





import numpy as np


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		lowerCamelCase :			Optional[Any]    		=np.array(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Dict    		=np.array(SCREAMING_SNAKE_CASE_		)

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

		return score


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		lowerCamelCase :			Tuple    		=np.array(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[Any]    		=np.array(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			Dict    		=predict - actual
		lowerCamelCase :			Dict    		=np.square(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=square_diff.mean()
		return score


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  str:
		lowerCamelCase :			List[Any]    		=np.array(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[str]    		=np.array(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			str    		=predict - actual
		lowerCamelCase :			int    		=np.square(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			str    		=square_diff.mean()
		lowerCamelCase :			Union[str, Any]    		=np.sqrt(SCREAMING_SNAKE_CASE_		)
		return score


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  int:
		lowerCamelCase :			Union[str, Any]    		=np.array(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Tuple    		=np.array(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			Tuple    		=np.log(predict + 1		)
		lowerCamelCase :			str    		=np.log(actual + 1		)

		lowerCamelCase :			str    		=log_predict - log_actual
		lowerCamelCase :			str    		=np.square(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Union[str, Any]    		=square_diff.mean()

		lowerCamelCase :			Union[str, Any]    		=np.sqrt(SCREAMING_SNAKE_CASE_		)

		return score


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		lowerCamelCase :			List[Any]    		=np.array(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Dict    		=np.array(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			Optional[Any]    		=predict - actual
		lowerCamelCase :			Dict    		=np.sum(SCREAMING_SNAKE_CASE_		) / len(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Tuple    		=np.sum(SCREAMING_SNAKE_CASE_		) / len(SCREAMING_SNAKE_CASE_		)
		# print(numerator, denumerator)
		lowerCamelCase :			Tuple    		=float(SCREAMING_SNAKE_CASE_		) / denumerator * 1_0_0

		return score



def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[str]:
		return np.mean(np.array(SCREAMING_SNAKE_CASE_		) == np.array(SCREAMING_SNAKE_CASE_		)		)








import pytest

from datasets.utils.version import Version


@pytest.mark.parametrize(
    '''other, expected_equality'''				,							[
        (Version('''1.0.0'''		), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''		), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]				,							)
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[int]:
		lowerCamelCase :			str    		=Version('''1.0.0'''		)
		assert (version == other) is expected_equality
		assert (version != other) is not expected_equality
		assert (hash(SCREAMING_SNAKE_CASE_		) == hash(SCREAMING_SNAKE_CASE_		)) is expected_equality

