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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck_tile/core/config.hpp"
 #include "ck_tile/core/numeric/integer.hpp"
 #include "ck_tile/core/numeric/integral_constant.hpp"
 #include "ck_tile/core/algorithm/coordinate_transform.hpp"
 #include "ck_tile/core/container/container_helper.hpp"
 #include "ck_tile/core/numeric/math.hpp"
 #include "ck_tile/core/utility/functional.hpp"
 #include "ck_tile/core/utility/type_traits.hpp"
 #include "ck_tile/core/tensor/tile_distribution.hpp"
 #include "ck_tile/core/container/thread_buffer.hpp"
  
 namespace ck_tile {
  
 template <typename DataType_, typename StaticTileDistribution_>
 struct static_distributed_tensor
 {
     using DataType               = remove_cvref_t<DataType_>;
     using StaticTileDistribution = remove_cvref_t<StaticTileDistribution_>;
  
     static_assert(StaticTileDistribution::is_static(),
                   "wrong! StaticTileDistribution should be known at compile tile");
  
     using ThreadTensorDesc =
         remove_cvref_t<decltype(StaticTileDistribution{}.get_ys_to_d_descriptor())>;
     static constexpr index_t PackedSize =
         ck_tile::numeric_traits<remove_cvref_t<DataType>>::PackedSize;
  
     static constexpr index_t kThreadElementSpaceSize = ThreadTensorDesc{}.get_element_space_size();
     static_assert(0 < kThreadElementSpaceSize, "Make sure tile distribution is valid");
  
     CK_TILE_HOST_DEVICE static constexpr auto get_num_of_dimension()
     {
         return StaticTileDistribution::get_num_of_dimension_x();
     }
  
     CK_TILE_HOST_DEVICE static constexpr auto get_lengths()
     {
         return StaticTileDistribution::get_lengths();
     }
  
     CK_TILE_HOST_DEVICE static constexpr auto get_tile_distribution()
     {
         return StaticTileDistribution{};
     }
  
     CK_TILE_HOST_DEVICE static constexpr auto get_distributed_spans()
     {
         return StaticTileDistribution::get_distributed_spans();
     }
  
     CK_TILE_HOST_DEVICE void initialize(const DataType& x) { thread_buf_.initialize(x); }
  
     CK_TILE_HOST_DEVICE constexpr const auto& get_thread_buffer() const { return thread_buf_; }
  
     CK_TILE_HOST_DEVICE constexpr auto& get_thread_buffer() { return thread_buf_; }
  
     CK_TILE_HOST_DEVICE static constexpr index_t get_thread_buffer_size()
     {
         return kThreadElementSpaceSize / PackedSize;
     }
  
     template <index_t... YSliceOrigins, index_t... YSliceLengths>
     CK_TILE_HOST_DEVICE auto get_y_sliced_thread_data(sequence<YSliceOrigins...>,
                                                       sequence<YSliceLengths...>) const
     {
         static_assert(sizeof...(YSliceOrigins) == StaticTileDistribution::NDimY &&
                           sizeof...(YSliceLengths) == StaticTileDistribution::NDimY,
                       "wrong!");
  
         constexpr auto sliced_thread_tensor_desc =
             make_naive_tensor_descriptor_packed(make_tuple(YSliceLengths...));
  
         thread_buffer<DataType, sliced_thread_tensor_desc.get_element_space_size()>
             sliced_thread_data;
  
         static_ford<sequence<YSliceLengths...>>{}([&](auto idx) {
             constexpr auto idx_ys = idx + sequence<YSliceOrigins...>{};
  
             sliced_thread_data(
                 number<sliced_thread_tensor_desc.calculate_offset(idx) / PackedSize>{}) =
                 thread_buf_[number<ThreadTensorDesc{}.calculate_offset(idx_ys) / PackedSize>{}];
         });
  
         return sliced_thread_data;
     }
  
     template <index_t... YSliceOrigins, index_t... YSliceLengths, typename SlicedThreadData>
     CK_TILE_HOST_DEVICE void set_y_sliced_thread_data(sequence<YSliceOrigins...>,
                                                       sequence<YSliceLengths...>,
                                                       const SlicedThreadData& sliced_thread_data)
     {
         static_assert(sizeof...(YSliceOrigins) == StaticTileDistribution::NDimY &&
                           sizeof...(YSliceLengths) == StaticTileDistribution::NDimY,
                       "wrong!");
  
         constexpr auto sliced_thread_tensor_desc =
             make_naive_tensor_descriptor_packed(make_tuple(YSliceLengths...));
  
         static_ford<sequence<YSliceLengths...>>{}([&](auto idx) {
             constexpr auto idx_ys = idx + sequence<YSliceOrigins...>{};
  
             thread_buf_(number<ThreadTensorDesc{}.calculate_offset(idx_ys) / PackedSize>{}) =
                 sliced_thread_data[number<sliced_thread_tensor_desc.calculate_offset(idx) /
                                           PackedSize>{}];
         });
     }
  
     template <typename TileDistributedIndices>
     CK_TILE_HOST_DEVICE constexpr const DataType& operator[](TileDistributedIndices) const
     {
         static_assert(is_static_v<TileDistributedIndices>,
                       "wrong! Tile Distributed Indices should be static");
  
         constexpr auto y_idx = get_tile_distribution().get_y_indices_from_distributed_indices(
             TileDistributedIndices{});
  
         return thread_buf_[number<ThreadTensorDesc{}.calculate_offset(y_idx) / PackedSize>{}];
     }
  
     template <typename TileDistributedIndices>
     CK_TILE_HOST_DEVICE constexpr DataType& operator()(TileDistributedIndices)
     {
         static_assert(is_static_v<TileDistributedIndices>,
                       "wrong! Tile Distributed Indices should be static");
  
         constexpr auto y_idx = get_tile_distribution().get_y_indices_from_distributed_indices(
             TileDistributedIndices{});
  
         return thread_buf_(number<ThreadTensorDesc{}.calculate_offset(y_idx) / PackedSize>{});
     }
  
     //
     thread_buffer<DataType, get_thread_buffer_size()> thread_buf_;
 };
  
 template <typename DataType, typename StaticTileDistribution>
 CK_TILE_HOST_DEVICE constexpr auto make_static_distributed_tensor(const StaticTileDistribution&)
 {
     return static_distributed_tensor<remove_cvref_t<DataType>,
                                      remove_cvref_t<StaticTileDistribution>>{};
 }
  
 template <typename DataType, typename StaticTileDistribution, typename ThreadBuffer>
 CK_TILE_HOST_DEVICE constexpr auto make_static_distributed_tensor(const StaticTileDistribution&,
                                                                   ThreadBuffer&& thread_buffer_)
 {
     return static_distributed_tensor<remove_cvref_t<DataType>,
                                      remove_cvref_t<StaticTileDistribution>>{thread_buffer_};
 }
  
 // get X indices from tuple of tile_distributed_index<>
 template <typename StaticTileDistribution, typename DistributedIndices>
 CK_TILE_HOST_DEVICE constexpr auto
 get_x_indices_from_distributed_indices(StaticTileDistribution tile_distribution,
                                        DistributedIndices distributed_indices)
 {
     const auto partition_index = detail::get_partition_index(tile_distribution);
     constexpr auto y_indices =
         tile_distribution.get_y_indices_from_distributed_indices(distributed_indices);
  
     const auto x_coord = make_tensor_adaptor_coordinate(
         tile_distribution.get_ps_ys_to_xs_adaptor(),
         container_concat(partition_index, to_array<ck_tile::index_t, y_indices.size()>(y_indices)));
  
     return x_coord.get_bottom_index();
 }
  
 template <typename DataType, typename StaticTileDistribution, typename XIndicesPredicate>
 CK_TILE_HOST_DEVICE void
 set_tile_if(static_distributed_tensor<DataType, StaticTileDistribution>& out_tensor,
             DataType value,
             XIndicesPredicate predicate)
 {
     constexpr auto out_spans =
         static_distributed_tensor<DataType, StaticTileDistribution>::get_distributed_spans();
     sweep_tile_span(out_spans[number<0>{}], [&](auto idx0) {
         sweep_tile_span(out_spans[number<1>{}], [&](auto idx1) {
             constexpr auto distributed_indices = make_tuple(idx0, idx1);
             const auto x_indices = get_x_indices_from_distributed_indices(StaticTileDistribution{},
                                                                           distributed_indices);
  
             if(predicate(x_indices))
             {
                 out_tensor(distributed_indices) = value;
             }
         });
     });
 }
  
 // this function used inside span loop over
 template <typename YLengths, index_t XUnpacks>
 CK_TILE_HOST_DEVICE constexpr auto get_y_unpacks_from_x_unpacks(YLengths, number<XUnpacks>)
 {
     constexpr auto y_size  = reduce_on_sequence(YLengths{}, multiplies{}, number<1>{});
     constexpr auto y_packs = number<XUnpacks>{};
     static_assert(y_size % y_packs == 0);
     constexpr auto y_slice_size = y_size / y_packs;
  
     constexpr auto slice_info = slice_sequence(YLengths{}, number<y_slice_size>{});
     constexpr auto unpacks    = slice_info[number<1>{}];
     return unpacks;
 }
  
 namespace detail {
  
 // check if 2 static_distributed_tensor has same data type and size of element
 // but only difference in distribution
 template <typename X, typename Y>
 struct is_similiar_distributed_tensor
 {
     static constexpr bool value = false;
 };
  
 template <typename TypeX, typename DistX, typename TypeY, typename DistY>
 struct is_similiar_distributed_tensor<static_distributed_tensor<TypeX, DistX>,
                                       static_distributed_tensor<TypeY, DistY>>
 {
     using Tx                    = static_distributed_tensor<TypeX, DistX>;
     using Ty                    = static_distributed_tensor<TypeY, DistY>;
     static constexpr bool value = std::is_same_v<typename Tx::DataType, typename Ty::DataType> &&
                                   Tx::get_thread_buffer_size() == Ty::get_thread_buffer_size();
 };
  
 template <typename X, typename Y>
 inline constexpr bool is_similiar_distributed_tensor_v =
     is_similiar_distributed_tensor<X, Y>::value;
  
 } // namespace detail
  
 } // namespace ck_tile