





"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       snake_case   :					int = 1_000		)->		int:
       _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 < 1_000:
                     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 // 1_000] + 8
       return count


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













"""simple docstring"""


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_     :      List[str]								=load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
A_							,	A_							,	A_							,	A_     :      List[Any]								=train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

A_     :      str								=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_     :      Any								=[[1, 2, 1, 4], [2, 3, 4, 5]]

A_     :      int								=knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)






