






'''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'''






__A    	=   {str(digit): digit**5 for digit in range(1_0)}




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A  )  )




def        _SCREAMING_SNAKE_CASE  (				)    -> int:







   """simple docstring"""
   return sum(
       number
       for number in range(10_00  ,		1_00_00_00  )
       if number == digits_fifth_powers_sum(A  )  )


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