assert state["context_b"] == "finished b with a: started a"
    assert state["context_a"] == "finished a"


def test_context_b_raise():
    with pytest.raises(OtherDependencyError):
        client.get("/context_b_raise")
    assert state["context_b"] == "finished b with a: started a"
    assert state["context_a"] == "finished a"


def test_background_tasks():

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

          "type":       "lowercase",
          "language":   "irish"
        },
        "irish_keywords": {
          "type":       "keyword_marker",
          "keywords_path": "${fess.dictionary.path}en-ie/protwords.txt"
        },
        "irish_stemmer": {
          "type":       "stemmer",
          "language":   "irish"
        },
        "irish_override": {

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =
          createFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES);

    assertThat(stopwatch.elapsed(MILLISECONDS)).isIn(Range.closed(NOT_ENOUGH_MS, DELAY_MS * 2));
    // Is it still computing away anyway?
    assertThat(target.finished).isFalse();
    MILLISECONDS.sleep(ENOUGH_MS);
    assertThat(target.finished).isFalse();
  }

  public void testNewProxy_badMethodWithEnoughTime() throws Exception {
    SampleImpl target = new SampleImpl(DELAY_MS);

    assertThat(TWO_VALUES_STATS.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    // For datasets of many double values created from an array, we test many combinations of finite
    // and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double mean = Stats.of(values.asArray()).mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of %s", values).that(mean).isNaN();

	"github.com/minio/minio/internal/logger"
)

func shutdownHealMRFWithTimeout() {
	const shutdownTimeout = time.Minute

	finished := make(chan struct{})
	go func() {
		globalMRFState.shutdown()
		close(finished)
	}()
	select {
	case <-time.After(shutdownTimeout):
	case <-finished:
	}
}

func handleSignals() {
	// Custom exit function
	exit := func(success bool) {
		if globalLoggerOutput != nil {

   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link