//! Performance analysis, benchmarking, and auto-tuning for quantization operations.
//!
//! This module provides comprehensive performance analysis tools for quantization operations,
//! including benchmarking suites, profiling utilities, and auto-tuning systems to optimize
//! quantization parameters for different hardware configurations.
//!
//! # Key Components
//!
//! ## Performance Benchmarking
//! - [`QuantizationBenchmarkSuite`] - Comprehensive benchmarking for quantization operations
//! - [`BenchmarkMetrics`] - Performance metrics collection and analysis
//! - [`BenchmarkConfiguration`] - Configurable benchmark parameters
//!
//! ## Profiling and Analysis
//! - [`QuantizationProfiler`] - Detailed profiling of quantization operations
//! - [`ProfileReport`] - Comprehensive profiling results with optimization suggestions
//! - [`MemoryProfiler`] - Memory usage analysis for quantized operations
//!
//! ## Auto-tuning Systems
//! - [`AutoTuner`] - Automatic parameter optimization for quantization
//! - [`TuningStrategy`] - Different strategies for parameter optimization
//! - [`TuningResult`] - Results of auto-tuning with optimal parameters
//!
//! # Examples
//!
//! ## Basic Benchmarking
//! ```rust
//! use torsh_backend::quantization::{
//!     QuantizationBenchmarkSuite, BenchmarkConfiguration, QuantizationParams
//! };
//!
//! # fn example() -> Result<(), Box<dyn std::error::Error>> {
//! let benchmark_suite = QuantizationBenchmarkSuite::new();
//! let config = BenchmarkConfiguration::default();
//! let params = QuantizationParams::int8_symmetric();
//!
//! let metrics = benchmark_suite.benchmark_quantization(&params, &config)?;
//! println!("Quantization throughput: {:.2} GB/s", metrics.throughput_gbps);
//! println!("Average latency: {:.2} ms", metrics.average_latency_ms);
//! # Ok(())
//! # }
//! ```
//!
//! ## Performance Profiling
//! ```rust
//! use torsh_backend::quantization::{QuantizationProfiler, QuantizationParams};
//!
//! # fn example() -> Result<(), Box<dyn std::error::Error>> {
//! let profiler = QuantizationProfiler::new();
//! let params = QuantizationParams::int8_symmetric();
//!
//! let report = profiler.profile_operation(&params, &[1.0; 1000])?;
//! println!("Memory usage: {} bytes", report.peak_memory_usage);
//! println!("Compute efficiency: {:.1}%", report.compute_efficiency_percent);
//! # Ok(())
//! # }
//! ```
//!
//! ## Auto-tuning
//! ```rust
//! use torsh_backend::quantization::{AutoTuner, TuningStrategy};
//!
//! # fn example() -> Result<(), Box<dyn std::error::Error>> {
//! let auto_tuner = AutoTuner::new(TuningStrategy::LatencyOptimized);
//! let sample_data = vec![1.0; 10000];
//!
//! let result = auto_tuner.tune_parameters(&sample_data)?;
//! println!("Optimal quantization: {:?}", result.optimal_params);
//! println!("Expected speedup: {:.2}x", result.performance_improvement_factor);
//! # Ok(())
//! # }
//! ```

use crate::BackendResult;
use super::core::{QuantizationParams, QuantizedDType, QuantizationScheme};
use super::hardware::QuantizationHardwareFeatures;
use std::time::{Duration, Instant};
use std::collections::HashMap;

/// Comprehensive benchmarking suite for quantization operations.
///
/// This suite provides standardized benchmarks for different quantization schemes,
/// hardware configurations, and data patterns to evaluate quantization performance.
#[derive(Debug, Clone)]
pub struct QuantizationBenchmarkSuite {
    hardware_features: QuantizationHardwareFeatures,
    benchmark_cache: HashMap<String, BenchmarkMetrics>,
}

/// Performance metrics collected during benchmarking.
///
/// Provides comprehensive metrics for evaluating quantization performance
/// across different dimensions including throughput, latency, and resource usage.
#[derive(Debug, Clone, PartialEq)]
pub struct BenchmarkMetrics {
    /// Throughput in gigabytes per second
    pub throughput_gbps: f32,
    /// Average operation latency in milliseconds
    pub average_latency_ms: f32,
    /// Peak memory usage during operation in bytes
    pub peak_memory_usage: usize,
    /// Memory bandwidth utilization percentage
    pub memory_bandwidth_utilization: f32,
    /// Compute utilization percentage
    pub compute_utilization: f32,
    /// Number of operations per second
    pub operations_per_second: f64,
    /// Energy efficiency in operations per joule (if available)
    pub energy_efficiency: Option<f64>,
    /// Cache hit rate percentage
    pub cache_hit_rate: f32,
}

/// Configuration parameters for benchmarking operations.
///
/// Allows customization of benchmark execution including data sizes,
/// iteration counts, and measurement parameters.
#[derive(Debug, Clone)]
pub struct BenchmarkConfiguration {
    /// Number of benchmark iterations
    pub iterations: usize,
    /// Warm-up iterations before measurement
    pub warmup_iterations: usize,
    /// Data sizes to benchmark (in elements)
    pub data_sizes: Vec<usize>,
    /// Enable detailed memory profiling
    pub enable_memory_profiling: bool,
    /// Enable power measurement (if supported)
    pub enable_power_measurement: bool,
    /// Target confidence interval for measurements
    pub confidence_interval: f32,
}

/// Detailed profiler for quantization operations.
///
/// Provides in-depth analysis of quantization performance including
/// memory usage patterns, compute efficiency, and optimization opportunities.
#[derive(Debug)]
pub struct QuantizationProfiler {
    hardware_features: QuantizationHardwareFeatures,
    memory_tracker: MemoryProfiler,
}

/// Comprehensive profiling report with optimization suggestions.
///
/// Contains detailed analysis results and recommendations for improving
/// quantization performance based on observed patterns.
#[derive(Debug, Clone)]
pub struct ProfileReport {
    /// Peak memory usage during operation
    pub peak_memory_usage: usize,
    /// Average memory usage during operation
    pub average_memory_usage: usize,
    /// Memory allocation pattern analysis
    pub memory_allocation_pattern: MemoryAllocationPattern,
    /// Compute efficiency percentage
    pub compute_efficiency_percent: f32,
    /// Identified performance bottlenecks
    pub bottlenecks: Vec<PerformanceBottleneck>,
    /// Optimization suggestions
    pub optimization_suggestions: Vec<OptimizationSuggestion>,
    /// Hardware utilization breakdown
    pub hardware_utilization: HardwareUtilization,
}

/// Memory profiler for quantized operations.
///
/// Tracks memory allocation patterns, usage efficiency, and identifies
/// opportunities for memory optimization in quantization workflows.
#[derive(Debug)]
pub struct MemoryProfiler {
    peak_usage: usize,
    current_usage: usize,
    allocation_count: usize,
    deallocation_count: usize,
    allocation_history: Vec<MemoryAllocation>,
}

/// Automatic parameter tuning system for quantization.
///
/// Uses various optimization strategies to automatically determine optimal
/// quantization parameters for given hardware and data characteristics.
#[derive(Debug)]
pub struct AutoTuner {
    strategy: TuningStrategy,
    hardware_features: QuantizationHardwareFeatures,
    tuning_cache: HashMap<String, TuningResult>,
}

/// Strategies for auto-tuning quantization parameters.
///
/// Different optimization objectives for parameter tuning, allowing
/// users to prioritize different aspects of quantization performance.
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TuningStrategy {
    /// Optimize for minimum latency
    LatencyOptimized,
    /// Optimize for maximum throughput
    ThroughputOptimized,
    /// Optimize for minimum memory usage
    MemoryOptimized,
    /// Balance between accuracy and performance
    AccuracyBalanced,
    /// Optimize for energy efficiency
    EnergyOptimized,
    /// Custom optimization with user-defined weights
    Custom {
        latency_weight: f32,
        throughput_weight: f32,
        memory_weight: f32,
        accuracy_weight: f32,
    },
}

/// Results from auto-tuning with optimal parameters.
///
/// Contains the best parameters found during tuning along with
/// performance estimates and configuration details.
#[derive(Debug, Clone)]
pub struct TuningResult {
    /// Optimal quantization parameters
    pub optimal_params: QuantizationParams,
    /// Expected performance improvement factor
    pub performance_improvement_factor: f32,
    /// Estimated accuracy impact (relative to baseline)
    pub accuracy_impact: f32,
    /// Estimated memory savings percentage
    pub memory_savings_percent: f32,
    /// Tuning confidence score (0.0 to 1.0)
    pub confidence_score: f32,
    /// Alternative parameter configurations
    pub alternatives: Vec<(QuantizationParams, f32)>,
}

/// Memory allocation pattern analysis.
#[derive(Debug, Clone)]
pub struct MemoryAllocationPattern {
    pub fragmentation_level: f32,
    pub allocation_frequency: f32,
    pub peak_to_average_ratio: f32,
    pub temporal_locality: f32,
}

/// Identified performance bottleneck.
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
    pub bottleneck_type: BottleneckType,
    pub severity: BottleneckSeverity,
    pub description: String,
    pub impact_percentage: f32,
}

/// Types of performance bottlenecks.
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum BottleneckType {
    MemoryBandwidth,
    ComputeUtilization,
    CacheEfficiency,
    Synchronization,
    DataMovement,
    AlgorithmChoice,
}

/// Severity levels for performance bottlenecks.
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum BottleneckSeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Optimization suggestion with implementation details.
#[derive(Debug, Clone)]
pub struct OptimizationSuggestion {
    pub suggestion_type: OptimizationType,
    pub description: String,
    pub expected_improvement: f32,
    pub implementation_complexity: ComplexityLevel,
    pub prerequisites: Vec<String>,
}

/// Types of optimization suggestions.
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum OptimizationType {
    ParameterTuning,
    AlgorithmChange,
    MemoryOptimization,
    HardwareUtilization,
    BatchSizeOptimization,
    CacheOptimization,
}

/// Implementation complexity levels.
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum ComplexityLevel {
    Trivial,
    Simple,
    Moderate,
    Complex,
    Expert,
}

/// Hardware utilization breakdown.
#[derive(Debug, Clone)]
pub struct HardwareUtilization {
    pub cpu_utilization: f32,
    pub memory_utilization: f32,
    pub cache_utilization: f32,
    pub vector_unit_utilization: f32,
    pub gpu_utilization: Option<f32>,
}

/// Memory allocation tracking entry.
#[derive(Debug, Clone)]
pub struct MemoryAllocation {
    pub size: usize,
    pub timestamp: Instant,
    pub allocation_type: AllocationType,
}

/// Types of memory allocations.
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum AllocationType {
    InputBuffer,
    OutputBuffer,
    IntermediateBuffer,
    LookupTable,
    Workspace,
}

impl QuantizationBenchmarkSuite {
    /// Create a new benchmark suite with hardware detection.
    pub fn new() -> Self {
        Self {
            hardware_features: QuantizationHardwareFeatures::detect_current(),
            benchmark_cache: HashMap::new(),
        }
    }

    /// Create a benchmark suite with specific hardware features.
    pub fn with_hardware_features(features: QuantizationHardwareFeatures) -> Self {
        Self {
            hardware_features: features,
            benchmark_cache: HashMap::new(),
        }
    }

    /// Benchmark quantization operation with given parameters.
    pub fn benchmark_quantization(
        &mut self,
        params: &QuantizationParams,
        config: &BenchmarkConfiguration,
    ) -> BackendResult<BenchmarkMetrics> {
        let cache_key = format!("{:?}_{:?}", params, config.data_sizes);

        if let Some(cached_metrics) = self.benchmark_cache.get(&cache_key) {
            return Ok(cached_metrics.clone());
        }

        let mut total_metrics = BenchmarkMetrics::default();
        let mut valid_runs = 0;

        for &data_size in &config.data_sizes {
            let data = self.generate_benchmark_data(data_size)?;

            // Warm-up runs
            for _ in 0..config.warmup_iterations {
                let _ = self.run_quantization_benchmark(&data, params)?;
            }

            // Measurement runs
            let mut iteration_metrics = Vec::new();
            for _ in 0..config.iterations {
                let metrics = self.run_quantization_benchmark(&data, params)?;
                iteration_metrics.push(metrics);
            }

            if !iteration_metrics.is_empty() {
                let averaged_metrics = self.average_metrics(&iteration_metrics);
                total_metrics = self.combine_metrics(&total_metrics, &averaged_metrics);
                valid_runs += 1;
            }
        }

        if valid_runs > 0 {
            total_metrics = self.normalize_metrics(&total_metrics, valid_runs);
            self.benchmark_cache.insert(cache_key, total_metrics.clone());
            Ok(total_metrics)
        } else {
            Err(crate::BackendError::QuantizationError(
                "No valid benchmark runs completed".to_string()
            ))
        }
    }

    /// Benchmark dequantization operation.
    pub fn benchmark_dequantization(
        &mut self,
        params: &QuantizationParams,
        config: &BenchmarkConfiguration,
    ) -> BackendResult<BenchmarkMetrics> {
        // Similar implementation to benchmark_quantization but for dequantization
        let mut total_metrics = BenchmarkMetrics::default();
        let mut valid_runs = 0;

        for &data_size in &config.data_sizes {
            let quantized_data = self.generate_quantized_benchmark_data(data_size, params)?;

            // Warm-up runs
            for _ in 0..config.warmup_iterations {
                let _ = self.run_dequantization_benchmark(&quantized_data, params)?;
            }

            // Measurement runs
            let mut iteration_metrics = Vec::new();
            for _ in 0..config.iterations {
                let metrics = self.run_dequantization_benchmark(&quantized_data, params)?;
                iteration_metrics.push(metrics);
            }

            if !iteration_metrics.is_empty() {
                let averaged_metrics = self.average_metrics(&iteration_metrics);
                total_metrics = self.combine_metrics(&total_metrics, &averaged_metrics);
                valid_runs += 1;
            }
        }

        if valid_runs > 0 {
            total_metrics = self.normalize_metrics(&total_metrics, valid_runs);
            Ok(total_metrics)
        } else {
            Err(crate::BackendError::QuantizationError(
                "No valid dequantization benchmark runs completed".to_string()
            ))
        }
    }

    /// Run comprehensive benchmark suite covering all supported operations.
    pub fn run_comprehensive_benchmark(
        &mut self,
        config: &BenchmarkConfiguration,
    ) -> BackendResult<HashMap<String, BenchmarkMetrics>> {
        let mut results = HashMap::new();

        // Test different quantization schemes
        let schemes = vec![
            QuantizationParams::int8_symmetric(),
            QuantizationParams::int8_asymmetric(),
            QuantizationParams::uint8_asymmetric(),
            QuantizationParams::int4_blockwise(),
        ];

        for (i, params) in schemes.iter().enumerate() {
            let scheme_name = format!("scheme_{}", i);

            // Benchmark quantization
            let quant_metrics = self.benchmark_quantization(params, config)?;
            results.insert(format!("{}_quantization", scheme_name), quant_metrics);

            // Benchmark dequantization
            let dequant_metrics = self.benchmark_dequantization(params, config)?;
            results.insert(format!("{}_dequantization", scheme_name), dequant_metrics);
        }

        Ok(results)
    }

    fn generate_benchmark_data(&self, size: usize) -> BackendResult<Vec<f32>> {
        // Generate representative benchmark data
        let mut data = Vec::with_capacity(size);
        for i in 0..size {
            // Use a mix of patterns to simulate real-world data
            let value = ((i as f32 * 0.1).sin() + (i as f32 * 0.01).cos()) * 10.0;
            data.push(value);
        }
        Ok(data)
    }

    fn generate_quantized_benchmark_data(
        &self,
        size: usize,
        params: &QuantizationParams,
    ) -> BackendResult<Vec<u8>> {
        let float_data = self.generate_benchmark_data(size)?;
        // Convert to quantized format based on params
        let mut quantized = Vec::with_capacity(size);
        for value in float_data {
            // Simplified quantization for benchmarking
            let quantized_value = ((value * params.scale[0]) + params.zero_point[0] as f32) as u8;
            quantized.push(quantized_value);
        }
        Ok(quantized)
    }

    fn run_quantization_benchmark(
        &self,
        data: &[f32],
        params: &QuantizationParams,
    ) -> BackendResult<BenchmarkMetrics> {
        let start_time = Instant::now();
        let start_memory = self.get_current_memory_usage();

        // Simulate quantization operation
        let mut quantized = Vec::with_capacity(data.len());
        for &value in data {
            let quantized_value = ((value * params.scale[0]) + params.zero_point[0] as f32) as u8;
            quantized.push(quantized_value);
        }

        let end_time = Instant::now();
        let end_memory = self.get_current_memory_usage();

        let duration = end_time.duration_since(start_time);
        let data_size_bytes = data.len() * std::mem::size_of::<f32>();

        Ok(BenchmarkMetrics {
            throughput_gbps: (data_size_bytes as f32) / (duration.as_secs_f32() * 1e9),
            average_latency_ms: duration.as_secs_f32() * 1000.0,
            peak_memory_usage: end_memory.saturating_sub(start_memory),
            memory_bandwidth_utilization: 85.0, // Placeholder
            compute_utilization: 75.0, // Placeholder
            operations_per_second: data.len() as f64 / duration.as_secs_f64(),
            energy_efficiency: None,
            cache_hit_rate: 90.0, // Placeholder
        })
    }

    fn run_dequantization_benchmark(
        &self,
        data: &[u8],
        params: &QuantizationParams,
    ) -> BackendResult<BenchmarkMetrics> {
        let start_time = Instant::now();
        let start_memory = self.get_current_memory_usage();

        // Simulate dequantization operation
        let mut dequantized = Vec::with_capacity(data.len());
        for &value in data {
            let dequantized_value = (value as f32 - params.zero_point[0] as f32) / params.scale[0];
            dequantized.push(dequantized_value);
        }

        let end_time = Instant::now();
        let end_memory = self.get_current_memory_usage();

        let duration = end_time.duration_since(start_time);
        let data_size_bytes = data.len() * std::mem::size_of::<u8>();

        Ok(BenchmarkMetrics {
            throughput_gbps: (data_size_bytes as f32) / (duration.as_secs_f32() * 1e9),
            average_latency_ms: duration.as_secs_f32() * 1000.0,
            peak_memory_usage: end_memory.saturating_sub(start_memory),
            memory_bandwidth_utilization: 80.0, // Placeholder
            compute_utilization: 70.0, // Placeholder
            operations_per_second: data.len() as f64 / duration.as_secs_f64(),
            energy_efficiency: None,
            cache_hit_rate: 88.0, // Placeholder
        })
    }

    fn get_current_memory_usage(&self) -> usize {
        // Platform-specific memory usage detection would go here
        // For now, return a placeholder value
        1024 * 1024 // 1MB placeholder
    }

    fn average_metrics(&self, metrics: &[BenchmarkMetrics]) -> BenchmarkMetrics {
        if metrics.is_empty() {
            return BenchmarkMetrics::default();
        }

        let count = metrics.len() as f32;
        BenchmarkMetrics {
            throughput_gbps: metrics.iter().map(|m| m.throughput_gbps).sum::<f32>() / count,
            average_latency_ms: metrics.iter().map(|m| m.average_latency_ms).sum::<f32>() / count,
            peak_memory_usage: metrics.iter().map(|m| m.peak_memory_usage).max().unwrap_or(0),
            memory_bandwidth_utilization: metrics.iter().map(|m| m.memory_bandwidth_utilization).sum::<f32>() / count,
            compute_utilization: metrics.iter().map(|m| m.compute_utilization).sum::<f32>() / count,
            operations_per_second: metrics.iter().map(|m| m.operations_per_second).sum::<f64>() / count as f64,
            energy_efficiency: None,
            cache_hit_rate: metrics.iter().map(|m| m.cache_hit_rate).sum::<f32>() / count,
        }
    }

    fn combine_metrics(&self, a: &BenchmarkMetrics, b: &BenchmarkMetrics) -> BenchmarkMetrics {
        BenchmarkMetrics {
            throughput_gbps: (a.throughput_gbps + b.throughput_gbps) / 2.0,
            average_latency_ms: (a.average_latency_ms + b.average_latency_ms) / 2.0,
            peak_memory_usage: a.peak_memory_usage.max(b.peak_memory_usage),
            memory_bandwidth_utilization: (a.memory_bandwidth_utilization + b.memory_bandwidth_utilization) / 2.0,
            compute_utilization: (a.compute_utilization + b.compute_utilization) / 2.0,
            operations_per_second: (a.operations_per_second + b.operations_per_second) / 2.0,
            energy_efficiency: None,
            cache_hit_rate: (a.cache_hit_rate + b.cache_hit_rate) / 2.0,
        }
    }

    fn normalize_metrics(&self, metrics: &BenchmarkMetrics, count: usize) -> BenchmarkMetrics {
        if count <= 1 {
            return metrics.clone();
        }

        let count_f32 = count as f32;
        BenchmarkMetrics {
            throughput_gbps: metrics.throughput_gbps / count_f32,
            average_latency_ms: metrics.average_latency_ms / count_f32,
            peak_memory_usage: metrics.peak_memory_usage,
            memory_bandwidth_utilization: metrics.memory_bandwidth_utilization / count_f32,
            compute_utilization: metrics.compute_utilization / count_f32,
            operations_per_second: metrics.operations_per_second / count as f64,
            energy_efficiency: None,
            cache_hit_rate: metrics.cache_hit_rate / count_f32,
        }
    }
}

impl QuantizationProfiler {
    /// Create a new profiler with hardware detection.
    pub fn new() -> Self {
        Self {
            hardware_features: QuantizationHardwareFeatures::detect_current(),
            memory_tracker: MemoryProfiler::new(),
        }
    }

    /// Profile a quantization operation and generate detailed report.
    pub fn profile_operation(
        &mut self,
        params: &QuantizationParams,
        data: &[f32],
    ) -> BackendResult<ProfileReport> {
        self.memory_tracker.reset();

        let start_time = Instant::now();

        // Track memory allocation for input
        self.memory_tracker.track_allocation(
            data.len() * std::mem::size_of::<f32>(),
            AllocationType::InputBuffer,
        );

        // Simulate quantization with profiling
        let output_size = data.len();
        self.memory_tracker.track_allocation(
            output_size,
            AllocationType::OutputBuffer,
        );

        // Perform operation analysis
        let mut quantized = Vec::with_capacity(data.len());
        for &value in data {
            let quantized_value = ((value * params.scale[0]) + params.zero_point[0] as f32) as u8;
            quantized.push(quantized_value);
        }

        let operation_time = start_time.elapsed();

        // Analyze performance characteristics
        let bottlenecks = self.identify_bottlenecks(params, data.len(), operation_time);
        let suggestions = self.generate_optimization_suggestions(&bottlenecks, params);
        let hardware_utilization = self.analyze_hardware_utilization();

        Ok(ProfileReport {
            peak_memory_usage: self.memory_tracker.get_peak_usage(),
            average_memory_usage: self.memory_tracker.get_average_usage(),
            memory_allocation_pattern: self.memory_tracker.analyze_allocation_pattern(),
            compute_efficiency_percent: self.calculate_compute_efficiency(operation_time, data.len()),
            bottlenecks,
            optimization_suggestions: suggestions,
            hardware_utilization,
        })
    }

    /// Profile memory usage patterns for batch operations.
    pub fn profile_batch_operations(
        &mut self,
        params: &QuantizationParams,
        batch_sizes: &[usize],
    ) -> BackendResult<Vec<ProfileReport>> {
        let mut reports = Vec::new();

        for &batch_size in batch_sizes {
            let data = vec![1.0; batch_size]; // Simplified data for profiling
            let report = self.profile_operation(params, &data)?;
            reports.push(report);
        }

        Ok(reports)
    }

    fn identify_bottlenecks(
        &self,
        params: &QuantizationParams,
        data_size: usize,
        operation_time: Duration,
    ) -> Vec<PerformanceBottleneck> {
        let mut bottlenecks = Vec::new();

        // Memory bandwidth bottleneck analysis
        let theoretical_bandwidth_gbps = 100.0; // Placeholder
        let actual_bandwidth = (data_size as f32 * std::mem::size_of::<f32>() as f32) /
                              (operation_time.as_secs_f32() * 1e9);

        if actual_bandwidth < theoretical_bandwidth_gbps * 0.5 {
            bottlenecks.push(PerformanceBottleneck {
                bottleneck_type: BottleneckType::MemoryBandwidth,
                severity: BottleneckSeverity::High,
                description: "Memory bandwidth utilization is below 50% of theoretical maximum".to_string(),
                impact_percentage: 60.0,
            });
        }

        // Compute utilization analysis
        if !self.hardware_features.supports_int8_simd && params.dtype == QuantizedDType::Int8 {
            bottlenecks.push(PerformanceBottleneck {
                bottleneck_type: BottleneckType::ComputeUtilization,
                severity: BottleneckSeverity::Medium,
                description: "Hardware lacks SIMD support for INT8 operations".to_string(),
                impact_percentage: 40.0,
            });
        }

        bottlenecks
    }

    fn generate_optimization_suggestions(
        &self,
        bottlenecks: &[PerformanceBottleneck],
        params: &QuantizationParams,
    ) -> Vec<OptimizationSuggestion> {
        let mut suggestions = Vec::new();

        for bottleneck in bottlenecks {
            match bottleneck.bottleneck_type {
                BottleneckType::MemoryBandwidth => {
                    suggestions.push(OptimizationSuggestion {
                        suggestion_type: OptimizationType::BatchSizeOptimization,
                        description: "Increase batch size to improve memory bandwidth utilization".to_string(),
                        expected_improvement: 25.0,
                        implementation_complexity: ComplexityLevel::Simple,
                        prerequisites: vec!["Available memory for larger batches".to_string()],
                    });
                }
                BottleneckType::ComputeUtilization => {
                    if self.hardware_features.supports_vnni {
                        suggestions.push(OptimizationSuggestion {
                            suggestion_type: OptimizationType::HardwareUtilization,
                            description: "Enable VNNI acceleration for INT8 operations".to_string(),
                            expected_improvement: 40.0,
                            implementation_complexity: ComplexityLevel::Moderate,
                            prerequisites: vec!["VNNI-compatible data layout".to_string()],
                        });
                    }
                }
                _ => {}
            }
        }

        suggestions
    }

    fn analyze_hardware_utilization(&self) -> HardwareUtilization {
        // Simplified hardware utilization analysis
        HardwareUtilization {
            cpu_utilization: 75.0,
            memory_utilization: 60.0,
            cache_utilization: 80.0,
            vector_unit_utilization: if self.hardware_features.supports_int8_simd { 85.0 } else { 0.0 },
            gpu_utilization: None,
        }
    }

    fn calculate_compute_efficiency(&self, operation_time: Duration, data_size: usize) -> f32 {
        // Simplified efficiency calculation
        let theoretical_ops_per_second = 1e9; // 1 GOP/s theoretical
        let actual_ops_per_second = data_size as f64 / operation_time.as_secs_f64();

        ((actual_ops_per_second / theoretical_ops_per_second) * 100.0) as f32
    }
}

impl AutoTuner {
    /// Create a new auto-tuner with specified strategy.
    pub fn new(strategy: TuningStrategy) -> Self {
        Self {
            strategy,
            hardware_features: QuantizationHardwareFeatures::detect_current(),
            tuning_cache: HashMap::new(),
        }
    }

    /// Tune quantization parameters for optimal performance.
    pub fn tune_parameters(&mut self, sample_data: &[f32]) -> BackendResult<TuningResult> {
        let data_hash = self.calculate_data_hash(sample_data);
        let cache_key = format!("{:?}_{}", self.strategy, data_hash);

        if let Some(cached_result) = self.tuning_cache.get(&cache_key) {
            return Ok(cached_result.clone());
        }

        let candidate_params = self.generate_candidate_parameters();
        let mut best_result = None;
        let mut best_score = f32::NEG_INFINITY;

        for params in candidate_params {
            let score = self.evaluate_parameters(&params, sample_data)?;

            if score > best_score {
                best_score = score;
                best_result = Some(params);
            }
        }

        let optimal_params = best_result.ok_or_else(|| {
            crate::BackendError::QuantizationError("No suitable parameters found".to_string())
        })?;

        let result = TuningResult {
            optimal_params: optimal_params.clone(),
            performance_improvement_factor: self.estimate_improvement_factor(&optimal_params, sample_data)?,
            accuracy_impact: self.estimate_accuracy_impact(&optimal_params, sample_data)?,
            memory_savings_percent: self.estimate_memory_savings(&optimal_params),
            confidence_score: 0.85, // Placeholder
            alternatives: Vec::new(),
        };

        self.tuning_cache.insert(cache_key, result.clone());
        Ok(result)
    }

    /// Tune parameters for specific hardware configuration.
    pub fn tune_for_hardware(
        &mut self,
        sample_data: &[f32],
        target_hardware: &QuantizationHardwareFeatures,
    ) -> BackendResult<TuningResult> {
        let original_features = self.hardware_features.clone();
        self.hardware_features = target_hardware.clone();

        let result = self.tune_parameters(sample_data);

        self.hardware_features = original_features;
        result
    }

    fn generate_candidate_parameters(&self) -> Vec<QuantizationParams> {
        let mut candidates = Vec::new();

        // Generate candidates based on hardware capabilities
        if self.hardware_features.supports_int8_simd {
            candidates.push(QuantizationParams::int8_symmetric());
            candidates.push(QuantizationParams::int8_asymmetric());
        }

        if self.hardware_features.supports_int4_operations {
            candidates.push(QuantizationParams::int4_blockwise());
        }

        candidates.push(QuantizationParams::uint8_asymmetric());

        // Add mixed precision candidates if supported
        if self.hardware_features.supports_mixed_precision {
            candidates.extend(self.generate_mixed_precision_candidates());
        }

        candidates
    }

    fn generate_mixed_precision_candidates(&self) -> Vec<QuantizationParams> {
        // Generate mixed precision quantization parameters
        vec![
            QuantizationParams {
                dtype: QuantizedDType::Mixed(vec![8, 4, 8]), // INT8 for weights, INT4 for activations
                scheme: QuantizationScheme::Symmetric,
                scale: vec![0.1, 0.05, 0.1],
                zero_point: vec![0, 0, 0],
                block_size: Some(64),
                channel_axis: None,
            }
        ]
    }

    fn evaluate_parameters(&self, params: &QuantizationParams, data: &[f32]) -> BackendResult<f32> {
        // Simulate quantization and evaluate based on strategy
        let simulated_performance = self.simulate_performance(params, data)?;
        let simulated_accuracy = self.simulate_accuracy_impact(params, data)?;
        let memory_usage = self.estimate_memory_usage(params, data.len());

        let score = match &self.strategy {
            TuningStrategy::LatencyOptimized => {
                -simulated_performance.average_latency_ms
            }
            TuningStrategy::ThroughputOptimized => {
                simulated_performance.throughput_gbps
            }
            TuningStrategy::MemoryOptimized => {
                -(memory_usage as f32) / 1e6 // Negative because we want to minimize memory
            }
            TuningStrategy::AccuracyBalanced => {
                simulated_performance.throughput_gbps * 0.6 + simulated_accuracy * 0.4
            }
            TuningStrategy::EnergyOptimized => {
                simulated_performance.energy_efficiency.unwrap_or(0.0) as f32
            }
            TuningStrategy::Custom {
                latency_weight,
                throughput_weight,
                memory_weight,
                accuracy_weight
            } => {
                -simulated_performance.average_latency_ms * latency_weight +
                simulated_performance.throughput_gbps * throughput_weight +
                -(memory_usage as f32) / 1e6 * memory_weight +
                simulated_accuracy * accuracy_weight
            }
        };

        Ok(score)
    }

    fn simulate_performance(&self, params: &QuantizationParams, data: &[f32]) -> BackendResult<BenchmarkMetrics> {
        // Simplified performance simulation
        let base_latency = 1.0; // 1ms base latency
        let latency_factor = match params.dtype {
            QuantizedDType::Int8 => if self.hardware_features.supports_int8_simd { 0.5 } else { 1.0 },
            QuantizedDType::UInt8 => 0.8,
            QuantizedDType::Int4 => if self.hardware_features.supports_int4_operations { 0.3 } else { 1.5 },
            _ => 1.0,
        };

        let latency = base_latency * latency_factor;
        let throughput = (data.len() as f32 * std::mem::size_of::<f32>() as f32) / (latency * 1e6);

        Ok(BenchmarkMetrics {
            throughput_gbps: throughput,
            average_latency_ms: latency,
            peak_memory_usage: data.len() * 2, // Simplified
            memory_bandwidth_utilization: 80.0,
            compute_utilization: 70.0,
            operations_per_second: data.len() as f64 / (latency as f64 / 1000.0),
            energy_efficiency: Some(1000.0), // Simplified
            cache_hit_rate: 85.0,
        })
    }

    fn simulate_accuracy_impact(&self, params: &QuantizationParams, _data: &[f32]) -> BackendResult<f32> {
        // Simplified accuracy impact simulation
        let accuracy_score = match params.dtype {
            QuantizedDType::Int8 => 0.95,
            QuantizedDType::UInt8 => 0.93,
            QuantizedDType::Int4 => 0.85,
            QuantizedDType::Binary => 0.70,
            _ => 0.90,
        };

        Ok(accuracy_score)
    }

    fn estimate_memory_usage(&self, params: &QuantizationParams, data_size: usize) -> usize {
        let element_size = match params.dtype {
            QuantizedDType::Int8 | QuantizedDType::UInt8 => 1,
            QuantizedDType::Int16 | QuantizedDType::UInt16 => 2,
            QuantizedDType::Int4 | QuantizedDType::UInt4 => 1, // Packed
            QuantizedDType::Binary => data_size / 8,
            _ => 1,
        };

        data_size * element_size
    }

    fn estimate_improvement_factor(&self, params: &QuantizationParams, data: &[f32]) -> BackendResult<f32> {
        let baseline_performance = self.simulate_performance(&QuantizationParams::int8_symmetric(), data)?;
        let optimized_performance = self.simulate_performance(params, data)?;

        Ok(baseline_performance.average_latency_ms / optimized_performance.average_latency_ms)
    }

    fn estimate_accuracy_impact(&self, params: &QuantizationParams, data: &[f32]) -> BackendResult<f32> {
        let baseline_accuracy = self.simulate_accuracy_impact(&QuantizationParams::int8_symmetric(), data)?;
        let optimized_accuracy = self.simulate_accuracy_impact(params, data)?;

        Ok((optimized_accuracy - baseline_accuracy) / baseline_accuracy)
    }

    fn estimate_memory_savings(&self, params: &QuantizationParams) -> f32 {
        let baseline_size = 4; // f32 baseline
        let quantized_size = match params.dtype {
            QuantizedDType::Int8 | QuantizedDType::UInt8 => 1,
            QuantizedDType::Int16 | QuantizedDType::UInt16 => 2,
            QuantizedDType::Int4 | QuantizedDType::UInt4 => 0.5,
            QuantizedDType::Binary => 0.125,
            _ => 1.0,
        };

        ((baseline_size - quantized_size) / baseline_size) * 100.0
    }

    fn calculate_data_hash(&self, data: &[f32]) -> u64 {
        // Simplified hash calculation for caching
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        data.len().hash(&mut hasher);

        // Sample a few values for hash to avoid hashing entire dataset
        let sample_indices = [0, data.len() / 4, data.len() / 2, 3 * data.len() / 4, data.len() - 1];
        for &idx in &sample_indices {
            if idx < data.len() {
                (data[idx] as u32).hash(&mut hasher);
            }
        }

        hasher.finish()
    }
}

impl MemoryProfiler {
    fn new() -> Self {
        Self {
            peak_usage: 0,
            current_usage: 0,
            allocation_count: 0,
            deallocation_count: 0,
            allocation_history: Vec::new(),
        }
    }

    fn reset(&mut self) {
        self.peak_usage = 0;
        self.current_usage = 0;
        self.allocation_count = 0;
        self.deallocation_count = 0;
        self.allocation_history.clear();
    }

    fn track_allocation(&mut self, size: usize, allocation_type: AllocationType) {
        self.current_usage += size;
        self.peak_usage = self.peak_usage.max(self.current_usage);
        self.allocation_count += 1;

        self.allocation_history.push(MemoryAllocation {
            size,
            timestamp: Instant::now(),
            allocation_type,
        });
    }

    fn get_peak_usage(&self) -> usize {
        self.peak_usage
    }

    fn get_average_usage(&self) -> usize {
        if self.allocation_history.is_empty() {
            0
        } else {
            self.allocation_history.iter().map(|a| a.size).sum::<usize>() / self.allocation_history.len()
        }
    }

    fn analyze_allocation_pattern(&self) -> MemoryAllocationPattern {
        let total_allocations = self.allocation_history.len();
        let avg_size = if total_allocations > 0 {
            self.allocation_history.iter().map(|a| a.size).sum::<usize>() / total_allocations
        } else {
            0
        };

        MemoryAllocationPattern {
            fragmentation_level: 0.1, // Simplified
            allocation_frequency: total_allocations as f32,
            peak_to_average_ratio: if avg_size > 0 { self.peak_usage as f32 / avg_size as f32 } else { 1.0 },
            temporal_locality: 0.8, // Simplified
        }
    }
}

impl Default for BenchmarkConfiguration {
    fn default() -> Self {
        Self {
            iterations: 10,
            warmup_iterations: 3,
            data_sizes: vec![1024, 4096, 16384, 65536],
            enable_memory_profiling: true,
            enable_power_measurement: false,
            confidence_interval: 0.95,
        }
    }
}

impl Default for BenchmarkMetrics {
    fn default() -> Self {
        Self {
            throughput_gbps: 0.0,
            average_latency_ms: 0.0,
            peak_memory_usage: 0,
            memory_bandwidth_utilization: 0.0,
            compute_utilization: 0.0,
            operations_per_second: 0.0,
            energy_efficiency: None,
            cache_hit_rate: 0.0,
        }
    }
}

impl QuantizationParams {
    /// Create parameters optimized for INT8 symmetric quantization.
    pub fn int8_symmetric() -> Self {
        Self {
            dtype: QuantizedDType::Int8,
            scheme: QuantizationScheme::Symmetric,
            scale: vec![0.1],
            zero_point: vec![0],
            block_size: None,
            channel_axis: None,
        }
    }

    /// Create parameters optimized for INT8 asymmetric quantization.
    pub fn int8_asymmetric() -> Self {
        Self {
            dtype: QuantizedDType::Int8,
            scheme: QuantizationScheme::Asymmetric,
            scale: vec![0.1],
            zero_point: vec![128],
            block_size: None,
            channel_axis: None,
        }
    }

    /// Create parameters optimized for UINT8 asymmetric quantization.
    pub fn uint8_asymmetric() -> Self {
        Self {
            dtype: QuantizedDType::UInt8,
            scheme: QuantizationScheme::Asymmetric,
            scale: vec![0.1],
            zero_point: vec![128],
            block_size: None,
            channel_axis: None,
        }
    }

    /// Create parameters optimized for INT4 block-wise quantization.
    pub fn int4_blockwise() -> Self {
        Self {
            dtype: QuantizedDType::Int4,
            scheme: QuantizationScheme::BlockWise,
            scale: vec![0.05],
            zero_point: vec![8],
            block_size: Some(32),
            channel_axis: None,
        }
    }
}

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

    #[test]
    fn test_benchmark_suite_creation() {
        let suite = QuantizationBenchmarkSuite::new();
        assert!(suite.benchmark_cache.is_empty());
    }

    #[test]
    fn test_benchmark_configuration_default() {
        let config = BenchmarkConfiguration::default();
        assert_eq!(config.iterations, 10);
        assert_eq!(config.warmup_iterations, 3);
        assert!(!config.data_sizes.is_empty());
        assert!(config.enable_memory_profiling);
    }

    #[test]
    fn test_profiler_creation() {
        let profiler = QuantizationProfiler::new();
        assert_eq!(profiler.memory_tracker.allocation_count, 0);
    }

    #[test]
    fn test_auto_tuner_creation() {
        let tuner = AutoTuner::new(TuningStrategy::LatencyOptimized);
        assert_eq!(tuner.strategy, TuningStrategy::LatencyOptimized);
        assert!(tuner.tuning_cache.is_empty());
    }

    #[test]
    fn test_tuning_strategy_comparison() {
        assert_eq!(TuningStrategy::LatencyOptimized, TuningStrategy::LatencyOptimized);
        assert_ne!(TuningStrategy::LatencyOptimized, TuningStrategy::ThroughputOptimized);
    }

    #[test]
    fn test_benchmark_metrics_default() {
        let metrics = BenchmarkMetrics::default();
        assert_eq!(metrics.throughput_gbps, 0.0);
        assert_eq!(metrics.peak_memory_usage, 0);
        assert!(metrics.energy_efficiency.is_none());
    }

    #[test]
    fn test_memory_profiler_tracking() {
        let mut profiler = MemoryProfiler::new();

        profiler.track_allocation(1024, AllocationType::InputBuffer);
        assert_eq!(profiler.current_usage, 1024);
        assert_eq!(profiler.peak_usage, 1024);
        assert_eq!(profiler.allocation_count, 1);

        profiler.track_allocation(2048, AllocationType::OutputBuffer);
        assert_eq!(profiler.current_usage, 3072);
        assert_eq!(profiler.peak_usage, 3072);
        assert_eq!(profiler.allocation_count, 2);
    }

    #[test]
    fn test_quantization_params_presets() {
        let int8_sym = QuantizationParams::int8_symmetric();
        assert_eq!(int8_sym.dtype, QuantizedDType::Int8);
        assert_eq!(int8_sym.scheme, QuantizationScheme::Symmetric);
        assert_eq!(int8_sym.zero_point[0], 0);

        let uint8_asym = QuantizationParams::uint8_asymmetric();
        assert_eq!(uint8_asym.dtype, QuantizedDType::UInt8);
        assert_eq!(uint8_asym.scheme, QuantizationScheme::Asymmetric);
        assert_eq!(uint8_asym.zero_point[0], 128);

        let int4_block = QuantizationParams::int4_blockwise();
        assert_eq!(int4_block.dtype, QuantizedDType::Int4);
        assert_eq!(int4_block.scheme, QuantizationScheme::BlockWise);
        assert_eq!(int4_block.block_size, Some(32));
    }

    #[test]
    fn test_bottleneck_severity_ordering() {
        assert!(BottleneckSeverity::Low < BottleneckSeverity::Medium);
        assert!(BottleneckSeverity::Medium < BottleneckSeverity::High);
        assert!(BottleneckSeverity::High < BottleneckSeverity::Critical);
    }

    #[test]
    fn test_complexity_level_ordering() {
        assert!(ComplexityLevel::Trivial < ComplexityLevel::Simple);
        assert!(ComplexityLevel::Simple < ComplexityLevel::Moderate);
        assert!(ComplexityLevel::Moderate < ComplexityLevel::Complex);
        assert!(ComplexityLevel::Complex < ComplexityLevel::Expert);
    }
}