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, 416 insertions, 0 deletions

diff --git a/absl/random/internal/distribution_test_util.cc b/absl/random/internal/distribution_test_util.cc
new file mode 100644
index 000000000000..85c8d596ebf8
--- /dev/null
+++ b/absl/random/internal/distribution_test_util.cc
@@ -0,0 +1,416 @@
+// 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.
+
+#include "absl/random/internal/distribution_test_util.h"
+
+#include <cassert>
+#include <cmath>
+#include <string>
+#include <vector>
+
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/macros.h"
+#include "absl/strings/str_cat.h"
+#include "absl/strings/str_format.h"
+
+namespace absl {
+namespace random_internal {
+namespace {
+
+#if defined(__EMSCRIPTEN__)
+// Workaround __EMSCRIPTEN__ error: llvm_fma_f64 not found.
+inline double fma(double x, double y, double z) { return (x * y) + z; }
+#endif
+
+}  // namespace
+
+DistributionMoments ComputeDistributionMoments(
+    absl::Span<const double> data_points) {
+  DistributionMoments result;
+
+  // Compute m1
+  for (double x : data_points) {
+    result.n++;
+    result.mean += x;
+  }
+  result.mean /= static_cast<double>(result.n);
+
+  // Compute m2, m3, m4
+  for (double x : data_points) {
+    double v = x - result.mean;
+    result.variance += v * v;
+    result.skewness += v * v * v;
+    result.kurtosis += v * v * v * v;
+  }
+  result.variance /= static_cast<double>(result.n - 1);
+
+  result.skewness /= static_cast<double>(result.n);
+  result.skewness /= std::pow(result.variance, 1.5);
+
+  result.kurtosis /= static_cast<double>(result.n);
+  result.kurtosis /= std::pow(result.variance, 2.0);
+  return result;
+
+  // When validating the min/max count, the following confidence intervals may
+  // be of use:
+  // 3.291 * stddev = 99.9% CI
+  // 2.576 * stddev = 99% CI
+  // 1.96 * stddev  = 95% CI
+  // 1.65 * stddev  = 90% CI
+}
+
+std::ostream& operator<<(std::ostream& os, const DistributionMoments& moments) {
+  return os << absl::StrFormat("mean=%f, stddev=%f, skewness=%f, kurtosis=%f",
+                               moments.mean, std::sqrt(moments.variance),
+                               moments.skewness, moments.kurtosis);
+}
+
+double InverseNormalSurvival(double x) {
+  // inv_sf(u) = -sqrt(2) * erfinv(2u-1)
+  static constexpr double kSqrt2 = 1.4142135623730950488;
+  return -kSqrt2 * absl::random_internal::erfinv(2 * x - 1.0);
+}
+
+bool Near(absl::string_view msg, double actual, double expected, double bound) {
+  assert(bound > 0.0);
+  double delta = fabs(expected - actual);
+  if (delta < bound) {
+    return true;
+  }
+
+  std::string formatted = absl::StrCat(
+      msg, " actual=", actual, " expected=", expected, " err=", delta / bound);
+  ABSL_RAW_LOG(INFO, "%s", formatted.c_str());
+  return false;
+}
+
+// TODO(absl-team): Replace with an "ABSL_HAVE_SPECIAL_MATH" and try
+// to use std::beta().  As of this writing P0226R1 is not implemented
+// in libc++: http://libcxx.llvm.org/cxx1z_status.html
+double beta(double p, double q) {
+  // Beta(x, y) = Gamma(x) * Gamma(y) / Gamma(x+y)
+  double lbeta = std::lgamma(p) + std::lgamma(q) - std::lgamma(p + q);
+  return std::exp(lbeta);
+}
+
+// Approximation to inverse of the Error Function in double precision.
+// (http://people.maths.ox.ac.uk/gilesm/files/gems_erfinv.pdf)
+double erfinv(double x) {
+#if !defined(__EMSCRIPTEN__)
+  using std::fma;
+#endif
+
+  double w = 0.0;
+  double p = 0.0;
+  w = -std::log((1.0 - x) * (1.0 + x));
+  if (w < 6.250000) {
+    w = w - 3.125000;
+    p = -3.6444120640178196996e-21;
+    p = fma(p, w, -1.685059138182016589e-19);
+    p = fma(p, w, 1.2858480715256400167e-18);
+    p = fma(p, w, 1.115787767802518096e-17);
+    p = fma(p, w, -1.333171662854620906e-16);
+    p = fma(p, w, 2.0972767875968561637e-17);
+    p = fma(p, w, 6.6376381343583238325e-15);
+    p = fma(p, w, -4.0545662729752068639e-14);
+    p = fma(p, w, -8.1519341976054721522e-14);
+    p = fma(p, w, 2.6335093153082322977e-12);
+    p = fma(p, w, -1.2975133253453532498e-11);
+    p = fma(p, w, -5.4154120542946279317e-11);
+    p = fma(p, w, 1.051212273321532285e-09);
+    p = fma(p, w, -4.1126339803469836976e-09);
+    p = fma(p, w, -2.9070369957882005086e-08);
+    p = fma(p, w, 4.2347877827932403518e-07);
+    p = fma(p, w, -1.3654692000834678645e-06);
+    p = fma(p, w, -1.3882523362786468719e-05);
+    p = fma(p, w, 0.0001867342080340571352);
+    p = fma(p, w, -0.00074070253416626697512);
+    p = fma(p, w, -0.0060336708714301490533);
+    p = fma(p, w, 0.24015818242558961693);
+    p = fma(p, w, 1.6536545626831027356);
+  } else if (w < 16.000000) {
+    w = std::sqrt(w) - 3.250000;
+    p = 2.2137376921775787049e-09;
+    p = fma(p, w, 9.0756561938885390979e-08);
+    p = fma(p, w, -2.7517406297064545428e-07);
+    p = fma(p, w, 1.8239629214389227755e-08);
+    p = fma(p, w, 1.5027403968909827627e-06);
+    p = fma(p, w, -4.013867526981545969e-06);
+    p = fma(p, w, 2.9234449089955446044e-06);
+    p = fma(p, w, 1.2475304481671778723e-05);
+    p = fma(p, w, -4.7318229009055733981e-05);
+    p = fma(p, w, 6.8284851459573175448e-05);
+    p = fma(p, w, 2.4031110387097893999e-05);
+    p = fma(p, w, -0.0003550375203628474796);
+    p = fma(p, w, 0.00095328937973738049703);
+    p = fma(p, w, -0.0016882755560235047313);
+    p = fma(p, w, 0.0024914420961078508066);
+    p = fma(p, w, -0.0037512085075692412107);
+    p = fma(p, w, 0.005370914553590063617);
+    p = fma(p, w, 1.0052589676941592334);
+    p = fma(p, w, 3.0838856104922207635);
+  } else {
+    w = std::sqrt(w) - 5.000000;
+    p = -2.7109920616438573243e-11;
+    p = fma(p, w, -2.5556418169965252055e-10);
+    p = fma(p, w, 1.5076572693500548083e-09);
+    p = fma(p, w, -3.7894654401267369937e-09);
+    p = fma(p, w, 7.6157012080783393804e-09);
+    p = fma(p, w, -1.4960026627149240478e-08);
+    p = fma(p, w, 2.9147953450901080826e-08);
+    p = fma(p, w, -6.7711997758452339498e-08);
+    p = fma(p, w, 2.2900482228026654717e-07);
+    p = fma(p, w, -9.9298272942317002539e-07);
+    p = fma(p, w, 4.5260625972231537039e-06);
+    p = fma(p, w, -1.9681778105531670567e-05);
+    p = fma(p, w, 7.5995277030017761139e-05);
+    p = fma(p, w, -0.00021503011930044477347);
+    p = fma(p, w, -0.00013871931833623122026);
+    p = fma(p, w, 1.0103004648645343977);
+    p = fma(p, w, 4.8499064014085844221);
+  }
+  return p * x;
+}
+
+namespace {
+
+// Direct implementation of AS63, BETAIN()
+// https://www.jstor.org/stable/2346797?seq=3#page_scan_tab_contents.
+//
+// BETAIN(x, p, q, beta)
+//  x:     the value of the upper limit x.
+//  p:     the value of the parameter p.
+//  q:     the value of the parameter q.
+//  beta:  the value of ln B(p, q)
+//
+double BetaIncompleteImpl(const double x, const double p, const double q,
+                          const double beta) {
+  if (p < (p + q) * x) {
+    // Incomplete beta function is symmetrical, so return the complement.
+    return 1. - BetaIncompleteImpl(1.0 - x, q, p, beta);
+  }
+
+  double psq = p + q;
+  const double kErr = 1e-14;
+  const double xc = 1. - x;
+  const double pre =
+      std::exp(p * std::log(x) + (q - 1.) * std::log(xc) - beta) / p;
+
+  double term = 1.;
+  double ai = 1.;
+  double result = 1.;
+  int ns = static_cast<int>(q + xc * psq);
+
+  // Use the soper reduction forumla.
+  double rx = (ns == 0) ? x : x / xc;
+  double temp = q - ai;
+  for (;;) {
+    term = term * temp * rx / (p + ai);
+    result = result + term;
+    temp = std::fabs(term);
+    if (temp < kErr && temp < kErr * result) {
+      return result * pre;
+    }
+    ai = ai + 1.;
+    --ns;
+    if (ns >= 0) {
+      temp = q - ai;
+      if (ns == 0) {
+        rx = x;
+      }
+    } else {
+      temp = psq;
+      psq = psq + 1.;
+    }
+  }
+
+  // NOTE: See also TOMS Alogrithm 708.
+  // http://www.netlib.org/toms/index.html
+  //
+  // NOTE: The NWSC library also includes BRATIO / ISUBX (p87)
+  // https://archive.org/details/DTIC_ADA261511/page/n75
+}
+
+// Direct implementation of AS109, XINBTA(p, q, beta, alpha)
+// https://www.jstor.org/stable/2346798?read-now=1&seq=4#page_scan_tab_contents
+// https://www.jstor.org/stable/2346887?seq=1#page_scan_tab_contents
+//
+// XINBTA(p, q, beta, alhpa)
+//  p:     the value of the parameter p.
+//  q:     the value of the parameter q.
+//  beta:  the value of ln B(p, q)
+//  alpha: the value of the lower tail area.
+//
+double BetaIncompleteInvImpl(const double p, const double q, const double beta,
+                             const double alpha) {
+  if (alpha < 0.5) {
+    // Inverse Incomplete beta function is symmetrical, return the complement.
+    return 1. - BetaIncompleteInvImpl(q, p, beta, 1. - alpha);
+  }
+  const double kErr = 1e-14;
+  double value = kErr;
+
+  // Compute the initial estimate.
+  {
+    double r = std::sqrt(-std::log(alpha * alpha));
+    double y =
+        r - fma(r, 0.27061, 2.30753) / fma(r, fma(r, 0.04481, 0.99229), 1.0);
+    if (p > 1. && q > 1.) {
+      r = (y * y - 3.) / 6.;
+      double s = 1. / (p + p - 1.);
+      double t = 1. / (q + q - 1.);
+      double h = 2. / s + t;
+      double w =
+          y * std::sqrt(h + r) / h - (t - s) * (r + 5. / 6. - t / (3. * h));
+      value = p / (p + q * std::exp(w + w));
+    } else {
+      r = q + q;
+      double t = 1.0 / (9. * q);
+      double u = 1.0 - t + y * std::sqrt(t);
+      t = r * (u * u * u);
+      if (t <= 0) {
+        value = 1.0 - std::exp((std::log((1.0 - alpha) * q) + beta) / q);
+      } else {
+        t = (4.0 * p + r - 2.0) / t;
+        if (t <= 1) {
+          value = std::exp((std::log(alpha * p) + beta) / p);
+        } else {
+          value = 1.0 - 2.0 / (t + 1.0);
+        }
+      }
+    }
+  }
+
+  // Solve for x using a modified newton-raphson method using the function
+  // BetaIncomplete.
+  {
+    value = std::max(value, kErr);
+    value = std::min(value, 1.0 - kErr);
+
+    const double r = 1.0 - p;
+    const double t = 1.0 - q;
+    double y;
+    double yprev = 0;
+    double sq = 1;
+    double prev = 1;
+    for (;;) {
+      if (value < 0 || value > 1.0) {
+        // Error case; value went infinite.
+        return std::numeric_limits<double>::infinity();
+      } else if (value == 0 || value == 1) {
+        y = value;
+      } else {
+        y = BetaIncompleteImpl(value, p, q, beta);
+        if (!std::isfinite(y)) {
+          return y;
+        }
+      }
+      y = (y - alpha) *
+          std::exp(beta + r * std::log(value) + t * std::log(1.0 - value));
+      if (y * yprev <= 0) {
+        prev = std::max(sq, std::numeric_limits<double>::min());
+      }
+      double g = 1.0;
+      for (;;) {
+        const double adj = g * y;
+        const double adj_sq = adj * adj;
+        if (adj_sq >= prev) {
+          g = g / 3.0;
+          continue;
+        }
+        const double tx = value - adj;
+        if (tx < 0 || tx > 1) {
+          g = g / 3.0;
+          continue;
+        }
+        if (prev < kErr) {
+          return value;
+        }
+        if (y * y < kErr) {
+          return value;
+        }
+        if (tx == value) {
+          return value;
+        }
+        if (tx == 0 || tx == 1) {
+          g = g / 3.0;
+          continue;
+        }
+        value = tx;
+        yprev = y;
+        break;
+      }
+    }
+  }
+
+  // NOTES: See also: Asymptotic inversion of the incomplete beta function.
+  // https://core.ac.uk/download/pdf/82140723.pdf
+  //
+  // NOTE: See the Boost library documentation as well:
+  // https://www.boost.org/doc/libs/1_52_0/libs/math/doc/sf_and_dist/html/math_toolkit/special/sf_beta/ibeta_function.html
+}
+
+}  // namespace
+
+double BetaIncomplete(const double x, const double p, const double q) {
+  // Error cases.
+  if (p < 0 || q < 0 || x < 0 || x > 1.0) {
+    return std::numeric_limits<double>::infinity();
+  }
+  if (x == 0 || x == 1) {
+    return x;
+  }
+  // ln(Beta(p, q))
+  double beta = std::lgamma(p) + std::lgamma(q) - std::lgamma(p + q);
+  return BetaIncompleteImpl(x, p, q, beta);
+}
+
+double BetaIncompleteInv(const double p, const double q, const double alpha) {
+  // Error cases.
+  if (p < 0 || q < 0 || alpha < 0 || alpha > 1.0) {
+    return std::numeric_limits<double>::infinity();
+  }
+  if (alpha == 0 || alpha == 1) {
+    return alpha;
+  }
+  // ln(Beta(p, q))
+  double beta = std::lgamma(p) + std::lgamma(q) - std::lgamma(p + q);
+  return BetaIncompleteInvImpl(p, q, beta, alpha);
+}
+
+// Given `num_trials` trials each with probability `p` of success, the
+// probability of no failures is `p^k`. To ensure the probability of a failure
+// is no more than `p_fail`, it must be that `p^k == 1 - p_fail`. This function
+// computes `p` from that equation.
+double RequiredSuccessProbability(const double p_fail, const int num_trials) {
+  double p = std::exp(std::log(1.0 - p_fail) / static_cast<double>(num_trials));
+  ABSL_ASSERT(p > 0);
+  return p;
+}
+
+double ZScore(double expected_mean, const DistributionMoments& moments) {
+  return (moments.mean - expected_mean) /
+         (std::sqrt(moments.variance) /
+          std::sqrt(static_cast<double>(moments.n)));
+}
+
+double MaxErrorTolerance(double acceptance_probability) {
+  double one_sided_pvalue = 0.5 * (1.0 - acceptance_probability);
+  const double max_err = InverseNormalSurvival(one_sided_pvalue);
+  ABSL_ASSERT(max_err > 0);
+  return max_err;
+}
+
+}  // namespace random_internal
+}  // namespace absl