






'''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 					lowercase__				(   __UpperCamelCase					)->				List[str]:
      for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  UpperCamelCase       						=      model_type_to_module_name(__UpperCamelCase					)

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

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

      return None







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase = None	,		__UpperCamelCase = False	,		__UpperCamelCase = False	,		__UpperCamelCase = None	,		__UpperCamelCase = None	,		__UpperCamelCase = None	,		__UpperCamelCase = False	,		**__UpperCamelCase	,		)->				str:
      UpperCamelCase       						=      get_file_from_repo(
          __UpperCamelCase	,		__UpperCamelCase	,		cache_dir=__UpperCamelCase	,		force_download=__UpperCamelCase	,		resume_download=__UpperCamelCase	,		proxies=__UpperCamelCase	,		use_auth_token=__UpperCamelCase	,		revision=__UpperCamelCase	,		local_files_only=__UpperCamelCase	,		)
      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(__UpperCamelCase	,		encoding="""utf-8"""					) as reader:
            return json.load(__UpperCamelCase					)






class        a_		:

  def __init__(   self   )						->   Union[str, 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(_SCREAMING_SNAKE_CASE   )
  def 						A__		(   cls    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      kwargs.pop("""config"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      kwargs.pop("""trust_remote_code"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      True

        UpperCamelCase						,UpperCamelCase       						=      FeatureExtractionMixin.get_feature_extractor_dict(_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      config_dict.get("""feature_extractor_type"""    ,	_SCREAMING_SNAKE_CASE   )
        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(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                    UpperCamelCase       						=      AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
              # It could be in `config.feature_extractor_type``
              UpperCamelCase       						=      getattr(_SCREAMING_SNAKE_CASE    ,	"""feature_extractor_type"""    ,	_SCREAMING_SNAKE_CASE   )
              if hasattr(_SCREAMING_SNAKE_CASE    ,	"""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(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      feature_extractor_auto_map is not None
        UpperCamelCase       						=      feature_extractor_class is not None or type(_SCREAMING_SNAKE_CASE   ) in FEATURE_EXTRACTOR_MAPPING
        UpperCamelCase       						=      resolve_trust_remote_code(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

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

        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__		(   _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






        FEATURE_EXTRACTOR_MAPPING.register(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )








'''simple docstring'''

from __future__ import annotations







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				tuple[float, list[float]]:
      UpperCamelCase       						=      list(range(len(__UpperCamelCase					)					)					)
      UpperCamelCase       						=      [v / w for v, w in zip(__UpperCamelCase	,		__UpperCamelCase					)]
      index.sort(key=lambda __UpperCamelCase					: ratio[i]	,		reverse=__UpperCamelCase					)

      UpperCamelCase       						=      0
      UpperCamelCase       						=      [0] * len(__UpperCamelCase					)
      for i in index:
            if weight[i] <= capacity:
                  UpperCamelCase       						=      1
                  max_value += value[i]
                  capacity -= weight[i]
            else:
                  UpperCamelCase       						=      capacity / weight[i]
                  max_value += value[i] * capacity / weight[i]
                  break

      return max_value, fractions


if __name__ == "__main__":
     import doctest

     doctest.testmod()
