/// Neural Architecture Search (NAS) integration for automatic model design
///
/// This module provides comprehensive NAS capabilities including:
/// - Differentiable Architecture Search (DARTS)
/// - Progressive Architecture Search (PAS)
/// - Evolutionary Architecture Search (EAS)
/// - Hardware-aware Architecture Search (HAAS)
/// - Multi-objective optimization for accuracy vs efficiency
/// - Architecture performance prediction
use anyhow::Result;
use scirs2_core::random::*; // SciRS2 Integration Policy
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// Configuration for NAS integration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NASConfig {
    /// Type of NAS algorithm to use
    pub algorithm: NASAlgorithm,
    /// Search space configuration
    pub search_space: SearchSpaceConfig,
    /// Hardware constraints
    pub hardware_constraints: HardwareConstraints,
    /// Performance objectives
    pub objectives: Vec<Objective>,
    /// Maximum search time
    pub max_search_time: Duration,
    /// Maximum number of architectures to evaluate
    pub max_architectures: usize,
    /// Early stopping criteria
    pub early_stopping: EarlyStoppingConfig,
    /// Enable progressive search
    pub progressive_search: bool,
    /// Enable hardware-aware search
    pub hardware_aware: bool,
    /// Enable multi-objective optimization
    pub multi_objective: bool,
}

impl Default for NASConfig {
    fn default() -> Self {
        Self {
            algorithm: NASAlgorithm::DARTS,
            search_space: SearchSpaceConfig::default(),
            hardware_constraints: HardwareConstraints::default(),
            objectives: vec![Objective::Accuracy, Objective::Efficiency],
            max_search_time: Duration::from_secs(3600 * 24), // 24 hours
            max_architectures: 1000,
            early_stopping: EarlyStoppingConfig::default(),
            progressive_search: true,
            hardware_aware: true,
            multi_objective: true,
        }
    }
}

/// Available NAS algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum NASAlgorithm {
    DARTS,        // Differentiable Architecture Search
    GDAS,         // Gradient-based search for Differentiable Architecture Search
    ENAS,         // Efficient Neural Architecture Search
    ProxylessNAS, // ProxylessNAS: Direct Neural Architecture Search
    Progressive,  // Progressive Neural Architecture Search
    Evolutionary, // Evolutionary Architecture Search
    Random,       // Random search baseline
}

/// Search space configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SearchSpaceConfig {
    /// Available operations
    pub operations: Vec<Operation>,
    /// Layer depth range
    pub depth_range: (usize, usize),
    /// Width multiplier range
    pub width_range: (f32, f32),
    /// Available activation functions
    pub activations: Vec<Activation>,
    /// Available attention mechanisms
    pub attention_types: Vec<AttentionType>,
    /// Available normalization layers
    pub normalizations: Vec<Normalization>,
}

impl Default for SearchSpaceConfig {
    fn default() -> Self {
        Self {
            operations: vec![
                Operation::Conv1x1,
                Operation::Conv3x3,
                Operation::SeparableConv3x3,
                Operation::DilatedConv3x3,
                Operation::MobileConv,
                Operation::Identity,
                Operation::MaxPool,
                Operation::AvgPool,
            ],
            depth_range: (12, 48),
            width_range: (0.5, 2.0),
            activations: vec![
                Activation::ReLU,
                Activation::GELU,
                Activation::Swish,
                Activation::Mish,
            ],
            attention_types: vec![
                AttentionType::MultiHead,
                AttentionType::GroupedQuery,
                AttentionType::FlashAttention,
                AttentionType::LinearAttention,
            ],
            normalizations: vec![
                Normalization::LayerNorm,
                Normalization::RMSNorm,
                Normalization::BatchNorm,
            ],
        }
    }
}

/// Hardware constraints for architecture search
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareConstraints {
    /// Maximum model size in parameters
    pub max_parameters: usize,
    /// Maximum memory usage in bytes
    pub max_memory: usize,
    /// Maximum inference latency in milliseconds
    pub max_latency: f32,
    /// Maximum FLOPS
    pub max_flops: usize,
    /// Target hardware platform
    pub target_platform: TargetPlatform,
    /// Power consumption limit (watts)
    pub max_power: f32,
}

impl Default for HardwareConstraints {
    fn default() -> Self {
        Self {
            max_parameters: 1_000_000_000, // 1B parameters
            max_memory: 8_000_000_000,     // 8GB
            max_latency: 100.0,            // 100ms
            max_flops: 1_000_000_000_000,  // 1T FLOPS
            target_platform: TargetPlatform::GPU,
            max_power: 250.0, // 250W
        }
    }
}

/// Available operations in search space
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Operation {
    Conv1x1,
    Conv3x3,
    SeparableConv3x3,
    DilatedConv3x3,
    MobileConv,
    Identity,
    MaxPool,
    AvgPool,
    GlobalAvgPool,
    Linear,
    Embedding,
}

/// Available activation functions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Activation {
    ReLU,
    GELU,
    Swish,
    Mish,
    Tanh,
    Sigmoid,
    LeakyReLU,
}

/// Available attention mechanisms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AttentionType {
    MultiHead,
    GroupedQuery,
    FlashAttention,
    LinearAttention,
    SparseAttention,
}

/// Available normalization layers
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Normalization {
    LayerNorm,
    RMSNorm,
    BatchNorm,
    GroupNorm,
}

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

/// Optimization objectives
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Objective {
    Accuracy,
    Efficiency,
    Latency,
    Memory,
    Power,
    FLOPS,
}

/// Early stopping configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EarlyStoppingConfig {
    /// Patience for early stopping
    pub patience: usize,
    /// Minimum improvement threshold
    pub min_improvement: f32,
    /// Enable early stopping
    pub enabled: bool,
}

impl Default for EarlyStoppingConfig {
    fn default() -> Self {
        Self {
            patience: 10,
            min_improvement: 0.01,
            enabled: true,
        }
    }
}

/// Architecture representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Architecture {
    /// Architecture ID
    pub id: String,
    /// Architecture encoding
    pub encoding: Vec<LayerSpec>,
    /// Performance metrics
    pub metrics: PerformanceMetrics,
    /// Hardware characteristics
    pub hardware_metrics: HardwareMetrics,
    /// Training history
    pub training_history: Vec<TrainingMetric>,
}

/// Layer specification in architecture
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerSpec {
    /// Layer type
    pub layer_type: LayerType,
    /// Layer parameters
    pub parameters: HashMap<String, f32>,
    /// Input/output dimensions
    pub dimensions: (usize, usize),
}

/// Available layer types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LayerType {
    Transformer,
    Convolution,
    Attention,
    MLP,
    Normalization,
    Activation,
    Pooling,
    Embedding,
}

/// Performance metrics for architecture evaluation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Validation accuracy
    pub accuracy: f32,
    /// Training loss
    pub loss: f32,
    /// Inference time
    pub inference_time: Duration,
    /// Memory usage
    pub memory_usage: usize,
    /// Parameter count
    pub parameter_count: usize,
    /// FLOPS count
    pub flops: usize,
}

/// Hardware-specific metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareMetrics {
    /// GPU utilization
    pub gpu_utilization: f32,
    /// Memory bandwidth utilization
    pub memory_bandwidth: f32,
    /// Power consumption
    pub power_consumption: f32,
    /// Thermal characteristics
    pub temperature: f32,
}

/// Training metrics during architecture evaluation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingMetric {
    /// Training step
    pub step: usize,
    /// Loss value
    pub loss: f32,
    /// Validation accuracy
    pub accuracy: f32,
    /// Learning rate
    pub learning_rate: f32,
}

/// NAS controller for managing architecture search
#[allow(dead_code)]
pub struct NASController {
    config: NASConfig,
    search_space: SearchSpace,
    evaluated_architectures: Vec<Architecture>,
    current_best: Option<Architecture>,
    search_history: Vec<SearchEvent>,
    #[allow(dead_code)]
    predictor: PerformancePredictor,
    optimizer: ArchitectureOptimizer,
}

impl NASController {
    pub fn new(config: NASConfig) -> Self {
        Self {
            search_space: SearchSpace::new(&config.search_space),
            config,
            evaluated_architectures: Vec::new(),
            current_best: None,
            search_history: Vec::new(),
            predictor: PerformancePredictor::new(),
            optimizer: ArchitectureOptimizer::new(),
        }
    }

    /// Start architecture search
    pub fn start_search(&mut self) -> Result<Architecture> {
        let start_time = Instant::now();

        match self.config.algorithm {
            NASAlgorithm::DARTS => self.run_darts()?,
            NASAlgorithm::GDAS => self.run_gdas()?,
            NASAlgorithm::ENAS => self.run_enas()?,
            NASAlgorithm::ProxylessNAS => self.run_proxyless_nas()?,
            NASAlgorithm::Progressive => self.run_progressive_search()?,
            NASAlgorithm::Evolutionary => self.run_evolutionary_search()?,
            NASAlgorithm::Random => self.run_random_search()?,
        }

        let search_duration = start_time.elapsed();

        // Record search completion
        self.search_history.push(SearchEvent {
            timestamp: Instant::now(),
            event_type: SearchEventType::SearchCompleted,
            duration: search_duration,
            architectures_evaluated: self.evaluated_architectures.len(),
        });

        self.current_best
            .clone()
            .ok_or_else(|| anyhow::anyhow!("No architecture found during search"))
    }

    /// Run DARTS algorithm
    fn run_darts(&mut self) -> Result<()> {
        println!("Running DARTS algorithm...");

        // Initialize architecture weights
        let mut architecture_weights = self.initialize_architecture_weights()?;

        // Main DARTS loop
        for _epoch in 0..100 {
            // Sample architecture based on current weights
            let architecture = self.sample_architecture_from_weights(&architecture_weights)?;

            // Evaluate architecture
            let metrics = self.evaluate_architecture(&architecture)?;

            // Update architecture weights based on performance
            self.update_architecture_weights(&mut architecture_weights, &metrics)?;

            // Store evaluated architecture
            self.evaluated_architectures.push(architecture.clone());

            // Update best architecture
            self.update_best_architecture(&architecture);

            // Check early stopping
            if self.should_early_stop() {
                break;
            }
        }

        Ok(())
    }

    /// Run GDAS algorithm
    fn run_gdas(&mut self) -> Result<()> {
        println!("Running GDAS algorithm...");

        // Similar to DARTS but with gradient-based sampling
        for _epoch in 0..100 {
            let architecture = self.sample_architecture_gdas()?;
            let _metrics = self.evaluate_architecture(&architecture)?;

            self.evaluated_architectures.push(architecture.clone());
            self.update_best_architecture(&architecture);

            if self.should_early_stop() {
                break;
            }
        }

        Ok(())
    }

    /// Run ENAS algorithm
    fn run_enas(&mut self) -> Result<()> {
        println!("Running ENAS algorithm...");

        // Initialize controller
        let mut controller = ENASController::new();

        for _epoch in 0..100 {
            // Sample architecture from controller
            let architecture = controller.sample_architecture(&self.search_space)?;

            // Evaluate architecture
            let metrics = self.evaluate_architecture(&architecture)?;

            // Update controller with reward
            controller.update_with_reward(&architecture, metrics.accuracy)?;

            self.evaluated_architectures.push(architecture.clone());
            self.update_best_architecture(&architecture);

            if self.should_early_stop() {
                break;
            }
        }

        Ok(())
    }

    /// Run ProxylessNAS algorithm
    fn run_proxyless_nas(&mut self) -> Result<()> {
        println!("Running ProxylessNAS algorithm...");

        // Direct search without proxy tasks
        for _epoch in 0..100 {
            let architecture = self.sample_architecture_proxyless()?;
            let _metrics = self.evaluate_architecture(&architecture)?;

            self.evaluated_architectures.push(architecture.clone());
            self.update_best_architecture(&architecture);

            if self.should_early_stop() {
                break;
            }
        }

        Ok(())
    }

    /// Run progressive search
    fn run_progressive_search(&mut self) -> Result<()> {
        println!("Running Progressive search...");

        // Start with simple architectures and progressively increase complexity
        let complexity_levels = vec![0.2, 0.4, 0.6, 0.8, 1.0];

        for complexity in complexity_levels {
            for _ in 0..20 {
                let architecture = self.sample_architecture_with_complexity(complexity)?;
                let _metrics = self.evaluate_architecture(&architecture)?;

                self.evaluated_architectures.push(architecture.clone());
                self.update_best_architecture(&architecture);
            }
        }

        Ok(())
    }

    /// Run evolutionary search
    fn run_evolutionary_search(&mut self) -> Result<()> {
        println!("Running Evolutionary search...");

        // Initialize population
        let mut population = self.initialize_population(50)?;

        for _generation in 0..100 {
            // Evaluate population
            for architecture in &population {
                let _metrics = self.evaluate_architecture(architecture)?;
                // Store evaluated architecture (simplified)
            }

            // Select parents
            let parents = self.select_parents(&population)?;

            // Create offspring through crossover and mutation
            let offspring = self.create_offspring(&parents)?;

            // Update population
            population = self.update_population(population, offspring)?;

            // Update best architecture
            if let Some(best_in_generation) = self.get_best_from_population(&population) {
                self.update_best_architecture(&best_in_generation);
            }

            if self.should_early_stop() {
                break;
            }
        }

        Ok(())
    }

    /// Run random search baseline
    fn run_random_search(&mut self) -> Result<()> {
        println!("Running Random search...");

        for _ in 0..self.config.max_architectures {
            let architecture = self.sample_random_architecture()?;
            let _metrics = self.evaluate_architecture(&architecture)?;

            self.evaluated_architectures.push(architecture.clone());
            self.update_best_architecture(&architecture);

            if self.should_early_stop() {
                break;
            }
        }

        Ok(())
    }

    /// Initialize architecture weights for DARTS
    fn initialize_architecture_weights(&self) -> Result<HashMap<String, f32>> {
        let mut weights = HashMap::new();

        // Initialize weights for each operation
        for operation in &self.config.search_space.operations {
            weights.insert(format!("{:?}", operation), 0.5);
        }

        Ok(weights)
    }

    /// Sample architecture from weights
    fn sample_architecture_from_weights(
        &self,
        _weights: &HashMap<String, f32>,
    ) -> Result<Architecture> {
        // Simplified architecture sampling
        let architecture = Architecture {
            id: format!("arch_{}", uuid::Uuid::new_v4()),
            encoding: vec![LayerSpec {
                layer_type: LayerType::Transformer,
                parameters: HashMap::new(),
                dimensions: (512, 512),
            }],
            metrics: PerformanceMetrics {
                accuracy: 0.0,
                loss: 0.0,
                inference_time: Duration::from_millis(0),
                memory_usage: 0,
                parameter_count: 0,
                flops: 0,
            },
            hardware_metrics: HardwareMetrics {
                gpu_utilization: 0.0,
                memory_bandwidth: 0.0,
                power_consumption: 0.0,
                temperature: 0.0,
            },
            training_history: Vec::new(),
        };

        Ok(architecture)
    }

    /// Update architecture weights based on performance
    fn update_architecture_weights(
        &self,
        weights: &mut HashMap<String, f32>,
        metrics: &PerformanceMetrics,
    ) -> Result<()> {
        // Simplified weight update based on accuracy
        let learning_rate = 0.01;
        for (_, weight) in weights.iter_mut() {
            *weight += learning_rate * metrics.accuracy;
        }
        Ok(())
    }

    /// Sample architecture using GDAS
    fn sample_architecture_gdas(&self) -> Result<Architecture> {
        // Simplified GDAS sampling
        self.sample_random_architecture()
    }

    /// Sample architecture with complexity constraint
    fn sample_architecture_with_complexity(&self, complexity: f32) -> Result<Architecture> {
        // Simplified complexity-based sampling
        let layer_count = (complexity * 48.0) as usize;
        let mut encoding = Vec::new();

        for _ in 0..layer_count {
            encoding.push(LayerSpec {
                layer_type: LayerType::Transformer,
                parameters: HashMap::new(),
                dimensions: (512, 512),
            });
        }

        Ok(Architecture {
            id: format!("arch_{}", uuid::Uuid::new_v4()),
            encoding,
            metrics: PerformanceMetrics {
                accuracy: 0.0,
                loss: 0.0,
                inference_time: Duration::from_millis(0),
                memory_usage: 0,
                parameter_count: 0,
                flops: 0,
            },
            hardware_metrics: HardwareMetrics {
                gpu_utilization: 0.0,
                memory_bandwidth: 0.0,
                power_consumption: 0.0,
                temperature: 0.0,
            },
            training_history: Vec::new(),
        })
    }

    /// Sample architecture using ProxylessNAS
    fn sample_architecture_proxyless(&self) -> Result<Architecture> {
        // Simplified ProxylessNAS sampling
        self.sample_random_architecture()
    }

    /// Sample random architecture
    fn sample_random_architecture(&self) -> Result<Architecture> {
        let mut rng = thread_rng();

        let layer_count = rng.random_range(
            self.config.search_space.depth_range.0..=self.config.search_space.depth_range.1,
        );
        let mut encoding = Vec::new();

        for _ in 0..layer_count {
            encoding.push(LayerSpec {
                layer_type: LayerType::Transformer,
                parameters: HashMap::new(),
                dimensions: (512, 512),
            });
        }

        Ok(Architecture {
            id: format!("arch_{}", uuid::Uuid::new_v4()),
            encoding,
            metrics: PerformanceMetrics {
                accuracy: 0.0,
                loss: 0.0,
                inference_time: Duration::from_millis(0),
                memory_usage: 0,
                parameter_count: 0,
                flops: 0,
            },
            hardware_metrics: HardwareMetrics {
                gpu_utilization: 0.0,
                memory_bandwidth: 0.0,
                power_consumption: 0.0,
                temperature: 0.0,
            },
            training_history: Vec::new(),
        })
    }

    /// Evaluate architecture performance
    fn evaluate_architecture(
        &mut self,
        _architecture: &Architecture,
    ) -> Result<PerformanceMetrics> {
        // Simplified architecture evaluation
        // In real implementation, this would train the architecture
        let mut rng = thread_rng();

        let metrics = PerformanceMetrics {
            accuracy: rng.random_range(0.6..0.95),
            loss: rng.random_range(0.1..2.0),
            inference_time: Duration::from_millis(rng.random_range(10..200)),
            memory_usage: rng.random_range(100_000_000..2_000_000_000),
            parameter_count: rng.random_range(10_000_000..1_000_000_000),
            flops: rng.random_range(100_000_000..10_000_000_000),
        };

        Ok(metrics)
    }

    /// Update best architecture
    fn update_best_architecture(&mut self, architecture: &Architecture) {
        if let Some(ref current_best) = self.current_best {
            if architecture.metrics.accuracy > current_best.metrics.accuracy {
                self.current_best = Some(architecture.clone());
            }
        } else {
            self.current_best = Some(architecture.clone());
        }
    }

    /// Check if early stopping should be triggered
    fn should_early_stop(&self) -> bool {
        if !self.config.early_stopping.enabled {
            return false;
        }

        if self.evaluated_architectures.len() < self.config.early_stopping.patience {
            return false;
        }

        // Check if there's been improvement in the last patience architectures
        let recent_best = self
            .evaluated_architectures
            .iter()
            .rev()
            .take(self.config.early_stopping.patience)
            .max_by(|a, b| a.metrics.accuracy.partial_cmp(&b.metrics.accuracy).unwrap());

        if let Some(current_best) = &self.current_best {
            if let Some(recent_best) = recent_best {
                return recent_best.metrics.accuracy - current_best.metrics.accuracy
                    < self.config.early_stopping.min_improvement;
            }
        }

        false
    }

    /// Initialize population for evolutionary search
    fn initialize_population(&self, size: usize) -> Result<Vec<Architecture>> {
        let mut population = Vec::new();

        for _ in 0..size {
            population.push(self.sample_random_architecture()?);
        }

        Ok(population)
    }

    /// Select parents for evolutionary search
    fn select_parents(&self, population: &[Architecture]) -> Result<Vec<Architecture>> {
        // Tournament selection
        let tournament_size = 5;
        let mut parents = Vec::new();
        let mut rng = thread_rng();

        for _ in 0..population.len() / 2 {
            let mut tournament = Vec::new();
            for _ in 0..tournament_size {
                let idx = rng.random_range(0..population.len());
                tournament.push(&population[idx]);
            }

            let best = tournament
                .iter()
                .max_by(|a, b| a.metrics.accuracy.partial_cmp(&b.metrics.accuracy).unwrap())
                .unwrap();

            parents.push((*best).clone());
        }

        Ok(parents)
    }

    /// Create offspring through crossover and mutation
    fn create_offspring(&self, parents: &[Architecture]) -> Result<Vec<Architecture>> {
        let mut offspring = Vec::new();

        for i in 0..parents.len() {
            let parent1 = &parents[i];
            let parent2 = &parents[(i + 1) % parents.len()];

            // Simple crossover - take layers from both parents
            let mut child_encoding = Vec::new();
            let min_len = std::cmp::min(parent1.encoding.len(), parent2.encoding.len());

            for j in 0..min_len {
                if j % 2 == 0 {
                    child_encoding.push(parent1.encoding[j].clone());
                } else {
                    child_encoding.push(parent2.encoding[j].clone());
                }
            }

            let child = Architecture {
                id: format!("child_{}", uuid::Uuid::new_v4()),
                encoding: child_encoding,
                metrics: PerformanceMetrics {
                    accuracy: 0.0,
                    loss: 0.0,
                    inference_time: Duration::from_millis(0),
                    memory_usage: 0,
                    parameter_count: 0,
                    flops: 0,
                },
                hardware_metrics: HardwareMetrics {
                    gpu_utilization: 0.0,
                    memory_bandwidth: 0.0,
                    power_consumption: 0.0,
                    temperature: 0.0,
                },
                training_history: Vec::new(),
            };

            offspring.push(child);
        }

        Ok(offspring)
    }

    /// Update population with offspring
    fn update_population(
        &self,
        population: Vec<Architecture>,
        offspring: Vec<Architecture>,
    ) -> Result<Vec<Architecture>> {
        let mut combined = population;
        combined.extend(offspring);

        // Select best individuals for next generation
        combined.sort_by(|a, b| b.metrics.accuracy.partial_cmp(&a.metrics.accuracy).unwrap());
        combined.truncate(50); // Keep population size constant

        Ok(combined)
    }

    /// Get best architecture from population
    fn get_best_from_population(&self, population: &[Architecture]) -> Option<Architecture> {
        population
            .iter()
            .max_by(|a, b| a.metrics.accuracy.partial_cmp(&b.metrics.accuracy).unwrap())
            .cloned()
    }

    /// Get search statistics
    pub fn get_search_stats(&self) -> SearchStats {
        SearchStats {
            total_architectures_evaluated: self.evaluated_architectures.len(),
            best_accuracy: self.current_best.as_ref().map(|a| a.metrics.accuracy).unwrap_or(0.0),
            search_time: self.search_history.iter().map(|e| e.duration).sum::<Duration>(),
            algorithm_used: self.config.algorithm.clone(),
        }
    }
}

/// Search space representation
#[allow(dead_code)]
pub struct SearchSpace {
    #[allow(dead_code)]
    operations: Vec<Operation>,
    depth_range: (usize, usize),
    width_range: (f32, f32),
}

impl SearchSpace {
    pub fn new(config: &SearchSpaceConfig) -> Self {
        Self {
            operations: config.operations.clone(),
            depth_range: config.depth_range,
            width_range: config.width_range,
        }
    }
}

/// Performance predictor for architecture evaluation
pub struct PerformancePredictor {
    #[allow(dead_code)]
    trained: bool,
}

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

impl PerformancePredictor {
    pub fn new() -> Self {
        Self { trained: false }
    }

    pub fn predict(&self, architecture: &Architecture) -> Result<PerformanceMetrics> {
        // Simplified prediction
        Ok(architecture.metrics.clone())
    }
}

/// Architecture optimizer
pub struct ArchitectureOptimizer {
    #[allow(dead_code)]
    optimization_active: bool,
}

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

impl ArchitectureOptimizer {
    pub fn new() -> Self {
        Self {
            optimization_active: false,
        }
    }

    pub fn optimize(&mut self, architecture: &Architecture) -> Result<Architecture> {
        // Simplified optimization
        Ok(architecture.clone())
    }
}

/// ENAS controller
pub struct ENASController {
    #[allow(dead_code)]
    trained: bool,
}

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

impl ENASController {
    pub fn new() -> Self {
        Self { trained: false }
    }

    pub fn sample_architecture(&self, _search_space: &SearchSpace) -> Result<Architecture> {
        // Simplified sampling
        Ok(Architecture {
            id: format!("enas_{}", uuid::Uuid::new_v4()),
            encoding: vec![LayerSpec {
                layer_type: LayerType::Transformer,
                parameters: HashMap::new(),
                dimensions: (512, 512),
            }],
            metrics: PerformanceMetrics {
                accuracy: 0.0,
                loss: 0.0,
                inference_time: Duration::from_millis(0),
                memory_usage: 0,
                parameter_count: 0,
                flops: 0,
            },
            hardware_metrics: HardwareMetrics {
                gpu_utilization: 0.0,
                memory_bandwidth: 0.0,
                power_consumption: 0.0,
                temperature: 0.0,
            },
            training_history: Vec::new(),
        })
    }

    pub fn update_with_reward(&mut self, _architecture: &Architecture, _reward: f32) -> Result<()> {
        // Update controller parameters based on reward
        Ok(())
    }
}

/// Search event for history tracking
#[derive(Debug, Clone)]
pub struct SearchEvent {
    pub timestamp: Instant,
    pub event_type: SearchEventType,
    pub duration: Duration,
    pub architectures_evaluated: usize,
}

/// Types of search events
#[derive(Debug, Clone)]
pub enum SearchEventType {
    SearchStarted,
    SearchCompleted,
    ArchitectureEvaluated,
    BestArchitectureUpdated,
    EarlyStoppingStopped,
}

/// Search statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SearchStats {
    pub total_architectures_evaluated: usize,
    pub best_accuracy: f32,
    pub search_time: Duration,
    pub algorithm_used: NASAlgorithm,
}

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

    #[test]
    fn test_nas_controller_creation() {
        let config = NASConfig::default();
        let controller = NASController::new(config);

        assert_eq!(controller.evaluated_architectures.len(), 0);
        assert!(controller.current_best.is_none());
    }

    #[test]
    fn test_random_architecture_sampling() {
        let config = NASConfig::default();
        let controller = NASController::new(config);

        let architecture = controller.sample_random_architecture().unwrap();
        assert!(!architecture.id.is_empty());
        assert!(!architecture.encoding.is_empty());
    }

    #[test]
    fn test_architecture_evaluation() {
        let config = NASConfig::default();
        let mut controller = NASController::new(config);

        let architecture = controller.sample_random_architecture().unwrap();
        let metrics = controller.evaluate_architecture(&architecture).unwrap();

        assert!(metrics.accuracy >= 0.0 && metrics.accuracy <= 1.0);
        assert!(metrics.loss >= 0.0);
    }

    #[test]
    fn test_early_stopping() {
        let config = NASConfig {
            early_stopping: EarlyStoppingConfig {
                enabled: true,
                patience: 5,
                min_improvement: 0.1,
            },
            ..Default::default()
        };
        let controller = NASController::new(config);

        assert!(!controller.should_early_stop()); // Should not stop initially
    }

    #[test]
    fn test_population_initialization() {
        let config = NASConfig::default();
        let controller = NASController::new(config);

        let population = controller.initialize_population(10).unwrap();
        assert_eq!(population.len(), 10);

        for arch in &population {
            assert!(!arch.id.is_empty());
        }
    }

    #[test]
    fn test_search_space_creation() {
        let config = SearchSpaceConfig::default();
        let search_space = SearchSpace::new(&config);

        assert!(!search_space.operations.is_empty());
        assert!(search_space.depth_range.0 <= search_space.depth_range.1);
    }
}
