from typing import List, Optional, Tuple, Union

import torch.nn.functional as F
from torch import Tensor

from torch_geometric.nn.aggr import Aggregation, MultiAggregation
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptPairTensor, Size, SparseTensor
from torch_geometric.utils import spmm


class SAGEConv(MessagePassing):
    r"""A variant of the GraphSAGE operator from the `"Inductive Representation
    Learning on Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper.

    .. math::
        \mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W}_2 \cdot
        \mathrm{mean}_{j \in \mathcal{N(i)}} \mathbf{x}_j

    If :obj:`project = True`, then :math:`\mathbf{x}_j` will first get
    projected via

    .. math::
        \mathbf{x}_j \leftarrow \sigma ( \mathbf{W}_3 \mathbf{x}_j +
        \mathbf{b})

    as described in Eq. (3) of the paper.

    Args:
        in_channels (int or tuple): Size of each input sample, or :obj:`-1` to
            derive the size from the first input(s) to the forward method.
            A tuple corresponds to the sizes of source and target
            dimensionalities.
        out_channels (int): Size of each output sample.
        aggr (str or Aggregation, optional): The aggregation scheme to use.
            Any aggregation of :obj:`torch_geometric.nn.aggr` can be used,
            *e.g.*, :obj:`"mean"`, :obj:`"max"`, or :obj:`"lstm"`.
            (default: :obj:`"mean"`)
        project (bool, optional): If set to :obj:`True`, the layer will apply a
            linear transformation followed by an activation function before
            aggregation (as described in Eq. (3) of the paper).
            (default: :obj:`True`)
        bias (bool, optional): If set to :obj:`False`, the layer will not learn
            an additive bias. (default: :obj:`True`)
        **kwargs (optional): Additional arguments of
            :class:`torch_geometric.nn.conv.MessagePassing`.

    Shapes:
        - **inputs:**
          node features :math:`(|\mathcal{V}|, F_{in})` or
          :math:`((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))`
          if bipartite,
          edge indices :math:`(2, |\mathcal{E}|)`
        - **outputs:** node features :math:`(|\mathcal{V}|, F_{out})` or
          :math:`(|\mathcal{V_t}|, F_{out})` if bipartite
    """

    def __init__(
        self,
        in_channels: Union[int, Tuple[int, int]],
        out_channels: int,
        aggr: Optional[Union[str, List[str], Aggregation]] = "mean",
        project: bool = True,
        bias: bool = True,
        **kwargs,
    ):
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.project = project

        if isinstance(in_channels, int):
            in_channels = (in_channels, in_channels)

        if aggr == "lstm":
            kwargs.setdefault("aggr_kwargs", {})
            kwargs["aggr_kwargs"].setdefault("in_channels", in_channels[0])
            kwargs["aggr_kwargs"].setdefault("out_channels", in_channels[0])

        super().__init__(aggr, **kwargs)

        if self.project:
            if in_channels[0] <= 0:
                raise ValueError(
                    f"'{self.__class__.__name__}' does not "
                    f"support lazy initialization with "
                    f"`project=True`"
                )
            self.lin = Linear(in_channels[0], in_channels[0], bias=True)

        if isinstance(self.aggr_module, MultiAggregation):
            aggr_out_channels = self.aggr_module.get_out_channels(
                in_channels[0]
            )
        else:
            aggr_out_channels = in_channels[0]

        self.lin_l = Linear(aggr_out_channels, out_channels, bias=bias)
        self.lin_r = Linear(in_channels[1], out_channels, bias=False)

        self.reset_parameters()

    def reset_parameters(self):
        super().reset_parameters()
        if self.project:
            self.lin.reset_parameters()
        self.lin_l.reset_parameters()
        self.lin_r.reset_parameters()

    def forward(
        self,
        x: Union[Tensor, OptPairTensor],
        edge_index: Adj,
        size: Size = None,
    ) -> Tensor:

        if isinstance(x, Tensor):
            x = (x, x)

        if self.project and hasattr(self, "lin"):
            x = (F.gelu(self.lin(x[0])), x[1])

        # propagate_type: (x: OptPairTensor)
        AX = self.propagate(edge_index, x=x, size=size)
        out = self.lin_l(AX)

        x_r = x[1]
        if x_r is not None:
            out = out + self.lin_r(x_r)

        return out

    def message(self, x_j: Tensor) -> Tensor:
        return x_j

    def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor:
        if isinstance(adj_t, SparseTensor):
            adj_t = adj_t.set_value(None, layout=None)
        return spmm(adj_t, x[0], reduce=self.aggr)

    def __repr__(self) -> str:
        return (
            f"{self.__class__.__name__}({self.in_channels}, "
            f"{self.out_channels}, aggr={self.aggr})"
        )