


import numpy as np
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:str   ,    lowercase__	:Any					)							->	List[Any]:
						__magic_name__					:				List[Any]				=   np.array(lowerCAmelCase_					)
						__magic_name__					:				Union[str, Any]				=   np.array(lowerCAmelCase_					)

						__magic_name__					:				List[Any]				=   abs(predict - actual					)
						__magic_name__					:				int				=   difference.mean()

						return score
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:List[str]   ,    lowercase__	:Union[str, Any]					)							->	List[Any]:
						__magic_name__					:				Union[str, Any]				=   np.array(lowerCAmelCase_					)
						__magic_name__					:				Union[str, Any]				=   np.array(lowerCAmelCase_					)

						__magic_name__					:				str				=   predict - actual
						__magic_name__					:				Union[str, Any]				=   np.square(lowerCAmelCase_					)

						__magic_name__					:				Tuple				=   square_diff.mean()
						return score
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Optional[int]   ,    lowercase__	:Any					)							->	Union[str, Any]:
						__magic_name__					:				int				=   np.array(lowerCAmelCase_					)
						__magic_name__					:				Optional[int]				=   np.array(lowerCAmelCase_					)

						__magic_name__					:				Optional[int]				=   predict - actual
						__magic_name__					:				Optional[Any]				=   np.square(lowerCAmelCase_					)
						__magic_name__					:				Any				=   square_diff.mean()
						__magic_name__					:				int				=   np.sqrt(lowerCAmelCase_					)
						return score
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict   ,    lowercase__	:str					)							->	List[str]:
						__magic_name__					:				str				=   np.array(lowerCAmelCase_					)
						__magic_name__					:				int				=   np.array(lowerCAmelCase_					)

						__magic_name__					:				List[Any]				=   np.log(predict + 1					)
						__magic_name__					:				List[Any]				=   np.log(actual + 1					)

						__magic_name__					:				Optional[Any]				=   log_predict - log_actual
						__magic_name__					:				int				=   np.square(lowerCAmelCase_					)
						__magic_name__					:				str				=   square_diff.mean()

						__magic_name__					:				List[Any]				=   np.sqrt(lowerCAmelCase_					)

						return score
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Union[str, Any]   ,    lowercase__	:int					)							->	Any:
						__magic_name__					:				List[Any]				=   np.array(lowerCAmelCase_					)
						__magic_name__					:				Union[str, Any]				=   np.array(lowerCAmelCase_					)

						__magic_name__					:				List[str]				=   predict - actual
						__magic_name__					:				Tuple				=   np.sum(lowerCAmelCase_					) / len(lowerCAmelCase_					)
						__magic_name__					:				Dict				=   np.sum(lowerCAmelCase_					) / len(lowerCAmelCase_					)
						# print(numerator, denumerator)
						__magic_name__					:				Optional[int]				=   float(lowerCAmelCase_					) / denumerator * 1_0_0

						return score

def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:List[str]   ,    lowercase__	:Any					)							->	Any:
						return np.mean(np.array(lowerCAmelCase_					) == np.array(lowerCAmelCase_					)					)






def       lowerCAmelCase			(		lowerCAmelCase_			)->			int:
				lowerCAmelCase_			:   Optional[int]     					=					len(lowerCAmelCase_			)
				if n in (0, 1):
								return 0
				return sum(query * (n - i - 1) for i, query in enumerate(sorted(lowerCAmelCase_			)			)			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()