("-inf", 200),
        ("nan", 200),
        ("0", 200),
        ("342", 200),
    ],
)
def test_allow_inf_nan_param_default(value: str, code: int):
    response = client.post(f"/?z={value}")
    assert response.status_code == code, response.text


@pytest.mark.parametrize(
    "value,code",
    [
        ("-1", 200),
        ("inf", 422),
        ("-inf", 422),
        ("nan", 422),
        ("0", 200),

          .that(Doubles.indexOf(new double[] {5.0, value, value, 5.0}, new double[] {value, value}))
          .isEqualTo(1);
    }
    assertThat(Doubles.indexOf(new double[] {5.0, NaN, NaN, 5.0}, new double[] {NaN, NaN}))
        .isEqualTo(-1);
  }

  public void testLastIndexOf() {
    assertThat(Doubles.lastIndexOf(EMPTY, 1.0)).isEqualTo(-1);
    assertThat(Doubles.lastIndexOf(ARRAY1, 2.0)).isEqualTo(-1);

    Our Id type is a prime example of this.

    >>> decimal_encoder(Decimal("1.0"))
    1.0

    >>> decimal_encoder(Decimal("1"))
    1

    >>> decimal_encoder(Decimal("NaN"))
    nan
    """
    exponent = dec_value.as_tuple().exponent
    if isinstance(exponent, int) and exponent >= 0:
        return int(dec_value)
    else:
        return float(dec_value)

        .comparingValuesUsing(QUANTILE_CORRESPONDENCE)
        .containsExactly(
            0, NaN,
            1, NaN,
            2, NaN,
            8, NaN,
            9, NaN,
            10, NaN);
  }

  public void testScale_index_compute_doubleCollection_nan() {
    assertThat(Quantiles.scale(10).index(5).compute(ONE_TO_FIVE_AND_NAN)).isNaN();
  }

        .comparingValuesUsing(QUANTILE_CORRESPONDENCE)
        .containsExactly(
            0, NaN,
            1, NaN,
            2, NaN,
            8, NaN,
            9, NaN,
            10, NaN);
  }

  public void testScale_index_compute_doubleCollection_nan() {
    assertThat(Quantiles.scale(10).index(5).compute(ONE_TO_FIVE_AND_NAN)).isNaN();
  }

            """
        ),
    ] = _Unset,
    allow_inf_nan: Annotated[
        Union[bool, None],
        Doc(
            """
            Allow `inf`, `-inf`, `nan`. Only applicable to numbers.
            """
        ),
    ] = _Unset,
    max_digits: Annotated[
        Union[int, None],
        Doc(
            """

def test_decimal_encoder_int():
    data = {"value": Decimal(2)}
    assert jsonable_encoder(data) == {"value": 2}


def test_decimal_encoder_nan():
    data = {"value": Decimal("NaN")}
    assert isnan(jsonable_encoder(data)["value"])


def test_decimal_encoder_infinity():
    data = {"value": Decimal("Infinity")}
    assert isinf(jsonable_encoder(data)["value"])

    assertThat(createSingleStats(Double.POSITIVE_INFINITY, 1.23).populationCovariance()).isNaN();
    assertThat(createSingleStats(Double.NEGATIVE_INFINITY, 1.23).populationCovariance()).isNaN();
    assertThat(createSingleStats(Double.NaN, 1.23).populationCovariance()).isNaN();
    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);

    assertThat(createSingleStats(Double.POSITIVE_INFINITY, 1.23).populationCovariance()).isNaN();
    assertThat(createSingleStats(Double.NEGATIVE_INFINITY, 1.23).populationCovariance()).isNaN();
    assertThat(createSingleStats(Double.NaN, 1.23).populationCovariance()).isNaN();
    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);

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