code: 
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    if "model" in orig_key:
      a__						=orig_key.replace('''model.'''			,   ''''''							)
    if "norm1" in orig_key:
      a__						=orig_key.replace('''norm1'''			,   '''attention.output.LayerNorm'''							)
    if "norm2" in orig_key:
      a__						=orig_key.replace('''norm2'''			,   '''output.LayerNorm'''							)
    if "norm" in orig_key:
      a__						=orig_key.replace('''norm'''			,   '''LayerNorm'''							)
    if "transformer" in orig_key:
      a__						=orig_key.split('''.'''							)[0].split('''_'''							)[-1]
      a__						=orig_key.replace(F"""transformer_{layer_num}"""			,   F"""encoder.layer.{layer_num}"""							)
    if "mha.attn" in orig_key:
      a__						=orig_key.replace('''mha.attn'''			,   '''attention.self'''							)
    if "mha" in orig_key:
      a__						=orig_key.replace('''mha'''			,   '''attention'''							)
    if "W_q" in orig_key:
      a__						=orig_key.replace('''W_q'''			,   '''self.query'''							)
    if "W_k" in orig_key:
      a__						=orig_key.replace('''W_k'''			,   '''self.key'''							)
    if "W_v" in orig_key:
      a__						=orig_key.replace('''W_v'''			,   '''self.value'''							)
    if "ff1" in orig_key:
      a__						=orig_key.replace('''ff1'''			,   '''intermediate.dense'''							)
    if "ff2" in orig_key:
      a__						=orig_key.replace('''ff2'''			,   '''output.dense'''							)
    if "ff" in orig_key:
      a__						=orig_key.replace('''ff'''			,   '''output.dense'''							)
    if "mlm_class" in orig_key:
      a__						=orig_key.replace('''mlm.mlm_class'''			,   '''cls.predictions.decoder'''							)
    if "mlm" in orig_key:
      a__						=orig_key.replace('''mlm'''			,   '''cls.predictions.transform'''							)
    if "cls" not in orig_key:
      a__						='''nystromformer.''' + orig_key
  
    return orig_key
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[Any]			,   _A   :				Any							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    for key in orig_state_dict.copy().keys():
      a__						=orig_state_dict.pop(lowercase_							)
  
      if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
        continue
      else:
        a__						=val
  
    a__						=orig_state_dict['''cls.predictions.decoder.bias''']
    a__						=(
        torch.arange(config.max_position_embeddings							).expand((1, -1)							) + 2
    )
  
    return orig_state_dict
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Any			,   _A   :				Tuple			,   _A   :				Any							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=torch.load(lowercase_			,   map_location='''cpu'''							)['''model_state_dict''']
    a__						=NystromformerConfig.from_json_file(lowercase_							)
    a__						=NystromformerForMaskedLM(lowercase_							)
  
    a__						=convert_checkpoint_helper(lowercase_			,   lowercase_							)
  
    model.load_state_dict(lowercase_							)
    model.eval()
    model.save_pretrained(lowercase_							)
  
    print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""							)
  
  
  if __name__ == "__main__":
         lowerCamelCase     =      argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Nystromformer pytorch checkpoint.'''
         )
         parser.add_argument(
             '''--config_file''',
             default=None,
             type=str,
             required=True,
             help='''The json file for Nystromformer model config.''',
         )
         parser.add_argument(
             '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
         )
         lowerCamelCase     =      parser.parse_args()
         convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
style_context: 
  
  import heapq
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				str			,   _A   :				Optional[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[(0, start)]  # cost from start node,end node
    a__						=set()
    while heap:
      ((a__),   (a__))						=heapq.heappop(_A							)
      if u in visited:
        continue
      visited.add(_A							)
      if u == end:
        return cost
      for v, c in graph[u]:
        if v in visited:
          continue
        a__						=cost + c
        heapq.heappush(_A			,   (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()
  