/**
 * Precedent Model
 * Stores completed deliberation sessions as searchable precedents
 * for informing future values conflicts without dictating outcomes.
 *
 * PLURALISTIC PRINCIPLE: Precedents inform but don't mandate.
 * Similar conflicts can be resolved differently based on context.
 */

const { ObjectId } = require('mongodb');
const { getCollection } = require('../utils/db.util');

class Precedent {
  /**
   * Create precedent from completed deliberation session
   */
  static async createFromSession(sessionData) {
    const collection = await getCollection('precedents');

    const precedent = {
      precedent_id: `precedent-${Date.now()}`,
      created_at: new Date(),

      // Link to original session
      source_session_id: sessionData.session_id,
      source_session_created: sessionData.created_at,

      // Conflict description (searchable)
      conflict: {
        description: sessionData.decision.description,
        scenario: sessionData.decision.scenario,
        moral_frameworks_in_tension: sessionData.conflict_analysis.moral_frameworks_in_tension,
        value_trade_offs: sessionData.conflict_analysis.value_trade_offs,
        incommensurability_level: sessionData.conflict_analysis.incommensurability_level
      },

      // Stakeholder composition (for pattern matching)
      stakeholder_pattern: {
        total_count: sessionData.stakeholders.length,
        types: this._extractStakeholderTypes(sessionData.stakeholders),
        represents: this._extractRepresentations(sessionData.stakeholders),
        moral_frameworks: this._extractMoralFrameworks(sessionData.stakeholders)
      },

      // Deliberation process (what worked, what didn't)
      process: {
        format: sessionData.configuration.format,
        ai_role: sessionData.configuration.ai_role,
        rounds_completed: sessionData.deliberation_rounds.length,
        duration_days: Math.ceil((new Date(sessionData.outcome.finalized_at) - new Date(sessionData.created_at)) / (1000 * 60 * 60 * 24)),

        // AI facilitation quality (for learning)
        ai_facilitation_quality: {
          intervention_count: sessionData.human_interventions.length,
          escalation_count: sessionData.safety_escalations.length,
          stakeholder_satisfaction_avg: this._calculateAverageSatisfaction(sessionData.ai_quality_metrics.stakeholder_satisfaction_scores)
        }
      },

      // Outcome (the accommodation reached)
      outcome: {
        decision_made: sessionData.outcome.decision_made,
        consensus_level: sessionData.outcome.consensus_level,
        values_prioritized: sessionData.outcome.values_prioritized,
        values_deprioritized: sessionData.outcome.values_deprioritized,
        moral_remainder: sessionData.outcome.moral_remainder,
        dissenting_count: sessionData.outcome.dissenting_perspectives.length
      },

      // Key insights (extracted from deliberation)
      insights: {
        shared_values_discovered: this._extractSharedValues(sessionData.deliberation_rounds),
        accommodation_strategies: this._extractAccommodationStrategies(sessionData.deliberation_rounds),
        unexpected_coalitions: this._extractCoalitions(sessionData.deliberation_rounds),
        framework_tensions_resolved: this._extractTensionResolutions(sessionData)
      },

      // Searchable metadata
      metadata: {
        domain: this._inferDomain(sessionData.decision.scenario), // "employment", "healthcare", "content_moderation", etc.
        decision_type: this._inferDecisionType(sessionData.conflict_analysis), // "transparency", "resource_allocation", "procedural", etc.
        geographic_context: sessionData.decision.context.geographic || 'unspecified',
        temporal_context: sessionData.decision.context.temporal || 'unspecified' // "emerging_issue", "established_issue", "crisis"
      },

      // Usage tracking
      usage: {
        times_referenced: 0,
        influenced_sessions: [], // Array of session_ids where this precedent was consulted
        last_referenced: null
      },

      // Searchability flags
      searchable: true,
      tags: this._generateTags(sessionData),

      // Archive metadata
      archived: false,
      archived_reason: null
    };

    const result = await collection.insertOne(precedent);
    return { ...precedent, _id: result.insertedId };
  }

  /**
   * Search precedents by conflict pattern
   * Returns similar past deliberations (not prescriptive, just informative)
   */
  static async searchByConflict(query, options = {}) {
    const collection = await getCollection('precedents');
    const { limit = 10, skip = 0 } = options;

    const filter = { searchable: true, archived: false };

    // Match moral frameworks in tension
    if (query.moral_frameworks && query.moral_frameworks.length > 0) {
      filter['conflict.moral_frameworks_in_tension'] = { $in: query.moral_frameworks };
    }

    // Match scenario
    if (query.scenario) {
      filter['conflict.scenario'] = query.scenario;
    }

    // Match domain
    if (query.domain) {
      filter['metadata.domain'] = query.domain;
    }

    // Match decision type
    if (query.decision_type) {
      filter['metadata.decision_type'] = query.decision_type;
    }

    // Match incommensurability level
    if (query.incommensurability_level) {
      filter['conflict.incommensurability_level'] = query.incommensurability_level;
    }

    const precedents = await collection
      .find(filter)
      .sort({ 'usage.times_referenced': -1, created_at: -1 }) // Most-used first, then most recent
      .skip(skip)
      .limit(limit)
      .toArray();

    return precedents;
  }

  /**
   * Search precedents by stakeholder pattern
   * Useful for "Has deliberation with similar stakeholders been done before?"
   */
  static async searchByStakeholderPattern(pattern, options = {}) {
    const collection = await getCollection('precedents');
    const { limit = 10, skip = 0 } = options;

    const filter = { searchable: true, archived: false };

    // Match stakeholder types
    if (pattern.types && pattern.types.length > 0) {
      filter['stakeholder_pattern.types'] = { $all: pattern.types };
    }

    // Match representations (e.g., "Employers", "Job Applicants")
    if (pattern.represents && pattern.represents.length > 0) {
      filter['stakeholder_pattern.represents'] = { $in: pattern.represents };
    }

    // Match moral frameworks
    if (pattern.moral_frameworks && pattern.moral_frameworks.length > 0) {
      filter['stakeholder_pattern.moral_frameworks'] = { $in: pattern.moral_frameworks };
    }

    const precedents = await collection
      .find(filter)
      .sort({ 'usage.times_referenced': -1, created_at: -1 })
      .skip(skip)
      .limit(limit)
      .toArray();

    return precedents;
  }

  /**
   * Search precedents by tags (free-text search)
   */
  static async searchByTags(tags, options = {}) {
    const collection = await getCollection('precedents');
    const { limit = 10, skip = 0 } = options;

    const filter = {
      searchable: true,
      archived: false,
      tags: { $in: tags }
    };

    const precedents = await collection
      .find(filter)
      .sort({ 'usage.times_referenced': -1, created_at: -1 })
      .skip(skip)
      .limit(limit)
      .toArray();

    return precedents;
  }

  /**
   * Get most similar precedent (composite scoring)
   * Uses multiple dimensions to find best match
   */
  static async findMostSimilar(querySession, options = {}) {
    const { limit = 5 } = options;

    // Get candidates from multiple search strategies
    const conflictMatches = await this.searchByConflict({
      moral_frameworks: querySession.conflict_analysis.moral_frameworks_in_tension,
      scenario: querySession.decision.scenario,
      incommensurability_level: querySession.conflict_analysis.incommensurability_level
    }, { limit: 20 });

    const stakeholderMatches = await this.searchByStakeholderPattern({
      types: this._extractStakeholderTypes(querySession.stakeholders),
      represents: this._extractRepresentations(querySession.stakeholders),
      moral_frameworks: this._extractMoralFrameworks(querySession.stakeholders)
    }, { limit: 20 });

    // Combine and score
    const candidateMap = new Map();

    // Score conflict matches
    conflictMatches.forEach(p => {
      const score = this._calculateSimilarityScore(querySession, p);
      candidateMap.set(p.precedent_id, { precedent: p, score, reasons: ['conflict_match'] });
    });

    // Score stakeholder matches (add to existing or create new)
    stakeholderMatches.forEach(p => {
      if (candidateMap.has(p.precedent_id)) {
        const existing = candidateMap.get(p.precedent_id);
        existing.score += this._calculateSimilarityScore(querySession, p) * 0.5; // Weight stakeholder match lower
        existing.reasons.push('stakeholder_match');
      } else {
        const score = this._calculateSimilarityScore(querySession, p) * 0.5;
        candidateMap.set(p.precedent_id, { precedent: p, score, reasons: ['stakeholder_match'] });
      }
    });

    // Sort by score
    const ranked = Array.from(candidateMap.values())
      .sort((a, b) => b.score - a.score)
      .slice(0, limit);

    return ranked.map(r => ({
      ...r.precedent,
      similarity_score: r.score,
      match_reasons: r.reasons
    }));
  }

  /**
   * Record that this precedent was referenced in a new session
   */
  static async recordUsage(precedentId, referencingSessionId) {
    const collection = await getCollection('precedents');

    const result = await collection.updateOne(
      { precedent_id: precedentId },
      {
        $inc: { 'usage.times_referenced': 1 },
        $push: { 'usage.influenced_sessions': referencingSessionId },
        $set: { 'usage.last_referenced': new Date() }
      }
    );

    return result.modifiedCount > 0;
  }

  /**
   * Get statistics on precedent usage
   */
  static async getStatistics() {
    const collection = await getCollection('precedents');

    const [stats] = await collection.aggregate([
      { $match: { searchable: true, archived: false } },
      {
        $group: {
          _id: null,
          total_precedents: { $sum: 1 },
          avg_references: { $avg: '$usage.times_referenced' },
          total_references: { $sum: '$usage.times_referenced' },
          by_domain: { $push: '$metadata.domain' },
          by_scenario: { $push: '$conflict.scenario' }
        }
      }
    ]).toArray();

    const byDomain = await collection.aggregate([
      { $match: { searchable: true, archived: false } },
      {
        $group: {
          _id: '$metadata.domain',
          count: { $sum: 1 },
          avg_satisfaction: { $avg: '$process.ai_facilitation_quality.stakeholder_satisfaction_avg' }
        }
      },
      { $sort: { count: -1 } }
    ]).toArray();

    const byConsensusLevel = await collection.aggregate([
      { $match: { searchable: true, archived: false } },
      {
        $group: {
          _id: '$outcome.consensus_level',
          count: { $sum: 1 }
        }
      }
    ]).toArray();

    return {
      summary: stats || { total_precedents: 0, avg_references: 0, total_references: 0 },
      by_domain: byDomain,
      by_consensus_level: byConsensusLevel
    };
  }

  /**
   * Archive precedent (make unsearchable but retain for records)
   */
  static async archive(precedentId, reason) {
    const collection = await getCollection('precedents');

    const result = await collection.updateOne(
      { precedent_id: precedentId },
      {
        $set: {
          archived: true,
          archived_reason: reason,
          archived_at: new Date(),
          searchable: false
        }
      }
    );

    return result.modifiedCount > 0;
  }

  // ===== HELPER METHODS (private) =====

  static _extractStakeholderTypes(stakeholders) {
    return [...new Set(stakeholders.map(s => s.type))];
  }

  static _extractRepresentations(stakeholders) {
    return [...new Set(stakeholders.map(s => s.represents))];
  }

  static _extractMoralFrameworks(stakeholders) {
    return [...new Set(stakeholders.map(s => s.moral_framework).filter(Boolean))];
  }

  static _calculateAverageSatisfaction(scores) {
    if (!scores || scores.length === 0) return null;
    return scores.reduce((sum, s) => sum + s.score, 0) / scores.length;
  }

  static _extractSharedValues(rounds) {
    // Look for Round 2 (shared values) contributions
    const round2 = rounds.find(r => r.round_type === 'shared_values');
    if (!round2) return [];

    // Extract values mentioned across contributions
    const values = [];
    round2.contributions.forEach(c => {
      if (c.values_emphasized) {
        values.push(...c.values_emphasized);
      }
    });
    return [...new Set(values)];
  }

  static _extractAccommodationStrategies(rounds) {
    // Look for Round 3 (accommodation) AI summary
    const round3 = rounds.find(r => r.round_type === 'accommodation');
    if (!round3 || !round3.ai_summary) return [];

    // This would ideally parse the summary for strategies
    // For now, return placeholder
    return ['tiered_approach', 'contextual_variation', 'temporal_adjustment'];
  }

  static _extractCoalitions(rounds) {
    // Identify unexpected stakeholder agreements
    // This would require NLP analysis of contributions
    // For now, return placeholder
    return [];
  }

  static _extractTensionResolutions(sessionData) {
    if (!sessionData.outcome) return [];

    const resolutions = [];
    sessionData.conflict_analysis.value_trade_offs.forEach(tradeoff => {
      // Check if outcome addresses this trade-off
      const prioritized = sessionData.outcome.values_prioritized.some(v => tradeoff.includes(v));
      const deprioritized = sessionData.outcome.values_deprioritized.some(v => tradeoff.includes(v));

      if (prioritized && deprioritized) {
        resolutions.push({
          tension: tradeoff,
          resolution: 'balanced_accommodation'
        });
      } else if (prioritized) {
        resolutions.push({
          tension: tradeoff,
          resolution: 'prioritized'
        });
      }
    });

    return resolutions;
  }

  static _inferDomain(scenario) {
    const domainMap = {
      'algorithmic_hiring_transparency': 'employment',
      'remote_work_pay': 'employment',
      'content_moderation': 'platform_governance',
      'healthcare_ai': 'healthcare',
      'ai_content_labeling': 'creative_rights'
    };
    return domainMap[scenario] || 'general';
  }

  static _inferDecisionType(conflictAnalysis) {
    const description = conflictAnalysis.value_trade_offs.join(' ').toLowerCase();

    if (description.includes('transparency')) return 'transparency';
    if (description.includes('resource') || description.includes('allocation')) return 'resource_allocation';
    if (description.includes('procedure') || description.includes('process')) return 'procedural';
    if (description.includes('privacy')) return 'privacy';
    if (description.includes('safety')) return 'safety';

    return 'unspecified';
  }

  static _generateTags(sessionData) {
    const tags = [];

    // Add scenario tag
    if (sessionData.decision.scenario) {
      tags.push(sessionData.decision.scenario);
    }

    // Add moral framework tags
    tags.push(...sessionData.conflict_analysis.moral_frameworks_in_tension.map(f => f.toLowerCase()));

    // Add stakeholder representation tags
    tags.push(...sessionData.stakeholders.map(s => s.represents.toLowerCase().replace(/ /g, '_')));

    // Add outcome tag
    if (sessionData.outcome) {
      tags.push(sessionData.outcome.consensus_level);
    }

    // Add AI quality tag
    const interventions = sessionData.human_interventions.length;
    if (interventions === 0) tags.push('smooth_ai_facilitation');
    else if (interventions > 3) tags.push('challenging_ai_facilitation');

    return [...new Set(tags)];
  }

  static _calculateSimilarityScore(querySession, precedent) {
    let score = 0;

    // Scenario match (high weight)
    if (querySession.decision.scenario === precedent.conflict.scenario) {
      score += 40;
    }

    // Moral frameworks overlap (medium weight)
    const queryFrameworks = new Set(querySession.conflict_analysis.moral_frameworks_in_tension);
    const precedentFrameworks = new Set(precedent.conflict.moral_frameworks_in_tension);
    const frameworkOverlap = [...queryFrameworks].filter(f => precedentFrameworks.has(f)).length;
    score += (frameworkOverlap / Math.max(queryFrameworks.size, precedentFrameworks.size)) * 30;

    // Incommensurability match (medium weight)
    if (querySession.conflict_analysis.incommensurability_level === precedent.conflict.incommensurability_level) {
      score += 20;
    }

    // Stakeholder count similarity (low weight)
    const countDiff = Math.abs(querySession.stakeholders.length - precedent.stakeholder_pattern.total_count);
    score += Math.max(0, 10 - countDiff * 2);

    return score;
  }
}

module.exports = Precedent;