//! # Advanced Mode Demonstration
//!
//! This example demonstrates the advanced capabilities of the Advanced Text Processing
//! Coordinator, showcasing the integration of multiple AI/ML techniques for enhanced
//! text processing performance.
//!
//! ## Features Demonstrated
//!
//! - Advanced-optimized text processing with neural ensembles
//! - Advanced semantic similarity with SIMD optimizations  
//! - Batch text classification with confidence estimation
//! - Dynamic topic modeling with quality metrics
//! - Comprehensive performance reporting
//! - Real-time adaptation and optimization

use scirs2_text::error::Result;
use scirs2_text::text_coordinator::{AdvancedTextConfig, AdvancedTextCoordinator};

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

    // Configure Advanced mode with all advanced features enabled
    let config = AdvancedTextConfig {
        enable_gpu_acceleration: true,
        enable_simd_optimizations: true,
        enable_neural_ensemble: true,
        enable_real_time_adaptation: true,
        enable_advanced_analytics: true,
        enable_multimodal: true,
        max_memory_usage_mb: 4096, // 4GB for this demo
        optimization_level: 3,     // Maximum optimization
        target_throughput: 2000.0, // 2000 docs/sec target
        enable_predictive_processing: true,
    };

    println!("📋 Configuration:");
    println!("  • GPU Acceleration: {}", config.enable_gpu_acceleration);
    println!(
        "  • SIMD Optimizations: {}",
        config.enable_simd_optimizations
    );
    println!("  • Neural Ensemble: {}", config.enable_neural_ensemble);
    println!(
        "  • Real-time Adaptation: {}",
        config.enable_real_time_adaptation
    );
    println!(
        "  • Advanced Analytics: {}",
        config.enable_advanced_analytics
    );
    println!(
        "  • Target Throughput: {} docs/sec",
        config.target_throughput
    );
    println!("  • Memory Limit: {} MB\n", config.max_memory_usage_mb);

    // Initialize the Advanced coordinator
    println!("🔧 Initializing Advanced Text Coordinator...");
    let coordinator = AdvancedTextCoordinator::new(config)?;
    println!("✅ Coordinator initialized successfully!\n");

    // Demo 1: Advanced-optimized text processing
    demo_advancedtext_processing(&coordinator)?;

    // Demo 2: Advanced semantic similarity
    demo_semantic_similarity(&coordinator)?;

    // Demo 3: Batch classification
    demo_batch_classification(&coordinator)?;

    // Demo 4: Dynamic topic modeling
    demo_topic_modeling(&coordinator)?;

    // Demo 5: Performance reporting
    demo_performance_reporting(&coordinator)?;

    println!("🎉 Advanced Mode Demo completed successfully!");
    println!("All advanced features demonstrated with optimal performance.");

    Ok(())
}

/// Demonstrates advanced-optimized text processing with full feature coordination
#[allow(dead_code)]
fn demo_advancedtext_processing(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("📊 Demo 1: Advanced-Optimized Text Processing");
    println!("==========================================");

    let sampletexts = vec![
        "Artificial intelligence is transforming the way we process and understand text data.".to_string(),
        "Machine learning algorithms can extract meaningful patterns from large corpora of documents.".to_string(),
        "Natural language processing combines computational linguistics with statistical analysis.".to_string(),
        "Deep learning models like transformers have revolutionized text understanding tasks.".to_string(),
        "The future of AI lies in developing more efficient and accurate language models.".to_string(),
    ];

    println!(
        "Processing {} documents with Advanced optimization...",
        sampletexts.len()
    );

    let start_time = std::time::Instant::now();
    let result = coordinator.advanced_processtext(&sampletexts)?;
    let processing_time = start_time.elapsed();

    println!("\n📈 Results:");
    println!("  • Processing Time: {processing_time:?}");
    println!(
        "  • Throughput: {:.2} docs/sec",
        result.performance_metrics.throughput
    );
    println!(
        "  • Memory Efficiency: {:.1}%",
        result.performance_metrics.memory_efficiency * 100.0
    );
    println!(
        "  • Accuracy Estimate: {:.1}%",
        result.performance_metrics.accuracy_estimate * 100.0
    );

    println!("\n🔧 Optimizations Applied:");
    for optimization in &result.optimizations_applied {
        println!("  • {optimization}");
    }

    println!("\n⏱️  Timing Breakdown:");
    println!(
        "  • Preprocessing: {:?}",
        result.timing_breakdown.preprocessing_time
    );
    println!(
        "  • Neural Processing: {:?}",
        result.timing_breakdown.neural_processing_time
    );
    println!(
        "  • Analytics: {:?}",
        result.timing_breakdown.analytics_time
    );
    println!(
        "  • Optimization: {:?}",
        result.timing_breakdown.optimization_time
    );

    println!("\n🎯 Confidence Scores:");
    for (metric, score) in &result.confidence_scores {
        println!("  • {}: {:.1}%", metric, score * 100.0);
    }

    println!(
        "\n📐 Vector Embeddings Shape: {:?}",
        result.primary_result.vectors.shape()
    );
    println!();

    Ok(())
}

/// Demonstrates advanced semantic similarity with multiple metrics
#[allow(dead_code)]
fn demo_semantic_similarity(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("🔍 Demo 2: Advanced Semantic Similarity");
    println!("=======================================");

    let text_pairs = [
        ("The cat sat on the mat", "A feline rested on the rug"),
        (
            "Machine learning is a subset of artificial intelligence",
            "AI includes machine learning as one of its components",
        ),
        ("The weather is sunny today", "It's raining heavily outside"),
    ];

    for (i, (text1, text2)) in text_pairs.iter().enumerate() {
        println!("\n📝 Text Pair {}:", i + 1);
        println!("  Text 1: \"{text1}\"");
        println!("  Text 2: \"{text2}\"");

        let result = coordinator.advanced_semantic_similarity(text1, text2)?;

        println!("\n📊 Similarity Metrics:");
        println!("  • Cosine Similarity: {:.3}", result.cosine_similarity);
        println!("  • Semantic Similarity: {:.3}", result.semantic_similarity);
        println!(
            "  • Contextual Similarity: {:.3}",
            result.contextual_similarity
        );
        println!("  • Confidence Score: {:.3}", result.confidence_score);
        println!("  • Processing Time: {:?}", result.processing_time);
    }

    println!();
    Ok(())
}

/// Demonstrates batch text classification with confidence estimation
#[allow(dead_code)]
fn demo_batch_classification(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("🏷️  Demo 3: Batch Text Classification");
    println!("===================================");

    let texts = vec![
        "This movie was absolutely fantastic! Great acting and storyline.".to_string(),
        "The service at this restaurant was terrible and the food was cold.".to_string(),
        "The new software update includes several bug fixes and performance improvements."
            .to_string(),
        "Breaking news: Major earthquake hits the coastal region.".to_string(),
    ];

    let categories = vec![
        "positive_review".to_string(),
        "negative_review".to_string(),
        "technology".to_string(),
        "news".to_string(),
    ];

    println!(
        "Classifying {} texts into {} categories...",
        texts.len(),
        categories.len()
    );
    println!("\n📊 Categories: {categories:?}");

    let result = coordinator.advanced_classify_batch(&texts, &categories)?;

    println!("\n📈 Classification Results:");
    println!(
        "  • Total Classifications: {}",
        result.classifications.len()
    );
    println!(
        "  • Average Confidence: {:.1}%",
        result.confidence_estimates.iter().sum::<f64>() / result.confidence_estimates.len() as f64
            * 100.0
    );
    println!("  • Processing Time: {:?}", result.processing_time);
    println!(
        "  • Throughput: {:.2} docs/sec",
        result.performance_metrics.throughput
    );

    for (i, confidence) in result.confidence_estimates.iter().enumerate() {
        println!("  • Text {}: {:.1}% confidence", i + 1, confidence * 100.0);
    }

    println!();
    Ok(())
}

/// Demonstrates dynamic topic modeling with quality metrics
#[allow(dead_code)]
fn demo_topic_modeling(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("📚 Demo 4: Dynamic Topic Modeling");
    println!("================================");

    let documents = vec![
        "Machine learning algorithms are used to build predictive models from data.".to_string(),
        "Deep neural networks can learn complex patterns in high-dimensional data.".to_string(),
        "Natural language processing helps computers understand human language.".to_string(),
        "Computer vision enables machines to interpret and analyze visual information.".to_string(),
        "Reinforcement learning trains agents to make decisions through trial and error."
            .to_string(),
        "Data science combines statistics, programming, and domain expertise.".to_string(),
        "Artificial intelligence aims to create systems that can perform human-like tasks."
            .to_string(),
    ];

    println!(
        "Analyzing {} documents for topic extraction...",
        documents.len()
    );

    let result = coordinator.advanced_topic_modeling(&documents, 3)?; // Extract 3 topics

    println!("\n📊 Topic Modeling Results:");
    println!("  • Processing Time: {:?}", result.processing_time);

    println!("\n📈 Quality Metrics:");
    println!(
        "  • Coherence Score: {:.3}",
        result.quality_metrics.coherence_score
    );
    println!(
        "  • Diversity Score: {:.3}",
        result.quality_metrics.diversity_score
    );
    println!(
        "  • Stability Score: {:.3}",
        result.quality_metrics.stability_score
    );
    println!(
        "  • Interpretability Score: {:.3}",
        result.quality_metrics.interpretability_score
    );

    println!();
    Ok(())
}

/// Demonstrates comprehensive performance reporting
#[allow(dead_code)]
fn demo_performance_reporting(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("📊 Demo 5: Performance Reporting");
    println!("===============================");

    let report = coordinator.get_performance_report()?;

    println!("\n🔧 Current Performance Metrics:");
    println!(
        "  • Processing Time: {:?}",
        report.current_metrics.processing_time
    );
    println!(
        "  • Throughput: {:.2} docs/sec",
        report.current_metrics.throughput
    );
    println!(
        "  • Memory Efficiency: {:.1}%",
        report.current_metrics.memory_efficiency * 100.0
    );
    println!(
        "  • Accuracy Estimate: {:.1}%",
        report.current_metrics.accuracy_estimate * 100.0
    );

    println!("\n🖥️  System Utilization:");
    println!("  • CPU: {:.1}%", report.system_utilization.cpu_utilization);
    println!(
        "  • Memory: {:.1}%",
        report.system_utilization.memory_utilization
    );
    println!("  • GPU: {:.1}%", report.system_utilization.gpu_utilization);
    println!(
        "  • Cache Hit Rate: {:.1}%",
        report.system_utilization.cache_hit_rate * 100.0
    );

    println!("\n💡 Optimization Recommendations:");
    for recommendation in &report.optimization_recommendations {
        println!(
            "  • [{}] {} (Impact: {:.0}%)",
            recommendation.category,
            recommendation.recommendation,
            recommendation.impact_estimate * 100.0
        );
    }

    println!("\n⚠️  Performance Bottlenecks:");
    for bottleneck in &report.bottleneck_analysis {
        println!(
            "  • {} ({:.0}% impact): {}",
            bottleneck.component,
            bottleneck.impact * 100.0,
            bottleneck.description
        );
        println!("    Suggestion: {}", bottleneck.suggested_fix);
    }

    println!();
    Ok(())
}
