import { Logger } from '../../utils/logger';
import { SQLiteMemoryCore } from '../sqlite-memory-core';
import { MemoryIntelligence } from '../memory-intelligence';
import { ToolExecutor } from '../tool-executor';
import { ToolRegistry } from '../tool-registry';

/**
 * @interface WorkerConfig
 * @description Configuration for a Worker Agent.
 */
export interface WorkerConfig {
  id: string;
  name: string;
  specialization: string; // e.g., 'data_processing', 'image_generation'
  vertexAiEndpoint?: string; // Specific Vertex AI model endpoint
  // Add other configuration for Pub/Sub topics, monitoring, etc.
}

/**
 * @interface WorkerOperations
 * @description Defines the operations available for a Worker Agent.
 */
export interface WorkerOperations {
  executeTask(task: any): Promise<any>;
  processWithAI(data: any, modelId?: string): Promise<any>;
  sendMessage(topic: string, message: any): Promise<void>;
  collectMetrics(): Promise<any>;
}

/**
 * @class WorkerAgent
 * @description Implements a specialized worker agent within the Hive-Mind swarm.
 */
export class WorkerAgent implements WorkerOperations {
  private config: WorkerConfig;
  private logger: Logger;
  private dbCore: SQLiteMemoryCore;
  private memoryIntelligence: MemoryIntelligence;
  private toolExecutor: ToolExecutor;
  private toolRegistry: ToolRegistry;
  // Placeholder for Vertex AI client
  // private vertexAiClient: any;
  // Placeholder for Pub/Sub client
  // private pubSubClient: any;

  constructor(
    config: WorkerConfig,
    dbCore: SQLiteMemoryCore,
    memoryIntelligence: MemoryIntelligence,
    toolExecutor: ToolExecutor,
    toolRegistry: ToolRegistry
  ) {
    this.config = config;
    this.logger = new Logger(`WorkerAgent:${config.id}`);
    this.dbCore = dbCore;
    this.memoryIntelligence = memoryIntelligence;
    this.toolExecutor = toolExecutor;
    this.toolRegistry = toolRegistry;

    this.logger.info(`Worker Agent ${config.name} (${config.id}) initialized with specialization: ${config.specialization}.`);
    // Initialize Vertex AI and Pub/Sub clients here (conceptual)
  }

  /**
   * Executes a given task assigned by the Queen.
   * @param {any} task The task to execute.
   * @returns {Promise<any>} The result of the task execution.
   */
  public async executeTask(task: any): Promise<any> {
    this.logger.info(`Worker ${this.config.name} executing task:`, task);
    // Example: Task might involve using a tool
    if (task.toolName && task.methodName) {
      try {
        const toolResult = await this.toolExecutor.executeTool(task.toolName, task.methodName, task.args || []);
        this.logger.debug(`Task completed with tool ${task.toolName}.${task.methodName}.`);
        return { status: 'completed', result: toolResult };
      } catch (error: any) {
        this.logger.error(`Error executing tool for task: ${error.message}`);
        return { status: 'failed', error: error.message };
      }
    } else if (task.aiProcessing) {
      const aiResult = await this.processWithAI(task.data, task.modelId);
      return { status: 'completed', result: aiResult };
    } else {
      // Simulate generic task execution
      await new Promise(resolve => setTimeout(resolve, 300));
      this.logger.debug('Generic task executed.');
      return { status: 'completed', result: `Task ${task.id} processed by ${this.config.name}` };
    }
  }

  /**
   * Processes data using a Vertex AI model endpoint.
   * @param {any} data The data to process.
   * @param {string} [modelId] Optional: Specific model ID to use. Defaults to worker's configured endpoint.
   * @returns {Promise<any>} The AI processing result.
   */
  public async processWithAI(data: any, modelId?: string): Promise<any> {
    const targetModel = modelId || this.config.vertexAiEndpoint || 'default_vertex_ai_model';
    this.logger.info(`Worker ${this.config.name} processing data with AI model: ${targetModel}`);
    // Placeholder for Vertex AI model inference
    await new Promise(resolve => setTimeout(resolve, 200)); // Simulate AI inference
    const aiResult = { processedData: data, modelUsed: targetModel, insights: 'simulated_ai_insights' };
    this.logger.debug('AI processing complete.', aiResult);
    return aiResult;
  }

  /**
   * Sends a message to a specified Pub/Sub topic.
   * @param {string} topic The Pub/Sub topic to publish to.
   * @param {any} message The message payload.
   * @returns {Promise<void>}
   */
  public async sendMessage(topic: string, message: any): Promise<void> {
    this.logger.info(`Worker ${this.config.name} sending message to Pub/Sub topic: ${topic}`);
    // Placeholder for Pub/Sub publish operation
    await new Promise(resolve => setTimeout(resolve, 50)); // Simulate Pub/Sub latency
    this.logger.debug('Message sent.', message);
  }

  /**
   * Collects performance metrics for the worker agent.
   * @returns {Promise<any>} Collected metrics.
   */
  public async collectMetrics(): Promise<any> {
    this.logger.info(`Worker ${this.config.name} collecting performance metrics...`);
    // Placeholder for Google Cloud Monitoring integration
    const metrics = {
      cpu_utilization: Math.random() * 100,
      memory_usage: Math.random() * 1024,
      tasks_completed: Math.floor(Math.random() * 100),
      latency: Math.random() * 50,
    };
    this.logger.debug('Metrics collected.', metrics);
    // Optionally, record metrics to SQLiteMemoryCore performance table
    // await this.dbCore.recordPerformanceMetric('worker_cpu_utilization', metrics.cpu_utilization, 'worker', this.config.id);
    return metrics;
  }
}