




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	:       Union[str, Any]	,							lowercase	:       Tuple	,							lowercase	:       List[Any]     ):


      '''simple docstring'''



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

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

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


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









def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int	,							lowercase	:       int     ):


      '''simple docstring'''



      return int((input_a, input_a).count(1     ) != 0     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      assert or_gate(0	,							0     ) == 0
      assert or_gate(0	,							1     ) == 1
      assert or_gate(1	,							0     ) == 1
      assert or_gate(1	,							1     ) == 1


if __name__ == "__main__":
 print(or_gate(0, 1))
 print(or_gate(1, 0))
 print(or_gate(0, 0))
 print(or_gate(1, 1))



