





"""simple docstring"""




def        __lowerCamelCase (							__a       :int   )		->    str:






		"""simple docstring"""

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser(
					    description=(
					        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
					        ''' Distillation'''
					    )
					)
					parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
					parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
					parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
					parser.add_argument('''--vocab_transform''', action='''store_true''')
					A   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

					for w in ["word_embeddings", "position_embeddings"]:
										A   : str			=				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										A   : Any			=				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)



