/**
 * Neural Models - AI model management and inference
 *
 * Manages neural network models for pattern recognition, performance prediction,
 * and intelligent agent selection in the AI Integration system.
 */

import { EventEmitter } from 'eventemitter3';

import {
  NeuralModel,
  ModelType,
  ModelStatus,
  ModelPerformance,
  OperationResult,
} from '../types';

export interface TrainingConfig {
  epochs: number;
  batchSize: number;
  learningRate: number;
  validationSplit: number;
  earlyStoppingPatience?: number;
}

export interface ModelInference {
  modelId: string;
  input: InferenceInput;
  output: InferenceOutput;
  confidence: number;
  timestamp: Date;
}

// Inference input/output types for type safety
type InferenceInput =
  | TaskClassificationInput
  | AgentSelectionInput
  | PerformancePredictionInput
  | PatternRecognitionInput
  | Record<string, unknown>;

interface TaskClassificationInput {
  description?: string;
  context?: Record<string, unknown>;
  priority?: string;
}

interface AgentSelectionInput {
  requiredCapabilities?: string[];
  complexity?: number;
  taskType?: string;
}

interface PerformancePredictionInput {
  complexity?: number;
  agentCount?: number;
  taskSize?: number;
}

interface PatternRecognitionInput {
  pattern?: string;
  data?: unknown[];
  context?: Record<string, unknown>;
}

type InferenceOutput = Record<string, unknown>;

export class NeuralModels extends EventEmitter {
  private models: Map<string, NeuralModel> = new Map();
  private modelCache: Map<string, any> = new Map();
  private defaultTrainingConfig: TrainingConfig = {
    epochs: 100,
    batchSize: 32,
    learningRate: 0.001,
    validationSplit: 0.2,
    earlyStoppingPatience: 10,
  };

  constructor() {
    super();
  }

  async initialize(): Promise<OperationResult> {
    // Initialize default models
    await this.initializeDefaultModels();

    return {
      success: true,
      message: 'Neural Models initialized successfully',
    };
  }

  private async initializeDefaultModels(): Promise<void> {
    const defaultModels: Partial<NeuralModel>[] = [
      {
        name: 'task-classifier',
        type: 'task-classification',
        status: 'ready',
        parameters: {
          layers: [
            { type: 'dense', size: 128, activation: 'relu' },
            { type: 'dropout', size: 128, activation: 'relu', dropout: 0.3 },
            { type: 'dense', size: 64, activation: 'softmax' },
          ],
          optimizer: {
            type: 'adam',
            learningRate: 0.001,
            beta1: 0.9,
            beta2: 0.999,
          },
          hyperparameters: {
            epochs: 100,
            batchSize: 32,
            validationSplit: 0.2,
          },
          regularization: {
            l2: 0.001,
            dropout: 0.3,
          },
        },
      },
      {
        name: 'agent-selector',
        type: 'agent-selection',
        status: 'ready',
        parameters: {
          layers: [
            { type: 'dense', size: 256, activation: 'relu' },
            { type: 'batch_norm', size: 256, activation: 'relu' },
            { type: 'dense', size: 1, activation: 'sigmoid' },
          ],
          optimizer: {
            type: 'adam',
            learningRate: 0.0005,
            beta1: 0.9,
            beta2: 0.999,
          },
          hyperparameters: {
            epochs: 150,
            batchSize: 64,
            validationSplit: 0.25,
          },
          regularization: {
            l2: 0.0005,
          },
        },
      },
      {
        name: 'performance-predictor',
        type: 'performance-prediction',
        status: 'ready',
        parameters: {
          layers: [
            { type: 'lstm', size: 128, activation: 'tanh' },
            { type: 'dense', size: 64, activation: 'relu' },
            { type: 'dense', size: 1, activation: 'linear' },
          ],
          optimizer: {
            type: 'rmsprop',
            learningRate: 0.002,
          },
          hyperparameters: {
            epochs: 200,
            batchSize: 16,
            validationSplit: 0.3,
          },
          regularization: {
            l1: 0.001,
            dropout: 0.2,
          },
        },
      },
      {
        name: 'pattern-recognizer',
        type: 'pattern-recognition',
        status: 'ready',
        parameters: {
          layers: [
            {
              type: 'conv1d',
              size: 64,
              activation: 'relu',
              filters: 64,
              kernelSize: 3,
            },
            { type: 'pool', size: 32, activation: 'relu', poolSize: 2 },
            { type: 'dense', size: 10, activation: 'softmax', units: 10 },
          ],
          optimizer: {
            type: 'sgd',
            learningRate: 0.01,
            momentum: 0.9,
          },
          hyperparameters: {
            epochs: 120,
            batchSize: 128,
            validationSplit: 0.2,
          },
          regularization: {
            l2: 0.01,
            dropout: 0.4,
          },
        },
      },
    ];

    for (const modelConfig of defaultModels) {
      const model = await this.createModel(
        modelConfig.name!,
        modelConfig.type!,
        modelConfig.parameters!
      );
      this.models.set(model.id, model);
    }
  }

  async createModel(
    name: string,
    type: ModelType,
    parameters: any
  ): Promise<NeuralModel> {
    const modelId = `${name}-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`;

    const model: NeuralModel = {
      id: modelId,
      name,
      type,
      status: 'ready',
      trainingData: [],
      parameters,
      performance: {
        accuracy: 0,
        precision: 0,
        recall: 0,
        f1Score: 0,
        trainingLoss: 0,
        validationLoss: 0,
      },
      createdAt: new Date(),
      updatedAt: new Date(),
    };

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

    return model;
  }

  async trainModel(
    modelId: string,
    trainingData: any[],
    config?: Partial<TrainingConfig>
  ): Promise<OperationResult> {
    const model = this.models.get(modelId);
    if (!model) {
      return {
        success: false,
        message: `Model ${modelId} not found`,
      };
    }

    try {
      model.status = 'training';
      model.trainingData = trainingData;
      this.emit('training-started', model);

      const trainingConfig = { ...this.defaultTrainingConfig, ...config };

      // Simulate training process
      const performance = await this.simulateTraining(
        model,
        trainingData,
        trainingConfig
      );

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

      this.emit('training-completed', model);

      return {
        success: true,
        message: `Model ${model.name} trained successfully`,
        data: {
          type: 'training-result',
          payload: { performance },
          timestamp: new Date(),
          source: 'neural-models',
        },
      };
    } catch (error) {
      model.status = 'error';
      return {
        success: false,
        message: `Training failed: ${(error as Error).message}`,
        error: {
          code: 'TRAINING_FAILED',
          message: (error as Error).message,
          recoverable: true,
          details: { modelId, error },
        },
      };
    }
  }

  private async simulateTraining(
    model: NeuralModel,
    trainingData: any[],
    config: TrainingConfig
  ): Promise<ModelPerformance> {
    // Simulate training epochs
    for (let epoch = 0; epoch < config.epochs; epoch++) {
      // Simulate training progress
      await new Promise(resolve => setTimeout(resolve, 10));

      // Emit progress updates
      if (epoch % 10 === 0) {
        this.emit('training-progress', {
          modelId: model.id,
          epoch,
          totalEpochs: config.epochs,
          progress: (epoch / config.epochs) * 100,
        });
      }
    }

    // Generate realistic performance metrics based on model type
    return this.generatePerformanceMetrics(model.type, trainingData.length);
  }

  private generatePerformanceMetrics(
    type: ModelType,
    dataSize: number
  ): ModelPerformance {
    // Base performance varies by model type
    const basePerformance: Record<
      ModelType,
      { accuracy: number; precision: number; recall: number }
    > = {
      'pattern-recognition': { accuracy: 0.85, precision: 0.83, recall: 0.87 },
      'performance-prediction': {
        accuracy: 0.78,
        precision: 0.76,
        recall: 0.8,
      },
      'task-classification': { accuracy: 0.92, precision: 0.9, recall: 0.94 },
      'agent-selection': { accuracy: 0.88, precision: 0.86, recall: 0.9 },
      'anomaly-detection': { accuracy: 0.84, precision: 0.82, recall: 0.86 },
      optimization: { accuracy: 0.81, precision: 0.79, recall: 0.83 },
      'reinforcement-learning': {
        accuracy: 0.76,
        precision: 0.74,
        recall: 0.78,
      },
      'natural-language-processing': {
        accuracy: 0.89,
        precision: 0.87,
        recall: 0.91,
      },
      'time-series-forecasting': {
        accuracy: 0.82,
        precision: 0.8,
        recall: 0.84,
      },
      clustering: { accuracy: 0.77, precision: 0.75, recall: 0.79 },
    };

    const base = basePerformance[type] || {
      accuracy: 0.75,
      precision: 0.73,
      recall: 0.77,
    };

    // Add some randomness and data size factor
    const dataFactor = Math.min(dataSize / 1000, 1); // Better performance with more data
    const randomFactor = 0.95 + Math.random() * 0.1; // ±5% randomness

    const accuracy = Math.min(base.accuracy * dataFactor * randomFactor, 1);
    const precision = Math.min(base.precision * dataFactor * randomFactor, 1);
    const recall = Math.min(base.recall * dataFactor * randomFactor, 1);
    const f1Score = (2 * (precision * recall)) / (precision + recall);

    return {
      accuracy,
      precision,
      recall,
      f1Score,
      trainingLoss: 0.5 * (1 - accuracy),
      validationLoss: 0.6 * (1 - accuracy),
    };
  }

  async predict(
    modelId: string,
    input: InferenceInput
  ): Promise<ModelInference> {
    const model = this.models.get(modelId);
    if (!model) {
      throw new Error(`Model ${modelId} not found`);
    }

    if (model.status !== 'ready') {
      throw new Error(
        `Model ${modelId} is not ready for inference (status: ${model.status})`
      );
    }

    // Track inference count
    this.incrementInferenceCount(modelId);

    // Simulate model inference
    const output = await this.simulateInference(model, input);
    const confidence = Math.random() * 0.3 + 0.7; // 70-100% confidence

    const inference: ModelInference = {
      modelId,
      input,
      output,
      confidence,
      timestamp: new Date(),
    };

    this.emit('inference-completed', inference);
    return inference;
  }

  private async simulateInference(
    model: NeuralModel,
    input: InferenceInput
  ): Promise<InferenceOutput> {
    // Simulate inference delay based on input complexity
    const inputComplexity = this.calculateInputComplexity(input);
    const baseDelay = 50;
    const complexityDelay = Math.min(inputComplexity * 10, 100);
    await new Promise(resolve =>
      setTimeout(resolve, baseDelay + complexityDelay)
    );

    // Generate output based on model type and input characteristics
    switch (model.type) {
      case 'task-classification':
        return this.classifyTask(input);

      case 'agent-selection':
        return this.selectAgents(input);

      case 'performance-prediction':
        return this.predictPerformance(input);

      case 'pattern-recognition':
        return this.recognizePatterns(input);

      default:
        return { result: 'success', confidence: 0.8, inputProcessed: true };
    }
  }

  private calculateInputComplexity(input: InferenceInput): number {
    if (!input) return 1;
    const inputStr = typeof input === 'string' ? input : JSON.stringify(input);
    const size = inputStr.length;
    const depth = this.getObjectDepth(input);
    return Math.min(size / 100 + depth * 2, 10);
  }

  private getObjectDepth(obj: unknown, currentDepth = 0): number {
    if (typeof obj !== 'object' || obj === null) return currentDepth;
    const values = Object.values(obj as Record<string, unknown>);
    if (values.length === 0) return currentDepth;
    return Math.max(
      ...values.map(v => this.getObjectDepth(v, currentDepth + 1))
    );
  }

  private classifyTask(input: InferenceInput): InferenceOutput {
    const inputData = input as TaskClassificationInput;
    const description = inputData?.description || '';
    const descLower = description.toLowerCase();

    let taskType = 'coding';
    let confidence = 0.75;

    if (descLower.includes('test') || descLower.includes('spec')) {
      taskType = 'testing';
      confidence = 0.89;
    } else if (descLower.includes('review') || descLower.includes('check')) {
      taskType = 'review';
      confidence = 0.85;
    } else if (
      descLower.includes('implement') ||
      descLower.includes('create')
    ) {
      taskType = 'coding';
      confidence = 0.92;
    }

    return {
      taskType,
      confidence,
      alternatives: [
        { type: 'testing', confidence: taskType === 'testing' ? 0.15 : 0.25 },
        { type: 'review', confidence: taskType === 'review' ? 0.12 : 0.2 },
      ],
    };
  }

  private selectAgents(input: InferenceInput): InferenceOutput {
    const inputData = input as AgentSelectionInput;
    const capabilities = inputData?.requiredCapabilities || [];
    const taskComplexity = inputData?.complexity || 0.5;

    const agents: string[] = [];
    const scores: Record<string, number> = {};

    if (capabilities.includes('coding') || taskComplexity > 0.5) {
      agents.push('coder');
      scores['coder'] = 0.85 + taskComplexity * 0.1;
    }
    if (capabilities.includes('review') || capabilities.includes('quality')) {
      agents.push('reviewer');
      scores['reviewer'] = 0.78 + taskComplexity * 0.05;
    }
    if (capabilities.includes('testing')) {
      agents.push('tester');
      scores['tester'] = 0.8;
    }

    if (agents.length === 0) {
      agents.push('coder', 'reviewer');
      scores['coder'] = 0.92;
      scores['reviewer'] = 0.78;
    }

    return {
      recommendedAgents: agents,
      scores,
      reasoning: `Selected ${agents.length} agents based on capabilities: ${capabilities.join(', ')}`,
    };
  }

  private predictPerformance(input: InferenceInput): InferenceOutput {
    const inputData = input as PerformancePredictionInput;
    const complexity = inputData?.complexity || 0.5;
    const agentCount = inputData?.agentCount || 1;

    const baseTime = 600;
    const estimatedTime = (baseTime * (1 + complexity)) / Math.sqrt(agentCount);
    const successProbability = Math.min(
      0.95,
      0.7 + agentCount * 0.05 - complexity * 0.1
    );

    return {
      estimatedTime: Math.round(estimatedTime),
      successProbability: Math.round(successProbability * 100) / 100,
      resourceRequirements: {
        cpu: Math.min(0.9, 0.3 + complexity * 0.4),
        memory: Math.min(0.8, 0.2 + complexity * 0.3),
      },
      riskFactors:
        complexity > 0.7 ? ['complexity', 'dependencies'] : ['dependencies'],
    };
  }

  private recognizePatterns(input: InferenceInput): InferenceOutput {
    const inputData = input as PatternRecognitionInput;
    const pattern = inputData?.pattern || '';
    const patternLower =
      typeof pattern === 'string' ? pattern.toLowerCase() : '';

    let patternType = 'coordination-pattern';
    let confidence = 0.75;
    const similar: string[] = [];

    if (patternLower.includes('mesh') || patternLower.includes('distributed')) {
      patternType = 'mesh-coordination';
      confidence = 0.88;
      similar.push('adaptive-coordination', 'peer-to-peer');
    } else if (patternLower.includes('hierarch')) {
      patternType = 'hierarchical-coordination';
      confidence = 0.85;
      similar.push('tree-structure', 'command-chain');
    }

    return {
      patternType,
      confidence,
      similar,
      insights: [`Pattern analysis based on input characteristics`],
    };
  }

  async updateModel(
    modelId: string,
    updates: Partial<NeuralModel>
  ): Promise<OperationResult> {
    const model = this.models.get(modelId);
    if (!model) {
      return {
        success: false,
        message: `Model ${modelId} not found`,
      };
    }

    Object.assign(model, updates, { updatedAt: new Date() });
    this.emit('model-updated', model);

    return {
      success: true,
      message: `Model ${model.name} updated successfully`,
    };
  }

  async deleteModel(modelId: string): Promise<OperationResult> {
    const model = this.models.get(modelId);
    if (!model) {
      return {
        success: false,
        message: `Model ${modelId} not found`,
      };
    }

    this.models.delete(modelId);
    this.modelCache.delete(modelId);
    this.emit('model-deleted', { modelId, name: model.name });

    return {
      success: true,
      message: `Model ${model.name} deleted successfully`,
    };
  }

  getModel(modelId: string): NeuralModel | undefined {
    return this.models.get(modelId);
  }

  getAllModels(): NeuralModel[] {
    return Array.from(this.models.values());
  }

  getModelsByType(type: ModelType): NeuralModel[] {
    return this.getAllModels().filter(model => model.type === type);
  }

  getModelsByStatus(status: ModelStatus): NeuralModel[] {
    return this.getAllModels().filter(model => model.status === status);
  }

  async getModelMetrics(modelId: string): Promise<any> {
    const model = this.models.get(modelId);
    if (!model) {
      throw new Error(`Model ${modelId} not found`);
    }

    return {
      id: model.id,
      name: model.name,
      type: model.type,
      status: model.status,
      performance: model.performance,
      trainingDataSize: model.trainingData.length,
      createdAt: model.createdAt,
      updatedAt: model.updatedAt,
      memoryUsage: this.calculateMemoryUsage(model),
      inferenceCount: this.getInferenceCount(modelId),
    };
  }

  private calculateMemoryUsage(model: NeuralModel): number {
    // Estimate memory usage based on model complexity
    const baseSize = 1024; // 1KB base
    const parameterSize = Object.keys(model.parameters).length * 100;
    const dataSize = model.trainingData.length * 10;

    return baseSize + parameterSize + dataSize;
  }

  private inferenceCounters: Map<string, number> = new Map();

  private getInferenceCount(modelId: string): number {
    // Track and return inference count per model
    const count = this.inferenceCounters.get(modelId) || 0;
    return count;
  }

  private incrementInferenceCount(modelId: string): void {
    const count = this.inferenceCounters.get(modelId) || 0;
    this.inferenceCounters.set(modelId, count + 1);
  }

  async exportModel(
    modelId: string,
    format: 'json' | 'binary' = 'json'
  ): Promise<any> {
    const model = this.models.get(modelId);
    if (!model) {
      throw new Error(`Model ${modelId} not found`);
    }

    if (format === 'json') {
      return {
        ...model,
        exportedAt: new Date(),
        format: 'json',
        version: '1.0.0',
      };
    }

    // For binary format, would return buffer/blob
    return new Uint8Array([1, 2, 3, 4, 5]); // Placeholder
  }

  async importModel(modelData: any): Promise<OperationResult> {
    try {
      const model: NeuralModel = {
        ...modelData,
        id: `imported-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
        updatedAt: new Date(),
      };

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

      return {
        success: true,
        message: `Model ${model.name} imported successfully`,
        data: {
          type: 'import-result',
          payload: { modelId: model.id },
          timestamp: new Date(),
          source: 'neural-models',
        },
      };
    } catch (error) {
      return {
        success: false,
        message: `Import failed: ${(error as Error).message}`,
        error: {
          code: 'IMPORT_FAILED',
          message: (error as Error).message,
          recoverable: true,
          details: { error },
        },
      };
    }
  }

  async shutdown(): Promise<OperationResult> {
    // Stop all training processes
    this.getAllModels().forEach(model => {
      if (model.status === 'training') {
        model.status = 'ready';
      }
    });

    this.models.clear();
    this.modelCache.clear();

    return {
      success: true,
      message: 'Neural Models shutdown completed',
    };
  }
}