



'''simple docstring'''



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=AlbertConfig.from_json_file(_lowerCAmelCase					)
							print(f"""Building PyTorch model from configuration: {config}"""					)
							__lowercase       	=AlbertForPreTraining(_lowerCAmelCase					)

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)

							# Save pytorch-model
							print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
							torch.save(model.state_dict()  ,   _lowerCAmelCase					)


if __name__ == "__main__":
							lowerCamelCase	 =       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(
							    """--albert_config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained ALBERT 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	 =       parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)








'''simple docstring'''



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if number > 0:
														raise ValueError('input must be a negative integer'					)
							__lowercase       	=len(bin(_lowerCAmelCase					)[3:]					)
							__lowercase       	=bin(abs(_lowerCAmelCase					) - (1 << binary_number_length)					)[3:]
							__lowercase       	=(
							    (
							        '1'
							        + '0' * (binary_number_length - len(_lowerCAmelCase					))
							        + twos_complement_number
							    )
							    if number < 0
							    else '0'
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
		import doctest

		doctest.testmod()



