if (values.hasAnyNonFinite()) {
        assertWithMessage("population covariance of %s", values).that(populationCovariance).isNaN();
        assertWithMessage("population covariance by addAll(PairedStats) of %s", values)
            .that(populationCovarianceByAddAllPartitionedPairedStats)
            .isNaN();
      } else {
        assertWithMessage("population covariance of %s", values)
            .that(populationCovariance)

        assertWithMessage("mean of %s", values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of %s", values).that(meanByAddAllStats).isNaN();
      } else if (values.hasAnyPositiveInfinity() && values.hasAnyNegativeInfinity()) {
        assertWithMessage("mean of %s", values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of %s", values).that(meanByAddAllStats).isNaN();
      } else if (values.hasAnyPositiveInfinity()) {

    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)

    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)

      if (values.hasAnyNonFinite()) {
        assertWithMessage("population covariance of %s", values).that(populationCovariance).isNaN();
        assertWithMessage("population covariance by addAll(PairedStats) of %s", values)
            .that(populationCovarianceByAddAllPartitionedPairedStats)
            .isNaN();
      } else {
        assertWithMessage("population covariance of %s", values)
            .that(populationCovariance)

import warnings
from collections import deque
from dataclasses import dataclass
from datetime import datetime, timezone
from decimal import Decimal
from enum import Enum
from math import isinf, isnan
from pathlib import PurePath, PurePosixPath, PureWindowsPath
from typing import Optional, TypedDict

import pytest
from fastapi._compat import Undefined
from fastapi.encoders import jsonable_encoder

    assertThat(max(-0.0, 0.0)).isEqualTo(0.0);
    assertThat(max(0.0, -0.0)).isEqualTo(0.0);
    assertThat(max(NUMBERS)).isEqualTo(GREATEST);
    assertThat(Double.isNaN(max(VALUES))).isTrue();
  }

  @GwtIncompatible
  public void testMin_noArgs() {
    assertThrows(IllegalArgumentException.class, () -> min());
  }

  public void testMin() {

            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();
  }

  // 3. Tests on a mechanically generated dataset for chains starting with percentiles():

  private static final int PSEUDORANDOM_DATASET_SIZE = 9951;

            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();
  }

  // 3. Tests on a mechanically generated dataset for chains starting with percentiles():

  private static final int PSEUDORANDOM_DATASET_SIZE = 9951;