#ifndef STAN_MATH_REV_MAT_FUN_MULTIPLY_HPP
#define STAN_MATH_REV_MAT_FUN_MULTIPLY_HPP

#include <stan/math/prim/mat/fun/Eigen.hpp>
#include <stan/math/prim/mat/fun/typedefs.hpp>
#include <stan/math/rev/scal/fun/value_of_rec.hpp>
#include <stan/math/rev/scal/fun/value_of.hpp>
#include <stan/math/rev/mat/fun/to_var.hpp>
#include <stan/math/rev/core.hpp>
#include <stan/math/prim/mat/fun/value_of_rec.hpp>
#include <stan/math/prim/mat/err/check_multiplicable.hpp>
#include <stan/math/prim/scal/err/check_not_nan.hpp>
#include <boost/math/tools/promotion.hpp>
#include <boost/utility/enable_if.hpp>
#include <boost/type_traits.hpp>

namespace stan {
namespace math {

/**
 * This is a subclass of the vari class for matrix
 * multiplication A * B where A is N by M and B
 * is M by K.
 *
 * The class stores the structure of each matrix,
 * the double values of A and B, and pointers to
 * the varis for A and B if A or B is a var. It
 * also instantiates and stores pointers to
 * varis for all elements of A * B.
 *
 * @tparam Ta Scalar type for matrix A
 * @tparam Ra Rows for matrix A
 * @tparam Ca Columns for matrix A, Rows for matrix B
 * @tparam Tb Scalar type for matrix B
 * @tparam Cb Columns for matrix B
 */
template <typename Ta, int Ra, int Ca, typename Tb, int Cb>
class multiply_mat_vari : public vari {
 public:
  int A_rows_;
  int A_cols_;
  int B_cols_;
  int A_size_;
  int B_size_;
  double* Ad_;
  double* Bd_;
  vari** variRefA_;
  vari** variRefB_;
  vari** variRefAB_;

  /**
   * Constructor for multiply_mat_vari.
   *
   * All memory allocated in
   * ChainableStack's stack_alloc arena.
   *
   * It is critical for the efficiency of this object
   * that the constructor create new varis that aren't
   * popped onto the var_stack_, but rather are
   * popped onto the var_nochain_stack_. This is
   * controlled to the second argument to
   * vari's constructor.
   *
   * @param A matrix
   * @param B matrix
   */
  multiply_mat_vari(const Eigen::Matrix<Ta, Ra, Ca>& A,
                    const Eigen::Matrix<Tb, Ca, Cb>& B)
      : vari(0.0),
        A_rows_(A.rows()),
        A_cols_(A.cols()),
        B_cols_(B.cols()),
        A_size_(A.size()),
        B_size_(B.size()),
        Ad_(ChainableStack::instance().memalloc_.alloc_array<double>(A_size_)),
        Bd_(ChainableStack::instance().memalloc_.alloc_array<double>(B_size_)),
        variRefA_(
            ChainableStack::instance().memalloc_.alloc_array<vari*>(A_size_)),
        variRefB_(
            ChainableStack::instance().memalloc_.alloc_array<vari*>(B_size_)),
        variRefAB_(ChainableStack::instance().memalloc_.alloc_array<vari*>(
            A_rows_ * B_cols_)) {
    using Eigen::Map;
    using Eigen::MatrixXd;
    for (size_type i = 0; i < A.size(); ++i) {
      variRefA_[i] = A.coeffRef(i).vi_;
      Ad_[i] = A.coeffRef(i).val();
    }
    for (size_type i = 0; i < B.size(); ++i) {
      variRefB_[i] = B.coeffRef(i).vi_;
      Bd_[i] = B.coeffRef(i).val();
    }
    MatrixXd AB = Map<MatrixXd>(Ad_, A_rows_, A_cols_)
                  * Map<MatrixXd>(Bd_, A_cols_, B_cols_);
    for (size_type i = 0; i < AB.size(); ++i)
      variRefAB_[i] = new vari(AB.coeffRef(i), false);
  }

  virtual void chain() {
    using Eigen::Map;
    using Eigen::MatrixXd;
    MatrixXd adjAB(A_rows_, B_cols_);
    MatrixXd adjA(A_rows_, A_cols_);
    MatrixXd adjB(A_cols_, B_cols_);

    for (size_type i = 0; i < adjAB.size(); ++i)
      adjAB(i) = variRefAB_[i]->adj_;
    adjA = adjAB * Map<MatrixXd>(Bd_, A_cols_, B_cols_).transpose();
    adjB = Map<MatrixXd>(Ad_, A_rows_, A_cols_).transpose() * adjAB;
    for (size_type i = 0; i < A_size_; ++i)
      variRefA_[i]->adj_ += adjA(i);
    for (size_type i = 0; i < B_size_; ++i)
      variRefB_[i]->adj_ += adjB(i);
  }
};

/**
 * This is a subclass of the vari class for matrix
 * multiplication A * B where A is 1 by M and B
 * is M by 1.
 *
 * The class stores the structure of each matrix,
 * the double values of A and B, and pointers to
 * the varis for A and B if A or B is a var. It
 * also instantiates and stores pointers to
 * varis for all elements of A * B.
 *
 * @tparam Ta Scalar type for matrix A
 * @tparam Ca Columns for matrix A, Rows for matrix B
 * @tparam Tb Scalar type for matrix B
 */
template <typename Ta, int Ca, typename Tb>
class multiply_mat_vari<Ta, 1, Ca, Tb, 1> : public vari {
 public:
  int size_;
  double* Ad_;
  double* Bd_;
  vari** variRefA_;
  vari** variRefB_;
  vari* variRefAB_;

  /**
   * Constructor for multiply_mat_vari.
   *
   * All memory allocated in
   * ChainableStack's stack_alloc arena.
   *
   * It is critical for the efficiency of this object
   * that the constructor create new varis that aren't
   * popped onto the var_stack_, but rather are
   * popped onto the var_nochain_stack_. This is
   * controlled to the second argument to
   * vari's constructor.
   *
   * @param A row vector
   * @param B vector
   */
  multiply_mat_vari(const Eigen::Matrix<Ta, 1, Ca>& A,
                    const Eigen::Matrix<Tb, Ca, 1>& B)
      : vari(0.0),
        size_(A.cols()),
        Ad_(ChainableStack::instance().memalloc_.alloc_array<double>(size_)),
        Bd_(ChainableStack::instance().memalloc_.alloc_array<double>(size_)),
        variRefA_(
            ChainableStack::instance().memalloc_.alloc_array<vari*>(size_)),
        variRefB_(
            ChainableStack::instance().memalloc_.alloc_array<vari*>(size_)) {
    using Eigen::Map;
    using Eigen::RowVectorXd;
    using Eigen::VectorXd;
    for (size_type i = 0; i < size_; ++i) {
      variRefA_[i] = A.coeffRef(i).vi_;
      Ad_[i] = A.coeffRef(i).val();
    }
    for (size_type i = 0; i < size_; ++i) {
      variRefB_[i] = B.coeffRef(i).vi_;
      Bd_[i] = B.coeffRef(i).val();
    }
    double AB = Map<RowVectorXd>(Ad_, 1, size_) * Map<VectorXd>(Bd_, size_, 1);
    variRefAB_ = new vari(AB, false);
  }

  virtual void chain() {
    using Eigen::Map;
    using Eigen::RowVectorXd;
    using Eigen::VectorXd;
    double adjAB;

    adjAB = variRefAB_->adj_;
    auto adjA = adjAB * Map<VectorXd>(Bd_, size_, 1).transpose();
    auto adjB = Map<RowVectorXd>(Ad_, 1, size_).transpose() * adjAB;
    for (size_type i = 0; i < size_; ++i)
      variRefA_[i]->adj_ += adjA(i);
    for (size_type i = 0; i < size_; ++i)
      variRefB_[i]->adj_ += adjB(i);
  }
};

/**
 * This is a subclass of the vari class for matrix
 * multiplication A * B where A is an N by M
 * matrix of double and B is M by K.
 *
 * The class stores the structure of each matrix,
 * the double values of A and B, and pointers to
 * the varis for A and B if A or B is a var. It
 * also instantiates and stores pointers to
 * varis for all elements of A * B.
 *
 * @tparam Ra Rows for matrix A
 * @tparam Ca Columns for matrix A, Rows for matrix B
 * @tparam Tb Scalar type for matrix B
 * @tparam Cb Columns for matrix B
 */
template <int Ra, int Ca, typename Tb, int Cb>
class multiply_mat_vari<double, Ra, Ca, Tb, Cb> : public vari {
 public:
  int A_rows_;
  int A_cols_;
  int B_cols_;
  int A_size_;
  int B_size_;
  double* Ad_;
  double* Bd_;
  vari** variRefB_;
  vari** variRefAB_;

  /**
   * Constructor for multiply_mat_vari.
   *
   * All memory allocated in
   * ChainableStack's stack_alloc arena.
   *
   * It is critical for the efficiency of this object
   * that the constructor create new varis that aren't
   * popped onto the var_stack_, but rather are
   * popped onto the var_nochain_stack_. This is
   * controlled to the second argument to
   * vari's constructor.
   *
   * @param A row vector
   * @param B vector
   */
  multiply_mat_vari(const Eigen::Matrix<double, Ra, Ca>& A,
                    const Eigen::Matrix<Tb, Ca, Cb>& B)
      : vari(0.0),
        A_rows_(A.rows()),
        A_cols_(A.cols()),
        B_cols_(B.cols()),
        A_size_(A.size()),
        B_size_(B.size()),
        Ad_(ChainableStack::instance().memalloc_.alloc_array<double>(A_size_)),
        Bd_(ChainableStack::instance().memalloc_.alloc_array<double>(B_size_)),
        variRefB_(
            ChainableStack::instance().memalloc_.alloc_array<vari*>(B_size_)),
        variRefAB_(ChainableStack::instance().memalloc_.alloc_array<vari*>(
            A_rows_ * B_cols_)) {
    using Eigen::Map;
    using Eigen::MatrixXd;
    for (size_type i = 0; i < A.size(); ++i)
      Ad_[i] = A.coeffRef(i);
    for (size_type i = 0; i < B.size(); ++i) {
      variRefB_[i] = B.coeffRef(i).vi_;
      Bd_[i] = B.coeffRef(i).val();
    }
    MatrixXd AB = Map<MatrixXd>(Ad_, A_rows_, A_cols_)
                  * Map<MatrixXd>(Bd_, A_cols_, B_cols_);
    for (size_type i = 0; i < AB.size(); ++i)
      variRefAB_[i] = new vari(AB.coeffRef(i), false);
  }

  virtual void chain() {
    using Eigen::Map;
    using Eigen::MatrixXd;
    MatrixXd adjAB(A_rows_, B_cols_);
    MatrixXd adjB(A_cols_, B_cols_);

    for (size_type i = 0; i < adjAB.size(); ++i)
      adjAB(i) = variRefAB_[i]->adj_;
    adjB = Map<MatrixXd>(Ad_, A_rows_, A_cols_).transpose() * adjAB;
    for (size_type i = 0; i < B_size_; ++i)
      variRefB_[i]->adj_ += adjB(i);
  }
};

/**
 * This is a subclass of the vari class for matrix
 * multiplication A * B where A is a double
 * row vector of length M and B is a vector of
 * length M.
 *
 * The class stores the structure of each matrix,
 * the double values of A and B, and pointers to
 * the varis for A and B if A or B is a var. It
 * also instantiates and stores pointers to
 * varis for all elements of A * B.
 *
 * @tparam Ca Columns for matrix A, Rows for matrix B
 * @tparam Tb Scalar type for matrix B
 */
template <int Ca, typename Tb>
class multiply_mat_vari<double, 1, Ca, Tb, 1> : public vari {
 public:
  int size_;
  double* Ad_;
  double* Bd_;
  vari** variRefB_;
  vari* variRefAB_;

  /**
   * Constructor for multiply_mat_vari.
   *
   * All memory allocated in
   * ChainableStack's stack_alloc arena.
   *
   * It is critical for the efficiency of this object
   * that the constructor create new varis that aren't
   * popped onto the var_stack_, but rather are
   * popped onto the var_nochain_stack_. This is
   * controlled to the second argument to
   * vari's constructor.
   *
   * @param A row vector
   * @param B vector
   */
  multiply_mat_vari(const Eigen::Matrix<double, 1, Ca>& A,
                    const Eigen::Matrix<Tb, Ca, 1>& B)
      : vari(0.0),
        size_(A.cols()),
        Ad_(ChainableStack::instance().memalloc_.alloc_array<double>(size_)),
        Bd_(ChainableStack::instance().memalloc_.alloc_array<double>(size_)),
        variRefB_(
            ChainableStack::instance().memalloc_.alloc_array<vari*>(size_)) {
    using Eigen::Map;
    using Eigen::RowVectorXd;
    using Eigen::VectorXd;
    for (size_type i = 0; i < size_; ++i)
      Ad_[i] = A.coeffRef(i);
    for (size_type i = 0; i < size_; ++i) {
      variRefB_[i] = B.coeffRef(i).vi_;
      Bd_[i] = B.coeffRef(i).val();
    }
    double AB = Eigen::Map<RowVectorXd>(Ad_, 1, size_)
                * Eigen::Map<VectorXd>(Bd_, size_, 1);
    variRefAB_ = new vari(AB, false);
  }

  virtual void chain() {
    using Eigen::Map;
    using Eigen::RowVectorXd;
    using Eigen::VectorXd;
    double adjAB;

    adjAB = variRefAB_->adj_;
    auto adjB = Map<RowVectorXd>(Ad_, 1, size_).transpose() * adjAB;
    for (size_type i = 0; i < size_; ++i)
      variRefB_[i]->adj_ += adjB(i);
  }
};

/**
 * This is a subclass of the vari class for matrix
 * multiplication A * B where A is N by M and B
 * is an M by K matrix of doubles.
 *
 * The class stores the structure of each matrix,
 * the double values of A and B, and pointers to
 * the varis for A and B if A or B is a var. It
 * also instantiates and stores pointers to
 * varis for all elements of A * B.
 *
 * @tparam Ta Scalar type for matrix A
 * @tparam Ra Rows for matrix A
 * @tparam Ca Columns for matrix A, Rows for matrix B
 * @tparam Cb Columns for matrix B
 */
template <typename Ta, int Ra, int Ca, int Cb>
class multiply_mat_vari<Ta, Ra, Ca, double, Cb> : public vari {
 public:
  int A_rows_;
  int A_cols_;
  int B_cols_;
  int A_size_;
  int B_size_;
  double* Ad_;
  double* Bd_;
  vari** variRefA_;
  vari** variRefAB_;

  /**
   * Constructor for multiply_mat_vari.
   *
   * All memory allocated in
   * ChainableStack's stack_alloc arena.
   *
   * It is critical for the efficiency of this object
   * that the constructor create new varis that aren't
   * popped onto the var_stack_, but rather are
   * popped onto the var_nochain_stack_. This is
   * controlled to the second argument to
   * vari's constructor.
   *
   * @param A row vector
   * @param B vector
   */
  multiply_mat_vari(const Eigen::Matrix<Ta, Ra, Ca>& A,
                    const Eigen::Matrix<double, Ca, Cb>& B)
      : vari(0.0),
        A_rows_(A.rows()),
        A_cols_(A.cols()),
        B_cols_(B.cols()),
        A_size_(A.size()),
        B_size_(B.size()),
        Ad_(ChainableStack::instance().memalloc_.alloc_array<double>(A_size_)),
        Bd_(ChainableStack::instance().memalloc_.alloc_array<double>(B_size_)),
        variRefA_(
            ChainableStack::instance().memalloc_.alloc_array<vari*>(A_size_)),
        variRefAB_(ChainableStack::instance().memalloc_.alloc_array<vari*>(
            A_rows_ * B_cols_)) {
    using Eigen::Map;
    using Eigen::MatrixXd;
    for (size_type i = 0; i < A_size_; ++i) {
      variRefA_[i] = A.coeffRef(i).vi_;
      Ad_[i] = A.coeffRef(i).val();
    }
    for (size_type i = 0; i < B_size_; ++i) {
      Bd_[i] = B.coeffRef(i);
    }
    MatrixXd AB = Map<MatrixXd>(Ad_, A_rows_, A_cols_)
                  * Map<MatrixXd>(Bd_, A_cols_, B_cols_);
    for (size_type i = 0; i < AB.size(); ++i)
      variRefAB_[i] = new vari(AB.coeffRef(i), false);
  }

  virtual void chain() {
    using Eigen::Map;
    using Eigen::MatrixXd;
    MatrixXd adjAB(A_rows_, B_cols_);
    MatrixXd adjA(A_rows_, A_cols_);

    for (size_type i = 0; i < adjAB.size(); ++i)
      adjAB(i) = variRefAB_[i]->adj_;
    adjA = adjAB * Map<MatrixXd>(Bd_, A_cols_, B_cols_).transpose();
    for (size_type i = 0; i < A_size_; ++i)
      variRefA_[i]->adj_ += adjA(i);
  }
};

/**
 * This is a subclass of the vari class for matrix
 * multiplication A * B where A is a row
 * vector of length M and B is a vector of length M
 * of doubles.
 *
 * The class stores the structure of each matrix,
 * the double values of A and B, and pointers to
 * the varis for A and B if A or B is a var. It
 * also instantiates and stores pointers to
 * varis for all elements of A * B.
 *
 * @tparam Ta Scalar type for matrix A
 * @tparam Ra Rows for matrix A
 * @tparam Ca Columns for matrix A, Rows for matrix B
 * @tparam Tb Scalar type for matrix B
 * @tparam Cb Columns for matrix B
 */
template <typename Ta, int Ca>
class multiply_mat_vari<Ta, 1, Ca, double, 1> : public vari {
 public:
  int size_;
  double* Ad_;
  double* Bd_;
  vari** variRefA_;
  vari* variRefAB_;

  /**
   * Constructor for multiply_mat_vari.
   *
   * All memory allocated in
   * ChainableStack's stack_alloc arena.
   *
   * It is critical for the efficiency of this object
   * that the constructor create new varis that aren't
   * popped onto the var_stack_, but rather are
   * popped onto the var_nochain_stack_. This is
   * controlled to the second argument to
   * vari's constructor.
   *
   * @param A row vector
   * @param B vector
   */
  multiply_mat_vari(const Eigen::Matrix<Ta, 1, Ca>& A,
                    const Eigen::Matrix<double, Ca, 1>& B)
      : vari(0.0),
        size_(A.cols()),
        Ad_(ChainableStack::instance().memalloc_.alloc_array<double>(size_)),
        Bd_(ChainableStack::instance().memalloc_.alloc_array<double>(size_)),
        variRefA_(
            ChainableStack::instance().memalloc_.alloc_array<vari*>(size_)) {
    using Eigen::Map;
    using Eigen::RowVectorXd;
    using Eigen::VectorXd;
    for (size_type i = 0; i < size_; ++i) {
      variRefA_[i] = A.coeffRef(i).vi_;
      Ad_[i] = A.coeffRef(i).val();
    }
    for (size_type i = 0; i < size_; ++i)
      Bd_[i] = B.coeffRef(i);
    double AB = Map<RowVectorXd>(Ad_, 1, size_) * Map<VectorXd>(Bd_, size_, 1);
    variRefAB_ = new vari(AB, false);
  }

  virtual void chain() {
    using Eigen::Map;
    using Eigen::RowVectorXd;
    using Eigen::VectorXd;
    double adjAB;

    adjAB = variRefAB_->adj_;
    auto adjA = adjAB * Map<VectorXd>(Bd_, size_, 1).transpose();
    for (size_type i = 0; i < size_; ++i)
      variRefA_[i]->adj_ += adjA(i);
  }
};

/**
 * Return the product of two scalars.
 * @tparam T1 scalar type of v
 * @tparam T2 scalar type of c
 * @param[in] v First scalar
 * @param[in] c Specified scalar
 * @return Product of scalars
 */
template <typename T1, typename T2>
inline typename boost::enable_if_c<
    (boost::is_scalar<T1>::value || boost::is_same<T1, var>::value)
        && (boost::is_scalar<T2>::value || boost::is_same<T2, var>::value),
    typename boost::math::tools::promote_args<T1, T2>::type>::type
multiply(const T1& v, const T2& c) {
  return v * c;
}

/**
 * Return the product of scalar and matrix.
 * @tparam T1 scalar type v
 * @tparam T2 scalar type matrix m
 * @tparam R2 Rows matrix m
 * @tparam C2 Columns matrix m
 * @param[in] c Specified scalar
 * @param[in] m Matrix
 * @return Product of scalar and matrix
 */
template <typename T1, typename T2, int R2, int C2>
inline Eigen::Matrix<var, R2, C2> multiply(const T1& c,
                                           const Eigen::Matrix<T2, R2, C2>& m) {
  // TODO(trangucci) pull out to eliminate overpromotion of one side
  // move to matrix.hpp w. promotion?
  return to_var(m) * to_var(c);
}

/**
 * Return the product of scalar and matrix.
 * @tparam T1 scalar type matrix m
 * @tparam T2 scalar type v
 * @tparam R1 Rows matrix m
 * @tparam C1 Columns matrix m
 * @param[in] c Specified scalar
 * @param[in] m Matrix
 * @return Product of scalar and matrix
 */
template <typename T1, int R1, int C1, typename T2>
inline Eigen::Matrix<var, R1, C1> multiply(const Eigen::Matrix<T1, R1, C1>& m,
                                           const T2& c) {
  // TODO(trangucci) pull out to eliminate overpromotion of one side
  // move to matrix.hpp w. promotion?
  return to_var(m) * to_var(c);
}

/**
 * Return the product of two matrices.
 * @tparam Ta scalar type matrix A
 * @tparam Ra Rows matrix A
 * @tparam Ca Columns matrix A
 * @tparam Tb scalar type matrix B
 * @tparam RB Rows matrix B
 * @tparam Cb Columns matrix B
 * @param[in] A Matrix
 * @param[in] B Matrix
 * @return Product of scalar and matrix.
 */
template <typename Ta, int Ra, int Ca, typename Tb, int Cb>
inline typename boost::enable_if_c<boost::is_same<Ta, var>::value
                                       || boost::is_same<Tb, var>::value,
                                   Eigen::Matrix<var, Ra, Cb> >::type
multiply(const Eigen::Matrix<Ta, Ra, Ca>& A,
         const Eigen::Matrix<Tb, Ca, Cb>& B) {
  check_multiplicable("multiply", "A", A, "B", B);
  check_not_nan("multiply", "A", A);
  check_not_nan("multiply", "B", B);

  // Memory managed with the arena allocator.
  multiply_mat_vari<Ta, Ra, Ca, Tb, Cb>* baseVari
      = new multiply_mat_vari<Ta, Ra, Ca, Tb, Cb>(A, B);
  Eigen::Matrix<var, Ra, Cb> AB_v(A.rows(), B.cols());
  for (size_type i = 0; i < AB_v.size(); ++i) {
    AB_v.coeffRef(i).vi_ = baseVari->variRefAB_[i];
  }
  return AB_v;
}

/**
 * Return the scalar product of a row vector and
 * a vector.
 * @tparam Ta scalar type row vector A
 * @tparam Ca Columns matrix A
 * @tparam Tb scalar type vector B
 * @param[in] A Row vector
 * @param[in] B Column vector
 * @return Scalar product of row vector and vector
 */
template <typename Ta, int Ca, typename Tb>
inline typename boost::enable_if_c<
    boost::is_same<Ta, var>::value || boost::is_same<Tb, var>::value, var>::type
multiply(const Eigen::Matrix<Ta, 1, Ca>& A, const Eigen::Matrix<Tb, Ca, 1>& B) {
  check_multiplicable("multiply", "A", A, "B", B);
  check_not_nan("multiply", "A", A);
  check_not_nan("multiply", "B", B);

  // Memory managed with the arena allocator.
  multiply_mat_vari<Ta, 1, Ca, Tb, 1>* baseVari
      = new multiply_mat_vari<Ta, 1, Ca, Tb, 1>(A, B);
  var AB_v;
  AB_v.vi_ = baseVari->variRefAB_;
  return AB_v;
}
}  // namespace math
}  // namespace stan
#endif
