trustformers_mobile/optimization/

ai_powered_optimizer.rs

//! AI-Powered Optimization Pipeline
//!
//! This module implements machine learning-driven optimization that learns from usage patterns
//! and dynamically adapts model architectures for optimal mobile performance.

use crate::scirs2_compat::random::legacy;
use crate::{MobileBackend, MobileConfig, PerformanceTier};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use trustformers_core::errors::Result;

// Helper functions for random number generation
fn random_usize(max: usize) -> usize {
    if max == 0 {
        return 0;
    }
    ((legacy::f64() * max as f64) as usize).min(max.saturating_sub(1))
}

fn random_f32() -> f32 {
    legacy::f32()
}

/// Neural Architecture Search for mobile-optimized model variants
#[derive(Debug, Clone)]
pub struct MobileNAS {
    search_config: NASConfig,
    architecture_candidates: Vec<MobileArchitecture>,
    performance_history: Vec<PerformanceRecord>,
    optimization_agent: ReinforcementLearningAgent,
}

/// Configuration for Neural Architecture Search
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NASConfig {
    /// Maximum search iterations
    pub max_iterations: usize,
    /// Performance metrics to optimize
    pub optimization_targets: Vec<OptimizationTarget>,
    /// Device constraints
    pub device_constraints: DeviceConstraints,
    /// Search strategy
    pub search_strategy: SearchStrategy,
    /// Early stopping criteria
    pub early_stopping: EarlyStoppingConfig,
}

/// Optimization targets for NAS
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum OptimizationTarget {
    /// Minimize inference latency
    Latency,
    /// Minimize memory usage
    Memory,
    /// Minimize power consumption
    Power,
    /// Maximize accuracy
    Accuracy,
    /// Minimize model size
    ModelSize,
    /// Minimize energy consumption
    Energy,
}

/// Device constraints for architecture search
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeviceConstraints {
    /// Maximum memory usage in MB
    pub max_memory_mb: usize,
    /// Maximum inference latency in ms
    pub max_latency_ms: f32,
    /// Target performance tier
    pub performance_tier: PerformanceTier,
    /// Available backends
    pub available_backends: Vec<MobileBackend>,
    /// Power budget
    pub power_budget_mw: f32,
}

/// Search strategy for architecture exploration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SearchStrategy {
    /// Random search baseline
    Random,
    /// Evolutionary algorithm
    Evolutionary {
        population_size: usize,
        mutation_rate: f32,
        crossover_rate: f32,
    },
    /// Reinforcement learning-based search
    ReinforcementLearning {
        learning_rate: f32,
        exploration_rate: f32,
        replay_buffer_size: usize,
    },
    /// Differentiable architecture search
    Differentiable {
        temperature: f32,
        gumbel_softmax: bool,
    },
    /// Progressive search with early pruning
    Progressive {
        stages: usize,
        pruning_threshold: f32,
    },
}

/// Early stopping configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EarlyStoppingConfig {
    /// Patience iterations
    pub patience: usize,
    /// Minimum improvement threshold
    pub min_improvement: f32,
    /// Monitor metric
    pub monitor_metric: OptimizationTarget,
}

/// Mobile architecture representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MobileArchitecture {
    /// Architecture ID
    pub id: String,
    /// Layer configuration
    pub layers: Vec<LayerConfig>,
    /// Skip connections
    pub skip_connections: Vec<SkipConnection>,
    /// Quantization scheme
    pub quantization: QuantizationConfig,
    /// Estimated metrics
    pub estimated_metrics: Option<ArchitectureMetrics>,
}

/// Layer configuration for mobile architectures
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerConfig {
    /// Layer type
    pub layer_type: LayerType,
    /// Input dimensions
    pub input_dim: Vec<usize>,
    /// Output dimensions
    pub output_dim: Vec<usize>,
    /// Layer-specific parameters
    pub parameters: HashMap<String, f32>,
    /// Activation function
    pub activation: ActivationType,
}

/// Mobile-optimized layer types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LayerType {
    /// Depthwise separable convolution
    DepthwiseSeparableConv {
        kernel_size: usize,
        stride: usize,
        dilation: usize,
    },
    /// Mobile inverted bottleneck
    MobileBottleneck {
        expansion_ratio: f32,
        kernel_size: usize,
        squeeze_excitation: bool,
    },
    /// Efficient channel attention
    EfficientChannelAttention {
        reduction_ratio: usize,
        use_gating: bool,
    },
    /// Mobile multi-head attention
    MobileMultiHeadAttention {
        num_heads: usize,
        head_dim: usize,
        sparse_attention: bool,
    },
    /// Group normalization (mobile-friendly)
    GroupNormalization { num_groups: usize },
    /// Mobile-optimized linear layer
    MobileLinear { use_bias: bool, quantized: bool },
}

/// Activation types optimized for mobile
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ActivationType {
    /// Swish activation (mobile-optimized)
    Swish,
    /// Hard swish (more efficient)
    HardSwish,
    /// ReLU6 (hardware-friendly)
    ReLU6,
    /// GELU approximation
    GeluApprox,
    /// Mish (if supported by hardware)
    Mish,
}

/// Skip connection configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SkipConnection {
    /// Source layer index
    pub from_layer: usize,
    /// Target layer index
    pub to_layer: usize,
    /// Connection type
    pub connection_type: ConnectionType,
}

/// Types of skip connections
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConnectionType {
    /// Direct residual connection
    Residual,
    /// Dense connection
    Dense,
    /// Attention-based connection
    Attention { num_heads: usize },
    /// Channel shuffle connection
    ChannelShuffle,
}

/// Quantization configuration for architecture
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationConfig {
    /// Per-layer quantization schemes
    pub layer_schemes: HashMap<usize, QuantizationScheme>,
    /// Mixed precision strategy
    pub mixed_precision: bool,
    /// Dynamic quantization
    pub dynamic_quantization: bool,
}

/// Quantization schemes for different layers
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum QuantizationScheme {
    /// 4-bit quantization
    Int4 { symmetric: bool },
    /// 8-bit quantization
    Int8 { symmetric: bool },
    /// 16-bit floating point
    FP16,
    /// Block-wise quantization
    BlockWise { block_size: usize },
    /// Full precision (no quantization)
    FP32,
}

/// Architecture performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArchitectureMetrics {
    /// Inference latency in milliseconds
    pub latency_ms: f32,
    /// Memory usage in MB
    pub memory_mb: f32,
    /// Power consumption in mW
    pub power_mw: f32,
    /// Model accuracy (if available)
    pub accuracy: Option<f32>,
    /// Model size in MB
    pub model_size_mb: f32,
    /// Energy consumption per inference in mJ
    pub energy_per_inference_mj: f32,
    /// Throughput (inferences per second)
    pub throughput_fps: f32,
}

/// Performance record for learning
#[derive(Debug, Clone)]
pub struct PerformanceRecord {
    /// Architecture that was evaluated
    pub architecture: MobileArchitecture,
    /// Measured performance metrics
    pub metrics: ArchitectureMetrics,
    /// Device configuration
    pub device_config: MobileConfig,
    /// Timestamp
    pub timestamp: std::time::SystemTime,
    /// User context (if available)
    pub user_context: Option<UserContext>,
}

/// User context for personalized optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UserContext {
    /// Usage patterns
    pub usage_patterns: Vec<UsagePattern>,
    /// Performance preferences
    pub preferences: UserPreferences,
    /// Device usage environment
    pub environment: DeviceEnvironment,
}

/// Usage pattern analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UsagePattern {
    /// Task type
    pub task_type: String,
    /// Frequency of use
    pub frequency: f32,
    /// Typical input characteristics
    pub input_characteristics: InputCharacteristics,
    /// Performance requirements
    pub performance_requirements: PerformanceRequirements,
}

/// Input characteristics for optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InputCharacteristics {
    /// Typical input sizes
    pub input_sizes: Vec<Vec<usize>>,
    /// Batch sizes commonly used
    pub common_batch_sizes: Vec<usize>,
    /// Data types
    pub data_types: Vec<String>,
}

/// Performance requirements from user perspective
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceRequirements {
    /// Maximum acceptable latency
    pub max_latency_ms: f32,
    /// Battery life importance (0.0-1.0)
    pub battery_importance: f32,
    /// Accuracy importance (0.0-1.0)
    pub accuracy_importance: f32,
}

/// User preferences for optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UserPreferences {
    /// Preferred optimization target
    pub primary_target: OptimizationTarget,
    /// Secondary optimization targets
    pub secondary_targets: Vec<OptimizationTarget>,
    /// Acceptable quality tradeoffs
    pub quality_tradeoffs: QualityTradeoffs,
}

/// Quality tradeoffs user is willing to accept
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QualityTradeoffs {
    /// Maximum accuracy loss acceptable (%)
    pub max_accuracy_loss: f32,
    /// Maximum latency increase acceptable (%)
    pub max_latency_increase: f32,
    /// Maximum memory increase acceptable (%)
    pub max_memory_increase: f32,
}

/// Device environment context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeviceEnvironment {
    /// Typical charging status
    pub charging_status: ChargingPattern,
    /// Network connectivity patterns
    pub network_patterns: NetworkPattern,
    /// Temperature environment
    pub thermal_environment: ThermalEnvironment,
}

/// Charging patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ChargingPattern {
    /// Frequently plugged in
    FrequentCharging,
    /// Moderate charging
    ModerateCharging,
    /// Infrequent charging
    InfrequentCharging,
}

/// Network connectivity patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum NetworkPattern {
    /// Mostly WiFi
    PrimarilyWiFi,
    /// Mixed WiFi/Cellular
    Mixed,
    /// Mostly Cellular
    PrimarilyCellular,
    /// Frequent offline usage
    FrequentOffline,
}

/// Thermal environment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ThermalEnvironment {
    /// Cool environment
    Cool,
    /// Moderate temperature
    Moderate,
    /// Warm environment
    Warm,
    /// Variable temperature
    Variable,
}

/// Reinforcement Learning agent for optimization
#[derive(Debug, Clone)]
pub struct ReinforcementLearningAgent {
    /// Agent configuration
    config: RLConfig,
    /// Q-value network (simplified representation)
    q_network: QNetwork,
    /// Experience replay buffer
    replay_buffer: Vec<Experience>,
    /// Current exploration rate
    exploration_rate: f32,
}

/// RL configuration
#[derive(Debug, Clone)]
pub struct RLConfig {
    /// Learning rate
    pub learning_rate: f32,
    /// Discount factor
    pub discount_factor: f32,
    /// Initial exploration rate
    pub initial_exploration_rate: f32,
    /// Exploration decay rate
    pub exploration_decay: f32,
    /// Minimum exploration rate
    pub min_exploration_rate: f32,
}

/// Q-Network representation (simplified)
#[derive(Debug, Clone)]
pub struct QNetwork {
    /// Network weights (simplified)
    weights: Vec<Vec<f32>>,
    /// Network architecture
    architecture: Vec<usize>,
}

/// Experience for replay buffer
#[derive(Debug, Clone)]
pub struct Experience {
    /// State (architecture features)
    pub state: Vec<f32>,
    /// Action (architecture modification)
    pub action: ArchitectureAction,
    /// Reward (performance improvement)
    pub reward: f32,
    /// Next state
    pub next_state: Vec<f32>,
    /// Done flag
    pub done: bool,
}

/// Actions that can be taken on architectures
#[derive(Debug, Clone)]
pub enum ArchitectureAction {
    /// Add a layer
    AddLayer {
        layer_type: LayerType,
        position: usize,
    },
    /// Remove a layer
    RemoveLayer { position: usize },
    /// Modify layer parameters
    ModifyLayer {
        position: usize,
        parameter: String,
        value: f32,
    },
    /// Change quantization scheme
    ChangeQuantization {
        layer: usize,
        scheme: QuantizationScheme,
    },
    /// Add skip connection
    AddSkipConnection {
        from: usize,
        to: usize,
        connection_type: ConnectionType,
    },
    /// Remove skip connection
    RemoveSkipConnection { from: usize, to: usize },
}

impl MobileNAS {
    /// Create new Neural Architecture Search engine
    pub fn new(config: NASConfig) -> Self {
        let rl_config = RLConfig {
            learning_rate: 0.001,
            discount_factor: 0.99,
            initial_exploration_rate: 1.0,
            exploration_decay: 0.995,
            min_exploration_rate: 0.1,
        };

        Self {
            search_config: config,
            architecture_candidates: Vec::new(),
            performance_history: Vec::new(),
            optimization_agent: ReinforcementLearningAgent::new(rl_config),
        }
    }

    /// Search for optimal mobile architecture
    pub fn search_optimal_architecture(
        &mut self,
        base_architecture: MobileArchitecture,
        user_context: Option<UserContext>,
    ) -> Result<MobileArchitecture> {
        let mut best_architecture = base_architecture.clone();
        let mut best_score = f32::NEG_INFINITY;
        let mut iterations_without_improvement = 0;

        for iteration in 0..self.search_config.max_iterations {
            // Generate candidate architecture
            let candidate = match &self.search_config.search_strategy {
                SearchStrategy::Random => self.generate_random_architecture(&base_architecture)?,
                SearchStrategy::Evolutionary { .. } => {
                    self.evolve_architecture(&best_architecture)?
                },
                SearchStrategy::ReinforcementLearning { .. } => {
                    self.rl_generate_architecture(&best_architecture)?
                },
                SearchStrategy::Differentiable { .. } => {
                    self.differentiable_search(&best_architecture)?
                },
                SearchStrategy::Progressive { .. } => {
                    self.progressive_search(&best_architecture, iteration)?
                },
            };

            // Evaluate candidate architecture
            let metrics = self.evaluate_architecture(&candidate)?;
            let score = self.calculate_fitness_score(&metrics, &user_context)?;

            // Update best architecture if improved
            if score > best_score {
                best_score = score;
                best_architecture = candidate.clone();
                iterations_without_improvement = 0;

                // Record performance for learning
                let record = PerformanceRecord {
                    architecture: candidate,
                    metrics,
                    device_config: MobileConfig::default(), // Would use actual device config
                    timestamp: std::time::SystemTime::now(),
                    user_context: user_context.clone(),
                };
                self.performance_history.push(record);
            } else {
                iterations_without_improvement += 1;
            }

            // Check early stopping
            if iterations_without_improvement >= self.search_config.early_stopping.patience {
                println!(
                    "Early stopping at iteration {} due to no improvement",
                    iteration
                );
                break;
            }

            // Update RL agent if using RL strategy
            if matches!(
                self.search_config.search_strategy,
                SearchStrategy::ReinforcementLearning { .. }
            ) {
                self.optimization_agent.update_from_experience(score)?;
            }
        }

        Ok(best_architecture)
    }

    /// Generate random architecture mutation
    fn generate_random_architecture(
        &self,
        base: &MobileArchitecture,
    ) -> Result<MobileArchitecture> {
        let mut candidate = base.clone();

        // Apply random mutations
        for _ in 0..3 {
            match random_usize(4) {
                0 => self.mutate_layer_params(&mut candidate)?,
                1 => self.mutate_quantization(&mut candidate)?,
                2 => self.mutate_skip_connections(&mut candidate)?,
                _ => self.mutate_architecture_structure(&mut candidate)?,
            }
        }

        Ok(candidate)
    }

    /// Evolutionary algorithm architecture generation
    fn evolve_architecture(&self, parent: &MobileArchitecture) -> Result<MobileArchitecture> {
        // Simple mutation-based evolution
        let mut offspring = parent.clone();

        // Apply mutations with probability
        if random_f32() < 0.3 {
            self.mutate_layer_params(&mut offspring)?;
        }
        if random_f32() < 0.2 {
            self.mutate_quantization(&mut offspring)?;
        }
        if random_f32() < 0.1 {
            self.mutate_skip_connections(&mut offspring)?;
        }

        Ok(offspring)
    }

    /// RL-based architecture generation
    fn rl_generate_architecture(
        &mut self,
        current: &MobileArchitecture,
    ) -> Result<MobileArchitecture> {
        let state = self.encode_architecture_state(current)?;
        let action = self.optimization_agent.select_action(&state)?;
        let mut new_architecture = current.clone();

        self.apply_architecture_action(&mut new_architecture, action)?;

        Ok(new_architecture)
    }

    /// Differentiable architecture search
    fn differentiable_search(&self, base: &MobileArchitecture) -> Result<MobileArchitecture> {
        // Simplified DARTS implementation
        let mut candidate = base.clone();

        // Apply gradual changes based on differentiable approximations
        for layer in &mut candidate.layers {
            // Adjust layer parameters based on gradient estimation
            if let Some(param) = layer.parameters.get_mut("channels") {
                *param *= 1.0 + (random_f32() - 0.5) * 0.1; // Small random adjustment
            }
        }

        Ok(candidate)
    }

    /// Progressive search with early pruning
    fn progressive_search(
        &self,
        base: &MobileArchitecture,
        iteration: usize,
    ) -> Result<MobileArchitecture> {
        let mut candidate = base.clone();

        // Progressive complexity increase
        let stage = iteration / (self.search_config.max_iterations / 4);
        match stage {
            0 => self.mutate_layer_params(&mut candidate)?,
            1 => self.mutate_quantization(&mut candidate)?,
            2 => self.mutate_skip_connections(&mut candidate)?,
            _ => self.mutate_architecture_structure(&mut candidate)?,
        }

        Ok(candidate)
    }

    /// Evaluate architecture performance
    fn evaluate_architecture(
        &self,
        architecture: &MobileArchitecture,
    ) -> Result<ArchitectureMetrics> {
        // Estimate performance metrics based on architecture
        let mut total_params = 0;
        let mut total_flops = 0;
        let mut memory_usage = 0;

        for layer in &architecture.layers {
            let (params, flops, memory) = self.estimate_layer_metrics(layer)?;
            total_params += params;
            total_flops += flops;
            memory_usage += memory;
        }

        // Estimate metrics based on hardware and architecture
        let latency_ms = self.estimate_latency(total_flops, &architecture.quantization)?;
        let memory_mb = memory_usage as f32 / (1024.0 * 1024.0);
        let power_mw = self.estimate_power_consumption(total_flops, latency_ms)?;
        let model_size_mb = (total_params * 4) as f32 / (1024.0 * 1024.0); // Assume FP32
        let energy_per_inference_mj = power_mw * latency_ms;
        let throughput_fps = 1000.0 / latency_ms;

        Ok(ArchitectureMetrics {
            latency_ms,
            memory_mb,
            power_mw,
            accuracy: None, // Would need actual evaluation
            model_size_mb,
            energy_per_inference_mj,
            throughput_fps,
        })
    }

    /// Calculate fitness score for architecture
    fn calculate_fitness_score(
        &self,
        metrics: &ArchitectureMetrics,
        user_context: &Option<UserContext>,
    ) -> Result<f32> {
        let mut score = 0.0;
        let mut total_weight = 0.0;

        // Weight based on optimization targets
        for &target in &self.search_config.optimization_targets {
            let (value, weight) = match target {
                OptimizationTarget::Latency => {
                    let normalized = 1.0 / (1.0 + metrics.latency_ms / 100.0);
                    (normalized, 1.0)
                },
                OptimizationTarget::Memory => {
                    let normalized = 1.0 / (1.0 + metrics.memory_mb / 512.0);
                    (normalized, 1.0)
                },
                OptimizationTarget::Power => {
                    let normalized = 1.0 / (1.0 + metrics.power_mw / 1000.0);
                    (normalized, 1.0)
                },
                OptimizationTarget::ModelSize => {
                    let normalized = 1.0 / (1.0 + metrics.model_size_mb / 100.0);
                    (normalized, 1.0)
                },
                OptimizationTarget::Energy => {
                    let normalized = 1.0 / (1.0 + metrics.energy_per_inference_mj / 10.0);
                    (normalized, 1.0)
                },
                OptimizationTarget::Accuracy => {
                    let normalized = metrics.accuracy.unwrap_or(0.8);
                    (normalized, 2.0) // Higher weight for accuracy
                },
            };

            score += value * weight;
            total_weight += weight;
        }

        // Adjust score based on user context
        if let Some(ref context) = user_context {
            score = self.adjust_score_for_user_context(score, metrics, context)?;
        }

        // Apply device constraints penalties
        score = self.apply_constraint_penalties(score, metrics)?;

        Ok(score / total_weight)
    }

    /// Adjust score based on user context
    fn adjust_score_for_user_context(
        &self,
        base_score: f32,
        metrics: &ArchitectureMetrics,
        context: &UserContext,
    ) -> Result<f32> {
        let mut adjusted_score = base_score;

        // Adjust based on user preferences
        match context.preferences.primary_target {
            OptimizationTarget::Latency if metrics.latency_ms > 50.0 => {
                adjusted_score *= 0.8; // Penalize high latency
            },
            OptimizationTarget::Memory if metrics.memory_mb > 256.0 => {
                adjusted_score *= 0.8; // Penalize high memory usage
            },
            OptimizationTarget::Power if metrics.power_mw > 500.0 => {
                adjusted_score *= 0.8; // Penalize high power consumption
            },
            _ => {},
        }

        // Consider usage patterns
        for pattern in &context.usage_patterns {
            if pattern.frequency > 0.5
                && metrics.latency_ms > pattern.performance_requirements.max_latency_ms
            {
                adjusted_score *= 0.9; // Penalize if doesn't meet frequent use case requirements
            }
        }

        Ok(adjusted_score)
    }

    /// Apply device constraint penalties
    fn apply_constraint_penalties(
        &self,
        base_score: f32,
        metrics: &ArchitectureMetrics,
    ) -> Result<f32> {
        let mut score = base_score;

        // Check memory constraints
        if metrics.memory_mb > self.search_config.device_constraints.max_memory_mb as f32 {
            score *= 0.5; // Heavy penalty for exceeding memory limit
        }

        // Check latency constraints
        if metrics.latency_ms > self.search_config.device_constraints.max_latency_ms {
            score *= 0.5; // Heavy penalty for exceeding latency limit
        }

        // Check power constraints
        if metrics.power_mw > self.search_config.device_constraints.power_budget_mw {
            score *= 0.7; // Moderate penalty for exceeding power budget
        }

        Ok(score)
    }

    /// Helper methods for mutations (simplified implementations)
    fn mutate_layer_params(&self, architecture: &mut MobileArchitecture) -> Result<()> {
        if !architecture.layers.is_empty() {
            let layer_idx = random_usize(architecture.layers.len());
            let layer = &mut architecture.layers[layer_idx];

            // Mutate a random parameter
            if !layer.parameters.is_empty() {
                let keys: Vec<_> = layer.parameters.keys().cloned().collect();
                let param_key = &keys[random_usize(keys.len())];
                if let Some(value) = layer.parameters.get_mut(param_key) {
                    *value *= 1.0 + (random_f32() - 0.5) * 0.2; // ±10% change
                }
            }
        }
        Ok(())
    }

    fn mutate_quantization(&self, architecture: &mut MobileArchitecture) -> Result<()> {
        if !architecture.layers.is_empty() {
            let layer_idx = random_usize(architecture.layers.len());
            let schemes = [
                QuantizationScheme::Int4 { symmetric: true },
                QuantizationScheme::Int8 { symmetric: true },
                QuantizationScheme::FP16,
                QuantizationScheme::FP32,
            ];
            let scheme = schemes[random_usize(schemes.len())].clone();
            architecture.quantization.layer_schemes.insert(layer_idx, scheme);
        }
        Ok(())
    }

    fn mutate_skip_connections(&self, _architecture: &mut MobileArchitecture) -> Result<()> {
        // Simplified skip connection mutation
        Ok(())
    }

    fn mutate_architecture_structure(&self, _architecture: &mut MobileArchitecture) -> Result<()> {
        // Simplified structure mutation
        Ok(())
    }

    fn estimate_layer_metrics(&self, layer: &LayerConfig) -> Result<(usize, usize, usize)> {
        // Simplified metric estimation
        let params =
            layer.input_dim.iter().product::<usize>() * layer.output_dim.iter().product::<usize>();
        let flops = params * 2; // Rough estimate
        let memory = params * 4; // Assume FP32
        Ok((params, flops, memory))
    }

    fn estimate_latency(
        &self,
        total_flops: usize,
        _quantization: &QuantizationConfig,
    ) -> Result<f32> {
        // Simplified latency estimation
        let base_latency = total_flops as f32 / 1_000_000.0; // Assume 1M FLOPS per ms
        Ok(base_latency)
    }

    fn estimate_power_consumption(&self, total_flops: usize, latency_ms: f32) -> Result<f32> {
        // Simplified power estimation
        let power = (total_flops as f32 / 1_000_000.0) * 100.0 + latency_ms * 10.0;
        Ok(power)
    }

    fn encode_architecture_state(&self, _architecture: &MobileArchitecture) -> Result<Vec<f32>> {
        // Simplified state encoding
        Ok(vec![0.5; 128]) // Dummy state vector
    }

    fn apply_architecture_action(
        &self,
        _architecture: &mut MobileArchitecture,
        _action: ArchitectureAction,
    ) -> Result<()> {
        // Simplified action application
        Ok(())
    }
}

impl ReinforcementLearningAgent {
    fn new(config: RLConfig) -> Self {
        Self {
            exploration_rate: config.initial_exploration_rate,
            config,
            q_network: QNetwork {
                weights: vec![vec![0.0; 128]; 64], // Simplified network
                architecture: vec![128, 64, 32, 16],
            },
            replay_buffer: Vec::new(),
        }
    }

    fn select_action(&mut self, _state: &[f32]) -> Result<ArchitectureAction> {
        // Simplified action selection
        let actions = vec![
            ArchitectureAction::ModifyLayer {
                position: 0,
                parameter: "channels".to_string(),
                value: 64.0,
            },
            // Add more actions...
        ];

        let action_idx = if random_f32() < self.exploration_rate {
            // Explore: random action
            random_usize(actions.len())
        } else {
            // Exploit: best action according to Q-network
            0 // Simplified: always pick first action
        };

        Ok(actions[action_idx].clone())
    }

    fn update_from_experience(&mut self, reward: f32) -> Result<()> {
        // Simplified Q-learning update
        self.exploration_rate = (self.exploration_rate * self.config.exploration_decay)
            .max(self.config.min_exploration_rate);

        // In a real implementation, this would update the Q-network weights
        // based on the experience and reward

        Ok(())
    }
}

impl Default for NASConfig {
    fn default() -> Self {
        Self {
            max_iterations: 100,
            optimization_targets: vec![
                OptimizationTarget::Latency,
                OptimizationTarget::Memory,
                OptimizationTarget::Power,
            ],
            device_constraints: DeviceConstraints {
                max_memory_mb: 512,
                max_latency_ms: 100.0,
                performance_tier: PerformanceTier::Mid,
                available_backends: vec![MobileBackend::CPU, MobileBackend::GPU],
                power_budget_mw: 1000.0,
            },
            search_strategy: SearchStrategy::Evolutionary {
                population_size: 20,
                mutation_rate: 0.1,
                crossover_rate: 0.7,
            },
            early_stopping: EarlyStoppingConfig {
                patience: 10,
                min_improvement: 0.01,
                monitor_metric: OptimizationTarget::Latency,
            },
        }
    }
}

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

    #[test]
    fn test_mobile_nas_creation() {
        let config = NASConfig::default();
        let nas = MobileNAS::new(config);
        assert_eq!(nas.architecture_candidates.len(), 0);
    }

    #[test]
    fn test_architecture_metrics() {
        let metrics = ArchitectureMetrics {
            latency_ms: 50.0,
            memory_mb: 128.0,
            power_mw: 500.0,
            accuracy: Some(0.9),
            model_size_mb: 25.0,
            energy_per_inference_mj: 25.0,
            throughput_fps: 20.0,
        };

        assert_eq!(metrics.latency_ms, 50.0);
        assert_eq!(metrics.throughput_fps, 20.0);
    }

    #[test]
    fn test_nas_config_default() {
        let config = NASConfig::default();
        assert_eq!(config.max_iterations, 100);
        assert!(config.optimization_targets.contains(&OptimizationTarget::Latency));
    }
}