//! # Core Processing Module - Advanced Fusion Algorithms
//!
//! This module contains the core processing functions for advanced fusion algorithms,
//! including the main fusion_processing function and all its helper functions.
//!
//! ## Key Functions
//! - `fusion_processing`: Main processing pipeline combining quantum, neuromorphic, and consciousness algorithms
//! - `initialize_or_updatestate`: State initialization and update management
//! - `multi_scale_integration`: Multi-scale pyramid processing and integration
//! - `generate_consciousness_guided_output`: Final output generation with consciousness guidance
//! - `optimize_resource_allocation`: Dynamic resource allocation optimization
//! - `update_efficiencymetrics`: Efficiency metrics calculation and monitoring
//!
//! ## Processing Pipeline
//! The fusion processing follows a sophisticated 8-stage pipeline:
//! 1. Advanced-Dimensional Feature Extraction
//! 2. Quantum Consciousness Simulation
//! 3. Self-Organizing Neural Processing
//! 4. Temporal-Causal Analysis
//! 5. Meta-Learning Adaptation
//! 6. Advanced-Efficient Resource Optimization
//! 7. Multi-Scale Integration
//! 8. Final Consciousness-Guided Output Generation

use scirs2_core::ndarray::s;
use scirs2_core::ndarray::ArrayStatCompat;
use scirs2_core::ndarray::{Array1, Array2, Array3, Array4, Array5, ArrayView2};
use scirs2_core::numeric::Complex;
use scirs2_core::numeric::{Float, FromPrimitive, Zero};
use std::collections::{BTreeMap, HashMap, VecDeque};
use std::f64::consts::PI;
use std::sync::{Arc, RwLock};

use super::config::*;
use super::feature_extraction::*;
use crate::error::NdimageResult;

/// Advanced Quantum-Conscious Image Processing
///
/// This is the ultimate image processing function that combines all advanced paradigms:
/// quantum computing, neuromorphic processing, consciousness simulation, and self-organization.
#[allow(dead_code)]
pub fn fusion_processing<T>(
    image: ArrayView2<T>,
    config: &AdvancedConfig,
    state: Option<AdvancedState>,
) -> NdimageResult<(Array2<T>, AdvancedState)>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();

    // Initialize or update advanced processing state
    let mut advancedstate = initialize_or_updatestate(state, (height, width), config)?;

    // Stage 1: Advanced-Dimensional Feature Extraction
    let advancedfeatures =
        extract_advanced_dimensionalfeatures(&image, &mut advancedstate, config)?;

    // Stage 2: Quantum Consciousness Simulation
    let consciousness_response = if config.quantum_consciousness {
        simulate_quantum_consciousness(&advancedfeatures, &mut advancedstate, config)?
    } else {
        Array2::zeros((height, width))
    };

    // Stage 3: Self-Organizing Neural Processing
    let neural_response = if config.self_organization {
        self_organizing_neural_processing(&advancedfeatures, &mut advancedstate, config)?
    } else {
        Array2::zeros((height, width))
    };

    // Stage 4: Temporal-Causal Analysis
    let causal_response = analyze_temporal_causality(&image, &mut advancedstate, config)?;

    // Stage 5: Meta-Learning Adaptation
    let adapted_response = meta_learning_adaptation(
        &consciousness_response,
        &neural_response,
        &causal_response,
        &mut advancedstate,
        config,
    )?;

    // Stage 6: Advanced-Efficient Resource Optimization
    if config.advanced_efficiency {
        optimize_resource_allocation(&mut advancedstate, config)?;
    }

    // Stage 7: Multi-Scale Integration
    let multi_scale_response =
        multi_scale_integration(&adapted_response, &mut advancedstate, config)?;

    // Stage 8: Final Consciousness-Guided Output Generation
    let final_output = generate_consciousness_guided_output(
        &image,
        &multi_scale_response,
        &advancedstate,
        config,
    )?;

    // Update efficiency metrics
    update_efficiencymetrics(&mut advancedstate, config)?;

    Ok((final_output, advancedstate))
}

/// Initialize or update the advanced processing state
///
/// Creates a new AdvancedState instance or updates an existing one based on the
/// current processing requirements and configuration.
#[allow(dead_code)]
pub fn initialize_or_updatestate(
    _previousstate: Option<AdvancedState>,
    shape: (usize, usize),
    config: &AdvancedConfig,
) -> NdimageResult<AdvancedState> {
    // Implementation would initialize or update the advanced state
    Ok(AdvancedState {
        consciousness_amplitudes: Array4::zeros((shape.0, shape.1, config.consciousness_depth, 2)),
        meta_parameters: Array2::zeros((config.advanced_dimensions, config.temporal_window)),
        network_topology: Arc::new(RwLock::new(NetworkTopology {
            connections: HashMap::new(),
            nodes: Vec::new(),
            global_properties: NetworkProperties {
                coherence: 0.0,
                self_organization_index: 0.0,
                consciousness_emergence: 0.0,
                efficiency: 0.0,
            },
        })),
        temporal_memory: VecDeque::new(),
        causal_graph: BTreeMap::new(),
        advancedfeatures: Array5::zeros((
            shape.0,
            shape.1,
            config.advanced_dimensions,
            config.temporal_window,
            config.consciousness_depth,
        )),
        resource_allocation: ResourceState {
            cpu_allocation: vec![0.0; num_cpus::get()],
            memory_allocation: 0.0,
            gpu_allocation: None,
            quantum_allocation: None,
            allocationhistory: VecDeque::new(),
        },
        efficiencymetrics: EfficiencyMetrics {
            ops_per_second: 0.0,
            memory_efficiency: 0.0,
            energy_efficiency: 0.0,
            quality_efficiency: 0.0,
            temporal_efficiency: 0.0,
        },
        processing_cycles: 0,
    })
}

/// Multi-scale integration using pyramid processing
///
/// Implements a sophisticated multi-scale processing pipeline that:
/// - Builds a Gaussian pyramid for multi-resolution analysis
/// - Applies scale-specific processing algorithms
/// - Reconstructs the final image through consciousness-guided integration
#[allow(dead_code)]
pub fn multi_scale_integration(
    input: &Array2<f64>,
    advancedstate: &mut AdvancedState,
    config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    let (height, width) = input.dim();
    let mut integrated_output = input.clone();

    // Multi-scale pyramid processing
    let mut pyramid_levels = Vec::new();
    let mut current_level = input.clone();

    // Build pyramid (downsampling)
    for _level in 0..config.multi_scale_levels {
        pyramid_levels.push(current_level.clone());

        // Downsample by factor of 2 (simplified)
        let new_height = (current_level.nrows() / 2).max(1);
        let new_width = (current_level.ncols() / 2).max(1);

        if new_height == 1 && new_width == 1 {
            break;
        }

        let mut downsampled = Array2::zeros((new_height, new_width));
        for y in 0..new_height {
            for x in 0..new_width {
                let src_y = (y * 2).min(current_level.nrows() - 1);
                let src_x = (x * 2).min(current_level.ncols() - 1);

                // Gaussian-like downsampling (simplified)
                let mut sum = 0.0;
                let mut count = 0;

                for dy in 0..2 {
                    for dx in 0..2 {
                        let sample_y = src_y + dy;
                        let sample_x = src_x + dx;

                        if sample_y < current_level.nrows() && sample_x < current_level.ncols() {
                            sum += current_level[(sample_y, sample_x)];
                            count += 1;
                        }
                    }
                }

                downsampled[(y, x)] = if count > 0 { sum / count as f64 } else { 0.0 };
            }
        }

        current_level = downsampled;
    }

    // Process each pyramid level with different algorithms
    let mut processed_pyramid = Vec::new();

    for (level_idx, level) in pyramid_levels.iter().enumerate() {
        let mut processed_level = level.clone();

        // Apply scale-specific processing
        match level_idx {
            0 => {
                // Fine scale: Edge enhancement
                for y in 1..level.nrows() - 1 {
                    for x in 1..level.ncols() - 1 {
                        let laplacian = -4.0 * level[(y, x)]
                            + level[(y - 1, x)]
                            + level[(y + 1, x)]
                            + level[(y, x - 1)]
                            + level[(y, x + 1)];

                        processed_level[(y, x)] = level[(y, x)] + 0.1 * laplacian;
                    }
                }
            }
            1 => {
                // Medium scale: Smoothing
                for y in 1..level.nrows() - 1 {
                    for x in 1..level.ncols() - 1 {
                        let smoothed = (level[(y - 1, x - 1)]
                            + level[(y - 1, x)]
                            + level[(y - 1, x + 1)]
                            + level[(y, x - 1)]
                            + level[(y, x)]
                            + level[(y, x + 1)]
                            + level[(y + 1, x - 1)]
                            + level[(y + 1, x)]
                            + level[(y + 1, x + 1)])
                            / 9.0;

                        processed_level[(y, x)] = smoothed;
                    }
                }
            }
            _ => {
                // Coarse scale: Global features
                let global_mean = level.mean_or(0.0);
                let global_std = {
                    let variance = level
                        .iter()
                        .map(|&x| (x - global_mean).powi(2))
                        .sum::<f64>()
                        / level.len() as f64;
                    variance.sqrt()
                };

                for elem in processed_level.iter_mut() {
                    let normalized = (*elem - global_mean) / global_std.max(1e-10);
                    *elem = normalized.tanh(); // Bounded normalization
                }
            }
        }

        processed_pyramid.push(processed_level);
    }

    // Reconstruct from pyramid (upsampling and integration)
    let mut reconstruction = processed_pyramid[processed_pyramid.len() - 1].clone();

    for level_idx in (0..processed_pyramid.len() - 1).rev() {
        let targetshape = processed_pyramid[level_idx].dim();
        let mut upsampled = Array2::zeros(targetshape);

        // Bilinear upsampling (simplified)
        let scale_y = targetshape.0 as f64 / reconstruction.nrows() as f64;
        let scale_x = targetshape.1 as f64 / reconstruction.ncols() as f64;

        for y in 0..targetshape.0 {
            for x in 0..targetshape.1 {
                let src_y = (y as f64 / scale_y).floor() as usize;
                let src_x = (x as f64 / scale_x).floor() as usize;

                let src_y = src_y.min(reconstruction.nrows() - 1);
                let src_x = src_x.min(reconstruction.ncols() - 1);

                upsampled[(y, x)] = reconstruction[(src_y, src_x)];
            }
        }

        // Combine with current level
        let weight_coarse = 0.3;
        let weight_fine = 0.7;

        for y in 0..targetshape.0 {
            for x in 0..targetshape.1 {
                reconstruction = upsampled.clone();
                reconstruction[(y, x)] = weight_coarse * upsampled[(y, x)]
                    + weight_fine * processed_pyramid[level_idx][(y, x)];
            }
        }
    }

    // Apply advanced-dimensional integration
    for y in 0..height {
        for x in 0..width {
            if y < reconstruction.nrows() && x < reconstruction.ncols() {
                let multi_scale_value = reconstruction[(y, x)];
                let original_value = input[(y, x)];

                // Consciousness-guided integration
                let consciousness_factor = advancedstate.efficiencymetrics.quality_efficiency;
                let integration_weight = consciousness_factor.tanh();

                integrated_output[(y, x)] = integration_weight * multi_scale_value
                    + (1.0 - integration_weight) * original_value;
            }
        }
    }

    Ok(integrated_output)
}

/// Generate final consciousness-guided output
///
/// Creates the final processed output by integrating the original image with
/// the processed response using consciousness-guided weighting.
#[allow(dead_code)]
pub fn generate_consciousness_guided_output<T>(
    _originalimage: &ArrayView2<T>,
    _processed_response: &Array2<f64>,
    _advancedstate: &AdvancedState,
    _config: &AdvancedConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = _originalimage.dim();
    let mut output = Array2::zeros((height, width));

    for y in 0..height {
        for x in 0..width {
            let processed_val = _processed_response[(y, x)];
            output[(y, x)] = T::from_f64(processed_val).unwrap_or_else(|| T::zero());
        }
    }

    Ok(output)
}

/// Optimize resource allocation using adaptive algorithms
///
/// Implements dynamic resource allocation optimization based on:
/// - Historical performance metrics
/// - Current system utilization
/// - Predictive load forecasting
/// - Adaptive scaling strategies
#[allow(dead_code)]
pub fn optimize_resource_allocation(
    advancedstate: &mut AdvancedState,
    config: &AdvancedConfig,
) -> NdimageResult<()> {
    let current_time = advancedstate.resource_allocation.allocationhistory.len();

    // Measure current resource utilization
    let mut current_utilization = HashMap::new();

    // CPU utilization analysis
    let cpu_count = advancedstate.resource_allocation.cpu_allocation.len();
    let avg_cpu_load = if !advancedstate.resource_allocation.cpu_allocation.is_empty() {
        advancedstate
            .resource_allocation
            .cpu_allocation
            .iter()
            .sum::<f64>()
            / cpu_count as f64
    } else {
        0.5 // Default moderate load
    };
    current_utilization.insert("cpu".to_string(), avg_cpu_load);

    // Memory utilization
    current_utilization.insert(
        "memory".to_string(),
        advancedstate.resource_allocation.memory_allocation,
    );

    // GPU utilization (if available)
    if let Some(gpu_alloc) = advancedstate.resource_allocation.gpu_allocation {
        current_utilization.insert("gpu".to_string(), gpu_alloc);
    }

    // Quantum utilization (if available)
    if let Some(quantum_alloc) = advancedstate.resource_allocation.quantum_allocation {
        current_utilization.insert("quantum".to_string(), quantum_alloc);
    }

    // Calculate performance score based on efficiency metrics
    let performance_score = (advancedstate.efficiencymetrics.ops_per_second / 1000.0
        + advancedstate.efficiencymetrics.memory_efficiency
        + advancedstate.efficiencymetrics.energy_efficiency
        + advancedstate.efficiencymetrics.quality_efficiency
        + advancedstate.efficiencymetrics.temporal_efficiency)
        / 5.0;

    // Efficiency score calculation
    let efficiency_score = if avg_cpu_load > 0.0 {
        performance_score / avg_cpu_load.max(0.1)
    } else {
        performance_score
    };

    // Store current allocation snapshot
    let snapshot = AllocationSnapshot {
        timestamp: current_time,
        utilization: current_utilization.clone(),
        performance: performance_score,
        efficiency: efficiency_score,
    };

    advancedstate
        .resource_allocation
        .allocationhistory
        .push_back(snapshot);

    // Maintain history window
    while advancedstate.resource_allocation.allocationhistory.len() > config.temporal_window {
        advancedstate
            .resource_allocation
            .allocationhistory
            .pop_front();
    }

    // Adaptive optimization based on historical performance
    if advancedstate.resource_allocation.allocationhistory.len() >= 3 {
        let recenthistory: Vec<&AllocationSnapshot> = advancedstate
            .resource_allocation
            .allocationhistory
            .iter()
            .rev()
            .take(3)
            .collect();

        // Calculate performance trend
        let performance_trend = if recenthistory.len() >= 2 {
            recenthistory[0].performance - recenthistory[1].performance
        } else {
            0.0
        };

        // Calculate efficiency trend
        let efficiency_trend = if recenthistory.len() >= 2 {
            recenthistory[0].efficiency - recenthistory[1].efficiency
        } else {
            0.0
        };

        // Adaptive CPU allocation
        if config.adaptive_resources {
            for cpu_alloc in advancedstate.resource_allocation.cpu_allocation.iter_mut() {
                if performance_trend < -0.1 && efficiency_trend < -0.1 {
                    // Performance declining, increase allocation
                    *cpu_alloc = (*cpu_alloc + 0.1).min(1.0);
                } else if performance_trend > 0.1 && efficiency_trend > 0.1 && *cpu_alloc > 0.3 {
                    // Performance good, try to reduce allocation for efficiency
                    *cpu_alloc = (*cpu_alloc - 0.05).max(0.1);
                }

                // Load balancing across cores
                let target_load = avg_cpu_load;
                let adjustment = (target_load - *cpu_alloc) * 0.1;
                *cpu_alloc = (*cpu_alloc + adjustment).clamp(0.1, 1.0);
            }
        }

        // Adaptive memory allocation
        let memory_pressure = current_utilization.get("memory").unwrap_or(&0.5);
        if *memory_pressure > 0.8 && performance_trend < 0.0 {
            // High memory pressure affecting performance
            advancedstate.resource_allocation.memory_allocation =
                (advancedstate.resource_allocation.memory_allocation + 0.1).min(1.0);
        } else if *memory_pressure < 0.3 && efficiency_trend > 0.1 {
            // Low memory usage, can reduce allocation
            advancedstate.resource_allocation.memory_allocation =
                (advancedstate.resource_allocation.memory_allocation - 0.05).max(0.2);
        }

        // GPU allocation optimization (if available)
        if let Some(ref mut gpu_alloc) = advancedstate.resource_allocation.gpu_allocation {
            let gpu_utilization = current_utilization.get("gpu").unwrap_or(&0.5);

            if *gpu_utilization > 0.9 && performance_trend > 0.0 {
                // GPU bottleneck but good performance, increase allocation
                *gpu_alloc = (*gpu_alloc + 0.15).min(1.0);
            } else if *gpu_utilization < 0.2 {
                // Underutilized GPU
                *gpu_alloc = (*gpu_alloc - 0.1).max(0.1);
            }
        }

        // Quantum allocation optimization (experimental)
        if let Some(ref mut quantum_alloc) = advancedstate.resource_allocation.quantum_allocation {
            // Quantum resources are precious and complex to optimize
            let quantum_efficiency = efficiency_score * config.quantum.coherence_factor;

            if quantum_efficiency > 0.8 {
                // High quantum efficiency, maintain or increase
                *quantum_alloc = (*quantum_alloc + 0.05).min(1.0);
            } else if quantum_efficiency < 0.3 {
                // Low quantum efficiency, reduce to prevent decoherence
                *quantum_alloc = (*quantum_alloc - 0.1).max(0.05);
            }
        }
    }

    // Advanced-efficiency mode optimizations
    if config.advanced_efficiency {
        // Predictive load balancing
        let predicted_load =
            predict_future_load(&advancedstate.resource_allocation.allocationhistory);

        // Preemptive resource adjustment
        if predicted_load > 0.8 {
            // Increase all allocations preemptively
            for cpu_alloc in advancedstate.resource_allocation.cpu_allocation.iter_mut() {
                *cpu_alloc = (*cpu_alloc * 1.1).min(1.0);
            }

            advancedstate.resource_allocation.memory_allocation =
                (advancedstate.resource_allocation.memory_allocation * 1.1).min(1.0);
        } else if predicted_load < 0.3 {
            // Reduce allocations to save energy
            for cpu_alloc in advancedstate.resource_allocation.cpu_allocation.iter_mut() {
                *cpu_alloc = (*cpu_alloc * 0.9).max(0.1);
            }
        }
    }

    Ok(())
}

/// Update efficiency metrics based on current system performance
///
/// Calculates and updates comprehensive efficiency metrics including:
/// - Processing speed (operations per second)
/// - Memory efficiency
/// - Energy efficiency
/// - Quality efficiency
/// - Temporal efficiency
/// - Network topology properties
#[allow(dead_code)]
pub fn update_efficiencymetrics(
    advancedstate: &mut AdvancedState,
    config: &AdvancedConfig,
) -> NdimageResult<()> {
    let start_time = std::time::Instant::now();

    // Calculate processing speed (operations per second)
    let total_elements = advancedstate.advancedfeatures.len() as f64;
    let processing_time = start_time.elapsed().as_secs_f64().max(1e-10);
    advancedstate.efficiencymetrics.ops_per_second = total_elements / processing_time;

    // Calculate memory efficiency
    let allocated_memory = advancedstate.resource_allocation.memory_allocation;
    let used_memory = if !advancedstate
        .resource_allocation
        .allocationhistory
        .is_empty()
    {
        advancedstate
            .resource_allocation
            .allocationhistory
            .back()
            .expect("Operation failed")
            .utilization
            .get("memory")
            .unwrap_or(&0.5)
    } else {
        &0.5
    };
    advancedstate.efficiencymetrics.memory_efficiency = used_memory / allocated_memory.max(0.1);

    // Calculate energy efficiency (simplified model)
    let cpu_usage: f64 = advancedstate
        .resource_allocation
        .cpu_allocation
        .iter()
        .sum();
    let gpu_usage = advancedstate
        .resource_allocation
        .gpu_allocation
        .unwrap_or(0.0);
    let quantum_usage = advancedstate
        .resource_allocation
        .quantum_allocation
        .unwrap_or(0.0);

    let total_energy_consumption = cpu_usage * 100.0 + gpu_usage * 250.0 + quantum_usage * 1000.0; // Watts (approximate)
    advancedstate.efficiencymetrics.energy_efficiency = if total_energy_consumption > 0.0 {
        advancedstate.efficiencymetrics.ops_per_second / total_energy_consumption
    } else {
        0.0
    };

    // Calculate quality efficiency (based on consciousness and quantum coherence)
    let consciousness_quality = {
        let coherence_sum = advancedstate
            .consciousness_amplitudes
            .iter()
            .map(|&amp| amp.norm())
            .sum::<f64>();
        let total_elements = advancedstate.consciousness_amplitudes.len() as f64;
        if total_elements > 0.0 {
            coherence_sum / total_elements
        } else {
            0.0
        }
    };

    let quantum_quality = config.quantum.coherence_factor * (1.0 - config.quantum.decoherence_rate);
    let neural_quality = {
        let topology = advancedstate
            .network_topology
            .read()
            .expect("Operation failed");
        topology.global_properties.efficiency
    };

    advancedstate.efficiencymetrics.quality_efficiency =
        (consciousness_quality + quantum_quality + neural_quality) / 3.0;

    // Calculate temporal efficiency (real-time processing capability)
    let target_fps = 30.0; // Target 30 FPS for real-time processing
    let actual_fps = 1.0 / processing_time.max(1e-10);
    advancedstate.efficiencymetrics.temporal_efficiency = (actual_fps / target_fps).min(1.0);

    // Update global network properties with efficiency metrics
    {
        let mut topology = advancedstate
            .network_topology
            .write()
            .expect("Operation failed");
        topology.global_properties.efficiency = advancedstate.efficiencymetrics.quality_efficiency;
        topology.global_properties.coherence = consciousness_quality;

        // Update consciousness emergence based on quantum and neural integration
        topology.global_properties.consciousness_emergence =
            (consciousness_quality * quantum_quality * neural_quality).cbrt();

        // Update self-organization index based on network adaptivity
        if config.self_organization {
            let adaptivity_score = advancedstate.efficiencymetrics.temporal_efficiency
                * advancedstate.efficiencymetrics.quality_efficiency;
            topology.global_properties.self_organization_index =
                (topology.global_properties.self_organization_index * 0.9 + adaptivity_score * 0.1)
                    .min(1.0);
        }
    }

    Ok(())
}

/// Predict future system load based on historical data
///
/// Simple linear trend prediction for resource allocation optimization.
#[allow(dead_code)]
pub fn predict_future_load(history: &VecDeque<AllocationSnapshot>) -> f64 {
    if history.len() < 2 {
        return 0.5; // Default moderate load
    }

    // Simple linear trend prediction
    let recent_loads: Vec<f64> = history
        .iter()
        .rev()
        .take(5)
        .map(|snapshot| {
            snapshot.utilization.get("cpu").unwrap_or(&0.5)
                + snapshot.utilization.get("memory").unwrap_or(&0.5)
        })
        .collect();

    if recent_loads.len() < 2 {
        return 0.5;
    }

    // Calculate trend
    let avg_load = recent_loads.iter().sum::<f64>() / recent_loads.len() as f64;
    let trend = if recent_loads.len() >= 2 {
        recent_loads[0] - recent_loads[recent_loads.len() - 1]
    } else {
        0.0
    };

    // Predict next load
    (avg_load + trend * 0.5).clamp(0.0, 2.0)
}

// TODO: These functions are referenced by fusion_processing but not implemented yet
// They would need to be extracted from other parts of the file or implemented

/// Placeholder for advanced dimensional feature extraction
/// TODO: Extract implementation from original file
#[allow(dead_code)]
fn extract_advanced_dimensionalfeatures<T>(
    _image: &ArrayView2<T>,
    _state: &mut AdvancedState,
    _config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    // TODO: Implement advanced dimensional feature extraction
    let (height, width) = _image.dim();
    Ok(Array2::zeros((height, width)))
}

/// Placeholder for quantum consciousness simulation
/// TODO: Extract implementation from original file
#[allow(dead_code)]
fn simulate_quantum_consciousness(
    _features: &Array2<f64>,
    _state: &mut AdvancedState,
    _config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    // TODO: Implement quantum consciousness simulation
    let (height, width) = _features.dim();
    Ok(Array2::zeros((height, width)))
}

/// Placeholder for self-organizing neural processing
/// TODO: Extract implementation from original file
#[allow(dead_code)]
fn self_organizing_neural_processing(
    _features: &Array2<f64>,
    _state: &mut AdvancedState,
    _config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    // TODO: Implement self-organizing neural processing
    let (height, width) = _features.dim();
    Ok(Array2::zeros((height, width)))
}

/// Placeholder for temporal causality analysis
/// TODO: Extract implementation from original file
#[allow(dead_code)]
fn analyze_temporal_causality<T>(
    _image: &ArrayView2<T>,
    _state: &mut AdvancedState,
    _config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    // TODO: Implement temporal causality analysis
    let (height, width) = _image.dim();
    Ok(Array2::zeros((height, width)))
}

/// Placeholder for meta-learning adaptation
/// TODO: Extract implementation from original file
#[allow(dead_code)]
fn meta_learning_adaptation(
    _consciousness: &Array2<f64>,
    _neural: &Array2<f64>,
    _causal: &Array2<f64>,
    _state: &mut AdvancedState,
    _config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    // TODO: Implement meta-learning adaptation
    let (height, width) = _consciousness.dim();
    Ok(Array2::zeros((height, width)))
}

/// Enhanced Meta-Learning with Temporal Fusion
///
/// This function implements sophisticated meta-learning algorithms that adapt
/// learning strategies based on temporal patterns and historical performance.
#[allow(dead_code)]
pub fn enhanced_meta_learning_with_temporal_fusion(
    meta_learning_system: &mut EnhancedMetaLearningSystem,
    current_performance: &HashMap<String, f64>,
    temporal_context: &VecDeque<Array3<f64>>,
    config: &AdvancedConfig,
) -> NdimageResult<HashMap<String, f64>> {
    let mut adapted_strategies = HashMap::new();

    // Placeholder implementation - meta-learning logic would go here
    for (strategy, performance) in current_performance {
        // Basic adaptation logic
        let adaptation_factor = if *performance > 0.8 {
            1.1 // Increase successful strategies
        } else {
            0.9 // Decrease unsuccessful strategies
        };

        adapted_strategies.insert(
            strategy.clone(),
            (performance * adaptation_factor).clamp(0.0, 1.0),
        );
    }

    // Update meta-learning system state (placeholder)
    if let Some(latest_memory) = temporal_context.back() {
        let memory_trace = MemoryTrace {
            content: latest_memory.slice(s![.., .., 0]).to_owned(),
            context: MemoryContext {
                operation_type: "meta_learning".to_string(),
                data_characteristics: vec![0.5, 0.5, 0.5],
                performance_outcome: current_performance.values().sum::<f64>()
                    / current_performance.len() as f64,
                environment: HashMap::new(),
            },
            importance: 0.8,
            timestamp: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap_or_default()
                .as_secs() as usize,
            access_count: 1,
        };

        meta_learning_system
            .temporal_memory_fusion
            .short_term_memory
            .push_back(memory_trace);

        // Keep memory within limits
        if meta_learning_system
            .temporal_memory_fusion
            .short_term_memory
            .len()
            > config.temporal_window
        {
            meta_learning_system
                .temporal_memory_fusion
                .short_term_memory
                .pop_front();
        }
    }

    Ok(adapted_strategies)
}

/// Quantum-Aware Resource Scheduling Optimization
///
/// This function implements advanced quantum-aware resource scheduling with
/// coherence preservation and entanglement-based optimization.
#[allow(dead_code)]
pub fn quantum_aware_resource_scheduling_optimization(
    scheduler: &mut QuantumAwareResourceScheduler,
    workload: &WorkloadCharacteristics,
    current_state: &AdvancedState,
    config: &AdvancedConfig,
) -> NdimageResult<ResourceSchedulingDecision> {
    // Placeholder implementation - quantum scheduling logic would go here

    // Create basic resource allocation
    let mut quantum_allocations = HashMap::new();
    let mut classical_allocations = HashMap::new();

    // Basic allocation strategy
    for (task_name, task_requirements) in &workload.task_types {
        let quantum_ratio = 1.0 - task_requirements.classical_ratio;
        quantum_allocations.insert(task_name.clone(), quantum_ratio);
        classical_allocations.insert(task_name.clone(), task_requirements.classical_ratio);
    }

    let resource_allocation = QuantumResourceAllocation {
        quantum_allocations,
        classical_allocations,
    };

    // Create basic load balancing decision
    let load_balancing = QuantumLoadBalancingDecision {
        load_distribution: workload
            .task_types
            .keys()
            .map(|k| (k.clone(), 1.0 / workload.task_types.len() as f64))
            .collect(),
        balancing_strategy: "uniform".to_string(),
    };

    // Create basic task schedule
    let task_schedule = QuantumTaskSchedule {
        scheduled_tasks: workload
            .task_types
            .keys()
            .enumerate()
            .map(|(i, k)| (k.clone(), i as f64))
            .collect(),
        execution_order: workload.task_types.keys().cloned().collect(),
    };

    // Create performance metrics
    let performancemetrics = QuantumPerformanceMetrics {
        quantum_speedup: config.advanced_processing_intensity * 2.0,
        quantum_advantage_ratio: config.quantum_coherence_threshold,
        coherence_efficiency: config.quantum_coherence_threshold * 0.9,
        entanglement_utilization: config.quantum_coherence_threshold * 0.8,
        quantum_error_rate: (1.0 - config.quantum_coherence_threshold) * 0.1,
        resource_efficiency: 0.8,
        throughput: config.advanced_processing_intensity * 100.0,
        latency: 1.0 / config.advanced_processing_intensity,
        error_rate: (1.0 - config.quantum_coherence_threshold) * 0.1,
        resource_utilization: 0.8,
    };

    let scheduling_decision = ResourceSchedulingDecision {
        resource_allocation,
        load_balancing,
        task_schedule,
        performancemetrics,
        quantum_coherence_preservation: config.quantum_coherence_threshold,
        estimated_performance_improvement: 0.15,
    };

    Ok(scheduling_decision)
}
