[libc++] Remove unused variable

[ldionne]

Summary

Since D100581, Clang started flagging this variable which is set but
never read. Based on comparing this function with __match_at_start_posix_nosubs
(which is very similar), I am pretty confident that __j was simply left
behind as an oversight in Howard's 6afe8b0a2336.

[ldionne]

Remove the warning in the test suite too.

[ldionne]

Remove j instead of silencing the error -- after some research, I think j was left behind as an oversight.

[• Quuxplusone]

If buildkite's happy, I'm happy!

[ldionne]

Fix issues in <random> tests

[• Quuxplusone]

libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp

185–186

Well, that's not going to work. ;)

203–207

Shouldn't you just comment these lines back in, so that the variables end up getting used/tested?
Line 197's comment is relevant, though.

[ldionne]

Hopefully fix remaining issues.

[ldionne]

libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp

203–207

I tested with ubsan enabled and I'm not seeing any issue.

libcxx/include/regex

 bool
 basic_regex<_CharT, _Traits>::__match_at_start_posix_subs(
         const _CharT* __first, const _CharT* __last,
         match_results<const _CharT*, _Allocator>& __m,
         regex_constants::match_flag_type __flags, bool __at_first) const
 {
     vector<__state> __states;
     __state __best_state;
-    ptrdiff_t __j = 0;
     ptrdiff_t __highest_j = 0;
     ptrdiff_t _Np = _VSTD::distance(__first, __last);
     __node* __st = __start_.get();
     if (__st)
     {
         sub_match<const _CharT*> __unmatched;
         __unmatched.first   = __last;
         __unmatched.second  = __last;
if (__st)
                 }
                 __matched = true;
                 if (__highest_j == _Np)
                     __states.clear();
                 else
                     __states.pop_back();
                 break;
             case __state::__accept_and_consume:
-                __j += __s.__current_ - __current;
                 __current = __s.__current_;
                 break;
             case __state::__repeat:
             case __state::__accept_but_not_consume:
                 break;
             case __state::__split:
                 {
                 __state __snext = __s;

libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval.pass.cpp

 
 // template<class _URNG> result_type operator()(_URNG& g);
 
 #include <random>
 #include <numeric>
 #include <vector>
 #include <cassert>
 
-#include "test_macros.h"
-
 template <class T>
 inline
 T
 sqr(T x)
 {
     return x * x;
 }
 
test4()
     double var = 0;
     double skew = 0;
     double kurtosis = 0;
     for (unsigned i = 0; i < u.size(); ++i)
     {
         double dbl = (u[i] - mean);
         double d2 = sqr(dbl);
         var += d2;
         skew += dbl * d2;
         kurtosis += d2 * d2;
     }
     var /= u.size();
-    //double dev = std::sqrt(var);
+    double dev = std::sqrt(var);
     // In this case:
     //   skew     computes to 0./0. == nan
     //   kurtosis computes to 0./0. == nan
     //   x_skew     == inf
     //   x_kurtosis == inf
-    //   These tests are commented out because UBSan warns about division by 0
-//    skew /= u.size() * dev * var;
-//    kurtosis /= u.size() * var * var;
-//    kurtosis -= 3;
+    skew /= u.size() * dev * var;
+    kurtosis /= u.size() * var * var;
+    kurtosis -= 3;
     double x_mean = d.t() * d.p();
     double x_var = x_mean*(1-d.p());
-//    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-//    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
     assert(mean == x_mean);
     assert(var == x_var);
-//    assert(skew == x_skew);
-//    assert(kurtosis == x_kurtosis);
+    // assert(skew == x_skew);
+    (void)skew; (void)x_skew;
+    // assert(kurtosis == x_kurtosis);
+    (void)kurtosis; (void)x_kurtosis;
 }
 
 void
 test5()
 {
     typedef std::binomial_distribution<> D;
     typedef std::mt19937 G;
     G g;
test5()
     {
         double dbl = (u[i] - mean);
         double d2 = sqr(dbl);
         var += d2;
         skew += dbl * d2;
         kurtosis += d2 * d2;
     }
     var /= u.size();
-//    double dev = std::sqrt(var);
+    double dev = std::sqrt(var);
     // In this case:
     //   skew     computes to 0./0. == nan
     //   kurtosis computes to 0./0. == nan
     //   x_skew     == -inf
     //   x_kurtosis == inf
-    //   These tests are commented out because UBSan warns about division by 0
-//    skew /= u.size() * dev * var;
-//    kurtosis /= u.size() * var * var;
-//    kurtosis -= 3;
+    skew /= u.size() * dev * var;
+    kurtosis /= u.size() * var * var;
+    kurtosis -= 3;
     double x_mean = d.t() * d.p();
     double x_var = x_mean*(1-d.p());
-//    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-//    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
     assert(mean == x_mean);
     assert(var == x_var);
-//    assert(skew == x_skew);
-//    assert(kurtosis == x_kurtosis);
+    // assert(skew == x_skew);
+    (void)skew; (void)x_skew;
+    // assert(kurtosis == x_kurtosis);
+    (void)kurtosis; (void)x_kurtosis;
 }
 
 void
 test6()
 {
     typedef std::binomial_distribution<> D;
     typedef std::mt19937 G;
     G g;
test9()
     {
         double dbl = (u[i] - mean);
         double d2 = sqr(dbl);
         var += d2;
         skew += dbl * d2;
         kurtosis += d2 * d2;
     }
     var /= u.size();
-//    double dev = std::sqrt(var);
+    double dev = std::sqrt(var);
     // In this case:
     //   skew     computes to 0./0. == nan
     //   kurtosis computes to 0./0. == nan
     //   x_skew     == inf
     //   x_kurtosis == inf
-    //   These tests are commented out because UBSan warns about division by 0
-//    skew /= u.size() * dev * var;
-//    kurtosis /= u.size() * var * var;
-//    kurtosis -= 3;
+    skew /= u.size() * dev * var;
+    kurtosis /= u.size() * var * var;
+    kurtosis -= 3;
     double x_mean = d.t() * d.p();
     double x_var = x_mean*(1-d.p());
-//    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-//    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
     assert(mean == x_mean);
     assert(var == x_var);
-//    assert(skew == x_skew);
-//    assert(kurtosis == x_kurtosis);
+    // assert(skew == x_skew);
+    (void)skew; (void)x_skew;
+    // assert(kurtosis == x_kurtosis);
+    (void)kurtosis; (void)x_kurtosis;
 }
 
 void
 test10()
 {
     typedef std::binomial_distribution<> D;
     typedef std::mt19937 G;
     G g;
test10()
     {
         double dbl = (u[i] - mean);
         double d2 = sqr(dbl);
         var += d2;
         skew += dbl * d2;
         kurtosis += d2 * d2;
     }
     var /= u.size();
-//    double dev = std::sqrt(var);
+    double dev = std::sqrt(var);
     // In this case:
     //   skew     computes to 0./0. == nan
     //   kurtosis computes to 0./0. == nan
     //   x_skew     == inf
     //   x_kurtosis == inf
-    //   These tests are commented out because UBSan warns about division by 0
-//    skew /= u.size() * dev * var;
-//    kurtosis /= u.size() * var * var;
-//    kurtosis -= 3;
+    skew /= u.size() * dev * var;
+    kurtosis /= u.size() * var * var;
+    kurtosis -= 3;
     double x_mean = d.t() * d.p();
     double x_var = x_mean*(1-d.p());
-//    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-//    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
     assert(mean == x_mean);
     assert(var == x_var);
-//    assert(skew == x_skew);
-//    assert(kurtosis == x_kurtosis);
+    // assert(skew == x_skew);
+    (void)skew; (void)x_skew;
+    // assert(kurtosis == x_kurtosis);
+    (void)kurtosis; (void)x_kurtosis;
 }
 
 void
 test11()
 {
     typedef std::binomial_distribution<> D;
     typedef std::mt19937 G;
     G g;
test11()
     {
         double dbl = (u[i] - mean);
         double d2 = sqr(dbl);
         var += d2;
         skew += dbl * d2;
         kurtosis += d2 * d2;
     }
     var /= u.size();
-//    double dev = std::sqrt(var);
+    double dev = std::sqrt(var);
     // In this case:
     //   skew     computes to 0./0. == nan
     //   kurtosis computes to 0./0. == nan
     //   x_skew     == -inf
     //   x_kurtosis == inf
-    //   These tests are commented out because UBSan warns about division by 0
-//    skew /= u.size() * dev * var;
-//    kurtosis /= u.size() * var * var;
-//    kurtosis -= 3;
+    skew /= u.size() * dev * var;
+    kurtosis /= u.size() * var * var;
+    kurtosis -= 3;
     double x_mean = d.t() * d.p();
     double x_var = x_mean*(1-d.p());
-//    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
-//    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
+    double x_skew = (1-2*d.p()) / std::sqrt(x_var);
+    double x_kurtosis = (1-6*d.p()*(1-d.p())) / x_var;
     assert(mean == x_mean);
     assert(var == x_var);
-//    assert(skew == x_skew);
-//    assert(kurtosis == x_kurtosis);
+    // assert(skew == x_skew);
+    (void)skew; (void)x_skew;
+    // assert(kurtosis == x_kurtosis);
+    (void)kurtosis; (void)x_kurtosis;
 }
 
 int main(int, char**)
 {
     test1();
     test2();
     test3();
     test4();

libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.negbin/eval.pass.cpp

 
 // template<class _URNG> result_type operator()(_URNG& g);
 
 #include <random>
 #include <numeric>
 #include <vector>
 #include <cassert>
 
-#include "test_macros.h"
-
 template <class T>
 inline
 T
 sqr(T x)
 {
     return x * x;
 }
 
test4()
     }
     var /= u.size();
     double dev = std::sqrt(var);
     skew /= u.size() * dev * var;
     kurtosis /= u.size() * var * var;
     kurtosis -= 3;
     double x_mean = d.k() * (1 - d.p()) / d.p();
     double x_var = x_mean / d.p();
-//    double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
-//    double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
+    double x_skew = (2 - d.p()) / std::sqrt(d.k() * (1 - d.p()));
+    double x_kurtosis = 6. / d.k() + sqr(d.p()) / (d.k() * (1 - d.p()));
     assert(mean == x_mean);
     assert(var == x_var);
+    // assert(skew == x_skew);
+    (void)skew; (void)x_skew;
+    // assert(kurtosis == x_kurtosis);
+    (void)kurtosis; (void)x_kurtosis;
 }
 
 void
 test5()
 {
     typedef std::negative_binomial_distribution<> D;
     typedef std::mt19937 G;
     G g;