"""simple docstring"""







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


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE									=						{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "gpt_neox"







				def __init__( self    :       Union[str, Any]			,					lowerCamelCase_    :       Union[str, Any]=5_0_4_3_2			,					lowerCamelCase_    :       Optional[Any]=6_1_4_4			,					lowerCamelCase_    :       int=4_4			,					lowerCamelCase_    :       List[Any]=6_4			,					lowerCamelCase_    :       str=2_4_5_7_6			,					lowerCamelCase_    :       Any="gelu"			,					lowerCamelCase_    :       Tuple=0.25			,					lowerCamelCase_    :       List[Any]=1_0_0_0_0			,					lowerCamelCase_    :       Tuple=0.0			,					lowerCamelCase_    :       Union[str, Any]=0.0			,					lowerCamelCase_    :       str=0.1			,					lowerCamelCase_    :       List[str]=2_0_4_8			,					lowerCamelCase_    :       List[str]=0.02			,					lowerCamelCase_    :       Tuple=1E-5			,					lowerCamelCase_    :       Optional[int]=True			,					lowerCamelCase_    :       Tuple=0			,					lowerCamelCase_    :       Optional[Any]=2			,					lowerCamelCase_    :       Optional[int]=False			,					lowerCamelCase_    :       str=True			,					lowerCamelCase_    :       Dict=None			,					**lowerCamelCase_    :       Tuple			,					):

					"""simple docstring"""






					super().__init__(bos_token_id=lowerCamelCase_			,					eos_token_id=lowerCamelCase_			,					**lowerCamelCase_						)
					_lowercase :    Optional[Any]							=							vocab_size
					_lowercase :    Union[str, Any]							=							max_position_embeddings
					_lowercase :    Optional[int]							=							hidden_size
					_lowercase :    Optional[Any]							=							num_hidden_layers
					_lowercase :    List[str]							=							num_attention_heads
					_lowercase :    int							=							intermediate_size
					_lowercase :    int							=							hidden_act
					_lowercase :    List[str]							=							rotary_pct
					_lowercase :    str							=							rotary_emb_base
					_lowercase :    Dict							=							attention_dropout
					_lowercase :    Any							=							hidden_dropout
					_lowercase :    List[Any]							=							classifier_dropout
					_lowercase :    Any							=							initializer_range
					_lowercase :    Any							=							layer_norm_eps
					_lowercase :    Optional[int]							=							use_cache
					_lowercase :    Union[str, Any]							=							tie_word_embeddings
					_lowercase :    int							=							use_parallel_residual
					_lowercase :    Any							=							rope_scaling
					self._rope_scaling_validation()

					if self.hidden_size % self.num_attention_heads != 0:
						raise ValueError(
						    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'						)







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					if self.rope_scaling is None:
						return

					if not isinstance(self.rope_scaling			,					lowerCamelCase_						) or len(self.rope_scaling						) != 2:
						raise ValueError(
						    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
						    F'''got {self.rope_scaling}'''						)
					_lowercase :    int							=							self.rope_scaling.get('type'			,					lowerCamelCase_						)
					_lowercase :    int							=							self.rope_scaling.get('factor'			,					lowerCamelCase_						)
					if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
					if rope_scaling_factor is None or not isinstance(lowerCamelCase_			,					lowerCamelCase_						) or rope_scaling_factor <= 1.0:
						raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)




"""simple docstring"""







from __future__ import annotations


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[True] * limit
	_lowercase :    Union[str, Any]							=							False
	_lowercase :    Any							=							False
	_lowercase :    List[Any]							=							True

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

	_lowercase :    Optional[Any]							=							[2]

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

	return primes


def      __lowerCAmelCase(		__UpperCAmelCase = 1_000_000			):






	"""simple docstring"""





	_lowercase :    Optional[Any]							=							prime_sieve(__UpperCAmelCase			)
	_lowercase :    int							=							0
	_lowercase :    List[Any]							=							0

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

			if sol in primes:
				_lowercase :    Optional[int]							=							j - i
				_lowercase :    Tuple							=							sol

	return largest


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



