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

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tests/test_image_transforms.py

@@ -21,7 +21,11 @@ from safetensors.torch import load_file
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import functional as F  # noqa: N812
 
-from lerobot.common.datasets.transforms import RandomSubsetApply, SharpnessJitter, get_image_transforms
+from lerobot.common.datasets.transforms import (
+    RandomSubsetApply,
+    SharpnessJitter,
+    get_image_transforms,
+)
 from lerobot.common.utils.utils import init_hydra_config, seeded_context
 from lerobot.scripts.visualize_image_transforms import visualize_transforms
 from tests.utils import DEFAULT_CONFIG_PATH, require_x86_64_kernel
@@ -51,7 +55,9 @@ def default_transforms():
 
 def test_get_image_transforms_no_transform(img_tensor_factory):
     img_tensor = img_tensor_factory()
-    tf_actual = get_image_transforms(brightness_min_max=(0.5, 0.5), max_num_transforms=0)
+    tf_actual = get_image_transforms(
+        brightness_min_max=(0.5, 0.5), max_num_transforms=0
+    )
     torch.testing.assert_close(tf_actual(img_tensor), img_tensor)
 
 
@@ -149,7 +155,9 @@ def test_get_image_transforms_random_order(img_tensor_factory):
         ("sharpness", [(0.5, 0.5), (2.0, 2.0)]),
     ],
 )
-def test_backward_compatibility_torchvision(img_tensor_factory, transform, min_max_values, single_transforms):
+def test_backward_compatibility_torchvision(
+    img_tensor_factory, transform, min_max_values, single_transforms
+):
     img_tensor = img_tensor_factory()
     for min_max in min_max_values:
         kwargs = {
@@ -268,23 +276,33 @@ def test_sharpness_jitter_invalid_range_max_smaller():
     ],
 )
 def test_visualize_image_transforms(repo_id, n_examples):
-    cfg = init_hydra_config(DEFAULT_CONFIG_PATH, overrides=[f"dataset_repo_id={repo_id}"])
+    cfg = init_hydra_config(
+        DEFAULT_CONFIG_PATH, overrides=[f"dataset_repo_id={repo_id}"]
+    )
     output_dir = Path(__file__).parent / "outputs" / "image_transforms"
     visualize_transforms(cfg, output_dir=output_dir, n_examples=n_examples)
     output_dir = output_dir / repo_id.split("/")[-1]
 
     # Check if the original frame image exists
-    assert (output_dir / "original_frame.png").exists(), "Original frame image was not saved."
+    assert (
+        output_dir / "original_frame.png"
+    ).exists(), "Original frame image was not saved."
 
     # Check if the transformed images exist for each transform type
     transforms = ["brightness", "contrast", "saturation", "hue", "sharpness"]
     for transform in transforms:
         transform_dir = output_dir / transform
         assert transform_dir.exists(), f"{transform} directory was not created."
-        assert any(transform_dir.iterdir()), f"No transformed images found in {transform} directory."
+        assert any(
+            transform_dir.iterdir()
+        ), f"No transformed images found in {transform} directory."
 
         # Check for specific files within each transform directory
-        expected_files = [f"{i}.png" for i in range(1, n_examples + 1)] + ["min.png", "max.png", "mean.png"]
+        expected_files = [f"{i}.png" for i in range(1, n_examples + 1)] + [
+            "min.png",
+            "max.png",
+            "mean.png",
+        ]
         for file_name in expected_files:
             assert (
                 transform_dir / file_name
@@ -292,7 +310,9 @@ def test_visualize_image_transforms(repo_id, n_examples):
 
     # Check if the combined transforms directory exists and contains the right files
     combined_transforms_dir = output_dir / "all"
-    assert combined_transforms_dir.exists(), "Combined transforms directory was not created."
+    assert (
+        combined_transforms_dir.exists()
+    ), "Combined transforms directory was not created."
     assert any(
         combined_transforms_dir.iterdir()
     ), "No transformed images found in combined transforms directory."