

"""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,
)


_lowerCAmelCase       						=		logging.get_logger(__name__)

_lowerCAmelCase       						=		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"""),
    ]
)

_lowerCAmelCase       						=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)

def 		lowerCamelCase__		(       _lowerCamelCase ):

    '''simple docstring'''



    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            _lowerCAmelCase							: str	=     model_type_to_module_name(A__ )

            _lowerCAmelCase							: Tuple	=     importlib.import_module(f""".{module_name}"""			, 'transformers.models' )
            try:
                return getattr(A__			, A__ )
            except AttributeError:
                continue

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(A__			, '__name__'			, A__ ) == 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.
    _lowerCAmelCase							: List[Any]	=     importlib.import_module('transformers' )
    if hasattr(A__			, A__ ):
        return getattr(A__			, A__ )

    return None

def 		lowerCamelCase__		(       _lowerCamelCase			, _lowerCamelCase = None			, _lowerCamelCase = False			, _lowerCamelCase = False			, _lowerCamelCase = None			, _lowerCamelCase = None			, _lowerCamelCase = None			, _lowerCamelCase = False			, **_lowerCamelCase			, ):

    '''simple docstring'''



    _lowerCAmelCase							: Optional[Any]	=     get_file_from_repo(
        A__			, A__			, cache_dir=A__			, force_download=A__			, resume_download=A__			, proxies=A__			, use_auth_token=A__			, revision=A__			, local_files_only=A__			, )
    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(A__			, encoding='utf-8' ) as reader:
        return json.load(A__ )







class 						__UpperCamelCase     :

  def __init__(		self   ):
      '''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(__lowerCamelCase   )
  def __lowerCamelCase		(		cls     ,_A     ,**_A   ):
      '''simple docstring'''

      _lowerCAmelCase							: Any	=     kwargs.pop('config'     ,__lowerCamelCase   )
      _lowerCAmelCase							: Tuple	=     kwargs.pop('trust_remote_code'     ,__lowerCamelCase   )
      _lowerCAmelCase							: str	=     True

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

      if feature_extractor_class is not None:
          _lowerCAmelCase							: int	=     feature_extractor_class_from_name(__lowerCamelCase   )

      _lowerCAmelCase							: List[str]	=     feature_extractor_auto_map is not None
      _lowerCAmelCase							: Tuple	=     feature_extractor_class is not None or type(__lowerCamelCase   ) in FEATURE_EXTRACTOR_MAPPING
      _lowerCAmelCase							: Optional[Any]	=     resolve_trust_remote_code(
          __lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase   )

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

      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 __lowerCamelCase		(		_A     ,_A   ):
      '''simple docstring'''

      FEATURE_EXTRACTOR_MAPPING.register(__lowerCamelCase     ,__lowerCamelCase   )








from collections.abc import Callable

import numpy as np





def 	__a     (      A__			:  Callable			,  A__			:  float			,  A__			:  float			,  A__			:  float			,  A__			:  float       ):
    SCREAMING_SNAKE_CASE										=   int(np.ceil((x_end - xa) / step_size       )       )
    SCREAMING_SNAKE_CASE										=   np.zeros((n + 1,)       )
    SCREAMING_SNAKE_CASE										=   ya
    SCREAMING_SNAKE_CASE										=   xa

    for k in range(A__       ):
        SCREAMING_SNAKE_CASE										=   y[k] + step_size * ode_func(A__			,  y[k]       )
        SCREAMING_SNAKE_CASE										=   y[k] + (
            (step_size / 2) * (ode_func(A__			,  y[k]       ) + ode_func(x + step_size			,  A__       ))
        )
        x += step_size

    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()