import torch
from torch import nn
from torch_geometric.nn import MessagePassing
from matplotlib.tri import Triangulation


def _import_boundary_conditions(x, boundary, boundary_mask):
    x[boundary_mask] = boundary


def plot_results_fn(x, pos, i, batch):
    x = x[batch == 0]
    pos = pos[batch == 0]
    tria = Triangulation(pos[:, 0].cpu(), pos[:, 1].cpu())
    import matplotlib.pyplot as plt

    plt.tricontourf(tria, x[:, 0].cpu(), levels=14)
    plt.colorbar()
    plt.savefig(f"out_{i:03d}.png")
    plt.axis("equal")
    plt.close()


class EncX(nn.Module):
    def __init__(self, x_ch, hidden):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(x_ch, hidden // 2),
            nn.SiLU(),
            nn.Linear(hidden // 2, hidden),
        )

    def forward(self, x):
        return self.net(x)


class EncC(nn.Module):
    def __init__(self, c_ch, hidden):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(c_ch, hidden // 2),
            nn.SiLU(),
            nn.Linear(hidden // 2, hidden),
        )

    def forward(self, c):
        return self.net(c)


class DecX(nn.Module):
    def __init__(self, hidden, out_ch):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(hidden, hidden // 2),
            nn.SiLU(),
            nn.Linear(hidden // 2, out_ch),
        )

    def forward(self, x):
        return self.net(x)


class ConditionalGNOBlock(MessagePassing):
    def __init__(self, hidden_ch, edge_ch=0, aggr="mean"):
        super().__init__(aggr=aggr, node_dim=0)
        # self.film_msg = FiLM(c_ch=hidden_ch, h_ch=hidden_ch)

        self.edge_attr_net = nn.Sequential(
            nn.Linear(edge_ch, hidden_ch // 2),
            nn.SiLU(),
            nn.Linear(hidden_ch // 2, hidden_ch),
            nn.Tanh(),
        )

        self.msg_proj = nn.Sequential(
            nn.Linear(hidden_ch, hidden_ch),
            nn.SiLU(),
            nn.Linear(hidden_ch, hidden_ch),
        )

        self.diff_net = nn.Sequential(
            nn.Linear(hidden_ch, hidden_ch),
            nn.SiLU(),
            nn.Linear(hidden_ch, hidden_ch),
        )

        self.x_net = nn.Sequential(
            nn.Linear(hidden_ch, hidden_ch),
            nn.SiLU(),
            nn.Linear(hidden_ch, hidden_ch),
        )

        self.c_ij_net = nn.Sequential(
            nn.Linear(hidden_ch, hidden_ch),
            nn.SiLU(),
            nn.Linear(hidden_ch, hidden_ch),
            nn.Tanh(),
        )

        self.balancing = nn.Parameter(torch.tensor(0.0))
        self.alpha = nn.Parameter(torch.tensor(1.0))

    def forward(self, x, c, edge_index, edge_attr=None):
        return self.propagate(edge_index, x=x, c=c, edge_attr=edge_attr)

    def message(self, x_i, x_j, c_i, c_j, edge_attr):
        c_ij = 0.5 * (c_i + c_j)
        alpha = torch.sigmoid(self.balancing)
        gate = torch.sigmoid(self.edge_attr_net(edge_attr))
        m = (
            alpha * self.diff_net(x_j - x_i)
            + (1 - alpha) * self.x_net(x_j) * gate
        )
        m = m * self.c_ij_net(c_ij)
        return m

    def update(self, aggr_out, x):
        return x + self.alpha * self.msg_proj(aggr_out)


class GatingGNO(nn.Module):
    """
    TODO: add doc
    """

    def __init__(
        self, x_ch_node, f_ch_node, hidden, layers, edge_ch=0, out_ch=1
    ):
        super().__init__()
        self.encoder_x = EncX(x_ch_node, hidden)
        self.encoder_c = EncC(f_ch_node, hidden)

        self.blocks = nn.ModuleList(
            [
                ConditionalGNOBlock(hidden_ch=hidden, edge_ch=edge_ch)
                for _ in range(layers)
            ]
        )
        self.dec = DecX(hidden, out_ch)

    def forward(
        self,
        x,
        c,
        boundary,
        boundary_mask,
        edge_index,
        edge_attr=None,
        unrolling_steps=1,
        plot_results=False,
        batch=None,
        pos=None,
    ):
        x = self.encoder_x(x)
        c = self.encoder_c(c)
        boundary = self.encoder_x(boundary)
        if plot_results:
            _import_boundary_conditions(x, boundary, boundary_mask)
            x_ = self.dec(x)
            plot_results_fn(x_, pos, 0, batch=batch)

        for _ in range(1, unrolling_steps + 1):
            _import_boundary_conditions(x, boundary, boundary_mask)
            for blk in self.blocks:
                x = blk(x, c, edge_index, edge_attr=edge_attr)
            if plot_results:
                x_ = self.dec(x)
                plot_results_fn(x_, pos, _, batch=batch)

        return self.dec(x)