



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



class 		lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""ClapFeatureExtractor"""
		snake_case_     =("""RobertaTokenizer""", """RobertaTokenizerFast""")



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						super().__init__(__lowerCamelCase		,__lowerCamelCase			)



		def __call__(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,**__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					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:
										lowerCAmelCase__					:    Tuple												=					self.tokenizer(__lowerCamelCase		,return_tensors=__lowerCamelCase		,**__lowerCamelCase			)

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

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



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      str:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      Any:

						"""simple docstring"""






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



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					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      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					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__					:    int												=					[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() = }""")

