chore(3p/abseil_cpp): unvendor abseil_cpp r/3786

we weren't actually using these sources anymore, okay?

Change-Id: If701571d9716de308d3512e1eb22c35db0877a66
Reviewed-on: https://cl.tvl.fyi/c/depot/+/5248
Tested-by: BuildkiteCI
Reviewed-by: grfn <grfn@gws.fyi>
Autosubmit: tazjin <tazjin@tvl.su>

1 files changed, 0 insertions, 232 deletions

diff --git a/third_party/abseil_cpp/absl/random/internal/chi_square.cc b/third_party/abseil_cpp/absl/random/internal/chi_square.cc
deleted file mode 100644
index 640d48cea6f4..000000000000
--- a/third_party/abseil_cpp/absl/random/internal/chi_square.cc
+++ /dev/null
@@ -1,232 +0,0 @@
-// 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/chi_square.h"
-
-#include <cmath>
-
-#include "absl/random/internal/distribution_test_util.h"
-
-namespace absl {
-ABSL_NAMESPACE_BEGIN
-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
-
-// Use Horner's method to evaluate a polynomial.
-template <typename T, unsigned N>
-inline T EvaluatePolynomial(T x, const T (&poly)[N]) {
-#if !defined(__EMSCRIPTEN__)
-  using std::fma;
-#endif
-  T p = poly[N - 1];
-  for (unsigned i = 2; i <= N; i++) {
-    p = fma(p, x, poly[N - i]);
-  }
-  return p;
-}
-
-static constexpr int kLargeDOF = 150;
-
-// Returns the probability of a normal z-value.
-//
-// Adapted from the POZ function in:
-//     Ibbetson D, Algorithm 209
-//     Collected Algorithms of the CACM 1963 p. 616
-//
-double POZ(double z) {
-  static constexpr double kP1[] = {
-      0.797884560593,  -0.531923007300, 0.319152932694,
-      -0.151968751364, 0.059054035642,  -0.019198292004,
-      0.005198775019,  -0.001075204047, 0.000124818987,
-  };
-  static constexpr double kP2[] = {
-      0.999936657524,  0.000535310849,  -0.002141268741, 0.005353579108,
-      -0.009279453341, 0.011630447319,  -0.010557625006, 0.006549791214,
-      -0.002034254874, -0.000794620820, 0.001390604284,  -0.000676904986,
-      -0.000019538132, 0.000152529290,  -0.000045255659,
-  };
-
-  const double kZMax = 6.0;  // Maximum meaningful z-value.
-  if (z == 0.0) {
-    return 0.5;
-  }
-  double x;
-  double y = 0.5 * std::fabs(z);
-  if (y >= (kZMax * 0.5)) {
-    x = 1.0;
-  } else if (y < 1.0) {
-    double w = y * y;
-    x = EvaluatePolynomial(w, kP1) * y * 2.0;
-  } else {
-    y -= 2.0;
-    x = EvaluatePolynomial(y, kP2);
-  }
-  return z > 0.0 ? ((x + 1.0) * 0.5) : ((1.0 - x) * 0.5);
-}
-
-// Approximates the survival function of the normal distribution.
-//
-// Algorithm 26.2.18, from:
-// [Abramowitz and Stegun, Handbook of Mathematical Functions,p.932]
-// http://people.math.sfu.ca/~cbm/aands/abramowitz_and_stegun.pdf
-//
-double normal_survival(double z) {
-  // Maybe replace with the alternate formulation.
-  // 0.5 * erfc((x - mean)/(sqrt(2) * sigma))
-  static constexpr double kR[] = {
-      1.0, 0.196854, 0.115194, 0.000344, 0.019527,
-  };
-  double r = EvaluatePolynomial(z, kR);
-  r *= r;
-  return 0.5 / (r * r);
-}
-
-}  // namespace
-
-// Calculates the critical chi-square value given degrees-of-freedom and a
-// p-value, usually using bisection. Also known by the name CRITCHI.
-double ChiSquareValue(int dof, double p) {
-  static constexpr double kChiEpsilon =
-      0.000001;  // Accuracy of the approximation.
-  static constexpr double kChiMax =
-      99999.0;  // Maximum chi-squared value.
-
-  const double p_value = 1.0 - p;
-  if (dof < 1 || p_value > 1.0) {
-    return 0.0;
-  }
-
-  if (dof > kLargeDOF) {
-    // For large degrees of freedom, use the normal approximation by
-    //     Wilson, E. B. and Hilferty, M. M. (1931)
-    //                     chi^2 - mean
-    //                Z = --------------
-    //                        stddev
-    const double z = InverseNormalSurvival(p_value);
-    const double mean = 1 - 2.0 / (9 * dof);
-    const double variance = 2.0 / (9 * dof);
-    // Cannot use this method if the variance is 0.
-    if (variance != 0) {
-      return std::pow(z * std::sqrt(variance) + mean, 3.0) * dof;
-    }
-  }
-
-  if (p_value <= 0.0) return kChiMax;
-
-  // Otherwise search for the p value by bisection
-  double min_chisq = 0.0;
-  double max_chisq = kChiMax;
-  double current = dof / std::sqrt(p_value);
-  while ((max_chisq - min_chisq) > kChiEpsilon) {
-    if (ChiSquarePValue(current, dof) < p_value) {
-      max_chisq = current;
-    } else {
-      min_chisq = current;
-    }
-    current = (max_chisq + min_chisq) * 0.5;
-  }
-  return current;
-}
-
-// Calculates the p-value (probability) of a given chi-square value
-// and degrees of freedom.
-//
-// Adapted from the POCHISQ function from:
-//     Hill, I. D. and Pike, M. C.  Algorithm 299
-//     Collected Algorithms of the CACM 1963 p. 243
-//
-double ChiSquarePValue(double chi_square, int dof) {
-  static constexpr double kLogSqrtPi =
-      0.5723649429247000870717135;  // Log[Sqrt[Pi]]
-  static constexpr double kInverseSqrtPi =
-      0.5641895835477562869480795;  // 1/(Sqrt[Pi])
-
-  // For large degrees of freedom, use the normal approximation by
-  //     Wilson, E. B. and Hilferty, M. M. (1931)
-  // Via Wikipedia:
-  //   By the Central Limit Theorem, because the chi-square distribution is the
-  //   sum of k independent random variables with finite mean and variance, it
-  //   converges to a normal distribution for large k.
-  if (dof > kLargeDOF) {
-    // Re-scale everything.
-    const double chi_square_scaled = std::pow(chi_square / dof, 1.0 / 3);
-    const double mean = 1 - 2.0 / (9 * dof);
-    const double variance = 2.0 / (9 * dof);
-    // If variance is 0, this method cannot be used.
-    if (variance != 0) {
-      const double z = (chi_square_scaled - mean) / std::sqrt(variance);
-      if (z > 0) {
-        return normal_survival(z);
-      } else if (z < 0) {
-        return 1.0 - normal_survival(-z);
-      } else {
-        return 0.5;
-      }
-    }
-  }
-
-  // The chi square function is >= 0 for any degrees of freedom.
-  // In other words, probability that the chi square function >= 0 is 1.
-  if (chi_square <= 0.0) return 1.0;
-
-  // If the degrees of freedom is zero, the chi square function is always 0 by
-  // definition. In other words, the probability that the chi square function
-  // is > 0 is zero (chi square values <= 0 have been filtered above).
-  if (dof < 1) return 0;
-
-  auto capped_exp = [](double x) { return x < -20 ? 0.0 : std::exp(x); };
-  static constexpr double kBigX = 20;
-
-  double a = 0.5 * chi_square;
-  const bool even = !(dof & 1);  // True if dof is an even number.
-  const double y = capped_exp(-a);
-  double s = even ? y : (2.0 * POZ(-std::sqrt(chi_square)));
-
-  if (dof <= 2) {
-    return s;
-  }
-
-  chi_square = 0.5 * (dof - 1.0);
-  double z = (even ? 1.0 : 0.5);
-  if (a > kBigX) {
-    double e = (even ? 0.0 : kLogSqrtPi);
-    double c = std::log(a);
-    while (z <= chi_square) {
-      e = std::log(z) + e;
-      s += capped_exp(c * z - a - e);
-      z += 1.0;
-    }
-    return s;
-  }
-
-  double e = (even ? 1.0 : (kInverseSqrtPi / std::sqrt(a)));
-  double c = 0.0;
-  while (z <= chi_square) {
-    e = e * (a / z);
-    c = c + e;
-    z += 1.0;
-  }
-  return c * y + s;
-}
-
-}  // namespace random_internal
-ABSL_NAMESPACE_END
-}  // namespace absl