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#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/fast_math.h"
#include "cutlass/kernel_hardware_info.hpp"
#include "cute/arch/cluster_sm90.hpp"
#include "cutlass/arch/mma_sm90.h"
#include "cutlass/epilogue/collective/detail.hpp"
#include "cutlass/gemm/gemm.h"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/gemm/kernel/sm90_tile_scheduler.hpp"
#include "cutlass/trace.h"

#include "cute/tensor.hpp"

///////////////////////////////////////////////////////////////////////////////

namespace cutlass::gemm::kernel {

namespace detail {

// IF_SWAP_AB<T>::value will be true only if:
//   class T has member SwapAB and T::SwapAB is true
template <typename T, typename = void>
struct IF_SWAP_AB { static constexpr bool value = false; };

template <typename T>
struct IF_SWAP_AB <T, void_t<decltype(T::SwapAB)>>
{ static constexpr bool value = T::SwapAB; };

} // namespace

///////////////////////////////////////////////////////////////////////////////

template <
  class ProblemShape_,
  class CollectiveMainloop_,
  class CollectiveEpilogue_,
  class TileScheduler_
>
class GemmUniversal<
  ProblemShape_,
  CollectiveMainloop_,
  CollectiveEpilogue_,
  TileScheduler_,
  cute::enable_if_t<cute::is_base_of_v<KernelTma, typename CollectiveMainloop_::DispatchPolicy::Schedule>>>
{
public:
  //
  // Type Aliases
  //
  using ProblemShape = ProblemShape_;
  static_assert(rank(ProblemShape{}) == 3 or rank(ProblemShape{}) == 4,
    "ProblemShape{} should be <M,N,K> or <M,N,K,L>");

  // Mainloop derived types
  using CollectiveMainloop = CollectiveMainloop_;
  using TileShape = typename CollectiveMainloop::TileShape;
  using TiledMma  = typename CollectiveMainloop::TiledMma;
  using ArchTag   = typename CollectiveMainloop::ArchTag;
  using ElementA  = typename CollectiveMainloop::ElementA;
  using StrideA   = typename CollectiveMainloop::StrideA;
  using ElementB  = typename CollectiveMainloop::ElementB;
  using StrideB   = typename CollectiveMainloop::StrideB;
  using DispatchPolicy = typename CollectiveMainloop::DispatchPolicy;
  using ElementAccumulator = typename CollectiveMainloop::ElementAccumulator;
  using ClusterShape = typename DispatchPolicy::ClusterShape;
  using MainloopArguments = typename CollectiveMainloop::Arguments;
  using MainloopParams = typename CollectiveMainloop::Params;
  static_assert(ArchTag::kMinComputeCapability >= 90);

  // Epilogue derived types
  using CollectiveEpilogue = CollectiveEpilogue_;
  using ElementC = typename CollectiveEpilogue::ElementC;
  using StrideC  = typename CollectiveEpilogue::StrideC;
  using ElementD = typename CollectiveEpilogue::ElementD;
  using StrideD  = typename CollectiveEpilogue::StrideD;
  using EpilogueArguments = typename CollectiveEpilogue::Params;
  using EpilogueParams = typename CollectiveEpilogue::Params;
  static_assert(cute::is_same_v<ElementAccumulator, typename CollectiveEpilogue::ElementAccumulator>,
    "Mainloop and epilogue do not agree on accumulator value type.");

  static_assert(cute::is_void_v<TileScheduler_> or cute::is_same_v<TileScheduler_, PersistentScheduler>,
    "TMA kernel does not support specializing the tile scheduler.");
  using TileScheduleTag = TileScheduler_;
  using TileScheduler = typename detail::TileSchedulerSelector<
    TileScheduler_, ArchTag, TileShape, ClusterShape>::Scheduler;
  using TileSchedulerArguments = typename TileScheduler::Arguments;

  static constexpr int SharedStorageSize = static_cast<int>(cute::max(
      sizeof(typename CollectiveMainloop::SharedStorage),
      sizeof(typename CollectiveEpilogue::SharedStorage)));

  static constexpr uint32_t MaxThreadsPerBlock = size(TiledMma{});
  static constexpr uint32_t MinBlocksPerMultiprocessor = 1;

  // Device side arguments
  struct Arguments {
    GemmUniversalMode mode{};
    ProblemShape problem_shape{};
    MainloopArguments mainloop{};
    EpilogueArguments epilogue{};
    KernelHardwareInfo hw_info{};
    TileSchedulerArguments scheduler{};
  };

  // Kernel entry point API
  struct Params {
    GemmUniversalMode mode;
    ProblemShape problem_shape;
    MainloopParams mainloop;
    EpilogueParams epilogue;
  };

  //
  // Methods
  //

  // Convert to underlying arguments. In this case, a simple copy for the aliased type.
  static
  Params
  to_underlying_arguments(Arguments const& args, void* workspace) {
    (void) workspace;
    auto problem_shape = args.problem_shape;
    if constexpr (detail::IF_SWAP_AB<CollectiveMainloop>::value) {
      // swap M/N
      get<0>(problem_shape) = get<1>(args.problem_shape);
      get<1>(problem_shape) = get<0>(args.problem_shape);
    }
    return {
      args.mode,
      problem_shape,
      CollectiveMainloop::to_underlying_arguments(args.problem_shape, args.mainloop, workspace),
      CollectiveEpilogue::to_underlying_arguments(args.problem_shape, args.epilogue, workspace)
    };
  }

  CUTLASS_HOST_DEVICE static
  bool
  can_implement(Arguments const& args) {
    bool implementable = (args.mode == GemmUniversalMode::kGemm) or
        (args.mode == GemmUniversalMode::kBatched && rank(ProblemShape{}) == 4);
    if (!implementable) {
      CUTLASS_TRACE_HOST("  CAN IMPLEMENT: Arguments or Problem Shape don't meet the requirements.\n");
      return implementable;
    }
    implementable &= CollectiveMainloop::can_implement(args.problem_shape, args.mainloop);
    implementable &= CollectiveEpilogue::can_implement(args.problem_shape, args.epilogue);
    return implementable;
  }

  static int
  get_workspace_size(Arguments const& args) {
    return 0;
  }

  static cutlass::Status
  initialize_workspace(Arguments const& args, void* workspace = nullptr, cudaStream_t stream = nullptr) {
    return Status::kSuccess;
  }

  // Computes the kernel launch grid shape based on runtime parameters
  static dim3
  get_grid_shape(Params const& params) {
    auto cluster_shape = ClusterShape{};
    auto tile_shape = TileShape{};
    auto problem_shape_MNKL = append<4>(params.problem_shape, Int<1>{});
    return TileScheduler::get_tiled_cta_shape_mnl(
        problem_shape_MNKL, tile_shape, cluster_shape);
  }

  static dim3
  get_block_shape() {
    return dim3(MaxThreadsPerBlock, 1, 1);
  }

  CUTLASS_DEVICE
  void
  operator()(Params const& params, char* smem_buf) {
    using namespace cute;
    using X = Underscore;

    // Any Tensor Op MMA Atom in the WGMMA ISA is arch conditional to sm90a.
    #if ! defined(__CUDA_ARCH_FEAT_SM90_ALL)
      if constexpr(size<0>(typename TiledMma::AtomShape_MNK{}) == 64) {
        printf("ERROR : Arch conditional MMA instruction used without targeting sm90a compute capability. Aborting.\n");
        return;
      }
    #endif

    // Preconditions
    static_assert(rank(StrideA{}) == 3, "StrideA must be rank-3: [M, K, L]. If batch mode is not needed, set L stride to Int<0>.");
    static_assert(rank(StrideB{}) == 3, "StrideB must be rank-3: [N, K, L]. If batch mode is not needed, set L stride to Int<0>.");
    static_assert(rank(StrideC{}) == 3, "StrideC must be rank-3: [M, N, L]. If batch mode is not needed, set L stride to Int<0>.");
    static_assert(rank(StrideD{}) == 3, "StrideD must be rank-3: [M, N, L]. If batch mode is not needed, set L stride to Int<0>.");

    int thread_idx = int(threadIdx.x);
    int warp_idx   = canonical_warp_idx_sync();
    int lane_predicate = cute::elect_one_sync();
    uint32_t block_rank_in_cluster = cute::block_rank_in_cluster();

    // Issue Tma Descriptor Prefetch from a single thread
    if ((warp_idx == 0) && lane_predicate) {
      CollectiveMainloop::prefetch_tma_descriptors(params.mainloop);
    }

    // Separate out problem shape for convenience
    // Optionally append 1s until problem shape is rank-4 in case its is only rank-3 (MNK)
    auto problem_shape_MNKL = append<4>(params.problem_shape, Int<1>{});
    auto M = get<0>(problem_shape_MNKL);
    auto N = get<1>(problem_shape_MNKL);
    auto K = get<2>(problem_shape_MNKL);
    auto L = get<3>(problem_shape_MNKL);

    // TMA requires special handling of strides to deal with coord codomain mapping
    // Represent the full tensors -- get these from TMA
    Tensor mA_mkl = params.mainloop.tma_load_a.get_tma_tensor(make_shape(M,K,L));                            // (m,k,l)
    Tensor mB_nkl = params.mainloop.tma_load_b.get_tma_tensor(make_shape(N,K,L));                            // (n,k,l)

    // Get the appropriate blocks for this thread block -- potential for thread block locality
    auto blk_shape = TileShape{};                                                                // (BLK_M,BLK_N,BLK_K)
    auto blk_coord = make_coord(_,_,_);                                                   // (m,n,k) -- defer the slice

    // Make tiled views
    Tensor gA_mkl = local_tile(mA_mkl, blk_shape, blk_coord, Step<_1, X,_1>{});                  // (BLK_M,BLK_K,m,k,l)
    Tensor gB_nkl = local_tile(mB_nkl, blk_shape, blk_coord, Step< X,_1,_1>{});                  // (BLK_N,BLK_K,n,k,l)

    // Compute m_coord, n_coord, and l_coord with their post-tiled shapes
    auto m_coord = idx2crd(int(blockIdx.x), shape<2>(gA_mkl));
    auto n_coord = idx2crd(int(blockIdx.y), shape<2>(gB_nkl));
    auto l_coord = idx2crd(int(blockIdx.z), shape<4>(gB_nkl));
    auto output_tile_coord = make_coord(m_coord, n_coord, _, l_coord);

    // Slice with m_coord and n_coord
    Tensor gA = gA_mkl(_,_,m_coord,_,l_coord);                                                       // (BLK_M,BLK_K,k)
    Tensor gB = gB_nkl(_,_,n_coord,_,l_coord);                                                       // (BLK_N,BLK_K,k)

    // Allocate the tiled_mma and the accumulators for the (M,N) blk_shape
    TiledMma tiled_mma;
    Tensor accumulators = partition_fragment_C(tiled_mma, take<0,2>(blk_shape));                   // (MMA,MMA_M,MMA_N)

    auto k_tile_iter  = cute::make_coord_iterator(shape<2>(gA));
    auto k_tile_count = size<2>(gA);

    // Perform the collective scoped MMA
    CollectiveMainloop collective_mma;
    collective_mma(
      gA, params.mainloop.tma_load_a,
      gB, params.mainloop.tma_load_b,
      accumulators,
      k_tile_iter, k_tile_count,
      thread_idx,
      block_rank_in_cluster,
      smem_buf,
      params.mainloop
    );

    constexpr int BLK_M_RANK = rank<0>(blk_shape);
    bool m_oob = int(blockIdx.x) >= size<2>(gA_mkl);
    auto m_max_coord = unwrap(cute::transform(make_seq<BLK_M_RANK>{}, [&](auto i) {
        return  m_oob ? 0 : get<i>(M) - get<0,i>(blk_shape) * get<i>(m_coord);
      }));

    constexpr int BLK_N_RANK = rank<1>(blk_shape);
    bool n_oob = int(blockIdx.y) >= size<2>(gB_nkl);
    auto n_max_coord = unwrap(cute::transform(make_seq<BLK_N_RANK>{}, [&](auto i) {
        return  n_oob ? 0 : get<i>(N) - get<1,i>(blk_shape) * get<i>(n_coord);
      }));
    auto residue_mnk = make_tuple(m_max_coord, n_max_coord, Int<0>{});

    // Epilogue and write to gD
    CollectiveEpilogue epilogue{params.epilogue};
    epilogue(
      problem_shape_MNKL,
      blk_shape,
      output_tile_coord,
      accumulators,
      tiled_mma,
      residue_mnk,
      thread_idx,
      smem_buf
    );
  }
};

///////////////////////////////////////////////////////////////////////////////

} // namespace cutlass::gemm::kernel
