chore(3p/abseil_cpp): unvendor abseil_cpp r/3786

we weren't actually using these sources anymore, okay?

Change-Id: If701571d9716de308d3512e1eb22c35db0877a66
Reviewed-on: https://cl.tvl.fyi/c/depot/+/5248
Tested-by: BuildkiteCI
Reviewed-by: grfn <grfn@gws.fyi>
Autosubmit: tazjin <tazjin@tvl.su>

1 files changed, 0 insertions, 619 deletions

diff --git a/third_party/abseil_cpp/absl/random/beta_distribution_test.cc b/third_party/abseil_cpp/absl/random/beta_distribution_test.cc
deleted file mode 100644
index 277e4dc6eed7..000000000000
--- a/third_party/abseil_cpp/absl/random/beta_distribution_test.cc
+++ /dev/null
@@ -1,619 +0,0 @@
-// Copyright 2017 The Abseil Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      https://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "absl/random/beta_distribution.h"
-
-#include <algorithm>
-#include <cstddef>
-#include <cstdint>
-#include <iterator>
-#include <random>
-#include <sstream>
-#include <string>
-#include <unordered_map>
-#include <vector>
-
-#include "gmock/gmock.h"
-#include "gtest/gtest.h"
-#include "absl/base/internal/raw_logging.h"
-#include "absl/random/internal/chi_square.h"
-#include "absl/random/internal/distribution_test_util.h"
-#include "absl/random/internal/pcg_engine.h"
-#include "absl/random/internal/sequence_urbg.h"
-#include "absl/random/random.h"
-#include "absl/strings/str_cat.h"
-#include "absl/strings/str_format.h"
-#include "absl/strings/str_replace.h"
-#include "absl/strings/strip.h"
-
-namespace {
-
-template <typename IntType>
-class BetaDistributionInterfaceTest : public ::testing::Test {};
-
-using RealTypes = ::testing::Types<float, double, long double>;
-TYPED_TEST_CASE(BetaDistributionInterfaceTest, RealTypes);
-
-TYPED_TEST(BetaDistributionInterfaceTest, SerializeTest) {
-  // The threshold for whether std::exp(1/a) is finite.
-  const TypeParam kSmallA =
-      1.0f / std::log((std::numeric_limits<TypeParam>::max)());
-  // The threshold for whether a * std::log(a) is finite.
-  const TypeParam kLargeA =
-      std::exp(std::log((std::numeric_limits<TypeParam>::max)()) -
-               std::log(std::log((std::numeric_limits<TypeParam>::max)())));
-  const TypeParam kLargeAPPC = std::exp(
-      std::log((std::numeric_limits<TypeParam>::max)()) -
-      std::log(std::log((std::numeric_limits<TypeParam>::max)())) - 10.0f);
-  using param_type = typename absl::beta_distribution<TypeParam>::param_type;
-
-  constexpr int kCount = 1000;
-  absl::InsecureBitGen gen;
-  const TypeParam kValues[] = {
-      TypeParam(1e-20), TypeParam(1e-12), TypeParam(1e-8), TypeParam(1e-4),
-      TypeParam(1e-3), TypeParam(0.1), TypeParam(0.25),
-      std::nextafter(TypeParam(0.5), TypeParam(0)),  // 0.5 - epsilon
-      std::nextafter(TypeParam(0.5), TypeParam(1)),  // 0.5 + epsilon
-      TypeParam(0.5), TypeParam(1.0),                //
-      std::nextafter(TypeParam(1), TypeParam(0)),    // 1 - epsilon
-      std::nextafter(TypeParam(1), TypeParam(2)),    // 1 + epsilon
-      TypeParam(12.5), TypeParam(1e2), TypeParam(1e8), TypeParam(1e12),
-      TypeParam(1e20),                        //
-      kSmallA,                                //
-      std::nextafter(kSmallA, TypeParam(0)),  //
-      std::nextafter(kSmallA, TypeParam(1)),  //
-      kLargeA,                                //
-      std::nextafter(kLargeA, TypeParam(0)),  //
-      std::nextafter(kLargeA, std::numeric_limits<TypeParam>::max()),
-      kLargeAPPC,  //
-      std::nextafter(kLargeAPPC, TypeParam(0)),
-      std::nextafter(kLargeAPPC, std::numeric_limits<TypeParam>::max()),
-      // Boundary cases.
-      std::numeric_limits<TypeParam>::max(),
-      std::numeric_limits<TypeParam>::epsilon(),
-      std::nextafter(std::numeric_limits<TypeParam>::min(),
-                     TypeParam(1)),                  // min + epsilon
-      std::numeric_limits<TypeParam>::min(),         // smallest normal
-      std::numeric_limits<TypeParam>::denorm_min(),  // smallest denorm
-      std::numeric_limits<TypeParam>::min() / 2,     // denorm
-      std::nextafter(std::numeric_limits<TypeParam>::min(),
-                     TypeParam(0)),  // denorm_max
-  };
-  for (TypeParam alpha : kValues) {
-    for (TypeParam beta : kValues) {
-      ABSL_INTERNAL_LOG(
-          INFO, absl::StrFormat("Smoke test for Beta(%a, %a)", alpha, beta));
-
-      param_type param(alpha, beta);
-      absl::beta_distribution<TypeParam> before(alpha, beta);
-      EXPECT_EQ(before.alpha(), param.alpha());
-      EXPECT_EQ(before.beta(), param.beta());
-
-      {
-        absl::beta_distribution<TypeParam> via_param(param);
-        EXPECT_EQ(via_param, before);
-        EXPECT_EQ(via_param.param(), before.param());
-      }
-
-      // Smoke test.
-      for (int i = 0; i < kCount; ++i) {
-        auto sample = before(gen);
-        EXPECT_TRUE(std::isfinite(sample));
-        EXPECT_GE(sample, before.min());
-        EXPECT_LE(sample, before.max());
-      }
-
-      // Validate stream serialization.
-      std::stringstream ss;
-      ss << before;
-      absl::beta_distribution<TypeParam> after(3.8f, 1.43f);
-      EXPECT_NE(before.alpha(), after.alpha());
-      EXPECT_NE(before.beta(), after.beta());
-      EXPECT_NE(before.param(), after.param());
-      EXPECT_NE(before, after);
-
-      ss >> after;
-
-#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \
-    defined(__ppc__) || defined(__PPC__)
-      if (std::is_same<TypeParam, long double>::value) {
-        // Roundtripping floating point values requires sufficient precision
-        // to reconstruct the exact value. It turns out that long double
-        // has some errors doing this on ppc.
-        if (alpha <= std::numeric_limits<double>::max() &&
-            alpha >= std::numeric_limits<double>::lowest()) {
-          EXPECT_EQ(static_cast<double>(before.alpha()),
-                    static_cast<double>(after.alpha()))
-              << ss.str();
-        }
-        if (beta <= std::numeric_limits<double>::max() &&
-            beta >= std::numeric_limits<double>::lowest()) {
-          EXPECT_EQ(static_cast<double>(before.beta()),
-                    static_cast<double>(after.beta()))
-              << ss.str();
-        }
-        continue;
-      }
-#endif
-
-      EXPECT_EQ(before.alpha(), after.alpha());
-      EXPECT_EQ(before.beta(), after.beta());
-      EXPECT_EQ(before, after)           //
-          << ss.str() << " "             //
-          << (ss.good() ? "good " : "")  //
-          << (ss.bad() ? "bad " : "")    //
-          << (ss.eof() ? "eof " : "")    //
-          << (ss.fail() ? "fail " : "");
-    }
-  }
-}
-
-TYPED_TEST(BetaDistributionInterfaceTest, DegenerateCases) {
-  // We use a fixed bit generator for distribution accuracy tests.  This allows
-  // these tests to be deterministic, while still testing the qualify of the
-  // implementation.
-  absl::random_internal::pcg64_2018_engine rng(0x2B7E151628AED2A6);
-
-  // Extreme cases when the params are abnormal.
-  constexpr int kCount = 1000;
-  const TypeParam kSmallValues[] = {
-      std::numeric_limits<TypeParam>::min(),
-      std::numeric_limits<TypeParam>::denorm_min(),
-      std::nextafter(std::numeric_limits<TypeParam>::min(),
-                     TypeParam(0)),  // denorm_max
-      std::numeric_limits<TypeParam>::epsilon(),
-  };
-  const TypeParam kLargeValues[] = {
-      std::numeric_limits<TypeParam>::max() * static_cast<TypeParam>(0.9999),
-      std::numeric_limits<TypeParam>::max() - 1,
-      std::numeric_limits<TypeParam>::max(),
-  };
-  {
-    // Small alpha and beta.
-    // Useful WolframAlpha plots:
-    //   * plot InverseBetaRegularized[x, 0.0001, 0.0001] from 0.495 to 0.505
-    //   * Beta[1.0, 0.0000001, 0.0000001]
-    //   * Beta[0.9999, 0.0000001, 0.0000001]
-    for (TypeParam alpha : kSmallValues) {
-      for (TypeParam beta : kSmallValues) {
-        int zeros = 0;
-        int ones = 0;
-        absl::beta_distribution<TypeParam> d(alpha, beta);
-        for (int i = 0; i < kCount; ++i) {
-          TypeParam x = d(rng);
-          if (x == 0.0) {
-            zeros++;
-          } else if (x == 1.0) {
-            ones++;
-          }
-        }
-        EXPECT_EQ(ones + zeros, kCount);
-        if (alpha == beta) {
-          EXPECT_NE(ones, 0);
-          EXPECT_NE(zeros, 0);
-        }
-      }
-    }
-  }
-  {
-    // Small alpha, large beta.
-    // Useful WolframAlpha plots:
-    //   * plot InverseBetaRegularized[x, 0.0001, 10000] from 0.995 to 1
-    //   * Beta[0, 0.0000001, 1000000]
-    //   * Beta[0.001, 0.0000001, 1000000]
-    //   * Beta[1, 0.0000001, 1000000]
-    for (TypeParam alpha : kSmallValues) {
-      for (TypeParam beta : kLargeValues) {
-        absl::beta_distribution<TypeParam> d(alpha, beta);
-        for (int i = 0; i < kCount; ++i) {
-          EXPECT_EQ(d(rng), 0.0);
-        }
-      }
-    }
-  }
-  {
-    // Large alpha, small beta.
-    // Useful WolframAlpha plots:
-    //   * plot InverseBetaRegularized[x, 10000, 0.0001] from 0 to 0.001
-    //   * Beta[0.99, 1000000, 0.0000001]
-    //   * Beta[1, 1000000, 0.0000001]
-    for (TypeParam alpha : kLargeValues) {
-      for (TypeParam beta : kSmallValues) {
-        absl::beta_distribution<TypeParam> d(alpha, beta);
-        for (int i = 0; i < kCount; ++i) {
-          EXPECT_EQ(d(rng), 1.0);
-        }
-      }
-    }
-  }
-  {
-    // Large alpha and beta.
-    absl::beta_distribution<TypeParam> d(std::numeric_limits<TypeParam>::max(),
-                                         std::numeric_limits<TypeParam>::max());
-    for (int i = 0; i < kCount; ++i) {
-      EXPECT_EQ(d(rng), 0.5);
-    }
-  }
-  {
-    // Large alpha and beta but unequal.
-    absl::beta_distribution<TypeParam> d(
-        std::numeric_limits<TypeParam>::max(),
-        std::numeric_limits<TypeParam>::max() * 0.9999);
-    for (int i = 0; i < kCount; ++i) {
-      TypeParam x = d(rng);
-      EXPECT_NE(x, 0.5f);
-      EXPECT_FLOAT_EQ(x, 0.500025f);
-    }
-  }
-}
-
-class BetaDistributionModel {
- public:
-  explicit BetaDistributionModel(::testing::tuple<double, double> p)
-      : alpha_(::testing::get<0>(p)), beta_(::testing::get<1>(p)) {}
-
-  double Mean() const { return alpha_ / (alpha_ + beta_); }
-
-  double Variance() const {
-    return alpha_ * beta_ / (alpha_ + beta_ + 1) / (alpha_ + beta_) /
-           (alpha_ + beta_);
-  }
-
-  double Kurtosis() const {
-    return 3 + 6 *
-                   ((alpha_ - beta_) * (alpha_ - beta_) * (alpha_ + beta_ + 1) -
-                    alpha_ * beta_ * (2 + alpha_ + beta_)) /
-                   alpha_ / beta_ / (alpha_ + beta_ + 2) / (alpha_ + beta_ + 3);
-  }
-
- protected:
-  const double alpha_;
-  const double beta_;
-};
-
-class BetaDistributionTest
-    : public ::testing::TestWithParam<::testing::tuple<double, double>>,
-      public BetaDistributionModel {
- public:
-  BetaDistributionTest() : BetaDistributionModel(GetParam()) {}
-
- protected:
-  template <class D>
-  bool SingleZTestOnMeanAndVariance(double p, size_t samples);
-
-  template <class D>
-  bool SingleChiSquaredTest(double p, size_t samples, size_t buckets);
-
-  absl::InsecureBitGen rng_;
-};
-
-template <class D>
-bool BetaDistributionTest::SingleZTestOnMeanAndVariance(double p,
-                                                        size_t samples) {
-  D dis(alpha_, beta_);
-
-  std::vector<double> data;
-  data.reserve(samples);
-  for (size_t i = 0; i < samples; i++) {
-    const double variate = dis(rng_);
-    EXPECT_FALSE(std::isnan(variate));
-    // Note that equality is allowed on both sides.
-    EXPECT_GE(variate, 0.0);
-    EXPECT_LE(variate, 1.0);
-    data.push_back(variate);
-  }
-
-  // We validate that the sample mean and sample variance are indeed from a
-  // Beta distribution with the given shape parameters.
-  const auto m = absl::random_internal::ComputeDistributionMoments(data);
-
-  // The variance of the sample mean is variance / n.
-  const double mean_stddev = std::sqrt(Variance() / static_cast<double>(m.n));
-
-  // The variance of the sample variance is (approximately):
-  //   (kurtosis - 1) * variance^2 / n
-  const double variance_stddev = std::sqrt(
-      (Kurtosis() - 1) * Variance() * Variance() / static_cast<double>(m.n));
-  // z score for the sample variance.
-  const double z_variance = (m.variance - Variance()) / variance_stddev;
-
-  const double max_err = absl::random_internal::MaxErrorTolerance(p);
-  const double z_mean = absl::random_internal::ZScore(Mean(), m);
-  const bool pass =
-      absl::random_internal::Near("z", z_mean, 0.0, max_err) &&
-      absl::random_internal::Near("z_variance", z_variance, 0.0, max_err);
-  if (!pass) {
-    ABSL_INTERNAL_LOG(
-        INFO,
-        absl::StrFormat(
-            "Beta(%f, %f), "
-            "mean: sample %f, expect %f, which is %f stddevs away, "
-            "variance: sample %f, expect %f, which is %f stddevs away.",
-            alpha_, beta_, m.mean, Mean(),
-            std::abs(m.mean - Mean()) / mean_stddev, m.variance, Variance(),
-            std::abs(m.variance - Variance()) / variance_stddev));
-  }
-  return pass;
-}
-
-template <class D>
-bool BetaDistributionTest::SingleChiSquaredTest(double p, size_t samples,
-                                                size_t buckets) {
-  constexpr double kErr = 1e-7;
-  std::vector<double> cutoffs, expected;
-  const double bucket_width = 1.0 / static_cast<double>(buckets);
-  int i = 1;
-  int unmerged_buckets = 0;
-  for (; i < buckets; ++i) {
-    const double p = bucket_width * static_cast<double>(i);
-    const double boundary =
-        absl::random_internal::BetaIncompleteInv(alpha_, beta_, p);
-    // The intention is to add `boundary` to the list of `cutoffs`. It becomes
-    // problematic, however, when the boundary values are not monotone, due to
-    // numerical issues when computing the inverse regularized incomplete
-    // Beta function. In these cases, we merge that bucket with its previous
-    // neighbor and merge their expected counts.
-    if ((cutoffs.empty() && boundary < kErr) ||
-        (!cutoffs.empty() && boundary <= cutoffs.back())) {
-      unmerged_buckets++;
-      continue;
-    }
-    if (boundary >= 1.0 - 1e-10) {
-      break;
-    }
-    cutoffs.push_back(boundary);
-    expected.push_back(static_cast<double>(1 + unmerged_buckets) *
-                       bucket_width * static_cast<double>(samples));
-    unmerged_buckets = 0;
-  }
-  cutoffs.push_back(std::numeric_limits<double>::infinity());
-  // Merge all remaining buckets.
-  expected.push_back(static_cast<double>(buckets - i + 1) * bucket_width *
-                     static_cast<double>(samples));
-  // Make sure that we don't merge all the buckets, making this test
-  // meaningless.
-  EXPECT_GE(cutoffs.size(), 3) << alpha_ << ", " << beta_;
-
-  D dis(alpha_, beta_);
-
-  std::vector<int32_t> counts(cutoffs.size(), 0);
-  for (int i = 0; i < samples; i++) {
-    const double x = dis(rng_);
-    auto it = std::upper_bound(cutoffs.begin(), cutoffs.end(), x);
-    counts[std::distance(cutoffs.begin(), it)]++;
-  }
-
-  // Null-hypothesis is that the distribution is beta distributed with the
-  // provided alpha, beta params (not estimated from the data).
-  const int dof = cutoffs.size() - 1;
-
-  const double chi_square = absl::random_internal::ChiSquare(
-      counts.begin(), counts.end(), expected.begin(), expected.end());
-  const bool pass =
-      (absl::random_internal::ChiSquarePValue(chi_square, dof) >= p);
-  if (!pass) {
-    for (int i = 0; i < cutoffs.size(); i++) {
-      ABSL_INTERNAL_LOG(
-          INFO, absl::StrFormat("cutoff[%d] = %f, actual count %d, expected %d",
-                                i, cutoffs[i], counts[i],
-                                static_cast<int>(expected[i])));
-    }
-
-    ABSL_INTERNAL_LOG(
-        INFO, absl::StrFormat(
-                  "Beta(%f, %f) %s %f, p = %f", alpha_, beta_,
-                  absl::random_internal::kChiSquared, chi_square,
-                  absl::random_internal::ChiSquarePValue(chi_square, dof)));
-  }
-  return pass;
-}
-
-TEST_P(BetaDistributionTest, TestSampleStatistics) {
-  static constexpr int kRuns = 20;
-  static constexpr double kPFail = 0.02;
-  const double p =
-      absl::random_internal::RequiredSuccessProbability(kPFail, kRuns);
-  static constexpr int kSampleCount = 10000;
-  static constexpr int kBucketCount = 100;
-  int failed = 0;
-  for (int i = 0; i < kRuns; ++i) {
-    if (!SingleZTestOnMeanAndVariance<absl::beta_distribution<double>>(
-            p, kSampleCount)) {
-      failed++;
-    }
-    if (!SingleChiSquaredTest<absl::beta_distribution<double>>(
-            0.005, kSampleCount, kBucketCount)) {
-      failed++;
-    }
-  }
-  // Set so that the test is not flaky at --runs_per_test=10000
-  EXPECT_LE(failed, 5);
-}
-
-std::string ParamName(
-    const ::testing::TestParamInfo<::testing::tuple<double, double>>& info) {
-  std::string name = absl::StrCat("alpha_", ::testing::get<0>(info.param),
-                                  "__beta_", ::testing::get<1>(info.param));
-  return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}});
-}
-
-INSTANTIATE_TEST_CASE_P(
-    TestSampleStatisticsCombinations, BetaDistributionTest,
-    ::testing::Combine(::testing::Values(0.1, 0.2, 0.9, 1.1, 2.5, 10.0, 123.4),
-                       ::testing::Values(0.1, 0.2, 0.9, 1.1, 2.5, 10.0, 123.4)),
-    ParamName);
-
-INSTANTIATE_TEST_CASE_P(
-    TestSampleStatistics_SelectedPairs, BetaDistributionTest,
-    ::testing::Values(std::make_pair(0.5, 1000), std::make_pair(1000, 0.5),
-                      std::make_pair(900, 1000), std::make_pair(10000, 20000),
-                      std::make_pair(4e5, 2e7), std::make_pair(1e7, 1e5)),
-    ParamName);
-
-// NOTE: absl::beta_distribution is not guaranteed to be stable.
-TEST(BetaDistributionTest, StabilityTest) {
-  // absl::beta_distribution stability relies on the stability of
-  // absl::random_interna::RandU64ToDouble, std::exp, std::log, std::pow,
-  // and std::sqrt.
-  //
-  // This test also depends on the stability of std::frexp.
-  using testing::ElementsAre;
-  absl::random_internal::sequence_urbg urbg({
-      0xffff00000000e6c8ull, 0xffff0000000006c8ull, 0x800003766295CFA9ull,
-      0x11C819684E734A41ull, 0x832603766295CFA9ull, 0x7fbe76c8b4395800ull,
-      0xB3472DCA7B14A94Aull, 0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull,
-      0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, 0x00035C904C70A239ull,
-      0x00009E0BCBAADE14ull, 0x0000000000622CA7ull, 0x4864f22c059bf29eull,
-      0x247856d8b862665cull, 0xe46e86e9a1337e10ull, 0xd8c8541f3519b133ull,
-      0xffe75b52c567b9e4ull, 0xfffff732e5709c5bull, 0xff1f7f0b983532acull,
-      0x1ec2e8986d2362caull, 0xC332DDEFBE6C5AA5ull, 0x6558218568AB9702ull,
-      0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, 0xECDD4775619F1510ull,
-      0x814c8e35fe9a961aull, 0x0c3cd59c9b638a02ull, 0xcb3bb6478a07715cull,
-      0x1224e62c978bbc7full, 0x671ef2cb04e81f6eull, 0x3c1cbd811eaf1808ull,
-      0x1bbc23cfa8fac721ull, 0xa4c2cda65e596a51ull, 0xb77216fad37adf91ull,
-      0x836d794457c08849ull, 0xe083df03475f49d7ull, 0xbc9feb512e6b0d6cull,
-      0xb12d74fdd718c8c5ull, 0x12ff09653bfbe4caull, 0x8dd03a105bc4ee7eull,
-      0x5738341045ba0d85ull, 0xf3fd722dc65ad09eull, 0xfa14fd21ea2a5705ull,
-      0xffe6ea4d6edb0c73ull, 0xD07E9EFE2BF11FB4ull, 0x95DBDA4DAE909198ull,
-      0xEAAD8E716B93D5A0ull, 0xD08ED1D0AFC725E0ull, 0x8E3C5B2F8E7594B7ull,
-      0x8FF6E2FBF2122B64ull, 0x8888B812900DF01Cull, 0x4FAD5EA0688FC31Cull,
-      0xD1CFF191B3A8C1ADull, 0x2F2F2218BE0E1777ull, 0xEA752DFE8B021FA1ull,
-  });
-
-  // Convert the real-valued result into a unit64 where we compare
-  // 5 (float) or 10 (double) decimal digits plus the base-2 exponent.
-  auto float_to_u64 = [](float d) {
-    int exp = 0;
-    auto f = std::frexp(d, &exp);
-    return (static_cast<uint64_t>(1e5 * f) * 10000) + std::abs(exp);
-  };
-  auto double_to_u64 = [](double d) {
-    int exp = 0;
-    auto f = std::frexp(d, &exp);
-    return (static_cast<uint64_t>(1e10 * f) * 10000) + std::abs(exp);
-  };
-
-  std::vector<uint64_t> output(20);
-  {
-    // Algorithm Joehnk (float)
-    absl::beta_distribution<float> dist(0.1f, 0.2f);
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return float_to_u64(dist(urbg)); });
-    EXPECT_EQ(44, urbg.invocations());
-    EXPECT_THAT(output,  //
-                testing::ElementsAre(
-                    998340000, 619030004, 500000001, 999990000, 996280000,
-                    500000001, 844740004, 847210001, 999970000, 872320000,
-                    585480007, 933280000, 869080042, 647670031, 528240004,
-                    969980004, 626050008, 915930002, 833440033, 878040015));
-  }
-
-  urbg.reset();
-  {
-    // Algorithm Joehnk (double)
-    absl::beta_distribution<double> dist(0.1, 0.2);
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return double_to_u64(dist(urbg)); });
-    EXPECT_EQ(44, urbg.invocations());
-    EXPECT_THAT(
-        output,  //
-        testing::ElementsAre(
-            99834713000000, 61903356870004, 50000000000001, 99999721170000,
-            99628374770000, 99999999990000, 84474397860004, 84721276240001,
-            99997407490000, 87232528120000, 58548364780007, 93328932910000,
-            86908237770042, 64767917930031, 52824581970004, 96998544140004,
-            62605946270008, 91593604380002, 83345031740033, 87804397230015));
-  }
-
-  urbg.reset();
-  {
-    // Algorithm Cheng 1
-    absl::beta_distribution<double> dist(0.9, 2.0);
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return double_to_u64(dist(urbg)); });
-    EXPECT_EQ(62, urbg.invocations());
-    EXPECT_THAT(
-        output,  //
-        testing::ElementsAre(
-            62069004780001, 64433204450001, 53607416560000, 89644295430008,
-            61434586310019, 55172615890002, 62187161490000, 56433684810003,
-            80454622050005, 86418558710003, 92920514700001, 64645184680001,
-            58549183380000, 84881283650005, 71078728590002, 69949694970000,
-            73157461710001, 68592191300001, 70747623900000, 78584696930005));
-  }
-
-  urbg.reset();
-  {
-    // Algorithm Cheng 2
-    absl::beta_distribution<double> dist(1.5, 2.5);
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return double_to_u64(dist(urbg)); });
-    EXPECT_EQ(54, urbg.invocations());
-    EXPECT_THAT(
-        output,  //
-        testing::ElementsAre(
-            75000029250001, 76751482860001, 53264575220000, 69193133650005,
-            78028324470013, 91573587560002, 59167523770000, 60658618560002,
-            80075870540000, 94141320460004, 63196592770003, 78883906300002,
-            96797992590001, 76907587800001, 56645167560000, 65408302280003,
-            53401156320001, 64731238570000, 83065573750001, 79788333820001));
-  }
-}
-
-// This is an implementation-specific test. If any part of the implementation
-// changes, then it is likely that this test will change as well.  Also, if
-// dependencies of the distribution change, such as RandU64ToDouble, then this
-// is also likely to change.
-TEST(BetaDistributionTest, AlgorithmBounds) {
-  {
-    absl::random_internal::sequence_urbg urbg(
-        {0x7fbe76c8b4395800ull, 0x8000000000000000ull});
-    // u=0.499, v=0.5
-    absl::beta_distribution<double> dist(1e-4, 1e-4);
-    double a = dist(urbg);
-    EXPECT_EQ(a, 2.0202860861567108529e-09);
-    EXPECT_EQ(2, urbg.invocations());
-  }
-
-  // Test that both the float & double algorithms appropriately reject the
-  // initial draw.
-  {
-    // 1/alpha = 1/beta = 2.
-    absl::beta_distribution<float> dist(0.5, 0.5);
-
-    // first two outputs are close to 1.0 - epsilon,
-    // thus:  (u ^ 2 + v ^ 2) > 1.0
-    absl::random_internal::sequence_urbg urbg(
-        {0xffff00000006e6c8ull, 0xffff00000007c7c8ull, 0x800003766295CFA9ull,
-         0x11C819684E734A41ull});
-    {
-      double y = absl::beta_distribution<double>(0.5, 0.5)(urbg);
-      EXPECT_EQ(4, urbg.invocations());
-      EXPECT_EQ(y, 0.9810668952633862) << y;
-    }
-
-    // ...and:  log(u) * a ~= log(v) * b ~= -0.02
-    // thus z ~= -0.02 + log(1 + e(~0))
-    //        ~= -0.02 + 0.69
-    // thus z > 0
-    urbg.reset();
-    {
-      float x = absl::beta_distribution<float>(0.5, 0.5)(urbg);
-      EXPECT_EQ(4, urbg.invocations());
-      EXPECT_NEAR(0.98106688261032104, x, 0.0000005) << x << "f";
-    }
-  }
-}
-
-}  // namespace