


'''simple docstring'''




def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   assert (
       isinstance(A		,	A      ) and number_of_steps > 0
   ), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
   if number_of_steps == 1:
      return 1
   _a		,						_a					:  int						=  1, 1
   for _ in range(number_of_steps - 1      ):
      _a		,						_a					:  Optional[Any]						=  current + previous, current
   return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()






'''simple docstring'''




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







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		"""ClapFeatureExtractor"""
 __lowerCAmelCase						: str							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Union[str, Any]:
    super().__init__(lowerCamelCase_		,      lowerCamelCase_      )




 def __call__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      **lowerCamelCase_      )      ->    List[Any]:
    _a					:  Any						=  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					:  Union[str, Any]						=  self.tokenizer(lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if audios is not None:
       _a					:  List[str]						=  self.feature_extractor(
           lowerCamelCase_		,      sampling_rate=lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if text is not None and audios is not None:
       _a					:  List[Any]						=  audio_features.input_features
       return encoding
    elif text is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**lowerCamelCase_      )		,      tensor_type=lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    Tuple:
    return self.tokenizer.batch_decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    List[str]:
    return self.tokenizer.decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 @property
 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Dict						=  self.tokenizer.model_input_names
    _a					:  Tuple						=  self.feature_extractor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names      )      )


