"""simple docstring"""







import math



def       A__    (	lowercase_     :	int						)  ->							bool:

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5      , int(math.sqrt(lowercase_						) + 1						)      , 6						):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True



def       A__    (	lowercase_     :	int = 1000      , lowercase_     :	int = 1000						)  ->							int:
   __lowerCamelCase    		=					[0, 0, 0]  # length, a, b
   for a in range((a_limit * -1) + 1      , lowercase_						):
      for b in range(2      , lowercase_						):
         if is_prime(lowercase_						):
            __lowerCamelCase    		=					0
            __lowerCamelCase    		=					0
            while is_prime((n**2) + (a * n) + b						):
               count += 1
               n += 1
            if count > longest[0]:
               __lowerCamelCase    		=					[count, a, b]
   __lowerCamelCase    		=					longest[1] * longest[2]
   return ans


if __name__ == "__main__":
     print(solution(1_0_0_0, 1_0_0_0))














"""simple docstring"""





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig
class  __lowerCamelCase     (	_a	):
   a   :					List[Any]			    =CustomConfig

   def __init__(       self			,					snake_case_   )		->							str:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.hidden_size			,					config.hidden_size   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[int]:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass







class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    =NoSuperInitConfig

   def __init__(       self			,					snake_case_   )		->							Optional[int]:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.attribute			,					config.attribute   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							int:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass




