# Find all one-step dependencies of those tests which are from //tensorflow
  # (since external deps will come from Python-level pip dependencies),
  # excluding dependencies and files that are known to be unneccessary.
  # This creates a list of targets under //tensorflow that are required for
  # TensorFlow python tests.

<?xml version="1.0" encoding="UTF-8"?>
<project name="deps" basedir=".">
	<property name="target.dir" value="${basedir}/target/deps" />
	<property name="webinf.dir" value="${basedir}/src/main/webapp/WEB-INF" />
	<property name="crawler.dir" value="${basedir}/src/main/webapp/WEB-INF/env/crawler" />
	<property name="suggest.dir" value="${basedir}/src/main/webapp/WEB-INF/env/suggest" />
	<property name="thumbnail.dir" value="${basedir}/src/main/webapp/WEB-INF/env/thumbnail" />

    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(1, 2, dataset(i));
    }
    return dummy;
  }

  @Benchmark
  double percentile90(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(90, 100, dataset(i));
    }

      String unused = interner.intern(Double.toHexString(Math.random()));
    }
    return reps;
  }

  @CanIgnoreReturnValue
  @Benchmark
  int stringIntern(int reps) {
    for (int i = 0; i < reps; i++) {
      String unused = Double.toHexString(Math.random()).intern();
    }
    return reps;
  }

    }
  }

  @Benchmark
  long log2(int reps) {
    long tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += Double.doubleToRawLongBits(DoubleMath.log2(positiveDoubles[j]));
    }
    return tmp;
  }

  @Benchmark
  long factorial(int reps) {
    long tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;

      longs[i] = RANDOM_SOURCE.nextLong();
    }
  }

  @Benchmark
  int log2(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += LongMath.log2(positive[j], mode);
    }
    return tmp;
  }

  @Benchmark
  int log10(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += LongMath.log10(positive[j], mode);

  }

  @Benchmark
  int asciiStringToUpperCase(int reps) {
    String string = noWorkToDo ? Ascii.toUpperCase(testString) : testString;

    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += Ascii.toUpperCase(string).length();
    }
    return dummy;
  }

  @Benchmark
  int asciiCharSequenceToUpperCase(int reps) {

  int hashString_4(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += Objects.hashCode(S0, S1, S2, S3);
    }
    return dummy;
  }

  @Benchmark
  int hashString_5(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += Objects.hashCode(S0, S1, S2, S3, S4);
    }
    return dummy;
  }

  @Benchmark
  int hashMixed_5(int reps) {
    int dummy = 0;

      list.add(listener, directExecutor());
    }
    return list.getImpl();
  }

  @Benchmark
  int addThenExecute_singleThreaded(int reps) {
    int returnValue = 0;
    for (int i = 0; i < reps; i++) {
      list = impl.newExecutionList();
      listenerLatch = new CountDownLatch(numListeners);
      for (int j = 0; j < numListeners; j++) {

    }
  }

  @Benchmark
  int roundToInt(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += DoubleMath.roundToInt(doubleInIntRange[j], mode);
    }
    return tmp;
  }

  @Benchmark
  long roundToLong(int reps) {
    long tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;