#ifndef DEVICE_REDUCE_BLOCKWISE_SECOND_CALL_HPP
#define DEVICE_REDUCE_BLOCKWISE_SECOND_CALL_HPP

#include <iostream>
#include <sstream>
#include "device.hpp"
#include "device_reduce.hpp"
#include "device_reduce_common.hpp"
#include "gridwise_2d_reduction_blockwise.hpp"

namespace ck {
namespace tensor_operation {
namespace device {

template <typename InDataType,
          typename AccDataType,
          typename OutDataType,
          index_t Rank,
          index_t NumReduceDim,
          typename ReduceOperation,
          typename InElementwiseOperation,
          typename AccElementwiseOperation,
          bool PropagateNan,
          bool NeedIndices,
          index_t BlockSize,
          index_t MThreadClusterSize,
          index_t KThreadClusterSize,
          index_t MThreadSliceSize,
          index_t KThreadSliceSize,
          index_t InSrcVectorDim,
          index_t InSrcVectorSize,
          index_t OutDstVectorSize>
struct DeviceReduceBlockWiseSecondCall
    : public DeviceReduce<InElementwiseOperation, AccElementwiseOperation>
{
    static_assert(Rank <= 6, "Bigger Rank size is not supported!");
    static_assert(BlockSize == MThreadClusterSize * KThreadClusterSize,
                  "Invalid thread cluster size assignments!");

    static_assert((InSrcVectorDim == 1 && KThreadSliceSize % InSrcVectorSize == 0) &&
                      (MThreadSliceSize % OutDstVectorSize == 0),
                  "Invalid thread slice sizes and/or vector sizes configuration, please check!");

    using IndexDataType = int32_t;

    static constexpr bool BetaIsZero = NeedIndices;

    static_assert(
        std::is_same<InDataType, AccDataType>::value,
        "InDataType and AccDataType should be the same to use DEviceReduceBlockWiseSecondCall!");

    static constexpr index_t NumInvariantDim = Rank - NumReduceDim;

    static constexpr index_t numDstDim = (NumInvariantDim == 0) ? 1 : NumInvariantDim;

    static constexpr int M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
    static constexpr int K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;

    static auto MakeSrc2dDescriptor(const std::vector<int>& inLengths,
                                    const std::vector<int>& inStrides)
    {
        const auto tupleSrcLengths = make_tuple_from_array(inLengths, Number<2>{});
        const auto tupleSrcStrides = make_tuple_from_array(inStrides, Number<2>{});

        const auto in_grid_desc_m_k =
            make_naive_tensor_descriptor(tupleSrcLengths, tupleSrcStrides);

        const auto invariantLength = in_grid_desc_m_k.GetLength(Number<0>{});
        const auto reduceLength    = in_grid_desc_m_k.GetLength(Number<1>{});

        const auto inPad_M =
            math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
        const auto inPad_K =
            math::integer_least_multiple(reduceLength, K_BlockTileSize) - reduceLength;

        auto in_grid_desc_m_k_padded = transform_tensor_descriptor(
            in_grid_desc_m_k,
            make_tuple(make_right_pad_transform(invariantLength, inPad_M),
                       make_right_pad_transform(reduceLength, inPad_K)),
            make_tuple(Sequence<0>{}, Sequence<1>{}),
            make_tuple(Sequence<0>{}, Sequence<1>{}));

        return (in_grid_desc_m_k_padded);
    };

    static auto MakeDst1dDescriptor(const std::vector<int>& outLengths,
                                    const std::vector<int>& outStrides)
    {
        const auto tupleDstLengths = make_tuple_from_array(outLengths, Number<numDstDim>{});
        const auto tupleDstStrides = make_tuple_from_array(outStrides, Number<numDstDim>{});

        auto outDesc = make_naive_tensor_descriptor(tupleDstLengths, tupleDstStrides);

        auto out_grid_desc_m = transform_tensor_descriptor(
            outDesc,
            make_tuple(make_merge_transform(tupleDstLengths)),
            make_tuple(typename arithmetic_sequence_gen<0, numDstDim, 1>::type{}),
            make_tuple(Sequence<0>{}));

        const auto invariantLength = out_grid_desc_m.GetLength(Number<0>{});

        const auto outPad =
            math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;

        auto out_grid_desc_m_padded = transform_tensor_descriptor(
            out_grid_desc_m,
            make_tuple(make_right_pad_transform(invariantLength, outPad)),
            make_tuple(Sequence<0>{}),
            make_tuple(Sequence<0>{}));
        return (out_grid_desc_m_padded);
    };

    struct Argument : public BaseArgument
    {
        Argument(const std::vector<int>& inLengths,
                 const std::vector<int>& inStrides,
                 const std::vector<int>& outLengths,
                 const std::vector<int>& outStrides,
                 float alpha,
                 float beta,
                 const InDataType* in_dev,
                 OutDataType* out_dev,
                 IndexDataType* out_indices_dev,
                 AccDataType* workspace_dev,
                 const InElementwiseOperation& in_elementwise_op,
                 const AccElementwiseOperation& acc_elementwise_op)
            : inLengths_(inLengths),
              inStrides_(inStrides),
              outLengths_(outLengths),
              outStrides_(outStrides),
              in_dev_{in_dev},
              out_dev_{out_dev},
              out_indices_dev_{out_indices_dev},
              in_elementwise_op_(in_elementwise_op),
              acc_elementwise_op_(acc_elementwise_op)
        {
            alpha_ = type_convert<AccDataType>(alpha);
            beta_  = type_convert<AccDataType>(beta);

            invariant_total_length = inLengths[0];
            reduce_total_length    = inLengths[1];

            invariant_lowest_length = inLengths[0];
            reduce_lowest_length    = inLengths[1];

            gridSize = math::integer_least_multiple(invariant_total_length, M_BlockTileSize) /
                       M_BlockTileSize;

            size_t ws_buf2_bytes_offset = math::integer_least_multiple(
                invariant_total_length * reduce_total_length * sizeof(AccDataType), 64);

            if constexpr(NeedIndices)
                workspace_indices_dev_ = reinterpret_cast<index_t*>(
                    reinterpret_cast<char*>(workspace_dev) + ws_buf2_bytes_offset);
            else
                workspace_indices_dev_ = nullptr;
        }

        std::vector<int> inLengths_;
        std::vector<int> inStrides_;
        std::vector<int> outLengths_;
        std::vector<int> outStrides_;

        AccDataType alpha_;
        AccDataType beta_;

        const InDataType* in_dev_;
        OutDataType* out_dev_;
        IndexDataType* out_indices_dev_;
        IndexDataType* workspace_indices_dev_;

        InElementwiseOperation in_elementwise_op_;
        AccElementwiseOperation acc_elementwise_op_;

        int invariant_lowest_length;
        int reduce_lowest_length;
        size_t invariant_total_length;
        size_t reduce_total_length;

        size_t gridSize;
    };

    struct Invoker : public BaseInvoker
    {
        float Run(const Argument& arg, int nrepeat = 1, hipStream_t stream_id = nullptr, bool measure_time = false)
        {
            const auto in_grid_desc_m_k = DeviceReduceBlockWiseSecondCall::MakeSrc2dDescriptor(
                arg.inLengths_, arg.inStrides_);
            const auto out_grid_desc_m = DeviceReduceBlockWiseSecondCall::MakeDst1dDescriptor(
                arg.outLengths_, arg.outStrides_);
            using InGridDesc_M_K = decltype(in_grid_desc_m_k);
            using OutGridDesc_M  = decltype(out_grid_desc_m);

            using GridwiseReduce = GridwiseReduction_mk_to_m_blockwise<InDataType,
                                                                       OutDataType,
                                                                       AccDataType,
                                                                       IndexDataType,
                                                                       InGridDesc_M_K,
                                                                       OutGridDesc_M,
                                                                       ReduceOperation,
                                                                       InElementwiseOperation,
                                                                       AccElementwiseOperation,
                                                                       PropagateNan,
                                                                       BetaIsZero,
                                                                       BlockSize,
                                                                       MThreadClusterSize,
                                                                       KThreadClusterSize,
                                                                       MThreadSliceSize,
                                                                       KThreadSliceSize,
                                                                       InSrcVectorDim,
                                                                       InSrcVectorSize,
                                                                       OutDstVectorSize>;

            float avg_time = 0;

            const auto kernel = kernel_reduce_blockwise_second_call<GridwiseReduce,
                                                                    NeedIndices,
                                                                    InDataType,
                                                                    OutDataType,
                                                                    AccDataType,
                                                                    IndexDataType,
                                                                    InGridDesc_M_K,
                                                                    OutGridDesc_M,
                                                                    InElementwiseOperation,
                                                                    AccElementwiseOperation>;

            avg_time = launch_and_time_kernel(kernel,
                                              nrepeat,
                                              dim3(arg.gridSize),
                                              dim3(BlockSize),
                                              0,
                        stream_id,
                        measure_time,
                                              in_grid_desc_m_k,
                                              out_grid_desc_m,
                                              arg.in_elementwise_op_,
                                              arg.acc_elementwise_op_,
                                              arg.alpha_,
                                              arg.in_dev_,
                                              arg.beta_,
                                              arg.out_dev_,
                                              arg.workspace_indices_dev_,
                                              arg.out_indices_dev_);

            return (avg_time);
        };

        float Run(const BaseArgument* p_arg, int nrepeat = 1, hipStream_t stream_id = nullptr, bool measure_time = false) override
        {
            return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat, stream_id, measure_time);
        };
    };

    bool IsSupportedArgument(const BaseArgument* p_arg) override
    {
        const Argument* pArg = dynamic_cast<const Argument*>(p_arg);

        if constexpr(InSrcVectorDim == 0)
            return (false);

        if(pArg->reduce_lowest_length % InSrcVectorSize != 0)
            return (false);

        // To improve
        if(pArg->invariant_lowest_length % OutDstVectorSize != 0)
            return (false);

        // cases with very small reduce_total_length should be handled by the ThreadWise method
        if(pArg->reduce_total_length / KThreadSliceSize < 2)
            return (false);

        return (true);
    };

    std::unique_ptr<BaseArgument>
    MakeArgumentPointer(const std::vector<int> inLengths,
                        const std::vector<int> inStrides,
                        const std::vector<int> outLengths,
                        const std::vector<int> outStrides,
                        const std::vector<int> reduceDims,
                        float alpha,
                        float beta,
                        const void* in_dev,
                        void* out_dev,
                        void* out_indices_dev,
                        void* workspace_dev,
                        const InElementwiseOperation in_elementwise_op,
                        const AccElementwiseOperation acc_elementwise_op) override
    {
        (void)reduceDims;

        return std::make_unique<Argument>(inLengths,
                                          inStrides,
                                          outLengths,
                                          outStrides,
                                          alpha,
                                          beta,
                                          static_cast<const InDataType*>(in_dev),
                                          static_cast<OutDataType*>(out_dev),
                                          static_cast<IndexDataType*>(out_indices_dev),
                                          static_cast<AccDataType*>(workspace_dev),
                                          in_elementwise_op,
                                          acc_elementwise_op);
    };

    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
    {
        return std::make_unique<Invoker>();
    };

    std::string GetTypeString() const override
    {
        auto str = std::stringstream();

        // clang-format off
        str << "DeviceReduceBlockWiseSecondCall<" << BlockSize << ",";
        str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
        str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
        str << "InSrcVectorDim_" << InSrcVectorDim << "_InSrcVectorSize_" << InSrcVectorSize << "_OutDstVectorSize_" << OutDstVectorSize << ">";
        // clang-format on

        return str.str();
    }
};

} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
