import { Logger } from '../../utils/logger';
import { ModelOrchestrator } from '../../integrations/vertex-ai/model-orchestrator';
import { ComputeCoordinator } from '../../integrations/gcp/compute-coordinator';
import { CommunicationCoordinator } from '../../integrations/gcp/communication-coordinator';

/**
 * @interface CoordinationEngineConfig
 * @description Configuration for the Intelligent Coordination Engine.
 */
export interface CoordinationEngineConfig {
  projectID: string;
  // Add configuration for load balancing, scaling, etc.
}

/**
 * @interface CoordinationEngineOperations
 * @description Defines operations for the Intelligent Coordination Engine.
 */
export interface CoordinationEngineOperations {
  distributeTask(task: any, workerPool: string[]): Promise<string>; // Returns assigned worker ID
  sendMessageToWorker(workerId: string, message: any): Promise<void>;
  receiveMessageFromWorker(workerId: string, handler: (message: any) => void): Promise<void>;
  syncSharedState(statePath: string, data: any): Promise<void>;
  getSharedState(statePath: string): Promise<any>;
}

/**
 * @class CoordinationEngine
 * @description Provides advanced swarm coordination capabilities, including intelligent task distribution and robust communication protocols.
 */
export class CoordinationEngine implements CoordinationEngineOperations {
  private config: CoordinationEngineConfig;
  private logger: Logger;
  private modelOrchestrator: ModelOrchestrator;
  private computeCoordinator: ComputeCoordinator;
  private communicationCoordinator: CommunicationCoordinator;

  constructor(
    config: CoordinationEngineConfig,
    modelOrchestrator: ModelOrchestrator,
    computeCoordinator: ComputeCoordinator,
    communicationCoordinator: CommunicationCoordinator
  ) {
    this.config = config;
    this.logger = new Logger('CoordinationEngine');
    this.modelOrchestrator = modelOrchestrator;
    this.computeCoordinator = computeCoordinator;
    this.communicationCoordinator = communicationCoordinator;
    this.logger.info('Intelligent Coordination Engine initialized.');
  }

  /**
   * Distributes a task to an optimal worker using Vertex AI optimization.
   * @param {any} task The task to distribute.
   * @param {string[]} workerPool A list of available worker IDs.
   * @returns {Promise<string>} The ID of the worker assigned the task.
   */
  public async distributeTask(task: any, workerPool: string[]): Promise<string> {
    this.logger.info(`Distributing task to optimal worker from pool of ${workerPool.length} workers...`);
    if (workerPool.length === 0) {
      throw new Error('No workers available for task distribution.');
    }

    // Simulate Vertex AI optimization for task partitioning and worker selection
    const optimalWorker = await this.modelOrchestrator.invokeModel(
      this.modelOrchestrator.config.defaultModel, // Use a general model for this
      `Given task: ${JSON.stringify(task)} and available workers: ${JSON.stringify(workerPool)}, select the optimal worker ID.`, 
      { temperature: 0.1 } // Low temperature for deterministic selection
    );

    // For simulation, just pick a random worker if AI doesn't return a specific one
    const assignedWorkerId = optimalWorker.output.includes('optimal_worker_id') 
      ? optimalWorker.output.split('optimal_worker_id:')[1].trim() 
      : workerPool[Math.floor(Math.random() * workerPool.length)];

    this.logger.debug(`Task distributed to worker: ${assignedWorkerId}`);
    return assignedWorkerId;
  }

  /**
   * Sends a message to a specific worker using Cloud Pub/Sub.
   * @param {string} workerId The ID of the worker.
   * @param {any} message The message payload.
   * @returns {Promise<void>}
   */
  public async sendMessageToWorker(workerId: string, message: any): Promise<void> {
    const topic = `worker-inbox-${workerId}`;
    await this.communicationCoordinator.publishMessage(topic, message);
    this.logger.debug(`Message sent to worker ${workerId}.`);
  }

  /**
   * Subscribes to messages from a specific worker using Cloud Pub/Sub.
   * @param {string} workerId The ID of the worker.
   * @param {(message: any) => void} handler The message handler function.
   * @returns {Promise<void>}
   */
  public async receiveMessageFromWorker(workerId: string, handler: (message: any) => void): Promise<void> {
    const topic = `worker-outbox-${workerId}`;
    await this.communicationCoordinator.subscribeToTopic(topic, handler);
    this.logger.debug(`Subscribed to messages from worker ${workerId}.`);
  }

  /**
   * Synchronizes shared state using Firebase Realtime Database.
   * @param {string} statePath The path in the database for the shared state.
   * @param {any} data The data to synchronize.
   * @returns {Promise<void>}
   */
  public async syncSharedState(statePath: string, data: any): Promise<void> {
    await this.communicationCoordinator.syncRealtimeData(statePath, data);
    this.logger.debug(`Shared state synced at ${statePath}.`);
  }

  /**
   * Retrieves shared state from Firebase Realtime Database.
   * @param {string} statePath The path in the database for the shared state.
   * @returns {Promise<any>} The retrieved shared state.
   */
  public async getSharedState(statePath: string): Promise<any> {
    const state = await this.communicationCoordinator.getRealtimeData(statePath);
    this.logger.debug(`Shared state retrieved from ${statePath}.`);
    return state;
  }

  /**
   * Manages real-time capacity across multiple GCP regions (conceptual).
   * @returns {Promise<void>}
   */
  public async manageRegionalCapacity(): Promise<void> {
    this.logger.info('Managing real-time capacity across GCP regions (conceptual)...');
    // This would involve:
    // - Monitoring load in different regions.
    // - Dynamically adjusting resource allocation (e.g., GKE clusters, Cloud Run instances).
    // - Routing tasks to less loaded regions.
  }

  /**
   * Implements predictive scaling based on historical workload patterns (conceptual).
   * @returns {Promise<void>}
   */
  public async predictiveScale(): Promise<void> {
    this.logger.info('Implementing predictive scaling based on workload patterns (conceptual)...');
    // This would involve:
    // - Analyzing historical task distribution and worker performance data.
    // - Using Vertex AI for forecasting future workload.
    // - Proactively scaling compute resources (e.g., GKE node pools, Cloud Run concurrency).
  }
}