# Copyright (c) OpenMMLab. All rights reserved.
import torch

from ..utils import ext_loader

ext_module = ext_loader.load_ext('_ext', ['bbox_overlaps'])


def _bbox_overlaps_cpu(bboxes1: torch.Tensor,
                       bboxes2: torch.Tensor,
                       mode: str = 'iou',
                       aligned: bool = False,
                       offset: int = 0) -> torch.Tensor:
    assert mode in ['iou', 'iof']

    if aligned:
        lt = torch.max(bboxes1[:, :2], bboxes2[:, :2])  # [rows, 2]
        rb = torch.min(bboxes1[:, 2:], bboxes2[:, 2:])  # [rows, 2]

        wh = (rb - lt + offset).clamp(min=0)  # [rows, 2]
        overlap = wh[:, 0] * wh[:, 1]
        area1 = (bboxes1[:, 2] - bboxes1[:, 0] + offset) * (
            bboxes1[:, 3] - bboxes1[:, 1] + offset)

        if mode == 'iou':
            area2 = (bboxes2[:, 2] - bboxes2[:, 0] + offset) * (
                bboxes2[:, 3] - bboxes2[:, 1] + offset)
            ious = overlap / (area1 + area2 - overlap)
        else:
            ious = overlap / area1
    else:
        lt = torch.max(bboxes1[:, None, :2], bboxes2[:, :2])  # [rows, cols, 2]
        rb = torch.min(bboxes1[:, None, 2:], bboxes2[:, 2:])  # [rows, cols, 2]

        wh = (rb - lt + offset).clamp(min=0)  # [rows, cols, 2]
        overlap = wh[:, :, 0] * wh[:, :, 1]
        area1 = (bboxes1[:, 2] - bboxes1[:, 0] + offset) * (
            bboxes1[:, 3] - bboxes1[:, 1] + offset)

        if mode == 'iou':
            area2 = (bboxes2[:, 2] - bboxes2[:, 0] + offset) * (
                bboxes2[:, 3] - bboxes2[:, 1] + offset)
            ious = overlap / (area1[:, None] + area2 - overlap)
        else:
            ious = overlap / (area1[:, None])

    return ious


def bbox_overlaps(bboxes1: torch.Tensor,
                  bboxes2: torch.Tensor,
                  mode: str = 'iou',
                  aligned: bool = False,
                  offset: int = 0) -> torch.Tensor:
    """Calculate overlap between two set of bboxes.

    If ``aligned`` is ``False``, then calculate the ious between each bbox
    of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
    bboxes1 and bboxes2.

    Args:
        bboxes1 (torch.Tensor): shape (m, 4) in <x1, y1, x2, y2> format or
            empty.
        bboxes2 (torch.Tensor): shape (n, 4) in <x1, y1, x2, y2> format or
            empty. If aligned is ``True``, then m and n must be equal.
        mode (str): "iou" (intersection over union) or iof (intersection over
            foreground).

    Returns:
        torch.Tensor: Return the ious betweens boxes. If ``aligned`` is
        ``False``, the shape of ious is (m, n) else (m, 1).

    Example:
        >>> bboxes1 = torch.FloatTensor([
        >>>     [0, 0, 10, 10],
        >>>     [10, 10, 20, 20],
        >>>     [32, 32, 38, 42],
        >>> ])
        >>> bboxes2 = torch.FloatTensor([
        >>>     [0, 0, 10, 20],
        >>>     [0, 10, 10, 19],
        >>>     [10, 10, 20, 20],
        >>> ])
        >>> bbox_overlaps(bboxes1, bboxes2)
        tensor([[0.5000, 0.0000, 0.0000],
                [0.0000, 0.0000, 1.0000],
                [0.0000, 0.0000, 0.0000]])

    Example:
        >>> empty = torch.FloatTensor([])
        >>> nonempty = torch.FloatTensor([
        >>>     [0, 0, 10, 9],
        >>> ])
        >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
        >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
        >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
    """

    mode_dict = {'iou': 0, 'iof': 1}
    assert mode in mode_dict.keys()
    mode_flag = mode_dict[mode]
    # Either the boxes are empty or the length of boxes' last dimension is 4
    assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0)
    assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0)
    assert offset == 1 or offset == 0

    rows = bboxes1.size(0)
    cols = bboxes2.size(0)

    if aligned:
        assert rows == cols
        ious = bboxes1.new_zeros(rows)
    else:
        ious = bboxes1.new_zeros((rows, cols))

    if rows * cols == 0:
        return ious

    if bboxes1.device.type == 'cpu' and torch.__version__ == 'parrots':
        return _bbox_overlaps_cpu(
            bboxes1, bboxes2, mode=mode, aligned=aligned, offset=offset)

    ext_module.bbox_overlaps(
        bboxes1, bboxes2, ious, mode=mode_flag, aligned=aligned, offset=offset)

    return ious