import { T as ToxicityLabel, M as MLDetectorConfig, a as MLAnalysisResult, H as HybridAnalysisResult } from '../types-Ct_ueYqw.js';
export { b as ToxicityPrediction } from '../types-Ct_ueYqw.js';
import { F as Filter } from '../Filter-BGcyIAvO.js';
import { F as FilterConfig, C as CheckProfanityResult } from '../types-B9c_ik4k.js';
export { HybridCheckResult, HybridCheckerConfig, MLCheckResult, MLCheckerConfig, RECOMMENDED_MODELS, createHybridChecker, createMLChecker, isTransformersAvailable, preloadModel } from './transformers.js';

/**
 * ML-based toxicity detection using TensorFlow.js.
 *
 * This module provides optional ML-based profanity/toxicity detection
 * using the TensorFlow.js toxicity model trained on the civil comments dataset.
 *
 * IMPORTANT: This requires optional peer dependencies:
 * - @tensorflow/tfjs
 * - @tensorflow-models/toxicity
 *
 * Install with: npm install @tensorflow/tfjs @tensorflow-models/toxicity
 *
 * @example
 * ```typescript
 * import { ToxicityDetector } from 'glin-profanity/ml';
 *
 * const detector = new ToxicityDetector({ threshold: 0.9 });
 * await detector.loadModel();
 *
 * const result = await detector.analyze('some text to check');
 * console.log(result.isToxic);
 * ```
 */

interface ToxicityModelPrediction {
    label: string;
    results: Array<{
        match: boolean | null;
        probabilities: Float32Array | number[];
    }>;
}
interface ToxicityModel {
    classify(sentences: string[]): Promise<ToxicityModelPrediction[]>;
}
/**
 * ML-based toxicity detector using TensorFlow.js.
 *
 * This class provides neural network-based toxicity detection that can
 * identify various types of harmful content including insults, threats,
 * identity attacks, and obscenity.
 *
 * The model is loaded lazily and cached for subsequent calls.
 */
declare class ToxicityDetector {
    private model;
    private loadingPromise;
    private config;
    private isAvailable;
    /**
     * All available toxicity labels.
     */
    static readonly ALL_LABELS: ToxicityLabel[];
    /**
     * Creates a new ToxicityDetector instance.
     *
     * @param config - Configuration options
     *
     * @example
     * ```typescript
     * // Basic usage with default threshold (0.85)
     * const detector = new ToxicityDetector();
     *
     * // Custom threshold for higher precision
     * const strictDetector = new ToxicityDetector({ threshold: 0.95 });
     *
     * // Check only specific categories
     * const customDetector = new ToxicityDetector({
     *   threshold: 0.8,
     *   labels: ['insult', 'threat', 'obscene'],
     * });
     * ```
     */
    constructor(config?: MLDetectorConfig);
    /**
     * Dynamic import wrapper to avoid TypeScript static analysis issues.
     * Uses Function constructor to bypass module resolution at compile time.
     * @internal
     */
    private dynamicImport;
    /**
     * Checks if TensorFlow.js and the toxicity model are available.
     * This performs a lazy check on first call and caches the result.
     *
     * @returns True if ML dependencies are available
     */
    checkAvailability(): Promise<boolean>;
    /**
     * Loads the toxicity model.
     * This is called automatically on first analyze() call if not called explicitly.
     *
     * @returns The loaded model
     * @throws Error if TensorFlow.js dependencies are not installed
     *
     * @example
     * ```typescript
     * const detector = new ToxicityDetector();
     *
     * // Explicitly preload model (optional)
     * await detector.loadModel();
     *
     * // Or let it load automatically on first use
     * const result = await detector.analyze('text');
     * ```
     */
    loadModel(): Promise<ToxicityModel>;
    private doLoadModel;
    /**
     * Analyzes text for toxicity using the ML model.
     *
     * @param text - Text to analyze
     * @returns Analysis result with predictions and scores
     *
     * @example
     * ```typescript
     * const detector = new ToxicityDetector();
     * const result = await detector.analyze('you are stupid');
     *
     * console.log(result.isToxic);           // true
     * console.log(result.overallScore);      // 0.92
     * console.log(result.matchedCategories); // ['insult', 'toxicity']
     * ```
     */
    analyze(text: string): Promise<MLAnalysisResult>;
    /**
     * Analyzes multiple texts in a batch for better performance.
     *
     * @param texts - Array of texts to analyze
     * @returns Array of analysis results
     *
     * @example
     * ```typescript
     * const detector = new ToxicityDetector();
     * const results = await detector.analyzeBatch([
     *   'hello friend',
     *   'you are terrible',
     *   'great work!',
     * ]);
     *
     * results.forEach((result, i) => {
     *   console.log(`Text ${i}: ${result.isToxic ? 'toxic' : 'clean'}`);
     * });
     * ```
     */
    analyzeBatch(texts: string[]): Promise<MLAnalysisResult[]>;
    /**
     * Simple boolean check for toxicity.
     *
     * @param text - Text to check
     * @returns True if text is detected as toxic
     *
     * @example
     * ```typescript
     * const detector = new ToxicityDetector();
     *
     * if (await detector.isToxic('some user input')) {
     *   console.log('Content flagged as toxic');
     * }
     * ```
     */
    isToxic(text: string): Promise<boolean>;
    /**
     * Gets the toxicity score for text (0-1).
     *
     * @param text - Text to score
     * @returns Toxicity score from 0 (clean) to 1 (highly toxic)
     */
    getScore(text: string): Promise<number>;
    /**
     * Disposes of the model to free memory.
     * The model will be reloaded on next analyze() call.
     */
    dispose(): void;
    /**
     * Gets the current configuration.
     */
    getConfig(): Required<MLDetectorConfig>;
    /**
     * Checks if the model is currently loaded.
     */
    isModelLoaded(): boolean;
}

/**
 * Hybrid filter combining rule-based and ML-based detection.
 *
 * This class provides the best of both worlds:
 * - Fast rule-based detection for common profanity
 * - ML-based detection for contextual toxicity
 *
 * @example
 * ```typescript
 * import { HybridFilter } from 'glin-profanity/ml';
 *
 * const filter = new HybridFilter({
 *   // Rule-based config
 *   languages: ['english'],
 *   detectLeetspeak: true,
 *   // ML config
 *   enableML: true,
 *   mlThreshold: 0.85,
 * });
 *
 * await filter.initialize();
 *
 * const result = await filter.checkProfanityAsync('some text');
 * console.log(result.isToxic);
 * ```
 */

/**
 * Hybrid filter configuration.
 */
interface HybridFilterConfig extends FilterConfig {
    /**
     * Enable ML-based detection.
     * Requires @tensorflow/tfjs and @tensorflow-models/toxicity.
     * @default false
     */
    enableML?: boolean;
    /**
     * ML confidence threshold.
     * @default 0.85
     */
    mlThreshold?: number;
    /**
     * Specific ML toxicity categories to check.
     */
    mlLabels?: ToxicityLabel[];
    /**
     * Preload ML model on initialization.
     * @default false
     */
    preloadML?: boolean;
    /**
     * How to combine rule-based and ML results.
     * - 'or': Flag if either method detects toxicity (more sensitive)
     * - 'and': Flag only if both methods detect toxicity (more precise)
     * - 'ml-override': Use ML result if available, fallback to rules
     * - 'rules-first': Use rules for speed, ML for borderline cases
     * @default 'or'
     */
    combinationMode?: 'or' | 'and' | 'ml-override' | 'rules-first';
    /**
     * Score threshold for "borderline" cases in rules-first mode.
     * If rule-based detection is uncertain (near this threshold),
     * ML will be used for confirmation.
     * @default 0.5
     */
    borderlineThreshold?: number;
}
/**
 * Hybrid profanity filter combining rule-based and ML detection.
 */
declare class HybridFilter {
    private ruleFilter;
    private mlDetector;
    private config;
    private mlInitialized;
    /**
     * Creates a new HybridFilter instance.
     *
     * @param config - Configuration options
     */
    constructor(config?: HybridFilterConfig);
    /**
     * Initializes the hybrid filter, loading the ML model if enabled.
     * Call this before using async methods for best performance.
     *
     * @example
     * ```typescript
     * const filter = new HybridFilter({ enableML: true });
     * await filter.initialize();
     * // Now ready for fast async checks
     * ```
     */
    initialize(): Promise<void>;
    /**
     * Checks if ML is available and initialized.
     */
    isMLReady(): boolean;
    /**
     * Synchronous profanity check using only rule-based detection.
     * Use this for fast, synchronous checks when ML isn't needed.
     *
     * @param text - Text to check
     * @returns True if profanity detected
     */
    isProfane(text: string): boolean;
    /**
     * Synchronous detailed check using only rule-based detection.
     *
     * @param text - Text to check
     * @returns Detailed profanity check result
     */
    checkProfanity(text: string): CheckProfanityResult;
    /**
     * Async profanity check using both rule-based and ML detection.
     *
     * @param text - Text to check
     * @returns Combined analysis result
     *
     * @example
     * ```typescript
     * const filter = new HybridFilter({
     *   enableML: true,
     *   combinationMode: 'or',
     * });
     * await filter.initialize();
     *
     * const result = await filter.checkProfanityAsync('some text');
     * if (result.isToxic) {
     *   console.log('Reason:', result.reason);
     *   console.log('Confidence:', result.confidence);
     * }
     * ```
     */
    checkProfanityAsync(text: string): Promise<HybridAnalysisResult>;
    /**
     * Simple async boolean check for toxicity.
     *
     * @param text - Text to check
     * @returns True if toxic
     */
    isToxicAsync(text: string): Promise<boolean>;
    /**
     * Analyzes text with ML only (if available).
     *
     * @param text - Text to analyze
     * @returns ML analysis result or null if ML not available
     */
    analyzeWithML(text: string): Promise<MLAnalysisResult | null>;
    /**
     * Batch analysis for multiple texts.
     *
     * @param texts - Array of texts to analyze
     * @returns Array of hybrid analysis results
     */
    checkProfanityBatchAsync(texts: string[]): Promise<HybridAnalysisResult[]>;
    private combineResults;
    /**
     * Gets the underlying rule-based filter.
     */
    getRuleFilter(): Filter;
    /**
     * Gets the underlying ML detector (if enabled).
     */
    getMLDetector(): ToxicityDetector | null;
    /**
     * Disposes of resources (ML model).
     */
    dispose(): void;
}

export { HybridAnalysisResult, HybridFilter, type HybridFilterConfig, MLAnalysisResult, MLDetectorConfig, ToxicityDetector, ToxicityLabel };