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, 430 deletions

diff --git a/third_party/abseil_cpp/absl/random/exponential_distribution_test.cc b/third_party/abseil_cpp/absl/random/exponential_distribution_test.cc
deleted file mode 100644
index 8e9e69b64b29..000000000000
--- a/third_party/abseil_cpp/absl/random/exponential_distribution_test.cc
+++ /dev/null
@@ -1,430 +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/exponential_distribution.h"
-
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <cstdint>
-#include <iterator>
-#include <limits>
-#include <random>
-#include <sstream>
-#include <string>
-#include <type_traits>
-#include <vector>
-
-#include "gmock/gmock.h"
-#include "gtest/gtest.h"
-#include "absl/base/internal/raw_logging.h"
-#include "absl/base/macros.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 {
-
-using absl::random_internal::kChiSquared;
-
-template <typename RealType>
-class ExponentialDistributionTypedTest : public ::testing::Test {};
-
-#if defined(__EMSCRIPTEN__)
-using RealTypes = ::testing::Types<float, double>;
-#else
-using RealTypes = ::testing::Types<float, double, long double>;
-#endif  // defined(__EMSCRIPTEN__)
-TYPED_TEST_CASE(ExponentialDistributionTypedTest, RealTypes);
-
-TYPED_TEST(ExponentialDistributionTypedTest, SerializeTest) {
-  using param_type =
-      typename absl::exponential_distribution<TypeParam>::param_type;
-
-  const TypeParam kParams[] = {
-      // Cases around 1.
-      1,                                           //
-      std::nextafter(TypeParam(1), TypeParam(0)),  // 1 - epsilon
-      std::nextafter(TypeParam(1), TypeParam(2)),  // 1 + epsilon
-      // Typical cases.
-      TypeParam(1e-8), TypeParam(1e-4), TypeParam(1), TypeParam(2),
-      TypeParam(1e4), TypeParam(1e8), TypeParam(1e20), TypeParam(2.5),
-      // 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
-      // There are some errors dealing with denorms on apple platforms.
-      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
-  };
-
-  constexpr int kCount = 1000;
-  absl::InsecureBitGen gen;
-
-  for (const TypeParam lambda : kParams) {
-    // Some values may be invalid; skip those.
-    if (!std::isfinite(lambda)) continue;
-    ABSL_ASSERT(lambda > 0);
-
-    const param_type param(lambda);
-
-    absl::exponential_distribution<TypeParam> before(lambda);
-    EXPECT_EQ(before.lambda(), param.lambda());
-
-    {
-      absl::exponential_distribution<TypeParam> via_param(param);
-      EXPECT_EQ(via_param, before);
-      EXPECT_EQ(via_param.param(), before.param());
-    }
-
-    // Smoke test.
-    auto sample_min = before.max();
-    auto sample_max = before.min();
-    for (int i = 0; i < kCount; i++) {
-      auto sample = before(gen);
-      EXPECT_GE(sample, before.min()) << before;
-      EXPECT_LE(sample, before.max()) << before;
-      if (sample > sample_max) sample_max = sample;
-      if (sample < sample_min) sample_min = sample;
-    }
-    if (!std::is_same<TypeParam, long double>::value) {
-      ABSL_INTERNAL_LOG(INFO,
-                        absl::StrFormat("Range {%f}: %f, %f, lambda=%f", lambda,
-                                        sample_min, sample_max, lambda));
-    }
-
-    std::stringstream ss;
-    ss << before;
-
-    if (!std::isfinite(lambda)) {
-      // Streams do not deserialize inf/nan correctly.
-      continue;
-    }
-    // Validate stream serialization.
-    absl::exponential_distribution<TypeParam> after(34.56f);
-
-    EXPECT_NE(before.lambda(), after.lambda());
-    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, particularly for values
-      // near {1.0 +/- epsilon}.
-      if (lambda <= std::numeric_limits<double>::max() &&
-          lambda >= std::numeric_limits<double>::lowest()) {
-        EXPECT_EQ(static_cast<double>(before.lambda()),
-                  static_cast<double>(after.lambda()))
-            << ss.str();
-      }
-      continue;
-    }
-#endif
-
-    EXPECT_EQ(before.lambda(), after.lambda())  //
-        << ss.str() << " "                      //
-        << (ss.good() ? "good " : "")           //
-        << (ss.bad() ? "bad " : "")             //
-        << (ss.eof() ? "eof " : "")             //
-        << (ss.fail() ? "fail " : "");
-  }
-}
-
-// http://www.itl.nist.gov/div898/handbook/eda/section3/eda3667.htm
-
-class ExponentialModel {
- public:
-  explicit ExponentialModel(double lambda)
-      : lambda_(lambda), beta_(1.0 / lambda) {}
-
-  double lambda() const { return lambda_; }
-
-  double mean() const { return beta_; }
-  double variance() const { return beta_ * beta_; }
-  double stddev() const { return std::sqrt(variance()); }
-  double skew() const { return 2; }
-  double kurtosis() const { return 6.0; }
-
-  double CDF(double x) { return 1.0 - std::exp(-lambda_ * x); }
-
-  // The inverse CDF, or PercentPoint function of the distribution
-  double InverseCDF(double p) {
-    ABSL_ASSERT(p >= 0.0);
-    ABSL_ASSERT(p < 1.0);
-    return -beta_ * std::log(1.0 - p);
-  }
-
- private:
-  const double lambda_;
-  const double beta_;
-};
-
-struct Param {
-  double lambda;
-  double p_fail;
-  int trials;
-};
-
-class ExponentialDistributionTests : public testing::TestWithParam<Param>,
-                                     public ExponentialModel {
- public:
-  ExponentialDistributionTests() : ExponentialModel(GetParam().lambda) {}
-
-  // SingleZTest provides a basic z-squared test of the mean vs. expected
-  // mean for data generated by the poisson distribution.
-  template <typename D>
-  bool SingleZTest(const double p, const size_t samples);
-
-  // SingleChiSquaredTest provides a basic chi-squared test of the normal
-  // distribution.
-  template <typename D>
-  double SingleChiSquaredTest();
-
-  // 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};
-};
-
-template <typename D>
-bool ExponentialDistributionTests::SingleZTest(const double p,
-                                               const size_t samples) {
-  D dis(lambda());
-
-  std::vector<double> data;
-  data.reserve(samples);
-  for (size_t i = 0; i < samples; i++) {
-    const double x = dis(rng_);
-    data.push_back(x);
-  }
-
-  const auto m = absl::random_internal::ComputeDistributionMoments(data);
-  const double max_err = absl::random_internal::MaxErrorTolerance(p);
-  const double z = absl::random_internal::ZScore(mean(), m);
-  const bool pass = absl::random_internal::Near("z", z, 0.0, max_err);
-
-  if (!pass) {
-    ABSL_INTERNAL_LOG(
-        INFO, absl::StrFormat("p=%f max_err=%f\n"
-                              " lambda=%f\n"
-                              " mean=%f vs. %f\n"
-                              " stddev=%f vs. %f\n"
-                              " skewness=%f vs. %f\n"
-                              " kurtosis=%f vs. %f\n"
-                              " z=%f vs. 0",
-                              p, max_err, lambda(), m.mean, mean(),
-                              std::sqrt(m.variance), stddev(), m.skewness,
-                              skew(), m.kurtosis, kurtosis(), z));
-  }
-  return pass;
-}
-
-template <typename D>
-double ExponentialDistributionTests::SingleChiSquaredTest() {
-  const size_t kSamples = 10000;
-  const int kBuckets = 50;
-
-  // The InverseCDF is the percent point function of the distribution, and can
-  // be used to assign buckets roughly uniformly.
-  std::vector<double> cutoffs;
-  const double kInc = 1.0 / static_cast<double>(kBuckets);
-  for (double p = kInc; p < 1.0; p += kInc) {
-    cutoffs.push_back(InverseCDF(p));
-  }
-  if (cutoffs.back() != std::numeric_limits<double>::infinity()) {
-    cutoffs.push_back(std::numeric_limits<double>::infinity());
-  }
-
-  D dis(lambda());
-
-  std::vector<int32_t> counts(cutoffs.size(), 0);
-  for (int j = 0; j < kSamples; j++) {
-    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 exponentially distributed
-  // with the provided lambda (not estimated from the data).
-  const int dof = static_cast<int>(counts.size()) - 1;
-
-  // Our threshold for logging is 1-in-50.
-  const double threshold = absl::random_internal::ChiSquareValue(dof, 0.98);
-
-  const double expected =
-      static_cast<double>(kSamples) / static_cast<double>(counts.size());
-
-  double chi_square = absl::random_internal::ChiSquareWithExpected(
-      std::begin(counts), std::end(counts), expected);
-  double p = absl::random_internal::ChiSquarePValue(chi_square, dof);
-
-  if (chi_square > threshold) {
-    for (int i = 0; i < cutoffs.size(); i++) {
-      ABSL_INTERNAL_LOG(
-          INFO, absl::StrFormat("%d : (%f) = %d", i, cutoffs[i], counts[i]));
-    }
-
-    ABSL_INTERNAL_LOG(INFO,
-                      absl::StrCat("lambda ", lambda(), "\n",     //
-                                   " expected ", expected, "\n",  //
-                                   kChiSquared, " ", chi_square, " (", p, ")\n",
-                                   kChiSquared, " @ 0.98 = ", threshold));
-  }
-  return p;
-}
-
-TEST_P(ExponentialDistributionTests, ZTest) {
-  const size_t kSamples = 10000;
-  const auto& param = GetParam();
-  const int expected_failures =
-      std::max(1, static_cast<int>(std::ceil(param.trials * param.p_fail)));
-  const double p = absl::random_internal::RequiredSuccessProbability(
-      param.p_fail, param.trials);
-
-  int failures = 0;
-  for (int i = 0; i < param.trials; i++) {
-    failures += SingleZTest<absl::exponential_distribution<double>>(p, kSamples)
-                    ? 0
-                    : 1;
-  }
-  EXPECT_LE(failures, expected_failures);
-}
-
-TEST_P(ExponentialDistributionTests, ChiSquaredTest) {
-  const int kTrials = 20;
-  int failures = 0;
-
-  for (int i = 0; i < kTrials; i++) {
-    double p_value =
-        SingleChiSquaredTest<absl::exponential_distribution<double>>();
-    if (p_value < 0.005) {  // 1/200
-      failures++;
-    }
-  }
-
-  // There is a 0.10% chance of producing at least one failure, so raise the
-  // failure threshold high enough to allow for a flake rate < 10,000.
-  EXPECT_LE(failures, 4);
-}
-
-std::vector<Param> GenParams() {
-  return {
-      Param{1.0, 0.02, 100},
-      Param{2.5, 0.02, 100},
-      Param{10, 0.02, 100},
-      // large
-      Param{1e4, 0.02, 100},
-      Param{1e9, 0.02, 100},
-      // small
-      Param{0.1, 0.02, 100},
-      Param{1e-3, 0.02, 100},
-      Param{1e-5, 0.02, 100},
-  };
-}
-
-std::string ParamName(const ::testing::TestParamInfo<Param>& info) {
-  const auto& p = info.param;
-  std::string name = absl::StrCat("lambda_", absl::SixDigits(p.lambda));
-  return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}});
-}
-
-INSTANTIATE_TEST_CASE_P(All, ExponentialDistributionTests,
-                        ::testing::ValuesIn(GenParams()), ParamName);
-
-// NOTE: absl::exponential_distribution is not guaranteed to be stable.
-TEST(ExponentialDistributionTest, StabilityTest) {
-  // absl::exponential_distribution stability relies on std::log1p and
-  // absl::uniform_real_distribution.
-  absl::random_internal::sequence_urbg urbg(
-      {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull,
-       0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull,
-       0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull,
-       0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull});
-
-  std::vector<int> output(14);
-
-  {
-    absl::exponential_distribution<double> dist;
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return static_cast<int>(10000.0 * dist(urbg)); });
-
-    EXPECT_EQ(14, urbg.invocations());
-    EXPECT_THAT(output,
-                testing::ElementsAre(0, 71913, 14375, 5039, 1835, 861, 25936,
-                                     804, 126, 12337, 17984, 27002, 0, 71913));
-  }
-
-  urbg.reset();
-  {
-    absl::exponential_distribution<float> dist;
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return static_cast<int>(10000.0f * dist(urbg)); });
-
-    EXPECT_EQ(14, urbg.invocations());
-    EXPECT_THAT(output,
-                testing::ElementsAre(0, 71913, 14375, 5039, 1835, 861, 25936,
-                                     804, 126, 12337, 17984, 27002, 0, 71913));
-  }
-}
-
-TEST(ExponentialDistributionTest, AlgorithmBounds) {
-  // Relies on absl::uniform_real_distribution, so some of these comments
-  // reference that.
-  absl::exponential_distribution<double> dist;
-
-  {
-    // This returns the smallest value >0 from absl::uniform_real_distribution.
-    absl::random_internal::sequence_urbg urbg({0x0000000000000001ull});
-    double a = dist(urbg);
-    EXPECT_EQ(a, 5.42101086242752217004e-20);
-  }
-
-  {
-    // This returns a value very near 0.5 from absl::uniform_real_distribution.
-    absl::random_internal::sequence_urbg urbg({0x7fffffffffffffefull});
-    double a = dist(urbg);
-    EXPECT_EQ(a, 0.693147180559945175204);
-  }
-
-  {
-    // This returns the largest value <1 from absl::uniform_real_distribution.
-    // WolframAlpha: ~39.1439465808987766283058547296341915292187253
-    absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFeFull});
-    double a = dist(urbg);
-    EXPECT_EQ(a, 36.7368005696771007251);
-  }
-  {
-    // This *ALSO* returns the largest value <1.
-    absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFFFull});
-    double a = dist(urbg);
-    EXPECT_EQ(a, 36.7368005696771007251);
-  }
-}
-
-}  // namespace