




"""simple docstring"""

import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def 						lowercase_					(						_UpperCAmelCase     ,					_UpperCAmelCase     ,					_UpperCAmelCase     ,					_UpperCAmelCase		):




	"""simple docstring"""






	A_     : Optional[Any]		      =						FunnelConfig.from_json_file(_lowerCamelCase		)
	print(f"""Building PyTorch model from configuration: {config}"""		)
	A_     : Optional[Any]		      =						FunnelBaseModel(_lowerCamelCase		) if base_model else FunnelModel(_lowerCamelCase		)

	# Load weights from tf checkpoint
	load_tf_weights_in_funnel(_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase		)

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


if __name__ == "__main__":
		_lowerCamelCase     :	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.'
		)
		parser.add_argument(
		    '--base_model', action='store_true', help='Whether you want just the base model (no decoder) or not.'
		)
		_lowerCamelCase     :	Union[str, Any]         =				parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
		)













"""simple docstring"""





import string







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				None:
  for key in range(len(string.ascii_uppercase ) ):
    lowerCamelCase_         =						''
    for symbol in message:
      if symbol in string.ascii_uppercase:
        lowerCamelCase_         =						string.ascii_uppercase.find(_lowerCamelCase )
        lowerCamelCase_         =						num - key
        if num < 0:
          lowerCamelCase_         =						num + len(string.ascii_uppercase )
        lowerCamelCase_         =						translated + string.ascii_uppercase[num]
      else:
        lowerCamelCase_         =						translated + symbol
    print(F'''Decryption using Key #{key}: {translated}''' )







def      lowerCamelCase__       (							)      ->				None:
  lowerCamelCase_         =						input('Encrypted message: ' )
  lowerCamelCase_         =						message.upper()
  decrypt(_lowerCamelCase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




