/*
// Copyright (c) 2016-2020 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
*/

//todo move to another folder

#pragma once

#include "api/memory.hpp"
#include "api/tensor.hpp"
#include "api/program.hpp"
#include "api/network.hpp"
#include <iostream>
#include <limits>
#include <random>
#include <algorithm>
#include <memory>
#include <gtest/gtest.h>
#include <api/primitive.hpp>
#include "float16.h"
#include "random_gen.h"
#include "api/concatenation.hpp"
#include "api/lrn.hpp"
#include "api/roi_pooling.hpp"
#include "api/scale.hpp"
#include "api/softmax.hpp"
#include "api/reorder.hpp"
#include "api/normalize.hpp"
#include "api/convolution.hpp"
#include "api/activation.hpp"
#include "api/pooling.hpp"

#include <chrono>

#define ARRAY_SIZE(a) (sizeof(a) / sizeof(a[0]))

namespace tests {
#define USE_RANDOM_SEED 0
#if USE_RANDOM_SEED
    std::random_device rnd_device;
    unsigned int const random_seed = rnd_device();
#else
    unsigned int const random_seed = 1337;
#endif

// rounds floating point number, fraction precision should be in the range [0,23]
// masks the bits:
// 1 11111111 11111111111111100000000
// |      |            |
// sign  exp        fraction
inline float float_round(float x, size_t fraction_precision = 15) {
    uint32_t mask = ~((1 << (23 - fraction_precision)) - 1);
    reinterpret_cast<uint32_t&>(x) &= mask;
    return x;
}

template<typename T>
using VF = std::vector<T>;        // float vector
template<typename T>
using VVF = std::vector<VF<T>>;        // feature map
template<typename T>
using VVVF = std::vector<VVF<T>>;        // 3d feature map
template<typename T>
using VVVVF = std::vector<VVVF<T>>;    // batch of 3d feature maps
template<typename T>
using VVVVVF = std::vector<VVVVF<T>>;    // split of bfyx filters
template<typename T>
using VVVVVVF = std::vector<VVVVVF<T>>;    // split of bfyx filters

template<typename T>
inline VF<T> flatten_4d(cldnn::format input_format, VVVVF<T> &data) {
    size_t a = data.size();
    size_t b = data[0].size();
    size_t c = data[0][0].size();
    size_t d = data[0][0][0].size();
    VF<T> vec(a * b * c * d, (T)(0.0f));
    size_t idx = 0;

    switch (input_format.value) {
        case cldnn::format::yxfb:
            for (size_t yi = 0; yi < c; ++yi)
                for (size_t xi = 0; xi < d; ++xi)
                    for (size_t fi = 0; fi < b; ++fi)
                        for (size_t bi = 0; bi < a; ++bi)
                            vec[idx++] = data[bi][fi][yi][xi];
            break;

        case cldnn::format::fyxb:
            for (size_t fi = 0; fi < b; ++fi)
                for (size_t yi = 0; yi < c; ++yi)
                    for (size_t xi = 0; xi < d; ++xi)
                        for (size_t bi = 0; bi < a; ++bi)
                            vec[idx++] = data[bi][fi][yi][xi];
            break;

        case cldnn::format::bfyx:
            for (size_t bi = 0; bi < a; ++bi)
                for (size_t fi = 0; fi < b; ++fi)
                    for (size_t yi = 0; yi < c; ++yi)
                        for (size_t xi = 0; xi < d; ++xi)
                            vec[idx++] = data[bi][fi][yi][xi];
            break;

        case cldnn::format::byxf:
            for (size_t bi = 0; bi < a; ++bi)
                for (size_t yi = 0; yi < c; ++yi)
                    for (size_t xi = 0; xi < d; ++xi)
                        for (size_t fi = 0; fi < b; ++fi)
                            vec[idx++] = data[bi][fi][yi][xi];
            break;

        default:
            assert(0);
    }
    return vec;
}

template<typename T>
inline VF<T> flatten_6d(cldnn::format input_format, VVVVVVF<T> &data) {
    size_t a = data.size();
    size_t b = data[0].size();
    size_t c = data[0][0].size();
    size_t d = data[0][0][0].size();
    size_t e = data[0][0][0][0].size();
    size_t f = data[0][0][0][0][0].size();
    VF<T> vec(a * b * c * d * e * f, (T)(0.0f));
    size_t idx = 0;

    switch (input_format.value) {
        case cldnn::format::bfwzyx:
            for (size_t bi = 0; bi < a; ++bi)
                for (size_t fi = 0; fi < b; ++fi)
                    for (size_t wi = 0; wi < c; ++wi)
                        for (size_t zi = 0; zi < d; ++zi)
                            for (size_t yi = 0; yi < e; ++yi)
                                for (size_t xi = 0; xi < f; ++xi)
                                    vec[idx++] = data[bi][fi][wi][zi][yi][xi];
            break;
        default:
            assert(0);
    }
    return vec;
}

template<typename T>
std::vector<T> generate_random_1d(size_t a, int min, int max, int k = 8) {
    static std::default_random_engine generator(random_seed);
    // 1/k is the resolution of the floating point numbers
    std::uniform_int_distribution<int> distribution(k * min, k * max);
    std::vector<T> v(a);

    for (size_t i = 0; i < a; ++i) {
        v[i] = (T)distribution(generator);
        v[i] /= k;
    }
    return v;
}

template<typename T>
std::vector<std::vector<T>> generate_random_2d(size_t a, size_t b, int min, int max, int k = 8) {
    std::vector<std::vector<T>> v(a);
    for (size_t i = 0; i < a; ++i)
        v[i] = generate_random_1d<T>(b, min, max, k);
    return v;
}

template<typename T>
std::vector<std::vector<std::vector<T>>> generate_random_3d(size_t a, size_t b, size_t c, int min, int max, int k = 8) {
    std::vector<std::vector<std::vector<T>>> v(a);
    for (size_t i = 0; i < a; ++i)
        v[i] = generate_random_2d<T>(b, c, min, max, k);
    return v;
}

// parameters order is assumed to be bfyx or bfyx
template<typename T>
std::vector<std::vector<std::vector<std::vector<T>>>> generate_random_4d(size_t a, size_t b, size_t c, size_t d, int min, int max, int k = 8) {
    std::vector<std::vector<std::vector<std::vector<T>>>> v(a);
    for (size_t i = 0; i < a; ++i)
        v[i] = generate_random_3d<T>(b, c, d, min, max, k);
    return v;
}

// parameters order is assumed to be sbfyx for filters when split > 1
template<typename T>
std::vector<std::vector<std::vector<std::vector<std::vector<T>>>>> generate_random_5d(size_t a, size_t b, size_t c, size_t d, size_t e, int min, int max, int k = 8) {
    std::vector<std::vector<std::vector<std::vector<std::vector<T>>>>> v(a);
    for (size_t i = 0; i < a; ++i)
        v[i] = generate_random_4d<T>(b, c, d, e, min, max, k);
    return v;
}

template<typename T>
VVVVVVF<T> generate_random_6d(size_t a, size_t b, size_t c, size_t d, size_t e, size_t f, int min, int max, int k = 8) {
    VVVVVVF<T> v(a);
    for (size_t i = 0; i < a; ++i)
        v[i] = generate_random_5d<T>(b, c, d, e, f, min, max, k);
    return v;
}

template <class T> void set_value(const cldnn::pointer<T>& ptr, uint32_t index, T value) { ptr[index] = value; }
template <class T> T    get_value(const cldnn::pointer<T>& ptr, uint32_t index) { return ptr[index]; }

template<typename T>
void set_values(const cldnn::memory& mem, std::initializer_list<T> args ){
    auto ptr = mem.pointer<T>();

    auto it = ptr.begin();
    for(auto x : args)
        *it++ = x;
}

template<typename T>
void set_values(const cldnn::memory& mem, std::vector<T> args) {
    auto ptr = mem.pointer<T>();

    auto it = ptr.begin();
    for (auto x : args)
        *it++ = x;
}

template<typename T>
void set_values_per_batch_and_feature(const cldnn::memory& mem, std::vector<T> args)
{
    auto mem_ptr = mem.pointer<T>();
    auto&& pitches = mem.get_layout().get_pitches();
    auto&& size = mem.get_layout().size;
    for (cldnn::tensor::value_type b = 0; b < size.batch[0]; ++b)
    {
        for (cldnn::tensor::value_type f = 0; f < size.feature[0]; ++f)
        {
            for (cldnn::tensor::value_type y = 0; y < size.spatial[1]; ++y)
            {
                for (cldnn::tensor::value_type x = 0; x < size.spatial[0]; ++x)
                {
                    unsigned int input_it = b*pitches.batch[0] + f*pitches.feature[0] + y*pitches.spatial[1] + x*pitches.spatial[0];
                    mem_ptr[input_it] = args[b*size.feature[0] + f];
                }
            }
        }
    }

}

template<typename T>
void set_random_values(const cldnn::memory& mem, bool sign = false, unsigned significand_bit = 8, unsigned scale = 1)
{
    auto ptr = mem.pointer<T>();

    std::mt19937 gen;
    for (auto it = ptr.begin(); it != ptr.end(); ++it)
    {
        *it = rnd_generators::gen_number<T>(gen, significand_bit, sign, false, scale);
    }
}

// Tries to construct a network, checking if an expected error appears
inline void check_exception_massage(const cldnn::engine& engine, cldnn::topology& topology, std::string msg_to_find)
{
    try {
        cldnn::network(engine, topology);
    }
    catch (std::exception & exc) {
        std::string msg(exc.what());
        if (msg.find(msg_to_find) != std::string::npos) {
            throw;
        }
        else {
            printf("%s\n", exc.what());
        }
    }
}

// Checks equality of floats.
// For values less than absoulte_error_limit, absolute error will be counted
// for others, the relatve error will be counted.
// Function returns false if error will exceed the threshold.
// Default values:
// relative_error_threshold = 1e-3
// absolute_error_threshold = 1e-6
// absoulte_error_limit = 1e-4
inline bool are_equal(
    const float ref_item,
    const float item,
    const float relative_error_threshold = 1e-3,
    const float absolute_error_threshold = 1e-6,
    const float absoulte_error_limit     = 1e-4) {

        if( fabs(item) < absoulte_error_limit) {
            if(fabs( item - ref_item ) > absolute_error_threshold) {
                std::cout << "Ref val: " << ref_item << "\tSecond val: " << item << std::endl;
                return false;
            }
        } else
            if(fabs(item - ref_item) / fabs(ref_item) > relative_error_threshold){
                std::cout << "Ref val: " << ref_item << "\tSecond val: " << item << std::endl;
                return false;
        }

        return true;
}

inline bool floating_point_equal(FLOAT16 x, FLOAT16 y, int max_ulps_diff = 4) {
    int16_t sign_bit_mask = 1;
    sign_bit_mask <<= 15;
    int16_t a = x.v, b = y.v;
    if ((a & sign_bit_mask) != (b & sign_bit_mask)) {
        a &= ~sign_bit_mask;
        b &= ~sign_bit_mask;
        return a == 0 && b == 0;
    }
    else {
        return std::abs(a - b) < (1 << (max_ulps_diff));
    }
}

inline bool floating_point_equal(float x, float y, int max_ulps_diff = 4) {
    int32_t sign_bit_mask = 1;
    sign_bit_mask <<= 31;
    int32_t a = reinterpret_cast<int32_t&>(x), b = reinterpret_cast<int32_t&>(y);
    if ((a & sign_bit_mask) != (b & sign_bit_mask)) {
        a &= ~sign_bit_mask;
        b &= ~sign_bit_mask;
        return a == 0 && b == 0;
    }
    else {
        return std::abs(a - b) < (1 << (max_ulps_diff));
    }
}

class test_params
{
public:

    test_params() :
        fmt(cldnn::format::bfyx)
    {
    }

    test_params(cldnn::data_types dt, cldnn::format input_format, int32_t batch_size, int32_t feature_size, cldnn::tensor input_size, cldnn::build_options const& options = cldnn::build_options()) :
        data_type(dt),
        fmt(input_format),
        network_build_options(options)
    {
        cldnn::tensor t = cldnn::tensor(batch_size, feature_size, input_size.spatial[0],  input_size.spatial[1] );
        input_layouts.push_back( cldnn::layout(dt, fmt, t) );
    }

    cldnn::data_types data_type;
    cldnn::format fmt;
    std::vector<cldnn::layout> input_layouts;

    void * opaque_custom_param = nullptr;

    cldnn::build_options network_build_options;

    std::string print();
    static std::string print_tensor(cldnn::tensor tensor);
};

struct pitches
{
    size_t b, f, y, x, z;
};

struct memory_desc
{
    pitches pitch;
    size_t offset;
};

const cldnn::engine & get_test_engine();

struct test_dump
{
    const std::string name() const;
    const std::string test_case_name() const;
private:
    const std::string test_case_name_str = ::testing::UnitTest::GetInstance()->current_test_info()->test_case_name();
    const std::string name_str = ::testing::UnitTest::GetInstance()->current_test_info()->name();
};

class generic_test : public ::testing::TestWithParam<std::tuple<std::shared_ptr<test_params>, std::shared_ptr<cldnn::primitive>>>
{

public:
    generic_test();

    void run_single_test();

    template<typename Type>
    void compare_buffers(const cldnn::memory& out, const cldnn::memory& ref);

    static size_t get_linear_index(const cldnn::layout & layout, size_t b, size_t f, size_t y, size_t x, const memory_desc& desc);
    static size_t get_linear_index(const cldnn::layout & layout, size_t b, size_t f, size_t z, size_t y, size_t x, const memory_desc& desc);
    static size_t get_linear_index_with_broadcast(const cldnn::layout& in_layout, size_t b, size_t f, size_t y, size_t x, const memory_desc& desc);

    static memory_desc get_linear_memory_desc(const cldnn::layout & layout);

    static std::vector<std::shared_ptr<test_params>> generate_generic_test_params(std::vector<std::shared_ptr<test_params>>& all_generic_params);

    static void dump_graph(const std::string test_name, cldnn::build_options& bo);

    virtual bool is_format_supported(cldnn::format format) = 0;

    virtual cldnn::tensor get_expected_output_tensor();

    struct custom_param_name_functor {
            std::string operator()(const ::testing::TestParamInfo<std::tuple<std::shared_ptr<test_params>, std::shared_ptr<cldnn::primitive>>>& info) {
                    return std::to_string(info.index);
            }
    };

protected:
    const cldnn::engine& engine = get_test_engine();
    std::shared_ptr<test_params> generic_params;
    test_dump test_info;
    std::shared_ptr<cldnn::primitive> layer_params;
    int max_ulps_diff_allowed; //Max number of ulps allowed between 2 values when comparing the output buffer and the reference buffer.
    bool random_values; // if set memory buffers will be filled with random values
    bool dump_graphs; // if set tests will dump graphs to file
    bool dump_memory; // if set memory buffers will be dumped to file
    virtual cldnn::memory generate_reference(const std::vector<cldnn::memory>& inputs) = 0;
    // Allows the test to override the random input data that the framework generates

    virtual void prepare_input_for_test(std::vector<cldnn::memory>& /*inputs*/) { }

    static std::vector<cldnn::data_types> test_data_types();
    static std::vector<cldnn::format> test_input_formats;
    static std::vector<cldnn::format> test_weight_formats;
    static std::vector<int32_t> test_batch_sizes;
    static std::vector<int32_t> test_feature_sizes;
    static std::vector<cldnn::tensor> test_input_sizes;
};

// When a test assertion such as EXPECT_EQ fails, Google-Test prints the argument values to help with debugging.
// It does this using a user - extensible value printer.
// This function will be used to print the test params in case of an error.
inline void PrintTupleTo(const std::tuple<std::shared_ptr<test_params>, std::shared_ptr<cldnn::primitive>>& t, ::std::ostream* os)
{
    std::stringstream str;

    auto test_param = std::get<0>(t);
    auto primitive = std::get<1>(t);

    str << std::endl << "Test params: " << test_param->print();

    str << "Layer params:\n"
        << "Output padding lower size: " << test_param->print_tensor(primitive->output_padding.lower_size())
        << " upper size: " << test_param->print_tensor(primitive->output_padding.upper_size()) << '\n';

    //TODO: do layers not have param dumping? we could consider adding it

    if (primitive->type == cldnn::concatenation::type_id())
    {
        auto dc = std::static_pointer_cast<cldnn::concatenation>(primitive);
        (void)dc;
    }
    else if(primitive->type == cldnn::lrn::type_id())
    {
        auto lrn = std::static_pointer_cast<cldnn::lrn >(primitive);
        std::string norm_region = (lrn->norm_region == cldnn::lrn_norm_region_across_channel) ? "across channel" : "within channel";
        str << "Norm region: " << norm_region
            << " Size: " << lrn->size
            << " Alpha: " << lrn->alpha
            << " Beta: " << lrn->beta
            << " K: " << lrn->k;
    }
    else if(primitive->type == cldnn::roi_pooling::type_id())
    {
        auto p = std::static_pointer_cast<cldnn::roi_pooling >(primitive);
        str << "Pooling mode: " << (p->mode == cldnn::pooling_mode::max ? "MAX" : "AVG")
            << " Pooled width: " << p->pooled_width
            << " Pooled height: " << p->pooled_height
            << " Spatial scale: " << p->spatial_scale
            << " Spatial bins x: " << p->spatial_bins_x
            << " Spatial bins y: " << p->spatial_bins_y
            << " Output dim: " << p->output_dim;
    }
    else if(primitive->type == cldnn::scale::type_id())
    {
        auto s = std::static_pointer_cast<cldnn::scale >(primitive);
        (void)s;
    }
    else if(primitive->type == cldnn::softmax::type_id())
    {
        auto sm = std::static_pointer_cast<cldnn::softmax >(primitive);
        (void)sm;
    }
    else if (primitive->type == cldnn::reorder::type_id())
    {
        auto reorder = std::static_pointer_cast<cldnn::reorder>(primitive);
        str << "Output data type: " << cldnn::data_type_traits::name(*reorder->output_data_type) << " Mean: " << reorder->mean << "Subtract per feature: " << "TODO" /*std::vector<float> subtract_per_feature*/;
    }
    else if (primitive->type == cldnn::normalize::type_id())
    {
        auto normalize = std::static_pointer_cast<cldnn::normalize>(primitive);
        std::string norm_region = normalize->across_spatial ? "across_spatial" : "within_spatial";
        str << "Norm region: " << norm_region << " Epsilon: " << normalize->epsilon << " Scale input id: " << normalize->scale_input;
    }
    else if (primitive->type == cldnn::convolution::type_id())
    {
        auto convolution = std::static_pointer_cast<cldnn::convolution>(primitive);
        str << "Stride x: " << convolution->stride.spatial[0] << " Stride y: " << convolution->stride.spatial[1]
            << " Dilation x: " << convolution->dilation.spatial[0] << " Dilation y: " << convolution->dilation.spatial[1]
            << " Input offset x: " << convolution->input_offset.spatial[0] << " Input offset y: " << convolution->input_offset.spatial[1];
    }
    else if (primitive->type == cldnn::activation::type_id())
    {
        auto activation = std::static_pointer_cast<cldnn::activation>(primitive);
        str << "Negative slope: " << activation->additional_params.a << " Negative slope input id: " << activation->additional_params_input;
    }
    else if (primitive->type == cldnn::pooling::type_id())
    {
        auto pooling = std::static_pointer_cast<cldnn::pooling>(primitive);
        std::string pooling_mode = (pooling->mode == cldnn::pooling_mode::max) ? "max" : "average";
        str << "Pooling mode: " << pooling_mode
            << " Input offset x: " << pooling->input_offset.spatial[0] << " Input offset y: " << pooling->input_offset.spatial[1]
            << " Stride x: " << pooling->stride.spatial[0] << " Stride y: " << pooling->stride.spatial[1]
            << " Size x: " << pooling->size.spatial[0] << " Size y: " << pooling->size.spatial[1];
    }
    else
    {
        throw std::runtime_error("Not implemented yet for this primitive.");
    }

    *os << str.str();
}

template <typename T, typename U>
T div_up(const T a, const U b) {
    assert(b);
    return (a + b - 1) / b;
}

inline void print_bin_blob(cldnn::memory& mem, std::string name)
{
    auto&& size = mem.get_layout().size;

    std::cerr << name;
    std::cerr << " shape: ";
    std::cerr << size.batch[0] << " ";
    std::cerr << size.feature[0] << " ";
    std::cerr << size.spatial[1] << " ";
    std::cerr << size.spatial[0] << " ";
    std::cerr << "(" << size.batch[0] * size.feature[0] * size.spatial[1] * size.spatial[0] << ")" << std::endl;

    auto mem_ptr = mem.pointer<uint32_t>();

    bool packed_ic = mem.get_layout().format == cldnn::format::b_fs_yx_32fp ? 1 : 0;
    int B = size.batch[0];
    int C = size.feature[0];
    int H = size.spatial[1];
    int W = size.spatial[0];

    for (cldnn::tensor::value_type b = 0; b < B; ++b)
    {
        for (cldnn::tensor::value_type f = 0; f < C; ++f)
        {
            for (cldnn::tensor::value_type y = 0; y < H; ++y)
            {
                for (cldnn::tensor::value_type x = 0; x < W; ++x)
                {
                    if (!packed_ic)
                    {
                        size_t input_it = b * C*H*W + f * W*H + y * W + x;
                        size_t elem = input_it / 32;
                        size_t bit = input_it % 32;
                        std::cerr << ((mem_ptr[elem] & (1 << bit)) >> bit) << " ";
                    }
                    else
                    {
                        size_t input_it = b * (C / 32)*W*H + (f / 32)*W*H + y * W + x;
                        size_t bit = f % 32;
                        std::cerr << ((mem_ptr[input_it] & (1 << bit)) >> bit) << " ";
                    }
                }
                std::cerr << std::endl;
            }
            std::cerr << std::endl;
        }
        std::cerr << "==============" << std::endl;
    }
}

inline void print_bin_blob_packed(cldnn::memory& mem, std::string name)
{
    auto&& size = mem.get_layout().size;

    std::cerr << name;
    std::cerr << " shape: ";
    std::cerr << size.batch[0] << " ";
    std::cerr << size.feature[0] << " ";
    std::cerr << size.spatial[1] << " ";
    std::cerr << size.spatial[0] << " ";
    std::cerr << "(" << size.batch[0] * size.feature[0] * size.spatial[1] * size.spatial[0] << ")" << std::endl;

    auto mem_ptr = mem.pointer<uint32_t>();

    int B = size.batch[0];
    int C = size.feature[0];
    int H = size.spatial[1];
    int W = size.spatial[0];

    for (cldnn::tensor::value_type b = 0; b < B; ++b)
    {
        for (cldnn::tensor::value_type f = 0; f < div_up(C, 32); ++f)
        {
            for (cldnn::tensor::value_type y = 0; y < H; ++y)
            {
                for (cldnn::tensor::value_type x = 0; x < W; ++x)
                {
                    size_t input_it = b * div_up(C, 32)*W*H + f * W*H + y * W + x;
                    std::cerr << mem_ptr[input_it] << " ";
                }
                std::cerr << std::endl;
            }
            std::cerr << std::endl;
        }
        std::cerr << "==============" << std::endl;
    }
}

inline void print_blob(cldnn::memory& mem, std::string name)
{
    auto&& size = mem.get_layout().size;

    std::cerr << name;
    std::cerr << " shape: ";
    std::cerr << size.batch[0] << " ";
    std::cerr << size.feature[0] << " ";
    std::cerr << size.spatial[1] << " ";
    std::cerr << size.spatial[0] << " ";
    std::cerr << "(" << size.batch[0] * size.feature[0] * size.spatial[1] * size.spatial[0] << ")" << std::endl;

    auto mem_ptr = mem.pointer<float>();

    int B = size.batch[0];
    int C = size.feature[0];
    int H = size.spatial[1];
    int W = size.spatial[0];

    for (cldnn::tensor::value_type b = 0; b < B; ++b)
    {
        for (cldnn::tensor::value_type f = 0; f < C; ++f)
        {
            for (cldnn::tensor::value_type y = 0; y < H; ++y)
            {
                for (cldnn::tensor::value_type x = 0; x < W; ++x)
                {
                    size_t input_it = b * C*W*H + f * W*H + y * W + x;
                    std::cerr << std::setw(4) << mem_ptr[input_it] << " ";
                }
                std::cerr << std::endl;
            }
            std::cerr << std::endl;
        }
        std::cerr << "==============" << std::endl;
    }
}
} // namespace tests

