





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase__   :				Optional[Any]      =   {
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__   :				str      =   [
      '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''Wav2Vec2ForAudioFrameClassification''',
      '''Wav2Vec2ForCTC''',
      '''Wav2Vec2ForMaskedLM''',
      '''Wav2Vec2ForPreTraining''',
      '''Wav2Vec2ForSequenceClassification''',
      '''Wav2Vec2ForXVector''',
      '''Wav2Vec2Model''',
      '''Wav2Vec2PreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__   :				Union[str, Any]      =   [
      '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFWav2Vec2ForCTC''',
      '''TFWav2Vec2Model''',
      '''TFWav2Vec2PreTrainedModel''',
      '''TFWav2Vec2ForSequenceClassification''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__   :				List[Any]      =   [
      '''FlaxWav2Vec2ForCTC''',
      '''FlaxWav2Vec2ForPreTraining''',
      '''FlaxWav2Vec2Model''',
      '''FlaxWav2Vec2PreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
  from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
  from .processing_wavaveca import WavaVecaProcessor
  from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavaveca import (
        WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavaVecaForAudioFrameClassification,
        WavaVecaForCTC,
        WavaVecaForMaskedLM,
        WavaVecaForPreTraining,
        WavaVecaForSequenceClassification,
        WavaVecaForXVector,
        WavaVecaModel,
        WavaVecaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_wavaveca import (
        TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFWavaVecaForCTC,
        TFWavaVecaForSequenceClassification,
        TFWavaVecaModel,
        TFWavaVecaPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_wavaveca import (
        FlaxWavaVecaForCTC,
        FlaxWavaVecaForPreTraining,
        FlaxWavaVecaModel,
        FlaxWavaVecaPreTrainedModel,
    )


else:
  import sys

  lowercase__   :				Union[str, Any]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str      )					->					float:

     def get_matched_characters(__snake_case  :			str			,							__snake_case  :			str      ) -> str:
          __A						:			Optional[int]								=	[]
          __A						:			Optional[Any]								=	min(len(_stra      )			,							len(_stra      )      ) // 2
          for i, l in enumerate(_stra      ):
               __A						:			str								=	int(max(0			,							i - limit      )      )
               __A						:			str								=	int(min(i + limit + 1			,							len(_stra      )      )      )
               if l in _stra[left:right]:
                    matched.append(__snake_case      )
                    __A						:			Dict								=	f'{_stra[0:_stra.index(__snake_case      )]} {_stra[_stra.index(__snake_case      ) + 1:]}'

          return "".join(__snake_case      )

     # matching characters
     __A						:			Any								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	len(__snake_case      )

     # transposition
     __A						:			Tuple								=	(
         len([(ca, ca) for ca, ca in zip(__snake_case			,							__snake_case      ) if ca != ca]      ) // 2
     )

     if not match_count:
          __A						:			int								=	0.0
     else:
          __A						:			Union[str, Any]								=	(
              1
              / 3
              * (
                  match_count / len(__snake_case      )
                  + match_count / len(__snake_case      )
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __A						:			Tuple								=	0
     for ca, ca in zip(stra[:4]			,							stra[:4]      ):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler('''hello''', '''world'''))