






'''simple docstring'''






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       int  ,		A  :       Any  )    -> List[Any]:







   """simple docstring"""
   # Initialise PyTorch model
   __snake_case				:      Any     							= RemBertConfig.from_json_file(A  )
   print('Building PyTorch model from configuration: {}'.format(str(A  )  )  )
   __snake_case				:      str     							= RemBertModel(A  )

   # Load weights from tf checkpoint
   load_tf_weights_in_rembert(A  ,		A  ,		A  )

   # Save pytorch-model
   print('Save PyTorch model to {}'.format(A  )  )
   torch.save(model.state_dict()  ,		A  )


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--rembert_config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained RemBERT model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      __A    	=   parser.parse_args()
      convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)







'''simple docstring'''






import math




class a_    :







   def __init__(self					,   __a=0)			->      Any:  # a graph with Node 0,1,...,N-1
      """simple docstring"""






      __snake_case				:      List[str]     							= n
      __snake_case				:      Tuple     							= [
          [math.inf for j in range(0					,   __a)] for i in range(0					,   __a)
      ]  # adjacency matrix for weight
      __snake_case				:      Union[str, Any]     							= [
          [math.inf for j in range(0					,   __a)] for i in range(0					,   __a)
      ]  # dp[i][j] stores minimum distance from i to j







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= w







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      for k in range(0					,   self.n):
         for i in range(0					,   self.n):
            for j in range(0					,   self.n):
               __snake_case				:      List[Any]     							= min(self.dp[i][j]					,   self.dp[i][k] + self.dp[k][j])







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      Optional[int]:
      """simple docstring"""






      return self.dp[u][v]


if __name__ == "__main__":
      __A    	=   Graph(5)
      graph.add_edge(0, 2, 9)
      graph.add_edge(0, 4, 1_0)
      graph.add_edge(1, 3, 5)
      graph.add_edge(2, 3, 7)
      graph.add_edge(3, 0, 1_0)
      graph.add_edge(3, 1, 2)
      graph.add_edge(3, 2, 1)
      graph.add_edge(3, 4, 6)
      graph.add_edge(4, 1, 3)
      graph.add_edge(4, 2, 4)
      graph.add_edge(4, 3, 9)
      graph.floyd_warshall()
      graph.show_min(1, 4)
      graph.show_min(0, 3)