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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# TODO(Dallas) Introduce Ensemble RMSE and MSE routines.

import torch
from typing import Union

Tensor = torch.Tensor


def mse(pred: Tensor, target: Tensor, dim: int = None) -> Union[Tensor, float]:
    """Calculates Mean Squared error between two tensors

    Parameters
    ----------
    pred : Tensor
        Input prediction tensor
    target : Tensor
        Target tensor
    dim : int, optional
        Reduction dimension. When None the losses are averaged or summed over all
        observations, by default None

    Returns
    -------
    Union[Tensor, float]
        Mean squared error value(s)
    """
    return torch.mean((pred - target) ** 2, dim=dim)


def rmse(pred: Tensor, target: Tensor, dim: int = None) -> Union[Tensor, float]:
    """Calculates Root mean Squared error between two tensors

    Parameters
    ----------
    pred : Tensor
        Input prediction tensor
    target : Tensor
        Target tensor
    dim : int, optional
        Reduction dimension. When None the losses are averaged or summed over all
        observations, by default None

    Returns
    -------
    Union[Tensor, float]
        Root mean squared error value(s)
    """
    return torch.sqrt(mse(pred, target, dim=dim))
