



from bisect import bisect
from itertools import accumulate







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Any:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		sorted(zip(lowerCamelCase_					,  lowerCamelCase_    )					,  key=lambda __snake_case    : x[0] / x[1]					,  reverse=lowerCamelCase_    )
      UpperCAmelCase_  :	List[str]				      =		[i[0] for i in r], [i[1] for i in r]
      UpperCAmelCase_  :	Tuple				      =		list(accumulate(lowerCamelCase_    )    )
      UpperCAmelCase_  :	str				      =		bisect(lowerCamelCase_					,  lowerCamelCase_    )
      return (
          0
          if k == 0
          else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
          if k != n
          else sum(vl[:k]    )
      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""ClapFeatureExtractor"""
       _lowerCamelCase			    =				("""RobertaTokenizer""", """RobertaTokenizerFast""")



       def __init__(     self     ,lowercase     ,lowercase):

             """simple docstring"""





             super().__init__(lowercase     ,lowercase)



       def __call__(     self     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		kwargs.pop("sampling_rate"     ,lowercase)

             if text is None and audios is None:
                   raise ValueError("You have to specify either text or audios. Both cannot be none.")

             if text is not None:
                   UpperCAmelCase_  :	List[str]				      =		self.tokenizer(lowercase     ,return_tensors=lowercase     ,**lowercase)

             if audios is not None:
                   UpperCAmelCase_  :	str				      =		self.feature_extractor(
                       lowercase     ,sampling_rate=lowercase     ,return_tensors=lowercase     ,**lowercase)

             if text is not None and audios is not None:
                   UpperCAmelCase_  :	Optional[int]				      =		audio_features.input_features
                   return encoding
             elif text is not None:
                   return encoding
             else:
                   return BatchEncoding(data=dict(**lowercase)     ,tensor_type=lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.batch_decode(*lowercase     ,**lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.decode(*lowercase     ,**lowercase)



       @property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.tokenizer.model_input_names
             UpperCAmelCase_  :	str				      =		self.feature_extractor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
