import torch
from torch import nn
from torch_geometric.nn import MessagePassing


# ---- FiLM that starts as identity and normalizes the target ----
class FiLM(nn.Module):
    def __init__(self, c_ch, h_ch):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(c_ch, 2 * h_ch), nn.SiLU(), nn.Linear(2 * h_ch, 2 * h_ch)
        )
        # init to identity: gamma≈0 (so 1+gamma=1), beta=0
        nn.init.zeros_(self.net[-1].weight)
        nn.init.zeros_(self.net[-1].bias)

    def forward(self, h, c):
        gb = self.net(c)
        gamma, beta = gb.chunk(2, dim=-1)
        return (1 + gamma) * h + beta


class ConditionalGNOBlock(MessagePassing):
    """
    Message passing with FiLM applied to the MESSAGE m_ij,
    using edge context c_ij = (c_i + c_j)/2.
    """

    def __init__(self, hidden_ch, edge_ch=0, aggr="add"):
        super().__init__(aggr=aggr, node_dim=0)
        # FiLM over the message (per-edge)
        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),
        )
        self.x_net = nn.Sequential(
            nn.Linear(hidden_ch, hidden_ch * 2),
            nn.SiLU(),
            nn.Linear(hidden_ch * 2, hidden_ch),
        )

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

    def update(self, aggr_out, x):
        return self.x_net(x) + aggr_out

    def message(self, x_j, c_i, c_j, edge_attr):
        # c_ij = (c_i + c_j)/2
        c_ij = 0.5 * (c_i + c_j)
        m = self.film_msg(x_j, c_ij)
        if edge_attr is not None:
            a_ij = self.edge_attr_net(edge_attr)
            m = m * a_ij
        return m


class GatingGNO(nn.Module):
    """
    In:
      x : [N, Cx]  (e.g., u or features to predict from)
      c : [N, Cf]  (conditioning field, e.g., conductivity)
    Out:
      y : [N, out_ch]
    """

    def __init__(
        self, x_ch_node, f_ch_node, hidden, layers, edge_ch=0, out_ch=1
    ):
        super().__init__()
        self.encoder_x = nn.Sequential(
            nn.Linear(x_ch_node, hidden // 2),
            nn.SiLU(),
            nn.Linear(hidden // 2, hidden),
        )
        self.encoder_c = nn.Sequential(
            nn.Linear(f_ch_node, hidden // 2),
            nn.SiLU(),
            nn.Linear(hidden // 2, hidden),
        )
        self.blocks = nn.ModuleList(
            [
                ConditionalGNOBlock(hidden_ch=hidden, edge_ch=edge_ch)
                for _ in range(layers)
            ]
        )
        self.dec = nn.Sequential(
            nn.Linear(hidden, hidden // 2),
            nn.SiLU(),
            nn.Linear(hidden // 2, out_ch),
        )

    def forward(self, x, c, edge_index, edge_attr=None):
        x = self.encoder_x(x)  # [N,H]
        c = self.encoder_c(c)  # [N,H]
        for blk in self.blocks:
            x = blk(x, c, edge_index, edge_attr=edge_attr)
        return self.dec(x)