#pragma once

bool run_gemm_add_multiply(const ProblemSize& problem_size, const ExecutionConfig& config)
{
    using namespace ck::literals;

    auto& [M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideE] = problem_size;

    auto f_host_tensor_descriptor =
        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
            if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
            {
                return HostTensorDescriptor({row, col}, {stride, 1_uz});
            }
            else
            {
                return HostTensorDescriptor({row, col}, {1_uz, stride});
            }
        };

    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
    Tensor<D0DataType> d0_m_n(f_host_tensor_descriptor(M, N, StrideD0, D0Layout{}));
    Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideD1, D1Layout{}));
    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));

    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
    std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
    std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;

    switch(config.init_method)
    {
    case 0: break;
    case 1:
        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
        d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
        d1_m_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-1, 1});
        break;
    default:
        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
        d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
        d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
    }

    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
    DeviceMem d0_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
    DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
    DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());

    a_device_buf.ToDevice(a_m_k.mData.data());
    b_device_buf.ToDevice(b_k_n.mData.data());
    d0_device_buf.ToDevice(d0_m_n.mData.data());
    d1_device_buf.ToDevice(d1_m_n.mData.data());

    auto a_element_op   = AElementOp{};
    auto b_element_op   = BElementOp{};
    auto cde_element_op = CDEElementOp{};

    // do GEMM
    auto device_op = DeviceOpInstance{};
    auto invoker   = device_op.MakeInvoker();
    auto argument =
        device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
                               b_device_buf.GetDeviceBuffer(),
                               {d0_device_buf.GetDeviceBuffer(), d1_device_buf.GetDeviceBuffer()},
                               e_device_buf.GetDeviceBuffer(),
                               M,
                               N,
                               K,
                               StrideA,
                               StrideB,
                               {StrideD0, StrideD1},
                               StrideE,
                               a_element_op,
                               b_element_op,
                               cde_element_op);

    if(!device_op.IsSupportedArgument(argument))
    {
        std::cout << "wrong! this device_op instance does not support this problem" << std::endl;
        return true;
    }

    float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});

    std::size_t flop      = 2_uz * M * N * K;
    std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
                            sizeof(D0DataType) * N + sizeof(D1DataType) * M * N +
                            sizeof(EDataType) * M * N;

    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;

    float gb_per_sec = num_btype / 1.E6 / ave_time;

    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
              << device_op.GetTypeString() << std::endl;

    if(config.do_verification)
    {
        Tensor<AccDataType> c_m_n({M, N});

        auto ref_gemm    = ReferenceGemmInstance{};
        auto ref_invoker = ref_gemm.MakeInvoker();

        auto ref_argument =
            ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});

        ref_invoker.Run(ref_argument);

        for(int m = 0; m < M; ++m)
        {
            for(int n = 0; n < N; ++n)
            {
                cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_m_n(m, n), d1_m_n(m, n));
            }
        }

        e_device_buf.FromDevice(e_m_n_device_result.mData.data());

        return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
    }

    return true;
}

bool run_gemm_add_multiply_example(int argc, char* argv[])
{
    ProblemSize problem_size;
    ExecutionConfig config;

    return !parse_cmd_args(argc, argv, problem_size, config) ||
           run_gemm_add_multiply(problem_size, config);
}
