#pragma once
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "host_conv.hpp"
#include "tensor_layout.hpp"
#include "device_tensor.hpp"
#include "device_conv_fwd_bias_activation.hpp"
#include "element_wise_operation.hpp"

namespace ck {
namespace tensor_operation {
namespace device {
namespace device_conv2d_fwd_bias_activation_atomic_add_instance {

using DeviceConvFwdBiasReluPtr =
    DeviceConvFwdBiasActivationPtr<ck::tensor_operation::element_wise::PassThrough,
                                   ck::tensor_operation::element_wise::PassThrough,
                                   ck::tensor_operation::element_wise::AddRelu>;

void add_device_conv2d_fwd_xdl_c_shuffle_bias_relu_atomic_add_nhwc_kyxc_nhwk_f16_instances(
    std::vector<DeviceConvFwdBiasReluPtr>&);

} // namespace device_conv2d_fwd_bias_activation_atomic_add_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

namespace ck {
namespace profiler {

void cpu_conv_bias_relu_atomic_add(ck::half_t* in_ptr,
                                   ck::half_t* weight_ptr,
                                   ck::half_t* output_ptr,
                                   ck::half_t* bias_ptr,
                                   const ck::index_t N,
                                   const ck::index_t K,
                                   const ck::index_t C,
                                   const ck::index_t Y,
                                   const ck::index_t X,
                                   const ck::index_t Hi,
                                   const ck::index_t Wi,
                                   const ck::index_t Ho,
                                   const ck::index_t Wo,
                                   const ck::index_t Stride,
                                   const ck::index_t Dilation,
                                   const ck::index_t Pad)
{

    const auto in_desc =
        HostTensorDescriptor(std::vector<std::size_t>{static_cast<std::size_t>(N),
                                                      static_cast<std::size_t>(Hi),
                                                      static_cast<std::size_t>(Wi),
                                                      static_cast<std::size_t>(C)});
    const auto wei_desc =
        HostTensorDescriptor(std::vector<std::size_t>{static_cast<std::size_t>(K),
                                                      static_cast<std::size_t>(Y),
                                                      static_cast<std::size_t>(X),
                                                      static_cast<std::size_t>(C)});
    const auto out_desc =
        HostTensorDescriptor(std::vector<std::size_t>{static_cast<std::size_t>(N),
                                                      static_cast<std::size_t>(Ho),
                                                      static_cast<std::size_t>(Wo),
                                                      static_cast<std::size_t>(K)});
    const auto bias_desc =
        HostTensorDescriptor(std::vector<std::size_t>{static_cast<std::size_t>(K)});

    auto f_k = [&](auto k) {
        for(int n = 0; n < N; ++n)
        {
            for(int ho = 0; ho < Ho; ++ho)
            {
                for(int wo = 0; wo < Wo; ++wo)
                {
                    double v = 0;
                    for(int c = 0; c < C; ++c)
                    {
                        for(int y = 0; y < Y; ++y)
                        {
                            int hi = ho * Stride + y * Dilation - Pad;
                            for(int x = 0; x < X; ++x)
                            {
                                int wi = wo * Stride + x * Dilation - Pad;
                                if(hi >= 0 && hi < Hi && wi >= 0 && wi < Wi)
                                {
                                    double in =
                                        in_ptr[in_desc.GetOffsetFromMultiIndex(n, hi, wi, c)];
                                    double wei =
                                        weight_ptr[wei_desc.GetOffsetFromMultiIndex(k, y, x, c)];

                                    v += in * wei;
                                }
                            }
                        }
                    }

                    v += bias_ptr[bias_desc.GetOffsetFromMultiIndex(k)];

                    v = v > 0 ? v : 0;

                    output_ptr[out_desc.GetOffsetFromMultiIndex(n, ho, wo, k)] = v;
                }
            }
        }
    };

    make_ParallelTensorFunctor(f_k, K)(std::thread::hardware_concurrency());
}

template <int NDimSpatial,
          typename InDataType,
          typename WeiDataType,
          typename OutDataType,
          typename InLayout,
          typename WeiLayout,
          typename OutLayout>
void profile_conv_fwd_bias_relu_atomic_add_impl(int do_verification,
                                                int init_method,
                                                bool do_log,
                                                int nrepeat,
                                                ck::index_t N,
                                                ck::index_t K,
                                                ck::index_t C,
                                                std::vector<ck::index_t> input_spatial_lengths,
                                                std::vector<ck::index_t> filter_spatial_lengths,
                                                std::vector<ck::index_t> output_spatial_lengths,
                                                std::vector<ck::index_t> conv_filter_strides,
                                                std::vector<ck::index_t> conv_filter_dilations,
                                                std::vector<ck::index_t> input_left_pads,
                                                std::vector<ck::index_t> input_right_pads)
{
    const ck::index_t Y = filter_spatial_lengths[0];
    const ck::index_t X = filter_spatial_lengths[1];

    const ck::index_t Hi = input_spatial_lengths[0];
    const ck::index_t Wi = input_spatial_lengths[1];

    const ck::index_t Ho = output_spatial_lengths[0];
    const ck::index_t Wo = output_spatial_lengths[1];

    auto f_host_tensor_descriptor =
        [](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W, auto layout) {
            if constexpr(is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
                         is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
                         is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::value)
            {
                return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
                                            std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
            }
            else if constexpr(is_same<decltype(layout), tensor_layout::convolution::NHWC>::value ||
                              is_same<decltype(layout), tensor_layout::convolution::KYXC>::value ||
                              is_same<decltype(layout), tensor_layout::convolution::NHWK>::value)
            {
                return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
                                            std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
            }
        };

    Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
    Tensor<WeiDataType> wei_k_c_y_x(f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
    Tensor<OutDataType> out_n_k_ho_wo_host_result(
        f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
    Tensor<OutDataType> out_n_k_ho_wo_device_result(
        f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));

    // bias: assume contiguous 1d vector
    Tensor<OutDataType> bias_k(
        HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(K)})));

    std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
    std::cout << "wei_k_c_y_x: " << wei_k_c_y_x.mDesc << std::endl;
    std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo_host_result.mDesc << std::endl;
    std::cout << "bias_k: " << bias_k.mDesc << std::endl;

    switch(init_method)
    {
    case 0: break;
    case 1:
        in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
        wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
        bias_k.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
        break;
    default:
        in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
        wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
        bias_k.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
    }

    using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
    using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
    using OutElementOp = ck::tensor_operation::element_wise::AddRelu;

    if(do_verification)
    {
        cpu_conv_bias_relu_atomic_add(in_n_c_hi_wi.mData.data(),
                                      wei_k_c_y_x.mData.data(),
                                      out_n_k_ho_wo_host_result.mData.data(),
                                      bias_k.mData.data(),
                                      N,
                                      K,
                                      C,
                                      Y,
                                      X,
                                      Hi,
                                      Wi,
                                      Ho,
                                      Wo,
                                      conv_filter_strides[0],
                                      conv_filter_dilations[0],
                                      input_left_pads[0]);
    }

    DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpace());
    DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_k_c_y_x.mDesc.GetElementSpace());
    DeviceMem out_device_buf(sizeof(OutDataType) *
                             out_n_k_ho_wo_device_result.mDesc.GetElementSpace());
    DeviceMem bias_device_buf(sizeof(OutDataType) * bias_k.mDesc.GetElementSpace());

    in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
    wei_device_buf.ToDevice(wei_k_c_y_x.mData.data());
    bias_device_buf.ToDevice(bias_k.mData.data());

    using DeviceConvFwdBiasReluPtr = ck::tensor_operation::device::
        DeviceConvFwdBiasActivationPtr<InElementOp, WeiElementOp, OutElementOp>;

    // add device operator instances
    std::vector<DeviceConvFwdBiasReluPtr> op_ptrs;

    if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, ck::half_t> &&
                 ck::is_same_v<ck::remove_cv_t<WeiDataType>, ck::half_t> &&
                 ck::is_same_v<ck::remove_cv_t<OutDataType>, ck::half_t>)
    {
        ck::tensor_operation::device::device_conv2d_fwd_bias_activation_atomic_add_instance::
            add_device_conv2d_fwd_xdl_c_shuffle_bias_relu_atomic_add_nhwc_kyxc_nhwk_f16_instances(
                op_ptrs);
    }

    if(op_ptrs.size() <= 0)
    {
        throw std::runtime_error("wrong! no device Conv instance found");
    }

    std::string best_conv_name;
    float best_ave_time   = 0;
    float best_tflops     = 0;
    float best_gb_per_sec = 0;

    // profile device Conv instances
    for(auto& op_ptr : op_ptrs)
    {
        auto argument_ptr = op_ptr->MakeArgumentPointer(
            static_cast<const InDataType*>(in_device_buf.GetDeviceBuffer()),
            static_cast<const WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
            static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
            static_cast<const OutDataType*>(bias_device_buf.GetDeviceBuffer()),
            N,
            K,
            C,
            input_spatial_lengths,
            filter_spatial_lengths,
            output_spatial_lengths,
            conv_filter_strides,
            conv_filter_dilations,
            input_left_pads,
            input_right_pads,
            InElementOp{},
            WeiElementOp{},
            OutElementOp{});

        auto invoker_ptr = op_ptr->MakeInvokerPointer();

        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
        {
            std::string conv_name = op_ptr->GetTypeString();

            float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);

            std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;

            std::size_t num_btype =
                sizeof(InDataType) * (N * C * Hi * Wi) + sizeof(WeiDataType) * (K * C * Y * X) +
                sizeof(OutDataType) * (N * K * Ho * Wo) + sizeof(OutDataType) * (K);

            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, " << conv_name << std::endl;

            if(tflops > best_tflops)
            {
                best_conv_name  = conv_name;
                best_tflops     = tflops;
                best_ave_time   = ave_time;
                best_gb_per_sec = gb_per_sec;
            }

            if(do_verification)
            {
                out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());

                check_error(out_n_k_ho_wo_host_result, out_n_k_ho_wo_device_result);

                if(do_log)
                {
                    LogRangeAsType<float>(std::cout << "in : ", in_n_c_hi_wi.mData, ",")
                        << std::endl;
                    LogRangeAsType<float>(std::cout << "wei: ", wei_k_c_y_x.mData, ",")
                        << std::endl;
                    LogRangeAsType<float>(
                        std::cout << "out_host  : ", out_n_k_ho_wo_host_result.mData, ",")
                        << std::endl;
                    LogRangeAsType<float>(
                        std::cout << "out_device: ", out_n_k_ho_wo_device_result.mData, ",")
                        << std::endl;
                }
            }
        }
    }

    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
              << best_gb_per_sec << " GB/s, " << best_conv_name << std::endl;
}

} // namespace profiler
} // namespace ck
