
'''simple docstring'''





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





def     snake_case							(						UpperCAmelCase		)->     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 "ff.0" in orig_key:
            __A    		=     orig_key.replace('ff.0' ,					'intermediate.dense'		)
      if "ff.2" in orig_key:
            __A    		=     orig_key.replace('ff.2' ,					'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 "backbone.backbone.encoders" in orig_key:
            __A    		=     orig_key.replace('backbone.backbone.encoders' ,					'encoder.layer'		)
      if "cls" not in orig_key:
            __A    		=     'mra.' + orig_key

      return orig_key





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     Optional[int]:







      """simple docstring"""
      for key in orig_state_dict.copy().keys():
            __A    		=     orig_state_dict.pop(UpperCAmelCase		)

            if ("pooler" in key) or ("sen_class" in key):
                  continue
            else:
                  __A    		=     val

      __A    		=     orig_state_dict['cls.predictions.decoder.bias']
      __A    		=     torch.arange(UpperCAmelCase		).expand((1, -1)		) + 2

      return orig_state_dict





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Optional[Any]:







      """simple docstring"""
      __A    		=     torch.load(UpperCAmelCase ,					map_location='cpu'		)['model_state_dict']
      __A    		=     MraConfig.from_json_file(UpperCAmelCase		)
      __A    		=     MraForMaskedLM(UpperCAmelCase		)

      __A    		=     convert_checkpoint_helper(config.max_position_embeddings ,					UpperCAmelCase		)

      print(model.load_state_dict(UpperCAmelCase		)		)
      model.eval()
      model.save_pretrained(UpperCAmelCase		)

      print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'		)


if __name__ == "__main__":
 a__						:				int           =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a__						:				Optional[int]           =    parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     Union[str, Any]:







      """simple docstring"""
      print('\nVertex Distance'		)
      for i in range(UpperCAmelCase		):
            if dist[i] != float('inf'		):
                  print(UpperCAmelCase ,					'\t' ,					int(dist[i]		) ,					end='\t'		)
            else:
                  print(UpperCAmelCase ,					'\t' ,					'INF' ,					end='\t'		)
            print()





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     List[Any]:







      """simple docstring"""
      __A    		=     float('inf'		)
      __A    		=     -1
      for i in range(UpperCAmelCase		):
            if (not vset[i]) and mdist[i] < min_val:
                  __A    		=     i
                  __A    		=     mdist[i]
      return min_ind





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Dict:







      """simple docstring"""
      __A    		=     [float('inf'		) for _ in range(UpperCAmelCase		)]
      __A    		=     [False for _ in range(UpperCAmelCase		)]
      __A    		=     0.0

      for _ in range(v - 1		):
            __A    		=     min_dist(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)
            __A    		=     True

            for i in range(UpperCAmelCase		):
                  if (
                      (not vset[i])
                      and graph[u][i] != float('inf'		)
                      and mdist[u] + graph[u][i] < mdist[i]
                  ):
                        __A    		=     mdist[u] + graph[u][i]

      print_dist(UpperCAmelCase ,					UpperCAmelCase		)


if __name__ == "__main__":
 a__						:				Tuple           =    int(input("Enter number of vertices: ").strip())
 a__						:				List[str]           =    int(input("Enter number of edges: ").strip())

 a__						:				int           =    [[float("inf") for i in range(V)] for j in range(V)]

 for i in range(V):
  a__						:				Optional[int]           =    0.0

 for i in range(E):
  print("\nEdge ", i + 1)
  a__						:				Union[str, Any]           =    int(input("Enter source:").strip())
  a__						:				Tuple           =    int(input("Enter destination:").strip())
  a__						:				int           =    float(input("Enter weight:").strip())
  a__						:				Dict           =    weight

 a__						:				int           =    int(input("\nEnter shortest path source:").strip())
 dijkstra(graph, V, gsrc)






