


"""simple docstring"""






import os
import sys
import time

from . import transposition_cipher as trans_cipher
def    A_ ( ):






     """simple docstring"""

     _a     						=					'''Prehistoric Men.txt'''
     _a     						=					'''Output.txt'''
     _a     						=					int(input('''Enter key: '''							)							)
     _a     						=					input('''Encrypt/Decrypt [e/d]: '''							)

     if not os.path.exists(_lowerCAmelCase							):
          print(f'File {input_file} does not exist. Quitting...'							)
          sys.exit()
     if os.path.exists(_lowerCAmelCase							):
          print(f'Overwrite {output_file}? [y/n]'							)
          _a     						=					input('''> '''							)
          if not response.lower().startswith('''y'''							):
               sys.exit()

     _a     						=					time.time()
     if mode.lower().startswith('''e'''							):
          with open(_lowerCAmelCase							) as f:
               _a     						=					f.read()
          _a     						=					trans_cipher.encrypt_message(_lowerCAmelCase,  _lowerCAmelCase							)
     elif mode.lower().startswith('''d'''							):
          with open(_lowerCAmelCase							) as f:
               _a     						=					f.read()
          _a     						=					trans_cipher.decrypt_message(_lowerCAmelCase,  _lowerCAmelCase							)

     with open(_lowerCAmelCase,  '''w'''							) as output_obj:
          output_obj.write(_lowerCAmelCase							)

     _a     						=					round(time.time() - start_time,  2							)
     print(('''Done (''', total_time, '''seconds )''')							)


if __name__ == "__main__":
   main()



"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

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


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