





'''simple docstring'''

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					str						    =   'ClapFeatureExtractor'
       _snake_case     :					Tuple						    =   ('RobertaTokenizer', 'RobertaTokenizerFast')





       def __init__(							self       :		int	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            super().__init__(lowerCAmelCase__	,			lowerCAmelCase__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	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:
                 _UpperCamelCase       =	self.tokenizer(lowerCAmelCase__	,			return_tensors=lowerCAmelCase__	,			**lowerCAmelCase__					)

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

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





       def snake_case__						(							self       :		Tuple	,			*lowerCAmelCase__       :		str	,			**lowerCAmelCase__       :		List[str]					)   ->					int:

            '''simple docstring'''




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





       def snake_case__						(							self       :		str	,			*lowerCAmelCase__       :		List[str]	,			**lowerCAmelCase__       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




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







       @property
       def snake_case__						(							self       :		Dict					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	self.tokenizer.model_input_names
            _UpperCamelCase       =	self.feature_extractor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names					)					)










'''simple docstring'''

def  a__    (    lowercase :					int				)		->		int:





     """simple docstring"""

     if not isinstance(lowercase, lowercase				):
          raise TypeError('''Input value must be an \'int\' type'''				)

     _UpperCamelCase       =	0
     while number:
          position += 1
          number >>= 1

     return position


if __name__ == "__main__":
   import doctest

   doctest.testmod()



