
'''simple docstring'''
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if not all(char in "01" for char in bin_string       ):
        raise ValueError("Non-binary value was passed to the function"       )
    if not bin_string:
        raise ValueError("Empty string was passed to the function"       )
    snake_case_ :    Any     			=						""
    while len(_lowercase       ) % 3 != 0:
        snake_case_ :    Optional[int]     			=						"0" + bin_string
    snake_case_ :    int     			=						[
        bin_string[index : index + 3]
        for index in range(len(_lowercase       )       )
        if index % 3 == 0
    ]
    for bin_group in bin_string_in_3_list:
        snake_case_ :    Dict     			=						0
        for index, val in enumerate(_lowercase       ):
            oct_val += int(2 ** (2 - index) * int(_lowercase       )       )
        oct_string += str(_lowercase       )
    return oct_string


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

  testmod()




'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""ClapFeatureExtractor"""
 UpperCAmelCase_		    =						("""RobertaTokenizer""", """RobertaTokenizerFast""")





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





 def __call__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       **lowercase_):
     snake_case_ :    Any     			=						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:
         snake_case_ :    Optional[int]     			=						self.tokenizer(lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

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

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





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.batch_decode(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.decode(*lowercase_ ,       **lowercase_)





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.tokenizer.model_input_names
     snake_case_ :    int     			=						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))




