


import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase : Dict 			=				logging.get_logger(__name__)


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		RobertaPreLayerNormConfig.from_pretrained(
           SCREAMING_SNAKE_CASE_							,			architectures=['''RobertaPreLayerNormForMaskedLM''']       )

       # convert state_dict
       UpperCamelCase    :	int   	=		torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE_							,			filename='''pytorch_model.bin'''       )       )
       UpperCamelCase    :	Union[str, Any]   	=		{}
       for tensor_key, tensor_value in original_state_dict.items():
              # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
              if tensor_key.startswith('''roberta.'''       ):
                     UpperCamelCase    :	Tuple   	=		'''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''       ) :]

              # The original implementation contains weights which are not used, remove them from the state_dict
              if tensor_key.endswith('''.self.LayerNorm.weight'''       ) or tensor_key.endswith('''.self.LayerNorm.bias'''       ):
                     continue

              UpperCamelCase    :	Union[str, Any]   	=		tensor_value

       UpperCamelCase    :	Tuple   	=		RobertaPreLayerNormForMaskedLM.from_pretrained(
           pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_							,			config=SCREAMING_SNAKE_CASE_							,			state_dict=SCREAMING_SNAKE_CASE_       )
       model.save_pretrained(SCREAMING_SNAKE_CASE_       )

       # convert tokenizer
       UpperCamelCase    :	Optional[Any]   	=		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
  __UpperCAmelCase : str 			=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint-repo",
      default=None,
      type=str,
      required=True,
      help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  __UpperCAmelCase : Union[str, Any] 			=				parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





def      a	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       if a < 0 or b < 0:
              raise ValueError('''the value of both inputs must be positive'''       )
       UpperCamelCase    :	int   	=		str(bin(SCREAMING_SNAKE_CASE_       )       )[2:]  # remove the leading "0b"
       UpperCamelCase    :	List[str]   	=		str(bin(SCREAMING_SNAKE_CASE_       )       )[2:]
       UpperCamelCase    :	Tuple   	=		max(len(SCREAMING_SNAKE_CASE_       )							,			len(SCREAMING_SNAKE_CASE_       )       )
       return "0b" + "".join(
           str(int('''1''' in (char_a, char_b)       )       )
           for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_       )							,			b_binary.zfill(SCREAMING_SNAKE_CASE_       )       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

