/**
 * RLHF (Reinforcement Learning from Human Feedback) Alignment System
 * Aligns AI behavior with human preferences through feedback
 */

export interface HumanFeedback {
    id: string;
    taskId: string;
    agentId: string;
    rating: number; // 1-5
    feedback: string;
    timestamp: Date;
    category: 'helpful' | 'harmless' | 'honest';
}

export interface PreferenceComparison {
    id: string;
    responseA: string;
    responseB: string;
    preferred: 'A' | 'B' | 'equal';
    reason?: string;
    timestamp: Date;
}

export interface RewardModel {
    weights: Map<string, number>;
    bias: number;
    accuracy: number;
}

export class RLHFAlignmentSystem {
    private feedbackLog: HumanFeedback[] = [];
    private preferences: PreferenceComparison[] = [];
    private rewardModel: RewardModel = {
        weights: new Map(),
        bias: 0,
        accuracy: 0,
    };

    /**
     * Collect human feedback
     */
    collectFeedback(feedback: Omit<HumanFeedback, 'id' | 'timestamp'>): string {
        const fullFeedback: HumanFeedback = {
            ...feedback,
            id: `feedback_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
            timestamp: new Date(),
        };

        this.feedbackLog.push(fullFeedback);

        // Keep only last 1000 feedbacks
        if (this.feedbackLog.length > 1000) {
            this.feedbackLog.shift();
        }

        return fullFeedback.id;
    }

    /**
     * Collect preference comparison
     */
    collectPreference(comparison: Omit<PreferenceComparison, 'id' | 'timestamp'>): string {
        const fullComparison: PreferenceComparison = {
            ...comparison,
            id: `pref_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
            timestamp: new Date(),
        };

        this.preferences.push(fullComparison);

        // Retrain reward model periodically
        if (this.preferences.length % 10 === 0) {
            this.trainRewardModel();
        }

        return fullComparison.id;
    }

    /**
     * Train reward model from preferences
     */
    private trainRewardModel(): void {
        if (this.preferences.length < 10) return;

        // Simple reward model training
        // In production, this would use proper ML techniques

        const features = new Map<string, number>();

        this.preferences.forEach(pref => {
            const preferred = pref.preferred === 'A' ? pref.responseA : pref.responseB;
            const notPreferred = pref.preferred === 'A' ? pref.responseB : pref.responseA;

            // Extract simple features (length, politeness markers, etc.)
            const prefLength = preferred.length;
            const notPrefLength = notPreferred.length;

            features.set('length_preference', (features.get('length_preference') || 0) +
                (prefLength > notPrefLength ? 1 : -1));

            // Check for politeness markers
            const politeWords = ['please', 'thank', 'appreciate', 'kindly'];
            const prefPolite = politeWords.some(word => preferred.toLowerCase().includes(word));
            const notPrefPolite = politeWords.some(word => notPreferred.toLowerCase().includes(word));

            if (prefPolite && !notPrefPolite) {
                features.set('politeness', (features.get('politeness') || 0) + 1);
            }
        });

        // Update reward model weights
        features.forEach((value, feature) => {
            this.rewardModel.weights.set(feature, value / this.preferences.length);
        });

        console.log(`🎯 Reward model updated with ${this.preferences.length} preferences`);
    }

    /**
     * Predict reward for a response
     */
    predictReward(response: string): number {
        let reward = this.rewardModel.bias;

        // Apply learned weights
        const length = response.length;
        reward += (this.rewardModel.weights.get('length_preference') || 0) * length / 1000;

        const politeWords = ['please', 'thank', 'appreciate', 'kindly'];
        const isPolite = politeWords.some(word => response.toLowerCase().includes(word));
        if (isPolite) {
            reward += this.rewardModel.weights.get('politeness') || 0;
        }

        return reward;
    }

    /**
     * Optimize response based on learned preferences
     */
    optimizeResponse(candidates: string[]): string {
        if (candidates.length === 0) return '';
        if (candidates.length === 1) return candidates[0];

        // Score each candidate
        const scored = candidates.map(candidate => ({
            response: candidate,
            reward: this.predictReward(candidate),
        }));

        // Return highest scoring
        scored.sort((a, b) => b.reward - a.reward);
        return scored[0].response;
    }

    /**
     * Check alignment with safety constraints
     */
    checkSafetyConstraints(response: string): {
        safe: boolean;
        violations: string[];
    } {
        const violations: string[] = [];

        // Check for harmful content patterns
        const harmfulPatterns = [
            /\b(hack|exploit|bypass)\b/i,
            /\b(illegal|unlawful)\b/i,
            /\b(violence|harm|hurt)\b/i,
        ];

        harmfulPatterns.forEach((pattern, index) => {
            if (pattern.test(response)) {
                violations.push(`Potential harmful content detected (pattern ${index + 1})`);
            }
        });

        // Check for dishonest patterns
        if (response.includes('I am certain') && response.includes('probably')) {
            violations.push('Contradictory certainty claims');
        }

        return {
            safe: violations.length === 0,
            violations,
        };
    }

    /**
     * Get feedback statistics
     */
    getFeedbackStatistics(agentId?: string): {
        totalFeedback: number;
        avgRating: number;
        categoryBreakdown: Record<string, number>;
        recentTrend: 'improving' | 'declining' | 'stable';
    } {
        const relevant = agentId
            ? this.feedbackLog.filter(f => f.agentId === agentId)
            : this.feedbackLog;

        const avgRating = relevant.length > 0
            ? relevant.reduce((sum, f) => sum + f.rating, 0) / relevant.length
            : 0;

        const categoryBreakdown = relevant.reduce((acc, f) => {
            acc[f.category] = (acc[f.category] || 0) + 1;
            return acc;
        }, {} as Record<string, number>);

        // Analyze trend (last 20 vs previous 20)
        const recent = relevant.slice(-20);
        const previous = relevant.slice(-40, -20);

        const recentAvg = recent.length > 0
            ? recent.reduce((sum, f) => sum + f.rating, 0) / recent.length
            : 0;
        const previousAvg = previous.length > 0
            ? previous.reduce((sum, f) => sum + f.rating, 0) / previous.length
            : 0;

        let trend: 'improving' | 'declining' | 'stable' = 'stable';
        if (recentAvg > previousAvg + 0.2) trend = 'improving';
        else if (recentAvg < previousAvg - 0.2) trend = 'declining';

        return {
            totalFeedback: relevant.length,
            avgRating,
            categoryBreakdown,
            recentTrend: trend,
        };
    }

    /**
     * Apply alignment corrections
     */
    async applyAlignmentCorrections(agentId: string): Promise<string[]> {
        const stats = this.getFeedbackStatistics(agentId);
        const corrections: string[] = [];

        if (stats.avgRating < 3) {
            corrections.push('Overall performance below acceptable threshold');
        }

        if (stats.categoryBreakdown.harmless && stats.categoryBreakdown.harmless < stats.totalFeedback * 0.8) {
            corrections.push('Increase safety measures');
        }

        if (stats.categoryBreakdown.honest && stats.categoryBreakdown.honest < stats.totalFeedback * 0.8) {
            corrections.push('Improve honesty and transparency');
        }

        if (stats.recentTrend === 'declining') {
            corrections.push('Performance declining - review recent changes');
        }

        return corrections;
    }
}

export const rlhfAlignmentSystem = new RLHFAlignmentSystem();