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+// 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/log_uniform_int_distribution.h"
+
+#include <cstddef>
+#include <cstdint>
+#include <iterator>
+#include <random>
+#include <sstream>
+#include <string>
+#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/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 LogUniformIntDistributionTypeTest : public ::testing::Test {};
+
+using IntTypes = ::testing::Types<int8_t, int16_t, int32_t, int64_t, //
+ uint8_t, uint16_t, uint32_t, uint64_t>;
+TYPED_TEST_CASE(LogUniformIntDistributionTypeTest, IntTypes);
+
+TYPED_TEST(LogUniformIntDistributionTypeTest, SerializeTest) {
+ using param_type =
+ typename absl::log_uniform_int_distribution<TypeParam>::param_type;
+ using Limits = std::numeric_limits<TypeParam>;
+
+ constexpr int kCount = 1000;
+ absl::InsecureBitGen gen;
+ for (const auto& param : {
+ param_type(0, 1), //
+ param_type(0, 2), //
+ param_type(0, 2, 10), //
+ param_type(9, 32, 4), //
+ param_type(1, 101, 10), //
+ param_type(1, Limits::max() / 2), //
+ param_type(0, Limits::max() - 1), //
+ param_type(0, Limits::max(), 2), //
+ param_type(0, Limits::max(), 10), //
+ param_type(Limits::min(), 0), //
+ param_type(Limits::lowest(), Limits::max()), //
+ param_type(Limits::min(), Limits::max()), //
+ }) {
+ // Validate parameters.
+ const auto min = param.min();
+ const auto max = param.max();
+ const auto base = param.base();
+ absl::log_uniform_int_distribution<TypeParam> before(min, max, base);
+ EXPECT_EQ(before.min(), param.min());
+ EXPECT_EQ(before.max(), param.max());
+ EXPECT_EQ(before.base(), param.base());
+
+ {
+ absl::log_uniform_int_distribution<TypeParam> via_param(param);
+ EXPECT_EQ(via_param, before);
+ }
+
+ // Validate stream serialization.
+ std::stringstream ss;
+ ss << before;
+
+ absl::log_uniform_int_distribution<TypeParam> after(3, 6, 17);
+
+ EXPECT_NE(before.max(), after.max());
+ EXPECT_NE(before.base(), after.base());
+ EXPECT_NE(before.param(), after.param());
+ EXPECT_NE(before, after);
+
+ ss >> after;
+
+ EXPECT_EQ(before.min(), after.min());
+ EXPECT_EQ(before.max(), after.max());
+ EXPECT_EQ(before.base(), after.base());
+ 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));
+ }
+}
+
+using log_uniform_i32 = absl::log_uniform_int_distribution<int32_t>;
+
+class LogUniformIntChiSquaredTest
+ : public testing::TestWithParam<log_uniform_i32::param_type> {
+ public:
+ // The ChiSquaredTestImpl provides a chi-squared goodness of fit test for
+ // data generated by the log-uniform-int distribution.
+ double ChiSquaredTestImpl();
+
+ absl::InsecureBitGen rng_;
+};
+
+double LogUniformIntChiSquaredTest::ChiSquaredTestImpl() {
+ using absl::random_internal::kChiSquared;
+
+ const auto& param = GetParam();
+
+ // Check the distribution of L=log(log_uniform_int_distribution, base),
+ // expecting that L is roughly uniformly distributed, that is:
+ //
+ // P[L=0] ~= P[L=1] ~= ... ~= P[L=log(max)]
+ //
+ // For a total of X entries, each bucket should contain some number of samples
+ // in the interval [X/k - a, X/k + a].
+ //
+ // Where `a` is approximately sqrt(X/k). This is validated by bucketing
+ // according to the log function and using a chi-squared test for uniformity.
+
+ const bool is_2 = (param.base() == 2);
+ const double base_log = 1.0 / std::log(param.base());
+ const auto bucket_index = [base_log, is_2, &param](int32_t x) {
+ uint64_t y = static_cast<uint64_t>(x) - param.min();
+ return (y == 0) ? 0
+ : is_2 ? static_cast<int>(1 + std::log2(y))
+ : static_cast<int>(1 + std::log(y) * base_log);
+ };
+ const int max_bucket = bucket_index(param.max()); // inclusive
+ const size_t trials = 15 + (max_bucket + 1) * 10;
+
+ log_uniform_i32 dist(param);
+
+ std::vector<int64_t> buckets(max_bucket + 1);
+ for (size_t i = 0; i < trials; ++i) {
+ const auto sample = dist(rng_);
+ // Check the bounds.
+ ABSL_ASSERT(sample <= dist.max());
+ ABSL_ASSERT(sample >= dist.min());
+ // Convert the output of the generator to one of num_bucket buckets.
+ int bucket = bucket_index(sample);
+ ABSL_ASSERT(bucket <= max_bucket);
+ ++buckets[bucket];
+ }
+
+ // The null-hypothesis is that the distribution is uniform with respect to
+ // log-uniform-int bucketization.
+ const int dof = buckets.size() - 1;
+ const double expected = trials / static_cast<double>(buckets.size());
+
+ const double threshold = absl::random_internal::ChiSquareValue(dof, 0.98);
+
+ double chi_square = absl::random_internal::ChiSquareWithExpected(
+ std::begin(buckets), std::end(buckets), expected);
+
+ const double p = absl::random_internal::ChiSquarePValue(chi_square, dof);
+
+ if (chi_square > threshold) {
+ ABSL_INTERNAL_LOG(INFO, "values");
+ for (size_t i = 0; i < buckets.size(); i++) {
+ ABSL_INTERNAL_LOG(INFO, absl::StrCat(i, ": ", buckets[i]));
+ }
+ ABSL_INTERNAL_LOG(INFO,
+ absl::StrFormat("trials=%d\n"
+ "%s(data, %d) = %f (%f)\n"
+ "%s @ 0.98 = %f",
+ trials, kChiSquared, dof, chi_square, p,
+ kChiSquared, threshold));
+ }
+ return p;
+}
+
+TEST_P(LogUniformIntChiSquaredTest, MultiTest) {
+ const int kTrials = 5;
+
+ int failures = 0;
+ for (int i = 0; i < kTrials; i++) {
+ double p_value = ChiSquaredTestImpl();
+ if (p_value < 0.005) {
+ 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);
+}
+
+// Generate the parameters for the test.
+std::vector<log_uniform_i32::param_type> GenParams() {
+ using Param = log_uniform_i32::param_type;
+ using Limits = std::numeric_limits<int32_t>;
+
+ return std::vector<Param>{
+ Param{0, 1, 2},
+ Param{1, 1, 2},
+ Param{0, 2, 2},
+ Param{0, 3, 2},
+ Param{0, 4, 2},
+ Param{0, 9, 10},
+ Param{0, 10, 10},
+ Param{0, 11, 10},
+ Param{1, 10, 10},
+ Param{0, (1 << 8) - 1, 2},
+ Param{0, (1 << 8), 2},
+ Param{0, (1 << 30) - 1, 2},
+ Param{-1000, 1000, 10},
+ Param{0, Limits::max(), 2},
+ Param{0, Limits::max(), 3},
+ Param{0, Limits::max(), 10},
+ Param{Limits::min(), 0},
+ Param{Limits::min(), Limits::max(), 2},
+ };
+}
+
+std::string ParamName(
+ const ::testing::TestParamInfo<log_uniform_i32::param_type>& info) {
+ const auto& p = info.param;
+ std::string name =
+ absl::StrCat("min_", p.min(), "__max_", p.max(), "__base_", p.base());
+ return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}});
+}
+
+INSTANTIATE_TEST_SUITE_P(, LogUniformIntChiSquaredTest,
+ ::testing::ValuesIn(GenParams()), ParamName);
+
+// NOTE: absl::log_uniform_int_distribution is not guaranteed to be stable.
+TEST(LogUniformIntDistributionTest, StabilityTest) {
+ using testing::ElementsAre;
+ // absl::uniform_int_distribution stability relies on
+ // absl::random_internal::LeadingSetBit, std::log, std::pow.
+ absl::random_internal::sequence_urbg urbg(
+ {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull,
+ 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull,
+ 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull,
+ 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull});
+
+ std::vector<int> output(6);
+
+ {
+ absl::log_uniform_int_distribution<int32_t> dist(0, 256);
+ std::generate(std::begin(output), std::end(output),
+ [&] { return dist(urbg); });
+ EXPECT_THAT(output, ElementsAre(256, 66, 4, 6, 57, 103));
+ }
+ urbg.reset();
+ {
+ absl::log_uniform_int_distribution<int32_t> dist(0, 256, 10);
+ std::generate(std::begin(output), std::end(output),
+ [&] { return dist(urbg); });
+ EXPECT_THAT(output, ElementsAre(8, 4, 0, 0, 0, 69));
+ }
+}
+
+} // namespace
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