/**
 * Neural Pattern Engine Integration with Hive Mind
 * 
 * Integrates the NeuralPatternEngine with the Hive Mind collective intelligence system.
 * Configures pattern recognition, learning, and optimization capabilities.
 */

import { NeuralPatternEngine, NeuralConfig, PatternPrediction } from '../../coordination/neural-pattern-engine.ts';
import { EventBus } from '../../core/event-bus.ts';
import { Logger } from '../../core/logger.ts';
import { TaskDefinition, TaskResult, AgentState } from '../../swarm/types.ts';
import { PatternType, LearningPattern } from '../types.js';

// Configuration for the neural integration
export interface NeuralIntegrationConfig {
  // Core neural engine configuration
  neuralConfig: Partial<NeuralConfig>;
  
  // Integration settings
  taskLearningEnabled: boolean;
  behaviorAnalysisEnabled: boolean;
  coordinationOptimizationEnabled: boolean;
  emergentPatternDetectionEnabled: boolean;
  
  // Advanced settings
  confidenceThreshold: number;
  adaptiveLearning: boolean;
  continuousOptimization: boolean;
  transferLearningEnabled: boolean;
}

// Default configuration
const DEFAULT_CONFIG: NeuralIntegrationConfig = {
  neuralConfig: {
    modelUpdateInterval: 300000, // 5 minutes
    confidenceThreshold: 0.7,
    trainingBatchSize: 32,
    maxTrainingEpochs: 50,
    learningRate: 0.001,
    enableWasmAcceleration: true,
    patternCacheSize: 1000,
    autoRetraining: true,
    qualityThreshold: 0.7
  },
  taskLearningEnabled: true,
  behaviorAnalysisEnabled: true,
  coordinationOptimizationEnabled: true,
  emergentPatternDetectionEnabled: true,
  confidenceThreshold: 0.6,
  adaptiveLearning: true,
  continuousOptimization: true,
  transferLearningEnabled: false
};

/**
 * Neural integration for the Hive Mind system
 * Connects the neural pattern engine to the collective intelligence
 */
export class NeuralIntegration {
  private readonly logger: Logger;
  private readonly eventBus: EventBus;
  private readonly neuralEngine: NeuralPatternEngine;
  private readonly config: NeuralIntegrationConfig;
  
  private observedPatterns: Map<string, LearningPattern> = new Map();
  private optimizationTimer?: NodeJS.Timeout;
  
  constructor(
    neuralEngine: NeuralPatternEngine,
    config: Partial<NeuralIntegrationConfig> = {}
  ) {
    this.logger = new Logger('NeuralIntegration');
    this.eventBus = EventBus.getInstance();
    this.neuralEngine = neuralEngine;
    this.config = { ...DEFAULT_CONFIG, ...config };
    
    this.setupEventListeners();
  }

  /**
   * Set up event listeners for neural integration
   */
  private setupEventListeners() {
    // Listen for task completions to learn patterns
    if (this.config.taskLearningEnabled) {
      this.eventBus.on('task:completed', (data: { 
        task: TaskDefinition; 
        result: TaskResult; 
        agent: AgentState 
      }) => {
        this.learnFromTaskCompletion(data.task, data.result, data.agent);
      });
    }
    
    // Listen for agent behavior changes
    if (this.config.behaviorAnalysisEnabled) {
      this.eventBus.on('agent:behavior_change', (data: { 
        agent: AgentState; 
        metrics: any 
      }) => {
        this.analyzeAgentBehavior(data.agent, data.metrics);
      });
    }
    
    // Listen for coordination patterns
    if (this.config.coordinationOptimizationEnabled) {
      this.eventBus.on('coordination:pattern', (data: { 
        pattern: string; 
        context: any;
        outcome: 'success' | 'failure';
        metrics: any;
      }) => {
        this.learnCoordinationPattern(data.pattern, data.context, data.outcome, data.metrics);
      });
    }
    
    // Set up optimization timer
    if (this.config.continuousOptimization) {
      this.optimizationTimer = setInterval(() => {
        this.optimizeNeuralPatterns();
      }, 30 * 60 * 1000); // 30 minutes
    }
  }

  /**
   * Learn from completed tasks
   */
  private async learnFromTaskCompletion(
    task: TaskDefinition,
    result: TaskResult,
    agent: AgentState
  ): Promise<void> {
    // Determine success based on quality and completeness thresholds
    const success = result.quality >= 0.7 && result.completeness >= 0.8;
    
    this.logger.debug('Learning from task completion', {
      taskId: task.id,
      success: success,
      quality: result.quality,
      completeness: result.completeness,
      agentId: agent.id
    });
    
    try {
      // Extract context for learning
      const context = {
        taskType: task.type,
        agentCapabilities: Object.keys(agent.capabilities),
        environment: agent.environment,
        historicalPerformance: [agent.metrics.successRate],
        resourceUsage: {
          responseTime: agent.metrics.responseTime,
          errorRate: agent.metrics.tasksFailed / (agent.metrics.tasksCompleted + agent.metrics.tasksFailed) || 0,
          cpu: agent.metrics.cpuUsage,
          memory: agent.metrics.memoryUsage
        },
        communicationPatterns: [],
        outcomes: [success ? 'success' : 'failure']
      };
      
      // Analyze task completion
      const taskPrediction = await this.neuralEngine.predictTaskMetrics(context);
      
      // Update observed patterns
      this.updateObservedPattern('task_completion', {
        context,
        outcome: {
          success: success,
          executionTime: result.executionTime,
          quality: result.accuracy
        },
        prediction: taskPrediction
      });
      
      this.logger.debug('Task learning complete', {
        taskId: task.id,
        confidence: taskPrediction.confidence
      });
    } catch (error) {
      this.logger.error('Error learning from task completion', { error });
    }
  }

  /**
   * Analyze agent behavior
   */
  private async analyzeAgentBehavior(
    agent: AgentState,
    metrics: any
  ): Promise<void> {
    try {
      // Get behavior prediction
      const prediction = await this.neuralEngine.analyzeAgentBehavior(agent, metrics);
      
      // If anomaly detected with high confidence, emit event
      if (prediction.confidence > this.config.confidenceThreshold && 
          prediction.prediction[0] > 0.7) {
        this.eventBus.emit('agent:anomaly_detected', {
          agentId: agent.id,
          anomalyScore: prediction.prediction[0],
          confidence: prediction.confidence,
          reasoning: prediction.reasoning
        });
      }
      
      // Update observed patterns
      this.updateObservedPattern('agent_behavior', {
        agentId: agent.id,
        metrics,
        prediction
      });
    } catch (error) {
      this.logger.error('Error analyzing agent behavior', { error });
    }
  }

  /**
   * Learn coordination patterns
   */
  private async learnCoordinationPattern(
    pattern: string,
    context: any,
    outcome: 'success' | 'failure',
    metrics: any
  ): Promise<void> {
    try {
      // Predict optimal coordination mode
      const prediction = await this.neuralEngine.predictCoordinationMode({
        taskType: context.taskType || 'unknown',
        agentCapabilities: context.agentCapabilities || [],
        environment: context.environment || {},
        historicalPerformance: context.historicalPerformance || [0.5],
        resourceUsage: context.resourceUsage || {},
        communicationPatterns: context.communicationPatterns || [],
        outcomes: [outcome]
      });
      
      // Update observed patterns
      this.updateObservedPattern('coordination', {
        pattern,
        context,
        outcome,
        metrics,
        prediction
      });
      
      // If emergent pattern detection is enabled
      if (this.config.emergentPatternDetectionEnabled && 
          prediction.confidence > this.config.confidenceThreshold) {
        this.detectEmergentPatterns(pattern, context, prediction);
      }
    } catch (error) {
      this.logger.error('Error learning coordination pattern', { error });
    }
  }

  /**
   * Detect emergent patterns
   */
  private detectEmergentPatterns(
    pattern: string, 
    context: any, 
    prediction: PatternPrediction
  ): void {
    // This would implement more sophisticated pattern detection algorithms
    // For now, we'll just log the detection
    this.logger.info('Potential emergent pattern detected', {
      pattern,
      confidence: prediction.confidence,
      reasoning: prediction.reasoning
    });
    
    this.eventBus.emit('pattern:emergence_detected', {
      pattern,
      context,
      prediction,
      timestamp: new Date()
    });
  }

  /**
   * Update observed pattern in the pattern store
   */
  private updateObservedPattern(
    type: string, 
    data: any
  ): void {
    const patternId = `${type}:${Date.now()}`;
    
    const pattern: LearningPattern = {
      id: patternId,
      swarmId: data.swarmId || 'unknown',
      patternType: data.outcome?.success ? 'success' : 'failure',
      context: data.context || {},
      outcome: data.outcome || {},
      confidence: data.prediction?.confidence || 0.5,
      frequency: 1,
      lastObserved: new Date(),
      applicableScenarios: [type],
      recommendations: []
    };
    
    this.observedPatterns.set(patternId, pattern);
    
    // Limit the size of the pattern store
    if (this.observedPatterns.size > 1000) {
      // Remove the oldest pattern
      const oldestKey = Array.from(this.observedPatterns.keys())[0];
      this.observedPatterns.delete(oldestKey);
    }
  }

  /**
   * Optimize neural patterns periodically
   */
  private async optimizeNeuralPatterns(): Promise<void> {
    this.logger.info('Starting neural pattern optimization');
    
    try {
      // Get all patterns
      const patterns = this.neuralEngine.getAllPatterns();
      
      // Find patterns that need optimization
      const patternsForOptimization = patterns.filter(pattern => {
        return pattern.accuracy < this.config.neuralConfig.qualityThreshold!;
      });
      
      if (patternsForOptimization.length > 0) {
        this.logger.info(`Optimizing ${patternsForOptimization.length} neural patterns`);
        
        // Optimize each pattern
        for (const pattern of patternsForOptimization) {
          await this.optimizePattern(pattern.id);
        }
        
        this.logger.info('Neural pattern optimization completed');
      } else {
        this.logger.info('No neural patterns require optimization');
      }
    } catch (error) {
      this.logger.error('Error during neural pattern optimization', { error });
    }
  }

  /**
   * Optimize a specific pattern
   */
  private async optimizePattern(patternId: string): Promise<void> {
    try {
      // Get pattern metrics
      const metrics = this.neuralEngine.getPatternMetrics(patternId);
      if (!metrics) return;
      
      this.logger.debug('Optimizing pattern', { patternId, accuracy: metrics.accuracy });
      
      // Emit optimization event
      this.eventBus.emit('pattern:optimizing', {
        patternId,
        currentAccuracy: metrics.accuracy
      });
      
      // For now, this is just a placeholder for actual optimization logic
      // In a real implementation, this would involve hyperparameter tuning, etc.
      
      this.eventBus.emit('pattern:optimized', {
        patternId,
        previousAccuracy: metrics.accuracy,
        newAccuracy: metrics.accuracy // This would normally be updated
      });
    } catch (error) {
      this.logger.error('Error optimizing neural pattern', { patternId, error });
    }
  }

  /**
   * Get all observed learning patterns
   */
  public getObservedPatterns(): LearningPattern[] {
    return Array.from(this.observedPatterns.values());
  }

  /**
   * Get neural pattern by type
   */
  public async getNeuralPatterns(type: PatternType): Promise<any[]> {
    try {
      return await this.neuralEngine.getAllPatterns()
        .filter(pattern => pattern.type === type);
    } catch (error) {
      this.logger.error('Error getting neural patterns', { type, error });
      return [];
    }
  }

  /**
   * Clean up resources
   */
  public shutdown(): void {
    // Clear the optimization timer
    if (this.optimizationTimer) {
      clearInterval(this.optimizationTimer);
    }
    
    this.logger.info('Neural integration shutdown complete');
  }
}