


import argparse

import torch

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


logging.set_verbosity_info()






def 			UpperCamelCase (     __magic_name__					:							Optional[Any]   ,   __magic_name__					:							List[Any]   ,   __magic_name__					:							str					)       ->  List[Any]:
		"""simple docstring"""




		lowercase__					      =		LxmertConfig.from_json_file(__magic_name__					)
		print(f'''Building PyTorch model from configuration: {config}'''					)
		lowercase__					      =		LxmertForPreTraining(__magic_name__					)

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

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


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





A	:		Union[str, Any]    =       [
    (1_0_0_0, 'M'),
    (9_0_0, 'CM'),
    (5_0_0, 'D'),
    (4_0_0, 'CD'),
    (1_0_0, 'C'),
    (9_0, 'XC'),
    (5_0, 'L'),
    (4_0, 'XL'),
    (1_0, 'X'),
    (9, 'IX'),
    (5, 'V'),
    (4, 'IV'),
    (1, 'I'),
]






def 			UpperCamelCase (     __magic_name__					:							str					)       ->  int:
		"""simple docstring"""




		lowercase__					      =		{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
		lowercase__					      =		0
		lowercase__					      =		0
		while place < len(__magic_name__					):
				if (place + 1 < len(__magic_name__					)) and (vals[roman[place]] < vals[roman[place + 1]]):
						total += vals[roman[place + 1]] - vals[roman[place]]
						place += 2
				else:
						total += vals[roman[place]]
						place += 1
		return total






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		[]
		for arabic, roman in ROMAN:
				((lowercase__)      ,     (lowercase__))					      =		divmod(__magic_name__   ,   __magic_name__					)
				result.append(roman * factor					)
				if number == 0:
						break
		return "".join(__magic_name__					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

