





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


UpperCamelCase__      =logging.get_logger(__name__)

UpperCamelCase__      ={
    '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__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    'gpt_neox'




       def __init__(      self    ,						__lowerCamelCase=5_0_4_3_2    ,						__lowerCamelCase=6_1_4_4    ,						__lowerCamelCase=4_4    ,						__lowerCamelCase=6_4    ,						__lowerCamelCase=2_4_5_7_6    ,						__lowerCamelCase="gelu"    ,						__lowerCamelCase=0.25    ,						__lowerCamelCase=1_0_0_0_0    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=2_0_4_8    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=1E-5    ,						__lowerCamelCase=True    ,						__lowerCamelCase=0    ,						__lowerCamelCase=2    ,						__lowerCamelCase=False    ,						__lowerCamelCase=True    ,						__lowerCamelCase=None    ,						**__lowerCamelCase    ,						)			->     Union[str, Any]:
            super().__init__(bos_token_id=__lowerCamelCase    ,						eos_token_id=__lowerCamelCase    ,						**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							vocab_size
            _SCREAMING_SNAKE_CASE       :	Any						    =							max_position_embeddings
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							hidden_size
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							num_hidden_layers
            _SCREAMING_SNAKE_CASE       :	int						    =							num_attention_heads
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							intermediate_size
            _SCREAMING_SNAKE_CASE       :	int						    =							hidden_act
            _SCREAMING_SNAKE_CASE       :	Any						    =							rotary_pct
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							rotary_emb_base
            _SCREAMING_SNAKE_CASE       :	str						    =							attention_dropout
            _SCREAMING_SNAKE_CASE       :	Any						    =							hidden_dropout
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							classifier_dropout
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							initializer_range
            _SCREAMING_SNAKE_CASE       :	int						    =							layer_norm_eps
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							use_cache
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							tie_word_embeddings
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							use_parallel_residual
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							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							)			->     Union[str, Any]:
            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}"""							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.rope_scaling.get("type"    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							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}"""							)






from __future__ import annotations

UpperCamelCase__      =[True] * 100_0001
UpperCamelCase__      =2
while i * i <= 100_0000:
       if seive[i]:
              for j in range(i * i, 100_0001, i):
                     UpperCamelCase__      =False
       i += 1






def     lowerCamelCase__			(__lowerCamelCase	):
     return seive[n]






def     lowerCamelCase__			(__lowerCamelCase	):
     return any(digit in "02468" for digit in str(__lowerCamelCase	)	)






def     lowerCamelCase__			(__lowerCamelCase = 1000000	):
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[2]  # result already includes the number 2.
     for num in range(3,       limit + 1,       2	):
          if is_prime(__lowerCamelCase	) and not contains_an_even_digit(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	List[Any]						    =							str(__lowerCamelCase	)
               _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[int(str_num[j:] + str_num[:j]	) for j in range(len(__lowerCamelCase	)	)]
               if all(is_prime(__lowerCamelCase	) for i in list_nums	):
                    result.append(__lowerCamelCase	)
     return result






def     lowerCamelCase__			():
     return len(find_circular_primes()	)


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