Removal of deprecated vision methods and specify deprecation versions (#24570)

* Removal of deprecated methods and specify versions

* Fix tests

docs/source/en/model_doc/conditional_detr.md

@@ -43,7 +43,6 @@ This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The o
 
 [[autodoc]] ConditionalDetrImageProcessor
     - preprocess
-    - pad_and_create_pixel_mask
     - post_process_object_detection
     - post_process_instance_segmentation
     - post_process_semantic_segmentation
@@ -53,7 +52,6 @@ This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The o
 
 [[autodoc]] ConditionalDetrFeatureExtractor
     - __call__
-    - pad_and_create_pixel_mask
     - post_process_object_detection
     - post_process_instance_segmentation
     - post_process_semantic_segmentation

docs/source/en/model_doc/deformable_detr.md

@@ -52,14 +52,12 @@ If you're interested in submitting a resource to be included here, please feel f
 
 [[autodoc]] DeformableDetrImageProcessor
     - preprocess
-    - pad_and_create_pixel_mask
     - post_process_object_detection
 
 ## DeformableDetrFeatureExtractor
 
 [[autodoc]] DeformableDetrFeatureExtractor
     - __call__
-    - pad_and_create_pixel_mask
     - post_process_object_detection
 
 ## DeformableDetrConfig

docs/source/en/model_doc/detr.md

@@ -190,7 +190,6 @@ If you're interested in submitting a resource to be included here, please feel f
 
 [[autodoc]] DetrFeatureExtractor
     - __call__
-    - pad_and_create_pixel_mask
     - post_process_object_detection
     - post_process_semantic_segmentation
     - post_process_instance_segmentation

docs/source/en/preprocessing.md

@@ -449,13 +449,13 @@ or segmentation maps.
 ### Pad
 
 In some cases, for instance, when fine-tuning [DETR](./model_doc/detr), the model applies scale augmentation at training
-time. This may cause images to be different sizes in a batch. You can use [`DetrImageProcessor.pad_and_create_pixel_mask`]
+time. This may cause images to be different sizes in a batch. You can use [`DetrImageProcessor.pad`]
 from [`DetrImageProcessor`] and define a custom `collate_fn` to batch images together.
 
 ```py
 >>> def collate_fn(batch):
 ...     pixel_values = [item["pixel_values"] for item in batch]
-...     encoding = image_processor.pad_and_create_pixel_mask(pixel_values, return_tensors="pt")
+...     encoding = image_processor.pad(pixel_values, return_tensors="pt")
 ...     labels = [item["labels"] for item in batch]
 ...     batch = {}
 ...     batch["pixel_values"] = encoding["pixel_values"]

docs/source/en/tasks/object_detection.md

@@ -305,7 +305,7 @@ to indicate which pixels are real (1) and which are padding (0).
 ```py
 >>> def collate_fn(batch):
 ...     pixel_values = [item["pixel_values"] for item in batch]
-...     encoding = image_processor.pad_and_create_pixel_mask(pixel_values, return_tensors="pt")
+...     encoding = image_processor.pad(pixel_values, return_tensors="pt")
 ...     labels = [item["labels"] for item in batch]
 ...     batch = {}
 ...     batch["pixel_values"] = encoding["pixel_values"]

docs/source/ko/preprocessing.md

@@ -456,12 +456,12 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 예를 들어, [DETR](./model_doc/detr)와 같은 경우에는 모델이 훈련할 때 크기 조정 증강을 적용합니다.
 이로 인해 배치 내 이미지 크기가 달라질 수 있습니다.
-[`DetrImageProcessor`]의 [`DetrImageProcessor.pad_and_create_pixel_mask`]를 사용하고 사용자 정의 `collate_fn`을 정의해서 배치 이미지를 처리할 수 있습니다.
+[`DetrImageProcessor`]의 [`DetrImageProcessor.pad`]를 사용하고 사용자 정의 `collate_fn`을 정의해서 배치 이미지를 처리할 수 있습니다.
 
 ```py
 >>> def collate_fn(batch):
 ...     pixel_values = [item["pixel_values"] for item in batch]
-...     encoding = image_processor.pad_and_create_pixel_mask(pixel_values, return_tensors="pt")
+...     encoding = image_processor.pad(pixel_values, return_tensors="pt")
 ...     labels = [item["labels"] for item in batch]
 ...     batch = {}
 ...     batch["pixel_values"] = encoding["pixel_values"]

docs/source/ko/tasks/object_detection.md

@@ -297,7 +297,7 @@ DatasetDict({
 ```py
 >>> def collate_fn(batch):
 ...     pixel_values = [item["pixel_values"] for item in batch]
-...     encoding = image_processor.pad_and_create_pixel_mask(pixel_values, return_tensors="pt")
+...     encoding = image_processor.pad(pixel_values, return_tensors="pt")
 ...     labels = [item["labels"] for item in batch]
 ...     batch = {}
 ...     batch["pixel_values"] = encoding["pixel_values"]

src/transformers/image_transforms.py

@@ -24,7 +24,6 @@ from .image_utils import (
     get_channel_dimension_axis,
     get_image_size,
     infer_channel_dimension_format,
-    to_numpy_array,
 )
 from .utils import ExplicitEnum, TensorType, is_jax_tensor, is_tf_tensor, is_torch_tensor
 from .utils.import_utils import (
@@ -345,18 +344,6 @@ def normalize(
         data_format (`ChannelDimension`, *optional*):
             The channel dimension format of the output image. If unset, will use the inferred format from the input.
     """
-    requires_backends(normalize, ["vision"])
-
-    if isinstance(image, PIL.Image.Image):
-        warnings.warn(
-            "PIL.Image.Image inputs are deprecated and will be removed in v4.26.0. Please use numpy arrays instead.",
-            FutureWarning,
-        )
-        # Convert PIL image to numpy array with the same logic as in the previous feature extractor normalize -
-        # casting to numpy array and dividing by 255.
-        image = to_numpy_array(image)
-        image = rescale(image, scale=1 / 255)
-
     if not isinstance(image, np.ndarray):
         raise ValueError("image must be a numpy array")
 
@@ -418,14 +405,9 @@ def center_crop(
     """
     requires_backends(center_crop, ["vision"])
 
-    if isinstance(image, PIL.Image.Image):
-        warnings.warn(
-            "PIL.Image.Image inputs are deprecated and will be removed in v4.26.0. Please use numpy arrays instead.",
-            FutureWarning,
-        )
-        image = to_numpy_array(image)
-        return_numpy = False if return_numpy is None else return_numpy
-    else:
+    if return_numpy is not None:
+        warnings.warn("return_numpy is deprecated and will be removed in v.4.33", FutureWarning)
+
     return_numpy = True if return_numpy is None else return_numpy
 
     if not isinstance(image, np.ndarray):

src/transformers/models/beit/image_processing_beit.py

@@ -128,15 +128,6 @@ class BeitImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
         self.do_reduce_labels = do_reduce_labels
 
-    @property
-    def reduce_labels(self) -> bool:
-        warnings.warn(
-            "The `reduce_labels` property is deprecated and will be removed in v4.27. Please use"
-            " `do_reduce_labels` instead.",
-            FutureWarning,
-        )
-        return self.do_reduce_labels
-
     @classmethod
     def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
         """

src/transformers/models/bridgetower/image_processing_bridgetower.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 """Image processor class for BridgeTower."""
 
-import warnings
 from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import numpy as np
@@ -352,42 +351,6 @@ class BridgeTowerImageProcessor(BaseImageProcessor):
 
         return BatchFeature(data=data, tensor_type=return_tensors)
 
-    # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor.pad_and_create_pixel_mask
-    def pad_and_create_pixel_mask(
-        self,
-        pixel_values_list: List[ImageInput],
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        data_format: Optional[ChannelDimension] = None,
-    ) -> BatchFeature:
-        """
-        Pads a batch of images with zeros to the size of largest height and width in the batch and returns their
-        corresponding pixel mask.
-
-        Args:
-            images (`List[np.ndarray]`):
-                Batch of images to pad.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                    - Unset: Return a list of `np.ndarray`.
-                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
-        """
-        warnings.warn(
-            "This method is deprecated and will be removed in v4.26.0. Please use pad instead.", FutureWarning
-        )
-        # pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
-        images = [to_numpy_array(image) for image in pixel_values_list]
-        return self.pad(
-            images=images,
-            return_pixel_mask=True,
-            return_tensors=return_tensors,
-            data_format=data_format,
-        )
-
     def preprocess(
         self,
         images: ImageInput,

src/transformers/models/conditional_detr/image_processing_conditional_detr.py

@@ -820,15 +820,6 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
         self.do_pad = do_pad
 
-    @property
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.max_size
-    def max_size(self):
-        logger.warning(
-            "The `max_size` parameter is deprecated and will be removed in v4.27. "
-            "Please specify in `size['longest_edge'] instead`.",
-        )
-        return self.size["longest_edge"]
-
     @classmethod
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->ConditionalDetr
     def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
@@ -873,7 +864,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
     def prepare(self, image, target, return_segmentation_masks=False, masks_path=None):
         logger.warning_once(
-            "The `prepare` method is deprecated and will be removed in a future version. "
+            "The `prepare` method is deprecated and will be removed in a v4.33. "
             "Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
             "does not return the image anymore.",
         )
@@ -882,23 +873,17 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
     def convert_coco_poly_to_mask(self, *args, **kwargs):
-        logger.warning_once(
-            "The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
         return convert_coco_poly_to_mask(*args, **kwargs)
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection with DETR->ConditionalDetr
     def prepare_coco_detection(self, *args, **kwargs):
-        logger.warning_once(
-            "The `prepare_coco_detection` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
         return prepare_coco_detection_annotation(*args, **kwargs)
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
     def prepare_coco_panoptic(self, *args, **kwargs):
-        logger.warning_once(
-            "The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
         return prepare_coco_panoptic_annotation(*args, **kwargs)
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
@@ -979,40 +964,6 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
         """
         return normalize_annotation(annotation, image_size=image_size)
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad_and_create_pixel_mask
-    def pad_and_create_pixel_mask(
-        self,
-        pixel_values_list: List[ImageInput],
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        data_format: Optional[ChannelDimension] = None,
-    ) -> BatchFeature:
-        """
-        Pads a batch of images with zeros to the size of largest height and width in the batch and returns their
-        corresponding pixel mask.
-
-        Args:
-            images (`List[np.ndarray]`):
-                Batch of images to pad.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                    - Unset: Return a list of `np.ndarray`.
-                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
-        """
-        logger.warning_once("This method is deprecated and will be removed in v4.27.0. Please use pad instead.")
-        # pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
-        images = [to_numpy_array(image) for image in pixel_values_list]
-        return self.pad(
-            images=images,
-            return_pixel_mask=True,
-            return_tensors=return_tensors,
-            data_format=data_format,
-        )
-
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image
     def _pad_image(
         self,

src/transformers/models/deformable_detr/image_processing_deformable_detr.py

@@ -818,15 +818,6 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
         self.do_pad = do_pad
 
-    @property
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.max_size
-    def max_size(self):
-        logger.warning(
-            "The `max_size` parameter is deprecated and will be removed in v4.27. "
-            "Please specify in `size['longest_edge'] instead`.",
-        )
-        return self.size["longest_edge"]
-
     @classmethod
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->DeformableDetr
     def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
@@ -871,7 +862,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
     def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
         logger.warning_once(
-            "The `prepare` method is deprecated and will be removed in a future version. "
+            "The `prepare` method is deprecated and will be removed in a v4.33. "
             "Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
             "does not return the image anymore.",
         )
@@ -880,23 +871,17 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
     def convert_coco_poly_to_mask(self, *args, **kwargs):
-        logger.warning_once(
-            "The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
         return convert_coco_poly_to_mask(*args, **kwargs)
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection
     def prepare_coco_detection(self, *args, **kwargs):
-        logger.warning_once(
-            "The `prepare_coco_detection` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
         return prepare_coco_detection_annotation(*args, **kwargs)
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
     def prepare_coco_panoptic(self, *args, **kwargs):
-        logger.warning_once(
-            "The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
         return prepare_coco_panoptic_annotation(*args, **kwargs)
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
@@ -977,40 +962,6 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
         """
         return normalize_annotation(annotation, image_size=image_size)
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad_and_create_pixel_mask
-    def pad_and_create_pixel_mask(
-        self,
-        pixel_values_list: List[ImageInput],
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        data_format: Optional[ChannelDimension] = None,
-    ) -> BatchFeature:
-        """
-        Pads a batch of images with zeros to the size of largest height and width in the batch and returns their
-        corresponding pixel mask.
-
-        Args:
-            images (`List[np.ndarray]`):
-                Batch of images to pad.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                    - Unset: Return a list of `np.ndarray`.
-                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
-        """
-        logger.warning_once("This method is deprecated and will be removed in v4.27.0. Please use pad instead.")
-        # pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
-        images = [to_numpy_array(image) for image in pixel_values_list]
-        return self.pad(
-            images=images,
-            return_pixel_mask=True,
-            return_tensors=return_tensors,
-            data_format=data_format,
-        )
-
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image
     def _pad_image(
         self,

src/transformers/models/deta/image_processing_deta.py

@@ -544,7 +544,7 @@ class DetaImageProcessor(BaseImageProcessor):
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
     def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
         logger.warning_once(
-            "The `prepare` method is deprecated and will be removed in a future version. "
+            "The `prepare` method is deprecated and will be removed in a v4.33. "
             "Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
             "does not return the image anymore.",
         )
@@ -553,23 +553,17 @@ class DetaImageProcessor(BaseImageProcessor):
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
     def convert_coco_poly_to_mask(self, *args, **kwargs):
-        logger.warning_once(
-            "The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
         return convert_coco_poly_to_mask(*args, **kwargs)
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection
     def prepare_coco_detection(self, *args, **kwargs):
-        logger.warning_once(
-            "The `prepare_coco_detection` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
         return prepare_coco_detection_annotation(*args, **kwargs)
 
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
     def prepare_coco_panoptic(self, *args, **kwargs):
-        logger.warning_once(
-            "The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
         return prepare_coco_panoptic_annotation(*args, **kwargs)
 
     def resize(
@@ -641,40 +635,6 @@ class DetaImageProcessor(BaseImageProcessor):
         """
         return normalize_annotation(annotation, image_size=image_size)
 
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad_and_create_pixel_mask
-    def pad_and_create_pixel_mask(
-        self,
-        pixel_values_list: List[ImageInput],
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        data_format: Optional[ChannelDimension] = None,
-    ) -> BatchFeature:
-        """
-        Pads a batch of images with zeros to the size of largest height and width in the batch and returns their
-        corresponding pixel mask.
-
-        Args:
-            images (`List[np.ndarray]`):
-                Batch of images to pad.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                    - Unset: Return a list of `np.ndarray`.
-                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
-        """
-        logger.warning_once("This method is deprecated and will be removed in v4.27.0. Please use pad instead.")
-        # pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
-        images = [to_numpy_array(image) for image in pixel_values_list]
-        return self.pad(
-            images=images,
-            return_pixel_mask=True,
-            return_tensors=return_tensors,
-            data_format=data_format,
-        )
-
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image
     def _pad_image(
         self,

src/transformers/models/detr/image_processing_detr.py

@@ -802,14 +802,6 @@ class DetrImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
         self.do_pad = do_pad
 
-    @property
-    def max_size(self):
-        logger.warning(
-            "The `max_size` parameter is deprecated and will be removed in v4.27. "
-            "Please specify in `size['longest_edge'] instead`.",
-        )
-        return self.size["longest_edge"]
-
     @classmethod
     def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
         """
@@ -851,7 +843,7 @@ class DetrImageProcessor(BaseImageProcessor):
 
     def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
         logger.warning_once(
-            "The `prepare` method is deprecated and will be removed in a future version. "
+            "The `prepare` method is deprecated and will be removed in a v4.33. "
             "Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
             "does not return the image anymore.",
         )
@@ -859,21 +851,15 @@ class DetrImageProcessor(BaseImageProcessor):
         return image, target
 
     def convert_coco_poly_to_mask(self, *args, **kwargs):
-        logger.warning_once(
-            "The `convert_coco_poly_to_mask` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
         return convert_coco_poly_to_mask(*args, **kwargs)
 
     def prepare_coco_detection(self, *args, **kwargs):
-        logger.warning_once(
-            "The `prepare_coco_detection` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
         return prepare_coco_detection_annotation(*args, **kwargs)
 
     def prepare_coco_panoptic(self, *args, **kwargs):
-        logger.warning_once(
-            "The `prepare_coco_panoptic` method is deprecated and will be removed in a future version. "
-        )
+        logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
         return prepare_coco_panoptic_annotation(*args, **kwargs)
 
     def resize(
@@ -949,39 +935,6 @@ class DetrImageProcessor(BaseImageProcessor):
         """
         return normalize_annotation(annotation, image_size=image_size)
 
-    def pad_and_create_pixel_mask(
-        self,
-        pixel_values_list: List[ImageInput],
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        data_format: Optional[ChannelDimension] = None,
-    ) -> BatchFeature:
-        """
-        Pads a batch of images with zeros to the size of largest height and width in the batch and returns their
-        corresponding pixel mask.
-
-        Args:
-            images (`List[np.ndarray]`):
-                Batch of images to pad.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                    - Unset: Return a list of `np.ndarray`.
-                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
-        """
-        logger.warning_once("This method is deprecated and will be removed in v4.27.0. Please use pad instead.")
-        # pad expects a list of np.ndarray, but the previous feature extractors expected torch tensors
-        images = [to_numpy_array(image) for image in pixel_values_list]
-        return self.pad(
-            images=images,
-            return_pixel_mask=True,
-            return_tensors=return_tensors,
-            data_format=data_format,
-        )
-
     def _pad_image(
         self,
         image: np.ndarray,

src/transformers/models/donut/image_processing_donut.py

@@ -151,12 +151,6 @@ class DonutImageProcessor(BaseImageProcessor):
 
         return image
 
-    def rotate_image(self, *args, **kwargs):
-        logger.info(
-            "rotate_image is deprecated and will be removed in version 4.27. Please use align_long_axis instead."
-        )
-        return self.align_long_axis(*args, **kwargs)
-
     def pad_image(
         self,
         image: np.ndarray,

src/transformers/models/mask2former/image_processing_mask2former.py

@@ -29,7 +29,6 @@ from ...image_transforms import (
     rescale,
     resize,
     to_channel_dimension_format,
-    to_numpy_array,
 )
 from ...image_utils import (
     ChannelDimension,
@@ -38,6 +37,7 @@ from ...image_utils import (
     get_image_size,
     infer_channel_dimension_format,
     is_batched,
+    to_numpy_array,
     valid_images,
 )
 from ...utils import (
@@ -441,24 +441,6 @@ class Mask2FormerImageProcessor(BaseImageProcessor):
             image_processor_dict["size_divisibility"] = kwargs.pop("size_divisibility")
         return super().from_dict(image_processor_dict, **kwargs)
 
-    @property
-    def size_divisibility(self):
-        warnings.warn(
-            "The `size_divisibility` property is deprecated and will be removed in v4.27. Please use "
-            "`size_divisor` instead.",
-            FutureWarning,
-        )
-        return self.size_divisor
-
-    @property
-    def max_size(self):
-        warnings.warn(
-            "The `max_size` property is deprecated and will be removed in v4.27. Please use size['longest_edge']"
-            " instead.",
-            FutureWarning,
-        )
-        return self.size["longest_edge"]
-
     def resize(
         self,
         image: np.ndarray,
@@ -789,7 +771,6 @@ class Mask2FormerImageProcessor(BaseImageProcessor):
         ignore_index: Optional[int] = None,
         reduce_labels: bool = False,
         return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs,
     ):
         """
         Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
@@ -840,12 +821,6 @@ class Mask2FormerImageProcessor(BaseImageProcessor):
         """
         ignore_index = self.ignore_index if ignore_index is None else ignore_index
         reduce_labels = self.reduce_labels if reduce_labels is None else reduce_labels
-
-        if "pad_and_return_pixel_mask" in kwargs:
-            warnings.warn(
-                "The `pad_and_return_pixel_mask` argument has no effect and will be removed in v4.27", FutureWarning
-            )
-
         pixel_values_list = [to_numpy_array(pixel_values) for pixel_values in pixel_values_list]
         encoded_inputs = self.pad(pixel_values_list, return_tensors=return_tensors)
 

src/transformers/models/maskformer/image_processing_maskformer.py

@@ -29,7 +29,6 @@ from ...image_transforms import (
     rescale,
     resize,
     to_channel_dimension_format,
-    to_numpy_array,
 )
 from ...image_utils import (
     ChannelDimension,
@@ -38,6 +37,7 @@ from ...image_utils import (
     get_image_size,
     infer_channel_dimension_format,
     make_list_of_images,
+    to_numpy_array,
     valid_images,
 )
 from ...utils import (
@@ -452,33 +452,6 @@ class MaskFormerImageProcessor(BaseImageProcessor):
             image_processor_dict["size_divisibility"] = kwargs.pop("size_divisibility")
         return super().from_dict(image_processor_dict, **kwargs)
 
-    @property
-    def size_divisibility(self):
-        warnings.warn(
-            "The `size_divisibility` property is deprecated and will be removed in v4.27. Please use "
-            "`size_divisor` instead.",
-            FutureWarning,
-        )
-        return self.size_divisor
-
-    @property
-    def max_size(self):
-        warnings.warn(
-            "The `max_size` property is deprecated and will be removed in v4.27. Please use size['longest_edge']"
-            " instead.",
-            FutureWarning,
-        )
-        return self.size["longest_edge"]
-
-    @property
-    def reduce_labels(self):
-        warnings.warn(
-            "The `reduce_labels` property is deprecated and will be removed in v4.27. Please use "
-            "`do_reduce_labels` instead.",
-            FutureWarning,
-        )
-        return self.do_reduce_labels
-
     def resize(
         self,
         image: np.ndarray,
@@ -820,7 +793,6 @@ class MaskFormerImageProcessor(BaseImageProcessor):
         ignore_index: Optional[int] = None,
         reduce_labels: bool = False,
         return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs,
     ):
         """
         Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
@@ -869,10 +841,6 @@ class MaskFormerImageProcessor(BaseImageProcessor):
               `annotations` are provided). They identify the labels of `mask_labels`, e.g. the label of
               `mask_labels[i][j]` if `class_labels[i][j]`.
         """
-        if "pad_and_return_pixel_mask" in kwargs:
-            warnings.warn(
-                "The `pad_and_return_pixel_mask` argument has no effect and will be removed in v4.27", FutureWarning
-            )
         ignore_index = self.ignore_index if ignore_index is None else ignore_index
         reduce_labels = self.do_reduce_labels if reduce_labels is None else reduce_labels
 

src/transformers/models/oneformer/image_processing_oneformer.py

@@ -30,7 +30,6 @@ from ...image_transforms import (
     rescale,
     resize,
     to_channel_dimension_format,
-    to_numpy_array,
 )
 from ...image_utils import (
     ChannelDimension,
@@ -39,6 +38,7 @@ from ...image_utils import (
     get_image_size,
     infer_channel_dimension_format,
     make_list_of_images,
+    to_numpy_array,
     valid_images,
 )
 from ...utils import (
@@ -881,7 +881,6 @@ class OneFormerImageProcessor(BaseImageProcessor):
         ignore_index: Optional[int] = None,
         reduce_labels: bool = False,
         return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs,
     ):
         """
         Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
@@ -935,11 +934,6 @@ class OneFormerImageProcessor(BaseImageProcessor):
             - **text_inputs** -- Optional list of text string entries to be fed to a model (when `annotations` are
               provided). They identify the binary masks present in the image.
         """
-        if "pad_and_return_pixel_mask" in kwargs:
-            warnings.warn(
-                "The `pad_and_return_pixel_mask` argument has no effect and will be removed in v4.27", FutureWarning
-            )
-
         ignore_index = self.ignore_index if ignore_index is None else ignore_index
         reduce_labels = self.do_reduce_labels if reduce_labels is None else reduce_labels
         pixel_values_list = [to_numpy_array(pixel_values) for pixel_values in pixel_values_list]

src/transformers/models/owlvit/image_processing_owlvit.py

@@ -27,7 +27,6 @@ from ...image_transforms import (
     rescale,
     resize,
     to_channel_dimension_format,
-    to_numpy_array,
 )
 from ...image_utils import (
     OPENAI_CLIP_MEAN,
@@ -36,6 +35,7 @@ from ...image_utils import (
     ImageInput,
     PILImageResampling,
     make_list_of_images,
+    to_numpy_array,
     valid_images,
 )
 from ...utils import TensorType, is_torch_available, logging

src/transformers/models/segformer/image_processing_segformer.py

@@ -116,15 +116,6 @@ class SegformerImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
         self.do_reduce_labels = do_reduce_labels
 
-    @property
-    def reduce_labels(self):
-        warnings.warn(
-            "The `reduce_labels` property is deprecated and will be removed in a v4.27. Please use "
-            "`do_reduce_labels` instead.",
-            FutureWarning,
-        )
-        return self.do_reduce_labels
-
     @classmethod
     def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
         """