






'''simple docstring'''
import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


__magic_name__		:     Any					=    logging.get_logger(__name__)

__magic_name__		:     List[str]					=    {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__		:     List[str]					=    {
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
__magic_name__		:     Optional[int]					=    {
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
__magic_name__		:     List[Any]					=    {
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

__magic_name__		:     str					=    {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
__magic_name__		:     List[Any]					=    {
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
__magic_name__		:     Union[str, Any]					=    {
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


__magic_name__		:     Tuple					=    {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__magic_name__		:     Optional[Any]					=    {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__magic_name__		:     List[Any]					=    {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}







class 	UpperCamelCase__					(     a_			):

		"""simple docstring"""
		UpperCAmelCase__     			=						VOCAB_FILES_NAMES
		UpperCAmelCase__     			=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__     			=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__     			=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 	UpperCamelCase__					(     a_			):

		"""simple docstring"""
		UpperCAmelCase__     			=						VOCAB_FILES_NAMES
		UpperCAmelCase__     			=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__     			=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__     			=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





__magic_name__		:     Tuple					=    collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

__magic_name__		:     Any					=    collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


__magic_name__		:     Optional[Any]					=    R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `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\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate 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. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate 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. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate 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. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''







@add_start_docstrings(a_			)
class 	UpperCamelCase__					:

		"""simple docstring"""




		def __call__( self						:						int							,    __A						:						Tuple							,    __A						:						List[str] = None							,    __A						:						Dict = None							,    __A						:						Optional[Any] = False							,    __A						:						Optional[int] = False							,    __A						:						Optional[int] = None							,    __A						:						List[str] = None							,    __A						:						List[Any] = None							,    **__A						:						List[str]							,    ):







							"""simple docstring"""
							if titles is None and texts is None:
												return super().__call__(
												    __UpperCamelCase							,    padding=__UpperCamelCase							,    truncation=__UpperCamelCase							,    max_length=__UpperCamelCase							,    return_tensors=__UpperCamelCase							,    return_attention_mask=__UpperCamelCase							,    **__UpperCamelCase							,    )
							elif titles is None or texts is None:
												_lowercase			= titles if texts is None else texts
												return super().__call__(
												    __UpperCamelCase							,    __UpperCamelCase							,    padding=__UpperCamelCase							,    truncation=__UpperCamelCase							,    max_length=__UpperCamelCase							,    return_tensors=__UpperCamelCase							,    return_attention_mask=__UpperCamelCase							,    **__UpperCamelCase							,    )
							_lowercase			= titles if not isinstance(__UpperCamelCase							,    __UpperCamelCase    ) else [titles]
							_lowercase			= texts if not isinstance(__UpperCamelCase							,    __UpperCamelCase    ) else [texts]
							_lowercase			= len(__UpperCamelCase    )
							_lowercase			= questions if not isinstance(__UpperCamelCase							,    __UpperCamelCase    ) else [questions] * n_passages
							if len(__UpperCamelCase    ) != len(__UpperCamelCase    ):
												raise ValueError(
												    f"""There should be as many titles than texts but got {len(__UpperCamelCase    )} titles and {len(__UpperCamelCase    )} texts."""    )
							_lowercase			= super().__call__(__UpperCamelCase							,    __UpperCamelCase							,    padding=__UpperCamelCase							,    truncation=__UpperCamelCase    )["input_ids"]
							_lowercase			= super().__call__(__UpperCamelCase							,    add_special_tokens=__UpperCamelCase							,    padding=__UpperCamelCase							,    truncation=__UpperCamelCase    )["input_ids"]
							_lowercase			= {
							    "input_ids": [
							        (encoded_question_and_title + encoded_text)[:max_length]
							        if max_length is not None and truncation
							        else encoded_question_and_title + encoded_text
							        for encoded_question_and_title, encoded_text in zip(__UpperCamelCase							,    __UpperCamelCase    )
							    ]
							}
							if return_attention_mask is not False:
												_lowercase			= []
												for input_ids in encoded_inputs["input_ids"]:
																	attention_mask.append([int(input_id != self.pad_token_id    ) for input_id in input_ids]    )
												_lowercase			= attention_mask
							return self.pad(__UpperCamelCase							,    padding=__UpperCamelCase							,    max_length=__UpperCamelCase							,    return_tensors=__UpperCamelCase    )




		def       snake_case     ( self						:						Dict							,    __A						:						str							,    __A						:						List[Any]							,    __A						:						int = 1_6							,    __A						:						Dict = 6_4							,    __A						:						List[str] = 4							,    ):







							"""simple docstring"""
							_lowercase			= reader_input["input_ids"]
							_lowercase	,			_lowercase	,			_lowercase			= reader_output[:3]
							_lowercase			= len(__UpperCamelCase    )
							_lowercase			= sorted(range(__UpperCamelCase    )							,    reverse=__UpperCamelCase							,    key=relevance_logits.__getitem__    )
							_lowercase			= []
							for doc_id in sorted_docs:
												_lowercase			= list(input_ids[doc_id]    )
												# assuming question & title information is at the beginning of the sequence
												_lowercase			= sequence_ids.index(self.sep_token_id							,    2    ) + 1  # second sep id
												if sequence_ids[-1] == self.pad_token_id:
																	_lowercase			= sequence_ids.index(self.pad_token_id    )
												else:
																	_lowercase			= len(__UpperCamelCase    )

												_lowercase			= self._get_best_spans(
												    start_logits=start_logits[doc_id][passage_offset:sequence_len]							,    end_logits=end_logits[doc_id][passage_offset:sequence_len]							,    max_answer_length=__UpperCamelCase							,    top_spans=__UpperCamelCase							,    )
												for start_index, end_index in best_spans:
																	start_index += passage_offset
																	end_index += passage_offset
																	nbest_spans_predictions.append(
																	    DPRSpanPrediction(
																	        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]							,    relevance_score=relevance_logits[doc_id]							,    doc_id=__UpperCamelCase							,    start_index=__UpperCamelCase							,    end_index=__UpperCamelCase							,    text=self.decode(sequence_ids[start_index : end_index + 1]    )							,    )    )
												if len(__UpperCamelCase    ) >= num_spans:
																	break
							return nbest_spans_predictions[:num_spans]




		def       snake_case     ( self						:						Dict							,    __A						:						Optional[Any]							,    __A						:						Tuple							,    __A						:						List[Any]							,    __A						:						int							,    ):







							"""simple docstring"""
							_lowercase			= []
							for start_index, start_score in enumerate(__UpperCamelCase    ):
												for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]    ):
																	scores.append(((start_index, start_index + answer_length), start_score + end_score)    )
							_lowercase			= sorted(__UpperCamelCase							,    key=lambda __A    : x[1]							,    reverse=__UpperCamelCase    )
							_lowercase			= []
							for (start_index, end_index), score in scores:
												if start_index > end_index:
																	raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""    )
												_lowercase			= end_index - start_index + 1
												if length > max_answer_length:
																	raise ValueError(f"""Span is too long: {length} > {max_answer_length}"""    )
												if any(
												    start_index <= prev_start_index <= prev_end_index <= end_index
												    or prev_start_index <= start_index <= end_index <= prev_end_index
												    for (prev_start_index, prev_end_index) in chosen_span_intervals    ):
																	continue
												chosen_span_intervals.append((start_index, end_index)    )

												if len(__UpperCamelCase    ) == top_spans:
																	break
							return chosen_span_intervals







@add_end_docstrings(a_			)
class 	UpperCamelCase__					(     a_      ,     a_			):

		"""simple docstring"""
		UpperCAmelCase__     			=						VOCAB_FILES_NAMES
		UpperCAmelCase__     			=						READER_PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__     			=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__     			=						READER_PRETRAINED_INIT_CONFIGURATION
		UpperCAmelCase__     			=						['input_ids', 'attention_mask']









import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowerCamelCase      :       Optional[Any]    =						logging.get_logger(__name__)

lowerCamelCase      :       Any    =						OrderedDict(
    [
        ('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
        ('''beit''', '''BeitFeatureExtractor'''),
        ('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
        ('''clap''', '''ClapFeatureExtractor'''),
        ('''clip''', '''CLIPFeatureExtractor'''),
        ('''clipseg''', '''ViTFeatureExtractor'''),
        ('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
        ('''convnext''', '''ConvNextFeatureExtractor'''),
        ('''cvt''', '''ConvNextFeatureExtractor'''),
        ('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
        ('''data2vec-vision''', '''BeitFeatureExtractor'''),
        ('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
        ('''deit''', '''DeiTFeatureExtractor'''),
        ('''detr''', '''DetrFeatureExtractor'''),
        ('''dinat''', '''ViTFeatureExtractor'''),
        ('''donut-swin''', '''DonutFeatureExtractor'''),
        ('''dpt''', '''DPTFeatureExtractor'''),
        ('''encodec''', '''EncodecFeatureExtractor'''),
        ('''flava''', '''FlavaFeatureExtractor'''),
        ('''glpn''', '''GLPNFeatureExtractor'''),
        ('''groupvit''', '''CLIPFeatureExtractor'''),
        ('''hubert''', '''Wav2Vec2FeatureExtractor'''),
        ('''imagegpt''', '''ImageGPTFeatureExtractor'''),
        ('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
        ('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
        ('''levit''', '''LevitFeatureExtractor'''),
        ('''maskformer''', '''MaskFormerFeatureExtractor'''),
        ('''mctct''', '''MCTCTFeatureExtractor'''),
        ('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
        ('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
        ('''mobilevit''', '''MobileViTFeatureExtractor'''),
        ('''nat''', '''ViTFeatureExtractor'''),
        ('''owlvit''', '''OwlViTFeatureExtractor'''),
        ('''perceiver''', '''PerceiverFeatureExtractor'''),
        ('''poolformer''', '''PoolFormerFeatureExtractor'''),
        ('''regnet''', '''ConvNextFeatureExtractor'''),
        ('''resnet''', '''ConvNextFeatureExtractor'''),
        ('''segformer''', '''SegformerFeatureExtractor'''),
        ('''sew''', '''Wav2Vec2FeatureExtractor'''),
        ('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
        ('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
        ('''speecht5''', '''SpeechT5FeatureExtractor'''),
        ('''swiftformer''', '''ViTFeatureExtractor'''),
        ('''swin''', '''ViTFeatureExtractor'''),
        ('''swinv2''', '''ViTFeatureExtractor'''),
        ('''table-transformer''', '''DetrFeatureExtractor'''),
        ('''timesformer''', '''VideoMAEFeatureExtractor'''),
        ('''tvlt''', '''TvltFeatureExtractor'''),
        ('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
        ('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
        ('''van''', '''ConvNextFeatureExtractor'''),
        ('''videomae''', '''VideoMAEFeatureExtractor'''),
        ('''vilt''', '''ViltFeatureExtractor'''),
        ('''vit''', '''ViTFeatureExtractor'''),
        ('''vit_mae''', '''ViTFeatureExtractor'''),
        ('''vit_msn''', '''ViTFeatureExtractor'''),
        ('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
        ('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
        ('''whisper''', '''WhisperFeatureExtractor'''),
        ('''xclip''', '''CLIPFeatureExtractor'''),
        ('''yolos''', '''YolosFeatureExtractor'''),
    ]
)

lowerCamelCase      :       Optional[int]    =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def    __lowerCAmelCase   (    __snake_case							):
     for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
          if class_name in extractors:
               __lowerCAmelCase             =       model_type_to_module_name(__snake_case							)

               __lowerCAmelCase             =       importlib.import_module(F""".{module_name}"""       ,			"transformers.models"							)
               try:
                    return getattr(__snake_case       ,			__snake_case							)
               except AttributeError:
                    continue

     for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
          if getattr(__snake_case       ,			"__name__"       ,			__snake_case							) == class_name:
               return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
     __lowerCAmelCase             =       importlib.import_module("transformers"							)
     if hasattr(__snake_case       ,			__snake_case							):
          return getattr(__snake_case       ,			__snake_case							)

     return None





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case = None       ,			__snake_case = False       ,			__snake_case = False       ,			__snake_case = None       ,			__snake_case = None       ,			__snake_case = None       ,			__snake_case = False       ,			**__snake_case       ,			):
     __lowerCAmelCase             =       get_file_from_repo(
         __snake_case       ,			__snake_case       ,			cache_dir=__snake_case       ,			force_download=__snake_case       ,			resume_download=__snake_case       ,			proxies=__snake_case       ,			use_auth_token=__snake_case       ,			revision=__snake_case       ,			local_files_only=__snake_case       ,			)
     if resolved_config_file is None:
          logger.info(
              "Could not locate the feature extractor configuration file, will try to use the model config instead."							)
          return {}

     with open(__snake_case       ,			encoding="utf-8"							) as reader:
          return json.load(__snake_case							)



class 				_UpperCamelCase      :


      def __init__(						self						)->						Union[str, Any]:
           raise EnvironmentError(
               "AutoFeatureExtractor is designed to be instantiated "
               "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."						)


      @classmethod
      @replace_list_option_in_docstrings(__UpperCamelCase						)
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       kwargs.pop("config"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       kwargs.pop("trust_remote_code"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       True

           __lowerCAmelCase      ,			__lowerCAmelCase             =       FeatureExtractionMixin.get_feature_extractor_dict(__UpperCamelCase       ,    **__UpperCamelCase						)
           __lowerCAmelCase             =       config_dict.get("feature_extractor_type"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       None
           if "AutoFeatureExtractor" in config_dict.get("auto_map"       ,    {}						):
                __lowerCAmelCase             =       config_dict["auto_map"]["AutoFeatureExtractor"]

           # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
           if feature_extractor_class is None and feature_extractor_auto_map is None:
                if not isinstance(__UpperCamelCase       ,    __UpperCamelCase						):
                     __lowerCAmelCase             =       AutoConfig.from_pretrained(__UpperCamelCase       ,    **__UpperCamelCase						)
                # It could be in `config.feature_extractor_type``
                __lowerCAmelCase             =       getattr(__UpperCamelCase       ,    "feature_extractor_type"       ,    __UpperCamelCase						)
                if hasattr(__UpperCamelCase       ,    "auto_map"						) and "AutoFeatureExtractor" in config.auto_map:
                     __lowerCAmelCase             =       config.auto_map["AutoFeatureExtractor"]

           if feature_extractor_class is not None:
                __lowerCAmelCase             =       feature_extractor_class_from_name(__UpperCamelCase						)

           __lowerCAmelCase             =       feature_extractor_auto_map is not None
           __lowerCAmelCase             =       feature_extractor_class is not None or type(__UpperCamelCase						) in FEATURE_EXTRACTOR_MAPPING
           __lowerCAmelCase             =       resolve_trust_remote_code(
               __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)

           if has_remote_code and trust_remote_code:
                __lowerCAmelCase             =       get_class_from_dynamic_module(
                    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						)
                __lowerCAmelCase             =       kwargs.pop("code_revision"       ,    __UpperCamelCase						)
                if os.path.isdir(__UpperCamelCase						):
                     feature_extractor_class.register_for_auto_class()
                return feature_extractor_class.from_dict(__UpperCamelCase       ,    **__UpperCamelCase						)
           elif feature_extractor_class is not None:
                return feature_extractor_class.from_dict(__UpperCamelCase       ,    **__UpperCamelCase						)
           # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
           elif type(__UpperCamelCase						) in FEATURE_EXTRACTOR_MAPPING:
                __lowerCAmelCase             =       FEATURE_EXTRACTOR_MAPPING[type(__UpperCamelCase						)]
                return feature_extractor_class.from_dict(__UpperCamelCase       ,    **__UpperCamelCase						)

           raise ValueError(
               F"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
               F"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
               F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()						)}"""						)


      @staticmethod
      def 			__UpperCAmelCase		(						__UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           FEATURE_EXTRACTOR_MAPPING.register(__UpperCamelCase       ,    __UpperCamelCase						)






