


"""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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''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__":
	A     :   Optional[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."
	)
	A     :   int			    =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






"""simple docstring"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    set(__lowerCamelCase  ), [start]

   while stack:
      UpperCAmelCase_					:					Any			     =    stack.pop()
      explored.add(__lowerCamelCase  )
      # 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(__lowerCamelCase  )
   return explored


_a		       =							{
    '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'))


