

'''simple docstring'''




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






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:int      	=					"""ClapFeatureExtractor"""
 __SCREAMING_SNAKE_CASE					:List[str]      	=					("""RobertaTokenizer""", """RobertaTokenizerFast""")



 def __init__(   self		:   int			,				a__		:   str			,				a__		:   List[Any]      ):
      super().__init__(a__			,				a__      )



 def __call__(   self		:   List[str]			,				a__		:   List[Any]=None			,				a__		:   Any=None			,				a__		:   str=None			,				**a__		:   int      ):
      __magic_name__						       =    kwargs.pop('''sampling_rate'''			,				a__      )

      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:
           __magic_name__						       =    self.tokenizer(a__			,				return_tensors=a__			,				**a__      )

      if audios is not None:
           __magic_name__						       =    self.feature_extractor(
               a__			,				sampling_rate=a__			,				return_tensors=a__			,				**a__      )

      if text is not None and audios is not None:
           __magic_name__						       =    audio_features.input_features
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**a__      )			,				tensor_type=a__      )



 def 			snake_case__  (   self		:   List[Any]			,				*a__		:   Tuple			,				**a__		:   List[Any]      ):
      return self.tokenizer.batch_decode(*a__			,				**a__      )



 def 			snake_case__  (   self		:   List[str]			,				*a__		:   int			,				**a__		:   Union[str, Any]      ):
      return self.tokenizer.decode(*a__			,				**a__      )



 @property
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.tokenizer.model_input_names
      __magic_name__						       =    self.feature_extractor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names      )      )









'''simple docstring'''




import functools




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     __magic_name__						       =    len(a			)

     @functools.cache
     def min_distance(a		,						a			) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __magic_name__						       =    int(worda[indexa] != worda[indexa]			)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1		,						a			)		,						1 + min_distance(a		,						indexa + 1			)		,						diff + min_distance(indexa + 1		,						indexa + 1			)		,						)

     return min_distance(0		,						0			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






