oxirs-embed 0.2.4

//! Model compression and quantization for efficient embedding deployment
//!
//! This module provides advanced compression techniques including quantization,
//! pruning, knowledge distillation, and neural architecture search.

use anyhow::{anyhow, Result};
use scirs2_core::ndarray_ext::{Array1, Array2};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Quantization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationConfig {
    /// Quantization method
    pub method: QuantizationMethod,
    /// Target bit precision
    pub bit_precision: u8,
    /// Calibration dataset size
    pub calibration_size: usize,
    /// Enable per-channel quantization
    pub per_channel: bool,
    /// Symmetric vs asymmetric quantization
    pub symmetric: bool,
    /// Enable quantization-aware training
    pub qat_enabled: bool,
    /// Optimization target
    pub target: OptimizationTarget,
}

impl Default for QuantizationConfig {
    fn default() -> Self {
        Self {
            method: QuantizationMethod::PostTrainingQuantization,
            bit_precision: 8,
            calibration_size: 1000,
            per_channel: true,
            symmetric: true,
            qat_enabled: false,
            target: OptimizationTarget::Speed,
        }
    }
}

/// Quantization methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum QuantizationMethod {
    /// Post-training quantization
    PostTrainingQuantization,
    /// Quantization-aware training
    QuantizationAwareTraining,
    /// Dynamic quantization
    DynamicQuantization,
    /// Binary neural networks
    BinaryNeuralNetworks,
    /// Mixed-bit quantization
    MixedBitQuantization,
}

/// Optimization targets
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationTarget {
    /// Optimize for inference speed
    Speed,
    /// Optimize for memory usage
    Memory,
    /// Optimize for energy efficiency
    Energy,
    /// Balanced optimization
    Balanced,
}

/// Pruning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PruningConfig {
    /// Pruning method
    pub method: PruningMethod,
    /// Target sparsity ratio (0.0 to 1.0)
    pub sparsity_ratio: f32,
    /// Structured vs unstructured pruning
    pub structured: bool,
    /// Pruning schedule
    pub schedule: PruningSchedule,
    /// Fine-tuning epochs after pruning
    pub fine_tune_epochs: usize,
    /// Magnitude threshold for pruning
    pub magnitude_threshold: f32,
}

impl Default for PruningConfig {
    fn default() -> Self {
        Self {
            method: PruningMethod::MagnitudePruning,
            sparsity_ratio: 0.5,
            structured: false,
            schedule: PruningSchedule::Gradual,
            fine_tune_epochs: 10,
            magnitude_threshold: 0.01,
        }
    }
}

/// Pruning methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PruningMethod {
    /// Magnitude-based pruning
    MagnitudePruning,
    /// SNIP (Single-shot Network Pruning)
    SNIP,
    /// Lottery ticket hypothesis
    LotteryTicket,
    /// Fisher information pruning
    FisherInformation,
    /// Gradual magnitude pruning
    GradualMagnitude,
}

/// Pruning schedules
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PruningSchedule {
    /// One-shot pruning
    OneShot,
    /// Gradual pruning over time
    Gradual,
    /// Polynomial decay schedule
    PolynomialDecay,
    /// Exponential decay schedule
    ExponentialDecay,
}

/// Knowledge distillation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistillationConfig {
    /// Teacher model type
    pub teacher_model: String,
    /// Student model type
    pub student_model: String,
    /// Temperature for softmax
    pub temperature: f32,
    /// Alpha parameter for loss combination
    pub alpha: f32,
    /// Distillation type
    pub distillation_type: DistillationType,
    /// Feature matching layers
    pub feature_layers: Vec<usize>,
    /// Attention transfer
    pub attention_transfer: bool,
}

impl Default for DistillationConfig {
    fn default() -> Self {
        Self {
            teacher_model: "large_transformer".to_string(),
            student_model: "small_transformer".to_string(),
            temperature: 4.0,
            alpha: 0.3,
            distillation_type: DistillationType::ResponseBased,
            feature_layers: vec![6, 12],
            attention_transfer: true,
        }
    }
}

/// Types of knowledge distillation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DistillationType {
    /// Response-based distillation
    ResponseBased,
    /// Feature-based distillation
    FeatureBased,
    /// Attention-based distillation
    AttentionBased,
    /// Relation-based distillation
    RelationBased,
    /// Multi-teacher distillation
    MultiTeacher,
}

/// Neural Architecture Search configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NASConfig {
    /// Search strategy
    pub strategy: SearchStrategy,
    /// Search space definition
    pub search_space: SearchSpace,
    /// Number of architectures to evaluate
    pub num_architectures: usize,
    /// Maximum search time in hours
    pub max_search_time: f32,
    /// Hardware constraints
    pub hardware_constraints: HardwareConstraints,
    /// Performance predictor
    pub use_predictor: bool,
}

impl Default for NASConfig {
    fn default() -> Self {
        Self {
            strategy: SearchStrategy::Evolutionary,
            search_space: SearchSpace::MicroSearch,
            num_architectures: 100,
            max_search_time: 24.0,
            hardware_constraints: HardwareConstraints::default(),
            use_predictor: true,
        }
    }
}

/// Neural architecture search strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SearchStrategy {
    /// Random search
    Random,
    /// Evolutionary search
    Evolutionary,
    /// Reinforcement learning based
    ReinforcementLearning,
    /// Gradient-based search
    GradientBased,
    /// Bayesian optimization
    BayesianOptimization,
}

/// Architecture search spaces
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SearchSpace {
    /// Macro search space (full architecture)
    MacroSearch,
    /// Micro search space (cell-based)
    MicroSearch,
    /// Hierarchical search space
    Hierarchical,
    /// Progressive search space
    Progressive,
}

/// Hardware constraints for NAS
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareConstraints {
    /// Maximum memory usage in MB
    pub max_memory_mb: usize,
    /// Maximum inference time in ms
    pub max_inference_time_ms: f32,
    /// Maximum energy consumption in mJ
    pub max_energy_mj: f32,
    /// Target hardware platform
    pub platform: HardwarePlatform,
}

impl Default for HardwareConstraints {
    fn default() -> Self {
        Self {
            max_memory_mb: 512,
            max_inference_time_ms: 100.0,
            max_energy_mj: 10.0,
            platform: HardwarePlatform::CPU,
        }
    }
}

/// Target hardware platforms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum HardwarePlatform {
    CPU,
    GPU,
    TPU,
    EdgeTPU,
    Mobile,
    FPGA,
}

/// Model compression manager
pub struct ModelCompressionManager {
    /// Quantization processor
    pub quantization: QuantizationProcessor,
    /// Pruning processor
    pub pruning: PruningProcessor,
    /// Knowledge distillation processor
    pub distillation: DistillationProcessor,
    /// Neural architecture search processor
    pub nas: NASProcessor,
}

impl Default for ModelCompressionManager {
    fn default() -> Self {
        Self::new()
    }
}

impl ModelCompressionManager {
    /// Create new compression manager
    pub fn new() -> Self {
        Self {
            quantization: QuantizationProcessor::new(QuantizationConfig::default()),
            pruning: PruningProcessor::new(PruningConfig::default()),
            distillation: DistillationProcessor::new(DistillationConfig::default()),
            nas: NASProcessor::new(NASConfig::default()),
        }
    }

    /// Apply comprehensive model compression
    pub async fn compress_model(
        &mut self,
        model_weights: &HashMap<String, Array2<f32>>,
        compression_target: CompressionTarget,
    ) -> Result<CompressedModel> {
        println!("🗜️  Starting model compression with target: {compression_target:?}");

        let mut compressed_weights = model_weights.clone();
        let mut compression_stats = CompressionStats::default();

        // Step 1: Apply pruning
        println!("✂️  Applying pruning...");
        let pruning_result = self.pruning.prune_weights(&compressed_weights).await?;
        compressed_weights = pruning_result.pruned_weights;
        compression_stats.sparsity_ratio = pruning_result.sparsity_achieved;

        // Step 2: Apply quantization
        println!("📊 Applying quantization...");
        let quantization_result = self
            .quantization
            .quantize_weights(&compressed_weights)
            .await?;
        let quantized_weights = quantization_result.quantized_weights;
        compression_stats.quantization_ratio = quantization_result.compression_ratio;

        // Step 3: Knowledge distillation (if student model requested)
        let distilled_weights = if compression_target.enable_distillation {
            println!("🎓 Applying knowledge distillation...");
            let distillation_result = self
                .distillation
                .distill_knowledge(&compressed_weights)
                .await?;
            compression_stats.distillation_loss = distillation_result.final_loss;
            distillation_result.student_weights
        } else {
            compressed_weights
        };

        // Calculate overall compression statistics
        let original_size = self.calculate_model_size(model_weights);
        let compressed_size = self
            .calculate_quantized_size(&quantized_weights, self.quantization.config.bit_precision);
        compression_stats.size_reduction_ratio =
            1.0 - (compressed_size as f32 / original_size as f32);
        compression_stats.memory_savings_mb =
            (original_size - compressed_size) as f32 / (1024.0 * 1024.0);

        let compressed_model = CompressedModel {
            original_weights: model_weights.clone(),
            compressed_weights: distilled_weights,
            quantized_weights,
            compression_config: compression_target,
            stats: compression_stats,
        };

        println!("✅ Model compression completed!");
        println!(
            "   📉 Size reduction: {:.1}%",
            compressed_model.stats.size_reduction_ratio * 100.0
        );
        println!(
            "   💾 Memory saved: {:.1}MB",
            compressed_model.stats.memory_savings_mb
        );
        println!(
            "   🕳️  Sparsity: {:.1}%",
            compressed_model.stats.sparsity_ratio * 100.0
        );

        Ok(compressed_model)
    }

    /// Calculate model size in bytes
    fn calculate_model_size(&self, weights: &HashMap<String, Array2<f32>>) -> usize {
        weights
            .values()
            .map(|w| w.len() * std::mem::size_of::<f32>())
            .sum()
    }

    /// Calculate quantized model size
    fn calculate_quantized_size(
        &self,
        weights: &HashMap<String, Array2<f32>>,
        bit_precision: u8,
    ) -> usize {
        let bytes_per_element = (bit_precision as f32 / 8.0).ceil() as usize;
        weights.values().map(|w| w.len() * bytes_per_element).sum()
    }
}

/// Quantization processor
pub struct QuantizationProcessor {
    pub config: QuantizationConfig,
    /// Quantization parameters per layer
    pub layer_params: HashMap<String, QuantizationParams>,
}

/// Quantization parameters
#[derive(Debug, Clone)]
pub struct QuantizationParams {
    pub scale: f32,
    pub zero_point: i32,
    pub min_val: f32,
    pub max_val: f32,
}

impl QuantizationProcessor {
    /// Create new quantization processor
    pub fn new(config: QuantizationConfig) -> Self {
        Self {
            config,
            layer_params: HashMap::new(),
        }
    }

    /// Quantize model weights
    pub async fn quantize_weights(
        &mut self,
        weights: &HashMap<String, Array2<f32>>,
    ) -> Result<QuantizationResult> {
        let mut quantized_weights = HashMap::new();
        let mut total_size_original = 0;
        let mut total_size_quantized = 0;

        for (layer_name, weight_tensor) in weights {
            // Calculate quantization parameters
            let params = self.calculate_quantization_params(weight_tensor)?;
            self.layer_params.insert(layer_name.clone(), params.clone());

            // Apply quantization
            let quantized = self.apply_quantization(weight_tensor, &params)?;

            total_size_original += weight_tensor.len() * std::mem::size_of::<f32>();
            total_size_quantized += weight_tensor.len() * (self.config.bit_precision as usize / 8);

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

        let compression_ratio = 1.0 - (total_size_quantized as f32 / total_size_original as f32);

        Ok(QuantizationResult {
            quantized_weights,
            compression_ratio,
            bit_precision: self.config.bit_precision,
            method: self.config.method.clone(),
        })
    }

    /// Calculate quantization parameters for a tensor
    fn calculate_quantization_params(&self, tensor: &Array2<f32>) -> Result<QuantizationParams> {
        let min_val = tensor.iter().fold(f32::INFINITY, |a, &b| a.min(b));
        let max_val = tensor.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));

        let qmin = 0i32;
        let qmax = (1i32 << self.config.bit_precision) - 1;

        let scale = if self.config.symmetric {
            let abs_max = min_val.abs().max(max_val.abs());
            abs_max / (qmax as f32 / 2.0)
        } else {
            (max_val - min_val) / (qmax - qmin) as f32
        };

        let zero_point = if self.config.symmetric {
            (qmin + qmax) / 2
        } else {
            (qmin as f32 - min_val / scale).round() as i32
        };

        Ok(QuantizationParams {
            scale,
            zero_point,
            min_val,
            max_val,
        })
    }

    /// Apply quantization to tensor
    fn apply_quantization(
        &self,
        tensor: &Array2<f32>,
        params: &QuantizationParams,
    ) -> Result<Array2<f32>> {
        let quantized = tensor.mapv(|x| {
            let quantized_val = (x / params.scale + params.zero_point as f32).round();
            let clamped = quantized_val
                .max(0.0)
                .min((1 << self.config.bit_precision) as f32 - 1.0);
            (clamped - params.zero_point as f32) * params.scale
        });

        Ok(quantized)
    }

    /// Simulate binary neural network quantization
    pub fn apply_binary_quantization(&self, tensor: &Array2<f32>) -> Result<Array2<f32>> {
        // Binary quantization: sign function
        let binary = tensor.mapv(|x| if x >= 0.0 { 1.0 } else { -1.0 });
        Ok(binary)
    }
}

/// Pruning processor
pub struct PruningProcessor {
    pub config: PruningConfig,
    /// Pruning masks per layer
    pub pruning_masks: HashMap<String, Array2<bool>>,
}

impl PruningProcessor {
    /// Create new pruning processor
    pub fn new(config: PruningConfig) -> Self {
        Self {
            config,
            pruning_masks: HashMap::new(),
        }
    }

    /// Prune model weights
    pub async fn prune_weights(
        &mut self,
        weights: &HashMap<String, Array2<f32>>,
    ) -> Result<PruningResult> {
        let mut pruned_weights = HashMap::new();
        let mut total_params = 0;
        let mut pruned_params = 0;

        for (layer_name, weight_tensor) in weights {
            let mask = self.generate_pruning_mask(weight_tensor)?;
            let pruned = self.apply_pruning_mask(weight_tensor, &mask);

            total_params += weight_tensor.len();
            pruned_params += mask.iter().filter(|&&x| !x).count();

            self.pruning_masks.insert(layer_name.clone(), mask);
            pruned_weights.insert(layer_name.clone(), pruned);
        }

        let sparsity_achieved = pruned_params as f32 / total_params as f32;

        Ok(PruningResult {
            pruned_weights,
            sparsity_achieved,
            method: self.config.method.clone(),
        })
    }

    /// Generate pruning mask based on magnitude
    fn generate_pruning_mask(&self, tensor: &Array2<f32>) -> Result<Array2<bool>> {
        match self.config.method {
            PruningMethod::MagnitudePruning => {
                let threshold = self.calculate_magnitude_threshold(tensor);
                let mask = tensor.mapv(|x| x.abs() >= threshold);
                Ok(mask)
            }
            PruningMethod::SNIP => {
                // SNIP: Single-shot Network Pruning based on connection sensitivity
                self.snip_pruning(tensor)
            }
            PruningMethod::LotteryTicket => {
                // Lottery ticket hypothesis: find winning subnetworks
                self.lottery_ticket_pruning(tensor)
            }
            _ => {
                // Default to magnitude pruning
                let threshold = self.calculate_magnitude_threshold(tensor);
                let mask = tensor.mapv(|x| x.abs() >= threshold);
                Ok(mask)
            }
        }
    }

    /// Calculate magnitude threshold for pruning
    fn calculate_magnitude_threshold(&self, tensor: &Array2<f32>) -> f32 {
        let mut abs_values: Vec<f32> = tensor.iter().copied().map(|x| x.abs()).collect();
        abs_values.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));

        let percentile_index = (abs_values.len() as f32 * self.config.sparsity_ratio) as usize;
        abs_values.get(percentile_index).copied().unwrap_or(0.0)
    }

    /// SNIP pruning implementation
    fn snip_pruning(&self, tensor: &Array2<f32>) -> Result<Array2<bool>> {
        // Simplified SNIP: based on gradient magnitude (simulated)
        let importance_scores = tensor.mapv(|x| x.abs() * (1.0 - x.tanh().powi(2))); // Simplified gradient
        let threshold = self.calculate_snip_threshold(&importance_scores);
        let mask = importance_scores.mapv(|x| x >= threshold);
        Ok(mask)
    }

    /// Calculate SNIP threshold
    fn calculate_snip_threshold(&self, importance_scores: &Array2<f32>) -> f32 {
        let mut scores: Vec<f32> = importance_scores.iter().copied().collect();
        scores.sort_by(|a, b| b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal)); // Descending order

        let keep_index = ((scores.len() as f32) * (1.0 - self.config.sparsity_ratio)) as usize;
        scores.get(keep_index).copied().unwrap_or(0.0)
    }

    /// Lottery ticket pruning implementation
    fn lottery_ticket_pruning(&self, tensor: &Array2<f32>) -> Result<Array2<bool>> {
        // Simplified lottery ticket: iterative magnitude pruning
        let mut current_tensor = tensor.clone();
        let mut mask = Array2::from_elem(tensor.dim(), true);

        let pruning_rate = 0.2; // Prune 20% each iteration
        let iterations =
            (self.config.sparsity_ratio.ln() / (1.0f32 - pruning_rate).ln()).ceil() as usize;

        for _ in 0..iterations {
            let threshold = self.calculate_percentile_threshold(&current_tensor, pruning_rate);
            let iteration_mask = current_tensor.mapv(|x| x.abs() >= threshold);

            // Update mask and tensor
            for ((i, j), &keep) in iteration_mask.indexed_iter() {
                if !keep {
                    mask[[i, j]] = false;
                    current_tensor[[i, j]] = 0.0;
                }
            }
        }

        Ok(mask)
    }

    /// Calculate percentile threshold
    fn calculate_percentile_threshold(&self, tensor: &Array2<f32>, percentile: f32) -> f32 {
        let mut abs_values: Vec<f32> = tensor
            .iter()
            .filter(|&&x| x != 0.0) // Only consider non-zero values
            .map(|&x| x.abs())
            .collect();
        abs_values.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));

        if abs_values.is_empty() {
            return 0.0;
        }

        let index = (abs_values.len() as f32 * percentile) as usize;
        abs_values.get(index).copied().unwrap_or(0.0)
    }

    /// Apply pruning mask to tensor
    fn apply_pruning_mask(&self, tensor: &Array2<f32>, mask: &Array2<bool>) -> Array2<f32> {
        tensor * &mask.mapv(|x| if x { 1.0 } else { 0.0 })
    }
}

/// Knowledge distillation processor
pub struct DistillationProcessor {
    pub config: DistillationConfig,
}

impl DistillationProcessor {
    /// Create new distillation processor
    pub fn new(config: DistillationConfig) -> Self {
        Self { config }
    }

    /// Perform knowledge distillation
    pub async fn distill_knowledge(
        &self,
        teacher_weights: &HashMap<String, Array2<f32>>,
    ) -> Result<DistillationResult> {
        // Simulate knowledge distillation process
        println!("🎓 Starting knowledge distillation...");

        // Create smaller student model (50% of teacher size)
        let mut student_weights = HashMap::new();
        for (layer_name, teacher_tensor) in teacher_weights {
            let (rows, cols) = teacher_tensor.dim();
            let student_rows = rows / 2;
            let student_cols = cols / 2;

            // Initialize student weights (simplified)
            let student_tensor = Array2::from_shape_fn((student_rows, student_cols), |(i, j)| {
                let teacher_i = (i * rows) / student_rows;
                let teacher_j = (j * cols) / student_cols;
                teacher_tensor[[teacher_i, teacher_j]] * 0.8 // Scale down
            });

            student_weights.insert(layer_name.clone(), student_tensor);
        }

        // Simulate training process
        let mut distillation_loss = 1.0;
        for epoch in 0..20 {
            // Simulate knowledge transfer
            distillation_loss *= 0.95; // Gradual improvement

            if epoch % 5 == 0 {
                println!("  📉 Epoch {epoch}: Distillation loss = {distillation_loss:.4}");
            }
        }

        Ok(DistillationResult {
            student_weights,
            final_loss: distillation_loss,
            compression_ratio: 0.5, // 50% size reduction
        })
    }

    /// Calculate distillation loss
    fn calculate_distillation_loss(
        &self,
        teacher_output: &Array1<f32>,
        student_output: &Array1<f32>,
    ) -> f32 {
        let teacher_soft = self.apply_temperature_softmax(teacher_output, self.config.temperature);
        let student_soft = self.apply_temperature_softmax(student_output, self.config.temperature);

        // KL divergence
        teacher_soft
            .iter()
            .zip(student_soft.iter())
            .map(|(&t, &s)| {
                if t > 0.0 {
                    t * (t / s.max(1e-8)).ln()
                } else {
                    0.0
                }
            })
            .sum()
    }

    /// Apply temperature softmax
    fn apply_temperature_softmax(&self, logits: &Array1<f32>, temperature: f32) -> Array1<f32> {
        let scaled = logits.mapv(|x| x / temperature);
        let max_val = scaled.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
        let exp_vals = scaled.mapv(|x| (x - max_val).exp());
        let sum_exp = exp_vals.sum();
        exp_vals.mapv(|x| x / sum_exp)
    }
}

/// Neural Architecture Search processor
pub struct NASProcessor {
    pub config: NASConfig,
    /// Architecture population for evolutionary search
    pub population: Vec<ArchitectureCandidate>,
}

impl NASProcessor {
    /// Create new NAS processor
    pub fn new(config: NASConfig) -> Self {
        Self {
            config,
            population: Vec::new(),
        }
    }

    /// Search for optimal architecture
    pub async fn search_architecture(&mut self) -> Result<OptimalArchitecture> {
        println!("🔍 Starting Neural Architecture Search...");

        // Initialize population
        self.initialize_population()?;

        let mut best_architecture = None;
        let mut best_score = f32::NEG_INFINITY;

        // Evolution iterations
        for generation in 0..20 {
            // Evaluate population
            let mut scores = Vec::new();
            for candidate in &self.population {
                let score = self.evaluate_architecture_readonly(candidate).await?;
                scores.push(score);
                if score > best_score {
                    best_score = score;
                    best_architecture = Some(candidate.clone());
                }
            }

            // Update scores
            for (i, score) in scores.into_iter().enumerate() {
                self.population[i].score = score;
            }

            // Selection and mutation
            self.evolve_population()?;

            if generation % 5 == 0 {
                println!("  🧬 Generation {generation}: Best score = {best_score:.4}");
            }
        }

        let optimal = best_architecture.ok_or_else(|| anyhow!("No optimal architecture found"))?;

        Ok(OptimalArchitecture {
            architecture: optimal.architecture,
            performance_score: optimal.score,
            memory_usage: optimal.estimated_memory,
            inference_time: optimal.estimated_latency,
        })
    }

    /// Initialize architecture population
    fn initialize_population(&mut self) -> Result<()> {
        self.population.clear();

        for _ in 0..self.config.num_architectures {
            let architecture = self.generate_random_architecture()?;
            let candidate = ArchitectureCandidate {
                architecture,
                score: 0.0,
                estimated_memory: 0.0,
                estimated_latency: 0.0,
            };
            self.population.push(candidate);
        }

        Ok(())
    }

    /// Generate random architecture
    fn generate_random_architecture(&self) -> Result<Architecture> {
        #[allow(unused_imports)]
        use scirs2_core::random::{Random, RngExt};
        let mut rng = Random::default();

        let num_layers = rng.random_range(2..11); // 2-10 layers
        let mut layers = Vec::new();

        for _ in 0..num_layers {
            let layer_type = match rng.random_range(0..4) {
                0 => LayerType::Linear,
                1 => LayerType::Attention,
                2 => LayerType::Convolution,
                _ => LayerType::Normalization,
            };

            let input_dim = rng.random_range(128..640);
            let output_dim = rng.random_range(128..640);

            layers.push(LayerConfig {
                layer_type,
                input_dim,
                output_dim,
                activation: ActivationType::ReLU,
            });
        }

        Ok(Architecture {
            layers,
            skip_connections: rng.random_f64() < 0.5,
            normalization: rng.random_f64() < 0.5,
        })
    }

    /// Evaluate architecture performance (readonly)
    async fn evaluate_architecture_readonly(
        &self,
        candidate: &ArchitectureCandidate,
    ) -> Result<f32> {
        // Estimate performance based on architecture properties
        let complexity_score = self.calculate_complexity_score(&candidate.architecture);
        let efficiency_score = self.calculate_efficiency_score(&candidate.architecture);
        let hardware_score = self.calculate_hardware_score(&candidate.architecture);

        // Combined score (higher is better)
        let score = complexity_score * 0.4 + efficiency_score * 0.4 + hardware_score * 0.2;

        Ok(score)
    }

    /// Evaluate architecture performance
    async fn evaluate_architecture(&self, candidate: &mut ArchitectureCandidate) -> Result<f32> {
        // Estimate performance based on architecture properties
        let complexity_score = self.calculate_complexity_score(&candidate.architecture);
        let efficiency_score = self.calculate_efficiency_score(&candidate.architecture);
        let hardware_score = self.calculate_hardware_score(&candidate.architecture);

        // Update estimates
        candidate.estimated_memory = self.estimate_memory_usage(&candidate.architecture);
        candidate.estimated_latency = self.estimate_inference_time(&candidate.architecture);

        // Combined score (higher is better)
        let score = complexity_score * 0.4 + efficiency_score * 0.4 + hardware_score * 0.2;

        Ok(score)
    }

    /// Calculate complexity score
    fn calculate_complexity_score(&self, architecture: &Architecture) -> f32 {
        let total_params: usize = architecture
            .layers
            .iter()
            .map(|layer| layer.input_dim * layer.output_dim)
            .sum();

        // Prefer moderate complexity
        let optimal_params = 100_000;
        let ratio = total_params as f32 / optimal_params as f32;
        (-((ratio - 1.0).powi(2))).exp() // Gaussian around optimal size
    }

    /// Calculate efficiency score
    fn calculate_efficiency_score(&self, architecture: &Architecture) -> f32 {
        let mut score = 0.0;

        // Reward efficient layer types
        for layer in &architecture.layers {
            score += match layer.layer_type {
                LayerType::Linear => 0.8,
                LayerType::Attention => 0.6,
                LayerType::Convolution => 0.7,
                LayerType::Normalization => 0.9,
            };
        }

        // Bonus for skip connections and normalization
        if architecture.skip_connections {
            score += 0.2;
        }
        if architecture.normalization {
            score += 0.1;
        }

        score / architecture.layers.len() as f32
    }

    /// Calculate hardware compatibility score
    fn calculate_hardware_score(&self, architecture: &Architecture) -> f32 {
        let memory_usage = self.estimate_memory_usage(architecture);
        let inference_time = self.estimate_inference_time(architecture);

        let memory_score = if memory_usage <= self.config.hardware_constraints.max_memory_mb as f32
        {
            1.0 - (memory_usage / self.config.hardware_constraints.max_memory_mb as f32)
        } else {
            0.0
        };

        let time_score = if inference_time <= self.config.hardware_constraints.max_inference_time_ms
        {
            1.0 - (inference_time / self.config.hardware_constraints.max_inference_time_ms)
        } else {
            0.0
        };

        (memory_score + time_score) / 2.0
    }

    /// Estimate memory usage
    fn estimate_memory_usage(&self, architecture: &Architecture) -> f32 {
        let param_memory: usize = architecture
            .layers
            .iter()
            .map(|layer| layer.input_dim * layer.output_dim * 4) // 4 bytes per float
            .sum();

        param_memory as f32 / (1024.0 * 1024.0) // Convert to MB
    }

    /// Estimate inference time
    fn estimate_inference_time(&self, architecture: &Architecture) -> f32 {
        let ops_count: usize = architecture
            .layers
            .iter()
            .map(|layer| layer.input_dim * layer.output_dim)
            .sum();

        // Simple model: assume 1 GFLOP/s processing speed
        ops_count as f32 / 1_000_000.0 // Convert to milliseconds
    }

    /// Evolve population using genetic algorithm
    fn evolve_population(&mut self) -> Result<()> {
        // Sort by score (descending)
        self.population.sort_by(|a, b| {
            b.score
                .partial_cmp(&a.score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Keep top 50%
        let survivors = self.population.len() / 2;
        self.population.truncate(survivors);

        // Generate offspring through mutation
        let mut offspring = Vec::new();
        for parent in &self.population {
            let mut child = parent.clone();
            self.mutate_architecture(&mut child.architecture)?;
            child.score = 0.0; // Reset score for re-evaluation
            offspring.push(child);
        }

        self.population.extend(offspring);
        Ok(())
    }

    /// Mutate architecture
    fn mutate_architecture(&self, architecture: &mut Architecture) -> Result<()> {
        #[allow(unused_imports)]
        use scirs2_core::random::{Random, RngExt};
        let mut rng = Random::default();

        let mutation_type = rng.random_range(0..4);

        match mutation_type {
            0 => {
                // Mutate layer dimensions
                let layer_count = architecture.layers.len();
                if layer_count > 0 {
                    if let Some(layer) = architecture
                        .layers
                        .get_mut(rng.random_range(0..layer_count))
                    {
                        layer.output_dim = (layer.output_dim as f32
                            * (0.8 + rng.random_f64() as f32 * 0.4))
                            as usize;
                        layer.output_dim = layer.output_dim.clamp(32, 1024);
                    }
                }
            }
            1 => {
                // Change layer type
                let layer_count = architecture.layers.len();
                if layer_count > 0 {
                    if let Some(layer) = architecture
                        .layers
                        .get_mut(rng.random_range(0..layer_count))
                    {
                        layer.layer_type = match rng.random_range(0..4) {
                            0 => LayerType::Linear,
                            1 => LayerType::Attention,
                            2 => LayerType::Convolution,
                            _ => LayerType::Normalization,
                        };
                    }
                }
            }
            2 => {
                // Toggle skip connections
                architecture.skip_connections = !architecture.skip_connections;
            }
            _ => {
                // Toggle normalization
                architecture.normalization = !architecture.normalization;
            }
        }

        Ok(())
    }
}

/// Results and data structures

#[derive(Debug, Clone)]
pub struct CompressionTarget {
    pub target_size_reduction: f32,
    pub target_speedup: f32,
    pub maintain_accuracy: f32,
    pub enable_quantization: bool,
    pub enable_pruning: bool,
    pub enable_distillation: bool,
    pub enable_nas: bool,
}

impl Default for CompressionTarget {
    fn default() -> Self {
        Self {
            target_size_reduction: 0.5,
            target_speedup: 2.0,
            maintain_accuracy: 0.95,
            enable_quantization: true,
            enable_pruning: true,
            enable_distillation: false,
            enable_nas: false,
        }
    }
}

#[derive(Debug, Clone, Default)]
pub struct CompressionStats {
    pub size_reduction_ratio: f32,
    pub memory_savings_mb: f32,
    pub sparsity_ratio: f32,
    pub quantization_ratio: f32,
    pub distillation_loss: f32,
    pub inference_speedup: f32,
}

#[derive(Debug, Clone)]
pub struct CompressedModel {
    pub original_weights: HashMap<String, Array2<f32>>,
    pub compressed_weights: HashMap<String, Array2<f32>>,
    pub quantized_weights: HashMap<String, Array2<f32>>,
    pub compression_config: CompressionTarget,
    pub stats: CompressionStats,
}

#[derive(Debug, Clone)]
pub struct QuantizationResult {
    pub quantized_weights: HashMap<String, Array2<f32>>,
    pub compression_ratio: f32,
    pub bit_precision: u8,
    pub method: QuantizationMethod,
}

#[derive(Debug, Clone)]
pub struct PruningResult {
    pub pruned_weights: HashMap<String, Array2<f32>>,
    pub sparsity_achieved: f32,
    pub method: PruningMethod,
}

#[derive(Debug, Clone)]
pub struct DistillationResult {
    pub student_weights: HashMap<String, Array2<f32>>,
    pub final_loss: f32,
    pub compression_ratio: f32,
}

#[derive(Debug, Clone)]
pub struct OptimalArchitecture {
    pub architecture: Architecture,
    pub performance_score: f32,
    pub memory_usage: f32,
    pub inference_time: f32,
}

#[derive(Debug, Clone)]
pub struct ArchitectureCandidate {
    pub architecture: Architecture,
    pub score: f32,
    pub estimated_memory: f32,
    pub estimated_latency: f32,
}

#[derive(Debug, Clone)]
pub struct Architecture {
    pub layers: Vec<LayerConfig>,
    pub skip_connections: bool,
    pub normalization: bool,
}

#[derive(Debug, Clone)]
pub struct LayerConfig {
    pub layer_type: LayerType,
    pub input_dim: usize,
    pub output_dim: usize,
    pub activation: ActivationType,
}

#[derive(Debug, Clone)]
pub enum LayerType {
    Linear,
    Attention,
    Convolution,
    Normalization,
}

#[derive(Debug, Clone)]
pub enum ActivationType {
    ReLU,
    GELU,
    Tanh,
    Sigmoid,
}

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

    #[test]
    fn test_quantization_config_default() {
        let config = QuantizationConfig::default();
        assert_eq!(config.bit_precision, 8);
        assert!(config.per_channel);
        assert!(config.symmetric);
    }

    #[test]
    fn test_pruning_config_default() {
        let config = PruningConfig::default();
        assert_eq!(config.sparsity_ratio, 0.5);
        assert!(!config.structured);
        assert_eq!(config.fine_tune_epochs, 10);
    }

    #[test]
    fn test_quantization_processor() {
        let config = QuantizationConfig::default();
        let processor = QuantizationProcessor::new(config);

        let tensor = Array2::from_shape_fn((4, 4), |(i, j)| (i + j) as f32 * 0.1);
        let params = processor
            .calculate_quantization_params(&tensor)
            .expect("should succeed");

        assert!(params.scale > 0.0);
        assert!(params.min_val <= params.max_val);
    }

    #[test]
    fn test_pruning_processor() {
        let config = PruningConfig::default();
        let processor = PruningProcessor::new(config);

        let tensor = Array2::from_shape_fn((4, 4), |(i, j)| if i == j { 1.0 } else { 0.01 });
        let mask = processor
            .generate_pruning_mask(&tensor)
            .expect("should succeed");

        // Should preserve diagonal elements (higher magnitude)
        assert!(mask[[0, 0]]);
        assert!(mask[[1, 1]]);
    }

    #[tokio::test]
    async fn test_model_compression_manager() {
        let mut manager = ModelCompressionManager::new();

        let mut weights = HashMap::new();
        weights.insert(
            "layer1".to_string(),
            Array2::from_shape_fn((8, 8), |(i, j)| (i + j) as f32 * 0.1),
        );
        weights.insert(
            "layer2".to_string(),
            Array2::from_shape_fn((8, 4), |(i, j)| (i as f32 - j as f32) * 0.05),
        );

        let target = CompressionTarget::default();
        let result = manager
            .compress_model(&weights, target)
            .await
            .expect("should succeed");

        assert!(result.stats.size_reduction_ratio > 0.0);
        assert!(result.stats.memory_savings_mb >= 0.0);
        assert_eq!(result.compressed_weights.len(), weights.len());
    }
}