import torch

from ..utils import _log_api_usage_once
from ._utils import _upcast_non_float, _loss_inter_union


def generalized_box_iou_loss(
    boxes1: torch.Tensor,
    boxes2: torch.Tensor,
    reduction: str = "none",
    eps: float = 1e-7,
) -> torch.Tensor:

    """
    Gradient-friendly IoU loss with an additional penalty that is non-zero when the
    boxes do not overlap and scales with the size of their smallest enclosing box.
    This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.

    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
    ``0 <= x1 < x2`` and ``0 <= y1 < y2``, and The two boxes should have the
    same dimensions.

    Args:
        boxes1 (Tensor[N, 4] or Tensor[4]): first set of boxes
        boxes2 (Tensor[N, 4] or Tensor[4]): second set of boxes
        reduction (string, optional): Specifies the reduction to apply to the output:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: No reduction will be
            applied to the output. ``'mean'``: The output will be averaged.
            ``'sum'``: The output will be summed. Default: ``'none'``
        eps (float): small number to prevent division by zero. Default: 1e-7

    Returns:
        Tensor: Loss tensor with the reduction option applied.

    Reference:
        Hamid Rezatofighi et. al: Generalized Intersection over Union:
        A Metric and A Loss for Bounding Box Regression:
        https://arxiv.org/abs/1902.09630
    """

    # Original implementation from https://github.com/facebookresearch/fvcore/blob/bfff2ef/fvcore/nn/giou_loss.py

    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
        _log_api_usage_once(generalized_box_iou_loss)

    boxes1 = _upcast_non_float(boxes1)
    boxes2 = _upcast_non_float(boxes2)
    intsctk, unionk = _loss_inter_union(boxes1, boxes2)
    iouk = intsctk / (unionk + eps)

    x1, y1, x2, y2 = boxes1.unbind(dim=-1)
    x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)

    # smallest enclosing box
    xc1 = torch.min(x1, x1g)
    yc1 = torch.min(y1, y1g)
    xc2 = torch.max(x2, x2g)
    yc2 = torch.max(y2, y2g)

    area_c = (xc2 - xc1) * (yc2 - yc1)
    miouk = iouk - ((area_c - unionk) / (area_c + eps))

    loss = 1 - miouk

    if reduction == "mean":
        loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum()
    elif reduction == "sum":
        loss = loss.sum()

    return loss
