


def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            a						:Tuple 							=       F'''Expected string as input, found {type(UpperCAmelCase_						)}'''
            raise ValueError(UpperCAmelCase_						)
      if not isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            a						:List[Any] 							=       F'''Expected boolean as use_pascal parameter, found {type(UpperCAmelCase_						)}'''
            raise ValueError(UpperCAmelCase_						)

      a						:int 							=       input_str.split('''_'''						)

      a						:Any 							=       0 if use_pascal else 1

      a						:int 							=       words[start_index:]

      a						:Tuple 							=       [word[0].upper() + word[1:] for word in words_to_capitalize]

      a						:Optional[int] 							=       '''''' if use_pascal else words[0]

      return "".join([initial_word, *capitalized_words]						)


if __name__ == "__main__":
     from doctest import testmod

     testmod()









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







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'ClapFeatureExtractor'
 SCREAMING_SNAKE_CASE__      =       ('RobertaTokenizer', 'RobertaTokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  **_lowerCamelCase    ):
       a						:Dict 							=       kwargs.pop('''sampling_rate'''					,  _lowerCamelCase    )

       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:
             a						:Optional[int] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if audios is not None:
             a						:Tuple 							=       self.feature_extractor(
                 _lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and audios is not None:
             a						:Union[str, Any] 							=       audio_features.input_features
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )					,  tensor_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.tokenizer.model_input_names
       a						:str 							=       self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )





