





'''simple docstring'''





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



class      _SCREAMING_SNAKE_CASE		(			UpperCAmelCase_					):







 '''simple docstring'''
 lowercase_	=    "ClapFeatureExtractor"
 lowercase_	=    ("RobertaTokenizer", "RobertaTokenizerFast")

 def __init__(self       :     Dict ,				UpperCAmelCase_       :     List[str] ,				UpperCAmelCase_       :     Optional[int]) ->int:



   '''simple docstring'''



   super().__init__(_lowercase ,				_lowercase)

 def __call__(self       :     Optional[int] ,				UpperCAmelCase_       :     List[Any]=None ,				UpperCAmelCase_       :     Dict=None ,				UpperCAmelCase_       :     str=None ,				**UpperCAmelCase_       :     Union[str, Any]) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						=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:
     lowerCamelCase__:   Dict    						=self.tokenizer(_lowercase ,				return_tensors=_lowercase ,				**_lowercase)

   if audios is not None:
     lowerCamelCase__:   int    						=self.feature_extractor(
         _lowercase ,				sampling_rate=_lowercase ,				return_tensors=_lowercase ,				**_lowercase)

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

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any] ,				*UpperCAmelCase_       :     Dict ,				**UpperCAmelCase_       :     Optional[int]) ->List[str]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str] ,				*UpperCAmelCase_       :     str ,				**UpperCAmelCase_       :     Optional[int]) ->Optional[int]:



   '''simple docstring'''



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







 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						=self.tokenizer.model_input_names
   lowerCamelCase__:   int    						=self.feature_extractor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))








def   lowerCAmelCase_				( __a			,					__a	)  ->	float:







  """simple docstring"""





  if density <= 0:
    raise ValueError("Impossible fluid density"	)
  if bulk_modulus <= 0:
    raise ValueError("Impossible bulk modulus"	)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
     import doctest

     doctest.testmod()

