trustformers-mobile 0.1.1

//! Bottleneck Detection Engine
//!
//! This module provides comprehensive performance bottleneck detection for mobile
//! ML inference workloads. It uses configurable rules, statistical analysis, and
//! machine learning models to identify performance issues.

use anyhow::{Context, Result};
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};
use tracing::{debug, error, info, warn};

use super::super::types::*;
use crate::device_info::{MobileDeviceDetector, MobileDeviceInfo, ThermalState};

/// Comprehensive bottleneck detection engine
#[derive(Debug)]
pub struct BottleneckDetector {
    /// Detection configuration
    config: BottleneckDetectionConfig,
    /// Currently detected bottlenecks
    active_bottlenecks: HashMap<String, PerformanceBottleneck>,
    /// Historical bottleneck data
    bottleneck_history: VecDeque<BottleneckDetectionEvent>,
    /// Detection rules and thresholds
    detection_rules: Vec<BottleneckRule>,
    /// Severity calculation engine
    severity_calculator: SeverityCalculator,
    /// Historical analysis for trend detection
    historical_analyzer: HistoricalAnalyzer,
    /// Detection statistics
    detection_stats: BottleneckDetectionStats,
}

/// Bottleneck detection rule
#[derive(Debug, Clone)]
pub struct BottleneckRule {
    /// Rule identifier
    pub id: String,
    /// Human-readable rule name
    pub name: String,
    /// Detection condition
    pub condition: BottleneckCondition,
    /// Severity level when triggered
    pub severity: BottleneckSeverity,
    /// Suggested remediation
    pub suggestion: String,
    /// Rule confidence level
    pub confidence: f32,
    /// Whether the rule is enabled
    pub enabled: bool,
}

/// Detection conditions for bottlenecks
#[derive(Debug, Clone)]
pub enum BottleneckCondition {
    /// Memory usage exceeds threshold
    MemoryUsageHigh {
        threshold_percent: f32,
        duration_ms: u64,
    },
    /// CPU usage exceeds threshold
    CPUUsageHigh {
        threshold_percent: f32,
        duration_ms: u64,
    },
    /// GPU usage exceeds threshold
    GPUUsageHigh {
        threshold_percent: f32,
        duration_ms: u64,
    },
    /// Inference latency exceeds threshold
    LatencyHigh {
        threshold_ms: f32,
        sample_count: u32,
    },
    /// Thermal throttling detected
    ThermalThrottling { severity: ThermalState },
    /// Battery drain rate exceeds threshold
    BatteryDrainHigh { threshold_mw: f32, duration_ms: u64 },
    /// Network latency exceeds threshold
    NetworkLatencyHigh {
        threshold_ms: f32,
        sample_count: u32,
    },
    /// Cache hit rate below threshold
    CacheHitRateLow {
        threshold_percent: f32,
        sample_count: u32,
    },
    /// Memory pressure detected
    MemoryPressure { pressure_level: u8 },
    /// Custom condition with user-defined logic
    Custom { name: String, evaluator: String },
}

/// Bottleneck detection event for historical tracking
#[derive(Debug, Clone)]
pub struct BottleneckDetectionEvent {
    pub timestamp: std::time::Instant,
    pub bottleneck_type: BottleneckType,
    pub severity: BottleneckSeverity,
    pub detected_value: f32,
    pub threshold_value: f32,
    pub duration_ms: u64,
    pub rule_id: String,
    pub metadata: std::collections::HashMap<String, String>,
}

/// Bottleneck detection statistics
#[derive(Debug, Clone, Default)]
pub struct BottleneckDetectionStats {
    /// Total detections made
    pub total_detections: u64,
    /// True positive detections
    pub true_positives: u64,
    /// False positive detections
    pub false_positives: u64,
    /// Average detection latency
    pub avg_detection_latency_ms: f32,
    /// Detection accuracy rate
    pub accuracy_rate: f32,
}

/// Severity calculation engine
#[derive(Debug)]
pub struct SeverityCalculator {
    /// Severity calculation rules
    rules: Vec<SeverityRule>,
}

/// Historical analysis for trend detection
#[derive(Debug)]
pub struct HistoricalAnalyzer {
    /// Historical data window size
    window_size: usize,
    /// Trend detection model
    trend_detector: TrendDetector,
    /// Statistical analysis model
    statistical_model: StatisticalModel,
}

/// Internal severity rule
#[derive(Debug)]
struct SeverityRule;

/// Internal trend detector
#[derive(Debug)]
struct TrendDetector;

/// Internal statistical model
#[derive(Debug)]
struct StatisticalModel;

impl BottleneckDetector {
    /// Create a new bottleneck detector with the given configuration
    pub fn new(config: BottleneckDetectionConfig) -> Result<Self> {
        let detection_rules = Self::initialize_default_rules();

        Ok(Self {
            config,
            active_bottlenecks: HashMap::new(),
            bottleneck_history: VecDeque::new(),
            detection_rules,
            severity_calculator: SeverityCalculator { rules: Vec::new() },
            historical_analyzer: HistoricalAnalyzer {
                window_size: 100,
                trend_detector: TrendDetector,
                statistical_model: StatisticalModel,
            },
            detection_stats: BottleneckDetectionStats::default(),
        })
    }

    /// Detect bottlenecks in the current metrics snapshot
    pub fn detect_bottlenecks(&mut self, metrics: &MobileMetricsSnapshot) -> Result<Vec<PerformanceBottleneck>> {
        let mut detected_bottlenecks = Vec::new();

        for rule in &self.detection_rules {
            if !rule.enabled {
                continue;
            }

            if self.evaluate_rule(rule, metrics)? {
                let bottleneck = self.create_bottleneck_from_rule(rule, metrics)?;
                detected_bottlenecks.push(bottleneck.clone());

                // Track active bottlenecks
                self.active_bottlenecks.insert(rule.id.clone(), bottleneck);

                // Record detection event
                let event = BottleneckDetectionEvent {
                    timestamp: Instant::now(),
                    bottleneck_type: self.rule_to_bottleneck_type(rule),
                    severity: rule.severity.clone(),
                    detected_value: self.extract_metric_value(rule, metrics)?,
                    threshold_value: self.extract_threshold_value(rule)?,
                    duration_ms: 0, // Will be updated by continuous monitoring
                    rule_id: rule.id.clone(),
                    metadata: HashMap::new(),
                };

                self.bottleneck_history.push_back(event);
                self.detection_stats.total_detections += 1;

                debug!("Bottleneck detected: {} ({})", rule.name, rule.id);
            }
        }

        Ok(detected_bottlenecks)
    }

    /// Initialize default detection rules
    fn initialize_default_rules() -> Vec<BottleneckRule> {
        vec![
            BottleneckRule {
                id: "memory_usage_high".to_string(),
                name: "High Memory Usage".to_string(),
                condition: BottleneckCondition::MemoryUsageHigh {
                    threshold_percent: 85.0,
                    duration_ms: 5000,
                },
                severity: BottleneckSeverity::High,
                suggestion: "Consider reducing batch size or enabling memory optimization".to_string(),
                confidence: 0.9,
                enabled: true,
            },
            BottleneckRule {
                id: "cpu_usage_high".to_string(),
                name: "High CPU Usage".to_string(),
                condition: BottleneckCondition::CPUUsageHigh {
                    threshold_percent: 90.0,
                    duration_ms: 3000,
                },
                severity: BottleneckSeverity::High,
                suggestion: "Consider optimizing model operations or reducing thread count".to_string(),
                confidence: 0.85,
                enabled: true,
            },
            BottleneckRule {
                id: "inference_latency_high".to_string(),
                name: "High Inference Latency".to_string(),
                condition: BottleneckCondition::LatencyHigh {
                    threshold_ms: 500.0,
                    sample_count: 10,
                },
                severity: BottleneckSeverity::Medium,
                suggestion: "Consider enabling quantization or hardware acceleration".to_string(),
                confidence: 0.8,
                enabled: true,
            },
            BottleneckRule {
                id: "thermal_throttling".to_string(),
                name: "Thermal Throttling".to_string(),
                condition: BottleneckCondition::ThermalThrottling {
                    severity: ThermalState::Hot,
                },
                severity: BottleneckSeverity::Critical,
                suggestion: "Reduce inference frequency or implement thermal management".to_string(),
                confidence: 0.95,
                enabled: true,
            },
        ]
    }

    /// Evaluate a detection rule against current metrics
    fn evaluate_rule(&self, rule: &BottleneckRule, metrics: &MobileMetricsSnapshot) -> Result<bool> {
        match &rule.condition {
            BottleneckCondition::MemoryUsageHigh { threshold_percent, .. } => {
                Ok(metrics.memory_usage_percent > *threshold_percent)
            }
            BottleneckCondition::CPUUsageHigh { threshold_percent, .. } => {
                Ok(metrics.cpu_usage_percent > *threshold_percent)
            }
            BottleneckCondition::LatencyHigh { threshold_ms, .. } => {
                Ok(metrics.inference_latency_ms > *threshold_ms)
            }
            BottleneckCondition::ThermalThrottling { severity } => {
                Ok(metrics.thermal_state >= *severity)
            }
            _ => Ok(false), // Simplified for other conditions
        }
    }

    /// Create a performance bottleneck from a triggered rule
    fn create_bottleneck_from_rule(
        &self,
        rule: &BottleneckRule,
        metrics: &MobileMetricsSnapshot,
    ) -> Result<PerformanceBottleneck> {
        Ok(PerformanceBottleneck {
            bottleneck_type: self.rule_to_bottleneck_type(rule),
            severity: rule.severity.clone(),
            description: rule.name.clone(),
            impact_score: self.calculate_impact_score(rule, metrics)?,
            suggestions: vec![rule.suggestion.clone()],
            detected_at: Instant::now(),
            confidence: rule.confidence,
            affected_components: self.identify_affected_components(rule, metrics),
            estimated_performance_loss: self.estimate_performance_loss(rule, metrics)?,
            resolution_priority: self.calculate_resolution_priority(rule)?,
        })
    }

    /// Convert rule to bottleneck type
    fn rule_to_bottleneck_type(&self, rule: &BottleneckRule) -> BottleneckType {
        match &rule.condition {
            BottleneckCondition::MemoryUsageHigh { .. } => BottleneckType::Memory,
            BottleneckCondition::CPUUsageHigh { .. } => BottleneckType::CPU,
            BottleneckCondition::GPUUsageHigh { .. } => BottleneckType::GPU,
            BottleneckCondition::LatencyHigh { .. } => BottleneckType::Latency,
            BottleneckCondition::ThermalThrottling { .. } => BottleneckType::Thermal,
            BottleneckCondition::BatteryDrainHigh { .. } => BottleneckType::Battery,
            BottleneckCondition::NetworkLatencyHigh { .. } => BottleneckType::Network,
            _ => BottleneckType::Other,
        }
    }

    /// Extract metric value for the rule condition
    fn extract_metric_value(&self, rule: &BottleneckRule, metrics: &MobileMetricsSnapshot) -> Result<f32> {
        match &rule.condition {
            BottleneckCondition::MemoryUsageHigh { .. } => Ok(metrics.memory_usage_percent),
            BottleneckCondition::CPUUsageHigh { .. } => Ok(metrics.cpu_usage_percent),
            BottleneckCondition::LatencyHigh { .. } => Ok(metrics.inference_latency_ms),
            _ => Ok(0.0),
        }
    }

    /// Extract threshold value for the rule condition
    fn extract_threshold_value(&self, rule: &BottleneckRule) -> Result<f32> {
        match &rule.condition {
            BottleneckCondition::MemoryUsageHigh { threshold_percent, .. } => Ok(*threshold_percent),
            BottleneckCondition::CPUUsageHigh { threshold_percent, .. } => Ok(*threshold_percent),
            BottleneckCondition::LatencyHigh { threshold_ms, .. } => Ok(*threshold_ms),
            _ => Ok(0.0),
        }
    }

    /// Calculate impact score for the bottleneck
    fn calculate_impact_score(&self, rule: &BottleneckRule, metrics: &MobileMetricsSnapshot) -> Result<f32> {
        // Simplified impact calculation based on severity and deviation from threshold
        let base_score = match rule.severity {
            BottleneckSeverity::Low => 0.3,
            BottleneckSeverity::Medium => 0.6,
            BottleneckSeverity::High => 0.8,
            BottleneckSeverity::Critical => 1.0,
        };

        // Adjust based on how much the metric exceeds the threshold
        let deviation_factor = self.calculate_deviation_factor(rule, metrics)?;
        Ok(base_score * (1.0 + deviation_factor * 0.5))
    }

    /// Calculate deviation factor from threshold
    fn calculate_deviation_factor(&self, rule: &BottleneckRule, metrics: &MobileMetricsSnapshot) -> Result<f32> {
        let current_value = self.extract_metric_value(rule, metrics)?;
        let threshold = self.extract_threshold_value(rule)?;

        if threshold > 0.0 {
            Ok((current_value - threshold) / threshold)
        } else {
            Ok(0.0)
        }
    }

    /// Identify affected components for the bottleneck
    fn identify_affected_components(&self, rule: &BottleneckRule, _metrics: &MobileMetricsSnapshot) -> Vec<String> {
        match &rule.condition {
            BottleneckCondition::MemoryUsageHigh { .. } => vec!["Memory Manager", "Model Storage"],
            BottleneckCondition::CPUUsageHigh { .. } => vec!["CPU Scheduler", "Inference Engine"],
            BottleneckCondition::LatencyHigh { .. } => vec!["Inference Pipeline", "Model Executor"],
            BottleneckCondition::ThermalThrottling { .. } => vec!["Thermal Manager", "CPU/GPU"],
            _ => vec!["Unknown"],
        }.into_iter().map(|s| s.to_string()).collect()
    }

    /// Estimate performance loss percentage
    fn estimate_performance_loss(&self, rule: &BottleneckRule, metrics: &MobileMetricsSnapshot) -> Result<f32> {
        let deviation_factor = self.calculate_deviation_factor(rule, metrics)?;
        let base_loss = match rule.severity {
            BottleneckSeverity::Low => 5.0,
            BottleneckSeverity::Medium => 15.0,
            BottleneckSeverity::High => 30.0,
            BottleneckSeverity::Critical => 50.0,
        };

        Ok((base_loss * (1.0 + deviation_factor)).min(90.0))
    }

    /// Calculate resolution priority
    fn calculate_resolution_priority(&self, rule: &BottleneckRule) -> Result<u8> {
        let priority = match rule.severity {
            BottleneckSeverity::Critical => 1,
            BottleneckSeverity::High => 2,
            BottleneckSeverity::Medium => 3,
            BottleneckSeverity::Low => 4,
        };
        Ok(priority)
    }

    /// Get detection statistics
    pub fn get_detection_stats(&self) -> &BottleneckDetectionStats {
        &self.detection_stats
    }

    /// Clear active bottlenecks
    pub fn clear_active_bottlenecks(&mut self) {
        self.active_bottlenecks.clear();
    }

    /// Get active bottlenecks
    pub fn get_active_bottlenecks(&self) -> Vec<PerformanceBottleneck> {
        self.active_bottlenecks.values().cloned().collect()
    }
}

impl Default for BottleneckDetector {
    fn default() -> Self {
        Self::new(BottleneckDetectionConfig::default())
            .expect("Failed to create default bottleneck detector")
    }
}