//! Smart caching and prefetching systems for tensor operations
//!
//! This module contains intelligent caching systems including smart cache management,
//! predictive prefetching, cache optimization, and access pattern analysis.

use super::*;
use crate::error::{CoreError, CoreResult};

#[cfg(feature = "gpu")]
use std::collections::HashMap;
#[cfg(feature = "gpu")]
use std::time::{Duration, Instant};

#[cfg(feature = "gpu")]
use crate::gpu::{
    auto_tuning::{KernelParameters, PerformanceMetrics},
    tensor_cores::{TensorCoreConfig, TensorOperation},
};

#[cfg(all(feature = "serde", feature = "gpu"))]
#[cfg(feature = "serialization")]
use serde::{Deserialize, Serialize};

/// Smart cache system for optimized configurations
#[allow(dead_code)]
#[derive(Debug)]
pub struct SmartCacheSystem {
    /// Cached configurations
    configuration_cache: HashMap<String, CachedConfiguration>,
    /// Cache analytics
    #[allow(dead_code)]
    cache_analytics: CacheAnalytics,
    /// Eviction policy
    #[allow(dead_code)]
    eviction_policy: EvictionPolicy,
    /// Prefetch engine
    #[allow(dead_code)]
    prefetch_engine: PrefetchEngine,
    /// Cache optimization
    #[allow(dead_code)]
    cache_optimizer: CacheOptimizer,
}

/// Cached configuration entry
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct CachedConfiguration {
    /// Configuration ID
    pub id: String,
    /// Tensor core configuration
    pub tensor_config: TensorCoreConfig,
    /// Kernel parameters
    pub kernel_params: KernelParameters,
    /// Performance metrics
    pub performance: PerformanceMetrics,
    /// Usage statistics
    pub usage_stats: UsageStatistics,
    /// Cache timestamp
    pub cached_at: Instant,
    /// Last access time
    pub last_accessed: Instant,
}

/// Usage statistics for cache entries
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct UsageStatistics {
    /// Access count
    pub access_count: u64,
    /// Hit rate
    pub hit_rate: f64,
    /// Average performance improvement
    pub avg_improvement: f64,
    /// Success rate
    pub success_rate: f64,
}

/// Cache analytics and metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct CacheAnalytics {
    /// Cache hit rate
    pub hit_rate: f64,
    /// Miss rate
    pub miss_rate: f64,
    /// Average lookup time
    pub avg_lookup_time: Duration,
    /// Cache utilization
    pub utilization: f64,
    /// Eviction rate
    pub eviction_rate: f64,
}

/// Cache eviction policies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum EvictionPolicy {
    LRU,  // Least Recently Used
    LFU,  // Least Frequently Used
    FIFO, // First In, First Out
    Random,
    TTL,      // Time To Live
    Adaptive, // AI-driven adaptive eviction
}

/// Predictive prefetch engine
#[allow(dead_code)]
#[derive(Debug)]
pub struct PrefetchEngine {
    /// Prefetch algorithms
    #[allow(dead_code)]
    prefetch_algorithms: Vec<PrefetchAlgorithm>,
    /// Access pattern analyzer
    #[allow(dead_code)]
    pattern_analyzer: AccessPatternAnalyzer,
    /// Prefetch decisions
    #[allow(dead_code)]
    prefetch_decisions: Vec<PrefetchDecision>,
    /// Prefetch effectiveness
    #[allow(dead_code)]
    prefetch_metrics: PrefetchMetrics,
}

/// Prefetch algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum PrefetchAlgorithm {
    SequentialPrefetch,
    StridePrefetch,
    PatternBasedPrefetch,
    MLBasedPrefetch,
    GraphBasedPrefetch,
}

/// Access pattern analyzer
#[allow(dead_code)]
#[derive(Debug)]
pub struct AccessPatternAnalyzer {
    /// Detected patterns
    patterns: Vec<AccessPattern>,
    /// Pattern confidence
    pattern_confidence: HashMap<String, f64>,
    /// Pattern predictions
    pattern_predictions: Vec<PatternPrediction>,
}

/// Access patterns for cache optimization
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AccessPattern {
    Sequential,
    Random,
    Temporal,
    Spatial,
    LoopingPattern,
    Custom(String),
}

/// Pattern prediction
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PatternPrediction {
    /// Pattern type
    pub pattern: AccessPattern,
    /// Predicted next access
    pub next_access: String,
    /// Confidence score
    pub confidence: f64,
    /// Prediction timestamp
    pub timestamp: Instant,
}

/// Prefetch decision
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PrefetchDecision {
    /// Item to prefetch
    pub item_id: String,
    /// Prefetch algorithm used
    pub algorithm: PrefetchAlgorithm,
    /// Decision confidence
    pub confidence: f64,
    /// Decision time
    pub timestamp: Instant,
    /// Success indicator
    pub success: Option<bool>,
}

/// Prefetch effectiveness metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PrefetchMetrics {
    /// Prefetch accuracy
    pub accuracy: f64,
    /// Prefetch hit rate
    pub hit_rate: f64,
    /// Bandwidth saved
    pub bandwidth_saved: f64,
    /// Latency reduction
    pub latency_reduction: Duration,
}

/// Cache optimizer for intelligent cache management
#[allow(dead_code)]
#[derive(Debug)]
pub struct CacheOptimizer {
    /// Optimization strategies
    optimization_strategies: Vec<CacheOptimizationStrategy>,
    /// Cache performance model
    performance_model: CachePerformanceModel,
    /// Optimization history
    optimization_history: Vec<CacheOptimizationDecision>,
}

/// Cache optimization strategies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum CacheOptimizationStrategy {
    SizeOptimization,
    ReplacementOptimization,
    PrefetchOptimization,
    PartitioningOptimization,
    CompressionOptimization,
}

/// Cache performance model
#[allow(dead_code)]
#[derive(Debug)]
pub struct CachePerformanceModel {
    /// Model parameters
    parameters: HashMap<String, f64>,
    /// Performance predictions
    predictions: HashMap<String, f64>,
    /// Model accuracy
    accuracy: f64,
}

/// Cache optimization decision
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct CacheOptimizationDecision {
    /// Optimization type
    pub optimization_type: CacheOptimizationStrategy,
    /// Parameters changed
    pub parameters: HashMap<String, f64>,
    /// Expected improvement
    pub expected_improvement: f64,
    /// Actual improvement
    pub actual_improvement: Option<f64>,
    /// Decision time
    pub timestamp: Instant,
}

// Implementation blocks

impl SmartCacheSystem {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            configuration_cache: HashMap::new(),
            cache_analytics: CacheAnalytics {
                hit_rate: 0.0,
                miss_rate: 1.0,
                avg_lookup_time: Duration::from_micros(10),
                utilization: 0.0,
                eviction_rate: 0.0,
            },
            eviction_policy: EvictionPolicy::Adaptive,
            prefetch_engine: PrefetchEngine::new()?,
            cache_optimizer: CacheOptimizer::new()?,
        })
    }

    pub fn lookup_configuration(
        &mut self,
        operation: &TensorOperation,
    ) -> CoreResult<Option<CachedConfiguration>> {
        let cache_key = self.generate_cache_key(operation)?;

        if let Some(cached_config) = self.configuration_cache.get_mut(&cache_key) {
            // Update access statistics
            cached_config.last_accessed = Instant::now();
            cached_config.usage_stats.access_count += 1;

            // Clone the config before updating analytics to avoid borrow conflicts
            let config_clone = cached_config.clone();

            // Update analytics
            self.update_hit_statistics();

            Ok(Some(config_clone))
        } else {
            // Cache miss
            self.update_miss_statistics();

            // Try to predict what should be prefetched
            self.predict_and_prefetch(operation)?;

            Ok(None)
        }
    }

    pub fn store_configuration(
        &mut self,
        operation: &TensorOperation,
        config: TensorCoreConfig,
        kernel_params: KernelParameters,
        performance: PerformanceMetrics,
    ) -> CoreResult<()> {
        let cache_key = self.generate_cache_key(operation)?;

        let cached_config = CachedConfiguration {
            id: cache_key.clone(),
            tensor_config: config,
            kernel_params,
            performance,
            usage_stats: UsageStatistics {
                access_count: 1,
                hit_rate: 0.0,
                avg_improvement: 0.0,
                success_rate: 1.0,
            },
            cached_at: Instant::now(),
            last_accessed: Instant::now(),
        };

        // Check if cache is full and eviction is needed
        if self.configuration_cache.len() >= self.get_max_cache_size() {
            self.evict_entry()?;
        }

        self.configuration_cache.insert(cache_key, cached_config);
        self.update_cache_analytics();

        Ok(())
    }

    fn generate_cache_key(&self, operation: &TensorOperation) -> CoreResult<String> {
        // Create a unique key based on operation characteristics
        let key = format!(
            "{}_{}_{}_{}_{}",
            operation.dimensions.0, // batch_size equivalent
            operation.dimensions.1, // sequence_length equivalent
            operation.dimensions.2, // hidden_size equivalent
            format!("{:?}", operation.input_type).len() as u8, // dtype equivalent (using debug format length)
            format!("{:?}", operation.op_type).len() as u8 // operation_type equivalent (using debug format length)
        );
        Ok(key)
    }

    fn get_max_cache_size(&self) -> usize {
        1000 // Configurable cache size limit
    }

    fn evict_entry(&mut self) -> CoreResult<()> {
        match self.eviction_policy {
            EvictionPolicy::LRU => self.evict_lru(),
            EvictionPolicy::LFU => self.evict_lfu(),
            EvictionPolicy::FIFO => self.evict_fifo(),
            EvictionPolicy::Random => self.evict_random(),
            EvictionPolicy::TTL => self.evict_ttl(),
            EvictionPolicy::Adaptive => self.evict_adaptive(),
        }
    }

    fn evict_lru(&mut self) -> CoreResult<()> {
        // Find least recently used entry
        let mut oldest_key = None;
        let mut oldest_time = Instant::now();

        for (key, config) in &self.configuration_cache {
            if config.last_accessed < oldest_time {
                oldest_time = config.last_accessed;
                oldest_key = Some(key.clone());
            }
        }

        if let Some(key) = oldest_key {
            self.configuration_cache.remove(&key);
        }

        Ok(())
    }

    fn evict_lfu(&mut self) -> CoreResult<()> {
        // Find least frequently used entry
        let mut lfu_key = None;
        let mut min_access_count = u64::MAX;

        for (key, config) in &self.configuration_cache {
            if config.usage_stats.access_count < min_access_count {
                min_access_count = config.usage_stats.access_count;
                lfu_key = Some(key.clone());
            }
        }

        if let Some(key) = lfu_key {
            self.configuration_cache.remove(&key);
        }

        Ok(())
    }

    fn evict_fifo(&mut self) -> CoreResult<()> {
        // Find first cached entry
        let mut oldest_key = None;
        let mut oldest_time = Instant::now();

        for (key, config) in &self.configuration_cache {
            if config.cached_at < oldest_time {
                oldest_time = config.cached_at;
                oldest_key = Some(key.clone());
            }
        }

        if let Some(key) = oldest_key {
            self.configuration_cache.remove(&key);
        }

        Ok(())
    }

    fn evict_random(&mut self) -> CoreResult<()> {
        // Remove a random entry
        if !self.configuration_cache.is_empty() {
            let keys: Vec<_> = self.configuration_cache.keys().cloned().collect();
            let random_index = rand_index(keys.len());
            if let Some(key) = keys.get(random_index) {
                self.configuration_cache.remove(key);
            }
        }
        Ok(())
    }

    fn evict_ttl(&mut self) -> CoreResult<()> {
        // Remove entries older than TTL
        let ttl = Duration::from_secs(3600); // 1 hour TTL
        let cutoff_time = Instant::now() - ttl;

        let keys_to_remove: Vec<_> = self
            .configuration_cache
            .iter()
            .filter(|(_, config)| config.cached_at < cutoff_time)
            .map(|(key, _)| key.clone())
            .collect();

        for key in keys_to_remove {
            self.configuration_cache.remove(&key);
        }

        // If no expired entries, fall back to LRU
        if self.configuration_cache.len() >= self.get_max_cache_size() {
            self.evict_lru()?;
        }

        Ok(())
    }

    fn evict_adaptive(&mut self) -> CoreResult<()> {
        // AI-driven adaptive eviction based on usage patterns and performance
        let mut eviction_scores: Vec<(String, f64)> = Vec::new();

        for (key, config) in &self.configuration_cache {
            let age_factor = config.cached_at.elapsed().as_secs_f64() / 3600.0; // Hours
            let access_factor = 1.0 / (config.usage_stats.access_count as f64 + 1.0);
            let performance_factor = 1.0 / (config.performance.throughput + 1.0);
            let success_factor = 1.0 - config.usage_stats.success_rate;

            // Combined eviction score (higher = more likely to evict)
            let score = age_factor + access_factor + performance_factor + success_factor;
            eviction_scores.push((key.clone(), score));
        }

        // Sort by eviction score and remove the highest scoring entry
        eviction_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).expect("Operation failed"));

        if let Some((key, _)) = eviction_scores.first() {
            self.configuration_cache.remove(key);
        }

        Ok(())
    }

    fn update_hit_statistics(&mut self) {
        let total_hits = self.cache_analytics.hit_rate * 100.0 + 1.0;
        let total_accesses = total_hits / self.cache_analytics.hit_rate.max(0.01);
        self.cache_analytics.hit_rate = total_hits / (total_accesses + 1.0);
        self.cache_analytics.miss_rate = 1.0 - self.cache_analytics.hit_rate;
    }

    fn update_miss_statistics(&mut self) {
        let total_misses = self.cache_analytics.miss_rate * 100.0 + 1.0;
        let total_accesses = total_misses / self.cache_analytics.miss_rate.max(0.01);
        self.cache_analytics.miss_rate = total_misses / (total_accesses + 1.0);
        self.cache_analytics.hit_rate = 1.0 - self.cache_analytics.miss_rate;
    }

    fn update_cache_analytics(&mut self) {
        self.cache_analytics.utilization =
            self.configuration_cache.len() as f64 / self.get_max_cache_size() as f64;
    }

    fn predict_and_prefetch(&mut self, operation: &TensorOperation) -> CoreResult<()> {
        // Analyze access patterns and predict what to prefetch
        let pattern = self.analyze_access_pattern(operation)?;

        if let Some(prediction) = self.predict_next_access(&pattern)? {
            // Prefetch the predicted configuration
            self.prefetch_configuration(&prediction)?;
        }

        Ok(())
    }

    fn analyze_access_pattern(&self, operation: &TensorOperation) -> CoreResult<AccessPattern> {
        // Simplified pattern analysis - map from actual TensorOperation fields
        if operation.dimensions.0 > 32 {
            // batch_size equivalent
            Ok(AccessPattern::Sequential)
        } else if operation.dimensions.1 > 512 {
            // sequence_length equivalent
            Ok(AccessPattern::Temporal)
        } else {
            Ok(AccessPattern::Random)
        }
    }

    fn predict_next_access(&self, pattern: &AccessPattern) -> CoreResult<Option<String>> {
        // Simplified prediction based on pattern
        match pattern {
            AccessPattern::Sequential => {
                // Predict next sequential access
                Ok(Some("next_sequential_config".to_string()))
            }
            AccessPattern::Temporal => {
                // Predict based on temporal locality
                Ok(Some("temporal_config".to_string()))
            }
            _ => Ok(None),
        }
    }

    fn prefetch_configuration(&mut self, config_id: &str) -> CoreResult<()> {
        // Simplified prefetching - in practice would actually fetch and prepare configuration
        println!("Prefetching configuration: {}", config_id);
        Ok(())
    }

    /// Get cache statistics
    pub fn get_cache_analytics(&self) -> &CacheAnalytics {
        &self.cache_analytics
    }

    /// Clear the cache
    pub fn clear_cache(&mut self) {
        self.configuration_cache.clear();
        self.cache_analytics = CacheAnalytics {
            hit_rate: 0.0,
            miss_rate: 1.0,
            avg_lookup_time: Duration::from_micros(10),
            utilization: 0.0,
            eviction_rate: 0.0,
        };
    }

    /// Get cache size
    pub fn cache_size(&self) -> usize {
        self.configuration_cache.len()
    }

    /// Optimize cache performance
    pub fn optimize_cache(&mut self) -> CoreResult<()> {
        // Analyze cache performance and adjust parameters
        let current_hit_rate = self.cache_analytics.hit_rate;

        if current_hit_rate < 0.5 {
            // Low hit rate - consider changing eviction policy
            self.eviction_policy = EvictionPolicy::Adaptive;
        } else if current_hit_rate > 0.9 {
            // Very high hit rate - might increase cache size if beneficial
            // This would be implemented based on memory constraints
        }

        Ok(())
    }
}

impl PrefetchEngine {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            prefetch_algorithms: vec![PrefetchAlgorithm::MLBasedPrefetch],
            pattern_analyzer: AccessPatternAnalyzer {
                patterns: vec![],
                pattern_confidence: HashMap::new(),
                pattern_predictions: vec![],
            },
            prefetch_decisions: Vec::new(),
            prefetch_metrics: PrefetchMetrics {
                accuracy: 0.8,
                hit_rate: 0.7,
                bandwidth_saved: 0.3,
                latency_reduction: Duration::from_millis(5),
            },
        })
    }

    /// Analyze access patterns and make prefetch decisions
    pub fn analyze_and_prefetch(
        &mut self,
        access_history: &[String],
    ) -> CoreResult<Vec<PrefetchDecision>> {
        let patterns = self.detect_patterns(access_history)?;
        let mut decisions = Vec::new();

        for pattern in patterns {
            if let Some(decision) = self.make_prefetch_decision(&pattern)? {
                decisions.push(decision);
            }
        }

        self.prefetch_decisions.extend(decisions.clone());
        Ok(decisions)
    }

    fn detect_patterns(&mut self, access_history: &[String]) -> CoreResult<Vec<AccessPattern>> {
        let mut detected_patterns = Vec::new();

        // Simple pattern detection
        if access_history.len() > 3 {
            // Check for sequential pattern
            let mut is_sequential = true;
            for i in 1..access_history.len() {
                if !self.is_sequential_access(&access_history[i - 1], &access_history[i]) {
                    is_sequential = false;
                    break;
                }
            }

            if is_sequential {
                detected_patterns.push(AccessPattern::Sequential);
                self.pattern_analyzer
                    .pattern_confidence
                    .insert("sequential".to_string(), 0.9);
            }

            // Check for looping pattern
            if self.has_looping_pattern(access_history) {
                detected_patterns.push(AccessPattern::LoopingPattern);
                self.pattern_analyzer
                    .pattern_confidence
                    .insert("looping".to_string(), 0.8);
            }
        }

        self.pattern_analyzer
            .patterns
            .extend(detected_patterns.clone());
        Ok(detected_patterns)
    }

    fn is_sequential_access(&self, prev: &str, curr: &str) -> bool {
        // Simplified sequential detection based on string similarity/ordering
        curr > prev && curr.len() == prev.len()
    }

    fn has_looping_pattern(&self, access_history: &[String]) -> bool {
        // Check if there's a repeating pattern in access history
        if access_history.len() < 6 {
            return false;
        }

        let half_len = access_history.len() / 2;
        let first_half = &access_history[0..half_len];
        let second_half = &access_history[half_len..2 * half_len];

        first_half == second_half
    }

    fn make_prefetch_decision(
        &self,
        pattern: &AccessPattern,
    ) -> CoreResult<Option<PrefetchDecision>> {
        match pattern {
            AccessPattern::Sequential => Ok(Some(PrefetchDecision {
                item_id: "next_sequential_item".to_string(),
                algorithm: PrefetchAlgorithm::SequentialPrefetch,
                confidence: 0.9,
                timestamp: Instant::now(),
                success: None,
            })),
            AccessPattern::LoopingPattern => Ok(Some(PrefetchDecision {
                item_id: "loop_start_item".to_string(),
                algorithm: PrefetchAlgorithm::PatternBasedPrefetch,
                confidence: 0.8,
                timestamp: Instant::now(),
                success: None,
            })),
            _ => Ok(None),
        }
    }

    /// Update prefetch metrics based on actual results
    pub fn update_prefetch_effectiveness(&mut self, decision_id: &str, was_successful: bool) {
        // Find the decision and update its success status
        for decision in &mut self.prefetch_decisions {
            if decision.item_id == decision_id {
                decision.success = Some(was_successful);
                break;
            }
        }

        // Update overall metrics
        self.recalculate_metrics();
    }

    fn recalculate_metrics(&mut self) {
        let total_decisions = self.prefetch_decisions.len();
        if total_decisions == 0 {
            return;
        }

        let successful_decisions = self
            .prefetch_decisions
            .iter()
            .filter(|d| d.success == Some(true))
            .count();

        self.prefetch_metrics.accuracy = successful_decisions as f64 / total_decisions as f64;
        self.prefetch_metrics.hit_rate = self.prefetch_metrics.accuracy; // Simplified

        // Update bandwidth and latency estimates based on success rate
        self.prefetch_metrics.bandwidth_saved = self.prefetch_metrics.accuracy * 0.4;
        self.prefetch_metrics.latency_reduction =
            Duration::from_millis((self.prefetch_metrics.accuracy * 10.0) as u64);
    }

    /// Get prefetch effectiveness metrics
    pub fn get_prefetch_metrics(&self) -> &PrefetchMetrics {
        &self.prefetch_metrics
    }
}

impl CacheOptimizer {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            optimization_strategies: vec![CacheOptimizationStrategy::SizeOptimization],
            performance_model: CachePerformanceModel {
                parameters: HashMap::new(),
                predictions: HashMap::new(),
                accuracy: 0.85,
            },
            optimization_history: Vec::new(),
        })
    }

    /// Optimize cache performance based on current metrics
    pub fn optimize(
        &mut self,
        cache_analytics: &CacheAnalytics,
    ) -> CoreResult<Vec<CacheOptimizationDecision>> {
        let mut decisions = Vec::new();

        // Analyze current performance and suggest optimizations
        if cache_analytics.hit_rate < 0.5 {
            // Low hit rate - try size optimization
            let decision = self.optimize_cache_size(cache_analytics)?;
            decisions.push(decision);
        }

        if cache_analytics.avg_lookup_time > Duration::from_millis(1) {
            // High lookup time - try partitioning optimization
            let decision = self.optimize_partitioning(cache_analytics)?;
            decisions.push(decision);
        }

        if cache_analytics.utilization > 0.9 {
            // High utilization - try compression optimization
            let decision = self.optimize_compression(cache_analytics)?;
            decisions.push(decision);
        }

        self.optimization_history.extend(decisions.clone());
        Ok(decisions)
    }

    fn optimize_cache_size(
        &self,
        analytics: &CacheAnalytics,
    ) -> CoreResult<CacheOptimizationDecision> {
        let current_size = 1000.0; // Current cache size estimate
        let suggested_size = current_size * (1.0 + (0.5 - analytics.hit_rate));

        let mut parameters = HashMap::new();
        parameters.insert("cache_size".to_string(), suggested_size);

        Ok(CacheOptimizationDecision {
            optimization_type: CacheOptimizationStrategy::SizeOptimization,
            parameters,
            expected_improvement: 0.1, // 10% improvement expected
            actual_improvement: None,
            timestamp: Instant::now(),
        })
    }

    fn optimize_partitioning(
        &self,
        analytics: &CacheAnalytics,
    ) -> CoreResult<CacheOptimizationDecision> {
        let mut parameters = HashMap::new();
        parameters.insert("partition_count".to_string(), 4.0);
        parameters.insert("partition_strategy".to_string(), 1.0); // Strategy enum as number

        Ok(CacheOptimizationDecision {
            optimization_type: CacheOptimizationStrategy::PartitioningOptimization,
            parameters,
            expected_improvement: 0.05, // 5% improvement expected
            actual_improvement: None,
            timestamp: Instant::now(),
        })
    }

    fn optimize_compression(
        &self,
        analytics: &CacheAnalytics,
    ) -> CoreResult<CacheOptimizationDecision> {
        let mut parameters = HashMap::new();
        parameters.insert("compression_ratio".to_string(), 0.7);
        parameters.insert("compression_algorithm".to_string(), 2.0); // LZ4

        Ok(CacheOptimizationDecision {
            optimization_type: CacheOptimizationStrategy::CompressionOptimization,
            parameters,
            expected_improvement: 0.15, // 15% improvement expected (space savings)
            actual_improvement: None,
            timestamp: Instant::now(),
        })
    }

    /// Update optimization results with actual performance
    pub fn update_optimization_result(&mut self, optimization_id: usize, actual_improvement: f64) {
        if let Some(decision) = self.optimization_history.get_mut(optimization_id) {
            decision.actual_improvement = Some(actual_improvement);
        }

        // Update performance model accuracy
        self.update_model_accuracy();
    }

    fn update_model_accuracy(&mut self) {
        let predictions_with_actuals: Vec<_> = self
            .optimization_history
            .iter()
            .filter(|d| d.actual_improvement.is_some())
            .collect();

        if !predictions_with_actuals.is_empty() {
            let total_error: f64 = predictions_with_actuals
                .iter()
                .map(|d| {
                    let actual = d.actual_improvement.expect("Operation failed");
                    let predicted = d.expected_improvement;
                    (actual - predicted).abs()
                })
                .sum();

            let mean_error = total_error / predictions_with_actuals.len() as f64;
            self.performance_model.accuracy = 1.0 - mean_error.min(1.0);
        }
    }
}

impl Default for AccessPatternAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

impl AccessPatternAnalyzer {
    pub fn new() -> Self {
        Self {
            patterns: Vec::new(),
            pattern_confidence: HashMap::new(),
            pattern_predictions: Vec::new(),
        }
    }

    /// Analyze access patterns from historical data
    pub fn analyze_patterns(
        &mut self,
        access_history: &[String],
    ) -> CoreResult<Vec<AccessPattern>> {
        let mut detected_patterns = Vec::new();

        // Spatial locality analysis
        if self.has_spatial_locality(access_history) {
            detected_patterns.push(AccessPattern::Spatial);
            self.pattern_confidence.insert("spatial".to_string(), 0.8);
        }

        // Temporal locality analysis
        if self.has_temporal_locality(access_history) {
            detected_patterns.push(AccessPattern::Temporal);
            self.pattern_confidence.insert("temporal".to_string(), 0.7);
        }

        // Sequential access analysis
        if self.has_sequential_access(access_history) {
            detected_patterns.push(AccessPattern::Sequential);
            self.pattern_confidence
                .insert("sequential".to_string(), 0.9);
        }

        self.patterns.extend(detected_patterns.clone());
        Ok(detected_patterns)
    }

    fn has_spatial_locality(&self, access_history: &[String]) -> bool {
        // Check if accesses are spatially close (simplified based on string similarity)
        if access_history.len() < 3 {
            return false;
        }

        let mut similar_pairs = 0;
        for i in 1..access_history.len() {
            if self.strings_are_similar(&access_history[i - 1], &access_history[i]) {
                similar_pairs += 1;
            }
        }

        similar_pairs as f64 / (access_history.len() - 1) as f64 > 0.6
    }

    fn has_temporal_locality(&self, access_history: &[String]) -> bool {
        // Check for repeated accesses within a time window
        let mut unique_accesses = std::collections::HashSet::new();
        let mut repeated_accesses = 0;

        for access in access_history {
            if unique_accesses.contains(access) {
                repeated_accesses += 1;
            } else {
                unique_accesses.insert(access.clone());
            }
        }

        repeated_accesses as f64 / access_history.len() as f64 > 0.3
    }

    fn has_sequential_access(&self, access_history: &[String]) -> bool {
        // Check if accesses follow a sequential pattern
        if access_history.len() < 3 {
            return false;
        }

        let mut sequential_count = 0;
        for i in 1..access_history.len() {
            if access_history[i] > access_history[i - 1] {
                sequential_count += 1;
            }
        }

        sequential_count as f64 / (access_history.len() - 1) as f64 > 0.8
    }

    fn strings_are_similar(&self, a: &str, b: &str) -> bool {
        // Simple similarity check based on string distance
        if a.len() != b.len() {
            return false;
        }

        let mut differences = 0;
        for (char_a, char_b) in a.chars().zip(b.chars()) {
            if char_a != char_b {
                differences += 1;
            }
        }

        differences <= a.len() / 3 // Allow up to 1/3 of characters to be different
    }

    /// Generate predictions based on detected patterns
    pub fn predict_next_accesses(
        &mut self,
        current_access: &str,
    ) -> CoreResult<Vec<PatternPrediction>> {
        let mut predictions = Vec::new();

        for pattern in &self.patterns {
            if let Some(confidence) = self.pattern_confidence.get(&pattern_to_string(pattern)) {
                let prediction = match pattern {
                    AccessPattern::Sequential => PatternPrediction {
                        pattern: pattern.clone(),
                        next_access: self.predict_sequential_next(current_access),
                        confidence: *confidence,
                        timestamp: Instant::now(),
                    },
                    AccessPattern::Temporal => PatternPrediction {
                        pattern: pattern.clone(),
                        next_access: self.predict_temporal_next(current_access),
                        confidence: *confidence,
                        timestamp: Instant::now(),
                    },
                    _ => {
                        PatternPrediction {
                            pattern: pattern.clone(),
                            next_access: current_access.to_string(),
                            confidence: *confidence * 0.5, // Lower confidence for generic patterns
                            timestamp: Instant::now(),
                        }
                    }
                };
                predictions.push(prediction);
            }
        }

        self.pattern_predictions.extend(predictions.clone());
        Ok(predictions)
    }

    fn predict_sequential_next(&self, current: &str) -> String {
        // Simple sequential prediction - increment the string
        format!("{}_next", current)
    }

    fn predict_temporal_next(&self, current: &str) -> String {
        // Temporal prediction based on historical patterns
        format!("{}_temporal", current)
    }
}

// Helper functions

fn pattern_to_string(pattern: &AccessPattern) -> String {
    match pattern {
        AccessPattern::Sequential => "sequential".to_string(),
        AccessPattern::Random => "random".to_string(),
        AccessPattern::Temporal => "temporal".to_string(),
        AccessPattern::Spatial => "spatial".to_string(),
        AccessPattern::LoopingPattern => "looping".to_string(),
        AccessPattern::Custom(name) => name.clone(),
    }
}

fn rand_index(max: usize) -> usize {
    // Simple pseudo-random index generation
    use std::collections::hash_map::DefaultHasher;
    use std::hash::{Hash, Hasher};
    use std::time::SystemTime;

    let mut hasher = DefaultHasher::new();
    SystemTime::now().hash(&mut hasher);
    let hash = hasher.finish();
    (hash as usize) % max.max(1)
}

// Default implementations

impl Default for UsageStatistics {
    fn default() -> Self {
        Self {
            access_count: 0,
            hit_rate: 0.0,
            avg_improvement: 0.0,
            success_rate: 0.0,
        }
    }
}

impl Default for CacheAnalytics {
    fn default() -> Self {
        Self {
            hit_rate: 0.0,
            miss_rate: 1.0,
            avg_lookup_time: Duration::from_micros(10),
            utilization: 0.0,
            eviction_rate: 0.0,
        }
    }
}

impl Default for PrefetchMetrics {
    fn default() -> Self {
        Self {
            accuracy: 0.0,
            hit_rate: 0.0,
            bandwidth_saved: 0.0,
            latency_reduction: Duration::default(),
        }
    }
}
