/**
 * Copyright (c) Facebook, Inc. and its affiliates.
 *
 * This source code is licensed under the MIT license found in the
 * LICENSE file in the root directory of this source tree.
 */

// -*- c++ -*-

#include <algorithm>
#include <faiss/impl/Vanama.h>
#include <omp.h>
#include <string>
#include <unordered_map>

#include <faiss/impl/AuxIndexStructures.h>
#include <faiss/impl/DistanceComputer.h>
#include <faiss/impl/IDSelector.h>
#include <faiss/utils/prefetch.h>

#include <faiss/impl/platform_macros.h>

#ifdef __AVX2__
#include <immintrin.h>

#include <limits>
#include <type_traits>
#endif

#define chronoElapsedTime(start)                               \
    std::chrono::duration_cast<std::chrono::microseconds>(     \
            std::chrono::high_resolution_clock::now() - start) \
            .count()

namespace faiss {

/**************************************************************
 * Vanama structure implementation
 **************************************************************/

int Vanama::nb_neighbors(int layer_no) const {
    return cum_nneighbor_per_level[layer_no + 1] -
            cum_nneighbor_per_level[layer_no];
}

void Vanama::set_nb_neighbors(int level_no, int n) {
    FAISS_THROW_IF_NOT(levels.size() == 0);
    int cur_n = nb_neighbors(level_no);
    for (int i = level_no + 1; i < cum_nneighbor_per_level.size(); i++) {
        cum_nneighbor_per_level[i] += n - cur_n;
    }
}

int Vanama::cum_nb_neighbors(int layer_no) const {
    return cum_nneighbor_per_level[layer_no];
}

void Vanama::neighbor_range(idx_t no, int layer_no, size_t* begin, size_t* end)
        const {
    size_t o = offsets[no];
    *begin = o + cum_nb_neighbors(layer_no);
    *end = o + cum_nb_neighbors(layer_no + 1);
}

Vanama::Vanama(int M) : rng(12345) {
    set_default_probas(M, 1.0 / log(M));
    offsets.push_back(0);

    M_ = M;
}

int Vanama::random_level() {
    double f = rng.rand_float();
    // could be a bit faster with bissection
    for (int level = 0; level < assign_probas.size(); level++) {
        if (f < assign_probas[level]) {
            return level;
        }
        f -= assign_probas[level];
    }
    // happens with exponentially low probability
    return assign_probas.size() - 1;
}

void Vanama::set_default_probas(int M, float levelMult) {
    int nn = 0;
    cum_nneighbor_per_level.push_back(0);
    for (int level = 0;; level++) {
        float proba = exp(-level / levelMult) * (1 - exp(-1 / levelMult));
        if (proba < 1e-9)
            break;
        assign_probas.push_back(proba);
        nn += level == 0 ? M * 2 : M;
        cum_nneighbor_per_level.push_back(nn);
    }
}

void Vanama::clear_neighbor_tables(int level) {
    for (int i = 0; i < levels.size(); i++) {
        size_t begin, end;
        neighbor_range(i, level, &begin, &end);
        for (size_t j = begin; j < end; j++) {
            neighbors[j] = -1;
        }
    }
}

void Vanama::reset() {
    max_level = -1;
    entry_point = -1;
    offsets.clear();
    offsets.push_back(0);
    levels.clear();
    neighbors.clear();
}

void Vanama::print_neighbor_stats(int level) const {
    FAISS_THROW_IF_NOT(level < cum_nneighbor_per_level.size());
    printf("stats on level %d, max %d neighbors per vertex:\n",
           level,
           nb_neighbors(level));
    size_t tot_neigh = 0, tot_common = 0, tot_reciprocal = 0, n_node = 0;
    #pragma omp parallel for reduction(+: tot_neigh) reduction(+: tot_common) \
  reduction(+: tot_reciprocal) reduction(+: n_node)
    for (int i = 0; i < levels.size(); i++) {
        if (levels[i] > level) {
            n_node++;
            size_t begin, end;
            neighbor_range(i, level, &begin, &end);
            std::unordered_set<int> neighset;
            for (size_t j = begin; j < end; j++) {
                if (neighbors[j] < 0)
                    break;
                neighset.insert(neighbors[j]);
            }
            int n_neigh = neighset.size();
            int n_common = 0;
            int n_reciprocal = 0;
            for (size_t j = begin; j < end; j++) {
                storage_idx_t i2 = neighbors[j];
                if (i2 < 0)
                    break;
                FAISS_ASSERT(i2 != i);
                size_t begin2, end2;
                neighbor_range(i2, level, &begin2, &end2);
                for (size_t j2 = begin2; j2 < end2; j2++) {
                    storage_idx_t i3 = neighbors[j2];
                    if (i3 < 0)
                        break;
                    if (i3 == i) {
                        n_reciprocal++;
                        continue;
                    }
                    if (neighset.count(i3)) {
                        neighset.erase(i3);
                        n_common++;
                    }
                }
            }
            tot_neigh += n_neigh;
            tot_common += n_common;
            tot_reciprocal += n_reciprocal;
        }
    }
    float normalizer = n_node;
    printf("   nb of nodes at that level %zd\n", n_node);
    printf("   neighbors per node: %.2f (%zd)\n",
           tot_neigh / normalizer,
           tot_neigh);
    printf("   nb of reciprocal neighbors: %.2f\n",
           tot_reciprocal / normalizer);
    printf("   nb of neighbors that are also neighbor-of-neighbors: %.2f (%zd)\n",
           tot_common / normalizer,
           tot_common);
}

void Vanama::fill_with_random_links(size_t n) {
    int max_level = prepare_level_tab(n);
    RandomGenerator rng2(456);

    for (int level = max_level - 1; level >= 0; --level) {
        std::vector<int> elts;
        for (int i = 0; i < n; i++) {
            if (levels[i] > level) {
                elts.push_back(i);
            }
        }
        printf("linking %zd elements in level %d\n", elts.size(), level);

        if (elts.size() == 1)
            continue;

        for (int ii = 0; ii < elts.size(); ii++) {
            int i = elts[ii];
            size_t begin, end;
            neighbor_range(i, 0, &begin, &end);
            for (size_t j = begin; j < end; j++) {
                int other = 0;
                do {
                    other = elts[rng2.rand_int(elts.size())];
                } while (other == i);

                neighbors[j] = other;
            }
        }
    }
}

    int Vanama::prepare_level_tab(size_t n, bool preset_levels) {
    size_t n0 = offsets.size() - 1;

    if (preset_levels) {
        FAISS_ASSERT(n0 + n == levels.size());
    } else {
        FAISS_ASSERT(n0 == levels.size());
        for (int i = 0; i < n; i++) {
            int pt_level = random_level();
            //int pt_level=0;
            levels.push_back(pt_level + 1);
        }
    }

    int max_level = 0;
    for (int i = 0; i < n; i++) {
        //int pt_level = levels[i + n0] - 1;
        int pt_level=0;
        // if (pt_level > max_level)
        //     max_level = pt_level;
        offsets.push_back(offsets.back() + cum_nb_neighbors(pt_level + 1));
        neighbors.resize(offsets.back(), -1);
    }

    return max_level;
}
/** Enumerate vertices from nearest to farthest from query, keep a
 * neighbor only if there is no previous neighbor that is closer to
 * that vertex than the query.
 */
void Vanama::shrink_neighbor_list(
        DistanceComputer& qdis,
        std::priority_queue<NodeDistFarther>& input,
        std::vector<NodeDistFarther>& output,
        int max_size) {

    double alpha = 1.1;

    std::priority_queue<NodeDistCloser> input2;
    std::priority_queue<NodeDistFarther> input1;
    //std::vector<NodeDistFarther> output2;

    while (!input.empty()) {
        input2.emplace(input.top().d, input.top().id);
        input1.emplace(input.top().d, input.top().id);
        input.pop();
    }
    NodeDistCloser p_star = input2.top();

    while((output.size() < max_size) && (!input1.empty())) {
        NodeDistFarther p_prime = input1.top();
        input1.pop();

        float dist_pstar_pprime = qdis.symmetric_dis(p_star.id, p_prime.id);

        if ( (alpha * dist_pstar_pprime) > p_prime.d) {
                output.push_back(p_prime);
            }
    }

    // for (NodeDistCloser curr1 : output2) {
    //     output.emplace_back(curr1.d, curr1.id);
    // }
}



namespace {

using storage_idx_t = Vanama::storage_idx_t;
using NodeDistCloser = Vanama::NodeDistCloser;
using NodeDistFarther = Vanama::NodeDistFarther;

/**************************************************************
 * Addition subroutines
 **************************************************************/

/// remove neighbors from the list to make it smaller than max_size
void shrink_neighbor_list(
        DistanceComputer& qdis,
        std::priority_queue<NodeDistCloser>& resultSet1,
        int max_size) {
    if (resultSet1.size() < max_size) {
        return;
    }
    std::priority_queue<NodeDistFarther> resultSet;
    std::vector<NodeDistFarther> returnlist;
    while (resultSet1.size() > 0) {
        resultSet.emplace(resultSet1.top().d, resultSet1.top().id);
        resultSet1.pop();
    }

    Vanama::shrink_neighbor_list(qdis, resultSet, returnlist, max_size);

    for (NodeDistFarther curen2 : returnlist) {
        resultSet1.emplace(curen2.d, curen2.id);
    }
}

/// add a link between two elements, possibly shrinking the list
/// of links to make room for it.
void add_link(
        Vanama& vanama,
        DistanceComputer& qdis,
        storage_idx_t src,
        storage_idx_t dest,
        int level) {
    size_t begin, end;
    vanama.neighbor_range(src, level, &begin, &end);
    if (vanama.neighbors[end - 1] == -1) {
        // there is enough room, find a slot to add it
        size_t i = end;
        while (i > begin) {
            if (vanama.neighbors[i - 1] != -1)
                break;
            i--;
        }
        vanama.neighbors[i] = dest;
        return;
    }

    // otherwise we let them fight out which to keep

    // copy to resultSet...
    std::priority_queue<NodeDistCloser> resultSet;

    auto dist = qdis.symmetric_dis(src,dest);

    resultSet.emplace(dist, dest);
    for (size_t i = begin; i < end; i++) { // HERE WAS THE BUG
        storage_idx_t neigh = vanama.neighbors[i];

	dist = qdis.symmetric_dis(src,neigh);

        resultSet.emplace(dist, neigh);
    }

    shrink_neighbor_list(qdis, resultSet, end - begin);

    // ...and back
    size_t i = begin;
    while (resultSet.size()) {
        vanama.neighbors[i++] = resultSet.top().id;
        resultSet.pop();
    }
    // they may have shrunk more than just by 1 element
    while (i < end) {
        vanama.neighbors[i++] = -1;
    }
}
    void search_neighbors_to_add(
            Vanama& vanama,
            DistanceComputer& qdis,
            std::priority_queue<NodeDistCloser>& results,
            int entry_point,
            float d_entry_point,
            int level,
            VisitedTable& vt);
/// search neighbors on a single level, starting from an entry point
void search_neighbors_to_add(
        Vanama& vanama,
        DistanceComputer& qdis,
        std::priority_queue<NodeDistCloser>& results,
        int entry_point,
        float d_entry_point,
        int level,
        VisitedTable& vt) {
    // top is nearest candidate
    std::priority_queue<NodeDistFarther> candidates;

    NodeDistFarther ev(d_entry_point, entry_point);
    candidates.push(ev);
    results.emplace(d_entry_point, entry_point);
    vt.set(entry_point);

    while (!candidates.empty()) {
        // get nearest
        const NodeDistFarther& currEv = candidates.top();

        if (currEv.d > results.top().d) {
            break;
        }
        int currNode = currEv.id;
        candidates.pop();

        // loop over neighbors
        size_t begin, end;
        vanama.neighbor_range(currNode, level, &begin, &end);
        for (size_t i = begin; i < end; i++) {
            storage_idx_t nodeId = vanama.neighbors[i];
            if (nodeId < 0)
                break;
            if (vt.get(nodeId))
                continue;
            vt.set(nodeId);


            float dis = qdis(nodeId);

            NodeDistFarther evE1(dis, nodeId);

            if (results.size() < vanama.efConstruction || results.top().d > dis) {
                results.emplace(dis, nodeId);
                candidates.emplace(dis, nodeId);
                if (results.size() > vanama.efConstruction) {
                    results.pop();
                }
            }
        }
    }
    vt.advance();
}

/**************************************************************
 * Searching subroutines
 **************************************************************/

/// greedily update a nearest vector at a given level
void greedy_update_nearest(
        const Vanama& vanama,
        DistanceComputer& qdis,
        int level,
        storage_idx_t& nearest,
        float& d_nearest) {
    for (;;) {
        storage_idx_t prev_nearest = nearest;

        size_t begin, end;
        vanama.neighbor_range(nearest, level, &begin, &end);
        for (size_t i = begin; i < end; i++) {

            storage_idx_t v = vanama.neighbors[i];
            if (v < 0)
                break;
            float dis = qdis(v);
            if (dis < d_nearest) {
                nearest = v;
                d_nearest = dis;
            }
        }
        if (nearest == prev_nearest) {
            return;
        }
    }
}

} // namespace

/// Finds neighbors and builds links with them, starting from an entry
/// point. The own neighbor list is assumed to be locked.
void Vanama::add_links_starting_from(
        DistanceComputer& ptdis,
        storage_idx_t pt_id,
        storage_idx_t nearest,
        float d_nearest,
        int level,
        omp_lock_t* locks,
        VisitedTable& vt) {
    std::priority_queue<NodeDistCloser> link_targets;

    search_neighbors_to_add(
            *this, ptdis, link_targets, nearest, d_nearest, level, vt);


    // but we can afford only this many neighbors
    int M = nb_neighbors(level);


    ::faiss::shrink_neighbor_list(ptdis, link_targets, M);

    std::vector<storage_idx_t> neighbors;
    neighbors.reserve(link_targets.size());
    while (!link_targets.empty()) {
        storage_idx_t other_id = link_targets.top().id;
        add_link(*this, ptdis, pt_id, other_id, level);
        neighbors.push_back(other_id);
        link_targets.pop();
    }

    omp_unset_lock(&locks[pt_id]);
    for (storage_idx_t other_id : neighbors) {
        omp_set_lock(&locks[other_id]);
        add_link(*this, ptdis, other_id, pt_id, level);
        omp_unset_lock(&locks[other_id]);
    }
    omp_set_lock(&locks[pt_id]);

}

/**************************************************************
 * Building, parallel
 **************************************************************/

void Vanama::add_with_locks(
        DistanceComputer& ptdis,
        int pt_level,
        int pt_id,
        std::vector<omp_lock_t>& locks,
        VisitedTable& vt) {
    //  greedy search on upper levels

    storage_idx_t nearest;
    #pragma omp critical
    {
        nearest = entry_point;

        if (nearest == -1) {
            max_level = pt_level;
            entry_point = pt_id;
        }
    }

    if (nearest < 0) {
        return;
    }

    omp_set_lock(&locks[pt_id]);

    int level = max_level; // level at which we start adding neighbors
    float d_nearest = ptdis(nearest);
    auto greedy_start = std::chrono::high_resolution_clock::now();
    /*for (; level > pt_level; level--) {
        greedy_update_nearest(*this, ptdis, level, nearest, d_nearest);
    }*/
    greedy_update_nearest(*this, ptdis, level, nearest, d_nearest);

    for (; level >= 0; level--) {
        //greedy_update_nearest(*this, ptdis, level, nearest, d_nearest);
        add_links_starting_from(
                ptdis, pt_id, nearest, d_nearest, level, locks.data(), vt);
    }

    omp_unset_lock(&locks[pt_id]);

    if (pt_level > max_level) {
        max_level = pt_level;
        entry_point = pt_id;
    }
}

/**************************************************************
 * Searching
 **************************************************************/

namespace {

using MinimaxHeap = Vanama::MinimaxHeap;
using Node = Vanama::Node;
/** Do a BFS on the candidates list */

int search_from_candidates(
        const Vanama& vanama,
        DistanceComputer& qdis,
        int k,
        idx_t* I,
        float* D,
        MinimaxHeap& candidates,
        VisitedTable& vt,
        HNSWStats& stats,
        int level,
        int nres_in = 0,
        const SearchParametersHNSW* params = nullptr) {
    int nres = nres_in;
    int ndis = 0;

    // can be overridden by search params
    bool do_dis_check = params ? params->check_relative_distance
                               : vanama.check_relative_distance;
    int efSearch = params ? params->efSearch : vanama.efSearch;
    const IDSelector* sel = params ? params->sel : nullptr;

    for (int i = 0; i < candidates.size(); i++) {
        idx_t v1 = candidates.ids[i];
        float d = candidates.dis[i];
        FAISS_ASSERT(v1 >= 0);
        if (!sel || sel->is_member(v1)) {
            if (nres < k) {
                faiss::maxheap_push(++nres, D, I, d, v1);
            } else if (d < D[0]) {
                faiss::maxheap_replace_top(nres, D, I, d, v1);
            }
        }
        vt.set(v1);
    }

    int nstep = 0;

    while (candidates.size() > 0) {
        float d0 = 0;
        int v0 = candidates.pop_min(&d0);

        if (do_dis_check) {
            // tricky stopping condition: there are more that ef
            // distances that are processed already that are smaller
            // than d0

            int n_dis_below = candidates.count_below(d0);
            if (n_dis_below >= efSearch) {
                break;
            }
        }

        size_t begin, end;
        vanama.neighbor_range(v0, level, &begin, &end);

        // // baseline version
        // for (size_t j = begin; j < end; j++) {
        //     int v1 = vanama.neighbors[j];
        //     if (v1 < 0)
        //         break;
        //     if (vt.get(v1)) {
        //         continue;
        //     }
        //     vt.set(v1);
        //     ndis++;
        //     float d = qdis(v1);
        //     if (!sel || sel->is_member(v1)) {
        //         if (nres < k) {
        //             faiss::maxheap_push(++nres, D, I, d, v1);
        //         } else if (d < D[0]) {
        //             faiss::maxheap_replace_top(nres, D, I, d, v1);
        //         }
        //     }
        //     candidates.push(v1, d);
        // }

        // the following version processes 4 neighbors at a time
        size_t jmax = begin;
        for (size_t j = begin; j < end; j++) {
            int v1 = vanama.neighbors[j];
            if (v1 < 0)
                break;

            prefetch_L2(vt.visited.data() + v1);
            jmax += 1;
        }

        int counter = 0;
        size_t saved_j[4];

        ndis += jmax - begin;

        auto add_to_heap = [&](const size_t idx, const float dis) {
            if (!sel || sel->is_member(idx)) {
                if (nres < k) {
                    faiss::maxheap_push(++nres, D, I, dis, idx);
                } else if (dis < D[0]) {
                    faiss::maxheap_replace_top(nres, D, I, dis, idx);
                }
            }
            candidates.push(idx, dis);
        };

        for (size_t j = begin; j < jmax; j++) {
            int v1 = vanama.neighbors[j];

            bool vget = vt.get(v1);
            vt.set(v1);
            saved_j[counter] = v1;
            counter += vget ? 0 : 1;

            if (counter == 4) {
                float dis[4];
                qdis.distances_batch_4(
                        saved_j[0],
                        saved_j[1],
                        saved_j[2],
                        saved_j[3],
                        dis[0],
                        dis[1],
                        dis[2],
                        dis[3]);

                for (size_t id4 = 0; id4 < 4; id4++) {
                    add_to_heap(saved_j[id4], dis[id4]);
                }

                counter = 0;
            }
        }

        for (size_t icnt = 0; icnt < counter; icnt++) {
            float dis = qdis(saved_j[icnt]);
            add_to_heap(saved_j[icnt], dis);
        }

        nstep++;
        if (!do_dis_check && nstep > efSearch) {
            break;
        }
    }

    if (level == 0) {
        stats.n1++;
        if (candidates.size() == 0) {
            stats.n2++;
        }
        stats.n3 += ndis;
    }

    return nres;
}

std::priority_queue<Vanama::Node> search_from_candidate_unbounded(
        const Vanama& vanama,
        const Node& node,
        DistanceComputer& qdis,
        int ef,
        VisitedTable* vt,
        HNSWStats& stats) {
    int ndis = 0;
    std::priority_queue<Node> top_candidates;
    std::priority_queue<Node, std::vector<Node>, std::greater<Node>> candidates;

    top_candidates.push(node);
    candidates.push(node);

    vt->set(node.second);

    while (!candidates.empty()) {
        float d0;
        storage_idx_t v0;
        std::tie(d0, v0) = candidates.top();

        if (d0 > top_candidates.top().first) {
            break;
        }

        candidates.pop();

        size_t begin, end;
        vanama.neighbor_range(v0, 0, &begin, &end);

        // // baseline version
        // for (size_t j = begin; j < end; ++j) {
        //     int v1 = vanama.neighbors[j];
        //
        //     if (v1 < 0) {
        //         break;
        //     }
        //     if (vt->get(v1)) {
        //         continue;
        //     }
        //
        //     vt->set(v1);
        //
        //     float d1 = qdis(v1);
        //     ++ndis;
        //
        //     if (top_candidates.top().first > d1 ||
        //         top_candidates.size() < ef) {
        //         candidates.emplace(d1, v1);
        //         top_candidates.emplace(d1, v1);
        //
        //         if (top_candidates.size() > ef) {
        //             top_candidates.pop();
        //         }
        //     }
        // }

        // the following version processes 4 neighbors at a time
        size_t jmax = begin;
        for (size_t j = begin; j < end; j++) {
            int v1 = vanama.neighbors[j];
            if (v1 < 0)
                break;

            prefetch_L2(vt->visited.data() + v1);
            jmax += 1;
        }

        int counter = 0;
        size_t saved_j[4];

        ndis += jmax - begin;

        auto add_to_heap = [&](const size_t idx, const float dis) {
            if (top_candidates.top().first > dis ||
                top_candidates.size() < ef) {
                candidates.emplace(dis, idx);
                top_candidates.emplace(dis, idx);

                if (top_candidates.size() > ef) {
                    top_candidates.pop();
                }
            }
        };

        for (size_t j = begin; j < jmax; j++) {
            int v1 = vanama.neighbors[j];

            bool vget = vt->get(v1);
            vt->set(v1);
            saved_j[counter] = v1;
            counter += vget ? 0 : 1;

            if (counter == 4) {
                float dis[4];
                qdis.distances_batch_4(
                        saved_j[0],
                        saved_j[1],
                        saved_j[2],
                        saved_j[3],
                        dis[0],
                        dis[1],
                        dis[2],
                        dis[3]);

                for (size_t id4 = 0; id4 < 4; id4++) {
                    add_to_heap(saved_j[id4], dis[id4]);
                }

                counter = 0;
            }
        }

        for (size_t icnt = 0; icnt < counter; icnt++) {
            float dis = qdis(saved_j[icnt]);
            add_to_heap(saved_j[icnt], dis);
        }
    }

    ++stats.n1;
    if (candidates.size() == 0) {
        ++stats.n2;
    }
    stats.n3 += ndis;

    return top_candidates;
}

} // anonymous namespace

HNSWStats Vanama::search(
        DistanceComputer& qdis,
        int k,
        idx_t* I,
        float* D,
        VisitedTable& vt,
        const SearchParametersHNSW* params) const {
    HNSWStats stats;
    if (entry_point == -1) {
        return stats;
    }
    if (upper_beam == 1) {
        //  greedy search on upper levels
        storage_idx_t nearest = entry_point;
        float d_nearest = qdis(nearest);

        for (int level = max_level; level >= 1; level--) {
            greedy_update_nearest(*this, qdis, level, nearest, d_nearest);
        }


        int ef = std::max(efSearch, k);
        if (search_bounded_queue) { // this is the most common branch
            MinimaxHeap candidates(ef);

            candidates.push(nearest, d_nearest);

            search_from_candidates(
                    *this, qdis, k, I, D, candidates, vt, stats, 0, 0, params);

        } else {
            std::priority_queue<Node> top_candidates =
                    search_from_candidate_unbounded(
                            *this,
                            Node(d_nearest, nearest),
                            qdis,
                            ef,
                            &vt,
                            stats);

            while (top_candidates.size() > k) {
                top_candidates.pop();
            }

            int nres = 0;
            while (!top_candidates.empty()) {
                float d;
                storage_idx_t label;
                std::tie(d, label) = top_candidates.top();
                faiss::maxheap_push(++nres, D, I, d, label);
                top_candidates.pop();
            }
        }

        vt.advance();

    } else {
        int candidates_size = upper_beam;
        MinimaxHeap candidates(candidates_size);

        std::vector<idx_t> I_to_next(candidates_size);
        std::vector<float> D_to_next(candidates_size);

        int nres = 1;
        I_to_next[0] = entry_point;
        D_to_next[0] = qdis(entry_point);

        for (int level = max_level; level >= 0; level--) {
            // copy I, D -> candidates

            candidates.clear();

            for (int i = 0; i < nres; i++) {
                candidates.push(I_to_next[i], D_to_next[i]);
            }

            if (level == 0) {
                nres = search_from_candidates(
                        *this, qdis, k, I, D, candidates, vt, stats, 0);
            } else {
                nres = search_from_candidates(
                        *this,
                        qdis,
                        candidates_size,
                        I_to_next.data(),
                        D_to_next.data(),
                        candidates,
                        vt,
                        stats,
                        level);
            }
            vt.advance();
        }
    }

    return stats;
}

void Vanama::search_level_0(
        DistanceComputer& qdis,
        int k,
        idx_t* idxi,
        float* simi,
        idx_t nprobe,
        const storage_idx_t* nearest_i,
        const float* nearest_d,
        int search_type,
        HNSWStats& search_stats,
        VisitedTable& vt) const {
    const Vanama& vanama = *this;

    if (search_type == 1) {
        int nres = 0;

        for (int j = 0; j < nprobe; j++) {
            storage_idx_t cj = nearest_i[j];

            if (cj < 0)
                break;

            if (vt.get(cj))
                continue;

            int candidates_size = std::max(vanama.efSearch, int(k));
            MinimaxHeap candidates(candidates_size);

            candidates.push(cj, nearest_d[j]);

            nres = search_from_candidates(
                    vanama,
                    qdis,
                    k,
                    idxi,
                    simi,
                    candidates,
                    vt,
                    search_stats,
                    0,
                    nres);
        }
    } else if (search_type == 2) {
        int candidates_size = std::max(vanama.efSearch, int(k));
        candidates_size = std::max(candidates_size, int(nprobe));

        MinimaxHeap candidates(candidates_size);
        for (int j = 0; j < nprobe; j++) {
            storage_idx_t cj = nearest_i[j];

            if (cj < 0)
                break;
            candidates.push(cj, nearest_d[j]);
        }

        search_from_candidates(
                vanama, qdis, k, idxi, simi, candidates, vt, search_stats, 0);
    }
}

void Vanama::permute_entries(const idx_t* map) {
    // remap levels
    storage_idx_t ntotal = levels.size();
    std::vector<storage_idx_t> imap(ntotal); // inverse mapping
    // map: new index -> old index
    // imap: old index -> new index
    for (int i = 0; i < ntotal; i++) {
        assert(map[i] >= 0 && map[i] < ntotal);
        imap[map[i]] = i;
    }
    if (entry_point != -1) {
        entry_point = imap[entry_point];
    }
    std::vector<int> new_levels(ntotal);
    std::vector<size_t> new_offsets(ntotal + 1);
    std::vector<storage_idx_t> new_neighbors(neighbors.size());
    size_t no = 0;
    for (int i = 0; i < ntotal; i++) {
        storage_idx_t o = map[i]; // corresponding "old" index
        new_levels[i] = levels[o];
        for (size_t j = offsets[o]; j < offsets[o + 1]; j++) {
            storage_idx_t neigh = neighbors[j];
            new_neighbors[no++] = neigh >= 0 ? imap[neigh] : neigh;
        }
        new_offsets[i + 1] = no;
    }
    assert(new_offsets[ntotal] == offsets[ntotal]);
    // swap everyone
    std::swap(levels, new_levels);
    std::swap(offsets, new_offsets);
    std::swap(neighbors, new_neighbors);
}

/**************************************************************
 * MinimaxHeap
 **************************************************************/

void Vanama::MinimaxHeap::push(storage_idx_t i, float v) {
    if (k == n) {
        if (v >= dis[0])
            return;
        if (ids[0] != -1) {
            --nvalid;
        }
        faiss::heap_pop<HC>(k--, dis.data(), ids.data());
    }
    faiss::heap_push<HC>(++k, dis.data(), ids.data(), v, i);
    ++nvalid;
}

float Vanama::MinimaxHeap::max() const {
    return dis[0];
}

int Vanama::MinimaxHeap::size() const {
    return nvalid;
}

void Vanama::MinimaxHeap::clear() {
    nvalid = k = 0;
}

#ifdef __AVX2__
int Vanama::MinimaxHeap::pop_min(float* vmin_out) {
    assert(k > 0);
    static_assert(
            std::is_same<storage_idx_t, int32_t>::value,
            "This code expects storage_idx_t to be int32_t");

    int32_t min_idx = -1;
    float min_dis = std::numeric_limits<float>::infinity();

    size_t iii = 0;

    __m256i min_indices = _mm256_setr_epi32(-1, -1, -1, -1, -1, -1, -1, -1);
    __m256 min_distances =
            _mm256_set1_ps(std::numeric_limits<float>::infinity());
    __m256i current_indices = _mm256_setr_epi32(0, 1, 2, 3, 4, 5, 6, 7);
    __m256i offset = _mm256_set1_epi32(8);

    // The baseline version is available in non-AVX2 branch.

    // The following loop tracks the rightmost index with the min distance.
    // -1 index values are ignored.
    const int k8 = (k / 8) * 8;
    for (; iii < k8; iii += 8) {
        __m256i indices =
                _mm256_loadu_si256((const __m256i*)(ids.data() + iii));
        __m256 distances = _mm256_loadu_ps(dis.data() + iii);

        // This mask filters out -1 values among indices.
        __m256i m1mask = _mm256_cmpgt_epi32(_mm256_setzero_si256(), indices);

        __m256i dmask = _mm256_castps_si256(
                _mm256_cmp_ps(min_distances, distances, _CMP_LT_OS));
        __m256 finalmask = _mm256_castsi256_ps(_mm256_or_si256(m1mask, dmask));

        const __m256i min_indices_new = _mm256_castps_si256(_mm256_blendv_ps(
                _mm256_castsi256_ps(current_indices),
                _mm256_castsi256_ps(min_indices),
                finalmask));

        const __m256 min_distances_new =
                _mm256_blendv_ps(distances, min_distances, finalmask);

        min_indices = min_indices_new;
        min_distances = min_distances_new;

        current_indices = _mm256_add_epi32(current_indices, offset);
    }

    // Vectorizing is doable, but is not practical
    int32_t vidx8[8];
    float vdis8[8];
    _mm256_storeu_ps(vdis8, min_distances);
    _mm256_storeu_si256((__m256i*)vidx8, min_indices);

    for (size_t j = 0; j < 8; j++) {
        if (min_dis > vdis8[j] || (min_dis == vdis8[j] && min_idx < vidx8[j])) {
            min_idx = vidx8[j];
            min_dis = vdis8[j];
        }
    }

    // process last values. Vectorizing is doable, but is not practical
    for (; iii < k; iii++) {
        if (ids[iii] != -1 && dis[iii] <= min_dis) {
            min_dis = dis[iii];
            min_idx = iii;
        }
    }

    if (min_idx == -1) {
        return -1;
    }

    if (vmin_out) {
        *vmin_out = min_dis;
    }
    int ret = ids[min_idx];
    ids[min_idx] = -1;
    --nvalid;
    return ret;
}

#else

// baseline non-vectorized version
int Vanama::MinimaxHeap::pop_min(float* vmin_out) {
    assert(k > 0);
    // returns min. This is an O(n) operation
    int i = k - 1;
    while (i >= 0) {
        if (ids[i] != -1) {
            break;
        }
        i--;
    }
    if (i == -1) {
        return -1;
    }
    int imin = i;
    float vmin = dis[i];
    i--;
    while (i >= 0) {
        if (ids[i] != -1 && dis[i] < vmin) {
            vmin = dis[i];
            imin = i;
        }
        i--;
    }
    if (vmin_out) {
        *vmin_out = vmin;
    }
    int ret = ids[imin];
    ids[imin] = -1;
    --nvalid;

    return ret;
}
#endif

int Vanama::MinimaxHeap::count_below(float thresh) {
    int n_below = 0;
    for (int i = 0; i < k; i++) {
        if (dis[i] < thresh) {
            n_below++;
        }
    }

    return n_below;
}

} // namespace faiss
