from typing import Optional

import torch


def knn(
    x: torch.Tensor,
    y: torch.Tensor,
    k: int,
    batch_x: Optional[torch.Tensor] = None,
    batch_y: Optional[torch.Tensor] = None,
    cosine: bool = False,
    num_workers: int = 1,
    batch_size: Optional[int] = None,
) -> torch.Tensor:
    r"""Finds for each element in :obj:`y` the :obj:`k` nearest points in
    :obj:`x`.

    Args:
        x (Tensor): Node feature matrix
            :math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
        y (Tensor): Node feature matrix
            :math:`\mathbf{X} \in \mathbb{R}^{M \times F}`.
        k (int): The number of neighbors.
        batch_x (LongTensor, optional): Batch vector
            :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
            node to a specific example. :obj:`batch_x` needs to be sorted.
            (default: :obj:`None`)
        batch_y (LongTensor, optional): Batch vector
            :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M`, which assigns each
            node to a specific example. :obj:`batch_y` needs to be sorted.
            (default: :obj:`None`)
        cosine (boolean, optional): If :obj:`True`, will use the Cosine
            distance instead of the Euclidean distance to find nearest
            neighbors. (default: :obj:`False`)
        num_workers (int): Number of workers to use for computation. Has no
            effect in case :obj:`batch_x` or :obj:`batch_y` is not
            :obj:`None`, or the input lies on the GPU. (default: :obj:`1`)
        batch_size (int, optional): The number of examples :math:`B`.
            Automatically calculated if not given. (default: :obj:`None`)

    :rtype: :class:`LongTensor`

    .. code-block:: python

        import torch
        from torch_cluster import knn

        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_y = torch.tensor([0, 0])
        assign_index = knn(x, y, 2, batch_x, batch_y)
    """
    if x.numel() == 0 or y.numel() == 0:
        return torch.empty(2, 0, dtype=torch.long, device=x.device)

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

    if batch_size is None:
        batch_size = 1
        if batch_x is not None:
            assert x.size(0) == batch_x.numel()
            batch_size = int(batch_x.max()) + 1
        if batch_y is not None:
            assert y.size(0) == batch_y.numel()
            batch_size = max(batch_size, int(batch_y.max()) + 1)
    assert batch_size > 0

    ptr_x: Optional[torch.Tensor] = None
    ptr_y: Optional[torch.Tensor] = None
    if batch_size > 1:
        assert batch_x is not None
        assert batch_y is not None
        arange = torch.arange(batch_size + 1, device=x.device)
        ptr_x = torch.bucketize(arange, batch_x)
        ptr_y = torch.bucketize(arange, batch_y)

    return torch.ops.torch_cluster.knn(x, y, ptr_x, ptr_y, k, cosine,
                                       num_workers)


def knn_graph(
    x: torch.Tensor,
    k: int,
    batch: Optional[torch.Tensor] = None,
    loop: bool = False,
    flow: str = 'source_to_target',
    cosine: bool = False,
    num_workers: int = 1,
    batch_size: Optional[int] = None,
) -> torch.Tensor:
    r"""Computes graph edges to the nearest :obj:`k` points.

    Args:
        x (Tensor): Node feature matrix
            :math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
        k (int): The number of neighbors.
        batch (LongTensor, optional): Batch vector
            :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
            node to a specific example. :obj:`batch` needs to be sorted.
            (default: :obj:`None`)
        loop (bool, optional): If :obj:`True`, the graph will contain
            self-loops. (default: :obj:`False`)
        flow (string, optional): The flow direction when used in combination
            with message passing (:obj:`"source_to_target"` or
            :obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
        cosine (boolean, optional): If :obj:`True`, will use the Cosine
            distance instead of Euclidean distance to find nearest neighbors.
            (default: :obj:`False`)
        num_workers (int): Number of workers to use for computation. Has no
            effect in case :obj:`batch` is not :obj:`None`, or the input lies
            on the GPU. (default: :obj:`1`)
        batch_size (int, optional): The number of examples :math:`B`.
            Automatically calculated if not given. (default: :obj:`None`)

    :rtype: :class:`LongTensor`

    .. code-block:: python

        import torch
        from torch_cluster import knn_graph

        x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
        batch = torch.tensor([0, 0, 0, 0])
        edge_index = knn_graph(x, k=2, batch=batch, loop=False)
    """

    assert flow in ['source_to_target', 'target_to_source']
    edge_index = knn(x, x, k if loop else k + 1, batch, batch, cosine,
                     num_workers, batch_size)

    if flow == 'source_to_target':
        row, col = edge_index[1], edge_index[0]
    else:
        row, col = edge_index[0], edge_index[1]

    if not loop:
        mask = row != col
        row, col = row[mask], col[mask]

    return torch.stack([row, col], dim=0)