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diff --git a/third_party/abseil_cpp/absl/random/discrete_distribution.h b/third_party/abseil_cpp/absl/random/discrete_distribution.h
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--- a/third_party/abseil_cpp/absl/random/discrete_distribution.h
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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.
-
-#ifndef ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_
-#define ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_
-
-#include <cassert>
-#include <cmath>
-#include <istream>
-#include <limits>
-#include <numeric>
-#include <type_traits>
-#include <utility>
-#include <vector>
-
-#include "absl/random/bernoulli_distribution.h"
-#include "absl/random/internal/iostream_state_saver.h"
-#include "absl/random/uniform_int_distribution.h"
-
-namespace absl {
-ABSL_NAMESPACE_BEGIN
-
-// absl::discrete_distribution
-//
-// A discrete distribution produces random integers i, where 0 <= i < n
-// distributed according to the discrete probability function:
-//
-//     P(i|p0,...,pn−1)=pi
-//
-// This class is an implementation of discrete_distribution (see
-// [rand.dist.samp.discrete]).
-//
-// The algorithm used is Walker's Aliasing algorithm, described in Knuth, Vol 2.
-// absl::discrete_distribution takes O(N) time to precompute the probabilities
-// (where N is the number of possible outcomes in the distribution) at
-// construction, and then takes O(1) time for each variate generation.  Many
-// other implementations also take O(N) time to construct an ordered sequence of
-// partial sums, plus O(log N) time per variate to binary search.
-//
-template <typename IntType = int>
-class discrete_distribution {
- public:
-  using result_type = IntType;
-
-  class param_type {
-   public:
-    using distribution_type = discrete_distribution;
-
-    param_type() { init(); }
-
-    template <typename InputIterator>
-    explicit param_type(InputIterator begin, InputIterator end)
-        : p_(begin, end) {
-      init();
-    }
-
-    explicit param_type(std::initializer_list<double> weights) : p_(weights) {
-      init();
-    }
-
-    template <class UnaryOperation>
-    explicit param_type(size_t nw, double xmin, double xmax,
-                        UnaryOperation fw) {
-      if (nw > 0) {
-        p_.reserve(nw);
-        double delta = (xmax - xmin) / static_cast<double>(nw);
-        assert(delta > 0);
-        double t = delta * 0.5;
-        for (size_t i = 0; i < nw; ++i) {
-          p_.push_back(fw(xmin + i * delta + t));
-        }
-      }
-      init();
-    }
-
-    const std::vector<double>& probabilities() const { return p_; }
-    size_t n() const { return p_.size() - 1; }
-
-    friend bool operator==(const param_type& a, const param_type& b) {
-      return a.probabilities() == b.probabilities();
-    }
-
-    friend bool operator!=(const param_type& a, const param_type& b) {
-      return !(a == b);
-    }
-
-   private:
-    friend class discrete_distribution;
-
-    void init();
-
-    std::vector<double> p_;                     // normalized probabilities
-    std::vector<std::pair<double, size_t>> q_;  // (acceptance, alternate) pairs
-
-    static_assert(std::is_integral<result_type>::value,
-                  "Class-template absl::discrete_distribution<> must be "
-                  "parameterized using an integral type.");
-  };
-
-  discrete_distribution() : param_() {}
-
-  explicit discrete_distribution(const param_type& p) : param_(p) {}
-
-  template <typename InputIterator>
-  explicit discrete_distribution(InputIterator begin, InputIterator end)
-      : param_(begin, end) {}
-
-  explicit discrete_distribution(std::initializer_list<double> weights)
-      : param_(weights) {}
-
-  template <class UnaryOperation>
-  explicit discrete_distribution(size_t nw, double xmin, double xmax,
-                                 UnaryOperation fw)
-      : param_(nw, xmin, xmax, std::move(fw)) {}
-
-  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);
-
-  const 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 static_cast<result_type>(param_.n());
-  }  // inclusive
-
-  // NOTE [rand.dist.sample.discrete] returns a std::vector<double> not a
-  // const std::vector<double>&.
-  const std::vector<double>& probabilities() const {
-    return param_.probabilities();
-  }
-
-  friend bool operator==(const discrete_distribution& a,
-                         const discrete_distribution& b) {
-    return a.param_ == b.param_;
-  }
-  friend bool operator!=(const discrete_distribution& a,
-                         const discrete_distribution& b) {
-    return a.param_ != b.param_;
-  }
-
- private:
-  param_type param_;
-};
-
-// --------------------------------------------------------------------------
-// Implementation details only below
-// --------------------------------------------------------------------------
-
-namespace random_internal {
-
-// Using the vector `*probabilities`, whose values are the weights or
-// probabilities of an element being selected, constructs the proportional
-// probabilities used by the discrete distribution.  `*probabilities` will be
-// scaled, if necessary, so that its entries sum to a value sufficiently close
-// to 1.0.
-std::vector<std::pair<double, size_t>> InitDiscreteDistribution(
-    std::vector<double>* probabilities);
-
-}  // namespace random_internal
-
-template <typename IntType>
-void discrete_distribution<IntType>::param_type::init() {
-  if (p_.empty()) {
-    p_.push_back(1.0);
-    q_.emplace_back(1.0, 0);
-  } else {
-    assert(n() <= (std::numeric_limits<IntType>::max)());
-    q_ = random_internal::InitDiscreteDistribution(&p_);
-  }
-}
-
-template <typename IntType>
-template <typename URBG>
-typename discrete_distribution<IntType>::result_type
-discrete_distribution<IntType>::operator()(
-    URBG& g,  // NOLINT(runtime/references)
-    const param_type& p) {
-  const auto idx = absl::uniform_int_distribution<result_type>(0, p.n())(g);
-  const auto& q = p.q_[idx];
-  const bool selected = absl::bernoulli_distribution(q.first)(g);
-  return selected ? idx : static_cast<result_type>(q.second);
-}
-
-template <typename CharT, typename Traits, typename IntType>
-std::basic_ostream<CharT, Traits>& operator<<(
-    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
-    const discrete_distribution<IntType>& x) {
-  auto saver = random_internal::make_ostream_state_saver(os);
-  const auto& probabilities = x.param().probabilities();
-  os << probabilities.size();
-
-  os.precision(random_internal::stream_precision_helper<double>::kPrecision);
-  for (const auto& p : probabilities) {
-    os << os.fill() << p;
-  }
-  return os;
-}
-
-template <typename CharT, typename Traits, typename IntType>
-std::basic_istream<CharT, Traits>& operator>>(
-    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
-    discrete_distribution<IntType>& x) {    // NOLINT(runtime/references)
-  using param_type = typename discrete_distribution<IntType>::param_type;
-  auto saver = random_internal::make_istream_state_saver(is);
-
-  size_t n;
-  std::vector<double> p;
-
-  is >> n;
-  if (is.fail()) return is;
-  if (n > 0) {
-    p.reserve(n);
-    for (IntType i = 0; i < n && !is.fail(); ++i) {
-      auto tmp = random_internal::read_floating_point<double>(is);
-      if (is.fail()) return is;
-      p.push_back(tmp);
-    }
-  }
-  x.param(param_type(p.begin(), p.end()));
-  return is;
-}
-
-ABSL_NAMESPACE_END
-}  // namespace absl
-
-#endif  // ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_