diff --git a/src/transformers/models/dpt/image_processing_dpt.py b/src/transformers/models/dpt/image_processing_dpt.py
index 2209def01e9c26..e1fd62f32d66e1 100644
--- a/src/transformers/models/dpt/image_processing_dpt.py
+++ b/src/transformers/models/dpt/image_processing_dpt.py
@@ -282,8 +282,8 @@ def _get_pad(size, size_divisor):
 
         return pad(image, ((pad_size_left, pad_size_right), (pad_size_top, pad_size_bottom)), data_format=data_format)
 
+    # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.reduce_label
     def reduce_label(self, label: ImageInput) -> np.ndarray:
-        # Copied from transformers.models.beit.image_processing_beit
         label = to_numpy_array(label)
         # Avoid using underflow conversion
         label[label == 0] = 255
@@ -309,8 +309,6 @@ def _preprocess(
         size_divisor: int = None,
         input_data_format: Optional[Union[str, ChannelDimension]] = None,
     ):
-        # Adapted from transformers.models.beit.image_processing_beit
-
         if do_reduce_labels:
             image = self.reduce_label(image)
 
@@ -354,7 +352,6 @@ def _preprocess_image(
         input_data_format: Optional[Union[str, ChannelDimension]] = None,
     ) -> np.ndarray:
         """Preprocesses a single image."""
-        # Adapted from transformers.models.beit.image_processing_beit
         # All transformations expect numpy arrays.
         image = to_numpy_array(image)
         if is_scaled_image(image) and do_rescale:
@@ -399,7 +396,6 @@ def _preprocess_segmentation_map(
         input_data_format: Optional[Union[str, ChannelDimension]] = None,
     ):
         """Preprocesses a single segmentation map."""
-        # Adapted from transformers.models.beit.image_processing_beit
         # All transformations expect numpy arrays.
         segmentation_map = to_numpy_array(segmentation_map)
         # Add an axis to the segmentation maps for transformations.
@@ -429,8 +425,8 @@ def _preprocess_segmentation_map(
         segmentation_map = segmentation_map.astype(np.int64)
         return segmentation_map
 
+    # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.__call__
     def __call__(self, images, segmentation_maps=None, **kwargs):
-        # Copied from transformers.models.beit.image_processing_beit
         # Overrides the `__call__` method of the `Preprocessor` class such that the images and segmentation maps can both
         # be passed in as positional arguments.
         return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs)
diff --git a/tests/models/dpt/test_image_processing_dpt.py b/tests/models/dpt/test_image_processing_dpt.py
index 1c88df576cb983..713c722a4c2b5d 100644
--- a/tests/models/dpt/test_image_processing_dpt.py
+++ b/tests/models/dpt/test_image_processing_dpt.py
@@ -90,8 +90,8 @@ def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=F
         )
 
 
+# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_single_inputs
 def prepare_semantic_single_inputs():
-    # Copied from transformers.tests.models.beit.test_image_processing_beit
     dataset = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
 
     image = Image.open(dataset[0]["file"])
@@ -100,8 +100,8 @@ def prepare_semantic_single_inputs():
     return image, map
 
 
+# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_batch_inputs
 def prepare_semantic_batch_inputs():
-    # Copied from transformers.tests.models.beit.test_image_processing_beit
     ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
 
     image1 = Image.open(ds[0]["file"])
@@ -171,8 +171,8 @@ def test_keep_aspect_ratio(self):
 
         self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672])
 
+    # Copied from transformers.tests.models.beit.test_image_processing_beit.BeitImageProcessingTest.test_call_segmentation_maps
     def test_call_segmentation_maps(self):
-        # Copied from transformers.tests.models.beit.test_image_processing_beit
         # Initialize image_processor
         image_processor = self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
@@ -278,8 +278,8 @@ def test_call_segmentation_maps(self):
         self.assertTrue(encoding["labels"].min().item() >= 0)
         self.assertTrue(encoding["labels"].max().item() <= 255)
 
+    # Copied from transformers.tests.models.beit.test_image_processing_beit.BeitImageProcessingTest.test_reduce_labels
     def test_reduce_labels(self):
-        # Copied from transformers.tests.models.beit.test_image_processing_beit
         # Initialize image_processor
         image_processor = self.image_processing_class(**self.image_processor_dict)