// SPDX-License-Identifier: Apache-2.0
// 
// Copyright 2008-2016 Conrad Sanderson (https://conradsanderson.id.au)
// Copyright 2008-2016 National ICT Australia (NICTA)
// 
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// https://www.apache.org/licenses/LICENSE-2.0
// 
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// ------------------------------------------------------------------------


//! \addtogroup gemm_mixed
//! @{



//! \brief
//! Matrix multiplication where the matrices have differing element types.
//! Uses caching for speedup.
//! Matrix 'C' is assumed to have been set to the correct size (ie. taking into account transposes)

template<const bool do_trans_A=false, const bool do_trans_B=false, const bool use_alpha=false, const bool use_beta=false>
struct gemm_mixed_large
  {
  template<typename out_eT, typename in_eT1, typename in_eT2>
  arma_hot
  inline
  static
  void
  apply
    (
          Mat<out_eT>& C,
    const Mat<in_eT1>& A,
    const Mat<in_eT2>& B,
    const out_eT       alpha = out_eT(1),
    const out_eT       beta  = out_eT(0)
    )
    {
    arma_debug_sigprint();
    
    const uword A_n_rows = A.n_rows;
    const uword A_n_cols = A.n_cols;
    
    const uword B_n_rows = B.n_rows;
    const uword B_n_cols = B.n_cols;
    
    if( (do_trans_A == false) && (do_trans_B == false) )
      {
      podarray<in_eT1> tmp(A_n_cols);
      in_eT1* A_rowdata = tmp.memptr();
      
      #if defined(ARMA_USE_OPENMP)
      const bool use_mp = (B_n_cols >= 2) && (B.n_elem >= 8192) && (mp_thread_limit::in_parallel() == false);
      #else
      const bool use_mp = false;
      #endif
      
      if(use_mp)
        {
        #if defined(ARMA_USE_OPENMP)
          {
          const int n_threads = int( (std::min)( uword(mp_thread_limit::get()), uword(B_n_cols) ) );
          
          for(uword row_A=0; row_A < A_n_rows; ++row_A)
            {
            tmp.copy_row(A, row_A);
            
            #pragma omp parallel for schedule(static) num_threads(n_threads)
            for(uword col_B=0; col_B < B_n_cols; ++col_B)
              {
              const in_eT2* B_coldata = B.colptr(col_B);
              
              out_eT acc = out_eT(0);
              for(uword i=0; i < B_n_rows; ++i)
                {
                acc += upgrade_val<in_eT1,in_eT2>::apply(A_rowdata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
                }
              
                   if( (use_alpha == false) && (use_beta == false) )  { C.at(row_A,col_B) =       acc;                          }
              else if( (use_alpha == true ) && (use_beta == false) )  { C.at(row_A,col_B) = alpha*acc;                          }
              else if( (use_alpha == false) && (use_beta == true ) )  { C.at(row_A,col_B) =       acc + beta*C.at(row_A,col_B); }
              else if( (use_alpha == true ) && (use_beta == true ) )  { C.at(row_A,col_B) = alpha*acc + beta*C.at(row_A,col_B); }
              }
            }
          }
        #endif
        }
      else
        {
        for(uword row_A=0; row_A < A_n_rows; ++row_A)
          {
          tmp.copy_row(A, row_A);
            
          for(uword col_B=0; col_B < B_n_cols; ++col_B)
            {
            const in_eT2* B_coldata = B.colptr(col_B);
            
            out_eT acc = out_eT(0);
            for(uword i=0; i < B_n_rows; ++i)
              {
              acc += upgrade_val<in_eT1,in_eT2>::apply(A_rowdata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
              }
          
                 if( (use_alpha == false) && (use_beta == false) )  { C.at(row_A,col_B) =       acc;                          }
            else if( (use_alpha == true ) && (use_beta == false) )  { C.at(row_A,col_B) = alpha*acc;                          }
            else if( (use_alpha == false) && (use_beta == true ) )  { C.at(row_A,col_B) =       acc + beta*C.at(row_A,col_B); }
            else if( (use_alpha == true ) && (use_beta == true ) )  { C.at(row_A,col_B) = alpha*acc + beta*C.at(row_A,col_B); }
            }
          }
        }
      }
    else
    if( (do_trans_A == true) && (do_trans_B == false) )
      {
      #if defined(ARMA_USE_OPENMP)
      const bool use_mp = (B_n_cols >= 2) && (B.n_elem >= 8192) && (mp_thread_limit::in_parallel() == false);
      #else
      const bool use_mp = false;
      #endif
      
      if(use_mp)
        {
        #if defined(ARMA_USE_OPENMP)
          {
          const int n_threads = int( (std::min)( uword(mp_thread_limit::get()), uword(B_n_cols) ) );
          
          for(uword col_A=0; col_A < A_n_cols; ++col_A)
            {
            // col_A is interpreted as row_A when storing the results in matrix C
            
            const in_eT1* A_coldata = A.colptr(col_A);
            
            #pragma omp parallel for schedule(static) num_threads(n_threads)
            for(uword col_B=0; col_B < B_n_cols; ++col_B)
              {
              const in_eT2* B_coldata = B.colptr(col_B);
              
              out_eT acc = out_eT(0);
              for(uword i=0; i < B_n_rows; ++i)
                {
                acc += upgrade_val<in_eT1,in_eT2>::apply(A_coldata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
                }
            
                   if( (use_alpha == false) && (use_beta == false) )  { C.at(col_A,col_B) =       acc;                          }
              else if( (use_alpha == true ) && (use_beta == false) )  { C.at(col_A,col_B) = alpha*acc;                          }
              else if( (use_alpha == false) && (use_beta == true ) )  { C.at(col_A,col_B) =       acc + beta*C.at(col_A,col_B); }
              else if( (use_alpha == true ) && (use_beta == true ) )  { C.at(col_A,col_B) = alpha*acc + beta*C.at(col_A,col_B); }
              }
            }
          }
        #endif
        }
      else
        {
        for(uword col_A=0; col_A < A_n_cols; ++col_A)
          {
          // col_A is interpreted as row_A when storing the results in matrix C
          
          const in_eT1* A_coldata = A.colptr(col_A);
          
          for(uword col_B=0; col_B < B_n_cols; ++col_B)
            {
            const in_eT2* B_coldata = B.colptr(col_B);
            
            out_eT acc = out_eT(0);
            for(uword i=0; i < B_n_rows; ++i)
              {
              acc += upgrade_val<in_eT1,in_eT2>::apply(A_coldata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
              }
          
                 if( (use_alpha == false) && (use_beta == false) )  { C.at(col_A,col_B) =       acc;                          }
            else if( (use_alpha == true ) && (use_beta == false) )  { C.at(col_A,col_B) = alpha*acc;                          }
            else if( (use_alpha == false) && (use_beta == true ) )  { C.at(col_A,col_B) =       acc + beta*C.at(col_A,col_B); }
            else if( (use_alpha == true ) && (use_beta == true ) )  { C.at(col_A,col_B) = alpha*acc + beta*C.at(col_A,col_B); }
            }
          }
        }
      }
    else
    if( (do_trans_A == false) && (do_trans_B == true) )
      {
      Mat<in_eT2> B_tmp;
      
      op_strans::apply_mat_noalias(B_tmp, B);
      
      gemm_mixed_large<false, false, use_alpha, use_beta>::apply(C, A, B_tmp, alpha, beta);
      }
    else
    if( (do_trans_A == true) && (do_trans_B == true) )
      {
      // mat B_tmp = trans(B);
      // dgemm_arma<true, false,  use_alpha, use_beta>::apply(C, A, B_tmp, alpha, beta);
      
      
      // By using the trans(A)*trans(B) = trans(B*A) equivalency,
      // transpose operations are not needed
      
      podarray<in_eT2> tmp(B_n_cols);
      in_eT2* B_rowdata = tmp.memptr();
      
      for(uword row_B=0; row_B < B_n_rows; ++row_B)
        {
        tmp.copy_row(B, row_B);
        
        for(uword col_A=0; col_A < A_n_cols; ++col_A)
          {
          const in_eT1* A_coldata = A.colptr(col_A);
          
          out_eT acc = out_eT(0);
          for(uword i=0; i < A_n_rows; ++i)
            {
            acc += upgrade_val<in_eT1,in_eT2>::apply(B_rowdata[i]) * upgrade_val<in_eT1,in_eT2>::apply(A_coldata[i]);
            }
          
               if( (use_alpha == false) && (use_beta == false) )  { C.at(col_A,row_B) =       acc;                          }
          else if( (use_alpha == true ) && (use_beta == false) )  { C.at(col_A,row_B) = alpha*acc;                          }
          else if( (use_alpha == false) && (use_beta == true ) )  { C.at(col_A,row_B) =       acc + beta*C.at(col_A,row_B); }
          else if( (use_alpha == true ) && (use_beta == true ) )  { C.at(col_A,row_B) = alpha*acc + beta*C.at(col_A,row_B); }
          }
        }
      
      }
    }
    
  };



//! \brief
//! Matrix multiplication where the matrices have differing element types.

template<const bool do_trans_A=false, const bool do_trans_B=false, const bool use_alpha=false, const bool use_beta=false>
struct gemm_mixed
  {
  //! immediate multiplication of matrices A and B, storing the result in C
  template<typename out_eT, typename in_eT1, typename in_eT2>
  inline
  static
  void
  apply
    (
          Mat<out_eT>& C,
    const Mat<in_eT1>& A,
    const Mat<in_eT2>& B,
    const out_eT       alpha = out_eT(1),
    const out_eT       beta  = out_eT(0)
    )
    {
    arma_debug_sigprint();
    
    if((is_cx<in_eT1>::yes && do_trans_A) || (is_cx<in_eT2>::yes && do_trans_B))
      {
      // better-than-nothing handling of hermitian transpose
      
      Mat<in_eT1> tmp_A;
      Mat<in_eT2> tmp_B;
      
      const bool predo_trans_A = ( (do_trans_A == true) && (is_cx<in_eT1>::yes) );
      const bool predo_trans_B = ( (do_trans_B == true) && (is_cx<in_eT2>::yes) );
      
      if(predo_trans_A)  { op_htrans::apply_mat_noalias(tmp_A, A); }
      if(predo_trans_B)  { op_htrans::apply_mat_noalias(tmp_B, B); }
      
      const Mat<in_eT1>& AA = (predo_trans_A == false) ? A : tmp_A;
      const Mat<in_eT2>& BB = (predo_trans_B == false) ? B : tmp_B;
      
      gemm_mixed_large<((predo_trans_A) ? false : do_trans_A), ((predo_trans_B) ? false : do_trans_B), use_alpha, use_beta>::apply(C, AA, BB, alpha, beta);
      }
    else
      {
      gemm_mixed_large<do_trans_A, do_trans_B, use_alpha, use_beta>::apply(C, A, B, alpha, beta);
      }
    }
  
  
  };



//! @}