






def    a_       (					_A					,      _A     )	->	str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'     )

				snake_case__													=   str(bin(__snake_case     )     )
				binary_number += "0" * shift_amount
				return binary_number
def    a_       (					_A					,      _A     )	->	str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'     )

				snake_case__													=   str(bin(__snake_case     )     )[2:]
				if shift_amount >= len(__snake_case     ):
								return "0b0"
				snake_case__													=   binary_number[: len(__snake_case     ) - shift_amount]
				return "0b" + shifted_binary_number
def    a_       (					_A					,      _A     )	->	str:







				"""simple docstring"""
				if number >= 0:  # Get binary representation of positive number
								snake_case__													=   "0" + str(bin(__snake_case     )     ).strip('-'     )[2:]
				else:  # Get binary (2's complement) representation of negative number
								snake_case__													=   len(bin(__snake_case     )[3:]     )  # Find 2's complement of number
								snake_case__													=   bin(abs(__snake_case     ) - (1 << binary_number_length)     )[3:]
								snake_case__													=   (
								    "1" + "0" * (binary_number_length - len(__snake_case     )) + binary_number
								)

				if shift_amount >= len(__snake_case     ):
								return "0b" + binary_number[0] * len(__snake_case     )
				return (
				    "0b"
				    + binary_number[0] * shift_amount
				    + binary_number[: len(__snake_case     ) - shift_amount]
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()






"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


if __name__ == "__main__":
   _A			:       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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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."""
   )
   _A			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


