.of(SIXTEEN_SQUARES_MEDIAN);
  }

  public void testMedian_computeInPlace() {
    double[] dataset = Doubles.toArray(SIXTEEN_SQUARES_DOUBLES);
    assertThat(median().computeInPlace(dataset)).isWithin(ALLOWED_ERROR).of(SIXTEEN_SQUARES_MEDIAN);
    assertThat(dataset).usingExactEquality().containsExactlyElementsIn(SIXTEEN_SQUARES_DOUBLES);
  }

  public void testQuartiles_index_compute_doubleCollection() {

        .of(SIXTEEN_SQUARES_MEDIAN);
  }

  public void testMedian_computeInPlace() {
    double[] dataset = Doubles.toArray(SIXTEEN_SQUARES_DOUBLES);
    assertThat(median().computeInPlace(dataset)).isWithin(ALLOWED_ERROR).of(SIXTEEN_SQUARES_MEDIAN);
    assertThat(dataset).usingExactEquality().containsExactlyElementsIn(SIXTEEN_SQUARES_DOUBLES);
  }

  public void testQuartiles_index_compute_doubleCollection() {

/**
 * Provides a fluent API for calculating <a
 * href="http://en.wikipedia.org/wiki/Quantile">quantiles</a>.
 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * <pre>{@code
 * double myMedian = median().compute(myDataset);
 * }</pre>
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * <pre>{@code

          // Exactly two elements left.
          swap(array, from, to);
        }
        return array[k];
      } else {
        int midIndex = (from + to) >>> 1;
        // Choose the median of the elements at the from, to and mid indexes,
        // and rearrange so that array[from]<=array[from+1], and
        // array[to] => array[from + 1].

        swap(array, midIndex, from + 1);

          // Exactly two elements left.
          swap(array, from, to);
        }
        return array[k];
      } else {
        int midIndex = (from + to) >>> 1;
        // Choose the median of the elements at the from, to and mid indexes,
        // and rearrange so that array[from]<=array[from+1], and
        // array[to] => array[from + 1].

        swap(array, midIndex, from + 1);

  @Override
  protected void setUp() {
    dataset = new double[DATASET_SIZE];
    for (int i = 0; i < DATASET_SIZE; i++) {
      dataset[i] = RNG.nextDouble();
    }
  }

  public void testSingleQuantile_median() {
    double referenceValue = REFERENCE_ALGORITHM.singleQuantile(1, 2, dataset.clone());
    for (QuantilesAlgorithm algorithm : NON_REFERENCE_ALGORITHMS) {

  @Override
  protected void setUp() {
    dataset = new double[DATASET_SIZE];
    for (int i = 0; i < DATASET_SIZE; i++) {
      dataset[i] = RNG.nextDouble();
    }
  }

  public void testSingleQuantile_median() {
    double referenceValue = REFERENCE_ALGORITHM.singleQuantile(1, 2, dataset.clone());
    for (QuantilesAlgorithm algorithm : NON_REFERENCE_ALGORITHMS) {

    // dataset which is already sorted or partially sorted is cheating.
    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) {

    // dataset which is already sorted or partially sorted is cheating.
    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) {

app = FastAPI()

media_type = "application/vnd.api+json"


# NOTE: These are not valid JSON:API resources
# but they are fine for testing requestBody with custom media_type
class Product(BaseModel):
    name: str
    price: float


class Shop(BaseModel):
    name: str


@app.post("/products")
async def create_product(data: Product = Body(media_type=media_type, embed=True)):