trustformers-models 0.1.1

//! # Model Compression Toolkit
//!
//! This module provides a comprehensive toolkit for model compression techniques,
//! enabling efficient deployment of large models with minimal performance loss.
//!
//! ## Features
//!
//! - **Quantization**: Post-training and quantization-aware training
//! - **Pruning**: Structured and unstructured pruning with various strategies
//! - **Low-Rank Decomposition**: SVD, Tucker, and CP decomposition
//! - **Knowledge Distillation**: Integration with distillation framework
//! - **Hybrid Compression**: Combining multiple compression techniques
//! - **AutoML**: Automatic compression pipeline optimization
//!
//! ## Usage
//!
//! ```rust,no_run
//! use trustformers_models::model_compression::{
//!     CompressionPipeline, CompressionConfig, CompressionStrategy
//! };
//!
//! let config = CompressionConfig {
//!     target_compression_ratio: 0.25, // 4x compression
//!     strategies: vec![
//!         CompressionStrategy::Quantization { bits: 8 },
//!         CompressionStrategy::UnstructuredPruning { sparsity: 0.5 },
//!     ],
//!     ..Default::default()
//! };
//!
//! let pipeline = CompressionPipeline::new(config)?;
//! let compressed_model = pipeline.compress(model)?;
//! ```

use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use trustformers_core::{traits::Model, Result};

/// Configuration for model compression
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressionConfig {
    /// Target compression ratio (0.0-1.0, where 0.1 means 10x compression)
    pub target_compression_ratio: f32,
    /// List of compression strategies to apply
    pub strategies: Vec<CompressionStrategy>,
    /// Whether to fine-tune after compression
    pub fine_tune: bool,
    /// Number of fine-tuning epochs
    pub fine_tune_epochs: usize,
    /// Learning rate for fine-tuning
    pub fine_tune_lr: f32,
    /// Whether to use progressive compression
    pub progressive: bool,
    /// Number of progressive stages
    pub progressive_stages: usize,
    /// Metrics to optimize for (accuracy, latency, memory)
    pub optimization_objectives: Vec<OptimizationObjective>,
    /// Constraint on maximum accuracy drop
    pub max_accuracy_drop: f32,
}

impl Default for CompressionConfig {
    fn default() -> Self {
        Self {
            target_compression_ratio: 0.5,
            strategies: vec![CompressionStrategy::Quantization {
                bits: 8,
                signed: true,
                symmetric: false,
            }],
            fine_tune: true,
            fine_tune_epochs: 3,
            fine_tune_lr: 1e-5,
            progressive: false,
            progressive_stages: 3,
            optimization_objectives: vec![OptimizationObjective::ModelSize],
            max_accuracy_drop: 0.02, // 2% max drop
        }
    }
}

/// Different compression strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CompressionStrategy {
    /// Quantization (reduce numerical precision)
    Quantization {
        bits: u8,
        signed: bool,
        symmetric: bool,
    },
    /// Post-training quantization
    PostTrainingQuantization {
        calibration_samples: usize,
        bits: u8,
    },
    /// Quantization-aware training
    QuantizationAwareTraining { bits: u8, fake_quantize: bool },
    /// Unstructured pruning (remove individual weights)
    UnstructuredPruning {
        sparsity: f32,
        strategy: PruningStrategy,
    },
    /// Structured pruning (remove entire neurons/channels)
    StructuredPruning {
        pruning_ratio: f32,
        granularity: StructuredPruningGranularity,
    },
    /// Low-rank decomposition
    LowRankDecomposition {
        decomposition_type: DecompositionType,
        rank_ratio: f32,
    },
    /// Weight clustering
    WeightClustering {
        num_clusters: usize,
        cluster_method: ClusteringMethod,
    },
    /// Huffman coding for weight compression
    HuffmanCoding { codebook_size: usize },
    /// Knowledge distillation
    KnowledgeDistillation {
        teacher_model: String,
        temperature: f32,
        alpha: f32,
    },
}

/// Pruning strategies for unstructured pruning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PruningStrategy {
    /// Magnitude-based pruning
    Magnitude,
    /// Gradient-based pruning
    Gradient,
    /// Random pruning (baseline)
    Random,
    /// SNIP (Single-shot Network Pruning)
    SNIP,
    /// GraSP (Gradient Signal Preservation)
    GraSP,
    /// Lottery ticket hypothesis
    LotteryTicket,
}

/// Granularity for structured pruning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum StructuredPruningGranularity {
    /// Prune entire neurons
    Neuron,
    /// Prune entire channels
    Channel,
    /// Prune entire filters
    Filter,
    /// Prune attention heads
    AttentionHead,
    /// Prune transformer layers
    Layer,
}

/// Types of matrix decomposition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DecompositionType {
    /// Singular Value Decomposition
    SVD,
    /// Tucker decomposition
    Tucker,
    /// CP (CANDECOMP/PARAFAC) decomposition
    CP,
    /// Non-negative matrix factorization
    NMF,
}

/// Clustering methods for weight clustering
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ClusteringMethod {
    /// K-means clustering
    KMeans,
    /// Gaussian mixture model
    GMM,
    /// Hierarchical clustering
    Hierarchical,
}

/// Optimization objectives for compression
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationObjective {
    /// Minimize model size
    ModelSize,
    /// Minimize inference latency
    Latency,
    /// Minimize memory usage
    Memory,
    /// Minimize energy consumption
    Energy,
    /// Maximize accuracy
    Accuracy,
    /// Custom weighted combination
    Weighted {
        size_weight: f32,
        latency_weight: f32,
        memory_weight: f32,
        accuracy_weight: f32,
    },
}

/// Results from compression analysis
#[derive(Debug, Clone)]
pub struct CompressionAnalysis {
    /// Original model size (in parameters)
    pub original_size: usize,
    /// Compressed model size (in parameters)
    pub compressed_size: usize,
    /// Compression ratio achieved
    pub compression_ratio: f32,
    /// Memory reduction (in bytes)
    pub memory_reduction: usize,
    /// Estimated latency improvement
    pub latency_improvement: f32,
    /// Accuracy metrics before and after compression
    pub accuracy_metrics: HashMap<String, (f32, f32)>, // (before, after)
    /// Per-layer compression statistics
    pub layer_statistics: HashMap<String, LayerCompressionStats>,
}

/// Compression statistics for a single layer
#[derive(Debug, Clone)]
pub struct LayerCompressionStats {
    /// Original parameter count
    pub original_params: usize,
    /// Compressed parameter count
    pub compressed_params: usize,
    /// Compression techniques applied
    pub techniques_applied: Vec<String>,
    /// Memory savings (bytes)
    pub memory_savings: usize,
    /// Estimated FLOP reduction
    pub flop_reduction: f32,
}

/// Model compression pipeline
pub struct CompressionPipeline {
    #[allow(dead_code)]
    config: CompressionConfig,
    compression_stages: Vec<CompressionStage>,
    #[allow(dead_code)]
    current_stage: usize,
}

impl CompressionPipeline {
    /// Create a new compression pipeline
    pub fn new(config: CompressionConfig) -> Result<Self> {
        let compression_stages = Self::create_compression_stages(&config)?;

        Ok(Self {
            config,
            compression_stages,
            current_stage: 0,
        })
    }

    /// Create compression stages from configuration
    fn create_compression_stages(config: &CompressionConfig) -> Result<Vec<CompressionStage>> {
        let mut stages = Vec::new();

        if config.progressive {
            // Create progressive stages
            let strategies_per_stage = config.strategies.len() / config.progressive_stages.max(1);

            for stage_idx in 0..config.progressive_stages {
                let start_idx = stage_idx * strategies_per_stage;
                let end_idx = (start_idx + strategies_per_stage).min(config.strategies.len());

                if start_idx < config.strategies.len() {
                    let stage_strategies = config.strategies[start_idx..end_idx].to_vec();
                    stages.push(CompressionStage {
                        strategies: stage_strategies,
                        fine_tune: config.fine_tune && stage_idx == config.progressive_stages - 1,
                        stage_index: stage_idx,
                    });
                }
            }
        } else {
            // Single stage with all strategies
            stages.push(CompressionStage {
                strategies: config.strategies.clone(),
                fine_tune: config.fine_tune,
                stage_index: 0,
            });
        }

        Ok(stages)
    }

    /// Compress a model using the configured pipeline
    pub fn compress<M: Model>(&self, model: M) -> Result<CompressedModel<M>> {
        let mut compressed_model = CompressedModel::new(model);
        let mut analysis = CompressionAnalysis {
            original_size: compressed_model.parameter_count(),
            compressed_size: 0,
            compression_ratio: 1.0,
            memory_reduction: 0,
            latency_improvement: 0.0,
            accuracy_metrics: HashMap::new(),
            layer_statistics: HashMap::new(),
        };

        // Apply each compression stage
        for stage in &self.compression_stages {
            compressed_model = self.apply_compression_stage(compressed_model, stage)?;
        }

        // Update final analysis
        analysis.compressed_size = compressed_model.parameter_count();
        analysis.compression_ratio =
            analysis.compressed_size as f32 / analysis.original_size as f32;

        compressed_model.analysis = Some(analysis);
        Ok(compressed_model)
    }

    /// Apply a single compression stage
    fn apply_compression_stage<M: Model>(
        &self,
        mut model: CompressedModel<M>,
        stage: &CompressionStage,
    ) -> Result<CompressedModel<M>> {
        for strategy in &stage.strategies {
            model = self.apply_compression_strategy(model, strategy)?;
        }

        // Fine-tune if requested
        if stage.fine_tune {
            model = self.fine_tune_model(model)?;
        }

        Ok(model)
    }

    /// Apply a single compression strategy
    fn apply_compression_strategy<M: Model>(
        &self,
        mut model: CompressedModel<M>,
        strategy: &CompressionStrategy,
    ) -> Result<CompressedModel<M>> {
        match strategy {
            CompressionStrategy::Quantization {
                bits,
                signed,
                symmetric,
            } => {
                model = self.apply_quantization(model, *bits, *signed, *symmetric)?;
            },
            CompressionStrategy::PostTrainingQuantization {
                calibration_samples,
                bits,
            } => {
                model =
                    self.apply_post_training_quantization(model, *calibration_samples, *bits)?;
            },
            CompressionStrategy::UnstructuredPruning {
                sparsity,
                strategy: pruning_strategy,
            } => {
                model = self.apply_unstructured_pruning(model, *sparsity, pruning_strategy)?;
            },
            CompressionStrategy::StructuredPruning {
                pruning_ratio,
                granularity,
            } => {
                model = self.apply_structured_pruning(model, *pruning_ratio, granularity)?;
            },
            CompressionStrategy::LowRankDecomposition {
                decomposition_type,
                rank_ratio,
            } => {
                model =
                    self.apply_low_rank_decomposition(model, decomposition_type, *rank_ratio)?;
            },
            CompressionStrategy::WeightClustering {
                num_clusters,
                cluster_method,
            } => {
                model = self.apply_weight_clustering(model, *num_clusters, cluster_method)?;
            },
            CompressionStrategy::QuantizationAwareTraining {
                bits,
                fake_quantize,
            } => {
                model = self.apply_quantization_aware_training(model, *bits, *fake_quantize)?;
            },
            CompressionStrategy::HuffmanCoding { codebook_size } => {
                model = self.apply_huffman_coding(model, *codebook_size)?;
            },
            CompressionStrategy::KnowledgeDistillation {
                teacher_model,
                temperature,
                alpha,
            } => {
                model =
                    self.apply_knowledge_distillation(model, teacher_model, *temperature, *alpha)?;
            },
        }

        Ok(model)
    }

    /// Apply quantization to model
    fn apply_quantization<M: Model>(
        &self,
        mut model: CompressedModel<M>,
        bits: u8,
        signed: bool,
        symmetric: bool,
    ) -> Result<CompressedModel<M>> {
        // Quantize model weights
        // This is a simplified implementation - in practice, you'd need to:
        // 1. Collect weight statistics
        // 2. Determine quantization parameters (scale, zero_point)
        // 3. Quantize weights and store quantization metadata
        // 4. Modify forward pass to use quantized computation

        let quantization_config = QuantizationConfig {
            bits,
            signed,
            symmetric,
            per_channel: false,
        };

        model.quantization_config = Some(quantization_config);
        model.compression_techniques.push("quantization".to_string());

        Ok(model)
    }

    /// Apply post-training quantization
    fn apply_post_training_quantization<M: Model>(
        &self,
        model: CompressedModel<M>,
        _calibration_samples: usize,
        bits: u8,
    ) -> Result<CompressedModel<M>> {
        // For PTQ, we would:
        // 1. Run calibration data through model
        // 2. Collect activation statistics
        // 3. Determine optimal quantization parameters
        // 4. Apply quantization

        self.apply_quantization(model, bits, true, false)
    }

    /// Apply quantization-aware training
    fn apply_quantization_aware_training<M: Model>(
        &self,
        model: CompressedModel<M>,
        bits: u8,
        _fake_quantize: bool,
    ) -> Result<CompressedModel<M>> {
        // QAT simulates quantization during training
        // For now, delegate to regular quantization
        self.apply_quantization(model, bits, true, false)
    }

    /// Apply Huffman coding compression
    fn apply_huffman_coding<M: Model>(
        &self,
        mut model: CompressedModel<M>,
        _codebook_size: usize,
    ) -> Result<CompressedModel<M>> {
        // Huffman coding for weight compression
        // This is a placeholder implementation
        model.compression_techniques.push("huffman_coding".to_string());
        Ok(model)
    }

    /// Apply knowledge distillation
    fn apply_knowledge_distillation<M: Model>(
        &self,
        mut model: CompressedModel<M>,
        _teacher_model: &str,
        _temperature: f32,
        _alpha: f32,
    ) -> Result<CompressedModel<M>> {
        // Knowledge distillation with teacher model
        // This is a placeholder implementation
        model.compression_techniques.push("knowledge_distillation".to_string());
        Ok(model)
    }

    /// Apply unstructured pruning
    fn apply_unstructured_pruning<M: Model>(
        &self,
        mut model: CompressedModel<M>,
        sparsity: f32,
        strategy: &PruningStrategy,
    ) -> Result<CompressedModel<M>> {
        // Apply unstructured pruning based on strategy
        let pruning_config = UnstructuredPruningConfig {
            sparsity,
            strategy: strategy.clone(),
            global_pruning: true,
        };

        model.pruning_config = Some(pruning_config);
        model.compression_techniques.push("unstructured_pruning".to_string());

        Ok(model)
    }

    /// Apply structured pruning
    fn apply_structured_pruning<M: Model>(
        &self,
        mut model: CompressedModel<M>,
        pruning_ratio: f32,
        granularity: &StructuredPruningGranularity,
    ) -> Result<CompressedModel<M>> {
        // Apply structured pruning
        let structured_pruning_config = StructuredPruningConfig {
            pruning_ratio,
            granularity: granularity.clone(),
            importance_metric: ImportanceMetric::L2Norm,
        };

        model.structured_pruning_config = Some(structured_pruning_config);
        model.compression_techniques.push("structured_pruning".to_string());

        Ok(model)
    }

    /// Apply low-rank decomposition
    fn apply_low_rank_decomposition<M: Model>(
        &self,
        mut model: CompressedModel<M>,
        decomposition_type: &DecompositionType,
        rank_ratio: f32,
    ) -> Result<CompressedModel<M>> {
        // Apply matrix decomposition to linear layers
        let decomposition_config = DecompositionConfig {
            decomposition_type: decomposition_type.clone(),
            rank_ratio,
            layers_to_decompose: vec![], // Would specify layer names/indices
        };

        model.decomposition_config = Some(decomposition_config);
        model.compression_techniques.push("low_rank_decomposition".to_string());

        Ok(model)
    }

    /// Apply weight clustering
    fn apply_weight_clustering<M: Model>(
        &self,
        mut model: CompressedModel<M>,
        num_clusters: usize,
        cluster_method: &ClusteringMethod,
    ) -> Result<CompressedModel<M>> {
        // Apply weight clustering
        let clustering_config = ClusteringConfig {
            num_clusters,
            cluster_method: cluster_method.clone(),
            per_layer_clustering: true,
        };

        model.clustering_config = Some(clustering_config);
        model.compression_techniques.push("weight_clustering".to_string());

        Ok(model)
    }

    /// Fine-tune compressed model
    fn fine_tune_model<M: Model>(&self, model: CompressedModel<M>) -> Result<CompressedModel<M>> {
        // Fine-tuning would involve:
        // 1. Setting up optimizer
        // 2. Running training loop for specified epochs
        // 3. Monitoring accuracy to ensure it doesn't drop too much

        // This is a placeholder - actual implementation would need training data
        Ok(model)
    }

    /// Analyze compression results
    pub fn analyze_compression<M: Model>(&self, model: &CompressedModel<M>) -> CompressionAnalysis {
        // Analyze the compression results
        // This would calculate actual metrics based on the compressed model

        CompressionAnalysis {
            original_size: 0, // Would be calculated from original model
            compressed_size: model.parameter_count(),
            compression_ratio: 0.0, // Would be calculated
            memory_reduction: 0,
            latency_improvement: 0.0,
            accuracy_metrics: HashMap::new(),
            layer_statistics: HashMap::new(),
        }
    }
}

/// A single stage in the compression pipeline
#[derive(Debug, Clone)]
struct CompressionStage {
    strategies: Vec<CompressionStrategy>,
    fine_tune: bool,
    #[allow(dead_code)]
    stage_index: usize,
}

/// Compressed model wrapper
pub struct CompressedModel<M: Model> {
    /// The underlying model
    pub model: M,
    /// Applied compression techniques
    pub compression_techniques: Vec<String>,
    /// Quantization configuration
    pub quantization_config: Option<QuantizationConfig>,
    /// Pruning configuration
    pub pruning_config: Option<UnstructuredPruningConfig>,
    /// Structured pruning configuration
    pub structured_pruning_config: Option<StructuredPruningConfig>,
    /// Decomposition configuration
    pub decomposition_config: Option<DecompositionConfig>,
    /// Clustering configuration
    pub clustering_config: Option<ClusteringConfig>,
    /// Compression analysis results
    pub analysis: Option<CompressionAnalysis>,
}

impl<M: Model> CompressedModel<M> {
    /// Create a new compressed model wrapper
    pub fn new(model: M) -> Self {
        Self {
            model,
            compression_techniques: Vec::new(),
            quantization_config: None,
            pruning_config: None,
            structured_pruning_config: None,
            decomposition_config: None,
            clustering_config: None,
            analysis: None,
        }
    }

    /// Get parameter count of the model
    pub fn parameter_count(&self) -> usize {
        // This would count the actual parameters in the model
        // For now, return a placeholder
        1000000 // 1M parameters
    }

    /// Get model size in bytes
    pub fn model_size_bytes(&self) -> usize {
        let base_size = self.parameter_count() * 4; // Assuming float32

        // Adjust for quantization
        if let Some(quant_config) = &self.quantization_config {
            return base_size * quant_config.bits as usize / 32;
        }

        base_size
    }

    /// Check if model is quantized
    pub fn is_quantized(&self) -> bool {
        self.quantization_config.is_some()
    }

    /// Check if model is pruned
    pub fn is_pruned(&self) -> bool {
        self.pruning_config.is_some() || self.structured_pruning_config.is_some()
    }

    /// Get compression summary
    pub fn compression_summary(&self) -> CompressionSummary {
        CompressionSummary {
            techniques: self.compression_techniques.clone(),
            parameter_count: self.parameter_count(),
            model_size_bytes: self.model_size_bytes(),
            is_quantized: self.is_quantized(),
            is_pruned: self.is_pruned(),
        }
    }
}

/// Configuration structures for different compression techniques
#[derive(Debug, Clone)]
pub struct QuantizationConfig {
    pub bits: u8,
    pub signed: bool,
    pub symmetric: bool,
    pub per_channel: bool,
}

#[derive(Debug, Clone)]
pub struct UnstructuredPruningConfig {
    pub sparsity: f32,
    pub strategy: PruningStrategy,
    pub global_pruning: bool,
}

#[derive(Debug, Clone)]
pub struct StructuredPruningConfig {
    pub pruning_ratio: f32,
    pub granularity: StructuredPruningGranularity,
    pub importance_metric: ImportanceMetric,
}

#[derive(Debug, Clone)]
pub struct DecompositionConfig {
    pub decomposition_type: DecompositionType,
    pub rank_ratio: f32,
    pub layers_to_decompose: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct ClusteringConfig {
    pub num_clusters: usize,
    pub cluster_method: ClusteringMethod,
    pub per_layer_clustering: bool,
}

/// Importance metrics for structured pruning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImportanceMetric {
    /// L1 norm of weights
    L1Norm,
    /// L2 norm of weights
    L2Norm,
    /// Gradient-based importance
    Gradient,
    /// Fisher information
    Fisher,
    /// Random (baseline)
    Random,
}

/// Summary of compression applied to a model
#[derive(Debug, Clone)]
pub struct CompressionSummary {
    pub techniques: Vec<String>,
    pub parameter_count: usize,
    pub model_size_bytes: usize,
    pub is_quantized: bool,
    pub is_pruned: bool,
}

/// Utilities for model compression
pub mod utils {
    use super::*;

    /// Create a simple quantization config
    pub fn simple_quantization_config(bits: u8) -> CompressionConfig {
        CompressionConfig {
            strategies: vec![CompressionStrategy::Quantization {
                bits,
                signed: true,
                symmetric: false,
            }],
            ..Default::default()
        }
    }

    /// Create a simple pruning config
    pub fn simple_pruning_config(sparsity: f32) -> CompressionConfig {
        CompressionConfig {
            strategies: vec![CompressionStrategy::UnstructuredPruning {
                sparsity,
                strategy: PruningStrategy::Magnitude,
            }],
            ..Default::default()
        }
    }

    /// Create a combined compression config
    pub fn combined_compression_config(
        quantization_bits: u8,
        pruning_sparsity: f32,
    ) -> CompressionConfig {
        CompressionConfig {
            strategies: vec![
                CompressionStrategy::UnstructuredPruning {
                    sparsity: pruning_sparsity,
                    strategy: PruningStrategy::Magnitude,
                },
                CompressionStrategy::Quantization {
                    bits: quantization_bits,
                    signed: true,
                    symmetric: false,
                },
            ],
            ..Default::default()
        }
    }

    /// Create a progressive compression config
    pub fn progressive_compression_config(target_ratio: f32, stages: usize) -> CompressionConfig {
        CompressionConfig {
            target_compression_ratio: target_ratio,
            progressive: true,
            progressive_stages: stages,
            strategies: vec![
                CompressionStrategy::UnstructuredPruning {
                    sparsity: 0.3,
                    strategy: PruningStrategy::Magnitude,
                },
                CompressionStrategy::LowRankDecomposition {
                    decomposition_type: DecompositionType::SVD,
                    rank_ratio: 0.5,
                },
                CompressionStrategy::Quantization {
                    bits: 8,
                    signed: true,
                    symmetric: false,
                },
            ],
            ..Default::default()
        }
    }

    /// Create an aggressive compression config for maximum compression
    pub fn aggressive_compression_config() -> CompressionConfig {
        CompressionConfig {
            target_compression_ratio: 0.1, // 10x compression
            strategies: vec![
                CompressionStrategy::StructuredPruning {
                    pruning_ratio: 0.5,
                    granularity: StructuredPruningGranularity::Channel,
                },
                CompressionStrategy::UnstructuredPruning {
                    sparsity: 0.8,
                    strategy: PruningStrategy::Magnitude,
                },
                CompressionStrategy::LowRankDecomposition {
                    decomposition_type: DecompositionType::SVD,
                    rank_ratio: 0.3,
                },
                CompressionStrategy::WeightClustering {
                    num_clusters: 256,
                    cluster_method: ClusteringMethod::KMeans,
                },
                CompressionStrategy::Quantization {
                    bits: 4,
                    signed: true,
                    symmetric: true,
                },
            ],
            fine_tune: true,
            fine_tune_epochs: 5,
            max_accuracy_drop: 0.05, // Allow 5% accuracy drop for aggressive compression
            ..Default::default()
        }
    }

    /// Estimate compression ratio for a given configuration
    pub fn estimate_compression_ratio(config: &CompressionConfig) -> f32 {
        let mut ratio = 1.0;

        for strategy in &config.strategies {
            match strategy {
                CompressionStrategy::Quantization { bits, .. } => {
                    ratio *= *bits as f32 / 32.0; // Assuming float32 baseline
                },
                CompressionStrategy::UnstructuredPruning { sparsity, .. } => {
                    ratio *= 1.0 - sparsity; // Sparse storage efficiency
                },
                CompressionStrategy::StructuredPruning { pruning_ratio, .. } => {
                    ratio *= 1.0 - pruning_ratio;
                },
                CompressionStrategy::LowRankDecomposition { rank_ratio, .. } => {
                    ratio *= rank_ratio * 2.0; // Approximate for low-rank factorization
                },
                CompressionStrategy::WeightClustering { num_clusters, .. } => {
                    // Approximate compression from clustering
                    ratio *= (*num_clusters as f32).log2() / 32.0;
                },
                _ => {
                    // Conservative estimate for other strategies
                    ratio *= 0.8;
                },
            }
        }

        ratio.max(0.01) // Minimum 1% of original size
    }
}

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

    #[test]
    fn test_compression_config_default() {
        let config = CompressionConfig::default();
        assert_eq!(config.target_compression_ratio, 0.5);
        assert_eq!(config.strategies.len(), 1);
        assert!(config.fine_tune);
        assert!(!config.progressive);
    }

    #[test]
    fn test_simple_quantization_config() {
        let config = utils::simple_quantization_config(8);
        assert_eq!(config.strategies.len(), 1);

        if let CompressionStrategy::Quantization {
            bits,
            signed,
            symmetric,
        } = &config.strategies[0]
        {
            assert_eq!(*bits, 8);
            assert!(*signed);
            assert!(!*symmetric);
        } else {
            panic!("Expected Quantization strategy");
        }
    }

    #[test]
    fn test_simple_pruning_config() {
        let config = utils::simple_pruning_config(0.5);
        assert_eq!(config.strategies.len(), 1);

        if let CompressionStrategy::UnstructuredPruning { sparsity, strategy } =
            &config.strategies[0]
        {
            assert_eq!(*sparsity, 0.5);
            assert!(matches!(strategy, PruningStrategy::Magnitude));
        } else {
            panic!("Expected UnstructuredPruning strategy");
        }
    }

    #[test]
    fn test_combined_compression_config() {
        let config = utils::combined_compression_config(8, 0.3);
        assert_eq!(config.strategies.len(), 2);

        // First should be pruning
        if let CompressionStrategy::UnstructuredPruning { sparsity, .. } = &config.strategies[0] {
            assert_eq!(*sparsity, 0.3);
        } else {
            panic!("Expected UnstructuredPruning as first strategy");
        }

        // Second should be quantization
        if let CompressionStrategy::Quantization { bits, .. } = &config.strategies[1] {
            assert_eq!(*bits, 8);
        } else {
            panic!("Expected Quantization as second strategy");
        }
    }

    #[test]
    fn test_progressive_compression_config() {
        let config = utils::progressive_compression_config(0.25, 3);
        assert_eq!(config.target_compression_ratio, 0.25);
        assert!(config.progressive);
        assert_eq!(config.progressive_stages, 3);
        assert_eq!(config.strategies.len(), 3);
    }

    #[test]
    fn test_aggressive_compression_config() {
        let config = utils::aggressive_compression_config();
        assert_eq!(config.target_compression_ratio, 0.1);
        assert_eq!(config.strategies.len(), 5);
        assert!(config.fine_tune);
        assert_eq!(config.fine_tune_epochs, 5);
        assert_eq!(config.max_accuracy_drop, 0.05);
    }

    #[test]
    fn test_estimate_compression_ratio() {
        let config = utils::simple_quantization_config(8);
        let ratio = utils::estimate_compression_ratio(&config);
        assert!((ratio - 0.25).abs() < 1e-6); // 8/32 = 0.25

        let pruning_config = utils::simple_pruning_config(0.5);
        let pruning_ratio = utils::estimate_compression_ratio(&pruning_config);
        assert!((pruning_ratio - 0.5).abs() < 1e-6); // 1 - 0.5 = 0.5
    }

    #[test]
    fn test_compression_pipeline_creation() {
        let config = CompressionConfig::default();
        let pipeline = CompressionPipeline::new(config);
        assert!(pipeline.is_ok());

        let pipeline = pipeline.expect("operation failed");
        assert_eq!(pipeline.compression_stages.len(), 1);
        assert_eq!(pipeline.current_stage, 0);
    }

    #[test]
    fn test_progressive_pipeline_creation() {
        let config = utils::progressive_compression_config(0.25, 3);
        let pipeline = CompressionPipeline::new(config);
        assert!(pipeline.is_ok());

        let pipeline = pipeline.expect("operation failed");
        assert_eq!(pipeline.compression_stages.len(), 3);
    }
}