



def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int				)->str:
      _lowerCAmelCase 			=  int(_SCREAMING_SNAKE_CASE				)
      if decimal in (0, 1):  # Exit cases for the recursion
            return str(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase			,   _lowerCAmelCase 			=  divmod(_SCREAMING_SNAKE_CASE     ,				2				)
      return binary_recursive(_SCREAMING_SNAKE_CASE				) + str(_SCREAMING_SNAKE_CASE				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str				)->str:
      _lowerCAmelCase 			=  str(_SCREAMING_SNAKE_CASE				).strip()
      if not number:
            raise ValueError('''No input value was provided'''				)
      _lowerCAmelCase 			=  '''-''' if number.startswith('''-'''				) else ''''''
      _lowerCAmelCase 			=  number.lstrip('''-'''				)
      if not number.isnumeric():
            raise ValueError('''Input value is not an integer'''				)
      return f'''{negative}0b{binary_recursive(int(_SCREAMING_SNAKE_CASE				)				)}'''


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

    testmod()




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





class 		UpperCAmelCase						(						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			):
         _lowerCAmelCase 			=  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:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

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

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





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer.model_input_names
         _lowerCAmelCase 			=  self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names			)			)