1 files changed, 259 insertions, 0 deletions
diff --git a/third_party/abseil_cpp/absl/random/uniform_int_distribution_test.cc b/third_party/abseil_cpp/absl/random/uniform_int_distribution_test.cc
new file mode 100644
index 0000000000..276d72ad20
--- /dev/null
+++ b/third_party/abseil_cpp/absl/random/uniform_int_distribution_test.cc
@@ -0,0 +1,259 @@
+// 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/uniform_int_distribution.h"
+
+#include <cmath>
+#include <cstdint>
+#include <iterator>
+#include <random>
+#include <sstream>
+#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"
+
+namespace {
+
+template <typename IntType>
+class UniformIntDistributionTest : public ::testing::Test {};
+
+using IntTypes = ::testing::Types<int8_t, uint8_t, int16_t, uint16_t, int32_t,
+                                  uint32_t, int64_t, uint64_t>;
+TYPED_TEST_SUITE(UniformIntDistributionTest, IntTypes);
+
+TYPED_TEST(UniformIntDistributionTest, ParamSerializeTest) {
+  // This test essentially ensures that the parameters serialize,
+  // not that the values generated cover the full range.
+  using Limits = std::numeric_limits<TypeParam>;
+  using param_type =
+      typename absl::uniform_int_distribution<TypeParam>::param_type;
+  const TypeParam kMin = std::is_unsigned<TypeParam>::value ? 37 : -105;
+  const TypeParam kNegOneOrZero = std::is_unsigned<TypeParam>::value ? 0 : -1;
+
+  constexpr int kCount = 1000;
+  absl::InsecureBitGen gen;
+  for (const auto& param : {
+           param_type(),
+           param_type(2, 2),  // Same
+           param_type(9, 32),
+           param_type(kMin, 115),
+           param_type(kNegOneOrZero, Limits::max()),
+           param_type(Limits::min(), Limits::max()),
+           param_type(Limits::lowest(), Limits::max()),
+           param_type(Limits::min() + 1, Limits::max() - 1),
+       }) {
+    const auto a = param.a();
+    const auto b = param.b();
+    absl::uniform_int_distribution<TypeParam> before(a, b);
+    EXPECT_EQ(before.a(), param.a());
+    EXPECT_EQ(before.b(), param.b());
+
+    {
+      // Initialize via param_type
+      absl::uniform_int_distribution<TypeParam> via_param(param);
+      EXPECT_EQ(via_param, before);
+    }
+
+    // Initialize via iostreams
+    std::stringstream ss;
+    ss << before;
+
+    absl::uniform_int_distribution<TypeParam> after(Limits::min() + 3,
+                                                    Limits::max() - 5);
+
+    EXPECT_NE(before.a(), after.a());
+    EXPECT_NE(before.b(), after.b());
+    EXPECT_NE(before.param(), after.param());
+    EXPECT_NE(before, after);
+
+    ss >> after;
+
+    EXPECT_EQ(before.a(), after.a());
+    EXPECT_EQ(before.b(), after.b());
+    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;
+      }
+    }
+    std::string msg = absl::StrCat("Range: ", +sample_min, ", ", +sample_max);
+    ABSL_RAW_LOG(INFO, "%s", msg.c_str());
+  }
+}
+
+TYPED_TEST(UniformIntDistributionTest, ViolatesPreconditionsDeathTest) {
+#if GTEST_HAS_DEATH_TEST
+  // Hi < Lo
+  EXPECT_DEBUG_DEATH({ absl::uniform_int_distribution<TypeParam> dist(10, 1); },
+                     "");
+#endif  // GTEST_HAS_DEATH_TEST
+#if defined(NDEBUG)
+  // opt-mode, for invalid parameters, will generate a garbage value,
+  // but should not enter an infinite loop.
+  absl::InsecureBitGen gen;
+  absl::uniform_int_distribution<TypeParam> dist(10, 1);
+  auto x = dist(gen);
+
+  // Any value will generate a non-empty string.
+  EXPECT_FALSE(absl::StrCat(+x).empty()) << x;
+#endif  // NDEBUG
+}
+
+TYPED_TEST(UniformIntDistributionTest, TestMoments) {
+  constexpr int kSize = 100000;
+  using Limits = std::numeric_limits<TypeParam>;
+  using param_type =
+      typename absl::uniform_int_distribution<TypeParam>::param_type;
+
+  // 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};
+
+  std::vector<double> values(kSize);
+  for (const auto& param :
+       {param_type(0, Limits::max()), param_type(13, 127)}) {
+    absl::uniform_int_distribution<TypeParam> dist(param);
+    for (int i = 0; i < kSize; i++) {
+      const auto sample = dist(rng);
+      ASSERT_LE(dist.param().a(), sample);
+      ASSERT_GE(dist.param().b(), sample);
+      values[i] = sample;
+    }
+
+    auto moments = absl::random_internal::ComputeDistributionMoments(values);
+    const double a = dist.param().a();
+    const double b = dist.param().b();
+    const double n = (b - a + 1);
+    const double mean = (a + b) / 2;
+    const double var = ((b - a + 1) * (b - a + 1) - 1) / 12;
+    const double kurtosis = 3 - 6 * (n * n + 1) / (5 * (n * n - 1));
+
+    // TODO(ahh): this is not the right bound
+    // empirically validated with --runs_per_test=10000.
+    EXPECT_NEAR(mean, moments.mean, 0.01 * var);
+    EXPECT_NEAR(var, moments.variance, 0.015 * var);
+    EXPECT_NEAR(0.0, moments.skewness, 0.025);
+    EXPECT_NEAR(kurtosis, moments.kurtosis, 0.02 * kurtosis);
+  }
+}
+
+TYPED_TEST(UniformIntDistributionTest, ChiSquaredTest50) {
+  using absl::random_internal::kChiSquared;
+
+  constexpr size_t kTrials = 1000;
+  constexpr int kBuckets = 50;  // inclusive, so actally +1
+  constexpr double kExpected =
+      static_cast<double>(kTrials) / static_cast<double>(kBuckets);
+
+  // Empirically validated with --runs_per_test=10000.
+  const int kThreshold =
+      absl::random_internal::ChiSquareValue(kBuckets, 0.999999);
+
+  const TypeParam min = std::is_unsigned<TypeParam>::value ? 37 : -37;
+  const TypeParam max = min + kBuckets;
+
+  // 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};
+
+  absl::uniform_int_distribution<TypeParam> dist(min, max);
+
+  std::vector<int32_t> counts(kBuckets + 1, 0);
+  for (size_t i = 0; i < kTrials; i++) {
+    auto x = dist(rng);
+    counts[x - min]++;
+  }
+  double chi_square = absl::random_internal::ChiSquareWithExpected(
+      std::begin(counts), std::end(counts), kExpected);
+  if (chi_square > kThreshold) {
+    double p_value =
+        absl::random_internal::ChiSquarePValue(chi_square, kBuckets);
+
+    // Chi-squared test failed. Output does not appear to be uniform.
+    std::string msg;
+    for (const auto& a : counts) {
+      absl::StrAppend(&msg, a, "\n");
+    }
+    absl::StrAppend(&msg, kChiSquared, " p-value ", p_value, "\n");
+    absl::StrAppend(&msg, "High ", kChiSquared, " value: ", chi_square, " > ",
+                    kThreshold);
+    ABSL_RAW_LOG(INFO, "%s", msg.c_str());
+    FAIL() << msg;
+  }
+}
+
+TEST(UniformIntDistributionTest, StabilityTest) {
+  // absl::uniform_int_distribution stability relies only on integer operations.
+  absl::random_internal::sequence_urbg urbg(
+      {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull,
+       0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull,
+       0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull,
+       0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull});
+
+  std::vector<int> output(12);
+
+  {
+    absl::uniform_int_distribution<int32_t> dist(0, 4);
+    for (auto& v : output) {
+      v = dist(urbg);
+    }
+  }
+  EXPECT_EQ(12, urbg.invocations());
+  EXPECT_THAT(output, testing::ElementsAre(4, 4, 3, 2, 1, 0, 1, 4, 3, 1, 3, 1));
+
+  {
+    urbg.reset();
+    absl::uniform_int_distribution<int32_t> dist(0, 100);
+    for (auto& v : output) {
+      v = dist(urbg);
+    }
+  }
+  EXPECT_EQ(12, urbg.invocations());
+  EXPECT_THAT(output, testing::ElementsAre(97, 86, 75, 41, 36, 16, 38, 92, 67,
+                                           30, 80, 38));
+
+  {
+    urbg.reset();
+    absl::uniform_int_distribution<int32_t> dist(0, 10000);
+    for (auto& v : output) {
+      v = dist(urbg);
+    }
+  }
+  EXPECT_EQ(12, urbg.invocations());
+  EXPECT_THAT(output, testing::ElementsAre(9648, 8562, 7439, 4089, 3571, 1602,
+                                           3813, 9195, 6641, 2986, 7956, 3765));
+}
+
+}  // namespace