


"""simple docstring"""


def  __snake_case       (   UpperCamelCase__	)      ->		bool:







  """simple docstring"""





  if number < 0:
    raise ValueError('number must not be negative'	)
  return number & (number - 1) == 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()









"""simple docstring"""


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



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = 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:
										A					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

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

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







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





