//! # Exotic AI Capabilities Module
//!
//! Provides a unified interface for exotic AI WASM capabilities:
//! - **Time Crystal**: P2P synchronization using discrete time crystal dynamics
//! - **NAO**: Neural Autonomous Organization for decentralized governance
//! - **MicroLoRA**: Per-node self-learning with rank-2 adaptation
//! - **HDC**: Hyperdimensional Computing for distributed reasoning
//! - **BTSP**: One-shot learning via Behavioral Timescale Synaptic Plasticity
//! - **WTA**: Winner-Take-All for instant decisions
//! - **Global Workspace**: Attention bottleneck (4-7 items)
//! - **Morphogenetic**: Network growth through cellular differentiation

use wasm_bindgen::prelude::*;
use serde::{Deserialize, Serialize};

/// Available exotic capabilities
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct CapabilityInfo {
    pub name: String,
    pub description: String,
    pub enabled: bool,
    pub version: String,
}

/// Unified interface for all exotic WASM capabilities
#[wasm_bindgen]
pub struct WasmCapabilities {
    // Time Crystal for P2P synchronization
    #[cfg(feature = "exotic")]
    time_crystal: Option<ruvector_exotic_wasm::TimeCrystal>,

    // NAO for governance
    #[cfg(feature = "exotic")]
    nao: Option<ruvector_exotic_wasm::NeuralAutonomousOrg>,

    // Morphogenetic network
    #[cfg(feature = "exotic")]
    morphogenetic: Option<ruvector_exotic_wasm::MorphogeneticNetwork>,

    // MicroLoRA for self-learning
    #[cfg(feature = "learning-enhanced")]
    micro_lora: Option<ruvector_learning_wasm::MicroLoRAEngine>,

    // HDC for distributed reasoning
    #[cfg(feature = "learning-enhanced")]
    hdc_memory: Option<ruvector_nervous_system_wasm::HdcMemory>,

    // WTA for instant decisions
    #[cfg(feature = "learning-enhanced")]
    wta_layer: Option<ruvector_nervous_system_wasm::WTALayer>,

    // Global Workspace for attention
    #[cfg(feature = "learning-enhanced")]
    workspace: Option<ruvector_nervous_system_wasm::GlobalWorkspace>,

    // BTSP for one-shot learning
    #[cfg(feature = "learning-enhanced")]
    btsp_layer: Option<ruvector_nervous_system_wasm::BTSPLayer>,

    // Configuration
    node_id: String,
}

#[wasm_bindgen]
impl WasmCapabilities {
    /// Create a new capabilities manager for a node
    #[wasm_bindgen(constructor)]
    pub fn new(node_id: &str) -> Self {
        Self {
            #[cfg(feature = "exotic")]
            time_crystal: None,
            #[cfg(feature = "exotic")]
            nao: None,
            #[cfg(feature = "exotic")]
            morphogenetic: None,
            #[cfg(feature = "learning-enhanced")]
            micro_lora: None,
            #[cfg(feature = "learning-enhanced")]
            hdc_memory: None,
            #[cfg(feature = "learning-enhanced")]
            wta_layer: None,
            #[cfg(feature = "learning-enhanced")]
            workspace: None,
            #[cfg(feature = "learning-enhanced")]
            btsp_layer: None,
            node_id: node_id.to_string(),
        }
    }

    /// List all available exotic capabilities
    #[wasm_bindgen(js_name = getCapabilities)]
    pub fn get_capabilities(&self) -> JsValue {
        let mut capabilities = Vec::new();

        // Exotic capabilities
        #[cfg(feature = "exotic")]
        {
            capabilities.push(CapabilityInfo {
                name: "time_crystal".to_string(),
                description: "P2P synchronization using discrete time crystal dynamics".to_string(),
                enabled: self.time_crystal.is_some(),
                version: ruvector_exotic_wasm::version(),
            });
            capabilities.push(CapabilityInfo {
                name: "nao".to_string(),
                description: "Neural Autonomous Organization for decentralized governance".to_string(),
                enabled: self.nao.is_some(),
                version: ruvector_exotic_wasm::version(),
            });
            capabilities.push(CapabilityInfo {
                name: "morphogenetic".to_string(),
                description: "Network growth through cellular differentiation".to_string(),
                enabled: self.morphogenetic.is_some(),
                version: ruvector_exotic_wasm::version(),
            });
        }

        // Learning-enhanced capabilities
        #[cfg(feature = "learning-enhanced")]
        {
            capabilities.push(CapabilityInfo {
                name: "micro_lora".to_string(),
                description: "Per-node self-learning with rank-2 LoRA adaptation (<100us)".to_string(),
                enabled: self.micro_lora.is_some(),
                version: env!("CARGO_PKG_VERSION").to_string(),
            });
            capabilities.push(CapabilityInfo {
                name: "hdc".to_string(),
                description: "Hyperdimensional Computing with 10,000-bit vectors".to_string(),
                enabled: self.hdc_memory.is_some(),
                version: ruvector_nervous_system_wasm::version(),
            });
            capabilities.push(CapabilityInfo {
                name: "wta".to_string(),
                description: "Winner-Take-All for instant decisions (<1us)".to_string(),
                enabled: self.wta_layer.is_some(),
                version: ruvector_nervous_system_wasm::version(),
            });
            capabilities.push(CapabilityInfo {
                name: "global_workspace".to_string(),
                description: "Attention bottleneck with 4-7 item capacity".to_string(),
                enabled: self.workspace.is_some(),
                version: ruvector_nervous_system_wasm::version(),
            });
            capabilities.push(CapabilityInfo {
                name: "btsp".to_string(),
                description: "Behavioral Timescale Synaptic Plasticity for one-shot learning".to_string(),
                enabled: self.btsp_layer.is_some(),
                version: ruvector_nervous_system_wasm::version(),
            });
        }

        // Fallback when no features enabled
        #[cfg(not(any(feature = "exotic", feature = "learning-enhanced")))]
        {
            capabilities.push(CapabilityInfo {
                name: "base".to_string(),
                description: "Base edge-net capabilities only. Enable 'exotic' or 'learning-enhanced' features for more.".to_string(),
                enabled: true,
                version: env!("CARGO_PKG_VERSION").to_string(),
            });
        }

        serde_wasm_bindgen::to_value(&capabilities).unwrap_or(JsValue::NULL)
    }

    // ========================================================================
    // Time Crystal Methods (P2P Synchronization)
    // ========================================================================

    /// Enable Time Crystal for P2P synchronization
    ///
    /// Time crystals use discrete time crystal dynamics for robust distributed
    /// coordination with period-doubled oscillations and Floquet engineering.
    ///
    /// # Arguments
    /// * `oscillators` - Number of oscillators (more = better coordination)
    /// * `period_ms` - Base oscillation period in milliseconds
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = enableTimeCrystal)]
    pub fn enable_time_crystal(&mut self, oscillators: usize, period_ms: u32) -> bool {
        let mut crystal = ruvector_exotic_wasm::TimeCrystal::new(oscillators, period_ms);
        crystal.crystallize();
        self.time_crystal = Some(crystal);
        true
    }

    /// Get the current time crystal synchronization level (0.0 - 1.0)
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = getTimeCrystalSync)]
    pub fn get_time_crystal_sync(&self) -> f32 {
        self.time_crystal
            .as_ref()
            .map(|c| c.order_parameter())
            .unwrap_or(0.0)
    }

    /// Tick the time crystal and get coordination pattern
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = tickTimeCrystal)]
    pub fn tick_time_crystal(&mut self) -> JsValue {
        if let Some(ref mut crystal) = self.time_crystal {
            let pattern = crystal.tick();
            serde_wasm_bindgen::to_value(&pattern).unwrap_or(JsValue::NULL)
        } else {
            JsValue::NULL
        }
    }

    /// Check if time crystal is crystallized (stable coordination)
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = isTimeCrystalStable)]
    pub fn is_time_crystal_stable(&self) -> bool {
        self.time_crystal
            .as_ref()
            .map(|c| c.is_crystallized())
            .unwrap_or(false)
    }

    // ========================================================================
    // NAO Methods (Decentralized Governance)
    // ========================================================================

    /// Enable Neural Autonomous Organization for decentralized governance
    ///
    /// NAO provides stake-weighted quadratic voting with oscillatory
    /// synchronization for coherent collective decision-making.
    ///
    /// # Arguments
    /// * `quorum` - Required quorum for proposals (0.0 - 1.0)
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = enableNAO)]
    pub fn enable_nao(&mut self, quorum: f32) -> bool {
        let mut nao = ruvector_exotic_wasm::NeuralAutonomousOrg::new(quorum.clamp(0.0, 1.0));
        // Register this node as a member
        nao.add_member(&self.node_id, 100);
        self.nao = Some(nao);
        true
    }

    /// Add a member to the NAO
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = addNAOMember)]
    pub fn add_nao_member(&mut self, member_id: &str, stake: u64) -> bool {
        if let Some(ref mut nao) = self.nao {
            nao.add_member(member_id, stake);
            true
        } else {
            false
        }
    }

    /// Propose an action in the NAO
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = proposeNAO)]
    pub fn propose_nao(&mut self, action: &str) -> String {
        if let Some(ref mut nao) = self.nao {
            nao.propose(action)
        } else {
            String::new()
        }
    }

    /// Vote on a NAO proposal
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = voteNAO)]
    pub fn vote_nao(&mut self, proposal_id: &str, weight: f32) -> bool {
        if let Some(ref mut nao) = self.nao {
            nao.vote(proposal_id, &self.node_id, weight.clamp(0.0, 1.0))
        } else {
            false
        }
    }

    /// Execute a NAO proposal if quorum reached
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = executeNAO)]
    pub fn execute_nao(&mut self, proposal_id: &str) -> bool {
        if let Some(ref mut nao) = self.nao {
            nao.execute(proposal_id)
        } else {
            false
        }
    }

    /// Get NAO synchronization level
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = getNAOSync)]
    pub fn get_nao_sync(&self) -> f32 {
        self.nao
            .as_ref()
            .map(|n| n.synchronization())
            .unwrap_or(0.0)
    }

    /// Tick the NAO dynamics
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = tickNAO)]
    pub fn tick_nao(&mut self, dt: f32) {
        if let Some(ref mut nao) = self.nao {
            nao.tick(dt);
        }
    }

    // ========================================================================
    // MicroLoRA Methods (Self-Learning)
    // ========================================================================

    /// Enable MicroLoRA for per-node self-learning
    ///
    /// MicroLoRA provides rank-2 LoRA adaptation with <100us latency
    /// for real-time per-operator learning.
    ///
    /// # Arguments
    /// * `dim` - Embedding dimension for the LoRA adapter
    /// * `rank` - Rank of the adaptation (typically 2-4)
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = enableMicroLoRA)]
    pub fn enable_micro_lora(&mut self, dim: usize, rank: usize) -> bool {
        let config = ruvector_learning_wasm::LoRAConfig {
            dim,
            rank: rank.max(2),
            alpha: 0.1,
            learning_rate: 0.01,
            dropout: 0.0,
        };
        self.micro_lora = Some(ruvector_learning_wasm::MicroLoRAEngine::new(config));
        true
    }

    /// Adapt the MicroLoRA weights with a gradient
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = adaptMicroLoRA)]
    pub fn adapt_micro_lora(&mut self, _operator_type: &str, gradient: &[f32]) -> bool {
        if let Some(ref mut lora) = self.micro_lora {
            lora.adapt(gradient);
            true
        } else {
            false
        }
    }

    /// Apply MicroLoRA to get adapted output
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = applyMicroLoRA)]
    pub fn apply_micro_lora(&mut self, _operator_type: &str, input: &[f32]) -> Vec<f32> {
        if let Some(ref mut lora) = self.micro_lora {
            lora.forward(input)
        } else {
            input.to_vec()
        }
    }

    // ========================================================================
    // HDC Methods (Hyperdimensional Computing)
    // ========================================================================

    /// Enable HDC memory for distributed reasoning
    ///
    /// HDC uses 10,000-bit binary hypervectors for efficient semantic
    /// operations with <50ns bind time.
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = enableHDC)]
    pub fn enable_hdc(&mut self) -> bool {
        self.hdc_memory = Some(ruvector_nervous_system_wasm::HdcMemory::new());
        true
    }

    /// Store a pattern in HDC memory
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = storeHDC)]
    pub fn store_hdc(&mut self, key: &str) -> bool {
        if let Some(ref mut memory) = self.hdc_memory {
            let hv = ruvector_nervous_system_wasm::Hypervector::random();
            memory.store(key, hv);
            true
        } else {
            false
        }
    }

    /// Retrieve from HDC memory with similarity threshold
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = retrieveHDC)]
    pub fn retrieve_hdc(&self, _key: &str, threshold: f32) -> JsValue {
        if let Some(ref memory) = self.hdc_memory {
            let query = ruvector_nervous_system_wasm::Hypervector::random();
            // retrieve already returns JsValue
            memory.retrieve(&query, threshold)
        } else {
            JsValue::NULL
        }
    }

    // ========================================================================
    // WTA Methods (Winner-Take-All)
    // ========================================================================

    /// Enable WTA layer for instant decisions
    ///
    /// WTA provides <1us decision time with lateral inhibition.
    ///
    /// # Arguments
    /// * `num_neurons` - Number of competing neurons
    /// * `inhibition` - Lateral inhibition strength (0.0 - 1.0)
    /// * `threshold` - Activation threshold
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = enableWTA)]
    pub fn enable_wta(&mut self, num_neurons: usize, inhibition: f32, threshold: f32) -> bool {
        match ruvector_nervous_system_wasm::WTALayer::new(num_neurons, threshold, inhibition) {
            Ok(layer) => {
                self.wta_layer = Some(layer);
                true
            }
            Err(_) => false,
        }
    }

    /// Compete to find the winner
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = competeWTA)]
    pub fn compete_wta(&mut self, activations: &[f32]) -> i32 {
        if let Some(ref mut wta) = self.wta_layer {
            wta.compete(activations).unwrap_or(-1)
        } else {
            -1
        }
    }

    // ========================================================================
    // Global Workspace Methods (Attention)
    // ========================================================================

    /// Enable Global Workspace for attention bottleneck
    ///
    /// Based on Global Workspace Theory with 4-7 item capacity
    /// (Miller's Law: 7 +/- 2).
    ///
    /// # Arguments
    /// * `capacity` - Workspace capacity (typically 4-7)
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = enableGlobalWorkspace)]
    pub fn enable_global_workspace(&mut self, capacity: usize) -> bool {
        self.workspace = Some(ruvector_nervous_system_wasm::GlobalWorkspace::new(
            capacity.clamp(4, 9),
        ));
        true
    }

    /// Broadcast item to the global workspace
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = broadcastToWorkspace)]
    pub fn broadcast_to_workspace(
        &mut self,
        content: &[f32],
        salience: f32,
        source_module: u16,
    ) -> bool {
        if let Some(ref mut workspace) = self.workspace {
            let item = ruvector_nervous_system_wasm::WorkspaceItem::new(
                content,
                salience,
                source_module,
                js_sys::Date::now() as u64,
            );
            workspace.broadcast(item)
        } else {
            false
        }
    }

    /// Get current workspace contents
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = getWorkspaceContents)]
    pub fn get_workspace_contents(&self) -> JsValue {
        if let Some(ref workspace) = self.workspace {
            // retrieve() returns JsValue already
            workspace.retrieve()
        } else {
            JsValue::NULL
        }
    }

    // ========================================================================
    // BTSP Methods (One-Shot Learning)
    // ========================================================================

    /// Enable BTSP layer for one-shot learning
    ///
    /// BTSP (Behavioral Timescale Synaptic Plasticity) enables immediate
    /// pattern association without iterative training.
    ///
    /// # Arguments
    /// * `input_dim` - Input dimension
    /// * `time_constant` - Synaptic time constant (ms)
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = enableBTSP)]
    pub fn enable_btsp(&mut self, input_dim: usize, time_constant: f32) -> bool {
        self.btsp_layer = Some(ruvector_nervous_system_wasm::BTSPLayer::new(
            input_dim,
            time_constant,
        ));
        true
    }

    /// One-shot associate a pattern
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = oneShotAssociate)]
    pub fn one_shot_associate(&mut self, pattern: &[f32], target: f32) -> bool {
        if let Some(ref mut btsp) = self.btsp_layer {
            btsp.one_shot_associate(pattern, target).is_ok()
        } else {
            false
        }
    }

    /// Forward pass through BTSP layer (returns scalar output)
    #[cfg(feature = "learning-enhanced")]
    #[wasm_bindgen(js_name = forwardBTSP)]
    pub fn forward_btsp(&self, input: &[f32]) -> f32 {
        if let Some(ref btsp) = self.btsp_layer {
            btsp.forward(input).unwrap_or(0.0)
        } else {
            0.0
        }
    }

    // ========================================================================
    // Morphogenetic Methods (Network Growth)
    // ========================================================================

    /// Enable Morphogenetic Network for emergent topology
    ///
    /// Uses cellular differentiation through morphogen gradients
    /// for self-organizing network growth.
    ///
    /// # Arguments
    /// * `width` - Grid width
    /// * `height` - Grid height
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = enableMorphogenetic)]
    pub fn enable_morphogenetic(&mut self, width: i32, height: i32) -> bool {
        let mut network = ruvector_exotic_wasm::MorphogeneticNetwork::new(width, height);
        // Seed initial cell at center
        network.seed_cell(width / 2, height / 2, ruvector_exotic_wasm::CellType::Stem);
        self.morphogenetic = Some(network);
        true
    }

    /// Grow the morphogenetic network
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = growMorphogenetic)]
    pub fn grow_morphogenetic(&mut self, rate: f32) {
        if let Some(ref mut network) = self.morphogenetic {
            network.grow(rate);
        }
    }

    /// Differentiate cells in the network
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = differentiateMorphogenetic)]
    pub fn differentiate_morphogenetic(&mut self) {
        if let Some(ref mut network) = self.morphogenetic {
            network.differentiate();
        }
    }

    /// Prune weak connections
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = pruneMorphogenetic)]
    pub fn prune_morphogenetic(&mut self, threshold: f32) {
        if let Some(ref mut network) = self.morphogenetic {
            network.prune(threshold);
        }
    }

    /// Get morphogenetic network cell count
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = getMorphogeneticCellCount)]
    pub fn get_morphogenetic_cell_count(&self) -> usize {
        self.morphogenetic
            .as_ref()
            .map(|n| n.cell_count())
            .unwrap_or(0)
    }

    /// Get morphogenetic network statistics
    #[cfg(feature = "exotic")]
    #[wasm_bindgen(js_name = getMorphogeneticStats)]
    pub fn get_morphogenetic_stats(&self) -> JsValue {
        if let Some(ref network) = self.morphogenetic {
            let stats = network.stats();
            serde_wasm_bindgen::to_value(&stats).unwrap_or(JsValue::NULL)
        } else {
            JsValue::NULL
        }
    }

    // ========================================================================
    // Utility Methods
    // ========================================================================

    /// Get a summary of all enabled capabilities
    #[wasm_bindgen(js_name = getSummary)]
    pub fn get_summary(&self) -> JsValue {
        let mut summary = serde_json::json!({
            "node_id": self.node_id,
            "capabilities": {}
        });

        #[cfg(feature = "exotic")]
        {
            summary["capabilities"]["time_crystal"] = serde_json::json!({
                "enabled": self.time_crystal.is_some(),
                "sync_level": self.time_crystal.as_ref().map(|c| c.order_parameter()).unwrap_or(0.0),
            });
            summary["capabilities"]["nao"] = serde_json::json!({
                "enabled": self.nao.is_some(),
                "member_count": self.nao.as_ref().map(|n| n.member_count()).unwrap_or(0),
            });
            summary["capabilities"]["morphogenetic"] = serde_json::json!({
                "enabled": self.morphogenetic.is_some(),
                "cell_count": self.morphogenetic.as_ref().map(|n| n.cell_count()).unwrap_or(0),
            });
        }

        #[cfg(feature = "learning-enhanced")]
        {
            summary["capabilities"]["micro_lora"] = serde_json::json!({
                "enabled": self.micro_lora.is_some(),
            });
            summary["capabilities"]["hdc"] = serde_json::json!({
                "enabled": self.hdc_memory.is_some(),
            });
            summary["capabilities"]["wta"] = serde_json::json!({
                "enabled": self.wta_layer.is_some(),
            });
            summary["capabilities"]["global_workspace"] = serde_json::json!({
                "enabled": self.workspace.is_some(),
            });
            summary["capabilities"]["btsp"] = serde_json::json!({
                "enabled": self.btsp_layer.is_some(),
            });
        }

        serde_wasm_bindgen::to_value(&summary).unwrap_or(JsValue::NULL)
    }

    /// Step all enabled capabilities forward (for main loop integration)
    #[wasm_bindgen]
    pub fn step(&mut self, dt: f32) {
        #[cfg(feature = "exotic")]
        {
            if let Some(ref mut crystal) = self.time_crystal {
                crystal.tick();
            }
            if let Some(ref mut nao) = self.nao {
                nao.tick(dt);
            }
            if let Some(ref mut network) = self.morphogenetic {
                network.grow(0.01);
            }
        }
    }
}

/// Stub implementations when features are not enabled
#[cfg(not(feature = "exotic"))]
#[wasm_bindgen]
impl WasmCapabilities {
    #[wasm_bindgen(js_name = enableTimeCrystal)]
    pub fn enable_time_crystal(&mut self, _oscillators: usize, _period_ms: u32) -> bool {
        false
    }

    #[wasm_bindgen(js_name = getTimeCrystalSync)]
    pub fn get_time_crystal_sync(&self) -> f32 {
        0.0
    }

    #[wasm_bindgen(js_name = tickTimeCrystal)]
    pub fn tick_time_crystal(&mut self) -> JsValue {
        JsValue::NULL
    }

    #[wasm_bindgen(js_name = isTimeCrystalStable)]
    pub fn is_time_crystal_stable(&self) -> bool {
        false
    }

    #[wasm_bindgen(js_name = enableNAO)]
    pub fn enable_nao(&mut self, _quorum: f32) -> bool {
        false
    }

    #[wasm_bindgen(js_name = addNAOMember)]
    pub fn add_nao_member(&mut self, _member_id: &str, _stake: u64) -> bool {
        false
    }

    #[wasm_bindgen(js_name = proposeNAO)]
    pub fn propose_nao(&mut self, _action: &str) -> String {
        String::new()
    }

    #[wasm_bindgen(js_name = voteNAO)]
    pub fn vote_nao(&mut self, _proposal_id: &str, _weight: f32) -> bool {
        false
    }

    #[wasm_bindgen(js_name = executeNAO)]
    pub fn execute_nao(&mut self, _proposal_id: &str) -> bool {
        false
    }

    #[wasm_bindgen(js_name = getNAOSync)]
    pub fn get_nao_sync(&self) -> f32 {
        0.0
    }

    #[wasm_bindgen(js_name = tickNAO)]
    pub fn tick_nao(&mut self, _dt: f32) {}

    #[wasm_bindgen(js_name = enableMorphogenetic)]
    pub fn enable_morphogenetic(&mut self, _width: i32, _height: i32) -> bool {
        false
    }

    #[wasm_bindgen(js_name = growMorphogenetic)]
    pub fn grow_morphogenetic(&mut self, _rate: f32) {}

    #[wasm_bindgen(js_name = differentiateMorphogenetic)]
    pub fn differentiate_morphogenetic(&mut self) {}

    #[wasm_bindgen(js_name = pruneMorphogenetic)]
    pub fn prune_morphogenetic(&mut self, _threshold: f32) {}

    #[wasm_bindgen(js_name = getMorphogeneticCellCount)]
    pub fn get_morphogenetic_cell_count(&self) -> usize {
        0
    }

    #[wasm_bindgen(js_name = getMorphogeneticStats)]
    pub fn get_morphogenetic_stats(&self) -> JsValue {
        JsValue::NULL
    }
}

#[cfg(not(feature = "learning-enhanced"))]
#[wasm_bindgen]
impl WasmCapabilities {
    #[wasm_bindgen(js_name = enableMicroLoRA)]
    pub fn enable_micro_lora(&mut self, _dim: usize, _rank: usize) -> bool {
        false
    }

    #[wasm_bindgen(js_name = adaptMicroLoRA)]
    pub fn adapt_micro_lora(&mut self, _operator_type: &str, _gradient: &[f32]) -> bool {
        false
    }

    #[wasm_bindgen(js_name = applyMicroLoRA)]
    pub fn apply_micro_lora(&mut self, _operator_type: &str, input: &[f32]) -> Vec<f32> {
        input.to_vec()
    }

    #[wasm_bindgen(js_name = enableHDC)]
    pub fn enable_hdc(&mut self) -> bool {
        false
    }

    #[wasm_bindgen(js_name = storeHDC)]
    pub fn store_hdc(&mut self, _key: &str) -> bool {
        false
    }

    #[wasm_bindgen(js_name = retrieveHDC)]
    pub fn retrieve_hdc(&self, _key: &str, _threshold: f32) -> JsValue {
        JsValue::NULL
    }

    #[wasm_bindgen(js_name = enableWTA)]
    pub fn enable_wta(&mut self, _num_neurons: usize, _inhibition: f32, _threshold: f32) -> bool {
        false
    }

    #[wasm_bindgen(js_name = competeWTA)]
    pub fn compete_wta(&mut self, _activations: &[f32]) -> i32 {
        -1
    }

    #[wasm_bindgen(js_name = enableGlobalWorkspace)]
    pub fn enable_global_workspace(&mut self, _capacity: usize) -> bool {
        false
    }

    #[wasm_bindgen(js_name = broadcastToWorkspace)]
    pub fn broadcast_to_workspace(
        &mut self,
        _content: &[f32],
        _salience: f32,
        _source_module: u16,
    ) -> bool {
        false
    }

    #[wasm_bindgen(js_name = getWorkspaceContents)]
    pub fn get_workspace_contents(&self) -> JsValue {
        JsValue::NULL
    }

    #[wasm_bindgen(js_name = enableBTSP)]
    pub fn enable_btsp(&mut self, _input_dim: usize, _time_constant: f32) -> bool {
        false
    }

    #[wasm_bindgen(js_name = oneShotAssociate)]
    pub fn one_shot_associate(&mut self, _pattern: &[f32], _target: f32) -> bool {
        false
    }

    #[wasm_bindgen(js_name = forwardBTSP)]
    pub fn forward_btsp(&self, _input: &[f32]) -> f32 {
        0.0
    }
}

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

    #[test]
    fn test_capabilities_creation() {
        let caps = WasmCapabilities::new("test-node");
        assert_eq!(caps.node_id, "test-node");
    }
}