


"""simple docstring"""






import string


def     _lowercase       (						__snake_case			)		->     None:
			for key in range(len(string.ascii_uppercase			)			):
						__lowerCAmelCase   :						int				= ""
						for symbol in message:
									if symbol in string.ascii_uppercase:
												__lowerCAmelCase   :						List[str]				= string.ascii_uppercase.find(__snake_case			)
												__lowerCAmelCase   :						Tuple				= num - key
												if num < 0:
															__lowerCAmelCase   :						List[Any]				= num + len(string.ascii_uppercase			)
												__lowerCAmelCase   :						str				= translated + string.ascii_uppercase[num]
									else:
												__lowerCAmelCase   :						str				= translated + symbol
						print(F"""Decryption using Key #{key}: {translated}"""			)


def     _lowercase       (						)		->     None:
			__lowerCAmelCase   :						Optional[int]				= input("Encrypted message: "			)
			__lowerCAmelCase   :						str				= message.upper()
			decrypt(__snake_case			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()




"""simple docstring"""






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= AlbertForPreTraining(__snake_case			)

			# Load weights from tf checkpoint
			load_tf_weights_in_albert(__snake_case      ,__snake_case      ,__snake_case			)

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


if __name__ == "__main__":
	__snake_case							:     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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This 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							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)