about summary refs log tree commit diff
path: root/third_party/abseil_cpp/absl/random/gaussian_distribution_test.cc
diff options
context:
space:
mode:
Diffstat (limited to 'third_party/abseil_cpp/absl/random/gaussian_distribution_test.cc')
-rw-r--r--third_party/abseil_cpp/absl/random/gaussian_distribution_test.cc579
1 files changed, 0 insertions, 579 deletions
diff --git a/third_party/abseil_cpp/absl/random/gaussian_distribution_test.cc b/third_party/abseil_cpp/absl/random/gaussian_distribution_test.cc
deleted file mode 100644
index 02ac578a5c18..000000000000
--- a/third_party/abseil_cpp/absl/random/gaussian_distribution_test.cc
+++ /dev/null
@@ -1,579 +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/gaussian_distribution.h"
-
-#include <algorithm>
-#include <cmath>
-#include <cstddef>
-#include <ios>
-#include <iterator>
-#include <random>
-#include <string>
-#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/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 GaussianDistributionInterfaceTest : public ::testing::Test {};
-
-using RealTypes = ::testing::Types<float, double, long double>;
-TYPED_TEST_CASE(GaussianDistributionInterfaceTest, RealTypes);
-
-TYPED_TEST(GaussianDistributionInterfaceTest, SerializeTest) {
-  using param_type =
-      typename absl::gaussian_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
-      // Arbitrary values.
-      TypeParam(1e-8), TypeParam(1e-4), TypeParam(2), TypeParam(1e4),
-      TypeParam(1e8), TypeParam(1e20), TypeParam(2.5),
-      // Boundary cases.
-      std::numeric_limits<TypeParam>::infinity(),
-      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,
-      std::nextafter(std::numeric_limits<TypeParam>::min(),
-                     TypeParam(0)),  // denorm_max
-  };
-
-  constexpr int kCount = 1000;
-  absl::InsecureBitGen gen;
-
-  // Use a loop to generate the combinations of {+/-x, +/-y}, and assign x, y to
-  // all values in kParams,
-  for (const auto mod : {0, 1, 2, 3}) {
-    for (const auto x : kParams) {
-      if (!std::isfinite(x)) continue;
-      for (const auto y : kParams) {
-        const TypeParam mean = (mod & 0x1) ? -x : x;
-        const TypeParam stddev = (mod & 0x2) ? -y : y;
-        const param_type param(mean, stddev);
-
-        absl::gaussian_distribution<TypeParam> before(mean, stddev);
-        EXPECT_EQ(before.mean(), param.mean());
-        EXPECT_EQ(before.stddev(), param.stddev());
-
-        {
-          absl::gaussian_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);
-          if (sample > sample_max) sample_max = sample;
-          if (sample < sample_min) sample_min = sample;
-          EXPECT_GE(sample, before.min()) << before;
-          EXPECT_LE(sample, before.max()) << before;
-        }
-        if (!std::is_same<TypeParam, long double>::value) {
-          ABSL_INTERNAL_LOG(
-              INFO, absl::StrFormat("Range{%f, %f}: %f, %f", mean, stddev,
-                                    sample_min, sample_max));
-        }
-
-        std::stringstream ss;
-        ss << before;
-
-        if (!std::isfinite(mean) || !std::isfinite(stddev)) {
-          // Streams do not parse inf/nan.
-          continue;
-        }
-
-        // Validate stream serialization.
-        absl::gaussian_distribution<TypeParam> after(-0.53f, 2.3456f);
-
-        EXPECT_NE(before.mean(), after.mean());
-        EXPECT_NE(before.stddev(), after.stddev());
-        EXPECT_NE(before.param(), after.param());
-        EXPECT_NE(before, after);
-
-        ss >> after;
-
-#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \
-    defined(__ppc__) || defined(__PPC__) || defined(__EMSCRIPTEN__)
-        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}.
-          //
-          // Emscripten is even worse, implementing long double as a 128-bit
-          // type, but shipping with a strtold() that doesn't support that.
-          if (mean <= std::numeric_limits<double>::max() &&
-              mean >= std::numeric_limits<double>::lowest()) {
-            EXPECT_EQ(static_cast<double>(before.mean()),
-                      static_cast<double>(after.mean()))
-                << ss.str();
-          }
-          if (stddev <= std::numeric_limits<double>::max() &&
-              stddev >= std::numeric_limits<double>::lowest()) {
-            EXPECT_EQ(static_cast<double>(before.stddev()),
-                      static_cast<double>(after.stddev()))
-                << ss.str();
-          }
-          continue;
-        }
-#endif
-
-        EXPECT_EQ(before.mean(), after.mean());
-        EXPECT_EQ(before.stddev(), after.stddev())  //
-            << ss.str() << " "                      //
-            << (ss.good() ? "good " : "")           //
-            << (ss.bad() ? "bad " : "")             //
-            << (ss.eof() ? "eof " : "")             //
-            << (ss.fail() ? "fail " : "");
-      }
-    }
-  }
-}
-
-// http://www.itl.nist.gov/div898/handbook/eda/section3/eda3661.htm
-
-class GaussianModel {
- public:
-  GaussianModel(double mean, double stddev) : mean_(mean), stddev_(stddev) {}
-
-  double mean() const { return mean_; }
-  double variance() const { return stddev() * stddev(); }
-  double stddev() const { return stddev_; }
-  double skew() const { return 0; }
-  double kurtosis() const { return 3.0; }
-
-  // The inverse CDF, or PercentPoint function.
-  double InverseCDF(double p) {
-    ABSL_ASSERT(p >= 0.0);
-    ABSL_ASSERT(p < 1.0);
-    return mean() + stddev() * -absl::random_internal::InverseNormalSurvival(p);
-  }
-
- private:
-  const double mean_;
-  const double stddev_;
-};
-
-struct Param {
-  double mean;
-  double stddev;
-  double p_fail;  // Z-Test probability of failure.
-  int trials;     // Z-Test trials.
-};
-
-// GaussianDistributionTests implements a z-test for the gaussian
-// distribution.
-class GaussianDistributionTests : public testing::TestWithParam<Param>,
-                                  public GaussianModel {
- public:
-  GaussianDistributionTests()
-      : GaussianModel(GetParam().mean, GetParam().stddev) {}
-
-  // 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 GaussianDistributionTests::SingleZTest(const double p,
-                                            const size_t samples) {
-  D dis(mean(), stddev());
-
-  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 double max_err = absl::random_internal::MaxErrorTolerance(p);
-  const auto m = absl::random_internal::ComputeDistributionMoments(data);
-  const double z = absl::random_internal::ZScore(mean(), m);
-  const bool pass = absl::random_internal::Near("z", z, 0.0, max_err);
-
-  // NOTE: Informational statistical test:
-  //
-  // Compute the Jarque-Bera test statistic given the excess skewness
-  // and kurtosis. The statistic is drawn from a chi-square(2) distribution.
-  // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test
-  //
-  // The null-hypothesis (normal distribution) is rejected when
-  // (p = 0.05 => jb > 5.99)
-  // (p = 0.01 => jb > 9.21)
-  // NOTE: JB has a large type-I error rate, so it will reject the
-  // null-hypothesis even when it is true more often than the z-test.
-  //
-  const double jb =
-      static_cast<double>(m.n) / 6.0 *
-      (std::pow(m.skewness, 2.0) + std::pow(m.kurtosis - 3.0, 2.0) / 4.0);
-
-  if (!pass || jb > 9.21) {
-    ABSL_INTERNAL_LOG(
-        INFO, absl::StrFormat("p=%f max_err=%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\n"
-                              " jb=%f vs. 9.21",
-                              p, max_err, m.mean, mean(), std::sqrt(m.variance),
-                              stddev(), m.skewness, skew(), m.kurtosis,
-                              kurtosis(), z, jb));
-  }
-  return pass;
-}
-
-template <typename D>
-double GaussianDistributionTests::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(mean(), stddev());
-
-  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 a gaussian distribution
-  // with the provided mean and stddev (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);
-
-  // Log if the chi_square value is above the threshold.
-  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("mean=", mean(), " stddev=", stddev(), "\n",   //
-                           " expected ", expected, "\n",                  //
-                           kChiSquared, " ", chi_square, " (", p, ")\n",  //
-                           kChiSquared, " @ 0.98 = ", threshold));
-  }
-  return p;
-}
-
-TEST_P(GaussianDistributionTests, ZTest) {
-  // TODO(absl-team): Run these tests against std::normal_distribution<double>
-  // to validate outcomes are similar.
-  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::gaussian_distribution<double>>(p, kSamples) ? 0 : 1;
-  }
-  EXPECT_LE(failures, expected_failures);
-}
-
-TEST_P(GaussianDistributionTests, ChiSquaredTest) {
-  const int kTrials = 20;
-  int failures = 0;
-
-  for (int i = 0; i < kTrials; i++) {
-    double p_value =
-        SingleChiSquaredTest<absl::gaussian_distribution<double>>();
-    if (p_value < 0.0025) {  // 1/400
-      failures++;
-    }
-  }
-  // There is a 0.05% chance of producing at least one failure, so raise the
-  // failure threshold high enough to allow for a flake rate of less than one in
-  // 10,000.
-  EXPECT_LE(failures, 4);
-}
-
-std::vector<Param> GenParams() {
-  return {
-      // Mean around 0.
-      Param{0.0, 1.0, 0.01, 100},
-      Param{0.0, 1e2, 0.01, 100},
-      Param{0.0, 1e4, 0.01, 100},
-      Param{0.0, 1e8, 0.01, 100},
-      Param{0.0, 1e16, 0.01, 100},
-      Param{0.0, 1e-3, 0.01, 100},
-      Param{0.0, 1e-5, 0.01, 100},
-      Param{0.0, 1e-9, 0.01, 100},
-      Param{0.0, 1e-17, 0.01, 100},
-
-      // Mean around 1.
-      Param{1.0, 1.0, 0.01, 100},
-      Param{1.0, 1e2, 0.01, 100},
-      Param{1.0, 1e-2, 0.01, 100},
-
-      // Mean around 100 / -100
-      Param{1e2, 1.0, 0.01, 100},
-      Param{-1e2, 1.0, 0.01, 100},
-      Param{1e2, 1e6, 0.01, 100},
-      Param{-1e2, 1e6, 0.01, 100},
-
-      // More extreme
-      Param{1e4, 1e4, 0.01, 100},
-      Param{1e8, 1e4, 0.01, 100},
-      Param{1e12, 1e4, 0.01, 100},
-  };
-}
-
-std::string ParamName(const ::testing::TestParamInfo<Param>& info) {
-  const auto& p = info.param;
-  std::string name = absl::StrCat("mean_", absl::SixDigits(p.mean), "__stddev_",
-                                  absl::SixDigits(p.stddev));
-  return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}});
-}
-
-INSTANTIATE_TEST_SUITE_P(All, GaussianDistributionTests,
-                         ::testing::ValuesIn(GenParams()), ParamName);
-
-// NOTE: absl::gaussian_distribution is not guaranteed to be stable.
-TEST(GaussianDistributionTest, StabilityTest) {
-  // absl::gaussian_distribution stability relies on the underlying zignor
-  // data, absl::random_interna::RandU64ToDouble, std::exp, std::log, and
-  // std::abs.
-  absl::random_internal::sequence_urbg urbg(
-      {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull,
-       0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull,
-       0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull,
-       0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull});
-
-  std::vector<int> output(11);
-
-  {
-    absl::gaussian_distribution<double> dist;
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return static_cast<int>(10000000.0 * dist(urbg)); });
-
-    EXPECT_EQ(13, urbg.invocations());
-    EXPECT_THAT(output,  //
-                testing::ElementsAre(1494, 25518841, 9991550, 1351856,
-                                     -20373238, 3456682, 333530, -6804981,
-                                     -15279580, -16459654, 1494));
-  }
-
-  urbg.reset();
-  {
-    absl::gaussian_distribution<float> dist;
-    std::generate(std::begin(output), std::end(output),
-                  [&] { return static_cast<int>(1000000.0f * dist(urbg)); });
-
-    EXPECT_EQ(13, urbg.invocations());
-    EXPECT_THAT(
-        output,  //
-        testing::ElementsAre(149, 2551884, 999155, 135185, -2037323, 345668,
-                             33353, -680498, -1527958, -1645965, 149));
-  }
-}
-
-// 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(GaussianDistributionTest, AlgorithmBounds) {
-  absl::gaussian_distribution<double> dist;
-
-  // In ~95% of cases, a single value is used to generate the output.
-  // for all inputs where |x| < 0.750461021389 this should be the case.
-  //
-  // The exact constraints are based on the ziggurat tables, and any
-  // changes to the ziggurat tables may require adjusting these bounds.
-  //
-  // for i in range(0, len(X)-1):
-  //   print i, X[i+1]/X[i], (X[i+1]/X[i] > 0.984375)
-  //
-  // 0.125 <= |values| <= 0.75
-  const uint64_t kValues[] = {
-      0x1000000000000100ull, 0x2000000000000100ull, 0x3000000000000100ull,
-      0x4000000000000100ull, 0x5000000000000100ull, 0x6000000000000100ull,
-      // negative values
-      0x9000000000000100ull, 0xa000000000000100ull, 0xb000000000000100ull,
-      0xc000000000000100ull, 0xd000000000000100ull, 0xe000000000000100ull};
-
-  // 0.875 <= |values| <= 0.984375
-  const uint64_t kExtraValues[] = {
-      0x7000000000000100ull, 0x7800000000000100ull,  //
-      0x7c00000000000100ull, 0x7e00000000000100ull,  //
-      // negative values
-      0xf000000000000100ull, 0xf800000000000100ull,  //
-      0xfc00000000000100ull, 0xfe00000000000100ull};
-
-  auto make_box = [](uint64_t v, uint64_t box) {
-    return (v & 0xffffffffffffff80ull) | box;
-  };
-
-  // The box is the lower 7 bits of the value. When the box == 0, then
-  // the algorithm uses an escape hatch to select the result for large
-  // outputs.
-  for (uint64_t box = 0; box < 0x7f; box++) {
-    for (const uint64_t v : kValues) {
-      // Extra values are added to the sequence to attempt to avoid
-      // infinite loops from rejection sampling on bugs/errors.
-      absl::random_internal::sequence_urbg urbg(
-          {make_box(v, box), 0x0003eb76f6f7f755ull, 0x5FCEA50FDB2F953Bull});
-
-      auto a = dist(urbg);
-      EXPECT_EQ(1, urbg.invocations()) << box << " " << std::hex << v;
-      if (v & 0x8000000000000000ull) {
-        EXPECT_LT(a, 0.0) << box << " " << std::hex << v;
-      } else {
-        EXPECT_GT(a, 0.0) << box << " " << std::hex << v;
-      }
-    }
-    if (box > 10 && box < 100) {
-      // The center boxes use the fast algorithm for more
-      // than 98.4375% of values.
-      for (const uint64_t v : kExtraValues) {
-        absl::random_internal::sequence_urbg urbg(
-            {make_box(v, box), 0x0003eb76f6f7f755ull, 0x5FCEA50FDB2F953Bull});
-
-        auto a = dist(urbg);
-        EXPECT_EQ(1, urbg.invocations()) << box << " " << std::hex << v;
-        if (v & 0x8000000000000000ull) {
-          EXPECT_LT(a, 0.0) << box << " " << std::hex << v;
-        } else {
-          EXPECT_GT(a, 0.0) << box << " " << std::hex << v;
-        }
-      }
-    }
-  }
-
-  // When the box == 0, the fallback algorithm uses a ratio of uniforms,
-  // which consumes 2 additional values from the urbg.
-  // Fallback also requires that the initial value be > 0.9271586026096681.
-  auto make_fallback = [](uint64_t v) { return (v & 0xffffffffffffff80ull); };
-
-  double tail[2];
-  {
-    // 0.9375
-    absl::random_internal::sequence_urbg urbg(
-        {make_fallback(0x7800000000000000ull), 0x13CCA830EB61BD96ull,
-         0x00000076f6f7f755ull});
-    tail[0] = dist(urbg);
-    EXPECT_EQ(3, urbg.invocations());
-    EXPECT_GT(tail[0], 0);
-  }
-  {
-    // -0.9375
-    absl::random_internal::sequence_urbg urbg(
-        {make_fallback(0xf800000000000000ull), 0x13CCA830EB61BD96ull,
-         0x00000076f6f7f755ull});
-    tail[1] = dist(urbg);
-    EXPECT_EQ(3, urbg.invocations());
-    EXPECT_LT(tail[1], 0);
-  }
-  EXPECT_EQ(tail[0], -tail[1]);
-  EXPECT_EQ(418610, static_cast<int64_t>(tail[0] * 100000.0));
-
-  // When the box != 0, the fallback algorithm computes a wedge function.
-  // Depending on the box, the threshold for varies as high as
-  // 0.991522480228.
-  {
-    // 0.9921875, 0.875
-    absl::random_internal::sequence_urbg urbg(
-        {make_box(0x7f00000000000000ull, 120), 0xe000000000000001ull,
-         0x13CCA830EB61BD96ull});
-    tail[0] = dist(urbg);
-    EXPECT_EQ(2, urbg.invocations());
-    EXPECT_GT(tail[0], 0);
-  }
-  {
-    // -0.9921875, 0.875
-    absl::random_internal::sequence_urbg urbg(
-        {make_box(0xff00000000000000ull, 120), 0xe000000000000001ull,
-         0x13CCA830EB61BD96ull});
-    tail[1] = dist(urbg);
-    EXPECT_EQ(2, urbg.invocations());
-    EXPECT_LT(tail[1], 0);
-  }
-  EXPECT_EQ(tail[0], -tail[1]);
-  EXPECT_EQ(61948, static_cast<int64_t>(tail[0] * 100000.0));
-
-  // Fallback rejected, try again.
-  {
-    // -0.9921875, 0.0625
-    absl::random_internal::sequence_urbg urbg(
-        {make_box(0xff00000000000000ull, 120), 0x1000000000000001,
-         make_box(0x1000000000000100ull, 50), 0x13CCA830EB61BD96ull});
-    dist(urbg);
-    EXPECT_EQ(3, urbg.invocations());
-  }
-}
-
-}  // namespace