/**
 * Psycho-Symbolic Reasoning MCP Tools
 * Integrates hybrid AI reasoning combining symbolic logic with psychological patterns
 */

import { Tool } from '@modelcontextprotocol/sdk/types.js';
import * as fs from 'fs';
import * as path from 'path';
import { fileURLToPath } from 'url';

const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);

// Knowledge graph storage
const knowledgeGraph = new Map<string, any>();
const reasoningPaths = new Map<string, any>();

export class PsychoSymbolicTools {
  getTools(): Tool[] {
    return [
      {
        name: 'psycho_symbolic_reason',
        description: 'Perform psycho-symbolic reasoning on a query',
        inputSchema: {
          type: 'object',
          properties: {
            query: {
              type: 'string',
              description: 'The reasoning query'
            },
            context: {
              type: 'object',
              description: 'Additional context for reasoning',
              default: {}
            },
            depth: {
              type: 'number',
              description: 'Maximum reasoning depth',
              default: 5,
              minimum: 1,
              maximum: 10
            }
          },
          required: ['query']
        }
      },
      {
        name: 'knowledge_graph_query',
        description: 'Query the knowledge graph',
        inputSchema: {
          type: 'object',
          properties: {
            query: {
              type: 'string',
              description: 'Graph query in natural language'
            },
            filters: {
              type: 'object',
              description: 'Query filters'
            },
            limit: {
              type: 'number',
              description: 'Maximum results',
              default: 10,
              minimum: 1,
              maximum: 100
            }
          },
          required: ['query']
        }
      },
      {
        name: 'add_knowledge',
        description: 'Add new knowledge to the graph',
        inputSchema: {
          type: 'object',
          properties: {
            subject: {
              type: 'string',
              description: 'Subject entity'
            },
            predicate: {
              type: 'string',
              description: 'Relationship type'
            },
            object: {
              type: 'string',
              description: 'Object entity'
            },
            metadata: {
              type: 'object',
              description: 'Additional metadata'
            }
          },
          required: ['subject', 'predicate', 'object']
        }
      },
      {
        name: 'analyze_reasoning_path',
        description: 'Analyze and explain a reasoning path',
        inputSchema: {
          type: 'object',
          properties: {
            query: {
              type: 'string',
              description: 'Original query'
            },
            showSteps: {
              type: 'boolean',
              description: 'Show detailed steps',
              default: true
            },
            includeConfidence: {
              type: 'boolean',
              description: 'Include confidence scores',
              default: true
            }
          },
          required: ['query']
        }
      },
      {
        name: 'detect_contradictions',
        description: 'Detect logical contradictions in knowledge',
        inputSchema: {
          type: 'object',
          properties: {
            domain: {
              type: 'string',
              description: 'Knowledge domain to check'
            },
            depth: {
              type: 'number',
              description: 'Analysis depth',
              default: 3
            }
          }
        }
      },
      {
        name: 'cognitive_pattern_analysis',
        description: 'Analyze cognitive patterns in reasoning',
        inputSchema: {
          type: 'object',
          properties: {
            pattern: {
              type: 'string',
              enum: ['convergent', 'divergent', 'lateral', 'systems', 'critical', 'abstract'],
              description: 'Cognitive pattern type'
            },
            data: {
              type: 'object',
              description: 'Data to analyze'
            }
          },
          required: ['pattern']
        }
      }
    ];
  }

  async handleToolCall(name: string, args: any): Promise<any> {
    switch (name) {
      case 'psycho_symbolic_reason':
        return this.performReasoning(args.query, args.context, args.depth);

      case 'knowledge_graph_query':
        return this.queryKnowledgeGraph(args.query, args.filters, args.limit);

      case 'add_knowledge':
        return this.addKnowledge(args.subject, args.predicate, args.object, args.metadata);

      case 'analyze_reasoning_path':
        return this.analyzeReasoningPath(args.query, args.showSteps, args.includeConfidence);

      case 'detect_contradictions':
        return this.detectContradictions(args.domain, args.depth);

      case 'cognitive_pattern_analysis':
        return this.analyzeCognitivePattern(args.pattern, args.data);

      default:
        throw new Error(`Unknown psycho-symbolic tool: ${name}`);
    }
  }

  private async performReasoning(query: string, context: any = {}, depth: number = 5): Promise<any> {
    const reasoningSteps = [];
    let currentDepth = 0;
    let currentQuery = query;
    const visited = new Set<string>();

    // Initialize reasoning with psychological patterns
    const cognitivePatterns = this.identifyCognitivePattern(query);
    reasoningSteps.push({
      type: 'pattern_identification',
      patterns: cognitivePatterns,
      confidence: 0.85
    });

    // Symbolic logic processing
    while (currentDepth < depth) {
      if (visited.has(currentQuery)) break;
      visited.add(currentQuery);

      // Extract logical components
      const logicalComponents = this.extractLogicalComponents(currentQuery);
      reasoningSteps.push({
        type: 'logical_decomposition',
        components: logicalComponents,
        depth: currentDepth
      });

      // Apply inference rules
      const inferences = this.applyInferenceRules(logicalComponents, context);
      reasoningSteps.push({
        type: 'inference',
        rules: inferences.rules,
        conclusions: inferences.conclusions
      });

      // Check for contradictions
      const contradictions = this.checkContradictions(inferences.conclusions);
      if (contradictions.length > 0) {
        reasoningSteps.push({
          type: 'contradiction_resolution',
          contradictions,
          resolution: this.resolveContradictions(contradictions)
        });
      }

      // Generate next query level
      const nextQuery = this.generateNextQuery(inferences.conclusions);
      if (!nextQuery) break;

      currentQuery = nextQuery;
      currentDepth++;
    }

    // Synthesize final answer
    const synthesis = this.synthesizeAnswer(reasoningSteps, query);

    // Store reasoning path
    reasoningPaths.set(query, {
      steps: reasoningSteps,
      synthesis,
      timestamp: Date.now()
    });

    return {
      answer: synthesis.answer,
      confidence: synthesis.confidence,
      reasoning: reasoningSteps,
      patterns: cognitivePatterns,
      depth: currentDepth
    };
  }

  private identifyCognitivePattern(query: string): string[] {
    const patterns = [];
    const lowerQuery = query.toLowerCase();

    if (lowerQuery.includes('why') || lowerQuery.includes('cause')) {
      patterns.push('causal');
    }
    if (lowerQuery.includes('how') || lowerQuery.includes('process')) {
      patterns.push('procedural');
    }
    if (lowerQuery.includes('what if') || lowerQuery.includes('suppose')) {
      patterns.push('hypothetical');
    }
    if (lowerQuery.includes('compare') || lowerQuery.includes('difference')) {
      patterns.push('comparative');
    }
    if (lowerQuery.includes('all') || lowerQuery.includes('every')) {
      patterns.push('universal');
    }
    if (lowerQuery.includes('some') || lowerQuery.includes('exist')) {
      patterns.push('existential');
    }

    if (patterns.length === 0) {
      patterns.push('exploratory');
    }

    return patterns;
  }

  private extractLogicalComponents(query: string): any {
    return {
      predicates: this.extractPredicates(query),
      quantifiers: this.extractQuantifiers(query),
      operators: this.extractLogicalOperators(query),
      entities: this.extractEntities(query)
    };
  }

  private extractPredicates(text: string): string[] {
    // Simple predicate extraction
    const predicates = [];
    const words = text.split(/\s+/);
    for (let i = 0; i < words.length - 1; i++) {
      if (words[i + 1] && /^(is|are|was|were|has|have|had)$/i.test(words[i])) {
        predicates.push(`${words[i]} ${words[i + 1]}`);
      }
    }
    return predicates;
  }

  private extractQuantifiers(text: string): string[] {
    const quantifiers = [];
    const patterns = /\b(all|every|some|none|any|most|few|many)\b/gi;
    const matches = text.match(patterns);
    return matches || [];
  }

  private extractLogicalOperators(text: string): string[] {
    const operators = [];
    const patterns = /\b(and|or|not|if|then|implies|equals)\b/gi;
    const matches = text.match(patterns);
    return matches || [];
  }

  private extractEntities(text: string): string[] {
    // Simple entity extraction (would use NER in production)
    const words = text.split(/\s+/);
    return words.filter(word =>
      word.length > 3 &&
      /^[A-Z]/.test(word) &&
      !['What', 'When', 'Where', 'Why', 'How', 'Who'].includes(word)
    );
  }

  private applyInferenceRules(components: any, context: any): any {
    const rules = [];
    const conclusions = [];

    // Modus Ponens
    if (components.operators.includes('if') && components.operators.includes('then')) {
      rules.push('modus_ponens');
      conclusions.push('conditional_inference');
    }

    // Universal Instantiation
    if (components.quantifiers.some(q => /^(all|every)$/i.test(q))) {
      rules.push('universal_instantiation');
      conclusions.push('specific_instance');
    }

    // Existential Generalization
    if (components.quantifiers.some(q => /^(some|exist)$/i.test(q))) {
      rules.push('existential_generalization');
      conclusions.push('existence_claim');
    }

    return { rules, conclusions };
  }

  private checkContradictions(conclusions: string[]): any[] {
    const contradictions = [];

    // Check for logical contradictions
    for (let i = 0; i < conclusions.length; i++) {
      for (let j = i + 1; j < conclusions.length; j++) {
        if (this.areContradictory(conclusions[i], conclusions[j])) {
          contradictions.push({
            statement1: conclusions[i],
            statement2: conclusions[j],
            type: 'logical'
          });
        }
      }
    }

    return contradictions;
  }

  private areContradictory(s1: string, s2: string): boolean {
    // Simplified contradiction detection
    return (s1.includes('not') && s2 === s1.replace('not', '').trim()) ||
           (s2.includes('not') && s1 === s2.replace('not', '').trim());
  }

  private resolveContradictions(contradictions: any[]): any {
    return {
      method: 'consistency_restoration',
      resolution: contradictions.map(c => ({
        original: c,
        resolved: 'requires_context_disambiguation'
      }))
    };
  }

  private generateNextQuery(conclusions: string[]): string | null {
    if (conclusions.length === 0) return null;

    // Generate follow-up query based on conclusions
    const lastConclusion = conclusions[conclusions.length - 1];
    if (lastConclusion === 'conditional_inference') {
      return 'What are the conditions?';
    } else if (lastConclusion === 'existence_claim') {
      return 'What examples exist?';
    }

    return null;
  }

  private synthesizeAnswer(steps: any[], originalQuery: string): any {
    const insights = [];
    let confidence = 0.5;

    for (const step of steps) {
      if (step.type === 'inference') {
        insights.push(...step.conclusions);
        confidence += 0.1;
      }
      if (step.type === 'pattern_identification') {
        confidence += step.confidence * 0.2;
      }
    }

    confidence = Math.min(confidence, 1.0);

    return {
      answer: `Based on ${steps.length} reasoning steps, ${insights.join(', ')}`,
      confidence,
      insights
    };
  }

  private async queryKnowledgeGraph(query: string, filters: any = {}, limit: number = 10): Promise<any> {
    const results = [];

    // Search through knowledge graph
    for (const [key, value] of knowledgeGraph.entries()) {
      if (this.matchesQuery(value, query, filters)) {
        results.push({
          id: key,
          ...value
        });

        if (results.length >= limit) break;
      }
    }

    return {
      query,
      results,
      total: results.length
    };
  }

  private matchesQuery(item: any, query: string, filters: any): boolean {
    const queryLower = query.toLowerCase();

    // Check if any field contains the query
    const itemStr = JSON.stringify(item).toLowerCase();
    if (!itemStr.includes(queryLower)) return false;

    // Apply filters
    for (const [key, value] of Object.entries(filters)) {
      if (item[key] !== value) return false;
    }

    return true;
  }

  private async addKnowledge(subject: string, predicate: string, object: string, metadata: any = {}): Promise<any> {
    const id = `${subject}-${predicate}-${object}-${Date.now()}`;

    const knowledge = {
      subject,
      predicate,
      object,
      metadata,
      timestamp: Date.now()
    };

    knowledgeGraph.set(id, knowledge);

    return {
      id,
      knowledge,
      status: 'added'
    };
  }

  private async analyzeReasoningPath(query: string, showSteps: boolean = true, includeConfidence: boolean = true): Promise<any> {
    const path = reasoningPaths.get(query);

    if (!path) {
      // Generate new reasoning path
      const result = await this.performReasoning(query, {}, 5);
      return {
        query,
        path: showSteps ? result.reasoning : null,
        confidence: includeConfidence ? result.confidence : null,
        answer: result.answer
      };
    }

    return {
      query,
      path: showSteps ? path.steps : null,
      confidence: includeConfidence ? path.synthesis.confidence : null,
      answer: path.synthesis.answer
    };
  }

  private async detectContradictions(domain?: string, depth: number = 3): Promise<any> {
    const contradictions = [];
    const items = [];

    // Collect items from domain
    for (const [key, value] of knowledgeGraph.entries()) {
      if (!domain || value.metadata?.domain === domain) {
        items.push(value);
      }
    }

    // Check for contradictions
    for (let i = 0; i < items.length; i++) {
      for (let j = i + 1; j < items.length; j++) {
        const contradiction = this.detectItemContradiction(items[i], items[j]);
        if (contradiction) {
          contradictions.push(contradiction);
        }
      }
    }

    return {
      domain,
      contradictions,
      total: contradictions.length
    };
  }

  private detectItemContradiction(item1: any, item2: any): any | null {
    // Check for direct contradictions
    if (item1.subject === item2.subject &&
        item1.predicate === item2.predicate &&
        item1.object !== item2.object) {
      return {
        type: 'direct',
        item1,
        item2,
        conflict: 'conflicting_objects'
      };
    }

    // Check for inverse relationships
    if (item1.subject === item2.object &&
        item1.object === item2.subject &&
        this.areInversePredicates(item1.predicate, item2.predicate)) {
      return {
        type: 'inverse',
        item1,
        item2,
        conflict: 'inverse_relationship'
      };
    }

    return null;
  }

  private areInversePredicates(p1: string, p2: string): boolean {
    const inversePairs = [
      ['causes', 'prevents'],
      ['increases', 'decreases'],
      ['supports', 'opposes'],
      ['enables', 'blocks']
    ];

    for (const [a, b] of inversePairs) {
      if ((p1 === a && p2 === b) || (p1 === b && p2 === a)) {
        return true;
      }
    }

    return false;
  }

  private async analyzeCognitivePattern(pattern: string, data: any = {}): Promise<any> {
    const analysis = {
      pattern,
      characteristics: [],
      strengths: [],
      limitations: [],
      applications: []
    };

    switch (pattern) {
      case 'convergent':
        analysis.characteristics = ['focused', 'analytical', 'logical'];
        analysis.strengths = ['problem-solving', 'decision-making'];
        analysis.limitations = ['limited_creativity', 'single_solution_focus'];
        analysis.applications = ['mathematics', 'engineering', 'diagnosis'];
        break;

      case 'divergent':
        analysis.characteristics = ['creative', 'exploratory', 'flexible'];
        analysis.strengths = ['innovation', 'multiple_solutions'];
        analysis.limitations = ['lack_of_focus', 'difficulty_concluding'];
        analysis.applications = ['brainstorming', 'art', 'research'];
        break;

      case 'lateral':
        analysis.characteristics = ['indirect', 'creative', 'unconventional'];
        analysis.strengths = ['breakthrough_thinking', 'novel_solutions'];
        analysis.limitations = ['unpredictable', 'hard_to_validate'];
        analysis.applications = ['innovation', 'problem_reframing'];
        break;

      case 'systems':
        analysis.characteristics = ['holistic', 'interconnected', 'complex'];
        analysis.strengths = ['comprehensive_understanding', 'emergence_recognition'];
        analysis.limitations = ['complexity', 'computational_intensity'];
        analysis.applications = ['ecology', 'economics', 'organizations'];
        break;

      case 'critical':
        analysis.characteristics = ['analytical', 'evaluative', 'skeptical'];
        analysis.strengths = ['error_detection', 'quality_assurance'];
        analysis.limitations = ['negativity_bias', 'analysis_paralysis'];
        analysis.applications = ['review', 'validation', 'debugging'];
        break;

      case 'abstract':
        analysis.characteristics = ['conceptual', 'theoretical', 'generalizable'];
        analysis.strengths = ['pattern_recognition', 'transferable_knowledge'];
        analysis.limitations = ['practical_application', 'concrete_examples'];
        analysis.applications = ['theory', 'mathematics', 'philosophy'];
        break;
    }

    // Apply pattern to data if provided
    if (data && Object.keys(data).length > 0) {
      (analysis as any).application = this.applyPatternToData(pattern, data);
    }

    return analysis;
  }

  private applyPatternToData(pattern: string, data: any): any {
    // Simplified pattern application
    return {
      pattern,
      data_summary: `Analyzed ${Object.keys(data).length} data points`,
      insights: [`${pattern} thinking applied to data`],
      recommendations: [`Consider ${pattern} approach for this problem`]
    };
  }
}

export default PsychoSymbolicTools;