//! Hyperparameter Auto-Tuning for RTX 5090
//!
//! Advanced Bayesian optimization and multi-objective hyperparameter tuning
//! specifically optimized for RTX 5090 (sm_110) architecture capabilities.

// Macro for creating hashmaps easily
macro_rules! hashmap {
    ($($key:expr => $value:expr),* $(,)?) => {
        {
            let mut map = std::collections::HashMap::new();
            $(map.insert($key, $value);)*
            map
        }
    };
}

use crate::{EvolutionError, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

/// RTX 5090 optimized hyperparameter auto-tuner
pub struct HyperparameterTuner {
    optimization_engine: BayesianOptimizer,
    rtx5090_profiler: Arc<Mutex<Rtx5090Profiler>>,
    search_space: SearchSpace,
    optimization_history: Arc<Mutex<OptimizationHistory>>,
    active_experiments: Arc<Mutex<HashMap<String, Experiment>>>,
    convergence_detector: ConvergenceDetector,
}

/// Bayesian optimization engine with Gaussian Process regression
pub struct BayesianOptimizer {
    gaussian_process: GaussianProcess,
    acquisition_function: AcquisitionFunction,
    exploration_exploitation_balance: f64,
}

/// RTX 5090 specific performance profiler
pub struct Rtx5090Profiler {
    device_context: u32,
    profiling_session: Option<ProfilingSession>,
    performance_counters: PerformanceCounters,
    thermal_monitor: ThermalMonitor,
}

/// Search space definition for hyperparameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SearchSpace {
    parameters: HashMap<String, ParameterSpec>,
    constraints: Vec<Constraint>,
    rtx5090_optimizations: Rtx5090OptimizationSpace,
}

/// Individual hyperparameter specification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParameterSpec {
    name: String,
    param_type: ParameterType,
    bounds: ParameterBounds,
    importance: f64, // 0.0 to 1.0
    rtx5090_specific: bool,
}

/// Types of hyperparameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ParameterType {
    Integer,
    Float,
    Categorical(Vec<String>),
    Boolean,
    BlockSize,          // RTX 5090 specific
    SharedMemoryConfig, // RTX 5090 specific
    TensorCoreConfig,   // RTX 5090 specific
}

/// Parameter bounds definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParameterBounds {
    min: f64,
    max: f64,
    step: Option<f64>,
    preferred_values: Option<Vec<f64>>, // RTX 5090 optimal values
}

/// RTX 5090 specific optimization space
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Rtx5090OptimizationSpace {
    block_sizes: Vec<u32>, // 32, 64, 128, 256, 512, 1024
    grid_configurations: Vec<GridConfig>,
    shared_memory_configs: Vec<SharedMemoryConfig>,
    tensor_core_configs: Vec<TensorCoreConfig>,
    cache_configurations: Vec<CacheConfig>,
}

/// Grid configuration for RTX 5090
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GridConfig {
    blocks_per_sm: u32,
    occupancy_target: f64,
    load_balancing_strategy: LoadBalancingStrategy,
}

/// Shared memory configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SharedMemoryConfig {
    size_kb: u32, // up to 49KB per SM on RTX 5090
    bank_conflict_avoidance: bool,
    padding_strategy: PaddingStrategy,
}

/// Tensor Core configuration for RTX 5090 4th gen
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorCoreConfig {
    precision: TensorPrecision,
    matrix_sizes: Vec<(u32, u32, u32)>, // M, N, K dimensions
    utilization_strategy: TensorUtilizationStrategy,
}

/// Cache configuration for RTX 5090's 128MB L2 cache
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheConfig {
    l1_cache_preference: L1CachePreference,
    l2_cache_strategy: L2CacheStrategy,
    memory_access_pattern: MemoryAccessPattern,
}

/// Load balancing strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LoadBalancingStrategy {
    EqualWork,
    AdaptiveWorkStealing,
    LocalityAware,
    Rtx5090Optimized,
}

/// Memory padding strategies to avoid bank conflicts
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PaddingStrategy {
    None,
    Static(u32),
    Dynamic,
    Rtx5090Aligned,
}

/// Tensor Core precision options
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TensorPrecision {
    FP16,
    BF16,
    TF32,
    FP8, // RTX 5090 supports FP8
    Mixed,
}

/// Tensor Core utilization strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TensorUtilizationStrategy {
    Maximum,
    Balanced,
    PowerEfficient,
    ThermalConstrained,
}

/// L1 cache preferences
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum L1CachePreference {
    SharedMemory,
    L1Cache,
    Balanced,
}

/// L2 cache strategies for RTX 5090's large 128MB L2
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum L2CacheStrategy {
    StreamingOptimized,
    LatencyOptimized,
    BandwidthOptimized,
    Rtx5090Default,
}

/// Memory access patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MemoryAccessPattern {
    Sequential,
    Strided,
    Random,
    CoalescedOptimal,
}

/// Constraint on parameter combinations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Constraint {
    constraint_type: ConstraintType,
    parameters: Vec<String>,
    condition: String,
}

/// Types of constraints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConstraintType {
    ResourceLimit, // e.g., shared memory limit
    Performance,   // e.g., occupancy requirements
    Thermal,       // RTX 5090 thermal constraints
    Power,         // RTX 5090 power limits
}

/// Experiment tracking
#[derive(Debug, Clone)]
pub struct Experiment {
    id: String,
    parameters: HashMap<String, f64>,
    objective_values: Vec<f64>,
    start_time: Instant,
    duration: Option<Duration>,
    status: ExperimentStatus,
    rtx5090_metrics: Rtx5090Metrics,
}

/// Experiment status
#[derive(Debug, Clone)]
pub enum ExperimentStatus {
    Running,
    Completed,
    Failed(String),
    Cancelled,
}

/// RTX 5090 specific performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Rtx5090Metrics {
    sm_efficiency: f64,
    tensor_core_utilization: f64,
    memory_bandwidth_achieved: f64, // GB/s
    l2_cache_hit_rate: f64,
    power_efficiency: f64, // Performance per watt
    thermal_efficiency: f64,
    cuda_core_utilization: f64,
    rt_core_utilization: f64, // Ray tracing cores
}

/// Optimization history for learning
pub struct OptimizationHistory {
    experiments: Vec<CompletedExperiment>,
    best_configurations: HashMap<String, Configuration>,
    convergence_data: ConvergenceData,
    rtx5090_specific_learnings: Rtx5090Learnings,
}

/// Completed experiment record
#[derive(Debug, Clone)]
pub struct CompletedExperiment {
    parameters: HashMap<String, f64>,
    objectives: Vec<f64>,
    execution_time: Duration,
    rtx5090_profile: Rtx5090Metrics,
    timestamp: Instant,
}

/// Best configuration for specific objectives
#[derive(Debug, Clone)]
pub struct Configuration {
    parameters: HashMap<String, f64>,
    performance_score: f64,
    validation_runs: u32,
    confidence: f64,
}

/// Convergence tracking data
#[derive(Debug, Clone)]
pub struct ConvergenceData {
    iterations: u32,
    best_objective_history: Vec<f64>,
    convergence_rate: f64,
    estimated_iterations_remaining: Option<u32>,
}

/// RTX 5090 specific learnings
#[derive(Debug, Clone)]
pub struct Rtx5090Learnings {
    optimal_block_sizes_by_workload: HashMap<String, u32>,
    tensor_core_sweet_spots: Vec<TensorCoreConfig>,
    thermal_aware_configurations: Vec<Configuration>,
    power_efficient_settings: Vec<Configuration>,
}

/// Gaussian Process for Bayesian optimization
pub struct GaussianProcess {
    kernel: RbfKernel,
    noise_variance: f64,
    training_data: Vec<TrainingPoint>,
    hyperparameters: GpHyperparameters,
}

/// RBF kernel for Gaussian Process
pub struct RbfKernel {
    length_scale: f64,
    signal_variance: f64,
}

/// Training point for GP
#[derive(Debug, Clone)]
pub struct TrainingPoint {
    input: Vec<f64>,
    output: f64,
    rtx5090_context: Rtx5090Context,
}

/// RTX 5090 context for training points
#[derive(Debug, Clone)]
pub struct Rtx5090Context {
    temperature: f64,
    power_consumption: f64,
    memory_temperature: f64,
    fan_speed: f64,
}

/// GP hyperparameters
#[derive(Debug, Clone)]
pub struct GpHyperparameters {
    length_scale_bounds: (f64, f64),
    signal_variance_bounds: (f64, f64),
    noise_variance_bounds: (f64, f64),
}

/// Acquisition function for next point selection
pub struct AcquisitionFunction {
    function_type: AcquisitionType,
    exploration_parameter: f64,
}

/// Types of acquisition functions
#[derive(Debug, Clone)]
pub enum AcquisitionType {
    ExpectedImprovement,
    UpperConfidenceBound,
    ProbabilityOfImprovement,
    EntropySearch,
    Rtx5090Aware, // Custom function considering RTX 5090 constraints
}

/// Convergence detector
pub struct ConvergenceDetector {
    patience: u32,
    min_improvement: f64,
    convergence_window: u32,
    rtx5090_thermal_limit: f64,
}

/// Performance counters for profiling
pub struct PerformanceCounters {
    sm_utilization: f64,
    memory_throughput: f64,
    instruction_throughput: f64,
    tensor_ops_per_second: f64,
    l2_cache_metrics: L2CacheMetrics,
}

/// L2 cache metrics for RTX 5090's 128MB L2
#[derive(Debug, Clone)]
pub struct L2CacheMetrics {
    hit_rate: f64,
    bandwidth_utilization: f64,
    access_pattern_efficiency: f64,
}

/// Thermal monitoring for RTX 5090
pub struct ThermalMonitor {
    gpu_temperature: f64,
    memory_temperature: f64,
    hotspot_temperature: f64,
    thermal_throttling_active: bool,
}

/// Profiling session
pub struct ProfilingSession {
    session_id: u32,
    active_kernels: Vec<String>,
    start_time: Instant,
}

impl HyperparameterTuner {
    /// Create new hyperparameter tuner optimized for RTX 5090
    pub async fn new() -> Result<Self> {
        let optimization_engine = BayesianOptimizer::new()?;
        let rtx5090_profiler = Arc::new(Mutex::new(Rtx5090Profiler::new()?));
        let search_space = SearchSpace::rtx5090_optimized();
        let optimization_history = Arc::new(Mutex::new(OptimizationHistory::new()));
        let active_experiments = Arc::new(Mutex::new(HashMap::new()));
        let convergence_detector = ConvergenceDetector::new();

        Ok(Self {
            optimization_engine,
            rtx5090_profiler,
            search_space,
            optimization_history,
            active_experiments,
            convergence_detector,
        })
    }

    /// Start autonomous hyperparameter tuning
    pub async fn start_autonomous_tuning(
        &self,
        objective_function: ObjectiveFunction,
    ) -> Result<()> {
        let mut iteration = 0u32;
        let max_iterations = 100u32;

        // Initialize with RTX 5090 optimal starting points
        self.initialize_with_rtx5090_defaults().await?;

        while iteration < max_iterations {
            // Generate next parameter configuration using Bayesian optimization
            let next_config = self.suggest_next_configuration().await?;

            // Check RTX 5090 thermal and power constraints
            if !self.check_rtx5090_constraints(&next_config).await? {
                iteration += 1;
                continue;
            }

            // Run experiment
            let experiment_result = self
                .run_experiment(next_config, &objective_function)
                .await?;

            // Update optimization history
            self.update_optimization_history(experiment_result).await?;

            // Check for convergence
            if self.check_convergence().await? {
                println!(
                    "🎯 Hyperparameter tuning converged after {} iterations",
                    iteration
                );
                break;
            }

            iteration += 1;

            // Adaptive delay based on RTX 5090 thermal state
            let delay = self.calculate_adaptive_delay().await?;
            tokio::time::sleep(delay).await;
        }

        // Report best configurations found
        self.report_best_configurations().await?;

        Ok(())
    }

    /// Initialize with RTX 5090 optimal starting points
    async fn initialize_with_rtx5090_defaults(&self) -> Result<()> {
        let rtx5090_defaults = vec![
            // Compute-heavy workloads
            self.create_config(hashmap! {
                "block_size" => 1024.0,
                "shared_memory_kb" => 48.0,
                "tensor_precision" => 2.0, // TF32
                "l2_cache_strategy" => 1.0, // LatencyOptimized
            }),
            // Memory-bound workloads
            self.create_config(hashmap! {
                "block_size" => 512.0,
                "shared_memory_kb" => 32.0,
                "tensor_precision" => 1.0, // FP16
                "l2_cache_strategy" => 2.0, // BandwidthOptimized
            }),
            // Balanced workloads
            self.create_config(hashmap! {
                "block_size" => 256.0,
                "shared_memory_kb" => 24.0,
                "tensor_precision" => 4.0, // Mixed
                "l2_cache_strategy" => 0.0, // StreamingOptimized
            }),
        ];

        // Run initial experiments with defaults
        for config in rtx5090_defaults {
            let objective_fn = ObjectiveFunction::MultiObjective {
                weights: vec![0.4, 0.3, 0.2, 0.1], // Performance, efficiency, thermal, power
            };
            self.run_experiment(config, &objective_fn).await?;
        }

        Ok(())
    }

    /// Suggest next configuration using Bayesian optimization
    async fn suggest_next_configuration(&self) -> Result<HashMap<String, f64>> {
        let history = self
            .optimization_history
            .lock()
            .map_err(|_| EvolutionError::AnalysisError("History mutex poisoned".to_string()))?;

        // Use Gaussian Process to predict performance
        let gp_prediction = self
            .optimization_engine
            .predict_performance(&history.experiments)?;

        // Apply RTX 5090 specific acquisition function
        let next_point = self
            .optimization_engine
            .acquisition_function
            .select_next_point(&gp_prediction, &self.search_space)?;

        Ok(next_point)
    }

    /// Check RTX 5090 specific constraints
    async fn check_rtx5090_constraints(&self, config: &HashMap<String, f64>) -> Result<bool> {
        let profiler = self
            .rtx5090_profiler
            .lock()
            .map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;

        // Check thermal constraints
        if profiler.thermal_monitor.gpu_temperature > 83.0 {
            return Ok(false); // Too hot for aggressive optimization
        }

        // Check power constraints (RTX 5090 TGP is 575W)
        let estimated_power = self.estimate_power_consumption(config)?;
        if estimated_power > 550.0 {
            return Ok(false); // Too close to power limit
        }

        // Check shared memory constraints (49KB per SM)
        if let Some(shared_mem) = config.get("shared_memory_kb") {
            if *shared_mem > 49.0 {
                return Ok(false); // Exceeds RTX 5090 shared memory limit
            }
        }

        // Check block size constraints for RTX 5090
        if let Some(block_size) = config.get("block_size") {
            if *block_size > 1024.0 || *block_size < 32.0 {
                return Ok(false); // Outside valid range
            }
        }

        Ok(true)
    }

    /// Run experiment with given configuration
    async fn run_experiment(
        &self,
        config: HashMap<String, f64>,
        objective_fn: &ObjectiveFunction,
    ) -> Result<CompletedExperiment> {
        let experiment_id = uuid::Uuid::new_v4().to_string();
        let start_time = Instant::now();

        // Apply configuration to RTX 5090
        self.apply_configuration(&config).await?;

        // Start profiling
        let mut profiler = self
            .rtx5090_profiler
            .lock()
            .map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;
        profiler.start_profiling_session(&experiment_id)?;
        drop(profiler); // Release lock

        // Run objective function evaluation
        let objectives = objective_fn.evaluate(&config).await?;

        // Collect RTX 5090 metrics
        let rtx5090_metrics = self.collect_rtx5090_metrics().await?;

        // Stop profiling
        let mut profiler = self
            .rtx5090_profiler
            .lock()
            .map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;
        profiler.stop_profiling_session()?;

        let execution_time = start_time.elapsed();

        Ok(CompletedExperiment {
            parameters: config,
            objectives,
            execution_time,
            rtx5090_profile: rtx5090_metrics,
            timestamp: start_time,
        })
    }

    /// Collect comprehensive RTX 5090 metrics
    async fn collect_rtx5090_metrics(&self) -> Result<Rtx5090Metrics> {
        let profiler = self
            .rtx5090_profiler
            .lock()
            .map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;

        Ok(Rtx5090Metrics {
            sm_efficiency: profiler.performance_counters.sm_utilization,
            tensor_core_utilization: profiler.performance_counters.tensor_ops_per_second / 1000.0, // Normalize
            memory_bandwidth_achieved: profiler.performance_counters.memory_throughput,
            l2_cache_hit_rate: profiler.performance_counters.l2_cache_metrics.hit_rate,
            power_efficiency: profiler.performance_counters.instruction_throughput / 400.0, // Normalize to watts
            thermal_efficiency: 1.0 - (profiler.thermal_monitor.gpu_temperature - 30.0) / 53.0, // 30-83°C range
            cuda_core_utilization: profiler.performance_counters.sm_utilization,
            rt_core_utilization: 0.0, // Would be measured in real RT workloads
        })
    }

    /// Update optimization history with new results
    async fn update_optimization_history(&self, experiment: CompletedExperiment) -> Result<()> {
        let mut history = self
            .optimization_history
            .lock()
            .map_err(|_| EvolutionError::AnalysisError("History mutex poisoned".to_string()))?;

        history.experiments.push(experiment.clone());

        // Update best configuration if this is better
        let performance_score =
            experiment.objectives.iter().sum::<f64>() / experiment.objectives.len() as f64;

        if let Some(current_best) = history.best_configurations.get("overall") {
            if performance_score > current_best.performance_score {
                history.best_configurations.insert(
                    "overall".to_string(),
                    Configuration {
                        parameters: experiment.parameters.clone(),
                        performance_score,
                        validation_runs: 1,
                        confidence: 0.8,
                    },
                );
            }
        } else {
            history.best_configurations.insert(
                "overall".to_string(),
                Configuration {
                    parameters: experiment.parameters.clone(),
                    performance_score,
                    validation_runs: 1,
                    confidence: 0.8,
                },
            );
        }

        // Update RTX 5090 specific learnings
        self.update_rtx5090_learnings(&mut history.rtx5090_specific_learnings, &experiment)?;

        Ok(())
    }

    /// Update RTX 5090 specific optimization learnings
    fn update_rtx5090_learnings(
        &self,
        learnings: &mut Rtx5090Learnings,
        experiment: &CompletedExperiment,
    ) -> Result<()> {
        // Extract workload type from parameters
        let workload_type = if experiment.rtx5090_profile.tensor_core_utilization > 0.5 {
            "tensor_heavy"
        } else if experiment.rtx5090_profile.memory_bandwidth_achieved > 800.0 {
            "memory_bound"
        } else {
            "compute_bound"
        };

        // Update optimal block sizes by workload
        if let Some(block_size) = experiment.parameters.get("block_size") {
            let performance_score = experiment.objectives.iter().sum::<f64>();
            if let Some(current_best) = learnings.optimal_block_sizes_by_workload.get(workload_type)
            {
                // Update if this configuration performed better
                // (In real implementation, would track performance scores)
                learnings
                    .optimal_block_sizes_by_workload
                    .insert(workload_type.to_string(), *block_size as u32);
            } else {
                learnings
                    .optimal_block_sizes_by_workload
                    .insert(workload_type.to_string(), *block_size as u32);
            }
        }

        // Update thermal-aware configurations
        if experiment.rtx5090_profile.thermal_efficiency > 0.8 {
            learnings.thermal_aware_configurations.push(Configuration {
                parameters: experiment.parameters.clone(),
                performance_score: experiment.objectives.iter().sum(),
                validation_runs: 1,
                confidence: 0.9,
            });
        }

        Ok(())
    }

    /// Check for optimization convergence
    async fn check_convergence(&self) -> Result<bool> {
        let history = self
            .optimization_history
            .lock()
            .map_err(|_| EvolutionError::AnalysisError("History mutex poisoned".to_string()))?;

        if history.experiments.len() < 10 {
            return Ok(false); // Need minimum experiments
        }

        // Check if best performance hasn't improved in last N iterations
        let recent_experiments =
            &history.experiments[history.experiments.len().saturating_sub(10)..];
        let recent_best = recent_experiments
            .iter()
            .map(|exp| exp.objectives.iter().sum::<f64>())
            .fold(f64::NEG_INFINITY, f64::max);

        let overall_best = history
            .experiments
            .iter()
            .map(|exp| exp.objectives.iter().sum::<f64>())
            .fold(f64::NEG_INFINITY, f64::max);

        // Converged if recent best is within 1% of overall best
        let improvement = (recent_best - overall_best) / overall_best;
        Ok(improvement.abs() < 0.01)
    }

    /// Calculate adaptive delay based on RTX 5090 thermal state
    async fn calculate_adaptive_delay(&self) -> Result<Duration> {
        let profiler = self
            .rtx5090_profiler
            .lock()
            .map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;

        let base_delay = Duration::from_millis(500);

        // Scale delay based on temperature
        let temp_factor = if profiler.thermal_monitor.gpu_temperature > 80.0 {
            3.0 // Slow down if getting hot
        } else if profiler.thermal_monitor.gpu_temperature > 75.0 {
            2.0
        } else {
            1.0
        };

        Ok(Duration::from_millis(
            (base_delay.as_millis() as f64 * temp_factor) as u64,
        ))
    }

    /// Report best configurations found
    async fn report_best_configurations(&self) -> Result<()> {
        let history = self
            .optimization_history
            .lock()
            .map_err(|_| EvolutionError::AnalysisError("History mutex poisoned".to_string()))?;

        println!("🏆 RTX 5090 Hyperparameter Tuning Results:");
        println!("==========================================");

        for (config_name, config) in &history.best_configurations {
            println!("📊 Best {} configuration:", config_name);
            println!("   Performance Score: {:.3}", config.performance_score);
            println!("   Confidence: {:.1}%", config.confidence * 100.0);
            println!("   Parameters:");
            for (param, value) in &config.parameters {
                println!("     {}: {:.2}", param, value);
            }
            println!();
        }

        // RTX 5090 specific insights
        println!("🔥 RTX 5090 Optimization Insights:");
        for (workload, block_size) in &history
            .rtx5090_specific_learnings
            .optimal_block_sizes_by_workload
        {
            println!(
                "   {} workloads: {} threads per block",
                workload, block_size
            );
        }

        println!(
            "   Found {} thermal-efficient configurations",
            history
                .rtx5090_specific_learnings
                .thermal_aware_configurations
                .len()
        );

        Ok(())
    }

    // Helper methods
    fn create_config(&self, params: HashMap<&str, f64>) -> HashMap<String, f64> {
        params
            .into_iter()
            .map(|(k, v)| (k.to_string(), v))
            .collect()
    }

    async fn apply_configuration(&self, _config: &HashMap<String, f64>) -> Result<()> {
        // In real implementation, would apply configuration to GPU
        Ok(())
    }

    fn estimate_power_consumption(&self, _config: &HashMap<String, f64>) -> Result<f64> {
        // Estimate power based on configuration
        Ok(450.0) // Placeholder
    }
}

/// Objective function for hyperparameter optimization
pub enum ObjectiveFunction {
    SingleObjective(Box<dyn Fn(&HashMap<String, f64>) -> f64 + Send + Sync>),
    MultiObjective { weights: Vec<f64> },
}

impl ObjectiveFunction {
    async fn evaluate(&self, config: &HashMap<String, f64>) -> Result<Vec<f64>> {
        match self {
            ObjectiveFunction::SingleObjective(func) => Ok(vec![func(config)]),
            ObjectiveFunction::MultiObjective { weights } => {
                // Multi-objective evaluation
                let performance = self.evaluate_performance(config).await?;
                let efficiency = self.evaluate_efficiency(config).await?;
                let thermal = self.evaluate_thermal(config).await?;
                let power = self.evaluate_power(config).await?;

                Ok(vec![performance, efficiency, thermal, power])
            }
        }
    }

    async fn evaluate_performance(&self, _config: &HashMap<String, f64>) -> Result<f64> {
        // Evaluate performance objective
        Ok(0.8) // Placeholder
    }

    async fn evaluate_efficiency(&self, _config: &HashMap<String, f64>) -> Result<f64> {
        // Evaluate efficiency objective
        Ok(0.7) // Placeholder
    }

    async fn evaluate_thermal(&self, _config: &HashMap<String, f64>) -> Result<f64> {
        // Evaluate thermal objective
        Ok(0.9) // Placeholder
    }

    async fn evaluate_power(&self, _config: &HashMap<String, f64>) -> Result<f64> {
        // Evaluate power efficiency objective
        Ok(0.75) // Placeholder
    }
}

// Implementation for various components...

impl SearchSpace {
    fn rtx5090_optimized() -> Self {
        let mut parameters = HashMap::new();

        parameters.insert(
            "block_size".to_string(),
            ParameterSpec {
                name: "block_size".to_string(),
                param_type: ParameterType::BlockSize,
                bounds: ParameterBounds {
                    min: 32.0,
                    max: 1024.0,
                    step: Some(32.0),
                    preferred_values: Some(vec![256.0, 512.0, 1024.0]), // RTX 5090 sweet spots
                },
                importance: 0.9,
                rtx5090_specific: true,
            },
        );

        parameters.insert(
            "shared_memory_kb".to_string(),
            ParameterSpec {
                name: "shared_memory_kb".to_string(),
                param_type: ParameterType::SharedMemoryConfig,
                bounds: ParameterBounds {
                    min: 0.0,
                    max: 49.0, // RTX 5090 limit
                    step: Some(1.0),
                    preferred_values: Some(vec![24.0, 32.0, 48.0]),
                },
                importance: 0.8,
                rtx5090_specific: true,
            },
        );

        Self {
            parameters,
            constraints: vec![],
            rtx5090_optimizations: Rtx5090OptimizationSpace {
                block_sizes: vec![32, 64, 128, 256, 512, 1024],
                grid_configurations: vec![],
                shared_memory_configs: vec![],
                tensor_core_configs: vec![],
                cache_configurations: vec![],
            },
        }
    }
}

impl BayesianOptimizer {
    fn new() -> Result<Self> {
        Ok(Self {
            gaussian_process: GaussianProcess::new(),
            acquisition_function: AcquisitionFunction::new(),
            exploration_exploitation_balance: 0.1,
        })
    }

    fn predict_performance(&self, _experiments: &[CompletedExperiment]) -> Result<Vec<f64>> {
        // GP prediction
        Ok(vec![0.8, 0.1]) // Mean and variance
    }
}

impl GaussianProcess {
    fn new() -> Self {
        Self {
            kernel: RbfKernel {
                length_scale: 1.0,
                signal_variance: 1.0,
            },
            noise_variance: 0.01,
            training_data: vec![],
            hyperparameters: GpHyperparameters {
                length_scale_bounds: (0.1, 10.0),
                signal_variance_bounds: (0.1, 10.0),
                noise_variance_bounds: (0.001, 1.0),
            },
        }
    }
}

impl AcquisitionFunction {
    fn new() -> Self {
        Self {
            function_type: AcquisitionType::Rtx5090Aware,
            exploration_parameter: 0.1,
        }
    }

    fn select_next_point(
        &self,
        _prediction: &[f64],
        _search_space: &SearchSpace,
    ) -> Result<HashMap<String, f64>> {
        // Select next point to evaluate
        Ok(hashmap! {
            "block_size".to_string() => 512.0,
            "shared_memory_kb".to_string() => 32.0,
        })
    }
}

impl Rtx5090Profiler {
    fn new() -> Result<Self> {
        Ok(Self {
            device_context: 0,
            profiling_session: None,
            performance_counters: PerformanceCounters {
                sm_utilization: 0.0,
                memory_throughput: 0.0,
                instruction_throughput: 0.0,
                tensor_ops_per_second: 0.0,
                l2_cache_metrics: L2CacheMetrics {
                    hit_rate: 0.0,
                    bandwidth_utilization: 0.0,
                    access_pattern_efficiency: 0.0,
                },
            },
            thermal_monitor: ThermalMonitor {
                gpu_temperature: 65.0,
                memory_temperature: 70.0,
                hotspot_temperature: 75.0,
                thermal_throttling_active: false,
            },
        })
    }

    fn start_profiling_session(&mut self, session_id: &str) -> Result<()> {
        self.profiling_session = Some(ProfilingSession {
            session_id: session_id.parse().unwrap_or(0),
            active_kernels: vec![],
            start_time: Instant::now(),
        });
        Ok(())
    }

    fn stop_profiling_session(&mut self) -> Result<()> {
        self.profiling_session = None;
        Ok(())
    }
}

impl OptimizationHistory {
    fn new() -> Self {
        Self {
            experiments: Vec::new(),
            best_configurations: HashMap::new(),
            convergence_data: ConvergenceData {
                iterations: 0,
                best_objective_history: vec![],
                convergence_rate: 0.0,
                estimated_iterations_remaining: None,
            },
            rtx5090_specific_learnings: Rtx5090Learnings {
                optimal_block_sizes_by_workload: HashMap::new(),
                tensor_core_sweet_spots: vec![],
                thermal_aware_configurations: vec![],
                power_efficient_settings: vec![],
            },
        }
    }
}

impl ConvergenceDetector {
    fn new() -> Self {
        Self {
            patience: 20,
            min_improvement: 0.01,
            convergence_window: 10,
            rtx5090_thermal_limit: 83.0,
        }
    }
}

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

    #[tokio::test]
    async fn test_hyperparameter_tuner_creation() {
        let tuner = HyperparameterTuner::new().await;
        assert!(tuner.is_ok());
    }

    #[test]
    fn test_rtx5090_search_space() {
        let search_space = SearchSpace::rtx5090_optimized();
        assert!(search_space.parameters.contains_key("block_size"));
        assert!(search_space.parameters.contains_key("shared_memory_kb"));

        let block_size_param = &search_space.parameters["block_size"];
        assert!(block_size_param.rtx5090_specific);
        assert_eq!(block_size_param.bounds.max, 1024.0);
    }

    #[tokio::test]
    async fn test_objective_function_evaluation() {
        let obj_fn = ObjectiveFunction::MultiObjective {
            weights: vec![0.25, 0.25, 0.25, 0.25],
        };

        let config = hashmap! {
            "block_size".to_string() => 256.0,
            "shared_memory_kb".to_string() => 32.0,
        };

        let result = obj_fn.evaluate(&config).await;
        assert!(result.is_ok());
        assert_eq!(result.unwrap().len(), 4); // 4 objectives
    }
}