


def   _lowerCAmelCase				(   ) ->   int:





	"""simple docstring"""







	return 1





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	return 0 if x < 0 else two_pence(x - 2						) + one_pence()





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]						) ->   int:





	"""simple docstring"""







	return 0 if x < 0 else five_pence(x - 5						) + two_pence(_SCREAMING_SNAKE_CASE						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   int:





	"""simple docstring"""







	return 0 if x < 0 else ten_pence(x - 10						) + five_pence(_SCREAMING_SNAKE_CASE						)





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	return 0 if x < 0 else twenty_pence(x - 20						) + ten_pence(_SCREAMING_SNAKE_CASE						)





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]						) ->   int:





	"""simple docstring"""







	return 0 if x < 0 else fifty_pence(x - 50						) + twenty_pence(_SCREAMING_SNAKE_CASE						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   int:





	"""simple docstring"""







	return 0 if x < 0 else one_pound(x - 1_00						) + fifty_pence(_SCREAMING_SNAKE_CASE						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]						) ->   int:





	"""simple docstring"""







	return 0 if x < 0 else two_pound(x - 2_00						) + one_pound(_SCREAMING_SNAKE_CASE						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any] = 2_00						) ->   int:





	"""simple docstring"""







	return two_pound(_SCREAMING_SNAKE_CASE						)


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







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







	return np.linalg.norm(np.array(UpperCamelCase__						) - np.array(UpperCamelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







	A								=				zip(UpperCamelCase__							,					UpperCamelCase__						)
	# List of distances of all points from the point to be classified
	A								=				[]
	for data_point in data:
		A								=				euclidean_distance(data_point[0]							,					UpperCamelCase__						)
		distances.append((distance, data_point[1])						)
	# Choosing 'k' points with the least distances.
	A								=				[i[1] for i in sorted(UpperCamelCase__						)[:k]]
	# Most commonly occurring class among them
	# is the class into which the point is classified
	A								=				Counter(UpperCamelCase__						).most_common(1						)[0][0]
	return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



