absl/random: run clang-format.

In order to avoid unrelated diffs in future changes that touch this code.

PiperOrigin-RevId: 694625990
Change-Id: I51ac54438a977f38c54c7638701d9ff3a259a58b

absl/random/distributions_test.cc

@@ -97,15 +97,14 @@ void CheckArgsInferType() {
 
 template <typename A, typename B, typename ExplicitRet>
 auto ExplicitUniformReturnT(int) -> decltype(absl::Uniform<ExplicitRet>(
-                                     std::declval<absl::InsecureBitGen&>(),
-                                     std::declval<A>(), std::declval<B>()));
+    std::declval<absl::InsecureBitGen&>(), std::declval<A>(),
+    std::declval<B>()));
 
 template <typename, typename, typename ExplicitRet>
 Invalid ExplicitUniformReturnT(...);
 
 template <typename TagType, typename A, typename B, typename ExplicitRet>
-auto ExplicitTaggedUniformReturnT(int)
-    -> decltype(absl::Uniform<ExplicitRet>(
+auto ExplicitTaggedUniformReturnT(int) -> decltype(absl::Uniform<ExplicitRet>(
     std::declval<TagType>(), std::declval<absl::InsecureBitGen&>(),
     std::declval<A>(), std::declval<B>()));
 

absl/random/examples_test.cc

@@ -96,4 +96,3 @@ TEST(Examples, CreateingCorrelatedVariateSequences) {
     EXPECT_EQ(absl::Uniform<uint32_t>(gen_1), absl::Uniform<uint32_t>(gen_2));
   }
 }
-

absl/random/generators_test.cc

@@ -117,7 +117,6 @@ void TestBernoulli(URBG* gen) {
   absl::Bernoulli(*gen, 0.5);
 }
 
-
 template <typename URBG>
 void TestZipf(URBG* gen) {
   absl::Zipf<int>(*gen, 100);

absl/random/internal/chi_square.cc

@@ -25,9 +25,7 @@ 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;
-}
+inline double fma(double x, double y, double z) { return (x * y) + z; }
 #endif
 
 // Use Horner's method to evaluate a polynomial.
@@ -106,8 +104,7 @@ double normal_survival(double z) {
 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.
+  static constexpr double kChiMax = 99999.0;  // Maximum chi-squared value.
 
   const double p_value = 1.0 - p;
   if (dof < 1 || p_value > 1.0) {

absl/random/internal/chi_square_test.cc

@@ -291,8 +291,7 @@ TEST(ChiSquareTest, TableData) {
       /*97*/ {115.223, 120.990, 126.141, 132.309, 145.789},
       /*98*/ {116.315, 122.108, 127.282, 133.476, 147.010},
       /*99*/ {117.407, 123.225, 128.422, 134.642, 148.230},
-      /*100*/ {118.498, 124.342, 129.561, 135.807, 149.449}
-      /**/};
+      /*100*/ {118.498, 124.342, 129.561, 135.807, 149.449} /**/};
 
   //    0.90      0.95     0.975      0.99     0.999
   for (int i = 0; i < ABSL_ARRAYSIZE(data); i++) {

absl/random/internal/distribution_caller.h

@@ -17,8 +17,8 @@
 #ifndef ABSL_RANDOM_INTERNAL_DISTRIBUTION_CALLER_H_
 #define ABSL_RANDOM_INTERNAL_DISTRIBUTION_CALLER_H_
 
-#include <utility>
 #include <type_traits>
+#include <utility>
 
 #include "absl/base/config.h"
 #include "absl/base/internal/fast_type_id.h"

absl/random/internal/explicit_seed_seq_test.cc

@@ -29,14 +29,18 @@ using ::absl::random_internal::ExplicitSeedSeq;
 template <typename Sseq>
 bool ConformsToInterface() {
   // Check that the SeedSequence can be default-constructed.
-  { Sseq default_constructed_seq; }
+  {
+    Sseq default_constructed_seq;
+  }
   // Check that the SeedSequence can be constructed with two iterators.
   {
     uint32_t init_array[] = {1, 3, 5, 7, 9};
     Sseq iterator_constructed_seq(init_array, &init_array[5]);
   }
   // Check that the SeedSequence can be std::initializer_list-constructed.
-  { Sseq list_constructed_seq = {1, 3, 5, 7, 9, 11, 13}; }
+  {
+    Sseq list_constructed_seq = {1, 3, 5, 7, 9, 11, 13};
+  }
   // Check that param() and size() return state provided to constructor.
   {
     uint32_t init_array[] = {1, 2, 3, 4, 5};

absl/random/internal/gaussian_distribution_gentables.cc

@@ -16,8 +16,6 @@
 //
 // $ blaze run :gaussian_distribution_gentables > gaussian_distribution.cc
 //
-#include "absl/random/gaussian_distribution.h"
-
 #include <cmath>
 #include <cstddef>
 #include <iostream>
@@ -25,6 +23,7 @@
 #include <string>
 
 #include "absl/base/macros.h"
+#include "absl/random/gaussian_distribution.h"
 
 namespace absl {
 ABSL_NAMESPACE_BEGIN

absl/random/internal/randen_benchmarks.cc

@@ -12,8 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 //
-#include "absl/random/internal/randen.h"
-
 #include <cstdint>
 #include <cstdio>
 #include <cstring>
@@ -21,6 +19,7 @@
 #include "absl/base/internal/raw_logging.h"
 #include "absl/random/internal/nanobenchmark.h"
 #include "absl/random/internal/platform.h"
+#include "absl/random/internal/randen.h"
 #include "absl/random/internal/randen_engine.h"
 #include "absl/random/internal/randen_hwaes.h"
 #include "absl/random/internal/randen_slow.h"

absl/random/internal/salted_seed_seq_test.cc

@@ -33,14 +33,18 @@ namespace {
 template <typename Sseq>
 void ConformsToInterface() {
   // Check that the SeedSequence can be default-constructed.
-  { Sseq default_constructed_seq; }
+  {
+    Sseq default_constructed_seq;
+  }
   // Check that the SeedSequence can be constructed with two iterators.
   {
     uint32_t init_array[] = {1, 3, 5, 7, 9};
     Sseq iterator_constructed_seq(std::begin(init_array), std::end(init_array));
   }
   // Check that the SeedSequence can be std::initializer_list-constructed.
-  { Sseq list_constructed_seq = {1, 3, 5, 7, 9, 11, 13}; }
+  {
+    Sseq list_constructed_seq = {1, 3, 5, 7, 9, 11, 13};
+  }
   // Check that param() and size() return state provided to constructor.
   {
     uint32_t init_array[] = {1, 2, 3, 4, 5};

absl/random/internal/traits_test.cc

@@ -104,14 +104,11 @@ void CheckWideningConvertsTo() {
 TEST(TraitsTest, IsWideningConvertibleTest) {
   constexpr bool kInvalid = false;
 
-  CheckWideningConvertsToSelf<
-      uint8_t, uint16_t, uint32_t, uint64_t,
-      int8_t,  int16_t,  int32_t,  int64_t,
-      float,   double>();
-  CheckNotWideningConvertibleWithSigned<
-      uint8_t, uint16_t, uint32_t, uint64_t>();
-  CheckWideningConvertsToLargerTypes<
-      uint8_t, uint16_t, uint32_t, uint64_t>();
+  CheckWideningConvertsToSelf<uint8_t, uint16_t, uint32_t, uint64_t, int8_t,
+                              int16_t, int32_t, int64_t, float, double>();
+  CheckNotWideningConvertibleWithSigned<uint8_t, uint16_t, uint32_t,
+                                        uint64_t>();
+  CheckWideningConvertsToLargerTypes<uint8_t, uint16_t, uint32_t, uint64_t>();
 
   CheckWideningConvertsTo<float, double>();
   CheckWideningConvertsTo<uint16_t, float>();

absl/random/internal/uniform_helper.h

@@ -201,8 +201,8 @@ is_uniform_range_valid(FloatType a, FloatType b) {
 }
 
 template <typename IntType>
-absl::enable_if_t<IsIntegral<IntType>::value, bool>
-is_uniform_range_valid(IntType a, IntType b) {
+absl::enable_if_t<IsIntegral<IntType>::value, bool> is_uniform_range_valid(
+    IntType a, IntType b) {
   return a <= b;
 }
 

absl/random/internal/wide_multiply.h

@@ -75,7 +75,6 @@ inline U256 MultiplyU128ToU256(uint128 a, uint128 b) {
           c00 + (c64a << 64) + (c64b << 64)};
 }
 
-
 template <>
 struct wide_multiply<uint128> {
   using input_type = uint128;

absl/random/poisson_distribution_test.cc

@@ -106,7 +106,6 @@ TYPED_TEST(PoissonDistributionInterfaceTest, SerializeTest) {
                      0.0),  // denorm_max
   };
 
-
   constexpr int kCount = 1000;
   absl::InsecureBitGen gen;
   for (const double m : kParams) {

absl/random/seed_sequences.h

@@ -80,8 +80,7 @@ using SeedSeq = random_internal::SaltedSeedSeq<std::seed_seq>;
 //
 template <typename URBG>
 SeedSeq CreateSeedSeqFrom(URBG* urbg) {
-  SeedSeq::result_type
-      seed_material[random_internal::kEntropyBlocksNeeded];
+  SeedSeq::result_type seed_material[random_internal::kEntropyBlocksNeeded];
 
   if (!random_internal::ReadSeedMaterialFromURBG(
           urbg, absl::MakeSpan(seed_material))) {

absl/random/uniform_int_distribution_test.cc

@@ -114,8 +114,8 @@ TYPED_TEST(UniformIntDistributionTest, ParamSerializeTest) {
 TYPED_TEST(UniformIntDistributionTest, ViolatesPreconditionsDeathTest) {
 #if GTEST_HAS_DEATH_TEST
   // Hi < Lo
-  EXPECT_DEBUG_DEATH({ absl::uniform_int_distribution<TypeParam> dist(10, 1); },
-                     "");
+  EXPECT_DEBUG_DEATH(
+      { absl::uniform_int_distribution<TypeParam> dist(10, 1); }, "");
 #endif  // GTEST_HAS_DEATH_TEST
 #if defined(NDEBUG)
   // opt-mode, for invalid parameters, will generate a garbage value,

absl/random/uniform_real_distribution_test.cc

@@ -190,7 +190,7 @@ TYPED_TEST(UniformRealDistributionTest, ParamSerializeTest) {
 
 #ifdef _MSC_VER
 #pragma warning(push)
-#pragma warning(disable:4756)  // Constant arithmetic overflow.
+#pragma warning(disable : 4756)  // Constant arithmetic overflow.
 #endif
 TYPED_TEST(UniformRealDistributionTest, ViolatesPreconditionsDeathTest) {
   using DistributionType = absl::uniform_real_distribution<TypeParam>;

absl/random/zipf_distribution_test.cc

@@ -176,15 +176,15 @@ class ZipfModel {
       const double x = v_ + i;
 
       // H(n, q-1)
-      const double hnqm1 =
-          (q_ == 2.0) ? (1.0 / x)
-                      : (q_ == 3.0) ? (1.0 / (x * x)) : std::pow(x, -qm1);
+      const double hnqm1 = (q_ == 2.0)   ? (1.0 / x)
+                           : (q_ == 3.0) ? (1.0 / (x * x))
+                                         : std::pow(x, -qm1);
       sum_hnq_m1 += hnqm1;
 
       // H(n, q)
-      const double hnq =
-          (q_ == 2.0) ? (1.0 / (x * x))
-                      : (q_ == 3.0) ? (1.0 / (x * x * x)) : std::pow(x, -q_);
+      const double hnq = (q_ == 2.0)   ? (1.0 / (x * x))
+                         : (q_ == 3.0) ? (1.0 / (x * x * x))
+                                       : std::pow(x, -q_);
       sum_hnq_ += hnq;
       hnq_.push_back(hnq);
       if (i > 1000 && hnq <= 1e-10) {