






"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  int = 10_00   ) ->    int:





  '''simple docstring'''





  lowercase							   =	[0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
  # number of letters in twenty, thirty, ..., ninety (0 for numbers less than
  # 20 due to inconsistency in teens)
  lowercase							   =	[0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

  lowercase							   =	0

  for i in range(1		, n + 1   ):
    if i < 10_00:
      if i >= 1_00:
        # add number of letters for "n hundred"
        count += ones_counts[i // 1_00] + 7

        if i % 1_00 != 0:
          # add number of letters for "and" if number is not multiple
          # of 100
          count += 3

      if 0 < i % 1_00 < 20:
        # add number of letters for one, two, three, ..., nineteen
        # (could be combined with below if not for inconsistency in
        # teens)
        count += ones_counts[i % 1_00]
      else:
        # add number of letters for twenty, twenty one, ..., ninety
        # nine
        count += ones_counts[i % 10]
        count += tens_counts[(i % 1_00 - i % 10) // 10]
    else:
      count += ones_counts[i // 10_00] + 8
  return count


if __name__ == "__main__":
    print(solution(int(input().strip())))










"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase		:  str						=		{
    'configuration_wav2vec2_conformer': [
        'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Wav2Vec2ConformerConfig',
    ],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  str						=		[
        'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Wav2Vec2ConformerForAudioFrameClassification',
        'Wav2Vec2ConformerForCTC',
        'Wav2Vec2ConformerForPreTraining',
        'Wav2Vec2ConformerForSequenceClassification',
        'Wav2Vec2ConformerForXVector',
        'Wav2Vec2ConformerModel',
        'Wav2Vec2ConformerPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_wavaveca_conformer import (
        WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        WavaVecaConformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_wavaveca_conformer import (
            WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavaVecaConformerForAudioFrameClassification,
            WavaVecaConformerForCTC,
            WavaVecaConformerForPreTraining,
            WavaVecaConformerForSequenceClassification,
            WavaVecaConformerForXVector,
            WavaVecaConformerModel,
            WavaVecaConformerPreTrainedModel,
        )

else:
    import sys

    _UpperCamelCase		:  Union[str, Any]						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


