


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_						( __lowercase			,       __lowercase			,       __lowercase       ):
   # Initialise PyTorch model
   UpperCAmelCase_							:      str						=   MobileBertConfig.from_json_file(__lowercase       )
   print(F'''Building PyTorch model from configuration: {config}'''       )
   UpperCAmelCase_							:      int						=   MobileBertForPreTraining(__lowercase       )
   # Load weights from tf checkpoint
   UpperCAmelCase_							:      Dict						=   load_tf_weights_in_mobilebert(__lowercase			,       __lowercase			,       __lowercase       )
   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
   torch.save(model.state_dict()			,       __lowercase       )


if __name__ == "__main__":
       __UpperCamelCase				:  Tuple      					=							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.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



def      snake_case_						( __lowercase       ):
   return " ".join(
       ''''''.join(word[::-1]       ) if len(__lowercase       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('Hey wollef sroirraw'))