



'''simple docstring'''






from __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						int  )      ->   list[int]:
							UpperCAmelCase :       Tuple 							=		[True] * limit
							UpperCAmelCase :       Any 							=		False
							UpperCAmelCase :       Optional[int] 							=		False
							UpperCAmelCase :       Optional[Any] 							=		True

							for i in range(3   ,      int(limit**0.5 + 1  )   ,      2  ):
														UpperCAmelCase :       Optional[int] 							=		i * 2
														while index < limit:
																					UpperCAmelCase :       int 							=		False
																					UpperCAmelCase :       Optional[Any] 							=		index + i

							UpperCAmelCase :       Optional[int] 							=		[2]

							for i in range(3   ,      _lowerCAmelCase   ,      2  ):
														if is_prime[i]:
																					primes.append(_lowerCAmelCase  )

							return primes


def  snake_case_     (					_lowerCAmelCase  :						int = 1000000  )      ->   int:
							UpperCAmelCase :       Optional[int] 							=		prime_sieve(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		0
							UpperCAmelCase :       List[Any] 							=		0

							for i in range(len(_lowerCAmelCase  )  ):
														for j in range(i + length   ,      len(_lowerCAmelCase  )  ):
																					UpperCAmelCase :       Any 							=		sum(primes[i:j]  )
																					if sol >= ceiling:
																												break

																					if sol in primes:
																												UpperCAmelCase :       List[str] 							=		j - i
																												UpperCAmelCase :       str 							=		sol

							return largest


if __name__ == "__main__":
						print(F"{solution() = }")








from ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache






