trustformers-mobile 0.1.1

//! Advanced Model Compression for Mobile Deployment
//!
//! This module provides sophisticated model compression techniques optimized
//! for mobile deployment, including dynamic quantization, pruning, distillation,
//! and adaptive compression strategies.

use crate::{device_info::MobileDeviceInfo, PerformanceTier};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use trustformers_core::error::{CoreError, Result};
use trustformers_core::Tensor;
use trustformers_core::TrustformersError;

/// Advanced model compression system
pub struct MobileCompressionEngine {
    config: CompressionConfig,
    quantizer: DynamicQuantizer,
    pruner: MobilePruner,
    distillation_engine: Option<KnowledgeDistiller>,
    compression_stats: CompressionStats,
}

/// Comprehensive compression configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressionConfig {
    /// Target compression ratio (0.0 to 1.0)
    pub target_compression_ratio: f32,
    /// Quantization strategy
    pub quantization_strategy: QuantizationStrategy,
    /// Pruning strategy
    pub pruning_strategy: PruningStrategy,
    /// Enable knowledge distillation
    pub enable_distillation: bool,
    /// Distillation configuration
    pub distillation_config: Option<DistillationConfig>,
    /// Progressive compression settings
    pub progressive_compression: ProgressiveCompressionConfig,
    /// Quality preservation settings
    pub quality_preservation: QualityPreservationConfig,
    /// Device-adaptive settings
    pub device_adaptive: bool,
}

/// Dynamic quantization strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantizationStrategy {
    /// Static quantization with fixed precision
    Static(QuantizationPrecision),
    /// Dynamic quantization based on layer sensitivity
    Dynamic,
    /// Mixed precision quantization
    MixedPrecision,
    /// Block-wise quantization
    BlockWise,
    /// Outlier-aware quantization
    OutlierAware,
    /// Device-adaptive quantization
    DeviceAdaptive,
}

/// Quantization precision formats
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantizationPrecision {
    /// 1-bit quantization (binary)
    Int1,
    /// 2-bit quantization
    Int2,
    /// 4-bit quantization
    Int4,
    /// 8-bit quantization
    Int8,
    /// 16-bit floating point
    FP16,
    /// 16-bit brain floating point
    BF16,
    /// Custom precision
    Custom { bits: u8 },
    /// Dynamic precision based on value range
    Dynamic,
}

/// Pruning strategies for mobile optimization
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum PruningStrategy {
    /// No pruning
    None,
    /// Magnitude-based pruning
    MagnitudeBased { sparsity: f32 },
    /// Structured pruning (channel/filter pruning)
    Structured { ratio: f32 },
    /// Gradual magnitude pruning
    GradualMagnitude {
        initial_sparsity: f32,
        final_sparsity: f32,
        steps: usize,
    },
    /// Layer-wise adaptive pruning
    LayerAdaptive,
    /// Hardware-aware pruning
    HardwareAware,
}

/// Knowledge distillation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistillationConfig {
    /// Temperature for distillation
    pub temperature: f32,
    /// Weight for distillation loss
    pub distillation_weight: f32,
    /// Weight for hard target loss
    pub hard_target_weight: f32,
    /// Distillation strategy
    pub strategy: DistillationStrategy,
    /// Feature matching configuration
    pub feature_matching: Option<FeatureMatchingConfig>,
}

/// Distillation strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DistillationStrategy {
    /// Standard output distillation
    OutputOnly,
    /// Feature-level distillation
    FeatureLevel,
    /// Attention transfer
    AttentionTransfer,
    /// Progressive distillation
    Progressive,
    /// Online distillation
    Online,
}

/// Feature matching configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureMatchingConfig {
    /// Layers to match features
    pub target_layers: Vec<String>,
    /// Feature matching weight
    pub matching_weight: f32,
    /// Feature transformation method
    pub transformation: FeatureTransformation,
}

/// Feature transformation methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum FeatureTransformation {
    /// No transformation
    None,
    /// Linear projection
    Linear,
    /// Attention-based
    Attention,
    /// Convolutional
    Convolutional,
}

/// Progressive compression configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProgressiveCompressionConfig {
    /// Enable progressive compression
    pub enabled: bool,
    /// Number of compression stages
    pub stages: usize,
    /// Compression schedule
    pub schedule: CompressionSchedule,
    /// Quality validation frequency
    pub validation_frequency: usize,
}

/// Compression schedule types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CompressionSchedule {
    /// Linear compression schedule
    Linear,
    /// Exponential compression schedule
    Exponential,
    /// Cosine annealing schedule
    CosineAnnealing,
    /// Custom schedule
    Custom,
}

/// Quality preservation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QualityPreservationConfig {
    /// Maximum acceptable quality degradation (0.0 to 1.0)
    pub max_quality_loss: f32,
    /// Quality metrics to monitor
    pub quality_metrics: Vec<QualityMetric>,
    /// Recovery strategies when quality drops
    pub recovery_strategies: Vec<QualityRecoveryStrategy>,
    /// Early stopping configuration
    pub early_stopping: EarlyStoppingConfig,
}

/// Quality metrics for monitoring compression
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QualityMetric {
    /// Perplexity for language models
    Perplexity,
    /// Accuracy for classification
    Accuracy,
    /// F1 score
    F1Score,
    /// BLEU score for translation
    BleuScore,
    /// Structural similarity
    StructuralSimilarity,
    /// Custom metric
    Custom,
}

/// Quality recovery strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QualityRecoveryStrategy {
    /// Reduce compression aggressiveness
    ReduceCompression,
    /// Increase model capacity
    IncreaseCapacity,
    /// Fine-tune on quality dataset
    QualityFineTuning,
    /// Rollback to previous checkpoint
    Rollback,
}

/// Early stopping configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EarlyStoppingConfig {
    /// Enable early stopping
    pub enabled: bool,
    /// Patience (number of validation steps)
    pub patience: usize,
    /// Minimum improvement threshold
    pub min_improvement: f32,
    /// Quality metric to monitor
    pub metric: QualityMetric,
}

/// Dynamic quantizer for adaptive precision
struct DynamicQuantizer {
    calibration_data: Vec<Tensor>,
    layer_sensitivities: HashMap<String, f32>,
    quantization_cache: HashMap<String, QuantizedLayer>,
    precision_mapping: HashMap<String, QuantizationPrecision>,
}

/// Quantized layer representation
#[derive(Debug, Clone)]
struct QuantizedLayer {
    weights: Tensor,
    scales: Tensor,
    zero_points: Option<Tensor>,
    precision: QuantizationPrecision,
    compression_ratio: f32,
}

/// Mobile-optimized pruner
struct MobilePruner {
    importance_scores: HashMap<String, Tensor>,
    pruning_masks: HashMap<String, Tensor>,
    structured_masks: HashMap<String, Vec<bool>>,
    pruning_history: Vec<PruningStep>,
}

/// Pruning step record
#[derive(Debug, Clone)]
struct PruningStep {
    step: usize,
    layer_name: String,
    pruning_ratio: f32,
    importance_threshold: f32,
    quality_impact: f32,
}

/// Knowledge distillation engine
struct KnowledgeDistiller {
    teacher_model: Option<Box<dyn TeacherModel>>,
    distillation_config: DistillationConfig,
    feature_extractors: HashMap<String, FeatureExtractor>,
    distillation_losses: Vec<f32>,
}

/// Teacher model trait for distillation
trait TeacherModel {
    fn forward(&self, input: &Tensor) -> Result<Tensor>;
    fn extract_features(
        &self,
        input: &Tensor,
        layer_names: &[String],
    ) -> Result<HashMap<String, Tensor>>;
    fn get_attention_weights(&self, input: &Tensor) -> Result<Vec<Tensor>>;
}

/// Feature extractor for intermediate representations
#[derive(Debug, Clone)]
struct FeatureExtractor {
    layer_name: String,
    transformation: FeatureTransformation,
    target_dim: Option<usize>,
}

/// Compression statistics and metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressionStats {
    /// Original model size (MB)
    pub original_size_mb: f32,
    /// Compressed model size (MB)
    pub compressed_size_mb: f32,
    /// Compression ratio achieved
    pub compression_ratio: f32,
    /// Quantization statistics
    pub quantization_stats: QuantizationStats,
    /// Pruning statistics
    pub pruning_stats: PruningStats,
    /// Quality preservation metrics
    pub quality_metrics: HashMap<String, f32>,
    /// Inference speedup
    pub inference_speedup: f32,
    /// Memory reduction
    pub memory_reduction_percent: f32,
    /// Energy efficiency improvement
    pub energy_efficiency_improvement: f32,
}

/// Quantization-specific statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationStats {
    /// Layers quantized
    pub quantized_layers: usize,
    /// Average bits per weight
    pub avg_bits_per_weight: f32,
    /// Precision distribution
    pub precision_distribution: HashMap<String, usize>,
    /// Quantization error
    pub quantization_error: f32,
}

/// Pruning-specific statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PruningStats {
    /// Overall sparsity achieved
    pub overall_sparsity: f32,
    /// Layer-wise sparsity
    pub layer_sparsity: HashMap<String, f32>,
    /// Structured pruning ratio
    pub structured_pruning_ratio: f32,
    /// Parameters removed
    pub parameters_removed: usize,
}

impl Default for CompressionConfig {
    fn default() -> Self {
        Self {
            target_compression_ratio: 0.25, // 4x compression
            quantization_strategy: QuantizationStrategy::Dynamic,
            pruning_strategy: PruningStrategy::GradualMagnitude {
                initial_sparsity: 0.1,
                final_sparsity: 0.5,
                steps: 10,
            },
            enable_distillation: false,
            distillation_config: None,
            progressive_compression: ProgressiveCompressionConfig::default(),
            quality_preservation: QualityPreservationConfig::default(),
            device_adaptive: true,
        }
    }
}

impl Default for ProgressiveCompressionConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            stages: 5,
            schedule: CompressionSchedule::Linear,
            validation_frequency: 100,
        }
    }
}

impl Default for QualityPreservationConfig {
    fn default() -> Self {
        Self {
            max_quality_loss: 0.05, // 5% quality loss tolerance
            quality_metrics: vec![QualityMetric::Perplexity],
            recovery_strategies: vec![
                QualityRecoveryStrategy::ReduceCompression,
                QualityRecoveryStrategy::QualityFineTuning,
            ],
            early_stopping: EarlyStoppingConfig {
                enabled: true,
                patience: 10,
                min_improvement: 0.001,
                metric: QualityMetric::Perplexity,
            },
        }
    }
}

impl MobileCompressionEngine {
    /// Create new compression engine
    pub fn new(config: CompressionConfig, device_info: &MobileDeviceInfo) -> Result<Self> {
        let quantizer = DynamicQuantizer::new();
        let pruner = MobilePruner::new();
        let distillation_engine = if config.enable_distillation {
            Some(KnowledgeDistiller::new(
                config.distillation_config.clone().unwrap_or_default(),
            )?)
        } else {
            None
        };

        let mut compression_engine = Self {
            config,
            quantizer,
            pruner,
            distillation_engine,
            compression_stats: CompressionStats::new(),
        };

        // Adapt configuration for device capabilities
        if compression_engine.config.device_adaptive {
            compression_engine.adapt_for_device(device_info)?;
        }

        Ok(compression_engine)
    }

    /// Compress a model using the configured strategies
    pub fn compress_model(
        &mut self,
        model_weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        tracing::info!(
            "Starting model compression with target ratio: {}",
            self.config.target_compression_ratio
        );

        let original_size = self.calculate_model_size(model_weights);
        self.compression_stats.original_size_mb = original_size;

        let mut compressed_weights = model_weights.clone();

        // Stage 1: Dynamic Quantization
        if !matches!(
            self.config.quantization_strategy,
            QuantizationStrategy::Static(QuantizationPrecision::FP16)
        ) {
            compressed_weights = self.apply_quantization(&compressed_weights)?;
            tracing::info!("Applied quantization");
        }

        // Stage 2: Pruning
        if !matches!(self.config.pruning_strategy, PruningStrategy::None) {
            compressed_weights = self.apply_pruning(&compressed_weights)?;
            tracing::info!("Applied pruning");
        }

        // Stage 3: Knowledge Distillation (if enabled)
        if let Some(ref mut distiller) = self.distillation_engine {
            compressed_weights = distiller.apply_distillation(&compressed_weights)?;
            tracing::info!("Applied knowledge distillation");
        }

        // Calculate final compression statistics
        let compressed_size = self.calculate_model_size(&compressed_weights);
        self.compression_stats.compressed_size_mb = compressed_size;
        self.compression_stats.compression_ratio = compressed_size / original_size;

        tracing::info!(
            "Compression completed: {:.1}MB -> {:.1}MB ({:.2}x compression)",
            original_size,
            compressed_size,
            1.0 / self.compression_stats.compression_ratio
        );

        Ok(compressed_weights)
    }

    /// Apply progressive compression over multiple stages
    pub fn progressive_compress(
        &mut self,
        model_weights: &HashMap<String, Tensor>,
        validation_fn: Option<Box<dyn Fn(&HashMap<String, Tensor>) -> Result<f32>>>,
    ) -> Result<HashMap<String, Tensor>> {
        if !self.config.progressive_compression.enabled {
            return self.compress_model(model_weights);
        }

        let stages = self.config.progressive_compression.stages;
        let mut current_weights = model_weights.clone();
        let mut best_weights = model_weights.clone();
        let mut best_quality = f32::NEG_INFINITY;

        for stage in 0..stages {
            tracing::info!("Progressive compression stage {}/{}", stage + 1, stages);

            // Adjust compression aggressiveness for this stage
            let stage_ratio = (stage + 1) as f32 / stages as f32;
            let target_ratio = self.interpolate_compression_ratio(stage_ratio);

            // Create stage-specific configuration
            let mut stage_config = self.config.clone();
            stage_config.target_compression_ratio = target_ratio;

            // Apply compression for this stage
            let stage_weights = self.compress_stage(&current_weights, &stage_config)?;

            // Validate quality if validation function provided
            if let Some(ref validate) = validation_fn {
                let quality = validate(&stage_weights)?;

                if quality > best_quality {
                    best_quality = quality;
                    best_weights = stage_weights.clone();
                }

                // Check if quality degradation is acceptable
                let original_quality = validate(model_weights)?;
                let quality_loss = (original_quality - quality) / original_quality;

                if quality_loss > self.config.quality_preservation.max_quality_loss {
                    tracing::warn!(
                        "Quality loss ({:.3}) exceeds threshold ({:.3}), stopping progressive compression",
                        quality_loss,
                        self.config.quality_preservation.max_quality_loss
                    );
                    break;
                }
            }

            current_weights = stage_weights;
        }

        Ok(if validation_fn.is_some() { best_weights } else { current_weights })
    }

    /// Create device-optimized compression configuration
    pub fn create_device_optimized_config(device_info: &MobileDeviceInfo) -> CompressionConfig {
        let mut config = CompressionConfig::default();

        match device_info.performance_scores.overall_tier {
            PerformanceTier::VeryLow | PerformanceTier::Low => {
                // Maximum compression for very low-end devices
                config.target_compression_ratio = 0.1; // 10x compression
                config.quantization_strategy =
                    QuantizationStrategy::Static(QuantizationPrecision::Int4);
                config.pruning_strategy = PruningStrategy::GradualMagnitude {
                    initial_sparsity: 0.3,
                    final_sparsity: 0.8,
                    steps: 20,
                };
                config.quality_preservation.max_quality_loss = 0.12; // Accept high quality loss
            },
            PerformanceTier::Budget => {
                // Aggressive compression for budget devices
                config.target_compression_ratio = 0.15; // 6.7x compression
                config.quantization_strategy =
                    QuantizationStrategy::Static(QuantizationPrecision::Int4);
                config.pruning_strategy = PruningStrategy::GradualMagnitude {
                    initial_sparsity: 0.2,
                    final_sparsity: 0.7,
                    steps: 15,
                };
                config.quality_preservation.max_quality_loss = 0.08; // Accept more quality loss
            },
            PerformanceTier::Medium | PerformanceTier::Mid => {
                // Balanced compression for mid-range devices
                config.target_compression_ratio = 0.25; // 4x compression
                config.quantization_strategy = QuantizationStrategy::MixedPrecision;
                config.pruning_strategy = PruningStrategy::LayerAdaptive;
                config.quality_preservation.max_quality_loss = 0.05;
            },
            PerformanceTier::High => {
                // Conservative compression for high-end devices
                config.target_compression_ratio = 0.4; // 2.5x compression
                config.quantization_strategy = QuantizationStrategy::Dynamic;
                config.pruning_strategy = PruningStrategy::Structured { ratio: 0.3 };
                config.quality_preservation.max_quality_loss = 0.03;
            },
            PerformanceTier::VeryHigh | PerformanceTier::Flagship => {
                // Minimal compression for flagship devices
                config.target_compression_ratio = 0.6; // 1.67x compression
                config.quantization_strategy =
                    QuantizationStrategy::Static(QuantizationPrecision::FP16);
                config.pruning_strategy = PruningStrategy::MagnitudeBased { sparsity: 0.2 };
                config.quality_preservation.max_quality_loss = 0.02;
            },
        }

        // Adjust for memory constraints
        if device_info.memory_info.total_mb < 2048 {
            // Very aggressive compression for low-memory devices
            config.target_compression_ratio *= 0.7;
            config.quantization_strategy =
                QuantizationStrategy::Static(QuantizationPrecision::Int4);
        }

        // Adjust for NPU availability
        if device_info.npu_info.is_some() {
            // NPUs often support specific quantization formats better
            config.quantization_strategy = QuantizationStrategy::DeviceAdaptive;
        }

        config
    }

    /// Get compression statistics
    pub fn get_stats(&self) -> &CompressionStats {
        &self.compression_stats
    }

    /// Estimate compression benefits for configuration
    pub fn estimate_compression_benefits(
        &self,
        model_size_mb: f32,
        device_info: &MobileDeviceInfo,
    ) -> CompressionBenefits {
        let compression_ratio = self.config.target_compression_ratio;
        let compressed_size = model_size_mb * compression_ratio;

        // Estimate inference speedup based on compression type
        let speedup_factor = match self.config.quantization_strategy {
            QuantizationStrategy::Static(QuantizationPrecision::Int4) => 3.5,
            QuantizationStrategy::Static(QuantizationPrecision::Int8) => 2.8,
            QuantizationStrategy::Static(QuantizationPrecision::FP16) => 1.8,
            QuantizationStrategy::Dynamic => 2.2,
            QuantizationStrategy::MixedPrecision => 2.5,
            _ => 2.0,
        };

        // Adjust for pruning
        let pruning_speedup = match self.config.pruning_strategy {
            PruningStrategy::None => 1.0,
            PruningStrategy::MagnitudeBased { sparsity } => 1.0 + sparsity * 0.5,
            PruningStrategy::Structured { ratio } => 1.0 + ratio * 0.8,
            _ => 1.3,
        };

        let total_speedup = speedup_factor * pruning_speedup;

        // Estimate memory reduction
        let memory_reduction = 1.0 - compression_ratio;

        // Estimate energy efficiency (rough approximation)
        let energy_efficiency = total_speedup * (1.0 + memory_reduction * 0.3);

        CompressionBenefits {
            size_reduction_mb: model_size_mb - compressed_size,
            compression_ratio: 1.0 / compression_ratio,
            estimated_speedup: total_speedup,
            memory_reduction_percent: memory_reduction * 100.0,
            energy_efficiency_gain: energy_efficiency,
            estimated_quality_loss: self.estimate_quality_loss(),
        }
    }

    // Private implementation methods

    fn adapt_for_device(&mut self, device_info: &MobileDeviceInfo) -> Result<()> {
        // Adjust quantization strategy based on device capabilities
        if device_info.supports_feature("int4") {
            // Device supports INT4, can use aggressive quantization
            if matches!(
                self.config.quantization_strategy,
                QuantizationStrategy::Dynamic
            ) {
                self.config.quantization_strategy = QuantizationStrategy::MixedPrecision;
            }
        } else if !device_info.supports_feature("int8") {
            // Fallback to FP16 if INT8 not supported
            self.config.quantization_strategy =
                QuantizationStrategy::Static(QuantizationPrecision::FP16);
        }

        // Adjust pruning based on device characteristics
        if device_info.memory_info.is_low_memory_device {
            // More aggressive pruning for low-memory devices
            if let PruningStrategy::GradualMagnitude {
                initial_sparsity,
                final_sparsity,
                steps,
            } = self.config.pruning_strategy
            {
                self.config.pruning_strategy = PruningStrategy::GradualMagnitude {
                    initial_sparsity: initial_sparsity * 1.5,
                    final_sparsity: (final_sparsity * 1.3).min(0.8),
                    steps,
                };
            }
        }

        tracing::info!(
            "Adapted compression configuration for device: {:?}",
            device_info.basic_info.model
        );
        Ok(())
    }

    fn apply_quantization(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        match self.config.quantization_strategy {
            QuantizationStrategy::Static(precision) => {
                self.quantizer.apply_static_quantization(weights, precision)
            },
            QuantizationStrategy::Dynamic => self.quantizer.apply_dynamic_quantization(weights),
            QuantizationStrategy::MixedPrecision => {
                self.quantizer.apply_mixed_precision_quantization(weights)
            },
            QuantizationStrategy::BlockWise => self.quantizer.apply_blockwise_quantization(weights),
            QuantizationStrategy::OutlierAware => {
                self.quantizer.apply_outlier_aware_quantization(weights)
            },
            QuantizationStrategy::DeviceAdaptive => {
                self.quantizer.apply_device_adaptive_quantization(weights)
            },
        }
    }

    fn apply_pruning(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        match self.config.pruning_strategy {
            PruningStrategy::None => Ok(weights.clone()),
            PruningStrategy::MagnitudeBased { sparsity } => {
                self.pruner.apply_magnitude_pruning(weights, sparsity)
            },
            PruningStrategy::Structured { ratio } => {
                self.pruner.apply_structured_pruning(weights, ratio)
            },
            PruningStrategy::GradualMagnitude {
                initial_sparsity,
                final_sparsity,
                steps,
            } => {
                self.pruner
                    .apply_gradual_pruning(weights, initial_sparsity, final_sparsity, steps)
            },
            PruningStrategy::LayerAdaptive => self.pruner.apply_layer_adaptive_pruning(weights),
            PruningStrategy::HardwareAware => self.pruner.apply_hardware_aware_pruning(weights),
        }
    }

    fn compress_stage(
        &mut self,
        weights: &HashMap<String, Tensor>,
        config: &CompressionConfig,
    ) -> Result<HashMap<String, Tensor>> {
        // Temporarily override config for this stage
        let original_config = self.config.clone();
        self.config = config.clone();

        let result = self.compress_model(weights);

        // Restore original config
        self.config = original_config;

        result
    }

    fn interpolate_compression_ratio(&self, stage_ratio: f32) -> f32 {
        match self.config.progressive_compression.schedule {
            CompressionSchedule::Linear => {
                1.0 - (1.0 - self.config.target_compression_ratio) * stage_ratio
            },
            CompressionSchedule::Exponential => {
                1.0 - (1.0 - self.config.target_compression_ratio) * stage_ratio.powf(2.0)
            },
            CompressionSchedule::CosineAnnealing => {
                let angle = stage_ratio * std::f32::consts::PI / 2.0;
                1.0 - (1.0 - self.config.target_compression_ratio) * angle.sin()
            },
            CompressionSchedule::Custom => {
                // Implement custom schedule logic
                self.config.target_compression_ratio
            },
        }
    }

    fn calculate_model_size(&self, weights: &HashMap<String, Tensor>) -> f32 {
        let total_params: usize = weights
            .values()
            .map(|tensor| {
                // Calculate number of elements from tensor shape
                tensor.shape().iter().product::<usize>()
            })
            .sum();

        // Assume FP32 weights (4 bytes per parameter)
        (total_params * 4) as f32 / (1024.0 * 1024.0) // Convert to MB
    }

    fn estimate_quality_loss(&self) -> f32 {
        // Rough estimation based on compression aggressiveness
        let quantization_loss = match self.config.quantization_strategy {
            QuantizationStrategy::Static(QuantizationPrecision::Int1) => 0.15,
            QuantizationStrategy::Static(QuantizationPrecision::Int4) => 0.05,
            QuantizationStrategy::Static(QuantizationPrecision::Int8) => 0.02,
            QuantizationStrategy::Static(QuantizationPrecision::FP16) => 0.01,
            QuantizationStrategy::Dynamic => 0.03,
            QuantizationStrategy::MixedPrecision => 0.025,
            _ => 0.03,
        };

        let pruning_loss = match self.config.pruning_strategy {
            PruningStrategy::None => 0.0,
            PruningStrategy::MagnitudeBased { sparsity } => sparsity * 0.1,
            PruningStrategy::Structured { ratio } => ratio * 0.08,
            _ => 0.04,
        };

        quantization_loss + pruning_loss
    }
}

/// Compression benefits estimation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressionBenefits {
    /// Size reduction in MB
    pub size_reduction_mb: f32,
    /// Compression ratio (e.g., 4.0 for 4x compression)
    pub compression_ratio: f32,
    /// Estimated inference speedup
    pub estimated_speedup: f32,
    /// Memory reduction percentage
    pub memory_reduction_percent: f32,
    /// Energy efficiency gain
    pub energy_efficiency_gain: f32,
    /// Estimated quality loss
    pub estimated_quality_loss: f32,
}

// Implementation stubs for compression components

impl DynamicQuantizer {
    fn new() -> Self {
        Self {
            calibration_data: Vec::new(),
            layer_sensitivities: HashMap::new(),
            quantization_cache: HashMap::new(),
            precision_mapping: HashMap::new(),
        }
    }

    fn apply_static_quantization(
        &mut self,
        weights: &HashMap<String, Tensor>,
        precision: QuantizationPrecision,
    ) -> Result<HashMap<String, Tensor>> {
        // Static quantization implementation
        let mut quantized = HashMap::new();
        for (name, tensor) in weights {
            quantized.insert(name.clone(), self.quantize_tensor(tensor, precision)?);
        }
        Ok(quantized)
    }

    fn apply_dynamic_quantization(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        // Dynamic quantization based on layer sensitivity
        let mut quantized = HashMap::new();
        for (name, tensor) in weights {
            let precision = self.determine_layer_precision(name);
            quantized.insert(name.clone(), self.quantize_tensor(tensor, precision)?);
        }
        Ok(quantized)
    }

    fn apply_mixed_precision_quantization(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        // Mixed precision quantization
        let mut quantized = HashMap::new();
        for (name, tensor) in weights {
            let precision = if name.contains("attention") {
                QuantizationPrecision::FP16 // Higher precision for attention layers
            } else if name.contains("embed") {
                QuantizationPrecision::Int8 // Medium precision for embeddings
            } else {
                QuantizationPrecision::Int4 // Lower precision for other layers
            };
            quantized.insert(name.clone(), self.quantize_tensor(tensor, precision)?);
        }
        Ok(quantized)
    }

    fn apply_blockwise_quantization(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        // Block-wise quantization implementation
        let mut quantized = HashMap::new();
        let block_size = 32; // Configurable block size

        for (name, tensor) in weights {
            let data = tensor.data()?;
            let mut quantized_data = Vec::new();

            // Process tensor in blocks
            for chunk in data.chunks(block_size) {
                // Find min/max for this block
                let min_val = chunk.iter().fold(f32::INFINITY, |min, &val| min.min(val));
                let max_val = chunk.iter().fold(f32::NEG_INFINITY, |max, &val| max.max(val));

                // Quantize block using min/max
                let scale = (max_val - min_val) / 255.0; // 8-bit quantization
                let zero_point = (-min_val / scale).round() as i32;

                for &value in chunk {
                    let quantized = ((value / scale) + zero_point as f32).round().clamp(0.0, 255.0);
                    let dequantized = (quantized - zero_point as f32) * scale;
                    quantized_data.push(dequantized);
                }
            }

            let quantized_tensor = Tensor::from_vec(quantized_data, &tensor.shape().to_vec())
                .map_err(|e| {
                    TrustformersError::runtime_error(format!(
                        "Failed to create quantized tensor: {}",
                        e
                    ))
                })?;
            quantized.insert(name.clone(), quantized_tensor);
        }

        Ok(quantized)
    }

    fn apply_outlier_aware_quantization(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        // Outlier-aware quantization implementation
        let mut quantized = HashMap::new();
        let outlier_threshold = 0.01; // Top 1% as outliers

        for (name, tensor) in weights {
            let data = tensor.data()?;
            let mut sorted_data = data.to_vec();
            sorted_data.sort_by(|a, b| a.abs().partial_cmp(&b.abs()).expect("Operation failed"));

            // Find outlier threshold
            let outlier_idx = ((1.0 - outlier_threshold) * sorted_data.len() as f32) as usize;
            let outlier_threshold_val = sorted_data[outlier_idx].abs();

            // Separate outliers from regular values
            let mut quantized_data = Vec::new();
            for value in data {
                if value.abs() > outlier_threshold_val {
                    // Keep outliers in full precision
                    quantized_data.push(value);
                } else {
                    // Quantize regular values more aggressively
                    let sign = value.signum();
                    let abs_val = value.abs();
                    let quantized_abs = (abs_val * 127.0 / outlier_threshold_val).round() / 127.0
                        * outlier_threshold_val;
                    quantized_data.push(sign * quantized_abs);
                }
            }

            let quantized_tensor = Tensor::from_vec(quantized_data, &tensor.shape().to_vec())
                .map_err(|e| {
                    TrustformersError::runtime_error(format!(
                        "Failed to create quantized tensor: {}",
                        e
                    ))
                })?;
            quantized.insert(name.clone(), quantized_tensor);
        }

        Ok(quantized)
    }

    fn apply_device_adaptive_quantization(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        // Device-adaptive quantization implementation
        let mut quantized = HashMap::new();

        // Determine device capabilities (simplified)
        let device_memory_gb = 4.0; // Assume 4GB device memory
        let has_hardware_acceleration = true;

        // Choose quantization precision based on device
        let precision = if device_memory_gb < 2.0 {
            QuantizationPrecision::Int4 // Ultra low memory
        } else if device_memory_gb < 4.0 {
            QuantizationPrecision::Int8 // Low memory
        } else if has_hardware_acceleration {
            QuantizationPrecision::FP16 // Good balance with acceleration
        } else {
            QuantizationPrecision::Int8 // Default
        };

        for (name, tensor) in weights {
            let quantized_tensor = self.quantize_tensor_with_precision(tensor, precision)?;
            quantized.insert(name.clone(), quantized_tensor);
        }

        Ok(quantized)
    }

    fn quantize_tensor(
        &self,
        tensor: &Tensor,
        _precision: QuantizationPrecision,
    ) -> Result<Tensor> {
        // Simplified quantization - in practice would implement actual quantization
        Ok(tensor.clone())
    }

    fn quantize_tensor_with_precision(
        &self,
        tensor: &Tensor,
        precision: QuantizationPrecision,
    ) -> Result<Tensor> {
        // Quantize tensor with specified precision
        let data = tensor.data()?;
        let mut quantized_data = Vec::new();

        match precision {
            QuantizationPrecision::Int4 => {
                // 4-bit quantization
                let min_val = data.iter().fold(f32::INFINITY, |min, val| min.min(*val));
                let max_val = data.iter().fold(f32::NEG_INFINITY, |max, val| max.max(*val));
                let scale = (max_val - min_val) / 15.0; // 4-bit has 16 levels (0-15)

                for value in data {
                    let quantized = ((value - min_val) / scale).round().clamp(0.0, 15.0);
                    let dequantized = quantized * scale + min_val;
                    quantized_data.push(dequantized);
                }
            },
            QuantizationPrecision::Int8 => {
                // 8-bit quantization
                let min_val = data.iter().fold(f32::INFINITY, |min, val| min.min(*val));
                let max_val = data.iter().fold(f32::NEG_INFINITY, |max, val| max.max(*val));
                let scale = (max_val - min_val) / 255.0; // 8-bit has 256 levels (0-255)

                for value in data {
                    let quantized = ((value - min_val) / scale).round().clamp(0.0, 255.0);
                    let dequantized = quantized * scale + min_val;
                    quantized_data.push(dequantized);
                }
            },
            QuantizationPrecision::FP16 => {
                // 16-bit float quantization
                for value in data {
                    let fp16_value = half::f16::from_f32(value);
                    quantized_data.push(fp16_value.to_f32());
                }
            },
            QuantizationPrecision::Int1 => {
                // 1-bit quantization (binary)
                let mean = data.iter().sum::<f32>() / data.len() as f32;
                for value in data {
                    let quantized = if value >= mean { 1.0 } else { -1.0 };
                    quantized_data.push(quantized);
                }
            },
            QuantizationPrecision::Int2 => {
                // 2-bit quantization
                let min_val = data.iter().fold(f32::INFINITY, |min, val| min.min(*val));
                let max_val = data.iter().fold(f32::NEG_INFINITY, |max, val| max.max(*val));
                let scale = (max_val - min_val) / 3.0; // 2-bit has 4 levels (0-3)

                for value in data {
                    let quantized = ((value - min_val) / scale).round().clamp(0.0, 3.0);
                    let dequantized = quantized * scale + min_val;
                    quantized_data.push(dequantized);
                }
            },
            QuantizationPrecision::BF16 => {
                // BFloat16 quantization
                for value in data {
                    // Simulate BF16 by truncating mantissa
                    let bits = value.to_bits();
                    let bf16_bits = bits & 0xFFFF0000; // Keep only sign, exponent, and 7 bits of mantissa
                    let bf16_value = f32::from_bits(bf16_bits);
                    quantized_data.push(bf16_value);
                }
            },
            QuantizationPrecision::Custom { bits } => {
                // Custom bit quantization
                let levels = (1u32 << bits) - 1; // 2^bits - 1
                let min_val = data.iter().fold(f32::INFINITY, |min, val| min.min(*val));
                let max_val = data.iter().fold(f32::NEG_INFINITY, |max, val| max.max(*val));
                let scale = (max_val - min_val) / levels as f32;

                for value in data {
                    let quantized = ((value - min_val) / scale).round().clamp(0.0, levels as f32);
                    let dequantized = quantized * scale + min_val;
                    quantized_data.push(dequantized);
                }
            },
            QuantizationPrecision::Dynamic => {
                // Dynamic precision based on value range
                let abs_max = data.iter().fold(0.0f32, |max, val| max.max(val.abs()));

                if abs_max > 10.0 {
                    // Use FP16 for large values
                    for value in data {
                        let fp16_value = half::f16::from_f32(value);
                        quantized_data.push(fp16_value.to_f32());
                    }
                } else if abs_max > 1.0 {
                    // Use INT8 for medium values
                    let scale = abs_max / 127.0;
                    for value in data {
                        let quantized = (value / scale).round().clamp(-127.0, 127.0);
                        let dequantized = quantized * scale;
                        quantized_data.push(dequantized);
                    }
                } else {
                    // Use INT4 for small values
                    let scale = abs_max / 7.0;
                    for value in data {
                        let quantized = (value / scale).round().clamp(-7.0, 7.0);
                        let dequantized = quantized * scale;
                        quantized_data.push(dequantized);
                    }
                }
            },
        }

        let quantized_tensor = Tensor::from_vec(quantized_data, &tensor.shape()).map_err(|e| {
            TrustformersError::runtime_error(format!("Failed to create quantized tensor: {}", e))
        })?;

        Ok(quantized_tensor)
    }

    fn determine_layer_precision(&self, layer_name: &str) -> QuantizationPrecision {
        // Determine precision based on layer sensitivity
        if layer_name.contains("output") || layer_name.contains("classifier") {
            QuantizationPrecision::FP16 // Higher precision for output layers
        } else if layer_name.contains("attention") {
            QuantizationPrecision::Int8
        } else {
            QuantizationPrecision::Int4
        }
    }
}

impl MobilePruner {
    fn new() -> Self {
        Self {
            importance_scores: HashMap::new(),
            pruning_masks: HashMap::new(),
            structured_masks: HashMap::new(),
            pruning_history: Vec::new(),
        }
    }

    fn apply_magnitude_pruning(
        &mut self,
        weights: &HashMap<String, Tensor>,
        sparsity: f32,
    ) -> Result<HashMap<String, Tensor>> {
        // Magnitude-based pruning implementation
        let mut pruned = HashMap::new();
        for (name, tensor) in weights {
            pruned.insert(name.clone(), self.prune_by_magnitude(tensor, sparsity)?);
        }
        Ok(pruned)
    }

    fn apply_structured_pruning(
        &mut self,
        weights: &HashMap<String, Tensor>,
        ratio: f32,
    ) -> Result<HashMap<String, Tensor>> {
        // Structured pruning implementation
        let mut pruned = HashMap::new();

        for (name, tensor) in weights {
            let data = tensor.data()?;
            let shape = tensor.shape();

            // For 2D tensors (matrices), prune entire rows or columns
            if shape.len() == 2 {
                let rows = shape[0];
                let cols = shape[1];
                let target_rows = ((1.0 - ratio) * rows as f32) as usize;

                // Calculate row norms
                let mut row_norms = Vec::new();
                for i in 0..rows {
                    let mut norm: f32 = 0.0;
                    for j in 0..cols {
                        let val = data[i * cols + j];
                        norm += val * val;
                    }
                    row_norms.push((norm.sqrt(), i));
                }

                // Sort by norm and keep top rows
                row_norms.sort_by(|a, b| b.0.partial_cmp(&a.0).expect("Operation failed"));
                let kept_rows: Vec<usize> =
                    row_norms.iter().take(target_rows).map(|(_, idx)| *idx).collect();

                // Create pruned tensor
                let mut pruned_data = Vec::new();
                for &row_idx in &kept_rows {
                    for j in 0..cols {
                        pruned_data.push(data[row_idx * cols + j]);
                    }
                }

                let pruned_tensor =
                    Tensor::from_vec(pruned_data, &[target_rows, cols]).map_err(|e| {
                        TrustformersError::runtime_error(format!(
                            "Failed to create pruned tensor: {}",
                            e
                        ))
                    })?;
                pruned.insert(name.clone(), pruned_tensor);
            } else {
                // For other dimensions, fall back to magnitude pruning
                let pruned_tensor = self.prune_by_magnitude(tensor, ratio)?;
                pruned.insert(name.clone(), pruned_tensor);
            }
        }

        Ok(pruned)
    }

    fn apply_gradual_pruning(
        &mut self,
        weights: &HashMap<String, Tensor>,
        _initial: f32,
        final_sparsity: f32,
        _steps: usize,
    ) -> Result<HashMap<String, Tensor>> {
        // Gradual pruning implementation - simplified to final sparsity
        self.apply_magnitude_pruning(weights, final_sparsity)
    }

    fn apply_layer_adaptive_pruning(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        // Layer-adaptive pruning implementation
        let mut pruned = HashMap::new();
        for (name, tensor) in weights {
            let sparsity = self.determine_layer_sparsity(name);
            pruned.insert(name.clone(), self.prune_by_magnitude(tensor, sparsity)?);
        }
        Ok(pruned)
    }

    fn apply_hardware_aware_pruning(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        // Hardware-aware pruning implementation
        Ok(weights.clone()) // Placeholder
    }

    fn prune_by_magnitude(&self, tensor: &Tensor, sparsity: f32) -> Result<Tensor> {
        // Simplified magnitude pruning - in practice would implement actual pruning
        // For now, just return the original tensor
        Ok(tensor.clone())
    }

    fn determine_layer_sparsity(&self, layer_name: &str) -> f32 {
        // Determine sparsity based on layer type
        if layer_name.contains("attention") {
            0.3 // Lower sparsity for attention layers
        } else if layer_name.contains("embed") {
            0.2 // Lower sparsity for embeddings
        } else {
            0.6 // Higher sparsity for other layers
        }
    }
}

impl KnowledgeDistiller {
    fn new(config: DistillationConfig) -> Result<Self> {
        Ok(Self {
            teacher_model: None,
            distillation_config: config,
            feature_extractors: HashMap::new(),
            distillation_losses: Vec::new(),
        })
    }

    fn apply_distillation(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        // Knowledge distillation implementation
        Ok(weights.clone()) // Placeholder
    }
}

impl Default for DistillationConfig {
    fn default() -> Self {
        Self {
            temperature: 4.0,
            distillation_weight: 0.8,
            hard_target_weight: 0.2,
            strategy: DistillationStrategy::OutputOnly,
            feature_matching: None,
        }
    }
}

impl CompressionStats {
    fn new() -> Self {
        Self {
            original_size_mb: 0.0,
            compressed_size_mb: 0.0,
            compression_ratio: 1.0,
            quantization_stats: QuantizationStats::new(),
            pruning_stats: PruningStats::new(),
            quality_metrics: HashMap::new(),
            inference_speedup: 1.0,
            memory_reduction_percent: 0.0,
            energy_efficiency_improvement: 1.0,
        }
    }
}

impl QuantizationStats {
    fn new() -> Self {
        Self {
            quantized_layers: 0,
            avg_bits_per_weight: 32.0, // FP32 default
            precision_distribution: HashMap::new(),
            quantization_error: 0.0,
        }
    }
}

impl PruningStats {
    fn new() -> Self {
        Self {
            overall_sparsity: 0.0,
            layer_sparsity: HashMap::new(),
            structured_pruning_ratio: 0.0,
            parameters_removed: 0,
        }
    }
}

/// Utility functions for mobile compression
pub struct CompressionUtils;

impl CompressionUtils {
    /// Calculate theoretical compression ratio for precision
    pub fn calculate_precision_compression_ratio(
        from: QuantizationPrecision,
        to: QuantizationPrecision,
    ) -> f32 {
        let from_bits = Self::precision_to_bits(from);
        let to_bits = Self::precision_to_bits(to);
        from_bits as f32 / to_bits as f32
    }

    /// Convert precision enum to bit count
    pub fn precision_to_bits(precision: QuantizationPrecision) -> u8 {
        match precision {
            QuantizationPrecision::Int1 => 1,
            QuantizationPrecision::Int2 => 2,
            QuantizationPrecision::Int4 => 4,
            QuantizationPrecision::Int8 => 8,
            QuantizationPrecision::FP16 | QuantizationPrecision::BF16 => 16,
            QuantizationPrecision::Custom { bits } => bits,
            QuantizationPrecision::Dynamic => 8, // Default to 8-bit for dynamic precision
        }
    }

    /// Estimate memory bandwidth savings
    pub fn estimate_bandwidth_savings(
        original_precision: QuantizationPrecision,
        compressed_precision: QuantizationPrecision,
        model_size_mb: f32,
    ) -> f32 {
        let compression_ratio =
            Self::calculate_precision_compression_ratio(original_precision, compressed_precision);
        model_size_mb * (1.0 - 1.0 / compression_ratio)
    }
}

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

    #[test]
    fn test_compression_config_default() {
        let config = CompressionConfig::default();
        assert_eq!(config.target_compression_ratio, 0.25);
        assert!(matches!(
            config.quantization_strategy,
            QuantizationStrategy::Dynamic
        ));
        assert!(config.device_adaptive);
    }

    #[test]
    fn test_quantization_precision_ordering() {
        assert!(
            CompressionUtils::precision_to_bits(QuantizationPrecision::Int1)
                < CompressionUtils::precision_to_bits(QuantizationPrecision::Int4)
        );
        assert!(
            CompressionUtils::precision_to_bits(QuantizationPrecision::Int4)
                < CompressionUtils::precision_to_bits(QuantizationPrecision::Int8)
        );
        assert!(
            CompressionUtils::precision_to_bits(QuantizationPrecision::Int8)
                < CompressionUtils::precision_to_bits(QuantizationPrecision::FP16)
        );
    }

    #[test]
    fn test_compression_ratio_calculation() {
        let ratio = CompressionUtils::calculate_precision_compression_ratio(
            QuantizationPrecision::FP16,
            QuantizationPrecision::Int8,
        );
        assert_eq!(ratio, 2.0); // 16-bit to 8-bit = 2x compression
    }

    #[test]
    fn test_device_optimized_config() {
        let device_info =
            crate::device_info::MobileDeviceDetector::detect().expect("Operation failed");
        let config = MobileCompressionEngine::create_device_optimized_config(&device_info);
        assert!(config.target_compression_ratio > 0.0);
        assert!(config.target_compression_ratio <= 1.0);
    }

    #[test]
    fn test_compression_benefits_estimation() {
        let config = CompressionConfig::default();
        let device_info =
            crate::device_info::MobileDeviceDetector::detect().expect("Operation failed");
        let engine = MobileCompressionEngine::new(config, &device_info).expect("Operation failed");

        let benefits = engine.estimate_compression_benefits(100.0, &device_info);
        assert!(benefits.compression_ratio > 1.0);
        assert!(benefits.size_reduction_mb > 0.0);
        assert!(benefits.estimated_speedup > 1.0);
    }

    #[test]
    fn test_progressive_compression_config() {
        let config = ProgressiveCompressionConfig::default();
        assert!(config.enabled);
        assert!(config.stages > 1);
        assert!(matches!(config.schedule, CompressionSchedule::Linear));
    }

    #[test]
    fn test_quality_preservation_config() {
        let config = QualityPreservationConfig::default();
        assert!(config.max_quality_loss > 0.0);
        assert!(config.max_quality_loss < 1.0);
        assert!(!config.quality_metrics.is_empty());
        assert!(config.early_stopping.enabled);
    }

    #[test]
    fn test_bandwidth_savings_estimation() {
        let savings = CompressionUtils::estimate_bandwidth_savings(
            QuantizationPrecision::FP16,
            QuantizationPrecision::Int8,
            100.0,
        );
        assert!(savings > 0.0);
        assert!(savings < 100.0);
    }

    #[test]
    fn test_compression_stats() {
        let stats = CompressionStats::new();
        assert_eq!(stats.compression_ratio, 1.0);
        assert_eq!(stats.inference_speedup, 1.0);
        assert_eq!(stats.memory_reduction_percent, 0.0);
    }
}