
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,
)


UpperCAmelCase__		:     str     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     str     			=      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'),
    ]
)

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






def   lowerCamelCase__							(					a      )      ->   int:
   for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
      if class_name in extractors:
         _A:  List[str]					=       model_type_to_module_name(snake_case_      )

         _A:  List[Any]					=       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.
   _A:  List[Any]					=       importlib.import_module('''transformers'''      )
   if hasattr(snake_case_		,		snake_case_      ):
      return getattr(snake_case_		,		snake_case_      )

   return None






def   lowerCamelCase__							(					a		,		a = None		,		a = False		,		a = False		,		a = None		,		a = None		,		a = None		,		a = False		,		**a		,		)      ->   Optional[Any]:
   _A:  Optional[int]					=       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   :





   '''simple docstring'''


   def __init__(					self     :							Optional[Any]     ):

      """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(_a     )
   def  __magic_name__				(					cls     :							Union[str, Any]			,			lowerCAmelCase_     :							List[Any]			,			**lowerCAmelCase_     :							List[Any]     ):

      """simple docstring"""
      _A:  Tuple					=       kwargs.pop('''config'''			,			_a     )
      _A:  Tuple					=       kwargs.pop('''trust_remote_code'''			,			_a     )
      _A:  List[Any]					=       True

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

      if feature_extractor_class is not None:
         _A:  Optional[Any]					=       feature_extractor_class_from_name(_a     )

      _A:  List[Any]					=       feature_extractor_auto_map is not None
      _A:  Union[str, Any]					=       feature_extractor_class is not None or type(_a     ) in FEATURE_EXTRACTOR_MAPPING
      _A:  Optional[int]					=       resolve_trust_remote_code(
          _a			,			_a			,			_a			,			_a     )

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

      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  __magic_name__				(					lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      FEATURE_EXTRACTOR_MAPPING.register(_a			,			_a     )









def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      while b:
            _A			,   _A							:     List[str]			=						b, a % b
      return a







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      return a if b == 0 else euclidean_gcd_recursive(snake_case_,a % b     )







def 				lowerCAmelCase_	(     ):
      print(f'''euclidean_gcd(3, 5) = {euclidean_gcd(3,5     )}'''     )
      print(f'''euclidean_gcd(5, 3) = {euclidean_gcd(5,3     )}'''     )
      print(f'''euclidean_gcd(1, 3) = {euclidean_gcd(1,3     )}'''     )
      print(f'''euclidean_gcd(3, 6) = {euclidean_gcd(3,6     )}'''     )
      print(f'''euclidean_gcd(6, 3) = {euclidean_gcd(6,3     )}'''     )

      print(f'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3,5     )}'''     )
      print(f'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5,3     )}'''     )
      print(f'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1,3     )}'''     )
      print(f'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3,6     )}'''     )
      print(f'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6,3     )}'''     )


if __name__ == "__main__":
  main()

