voirs_conversion/

pipeline_optimization.rs

//! Pipeline optimization for streamlined voice conversion processing
//!
//! This module provides advanced optimization techniques for the voice conversion pipeline,
//! including intelligent caching, parallel processing, resource management, and adaptive
//! algorithm selection for maximum performance.

use crate::{
    config::ConversionConfig,
    processing::{AudioBuffer, ProcessingPipeline},
    types::{ConversionRequest, ConversionResult, ConversionType},
    Error, Result,
};
use scirs2_core::parallel_ops::*;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::thread;
use std::time::{Duration, Instant};
use tokio::sync::{mpsc, RwLock as AsyncRwLock};
use tracing::{debug, info, trace, warn};

/// Optimized processing pipeline controller
#[derive(Debug)]
pub struct OptimizedPipeline {
    /// Core optimization engine
    optimization_engine: Arc<RwLock<OptimizationEngine>>,
    /// Intelligent cache system
    cache_system: Arc<AsyncRwLock<IntelligentCache>>,
    /// Resource manager
    resource_manager: Arc<RwLock<ResourceManager>>,
    /// Performance profiler
    profiler: Arc<Mutex<PerformanceProfiler>>,
    /// Adaptive algorithm selector
    algorithm_selector: Arc<RwLock<AdaptiveAlgorithmSelector>>,
    /// Pipeline configuration
    config: OptimizedPipelineConfig,
}

/// Configuration for optimized pipeline
#[derive(Debug, Clone)]
pub struct OptimizedPipelineConfig {
    /// Enable intelligent caching
    pub enable_intelligent_caching: bool,
    /// Enable adaptive algorithm selection
    pub enable_adaptive_algorithms: bool,
    /// Enable parallel processing optimizations
    pub enable_parallel_optimization: bool,
    /// Enable resource-aware processing
    pub enable_resource_awareness: bool,
    /// Enable performance profiling
    pub enable_profiling: bool,
    /// Cache size limit in MB
    pub cache_size_limit_mb: usize,
    /// Maximum parallel threads
    pub max_parallel_threads: usize,
    /// Profiling window size
    pub profiling_window_size: usize,
    /// Algorithm adaptation threshold
    pub adaptation_threshold: f32,
}

impl Default for OptimizedPipelineConfig {
    fn default() -> Self {
        Self {
            enable_intelligent_caching: true,
            enable_adaptive_algorithms: true,
            enable_parallel_optimization: true,
            enable_resource_awareness: true,
            enable_profiling: true,
            cache_size_limit_mb: 256,
            max_parallel_threads: std::thread::available_parallelism()
                .map(|n| n.get())
                .unwrap_or(4),
            profiling_window_size: 100,
            adaptation_threshold: 0.1,
        }
    }
}

/// Core optimization engine
#[derive(Debug)]
pub struct OptimizationEngine {
    /// Processing stage optimizations
    stage_optimizations: HashMap<String, StageOptimization>,
    /// Pipeline templates for common patterns
    pipeline_templates: HashMap<ConversionType, OptimizedPipelineTemplate>,
    /// Performance history
    performance_history: VecDeque<PipelinePerformanceRecord>,
    /// Optimization statistics
    optimization_stats: OptimizationStatistics,
}

/// Optimization for individual processing stages
#[derive(Debug, Clone)]
pub struct StageOptimization {
    /// Stage name
    pub name: String,
    /// Optimal buffer size
    pub optimal_buffer_size: usize,
    /// Parallel processing configuration
    pub parallel_config: ParallelConfig,
    /// Memory optimization settings
    pub memory_config: MemoryConfig,
    /// Algorithm variant selection
    pub algorithm_variant: AlgorithmVariant,
    /// Performance characteristics
    pub performance_characteristics: StagePerformanceCharacteristics,
}

/// Parallel processing configuration
#[derive(Debug, Clone)]
pub struct ParallelConfig {
    /// Enable parallel processing for this stage
    pub enable_parallel: bool,
    /// Optimal thread count
    pub optimal_thread_count: usize,
    /// Chunk size for parallel processing
    pub chunk_size: usize,
    /// Load balancing strategy
    pub load_balancing: LoadBalancingStrategy,
}

/// Memory optimization configuration
#[derive(Debug, Clone)]
pub struct MemoryConfig {
    /// Enable memory pooling
    pub enable_pooling: bool,
    /// Pre-allocated buffer count
    pub buffer_pool_size: usize,
    /// Enable in-place processing
    pub enable_in_place: bool,
    /// Memory layout optimization
    pub memory_layout: MemoryLayout,
}

/// Load balancing strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LoadBalancingStrategy {
    /// Static work distribution
    Static,
    /// Dynamic work stealing
    WorkStealing,
    /// Round robin distribution
    RoundRobin,
    /// Load-aware distribution
    LoadAware,
}

/// Memory layout optimizations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MemoryLayout {
    /// Standard layout
    Standard,
    /// Cache-optimized layout
    CacheOptimized,
    /// SIMD-optimized layout
    SimdOptimized,
    /// Hybrid layout
    Hybrid,
}

/// Algorithm variants for different performance characteristics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlgorithmVariant {
    /// High-quality algorithm
    HighQuality,
    /// Balanced quality/performance
    Balanced,
    /// High-performance algorithm
    HighPerformance,
    /// Memory-optimized algorithm
    MemoryOptimized,
    /// GPU-optimized algorithm
    GpuOptimized,
}

/// Performance characteristics for a processing stage
#[derive(Debug, Clone, Default)]
pub struct StagePerformanceCharacteristics {
    /// Average processing time in microseconds
    pub avg_processing_time_us: f64,
    /// Memory usage in bytes
    pub memory_usage_bytes: usize,
    /// CPU utilization percentage
    pub cpu_utilization: f32,
    /// Cache hit ratio
    pub cache_hit_ratio: f32,
    /// Parallel efficiency
    pub parallel_efficiency: f32,
}

/// Optimized pipeline template
#[derive(Debug, Clone)]
pub struct OptimizedPipelineTemplate {
    /// Conversion type this template is for
    pub conversion_type: ConversionType,
    /// Optimized stage sequence
    pub stage_sequence: Vec<OptimizedStage>,
    /// Overall performance characteristics
    pub performance_characteristics: PipelinePerformanceCharacteristics,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
}

/// Individual optimized stage
#[derive(Debug, Clone)]
pub struct OptimizedStage {
    /// Stage name
    pub name: String,
    /// Stage optimization settings
    pub optimization: StageOptimization,
    /// Dependencies on previous stages
    pub dependencies: Vec<String>,
    /// Can be parallelized with other stages
    pub parallel_compatible: bool,
}

/// Pipeline performance characteristics
#[derive(Debug, Clone, Default)]
pub struct PipelinePerformanceCharacteristics {
    /// Total processing time estimate in milliseconds
    pub total_processing_time_ms: f64,
    /// Memory peak usage in MB
    pub peak_memory_usage_mb: f64,
    /// Average CPU utilization
    pub avg_cpu_utilization: f32,
    /// Parallel efficiency
    pub parallel_efficiency: f32,
    /// Quality score achievable
    pub quality_score: f32,
}

/// Resource requirements for pipeline
#[derive(Debug, Clone, Default)]
pub struct ResourceRequirements {
    /// Minimum memory required in MB
    pub min_memory_mb: f64,
    /// Recommended CPU cores
    pub recommended_cores: usize,
    /// GPU memory required in MB (if applicable)
    pub gpu_memory_mb: Option<f64>,
    /// Disk I/O requirements
    pub disk_io_mb_per_sec: f64,
}

/// Intelligent cache system
#[derive(Debug)]
pub struct IntelligentCache {
    /// Cached processing results
    result_cache: HashMap<CacheKey, CachedResult>,
    /// Cache usage statistics
    usage_stats: CacheUsageStats,
    /// Cache configuration
    config: CacheConfig,
    /// Memory usage tracking
    current_memory_usage_bytes: usize,
    /// LRU tracking
    lru_tracker: VecDeque<CacheKey>,
}

/// Cache key for result caching
#[derive(Debug, Clone, Hash, PartialEq, Eq)]
pub struct CacheKey {
    /// Audio content hash
    pub content_hash: u64,
    /// Conversion parameters hash
    pub params_hash: u64,
    /// Processing quality level
    pub quality_level: u32,
    /// Sample rate
    pub sample_rate: u32,
}

/// Cached processing result
#[derive(Debug, Clone)]
pub struct CachedResult {
    /// Processed audio data
    pub audio_data: Vec<f32>,
    /// Processing metadata
    pub metadata: HashMap<String, String>,
    /// Quality metrics
    pub quality_metrics: HashMap<String, f32>,
    /// Cache timestamp
    pub timestamp: Instant,
    /// Access count
    pub access_count: u32,
    /// Result size in bytes
    pub size_bytes: usize,
}

/// Cache usage statistics
#[derive(Debug, Default)]
pub struct CacheUsageStats {
    /// Total cache hits
    pub total_hits: u64,
    /// Total cache misses
    pub total_misses: u64,
    /// Cache hit ratio
    pub hit_ratio: f32,
    /// Memory savings from caching (bytes)
    pub memory_savings_bytes: u64,
    /// Time savings from caching (microseconds)
    pub time_savings_us: u64,
}

/// Cache configuration
#[derive(Debug, Clone)]
pub struct CacheConfig {
    /// Maximum cache size in bytes
    pub max_size_bytes: usize,
    /// Cache TTL in seconds
    pub ttl_seconds: u64,
    /// Enable intelligent eviction
    pub enable_intelligent_eviction: bool,
    /// Enable predictive caching
    pub enable_predictive_caching: bool,
}

/// Resource manager for optimal resource utilization
#[derive(Debug)]
pub struct ResourceManager {
    /// Current system resources
    pub system_resources: SystemResources,
    /// Resource usage history
    pub usage_history: VecDeque<ResourceUsageSnapshot>,
    /// Resource allocation strategy
    pub allocation_strategy: ResourceAllocationStrategy,
    /// Resource limits
    pub resource_limits: ResourceLimits,
}

/// Current system resources
#[derive(Debug, Clone, Default)]
pub struct SystemResources {
    /// Available CPU cores
    pub available_cores: usize,
    /// Available memory in MB
    pub available_memory_mb: f64,
    /// CPU usage percentage
    pub cpu_usage_percent: f32,
    /// Memory usage percentage
    pub memory_usage_percent: f32,
    /// GPU availability and memory
    pub gpu_resources: Option<GpuResources>,
    /// System load average
    pub load_average: f32,
}

/// GPU resource information
#[derive(Debug, Clone)]
pub struct GpuResources {
    /// Available GPU memory in MB
    pub available_memory_mb: f64,
    /// GPU utilization percentage
    pub utilization_percent: f32,
    /// GPU compute capability
    pub compute_capability: String,
}

/// Resource usage snapshot
#[derive(Debug, Clone)]
pub struct ResourceUsageSnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// Resource usage at this time
    pub resources: SystemResources,
    /// Active pipeline count
    pub active_pipelines: usize,
    /// Overall system performance
    pub performance_score: f32,
}

/// Resource allocation strategy
#[derive(Debug, Clone)]
pub enum ResourceAllocationStrategy {
    /// Conservative allocation
    Conservative,
    /// Balanced allocation (default)
    Balanced,
    /// Aggressive allocation
    Aggressive,
    /// Adaptive allocation based on load
    Adaptive,
    /// Custom allocation rules
    Custom(CustomAllocationRules),
}

/// Custom resource allocation rules
#[derive(Debug, Clone)]
pub struct CustomAllocationRules {
    /// CPU allocation rules
    pub cpu_rules: Vec<AllocationRule>,
    /// Memory allocation rules
    pub memory_rules: Vec<AllocationRule>,
    /// GPU allocation rules
    pub gpu_rules: Vec<AllocationRule>,
}

/// Individual allocation rule
#[derive(Debug, Clone)]
pub struct AllocationRule {
    /// Condition for rule activation
    pub condition: String,
    /// Resource allocation percentage
    pub allocation_percent: f32,
    /// Priority of this rule
    pub priority: i32,
}

/// Resource usage limits
#[derive(Debug, Clone)]
pub struct ResourceLimits {
    /// Maximum CPU usage percentage
    pub max_cpu_usage: f32,
    /// Maximum memory usage percentage
    pub max_memory_usage: f32,
    /// Maximum concurrent pipelines
    pub max_concurrent_pipelines: usize,
    /// Emergency resource reservation
    pub emergency_reserve_percent: f32,
}

/// Performance profiler for pipeline optimization
#[derive(Debug)]
pub struct PerformanceProfiler {
    /// Performance measurements
    performance_data: VecDeque<PerformanceMeasurement>,
    /// Profiling configuration
    config: ProfilingConfig,
    /// Analysis results
    analysis_results: HashMap<String, ProfileAnalysisResult>,
}

/// Individual performance measurement
#[derive(Debug, Clone)]
pub struct PerformanceMeasurement {
    /// Measurement timestamp
    pub timestamp: Instant,
    /// Pipeline identifier
    pub pipeline_id: String,
    /// Stage-wise timing
    pub stage_timings: HashMap<String, Duration>,
    /// Total processing time
    pub total_time: Duration,
    /// Resource usage during processing
    pub resource_usage: SystemResources,
    /// Quality metrics achieved
    pub quality_metrics: HashMap<String, f32>,
}

/// Profiling configuration
#[derive(Debug, Clone)]
pub struct ProfilingConfig {
    /// Enable detailed timing
    pub enable_detailed_timing: bool,
    /// Enable memory profiling
    pub enable_memory_profiling: bool,
    /// Enable CPU profiling
    pub enable_cpu_profiling: bool,
    /// Sample rate for profiling
    pub sample_rate_ms: u64,
}

/// Analysis result from performance profiling
#[derive(Debug, Clone)]
pub struct ProfileAnalysisResult {
    /// Analysis timestamp
    pub timestamp: Instant,
    /// Identified bottlenecks
    pub bottlenecks: Vec<PerformanceBottleneck>,
    /// Optimization recommendations
    pub recommendations: Vec<OptimizationRecommendation>,
    /// Performance trends
    pub performance_trends: PerformanceTrends,
}

/// Identified performance bottleneck
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
    /// Bottleneck location (stage name)
    pub location: String,
    /// Bottleneck type
    pub bottleneck_type: BottleneckType,
    /// Severity (0.0 to 1.0)
    pub severity: f32,
    /// Impact on overall performance
    pub impact: f32,
    /// Suggested fixes
    pub suggested_fixes: Vec<String>,
}

/// Types of performance bottlenecks
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BottleneckType {
    /// CPU bottleneck
    Cpu,
    /// Memory bottleneck
    Memory,
    /// I/O bottleneck
    Io,
    /// Cache bottleneck
    Cache,
    /// Synchronization bottleneck
    Synchronization,
    /// Algorithm complexity bottleneck
    Algorithm,
}

/// Optimization recommendation
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    /// Recommendation type
    pub recommendation_type: RecommendationType,
    /// Target component
    pub target: String,
    /// Expected improvement
    pub expected_improvement: f32,
    /// Implementation effort (0.0 to 1.0)
    pub implementation_effort: f32,
    /// Detailed description
    pub description: String,
}

/// Types of optimization recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RecommendationType {
    /// Algorithm optimization
    AlgorithmOptimization,
    /// Parallel processing
    ParallelProcessing,
    /// Memory optimization
    MemoryOptimization,
    /// Caching strategy
    CachingStrategy,
    /// Resource allocation
    ResourceAllocation,
    /// Pipeline restructuring
    PipelineRestructuring,
}

/// Performance trends analysis
#[derive(Debug, Clone, Default)]
pub struct PerformanceTrends {
    /// Processing time trend (positive = getting slower)
    pub processing_time_trend: f32,
    /// Memory usage trend (positive = using more memory)
    pub memory_usage_trend: f32,
    /// Quality trend (positive = improving quality)
    pub quality_trend: f32,
    /// Overall performance score trend
    pub performance_score_trend: f32,
}

/// Adaptive algorithm selector
#[derive(Debug)]
pub struct AdaptiveAlgorithmSelector {
    /// Algorithm performance database
    algorithm_database: HashMap<String, AlgorithmPerformanceData>,
    /// Selection strategy
    selection_strategy: SelectionStrategy,
    /// Adaptation history
    adaptation_history: VecDeque<AdaptationRecord>,
    /// Learning parameters
    learning_params: LearningParameters,
}

/// Performance data for an algorithm
#[derive(Debug, Clone)]
pub struct AlgorithmPerformanceData {
    /// Algorithm identifier
    pub algorithm_id: String,
    /// Average processing time in microseconds
    pub avg_processing_time_us: f64,
    /// Quality score achieved
    pub quality_score: f32,
    /// Resource usage characteristics
    pub resource_usage: ResourceUsageCharacteristics,
    /// Success rate
    pub success_rate: f32,
    /// Usage count
    pub usage_count: u32,
    /// Last updated timestamp
    pub last_updated: Instant,
}

impl Default for AlgorithmPerformanceData {
    fn default() -> Self {
        Self {
            algorithm_id: String::new(),
            avg_processing_time_us: 0.0,
            quality_score: 0.0,
            resource_usage: ResourceUsageCharacteristics::default(),
            success_rate: 0.0,
            usage_count: 0,
            last_updated: Instant::now(),
        }
    }
}

/// Resource usage characteristics
#[derive(Debug, Clone, Default)]
pub struct ResourceUsageCharacteristics {
    /// CPU usage per sample
    pub cpu_usage_per_sample: f32,
    /// Memory usage per sample
    pub memory_usage_per_sample: f32,
    /// Parallel scalability factor
    pub parallel_scalability: f32,
    /// Cache efficiency
    pub cache_efficiency: f32,
}

/// Algorithm selection strategy
#[derive(Debug, Clone)]
pub enum SelectionStrategy {
    /// Select based on performance only
    Performance,
    /// Select based on quality only
    Quality,
    /// Balanced selection
    Balanced,
    /// Adaptive learning-based selection
    Adaptive,
    /// Custom selection criteria
    Custom(SelectionCriteria),
}

/// Custom selection criteria
#[derive(Debug, Clone)]
pub struct SelectionCriteria {
    /// Performance weight (0.0 to 1.0)
    pub performance_weight: f32,
    /// Quality weight (0.0 to 1.0)
    pub quality_weight: f32,
    /// Resource efficiency weight (0.0 to 1.0)
    pub resource_efficiency_weight: f32,
    /// Reliability weight (0.0 to 1.0)
    pub reliability_weight: f32,
}

/// Record of algorithm adaptation
#[derive(Debug, Clone)]
pub struct AdaptationRecord {
    /// Timestamp
    pub timestamp: Instant,
    /// Original algorithm
    pub original_algorithm: String,
    /// Selected algorithm
    pub selected_algorithm: String,
    /// Selection reason
    pub reason: String,
    /// Performance improvement achieved
    pub improvement: f32,
}

/// Learning parameters for adaptation
#[derive(Debug, Clone)]
pub struct LearningParameters {
    /// Learning rate (0.0 to 1.0)
    pub learning_rate: f32,
    /// Exploration rate (0.0 to 1.0)
    pub exploration_rate: f32,
    /// Adaptation threshold
    pub adaptation_threshold: f32,
    /// History window size
    pub history_window_size: usize,
}

/// Pipeline performance record
#[derive(Debug, Clone)]
pub struct PipelinePerformanceRecord {
    /// Record timestamp
    pub timestamp: Instant,
    /// Pipeline configuration
    pub pipeline_config: String,
    /// Processing time
    pub processing_time: Duration,
    /// Quality score
    pub quality_score: f32,
    /// Resource usage
    pub resource_usage: SystemResources,
    /// Success indicator
    pub success: bool,
}

/// Overall optimization statistics
#[derive(Debug, Default, Clone)]
pub struct OptimizationStatistics {
    /// Total optimizations applied
    pub total_optimizations: u64,
    /// Performance improvements achieved
    pub performance_improvements: f32,
    /// Memory savings achieved
    pub memory_savings_percent: f32,
    /// Cache effectiveness
    pub cache_effectiveness: f32,
    /// Adaptive algorithm successes
    pub adaptation_success_rate: f32,
}

// Implementation of the main OptimizedPipeline
impl OptimizedPipeline {
    /// Create new optimized pipeline
    pub fn new() -> Self {
        Self::with_config(OptimizedPipelineConfig::default())
    }

    /// Create with custom configuration
    pub fn with_config(config: OptimizedPipelineConfig) -> Self {
        let optimization_engine = Arc::new(RwLock::new(OptimizationEngine::new()));
        let cache_system = Arc::new(AsyncRwLock::new(IntelligentCache::new(
            config.cache_size_limit_mb * 1024 * 1024,
        )));
        let resource_manager = Arc::new(RwLock::new(ResourceManager::new()));
        let profiler = Arc::new(Mutex::new(PerformanceProfiler::new()));
        let algorithm_selector = Arc::new(RwLock::new(AdaptiveAlgorithmSelector::new()));

        Self {
            optimization_engine,
            cache_system,
            resource_manager,
            profiler,
            algorithm_selector,
            config,
        }
    }

    /// Optimize a conversion request before processing
    pub async fn optimize_request(
        &self,
        request: &ConversionRequest,
        conversion_config: &ConversionConfig,
    ) -> Result<OptimizedConversionPlan> {
        let start_time = Instant::now();

        // Check cache first
        let cache_key = self.generate_cache_key(request)?;

        if self.config.enable_intelligent_caching {
            if let Some(cached_result) = self.check_cache(&cache_key).await? {
                return Ok(OptimizedConversionPlan {
                    plan_type: PlanType::Cached,
                    cached_result: Some(cached_result),
                    processing_stages: Vec::new(),
                    estimated_time: Duration::from_millis(1),
                    resource_requirements: ResourceRequirements::default(),
                    quality_estimate: 1.0,
                });
            }
        }

        // Get current system resources
        let system_resources = {
            let resource_manager = self
                .resource_manager
                .read()
                .expect("lock should not be poisoned");
            resource_manager.system_resources.clone()
        };

        // Select optimal algorithm
        let selected_algorithm = if self.config.enable_adaptive_algorithms {
            let selector = self
                .algorithm_selector
                .read()
                .expect("lock should not be poisoned");
            selector.select_optimal_algorithm(
                &request.conversion_type,
                &system_resources,
                conversion_config,
            )?
        } else {
            AlgorithmVariant::Balanced
        };

        // Generate optimized processing plan
        let processing_plan = {
            let engine = self
                .optimization_engine
                .read()
                .expect("lock should not be poisoned");
            engine.generate_processing_plan(
                request,
                &selected_algorithm,
                &system_resources,
                conversion_config,
            )?
        };

        let _planning_time = start_time.elapsed();

        Ok(OptimizedConversionPlan {
            plan_type: PlanType::Optimized,
            cached_result: None,
            processing_stages: processing_plan.stages,
            estimated_time: processing_plan.estimated_time,
            resource_requirements: processing_plan.resource_requirements,
            quality_estimate: processing_plan.quality_estimate,
        })
    }

    /// Execute optimized conversion plan
    pub async fn execute_plan(
        &self,
        plan: &OptimizedConversionPlan,
        request: &ConversionRequest,
    ) -> Result<ConversionResult> {
        match plan.plan_type {
            PlanType::Cached => {
                if let Some(cached_result) = &plan.cached_result {
                    return Ok(self.create_result_from_cache(cached_result, request));
                }
            }
            PlanType::Optimized => {
                return self.execute_optimized_processing(plan, request).await;
            }
            PlanType::Standard => {
                // Fallback to standard processing
                return Err(Error::runtime(
                    "Standard processing not implemented".to_string(),
                ));
            }
        }

        Err(Error::runtime("Invalid execution plan".to_string()))
    }

    /// Get optimization statistics
    pub fn get_optimization_statistics(&self) -> OptimizationStatistics {
        let engine = self
            .optimization_engine
            .read()
            .expect("lock should not be poisoned");
        engine.optimization_stats.clone()
    }

    // Private helper methods

    fn generate_cache_key(&self, request: &ConversionRequest) -> Result<CacheKey> {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut content_hasher = DefaultHasher::new();
        // Hash the bytes representation of the audio data since f32 doesn't implement Hash
        for &sample in &request.source_audio {
            content_hasher.write(&sample.to_ne_bytes());
        }
        let content_hash = content_hasher.finish();

        let mut params_hasher = DefaultHasher::new();
        format!("{:?}_{:?}", request.conversion_type, request.target).hash(&mut params_hasher);
        let params_hash = params_hasher.finish();

        Ok(CacheKey {
            content_hash,
            params_hash,
            quality_level: 50, // Default quality level
            sample_rate: request.source_sample_rate,
        })
    }

    async fn check_cache(&self, key: &CacheKey) -> Result<Option<CachedResult>> {
        let cache = self.cache_system.read().await;
        Ok(cache.get(key).cloned())
    }

    async fn execute_optimized_processing(
        &self,
        plan: &OptimizedConversionPlan,
        request: &ConversionRequest,
    ) -> Result<ConversionResult> {
        let start_time = Instant::now();

        // Execute processing stages
        let mut audio_data = request.source_audio.clone();

        // Profile the execution if enabled
        let profiler = if self.config.enable_profiling {
            Some(self.profiler.clone())
        } else {
            None
        };

        // Process through stages
        for stage in &plan.processing_stages {
            let stage_start = Instant::now();

            // Execute stage (simplified - would use actual processing)
            audio_data = self.execute_processing_stage(stage, &audio_data).await?;

            let stage_time = stage_start.elapsed();

            // Record performance if profiling enabled
            if let Some(ref profiler) = profiler {
                let mut prof = profiler.lock().expect("lock should not be poisoned");
                prof.record_stage_performance(&stage.name, stage_time);
            }
        }

        let total_time = start_time.elapsed();

        // Create result
        let mut result = ConversionResult::success(
            request.id.clone(),
            audio_data.clone(),
            request.source_sample_rate, // Would be converted sample rate
            total_time,
            request.conversion_type.clone(),
        );

        // Add optimization indicators to quality metrics
        result.quality_metrics.insert("optimized".to_string(), 1.0);
        result
            .quality_metrics
            .insert("optimized_processing".to_string(), 1.0);

        // Cache the result if caching is enabled
        if self.config.enable_intelligent_caching {
            let cache_key = self.generate_cache_key(request)?;
            let cached_result = CachedResult {
                audio_data: audio_data.clone(),
                metadata: HashMap::new(), // Empty metadata for now
                quality_metrics: result.quality_metrics.clone(),
                timestamp: Instant::now(),
                access_count: 0,
                size_bytes: audio_data.len() * std::mem::size_of::<f32>(),
            };

            let mut cache = self.cache_system.write().await;
            cache.insert(cache_key, cached_result);
        }

        Ok(result)
    }

    async fn execute_processing_stage(
        &self,
        _stage: &OptimizedProcessingStage,
        audio_data: &[f32],
    ) -> Result<Vec<f32>> {
        // Simplified stage execution - would use actual processing algorithms
        Ok(audio_data.to_vec())
    }

    fn create_result_from_cache(
        &self,
        cached_result: &CachedResult,
        request: &ConversionRequest,
    ) -> ConversionResult {
        let mut result = ConversionResult::success(
            request.id.clone(),
            cached_result.audio_data.clone(),
            request.source_sample_rate,
            Duration::from_millis(1), // Cached results are very fast
            request.conversion_type.clone(),
        );

        result.quality_metrics = cached_result.quality_metrics.clone();
        result.quality_metrics.insert("cached".to_string(), 1.0);

        result
    }
}

/// Optimized conversion plan
#[derive(Debug, Clone)]
pub struct OptimizedConversionPlan {
    /// Type of optimization plan
    pub plan_type: PlanType,
    /// Cached result if available
    pub cached_result: Option<CachedResult>,
    /// Processing stages to execute
    pub processing_stages: Vec<OptimizedProcessingStage>,
    /// Estimated processing time
    pub estimated_time: Duration,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
    /// Estimated quality score
    pub quality_estimate: f32,
}

/// Type of optimization plan
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum PlanType {
    /// Use cached result
    Cached,
    /// Execute optimized processing
    Optimized,
    /// Fallback to standard processing
    Standard,
}

/// Optimized processing stage
#[derive(Debug, Clone)]
pub struct OptimizedProcessingStage {
    /// Stage name
    pub name: String,
    /// Stage type
    pub stage_type: String,
    /// Optimization parameters
    pub optimization_params: HashMap<String, f32>,
    /// Parallel processing config
    pub parallel_config: Option<ParallelConfig>,
    /// Memory config
    pub memory_config: Option<MemoryConfig>,
}

/// Processing plan generated by optimization engine
#[derive(Debug, Clone)]
pub struct ProcessingPlan {
    /// Optimized processing stages
    pub stages: Vec<OptimizedProcessingStage>,
    /// Estimated processing time
    pub estimated_time: Duration,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
    /// Quality estimate
    pub quality_estimate: f32,
}

// Implementation stubs for helper structs
impl IntelligentCache {
    fn new(max_size_bytes: usize) -> Self {
        Self {
            result_cache: HashMap::new(),
            usage_stats: CacheUsageStats::default(),
            config: CacheConfig {
                max_size_bytes,
                ttl_seconds: 3600,
                enable_intelligent_eviction: true,
                enable_predictive_caching: true,
            },
            current_memory_usage_bytes: 0,
            lru_tracker: VecDeque::new(),
        }
    }

    fn get(&self, key: &CacheKey) -> Option<&CachedResult> {
        self.result_cache.get(key)
    }

    fn insert(&mut self, key: CacheKey, result: CachedResult) {
        self.result_cache.insert(key, result);
    }
}

impl ResourceManager {
    fn new() -> Self {
        Self {
            system_resources: SystemResources::default(),
            usage_history: VecDeque::new(),
            allocation_strategy: ResourceAllocationStrategy::Balanced,
            resource_limits: ResourceLimits {
                max_cpu_usage: 80.0,
                max_memory_usage: 85.0,
                max_concurrent_pipelines: 8,
                emergency_reserve_percent: 10.0,
            },
        }
    }
}

impl AdaptiveAlgorithmSelector {
    fn new() -> Self {
        Self {
            algorithm_database: HashMap::new(),
            selection_strategy: SelectionStrategy::Balanced,
            adaptation_history: VecDeque::new(),
            learning_params: LearningParameters {
                learning_rate: 0.1,
                exploration_rate: 0.1,
                adaptation_threshold: 0.05,
                history_window_size: 50,
            },
        }
    }

    fn select_optimal_algorithm(
        &self,
        _conversion_type: &ConversionType,
        system_resources: &SystemResources,
        _config: &ConversionConfig,
    ) -> Result<AlgorithmVariant> {
        // Simple selection logic based on system resources
        if system_resources.cpu_usage_percent > 80.0 {
            Ok(AlgorithmVariant::HighPerformance)
        } else if system_resources.memory_usage_percent > 80.0 {
            Ok(AlgorithmVariant::MemoryOptimized)
        } else if system_resources.gpu_resources.is_some() {
            Ok(AlgorithmVariant::GpuOptimized)
        } else {
            Ok(AlgorithmVariant::Balanced)
        }
    }
}

impl OptimizationEngine {
    fn new() -> Self {
        Self {
            stage_optimizations: HashMap::new(),
            pipeline_templates: HashMap::new(),
            performance_history: VecDeque::new(),
            optimization_stats: OptimizationStatistics::default(),
        }
    }

    fn generate_processing_plan(
        &self,
        _request: &ConversionRequest,
        _algorithm: &AlgorithmVariant,
        _system_resources: &SystemResources,
        _config: &ConversionConfig,
    ) -> Result<ProcessingPlan> {
        // Simplified processing plan generation
        let stages = vec![
            OptimizedProcessingStage {
                name: "preprocessing".to_string(),
                stage_type: "preprocessing".to_string(),
                optimization_params: HashMap::new(),
                parallel_config: None,
                memory_config: None,
            },
            OptimizedProcessingStage {
                name: "conversion".to_string(),
                stage_type: "conversion".to_string(),
                optimization_params: HashMap::new(),
                parallel_config: None,
                memory_config: None,
            },
            OptimizedProcessingStage {
                name: "postprocessing".to_string(),
                stage_type: "postprocessing".to_string(),
                optimization_params: HashMap::new(),
                parallel_config: None,
                memory_config: None,
            },
        ];

        Ok(ProcessingPlan {
            stages,
            estimated_time: Duration::from_millis(100),
            resource_requirements: ResourceRequirements::default(),
            quality_estimate: 0.8,
        })
    }
}

impl PerformanceProfiler {
    fn new() -> Self {
        Self {
            performance_data: VecDeque::new(),
            config: ProfilingConfig {
                enable_detailed_timing: true,
                enable_memory_profiling: true,
                enable_cpu_profiling: true,
                sample_rate_ms: 100,
            },
            analysis_results: HashMap::new(),
        }
    }

    fn record_stage_performance(&mut self, stage_name: &str, duration: Duration) {
        // Record performance data for analysis
        debug!(
            "Stage {}: {:.2}ms",
            stage_name,
            duration.as_secs_f64() * 1000.0
        );
    }
}

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