

'''simple docstring'''




import numpy as np


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				str  ):
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )

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

  return score


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Any  ):
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )

  UpperCAmelCase  							=		predict - actual
  UpperCAmelCase  							=		np.square(SCREAMING_SNAKE_CASE  )

  UpperCAmelCase  							=		square_diff.mean()
  return score


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				Optional[int]  ):
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )

  UpperCAmelCase  							=		predict - actual
  UpperCAmelCase  							=		np.square(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		square_diff.mean()
  UpperCAmelCase  							=		np.sqrt(SCREAMING_SNAKE_CASE  )
  return score


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Any    ,				SCREAMING_SNAKE_CASE :				Dict  ):
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )

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

  UpperCAmelCase  							=		log_predict - log_actual
  UpperCAmelCase  							=		np.square(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		square_diff.mean()

  UpperCAmelCase  							=		np.sqrt(SCREAMING_SNAKE_CASE  )

  return score


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				int  ):
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		np.array(SCREAMING_SNAKE_CASE  )

  UpperCAmelCase  							=		predict - actual
  UpperCAmelCase  							=		np.sum(SCREAMING_SNAKE_CASE  ) / len(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		np.sum(SCREAMING_SNAKE_CASE  ) / len(SCREAMING_SNAKE_CASE  )
  # print(numerator, denumerator)
  UpperCAmelCase  							=		float(SCREAMING_SNAKE_CASE  ) / denumerator * 100

  return score



def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[Any]    ,				SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
  return np.mean(np.array(SCREAMING_SNAKE_CASE  ) == np.array(SCREAMING_SNAKE_CASE  )  )





'''simple docstring'''




from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a			:	Optional[int]								=  TypeVar('DatasetType', Dataset, IterableDataset)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				DatasetType    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				int  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    return _split_by_node_map_style_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )
  else:
    return _split_by_node_iterable_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )


