






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_					=	{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
      'AdaptiveEmbedding',
      'TransfoXLForSequenceClassification',
      'TransfoXLLMHeadModel',
      'TransfoXLModel',
      'TransfoXLPreTrainedModel',
      'load_tf_weights_in_transfo_xl',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFAdaptiveEmbedding',
      'TFTransfoXLForSequenceClassification',
      'TFTransfoXLLMHeadModel',
      'TFTransfoXLMainLayer',
      'TFTransfoXLModel',
      'TFTransfoXLPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
  from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_transfo_xl import (
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        AdaptiveEmbedding,
        TransfoXLForSequenceClassification,
        TransfoXLLMHeadModel,
        TransfoXLModel,
        TransfoXLPreTrainedModel,
        load_tf_weights_in_transfo_xl,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_transfo_xl import (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAdaptiveEmbedding,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLMainLayer,
        TFTransfoXLModel,
        TFTransfoXLPreTrainedModel,
    )

else:
  import sys

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''
def 	_a(					UpperCamelCase__			:  Optional[Any]=2_8_1_2_3							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[Any]													=[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__       :			List[str]													=set()
      SCREAMING_SNAKE_CASE__       :			str													=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())