// Add small multiples of MIN_VALUE and MIN_NORMAL
    for (int scale = 1; scale <= 4; scale++) {
      for (double d : Doubles.asList(Double.MIN_VALUE, Double.MIN_NORMAL)) {
        fractionalBuilder.add(d * scale).add(-d * scale);
      }
    }
    for (int i = Double.MIN_EXPONENT; i <= Double.MAX_EXPONENT; i++) {
      for (int direction : new int[] {1, -1}) {

   *
   * <p>Zero is treated as having log2 == 0.
   */
  static double randomDouble(int maxExponent) {
    double result = RANDOM_SOURCE.nextDouble();
    result = Math.scalb(result, RANDOM_SOURCE.nextInt(maxExponent + 1));
    return RANDOM_SOURCE.nextBoolean() ? result : -result;
  }

  /** Returns a random integer between zero and {@code MAX_EXPONENT}. */
  static int randomExponent() {

    for (int exp : asList(-1022, -50, -1, 0, 1, 2, 3, 4, 100, 1022, 1023)) {
      for (RoundingMode mode : asList(HALF_EVEN, HALF_UP, HALF_DOWN)) {
        double x = Math.scalb(Math.sqrt(2) + 0.001, exp);
        double y = Math.scalb(Math.sqrt(2) - 0.001, exp);
        if (exp < 0) {
          assertEquals(exp + 1, DoubleMath.log2(x, mode));
          assertEquals(exp, DoubleMath.log2(y, mode));
        } else {

    // Add small multiples of MIN_VALUE and MIN_NORMAL
    for (int scale = 1; scale <= 4; scale++) {
      for (double d : Doubles.asList(Double.MIN_VALUE, Double.MIN_NORMAL)) {
        fractionalBuilder.add(d * scale).add(-d * scale);
      }
    }
    for (int i = Double.MIN_EXPONENT; i <= Double.MAX_EXPONENT; i++) {
      for (int direction : new int[] {1, -1}) {

   *
   * <p>Zero is treated as having log2 == 0.
   */
  static double randomDouble(int maxExponent) {
    double result = RANDOM_SOURCE.nextDouble();
    result = Math.scalb(result, RANDOM_SOURCE.nextInt(maxExponent + 1));
    return RANDOM_SOURCE.nextBoolean() ? result : -result;
  }

  /** Returns a random integer between zero and {@code MAX_EXPONENT}. */
  static int randomExponent() {

    for (int exp : asList(-1022, -50, -1, 0, 1, 2, 3, 4, 100, 1022, 1023)) {
      for (RoundingMode mode : asList(HALF_EVEN, HALF_UP, HALF_DOWN)) {
        double x = Math.scalb(Math.sqrt(2) + 0.001, exp);
        double y = Math.scalb(Math.sqrt(2) - 0.001, exp);
        if (exp < 0) {
          assertEquals(exp + 1, DoubleMath.log2(x, mode));
          assertEquals(exp, DoubleMath.log2(y, mode));
        } else {

    assertThrows(IllegalArgumentException.class, () -> intermediate.indexes(1, 11, 3));
  }

  public void testScale_indexes_collection_negative() {
    Quantiles.Scale intermediate = Quantiles.scale(10);
    assertThrows(
        IllegalArgumentException.class, () -> intermediate.indexes(ImmutableList.of(1, -1, 3)));
  }

  public void testScale_indexes_collection_tooHigh() {

    assertThrows(IllegalArgumentException.class, () -> intermediate.indexes(1, 11, 3));
  }

  public void testScale_indexes_collection_negative() {
    Quantiles.Scale intermediate = Quantiles.scale(10);
    assertThrows(
        IllegalArgumentException.class, () -> intermediate.indexes(ImmutableList.of(1, -1, 3)));
  }

  public void testScale_indexes_collection_tooHigh() {

        findLatest("scala-library", "org.scala-lang:scala-library:2.13.+", versionProperties)
        val scalaVersion = VersionNumber.parse(versionProperties["scala-library"] as String)
        versionProperties["scala"] = "${scalaVersion.major}.${scalaVersion.minor}"

        // The latest released version is 2.0.0-M1, which is excluded by "don't use snapshot" strategy

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/autoscaling/v2beta2";

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to