
from math import sqrt




def 		A_	(							snake_case							:				int	) ->      list[int]:



						'''simple docstring'''

						__UpperCamelCase       					=     [1]
						for i in range(2	, int(sqrt(snake_case	)	) + 1	, 1	):
												if number % i == 0:
																		values.append(snake_case	)
																		if int(number // i	) != i:
																								values.append(int(number // i	)	)
						return sorted(snake_case	)




def 		A_	(							snake_case							:				int	) ->      bool:



						'''simple docstring'''

						return sum(factors(snake_case	)	) > n




def 		A_	(							snake_case							:				int	) ->      bool:



						'''simple docstring'''

						__UpperCamelCase       					=     factors(snake_case	)
						__UpperCamelCase       					=     len(snake_case	)
						__UpperCamelCase       					=     [[0 for i in range(number + 1	)] for j in range(r + 1	)]
						for i in range(r + 1	):
												__UpperCamelCase       					=     True

						for i in range(1	, number + 1	):
												__UpperCamelCase       					=     False

						for i in range(1	, r + 1	):
												for j in range(1	, number + 1	):
																		if j < values[i - 1]:
																								__UpperCamelCase       					=     subset[i - 1][j]
																		else:
																								__UpperCamelCase       					=     subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

						return subset[r][number] != 0




def 		A_	(							snake_case							:				int	) ->      bool:



						'''simple docstring'''

						return abundant(snake_case	) and not semi_perfect(snake_case	)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				for number in (6_9, 7_0, 7_1):
								print(F"{number} is {'' if weird(number) else 'not '}weird.")






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					CustomConfig



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													super().__init__(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     torch.nn.Linear(config.hidden_size       ,		config.hidden_size     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[str]:





													'''simple docstring'''






													return self.linear(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Any:





													'''simple docstring'''






													pass






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					NoSuperInitConfig



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													super().__init__(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     torch.nn.Linear(config.attribute       ,		config.attribute     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[str]:





													'''simple docstring'''






													return self.linear(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													pass




