/**
 * Embedding functions for converting data to vectors using Transformers.js
 * Complete rewrite to eliminate TensorFlow.js and use ONNX-based models
 */
import { isBrowser } from './environment.js';
// @ts-ignore - Transformers.js is now the primary embedding library
import { pipeline, env } from '@huggingface/transformers';
/**
 * Detect the best available GPU device for the current environment
 */
export async function detectBestDevice() {
    // Browser environment - check for WebGPU support
    if (isBrowser()) {
        if (typeof navigator !== 'undefined' && 'gpu' in navigator) {
            try {
                const adapter = await navigator.gpu?.requestAdapter();
                if (adapter) {
                    return 'webgpu';
                }
            }
            catch (error) {
                // WebGPU not available or failed to initialize
            }
        }
        return 'cpu';
    }
    // Node.js environment - check for CUDA support
    try {
        // Check if ONNX Runtime GPU packages are available
        // This is a simple heuristic - in production you might want more sophisticated detection
        const hasGpu = process.env.CUDA_VISIBLE_DEVICES !== undefined ||
            process.env.ONNXRUNTIME_GPU_ENABLED === 'true';
        return hasGpu ? 'cuda' : 'cpu';
    }
    catch (error) {
        return 'cpu';
    }
}
/**
 * Resolve device string to actual device configuration
 */
export async function resolveDevice(device = 'auto') {
    if (device === 'auto') {
        return await detectBestDevice();
    }
    // Map 'gpu' to appropriate GPU type for current environment
    if (device === 'gpu') {
        const detected = await detectBestDevice();
        return detected === 'cpu' ? 'cpu' : detected;
    }
    return device;
}
export class TransformerEmbedding {
    /**
     * Create a new TransformerEmbedding instance
     */
    constructor(options = {}) {
        this.extractor = null;
        this.initialized = false;
        this.verbose = true;
        this.verbose = options.verbose !== undefined ? options.verbose : true;
        // PRODUCTION-READY MODEL CONFIGURATION
        // Priority order: explicit option > environment variable > smart default
        let localFilesOnly;
        if (options.localFilesOnly !== undefined) {
            // 1. Explicit option takes highest priority
            localFilesOnly = options.localFilesOnly;
        }
        else if (process.env.BRAINY_ALLOW_REMOTE_MODELS !== undefined) {
            // 2. Environment variable override
            localFilesOnly = process.env.BRAINY_ALLOW_REMOTE_MODELS !== 'true';
        }
        else if (process.env.NODE_ENV === 'development') {
            // 3. Development mode allows remote models
            localFilesOnly = false;
        }
        else if (isBrowser()) {
            // 4. Browser defaults to allowing remote models
            localFilesOnly = false;
        }
        else {
            // 5. Node.js production: try local first, but allow remote as fallback
            // This is the NEW production-friendly default
            localFilesOnly = false;
        }
        this.options = {
            model: options.model || 'Xenova/all-MiniLM-L6-v2',
            verbose: this.verbose,
            cacheDir: options.cacheDir || './models',
            localFilesOnly: localFilesOnly,
            dtype: options.dtype || 'fp32',
            device: options.device || 'auto'
        };
        if (this.verbose) {
            this.logger('log', `Embedding config: localFilesOnly=${localFilesOnly}, model=${this.options.model}, cacheDir=${this.options.cacheDir}`);
        }
        // Configure transformers.js environment
        if (!isBrowser()) {
            // Set cache directory for Node.js
            env.cacheDir = this.options.cacheDir;
            // Prioritize local models for offline operation
            env.allowRemoteModels = !this.options.localFilesOnly;
            env.allowLocalModels = true;
        }
        else {
            // Browser configuration
            // Allow both local and remote models, but prefer local if available
            env.allowLocalModels = true;
            env.allowRemoteModels = true;
            // Force the configuration to ensure it's applied
            if (this.verbose) {
                this.logger('log', `Browser env config - allowLocalModels: ${env.allowLocalModels}, allowRemoteModels: ${env.allowRemoteModels}, localFilesOnly: ${this.options.localFilesOnly}`);
            }
        }
    }
    /**
     * Get the default cache directory for models
     */
    async getDefaultCacheDir() {
        if (isBrowser()) {
            return './models'; // Browser default
        }
        // Check for bundled models in the package
        const possiblePaths = [
            // In the installed package
            './node_modules/@soulcraft/brainy/models',
            // In development/source
            './models',
            './dist/../models',
            // Alternative locations
            '../models',
            '../../models'
        ];
        // Check if we're in Node.js and try to find the bundled models
        if (typeof process !== 'undefined' && process.versions?.node) {
            try {
                // Use dynamic import instead of require for ES modules compatibility
                const { createRequire } = await import('module');
                const require = createRequire(import.meta.url);
                const path = require('path');
                const fs = require('fs');
                // Try to resolve the package location
                try {
                    const brainyPackagePath = require.resolve('@soulcraft/brainy/package.json');
                    const brainyPackageDir = path.dirname(brainyPackagePath);
                    const bundledModelsPath = path.join(brainyPackageDir, 'models');
                    if (fs.existsSync(bundledModelsPath)) {
                        this.logger('log', `Using bundled models from package: ${bundledModelsPath}`);
                        return bundledModelsPath;
                    }
                }
                catch (e) {
                    // Not installed as package, continue
                }
                // Try relative paths from current location
                for (const relativePath of possiblePaths) {
                    const fullPath = path.resolve(relativePath);
                    if (fs.existsSync(fullPath)) {
                        this.logger('log', `Using bundled models from: ${fullPath}`);
                        return fullPath;
                    }
                }
            }
            catch (error) {
                // Silently fall back to default path if module detection fails
            }
        }
        // Fallback to default cache directory
        return './models';
    }
    /**
     * Check if we're running in a test environment
     */
    isTestEnvironment() {
        // Always use real implementation - no more mocking
        return false;
    }
    /**
     * Log message only if verbose mode is enabled
     */
    logger(level, message, ...args) {
        if (level === 'error' || this.verbose) {
            console[level](`[TransformerEmbedding] ${message}`, ...args);
        }
    }
    /**
     * Initialize the embedding model
     */
    async init() {
        if (this.initialized) {
            return;
        }
        // Always use real implementation - no mocking
        try {
            // Resolve device configuration and cache directory
            const device = await resolveDevice(this.options.device);
            const cacheDir = this.options.cacheDir === './models'
                ? await this.getDefaultCacheDir()
                : this.options.cacheDir;
            this.logger('log', `Loading Transformer model: ${this.options.model} on device: ${device}`);
            const startTime = Date.now();
            // Load the feature extraction pipeline with GPU support
            const pipelineOptions = {
                cache_dir: cacheDir,
                local_files_only: isBrowser() ? false : this.options.localFilesOnly,
                dtype: this.options.dtype
            };
            // Add device configuration for GPU acceleration
            if (device !== 'cpu') {
                pipelineOptions.device = device;
                this.logger('log', `🚀 GPU acceleration enabled: ${device}`);
            }
            if (this.verbose) {
                this.logger('log', `Pipeline options: ${JSON.stringify(pipelineOptions)}`);
            }
            try {
                this.extractor = await pipeline('feature-extraction', this.options.model, pipelineOptions);
            }
            catch (gpuError) {
                // Fallback to CPU if GPU initialization fails
                if (device !== 'cpu') {
                    this.logger('warn', `GPU initialization failed, falling back to CPU: ${gpuError?.message || gpuError}`);
                    const cpuOptions = { ...pipelineOptions };
                    delete cpuOptions.device;
                    this.extractor = await pipeline('feature-extraction', this.options.model, cpuOptions);
                }
                else {
                    // PRODUCTION-READY ERROR HANDLING
                    // If local_files_only is true and models are missing, try enabling remote downloads
                    if (pipelineOptions.local_files_only && gpuError?.message?.includes('local_files_only')) {
                        this.logger('warn', 'Local models not found, attempting remote download as fallback...');
                        try {
                            const remoteOptions = { ...pipelineOptions, local_files_only: false };
                            this.extractor = await pipeline('feature-extraction', this.options.model, remoteOptions);
                            this.logger('log', '✅ Successfully downloaded and loaded model from remote');
                            // Update the configuration to reflect what actually worked
                            this.options.localFilesOnly = false;
                        }
                        catch (remoteError) {
                            // Both local and remote failed - throw comprehensive error
                            const errorMsg = `Failed to load embedding model "${this.options.model}". ` +
                                `Local models not found and remote download failed. ` +
                                `To fix: 1) Set BRAINY_ALLOW_REMOTE_MODELS=true, ` +
                                `2) Run "npm run download-models", or ` +
                                `3) Use a custom embedding function.`;
                            throw new Error(errorMsg);
                        }
                    }
                    else {
                        throw gpuError;
                    }
                }
            }
            const loadTime = Date.now() - startTime;
            this.logger('log', `✅ Model loaded successfully in ${loadTime}ms`);
            this.initialized = true;
        }
        catch (error) {
            this.logger('error', 'Failed to initialize Transformer embedding model:', error);
            throw new Error(`Transformer embedding initialization failed: ${error}`);
        }
    }
    /**
     * Generate embeddings for text data
     */
    async embed(data) {
        if (!this.initialized) {
            await this.init();
        }
        try {
            // Handle different input types
            let textToEmbed;
            if (typeof data === 'string') {
                // Handle empty string case
                if (data.trim() === '') {
                    // Return a zero vector of 384 dimensions (all-MiniLM-L6-v2 standard)
                    return new Array(384).fill(0);
                }
                textToEmbed = [data];
            }
            else if (Array.isArray(data) && data.every((item) => typeof item === 'string')) {
                // Handle empty array or array with empty strings
                if (data.length === 0 || data.every((item) => item.trim() === '')) {
                    return new Array(384).fill(0);
                }
                // Filter out empty strings
                textToEmbed = data.filter((item) => item.trim() !== '');
                if (textToEmbed.length === 0) {
                    return new Array(384).fill(0);
                }
            }
            else {
                throw new Error('TransformerEmbedding only supports string or string[] data');
            }
            // Ensure the extractor is available
            if (!this.extractor) {
                throw new Error('Transformer embedding model is not available');
            }
            // Generate embeddings with mean pooling and normalization
            const result = await this.extractor(textToEmbed, {
                pooling: 'mean',
                normalize: true
            });
            // Extract the embedding data
            let embedding;
            if (textToEmbed.length === 1) {
                // Single text input - return first embedding
                embedding = Array.from(result.data.slice(0, 384));
            }
            else {
                // Multiple texts - return first embedding (maintain compatibility)
                embedding = Array.from(result.data.slice(0, 384));
            }
            // Validate embedding dimensions
            if (embedding.length !== 384) {
                this.logger('warn', `Unexpected embedding dimension: ${embedding.length}, expected 384`);
                // Pad or truncate to 384 dimensions
                if (embedding.length < 384) {
                    embedding = [...embedding, ...new Array(384 - embedding.length).fill(0)];
                }
                else {
                    embedding = embedding.slice(0, 384);
                }
            }
            return embedding;
        }
        catch (error) {
            this.logger('error', 'Error generating embeddings:', error);
            throw new Error(`Failed to generate embeddings: ${error}`);
        }
    }
    /**
     * Dispose of the model and free resources
     */
    async dispose() {
        if (this.extractor && typeof this.extractor.dispose === 'function') {
            await this.extractor.dispose();
        }
        this.extractor = null;
        this.initialized = false;
    }
    /**
     * Get the dimension of embeddings produced by this model
     */
    getDimension() {
        return 384;
    }
    /**
     * Check if the model is initialized
     */
    isInitialized() {
        return this.initialized;
    }
}
// Legacy alias for backward compatibility
export const UniversalSentenceEncoder = TransformerEmbedding;
/**
 * Create a new embedding model instance
 */
export function createEmbeddingModel(options) {
    return new TransformerEmbedding(options);
}
/**
 * Default embedding function using the lightweight transformer model
 */
export const defaultEmbeddingFunction = async (data) => {
    const embedder = new TransformerEmbedding({ verbose: false });
    return await embedder.embed(data);
};
/**
 * Create an embedding function with custom options
 */
export function createEmbeddingFunction(options = {}) {
    const embedder = new TransformerEmbedding(options);
    return async (data) => {
        return await embedder.embed(data);
    };
}
/**
 * Batch embedding function for processing multiple texts efficiently
 */
export async function batchEmbed(texts, options = {}) {
    const embedder = new TransformerEmbedding(options);
    await embedder.init();
    const embeddings = [];
    // Process in batches for memory efficiency
    const batchSize = 32;
    for (let i = 0; i < texts.length; i += batchSize) {
        const batch = texts.slice(i, i + batchSize);
        for (const text of batch) {
            const embedding = await embedder.embed(text);
            embeddings.push(embedding);
        }
    }
    await embedder.dispose();
    return embeddings;
}
/**
 * Embedding functions for specific model types
 */
export const embeddingFunctions = {
    /** Default lightweight model (all-MiniLM-L6-v2, 384 dimensions) */
    default: defaultEmbeddingFunction,
    /** Create custom embedding function */
    create: createEmbeddingFunction,
    /** Batch processing */
    batch: batchEmbed
};
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