use micro_cartan_attn::{CartanAttention, CartanConfig};
use ndarray::{Array2, Array3};

// Define a custom Cartan-based layer
#[derive(Debug, Clone)]
pub struct AdvancedCartanLayer {
    cartan_matrix: Array2<f32>,
    attention: CartanAttention,
    feedforward: FeedForward,
    layer_norm: LayerNorm,
    dropout_rate: f32,
}

impl AdvancedCartanLayer {
    pub fn new(config: &CartanConfig) -> Self {
        // Initialize with custom Cartan matrix for your domain
        let cartan_matrix = Self::create_domain_specific_matrix(config);
        
        Self {
            cartan_matrix,
            attention: CartanAttention::new(config),
            feedforward: FeedForward::new(config.hidden_size, config.intermediate_size),
            layer_norm: LayerNorm::new(config.hidden_size),
            dropout_rate: config.dropout_rate,
        }
    }
    
    fn create_domain_specific_matrix(config: &CartanConfig) -> Array2<f32> {
        // Create specialized Cartan matrix based on your problem domain
        let size = config.cartan_rank;
        let mut matrix = Array2::zeros((size, size));
        
        // Example: A₃ Cartan matrix for 4D rotational symmetries
        for i in 0..size {
            for j in 0..size {
                if i == j {
                    matrix[[i, j]] = 2.0;
                } else if (i as i32 - j as i32).abs() == 1 {
                    matrix[[i, j]] = -1.0;
                }
            }
        }
        
        matrix
    }
    
    pub fn forward(&mut self, input: &Array3<f32>) -> Array3<f32> {
        // Multi-head attention with Cartan structure
        let attention_output = self.attention.forward_with_matrix(input, &self.cartan_matrix);
        
        // Residual connection and layer normalization
        let normalized = self.layer_norm.forward(&(input + &attention_output));
        
        // Feed-forward network
        let ff_output = self.feedforward.forward(&normalized);
        
        // Final residual connection
        normalized + ff_output
    }
}

// Build complex architectures by composing layers
pub struct CustomNeuralArchitecture {
    embedding: EmbeddingLayer,
    cartan_layers: Vec<AdvancedCartanLayer>,
    output_projection: Linear,
    architecture_type: ArchitectureType,
}

#[derive(Debug, Clone)]
pub enum ArchitectureType {
    Encoder,
    Decoder,
    EncoderDecoder,
    Custom(String),
}

impl CustomNeuralArchitecture {
    pub fn build_transformer_variant(config: &ArchitectureConfig) -> Self {
        let mut layers = Vec::new();
        
        for i in 0..config.num_layers {
            let layer_config = CartanConfig {
                cartan_rank: config.cartan_rank,
                hidden_size: config.hidden_size,
                num_attention_heads: config.num_heads,
                layer_index: i,
                ..Default::default()
            };
            
            layers.push(AdvancedCartanLayer::new(&layer_config));
        }
        
        Self {
            embedding: EmbeddingLayer::new(config.vocab_size, config.hidden_size),
            cartan_layers: layers,
            output_projection: Linear::new(config.hidden_size, config.output_size),
            architecture_type: ArchitectureType::Encoder,
        }
    }
    
    pub fn forward_with_gradient_checkpointing(&mut self, input: &Array2<i32>) -> Array2<f32> {
        let mut hidden = self.embedding.forward(input);
        
        // Process through Cartan layers with gradient checkpointing
        for (i, layer) in self.cartan_layers.iter_mut().enumerate() {
            if i % 2 == 0 {
                // Checkpoint every other layer to save memory
                hidden = checkpoint(|| layer.forward(&hidden));
            } else {
                hidden = layer.forward(&hidden);
            }
        }
        
        self.output_projection.forward(&hidden)
    }
}

pub struct AdaptiveCartanTrainer {
    base_matrices: HashMap<String, Array2<f32>>,
    adaptation_rate: f32,
    performance_history: VecDeque<f32>,
}

impl AdaptiveCartanTrainer {
    pub fn adapt_matrix_during_training(
        &mut self,
        layer_name: &str,
        gradient_info: &GradientInfo,
        loss: f32,
    ) -> Array2<f32> {
        let base_matrix = self.base_matrices.get(layer_name).unwrap();
        
        // Analyze gradient flow patterns
        let gradient_condition = gradient_info.compute_condition_number();
        
        // Adapt matrix based on training dynamics
        if gradient_condition > 100.0 {
            // Poor conditioning - increase off-diagonal suppression
            self.strengthen_diagonal(base_matrix)
        } else if loss > self.performance_history.back().unwrap_or(&f32::INFINITY) {
            // Performance degrading - explore different structure
            self.introduce_structural_variation(base_matrix)
        } else {
            base_matrix.clone()
        }
    }
    
    fn strengthen_diagonal(&self, matrix: &Array2<f32>) -> Array2<f32> {
        let mut adapted = matrix.clone();
        let diagonal_boost = 0.1;
        
        for i in 0..matrix.nrows() {
            adapted[[i, i]] += diagonal_boost;
        }
        
        adapted
    }
    
    fn introduce_structural_variation(&self, matrix: &Array2<f32>) -> Array2<f32> {
        // Introduce controlled structural changes
        let mut adapted = matrix.clone();
        let variation_strength = 0.05;
        
        // Add small random perturbations to explore nearby structures
        for i in 0..matrix.nrows() {
            for j in 0..matrix.ncols() {
                if i != j {
                    let perturbation = (rand::random::<f32>() - 0.5) * variation_strength;
                    adapted[[i, j]] += perturbation;
                }
            }
        }
        
        adapted
    }
}

use micro_swarm::{SwarmCoordinator, NodeId, TaskPriority};
use std::collections::HashMap;
use tokio::sync::mpsc;

pub struct HierarchicalSwarm {
    coordinator: SwarmCoordinator,
    regional_leaders: HashMap<String, NodeId>,
    load_balancer: DynamicLoadBalancer,
    fault_detector: ByzantineFaultDetector,
}

impl HierarchicalSwarm {
    pub async fn new(regions: Vec<String>, nodes_per_region: usize) -> Self {
        let mut coordinator = SwarmCoordinator::new();
        let mut regional_leaders = HashMap::new();
        
        // Create hierarchical structure
        for region in &regions {
            let leader_id = coordinator.spawn_leader_node(region).await;
            regional_leaders.insert(region.clone(), leader_id);
            
            // Spawn worker nodes under each leader
            for i in 0..nodes_per_region {
                let worker_id = coordinator.spawn_worker_node(&leader_id).await;
                coordinator.register_hierarchy(leader_id, worker_id).await;
            }
        }
        
        Self {
            coordinator,
            regional_leaders,
            load_balancer: DynamicLoadBalancer::new(),
            fault_detector: ByzantineFaultDetector::new(),
        }
    }
    
    pub async fn distribute_computation(
        &mut self,
        computation: ComputationGraph,
    ) -> Result<ComputationResult, SwarmError> {
        // Analyze computation requirements
        let requirements = self.analyze_computation_requirements(&computation);
        
        // Create execution plan with fault tolerance
        let execution_plan = self.create_fault_tolerant_plan(requirements).await?;
        
        // Execute with real-time monitoring
        self.execute_with_monitoring(execution_plan).await
    }
    
    async fn create_fault_tolerant_plan(
        &self,
        requirements: ComputationRequirements,
    ) -> Result<ExecutionPlan, SwarmError> {
        let mut plan = ExecutionPlan::new();
        
        // Assign tasks with replication for fault tolerance
        for task in requirements.tasks {
            let primary_region = self.load_balancer.select_optimal_region(&task);
            let backup_regions = self.select_backup_regions(&primary_region, 2);
            
            plan.add_replicated_task(task, primary_region, backup_regions);
        }
        
        // Add Byzantine fault detection checkpoints
        plan.add_consensus_checkpoints(self.fault_detector.checkpoint_interval());
        
        Ok(plan)
    }
}

pub struct MeshSwarmNetwork {
    nodes: HashMap<NodeId, MeshNode>,
    gossip_protocol: GossipProtocol,
    routing_table: DistributedRoutingTable,
    message_queue: PriorityQueue<SwarmMessage>,
}

impl MeshSwarmNetwork {
    pub async fn bootstrap_network(initial_nodes: Vec<NodeConfig>) -> Self {
        let mut network = Self::new();
        
        // Bootstrap initial connectivity
        for node_config in initial_nodes {
            let node = MeshNode::new(node_config).await;
            network.add_node(node).await;
        }
        
        // Establish initial connections using gossip
        network.gossip_protocol.bootstrap_connections().await;
        
        network
    }
    
    pub async fn execute_distributed_learning(
        &mut self,
        model: &DistributedModel,
        training_data: &[TrainingBatch],
    ) -> Result<ModelWeights, SwarmError> {
        // Partition data across nodes
        let data_partitions = self.partition_training_data(training_data);
        
        // Initialize federated learning round
        let mut global_weights = model.get_weights();
        let num_rounds = 10;
        
        for round in 0..num_rounds {
            // Broadcast current global weights
            self.broadcast_weights(&global_weights).await?;
            
            // Each node trains locally
            let local_updates = self.collect_local_updates(&data_partitions).await?;
            
            // Aggregate updates using Byzantine-resilient averaging
            global_weights = self.byzantine_resilient_averaging(local_updates)?;
            
            // Update routing table based on node performance
            self.update_routing_based_on_performance().await;
        }
        
        Ok(global_weights)
    }
    
    async fn byzantine_resilient_averaging(
        &self,
        updates: Vec<LocalUpdate>,
    ) -> Result<ModelWeights, SwarmError> {
        // Use Krum algorithm for Byzantine-resilient aggregation
        let krum_parameter = (updates.len() as f32 * 0.66) as usize;
        
        let mut distances = Vec::new();
        for (i, update_i) in updates.iter().enumerate() {
            let mut node_distances = Vec::new();
            
            for (j, update_j) in updates.iter().enumerate() {
                if i != j {
                    let distance = self.compute_weight_distance(&update_i.weights, &update_j.weights);
                    node_distances.push((j, distance));
                }
            }
            
            // Sort by distance and sum k closest
            node_distances.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap());
            let sum_distance: f32 = node_distances.iter()
                .take(krum_parameter)
                .map(|(_, dist)| dist)
                .sum();
            
            distances.push((i, sum_distance));
        }
        
        // Select update with minimum sum of distances
        distances.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap());
        let selected_update = &updates[distances[0].0];
        
        Ok(selected_update.weights.clone())
    }
}

pub struct PBFTConsensus {
    node_id: NodeId,
    view_number: u64,
    sequence_number: u64,
    primary_id: NodeId,
    message_log: MessageLog,
    state_machine: StateMachine,
    timer: ConsensusTimer,
}

impl PBFTConsensus {
    pub async fn propose_value(&mut self, value: ConsensusValue) -> Result<(), ConsensusError> {
        if self.node_id != self.primary_id {
            return Err(ConsensusError::NotPrimary);
        }
        
        // Phase 1: Pre-prepare
        let pre_prepare = PrePrepareMessage {
            view: self.view_number,
            sequence: self.sequence_number,
            value: value.clone(),
            primary_signature: self.sign_message(&value),
        };
        
        self.broadcast_to_all(ConsensusMessage::PrePrepare(pre_prepare)).await?;
        self.sequence_number += 1;
        
        Ok(())
    }
    
    pub async fn handle_pre_prepare(
        &mut self,
        msg: PrePrepareMessage,
    ) -> Result<(), ConsensusError> {
        // Validate message
        if !self.validate_pre_prepare(&msg) {
            return Err(ConsensusError::InvalidMessage);
        }
        
        // Phase 2: Prepare
        let prepare = PrepareMessage {
            view: msg.view,
            sequence: msg.sequence,
            value_hash: self.hash_value(&msg.value),
            node_signature: self.sign_prepare(&msg),
        };
        
        self.broadcast_to_all(ConsensusMessage::Prepare(prepare)).await?;
        self.message_log.add_prepare(msg.sequence, prepare);
        
        // Check if we have enough prepare messages
        if self.message_log.count_prepares(msg.sequence) >= self.required_prepares() {
            self.send_commit(msg.sequence, msg.value).await?;
        }
        
        Ok(())
    }
    
    async fn send_commit(&mut self, sequence: u64, value: ConsensusValue) -> Result<(), ConsensusError> {
        let commit = CommitMessage {
            view: self.view_number,
            sequence,
            value_hash: self.hash_value(&value),
            node_signature: self.sign_commit(&value),
        };
        
        self.broadcast_to_all(ConsensusMessage::Commit(commit)).await?;
        self.message_log.add_commit(sequence, commit);
        
        // Check if we can execute
        if self.message_log.count_commits(sequence) >= self.required_commits() {
            self.execute_value(value).await?;
        }
        
        Ok(())
    }
    
    fn required_prepares(&self) -> usize {
        // Need 2f+1 prepare messages where f is max Byzantine nodes
        (self.total_nodes() * 2 + 2) / 3
    }
    
    fn required_commits(&self) -> usize {
        // Need 2f+1 commit messages
        (self.total_nodes() * 2 + 2) / 3
    }
}

use std::collections::HashMap;
use micro_core::memory::MemoryPool;

pub struct SmartCheckpointer {
    checkpoint_strategy: CheckpointStrategy,
    memory_budget: usize,
    computation_cost_model: ComputationCostModel,
    recomputation_cache: HashMap<LayerId, CachedComputation>,
}

#[derive(Debug, Clone)]
pub enum CheckpointStrategy {
    Every(usize),           // Checkpoint every N layers
    Adaptive(f32),          // Adaptive based on memory pressure
    CostOptimal,            // Minimize time-memory tradeoff
    Custom(Box<dyn Fn(usize, usize) -> bool>), // Custom strategy
}

impl SmartCheckpointer {
    pub fn new(memory_budget_mb: usize) -> Self {
        Self {
            checkpoint_strategy: CheckpointStrategy::CostOptimal,
            memory_budget: memory_budget_mb * 1024 * 1024, // Convert to bytes
            computation_cost_model: ComputationCostModel::new(),
            recomputation_cache: HashMap::new(),
        }
    }
    
    pub fn should_checkpoint(
        &self,
        layer_id: LayerId,
        current_memory_usage: usize,
        forward_cost: f32,
    ) -> bool {
        match &self.checkpoint_strategy {
            CheckpointStrategy::Every(n) => layer_id.index() % n == 0,
            CheckpointStrategy::Adaptive(threshold) => {
                let memory_pressure = current_memory_usage as f32 / self.memory_budget as f32;
                memory_pressure > *threshold
            },
            CheckpointStrategy::CostOptimal => {
                self.compute_optimal_checkpoint_decision(layer_id, current_memory_usage, forward_cost)
            },
            CheckpointStrategy::Custom(func) => {
                func(layer_id.index(), current_memory_usage)
            },
        }
    }
    
    fn compute_optimal_checkpoint_decision(
        &self,
        layer_id: LayerId,
        current_memory: usize,
        forward_cost: f32,
    ) -> bool {
        // Chen et al. optimal checkpointing strategy
        // Minimize: memory_cost + recomputation_cost
        
        let remaining_layers = self.estimate_remaining_layers(layer_id);
        let memory_if_checkpoint = self.estimate_memory_after_checkpoint(current_memory);
        let memory_if_no_checkpoint = current_memory + self.estimate_activation_size(layer_id);
        
        let recomputation_cost = forward_cost * self.estimate_recomputation_probability(layer_id);
        let memory_savings = memory_if_no_checkpoint - memory_if_checkpoint;
        
        // Checkpoint if memory savings justify recomputation cost
        let memory_value = memory_savings as f32 / self.memory_budget as f32;
        memory_value > recomputation_cost * 0.1 // Tunable parameter
    }
    
    pub fn forward_with_checkpointing<F, T>(
        &mut self,
        layers: &[F],
        input: T,
        memory_pool: &mut MemoryPool,
    ) -> T 
    where
        F: Fn(T) -> T + Clone,
        T: Clone,
    {
        let mut activations = Vec::new();
        let mut current = input;
        
        // Forward pass with selective checkpointing
        for (i, layer) in layers.iter().enumerate() {
            let layer_id = LayerId::new(i);
            let memory_usage = memory_pool.current_usage();
            let forward_cost = self.computation_cost_model.estimate_cost(layer_id);
            
            if self.should_checkpoint(layer_id, memory_usage, forward_cost) {
                // Store activation for this layer
                activations.push((i, current.clone()));
                current = layer(current);
            } else {
                // Don't store, will recompute if needed
                current = layer(current);
            }
        }
        
        current
    }
    
    pub fn backward_with_recomputation<F, T>(
        &mut self,
        layers: &[F],
        stored_activations: &[(usize, T)],
        gradient: T,
    ) -> Vec<T>
    where
        F: Fn(T) -> T + Clone,
        T: Clone,
    {
        let mut gradients = Vec::new();
        let mut current_grad = gradient;
        
        // Backward pass with smart recomputation
        for i in (0..layers.len()).rev() {
            let layer_id = LayerId::new(i);
            
            // Check if we have stored activation
            if let Some((_, activation)) = stored_activations.iter().find(|(idx, _)| *idx == i) {
                // Use stored activation
                let layer_grad = self.compute_layer_gradient(&layers[i], activation, &current_grad);
                gradients.push(layer_grad.clone());
                current_grad = layer_grad;
            } else {
                // Recompute activation
                let activation = self.recompute_activation(layers, i);
                let layer_grad = self.compute_layer_gradient(&layers[i], &activation, &current_grad);
                gradients.push(layer_grad.clone());
                current_grad = layer_grad;
            }
        }
        
        gradients.reverse();
        gradients
    }
}

pub struct MixedPrecisionTrainer {
    loss_scaler: DynamicLossScaler,
    fp16_layers: HashSet<LayerId>,
    gradient_clipper: GradientClipper,
    overflow_detector: OverflowDetector,
}

impl MixedPrecisionTrainer {
    pub fn new() -> Self {
        Self {
            loss_scaler: DynamicLossScaler::new(2.0_f32.powi(15)), // Initial scale
            fp16_layers: HashSet::new(),
            gradient_clipper: GradientClipper::new(1.0),
            overflow_detector: OverflowDetector::new(),
        }
    }
    
    pub fn train_step(
        &mut self,
        model: &mut Model,
        batch: &TrainingBatch,
    ) -> Result<TrainingMetrics, TrainingError> {
        // Forward pass in mixed precision
        let loss = self.forward_mixed_precision(model, batch)?;
        
        // Scale loss to prevent underflow
        let scaled_loss = self.loss_scaler.scale_loss(loss);
        
        // Backward pass
        let scaled_gradients = self.backward_pass(model, scaled_loss)?;
        
        // Unscale gradients and check for overflow
        let gradients = self.loss_scaler.unscale_gradients(scaled_gradients)?;
        
        if self.overflow_detector.has_overflow(&gradients) {
            // Skip update and reduce loss scale
            self.loss_scaler.reduce_scale();
            return Ok(TrainingMetrics::overflow_step());
        }
        
        // Clip gradients
        let clipped_gradients = self.gradient_clipper.clip(gradients);
        
        // Apply gradients
        model.apply_gradients(clipped_gradients)?;
        
        // Increase loss scale if stable
        self.loss_scaler.maybe_increase_scale();
        
        Ok(TrainingMetrics::successful_step(loss))
    }
    
    fn forward_mixed_precision(
        &self,
        model: &Model,
        batch: &TrainingBatch,
    ) -> Result<f32, TrainingError> {
        let mut current = batch.inputs.clone();
        
        for (i, layer) in model.layers.iter().enumerate() {
            let layer_id = LayerId::new(i);
            
            if self.fp16_layers.contains(&layer_id) {
                // Convert to FP16 for computation
                current = current.to_half_precision();
                current = layer.forward(current);
                // Convert back to FP32 for numerical stability
                current = current.to_single_precision();
            } else {
                // Keep in FP32
                current = layer.forward(current);
            }
        }
        
        let loss = model.compute_loss(&current, &batch.targets);
        Ok(loss)
    }
    
    pub fn auto_select_fp16_layers(&mut self, model: &Model) {
        // Automatically select which layers to use FP16 based on sensitivity analysis
        for (i, layer) in model.layers.iter().enumerate() {
            let layer_id = LayerId::new(i);
            
            // Check if layer is suitable for FP16
            if self.is_fp16_suitable(layer) {
                self.fp16_layers.insert(layer_id);
            }
        }
    }
    
    fn is_fp16_suitable(&self, layer: &Layer) -> bool {
        match layer.layer_type() {
            LayerType::Linear | LayerType::Convolution => true,
            LayerType::LayerNorm | LayerType::BatchNorm => false, // Keep in FP32 for stability
            LayerType::Attention => true, // Can use FP16 for most attention operations
            LayerType::Embedding => false, // Keep embeddings in FP32
            _ => false,
        }
    }
}

pub struct DynamicLossScaler {
    scale: f32,
    growth_factor: f32,
    backoff_factor: f32,
    growth_interval: usize,
    steps_since_overflow: usize,
}

impl DynamicLossScaler {
    pub fn new(initial_scale: f32) -> Self {
        Self {
            scale: initial_scale,
            growth_factor: 2.0,
            backoff_factor: 0.5,
            growth_interval: 2000,
            steps_since_overflow: 0,
        }
    }
    
    pub fn scale_loss(&self, loss: f32) -> f32 {
        loss * self.scale
    }
    
    pub fn unscale_gradients(&self, gradients: Vec<Tensor>) -> Result<Vec<Tensor>, TrainingError> {
        let mut unscaled = Vec::new();
        
        for grad in gradients {
            let unscaled_grad = grad / self.scale;
            unscaled.push(unscaled_grad);
        }
        
        Ok(unscaled)
    }
    
    pub fn reduce_scale(&mut self) {
        self.scale *= self.backoff_factor;
        self.steps_since_overflow = 0;
        
        // Ensure scale doesn't become too small
        if self.scale < 1.0 {
            self.scale = 1.0;
        }
    }
    
    pub fn maybe_increase_scale(&mut self) {
        self.steps_since_overflow += 1;
        
        if self.steps_since_overflow >= self.growth_interval {
            self.scale *= self.growth_factor;
            self.steps_since_overflow = 0;
            
            // Cap maximum scale
            if self.scale > 2.0_f32.powi(24) {
                self.scale = 2.0_f32.powi(24);
            }
        }
    }
}

use std::sync::Arc;
use tokio::sync::RwLock;

pub struct CompressedDataParallel {
    world_size: usize,
    rank: usize,
    compression_method: CompressionMethod,
    communication_backend: CommunicationBackend,
    gradient_buffer: Arc<RwLock<GradientBuffer>>,
    error_feedback: ErrorFeedback,
}

#[derive(Debug, Clone)]
pub enum CompressionMethod {
    TopK(usize),                    // Keep top K gradients
    RandomK(usize),                 // Random sparsification
    Quantization { bits: u8 },      // Quantize to N bits
    SignSGD,                        // Sign-based compression
    PowerSGD { rank: usize },       // Low-rank approximation
    Hybrid(Vec<CompressionMethod>), // Adaptive combination
}

impl CompressedDataParallel {
    pub async fn new(
        world_size: usize,
        rank: usize,
        compression_method: CompressionMethod,
    ) -> Self {
        Self {
            world_size,
            rank,
            compression_method,
            communication_backend: CommunicationBackend::new(world_size, rank).await,
            gradient_buffer: Arc::new(RwLock::new(GradientBuffer::new())),
            error_feedback: ErrorFeedback::new(),
        }
    }
    
    pub async fn all_reduce_compressed(
        &mut self,
        gradients: Vec<Tensor>,
    ) -> Result<Vec<Tensor>, DistributedError> {
        // Compress gradients
        let compressed = self.compress_gradients(gradients).await?;
        
        // All-reduce compressed gradients
        let reduced_compressed = self.communication_backend
            .all_reduce_compressed(compressed).await?;
        
        // Decompress and apply error feedback
        let decompressed = self.decompress_gradients(reduced_compressed).await?;
        let corrected = self.error_feedback.apply_correction(decompressed);
        
        Ok(corrected)
    }
    
    async fn compress_gradients(
        &self,
        gradients: Vec<Tensor>,
    ) -> Result<CompressedGradients, CompressionError> {
        match &self.compression_method {
            CompressionMethod::TopK(k) => {
                self.compress_topk(gradients, *k).await
            },
            CompressionMethod::RandomK(k) => {
                self.compress_randomk(gradients, *k).await
            },
            CompressionMethod::Quantization { bits } => {
                self.compress_quantize(gradients, *bits).await
            },
            CompressionMethod::SignSGD => {
                self.compress_sign(gradients).await
            },
            CompressionMethod::PowerSGD { rank } => {
                self.compress_powersgd(gradients, *rank).await
            },
            CompressionMethod::Hybrid(methods) => {
                self.compress_adaptive(gradients, methods).await
            },
        }
    }
    
    async fn compress_topk(
        &self,
        gradients: Vec<Tensor>,
        k: usize,
    ) -> Result<CompressedGradients, CompressionError> {
        let mut compressed = CompressedGradients::new();
        
        for (i, grad) in gradients.into_iter().enumerate() {
            // Flatten gradient
            let flat_grad = grad.flatten();
            let grad_size = flat_grad.len();
            
            // Find top-K elements by magnitude
            let mut indexed_values: Vec<(usize, f32)> = flat_grad
                .into_iter()
                .enumerate()
                .map(|(idx, val)| (idx, val))
                .collect();
            
            // Sort by absolute value, descending
            indexed_values.sort_by(|a, b| b.1.abs().partial_cmp(&a.1.abs()).unwrap());
            
            // Keep only top-K
            indexed_values.truncate(k);
            
            // Store as sparse representation
            let indices: Vec<u32> = indexed_values.iter().map(|(idx, _)| *idx as u32).collect();
            let values: Vec<f32> = indexed_values.iter().map(|(_, val)| *val).collect();
            
            compressed.add_sparse_tensor(i, SparseGradient {
                indices,
                values,
                shape: grad.shape().to_vec(),
                compression_ratio: k as f32 / grad_size as f32,
            });
        }
        
        Ok(compressed)
    }
    
    async fn compress_powersgd(
        &self,
        gradients: Vec<Tensor>,
        rank: usize,
    ) -> Result<CompressedGradients, CompressionError> {
        let mut compressed = CompressedGradients::new();
        
        for (i, grad) in gradients.into_iter().enumerate() {
            // Reshape gradient to matrix
            let matrix = grad.as_matrix();
            let (m, n) = matrix.dim();
            
            if rank >= std::cmp::min(m, n) {
                // Rank too large, use original gradient
                compressed.add_dense_tensor(i, grad);
                continue;
            }
            
            // Compute SVD approximation
            let (u, s, vt) = matrix.svd()?;
            
            // Keep only top 'rank' components
            let u_compressed = u.slice(s![.., ..rank]);
            let s_compressed = s.slice(s![..rank]);
            let vt_compressed = vt.slice(s![..rank, ..]);
            
            // Store low-rank factors
            compressed.add_lowrank_tensor(i, LowRankGradient {
                u: u_compressed.to_owned(),
                s: s_compressed.to_owned(),
                vt: vt_compressed.to_owned(),
                original_shape: grad.shape().to_vec(),
                compression_ratio: (2 * rank * (m + n)) as f32 / (m * n) as f32,
            });
        }
        
        Ok(compressed)
    }
    
    async fn compress_adaptive(
        &self,
        gradients: Vec<Tensor>,
        methods: &[CompressionMethod],
    ) -> Result<CompressedGradients, CompressionError> {
        // Choose compression method adaptively based on gradient properties
        let mut compressed = CompressedGradients::new();
        
        for (i, grad) in gradients.into_iter().enumerate() {
            let grad_stats = self.compute_gradient_statistics(&grad);
            let best_method = self.select_best_compression_method(&grad_stats, methods);
            
            // Apply selected compression method
            let single_grad_compressed = match best_method {
                CompressionMethod::TopK(k) => {
                    self.compress_topk(vec![grad], k).await?.get_tensor(0).unwrap()
                },
                CompressionMethod::Quantization { bits } => {
                    self.compress_quantize(vec![grad], bits).await?.get_tensor(0).unwrap()
                },
                // ... other methods
                _ => return Err(CompressionError::UnsupportedMethod),
            };
            
            compressed.add_tensor(i, single_grad_compressed);
        }
        
        Ok(compressed)
    }
    
    fn compute_gradient_statistics(&self, grad: &Tensor) -> GradientStatistics {
        let flat = grad.flatten();
        let mean = flat.iter().sum::<f32>() / flat.len() as f32;
        let variance = flat.iter()
            .map(|x| (x - mean).powi(2))
            .sum::<f32>() / flat.len() as f32;
        
        let sparsity = flat.iter().filter(|&&x| x.abs() < 1e-6).count() as f32 / flat.len() as f32;
        
        GradientStatistics {
            mean,
            variance,
            sparsity,
            l2_norm: flat.iter().map(|x| x.powi(2)).sum::<f32>().sqrt(),
            size: flat.len(),
        }
    }
    
    fn select_best_compression_method(
        &self,
        stats: &GradientStatistics,
        available_methods: &[CompressionMethod],
    ) -> CompressionMethod {
        // Simple heuristic-based selection
        if stats.sparsity > 0.9 {
            // Very sparse - use TopK
            CompressionMethod::TopK((stats.size as f32 * 0.01) as usize)
        } else if stats.variance < 0.1 {
            // Low variance - quantization works well
            CompressionMethod::Quantization { bits: 8 }
        } else {
            // Default to PowerSGD for dense gradients
            CompressionMethod::PowerSGD { rank: 4 }
        }
    }
}

use std::arch::x86_64::*;

pub struct SIMDCartanKernel {
    simd_width: usize,
    use_avx512: bool,
    use_fma: bool,
}

impl SIMDCartanKernel {
    pub fn new() -> Self {
        Self {
            simd_width: Self::detect_simd_width(),
            use_avx512: Self::has_avx512(),
            use_fma: Self::has_fma(),
        }
    }
    
    fn detect_simd_width() -> usize {
        if Self::has_avx512() {
            16 // 512 bits / 32 bits per f32
        } else if Self::has_avx2() {
            8  // 256 bits / 32 bits per f32
        } else {
            4  // 128 bits / 32 bits per f32 (SSE)
        }
    }
    
    pub unsafe fn cartan_matrix_vector_multiply(
        &self,
        cartan_matrix: &[f32],
        vector: &[f32],
        result: &mut [f32],
        n: usize,
    ) {
        if self.use_avx512 {
            self.cartan_matvec_avx512(cartan_matrix, vector, result, n);
        } else if Self::has_avx2() {
            self.cartan_matvec_avx2(cartan_matrix, vector, result, n);
        } else {
            self.cartan_matvec_sse(cartan_matrix, vector, result, n);
        }
    }
    
    #[target_feature(enable = "avx512f")]
    unsafe fn cartan_matvec_avx512(
        &self,
        matrix: &[f32],
        vector: &[f32],
        result: &mut [f32],
        n: usize,
    ) {
        let simd_width = 16;
        let remainder = n % simd_width;
        let vectorized_size = n - remainder;
        
        for i in 0..n {
            let mut sum = _mm512_setzero_ps();
            
            // Process 16 elements at a time
            for j in (0..vectorized_size).step_by(simd_width) {
                let matrix_row = &matrix[i * n + j..];
                let vec_chunk = &vector[j..];
                
                let m_vec = _mm512_loadu_ps(matrix_row.as_ptr());
                let v_vec = _mm512_loadu_ps(vec_chunk.as_ptr());
                
                if self.use_fma {
                    sum = _mm512_fmadd_ps(m_vec, v_vec, sum);
                } else {
                    let prod = _mm512_mul_ps(m_vec, v_vec);
                    sum = _mm512_add_ps(sum, prod);
                }
            }
            
            // Horizontal sum of the vector
            let mut sum_scalar = self.horizontal_sum_avx512(sum);
            
            // Handle remainder elements
            for j in vectorized_size..n {
                sum_scalar += matrix[i * n + j] * vector[j];
            }
            
            result[i] = sum_scalar;
        }
    }
    
    #[target_feature(enable = "avx2")]
    unsafe fn cartan_matvec_avx2(
        &self,
        matrix: &[f32],
        vector: &[f32],
        result: &mut [f32],
        n: usize,
    ) {
        let simd_width = 8;
        let remainder = n % simd_width;
        let vectorized_size = n - remainder;
        
        for i in 0..n {
            let mut sum = _mm256_setzero_ps();
            
            // Process 8 elements at a time
            for j in (0..vectorized_size).step_by(simd_width) {
                let matrix_row = &matrix[i * n + j..];
                let vec_chunk = &vector[j..];
                
                let m_vec = _mm256_loadu_ps(matrix_row.as_ptr());
                let v_vec = _mm256_loadu_ps(vec_chunk.as_ptr());
                
                if self.use_fma {
                    sum = _mm256_fmadd_ps(m_vec, v_vec, sum);
                } else {
                    let prod = _mm256_mul_ps(m_vec, v_vec);
                    sum = _mm256_add_ps(sum, prod);
                }
            }
            
            // Horizontal sum
            let mut sum_scalar = self.horizontal_sum_avx2(sum);
            
            // Handle remainder
            for j in vectorized_size..n {
                sum_scalar += matrix[i * n + j] * vector[j];
            }
            
            result[i] = sum_scalar;
        }
    }
    
    unsafe fn horizontal_sum_avx512(&self, vec: __m512) -> f32 {
        let sum256_low = _mm512_castps512_ps256(vec);
        let sum256_high = _mm512_extractf32x8_ps(vec, 1);
        let sum256 = _mm256_add_ps(sum256_low, sum256_high);
        self.horizontal_sum_avx2(sum256)
    }
    
    unsafe fn horizontal_sum_avx2(&self, vec: __m256) -> f32 {
        let sum128_low = _mm256_castps256_ps128(vec);
        let sum128_high = _mm256_extractf128_ps(vec, 1);
        let sum128 = _mm_add_ps(sum128_low, sum128_high);
        
        let sum64 = _mm_hadd_ps(sum128, sum128);
        let sum32 = _mm_hadd_ps(sum64, sum64);
        
        _mm_cvtss_f32(sum32)
    }
    
    fn has_avx512() -> bool {
        is_x86_feature_detected!("avx512f")
    }
    
    fn has_avx2() -> bool {
        is_x86_feature_detected!("avx2")
    }
    
    fn has_fma() -> bool {
        is_x86_feature_detected!("fma")
    }
}

pub struct SIMDAttentionKernel {
    kernel: SIMDCartanKernel,
    softmax_kernel: SIMDSoftmaxKernel,
    cache: AttentionCache,
}

impl SIMDAttentionKernel {
    pub unsafe fn multi_head_attention_forward(
        &mut self,
        query: &[f32],      // [batch_size, seq_len, d_model]
        key: &[f32],        // [batch_size, seq_len, d_model]
        value: &[f32],      // [batch_size, seq_len, d_model]
        output: &mut [f32], // [batch_size, seq_len, d_model]
        params: &AttentionParams,
    ) {
        let batch_size = params.batch_size;
        let seq_len = params.seq_len;
        let d_model = params.d_model;
        let num_heads = params.num_heads;
        let d_k = d_model / num_heads;
        
        // Allocate temporary buffers
        let mut q_proj = vec![0.0f32; batch_size * seq_len * d_model];
        let mut k_proj = vec![0.0f32; batch_size * seq_len * d_model];
        let mut v_proj = vec![0.0f32; batch_size * seq_len * d_model];
        let mut attention_scores = vec![0.0f32; batch_size * num_heads * seq_len * seq_len];
        let mut attention_probs = vec![0.0f32; batch_size * num_heads * seq_len * seq_len];
        let mut context = vec![0.0f32; batch_size * seq_len * d_model];
        
        // Project Q, K, V
        self.kernel.cartan_matrix_vector_multiply(
            &params.w_q,
            query,
            &mut q_proj,
            d_model,
        );
        
        self.kernel.cartan_matrix_vector_multiply(
            &params.w_k,
            key,
            &mut k_proj,
            d_model,
        );
        
        self.kernel.cartan_matrix_vector_multiply(
            &params.w_v,
            value,
            &mut v_proj,
            d_model,
        );
        
        // Compute attention for each head
        for batch in 0..batch_size {
            for head in 0..num_heads {
                let q_head_offset = batch * seq_len * d_model + head * d_k;
                let k_head_offset = batch * seq_len * d_model + head * d_k;
                let v_head_offset = batch * seq_len * d_model + head * d_k;
                let score_offset = batch * num_heads * seq_len * seq_len + 
                                 head * seq_len * seq_len;
                
                // Q * K^T
                self.compute_attention_scores(
                    &q_proj[q_head_offset..],
                    &k_proj[k_head_offset..],
                    &mut attention_scores[score_offset..],
                    seq_len,
                    d_k,
                );
                
                // Apply scaling
                let scale = 1.0 / (d_k as f32).sqrt();
                self.scale_attention_scores(
                    &mut attention_scores[score_offset..],
                    seq_len * seq_len,
                    scale,
                );
                
                // Softmax
                self.softmax_kernel.softmax_2d(
                    &attention_scores[score_offset..],
                    &mut attention_probs[score_offset..],
                    seq_len,
                    seq_len,
                );
                
                // Attention * V
                self.apply_attention_to_values(
                    &attention_probs[score_offset..],
                    &v_proj[v_head_offset..],
                    &mut context[batch * seq_len * d_model + head * d_k..],
                    seq_len,
                    d_k,
                );
            }
        }
        
        // Final projection
        self.kernel.cartan_matrix_vector_multiply(
            &params.w_o,
            &context,
            output,
            d_model,
        );
    }
    
    #[target_feature(enable = "avx2")]
    unsafe fn compute_attention_scores(
        &self,
        query: &[f32],      // [seq_len, d_k]
        key: &[f32],        // [seq_len, d_k]
        scores: &mut [f32], // [seq_len, seq_len]
        seq_len: usize,
        d_k: usize,
    ) {
        for i in 0..seq_len {
            for j in 0..seq_len {
                let mut score = _mm256_setzero_ps();
                let simd_width = 8;
                let remainder = d_k % simd_width;
                let vectorized_size = d_k - remainder;
                
                // Vectorized dot product
                for k in (0..vectorized_size).step_by(simd_width) {
                    let q_vec = _mm256_loadu_ps(&query[i * d_k + k]);
                    let k_vec = _mm256_loadu_ps(&key[j * d_k + k]);
                    
                    score = _mm256_fmadd_ps(q_vec, k_vec, score);
                }
                
                // Horizontal sum
                let mut score_scalar = self.kernel.horizontal_sum_avx2(score);
                
                // Handle remainder
                for k in vectorized_size..d_k {
                    score_scalar += query[i * d_k + k] * key[j * d_k + k];
                }
                
                scores[i * seq_len + j] = score_scalar;
            }
        }
    }
    
    #[target_feature(enable = "avx2")]
    unsafe fn scale_attention_scores(
        &self,
        scores: &mut [f32],
        len: usize,
        scale: f32,
    ) {
        let scale_vec = _mm256_set1_ps(scale);
        let simd_width = 8;
        let remainder = len % simd_width;
        let vectorized_size = len - remainder;
        
        // Vectorized scaling
        for i in (0..vectorized_size).step_by(simd_width) {
            let score_vec = _mm256_loadu_ps(&scores[i]);
            let scaled = _mm256_mul_ps(score_vec, scale_vec);
            _mm256_storeu_ps(&mut scores[i], scaled);
        }
        
        // Handle remainder
        for i in vectorized_size..len {
            scores[i] *= scale;
        }
    }
}

use wasm_bindgen::prelude::*;

#[cfg(target_arch = "wasm32")]
use std::arch::wasm32::*;

#[wasm_bindgen]
pub struct WasmCartanKernel {
    use_simd: bool,
}

#[wasm_bindgen]
impl WasmCartanKernel {
    #[wasm_bindgen(constructor)]
    pub fn new() -> Self {
        Self {
            use_simd: Self::has_simd_support(),
        }
    }
    
    fn has_simd_support() -> bool {
        #[cfg(target_arch = "wasm32")]
        {
            // Check if SIMD is available in WASM
            std::arch::is_wasm_feature_detected!("simd128")
        }
        #[cfg(not(target_arch = "wasm32"))]
        {
            false
        }
    }
    
    #[wasm_bindgen]
    pub fn cartan_attention_forward(
        &self,
        query: &[f32],
        key: &[f32],
        value: &[f32],
        cartan_matrix: &[f32],
        output: &mut [f32],
        seq_len: usize,
        d_model: usize,
    ) -> Result<(), JsValue> {
        #[cfg(target_arch = "wasm32")]
        {
            if self.use_simd {
                unsafe {
                    self.cartan_attention_simd(
                        query, key, value, cartan_matrix, output, seq_len, d_model
                    )?;
                }
            } else {
                self.cartan_attention_scalar(
                    query, key, value, cartan_matrix, output, seq_len, d_model
                )?;
            }
        }
        #[cfg(not(target_arch = "wasm32"))]
        {
            return Err(JsValue::from_str("WASM-only function"));
        }
        
        Ok(())
    }
    
    #[cfg(target_arch = "wasm32")]
    unsafe fn cartan_attention_simd(
        &self,
        query: &[f32],
        key: &[f32],
        value: &[f32],
        cartan_matrix: &[f32],
        output: &mut [f32],
        seq_len: usize,
        d_model: usize,
    ) -> Result<(), JsValue> {
        let simd_width = 4; // WASM SIMD is 128-bit
        
        // Apply Cartan structure to attention computation
        for i in 0..seq_len {
            for j in 0..seq_len {
                let mut attention_weight = v128_const(0.0, 0.0, 0.0, 0.0);
                
                // Vectorized attention computation with Cartan matrix
                for k in (0..d_model).step_by(simd_width) {
                    let q_vec = v128_load(&query[i * d_model + k] as *const f32);
                    let k_vec = v128_load(&key[j * d_model + k] as *const f32);
                    let cartan_vec = v128_load(&cartan_matrix[k] as *const f32);
                    
                    // Apply Cartan transformation: C * (Q · K)
                    let qk_product = f32x4_mul(q_vec, k_vec);
                    let cartan_weighted = f32x4_mul(qk_product, cartan_vec);
                    
                    attention_weight = f32x4_add(attention_weight, cartan_weighted);
                }
                
                // Horizontal sum for final attention weight
                let weight_scalar = self.horizontal_sum_wasm_simd(attention_weight);
                
                // Apply attention weight to values
                for k in (0..d_model).step_by(simd_width) {
                    let v_vec = v128_load(&value[j * d_model + k] as *const f32);
                    let weight_vec = f32x4_splat(weight_scalar);
                    let weighted_value = f32x4_mul(v_vec, weight_vec);
                    
                    // Accumulate in output
                    let current_output = v128_load(&output[i * d_model + k] as *const f32);
                    let new_output = f32x4_add(current_output, weighted_value);
                    v128_store(&mut output[i * d_model + k] as *mut f32, new_output);
                }
            }
        }
        
        Ok(())
    }
    
    #[cfg(target_arch = "wasm32")]
    fn horizontal_sum_wasm_simd(&self, vec: v128) -> f32 {
        // Extract individual lanes and sum them
        let lane0 = f32x4_extract_lane::<0>(vec);
        let lane1 = f32x4_extract_lane::<1>(vec);
        let lane2 = f32x4_extract_lane::<2>(vec);
        let lane3 = f32x4_extract_lane::<3>(vec);
        
        lane0 + lane1 + lane2 + lane3
    }
    
    fn cartan_attention_scalar(
        &self,
        query: &[f32],
        key: &[f32],
        value: &[f32],
        cartan_matrix: &[f32],
        output: &mut [f32],
        seq_len: usize,
        d_model: usize,
    ) -> Result<(), JsValue> {
        // Fallback scalar implementation
        for i in 0..seq_len {
            for j in 0..seq_len {
                let mut attention_weight = 0.0;
                
                for k in 0..d_model {
                    let qk_product = query[i * d_model + k] * key[j * d_model + k];
                    attention_weight += cartan_matrix[k] * qk_product;
                }
                
                for k in 0..d_model {
                    output[i * d_model + k] += attention_weight * value[j * d_model + k];
                }
            }
        }
        
        Ok(())
    }
}

// JavaScript interface for web deployment
#[wasm_bindgen]
extern "C" {
    #[wasm_bindgen(js_namespace = console)]
    fn log(s: &str);
    
    #[wasm_bindgen(js_namespace = performance)]
    fn now() -> f64;
}

#[wasm_bindgen]
pub struct WasmPerformanceProfiler {
    start_time: f64,
    checkpoints: Vec<(String, f64)>,
}

#[wasm_bindgen]
impl WasmPerformanceProfiler {
    #[wasm_bindgen(constructor)]
    pub fn new() -> Self {
        Self {
            start_time: now(),
            checkpoints: Vec::new(),
        }
    }
    
    #[wasm_bindgen]
    pub fn checkpoint(&mut self, name: &str) {
        let current_time = now();
        self.checkpoints.push((name.to_string(), current_time - self.start_time));
    }
    
    #[wasm_bindgen]
    pub fn get_report(&self) -> String {
        let mut report = String::from("Performance Report:\n");
        
        for (name, time) in &self.checkpoints {
            report.push_str(&format!("{}: {:.2}ms\n", name, time));
        }
        
        report
    }
}

use prometheus::{Counter, Histogram, Gauge, Registry};
use tracing::{info, warn, error, debug};
use opentelemetry::trace::{TraceProvider, Tracer};
use serde_json::json;

pub struct ProductionMetrics {
    requests_total: Counter,
    request_duration: Histogram,
    active_connections: Gauge,
    model_memory_usage: Gauge,
    gpu_utilization: Gauge,
    inference_latency: Histogram,
    error_rate: Counter,
    throughput: Gauge,
}

impl ProductionMetrics {
    pub fn new(registry: &Registry) -> Self {
        let requests_total = Counter::new(
            "cartan_requests_total",
            "Total number of inference requests"
        ).unwrap();
        
        let request_duration = Histogram::with_opts(
            prometheus::HistogramOpts::new(
                "cartan_request_duration_seconds",
                "Request duration in seconds"
            ).buckets(vec![0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0, 5.0])
        ).unwrap();
        
        let active_connections = Gauge::new(
            "cartan_active_connections",
            "Number of active connections"
        ).unwrap();
        
        let model_memory_usage = Gauge::new(
            "cartan_model_memory_bytes",
            "Model memory usage in bytes"
        ).unwrap();
        
        let gpu_utilization = Gauge::new(
            "cartan_gpu_utilization_percent",
            "GPU utilization percentage"
        ).unwrap();
        
        let inference_latency = Histogram::with_opts(
            prometheus::HistogramOpts::new(
                "cartan_inference_latency_seconds",
                "Inference latency in seconds"
            ).buckets(vec![0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1])
        ).unwrap();
        
        let error_rate = Counter::new(
            "cartan_errors_total",
            "Total number of errors"
        ).unwrap();
        
        let throughput = Gauge::new(
            "cartan_throughput_rps",
            "Requests per second throughput"
        ).unwrap();
        
        // Register all metrics
        registry.register(Box::new(requests_total.clone())).unwrap();
        registry.register(Box::new(request_duration.clone())).unwrap();
        registry.register(Box::new(active_connections.clone())).unwrap();
        registry.register(Box::new(model_memory_usage.clone())).unwrap();
        registry.register(Box::new(gpu_utilization.clone())).unwrap();
        registry.register(Box::new(inference_latency.clone())).unwrap();
        registry.register(Box::new(error_rate.clone())).unwrap();
        registry.register(Box::new(throughput.clone())).unwrap();
        
        Self {
            requests_total,
            request_duration,
            active_connections,
            model_memory_usage,
            gpu_utilization,
            inference_latency,
            error_rate,
            throughput,
        }
    }
    
    pub fn record_request(&self, duration: f64, success: bool) {
        self.requests_total.inc();
        self.request_duration.observe(duration);
        
        if !success {
            self.error_rate.inc();
        }
    }
    
    pub fn update_system_metrics(&self, system_info: &SystemInfo) {
        self.model_memory_usage.set(system_info.model_memory_bytes as f64);
        self.gpu_utilization.set(system_info.gpu_utilization_percent);
        self.active_connections.set(system_info.active_connections as f64);
        self.throughput.set(system_info.requests_per_second);
    }
}

pub struct ProductionInferenceService {
    models: HashMap<String, Arc<CartanModel>>,
    metrics: ProductionMetrics,
    tracer: Box<dyn Tracer + Send + Sync>,
    circuit_breaker: CircuitBreaker,
    rate_limiter: RateLimiter,
    health_checker: HealthChecker,
}

impl ProductionInferenceService {
    pub async fn serve_inference(
        &self,
        request: InferenceRequest,
    ) -> Result<InferenceResponse, ServiceError> {
        let start_time = std::time::Instant::now();
        let span = self.tracer.start("inference_request");
        
        // Rate limiting
        if !self.rate_limiter.try_acquire().await {
            self.metrics.error_rate.inc();
            return Err(ServiceError::RateLimited);
        }
        
        // Circuit breaker check
        if !self.circuit_breaker.is_available() {
            self.metrics.error_rate.inc();
            warn!("Circuit breaker open, rejecting request");
            return Err(ServiceError::CircuitBreakerOpen);
        }
        
        // Input validation
        if let Err(e) = self.validate_request(&request) {
            self.metrics.error_rate.inc();
            error!("Invalid request: {:?}", e);
            return Err(ServiceError::InvalidRequest(e));
        }
        
        let result = async {
            // Get model
            let model = self.models.get(&request.model_name)
                .ok_or(ServiceError::ModelNotFound)?;
            
            // Run inference with timeout
            let inference_future = model.predict(&request.input);
            let response = tokio::time::timeout(
                Duration::from_secs(30),
                inference_future
            ).await??;
            
            Ok(response)
        }.await;
        
        let duration = start_time.elapsed().as_secs_f64();
        let success = result.is_ok();
        
        // Record metrics
        self.metrics.record_request(duration, success);
        
        // Update circuit breaker
        if success {
            self.circuit_breaker.record_success();
        } else {
            self.circuit_breaker.record_failure();
        }
        
        // Structured logging
        info!(
            request_id = %request.id,
            model_name = %request.model_name,
            duration_ms = duration * 1000.0,
            success = success,
            "Inference request completed"
        );
        
        result
    }
    
    fn validate_request(&self, request: &InferenceRequest) -> Result<(), ValidationError> {
        // Input size validation
        if request.input.len() > 10000 {
            return Err(ValidationError::InputTooLarge);
        }
        
        // Model name validation
        if !self.models.contains_key(&request.model_name) {
            return Err(ValidationError::InvalidModelName);
        }
        
        // Format validation
        if !self.is_valid_input_format(&request.input) {
            return Err(ValidationError::InvalidFormat);
        }
        
        Ok(())
    }
    
    pub async fn health_check(&self) -> HealthStatus {
        let mut status = HealthStatus::new();
        
        // Check model availability
        let model_health = self.check_model_health().await;
        status.add_check("models", model_health);
        
        // Check GPU availability
        let gpu_health = self.check_gpu_health().await;
        status.add_check("gpu", gpu_health);
        
        // Check memory usage
        let memory_health = self.check_memory_health().await;
        status.add_check("memory", memory_health);
        
        // Check external dependencies
        let deps_health = self.check_dependencies_health().await;
        status.add_check("dependencies", deps_health);
        
        status
    }
    
    async fn check_model_health(&self) -> CheckResult {
        for (name, model) in &self.models {
            match model.health_check().await {
                Ok(_) => continue,
                Err(e) => {
                    return CheckResult::Unhealthy(format!("Model {} unhealthy: {}", name, e));
                }
            }
        }
        CheckResult::Healthy
    }
    
    async fn check_gpu_health(&self) -> CheckResult {
        match self.get_gpu_utilization().await {
            Ok(utilization) if utilization < 95.0 => CheckResult::Healthy,
            Ok(utilization) => CheckResult::Warning(format!("High GPU utilization: {}%", utilization)),
            Err(e) => CheckResult::Unhealthy(format!("GPU check failed: {}", e)),
        }
    }
}

// wasm-edge-deployment/src/lib.rs
use wasm_bindgen::prelude::*;
use js_sys::{Promise, Uint8Array};
use web_sys::{console, performance};
use serde::{Deserialize, Serialize};

#[wasm_bindgen]
pub struct EdgeCartanInference {
    model: CompactCartanModel,
    quantization: QuantizationConfig,
    cache: InferenceCache,
    performance_monitor: PerformanceMonitor,
}

#[derive(Serialize, Deserialize)]
pub struct CompactCartanModel {
    weights: Vec<i8>,           // Quantized weights
    scales: Vec<f32>,           // Quantization scales
    cartan_structure: Vec<u16>, // Compressed Cartan matrix indices
    layer_configs: Vec<LayerConfig>,
}

#[wasm_bindgen]
impl EdgeCartanInference {
    #[wasm_bindgen(constructor)]
    pub fn new(model_data: &[u8]) -> Result<EdgeCartanInference, JsValue> {
        console::log_1(&"Initializing Edge Cartan Inference...".into());
        
        let model = Self::deserialize_compact_model(model_data)?;
        
        Ok(EdgeCartanInference {
            model,
            quantization: QuantizationConfig::int8_default(),
            cache: InferenceCache::new(100), // Cache last 100 results
            performance_monitor: PerformanceMonitor::new(),
        })
    }
    
    #[wasm_bindgen]
    pub fn predict(&mut self, input_data: &[f32]) -> Promise {
        let input = input_data.to_vec();
        let mut model = self.model.clone();
        let mut cache = self.cache.clone();
        let mut perf_monitor = self.performance_monitor.clone();
        
        wasm_bindgen_futures::future_to_promise(async move {
            let start_time = performance::now();
            
            // Check cache first
            let cache_key = Self::compute_cache_key(&input);
            if let Some(cached_result) = cache.get(&cache_key) {
                perf_monitor.record_cache_hit();
                return Ok(JsValue::from_serde(&cached_result)?);
            }
            
            // Run inference
            let result = Self::run_quantized_inference(&model, &input).await?;
            
            // Cache result
            cache.insert(cache_key, result.clone());
            
            let duration = performance::now() - start_time;
            perf_monitor.record_inference(duration);
            
            console::log_1(&format!("Inference completed in {:.2}ms", duration).into());
            
            Ok(JsValue::from_serde(&result)?)
        })
    }
    
    async fn run_quantized_inference(
        model: &CompactCartanModel,
        input: &[f32],
    ) -> Result<InferenceResult, JsValue> {
        let mut current_activation = input.to_vec();
        
        for (layer_idx, layer_config) in model.layer_configs.iter().enumerate() {
            match layer_config.layer_type {
                LayerType::LinearQuantized => {
                    current_activation = Self::quantized_linear_forward(
                        &current_activation,
                        &model.weights,
                        &model.scales,
                        layer_config,
                    )?;
                },
                LayerType::CartanAttentionQuantized => {
                    current_activation = Self::quantized_cartan_attention_forward(
                        &current_activation,
                        &model.cartan_structure,
                        &model.weights,
                        &model.scales,
                        layer_config,
                    )?;
                },
                LayerType::LayerNorm => {
                    current_activation = Self::layer_norm_forward(
                        &current_activation,
                        layer_config,
                    )?;
                },
            }
            
            // Apply activation function
            if let Some(activation) = &layer_config.activation {
                current_activation = Self::apply_activation(&current_activation, activation)?;
            }
        }
        
        Ok(InferenceResult {
            output: current_activation,
            confidence: Self::compute_confidence(&current_activation),
            latency_ms: 0.0, // Will be filled by caller
        })
    }
    
    fn quantized_linear_forward(
        input: &[f32],
        quantized_weights: &[i8],
        scales: &[f32],
        config: &LayerConfig,
    ) -> Result<Vec<f32>, JsValue> {
        let input_size = config.input_size;
        let output_size = config.output_size;
        let mut output = vec![0.0f32; output_size];
        
        for i in 0..output_size {
            let mut sum = 0.0;
            let weight_offset = i * input_size;
            let scale = scales[i];
            
            // Quantized matrix multiplication
            for j in 0..input_size {
                let quantized_weight = quantized_weights[weight_offset + j] as f32;
                let dequantized_weight = quantized_weight * scale;
                sum += input[j] * dequantized_weight;
            }
            
            output[i] = sum;
        }
        
        Ok(output)
    }
    
    fn quantized_cartan_attention_forward(
        input: &[f32],
        cartan_structure: &[u16],
        quantized_weights: &[i8],
        scales: &[f32],
        config: &LayerConfig,
    ) -> Result<Vec<f32>, JsValue> {
        let seq_len = config.seq_len;
        let d_model = config.d_model;
        let num_heads = config.num_heads;
        let d_k = d_model / num_heads;
        
        let mut output = vec![0.0f32; seq_len * d_model];
        
        // Simplified quantized multi-head attention with Cartan structure
        for head in 0..num_heads {
            let head_offset = head * d_k;
            
            for i in 0..seq_len {
                for j in 0..seq_len {
                    let mut attention_weight = 0.0;
                    
                    // Apply Cartan structure to attention computation
                    for k in 0..d_k {
                        let cartan_idx = cartan_structure[k] as usize;
                        let q_val = input[i * d_model + head_offset + k];
                        let k_val = input[j * d_model + head_offset + k];
                        
                        // Use Cartan matrix element (simplified)
                        let cartan_weight = if cartan_idx < scales.len() {
                            scales[cartan_idx]
                        } else {
                            1.0
                        };
                        
                        attention_weight += cartan_weight * q_val * k_val;
                    }
                    
                    // Apply softmax normalization (simplified)
                    attention_weight = attention_weight.exp();
                    
                    // Apply to values
                    for k in 0..d_k {
                        let v_val = input[j * d_model + head_offset + k];
                        output[i * d_model + head_offset + k] += attention_weight * v_val;
                    }
                }
            }
        }
        
        Ok(output)
    }
    
    #[wasm_bindgen]
    pub fn get_performance_stats(&self) -> JsValue {
        JsValue::from_serde(&self.performance_monitor.get_stats()).unwrap()
    }
    
    #[wasm_bindgen]
    pub fn optimize_for_device(&mut self, device_info: &JsValue) -> Result<(), JsValue> {
        let device: DeviceInfo = device_info.into_serde()?;
        
        // Adjust model based on device capabilities
        if device.memory_mb < 512 {
            // Very limited memory - use aggressive quantization
            self.quantization = QuantizationConfig::int4_aggressive();
        } else if device.memory_mb < 1024 {
            // Limited memory - use moderate quantization
            self.quantization = QuantizationConfig::int8_moderate();
        } else {
            // Sufficient memory - use light quantization
            self.quantization = QuantizationConfig::int8_default();
        }
        
        // Adjust cache size based on available memory
        let cache_size = std::cmp::min(1000, device.memory_mb / 4);
        self.cache = InferenceCache::new(cache_size);
        
        console::log_1(&format!(
            "Optimized for device: {}MB memory, cache size: {}",
            device.memory_mb, cache_size
        ).into());
        
        Ok(())
    }
}

// JavaScript integration helpers
#[wasm_bindgen]
extern "C" {
    #[wasm_bindgen(js_namespace = ["window", "navigator"])]
    fn hardwareConcurrency() -> u32;
    
    #[wasm_bindgen(js_namespace = ["window", "performance", "memory"])]
    fn usedJSHeapSize() -> u32;
    
    #[wasm_bindgen(js_namespace = ["window", "performance", "memory"])]
    fn totalJSHeapSize() -> u32;
}

#[derive(Clone)]
pub struct PerformanceMonitor {
    inference_times: Vec<f64>,
    cache_hits: u32,
    cache_misses: u32,
    total_inferences: u32,
}

impl PerformanceMonitor {
    pub fn new() -> Self {
        Self {
            inference_times: Vec::new(),
            cache_hits: 0,
            cache_misses: 0,
            total_inferences: 0,
        }
    }
    
    pub fn record_inference(&mut self, duration_ms: f64) {
        self.inference_times.push(duration_ms);
        self.total_inferences += 1;
        
        // Keep only last 100 measurements
        if self.inference_times.len() > 100 {
            self.inference_times.remove(0);
        }
    }
    
    pub fn record_cache_hit(&mut self) {
        self.cache_hits += 1;
    }
    
    pub fn record_cache_miss(&mut self) {
        self.cache_misses += 1;
    }
    
    pub fn get_stats(&self) -> PerformanceStats {
        let avg_inference_time = if !self.inference_times.is_empty() {
            self.inference_times.iter().sum::<f64>() / self.inference_times.len() as f64
        } else {
            0.0
        };
        
        let cache_hit_rate = if self.cache_hits + self.cache_misses > 0 {
            self.cache_hits as f64 / (self.cache_hits + self.cache_misses) as f64
        } else {
            0.0
        };
        
        PerformanceStats {
            avg_inference_time_ms: avg_inference_time,
            cache_hit_rate,
            total_inferences: self.total_inferences,
            memory_usage_mb: unsafe { usedJSHeapSize() } as f64 / 1024.0 / 1024.0,
        }
    }
}

#[derive(Serialize, Deserialize)]
pub struct PerformanceStats {
    pub avg_inference_time_ms: f64,
    pub cache_hit_rate: f64,
    pub total_inferences: u32,
    pub memory_usage_mb: f64,
}