# 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));
    }

      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
  long stopwatch(int reps) {
    long total = 0;
    for (int i = 0; i < reps; i++) {
      Stopwatch s = Stopwatch.createStarted();
      // here is where you would do something
      total += s.elapsed(NANOSECONDS);
    }
    return total;
  }

  @Benchmark
  long manual(int reps) {
    long total = 0;
    for (int i = 0; i < reps; i++) {
      long start = System.nanoTime();

    }
  }

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

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

  int copyArrayList(int reps) {
    int size = this.size;
    int dummy = 0;
    for (int rep = 0; rep < reps; rep++) {
      List<Object> builder = new ArrayList<>();
      for (int i = 0; i < size; i++) {
        builder.add(OBJECT);
      }
      dummy += ImmutableList.copyOf(builder).size();
    }
    return dummy;
  }

  @Benchmark
  int copyPreSizedArrayList(int reps) {
    int size = this.size;
    int tmp = 0;

  }

  @Benchmark
  int hashUtf8(int reps) {
    int res = 0;
    for (int i = 0; i < reps; i++) {
      res +=
          System.identityHashCode(
              hashFunctionEnum.getHashFunction().hashString(strings[i & SAMPLE_MASK], UTF_8));
    }
    return res;
  }

  @Benchmark
  int hashUtf8Hasher(int reps) {
    int res = 0;
    for (int i = 0; i < reps; i++) {
      res +=

  @SuppressWarnings("InlineMeInliner") // String.repeat unavailable under Java 8
  void setUp() {
    originalString = Strings.repeat("x", length);
  }

  @Benchmark
  void oldRepeat(long reps) {
    for (int i = 0; i < reps; i++) {
      String x = oldRepeat(originalString, count);
      if (x.length() != (originalString.length() * count)) {
        throw new RuntimeException("Wrong length: " + x);
      }
    }
  }

    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros",
        ":tf_shape_internal",
        "//tensorflow/core:framework",
    ],
)

cc_library(
    name = "tf_shape_internal",
    hdrs = ["tf_shape_internal.h"],
    copts = tf_copts(),
    visibility = ["//tensorflow:internal"],
    deps = [
        ":conversion_macros",
        "//tensorflow/core:framework",