import {
  AugmentationType,
  IActivationAugmentation,
  AugmentationResponse
} from '../types/augmentations.js'
import { BrainyData } from '../brainyData.js'
import { GraphNoun, GraphVerb, NounType, VerbType } from '../types/graphTypes.js'

/**
 * LLMTrainingActivationAugmentation
 *
 * An activation augmentation that provides actions for exporting Brainy graph data
 * to train and export a Language Learning Model (LLM).
 */
export class LLMTrainingActivationAugmentation implements IActivationAugmentation {
  readonly name: string
  readonly description: string
  enabled: boolean = true
  private isInitialized = false
  private brainyData: BrainyData | null = null

  constructor(name: string = 'llm-training-activation') {
    this.name = name
    this.description = 'Activation augmentation for training and exporting LLMs using Brainy graph data'
  }

  getType(): AugmentationType {
    return AugmentationType.ACTIVATION
  }

  /**
   * Initialize the augmentation
   */
  async initialize(): Promise<void> {
    if (this.isInitialized) {
      return
    }

    this.isInitialized = true
  }

  /**
   * Shut down the augmentation
   */
  async shutDown(): Promise<void> {
    this.isInitialized = false
  }

  /**
   * Get the status of the augmentation
   */
  async getStatus(): Promise<'active' | 'inactive' | 'error'> {
    return this.isInitialized ? 'active' : 'inactive'
  }

  /**
   * Set the Brainy data instance to use for accessing graph data
   * @param data The BrainyData instance
   */
  setBrainyData(data: BrainyData): void {
    this.brainyData = data
  }

  /**
   * Trigger an action based on a processed command or internal state
   * @param actionName The name of the action to trigger
   * @param parameters Optional parameters for the action
   */
  // This is the interface method that returns a synchronous response
  triggerAction(
    actionName: string,
    parameters?: Record<string, unknown>
  ): AugmentationResponse<unknown> {
    if (!this.brainyData) {
      return {
        success: false,
        data: null,
        error: 'BrainyData instance not set'
      }
    }

    // Start the async operation
    this.triggerActionAsync(actionName, parameters);

    // Return a placeholder response
    return {
      success: true,
      data: { status: 'processing', actionName, parameters },
    };
  }

  // This is the actual implementation that handles the async operations
  private async triggerActionAsync(
    actionName: string,
    parameters?: Record<string, unknown>
  ): Promise<void> {
    try {
      let result: AugmentationResponse<unknown>;

      switch (actionName) {
        case 'exportGraphData':
          result = await this.handleExportGraphData(parameters || {});
          break;
        case 'generateTrainingData':
          result = await this.handleGenerateTrainingData(parameters || {});
          break;
        case 'trainModel':
          result = await this.handleTrainModel(parameters || {});
          break;
        case 'exportModel':
          result = await this.handleExportModel(parameters || {});
          break;
        default:
          result = {
            success: false,
            data: null,
            error: `Unknown action: ${actionName}`
          };
      }

      // Here you would typically emit an event or update a status property
      // with the result of the async operation
      console.log(`Action ${actionName} completed:`, result);
    } catch (error) {
      console.error(`Error executing action ${actionName}:`, error);
    }
  }

  /**
   * Handle the exportGraphData action
   * @param parameters Action parameters
   */
  private async handleExportGraphData(
    parameters: Record<string, unknown>
  ): Promise<AugmentationResponse<unknown>> {
    const format = parameters.format as string || 'json'
    const includeNouns = parameters.includeNouns as boolean || true
    const includeVerbs = parameters.includeVerbs as boolean || true
    const nounTypes = parameters.nounTypes as NounType[] || Object.values(NounType)
    const verbTypes = parameters.verbTypes as VerbType[] || Object.values(VerbType)

    try {
      const exportData: Record<string, unknown> = {}

      // Export nouns if requested
      if (includeNouns) {
        const nouns: GraphNoun[] = []
        // Get all nouns from BrainyData
        // Use search to get all nouns of the specified types
        for (const nounType of nounTypes) {
          try {
            // Search for nouns of this type with a high limit to get all
            const searchResults = await this.brainyData!.searchByNounTypes(
              '', // Empty query to match all
              1000, // High limit to get as many as possible
              [nounType as string]
            );

            // Add the results to our nouns array
            if (searchResults && Array.isArray(searchResults)) {
              for (const result of searchResults) {
                // SearchResult might not have data directly, use metadata instead
                if (result.metadata) {
                  nouns.push(result.metadata as unknown as GraphNoun);
                }
              }
            }
          } catch (error) {
            console.warn(`Failed to get nouns of type ${nounType}:`, error);
          }
        }

        exportData.nouns = nouns
      }

      // Export verbs if requested
      if (includeVerbs) {
        try {
          const verbs = await this.brainyData!.getAllVerbs() || []
          exportData.verbs = verbs.filter(verb => {
            // Check for verb type in either verb.verb or verb.type
            const verbType = (verb as any).verb || (verb as any).type;
            return typeof verbType === 'string' && verbTypes.includes(verbType as VerbType);
          })
        } catch (error) {
          console.warn('Failed to get all verbs:', error);
          exportData.verbs = [];
        }
      }

      // Format the export data
      if (format === 'json') {
        return {
          success: true,
          data: exportData
        }
      } else if (format === 'csv') {
        // Convert to CSV format (simplified)
        const csvData = this.convertToCSV(exportData)
        return {
          success: true,
          data: csvData
        }
      } else {
        return {
          success: false,
          data: null,
          error: `Unsupported format: ${format}`
        }
      }
    } catch (error) {
      return {
        success: false,
        data: null,
        error: `Failed to export graph data: ${error}`
      }
    }
  }

  /**
   * Handle the generateTrainingData action
   * @param parameters Action parameters
   */
  private async handleGenerateTrainingData(
    parameters: Record<string, unknown>
  ): Promise<AugmentationResponse<unknown>> {
    const format = parameters.format as string || 'jsonl'
    const includeEmbeddings = parameters.includeEmbeddings as boolean || true
    const maxSamples = parameters.maxSamples as number || 1000

    try {
      // First, export the graph data
      const exportResult = await this.handleExportGraphData({
        format: 'json',
        includeNouns: true,
        includeVerbs: true
      })

      if (!exportResult.success) {
        return exportResult
      }

      const graphData = exportResult.data as Record<string, unknown>
      const nouns = graphData.nouns as GraphNoun[] || []
      const verbs = graphData.verbs as GraphVerb[] || []

      // Generate training samples
      const trainingSamples = this.generateTrainingSamples(nouns, verbs, maxSamples, includeEmbeddings)

      // Format the training data
      if (format === 'jsonl') {
        const jsonlData = trainingSamples.map(sample => JSON.stringify(sample)).join('\n')
        return {
          success: true,
          data: jsonlData
        }
      } else if (format === 'json') {
        return {
          success: true,
          data: trainingSamples
        }
      } else {
        return {
          success: false,
          data: null,
          error: `Unsupported format: ${format}`
        }
      }
    } catch (error) {
      return {
        success: false,
        data: null,
        error: `Failed to generate training data: ${error}`
      }
    }
  }

  /**
   * Handle the trainModel action
   * @param parameters Action parameters
   */
  private async handleTrainModel(
    parameters: Record<string, unknown>
  ): Promise<AugmentationResponse<unknown>> {
    const modelType = parameters.modelType as string || 'default'
    const epochs = parameters.epochs as number || 10
    const batchSize = parameters.batchSize as number || 32
    const learningRate = parameters.learningRate as number || 0.001

    try {
      // First, generate the training data
      const trainingDataResult = await this.handleGenerateTrainingData({
        format: 'json'
      })

      if (!trainingDataResult.success) {
        return trainingDataResult
      }

      const trainingSamples = trainingDataResult.data as any[]

      // In a real implementation, this would call an actual LLM training library
      // For now, we'll just simulate the training process
      const trainingResult = this.simulateTraining(
        trainingSamples, 
        modelType, 
        epochs, 
        batchSize, 
        learningRate
      )

      return {
        success: true,
        data: trainingResult
      }
    } catch (error) {
      return {
        success: false,
        data: null,
        error: `Failed to train model: ${error}`
      }
    }
  }

  /**
   * Handle the exportModel action
   * @param parameters Action parameters
   */
  private async handleExportModel(
    parameters: Record<string, unknown>
  ): Promise<AugmentationResponse<unknown>> {
    const format = parameters.format as string || 'onnx'
    const modelId = parameters.modelId as string

    try {
      // In a real implementation, this would export the trained model
      // For now, we'll just return a simulated export result
      const exportResult = {
        modelId: modelId || 'default-model',
        format,
        timestamp: new Date().toISOString(),
        size: '125MB',
        parameters: '7B',
        url: `https://example.com/models/${modelId || 'default-model'}.${format}`
      }

      return {
        success: true,
        data: exportResult
      }
    } catch (error) {
      return {
        success: false,
        data: null,
        error: `Failed to export model: ${error}`
      }
    }
  }

  /**
   * Generates an expressive output or response from Brainy
   * @param knowledgeId The identifier of the knowledge to express
   * @param format The desired output format (e.g., 'text', 'json')
   */
  generateOutput(
    knowledgeId: string,
    format: string
  ): AugmentationResponse<string | Record<string, unknown>> {
    if (!this.brainyData) {
      return {
        success: false,
        data: '',
        error: 'BrainyData instance not set'
      }
    }

    // Start the async operation
    this.generateOutputAsync(knowledgeId, format);

    // Return a placeholder response
    return {
      success: true,
      data: { status: 'processing', knowledgeId, format },
    };
  }

  // This is the actual implementation that handles the async operations
  private async generateOutputAsync(
    knowledgeId: string,
    format: string
  ): Promise<void> {
    try {
      // Get the knowledge from BrainyData
      const knowledge = await this.brainyData!.get(knowledgeId);
      let result: AugmentationResponse<string | Record<string, unknown>>;

      if (!knowledge) {
        result = {
          success: false,
          data: '',
          error: `Knowledge not found: ${knowledgeId}`
        };
      } else {
        // Format the output
        if (format === 'text') {
          // Generate a text representation of the knowledge
          const text = this.generateTextFromKnowledge(knowledge);
          result = {
            success: true,
            data: text
          };
        } else if (format === 'json') {
          // Return the knowledge as JSON
          // Convert VectorDocument to a plain object to avoid type issues
          const knowledgeObj = { ...knowledge };
          result = {
            success: true,
            data: knowledgeObj
          };
        } else {
          result = {
            success: false,
            data: '',
            error: `Unsupported format: ${format}`
          };
        }
      }

      // Here you would typically emit an event or update a status property
      // with the result of the async operation
      console.log(`Generate output for ${knowledgeId} completed:`, result);
    } catch (error) {
      console.error(`Failed to generate output for ${knowledgeId}:`, error);
    }
  }

  /**
   * Interacts with an external system or API
   * @param systemId The identifier of the external system
   * @param payload The data to send to the external system
   */
  interactExternal(
    systemId: string,
    payload: Record<string, unknown>
  ): AugmentationResponse<unknown> {
    // This method would interact with external LLM training systems or APIs
    // For now, we'll just return a simulated response
    return {
      success: true,
      data: {
        systemId,
        status: 'processing',
        message: 'Interaction with external system initiated',
        timestamp: new Date().toISOString(),
        payload
      }
    }
  }

  /**
   * Helper method to convert data to CSV format
   * @param data The data to convert
   * @returns CSV formatted string
   */
  private convertToCSV(data: Record<string, unknown>): string {
    // Simplified CSV conversion - in a real implementation, this would be more robust
    let csv = ''

    // Handle nouns
    if (data.nouns && Array.isArray(data.nouns) && data.nouns.length > 0) {
      const nouns = data.nouns as GraphNoun[]
      // Add header
      csv += 'id,noun,label,createdAt\n'

      // Add rows
      for (const noun of nouns) {
        csv += `${noun.id},${noun.noun},${noun.label || ''},${noun.createdAt.seconds}\n`
      }

      csv += '\n'
    }

    // Handle verbs
    if (data.verbs && Array.isArray(data.verbs) && data.verbs.length > 0) {
      const verbs = data.verbs as GraphVerb[]
      // Add header
      csv += 'id,source,target,verb,label,createdAt\n'

      // Add rows
      for (const verb of verbs) {
        csv += `${verb.id},${verb.source},${verb.target},${verb.verb},${verb.label || ''},${verb.createdAt.seconds}\n`
      }
    }

    return csv
  }

  /**
   * Helper method to generate training samples from graph data
   * @param nouns The graph nouns
   * @param verbs The graph verbs
   * @param maxSamples Maximum number of samples to generate
   * @param includeEmbeddings Whether to include embeddings in the samples
   * @returns Array of training samples
   */
  private generateTrainingSamples(
    nouns: GraphNoun[],
    verbs: GraphVerb[],
    maxSamples: number,
    includeEmbeddings: boolean
  ): any[] {
    const samples = []

    // Create a map of noun IDs to nouns for quick lookup
    const nounMap = new Map<string, GraphNoun>()
    for (const noun of nouns) {
      nounMap.set(noun.id, noun)
    }

    // Generate samples from verbs (relationships)
    for (const verb of verbs) {
      if (samples.length >= maxSamples) break

      const sourceNoun = nounMap.get(verb.source)
      const targetNoun = nounMap.get(verb.target)

      if (sourceNoun && targetNoun) {
        // Create a training sample
        const sample: Record<string, unknown> = {
          input: `What is the relationship between ${sourceNoun.label || sourceNoun.id} and ${targetNoun.label || targetNoun.id}?`,
          output: `${sourceNoun.label || sourceNoun.id} ${verb.verb} ${targetNoun.label || targetNoun.id}`
        }

        // Add embeddings if requested
        if (includeEmbeddings) {
          sample.sourceEmbedding = sourceNoun.embedding
          sample.targetEmbedding = targetNoun.embedding
          sample.verbEmbedding = verb.embedding
        }

        samples.push(sample)
      }
    }

    // Generate samples from nouns (entities)
    for (const noun of nouns) {
      if (samples.length >= maxSamples) break

      // Create a training sample
      const sample: Record<string, unknown> = {
        input: `What type of entity is ${noun.label || noun.id}?`,
        output: `${noun.label || noun.id} is a ${noun.noun}`
      }

      // Add embeddings if requested
      if (includeEmbeddings) {
        sample.embedding = noun.embedding
      }

      samples.push(sample)
    }

    return samples
  }

  /**
   * Helper method to simulate training an LLM
   * @param trainingSamples The training samples
   * @param modelType The type of model to train
   * @param epochs Number of training epochs
   * @param batchSize Batch size for training
   * @param learningRate Learning rate for training
   * @returns Simulated training result
   */
  private simulateTraining(
    trainingSamples: any[],
    modelType: string,
    epochs: number,
    batchSize: number,
    learningRate: number
  ): Record<string, unknown> {
    // In a real implementation, this would use an actual LLM training library
    // For now, we'll just simulate the training process
    return {
      modelId: `${modelType}-${new Date().getTime()}`,
      trainingSamples: trainingSamples.length,
      epochs,
      batchSize,
      learningRate,
      trainingTime: `${Math.floor(Math.random() * 100) + 50}s`,
      loss: Math.random() * 0.5,
      accuracy: 0.5 + Math.random() * 0.5,
      timestamp: new Date().toISOString()
    }
  }

  /**
   * Helper method to generate text from knowledge
   * @param knowledge The knowledge to generate text from
   * @returns Generated text
   */
  private generateTextFromKnowledge(knowledge: any): string {
    // In a real implementation, this would generate natural language text
    // For now, we'll just return a simple string
    if (knowledge.noun) {
      return `This is a ${knowledge.noun} called "${knowledge.label || knowledge.id}".`
    } else if (knowledge.verb) {
      return `This is a ${knowledge.verb} relationship from "${knowledge.source}" to "${knowledge.target}".`
    } else {
      return `This is an entity with ID "${knowledge.id}".`
    }
  }
}

/**
 * Factory function to create an LLM training augmentation
 * @param options Additional options
 * @returns The created augmentation
 */
export async function createLLMTrainingAugmentation(
  options: {
    name?: string,
    brainyData?: BrainyData
  } = {}
): Promise<LLMTrainingActivationAugmentation> {
  // Create the activation augmentation
  const activation = new LLMTrainingActivationAugmentation(options.name)
  await activation.initialize()

  // Set the BrainyData instance if provided
  if (options.brainyData) {
    activation.setBrainyData(options.brainyData)
  }

  return activation
}
