def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Dict=2_81_23     )							-> str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[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

  SCREAMING_SNAKE_CASE__      							=				set()
  SCREAMING_SNAKE_CASE__      							=				0

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

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

  return res


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

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
