
import numpy as np




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int		,      SCREAMING_SNAKE_CASE_  :  Optional[Any]					)     ->    Optional[Any]:
						_lowercase									=				np.array(__A					)
						_lowercase									=				np.array(__A					)

						_lowercase									=				abs(predict - actual					)
						_lowercase									=				difference.mean()

						return score




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Optional[Any]		,      SCREAMING_SNAKE_CASE_  :  List[Any]					)     ->    str:
						_lowercase									=				np.array(__A					)
						_lowercase									=				np.array(__A					)

						_lowercase									=				predict - actual
						_lowercase									=				np.square(__A					)

						_lowercase									=				square_diff.mean()
						return score




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Optional[int]		,      SCREAMING_SNAKE_CASE_  :  Tuple					)     ->    List[Any]:
						_lowercase									=				np.array(__A					)
						_lowercase									=				np.array(__A					)

						_lowercase									=				predict - actual
						_lowercase									=				np.square(__A					)
						_lowercase									=				square_diff.mean()
						_lowercase									=				np.sqrt(__A					)
						return score




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  List[Any]		,      SCREAMING_SNAKE_CASE_  :  Optional[Any]					)     ->    Any:
						_lowercase									=				np.array(__A					)
						_lowercase									=				np.array(__A					)

						_lowercase									=				np.log(predict + 1					)
						_lowercase									=				np.log(actual + 1					)

						_lowercase									=				log_predict - log_actual
						_lowercase									=				np.square(__A					)
						_lowercase									=				square_diff.mean()

						_lowercase									=				np.sqrt(__A					)

						return score




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Dict		,      SCREAMING_SNAKE_CASE_  :  Optional[Any]					)     ->    Optional[int]:
						_lowercase									=				np.array(__A					)
						_lowercase									=				np.array(__A					)

						_lowercase									=				predict - actual
						_lowercase									=				np.sum(__A					) / len(__A					)
						_lowercase									=				np.sum(__A					) / len(__A					)
						# print(numerator, denumerator)
						_lowercase									=				float(__A					) / denumerator * 1_00

						return score




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  List[Any]		,      SCREAMING_SNAKE_CASE_  :  List[Any]					)     ->    Any:
						return np.mean(np.array(__A					) == np.array(__A					)					)

import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A		:   Optional[int]         =    TypeVar('''T''', bound='''TaskTemplate''')



@dataclass(frozen=_a						)
class       a_   ( abc.ABC						):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			a  : str
			a  : ClassVar[Features]
			a  : ClassVar[Features]






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									return copy.deepcopy(self     )






			@property
			def       UpperCamelCase_							(			self     ):
									return Features(**self.input_schema							, **self.label_schema     )






			@property
			@abc.abstractmethod
			def       UpperCamelCase_							(			self     ):
									raise NotImplementedError






			@classmethod
			def       UpperCamelCase_							(			cls							, __UpperCamelCase     ):
									_lowercase									=				{f.name for f in dataclasses.fields(cls     )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}     )