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diff --git a/third_party/abseil_cpp/absl/random/zipf_distribution.h b/third_party/abseil_cpp/absl/random/zipf_distribution.h
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--- a/third_party/abseil_cpp/absl/random/zipf_distribution.h
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@@ -1,271 +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_ZIPF_DISTRIBUTION_H_
-#define ABSL_RANDOM_ZIPF_DISTRIBUTION_H_
-
-#include <cassert>
-#include <cmath>
-#include <istream>
-#include <limits>
-#include <ostream>
-#include <type_traits>
-
-#include "absl/random/internal/iostream_state_saver.h"
-#include "absl/random/uniform_real_distribution.h"
-
-namespace absl {
-ABSL_NAMESPACE_BEGIN
-
-// absl::zipf_distribution produces random integer-values in the range [0, k],
-// distributed according to the discrete probability function:
-//
-//  P(x) = (v + x) ^ -q
-//
-// The parameter `v` must be greater than 0 and the parameter `q` must be
-// greater than 1. If either of these parameters take invalid values then the
-// behavior is undefined.
-//
-// IntType is the result_type generated by the generator. It must be of integral
-// type; a static_assert ensures this is the case.
-//
-// The implementation is based on W.Hormann, G.Derflinger:
-//
-// "Rejection-Inversion to Generate Variates from Monotone Discrete
-// Distributions"
-//
-// http://eeyore.wu-wien.ac.at/papers/96-04-04.wh-der.ps.gz
-//
-template <typename IntType = int>
-class zipf_distribution {
- public:
-  using result_type = IntType;
-
-  class param_type {
-   public:
-    using distribution_type = zipf_distribution;
-
-    // Preconditions: k > 0, v > 0, q > 1
-    // The precondidtions are validated when NDEBUG is not defined via
-    // a pair of assert() directives.
-    // If NDEBUG is defined and either or both of these parameters take invalid
-    // values, the behavior of the class is undefined.
-    explicit param_type(result_type k = (std::numeric_limits<IntType>::max)(),
-                        double q = 2.0, double v = 1.0);
-
-    result_type k() const { return k_; }
-    double q() const { return q_; }
-    double v() const { return v_; }
-
-    friend bool operator==(const param_type& a, const param_type& b) {
-      return a.k_ == b.k_ && a.q_ == b.q_ && a.v_ == b.v_;
-    }
-    friend bool operator!=(const param_type& a, const param_type& b) {
-      return !(a == b);
-    }
-
-   private:
-    friend class zipf_distribution;
-    inline double h(double x) const;
-    inline double hinv(double x) const;
-    inline double compute_s() const;
-    inline double pow_negative_q(double x) const;
-
-    // Parameters here are exactly the same as the parameters of Algorithm ZRI
-    // in the paper.
-    IntType k_;
-    double q_;
-    double v_;
-
-    double one_minus_q_;  // 1-q
-    double s_;
-    double one_minus_q_inv_;  // 1 / 1-q
-    double hxm_;              // h(k + 0.5)
-    double hx0_minus_hxm_;    // h(x0) - h(k + 0.5)
-
-    static_assert(std::is_integral<IntType>::value,
-                  "Class-template absl::zipf_distribution<> must be "
-                  "parameterized using an integral type.");
-  };
-
-  zipf_distribution()
-      : zipf_distribution((std::numeric_limits<IntType>::max)()) {}
-
-  explicit zipf_distribution(result_type k, double q = 2.0, double v = 1.0)
-      : param_(k, q, v) {}
-
-  explicit zipf_distribution(const param_type& p) : param_(p) {}
-
-  void reset() {}
-
-  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);
-
-  result_type k() const { return param_.k(); }
-  double q() const { return param_.q(); }
-  double v() const { return param_.v(); }
-
-  param_type param() const { return param_; }
-  void param(const param_type& p) { param_ = p; }
-
-  result_type(min)() const { return 0; }
-  result_type(max)() const { return k(); }
-
-  friend bool operator==(const zipf_distribution& a,
-                         const zipf_distribution& b) {
-    return a.param_ == b.param_;
-  }
-  friend bool operator!=(const zipf_distribution& a,
-                         const zipf_distribution& b) {
-    return a.param_ != b.param_;
-  }
-
- private:
-  param_type param_;
-};
-
-// --------------------------------------------------------------------------
-// Implementation details follow
-// --------------------------------------------------------------------------
-
-template <typename IntType>
-zipf_distribution<IntType>::param_type::param_type(
-    typename zipf_distribution<IntType>::result_type k, double q, double v)
-    : k_(k), q_(q), v_(v), one_minus_q_(1 - q) {
-  assert(q > 1);
-  assert(v > 0);
-  assert(k > 0);
-  one_minus_q_inv_ = 1 / one_minus_q_;
-
-  // Setup for the ZRI algorithm (pg 17 of the paper).
-  // Compute: h(i max) => h(k + 0.5)
-  constexpr double kMax = 18446744073709549568.0;
-  double kd = static_cast<double>(k);
-  // TODO(absl-team): Determine if this check is needed, and if so, add a test
-  // that fails for k > kMax
-  if (kd > kMax) {
-    // Ensure that our maximum value is capped to a value which will
-    // round-trip back through double.
-    kd = kMax;
-  }
-  hxm_ = h(kd + 0.5);
-
-  // Compute: h(0)
-  const bool use_precomputed = (v == 1.0 && q == 2.0);
-  const double h0x5 = use_precomputed ? (-1.0 / 1.5)  // exp(-log(1.5))
-                                      : h(0.5);
-  const double elogv_q = (v_ == 1.0) ? 1 : pow_negative_q(v_);
-
-  // h(0) = h(0.5) - exp(log(v) * -q)
-  hx0_minus_hxm_ = (h0x5 - elogv_q) - hxm_;
-
-  // And s
-  s_ = use_precomputed ? 0.46153846153846123 : compute_s();
-}
-
-template <typename IntType>
-double zipf_distribution<IntType>::param_type::h(double x) const {
-  // std::exp(one_minus_q_ * std::log(v_ + x)) * one_minus_q_inv_;
-  x += v_;
-  return (one_minus_q_ == -1.0)
-             ? (-1.0 / x)  // -exp(-log(x))
-             : (std::exp(std::log(x) * one_minus_q_) * one_minus_q_inv_);
-}
-
-template <typename IntType>
-double zipf_distribution<IntType>::param_type::hinv(double x) const {
-  // std::exp(one_minus_q_inv_ * std::log(one_minus_q_ * x)) - v_;
-  return -v_ + ((one_minus_q_ == -1.0)
-                    ? (-1.0 / x)  // exp(-log(-x))
-                    : std::exp(one_minus_q_inv_ * std::log(one_minus_q_ * x)));
-}
-
-template <typename IntType>
-double zipf_distribution<IntType>::param_type::compute_s() const {
-  // 1 - hinv(h(1.5) - std::exp(std::log(v_ + 1) * -q_));
-  return 1.0 - hinv(h(1.5) - pow_negative_q(v_ + 1.0));
-}
-
-template <typename IntType>
-double zipf_distribution<IntType>::param_type::pow_negative_q(double x) const {
-  // std::exp(std::log(x) * -q_);
-  return q_ == 2.0 ? (1.0 / (x * x)) : std::exp(std::log(x) * -q_);
-}
-
-template <typename IntType>
-template <typename URBG>
-typename zipf_distribution<IntType>::result_type
-zipf_distribution<IntType>::operator()(
-    URBG& g, const param_type& p) {  // NOLINT(runtime/references)
-  absl::uniform_real_distribution<double> uniform_double;
-  double k;
-  for (;;) {
-    const double v = uniform_double(g);
-    const double u = p.hxm_ + v * p.hx0_minus_hxm_;
-    const double x = p.hinv(u);
-    k = rint(x);              // std::floor(x + 0.5);
-    if (k > p.k()) continue;  // reject k > max_k
-    if (k - x <= p.s_) break;
-    const double h = p.h(k + 0.5);
-    const double r = p.pow_negative_q(p.v_ + k);
-    if (u >= h - r) break;
-  }
-  IntType ki = static_cast<IntType>(k);
-  assert(ki <= p.k_);
-  return ki;
-}
-
-template <typename CharT, typename Traits, typename IntType>
-std::basic_ostream<CharT, Traits>& operator<<(
-    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
-    const zipf_distribution<IntType>& x) {
-  using stream_type =
-      typename random_internal::stream_format_type<IntType>::type;
-  auto saver = random_internal::make_ostream_state_saver(os);
-  os.precision(random_internal::stream_precision_helper<double>::kPrecision);
-  os << static_cast<stream_type>(x.k()) << os.fill() << x.q() << os.fill()
-     << x.v();
-  return os;
-}
-
-template <typename CharT, typename Traits, typename IntType>
-std::basic_istream<CharT, Traits>& operator>>(
-    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
-    zipf_distribution<IntType>& x) {        // NOLINT(runtime/references)
-  using result_type = typename zipf_distribution<IntType>::result_type;
-  using param_type = typename zipf_distribution<IntType>::param_type;
-  using stream_type =
-      typename random_internal::stream_format_type<IntType>::type;
-  stream_type k;
-  double q;
-  double v;
-
-  auto saver = random_internal::make_istream_state_saver(is);
-  is >> k >> q >> v;
-  if (!is.fail()) {
-    x.param(param_type(static_cast<result_type>(k), q, v));
-  }
-  return is;
-}
-
-ABSL_NAMESPACE_END
-}  // namespace absl
-
-#endif  // ABSL_RANDOM_ZIPF_DISTRIBUTION_H_