[libc++] Remove unused variable warnings

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 6afe8b0a23.

Also workaround some unused variable warnings in the <random> tests.
It's pretty lame that we're not asserting the skew and kurtosis of
the binomial and negative binomial distributions, but that should be
tackled separately.

Differential Revision: https://reviews.llvm.org/D103533

libcxx/include/regex

@@ -5884,7 +5884,6 @@ basic_regex<_CharT, _Traits>::__match_at_start_posix_subs(
 {
     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();
@@ -5945,7 +5944,6 @@ basic_regex<_CharT, _Traits>::__match_at_start_posix_subs(
                     __states.pop_back();
                 break;
             case __state::__accept_and_consume:
-                __j += __s.__current_ - __current;
                 __current = __s.__current_;
                 break;
             case __state::__repeat:

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

@@ -20,8 +20,6 @@
 #include <vector>
 #include <cassert>
 
-#include "test_macros.h"
-
 template <class T>
 inline
 T
@@ -188,24 +186,25 @@ test4()
         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
@@ -237,24 +236,25 @@ test5()
         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
@@ -390,24 +390,25 @@ test9()
         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
@@ -439,24 +440,25 @@ test10()
         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
@@ -488,24 +490,25 @@ test11()
         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**)

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

@@ -20,8 +20,6 @@
 #include <vector>
 #include <cassert>
 
-#include "test_macros.h"
-
 template <class T>
 inline
 T
@@ -194,10 +192,14 @@ test4()
     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