





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 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   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__":
    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)










'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					int					,				__snake_case    :					int     )	->	float:
     __A		:					Dict						=			(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total






def 			_lowerCAmelCase     (    )	->	Union[str, Any]:
     print(sum_of_series(1					,				1					,				10     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()