




"""simple docstring"""







def 							lowercase					(snake_case__   : List[Any]      ,       snake_case__   : Any      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Optional[int] )     ->  Any:


				'''simple docstring'''






				global f  # a global dp table for knapsack
				if f[i][j] < 0:
								if j < wt[i - 1]:
												lowerCAmelCase  =				mf_knapsack(i - 1      ,       snake_case__      ,       snake_case__      ,       snake_case__ )
								else:
												lowerCAmelCase  =				max(
												    mf_knapsack(i - 1      ,       snake_case__      ,       snake_case__      ,       snake_case__ )      ,       mf_knapsack(i - 1      ,       snake_case__      ,       snake_case__      ,       j - wt[i - 1] ) + val[i - 1]      ,       )
								lowerCAmelCase  =				val
				return f[i][j]





def 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : str      ,       snake_case__   : int      ,       snake_case__   : Dict )     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				[[0] * (w + 1) for _ in range(n + 1 )]

				for i in range(1      ,       n + 1 ):
								for w_ in range(1      ,       w + 1 ):
												if wt[i - 1] <= w_:
																lowerCAmelCase  =				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]      ,       dp[i - 1][w_] )
												else:
																lowerCAmelCase  =				dp[i - 1][w_]

				return dp[n][w_], dp





def 							lowercase					(snake_case__   : int      ,       snake_case__   : list      ,       snake_case__   : list )     ->  Union[str, Any]:


				'''simple docstring'''






				if not (isinstance(snake_case__      ,       (list, tuple) ) and isinstance(snake_case__      ,       (list, tuple) )):
								raise ValueError(
								    """Both the weights and values vectors must be either lists or tuples""" )

				lowerCAmelCase  =				len(snake_case__ )
				if num_items != len(snake_case__ ):
								lowerCAmelCase  =				(
								    """The number of weights must be the same as the number of values.\n"""
								    f'''But got {num_items} weights and {len(snake_case__ )} values'''
								)
								raise ValueError(snake_case__ )
				for i in range(snake_case__ ):
								if not isinstance(wt[i]      ,       snake_case__ ):
												lowerCAmelCase  =				(
												    """All weights must be integers but got weight of """
												    f'''type {type(wt[i] )} at index {i}'''
												)
												raise TypeError(snake_case__ )

				lowerCAmelCase ,						lowerCAmelCase  =				knapsack(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				set()
				_construct_solution(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )

				return optimal_val, example_optional_set





def 							lowercase					(snake_case__   : list      ,       snake_case__   : list      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : set )     ->  Union[str, Any]:


				'''simple docstring'''






				if i > 0 and j > 0:
								if dp[i - 1][j] == dp[i][j]:
												_construct_solution(snake_case__      ,       snake_case__      ,       i - 1      ,       snake_case__      ,       snake_case__ )
								else:
												optimal_set.add(snake_case__ )
												_construct_solution(snake_case__      ,       snake_case__      ,       i - 1      ,       j - wt[i - 1]      ,       snake_case__ )


if __name__ == "__main__":
		a      =							[3, 2, 4, 4]
		a      =							[4, 3, 2, 3]
		a      =							4
		a      =							6
		a      =							[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
		a   ,       a      =							knapsack(w, wt, val, n)
		print(optimal_solution)
		print(mf_knapsack(n, wt, val, w))  # switched the n and w

		# testing the dynamic programming problem with example
		# the optimal subset for the above example are items 3 and 4
		a   ,       a      =							knapsack_with_example_solution(w, wt, val)
		assert optimal_solution == 8
		assert optimal_subset == {3, 4}
		print('optimal_value = ', optimal_solution)
		print('An optimal subset corresponding to the optimal value', optimal_subset)











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      =							{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BioGptForCausalLM',
		    'BioGptForTokenClassification',
		    'BioGptForSequenceClassification',
		    'BioGptModel',
		    'BioGptPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





