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#pragma once
#include <library/cpp/random_provider/random_provider.h>
#include <util/generic/maybe.h>
#include <util/generic/ptr.h>
#include <util/generic/string.h>
#include <util/generic/strbuf.h>
#include <util/generic/vector.h>
#include <util/system/types.h>
#include <util/system/yassert.h>
#include <array>
#include <limits>
#include <tuple>
#include <type_traits>
#include <variant>
namespace NKikimr::NMiniKQL {
namespace NPrivate {
template <typename T, typename = void>
struct TRandomDataGenerator {
static_assert(sizeof(T) == 0,
"TRandomDataGenerator is not specialized for this type. "
"Add a specialization of TRandomDataGenerator<T> to support it.");
};
template <>
struct TRandomDataGenerator<bool> {
struct TSettings {
double TrueProbability = 0.5;
};
static bool Generate(IRandomProvider& provider, const TSettings& settings) {
Y_ENSURE(settings.TrueProbability >= 0.0 && settings.TrueProbability <= 1.0);
return provider.GenRandReal2() < settings.TrueProbability;
}
};
template <typename T>
requires std::is_integral_v<T> && (!std::is_same_v<T, bool>)
struct TRandomDataGenerator<T> {
struct TSettings {
T Min = std::numeric_limits<T>::min();
T Max = std::numeric_limits<T>::max();
};
static T Generate(IRandomProvider& provider, const TSettings& settings) {
Y_ENSURE(settings.Min < settings.Max);
const auto range = static_cast<ui64>(settings.Max) - static_cast<ui64>(settings.Min);
return static_cast<T>(static_cast<ui64>(settings.Min) + provider.GenRand64() % range);
}
};
template <typename T>
requires std::is_floating_point_v<T>
struct TRandomDataGenerator<T> {
struct TSettings {
T Min = T(0);
T Max = T(1);
};
static T Generate(IRandomProvider& provider, const TSettings& settings) {
Y_ENSURE(settings.Min < settings.Max);
return static_cast<T>(
settings.Min +
provider.GenRandReal2() * static_cast<double>(settings.Max - settings.Min));
}
};
template <>
struct TRandomDataGenerator<TString> {
struct TSettings {
size_t MinSize = 0;
size_t MaxSize = 32;
TStringBuf Alphabet = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789";
};
static TString Generate(IRandomProvider& provider, const TSettings& settings) {
Y_ENSURE(settings.MinSize < settings.MaxSize);
Y_ENSURE(!settings.Alphabet.empty());
const size_t len = settings.MinSize + provider.GenRand64() % (settings.MaxSize - settings.MinSize);
TString result(len, '\0');
for (size_t i = 0; i < len; ++i) {
result[i] = settings.Alphabet[provider.GenRand64() % settings.Alphabet.size()];
}
return result;
}
};
template <typename T>
struct TRandomDataGenerator<TMaybe<T>> {
struct TSettings {
double NullProbability = 0.1;
typename TRandomDataGenerator<T>::TSettings Inner{};
};
static TMaybe<T> Generate(IRandomProvider& provider, const TSettings& settings) {
Y_ENSURE(settings.NullProbability >= 0.0 && settings.NullProbability <= 1.0);
if (provider.GenRandReal2() < settings.NullProbability) {
return Nothing();
}
return TRandomDataGenerator<T>::Generate(provider, settings.Inner);
}
};
template <typename... Ts>
struct TRandomDataGenerator<std::tuple<Ts...>> {
using TSettings = std::tuple<typename TRandomDataGenerator<Ts>::TSettings...>;
static std::tuple<Ts...> Generate(IRandomProvider& provider, const TSettings& settings) {
return [&]<size_t... Is>(std::index_sequence<Is...>) {
return std::make_tuple(TRandomDataGenerator<Ts>::Generate(provider, std::get<Is>(settings))...);
}(std::index_sequence_for<Ts...>{});
}
};
template <typename... Ts>
struct TRandomDataGenerator<std::variant<Ts...>> {
struct TSettings {
std::array<double, sizeof...(Ts)> Weights = [] {
std::array<double, sizeof...(Ts)> w{};
w.fill(1.0);
return w;
}();
std::tuple<typename TRandomDataGenerator<Ts>::TSettings...> InnerSettings{};
};
static std::variant<Ts...> Generate(IRandomProvider& provider, const TSettings& settings) {
const size_t idx = SelectWeightedIndex(provider, settings.Weights);
std::variant<Ts...> result;
[&]<size_t... Is>(std::index_sequence<Is...>) {
Y_UNUSED(((Is == idx && (result = std::variant<Ts...>(
std::in_place_index<Is>,
TRandomDataGenerator<Ts>::Generate(
provider, std::get<Is>(settings.InnerSettings))), true)) ||
...));
}(std::index_sequence_for<Ts...>{});
return result;
}
private:
static size_t SelectWeightedIndex(IRandomProvider& provider, const std::array<double, sizeof...(Ts)>& weights) {
double total = 0.0;
for (double w : weights) {
Y_ENSURE(w >= 0.0);
total += w;
}
Y_ENSURE(total > 0.0);
const double roll = provider.GenRandReal2() * total;
double cumulative = 0.0;
for (size_t i = 0; i + 1 < sizeof...(Ts); ++i) {
cumulative += weights[i];
if (roll < cumulative) {
return i;
}
}
return sizeof...(Ts) - 1;
}
};
} // namespace NPrivate
template <typename T>
using TGeneratorSettings = typename NPrivate::TRandomDataGenerator<T>::TSettings;
template <typename T>
TVector<T> GenerateRandomData(TIntrusivePtr<IRandomProvider> provider, TGeneratorSettings<T> settings, size_t count) {
TVector<T> result;
result.reserve(count);
for (size_t i = 0; i < count; ++i) {
result.push_back(NPrivate::TRandomDataGenerator<T>::Generate(*provider, settings));
}
return result;
}
template <typename T>
TVector<T> GenerateRandomData(TIntrusivePtr<IRandomProvider> provider, size_t count) {
return GenerateRandomData<T>(std::move(provider), TGeneratorSettings<T>{}, count);
}
} // namespace NKikimr::NMiniKQL
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