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

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

lerobot/common/datasets/transforms.py

@@ -57,7 +57,9 @@ class RandomSubsetApply(Transform):
         elif not isinstance(n_subset, int):
             raise TypeError("n_subset should be an int or None")
         elif not (1 <= n_subset <= len(transforms)):
-            raise ValueError(f"n_subset should be in the interval [1, {len(transforms)}]")
+            raise ValueError(
+                f"n_subset should be in the interval [1, {len(transforms)}]"
+            )
 
         self.transforms = transforms
         total = sum(p)
@@ -116,16 +118,22 @@ class SharpnessJitter(Transform):
     def _check_input(self, sharpness):
         if isinstance(sharpness, (int, float)):
             if sharpness < 0:
-                raise ValueError("If sharpness is a single number, it must be non negative.")
+                raise ValueError(
+                    "If sharpness is a single number, it must be non negative."
+                )
             sharpness = [1.0 - sharpness, 1.0 + sharpness]
             sharpness[0] = max(sharpness[0], 0.0)
         elif isinstance(sharpness, collections.abc.Sequence) and len(sharpness) == 2:
             sharpness = [float(v) for v in sharpness]
         else:
-            raise TypeError(f"{sharpness=} should be a single number or a sequence with length 2.")
+            raise TypeError(
+                f"{sharpness=} should be a single number or a sequence with length 2."
+            )
 
         if not 0.0 <= sharpness[0] <= sharpness[1]:
-            raise ValueError(f"sharpnesss values should be between (0., inf), but got {sharpness}.")
+            raise ValueError(
+                f"sharpnesss values should be between (0., inf), but got {sharpness}."
+            )
 
         return float(sharpness[0]), float(sharpness[1])
 
@@ -134,7 +142,9 @@ class SharpnessJitter(Transform):
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         sharpness_factor = self._generate_value(self.sharpness[0], self.sharpness[1])
-        return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
+        return self._call_kernel(
+            F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor
+        )
 
 
 def get_image_transforms(
@@ -185,7 +195,11 @@ def get_image_transforms(
             raise ValueError("The interpolation passed is not supported")
         # Weight for resizing is always 1
         weights.append(1.0)
-        transforms.append(v2.Resize(size=(image_size[0], image_size[1]), interpolation=interpolation_mode))
+        transforms.append(
+            v2.Resize(
+                size=(image_size[0], image_size[1]), interpolation=interpolation_mode
+            )
+        )
     if brightness_min_max is not None and brightness_weight > 0.0:
         weights.append(brightness_weight)
         transforms.append(v2.ColorJitter(brightness=brightness_min_max))
@@ -219,4 +233,6 @@ def get_image_transforms(
         return v2.Identity()
     else:
         # TODO(rcadene, aliberts): add v2.ToDtype float16?
-        return RandomSubsetApply(transforms, p=weights, n_subset=n_subset, random_order=random_order)
+        return RandomSubsetApply(
+            transforms, p=weights, n_subset=n_subset, random_order=random_order
+        )