

"""simple docstring"""





def   _A  (  UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	Optional[Any])     ->						Optional[int]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	List[str],      UpperCamelCase_					:	int,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Union[str, Any])     ->						int:
   '''simple docstring'''


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

   return dp[n][w_], dp




def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	list,      UpperCamelCase_					:	list)     ->						Any:
   '''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")

   __lowercase								=						len(UpperCamelCase_)
   if num_items != len(UpperCamelCase_):
      __lowercase								=						(
          "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_):
         __lowercase								=						(
             "All weights must be integers but got weight of "
             F"""type {type(wt[i])} at index {i}"""
         )
         raise TypeError(UpperCamelCase_)

   __lowercase		,__lowercase								=						knapsack(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)
   __lowercase								=						set()
   _construct_solution(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)

   return optimal_val, example_optional_set




def   _A  (  UpperCamelCase_					:	list,      UpperCamelCase_					:	list,      UpperCamelCase_					:	int,      UpperCamelCase_					:	int,      UpperCamelCase_					:	set)     ->						List[Any]:
   '''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__":
   _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"""





import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      **UpperCamelCase_					:	Optional[int])     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						AutoConfig.from_pretrained(UpperCamelCase_,      **UpperCamelCase_)
   __lowercase								=						AutoModelForSeqaSeqLM.from_config(UpperCamelCase_)
   model.save_pretrained(UpperCamelCase_)
   AutoTokenizer.from_pretrained(UpperCamelCase_).save_pretrained(UpperCamelCase_)
   return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)




