




def 						lowerCAmelCase		(		_lowerCAmelCase							:   Tuple=2_8123	):




 """simple docstring"""







 UpperCAmelCase__							 =						[1] * (limit + 1)

 for i in range(2						,							int(limit**0.5	) + 1	):
  sum_divs[i * i] += i
  for k in range(i + 1						,							limit // i + 1	):
   sum_divs[k * i] += k + i

 UpperCAmelCase__							 =						set()
 UpperCAmelCase__							 =						0

 for n in range(1						,							limit + 1	):
  if sum_divs[n] > n:
   abundants.add(_lowerCAmelCase	)

  if not any((n - a in abundants) for a in abundants	):
   res += n

 return res


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











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Dict		=   argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  _lowerCAmelCase						:   Union[str, Any]		=   parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase						:   Union[str, Any]		=   os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
    _lowerCAmelCase						:   List[str]		=   F'''./DialoGPT-{MODEL}'''
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )





