from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE						:   int         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   List[Any]         =			{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'pegasus'
							_lowerCamelCase						=		['past_key_values']
							_lowerCamelCase						=		{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



							def __init__(						self				,    lowercase_=50_265				,    lowercase_=1_024				,    lowercase_=12				,    lowercase_=4_096				,    lowercase_=16				,    lowercase_=12				,    lowercase_=4_096				,    lowercase_=16				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=True				,    lowercase_=True				,    lowercase_="gelu"				,    lowercase_=1_024				,    lowercase_=0.1				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.02				,    lowercase_=0				,    lowercase_=False				,    lowercase_=0				,    lowercase_=1				,    lowercase_=1				,    **lowercase_				,    ):
													_snake_case						:					List[Any]    =   vocab_size
													_snake_case						:					Any    =   max_position_embeddings
													_snake_case						:					Tuple    =   d_model
													_snake_case						:					Optional[int]    =   encoder_ffn_dim
													_snake_case						:					List[Any]    =   encoder_layers
													_snake_case						:					int    =   encoder_attention_heads
													_snake_case						:					Optional[Any]    =   decoder_ffn_dim
													_snake_case						:					str    =   decoder_layers
													_snake_case						:					Any    =   decoder_attention_heads
													_snake_case						:					str    =   dropout
													_snake_case						:					int    =   attention_dropout
													_snake_case						:					Dict    =   activation_dropout
													_snake_case						:					str    =   activation_function
													_snake_case						:					Any    =   init_std
													_snake_case						:					Any    =   encoder_layerdrop
													_snake_case						:					List[str]    =   decoder_layerdrop
													_snake_case						:					Any    =   use_cache
													_snake_case						:					Union[str, Any]    =   encoder_layers
													_snake_case						:					Optional[int]    =   scale_embedding  # scale factor will be sqrt(d_model) if True
													super().__init__(
													    pad_token_id=lowercase_				,    eos_token_id=lowercase_				,    is_encoder_decoder=lowercase_				,    decoder_start_token_id=lowercase_				,    forced_eos_token_id=lowercase_				,    **lowercase_				,    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self.encoder_attention_heads



							@property
							def 	UpperCamelCase	(						self    ):
													return self.d_model
def  snake_case						(__lowercase						)	->	list[int]:

						'''simple docstring'''



						if num <= 0:
												raise ValueError("Input must be a positive integer"						)

						_snake_case						:					Any    =   [True] * (num + 1)

						_snake_case						:					str    =   2
						while p * p <= num:
												if primes[p]:
																		for i in range(p * p ,      num + 1 ,      __lowercase						):
																								_snake_case						:					Optional[int]    =   False
												p += 1

						return [prime for prime in range(2 ,      num + 1						) if primes[prime]]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__SCREAMING_SNAKE_CASE						:   Any         =			int(input('Enter a positive integer: ').strip())
				print(prime_sieve_eratosthenes(user_num))