//! VICReg SSL Framework Integration Example
//!
//! Demonstrates how VICReg integrates with the unified SSL training framework,
//! similar to other SSL methods like Barlow Twins, BYOL, and MoCo v3.

use rtx_transformers::prelude::*;
use rtx_transformers::ssl::*;

fn main() -> Result<()> {
    println!("🔥 VICReg SSL Framework Integration Demo");
    println!("========================================");

    let device = Device::cuda(0).unwrap_or(Device::default());

    // Demo 1: VICReg through SSL Framework
    demo_ssl_framework_integration(&device)?;

    // Demo 2: Comparison with other SSL methods
    demo_ssl_method_comparison(&device)?;

    // Demo 3: VICReg-specific metrics
    demo_vicreg_metrics(&device)?;

    println!("✅ All VICReg SSL integration demos completed!");

    Ok(())
}

/// Demo 1: VICReg integrated with the SSL training framework
fn demo_ssl_framework_integration(device: &Device) -> Result<()> {
    println!("\n📚 Demo 1: SSL Framework Integration");
    println!("-----------------------------------");

    // Create VICReg configuration
    let vicreg_config = VICRegConfig::new(2048, vec![8192, 8192, 8192])
        .with_sim_coeff(25.0)
        .with_std_coeff(25.0)
        .with_cov_coeff(1.0)
        .with_variance_target(1.0);

    // Create SSL training configuration with VICReg
    let ssl_config = SSLTrainingConfig {
        method: SSLMethod::VICReg(vicreg_config),
        learning_rate: 1e-3,
        batch_size: 32,
        epochs: 100,
        ..Default::default()
    };

    println!("✅ SSL Configuration created with VICReg method");
    println!("   - Learning rate: {}", ssl_config.learning_rate);
    println!("   - Batch size: {}", ssl_config.batch_size);
    println!("   - Epochs: {}", ssl_config.epochs);

    // In a real implementation, you would create the SSL trainer:
    // let backbone = VisionBackbone::resnet50(device);
    // let trainer = SSLTrainer::new(backbone, None, ssl_config, device)?;

    println!("   - VICReg integrated as SSL method ✅");

    Ok(())
}

/// Demo 2: Compare VICReg configuration with other SSL methods
fn demo_ssl_method_comparison(device: &Device) -> Result<()> {
    println!("\n📊 Demo 2: SSL Method Comparison");
    println!("--------------------------------");

    // VICReg configuration
    let vicreg_config = VICRegConfig::default();
    let vicreg_method = SSLMethod::VICReg(vicreg_config);

    // Barlow Twins configuration (for comparison)
    let barlow_config = BarlowTwinsConfig::default();
    let barlow_method = SSLMethod::BarlowTwins(barlow_config);

    println!("🔬 Method Characteristics:");
    println!("   VICReg:");
    println!("     - No momentum encoders needed");
    println!("     - Explicit variance & covariance regularization");
    println!("     - Three loss terms (invariance, variance, covariance)");
    println!("     - Hyperparameters: λ=25, μ=25, ν=1");

    println!("   Barlow Twins:");
    println!("     - Cross-correlation matrix approach");
    println!("     - Redundancy reduction principle");
    println!("     - Two loss terms (invariance, redundancy)");
    println!("     - Hyperparameter: λ=0.005");

    match vicreg_method {
        SSLMethod::VICReg(config) => {
            println!(
                "✅ VICReg method configured with {} expander layers",
                config.expander_dims.len()
            );
        }
        _ => unreachable!(),
    }

    Ok(())
}

/// Demo 3: VICReg-specific metrics tracking
fn demo_vicreg_metrics(device: &Device) -> Result<()> {
    println!("\n📈 Demo 3: VICReg Metrics");
    println!("------------------------");

    // Create sample representations
    let batch_size = 64;
    let feature_dim = 8192;
    let y1 = Tensor::randn(vec![batch_size, feature_dim], DType::F32, device)?;
    let y2 = Tensor::randn(vec![batch_size, feature_dim], DType::F32, device)?;

    // Compute VICReg loss components
    let config = VICRegConfig::default();
    let loss_result = compute_vicreg_loss(&y1, &y2, &config)?;

    // Create VICReg-specific metrics (as would be done in SSL trainer)
    let vicreg_metrics = MethodMetrics::VICReg {
        invariance_loss: loss_result.invariance_loss.to_scalar::<f32>()?,
        variance_loss: (loss_result.variance_loss_y1.to_scalar::<f32>()?
            + loss_result.variance_loss_y2.to_scalar::<f32>()?)
            / 2.0,
        covariance_loss: (loss_result.covariance_loss_y1.to_scalar::<f32>()?
            + loss_result.covariance_loss_y2.to_scalar::<f32>()?)
            / 2.0,
    };

    // Display metrics as they would appear in training logs
    match vicreg_metrics {
        MethodMetrics::VICReg {
            invariance_loss,
            variance_loss,
            covariance_loss,
        } => {
            println!("📊 VICReg Training Metrics:");
            println!("   - Invariance Loss: {:.6}", invariance_loss);
            println!("   - Variance Loss:   {:.6}", variance_loss);
            println!("   - Covariance Loss: {:.6}", covariance_loss);
            println!(
                "   - Total Loss:      {:.6}",
                loss_result.total_loss.to_scalar::<f32>()?
            );

            // Interpretation
            println!("\n🔍 Metric Interpretation:");
            println!("   - Invariance: Lower = more similar augmented views");
            println!("   - Variance: Lower = better dimensional variance maintenance");
            println!("   - Covariance: Lower = better feature decorrelation");
        }
        _ => unreachable!(),
    }

    // Demonstrate loss component weighting
    println!("\n⚖️  Loss Component Weighting:");
    println!(
        "   - Invariance × λ({}) = {:.6}",
        config.sim_coeff,
        loss_result.invariance_loss.to_scalar::<f32>()? * config.sim_coeff
    );
    println!(
        "   - Variance × μ({}) = {:.6}",
        config.std_coeff,
        (loss_result.variance_loss_y1.to_scalar::<f32>()?
            + loss_result.variance_loss_y2.to_scalar::<f32>()?)
            * config.std_coeff
    );
    println!(
        "   - Covariance × ν({}) = {:.6}",
        config.cov_coeff,
        (loss_result.covariance_loss_y1.to_scalar::<f32>()?
            + loss_result.covariance_loss_y2.to_scalar::<f32>()?)
            * config.cov_coeff
    );

    Ok(())
}

/// Example of how VICReg would be used in a complete training loop
#[allow(dead_code)]
fn example_training_loop() -> Result<()> {
    let device = Device::cuda(0).unwrap_or(Device::default());

    // VICReg configuration
    let vicreg_config = VICRegConfig::new(2048, vec![8192, 8192, 8192])
        .with_sim_coeff(25.0)
        .with_std_coeff(25.0)
        .with_cov_coeff(1.0);

    // SSL training configuration
    let ssl_config = SSLTrainingConfig {
        method: SSLMethod::VICReg(vicreg_config),
        learning_rate: 1e-3,
        batch_size: 256, // VICReg works well with moderate batch sizes
        epochs: 300,
        ..Default::default()
    };

    // In practice:
    // let backbone = VisionBackbone::resnet50(&device);
    // let mut trainer = SSLTrainer::new(backbone, None, ssl_config, &device)?;

    // Training loop:
    // for epoch in 0..ssl_config.epochs {
    //     // Load batch of images
    //     let images = load_batch(ssl_config.batch_size)?;
    //
    //     // Training step
    //     let metrics = trainer.train_step(&images, Some(epoch))?;
    //
    //     // Log VICReg-specific metrics
    //     if let MethodMetrics::VICReg { invariance_loss, variance_loss, covariance_loss } = metrics.method_metrics {
    //         log::info!("Epoch {}: Inv={:.4}, Var={:.4}, Cov={:.4}",
    //                   epoch, invariance_loss, variance_loss, covariance_loss);
    //     }
    // }

    Ok(())
}