import { Logger } from '../../utils/logger';
import { ModelOrchestrator } from '../../integrations/vertex-ai/model-orchestrator';

/**
 * @interface VertexAiPerformanceOptimizerConfig
 * @description Configuration for the Vertex AI Performance Optimizer.
 */
export interface VertexAiPerformanceOptimizerConfig {
  projectID: string;
  // Add configuration for auto-scaling thresholds, cost targets, etc.
}

/**
 * @interface VertexAiPerformanceOptimizerOperations
 * @description Defines operations for optimizing Vertex AI model performance and cost.
 */
export interface VertexAiPerformanceOptimizerOperations {
  optimizeModelEndpoints(): Promise<void>;
  optimizeBatchPredictions(jobConfig: any): Promise<any>;
  analyzeCostAndRecommendOptimizations(): Promise<any>;
  benchmarkModel(modelId: string, testData: any): Promise<any>;
  predictiveMaintenance(modelId: string): Promise<any>;
}

/**
 * @class VertexAiPerformanceOptimizer
 * @description Provides enterprise-grade optimization for Vertex AI model performance and cost.
 */
export class VertexAiPerformanceOptimizer implements VertexAiPerformanceOptimizerOperations {
  private config: VertexAiPerformanceOptimizerConfig;
  private logger: Logger;
  private modelOrchestrator: ModelOrchestrator;

  constructor(config: VertexAiPerformanceOptimizerConfig, modelOrchestrator: ModelOrchestrator) {
    this.config = config;
    this.logger = new Logger('VertexAiPerformanceOptimizer');
    this.modelOrchestrator = modelOrchestrator;
    this.logger.info('Vertex AI Performance Optimizer initialized.');
  }

  /**
   * Optimizes Vertex AI model endpoints for auto-scaling based on real-time demand patterns (conceptual).
   * @returns {Promise<void>}
   */
  public async optimizeModelEndpoints(): Promise<void> {
    this.logger.info('Optimizing Vertex AI model endpoints for auto-scaling (conceptual)...');
    // This would involve:
    // - Configuring auto-scaling policies for deployed model endpoints.
    // - Monitoring traffic and latency to dynamically adjust resources.
    // - Potentially using Vertex AI's own auto-scaling features.
    await new Promise(resolve => setTimeout(resolve, 200));
    this.logger.debug('Model endpoint optimization complete.');
  }

  /**
   * Optimizes batch prediction jobs for cost and performance (conceptual).
   * @param {any} jobConfig Configuration for the batch prediction job.
   * @returns {Promise<any>} The optimized job results.
   */
  public async optimizeBatchPredictions(jobConfig: any): Promise<any> {
    this.logger.info('Optimizing Vertex AI batch predictions (conceptual)...', jobConfig);
    // This would involve:
    // - Selecting optimal machine types and accelerators.
    // - Parallelizing inference across multiple nodes.
    // - Implementing data partitioning and efficient I/O.
    await new Promise(resolve => setTimeout(resolve, 500));
    const optimizedJobResult = { ...jobConfig, status: 'optimized', estimatedCostReduction: '20%' };
    this.logger.debug('Batch prediction optimization complete.', optimizedJobResult);
    return optimizedJobResult;
  }

  /**
   * Analyzes Vertex AI costs and recommends optimizations using AI insights (conceptual).
   * @returns {Promise<any>} Cost analysis and recommendations.
   */
  public async analyzeCostAndRecommendOptimizations(): Promise<any> {
    this.logger.info('Analyzing Vertex AI costs and recommending optimizations (conceptual)...');
    // This would involve:
    // - Ingesting billing data from Cloud Billing.
    // - Using an AI model to identify cost-saving opportunities (e.g., underutilized endpoints, inefficient models).
    await new Promise(resolve => setTimeout(resolve, 300));
    const recommendations = { 
      totalCostLastMonth: '$1500',
      recommendations: [
        'Downscale underutilized endpoints',
        'Switch to cheaper models for non-critical tasks',
        'Implement caching for repetitive inferences'
      ]
    };
    this.logger.debug('Cost analysis complete.', recommendations);
    return recommendations;
  }

  /**
   * Benchmarks a Vertex AI model against SLA targets and user expectations (conceptual).
   * @param {string} modelId The ID of the model to benchmark.
   * @param {any} testData Test data for benchmarking.
   * @returns {Promise<any>} Benchmarking results.
   */
  public async benchmarkModel(modelId: string, testData: any): Promise<any> {
    this.logger.info(`Benchmarking Vertex AI model ${modelId} (conceptual)...`);
    const startTime = Date.now();
    await this.modelOrchestrator.invokeModel(modelId, testData);
    const endTime = Date.now();
    const latency = endTime - startTime;
    const throughput = 1 / (latency / 1000); // Simplified

    const benchmarkResults = {
      modelId,
      latencyMs: latency,
      throughputOpsPerSec: throughput,
      slaTargetLatencyMs: 200,
      slaMet: latency <= 200,
    };
    this.logger.debug('Model benchmarking complete.', benchmarkResults);
    return benchmarkResults;
  }

  /**
   * Performs predictive maintenance for Vertex AI model deployments and infrastructure (conceptual).
   * @param {string} modelId The ID of the model to monitor.
   * @returns {Promise<any>} Predictive maintenance insights.
   */
  public async predictiveMaintenance(modelId: string): Promise<any> {
    this.logger.info(`Performing predictive maintenance for Vertex AI model ${modelId} (conceptual)...`);
    // This would involve:
    // - Analyzing historical performance data and logs for anomalies.
    // - Using machine learning models to predict potential failures or degradation.
    // - Proactively triggering alerts or maintenance actions.
    await new Promise(resolve => setTimeout(resolve, 400));
    const maintenanceInsights = { 
      modelId,
      prediction: 'No immediate issues detected',
      confidence: 0.95,
      suggestedAction: 'Monitor performance trends'
    };
    this.logger.debug('Predictive maintenance insights:', maintenanceInsights);
    return maintenanceInsights;
  }
}