
'''simple docstring'''
def 		A_(     A						:		Optional[int]):
							UpperCamelCase   			=     int(__lowercase)
							if decimal in (0, 1):  # Exit cases for the recursion
														return str(__lowercase)
							UpperCamelCase			,	UpperCamelCase   			=     divmod(__lowercase		,     2)
							return binary_recursive(__lowercase) + str(__lowercase)







def 		A_(     A						:		List[Any]):
							UpperCamelCase   			=     str(__lowercase).strip()
							if not number:
														raise ValueError('No input value was provided')
							UpperCamelCase   			=     '-' if number.startswith('-') else ''
							UpperCamelCase   			=     number.lstrip('-')
							if not number.isnumeric():
														raise ValueError('Input value is not an integer')
							return f'''{negative}0b{binary_recursive(int(__lowercase))}'''


if __name__ == "__main__":
						from doctest import testmod

						testmod()












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_						=  MobileBertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  MobileBertForPreTraining(__lowercase)
   # Load weights from tf checkpoint
   UpperCamelCase_						=  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__":
 snake_case__   :						int													=   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__   :						Optional[Any]													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





