lowerCamelCase_    :		Tuple							=	[0, 2, 4, 6, 8]
lowerCamelCase_    :		Any							=	[1, 3, 5, 7, 9]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					if remaining_length == 0:
										if digits[0] == 0 or digits[-1] == 0:
															return 0

										for i in range(length // 2 - 1				,    -1				,    -1						):
															remainder += digits[i] + digits[length - i - 1]

															if remainder % 2 == 0:
																				return 0

															remainder //= 10

										return 1

					if remaining_length == 1:
										if remainder % 2 == 0:
															return 0

										__a		 =       0
										for digit in range(10						):
															__a		 =       digit
															result += reversible_numbers(
															    0				,    (remainder + 2 * digit) // 10				,    __lowerCamelCase				,    __lowerCamelCase						)
										return result

					__a		 =       0
					for digita in range(10						):
										__a		 =       digita

										if (remainder + digita) % 2 == 0:
															__a		 =       ODD_DIGITS
										else:
															__a		 =       EVEN_DIGITS

										for digita in other_parity_digits:
															__a		 =       digita
															result += reversible_numbers(
															    remaining_length - 2				,    (remainder + digita + digita) // 10				,    __lowerCamelCase				,    __lowerCamelCase				,    )
					return result
def 							lowerCAmelCase(     __lowerCamelCase = 9						):
					__a		 =       0
					for length in range(1				,    max_power + 1						):
										result += reversible_numbers(__lowerCamelCase				,    0				,    [0] * length				,    __lowerCamelCase						)
					return result


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





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 							lowerCAmelCase(     __lowerCamelCase						):
					return (data["data"], data["target"])
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       XGBRegressor(verbosity=0				,    random_state=42						)
					xgb.fit(__lowerCamelCase				,    __lowerCamelCase						)
					# Predict target for test data
					__a		 =       xgb.predict(__lowerCamelCase						)
					__a		 =       predictions.reshape(len(__lowerCamelCase						)				,    1						)
					return predictions
def 							lowerCAmelCase(     ):
					__a		 =       fetch_california_housing()
					__a ,				__a		 =       data_handling(__lowerCamelCase						)
					__a ,				__a ,				__a ,				__a		 =       train_test_split(
					    __lowerCamelCase				,    __lowerCamelCase				,    test_size=0.25				,    random_state=1						)
					__a		 =       xgboost(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)
					# Error printing
					print(f'''Mean Absolute Error : {mean_absolute_error(__lowerCamelCase				,    __lowerCamelCase						)}'''						)
					print(f'''Mean Square Error  : {mean_squared_error(__lowerCamelCase				,    __lowerCamelCase						)}'''						)


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()




