
"""simple docstring"""






import os
import sys
import time

from . import transposition_cipher as trans_cipher
def    UpperCamelCase						(					)						->None:

   """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(UpperCAmelCase     ):
      print(F'''File {input_file} does not exist. Quitting...'''     )
      sys.exit()
   if os.path.exists(UpperCAmelCase     ):
      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(UpperCAmelCase     ) as f:
         a_						   =     f.read()
      a_						   =     trans_cipher.encrypt_message(UpperCAmelCase ,   UpperCAmelCase     )
   elif mode.lower().startswith("d"     ):
      with open(UpperCAmelCase     ) as f:
         a_						   =     f.read()
      a_						   =     trans_cipher.decrypt_message(UpperCAmelCase ,   UpperCAmelCase     )

   with open(UpperCAmelCase ,   "w"     ) as output_obj:
      output_obj.write(UpperCAmelCase     )

   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    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     RoFormerConfig.from_json_file(UpperCAmelCase     )
   print(F'''Building PyTorch model from configuration: {config}'''     )
   a_						   =     RoFormerForMaskedLM(UpperCAmelCase     )

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

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


if __name__ == "__main__":
      UpperCamelCase_						   =   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.'
      )
      UpperCamelCase_						   =   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)