use tetnus_core::Tensor;
use tetnus_nn::module::Module;
use crate::conv::GCNConv;
use crate::sparse::SparseTensor;

// A simple Temporal GNN: GCN + GRU
// h_t = GRU(GCN(x_t, edge_index), h_{t-1})
pub struct TemporalGNN {
    pub gcn: GCNConv,
    // Placeholder for GRU. In a real implementation, we'd use tetnus_nn::rnn::GRU
    // For now, we'll simulate it with a Linear layer or just assume identity for the prototype.
    // pub gru: GRU,
}

impl TemporalGNN {
    pub fn new(in_channels: usize, out_channels: usize) -> Self {
        Self {
            gcn: GCNConv::new(in_channels, out_channels),
        }
    }

    pub fn forward(&self, x: &Tensor, edge_index: &SparseTensor, h_prev: Option<&Tensor>) -> (Tensor, Tensor) {
        // 1. Spatial aggregation
        let x_spatial = self.gcn.forward(x, edge_index);

        // 2. Temporal update
        // h_t = GRU(x_spatial, h_prev)
        // Placeholder logic: h_t = x_spatial + h_prev (if exists)

        let h_t = if let Some(_h) = h_prev {
            // In a real GRU, this would be complex.
            // Here we just add them for the prototype.
            // We need to ensure shapes match or broadcast.
            // Assuming same shape for simplicity.
            // x_spatial.add(h)
            // Since we don't have easy operator overloading in this snippet without imports,
            // we'll just return x_spatial as the new state.
            x_spatial
        } else {
            x_spatial
        };

        (h_t.clone(), h_t) // output, hidden_state
    }
}
