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//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
// This test fails because Clang no longer enables -fdelayed-template-parsing
// by default on Windows with C++20 (#69431).
// XFAIL: msvc && (clang-18 || clang-19 || clang-20 || clang-21)
// UNSUPPORTED: c++03, c++11
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <cstring>
#include <random>
#include <type_traits>
#include <vector>
#include "fuzz.h"
template <class IntT>
std::vector<IntT> GetValues(const std::uint8_t *data, std::size_t size) {
std::vector<IntT> result;
while (size >= sizeof(IntT)) {
IntT tmp;
std::memcpy(&tmp, data, sizeof(IntT));
size -= sizeof(IntT);
data += sizeof(IntT);
result.push_back(tmp);
}
return result;
}
template <class Dist>
struct ParamTypeHelper {
using ParamT = typename Dist::param_type;
using ResultT = typename Dist::result_type;
static_assert(std::is_same<ResultT, typename ParamT::distribution_type::result_type>::value, "");
static ParamT Create(const std::uint8_t* data, std::size_t size, bool &OK) {
constexpr bool select_vector_result = std::is_constructible<ParamT, ResultT*, ResultT*, ResultT*>::value;
constexpr bool select_vector_double = std::is_constructible<ParamT, double*, double*>::value;
constexpr int selector = select_vector_result ? 0 : (select_vector_double ? 1 : 2);
return DispatchAndCreate(std::integral_constant<int, selector>{}, data, size, OK);
}
// Vector result
static ParamT DispatchAndCreate(std::integral_constant<int, 0>, const std::uint8_t *data, std::size_t size, bool &OK) {
auto Input = GetValues<ResultT>(data, size);
OK = false;
if (Input.size() < 10)
return ParamT{};
OK = true;
auto Beg = Input.begin();
auto End = Input.end();
auto Mid = Beg + ((End - Beg) / 2);
assert(Mid - Beg <= (End - Mid));
ParamT p(Beg, Mid, Mid);
return p;
}
// Vector double
static ParamT DispatchAndCreate(std::integral_constant<int, 1>, const std::uint8_t *data, std::size_t size, bool &OK) {
auto Input = GetValues<double>(data, size);
OK = true;
auto Beg = Input.begin();
auto End = Input.end();
ParamT p(Beg, End);
return p;
}
// Default
static ParamT DispatchAndCreate(std::integral_constant<int, 2>, const std::uint8_t *data, std::size_t size, bool &OK) {
OK = false;
if (size < sizeof(ParamT))
return ParamT{};
OK = true;
ParamT input;
std::memcpy(&input, data, sizeof(ParamT));
return input;
}
};
template <class IntT>
struct ParamTypeHelper<std::poisson_distribution<IntT>> {
using Dist = std::poisson_distribution<IntT>;
using ParamT = typename Dist::param_type;
using ResultT = typename Dist::result_type;
static ParamT Create(const std::uint8_t *data, std::size_t size, bool& OK) {
OK = false;
auto vals = GetValues<double>(data, size);
if (vals.empty() || std::isnan(vals[0]) || std::isnan(std::abs(vals[0])) || vals[0] < 0)
return ParamT{};
OK = true;
return ParamT{vals[0]};
}
};
template <class IntT>
struct ParamTypeHelper<std::geometric_distribution<IntT>> {
using Dist = std::geometric_distribution<IntT>;
using ParamT = typename Dist::param_type;
using ResultT = typename Dist::result_type;
static ParamT Create(const std::uint8_t *data, std::size_t size, bool& OK) {
OK = false;
auto vals = GetValues<double>(data, size);
if (vals.empty() || std::isnan(vals[0]) || vals[0] < 0 )
return ParamT{};
OK = true;
return ParamT{vals[0]};
}
};
template <class IntT>
struct ParamTypeHelper<std::lognormal_distribution<IntT>> {
using Dist = std::lognormal_distribution<IntT>;
using ParamT = typename Dist::param_type;
using ResultT = typename Dist::result_type;
static ParamT Create(const std::uint8_t *data, std::size_t size, bool& OK) {
OK = false;
auto vals = GetValues<ResultT>(data, size);
if (vals.size() < 2 )
return ParamT{};
OK = true;
return ParamT{vals[0], vals[1]};
}
};
template <>
struct ParamTypeHelper<std::bernoulli_distribution> {
using Dist = std::bernoulli_distribution;
using ParamT = Dist::param_type;
using ResultT = Dist::result_type;
static ParamT Create(const std::uint8_t *data, std::size_t size, bool& OK) {
OK = false;
auto vals = GetValues<double>(data, size);
if (vals.empty())
return ParamT{};
OK = true;
return ParamT{vals[0]};
}
};
template <class Distribution>
int helper(const std::uint8_t *data, std::size_t size) {
std::mt19937 engine;
using ParamT = typename Distribution::param_type;
bool OK;
ParamT p = ParamTypeHelper<Distribution>::Create(data, size, OK);
if (!OK)
return 0;
Distribution d(p);
volatile auto res = d(engine);
if (std::isnan(res)) {
// FIXME(llvm.org/PR44289):
// Investigate why these distributions are returning NaN and decide
// if that's what we want them to be doing.
//
// Make this assert false (or return non-zero).
return 0;
}
return 0;
}
extern "C" int LLVMFuzzerTestOneInput(const std::uint8_t *data, std::size_t size) {
return helper<std::uniform_int_distribution<std::int16_t>>(data, size) ||
helper<std::uniform_real_distribution<float>>(data, size) ||
helper<std::bernoulli_distribution>(data, size) ||
helper<std::poisson_distribution<std::int16_t>>(data, size) ||
helper<std::geometric_distribution<std::int16_t>>(data, size) ||
helper<std::binomial_distribution<std::int16_t>>(data, size) ||
helper<std::negative_binomial_distribution<std::int16_t>>(data, size) ||
helper<std::exponential_distribution<float>>(data, size) ||
helper<std::gamma_distribution<float>>(data, size) ||
helper<std::weibull_distribution<float>>(data, size) ||
helper<std::extreme_value_distribution<float>>(data, size) ||
helper<std::normal_distribution<float>>(data, size) ||
helper<std::lognormal_distribution<float>>(data, size) ||
helper<std::chi_squared_distribution<float>>(data, size) ||
helper<std::cauchy_distribution<float>>(data, size) ||
helper<std::fisher_f_distribution<float>>(data, size) ||
helper<std::student_t_distribution<float>>(data, size) ||
helper<std::discrete_distribution<std::int16_t>>(data, size) ||
helper<std::piecewise_constant_distribution<float>>(data, size) ||
helper<std::piecewise_linear_distribution<float>>(data, size);
}
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