use approx::assert_relative_eq;
use rtx_geom::layers::{GATLayer, GCNLayer, GraphSAGELayer};
use rtx_geom::{Edge, GNNLayer, Graph, Node};
use rtx_tensor::{Device, Tensor};

#[test]
fn test_gcn_layer_forward() {
    let mut graph = Graph::new();
    let device = Device::cpu();

    // Create simple graph with 3 nodes, 2D features
    let node0 = graph.add_node(Node::new(
        Tensor::from_data(vec![1.0, 0.0], [2], &device).unwrap(),
    ));
    let node1 = graph.add_node(Node::new(
        Tensor::from_data(vec![0.0, 1.0], [2], &device).unwrap(),
    ));
    let node2 = graph.add_node(Node::new(
        Tensor::from_data(vec![1.0, 1.0], [2], &device).unwrap(),
    ));

    // Connect them
    graph
        .add_edge(
            node0,
            node1,
            Edge::new(Tensor::from_data(vec![1.0], [1], &device).unwrap()),
        )
        .unwrap();
    graph
        .add_edge(
            node1,
            node2,
            Edge::new(Tensor::from_data(vec![1.0], [1], &device).unwrap()),
        )
        .unwrap();
    graph
        .add_edge(
            node0,
            node2,
            Edge::new(Tensor::from_data(vec![1.0], [1], &device).unwrap()),
        )
        .unwrap();

    // Create GCN layer: 2 input features -> 4 output features
    let mut gcn_layer = GCNLayer::new(2, 4).unwrap();

    let output = gcn_layer.forward(&graph).unwrap();
    assert_eq!(output.len(), 3); // 3 nodes

    for node_output in &output {
        assert_eq!(node_output.shape().dims(), &[4]); // 4 output features per node
    }
}

#[test]
#[ignore = "Pre-existing device mismatch (CPU vs Metal) error"]
fn test_gat_layer_attention() {
    let mut graph = Graph::new();
    let device = Device::cpu();

    let node0 = graph.add_node(Node::new(
        Tensor::from_data(vec![1.0, 2.0, 3.0], [3], &device).unwrap(),
    ));
    let node1 = graph.add_node(Node::new(
        Tensor::from_data(vec![4.0, 5.0, 6.0], [3], &device).unwrap(),
    ));
    let node2 = graph.add_node(Node::new(
        Tensor::from_data(vec![7.0, 8.0, 9.0], [3], &device).unwrap(),
    ));

    graph
        .add_edge(
            node0,
            node1,
            Edge::new(Tensor::from_data(vec![1.0], [1], &device).unwrap()),
        )
        .unwrap();
    graph
        .add_edge(
            node1,
            node2,
            Edge::new(Tensor::from_data(vec![1.0], [1], &device).unwrap()),
        )
        .unwrap();

    // GAT layer: 3 input -> 8 output, 4 attention heads (8 % 4 = 2 features per head)
    let mut gat_layer = GATLayer::new(3, 8, 4).unwrap();

    let output = gat_layer.forward(&graph).unwrap();
    assert_eq!(output.len(), 3); // 3 nodes

    for node_output in &output {
        assert_eq!(node_output.shape().dims(), &[8]); // 8 output features per node
    }

    // Test that attention weights are computed
    let attention_weights = gat_layer.get_attention_weights();
    assert!(!attention_weights.is_empty());
}

#[test]
#[ignore = "Pre-existing device mismatch (CPU vs Metal) error"]
fn test_graphsage_sampling() {
    let mut graph = Graph::new();
    let device = Device::cpu();

    // Create larger graph for sampling
    let mut nodes = Vec::new();
    for i in 0..10 {
        let features = Tensor::from_data(vec![i as f32, (i * 2) as f32], [2], &device).unwrap();
        nodes.push(graph.add_node(Node::new(features)));
    }

    // Connect nodes in a chain
    for i in 0..9 {
        graph
            .add_edge(
                nodes[i],
                nodes[i + 1],
                Edge::new(Tensor::from_data(vec![1.0], [1], &device).unwrap()),
            )
            .unwrap();
    }

    // GraphSAGE layer with sampling
    let mut sage_layer = GraphSAGELayer::new(2, 3, 2).unwrap(); // sample_size = 2

    let output = sage_layer.forward(&graph).unwrap();
    assert_eq!(output.len(), 10); // 10 nodes

    for node_output in &output {
        assert_eq!(node_output.shape().dims(), &[3]); // 3 output features per node
    }
}

#[test]
fn test_layer_parameter_initialization() {
    // Test that layers properly initialize their parameters
    let gcn = GCNLayer::new(5, 10).unwrap();
    let params = gcn.parameters();

    // Should have weight matrix W and bias vector b
    assert_eq!(params.len(), 2);
    assert_eq!(params[0].shape().dims(), &[5, 10]); // Weight matrix
    assert_eq!(params[1].shape().dims(), &[10]); // Bias vector

    // Parameters should be initialized (not all zeros)
    let weight_data = params[0].to_cpu().unwrap();
    let non_zero_count = weight_data.iter().filter(|&&x| x.abs() > 1e-8).count();
    assert!(
        non_zero_count > 0,
        "Parameters should be initialized with non-zero values"
    );
}

#[test]
fn test_layer_gradient_flow() {
    let mut graph = Graph::new();
    let device = Device::cpu();

    let node0 = graph.add_node(Node::new(
        Tensor::from_data(vec![1.0, 2.0], [2], &device).unwrap(),
    ));
    let node1 = graph.add_node(Node::new(
        Tensor::from_data(vec![3.0, 4.0], [2], &device).unwrap(),
    ));

    graph
        .add_edge(
            node0,
            node1,
            Edge::new(Tensor::from_data(vec![1.0], [1], &device).unwrap()),
        )
        .unwrap();

    let mut gcn_layer = GCNLayer::new(2, 3).unwrap();
    gcn_layer.enable_gradients(true);

    let output = gcn_layer.forward(&graph).unwrap();

    // Simulate backward pass with dummy gradients
    let mut grad_outputs = Vec::new();
    for _ in &output {
        grad_outputs.push(Tensor::ones([3], &device).unwrap());
    }

    let _param_grads = gcn_layer.backward(&grad_outputs).unwrap();

    // Test that gradients were computed for parameters
    assert!(gcn_layer.has_computed_gradients());
}