[pre-commit.ci] pre-commit suggestions (#17983)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: Jirka B <j.borovec+github@gmail.com>

.actions/assistant.py

@@ -196,13 +196,12 @@ def load_readme_description(path_dir: str, homepage: str, version: str) -> str:
     skip_begin = r"<!-- following section will be skipped from PyPI description -->"
     skip_end = r"<!-- end skipping PyPI description -->"
     # todo: wrap content as commented description
-    text = re.sub(rf"{skip_begin}.+?{skip_end}", "<!--  -->", text, flags=re.IGNORECASE + re.DOTALL)
+    return re.sub(rf"{skip_begin}.+?{skip_end}", "<!--  -->", text, flags=re.IGNORECASE + re.DOTALL)
 
     # # https://github.com/Borda/pytorch-lightning/releases/download/1.1.0a6/codecov_badge.png
     # github_release_url = os.path.join(homepage, "releases", "download", version)
     # # download badge and replace url with local file
     # text = _parse_for_badge(text, github_release_url)
-    return text
 
 
 def distribute_version(src_folder: str, ver_file: str = "version.info") -> None:

.pre-commit-config.yaml

@@ -50,20 +50,20 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.1
+    rev: v3.8.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
         name: Upgrade code
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.6.3
+    rev: v1.7.3
     hooks:
       - id: docformatter
         args: [--in-place, --wrap-summaries=115, --wrap-descriptions=120]
 
   - repo: https://github.com/asottile/yesqa
-    rev: v1.4.0
+    rev: v1.5.0
     hooks:
       - id: yesqa
         name: Unused noqa
@@ -89,7 +89,7 @@ repos:
         exclude: docs/source-app
 
   - repo: https://github.com/asottile/blacken-docs
-    rev: 1.13.0
+    rev: 1.14.0
     hooks:
       - id: blacken-docs
         args: [--line-length=120]
@@ -111,8 +111,8 @@ repos:
               README.md
           )$
 
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: 'v0.0.262'
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: 'v0.0.276'
     hooks:
       - id: ruff
         args: ["--fix"]

examples/pytorch/basics/backbone_image_classifier.py

@@ -50,8 +50,7 @@ class Backbone(torch.nn.Module):
     def forward(self, x):
         x = x.view(x.size(0), -1)
         x = torch.relu(self.l1(x))
-        x = torch.relu(self.l2(x))
-        return x
+        return torch.relu(self.l2(x))
 
 
 class LitClassifier(LightningModule):

examples/pytorch/domain_templates/computer_vision_fine_tuning.py

@@ -215,9 +215,7 @@ class TransferLearningModel(LightningModule):
         x = x.squeeze(-1).squeeze(-1)
 
         # 2. Classifier (returns logits):
-        x = self.fc(x)
-
-        return x
+        return self.fc(x)
 
     def loss(self, logits, labels):
         return self.loss_func(input=logits, target=labels)

examples/pytorch/domain_templates/generative_adversarial_net.py

@@ -64,8 +64,7 @@ class Generator(nn.Module):
 
     def forward(self, z):
         img = self.model(z)
-        img = img.view(img.size(0), *self.img_shape)
-        return img
+        return img.view(img.size(0), *self.img_shape)
 
 
 class Discriminator(nn.Module):

examples/pytorch/ipu/mnist_sample.py

@@ -33,8 +33,7 @@ class LitClassifier(LightningModule):
     def forward(self, x):
         x = x.view(x.size(0), -1)
         x = torch.relu(self.l1(x))
-        x = torch.relu(self.l2(x))
-        return x
+        return torch.relu(self.l2(x))
 
     def training_step(self, batch, batch_idx):
         x, y = batch

src/lightning/app/utilities/app_commands.py

@@ -80,7 +80,7 @@ def _extract_commands_from_file(file_name: str) -> CommandLines:
 
 
 def _execute_app_commands(cl: CommandLines) -> None:
-    """open a subprocess shell to execute app commands.
+    """Open a subprocess shell to execute app commands.
 
     The calling app environment is used in the current environment the code is running in
     """

src/lightning/app/utilities/app_helpers.py

@@ -49,7 +49,7 @@ logger = logging.getLogger(__name__)
 
 @dataclass
 class StateEntry:
-    """dataclass used to keep track the latest state shared through the app REST API."""
+    """Dataclass used to keep track the latest state shared through the app REST API."""
 
     app_state: Mapping = field(default_factory=dict)
     served_state: Mapping = field(default_factory=dict)
@@ -76,32 +76,32 @@ class StateStore(ABC):
 
     @abstractmethod
     def get_app_state(self, k: str) -> Mapping:
-        """returns a stored appstate for an input key 'k'."""
+        """Returns a stored appstate for an input key 'k'."""
         pass
 
     @abstractmethod
     def get_served_state(self, k: str) -> Mapping:
-        """returns a last served app state for an input key 'k'."""
+        """Returns a last served app state for an input key 'k'."""
         pass
 
     @abstractmethod
     def get_served_session_id(self, k: str) -> str:
-        """returns session id for state of a key 'k'."""
+        """Returns session id for state of a key 'k'."""
         pass
 
     @abstractmethod
     def set_app_state(self, k: str, v: Mapping):
-        """sets the app state for state of a key 'k'."""
+        """Sets the app state for state of a key 'k'."""
         pass
 
     @abstractmethod
     def set_served_state(self, k: str, v: Mapping):
-        """sets the served state for state of a key 'k'."""
+        """Sets the served state for state of a key 'k'."""
         pass
 
     @abstractmethod
     def set_served_session_id(self, k: str, v: str):
-        """sets the session id for state of a key 'k'."""
+        """Sets the session id for state of a key 'k'."""
         pass
 
 

src/lightning/app/utilities/login.py

@@ -71,7 +71,7 @@ class Auth:
             return True
 
     def save(self, token: str = "", user_id: str = "", api_key: str = "", username: str = "") -> None:
-        """save credentials to disk."""
+        """Save credentials to disk."""
         self.secrets_file.parent.mkdir(exist_ok=True, parents=True)
         with self.secrets_file.open("w") as f:
             json.dump(
@@ -98,7 +98,7 @@ class Auth:
 
     @property
     def auth_header(self) -> Optional[str]:
-        """authentication header used by lightning-cloud client."""
+        """Authentication header used by lightning-cloud client."""
         if self.api_key:
             token = f"{self.user_id}:{self.api_key}"
             return f"Basic {base64.b64encode(token.encode('ascii')).decode('ascii')}"  # E501
@@ -108,7 +108,7 @@ class Auth:
         )
 
     def _run_server(self) -> None:
-        """start a server to complete authentication."""
+        """Start a server to complete authentication."""
         AuthServer().login_with_browser(self)
 
     def authenticate(self) -> Optional[str]:

src/lightning/app/utilities/openapi.py

@@ -29,7 +29,7 @@ def _duplicate_checker(js):
 
 
 def string2dict(text):
-    """string2dict parses a JSON string into a dictionary, ensuring no keys are duplicated by accident."""
+    """String2dict parses a JSON string into a dictionary, ensuring no keys are duplicated by accident."""
     if not isinstance(text, str):
         text = text.decode("utf-8")
     try:

src/lightning/pytorch/loops/training_epoch_loop.py

@@ -298,7 +298,7 @@ class _TrainingEpochLoop(loops._Loop):
         return not accumulation_done and strategy_accumulates_on_final_batch
 
     def update_lr_schedulers(self, interval: str, update_plateau_schedulers: bool) -> None:
-        """updates the lr schedulers based on the given interval."""
+        """Updates the lr schedulers based on the given interval."""
         if interval == "step" and self._should_accumulate():
             return
         self._update_learning_rates(interval=interval, update_plateau_schedulers=update_plateau_schedulers)

tests/tests_app/core/test_lightning_api.py

@@ -499,9 +499,8 @@ def target():
 
 
 async def async_request(url: str, data: InputRequestModel):
-    async with aiohttp.ClientSession() as session:
-        async with session.post(url, json=data.dict()) as result:
-            return await result.json()
+    async with aiohttp.ClientSession() as session, session.post(url, json=data.dict()) as result:
+        return await result.json()
 
 
 @pytest.mark.xfail(strict=False, reason="No idea why... need to be fixed")  # fixme

tests/tests_fabric/helpers/dataloaders.py

@@ -38,7 +38,7 @@ class CustomInfDataloader:
 
 class CustomNotImplementedErrorDataloader(CustomInfDataloader):
     def __len__(self):
-        """raise NotImplementedError."""
+        """Raise NotImplementedError."""
         raise NotImplementedError
 
     def __next__(self):

tests/tests_fabric/loggers/test_tensorboard.py

@@ -147,7 +147,7 @@ def test_tensorboard_log_hparams_and_metrics(tmpdir):
 
 @pytest.mark.parametrize("example_input_array", [None, torch.rand(2, 32)])
 def test_tensorboard_log_graph(tmpdir, example_input_array):
-    """test that log graph works with both model.example_input_array and if array is passed externally."""
+    """Test that log graph works with both model.example_input_array and if array is passed externally."""
     # TODO(fabric): Test both nn.Module and LightningModule
     # TODO(fabric): Assert _apply_batch_transfer_handler is calling the batch transfer hooks
     model = BoringModel()
@@ -170,7 +170,7 @@ def test_tensorboard_log_graph(tmpdir, example_input_array):
 
 @pytest.mark.skipif(not _TENSORBOARD_AVAILABLE, reason=str(_TENSORBOARD_AVAILABLE))
 def test_tensorboard_log_graph_warning_no_example_input_array(tmpdir):
-    """test that log graph throws warning if model.example_input_array is None."""
+    """Test that log graph throws warning if model.example_input_array is None."""
     model = BoringModel()
     model.example_input_array = None
     logger = TensorBoardLogger(tmpdir, log_graph=True)

tests/tests_pytorch/helpers/dataloaders.py

@@ -38,7 +38,7 @@ class CustomInfDataloader:
 
 class CustomNotImplementedErrorDataloader(CustomInfDataloader):
     def __len__(self):
-        """raise NotImplementedError."""
+        """Raise NotImplementedError."""
         raise NotImplementedError
 
     def __next__(self):

tests/tests_pytorch/loggers/test_tensorboard.py

@@ -210,7 +210,7 @@ def test_tensorboard_log_omegaconf_hparams_and_metrics(tmpdir):
 
 @pytest.mark.parametrize("example_input_array", [None, torch.rand(2, 32)])
 def test_tensorboard_log_graph(tmpdir, example_input_array):
-    """test that log graph works with both model.example_input_array and if array is passed externally."""
+    """Test that log graph works with both model.example_input_array and if array is passed externally."""
     model = BoringModel()
     if example_input_array is not None:
         model.example_input_array = None
@@ -221,7 +221,7 @@ def test_tensorboard_log_graph(tmpdir, example_input_array):
 
 @pytest.mark.skipif(not _TENSORBOARD_AVAILABLE, reason=str(_TENSORBOARD_AVAILABLE))
 def test_tensorboard_log_graph_warning_no_example_input_array(tmpdir):
-    """test that log graph throws warning if model.example_input_array is None."""
+    """Test that log graph throws warning if model.example_input_array is None."""
     model = BoringModel()
     model.example_input_array = None
     logger = TensorBoardLogger(tmpdir, log_graph=True)

tests/tests_pytorch/profilers/test_profiler.py

@@ -40,7 +40,7 @@ def _get_python_cprofile_total_duration(profile):
 
 
 def _sleep_generator(durations):
-    """the profile_iterable method needs an iterable in which we can ensure that we're properly timing how long it
+    """The profile_iterable method needs an iterable in which we can ensure that we're properly timing how long it
     takes to call __next__"""
     for duration in durations:
         time.sleep(duration)
@@ -277,7 +277,7 @@ def test_advanced_profiler_durations(advanced_profiler, action: str, expected: l
 
 @pytest.mark.flaky(reruns=3)
 def test_advanced_profiler_overhead(advanced_profiler, n_iter=5):
-    """ensure that the profiler doesn't introduce too much overhead during training."""
+    """Ensure that the profiler doesn't introduce too much overhead during training."""
     for _ in range(n_iter):
         with advanced_profiler.profile("no-op"):
             pass
@@ -289,7 +289,7 @@ def test_advanced_profiler_overhead(advanced_profiler, n_iter=5):
 
 
 def test_advanced_profiler_describe(tmpdir, advanced_profiler):
-    """ensure the profiler won't fail when reporting the summary."""
+    """Ensure the profiler won't fail when reporting the summary."""
     # record at least one event
     with advanced_profiler.profile("test"):
         pass

tests/tests_pytorch/trainer/logging_/test_logger_connector.py

@@ -266,7 +266,7 @@ def test_fx_validator_integration(tmpdir):
 
 @pytest.mark.parametrize("add_dataloader_idx", [False, True])
 def test_auto_add_dataloader_idx(tmpdir, add_dataloader_idx):
-    """test that auto_add_dataloader_idx argument works."""
+    """Test that auto_add_dataloader_idx argument works."""
 
     class TestModel(BoringModel):
         def val_dataloader(self):

tests/tests_pytorch/trainer/test_trainer.py

@@ -245,7 +245,7 @@ def test_gradient_accumulation_scheduling_last_batch(tmpdir, accumulate_grad_bat
 
 
 def test_loading_meta_tags(tmpdir):
-    """test for backward compatibility to meta_tags.csv."""
+    """Test for backward compatibility to meta_tags.csv."""
     hparams = {
         "batch_size": 32,
         "learning_rate": 0.001 * 8,