//! Real-world Advanced Mode Integration Demo
//!
//! This example demonstrates practical integration of Advanced mode features
//! in realistic scenarios, showing how to build intelligent I/O systems that
//! adapt to changing conditions and optimize performance automatically.

use scirs2_io::error::Result;
use scirs2_io::neural_adaptive_io::{
    AdvancedIoProcessor, NeuralAdaptiveIoController, SystemMetrics,
};
use scirs2_io::quantum_inspired_io::QuantumParallelProcessor;
use std::collections::VecDeque;
use std::sync::{Arc, Mutex};
use std::thread;
use std::time::{Duration, Instant};

/// Intelligent I/O Manager that combines neural and quantum approaches
pub struct IntelligentIoManager {
    #[allow(dead_code)]
    neural_controller: NeuralAdaptiveIoController,
    quantum_processor: QuantumParallelProcessor,
    advanced_think_processor: AdvancedIoProcessor,
    performance_history: Arc<Mutex<VecDeque<f64>>>,
    current_mode: ProcessingMode,
}

#[derive(Debug, Clone, Copy)]
pub enum ProcessingMode {
    Neural,
    Quantum,
    Advanced,
    Adaptive,
}

impl Default for IntelligentIoManager {
    fn default() -> Self {
        Self::new()
    }
}

impl IntelligentIoManager {
    pub fn new() -> Self {
        Self {
            neural_controller: NeuralAdaptiveIoController::new(),
            quantum_processor: QuantumParallelProcessor::new(8),
            advanced_think_processor: AdvancedIoProcessor::new(),
            performance_history: Arc::new(Mutex::new(VecDeque::with_capacity(1000))),
            current_mode: ProcessingMode::Adaptive,
        }
    }

    /// Process data intelligently, choosing the best approach
    pub fn process_intelligently(&mut self, data: &[u8]) -> Result<Vec<u8>> {
        let start_time = Instant::now();

        // Get current system metrics
        let metrics = self.collect_system_metrics();

        // Choose processing strategy based on data and system characteristics
        let chosen_mode = self.choose_processing_mode(data, &metrics);

        // Process data using chosen strategy
        let result = match chosen_mode {
            ProcessingMode::Neural => self.process_neural_adaptive(data),
            ProcessingMode::Quantum => self.process_quantum_parallel(data),
            ProcessingMode::Advanced => self.process_advanced_think(data),
            ProcessingMode::Adaptive => self.process_adaptive_hybrid(data, &metrics),
        }?;

        // Record performance for future optimization
        let processing_time = start_time.elapsed();
        self.record_performance_metrics(data.len(), result.len(), processing_time, chosen_mode);

        self.current_mode = chosen_mode;
        Ok(result)
    }

    /// Neural adaptive processing
    fn process_neural_adaptive(&mut self, data: &[u8]) -> Result<Vec<u8>> {
        self.advanced_think_processor.process_data_adaptive(data)
    }

    /// Quantum-inspired parallel processing
    fn process_quantum_parallel(&mut self, data: &[u8]) -> Result<Vec<u8>> {
        self.quantum_processor.process_quantum_parallel(data)
    }

    /// advanced integrated processing
    fn process_advanced_think(&mut self, data: &[u8]) -> Result<Vec<u8>> {
        self.advanced_think_processor.process_data_adaptive(data)
    }

    /// Adaptive hybrid processing
    fn process_adaptive_hybrid(&mut self, data: &[u8], metrics: &SystemMetrics) -> Result<Vec<u8>> {
        // Analyze data characteristics
        let data_entropy = self.calculate_data_entropy(data);
        let data_size_class = self.classify_data_size(data.len());

        // Choose strategy based on data and system state
        if data_entropy > 0.7 && metrics.cpu_usage < 0.6 {
            // High entropy data with available CPU - use quantum
            self.quantum_processor.process_quantum_parallel(data)
        } else if data_size_class == DataSizeClass::Large && metrics.memory_usage < 0.7 {
            // Large data with available memory - use neural adaptive
            self.advanced_think_processor.process_data_adaptive(data)
        } else {
            // Default to advanced for balanced approach
            self.advanced_think_processor.process_data_adaptive(data)
        }
    }

    /// Choose optimal processing mode based on conditions
    fn choose_processing_mode(&self, data: &[u8], metrics: &SystemMetrics) -> ProcessingMode {
        let data_entropy = self.calculate_data_entropy(data);
        let data_size = data.len();

        // Decision tree based on data characteristics and system state
        if data_size > 100_000 {
            // Large data - prefer neural adaptive for better memory management
            ProcessingMode::Neural
        } else if data_entropy > 0.8 && metrics.cpu_usage < 0.5 {
            // High entropy with available CPU - quantum excels here
            ProcessingMode::Quantum
        } else if metrics.memory_usage > 0.8 || metrics.cpu_usage > 0.9 {
            // Resource constrained - use adaptive strategy
            ProcessingMode::Advanced
        } else {
            // Balanced conditions - use adaptive hybrid
            ProcessingMode::Adaptive
        }
    }

    /// Collect real system metrics (simplified for demo)
    fn collect_system_metrics(&self) -> SystemMetrics {
        // In real implementation, this would collect actual system metrics
        SystemMetrics {
            cpu_usage: 0.6 + (Instant::now().elapsed().as_millis() % 40) as f32 / 100.0,
            memory_usage: 0.5 + (Instant::now().elapsed().as_millis() % 30) as f32 / 100.0,
            disk_usage: 0.4 + (Instant::now().elapsed().as_millis() % 20) as f32 / 100.0,
            network_usage: 0.3 + (Instant::now().elapsed().as_millis() % 25) as f32 / 100.0,
            cache_hit_ratio: 0.7 + (Instant::now().elapsed().as_millis() % 15) as f32 / 100.0,
            throughput: 0.5 + (Instant::now().elapsed().as_millis() % 35) as f32 / 100.0,
            load_average: 0.6,
            available_memory_ratio: 0.4,
        }
    }

    /// Calculate Shannon entropy of data
    fn calculate_data_entropy(&self, 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]
    }

    /// Classify data size for processing decisions
    fn classify_data_size(&self, size: usize) -> DataSizeClass {
        match size {
            0..=1_000 => DataSizeClass::Small,
            1_001..=50_000 => DataSizeClass::Medium,
            50_001..=1_000_000 => DataSizeClass::Large,
            _ => DataSizeClass::VeryLarge,
        }
    }

    /// Record performance metrics for analysis
    fn record_performance_metrics(
        &self,
        input_size: usize,
        output_size: usize,
        processing_time: Duration,
        mode: ProcessingMode,
    ) {
        let throughput = (input_size as f64) / (processing_time.as_secs_f64() * 1024.0 * 1024.0);

        let mut history = self.performance_history.lock().expect("Operation failed");
        history.push_back(throughput);
        if history.len() > 1000 {
            history.pop_front();
        }

        println!("📊 Performance Metrics:");
        println!("   Mode: {:?}", mode);
        println!("   Input Size: {} bytes", input_size);
        println!("   Output Size: {} bytes", output_size);
        println!("   Processing Time: {:.2} ms", processing_time.as_millis());
        println!("   Throughput: {:.2} MB/s", throughput);
        println!(
            "   Compression Ratio: {:.3}",
            output_size as f32 / input_size as f32
        );
    }

    /// Get performance analytics
    pub fn get_performance_analytics(&self) -> PerformanceAnalytics {
        let history = self.performance_history.lock().expect("Operation failed");

        if history.is_empty() {
            return PerformanceAnalytics::default();
        }

        let total_ops = history.len();
        let avg_throughput = history.iter().sum::<f64>() / total_ops as f64;
        let recent_throughput =
            history.iter().rev().take(10).sum::<f64>() / 10.0_f64.min(total_ops as f64);

        let max_throughput = history.iter().fold(0.0_f64, |a, &b| a.max(b));
        let min_throughput = history.iter().fold(f64::INFINITY, |a, &b| a.min(b));

        PerformanceAnalytics {
            total_operations: total_ops,
            average_throughput: avg_throughput,
            recent_throughput,
            max_throughput,
            min_throughput,
            current_mode: self.current_mode,
            improvement_trend: if recent_throughput > avg_throughput {
                TrendDirection::Improving
            } else {
                TrendDirection::Stable
            },
        }
    }

    /// Optimize all processors based on performance history
    pub fn optimize_all_processors(&mut self) -> Result<()> {
        // Optimize quantum processor
        self.quantum_processor.optimize_parameters()?;

        // Get performance stats from all processors
        let neural_stats = self.advanced_think_processor.get_performance_stats();
        let quantum_stats = self.quantum_processor.get_performance_stats();

        println!("🔧 Optimization Complete:");
        println!(
            "   Neural Improvement: {:.2}x",
            neural_stats.improvement_ratio
        );
        println!(
            "   Quantum Efficiency: {:.1}%",
            quantum_stats.average_efficiency * 100.0
        );

        Ok(())
    }
}

#[derive(Debug, Clone, Copy, PartialEq)]
enum DataSizeClass {
    Small,
    Medium,
    Large,
    VeryLarge,
}

#[derive(Debug, Clone)]
pub struct PerformanceAnalytics {
    pub total_operations: usize,
    pub average_throughput: f64,
    pub recent_throughput: f64,
    pub max_throughput: f64,
    pub min_throughput: f64,
    pub current_mode: ProcessingMode,
    pub improvement_trend: TrendDirection,
}

impl Default for PerformanceAnalytics {
    fn default() -> Self {
        Self {
            total_operations: 0,
            average_throughput: 0.0,
            recent_throughput: 0.0,
            max_throughput: 0.0,
            min_throughput: 0.0,
            current_mode: ProcessingMode::Adaptive,
            improvement_trend: TrendDirection::Stable,
        }
    }
}

#[allow(dead_code)]
#[derive(Debug, Clone, Copy)]
pub enum TrendDirection {
    Improving,
    Stable,
    Declining,
}

/// Workload simulation for different scenarios
pub struct WorkloadSimulator {
    manager: IntelligentIoManager,
}

impl Default for WorkloadSimulator {
    fn default() -> Self {
        Self::new()
    }
}

impl WorkloadSimulator {
    pub fn new() -> Self {
        Self {
            manager: IntelligentIoManager::new(),
        }
    }

    /// Simulate a scientific computing workload
    pub fn simulate_scientific_computing(&mut self) -> Result<()> {
        println!("🔬 Scientific Computing Workload Simulation");
        println!("==========================================");

        let scenarios = vec![
            ("Small Dataset Analysis", generate_scientific_data(5_000)),
            ("Medium Simulation Data", generate_scientific_data(25_000)),
            (
                "Large Experimental Results",
                generate_scientific_data(100_000),
            ),
            (
                "High-Frequency Time Series",
                generate_time_series_data(50_000),
            ),
            ("Genomic Sequence Data", generate_genomic_data(30_000)),
        ];

        for (name, data) in scenarios {
            println!("\n📊 Processing: {}", name);
            let start = Instant::now();
            let result = self.manager.process_intelligently(&data)?;
            let total_time = start.elapsed();

            println!("   Total Time: {:.2} ms", total_time.as_millis());
            println!(
                "   Data Reduction: {:.1}%",
                (1.0 - result.len() as f32 / data.len() as f32) * 100.0
            );
        }

        Ok(())
    }

    /// Simulate real-time data streaming
    pub fn simulate_real_time_streaming(&mut self) -> Result<()> {
        println!("\n\n📡 Real-time Data Streaming Simulation");
        println!("=====================================");

        let chunk_size = 8192;
        let num_chunks = 50;

        println!(
            "Processing {} chunks of {} bytes each...",
            num_chunks, chunk_size
        );

        let mut total_processing_time = Duration::default();
        let mut total_data_processed = 0;

        for i in 0..num_chunks {
            let chunk_data = generate_streaming_data(chunk_size, i);

            let start = Instant::now();
            let _result = self.manager.process_intelligently(&chunk_data)?;
            let chunk_time = start.elapsed();

            total_processing_time += chunk_time;
            total_data_processed += chunk_data.len();

            if i % 10 == 0 {
                println!("   Chunk {}: {:.2} ms", i, chunk_time.as_millis());
            }

            // Simulate real-time constraints
            thread::sleep(Duration::from_millis(10));
        }

        let avg_throughput =
            (total_data_processed as f64) / (total_processing_time.as_secs_f64() * 1024.0 * 1024.0);

        println!("\n📈 Streaming Performance Summary:");
        println!(
            "   Total Data: {:.2} MB",
            total_data_processed as f64 / (1024.0 * 1024.0)
        );
        println!(
            "   Total Processing Time: {:.2} ms",
            total_processing_time.as_millis()
        );
        println!("   Average Throughput: {:.2} MB/s", avg_throughput);

        Ok(())
    }

    /// Simulate enterprise data processing
    pub fn simulate_enterprise_processing(&mut self) -> Result<()> {
        println!("\n\n🏢 Enterprise Data Processing Simulation");
        println!("=======================================");

        // Simulate different enterprise workloads
        let workloads = vec![
            ("Database Backup", generate_database_backup_data(80_000)),
            ("Log File Analysis", generate_log_data(60_000)),
            ("Document Processing", generate_document_data(40_000)),
            ("Financial Transactions", generate_financial_data(35_000)),
            ("Customer Analytics", generate_analytics_data(55_000)),
        ];

        for (workload_name, data) in workloads {
            println!("\n💼 Processing: {}", workload_name);

            let start = Instant::now();
            let result = self.manager.process_intelligently(&data)?;
            let processing_time = start.elapsed();

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

            println!("   Processing Time: {:.2} ms", processing_time.as_millis());
            println!("   Throughput: {:.2} MB/s", throughput);
            println!(
                "   Space Efficiency: {:.1}%",
                (1.0 - result.len() as f32 / data.len() as f32) * 100.0
            );
        }

        // Periodic optimization
        println!("\n🔧 Running Optimization...");
        self.manager.optimize_all_processors()?;

        Ok(())
    }

    /// Get comprehensive analytics
    pub fn get_analytics(&self) -> PerformanceAnalytics {
        self.manager.get_performance_analytics()
    }
}

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

    let mut simulator = WorkloadSimulator::new();

    // Run different workload simulations
    simulator.simulate_scientific_computing()?;
    simulator.simulate_real_time_streaming()?;
    simulator.simulate_enterprise_processing()?;

    // Display final analytics
    let analytics = simulator.get_analytics();
    println!("\n\n📊 Final Performance Analytics");
    println!("=============================");
    println!("Total Operations: {}", analytics.total_operations);
    println!(
        "Average Throughput: {:.2} MB/s",
        analytics.average_throughput
    );
    println!("Recent Throughput: {:.2} MB/s", analytics.recent_throughput);
    println!("Peak Throughput: {:.2} MB/s", analytics.max_throughput);
    println!("Current Mode: {:?}", analytics.current_mode);
    println!("Performance Trend: {:?}", analytics.improvement_trend);

    println!("\n🎉 Integration Demo Complete!");
    println!("The Advanced mode successfully adapted to different workloads,");
    println!("demonstrating intelligent strategy selection and performance optimization.");

    Ok(())
}

// Data generation functions for different scenarios

#[allow(dead_code)]
fn generate_scientific_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            // Simulate scientific data with patterns
            let base = (i as f32 * 0.1).sin() * 127.0 + 128.0;
            let noise = ((i * 17) % 256) as f32 * 0.1;
            (base + noise) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_time_series_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            // Simulate time series with trends and seasonality
            let trend = (i as f32 / size as f32) * 50.0;
            let seasonal = (i as f32 * 2.0 * std::f32::consts::PI / 100.0).sin() * 30.0;
            let noise = ((i * 23 + 7) % 256) as f32 * 0.2;
            (128.0 + trend + seasonal + noise) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_genomic_data(size: usize) -> Vec<u8> {
    // Simulate genomic sequences (A, T, G, C)
    (0..size)
        .map(|i| match (i * 31 + 13) % 4 {
            0 => b'A',
            1 => b'T',
            2 => b'G',
            _ => b'C',
        })
        .collect()
}

#[allow(dead_code)]
fn generate_streaming_data(size: usize, chunkid: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            // Simulate streaming data with chunk correlation
            let chunk_factor = (chunkid as f32 * 0.1).cos() * 50.0;
            let local_pattern = ((i * 7 + chunkid * 3) % 256) as f32;
            (chunk_factor + local_pattern) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_database_backup_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            // Simulate structured database data
            if i % 100 < 20 {
                // Headers/metadata
                ((i * 11) % 128) as u8
            } else {
                // Data content
                ((i * 13 + 7) % 256) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_log_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            // Simulate log file patterns
            if i % 50 < 10 {
                // Timestamps
                (i % 10 + 48) as u8 // ASCII digits
            } else {
                // Log content
                ((i * 19 + 37) % 94 + 32) as u8 // Printable ASCII
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_document_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            // Simulate document text
            ((i * 7 + 11) % 94 + 32) as u8 // Printable ASCII
        })
        .collect()
}

#[allow(dead_code)]
fn generate_financial_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            // Simulate financial transaction data
            if i % 20 < 8 {
                // Amount digits
                ((i * 3) % 10 + 48) as u8
            } else {
                // Other transaction data
                ((i * 17 + 23) % 256) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_analytics_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            // Simulate analytics data with correlations
            let base_value = (i as f32 / 1000.0).sin() * 100.0 + 128.0;
            let correlation = ((i / 10) as f32 * 0.5).cos() * 20.0;
            (base_value + correlation) as u8
        })
        .collect()
}
