////////////////////////////////////////////////////////////////////////////////
// Copyright 上海赜睿信息科技有限公司(Zilliz) - All Rights Reserved
// Unauthorized copying of this file, via any medium is strictly prohibited.
// Proprietary and confidential.
////////////////////////////////////////////////////////////////////////////////

#include <gtest/gtest.h>

#include "wrapper/Operand.h"
#include "wrapper/Index.h"
#include "wrapper/IndexBuilder.h"

using namespace zilliz::milvus::engine;


TEST(operand_test, Wrapper_Test) {
    using std::cout;
    using std::endl;

    auto opd = std::make_shared<Operand>();
    opd->index_type = "IVF";
    opd->preproc = "OPQ";
    opd->postproc = "PQ";
    opd->metric_type = "L2";
    opd->d = 64;

    auto opd_str = operand_to_str(opd);
    auto new_opd = str_to_operand(opd_str);

    // TODO: fix all place where using opd to build index.
    assert(new_opd->get_index_type(10000) == opd->get_index_type(10000));
}

TEST(build_test, Wrapper_Test) {
    // dimension of the vectors to index
    int d = 3;

    // make a set of nt training vectors in the unit cube
    size_t nt = 10000;

    // a reasonable number of cetroids to index nb vectors
    int ncentroids = 16;

    std::random_device rd;
    std::mt19937 gen(rd());

    std::vector<float> xb;
    std::vector<long> ids;

    //prepare train data
    std::uniform_real_distribution<> dis_xt(-1.0, 1.0);
    std::vector<float> xt(nt * d);
    for (size_t i = 0; i < nt * d; i++) {
        xt[i] = dis_xt(gen);
    }

    //train the index
    auto opd = std::make_shared<Operand>();
    opd->index_type = "IVF";
    opd->d = d;
    opd->ncent = ncentroids;
    IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
    auto index_1 = index_builder_1->build_all(0, xb, ids, nt, xt);
    ASSERT_TRUE(index_1 != nullptr);

    // size of the database we plan to index
    size_t nb = 100000;

    //prepare raw data
    xb.resize(nb);
    ids.resize(nb);
    for (size_t i = 0; i < nb; i++) {
        xb[i] = dis_xt(gen);
        ids[i] = i;
    }
    index_1->add_with_ids(nb, xb.data(), ids.data());

    //search in first quadrant
    int nq = 1, k = 10;
    std::vector<float> xq = {0.5, 0.5, 0.5};
    float *result_dists = new float[k];
    long *result_ids = new long[k];
    index_1->search(nq, xq.data(), k, result_dists, result_ids);

    for (int i = 0; i < k; i++) {
        if (result_ids[i] < 0) {
            ASSERT_TRUE(false);
            break;
        }

        long id = result_ids[i];
        std::cout << "No." << id << " [" << xb[id * 3] << ", " << xb[id * 3 + 1] << ", "
                  << xb[id * 3 + 2] << "] distance = " << result_dists[i] << std::endl;

        //makesure result vector is in first quadrant
        ASSERT_TRUE(xb[id * 3] > 0.0);
        ASSERT_TRUE(xb[id * 3 + 1] > 0.0);
        ASSERT_TRUE(xb[id * 3 + 2] > 0.0);
    }

    delete[] result_dists;
    delete[] result_ids;
}

TEST(gpu_build_test, Wrapper_Test) {
    using std::vector;

    int d = 256;
    int nb = 3 * 1000 * 100;
    int nq = 100;
    vector<float> xb(d * nb);
    vector<float> xq(d * nq);
    vector<long> ids(nb);

    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<> dis_xt(-1.0, 1.0);
    for (auto &e : xb) { e = float(dis_xt(gen)); }
    for (auto &e : xq) { e = float(dis_xt(gen)); }
    for (int i = 0; i < nb; ++i) { ids[i] = i; }

    auto opd = std::make_shared<Operand>();
    opd->index_type = "IVF";
    opd->d = d;
    opd->ncent = 256;

    IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
    auto index_1 = index_builder_1->build_all(nb, xb.data(), ids.data());
    assert(index_1->ntotal == nb);
    assert(index_1->dim == d);

    // sanity check: search 5 first vectors of xb
    int k = 1;
    vector<long> I(5 * k);
    vector<float> D(5 * k);
    index_1->search(5, xb.data(), k, D.data(), I.data());
    for (int i = 0; i < 5; ++i) { assert(i == I[i]); }
}