


import random

from .binary_exp_mod import bin_exp_mod




def    a   (							A__      ,    A__=1_0_0_0							)	-> List[str]:




      '''simple docstring'''

      if n < 2:
            return False

      if n % 2 == 0:
            return n == 2

      # this means n is odd
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		n - 1
      SCREAMING_SNAKE_CASE__       :  str						     =		0
      while d % 2 == 0:
            d /= 2
            exp += 1

      # n - 1=d*(2**exp)
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		0
      while count < prec:
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		random.randint(2      ,    n - 1							)
            SCREAMING_SNAKE_CASE__       :  Any						     =		bin_exp_mod(A__      ,    A__      ,    A__							)
            if b != 1:
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		True
                  for _ in range(A__							):
                        if b == n - 1:
                              SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		False
                              break
                        SCREAMING_SNAKE_CASE__       :  List[str]						     =		b * b
                        b %= n
                  if flag:
                        return False
                  count += 1
      return True


if __name__ == "__main__":
    a_ :Tuple  							=			abs(int(input('Enter bound : ').strip()))
    print('Here\'s the list of primes:')
    print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))














from ... import PretrainedConfig


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






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
      __snake_case	    =    '''nezha'''







      def __init__( self :		Union[str, Any]					,   __UpperCAmelCase :		List[str]=21_128					,   __UpperCAmelCase :		Any=768					,   __UpperCAmelCase :		int=12					,   __UpperCAmelCase :		Optional[int]=12					,   __UpperCAmelCase :		Tuple=3_072					,   __UpperCAmelCase :		Union[str, Any]="gelu"					,   __UpperCAmelCase :		List[str]=0.1					,   __UpperCAmelCase :		Any=0.1					,   __UpperCAmelCase :		str=512					,   __UpperCAmelCase :		List[Any]=64					,   __UpperCAmelCase :		Tuple=2					,   __UpperCAmelCase :		List[Any]=0.02					,   __UpperCAmelCase :		List[str]=1e-1_2					,   __UpperCAmelCase :		int=0.1					,   __UpperCAmelCase :		Dict=0					,   __UpperCAmelCase :		Any=2					,   __UpperCAmelCase :		Optional[Any]=3					,   __UpperCAmelCase :		int=True					,   **__UpperCAmelCase :		Optional[Any]					,   )       ->Any:




       """simple docstring"""


       super().__init__(pad_token_id=__UpperCAmelCase					,   bos_token_id=__UpperCAmelCase					,   eos_token_id=__UpperCAmelCase					,   **__UpperCAmelCase    )

       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


