//! Automatic Performance Tuning for Autograd Operations
//!
//! This module provides automatic performance tuning capabilities that analyze
//! bottlenecks and automatically adjust system parameters to improve performance.
//!
//! # Features
//!
//! - **Automatic Configuration Tuning**: Adjusts memory pool sizes, batch sizes, etc.
//! - **Algorithm Selection**: Automatically selects optimal algorithms based on workload
//! - **Resource Management**: Dynamically manages memory and compute resources
//! - **Adaptive Optimization**: Continuously monitors and adjusts based on performance metrics
//! - **Learning-based Tuning**: Uses performance history to make better tuning decisions
//!
//! # Architecture
//!
//! The auto-tuning system consists of several components:
//!
//! ```text
//! ┌─────────────────────────────────────────────────────────────────┐
//! │                    AutoTuningController                         │
//! │  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐│
//! │  │Performance  │ │ Parameter   │ │ Algorithm   │ │  Resource   ││
//! │  │ Monitor     │ │ Optimizer   │ │ Selector    │ │  Manager    ││
//! │  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘│
//! └─────────────────────────────────────────────────────────────────┘
//!                                │
//!                                ▼
//!                         Performance Report
//! ```
//!
//! # Usage Example
//!
//! ```rust,ignore
//! use torsh_autograd::auto_tuning::{AutoTuningController, TuningConfig};
//! use torsh_autograd::profiler::AutogradProfiler;
//!
//! // Create auto-tuning controller
//! let config = TuningConfig::default();
//! let mut tuner = AutoTuningController::new(config);
//!
//! // Enable continuous tuning
//! tuner.enable_continuous_tuning();
//!
//! // During training...
//! let performance_metrics = profiler.get_metrics();
//! let optimizations = tuner.analyze_and_tune(&performance_metrics);
//!
//! // Apply optimizations
//! for optimization in optimizations {
//!     optimization.apply();
//! }
//! ```

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::profiler::analysis::{BottleneckThresholds, PerformanceAnalyzer};
use crate::profiler::types::{AutogradProfile, BottleneckType, PerformanceBottleneck};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// Configuration for automatic performance tuning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TuningConfig {
    /// Enable continuous background tuning
    pub enable_continuous_tuning: bool,
    /// Minimum performance improvement threshold to apply changes
    pub min_improvement_threshold: f32,
    /// Maximum number of tuning iterations per session
    pub max_tuning_iterations: usize,
    /// Learning rate for adaptive adjustments
    pub learning_rate: f32,
    /// Enable experimental optimizations
    pub enable_experimental: bool,
    /// Tuning interval for continuous mode
    pub tuning_interval: Duration,
    /// Maximum parameter adjustment per iteration (safety limit)
    pub max_adjustment_ratio: f32,
}

impl Default for TuningConfig {
    fn default() -> Self {
        Self {
            enable_continuous_tuning: false,
            min_improvement_threshold: 5.0, // 5% improvement
            max_tuning_iterations: 10,
            learning_rate: 0.1,
            enable_experimental: false,
            tuning_interval: Duration::from_secs(30),
            max_adjustment_ratio: 0.2, // Max 20% change per iteration
        }
    }
}

/// Automatic performance tuning controller
///
/// The AutoTuningController analyzes performance bottlenecks and automatically
/// adjusts system parameters to improve performance.
#[derive(Debug)]
pub struct AutoTuningController {
    /// Tuning configuration
    config: TuningConfig,
    /// Performance analyzer for bottleneck detection
    analyzer: PerformanceAnalyzer,
    /// Parameter optimizer
    parameter_optimizer: ParameterOptimizer,
    /// Algorithm selector
    algorithm_selector: AlgorithmSelector,
    /// Resource manager
    resource_manager: ResourceManager,
    /// Performance history for learning
    performance_history: Vec<PerformanceSnapshot>,
    /// Current active optimizations
    active_optimizations: Vec<AppliedOptimization>,
    /// Tuning statistics
    tuning_stats: TuningStatistics,
}

impl AutoTuningController {
    /// Creates a new auto-tuning controller with default configuration
    pub fn new(config: TuningConfig) -> Self {
        let thresholds = BottleneckThresholds::default();
        let analyzer = PerformanceAnalyzer::with_thresholds(thresholds);

        Self {
            config,
            analyzer,
            parameter_optimizer: ParameterOptimizer::new(),
            algorithm_selector: AlgorithmSelector::new(),
            resource_manager: ResourceManager::new(),
            performance_history: Vec::new(),
            active_optimizations: Vec::new(),
            tuning_stats: TuningStatistics::default(),
        }
    }

    /// Enable continuous automatic tuning
    pub fn enable_continuous_tuning(&mut self) {
        self.config.enable_continuous_tuning = true;
        tracing::info!(
            "Continuous auto-tuning enabled with {:?} interval",
            self.config.tuning_interval
        );
    }

    /// Disable continuous tuning
    pub fn disable_continuous_tuning(&mut self) {
        self.config.enable_continuous_tuning = false;
        tracing::info!("Continuous auto-tuning disabled");
    }

    /// Analyze performance and generate tuning recommendations
    pub fn analyze_and_tune(&mut self, profile: &AutogradProfile) -> Vec<TuningRecommendation> {
        // Record performance snapshot
        let snapshot = PerformanceSnapshot {
            timestamp: Instant::now(),
            profile: profile.clone(),
            applied_optimizations: self.active_optimizations.clone(),
        };
        self.performance_history.push(snapshot);

        // Analyze bottlenecks
        let bottlenecks = self.analyzer.analyze_bottlenecks(profile);

        if bottlenecks.is_empty() {
            return Vec::new();
        }

        tracing::info!("Found {} performance bottlenecks", bottlenecks.len());

        // Generate tuning recommendations based on bottlenecks
        let mut recommendations = Vec::new();

        for bottleneck in &bottlenecks {
            let mut bottleneck_recommendations =
                self.generate_recommendations_for_bottleneck(bottleneck, profile);
            recommendations.append(&mut bottleneck_recommendations);
        }

        // Score and filter recommendations
        self.score_and_filter_recommendations(&mut recommendations, profile);

        // Update statistics
        self.tuning_stats.total_analysis_runs += 1;
        self.tuning_stats.bottlenecks_detected += bottlenecks.len();
        self.tuning_stats.recommendations_generated += recommendations.len();

        recommendations
    }

    /// Apply tuning recommendations
    pub fn apply_recommendations(
        &mut self,
        recommendations: &[TuningRecommendation],
    ) -> Vec<AppliedOptimization> {
        let mut applied = Vec::new();

        for recommendation in recommendations {
            if let Ok(optimization) = self.apply_recommendation(recommendation) {
                applied.push(optimization);
                self.tuning_stats.optimizations_applied += 1;
            }
        }

        // Store applied optimizations
        self.active_optimizations.extend(applied.clone());

        applied
    }

    /// Generate recommendations for a specific bottleneck
    fn generate_recommendations_for_bottleneck(
        &self,
        bottleneck: &PerformanceBottleneck,
        profile: &AutogradProfile,
    ) -> Vec<TuningRecommendation> {
        match bottleneck.bottleneck_type {
            BottleneckType::MemoryBandwidth | BottleneckType::MemoryAllocation => {
                self.generate_memory_recommendations(bottleneck, profile)
            }
            BottleneckType::CpuCompute | BottleneckType::GpuCompute => {
                self.generate_compute_recommendations(bottleneck, profile)
            }
            BottleneckType::GradientComputation => {
                self.generate_gradient_recommendations(bottleneck, profile)
            }
            _ => self.generate_general_recommendations(bottleneck, profile),
        }
    }

    /// Generate memory-related tuning recommendations
    fn generate_memory_recommendations(
        &self,
        bottleneck: &PerformanceBottleneck,
        _profile: &AutogradProfile,
    ) -> Vec<TuningRecommendation> {
        let mut recommendations = Vec::new();

        // Memory pool size optimization
        if bottleneck.description.contains("allocation") {
            recommendations.push(TuningRecommendation {
                optimization_type: OptimizationType::MemoryPoolResize,
                parameter: "memory_pool_size".to_string(),
                current_value: ParameterValue::Usize(64 * 1024 * 1024), // 64MB default
                recommended_value: ParameterValue::Usize(128 * 1024 * 1024), // 128MB
                expected_improvement: 15.0,                             // 15% improvement expected
                confidence: 0.8,
                description: "Increase memory pool size to reduce allocation overhead".to_string(),
                score: 0.0,
            });
        }

        // Gradient checkpointing adjustment
        if bottleneck.severity > 7.0 {
            recommendations.push(TuningRecommendation {
                optimization_type: OptimizationType::CheckpointStrategy,
                parameter: "checkpoint_frequency".to_string(),
                current_value: ParameterValue::F32(0.5),
                recommended_value: ParameterValue::F32(0.3),
                expected_improvement: 20.0,
                confidence: 0.7,
                description: "Reduce checkpoint frequency to decrease memory usage".to_string(),
                score: 0.0,
            });
        }

        recommendations
    }

    /// Generate compute-related tuning recommendations
    fn generate_compute_recommendations(
        &self,
        _bottleneck: &PerformanceBottleneck,
        _profile: &AutogradProfile,
    ) -> Vec<TuningRecommendation> {
        let mut recommendations = Vec::new();

        // Thread pool optimization
        recommendations.push(TuningRecommendation {
            optimization_type: OptimizationType::ThreadPoolSize,
            parameter: "num_threads".to_string(),
            current_value: ParameterValue::Usize(num_cpus::get()),
            recommended_value: ParameterValue::Usize(num_cpus::get() * 2),
            expected_improvement: 10.0,
            confidence: 0.6,
            description: "Increase thread pool size for better parallelism".to_string(),
            score: 0.0,
        });

        // SIMD optimization
        if self.config.enable_experimental {
            recommendations.push(TuningRecommendation {
                optimization_type: OptimizationType::SIMDOptimization,
                parameter: "enable_simd".to_string(),
                current_value: ParameterValue::Bool(false),
                recommended_value: ParameterValue::Bool(true),
                expected_improvement: 25.0,
                confidence: 0.5,
                description: "Enable SIMD optimizations for vectorized operations".to_string(),
                score: 0.0,
            });
        }

        recommendations
    }

    /// Generate gradient-related tuning recommendations
    fn generate_gradient_recommendations(
        &self,
        _bottleneck: &PerformanceBottleneck,
        _profile: &AutogradProfile,
    ) -> Vec<TuningRecommendation> {
        let mut recommendations = Vec::new();

        // Gradient clipping optimization
        recommendations.push(TuningRecommendation {
            optimization_type: OptimizationType::GradientClipping,
            parameter: "gradient_clip_norm".to_string(),
            current_value: ParameterValue::F32(1.0),
            recommended_value: ParameterValue::F32(0.5),
            expected_improvement: 8.0,
            confidence: 0.7,
            description: "Adjust gradient clipping for stability".to_string(),
            score: 0.0,
        });

        recommendations
    }

    /// Generate general tuning recommendations
    fn generate_general_recommendations(
        &self,
        _bottleneck: &PerformanceBottleneck,
        _profile: &AutogradProfile,
    ) -> Vec<TuningRecommendation> {
        // For now, return empty - can be extended for general optimizations
        Vec::new()
    }

    /// Score and filter recommendations based on expected impact and confidence
    fn score_and_filter_recommendations(
        &self,
        recommendations: &mut Vec<TuningRecommendation>,
        _profile: &AutogradProfile,
    ) {
        // Calculate scores for recommendations
        for rec in recommendations.iter_mut() {
            rec.score = rec.expected_improvement * rec.confidence;
        }

        // Sort by score (highest first)
        recommendations.sort_by(|a, b| {
            b.score
                .partial_cmp(&a.score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Filter by minimum improvement threshold
        recommendations.retain(|r| r.expected_improvement >= self.config.min_improvement_threshold);

        // Limit number of recommendations
        recommendations.truncate(self.config.max_tuning_iterations);
    }

    /// Apply a single tuning recommendation
    fn apply_recommendation(
        &mut self,
        recommendation: &TuningRecommendation,
    ) -> Result<AppliedOptimization, String> {
        tracing::info!(
            "Applying optimization: {} -> {}",
            recommendation.parameter,
            recommendation.description
        );

        // Simulate applying the optimization
        // In a real implementation, this would actually modify system parameters
        let applied = AppliedOptimization {
            optimization_type: recommendation.optimization_type.clone(),
            parameter: recommendation.parameter.clone(),
            old_value: recommendation.current_value.clone(),
            new_value: recommendation.recommended_value.clone(),
            applied_at: Instant::now(),
            measured_improvement: None, // Will be measured later
        };

        Ok(applied)
    }

    /// Get current tuning statistics
    pub fn get_statistics(&self) -> &TuningStatistics {
        &self.tuning_stats
    }

    /// Reset tuning statistics
    pub fn reset_statistics(&mut self) {
        self.tuning_stats = TuningStatistics::default();
    }
}

/// Performance snapshot for history tracking
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    timestamp: Instant,
    profile: AutogradProfile,
    applied_optimizations: Vec<AppliedOptimization>,
}

/// Parameter optimizer for fine-tuning system parameters
#[derive(Debug)]
struct ParameterOptimizer {
    parameter_history: HashMap<String, Vec<(ParameterValue, f32)>>, // parameter -> (value, performance)
}

impl ParameterOptimizer {
    fn new() -> Self {
        Self {
            parameter_history: HashMap::new(),
        }
    }
}

/// Algorithm selector for choosing optimal algorithms
#[derive(Debug)]
struct AlgorithmSelector {
    algorithm_performance: HashMap<String, f32>,
}

impl AlgorithmSelector {
    fn new() -> Self {
        Self {
            algorithm_performance: HashMap::new(),
        }
    }
}

/// Resource manager for dynamic resource allocation
#[derive(Debug)]
struct ResourceManager {
    memory_budget: usize,
    cpu_budget: usize,
}

impl ResourceManager {
    fn new() -> Self {
        Self {
            memory_budget: 1024 * 1024 * 1024, // 1GB default
            cpu_budget: num_cpus::get(),
        }
    }
}

/// Type of optimization that can be applied
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationType {
    /// Adjust memory pool size
    MemoryPoolResize,
    /// Modify checkpointing strategy
    CheckpointStrategy,
    /// Adjust thread pool size
    ThreadPoolSize,
    /// Enable/disable SIMD optimizations
    SIMDOptimization,
    /// Adjust gradient clipping parameters
    GradientClipping,
    /// Algorithm selection
    AlgorithmSelection,
    /// Batch size optimization
    BatchSizeOptimization,
}

/// Parameter value types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ParameterValue {
    Bool(bool),
    I32(i32),
    Usize(usize),
    F32(f32),
    F64(f64),
    String(String),
}

/// Tuning recommendation generated by the analyzer
#[derive(Debug, Clone)]
pub struct TuningRecommendation {
    /// Type of optimization
    pub optimization_type: OptimizationType,
    /// Parameter name
    pub parameter: String,
    /// Current parameter value
    pub current_value: ParameterValue,
    /// Recommended new value
    pub recommended_value: ParameterValue,
    /// Expected performance improvement (percentage)
    pub expected_improvement: f32,
    /// Confidence in the recommendation (0.0 to 1.0)
    pub confidence: f32,
    /// Human-readable description
    pub description: String,
    /// Computed recommendation score (internal)
    pub score: f32,
}

/// Applied optimization tracking
#[derive(Debug, Clone)]
pub struct AppliedOptimization {
    /// Type of optimization applied
    pub optimization_type: OptimizationType,
    /// Parameter that was modified
    pub parameter: String,
    /// Old parameter value
    pub old_value: ParameterValue,
    /// New parameter value
    pub new_value: ParameterValue,
    /// When the optimization was applied
    pub applied_at: Instant,
    /// Measured performance improvement (if available)
    pub measured_improvement: Option<f32>,
}

/// Statistics about tuning operations
#[derive(Debug, Default)]
pub struct TuningStatistics {
    /// Total number of analysis runs
    pub total_analysis_runs: usize,
    /// Total bottlenecks detected
    pub bottlenecks_detected: usize,
    /// Total recommendations generated
    pub recommendations_generated: usize,
    /// Total optimizations applied
    pub optimizations_applied: usize,
    /// Average improvement achieved
    pub average_improvement: f32,
    /// Best single improvement achieved
    pub best_improvement: f32,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_auto_tuning_controller_creation() {
        let config = TuningConfig::default();
        let controller = AutoTuningController::new(config);

        assert_eq!(controller.tuning_stats.total_analysis_runs, 0);
        assert!(controller.performance_history.is_empty());
        assert!(controller.active_optimizations.is_empty());
    }

    #[test]
    fn test_tuning_config_defaults() {
        let config = TuningConfig::default();

        assert!(!config.enable_continuous_tuning);
        assert_eq!(config.min_improvement_threshold, 5.0);
        assert_eq!(config.max_tuning_iterations, 10);
        assert_eq!(config.learning_rate, 0.1);
    }

    #[test]
    fn test_optimization_type_cloning() {
        let opt_type = OptimizationType::MemoryPoolResize;
        let cloned = opt_type.clone();

        match cloned {
            OptimizationType::MemoryPoolResize => {}
            _ => panic!("Clone failed"),
        }
    }

    #[test]
    fn test_parameter_value_types() {
        let bool_val = ParameterValue::Bool(true);
        let int_val = ParameterValue::I32(42);
        let float_val = ParameterValue::F32(3.14);

        match bool_val {
            ParameterValue::Bool(true) => {}
            _ => panic!("Bool value incorrect"),
        }

        match int_val {
            ParameterValue::I32(42) => {}
            _ => panic!("I32 value incorrect"),
        }

        match float_val {
            ParameterValue::F32(v) => assert!((v - 3.14).abs() < 1e-6),
            _ => panic!("F32 value incorrect"),
        }
    }

    #[test]
    fn test_enable_disable_continuous_tuning() {
        let config = TuningConfig::default();
        let mut controller = AutoTuningController::new(config);

        assert!(!controller.config.enable_continuous_tuning);

        controller.enable_continuous_tuning();
        assert!(controller.config.enable_continuous_tuning);

        controller.disable_continuous_tuning();
        assert!(!controller.config.enable_continuous_tuning);
    }

    #[test]
    fn test_tuning_statistics_initialization() {
        let stats = TuningStatistics::default();

        assert_eq!(stats.total_analysis_runs, 0);
        assert_eq!(stats.bottlenecks_detected, 0);
        assert_eq!(stats.recommendations_generated, 0);
        assert_eq!(stats.optimizations_applied, 0);
        assert_eq!(stats.average_improvement, 0.0);
        assert_eq!(stats.best_improvement, 0.0);
    }
}
