# coding=utf-8
# Copyright 2021 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.
""" AutoProcessor class. """
import importlib
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...file_utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_list_of_files
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    config_class_to_model_type,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING_NAMES, AutoFeatureExtractor
from .tokenization_auto import TOKENIZER_MAPPING_NAMES, AutoTokenizer


PROCESSOR_MAPPING_NAMES = OrderedDict(
    [
        ("clip", "CLIPProcessor"),
        ("layoutlmv2", "LayoutLMv2Processor"),
        ("layoutxlm", "LayoutXLMProcessor"),
        ("speech_to_text", "Speech2TextProcessor"),
        ("speech_to_text_2", "Speech2Text2Processor"),
        ("trocr", "TrOCRProcessor"),
        ("wav2vec2", "Wav2Vec2Processor"),
        ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
    ]
)

PROCESSOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def processor_class_from_name(class_name: str):
    for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
        if class_name in processors:
            module_name = model_type_to_module_name(module_name)

            module = importlib.import_module(f".{module_name}", "transformers.models")
            return getattr(module, class_name)
            break

    return None


class AutoProcessor:
    r"""
    This is a generic processor class that will be instantiated as one of the processor classes of the library when
    created with the :meth:`AutoProcessor.from_pretrained` class method.

    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoProcessor is designed to be instantiated "
            "using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."
        )

    @classmethod
    @replace_list_option_in_docstrings(PROCESSOR_MAPPING_NAMES)
    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        r"""
        Instantiate one of the processor classes of the library from a pretrained model vocabulary.

        The processor class to instantiate is selected based on the :obj:`model_type` property of the config object
        (either passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible):

        List options

        For other types of models, this class will return the appropriate tokenizer (if available) or feature
        extractor.

        Params:
            pretrained_model_name_or_path (:obj:`str` or :obj:`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 processor files saved using the :obj:`save_pretrained()` method,
                  e.g., ``./my_model_directory/``.
            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
                Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
                standard cache should not be used.
            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to force to (re-)download the feature extractor files and override the cached versions
                if they exist.
            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to delete incompletely received file. Attempts to resume the download if such a file
                exists.
            proxies (:obj:`Dict[str, str]`, `optional`):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
            use_auth_token (:obj:`str` or `bool`, `optional`):
                The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
                generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
            revision (:obj:`str`, `optional`, defaults to :obj:`"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 (:obj:`bool`, `optional`, defaults to :obj:`False`):
                If :obj:`False`, then this function returns just the final feature extractor object. If :obj:`True`,
                then this functions returns a :obj:`Tuple(feature_extractor, unused_kwargs)` where `unused_kwargs` is a
                dictionary consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the
                part of ``kwargs`` which has not been used to update ``feature_extractor`` and is otherwise ignored.
            kwargs (:obj:`Dict[str, Any]`, `optional`):
                The values in kwargs of any keys which are feature extractor attributes will be used to override the
                loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
                controlled by the ``return_unused_kwargs`` keyword parameter.

        .. note::

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

        Examples::

            >>> from transformers import AutoProcessor

            >>> # Download processor from huggingface.co and cache.
            >>> processor = AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h')

            >>> # If processor files are in a directory (e.g. processor was saved using `save_pretrained('./test/saved_model/')`)
            >>> processor = AutoProcessor.from_pretrained('./test/saved_model/')

        """
        config = kwargs.pop("config", None)
        kwargs["_from_auto"] = True

        # First, let's see if we have a preprocessor config.
        # get_list_of_files only takes three of the kwargs we have, so we filter them.
        get_list_of_files_kwargs = {
            key: kwargs[key] for key in ["revision", "use_auth_token", "local_files_only"] if key in kwargs
        }
        model_files = get_list_of_files(pretrained_model_name_or_path, **get_list_of_files_kwargs)
        if FEATURE_EXTRACTOR_NAME in model_files:
            config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
            if "processor_class" in config_dict:
                processor_class = processor_class_from_name(config_dict["processor_class"])
                return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)

        # Otherwise, load config, if it can be loaded.
        if not isinstance(config, PretrainedConfig):
            config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)

        model_type = config_class_to_model_type(type(config).__name__)

        if getattr(config, "processor_class", None) is not None:
            processor_class = config.processor_class
            return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)

        model_type = config_class_to_model_type(type(config).__name__)
        if model_type is not None and model_type in PROCESSOR_MAPPING_NAMES:
            return PROCESSOR_MAPPING[type(config)].from_pretrained(pretrained_model_name_or_path, **kwargs)

        # At this stage there doesn't seem to be a `Processor` class available for this model, so let's try a tokenizer
        if model_type in TOKENIZER_MAPPING_NAMES:
            return AutoTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)

        # At this stage there doesn't seem to be a `Processor` class available for this model, so let's try a tokenizer
        if model_type in FEATURE_EXTRACTOR_MAPPING_NAMES:
            return AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)

        all_model_types = set(
            PROCESSOR_MAPPING_NAMES.keys() + TOKENIZER_MAPPING_NAMES.keys() + FEATURE_EXTRACTOR_MAPPING_NAMES.keys()
        )
        all_model_types = list(all_model_types)
        all_model_types.sort()
        raise ValueError(
            f"Unrecognized processor in {pretrained_model_name_or_path}. Should have a `processor_type` key in "
            f"its {FEATURE_EXTRACTOR_NAME}, or one of the following `model_type` keys in its {CONFIG_NAME}: "
            f"{', '.join(all_model_types)}"
        )
