# coding=utf-8
# Copyright 2022 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.
"""
Processing saving/loading class for common processors.
"""

import copy
import inspect
import json
import os
import warnings
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, TypedDict, Union

import numpy as np

from .dynamic_module_utils import custom_object_save
from .image_utils import ChannelDimension, is_vision_available


if is_vision_available():
    from .image_utils import PILImageResampling

from .tokenization_utils_base import (
    PaddingStrategy,
    PreTrainedTokenizerBase,
    TruncationStrategy,
)
from .utils import (
    PROCESSOR_NAME,
    PushToHubMixin,
    TensorType,
    add_model_info_to_auto_map,
    add_model_info_to_custom_pipelines,
    cached_file,
    copy_func,
    direct_transformers_import,
    download_url,
    is_offline_mode,
    is_remote_url,
    logging,
)


logger = logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
transformers_module = direct_transformers_import(Path(__file__).parent)


AUTO_TO_BASE_CLASS_MAPPING = {
    "AutoTokenizer": "PreTrainedTokenizerBase",
    "AutoFeatureExtractor": "FeatureExtractionMixin",
    "AutoImageProcessor": "ImageProcessingMixin",
}


class TextKwargs(TypedDict, total=False):
    """
    Keyword arguments for text processing. For extended documentation, check out tokenization_utils_base methods and
    docstrings associated.

    Attributes:
        add_special_tokens (`bool`, *optional*)
            Whether or not to add special tokens when encoding the sequences.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*)
            Activates and controls padding.
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*):
            Activates and controls truncation.
        max_length (`int`, *optional*):
            Controls the maximum length to use by one of the truncation/padding parameters.
        stride (`int`, *optional*):
            If set, the overflowing tokens will contain some tokens from the end of the truncated sequence.
        is_split_into_words (`bool`, *optional*):
            Whether or not the input is already pre-tokenized.
        pad_to_multiple_of (`int`, *optional*):
            If set, will pad the sequence to a multiple of the provided value.
        return_token_type_ids (`bool`, *optional*):
            Whether to return token type IDs.
        return_attention_mask (`bool`, *optional*):
            Whether to return the attention mask.
        return_overflowing_tokens (`bool`, *optional*):
            Whether or not to return overflowing token sequences.
        return_special_tokens_mask (`bool`, *optional*):
            Whether or not to return special tokens mask information.
        return_offsets_mapping (`bool`, *optional*):
            Whether or not to return `(char_start, char_end)` for each token.
        return_length (`bool`, *optional*):
            Whether or not to return the lengths of the encoded inputs.
        verbose (`bool`, *optional*):
            Whether or not to print more information and warnings.
        padding_side (`str`, *optional*):
            The side on which padding will be applied.
    """

    add_special_tokens: Optional[bool]
    padding: Union[bool, str, PaddingStrategy]
    truncation: Union[bool, str, TruncationStrategy]
    max_length: Optional[int]
    stride: Optional[int]
    is_split_into_words: Optional[bool]
    pad_to_multiple_of: Optional[int]
    return_token_type_ids: Optional[bool]
    return_attention_mask: Optional[bool]
    return_overflowing_tokens: Optional[bool]
    return_special_tokens_mask: Optional[bool]
    return_offsets_mapping: Optional[bool]
    return_length: Optional[bool]
    verbose: Optional[bool]
    padding_side: Optional[str]


class ImagesKwargs(TypedDict, total=False):
    """
    Keyword arguments for image processing. For extended documentation, check the appropriate ImageProcessor
    class methods and docstrings.

    Attributes:
        do_resize (`bool`, *optional*):
            Whether to resize the image.
        size (`Dict[str, int]`, *optional*):
            Resize the shorter side of the input to `size["shortest_edge"]`.
        size_divisor (`int`, *optional*):
            The size by which to make sure both the height and width can be divided.
        crop_size (`Dict[str, int]`, *optional*):
            Desired output size when applying center-cropping.
        resample (`PILImageResampling`, *optional*):
            Resampling filter to use if resizing the image.
        do_rescale (`bool`, *optional*):
            Whether to rescale the image by the specified scale `rescale_factor`.
        rescale_factor (`int` or `float`, *optional*):
            Scale factor to use if rescaling the image.
        do_normalize (`bool`, *optional*):
            Whether to normalize the image.
        image_mean (`float` or `List[float]`, *optional*):
            Mean to use if normalizing the image.
        image_std (`float` or `List[float]`, *optional*):
            Standard deviation to use if normalizing the image.
        do_pad (`bool`, *optional*):
            Whether to pad the image to the `(max_height, max_width)` of the images in the batch.
        do_center_crop (`bool`, *optional*):
            Whether to center crop the image.
        data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the output image.
        input_data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the input image.
    """

    do_resize: Optional[bool]
    size: Optional[Dict[str, int]]
    size_divisor: Optional[int]
    crop_size: Optional[Dict[str, int]]
    resample: Optional[Union["PILImageResampling", int]]
    do_rescale: Optional[bool]
    rescale_factor: Optional[float]
    do_normalize: Optional[bool]
    image_mean: Optional[Union[float, List[float]]]
    image_std: Optional[Union[float, List[float]]]
    do_pad: Optional[bool]
    do_center_crop: Optional[bool]
    data_format: Optional[ChannelDimension]
    input_data_format: Optional[Union[str, ChannelDimension]]


class VideosKwargs(TypedDict, total=False):
    """
    Keyword arguments for video processing.

    Attributes:
        do_resize (`bool`):
            Whether to resize the image.
        size (`Dict[str, int]`, *optional*):
            Resize the shorter side of the input to `size["shortest_edge"]`.
        size_divisor (`int`, *optional*):
            The size by which to make sure both the height and width can be divided.
        resample (`PILImageResampling`, *optional*):
            Resampling filter to use if resizing the image.
        do_rescale (`bool`, *optional*):
            Whether to rescale the image by the specified scale `rescale_factor`.
        rescale_factor (`int` or `float`, *optional*):
            Scale factor to use if rescaling the image.
        do_normalize (`bool`, *optional*):
            Whether to normalize the image.
        image_mean (`float` or `List[float]`, *optional*):
            Mean to use if normalizing the image.
        image_std (`float` or `List[float]`, *optional*):
            Standard deviation to use if normalizing the image.
        do_pad (`bool`, *optional*):
            Whether to pad the image to the `(max_height, max_width)` of the images in the batch.
        do_center_crop (`bool`, *optional*):
            Whether to center crop the image.
        data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the output image.
        input_data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the input image.
    """

    do_resize: Optional[bool]
    size: Optional[Dict[str, int]]
    size_divisor: Optional[int]
    resample: Optional["PILImageResampling"]
    do_rescale: Optional[bool]
    rescale_factor: Optional[float]
    do_normalize: Optional[bool]
    image_mean: Optional[Union[float, List[float]]]
    image_std: Optional[Union[float, List[float]]]
    do_pad: Optional[bool]
    do_center_crop: Optional[bool]
    data_format: Optional[ChannelDimension]
    input_data_format: Optional[Union[str, ChannelDimension]]


class AudioKwargs(TypedDict, total=False):
    """
    Keyword arguments for audio processing.

    Attributes:
        sampling_rate (`int`, *optional*):
            The sampling rate at which the `raw_speech` input was sampled.
        raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
            The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
            values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not
            stereo, i.e. single float per timestep.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding
            index) among:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                sequence if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'`
        max_length (`int`, *optional*):
            Maximum length of the returned list and optionally padding length (see above).
        truncation (`bool`, *optional*):
            Activates truncation to cut input sequences longer than *max_length* to *max_length*.
        pad_to_multiple_of (`int`, *optional*):
            If set, will pad the sequence to a multiple of the provided value.
        return_attention_mask (`bool`, *optional*):
            Whether or not [`~ASTFeatureExtractor.__call__`] should return `attention_mask`.
    """

    sampling_rate: Optional[int]
    raw_speech: Optional[Union["np.ndarray", List[float], List["np.ndarray"], List[List[float]]]]
    padding: Optional[Union[bool, str, PaddingStrategy]]
    max_length: Optional[int]
    truncation: Optional[bool]
    pad_to_multiple_of: Optional[int]
    return_attention_mask: Optional[bool]


class CommonKwargs(TypedDict, total=False):
    return_tensors: Optional[Union[str, TensorType]]


class ProcessingKwargs(TextKwargs, ImagesKwargs, VideosKwargs, AudioKwargs, CommonKwargs, total=False):
    """
    Base class for kwargs passing to processors.
    A model should have its own `ModelProcessorKwargs` class that inherits from `ProcessingKwargs` to provide:
        1) Additional typed keys and that this model requires to process inputs.
        2) Default values for existing keys under a `_defaults` attribute.
    New keys have to be defined as follows to ensure type hinting is done correctly.

    ```python
    # adding a new image kwarg for this model
    class ModelImagesKwargs(ImagesKwargs, total=False):
        new_image_kwarg: Optional[bool]

    class ModelProcessorKwargs(ProcessingKwargs, total=False):
        images_kwargs: ModelImagesKwargs
        _defaults = {
            "images_kwargs: {
                "new_image_kwarg": False,
            }
            "text_kwargs": {
                "padding": "max_length",
            },
        }

    ```
    """

    common_kwargs: CommonKwargs = {
        **CommonKwargs.__annotations__,
    }
    text_kwargs: TextKwargs = {
        **TextKwargs.__annotations__,
    }
    images_kwargs: ImagesKwargs = {
        **ImagesKwargs.__annotations__,
    }
    videos_kwargs: VideosKwargs = {
        **VideosKwargs.__annotations__,
    }
    audio_kwargs: AudioKwargs = {
        **AudioKwargs.__annotations__,
    }


class ProcessorMixin(PushToHubMixin):
    """
    This is a mixin used to provide saving/loading functionality for all processor classes.
    """

    attributes = ["feature_extractor", "tokenizer"]
    optional_attributes = ["chat_template"]
    # Names need to be attr_class for attr in attributes
    feature_extractor_class = None
    tokenizer_class = None
    _auto_class = None
    valid_kwargs: List[str] = []

    # args have to match the attributes class attribute
    def __init__(self, *args, **kwargs):
        # First, extract optional attributes from kwargs if present
        # Optional attributes can never be positional arguments
        for optional_attribute in self.optional_attributes:
            setattr(self, optional_attribute, kwargs.pop(optional_attribute, None))
        # Sanitize args and kwargs
        for key in kwargs:
            if key not in self.attributes:
                raise TypeError(f"Unexpected keyword argument {key}.")
        for arg, attribute_name in zip(args, self.attributes):
            if attribute_name in kwargs:
                raise TypeError(f"Got multiple values for argument {attribute_name}.")
            else:
                kwargs[attribute_name] = arg

        if len(kwargs) != len(self.attributes):
            raise ValueError(
                f"This processor requires {len(self.attributes)} arguments: {', '.join(self.attributes)}. Got "
                f"{len(args)} arguments instead."
            )

        # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
        for attribute_name, arg in kwargs.items():
            class_name = getattr(self, f"{attribute_name}_class")
            # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
            class_name = AUTO_TO_BASE_CLASS_MAPPING.get(class_name, class_name)
            if isinstance(class_name, tuple):
                proper_class = tuple(getattr(transformers_module, n) for n in class_name if n is not None)
            else:
                proper_class = getattr(transformers_module, class_name)

            if not isinstance(arg, proper_class):
                raise ValueError(
                    f"Received a {type(arg).__name__} for argument {attribute_name}, but a {class_name} was expected."
                )

            setattr(self, attribute_name, arg)

    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 processor instance.
        """
        output = copy.deepcopy(self.__dict__)

        # Get the kwargs in `__init__`.
        sig = inspect.signature(self.__init__)
        # Only save the attributes that are presented in the kwargs of `__init__`.
        attrs_to_save = sig.parameters
        # Don't save attributes like `tokenizer`, `image processor` etc.
        attrs_to_save = [x for x in attrs_to_save if x not in self.__class__.attributes]
        # extra attributes to be kept
        attrs_to_save += ["auto_map"]

        output = {k: v for k, v in output.items() if k in attrs_to_save}

        output["processor_class"] = self.__class__.__name__

        if "tokenizer" in output:
            del output["tokenizer"]
        if "image_processor" in output:
            del output["image_processor"]
        if "feature_extractor" in output:
            del output["feature_extractor"]

        # Some attributes have different names but containing objects that are not simple strings
        output = {
            k: v
            for k, v in output.items()
            if not (isinstance(v, PushToHubMixin) or v.__class__.__name__ == "BeamSearchDecoderCTC")
        }

        return output

    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()

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

    def to_json_file(self, json_file_path: Union[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 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):
        attributes_repr = [f"- {name}: {repr(getattr(self, name))}" for name in self.attributes]
        attributes_repr = "\n".join(attributes_repr)
        return f"{self.__class__.__name__}:\n{attributes_repr}\n\n{self.to_json_string()}"

    def save_pretrained(self, save_directory, push_to_hub: bool = False, **kwargs):
        """
        Saves the attributes of this processor (feature extractor, tokenizer...) in the specified directory so that it
        can be reloaded using the [`~ProcessorMixin.from_pretrained`] method.

        <Tip>

        This class method is simply calling [`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] and
        [`~tokenization_utils_base.PreTrainedTokenizerBase.save_pretrained`]. Please refer to the docstrings of the
        methods above for more information.

        </Tip>

        Args:
            save_directory (`str` or `os.PathLike`):
                Directory where the feature extractor JSON file and the tokenizer files will be saved (directory 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.
        """
        use_auth_token = kwargs.pop("use_auth_token", None)

        if use_auth_token is not None:
            warnings.warn(
                "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
                FutureWarning,
            )
            if kwargs.get("token", None) is not None:
                raise ValueError(
                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
                )
            kwargs["token"] = use_auth_token

        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 = self._create_repo(repo_id, **kwargs)
            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:
            attrs = [getattr(self, attribute_name) for attribute_name in self.attributes]
            configs = [(a.init_kwargs if isinstance(a, PreTrainedTokenizerBase) else a) for a in attrs]
            configs.append(self)
            custom_object_save(self, save_directory, config=configs)

        for attribute_name in self.attributes:
            attribute = getattr(self, attribute_name)
            # Include the processor class in the attribute config so this processor can then be reloaded with the
            # `AutoProcessor` API.
            if hasattr(attribute, "_set_processor_class"):
                attribute._set_processor_class(self.__class__.__name__)
            attribute.save_pretrained(save_directory)

        if self._auto_class is not None:
            # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
            for attribute_name in self.attributes:
                attribute = getattr(self, attribute_name)
                if isinstance(attribute, PreTrainedTokenizerBase):
                    del attribute.init_kwargs["auto_map"]

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

        # For now, let's not save to `processor_config.json` if the processor doesn't have extra attributes and
        # `auto_map` is not specified.
        if set(self.to_dict().keys()) != {"processor_class"}:
            self.to_json_file(output_processor_file)
            logger.info(f"processor saved in {output_processor_file}")

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

        if set(self.to_dict().keys()) == {"processor_class"}:
            return []
        return [output_processor_file]

    @classmethod
    def get_processor_dict(
        cls, pretrained_model_name_or_path: Union[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
        processor of type [`~processing_utils.ProcessingMixin`] using `from_args_and_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.

        Returns:
            `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the processor object.
        """
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", None)
        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", "")

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

        user_agent = {"file_type": "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):
            processor_file = os.path.join(pretrained_model_name_or_path, PROCESSOR_NAME)
        if os.path.isfile(pretrained_model_name_or_path):
            resolved_processor_file = pretrained_model_name_or_path
            is_local = True
        elif is_remote_url(pretrained_model_name_or_path):
            processor_file = pretrained_model_name_or_path
            resolved_processor_file = download_url(pretrained_model_name_or_path)
        else:
            processor_file = PROCESSOR_NAME
            try:
                # Load from local folder or from cache or download from model Hub and cache
                resolved_processor_file = cached_file(
                    pretrained_model_name_or_path,
                    processor_file,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    proxies=proxies,
                    resume_download=resume_download,
                    local_files_only=local_files_only,
                    token=token,
                    user_agent=user_agent,
                    revision=revision,
                    subfolder=subfolder,
                    _raise_exceptions_for_missing_entries=False,
                )
            except EnvironmentError:
                # 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 EnvironmentError(
                    f"Can't load 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 {PROCESSOR_NAME} file"
                )

        # Existing processors on the Hub created before #27761 being merged don't have `processor_config.json` (if not
        # updated afterward), and we need to keep `from_pretrained` work. So here it fallbacks to the empty dict.
        # (`cached_file` called using `_raise_exceptions_for_missing_entries=False` to avoid exception)
        # However, for models added in the future, we won't get the expected error if this file is missing.
        if resolved_processor_file is None:
            return {}, kwargs

        try:
            # Load processor dict
            with open(resolved_processor_file, "r", encoding="utf-8") as reader:
                text = reader.read()
            processor_dict = json.loads(text)

        except json.JSONDecodeError:
            raise EnvironmentError(
                f"It looks like the config file at '{resolved_processor_file}' is not a valid JSON file."
            )

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

        if not is_local:
            if "auto_map" in processor_dict:
                processor_dict["auto_map"] = add_model_info_to_auto_map(
                    processor_dict["auto_map"], pretrained_model_name_or_path
                )
            if "custom_pipelines" in processor_dict:
                processor_dict["custom_pipelines"] = add_model_info_to_custom_pipelines(
                    processor_dict["custom_pipelines"], pretrained_model_name_or_path
                )

        return processor_dict, kwargs

    @classmethod
    def from_args_and_dict(cls, args, processor_dict: Dict[str, Any], **kwargs):
        """
        Instantiates a type of [`~processing_utils.ProcessingMixin`] from a Python dictionary of parameters.

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

        Returns:
            [`~processing_utils.ProcessingMixin`]: The processor object instantiated from those
            parameters.
        """
        processor_dict = processor_dict.copy()
        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)

        # We have to pop up some unused (but specific) kwargs and then validate that it doesn't contain unused kwargs
        # If we don't pop, some specific kwargs will raise a warning
        if "processor_class" in processor_dict:
            del processor_dict["processor_class"]

        if "auto_map" in processor_dict:
            del processor_dict["auto_map"]

        unused_kwargs = cls.validate_init_kwargs(processor_config=processor_dict, valid_kwargs=cls.valid_kwargs)
        processor = cls(*args, **processor_dict)

        # Update processor with kwargs if needed
        for key in set(kwargs.keys()):
            if hasattr(processor, key):
                setattr(processor, key, kwargs.pop(key))

        kwargs.update(unused_kwargs)
        logger.info(f"Processor {processor}")
        if return_unused_kwargs:
            return processor, kwargs
        else:
            return processor

    def _merge_kwargs(
        self,
        ModelProcessorKwargs: ProcessingKwargs,
        tokenizer_init_kwargs: Optional[Dict] = None,
        **kwargs,
    ) -> Dict[str, Dict]:
        """
        Method to merge dictionaries of kwargs cleanly separated by modality within a Processor instance.
        The order of operations is as follows:
            1) kwargs passed as before have highest priority to preserve BC.
                ```python
                high_priority_kwargs = {"crop_size" = (224, 224), "padding" = "max_length"}
                processor(..., **high_priority_kwargs)
                ```
            2) kwargs passed as modality-specific kwargs have second priority. This is the recommended API.
                ```python
                processor(..., text_kwargs={"padding": "max_length"}, images_kwargs={"crop_size": (224, 224)}})
                ```
            3) kwargs passed during instantiation of a modality processor have fourth priority.
                ```python
                tokenizer = tokenizer_class(..., {"padding": "max_length"})
                image_processor = image_processor_class(...)
                processor(tokenizer, image_processor) # will pass max_length unless overriden by kwargs at call
                ```
            4) defaults kwargs specified at processor level have lowest priority.
                ```python
                class MyProcessingKwargs(ProcessingKwargs, CommonKwargs, TextKwargs, ImagesKwargs, total=False):
                    _defaults = {
                        "text_kwargs": {
                            "padding": "max_length",
                            "max_length": 64,
                        },
                    }
                ```
        Args:
            ModelProcessorKwargs (`ProcessingKwargs`):
                Typed dictionary of kwargs specifically required by the model passed.
            tokenizer_init_kwargs (`Dict`, *optional*):
                Dictionary of kwargs the tokenizer was instantiated with and need to take precedence over defaults.

        Returns:
            output_kwargs (`Dict`):
                Dictionary of per-modality kwargs to be passed to each modality-specific processor.

        """
        # Initialize dictionaries
        output_kwargs = {
            "text_kwargs": {},
            "images_kwargs": {},
            "audio_kwargs": {},
            "videos_kwargs": {},
            "common_kwargs": {},
        }

        default_kwargs = {
            "text_kwargs": {},
            "images_kwargs": {},
            "audio_kwargs": {},
            "videos_kwargs": {},
            "common_kwargs": {},
        }

        # get defaults from set model processor kwargs if they exist
        for modality in default_kwargs:
            default_kwargs[modality] = ModelProcessorKwargs._defaults.get(modality, {}).copy()
            # update defaults with arguments from tokenizer init
            for modality_key in ModelProcessorKwargs.__annotations__[modality].__annotations__.keys():
                # init with tokenizer init kwargs if necessary
                if modality_key in tokenizer_init_kwargs:
                    default_kwargs[modality][modality_key] = tokenizer_init_kwargs[modality_key]
        # now defaults kwargs are updated with the tokenizers defaults.
        # pass defaults to output dictionary
        output_kwargs.update(default_kwargs)

        # update modality kwargs with passed kwargs
        non_modality_kwargs = set(kwargs) - set(output_kwargs)
        for modality in output_kwargs:
            for modality_key in ModelProcessorKwargs.__annotations__[modality].__annotations__.keys():
                # check if we received a structured kwarg dict or not to handle it correctly
                if modality in kwargs:
                    kwarg_value = kwargs[modality].pop(modality_key, "__empty__")
                    # check if this key was passed as a flat kwarg.
                    if kwarg_value != "__empty__" and modality_key in non_modality_kwargs:
                        raise ValueError(
                            f"Keyword argument {modality_key} was passed two times: in a dictionary for {modality} and as a **kwarg."
                        )
                elif modality_key in kwargs:
                    kwarg_value = kwargs.pop(modality_key, "__empty__")
                else:
                    kwarg_value = "__empty__"
                if kwarg_value != "__empty__":
                    output_kwargs[modality][modality_key] = kwarg_value
        # if something remains in kwargs, it belongs to common after flattening
        if set(kwargs) & set(default_kwargs):
            # here kwargs is dictionary-based since it shares keys with default set
            [output_kwargs["common_kwargs"].update(subdict) for _, subdict in kwargs.items()]
        else:
            # here it's a flat dict
            output_kwargs["common_kwargs"].update(kwargs)

        # all modality-specific kwargs are updated with common kwargs
        for modality in output_kwargs:
            output_kwargs[modality].update(output_kwargs["common_kwargs"])
        return output_kwargs

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Union[str, os.PathLike],
        cache_dir: Optional[Union[str, os.PathLike]] = None,
        force_download: bool = False,
        local_files_only: bool = False,
        token: Optional[Union[str, bool]] = None,
        revision: str = "main",
        **kwargs,
    ):
        r"""
        Instantiate a processor associated with a pretrained model.

        <Tip>

        This class method is simply calling the feature extractor
        [`~feature_extraction_utils.FeatureExtractionMixin.from_pretrained`], image processor
        [`~image_processing_utils.ImageProcessingMixin`] and the tokenizer
        [`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`] methods. Please refer to the docstrings of the
        methods above for more information.

        </Tip>

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

                - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
                  huggingface.co.
                - a path to a *directory* containing a feature extractor file saved using the
                  [`~SequenceFeatureExtractor.save_pretrained`] method, e.g., `./my_model_directory/`.
                - a path or url to a saved feature extractor JSON *file*, e.g.,
                  `./my_model_directory/preprocessor_config.json`.
            **kwargs
                Additional keyword arguments passed along to both
                [`~feature_extraction_utils.FeatureExtractionMixin.from_pretrained`] and
                [`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`].
        """
        kwargs["cache_dir"] = cache_dir
        kwargs["force_download"] = force_download
        kwargs["local_files_only"] = local_files_only
        kwargs["revision"] = revision

        use_auth_token = kwargs.pop("use_auth_token", None)
        if use_auth_token is not None:
            warnings.warn(
                "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
                FutureWarning,
            )
            if token is not None:
                raise ValueError(
                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
                )
            token = use_auth_token

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

        args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
        processor_dict, kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs)

        return cls.from_args_and_dict(args, processor_dict, **kwargs)

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

        <Tip warning={true}>

        This API is experimental and may have some slight breaking changes in the next releases.

        </Tip>

        Args:
            auto_class (`str` or `type`, *optional*, defaults to `"AutoProcessor"`):
                The auto class to register this new feature extractor 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

    @classmethod
    def _get_arguments_from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        args = []
        for attribute_name in cls.attributes:
            class_name = getattr(cls, f"{attribute_name}_class")
            if isinstance(class_name, tuple):
                classes = tuple(getattr(transformers_module, n) if n is not None else None for n in class_name)
                use_fast = kwargs.get("use_fast", True)
                if use_fast and classes[1] is not None:
                    attribute_class = classes[1]
                else:
                    attribute_class = classes[0]
            else:
                attribute_class = getattr(transformers_module, class_name)

            args.append(attribute_class.from_pretrained(pretrained_model_name_or_path, **kwargs))
        return args

    @property
    def model_input_names(self):
        first_attribute = getattr(self, self.attributes[0])
        return getattr(first_attribute, "model_input_names", None)

    @staticmethod
    def validate_init_kwargs(processor_config, valid_kwargs):
        kwargs_from_config = processor_config.keys()
        unused_kwargs = {}
        unused_keys = set(kwargs_from_config) - set(valid_kwargs)
        if unused_keys:
            unused_key_str = ", ".join(unused_keys)
            logger.warning(
                f"Some kwargs in processor config are unused and will not have any effect: {unused_key_str}. "
            )
            unused_kwargs = {k: processor_config[k] for k in unused_keys}
        return unused_kwargs

    def apply_chat_template(
        self,
        conversation: Union[List[Dict[str, str]]],
        chat_template: Optional[str] = None,
        tokenize: bool = False,
        **kwargs,
    ) -> str:
        """
        Similar to the `apply_chat_template` method on tokenizers, this method applies a Jinja template to input
        conversations to turn them into a single tokenizable string.

        Args:
            conversation (`List[Dict, str, str]`):
                The conversation to format.
            chat_template (`Optional[str]`, *optional*):
                The Jinja template to use for formatting the conversation. If not provided, the default chat template
                is used.
            tokenize (`bool`, *optional*, defaults to `False`):
                Whether to tokenize the output or not.
            **kwargs:
                Additional keyword arguments
        """

        if chat_template is None:
            if self.chat_template is not None:
                chat_template = self.chat_template
            elif getattr(self, "default_chat_template", None) is not None:
                logger.warning_once(
                    "No chat template is set for this processor, falling back to a default class-level template. This is "
                    "very error-prone, because models are often trained with templates different from the class default! "
                    "Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which "
                    "point any code depending on them will stop working. We recommend setting a valid chat template before "
                    "then to ensure that this model continues working without issues."
                )
                chat_template = self.default_chat_template
            else:
                raise ValueError(
                    "No chat template is set for this processor. Please either set the `chat_template` attribute, "
                    "or provide a chat template as an argument. See "
                    "https://huggingface.co/docs/transformers/main/en/chat_templating for more information."
                )
        return self.tokenizer.apply_chat_template(
            conversation, chat_template=chat_template, tokenize=tokenize, **kwargs
        )


ProcessorMixin.push_to_hub = copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
    ProcessorMixin.push_to_hub.__doc__ = ProcessorMixin.push_to_hub.__doc__.format(
        object="processor", object_class="AutoProcessor", object_files="processor files"
    )
