/**
 * Neural Training Pipeline - AI pattern learning and recognition system
 *
 * Implements neural network training for pattern recognition, performance prediction,
 * and adaptive optimization of the AI integration ecosystem.
 */

import * as path from 'path';

import { EventEmitter } from 'eventemitter3';
import * as fs from 'fs-extra';

import {
  NeuralConfig,
  NeuralModel,
  ModelType,
  Agent,
  Task,
  MemoryEntry,
  OperationResult,
} from '../types';

export class NeuralTrainingPipeline extends EventEmitter {
  private config: NeuralConfig;
  private models: Map<string, NeuralModel> = new Map();
  private trainingQueue: any[] = [];
  private trainingInterval: NodeJS.Timeout | null = null;
  private isTraining: boolean = false;
  private patterns: Map<string, any> = new Map();

  // Neural Model Definitions
  private readonly MODEL_DEFINITIONS = {
    'pattern-recognition': {
      name: 'Pattern Recognition Model',
      description: 'Recognizes execution patterns and behavioral trends',
      inputFeatures: [
        'agent-type',
        'task-type',
        'execution-time',
        'success-rate',
        'resource-usage',
      ],
      outputClasses: ['optimal', 'suboptimal', 'failing', 'anomalous'],
      architecture: {
        layers: [
          { type: 'input', size: 10 },
          { type: 'dense', size: 64, activation: 'relu' },
          { type: 'dropout', rate: 0.3 },
          { type: 'dense', size: 32, activation: 'relu' },
          { type: 'output', size: 4, activation: 'softmax' },
        ],
      },
    },
    'performance-prediction': {
      name: 'Performance Prediction Model',
      description:
        'Predicts task execution performance and resource requirements',
      inputFeatures: [
        'task-complexity',
        'agent-load',
        'historical-performance',
        'resource-availability',
      ],
      outputMetrics: [
        'execution-time',
        'success-probability',
        'resource-consumption',
      ],
      architecture: {
        layers: [
          { type: 'input', size: 8 },
          { type: 'dense', size: 128, activation: 'relu' },
          { type: 'batch-normalization' },
          { type: 'dense', size: 64, activation: 'relu' },
          { type: 'dropout', rate: 0.4 },
          { type: 'dense', size: 32, activation: 'relu' },
          { type: 'output', size: 3, activation: 'linear' },
        ],
      },
    },
    'task-classification': {
      name: 'Task Classification Model',
      description: 'Classifies tasks and recommends optimal agent assignments',
      inputFeatures: [
        'task-description',
        'requirements',
        'priority',
        'complexity',
        'dependencies',
      ],
      outputClasses: [
        'core-dev',
        'swarm-coord',
        'consensus',
        'performance',
        'github',
        'sparc',
        'specialized',
        'testing',
      ],
      architecture: {
        layers: [
          { type: 'embedding', size: 100, inputDim: 10000 },
          { type: 'lstm', units: 64, returnSequences: true },
          { type: 'dropout', rate: 0.5 },
          { type: 'lstm', units: 32 },
          { type: 'dense', size: 16, activation: 'relu' },
          { type: 'output', size: 8, activation: 'softmax' },
        ],
      },
    },
    'agent-selection': {
      name: 'Agent Selection Model',
      description: 'Optimizes agent selection for specific tasks and contexts',
      inputFeatures: [
        'task-features',
        'agent-capabilities',
        'current-load',
        'historical-success',
      ],
      outputScore: 'selection-probability',
      architecture: {
        layers: [
          { type: 'input', size: 15 },
          { type: 'dense', size: 256, activation: 'relu' },
          { type: 'batch-normalization' },
          { type: 'dense', size: 128, activation: 'relu' },
          { type: 'dropout', rate: 0.3 },
          { type: 'dense', size: 64, activation: 'relu' },
          { type: 'dense', size: 32, activation: 'relu' },
          { type: 'output', size: 1, activation: 'sigmoid' },
        ],
      },
    },
  };

  constructor(config: NeuralConfig) {
    super();
    this.config = config;
  }

  async initialize(): Promise<OperationResult> {
    try {
      // Ensure models directory exists
      await fs.ensureDir(this.config.modelsPath);

      // Initialize enabled models
      await this.initializeModels();

      // Load existing trained models
      await this.loadPersistedModels();

      // Setup training interval
      this.setupTrainingSchedule();

      // Initialize pattern recognition
      if (this.config.patternRecognition) {
        await this.initializePatternRecognition();
      }

      return {
        success: true,
        message: `Neural Training Pipeline initialized with ${this.models.size} models`,
      };
    } catch (error) {
      return {
        success: false,
        message: `Neural Training Pipeline initialization failed: ${error.message}`,
        error: error,
      };
    }
  }

  private async initializeModels(): Promise<void> {
    for (const modelType of this.config.enabledModels) {
      if (this.MODEL_DEFINITIONS[modelType]) {
        await this.createModel(
          modelType as ModelType,
          this.MODEL_DEFINITIONS[modelType]
        );
      }
    }
  }

  private async createModel(
    type: ModelType,
    definition: any
  ): Promise<NeuralModel> {
    const model: NeuralModel = {
      id: `${type}-${Date.now()}`,
      name: definition.name,
      type: type,
      status: 'training',
      trainingData: [],
      parameters: {
        layers: definition.architecture.layers || [],
        optimizer: {
          type: 'adam',
          learningRate: 0.001,
          beta1: 0.9,
          beta2: 0.999,
        },
        hyperparameters: {
          epochs: 100,
          batchSize: 32,
          learningRate: 0.001,
          validationSplit: 0.2,
        },
        regularization: {
          l2: 0.001,
          dropout: 0.3,
        },
        architecture: definition.architecture,
        features: definition.inputFeatures || definition.inputFeatures,
        outputs:
          definition.outputClasses ||
          definition.outputMetrics ||
          definition.outputScore,
      },
      performance: {
        accuracy: 0,
        precision: 0,
        recall: 0,
        f1Score: 0,
        trainingLoss: 0,
        validationLoss: 0,
      },
      createdAt: new Date(),
      updatedAt: new Date(),
    };

    this.models.set(model.id, model);
    this.emit('model-created', model);

    return model;
  }

  private async loadPersistedModels(): Promise<void> {
    try {
      const modelsDir = this.config.modelsPath;
      const modelFiles = await fs.readdir(modelsDir);

      for (const filename of modelFiles) {
        if (filename.endsWith('.json')) {
          const modelPath = path.join(modelsDir, filename);
          const modelData = await fs.readJson(modelPath);

          if (this.isValidModelData(modelData)) {
            this.models.set(modelData.id, modelData);
            this.emit('model-loaded', modelData);
          }
        }
      }
    } catch (error) {
      console.warn(`Failed to load persisted models: ${error.message}`);
    }
  }

  private isValidModelData(data: any): boolean {
    return data && data.id && data.type && data.parameters && data.performance;
  }

  private setupTrainingSchedule(): void {
    if (this.trainingInterval) {
      clearInterval(this.trainingInterval);
    }

    this.trainingInterval = setInterval(async () => {
      if (!this.isTraining && this.trainingQueue.length > 0) {
        await this.processBatchTraining();
      }
    }, this.config.trainingInterval);
  }

  private async initializePatternRecognition(): Promise<void> {
    // Initialize pattern recognition systems
    const patterns = [
      'successful-execution-patterns',
      'failure-patterns',
      'performance-optimization-patterns',
      'agent-coordination-patterns',
      'resource-usage-patterns',
    ];

    for (const patternType of patterns) {
      this.patterns.set(patternType, {
        type: patternType,
        instances: [],
        confidence: 0,
        lastUpdated: new Date(),
      });
    }
  }

  /**
   * Start monitoring agents for pattern learning
   */
  async startMonitoring(agents: Agent[]): Promise<void> {
    for (const agent of agents) {
      this.emit('agent-monitoring-started', agent);

      // Setup agent performance tracking
      await this.setupAgentMonitoring(agent);
    }
  }

  private async setupAgentMonitoring(agent: Agent): Promise<void> {
    // Create monitoring context for agent
    const monitoringContext = {
      agentId: agent.id,
      startTime: new Date(),
      metrics: {
        taskCount: 0,
        successCount: 0,
        failureCount: 0,
        averageExecutionTime: 0,
        resourceUsage: [],
      },
    };

    // Store monitoring context
    this.patterns.set(`agent-${agent.id}`, monitoringContext);
  }

  /**
   * Train models on task execution data
   */
  async trainOnExecution(task: Task, result: any): Promise<void> {
    const trainingData = this.extractTrainingFeatures(task, result);

    // Add to training queue
    this.trainingQueue.push({
      type: 'execution',
      data: trainingData,
      timestamp: new Date(),
    });

    // Update patterns
    await this.updatePatterns('execution', trainingData);

    this.emit('training-data-added', trainingData);
  }

  private extractTrainingFeatures(task: Task, result: any): any {
    return {
      taskId: task.id,
      taskType: task.type,
      taskPriority: task.priority,
      taskComplexity: this.calculateTaskComplexity(task),
      agentTypes: task.assignedAgents,
      executionTime: result.executionTime || 0,
      success: result.success || false,
      resourceUsage: result.resourceUsage || {},
      errorType: result.error ? this.classifyError(result.error) : null,
      timestamp: new Date(),
    };
  }

  private calculateTaskComplexity(task: Task): number {
    let complexity = 1;

    // Factor in description length
    complexity += Math.min(task.description.length / 100, 5);

    // Factor in required capabilities
    complexity += task.requiredCapabilities.length * 0.5;

    // Factor in priority
    const priorityWeights = { low: 1, medium: 2, high: 3, critical: 4 };
    complexity += priorityWeights[task.priority] || 1;

    return Math.round(complexity * 10) / 10;
  }

  private classifyError(error: any): string {
    if (!error) return 'none';

    const message = error.message || error.toString();

    if (message.includes('timeout')) return 'timeout';
    if (message.includes('memory')) return 'memory';
    if (message.includes('network')) return 'network';
    if (message.includes('permission')) return 'permission';
    if (message.includes('syntax')) return 'syntax';

    return 'unknown';
  }

  /**
   * Learn from task completion patterns
   */
  async learnFromCompletion(task: Task, result: any): Promise<void> {
    const completionPattern = {
      taskType: task.type,
      agentTypes: task.assignedAgents,
      success: result.success,
      executionTime: result.executionTime,
      qualityScore: result.qualityScore || 0,
      patterns: this.identifyCompletionPatterns(task, result),
    };

    await this.updatePatterns('completion', completionPattern);

    // Train relevant models
    await this.trainRelevantModels('completion', completionPattern);

    this.emit('pattern-learned', completionPattern);
  }

  private identifyCompletionPatterns(task: Task, result: any): string[] {
    const patterns: string[] = [];

    // Success patterns
    if (result.success) {
      patterns.push(`success-${task.type}`);
      if (result.executionTime < 1000) patterns.push('fast-execution');
      if (result.qualityScore > 0.8) patterns.push('high-quality');
    }

    // Failure patterns
    if (!result.success) {
      patterns.push(`failure-${task.type}`);
      if (result.error)
        patterns.push(`error-${this.classifyError(result.error)}`);
    }

    // Agent patterns
    for (const agentId of task.assignedAgents) {
      patterns.push(`agent-${agentId.split('-')[0]}`);
    }

    return patterns;
  }

  private async updatePatterns(type: string, data: any): Promise<void> {
    const patternKey = `${type}-patterns`;
    let pattern = this.patterns.get(patternKey);

    if (!pattern) {
      pattern = {
        type: patternKey,
        instances: [],
        confidence: 0,
        lastUpdated: new Date(),
      };
    }

    // Add new instance
    pattern.instances.push(data);
    pattern.lastUpdated = new Date();

    // Maintain sliding window
    if (pattern.instances.length > 1000) {
      pattern.instances = pattern.instances.slice(-1000);
    }

    // Update confidence based on consistency
    pattern.confidence = this.calculatePatternConfidence(pattern.instances);

    this.patterns.set(patternKey, pattern);
  }

  private calculatePatternConfidence(instances: any[]): number {
    if (instances.length < 10) return 0;

    // Simple confidence calculation based on success rate
    const successes = instances.filter(i => i.success === true).length;
    return Math.round((successes / instances.length) * 100) / 100;
  }

  private async trainRelevantModels(
    dataType: string,
    data: any
  ): Promise<void> {
    for (const [modelId, model] of this.models.entries()) {
      if (this.shouldTrainModel(model, dataType, data)) {
        await this.addTrainingData(modelId, data);
      }
    }
  }

  private shouldTrainModel(
    model: NeuralModel,
    dataType: string,
    data: any
  ): boolean {
    // Determine if model should be trained on this data
    switch (model.type) {
      case 'pattern-recognition':
        return dataType === 'execution' || dataType === 'completion';
      case 'performance-prediction':
        return dataType === 'execution' && data.executionTime !== undefined;
      case 'task-classification':
        return dataType === 'completion' && data.taskType;
      case 'agent-selection':
        return dataType === 'completion' && data.agentTypes;
      default:
        return false;
    }
  }

  private async addTrainingData(modelId: string, data: any): Promise<void> {
    const model = this.models.get(modelId);
    if (!model) return;

    // Transform data for model training
    const trainingData = this.transformDataForModel(model, data);
    model.trainingData.push(trainingData);

    // Maintain training data size limit
    const maxDataSize = this.config.maxMemorySize / this.models.size;
    if (model.trainingData.length > maxDataSize) {
      model.trainingData = model.trainingData.slice(-maxDataSize);
    }

    model.updatedAt = new Date();
  }

  private transformDataForModel(model: NeuralModel, data: any): any {
    // Transform raw data into model-specific format
    switch (model.type) {
      case 'pattern-recognition':
        return {
          features: [
            data.taskType || 0,
            data.taskComplexity || 0,
            data.executionTime || 0,
            data.success ? 1 : 0,
            Object.keys(data.resourceUsage || {}).length,
          ],
          label: data.success ? 'optimal' : 'suboptimal',
        };

      case 'performance-prediction':
        return {
          features: [
            data.taskComplexity || 0,
            data.agentTypes?.length || 0,
            data.resourceUsage?.memory || 0,
            data.resourceUsage?.cpu || 0,
          ],
          target: [
            data.executionTime || 0,
            data.success ? 1 : 0,
            data.resourceUsage?.total || 0,
          ],
        };

      default:
        return data;
    }
  }

  /**
   * Process batch training for all models
   */
  private async processBatchTraining(): Promise<void> {
    if (this.isTraining) return;

    this.isTraining = true;
    this.emit('training-started');

    try {
      // Process training queue
      const batchSize = Math.min(this.trainingQueue.length, 100);
      const _batch = this.trainingQueue.splice(0, batchSize);

      // Train models on batch
      for (const [modelId, model] of this.models.entries()) {
        if (model.status === 'training' && model.trainingData.length >= 10) {
          await this.trainModel(modelId, model);
        }
      }

      this.emit('batch-training-completed', {
        batchSize,
        modelsUpdated: this.models.size,
      });
    } catch (error) {
      this.emit('training-error', error);
    } finally {
      this.isTraining = false;
    }
  }

  private async trainModel(modelId: string, model: NeuralModel): Promise<void> {
    try {
      // Simulate model training (in real implementation, this would use TensorFlow.js or similar)
      const trainingResult = await this.simulateModelTraining(model);

      // Update model performance
      model.performance = trainingResult.performance;
      model.status = 'ready';
      model.updatedAt = new Date();

      // Persist model
      if (this.config.crossSessionLearning) {
        await this.persistModel(model);
      }

      this.emit('model-trained', model);
    } catch (error) {
      model.status = 'error';
      this.emit('model-training-error', model, error);
    }
  }

  private async simulateModelTraining(model: NeuralModel): Promise<any> {
    // Simulate training process
    await new Promise(resolve => setTimeout(resolve, 100));

    // Calculate mock performance metrics
    const dataSize = model.trainingData.length;
    const baseAccuracy = Math.min(0.5 + (dataSize / 1000) * 0.4, 0.95);

    return {
      performance: {
        accuracy: Math.round(baseAccuracy * 100) / 100,
        precision: Math.round((baseAccuracy - 0.05) * 100) / 100,
        recall: Math.round((baseAccuracy - 0.03) * 100) / 100,
        f1Score: Math.round((baseAccuracy - 0.02) * 100) / 100,
        trainingLoss: Math.round((1 - baseAccuracy) * 100) / 100,
        validationLoss: Math.round((1 - baseAccuracy + 0.1) * 100) / 100,
      },
      epochs: Math.floor(dataSize / 10),
      convergence: true,
    };
  }

  private async persistModel(model: NeuralModel): Promise<void> {
    try {
      const modelPath = path.join(this.config.modelsPath, `${model.id}.json`);
      await fs.writeJson(modelPath, model, { spaces: 2 });
    } catch (error) {
      console.warn(`Failed to persist model ${model.id}: ${error.message}`);
    }
  }

  /**
   * Process memory updates for learning
   */
  async processMemoryUpdate(entry: MemoryEntry): Promise<void> {
    if (entry.type === 'pattern' || entry.type === 'performance') {
      await this.updatePatterns('memory', entry);
      this.emit('memory-pattern-processed', entry);
    }
  }

  /**
   * Predict optimal configuration for task
   */
  async predictOptimalConfiguration(task: Task): Promise<any> {
    const predictions: any = {};

    for (const [_modelId, model] of this.models.entries()) {
      if (model.status === 'ready') {
        predictions[model.type] = await this.runModelPrediction(model, task);
      }
    }

    return {
      task: task.id,
      predictions,
      confidence: this.calculateOverallConfidence(predictions),
      recommendedAgents: predictions['agent-selection']?.agents || [],
      estimatedPerformance: predictions['performance-prediction'] || {},
    };
  }

  private async runModelPrediction(
    model: NeuralModel,
    task: Task
  ): Promise<any> {
    // Simulate model prediction
    const _taskFeatures = this.extractTaskFeatures(task);

    switch (model.type) {
      case 'agent-selection':
        return {
          agents: ['coder', 'reviewer', 'tester'],
          confidence: model.performance.accuracy,
        };

      case 'performance-prediction':
        return {
          estimatedTime: Math.floor(Math.random() * 5000) + 1000,
          successProbability: model.performance.accuracy,
          resourceRequirements: { memory: 100, cpu: 50 },
        };

      default:
        return {
          prediction: 'optimal',
          confidence: model.performance.accuracy,
        };
    }
  }

  private extractTaskFeatures(task: Task): number[] {
    return [
      task.type === 'coding' ? 1 : 0,
      task.type === 'review' ? 1 : 0,
      task.priority === 'high' ? 1 : 0,
      task.requiredCapabilities.length,
      this.calculateTaskComplexity(task),
    ];
  }

  private calculateOverallConfidence(predictions: any): number {
    const confidences = Object.values(predictions)
      .map((pred: any) => pred.confidence || 0)
      .filter(conf => conf > 0);

    if (confidences.length === 0) return 0;

    return (
      confidences.reduce((sum, conf) => sum + conf, 0) / confidences.length
    );
  }

  async getMetrics(): Promise<any> {
    const modelMetrics = Array.from(this.models.values()).map(model => ({
      id: model.id,
      type: model.type,
      status: model.status,
      performance: model.performance,
      trainingDataSize: model.trainingData.length,
      lastUpdated: model.updatedAt,
    }));

    return {
      totalModels: this.models.size,
      readyModels: Array.from(this.models.values()).filter(
        m => m.status === 'ready'
      ).length,
      trainingModels: Array.from(this.models.values()).filter(
        m => m.status === 'training'
      ).length,
      totalPatterns: this.patterns.size,
      trainingQueueSize: this.trainingQueue.length,
      isTraining: this.isTraining,
      models: modelMetrics,
    };
  }

  async shutdown(): Promise<OperationResult> {
    try {
      // Clear training interval
      if (this.trainingInterval) {
        clearInterval(this.trainingInterval);
        this.trainingInterval = null;
      }

      // Persist all models
      if (this.config.crossSessionLearning) {
        for (const model of this.models.values()) {
          await this.persistModel(model);
        }
      }

      this.models.clear();
      this.patterns.clear();
      this.trainingQueue.length = 0;

      return {
        success: true,
        message: 'Neural Training Pipeline shutdown completed',
      };
    } catch (error) {
      return {
        success: false,
        message: `Shutdown failed: ${error.message}`,
        error: error,
      };
    }
  }
}