





'''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						: Any		=   logging.get_logger(__name__)


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=RobertaPreLayerNormConfig.from_pretrained(
      __SCREAMING_SNAKE_CASE  ,      architectures=['''RobertaPreLayerNormForMaskedLM''']   )

  # convert state_dict
  _UpperCamelCase       			=torch.load(hf_hub_download(repo_id=__SCREAMING_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=__SCREAMING_SNAKE_CASE  ,      config=__SCREAMING_SNAKE_CASE  ,      state_dict=__SCREAMING_SNAKE_CASE   )
  model.save_pretrained(__SCREAMING_SNAKE_CASE   )

  # convert tokenizer
  _UpperCamelCase       			=AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )
  tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      __lowerCamelCase						: Dict		=   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						: List[Any]		=   parser.parse_args()
      convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)













'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return 1 if digit in (0, 1) else (digit * factorial(digit - 1   ))


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=0
  _UpperCamelCase       			=number
  while duplicate > 0:
    _UpperCamelCase			,     _UpperCamelCase       			=divmod(__SCREAMING_SNAKE_CASE  ,      10   )
    fact_sum += factorial(__SCREAMING_SNAKE_CASE   )
  return fact_sum == number


if __name__ == "__main__":
      print('Program to check whether a number is a Krisnamurthy Number or not.')
      __lowerCamelCase						: Optional[Any]		=   int(input('Enter number: ').strip())
      print(
          F"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
      )






