/**
 * Universal Neural Import API
 * 
 * ALWAYS uses neural matching to map ANY data to our strict NounTypes and VerbTypes
 * Never falls back to rules - neural matching is MANDATORY
 * 
 * Handles:
 * - Strings (text, JSON, CSV, YAML, Markdown)
 * - Files (local paths, any format)
 * - URLs (web pages, APIs, documents)
 * - Objects (structured data)
 * - Binary data (images, PDFs via extraction)
 */

import { NounType, VerbType } from '../types/graphTypes.js'
import { Vector } from '../coreTypes.js'
import type { Brainy } from '../brainy.js'
import type { Entity, Relation } from '../types/brainy.types.js'
import { BrainyTypes, getBrainyTypes } from '../augmentations/typeMatching/brainyTypes.js'
import { NeuralImportAugmentation } from '../augmentations/neuralImport.js'

export interface ImportSource {
  type: 'string' | 'file' | 'url' | 'object' | 'binary'
  data: any
  format?: string  // Optional hint about format
  metadata?: any   // Additional context
}

export interface NeuralImportResult {
  entities: Array<{
    id: string
    type: NounType
    data: any
    vector: Vector
    confidence: number
    metadata: any
  }>
  relationships: Array<{
    id: string
    from: string
    to: string
    type: VerbType
    weight: number
    confidence: number
    metadata?: any
  }>
  stats: {
    totalProcessed: number
    entitiesCreated: number
    relationshipsCreated: number
    averageConfidence: number
    processingTimeMs: number
  }
}

export class UniversalImportAPI {
  private brain: Brainy<any>
  private typeMatcher!: BrainyTypes
  private neuralImport: NeuralImportAugmentation
  private embedCache = new Map<string, Vector>()
  
  constructor(brain: Brainy<any>) {
    this.brain = brain
    this.neuralImport = new NeuralImportAugmentation({
      confidenceThreshold: 0.0,  // Accept ALL confidence levels - never reject
      enableWeights: true,
      skipDuplicates: false  // Process everything
    })
  }
  
  /**
   * Initialize the neural import system
   */
  async init(): Promise<void> {
    this.typeMatcher = await getBrainyTypes()
    // Neural import initializes itself
  }
  
  /**
   * Universal import - handles ANY data source
   * ALWAYS uses neural matching, NEVER falls back
   */
  async import(source: ImportSource | string | any): Promise<NeuralImportResult> {
    const startTime = Date.now()
    
    // Normalize source
    const normalizedSource = this.normalizeSource(source)
    
    // Extract data based on source type
    const extractedData = await this.extractData(normalizedSource)
    
    // Neural processing - MANDATORY
    const neuralResults = await this.neuralProcess(extractedData)
    
    // Store in brain
    const result = await this.storeInBrain(neuralResults)
    
    result.stats.processingTimeMs = Date.now() - startTime
    
    return result
  }
  
  /**
   * Import from URL - fetches and processes
   */
  async importFromURL(url: string): Promise<NeuralImportResult> {
    const response = await fetch(url)
    const contentType = response.headers.get('content-type') || 'text/plain'
    
    let data: any
    if (contentType.includes('json')) {
      data = await response.json()
    } else if (contentType.includes('text') || contentType.includes('html')) {
      data = await response.text()
    } else {
      // Binary data
      const buffer = await response.arrayBuffer()
      data = new Uint8Array(buffer)
    }
    
    return this.import({
      type: 'url',
      data,
      format: contentType,
      metadata: { url, fetchedAt: Date.now() }
    })
  }
  
  /**
   * Import from file - reads and processes
   * Note: In browser environment, use File API instead
   */
  async importFromFile(filePath: string): Promise<NeuralImportResult> {
    // Read the actual file content
    const { readFileSync } = await import('fs')
    const ext = filePath.split('.').pop()?.toLowerCase() || 'txt'
    
    try {
      const fileContent = readFileSync(filePath, 'utf-8')
      
      return this.import({
        type: 'file',
        data: fileContent,  // Actual file content
        format: ext,
        metadata: { 
          path: filePath,
          importedAt: Date.now(),
          fileSize: fileContent.length
        }
      })
    } catch (error) {
      throw new Error(`Failed to read file ${filePath}: ${(error as Error).message}`)
    }
  }
  
  /**
   * Normalize any input to ImportSource
   */
  private normalizeSource(source: any): ImportSource {
    // Already normalized
    if (source && typeof source === 'object' && 'type' in source && 'data' in source) {
      return source as ImportSource
    }
    
    // String input
    if (typeof source === 'string') {
      // Check if it's a URL
      if (source.startsWith('http://') || source.startsWith('https://')) {
        return { type: 'url', data: source }
      }
      
      // Check if it looks like a file path
      if (source.includes('/') || source.includes('\\') || source.includes('.')) {
        // Assume it's a file path reference
        return { type: 'file', data: source }
      }
      
      // Treat as raw string data
      return { type: 'string', data: source }
    }
    
    // Object/Array input
    if (typeof source === 'object') {
      return { type: 'object', data: source }
    }
    
    // Default to string
    return { type: 'string', data: String(source) }
  }
  
  /**
   * Extract structured data from source
   */
  private async extractData(source: ImportSource): Promise<any[]> {
    switch (source.type) {
      case 'url':
        // URL is in data field, need to fetch
        return this.extractFromURL(source.data)
      
      case 'file':
        // File path is in data field, need to read
        return this.extractFromFile(source.data)
      
      case 'string':
        return this.extractFromString(source.data, source.format)
      
      case 'object':
        return Array.isArray(source.data) ? source.data : [source.data]
      
      case 'binary':
        return this.extractFromBinary(source.data, source.format)
      
      default:
        // Unknown type, treat as object
        return [source.data]
    }
  }
  
  /**
   * Extract data from URL
   */
  private async extractFromURL(url: string): Promise<any[]> {
    const result = await this.importFromURL(url)
    return result.entities.map(e => e.data)
  }
  
  /**
   * Extract data from file
   */
  private async extractFromFile(filePath: string): Promise<any[]> {
    const result = await this.importFromFile(filePath)
    return result.entities.map(e => e.data)
  }
  
  /**
   * Extract data from string based on format
   */
  private extractFromString(data: string, format?: string): any[] {
    // Try to detect format if not provided
    const detectedFormat = format || this.detectFormat(data)
    
    switch (detectedFormat) {
      case 'json':
        try {
          const parsed = JSON.parse(data)
          return Array.isArray(parsed) ? parsed : [parsed]
        } catch {
          // Not valid JSON, treat as text
          return this.extractFromText(data)
        }
      
      case 'csv':
        return this.parseCSV(data)
      
      case 'yaml':
      case 'yml':
        return this.parseYAML(data)
      
      case 'markdown':
      case 'md':
        return this.parseMarkdown(data)
      
      case 'xml':
      case 'html':
        return this.parseHTML(data)
      
      default:
        return this.extractFromText(data)
    }
  }
  
  /**
   * Extract from binary data (images, PDFs, etc)
   */
  private async extractFromBinary(data: Uint8Array, format?: string): Promise<any[]> {
    // For now, create a single entity representing the binary data
    // In production, would use OCR, image recognition, PDF extraction, etc.
    return [{
      type: 'binary',
      format: format || 'unknown',
      size: data.length,
      hash: await this.hashBinary(data),
      extractedAt: Date.now()
    }]
  }
  
  /**
   * Extract entities from plain text
   */
  private extractFromText(text: string): any[] {
    // Split into meaningful chunks
    const chunks: any[] = []
    
    // Split by paragraphs
    const paragraphs = text.split(/\n\n+/)
    for (const para of paragraphs) {
      if (para.trim()) {
        chunks.push({
          text: para.trim(),
          type: 'paragraph',
          length: para.length
        })
      }
    }
    
    // If no paragraphs, split by sentences
    if (chunks.length === 0) {
      const sentences = text.match(/[^.!?]+[.!?]+/g) || [text]
      for (const sentence of sentences) {
        if (sentence.trim()) {
          chunks.push({
            text: sentence.trim(),
            type: 'sentence',
            length: sentence.length
          })
        }
      }
    }
    
    return chunks
  }
  
  /**
   * Neural processing - CORE of the system
   * ALWAYS uses embeddings and neural matching
   */
  private async neuralProcess(data: any[]): Promise<{
    entities: Map<string, any>
    relationships: Map<string, any>
  }> {
    const entities = new Map<string, any>()
    const relationships = new Map<string, any>()
    
    for (const item of data) {
      // Generate embedding for the item
      const embedding = await this.generateEmbedding(item)
      
      // Neural type matching - MANDATORY
      const nounMatch = await this.typeMatcher.matchNounType(item)
      
      // Never reject based on confidence - we ALWAYS accept the best match
      const entityId = this.generateId(item)
      
      entities.set(entityId, {
        id: entityId,
        type: nounMatch.type as NounType,  // Always use the neural match
        data: item,
        vector: embedding,
        confidence: nounMatch.confidence,
        metadata: {
          ...item,
          _neuralMatch: nounMatch,
          _importedAt: Date.now()
        }
      })
      
      // Detect relationships using neural matching
      await this.detectNeuralRelationships(item, entityId, entities, relationships)
    }
    
    return { entities, relationships }
  }
  
  /**
   * Generate embedding for any data
   */
  private async generateEmbedding(data: any): Promise<Vector> {
    // Convert to string for embedding
    const text = this.dataToText(data)
    
    // Check cache
    if (this.embedCache.has(text)) {
      return this.embedCache.get(text)!
    }
    
    // Generate new embedding
    const embedding = await (this.brain as any).embed(text)
    
    // Cache it
    this.embedCache.set(text, embedding)
    
    return embedding
  }
  
  /**
   * Convert any data to text for embedding
   */
  private dataToText(data: any): string {
    if (typeof data === 'string') return data
    
    if (typeof data === 'object') {
      // Extract meaningful text from object
      const parts: string[] = []
      
      // Priority fields
      const priorityFields = ['name', 'title', 'description', 'text', 'content', 'label', 'value']
      for (const field of priorityFields) {
        if (data[field]) {
          parts.push(String(data[field]))
        }
      }
      
      // Add other fields
      for (const [key, value] of Object.entries(data)) {
        if (!priorityFields.includes(key) && value) {
          if (typeof value === 'string' || typeof value === 'number') {
            parts.push(`${key}: ${value}`)
          }
        }
      }
      
      return parts.join(' ')
    }
    
    return JSON.stringify(data)
  }
  
  /**
   * Detect relationships using neural matching
   */
  private async detectNeuralRelationships(
    item: any,
    sourceId: string,
    entities: Map<string, any>,
    relationships: Map<string, any>
  ): Promise<void> {
    if (typeof item !== 'object') return
    
    // Look for references to other entities
    for (const [key, value] of Object.entries(item)) {
      // Check if this looks like a reference
      if (this.looksLikeReference(key, value)) {
        // Find or predict target entity
        const targetId = String(value)
        
        // Neural verb type matching
        const verbMatch = await this.typeMatcher.matchVerbType(
          item,  // source object
          { id: targetId },  // target (we may not have full data)
          key  // field name as context
        )
        
        // Always create relationship with neural match
        const relationId = `${sourceId}_${verbMatch.type}_${targetId}`
        relationships.set(relationId, {
          id: relationId,
          from: sourceId,
          to: targetId,
          type: verbMatch.type as VerbType,
          weight: verbMatch.confidence,  // Use confidence as weight
          confidence: verbMatch.confidence,
          metadata: {
            field: key,
            _neuralMatch: verbMatch,
            _importedAt: Date.now()
          }
        })
      }
      
      // Handle arrays of references
      if (Array.isArray(value)) {
        for (const item of value) {
          if (this.looksLikeReference(key, item)) {
            const targetId = String(item)
            const verbMatch = await this.typeMatcher.matchVerbType(
              item,
              { id: targetId },
              key
            )
            
            const relationId = `${sourceId}_${verbMatch.type}_${targetId}`
            relationships.set(relationId, {
              id: relationId,
              from: sourceId,
              to: targetId,
              type: verbMatch.type as VerbType,
              weight: verbMatch.confidence,
              confidence: verbMatch.confidence,
              metadata: {
                field: key,
                array: true,
                _neuralMatch: verbMatch,
                _importedAt: Date.now()
              }
            })
          }
        }
      }
    }
  }
  
  /**
   * Check if a field looks like a reference
   */
  private looksLikeReference(key: string, value: any): boolean {
    // Field name patterns that suggest references
    const refPatterns = [
      /[Ii]d$/,          // ends with Id or id
      /_id$/,            // ends with _id
      /^parent/i,        // starts with parent
      /^child/i,         // starts with child
      /^related/i,       // starts with related
      /^ref/i,           // starts with ref
      /^link/i,          // starts with link
      /^target/i,        // starts with target
      /^source/i,        // starts with source
    ]
    
    // Check if field name matches patterns
    const fieldLooksLikeRef = refPatterns.some(pattern => pattern.test(key))
    
    // Check if value looks like an ID
    const valueLooksLikeId = (
      typeof value === 'string' || 
      typeof value === 'number'
    ) && String(value).length > 0
    
    return fieldLooksLikeRef && valueLooksLikeId
  }
  
  /**
   * Store processed data in brain
   */
  private async storeInBrain(neuralResults: {
    entities: Map<string, any>
    relationships: Map<string, any>
  }): Promise<NeuralImportResult> {
    const result: NeuralImportResult = {
      entities: [],
      relationships: [],
      stats: {
        totalProcessed: neuralResults.entities.size + neuralResults.relationships.size,
        entitiesCreated: 0,
        relationshipsCreated: 0,
        averageConfidence: 0,
        processingTimeMs: 0
      }
    }
    
    let totalConfidence = 0
    
    // Store entities
    for (const entity of neuralResults.entities.values()) {
      const id = await this.brain.add({
        data: entity.data,
        type: entity.type,
        metadata: entity.metadata,
        vector: entity.vector
      })
      
      // Update entity ID for relationship mapping
      entity.id = id
      
      result.entities.push({
        ...entity,
        id
      })
      
      result.stats.entitiesCreated++
      totalConfidence += entity.confidence
    }
    
    // Store relationships
    for (const relation of neuralResults.relationships.values()) {
      // Map to actual entity IDs
      const sourceEntity = Array.from(neuralResults.entities.values())
        .find(e => e.id === relation.from)
      const targetEntity = Array.from(neuralResults.entities.values())
        .find(e => e.id === relation.to)
      
      if (sourceEntity && targetEntity) {
        const id = await this.brain.relate({
          from: sourceEntity.id,
          to: targetEntity.id,
          type: relation.type,
          weight: relation.weight,
          metadata: relation.metadata
        })
        
        result.relationships.push({
          ...relation,
          id,
          from: sourceEntity.id,
          to: targetEntity.id
        })
        
        result.stats.relationshipsCreated++
        totalConfidence += relation.confidence
      }
    }
    
    // Calculate average confidence
    const totalItems = result.stats.entitiesCreated + result.stats.relationshipsCreated
    result.stats.averageConfidence = totalItems > 0 ? totalConfidence / totalItems : 0
    
    return result
  }
  
  // Helper methods for parsing different formats
  
  private detectFormat(data: string): string {
    const trimmed = data.trim()
    
    // JSON
    if ((trimmed.startsWith('{') && trimmed.endsWith('}')) ||
        (trimmed.startsWith('[') && trimmed.endsWith(']'))) {
      return 'json'
    }
    
    // CSV (has commas and newlines)
    if (trimmed.includes(',') && trimmed.includes('\n')) {
      return 'csv'
    }
    
    // YAML (has colons and indentation)
    if (trimmed.includes(':') && (trimmed.includes('\n  ') || trimmed.includes('\n\t'))) {
      return 'yaml'
    }
    
    // Markdown (has headers)
    if (trimmed.includes('#') || trimmed.includes('```')) {
      return 'markdown'
    }
    
    // HTML/XML
    if (trimmed.includes('<') && trimmed.includes('>')) {
      return trimmed.toLowerCase().includes('<!doctype html') ? 'html' : 'xml'
    }
    
    return 'text'
  }
  
  private parseCSV(data: string): any[] {
    // Reuse the CSV parser from neural import
    const lines = data.split('\n').filter(l => l.trim())
    if (lines.length === 0) return []
    
    const headers = lines[0].split(',').map(h => h.trim())
    const results = []
    
    for (let i = 1; i < lines.length; i++) {
      const values = lines[i].split(',').map(v => v.trim())
      const obj: any = {}
      headers.forEach((header, index) => {
        obj[header] = values[index] || ''
      })
      results.push(obj)
    }
    
    return results
  }
  
  private parseYAML(data: string): any[] {
    // Simple YAML parser
    const results = []
    const lines = data.split('\n')
    let current: any = null
    
    for (const line of lines) {
      const trimmed = line.trim()
      if (!trimmed || trimmed.startsWith('#')) continue
      
      if (trimmed.startsWith('- ')) {
        // Array item
        const value = trimmed.substring(2)
        if (!current) {
          results.push(value)
        } else {
          if (!current._items) current._items = []
          current._items.push(value)
        }
      } else if (trimmed.includes(':')) {
        // Key-value
        const [key, ...valueParts] = trimmed.split(':')
        const value = valueParts.join(':').trim()
        
        if (!current) {
          current = {}
          results.push(current)
        }
        current[key.trim()] = value
      }
    }
    
    return results.length > 0 ? results : [{ text: data }]
  }
  
  private parseMarkdown(data: string): any[] {
    const results = []
    const lines = data.split('\n')
    
    let current: any = null
    let inCodeBlock = false
    
    for (const line of lines) {
      if (line.startsWith('```')) {
        inCodeBlock = !inCodeBlock
        if (inCodeBlock && current) {
          current.code = ''
        }
        continue
      }
      
      if (inCodeBlock && current) {
        current.code += line + '\n'
      } else if (line.startsWith('#')) {
        // Header
        const level = line.match(/^#+/)?.[0].length || 1
        const text = line.replace(/^#+\s*/, '')
        current = {
          type: 'heading',
          level,
          text
        }
        results.push(current)
      } else if (line.trim()) {
        // Paragraph
        if (!current || current.type !== 'paragraph') {
          current = {
            type: 'paragraph',
            text: ''
          }
          results.push(current)
        }
        current.text += line + ' '
      }
    }
    
    return results
  }
  
  private parseHTML(data: string): any[] {
    // Simple HTML text extraction
    const text = data
      .replace(/<script\b[^<]*(?:(?!<\/script>)<[^<]*)*<\/script>/gi, '')  // Remove scripts
      .replace(/<style\b[^<]*(?:(?!<\/style>)<[^<]*)*<\/style>/gi, '')      // Remove styles  
      .replace(/<[^>]+>/g, ' ')                                              // Remove tags
      .replace(/\s+/g, ' ')                                                 // Normalize whitespace
      .trim()
    
    return this.extractFromText(text)
  }
  
  private generateId(data: any): string {
    // Generate deterministic ID based on content
    const text = this.dataToText(data)
    const hash = this.simpleHash(text)
    return `import_${hash}_${Date.now()}`
  }
  
  private simpleHash(text: string): string {
    let hash = 0
    for (let i = 0; i < text.length; i++) {
      const char = text.charCodeAt(i)
      hash = ((hash << 5) - hash) + char
      hash = hash & hash
    }
    return Math.abs(hash).toString(36)
  }
  
  private async hashBinary(data: Uint8Array): Promise<string> {
    // Simple binary hash
    let hash = 0
    for (let i = 0; i < Math.min(data.length, 1000); i++) {
      hash = ((hash << 5) - hash) + data[i]
      hash = hash & hash
    }
    return Math.abs(hash).toString(36)
  }
}