


'''simple docstring'''







import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      CustomConfig


    def __init__(	self    ,   a       ):
          super().__init__(a       )
          UpperCamelCase__      =							torch.nn.Linear(config.hidden_size    ,   config.hidden_size       )


    def   __a				(	self    ,   a       ):
          return self.linear(a       )


    def   __a				(	self    ,   a       ):
          pass

class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      NoSuperInitConfig


    def __init__(	self    ,   a       ):
          super().__init__(a       )
          UpperCamelCase__      =							torch.nn.Linear(config.attribute    ,   config.attribute       )


    def   __a				(	self    ,   a       ):
          return self.linear(a       )


    def   __a				(	self    ,   a       ):
          pass









'''simple docstring'''







import itertools







def _UpperCamelCase			(	__A = 1000000       )				->						int:


      '''simple docstring'''



      UpperCamelCase__      =							[1]

      for i in itertools.count(len(__A       )       ):
            UpperCamelCase__      =							0
            for j in itertools.count(1       ):
                  UpperCamelCase__      =							-1 if j % 2 == 0 else +1
                  UpperCamelCase__      =							(j * j * 3 - j) // 2
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number
                  index += j
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number

            if item == 0:
                  return i
            partitions.append(__A       )

      return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")





