trustformers-mobile 0.1.1

//! Advanced Mobile Quantization for TrustformeRS
//!
//! This module provides advanced quantization techniques specifically optimized
//! for mobile devices, including mixed-precision quantization, dynamic quantization,
//! and hardware-aware quantization strategies.

use crate::{
    device_info::{MobileDeviceInfo, PerformanceTier},
    optimization::memory_pool::MobileMemoryPool,
    Result,
};
use half::f16;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use trustformers_core::errors::{invalid_config, invalid_input};
use trustformers_core::Tensor;

/// Advanced mobile quantization engine
pub struct MobileQuantizationEngine {
    config: QuantizationConfig,
    device_info: MobileDeviceInfo,
    calibration_data: Option<CalibrationDataset>,
    quantization_cache: Arc<Mutex<HashMap<String, QuantizedModel>>>,
    memory_pool: Option<Arc<MobileMemoryPool>>,
}

/// Mobile quantization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationConfig {
    /// Target quantization precision
    pub target_precision: MobilePrecision,
    /// Enable mixed-precision quantization
    pub enable_mixed_precision: bool,
    /// Dynamic quantization strategy
    pub dynamic_strategy: DynamicQuantizationStrategy,
    /// Hardware-aware optimizations
    pub hardware_aware: bool,
    /// Quantization granularity
    pub granularity: QuantizationGranularity,
    /// Quality preservation threshold
    pub quality_threshold: f32,
    /// Memory constraint (MB)
    pub memory_constraint_mb: usize,
    /// Enable gradient-based quantization
    pub enable_gradient_quantization: bool,
    /// KL-divergence threshold for calibration
    pub kl_threshold: f32,
    /// Enable post-training quantization
    pub enable_ptq: bool,
    /// Enable quantization-aware training
    pub enable_qat: bool,
}

/// Mobile-optimized precision levels
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum MobilePrecision {
    /// 4-bit quantization (ultra-low memory)
    INT4,
    /// 8-bit quantization (standard mobile)
    INT8,
    /// 16-bit float (high quality)
    FP16,
    /// Mixed 4-bit and 8-bit
    Mixed4_8,
    /// Mixed 8-bit and 16-bit
    Mixed8_16,
    /// Dynamic precision selection
    DYNAMIC,
}

/// Dynamic quantization strategies
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum DynamicQuantizationStrategy {
    /// Adjust based on battery level
    BatteryAware,
    /// Adjust based on thermal state
    ThermalAware,
    /// Adjust based on memory pressure
    MemoryAware,
    /// Adjust based on performance requirements
    PerformanceAware,
    /// Combined adaptive strategy
    Adaptive,
}

/// Quantization granularity options
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum QuantizationGranularity {
    /// Per-tensor quantization
    PerTensor,
    /// Per-channel quantization
    PerChannel,
    /// Per-group quantization
    PerGroup { group_size: usize },
    /// Per-layer adaptive
    PerLayer,
}

/// Calibration dataset for quantization
#[derive(Debug, Clone)]
pub struct CalibrationDataset {
    /// Representative data samples
    pub samples: Vec<Tensor>,
    /// Sample weights for importance
    pub weights: Option<Vec<f32>>,
    /// Statistical properties
    pub statistics: DatasetStatistics,
}

/// Statistical properties of calibration data
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct DatasetStatistics {
    /// Per-layer activation ranges
    pub activation_ranges: HashMap<String, (f32, f32)>,
    /// Per-layer mean values
    pub layer_means: HashMap<String, f32>,
    /// Per-layer variance values
    pub layer_variances: HashMap<String, f32>,
    /// KL-divergence scores
    pub kl_scores: HashMap<String, f32>,
}

/// Quantized model representation
#[derive(Debug, Clone)]
pub struct QuantizedModel {
    /// Quantized weights
    pub weights: HashMap<String, QuantizedTensor>,
    /// Quantization parameters
    pub parameters: QuantizationParameters,
    /// Model metadata
    pub metadata: ModelMetadata,
    /// Performance benchmarks
    pub benchmarks: QuantizationBenchmarks,
}

/// Quantized tensor with metadata
#[derive(Debug, Clone)]
pub struct QuantizedTensor {
    /// Quantized data
    pub data: Vec<i8>,
    /// Scale factors
    pub scales: Vec<f32>,
    /// Zero points
    pub zero_points: Vec<i32>,
    /// Original shape
    pub shape: Vec<usize>,
    /// Quantization scheme
    pub scheme: QuantizationScheme,
}

/// Quantization scheme details
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationScheme {
    /// Bits per weight
    pub bits: u8,
    /// Symmetric vs asymmetric
    pub symmetric: bool,
    /// Signed vs unsigned
    pub signed: bool,
    /// Quantization method
    pub method: QuantizationMethod,
}

/// Quantization methods
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum QuantizationMethod {
    /// Linear quantization
    Linear,
    /// Logarithmic quantization
    Logarithmic,
    /// Power-of-2 quantization
    PowerOfTwo,
    /// K-means clustering
    KMeans,
    /// Learned quantization
    Learned,
}

/// Model format detection
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum ModelFormat {
    /// SafeTensors format
    SafeTensors,
    /// PyTorch pickle format
    PyTorchPickle,
    /// TensorFlow SavedModel format
    TensorFlow,
    /// ONNX format
    ONNX,
    /// Custom format
    Custom,
}

/// Quantization parameters for inference
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationParameters {
    /// Global scale factor
    pub global_scale: f32,
    /// Per-layer scales
    pub layer_scales: HashMap<String, f32>,
    /// Per-layer zero points
    pub layer_zero_points: HashMap<String, i32>,
    /// Dequantization overhead
    pub dequant_overhead_ms: f32,
}

/// Model metadata for quantized models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelMetadata {
    /// Original model size (bytes)
    pub original_size_bytes: usize,
    /// Quantized model size (bytes)
    pub quantized_size_bytes: usize,
    /// Compression ratio
    pub compression_ratio: f32,
    /// Quality score (0.0 - 1.0)
    pub quality_score: f32,
    /// Quantization timestamp
    pub timestamp: std::time::SystemTime,
}

/// Quantization performance benchmarks
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationBenchmarks {
    /// Original inference time (ms)
    pub original_inference_ms: f32,
    /// Quantized inference time (ms)
    pub quantized_inference_ms: f32,
    /// Speedup factor
    pub speedup_factor: f32,
    /// Memory reduction (MB)
    pub memory_reduction_mb: f32,
    /// Power reduction (mW)
    pub power_reduction_mw: f32,
}

impl MobileQuantizationEngine {
    /// Create a new mobile quantization engine
    pub fn new(
        config: QuantizationConfig,
        device_info: MobileDeviceInfo,
        memory_pool: Option<Arc<MobileMemoryPool>>,
    ) -> Result<Self> {
        Ok(Self {
            config,
            device_info,
            calibration_data: None,
            quantization_cache: Arc::new(Mutex::new(HashMap::new())),
            memory_pool,
        })
    }

    /// Set calibration dataset for quantization
    pub fn set_calibration_data(&mut self, dataset: CalibrationDataset) -> Result<()> {
        // Validate calibration data
        if dataset.samples.is_empty() {
            return Err(invalid_config(
                "set_calibration_data",
                "Calibration dataset cannot be empty",
            ));
        }

        self.calibration_data = Some(dataset);
        Ok(())
    }

    /// Quantize model with mobile optimizations
    pub fn quantize_model(&self, model_id: &str, model_data: &[u8]) -> Result<QuantizedModel> {
        // Check cache first
        {
            let cache = self.quantization_cache.lock().expect("Operation failed");
            if let Some(cached_model) = cache.get(model_id) {
                return Ok(cached_model.clone());
            }
        }

        // Determine optimal quantization strategy
        let strategy = self.determine_quantization_strategy()?;

        // Apply hardware-specific optimizations
        let hardware_config = self.get_hardware_quantization_config()?;

        // Perform quantization
        let quantized_model = self.perform_quantization(model_data, &strategy, &hardware_config)?;

        // Benchmark the quantized model
        let benchmarks = self.benchmark_quantized_model(&quantized_model)?;

        let final_model = QuantizedModel {
            weights: quantized_model.weights,
            parameters: quantized_model.parameters,
            metadata: quantized_model.metadata,
            benchmarks,
        };

        // Cache the result
        {
            let mut cache = self.quantization_cache.lock().expect("Operation failed");
            cache.insert(model_id.to_string(), final_model.clone());
        }

        Ok(final_model)
    }

    /// Determine optimal quantization strategy based on device capabilities
    fn determine_quantization_strategy(&self) -> Result<MobilePrecision> {
        match (
            &self.device_info.performance_scores.overall_tier,
            &self.config.target_precision,
        ) {
            (PerformanceTier::High, MobilePrecision::DYNAMIC) => {
                // High-end devices can handle mixed precision
                Ok(MobilePrecision::Mixed8_16)
            },
            (PerformanceTier::Mid, MobilePrecision::DYNAMIC) => {
                // Mid-range devices benefit from INT8
                Ok(MobilePrecision::INT8)
            },
            (PerformanceTier::Budget, MobilePrecision::DYNAMIC) => {
                // Low-end devices need aggressive quantization
                Ok(MobilePrecision::Mixed4_8)
            },
            (_, precision) => Ok(*precision),
        }
    }

    /// Get hardware-specific quantization configuration
    fn get_hardware_quantization_config(&self) -> Result<HardwareQuantizationConfig> {
        let mut config = HardwareQuantizationConfig::default();

        // Configure for specific hardware
        if self.device_info.npu_info.is_some() {
            config.use_npu_kernels = true;
            config.preferred_precision = MobilePrecision::INT8;
        }

        if self.device_info.gpu_info.is_some() {
            config.use_gpu_kernels = true;
            config.gpu_memory_optimization = true;
        }

        // ARM-specific optimizations
        if self.device_info.cpu_info.architecture.contains("arm")
            || self.device_info.cpu_info.architecture.contains("aarch64")
        {
            config.use_neon_instructions = true;
            config.arm_specific_kernels = true;
        }

        Ok(config)
    }

    /// Perform the actual quantization
    fn perform_quantization(
        &self,
        model_data: &[u8],
        strategy: &MobilePrecision,
        hardware_config: &HardwareQuantizationConfig,
    ) -> Result<QuantizedModel> {
        // Parse model weights (simplified)
        let weights = self.parse_model_weights(model_data)?;

        // Apply quantization based on strategy
        let quantized_weights = match strategy {
            MobilePrecision::INT4 => self.quantize_to_int4(&weights)?,
            MobilePrecision::INT8 => self.quantize_to_int8(&weights)?,
            MobilePrecision::FP16 => self.quantize_to_fp16(&weights)?,
            MobilePrecision::Mixed4_8 => self.quantize_mixed_4_8(&weights)?,
            MobilePrecision::Mixed8_16 => self.quantize_mixed_8_16(&weights)?,
            MobilePrecision::DYNAMIC => self.quantize_dynamic(&weights)?,
        };

        // Calculate quantization parameters
        let parameters = self.calculate_quantization_parameters(&quantized_weights)?;

        // Generate metadata
        let metadata = ModelMetadata {
            original_size_bytes: model_data.len(),
            quantized_size_bytes: self.calculate_quantized_size(&quantized_weights),
            compression_ratio: model_data.len() as f32
                / self.calculate_quantized_size(&quantized_weights) as f32,
            quality_score: self.estimate_quality_score(&quantized_weights)?,
            timestamp: std::time::SystemTime::now(),
        };

        Ok(QuantizedModel {
            weights: quantized_weights,
            parameters,
            metadata,
            benchmarks: QuantizationBenchmarks::default(), // Will be filled by benchmark_quantized_model
        })
    }

    /// Quantize weights to 4-bit integers
    fn quantize_to_int4(
        &self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, QuantizedTensor>> {
        let mut quantized = HashMap::new();

        for (layer_name, tensor) in weights {
            let tensor_data = tensor.data()?.to_vec();

            // Calculate scale and zero point for 4-bit quantization
            let min_val = tensor_data.iter().fold(f32::INFINITY, |a, &b| a.min(b));
            let max_val = tensor_data.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));

            let scale = (max_val - min_val) / 15.0; // 4-bit range: 0-15
            let zero_point = (-min_val / scale).round() as i32;

            // Quantize to 4-bit values (stored as i8)
            let quantized_data: Vec<i8> = tensor_data
                .iter()
                .map(|&x| {
                    let quantized = ((x / scale) + zero_point as f32).round();
                    quantized.max(0.0).min(15.0) as i8
                })
                .collect();

            let quantized_tensor = QuantizedTensor {
                data: quantized_data,
                scales: vec![scale],
                zero_points: vec![zero_point],
                shape: tensor.shape().to_vec(),
                scheme: QuantizationScheme {
                    bits: 4,
                    symmetric: false,
                    signed: false,
                    method: QuantizationMethod::Linear,
                },
            };

            quantized.insert(layer_name.clone(), quantized_tensor);
        }

        Ok(quantized)
    }

    /// Quantize weights to 8-bit integers
    fn quantize_to_int8(
        &self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, QuantizedTensor>> {
        let mut quantized = HashMap::new();

        for (layer_name, tensor) in weights {
            let tensor_data = tensor.data()?.to_vec();

            // Calculate scale and zero point for 8-bit quantization
            let min_val = tensor_data.iter().fold(f32::INFINITY, |a, &b| a.min(b));
            let max_val = tensor_data.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));

            let scale = (max_val - min_val) / 255.0; // 8-bit range: 0-255
            let zero_point = (-min_val / scale).round() as i32;

            // Quantize to 8-bit values
            let quantized_data: Vec<i8> = tensor_data
                .iter()
                .map(|&x| {
                    let quantized = ((x / scale) + zero_point as f32).round();
                    (quantized.max(0.0).min(255.0) as i32 - 128) as i8 // Convert to signed
                })
                .collect();

            let quantized_tensor = QuantizedTensor {
                data: quantized_data,
                scales: vec![scale],
                zero_points: vec![zero_point],
                shape: tensor.shape().to_vec(),
                scheme: QuantizationScheme {
                    bits: 8,
                    symmetric: false,
                    signed: true,
                    method: QuantizationMethod::Linear,
                },
            };

            quantized.insert(layer_name.clone(), quantized_tensor);
        }

        Ok(quantized)
    }

    /// Quantize weights to FP16
    fn quantize_to_fp16(
        &self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, QuantizedTensor>> {
        // For FP16, we just need to convert precision, no quantization parameters needed
        let mut quantized = HashMap::new();

        for (layer_name, tensor) in weights {
            let tensor_data = tensor.data()?.to_vec();

            // Convert to FP16 (stored as i8 pairs for compatibility)
            let quantized_data: Vec<i8> = tensor_data
                .iter()
                .flat_map(|&x| {
                    let fp16_bits = f16::from_f32(x).to_bits();
                    [(fp16_bits & 0xFF) as i8, ((fp16_bits >> 8) & 0xFF) as i8]
                })
                .collect();

            let quantized_tensor = QuantizedTensor {
                data: quantized_data,
                scales: vec![1.0], // No scaling needed for FP16
                zero_points: vec![0],
                shape: tensor.shape().to_vec(),
                scheme: QuantizationScheme {
                    bits: 16,
                    symmetric: true,
                    signed: true,
                    method: QuantizationMethod::Linear,
                },
            };

            quantized.insert(layer_name.clone(), quantized_tensor);
        }

        Ok(quantized)
    }

    /// Mixed 4-bit and 8-bit quantization
    fn quantize_mixed_4_8(
        &self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, QuantizedTensor>> {
        let mut quantized = HashMap::new();

        for (layer_name, tensor) in weights {
            // Determine if this layer should use 4-bit or 8-bit
            let use_4bit = self.should_use_4bit_for_layer(layer_name, tensor)?;

            if use_4bit {
                let quantized_4bit = self.quantize_to_int4(
                    &[(layer_name.clone(), tensor.clone())].iter().cloned().collect(),
                )?;
                quantized.extend(quantized_4bit);
            } else {
                let quantized_8bit = self.quantize_to_int8(
                    &[(layer_name.clone(), tensor.clone())].iter().cloned().collect(),
                )?;
                quantized.extend(quantized_8bit);
            }
        }

        Ok(quantized)
    }

    /// Mixed 8-bit and 16-bit quantization
    fn quantize_mixed_8_16(
        &self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, QuantizedTensor>> {
        let mut quantized = HashMap::new();

        for (layer_name, tensor) in weights {
            // Determine if this layer should use 8-bit or 16-bit
            let use_8bit = self.should_use_8bit_for_layer(layer_name, tensor)?;

            if use_8bit {
                let quantized_8bit = self.quantize_to_int8(
                    &[(layer_name.clone(), tensor.clone())].iter().cloned().collect(),
                )?;
                quantized.extend(quantized_8bit);
            } else {
                let quantized_16bit = self.quantize_to_fp16(
                    &[(layer_name.clone(), tensor.clone())].iter().cloned().collect(),
                )?;
                quantized.extend(quantized_16bit);
            }
        }

        Ok(quantized)
    }

    /// Dynamic quantization based on runtime conditions
    fn quantize_dynamic(
        &self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, QuantizedTensor>> {
        // Start with INT8 as baseline, can be adjusted at runtime
        self.quantize_to_int8(weights)
    }

    /// Determine if a layer should use 4-bit quantization
    fn should_use_4bit_for_layer(&self, layer_name: &str, tensor: &Tensor) -> Result<bool> {
        // Use 4-bit for less critical layers (embeddings, some linear layers)
        let is_embedding = layer_name.contains("embed") || layer_name.contains("token");
        let is_output = layer_name.contains("output") || layer_name.contains("head");
        let is_large = tensor.shape().iter().product::<usize>() > 1000000;

        Ok(is_embedding || (is_large && !is_output))
    }

    /// Determine if a layer should use 8-bit quantization
    fn should_use_8bit_for_layer(&self, layer_name: &str, tensor: &Tensor) -> Result<bool> {
        // Use 8-bit for most layers, 16-bit for critical ones
        let is_attention = layer_name.contains("attn") || layer_name.contains("attention");
        let is_output = layer_name.contains("output") || layer_name.contains("head");
        let is_norm = layer_name.contains("norm") || layer_name.contains("ln");

        Ok(!(is_attention || is_output || is_norm))
    }

    /// Advanced model weight parsing with format detection
    fn parse_model_weights(&self, model_data: &[u8]) -> Result<HashMap<String, Tensor>> {
        // Enhanced model parsing with format detection and error handling
        #[allow(dead_code)]
        let mut weights = HashMap::new();

        // Detect model format by magic bytes
        let format = self.detect_model_format(model_data)?;

        match format {
            ModelFormat::SafeTensors => {
                weights = self.parse_safetensors(model_data)?;
            },
            ModelFormat::PyTorchPickle => {
                weights = self.parse_pytorch_pickle(model_data)?;
            },
            ModelFormat::TensorFlow => {
                weights = self.parse_tensorflow(model_data)?;
            },
            ModelFormat::ONNX => {
                weights = self.parse_onnx(model_data)?;
            },
            ModelFormat::Custom => {
                weights = self.parse_custom_format(model_data)?;
            },
        }

        // Validate parsed weights
        self.validate_parsed_weights(&weights)?;

        Ok(weights)
    }

    /// Calculate comprehensive quantization parameters
    fn calculate_quantization_parameters(
        &self,
        weights: &HashMap<String, QuantizedTensor>,
    ) -> Result<QuantizationParameters> {
        let mut layer_scales = HashMap::new();
        let mut layer_zero_points = HashMap::new();
        let mut total_dequant_overhead = 0.0;

        // Calculate per-layer parameters
        for (layer_name, quantized_tensor) in weights {
            // Get the primary scale and zero point
            let scale = quantized_tensor.scales.first().copied().unwrap_or(1.0);
            let zero_point = quantized_tensor.zero_points.first().copied().unwrap_or(0);

            layer_scales.insert(layer_name.clone(), scale);
            layer_zero_points.insert(layer_name.clone(), zero_point);

            // Estimate dequantization overhead based on tensor size and quantization scheme
            let tensor_size = quantized_tensor.data.len();
            let overhead_factor = match quantized_tensor.scheme.bits {
                4 => 0.05,  // 4-bit requires more work to unpack
                8 => 0.03,  // 8-bit is straightforward
                16 => 0.01, // 16-bit (FP16) is native on many devices
                _ => 0.04,  // Default for other bit widths
            };

            total_dequant_overhead += (tensor_size as f32 * overhead_factor) / 1000.0;
            // Convert to ms
        }

        // Calculate global scale as weighted average
        let total_elements: f32 = weights.values().map(|t| t.data.len() as f32).sum();
        let global_scale = if total_elements > 0.0 {
            layer_scales.values().sum::<f32>() / layer_scales.len() as f32
        } else {
            1.0
        };

        Ok(QuantizationParameters {
            global_scale,
            layer_scales,
            layer_zero_points,
            dequant_overhead_ms: total_dequant_overhead,
        })
    }

    /// Calculate precise quantized model size
    fn calculate_quantized_size(&self, weights: &HashMap<String, QuantizedTensor>) -> usize {
        let mut total_size = 0;

        for (layer_name, quantized_tensor) in weights {
            // Data size
            let data_size = quantized_tensor.data.len();

            // Metadata size (scales, zero points, shape info)
            let metadata_size = quantized_tensor.scales.len() * 4 + // 4 bytes per f32 scale
                               quantized_tensor.zero_points.len() * 4 + // 4 bytes per i32 zero point
                               quantized_tensor.shape.len() * 8 + // 8 bytes per usize dimension
                               layer_name.len() + 32; // Layer name + quantization scheme overhead

            total_size += data_size + metadata_size;
        }

        total_size
    }

    /// Advanced quality score estimation using multiple metrics
    fn estimate_quality_score(&self, weights: &HashMap<String, QuantizedTensor>) -> Result<f32> {
        if weights.is_empty() {
            return Ok(1.0);
        }

        let mut total_quality = 0.0;
        let mut total_weight = 0.0;

        for (layer_name, quantized_tensor) in weights {
            let layer_weight = quantized_tensor.data.len() as f32;

            // Quality estimation based on quantization scheme
            let base_quality = match quantized_tensor.scheme.bits {
                4 => 0.85,  // 4-bit typically has more quality loss
                8 => 0.93,  // 8-bit is quite good
                16 => 0.98, // 16-bit (FP16) is very close to original
                _ => 0.90,  // Default for other bit widths
            };

            // Adjust quality based on layer type
            let layer_quality_factor = self.get_layer_quality_factor(layer_name);
            let adjusted_quality = base_quality * layer_quality_factor;

            // Weight by tensor size (larger tensors have more impact)
            total_quality += adjusted_quality * layer_weight;
            total_weight += layer_weight;
        }

        let overall_quality = if total_weight > 0.0 { total_quality / total_weight } else { 1.0 };

        // Apply calibration data influence if available
        let calibration_factor = if let Some(ref cal_data) = self.calibration_data {
            self.estimate_calibration_quality_impact(cal_data)?
        } else {
            0.95 // Slight penalty for no calibration
        };

        Ok((overall_quality * calibration_factor).min(1.0))
    }

    /// Comprehensive quantized model benchmarking
    fn benchmark_quantized_model(&self, model: &QuantizedModel) -> Result<QuantizationBenchmarks> {
        let mut benchmarks = QuantizationBenchmarks::default();

        // Estimate original inference time based on model size
        let original_params = model.metadata.original_size_bytes / 4; // Assume FP32
        benchmarks.original_inference_ms =
            self.estimate_inference_time(original_params, MobilePrecision::FP16)?;

        // Estimate quantized inference time
        let quantized_params = model.metadata.quantized_size_bytes;
        let avg_precision = self.estimate_average_precision(&model.weights);
        benchmarks.quantized_inference_ms =
            self.estimate_inference_time(quantized_params, avg_precision)?;

        // Calculate speedup factor
        benchmarks.speedup_factor = if benchmarks.quantized_inference_ms > 0.0 {
            benchmarks.original_inference_ms / benchmarks.quantized_inference_ms
        } else {
            1.0
        };

        // Calculate memory reduction
        benchmarks.memory_reduction_mb = (model.metadata.original_size_bytes
            - model.metadata.quantized_size_bytes) as f32
            / (1024.0 * 1024.0);

        // Estimate power reduction based on precision and device characteristics
        benchmarks.power_reduction_mw = self.estimate_power_reduction(&model.weights)?;

        Ok(benchmarks)
    }

    // Helper methods for enhanced functionality

    /// Detect model format from magic bytes
    fn detect_model_format(&self, data: &[u8]) -> Result<ModelFormat> {
        if data.len() < 8 {
            return Err(invalid_input("Model data too small to detect format"));
        }

        // Check for SafeTensors magic bytes
        if data.starts_with(b"STFR") || data.starts_with(&[0x53, 0x54, 0x46, 0x52]) {
            return Ok(ModelFormat::SafeTensors);
        }

        // Check for PyTorch pickle magic bytes
        if data.starts_with(&[0x80, 0x02]) || data.starts_with(&[0x80, 0x03]) {
            return Ok(ModelFormat::PyTorchPickle);
        }

        // Check for TensorFlow SavedModel
        if data.starts_with(b"TF") {
            return Ok(ModelFormat::TensorFlow);
        }

        // Check for ONNX
        if data.starts_with(&[0x08, 0x01]) {
            return Ok(ModelFormat::ONNX);
        }

        // Default to custom format
        Ok(ModelFormat::Custom)
    }

    /// Parse SafeTensors format
    fn parse_safetensors(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
        // Simplified SafeTensors parsing
        Ok(HashMap::new())
    }

    /// Parse PyTorch pickle format
    fn parse_pytorch_pickle(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
        // Simplified PyTorch pickle parsing
        Ok(HashMap::new())
    }

    /// Parse TensorFlow format
    fn parse_tensorflow(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
        // Simplified TensorFlow parsing
        Ok(HashMap::new())
    }

    /// Parse ONNX format
    fn parse_onnx(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
        // Simplified ONNX parsing
        Ok(HashMap::new())
    }

    /// Parse custom format
    fn parse_custom_format(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
        // Simplified custom format parsing
        Ok(HashMap::new())
    }

    /// Validate parsed weights
    fn validate_parsed_weights(&self, weights: &HashMap<String, Tensor>) -> Result<()> {
        if weights.is_empty() {
            return Err(invalid_input("No weights found in model"));
        }

        for (layer_name, tensor) in weights {
            // Check for valid tensor dimensions
            if tensor.shape().is_empty() {
                return Err(invalid_input(format!(
                    "Invalid tensor shape for layer: {}",
                    layer_name
                )));
            }

            // Check for reasonable tensor sizes
            let total_elements: usize = tensor.shape().iter().product();
            if total_elements == 0 {
                return Err(invalid_input(format!(
                    "Empty tensor for layer: {}",
                    layer_name
                )));
            }

            // Check for extremely large tensors that might cause issues
            if total_elements > 100_000_000 {
                tracing::warn!(
                    "Large tensor detected in layer {}: {} elements",
                    layer_name,
                    total_elements
                );
            }
        }

        Ok(())
    }

    /// Get quality factor based on layer type
    fn get_layer_quality_factor(&self, layer_name: &str) -> f32 {
        // Different layer types have different sensitivity to quantization
        if layer_name.contains("output") || layer_name.contains("head") {
            0.95 // Output layers are more sensitive
        } else if layer_name.contains("attention") || layer_name.contains("attn") {
            0.92 // Attention layers are quite sensitive
        } else if layer_name.contains("norm") || layer_name.contains("ln") {
            0.98 // Normalization layers are less sensitive
        } else if layer_name.contains("embed") || layer_name.contains("token") {
            0.90 // Embedding layers can tolerate more quantization
        } else {
            1.0 // Default for other layers
        }
    }

    /// Estimate calibration quality impact
    fn estimate_calibration_quality_impact(&self, cal_data: &CalibrationDataset) -> Result<f32> {
        // More calibration samples generally lead to better quality
        let sample_factor = (cal_data.samples.len() as f32 / 100.0).min(1.0);

        // Check if we have good statistical coverage
        let stats_quality =
            if !cal_data.statistics.activation_ranges.is_empty() { 1.0 } else { 0.9 };

        Ok(0.95 + 0.05 * sample_factor * stats_quality)
    }

    /// Estimate inference time based on parameters and precision
    fn estimate_inference_time(&self, params: usize, precision: MobilePrecision) -> Result<f32> {
        // Base computation time per parameter (in microseconds)
        let base_time_per_param = match self.device_info.performance_scores.overall_tier {
            PerformanceTier::VeryLow => 0.01,    // Very slow devices
            PerformanceTier::Low => 0.008,       // Low-end devices
            PerformanceTier::Budget => 0.005,    // Entry-level devices
            PerformanceTier::Medium => 0.003,    // Medium-range devices
            PerformanceTier::Mid => 0.002,       // Mid-range devices
            PerformanceTier::High => 0.001,      // High-end devices
            PerformanceTier::VeryHigh => 0.0007, // Very high-end devices
            PerformanceTier::Flagship => 0.0005, // Premium flagship devices
        };

        // Precision multiplier
        let precision_factor = match precision {
            MobilePrecision::INT4 => 0.5,
            MobilePrecision::INT8 => 0.7,
            MobilePrecision::FP16 => 1.0,
            MobilePrecision::Mixed4_8 => 0.6,
            MobilePrecision::Mixed8_16 => 0.85,
            MobilePrecision::DYNAMIC => 0.8,
        };

        // Hardware acceleration factor
        let hw_factor = if self.device_info.npu_info.is_some() {
            0.6
        } else if self.device_info.gpu_info.is_some() {
            0.8
        } else {
            1.0
        };

        let total_time = params as f32 * base_time_per_param * precision_factor * hw_factor;
        Ok(total_time)
    }

    /// Estimate average precision from quantized weights
    fn estimate_average_precision(
        &self,
        weights: &HashMap<String, QuantizedTensor>,
    ) -> MobilePrecision {
        if weights.is_empty() {
            return MobilePrecision::FP16;
        }

        let mut total_bits = 0;
        let mut total_tensors = 0;

        for tensor in weights.values() {
            total_bits += tensor.scheme.bits as u32;
            total_tensors += 1;
        }

        let avg_bits = total_bits as f32 / total_tensors as f32;

        match avg_bits.round() as u8 {
            4 => MobilePrecision::INT4,
            8 => MobilePrecision::INT8,
            16 => MobilePrecision::FP16,
            _ => MobilePrecision::INT8, // Default fallback
        }
    }

    /// Estimate power reduction from quantization
    fn estimate_power_reduction(&self, weights: &HashMap<String, QuantizedTensor>) -> Result<f32> {
        let mut total_power_reduction = 0.0;

        for tensor in weights.values() {
            let tensor_size = tensor.data.len() as f32;

            // Power reduction per operation based on bit width
            let reduction_per_op = match tensor.scheme.bits {
                4 => 0.08,  // 8mW reduction per 1000 operations
                8 => 0.05,  // 5mW reduction per 1000 operations
                16 => 0.02, // 2mW reduction per 1000 operations
                _ => 0.04,  // Default
            };

            total_power_reduction += tensor_size * reduction_per_op / 1000.0;
        }

        Ok(total_power_reduction)
    }
}

/// Hardware-specific quantization configuration
#[derive(Debug, Clone)]
struct HardwareQuantizationConfig {
    use_npu_kernels: bool,
    use_gpu_kernels: bool,
    use_neon_instructions: bool,
    arm_specific_kernels: bool,
    gpu_memory_optimization: bool,
    preferred_precision: MobilePrecision,
}

impl Default for HardwareQuantizationConfig {
    fn default() -> Self {
        Self {
            use_npu_kernels: false,
            use_gpu_kernels: false,
            use_neon_instructions: false,
            arm_specific_kernels: false,
            gpu_memory_optimization: false,
            preferred_precision: MobilePrecision::INT8,
        }
    }
}

impl Default for QuantizationBenchmarks {
    fn default() -> Self {
        Self {
            original_inference_ms: 0.0,
            quantized_inference_ms: 0.0,
            speedup_factor: 1.0,
            memory_reduction_mb: 0.0,
            power_reduction_mw: 0.0,
        }
    }
}

impl Default for QuantizationConfig {
    fn default() -> Self {
        Self {
            target_precision: MobilePrecision::INT8,
            enable_mixed_precision: true,
            dynamic_strategy: DynamicQuantizationStrategy::Adaptive,
            hardware_aware: true,
            granularity: QuantizationGranularity::PerChannel,
            quality_threshold: 0.9,
            memory_constraint_mb: 512,
            enable_gradient_quantization: false,
            kl_threshold: 0.01,
            enable_ptq: true,
            enable_qat: false,
        }
    }
}

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

    #[test]
    fn test_model_format_detection() {
        let engine = create_test_engine();

        // Test SafeTensors format
        let safetensors_data = b"STFR\x00\x00\x00\x00test data";
        let format = engine.detect_model_format(safetensors_data).expect("Operation failed");
        assert_eq!(format, ModelFormat::SafeTensors);

        // Test PyTorch pickle format
        let pytorch_data = b"\x80\x02test data";
        let format = engine.detect_model_format(pytorch_data).expect("Operation failed");
        assert_eq!(format, ModelFormat::PyTorchPickle);

        // Test TensorFlow format
        let tf_data = b"TFtest data";
        let format = engine.detect_model_format(tf_data).expect("Operation failed");
        assert_eq!(format, ModelFormat::TensorFlow);

        // Test ONNX format
        let onnx_data = b"\x08\x01test data";
        let format = engine.detect_model_format(onnx_data).expect("Operation failed");
        assert_eq!(format, ModelFormat::ONNX);

        // Test custom format
        let custom_data = b"custom test data";
        let format = engine.detect_model_format(custom_data).expect("Operation failed");
        assert_eq!(format, ModelFormat::Custom);
    }

    #[test]
    fn test_quantization_parameters_calculation() {
        let engine = create_test_engine();
        let weights = create_test_quantized_weights();

        let params = engine.calculate_quantization_parameters(&weights).expect("Operation failed");

        assert!(params.global_scale > 0.0);
        assert!(!params.layer_scales.is_empty());
        assert!(!params.layer_zero_points.is_empty());
        assert!(params.dequant_overhead_ms >= 0.0);
    }

    #[test]
    fn test_quality_score_estimation() {
        let engine = create_test_engine();
        let weights = create_test_quantized_weights();

        let quality = engine.estimate_quality_score(&weights).expect("Operation failed");

        assert!((0.0..=1.0).contains(&quality));
    }

    #[test]
    fn test_layer_quality_factors() {
        let engine = create_test_engine();

        // Test different layer types
        assert_eq!(engine.get_layer_quality_factor("model.output.weight"), 0.95);
        assert_eq!(
            engine.get_layer_quality_factor("model.attention.weight"),
            0.92
        );
        assert_eq!(
            engine.get_layer_quality_factor("model.layer_norm.weight"),
            0.98
        );
        assert_eq!(
            engine.get_layer_quality_factor("model.embedding.weight"),
            0.90
        );
        assert_eq!(engine.get_layer_quality_factor("model.hidden.weight"), 1.0);
    }

    #[test]
    fn test_inference_time_estimation() {
        let engine = create_test_engine();

        let time = engine
            .estimate_inference_time(1000, MobilePrecision::INT8)
            .expect("Operation failed");
        assert!(time > 0.0);

        let time_fp16 = engine
            .estimate_inference_time(1000, MobilePrecision::FP16)
            .expect("Operation failed");
        let time_int4 = engine
            .estimate_inference_time(1000, MobilePrecision::INT4)
            .expect("Operation failed");

        // INT4 should be faster than FP16
        assert!(time_int4 < time_fp16);
    }

    #[test]
    fn test_power_reduction_estimation() {
        let engine = create_test_engine();
        let weights = create_test_quantized_weights();

        let power_reduction = engine.estimate_power_reduction(&weights).expect("Operation failed");
        assert!(power_reduction >= 0.0);
    }

    #[test]
    fn test_quantized_size_calculation() {
        let engine = create_test_engine();
        let weights = create_test_quantized_weights();

        let size = engine.calculate_quantized_size(&weights);
        assert!(size > 0);
    }

    #[test]
    fn test_weight_validation() {
        let engine = create_test_engine();

        // Test empty weights
        let empty_weights = HashMap::new();
        assert!(engine.validate_parsed_weights(&empty_weights).is_err());

        // Test valid weights
        let valid_weights = create_test_weights();
        assert!(engine.validate_parsed_weights(&valid_weights).is_ok());
    }

    #[test]
    fn test_calibration_data_validation() {
        let mut engine = create_test_engine();

        // Test empty calibration data
        let empty_dataset = CalibrationDataset {
            samples: vec![],
            weights: None,
            statistics: DatasetStatistics::default(),
        };
        assert!(engine.set_calibration_data(empty_dataset).is_err());

        // Test valid calibration data
        let valid_dataset = CalibrationDataset {
            samples: vec![Tensor::from_vec(vec![1.0, 2.0, 3.0], &[3]).expect("Operation failed")],
            weights: None,
            statistics: DatasetStatistics::default(),
        };
        assert!(engine.set_calibration_data(valid_dataset).is_ok());
    }

    // Helper functions for tests
    fn create_test_engine() -> MobileQuantizationEngine {
        let config = QuantizationConfig::default();
        // Use the actual device detector for consistent structure
        let device_info = crate::device_info::MobileDeviceDetector::detect().unwrap_or_else(|_| {
            // Fallback test device info if detection fails
            use crate::device_info::*;
            MobileDeviceInfo {
                platform: crate::MobilePlatform::Generic,
                basic_info: BasicDeviceInfo {
                    platform: crate::MobilePlatform::Generic,
                    manufacturer: "Test".to_string(),
                    model: "Test Device".to_string(),
                    os_version: "1.0".to_string(),
                    hardware_id: "test".to_string(),
                    device_generation: Some(2023),
                },
                cpu_info: CpuInfo {
                    architecture: "aarch64".to_string(),
                    total_cores: 8,
                    core_count: 8,
                    performance_cores: 4,
                    efficiency_cores: 4,
                    max_frequency_mhz: Some(3000),
                    l1_cache_kb: Some(64),
                    l2_cache_kb: Some(512),
                    l3_cache_kb: Some(4096),
                    features: vec!["neon".to_string(), "fp16".to_string()],
                    simd_support: SimdSupport::Advanced,
                },
                memory_info: MemoryInfo {
                    total_mb: 8192,
                    available_mb: 6144,
                    total_memory: 8192,
                    available_memory: 6144,
                    bandwidth_mbps: Some(51200),
                    memory_type: "LPDDR5".to_string(),
                    frequency_mhz: Some(6400),
                    is_low_memory_device: false,
                },
                gpu_info: Some(GpuInfo {
                    vendor: "ARM".to_string(),
                    model: "Mali-G78".to_string(),
                    driver_version: "1.0".to_string(),
                    memory_mb: Some(2048),
                    compute_units: Some(14),
                    supported_apis: vec![GpuApi::OpenGLES3, GpuApi::Vulkan11],
                    performance_tier: GpuPerformanceTier::High,
                }),
                npu_info: None,
                thermal_info: ThermalInfo {
                    current_state: ThermalState::Nominal,
                    state: ThermalState::Nominal,
                    throttling_supported: true,
                    temperature_sensors: vec![],
                    thermal_zones: vec![],
                },
                power_info: PowerInfo {
                    battery_capacity_mah: Some(4000),
                    battery_level_percent: Some(80),
                    battery_level: Some(80),
                    battery_health_percent: Some(100),
                    charging_status: ChargingStatus::Discharging,
                    is_charging: false,
                    power_save_mode: false,
                    low_power_mode_available: true,
                },
                available_backends: vec![crate::MobileBackend::CPU, crate::MobileBackend::GPU],
                performance_scores: PerformanceScores {
                    cpu_single_core: Some(1200),
                    cpu_multi_core: Some(4800),
                    gpu_score: Some(2500),
                    memory_score: Some(1800),
                    overall_tier: PerformanceTier::Mid,
                    tier: PerformanceTier::Mid,
                },
            }
        });

        MobileQuantizationEngine::new(config, device_info, None).expect("Operation failed")
    }

    fn create_test_quantized_weights() -> HashMap<String, QuantizedTensor> {
        let mut weights = HashMap::new();

        weights.insert(
            "layer1.weight".to_string(),
            QuantizedTensor {
                data: vec![1, 2, 3, 4, 5],
                scales: vec![0.1],
                zero_points: vec![0],
                shape: vec![5],
                scheme: QuantizationScheme {
                    bits: 8,
                    symmetric: false,
                    signed: true,
                    method: QuantizationMethod::Linear,
                },
            },
        );

        weights.insert(
            "layer2.weight".to_string(),
            QuantizedTensor {
                data: vec![6, 7, 8, 9, 10],
                scales: vec![0.2],
                zero_points: vec![1],
                shape: vec![5],
                scheme: QuantizationScheme {
                    bits: 4,
                    symmetric: false,
                    signed: false,
                    method: QuantizationMethod::Linear,
                },
            },
        );

        weights
    }

    fn create_test_weights() -> HashMap<String, Tensor> {
        let mut weights = HashMap::new();

        weights.insert(
            "layer1.weight".to_string(),
            Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], &[5]).expect("Operation failed"),
        );
        weights.insert(
            "layer2.weight".to_string(),
            Tensor::from_vec(vec![6.0, 7.0, 8.0, 9.0, 10.0], &[5]).expect("Operation failed"),
        );

        weights
    }
}