import torch
import torch.nn as nn


def smoothl1_loss(error, delta=1.0):
    """Smooth L1 loss.
    x = error = pred - gt or dist(pred,gt)
    0.5 * |x|^2                 if |x|<=d
    |x| - 0.5 * d               if |x|>d
    """
    diff = torch.abs(error)
    loss = torch.where(diff < delta, 0.5 * diff * diff / delta, diff - 0.5 * delta)
    return loss


def l1_loss(error):
    loss = torch.abs(error)
    return loss


class SigmoidFocalClassificationLoss(nn.Module):
    """
    Sigmoid focal cross entropy loss.
    """

    def __init__(self, gamma: float = 2.0, alpha: float = 0.25):
        """
        Args:
            gamma: Weighting parameter to balance loss for hard and easy examples.
            alpha: Weighting parameter to balance loss for positive and negative examples.
        """
        super(SigmoidFocalClassificationLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma

    @staticmethod
    def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor):
        """ PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits:
            max(x, 0) - x * z + log(1 + exp(-abs(x))) in
            https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits

        Args:
            input: (B, #proposals, #classes) float tensor.
                Predicted logits for each class
            target: (B, #proposals, #classes) float tensor.
                One-hot encoded classification targets

        Returns:
            loss: (B, #proposals, #classes) float tensor.
                Sigmoid cross entropy loss without reduction
        """
        loss = torch.clamp(input, min=0) - input * target + \
               torch.log1p(torch.exp(-torch.abs(input)))
        return loss

    def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor):
        """
        Args:
            input: (B, #proposals, #classes) float tensor.
                Predicted logits for each class
            target: (B, #proposals, #classes) float tensor.
                One-hot encoded classification targets
            weights: (B, #proposals) float tensor.
                Anchor-wise weights.

        Returns:
            weighted_loss: (B, #proposals, #classes) float tensor after weighting.
        """
        pred_sigmoid = torch.sigmoid(input)
        alpha_weight = target * self.alpha + (1 - target) * (1 - self.alpha)
        pt = target * (1.0 - pred_sigmoid) + (1.0 - target) * pred_sigmoid
        focal_weight = alpha_weight * torch.pow(pt, self.gamma)

        bce_loss = self.sigmoid_cross_entropy_with_logits(input, target)

        loss = focal_weight * bce_loss

        weights = weights.unsqueeze(-1)
        assert weights.shape.__len__() == loss.shape.__len__()

        return loss * weights
