



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification







def  _snake_case			(							__snake_case     )  ->  Union[str, Any]:






      '''simple docstring'''







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







def  _snake_case			(							__snake_case     )  ->  str:






      '''simple docstring'''







      UpperCAmelCase_   ,   UpperCAmelCase_	:						Tuple					   =		emb.weight.shape
      UpperCAmelCase_	:						Dict					   =		nn.Linear(__snake_case       ,	__snake_case       ,	bias=__snake_case     )
      UpperCAmelCase_	:						str					   =		emb.weight.data
      return lin_layer







def  _snake_case			(							__snake_case       ,	__snake_case="uclanlp/plbart-base"       ,	__snake_case=False       ,	__snake_case=False     )  ->  List[str]:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		torch.load(__snake_case       ,	map_location="cpu"     )["model"]
      remove_ignore_keys_(__snake_case     )
      UpperCAmelCase_	:						List[Any]					   =		state_dict["encoder.embed_tokens.weight"].shape[0]

      UpperCAmelCase_	:						Tuple					   =		PLBartConfig.from_pretrained(__snake_case       ,	vocab_size=__snake_case     )

      UpperCAmelCase_	:						Any					   =		state_dict["decoder.embed_tokens.weight"]
      if not classification:
            UpperCAmelCase_	:						Any					   =		PLBartForConditionalGeneration(__snake_case     )
            model.model.load_state_dict(__snake_case     )
            if finetuned:
                  UpperCAmelCase_	:						List[Any]					   =		make_linear_from_emb(model.model.shared     )

      else:
            UpperCAmelCase_	:						List[str]					   =		{}
            for key, value in state_dict.copy().items():
                  if key.startswith("classification_heads.sentence_classification_head"     ):
                        UpperCAmelCase_	:						Tuple					   =		value
                        state_dict.pop(__snake_case     )
            UpperCAmelCase_	:						Any					   =		PLBartForSequenceClassification(__snake_case     )
            model.model.load_state_dict(__snake_case     )
            model.classification_head.load_state_dict(__snake_case     )

      return model


if __name__ == "__main__":
   __lowerCamelCase								=      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('''fairseq_path''', type=str, help='''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='''uclanlp/plbart-base''',
       type=str,
       help='''Which huggingface architecture to use: plbart-base''',
   )
   parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
   parser.add_argument(
       '''--classification''', action='''store_true''', help='''whether the model is a classification checkpoint'''
   )
   __lowerCamelCase								=      parser.parse_args()
   __lowerCamelCase								=      convert_fairseq_plbart_checkpoint_from_disk(
       args.fairseq_path,
       hf_config_path=args.hf_config,
       finetuned=args.finetuned,
       classification=args.classification,
   )
   model.save_pretrained(args.pytorch_dump_folder_path)





import heapq







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		[(0, start)]  # cost from start node,end node
      UpperCAmelCase_	:						List[Any]					   =		set()
      while heap:
            ((UpperCAmelCase_)   ,   (UpperCAmelCase_))	:						str					   =		heapq.heappop(__snake_case     )
            if u in visited:
                  continue
            visited.add(__snake_case     )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  UpperCAmelCase_	:						Tuple					   =		cost + c
                  heapq.heappush(__snake_case       ,	(next_item, v)     )
      return -1


__lowerCamelCase								=      {
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

__lowerCamelCase								=      dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
   import doctest

   doctest.testmod()
