// SPDX-License-Identifier: Apache-2.0
//
// Copyright 2008-2016 Conrad Sanderson (http://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
// http://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.
// ------------------------------------------------------------------------

namespace newarp {

template <typename eT, int SelectionRule, typename OpType>
inline void GenEigsSolver<eT, SelectionRule, OpType>::fill_rand(eT* dest, const uword N,
                                                                const uword seed_val) {
  arma_extra_debug_sigprint();

  typedef typename std::mt19937_64::result_type seed_type;

  local_rng.seed(seed_type(seed_val));

  std::uniform_real_distribution<double> dist(-1.0, +1.0);

  for (uword i = 0; i < N; ++i) {
    dest[i] = eT(dist(local_rng));
  }
}

template <typename eT, int SelectionRule, typename OpType>
inline void GenEigsSolver<eT, SelectionRule, OpType>::factorise_from(uword from_k,
                                                                     uword to_m,
                                                                     const Col<eT>& fk) {
  arma_extra_debug_sigprint();

  if (to_m <= from_k) {
    return;
  }

  fac_f = fk;

  Col<eT> w(dim_n, arma_zeros_indicator());
  eT beta = norm(fac_f);
  // Keep the upperleft k x k submatrix of H and set other elements to 0
  fac_H.tail_cols(ncv - from_k).zeros();
  fac_H.submat(span(from_k, ncv - 1), span(0, from_k - 1)).zeros();
  for (uword i = from_k; i <= to_m - 1; i++) {
    bool restart = false;
    // If beta = 0, then the next V is not full rank
    // We need to generate a new residual vector that is orthogonal
    // to the current V, which we call a restart
    if (beta < eps) {
      // // Generate new random vector for fac_f
      // blas_int idist = 2;
      // blas_int iseed[4] = {1, 3, 5, 7};
      // iseed[0] = (i + 100) % 4095;
      // blas_int n = dim_n;
      // lapack::larnv(&idist, &iseed[0], &n, fac_f.memptr());

      // Generate new random vector for fac_f
      fill_rand(fac_f.memptr(), dim_n, i + 1);

      // f <- f - V * V' * f, so that f is orthogonal to V
      Mat<eT> Vs(fac_V.memptr(), dim_n, i, false);  // First i columns
      Col<eT> Vf = Vs.t() * fac_f;
      fac_f -= Vs * Vf;
      // beta <- ||f||
      beta = norm(fac_f);

      restart = true;
    }

    // v <- f / ||f||
    fac_V.col(i) = fac_f / beta;  // The (i+1)-th column

    // Note that H[i+1, i] equals to the unrestarted beta
    if (restart) {
      fac_H(i, i - 1) = 0.0;
    } else {
      fac_H(i, i - 1) = beta;
    }

    // w <- A * v, v = fac_V.col(i)
    op.perform_op(fac_V.colptr(i), w.memptr());
    nmatop++;

    // First i+1 columns of V
    Mat<eT> Vs(fac_V.memptr(), dim_n, i + 1, false);
    // h = fac_H(0:i, i)
    Col<eT> h(fac_H.colptr(i), i + 1, false);
    // h <- V' * w
    h = Vs.t() * w;

    // f <- w - V * h
    fac_f = w - Vs * h;
    beta = norm(fac_f);

    if (beta > 0.717 * norm(h)) {
      continue;
    }

    // f/||f|| is going to be the next column of V, so we need to test
    // whether V' * (f/||f||) ~= 0
    Col<eT> Vf = Vs.t() * fac_f;
    // If not, iteratively correct the residual
    uword count = 0;
    while (count < 5 && abs(Vf).max() > approx0 * beta) {
      // f <- f - V * Vf
      fac_f -= Vs * Vf;
      // h <- h + Vf
      h += Vf;
      // beta <- ||f||
      beta = norm(fac_f);

      Vf = Vs.t() * fac_f;
      count++;
    }
  }
}

template <typename eT, int SelectionRule, typename OpType>
inline void GenEigsSolver<eT, SelectionRule, OpType>::restart(uword k) {
  arma_extra_debug_sigprint();

  if (k >= ncv) {
    return;
  }

  DoubleShiftQR<eT> decomp_ds(ncv);
  UpperHessenbergQR<eT> decomp;

  Mat<eT> Q(ncv, ncv, fill::eye);

  for (uword i = k; i < ncv; i++) {
    if (cx_attrib::is_complex(ritz_val(i), eT(0)) && (i < (ncv - 1)) &&
        cx_attrib::is_conj(ritz_val(i), ritz_val(i + 1), eT(0))) {
      // H - mu * I = Q1 * R1
      // H <- R1 * Q1 + mu * I = Q1' * H * Q1
      // H - conj(mu) * I = Q2 * R2
      // H <- R2 * Q2 + conj(mu) * I = Q2' * H * Q2
      //
      // (H - mu * I) * (H - conj(mu) * I) = Q1 * Q2 * R2 * R1 = Q * R
      eT s = 2 * ritz_val(i).real();
      eT t = std::norm(ritz_val(i));
      decomp_ds.compute(fac_H, s, t);

      // Q -> Q * Qi
      decomp_ds.apply_YQ(Q);
      // H -> Q'HQ
      fac_H = decomp_ds.matrix_QtHQ();

      i++;
    } else {
      // QR decomposition of H - mu * I, mu is real
      fac_H.diag() -= ritz_val(i).real();
      decomp.compute(fac_H);

      // Q -> Q * Qi
      decomp.apply_YQ(Q);
      // H -> Q'HQ = RQ + mu * I
      fac_H = decomp.matrix_RQ();
      fac_H.diag() += ritz_val(i).real();
    }
  }

  // V -> VQ
  // Q has some elements being zero
  // The first (ncv - k + i) elements of the i-th column of Q are non-zero
  Mat<eT> Vs(dim_n, k + 1, arma_nozeros_indicator());
  uword nnz;
  for (uword i = 0; i < k; i++) {
    nnz = ncv - k + i + 1;
    Mat<eT> V(fac_V.memptr(), dim_n, nnz, false);
    Col<eT> q(Q.colptr(i), nnz, false);
    Col<eT> v(Vs.colptr(i), dim_n, false);
    v = V * q;
  }

  Vs.col(k) = fac_V * Q.col(k);
  fac_V.head_cols(k + 1) = Vs;

  Col<eT> fk = fac_f * Q(ncv - 1, k - 1) + fac_V.col(k) * fac_H(k, k - 1);
  factorise_from(k, ncv, fk);
  retrieve_ritzpair();
}

template <typename eT, int SelectionRule, typename OpType>
inline uword GenEigsSolver<eT, SelectionRule, OpType>::num_converged(eT tol) {
  arma_extra_debug_sigprint();

  // thresh = tol * max(prec, abs(theta)), theta for ritz value
  const eT f_norm = arma::norm(fac_f);
  for (uword i = 0; i < nev; i++) {
    eT thresh = tol * (std::max)(approx0, std::abs(ritz_val(i)));
    eT resid = std::abs(ritz_est(i)) * f_norm;
    ritz_conv[i] = (resid < thresh);
  }

  return std::count(ritz_conv.begin(), ritz_conv.end(), true);
}

template <typename eT, int SelectionRule, typename OpType>
inline uword GenEigsSolver<eT, SelectionRule, OpType>::nev_adjusted(uword nconv) {
  arma_extra_debug_sigprint();

  uword nev_new = nev;

  for (uword i = nev; i < ncv; i++) {
    if (std::abs(ritz_est(i)) < eps) {
      nev_new++;
    }
  }
  // Adjust nev_new again, according to dnaup2.f line 660~674 in ARPACK
  nev_new += (std::min)(nconv, (ncv - nev_new) / 2);
  if (nev_new == 1 && ncv >= 6) {
    nev_new = ncv / 2;
  } else if (nev_new == 1 && ncv > 3) {
    nev_new = 2;
  }

  if (nev_new > ncv - 2) {
    nev_new = ncv - 2;
  }

  // Increase nev by one if ritz_val[nev - 1] and
  // ritz_val[nev] are conjugate pairs
  if (cx_attrib::is_complex(ritz_val(nev_new - 1), eps) &&
      cx_attrib::is_conj(ritz_val(nev_new - 1), ritz_val(nev_new), eps)) {
    nev_new++;
  }

  return nev_new;
}

template <typename eT, int SelectionRule, typename OpType>
inline void GenEigsSolver<eT, SelectionRule, OpType>::retrieve_ritzpair() {
  arma_extra_debug_sigprint();

  UpperHessenbergEigen<eT> decomp(fac_H);

  Col<std::complex<eT> > evals = decomp.eigenvalues();
  Mat<std::complex<eT> > evecs = decomp.eigenvectors();

  SortEigenvalue<std::complex<eT>, SelectionRule> sorting(evals.memptr(), evals.n_elem);
  std::vector<uword> ind = sorting.index();

  // Copy the ritz values and vectors to ritz_val and ritz_vec, respectively
  for (uword i = 0; i < ncv; i++) {
    ritz_val(i) = evals(ind[i]);
    ritz_est(i) = evecs(ncv - 1, ind[i]);
  }
  for (uword i = 0; i < nev; i++) {
    ritz_vec.col(i) = evecs.col(ind[i]);
  }
}

template <typename eT, int SelectionRule, typename OpType>
inline void GenEigsSolver<eT, SelectionRule, OpType>::sort_ritzpair() {
  arma_extra_debug_sigprint();

  // SortEigenvalue< std::complex<eT>, EigsSelect::LARGEST_MAGN >
  // sorting(ritz_val.memptr(), nev);

  // sort Ritz values according to SelectionRule, to be consistent with ARPACK
  SortEigenvalue<std::complex<eT>, SelectionRule> sorting(ritz_val.memptr(), nev);

  std::vector<uword> ind = sorting.index();

  Col<std::complex<eT> > new_ritz_val(ncv, arma_zeros_indicator());
  Mat<std::complex<eT> > new_ritz_vec(ncv, nev, arma_nozeros_indicator());
  std::vector<bool> new_ritz_conv(nev);

  for (uword i = 0; i < nev; i++) {
    new_ritz_val(i) = ritz_val(ind[i]);
    new_ritz_vec.col(i) = ritz_vec.col(ind[i]);
    new_ritz_conv[i] = ritz_conv[ind[i]];
  }

  ritz_val.swap(new_ritz_val);
  ritz_vec.swap(new_ritz_vec);
  ritz_conv.swap(new_ritz_conv);
}

template <typename eT, int SelectionRule, typename OpType>
inline GenEigsSolver<eT, SelectionRule, OpType>::GenEigsSolver(const OpType& op_,
                                                               uword nev_, uword ncv_)
    : op(op_),
      nev(nev_),
      dim_n(op.n_rows),
      ncv(ncv_ > dim_n ? dim_n : ncv_),
      nmatop(0),
      niter(0),
      eps(std::numeric_limits<eT>::epsilon()),
      approx0(std::pow(eps, eT(2.0) / 3)) {
  arma_extra_debug_sigprint();

  arma_debug_check((nev_ < 1 || nev_ > dim_n - 2),
                   "newarp::GenEigsSolver: nev must satisfy 1 <= nev <= n - 2, n is the "
                   "size of matrix");
  arma_debug_check((ncv_ < nev_ + 2 || ncv_ > dim_n),
                   "newarp::GenEigsSolver: ncv must satisfy nev + 2 <= ncv <= n, n is "
                   "the size of matrix");
}

template <typename eT, int SelectionRule, typename OpType>
inline void GenEigsSolver<eT, SelectionRule, OpType>::init(eT* init_resid) {
  arma_extra_debug_sigprint();

  // Reset all matrices/vectors to zero
  fac_V.zeros(dim_n, ncv);
  fac_H.zeros(ncv, ncv);
  fac_f.zeros(dim_n);
  ritz_val.zeros(ncv);
  ritz_vec.zeros(ncv, nev);
  ritz_est.zeros(ncv);
  ritz_conv.assign(nev, false);

  nmatop = 0;
  niter = 0;

  Col<eT> r(init_resid, dim_n, false);
  // The first column of fac_V
  Col<eT> v(fac_V.colptr(0), dim_n, false);
  eT rnorm = norm(r);
  arma_check((rnorm < eps),
             "newarp::GenEigsSolver::init(): initial residual vector cannot be zero");
  v = r / rnorm;

  Col<eT> w(dim_n, arma_zeros_indicator());
  op.perform_op(v.memptr(), w.memptr());
  nmatop++;

  fac_H(0, 0) = dot(v, w);
  fac_f = w - v * fac_H(0, 0);
}

template <typename eT, int SelectionRule, typename OpType>
inline void GenEigsSolver<eT, SelectionRule, OpType>::init() {
  arma_extra_debug_sigprint();

  // podarray<eT> init_resid(dim_n);
  // blas_int idist = 2;                // Uniform(-1, 1)
  // blas_int iseed[4] = {1, 3, 5, 7};  // Fixed random seed
  // blas_int n = dim_n;
  // lapack::larnv(&idist, &iseed[0], &n, init_resid.memptr());
  // init(init_resid.memptr());

  podarray<eT> init_resid(dim_n);

  fill_rand(init_resid.memptr(), dim_n, 0);

  init(init_resid.memptr());
}

template <typename eT, int SelectionRule, typename OpType>
inline uword GenEigsSolver<eT, SelectionRule, OpType>::compute(uword maxit, eT tol) {
  arma_extra_debug_sigprint();

  // The m-step Arnoldi factorisation
  factorise_from(1, ncv, fac_f);
  retrieve_ritzpair();
  // Restarting
  uword i, nconv = 0, nev_adj;
  for (i = 0; i < maxit; i++) {
    nconv = num_converged(tol);
    if (nconv >= nev) {
      break;
    }

    nev_adj = nev_adjusted(nconv);
    restart(nev_adj);
  }
  // Sorting results
  sort_ritzpair();

  niter = i + 1;

  return (std::min)(nev, nconv);
}

template <typename eT, int SelectionRule, typename OpType>
inline Col<std::complex<eT> > GenEigsSolver<eT, SelectionRule, OpType>::eigenvalues() {
  arma_extra_debug_sigprint();

  uword nconv = std::count(ritz_conv.begin(), ritz_conv.end(), true);
  Col<std::complex<eT> > res(nconv, arma_zeros_indicator());

  if (nconv > 0) {
    uword j = 0;
    for (uword i = 0; i < nev; i++) {
      if (ritz_conv[i]) {
        res(j) = ritz_val(i);
        j++;
      }
    }
  }

  return res;
}

template <typename eT, int SelectionRule, typename OpType>
inline Mat<std::complex<eT> > GenEigsSolver<eT, SelectionRule, OpType>::eigenvectors(
    uword nvec) {
  arma_extra_debug_sigprint();

  uword nconv = std::count(ritz_conv.begin(), ritz_conv.end(), true);
  nvec = (std::min)(nvec, nconv);
  Mat<std::complex<eT> > res(dim_n, nvec);

  if (nvec > 0) {
    Mat<std::complex<eT> > ritz_vec_conv(ncv, nvec, arma_zeros_indicator());
    uword j = 0;
    for (uword i = 0; (i < nev) && (j < nvec); i++) {
      if (ritz_conv[i]) {
        ritz_vec_conv.col(j) = ritz_vec.col(i);
        j++;
      }
    }

    res = fac_V * ritz_vec_conv;
  }

  return res;
}

}  // namespace newarp
