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

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

Author: pre-commit-ci[bot]

src/pytorch_lightning/callbacks/progress/base.py

@@ -222,7 +222,9 @@ def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: O
         if not trainer.is_global_zero:
             self.disable()
 
-    def get_metrics(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> Dict[str, Union[int, str, float, Dict[str, float]]]:
+    def get_metrics(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
+    ) -> Dict[str, Union[int, str, float, Dict[str, float]]]:
         r"""
         Combines progress bar metrics collected from the trainer with standard metrics from get_standard_metrics.
         Implement this to override the items displayed in the progress bar.

src/pytorch_lightning/trainer/connectors/data_connector.py

@@ -439,7 +439,9 @@ def _request_dataloader(self, stage: RunningStage) -> Union[DataLoader, List[Dat
         return dataloader
 
     @staticmethod
-    def _resolve_overfit_batches(dataloaders: Union[Collection[DataLoader], DataLoader], mode: RunningStage) -> Collection[DataLoader]:
+    def _resolve_overfit_batches(
+        dataloaders: Union[Collection[DataLoader], DataLoader], mode: RunningStage
+    ) -> Collection[DataLoader]:
         all_have_sequential_sampler = True
 
         def resolve_has_no_sequential_sampler(dataloader: DataLoader):

src/pytorch_lightning/trainer/trainer.py

@@ -634,12 +634,11 @@ def _setup_on_init(self) -> None:
         self.num_test_batches: List[Union[int, float]] = []
         self.num_val_batches: List[Union[int, float]] = []
         self.num_predict_batches: List[Union[int, float]] = []
-        
+
         self.test_dataloaders: Optional[List[DataLoader]] = None
         self.val_dataloaders: Optional[List[DataLoader]] = None
         self._last_train_dl_reload_epoch = float("-inf")
         self._last_val_dl_reload_epoch = float("-inf")
-        
 
     def _call_and_handle_interrupt(self, trainer_fn: Callable, *args: Any, **kwargs: Any) -> Any:
         r"""
@@ -986,7 +985,7 @@ def _predict_impl(
         self.state.status = TrainerStatus.RUNNING
         self.predicting = True
 
-        self.predict_loop.return_predictions = return_predictions # type: ignore
+        self.predict_loop.return_predictions = return_predictions  # type: ignore
 
         # if a datamodule comes in as the second arg, then fix it for the user
         if isinstance(dataloaders, LightningDataModule):
@@ -1451,7 +1450,7 @@ def __set_ckpt_path(self, ckpt_path: Optional[str], model_provided: bool, model_
                     " or last checkpoint available. No checkpoint will be loaded."
                 )
                 return None
-            ckpt_path = max(candidates_ts.keys(), key=partial(operator.getitem, candidates_ts)) # type: ignore
+            ckpt_path = max(candidates_ts.keys(), key=partial(operator.getitem, candidates_ts))  # type: ignore
 
         if not ckpt_path:
             raise MisconfigurationException(
@@ -2128,7 +2127,7 @@ def data_parallel_device_ids(self) -> Optional[List[int]]:
         return self.device_ids if isinstance(self.accelerator, CUDAAccelerator) else None
 
     @property
-    def lightning_module(self) -> "pl.LightningModule": # type: ignore
+    def lightning_module(self) -> "pl.LightningModule":  # type: ignore
         # TODO: this is actually an optional return
         assert self.strategy.lightning_module is not None
         return self.strategy.lightning_module