"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A			   = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = ["XLNetTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = ["XLNetTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XLNetForMultipleChoice",
           "XLNetForQuestionAnswering",
           "XLNetForQuestionAnsweringSimple",
           "XLNetForSequenceClassification",
           "XLNetForTokenClassification",
           "XLNetLMHeadModel",
           "XLNetModel",
           "XLNetPreTrainedModel",
           "load_tf_weights_in_xlnet",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXLNetForMultipleChoice",
           "TFXLNetForQuestionAnsweringSimple",
           "TFXLNetForSequenceClassification",
           "TFXLNetForTokenClassification",
           "TFXLNetLMHeadModel",
           "TFXLNetMainLayer",
           "TFXLNetModel",
           "TFXLNetPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet import XLNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet_fast import XLNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlnet import (
                  XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLNetForMultipleChoice,
                  XLNetForQuestionAnswering,
                  XLNetForQuestionAnsweringSimple,
                  XLNetForSequenceClassification,
                  XLNetForTokenClassification,
                  XLNetLMHeadModel,
                  XLNetModel,
                  XLNetPreTrainedModel,
                  load_tf_weights_in_xlnet,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlnet import (
                  TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLNetForMultipleChoice,
                  TFXLNetForQuestionAnsweringSimple,
                  TFXLNetForSequenceClassification,
                  TFXLNetForTokenClassification,
                  TFXLNetLMHeadModel,
                  TFXLNetMainLayer,
                  TFXLNetModel,
                  TFXLNetPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase=2_8_1_2_3 )  ->  Any:
   lowercase__:   Optional[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

   lowercase__:   Union[str, Any]					=      set()
   lowercase__:   Optional[Any]					=      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())
