src/transformers/models/auto/image_processing_auto.py/AutoImageProcessor/from_pretrained
class AutoImageProcessor:    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        r"""
        Instantiate one of the image processor classes of the library from a pretrained model vocabulary.

        The image processor class to instantiate is selected based on the `model_type` property of the config object
        (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's
        missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

        List options

        Params:
            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. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
                  namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
                - 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 or url 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.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to delete incompletely received file. Attempts to resume the download if such a file
                exists.
            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.
            use_auth_token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
                when running `huggingface-cli 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.
            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.
            trust_remote_code (`bool`, *optional*, defaults to `False`):
                Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
                should only be set to `True` for repositories you trust and in which you have read the code, as it will
                execute code present on the Hub on your local machine.
            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.

        <Tip>

        Passing `use_auth_token=True` is required when you want to use a private model.

        </Tip>

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor

        >>> # Download image processor from huggingface.co and cache.
        >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")

        >>> # If image processor files are in a directory (e.g. image processor was saved using *save_pretrained('./test/saved_model/')*)
        >>> image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")
        ```"""
        config = kwargs.pop("config", None)
        trust_remote_code = kwargs.pop("trust_remote_code", False)
        kwargs["_from_auto"] = True

        config_dict, _ = ImageProcessingMixin.get_image_processor_dict(pretrained_model_name_or_path, **kwargs)
        image_processor_class = config_dict.get("image_processor_type", None)
        image_processor_auto_map = None
        if "AutoImageProcessor" in config_dict.get("auto_map", {}):
            image_processor_auto_map = config_dict["auto_map"]["AutoImageProcessor"]

        # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
        # and if so, infer the image processor class from there.
        if image_processor_class is None and image_processor_auto_map is None:
            feature_extractor_class = config_dict.pop("feature_extractor_type", None)
            if feature_extractor_class is not None:
                logger.warning(
                    "Could not find image processor class in the image processor config or the model config. Loading"
                    " based on pattern matching with the model's feature extractor configuration."
                )
                image_processor_class = feature_extractor_class.replace("FeatureExtractor", "ImageProcessor")
            if "AutoFeatureExtractor" in config_dict.get("auto_map", {}):
                feature_extractor_auto_map = config_dict["auto_map"]["AutoFeatureExtractor"]
                image_processor_auto_map = feature_extractor_auto_map.replace("FeatureExtractor", "ImageProcessor")
                logger.warning(
                    "Could not find image processor auto map in the image processor config or the model config."
                    " Loading based on pattern matching with the model's feature extractor configuration."
                )

        # If we don't find the image processor class in the image processor config, let's try the model config.
        if image_processor_class is None and image_processor_auto_map is None:
            if not isinstance(config, PretrainedConfig):
                config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
            # It could be in `config.image_processor_type``
            image_processor_class = getattr(config, "image_processor_type", None)
            if hasattr(config, "auto_map") and "AutoImageProcessor" in config.auto_map:
                image_processor_auto_map = config.auto_map["AutoImageProcessor"]

        if image_processor_class is not None:
            # If we have custom code for a image processor, we get the proper class.
            if image_processor_auto_map is not None:
                if not trust_remote_code:
                    raise ValueError(
                        f"Loading {pretrained_model_name_or_path} requires you to execute the image processor file "
                        "in that repo on your local machine. Make sure you have read the code there to avoid "
                        "malicious use, then set the option `trust_remote_code=True` to remove this error."
                    )
                if kwargs.get("revision", None) is None:
                    logger.warning(
                        "Explicitly passing a `revision` is encouraged when loading a image processor with custom "
                        "code to ensure no malicious code has been contributed in a newer revision."
                    )

                module_file, class_name = image_processor_auto_map.split(".")
                image_processor_class = get_class_from_dynamic_module(
                    pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
                )
                image_processor_class.register_for_auto_class()
            else:
                image_processor_class = image_processor_class_from_name(image_processor_class)

            return image_processor_class.from_dict(config_dict, **kwargs)
        # Last try: we use the IMAGE_PROCESSOR_MAPPING.
        elif type(config) in IMAGE_PROCESSOR_MAPPING:
            image_processor_class = IMAGE_PROCESSOR_MAPPING[type(config)]
            return image_processor_class.from_dict(config_dict, **kwargs)

        raise ValueError(
            f"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
            f"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
            f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys())}"
        )