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, 427 deletions

diff --git a/third_party/abseil_cpp/absl/random/zipf_distribution_test.cc b/third_party/abseil_cpp/absl/random/zipf_distribution_test.cc
deleted file mode 100644
index f8cf70e0dddf..000000000000
--- a/third_party/abseil_cpp/absl/random/zipf_distribution_test.cc
+++ /dev/null
@@ -1,427 +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/zipf_distribution.h"
-
-#include <algorithm>
-#include <cstddef>
-#include <cstdint>
-#include <iterator>
-#include <random>
-#include <string>
-#include <utility>
-#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/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_replace.h"
-#include "absl/strings/strip.h"
-
-namespace {
-
-using ::absl::random_internal::kChiSquared;
-using ::testing::ElementsAre;
-
-template <typename IntType>
-class ZipfDistributionTypedTest : public ::testing::Test {};
-
-using IntTypes = ::testing::Types<int, int8_t, int16_t, int32_t, int64_t,
-                                  uint8_t, uint16_t, uint32_t, uint64_t>;
-TYPED_TEST_CASE(ZipfDistributionTypedTest, IntTypes);
-
-TYPED_TEST(ZipfDistributionTypedTest, SerializeTest) {
-  using param_type = typename absl::zipf_distribution<TypeParam>::param_type;
-
-  constexpr int kCount = 1000;
-  absl::InsecureBitGen gen;
-  for (const auto& param : {
-           param_type(),
-           param_type(32),
-           param_type(100, 3, 2),
-           param_type(std::numeric_limits<TypeParam>::max(), 4, 3),
-           param_type(std::numeric_limits<TypeParam>::max() / 2),
-       }) {
-    // Validate parameters.
-    const auto k = param.k();
-    const auto q = param.q();
-    const auto v = param.v();
-
-    absl::zipf_distribution<TypeParam> before(k, q, v);
-    EXPECT_EQ(before.k(), param.k());
-    EXPECT_EQ(before.q(), param.q());
-    EXPECT_EQ(before.v(), param.v());
-
-    {
-      absl::zipf_distribution<TypeParam> via_param(param);
-      EXPECT_EQ(via_param, before);
-    }
-
-    // Validate stream serialization.
-    std::stringstream ss;
-    ss << before;
-    absl::zipf_distribution<TypeParam> after(4, 5.5, 4.4);
-
-    EXPECT_NE(before.k(), after.k());
-    EXPECT_NE(before.q(), after.q());
-    EXPECT_NE(before.v(), after.v());
-    EXPECT_NE(before.param(), after.param());
-    EXPECT_NE(before, after);
-
-    ss >> after;
-
-    EXPECT_EQ(before.k(), after.k());
-    EXPECT_EQ(before.q(), after.q());
-    EXPECT_EQ(before.v(), after.v());
-    EXPECT_EQ(before.param(), after.param());
-    EXPECT_EQ(before, after);
-
-    // Smoke test.
-    auto sample_min = after.max();
-    auto sample_max = after.min();
-    for (int i = 0; i < kCount; i++) {
-      auto sample = after(gen);
-      EXPECT_GE(sample, after.min());
-      EXPECT_LE(sample, after.max());
-      if (sample > sample_max) sample_max = sample;
-      if (sample < sample_min) sample_min = sample;
-    }
-    ABSL_INTERNAL_LOG(INFO,
-                      absl::StrCat("Range: ", +sample_min, ", ", +sample_max));
-  }
-}
-
-class ZipfModel {
- public:
-  ZipfModel(size_t k, double q, double v) : k_(k), q_(q), v_(v) {}
-
-  double mean() const { return mean_; }
-
-  // For the other moments of the Zipf distribution, see, for example,
-  // http://mathworld.wolfram.com/ZipfDistribution.html
-
-  // PMF(k) = (1 / k^s) / H(N,s)
-  // Returns the probability that any single invocation returns k.
-  double PMF(size_t i) { return i >= hnq_.size() ? 0.0 : hnq_[i] / sum_hnq_; }
-
-  // CDF = H(k, s) / H(N,s)
-  double CDF(size_t i) {
-    if (i >= hnq_.size()) {
-      return 1.0;
-    }
-    auto it = std::begin(hnq_);
-    double h = 0.0;
-    for (const auto end = it; it != end; it++) {
-      h += *it;
-    }
-    return h / sum_hnq_;
-  }
-
-  // The InverseCDF returns the k values which bound p on the upper and lower
-  // bound. Since there is no closed-form solution, this is implemented as a
-  // bisction of the cdf.
-  std::pair<size_t, size_t> InverseCDF(double p) {
-    size_t min = 0;
-    size_t max = hnq_.size();
-    while (max > min + 1) {
-      size_t target = (max + min) >> 1;
-      double x = CDF(target);
-      if (x > p) {
-        max = target;
-      } else {
-        min = target;
-      }
-    }
-    return {min, max};
-  }
-
-  // Compute the probability totals, which are based on the generalized harmonic
-  // number, H(N,s).
-  //   H(N,s) == SUM(k=1..N, 1 / k^s)
-  //
-  // In the limit, H(N,s) == zetac(s) + 1.
-  //
-  // NOTE: The mean of a zipf distribution could be computed here as well.
-  // Mean :=  H(N, s-1) / H(N,s).
-  // Given the parameter v = 1, this gives the following function:
-  // (Hn(100, 1) - Hn(1,1)) / (Hn(100,2) - Hn(1,2)) = 6.5944
-  //
-  void Init() {
-    if (!hnq_.empty()) {
-      return;
-    }
-    hnq_.clear();
-    hnq_.reserve(std::min(k_, size_t{1000}));
-
-    sum_hnq_ = 0;
-    double qm1 = q_ - 1.0;
-    double sum_hnq_m1 = 0;
-    for (size_t i = 0; i < k_; i++) {
-      // Partial n-th generalized harmonic number
-      const double x = v_ + i;
-
-      // H(n, q-1)
-      const double hnqm1 =
-          (q_ == 2.0) ? (1.0 / x)
-                      : (q_ == 3.0) ? (1.0 / (x * x)) : std::pow(x, -qm1);
-      sum_hnq_m1 += hnqm1;
-
-      // H(n, q)
-      const double hnq =
-          (q_ == 2.0) ? (1.0 / (x * x))
-                      : (q_ == 3.0) ? (1.0 / (x * x * x)) : std::pow(x, -q_);
-      sum_hnq_ += hnq;
-      hnq_.push_back(hnq);
-      if (i > 1000 && hnq <= 1e-10) {
-        // The harmonic number is too small.
-        break;
-      }
-    }
-    assert(sum_hnq_ > 0);
-    mean_ = sum_hnq_m1 / sum_hnq_;
-  }
-
- private:
-  const size_t k_;
-  const double q_;
-  const double v_;
-
-  double mean_;
-  std::vector<double> hnq_;
-  double sum_hnq_;
-};
-
-using zipf_u64 = absl::zipf_distribution<uint64_t>;
-
-class ZipfTest : public testing::TestWithParam<zipf_u64::param_type>,
-                 public ZipfModel {
- public:
-  ZipfTest() : ZipfModel(GetParam().k(), GetParam().q(), GetParam().v()) {}
-
-  // 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};
-};
-
-TEST_P(ZipfTest, ChiSquaredTest) {
-  const auto& param = GetParam();
-  Init();
-
-  size_t trials = 10000;
-
-  // Find the split-points for the buckets.
-  std::vector<size_t> points;
-  std::vector<double> expected;
-  {
-    double last_cdf = 0.0;
-    double min_p = 1.0;
-    for (double p = 0.01; p < 1.0; p += 0.01) {
-      auto x = InverseCDF(p);
-      if (points.empty() || points.back() < x.second) {
-        const double p = CDF(x.second);
-        points.push_back(x.second);
-        double q = p - last_cdf;
-        expected.push_back(q);
-        last_cdf = p;
-        if (q < min_p) {
-          min_p = q;
-        }
-      }
-    }
-    if (last_cdf < 0.999) {
-      points.push_back(std::numeric_limits<size_t>::max());
-      double q = 1.0 - last_cdf;
-      expected.push_back(q);
-      if (q < min_p) {
-        min_p = q;
-      }
-    } else {
-      points.back() = std::numeric_limits<size_t>::max();
-      expected.back() += (1.0 - last_cdf);
-    }
-    // The Chi-Squared score is not completely scale-invariant; it works best
-    // when the small values are in the small digits.
-    trials = static_cast<size_t>(8.0 / min_p);
-  }
-  ASSERT_GT(points.size(), 0);
-
-  // Generate n variates and fill the counts vector with the count of their
-  // occurrences.
-  std::vector<int64_t> buckets(points.size(), 0);
-  double avg = 0;
-  {
-    zipf_u64 dis(param);
-    for (size_t i = 0; i < trials; i++) {
-      uint64_t x = dis(rng_);
-      ASSERT_LE(x, dis.max());
-      ASSERT_GE(x, dis.min());
-      avg += static_cast<double>(x);
-      auto it = std::upper_bound(std::begin(points), std::end(points),
-                                 static_cast<size_t>(x));
-      buckets[std::distance(std::begin(points), it)]++;
-    }
-    avg = avg / static_cast<double>(trials);
-  }
-
-  // Validate the output using the Chi-Squared test.
-  for (auto& e : expected) {
-    e *= trials;
-  }
-
-  // The null-hypothesis is that the distribution is a poisson distribution with
-  // the provided mean (not estimated from the data).
-  const int dof = static_cast<int>(expected.size()) - 1;
-
-  // NOTE: This test runs about 15x per invocation, so a value of 0.9995 is
-  // approximately correct for a test suite failure rate of 1 in 100.  In
-  // practice we see failures slightly higher than that.
-  const double threshold = absl::random_internal::ChiSquareValue(dof, 0.9999);
-
-  const double chi_square = absl::random_internal::ChiSquare(
-      std::begin(buckets), std::end(buckets), std::begin(expected),
-      std::end(expected));
-
-  const double p_actual =
-      absl::random_internal::ChiSquarePValue(chi_square, dof);
-
-  // Log if the chi_squared value is above the threshold.
-  if (chi_square > threshold) {
-    ABSL_INTERNAL_LOG(INFO, "values");
-    for (size_t i = 0; i < expected.size(); i++) {
-      ABSL_INTERNAL_LOG(INFO, absl::StrCat(points[i], ": ", buckets[i],
-                                           " vs. E=", expected[i]));
-    }
-    ABSL_INTERNAL_LOG(INFO, absl::StrCat("trials ", trials));
-    ABSL_INTERNAL_LOG(INFO,
-                      absl::StrCat("mean ", avg, " vs. expected ", mean()));
-    ABSL_INTERNAL_LOG(INFO, absl::StrCat(kChiSquared, "(data, ", dof, ") = ",
-                                         chi_square, " (", p_actual, ")"));
-    ABSL_INTERNAL_LOG(INFO,
-                      absl::StrCat(kChiSquared, " @ 0.9995 = ", threshold));
-    FAIL() << kChiSquared << " value of " << chi_square
-           << " is above the threshold.";
-  }
-}
-
-std::vector<zipf_u64::param_type> GenParams() {
-  using param = zipf_u64::param_type;
-  const auto k = param().k();
-  const auto q = param().q();
-  const auto v = param().v();
-  const uint64_t k2 = 1 << 10;
-  return std::vector<zipf_u64::param_type>{
-      // Default
-      param(k, q, v),
-      // vary K
-      param(4, q, v), param(1 << 4, q, v), param(k2, q, v),
-      // vary V
-      param(k2, q, 0.5), param(k2, q, 1.5), param(k2, q, 2.5), param(k2, q, 10),
-      // vary Q
-      param(k2, 1.5, v), param(k2, 3, v), param(k2, 5, v), param(k2, 10, v),
-      // Vary V & Q
-      param(k2, 1.5, 0.5), param(k2, 3, 1.5), param(k, 10, 10)};
-}
-
-std::string ParamName(
-    const ::testing::TestParamInfo<zipf_u64::param_type>& info) {
-  const auto& p = info.param;
-  std::string name = absl::StrCat("k_", p.k(), "__q_", absl::SixDigits(p.q()),
-                                  "__v_", absl::SixDigits(p.v()));
-  return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}});
-}
-
-INSTANTIATE_TEST_SUITE_P(All, ZipfTest, ::testing::ValuesIn(GenParams()),
-                         ParamName);
-
-// NOTE: absl::zipf_distribution is not guaranteed to be stable.
-TEST(ZipfDistributionTest, StabilityTest) {
-  // absl::zipf_distribution stability relies on
-  // absl::uniform_real_distribution, std::log, std::exp, std::log1p
-  absl::random_internal::sequence_urbg urbg(
-      {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull,
-       0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull,
-       0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull,
-       0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull});
-
-  std::vector<int> output(10);
-
-  {
-    absl::zipf_distribution<int32_t> dist;
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return dist(urbg); });
-    EXPECT_THAT(output, ElementsAre(10031, 0, 0, 3, 6, 0, 7, 47, 0, 0));
-  }
-  urbg.reset();
-  {
-    absl::zipf_distribution<int32_t> dist(std::numeric_limits<int32_t>::max(),
-                                          3.3);
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return dist(urbg); });
-    EXPECT_THAT(output, ElementsAre(44, 0, 0, 0, 0, 1, 0, 1, 3, 0));
-  }
-}
-
-TEST(ZipfDistributionTest, AlgorithmBounds) {
-  absl::zipf_distribution<int32_t> dist;
-
-  // Small values from absl::uniform_real_distribution map to larger Zipf
-  // distribution values.
-  const std::pair<uint64_t, int32_t> kInputs[] = {
-      {0xffffffffffffffff, 0x0}, {0x7fffffffffffffff, 0x0},
-      {0x3ffffffffffffffb, 0x1}, {0x1ffffffffffffffd, 0x4},
-      {0xffffffffffffffe, 0x9},  {0x7ffffffffffffff, 0x12},
-      {0x3ffffffffffffff, 0x25}, {0x1ffffffffffffff, 0x4c},
-      {0xffffffffffffff, 0x99},  {0x7fffffffffffff, 0x132},
-      {0x3fffffffffffff, 0x265}, {0x1fffffffffffff, 0x4cc},
-      {0xfffffffffffff, 0x999},  {0x7ffffffffffff, 0x1332},
-      {0x3ffffffffffff, 0x2665}, {0x1ffffffffffff, 0x4ccc},
-      {0xffffffffffff, 0x9998},  {0x7fffffffffff, 0x1332f},
-      {0x3fffffffffff, 0x2665a}, {0x1fffffffffff, 0x4cc9e},
-      {0xfffffffffff, 0x998e0},  {0x7ffffffffff, 0x133051},
-      {0x3ffffffffff, 0x265ae4}, {0x1ffffffffff, 0x4c9ed3},
-      {0xffffffffff, 0x98e223},  {0x7fffffffff, 0x13058c4},
-      {0x3fffffffff, 0x25b178e}, {0x1fffffffff, 0x4a062b2},
-      {0xfffffffff, 0x8ee23b8},  {0x7ffffffff, 0x10b21642},
-      {0x3ffffffff, 0x1d89d89d}, {0x1ffffffff, 0x2fffffff},
-      {0xffffffff, 0x45d1745d},  {0x7fffffff, 0x5a5a5a5a},
-      {0x3fffffff, 0x69ee5846},  {0x1fffffff, 0x73ecade3},
-      {0xfffffff, 0x79a9d260},   {0x7ffffff, 0x7cc0532b},
-      {0x3ffffff, 0x7e5ad146},   {0x1ffffff, 0x7f2c0bec},
-      {0xffffff, 0x7f95adef},    {0x7fffff, 0x7fcac0da},
-      {0x3fffff, 0x7fe55ae2},    {0x1fffff, 0x7ff2ac0e},
-      {0xfffff, 0x7ff955ae},     {0x7ffff, 0x7ffcaac1},
-      {0x3ffff, 0x7ffe555b},     {0x1ffff, 0x7fff2aac},
-      {0xffff, 0x7fff9556},      {0x7fff, 0x7fffcaab},
-      {0x3fff, 0x7fffe555},      {0x1fff, 0x7ffff2ab},
-      {0xfff, 0x7ffff955},       {0x7ff, 0x7ffffcab},
-      {0x3ff, 0x7ffffe55},       {0x1ff, 0x7fffff2b},
-      {0xff, 0x7fffff95},        {0x7f, 0x7fffffcb},
-      {0x3f, 0x7fffffe5},        {0x1f, 0x7ffffff3},
-      {0xf, 0x7ffffff9},         {0x7, 0x7ffffffd},
-      {0x3, 0x7ffffffe},         {0x1, 0x7fffffff},
-  };
-
-  for (const auto& instance : kInputs) {
-    absl::random_internal::sequence_urbg urbg({instance.first});
-    EXPECT_EQ(instance.second, dist(urbg));
-  }
-}
-
-}  // namespace