/**
 * 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.
 */

#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <random>

#include <sys/time.h>

#include <faiss/IndexFlat.h>
#include <faiss/IndexIVFPQ.h>
#include <faiss/IndexPQ.h>
#include <faiss/index_io.h>

double elapsed() {
    struct timeval tv;
    gettimeofday(&tv, nullptr);
    return tv.tv_sec + tv.tv_usec * 1e-6;
}

int main() {
    double t0 = elapsed();

    // dimension of the vectors to index
    int d = 64;

    // size of the database we plan to index
    size_t nb = 1000 * 1000;
    size_t add_bs = 10000; // # size of the blocks to add

    // make a set of nt training vectors in the unit cube
    // (could be the database)
    size_t nt = 100 * 1000;

    //---------------------------------------------------------------
    // Define the core quantizer
    // We choose a multiple inverted index for faster training with less data
    // and because it usually offers best accuracy/speed trade-offs
    //
    // We here assume that its lifespan of this coarse quantizer will cover the
    // lifespan of the inverted-file quantizer IndexIVFFlat below
    // With dynamic allocation, one may give the responsibility to free the
    // quantizer to the inverted-file index (with attribute do_delete_quantizer)
    //
    // Note: a regular clustering algorithm would be defined as:
    //       faiss::IndexFlatL2 coarse_quantizer (d);
    //
    // Use nhash=2 subquantizers used to define the product coarse quantizer
    // Number of bits: we will have 2^nbits_coarse centroids per subquantizer
    //                 meaning (2^12)^nhash distinct inverted lists
    //
    // The parameter bytes_per_code is determined by the memory
    // constraint, the dataset will use nb * (bytes_per_code + 8)
    // bytes.
    //
    // The parameter nbits_subq is determined by the size of the dataset to
    // index.
    //
    size_t nhash = 2;
    size_t nbits_subq = 9;
    size_t ncentroids = 1 << (nhash * nbits_subq); // total # of centroids
    int bytes_per_code = 16;

    faiss::MultiIndexQuantizer coarse_quantizer(d, nhash, nbits_subq);

    printf("IMI (%ld,%ld): %ld virtual centroids (target: %ld base vectors)",
           nhash,
           nbits_subq,
           ncentroids,
           nb);

    // the coarse quantizer should not be dealloced before the index
    // 4 = nb of bytes per code (d must be a multiple of this)
    // 8 = nb of bits per sub-code (almost always 8)
    faiss::IndexIVFPQ index(
            &coarse_quantizer, d, ncentroids, bytes_per_code, 8);
    index.quantizer_trains_alone = true;

    // define the number of probes. 2048 is for high-dim, overkill in practice
    // Use 4-1024 depending on the trade-off speed accuracy that you want
    index.nprobe = 2048;

    std::mt19937 rng;
    std::uniform_real_distribution<> distrib;

    { // training.

        // The distribution of the training vectors should be the same
        // as the database vectors. It could be a sub-sample of the
        // database vectors, if sampling is not biased. Here we just
        // randomly generate the vectors.

        printf("[%.3f s] Generating %ld vectors in %dD for training\n",
               elapsed() - t0,
               nt,
               d);

        std::vector<float> trainvecs(nt * d);
        for (size_t i = 0; i < nt; i++) {
            for (size_t j = 0; j < d; j++) {
                trainvecs[i * d + j] = distrib(rng);
            }
        }

        printf("[%.3f s] Training the index\n", elapsed() - t0);
        index.verbose = true;
        index.train(nt, trainvecs.data());
    }

    // the index can be re-loaded later with
    // faiss::Index * idx = faiss::read_index("/tmp/trained_index.faissindex");
    faiss::write_index(&index, "/tmp/trained_index.faissindex");

    size_t nq;
    std::vector<float> queries;

    { // populating the database
        printf("[%.3f s] Building a dataset of %ld vectors to index\n",
               elapsed() - t0,
               nb);

        std::vector<float> database(nb * d);
        std::vector<faiss::idx_t> ids(nb);
        for (size_t i = 0; i < nb; i++) {
            for (size_t j = 0; j < d; j++) {
                database[i * d + j] = distrib(rng);
            }
            ids[i] = 8760000000L + i;
        }

        printf("[%.3f s] Adding the vectors to the index\n", elapsed() - t0);

        for (size_t begin = 0; begin < nb; begin += add_bs) {
            size_t end = std::min(begin + add_bs, nb);
            index.add_with_ids(
                    end - begin,
                    database.data() + d * begin,
                    ids.data() + begin);
        }

        // remember a few elements from the database as queries
        int i0 = 1234;
        int i1 = 1244;

        nq = i1 - i0;
        queries.resize(nq * d);
        for (int i = i0; i < i1; i++) {
            for (int j = 0; j < d; j++) {
                queries[(i - i0) * d + j] = database[i * d + j];
            }
        }
    }

    // A few notes on the internal format of the index:
    //
    // - the positing lists for PQ codes are index.codes, which is a
    //    std::vector < std::vector<uint8_t> >
    //   if n is the length of posting list #i, codes[i] has length
    //   bytes_per_code * n
    //
    // - the corresponding ids are stored in index.ids
    //
    // - given a vector float *x, finding which k centroids are
    //   closest to it (ie to find the nearest neighbors) can be done with
    //
    //   faiss::idx_t *centroid_ids = new faiss::idx_t[k];
    //   float *distances = new float[k];
    //   index.quantizer->search (1, x, k, dis, centroids_ids);
    //

    faiss::write_index(&index, "/tmp/populated_index.faissindex");

    { // searching the database
        int k = 5;
        printf("[%.3f s] Searching the %d nearest neighbors "
               "of %ld vectors in the index\n",
               elapsed() - t0,
               k,
               nq);

        std::vector<faiss::idx_t> nns(k * nq);
        std::vector<float> dis(k * nq);

        index.search(nq, queries.data(), k, dis.data(), nns.data());

        printf("[%.3f s] Query results (vector ids, then distances):\n",
               elapsed() - t0);

        for (int i = 0; i < nq; i++) {
            printf("query %2d: ", i);
            for (int j = 0; j < k; j++) {
                printf("%7ld ", nns[j + i * k]);
            }
            printf("\n     dis: ");
            for (int j = 0; j < k; j++) {
                printf("%7g ", dis[j + i * k]);
            }
            printf("\n");
        }
    }
    return 0;
}
