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, 275 deletions

diff --git a/third_party/abseil_cpp/absl/random/gaussian_distribution.h b/third_party/abseil_cpp/absl/random/gaussian_distribution.h
deleted file mode 100644
index 4b07a5c0af9d..000000000000
--- a/third_party/abseil_cpp/absl/random/gaussian_distribution.h
+++ /dev/null
@@ -1,275 +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.
-
-#ifndef ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_
-#define ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_
-
-// absl::gaussian_distribution implements the Ziggurat algorithm
-// for generating random gaussian numbers.
-//
-// Implementation based on "The Ziggurat Method for Generating Random Variables"
-// by George Marsaglia and Wai Wan Tsang: http://www.jstatsoft.org/v05/i08/
-//
-
-#include <cmath>
-#include <cstdint>
-#include <istream>
-#include <limits>
-#include <type_traits>
-
-#include "absl/base/config.h"
-#include "absl/random/internal/fast_uniform_bits.h"
-#include "absl/random/internal/generate_real.h"
-#include "absl/random/internal/iostream_state_saver.h"
-
-namespace absl {
-ABSL_NAMESPACE_BEGIN
-namespace random_internal {
-
-// absl::gaussian_distribution_base implements the underlying ziggurat algorithm
-// using the ziggurat tables generated by the gaussian_distribution_gentables
-// binary.
-//
-// The specific algorithm has some of the improvements suggested by the
-// 2005 paper, "An Improved Ziggurat Method to Generate Normal Random Samples",
-// Jurgen A Doornik.  (https://www.doornik.com/research/ziggurat.pdf)
-class ABSL_DLL gaussian_distribution_base {
- public:
-  template <typename URBG>
-  inline double zignor(URBG& g);  // NOLINT(runtime/references)
-
- private:
-  friend class TableGenerator;
-
-  template <typename URBG>
-  inline double zignor_fallback(URBG& g,  // NOLINT(runtime/references)
-                                bool neg);
-
-  // Constants used for the gaussian distribution.
-  static constexpr double kR = 3.442619855899;  // Start of the tail.
-  static constexpr double kRInv = 0.29047645161474317;  // ~= (1.0 / kR) .
-  static constexpr double kV = 9.91256303526217e-3;
-  static constexpr uint64_t kMask = 0x07f;
-
-  // The ziggurat tables store the pdf(f) and inverse-pdf(x) for equal-area
-  // points on one-half of the normal distribution, where the pdf function,
-  // pdf = e ^ (-1/2 *x^2), assumes that the mean = 0 & stddev = 1.
-  //
-  // These tables are just over 2kb in size; larger tables might improve the
-  // distributions, but also lead to more cache pollution.
-  //
-  // x = {3.71308, 3.44261, 3.22308, ..., 0}
-  // f = {0.00101, 0.00266, 0.00554, ..., 1}
-  struct Tables {
-    double x[kMask + 2];
-    double f[kMask + 2];
-  };
-  static const Tables zg_;
-  random_internal::FastUniformBits<uint64_t> fast_u64_;
-};
-
-}  // namespace random_internal
-
-// absl::gaussian_distribution:
-// Generates a number conforming to a Gaussian distribution.
-template <typename RealType = double>
-class gaussian_distribution : random_internal::gaussian_distribution_base {
- public:
-  using result_type = RealType;
-
-  class param_type {
-   public:
-    using distribution_type = gaussian_distribution;
-
-    explicit param_type(result_type mean = 0, result_type stddev = 1)
-        : mean_(mean), stddev_(stddev) {}
-
-    // Returns the mean distribution parameter.  The mean specifies the location
-    // of the peak.  The default value is 0.0.
-    result_type mean() const { return mean_; }
-
-    // Returns the deviation distribution parameter.  The default value is 1.0.
-    result_type stddev() const { return stddev_; }
-
-    friend bool operator==(const param_type& a, const param_type& b) {
-      return a.mean_ == b.mean_ && a.stddev_ == b.stddev_;
-    }
-
-    friend bool operator!=(const param_type& a, const param_type& b) {
-      return !(a == b);
-    }
-
-   private:
-    result_type mean_;
-    result_type stddev_;
-
-    static_assert(
-        std::is_floating_point<RealType>::value,
-        "Class-template absl::gaussian_distribution<> must be parameterized "
-        "using a floating-point type.");
-  };
-
-  gaussian_distribution() : gaussian_distribution(0) {}
-
-  explicit gaussian_distribution(result_type mean, result_type stddev = 1)
-      : param_(mean, stddev) {}
-
-  explicit gaussian_distribution(const param_type& p) : param_(p) {}
-
-  void reset() {}
-
-  // Generating functions
-  template <typename URBG>
-  result_type operator()(URBG& g) {  // NOLINT(runtime/references)
-    return (*this)(g, param_);
-  }
-
-  template <typename URBG>
-  result_type operator()(URBG& g,  // NOLINT(runtime/references)
-                         const param_type& p);
-
-  param_type param() const { return param_; }
-  void param(const param_type& p) { param_ = p; }
-
-  result_type(min)() const {
-    return -std::numeric_limits<result_type>::infinity();
-  }
-  result_type(max)() const {
-    return std::numeric_limits<result_type>::infinity();
-  }
-
-  result_type mean() const { return param_.mean(); }
-  result_type stddev() const { return param_.stddev(); }
-
-  friend bool operator==(const gaussian_distribution& a,
-                         const gaussian_distribution& b) {
-    return a.param_ == b.param_;
-  }
-  friend bool operator!=(const gaussian_distribution& a,
-                         const gaussian_distribution& b) {
-    return a.param_ != b.param_;
-  }
-
- private:
-  param_type param_;
-};
-
-// --------------------------------------------------------------------------
-// Implementation details only below
-// --------------------------------------------------------------------------
-
-template <typename RealType>
-template <typename URBG>
-typename gaussian_distribution<RealType>::result_type
-gaussian_distribution<RealType>::operator()(
-    URBG& g,  // NOLINT(runtime/references)
-    const param_type& p) {
-  return p.mean() + p.stddev() * static_cast<result_type>(zignor(g));
-}
-
-template <typename CharT, typename Traits, typename RealType>
-std::basic_ostream<CharT, Traits>& operator<<(
-    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
-    const gaussian_distribution<RealType>& x) {
-  auto saver = random_internal::make_ostream_state_saver(os);
-  os.precision(random_internal::stream_precision_helper<RealType>::kPrecision);
-  os << x.mean() << os.fill() << x.stddev();
-  return os;
-}
-
-template <typename CharT, typename Traits, typename RealType>
-std::basic_istream<CharT, Traits>& operator>>(
-    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
-    gaussian_distribution<RealType>& x) {   // NOLINT(runtime/references)
-  using result_type = typename gaussian_distribution<RealType>::result_type;
-  using param_type = typename gaussian_distribution<RealType>::param_type;
-
-  auto saver = random_internal::make_istream_state_saver(is);
-  auto mean = random_internal::read_floating_point<result_type>(is);
-  if (is.fail()) return is;
-  auto stddev = random_internal::read_floating_point<result_type>(is);
-  if (!is.fail()) {
-    x.param(param_type(mean, stddev));
-  }
-  return is;
-}
-
-namespace random_internal {
-
-template <typename URBG>
-inline double gaussian_distribution_base::zignor_fallback(URBG& g, bool neg) {
-  using random_internal::GeneratePositiveTag;
-  using random_internal::GenerateRealFromBits;
-
-  // This fallback path happens approximately 0.05% of the time.
-  double x, y;
-  do {
-    // kRInv = 1/r, U(0, 1)
-    x = kRInv *
-        std::log(GenerateRealFromBits<double, GeneratePositiveTag, false>(
-            fast_u64_(g)));
-    y = -std::log(
-        GenerateRealFromBits<double, GeneratePositiveTag, false>(fast_u64_(g)));
-  } while ((y + y) < (x * x));
-  return neg ? (x - kR) : (kR - x);
-}
-
-template <typename URBG>
-inline double gaussian_distribution_base::zignor(
-    URBG& g) {  // NOLINT(runtime/references)
-  using random_internal::GeneratePositiveTag;
-  using random_internal::GenerateRealFromBits;
-  using random_internal::GenerateSignedTag;
-
-  while (true) {
-    // We use a single uint64_t to generate both a double and a strip.
-    // These bits are unused when the generated double is > 1/2^5.
-    // This may introduce some bias from the duplicated low bits of small
-    // values (those smaller than 1/2^5, which all end up on the left tail).
-    uint64_t bits = fast_u64_(g);
-    int i = static_cast<int>(bits & kMask);  // pick a random strip
-    double j = GenerateRealFromBits<double, GenerateSignedTag, false>(
-        bits);  // U(-1, 1)
-    const double x = j * zg_.x[i];
-
-    // Retangular box. Handles >97% of all cases.
-    // For any given box, this handles between 75% and 99% of values.
-    // Equivalent to U(01) < (x[i+1] / x[i]), and when i == 0, ~93.5%
-    if (std::abs(x) < zg_.x[i + 1]) {
-      return x;
-    }
-
-    // i == 0: Base box. Sample using a ratio of uniforms.
-    if (i == 0) {
-      // This path happens about 0.05% of the time.
-      return zignor_fallback(g, j < 0);
-    }
-
-    // i > 0: Wedge samples using precomputed values.
-    double v = GenerateRealFromBits<double, GeneratePositiveTag, false>(
-        fast_u64_(g));  // U(0, 1)
-    if ((zg_.f[i + 1] + v * (zg_.f[i] - zg_.f[i + 1])) <
-        std::exp(-0.5 * x * x)) {
-      return x;
-    }
-
-    // The wedge was missed; reject the value and try again.
-  }
-}
-
-}  // namespace random_internal
-ABSL_NAMESPACE_END
-}  // namespace absl
-
-#endif  // ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_