





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						BertConfig.from_json_file(_SCREAMING_SNAKE_CASE     )
      print(F"""Building PyTorch model from configuration: {config}"""     )
      SCREAMING_SNAKE_CASE		=						BertForPreTraining(_SCREAMING_SNAKE_CASE     )

      # Load weights from tf checkpoint
      load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
      torch.save(model.state_dict()			,						_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	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(
          """--bert_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained BERT 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."""
      )
      SCREAMING_SNAKE_CASE_						       =	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   int	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{}






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		None:




           '''simple docstring'''


           print(self.vertex	)
           for i in self.vertex:
                 print(lowerCamelCase__						,""" -> """						,""" -> """.join([str(lowerCamelCase__	) for j in self.vertex[i]]	)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int	)					->		None:




           '''simple docstring'''


           if from_vertex in self.vertex:
                 self.vertex[from_vertex].append(lowerCamelCase__	)
           else:
                 # else make a new vertex
                 SCREAMING_SNAKE_CASE		=						[to_vertex]






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[False] * len(self.vertex	)

           # call the recursive helper function
           for i in range(len(self.vertex	)	):
                 if not visited[i]:
                       self.dfs_recursive(lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   list	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						True

           print(lowerCamelCase__						,end=""" """	)

           # Recur for all the vertices that are adjacent to this node
           for i in self.vertex:
                 if not visited[i]:
                       self.dfs_recursive(lowerCamelCase__						,lowerCamelCase__	)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	Graph()
      g.add_edge(0, 1)
      g.add_edge(0, 2)
      g.add_edge(1, 2)
      g.add_edge(2, 0)
      g.add_edge(2, 3)
      g.add_edge(3, 3)

      g.print_graph()
      print("""DFS:""")
      g.dfs()

      # OUTPUT:
      # 0  ->  1 -> 2
      # 1  ->  2
      # 2  ->  0 -> 3
      # 3  ->  3
      # DFS:
      #  0 1 2 3


