



"""simple docstring"""







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


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Dict							  =     "biogpt"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=42384						,	SCREAMING_SNAKE_CASE__=1024						,	SCREAMING_SNAKE_CASE__=24						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=4096						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=1024						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-12						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	**SCREAMING_SNAKE_CASE__						,	)      ->	int:
				A__											=					vocab_size
				A__											=					max_position_embeddings
				A__											=					hidden_size
				A__											=					num_hidden_layers
				A__											=					num_attention_heads
				A__											=					intermediate_size
				A__											=					hidden_act
				A__											=					hidden_dropout_prob
				A__											=					attention_probs_dropout_prob
				A__											=					initializer_range
				A__											=					layer_norm_eps
				A__											=					scale_embedding
				A__											=					use_cache
				A__											=					layerdrop
				A__											=					activation_dropout
				super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)





'''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())))




