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diff --git a/third_party/abseil_cpp/absl/random/bernoulli_distribution.h b/third_party/abseil_cpp/absl/random/bernoulli_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_BERNOULLI_DISTRIBUTION_H_
+#define ABSL_RANDOM_BERNOULLI_DISTRIBUTION_H_
+
+#include <cstdint>
+#include <istream>
+#include <limits>
+
+#include "absl/base/optimization.h"
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/random/internal/iostream_state_saver.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::bernoulli_distribution is a drop in replacement for
+// std::bernoulli_distribution. It guarantees that (given a perfect
+// UniformRandomBitGenerator) the acceptance probability is *exactly* equal to
+// the given double.
+//
+// The implementation assumes that double is IEEE754
+class bernoulli_distribution {
+ public:
+  using result_type = bool;
+
+  class param_type {
+   public:
+    using distribution_type = bernoulli_distribution;
+
+    explicit param_type(double p = 0.5) : prob_(p) {
+      assert(p >= 0.0 && p <= 1.0);
+    }
+
+    double p() const { return prob_; }
+
+    friend bool operator==(const param_type& p1, const param_type& p2) {
+      return p1.p() == p2.p();
+    }
+    friend bool operator!=(const param_type& p1, const param_type& p2) {
+      return p1.p() != p2.p();
+    }
+
+   private:
+    double prob_;
+  };
+
+  bernoulli_distribution() : bernoulli_distribution(0.5) {}
+
+  explicit bernoulli_distribution(double p) : param_(p) {}
+
+  explicit bernoulli_distribution(param_type p) : param_(p) {}
+
+  // no-op
+  void reset() {}
+
+  template <typename URBG>
+  bool operator()(URBG& g) {  // NOLINT(runtime/references)
+    return Generate(param_.p(), g);
+  }
+
+  template <typename URBG>
+  bool operator()(URBG& g,  // NOLINT(runtime/references)
+                  const param_type& param) {
+    return Generate(param.p(), g);
+  }
+
+  param_type param() const { return param_; }
+  void param(const param_type& param) { param_ = param; }
+
+  double p() const { return param_.p(); }
+
+  result_type(min)() const { return false; }
+  result_type(max)() const { return true; }
+
+  friend bool operator==(const bernoulli_distribution& d1,
+                         const bernoulli_distribution& d2) {
+    return d1.param_ == d2.param_;
+  }
+
+  friend bool operator!=(const bernoulli_distribution& d1,
+                         const bernoulli_distribution& d2) {
+    return d1.param_ != d2.param_;
+  }
+
+ private:
+  static constexpr uint64_t kP32 = static_cast<uint64_t>(1) << 32;
+
+  template <typename URBG>
+  static bool Generate(double p, URBG& g);  // NOLINT(runtime/references)
+
+  param_type param_;
+};
+
+template <typename CharT, typename Traits>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const bernoulli_distribution& x) {
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os.precision(random_internal::stream_precision_helper<double>::kPrecision);
+  os << x.p();
+  return os;
+}
+
+template <typename CharT, typename Traits>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+    bernoulli_distribution& x) {            // NOLINT(runtime/references)
+  auto saver = random_internal::make_istream_state_saver(is);
+  auto p = random_internal::read_floating_point<double>(is);
+  if (!is.fail()) {
+    x.param(bernoulli_distribution::param_type(p));
+  }
+  return is;
+}
+
+template <typename URBG>
+bool bernoulli_distribution::Generate(double p,
+                                      URBG& g) {  // NOLINT(runtime/references)
+  random_internal::FastUniformBits<uint32_t> fast_u32;
+
+  while (true) {
+    // There are two aspects of the definition of `c` below that are worth
+    // commenting on.  First, because `p` is in the range [0, 1], `c` is in the
+    // range [0, 2^32] which does not fit in a uint32_t and therefore requires
+    // 64 bits.
+    //
+    // Second, `c` is constructed by first casting explicitly to a signed
+    // integer and then converting implicitly to an unsigned integer of the same
+    // size.  This is done because the hardware conversion instructions produce
+    // signed integers from double; if taken as a uint64_t the conversion would
+    // be wrong for doubles greater than 2^63 (not relevant in this use-case).
+    // If converted directly to an unsigned integer, the compiler would end up
+    // emitting code to handle such large values that are not relevant due to
+    // the known bounds on `c`.  To avoid these extra instructions this
+    // implementation converts first to the signed type and then use the
+    // implicit conversion to unsigned (which is a no-op).
+    const uint64_t c = static_cast<int64_t>(p * kP32);
+    const uint32_t v = fast_u32(g);
+    // FAST PATH: this path fails with probability 1/2^32.  Note that simply
+    // returning v <= c would approximate P very well (up to an absolute error
+    // of 1/2^32); the slow path (taken in that range of possible error, in the
+    // case of equality) eliminates the remaining error.
+    if (ABSL_PREDICT_TRUE(v != c)) return v < c;
+
+    // It is guaranteed that `q` is strictly less than 1, because if `q` were
+    // greater than or equal to 1, the same would be true for `p`. Certainly `p`
+    // cannot be greater than 1, and if `p == 1`, then the fast path would
+    // necessary have been taken already.
+    const double q = static_cast<double>(c) / kP32;
+
+    // The probability of acceptance on the fast path is `q` and so the
+    // probability of acceptance here should be `p - q`.
+    //
+    // Note that `q` is obtained from `p` via some shifts and conversions, the
+    // upshot of which is that `q` is simply `p` with some of the
+    // least-significant bits of its mantissa set to zero. This means that the
+    // difference `p - q` will not have any rounding errors. To see why, pretend
+    // that double has 10 bits of resolution and q is obtained from `p` in such
+    // a way that the 4 least-significant bits of its mantissa are set to zero.
+    // For example:
+    //   p   = 1.1100111011 * 2^-1
+    //   q   = 1.1100110000 * 2^-1
+    // p - q = 1.011        * 2^-8
+    // The difference `p - q` has exactly the nonzero mantissa bits that were
+    // "lost" in `q` producing a number which is certainly representable in a
+    // double.
+    const double left = p - q;
+
+    // By construction, the probability of being on this slow path is 1/2^32, so
+    // P(accept in slow path) = P(accept| in slow path) * P(slow path),
+    // which means the probability of acceptance here is `1 / (left * kP32)`:
+    const double here = left * kP32;
+
+    // The simplest way to compute the result of this trial is to repeat the
+    // whole algorithm with the new probability. This terminates because even
+    // given  arbitrarily unfriendly "random" bits, each iteration either
+    // multiplies a tiny probability by 2^32 (if c == 0) or strips off some
+    // number of nonzero mantissa bits. That process is bounded.
+    if (here == 0) return false;
+    p = here;
+  }
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_BERNOULLI_DISTRIBUTION_H_