//! Comprehensive advanced Integration Demonstration
//!
//! This example showcases the complete integration of all advanced components
//! working together in a comprehensive real-world scenario. It demonstrates:
//! - Unified intelligence coordination across all systems
//! - Adaptive resource orchestration with emergent optimization
//! - Cross-system performance correlation and meta-learning
//! - Advanced pattern recognition driving system-wide optimizations
//! - Real-time performance intelligence and autonomous improvement

use scirs2_io::advanced_coordinator::AdvancedCoordinator;
use scirs2_io::enhanced_algorithms::AdvancedPatternRecognizer;
use scirs2_io::error::Result;
use scirs2_io::neural_adaptive_io::NeuralAdaptiveIoController;
use scirs2_io::quantum_inspired_io::QuantumParallelProcessor;
use std::time::{Duration, Instant};

#[allow(dead_code)]
fn main() -> Result<()> {
    println!("🌟 SciRS2-IO advanced Comprehensive Integration Demo");
    println!("=====================================================\n");

    // Phase 1: System Initialization and Intelligence Baseline
    demonstrate_intelligent_initialization()?;

    // Phase 2: Adaptive Multi-System Processing
    demonstrate_adaptive_multi_system_processing()?;

    // Phase 3: Emergent Optimization Discovery
    demonstrate_emergent_optimization_discovery()?;

    // Phase 4: Cross-System Meta-Learning
    demonstrate_cross_system_meta_learning()?;

    // Phase 5: Real-World Workflow Optimization
    demonstrate_real_world_workflow_optimization()?;

    // Phase 6: Autonomous System Evolution
    demonstrate_autonomous_system_evolution()?;

    println!("\n🎯 Comprehensive Integration Demo Complete!");
    println!("Advanced advanced Capabilities Demonstrated:");
    println!("  ✅ Unified intelligence coordination");
    println!("  ✅ Emergent optimization discovery");
    println!("  ✅ Cross-system meta-learning");
    println!("  ✅ Autonomous system evolution");
    println!("  ✅ Real-world performance optimization");

    Ok(())
}

/// Demonstrate intelligent system initialization with capability assessment
#[allow(dead_code)]
fn demonstrate_intelligent_initialization() -> Result<()> {
    println!("🚀 Phase 1: Intelligent System Initialization");
    println!("==============================================");

    let start = Instant::now();

    // Initialize advanced Coordinator
    let coordinator = AdvancedCoordinator::new()?;
    println!("✅ advanced Coordinator initialized");

    // Initialize Advanced Pattern Recognizer
    let pattern_recognizer = AdvancedPatternRecognizer::new();
    println!("✅ Advanced Pattern Recognizer initialized");

    // Initialize Neural Adaptive Controller
    let _neural_controller = NeuralAdaptiveIoController::new();
    println!("✅ Neural Adaptive Controller initialized");

    // Initialize Quantum Parallel Processor
    let _quantum_processor = QuantumParallelProcessor::new(8);
    println!("✅ Quantum Parallel Processor initialized");

    // Platform capability assessment
    let capabilities = scirs2_core::simd_ops::PlatformCapabilities::detect();
    println!("✅ Platform capabilities assessed:");
    println!("   - SIMD Available: {}", capabilities.simd_available);
    println!("   - AVX Support: {}", capabilities.avx2_available);
    println!("   - CUDA Available: {}", capabilities.cuda_available);

    let init_time = start.elapsed();
    println!("📊 Initialization completed in {:?}\n", init_time);

    Ok(())
}

/// Demonstrate adaptive multi-system processing with intelligent coordination
#[allow(dead_code)]
fn demonstrate_adaptive_multi_system_processing() -> Result<()> {
    println!("🧠 Phase 2: Adaptive Multi-System Processing");
    println!("=============================================");

    // Create diverse test datasets that will trigger different optimization strategies
    let datasets = vec![
        ("repetitive_data", generate_repetitive_data(10000)),
        ("random_data", generate_random_data(10000)),
        ("sequential_data", generate_sequential_data(10000)),
        ("fractal_data", generate_fractal_data(10000)),
        ("mixed_pattern_data", generate_mixed_pattern_data(10000)),
    ];

    let mut coordinator = AdvancedCoordinator::new()?;
    let mut pattern_recognizer = AdvancedPatternRecognizer::new();

    for (dataset_name, data) in datasets {
        println!("📊 Processing dataset: {}", dataset_name);

        let start = Instant::now();

        // Analyze patterns to inform processing strategy
        let pattern_analysis = pattern_recognizer.analyze_patterns(&data)?;
        println!(
            "   - Pattern analysis completed ({} patterns detected)",
            pattern_analysis.pattern_scores.len()
        );

        // Process using advanced coordination
        let processing_result = coordinator.process_with_advanced_intelligence(&data)?;

        let processing_time = start.elapsed();
        println!("   - Processing completed in {:?}", processing_time);
        println!(
            "   - Efficiency score: {:.3}",
            processing_result.efficiency_score()
        );

        // Show optimization recommendations
        if !pattern_analysis.optimization_recommendations.is_empty() {
            println!("   - Optimization recommendations:");
            for rec in &pattern_analysis.optimization_recommendations {
                println!(
                    "     • {}: {} (confidence: {:.2})",
                    rec.optimization_type, rec.reason, rec.confidence
                );
            }
        }

        println!();
    }

    Ok(())
}

/// Demonstrate emergent optimization discovery through pattern correlation
#[allow(dead_code)]
fn demonstrate_emergent_optimization_discovery() -> Result<()> {
    println!("🔍 Phase 3: Emergent Optimization Discovery");
    println!("===========================================");

    let mut pattern_recognizer = AdvancedPatternRecognizer::new();
    let mut coordinator = AdvancedCoordinator::new()?;

    // Process multiple related datasets to discover emergent patterns
    let related_datasets = [
        generate_correlated_data_sequence(5000, 1),
        generate_correlated_data_sequence(5000, 2),
        generate_correlated_data_sequence(5000, 3),
    ];

    let mut emergent_patterns = Vec::new();
    let mut meta_patterns = Vec::new();

    for (i, data) in related_datasets.iter().enumerate() {
        println!("🔬 Analyzing dataset sequence {}", i + 1);

        let analysis = pattern_recognizer.analyze_patterns(data)?;

        // Collect emergent patterns
        for pattern in &analysis.emergent_patterns {
            println!(
                "   ✨ Emergent pattern detected: {} (confidence: {:.3})",
                pattern.pattern_type, pattern.confidence
            );
            emergent_patterns.push(pattern.clone());
        }

        // Collect meta-patterns
        for meta_pattern in &analysis.meta_patterns {
            println!(
                "   🌐 Meta-pattern discovered: {} patterns correlated (strength: {:.3})",
                meta_pattern.pattern_combination.len(),
                meta_pattern.correlation_strength
            );
            meta_patterns.push(meta_pattern.clone());
        }

        // Apply discovered optimizations
        let processing_result = coordinator.process_with_emergent_optimization(data, &analysis)?;
        println!(
            "   📈 Emergent optimization applied - efficiency: {:.3}",
            processing_result.efficiency_score()
        );
        println!();
    }

    println!("📋 Emergent Discovery Summary:");
    println!("   - Total emergent patterns: {}", emergent_patterns.len());
    println!("   - Total meta-patterns: {}", meta_patterns.len());
    println!("   - System intelligence increased through pattern discovery\n");

    Ok(())
}

/// Demonstrate cross-system meta-learning and knowledge transfer
#[allow(dead_code)]
fn demonstrate_cross_system_meta_learning() -> Result<()> {
    println!("🎓 Phase 4: Cross-System Meta-Learning");
    println!("======================================");

    let mut coordinator = AdvancedCoordinator::new()?;

    // Simulate learning from different domains
    let domain_data = vec![
        ("scientific_simulation", generate_scientific_data(8000)),
        ("financial_timeseries", generate_financial_data(8000)),
        ("image_processing", generate_image_like_data(8000)),
        ("network_traffic", generate_network_data(8000)),
    ];

    println!("🧠 Training meta-learning system across domains...");

    for (domain, data) in domain_data {
        println!("   📚 Learning from domain: {}", domain);

        let start = Instant::now();

        // Process and learn from this domain
        let learning_result = coordinator.learn_from_domain(&data, domain)?;

        let learning_time = start.elapsed();
        println!("     - Learning completed in {:?}", learning_time);
        println!(
            "     - Knowledge patterns extracted: {}",
            learning_result.pattern_count()
        );
        println!(
            "     - Transferable optimizations: {}",
            learning_result.optimization_count()
        );
    }

    // Demonstrate knowledge transfer to new, unseen data
    println!("\n🔄 Demonstrating knowledge transfer to unseen data...");
    let unseen_data = generate_hybrid_domain_data(10000);

    let transfer_result = coordinator.apply_transferred_knowledge(&unseen_data)?;
    println!("   ✅ Knowledge transfer successful");
    println!(
        "   📊 Performance improvement: {:.1}%",
        transfer_result.improvement_percentage()
    );
    println!(
        "   🎯 Confidence in transfer: {:.3}",
        transfer_result.confidence()
    );
    println!();

    Ok(())
}

/// Demonstrate real-world workflow optimization scenarios
#[allow(dead_code)]
fn demonstrate_real_world_workflow_optimization() -> Result<()> {
    println!("🏭 Phase 5: Real-World Workflow Optimization");
    println!("============================================");

    // Simulate real-world scenarios
    let workflows = vec![
        ("large_file_processing", simulate_large_file_workflow()),
        ("streaming_data_pipeline", simulate_streaming_workflow()),
        (
            "batch_scientific_computing",
            simulate_batch_computing_workflow(),
        ),
        (
            "real_time_analytics",
            simulate_realtime_analytics_workflow(),
        ),
    ];

    let mut coordinator = AdvancedCoordinator::new()?;

    for (workflow_name, workflow_data) in workflows {
        println!("⚙️  Optimizing workflow: {}", workflow_name);

        let start = Instant::now();

        // Apply advanced optimization to workflow
        let optimization_result = coordinator.optimize_workflow(&workflow_data, workflow_name)?;

        let optimization_time = start.elapsed();
        println!("   ✅ Workflow optimized in {:?}", optimization_time);
        println!(
            "   📈 Performance gain: {:.1}%",
            optimization_result.performance_gain()
        );
        println!(
            "   💾 Memory efficiency: {:.1}%",
            optimization_result.memory_efficiency()
        );
        println!(
            "   ⚡ Energy savings: {:.1}%",
            optimization_result.energy_savings()
        );

        // Show specific optimizations applied
        for optimization in optimization_result.applied_optimizations() {
            println!(
                "     • {}: {}",
                optimization.name(),
                optimization.description()
            );
        }
        println!();
    }

    Ok(())
}

/// Demonstrate autonomous system evolution and self-improvement
#[allow(dead_code)]
fn demonstrate_autonomous_system_evolution() -> Result<()> {
    println!("🔄 Phase 6: Autonomous System Evolution");
    println!("======================================");

    let mut coordinator = AdvancedCoordinator::new()?;

    // Enable autonomous evolution mode
    coordinator.enable_autonomous_evolution()?;
    println!("✅ Autonomous evolution mode enabled");

    // Simulate extended operation with continuous improvement
    println!("🔬 Simulating extended operation with continuous learning...");

    let evolution_cycles = 5;
    for cycle in 1..=evolution_cycles {
        println!("   🔄 Evolution cycle {}/{}", cycle, evolution_cycles);

        // Generate varied workload
        let workload = generate_varied_workload(cycle * 2000);

        let start = Instant::now();

        // Process with autonomous improvement
        let evolution_result = coordinator.process_with_evolution(&workload)?;

        let cycle_time = start.elapsed();
        println!("     - Cycle completed in {:?}", cycle_time);
        println!(
            "     - System efficiency: {:.3}",
            evolution_result.system_efficiency()
        );
        println!(
            "     - New adaptations discovered: {}",
            evolution_result.new_adaptations()
        );

        // Show system improvements
        let improvements = evolution_result.system_improvements();
        if !improvements.is_empty() {
            println!("     - System improvements this cycle:");
            for improvement in improvements {
                println!(
                    "       • {}: +{:.1}% efficiency",
                    improvement.component(),
                    improvement.efficiency_gain()
                );
            }
        }

        std::thread::sleep(Duration::from_millis(100)); // Simulate processing time
    }

    // Show final evolution summary
    let evolution_summary = coordinator.get_evolution_summary()?;
    println!("\n📊 Autonomous Evolution Summary:");
    println!(
        "   - Total adaptations discovered: {}",
        evolution_summary.total_adaptations()
    );
    println!(
        "   - Overall efficiency improvement: {:.1}%",
        evolution_summary.overall_improvement()
    );
    println!(
        "   - System intelligence level: {:.3}",
        evolution_summary.intelligence_level()
    );
    println!(
        "   - Autonomous capabilities unlocked: {}",
        evolution_summary.autonomous_capabilities()
    );

    Ok(())
}

// Helper functions for generating test data

#[allow(dead_code)]
fn generate_repetitive_data(size: usize) -> Vec<u8> {
    let pattern = [1, 2, 3, 4, 5];
    (0..size).map(|i| pattern[i % pattern.len()]).collect()
}

#[allow(dead_code)]
fn generate_random_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|_| scirs2_core::random::random::<u8>())
        .collect()
}

#[allow(dead_code)]
fn generate_sequential_data(size: usize) -> Vec<u8> {
    (0..size).map(|i| (i % 256) as u8).collect()
}

#[allow(dead_code)]
fn generate_fractal_data(size: usize) -> Vec<u8> {
    let mut data = Vec::with_capacity(size);
    for i in 0..size {
        let value = ((i as f64).sin() * 127.0 + 128.0) as u8;
        data.push(value);
    }
    data
}

#[allow(dead_code)]
fn generate_mixed_pattern_data(size: usize) -> Vec<u8> {
    let mut data = Vec::with_capacity(size);
    for i in 0..size {
        let value = match i % 4 {
            0 => (i % 256) as u8,
            1 => scirs2_core::random::random::<u8>(),
            2 => ((i / 4) % 256) as u8,
            _ => 128,
        };
        data.push(value);
    }
    data
}

#[allow(dead_code)]
fn generate_correlated_data_sequence(size: usize, sequenceid: usize) -> Vec<u8> {
    (0..size)
        .map(|i| ((i + sequenceid * 100) % 256) as u8)
        .collect()
}

#[allow(dead_code)]
fn generate_scientific_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let t = i as f64 / 100.0;
            ((t.sin() * t.cos() * 127.0) + 128.0) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_financial_data(size: usize) -> Vec<u8> {
    let mut data = Vec::with_capacity(size);
    let mut value = 128.0;
    for _ in 0..size {
        value += (scirs2_core::random::random::<f64>() - 0.5) * 10.0;
        value = value.clamp(0.0, 255.0);
        data.push(value as u8);
    }
    data
}

#[allow(dead_code)]
fn generate_image_like_data(size: usize) -> Vec<u8> {
    let width = (size as f64).sqrt() as usize;
    let mut data = Vec::with_capacity(size);
    for y in 0..width {
        for x in 0..width {
            let distance = ((x as f64 - width as f64 / 2.0).powi(2)
                + (y as f64 - width as f64 / 2.0).powi(2))
            .sqrt();
            let value = ((distance * 8.0).sin() * 127.0 + 128.0) as u8;
            data.push(value);
        }
    }
    data.resize(size, 0);
    data
}

#[allow(dead_code)]
fn generate_network_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 100 < 10 {
                // Simulate packet headers
                scirs2_core::random::random::<u8>()
            } else {
                (i % 256) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_hybrid_domain_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| match i % 5 {
            0..=1 => generate_scientific_data(1)[0],
            2 => generate_financial_data(1)[0],
            3 => generate_image_like_data(1)[0],
            _ => generate_network_data(1)[0],
        })
        .collect()
}

// Workflow simulation functions

#[allow(dead_code)]
fn simulate_large_file_workflow() -> Vec<u8> {
    generate_mixed_pattern_data(50000)
}

#[allow(dead_code)]
fn simulate_streaming_workflow() -> Vec<u8> {
    generate_sequential_data(20000)
}

#[allow(dead_code)]
fn simulate_batch_computing_workflow() -> Vec<u8> {
    generate_scientific_data(30000)
}

#[allow(dead_code)]
fn simulate_realtime_analytics_workflow() -> Vec<u8> {
    generate_financial_data(15000)
}

#[allow(dead_code)]
fn generate_varied_workload(size: usize) -> Vec<u8> {
    let mut data = Vec::with_capacity(size);
    for i in 0..size {
        let value = match i % 6 {
            0 => generate_repetitive_data(1)[0],
            1 => generate_random_data(1)[0],
            2 => generate_sequential_data(1)[0],
            3 => generate_fractal_data(1)[0],
            4 => generate_scientific_data(1)[0],
            _ => generate_network_data(1)[0],
        };
        data.push(value);
    }
    data
}
