import torch

if torch.cuda.is_available():
    import nearest_cuda


def nearest(x, y, batch_x=None, batch_y=None):
    """Finds for each element in `x` its nearest point in `y`.

    Args:
        x (Tensor): D-dimensional point features.
        y (Tensor): D-dimensional point features.
        batch_x (LongTensor, optional): Vector that maps each point to its
            example identifier. If :obj:`None`, all points belong to the same
            example. If not :obj:`None`, points in the same example need to
            have contiguous memory layout and :obj:`batch` needs to be
            ascending. (default: :obj:`None`)
        batch_y (LongTensor, optional): See `batch_x` (default: :obj:`None`)

    Examples::

        >>> x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
        >>> batch_x = torch.Tensor([0, 0, 0, 0])
        >>> y = torch.Tensor([[-1, 0], [1, 0]])
        >>> batch_x = torch.Tensor([0, 0])
        >>> out = fps(x, y, batch_x, batch_y)
    """

    if batch_x is None:
        batch_x = x.new_zeros(x.size(0), dtype=torch.long)

    if batch_y is None:
        batch_y = y.new_zeros(y.size(0), dtype=torch.long)

    x = x.view(-1, 1) if x.dim() == 1 else x
    y = y.view(-1, 1) if y.dim() == 1 else y

    assert x.is_cuda
    assert x.dim() == 2 and batch_x.dim() == 1
    assert y.dim() == 2 and batch_y.dim() == 1
    assert x.size(1) == y.size(1)
    assert x.size(0) == batch_x.size(0)
    assert y.size(0) == batch_y.size(0)

    op = nearest_cuda.nearest if x.is_cuda else None
    out = op(x, y, batch_x, batch_y)

    return out