



def        lowercase_							(A : int		,					A : int      ):
       return int((input_a, input_a).count(0      ) == 0      )
def        lowercase_							():
       assert and_gate(0		,					0      ) == 0
       assert and_gate(0		,					1      ) == 0
       assert and_gate(1		,					0      ) == 0
       assert and_gate(1		,					1      ) == 1


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











import argparse

import torch

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


logging.set_verbosity_info()
def        lowercase_							(A : str		,					A : List[Any]		,					A : Any      ):
       # Initialise PyTorch model
       snake_case__							:	List[Any]   	=	LxmertConfig.from_json_file(A      )
       print(F'''Building PyTorch model from configuration: {config}'''      )
       snake_case__							:	List[str]   	=	LxmertForPreTraining(A      )

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

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


if __name__ == "__main__":
 a_   :Union[str, 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_   :Optional[int]        =      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






