import functools

import torch.nn.functional as F


def reduce_loss(loss, reduction):
    """Reduce loss as specified.

    Args:
        loss (Tensor): Elementwise loss tensor.
        reduction (str): Options are "none", "mean" and "sum".

    Returns:
        Tensor: Reduced loss tensor.
    """
    reduction_enum = F._Reduction.get_enum(reduction)
    # none: 0, elementwise_mean:1, sum: 2
    if reduction_enum == 0:
        return loss
    if reduction_enum == 1:
        return loss.mean()

    return loss.sum()


def mask_reduce_loss(loss, weight=None, reduction='mean', sample_wise=False):
    """Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights. Default: None.
        reduction (str): Same as built-in losses of PyTorch. Options are
            "none", "mean" and "sum". Default: 'mean'.
        sample_wise (bool): Whether calculate the loss sample-wise. This
            argument only takes effect when `reduction` is 'mean' and `weight`
            (argument of `forward()`) is not None. It will first reduces loss
            with 'mean' per-sample, and then it means over all the samples.
            Default: False.

    Returns:
        Tensor: Processed loss values.
    """
    # if weight is specified, apply element-wise weight
    if weight is not None:
        assert weight.dim() == loss.dim()
        assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
        loss = loss * weight

    # if weight is not specified or reduction is sum, just reduce the loss
    if weight is None or reduction == 'sum':
        loss = reduce_loss(loss, reduction)
    # if reduction is mean, then compute mean over masked region
    elif reduction == 'mean':
        # expand weight from N1HW to NCHW
        if weight.size(1) == 1:
            weight = weight.expand_as(loss)
        # small value to prevent division by zero
        eps = 1e-12

        # perform sample-wise mean
        if sample_wise:
            weight = weight.sum(dim=[1, 2, 3], keepdim=True)  # NCHW to N111
            loss = (loss / (weight + eps)).sum() / weight.size(0)
        # perform pixel-wise mean
        else:
            loss = loss.sum() / (weight.sum() + eps)

    return loss


def masked_loss(loss_func):
    """Create a masked version of a given loss function.

    To use this decorator, the loss function must have the signature like
    `loss_func(pred, target, **kwargs)`. The function only needs to compute
    element-wise loss without any reduction. This decorator will add weight
    and reduction arguments to the function. The decorated function will have
    the signature like `loss_func(pred, target, weight=None, reduction='mean',
    avg_factor=None, **kwargs)`.

    :Example:

    >>> import torch
    >>> @masked_loss
    >>> def l1_loss(pred, target):
    >>>     return (pred - target).abs()

    >>> pred = torch.Tensor([0, 2, 3])
    >>> target = torch.Tensor([1, 1, 1])
    >>> weight = torch.Tensor([1, 0, 1])

    >>> l1_loss(pred, target)
    tensor(1.3333)
    >>> l1_loss(pred, target, weight)
    tensor(1.5000)
    >>> l1_loss(pred, target, reduction='none')
    tensor([1., 1., 2.])
    >>> l1_loss(pred, target, weight, reduction='sum')
    tensor(3.)
    """

    @functools.wraps(loss_func)
    def wrapper(pred,
                target,
                weight=None,
                reduction='mean',
                sample_wise=False,
                **kwargs):
        # get element-wise loss
        loss = loss_func(pred, target, **kwargs)
        loss = mask_reduce_loss(loss, weight, reduction, sample_wise)
        return loss

    return wrapper
