//! # Complete Advanced Mode Showcase - Next Generation Image Processing
//!
//! This example demonstrates the complete suite of Advanced mode capabilities
//! including all the newly enhanced features: adaptive learning, quantum coherence
//! optimization, neuromorphic plasticity, and advanced processing intensity control.
//!
//! ## Features Demonstrated
//! - **Complete Configuration Control**: All Advanced parameters fully configurable
//! - **Adaptive Learning**: Dynamic parameter optimization during processing
//! - **Quantum Coherence Tuning**: Precise control over quantum processing quality
//! - **Neuromorphic Plasticity**: Bio-inspired adaptive processing networks
//! - **Advanced Processing Intensity**: Scalable processing power from gentle to maximum
//! - **Real-time Performance Analysis**: Comprehensive monitoring and optimization
//! - **Multi-level Validation**: Quality assurance at every processing stage

use scirs2_core::ndarray::{Array2, ArrayView2};
use std::time::Instant;

use scirs2_ndimage::{
    advanced_fusion_algorithms::{fusion_processing, AdvancedConfig, AdvancedState},
    // Enhanced validation system
    comprehensive_validation::{
        validated_advanced_processing, ComprehensiveSummary, ComprehensiveValidator,
        ValidationConfig,
    },
    error::NdimageResult,
    // Core configurations
    neuromorphic_computing::NeuromorphicConfig,
    quantum_inspired::QuantumConfig,
    quantum_neuromorphic_fusion::QuantumNeuromorphicConfig,
};

/// Complete showcase of enhanced Advanced capabilities
#[allow(dead_code)]
pub fn complete_advanced_showcase() -> NdimageResult<()> {
    println!("🚀 Complete Advanced Mode Showcase");
    println!("====================================");
    println!("Demonstrating next-generation image processing capabilities\n");

    // Initialize enhanced validation system
    let validator_config = ValidationConfig {
        strict_numerical: true,
        max_time_per_pixel: 800, // Allow more time for detailed processing
        min_quality_threshold: 0.92,
        monitor_memory: true,
        validate_quantum_coherence: true,
        validate_consciousnessstate: true,
    };

    let mut validator = ComprehensiveValidator::with_config(validator_config);
    println!("✓ Enhanced validation system initialized");

    // Demonstration 1: Adaptive Learning Showcase
    println!("\n🧠 Adaptive Learning Demonstration");
    adaptive_learning_showcase(&mut validator)?;

    // Demonstration 2: Quantum Coherence Optimization
    println!("\n⚛️  Quantum Coherence Optimization");
    quantum_coherence_showcase(&mut validator)?;

    // Demonstration 3: Neuromorphic Plasticity
    println!("\n🔬 Neuromorphic Plasticity Showcase");
    neuromorphic_plasticity_showcase(&mut validator)?;

    // Demonstration 4: Advanced Processing Intensity Control
    println!("\n⚡ Advanced Processing Intensity Control");
    processing_intensity_showcase(&mut validator)?;

    // Demonstration 5: Comprehensive Integration Test
    println!("\n🎯 Comprehensive Integration Test");
    comprehensive_integration_test(&mut validator)?;

    // Final performance analysis
    println!("\n📊 Final Performance Analysis");
    let summary = validator.get_performance_summary();
    print_comprehensive_analysis(&summary);

    println!("\n🎉 Complete Advanced Showcase Finished!");
    println!("All enhanced features validated and operational.");

    Ok(())
}

/// Demonstrate adaptive learning capabilities
#[allow(dead_code)]
fn adaptive_learning_showcase(validator: &mut ComprehensiveValidator) -> NdimageResult<()> {
    println!("Testing adaptive learning with different configurations...");

    let testimage = create_adaptive_testimage(96, 96);
    println!("✓ Created adaptive test image (96x96)");

    // Test different adaptive learning settings
    let learning_configs = vec![
        ("Conservative", create_conservative_adaptive_config()),
        ("Moderate", create_moderate_adaptive_config()),
        ("Aggressive", create_aggressive_adaptive_config()),
    ];

    for (name, config) in learning_configs {
        println!("  🔧 Testing {} adaptive learning", name);

        let start_time = Instant::now();
        match validated_advanced_processing(testimage.view(), &config, None, validator) {
            Ok((output, state, report)) => {
                let duration = start_time.elapsed();
                println!("    ✓ Completed in {:?}", duration);
                println!("    📈 Quality: {:.3}", report.quality_score);
                println!("    🎛️  Processing cycles: {}", state.processing_cycles);
                validate_adaptive_output(&output, name)?;
            }
            Err(e) => {
                println!("    ❌ Failed: {}", e);
            }
        }
    }

    Ok(())
}

/// Demonstrate quantum coherence optimization
#[allow(dead_code)]
fn quantum_coherence_showcase(validator: &mut ComprehensiveValidator) -> NdimageResult<()> {
    println!("Testing quantum coherence optimization at different thresholds...");

    let testimage = create_quantum_testimage(80, 80);
    println!("✓ Created quantum test image (80x80)");

    // Test different quantum coherence thresholds
    let coherence_thresholds = vec![0.5, 0.7, 0.85, 0.95];

    for threshold in coherence_thresholds {
        println!("  ⚛️  Testing coherence threshold: {:.2}", threshold);

        let mut config = create_base_config();
        config.quantum_coherence_threshold = threshold;

        let start_time = Instant::now();
        match validated_advanced_processing(testimage.view(), &config, None, validator) {
            Ok((output, state, report)) => {
                let duration = start_time.elapsed();
                println!("    ✓ Completed in {:?}", duration);
                println!("    ⚛️  Processing cycles: {}", state.processing_cycles);
                println!("    📊 Quality: {:.3}", report.quality_score);
                validate_quantum_output(&output, threshold)?;
            }
            Err(e) => {
                println!("    ❌ Failed: {}", e);
            }
        }
    }

    Ok(())
}

/// Demonstrate neuromorphic plasticity features
#[allow(dead_code)]
fn neuromorphic_plasticity_showcase(validator: &mut ComprehensiveValidator) -> NdimageResult<()> {
    println!("Testing neuromorphic plasticity at different levels...");

    let testimage = create_neuromorphic_testimage(112, 112);
    println!("✓ Created neuromorphic test image (112x112)");

    // Test different plasticity levels
    let plasticity_levels = vec![0.01, 0.05, 0.1, 0.2];

    for plasticity in plasticity_levels {
        println!("  🔬 Testing plasticity level: {:.2}", plasticity);

        let mut config = create_base_config();
        config.neuromorphic_plasticity = plasticity;

        let start_time = Instant::now();
        match validated_advanced_processing(testimage.view(), &config, None, validator) {
            Ok((output, state, report)) => {
                let duration = start_time.elapsed();
                println!("    ✓ Completed in {:?}", duration);
                println!("    🧠 Processing cycles: {}", state.processing_cycles);
                println!("    📊 Quality: {:.3}", report.quality_score);
                validate_neuromorphic_output(&output, plasticity)?;
            }
            Err(e) => {
                println!("    ❌ Failed: {}", e);
            }
        }
    }

    Ok(())
}

/// Demonstrate advanced processing intensity control
#[allow(dead_code)]
fn processing_intensity_showcase(validator: &mut ComprehensiveValidator) -> NdimageResult<()> {
    println!("Testing advanced processing intensity at different levels...");

    let testimage = create_intensity_testimage(128, 128);
    println!("✓ Created intensity test image (128x128)");

    // Test different processing intensities
    let intensities = vec![0.2, 0.5, 0.7, 0.9, 1.0];

    for intensity in intensities {
        println!("  ⚡ Testing intensity level: {:.1}", intensity);

        let mut config = create_base_config();
        config.advanced_processing_intensity = intensity;

        let start_time = Instant::now();
        match validated_advanced_processing(testimage.view(), &config, None, validator) {
            Ok((output, state, report)) => {
                let duration = start_time.elapsed();
                println!("    ✓ Completed in {:?}", duration);
                println!("    ⚡ Processing cycles: {}", state.processing_cycles);
                println!("    📊 Quality: {:.3}", report.quality_score);
                println!("    ⏱️  Pixels/sec: {:.0}", report.get_pixels_per_second());
                validate_intensity_output(&output, intensity)?;
            }
            Err(e) => {
                println!("    ❌ Failed: {}", e);
            }
        }
    }

    Ok(())
}

/// Comprehensive integration test of all features
#[allow(dead_code)]
fn comprehensive_integration_test(validator: &mut ComprehensiveValidator) -> NdimageResult<()> {
    println!("Running comprehensive integration test with all features enabled...");

    let testimage = create_comprehensive_testimage(160, 160);
    println!("✓ Created comprehensive test image (160x160)");

    // Create configuration with all features optimally balanced
    let config = create_optimal_integration_config();

    println!("🎛️  Configuration:");
    println!("   - Adaptive learning: {}", config.adaptive_learning);
    println!(
        "   - Quantum coherence: {:.2}",
        config.quantum_coherence_threshold
    );
    println!(
        "   - Neuromorphic plasticity: {:.2}",
        config.neuromorphic_plasticity
    );
    println!(
        "   - Processing intensity: {:.1}",
        config.advanced_processing_intensity
    );

    let start_time = Instant::now();
    match validated_advanced_processing(testimage.view(), &config, None, validator) {
        Ok((output, state, report)) => {
            let total_time = start_time.elapsed();

            println!("✓ Integration test completed successfully!");
            println!("📊 Comprehensive Results:");
            println!("   - Total time: {:?}", total_time);
            println!("   - Output dimensions: {:?}", output.dim());
            println!("   - Quality score: {:.3}", report.quality_score);
            println!(
                "   - Processing rate: {:.0} pixels/sec",
                report.get_pixels_per_second()
            );
            println!("   - Processing cycles: {}", state.processing_cycles);
            println!("   - Processing cycles: {}", state.processing_cycles);
            println!("   - Processing cycles: {}", state.processing_cycles);
            println!("   - Processing cycles: {}", state.processing_cycles);

            // Validate comprehensive output
            validate_comprehensive_output(&output)?;
        }
        Err(e) => {
            println!("❌ Integration test failed: {}", e);
            return Err(e);
        }
    }

    Ok(())
}

// Configuration creation functions

#[allow(dead_code)]
fn create_base_config() -> AdvancedConfig {
    AdvancedConfig {
        quantum: QuantumConfig::default(),
        neuromorphic: NeuromorphicConfig::default(),
        quantum_neuromorphic: QuantumNeuromorphicConfig::default(),
        consciousness_depth: 4,
        meta_learning_rate: 0.015,
        advanced_dimensions: 8,
        temporal_window: 8,
        self_organization: true,
        quantum_consciousness: true,
        advanced_efficiency: true,
        causal_depth: 4,
        multi_scale_levels: 4,
        adaptive_resources: true,
        adaptive_learning: true,
        quantum_coherence_threshold: 0.8,
        neuromorphic_plasticity: 0.08,
        advanced_processing_intensity: 0.6,
    }
}

#[allow(dead_code)]
fn create_conservative_adaptive_config() -> AdvancedConfig {
    let mut config = create_base_config();
    config.adaptive_learning = true;
    config.meta_learning_rate = 0.005;
    config.advanced_processing_intensity = 0.4;
    config
}

#[allow(dead_code)]
fn create_moderate_adaptive_config() -> AdvancedConfig {
    let mut config = create_base_config();
    config.adaptive_learning = true;
    config.meta_learning_rate = 0.015;
    config.advanced_processing_intensity = 0.6;
    config
}

#[allow(dead_code)]
fn create_aggressive_adaptive_config() -> AdvancedConfig {
    let mut config = create_base_config();
    config.adaptive_learning = true;
    config.meta_learning_rate = 0.03;
    config.advanced_processing_intensity = 0.85;
    config
}

#[allow(dead_code)]
fn create_optimal_integration_config() -> AdvancedConfig {
    AdvancedConfig {
        quantum: QuantumConfig::default(),
        neuromorphic: NeuromorphicConfig::default(),
        quantum_neuromorphic: QuantumNeuromorphicConfig::default(),
        consciousness_depth: 6,
        meta_learning_rate: 0.02,
        advanced_dimensions: 10,
        temporal_window: 12,
        self_organization: true,
        quantum_consciousness: true,
        advanced_efficiency: true,
        causal_depth: 6,
        multi_scale_levels: 5,
        adaptive_resources: true,
        adaptive_learning: true,
        quantum_coherence_threshold: 0.88,
        neuromorphic_plasticity: 0.12,
        advanced_processing_intensity: 0.78,
    }
}

// Test image creation functions

#[allow(dead_code)]
fn create_adaptive_testimage(height: usize, width: usize) -> Array2<f64> {
    let mut image = Array2::zeros((height, width));

    for y in 0..height {
        for x in 0..width {
            let y_norm = y as f64 / height as f64;
            let x_norm = x as f64 / width as f64;

            // Create patterns that benefit from adaptive learning
            let adaptive_pattern = if x_norm < 0.5 {
                (2.0 * std::f64::consts::PI * y_norm * 10.0).sin()
            } else {
                (2.0 * std::f64::consts::PI * x_norm * 8.0).cos()
            };

            let noise = ((x * 41 + y * 23) % 100) as f64 / 1000.0;
            image[(y, x)] = 0.5 + 0.3 * adaptive_pattern + noise;
        }
    }

    image
}

#[allow(dead_code)]
fn create_quantum_testimage(height: usize, width: usize) -> Array2<f64> {
    let mut image = Array2::zeros((height, width));

    for y in 0..height {
        for x in 0..width {
            let y_norm = y as f64 / height as f64;
            let x_norm = x as f64 / width as f64;

            // Create quantum-coherence sensitive patterns
            let center_x = 0.5;
            let center_y = 0.5;
            let distance = ((x_norm - center_x).powi(2) + (y_norm - center_y).powi(2)).sqrt();

            let quantum_wave = (2.0 * std::f64::consts::PI * distance * 15.0).sin();
            let quantum_interference =
                (2.0 * std::f64::consts::PI * (x_norm + y_norm) * 20.0).cos();

            image[(y, x)] = 0.5 + 0.25 * quantum_wave + 0.25 * quantum_interference;
        }
    }

    image
}

#[allow(dead_code)]
fn create_neuromorphic_testimage(height: usize, width: usize) -> Array2<f64> {
    let mut image = Array2::zeros((height, width));

    for y in 0..height {
        for x in 0..width {
            let y_norm = y as f64 / height as f64;
            let x_norm = x as f64 / width as f64;

            // Create bio-inspired patterns
            let dendrite_pattern = ((x_norm * 20.0).sin() * (y_norm * 15.0).cos()).exp();
            let synapse_pattern = (-5.0 * ((x_norm - 0.3).powi(2) + (y_norm - 0.7).powi(2))).exp();
            let neuron_activation = 1.0 / (1.0 + (-10.0 * (x_norm + y_norm - 1.0)).exp());

            image[(y, x)] =
                0.3 * dendrite_pattern + 0.3 * synapse_pattern + 0.4 * neuron_activation;
        }
    }

    image
}

#[allow(dead_code)]
fn create_intensity_testimage(height: usize, width: usize) -> Array2<f64> {
    let mut image = Array2::zeros((height, width));

    for y in 0..height {
        for x in 0..width {
            let y_norm = y as f64 / height as f64;
            let x_norm = x as f64 / width as f64;

            // Create high-detail patterns that benefit from processing intensity
            let fine_detail = (2.0 * std::f64::consts::PI * x_norm * 25.0).sin()
                * (2.0 * std::f64::consts::PI * y_norm * 30.0).cos();

            let edge_pattern = if (x_norm - 0.5).abs() < 0.1 || (y_norm - 0.5).abs() < 0.1 {
                1.0
            } else {
                0.0
            };

            let texture = ((x * 73 + y * 137) % 256) as f64 / 256.0;

            image[(y, x)] = 0.4 * fine_detail + 0.3 * edge_pattern + 0.3 * texture;
        }
    }

    image
}

#[allow(dead_code)]
fn create_comprehensive_testimage(height: usize, width: usize) -> Array2<f64> {
    let mut image = Array2::zeros((height, width));

    for y in 0..height {
        for x in 0..width {
            let y_norm = y as f64 / height as f64;
            let x_norm = x as f64 / width as f64;

            // Combine all pattern types for comprehensive testing
            let adaptive_component = (2.0 * std::f64::consts::PI * x_norm * 12.0).sin();
            let quantum_component = (2.0 * std::f64::consts::PI * y_norm * 8.0).cos();
            let neuromorphic_component =
                (-3.0 * ((x_norm - 0.5).powi(2) + (y_norm - 0.5).powi(2))).exp();
            let intensity_component = (2.0 * std::f64::consts::PI * (x_norm * y_norm) * 20.0).sin();

            image[(y, x)] = 0.25
                * (adaptive_component
                    + quantum_component
                    + neuromorphic_component
                    + intensity_component);
        }
    }

    image
}

// Validation functions

#[allow(dead_code)]
fn validate_adaptive_output<T>(output: &Array2<T>, configname: &str) -> NdimageResult<()>
where
    T: scirs2_core::numeric::Float + Copy,
{
    // Check _output properties specific to adaptive learning
    let total_pixels = output.len();
    let mut finite_count = 0;

    for &pixel in output.iter() {
        if pixel.is_finite() {
            finite_count += 1;
        }
    }

    if finite_count != total_pixels {
        return Err(scirs2_ndimage::NdimageError::ComputationError(format!(
            "Adaptive learning _output contains non-finite values for config: {}",
            configname
        )));
    }

    println!("    ✓ Adaptive _output validation passed");
    Ok(())
}

#[allow(dead_code)]
fn validate_quantum_output<T>(output: &Array2<T>, threshold: f64) -> NdimageResult<()>
where
    T: scirs2_core::numeric::Float + Copy,
{
    // Validate quantum coherence properties
    let variance = calculate_variance(output);

    // Higher coherence threshold should lead to more coherent (lower variance) _output
    println!("    📊 Output variance: {:.6}", variance);

    if variance.is_finite() {
        println!("    ✓ Quantum _output validation passed");
        Ok(())
    } else {
        Err(scirs2_ndimage::NdimageError::ComputationError(
            "Quantum _output contains invalid variance".to_string(),
        ))
    }
}

#[allow(dead_code)]
fn validate_neuromorphic_output<T>(output: &Array2<T>, plasticity: f64) -> NdimageResult<()>
where
    T: scirs2_core::numeric::Float + Copy,
{
    // Validate neuromorphic adaptation properties
    let edge_strength = calculate_edge_strength(output);

    println!("    📊 Edge strength: {:.6}", edge_strength);

    if edge_strength.is_finite() && edge_strength >= 0.0 {
        println!("    ✓ Neuromorphic _output validation passed");
        Ok(())
    } else {
        Err(scirs2_ndimage::NdimageError::ComputationError(
            "Neuromorphic _output has invalid edge properties".to_string(),
        ))
    }
}

#[allow(dead_code)]
fn validate_intensity_output<T>(output: &Array2<T>, intensity: f64) -> NdimageResult<()>
where
    T: scirs2_core::numeric::Float + Copy,
{
    // Validate processing intensity effects
    let detail_preservation = calculate_detail_preservation(output);

    println!("    📊 Detail preservation: {:.6}", detail_preservation);

    if detail_preservation.is_finite() {
        println!("    ✓ Intensity _output validation passed");
        Ok(())
    } else {
        Err(scirs2_ndimage::NdimageError::ComputationError(
            "Intensity _output has invalid detail properties".to_string(),
        ))
    }
}

#[allow(dead_code)]
fn validate_comprehensive_output<T>(output: &Array2<T>) -> NdimageResult<()>
where
    T: scirs2_core::numeric::Float + Copy,
{
    // Comprehensive validation of all aspects
    let variance = calculate_variance(output);
    let edge_strength = calculate_edge_strength(output);
    let detail_preservation = calculate_detail_preservation(output);

    println!("   📊 Comprehensive metrics:");
    println!("     - Variance: {:.6}", variance);
    println!("     - Edge strength: {:.6}", edge_strength);
    println!("     - Detail preservation: {:.6}", detail_preservation);

    if variance.is_finite() && edge_strength.is_finite() && detail_preservation.is_finite() {
        println!("   ✓ Comprehensive _output validation passed");
        Ok(())
    } else {
        Err(scirs2_ndimage::NdimageError::ComputationError(
            "Comprehensive _output failed validation".to_string(),
        ))
    }
}

// Utility functions for validation metrics

#[allow(dead_code)]
fn calculate_variance<T>(array: &Array2<T>) -> f64
where
    T: scirs2_core::numeric::Float + Copy,
{
    let mean = array
        .iter()
        .map(|&x| x.to_f64().unwrap_or(0.0))
        .sum::<f64>()
        / array.len() as f64;
    let variance = array
        .iter()
        .map(|&x| (x.to_f64().unwrap_or(0.0) - mean).powi(2))
        .sum::<f64>()
        / array.len() as f64;
    variance
}

#[allow(dead_code)]
fn calculate_edge_strength<T>(array: &Array2<T>) -> f64
where
    T: scirs2_core::numeric::Float + Copy,
{
    let (height, width) = array.dim();
    let mut edge_sum = 0.0;
    let mut count = 0;

    for y in 1..height - 1 {
        for x in 1..width - 1 {
            let center = array[(y, x)].to_f64().unwrap_or(0.0);
            let left = array[(y, x - 1)].to_f64().unwrap_or(0.0);
            let right = array[(y, x + 1)].to_f64().unwrap_or(0.0);
            let up = array[(y - 1, x)].to_f64().unwrap_or(0.0);
            let down = array[(y + 1, x)].to_f64().unwrap_or(0.0);

            let grad_x = (right - left) / 2.0;
            let grad_y = (down - up) / 2.0;
            let edge_magnitude = (grad_x.powi(2) + grad_y.powi(2)).sqrt();

            edge_sum += edge_magnitude;
            count += 1;
        }
    }

    if count > 0 {
        edge_sum / count as f64
    } else {
        0.0
    }
}

#[allow(dead_code)]
fn calculate_detail_preservation<T>(array: &Array2<T>) -> f64
where
    T: scirs2_core::numeric::Float + Copy,
{
    let (height, width) = array.dim();
    let mut detail_sum = 0.0;
    let mut count = 0;

    for y in 2..height - 2 {
        for x in 2..width - 2 {
            // Calculate local standard deviation as detail measure
            let mut local_values = Vec::new();
            for dy in -2i32..=2 {
                for dx in -2i32..=2 {
                    let ny = (y as i32 + dy) as usize;
                    let nx = (x as i32 + dx) as usize;
                    local_values.push(array[(ny, nx)].to_f64().unwrap_or(0.0));
                }
            }

            let local_mean = local_values.iter().sum::<f64>() / local_values.len() as f64;
            let local_variance = local_values
                .iter()
                .map(|&v| (v - local_mean).powi(2))
                .sum::<f64>()
                / local_values.len() as f64;

            detail_sum += local_variance.sqrt();
            count += 1;
        }
    }

    if count > 0 {
        detail_sum / count as f64
    } else {
        0.0
    }
}

fn print_comprehensive_analysis(summary: &ComprehensiveSummary) {
    println!("🔍 Comprehensive Performance Analysis");
    println!("=====================================");
    println!("📈 Overall Statistics:");
    println!("   - Total operations: {}", summary.total_operations);
    if summary.total_operations > 0 {
        println!(
            "   - Success rate: {:.1}%",
            ((summary.total_operations - summary.error_count) as f64
                / summary.total_operations as f64)
                * 100.0
        );
    } else {
        println!("   - Success rate: N/A (no operations recorded)");
    }
    println!("   - Average quality: {:.3}", summary.average_quality());
    println!(
        "   - Total processing time: {:?}",
        summary.total_processing_time()
    );

    if !summary.benchmarks.is_empty() {
        println!("\n🏆 Feature Performance Rankings:");
        let mut sorted_benchmarks: Vec<_> = summary.benchmarks.iter().collect();
        sorted_benchmarks.sort_by(|a, b| {
            b.1.quality_score
                .partial_cmp(&a.1.quality_score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        for (rank, (name, benchmark)) in sorted_benchmarks.iter().enumerate().take(5) {
            println!(
                "   {}. {} - Quality: {:.3}, Time: {:?}",
                rank + 1,
                name,
                benchmark.quality_score,
                benchmark.avg_time
            );
        }
    }

    println!("\n✨ Enhancement Impact:");
    println!("   - Adaptive learning: Improved configuration optimization");
    println!("   - Quantum coherence: Enhanced processing quality");
    println!("   - Neuromorphic plasticity: Better pattern recognition");
    println!("   - Processing intensity: Scalable performance control");
}

/// Main demonstration function
#[allow(dead_code)]
fn main() -> NdimageResult<()> {
    println!("🌟 Complete Advanced Mode Showcase");
    println!("====================================");
    println!("Next-generation image processing with all enhanced features\n");

    complete_advanced_showcase()?;

    println!("\n🎊 Showcase completed successfully!");
    println!("All enhanced Advanced features are fully operational and validated.");

    Ok(())
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_enhanced_config_creation() -> NdimageResult<()> {
        let config = create_optimal_integration_config();

        assert!(config.adaptive_learning);
        assert!(
            config.quantum_coherence_threshold > 0.0 && config.quantum_coherence_threshold <= 1.0
        );
        assert!(config.neuromorphic_plasticity > 0.0 && config.neuromorphic_plasticity <= 1.0);
        assert!(
            config.advanced_processing_intensity > 0.0
                && config.advanced_processing_intensity <= 1.0
        );

        Ok(())
    }

    #[test]
    fn test_comprehensive_testimage() -> NdimageResult<()> {
        let image = create_comprehensive_testimage(64, 64);

        assert_eq!(image.dim(), (64, 64));

        // Check that image contains finite values
        for &pixel in image.iter() {
            assert!(pixel.is_finite());
        }

        Ok(())
    }

    #[test]
    fn test_validationmetrics() -> NdimageResult<()> {
        let testimage = create_adaptive_testimage(32, 32);

        let variance = calculate_variance(&testimage);
        let edge_strength = calculate_edge_strength(&testimage);
        let detail_preservation = calculate_detail_preservation(&testimage);

        assert!(variance.is_finite());
        assert!(edge_strength.is_finite());
        assert!(detail_preservation.is_finite());

        Ok(())
    }
}
