import torch
from torch.autograd import Function

from . import ball_query_ext


class BallQuery(Function):
    """Ball Query.

    Find nearby points in spherical space.
    """

    @staticmethod
    def forward(ctx, min_radius: float, max_radius: float, sample_num: int,
                xyz: torch.Tensor, center_xyz: torch.Tensor) -> torch.Tensor:
        """forward.

        Args:
            min_radius (float): minimum radius of the balls.
            max_radius (float): maximum radius of the balls.
            sample_num (int): maximum number of features in the balls.
            xyz (Tensor): (B, N, 3) xyz coordinates of the features.
            center_xyz (Tensor): (B, npoint, 3) centers of the ball query.

        Returns:
            Tensor: (B, npoint, nsample) tensor with the indices of
                the features that form the query balls.
        """
        assert center_xyz.is_contiguous()
        assert xyz.is_contiguous()
        assert min_radius < max_radius

        B, N, _ = xyz.size()
        npoint = center_xyz.size(1)
        idx = torch.cuda.IntTensor(B, npoint, sample_num).zero_()

        ball_query_ext.ball_query_wrapper(B, N, npoint, min_radius, max_radius,
                                          sample_num, center_xyz, xyz, idx)
        ctx.mark_non_differentiable(idx)
        return idx

    @staticmethod
    def backward(ctx, a=None):
        return None, None, None, None


ball_query = BallQuery.apply
