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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck_tile/core.hpp"
 #include "ck_tile/ops/common.hpp"
 #include "ck_tile/ops/elementwise/pipeline/elementwise_pipeline_problem.hpp"
 #include "ck_tile/ops/elementwise/pipeline/elementwise_pipeline_default_policy.hpp"
 namespace ck_tile {
  
 template <typename Problem_, typename Policy_>
 struct ElementWiseKernel
 {
     using Problem = ck_tile::remove_cvref_t<Problem_>;
     using Policy  = ck_tile::remove_cvref_t<Policy_>;
  
     using XDataType            = ck_tile::remove_cvref_t<typename Problem::XDataType>;
     using ComputeDataType      = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
     using YDataType            = ck_tile::remove_cvref_t<typename Problem::YDataType>;
     using ElementWiseOperation = ck_tile::remove_cvref_t<typename Problem::ElementWiseOperation>;
  
     static constexpr index_t kBlockSize = Problem::BlockShape::kBlockSize;
     CK_TILE_HOST static constexpr auto BlockSize()
     {
         return is_wave32() ? kBlockSize / 2 : kBlockSize;
     }
  
     template <typename... XDataType, typename Dims>
     CK_TILE_DEVICE void operator()(const Dims lens,
                                    const Dims input_strides,
                                    const Dims output_strides,
                                    const tuple<XDataType...>& input_tensors,
                                    YDataType* p_y) const
     {
         using S = typename Problem::BlockShape;
  
         // Setup block-level coordinates and transforms
         const index_t iM           = get_block_id() * S::kBlockM;
         const auto merge_transform = make_merge_transform(lens);
  
         // Load all input tiles into registers.
         // The lambda structure here is intended to minimize the lifetime
         // of intermediate objects (views, windows) used for loading.
         const auto x_tiles = ck_tile::generate_tuple(
             [&](auto i) {
                 const auto tensor_view = make_naive_tensor_view<address_space_enum::global>(
                     input_tensors.get(i), lens, input_strides, number<S::kVectorM>{}, number<1>{});
  
                 const auto transformed_tensor = pad_tensor_view(
                     transform_tensor_view(tensor_view,
                                           ck_tile::make_tuple(merge_transform),
                                           ck_tile::make_tuple(make_index_sequence<Dims::size()>{}),
                                           ck_tile::make_tuple(sequence<0>{})),
                     ck_tile::make_tuple(number<S::kBlockM>{}),
                     sequence<Problem::kPad>{});
  
                 const auto x_window =
                     make_tile_window(transformed_tensor,
                                      ck_tile::make_tuple(number<S::kBlockM>{}),
                                      {iM},
                                      Policy::template MakeXBlockTileDistribution<Problem>());
  
                 return load_tile(x_window);
             },
             number<sizeof...(XDataType)>{});
  
         // Setup output tile in registers.
         const auto& x_tile0 = x_tiles.get(number<0>{});
         auto y_tile = make_static_distributed_tensor<YDataType>(x_tile0.get_tile_distribution());
  
         // Perform element-wise computation.
         const auto spans = x_tile0.get_distributed_spans();
         sweep_tile_span(spans[number<0>{}], [&](auto idx) {
             const auto tile_idx = make_tuple(idx);
             apply(
                 [&](auto&&... tiles) {
                     ElementWiseOperation{}(y_tile(tile_idx),
                                            type_convert<ComputeDataType>(tiles[tile_idx])...);
                 },
                 x_tiles);
         });
  
         // Setup output window and store the result tile.
         const auto y_m_n = make_naive_tensor_view<address_space_enum::global>(
             p_y, lens, output_strides, number<S::kVectorM>{});
  
         const auto transformed_y_m_n = pad_tensor_view(
             transform_tensor_view(y_m_n,
                                   ck_tile::make_tuple(merge_transform),
                                   ck_tile::make_tuple(make_index_sequence<Dims::size()>{}),
                                   ck_tile::make_tuple(sequence<0>{})),
             ck_tile::make_tuple(number<S::kBlockM>{}),
             sequence<Problem::kPad>{});
  
         auto y_window = make_tile_window(transformed_y_m_n,
                                          make_tuple(number<S::kBlockM>{}),
                                          {iM},
                                          y_tile.get_tile_distribution());
  
         store_tile(y_window, cast_tile<YDataType>(y_tile));
     }
  
     template <typename... Ints>
     CK_TILE_HOST static bool IsSupportedArgument(const ck_tile::tuple<Ints...>& input_sizes)
     {
         int total_elements  = 1;
         const auto kVectorM = Problem_::BlockShape::kVectorM;
  
         apply([&](auto&&... args) { ((total_elements *= args), ...); }, input_sizes);
  
         if((total_elements % kVectorM) != 0)
         {
             if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
             {
                 CK_TILE_ERROR("Conditions not met: total number of input elements (",
                               total_elements,
                               ") should be multiple of the vectorization size (",
                               kVectorM,
                               ")");
             }
             return false;
         }
  
         return true;
     }
 };
  
 } // namespace ck_tile