





"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  MobileBertConfig.from_json_file(_UpperCAmelCase					)
  print(f"""Building PyTorch model from configuration: {config}"""					)
  A_  :       List[Any]										=  MobileBertForPreTraining(_UpperCAmelCase					)
  # Load weights from tf checkpoint
  A_  :       List[str]										=  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__":
   lowerCamelCase_     :						Optional[Any]						=					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.'
   )
   lowerCamelCase_     :						List[Any]						=					parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)






"""simple docstring"""




def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  if isinstance(_UpperCAmelCase ,					_UpperCAmelCase					):
    raise TypeError('\'float\' object cannot be interpreted as an integer'					)
  if isinstance(_UpperCAmelCase ,					_UpperCAmelCase					):
    raise TypeError('\'str\' object cannot be interpreted as an integer'					)

  if num == 0:
    return "0b0"

  A_  :       str										=  False

  if num < 0:
    A_  :       Dict										=  True
    A_  :       Union[str, Any]										=  -num

  A_  :       list[int]										=  []
  while num > 0:
    binary.insert(0 ,					num % 2					)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(_UpperCAmelCase					) for e in binary					)

  return "0b" + "".join(str(_UpperCAmelCase					) for e in binary					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()