
from __future__ import annotations




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



						'''simple docstring'''

						if counter == 0 or capacity == 0:
												return 0

						# If weight of the nth item is more than Knapsack of capacity,
						#   then this item cannot be included in the optimal solution,
						# else return the maximum of two cases:
						#   (1) nth item included
						#   (2) not included
						if weights[counter - 1] > capacity:
												return knapsack(snake_case	, snake_case	, snake_case	, counter - 1	)
						else:
												__UpperCamelCase       					=     capacity - weights[counter - 1]
												__UpperCamelCase       					=     values[counter - 1] + knapsack(
												    snake_case	, snake_case	, snake_case	, counter - 1	)
												__UpperCamelCase       					=     knapsack(snake_case	, snake_case	, snake_case	, counter - 1	)
												return max(snake_case	, snake_case	)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






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




