// Simple validation script for TripletLoss implementation

fn main() {
    println!("TripletLoss implementation validation");
    
    // Test that we can create the basic types
    use rtx_losses::{TripletLoss, MinimalTensor, MinimalDevice, DistanceMetric};
    
    let device = MinimalDevice::cpu();
    let loss = TripletLoss::new()
        .with_margin(1.0)
        .with_distance_metric(DistanceMetric::Euclidean);
    
    println!("✓ Created TripletLoss with margin: {}", loss.margin());
    println!("✓ Distance metric: {:?}", loss.distance_metric());
    
    // Test basic tensor creation
    match MinimalTensor::from_data(vec![0.0, 1.0], &[1, 2], &device) {
        Ok(tensor) => println!("✓ Created tensor with shape: {:?}", tensor.shape()),
        Err(e) => println!("✗ Failed to create tensor: {}", e),
    }
    
    // Test basic forward pass
    let anchor = MinimalTensor::from_data(vec![0.0, 0.0], &[1, 2], &device).unwrap();
    let positive = MinimalTensor::from_data(vec![1.0, 0.0], &[1, 2], &device).unwrap();
    let negative = MinimalTensor::from_data(vec![3.0, 0.0], &[1, 2], &device).unwrap();
    
    match loss.forward_triplets(&anchor, &positive, &negative) {
        Ok(result) => {
            let loss_value = result.item().unwrap();
            println!("✓ Forward pass successful, loss: {}", loss_value);
            if loss_value >= 0.0 && loss_value.is_finite() {
                println!("✓ Loss value is valid (non-negative and finite)");
            }
        },
        Err(e) => println!("✗ Forward pass failed: {}", e),
    }
    
    println!("TripletLoss validation complete!");
}