


'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							0
       while num > 0:
              digit_sum += num % 1_0
              num //= 1_0
       return digit_sum






def       lowercase					(_A = 1_0_0       ):







       """simple docstring"""







       _lowerCAmelCase     :				Dict       =							1
       _lowerCAmelCase     :				str       =							2

       for i in range(2 ,    max_n + 1       ):
              _lowerCAmelCase     :				Optional[Any]       =							pre_numerator
              _lowerCAmelCase     :				str       =							2 * i // 3 if i % 3 == 0 else 1
              _lowerCAmelCase     :				int       =							cur_numerator
              _lowerCAmelCase     :				str       =							e_cont * pre_numerator + temp

       return sum_digits(__lowerCAmelCase       )


if __name__ == "__main__":
       print(F'''{solution() = }''')







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__  :  Optional[Any]					=       {
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InformerForPrediction""",
          """InformerModel""",
          """InformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_informer import (
                INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                InformerForPrediction,
                InformerModel,
                InformerPreTrainedModel,
            )

else:
      import sys

      __magic_name__  :  Any					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

