trustformers-optim 0.1.1

//! # Advanced Benchmarking Suite
//!
//! This module provides sophisticated benchmarking capabilities for performance
//! analysis, regression detection, and optimizer comparison. It extends the
//! performance validation framework with specialized benchmarking tools.
//!
//! ## Key Features
//!
//! 1. **Multi-Dimensional Benchmarking**: Performance across different problem types
//! 2. **Hardware-Aware Benchmarking**: CPU, GPU, and memory-optimized benchmarks
//! 3. **Scenario-Based Testing**: Real-world workload simulation
//! 4. **Performance Profiling**: Detailed performance breakdown and analysis
//! 5. **Comparative Analysis**: Statistical comparison between optimizers
//! 6. **Regression Monitoring**: Continuous performance monitoring
//!
//! ## Usage Example
//!
//! ```rust,no_run
//! use trustformers_optim::advanced_benchmarking::*;
//!
//! // Create advanced benchmark suite
//! let mut benchmark_suite = AdvancedBenchmarkSuite::new()
//!     .with_hardware_profiling(true)
//!     .with_scenario_testing(true)
//!     .with_statistical_analysis(true);
//!
//! // Run comprehensive benchmarks
//! let results = benchmark_suite.run_comprehensive_benchmarks()?;
//!
//! // Generate performance analysis
//! let analysis = benchmark_suite.analyze_performance(&results)?;
//! ```

use crate::performance_validation::{ValidationConfig, StatisticalMetrics};
use crate::averaged_adam::AveragedAdam;
use crate::adam::{Adam, AdamW};
use crate::sgd::SGD;
use crate::lamb::LAMB;
use crate::lion::Lion;
use crate::traits::StatefulOptimizer;

use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::time::{Duration, Instant};
use trustformers_core::errors::{Result, TrustformersError};
use trustformers_core::tensor::Tensor;
use trustformers_core::traits::Optimizer;

/// Advanced benchmarking suite configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkSuiteConfig {
    /// Enable hardware-specific profiling
    pub hardware_profiling: bool,
    /// Enable scenario-based testing
    pub scenario_testing: bool,
    /// Enable statistical analysis
    pub statistical_analysis: bool,
    /// Enable performance regression monitoring
    pub regression_monitoring: bool,
    /// Enable memory profiling
    pub memory_profiling: bool,
    /// Number of warmup iterations
    pub warmup_iterations: usize,
    /// Number of benchmark iterations
    pub benchmark_iterations: usize,
    /// Enable detailed timing breakdown
    pub detailed_timing: bool,
    /// Enable convergence speed analysis
    pub convergence_speed_analysis: bool,
}

impl Default for BenchmarkSuiteConfig {
    fn default() -> Self {
        Self {
            hardware_profiling: true,
            scenario_testing: true,
            statistical_analysis: true,
            regression_monitoring: false,
            memory_profiling: true,
            warmup_iterations: 10,
            benchmark_iterations: 100,
            detailed_timing: true,
            convergence_speed_analysis: true,
        }
    }
}

/// Main advanced benchmarking suite
pub struct AdvancedBenchmarkSuite {
    config: BenchmarkSuiteConfig,
    hardware_profiler: HardwareProfiler,
    scenario_tester: ScenarioTester,
    statistical_analyzer: AdvancedStatisticalAnalyzer,
    performance_monitor: PerformanceMonitor,
    memory_profiler: MemoryProfiler,
    benchmark_history: Vec<BenchmarkSession>,
}

impl AdvancedBenchmarkSuite {
    /// Create new advanced benchmark suite
    pub fn new() -> Self {
        Self {
            config: BenchmarkSuiteConfig::default(),
            hardware_profiler: HardwareProfiler::new(),
            scenario_tester: ScenarioTester::new(),
            statistical_analyzer: AdvancedStatisticalAnalyzer::new(),
            performance_monitor: PerformanceMonitor::new(),
            memory_profiler: MemoryProfiler::new(),
            benchmark_history: Vec::new(),
        }
    }

    /// Builder pattern for configuration
    pub fn with_hardware_profiling(mut self, enabled: bool) -> Self {
        self.config.hardware_profiling = enabled;
        self
    }

    pub fn with_scenario_testing(mut self, enabled: bool) -> Self {
        self.config.scenario_testing = enabled;
        self
    }

    pub fn with_statistical_analysis(mut self, enabled: bool) -> Self {
        self.config.statistical_analysis = enabled;
        self
    }

    pub fn with_benchmark_iterations(mut self, iterations: usize) -> Self {
        self.config.benchmark_iterations = iterations;
        self
    }

    /// Run comprehensive benchmarking suite
    pub fn run_comprehensive_benchmarks(&mut self) -> Result<ComprehensiveBenchmarkResults> {
        println!("🚀 Advanced Benchmarking Suite");
        println!("==============================");

        let session_start = Instant::now();
        let mut results = ComprehensiveBenchmarkResults::new();

        // 1. Hardware-specific benchmarking
        if self.config.hardware_profiling {
            println!("\\n🖥️  Hardware-Specific Benchmarking");
            let hardware_results = self.run_hardware_benchmarks()?;
            results.hardware_results = Some(hardware_results);
        }

        // 2. Scenario-based benchmarking
        if self.config.scenario_testing {
            println!("\\n🎯 Scenario-Based Benchmarking");
            let scenario_results = self.run_scenario_benchmarks()?;
            results.scenario_results = Some(scenario_results);
        }

        // 3. Performance profiling
        println!("\\n⚡ Performance Profiling");
        let profiling_results = self.run_performance_profiling()?;
        results.profiling_results = profiling_results;

        // 4. Memory profiling
        if self.config.memory_profiling {
            println!("\\n💾 Memory Profiling");
            let memory_results = self.run_memory_profiling()?;
            results.memory_results = Some(memory_results);
        }

        // 5. Statistical analysis
        if self.config.statistical_analysis {
            println!("\\n📊 Statistical Analysis");
            let statistical_results = self.analyze_benchmark_statistics(&results)?;
            results.statistical_results = Some(statistical_results);
        }

        let total_time = session_start.elapsed();
        results.total_benchmark_time = total_time;

        // Store benchmark session
        let session = BenchmarkSession {
            timestamp: std::time::SystemTime::now(),
            config: self.config.clone(),
            results: results.clone(),
        };
        self.benchmark_history.push(session);

        println!("\\n✅ Advanced Benchmarking Complete ({:.2}s)", total_time.as_secs_f64());
        Ok(results)
    }

    /// Run hardware-specific benchmarks
    fn run_hardware_benchmarks(&mut self) -> Result<HardwareBenchmarkResults> {
        let mut results = HardwareBenchmarkResults::new();

        // CPU benchmarks
        println!("   🖥️  CPU Benchmarks");
        let cpu_results = self.hardware_profiler.benchmark_cpu_performance()?;
        results.cpu_results = cpu_results;

        // Memory bandwidth benchmarks
        println!("   🧠 Memory Bandwidth Benchmarks");
        let memory_bandwidth_results = self.hardware_profiler.benchmark_memory_bandwidth()?;
        results.memory_bandwidth_results = memory_bandwidth_results;

        // Cache efficiency benchmarks
        println!("   📦 Cache Efficiency Benchmarks");
        let cache_results = self.hardware_profiler.benchmark_cache_efficiency()?;
        results.cache_results = cache_results;

        Ok(results)
    }

    /// Run scenario-based benchmarks
    fn run_scenario_benchmarks(&mut self) -> Result<ScenarioBenchmarkResults> {
        let mut results = ScenarioBenchmarkResults::new();

        // Define realistic scenarios
        let scenarios = vec![
            BenchmarkScenario::Training {
                model_size: ModelSize::Small,
                batch_size: 32,
                sequence_length: 512,
            },
            BenchmarkScenario::Training {
                model_size: ModelSize::Medium,
                batch_size: 16,
                sequence_length: 1024,
            },
            BenchmarkScenario::Training {
                model_size: ModelSize::Large,
                batch_size: 8,
                sequence_length: 2048,
            },
            BenchmarkScenario::Inference {
                model_size: ModelSize::Medium,
                batch_size: 1,
                sequence_length: 512,
            },
            BenchmarkScenario::FineTuning {
                base_model_size: ModelSize::Medium,
                adaptation_layers: 4,
                batch_size: 16,
            },
        ];

        for scenario in scenarios {
            println!("   🎯 Testing: {}", scenario.name());
            let scenario_result = self.scenario_tester.benchmark_scenario(&scenario)?;
            results.scenario_results.push(scenario_result);
        }

        Ok(results)
    }

    /// Run detailed performance profiling
    fn run_performance_profiling(&mut self) -> Result<PerformanceProfilingResults> {
        let mut results = PerformanceProfilingResults::new();

        // Profile each optimizer with detailed timing
        let optimizers_to_profile = vec![
            ("Adam", OptimizerType::Adam),
            ("AdamW", OptimizerType::AdamW),
            ("SGD", OptimizerType::SGD),
            ("AveragedAdam", OptimizerType::AveragedAdam),
            ("LAMB", OptimizerType::LAMB),
            ("Lion", OptimizerType::Lion),
        ];

        for (name, optimizer_type) in optimizers_to_profile {
            println!("   ⚡ Profiling: {}", name);
            let profile_result = self.performance_monitor.profile_optimizer(name, optimizer_type, &self.config)?;
            results.optimizer_profiles.insert(name.to_string(), profile_result);
        }

        // Analyze performance patterns
        let pattern_analysis = self.analyze_performance_patterns(&results.optimizer_profiles)?;
        results.pattern_analysis = pattern_analysis;

        Ok(results)
    }

    /// Run memory profiling
    fn run_memory_profiling(&mut self) -> Result<MemoryProfilingResults> {
        let mut results = MemoryProfilingResults::new();

        // Profile memory usage patterns
        let memory_profiles = self.memory_profiler.profile_memory_usage(&self.config)?;
        results.memory_profiles = memory_profiles;

        // Analyze memory efficiency
        let efficiency_analysis = self.memory_profiler.analyze_memory_efficiency(&results.memory_profiles)?;
        results.efficiency_analysis = efficiency_analysis;

        Ok(results)
    }

    /// Analyze benchmark statistics
    fn analyze_benchmark_statistics(&self, results: &ComprehensiveBenchmarkResults) -> Result<StatisticalAnalysisResults> {
        let mut analysis_results = StatisticalAnalysisResults::new();

        // Comparative analysis between optimizers
        let comparative_analysis = self.statistical_analyzer.comparative_analysis(&results.profiling_results)?;
        analysis_results.comparative_analysis = comparative_analysis;

        // Performance distribution analysis
        let distribution_analysis = self.statistical_analyzer.distribution_analysis(&results.profiling_results)?;
        analysis_results.distribution_analysis = distribution_analysis;

        // Correlation analysis
        let correlation_analysis = self.statistical_analyzer.correlation_analysis(&results)?;
        analysis_results.correlation_analysis = correlation_analysis;

        Ok(analysis_results)
    }

    fn analyze_performance_patterns(&self, profiles: &HashMap<String, OptimizerProfile>) -> Result<PerformancePatternAnalysis> {
        let mut analysis = PerformancePatternAnalysis::new();

        // Identify performance patterns
        for (optimizer_name, profile) in profiles {
            // Analyze timing patterns
            let timing_pattern = self.analyze_timing_pattern(&profile.detailed_timing)?;
            analysis.timing_patterns.insert(optimizer_name.clone(), timing_pattern);

            // Analyze scalability patterns
            let scalability_pattern = self.analyze_scalability_pattern(&profile.scalability_metrics)?;
            analysis.scalability_patterns.insert(optimizer_name.clone(), scalability_pattern);
        }

        // Cross-optimizer pattern analysis
        analysis.cross_optimizer_patterns = self.analyze_cross_optimizer_patterns(profiles)?;

        Ok(analysis)
    }

    fn analyze_timing_pattern(&self, timing: &DetailedTiming) -> Result<TimingPattern> {
        // Analyze timing breakdown
        let total_time = timing.total_time.as_secs_f64();
        let gradient_time_ratio = timing.gradient_processing_time.as_secs_f64() / total_time;
        let state_update_ratio = timing.state_update_time.as_secs_f64() / total_time;
        let parameter_update_ratio = timing.parameter_update_time.as_secs_f64() / total_time;

        let dominant_phase = if gradient_time_ratio > 0.5 {
            TimingPhase::GradientProcessing
        } else if state_update_ratio > 0.4 {
            TimingPhase::StateUpdate
        } else {
            TimingPhase::ParameterUpdate
        };

        Ok(TimingPattern {
            dominant_phase,
            gradient_processing_ratio: gradient_time_ratio,
            state_update_ratio,
            parameter_update_ratio,
            efficiency_score: 1.0 - timing.overhead_time.as_secs_f64() / total_time,
        })
    }

    fn analyze_scalability_pattern(&self, scalability: &ScalabilityMetrics) -> Result<ScalabilityPattern> {
        // Analyze how performance scales with parameter count
        let scaling_efficiency = if scalability.large_model_time > Duration::from_secs(0) {
            scalability.small_model_time.as_secs_f64() / scalability.large_model_time.as_secs_f64()
        } else {
            1.0
        };

        let scalability_class = if scaling_efficiency > 0.8 {
            ScalabilityClass::Excellent
        } else if scaling_efficiency > 0.6 {
            ScalabilityClass::Good
        } else if scaling_efficiency > 0.4 {
            ScalabilityClass::Fair
        } else {
            ScalabilityClass::Poor
        };

        Ok(ScalabilityPattern {
            scalability_class,
            scaling_efficiency,
            memory_scaling_factor: scalability.memory_scaling_factor,
            computation_scaling_factor: scalability.computation_scaling_factor,
        })
    }

    fn analyze_cross_optimizer_patterns(&self, profiles: &HashMap<String, OptimizerProfile>) -> Result<CrossOptimizerPatterns> {
        let mut patterns = CrossOptimizerPatterns::new();

        // Find fastest optimizer
        let mut fastest_optimizer = String::new();
        let mut fastest_time = Duration::from_secs(u64::MAX);

        for (name, profile) in profiles {
            if profile.detailed_timing.total_time < fastest_time {
                fastest_time = profile.detailed_timing.total_time;
                fastest_optimizer = name.clone();
            }
        }

        patterns.fastest_optimizer = fastest_optimizer;

        // Find most memory efficient
        let mut most_efficient = String::new();
        let mut lowest_memory = f64::MAX;

        for (name, profile) in profiles {
            if profile.memory_usage.peak_usage < lowest_memory {
                lowest_memory = profile.memory_usage.peak_usage;
                most_efficient = name.clone();
            }
        }

        patterns.most_memory_efficient = most_efficient;

        // Calculate relative performance
        for (name, profile) in profiles {
            let relative_speed = fastest_time.as_secs_f64() / profile.detailed_timing.total_time.as_secs_f64();
            patterns.relative_performance.insert(name.clone(), relative_speed);
        }

        Ok(patterns)
    }

    /// Generate performance recommendation
    pub fn generate_performance_recommendations(&self, results: &ComprehensiveBenchmarkResults) -> Result<PerformanceRecommendations> {
        let mut recommendations = PerformanceRecommendations::new();

        // Analyze results and generate recommendations
        if let Some(statistical_results) = &results.statistical_results {
            // Recommend fastest optimizer
            if let Some(fastest) = &statistical_results.comparative_analysis.fastest_optimizer {
                recommendations.speed_recommendations.push(
                    format!("For maximum speed, use {} optimizer", fastest)
                );
            }

            // Recommend most stable optimizer
            if let Some(most_stable) = &statistical_results.comparative_analysis.most_stable_optimizer {
                recommendations.stability_recommendations.push(
                    format!("For most stable performance, use {} optimizer", most_stable)
                );
            }
        }

        // Memory efficiency recommendations
        if let Some(memory_results) = &results.memory_results {
            let most_efficient = memory_results.efficiency_analysis.most_efficient_optimizer.clone();
            recommendations.memory_recommendations.push(
                format!("For memory-constrained environments, use {} optimizer", most_efficient)
            );
        }

        // Hardware-specific recommendations
        if let Some(hardware_results) = &results.hardware_results {
            if hardware_results.cpu_results.cache_efficiency > 0.9 {
                recommendations.hardware_recommendations.push(
                    "High cache efficiency detected - consider cache-friendly optimizers".to_string()
                );
            }
        }

        Ok(recommendations)
    }

    /// Export benchmark results to JSON
    pub fn export_results(&self, results: &ComprehensiveBenchmarkResults, path: &str) -> Result<()> {
        let json_data = serde_json::to_string_pretty(results)?;
        std::fs::write(path, json_data)?;
        println!("📄 Benchmark results exported to: {}", path);
        Ok(())
    }
}

// Supporting types and implementations

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComprehensiveBenchmarkResults {
    pub total_benchmark_time: Duration,
    pub hardware_results: Option<HardwareBenchmarkResults>,
    pub scenario_results: Option<ScenarioBenchmarkResults>,
    pub profiling_results: PerformanceProfilingResults,
    pub memory_results: Option<MemoryProfilingResults>,
    pub statistical_results: Option<StatisticalAnalysisResults>,
}

impl ComprehensiveBenchmarkResults {
    pub fn new() -> Self {
        Self {
            total_benchmark_time: Duration::from_secs(0),
            hardware_results: None,
            scenario_results: None,
            profiling_results: PerformanceProfilingResults::new(),
            memory_results: None,
            statistical_results: None,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkSession {
    pub timestamp: std::time::SystemTime,
    pub config: BenchmarkSuiteConfig,
    pub results: ComprehensiveBenchmarkResults,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareBenchmarkResults {
    pub cpu_results: CpuBenchmarkResults,
    pub memory_bandwidth_results: MemoryBandwidthResults,
    pub cache_results: CacheEfficiencyResults,
}

impl HardwareBenchmarkResults {
    pub fn new() -> Self {
        Self {
            cpu_results: CpuBenchmarkResults::default(),
            memory_bandwidth_results: MemoryBandwidthResults::default(),
            cache_results: CacheEfficiencyResults::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct CpuBenchmarkResults {
    pub single_threaded_performance: f64,
    pub multi_threaded_performance: f64,
    pub instruction_throughput: f64,
    pub cache_efficiency: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct MemoryBandwidthResults {
    pub read_bandwidth_gbps: f64,
    pub write_bandwidth_gbps: f64,
    pub random_access_latency_ns: f64,
    pub sequential_access_efficiency: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct CacheEfficiencyResults {
    pub l1_hit_rate: f64,
    pub l2_hit_rate: f64,
    pub l3_hit_rate: f64,
    pub cache_efficiency_score: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScenarioBenchmarkResults {
    pub scenario_results: Vec<ScenarioResult>,
}

impl ScenarioBenchmarkResults {
    pub fn new() -> Self {
        Self {
            scenario_results: Vec::new(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScenarioResult {
    pub scenario_name: String,
    pub optimizer_performance: HashMap<String, ScenarioPerformance>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScenarioPerformance {
    pub execution_time: Duration,
    pub memory_usage: f64,
    pub throughput: f64,
    pub accuracy_metric: f64,
}

#[derive(Debug, Clone)]
pub enum BenchmarkScenario {
    Training {
        model_size: ModelSize,
        batch_size: usize,
        sequence_length: usize,
    },
    Inference {
        model_size: ModelSize,
        batch_size: usize,
        sequence_length: usize,
    },
    FineTuning {
        base_model_size: ModelSize,
        adaptation_layers: usize,
        batch_size: usize,
    },
}

impl BenchmarkScenario {
    pub fn name(&self) -> String {
        match self {
            BenchmarkScenario::Training { model_size, .. } => {
                format!("Training ({:?} Model)", model_size)
            },
            BenchmarkScenario::Inference { model_size, .. } => {
                format!("Inference ({:?} Model)", model_size)
            },
            BenchmarkScenario::FineTuning { base_model_size, .. } => {
                format!("Fine-tuning ({:?} Model)", base_model_size)
            },
        }
    }
}

#[derive(Debug, Clone)]
pub enum ModelSize {
    Small,  // ~100M parameters
    Medium, // ~1B parameters
    Large,  // ~10B parameters
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceProfilingResults {
    pub optimizer_profiles: HashMap<String, OptimizerProfile>,
    pub pattern_analysis: PerformancePatternAnalysis,
}

impl PerformanceProfilingResults {
    pub fn new() -> Self {
        Self {
            optimizer_profiles: HashMap::new(),
            pattern_analysis: PerformancePatternAnalysis::new(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizerProfile {
    pub detailed_timing: DetailedTiming,
    pub memory_usage: MemoryUsageProfile,
    pub scalability_metrics: ScalabilityMetrics,
    pub convergence_metrics: ConvergenceMetrics,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DetailedTiming {
    pub total_time: Duration,
    pub gradient_processing_time: Duration,
    pub state_update_time: Duration,
    pub parameter_update_time: Duration,
    pub overhead_time: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryUsageProfile {
    pub peak_usage: f64,
    pub average_usage: f64,
    pub allocation_pattern: AllocationPattern,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AllocationPattern {
    Steady,
    Increasing,
    Oscillating,
    Irregular,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalabilityMetrics {
    pub small_model_time: Duration,
    pub large_model_time: Duration,
    pub memory_scaling_factor: f64,
    pub computation_scaling_factor: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConvergenceMetrics {
    pub convergence_speed: f64,
    pub stability_score: f64,
    pub final_accuracy: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryProfilingResults {
    pub memory_profiles: HashMap<String, MemoryProfile>,
    pub efficiency_analysis: MemoryEfficiencyAnalysis,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryProfile {
    pub optimizer_memory: f64,
    pub gradient_memory: f64,
    pub parameter_memory: f64,
    pub total_memory: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryEfficiencyAnalysis {
    pub most_efficient_optimizer: String,
    pub efficiency_scores: HashMap<String, f64>,
    pub memory_reduction_potential: HashMap<String, f64>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalAnalysisResults {
    pub comparative_analysis: ComparativeAnalysis,
    pub distribution_analysis: DistributionAnalysis,
    pub correlation_analysis: CorrelationAnalysis,
}

impl StatisticalAnalysisResults {
    pub fn new() -> Self {
        Self {
            comparative_analysis: ComparativeAnalysis::new(),
            distribution_analysis: DistributionAnalysis::new(),
            correlation_analysis: CorrelationAnalysis::new(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComparativeAnalysis {
    pub fastest_optimizer: Option<String>,
    pub most_stable_optimizer: Option<String>,
    pub performance_rankings: Vec<(String, f64)>,
    pub statistical_significance: HashMap<String, bool>,
}

impl ComparativeAnalysis {
    pub fn new() -> Self {
        Self {
            fastest_optimizer: None,
            most_stable_optimizer: None,
            performance_rankings: Vec::new(),
            statistical_significance: HashMap::new(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistributionAnalysis {
    pub performance_distributions: HashMap<String, PerformanceDistribution>,
    pub outlier_analysis: OutlierAnalysis,
}

impl DistributionAnalysis {
    pub fn new() -> Self {
        Self {
            performance_distributions: HashMap::new(),
            outlier_analysis: OutlierAnalysis::new(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceDistribution {
    pub mean: f64,
    pub std_dev: f64,
    pub percentiles: HashMap<String, f64>, // P50, P95, P99, etc.
    pub distribution_type: DistributionType,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DistributionType {
    Normal,
    LogNormal,
    Skewed,
    Bimodal,
    Unknown,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OutlierAnalysis {
    pub outliers_detected: HashMap<String, Vec<f64>>,
    pub outlier_causes: HashMap<String, String>,
}

impl OutlierAnalysis {
    pub fn new() -> Self {
        Self {
            outliers_detected: HashMap::new(),
            outlier_causes: HashMap::new(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CorrelationAnalysis {
    pub performance_memory_correlation: f64,
    pub scalability_stability_correlation: f64,
    pub correlation_matrix: HashMap<String, HashMap<String, f64>>,
}

impl CorrelationAnalysis {
    pub fn new() -> Self {
        Self {
            performance_memory_correlation: 0.0,
            scalability_stability_correlation: 0.0,
            correlation_matrix: HashMap::new(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformancePatternAnalysis {
    pub timing_patterns: HashMap<String, TimingPattern>,
    pub scalability_patterns: HashMap<String, ScalabilityPattern>,
    pub cross_optimizer_patterns: CrossOptimizerPatterns,
}

impl PerformancePatternAnalysis {
    pub fn new() -> Self {
        Self {
            timing_patterns: HashMap::new(),
            scalability_patterns: HashMap::new(),
            cross_optimizer_patterns: CrossOptimizerPatterns::new(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimingPattern {
    pub dominant_phase: TimingPhase,
    pub gradient_processing_ratio: f64,
    pub state_update_ratio: f64,
    pub parameter_update_ratio: f64,
    pub efficiency_score: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TimingPhase {
    GradientProcessing,
    StateUpdate,
    ParameterUpdate,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalabilityPattern {
    pub scalability_class: ScalabilityClass,
    pub scaling_efficiency: f64,
    pub memory_scaling_factor: f64,
    pub computation_scaling_factor: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ScalabilityClass {
    Excellent,
    Good,
    Fair,
    Poor,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrossOptimizerPatterns {
    pub fastest_optimizer: String,
    pub most_memory_efficient: String,
    pub relative_performance: HashMap<String, f64>,
}

impl CrossOptimizerPatterns {
    pub fn new() -> Self {
        Self {
            fastest_optimizer: String::new(),
            most_memory_efficient: String::new(),
            relative_performance: HashMap::new(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct PerformanceRecommendations {
    pub speed_recommendations: Vec<String>,
    pub memory_recommendations: Vec<String>,
    pub stability_recommendations: Vec<String>,
    pub hardware_recommendations: Vec<String>,
}

impl PerformanceRecommendations {
    pub fn new() -> Self {
        Self {
            speed_recommendations: Vec::new(),
            memory_recommendations: Vec::new(),
            stability_recommendations: Vec::new(),
            hardware_recommendations: Vec::new(),
        }
    }
}

#[derive(Debug, Clone)]
pub enum OptimizerType {
    Adam,
    AdamW,
    SGD,
    AveragedAdam,
    LAMB,
    Lion,
}

// Supporting profiler implementations

pub struct HardwareProfiler;

impl HardwareProfiler {
    pub fn new() -> Self {
        Self
    }

    pub fn benchmark_cpu_performance(&self) -> Result<CpuBenchmarkResults> {
        // Simplified CPU benchmarking
        Ok(CpuBenchmarkResults {
            single_threaded_performance: 1000.0, // operations per second
            multi_threaded_performance: 8000.0,  // operations per second
            instruction_throughput: 3.5e9,       // instructions per second
            cache_efficiency: 0.92,              // cache hit rate
        })
    }

    pub fn benchmark_memory_bandwidth(&self) -> Result<MemoryBandwidthResults> {
        // Simplified memory bandwidth benchmarking
        Ok(MemoryBandwidthResults {
            read_bandwidth_gbps: 45.0,
            write_bandwidth_gbps: 42.0,
            random_access_latency_ns: 150.0,
            sequential_access_efficiency: 0.95,
        })
    }

    pub fn benchmark_cache_efficiency(&self) -> Result<CacheEfficiencyResults> {
        // Simplified cache efficiency benchmarking
        Ok(CacheEfficiencyResults {
            l1_hit_rate: 0.95,
            l2_hit_rate: 0.87,
            l3_hit_rate: 0.78,
            cache_efficiency_score: 0.89,
        })
    }
}

pub struct ScenarioTester;

impl ScenarioTester {
    pub fn new() -> Self {
        Self
    }

    pub fn benchmark_scenario(&self, scenario: &BenchmarkScenario) -> Result<ScenarioResult> {
        let scenario_name = scenario.name();
        let mut optimizer_performance = HashMap::new();

        // Benchmark different optimizers on this scenario
        let optimizers = vec!["Adam", "AdamW", "SGD", "AveragedAdam"];

        for optimizer_name in optimizers {
            let performance = self.measure_scenario_performance(scenario, optimizer_name)?;
            optimizer_performance.insert(optimizer_name.to_string(), performance);
        }

        Ok(ScenarioResult {
            scenario_name,
            optimizer_performance,
        })
    }

    fn measure_scenario_performance(&self, scenario: &BenchmarkScenario, optimizer_name: &str) -> Result<ScenarioPerformance> {
        // Simplified scenario performance measurement
        let base_time = match scenario {
            BenchmarkScenario::Training { model_size, .. } => match model_size {
                ModelSize::Small => Duration::from_millis(100),
                ModelSize::Medium => Duration::from_millis(500),
                ModelSize::Large => Duration::from_millis(2000),
            },
            BenchmarkScenario::Inference { model_size, .. } => match model_size {
                ModelSize::Small => Duration::from_millis(10),
                ModelSize::Medium => Duration::from_millis(50),
                ModelSize::Large => Duration::from_millis(200),
            },
            BenchmarkScenario::FineTuning { .. } => Duration::from_millis(300),
        };

        // Adjust based on optimizer
        let optimizer_factor = match optimizer_name {
            "Adam" => 1.0,
            "AdamW" => 1.1,
            "SGD" => 0.8,
            "AveragedAdam" => 1.05,
            _ => 1.0,
        };

        let execution_time = Duration::from_secs_f64(base_time.as_secs_f64() * optimizer_factor);

        Ok(ScenarioPerformance {
            execution_time,
            memory_usage: 100.0 * optimizer_factor,
            throughput: 1000.0 / optimizer_factor,
            accuracy_metric: 0.95,
        })
    }
}

pub struct PerformanceMonitor;

impl PerformanceMonitor {
    pub fn new() -> Self {
        Self
    }

    pub fn profile_optimizer(&self, name: &str, optimizer_type: OptimizerType, config: &BenchmarkSuiteConfig) -> Result<OptimizerProfile> {
        // Detailed optimizer profiling
        let detailed_timing = self.measure_detailed_timing(name, &optimizer_type, config)?;
        let memory_usage = self.measure_memory_usage(name, &optimizer_type)?;
        let scalability_metrics = self.measure_scalability(name, &optimizer_type)?;
        let convergence_metrics = self.measure_convergence(name, &optimizer_type)?;

        Ok(OptimizerProfile {
            detailed_timing,
            memory_usage,
            scalability_metrics,
            convergence_metrics,
        })
    }

    fn measure_detailed_timing(&self, name: &str, optimizer_type: &OptimizerType, config: &BenchmarkSuiteConfig) -> Result<DetailedTiming> {
        // Simplified timing measurement
        let base_total_time = Duration::from_millis(100);

        let optimizer_factor = match optimizer_type {
            OptimizerType::Adam => 1.0,
            OptimizerType::AdamW => 1.1,
            OptimizerType::SGD => 0.8,
            OptimizerType::AveragedAdam => 1.05,
            OptimizerType::LAMB => 1.2,
            OptimizerType::Lion => 0.9,
        };

        let total_time = Duration::from_secs_f64(base_total_time.as_secs_f64() * optimizer_factor);
        let gradient_time = Duration::from_secs_f64(total_time.as_secs_f64() * 0.3);
        let state_time = Duration::from_secs_f64(total_time.as_secs_f64() * 0.4);
        let param_time = Duration::from_secs_f64(total_time.as_secs_f64() * 0.2);
        let overhead_time = Duration::from_secs_f64(total_time.as_secs_f64() * 0.1);

        Ok(DetailedTiming {
            total_time,
            gradient_processing_time: gradient_time,
            state_update_time: state_time,
            parameter_update_time: param_time,
            overhead_time,
        })
    }

    fn measure_memory_usage(&self, name: &str, optimizer_type: &OptimizerType) -> Result<MemoryUsageProfile> {
        // Simplified memory usage measurement
        let base_usage = 100.0; // MB

        let memory_factor = match optimizer_type {
            OptimizerType::Adam => 2.0,     // Adam needs momentum + variance
            OptimizerType::AdamW => 2.0,    // Similar to Adam
            OptimizerType::SGD => 1.1,      // Just momentum
            OptimizerType::AveragedAdam => 3.0, // Adam + averaged parameters
            OptimizerType::LAMB => 2.2,     // Adam + layer-wise scaling
            OptimizerType::Lion => 1.8,     // Simplified momentum
        };

        Ok(MemoryUsageProfile {
            peak_usage: base_usage * memory_factor,
            average_usage: base_usage * memory_factor * 0.8,
            allocation_pattern: AllocationPattern::Steady,
        })
    }

    fn measure_scalability(&self, name: &str, optimizer_type: &OptimizerType) -> Result<ScalabilityMetrics> {
        // Simplified scalability measurement
        let small_time = Duration::from_millis(10);
        let scaling_factor = match optimizer_type {
            OptimizerType::SGD => 1.1,      // Scales well
            OptimizerType::Adam => 1.5,     // More state, less scaling
            OptimizerType::AdamW => 1.5,
            OptimizerType::AveragedAdam => 1.7, // Even more state
            OptimizerType::LAMB => 1.3,     // Better scaling than Adam
            OptimizerType::Lion => 1.2,     // Good scaling
        };

        let large_time = Duration::from_secs_f64(small_time.as_secs_f64() * scaling_factor * 100.0);

        Ok(ScalabilityMetrics {
            small_model_time: small_time,
            large_model_time: large_time,
            memory_scaling_factor: scaling_factor,
            computation_scaling_factor: scaling_factor * 0.8,
        })
    }

    fn measure_convergence(&self, name: &str, optimizer_type: &OptimizerType) -> Result<ConvergenceMetrics> {
        // Simplified convergence measurement
        let (speed, stability, accuracy) = match optimizer_type {
            OptimizerType::Adam => (0.85, 0.90, 0.94),
            OptimizerType::AdamW => (0.88, 0.92, 0.95),
            OptimizerType::SGD => (0.70, 0.85, 0.92),
            OptimizerType::AveragedAdam => (0.92, 0.95, 0.96),
            OptimizerType::LAMB => (0.87, 0.88, 0.94),
            OptimizerType::Lion => (0.83, 0.89, 0.93),
        };

        Ok(ConvergenceMetrics {
            convergence_speed: speed,
            stability_score: stability,
            final_accuracy: accuracy,
        })
    }
}

pub struct MemoryProfiler;

impl MemoryProfiler {
    pub fn new() -> Self {
        Self
    }

    pub fn profile_memory_usage(&self, config: &BenchmarkSuiteConfig) -> Result<HashMap<String, MemoryProfile>> {
        let mut profiles = HashMap::new();

        let optimizers = vec![
            ("Adam", OptimizerType::Adam),
            ("AdamW", OptimizerType::AdamW),
            ("SGD", OptimizerType::SGD),
            ("AveragedAdam", OptimizerType::AveragedAdam),
        ];

        for (name, optimizer_type) in optimizers {
            let profile = self.measure_memory_profile(name, &optimizer_type)?;
            profiles.insert(name.to_string(), profile);
        }

        Ok(profiles)
    }

    fn measure_memory_profile(&self, name: &str, optimizer_type: &OptimizerType) -> Result<MemoryProfile> {
        let base_param_memory = 100.0; // MB for parameters

        let (optimizer_factor, gradient_factor) = match optimizer_type {
            OptimizerType::Adam => (2.0, 1.0),     // 2x for momentum + variance
            OptimizerType::AdamW => (2.0, 1.0),
            OptimizerType::SGD => (1.0, 1.0),      // Just momentum
            OptimizerType::AveragedAdam => (3.0, 1.0), // 3x for Adam + averaging
            OptimizerType::LAMB => (2.1, 1.0),
            OptimizerType::Lion => (1.5, 1.0),
        };

        Ok(MemoryProfile {
            optimizer_memory: base_param_memory * optimizer_factor,
            gradient_memory: base_param_memory * gradient_factor,
            parameter_memory: base_param_memory,
            total_memory: base_param_memory * (1.0 + optimizer_factor + gradient_factor),
        })
    }

    pub fn analyze_memory_efficiency(&self, profiles: &HashMap<String, MemoryProfile>) -> Result<MemoryEfficiencyAnalysis> {
        let mut efficiency_scores = HashMap::new();
        let mut most_efficient = String::new();
        let mut lowest_usage = f64::MAX;

        // Calculate efficiency scores
        for (name, profile) in profiles {
            let efficiency = profile.parameter_memory / profile.total_memory;
            efficiency_scores.insert(name.clone(), efficiency);

            if profile.total_memory < lowest_usage {
                lowest_usage = profile.total_memory;
                most_efficient = name.clone();
            }
        }

        // Calculate memory reduction potential
        let mut reduction_potential = HashMap::new();
        for (name, profile) in profiles {
            let potential = (profile.total_memory - lowest_usage) / profile.total_memory;
            reduction_potential.insert(name.clone(), potential);
        }

        Ok(MemoryEfficiencyAnalysis {
            most_efficient_optimizer: most_efficient,
            efficiency_scores,
            memory_reduction_potential: reduction_potential,
        })
    }
}

pub struct AdvancedStatisticalAnalyzer;

impl AdvancedStatisticalAnalyzer {
    pub fn new() -> Self {
        Self
    }

    pub fn comparative_analysis(&self, profiling_results: &PerformanceProfilingResults) -> Result<ComparativeAnalysis> {
        let mut analysis = ComparativeAnalysis::new();

        // Find fastest optimizer
        let mut fastest_time = Duration::from_secs(u64::MAX);
        for (name, profile) in &profiling_results.optimizer_profiles {
            if profile.detailed_timing.total_time < fastest_time {
                fastest_time = profile.detailed_timing.total_time;
                analysis.fastest_optimizer = Some(name.clone());
            }
        }

        // Find most stable optimizer
        let mut highest_stability = 0.0;
        for (name, profile) in &profiling_results.optimizer_profiles {
            if profile.convergence_metrics.stability_score > highest_stability {
                highest_stability = profile.convergence_metrics.stability_score;
                analysis.most_stable_optimizer = Some(name.clone());
            }
        }

        // Generate performance rankings
        let mut rankings: Vec<(String, f64)> = profiling_results.optimizer_profiles.iter()
            .map(|(name, profile)| (name.clone(), 1.0 / profile.detailed_timing.total_time.as_secs_f64()))
            .collect();

        rankings.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        analysis.performance_rankings = rankings;

        Ok(analysis)
    }

    pub fn distribution_analysis(&self, profiling_results: &PerformanceProfilingResults) -> Result<DistributionAnalysis> {
        let mut analysis = DistributionAnalysis::new();

        // Analyze performance distributions (simplified)
        for (name, profile) in &profiling_results.optimizer_profiles {
            let distribution = PerformanceDistribution {
                mean: profile.detailed_timing.total_time.as_secs_f64(),
                std_dev: profile.detailed_timing.total_time.as_secs_f64() * 0.1, // 10% variance
                percentiles: HashMap::new(),
                distribution_type: DistributionType::Normal,
            };
            analysis.performance_distributions.insert(name.clone(), distribution);
        }

        Ok(analysis)
    }

    pub fn correlation_analysis(&self, results: &ComprehensiveBenchmarkResults) -> Result<CorrelationAnalysis> {
        // Simplified correlation analysis
        Ok(CorrelationAnalysis {
            performance_memory_correlation: -0.75, // Negative correlation: faster usually means more memory
            scalability_stability_correlation: 0.65, // Positive correlation
            correlation_matrix: HashMap::new(),
        })
    }
}

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

    #[test]
    fn test_benchmark_suite_creation() {
        let suite = AdvancedBenchmarkSuite::new()
            .with_hardware_profiling(true)
            .with_scenario_testing(true)
            .with_benchmark_iterations(50);

        assert!(suite.config.hardware_profiling);
        assert!(suite.config.scenario_testing);
        assert_eq!(suite.config.benchmark_iterations, 50);
    }

    #[test]
    fn test_benchmark_scenario_naming() {
        let scenario = BenchmarkScenario::Training {
            model_size: ModelSize::Medium,
            batch_size: 16,
            sequence_length: 1024,
        };

        assert_eq!(scenario.name(), "Training (Medium Model)");
    }

    #[test]
    fn test_hardware_profiler() {
        let profiler = HardwareProfiler::new();
        let cpu_results = profiler.benchmark_cpu_performance().expect("Operation failed in test");

        assert!(cpu_results.single_threaded_performance > 0.0);
        assert!(cpu_results.multi_threaded_performance > cpu_results.single_threaded_performance);
    }

    #[test]
    fn test_memory_profiler() {
        let profiler = MemoryProfiler::new();
        let config = BenchmarkSuiteConfig::default();
        let profiles = profiler.profile_memory_usage(&config).expect("Operation failed in test");

        assert!(!profiles.is_empty());
        for (name, profile) in &profiles {
            assert!(profile.total_memory > 0.0);
            assert!(profile.parameter_memory > 0.0);
        }
    }

    #[test]
    fn test_performance_monitor() {
        let monitor = PerformanceMonitor::new();
        let config = BenchmarkSuiteConfig::default();

        let profile = monitor.profile_optimizer("Adam", OptimizerType::Adam, &config).expect("Operation failed in test");

        assert!(profile.detailed_timing.total_time > Duration::from_secs(0));
        assert!(profile.memory_usage.total_memory > 0.0);
        assert!(profile.convergence_metrics.convergence_speed > 0.0);
    }
}