






'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class  _snake_case (					lowerCAmelCase__       ):

					"""simple docstring"""





					_UpperCamelCase					=   '''Wav2Vec2FeatureExtractor'''
					_UpperCamelCase					=   '''AutoTokenizer'''




					def __init__(  self ,			UpperCAmelCase__ ,			UpperCAmelCase__  )   ->						Optional[Any]:
										super().__init__(__lowerCAmelCase ,			__lowerCAmelCase  )
										a_						= self.feature_extractor
										a_						= False




					@classmethod
					def 			__SCREAMING_SNAKE_CASE			(  cls ,			UpperCAmelCase__ ,			**UpperCAmelCase__  )   ->						Any:


										try:
															return super().from_pretrained(__lowerCAmelCase ,			**__lowerCAmelCase  )
										except OSError:
															warnings.warn(
															    F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
															    ' include a `tokenizer_class` attribute is deprecated and will be '
															    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
															    ' attribute to either your `config.json` or `tokenizer_config.json` '
															    'file to suppress this warning: ' ,			__lowerCAmelCase ,			)

															a_						= WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase ,			**__lowerCAmelCase  )
															a_						= WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase ,			**__lowerCAmelCase  )

															return cls(feature_extractor=__lowerCAmelCase ,			tokenizer=__lowerCAmelCase  )




					def __call__(  self ,			*UpperCAmelCase__ ,			**UpperCAmelCase__  )   ->						Tuple:


										if self._in_target_context_manager:
															return self.current_processor(*__lowerCAmelCase ,			**__lowerCAmelCase  )

										if "raw_speech" in kwargs:
															warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'  )
															a_						= kwargs.pop('raw_speech'  )
										else:
															a_						= kwargs.pop('audio' ,			__lowerCAmelCase  )
										a_						= kwargs.pop('sampling_rate' ,			__lowerCAmelCase  )
										a_						= kwargs.pop('text' ,			__lowerCAmelCase  )
										if len(__lowerCAmelCase  ) > 0:
															a_						= args[0]
															a_						= args[1:]

										if audio is None and text is None:
															raise ValueError('You need to specify either an `audio` or `text` input to process.'  )

										if audio is not None:
															a_						= self.feature_extractor(__lowerCAmelCase ,			*__lowerCAmelCase ,			sampling_rate=__lowerCAmelCase ,			**__lowerCAmelCase  )
										if text is not None:
															a_						= self.tokenizer(__lowerCAmelCase ,			**__lowerCAmelCase  )

										if text is None:
															return inputs
										elif audio is None:
															return encodings
										else:
															a_						= encodings['input_ids']
															return inputs




					def 			__SCREAMING_SNAKE_CASE			(  self ,			*UpperCAmelCase__ ,			**UpperCAmelCase__  )   ->						Tuple:


										if self._in_target_context_manager:
															return self.current_processor.pad(*__lowerCAmelCase ,			**__lowerCAmelCase  )

										a_						= kwargs.pop('input_features' ,			__lowerCAmelCase  )
										a_						= kwargs.pop('labels' ,			__lowerCAmelCase  )
										if len(__lowerCAmelCase  ) > 0:
															a_						= args[0]
															a_						= args[1:]

										if input_features is not None:
															a_						= self.feature_extractor.pad(__lowerCAmelCase ,			*__lowerCAmelCase ,			**__lowerCAmelCase  )
										if labels is not None:
															a_						= self.tokenizer.pad(__lowerCAmelCase ,			**__lowerCAmelCase  )

										if labels is None:
															return input_features
										elif input_features is None:
															return labels
										else:
															a_						= labels['input_ids']
															return input_features




					def 			__SCREAMING_SNAKE_CASE			(  self ,			*UpperCAmelCase__ ,			**UpperCAmelCase__  )   ->						List[Any]:
										return self.tokenizer.batch_decode(*__lowerCAmelCase ,			**__lowerCAmelCase  )




					def 			__SCREAMING_SNAKE_CASE			(  self ,			*UpperCAmelCase__ ,			**UpperCAmelCase__  )   ->						str:
										return self.tokenizer.decode(*__lowerCAmelCase ,			**__lowerCAmelCase  )




					@contextmanager
					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						Union[str, Any]:
										warnings.warn(
										    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
										    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
										    'your audio inputs, or in a separate call.'  )
										a_						= True
										a_						= self.tokenizer
										yield
										a_						= self.feature_extractor
										a_						= False


'''simple docstring'''




from __future__ import annotations

import math






def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


		if num <= 0:
				lowerCAmelCase     =    f"{num}: Invalid input, please enter a positive integer."
				raise ValueError(_A					)

		lowerCAmelCase     =    [True] * (num + 1)
		lowerCAmelCase     =    []
		lowerCAmelCase     =    2
		lowerCAmelCase     =    int(math.sqrt(_A					)					)

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

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

				start += 1

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

		return prime


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




