




'''simple docstring'''




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()
_lowerCAmelCase         =						logging.get_logger(__name__)




def 						UpperCamelCase	(			a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     __magic_name__						       =    RobertaPreLayerNormConfig.from_pretrained(
         a		,						architectures=['''RobertaPreLayerNormForMaskedLM''']			)

     # convert state_dict
     __magic_name__						       =    torch.load(hf_hub_download(repo_id=a		,						filename='''pytorch_model.bin'''			)			)
     __magic_name__						       =    {}
     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.'''			):
               __magic_name__						       =    '''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

          __magic_name__						       =    tensor_value

     __magic_name__						       =    RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=a		,						config=a		,						state_dict=a			)
     model.save_pretrained(a			)

     # convert tokenizer
     __magic_name__						       =    AutoTokenizer.from_pretrained(a			)
     tokenizer.save_pretrained(a			)


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









'''simple docstring'''




from __future__ import annotations

from fractions import Fraction




def 						UpperCamelCase	(			a		,						a			)				->     bool:






     '''simple docstring'''
     return (
         num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
     )




def 						UpperCamelCase	(			a			)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     __magic_name__						       =    11
     __magic_name__						       =    int('''1''' + '''0''' * digit_len			)
     for num in range(a		,						a			):
          while den <= 99:
               if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                    if is_digit_cancelling(a		,						a			):
                         solutions.append(F'''{num}/{den}'''			)
               den += 1
          num += 1
          __magic_name__						       =    10
     return solutions




def 						UpperCamelCase	(			a = 2			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    1.0
     for fraction in fraction_list(a			):
          __magic_name__						       =    Fraction(a			)
          result *= frac.denominator / frac.numerator
     return int(a			)


if __name__ == "__main__":
      print(solution())






