// ElevenLabs API integration with advanced features
interface VoiceGenerationConfig {
  text: string
  voiceId: string
  modelId: 'eleven_monolingual_v1' | 'eleven_multilingual_v2' | 'eleven_turbo_v2'
  voiceSettings: {
    stability: number
    similarityBoost: number
    style: number
    useSpeakerBoost: boolean
  }
  outputFormat?: 'mp3_44100_128' | 'pcm_16000' | 'pcm_22050' | 'pcm_24000'
}
export class ElevenLabsClient {
  private apiKey: string
  private baseUrl = 'https://api.elevenlabs.io/v1'
  constructor(apiKey: string) {
    this.apiKey = apiKey
  }
  async generateSpeech(config: VoiceGenerationConfig): Promise {
    const response = await fetch(
      ${this.baseUrl}/text-to-speech/${config.voiceId},
      {
        method: 'POST',
        headers: {
          'Accept': 'audio/mpeg',
          'Content-Type': 'application/json',
          'xi-api-key': this.apiKey
        },
        body: JSON.stringify({
          text: config.text,
          model_id: config.modelId,
          voice_settings: config.voiceSettings,
          output_format: config.outputFormat
        })
      }
    )
    if (!response.ok) {
      throw new Error(ElevenLabs API error: ${response.statusText})
    }
    return await response.arrayBuffer()
  }
  async streamSpeech(config: VoiceGenerationConfig): Promise {
    const response = await fetch(
      ${this.baseUrl}/text-to-speech/${config.voiceId}/stream,
      {
        method: 'POST',
        headers: {
          'Accept': 'audio/mpeg',
          'Content-Type': 'application/json',
          'xi-api-key': this.apiKey
        },
        body: JSON.stringify({
          text: config.text,
          model_id: config.modelId,
          voice_settings: config.voiceSettings
        })
      }
    )
    if (!response.body) {
      throw new Error('No response body')
    }
    return response.body
  }
  async cloneVoice(name: string, audioFiles: File[]): Promise {
    const formData = new FormData()
    formData.append('name', name)
    audioFiles.forEach((file, index) => {
      formData.append(files[${index}], file)
    })
    const response = await fetch(${this.baseUrl}/voices/add, {
      method: 'POST',
      headers: {
        'xi-api-key': this.apiKey
      },
      body: formData
    })
    const data = await response.json()
    return data.voice_id
  }
  async getVoices() {
    const response = await fetch(${this.baseUrl}/voices, {
      headers: {
        'xi-api-key': this.apiKey
      }
    })
    return await response.json()
  }
}

// Real-time voice synthesis with WebSocket
export class RealtimeVoiceClient {
  private ws: WebSocket | null = null
  private audioContext: AudioContext
  private audioQueue: AudioBuffer[] = []
  constructor() {
    this.audioContext = new AudioContext()
  }
  async connect(voiceId: string, apiKey: string) {
    this.ws = new WebSocket(
      wss://api.elevenlabs.io/v1/text-to-speech/${voiceId}/stream-input?model_id=eleven_turbo_v2
    )
    this.ws.onopen = () => {
      this.ws?.send(JSON.stringify({
        text: ' ',
        voice_settings: {
          stability: 0.5,
          similarity_boost: 0.75
        },
        xi_api_key: apiKey
      }))
    }
    this.ws.onmessage = async (event) => {
      const audioData = await event.data.arrayBuffer()
      const audioBuffer = await this.audioContext.decodeAudioData(audioData)
      this.playAudio(audioBuffer)
    }
  }
  sendText(text: string) {
    if (this.ws?.readyState === WebSocket.OPEN) {
      this.ws.send(JSON.stringify({ text }))
    }
  }
  private playAudio(buffer: AudioBuffer) {
    const source = this.audioContext.createBufferSource()
    source.buffer = buffer
    source.connect(this.audioContext.destination)
    source.start()
  }
  disconnect() {
    this.ws?.close()
  }
}

// Luma Ray3 AI video generation
interface VideoGenerationParams {
  prompt: string
  duration: number
  style: 'cinematic' | 'documentary' | 'animated' | 'realistic'
  resolution: '720p' | '1080p' | '4k'
  fps: 24 | 30 | 60
  aspectRatio: '16:9' | '9:16' | '1:1' | '4:3'
  seed?: number
}
interface VideoGenerationResponse {
  id: string
  status: 'processing' | 'completed' | 'failed'
  videoUrl?: string
  thumbnailUrl?: string
  progress?: number
}
export class LumaRay3Client {
  private apiKey: string
  private baseUrl = 'https://api.lumalabs.ai/v1'
  constructor(apiKey: string) {
    this.apiKey = apiKey
  }
  async generateVideo(params: VideoGenerationParams): Promise {
    const response = await fetch(${this.baseUrl}/generate, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': Bearer ${this.apiKey}
      },
      body: JSON.stringify({
        prompt: params.prompt,
        duration: params.duration,
        style: params.style,
        resolution: params.resolution,
        fps: params.fps,
        aspect_ratio: params.aspectRatio,
        seed: params.seed
      })
    })
    return await response.json()
  }
  async getVideoStatus(videoId: string): Promise {
    const response = await fetch(${this.baseUrl}/videos/${videoId}, {
      headers: {
        'Authorization': Bearer ${this.apiKey}
      }
    })
    return await response.json()
  }
  async waitForCompletion(videoId: string, timeout = 300000): Promise {
    const startTime = Date.now()
    while (Date.now() - startTime < timeout) {
      const status = await this.getVideoStatus(videoId)
      if (status.status === 'completed' && status.videoUrl) {
        return status.videoUrl
      }
      if (status.status === 'failed') {
        throw new Error('Video generation failed')
      }
      await new Promise(resolve => setTimeout(resolve, 5000))
    }
    throw new Error('Video generation timeout')
  }
  async enhanceVideo(videoUrl: string, options: {
    upscale?: boolean
    denoise?: boolean
    stabilize?: boolean
    colorGrade?: string
  }): Promise {
    const response = await fetch(${this.baseUrl}/enhance, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': Bearer ${this.apiKey}
      },
      body: JSON.stringify({
        video_url: videoUrl,
        ...options
      })
    })
    const data = await response.json()
    return data.enhanced_url
  }
}

// Advanced FFmpeg wrapper for Node.js
import ffmpeg from 'fluent-ffmpeg'
import { PassThrough } from 'stream'
export class VideoProcessor {
  async convertFormat(inputPath: string, outputPath: string, format: string): Promise {
    return new Promise((resolve, reject) => {
      ffmpeg(inputPath)
        .output(outputPath)
        .videoCodec('libx264')
        .audioCodec('aac')
        .format(format)
        .on('end', () => resolve(outputPath))
        .on('error', reject)
        .run()
    })
  }
  async generateThumbnails(videoPath: string, count: number): Promise {
    const timestamps = Array.from({ length: count }, (_, i) =>
      ${Math.floor((100 / count) * i)}%
    )
    return new Promise((resolve, reject) => {
      const filenames: string[] = []
      ffmpeg(videoPath)
        .screenshots({
          timestamps,
          filename: 'thumbnail-%i.png',
          folder: './thumbnails'
        })
        .on('filenames', (names) => {
          filenames.push(...names.map(n => ./thumbnails/${n}))
        })
        .on('end', () => resolve(filenames))
        .on('error', reject)
    })
  }
  async extractAudio(videoPath: string, outputPath: string): Promise {
    return new Promise((resolve, reject) => {
      ffmpeg(videoPath)
        .output(outputPath)
        .noVideo()
        .audioCodec('libmp3lame')
        .audioBitrate('320k')
        .on('end', () => resolve(outputPath))
        .on('error', reject)
        .run()
    })
  }
  async compressVideo(inputPath: string, outputPath: string, quality: 'low' | 'medium' | 'high'): Promise {
    const crf = quality === 'low' ? 28 : quality === 'medium' ? 23 : 18
    return new Promise((resolve, reject) => {
      ffmpeg(inputPath)
        .output(outputPath)
        .videoCodec('libx264')
        .outputOptions([
          -crf ${crf},
          '-preset slow',
          '-movflags +faststart'
        ])
        .on('end', () => resolve(outputPath))
        .on('error', reject)
        .run()
    })
  }
  async addWatermark(videoPath: string, watermarkPath: string, outputPath: string): Promise {
    return new Promise((resolve, reject) => {
      ffmpeg(videoPath)
        .input(watermarkPath)
        .complexFilter([
          '[1:v]scale=100:-1[watermark]',
          '[0:v][watermark]overlay=W-w-10:H-h-10'
        ])
        .output(outputPath)
        .on('end', () => resolve(outputPath))
        .on('error', reject)
        .run()
    })
  }
  streamVideoTranscode(inputPath: string, format: string): PassThrough {
    const stream = new PassThrough()
    ffmpeg(inputPath)
      .format(format)
      .videoCodec('libx264')
      .audioCodec('aac')
      .outputOptions([
        '-movflags frag_keyframe+empty_moov',
        '-preset ultrafast'
      ])
      .on('error', (err) => stream.destroy(err))
      .pipe(stream)
    return stream
  }
}

// Client-side video processing with FFmpeg.wasm
import { FFmpeg } from '@ffmpeg/ffmpeg'
import { fetchFile, toBlobURL } from '@ffmpeg/util'
export class ClientVideoProcessor {
  private ffmpeg: FFmpeg
  constructor() {
    this.ffmpeg = new FFmpeg()
  }
  async initialize() {
    const baseURL = 'https://unpkg.com/@ffmpeg/core@0.12.6/dist/esm'
    await this.ffmpeg.load({
      coreURL: await toBlobURL(${baseURL}/ffmpeg-core.js, 'text/javascript'),
      wasmURL: await toBlobURL(${baseURL}/ffmpeg-core.wasm, 'application/wasm')
    })
  }
  async convertVideo(file: File, outputFormat: string): Promise {
    await this.ffmpeg.writeFile('input.mp4', await fetchFile(file))
    await this.ffmpeg.exec([
      '-i', 'input.mp4',
      '-c:v', 'libx264',
      '-c:a', 'aac',
      output.${outputFormat}
    ])
    const data = await this.ffmpeg.readFile(output.${outputFormat})
    return new Blob([data], { type: video/${outputFormat} })
  }
  async trimVideo(file: File, startTime: number, duration: number): Promise {
    await this.ffmpeg.writeFile('input.mp4', await fetchFile(file))
    await this.ffmpeg.exec([
      '-i', 'input.mp4',
      '-ss', startTime.toString(),
      '-t', duration.toString(),
      '-c', 'copy',
      'output.mp4'
    ])
    const data = await this.ffmpeg.readFile('output.mp4')
    return new Blob([data], { type: 'video/mp4' })
  }
  async extractFrame(file: File, timestamp: number): Promise {
    await this.ffmpeg.writeFile('input.mp4', await fetchFile(file))
    await this.ffmpeg.exec([
      '-i', 'input.mp4',
      '-ss', timestamp.toString(),
      '-vframes', '1',
      'frame.png'
    ])
    const data = await this.ffmpeg.readFile('frame.png')
    return new Blob([data], { type: 'image/png' })
  }
}

// Real-time audio processing with WebRTC
export class WebRTCAudioProcessor {
  private peerConnection: RTCPeerConnection | null = null
  private audioContext: AudioContext
  private analyser: AnalyserNode
  private gainNode: GainNode
  constructor() {
    this.audioContext = new AudioContext()
    this.analyser = this.audioContext.createAnalyser()
    this.gainNode = this.audioContext.createGain()
    this.analyser.fftSize = 2048
    this.analyser.connect(this.audioContext.destination)
  }
  async initializePeerConnection(config?: RTCConfiguration) {
    this.peerConnection = new RTCPeerConnection(config)
    const stream = await navigator.mediaDevices.getUserMedia({
      audio: {
        echoCancellation: true,
        noiseSuppression: true,
        autoGainControl: true,
        sampleRate: 48000
      }
    })
    const source = this.audioContext.createMediaStreamSource(stream)
    source.connect(this.gainNode)
    this.gainNode.connect(this.analyser)
    stream.getTracks().forEach(track => {
      this.peerConnection?.addTrack(track, stream)
    })
    return this.peerConnection
  }
  async applyNoiseSupression(intensity: number) {
    const stream = await navigator.mediaDevices.getUserMedia({
      audio: {
        noiseSuppression: true,
        echoCancellation: true
      }
    })
    const processor = this.audioContext.createScriptProcessor(4096, 1, 1)
    const source = this.audioContext.createMediaStreamSource(stream)
    processor.onaudioprocess = (e) => {
      const input = e.inputBuffer.getChannelData(0)
      const output = e.outputBuffer.getChannelData(0)
      for (let i = 0; i < input.length; i++) {
        output[i] = input[i] * (1 - intensity)
      }
    }
    source.connect(processor)
    processor.connect(this.audioContext.destination)
  }
  getAudioLevels(): Uint8Array {
    const dataArray = new Uint8Array(this.analyser.frequencyBinCount)
    this.analyser.getByteTimeDomainData(dataArray)
    return dataArray
  }
  setVolume(volume: number) {
    this.gainNode.gain.value = Math.max(0, Math.min(1, volume))
  }
}

// Audio Worklet for real-time processing
// audio-processor.worklet.ts
class AudioProcessor extends AudioWorkletProcessor {
  process(inputs: Float32Array[][], outputs: Float32Array[][], parameters: Record) {
    const input = inputs[0]
    const output = outputs[0]
    for (let channel = 0; channel < input.length; channel++) {
      const inputChannel = input[channel]
      const outputChannel = output[channel]
      for (let i = 0; i < inputChannel.length; i++) {
        // Apply gain and compression
        const gain = parameters.gain?.[i] ?? 1
        const threshold = parameters.threshold?.[i] ?? 0.8
        let sample = inputChannel[i] * gain
        // Simple compression
        if (Math.abs(sample) > threshold) {
          sample = threshold * Math.sign(sample)
        }
        outputChannel[i] = sample
      }
    }
    return true
  }
}
registerProcessor('audio-processor', AudioProcessor)

// HLS/DASH streaming implementation
export class AdaptiveStreamingManager {
  private hls: any
  private currentQuality: string = 'auto'
  async initializeHLS(videoElement: HTMLVideoElement, manifestUrl: string) {
    const Hls = (await import('hls.js')).default
    if (Hls.isSupported()) {
      this.hls = new Hls({
        enableWorker: true,
        lowLatencyMode: true,
        backBufferLength: 90
      })
      this.hls.loadSource(manifestUrl)
      this.hls.attachMedia(videoElement)
      this.hls.on(Hls.Events.MANIFEST_PARSED, () => {
        this.setupQualityLevels()
      })
      this.hls.on(Hls.Events.ERROR, (event: any, data: any) => {
        if (data.fatal) {
          this.handleStreamingError(data)
        }
      })
    } else if (videoElement.canPlayType('application/vnd.apple.mpegurl')) {
      videoElement.src = manifestUrl
    }
  }
  private setupQualityLevels() {
    if (!this.hls) return
    const levels = this.hls.levels.map((level: any, index: number) => ({
      index,
      height: level.height,
      bitrate: level.bitrate,
      label: ${level.height}p
    }))
    return levels
  }
  setQuality(qualityIndex: number) {
    if (!this.hls) return
    if (qualityIndex === -1) {
      this.hls.currentLevel = -1 // Auto
      this.currentQuality = 'auto'
    } else {
      this.hls.currentLevel = qualityIndex
      this.currentQuality = this.hls.levels[qualityIndex].height + 'p'
    }
  }
  private handleStreamingError(data: any) {
    switch (data.type) {
      case 'networkError':
        this.hls.startLoad()
        break
      case 'mediaError':
        this.hls.recoverMediaError()
        break
      default:
        this.hls.destroy()
        break
    }
  }
}

// AI-powered audio enhancement
export class AudioEnhancer {
  private audioContext: AudioContext
  constructor() {
    this.audioContext = new AudioContext()
  }
  async enhanceAudio(audioBuffer: AudioBuffer): Promise {
    const response = await fetch('/api/audio/enhance', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        audioData: this.bufferToArray(audioBuffer),
        sampleRate: audioBuffer.sampleRate,
        options: {
          noiseReduction: true,
          normalization: true,
          compression: true
        }
      })
    })
    const enhancedData = await response.json()
    return this.arrayToBuffer(enhancedData.audio, audioBuffer.sampleRate)
  }
  async separateVocals(audioBuffer: AudioBuffer): Promise<{
    vocals: AudioBuffer
    instrumental: AudioBuffer
  }> {
    const response = await fetch('/api/audio/separate', {
      method: 'POST',
      body: this.bufferToBlob(audioBuffer)
    })
    const data = await response.json()
    return {
      vocals: await this.urlToBuffer(data.vocalsUrl),
      instrumental: await this.urlToBuffer(data.instrumentalUrl)
    }
  }
  private bufferToArray(buffer: AudioBuffer): Float32Array {
    return buffer.getChannelData(0)
  }
  private arrayToBuffer(array: Float32Array, sampleRate: number): AudioBuffer {
    const buffer = this.audioContext.createBuffer(1, array.length, sampleRate)
    buffer.copyToChannel(array, 0)
    return buffer
  }
  private bufferToBlob(buffer: AudioBuffer): Blob {
    const interleaved = this.interleave(buffer)
    const dataview = this.encodeWAV(interleaved, buffer.sampleRate)
    return new Blob([dataview], { type: 'audio/wav' })
  }
  private interleave(buffer: AudioBuffer): Float32Array {
    const length = buffer.length * buffer.numberOfChannels
    const result = new Float32Array(length)
    let offset = 0
    for (let i = 0; i < buffer.length; i++) {
      for (let channel = 0; channel < buffer.numberOfChannels; channel++) {
        result[offset++] = buffer.getChannelData(channel)[i]
      }
    }
    return result
  }
  private encodeWAV(samples: Float32Array, sampleRate: number): DataView {
    const buffer = new ArrayBuffer(44 + samples.length * 2)
    const view = new DataView(buffer)
    // WAV header
    this.writeString(view, 0, 'RIFF')
    view.setUint32(4, 36 + samples.length * 2, true)
    this.writeString(view, 8, 'WAVE')
    this.writeString(view, 12, 'fmt ')
    view.setUint32(16, 16, true)
    view.setUint16(20, 1, true)
    view.setUint16(22, 1, true)
    view.setUint32(24, sampleRate, true)
    view.setUint32(28, sampleRate * 2, true)
    view.setUint16(32, 2, true)
    view.setUint16(34, 16, true)
    this.writeString(view, 36, 'data')
    view.setUint32(40, samples.length * 2, true)
    // Audio data
    let offset = 44
    for (let i = 0; i < samples.length; i++) {
      const s = Math.max(-1, Math.min(1, samples[i]))
      view.setInt16(offset, s < 0 ? s * 0x8000 : s * 0x7FFF, true)
      offset += 2
    }
    return view
  }
  private writeString(view: DataView, offset: number, string: string) {
    for (let i = 0; i < string.length; i++) {
      view.setUint8(offset + i, string.charCodeAt(i))
    }
  }
  private async urlToBuffer(url: string): Promise {
    const response = await fetch(url)
    const arrayBuffer = await response.arrayBuffer()
    return await this.audioContext.decodeAudioData(arrayBuffer)
  }
}

// Complete media processing pipeline
export class MediaPipeline {
  async processUserVideo(file: File): Promise<{
    original: string
    compressed: string
    thumbnail: string
    hls: string
  }> {
    // 1. Upload original
    const originalUrl = await this.uploadToStorage(file)
    // 2. Generate compressed version
    const compressedUrl = await this.compressVideo(originalUrl)
    // 3. Generate thumbnail
    const thumbnailUrl = await this.generateThumbnail(originalUrl)
    // 4. Create HLS stream
    const hlsUrl = await this.generateHLS(originalUrl)
    return {
      original: originalUrl,
      compressed: compressedUrl,
      thumbnail: thumbnailUrl,
      hls: hlsUrl
    }
  }
  private async uploadToStorage(file: File): Promise {
    const formData = new FormData()
    formData.append('file', file)
    const response = await fetch('/api/upload/video', {
      method: 'POST',
      body: formData
    })
    const data = await response.json()
    return data.url
  }
  private async compressVideo(url: string): Promise {
    const response = await fetch('/api/video/compress', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ url, quality: 'medium' })
    })
    const data = await response.json()
    return data.compressedUrl
  }
  private async generateThumbnail(url: string): Promise {
    const response = await fetch('/api/video/thumbnail', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ url, timestamp: 1 })
    })
    const data = await response.json()
    return data.thumbnailUrl
  }
  private async generateHLS(url: string): Promise {
    const response = await fetch('/api/video/hls', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ url })
    })
    const data = await response.json()
    return data.manifestUrl
  }
}