



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

class        snake_case__    (__snake_case		):







						"""simple docstring"""




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

						def __init__(       self ,       __lowercase ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									super().__init__(UpperCamelCase__ ,       UpperCamelCase__  )

						def __call__(       self ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       **__lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						Dict							=  kwargs.pop("""sampling_rate""" ,       UpperCamelCase__  )

									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__       :						Any							=  self.tokenizer(UpperCamelCase__ ,       return_tensors=UpperCamelCase__ ,       **UpperCamelCase__  )

									if audios is not None:
												a__       :						Optional[int]							=  self.feature_extractor(
												    UpperCamelCase__ ,       sampling_rate=UpperCamelCase__ ,       return_tensors=UpperCamelCase__ ,       **UpperCamelCase__  )

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

						def 							SCREAMING_SNAKE_CASE__(       self ,       *__lowercase ,       **__lowercase  ) ->				int:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       *__lowercase ,       **__lowercase  ) ->				List[str]:
									"""simple docstring"""




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

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						str							=  self.tokenizer.model_input_names
									a__       :						Any							=  self.feature_extractor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names  )  )









from __future__ import annotations

import math

def      lowerCAmelCase_					(  _lowercase    :       int)							->	list[int]:







			"""simple docstring"""

			if num <= 0:
						a__       :						Tuple							=  F'''{num}: Invalid input, please enter a positive integer.'''
						raise ValueError(_lowercase)

			a__       :						List[Any]							=  [True] * (num + 1)
			a__       :						List[str]							=  []
			a__       :						List[Any]							=  2
			a__       :						Optional[int]							=  int(math.sqrt(_lowercase))

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(_lowercase)

									# Set multiples of start be False
									for i in range(start * start						,       num + 1						,       _lowercase):
												if sieve[i] is True:
															a__       :						Optional[int]							=  False

						start += 1

			for j in range(end + 1						,       num + 1):
						if sieve[j] is True:
									prime.append(_lowercase)

			return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("Enter a positive integer: ").strip())))




