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

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

src/lightning/pytorch/callbacks/timer.py

@@ -111,8 +111,8 @@ def __init__(
         self._duration = duration.total_seconds() if duration is not None else None
         self._interval = interval
         self._verbose = verbose
-        self._start_time: dict[RunningStage, Optional[float]] = {stage: None for stage in RunningStage}
-        self._end_time: dict[RunningStage, Optional[float]] = {stage: None for stage in RunningStage}
+        self._start_time: dict[RunningStage, Optional[float]] = dict.fromkeys(RunningStage)
+        self._end_time: dict[RunningStage, Optional[float]] = dict.fromkeys(RunningStage)
         self._offset = 0
 
     def start_time(self, stage: str = RunningStage.TRAINING) -> Optional[float]:

tests/tests_pytorch/helpers/datasets.py

@@ -148,7 +148,7 @@ def __init__(self, root: str, num_samples: int = 100, digits: Optional[Sequence]
 
     @staticmethod
     def _prepare_subset(full_data: Tensor, full_targets: Tensor, num_samples: int, digits: Sequence):
-        classes = {d: 0 for d in digits}
+        classes = dict.fromkeys(digits, 0)
         indexes = []
         for idx, target in enumerate(full_targets):
             label = target.item()

tests/tests_pytorch/trainer/connectors/test_data_connector.py

@@ -497,7 +497,7 @@ def test_dataloader_source_request_from_module():
 
 
 @pytest.mark.parametrize(
-    "hook_name", ("on_before_batch_transfer", "transfer_batch_to_device", "on_after_batch_transfer")
+    "hook_name", ["on_before_batch_transfer", "transfer_batch_to_device", "on_after_batch_transfer"]
 )
 class TestDataHookSelector:
     def overridden_func(self, batch, *args, **kwargs):

tests/tests_pytorch/trainer/logging_/test_logger_connector.py

@@ -246,7 +246,7 @@ def test_fx_validator_integration(tmp_path):
     })
     trainer.test(model, verbose=False)
 
-    not_supported.update({k: "result collection is not registered yet" for k in not_supported})
+    not_supported.update(dict.fromkeys(not_supported, "result collection is not registered yet"))
     not_supported.update({
         "predict_dataloader": "result collection is not registered yet",
         "on_predict_model_eval": "result collection is not registered yet",