import torch

from . import roiaware_pool3d_ext


def points_in_boxes_gpu(points, boxes):
    """Find points that are in boxes (CUDA)

    Args:
        points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR coordinate
        boxes (torch.Tensor): [B, T, 7],
            num_valid_boxes <= T, [x, y, z, w, l, h, ry] in LiDAR coordinate,
            (x, y, z) is the bottom center

    Returns:
        box_idxs_of_pts (torch.Tensor): (B, M), default background = -1
    """
    assert boxes.shape[0] == points.shape[0]
    assert boxes.shape[2] == 7
    batch_size, num_points, _ = points.shape

    box_idxs_of_pts = points.new_zeros((batch_size, num_points),
                                       dtype=torch.int).fill_(-1)

    # If manually put the tensor 'points' or 'boxes' on a device
    # which is not the current device, some temporary variables
    # will be created on the current device in the cuda op,
    # and the output will be incorrect.
    # Therefore, we force the current device to be the same
    # as the device of the tensors if it was not.
    # Please refer to https://github.com/open-mmlab/mmdetection3d/issues/305
    # for the incorrect output before the fix.
    points_device = points.get_device()
    assert points_device == boxes.get_device(), \
        'Points and boxes should be put on the same device'
    if torch.cuda.current_device() != points_device:
        torch.cuda.set_device(points_device)

    roiaware_pool3d_ext.points_in_boxes_gpu(boxes.contiguous(),
                                            points.contiguous(),
                                            box_idxs_of_pts)

    return box_idxs_of_pts


def points_in_boxes_cpu(points, boxes):
    """Find points that are in boxes (CPU)

    Note:
        Currently, the output of this function is different from that of
        points_in_boxes_gpu.

    Args:
        points (torch.Tensor): [npoints, 3]
        boxes (torch.Tensor): [N, 7], in LiDAR coordinate,
            (x, y, z) is the bottom center

    Returns:
        point_indices (torch.Tensor): (N, npoints)
    """
    # TODO: Refactor this function as a CPU version of points_in_boxes_gpu
    assert boxes.shape[1] == 7
    assert points.shape[1] == 3

    point_indices = points.new_zeros((boxes.shape[0], points.shape[0]),
                                     dtype=torch.int)
    roiaware_pool3d_ext.points_in_boxes_cpu(boxes.float().contiguous(),
                                            points.float().contiguous(),
                                            point_indices)

    return point_indices


def points_in_boxes_batch(points, boxes):
    """Find points that are in boxes (CUDA)

    Args:
        points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR coordinate
        boxes (torch.Tensor): [B, T, 7],
            num_valid_boxes <= T, [x, y, z, w, l, h, ry] in LiDAR coordinate,
            (x, y, z) is the bottom center.

    Returns:
        box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0
    """
    assert boxes.shape[0] == points.shape[0]
    assert boxes.shape[2] == 7
    batch_size, num_points, _ = points.shape
    num_boxes = boxes.shape[1]

    box_idxs_of_pts = points.new_zeros((batch_size, num_points, num_boxes),
                                       dtype=torch.int).fill_(0)

    # Same reason as line 25-32
    points_device = points.get_device()
    assert points_device == boxes.get_device(), \
        'Points and boxes should be put on the same device'
    if torch.cuda.current_device() != points_device:
        torch.cuda.set_device(points_device)

    roiaware_pool3d_ext.points_in_boxes_batch(boxes.contiguous(),
                                              points.contiguous(),
                                              box_idxs_of_pts)

    return box_idxs_of_pts
