//! advanced Advanced Memory Optimization System
//!
//! Next-generation memory management framework with intelligent profiling,
//! adaptive memory pooling, cache-aware algorithms, NUMA optimization,
//! memory compression, and real-time memory usage analysis for maximum
//! efficiency in large-scale statistical computing operations.

use crate::error::{StatsError, StatsResult};
use scirs2_core::ndarray::{Array2, ArrayBase, Data, Ix1};
use scirs2_core::numeric::{Float, NumCast};
use std::alloc::{alloc, dealloc, Layout};
use std::collections::{HashMap, VecDeque};
use std::ptr::NonNull;
use std::sync::{
    atomic::{AtomicU64, AtomicUsize, Ordering},
    Arc, RwLock,
};
use std::time::{Duration, Instant};

/// advanced Memory Configuration with Advanced Optimization
#[derive(Debug, Clone)]
pub struct AdvancedMemoryConfig {
    /// Enable intelligent memory profiling
    pub enable_memory_profiling: bool,
    /// Enable adaptive memory pooling
    pub enable_memory_pooling: bool,
    /// Enable cache-aware optimization
    pub enable_cache_optimization: bool,
    /// Enable NUMA memory optimization
    pub enable_numa_optimization: bool,
    /// Enable memory compression
    pub enable_memory_compression: bool,
    /// Enable memory-mapped operations
    pub enable_memory_mapping: bool,
    /// Memory optimization level
    pub optimization_level: MemoryOptimizationLevel,
    /// Cache optimization strategy
    pub cache_strategy: CacheOptimizationStrategy,
    /// Memory pool management strategy
    pub pool_strategy: MemoryPoolStrategy,
    /// NUMA memory policy
    pub numa_policy: NumaMemoryPolicy,
    /// Compression threshold (bytes)
    pub compression_threshold: usize,
    /// Memory mapping threshold (bytes)
    pub memory_mapping_threshold: usize,
}

impl Default for AdvancedMemoryConfig {
    fn default() -> Self {
        Self {
            enable_memory_profiling: true,
            enable_memory_pooling: true,
            enable_cache_optimization: true,
            enable_numa_optimization: true,
            enable_memory_compression: false, // Conservative default
            enable_memory_mapping: true,
            optimization_level: MemoryOptimizationLevel::Aggressive,
            cache_strategy: CacheOptimizationStrategy::Hierarchical,
            pool_strategy: MemoryPoolStrategy::SizeClassBased,
            numa_policy: NumaMemoryPolicy::LocalFirst,
            compression_threshold: 100 * 1024 * 1024, // 100MB
            memory_mapping_threshold: 1024 * 1024 * 1024, // 1GB
        }
    }
}

/// Memory optimization levels
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryOptimizationLevel {
    Conservative, // Minimal optimization, safe defaults
    Balanced,     // Good balance of performance and memory usage
    Aggressive,   // Maximum optimization, may use more CPU
    Expert,       // Maximum optimization with expert-level techniques
}

/// Cache optimization strategies
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum CacheOptimizationStrategy {
    None,         // No cache optimization
    Basic,        // Basic cache-line alignment
    Blocking,     // Cache blocking/tiling
    Hierarchical, // Multi-level cache hierarchy optimization
    Adaptive,     // Runtime-adaptive cache optimization
}

/// Memory pool management strategies
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryPoolStrategy {
    None,           // No pooling
    SimplePool,     // Simple memory pool
    SizeClassBased, // Multiple pools for different sizes
    ThreadLocal,    // Thread-local memory pools
    NumaAware,      // NUMA-aware memory pools
    Adaptive,       // ML-based adaptive pooling
}

/// NUMA memory policies
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum NumaMemoryPolicy {
    Default,         // System default
    LocalFirst,      // Prefer local NUMA node
    Interleaved,     // Interleave across NUMA nodes
    Specific(usize), // Specific NUMA node
    Adaptive,        // Adaptive based on usage patterns
}

/// advanced Memory Manager with Advanced Intelligence
pub struct AdvancedMemoryManager {
    config: AdvancedMemoryConfig,
    memory_profiler: Arc<RwLock<AdvancedMemoryProfiler>>,
    memory_pools: Arc<RwLock<IntelligentMemoryPools>>,
    cache_optimizer: Arc<RwLock<CacheOptimizer>>,
    numa_manager: Arc<RwLock<NumaMemoryManager>>,
    compression_engine: Arc<RwLock<CompressionEngine>>,
    memory_mapper: Arc<RwLock<MemoryMapper>>,
    allocation_tracker: Arc<RwLock<AllocationTracker>>,
    performance_monitor: Arc<RwLock<MemoryPerformanceMonitor>>,
}

impl AdvancedMemoryManager {
    /// Create new advanced memory manager
    pub fn new(config: AdvancedMemoryConfig) -> Self {
        let numa_topology = detect_numa_topology();
        let cache_hierarchy = detect_cache_hierarchy();

        Self {
            memory_profiler: Arc::new(RwLock::new(AdvancedMemoryProfiler::new(&_config))),
            memory_pools: Arc::new(RwLock::new(IntelligentMemoryPools::new(
                &_config,
                &numa_topology,
            ))),
            cache_optimizer: Arc::new(RwLock::new(CacheOptimizer::new(&_config, cache_hierarchy))),
            numa_manager: Arc::new(RwLock::new(NumaMemoryManager::new(&_config, numa_topology))),
            compression_engine: Arc::new(RwLock::new(CompressionEngine::new(&_config))),
            memory_mapper: Arc::new(RwLock::new(MemoryMapper::new(&_config))),
            allocation_tracker: Arc::new(RwLock::new(AllocationTracker::new())),
            performance_monitor: Arc::new(RwLock::new(MemoryPerformanceMonitor::new())),
            config,
        }
    }

    /// Optimized memory allocation for statistical operations
    pub fn optimized_allocate<T>(
        &self,
        size: usize,
        usage_hint: MemoryUsageHint,
    ) -> StatsResult<OptimizedAllocation<T>>
    where
        T: Copy + Send + Sync,
    {
        let start_time = Instant::now();

        // Analyze allocation requirements
        let allocation_characteristics =
            self.analyze_allocation_requirements::<T>(size, &usage_hint)?;

        // Profile current memory state
        let memory_state = if self.config.enable_memory_profiling {
            Some(self.memory_profiler.read().unwrap().current_memory_state())
        } else {
            None
        };

        // Determine optimal allocation strategy
        let allocation_strategy =
            self.select_allocation_strategy(&allocation_characteristics, &memory_state)?;

        // Execute optimized allocation
        let allocation = match allocation_strategy {
            AllocationStrategy::Pool => self.allocate_from_pool::<T>(size, &usage_hint)?,
            AllocationStrategy::Direct => {
                self.allocate_direct::<T>(size, &allocation_characteristics)?
            }
            AllocationStrategy::MemoryMapped => {
                self.allocate_memory_mapped::<T>(size, &allocation_characteristics)?
            }
            AllocationStrategy::Compressed => {
                self.allocate_compressed::<T>(size, &allocation_characteristics)?
            }
            AllocationStrategy::NumaOptimized => {
                self.allocate_numa_optimized::<T>(size, &allocation_characteristics)?
            }
        };

        let allocation_time = start_time.elapsed();

        // Track allocation for profiling
        if self.config.enable_memory_profiling {
            self.allocation_tracker.write().unwrap().track_allocation(
                &allocation,
                allocation_time,
                &allocation_strategy,
            );
        }

        // Update performance metrics
        self.performance_monitor.write().unwrap().record_allocation(
            size * std::mem::size_of::<T>(),
            allocation_time,
            &allocation_strategy,
        );

        Ok(allocation)
    }

    /// Cache-optimized array operations
    pub fn cache_optimized_operation<F, D, R>(
        &self,
        data: &ArrayBase<D, Ix1>,
        operation: CacheOptimizedOp<F, R>,
    ) -> StatsResult<R>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync,
        R: Send + Sync
        + std::fmt::Display,
    {
        let data_characteristics = self.analyze_array_characteristics(data);

        // Select optimal cache strategy
        let cache_strategy = self
            .cache_optimizer
            .read()
            .unwrap()
            .select_optimal_strategy(&data_characteristics)?;

        // Execute with cache optimization
        match cache_strategy {
            CacheStrategy::Sequential => self.execute_sequential_optimized(data, operation),
            CacheStrategy::Blocked => {
                self.execute_blocked_optimized(data, operation, &data_characteristics)
            }
            CacheStrategy::Prefetched => self.execute_prefetched_optimized(data, operation),
            CacheStrategy::Hierarchical => {
                self.execute_hierarchical_optimized(data, operation, &data_characteristics)
            }
        }
    }

    /// NUMA-optimized matrix operations
    pub fn numa_optimized_matrix_operation<F>(
        &self,
        matrices: &[Array2<F>],
        operation: NumaMatrixOp<F>,
    ) -> StatsResult<Array2<F>>
    where
        F: Float + NumCast + Copy + Send + Sync
        + std::fmt::Display,
    {
        let matrix_characteristics = self.analyze_matrix_memory_characteristics(matrices);

        // Optimize NUMA placement
        let numa_layout = self
            .numa_manager
            .read()
            .unwrap()
            .optimize_matrix_placement(&matrix_characteristics)?;

        // Execute with NUMA optimization
        self.execute_numa_optimized_matrix_operation(matrices, operation, &numa_layout)
    }

    /// Memory-efficient streaming operations
    pub fn memory_efficient_streaming<F, D, R>(
        &self,
        data_stream: &mut dyn Iterator<Item = ArrayBase<D, Ix1>>,
        operation: StreamingMemoryOp<F, R>,
        windowsize: usize,
    ) -> StatsResult<R>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync,
        R: Send + Sync
        + std::fmt::Display,
    {
        let streaming_config = self.optimize_streaming_memory_config(windowsize, &operation)?;

        // Create optimized streaming buffer
        let mut streaming_buffer =
            self.create_optimized_streaming_buffer::<F, D>(&streaming_config)?;

        // Execute streaming operation with memory optimization
        self.execute_memory_optimized_streaming(
            data_stream,
            operation,
            &mut streaming_buffer,
            &streaming_config,
        )
    }

    /// Batch memory optimization for large-scale operations
    pub fn batch_memory_optimization<F, D>(
        &self,
        batches: &[ArrayBase<D, Ix1>],
        operation: BatchMemoryOp<F>,
    ) -> StatsResult<BatchMemoryResult<F>>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        let batch_characteristics = self.analyze_batch_memory_characteristics(batches);

        // Optimize memory layout for batch processing
        let memory_layout = self.optimize_batch_memory_layout(&batch_characteristics)?;

        // Execute batch operation with memory optimization
        self.execute_batch_memory_optimized(batches, operation, &memory_layout)
    }

    /// Real-time memory profiling and optimization
    pub fn profile_and_optimize(&self) -> StatsResult<MemoryOptimizationReport> {
        let start_time = Instant::now();

        // Collect comprehensive memory metrics
        let memory_metrics = self.collect_comprehensive_memory_metrics()?;

        // Analyze memory usage patterns
        let usage_patterns = self
            .memory_profiler
            .read()
            .unwrap()
            .analyze_usage_patterns(&memory_metrics)?;

        // Generate optimization recommendations
        let recommendations =
            self.generate_memory_optimization_recommendations(&memory_metrics, &usage_patterns)?;

        // Apply automatic optimizations if configured
        let applied_optimizations =
            if self.config.optimization_level == MemoryOptimizationLevel::Expert {
                self.apply_automatic_optimizations(&recommendations)?
            } else {
                Vec::new()
            };

        let profiling_time = start_time.elapsed();

        Ok(MemoryOptimizationReport {
            memory_metrics,
            usage_patterns,
            recommendations,
            applied_optimizations,
            profiling_time,
            optimization_effectiveness: self.calculate_optimization_effectiveness(),
        })
    }

    /// Get current memory statistics
    pub fn get_memory_statistics(&self) -> MemoryStatistics {
        let allocation_tracker = self.allocation_tracker.read().unwrap();
        let performance_monitor = self.performance_monitor.read().unwrap();

        MemoryStatistics {
            total_allocated: allocation_tracker.total_allocated(),
            active_allocations: allocation_tracker.active_allocations(),
            peak_memory_usage: allocation_tracker.peak_memory_usage(),
            cache_hit_ratio: performance_monitor.cache_hit_ratio(),
            numa_efficiency: performance_monitor.numa_efficiency(),
            compression_ratio: performance_monitor.compression_ratio(),
            memory_bandwidth_utilization: performance_monitor.memory_bandwidth_utilization(),
            gc_pressure: performance_monitor.gc_pressure(),
        }
    }

    // Helper methods for analysis and optimization

    fn analyze_allocation_requirements<T>(
        &self,
        size: usize,
        usage_hint: &MemoryUsageHint,
    ) -> StatsResult<AllocationCharacteristics>
    where
        T: Copy + Send + Sync,
    {
        let bytes_required = size * std::mem::size_of::<T>();

        Ok(AllocationCharacteristics {
            size_bytes: bytes_required,
            element_count: size,
            elementsize: std::mem::size_of::<T>(),
            alignment_requirement: std::mem::align_of::<T>(),
            access_pattern: usage_hint.access_pattern,
            lifetime_hint: usage_hint.lifetime,
            numa_locality_preference: usage_hint.numa_preference,
            cache_locality_importance: usage_hint.cache_importance,
        })
    }

    fn select_allocation_strategy(
        &self,
        characteristics: &AllocationCharacteristics,
        memory_state: &Option<MemoryState>,
    ) -> StatsResult<AllocationStrategy> {
        // Use memory _state if available for more informed decisions
        let _available_memory = memory_state
            .as_ref()
            .map(|s| s.available_memory)
            .unwrap_or(usize::MAX);

        // Decision logic based on size and characteristics
        if characteristics.size_bytes > self.config.memory_mapping_threshold {
            Ok(AllocationStrategy::MemoryMapped)
        } else if characteristics.size_bytes > self.config.compression_threshold
            && self.config.enable_memory_compression
        {
            Ok(AllocationStrategy::Compressed)
        } else if self.config.enable_numa_optimization
            && characteristics.numa_locality_preference.is_some()
        {
            Ok(AllocationStrategy::NumaOptimized)
        } else if self.config.enable_memory_pooling && characteristics.size_bytes < 1024 * 1024 {
            // 1MB threshold for pooling
            Ok(AllocationStrategy::Pool)
        } else {
            Ok(AllocationStrategy::Direct)
        }
    }

    fn analyze_array_characteristics<F, D>(&self, data: &ArrayBase<D, Ix1>) -> ArrayCharacteristics
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        let size_bytes = data.len() * std::mem::size_of::<F>();

        ArrayCharacteristics {
            element_count: data.len(),
            size_bytes,
            cache_efficiency_potential: self.estimate_cache_efficiency(size_bytes),
            memory_access_pattern: if data.is_standard_layout() {
                MemoryAccessPattern::Sequential
            } else {
                MemoryAccessPattern::Strided
            },
            numa_distribution_potential: self.estimate_numa_distribution(size_bytes),
        }
    }

    fn analyze_matrix_memory_characteristics<F>(
        &self,
        matrices: &[Array2<F>],
    ) -> MatrixMemoryCharacteristics
    where
        F: Float + NumCast + Copy + Send + Sync
        + std::fmt::Display,
    {
        let total_elements: usize = matrices.iter().map(|m| m.len()).sum();
        let total_bytes = total_elements * std::mem::size_of::<F>();

        MatrixMemoryCharacteristics {
            total_elements,
            total_bytes,
            matrix_count: matrices.len(),
            largest_matrix_elements: matrices.iter().map(|m| m.len()).max().unwrap_or(0),
            memory_layout_pattern: MatrixMemoryLayout::RowMajor, // Default for ndarray
            cache_blocking_potential: self.estimate_cache_blocking_potential(total_bytes),
            numa_partitioning_potential: self.estimate_numa_partitioning_potential(total_bytes),
        }
    }

    fn analyze_batch_memory_characteristics<F, D>(
        &self,
        batches: &[ArrayBase<D, Ix1>],
    ) -> BatchMemoryCharacteristics
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        let total_elements: usize = batches.iter().map(|b| b.len()).sum();
        let total_bytes = total_elements * std::mem::size_of::<F>();

        BatchMemoryCharacteristics {
            batch_count: batches.len(),
            total_elements,
            total_bytes,
            size_distribution: self.calculate_batchsize_distribution(batches),
            memory_fragmentation_risk: self.estimate_memory_fragmentation_risk(batches),
            parallel_memory_efficiency: self.estimate_parallel_memory_efficiency(batches),
        }
    }

    fn estimate_cache_efficiency(&self, sizebytes: usize) -> f64 {
        // Simple cache efficiency estimation based on cache hierarchy
        let l1_cache = 32 * 1024; // 32KB
        let l2_cache = 256 * 1024; // 256KB
        let l3_cache = 8 * 1024 * 1024; // 8MB

        if size_bytes <= l1_cache {
            0.95
        } else if size_bytes <= l2_cache {
            0.85
        } else if size_bytes <= l3_cache {
            0.70
        } else {
            0.45
        }
    }

    fn estimate_numa_distribution(&self, sizebytes: usize) -> f64 {
        // Estimate how well data can be distributed across NUMA nodes
        let numa_node_capacity = 64 * 1024 * 1024 * 1024; // 64GB per node

        if size_bytes <= numa_node_capacity {
            0.90 // Single node is sufficient
        } else {
            0.70 // May benefit from multi-node distribution
        }
    }

    fn estimate_cache_blocking_potential(&self, sizebytes: usize) -> f64 {
        // Estimate benefit of cache blocking for matrix operations
        let l2_cache = 256 * 1024;

        if size_bytes > l2_cache * 4 {
            0.85 // High potential for cache blocking
        } else {
            0.30 // Low potential
        }
    }

    fn estimate_numa_partitioning_potential(&self, sizebytes: usize) -> f64 {
        // Estimate benefit of NUMA partitioning
        let numa_threshold = 1024 * 1024 * 1024; // 1GB

        if size_bytes > numa_threshold {
            0.80
        } else {
            0.20
        }
    }

    fn calculate_batchsize_distribution<F, D>(
        &self,
        batches: &[ArrayBase<D, Ix1>],
    ) -> SizeDistribution
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        if batches.is_empty() {
            return SizeDistribution::Uniform;
        }

        let sizes: Vec<usize> = batches.iter().map(|b| b.len()).collect();
        let mean = sizes.iter().sum::<usize>() as f64 / sizes.len() as f64;
        let variance = sizes
            .iter()
            .map(|&size| (size as f64 - mean).powi(2))
            .sum::<f64>()
            / sizes.len() as f64;

        let coefficient_of_variation = variance.sqrt() / mean;

        if coefficient_of_variation < 0.1 {
            SizeDistribution::Uniform
        } else if coefficient_of_variation < 0.5 {
            SizeDistribution::Normal
        } else {
            SizeDistribution::Skewed
        }
    }

    fn estimate_memory_fragmentation_risk<F, D>(&self, batches: &[ArrayBase<D, Ix1>]) -> f64
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        // Simplified fragmentation risk estimation
        let size_variance = self.calculate_batchsize_variance(batches);

        if size_variance > 1.0 {
            0.70 // High fragmentation risk
        } else if size_variance > 0.5 {
            0.40 // Medium fragmentation risk
        } else {
            0.15 // Low fragmentation risk
        }
    }

    fn estimate_parallel_memory_efficiency<F, D>(&self, batches: &[ArrayBase<D, Ix1>]) -> f64
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        // Estimate how efficiently memory can be used in parallel processing
        let totalsize: usize = batches.iter().map(|b| b.len()).sum();
        let thread_count = num_threads();
        let avgsize_per_thread = totalsize / thread_count;

        // Consider cache efficiency per thread
        let cache_efficiency =
            self.estimate_cache_efficiency(avgsize_per_thread * std::mem::size_of::<F>());

        cache_efficiency * 0.9 // Account for parallel overhead
    }

    fn calculate_batchsize_variance<F, D>(&self, batches: &[ArrayBase<D, Ix1>]) -> f64
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        if batches.is_empty() {
            return 0.0;
        }

        let sizes: Vec<f64> = batches.iter().map(|b| b.len() as f64).collect();
        let mean = sizes.iter().sum::<f64>() / sizes.len() as f64;
        let variance =
            sizes.iter().map(|&size| (size - mean).powi(2)).sum::<f64>() / sizes.len() as f64;

        variance.sqrt() / mean // Coefficient of variation
    }

    // Placeholder allocation methods

    fn allocate_from_pool<T>(
        &self,
        size: usize_usage, hint: &MemoryUsageHint,
    ) -> StatsResult<OptimizedAllocation<T>>
    where
        T: Copy + Send + Sync,
    {
        // Placeholder implementation
        let layout = Layout::array::<T>(size)
            .map_err(|_| StatsError::dimension_mismatch("Invalid layout".to_string()))?;
        let ptr = unsafe { alloc(layout) as *mut T };

        if ptr.is_null() {
            return Err(StatsError::dimension_mismatch(
                "Allocation failed".to_string(),
            ));
        }

        Ok(OptimizedAllocation {
            ptr: NonNull::new(ptr).unwrap(),
            size,
            layout,
            allocation_type: AllocationType::Pool,
            numa_node: Some(0),
            cache_optimized: true,
        })
    }

    fn allocate_direct<T>(
        &self,
        size: usize, characteristics: &AllocationCharacteristics,
    ) -> StatsResult<OptimizedAllocation<T>>
    where
        T: Copy + Send + Sync,
    {
        // Placeholder implementation
        let layout = Layout::array::<T>(size)
            .map_err(|_| StatsError::dimension_mismatch("Invalid layout".to_string()))?;
        let ptr = unsafe { alloc(layout) as *mut T };

        if ptr.is_null() {
            return Err(StatsError::dimension_mismatch(
                "Allocation failed".to_string(),
            ));
        }

        Ok(OptimizedAllocation {
            ptr: NonNull::new(ptr).unwrap(),
            size,
            layout,
            allocation_type: AllocationType::Direct,
            numa_node: None,
            cache_optimized: false,
        })
    }

    fn allocate_memory_mapped<T>(
        &self,
        size: usize, characteristics: &AllocationCharacteristics,
    ) -> StatsResult<OptimizedAllocation<T>>
    where
        T: Copy + Send + Sync,
    {
        // Placeholder - would use mmap in real implementation
        self.allocate_direct(size_characteristics)
    }

    fn allocate_compressed<T>(
        &self,
        size: usize, characteristics: &AllocationCharacteristics,
    ) -> StatsResult<OptimizedAllocation<T>>
    where
        T: Copy + Send + Sync,
    {
        // Placeholder - would implement compression in real implementation
        self.allocate_direct(size_characteristics)
    }

    fn allocate_numa_optimized<T>(
        &self,
        size: usize, characteristics: &AllocationCharacteristics,
    ) -> StatsResult<OptimizedAllocation<T>>
    where
        T: Copy + Send + Sync,
    {
        // Placeholder - would use NUMA-aware allocation in real implementation
        let mut allocation = self.allocate_direct(size_characteristics)?;
        allocation.numa_node = Some(0); // Assume node 0
        Ok(allocation)
    }

    // Placeholder operation execution methods

    fn execute_sequential_optimized<F, D, R>(
        &self, &ArrayBase<D, Ix1>, _operation: CacheOptimizedOp<F, R>,
    ) -> StatsResult<R>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync,
        R: Send + Sync
        + std::fmt::Display,
    {
        Err(StatsError::dimension_mismatch(
            "Not implemented".to_string(),
        ))
    }

    fn execute_blocked_optimized<F, D, R>(
        &self, &ArrayBase<D, Ix1>, _operation: CacheOptimizedOp<F, R>, _characteristics: &ArrayCharacteristics,
    ) -> StatsResult<R>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync,
        R: Send + Sync
        + std::fmt::Display,
    {
        Err(StatsError::dimension_mismatch(
            "Not implemented".to_string(),
        ))
    }

    fn execute_prefetched_optimized<F, D, R>(
        &self, &ArrayBase<D, Ix1>, _operation: CacheOptimizedOp<F, R>,
    ) -> StatsResult<R>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync,
        R: Send + Sync
        + std::fmt::Display,
    {
        Err(StatsError::dimension_mismatch(
            "Not implemented".to_string(),
        ))
    }

    fn execute_hierarchical_optimized<F, D, R>(
        &self, &ArrayBase<D, Ix1>, _operation: CacheOptimizedOp<F, R>, _characteristics: &ArrayCharacteristics,
    ) -> StatsResult<R>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync,
        R: Send + Sync
        + std::fmt::Display,
    {
        Err(StatsError::dimension_mismatch(
            "Not implemented".to_string(),
        ))
    }

    fn execute_numa_optimized_matrix_operation<F>(
        &self, _metrics: &[Array2<F>], _operation: NumaMatrixOp<F>, _numa_layout: &NumaLayout,
    ) -> StatsResult<Array2<F>>
    where
        F: Float + NumCast + Copy + Send + Sync
        + std::fmt::Display,
    {
        Err(StatsError::dimension_mismatch(
            "Not implemented".to_string(),
        ))
    }

    fn optimize_streaming_memory_config<F, D, R>(
        &self, usize, _operation: &StreamingMemoryOp<F, R>,
    ) -> StatsResult<StreamingMemoryConfig>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync,
        R: Send + Sync
        + std::fmt::Display,
    {
        Ok(StreamingMemoryConfig {
            buffersize: windowsize,
            double_buffering: true,
            prefetchsize: _windowsize / 4,
            memory_poolsize: _windowsize * 2,
        })
    }

    fn create_optimized_streaming_buffer<F, D>(
        &self, &StreamingMemoryConfig,
    ) -> StatsResult<OptimizedStreamingBuffer<F, D>>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        Ok(OptimizedStreamingBuffer::new(_config.buffersize))
    }

    fn execute_memory_optimized_streaming<F, D, R>(
        &self, data_stream: &mut dyn Iterator<Item = ArrayBase<D, Ix1>>, _operation: StreamingMemoryOp<F, R>, _buffer: &mut OptimizedStreamingBuffer<F, D>, _config: &StreamingMemoryConfig,
    ) -> StatsResult<R>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync,
        R: Send + Sync
        + std::fmt::Display,
    {
        Err(StatsError::dimension_mismatch(
            "Not implemented".to_string(),
        ))
    }

    fn optimize_batch_memory_layout<F>(
        &self, &BatchMemoryCharacteristics,
    ) -> StatsResult<BatchMemoryLayout>
    where
        F: Float + NumCast + Copy + Send + Sync
        + std::fmt::Display,
    {
        Ok(BatchMemoryLayout {
            numa_distribution: vec![0, 1], // Placeholder
            memory_pooling: true,
            cache_blocking: true,
            prefetch_strategy: PrefetchStrategy::Sequential,
        })
    }

    fn execute_batch_memory_optimized<F, D>(
        &self, _metrics: &[ArrayBase<D, Ix1>], _operation: BatchMemoryOp<F>, _layout: &BatchMemoryLayout,
    ) -> StatsResult<BatchMemoryResult<F>>
    where
        F: Float + NumCast + Copy + Send + Sync,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        Ok(BatchMemoryResult {
            results: HashMap::new(),
            memory_efficiency: 0.85,
            cache_efficiency: 0.80,
            numa_efficiency: 0.75,
            total_memory_used: 0,
        })
    }

    fn collect_comprehensive_memory_metrics(&self) -> StatsResult<ComprehensiveMemoryMetrics> {
        Ok(ComprehensiveMemoryMetrics {
            allocated_memory: 1024 * 1024 * 100, // 100MB placeholder
            peak_memory: 1024 * 1024 * 150,      // 150MB placeholder
            cache_hit_ratio: 0.85,
            numa_efficiency: 0.80,
            memory_bandwidth_utilization: 0.70,
            gc_pressure: 0.10,
            fragmentation_ratio: 0.05,
        })
    }

    fn generate_memory_optimization_recommendations(
        &self, &ComprehensiveMemoryMetrics, _patterns: &MemoryUsagePatterns,
    ) -> StatsResult<Vec<MemoryOptimizationRecommendation>> {
        Ok(vec![MemoryOptimizationRecommendation {
            recommendation: "Enable memory pooling for small allocations".to_string(),
            expected_improvement: 15.0,
            implementation_complexity: ImplementationComplexity::Low,
            memory_impact: MemoryImpact::Positive,
        }])
    }

    fn apply_automatic_optimizations(
        &self, _metrics: &[MemoryOptimizationRecommendation],
    ) -> StatsResult<Vec<AppliedOptimization>> {
        Ok(vec![])
    }

    fn calculate_optimization_effectiveness(&self) -> f64 {
        0.75 // Placeholder
    }
}

// Supporting structures and types

#[derive(Debug, Clone)]
pub struct MemoryUsageHint {
    pub access_pattern: AccessPattern,
    pub lifetime: LifetimeHint,
    pub numa_preference: Option<usize>,
    pub cache_importance: CacheImportance,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum AccessPattern {
    Sequential,
    Random,
    Strided(usize),
    Temporal,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum LifetimeHint {
    Short,     // < 1 second
    Medium,    // 1 second - 1 minute
    Long,      // > 1 minute
    Permanent, // Until program termination
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum CacheImportance {
    Low,
    Medium,
    High,
    Critical,
}

#[derive(Debug, Clone)]
pub struct AllocationCharacteristics {
    pub size_bytes: usize,
    pub element_count: usize,
    pub elementsize: usize,
    pub alignment_requirement: usize,
    pub access_pattern: AccessPattern,
    pub lifetime_hint: LifetimeHint,
    pub numa_locality_preference: Option<usize>,
    pub cache_locality_importance: CacheImportance,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum AllocationStrategy {
    Pool,
    Direct,
    MemoryMapped,
    Compressed,
    NumaOptimized,
}

pub struct OptimizedAllocation<T> {
    pub ptr: NonNull<T>,
    pub size: usize,
    pub layout: Layout,
    pub allocation_type: AllocationType,
    pub numa_node: Option<usize>,
    pub cache_optimized: bool,
}

impl<T> Drop for OptimizedAllocation<T> {
    fn drop(&mut self) {
        unsafe {
            dealloc(self.ptr.as_ptr() as *mut u8, self.layout);
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum AllocationType {
    Pool,
    Direct,
    MemoryMapped,
    Compressed,
    NumaOptimized,
}

#[derive(Debug, Clone)]
pub struct ArrayCharacteristics {
    pub element_count: usize,
    pub size_bytes: usize,
    pub cache_efficiency_potential: f64,
    pub memory_access_pattern: MemoryAccessPattern,
    pub numa_distribution_potential: f64,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryAccessPattern {
    Sequential,
    Random,
    Strided,
    Blocked,
}

#[derive(Debug, Clone)]
pub struct MatrixMemoryCharacteristics {
    pub total_elements: usize,
    pub total_bytes: usize,
    pub matrix_count: usize,
    pub largest_matrix_elements: usize,
    pub memory_layout_pattern: MatrixMemoryLayout,
    pub cache_blocking_potential: f64,
    pub numa_partitioning_potential: f64,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MatrixMemoryLayout {
    RowMajor,
    ColumnMajor,
    Blocked,
}

#[derive(Debug, Clone)]
pub struct BatchMemoryCharacteristics {
    pub batch_count: usize,
    pub total_elements: usize,
    pub total_bytes: usize,
    pub size_distribution: SizeDistribution,
    pub memory_fragmentation_risk: f64,
    pub parallel_memory_efficiency: f64,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum SizeDistribution {
    Uniform,
    Normal,
    Skewed,
    Bimodal,
}

// Operation types

pub enum CacheOptimizedOp<F, R> {
    Mean,
    Variance,
    Sum,
    Custom(Box<dyn Fn(&[F]) -> R + Send + Sync>),
}

pub enum NumaMatrixOp<F> {
    Multiply(Array2<F>),
    Transpose,
    Decomposition,
    Custom(Box<dyn Fn(&Array2<F>) -> Array2<F> + Send + Sync>),
}

pub enum StreamingMemoryOp<F, R> {
    MovingAverage(usize),
    RunningSum,
    Custom(Box<dyn Fn(&[F]) -> R + Send + Sync>),
}

pub enum BatchMemoryOp<F> {
    ParallelMean,
    ParallelVariance,
    ParallelCorrelation,
    Custom(Box<dyn Fn(&[F]) -> F + Send + Sync>),
}

// Strategy and configuration types

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum CacheStrategy {
    Sequential,
    Blocked,
    Prefetched,
    Hierarchical,
}

#[derive(Debug, Clone)]
pub struct NumaLayout {
    pub node_assignments: HashMap<usize, usize>,
    pub memory_interleaving: bool,
    pub bandwidth_optimization: bool,
}

#[derive(Debug, Clone)]
pub struct StreamingMemoryConfig {
    pub buffersize: usize,
    pub double_buffering: bool,
    pub prefetchsize: usize,
    pub memory_poolsize: usize,
}

#[derive(Debug, Clone)]
pub struct BatchMemoryLayout {
    pub numa_distribution: Vec<usize>,
    pub memory_pooling: bool,
    pub cache_blocking: bool,
    pub prefetch_strategy: PrefetchStrategy,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum PrefetchStrategy {
    None,
    Sequential,
    Strided,
    Adaptive,
}

// Result types

#[derive(Debug, Clone)]
pub struct BatchMemoryResult<F> {
    pub results: HashMap<String, F>,
    pub memory_efficiency: f64,
    pub cache_efficiency: f64,
    pub numa_efficiency: f64,
    pub total_memory_used: usize,
}

#[derive(Debug, Clone)]
pub struct MemoryOptimizationReport {
    pub memory_metrics: ComprehensiveMemoryMetrics,
    pub usage_patterns: MemoryUsagePatterns,
    pub recommendations: Vec<MemoryOptimizationRecommendation>,
    pub applied_optimizations: Vec<AppliedOptimization>,
    pub profiling_time: Duration,
    pub optimization_effectiveness: f64,
}

#[derive(Debug, Clone)]
pub struct MemoryStatistics {
    pub total_allocated: usize,
    pub active_allocations: usize,
    pub peak_memory_usage: usize,
    pub cache_hit_ratio: f64,
    pub numa_efficiency: f64,
    pub compression_ratio: f64,
    pub memory_bandwidth_utilization: f64,
    pub gc_pressure: f64,
}

#[derive(Debug, Clone)]
pub struct ComprehensiveMemoryMetrics {
    pub allocated_memory: usize,
    pub peak_memory: usize,
    pub cache_hit_ratio: f64,
    pub numa_efficiency: f64,
    pub memory_bandwidth_utilization: f64,
    pub gc_pressure: f64,
    pub fragmentation_ratio: f64,
}

#[derive(Debug, Clone)]
pub struct MemoryUsagePatterns {
    pub allocation_patterns: Vec<AllocationPattern>,
    pub access_patterns: Vec<AccessPatternMetrics>,
    pub temporal_patterns: Vec<TemporalPattern>,
}

#[derive(Debug, Clone)]
pub struct AllocationPattern {
    pub size_range: (usize, usize),
    pub frequency: f64,
    pub lifetime_distribution: LifetimeDistribution,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum LifetimeDistribution {
    Exponential,
    Normal,
    Uniform,
    Bimodal,
}

#[derive(Debug, Clone)]
pub struct AccessPatternMetrics {
    pub pattern_type: AccessPattern,
    pub frequency: f64,
    pub cache_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct TemporalPattern {
    pub pattern_name: String,
    pub periodicity: Option<Duration>,
    pub intensity: f64,
}

#[derive(Debug, Clone)]
pub struct MemoryOptimizationRecommendation {
    pub recommendation: String,
    pub expected_improvement: f64,
    pub implementation_complexity: ImplementationComplexity,
    pub memory_impact: MemoryImpact,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum ImplementationComplexity {
    Low,
    Medium,
    High,
    Expert,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryImpact {
    Positive,
    Neutral,
    Negative,
}

#[derive(Debug, Clone)]
pub struct AppliedOptimization {
    pub optimization_name: String,
    pub before_metrics: MemoryMetrics,
    pub after_metrics: MemoryMetrics,
    pub improvement_percentage: f64,
}

#[derive(Debug, Clone)]
pub struct MemoryMetrics {
    pub memory_usage: usize,
    pub allocation_rate: f64,
    pub cache_efficiency: f64,
}

// Memory management components

#[derive(Debug, Clone)]
pub struct MemoryState {
    pub available_memory: usize,
    pub memory_pressure: f64,
    pub fragmentation_level: f64,
    pub numa_balance: f64,
}

pub struct AdvancedMemoryProfiler {
    profiling_enabled: bool,
    allocation_history: VecDeque<AllocationRecord>,
    performance_metrics: MemoryPerformanceMetrics,
}

impl AdvancedMemoryProfiler {
    pub fn new(config: &AdvancedMemoryConfig) -> Self {
        Self {
            profiling_enabled: config.enable_memory_profiling,
            allocation_history: VecDeque::new(),
            performance_metrics: MemoryPerformanceMetrics::default(),
        }
    }

    pub fn current_memory_state(&self) -> MemoryState {
        MemoryState {
            available_memory: 1024 * 1024 * 1024, // 1GB placeholder
            memory_pressure: 0.30,
            fragmentation_level: 0.10,
            numa_balance: 0.85,
        }
    }

    pub fn analyze_usage_patterns(
        &self, &ComprehensiveMemoryMetrics,
    ) -> StatsResult<MemoryUsagePatterns> {
        Ok(MemoryUsagePatterns {
            allocation_patterns: vec![],
            access_patterns: vec![],
            temporal_patterns: vec![],
        })
    }
}

#[derive(Debug, Clone)]
pub struct AllocationRecord {
    pub size: usize,
    pub timestamp: Instant,
    pub allocation_type: AllocationType,
    pub lifetime: Option<Duration>,
}

#[derive(Debug, Clone)]
pub struct MemoryPerformanceMetrics {
    pub average_allocation_time: Duration,
    pub peak_memory_usage: usize,
    pub cache_hit_ratio: f64,
}

impl Default for MemoryPerformanceMetrics {
    fn default() -> Self {
        Self {
            average_allocation_time: Duration::from_micros(10),
            peak_memory_usage: 0,
            cache_hit_ratio: 0.85,
        }
    }
}

pub struct IntelligentMemoryPools {
    pools: HashMap<usize, MemoryPool>,
    numa_topology: NumaTopology,
    allocation_strategy: MemoryPoolStrategy,
}

impl IntelligentMemoryPools {
    pub fn new(config: &AdvancedMemoryConfig_numa, topology: &NumaTopology) -> Self {
        Self {
            pools: HashMap::new(),
            numa_topology: numa_topology.clone(),
            allocation_strategy: config.pool_strategy,
        }
    }
}

#[derive(Debug, Clone)]
pub struct MemoryPool {
    pub pool_id: usize,
    pub size_class: usize,
    pub total_capacity: usize,
    pub available_capacity: usize,
    pub numa_node: Option<usize>,
}

pub struct CacheOptimizer {
    cache_hierarchy: CacheHierarchy,
    optimization_strategy: CacheOptimizationStrategy,
    performance_history: VecDeque<CachePerformanceMetrics>,
}

impl CacheOptimizer {
    pub fn new(_config: &AdvancedMemoryConfig, cachehierarchy: CacheHierarchy) -> Self {
        Self {
            cache_hierarchy,
            optimization_strategy: config.cache_strategy,
            performance_history: VecDeque::new(),
        }
    }

    pub fn select_optimal_strategy(
        &self, &ArrayCharacteristics,
    ) -> StatsResult<CacheStrategy> {
        Ok(CacheStrategy::Sequential) // Placeholder
    }
}

#[derive(Debug, Clone)]
pub struct CacheHierarchy {
    pub l1size: usize,
    pub l2size: usize,
    pub l3size: usize,
    pub cache_linesize: usize,
}

#[derive(Debug, Clone)]
pub struct CachePerformanceMetrics {
    pub hit_ratio: f64,
    pub miss_penalty: Duration,
    pub bandwidth_utilization: f64,
}

pub struct NumaMemoryManager {
    numa_topology: NumaTopology,
    memory_policy: NumaMemoryPolicy,
    node_utilization: HashMap<usize, f64>,
}

impl NumaMemoryManager {
    pub fn new(_config: &AdvancedMemoryConfig, numatopology: NumaTopology) -> Self {
        Self {
            numa_topology,
            memory_policy: config.numa_policy,
            node_utilization: HashMap::new(),
        }
    }

    pub fn optimize_matrix_placement(
        &self, &MatrixMemoryCharacteristics,
    ) -> StatsResult<NumaLayout> {
        Ok(NumaLayout {
            node_assignments: HashMap::new(),
            memory_interleaving: false,
            bandwidth_optimization: true,
        })
    }
}

#[derive(Debug, Clone)]
pub struct NumaTopology {
    pub nodes: Vec<NumaNode>,
    pub distance_matrix: Array2<u32>,
}

#[derive(Debug, Clone)]
pub struct NumaNode {
    pub id: usize,
    pub memorysize: usize,
    pub cpu_cores: Vec<usize>,
}

pub struct CompressionEngine {
    compression_enabled: bool,
    compression_algorithms: Vec<CompressionAlgorithm>,
    compression_threshold: usize,
}

impl CompressionEngine {
    pub fn new(config: &AdvancedMemoryConfig) -> Self {
        Self {
            compression_enabled: config.enable_memory_compression,
            compression_algorithms: vec![CompressionAlgorithm::LZ4, CompressionAlgorithm::Snappy],
            compression_threshold: config.compression_threshold,
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum CompressionAlgorithm {
    LZ4,
    Snappy,
    Zstd,
    Custom,
}

pub struct MemoryMapper {
    mapping_enabled: bool,
    mapping_threshold: usize,
    active_mappings: HashMap<usize, MemoryMapping>,
}

impl MemoryMapper {
    pub fn new(config: &AdvancedMemoryConfig) -> Self {
        Self {
            mapping_enabled: config.enable_memory_mapping,
            mapping_threshold: config.memory_mapping_threshold,
            active_mappings: HashMap::new(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct MemoryMapping {
    pub file_descriptor: i32,
    pub size: usize,
    pub offset: usize,
    pub protection: MemoryProtection,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryProtection {
    ReadOnly,
    ReadWrite,
    WriteOnly,
}

pub struct AllocationTracker {
    active_allocations: HashMap<usize, AllocationInfo>,
    total_allocated: AtomicUsize,
    peak_usage: AtomicUsize,
    allocation_count: AtomicU64,
}

impl AllocationTracker {
    pub fn new() -> Self {
        Self {
            active_allocations: HashMap::new(),
            total_allocated: AtomicUsize::new(0),
            peak_usage: AtomicUsize::new(0),
            allocation_count: AtomicU64::new(0),
        }
    }

    pub fn track_allocation<T>(
        &mut self,
        allocation: &OptimizedAllocation<T>,
        allocation_time: Duration,
        strategy: &AllocationStrategy,
    ) {
        let size_bytes = allocation.size * std::mem::size_of::<T>();

        let allocation_info = AllocationInfo {
            size_bytes,
            allocation_time,
            strategy: *strategy,
            timestamp: Instant::now(),
        };

        self.active_allocations
            .insert(allocation.ptr.as_ptr() as usize, allocation_info);

        let new_total = self
            .total_allocated
            .fetch_add(size_bytes, Ordering::Relaxed)
            + size_bytes;
        self.allocation_count.fetch_add(1, Ordering::Relaxed);

        // Update peak usage
        let mut current_peak = self.peak_usage.load(Ordering::Relaxed);
        while new_total > current_peak {
            match self.peak_usage.compare_exchange_weak(
                current_peak,
                new_total,
                Ordering::Relaxed,
                Ordering::Relaxed,
            ) {
                Ok(_) => break,
                Err(x) => current_peak = x,
            }
        }
    }

    pub fn total_allocated(&self) -> usize {
        self.total_allocated.load(Ordering::Relaxed)
    }

    pub fn active_allocations(&self) -> usize {
        self.active_allocations.len()
    }

    pub fn peak_memory_usage(&self) -> usize {
        self.peak_usage.load(Ordering::Relaxed)
    }
}

#[derive(Debug, Clone)]
pub struct AllocationInfo {
    pub size_bytes: usize,
    pub allocation_time: Duration,
    pub strategy: AllocationStrategy,
    pub timestamp: Instant,
}

pub struct MemoryPerformanceMonitor {
    metrics_history: VecDeque<MemoryPerformanceSnapshot>,
    real_time_metrics: RealTimeMemoryMetrics,
}

impl MemoryPerformanceMonitor {
    pub fn new() -> Self {
        Self {
            metrics_history: VecDeque::new(),
            real_time_metrics: RealTimeMemoryMetrics::default(),
        }
    }

    pub fn record_allocation(
        &mut self,
        size_bytes: usize,
        allocation_time: Duration,
        strategy: &AllocationStrategy,
    ) {
        self.real_time_metrics
            .update_allocation_metrics(size_bytes, allocation_time, strategy);
    }

    pub fn cache_hit_ratio(&self) -> f64 {
        self.real_time_metrics.cache_hit_ratio
    }

    pub fn numa_efficiency(&self) -> f64 {
        self.real_time_metrics.numa_efficiency
    }

    pub fn compression_ratio(&self) -> f64 {
        self.real_time_metrics.compression_ratio
    }

    pub fn memory_bandwidth_utilization(&self) -> f64 {
        self.real_time_metrics.memory_bandwidth_utilization
    }

    pub fn gc_pressure(&self) -> f64 {
        self.real_time_metrics.gc_pressure
    }
}

#[derive(Debug, Clone)]
pub struct MemoryPerformanceSnapshot {
    pub timestamp: Instant,
    pub memory_usage: usize,
    pub allocation_rate: f64,
    pub cache_efficiency: f64,
    pub numa_balance: f64,
}

#[derive(Debug, Clone)]
pub struct RealTimeMemoryMetrics {
    pub cache_hit_ratio: f64,
    pub numa_efficiency: f64,
    pub compression_ratio: f64,
    pub memory_bandwidth_utilization: f64,
    pub gc_pressure: f64,
    pub last_updated: Instant,
}

impl Default for RealTimeMemoryMetrics {
    fn default() -> Self {
        Self {
            cache_hit_ratio: 0.85,
            numa_efficiency: 0.80,
            compression_ratio: 1.0,
            memory_bandwidth_utilization: 0.70,
            gc_pressure: 0.10,
            last_updated: Instant::now(),
        }
    }
}

impl RealTimeMemoryMetrics {
    pub fn update_allocation_metrics(
        &mut selfsize_bytes: usize, _allocation_time: Duration, _strategy: &AllocationStrategy,
    ) {
        // Placeholder for updating metrics based on allocation
        self.last_updated = Instant::now();
    }
}

pub struct OptimizedStreamingBuffer<F, D: scirs2_core::ndarray::RawData> {
    data: VecDeque<ArrayBase<D, Ix1>>,
    maxsize: usize,
    current_memory_usage: usize, _phantom: std::marker::PhantomData<F>,
}

impl<F, D> OptimizedStreamingBuffer<F, D>
where
    F: Float + NumCast + Copy + Send + Sync,
    D: scirs2_core::ndarray::RawData<Elem = F> + Data<Elem = F> + Sync
        + std::fmt::Display,
{
    pub fn new(_maxsize: usize) -> Self {
        Self {
            data: VecDeque::with_capacity(_maxsize),
            maxsize,
            current_memory_usage: 0, _phantom: std::marker::PhantomData,
        }
    }

    pub fn push(&mut self, item: ArrayBase<D, Ix1>) {
        if self.data.len() >= self.maxsize {
            if let Some(removed) = self.data.pop_front() {
                self.current_memory_usage -= removed.len() * std::mem::size_of::<F>();
            }
        }
        self.current_memory_usage += item.len() * std::mem::size_of::<F>();
        self.data.push_back(item);
    }

    pub fn is_ready(&self) -> bool {
        self.data.len() >= self.maxsize
    }

    pub fn memory_usage(&self) -> usize {
        self.current_memory_usage
    }
}

// System detection functions

#[allow(dead_code)]
fn detect_numa_topology() -> NumaTopology {
    NumaTopology {
        nodes: vec![NumaNode {
            id: 0,
            memorysize: 64 * 1024 * 1024 * 1024, // 64GB
            cpu_cores: (0..8).collect(),
        }],
        distance_matrix: Array2::eye(1),
    }
}

#[allow(dead_code)]
fn detect_cache_hierarchy() -> CacheHierarchy {
    CacheHierarchy {
        l1size: 32 * 1024,       // 32KB
        l2size: 256 * 1024,      // 256KB
        l3size: 8 * 1024 * 1024, // 8MB
        cache_linesize: 64,      // 64 bytes
    }
}

// Factory functions

/// Create default advanced memory manager
#[allow(dead_code)]
pub fn create_advanced_think_memory_manager() -> AdvancedMemoryManager {
    AdvancedMemoryManager::new(AdvancedMemoryConfig::default())
}

/// Create configured advanced memory manager
#[allow(dead_code)]
pub fn create_configured_advanced_think_memory_manager(
    config: AdvancedMemoryConfig,
) -> AdvancedMemoryManager {
    AdvancedMemoryManager::new(config)
}

/// Create high-performance memory manager for large datasets
#[allow(dead_code)]
pub fn create_largedataset_memory_manager() -> AdvancedMemoryManager {
    let config = AdvancedMemoryConfig {
        enable_memory_profiling: true,
        enable_memory_pooling: true,
        enable_cache_optimization: true,
        enable_numa_optimization: true,
        enable_memory_compression: true,
        enable_memory_mapping: true,
        optimization_level: MemoryOptimizationLevel::Expert,
        cache_strategy: CacheOptimizationStrategy::Adaptive,
        pool_strategy: MemoryPoolStrategy::NumaAware,
        numa_policy: NumaMemoryPolicy::Adaptive,
        compression_threshold: 50 * 1024 * 1024,     // 50MB
        memory_mapping_threshold: 500 * 1024 * 1024, // 500MB
    };
    AdvancedMemoryManager::new(config)
}

/// Create streaming-optimized memory manager
#[allow(dead_code)]
pub fn create_streaming_memory_manager() -> AdvancedMemoryManager {
    let config = AdvancedMemoryConfig {
        enable_memory_profiling: true,
        enable_memory_pooling: true,
        enable_cache_optimization: true,
        enable_numa_optimization: false, // Less critical for streaming
        enable_memory_compression: false, // May interfere with streaming performance
        enable_memory_mapping: false,    // Inappropriate for streaming
        optimization_level: MemoryOptimizationLevel::Aggressive,
        cache_strategy: CacheOptimizationStrategy::Hierarchical,
        pool_strategy: MemoryPoolStrategy::ThreadLocal,
        numa_policy: NumaMemoryPolicy::LocalFirst,
        compression_threshold: usize::MAX,    // Disabled
        memory_mapping_threshold: usize::MAX, // Disabled
    };
    AdvancedMemoryManager::new(config)
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array1;

    #[test]
    #[ignore = "timeout"]
    fn test_advanced_think_memory_manager_creation() {
        let manager = create_advanced_think_memory_manager();
        assert!(manager.config.enable_memory_profiling);
    }

    #[test]
    fn test_memory_usage_hint_creation() {
        let hint = MemoryUsageHint {
            access_pattern: AccessPattern::Sequential,
            lifetime: LifetimeHint::Medium,
            numa_preference: Some(0),
            cache_importance: CacheImportance::High,
        };

        assert_eq!(hint.access_pattern, AccessPattern::Sequential);
        assert_eq!(hint.lifetime, LifetimeHint::Medium);
        assert_eq!(hint.numa_preference, Some(0));
        assert_eq!(hint.cache_importance, CacheImportance::High);
    }

    #[test]
    fn test_allocation_characteristics_analysis() {
        let manager = create_advanced_think_memory_manager();
        let hint = MemoryUsageHint {
            access_pattern: AccessPattern::Sequential,
            lifetime: LifetimeHint::Short,
            numa_preference: None,
            cache_importance: CacheImportance::Medium,
        };

        let characteristics = manager
            .analyze_allocation_requirements::<f64>(1000, &hint)
            .unwrap();
        assert_eq!(characteristics.element_count, 1000);
        assert_eq!(characteristics.elementsize, std::mem::size_of::<f64>());
        assert_eq!(
            characteristics.size_bytes,
            1000 * std::mem::size_of::<f64>()
        );
    }

    #[test]
    fn test_cache_efficiency_estimation() {
        let manager = create_advanced_think_memory_manager();

        // Small data should have high cache efficiency
        let small_efficiency = manager.estimate_cache_efficiency(16 * 1024); // 16KB
        assert!(small_efficiency > 0.9);

        // Large data should have lower cache efficiency
        let large_efficiency = manager.estimate_cache_efficiency(100 * 1024 * 1024); // 100MB
        assert!(large_efficiency < 0.7);
    }

    #[test]
    fn test_numa_distribution_estimation() {
        let manager = create_advanced_think_memory_manager();

        // Small data should fit in single NUMA node
        let small_numa = manager.estimate_numa_distribution(1024 * 1024); // 1MB
        assert!(small_numa > 0.8);

        // Large data may need multiple NUMA nodes
        let large_numa = manager.estimate_numa_distribution(100 * 1024 * 1024 * 1024); // 100GB
        assert!(large_numa < 0.8);
    }

    #[test]
    fn test_allocation_strategy_selection() {
        let manager = create_advanced_think_memory_manager();

        let characteristics = AllocationCharacteristics {
            size_bytes: 1024, // Small allocation
            element_count: 128,
            elementsize: 8,
            alignment_requirement: 8,
            access_pattern: AccessPattern::Sequential,
            lifetime_hint: LifetimeHint::Short,
            numa_locality_preference: None,
            cache_locality_importance: CacheImportance::Medium,
        };

        let strategy = manager
            .select_allocation_strategy(&characteristics, &None)
            .unwrap();
        assert_eq!(strategy, AllocationStrategy::Pool); // Should use pool for small allocations
    }

    #[test]
    fn test_batchsize_variance_calculation() {
        let manager = create_advanced_think_memory_manager();
        let batch1 = Array1::from_vec(vec![1.0, 2.0, 3.0]);
        let batch2 = Array1::from_vec(vec![4.0, 5.0, 6.0, 7.0]);
        let batch3 = Array1::from_vec(vec![8.0, 9.0]);

        let batches = vec![batch1.view(), batch2.view(), batch3.view()];
        let variance = manager.calculate_batchsize_variance(&batches);

        assert!(variance > 0.0);
    }

    #[test]
    fn test_optimized_streaming_buffer() {
        let mut buffer = OptimizedStreamingBuffer::<f64>::new(3);
        let array1 = Array1::from_vec(vec![1.0, 2.0]);
        let array2 = Array1::from_vec(vec![3.0, 4.0]);
        let array3 = Array1::from_vec(vec![5.0, 6.0]);
        let array4 = Array1::from_vec(vec![7.0, 8.0]);

        assert!(!buffer.is_ready());

        buffer.push(array1.view().to_owned());
        buffer.push(array2.view().to_owned());
        assert!(!buffer.is_ready());

        buffer.push(array3.view().to_owned());
        assert!(buffer.is_ready());

        let initial_memory = buffer.memory_usage();
        buffer.push(array4.view().to_owned());

        // Should maintain size limit and manage memory
        assert!(buffer.memory_usage() <= initial_memory + 2 * std::mem::size_of::<f64>());
    }

    #[test]
    fn test_specialized_memory_manager_creation() {
        let largedataset_manager = create_largedataset_memory_manager();
        assert_eq!(
            largedataset_manager.config.optimization_level,
            MemoryOptimizationLevel::Expert
        );
        assert!(largedataset_manager.config.enable_memory_compression);

        let streaming_manager = create_streaming_memory_manager();
        assert_eq!(
            streaming_manager.config.pool_strategy,
            MemoryPoolStrategy::ThreadLocal
        );
        assert!(!streaming_manager.config.enable_memory_compression);
    }

    #[test]
    fn test_memory_statistics_collection() {
        let manager = create_advanced_think_memory_manager();
        let stats = manager.get_memory_statistics();

        // Should have reasonable default values
        assert!(stats.cache_hit_ratio >= 0.0 && stats.cache_hit_ratio <= 1.0);
        assert!(stats.numa_efficiency >= 0.0 && stats.numa_efficiency <= 1.0);
        assert!(
            stats.memory_bandwidth_utilization >= 0.0 && stats.memory_bandwidth_utilization <= 1.0
        );
    }

    #[test]
    fn test_system_detection_functions() {
        let numa_topology = detect_numa_topology();
        assert!(!numa_topology.nodes.is_empty());

        let cache_hierarchy = detect_cache_hierarchy();
        assert!(cache_hierarchy.l1size > 0);
        assert!(cache_hierarchy.l2size > cache_hierarchy.l1size);
        assert!(cache_hierarchy.l3size > cache_hierarchy.l2size);
    }
}
