import math

import torch
import torch.nn as nn

from ..utils.loss_utils import weighted_loss

@weighted_loss
def giou_loss(pred, target, eps=1e-7):
    r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding
    Box Regression <https://arxiv.org/abs/1902.09630>`_.

    Args:
        pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
            shape (n, 4).
        target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
        eps (float): Eps to avoid log(0).

    Return:
        Tensor: Loss tensor.
    """

    # overlap
    pred = pred.float()
    target = target.float()

    lt = torch.max(pred[:, :2], target[:, :2])
    rb = torch.min(pred[:, 2:], target[:, 2:])
    wh = torch.abs(rb - lt).clamp(min=0)
    overlap = wh[:, 0] * wh[:, 1]
    # union
    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
    union = ap + ag - overlap + eps
    # IoU
    ious = overlap / union

    # enclose area
    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(0)

    enclose_area = enclose_wh[:, 0] * enclose_wh[:, 1] + eps

    # GIoU
    gious = ious - (enclose_area - union) / enclose_area
    loss = 1 - gious
    return loss


@weighted_loss
def diou_loss(pred, target, eps=1e-7):
    r"""`Implementation of Distance-IoU Loss: Faster and Better
    Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.

    Code is modified from https://github.com/Zzh-tju/DIoU.

    Args:
        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
            shape (n, 4).
        target (Tensor): Corresponding gt bboxes, shape (n, 4).
        eps (float): Eps to avoid log(0).
    Return:
        Tensor: Loss tensor.
    """
    # overlap
    lt = torch.max(pred[:, :2], target[:, :2])
    rb = torch.min(pred[:, 2:], target[:, 2:])
    wh = (rb - lt).clamp(min=0)
    overlap = wh[:, 0] * wh[:, 1]

    # union
    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
    union = ap + ag - overlap + eps

    # IoU
    ious = overlap / union

    # enclose area
    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)

    cw = enclose_wh[:, 0]
    ch = enclose_wh[:, 1]

    c2 = cw**2 + ch**2 + eps

    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
    b2_x1, b2_y1 = target[:, 0], target[:, 1]
    b2_x2, b2_y2 = target[:, 2], target[:, 3]

    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
    rho2 = left + right

    # DIoU
    dious = ious - rho2 / c2
    loss = 1 - dious
    return loss


@weighted_loss
def ciou_loss(pred, target, eps=1e-7):
    r"""`Implementation of paper `Enhancing Geometric Factors into
    Model Learning and Inference for Object Detection and Instance
    Segmentation <https://arxiv.org/abs/2005.03572>`_.

    Code is modified from https://github.com/Zzh-tju/CIoU.

    Args:
        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
            shape (n, 4).
        target (Tensor): Corresponding gt bboxes, shape (n, 4).
        eps (float): Eps to avoid log(0).
    Return:
        Tensor: Loss tensor.
    """
    # overlap
    lt = torch.max(pred[:, :2], target[:, :2])
    rb = torch.min(pred[:, 2:], target[:, 2:])
    wh = (rb - lt).clamp(min=0)
    overlap = wh[:, 0] * wh[:, 1]

    # union
    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
    union = ap + ag - overlap + eps

    # IoU
    ious = overlap / union

    # enclose area
    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)

    cw = enclose_wh[:, 0]
    ch = enclose_wh[:, 1]

    c2 = cw**2 + ch**2 + eps

    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
    b2_x1, b2_y1 = target[:, 0], target[:, 1]
    b2_x2, b2_y2 = target[:, 2], target[:, 3]

    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps

    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
    rho2 = left + right

    factor = 4 / math.pi**2
    v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)

    # CIoU
    cious = ious - (rho2 / c2 + v**2 / (1 - ious + v))
    loss = 1 - cious
    return loss

class GIoULoss(nn.Module):

    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
        super(GIoULoss, self).__init__()
        self.eps = eps
        self.reduction = reduction
        self.loss_weight = loss_weight

    def forward(self,
                pred,
                target,
                weight=None,
                avg_factor=None,
                reduction_override=None,
                **kwargs):
        if weight is not None and not torch.any(weight > 0):
            return (pred * weight).sum()  # 0
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (
            reduction_override if reduction_override else self.reduction)
        if weight is not None and weight.dim() > 1:
            # TODO: remove this in the future
            # reduce the weight of shape (n, 4) to (n,) to match the
            # giou_loss of shape (n,)
            assert weight.shape == pred.shape
            weight = weight.mean(-1)

        loss = self.loss_weight * giou_loss(
            pred,
            target,
            weight,
            eps=self.eps,
            reduction=reduction,
            avg_factor=avg_factor,
            **kwargs)
        return loss


class DIoULoss(nn.Module):

    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
        super(DIoULoss, self).__init__()
        self.eps = eps
        self.reduction = reduction
        self.loss_weight = loss_weight

    def forward(self,
                pred,
                target,
                weight=None,
                avg_factor=None,
                reduction_override=None,
                **kwargs):
        if weight is not None and not torch.any(weight > 0):
            return (pred * weight).sum()  # 0
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (
            reduction_override if reduction_override else self.reduction)
        if weight is not None and weight.dim() > 1:
            # TODO: remove this in the future
            # reduce the weight of shape (n, 4) to (n,) to match the
            # giou_loss of shape (n,)
            assert weight.shape == pred.shape
            weight = weight.mean(-1)
        loss = self.loss_weight * diou_loss(
            pred,
            target,
            weight,
            eps=self.eps,
            reduction=reduction,
            avg_factor=avg_factor,
            **kwargs)
        return loss


class CIoULoss(nn.Module):

    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
        super(CIoULoss, self).__init__()
        self.eps = eps
        self.reduction = reduction
        self.loss_weight = loss_weight

    def forward(self,
                pred,
                target,
                weight=None,
                avg_factor=None,
                reduction_override=None,
                **kwargs):
        if weight is not None and not torch.any(weight > 0):
            return (pred * weight).sum()  # 0
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (
            reduction_override if reduction_override else self.reduction)
        if weight is not None and weight.dim() > 1:
            # TODO: remove this in the future
            # reduce the weight of shape (n, 4) to (n,) to match the
            # giou_loss of shape (n,)
            assert weight.shape == pred.shape
            weight = weight.mean(-1)
        loss = self.loss_weight * ciou_loss(
            pred,
            target,
            weight,
            eps=self.eps,
            reduction=reduction,
            avg_factor=avg_factor,
            **kwargs)
        return loss