





'''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,
)


lowercase__						:      List[str]	=    logging.get_logger(__name__)

lowercase__						:      Optional[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'),
    ]
)

lowercase__						:      List[str]	=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def  a__    (    lowercase :					str				)		->		Any:





     """simple docstring"""

     for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
          if class_name in extractors:
               _UpperCamelCase       =	model_type_to_module_name(lowercase				)

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

     for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
          if getattr(lowercase, '''__name__''', lowercase				) == 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.
     _UpperCamelCase       =	importlib.import_module('''transformers'''				)
     if hasattr(lowercase, lowercase				):
          return getattr(lowercase, lowercase				)

     return None







def  a__    (    lowercase :					Union[str, os.PathLike], lowercase :					Optional[Union[str, os.PathLike]] = None, lowercase :					bool = False, lowercase :					bool = False, lowercase :					Optional[Dict[str, str]] = None, lowercase :					Optional[Union[bool, str]] = None, lowercase :					Optional[str] = None, lowercase :					bool = False, **lowercase :					int, )		->		Tuple:





     """simple docstring"""

     _UpperCamelCase       =	get_file_from_repo(
         lowercase, lowercase, cache_dir=lowercase, force_download=lowercase, resume_download=lowercase, proxies=lowercase, use_auth_token=lowercase, revision=lowercase, local_files_only=lowercase, )
     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(lowercase, encoding='''utf-8'''				) as reader:
          return json.load(lowercase				)




class     __lowerCAmelCase					:






       """simple docstring"""





       def __init__(							self       :		Tuple					)   ->					int:

            '''simple docstring'''




            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 snake_case__						(							cls       :		int	,			lowerCAmelCase__       :		Optional[Any]	,			**lowerCAmelCase__       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	kwargs.pop('''config'''	,			__UpperCAmelCase					)
            _UpperCamelCase       =	kwargs.pop('''trust_remote_code'''	,			__UpperCAmelCase					)
            _UpperCamelCase       =	True

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

            if feature_extractor_class is not None:
                 _UpperCamelCase       =	feature_extractor_class_from_name(__UpperCAmelCase					)

            _UpperCamelCase       =	feature_extractor_auto_map is not None
            _UpperCamelCase       =	feature_extractor_class is not None or type(__UpperCAmelCase					) in FEATURE_EXTRACTOR_MAPPING
            _UpperCamelCase       =	resolve_trust_remote_code(
                __UpperCAmelCase	,			__UpperCAmelCase	,			__UpperCAmelCase	,			__UpperCAmelCase					)

            if has_remote_code and trust_remote_code:
                 _UpperCamelCase       =	get_class_from_dynamic_module(
                     __UpperCAmelCase	,			__UpperCAmelCase	,			**__UpperCAmelCase					)
                 _UpperCamelCase       =	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:
                 _UpperCamelCase       =	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 snake_case__						(							lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		Tuple					)   ->					Optional[int]:

            '''simple docstring'''




            FEATURE_EXTRACTOR_MAPPING.register(__UpperCAmelCase	,			__UpperCAmelCase					)










'''simple docstring'''

def  a__    (    lowercase :					int				)		->		int:





     """simple docstring"""

     if not isinstance(lowercase, lowercase				):
          raise TypeError('''Input value must be an \'int\' type'''				)

     _UpperCamelCase       =	0
     while number:
          position += 1
          number >>= 1

     return position


if __name__ == "__main__":
   import doctest

   doctest.testmod()



