// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H

// IWYU pragma: private
#include "../InternalHeaderCheck.h"

namespace Eigen {

template <typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjLhs, bool ConjRhs>
struct selfadjoint_rank1_update;

namespace internal {

/**********************************************************************
 * This file implements a general A * B product while
 * evaluating only one triangular part of the product.
 * This is a more general version of self adjoint product (C += A A^T)
 * as the level 3 SYRK Blas routine.
 **********************************************************************/

// forward declarations (defined at the end of this file)
template <typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs,
          int ResInnerStride, int UpLo>
struct tribb_kernel;

/* Optimized matrix-matrix product evaluating only one triangular half */
template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
          int RhsStorageOrder, bool ConjugateRhs, int ResStorageOrder, int ResInnerStride, int UpLo,
          int Version = Specialized>
struct general_matrix_matrix_triangular_product;

// as usual if the result is row major => we transpose the product
template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
          int RhsStorageOrder, bool ConjugateRhs, int ResInnerStride, int UpLo, int Version>
struct general_matrix_matrix_triangular_product<Index, LhsScalar, LhsStorageOrder, ConjugateLhs, RhsScalar,
                                                RhsStorageOrder, ConjugateRhs, RowMajor, ResInnerStride, UpLo,
                                                Version> {
  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
  static EIGEN_STRONG_INLINE void run(Index size, Index depth, const LhsScalar* lhs, Index lhsStride,
                                      const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resIncr,
                                      Index resStride, const ResScalar& alpha,
                                      level3_blocking<RhsScalar, LhsScalar>& blocking) {
    general_matrix_matrix_triangular_product<Index, RhsScalar, RhsStorageOrder == RowMajor ? ColMajor : RowMajor,
                                             ConjugateRhs, LhsScalar, LhsStorageOrder == RowMajor ? ColMajor : RowMajor,
                                             ConjugateLhs, ColMajor, ResInnerStride,
                                             UpLo == Lower ? Upper : Lower>::run(size, depth, rhs, rhsStride, lhs,
                                                                                 lhsStride, res, resIncr, resStride,
                                                                                 alpha, blocking);
  }
};

template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
          int RhsStorageOrder, bool ConjugateRhs, int ResInnerStride, int UpLo, int Version>
struct general_matrix_matrix_triangular_product<Index, LhsScalar, LhsStorageOrder, ConjugateLhs, RhsScalar,
                                                RhsStorageOrder, ConjugateRhs, ColMajor, ResInnerStride, UpLo,
                                                Version> {
  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
  static EIGEN_STRONG_INLINE void run(Index size, Index depth, const LhsScalar* lhs_, Index lhsStride,
                                      const RhsScalar* rhs_, Index rhsStride, ResScalar* res_, Index resIncr,
                                      Index resStride, const ResScalar& alpha,
                                      level3_blocking<LhsScalar, RhsScalar>& blocking) {
    if (size == 0) {
      return;
    }

    typedef gebp_traits<LhsScalar, RhsScalar> Traits;

    typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
    typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
    LhsMapper lhs(lhs_, lhsStride);
    RhsMapper rhs(rhs_, rhsStride);
    ResMapper res(res_, resStride, resIncr);

    Index kc = blocking.kc();
    // Ensure that mc >= nr and <= size
    Index mc = (std::min)(size, (std::max)(static_cast<decltype(blocking.mc())>(Traits::nr), blocking.mc()));

    // !!! mc must be a multiple of nr
    if (mc > Traits::nr) {
      using UnsignedIndex = typename make_unsigned<Index>::type;
      mc = (UnsignedIndex(mc) / Traits::nr) * Traits::nr;
    }

    std::size_t sizeA = kc * mc;
    std::size_t sizeB = kc * size;

    ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());

    gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
                  LhsStorageOrder>
        pack_lhs;
    gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
    tribb_kernel<LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs, ResInnerStride, UpLo>
        sybb;

    for (Index k2 = 0; k2 < depth; k2 += kc) {
      const Index actual_kc = (std::min)(k2 + kc, depth) - k2;

      // note that the actual rhs is the transpose/adjoint of mat
      pack_rhs(blockB, rhs.getSubMapper(k2, 0), actual_kc, size);

      for (Index i2 = 0; i2 < size; i2 += mc) {
        const Index actual_mc = (std::min)(i2 + mc, size) - i2;

        pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);

        // the selected actual_mc * size panel of res is split into three different part:
        //  1 - before the diagonal => processed with gebp or skipped
        //  2 - the actual_mc x actual_mc symmetric block => processed with a special kernel
        //  3 - after the diagonal => processed with gebp or skipped
        if (UpLo == Lower)
          gebp(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, (std::min)(size, i2), alpha, -1, -1, 0,
               0);

        sybb(res_ + resStride * i2 + resIncr * i2, resIncr, resStride, blockA, blockB + actual_kc * i2, actual_mc,
             actual_kc, alpha);

        if (UpLo == Upper) {
          Index j2 = i2 + actual_mc;
          gebp(res.getSubMapper(i2, j2), blockA, blockB + actual_kc * j2, actual_mc, actual_kc,
               (std::max)(Index(0), size - j2), alpha, -1, -1, 0, 0);
        }
      }
    }
  }
};

// Optimized packed Block * packed Block product kernel evaluating only one given triangular part
// This kernel is built on top of the gebp kernel:
// - the current destination block is processed per panel of actual_mc x BlockSize
//   where BlockSize is set to the minimal value allowing gebp to be as fast as possible
// - then, as usual, each panel is split into three parts along the diagonal,
//   the sub blocks above and below the diagonal are processed as usual,
//   while the triangular block overlapping the diagonal is evaluated into a
//   small temporary buffer which is then accumulated into the result using a
//   triangular traversal.
template <typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs,
          int ResInnerStride, int UpLo>
struct tribb_kernel {
  typedef gebp_traits<LhsScalar, RhsScalar, ConjLhs, ConjRhs> Traits;
  typedef typename Traits::ResScalar ResScalar;

  enum { BlockSize = meta_least_common_multiple<plain_enum_max(mr, nr), plain_enum_min(mr, nr)>::ret };
  void operator()(ResScalar* res_, Index resIncr, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB,
                  Index size, Index depth, const ResScalar& alpha) {
    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned> BufferMapper;
    ResMapper res(res_, resStride, resIncr);
    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel1;
    gebp_kernel<LhsScalar, RhsScalar, Index, BufferMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel2;

    Matrix<ResScalar, BlockSize, BlockSize, ColMajor> buffer;

    // let's process the block per panel of actual_mc x BlockSize,
    // again, each is split into three parts, etc.
    for (Index j = 0; j < size; j += BlockSize) {
      Index actualBlockSize = std::min<Index>(BlockSize, size - j);
      const RhsScalar* actual_b = blockB + j * depth;

      if (UpLo == Upper)
        gebp_kernel1(res.getSubMapper(0, j), blockA, actual_b, j, depth, actualBlockSize, alpha, -1, -1, 0, 0);

      // selfadjoint micro block
      {
        Index i = j;
        buffer.setZero();
        // 1 - apply the kernel on the temporary buffer
        gebp_kernel2(BufferMapper(buffer.data(), BlockSize), blockA + depth * i, actual_b, actualBlockSize, depth,
                     actualBlockSize, alpha, -1, -1, 0, 0);

        // 2 - triangular accumulation
        for (Index j1 = 0; j1 < actualBlockSize; ++j1) {
          typename ResMapper::LinearMapper r = res.getLinearMapper(i, j + j1);
          for (Index i1 = UpLo == Lower ? j1 : 0; UpLo == Lower ? i1 < actualBlockSize : i1 <= j1; ++i1)
            r(i1) += buffer(i1, j1);
        }
      }

      if (UpLo == Lower) {
        Index i = j + actualBlockSize;
        gebp_kernel1(res.getSubMapper(i, j), blockA + depth * i, actual_b, size - i, depth, actualBlockSize, alpha, -1,
                     -1, 0, 0);
      }
    }
  }
};

}  // end namespace internal

// high level API

template <typename MatrixType, typename ProductType, int UpLo, bool IsOuterProduct>
struct general_product_to_triangular_selector;

template <typename MatrixType, typename ProductType, int UpLo>
struct general_product_to_triangular_selector<MatrixType, ProductType, UpLo, true> {
  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta) {
    typedef typename MatrixType::Scalar Scalar;

    typedef internal::remove_all_t<typename ProductType::LhsNested> Lhs;
    typedef internal::blas_traits<Lhs> LhsBlasTraits;
    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
    typedef internal::remove_all_t<ActualLhs> ActualLhs_;
    internal::add_const_on_value_type_t<ActualLhs> actualLhs = LhsBlasTraits::extract(prod.lhs());

    typedef internal::remove_all_t<typename ProductType::RhsNested> Rhs;
    typedef internal::blas_traits<Rhs> RhsBlasTraits;
    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
    typedef internal::remove_all_t<ActualRhs> ActualRhs_;
    internal::add_const_on_value_type_t<ActualRhs> actualRhs = RhsBlasTraits::extract(prod.rhs());

    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) *
                         RhsBlasTraits::extractScalarFactor(prod.rhs().derived());

    if (!beta) mat.template triangularView<UpLo>().setZero();

    enum {
      StorageOrder = (internal::traits<MatrixType>::Flags & RowMajorBit) ? RowMajor : ColMajor,
      UseLhsDirectly = ActualLhs_::InnerStrideAtCompileTime == 1,
      UseRhsDirectly = ActualRhs_::InnerStrideAtCompileTime == 1
    };

    internal::gemv_static_vector_if<Scalar, Lhs::SizeAtCompileTime, Lhs::MaxSizeAtCompileTime, !UseLhsDirectly>
        static_lhs;
    ei_declare_aligned_stack_constructed_variable(
        Scalar, actualLhsPtr, actualLhs.size(),
        (UseLhsDirectly ? const_cast<Scalar*>(actualLhs.data()) : static_lhs.data()));
    if (!UseLhsDirectly) Map<typename ActualLhs_::PlainObject>(actualLhsPtr, actualLhs.size()) = actualLhs;

    internal::gemv_static_vector_if<Scalar, Rhs::SizeAtCompileTime, Rhs::MaxSizeAtCompileTime, !UseRhsDirectly>
        static_rhs;
    ei_declare_aligned_stack_constructed_variable(
        Scalar, actualRhsPtr, actualRhs.size(),
        (UseRhsDirectly ? const_cast<Scalar*>(actualRhs.data()) : static_rhs.data()));
    if (!UseRhsDirectly) Map<typename ActualRhs_::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;

    selfadjoint_rank1_update<
        Scalar, Index, StorageOrder, UpLo, LhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
        RhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex>::run(actualLhs.size(), mat.data(),
                                                                             mat.outerStride(), actualLhsPtr,
                                                                             actualRhsPtr, actualAlpha);
  }
};

template <typename MatrixType, typename ProductType, int UpLo>
struct general_product_to_triangular_selector<MatrixType, ProductType, UpLo, false> {
  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta) {
    typedef internal::remove_all_t<typename ProductType::LhsNested> Lhs;
    typedef internal::blas_traits<Lhs> LhsBlasTraits;
    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
    typedef internal::remove_all_t<ActualLhs> ActualLhs_;
    internal::add_const_on_value_type_t<ActualLhs> actualLhs = LhsBlasTraits::extract(prod.lhs());

    typedef internal::remove_all_t<typename ProductType::RhsNested> Rhs;
    typedef internal::blas_traits<Rhs> RhsBlasTraits;
    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
    typedef internal::remove_all_t<ActualRhs> ActualRhs_;
    internal::add_const_on_value_type_t<ActualRhs> actualRhs = RhsBlasTraits::extract(prod.rhs());

    typename ProductType::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) *
                                               RhsBlasTraits::extractScalarFactor(prod.rhs().derived());

    if (!beta) mat.template triangularView<UpLo>().setZero();

    enum {
      IsRowMajor = (internal::traits<MatrixType>::Flags & RowMajorBit) ? 1 : 0,
      LhsIsRowMajor = ActualLhs_::Flags & RowMajorBit ? 1 : 0,
      RhsIsRowMajor = ActualRhs_::Flags & RowMajorBit ? 1 : 0,
      SkipDiag = (UpLo & (UnitDiag | ZeroDiag)) != 0
    };

    Index size = mat.cols();
    if (SkipDiag) size--;
    Index depth = actualLhs.cols();

    typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor, typename Lhs::Scalar, typename Rhs::Scalar,
                                          MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime,
                                          ActualRhs_::MaxColsAtCompileTime>
        BlockingType;

    BlockingType blocking(size, size, depth, 1, false);

    internal::general_matrix_matrix_triangular_product<
        Index, typename Lhs::Scalar, LhsIsRowMajor ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
        typename Rhs::Scalar, RhsIsRowMajor ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
        IsRowMajor ? RowMajor : ColMajor, MatrixType::InnerStrideAtCompileTime,
        UpLo&(Lower | Upper)>::run(size, depth, &actualLhs.coeffRef(SkipDiag && (UpLo & Lower) == Lower ? 1 : 0, 0),
                                   actualLhs.outerStride(),
                                   &actualRhs.coeffRef(0, SkipDiag && (UpLo & Upper) == Upper ? 1 : 0),
                                   actualRhs.outerStride(),
                                   mat.data() +
                                       (SkipDiag ? (bool(IsRowMajor) != ((UpLo & Lower) == Lower) ? mat.innerStride()
                                                                                                  : mat.outerStride())
                                                 : 0),
                                   mat.innerStride(), mat.outerStride(), actualAlpha, blocking);
  }
};

template <typename MatrixType_, unsigned int Mode_>
template <typename ProductType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename TriangularViewImpl<MatrixType_, Mode_, Dense>::TriangularViewType&
TriangularViewImpl<MatrixType_, Mode_, Dense>::_assignProduct(
    const ProductType& prod, const typename TriangularViewImpl<MatrixType_, Mode_, Dense>::Scalar& alpha, bool beta) {
  EIGEN_STATIC_ASSERT((Mode_ & UnitDiag) == 0, WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED);
  eigen_assert(derived().nestedExpression().rows() == prod.rows() && derived().cols() == prod.cols());

  general_product_to_triangular_selector<MatrixType_, ProductType, Mode_,
                                         internal::traits<ProductType>::InnerSize == 1>::run(derived()
                                                                                                 .nestedExpression()
                                                                                                 .const_cast_derived(),
                                                                                             prod, alpha, beta);

  return derived();
}

}  // end namespace Eigen

#endif  // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H