Package upstream model with reproducible local MMEB evaluation

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# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import copy
import json
import os
from typing import Any, TypeVar

import numpy as np
from huggingface_hub import create_repo, is_offline_mode

from .dynamic_module_utils import custom_object_save
from .feature_extraction_utils import BatchFeature as BaseBatchFeature
from .image_utils import is_valid_image, load_image
from .utils import (
    IMAGE_PROCESSOR_NAME,
    PROCESSOR_NAME,
    PushToHubMixin,
    copy_func,
    logging,
    safe_load_json_file,
)
from .utils.hub import cached_file


ImageProcessorType = TypeVar("ImageProcessorType", bound="ImageProcessingMixin")


logger = logging.get_logger(__name__)


# TODO: Move BatchFeature to be imported by both image_processing_utils and image_processing_utils_fast
# We override the class string here, but logic is the same.
class BatchFeature(BaseBatchFeature):
    r"""
    Holds the output of the image processor specific `__call__` methods.
    This class is derived from a python dictionary and can be used as a dictionary.
    Args:
        data (`dict`):
            Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
        tensor_type (`Union[None, str, TensorType]`, *optional*):
            You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
            initialization.
    """


# TODO: (Amy) - factor out the common parts of this and the feature extractor
class ImageProcessingMixin(PushToHubMixin):
    """
    This is an image processor mixin used to provide saving/loading functionality for sequential and image feature
    extractors.
    """

    _auto_class = None

    def __init__(self, **kwargs):
        """Set elements of `kwargs` as attributes."""
        # This key was saved while we still used `XXXFeatureExtractor` for image processing. Now we use
        # `XXXImageProcessor`, this attribute and its value are misleading.
        kwargs.pop("feature_extractor_type", None)
        # Pop "processor_class", should not be saved with image processing config anymore
        kwargs.pop("processor_class", None)
        # Additional attributes without default values
        for key, value in kwargs.items():
            try:
                setattr(self, key, value)
            except AttributeError as err:
                logger.error(f"Can't set {key} with value {value} for {self}")
                raise err

    @classmethod
    def from_pretrained(
        cls: type[ImageProcessorType],
        pretrained_model_name_or_path: str | os.PathLike,
        cache_dir: str | os.PathLike | None = None,
        force_download: bool = False,
        local_files_only: bool = False,
        token: str | bool | None = None,
        revision: str = "main",
        **kwargs,
    ) -> ImageProcessorType:
        r"""
        Instantiate a type of [`~image_processing_utils.ImageProcessingMixin`] from an image processor.
        Args:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                This can be either:
                - a string, the *model id* of a pretrained image_processor hosted inside a model repo on
                  huggingface.co.
                - a path to a *directory* containing a image processor file saved using the
                  [`~image_processing_utils.ImageProcessingMixin.save_pretrained`] method, e.g.,
                  `./my_model_directory/`.
                - a path to a saved image processor JSON *file*, e.g.,
                  `./my_model_directory/preprocessor_config.json`.
            cache_dir (`str` or `os.PathLike`, *optional*):
                Path to a directory in which a downloaded pretrained model image processor should be cached if the
                standard cache should not be used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force to (re-)download the image processor files and override the cached versions if
                they exist.
            proxies (`dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
            token (`str` or `bool`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
                the token generated when running `hf auth login` (stored in `~/.huggingface`).
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.
                <Tip>
                To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>"`.
                </Tip>
            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                If `False`, then this function returns just the final image processor object. If `True`, then this
                functions returns a `Tuple(image_processor, unused_kwargs)` where *unused_kwargs* is a dictionary
                consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
                `kwargs` which has not been used to update `image_processor` and is otherwise ignored.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
                specify the folder name here.
            kwargs (`dict[str, Any]`, *optional*):
                The values in kwargs of any keys which are image processor attributes will be used to override the
                loaded values. Behavior concerning key/value pairs whose keys are *not* image processor attributes is
                controlled by the `return_unused_kwargs` keyword parameter.
        Returns:
            A image processor of type [`~image_processing_utils.ImageProcessingMixin`].
        Examples:
        ```python
        # We can't instantiate directly the base class *ImageProcessingMixin* so let's show the examples on a
        # derived class: *CLIPImageProcessor*
        image_processor = CLIPImageProcessor.from_pretrained(
            "openai/clip-vit-base-patch32"
        )  # Download image_processing_config from huggingface.co and cache.
        image_processor = CLIPImageProcessor.from_pretrained(
            "./test/saved_model/"
        )  # E.g. image processor (or model) was saved using *save_pretrained('./test/saved_model/')*
        image_processor = CLIPImageProcessor.from_pretrained("./test/saved_model/preprocessor_config.json")
        image_processor = CLIPImageProcessor.from_pretrained(
            "openai/clip-vit-base-patch32", do_normalize=False, foo=False
        )
        assert image_processor.do_normalize is False
        image_processor, unused_kwargs = CLIPImageProcessor.from_pretrained(
            "openai/clip-vit-base-patch32", do_normalize=False, foo=False, return_unused_kwargs=True
        )
        assert image_processor.do_normalize is False
        assert unused_kwargs == {"foo": False}
        ```"""
        kwargs["cache_dir"] = cache_dir
        kwargs["force_download"] = force_download
        kwargs["local_files_only"] = local_files_only
        kwargs["revision"] = revision

        if token is not None:
            kwargs["token"] = token

        image_processor_dict, kwargs = cls.get_image_processor_dict(pretrained_model_name_or_path, **kwargs)

        return cls.from_dict(image_processor_dict, **kwargs)

    def save_pretrained(self, save_directory: str | os.PathLike, push_to_hub: bool = False, **kwargs):
        """
        Save an image processor object to the directory `save_directory`, so that it can be re-loaded using the
        [`~image_processing_utils.ImageProcessingMixin.from_pretrained`] class method.
        Args:
            save_directory (`str` or `os.PathLike`):
                Directory where the image processor JSON file will be saved (will be created if it does not exist).
            push_to_hub (`bool`, *optional*, defaults to `False`):
                Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                namespace).
            kwargs (`dict[str, Any]`, *optional*):
                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
        """
        if os.path.isfile(save_directory):
            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

        os.makedirs(save_directory, exist_ok=True)

        if push_to_hub:
            commit_message = kwargs.pop("commit_message", None)
            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
            repo_id = create_repo(repo_id, exist_ok=True, **kwargs).repo_id
            files_timestamps = self._get_files_timestamps(save_directory)

        # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
        # loaded from the Hub.
        if self._auto_class is not None:
            custom_object_save(self, save_directory, config=self)

        # If we save using the predefined names, we can load using `from_pretrained`
        output_image_processor_file = os.path.join(save_directory, IMAGE_PROCESSOR_NAME)

        self.to_json_file(output_image_processor_file)
        logger.info(f"Image processor saved in {output_image_processor_file}")

        if push_to_hub:
            self._upload_modified_files(
                save_directory,
                repo_id,
                files_timestamps,
                commit_message=commit_message,
                token=kwargs.get("token"),
            )

        return [output_image_processor_file]

    @classmethod
    def get_image_processor_dict(
        cls, pretrained_model_name_or_path: str | os.PathLike, **kwargs
    ) -> tuple[dict[str, Any], dict[str, Any]]:
        """
        From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
        image processor of type [`~image_processor_utils.ImageProcessingMixin`] using `from_dict`.
        Parameters:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
                specify the folder name here.
            image_processor_filename (`str`, *optional*, defaults to `"config.json"`):
                The name of the file in the model directory to use for the image processor config.
        Returns:
            `tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the image processor object.
        """
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        proxies = kwargs.pop("proxies", None)
        token = kwargs.pop("token", None)
        local_files_only = kwargs.pop("local_files_only", False)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", "")
        image_processor_filename = kwargs.pop("image_processor_filename", IMAGE_PROCESSOR_NAME)

        from_pipeline = kwargs.pop("_from_pipeline", None)
        from_auto_class = kwargs.pop("_from_auto", False)

        user_agent = {"file_type": "image processor", "from_auto_class": from_auto_class}
        if from_pipeline is not None:
            user_agent["using_pipeline"] = from_pipeline

        if is_offline_mode() and not local_files_only:
            logger.info("Offline mode: forcing local_files_only=True")
            local_files_only = True

        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
        is_local = os.path.isdir(pretrained_model_name_or_path)
        if os.path.isdir(pretrained_model_name_or_path):
            image_processor_file = os.path.join(pretrained_model_name_or_path, image_processor_filename)
        if os.path.isfile(pretrained_model_name_or_path):
            resolved_image_processor_file = pretrained_model_name_or_path
            resolved_processor_file = None
            is_local = True
        else:
            image_processor_file = image_processor_filename
            try:
                resolved_processor_file = cached_file(
                    pretrained_model_name_or_path,
                    filename=PROCESSOR_NAME,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    token=token,
                    user_agent=user_agent,
                    revision=revision,
                    subfolder=subfolder,
                    _raise_exceptions_for_missing_entries=False,
                )
                resolved_image_processor_file = cached_file(
                    pretrained_model_name_or_path,
                    filename=image_processor_file,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    token=token,
                    user_agent=user_agent,
                    revision=revision,
                    subfolder=subfolder,
                    _raise_exceptions_for_missing_entries=False,
                )
            except OSError:
                # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
                # the original exception.
                raise
            except Exception:
                # For any other exception, we throw a generic error.
                raise OSError(
                    f"Can't load image processor for '{pretrained_model_name_or_path}'. If you were trying to load"
                    " it from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                    f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
                    f" directory containing a {image_processor_filename} file"
                )

        # Load image_processor dict. Priority goes as (nested config if found -> image processor config)
        # We are downloading both configs because almost all models have a `processor_config.json` but
        # not all of these are nested. We need to check if it was saved recebtly as nested or if it is legacy style
        image_processor_dict = None
        if resolved_processor_file is not None:
            processor_dict = safe_load_json_file(resolved_processor_file)
            if "image_processor" in processor_dict:
                image_processor_dict = processor_dict["image_processor"]

        if resolved_image_processor_file is not None and image_processor_dict is None:
            image_processor_dict = safe_load_json_file(resolved_image_processor_file)

        if image_processor_dict is None:
            raise OSError(
                f"Can't load image processor for '{pretrained_model_name_or_path}'. If you were trying to load"
                " it from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
                f" directory containing a {image_processor_filename} file"
            )

        if is_local:
            logger.info(f"loading configuration file {resolved_image_processor_file}")
        else:
            logger.info(
                f"loading configuration file {image_processor_file} from cache at {resolved_image_processor_file}"
            )

        return image_processor_dict, kwargs

    @classmethod
    def from_dict(cls, image_processor_dict: dict[str, Any], **kwargs):
        """
        Instantiates a type of [`~image_processing_utils.ImageProcessingMixin`] from a Python dictionary of parameters.
        Args:
            image_processor_dict (`dict[str, Any]`):
                Dictionary that will be used to instantiate the image processor object. Such a dictionary can be
                retrieved from a pretrained checkpoint by leveraging the
                [`~image_processing_utils.ImageProcessingMixin.to_dict`] method.
            kwargs (`dict[str, Any]`):
                Additional parameters from which to initialize the image processor object.
        Returns:
            [`~image_processing_utils.ImageProcessingMixin`]: The image processor object instantiated from those
            parameters.
        """
        image_processor_dict = image_processor_dict.copy()
        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
        image_processor_dict.update({k: v for k, v in kwargs.items() if k in cls.valid_kwargs.__annotations__})
        image_processor = cls(**image_processor_dict)

        # Apply extra kwargs to instance (BC for remote code, e.g. phi4_multimodal)
        extra_keys = []
        for key in reversed(list(kwargs.keys())):
            if hasattr(image_processor, key) and key not in cls.valid_kwargs.__annotations__:
                setattr(image_processor, key, kwargs.pop(key, None))
                extra_keys.append(key)
        if extra_keys:
            logger.warning_once(
                f"Image processor {cls.__name__}: kwargs {extra_keys} were applied for backward compatibility. "
                f"To avoid this warning, add them to valid_kwargs: create a custom TypedDict extending "
                f"ImagesKwargs with these keys and set it as the `valid_kwargs` class attribute."
            )

        logger.info(f"Image processor {image_processor}")
        if return_unused_kwargs:
            return image_processor, kwargs
        else:
            return image_processor

    def to_dict(self) -> dict[str, Any]:
        """
        Serializes this instance to a Python dictionary.
        Returns:
            `dict[str, Any]`: Dictionary of all the attributes that make up this image processor instance.
        """
        output = copy.deepcopy(self.__dict__)
        output["image_processor_type"] = self.__class__.__name__

        return output

    @classmethod
    def from_json_file(cls, json_file: str | os.PathLike):
        """
        Instantiates a image processor of type [`~image_processing_utils.ImageProcessingMixin`] from the path to a JSON
        file of parameters.
        Args:
            json_file (`str` or `os.PathLike`):
                Path to the JSON file containing the parameters.
        Returns:
            A image processor of type [`~image_processing_utils.ImageProcessingMixin`]: The image_processor object
            instantiated from that JSON file.
        """
        with open(json_file, encoding="utf-8") as reader:
            text = reader.read()
        image_processor_dict = json.loads(text)
        return cls(**image_processor_dict)

    def to_json_string(self) -> str:
        """
        Serializes this instance to a JSON string.
        Returns:
            `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
        """
        dictionary = self.to_dict()

        for key, value in dictionary.items():
            if isinstance(value, np.ndarray):
                dictionary[key] = value.tolist()

        return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"

    def to_json_file(self, json_file_path: str | os.PathLike):
        """
        Save this instance to a JSON file.
        Args:
            json_file_path (`str` or `os.PathLike`):
                Path to the JSON file in which this image_processor instance's parameters will be saved.
        """
        with open(json_file_path, "w", encoding="utf-8") as writer:
            writer.write(self.to_json_string())

    def __repr__(self):
        return f"{self.__class__.__name__} {self.to_json_string()}"

    @classmethod
    def register_for_auto_class(cls, auto_class="AutoImageProcessor"):
        """
        Register this class with a given auto class. This should only be used for custom image processors as the ones
        in the library are already mapped with `AutoImageProcessor `.
        Args:
            auto_class (`str` or `type`, *optional*, defaults to `"AutoImageProcessor "`):
                The auto class to register this new image processor with.
        """
        if not isinstance(auto_class, str):
            auto_class = auto_class.__name__

        import transformers.models.auto as auto_module

        if not hasattr(auto_module, auto_class):
            raise ValueError(f"{auto_class} is not a valid auto class.")

        cls._auto_class = auto_class

    def fetch_images(self, image_url_or_urls: str | list[str] | list[list[str]]):
        """
        Convert a single or a list of urls into the corresponding `PIL.Image` objects.
        If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
        returned.
        """
        if isinstance(image_url_or_urls, (list, tuple)):
            return [self.fetch_images(x) for x in image_url_or_urls]
        elif isinstance(image_url_or_urls, str):
            return load_image(image_url_or_urls)
        elif is_valid_image(image_url_or_urls):
            return image_url_or_urls
        else:
            raise TypeError(f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}")


ImageProcessingMixin.push_to_hub = copy_func(ImageProcessingMixin.push_to_hub)
if ImageProcessingMixin.push_to_hub.__doc__ is not None:
    ImageProcessingMixin.push_to_hub.__doc__ = ImageProcessingMixin.push_to_hub.__doc__.format(
        object="image processor", object_class="AutoImageProcessor", object_files="image processor file"
    )

	# Copyright 2020 The HuggingFace Inc. team.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import copy
	import json
	import os
	from typing import Any, TypeVar

	import numpy as np
	from huggingface_hub import create_repo, is_offline_mode

	from .dynamic_module_utils import custom_object_save
	from .feature_extraction_utils import BatchFeature as BaseBatchFeature
	from .image_utils import is_valid_image, load_image
	from .utils import (
	IMAGE_PROCESSOR_NAME,
	PROCESSOR_NAME,
	PushToHubMixin,
	copy_func,
	logging,
	safe_load_json_file,
	)
	from .utils.hub import cached_file


	ImageProcessorType = TypeVar("ImageProcessorType", bound="ImageProcessingMixin")


	logger = logging.get_logger(__name__)


	# TODO: Move BatchFeature to be imported by both image_processing_utils and image_processing_utils_fast
	# We override the class string here, but logic is the same.
	class BatchFeature(BaseBatchFeature):
	r"""
	Holds the output of the image processor specific `__call__` methods.

	This class is derived from a python dictionary and can be used as a dictionary.

	Args:
	data (`dict`):
	Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
	tensor_type (`Union[None, str, TensorType]`, optional):
	You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
	initialization.
	"""


	# TODO: (Amy) - factor out the common parts of this and the feature extractor
	class ImageProcessingMixin(PushToHubMixin):
	"""
	This is an image processor mixin used to provide saving/loading functionality for sequential and image feature
	extractors.
	"""

	_auto_class = None

	def __init__(self, **kwargs):
	"""Set elements of `kwargs` as attributes."""
	# This key was saved while we still used `XXXFeatureExtractor` for image processing. Now we use
	# `XXXImageProcessor`, this attribute and its value are misleading.
	kwargs.pop("feature_extractor_type", None)
	# Pop "processor_class", should not be saved with image processing config anymore
	kwargs.pop("processor_class", None)
	# Additional attributes without default values
	for key, value in kwargs.items():
	try:
	setattr(self, key, value)
	except AttributeError as err:
	logger.error(f"Can't set {key} with value {value} for {self}")
	raise err

	@classmethod
	def from_pretrained(
	cls: type[ImageProcessorType],
	pretrained_model_name_or_path: str \| os.PathLike,
	cache_dir: str \| os.PathLike \| None = None,
	force_download: bool = False,
	local_files_only: bool = False,
	token: str \| bool \| None = None,
	revision: str = "main",
	**kwargs,
	) -> ImageProcessorType:
	r"""
	Instantiate a type of [`~image_processing_utils.ImageProcessingMixin`] from an image processor.

	Args:
	pretrained_model_name_or_path (`str` or `os.PathLike`):
	This can be either:

	- a string, the model id of a pretrained image_processor hosted inside a model repo on
	huggingface.co.
	- a path to a directory containing a image processor file saved using the
	[`~image_processing_utils.ImageProcessingMixin.save_pretrained`] method, e.g.,
	`./my_model_directory/`.
	- a path to a saved image processor JSON file, e.g.,
	`./my_model_directory/preprocessor_config.json`.
	cache_dir (`str` or `os.PathLike`, optional):
	Path to a directory in which a downloaded pretrained model image processor should be cached if the
	standard cache should not be used.
	force_download (`bool`, optional, defaults to `False`):
	Whether or not to force to (re-)download the image processor files and override the cached versions if
	they exist.
	proxies (`dict[str, str]`, optional):
	A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
	'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
	token (`str` or `bool`, optional):
	The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
	the token generated when running `hf auth login` (stored in `~/.huggingface`).
	revision (`str`, optional, defaults to `"main"`):
	The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
	git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
	identifier allowed by git.


	<Tip>

	To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>"`.

	</Tip>

	return_unused_kwargs (`bool`, optional, defaults to `False`):
	If `False`, then this function returns just the final image processor object. If `True`, then this
	functions returns a `Tuple(image_processor, unused_kwargs)` where unused_kwargs is a dictionary
	consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
	`kwargs` which has not been used to update `image_processor` and is otherwise ignored.
	subfolder (`str`, optional, defaults to `""`):
	In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
	specify the folder name here.
	kwargs (`dict[str, Any]`, optional):
	The values in kwargs of any keys which are image processor attributes will be used to override the
	loaded values. Behavior concerning key/value pairs whose keys are not image processor attributes is
	controlled by the `return_unused_kwargs` keyword parameter.

	Returns:
	A image processor of type [`~image_processing_utils.ImageProcessingMixin`].

	Examples:

	```python
	# We can't instantiate directly the base class ImageProcessingMixin so let's show the examples on a
	# derived class: CLIPImageProcessor
	image_processor = CLIPImageProcessor.from_pretrained(
	"openai/clip-vit-base-patch32"
	) # Download image_processing_config from huggingface.co and cache.
	image_processor = CLIPImageProcessor.from_pretrained(
	"./test/saved_model/"
	) # E.g. image processor (or model) was saved using save_pretrained('./test/saved_model/')
	image_processor = CLIPImageProcessor.from_pretrained("./test/saved_model/preprocessor_config.json")
	image_processor = CLIPImageProcessor.from_pretrained(
	"openai/clip-vit-base-patch32", do_normalize=False, foo=False
	)
	assert image_processor.do_normalize is False
	image_processor, unused_kwargs = CLIPImageProcessor.from_pretrained(
	"openai/clip-vit-base-patch32", do_normalize=False, foo=False, return_unused_kwargs=True
	)
	assert image_processor.do_normalize is False
	assert unused_kwargs == {"foo": False}
	```"""
	kwargs["cache_dir"] = cache_dir
	kwargs["force_download"] = force_download
	kwargs["local_files_only"] = local_files_only
	kwargs["revision"] = revision

	if token is not None:
	kwargs["token"] = token

	image_processor_dict, kwargs = cls.get_image_processor_dict(pretrained_model_name_or_path, **kwargs)

	return cls.from_dict(image_processor_dict, **kwargs)

	def save_pretrained(self, save_directory: str \| os.PathLike, push_to_hub: bool = False, **kwargs):
	"""
	Save an image processor object to the directory `save_directory`, so that it can be re-loaded using the
	[`~image_processing_utils.ImageProcessingMixin.from_pretrained`] class method.

	Args:
	save_directory (`str` or `os.PathLike`):
	Directory where the image processor JSON file will be saved (will be created if it does not exist).
	push_to_hub (`bool`, optional, defaults to `False`):
	Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
	repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
	namespace).
	kwargs (`dict[str, Any]`, optional):
	Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
	"""
	if os.path.isfile(save_directory):
	raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

	os.makedirs(save_directory, exist_ok=True)

	if push_to_hub:
	commit_message = kwargs.pop("commit_message", None)
	repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
	repo_id = create_repo(repo_id, exist_ok=True, **kwargs).repo_id
	files_timestamps = self._get_files_timestamps(save_directory)

	# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
	# loaded from the Hub.
	if self._auto_class is not None:
	custom_object_save(self, save_directory, config=self)

	# If we save using the predefined names, we can load using `from_pretrained`
	output_image_processor_file = os.path.join(save_directory, IMAGE_PROCESSOR_NAME)

	self.to_json_file(output_image_processor_file)
	logger.info(f"Image processor saved in {output_image_processor_file}")

	if push_to_hub:
	self._upload_modified_files(
	save_directory,
	repo_id,
	files_timestamps,
	commit_message=commit_message,
	token=kwargs.get("token"),
	)

	return [output_image_processor_file]

	@classmethod
	def get_image_processor_dict(
	cls, pretrained_model_name_or_path: str \| os.PathLike, **kwargs
	) -> tuple[dict[str, Any], dict[str, Any]]:
	"""
	From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
	image processor of type [`~image_processor_utils.ImageProcessingMixin`] using `from_dict`.

	Parameters:
	pretrained_model_name_or_path (`str` or `os.PathLike`):
	The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
	subfolder (`str`, optional, defaults to `""`):
	In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
	specify the folder name here.
	image_processor_filename (`str`, optional, defaults to `"config.json"`):
	The name of the file in the model directory to use for the image processor config.

	Returns:
	`tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the image processor object.
	"""
	cache_dir = kwargs.pop("cache_dir", None)
	force_download = kwargs.pop("force_download", False)
	proxies = kwargs.pop("proxies", None)
	token = kwargs.pop("token", None)
	local_files_only = kwargs.pop("local_files_only", False)
	revision = kwargs.pop("revision", None)
	subfolder = kwargs.pop("subfolder", "")
	image_processor_filename = kwargs.pop("image_processor_filename", IMAGE_PROCESSOR_NAME)

	from_pipeline = kwargs.pop("_from_pipeline", None)
	from_auto_class = kwargs.pop("_from_auto", False)

	user_agent = {"file_type": "image processor", "from_auto_class": from_auto_class}
	if from_pipeline is not None:
	user_agent["using_pipeline"] = from_pipeline

	if is_offline_mode() and not local_files_only:
	logger.info("Offline mode: forcing local_files_only=True")
	local_files_only = True

	pretrained_model_name_or_path = str(pretrained_model_name_or_path)
	is_local = os.path.isdir(pretrained_model_name_or_path)
	if os.path.isdir(pretrained_model_name_or_path):
	image_processor_file = os.path.join(pretrained_model_name_or_path, image_processor_filename)
	if os.path.isfile(pretrained_model_name_or_path):
	resolved_image_processor_file = pretrained_model_name_or_path
	resolved_processor_file = None
	is_local = True
	else:
	image_processor_file = image_processor_filename
	try:
	resolved_processor_file = cached_file(
	pretrained_model_name_or_path,
	filename=PROCESSOR_NAME,
	cache_dir=cache_dir,
	force_download=force_download,
	proxies=proxies,
	local_files_only=local_files_only,
	token=token,
	user_agent=user_agent,
	revision=revision,
	subfolder=subfolder,
	_raise_exceptions_for_missing_entries=False,
	)
	resolved_image_processor_file = cached_file(
	pretrained_model_name_or_path,
	filename=image_processor_file,
	cache_dir=cache_dir,
	force_download=force_download,
	proxies=proxies,
	local_files_only=local_files_only,
	token=token,
	user_agent=user_agent,
	revision=revision,
	subfolder=subfolder,
	_raise_exceptions_for_missing_entries=False,
	)
	except OSError:
	# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
	# the original exception.
	raise
	except Exception:
	# For any other exception, we throw a generic error.
	raise OSError(
	f"Can't load image processor for '{pretrained_model_name_or_path}'. If you were trying to load"
	" it from 'https://huggingface.co/models', make sure you don't have a local directory with the"
	f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
	f" directory containing a {image_processor_filename} file"
	)

	# Load image_processor dict. Priority goes as (nested config if found -> image processor config)
	# We are downloading both configs because almost all models have a `processor_config.json` but
	# not all of these are nested. We need to check if it was saved recebtly as nested or if it is legacy style
	image_processor_dict = None
	if resolved_processor_file is not None:
	processor_dict = safe_load_json_file(resolved_processor_file)
	if "image_processor" in processor_dict:
	image_processor_dict = processor_dict["image_processor"]

	if resolved_image_processor_file is not None and image_processor_dict is None:
	image_processor_dict = safe_load_json_file(resolved_image_processor_file)

	if image_processor_dict is None:
	raise OSError(
	f"Can't load image processor for '{pretrained_model_name_or_path}'. If you were trying to load"
	" it from 'https://huggingface.co/models', make sure you don't have a local directory with the"
	f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
	f" directory containing a {image_processor_filename} file"
	)

	if is_local:
	logger.info(f"loading configuration file {resolved_image_processor_file}")
	else:
	logger.info(
	f"loading configuration file {image_processor_file} from cache at {resolved_image_processor_file}"
	)

	return image_processor_dict, kwargs

	@classmethod
	def from_dict(cls, image_processor_dict: dict[str, Any], **kwargs):
	"""
	Instantiates a type of [`~image_processing_utils.ImageProcessingMixin`] from a Python dictionary of parameters.

	Args:
	image_processor_dict (`dict[str, Any]`):
	Dictionary that will be used to instantiate the image processor object. Such a dictionary can be
	retrieved from a pretrained checkpoint by leveraging the
	[`~image_processing_utils.ImageProcessingMixin.to_dict`] method.
	kwargs (`dict[str, Any]`):
	Additional parameters from which to initialize the image processor object.

	Returns:
	[`~image_processing_utils.ImageProcessingMixin`]: The image processor object instantiated from those
	parameters.
	"""
	image_processor_dict = image_processor_dict.copy()
	return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
	image_processor_dict.update({k: v for k, v in kwargs.items() if k in cls.valid_kwargs.__annotations__})
	image_processor = cls(**image_processor_dict)

	# Apply extra kwargs to instance (BC for remote code, e.g. phi4_multimodal)
	extra_keys = []
	for key in reversed(list(kwargs.keys())):
	if hasattr(image_processor, key) and key not in cls.valid_kwargs.__annotations__:
	setattr(image_processor, key, kwargs.pop(key, None))
	extra_keys.append(key)
	if extra_keys:
	logger.warning_once(
	f"Image processor {cls.__name__}: kwargs {extra_keys} were applied for backward compatibility. "
	f"To avoid this warning, add them to valid_kwargs: create a custom TypedDict extending "
	f"ImagesKwargs with these keys and set it as the `valid_kwargs` class attribute."
	)

	logger.info(f"Image processor {image_processor}")
	if return_unused_kwargs:
	return image_processor, kwargs
	else:
	return image_processor

	def to_dict(self) -> dict[str, Any]:
	"""
	Serializes this instance to a Python dictionary.

	Returns:
	`dict[str, Any]`: Dictionary of all the attributes that make up this image processor instance.
	"""
	output = copy.deepcopy(self.__dict__)
	output["image_processor_type"] = self.__class__.__name__

	return output

	@classmethod
	def from_json_file(cls, json_file: str \| os.PathLike):
	"""
	Instantiates a image processor of type [`~image_processing_utils.ImageProcessingMixin`] from the path to a JSON
	file of parameters.

	Args:
	json_file (`str` or `os.PathLike`):
	Path to the JSON file containing the parameters.

	Returns:
	A image processor of type [`~image_processing_utils.ImageProcessingMixin`]: The image_processor object
	instantiated from that JSON file.
	"""
	with open(json_file, encoding="utf-8") as reader:
	text = reader.read()
	image_processor_dict = json.loads(text)
	return cls(**image_processor_dict)

	def to_json_string(self) -> str:
	"""
	Serializes this instance to a JSON string.

	Returns:
	`str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
	"""
	dictionary = self.to_dict()

	for key, value in dictionary.items():
	if isinstance(value, np.ndarray):
	dictionary[key] = value.tolist()

	return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"

	def to_json_file(self, json_file_path: str \| os.PathLike):
	"""
	Save this instance to a JSON file.

	Args:
	json_file_path (`str` or `os.PathLike`):
	Path to the JSON file in which this image_processor instance's parameters will be saved.
	"""
	with open(json_file_path, "w", encoding="utf-8") as writer:
	writer.write(self.to_json_string())

	def __repr__(self):
	return f"{self.__class__.__name__} {self.to_json_string()}"

	@classmethod
	def register_for_auto_class(cls, auto_class="AutoImageProcessor"):
	"""
	Register this class with a given auto class. This should only be used for custom image processors as the ones
	in the library are already mapped with `AutoImageProcessor `.



	Args:
	auto_class (`str` or `type`, optional, defaults to `"AutoImageProcessor "`):
	The auto class to register this new image processor with.
	"""
	if not isinstance(auto_class, str):
	auto_class = auto_class.__name__

	import transformers.models.auto as auto_module

	if not hasattr(auto_module, auto_class):
	raise ValueError(f"{auto_class} is not a valid auto class.")

	cls._auto_class = auto_class

	def fetch_images(self, image_url_or_urls: str \| list[str] \| list[list[str]]):
	"""
	Convert a single or a list of urls into the corresponding `PIL.Image` objects.

	If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
	returned.
	"""
	if isinstance(image_url_or_urls, (list, tuple)):
	return [self.fetch_images(x) for x in image_url_or_urls]
	elif isinstance(image_url_or_urls, str):
	return load_image(image_url_or_urls)
	elif is_valid_image(image_url_or_urls):
	return image_url_or_urls
	else:
	raise TypeError(f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}")


	ImageProcessingMixin.push_to_hub = copy_func(ImageProcessingMixin.push_to_hub)
	if ImageProcessingMixin.push_to_hub.__doc__ is not None:
	ImageProcessingMixin.push_to_hub.__doc__ = ImageProcessingMixin.push_to_hub.__doc__.format(
	object="image processor", object_class="AutoImageProcessor", object_files="image processor file"
	)