



'''simple docstring'''
import string






def A__   (					UpperCAmelCase_							):
						for key in range(len(string.ascii_uppercase							)							):
												_UpperCamelCase :		Optional[int]			    =       ''
												for symbol in message:
																		if symbol in string.ascii_uppercase:
																								_UpperCamelCase :		Tuple			    =       string.ascii_uppercase.find(UpperCAmelCase_							)
																								_UpperCamelCase :		List[str]			    =       num - key
																								if num < 0:
																														_UpperCamelCase :		List[str]			    =       num + len(string.ascii_uppercase							)
																								_UpperCamelCase :		Tuple			    =       translated + string.ascii_uppercase[num]
																		else:
																								_UpperCamelCase :		List[Any]			    =       translated + symbol
												print(f'Decryption using Key #{key}: {translated}'							)






def A__   (					):
						_UpperCamelCase :		Any			    =       input('Encrypted message: '							)
						_UpperCamelCase :		str			    =       message.upper()
						decrypt(UpperCAmelCase_							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()







'''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 A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						# Initialise PyTorch model
						_UpperCamelCase :		Any			    =       LxmertConfig.from_json_file(UpperCAmelCase_							)
						print(f'Building PyTorch model from configuration: {config}'							)
						_UpperCamelCase :		int			    =       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__":
					snake_case_			:     int  	=  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.'
					)
					snake_case_			:     List[str]  	=  parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


