import torch
import torch.nn as nn
from torch_geometric.nn import MessagePassing
from torch.nn.utils import spectral_norm


class DiffusionLayer(MessagePassing):
    """
    Modella: T_new = T_old + dt * Divergenza(Flusso)
    """

    def __init__(
        self,
        channels: int,
        **kwargs,
    ):
        super().__init__(aggr="add", **kwargs)

        self.conductivity_net = nn.Sequential(
            spectral_norm(nn.Linear(channels, channels, bias=False)),
            nn.GELU(),
            spectral_norm(nn.Linear(channels, channels, bias=False)),
        )

        self.phys_encoder = nn.Sequential(
            spectral_norm(nn.Linear(1, 8, bias=True)),
            nn.Tanh(),
            spectral_norm(nn.Linear(8, 1, bias=True)),
            nn.Softplus(),
        )

        self.alpha_param = nn.Parameter(torch.tensor(1e-2))

    @property
    def alpha(self):
        return torch.clamp(self.alpha_param, min=1e-7, max=1.0)

    def forward(self, x, edge_index, edge_weight, conductivity):
        edge_weight = edge_weight.unsqueeze(-1)
        conductance = self.phys_encoder(edge_weight)
        net_flux = self.propagate(edge_index, x=x, conductance=conductance)
        return x + self.alpha * net_flux

    def message(self, x_i, x_j, conductance):
        delta = x_j - x_i
        flux = delta * conductance
        flux = flux + self.conductivity_net(flux)
        return flux


class DiffusionNet(nn.Module):
    def __init__(
        self,
        input_dim=1,
        output_dim=1,
        hidden_dim=8,
        n_layers=4,
        shared_weights=False,
    ):
        super().__init__()

        # Encoder: Projects input temperature to hidden feature space
        self.enc = nn.Sequential(
            spectral_norm(nn.Linear(input_dim, hidden_dim, bias=True)),
            nn.GELU(),
            spectral_norm(nn.Linear(hidden_dim, hidden_dim, bias=True)),
        )

        self.scale_x = nn.Parameter(torch.zeros(hidden_dim))

        # Scale parameters for conditioning
        self.scale_edge_attr = nn.Parameter(torch.zeros(1))

        # If shared_weights is True, use the same DiffusionLayer multiple times
        if shared_weights:
            diffusion_layer = DiffusionLayer(hidden_dim)
            self.layers = torch.nn.ModuleList(
                [diffusion_layer for _ in range(n_layers)]
            )
        # If shared_weights is False, use separate DiffusionLayers
        else:
            # Stack of Diffusion Layers
            self.layers = torch.nn.ModuleList(
                [DiffusionLayer(hidden_dim) for _ in range(n_layers)]
            )

        # Decoder: Projects hidden features back to Temperature space
        self.dec = nn.Sequential(
            spectral_norm(nn.Linear(hidden_dim, hidden_dim, bias=True)),
            nn.GELU(),
            spectral_norm(nn.Linear(hidden_dim, output_dim, bias=True)),
            nn.Softplus(),  # Ensure positive temperature output
        )

        self.func = torch.nn.GELU()

        self.dt_param = nn.Parameter(torch.tensor(1e-2))

    @property
    def dt(self):
        return torch.clamp(self.dt_param, min=1e-5, max=0.5)

    def forward(self, x, edge_index, edge_attr, conductivity):
        # 1. Global Residual Connection setup
        # We save the input to add it back at the very end.
        # The network learns the correction (Delta T), not the absolute T.
        x_input = x
        # 2. Encode
        h = self.enc(x) * torch.exp(self.scale_x)

        # Scale edge attributes (learnable gating of physical conductivity)
        w = edge_attr * torch.exp(self.scale_edge_attr)

        # 4. Message Passing (Diffusion Steps)
        for layer in self.layers:
            # h is updated internally via residual connection in DiffusionLayer
            h = layer(h, edge_index, w, conductivity)
            h = self.func(h)

        # 5. Decode
        delta_x = self.dec(h)

        # 6. Final Update (Explicit Euler Step)
        # T_new = T_old + Correction
        return delta_x + x_input * self.dt