





from math import factorial, pi





def lowerCAmelCase__(__snake_case ,__snake_case = 30							)       ->   List[str]:







 '''simple docstring'''







 if not isinstance(__snake_case ,(int, float)							):
  raise ValueError('''maclaurin_sin() requires either an int or float for theta'''							)

 if not isinstance(__snake_case ,__snake_case							) or accuracy <= 0:
  raise ValueError('''maclaurin_sin() requires a positive int for accuracy'''							)

 lowerCamelCase__              =	float(__snake_case							)
 lowerCamelCase__              =	theta // (2 * pi)
 theta -= 2 * div * pi
 return sum(
     (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1							) for r in range(__snake_case							)							)





def lowerCAmelCase__(__snake_case ,__snake_case = 30							)       ->   str:







 '''simple docstring'''







 if not isinstance(__snake_case ,(int, float)							):
  raise ValueError('''maclaurin_cos() requires either an int or float for theta'''							)

 if not isinstance(__snake_case ,__snake_case							) or accuracy <= 0:
  raise ValueError('''maclaurin_cos() requires a positive int for accuracy'''							)

 lowerCamelCase__              =	float(__snake_case							)
 lowerCamelCase__              =	theta // (2 * pi)
 theta -= 2 * div * pi
 return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r							) for r in range(__snake_case							)							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(maclaurin_sin(10))
   print(maclaurin_sin(-10))
   print(maclaurin_sin(10, 15))
   print(maclaurin_sin(-10, 15))

   print(maclaurin_cos(5))
   print(maclaurin_cos(-5))
   print(maclaurin_cos(10, 15))
   print(maclaurin_cos(-10, 15))










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






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







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





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase ,						__lowerCAmelCase							)





			def __call__(					self ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	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__              =	self.tokenizer(__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

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

				if text is not None and audios is not None:
					lowerCamelCase__              =	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							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.tokenizer.model_input_names
				lowerCamelCase__              =	self.feature_extractor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names							)							)



