


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


UpperCAmelCase_				:			Optional[int]   =	logging.get_logger(__name__)

UpperCAmelCase_				:			Any   =	{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					List[Any]					       =						'''megatron-bert'''
      def __init__(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	int=2_9_0_5_6					,							SCREAMING_SNAKE_CASE__    :	Dict=1_0_2_4					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=2_4					,							SCREAMING_SNAKE_CASE__    :	List[str]=1_6					,							SCREAMING_SNAKE_CASE__    :	List[str]=4_0_9_6					,							SCREAMING_SNAKE_CASE__    :	str="gelu"					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.1					,							SCREAMING_SNAKE_CASE__    :	List[Any]=5_1_2					,							SCREAMING_SNAKE_CASE__    :	List[str]=2					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=0.02					,							SCREAMING_SNAKE_CASE__    :	Dict=1E-12					,							SCREAMING_SNAKE_CASE__    :	Dict=0					,							SCREAMING_SNAKE_CASE__    :	Dict="absolute"					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=True					,							**SCREAMING_SNAKE_CASE__    :	Optional[int]					,							)		->      str:
             super().__init__(pad_token_id=lowercase_					,							**lowercase_					)

             a_ :		Union[str, Any]							=  vocab_size
             a_ :		Union[str, Any]							=  hidden_size
             a_ :		Optional[Any]							=  num_hidden_layers
             a_ :		Dict							=  num_attention_heads
             a_ :		int							=  hidden_act
             a_ :		List[str]							=  intermediate_size
             a_ :		Tuple							=  hidden_dropout_prob
             a_ :		Optional[int]							=  attention_probs_dropout_prob
             a_ :		Union[str, Any]							=  max_position_embeddings
             a_ :		List[Any]							=  type_vocab_size
             a_ :		Optional[int]							=  initializer_range
             a_ :		Optional[Any]							=  layer_norm_eps
             a_ :		str							=  position_embedding_type
             a_ :		Any							=  use_cache






from __future__ import annotations

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		bool:

       """simple docstring"""

       return seive[n]







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		bool:

       """simple docstring"""

       return any(digit in '02468' for digit in str(__A    )    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int = 1_00_00_00    )     ->		list[int]:

       """simple docstring"""

       a_ :		Dict							=  [2]  # result already includes the number 2.
       for num in range(3  ,   limit + 1  ,   2    ):
              if is_prime(__A    ) and not contains_an_even_digit(__A    ):
                     a_ :		Dict							=  str(__A    )
                     a_ :		Any							=  [int(str_num[j:] + str_num[:j]    ) for j in range(len(__A    )    )]
                     if all(is_prime(__A    ) for i in list_nums    ):
                            result.append(__A    )
       return result







def 	SCREAMING_SNAKE_CASE_						(      )     ->		int:

       """simple docstring"""

       return len(find_circular_primes()    )


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


