//! Comprehensive Advanced Mode Showcase
//!
//! This example demonstrates the advanced neural-adaptive and quantum-inspired
//! I/O processing capabilities implemented in scirs2-io's Advanced mode.
//!
//! Features showcased:
//! - Neural adaptive I/O optimization with real-time learning
//! - Quantum-inspired parallel processing with multiple strategies
//! - Performance monitoring and adaptive parameter tuning
//! - Integration between neural and quantum approaches

use scirs2_io::error::Result;
use scirs2_io::neural_adaptive_io::{
    AdvancedIoProcessor, NeuralAdaptiveIoController, PerformanceFeedback, SystemMetrics,
};
use scirs2_io::quantum_inspired_io::QuantumParallelProcessor;
use std::time::Instant;

#[allow(dead_code)]
fn main() -> Result<()> {
    println!("🚀 SciRS2-IO Advanced Mode Comprehensive Showcase");
    println!("====================================================\n");

    // Demo 1: Neural Adaptive I/O Controller
    demonstrate_neural_adaptive_io()?;

    // Demo 2: Quantum-Inspired Parallel Processing
    demonstrate_quantum_parallel_processing()?;

    // Demo 3: Advanced Integrated Processor
    demonstrate_advanced_think_processor()?;

    // Demo 4: Performance Comparison
    demonstrate_performance_comparison()?;

    // Demo 5: Adaptive Learning Showcase
    demonstrate_adaptive_learning()?;

    println!("\n🎉 Advanced Mode Showcase Complete!");
    println!("The advanced I/O processing capabilities demonstrate:");
    println!("  ✅ Neural adaptation with real-time learning");
    println!("  ✅ Quantum-inspired optimization algorithms");
    println!("  ✅ Self-tuning performance optimization");
    println!("  ✅ Multi-strategy processing with automatic selection");
    println!("  ✅ Enterprise-grade monitoring and analytics");

    Ok(())
}

/// Demonstrate neural adaptive I/O controller capabilities
#[allow(dead_code)]
fn demonstrate_neural_adaptive_io() -> Result<()> {
    println!("🧠 Neural Adaptive I/O Controller Demo");
    println!("=====================================");

    let controller = NeuralAdaptiveIoController::new();

    // Simulate different system conditions
    let scenarios = vec![
        (
            "High CPU Load",
            SystemMetrics {
                cpu_usage: 0.9,
                memory_usage: 0.6,
                disk_usage: 0.4,
                network_usage: 0.3,
                cache_hit_ratio: 0.7,
                throughput: 0.3,
                load_average: 0.8,
                available_memory_ratio: 0.4,
            },
        ),
        (
            "Memory Constrained",
            SystemMetrics {
                cpu_usage: 0.5,
                memory_usage: 0.95,
                disk_usage: 0.6,
                network_usage: 0.4,
                cache_hit_ratio: 0.5,
                throughput: 0.4,
                load_average: 0.7,
                available_memory_ratio: 0.05,
            },
        ),
        (
            "Network Intensive",
            SystemMetrics {
                cpu_usage: 0.4,
                memory_usage: 0.5,
                disk_usage: 0.3,
                network_usage: 0.9,
                cache_hit_ratio: 0.8,
                throughput: 0.7,
                load_average: 0.5,
                available_memory_ratio: 0.5,
            },
        ),
        (
            "Balanced Load",
            SystemMetrics {
                cpu_usage: 0.6,
                memory_usage: 0.6,
                disk_usage: 0.5,
                network_usage: 0.5,
                cache_hit_ratio: 0.8,
                throughput: 0.6,
                load_average: 0.6,
                available_memory_ratio: 0.4,
            },
        ),
    ];

    for (scenario_name, metrics) in scenarios {
        println!("\n📊 Scenario: {}", scenario_name);

        let decisions = controller.get_optimization_decisions(&metrics)?;
        let concrete_params = decisions.to_concrete_params(8, 64 * 1024);

        println!("  Neural Network Recommendations:");
        println!(
            "    Thread Count Factor: {:.3}",
            decisions.thread_count_factor
        );
        println!(
            "    Buffer Size Factor: {:.3}",
            decisions.buffer_size_factor
        );
        println!("    Compression Level: {:.3}", decisions.compression_level);
        println!("    Cache Priority: {:.3}", decisions.cache_priority);
        println!("    SIMD Factor: {:.3}", decisions.simd_factor);

        println!("  Concrete Parameters:");
        println!("    Threads: {}", concrete_params.thread_count);
        println!("    Buffer Size: {} KB", concrete_params.buffer_size / 1024);
        println!(
            "    Compression: Level {}",
            concrete_params.compression_level
        );
        println!("    Use Cache: {}", concrete_params.use_cache);
        println!("    Use SIMD: {}", concrete_params.use_simd);

        // Simulate performance feedback
        let feedback = PerformanceFeedback {
            throughput_mbps: 50.0 + (decisions.thread_count_factor * 30.0),
            latency_ms: 10.0 + (1.0 - decisions.buffer_size_factor) * 20.0,
            cpu_efficiency: 0.7 + (decisions.simd_factor * 0.2),
            memory_efficiency: 0.6 + (decisions.cache_priority * 0.3),
            error_rate: 0.01 * (1.0 - decisions.compression_level),
        };

        controller.record_performance(metrics, decisions, feedback.clone())?;

        println!("  Simulated Performance:");
        println!("    Throughput: {:.1} MB/s", feedback.throughput_mbps);
        println!("    Latency: {:.1} ms", feedback.latency_ms);
        println!(
            "    CPU Efficiency: {:.1}%",
            feedback.cpu_efficiency * 100.0
        );
        println!(
            "    Memory Efficiency: {:.1}%",
            feedback.memory_efficiency * 100.0
        );
        println!("    Error Rate: {:.3}%", feedback.error_rate * 100.0);
    }

    let stats = controller.get_adaptation_stats();
    println!("\n📈 Neural Adaptation Statistics:");
    println!("  Total Adaptations: {}", stats.total_adaptations);
    println!(
        "  Baseline Throughput: {:.1} MB/s",
        stats.baseline_throughput
    );
    println!(
        "  Recent Avg Throughput: {:.1} MB/s",
        stats.recent_avg_throughput
    );
    println!("  Improvement Ratio: {:.2}x", stats.improvement_ratio);
    println!(
        "  Adaptation Effectiveness: {:.1}%",
        stats.adaptation_effectiveness * 100.0
    );

    Ok(())
}

/// Demonstrate quantum-inspired parallel processing
#[allow(dead_code)]
fn demonstrate_quantum_parallel_processing() -> Result<()> {
    println!("\n\n⚛️  Quantum-Inspired Parallel Processing Demo");
    println!("============================================");

    let mut processor = QuantumParallelProcessor::new(5);

    // Test different data patterns
    let testdatasets = vec![
        ("Random Data", generate_randomdata(1000)),
        ("Structured Data", generate_structureddata(1000)),
        ("Compressed Pattern", generate_compressed_pattern(1000)),
        ("High Entropy", generate_high_entropydata(1000)),
        ("Low Entropy", generate_low_entropydata(1000)),
    ];

    for (dataset_name, data) in testdatasets {
        println!("\n🔬 Processing: {}", dataset_name);

        let start_time = Instant::now();
        let result = processor.process_quantum_parallel(&data)?;
        let processing_time = start_time.elapsed();

        println!("  Original Size: {} bytes", data.len());
        println!("  Processed Size: {} bytes", result.len());
        println!("  Processing Time: {:.2} ms", processing_time.as_millis());
        println!(
            "  Throughput: {:.1} MB/s",
            (data.len() as f64) / (processing_time.as_secs_f64() * 1024.0 * 1024.0)
        );

        // Analyze quantum processing characteristics
        let entropy_original = calculate_entropy(&data);
        let entropy_processed = calculate_entropy(&result);
        println!(
            "  Entropy Change: {:.3} → {:.3}",
            entropy_original, entropy_processed
        );

        let compression_ratio = result.len() as f32 / data.len() as f32;
        println!("  Size Ratio: {:.3}", compression_ratio);
    }

    // Demonstrate quantum parameter optimization
    println!("\n🔧 Quantum Parameter Optimization");
    processor.optimize_parameters()?;

    let stats = processor.get_performance_stats();
    println!("  Quantum Performance Statistics:");
    println!("    Total Operations: {}", stats.total_operations);
    println!(
        "    Average Efficiency: {:.1}%",
        stats.average_efficiency * 100.0
    );
    println!(
        "    Recent Efficiency: {:.1}%",
        stats.recent_efficiency * 100.0
    );
    println!("    Quantum Coherence: {:.2}", stats.quantum_coherence);
    println!(
        "    Superposition Usage: {:.1}%",
        stats.superposition_usage * 100.0
    );
    println!(
        "    Entanglement Usage: {:.1}%",
        stats.entanglement_usage * 100.0
    );

    Ok(())
}

/// Demonstrate advanced integrated processor
#[allow(dead_code)]
fn demonstrate_advanced_think_processor() -> Result<()> {
    println!("\n\n🚀 Advanced Integrated Processor Demo");
    println!("=======================================");

    let mut processor = AdvancedIoProcessor::new();

    // Progressive data sizes to show scaling
    let datasizes = vec![100, 1000, 10000, 50000];

    for &size in &datasizes {
        println!("\n📊 Processing {} bytes of data", size);

        let testdata = generate_mixed_patterndata(size);

        let start_time = Instant::now();
        let result = processor.process_data_adaptive(&testdata)?;
        let processing_time = start_time.elapsed();

        let throughput = (size as f64) / (processing_time.as_secs_f64() * 1024.0 * 1024.0);

        println!("  Processing Time: {:.2} ms", processing_time.as_millis());
        println!("  Throughput: {:.1} MB/s", throughput);
        println!("  Output Size: {} bytes", result.len());
        println!(
            "  Efficiency Ratio: {:.3}",
            result.len() as f32 / testdata.len() as f32
        );
    }

    let performance_stats = processor.get_performance_stats();
    println!("\n📈 Advanced Performance Summary:");
    println!(
        "  Total Adaptations: {}",
        performance_stats.total_adaptations
    );
    println!(
        "  Baseline Performance: {:.1} MB/s",
        performance_stats.baseline_throughput
    );
    println!(
        "  Current Performance: {:.1} MB/s",
        performance_stats.recent_avg_throughput
    );
    println!(
        "  Overall Improvement: {:.2}x",
        performance_stats.improvement_ratio
    );
    println!(
        "  Adaptation Effectiveness: {:.1}%",
        performance_stats.adaptation_effectiveness * 100.0
    );

    Ok(())
}

/// Demonstrate performance comparison between approaches
#[allow(dead_code)]
fn demonstrate_performance_comparison() -> Result<()> {
    println!("\n\n⚡ Performance Comparison Demo");
    println!("=============================");

    let testdata = generate_benchmarkdata(10000);

    // Neural adaptive approach
    println!("\n🧠 Neural Adaptive Approach:");
    let mut neural_processor = AdvancedIoProcessor::new();
    let start = Instant::now();
    let neural_result = neural_processor.process_data_adaptive(&testdata)?;
    let neural_time = start.elapsed();

    println!("  Processing Time: {:.2} ms", neural_time.as_millis());
    println!(
        "  Throughput: {:.1} MB/s",
        (testdata.len() as f64) / (neural_time.as_secs_f64() * 1024.0 * 1024.0)
    );
    println!("  Output Size: {} bytes", neural_result.len());

    // Quantum-inspired approach
    println!("\n⚛️  Quantum-Inspired Approach:");
    let mut quantum_processor = QuantumParallelProcessor::new(5);
    let start = Instant::now();
    let quantum_result = quantum_processor.process_quantum_parallel(&testdata)?;
    let quantum_time = start.elapsed();

    println!("  Processing Time: {:.2} ms", quantum_time.as_millis());
    println!(
        "  Throughput: {:.1} MB/s",
        (testdata.len() as f64) / (quantum_time.as_secs_f64() * 1024.0 * 1024.0)
    );
    println!("  Output Size: {} bytes", quantum_result.len());

    // Performance comparison
    println!("\n📊 Comparison Summary:");
    let neural_throughput = (testdata.len() as f64) / (neural_time.as_secs_f64() * 1024.0 * 1024.0);
    let quantum_throughput =
        (testdata.len() as f64) / (quantum_time.as_secs_f64() * 1024.0 * 1024.0);

    if neural_throughput > quantum_throughput {
        println!(
            "  🏆 Neural approach is {:.2}x faster",
            neural_throughput / quantum_throughput
        );
    } else {
        println!(
            "  🏆 Quantum approach is {:.2}x faster",
            quantum_throughput / neural_throughput
        );
    }

    println!(
        "  Neural Compression: {:.3}",
        neural_result.len() as f32 / testdata.len() as f32
    );
    println!(
        "  Quantum Compression: {:.3}",
        quantum_result.len() as f32 / testdata.len() as f32
    );

    Ok(())
}

/// Demonstrate adaptive learning over time
#[allow(dead_code)]
fn demonstrate_adaptive_learning() -> Result<()> {
    println!("\n\n📚 Adaptive Learning Showcase");
    println!("============================");

    let controller = NeuralAdaptiveIoController::new();

    // Simulate learning over time with consistent workload pattern
    println!("\n🔄 Simulating Learning Over Time...");

    let base_metrics = SystemMetrics {
        cpu_usage: 0.7,
        memory_usage: 0.6,
        disk_usage: 0.5,
        network_usage: 0.4,
        cache_hit_ratio: 0.7,
        throughput: 0.5,
        load_average: 0.6,
        available_memory_ratio: 0.4,
    };

    let baseline_performance = 40.0;

    for iteration in 1..=10 {
        // Slightly vary the metrics to simulate real conditions
        let varied_metrics = SystemMetrics {
            cpu_usage: base_metrics.cpu_usage + (iteration as f32 * 0.01),
            memory_usage: base_metrics.memory_usage + (iteration as f32 * 0.005),
            disk_usage: base_metrics.disk_usage + (iteration as f32 * 0.003),
            network_usage: base_metrics.network_usage + (iteration as f32 * 0.002),
            cache_hit_ratio: base_metrics.cache_hit_ratio + (iteration as f32 * 0.01),
            throughput: base_metrics.throughput + (iteration as f32 * 0.015),
            load_average: base_metrics.load_average,
            available_memory_ratio: base_metrics.available_memory_ratio,
        };

        let decisions = controller.get_optimization_decisions(&varied_metrics)?;

        // Simulate improving performance as the network learns
        let performance_improvement = 1.0 + (iteration as f32 * 0.05);
        let current_performance = baseline_performance * performance_improvement;

        let feedback = PerformanceFeedback {
            throughput_mbps: current_performance,
            latency_ms: 15.0 - (iteration as f32 * 0.5),
            cpu_efficiency: 0.7 + (iteration as f32 * 0.02),
            memory_efficiency: 0.6 + (iteration as f32 * 0.025),
            error_rate: 0.02 / (iteration as f32),
        };

        controller.record_performance(varied_metrics, decisions, feedback.clone())?;

        println!(
            "  Iteration {}: Throughput {:.1} MB/s, Latency {:.1} ms",
            iteration, feedback.throughput_mbps, feedback.latency_ms
        );
    }

    let final_stats = controller.get_adaptation_stats();
    println!("\n📈 Learning Results:");
    println!("  Initial Performance: {:.1} MB/s", baseline_performance);
    println!(
        "  Final Performance: {:.1} MB/s",
        final_stats.recent_avg_throughput
    );
    println!(
        "  Performance Improvement: {:.1}%",
        ((final_stats.recent_avg_throughput / baseline_performance) - 1.0) * 100.0
    );
    println!(
        "  Adaptation Effectiveness: {:.1}%",
        final_stats.adaptation_effectiveness * 100.0
    );

    Ok(())
}

// Helper functions for generating test data

#[allow(dead_code)]
fn generate_randomdata(size: usize) -> Vec<u8> {
    (0..size).map(|i| ((i * 17 + 23) % 256) as u8).collect()
}

#[allow(dead_code)]
fn generate_structureddata(size: usize) -> Vec<u8> {
    (0..size).map(|i| (i % 64) as u8).collect()
}

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

#[allow(dead_code)]
fn generate_high_entropydata(size: usize) -> Vec<u8> {
    (0..size).map(|i| ((i * 157 + 73) % 256) as u8).collect()
}

#[allow(dead_code)]
fn generate_low_entropydata(size: usize) -> Vec<u8> {
    vec![42; size]
}

#[allow(dead_code)]
fn generate_mixed_patterndata(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 10 < 3 {
                ((i * 13) % 256) as u8
            } else {
                (i % 16) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_benchmarkdata(size: usize) -> Vec<u8> {
    (0..size).map(|i| ((i * 31 + i * i) % 256) as u8).collect()
}

#[allow(dead_code)]
fn calculate_entropy(data: &[u8]) -> f32 {
    let mut frequency = [0u32; 256];
    for &byte in data {
        frequency[byte as usize] += 1;
    }

    let len = data.len() as f32;
    let mut entropy = 0.0;

    for &freq in &frequency {
        if freq > 0 {
            let p = freq as f32 / len;
            entropy -= p * p.log2();
        }
    }

    entropy / 8.0 // Normalize to [0, 1]
}
