





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       =				logging.get_logger(__name__)






def 			_SCREAMING_SNAKE_CASE						(     SCREAMING_SNAKE_CASE						:str       ,       SCREAMING_SNAKE_CASE						:str       )     ->       str:
 __lowerCAmelCase	:			Dict         =  RobertaPreLayerNormConfig.from_pretrained(
     _snake_case       ,       architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

 # convert state_dict
 __lowerCAmelCase	:			str         =  torch.load(hf_hub_download(repo_id=_snake_case       ,       filename="""pytorch_model.bin"""       )       )
 __lowerCAmelCase	:			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."""       ):
   __lowerCAmelCase	:			Union[str, Any]         =  """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

  __lowerCAmelCase	:			List[Any]         =  tensor_value

 __lowerCAmelCase	:			Tuple         =  RobertaPreLayerNormForMaskedLM.from_pretrained(
     pretrained_model_name_or_path=_snake_case       ,       config=_snake_case       ,       state_dict=_snake_case       )
 model.save_pretrained(_snake_case       )

 # convert tokenizer
 __lowerCAmelCase	:			Any         =  AutoTokenizer.from_pretrained(_snake_case       )
 tokenizer.save_pretrained(_snake_case       )


if __name__ == "__main__":
      _UpperCAmelCase       =				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       =				parser.parse_args()
      convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)




def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     bool:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''				)
   if len(_snake_case				) == 0:
      raise ValueError('''Input list must be a non empty list'''				)
   if len(_snake_case				) == 1:
      return True
   _A      =       series[1] - series[0]
   for index in range(len(_snake_case				) - 1				):
      if series[index + 1] - series[index] != common_diff:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     float:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''				)
   if len(_snake_case				) == 0:
      raise ValueError('''Input list must be a non empty list'''				)
   _A      =       0
   for val in series:
      answer += val
   return answer / len(_snake_case				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



