






def    UpperCamelCase__  (   UpperCAmelCase = 1000     )     ->		int:







							"""simple docstring"""





							_a  :    str			=						[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  :    List[str]			=						[0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

							_a  :    Optional[int]			=						0

							for i in range(1    ,	n + 1     ):
														if i < 1000:
																					if i >= 100:
																												# add number of letters for "n hundred"
																												count += ones_counts[i // 100] + 7

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

																					if 0 < i % 100 < 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 % 100]
																					else:
																												# add number of letters for twenty, twenty one, ..., ninety
																												# nine
																												count += ones_counts[i % 10]
																												count += tens_counts[(i % 100 - i % 10) // 10]
														else:
																					count += ones_counts[i // 1000] + 8
							return count


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







import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil






@pytest.fixture
def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Union[str, Any]:







							"""simple docstring"""





							_a  :    Tuple			=						tmp_path / '''text.txt'''
							_a  :    Dict			=						textwrap.dedent(
							    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        '''     )
							with open(UpperCAmelCase    ,	'''w'''    ,	encoding='''utf-8'''     ) as f:
														f.write(UpperCAmelCase     )
							return str(UpperCAmelCase     )






@pytest.fixture
def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Optional[int]:







							"""simple docstring"""





							_a  :    List[Any]			=						tmp_path / '''text_with_image.txt'''
							with open(UpperCAmelCase    ,	'''w'''    ,	encoding='''utf-8'''     ) as f:
														f.write(UpperCAmelCase     )
							return str(UpperCAmelCase     )






@pytest.mark.parametrize('''keep_linebreaks'''    ,	[True, False]     )
def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Any:







							"""simple docstring"""





							with open(UpperCAmelCase    ,	encoding='''utf-8'''     ) as f:
														_a  :    str			=						f.read().splitlines(keepends=UpperCAmelCase     )
							_a  :    Tuple			=						Text(keep_linebreaks=UpperCAmelCase    ,	encoding='''utf-8'''     )
							_a  :    Optional[Any]			=						text._generate_tables([[text_file]]     )
							_a  :    str			=						pa.concat_tables([table for _, table in generator]     ).to_pydict()['''text''']
							assert generated_content == expected_content






@require_pil
def    UpperCamelCase__  (   UpperCAmelCase     )     ->		List[str]:







							"""simple docstring"""





							with open(UpperCAmelCase    ,	encoding='''utf-8'''     ) as f:
														_a  :    str			=						f.read().splitlines()[0]
							_a  :    Dict			=						Text(encoding='''utf-8'''    ,	features=Features({'''image''': Image()}     )     )
							_a  :    str			=						text._generate_tables([[text_file_with_image]]     )
							_a  :    int			=						pa.concat_tables([table for _, table in generator]     )
							assert pa_table.schema.field('''image'''     ).type == Image()()
							_a  :    Optional[int]			=						pa_table.to_pydict()['''image''']
							assert generated_content == [{"path": image_file, "bytes": None}]







@pytest.mark.parametrize('''sample_by'''    ,	['''line''', '''paragraph''', '''document''']     )
def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Optional[Any]:







							"""simple docstring"""





							with open(UpperCAmelCase    ,	encoding='''utf-8'''     ) as f:
														_a  :    Any			=						f.read()
							if sample_by == "line":
														_a  :    Any			=						expected_content.splitlines()
							elif sample_by == "paragraph":
														_a  :    Tuple			=						expected_content.split('''\n\n'''     )
							elif sample_by == "document":
														_a  :    Dict			=						[expected_content]
							_a  :    Dict			=						Text(sample_by=UpperCAmelCase    ,	encoding='''utf-8'''    ,	chunksize=100     )
							_a  :    Optional[int]			=						text._generate_tables([[text_file]]     )
							_a  :    List[Any]			=						pa.concat_tables([table for _, table in generator]     ).to_pydict()['''text''']
							assert generated_content == expected_content