From f596cb11adf231e6300856d27d0f4e6f25fd7958 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 29 Jan 2024 02:46:25 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../connectors/checkpoint_connector.py        |  5 +--
 .../connectors/test_checkpoint_connector.py   | 37 ++++++++++---------
 2 files changed, 22 insertions(+), 20 deletions(-)

diff --git a/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py b/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py
index 52aaa93fea104..623739cf728cb 100644
--- a/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py
+++ b/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py
@@ -14,7 +14,7 @@
 import logging
 import os
 import re
-from typing import Any, Dict, Optional, List
+from typing import Any, Dict, List, Optional
 
 import torch
 from fsspec.core import url_to_fs
@@ -525,8 +525,7 @@ def _get_dataloader_state_dicts(self) -> List[Dict[str, Any]]:
         combined_loader = self.trainer.fit_loop._combined_loader
         iterables = combined_loader.flattened if combined_loader is not None else []
         return [
-            train_dataloader.state_dict() for train_dataloader in iterables
-            if isinstance(train_dataloader, _Stateful)
+            train_dataloader.state_dict() for train_dataloader in iterables if isinstance(train_dataloader, _Stateful)
         ]
 
     @staticmethod
diff --git a/tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py b/tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py
index dc8353e57937b..89ddf3c4d0c15 100644
--- a/tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py
+++ b/tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py
@@ -17,14 +17,13 @@
 
 import pytest
 import torch
-from torch.utils.data import DataLoader
-
 from lightning.pytorch import Trainer
 from lightning.pytorch.callbacks import ModelCheckpoint
 from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
 from lightning.pytorch.trainer.states import TrainerFn
-from lightning.pytorch.utilities.migration.utils import _set_version
 from lightning.pytorch.utilities import CombinedLoader
+from lightning.pytorch.utilities.migration.utils import _set_version
+from torch.utils.data import DataLoader
 
 
 def test_preloaded_checkpoint_lifecycle(tmpdir):
@@ -239,22 +238,26 @@ def __init__(self, *args, **kwargs):
         super().__init__(RandomDataset(32, 64), *args, **kwargs)
 
 
-@pytest.mark.parametrize(("train_dataloaders", "expected_states"), [
-    # No dataloader
-    ([], None),
-    # Single stateful DataLoader
-    (StatefulDataLoader(), [{"label": 0}]),
-    # Single, not stateful DataLoader
-    (CombinedLoader(NotStatefulDataLoader()), None),
-    # Single stateful DataLoader
-    (CombinedLoader(StatefulDataLoader()), [{"label": 0}]),
-    # Multiple stateful DataLoaders
-    (CombinedLoader([StatefulDataLoader(3), StatefulDataLoader(1)]), [{"label": 3}, {"label": 1}]),
-    # Mix of stateful and not stateful DataLoaders
-    (CombinedLoader([NotStatefulDataLoader(3), StatefulDataLoader(1), NotStatefulDataLoader(2)]), [{"label": 1}]),
-])
+@pytest.mark.parametrize(
+    ("train_dataloaders", "expected_states"),
+    [
+        # No dataloader
+        ([], None),
+        # Single stateful DataLoader
+        (StatefulDataLoader(), [{"label": 0}]),
+        # Single, not stateful DataLoader
+        (CombinedLoader(NotStatefulDataLoader()), None),
+        # Single stateful DataLoader
+        (CombinedLoader(StatefulDataLoader()), [{"label": 0}]),
+        # Multiple stateful DataLoaders
+        (CombinedLoader([StatefulDataLoader(3), StatefulDataLoader(1)]), [{"label": 3}, {"label": 1}]),
+        # Mix of stateful and not stateful DataLoaders
+        (CombinedLoader([NotStatefulDataLoader(3), StatefulDataLoader(1), NotStatefulDataLoader(2)]), [{"label": 1}]),
+    ],
+)
 def test_train_dataloaders_restore(train_dataloaders, expected_states, tmp_path):
     """Test that the CheckpointConnector saves the state of stateful dataloaders and can reload them."""
+
     class DataLoaderModel(BoringModel):
         def training_step(self, batch, batch_idx):
             if isinstance(batch, list):