Fixup

src/transformers/models/conditional_detr/image_processing_conditional_detr.py

@@ -1322,8 +1322,8 @@ def preprocess(
             image_std (`float` or `List[float]`, *optional*, defaults to self.image_std):
                 Standard deviation to use when normalizing the image.
             do_pad (`bool`, *optional*, defaults to self.do_pad):
-                Whether to pad the image. If `True`, padding will be applied to the bottom and right of 
-                the image with zeros. If `pad_size` is provided, the image will be padded to the specified 
+                Whether to pad the image. If `True`, padding will be applied to the bottom and right of
+                the image with zeros. If `pad_size` is provided, the image will be padded to the specified
                 dimensions. Otherwise, the image will be padded to the maximum height and width of the batch.
             format (`str` or `AnnotationFormat`, *optional*, defaults to self.format):
                 Format of the annotations.

src/transformers/models/deformable_detr/image_processing_deformable_detr.py

@@ -145,7 +145,6 @@ def get_resize_output_image_size(
     return get_size_with_aspect_ratio(image_size, size, max_size)
 
 
-
 # Copied from transformers.models.detr.image_processing_detr.get_image_size_for_max_height_width
 def get_image_size_for_max_height_width(
     input_image: np.ndarray,
@@ -832,7 +831,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
             If `pad_size` is provided, the image will be padded to the specified dimensions.
             Otherwise, the image will be padded to the maximum height and width of the batch.
         pad_size (`Dict[str, int]`, *optional*):
-            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
             image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
     """
 
@@ -1325,8 +1324,8 @@ def preprocess(
             image_std (`float` or `List[float]`, *optional*, defaults to self.image_std):
                 Standard deviation to use when normalizing the image.
             do_pad (`bool`, *optional*, defaults to self.do_pad):
-                Whether to pad the image. If `True`, padding will be applied to the bottom and right of 
-                the image with zeros. If `pad_size` is provided, the image will be padded to the specified 
+                Whether to pad the image. If `True`, padding will be applied to the bottom and right of
+                the image with zeros. If `pad_size` is provided, the image will be padded to the specified
                 dimensions. Otherwise, the image will be padded to the maximum height and width of the batch.
             format (`str` or `AnnotationFormat`, *optional*, defaults to self.format):
                 Format of the annotations.

src/transformers/models/deta/image_processing_deta.py

@@ -139,7 +139,6 @@ def get_resize_output_image_size(
     return get_size_with_aspect_ratio(image_size, size, max_size)
 
 
-
 # Copied from transformers.models.detr.image_processing_detr.get_image_size_for_max_height_width
 def get_image_size_for_max_height_width(
     input_image: np.ndarray,
@@ -541,7 +540,7 @@ class DetaImageProcessor(BaseImageProcessor):
             If `pad_size` is provided, the image will be padded to the specified dimensions.
             Otherwise, the image will be padded to the maximum height and width of the batch.
         pad_size (`Dict[str, int]`, *optional*):
-            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
             image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
     """
 
@@ -972,8 +971,8 @@ def preprocess(
                 boxes from the format `(top_left_x, top_left_y, width, height)` to `(center_x, center_y, width, height)`
                 and in relative coordinates.
             do_pad (`bool`, *optional*, defaults to self.do_pad):
-                Whether to pad the image. If `True`, padding will be applied to the bottom and right of 
-                the image with zeros. If `pad_size` is provided, the image will be padded to the specified 
+                Whether to pad the image. If `True`, padding will be applied to the bottom and right of
+                the image with zeros. If `pad_size` is provided, the image will be padded to the specified
                 dimensions. Otherwise, the image will be padded to the maximum height and width of the batch.
             format (`str` or `AnnotationFormat`, *optional*, defaults to self.format):
                 Format of the annotations.
@@ -991,7 +990,7 @@ def preprocess(
                 - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                 - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
             pad_size (`Dict[str, int]`, *optional*):
-                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
                 image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
         """
         if "pad_and_return_pixel_mask" in kwargs:

src/transformers/models/detr/image_processing_detr.py

@@ -824,7 +824,7 @@ class DetrImageProcessor(BaseImageProcessor):
             If `pad_size` is provided, the image will be padded to the specified dimensions.
             Otherwise, the image will be padded to the maximum height and width of the batch.
         pad_size (`Dict[str, int]`, *optional*):
-            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
             image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
     """
 
@@ -1194,7 +1194,7 @@ def pad(
                 bounding boxes have not been converted to relative coordinates and `(centre_x, centre_y, width, height)`
                 format, the bounding boxes will not be updated.
             pad_size (`Dict[str, int]`, *optional*):
-                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
                 image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
         """
         pad_size = pad_size if pad_size is not None else self.pad_size
@@ -1303,8 +1303,8 @@ def preprocess(
             image_std (`float` or `List[float]`, *optional*, defaults to self.image_std):
                 Standard deviation to use when normalizing the image.
             do_pad (`bool`, *optional*, defaults to self.do_pad):
-                Whether to pad the image. If `True`, padding will be applied to the bottom and right of 
-                the image with zeros. If `pad_size` is provided, the image will be padded to the specified 
+                Whether to pad the image. If `True`, padding will be applied to the bottom and right of
+                the image with zeros. If `pad_size` is provided, the image will be padded to the specified
                 dimensions. Otherwise, the image will be padded to the maximum height and width of the batch.
             format (`str` or `AnnotationFormat`, *optional*, defaults to self.format):
                 Format of the annotations.
@@ -1322,7 +1322,7 @@ def preprocess(
                 - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                 - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
             pad_size (`Dict[str, int]`, *optional*):
-                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
                 image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
         """
         if "pad_and_return_pixel_mask" in kwargs:

src/transformers/models/grounding_dino/image_processing_grounding_dino.py

@@ -839,7 +839,7 @@ class GroundingDinoImageProcessor(BaseImageProcessor):
             If `pad_size` is provided, the image will be padded to the specified dimensions.
             Otherwise, the image will be padded to the maximum height and width of the batch.
         pad_size (`Dict[str, int]`, *optional*):
-            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
             image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
     """
 
@@ -1332,8 +1332,8 @@ def preprocess(
             image_std (`float` or `List[float]`, *optional*, defaults to self.image_std):
                 Standard deviation to use when normalizing the image.
             do_pad (`bool`, *optional*, defaults to self.do_pad):
-                Whether to pad the image. If `True`, padding will be applied to the bottom and right of 
-                the image with zeros. If `pad_size` is provided, the image will be padded to the specified 
+                Whether to pad the image. If `True`, padding will be applied to the bottom and right of
+                the image with zeros. If `pad_size` is provided, the image will be padded to the specified
                 dimensions. Otherwise, the image will be padded to the maximum height and width of the batch.
             format (`str` or `AnnotationFormat`, *optional*, defaults to self.format):
                 Format of the annotations.

src/transformers/models/yolos/image_processing_yolos.py

@@ -739,7 +739,7 @@ class YolosImageProcessor(BaseImageProcessor):
             If `pad_size` is provided, the image will be padded to the specified dimensions.
             Otherwise, the image will be padded to the maximum height and width of the batch.
         pad_size (`Dict[str, int]`, *optional*):
-            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+            The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
             image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
     """
 
@@ -1121,7 +1121,7 @@ def pad(
                 bounding boxes have not been converted to relative coordinates and `(centre_x, centre_y, width, height)`
                 format, the bounding boxes will not be updated.
             pad_size (`Dict[str, int]`, *optional*):
-                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
                 image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
         """
         pad_size = pad_size if pad_size is not None else self.pad_size
@@ -1230,8 +1230,8 @@ def preprocess(
                 boxes from the format `(top_left_x, top_left_y, width, height)` to `(center_x, center_y, width, height)`
                 and in relative coordinates.
             do_pad (`bool`, *optional*, defaults to self.do_pad):
-                Whether to pad the image. If `True`, padding will be applied to the bottom and right of 
-                the image with zeros. If `pad_size` is provided, the image will be padded to the specified 
+                Whether to pad the image. If `True`, padding will be applied to the bottom and right of
+                the image with zeros. If `pad_size` is provided, the image will be padded to the specified
                 dimensions. Otherwise, the image will be padded to the maximum height and width of the batch.
             format (`str` or `AnnotationFormat`, *optional*, defaults to self.format):
                 Format of the annotations.
@@ -1246,7 +1246,7 @@ def preprocess(
                 - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                 - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
             pad_size (`Dict[str, int]`, *optional*):
-                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any 
+                The size `{"height": int, "width" int}` to pad the images to. Must be larger than any
                 image in `images`. If not provided, the images will be padded to the largest height and width in the batch.
         """
         if "pad_and_return_pixel_mask" in kwargs:

tests/models/conditional_detr/test_image_processing_conditional_detr.py

@@ -512,9 +512,7 @@ def test_max_width_max_height_resizing_and_pad_strategy(self):
 
         # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100
         image_processor = ConditionalDetrImageProcessor(
-            size={"max_height": 100, "max_width": 100},
-            do_pad=True, 
-            pad_size={"height": 100, "width": 100}
+            size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100}
         )
         inputs = image_processor(images=[image_1], return_tensors="pt")
         self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100]))

tests/models/deformable_detr/test_image_processing_deformable_detr.py

@@ -514,9 +514,7 @@ def test_max_width_max_height_resizing_and_pad_strategy(self):
 
         # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100
         image_processor = DeformableDetrImageProcessor(
-            size={"max_height": 100, "max_width": 100},
-            do_pad=True, 
-            pad_size={"height": 100, "width": 100}
+            size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100}
         )
         inputs = image_processor(images=[image_1], return_tensors="pt")
         self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100]))

tests/models/deta/test_image_processing_deta.py

@@ -508,9 +508,7 @@ def test_max_width_max_height_resizing_and_pad_strategy(self):
 
         # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100
         image_processor = DetaImageProcessor(
-            size={"max_height": 100, "max_width": 100},
-            do_pad=True, 
-            pad_size={"height": 100, "width": 100}
+            size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100}
         )
         inputs = image_processor(images=[image_1], return_tensors="pt")
         self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100]))

tests/models/detr/test_image_processing_detr.py

@@ -568,9 +568,7 @@ def test_max_width_max_height_resizing_and_pad_strategy(self):
 
         # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100
         image_processor = DetrImageProcessor(
-            size={"max_height": 100, "max_width": 100},
-            do_pad=True, 
-            pad_size={"height": 100, "width": 100}
+            size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100}
         )
         inputs = image_processor(images=[image_1], return_tensors="pt")
         self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100]))

tests/models/grounding_dino/test_image_processing_grounding_dino.py

@@ -550,9 +550,7 @@ def test_max_width_max_height_resizing_and_pad_strategy(self):
 
         # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100
         image_processor = GroundingDinoImageProcessor(
-            size={"max_height": 100, "max_width": 100},
-            do_pad=True, 
-            pad_size={"height": 100, "width": 100}
+            size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100}
         )
         inputs = image_processor(images=[image_1], return_tensors="pt")
         self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100]))

tests/models/yolos/test_image_processing_yolos.py

@@ -568,9 +568,7 @@ def test_max_width_max_height_resizing_and_pad_strategy(self):
 
         # do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100
         image_processor = YolosImageProcessor(
-            size={"max_height": 100, "max_width": 100},
-            do_pad=True, 
-            pad_size={"height": 100, "width": 100}
+            size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100}
         )
         inputs = image_processor(images=[image_1], return_tensors="pt")
         self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100]))