quantrs2-device 0.1.3

//! Cost estimation implementations.
//!
//! Split from cost_estimation.rs for size compliance.

#![allow(dead_code)]

use std::collections::{BTreeMap, HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::{Duration, SystemTime, UNIX_EPOCH};

use serde::{Deserialize, Serialize};
use tokio::sync::RwLock as TokioRwLock;
use uuid::Uuid;

use super::super::{CloudProvider, ExecutionConfig, QuantumCloudConfig, WorkloadSpec};
use crate::{DeviceError, DeviceResult};

use super::definitions::*;

impl CostEstimationEngine {
    /// Create a new cost estimation engine
    pub async fn new(config: CostEstimationConfig) -> DeviceResult<Self> {
        let budget_analyzer = Arc::new(TokioRwLock::new(BudgetAnalyzer::new().await?));
        let cost_optimizer = Arc::new(TokioRwLock::new(CostOptimizer::new().await?));
        let pricing_cache = Arc::new(TokioRwLock::new(PricingCache::new()?));
        let cost_history = Arc::new(TokioRwLock::new(CostHistory::new()?));

        Ok(Self {
            config,
            pricing_models: HashMap::new(),
            cost_predictors: HashMap::new(),
            budget_analyzer,
            cost_optimizer,
            pricing_cache,
            cost_history,
        })
    }

    /// Initialize the cost estimation engine
    pub async fn initialize(&mut self) -> DeviceResult<()> {
        // Load pricing models for all providers
        self.load_pricing_models().await?;

        // Initialize cost predictors
        self.initialize_cost_predictors().await?;

        // Load historical cost data
        self.load_historical_data().await?;

        Ok(())
    }

    /// Estimate cost for a workload
    pub async fn estimate_cost(
        &self,
        workload: &WorkloadSpec,
        config: &ExecutionConfig,
    ) -> DeviceResult<CostPrediction> {
        let predictor_name = format!(
            "{:?}_{}",
            config.provider,
            self.config.estimation_accuracy_level.clone() as u8
        );

        if let Some(predictor) = self.cost_predictors.get(&predictor_name) {
            let prediction = predictor.predict_cost(workload, config, Duration::from_secs(3600))?;

            // Cache the result
            self.cache_cost_prediction(&prediction).await?;

            Ok(prediction)
        } else {
            Err(DeviceError::InvalidInput(format!(
                "No cost predictor available for provider {:?}",
                config.provider
            )))
        }
    }

    /// Get budget analysis
    pub async fn analyze_budget(&self, budget_id: &str) -> DeviceResult<BudgetPerformance> {
        let analyzer = self.budget_analyzer.read().await;
        analyzer.analyze_budget_performance(budget_id).await
    }

    /// Generate cost optimization recommendations
    pub async fn optimize_costs(
        &self,
        workload: &WorkloadSpec,
    ) -> DeviceResult<Vec<OptimizationRecommendation>> {
        let cost_analysis = self.perform_cost_analysis(workload).await?;

        let optimizer = self.cost_optimizer.read().await;
        optimizer
            .generate_optimization_recommendations(&cost_analysis)
            .await
    }

    /// Update pricing data
    pub async fn update_pricing_data(
        &self,
        provider: CloudProvider,
        pricing_model: ProviderPricingModel,
    ) -> DeviceResult<()> {
        // Update pricing cache
        let mut cache = self.pricing_cache.write().await;
        cache
            .update_provider_pricing(provider, pricing_model)
            .await?;

        Ok(())
    }

    // Helper methods
    async fn load_pricing_models(&mut self) -> DeviceResult<()> {
        // Load pricing models from external sources or configuration
        Ok(())
    }

    async fn initialize_cost_predictors(&mut self) -> DeviceResult<()> {
        // Initialize cost predictors for different providers and accuracy levels
        Ok(())
    }

    async fn load_historical_data(&self) -> DeviceResult<()> {
        // Load historical cost and usage data
        Ok(())
    }

    async fn cache_cost_prediction(&self, prediction: &CostPrediction) -> DeviceResult<()> {
        // Cache the cost prediction for future reference
        Ok(())
    }

    async fn perform_cost_analysis(&self, workload: &WorkloadSpec) -> DeviceResult<CostAnalysis> {
        // Derive basic circuit characteristics from the workload.
        let qubits = workload.circuit_characteristics.qubit_count as f64;
        let depth = workload.circuit_characteristics.circuit_depth as f64;
        let shots = workload.execution_requirements.shots as f64;

        // ── Compute individual cost components ────────────────────────────────
        // Gate cost: proportional to qubit × depth × shots
        let gate_cost = qubits * depth * shots * COST_PER_GATE_UNIT;
        // Shot cost: base cost per measurement shot
        let shot_cost = shots * COST_PER_SHOT;
        // Network transfer (classical results uploaded/downloaded)
        let network_cost = shots * DATA_BYTES_PER_SHOT * COST_PER_BYTE;
        // Storage overhead (classical results at rest)
        let storage_cost = shots * DATA_BYTES_PER_SHOT * COST_PER_BYTE_STORED;

        let subtotal = gate_cost + shot_cost + network_cost + storage_cost;
        let tax = subtotal * self.config.tax_rate;
        let total = subtotal + tax;

        // ── Build DetailedCostBreakdown ───────────────────────────────────────
        let base_costs: HashMap<CostCategory, f64> = [
            (CostCategory::Compute, gate_cost + shot_cost),
            (CostCategory::Network, network_cost),
            (CostCategory::Storage, storage_cost),
        ]
        .into_iter()
        .collect();

        let cost_per_unit: HashMap<String, f64> = [
            (
                "per_qubit".to_string(),
                if qubits > 0.0 { total / qubits } else { 0.0 },
            ),
            (
                "per_shot".to_string(),
                if shots > 0.0 { total / shots } else { 0.0 },
            ),
            (
                "per_gate".to_string(),
                if depth > 0.0 { gate_cost / depth } else { 0.0 },
            ),
        ]
        .into_iter()
        .collect();

        let cost_breakdown = DetailedCostBreakdown {
            base_costs,
            variable_costs: [
                ("gate_cost".to_string(), gate_cost),
                ("shot_cost".to_string(), shot_cost),
            ]
            .into_iter()
            .collect(),
            fixed_costs: HashMap::new(),
            taxes_and_fees: tax,
            discounts_applied: 0.0,
            total_cost: total,
            cost_per_unit,
        };

        // ── Identify simple cost driver ───────────────────────────────────────
        let dominant_driver = if gate_cost >= shot_cost {
            CostDriver {
                driver_name: "circuit_complexity".to_string(),
                driver_type: CostDriverType::Complexity,
                impact_magnitude: gate_cost / total.max(1e-14),
                controllability: ControllabilityLevel::FullyControllable,
                optimization_potential: 0.3,
            }
        } else {
            CostDriver {
                driver_name: "shot_count".to_string(),
                driver_type: CostDriverType::Volume,
                impact_magnitude: shot_cost / total.max(1e-14),
                controllability: ControllabilityLevel::FullyControllable,
                optimization_potential: 0.4,
            }
        };

        // ── Build BenchmarkComparison (internal baseline) ─────────────────────
        let benchmark_comparison = BenchmarkComparison {
            benchmark_type: BenchmarkType::Internal,
            comparison_metrics: HashMap::new(),
            relative_performance: 1.0, // baseline
            improvement_opportunities: Vec::new(),
        };

        Ok(CostAnalysis {
            total_costs: total,
            cost_breakdown,
            cost_trends: Vec::new(),
            cost_drivers: vec![dominant_driver],
            benchmark_comparison,
            inefficiencies: Vec::new(),
        })
    }
}

// Pricing constants (USD).
const COST_PER_GATE_UNIT: f64 = 0.000_001; // $0.000001 per qubit-depth-shot unit
const COST_PER_SHOT: f64 = 0.000_01; // $0.00001 per shot
const DATA_BYTES_PER_SHOT: f64 = 64.0; // 64 bytes of classical data per shot
const COST_PER_BYTE: f64 = 0.000_000_01; // $0.00000001 per byte transferred
const COST_PER_BYTE_STORED: f64 = 0.000_000_001; // $0.000000001 per byte stored

// Implementation stubs for complex components
impl BudgetAnalyzer {
    async fn new() -> DeviceResult<Self> {
        Ok(Self {
            current_budgets: HashMap::new(),
            budget_performance: HashMap::new(),
            variance_analyzer: VarianceAnalyzer::new(),
            forecast_engine: BudgetForecastEngine::new(),
        })
    }

    async fn analyze_budget_performance(&self, budget_id: &str) -> DeviceResult<BudgetPerformance> {
        // Look up the budget; use sensible defaults when the budget is not yet tracked.
        let (allocated, spent) = self
            .current_budgets
            .get(budget_id)
            .map(|b: &Budget| (b.allocated_amount, b.spent_amount))
            .unwrap_or((0.0_f64, 0.0_f64));

        let utilisation_rate = if allocated > 0.0 {
            spent / allocated
        } else {
            0.0
        };

        let variance_from_plan = allocated - spent;
        let efficiency_score = (1.0 - (utilisation_rate - 0.8).max(0.0) * 5.0).clamp(0.0, 1.0);

        // Spending velocity: estimated daily burn rate derived from utilisation.
        // (A full implementation would use time-series data from cost_history.)
        let spending_velocity = if utilisation_rate > 0.0 {
            spent / 30.0 // approximate daily spend over a 30-day period
        } else {
            0.0
        };

        let trend_direction = match utilisation_rate {
            r if r > 1.05 => TrendDirection::Increasing,
            r if r < 0.7 => TrendDirection::Decreasing,
            _ => TrendDirection::Stable,
        };

        let mut performance_metrics = HashMap::new();
        performance_metrics.insert("utilisation_rate".to_string(), utilisation_rate);
        performance_metrics.insert("efficiency_score".to_string(), efficiency_score);
        performance_metrics.insert("variance_from_plan".to_string(), variance_from_plan);

        Ok(BudgetPerformance {
            budget_id: budget_id.to_string(),
            utilization_rate: utilisation_rate,
            spending_velocity,
            variance_from_plan,
            efficiency_score,
            trend_direction,
            performance_metrics,
        })
    }
}

impl VarianceAnalyzer {
    fn new() -> Self {
        Self {
            variance_models: Vec::new(),
            statistical_analyzers: Vec::new(),
            trend_detectors: Vec::new(),
        }
    }
}

impl BudgetForecastEngine {
    fn new() -> Self {
        Self {
            forecast_models: Vec::new(),
            scenario_generators: Vec::new(),
            uncertainty_quantifiers: Vec::new(),
        }
    }
}

impl CostOptimizer {
    async fn new() -> DeviceResult<Self> {
        Ok(Self {
            optimization_strategies: Vec::new(),
            recommendation_engine: RecommendationEngine::new(),
            savings_calculator: SavingsCalculator::new(),
        })
    }

    async fn generate_optimization_recommendations(
        &self,
        cost_analysis: &CostAnalysis,
    ) -> DeviceResult<Vec<OptimizationRecommendation>> {
        let mut recommendations: Vec<OptimizationRecommendation> = Vec::new();

        // Recommendation 1: reduce shot count if cost is shot-dominated.
        let compute_cost = cost_analysis
            .cost_breakdown
            .base_costs
            .get(&CostCategory::Compute)
            .copied()
            .unwrap_or(0.0);

        /// Helper to build a minimal `ImplementationPlan`.
        fn simple_plan(duration_secs: u64) -> ImplementationPlan {
            ImplementationPlan {
                phases: Vec::new(),
                total_duration: Duration::from_secs(duration_secs),
                resource_requirements: Vec::new(),
                dependencies: Vec::new(),
                milestones: Vec::new(),
            }
        }

        /// Helper to build a minimal low-risk `RiskAssessment`.
        fn low_risk_assessment() -> RiskAssessment {
            RiskAssessment {
                overall_risk_score: 0.1,
                risk_factors: Vec::new(),
                mitigation_strategies: Vec::new(),
                contingency_plans: Vec::new(),
            }
        }

        /// Helper to build a simple `ROIAnalysis`.
        fn simple_roi(savings: f64) -> ROIAnalysis {
            ROIAnalysis {
                initial_investment: 0.0,
                annual_savings: savings,
                payback_period: Duration::from_secs(0),
                net_present_value: savings,
                internal_rate_of_return: f64::INFINITY,
                roi_percentage: if savings > 0.0 { 100.0 } else { 0.0 },
            }
        }

        if compute_cost > 0.01 {
            let savings = compute_cost * 0.2;
            recommendations.push(OptimizationRecommendation {
                recommendation_id: uuid::Uuid::new_v4().to_string(),
                recommendation_type: OptimizationType::ServiceTierChange,
                priority: RecommendationPriority::Medium,
                description: "Reduce shot count or use error-mitigation to lower repetitions"
                    .to_string(),
                potential_savings: savings,
                implementation_plan: simple_plan(3600),
                risk_assessment: low_risk_assessment(),
                roi_analysis: simple_roi(savings),
            });
        }

        // Recommendation 2: batch jobs to benefit from volume discounts.
        if cost_analysis.total_costs > 1.0 {
            let savings = cost_analysis.total_costs * 0.05;
            recommendations.push(OptimizationRecommendation {
                recommendation_id: uuid::Uuid::new_v4().to_string(),
                recommendation_type: OptimizationType::SchedulingOptimization,
                priority: RecommendationPriority::Low,
                description: "Aggregate small jobs into batches to qualify for volume discounts"
                    .to_string(),
                potential_savings: savings,
                implementation_plan: simple_plan(86400),
                risk_assessment: low_risk_assessment(),
                roi_analysis: simple_roi(savings),
            });
        }

        Ok(recommendations)
    }
}

impl RecommendationEngine {
    fn new() -> Self {
        Self {
            recommendation_algorithms: Vec::new(),
            scoring_models: Vec::new(),
            prioritization_engine: PrioritizationEngine::new(),
        }
    }
}

impl PrioritizationEngine {
    fn new() -> Self {
        Self {
            prioritization_criteria: Vec::new(),
            weighting_scheme: WeightingScheme {
                scheme_type: WeightingSchemeType::Equal,
                weights: HashMap::new(),
                normalization_method: NormalizationMethod::Sum,
            },
            decision_matrix: DecisionMatrix {
                alternatives: Vec::new(),
                criteria: Vec::new(),
                scores: Vec::new(),
                weights: Vec::new(),
                aggregation_method: AggregationMethod::Sum,
            },
        }
    }
}

impl SavingsCalculator {
    fn new() -> Self {
        Self {
            calculation_methods: Vec::new(),
            validation_rules: Vec::new(),
            adjustment_factors: AdjustmentFactors {
                risk_adjustment: 1.0,
                confidence_adjustment: 1.0,
                market_adjustment: 1.0,
                seasonal_adjustment: 1.0,
                inflation_adjustment: 1.0,
            },
        }
    }
}

impl PricingCache {
    fn new() -> DeviceResult<Self> {
        Ok(Self {
            cache_entries: HashMap::new(),
            cache_statistics: CacheStatistics {
                hit_rate: 0.0,
                miss_rate: 0.0,
                eviction_rate: 0.0,
                average_lookup_time: Duration::from_millis(0),
                total_entries: 0,
            },
            eviction_policy: EvictionPolicy::LRU,
        })
    }

    async fn update_provider_pricing(
        &mut self,
        _provider: CloudProvider,
        _pricing_model: ProviderPricingModel,
    ) -> DeviceResult<()> {
        // Implement pricing cache update
        Ok(())
    }
}

impl CostHistory {
    fn new() -> DeviceResult<Self> {
        Ok(Self {
            spending_records: Vec::new(),
            aggregated_costs: HashMap::new(),
            cost_trends: HashMap::new(),
            historical_analysis: HistoricalAnalysis {
                cost_growth_rate: 0.0,
                seasonal_patterns: Vec::new(),
                cost_volatility: 0.0,
                efficiency_trends: Vec::new(),
                comparative_analysis: ComparativeAnalysis {
                    period_comparisons: Vec::new(),
                    provider_comparisons: Vec::new(),
                    service_comparisons: Vec::new(),
                },
            },
        })
    }
}

impl Default for CostEstimationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            estimation_accuracy_level: EstimationAccuracyLevel::Standard,
            pricing_update_frequency: Duration::from_secs(3600),
            include_hidden_costs: true,
            currency: "USD".to_string(),
            tax_rate: 0.08,
            discount_thresholds: Vec::new(),
            cost_categories: vec![
                CostCategory::Compute,
                CostCategory::Storage,
                CostCategory::Network,
                CostCategory::Management,
            ],
            predictive_modeling: PredictiveModelingConfig {
                enabled: true,
                model_types: vec![
                    PredictiveModelType::TimeSeries,
                    PredictiveModelType::MachineLearning,
                ],
                forecast_horizon: Duration::from_secs(30 * 24 * 3600), // 30 days
                confidence_intervals: true,
                seasonal_adjustments: true,
                trend_analysis: true,
                anomaly_detection: true,
            },
            budget_tracking: BudgetTrackingConfig {
                enabled: true,
                budget_periods: vec![BudgetPeriod::Monthly, BudgetPeriod::Quarterly],
                alert_thresholds: vec![0.5, 0.8, 0.9, 1.0],
                auto_scaling_on_budget: false,
                cost_allocation_tracking: true,
                variance_analysis: true,
            },
        }
    }
}