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





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""ClapFeatureExtractor"""
      _UpperCAmelCase				:    List[str]           =			("""RobertaTokenizer""", """RobertaTokenizerFast""")
      def __init__(      self    ,  __magic_name__    ,  __magic_name__      ):
       super().__init__(__magic_name__    ,  __magic_name__      )
      def __call__(      self    ,  __magic_name__=None    ,  __magic_name__=None    ,  __magic_name__=None    ,  **__magic_name__      ):
       lowerCamelCase		:	List[Any]							=      kwargs.pop("""sampling_rate"""    ,  __magic_name__      )

       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		:	Union[str, Any]							=      self.tokenizer(__magic_name__    ,  return_tensors=__magic_name__    ,  **__magic_name__      )

       if audios is not None:
        lowerCamelCase		:	List[Any]							=      self.feature_extractor(
            __magic_name__    ,  sampling_rate=__magic_name__    ,  return_tensors=__magic_name__    ,  **__magic_name__      )

       if text is not None and audios is not None:
        lowerCamelCase		:	List[str]							=      audio_features.input_features
        return encoding
       elif text is not None:
        return encoding
       else:
        return BatchEncoding(data=dict(**__magic_name__      )    ,  tensor_type=__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  *__magic_name__    ,  **__magic_name__      ):
       return self.tokenizer.batch_decode(*__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  *__magic_name__    ,  **__magic_name__      ):
       return self.tokenizer.decode(*__magic_name__    ,  **__magic_name__      )






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






def     _a					(						lowerCamelCase = 100_0000							):
 lowerCamelCase		:	Any							=      set(range(3,	lowerCamelCase,	2							)							)
 primes.add(2							)
 for p in range(3,	lowerCamelCase,	2							):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p,	lowerCamelCase,	lowerCamelCase							)							)							)

 lowerCamelCase		:	Any							=      [float(lowerCamelCase							) for n in range(limit + 1							)]

 for p in primes:
  for n in range(lowerCamelCase,	limit + 1,	lowerCamelCase							):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]							)							)


if __name__ == "__main__":
      print(f'''{solution() = }''')




