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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/utility/functional.hpp"
 #include "ck_tile/core/algorithm/coordinate_transform.hpp"
 #include "ck_tile/core/algorithm/space_filling_curve.hpp"
 #include "ck_tile/core/container/container_helper.hpp"
 #include "ck_tile/core/container/thread_buffer.hpp"
 #include "ck_tile/core/container/statically_indexed_array.hpp"
 #include "ck_tile/core/numeric/math.hpp"
 #include "ck_tile/core/utility/type_traits.hpp"
 #include "ck_tile/core/tensor/tile_elementwise.hpp"
 #include "ck_tile/core/utility/transpose_vectors.hpp"
  
 namespace ck_tile {
 namespace detail {
  
 template <typename OutTensor, typename InTensor>
 CK_TILE_DEVICE void shuffle_tile_impl_in_thread(OutTensor& out_tensor, const InTensor& in_tensor)
 {
     constexpr auto I0 = number<0>{};
  
     using DataType = typename InTensor::DataType;
  
     constexpr auto y_in_desc  = InTensor::get_tile_distribution().get_ys_to_d_descriptor();
     constexpr auto y_out_desc = OutTensor::get_tile_distribution().get_ys_to_d_descriptor();
  
     // y_dim_out_to_in
     constexpr auto get_rh_major_minor_to_y = [](auto dstr_tensor) {
         using DstrEncode = typename decltype(dstr_tensor.get_tile_distribution())::DstrEncode;
  
         map<array<index_t, 2>, index_t> rh_major_minor_to_y_;
  
         static_for<0, DstrEncode::NDimY, 1>{}([&](auto i) {
             constexpr index_t rh_major = DstrEncode::ys_to_rhs_major_[i];
             constexpr index_t rh_minor = DstrEncode::ys_to_rhs_minor_[i];
  
             rh_major_minor_to_y_({rh_major, rh_minor}) = i;
         });
  
         return rh_major_minor_to_y_;
     };
  
     constexpr auto rh_major_minor_to_y_in  = get_rh_major_minor_to_y(InTensor{});
     constexpr auto rh_major_minor_to_y_out = get_rh_major_minor_to_y(OutTensor{});
  
     constexpr auto y_dim_out_to_in = [&] {
         map<index_t, index_t> y_dim_out_to_in_;
  
         for(const auto& [rh_major_minor, y_out] : rh_major_minor_to_y_out)
         {
             y_dim_out_to_in_(y_out) = rh_major_minor_to_y_in[rh_major_minor];
         }
  
         return y_dim_out_to_in_;
     }();
  
     //
     constexpr index_t NDimY = InTensor::get_tile_distribution().get_num_of_dimension_y();
  
     constexpr auto y_lengths = to_sequence(y_in_desc.get_lengths());
  
     // input and output vector dim in the order of input Y dims
     constexpr index_t y_dim_vec_in  = NDimY - 1;
     constexpr index_t y_dim_vec_out = y_dim_out_to_in[NDimY - 1];
  
     // vector lengths
     constexpr index_t vec_length_in  = y_lengths[y_dim_vec_in];
     constexpr index_t vec_length_out = y_lengths[y_dim_vec_out];
  
     // # of vectors
     constexpr index_t num_vec_in  = vec_length_out;
     constexpr index_t num_vec_out = vec_length_in;
  
     using InVec  = array<DataType, vec_length_in>;
     using OutVec = array<DataType, vec_length_out>;
  
     // using InVec  = typename InVec::type;
     // using OutVec = typename OutVec::type;
  
     // SFC
     constexpr auto scalars_per_access_arr = generate_array(
         [&](auto i) { return (i == y_dim_vec_in or i == y_dim_vec_out) ? y_lengths[i] : 1; },
         number<NDimY>{});
  
     constexpr auto scalars_per_access = TO_SEQUENCE(scalars_per_access_arr, NDimY);
  
     using SFC_Y = space_filling_curve<decltype(y_lengths),
                                       typename arithmetic_sequence_gen<0, NDimY, 1>::type,
                                       decltype(scalars_per_access)>;
  
     constexpr index_t num_access = SFC_Y::get_num_of_access();
  
     static_assert(num_access > 0, "wrong! num_access should be larger than 0");
  
     // in/out vectors to be transposed
     thread_buffer<InVec, num_vec_in> in_vectors;
     thread_buffer<OutVec, num_vec_out> out_vectors;
  
     // loop over SFC and do transpose
     static_for<0, num_access, 1>{}([&](auto iAccess) {
         // data index [y0, y1, ...] in the order of input tensor
         constexpr auto idx_y_start = SFC_Y::get_index(iAccess);
  
         // get input vectors
         static_for<0, num_vec_in, 1>{}([&](auto i) {
             constexpr auto idx_y_in = generate_tuple(
                 [&](auto ii) {
                     return ii == y_dim_vec_out ? idx_y_start[ii] + i : idx_y_start[ii];
                 },
                 number<NDimY>{});
  
             constexpr index_t in_offset = y_in_desc.calculate_offset(idx_y_in);
             static_assert(in_offset % vec_length_in == 0);
  
             in_vectors(i).template get_as<InVec>()(I0) =
                 in_tensor.get_thread_buffer()
                     .template get_as<InVec>()[number<in_offset / vec_length_in>{}];
         });
  
         // transpose
         transpose_vectors<DataType, num_vec_in, num_vec_out>{}(in_vectors, out_vectors);
  
         // set output vectors
         static_for<0, num_vec_out, 1>{}([&](auto i) {
             constexpr auto idx_y_out_tmp = generate_array(
                 [&](auto ii) {
                     return ii == y_dim_vec_in ? static_cast<index_t>(idx_y_start[ii]) + i
                                               : static_cast<index_t>(idx_y_start[ii]);
                 },
                 number<NDimY>{});
  
             constexpr auto idx_y_out =
                 container_reorder_given_new2old(idx_y_out_tmp, y_dim_out_to_in);
  
             constexpr index_t out_offset = y_out_desc.calculate_offset(idx_y_out);
             static_assert(out_offset % vec_length_out == 0);
  
             out_tensor.get_thread_buffer().template set_as<OutVec>(
                 number<out_offset / vec_length_out>{},
                 out_vectors[i].template get_as<OutVec>()[I0]);
         });
     });
 }
  
 } // namespace detail
  
 template <typename OutTensor, typename InTensor>
 CK_TILE_DEVICE void shuffle_tile(OutTensor& out, const InTensor& in)
 {
     using InDataType  = typename InTensor::DataType;
     using OutDataType = typename OutTensor::DataType;
  
     using InDstrEncode  = typename InTensor::StaticTileDistribution::DstrEncode;
     using OutDstrEncode = typename OutTensor::StaticTileDistribution::DstrEncode;
  
     // type convert
     const auto in_tmp = tile_elementwise_in(type_convert<OutDataType, InDataType>, in);
  
     // shuffle
     if constexpr(InDstrEncode::rs_lengths_ == OutDstrEncode::rs_lengths_ &&
                  InDstrEncode::hs_lengthss_ == OutDstrEncode::hs_lengthss_ &&
                  InDstrEncode::ps_to_rhss_major_ == OutDstrEncode::ps_to_rhss_major_ &&
                  InDstrEncode::ps_to_rhss_minor_ == OutDstrEncode::ps_to_rhss_minor_ &&
                  InDstrEncode::NDimY == OutDstrEncode::NDimY)
     {
         detail::shuffle_tile_impl_in_thread(out, in_tmp);
     }
     else
     {
         static_assert(false, "The shuffle should always happen!");
     }
 }
  
 } // namespace ck_tile