def    lowerCAmelCase_ (     __A,  __A,  __A,  __A							)     ->				List[Any]:







      '''simple docstring'''


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




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A							)     ->				Optional[Any]:







      '''simple docstring'''


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

      return dp[n][w_], dp




def    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				Optional[Any]:







      '''simple docstring'''


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

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

      UpperCAmelCase__		,      UpperCAmelCase__								=  knapsack(__A,  __A,  __A,  __A							)
      UpperCAmelCase__								=  set()
      _construct_solution(__A,  __A,  __A,  __A,  __A							)

      return optimal_val, example_optional_set




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A,  __A							)     ->				Optional[int]:







      '''simple docstring'''


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


if __name__ == "__main__":
      UpperCamelCase__											=					[3, 2, 4, 4]
      UpperCamelCase__											=					[4, 3, 2, 3]
      UpperCamelCase__											=					4
      UpperCamelCase__											=					6
      UpperCamelCase__											=					[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
      UpperCamelCase__							,							UpperCamelCase__											=					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__											=					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)




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def    lowerCAmelCase_ (     __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  [
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(__A,  __A							)




def    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__		,      UpperCAmelCase__								=  emb.weight.shape
      UpperCAmelCase__								=  nn.Linear(__A,  __A,  bias=__A							)
      UpperCAmelCase__								=  emb.weight.data
      return lin_layer




def    lowerCAmelCase_ (     __A,  __A="facebook/mbart-large-en-ro",  __A=False,  __A=False							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.load(__A,  map_location="cpu"							)["model"]
      remove_ignore_keys_(__A							)
      UpperCAmelCase__								=  state_dict["encoder.embed_tokens.weight"].shape[0]

      UpperCAmelCase__								=  MBartConfig.from_pretrained(__A,  vocab_size=__A							)
      if mbart_aa and finetuned:
            UpperCAmelCase__								=  "relu"

      UpperCAmelCase__								=  state_dict["decoder.embed_tokens.weight"]
      UpperCAmelCase__								=  MBartForConditionalGeneration(__A							)
      model.model.load_state_dict(__A							)

      if finetuned:
            UpperCAmelCase__								=  make_linear_from_emb(model.model.shared							)

      return model


if __name__ == "__main__":
      UpperCamelCase__											=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
      )
      parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument(
          '--hf_config',
          default='facebook/mbart-large-cc25',
          type=str,
          help='Which huggingface architecture to use: mbart-large',
      )
      parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
      parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
      UpperCamelCase__											=					parser.parse_args()
      UpperCamelCase__											=					convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)



