MAINT Enable ruff RET (flake8-return) rules and fix all violations

build_scripts/conditional_jb_build.py

@@ -27,9 +27,8 @@ def main():
             cwd=os.path.dirname(os.path.dirname(__file__)),  # Repository root
         )
         return result.returncode
-    else:
-        print("RUN_LONG_PRECOMMIT not set: Skipping full Jupyter Book build (fast validation runs instead)")
-        return 0
+    print("RUN_LONG_PRECOMMIT not set: Skipping full Jupyter Book build (fast validation runs instead)")
+    return 0
 
 
 if __name__ == "__main__":

build_scripts/prepare_package.py

@@ -110,9 +110,8 @@ def copy_frontend_to_package(frontend_dist: Path, backend_frontend: Path) -> boo
     if index_html.exists():
         print("✓ Frontend successfully copied to package")
         return True
-    else:
-        print("ERROR: index.html not found after copy")
-        return False
+    print("ERROR: index.html not found after copy")
+    return False
 
 
 def main():

build_scripts/validate_jupyter_book.py

@@ -327,9 +327,8 @@ def main():
             print(f"  • {error}", file=sys.stderr)
         print("=" * 80, file=sys.stderr)
         return 1
-    else:
-        print("\n[OK] All Jupyter Book validations passed!")
-        return 0
+    print("\n[OK] All Jupyter Book validations passed!")
+    return 0
 
 
 if __name__ == "__main__":

doc/deployment/deploy_hf_model_aml.ipynb

@@ -222,9 +222,7 @@
     "    # Generate a 5-char random alphanumeric string and append to '-'\n",
     "    random_suffix = \"-\" + \"\".join(random.choices(string.ascii_letters + string.digits, k=5))\n",
     "\n",
-    "    updated_name = f\"{base_name}{random_suffix}\"\n",
-    "\n",
-    "    return updated_name"
+    "    return f\"{base_name}{random_suffix}\""
    ]
   },
   {

doc/deployment/deploy_hf_model_aml.py

@@ -185,9 +185,7 @@ def get_updated_endpoint_name(endpoint_name):
     # Generate a 5-char random alphanumeric string and append to '-'
     random_suffix = "-" + "".join(random.choices(string.ascii_letters + string.digits, k=5))
 
-    updated_name = f"{base_name}{random_suffix}"
-
-    return updated_name
+    return f"{base_name}{random_suffix}"
 
 
 # %%

doc/deployment/download_and_register_hf_model_aml.ipynb

@@ -340,8 +340,7 @@
     "\n",
     "    # Find the model with the maximum version number\n",
     "    max_version = max(models, key=lambda x: int(x.version)).version\n",
-    "    model_max_version = str(int(max_version))\n",
-    "    return model_max_version"
+    "    return str(int(max_version))"
    ]
   },
   {

doc/deployment/download_and_register_hf_model_aml.py

@@ -262,8 +262,7 @@ def get_max_model_version(models: list) -> str:
 
     # Find the model with the maximum version number
     max_version = max(models, key=lambda x: int(x.version)).version
-    model_max_version = str(int(max_version))
-    return model_max_version
+    return str(int(max_version))
 
 
 # %%

frontend/dev.py

@@ -120,9 +120,7 @@ def start_backend(initializers: list[str] | None = None):
         cmd.extend(["--initializers"] + initializers)
 
     # Start backend
-    backend = subprocess.Popen(cmd, env=env)
-
-    return backend
+    return subprocess.Popen(cmd, env=env)
 
 
 def start_frontend():
@@ -134,9 +132,7 @@ def start_frontend():
 
     # Start frontend process
     npm_cmd = "npm.cmd" if is_windows() else "npm"
-    frontend = subprocess.Popen([npm_cmd, "run", "dev"])
-
-    return frontend
+    return subprocess.Popen([npm_cmd, "run", "dev"])
 
 
 def start_servers():
@@ -199,7 +195,7 @@ def main():
         if command == "stop":
             stop_servers()
             return
-        elif command == "restart":
+        if command == "restart":
             stop_servers()
             time.sleep(1)
         elif command == "start":

pyproject.toml

@@ -252,6 +252,7 @@ select = [
     "DOC",  # https://docs.astral.sh/ruff/rules/#pydoclint-doc
     "F401",  # unused-import
     "I",  # isort
+    "RET",  # https://docs.astral.sh/ruff/rules/#flake8-return-ret
     "W",  # https://docs.astral.sh/ruff/rules/#pycodestyle-w
 ]
 ignore = [

pyrit/analytics/text_matching.py

@@ -69,8 +69,7 @@ def is_match(self, *, target: str, text: str) -> bool:
             text = text.strip()
         if self._case_sensitive:
             return target in text
-        else:
-            return target.lower() in text.lower()
+        return target.lower() in text.lower()
 
 
 class ApproximateTextMatching(TextMatching):
@@ -141,9 +140,7 @@ def _calculate_ngram_overlap(self, *, target: str, text: str) -> float:
         matching_ngrams = sum(int(ngram in text_str) for ngram in target_ngrams)
 
         # Calculate proportion of matching n-grams
-        score = matching_ngrams / len(target_ngrams)
-
-        return score
+        return matching_ngrams / len(target_ngrams)
 
     def get_overlap_score(self, *, target: str, text: str) -> float:
         """

pyrit/auth/azure_auth.py

@@ -221,8 +221,7 @@ def get_azure_token_provider(scope: str) -> Callable[[], str]:
         >>> token = token_provider()  # Get current token
     """
     try:
-        token_provider = get_bearer_token_provider(DefaultAzureCredential(), scope)
-        return token_provider
+        return get_bearer_token_provider(DefaultAzureCredential(), scope)
     except Exception as e:
         logger.error(f"Failed to obtain token provider for '{scope}': {e}")
         raise
@@ -246,8 +245,7 @@ def get_azure_async_token_provider(scope: str):  # type: ignore[no-untyped-def]
         >>> token = await token_provider()  # Get current token (in async context)
     """
     try:
-        token_provider = get_async_bearer_token_provider(AsyncDefaultAzureCredential(), scope)
-        return token_provider
+        return get_async_bearer_token_provider(AsyncDefaultAzureCredential(), scope)
     except Exception as e:
         logger.error(f"Failed to obtain async token provider for '{scope}': {e}")
         raise
@@ -334,13 +332,12 @@ def get_speech_config(resource_id: Union[str, None], key: Union[str, None], regi
             subscription=key,
             region=region,
         )
-    elif resource_id and region:
+    if resource_id and region:
         return get_speech_config_from_default_azure_credential(
             resource_id=resource_id,
             region=region,
         )
-    else:
-        raise ValueError("Insufficient information provided for Azure Speech service.")
+    raise ValueError("Insufficient information provided for Azure Speech service.")
 
 
 def get_speech_config_from_default_azure_credential(resource_id: str, region: str) -> speechsdk.SpeechConfig:
@@ -370,11 +367,10 @@ def get_speech_config_from_default_azure_credential(resource_id: str, region: st
         azure_auth = AzureAuth(token_scope=get_default_azure_scope(""))
         token = azure_auth.get_token()
         authorization_token = "aad#" + resource_id + "#" + token
-        speech_config = speechsdk.SpeechConfig(
+        return speechsdk.SpeechConfig(
             auth_token=authorization_token,
             region=region,
         )
-        return speech_config
     except Exception as e:
         logger.error(f"Failed to get speech config for resource ID '{resource_id}' and region '{region}': {e}")
         raise

pyrit/auth/azure_storage_auth.py

@@ -34,12 +34,10 @@ async def get_user_delegation_key(blob_service_client: BlobServiceClient) -> Use
         delegation_key_start_time = datetime.now()
         delegation_key_expiry_time = delegation_key_start_time + timedelta(days=1)
 
-        user_delegation_key = await blob_service_client.get_user_delegation_key(
+        return await blob_service_client.get_user_delegation_key(
             key_start_time=delegation_key_start_time, key_expiry_time=delegation_key_expiry_time
         )
 
-        return user_delegation_key
-
     @staticmethod
     async def get_sas_token(container_url: str) -> str:
         """

pyrit/auth/copilot_authenticator.py

@@ -208,7 +208,7 @@ async def _get_cached_token_if_available_and_valid(self) -> Optional[dict[str, A
             if not cached_user:
                 logger.info("No user associated with cached token. Token invalidated.")
                 return None
-            elif cached_user != self._username:
+            if cached_user != self._username:
                 logger.info(
                     f"Cached token is for different user (cached: {cached_user}, current: {self._username}). "
                     "Token invalidated."

pyrit/auxiliary_attacks/gcg/attack/base/attack_manager.py

@@ -66,44 +66,41 @@ def default(self, obj: Any) -> Any:
 def get_embedding_layer(model: Any) -> Any:
     if isinstance(model, GPTJForCausalLM) or isinstance(model, GPT2LMHeadModel):
         return model.transformer.wte
-    elif isinstance(model, LlamaForCausalLM):
+    if isinstance(model, LlamaForCausalLM):
         return model.model.embed_tokens
-    elif isinstance(model, GPTNeoXForCausalLM):
+    if isinstance(model, GPTNeoXForCausalLM):
         return model.base_model.embed_in
-    elif isinstance(model, Phi3ForCausalLM):
+    if isinstance(model, Phi3ForCausalLM):
         return model.model.embed_tokens
-    else:
-        raise ValueError(f"Unknown model type: {type(model)}")
+    raise ValueError(f"Unknown model type: {type(model)}")
 
 
 def get_embedding_matrix(model: Any) -> Any:
     if isinstance(model, GPTJForCausalLM) or isinstance(model, GPT2LMHeadModel):
         return model.transformer.wte.weight
-    elif isinstance(model, LlamaForCausalLM):
+    if isinstance(model, LlamaForCausalLM):
         return model.model.embed_tokens.weight
-    elif isinstance(model, GPTNeoXForCausalLM):
+    if isinstance(model, GPTNeoXForCausalLM):
         return model.base_model.embed_in.weight  # type: ignore[union-attr, unused-ignore]
-    elif isinstance(model, MixtralForCausalLM) or isinstance(model, MistralForCausalLM):
+    if isinstance(model, MixtralForCausalLM) or isinstance(model, MistralForCausalLM):
         return model.model.embed_tokens.weight
-    elif isinstance(model, Phi3ForCausalLM):
+    if isinstance(model, Phi3ForCausalLM):
         return model.model.embed_tokens.weight
-    else:
-        raise ValueError(f"Unknown model type: {type(model)}")
+    raise ValueError(f"Unknown model type: {type(model)}")
 
 
 def get_embeddings(model: Any, input_ids: torch.Tensor) -> Any:
     if isinstance(model, GPTJForCausalLM) or isinstance(model, GPT2LMHeadModel):
         return model.transformer.wte(input_ids).half()
-    elif isinstance(model, LlamaForCausalLM):
+    if isinstance(model, LlamaForCausalLM):
         return model.model.embed_tokens(input_ids)
-    elif isinstance(model, GPTNeoXForCausalLM):
+    if isinstance(model, GPTNeoXForCausalLM):
         return model.base_model.embed_in(input_ids).half()  # type: ignore[operator, unused-ignore]
-    elif isinstance(model, MixtralForCausalLM) or isinstance(model, MistralForCausalLM):
+    if isinstance(model, MixtralForCausalLM) or isinstance(model, MistralForCausalLM):
         return model.model.embed_tokens(input_ids)
-    elif isinstance(model, Phi3ForCausalLM):
+    if isinstance(model, Phi3ForCausalLM):
         return model.model.embed_tokens(input_ids)
-    else:
-        raise ValueError(f"Unknown model type: {type(model)}")
+    raise ValueError(f"Unknown model type: {type(model)}")
 
 
 def get_nonascii_toks(tokenizer: Any, device: str = "cpu") -> torch.Tensor:
@@ -364,24 +361,21 @@ def logits(self, model: Any, test_controls: Any = None, return_ids: bool = False
             del locs, test_ids
             gc.collect()
             return model(input_ids=ids, attention_mask=attn_mask).logits, ids
-        else:
-            del locs, test_ids
-            logits = model(input_ids=ids, attention_mask=attn_mask).logits
-            del ids
-            gc.collect()
-            return logits
+        del locs, test_ids
+        logits = model(input_ids=ids, attention_mask=attn_mask).logits
+        del ids
+        gc.collect()
+        return logits
 
     def target_loss(self, logits: torch.Tensor, ids: torch.Tensor) -> torch.Tensor:
         crit = nn.CrossEntropyLoss(reduction="none")
         loss_slice = slice(self._target_slice.start - 1, self._target_slice.stop - 1)
-        loss = crit(logits[:, loss_slice, :].transpose(1, 2), ids[:, self._target_slice])
-        return loss  # type: ignore[no-any-return, unused-ignore]
+        return crit(logits[:, loss_slice, :].transpose(1, 2), ids[:, self._target_slice])  # type: ignore[no-any-return]
 
     def control_loss(self, logits: torch.Tensor, ids: torch.Tensor) -> torch.Tensor:
         crit = nn.CrossEntropyLoss(reduction="none")
         loss_slice = slice(self._control_slice.start - 1, self._control_slice.stop - 1)
-        loss = crit(logits[:, loss_slice, :].transpose(1, 2), ids[:, self._control_slice])
-        return loss  # type: ignore[no-any-return, unused-ignore]
+        return crit(logits[:, loss_slice, :].transpose(1, 2), ids[:, self._control_slice])  # type: ignore[no-any-return]
 
     @property
     def assistant_str(self) -> Any:
@@ -527,8 +521,7 @@ def logits(self, model: Any, test_controls: Any = None, return_ids: bool = False
         vals = [prompt.logits(model, test_controls, return_ids) for prompt in self._prompts]
         if return_ids:
             return [val[0] for val in vals], [val[1] for val in vals]
-        else:
-            return vals
+        return vals
 
     def target_loss(self, logits: list[torch.Tensor], ids: list[torch.Tensor]) -> torch.Tensor:
         return torch.cat(

pyrit/auxiliary_attacks/gcg/attack/gcg/gcg_attack.py

@@ -119,8 +119,7 @@ def sample_control(
         new_token_val = torch.gather(
             top_indices[new_token_pos], 1, torch.randint(0, topk, (batch_size, 1), device=grad.device)
         )
-        new_control_toks = original_control_toks.scatter_(1, new_token_pos.unsqueeze(-1), new_token_val)
-        return new_control_toks
+        return original_control_toks.scatter_(1, new_token_pos.unsqueeze(-1), new_token_val)
 
 
 class GCGMultiPromptAttack(MultiPromptAttack):

pyrit/auxiliary_attacks/gcg/experiments/train.py

@@ -305,19 +305,18 @@ def _create_attack(
                 mpa_batch_size=params.batch_size,
                 mpa_n_steps=params.n_steps,
             )
-        else:
-            return IndividualPromptAttack(
-                train_goals,
-                train_targets,
-                workers,
-                control_init=params.control_init,
-                logfile=f"{params.result_prefix}_{timestamp}.json",
-                managers=managers,
-                test_goals=getattr(params, "test_goals", []),
-                test_targets=getattr(params, "test_targets", []),
-                test_workers=test_workers,
-                mpa_deterministic=params.gbda_deterministic,
-                mpa_lr=params.learning_rate,
-                mpa_batch_size=params.batch_size,
-                mpa_n_steps=params.n_steps,
-            )
+        return IndividualPromptAttack(
+            train_goals,
+            train_targets,
+            workers,
+            control_init=params.control_init,
+            logfile=f"{params.result_prefix}_{timestamp}.json",
+            managers=managers,
+            test_goals=getattr(params, "test_goals", []),
+            test_targets=getattr(params, "test_targets", []),
+            test_workers=test_workers,
+            mpa_deterministic=params.gbda_deterministic,
+            mpa_lr=params.learning_rate,
+            mpa_batch_size=params.batch_size,
+            mpa_n_steps=params.n_steps,
+        )

pyrit/common/path.py

@@ -15,8 +15,7 @@ def get_default_data_path(dir: str) -> pathlib.Path:
     """
     if in_git_repo():
         return pathlib.Path(PYRIT_PATH, "..", dir).resolve()
-    else:
-        return pathlib.Path(user_data_dir(dir, "pyrit")).resolve()
+    return pathlib.Path(user_data_dir(dir, "pyrit")).resolve()
 
 
 def in_git_repo() -> bool:

pyrit/common/question_answer_helpers.py

@@ -15,5 +15,4 @@ def construct_evaluation_prompt(entry: QuestionAnsweringEntry) -> str:
         str: The constructed prompt string.
     """
     available_choices = "\n".join(f"index={c.index}, value={c.text}" for c in entry.choices)
-    prompt = f"Question:\n{entry.question}\n\nChoices:\n{available_choices}"
-    return prompt
+    return f"Question:\n{entry.question}\n\nChoices:\n{available_choices}"

pyrit/common/utils.py

@@ -75,8 +75,7 @@ def combine_list(list1: Union[str, List[str]], list2: Union[str, List[str]]) ->
         list2 = [list2]
 
     # Merge and keep only unique values
-    combined = list(set(list1 + list2))
-    return combined
+    return list(set(list1 + list2))
 
 
 def get_random_indices(*, start: int, size: int, proportion: float) -> List[int]:

pyrit/common/yaml_loadable.py

@@ -42,5 +42,4 @@ def from_yaml_file(cls: Type[T], file: Union[Path | str]) -> T:
         # otherwise, just instantiate directly with **yaml_data
         if hasattr(cls, "from_dict") and callable(getattr(cls, "from_dict")):
             return cls.from_dict(yaml_data)  # type: ignore
-        else:
-            return cls(**yaml_data)
+        return cls(**yaml_data)