#pragma once

#include "../jit_kernels/impls/sm90_fp8_gemm_1d1d.hpp"
#include "../jit_kernels/impls/sm90_fp8_gemm_1d2d.hpp"
#include "../jit_kernels/impls/sm100_fp8_gemm_1d1d.hpp"
#include "../jit_kernels/impls/sm100_fp8_gemm_1d2d.hpp"

#include "layout.hpp"

namespace deep_gemm::attention {

static void fp8_gemm_nt_skip_head_mid(const std::pair<torch::Tensor, torch::Tensor>& a,
                                      const std::pair<torch::Tensor, torch::Tensor>& b,
                                      const torch::Tensor& d,
                                      const std::tuple<int, int, int> &head_splits,
                                      std::optional<std::tuple<int, int, int>> recipe,
                                      const std::string& compiled_dims,
                                      const bool& disable_ue8m0_cast) {
    // Shape must be `[M, K] @ [N, K].T`
    const auto& major_a = get_major_type_ab(a.first);
    const auto& major_b = get_major_type_ab(b.first);
    if (fp8_requires_k_major()) {
        DG_HOST_ASSERT(major_a == cute::UMMA::Major::K);
        DG_HOST_ASSERT(major_b == cute::UMMA::Major::K);
    }

    // D must be N-major
    check_major_type_cd(d);

    // Type and shape checks
    const auto& [m , k ] = get_shape<2>(a.first);
    const auto& [n , k_] = get_shape<2>(b.first);
    const auto& [m_, n_] = get_shape<2>(d);
    DG_HOST_ASSERT(m == m_ and k == k_);
    DG_HOST_ASSERT(n > 0 and k > 0);
    DG_HOST_ASSERT(a.first.scalar_type() == torch::kFloat8_e4m3fn);
    DG_HOST_ASSERT(b.first.scalar_type() == torch::kFloat8_e4m3fn);
    DG_HOST_ASSERT(d.scalar_type() == torch::kBFloat16 or d.scalar_type() == torch::kFloat);

    // Check head splits and N
    const auto& [left, mid, right] = head_splits;
    DG_HOST_ASSERT(n % (left + right) == 0 and n_ == n + n / (left + right) * mid);

    // Do nothing if the problem is empty
    if (m == 0)
        return;

    // Transform SFA and SFB into compute-required layout
    if (not recipe.has_value())
        recipe = get_default_recipe(a.second.scalar_type(), b.second.scalar_type());
    DG_HOST_ASSERT(recipe.value() == std::make_tuple(1, 1, 128) or recipe.value() == std::make_tuple(1, 128, 128));
    const auto& sfa = layout::transform_sf_into_required_layout(a.second, m, k, recipe.value(), std::nullopt,  true, disable_ue8m0_cast);
    const auto& sfb = layout::transform_sf_into_required_layout(b.second, n, k, recipe.value(), std::nullopt, false, disable_ue8m0_cast);

    // Dispatch into different implements
    const auto& arch_major = device_runtime->get_arch_major();
    const auto& epilogue_type = fmt::format("EpilogueHeadSplits<{}, {}, {}>", left, mid, right);
    if (arch_major == 9 and sfa.scalar_type() == torch::kFloat and std::get<1>(recipe.value()) != 1) {
        sm90_fp8_gemm_1d2d(a.first, sfa, b.first, sfb, std::nullopt, d, m, n, k, major_a, major_b, compiled_dims, epilogue_type);
    } else if (arch_major == 10 and sfa.scalar_type() == torch::kInt) {
        sm100_fp8_gemm_1d1d(a.first, sfa, b.first, sfb, std::nullopt, d, m, n, k, major_a, major_b, compiled_dims, epilogue_type);
    } else if (arch_major == 10 and sfa.scalar_type() == torch::kFloat) {
        sm100_fp8_gemm_1d2d(a.first, sfa, b.first, sfb, std::nullopt, d, m, n, k, major_a, major_b, compiled_dims, epilogue_type);
    } else {
        DG_HOST_UNREACHABLE("Unsupported architecture or scaling factor types");
    }
}

static void register_apis(pybind11::module_& m) {
    m.def("fp8_gemm_nt_skip_head_mid", &fp8_gemm_nt_skip_head_mid,
          py::arg("a"), py::arg("b"), py::arg("d"), py::arg("head_splits"),
          py::arg("recipe") = std::nullopt,
          py::arg("compiled_dims") = "nk",
          py::arg("disable_ue8m0_cast") = false);
}

} // namespace deep_gemm::attention