





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_(			A				,      A				,      A   ):
	# Initialise PyTorch model
	UpperCAmelCase_								=     LxmertConfig.from_json_file(A   )
	print(f"""Building PyTorch model from configuration: {config}"""   )
	UpperCAmelCase_								=     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__":
				UpperCamelCase__   : 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."""
				)
				UpperCamelCase__   : int					  = parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def    A_(			A				,      A				,      **A   ):
	UpperCAmelCase_								=     AutoConfig.from_pretrained(A				,      **A   )
	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_config(A   )
	model.save_pretrained(A   )
	AutoTokenizer.from_pretrained(A   ).save_pretrained(A   )
	return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)


