/**
 * @file multiplicationSparse.hpp
 * @brief Sparse matrix multiplication operations for HDF5 matrices
 * @note 2026-03-07 Output datasets now inherit compression level from input datasets
 *         via setCompressionLevel() called before every createDataset() invocation.
 * @details This header file provides implementations for sparse matrix
 * multiplication operations on matrices stored in HDF5 format. The implementation
 * includes:
 * 
 * Key features:
 * - Sparse matrix multiplication
 * - Compressed storage formats
 * - Memory-efficient algorithms
 * - Parallel processing support
 * - Sparse-dense operations
 * 
 * Supported operations:
 * - Sparse-sparse multiplication
 * - Sparse-dense multiplication
 * - Block sparse multiplication
 * - Transposed sparse operations
 * - Mixed precision operations
 * 
 * Performance features:
 * - Sparse storage optimization
 * - Cache-friendly algorithms
 * - Multi-threaded processing
 * - I/O optimization
 * - Memory management
 * 
 * The implementation uses:
 * - Compressed Row/Column Storage (CRS/CCS)
 * - Block sparse algorithms
 * - HDF5 chunked storage
 * - Parallel I/O
 */

#ifndef BIGDATASTATMETH_HDF5_MULTIPLICATIONSPARSE_HPP
#define BIGDATASTATMETH_HDF5_MULTIPLICATIONSPARSE_HPP

#include <RcppEigen.h>
#include "H5Cpp.h"

namespace BigDataStatMeth {


// Eigen not allow blocks 
// inline Eigen::SparseMatrix<double> Bblock_matrix_mul_parallel_sparse(Eigen::SparseMatrix<double> A, Eigen::SparseMatrix<double> B,
//                                                                             int block_size, Rcpp::Nullable<int> threads);

// inline BigDataStatMeth::hdf5Dataset* multiplication( BigDataStatMeth::hdf5Dataset* dsA, BigDataStatMeth::hdf5Dataset* dsB, BigDataStatMeth::hdf5Dataset* dsC,
//                                                             hsize_t hdf5_block, hsize_t mem_block_size, bool bparal, bool browmajor, Rcpp::Nullable<int> threads);

/**
 * @brief Sparse matrix multiplication for HDF5 matrices
 * @details Performs sparse matrix multiplication C = A * B where A, B, and C are
 * HDF5 datasets, with optimizations for sparse data structures.
 * 
 * @param dsA First input matrix dataset
 * @param dsB Second input matrix dataset
 * @param dsC Output matrix dataset
 * @param hdf5_block Block size for HDF5 I/O operations
 * @param mem_block_size Block size for in-memory operations
 * @param bparal Whether to use parallel processing
 * @param browmajor Whether matrices are stored in row-major order
 * @param threads Number of threads for parallel processing
 */
inline BigDataStatMeth::hdf5Dataset* multiplicationSparse( BigDataStatMeth::hdf5Dataset* dsA, BigDataStatMeth::hdf5Dataset* dsB, BigDataStatMeth::hdf5Dataset* dsC,
                                                                  hsize_t hdf5_block, hsize_t mem_block_size, bool bparal, bool browmajor, Rcpp::Nullable<int> threads  = R_NilValue) 
{

        try {
            
            hsize_t K = dsA->nrows();
            hsize_t N = dsA->ncols();
            
            hsize_t M = dsB->nrows();
            // hsize_t L = dsB->ncols();
            
            if( dsA->nrows() == dsB->ncols())
            {
                
                hsize_t isize = hdf5_block + 1,
                        ksize = hdf5_block + 1,
                        jsize = hdf5_block + 1;
                
                std::vector<hsize_t> stride = {1, 1};
                std::vector<hsize_t> block = {1, 1};
                
                dsC->inheritCompressionLevel(dsA->getCompressionLevel());
                //.. 20260408 ..// dsC->createDataset( M, N, "real");  
                dsC->createDataset( N, M, "real");
                
                for (hsize_t ii = 0; ii < N; ii += hdf5_block)
                {
                    
                    if( ii + hdf5_block > N ) isize = N - ii;
                    // Això haurien de ser files i no per columnes
                    for (hsize_t jj = 0; jj < M; jj += hdf5_block)
                    {
                        
                        if( jj + hdf5_block > M) jsize = M - jj;
                        
                        for(hsize_t kk = 0; kk < K; kk += hdf5_block)
                        {
                            
                            if( kk + hdf5_block > K ) ksize = K - kk;
                            
                            
                            hsize_t iRowsA = std::min(hdf5_block,ksize),
                                    iColsA = std::min(hdf5_block,isize),
                                    iRowsB = std::min(hdf5_block,jsize),
                                    iColsB = std::min(hdf5_block,ksize);
                            
                            std::vector<double> vdA( iRowsA * iColsA ); 
                            dsA->readDatasetBlock( {kk, ii}, {iRowsA, iColsA}, stride, block, vdA.data() );
                            Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> A (vdA.data(), iRowsA, iColsA );
                            
                            std::vector<double> vdB( iRowsB * iColsB ); 
                            dsB->readDatasetBlock( {jj, kk}, {iRowsB, iColsB}, stride, block, vdB.data() );
                            Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> B (vdB.data(), iRowsB, iColsB );
                            
                            //.. 20260408 ..// std::vector<double> vdC( iRowsB * iColsA ); 
                            //.. 20260408 ..// dsC->readDatasetBlock( {jj, ii}, {iRowsB, iColsA}, stride, block, vdC.data() );
                            //.. 20260408 ..// Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> C (vdC.data(), iRowsB, iColsA );
                            //.. 20260408 ..// 
                            //.. 20260408 ..// // if( bparal == false) {
                            //.. 20260408 ..// //     C = C + B * A;
                            //.. 20260408 ..// // } else {
                            //.. 20260408 ..// //     C = C + Bblock_matrix_mul_parallel(B, A, mem_block_size, threads);
                            //.. 20260408 ..// // }
                            //.. 20260408 ..// 
                            //.. 20260408 ..// C = C + (B.sparseView() * A.sparseView()).toDense() ;
                            //.. 20260408 ..// 
                            //.. 20260408 ..// std::vector<hsize_t> offset = {jj,ii};
                            //.. 20260408 ..// std::vector<hsize_t> count = {iRowsB, iColsA};
                            //.. 20260408 ..// 
                            //.. 20260408 ..// dsC->writeDatasetBlock(Rcpp::wrap(C), offset, count, stride, block, false);
                            
                            
                            Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> C_accumulator =
                                Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>::Zero(iRowsB, iColsA);
                            
                            if (kk == 0) {
                                C_accumulator = (B.sparseView() * A.sparseView()).toDense();
                            } else {
                                std::vector<double> vdC(iRowsB * iColsA);
                                dsC->readDatasetBlock({jj, ii}, {iRowsB, iColsA}, stride, block, vdC.data());
                                Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> C_prev(vdC.data(), iRowsB, iColsA);
                                C_accumulator = C_prev + (B.sparseView() * A.sparseView()).toDense();
                            }
                            
                            // Write using vector overload — same pattern as multiplication.hpp
                            std::vector<double> vdC_final(iRowsB * iColsA);
                            Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> C_final_map(vdC_final.data(), iRowsB, iColsA);
                            C_final_map = C_accumulator;
                            
                            std::vector<hsize_t> offset = {jj, ii};
                            std::vector<hsize_t> count  = {iRowsB, iColsA};
                            dsC->writeDatasetBlock(vdC_final, offset, count, stride, block);
                            
                            
                            if( kk + hdf5_block > K ) ksize = hdf5_block + 1;
                        }
                        
                        if( jj + hdf5_block > M ) jsize = hdf5_block + 1;
                    }
                    
                    if( ii + hdf5_block > N ) isize = hdf5_block + 1;
                }
                
            }else {
                throw std::range_error("multiplicationSparse error: non-conformable arguments");
            }
            
        } catch(std::exception& ex) {
            throw std::runtime_error(std::string("multiplicationSparse: ") + ex.what());
        }
        
        return(dsC);

    }

}

#endif // BIGDATASTATMETH_HDF5_MULTIPLICATIONSPARSE_HPP
