



import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    # Initialise PyTorch model
    __a		 =       MobileBertConfig.from_json_file(_UpperCAmelCase       )
    print(f'Building PyTorch model from configuration: {config}'       )
    __a		 =       MobileBertForPreTraining(_UpperCAmelCase       )
    # Load weights from tf checkpoint
    __a		 =       load_tf_weights_in_mobilebert(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'       )
    torch.save(model.state_dict()     ,				_UpperCAmelCase       )


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





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       ''''''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       [chr(i + 65       ) for i in range(26       )]
    # Remove duplicate characters from key
    __a		 =       remove_duplicates(key.upper()       )
    __a		 =       len(_UpperCAmelCase       )
    # First fill cipher with key characters
    __a		 =       {alphabet[i]: char for i, char in enumerate(_UpperCAmelCase       )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase       )     ,				26       ):
        __a		 =       alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __a		 =       alphabet[i - offset]
        __a		 =       char
    return cipher_alphabet





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    return "".join(cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       {v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    ):
    __a		 =       input('''Enter message to encode or decode: '''       ).strip()
    __a		 =       input('''Enter keyword: '''       ).strip()
    __a		 =       input('''Encipher or decipher? E/D:'''       ).strip()[0].lower()
    try:
        __a		 =       {'''e''': encipher, '''d''': decipher}[option]
    except KeyError:
        raise KeyError('''invalid input option'''       )
    __a		 =       create_cipher_map(_UpperCAmelCase       )
    print(func(_UpperCAmelCase     ,				_UpperCAmelCase       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





