




'''simple docstring'''


def 					SCREAMING_SNAKE_CASE(   __lowercase = 1_0_0_0	)			-> int:
     A:		Optional[Any]          =		[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)
     A:		Any          =		[0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

     A:		Tuple          =		0

     for i in range(1		,				n + 1	):
          if i < 1_0_0_0:
               if i >= 1_0_0:
                    # add number of letters for "n hundred"
                    count += ones_counts[i // 1_0_0] + 7

                    if i % 1_0_0 != 0:
                         # add number of letters for "and" if number is not multiple
                         # of 100
                         count += 3

               if 0 < i % 1_0_0 < 2_0:
                    # 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_0_0]
               else:
                    # add number of letters for twenty, twenty one, ..., ninety
                    # nine
                    count += ones_counts[i % 1_0]
                    count += tens_counts[(i % 1_0_0 - i % 1_0) // 1_0]
          else:
               count += ones_counts[i // 1_0_0_0] + 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


__A     						= {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

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