





from sklearn.metrics import accuracy_score

import datasets


a__			:    Tuple			      =		'\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


a__			:    str			      =		'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


a__			:    Dict			      =		'\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,		_KWARGS_DESCRIPTION   )
class UpperCAmelCase_							(   datasets.Metric   ):


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       return datasets.MetricInfo(
           description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(
               {
                   '''predictions''': datasets.Sequence(datasets.Value('''int32'''   )   ),
                   '''references''': datasets.Sequence(datasets.Value('''int32'''   )   ),
               }
               if self.config_name == '''multilabel'''
               else {
                   '''predictions''': datasets.Value('''int32'''   ),
                   '''references''': datasets.Value('''int32'''   ),
               }   )   ,reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']   ,)




  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ,__snake_case=True   ,__snake_case=None   ):



       """simple docstring"""





       return {
           "accuracy": float(
               accuracy_score(__snake_case   ,__snake_case   ,normalize=__snake_case   ,sample_weight=__snake_case   )   )
       }










def     UpperCAmelCase_						(						_UpperCAmelCase       :int = 1000							)  ->							int:
     '''simple docstring'''


     A_					      =					[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_					      =					[0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

     A_					      =					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())))



