


from ... import PretrainedConfig


UpperCAmelCase_      : Optional[Any]  					=       {
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     str        =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
      snake_case__				:     Any        =						'''nezha'''
      def __init__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=2_1_1_2_8     ,							SCREAMING_SNAKE_CASE__				:       List[str]=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       List[str]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=1_2     ,							SCREAMING_SNAKE_CASE__				:       List[str]=3_0_7_2     ,							SCREAMING_SNAKE_CASE__				:       Any="gelu"     ,							SCREAMING_SNAKE_CASE__				:       List[str]=0.1     ,							SCREAMING_SNAKE_CASE__				:       Any=0.1     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       Tuple=6_4     ,							SCREAMING_SNAKE_CASE__				:       Tuple=2     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.02     ,							SCREAMING_SNAKE_CASE__				:       List[str]=1E-12     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0     ,							SCREAMING_SNAKE_CASE__				:       List[str]=2     ,							SCREAMING_SNAKE_CASE__				:       Dict=3     ,							SCREAMING_SNAKE_CASE__				:       str=True     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			int:
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__     ,							bos_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             a_					:		Union[str, Any]							=  vocab_size
             a_					:		Tuple							=  hidden_size
             a_					:		List[Any]							=  num_hidden_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		List[str]							=  hidden_act
             a_					:		Tuple							=  intermediate_size
             a_					:		int							=  hidden_dropout_prob
             a_					:		Dict							=  attention_probs_dropout_prob
             a_					:		List[Any]							=  max_position_embeddings
             a_					:		Optional[Any]							=  max_relative_position
             a_					:		Dict							=  type_vocab_size
             a_					:		str							=  initializer_range
             a_					:		List[Any]							=  layer_norm_eps
             a_					:		Optional[Any]							=  classifier_dropout
             a_					:		Optional[Any]							=  use_cache







'''simple docstring'''




from __future__ import annotations

import math



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if num <= 0:
						A_	:						List[Any]	=     f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(lowerCamelCase__    )

			A_	:						Dict	=     [True] * (num + 1)
			A_	:						List[Any]	=     []
			A_	:						Tuple	=     2
			A_	:						Optional[int]	=     int(math.sqrt(lowerCamelCase__    )    )

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

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

						start += 1

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

			return prime


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