Export of internal Abseil changes.

--
7a6ff16a85beb730c172d5d25cf1b5e1be885c56 by Laramie Leavitt <lar@google.com>:

Internal change.

PiperOrigin-RevId: 254454546

--
ff8f9bafaefc26d451f576ea4a06d150aed63f6f by Andy Soffer <asoffer@google.com>:

Internal changes

PiperOrigin-RevId: 254451562

--
deefc5b651b479ce36f0b4ef203e119c0c8936f2 by CJ Johnson <johnsoncj@google.com>:

Account for subtracting unsigned values from the size of InlinedVector

PiperOrigin-RevId: 254450625

--
3c677316a27bcadc17e41957c809ca472d5fef14 by Andy Soffer <asoffer@google.com>:

Add C++17's std::make_from_tuple to absl/utility/utility.h

PiperOrigin-RevId: 254411573

--
4ee3536a918830eeec402a28fc31a62c7c90b940 by CJ Johnson <johnsoncj@google.com>:

Adds benchmark for the rest of the InlinedVector public API

PiperOrigin-RevId: 254408378

--
e5a21a00700ee83498ff1efbf649169756463ee4 by CJ Johnson <johnsoncj@google.com>:

Updates the definition of InlinedVector::shrink_to_fit() to be exception safe and adds exception safety tests for it.

PiperOrigin-RevId: 254401387

--
2ea82e72b86d82d78b4e4712a63a55981b53c64b by Laramie Leavitt <lar@google.com>:

Use absl::InsecureBitGen in place of std::mt19937
in tests absl/random/...distribution_test.cc

PiperOrigin-RevId: 254289444

--
fa099e02c413a7ffda732415e8105cad26a90337 by Andy Soffer <asoffer@google.com>:

Internal changes

PiperOrigin-RevId: 254286334

--
ce34b7f36933b30cfa35b9c9a5697a792b5666e4 by Andy Soffer <asoffer@google.com>:

Internal changes

PiperOrigin-RevId: 254273059

--
6f9c473da7c2090c2e85a37c5f00622e8a912a89 by Jorg Brown <jorg@google.com>:

Change absl::container_internal::CompressedTuple to instantiate its
internal Storage class with the name of the type it's holding, rather
than the name of the Tuple.  This is not an externally-visible change,
other than less compiler memory is used and less debug information is
generated.

PiperOrigin-RevId: 254269285

--
8bd3c186bf2fc0c55d8a2dd6f28a5327502c9fba by Andy Soffer <asoffer@google.com>:

Adding short-hand IntervalClosed for IntervalClosedClosed and IntervalOpen for
IntervalOpenOpen.

PiperOrigin-RevId: 254252419

--
ea957f99b6a04fccd42aa05605605f3b44b1ecfd by Abseil Team <absl-team@google.com>:

Do not directly use __SIZEOF_INT128__.

In order to avoid linker errors when building with clang-cl (__fixunsdfti, __udivti3 and __fixunssfti are undefined), this CL uses ABSL_HAVE_INTRINSIC_INT128 which is not defined for clang-cl.

PiperOrigin-RevId: 254250739

--
89ab385cd26b34d64130bce856253aaba96d2345 by Andy Soffer <asoffer@google.com>:

Internal changes

PiperOrigin-RevId: 254242321

--
cffc793d93eca6d6bdf7de733847b6ab4a255ae9 by CJ Johnson <johnsoncj@google.com>:

Adds benchmark for InlinedVector::reserve(size_type)

PiperOrigin-RevId: 254199226

--
c90c7a9fa3c8f0c9d5114036979548b055ea2f2a by Gennadiy Rozental <rogeeff@google.com>:

Import of CCTZ from GitHub.

PiperOrigin-RevId: 254072387

--
c4c388beae016c9570ab54ffa1d52660e4a85b7b by Laramie Leavitt <lar@google.com>:

Internal cleanup.

PiperOrigin-RevId: 254062381

--
d3c992e221cc74e5372d0c8fa410170b6a43c062 by Tom Manshreck <shreck@google.com>:

Update distributions.h to Abseil standards

PiperOrigin-RevId: 254054946

--
d15ad0035c34ef11b14fadc5a4a2d3ec415f5518 by CJ Johnson <johnsoncj@google.com>:

Removes functions with only one caller from the implementation details of InlinedVector by manually inlining the definitions

PiperOrigin-RevId: 254005427

--
2f37e807efc3a8ef1f4b539bdd379917d4151520 by Andy Soffer <asoffer@google.com>:

Initial release of Abseil Random

PiperOrigin-RevId: 253999861

--
24ed1694b6430791d781ed533a8f8ccf6cac5856 by CJ Johnson <johnsoncj@google.com>:

Updates the definition of InlinedVector::assign(...)/InlinedVector::operator=(...) to new, exception-safe implementations with exception safety tests to boot

PiperOrigin-RevId: 253993691

--
5613d95f5a7e34a535cfaeadce801441e990843e by CJ Johnson <johnsoncj@google.com>:

Adds benchmarks for InlinedVector::shrink_to_fit()

PiperOrigin-RevId: 253989647

--
2a96ddfdac40bbb8cb6a7f1aeab90917067c6e63 by Abseil Team <absl-team@google.com>:

Initial release of Abseil Random

PiperOrigin-RevId: 253927497

--
bf1aff8fc9ffa921ad74643e9525ecf25b0d8dc1 by Andy Soffer <asoffer@google.com>:

Initial release of Abseil Random

PiperOrigin-RevId: 253920512

--
bfc03f4a3dcda3cf3a4b84bdb84cda24e3394f41 by Laramie Leavitt <lar@google.com>:

Internal change.

PiperOrigin-RevId: 253886486

--
05036cfcc078ca7c5f581a00dfb0daed568cbb69 by Eric Fiselier <ericwf@google.com>:

Don't include `winsock2.h` because it drags in `windows.h` and friends,
and they define awful macros like OPAQUE, ERROR, and more. This has the
potential to break abseil users.

Instead we only forward declare `timeval` and require Windows users
include `winsock2.h` themselves. This is both inconsistent and poor QoI, but so
including 'windows.h' is bad too.

PiperOrigin-RevId: 253852615
GitOrigin-RevId: 7a6ff16a85beb730c172d5d25cf1b5e1be885c56
Change-Id: Icd6aff87da26f29ec8915da856f051129987cef6

1 files changed, 254 insertions, 0 deletions

diff --git a/absl/random/poisson_distribution.h b/absl/random/poisson_distribution.h
new file mode 100644
index 0000000..7750b1c
--- /dev/null
+++ b/absl/random/poisson_distribution.h
@@ -0,0 +1,254 @@
+// 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_POISSON_DISTRIBUTION_H_
+#define ABSL_RANDOM_POISSON_DISTRIBUTION_H_
+
+#include <cassert>
+#include <cmath>
+#include <istream>
+#include <limits>
+#include <ostream>
+#include <type_traits>
+
+#include "absl/random/internal/distribution_impl.h"
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/random/internal/fastmath.h"
+#include "absl/random/internal/iostream_state_saver.h"
+
+namespace absl {
+
+// absl::poisson_distribution:
+// Generates discrete variates conforming to a Poisson distribution.
+//   p(n) = (mean^n / n!) exp(-mean)
+//
+// Depending on the parameter, the distribution selects one of the following
+// algorithms:
+// * The standard algorithm, attributed to Knuth, extended using a split method
+// for larger values
+// * The "Ratio of Uniforms as a convenient method for sampling from classical
+// discrete distributions", Stadlober, 1989.
+// http://www.sciencedirect.com/science/article/pii/0377042790903495
+//
+// NOTE: param_type.mean() is a double, which permits values larger than
+// poisson_distribution<IntType>::max(), however this should be avoided and
+// the distribution results are limited to the max() value.
+//
+// The goals of this implementation are to provide good performance while still
+// beig thread-safe: This limits the implementation to not using lgamma provided
+// by <math.h>.
+//
+template <typename IntType = int>
+class poisson_distribution {
+ public:
+  using result_type = IntType;
+
+  class param_type {
+   public:
+    using distribution_type = poisson_distribution;
+    explicit param_type(double mean = 1.0);
+
+    double mean() const { return mean_; }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.mean_ == b.mean_;
+    }
+
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    friend class poisson_distribution;
+
+    double mean_;
+    double emu_;  // e ^ -mean_
+    double lmu_;  // ln(mean_)
+    double s_;
+    double log_k_;
+    int split_;
+
+    static_assert(std::is_integral<IntType>::value,
+                  "Class-template absl::poisson_distribution<> must be "
+                  "parameterized using an integral type.");
+  };
+
+  poisson_distribution() : poisson_distribution(1.0) {}
+
+  explicit poisson_distribution(double mean) : param_(mean) {}
+
+  explicit poisson_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 0; }
+  result_type(max)() const { return (std::numeric_limits<result_type>::max)(); }
+
+  double mean() const { return param_.mean(); }
+
+  friend bool operator==(const poisson_distribution& a,
+                         const poisson_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const poisson_distribution& a,
+                         const poisson_distribution& b) {
+    return a.param_ != b.param_;
+  }
+
+ private:
+  param_type param_;
+  random_internal::FastUniformBits<uint64_t> fast_u64_;
+};
+
+// -----------------------------------------------------------------------------
+// Implementation details follow
+// -----------------------------------------------------------------------------
+
+template <typename IntType>
+poisson_distribution<IntType>::param_type::param_type(double mean)
+    : mean_(mean), split_(0) {
+  assert(mean >= 0);
+  assert(mean <= (std::numeric_limits<result_type>::max)());
+  // As a defensive measure, avoid large values of the mean.  The rejection
+  // algorithm used does not support very large values well.  It my be worth
+  // changing algorithms to better deal with these cases.
+  assert(mean <= 1e10);
+  if (mean_ < 10) {
+    // For small lambda, use the knuth method.
+    split_ = 1;
+    emu_ = std::exp(-mean_);
+  } else if (mean_ <= 50) {
+    // Use split-knuth method.
+    split_ = 1 + static_cast<int>(mean_ / 10.0);
+    emu_ = std::exp(-mean_ / static_cast<double>(split_));
+  } else {
+    // Use ratio of uniforms method.
+    constexpr double k2E = 0.7357588823428846;
+    constexpr double kSA = 0.4494580810294493;
+
+    lmu_ = std::log(mean_);
+    double a = mean_ + 0.5;
+    s_ = kSA + std::sqrt(k2E * a);
+    const double mode = std::ceil(mean_) - 1;
+    log_k_ = lmu_ * mode - absl::random_internal::StirlingLogFactorial(mode);
+  }
+}
+
+template <typename IntType>
+template <typename URBG>
+typename poisson_distribution<IntType>::result_type
+poisson_distribution<IntType>::operator()(
+    URBG& g,  // NOLINT(runtime/references)
+    const param_type& p) {
+  using random_internal::PositiveValueT;
+  using random_internal::RandU64ToDouble;
+  using random_internal::SignedValueT;
+
+  if (p.split_ != 0) {
+    // Use Knuth's algorithm with range splitting to avoid floating-point
+    // errors. Knuth's algorithm is: Ui is a sequence of uniform variates on
+    // (0,1); return the number of variates required for product(Ui) <
+    // exp(-lambda).
+    //
+    // The expected number of variates required for Knuth's method can be
+    // computed as follows:
+    // The expected value of U is 0.5, so solving for 0.5^n < exp(-lambda) gives
+    // the expected number of uniform variates
+    // required for a given lambda, which is:
+    //  lambda = [2, 5,  9, 10, 11, 12, 13, 14, 15, 16, 17]
+    //  n      = [3, 8, 13, 15, 16, 18, 19, 21, 22, 24, 25]
+    //
+    result_type n = 0;
+    for (int split = p.split_; split > 0; --split) {
+      double r = 1.0;
+      do {
+        r *= RandU64ToDouble<PositiveValueT, true>(fast_u64_(g));
+        ++n;
+      } while (r > p.emu_);
+      --n;
+    }
+    return n;
+  }
+
+  // Use ratio of uniforms method.
+  //
+  // Let u ~ Uniform(0, 1), v ~ Uniform(-1, 1),
+  //     a = lambda + 1/2,
+  //     s = 1.5 - sqrt(3/e) + sqrt(2(lambda + 1/2)/e),
+  //     x = s * v/u + a.
+  // P(floor(x) = k | u^2 < f(floor(x))/k), where
+  // f(m) = lambda^m exp(-lambda)/ m!, for 0 <= m, and f(m) = 0 otherwise,
+  // and k = max(f).
+  const double a = p.mean_ + 0.5;
+  for (;;) {
+    const double u =
+        RandU64ToDouble<PositiveValueT, false>(fast_u64_(g));  // (0, 1)
+    const double v =
+        RandU64ToDouble<SignedValueT, false>(fast_u64_(g));  // (-1, 1)
+    const double x = std::floor(p.s_ * v / u + a);
+    if (x < 0) continue;  // f(negative) = 0
+    const double rhs = x * p.lmu_;
+    // clang-format off
+    double s = (x <= 1.0) ? 0.0
+             : (x == 2.0) ? 0.693147180559945
+             : absl::random_internal::StirlingLogFactorial(x);
+    // clang-format on
+    const double lhs = 2.0 * std::log(u) + p.log_k_ + s;
+    if (lhs < rhs) {
+      return x > (max)() ? (max)()
+                         : static_cast<result_type>(x);  // f(x)/k >= u^2
+    }
+  }
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const poisson_distribution<IntType>& x) {
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os.precision(random_internal::stream_precision_helper<double>::kPrecision);
+  os << x.mean();
+  return os;
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+    poisson_distribution<IntType>& x) {     // NOLINT(runtime/references)
+  using param_type = typename poisson_distribution<IntType>::param_type;
+
+  auto saver = random_internal::make_istream_state_saver(is);
+  double mean = random_internal::read_floating_point<double>(is);
+  if (!is.fail()) {
+    x.param(param_type(mean));
+  }
+  return is;
+}
+
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_POISSON_DISTRIBUTION_H_