





from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__UpperCamelCase:	List[Any] = TypeVar("""T""")


class       __lowerCAmelCase					(       Generic[T]      ):


	'''simple docstring'''





	_A													=				42  # Cache store of keys
	_A													=				42  # References of the keys in cache
	_A													=				10  # Maximum capacity of cache

	def __init__(						self:						Tuple,					lowerCamelCase_:						int			):
			lowercase__					:      Optional[int]      =		deque()
			lowercase__					:      Optional[Any]      =		set()
			if not n:
					lowercase__					:      Tuple      =		sys.maxsize
			elif n < 0:
					raise ValueError('n should be an integer greater than 0.'			)
			else:
					lowercase__					:      List[Any]      =		n

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						T			):
			if x not in self.key_reference:
					if len(self.dq_store			) == LRUCache._MAX_CAPACITY:
							lowercase__					:      Dict      =		self.dq_store.pop()
							self.key_reference.remove(lowerCamelCase_			)
			else:
					self.dq_store.remove(lowerCamelCase_			)

			self.dq_store.appendleft(lowerCamelCase_			)
			self.key_reference.add(lowerCamelCase_			)

	def    snake_case__(						self:						Tuple			):
			for k in self.dq_store:
					print(lowerCamelCase_			)





	def __repr__(						self:						Any			):
			return F"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store			)}"""


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	LRUCache[str | int] = LRUCache(4)
			lru_cache.refer("""A""")
			lru_cache.refer(2)
			lru_cache.refer(3)
			lru_cache.refer("""A""")
			lru_cache.refer(4)
			lru_cache.refer(5)
			lru_cache.display()

			print(lru_cache)
			assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"









import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict			)   ->							tuple:




		'''simple docstring'''
		return (data["data"], data["target"])


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray			)   ->							np.ndarray:




		'''simple docstring'''
		lowercase__					:      List[str]      =		XGBRegressor(verbosity=0   ,    random_state=42			)
		xgb.fit(_lowercase   ,    _lowercase			)
		# Predict target for test data
		lowercase__					:      Union[str, Any]      =		xgb.predict(_lowercase			)
		lowercase__					:      Tuple      =		predictions.reshape(len(_lowercase			)   ,    1			)
		return predictions


def       SCREAMING_SNAKE_CASE__  (       )   ->							None:




		'''simple docstring'''
		lowercase__					:      List[str]      =		fetch_california_housing()
		lowercase__   ,				lowercase__					:      Optional[int]      =		data_handling(_lowercase			)
		lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[Any]      =		train_test_split(
		    _lowercase   ,    _lowercase   ,    test_size=0.25   ,    random_state=1			)
		lowercase__					:      Any      =		xgboost(_lowercase   ,    _lowercase   ,    _lowercase			)
		# Error printing
		print(f"""Mean Absolute Error : {mean_absolute_error(_lowercase   ,    _lowercase			)}"""			)
		print(f"""Mean Square Error  : {mean_squared_error(_lowercase   ,    _lowercase			)}"""			)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()


