/**
 * Neural Workflow for Pattern Recognition
 * 
 * Orchestrates the neural pattern recognition process with TensorFlow integration
 */

import { EventBus } from '../../core/event-bus.js';
import { Logger, logger } from '../../core/logger.js';
import { PatternRecognizer } from './pattern-recognizer.js';
import { NeuralManager } from './neural-manager.js';
import { PatternType, PatternMatch } from '../types.js';

export interface NeuralWorkflowConfig {
  // Workflow configuration options
  batchProcessing: boolean;
  parallelExecution: boolean;
  adaptiveLearning: boolean;
  reinforcementCycles: number;
  feedbackThreshold: number;
  enableOptimization: boolean;
}

export interface WorkflowResult {
  matches: PatternMatch[];
  analysis: {
    confidence: number;
    matchCount: number;
    executionTimeMs: number;
    tensorFlowUsed: boolean;
    optimizationApplied: boolean;
  };
  recommendations?: {
    action: string;
    confidence: number;
    reasoning: string;
  }[];
  feedback?: {
    metric: string;
    value: number;
    threshold: number;
    action: string;
  }[];
}

/**
 * Neural Workflow for orchestrating pattern recognition processes
 */
export class NeuralWorkflow {
  private logger: Logger;
  private eventBus: EventBus;
  private patternRecognizer: PatternRecognizer;
  private neuralManager: NeuralManager;
  private config: NeuralWorkflowConfig;
  private workflowCache = new Map<string, WorkflowResult>();
  
  constructor(
    patternRecognizer: PatternRecognizer,
    neuralManager: NeuralManager,
    eventBus: EventBus,
    config: NeuralWorkflowConfig
  ) {
    this.patternRecognizer = patternRecognizer;
    this.neuralManager = neuralManager;
    this.eventBus = eventBus;
    this.config = config;
    
    const loggerInstance = Logger.getInstance({
      level: 'info',
      format: 'json',
      destination: 'console'
    });
    this.logger = loggerInstance?.child({ component: 'NeuralWorkflow' }) || {
      debug: (msg: string, meta?: any) => console.debug(`[NeuralWorkflow] ${msg}`, meta),
      info: (msg: string, meta?: any) => console.info(`[NeuralWorkflow] ${msg}`, meta),
      warn: (msg: string, meta?: any) => console.warn(`[NeuralWorkflow] ${msg}`, meta),
      error: (msg: string, error?: any) => console.error(`[NeuralWorkflow] ${msg}`, error),
      configure: async () => {},
      child: (ctx: any) => this.logger
    } as any;
    
    this.setupEventListeners();
  }
  
  /**
   * Initialize the workflow
   */
  async initialize(): Promise<void> {
    this.logger.info('Initializing Neural Workflow');
    
    try {
      // Only NeuralManager has initialize method
      await this.neuralManager.initialize();
      
      this.eventBus.emit('neural:workflow:initialized', {
        timestamp: new Date().toISOString(),
        config: this.config
      });
      
      this.logger.info('Neural Workflow initialized successfully');
    } catch (error) {
      this.logger.error('Failed to initialize Neural Workflow', error);
      throw error;
    }
  }

  /**
   * Execute pattern recognition workflow
   */
  async executePatternRecognition(inputData: Record<string, unknown>): Promise<PatternMatch[]> {
    this.logger.info('Executing pattern recognition workflow');
    
    try {
      // PatternRecognizer.recognizePattern expects (swarmId, data, patternType)
      const swarmId = (inputData.swarmId as string) || 'default-swarm';
      const patternType = (inputData.patternType as PatternType) || 'coordination';
      const result = await this.patternRecognizer.recognizePattern(swarmId, inputData, patternType);
      
      this.eventBus.emit('neural:pattern:recognition:complete', {
        timestamp: new Date().toISOString(),
        patterns: result,
        inputData
      });
      
      return result;
    } catch (error) {
      this.logger.error('Pattern recognition workflow failed', error);
      throw error;
    }
  }

  /**
   * Execute neural processing workflow
   */
  async executeNeuralProcessing(inputData: Record<string, unknown>): Promise<any> {
    this.logger.info('Executing neural processing workflow');
    
    try {
      // Use NeuralManager's recognizePattern method as the main processing method
      const swarmId = (inputData.swarmId as string) || 'default-swarm';
      const patternType = (inputData.patternType as PatternType) || 'coordination';
      const result = await this.neuralManager.recognizePattern(swarmId, inputData, patternType);
      
      this.eventBus.emit('neural:processing:complete', {
        timestamp: new Date().toISOString(),
        result,
        inputData
      });
      
      return result;
    } catch (error) {
      this.logger.error('Neural processing workflow failed', error);
      throw error;
    }
  }

  /**
   * Execute combined pattern recognition and neural processing workflow
   */
  async executeCombinedWorkflow(inputData: Record<string, unknown>): Promise<{ pattern: PatternMatch[]; neural: any }> {
    this.logger.info('Executing combined neural workflow');
    
    try {
      const patternResult = await this.executePatternRecognition(inputData);
      let neuralResult;
      
      try {
        neuralResult = await this.executeNeuralProcessing(inputData);
      } catch (neuralError) {
        this.logger.error('Neural processing failed in combined workflow', neuralError);
        neuralResult = { error: 'Neural processing failed', details: neuralError };
      }
      
      const result = {
        pattern: patternResult,
        neural: neuralResult
      };
      
      this.eventBus.emit('neural:combined:workflow:complete', {
        timestamp: new Date().toISOString(),
        result,
        inputData
      });
      
      return result;
    } catch (error) {
      this.logger.error('Combined neural workflow failed', error);
      throw error;
    }
  }

  /**
   * Get workflow metrics
   */
  async getMetrics(): Promise<any> {
    this.logger.info('Getting neural workflow metrics');
    
    try {
      // Use available status methods instead of non-existent getMetrics
      const patternRecognitionMetrics = this.patternRecognizer.getStatus();
      const neuralProcessingMetrics = this.neuralManager.getStatus();
      
      const workflowMetrics = {
        patternRecognitionMetrics,
        neuralProcessingMetrics,
        cacheSize: this.workflowCache.size,
        config: this.config,
        recentWorkflows: Array.from(this.workflowCache.entries())
          .slice(-10)
          .map(([key, value]) => ({
            key,
            matchCount: value.matches.length,
            confidence: value.analysis.confidence,
            tensorFlowUsed: value.analysis.tensorFlowUsed
          }))
      };
      
      return workflowMetrics;
    } catch (error) {
      this.logger.error('Failed to get workflow metrics', error);
      throw error;
    }
  }

  /**
   * Shutdown the workflow
   */
  async shutdown(): Promise<void> {
    this.logger.info('Shutting down Neural Workflow');
    
    try {
      // Only NeuralManager has shutdown method
      await this.neuralManager.shutdown();
    } catch (error) {
      this.logger.error('Error shutting down neural manager', error);
    }
    
    // Clear cache
    this.workflowCache.clear();
    
    this.eventBus.emit('neural:workflow:shutdown', {
      timestamp: new Date().toISOString()
    });
    
    this.logger.info('Neural Workflow shutdown complete');
  }

  /**
   * Set up event listeners for the workflow
   */
  private setupEventListeners(): void {
    this.eventBus.on('neural:pattern:recognized', data => {
      this.handlePatternRecognition(data);
    });
    
    this.eventBus.on('neural:training:complete', data => {
      this.logger.info(`Training completed for ${data.modelName} with accuracy ${data.finalAccuracy}`);
      
      // Emit workflow updated event
      this.eventBus.emit('neural:workflow:updated', {
        component: 'training',
        modelName: data.modelName,
        accuracy: data.finalAccuracy,
        timestamp: new Date().toISOString()
      });
    });
  }
  
  /**
   * Handle pattern recognition events
   */
  private handlePatternRecognition(data: { patterns: PatternMatch[]; swarmId: string; patternType: PatternType; executionTimeMs?: number; optimized?: boolean }): void {
    const { patterns, swarmId, patternType } = data;
    
    if (patterns && patterns.length > 0) {
      // Cache the result for future reference
      const cacheKey = `${swarmId}:${patternType}:${Date.now()}`;
      this.workflowCache.set(cacheKey, {
        matches: patterns,
        analysis: {
          confidence: patterns.reduce((acc: number, p: PatternMatch) => acc + p.confidence, 0) / patterns.length,
          matchCount: patterns.length,
          executionTimeMs: data.executionTimeMs || 0,
          tensorFlowUsed: patterns.some((p: PatternMatch) => p.modelName.includes('tensorflow')),
          optimizationApplied: data.optimized || false
        }
      });
      
      // Clean up old cache entries
      this.cleanupCache();
    }
  }
  
  /**
   * Clean up old cache entries
   */
  private cleanupCache(): void {
    const maxCacheSize = 100;
    if (this.workflowCache.size > maxCacheSize) {
      // Remove oldest entries
      const sortedKeys = Array.from(this.workflowCache.keys()).sort();
      const keysToDelete = sortedKeys.slice(0, this.workflowCache.size - maxCacheSize);
      
      keysToDelete.forEach(key => this.workflowCache.delete(key));
    }
  }
  
  /**
   * Execute a neural pattern recognition workflow
   */
  async executeWorkflow(
    swarmId: string,
    data: Record<string, unknown>,
    patternType: PatternType,
    options: {
      priority?: 'high' | 'medium' | 'low';
      timeout?: number;
      optimizationLevel?: number;
    } = {}
  ): Promise<WorkflowResult> {
    const startTime = Date.now();
    
    try {
      this.logger.debug(`Starting neural workflow for ${patternType} patterns`);
      
      // Process data through the pattern recognizer
      const matches = await this.patternRecognizer.recognizePattern(swarmId, data, patternType);
      
      // Apply neural manager optimization if enabled
      if (this.config.enableOptimization && options.optimizationLevel && options.optimizationLevel > 0) {
        await this.neuralManager.optimizePatterns(swarmId);
      }
      
      // Generate recommendations based on matches
      const recommendations = this.generateRecommendations(matches, patternType);
      
      // Generate feedback metrics
      const feedback = this.generateFeedback(matches, patternType);
      
      // Create workflow result
      const result: WorkflowResult = {
        matches,
        analysis: {
          confidence: matches.length > 0 
            ? matches.reduce((acc, p) => acc + p.confidence, 0) / matches.length
            : 0,
          matchCount: matches.length,
          executionTimeMs: Date.now() - startTime,
          tensorFlowUsed: matches.some(p => p.modelName.includes('tensorflow')),
          optimizationApplied: !!options.optimizationLevel && options.optimizationLevel > 0
        },
        recommendations,
        feedback
      };
      
      // Emit workflow completed event
      this.eventBus.emit('neural:workflow:completed', {
        swarmId,
        patternType,
        matchCount: matches.length,
        executionTimeMs: result.analysis.executionTimeMs,
        timestamp: new Date().toISOString()
      });
      
      return result;
    } catch (error) {
      this.logger.error('Neural workflow execution failed:', error);
      
      // Return empty result
      return {
        matches: [],
        analysis: {
          confidence: 0,
          matchCount: 0,
          executionTimeMs: Date.now() - startTime,
          tensorFlowUsed: false,
          optimizationApplied: false
        }
      };
    }
  }
  
  /**
   * Execute pattern recognition in batch mode
   */
  async executeBatchWorkflow(
    swarmId: string,
    dataItems: Record<string, unknown>[],
    patternType: PatternType
  ): Promise<WorkflowResult[]> {
    try {
      this.logger.info(`Starting batch neural workflow for ${dataItems.length} items`);
      
      // Process items in parallel if enabled
      if (this.config.parallelExecution) {
        const results = await Promise.all(
          dataItems.map(item => this.executeWorkflow(swarmId, item, patternType))
        );
        return results;
      } else {
        // Process sequentially
        const results: WorkflowResult[] = [];
        for (const item of dataItems) {
          const result = await this.executeWorkflow(swarmId, item, patternType);
          results.push(result);
        }
        return results;
      }
    } catch (error) {
      this.logger.error('Batch workflow execution failed:', error);
      return [];
    }
  }
  
  /**
   * Generate recommendations based on pattern matches
   */
  private generateRecommendations(
    matches: PatternMatch[],
    patternType: PatternType
  ): { action: string; confidence: number; reasoning: string }[] {
    if (matches.length === 0) return [];
    
    const recommendations: { action: string; confidence: number; reasoning: string }[] = [];
    
    switch (patternType) {
      case 'coordination':
        // Recommend coordination strategies
        if (matches[0].confidence > 0.8) {
          recommendations.push({
            action: 'optimize_coordination',
            confidence: matches[0].confidence,
            reasoning: 'High confidence coordination pattern match suggests optimization opportunity'
          });
        }
        break;
        
      case 'optimization':
        // Recommend performance optimizations
        if (matches[0].confidence > 0.75) {
          recommendations.push({
            action: 'apply_performance_optimizations',
            confidence: matches[0].confidence,
            reasoning: 'Strong optimization pattern match detected'
          });
        }
        break;
        
      case 'prediction':
        // Recommend predictive scaling
        if (matches[0].confidence > 0.7) {
          recommendations.push({
            action: 'enable_predictive_scaling',
            confidence: matches[0].confidence,
            reasoning: 'Predictive pattern indicates resource scaling opportunity'
          });
        }
        break;
        
      case 'behavior':
        // Recommend behavior adaptations
        if (matches[0].confidence > 0.85) {
          recommendations.push({
            action: 'adapt_agent_behavior',
            confidence: matches[0].confidence,
            reasoning: 'Strong behavior pattern suggests adaptation opportunity'
          });
        }
        break;
    }
    
    return recommendations;
  }
  
  /**
   * Generate feedback metrics based on pattern matches
   */
  private generateFeedback(
    matches: PatternMatch[],
    // eslint-disable-next-line @typescript-eslint/no-unused-vars
    _patternType: PatternType
  ): { metric: string; value: number; threshold: number; action: string }[] {
    if (matches.length === 0) return [];
    
    const feedback: { metric: string; value: number; threshold: number; action: string }[] = [];
    
    // Calculate average confidence
    const avgConfidence = matches.reduce((acc, m) => acc + m.confidence, 0) / matches.length;
    
    // Check if feedback should be generated
    if (avgConfidence > this.config.feedbackThreshold) {
      feedback.push({
        metric: 'pattern_confidence',
        value: avgConfidence,
        threshold: this.config.feedbackThreshold,
        action: 'increase_recognition_weight'
      });
    }
    
    // Check TensorFlow usage
    const tensorFlowMatches = matches.filter(m => m.modelName.includes('tensorflow'));
    if (tensorFlowMatches.length > 0) {
      const tfConfidence = tensorFlowMatches.reduce((acc, m) => acc + m.confidence, 0) / tensorFlowMatches.length;
      
      feedback.push({
        metric: 'tensorflow_confidence',
        value: tfConfidence,
        threshold: 0.75,
        action: tfConfidence > 0.75 ? 'prioritize_tensorflow' : 'balance_approaches'
      });
    }
    
    return feedback;
  }
  
  /**
   * Apply reinforcement learning to improve pattern recognition
   */
  async applyReinforcement(
    // eslint-disable-next-line @typescript-eslint/no-unused-vars
    _swarmId: string,
    patternId: string,
    success: boolean,
    // eslint-disable-next-line @typescript-eslint/no-unused-vars
    _feedback?: { score: number; metadata: Record<string, unknown> }
  ): Promise<void> {
    try {
      // Update pattern usage with success/failure
      await this.patternRecognizer.updatePatternUsage(patternId, success);
      
      // If adaptive learning is enabled, apply reinforcement cycles
      if (this.config.adaptiveLearning && success) {
        // Apply reinforcement learning cycles
        for (let i = 0; i < this.config.reinforcementCycles; i++) {
          // Attempt to increase the pattern's confidence through usage
          await this.patternRecognizer.updatePatternUsage(patternId, true);
        }
        
        this.logger.debug(`Applied ${this.config.reinforcementCycles} reinforcement cycles to pattern ${patternId}`);
      }
      
      // Emit reinforcement event
      this.eventBus.emit('neural:reinforcement:applied', {
        patternId,
        success,
        reinforcementCycles: this.config.adaptiveLearning ? this.config.reinforcementCycles : 0,
        timestamp: new Date().toISOString()
      });
    } catch (error) {
      this.logger.error('Failed to apply reinforcement:', error);
    }
  }
  
  /**
   * Get the workflow statistics
   */
  getStatistics(): {
    cacheSize: number;
    config: NeuralWorkflowConfig;
    recentWorkflows: { key: string; matchCount: number; confidence: number; tensorFlowUsed: boolean }[];
  } {
    return {
      cacheSize: this.workflowCache.size,
      config: this.config,
      recentWorkflows: Array.from(this.workflowCache.entries())
        .slice(-5)
        .map(([key, value]) => ({
          key,
          matchCount: value.matches.length,
          confidence: value.analysis.confidence,
          tensorFlowUsed: value.analysis.tensorFlowUsed
        }))
    };
  }
}