# 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 importlib.util
import os
from pathlib import Path

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase


# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
spec = importlib.util.spec_from_file_location(
    "transformers", Path(__file__).parent / "__init__.py", submodule_search_locations=[Path(__file__).parent]
)
transformers_module = spec.loader.load_module()


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


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

    attributes = ["feature_extractor", "tokenizer"]
    # Names need to be attr_class for attr in attributes
    feature_extractor_class = None
    tokenizer_class = None
    _auto_class = None

    # args have to match the attributes class attribute
    def __init__(self, *args, **kwargs):
        # Sanitize args and kwargs
        for key in kwargs:
            if key not in self.attributes:
                raise TypeError(f"Unexepcted 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 __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}"

    def save_pretrained(self, save_directory):
        """
        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.PreTrainedTokenizer.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).
        """
        os.makedirs(save_directory, exist_ok=True)
        # 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]
            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"]

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, **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`] 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. 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 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`].
        """
        args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
        return cls(*args)

    @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
