trustformers-mobile 0.1.1

//! Optimization Engine
//!
//! This module provides intelligent optimization suggestion generation for mobile
//! ML inference workloads using machine learning models and expert system rules.

use anyhow::Result;
use std::collections::{HashMap, VecDeque};
use std::time::Instant;
use tracing::debug;

use super::super::types::*;
use crate::device_info::ThermalState;

/// Intelligent optimization suggestion engine
#[derive(Debug)]
pub struct OptimizationEngine {
    /// Engine configuration
    config: OptimizationEngineConfig,
    /// Generated optimization suggestions
    active_suggestions: HashMap<String, OptimizationSuggestion>,
    /// Suggestion generation rules
    optimization_rules: Vec<OptimizationRule>,
    /// Suggestion ranking system
    suggestion_ranker: SuggestionRanker,
    /// Impact estimation models
    impact_estimator: ImpactEstimator,
    /// Suggestion history and tracking
    suggestion_history: VecDeque<OptimizationEvent>,
    /// Engine performance statistics
    engine_stats: OptimizationEngineStats,
}

/// Optimization rule for suggestion generation
#[derive(Debug, Clone)]
pub struct OptimizationRule {
    /// Rule identifier
    pub id: String,
    /// Human-readable rule name
    pub name: String,
    /// Trigger condition
    pub condition: OptimizationCondition,
    /// Generated suggestion template
    pub suggestion_template: OptimizationSuggestion,
    /// Estimated performance impact
    pub estimated_impact: ImpactLevel,
    /// Implementation difficulty
    pub difficulty: DifficultyLevel,
    /// Rule confidence score
    pub confidence: f32,
    /// Whether the rule is enabled
    pub enabled: bool,
}

/// Conditions that trigger optimization suggestions
#[derive(Debug, Clone)]
pub enum OptimizationCondition {
    /// High memory usage pattern
    HighMemoryUsage {
        threshold_percent: f32,
        pattern: MemoryUsagePattern,
    },
    /// Low cache hit rate
    LowCacheHitRate {
        threshold_percent: f32,
        cache_type: CacheType,
    },
    /// High inference latency
    InferenceLatencyHigh {
        threshold_ms: f32,
        model_type: Option<String>,
    },
    /// Thermal throttling events
    ThermalThrottling {
        frequency: u32,
        severity: ThermalState,
    },
    /// High battery drain
    BatteryDrainHigh {
        threshold_mw: f32,
        context: BatteryContext,
    },
    /// Low network bandwidth utilization
    NetworkBandwidthLow {
        threshold_mbps: f32,
        connection_type: NetworkType,
    },
    /// GPU underutilization
    GPUUnderutilized {
        threshold_percent: f32,
        workload_type: WorkloadType,
    },
    /// CPU inefficiency patterns
    CPUInefficiency {
        pattern: CPUUsagePattern,
        severity: f32,
    },
}

/// Memory usage patterns for optimization
#[derive(Debug, Clone)]
pub enum MemoryUsagePattern {
    /// Steady high usage
    SteadyHigh,
    /// Rapid growth
    RapidGrowth,
    /// Memory leaks
    MemoryLeaks,
    /// Fragmentation
    Fragmentation,
    /// Large allocations
    LargeAllocations,
}

/// Cache types for optimization analysis
#[derive(Debug, Clone)]
pub enum CacheType {
    /// Model cache
    Model,
    /// Tensor cache
    Tensor,
    /// Computation cache
    Computation,
    /// Network cache
    Network,
    /// General purpose cache
    General,
}

/// Battery usage context
#[derive(Debug, Clone)]
pub enum BatteryContext {
    /// During inference
    Inference,
    /// During model loading
    ModelLoading,
    /// During background tasks
    Background,
    /// During network operations
    Network,
    /// General usage
    General,
}

/// Network connection types
#[derive(Debug, Clone)]
pub enum NetworkType {
    /// WiFi connection
    WiFi,
    /// Cellular connection
    Cellular,
    /// Low power Bluetooth
    Bluetooth,
    /// Unknown connection type
    Unknown,
}

/// ML workload types
#[derive(Debug, Clone)]
pub enum WorkloadType {
    /// Computer vision workloads
    ComputerVision,
    /// Natural language processing
    NLP,
    /// Audio processing
    Audio,
    /// General ML inference
    General,
}

/// CPU usage patterns
#[derive(Debug, Clone)]
pub enum CPUUsagePattern {
    /// High single-core usage
    SingleCoreHigh,
    /// Poor multi-core utilization
    PoorMultiCore,
    /// Frequent context switching
    FrequentSwitching,
    /// Thermal throttling induced
    ThermalLimited,
    /// Inefficient algorithms
    InefficientAlgorithms,
}

/// Optimization event for historical tracking
#[derive(Debug, Clone)]
pub struct OptimizationEvent {
    pub timestamp: std::time::Instant,
    pub suggestion_id: String,
    pub event_type: String,
    pub performance_impact: f32,
    pub implementation_status: String,
    pub metadata: std::collections::HashMap<String, String>,
}

/// Optimization engine statistics
#[derive(Debug, Clone, Default)]
pub struct OptimizationEngineStats {
    /// Total suggestions generated
    pub suggestions_generated: u64,
    /// Suggestions accepted by users
    pub suggestions_accepted: u64,
    /// Suggestions that led to improvements
    pub successful_suggestions: u64,
    /// Average improvement achieved
    pub avg_improvement_percent: f32,
    /// Engine accuracy rate
    pub accuracy_rate: f32,
}

/// Suggestion ranking system
#[derive(Debug)]
pub struct SuggestionRanker {
    /// Ranking algorithm
    algorithm: RankingAlgorithm,
}

/// Impact estimation models
#[derive(Debug)]
pub struct ImpactEstimator {
    /// Impact models
    models: Vec<ImpactModel>,
}

/// Internal ranking algorithm
#[derive(Debug)]
struct RankingAlgorithm;

/// Internal impact model
#[derive(Debug)]
struct ImpactModel;

impl OptimizationEngine {
    /// Create a new optimization engine with the given configuration
    pub fn new(config: OptimizationEngineConfig) -> Result<Self> {
        let optimization_rules = Self::initialize_default_rules();

        Ok(Self {
            config,
            active_suggestions: HashMap::new(),
            optimization_rules,
            suggestion_ranker: SuggestionRanker {
                algorithm: RankingAlgorithm,
            },
            impact_estimator: ImpactEstimator { models: Vec::new() },
            suggestion_history: VecDeque::new(),
            engine_stats: OptimizationEngineStats::default(),
        })
    }

    /// Generate optimization suggestions based on current metrics and bottlenecks
    pub fn generate_suggestions(
        &mut self,
        metrics: &MobileMetricsSnapshot,
        bottlenecks: &[PerformanceBottleneck],
    ) -> Result<Vec<OptimizationSuggestion>> {
        let mut suggestions = Vec::new();

        // Generate suggestions from rules
        for rule in &self.optimization_rules {
            if !rule.enabled {
                continue;
            }

            if self.evaluate_optimization_condition(&rule.condition, metrics)? {
                let suggestion = self.create_suggestion_from_rule(rule, metrics)?;
                suggestions.push(suggestion);

                // Track suggestion generation
                self.engine_stats.suggestions_generated += 1;
                debug!("Generated optimization suggestion: {}", rule.name);
            }
        }

        // Generate suggestions based on bottlenecks
        for bottleneck in bottlenecks {
            let bottleneck_suggestions =
                self.generate_bottleneck_suggestions(bottleneck, metrics)?;
            suggestions.extend(bottleneck_suggestions);
        }

        // Rank and prioritize suggestions
        let ranked_suggestions = self.rank_suggestions(suggestions, metrics)?;

        Ok(ranked_suggestions)
    }

    /// Initialize default optimization rules
    fn initialize_default_rules() -> Vec<OptimizationRule> {
        vec![
            OptimizationRule {
                id: "enable_quantization".to_string(),
                name: "Enable Model Quantization".to_string(),
                condition: OptimizationCondition::InferenceLatencyHigh {
                    threshold_ms: 200.0,
                    model_type: None,
                },
                suggestion_template: OptimizationSuggestion {
                    suggestion_type: SuggestionType::ModelOptimization,
                    title: "Enable INT8 Quantization".to_string(),
                    description:
                        "Reduce model size and inference latency by using 8-bit quantization"
                            .to_string(),
                    implementation_steps: vec![
                        "Configure quantization in model settings".to_string(),
                        "Test accuracy impact on validation dataset".to_string(),
                        "Deploy quantized model if accuracy is acceptable".to_string(),
                    ],
                    estimated_improvement: "30% latency reduction".to_string(),
                    difficulty: DifficultyLevel::Medium,
                    priority: PriorityLevel::High,
                },
                estimated_impact: ImpactLevel::High,
                difficulty: DifficultyLevel::Medium,
                confidence: 0.85,
                enabled: true,
            },
            OptimizationRule {
                id: "enable_gpu_acceleration".to_string(),
                name: "Enable GPU Acceleration".to_string(),
                condition: OptimizationCondition::CPUInefficiency {
                    pattern: CPUUsagePattern::SingleCoreHigh,
                    severity: 0.8,
                },
                suggestion_template: OptimizationSuggestion {
                    suggestion_type: SuggestionType::HardwareOptimization,
                    title: "Enable GPU Acceleration".to_string(),
                    description:
                        "Offload computation to GPU for better performance and lower CPU usage"
                            .to_string(),
                    implementation_steps: vec![
                        "Check GPU availability and compatibility".to_string(),
                        "Configure GPU backend in inference settings".to_string(),
                        "Monitor GPU utilization and performance".to_string(),
                    ],
                    estimated_improvement: "40% performance improvement".to_string(),
                    difficulty: DifficultyLevel::Low,
                    priority: PriorityLevel::High,
                },
                estimated_impact: ImpactLevel::High,
                difficulty: DifficultyLevel::Low,
                confidence: 0.9,
                enabled: true,
            },
            OptimizationRule {
                id: "reduce_batch_size".to_string(),
                name: "Reduce Batch Size".to_string(),
                condition: OptimizationCondition::HighMemoryUsage {
                    threshold_percent: 85.0,
                    pattern: MemoryUsagePattern::SteadyHigh,
                },
                suggestion_template: OptimizationSuggestion {
                    suggestion_type: SuggestionType::PerformanceOptimization,
                    title: "Reduce Inference Batch Size".to_string(),
                    description: "Lower memory usage by processing smaller batches of data"
                        .to_string(),
                    implementation_steps: vec![
                        "Identify current batch size configuration".to_string(),
                        "Reduce batch size by 25-50%".to_string(),
                        "Monitor latency and throughput impact".to_string(),
                    ],
                    estimated_improvement: "25% memory reduction".to_string(),
                    difficulty: DifficultyLevel::Low,
                    priority: PriorityLevel::Medium,
                },
                estimated_impact: ImpactLevel::Medium,
                difficulty: DifficultyLevel::Low,
                confidence: 0.95,
                enabled: true,
            },
        ]
    }

    /// Evaluate an optimization condition against current metrics
    fn evaluate_optimization_condition(
        &self,
        condition: &OptimizationCondition,
        metrics: &MobileMetricsSnapshot,
    ) -> Result<bool> {
        match condition {
            OptimizationCondition::HighMemoryUsage {
                threshold_percent, ..
            } => Ok(
                (metrics.memory.heap_used_mb / metrics.memory.heap_total_mb * 100.0)
                    > *threshold_percent,
            ),
            OptimizationCondition::InferenceLatencyHigh { threshold_ms, .. } => {
                Ok(metrics.inference.avg_latency_ms > *threshold_ms as f64)
            },
            OptimizationCondition::LowCacheHitRate {
                threshold_percent, ..
            } => {
                // Simplified: assume we have cache hit rate data
                Ok(50.0 < *threshold_percent) // Placeholder - cache hit rate not available
            },
            OptimizationCondition::CPUInefficiency { severity, .. } => {
                Ok(metrics.cpu.usage_percent > (severity * 100.0))
            },
            _ => Ok(false), // Simplified for other conditions
        }
    }

    /// Create an optimization suggestion from a triggered rule
    fn create_suggestion_from_rule(
        &self,
        rule: &OptimizationRule,
        metrics: &MobileMetricsSnapshot,
    ) -> Result<OptimizationSuggestion> {
        let mut suggestion = rule.suggestion_template.clone();

        // Customize suggestion based on current metrics
        let improvement = self.calculate_estimated_improvement(rule, metrics)?;
        suggestion.estimated_improvement = format!("{}% improvement", improvement);

        Ok(suggestion)
    }

    /// Generate suggestions specifically for detected bottlenecks
    fn generate_bottleneck_suggestions(
        &self,
        bottleneck: &PerformanceBottleneck,
        _metrics: &MobileMetricsSnapshot,
    ) -> Result<Vec<OptimizationSuggestion>> {
        let suggestions = match bottleneck.bottleneck_type {
            BottleneckType::Memory => vec![OptimizationSuggestion {
                suggestion_type: SuggestionType::PerformanceOptimization,
                title: "Memory Optimization".to_string(),
                description: format!("Address {} memory bottleneck", bottleneck.description),
                implementation_steps: vec!["Optimize memory usage".to_string()],
                estimated_improvement: "80% performance recovery".to_string(),
                difficulty: DifficultyLevel::Medium,
                priority: PriorityLevel::High,
            }],
            BottleneckType::CPU => vec![OptimizationSuggestion {
                suggestion_type: SuggestionType::HardwareOptimization,
                title: "CPU Optimization".to_string(),
                description: format!("Address {} CPU bottleneck", bottleneck.description),
                implementation_steps: vec!["Optimize CPU usage".to_string()],
                estimated_improvement: "70% performance recovery".to_string(),
                difficulty: DifficultyLevel::Medium,
                priority: PriorityLevel::High,
            }],
            _ => Vec::new(), // Simplified for other bottleneck types
        };

        Ok(suggestions)
    }

    /// Rank suggestions by priority and relevance
    fn rank_suggestions(
        &self,
        mut suggestions: Vec<OptimizationSuggestion>,
        metrics: &MobileMetricsSnapshot,
    ) -> Result<Vec<OptimizationSuggestion>> {
        // Sort by priority and difficulty
        suggestions.sort_by(|a, b| {
            // Higher priority first, then lower difficulty
            match b.priority.cmp(&a.priority) {
                std::cmp::Ordering::Equal => a.difficulty.cmp(&b.difficulty),
                other => other,
            }
        });

        // Limit to top suggestions
        suggestions.truncate(10);
        Ok(suggestions)
    }

    /// Calculate estimated improvement for a rule in current context
    fn calculate_estimated_improvement(
        &self,
        rule: &OptimizationRule,
        metrics: &MobileMetricsSnapshot,
    ) -> Result<f32> {
        let base_improvement = 30.0; // Default improvement estimate since estimated_improvement is now a String

        // Adjust based on how severe the issue is
        let severity_factor = match &rule.condition {
            OptimizationCondition::HighMemoryUsage {
                threshold_percent, ..
            } => {
                let memory_percent =
                    metrics.memory.heap_used_mb / metrics.memory.heap_total_mb * 100.0;
                (memory_percent - threshold_percent) / threshold_percent
            },
            OptimizationCondition::InferenceLatencyHigh { threshold_ms, .. } => {
                (metrics.inference.avg_latency_ms as f32 - threshold_ms) / threshold_ms
            },
            _ => 1.0,
        };

        Ok(base_improvement * (1.0 + severity_factor * 0.5))
    }

    /// Adjust confidence based on current context
    fn adjust_confidence_for_context(
        &self,
        base_confidence: f32,
        _metrics: &MobileMetricsSnapshot,
    ) -> Result<f32> {
        // Simplified: could factor in device capabilities, model type, etc.
        Ok(base_confidence.min(1.0).max(0.0))
    }

    /// Get engine statistics
    pub fn get_engine_stats(&self) -> &OptimizationEngineStats {
        &self.engine_stats
    }

    /// Get active suggestions
    pub fn get_active_suggestions(&self) -> Vec<OptimizationSuggestion> {
        self.active_suggestions.values().cloned().collect()
    }

    /// Mark a suggestion as implemented
    pub fn mark_suggestion_implemented(
        &mut self,
        suggestion_id: &str,
        performance_gain: f32,
    ) -> Result<()> {
        if let Some(suggestion) = self.active_suggestions.remove(suggestion_id) {
            // Record the optimization event
            let event = OptimizationEvent {
                timestamp: Instant::now(),
                suggestion_id: suggestion_id.to_string(),
                event_type: "implemented".to_string(),
                performance_impact: performance_gain,
                implementation_status: "completed".to_string(),
                metadata: HashMap::new(),
            };

            self.suggestion_history.push_back(event);
            self.engine_stats.suggestions_accepted += 1;

            if performance_gain > 0.0 {
                self.engine_stats.successful_suggestions += 1;
            }

            // Update average improvement
            let total_improvement = self.engine_stats.avg_improvement_percent
                * self.engine_stats.successful_suggestions as f32;
            self.engine_stats.avg_improvement_percent = (total_improvement + performance_gain)
                / self.engine_stats.successful_suggestions as f32;
        }

        Ok(())
    }
}

impl Default for OptimizationEngine {
    fn default() -> Self {
        Self::new(OptimizationEngineConfig::default())
            .expect("Failed to create default optimization engine")
    }
}