[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

tests/tests_pytorch/trainer/optimization/test_optimizers.py

@@ -12,9 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from unittest import mock
-from unittest.mock import call
-from unittest.mock import patch
-
+from unittest.mock import call, patch
 
 import pytest
 import torch
@@ -662,44 +660,44 @@ def lr_scheduler_step(*_): ...
 
 @patch("torch.optim.lr_scheduler.StepLR.step")
 def test_lr_scheduler_step_across_epoch_boundaries(mocked_sched, tmp_path):
-   class StepAcrossEpochsModel(LightningModule):
-       def __init__(self):
-           super().__init__()
-           self.layer = torch.nn.Linear(32, 2)
-
-       def forward(self, x):
-           return self.layer(x)
-
-       def training_step(self, batch, batch_idx):
-           return {"loss": torch.tensor(0.1, requires_grad=True)}
-
-       def configure_optimizers(self):
-           optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
-           scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
-           return {
-               "optimizer": optimizer,
-               "lr_scheduler": {
-                   "scheduler": scheduler,
-                   "interval": "step",
-                   "frequency": 5,  # Scheduler steps every 5 iterations
-               },
-           }
-
-   model = StepAcrossEpochsModel()
-
-   # Trainer configuration for cross-epoch testing
-   trainer = Trainer(
-       default_root_dir=tmp_path,
-       limit_train_batches=7,  # More than `frequency` iterations per epoch
-       max_epochs=3,  # Test across multiple epochs
-   )
-
-   # Fit the model
-   trainer.fit(model)
-
-   # Calculate the total number of steps (iterations) and expected scheduler calls
-   total_steps = 7 * 3  # Total iterations (7 batches per epoch * 3 epochs)
-   expected_steps = total_steps // 5  # Scheduler steps every 5 iterations
-
-   # Assert that the scheduler was called the expected number of times
-   assert mocked_sched.call_count == expected_steps
+    class StepAcrossEpochsModel(LightningModule):
+        def __init__(self):
+            super().__init__()
+            self.layer = torch.nn.Linear(32, 2)
+
+        def forward(self, x):
+            return self.layer(x)
+
+        def training_step(self, batch, batch_idx):
+            return {"loss": torch.tensor(0.1, requires_grad=True)}
+
+        def configure_optimizers(self):
+            optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
+            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
+            return {
+                "optimizer": optimizer,
+                "lr_scheduler": {
+                    "scheduler": scheduler,
+                    "interval": "step",
+                    "frequency": 5,  # Scheduler steps every 5 iterations
+                },
+            }
+
+    model = StepAcrossEpochsModel()
+
+    # Trainer configuration for cross-epoch testing
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        limit_train_batches=7,  # More than `frequency` iterations per epoch
+        max_epochs=3,  # Test across multiple epochs
+    )
+
+    # Fit the model
+    trainer.fit(model)
+
+    # Calculate the total number of steps (iterations) and expected scheduler calls
+    total_steps = 7 * 3  # Total iterations (7 batches per epoch * 3 epochs)
+    expected_steps = total_steps // 5  # Scheduler steps every 5 iterations
+
+    # Assert that the scheduler was called the expected number of times
+    assert mocked_sched.call_count == expected_steps