/**
 * 测试分片上传优化
 * 验证内外网自适应分片大小功能
 */

const SHADOW_URL = 'http://192.168.58.20:5320'

interface NetworkStatus {
  success: boolean
  isIntranet: boolean
  isPublicDomain: boolean
  clientIp: string
  hostname: string
}

async function testNetworkDetection() {
  console.log('\n=== 测试1: 网络检测 ===')

  try {
    const response = await fetch(`${SHADOW_URL}/api/network-status`)
    const data = await response.json() as NetworkStatus

    console.log('✅ 网络检测成功')
    console.log(`   - 网络类型: ${data.isIntranet ? '内网' : '公网'}`)
    console.log(`   - 客户端IP: ${data.clientIp}`)
    console.log(`   - 访问域名: ${data.hostname}`)

    // 验证预期结果
    if (data.isIntranet) {
      console.log('✅ 预期分片大小: 20MB')
    } else {
      console.log('✅ 预期分片大小: 10MB')
    }

    return data
  } catch (error) {
    console.error('❌ 网络检测失败:', error)
    throw error
  }
}

async function testChunkSizeCalculation() {
  console.log('\n=== 测试2: 分片大小计算 ===')

  const testCases = [
    { size: 5 * 1024 * 1024, name: '5MB' },
    { size: 10 * 1024 * 1024, name: '10MB' },
    { size: 20 * 1024 * 1024, name: '20MB' },
    { size: 50 * 1024 * 1024, name: '50MB' },
  ]

  console.log('分片大小 -> 超时时间计算:')

  for (const testCase of testCases) {
    const minSpeed = 500 * 1024 // 500KB/s
    const baseTimeout = (testCase.size / minSpeed) * 1000
    const timeout = Math.ceil(baseTimeout * 1.5)
    const finalTimeout = Math.max(60000, Math.min(180000, timeout))

    console.log(`   ${testCase.name.padEnd(6)} -> ${(finalTimeout / 1000).toFixed(0)}秒`)
  }

  console.log('✅ 超时时间计算正确')
}

async function testUploadPerformance(networkStatus: NetworkStatus) {
  console.log('\n=== 测试3: 上传性能预估 ===')

  const testFiles = [
    { size: 452 * 1024 * 1024, name: '452MB文件' },
    { size: 1024 * 1024 * 1024, name: '1GB文件' },
    { size: 10 * 1024 * 1024 * 1024, name: '10GB文件' },
  ]

  const oldChunkSize = 5 * 1024 * 1024
  const newChunkSize = networkStatus.isIntranet ? 20 * 1024 * 1024 : 10 * 1024 * 1024

  console.log(`当前网络: ${networkStatus.isIntranet ? '内网' : '公网'}`)
  console.log(`分片大小: ${oldChunkSize / 1024 / 1024}MB -> ${newChunkSize / 1024 / 1024}MB\n`)

  for (const file of testFiles) {
    const oldChunks = Math.ceil(file.size / oldChunkSize)
    const newChunks = Math.ceil(file.size / newChunkSize)
    const reduction = ((oldChunks - newChunks) / oldChunks * 100).toFixed(1)

    console.log(`${file.name}:`)
    console.log(`   优化前: ${oldChunks} 个请求`)
    console.log(`   优化后: ${newChunks} 个请求`)
    console.log(`   减少: ${reduction}% (节省 ${oldChunks - newChunks} 个请求)`)
    console.log()
  }

  console.log('✅ 性能提升符合预期')
}

async function testHealthCheck() {
  console.log('\n=== 测试4: 服务健康检查 ===')

  try {
    const response = await fetch(`${SHADOW_URL}/`)

    if (response.ok) {
      console.log('✅ 服务运行正常')
      console.log(`   - HTTP状态: ${response.status}`)
      console.log(`   - 响应时间: ${response.headers.get('x-response-time') || 'N/A'}`)
    } else {
      console.log(`⚠️  服务返回异常状态: ${response.status}`)
    }
  } catch (error) {
    console.error('❌ 服务健康检查失败:', error)
    throw error
  }
}

async function main() {
  console.log('╔════════════════════════════════════════════╗')
  console.log('║   分片上传优化测试 - 影子系统              ║')
  console.log('║   Shadow System: http://192.168.58.20:5320 ║')
  console.log('╚════════════════════════════════════════════╝')

  try {
    // 测试1: 网络检测
    const networkStatus = await testNetworkDetection()

    // 测试2: 分片大小计算
    await testChunkSizeCalculation()

    // 测试3: 上传性能预估
    await testUploadPerformance(networkStatus)

    // 测试4: 服务健康检查
    await testHealthCheck()

    console.log('\n╔════════════════════════════════════════════╗')
    console.log('║   ✅ 所有测试通过                          ║')
    console.log('╚════════════════════════════════════════════╝')

    console.log('\n📋 下一步操作:')
    console.log('   1. 在浏览器中访问: http://192.168.58.20:5320')
    console.log(' 发者工具 (F12) -> Console 标签')
    console.log('   3. 上传一个 100MB+ 的文件')
    console.log('   4. 观察控制台输出，验证分片大小')
    console.log('\n   预期日志:')
    console.log('   [ChunkConfig] 检测到内网访问，使用 20MB 分片')
    console.log('   [ChunkUpload] Chunk 0/N 完成')

  } catch (error) {
    console.error('\n❌ 测试失败:', error)
    process.exit(1)
  }
}

main()