code: 
  
  
  """simple docstring"""
  
  
  
  
  def 			a_   (  lowercase__   :int = 1000    ):
  			__lowerCamelCase        =			[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)
  			__lowerCamelCase        =			[0, 0, 6, 6, 5, 5, 5, 7, 6, 6]
  
  			__lowerCamelCase        =			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())))
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  __magic_name__	:   Optional[Any]          =							{
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   List[Any]          =							[
  					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'FlaubertForMultipleChoice',
  					    'FlaubertForQuestionAnswering',
  					    'FlaubertForQuestionAnsweringSimple',
  					    'FlaubertForSequenceClassification',
  					    'FlaubertForTokenClassification',
  					    'FlaubertModel',
  					    'FlaubertWithLMHeadModel',
  					    'FlaubertPreTrainedModel',
  					]
  
  try:
  					if not is_tf_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   Tuple          =							[
  					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'TFFlaubertForMultipleChoice',
  					    'TFFlaubertForQuestionAnsweringSimple',
  					    'TFFlaubertForSequenceClassification',
  					    'TFFlaubertForTokenClassification',
  					    'TFFlaubertModel',
  					    'TFFlaubertPreTrainedModel',
  					    'TFFlaubertWithLMHeadModel',
  					]
  
  
  if TYPE_CHECKING:
  					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  					from .tokenization_flaubert import FlaubertTokenizer
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_flaubert import (
  										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    FlaubertForMultipleChoice,
  										    FlaubertForQuestionAnswering,
  										    FlaubertForQuestionAnsweringSimple,
  										    FlaubertForSequenceClassification,
  										    FlaubertForTokenClassification,
  										    FlaubertModel,
  										    FlaubertPreTrainedModel,
  										    FlaubertWithLMHeadModel,
  										)
  
  					try:
  										if not is_tf_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_tf_flaubert import (
  										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    TFFlaubertForMultipleChoice,
  										    TFFlaubertForQuestionAnsweringSimple,
  										    TFFlaubertForSequenceClassification,
  										    TFFlaubertForTokenClassification,
  										    TFFlaubertModel,
  										    TFFlaubertPreTrainedModel,
  										    TFFlaubertWithLMHeadModel,
  										)
  
  else:
  					import sys
  
  					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  