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

class DiffusionLayer(MessagePassing):
    """
    Modella: T_new = T_old + dt * Divergenza(Flusso)
    """
    def __init__(
        self,
        channels: int,
        **kwargs,
    ):
        
        super().__init__(aggr='add', **kwargs)
        
        self.dt = nn.Parameter(torch.tensor(1e-4))
        self.conductivity_net = nn.Sequential(
            nn.Linear(channels, channels, bias=False),
            nn.GELU(),
            nn.Linear(channels, channels, bias=False),
        )
        
        self.phys_encoder = nn.Sequential(
            nn.Linear(1, 8, bias=False),
            nn.Tanh(),
            nn.Linear(8, 1, bias=False),
            nn.Softplus()
        )

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

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

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

        # Scale parameters for conditioning
        self.scale_edge_attr = nn.Parameter(torch.zeros(1))
        
        # 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(
            nn.Linear(hidden_dim, hidden_dim, bias=True),
            nn.GELU(),
            nn.Linear(hidden_dim, output_dim, bias=True),
            nn.Softplus(),  # Ensure positive temperature output
        )

        self.func = torch.nn.GELU()

    def forward(self, x, edge_index, edge_attr):
        # 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) 
            h = self.func(h)

        # 5. Decode
        delta_x = self.dec(h)
        
        # 6. Final Update (Explicit Euler Step)
        # T_new = T_old + Correction
        # return x_input + delta_x
        return delta_ddx