



"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Any:
							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					A         =					mf_knapsack(i - 1				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)
														else:
																					A         =					max(
																					    mf_knapsack(i - 1				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)				,      mf_knapsack(i - 1				,      lowerCamelCase__				,      lowerCamelCase__				,      j - wt[i - 1]			) + val[i - 1]				,      )
														A         =					val
							return f[i][j]

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							List[str]:
							A         =					[[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_:
																												A         =					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,      dp[i - 1][w_]			)
																					else:
																												A         =					dp[i - 1][w_]

							return dp[n][w_], dp

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							List[str]:
							if not (isinstance(lowerCamelCase__				,      (list, tuple)			) and isinstance(lowerCamelCase__				,      (list, tuple)			)):
														raise ValueError(
														    'Both the weights and values vectors must be either lists or tuples'			)

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

							A      ,  A         =					knapsack(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)
							A         =					set()
							_construct_solution(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)

							return optimal_val, example_optional_set

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							# for the current item i at a maximum weight j to be part of an optimal subset,
							# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
							# where i - 1 means considering only the previous items at the given maximum weight
							if i > 0 and j > 0:
														if dp[i - 1][j] == dp[i][j]:
																					_construct_solution(lowerCamelCase__				,      lowerCamelCase__				,      i - 1				,      lowerCamelCase__				,      lowerCamelCase__			)
														else:
																					optimal_set.add(lowerCamelCase__			)
																					_construct_solution(lowerCamelCase__				,      lowerCamelCase__				,      i - 1				,      j - wt[i - 1]				,      lowerCamelCase__			)


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 ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     """decision_transformer"""
		lowerCAmelCase_       :	Tuple 							=     ["""past_key_values"""]
		lowerCAmelCase_       :	Dict 							=     {
		    """max_position_embeddings""": """n_positions""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}



		def __init__( self  :    Optional[Any]      ,							snake_case  :    int=17      ,							snake_case  :    int=4      ,							snake_case  :    Union[str, Any]=128      ,							snake_case  :    Optional[Any]=4_096      ,							snake_case  :    List[Any]=True      ,							snake_case  :    str=1      ,							snake_case  :    str=1_024      ,							snake_case  :    List[str]=3      ,							snake_case  :    str=1      ,							snake_case  :    Any=None      ,							snake_case  :    Optional[int]="relu"      ,							snake_case  :    List[Any]=0.1      ,							snake_case  :    str=0.1      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    Union[str, Any]=1E-5      ,							snake_case  :    Optional[int]=0.02      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Tuple=True      ,							snake_case  :    Optional[Any]=50_256      ,							snake_case  :    List[Any]=50_256      ,							snake_case  :    List[str]=False      ,							snake_case  :    Any=False      ,							**snake_case  :    List[str]      ,							)			->						List[Any]:






									'''simple docstring'''

									A         =					state_dim
									A         =					act_dim
									A         =					hidden_size
									A         =					max_ep_len
									A         =					action_tanh
									A         =					vocab_size
									A         =					n_positions
									A         =					n_layer
									A         =					n_head
									A         =					n_inner
									A         =					activation_function
									A         =					resid_pdrop
									A         =					embd_pdrop
									A         =					attn_pdrop
									A         =					layer_norm_epsilon
									A         =					initializer_range
									A         =					scale_attn_weights
									A         =					use_cache
									A         =					scale_attn_by_inverse_layer_idx
									A         =					reorder_and_upcast_attn

									A         =					bos_token_id
									A         =					eos_token_id

									super().__init__(bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)



