
def        __UpperCamelCase					(    lowerCAmelCase__							:    Any=2_8_1_2_3  ):
 __a			:  Tuple		    =  [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

 __a			:  Union[str, Any]		    =  set()
 __a			:  Dict		    =  0

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

  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 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

    __a			:  Optional[Any]		    =  model(input_ids.to(snake_case_       ) ,    labels=labels.to(snake_case_       )       ).loss
    __a			:  Tuple		    =  -(labels.shape[-1] * loss.item())

    __a			:  Dict		    =  -84.9127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )
