quantrs2-device 0.1.3

//! Cost optimization engine implementations

use std::collections::HashMap;
use std::sync::RwLock;
use std::time::{Duration, SystemTime};

use quantrs2_circuit::prelude::Circuit;
use serde::{Deserialize, Serialize};
use std::collections::VecDeque;
use std::sync::Arc;

use crate::{job_scheduling::JobPriority, translation::HardwareBackend, DeviceError, DeviceResult};

use super::types::*;

impl CostOptimizationEngine {
    /// Create a new cost optimization engine
    pub fn new(config: CostOptimizationConfig) -> Self {
        Self {
            config: config.clone(),
            cost_estimator: Arc::new(RwLock::new(CostEstimator::new(&config.estimation_config))),
            budget_manager: Arc::new(RwLock::new(BudgetManager::new(&config.budget_config))),
            provider_comparator: Arc::new(RwLock::new(ProviderComparator::new(
                &config.provider_comparison,
            ))),
            predictive_modeler: Arc::new(RwLock::new(PredictiveModeler::new(
                &config.predictive_modeling,
            ))),
            resource_optimizer: Arc::new(RwLock::new(ResourceOptimizer::new(
                &config.resource_optimization,
            ))),
            cost_monitor: Arc::new(RwLock::new(CostMonitor::new(&config.monitoring_config))),
            alert_manager: Arc::new(RwLock::new(AlertManager::new(&config.alert_config))),
            optimization_cache: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Estimate cost for a circuit execution
    pub async fn estimate_cost<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        provider: HardwareBackend,
        shots: usize,
    ) -> DeviceResult<CostEstimate> {
        let mut estimator = self.cost_estimator.write().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire write lock on cost_estimator: {e}"
            ))
        })?;
        estimator.estimate_cost(circuit, provider, shots).await
    }

    /// Compare costs across providers
    pub async fn compare_providers<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        providers: Vec<HardwareBackend>,
        shots: usize,
    ) -> DeviceResult<ProviderComparisonResult> {
        let mut comparator = self.provider_comparator.write().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire write lock on provider_comparator: {e}"
            ))
        })?;
        comparator
            .compare_providers(circuit, providers, shots)
            .await
    }

    /// Optimize resource allocation for cost
    pub async fn optimize_resource_allocation(
        &self,
        requirements: &ResourceRequirements,
    ) -> DeviceResult<OptimizationResult> {
        let mut optimizer = self.resource_optimizer.write().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire write lock on resource_optimizer: {e}"
            ))
        })?;
        optimizer.optimize_allocation(requirements).await
    }

    /// Get current budget status
    pub async fn get_budget_status(&self) -> DeviceResult<BudgetStatus> {
        let budget_manager = self.budget_manager.read().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire read lock on budget_manager: {e}"
            ))
        })?;
        Ok(budget_manager.get_current_status())
    }

    /// Predict future costs
    pub async fn predict_costs(
        &self,
        prediction_horizon: Duration,
        features: HashMap<String, f64>,
    ) -> DeviceResult<PredictionResult> {
        let mut modeler = self.predictive_modeler.write().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire write lock on predictive_modeler: {e}"
            ))
        })?;
        modeler.predict_costs(prediction_horizon, features).await
    }

    /// Get optimization recommendations
    pub async fn get_optimization_recommendations(
        &self,
        context: OptimizationContext,
    ) -> DeviceResult<Vec<OptimizationRecommendation>> {
        // Analyze current usage patterns
        let budget_status = self.get_budget_status().await?;
        let cost_trends = self.analyze_cost_trends().await?;

        // Generate recommendations based on analysis
        let recommendations = self
            .generate_recommendations(&budget_status, &cost_trends, &context)
            .await?;

        Ok(recommendations)
    }

    /// Monitor costs in real-time
    pub async fn start_cost_monitoring(&self) -> DeviceResult<()> {
        let monitor = self.cost_monitor.clone();
        let alert_manager = self.alert_manager.clone();

        tokio::spawn(async move {
            loop {
                // Update monitoring metrics
                {
                    if let Ok(mut monitor_guard) = monitor.write() {
                        // Note: update_metrics is not async in the current implementation
                        // monitor_guard.update_metrics().await;
                        // For now, we'll use a synchronous call
                        monitor_guard.update_metrics_sync();
                    }
                }

                // Check for alerts
                {
                    if let Ok(mut alert_guard) = alert_manager.write() {
                        // Note: check_and_trigger_alerts is not async in the current implementation
                        // alert_guard.check_and_trigger_alerts().await;
                        // For now, we'll use a synchronous call
                        alert_guard.check_and_trigger_alerts_sync();
                    }
                }

                tokio::time::sleep(Duration::from_secs(60)).await;
            }
        });

        Ok(())
    }

    // Helper methods for implementation...

    async fn analyze_cost_trends(&self) -> DeviceResult<CostTrends> {
        // Implementation for cost trend analysis
        Ok(CostTrends::default())
    }

    async fn generate_recommendations(
        &self,
        budget_status: &BudgetStatus,
        cost_trends: &CostTrends,
        context: &OptimizationContext,
    ) -> DeviceResult<Vec<OptimizationRecommendation>> {
        // Implementation for generating optimization recommendations
        Ok(vec![])
    }
}

/// Resource requirements for optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    pub circuits: Vec<CircuitRequirement>,
    pub budget_constraints: Vec<BudgetConstraint>,
    pub time_constraints: Vec<TimeConstraint>,
    pub quality_requirements: Vec<QualityRequirement>,
}

/// Circuit requirement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CircuitRequirement {
    pub circuit_id: String,
    pub qubit_count: usize,
    pub gate_count: usize,
    pub shots: usize,
    pub priority: JobPriority,
    pub deadline: Option<SystemTime>,
}

/// Budget constraint
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BudgetConstraint {
    pub constraint_type: BudgetConstraintType,
    pub value: f64,
    pub scope: ConstraintScope,
}

/// Budget constraint types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum BudgetConstraintType {
    MaxTotalCost,
    MaxCostPerCircuit,
    MaxCostPerProvider,
    CostPerformanceRatio,
}

/// Constraint scopes
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ConstraintScope {
    Global,
    PerProvider,
    PerCircuit,
    PerTimeWindow(Duration),
}

/// Time constraint
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeConstraint {
    pub constraint_type: TimeConstraintType,
    pub value: Duration,
    pub scope: ConstraintScope,
}

/// Time constraint types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum TimeConstraintType {
    MaxExecutionTime,
    MaxQueueTime,
    Deadline,
    PreferredWindow,
}

/// Quality requirement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QualityRequirement {
    pub requirement_type: QualityRequirementType,
    pub value: f64,
    pub scope: ConstraintScope,
}

/// Quality requirement types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum QualityRequirementType {
    MinFidelity,
    MaxErrorRate,
    MinSuccessRate,
    ConsistencyLevel,
}

/// Optimization context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationContext {
    pub user_preferences: UserPreferences,
    pub historical_patterns: HistoricalPatterns,
    pub current_workload: CurrentWorkload,
    pub market_conditions: MarketConditions,
}

/// User preferences for optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UserPreferences {
    pub cost_sensitivity: f64, // 0.0 to 1.0
    pub time_sensitivity: f64,
    pub quality_sensitivity: f64,
    pub preferred_providers: Vec<HardwareBackend>,
    pub risk_tolerance: RiskTolerance,
}

/// Risk tolerance levels
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum RiskTolerance {
    Conservative,
    Moderate,
    Aggressive,
    Custom(f64),
}

/// Historical usage patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HistoricalPatterns {
    pub usage_frequency: HashMap<HardwareBackend, f64>,
    pub cost_patterns: HashMap<String, f64>,
    pub performance_history: HashMap<HardwareBackend, f64>,
    pub error_patterns: HashMap<String, f64>,
}

/// Current workload information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CurrentWorkload {
    pub pending_circuits: usize,
    pub queue_lengths: HashMap<HardwareBackend, usize>,
    pub resource_utilization: HashMap<HardwareBackend, f64>,
    pub estimated_completion_times: HashMap<HardwareBackend, Duration>,
}

/// Market conditions affecting costs
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MarketConditions {
    pub demand_levels: HashMap<HardwareBackend, DemandLevel>,
    pub pricing_trends: HashMap<HardwareBackend, PricingTrend>,
    pub capacity_utilization: HashMap<HardwareBackend, f64>,
    pub promotional_offers: Vec<PromotionalOffer>,
}

/// Demand levels
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum DemandLevel {
    Low,
    Normal,
    High,
    Peak,
}

/// Pricing trends
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum PricingTrend {
    Decreasing,
    Stable,
    Increasing,
    Volatile,
}

/// Promotional offers
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PromotionalOffer {
    pub provider: HardwareBackend,
    pub offer_type: OfferType,
    pub discount_percentage: f64,
    pub valid_until: SystemTime,
    pub conditions: Vec<String>,
}

/// Offer types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum OfferType {
    VolumeDiscount,
    FirstTimeUser,
    LoyaltyDiscount,
    OffPeakPricing,
    BundleOffer,
}

/// Cost trends analysis
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct CostTrends {
    pub overall_trend: TrendDirection,
    pub provider_trends: HashMap<HardwareBackend, TrendDirection>,
    pub seasonal_patterns: Vec<SeasonalPattern>,
    pub anomalies: Vec<CostAnomaly>,
}

/// Trend directions
#[derive(Debug, Clone, PartialEq, Default, Serialize, Deserialize)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    #[default]
    Stable,
    Volatile,
}

/// Seasonal patterns in costs
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SeasonalPattern {
    pub pattern_name: String,
    pub period: Duration,
    pub amplitude: f64,
    pub phase_offset: Duration,
}

/// Cost anomalies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostAnomaly {
    pub anomaly_type: AnomalyType,
    pub detected_at: SystemTime,
    pub severity: f64,
    pub description: String,
    pub affected_providers: Vec<HardwareBackend>,
}

/// Anomaly types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum AnomalyType {
    CostSpike,
    UnexpectedDiscount,
    ProviderOutage,
    QueueBottleneck,
    PerformanceDegradation,
}

/// Optimization recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    pub recommendation_type: RecommendationType,
    pub description: String,
    pub estimated_savings: f64,
    pub implementation_effort: ImplementationEffort,
    pub confidence_score: f64,
    pub action_items: Vec<ActionItem>,
}

/// Recommendation types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum RecommendationType {
    ProviderSwitch,
    TimingOptimization,
    BatchingOptimization,
    ResourceReallocation,
    BudgetAdjustment,
    QualityTradeoff,
}

/// Implementation effort levels
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ImplementationEffort {
    Low,
    Medium,
    High,
    VeryHigh,
}

/// Action items for implementing recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ActionItem {
    pub description: String,
    pub priority: ActionPriority,
    pub estimated_time: Duration,
    pub required_resources: Vec<String>,
}

/// Action priorities
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub enum ActionPriority {
    Low,
    Medium,
    High,
    Critical,
}

// Implementation stubs for component constructors
impl CostEstimator {
    fn new(_config: &CostEstimationConfig) -> Self {
        Self {
            models: HashMap::new(),
            historical_data: VecDeque::new(),
            ml_models: HashMap::new(),
            estimation_cache: HashMap::new(),
        }
    }

    async fn estimate_cost<const N: usize>(
        &mut self,
        _circuit: &Circuit<N>,
        _provider: HardwareBackend,
        _shots: usize,
    ) -> DeviceResult<CostEstimate> {
        // Placeholder implementation
        Ok(CostEstimate {
            total_cost: 10.0,
            cost_breakdown: CostBreakdown {
                execution_cost: 8.0,
                queue_cost: 1.0,
                setup_cost: 0.5,
                data_transfer_cost: 0.3,
                storage_cost: 0.2,
                additional_fees: HashMap::new(),
            },
            confidence_interval: (9.0, 11.0),
            metadata: CostEstimationMetadata {
                model_used: "linear".to_string(),
                timestamp: SystemTime::now(),
                confidence_level: 0.95,
                historical_accuracy: Some(0.92),
                factors_considered: vec!["shots".to_string(), "qubits".to_string()],
            },
        })
    }
}

impl BudgetManager {
    fn new(_config: &BudgetConfig) -> Self {
        Self {
            current_budget: BudgetStatus {
                total_budget: 10000.0,
                used_budget: 2500.0,
                remaining_budget: 7500.0,
                utilization_percentage: 25.0,
                daily_status: None,
                monthly_status: None,
                provider_breakdown: HashMap::new(),
            },
            budget_history: VecDeque::new(),
            spending_patterns: HashMap::new(),
            budget_alerts: Vec::new(),
        }
    }

    fn get_current_status(&self) -> BudgetStatus {
        self.current_budget.clone()
    }
}

impl ProviderComparator {
    fn new(_config: &ProviderComparisonConfig) -> Self {
        Self {
            comparison_cache: HashMap::new(),
            real_time_metrics: HashMap::new(),
            reliability_tracker: ReliabilityTracker {
                provider_reliability: HashMap::new(),
                incident_history: VecDeque::new(),
            },
        }
    }

    async fn compare_providers<const N: usize>(
        &mut self,
        _circuit: &Circuit<N>,
        providers: Vec<HardwareBackend>,
        _shots: usize,
    ) -> DeviceResult<ProviderComparisonResult> {
        // Placeholder implementation
        let mut provider_scores = HashMap::new();
        let mut detailed_metrics = HashMap::new();

        for provider in &providers {
            provider_scores.insert(*provider, 0.8);
            detailed_metrics.insert(
                *provider,
                ProviderMetrics {
                    cost_metrics: HashMap::new(),
                    performance_metrics: HashMap::new(),
                    reliability_metrics: HashMap::new(),
                    overall_score: 0.8,
                },
            );
        }

        Ok(ProviderComparisonResult {
            provider_scores,
            detailed_metrics,
            recommended_provider: providers[0],
            timestamp: SystemTime::now(),
        })
    }
}

impl PredictiveModeler {
    fn new(_config: &PredictiveModelingConfig) -> Self {
        Self {
            models: HashMap::new(),
            feature_store: FeatureStore {
                features: HashMap::new(),
                feature_metadata: HashMap::new(),
                derived_features: HashMap::new(),
            },
            model_performance: HashMap::new(),
            ensemble_config: EnsembleConfig {
                ensemble_method: EnsembleMethod::Averaging,
                model_weights: HashMap::new(),
                voting_strategy: VotingStrategy::Weighted,
                diversity_threshold: 0.1,
            },
        }
    }

    async fn predict_costs(
        &mut self,
        _prediction_horizon: Duration,
        _features: HashMap<String, f64>,
    ) -> DeviceResult<PredictionResult> {
        // Placeholder implementation
        Ok(PredictionResult {
            predicted_value: 15.0,
            confidence_interval: (12.0, 18.0),
            feature_contributions: HashMap::new(),
            model_used: "ensemble".to_string(),
            prediction_timestamp: SystemTime::now(),
        })
    }
}

impl ResourceOptimizer {
    fn new(_config: &ResourceOptimizationConfig) -> Self {
        Self {
            optimization_algorithms: HashMap::new(),
            constraint_solver: ConstraintSolver {
                solver_type: SolverType::InteriorPoint,
                tolerance: 1e-6,
                max_iterations: 1000,
            },
            optimization_history: VecDeque::new(),
            pareto_frontiers: HashMap::new(),
        }
    }

    async fn optimize_allocation(
        &mut self,
        _requirements: &ResourceRequirements,
    ) -> DeviceResult<OptimizationResult> {
        // Placeholder implementation
        Ok(OptimizationResult {
            solution: vec![0.8, 0.2],
            objective_values: vec![12.5],
            constraint_violations: vec![],
            optimization_status: OptimizationStatus::Optimal,
            iterations: 25,
            execution_time: Duration::from_millis(150),
            algorithm_used: "interior_point".to_string(),
        })
    }
}

impl CostMonitor {
    fn new(_config: &CostMonitoringConfig) -> Self {
        Self {
            monitoring_metrics: HashMap::new(),
            anomaly_detector: AnomalyDetector {
                detection_methods: vec![AnomalyDetectionMethod::StatisticalOutlier],
                anomaly_threshold: 2.0,
                detected_anomalies: VecDeque::new(),
            },
            trend_analyzer: TrendAnalyzer {
                trend_models: HashMap::new(),
                trend_detection_sensitivity: 0.1,
                forecasting_horizon: Duration::from_secs(24 * 3600),
            },
            dashboard_data: DashboardData {
                widget_data: HashMap::new(),
                last_updated: SystemTime::now(),
                update_frequency: Duration::from_secs(30),
            },
        }
    }

    async fn update_metrics(&mut self) {
        // Placeholder implementation for updating monitoring metrics
    }

    fn update_metrics_sync(&mut self) {
        // Placeholder implementation for updating monitoring metrics synchronously
    }
}

impl AlertManager {
    fn new(_config: &CostAlertConfig) -> Self {
        Self {
            active_alerts: HashMap::new(),
            alert_history: VecDeque::new(),
            notification_handlers: HashMap::new(),
            escalation_policies: HashMap::new(),
        }
    }

    async fn check_and_trigger_alerts(&mut self) {
        // Placeholder implementation for checking and triggering alerts
    }

    fn check_and_trigger_alerts_sync(&mut self) {
        // Placeholder implementation for checking and triggering alerts synchronously
    }
}

// Default implementation already exists in the struct definition

#[cfg(test)]
#[allow(clippy::pedantic, clippy::field_reassign_with_default)]
mod tests {
    use super::*;

    #[test]
    fn test_cost_optimization_config_default() {
        let config = CostOptimizationConfig::default();
        assert_eq!(config.budget_config.total_budget, 10000.0);
        assert!(config.estimation_config.enable_ml_estimation);
        assert_eq!(
            config.optimization_strategy,
            CostOptimizationStrategy::MaximizeCostPerformance
        );
    }

    #[test]
    fn test_budget_rollover_policy() {
        let policy = BudgetRolloverPolicy::PercentageRollover(0.2);
        match policy {
            BudgetRolloverPolicy::PercentageRollover(percentage) => {
                assert_eq!(percentage, 0.2);
            }
            _ => panic!("Expected PercentageRollover"),
        }
    }

    #[test]
    fn test_cost_model_creation() {
        let model = CostModel {
            model_type: CostModelType::Linear,
            base_cost_per_shot: 0.01,
            cost_per_qubit: 0.1,
            cost_per_gate: 0.001,
            cost_per_second: 0.1,
            setup_cost: 1.0,
            queue_time_multiplier: 0.1,
            time_based_pricing: None,
            volume_discounts: vec![],
            custom_factors: HashMap::new(),
        };

        assert_eq!(model.model_type, CostModelType::Linear);
        assert_eq!(model.base_cost_per_shot, 0.01);
    }

    #[tokio::test]
    async fn test_cost_optimization_engine_creation() {
        let config = CostOptimizationConfig::default();
        let _engine = CostOptimizationEngine::new(config);

        // Test passes if engine creates without error (no panic)
    }

    #[test]
    fn test_optimization_objectives() {
        let objectives = [
            OptimizationObjective::MinimizeCost,
            OptimizationObjective::MaximizeQuality,
            OptimizationObjective::MinimizeTime,
        ];

        assert_eq!(objectives.len(), 3);
        assert_eq!(objectives[0], OptimizationObjective::MinimizeCost);
    }
}