

import numpy as np







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= np.array(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= np.array(_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				Dict							= abs(predict - actual					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= difference.mean()

						return score







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= np.array(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Optional[int]							= np.array(_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				List[Any]							= predict - actual
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= np.square(_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				List[str]							= square_diff.mean()
						return score







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= np.array(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				str							= np.array(_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				List[str]							= predict - actual
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= np.square(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Tuple							= square_diff.mean()
						SCREAMING_SNAKE_CASE_:				Dict							= np.sqrt(_UpperCAmelCase					)
						return score







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				int							= np.array(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				str							= np.array(_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				Tuple							= np.log(predict + 1					)
						SCREAMING_SNAKE_CASE_:				int							= np.log(actual + 1					)

						SCREAMING_SNAKE_CASE_:				Tuple							= log_predict - log_actual
						SCREAMING_SNAKE_CASE_:				Tuple							= np.square(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				List[Any]							= square_diff.mean()

						SCREAMING_SNAKE_CASE_:				int							= np.sqrt(_UpperCAmelCase					)

						return score







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Tuple							= np.array(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= np.array(_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				List[str]							= predict - actual
						SCREAMING_SNAKE_CASE_:				List[str]							= np.sum(_UpperCAmelCase					) / len(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				str							= np.sum(_UpperCAmelCase					) / len(_UpperCAmelCase					)
						# print(numerator, denumerator)
						SCREAMING_SNAKE_CASE_:				Tuple							= float(_UpperCAmelCase					) / denumerator * 1_00

						return score







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						return np.mean(np.array(_UpperCAmelCase					) == np.array(_UpperCAmelCase					)					)









def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= []
						SCREAMING_SNAKE_CASE_:				Any							= 1

						for i in range(len(_UpperCAmelCase					)					):
												if i + 1 < len(_UpperCAmelCase					) and text[i] == text[i + 1]:
																		count += 1
												else:
																		encoded.append((text[i], count)					)
																		SCREAMING_SNAKE_CASE_:				List[Any]							= 1

						return encoded







def     A_   (					_UpperCAmelCase					):
						return "".join(char * length for char, length in encoded					)


if __name__ == "__main__":
						from doctest import testmod

						testmod(name="""run_length_encode""", verbose=True)
						testmod(name="""run_length_decode""", verbose=True)






