// Copyright (C) 2018-2024 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#include <intel_gpu/graph/network.hpp>
#include <intel_gpu/graph/topology.hpp>
#include <intel_gpu/primitives/input_layout.hpp>
#include <intel_gpu/primitives/matrix_nms.hpp>
#include <intel_gpu/primitives/mutable_data.hpp>
#include <intel_gpu/runtime/memory.hpp>

#include "test_utils.h"

using namespace cldnn;
using namespace tests;

namespace {

#define PAD       -1.0
#define PADI      -1
#define THRESHOLD 1e-3f

template <class T>
std::vector<T> convert(const std::vector<float>& v) {
    return {v.begin(), v.end()};
}

struct matrix_nms_test_inputs {
    int num_butches;
    int num_boxes;
    int num_classes;
    int num_selected_boxes;
    bool sort_result_across_batch;
    float score_threshold;
    int nms_top_k;
    int keep_top_k;
    int background_class;
    float gaussian_sigma;
    float post_threshold;
    bool normalized;
    std::vector<float> boxes_values;
    std::vector<float> scores_values;
    std::vector<float> expected_output;
    std::vector<int> expected_selected_boxes;
    std::vector<int> expected_valid_outputs;
    ov::op::v8::MatrixNms::SortResultType sort_result_type;
    ov::op::v8::MatrixNms::DecayFunction decay_function;
    std::string test_name;
};

using matrix_nms_test_params = std::tuple<matrix_nms_test_inputs, format::type, bool>;

template <class T>
struct matrix_nms_gpu_test : public testing::TestWithParam<matrix_nms_test_params> {
public:
    void test() {
        format::type blocked_format;
        matrix_nms_test_inputs test_inputs;
        bool is_caching_test;
        std::tie(test_inputs, blocked_format, is_caching_test) = testing::TestWithParam<matrix_nms_test_params>::GetParam();

        const auto data_type = ov::element::from<T>();
        const auto plain_format = format::bfyx;

        auto& engine = get_test_engine();

        auto boxes = engine.allocate_memory(
            {data_type, plain_format, tensor{test_inputs.num_butches, test_inputs.num_boxes, 1, 4}});
        auto scores = engine.allocate_memory(
            {data_type,
             plain_format,
             tensor{test_inputs.num_butches, test_inputs.num_classes, 1, test_inputs.num_boxes}});

        auto selected_boxes =
            engine.allocate_memory({data_types::i32, plain_format, tensor{test_inputs.num_selected_boxes, 1, 1, 1}});
        auto valid_outputs =
            engine.allocate_memory({data_types::i32, plain_format, tensor{test_inputs.num_butches, 1, 1, 1}});

        set_values(boxes, convert<T>(test_inputs.boxes_values));
        set_values(scores, convert<T>(test_inputs.scores_values));

        const ov::op::v8::MatrixNms::Attributes attrs(test_inputs.sort_result_type,
                                                      test_inputs.sort_result_across_batch,
                                                      ov::element::i32,
                                                      test_inputs.score_threshold,
                                                      test_inputs.nms_top_k,
                                                      test_inputs.keep_top_k,
                                                      test_inputs.background_class,
                                                      test_inputs.decay_function,
                                                      test_inputs.gaussian_sigma,
                                                      test_inputs.post_threshold,
                                                      test_inputs.normalized);

        topology topology;
        topology.add(input_layout("boxes", boxes->get_layout()));
        topology.add(input_layout("scores", scores->get_layout()));
        topology.add(mutable_data("selected_boxes", selected_boxes));
        topology.add(mutable_data("valid_outputs", valid_outputs));

        topology.add(reorder("reordered_boxes", input_info("boxes"), blocked_format, data_type));
        topology.add(reorder("reordered_scores", input_info("scores"), blocked_format, data_type));

        topology.add(matrix_nms("reordered_matrix_nms",
                                input_info("reordered_boxes"),
                                input_info("reordered_scores"),
                                input_info("selected_boxes"),
                                input_info("valid_outputs"),
                                attrs));
        topology.add(reorder("matrix_nms", input_info("reordered_matrix_nms"), plain_format, data_type));

        cldnn::network::ptr network = get_network(engine, topology, get_test_default_config(engine), get_test_stream_ptr(), is_caching_test);

        network->set_input_data("boxes", boxes);
        network->set_input_data("scores", scores);

        auto outputs = network->execute();

        auto output = outputs.at("matrix_nms").get_memory();
        cldnn::mem_lock<T> output_ptr(output, get_test_stream());

        cldnn::mem_lock<int> selected_boxes_ptr(selected_boxes, get_test_stream());
        cldnn::mem_lock<int> valid_outputs_ptr(valid_outputs, get_test_stream());

        const auto expected_output = convert<T>(test_inputs.expected_output);
        ASSERT_EQ(expected_output.size(), output_ptr.size());
        for (size_t i = 0; i < expected_output.size(); ++i) {
            ASSERT_NEAR(expected_output[i], output_ptr[i], THRESHOLD);
        }

        if (!is_caching_test) {
            ASSERT_EQ(test_inputs.expected_selected_boxes.size(), selected_boxes_ptr.size());
            for (size_t i = 0; i < test_inputs.expected_selected_boxes.size(); ++i) {
                ASSERT_EQ(test_inputs.expected_selected_boxes[i], selected_boxes_ptr[i]);
            }

            ASSERT_EQ(test_inputs.expected_valid_outputs.size(), valid_outputs_ptr.size());
            for (size_t i = 0; i < test_inputs.expected_valid_outputs.size(); ++i) {
                ASSERT_EQ(test_inputs.expected_valid_outputs[i], valid_outputs_ptr[i]);
            }
        }
    }

    static std::string PrintToStringParamName(const testing::TestParamInfo<matrix_nms_test_params>& info) {
        auto& test_inputs = std::get<0>(info.param);
        std::ostringstream result;

        auto sort_res_type_str =
            test_inputs.sort_result_type == ov::op::v8::MatrixNms::SortResultType::SCORE
                ? "score"
                : test_inputs.sort_result_type == ov::op::v8::MatrixNms::SortResultType::CLASSID ? "class_id" : "none";
        auto decay_function_str =
            test_inputs.decay_function == ov::op::v8::MatrixNms::DecayFunction::LINEAR
                ? "linear"
                : test_inputs.decay_function == ov::op::v8::MatrixNms::DecayFunction::GAUSSIAN ? "gaussian" : "none";

        result << "SortResultAcrossBatch=" << bool_to_str(test_inputs.sort_result_across_batch) << "_";
        result << "ScoreThreshold=" << test_inputs.score_threshold << "_";
        result << "NmsTopK=" << test_inputs.nms_top_k << "_";
        result << "KeepTopK=" << test_inputs.keep_top_k << "_";
        result << "BackgroundClass=" << test_inputs.background_class << "_";
        result << "GaussianSigma=" << test_inputs.gaussian_sigma << "_";
        result << "PostThreshold=" << test_inputs.post_threshold << "_";
        result << "Normalized=" << bool_to_str(test_inputs.normalized) << "_";
        result << "sort_result_type=" << sort_res_type_str << "_";
        result << "decay_function=" << decay_function_str << "_";
        result << "Format=" << fmt_to_str(std::get<1>(info.param)) << "_";
        result << "Cached=" << bool_to_str(std::get<2>(info.param));

        if (!test_inputs.test_name.empty())
            result << "_TN=" << test_inputs.test_name;

        return result.str();
    }
};

matrix_nms_test_inputs get_matrix_nms_smoke_inputs() {
    return {1,      // num_butches
            6,      // num_boxes
            2,      // num_classes
            3,      // num_selected_boxes
            false,  // sort_result_across_bch
            0.0f,   // score_threshold
            3,      // nms_top_k
            -1,     // keep_top_k
            0,      // background_class
            2.0f,   // gaussian_sigma
            0.0f,   // post_threshold
            true,   // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3},  // scores
            std::vector<float>{1.00,                                                              // expected_output
                               0.95,
                               0.00,
                               0.00,
                               1.00,
                               1.00,
                               1.00,
                               0.8,
                               0.00,
                               10.00,
                               1.00,
                               11.00,
                               1.00,
                               0.13636364,
                               0.0,
                               0.1,
                               1.0,
                               1.1},
            std::vector<int>{0, 3, 1},          // expected_selected_boxes
            std::vector<int>{3},                // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,  // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,  // decay_function
            "smoke"};
}

matrix_nms_test_inputs get_matrix_nms_gaussian_inputs() {
    return {1,      // num_butches
            6,      // num_boxes
            2,      // num_classes
            3,      // num_selected_boxes
            false,  // sort_result_across_bch
            0.0f,   // score_threshold
            3,      // nms_top_k
            -1,     // keep_top_k
            0,      // background_class
            2.0f,   // gaussian_sigma
            0.0f,   // post_threshold
            true,   // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3},  // scores
            std::vector<float>{1.00,                                                              // expected_output
                               0.95,
                               0.00,
                               0.00,
                               1.00,
                               1.00,
                               1.00,
                               0.8,
                               0.00,
                               10.00,
                               1.00,
                               11.00,
                               1.00,
                               0.1966116,
                               0.0,
                               0.1,
                               1.0,
                               1.1},
            std::vector<int>{0, 3, 1},            // expected_selected_boxes
            std::vector<int>{3},                  // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,    // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::GAUSSIAN,  // decay_function
            "gaussian"};
}

matrix_nms_test_inputs get_matrix_nms_two_batches_two_classes_inputs() {
    return {2,      // num_butches
            6,      // num_boxes
            2,      // num_classes
            6,      // num_selected_boxes
            false,  // sort_result_across_bch
            0.0f,   // score_threshold
            3,      // nms_top_k
            -1,     // keep_top_k
            0,      // background_class
            2.0f,   // gaussian_sigma
            0.0f,   // post_threshold
            true,   // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
                               0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // scores
                               0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3},
            std::vector<float>{1.00, 0.95,  0.00, 0.00,  1.00, 1.00,  // expected_output
                               1.00, 0.8,   0.00, 10.00, 1.00, 11.00,      1.00, 0.13636364, 0.0,  0.1,
                               1.0,  1.1,   1.00, 0.95,  0.00, 0.00,       1.00, 1.00,       1.00, 0.8,
                               0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0,  0.1,        1.0,  1.1},
            std::vector<int>{0, 3, 1, 6, 9, 7},  // expected_selected_boxes
            std::vector<int>{3, 3},              // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,   // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,   // decay_function
            "two_batches_two_classes"};
}

matrix_nms_test_inputs get_matrix_nms_two_batches_two_classes_by_score_cross_batch_inputs() {
    return {2,     // num_butches
            6,     // num_boxes
            2,     // num_classes
            12,    // num_selected_boxes
            true,  // sort_result_across_bch
            0.0f,  // score_threshold
            3,     // nms_top_k
            -1,    // keep_top_k
            -1,    // background_class
            2.0f,  // gaussian_sigma
            0.5f,  // post_threshold
            true,  // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
                               0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // scores
                               0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3},
            std::vector<float>{0.00, 0.95, 0.00, 10.00, 1.00, 11.00,  // expected_output
                               1.00, 0.95, 0.00, 0.00,  1.00, 1.00,  0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 1.00, 0.95,
                               0.00, 0.00, 1.00, 1.00,  PAD,  PAD,   PAD,  PAD,  PAD,  PAD,   PAD,  PAD,   PAD,  PAD,
                               PAD,  PAD,  0.00, 0.90,  0.00, 0.00,  1.00, 1.00, 0.00, 0.90,  0.00, 0.00,  1.00, 1.00,
                               1.00, 0.80, 0.00, 10.00, 1.00, 11.00, 1.00, 0.80, 0.00, 10.00, 1.00, 11.00, PAD,  PAD,
                               PAD,  PAD,  PAD,  PAD,   PAD,  PAD,   PAD,  PAD,  PAD,  PAD},
            std::vector<int>{3, 0, 9, 6, PADI, PADI, 0, 6, 3, 9, PADI, PADI},  // expected_selected_boxes
            std::vector<int>{4, 4},                                            // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,                                 // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,                                 // decay_function
            "two_batches_two_classes_by_score_cross_batch"};
}

matrix_nms_test_inputs get_matrix_nms_two_batches_two_classes_by_classid_cross_batch_inputs() {
    return {2,     // num_butches
            6,     // num_boxes
            2,     // num_classes
            12,    // num_selected_boxes
            true,  // sort_result_across_bch
            0.0f,  // score_threshold
            3,     // nms_top_k
            -1,    // keep_top_k
            -1,    // background_class
            2.0f,  // gaussian_sigma
            0.5f,  // post_threshold
            true,  // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
                               0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // scores
                               0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3},
            std::vector<float>{0.00, 0.95, 0.00, 10.00, 1.00, 11.00,  // expected_output
                               0.00, 0.90, 0.00, 0.00,  1.00, 1.00,  0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90,
                               0.00, 0.00, 1.00, 1.00,  PAD,  PAD,   PAD,  PAD,  PAD,  PAD,   PAD,  PAD,   PAD,  PAD,
                               PAD,  PAD,  1.00, 0.95,  0.00, 0.00,  1.00, 1.00, 1.00, 0.80,  0.00, 10.00, 1.00, 11.00,
                               1.00, 0.95, 0.00, 0.00,  1.00, 1.00,  1.00, 0.80, 0.00, 10.00, 1.00, 11.00, PAD,  PAD,
                               PAD,  PAD,  PAD,  PAD,   PAD,  PAD,   PAD,  PAD,  PAD,  PAD},
            std::vector<int>{3, 0, 9, 6, PADI, PADI, 0, 3, 6, 9, PADI, PADI},  // expected_selected_boxes
            std::vector<int>{4, 4},                                            // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::CLASSID,                              // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,                                 // decay_function
            "matrix_nms_two_batches_two_classes_by_classid_cross_batch"};
}

matrix_nms_test_inputs get_matrix_nms_by_keep_top_k_inputs() {
    return {2,      // num_butches
            6,      // num_boxes
            2,      // num_classes
            6,      // num_selected_boxes
            false,  // sort_result_across_bch
            0.0f,   // score_threshold
            3,      // nms_top_k
            3,      // keep_top_k
            0,      // background_class
            2.0f,   // gaussian_sigma
            0.0f,   // post_threshold
            true,   // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
                               0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // scores
                               0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3},
            std::vector<float>{1.00, 0.95,  0.00, 0.00,  1.00, 1.00,  // expected_output
                               1.00, 0.8,   0.00, 10.00, 1.00, 11.00,      1.00, 0.13636364, 0.0,  0.1,
                               1.0,  1.1,   1.00, 0.95,  0.00, 0.00,       1.00, 1.00,       1.00, 0.8,
                               0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0,  0.1,        1.0,  1.1},
            std::vector<int>{0, 3, 1, 6, 9, 7},    // expected_selected_boxes
            std::vector<int>{3, 3},                // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::CLASSID,  // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,     // decay_function
            "matrix_nms_by_keep_top_k"};
}

matrix_nms_test_inputs get_matrix_nms_background_inputs() {
    return {1,      // num_butches
            6,      // num_boxes
            2,      // num_classes
            6,      // num_selected_boxes
            false,  // sort_result_across_bch
            0.0f,   // score_threshold
            3,      // nms_top_k
            -1,     // keep_top_k
            -1,     // background_class
            2.0f,   // gaussian_sigma
            0.0f,   // post_threshold
            true,   // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3},  // scores
            std::vector<float>{0.00, 0.95, 0.0,  10.0, 1.0,  11.0,                                // expected_output
                               1.00, 0.95, 0.0,  0.0,  1.0,  1.0,        0.00, 0.9,  0.0,  0.0,
                               1.0,  1.0,  1.00, 0.8,  0.0,  10.0,       1.0,  11.0, 0.00, 0.13636364,
                               0.0,  0.1,  1.0,  1.1,  1.00, 0.13636364, 0.0,  0.1,  1.0,  1.1},
            std::vector<int>{3, 0, 0, 3, 1, 1},  // expected_selected_boxes
            std::vector<int>{6},                 // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,   // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,   // decay_function
            "matrix_nms_background"};
}

matrix_nms_test_inputs get_matrix_nms_flipped_coordinates_inputs() {
    return {1,      // num_butches
            6,      // num_boxes
            1,      // num_classes
            3,      // num_selected_boxes
            false,  // sort_result_across_bch
            0.0f,   // score_threshold
            3,      // nms_top_k
            -1,     // keep_top_k
            -1,     // background_class
            2.0f,   // gaussian_sigma
            0.0f,   // post_threshold
            true,   // normalized
            std::vector<float>{1.0, 1.0,  0.0, 0.0,  0.0, 0.1,  1.0, 1.1,  0.0, 0.9,   1.0, -0.1,  // boxes
                               0.0, 10.0, 1.0, 11.0, 1.0, 10.1, 0.0, 11.1, 1.0, 101.0, 0.0, 100.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3},  // scores
            std::vector<float>{0.00,
                               0.95,
                               0.0,
                               10.0,
                               1.0,
                               11.0,  // expected_output
                               0.00,
                               0.9,
                               1.0,
                               1.0,
                               0.0,
                               0.0,
                               0.00,
                               0.75,
                               0.0,
                               0.1,
                               1.0,
                               1.1},
            std::vector<int>{3, 0, 1},          // expected_selected_boxes
            std::vector<int>{3},                // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,  // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,  // decay_function
            "flipped_coordinates"};
}

matrix_nms_test_inputs get_matrix_nms_post_threshold_inputs() {
    return {1,      // num_butches
            6,      // num_boxes
            1,      // num_classes
            3,      // num_selected_boxes
            false,  // sort_result_across_bch
            0.0f,   // score_threshold
            3,      // nms_top_k
            -1,     // keep_top_k
            -1,     // background_class
            2.0f,   // gaussian_sigma
            0.8f,   // post_threshold
            true,   // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3},  // scores
            std::vector<float>{0.00,
                               0.95,
                               0.00,
                               10.00,
                               1.00,
                               11.00,  // expected_output
                               0.00,
                               0.9,
                               0.00,
                               0.00,
                               1.00,
                               1.00,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD},
            std::vector<int>{3, 0, PADI},       // expected_selected_boxes
            std::vector<int>{2},                // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,  // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,  // decay_function
            "post_threshold"};
}

matrix_nms_test_inputs get_matrix_nms_identical_boxes_inputs() {
    return {1,      // num_butches
            10,     // num_boxes
            1,      // num_classes
            3,      // num_selected_boxes
            false,  // sort_result_across_bch
            0.0f,   // score_threshold
            3,      // nms_top_k
            -1,     // keep_top_k
            -1,     // background_class
            2.0f,   // gaussian_sigma
            0.3f,   // post_threshold
            true,   // normalized
            std::vector<float>{0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,  // boxes
                               1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0,
                               0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0},
            std::vector<float>{0.4, 0.01, 0.2, 0.09, 0.15, 0.05, 0.02, 0.03, 0.05, 0.0},  // scores
            std::vector<float>{0.00,
                               0.40,
                               0.00,
                               0.00,
                               1.00,
                               1.00,  // expected_output
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD},
            std::vector<int>{0, PADI, PADI},    // expected_selected_boxes
            std::vector<int>{1},                // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,  // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,  // decay_function
            "identical_boxes"};
};

matrix_nms_test_inputs get_matrix_nms_top_k_inputs() {
    return {1,      // num_butches
            6,      // num_boxes
            1,      // num_classes
            2,      // num_selected_boxes
            false,  // sort_result_across_bch
            0.0f,   // score_threshold
            2,      // nms_top_k
            -1,     // keep_top_k
            -1,     // background_class
            2.0f,   // gaussian_sigma
            0.0f,   // post_threshold
            true,   // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3},  // scores
            std::vector<float>{0.00,
                               0.95,
                               0.00,
                               10.00,
                               1.00,
                               11.00,  // expected_output
                               0.00,
                               0.90,
                               0.00,
                               0.00,
                               1.00,
                               1.00},
            std::vector<int>{3, 0},             // expected_selected_boxes
            std::vector<int>{2},                // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,  // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,  // decay_function
            "matrix_nms_nms_top_k"};
}

matrix_nms_test_inputs get_matrix_nms_single_box_inputs() {
    return {1,                                                       // num_butches
            1,                                                       // num_boxes
            1,                                                       // num_classes
            1,                                                       // num_selected_boxes
            false,                                                   // sort_result_across_bch
            0.0f,                                                    // score_threshold
            3,                                                       // nms_top_k
            -1,                                                      // keep_top_k
            -1,                                                      // background_class
            2.0f,                                                    // gaussian_sigma
            0.0f,                                                    // post_threshold
            true,                                                    // normalized
            std::vector<float>{0.0, 0.0, 1.0, 1.0},                  // boxes
            std::vector<float>{0.9},                                 // scores
            std::vector<float>{0.00, 0.90, 0.00, 0.00, 1.00, 1.00},  // expected_output
            std::vector<int>{0},                                     // expected_selected_boxes
            std::vector<int>{1},                                     // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,                       // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,                       // decay_function
            "matrix_nms_single_box"};
}

matrix_nms_test_inputs get_matrix_nms_no_output_inputs() {
    return {1,      // num_butches
            6,      // num_boxes
            1,      // num_classes
            3,      // num_selected_boxes
            false,  // sort_result_across_bch
            2.0f,   // score_threshold
            3,      // nms_top_k
            -1,     // keep_top_k
            -1,     // background_class
            2.0f,   // gaussian_sigma
            0.0f,   // post_threshold
            true,   // normalized
            std::vector<float>{0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,  // boxes
                               0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0},
            std::vector<float>{0.9, 0.75, 0.6, 0.95, 0.5, 0.3},  // scores
            std::vector<float>{PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,  // expected_output
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD,
                               PAD},
            std::vector<int>{PADI, PADI, PADI},  // expected_selected_boxes
            std::vector<int>{0},                 // expected_valid_output
            ov::op::v8::MatrixNms::SortResultType::SCORE,   // sort_result_type
            ov::op::v8::MatrixNms::DecayFunction::LINEAR,   // decay_function
            "matrix_nms_no_output"};
}

const std::vector<format::type> layout_formats = {format::bfyx,
                                                  format::b_fs_yx_fsv16,
                                                  format::b_fs_yx_fsv32,
                                                  format::bs_fs_yx_bsv16_fsv16,
                                                  format::bs_fs_yx_bsv32_fsv32,
                                                  format::bs_fs_yx_bsv32_fsv16};

#ifdef RUN_ALL_MODEL_CACHING_TESTS
const std::vector<bool> run_caching_test = {false, true};
#else
const std::vector<bool> run_caching_test = {false};
#endif

#define INSTANTIATE_MATRIX_NMS_TEST_SUITE(input_type, func)                                                \
    using matrix_nms_gpu_test_##input_type##func = matrix_nms_gpu_test<input_type>;                        \
    TEST_P(matrix_nms_gpu_test_##input_type##func, test) {                                                 \
        test();                                                                                            \
    }                                                                                                      \
    INSTANTIATE_TEST_SUITE_P(matrix_nms_test_##input_type##func,                                           \
                             matrix_nms_gpu_test_##input_type##func,                                       \
                             testing::Combine(testing::Values(func()), testing::ValuesIn(layout_formats),  \
                                              testing::ValuesIn(run_caching_test)),                        \
                             matrix_nms_gpu_test_##input_type##func::PrintToStringParamName);

INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_smoke_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_gaussian_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_two_batches_two_classes_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_two_batches_two_classes_by_classid_cross_batch_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_two_batches_two_classes_by_score_cross_batch_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_by_keep_top_k_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_background_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_flipped_coordinates_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_post_threshold_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_identical_boxes_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_top_k_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_single_box_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float, get_matrix_nms_no_output_inputs)

using ov::float16;
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_smoke_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_gaussian_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_two_batches_two_classes_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_by_keep_top_k_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_two_batches_two_classes_by_classid_cross_batch_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_two_batches_two_classes_by_score_cross_batch_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_background_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_flipped_coordinates_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_post_threshold_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_identical_boxes_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_top_k_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_single_box_inputs)
INSTANTIATE_MATRIX_NMS_TEST_SUITE(float16, get_matrix_nms_no_output_inputs)

#ifndef RUN_ALL_MODEL_CACHING_TESTS
INSTANTIATE_TEST_SUITE_P(matrix_nms_test_float16get_matrix_nms_smoke_inputs_cached,
                         matrix_nms_gpu_test_float16get_matrix_nms_smoke_inputs,
                         testing::Combine(testing::Values(get_matrix_nms_smoke_inputs()), testing::ValuesIn(layout_formats),
                                          testing::Values(true)),
                         matrix_nms_gpu_test_float16get_matrix_nms_smoke_inputs::PrintToStringParamName);
#endif

#undef INSTANTIATE_MATRIX_NMS_TEST_SUITE

}  // namespace
