'''simple docstring'''




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:
    __lowerCamelCase          =  RemBertConfig.from_json_file(__snake_case    )
    print('''Building PyTorch model from configuration: {}'''.format(str(__snake_case    )    )    )
    __lowerCamelCase          =  RemBertModel(__snake_case    )

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

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__snake_case    )    )
    torch.save(model.state_dict()    ,  __snake_case    )


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(
      "--rembert_config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained RemBERT 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_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)

'''simple docstring'''




__UpperCAmelCase												="ABCDEFGHIJKLMNOPQRSTUVWXYZ"






def __lowerCAmelCase			(							)     ->   None:
    __lowerCamelCase          =  input('''Enter message: '''    )
    __lowerCamelCase          =  input('''Enter key [alphanumeric]: '''    )
    __lowerCamelCase          =  input('''Encrypt/Decrypt [e/d]: '''    )

    if mode.lower().startswith('''e'''    ):
        __lowerCamelCase          =  '''encrypt'''
        __lowerCamelCase          =  encrypt_message(UpperCamelCase__    ,  UpperCamelCase__    )
    elif mode.lower().startswith('''d'''    ):
        __lowerCamelCase          =  '''decrypt'''
        __lowerCamelCase          =  decrypt_message(UpperCamelCase__    ,  UpperCamelCase__    )

    print(f"""\n{mode.title()}ed message:"""    )
    print(UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    return translate_message(UpperCamelCase__    ,  UpperCamelCase__    ,  '''encrypt'''    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    return translate_message(UpperCamelCase__    ,  UpperCamelCase__    ,  '''decrypt'''    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  0
    __lowerCamelCase          =  key.upper()

    for symbol in message:
        __lowerCamelCase          =  LETTERS.find(symbol.upper()    )
        if num != -1:
            if mode == "encrypt":
                num += LETTERS.find(key[key_index]    )
            elif mode == "decrypt":
                num -= LETTERS.find(key[key_index]    )

            num %= len(UpperCamelCase__    )

            if symbol.isupper():
                translated.append(LETTERS[num]    )
            elif symbol.islower():
                translated.append(LETTERS[num].lower()    )

            key_index += 1
            if key_index == len(UpperCamelCase__    ):
                __lowerCamelCase          =  0
        else:
            translated.append(UpperCamelCase__    )
    return "".join(UpperCamelCase__    )


if __name__ == "__main__":
  main()
