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, 250 deletions

diff --git a/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc b/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc
deleted file mode 100644
index 6d007006ef48..000000000000
--- a/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc
+++ /dev/null
@@ -1,250 +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/discrete_distribution.h"
-
-#include <cmath>
-#include <cstddef>
-#include <cstdint>
-#include <iterator>
-#include <numeric>
-#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/pcg_engine.h"
-#include "absl/random/internal/sequence_urbg.h"
-#include "absl/random/random.h"
-#include "absl/strings/str_cat.h"
-#include "absl/strings/strip.h"
-
-namespace {
-
-template <typename IntType>
-class DiscreteDistributionTypeTest : 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(DiscreteDistributionTypeTest, IntTypes);
-
-TYPED_TEST(DiscreteDistributionTypeTest, ParamSerializeTest) {
-  using param_type =
-      typename absl::discrete_distribution<TypeParam>::param_type;
-
-  absl::discrete_distribution<TypeParam> empty;
-  EXPECT_THAT(empty.probabilities(), testing::ElementsAre(1.0));
-
-  absl::discrete_distribution<TypeParam> before({1.0, 2.0, 1.0});
-
-  // Validate that the probabilities sum to 1.0. We picked values which
-  // can be represented exactly to avoid floating-point roundoff error.
-  double s = 0;
-  for (const auto& x : before.probabilities()) {
-    s += x;
-  }
-  EXPECT_EQ(s, 1.0);
-  EXPECT_THAT(before.probabilities(), testing::ElementsAre(0.25, 0.5, 0.25));
-
-  // Validate the same data via an initializer list.
-  {
-    std::vector<double> data({1.0, 2.0, 1.0});
-
-    absl::discrete_distribution<TypeParam> via_param{
-        param_type(std::begin(data), std::end(data))};
-
-    EXPECT_EQ(via_param, before);
-  }
-
-  std::stringstream ss;
-  ss << before;
-  absl::discrete_distribution<TypeParam> after;
-
-  EXPECT_NE(before, after);
-
-  ss >> after;
-
-  EXPECT_EQ(before, after);
-}
-
-TYPED_TEST(DiscreteDistributionTypeTest, Constructor) {
-  auto fn = [](double x) { return x; };
-  {
-    absl::discrete_distribution<int> unary(0, 1.0, 9.0, fn);
-    EXPECT_THAT(unary.probabilities(), testing::ElementsAre(1.0));
-  }
-
-  {
-    absl::discrete_distribution<int> unary(2, 1.0, 9.0, fn);
-    // => fn(1.0 + 0 * 4 + 2) => 3
-    // => fn(1.0 + 1 * 4 + 2) => 7
-    EXPECT_THAT(unary.probabilities(), testing::ElementsAre(0.3, 0.7));
-  }
-}
-
-TEST(DiscreteDistributionTest, InitDiscreteDistribution) {
-  using testing::Pair;
-
-  {
-    std::vector<double> p({1.0, 2.0, 3.0});
-    std::vector<std::pair<double, size_t>> q =
-        absl::random_internal::InitDiscreteDistribution(&p);
-
-    EXPECT_THAT(p, testing::ElementsAre(1 / 6.0, 2 / 6.0, 3 / 6.0));
-
-    // Each bucket is p=1/3, so bucket 0 will send half it's traffic
-    // to bucket 2, while the rest will retain all of their traffic.
-    EXPECT_THAT(q, testing::ElementsAre(Pair(0.5, 2),  //
-                                        Pair(1.0, 1),  //
-                                        Pair(1.0, 2)));
-  }
-
-  {
-    std::vector<double> p({1.0, 2.0, 3.0, 5.0, 2.0});
-
-    std::vector<std::pair<double, size_t>> q =
-        absl::random_internal::InitDiscreteDistribution(&p);
-
-    EXPECT_THAT(p, testing::ElementsAre(1 / 13.0, 2 / 13.0, 3 / 13.0, 5 / 13.0,
-                                        2 / 13.0));
-
-    // A more complex bucketing solution: Each bucket has p=0.2
-    // So buckets 0, 1, 4 will send their alternate traffic elsewhere, which
-    // happens to be bucket 3.
-    // However, summing up that alternate traffic gives bucket 3 too much
-    // traffic, so it will send some traffic to bucket 2.
-    constexpr double b0 = 1.0 / 13.0 / 0.2;
-    constexpr double b1 = 2.0 / 13.0 / 0.2;
-    constexpr double b3 = (5.0 / 13.0 / 0.2) - ((1 - b0) + (1 - b1) + (1 - b1));
-
-    EXPECT_THAT(q, testing::ElementsAre(Pair(b0, 3),   //
-                                        Pair(b1, 3),   //
-                                        Pair(1.0, 2),  //
-                                        Pair(b3, 2),   //
-                                        Pair(b1, 3)));
-  }
-}
-
-TEST(DiscreteDistributionTest, ChiSquaredTest50) {
-  using absl::random_internal::kChiSquared;
-
-  constexpr size_t kTrials = 10000;
-  constexpr int kBuckets = 50;  // inclusive, so actally +1
-
-  // 1-in-100000 threshold, but remember, there are about 8 tests
-  // in this file. And the test could fail for other reasons.
-  // Empirically validated with --runs_per_test=10000.
-  const int kThreshold =
-      absl::random_internal::ChiSquareValue(kBuckets, 0.99999);
-
-  std::vector<double> weights(kBuckets, 0);
-  std::iota(std::begin(weights), std::end(weights), 1);
-  absl::discrete_distribution<int> dist(std::begin(weights), std::end(weights));
-
-  // 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<int32_t> counts(kBuckets, 0);
-  for (size_t i = 0; i < kTrials; i++) {
-    auto x = dist(rng);
-    counts[x]++;
-  }
-
-  // Scale weights.
-  double sum = 0;
-  for (double x : weights) {
-    sum += x;
-  }
-  for (double& x : weights) {
-    x = kTrials * (x / sum);
-  }
-
-  double chi_square =
-      absl::random_internal::ChiSquare(std::begin(counts), std::end(counts),
-                                       std::begin(weights), std::end(weights));
-
-  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 (size_t i = 0; i < counts.size(); i++) {
-      absl::StrAppend(&msg, i, ": ", counts[i], " vs ", weights[i], "\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(DiscreteDistributionTest, StabilityTest) {
-  // absl::discrete_distribution stabilitiy relies on
-  // absl::uniform_int_distribution and absl::bernoulli_distribution.
-  absl::random_internal::sequence_urbg urbg(
-      {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull,
-       0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull,
-       0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull,
-       0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull});
-
-  std::vector<int> output(6);
-
-  {
-    absl::discrete_distribution<int32_t> dist({1.0, 2.0, 3.0, 5.0, 2.0});
-    EXPECT_EQ(0, dist.min());
-    EXPECT_EQ(4, dist.max());
-    for (auto& v : output) {
-      v = dist(urbg);
-    }
-    EXPECT_EQ(12, urbg.invocations());
-  }
-
-  // With 12 calls to urbg, each call into discrete_distribution consumes
-  // precisely 2 values: one for the uniform call, and a second for the
-  // bernoulli.
-  //
-  // Given the alt mapping: 0=>3, 1=>3, 2=>2, 3=>2, 4=>3, we can
-  //
-  // uniform:      443210143131
-  // bernoulli: b0 000011100101
-  // bernoulli: b1 001111101101
-  // bernoulli: b2 111111111111
-  // bernoulli: b3 001111101111
-  // bernoulli: b4 001111101101
-  // ...
-  EXPECT_THAT(output, testing::ElementsAre(3, 3, 1, 3, 3, 3));
-
-  {
-    urbg.reset();
-    absl::discrete_distribution<int64_t> dist({1.0, 2.0, 3.0, 5.0, 2.0});
-    EXPECT_EQ(0, dist.min());
-    EXPECT_EQ(4, dist.max());
-    for (auto& v : output) {
-      v = dist(urbg);
-    }
-    EXPECT_EQ(12, urbg.invocations());
-  }
-  EXPECT_THAT(output, testing::ElementsAre(3, 3, 0, 3, 0, 4));
-}
-
-}  // namespace