//! Comprehensive demonstration of model compression techniques
#![allow(unused_variables)]
//!
//! This example shows how to use pruning, distillation, and compression pipelines
//! to reduce model size while maintaining accuracy.

use std::collections::HashMap;
use trustformers_core::compression::{
    // Pipeline
    AccuracyRetention,
    CompressionEvaluator,
    // Metrics
    CompressionMetrics,
    CompressionRatio,
    CompressionTargets,
    DistillationConfig,
    DistillationLoss,
    DistillationStrategy,
    FeatureDistiller,
    GradualPruner,
    HeadPruner,
    InferenceSpeedup,
    // Distillation
    KnowledgeDistiller,
    LayerPruner,
    // Pruning
    MagnitudePruner,
    ModelSizeReduction,
    PipelineBuilder,
    PruningConfig,
    PruningStrategy,
    SparsityMetric,
    StructuredPruner,
};
use trustformers_core::errors::Result;
use trustformers_core::tensor::Tensor;
use trustformers_core::traits::Model;

#[tokio::main]
async fn main() -> Result<()> {
    println!("TrustformeRS Model Compression Toolkit Demo");
    println!("==========================================\n");

    // Example 1: Basic magnitude pruning
    println!("Example 1: Magnitude-based Pruning");
    demo_magnitude_pruning()?;

    // Example 2: Structured pruning
    println!("\nExample 2: Structured Pruning");
    demo_structured_pruning()?;

    // Example 3: Gradual pruning
    println!("\nExample 3: Gradual Pruning Schedule");
    demo_gradual_pruning()?;

    // Example 4: Knowledge distillation
    println!("\nExample 4: Knowledge Distillation");
    demo_knowledge_distillation().await?;

    // Example 5: Feature distillation
    println!("\nExample 5: Feature-based Distillation");
    demo_feature_distillation().await?;

    // Example 6: Compression pipeline
    println!("\nExample 6: Full Compression Pipeline");
    demo_compression_pipeline().await?;

    // Example 7: Compression metrics
    println!("\nExample 7: Compression Metrics and Evaluation");
    demo_compression_metrics()?;

    // Example 8: Advanced techniques
    println!("\nExample 8: Advanced Compression Techniques");
    demo_advanced_compression().await?;

    Ok(())
}

fn demo_magnitude_pruning() -> Result<()> {
    println!("Demonstrating magnitude-based pruning...");

    // Create pruner with 50% sparsity
    let pruner = MagnitudePruner::new(0.5);

    // Create sample weights
    let weights = Tensor::from_vec(
        vec![0.1, -0.5, 0.02, 0.8, -0.03, 0.9, -0.01, 0.4, 0.001, -0.7],
        &[2, 5],
    )?;

    println!("Original weights:");
    println!("{:?}", weights.data());

    // Configure pruning
    let config = PruningConfig {
        target_sparsity: 0.5,
        iterative: false,
        fine_tune: false,
        ..Default::default()
    };

    // Apply pruning
    let pruned = pruner.prune_weights(&weights, &config)?;
    println!("\nPruned weights (50% sparsity):");
    println!("{:?}", pruned.to_vec_f32()?);

    // Calculate actual sparsity
    let data = pruned.to_vec_f32()?;
    let zero_count = data.iter().filter(|&&x| x.abs() < 1e-8).count();
    let sparsity = zero_count as f32 / data.len() as f32;
    println!("Actual sparsity: {:.1}%", sparsity * 100.0);

    Ok(())
}

fn demo_structured_pruning() -> Result<()> {
    println!("Demonstrating structured pruning (channel pruning)...");

    // Create structured pruner for dimension 0 (channels)
    let pruner = StructuredPruner::new(0);

    // Create sample convolutional weights [out_channels, in_channels, height, width]
    let weights = Tensor::from_vec(
        vec![
            // Channel 0 (weak)
            0.01, 0.02, -0.01, 0.03, // Channel 1 (strong)
            0.5, 0.6, 0.7, 0.8, // Channel 2 (weak)
            -0.02, 0.01, 0.02, -0.01, // Channel 3 (strong)
            0.9, -0.8, 0.7, -0.6,
        ],
        &[4, 1, 2, 2],
    )?;

    let config = PruningConfig {
        target_sparsity: 0.5, // Prune 50% of channels
        ..Default::default()
    };

    let pruned = pruner.prune_weights(&weights, &config)?;

    println!("Structured pruning removes entire channels:");
    println!("Original shape: {:?}", weights.shape());
    println!("Pruned has {} zero channels", 2);

    Ok(())
}

fn demo_gradual_pruning() -> Result<()> {
    println!("Demonstrating gradual pruning schedule...");

    // Create gradual pruner
    let pruner = GradualPruner::new(
        0.0,  // initial sparsity
        0.9,  // final sparsity
        1000, // begin step
        5000, // end step
        100,  // frequency
    );

    // Show sparsity at different training steps
    let steps = vec![0, 1000, 2000, 3000, 4000, 5000, 6000];

    println!("Sparsity schedule:");
    for step in steps {
        let sparsity = pruner.get_sparsity_at_step(step);
        println!("  Step {}: {:.1}% sparsity", step, sparsity * 100.0);
    }

    println!("\nGradual pruning allows the model to adapt during training.");

    Ok(())
}

async fn demo_knowledge_distillation() -> Result<()> {
    println!("Demonstrating knowledge distillation...");

    // Create knowledge distiller with temperature=3.0
    let distiller = KnowledgeDistiller::new(3.0).with_loss(DistillationLoss::KLDivergence);

    // Configure distillation
    let config = DistillationConfig {
        temperature: 3.0,
        alpha: 0.7, // 70% distillation loss, 30% task loss
        learning_rate: 1e-4,
        epochs: 10,
        batch_size: 32,
        ..Default::default()
    };

    println!("Distillation configuration:");
    println!("  Temperature: {}", config.temperature);
    println!("  Alpha (distillation weight): {}", config.alpha);
    println!("  Learning rate: {}", config.learning_rate);
    println!("  Epochs: {}", config.epochs);

    // Demonstrate soft target generation
    let teacher_logits = Tensor::from_vec(vec![2.0, 0.5, -1.0, 0.3], &[1, 4])?;
    let student_logits = Tensor::from_vec(vec![1.5, 0.3, -0.8, 0.2], &[1, 4])?;

    println!("\nTeacher logits: {:?}", teacher_logits.data());
    println!("Student logits: {:?}", student_logits.data());

    // In practice, would train student model here
    println!("\nKnowledge distillation transfers 'dark knowledge' from teacher to student.");

    Ok(())
}

async fn demo_feature_distillation() -> Result<()> {
    println!("Demonstrating feature-based distillation...");

    // Create layer mappings between teacher and student
    let mut layer_mappings = HashMap::new();
    layer_mappings.insert("teacher.layer4".to_string(), "student.layer2".to_string());
    layer_mappings.insert("teacher.layer8".to_string(), "student.layer4".to_string());
    layer_mappings.insert("teacher.layer12".to_string(), "student.layer6".to_string());

    let distiller = FeatureDistiller::new(layer_mappings.clone());

    println!("Layer mappings for feature distillation:");
    for (teacher_layer, student_layer) in &layer_mappings {
        println!("  {} -> {}", teacher_layer, student_layer);
    }

    println!("\nFeature distillation matches intermediate representations.");
    println!("This helps the student learn internal representations.");

    Ok(())
}

async fn demo_compression_pipeline() -> Result<()> {
    println!("Building comprehensive compression pipeline...");

    // Create pipeline with multiple stages
    let pipeline = PipelineBuilder::new()
        // Stage 1: Initial pruning
        .add_pruning(0.3)
        // Stage 2: Quantization
        .add_quantization(8)
        // Stage 3: Knowledge distillation
        .add_distillation("bert-base".to_string(), 4.0)
        // Stage 4: Fine pruning
        .add_pruning(0.6)
        // Stage 5: Fine-tuning
        .add_finetuning(5, 1e-5)
        // Set targets
        .target_ratio(10.0)
        .max_accuracy_loss(0.02)
        .build();

    println!("\nPipeline stages:");
    println!("1. Magnitude pruning (30% sparsity)");
    println!("2. INT8 quantization");
    println!("3. Knowledge distillation from bert-base");
    println!("4. Further pruning (60% sparsity)");
    println!("5. Fine-tuning (5 epochs)");

    println!("\nTarget compression ratio: 10x");
    println!("Maximum accuracy loss: 2%");

    // In practice, would run pipeline.compress(model).await

    Ok(())
}

fn demo_compression_metrics() -> Result<()> {
    println!("Evaluating compression metrics...");

    // Create evaluator
    let evaluator = CompressionEvaluator::new();

    // Create mock metrics
    let mut metrics = CompressionMetrics::new();

    // Set example values
    metrics.size_reduction = ModelSizeReduction {
        original_size_bytes: 400_000_000,  // 400MB
        compressed_size_bytes: 50_000_000, // 50MB
        percentage: 0.875,
        size_breakdown: HashMap::new(),
    };

    metrics.sparsity = SparsityMetric {
        overall: 0.7,
        structured_sparsity: 0.5,
        unstructured_sparsity: 0.2,
        layer_sparsity: HashMap::new(),
    };

    metrics.accuracy_retention = AccuracyRetention::new(0.95, 0.93);

    metrics.inference_speedup = InferenceSpeedup::calculate(100.0, 25.0);

    metrics.compression_ratio = CompressionRatio::calculate(
        400_000_000 * 8, // bits
        50_000_000 * 8,
    );

    // Print summary
    println!("\n{}", metrics.summary());

    // Check against targets
    let targets = CompressionTargets {
        min_size_reduction: 0.8,
        min_accuracy: 0.92,
        min_speedup: 3.0,
    };

    println!("\nChecking against targets:");
    println!(
        "  Minimum size reduction: {:.0}%",
        targets.min_size_reduction * 100.0
    );
    println!("  Minimum accuracy: {:.0}%", targets.min_accuracy * 100.0);
    println!("  Minimum speedup: {:.1}x", targets.min_speedup);

    if metrics.meets_targets(&targets) {
        println!("✅ All targets met!");
    } else {
        println!("❌ Some targets not met");
    }

    Ok(())
}

async fn demo_advanced_compression() -> Result<()> {
    println!("Advanced compression techniques...");

    // 1. Attention head pruning for transformers
    println!("\n1. Attention Head Pruning:");
    let head_pruner = HeadPruner::new(12, 64); // 12 attention heads with 64-dim head
    println!("   Pruning 4 out of 12 attention heads");
    println!("   This reduces multi-head attention compute by 33%");

    // 2. Combined distillation strategy
    println!("\n2. Combined Distillation Strategy:");
    let strategy = DistillationStrategy::Combined {
        response_weight: 0.5,
        feature_weight: 0.3,
        attention_weight: 0.2,
    };
    println!("   Response distillation: 50%");
    println!("   Feature distillation: 30%");
    println!("   Attention distillation: 20%");

    // 3. Layer pruning
    println!("\n3. Layer Pruning:");
    let mut importance_scores = HashMap::new();
    importance_scores.insert("encoder.layer.4".to_string(), 0.3);
    importance_scores.insert("encoder.layer.5".to_string(), 0.2);
    importance_scores.insert("encoder.layer.8".to_string(), 0.4);

    let layer_pruner = LayerPruner::with_importance_scores(importance_scores);
    println!("   Removing layers with low importance scores");
    println!("   This can reduce model depth significantly");

    // 4. Hardware-aware compression
    println!("\n4. Hardware-Aware Compression:");
    println!("   Structured pruning for GPU efficiency");
    println!("   INT8 quantization for CPU/edge devices");
    println!("   Channel-wise pruning for mobile deployment");

    Ok(())
}

// Helper functions for creating mock models
#[allow(dead_code)]
fn create_mock_model() -> MockModel {
    MockModel::new(10_000_000)
}

#[allow(dead_code)]
fn create_mock_teacher_model() -> MockModel {
    MockModel::new(100_000_000)
}

#[allow(dead_code)]
fn create_mock_student_model() -> MockModel {
    MockModel::new(10_000_000)
}

// Mock model implementation
#[allow(dead_code)]
struct MockModel {
    num_params: usize,
}

#[allow(dead_code)]
impl MockModel {
    fn new(num_params: usize) -> Self {
        Self { num_params }
    }
}

impl Model for MockModel {
    type Config = MockConfig;
    type Input = Tensor;
    type Output = Tensor;

    fn forward(&self, input: Self::Input) -> Result<Self::Output> {
        Ok(input)
    }

    fn load_pretrained(&mut self, _reader: &mut dyn std::io::Read) -> Result<()> {
        Ok(())
    }

    fn get_config(&self) -> &Self::Config {
        &MockConfig
    }

    fn num_parameters(&self) -> usize {
        self.num_params
    }
}

#[derive(serde::Serialize, serde::Deserialize)]
#[allow(dead_code)]
struct MockConfig;

impl trustformers_core::traits::Config for MockConfig {
    fn architecture(&self) -> &'static str {
        "mock"
    }
}
