


def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int = 4_0_0_0_0_0_0				)       ->  int:

						if n <= 1:
												return 0
						__magic_name__				:							str				=      0
						__magic_name__				:							Optional[int]				=      2
						__magic_name__				:							Optional[Any]				=      0
						while 4 * b + a <= n:
												__magic_name__		, __magic_name__				:							Union[str, Any]				=      b, 4 * b + a
												count += a
						return count + b


if __name__ == "__main__":
			print(F"""{solution() = }""")






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

# Load iris file
snake_case__     							= 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
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

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


