



'''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


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__      					=     {
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE__      					=     {
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE__      					=     {
    "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"
        ),
    },
}

SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :List[Any]						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Union[str, Any]						   =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :List[str]						   =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :Optional[int]						   =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Dict						   =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :Dict						   =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :Optional[int]						   =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





SCREAMING_SNAKE_CASE__      					=     collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

SCREAMING_SNAKE_CASE__      					=     collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


SCREAMING_SNAKE_CASE__      					=     R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "


@add_start_docstrings(UpperCamelCase   )
class 				snake_case :



       def __call__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = False     ,UpperCAmelCase_ = False     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,**UpperCAmelCase_     ,)  ->							BatchEncoding:
           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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = 16     ,UpperCAmelCase_ = 64     ,UpperCAmelCase_ = 4     ,)  ->							List[DPRSpanPrediction]:
           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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,)  ->							List[DPRSpanPrediction]:
           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 UpperCAmelCase_      : 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(UpperCamelCase   )
class 				snake_case (UpperCamelCase		,      UpperCamelCase   ):
       lowerCAmelCase__  :int						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Tuple						   =					READER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :Optional[Any]						   =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :Union[str, Any]						   =					READER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__  :Tuple						   =					["input_ids", "attention_mask"]









'''simple docstring'''





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,
)


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     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"),
    ]
)

SCREAMING_SNAKE_CASE__      					=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)




def        lowerCamelCase  (				_snake_case  :						str       ):





    '''simple docstring'''


    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            lowercase__		       =						model_type_to_module_name(_snake_case       )

            lowercase__		       =						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.
    lowercase__		       =						importlib.import_module("transformers"       )
    if hasattr(_snake_case     ,_snake_case       ):
        return getattr(_snake_case     ,_snake_case       )

    return None




def        lowerCamelCase  (				_snake_case  :						Union[str, os.PathLike]     ,_snake_case  :						Optional[Union[str, os.PathLike]] = None     ,_snake_case  :						bool = False     ,_snake_case  :						bool = False     ,_snake_case  :						Optional[Dict[str, str]] = None     ,_snake_case  :						Optional[Union[bool, str]] = None     ,_snake_case  :						Optional[str] = None     ,_snake_case  :						bool = False     ,**_snake_case  :						Union[str, Any]     ,):





    '''simple docstring'''


    lowercase__		       =						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 				snake_case :



       def __init__(				self      )  ->							List[str]:
           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 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						kwargs.pop("config"     ,UpperCAmelCase_      )
           lowercase__		       =						kwargs.pop("trust_remote_code"     ,UpperCAmelCase_      )
           lowercase__		       =						True

           lowercase__       ,       lowercase__		       =						FeatureExtractionMixin.get_feature_extractor_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )
           lowercase__		       =						config_dict.get("feature_extractor_type"     ,UpperCAmelCase_      )
           lowercase__		       =						None
           if "AutoFeatureExtractor" in config_dict.get("auto_map"     ,{}      ):
               lowercase__		       =						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_      ):
                   lowercase__		       =						AutoConfig.from_pretrained(UpperCAmelCase_     ,**UpperCAmelCase_      )
               # It could be in `config.feature_extractor_type``
               lowercase__		       =						getattr(UpperCAmelCase_     ,"feature_extractor_type"     ,UpperCAmelCase_      )
               if hasattr(UpperCAmelCase_     ,"auto_map"      ) and "AutoFeatureExtractor" in config.auto_map:
                   lowercase__		       =						config.auto_map["AutoFeatureExtractor"]

           if feature_extractor_class is not None:
               lowercase__		       =						feature_extractor_class_from_name(UpperCAmelCase_      )

           lowercase__		       =						feature_extractor_auto_map is not None
           lowercase__		       =						feature_extractor_class is not None or type(UpperCAmelCase_      ) in FEATURE_EXTRACTOR_MAPPING
           lowercase__		       =						resolve_trust_remote_code(
               UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )

           if has_remote_code and trust_remote_code:
               lowercase__		       =						get_class_from_dynamic_module(
                   UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )
               lowercase__		       =						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:
               lowercase__		       =						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 							_a      (				UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Any:
           FEATURE_EXTRACTOR_MAPPING.register(UpperCAmelCase_     ,UpperCAmelCase_      )




