




import argparse

import torch

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


logging.set_verbosity_info()

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[Any]     ,							lowercase     :	List[str]     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      lowerCamelCase_							=   LxmertConfig.from_json_file(__UpperCAmelCase					)
      print(f"""Building PyTorch model from configuration: {config}"""					)
      lowerCamelCase_							=   LxmertForPreTraining(__UpperCAmelCase					)

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

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


if __name__ == "__main__":
 lowerCamelCase    :		List[Any]			   =							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."
 )
 lowerCamelCase    :		Optional[int]			   =							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









from __future__ import annotations

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	dict     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_					,    lowerCamelCase_							=   set(lowercase					), [start]

      while stack:
            lowerCamelCase_							=   stack.pop()
            explored.add(lowercase					)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]					):
                  if adj not in explored:
                        stack.append(lowercase					)
      return explored


lowerCamelCase    :		int			   =       {
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(depth_first_search(G, "A"))



