



'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_		,						a_       		=emb.weight.shape
     a_       		=nn.Linear(lowercase__       ,	lowercase__       ,	bias=lowercase__       )
     a_       		=emb.weight.data
     return lin_layer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__="facebook/mbart-large-en-ro"       ,	lowercase__=False       ,	lowercase__=False       ):


     '''simple docstring'''







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

     a_       		=MBartConfig.from_pretrained(lowercase__       ,	vocab_size=lowercase__       )
     if mbart_aa and finetuned:
          a_       		="relu"

     a_       		=state_dict["decoder.embed_tokens.weight"]
     a_       		=MBartForConditionalGeneration(lowercase__       )
     model.model.load_state_dict(lowercase__       )

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

     return model


if __name__ == "__main__":
  lowercase							       =			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''')
  lowercase							       =			parser.parse_args()
  lowercase							       =			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)







'''simple docstring'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     print(F"""Vertex\tShortest Distance from vertex {src}"""       )
     for i, d in enumerate(lowercase__       ):
          print(F"""{i}\t\t{d}"""       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for j in range(lowercase__       ):
          a_		,						a_		,						a_       		=(graph[j][k] for k in ["src", "dst", "weight"])
          if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
               return True
     return False






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=[float("inf"       )] * vertex_count
     a_       		=0.0

     for _ in range(vertex_count - 1       ):
          for j in range(lowercase__       ):
               a_		,						a_		,						a_       		=(graph[j][k] for k in ["src", "dst", "weight"])

               if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
                    a_       		=distance[u] + w

     a_       		=check_negative_cycle(lowercase__       ,	lowercase__       ,	lowercase__       )
     if negative_cycle_exists:
          raise Exception("Negative cycle found"       )

     return distance


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowercase							       =			int(input('''Enter number of vertices: ''').strip())
  lowercase							       =			int(input('''Enter number of edges: ''').strip())

  lowercase							       =			[{} for _ in range(E)]

  for i in range(E):
    print('''Edge ''', i + 1)
    lowercase       ,      lowercase       ,      lowercase							       =			(
        int(x)
        for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
    )
    lowercase							       =			{'''src''': src, '''dst''': dest, '''weight''': weight}

  lowercase							       =			int(input('''\nEnter shortest path source:''').strip())
  lowercase							       =			bellman_ford(graph, V, E, source)
  print_distance(shortest_distance, 0)


