI92y6EhXTSJhv5c8HxFSmCljxOB9oMtdZ6EjXT2ymNHS7W7wZhuoRzrS3QrpQ+khtPUJ5xnd0GqIxWJ2Yc/t+aj3uCOdswIIEYTpSA9aSA/iefPhdOEC0REQ83Hfffb/N24EXnop935RcaTLz4suIT1fUSlsR3qYQvqxY2avFSAF76Eh+ziHk04D//Zv9u933pl7P2cRw8CYIwrpEcOrkE4Xrnq8toEUe6WTnfhHCulHjwKDg8X37+oSi90D7AsqKXWGDIsZpNyYMva4kO7SkU4RVz2ZNmAxIzScMzPcLLwrM+pjiKGphtl4qpB9oeWYu8GbjnRChjE8GzoIIT3IXGjThPTR4kj/7W+zf//d73LvFxUBp9wc6WEK6XIWjFNIN7Ef1NSIWB4gf1948UVg50779/Xrc4sJsh80NgIVFUoPUwUU0iOGdENTSA8Pr0I6HenqGT/eXnPDTayI3Ke2VvwQNbCwR4g76EgPn4wj3aV4KNtgfK3hjrAIIQXcgdRAxmleCBlx1FTThERc0+J6ZYjXqClmpBMyjHJypIfpjs4npJvoxC0mpB8+DLzyirh9221iG3VHuuwHvb22sy1IR7pOId2kjHQgtyPdRCE9Fiu+4OjTT4vtJz4BnHqquP3yyyP3M3wqO4X0iEHhKnxKjXahI10d8bjtanbTDnRC68FrYY/xOqRcyQjpdKSHBtsgfKqT1WisbgTgrqiUEXAZKaIU2RdcO9J76EgnJIt8QroKp6q8aG9rE/EIknKOdjl2DEilgL6+7L+bhBQP8wnpzzwjskZPOw348IfF3157LXeOdFQEHOmSBkRf6O8XP4BaId3pSB8ctGeCBNEXnI50U/qCyYvuAsWLSi++KLYXXmgL6Vu2jNzPcAGHQnrEoJAePrINDh0SY3oxGO2iByniuslJp4CrBy/RLoOD9ncRno9IuTF17FQAJSw2SuFKGdIN3d7Xjr6hvqL7Z+IsuOCrUrwUNGQb0AmtFi8Z6WkrnYnYYUGDkOMMF9JVOlXHjROuTiDb0RlktEtnpy3wmuJIl+Kh8+8mUUw8XL9ebC+4AJg3T7RxR4ftuHMSFUd6RYWI8gDEZ8YZ8eIU2Usl1+wMp7CtM2IllyPdlL5gsiMdKNwX0mm7L5x7LnDKKeL21q0j9y1XR3prayuuuOIK1NfXo7GxEVdffTWO5QucB7Bnzx7EYrGcP7/4xS8y++X6/yOPPKLrZRhHqbEiFBDVMXGicESn07nHvuEw2kUPXhYcNfw8HFm8ONLldw/njC9iHhy79eAln9uyLBw8Jk5sk8dw4FBFQ1UDqhIit9GNE1cuSirbjqghU1Ry0RdYUNJDxpHuYuHd9r52pCzhGhlfY7ioQ0hQ6Ix2SSbtL8pOATFIR3o6bYt1prhw5fEkEnYGs0kUE9JldMXZZwvx+cQTxe/bt2fv199vv9YoXLg6iy+yH4wda2ew+iGXI10WlBoa1DxHPoY70i3LvL5gqpBeKNrlzTfFZ6W2VojoJ58s/k5Hus0VV1yBrVu3Ys2aNXj88cfxwgsv4Nprr827/4wZM3DgwIGsn3/+53/GmDFj8Kd/+qdZ+z7wwANZ+61atUrXyzAOL0J6f7/d36fwOlAZiYTtLnfjxKUjXQ9ehHTDz8ORxcv5SBaUnPn2xDw4duvBiwu3o78D/SkxNVY6R4l/YrGYp0UW5T5sA7V4WXiX2dx68OJIl7MC6qvqUZU0ULwiJAx0CulA7mzoIES8mhoh5AP2azLRhSsd+yZRSEhPpYCNG8Xts88W2/nzxXa4kC7f70QinCxurzj7gup+kMuRHsTMDGCkI72nx46tCbMvdHWZL6QX6gvr1ontWWeJc40U0t98c2TUg+FOyKSOB922bRtWr16Nl19+GWeddRYA4K677sKll16K7373u5g6deqI+yQSCTQ3Z1+sPProo/jYxz6GMcM+JI2NjSP2LRekcNXeLtZ1kLNpciEFxspKOkBVM2WKENEPHADOOKPwvsxI14MU0t0UMzgzQw9eol2k2M6inrlw7NaHFA+P9BzBQGoAlYnKvPtKcauxuhHVyepAjq9caB7TjD3te1wJiHIfCulq8bLwrixmcGaGWrwUlGTBg/2AEAcytkJmNesQEN9+O3ghPRYTr+HoUfGapk83w4Xb12c7g6MgHlpWtti/bZuIphkzBliwQPxt/nzgySdHCumyzcePN7NgMBwppHd02AuOqi4oOR3p8vOoW9wa7kiXx1BZGc5it/KcEwVHuhshfelSsT3xRFE06u8XgsK0afa+hjshtTjS165di8bGxsyFOAAsX74c8Xgc6+SbV4QNGzZg06ZNuPrqq0f87/Of/zwmTJiAc845B/fffz+sXIs0jFIaG+3ZTMXEKylcNTdH4zwcJbwsOMpoFz14aYP9+8WWIq5anNEuxU7DFNLNx7Sxu7+/H52dnVk/UWV8zXhUxCsAFBev6ITWh5eZAWwHPXhpA7mPvA9Rg/xMH+w+iLSVLrivLHiwDQhxoNuRHqYTd/iCoyY40gHbIWi6eDg0ZAudEhnrsmSJPS133jyx3bkze195YRsVB15YjnTdhZ3hjnRnQSkMYS3X7IwwBH03FIp2+eMfxfbcc8U2mRQFOwB4553sfQ13pGsR0ltaWjBpWOdPJpNoampCixvVC8B//Md/YOHChTjvvPOy/v6Nb3wD//3f/401a9bgIx/5CP7u7/4Od911V8HHGk0X47GYexeo/H+ZGgC14jbSor/fLmRGZTyMCrJgKUXyQsh2ymGoJT6Q55be3uz1ZXIhz0cU0s3FtLH79ttvR0NDQ+ZnxowZ3l6QQcRiMddxClLApXClHtkGxYoZg6nBTKwIhXS1eIl22d+1P+s+RA2T64SzYyg9lPmc5yNTzGAbEGITtJCeTgfnDB8upIfpSK+stKNm5PdQU8XDmhrb7TjciStjXZYssf92wgli++672fvu2ye2TmeuyeTKSNfhSJfmm6CjXYY70sNaADZKGen5HOnd3cDmzeK2dKQD9noBw4X00eRIv+mmm/IuKiZ/3nzzTd8H1dvbi4cffjino+3rX/86zj//fJxxxhn4yle+ghtvvBHf+c53Cj7eaLoYB9yLuBSu9OG2DWQhLZnUu7B0OSKLl++9V3xfuqH1UFdnzzRzO0OGbRA8UR27b775ZnR0dGR+3h1+sREx3AqIjFLQh9tYkcM9h2HBQiKW4AKLivES7SL3kQuUEjVUJCoyC7gWKyrRkU5IDpxCumXpF9K7uoSYDpSXIz0Ws8VCeaFhqngYi9kC4nAn7uuvi+3ixfbfpB41/LutdIhFxf2Vy5Eu/+YX+ZkbHLRjlIIuKMm+RyHdPfmE9M2bxXs5ZUp2oSifkG747AxPGelf+tKX8Fd/9VcF95k9ezaam5txSOZZHGdoaAitra2u8lF/+ctfoqenB1deeWXRfZcuXYrbbrsN/f39qMqzgvPNN9+MG264IfN7Z2dnpMV0tyKuM9qFqEW2QTE3tDMfPa5tad/yxCmkD4+iGw6jXfQxZYr4jnHggL1uTi54PgqPqI7dVVVVef8XRdwKiIwU0YcsZuw/Vnjwlm0wqW4SEnGujqwStwWltJXm7AyNTBkzBUd6juBA1wGcNvm0vPsxXoeQHDjFw54ee5E8Va6p4UK6FGarqwsvkKYCp5BuWeE60gEhFra3mx/tAoj36MCBbAHRsmwh/TTHuVZqUYcOiSns8vtu1Bzpzr5QeXz9H1UFpdpa8Xnv7RVCdn198BnpliVemwn9ABAiend39t9MI1+0i3SjnzbsO0cuIT2VsoV0Q/uCJyF94sSJmOjCWr9s2TK0t7djw4YNWHJ8CsszzzyDdDqNpU4bfx7+4z/+Ax/+8IddPdemTZswbty4ghfbo+5inI700HHrhja8kBZp5Od6YEB8z8x3uujpsWdeRqW4HyWmTAHeest9YY/no+CJ6tg92nCbDU3xUB8z6sWF67sdhWc3sJihD+kuPzZwDF39XRhbNTbnfkd7jmIwLRYu42Kj6mke04zNhza7niHDaBdCHEjxsK/Pnn6cSAjhTwVSSJcu2CBFPKeQ3tUlMr+B8J24pjvSAVtAdArp774rCgHJJLBwof338eNFYaSvTwgKc+aIv0v3l6Hi4Qici42qFtIB8T6995642J81y+5vuuM+qqrs9mlvpyPdC4Uc6QBwyinZf88lpB88KMT0RMLYhQa1eGQXLlyIlStX4pprrsH69evx4osv4rrrrsMnPvEJTD2uZO3btw8LFizA+vXrs+67Y8cOvPDCC/ibv/mbEY/729/+Fj/5yU+wZcsW7NixAz/+8Y/xrW99C1/4whd0vAxjoSM9fGQRee/ewvtFragcJSor7fNqoYKG7Ae1tXYMCVGHPB/Jz3o+KKSbD8duvWScuEUc6Yx20ceMhuNCeieF9LAYUzkGjdWNAAq3g+wHE2onoDJRGcShlRXT6sUX032dhQdveb5iXyDEgfOCQl6ENDSoW4RQinXSkR6WkC6fNwgnfD6iJKTnEhClG33hQltoBsRnJVdOurygior7S+dio8DIolJQQjqQveCofP6wHOnynNPVZb6Qns+RvmWL2A4X0qWw5hR0ZD9obrYX6DUMbWETDz30EBYsWICLL74Yl156KS644ALcd999mf8PDg5i+/bt6Onpybrf/fffj+nTp+OSSy4Z8ZgVFRW4++67sWzZMixevBj33nsv7rjjDtx66626XoaRyPNqMeGKi43qQ457Bw+K2Vj5kOcD6WAnanEzM8AZ6xLGItujnXxr5TixLArpUYFjtz6mjT0uXHUVHrwp4upDOtLb+9rR1d+Vdz+2gV5OaBADxzvt7+Tdh/noejmhXrTB3o7CjhBGuxCSg2TSdp/LL8A6xEMppMttEG5Yp5AetgsXiL6Q/tprYjs8zgLInZMeNUe6zsVGgZFFpSCFdPk62tvtNqUjvTiyH3R1iXx7QIgB0pF+6qnZ+0tx0+kSjoAb1VO0ixeamprw8MMP5/3/zJkzYcnVdx1861vfwre+9a2c91m5ciVWrlyp7BijihSuhufxD4fClT6cs7H27QNmz869H4V0vUyfDmzY4M6RHpXCftRwcz7q6hIROwALe6bDsVsfGfGwo/DgnYl2YZSCcsZWjUVDVQM6+jvwbue7WDRxUc79KKTr5cSGE/H6wdcLirgUcPUiz0d7O/O3Qd9QH9r72gHwfETICMaOFV9ugxDSwxAPnY70sFy4wEghva4uvGMpRiFH+umnj9xfCgRSNBwasl9nVC5cnY506RwOwpEu/66TXI70sIX0jg7bxWmqkO5cK6K1VUQIHDokzmexWHbEEWCLlUeOiMzeyspICOlc/jCCOGOEcugZAMTf6UjXRyzmLt6FQrpe3DjSWVDSizwfFeoHThOJqWM+Ibo5sVF0lnfa38lZjACAwdQgjvSIC2eKuHrIxLsUyEmXIu7kOjNzGaNORsQtIKTv7xKuPAq4enDTBrKgVJWowrhqzQu7ERI1pICoU0jv6BCOTrkQfBCLbpnqSDd9gUXAuyNdioRSNDx4EEinhSAdlQXWdEe7OB3plmUXl4J2pIcd7SI/984oBFOLSomEHe8i3zfpRp87d+RaEuPHAxUV4rYUDSikEx1IAbe31z6XDOfoUXsmhaH5/JEn12ys4VBI14vXaBeiHulILySks5hBiC1cdQ92o7W3Nec+UjysTFSiqSZEB9goRrZDoXzu9zrFoDK9noO3Dty4oWW0Cx3penBT2HPmo8eYjUdINjqF9MZGIH5cpjl6lI70Qr+bxHAhvbcXePttcTuXkC5d5/JiVW4NzoUegXOxUZ1C+tGjopjS1yd+Dzoj3ZRoF0k8LuIRTEW2jzx35ctHB4RDVTp/pWhAIZ3ooKrKFqTyxSnIMb25WexP1OMmG5pCul6Gz4jLBaNd9CId6fL7TS6itm4OITqoTlZnXOZ72vfk3Ee6Q09oOAHxGL+i6UDmpBdypDvbgajHjRua0S56kf2ge7AbbX1tOffhrABCCqBTSE8kbFH2yJHwhHSTHOn5fjcJ2WbyfXv7beEwHzcud0TAcCE9AuLhCHRnpDujXWQ/qKkJxo2dy5EeVl+orraLa4DoByYXuIcL6fny0SVS3KSQTnTjjHfJhXSHnsBrQG0Ui3bp7BQ/gNHngEgj26BQPrcsZlDE1UNDg30tka8vyPbh+YiUOyc2HHeB5slJl8KiFLmIejJCeh5H+mBqMOPElTEwRC1uFhtlMUMvNRU1mFgrLnTzFTTkeYptQEgOdArpQLaAKKNd6Eg3W0gfnm3/1ltie9JJuUXPfI70KAkHQUa7BFlQAmxHeltb+H0hFhPrMkhM7gfAyL7w5ptiuyj32kQjFhylkE50QSE9fIpFu8j+39CQfd4j6pCLvO7ZI9ZnyYXsIzNnBnFE5Ynb85Hcj5ByZWbjTAD5BUSKh/qR4ng+8XB/135YsFCZqMSkuohklEYM+fl+r/M9pNKpnPvIWRsygoSoR763+fqC/LssABJCHEgBUYp7zgX2VJBLQCznjPR8v5vEcBeuU0jPhVM8TKejOYVX9oPeXvED6HekB7HQKGC/jnffFe0DmNMXTO4HwMi+sGOH2M6bl3t/OtJJUBQTrugA1U+xbGjGuuhn2jQRXTQ0lLugkUpRxA2CYn1Bno/YBqTckYKUm2gXooeMG7rIrIDp9dMZr6OJKWOmIBlPImWlMhEuTnoGe3C4R1x8UcTVR7GZAbKPsA0IyYEUECW6HOlhRbt0doa/wCIwUjA0dYFFIFv0TaeLC+ky7mVwUNwnAuLhCHK5BYf3DT+Y4EjfuVNs6+rCzUyOqpDe2WnPqpk7N/f+Ukjfvx/o6hI/gNFFJV4hRBTprqUjPTzmzBHbnTvtIqUTCun6iceBWbPEbTnGOTlwQIjsyaTR5+HIIwXyfEI6ixmECDIL/OUTcTsppOtmbpP4Er+7bTcGU4Mj/i8jX9gG+kjEE5mFXHe37R7xf1nMGFs5Fo3VjUEeWllxQn3hopIU2DkrgJAcBCWkHzhgx0oEKaQ73UimuHBz/W4Sss3SaREHUkxIr6iwZxns329Hu0TporWiQmSWS+rqxIW3KnI50oMS0mVf2LVLbMMsKAHRFdKlG33SpPxFFqcjXfaDsWONjnWgkB5RpCC1Z0/u/8txbwbjPbUxc6YYJ3p7cy92KYscbAO9OAsaw5H9Y8aM6Cx+HkVkYS9XG1gWHemESGS0Sz5HuhSumJGuj6ljp6ImWYOUlcrZDmyDYDhpvBAWth/dPuJ/sg1mNs5EzOTFtCLO7HEiH29H646c/6cjnZACBCWkbz9+jozFghG0a2uFOArYX+xNcqSbLCBWVtqfg8OHiwvpQHZOehQd6UD2Z191P5Cf+b4++8I+aEe6zH4Ps6AEREtId86oefttcTufGx3IFtIj0g8opEcUmQ29c6cQqoYjC2fMhdZHMmmLuHKcdCKLb4XOGcQ/8v3dkeM6kAJuMMi4MzlOOmlrA44dE7dZVCLlzpxxYtDY0boD1rDBO22lsbNNXLTOaZoT+LGVC/FYHPPGi5PWW0dHDt5SVJTOdaKH+ePnAwC2HxkppDMfPRjmTzjeBjmKGccGjqG1V7hg2Q6E5GC4YKhLSN+2TWzHjw/GFRSL2cK5zLymI909zgKIjMbJlwsN2EL6vn3RdKQD2UUl1f1gzBigulrcfuMNsQ06I11ikpBucsQRkNuR7qYfUEgnupk9W4ylx47Z51xJR4c986XQ55X4R76/FNLDo5AjfffxGeMsKOlFGi3eemtkYU+K69OmZc/8I6QcmT1uNpLxJLoHu7GvK3sq077Ofegb6kMynsw414kepBv67daR1b8dbRTSgyAjpOcQcWVBaVbjrECPqdxYMGEBAGBn684RMUc7W0UbjK8Zj/oqhXm3hIwWgnKkSyE9KBcuAIwbl/07Henuke20dq3YTptWWPSUAuKOHUB7u32fKKFTSI/F7PgbKaQH7UiXhB3t4ow5iUo/cArpbhzphw7Z0RqG9wMK6RGlstJ2pW8fdg0ihavJk42OFRoVOAXE4VBID4ZCQrrsG4Vm1BH/zJkjvud0ddlriUjczGokpFyoSFRk4hSGO3GlE3pW4ywk4wrzJckI5jXld6S/fVR8iaKQrpdCbmj5Nym2Ez1Mr5+OmmQNBtOD2N2enVWfaYMJbANCcqLbkS7drwMDYhukkD5cMDRJSDfdiSsLIK+8IrazihSEpZD+8stiW1endrHOINAppAO2kC4vMoPOSJeY5Eg3XUiX/eDwYVucLOTwnThRLH6XTgOvvir+RiGd6GL+8e+2+YR0utH1k09Ib2uzZ3PN4Qx9rTiF9OFuaNk35vM6UCtVVbbrf3hfoJBOSDb5nLjSHS1jR4g+8jnSuwe6ceDYAQC22E70IPvBrrZdI9zQsshEEVcv8VjcLmgMK+zJIpPsK4SQYQTlSJdIMTEInMJ5XZ34oh8WTuG8qkrtQpY6kCLvxo1iWyzXUoqFGzaI7dSpwp0UJXRmpAMjhfOwHOkU0t0j22hwENi0SdyWLuBcJBLCBQzYRSgK6UQXUpjKJ6RTuNKPfI+HZ0NLN/rkyeaf56LO7NliTZzubjsTHRCi+ptvitsLFoRzbOWE7AtyBqqEQjoh2eTLhpZOaAq4+snnSJezAppqmjCuZtyI+xF1TKufhtqKWgylh7CrbVfm74OpwUy0Cx3p+pHv8ZtH3sz6O2cFEFKEoIX0sKJdTBIPTXejA3Y7tbWJ7QknFN5fOtLlgpaGi4c5CcqRLgmqL4wZk13UCDvaJUpCem2t+AGESAMU7wsy3kUKOob3BQrpEWbRIrHdvDn771I8pCNdP1Ic3LVLLCYt2bJFbGUbEX1UVNjv8+uv239vaRFRI/E4ZwUEwWmnie1rr2X/nfE6hGRz8qSTAQCvH3o96+9bD28FQOEqCKTL9t2Od3Fs4Fjm77INZHY00Uc8Fs85O2NX2y4MpYdQW1GLafVmX0SNBuRnffgMGTrSCSmCUzBMJm3RSBVhCulOwdAk8TCIxVb9MrzdijnShy8sGrWFRoHghfSgFhuNx7Nfj0lFJdOFdCD7nFVZWfwcJoV0CYV0ooszzhDbV1/NjrSQsUKnnx78MZUbU6eKc2oqlV3QkGIi2yAYpIjrFNLleiSzZoU7I7FckJ91p5A+MGA71BcuDP6YCDGRM6ecCQDYeGAj0lY68/dNLZsAAGdMOSOMwyorJtZNxNSxU2HByrzvAPBaiziBnT6Zg3cQyKKSsw02HxJfphZMWIB4jJcpupFCuiwiAUAqncKWQ8IRsnACB29CcuIUD+vq1MdxNDRkC8dBRruY5EhXXaDQzXCx0K0jXWK4eJiTIIX0RGJk5IpOnM8Vdl+ImpDuLHhMmyYKE4WIWF/gN9QIc/LJogDe1mYvbtvdbTtAz+C1uHZiMWDJEnFbRpsBFNKDJpcbWsZrnXlm8MdTjixeLLavvSbWCQGArVuFmN7YWDgWjZByYuGEhahKVKGzvxO728QCfwePHcSBYwcQQwynTjo15CMsD5ZMEYP3xgMbM3/bdHATAArpQXH21LMBAC/vfznzt1f2i8H7rClnhXJM5YYs7L164NVMVv32o9vRM9iDuoo6OtIJyYdTPNSRaR2PZwt35epILya+mcZwt3QxIX3ixOyCCR3pI3EK6RMmBPuZcL6esPvC2LH27SjFHAHFZ2YA2Y50Z2a6oUTszEScVFUBp5wibsv1LDZvFu70yZOB5ubwjq2cGC6kW5btjJYCL9GLLBqtX2//TS5+fvbZwR9POTJ/PlBdDRw7Zq8ZIM9LZ54ZvXVzCNFFRaICp04WYvmGA2LgkI7ck8afhLrKCHw5HgVIIV22AeBwpDdTSA+Cc6adAwBYv289rONTK6WofvY0Dt5BMK9pHhqrG9Gf6sfrB8WX1w37RZ9Y3LwYiXgEohQICYOKCvu2ri+5TlG2XDPSo8bwdiomICYS2aKN4S7cnDhd27qF9CD7AUBHuh+cbTV9evH9nUJ6c7PxUU4U0iOOFAn/8AexlUIi3ejBcdZx09Qf/yi227YBra1CVDz55PCOq5w491wxO2PvXmDPHvE36Ug/i6a2QEgmgaVLxe3nnxdbzgogJDfnTjsXAPDcnucAAP+z938AAEumLgnrkMqOs6aKweGld18CILK5D3YfRDKe5KyAgFjcvBjJeBKHug9hT/sepK10RsSV7UP0EovFMgWNdfvWAbBnBchiEyGkCLqEdKdwF2S0i0mO9KjhbKfa2uyiRD6cLvQoCunO1zjahHSn85tCujechUCvjvQI9AMK6RFn+XKxXbNGbJ98Umzf//5wjqccufBCMcNoyxbgvfeAp58Wf7/gAmZzB0VdnT0z4IUXgN27xYLPiYT9d6KfD3xAbJ95RszM+P3vxe/nnx/eMRFiIpfMuQQA8OROMWj/fpfoLB+c/cHQjqnc+JMT/wTJeBI7WndgZ+tOPLXrKQDAudPP5ayAgKhOVmPZ9GUAgCfefgLr3luHjv4O1FfV4+SJdCIExfkzxCAt+8BTu8V22YxloR0Tyc3dd9+NmTNnorq6GkuXLsV651TMHPziF7/AggULUF1djVNPPRVPPPFE1v8ty8Itt9yCKVOmoKamBsuXL8fbclrhcVpbW3HFFVegvr4ejY2NuPrqq3Hs2DEQB5WVeh7X6YYNUkB0Cpdhi4fOY1i5MtzjcIMzjqKhwV2RxSmkRzHaxVls0SGkOz/7QX8e0/ZaRoFms+ciakK6H0c6hXSim4svFufnLVuAXbuA554Tf4/CODNaGD8eOEeYefD//h/wlLj+wMUXh3dM5YgsHj32GPDoo+L2+96XHdtG9CLbYM0akZW+a5e4tpAFP0KI4KKZFyEZT2JX2y68uPdFvLxPxFlQSA+O+qr6jIC4esdq/H4nixlh8OH5HwYA/Hr7r/Hr7b8GAFw671JUJCoK3Y0o5EMnfQiA6AdbDm3BG4ffQDKexIo5K0I+MuLk5z//OW644Qbceuut2LhxI04//XSsWLEChw4dyrn/Sy+9hE9+8pO4+uqr8eqrr2LVqlVYtWoVtmzZktnn29/+Nu68807cc889WLduHerq6rBixQr09fVl9rniiiuwdetWrFmzBo8//jheeOEFXHvttdpfb6T41Kf0PG5NjX07SAHRKXqZcCH13/8N3HwzcM89YR9JcZyLo7otsDjFc6eYGBWCFNKDzgZ/5x37djIZ7HMPJ2pCulMMd+NIj1g/oJAeccaPB847T9y+7DKgp0d8Zk/lrORAuewysb39diGmA8Cll4Z3POXIFVeI7W9+A9xxh7j9v/5XeMdTjixbJr57t7YCl18u/nbRRdEY6wkJkrFVYzOu9AseuAAWLJw55UxMqzffgTGa+LOT/gwA8J2XvoPH33ocAPCnc/80zEMqOy6fLwaLNbvW4N9e/DcAwKr5q0I8ovJjcfNinNhwInqHevEX//0XAIA/OeFPMK7GRSQBCYw77rgD11xzDa666iosWrQI99xzD2pra3H//ffn3P+HP/whVq5ciS9/+ctYuHAhbrvtNpx55pn40Y9+BEC40X/wgx/ga1/7Gi6//HKcdtpp+NnPfob9+/fjscceAwBs27YNq1evxk9+8hMsXboUF1xwAe666y488sgj2L9/f1Av3VxefBG49VbgW9/S8/hOATHIvGCnI72/P7jnzccHPiDeY6dIHQVmznS3nxQQJ0yI5nR23UJ6dbV9242zWSXz5olthQHF/agJ6XPm2LfdCOnO2RyzZqk/HsVQSB8F/N3fie2bb4rttddyYb+g+eu/FuPe7t3A4KAobnCh0WA55RTxvqdSwL59wsTxsY+FfVTlRTIJfO5z4vbevWL7N38T3vEQYjJfWvalrN9vOPeGkI6kfLny9CtRW1GLdzreQX+qH0umLGE2d8DMGz8vU9AAgOn103H5gstDPKLyIxaL4W/OFIP19qPbAQBXn3F1mIdEhjEwMIANGzZguWOKXzwex/Lly7F27dqc91m7dm3W/gCwYsWKzP67d+9GS0tL1j4NDQ1YunRpZp+1a9eisbERZzkWHFq+fDni8TjWrVuX83n7+/vR2dmZ9TNqOe884J/+SZ/ItmqV2AYt4sUdEpFTDCPu+MIXhJP/3nvd7S+F9AjEWeTEmZGuyzF+8cXic3nNNXoePx8//CFw1VXAxo3BPm8uxo61b0dBSJ89277tJrKookK07/vfbwucBkMhfRTwiU8Af/mX4vb55wP/+I/hHk85MnUqcNddov83NwM//nHYR1Se3H23MG9UVIj2cBY2STD8/d8LZzogZrp+5CPhHg8hpvKBWR/A9y75Hk4afxK+cv5X8Jen/mXYh1R2TKqbhH+/9N9RmajEpLpJuPfP7kWMToTAue/P7sOKOSuwZMoS/Opjv0J1srr4nYhSblh2A9534vsAAB87+WP45KmfDPmIiJMjR44glUph8rAvtpMnT0ZLS0vO+7S0tBTcX26L7TNp2CKXyWQSTU1NeZ/39ttvR0NDQ+ZnhhsnIsnN8uXA448D27YF/9z/8z/AnXcCKxjx5Jk77wSOHgXmz3e3/wc+IKIsPvpRvceli/p6IUD9/d/ru/j+3e+AgweBE0/U8/j5mDkTuP9+4dgLm6Ym4ZBfsCBbVDeVKVOEw+6znxUCmRvuu08sthaB2SchB/0QFcTjwEMPAT/6kVgDgdeA4XDNNcAnPyni0HStOUMKs3gxsH8/0NcXjULtaGTMGDHTtbNTz+w+QkYTNyy7ATcsoxM9TD6z+DP4yKKPoDJRicoEB+8wmDJ2ClZ/anXYh1HW1FbU4rnPPIfO/k40VHPwJqVz880344Yb7HGts7OTYrofZH5o0Jx/vvghpeElT3vmTDGdOsoizne+o/fxq6qiGXujkkQC2LxZfE7iEfFD//u/h30E2qCQPooYxyjD0KF4Gz7JJNshbGIxiuiEkOgwppKDBiGxWIwiuqFMmDABiUQCBw8ezPr7wYMH0ZzH6dfc3Fxwf7k9ePAgpjgWdjt48CAWL16c2Wf4YqZDQ0NobW3N+7xVVVWoKnfBixCvRFlEJ8HBc6sxRKSUQQghhBBCCCGElBeVlZVYsmQJnn766czf0uk0nn76aSyTeXrDWLZsWdb+ALBmzZrM/rNmzUJzc3PWPp2dnVi3bl1mn2XLlqG9vR0bNmzI7PPMM88gnU5j6dKlyl4fIYQQEiXoSCeEEEIIIYQQQgzlhhtuwGc+8xmcddZZOOecc/CDH/wA3d3duOqqqwAAV155JaZNm4bbb78dAPDFL34RF154Ib73ve/hsssuwyOPPIJXXnkF9913HwAxA+H666/HN7/5TcybNw+zZs3C17/+dUydOhWrji9yuXDhQqxcuRLXXHMN7rnnHgwODuK6667DJz7xCUx1s3gcIYQQMgqhkE4IIYQQQgghhBjKxz/+cRw+fBi33HILWlpasHjxYqxevTqzWOjevXsRd+TmnnfeeXj44Yfxta99DV/96lcxb948PPbYYzjFsWjejTfeiO7ublx77bVob2/HBRdcgNWrV6O62l7w96GHHsJ1112Hiy++GPF4HB/5yEdw5513BvfCCSGEEMOIWZZlhX0QQdPZ2YmGhgZ0dHSgvr4+7MMhhBBCcsLxyobvBSGEkCjA8cqG7wUhhJAo4GW8YkY6IYQQQgghhBBCCCGEEFIACumEEEIIIYQQQgghhBBCSAEopBNCCCGEEEIIIYQQQgghBaCQTgghhBBCCCGEEEIIIYQUgEI6IYQQQgghhBBCCCGEEFIACumEEEIIIYQQQgghhBBCSAEopBNCCCGEEEIIIYQQQgghBaCQTgghhBBCCCGEEEIIIYQUgEI6IYQQQgghhBBCCCGEEFIACumEEEIIIYQQQgghhBBCSAEopBNCCCGEEEIIIYQQQgghBUiGfQBhYFkWAKCzszPkIyGEEELyI8cpOW6VMxy7CSGERAGO3TYcuwkhhEQBL2N3WQrpXV1dAIAZM2aEfCSEEEJIcbq6utDQ0BD2YYQKx25CCCFRgmM3x25CCCHRws3YHbPKsFSeTqexf/9+jB07FrFYzNdjdXZ2YsaMGXj33XdRX1+v6AiDha8hfKJ+/ABfgwlE/fgBvobhWJaFrq4uTJ06FfF4eaexcezOhq8hfKJ+/ABfgwlE/fgBvobhcOy24didDV9D+ET9+AG+BhOI+vEDfA3D8TJ2l6UjPR6PY/r06Uofs76+PrIfPglfQ/hE/fgBvgYTiPrxA3wNTsrdzSbh2J0bvobwifrxA3wNJhD14wf4Gpxw7BZw7M4NX0P4RP34Ab4GE4j68QN8DU7cjt3lXSInhBBCCCGEEEIIIYQQQopAIZ0QQgghhBBCCCGEEEIIKQCFdJ9UVVXh1ltvRVVVVdiHUjJ8DeET9eMH+BpMIOrHD/A1kGAYDW3E1xA+UT9+gK/BBKJ+/ABfAwmG0dBGfA3hE/XjB/gaTCDqxw/wNfihLBcbJYQQQgghhBBCCCGEEELcQkc6IYQQQgghhBBCCCGEEFIACumEEEIIIYQQQgghhBBCSAEopBNCCCGEEEIIIYQQQgghBaCQTgghhBBCCCGEEEIIIYQUgEK6C+6++27MnDkT1dXVWLp0KdavX19w/1/84hdYsGABqqurceqpp+KJJ54I6Ejz4+U1PPjgg4jFYlk/1dXVAR5tNi+88AI+9KEPYerUqYjFYnjssceK3ue5557DmWeeiaqqKsydOxcPPvig9uMshNfX8Nxzz41og1gshpaWlmAOeBi33347zj77bIwdOxaTJk3CqlWrsH379qL3M6kvlPIaTOoLP/7xj3Haaaehvr4e9fX1WLZsGf7f//t/Be9j0vsPeH8NJr3/ufjXf/1XxGIxXH/99QX3M60dygWO3Ry7/cKxO/y+wLE7/HMRx24z2qFc4NjNsdsvHLvD7wscu8M/F3Hs1tsOFNKL8POf/xw33HADbr31VmzcuBGnn346VqxYgUOHDuXc/6WXXsInP/lJXH311Xj11VexatUqrFq1Clu2bAn4yG28vgYAqK+vx4EDBzI/77zzToBHnE13dzdOP/103H333a723717Ny677DK8//3vx6ZNm3D99dfjb/7mb/Dkk09qPtL8eH0Nku3bt2e1w6RJkzQdYWGef/55fP7zn8cf//hHrFmzBoODg7jkkkvQ3d2d9z6m9YVSXgNgTl+YPn06/vVf/xUbNmzAK6+8gg984AO4/PLLsXXr1pz7m/b+A95fA2DO+z+cl19+Gffeey9OO+20gvuZ2A7lAMfu8PsLx26O3Srg2B3+uYhjtxntUA5w7A6/v3Ds5titAo7d4Z+LOHZrbgeLFOScc86xPv/5z2d+T6VS1tSpU63bb7895/4f+9jHrMsuuyzrb0uXLrU++9nPaj3OQnh9DQ888IDV0NAQ0NF5A4D16KOPFtznxhtvtE4++eSsv3384x+3VqxYofHI3OPmNTz77LMWAKutrS2QY/LKoUOHLADW888/n3cfE/uCEzevweS+YFmWNW7cOOsnP/lJzv+Z/v5LCr0GU9//rq4ua968edaaNWusCy+80PriF7+Yd9+otMNog2O3WXDsNgOO3WbAsTscOHabD8dus+DYbQYcu82AY3c4mDp205FegIGBAWzYsAHLly/P/C0ej2P58uVYu3ZtzvusXbs2a38AWLFiRd79dVPKawCAY8eO4cQTT8SMGTOKVq5Mw7Q28MPixYsxZcoUfPCDH8SLL74Y9uFk6OjoAAA0NTXl3cf0dnDzGgAz+0IqlcIjjzyC7u5uLFu2LOc+pr//bl4DYOb7//nPfx6XXXbZiPc3F6a3w2iEY7dZ/cUtprWBHzh264Njd7hw7DajHUYjHLvN6i9uMa0N/MCxWx8cu8OFY7f6dqCQXoAjR44glUph8uTJWX+fPHly3syslpYWT/vrppTXMH/+fNx///349a9/jf/6r/9COp3Geeedh/feey+IQ/ZNvjbo7OxEb29vSEfljSlTpuCee+7Br371K/zqV7/CjBkzcNFFF2Hjxo1hHxrS6TSuv/56nH/++TjllFPy7mdaX3Di9jWY1hc2b96MMWPGoKqqCn/7t3+LRx99FIsWLcq5r6nvv5fXYNr7DwCPPPIINm7ciNtvv93V/qa2w2iGY7c5/cULHLv1wrGbY7cfOHab0Q6jGY7d5vQXL3Ds1gvHbo7dfuDYra8dksofkUSeZcuWZVWqzjvvPCxcuBD33nsvbrvtthCPrHyYP38+5s+fn/n9vPPOw86dO/H9738f//mf/xnikYmq4JYtW/A///M/oR6HH9y+BtP6wvz587Fp0yZ0dHTgl7/8JT7zmc/g+eefzzsgmoiX12Da+//uu+/ii1/8ItasWWPU4iuEAOb1l3KEY7deOHaHB8duQvRgWn8pRzh264Vjd3hw7NYHhfQCTJgwAYlEAgcPHsz6+8GDB9Hc3JzzPs3NzZ72100pr2E4FRUVOOOMM7Bjxw4dh6icfG1QX1+PmpqakI7KP+ecc07og+h1112Hxx9/HC+88AKmT59ecF/T+oLEy2sYTth9obKyEnPnzgUALFmyBC+//DJ++MMf4t577x2xr6nvv5fXMJyw3/8NGzbg0KFDOPPMMzN/S6VSeOGFF/CjH/0I/f39SCQSWfcxtR1GMxy7BWH3F69w7NYHx26O3X7h2G1GO4xmOHYLwu4vXuHYrQ+O3Ry7/cKxW187MNqlAJWVlViyZAmefvrpzN/S6TSefvrpvNlCy5Yty9ofANasWVMwi0gnpbyG4aRSKWzevBlTpkzRdZhKMa0NVLFp06bQ2sCyLFx33XV49NFH8cwzz2DWrFlF72NaO5TyGoZjWl9Ip9Po7+/P+T/T3v98FHoNwwn7/b/44ouxefNmbNq0KfNz1lln4YorrsCmTZtGDOZAdNphNMGxWxB2f/GKaW2gCo7d/uDYbWY/4NhNVMOxWxB2f/GKaW2gCo7d/uDYbWY/4NitEOXLl44yHnnkEauqqsp68MEHrTfeeMO69tprrcbGRqulpcWyLMv69Kc/bd10002Z/V988UUrmUxa3/3ud61t27ZZt956q1VRUWFt3rw5rJfg+TX88z//s/Xkk09aO3futDZs2GB94hOfsKqrq62tW7eGcvxdXV3Wq6++ar366qsWAOuOO+6wXn31Veudd96xLMuybrrpJuvTn/50Zv9du3ZZtbW11pe//GVr27Zt1t13320lEglr9erVoRy/ZXl/Dd///vetxx57zHr77betzZs3W1/84heteDxuPfXUU6Ec/+c+9zmroaHBeu6556wDBw5kfnp6ejL7mN4XSnkNJvWFm266yXr++eet3bt3W6+//rp10003WbFYzPr973+f89hNe/8ty/trMOn9z8fw1cOj0A7lAMfu8PsLx26O3Srg2B3+uYhjtxntUA5w7A6/v3Ds5titAo7d4Z+LOHbrbQcK6S646667rBNOOMGqrKy0zjnnHOuPf/xj5n8XXnih9ZnPfCZr///+7/+2TjrpJKuystI6+eSTrd/97ncBH/FIvLyG66+/PrPv5MmTrUsvvdTauHFjCEctePbZZy0AI37kMX/mM5+xLrzwwhH3Wbx4sVVZWWnNnj3beuCBBwI/7uHH4+U1/Nu//Zs1Z84cq7q62mpqarIuuugi65lnngnn4C0r57EDyHpfTe8LpbwGk/rCX//1X1snnniiVVlZaU2cONG6+OKLMwOhZZn//luW99dg0vufj+EDehTaoVzg2M2x2y8cu8PvCxy7wz8Xcew2ox3KBY7dHLv9wrE7/L7AsTv8cxHHbr3tELMsyyrdz04IIYQQQgghhBBCCCGEjG6YkU4IIYQQQgghhBBCCCGEFIBCOiGEEEIIIYQQQgghhBBSAArphBBCCCGEEEIIIYQQQkgBKKQTQgghhBBCCCGEEEIIIQWgkE4IIYQQQgghhBBCCCGEFIBCOiGEEEIIIYQQQgghhBBSAArphBBCCCGEEEIIIYQQQkgBKKQTQgghhBBCCCGEEEIIIQWgkE4IIYQQQgghhBBCCCGEFIBCOiGEEEIIIYQQQgghhBBSAArphBBCCCGEEEIIIYQQQkgBKKQTQgghhBBCCCGEEEIIIQX4/wG60XC3pjC+cgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plotting solution\n", "with torch.no_grad():\n",