import torch
import scipy.cluster

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])
        >>> cluster = nearest(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.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)

    if x.is_cuda:
        return nearest_cuda.nearest(x, y, batch_x, batch_y)

    # Rescale x and y.
    min_xy = min(x.min().item(), y.min().item())
    x, y = x - min_xy, y - min_xy

    max_xy = max(x.max().item(), y.max().item())
    x, y, = x / max_xy, y / max_xy

    # Concat batch/features to ensure no cross-links between examples exist.
    x = torch.cat([x, 2 * x.size(1) * batch_x.view(-1, 1).to(x.dtype)], dim=-1)
    y = torch.cat([y, 2 * y.size(1) * batch_y.view(-1, 1).to(y.dtype)], dim=-1)

    return torch.from_numpy(scipy.cluster.vq.vq(x, y)[0]).to(torch.long)