






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()
__a:     int   					=					logging.get_logger(__name__)




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						List[Any]:
       _UpperCAmelCase							=    RobertaPreLayerNormConfig.from_pretrained(
           __snake_case   ,      architectures=["""RobertaPreLayerNormForMaskedLM"""] )

       # convert state_dict
       _UpperCAmelCase							=    torch.load(hf_hub_download(repo_id=__snake_case   ,      filename="""pytorch_model.bin""" ) )
       _UpperCAmelCase							=    {}
       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							=    """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							=    tensor_value

       _UpperCAmelCase							=    RobertaPreLayerNormForMaskedLM.from_pretrained(
           pretrained_model_name_or_path=__snake_case   ,      config=__snake_case   ,      state_dict=__snake_case )
       model.save_pretrained(__snake_case )

       # convert tokenizer
       _UpperCAmelCase							=    AutoTokenizer.from_pretrained(__snake_case )
       tokenizer.save_pretrained(__snake_case )


if __name__ == "__main__":
  __a:     Tuple   					=					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.'''
  )
  __a:     Dict   					=					parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





def 				A__ (snake_case   :		int				)     ->						bool:
					if number < 0:
										raise ValueError("""number must not be negative"""				)
					return number & (number - 1) == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()






