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diff --git a/contrib/llvm-project/libcxx/include/__random/poisson_distribution.h b/contrib/llvm-project/libcxx/include/__random/poisson_distribution.h
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+//===----------------------------------------------------------------------===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef _LIBCPP___RANDOM_POISSON_DISTRIBUTION_H
+#define _LIBCPP___RANDOM_POISSON_DISTRIBUTION_H
+
+#include <__config>
+#include <__random/clamp_to_integral.h>
+#include <__random/exponential_distribution.h>
+#include <__random/is_valid.h>
+#include <__random/normal_distribution.h>
+#include <__random/uniform_real_distribution.h>
+#include <cmath>
+#include <iosfwd>
+#include <limits>
+
+#if !defined(_LIBCPP_HAS_NO_PRAGMA_SYSTEM_HEADER)
+# pragma GCC system_header
+#endif
+
+_LIBCPP_PUSH_MACROS
+#include <__undef_macros>
+
+_LIBCPP_BEGIN_NAMESPACE_STD
+
+template <class _IntType = int>
+class _LIBCPP_TEMPLATE_VIS poisson_distribution {
+ static_assert(__libcpp_random_is_valid_inttype<_IntType>::value, "IntType must be a supported integer type");
+
+public:
+ // types
+ typedef _IntType result_type;
+
+ class _LIBCPP_TEMPLATE_VIS param_type {
+ double __mean_;
+ double __s_;
+ double __d_;
+ double __l_;
+ double __omega_;
+ double __c0_;
+ double __c1_;
+ double __c2_;
+ double __c3_;
+ double __c_;
+
+ public:
+ typedef poisson_distribution distribution_type;
+
+ _LIBCPP_HIDE_FROM_ABI explicit param_type(double __mean = 1.0);
+
+ _LIBCPP_HIDE_FROM_ABI double mean() const { return __mean_; }
+
+ friend _LIBCPP_HIDE_FROM_ABI bool operator==(const param_type& __x, const param_type& __y) {
+ return __x.__mean_ == __y.__mean_;
+ }
+ friend _LIBCPP_HIDE_FROM_ABI bool operator!=(const param_type& __x, const param_type& __y) { return !(__x == __y); }
+
+ friend class poisson_distribution;
+ };
+
+private:
+ param_type __p_;
+
+public:
+ // constructors and reset functions
+#ifndef _LIBCPP_CXX03_LANG
+ _LIBCPP_HIDE_FROM_ABI poisson_distribution() : poisson_distribution(1.0) {}
+ _LIBCPP_HIDE_FROM_ABI explicit poisson_distribution(double __mean) : __p_(__mean) {}
+#else
+ _LIBCPP_HIDE_FROM_ABI explicit poisson_distribution(double __mean = 1.0) : __p_(__mean) {}
+#endif
+ _LIBCPP_HIDE_FROM_ABI explicit poisson_distribution(const param_type& __p) : __p_(__p) {}
+ _LIBCPP_HIDE_FROM_ABI void reset() {}
+
+ // generating functions
+ template <class _URNG>
+ _LIBCPP_HIDE_FROM_ABI result_type operator()(_URNG& __g) {
+ return (*this)(__g, __p_);
+ }
+ template <class _URNG>
+ _LIBCPP_HIDE_FROM_ABI result_type operator()(_URNG& __g, const param_type& __p);
+
+ // property functions
+ _LIBCPP_HIDE_FROM_ABI double mean() const { return __p_.mean(); }
+
+ _LIBCPP_HIDE_FROM_ABI param_type param() const { return __p_; }
+ _LIBCPP_HIDE_FROM_ABI void param(const param_type& __p) { __p_ = __p; }
+
+ _LIBCPP_HIDE_FROM_ABI result_type min() const { return 0; }
+ _LIBCPP_HIDE_FROM_ABI result_type max() const { return numeric_limits<result_type>::max(); }
+
+ friend _LIBCPP_HIDE_FROM_ABI bool operator==(const poisson_distribution& __x, const poisson_distribution& __y) {
+ return __x.__p_ == __y.__p_;
+ }
+ friend _LIBCPP_HIDE_FROM_ABI bool operator!=(const poisson_distribution& __x, const poisson_distribution& __y) {
+ return !(__x == __y);
+ }
+};
+
+template <class _IntType>
+poisson_distribution<_IntType>::param_type::param_type(double __mean)
+ // According to the standard `inf` is a valid input, but it causes the
+ // distribution to hang, so we replace it with the maximum representable
+ // mean.
+ : __mean_(isinf(__mean) ? numeric_limits<double>::max() : __mean) {
+ if (__mean_ < 10) {
+ __s_ = 0;
+ __d_ = 0;
+ __l_ = std::exp(-__mean_);
+ __omega_ = 0;
+ __c3_ = 0;
+ __c2_ = 0;
+ __c1_ = 0;
+ __c0_ = 0;
+ __c_ = 0;
+ } else {
+ __s_ = std::sqrt(__mean_);
+ __d_ = 6 * __mean_ * __mean_;
+ __l_ = std::trunc(__mean_ - 1.1484);
+ __omega_ = .3989423 / __s_;
+ double __b1 = .4166667E-1 / __mean_;
+ double __b2 = .3 * __b1 * __b1;
+ __c3_ = .1428571 * __b1 * __b2;
+ __c2_ = __b2 - 15. * __c3_;
+ __c1_ = __b1 - 6. * __b2 + 45. * __c3_;
+ __c0_ = 1. - __b1 + 3. * __b2 - 15. * __c3_;
+ __c_ = .1069 / __mean_;
+ }
+}
+
+template <class _IntType>
+template <class _URNG>
+_IntType poisson_distribution<_IntType>::operator()(_URNG& __urng, const param_type& __pr) {
+ static_assert(__libcpp_random_is_valid_urng<_URNG>::value, "");
+ double __tx;
+ uniform_real_distribution<double> __urd;
+ if (__pr.__mean_ < 10) {
+ __tx = 0;
+ for (double __p = __urd(__urng); __p > __pr.__l_; ++__tx)
+ __p *= __urd(__urng);
+ } else {
+ double __difmuk;
+ double __g = __pr.__mean_ + __pr.__s_ * normal_distribution<double>()(__urng);
+ double __u;
+ if (__g > 0) {
+ __tx = std::trunc(__g);
+ if (__tx >= __pr.__l_)
+ return std::__clamp_to_integral<result_type>(__tx);
+ __difmuk = __pr.__mean_ - __tx;
+ __u = __urd(__urng);
+ if (__pr.__d_ * __u >= __difmuk * __difmuk * __difmuk)
+ return std::__clamp_to_integral<result_type>(__tx);
+ }
+ exponential_distribution<double> __edist;
+ for (bool __using_exp_dist = false; true; __using_exp_dist = true) {
+ double __e;
+ if (__using_exp_dist || __g <= 0) {
+ double __t;
+ do {
+ __e = __edist(__urng);
+ __u = __urd(__urng);
+ __u += __u - 1;
+ __t = 1.8 + (__u < 0 ? -__e : __e);
+ } while (__t <= -.6744);
+ __tx = std::trunc(__pr.__mean_ + __pr.__s_ * __t);
+ __difmuk = __pr.__mean_ - __tx;
+ __using_exp_dist = true;
+ }
+ double __px;
+ double __py;
+ if (__tx < 10 && __tx >= 0) {
+ const double __fac[] = {1, 1, 2, 6, 24, 120, 720, 5040, 40320, 362880};
+ __px = -__pr.__mean_;
+ __py = std::pow(__pr.__mean_, (double)__tx) / __fac[static_cast<int>(__tx)];
+ } else {
+ double __del = .8333333E-1 / __tx;
+ __del -= 4.8 * __del * __del * __del;
+ double __v = __difmuk / __tx;
+ if (std::abs(__v) > 0.25)
+ __px = __tx * std::log(1 + __v) - __difmuk - __del;
+ else
+ __px = __tx * __v * __v *
+ (((((((.1250060 * __v + -.1384794) * __v + .1421878) * __v + -.1661269) * __v + .2000118) * __v +
+ -.2500068) *
+ __v +
+ .3333333) *
+ __v +
+ -.5) -
+ __del;
+ __py = .3989423 / std::sqrt(__tx);
+ }
+ double __r = (0.5 - __difmuk) / __pr.__s_;
+ double __r2 = __r * __r;
+ double __fx = -0.5 * __r2;
+ double __fy = __pr.__omega_ * (((__pr.__c3_ * __r2 + __pr.__c2_) * __r2 + __pr.__c1_) * __r2 + __pr.__c0_);
+ if (__using_exp_dist) {
+ if (__pr.__c_ * std::abs(__u) <= __py * std::exp(__px + __e) - __fy * std::exp(__fx + __e))
+ break;
+ } else {
+ if (__fy - __u * __fy <= __py * std::exp(__px - __fx))
+ break;
+ }
+ }
+ }
+ return std::__clamp_to_integral<result_type>(__tx);
+}
+
+template <class _CharT, class _Traits, class _IntType>
+_LIBCPP_HIDE_FROM_ABI basic_ostream<_CharT, _Traits>&
+operator<<(basic_ostream<_CharT, _Traits>& __os, const poisson_distribution<_IntType>& __x) {
+ __save_flags<_CharT, _Traits> __lx(__os);
+ typedef basic_ostream<_CharT, _Traits> _OStream;
+ __os.flags(_OStream::dec | _OStream::left | _OStream::fixed | _OStream::scientific);
+ return __os << __x.mean();
+}
+
+template <class _CharT, class _Traits, class _IntType>
+_LIBCPP_HIDE_FROM_ABI basic_istream<_CharT, _Traits>&
+operator>>(basic_istream<_CharT, _Traits>& __is, poisson_distribution<_IntType>& __x) {
+ typedef poisson_distribution<_IntType> _Eng;
+ typedef typename _Eng::param_type param_type;
+ __save_flags<_CharT, _Traits> __lx(__is);
+ typedef basic_istream<_CharT, _Traits> _Istream;
+ __is.flags(_Istream::dec | _Istream::skipws);
+ double __mean;
+ __is >> __mean;
+ if (!__is.fail())
+ __x.param(param_type(__mean));
+ return __is;
+}
+
+_LIBCPP_END_NAMESPACE_STD
+
+_LIBCPP_POP_MACROS
+
+#endif // _LIBCPP___RANDOM_POISSON_DISTRIBUTION_H