

from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
					import accelerate





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							if not is_accelerate_available():
														return method
							snake_case_          =   version.parse(accelerate.__version__				).base_version
							if version.parse(UpperCamelCase__				) < version.parse('0.17.0'				):
														return method

							def wrapper(self       ,							*UpperCamelCase__       ,							**UpperCamelCase__				):
														if hasattr(self       ,							'_hf_hook'				) and hasattr(self._hf_hook       ,							'pre_forward'				):
																					self._hf_hook.pre_forward(self				)
														return method(self       ,							*UpperCamelCase__       ,							**UpperCamelCase__				)

							return wrapper







def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   [[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_:
																												snake_case_          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]       ,							dp[i - 1][w_]				)
																					else:
																												snake_case_          =   dp[i - 1][w_]

							return dp[n][w_], dp





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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

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

							snake_case_    ,   snake_case_          =   knapsack(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   set()
							_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)

							return optimal_val, example_optional_set





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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


if __name__ == "__main__":
					_UpperCAmelCase		:       int						    =	[3, 2, 4, 4]
					_UpperCAmelCase		:       Tuple						    =	[4, 3, 2, 3]
					_UpperCAmelCase		:       Dict						    =	4
					_UpperCAmelCase		:       int						    =	6
					_UpperCAmelCase		:       Union[str, Any]						    =	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	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
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	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)




