

'''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,
)


_a					:			Union[str, Any]							=    logging.get_logger(__name__)

_a					:			int							=    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'),
    ]
)

_a					:			Dict							=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str    ):
  for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
    if class_name in extractors:
      UpperCAmelCase    				=						model_type_to_module_name(SCREAMING_SNAKE_CASE    )

      UpperCAmelCase    				=						importlib.import_module(f'''.{module_name}'''							, 'transformers.models'    )
      try:
        return getattr(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
      except AttributeError:
        continue

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

  return None


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Union[str, os.PathLike]							, SCREAMING_SNAKE_CASE   : Optional[Union[str, os.PathLike]] = None							, SCREAMING_SNAKE_CASE   : bool = False							, SCREAMING_SNAKE_CASE   : bool = False							, SCREAMING_SNAKE_CASE   : Optional[Dict[str, str]] = None							, SCREAMING_SNAKE_CASE   : Optional[Union[bool, str]] = None							, SCREAMING_SNAKE_CASE   : Optional[str] = None							, SCREAMING_SNAKE_CASE   : bool = False							, **SCREAMING_SNAKE_CASE   : Optional[Any]							, ):
  UpperCAmelCase    				=						get_file_from_repo(
      SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, cache_dir=SCREAMING_SNAKE_CASE							, force_download=SCREAMING_SNAKE_CASE							, resume_download=SCREAMING_SNAKE_CASE							, proxies=SCREAMING_SNAKE_CASE							, use_auth_token=SCREAMING_SNAKE_CASE							, revision=SCREAMING_SNAKE_CASE							, local_files_only=SCREAMING_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(SCREAMING_SNAKE_CASE							, encoding='utf-8'    ) as reader:
    return json.load(SCREAMING_SNAKE_CASE    )



class 							lowercase_						:

  '''simple docstring'''





  def __init__(  self  )				->					Tuple:


    """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     snake_case_      (  cls ,			a_ ,			**a_  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						kwargs.pop('config' ,			a_  )
    UpperCAmelCase    				=						kwargs.pop('trust_remote_code' ,			a_  )
    UpperCAmelCase    				=						True

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

    UpperCAmelCase    				=						feature_extractor_auto_map is not None
    UpperCAmelCase    				=						feature_extractor_class is not None or type(a_  ) in FEATURE_EXTRACTOR_MAPPING
    UpperCAmelCase    				=						resolve_trust_remote_code(
        a_ ,			a_ ,			a_ ,			a_  )

    if has_remote_code and trust_remote_code:
      UpperCAmelCase    				=						get_class_from_dynamic_module(
          a_ ,			a_ ,			**a_  )
      UpperCAmelCase    				=						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:
      UpperCAmelCase    				=						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     snake_case_      (  a_ ,			a_  )				->					Tuple:


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





'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  # "extended trapezoidal rule"
  # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
  UpperCAmelCase    				=						(boundary[1] - boundary[0]) / steps
  UpperCAmelCase    				=						boundary[0]
  UpperCAmelCase    				=						boundary[1]
  UpperCAmelCase    				=						make_points(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						0.0
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  for i in x_i:
    # print(i)
    y += h * f(SCREAMING_SNAKE_CASE    )
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  return y


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						a + h
  while x < (b - h):
    yield x
    UpperCAmelCase    				=						x + h


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):  # enter your function here
  UpperCAmelCase    				=						(x - 0) * (x - 0)
  return y


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						0.0  # Lower bound of integration
  UpperCAmelCase    				=						1.0  # Upper bound of integration
  UpperCAmelCase    				=						10.0  # define number of steps or resolution
  UpperCAmelCase    				=						[a, b]  # define boundary of integration
  UpperCAmelCase    				=						method_a(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  print(f'''y = {y}'''    )


if __name__ == "__main__":
  main()


