


'''simple docstring'''
import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Optional[Any]:
    # Initialise PyTorch model
    UpperCAmelCase__     :						Dict						=							LxmertConfig.from_json_file(lowerCAmelCase__		)
    print(F"""Building PyTorch model from configuration: {config}"""		)
    UpperCAmelCase__     :						str						=							LxmertForPreTraining(lowerCAmelCase__		)

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)

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


if __name__ == "__main__":
  UpperCamelCase__          = 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(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  UpperCamelCase__          = parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

from __future__ import annotations
def 		snake_case__	(		lowercase							,				lowercase					):
   print(F'''Vertex\tShortest Distance from vertex {src}'''					)
   for i, d in enumerate(lowercase					):
      print(F'''{i}\t\t{d}'''					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   for j in range(lowercase					):
      lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	(graph[j][k] for k in ["src", "dst", "weight"])
      if distance[u] != float("inf"					) and distance[u] + w < distance[v]:
         return True
   return False
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							int       =	[float("inf"					)] * vertex_count
   lowerCAmelCase_:							Dict       =	0.0

   for _ in range(vertex_count - 1					):
      for j in range(lowercase					):
         lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Any       =	(graph[j][k] for k in ["src", "dst", "weight"])

         if distance[u] != float("inf"					) and distance[u] + w < distance[v]:
            lowerCAmelCase_:							List[str]       =	distance[u] + w

   lowerCAmelCase_:							str       =	check_negative_cycle(lowercase							,				lowercase							,				lowercase					)
   if negative_cycle_exists:
      raise Exception("Negative cycle found"					)

   return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a  :      List[str]            =    int(input("""Enter number of vertices: """).strip())
 a  :      Any            =    int(input("""Enter number of edges: """).strip())

 a  :      list[dict[str, int]]            =    [{} for _ in range(E)]

 for i in range(E):
  print("""Edge """, i + 1)
  a     , a     , a  :      Optional[int]            =    (
      int(x)
      for x in input("""Enter source, destination, weight: """).strip().split(""" """)
  )
  a  :      Optional[int]            =    {"""src""": src, """dst""": dest, """weight""": weight}

 a  :      List[Any]            =    int(input("""\nEnter shortest path source:""").strip())
 a  :      Tuple            =    bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)