eval.cpp

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

#include <cmath>
#include <cstddef>
#include <string>
#include <vector>

#include "common_test_utils/ndarray.hpp"
#include "common_test_utils/test_assertions.hpp"
#include "common_test_utils/test_case.hpp"
#include "common_test_utils/test_tools.hpp"
#include "common_test_utils/type_prop.hpp"
#include "gmock/gmock.h"
#include "gtest/gtest.h"
#include "openvino/core/except.hpp"
#include "openvino/core/model.hpp"
#include "openvino/core/shape.hpp"
#include "openvino/core/type/element_type.hpp"
#include "openvino/op/abs.hpp"
#include "openvino/op/acos.hpp"
#include "openvino/op/add.hpp"
#include "openvino/op/asin.hpp"
#include "openvino/op/atan.hpp"
#include "openvino/op/broadcast.hpp"
#include "openvino/op/ceiling.hpp"
#include "openvino/op/concat.hpp"
#include "openvino/op/constant.hpp"
#include "openvino/op/convert.hpp"
#include "openvino/op/cos.hpp"
#include "openvino/op/cosh.hpp"
#include "openvino/op/cum_sum.hpp"
#include "openvino/op/erf.hpp"
#include "openvino/op/exp.hpp"
#include "openvino/op/fake_convert.hpp"
#include "openvino/op/fake_quantize.hpp"
#include "openvino/op/floor.hpp"
#include "openvino/op/gather.hpp"
#include "openvino/op/log.hpp"
#include "openvino/op/logical_not.hpp"
#include "openvino/op/max_pool.hpp"
#include "openvino/op/minimum.hpp"
#include "openvino/op/negative.hpp"
#include "openvino/op/parameter.hpp"
#include "openvino/op/range.hpp"
#include "openvino/op/reduce_min.hpp"
#include "openvino/op/relu.hpp"
#include "openvino/op/reshape.hpp"
#include "openvino/op/round.hpp"
#include "openvino/op/scatter_elements_update.hpp"
#include "openvino/op/scatter_update.hpp"
#include "openvino/op/shape_of.hpp"
#include "openvino/op/sigmoid.hpp"
#include "openvino/op/sign.hpp"
#include "openvino/op/sin.hpp"
#include "openvino/op/sinh.hpp"
#include "openvino/op/softmax.hpp"
#include "openvino/op/softsign.hpp"
#include "openvino/op/sqrt.hpp"
#include "openvino/op/squeeze.hpp"
#include "openvino/op/tan.hpp"
#include "openvino/op/tanh.hpp"
#include "openvino/op/topk.hpp"
#include "openvino/op/transpose.hpp"
#include "openvino/op/unsqueeze.hpp"
#include "openvino/runtime/tensor.hpp"
#include "sequence_generator.hpp"
#include "utils/eval_utils.hpp"

using namespace std;
using namespace ov;
using namespace testing;

namespace {
template <typename T>
std::vector<T> read_vector(const ov::Tensor& tv) {
    if (ov::element::from<T>() != tv.get_element_type()) {
        OPENVINO_THROW("read_vector type must match Tensor type");
    }
    size_t element_count = tv.get_size();
    size_t size = tv.get_byte_size();
    std::vector<T> rc(element_count);
    memcpy(rc.data(), tv.data(), size);
    return rc;
}
}  // namespace

#define ASSERT_FLOAT_VECTORS_EQ(expected, result)                       \
    ASSERT_EQ(expected.size(), result.size()) << "Array sizes differ."; \
    for (size_t i = 0; i < expected.size(); ++i) {                      \
        ASSERT_FLOAT_EQ(expected[i], result[i]) << "at index: " << i;   \
    }

TEST(eval, evaluate_shape_of) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1});
    auto so = make_shared<op::v0::ShapeOf>(p);
    auto model = make_shared<Model>(OutputVector{so}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3}, {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::i64);
    EXPECT_EQ(result.get_shape(), (Shape{2}));
    auto result_shape = read_vector<int64_t>(result);
    vector<int64_t> arg_shape{2, 3};
    ASSERT_EQ(result_shape, arg_shape);
}

TEST(eval, evaluate_dynamic_range_sum) {
    auto p_start = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{});
    auto p_stop = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{});
    auto p_step = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{});
    auto p1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{});
    auto range = make_shared<op::v0::Range>(p_start, p_stop, p_step);
    auto add = make_shared<op::v1::Add>(range, p1);
    auto model = make_shared<Model>(OutputVector{add}, ParameterVector{p_start, p_stop, p_step, p1});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>({}, {1.0f}),
                                      make_tensor<element::Type_t::f32>({}, {10.0f}),
                                      make_tensor<element::Type_t::f32>({}, {3.0f}),
                                      make_tensor<element::Type_t::f32>({}, {7.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> seq{8.0f, 11.0f, 14.0f};
    ASSERT_EQ(cval, seq);
}

TEST(eval, evaluate_dynamic_range_fp16_out) {
    auto p_start = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape{});
    auto p_stop = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape{});
    auto p_step = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape{});
    auto range = make_shared<op::v4::Range>(p_start, p_stop, p_step, ov::element::f16);
    auto model = make_shared<Model>(OutputVector{range}, ParameterVector{p_start, p_stop, p_step});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::i32>({}, {0}),
                                      make_tensor<element::Type_t::i32>({}, {3087}),
                                      make_tensor<element::Type_t::i32>({}, {1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f16);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3087}));
    auto cval = read_vector<ov::float16>(result_tensor);
    for (size_t i = 0; i < 3087; i++) {
        ASSERT_EQ(cval[i], ov::float16(i));
    }
}

TEST(eval, evaluate_broadcast_v3_bidirectional) {
    Shape shape_a{4, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = ov::op::v0::Constant::create<int32_t>(element::i32, Shape{3}, {2, 1, 4});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{4, 1}, {1.0f, 2.0f, 3.0f, 4.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (ov::Shape{2, 4, 4}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v3_bidirectional_target_rank_smaller_than_input) {
    Shape shape_a{1, 1, 1, 1, 1, 1, 1, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{4}, {1, 3, 1, 1});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(shape_a, {1.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{1, 1, 1, 1, 1, 3, 1, 1}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{1.0f, 1.0f, 1.0f};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v3_bidirectional_target_rank_smaller_than_input_2) {
    Shape shape_a{1, 3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = ov::op::v0::Constant::create<int32_t>(element::i32, Shape{2}, {3, 1});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{1, 3, 1}, {1.0f, 2.0f, 3.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{1, 3, 1}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{1.0f, 2.0f, 3.0f};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v3_bidirectional_dyn) {
    Shape shape_a{4, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::i32, shape_a);
    auto target_shape = make_shared<ov::op::v0::Parameter>(element::i32, Shape{3});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::i32>(Shape{4, 1}, {1, 2, 3, 4}),
                                      make_tensor<element::Type_t::i32>(Shape{3}, {2, 1, 4})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::i32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 4, 4}));
    auto result_val = read_vector<int32_t>(result);
    vector<int32_t> expec{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4,
                          1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v3_numpy) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 6});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape);
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 6}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{
        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
    };
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v3_numpy_dyn) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = make_shared<ov::op::v0::Parameter>(element::i32, Shape{3});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape);
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
                                      make_tensor<element::Type_t::i32>(Shape{3}, {2, 3, 6})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 6}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{
        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
    };
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v3_numpy_vs_bidi) {
    Shape in_shape{1, 4, 1};

    auto A = make_shared<ov::op::v0::Parameter>(element::f32, in_shape);
    auto target_shape = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{3}, {1, 4, 4});
    auto bcast_v3_num = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::NUMPY);
    auto model_num = make_shared<Model>(OutputVector{bcast_v3_num}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(in_shape, {1.0f, 2.0f, 3.0f, 4.0f})};
    ASSERT_TRUE(model_num->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{1, 4, 4}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
    ASSERT_EQ(expec, result_val);

    auto target_shape2 = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{2}, {1, 4});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape2, op::BroadcastType::BIDIRECTIONAL);
    auto model_bidi = make_shared<Model>(OutputVector{bcast_v3_num}, ParameterVector{A});

    auto result2 = ov::Tensor();
    auto out_vector2 = ov::TensorVector{result2};
    auto in_vector2 = ov::TensorVector{make_tensor<element::Type_t::f32>(in_shape, {1.0f, 2.0f, 3.0f, 4.0f})};
    ASSERT_TRUE(model_bidi->evaluate(out_vector2, in_vector2));
    result2 = out_vector.at(0);
    EXPECT_EQ(result2.get_element_type(), element::f32);
    EXPECT_EQ(result2.get_shape(), (Shape{1, 4, 4}));
    auto result_val2 = read_vector<float>(result2);
    vector<float> expec2{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
    ASSERT_EQ(expec2, result_val2);
}

TEST(eval, evaluate_broadcast_v3_bidi_3d) {
    Shape in_shape{1, 4, 1};

    auto A = make_shared<ov::op::v0::Parameter>(element::f32, in_shape);
    auto target_shape = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{3}, {1, 1, 3});
    auto bcast_v3_num = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
    auto model = make_shared<Model>(OutputVector{bcast_v3_num}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(in_shape, {1.0f, 2.0f, 3.0f, 4.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{1, 4, 3}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f};
    ASSERT_EQ(expec, result_val);
}

TEST(eval, evaluate_broadcast_v3_bidi_4d) {
    Shape in_shape{4, 1, 1};
    Shape expec_shape{1, 4, 2, 2};

    auto A = make_shared<ov::op::v0::Parameter>(element::f32, in_shape);
    auto target_shape = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{4}, {1, 1, 2, 2});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(in_shape, {1.0f, 2.0f, 3.0f, 4.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{1, 4, 2, 2}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v3_pdpd) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 6});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastModeSpec(op::BroadcastType::PDPD, 1));
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 6}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{
        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
    };
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v3_pdpd_dyn) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = make_shared<ov::op::v0::Parameter>(element::i32, Shape{3});
    auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastModeSpec(op::BroadcastType::PDPD, 1));
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
                                      make_tensor<element::Type_t::i32>(Shape{3}, {2, 3, 6})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 6}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{
        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
    };
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v1_numpy) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 6});
    auto bcast_v3 = make_shared<op::v1::Broadcast>(A, target_shape);
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 6}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{
        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
    };
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v1_numpy_dyn) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = make_shared<ov::op::v0::Parameter>(element::i64, Shape{3});
    auto bcast_v3 = make_shared<op::v1::Broadcast>(A, target_shape);
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
                                      make_tensor<element::Type_t::i64>(Shape{3}, {2, 3, 6})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 6}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{
        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
    };
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v1_pdpd) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 6});
    auto bcast_v3 =
        make_shared<op::v1::Broadcast>(A, target_shape, op::AutoBroadcastSpec(op::AutoBroadcastType::PDPD, 1));
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 6}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{
        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
    };
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v1_pdpd_dyn) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = make_shared<ov::op::v0::Parameter>(element::i64, Shape{3});
    auto bcast_v3 =
        make_shared<op::v1::Broadcast>(A, target_shape, op::AutoBroadcastSpec(op::AutoBroadcastType::PDPD, 1));
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
                                      make_tensor<element::Type_t::i64>(Shape{3}, {2, 3, 6})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 6}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{
        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
    };
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v1_explicit) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = ov::op::v0::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 1});
    auto axes_mapping = ov::op::v0::Constant::create<int32_t>(element::i32, Shape{2}, {1, 2});
    auto bcast_v3 = make_shared<op::v1::Broadcast>(A,
                                                   target_shape,
                                                   axes_mapping,
                                                   op::AutoBroadcastSpec(op::AutoBroadcastType::EXPLICIT));
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 1}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{1, 2, 3, 1, 2, 3};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v1_explicit_dyn) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = make_shared<ov::op::v0::Parameter>(element::i64, Shape{3});
    auto axes_mapping = make_shared<ov::op::v0::Parameter>(element::i32, Shape{2});

    auto bcast_v1 = make_shared<op::v1::Broadcast>(A,
                                                   target_shape,
                                                   axes_mapping,
                                                   op::AutoBroadcastSpec(op::AutoBroadcastType::EXPLICIT));
    auto model = make_shared<Model>(OutputVector{bcast_v1}, ParameterVector{A, target_shape, axes_mapping});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
                                      make_tensor<element::Type_t::i64>(Shape{3}, {2, 3, 1}),
                                      make_tensor<element::Type_t::i32>(Shape{2}, {1, 2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 1}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{1, 2, 3, 1, 2, 3};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_broadcast_v3_explicit_dyn) {
    Shape shape_a{3, 1};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape_a);
    auto target_shape = make_shared<ov::op::v0::Parameter>(element::i64, Shape{3});
    auto axes_mapping = make_shared<ov::op::v0::Parameter>(element::i32, Shape{2});

    auto bcast_v3 = make_shared<op::v3::Broadcast>(A,
                                                   target_shape,
                                                   axes_mapping,
                                                   op::BroadcastModeSpec(op::BroadcastType::EXPLICIT));
    auto model = make_shared<Model>(OutputVector{bcast_v3}, ParameterVector{A, target_shape, axes_mapping});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
                                      make_tensor<element::Type_t::i64>(Shape{3}, {2, 3, 1}),
                                      make_tensor<element::Type_t::i32>(Shape{2}, {1, 2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3, 1}));
    auto result_val = read_vector<float>(result);
    vector<float> expec{1, 2, 3, 1, 2, 3};
    ASSERT_EQ(result_val, expec);
}

class TestOpMultiOut : public op::Op {
public:
    OPENVINO_OP("TestOpMultiOut");
    TestOpMultiOut() = default;

    TestOpMultiOut(const Output<Node>& output_1, const Output<Node>& output_2) : Op({output_1, output_2}) {
        validate_and_infer_types();
    }

    void validate_and_infer_types() override {
        set_output_size(2);
        set_output_type(0, get_input_element_type(0), get_input_partial_shape(0));
        set_output_type(1, get_input_element_type(1), get_input_partial_shape(1));
    }

    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override {
        return std::make_shared<TestOpMultiOut>(new_args.at(0), new_args.at(1));
    }

    bool evaluate(ov::TensorVector& outputs, const ov::TensorVector& inputs) const override {
        memcpy(outputs[0].data(), inputs[0].data(), inputs[0].get_byte_size());
        memcpy(outputs[1].data(), inputs[1].data(), inputs[1].get_byte_size());
        return true;
    }
};

TEST(eval, test_op_multi_out) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{2, 3});
    auto p2 = make_shared<ov::op::v0::Parameter>(element::f64, PartialShape{2, 2});
    auto so = make_shared<TestOpMultiOut>(p, p2);
    auto model = make_shared<Model>(OutputVector{so->output(0), so->output(1)}, ParameterVector{p, p2});
    auto result = ov::Tensor(element::Type_t::f32, Shape{2, 3});
    auto result2 = ov::Tensor(element::Type_t::f64, Shape{2, 2});
    ov::TensorVector outs{result, result2};
    ov::TensorVector ins{make_tensor<element::Type_t::f32>(Shape{2, 3}),
                         make_tensor<element::Type_t::f64>(Shape{2, 2})};
    ASSERT_TRUE(model->evaluate(outs, ins));
    result = outs.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    EXPECT_EQ(result.get_shape(), (Shape{2, 3}));
    auto result_val = read_vector<float>(result);
    auto arg_val = read_vector<float>(ins[0]);
    ASSERT_EQ(result_val, arg_val);
    EXPECT_EQ(result2.get_element_type(), element::f64);
    EXPECT_EQ(result2.get_shape(), (Shape{2, 2}));
    auto result_val2 = read_vector<double>(result2);
    auto arg_val2 = read_vector<double>(ins[1]);
    ASSERT_EQ(result_val2, arg_val2);
}

TEST(eval, evaluate_reshape_v1) {
    auto data = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 5});
    auto pattern = make_shared<ov::op::v0::Parameter>(element::i64, Shape{2});
    auto dyn_reshape = make_shared<ov::op::v1::Reshape>(data, pattern, false);
    auto model = make_shared<Model>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>({2, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}),
                                      make_tensor<element::Type_t::i64>({2}, {5, 2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{5, 2}));
    auto computed_val = read_vector<float>(result_tensor);
    vector<float> expected_val{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
    ASSERT_EQ(computed_val, expected_val);
}

TEST(eval, evaluate_reshape_v1_negative_index) {
    auto data = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 5});
    auto pattern = make_shared<ov::op::v0::Parameter>(element::i64, Shape{2});
    auto dyn_reshape = make_shared<op::v1::Reshape>(data, pattern, false);
    auto model = make_shared<Model>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>({2, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}),
                                      make_tensor<element::Type_t::i64>({2}, {2, -1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{2, 5}));
    auto computed_val = read_vector<float>(result_tensor);
    vector<float> expected_val{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
    ASSERT_EQ(computed_val, expected_val);
}

TEST(eval, evaluate_reshape_v1_negative_index_zero_dim_zero_flag) {
    auto data = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 2, 2, 2});
    auto pattern = make_shared<ov::op::v0::Parameter>(element::i64, Shape{6});
    auto dyn_reshape = make_shared<op::v1::Reshape>(data, pattern, true);
    auto model = make_shared<Model>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>({2, 2, 2, 2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}),
        make_tensor<element::Type_t::i64>({6}, {2, 0, 1, -1, 1, 2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{2, 2, 1, 2, 1, 2}));
    auto computed_val = read_vector<float>(result_tensor);
    vector<float> expected_val{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
    ASSERT_EQ(computed_val, expected_val);
}

TEST(eval, evaluate_reshape_v1_pattern_int16) {
    auto data = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 2, 2, 2});
    auto pattern = make_shared<ov::op::v0::Parameter>(element::i16, Shape{6});
    auto dyn_reshape = make_shared<op::v1::Reshape>(data, pattern, true);
    auto model = make_shared<Model>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>({2, 2, 2, 2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}),
        make_tensor<element::Type_t::i16>({6}, {2, 0, 1, -1, 1, 2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{2, 2, 1, 2, 1, 2}));
    auto computed_val = read_vector<float>(result_tensor);
    vector<float> expected_val{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
    ASSERT_EQ(computed_val, expected_val);
}

TEST(eval, evaluate_reshape_v1_data_dynamic_shape) {
    constexpr auto exp_dtype = element::i32;

    auto data = make_shared<ov::op::v0::Parameter>(exp_dtype, PartialShape::dynamic());
    auto pattern = make_shared<ov::op::v0::Parameter>(element::i64, Shape{6});
    auto dyn_reshape = make_shared<op::v1::Reshape>(data, pattern, true);
    auto model = make_shared<Model>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::i32>(Shape{2, 2, 2}, {0, 1, 2, 3, 4, 5, 6, 7}),
                                      make_tensor<element::Type_t::i64>(pattern->get_shape(), {2, 0, 1, -1, 1, 1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), exp_dtype);
    EXPECT_EQ(result_tensor.get_shape(), Shape({2, 2, 1, 2, 1, 1}));
    EXPECT_THAT(read_vector<int32_t>(result_tensor), ElementsAre(0, 1, 2, 3, 4, 5, 6, 7));
}

TEST(eval, evaluate_reshape_v1_not_backward_compatible_and_in_out_size_not_eq) {
    constexpr auto exp_dtype = element::i32;
    auto data = make_shared<ov::op::v0::Parameter>(exp_dtype, PartialShape::dynamic());
    auto pattern = make_shared<ov::op::v0::Parameter>(element::i16, Shape{5});
    auto dyn_reshape = make_shared<op::v1::Reshape>(data, pattern, true);
    auto model = make_shared<Model>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::i32>(Shape{2, 2, 2}, {0, 1, 2, 3, 4, 5, 6, 7}),
                                      make_tensor<element::Type_t::i16>(pattern->get_shape(), {2, 1, 1, 1, 1})};

    OV_EXPECT_THROW(model->evaluate(out_vector, in_vector),
                    NodeValidationFailure,
                    HasSubstr("Requested output shape [2,1,1,1,1] is incompatible with input shape"));
}

TEST(eval, evaluate_convert) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1});
    auto convert = make_shared<op::v0::Convert>(p, element::i64);
    auto model = make_shared<Model>(OutputVector{convert}, ParameterVector{p});

    std::vector<std::vector<float>> inputs{{-1, 1}};
    std::vector<std::vector<int64_t>> expected_result{{-1, 1}};
    for (size_t i = 0; i < inputs.size(); i++) {
        auto result = ov::Tensor();
        auto out_vector = ov::TensorVector{result};
        auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{1, 2}, inputs[i])};
        ASSERT_TRUE(model->evaluate(out_vector, in_vector));
        result = out_vector.at(0);
        EXPECT_EQ(result.get_element_type(), element::i64);
        EXPECT_EQ(result.get_shape(), (Shape{1, 2}));
        auto result_data = read_vector<int64_t>(result);
        ASSERT_EQ(result_data, expected_result[i]);
    }
}

TEST(eval, evaluate_abs) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 3});
    auto abs = make_shared<ov::op::v0::Abs>(p);
    auto model = make_shared<Model>(OutputVector{abs}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3}, {0.0f, -1.0f, -2.0f, -3.0f, 4.0f, 5.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_erf) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 3});
    auto erf = make_shared<op::v0::Erf>(p);
    auto model = make_shared<Model>(OutputVector{erf}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3}, {0.0f, -1.0f, -2.0f, -3.0f, 4.0f, 5.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{std::erf(0.0f),
                        std::erf(-1.0f),
                        std::erf(-2.0f),
                        std::erf(-3.0f),
                        std::erf(4.0f),
                        std::erf(5.0f)};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_exp) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 3});
    auto exp = make_shared<op::v0::Exp>(p);
    auto model = make_shared<Model>(OutputVector{exp}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3}, {0.0f, -1.0f, -2.0f, -3.0f, 4.0f, 5.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{std::exp(0.0f),
                        std::exp(-1.0f),
                        std::exp(-2.0f),
                        std::exp(-3.0f),
                        std::exp(4.0f),
                        std::exp(5.0f)};
    ASSERT_FLOAT_VECTORS_EQ(expec, result_val);
}

TEST(eval, evaluate_floor) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 2});
    auto floor = make_shared<op::v0::Floor>(p);
    auto model = make_shared<Model>(OutputVector{floor}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 2}, {-2.5f, -2.0f, 0.3f, 4.8f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{-3.0f, -2.0f, 0.0f, 4.0f};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_floor_int32) {
    auto p = make_shared<ov::op::v0::Parameter>(element::i32, Shape{2, 2});
    auto floor = make_shared<op::v0::Floor>(p);
    auto model = make_shared<Model>(OutputVector{floor}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::i32>(Shape{2, 2}, {-2, -136314888, 0x40000010, 0x40000001})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::i32);
    auto result_val = read_vector<int32_t>(result);
    vector<int32_t> expec{-2, -136314888, 0x40000010, 0x40000001};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_log) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 2, 2});
    auto log = make_shared<op::v0::Log>(p);
    auto model = make_shared<Model>(OutputVector{log}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(Shape{2, 2, 2}, {0.125f, 0.25f, 0.5f, 1.f, 2.f, 4.f, 8.f, 16.f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{std::log(0.125f),
                        std::log(0.25f),
                        std::log(0.5f),
                        std::log(1.f),
                        std::log(2.f),
                        std::log(4.f),
                        std::log(8.f),
                        std::log(16.f)};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_negative_f32) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 5});
    auto negate = make_shared<op::v0::Negative>(p);
    auto model = make_shared<Model>(OutputVector{negate}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(Shape{2, 5},
                                          {1.35f, 8.76f, -8.0f, 17.234f, -2.121f, 1.0f, 8.7f, -8.92f, 17.0f, -1.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{-1.35f, -8.76f, 8.0f, -17.234f, 2.121f, -1.0f, -8.7f, 8.92f, -17.0f, 1.0f};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_negative_i32) {
    auto p = make_shared<ov::op::v0::Parameter>(element::i32, Shape{2, 5});
    auto negate = make_shared<op::v0::Negative>(p);
    auto model = make_shared<Model>(OutputVector{negate}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::i32>(Shape{2, 5}, {1, 8, -8, 17, -2, 1, 8, -8, 17, 0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::i32);
    auto result_val = read_vector<int32_t>(result);
    vector<int32_t> expec{-1, -8, 8, -17, 2, -1, -8, 8, -17, 0};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_relu_2Ffprop_f32) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 5});
    auto relu = make_shared<op::v0::Relu>(p);
    auto model = make_shared<Model>(OutputVector{relu}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(Shape{2, 5}, {1, 8, -8, 17, -0.5f, 0.1f, 8.5f, -8, 17, -0.5f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{1, 8, 0, 17, 0, 0.1f, 8.5f, 0, 17, 0};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_relu_2Ffprop_i32) {
    auto p = make_shared<ov::op::v0::Parameter>(element::i32, Shape{2, 5});
    auto relu = make_shared<op::v0::Relu>(p);
    auto model = make_shared<Model>(OutputVector{relu}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::i32>(Shape{2, 5}, {1, 8, -8, 17, -2, 1, 8, -8, 17, -1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::i32);
    auto result_val = read_vector<int32_t>(result);
    vector<int32_t> expec{1, 8, 0, 17, 0, 1, 8, 0, 17, 0};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_round) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{5});
    auto round = make_shared<op::v5::Round>(p, op::v5::Round::RoundMode::HALF_TO_EVEN);
    auto model = make_shared<Model>(OutputVector{round}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{5}, {0.9f, 2.5f, 2.3f, 1.5f, -4.5f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{1.0f, 2.0f, 2.0f, 2.0f, -4.0f};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_round_2D) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{3, 5});
    auto round = make_shared<op::v5::Round>(p, op::v5::Round::RoundMode::HALF_TO_EVEN);
    auto model = make_shared<Model>(OutputVector{round}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(
        Shape{3, 5},
        {0.1f, 0.5f, 0.9f, 1.2f, 1.5f, 1.8f, 2.3f, 2.5f, 2.7f, -1.1f, -1.5f, -1.9f, -2.2f, -2.5f, -2.8f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{0.f, 0.f, 1.f, 1.f, 2.f, 2.f, 2.f, 2.f, 3.f, -1.f, -2.f, -2.f, -2.f, -2.f, -3.f};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_sigmoid) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{1, 1, 2, 2});
    auto sigmoid = make_shared<op::v0::Sigmoid>(p);
    auto model = make_shared<Model>(OutputVector{sigmoid}, ParameterVector{p});
    float x1 = 1.0f;
    float x2 = 4.0f;
    float sigma1 = 1.0f / (1.0f + std::exp(-x1));
    float sigma2 = 1.0f / (1.0f + std::exp(-x2));
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{1, 1, 2, 2}, {x1, x2, x1, x2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{sigma1, sigma2, sigma1, sigma2};
    EXPECT_EQ(result_val.size(), expec.size());
}

TEST(eval, evaluate_sign) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 3});
    auto sign = make_shared<op::v0::Sign>(p);
    auto model = make_shared<Model>(OutputVector{sign}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3}, {1, -2, 0, -4.8f, 4.8f, -0.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{1, -1, 0, -1, 1, 0};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_sign_nan) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f16, Shape{2, 3});
    auto sign = make_shared<op::v0::Sign>(p);
    auto model = make_shared<Model>(OutputVector{sign}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f16>(Shape{2, 3},
                                          {std::numeric_limits<float16>::quiet_NaN(), -2, 0, -4.8f, 4.8f, -0.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), element::f16);
    EXPECT_THAT(read_vector<float16>(result),
                Pointwise(NanSensitiveFloatEq(),
                          std::vector<float16>{std::numeric_limits<float16>::quiet_NaN(), -1, 0, -1, 1, 0}));
}

TEST(eval, evaluate_sin) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{11});
    auto sin = make_shared<op::v0::Sin>(p);
    auto model = make_shared<Model>(OutputVector{sin}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(Shape{11},
                                          {0.f, 0.25f, -0.25f, 0.5f, -0.5f, 1.f, -1.f, 2.f, -2.f, 4.f, -4.f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{0.00000000f,
                        0.24740396f,
                        -0.24740396f,
                        0.47942554f,
                        -0.47942554f,
                        0.84147098f,
                        -0.84147098f,
                        0.90929743f,
                        -0.90929743f,
                        -0.75680250f,
                        0.75680250f};
    ASSERT_FLOAT_VECTORS_EQ(expec, result_val);
}

TEST(eval, evaluate_sinh) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{6});
    auto sinh = make_shared<op::v0::Sinh>(p);
    auto model = make_shared<Model>(OutputVector{sinh}, ParameterVector{p});
    vector<float> input{1.0f, 0.0f, -0.0f, -1.0f, 5.0f, -5.0f};
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{6}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
        return sinhf(x);
    });
    ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}

TEST(eval, evaluate_sqrt) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{6});
    auto sqrt = make_shared<op::v0::Sqrt>(p);
    auto model = make_shared<Model>(OutputVector{sqrt}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    vector<float> input{16, 4, 81, 100, 10000, 0};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{6}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{4, 2, 9, 10, 100, 0};
    ASSERT_FLOAT_VECTORS_EQ(expec, result_val);
}

TEST(eval, evaluate_acos) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{11});
    auto acos = make_shared<op::v0::Acos>(p);
    auto model = make_shared<Model>(OutputVector{acos}, ParameterVector{p});
    vector<float> input{-1.f, -0.75f, -0.5f, -0.25f, -0.125f, 0.f, 0.125f, 0.25f, 0.5f, 0.75f, 1.f};
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{11}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
        return std::acos(x);
    });
    ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}

TEST(eval, evaluate_asin) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{11});
    auto asin = make_shared<op::v0::Asin>(p);
    auto model = make_shared<Model>(OutputVector{asin}, ParameterVector{p});

    vector<float> input{-1.f, -0.75f, -0.5f, -0.25f, -0.125f, 0.f, 0.125f, 0.25f, 0.5f, 0.75f, 1.f};
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{11}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
        return std::asin(x);
    });

    ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}

TEST(eval, evaluate_atan) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{11});
    auto atan = make_shared<op::v0::Atan>(p);
    auto model = make_shared<Model>(OutputVector{atan}, ParameterVector{p});

    vector<float> input{-4.f, -2.f, -1.f, -0.5f, -0.25f, 0.f, 0.25f, 0.5f, 1.f, 2.f, 4.f};
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{11}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
        return std::atan(x);
    });

    ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}

TEST(eval, evaluate_ceiling) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 2});
    auto ceil = make_shared<op::v0::Ceiling>(p);
    auto model = make_shared<Model>(OutputVector{ceil}, ParameterVector{p});

    vector<float> input{-2.5f, -2.0f, 0.3f, 4.8f};
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 2}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    vector<float> expec{-2.0f, -2.0f, 1.0f, 5.0f};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_cos) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{11});
    auto cos = make_shared<op::v0::Cos>(p);
    auto model = make_shared<Model>(OutputVector{cos}, ParameterVector{p});

    vector<float> input{0.f, 0.25f, -0.25f, 0.5f, -0.5f, 1.f, -1.f, 2.f, -2.f, 4.f, -4.f};
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{11}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
        return std::cos(x);
    });

    ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}

TEST(eval, evaluate_cosh) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{6});
    auto cosh = make_shared<op::v0::Cosh>(p);
    auto model = make_shared<Model>(OutputVector{cosh}, ParameterVector{p});

    vector<float> input{1.0f, 0.0f, -0.0f, -1.0f, 5.0f, -5.0f};
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{6}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
        return std::cosh(x);
    });

    ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}

TEST(eval, evaluate_tan) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{11});
    auto tan = make_shared<op::v0::Tan>(p);
    auto model = make_shared<Model>(OutputVector{tan}, ParameterVector{p});

    vector<float> input{0.f, 0.25f, -0.25f, 0.5f, -0.5f, 1.f, -1.f, 2.f, -2.f, 4.f, -4.f};
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{11}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
        return std::tan(x);
    });

    ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}

TEST(eval, evaluate_tanh) {
    auto p = make_shared<ov::op::v0::Parameter>(element::f32, Shape{6});
    auto tanh = make_shared<op::v0::Tanh>(p);
    auto model = make_shared<Model>(OutputVector{tanh}, ParameterVector{p});

    vector<float> input{1.0f, 0.0f, -0.0f, -1.0f, 0.5f, -0.5f};
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{6}, input)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);
    EXPECT_EQ(result.get_element_type(), element::f32);
    auto result_val = read_vector<float>(result);
    std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
        return std::tanh(x);
    });

    ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}

TEST(eval, evaluate_logical_not_dynamic_input_shape) {
    const auto a = make_shared<ov::op::v0::Parameter>(element::boolean, PartialShape::dynamic());
    const auto op = make_shared<op::v1::LogicalNot>(a);
    const auto model = make_shared<Model>(OutputVector{op}, ParameterVector{a});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(Shape{2, 1, 2}, {0, 0, 1, 1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), element::boolean);
    EXPECT_EQ(result.get_shape(), Shape({2, 1, 2}));
    EXPECT_THAT(read_vector<char>(result), ElementsAre(1, 1, 0, 0));
}

TEST(eval, evaluate_logical_not) {
    auto p = make_shared<ov::op::v0::Parameter>(element::boolean, Shape{2, 2});
    auto logical_not = make_shared<op::v1::LogicalNot>(p);
    auto model = make_shared<Model>(OutputVector{logical_not}, ParameterVector{p});
    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(Shape{2, 2}, {1, 0, 1, 0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), element::boolean);
    auto result_val = read_vector<char>(result);
    vector<char> expec{0, 1, 0, 1};
    ASSERT_EQ(result_val, expec);
}

TEST(eval, evaluate_dynamic_gather_v1) {
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto gather = make_shared<op::v1::Gather>(arg1, arg2, arg3);
    auto model = make_shared<Model>(OutputVector{gather}, ParameterVector{arg1, arg2, arg3});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>({3}, {1.0f, 2.0f, 3.0f}),
                                      make_tensor<element::Type_t::i32>({2}, {1, 0}),
                                      make_tensor<element::Type_t::i32>({1}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{2}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{2.0f, 1.0f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_dynamic_gather_v1_scalar_axis) {
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());
    auto gather = make_shared<op::v1::Gather>(arg1, arg2, arg3);
    auto model = make_shared<Model>(OutputVector{gather}, ParameterVector{arg1, arg2, arg3});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>({3, 3}, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f, 3.0f, 3.1f, 3.2f}),
        make_tensor<element::Type_t::i32>({1, 2}, {0, 2}),
        make_tensor<element::Type_t::u64>({}, {1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 1, 2}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{1.0f, 1.2f, 2.0f, 2.2f, 3.0f, 3.2f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_dynamic_gather_v7) {
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    int64_t batch_dims = 1;
    int32_t axis = 1;
    auto gather = make_shared<op::v7::Gather>(arg1, arg2, arg3, batch_dims);
    auto model = make_shared<Model>(OutputVector{gather}, ParameterVector{arg1, arg2, arg3});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>({2, 3}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}),
                                      make_tensor<element::Type_t::i32>({2, 2}, {1, 0, 1, 0}),
                                      make_tensor<element::Type_t::i32>({1}, {axis})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{2, 2}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{2.0f, 1.0f, 5.0f, 4.0f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_dynamic_gather_v7_axis_scalar) {
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());
    int64_t batch_dims = 0;
    int64_t axis = 1;
    auto gather = make_shared<op::v7::Gather>(arg1, arg2, arg3, batch_dims);
    auto model = make_shared<Model>(OutputVector{gather}, ParameterVector{arg1, arg2, arg3});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>({3, 3}, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f, 3.0f, 3.1f, 3.2f}),
        make_tensor<element::Type_t::i32>({1, 2}, {0, 2}),
        make_tensor<element::Type_t::i64>({}, {axis})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 1, 2}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{1.0f, 1.2f, 2.0f, 2.2f, 3.0f, 3.2f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_dynamic_concat) {
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto concat = make_shared<op::v0::Concat>(NodeVector{arg1, arg2}, 1);
    auto model = make_shared<Model>(OutputVector{concat}, ParameterVector{arg1, arg2});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>({1, 1}, {1.0f}),
                                      make_tensor<element::Type_t::f32>({1, 2}, {8.0f, 10.0f})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{1, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{1.0f, 8.0f, 10.0f};
    ASSERT_EQ(cval, out);
}

TEST(eval, max_pool_v1_dynamic) {
    Shape window_shape{3};
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto model = make_shared<Model>(
        make_shared<op::v1::MaxPool>(A, Strides(), Shape(), Shape(), window_shape, op::RoundingType::FLOOR),
        ParameterVector{A});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>({1, 1, 14}, {0, 1, 0, 2, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{1, 1, 12}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{1, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 0};
}

template <class T>
class ScatterElementsUpdateEvalTest : public ::testing::Test {};
TYPED_TEST_SUITE_P(ScatterElementsUpdateEvalTest);

TYPED_TEST_P(ScatterElementsUpdateEvalTest, evaluate_static_scatter_elements_update_basic) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{2, 3};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update = make_shared<TypeParam>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}),
                         make_tensor<element::Type_t::i32>(indices_shape, {1, 0, 2, 0, 2, 1}),
                         make_tensor<element::Type_t::f32>(indices_shape, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f}),
                         make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{2.f, 1.1f, 0.0f, 1.f, 0.0f, 2.2f, 0.f, 2.1f, 1.2f};
    ASSERT_EQ(cval, out);
}

TYPED_TEST_P(ScatterElementsUpdateEvalTest, evaluate_dynamic_scatter_elements_update_basic) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{2, 3};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());

    auto scatter_elements_update = make_shared<TypeParam>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}),
                         make_tensor<element::Type_t::i32>(indices_shape, {1, 0, 2, 0, 2, 1}),
                         make_tensor<element::Type_t::f32>(indices_shape, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f}),
                         make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{2.f, 1.1f, 0.0f, 1.f, 0.0f, 2.2f, 0.f, 2.1f, 1.2f};
    ASSERT_EQ(cval, out);
}

TYPED_TEST_P(ScatterElementsUpdateEvalTest, evaluate_dynamic_scatter_elements_update_negative_axis) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{2, 3};
    const Shape axis_shape{};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());

    auto scatter_elements_update = make_shared<TypeParam>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}),
                         make_tensor<element::Type_t::i32>(indices_shape, {1, 0, 2, 0, 2, 1}),
                         make_tensor<element::Type_t::f32>(indices_shape, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f}),
                         make_tensor<element::Type_t::i64>(axis_shape, {-1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{1.1f, 1.0f, 1.2f, 2.0f, 2.2f, 2.1f, 0.0f, 0.0f, 0.0f};
    ASSERT_EQ(cval, out);
}

TYPED_TEST_P(ScatterElementsUpdateEvalTest, evaluate_dynamic_scatter_elements_update_1d_axis) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{2, 3};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());

    auto scatter_elements_update = make_shared<TypeParam>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}),
                         make_tensor<element::Type_t::i32>(indices_shape, {1, 0, 2, 0, 2, 1}),
                         make_tensor<element::Type_t::f32>(indices_shape, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f}),
                         make_tensor<element::Type_t::i64>({1}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{2.f, 1.1f, 0.0f, 1.f, 0.0f, 2.2f, 0.f, 2.1f, 1.2f};
    ASSERT_EQ(cval, out);
}

TYPED_TEST_P(ScatterElementsUpdateEvalTest, evaluate_dynamic_scatter_elements_update_one_elem_i32) {
    const Shape data_shape{3, 3, 3};
    const Shape indices_shape{1, 1, 1};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());

    auto scatter_elements_update = make_shared<TypeParam>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::i32>(data_shape, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                                                                        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}),
                         make_tensor<element::Type_t::i32>(indices_shape, {1}),
                         make_tensor<element::Type_t::i32>(indices_shape, {2}),
                         make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::i32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3, 3}));
    auto cval = read_vector<int32_t>(result_tensor);
    vector<int32_t> out{0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
    ASSERT_EQ(cval, out);
}

REGISTER_TYPED_TEST_SUITE_P(ScatterElementsUpdateEvalTest,
                            evaluate_dynamic_scatter_elements_update_one_elem_i32,
                            evaluate_dynamic_scatter_elements_update_1d_axis,
                            evaluate_dynamic_scatter_elements_update_negative_axis,
                            evaluate_dynamic_scatter_elements_update_basic,
                            evaluate_static_scatter_elements_update_basic);

using OpVersions = ::testing::Types<ov::op::v3::ScatterElementsUpdate, ov::op::v12::ScatterElementsUpdate>;
INSTANTIATE_TYPED_TEST_SUITE_P(eval, ScatterElementsUpdateEvalTest, OpVersions);

TEST(eval, evaluate_static_scatter_elements_update_reduction_sum) {
    const Shape data_shape{10};
    const Shape indices_shape{4};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::SUM);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(data_shape, {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f}),
        make_tensor<element::Type_t::i32>(indices_shape, {5, 0, 7, 5}),
        make_tensor<element::Type_t::f32>(indices_shape, {5.0f, 6.0f, 1.5f, -5.0f}),
        make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<float>(result_tensor);
    const vector<float> out{6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 8.5f, 8.0f, 9.0f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_prod_exclusive) {
    const Shape data_shape{10};
    const Shape indices_shape{4};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::PROD,
                                                        false);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(data_shape, {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f}),
        make_tensor<element::Type_t::i32>(indices_shape, {1, 9, 4, 9}),
        make_tensor<element::Type_t::f32>(indices_shape, {5.0f, 6.0f, 1.5f, -2.0f}),
        make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<float>(result_tensor);
    const vector<float> out{0.0f, 5.0f, 2.0f, 3.0f, 1.5f, 5.0f, 6.0f, 7.0f, 8.0f, -12.0f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_mean) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{2, 2};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MEAN,
                                                        true);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(data_shape, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f}),
        make_tensor<element::Type_t::i32>(indices_shape, {2, 2, 0, 1}),
        make_tensor<element::Type_t::f32>(indices_shape, {10.f, 21.f, 25.f, 38.f}),
        make_tensor<element::Type_t::i64>({}, {1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<float>(result_tensor);
    const vector<float> out{1.0f, 2.0f, 11.33333f, 14.5f, 21.5f, 6.0f, 7.0f, 8.0f, 9.0f};
    for (size_t i = 0; i < cval.size(); ++i)
        EXPECT_NEAR(cval[i], out[i], 1e-5f);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_mean_exclusive) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{2, 2};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MEAN,
                                                        false);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(data_shape, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f}),
        make_tensor<element::Type_t::i32>(indices_shape, {2, 2, 0, 1}),
        make_tensor<element::Type_t::f32>(indices_shape, {10.f, 21.f, 25.f, 38.f}),
        make_tensor<element::Type_t::i64>({}, {1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<float>(result_tensor);
    const vector<float> out{1.0f, 2.0f, 15.5f, 25.f, 38.f, 6.0f, 7.0f, 8.0f, 9.0f};
    for (size_t i = 0; i < cval.size(); ++i)
        EXPECT_NEAR(cval[i], out[i], 1e-5f);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_mean_ints) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{2, 2};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::i32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MEAN,
                                                        true);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::i32>(data_shape, {1, 2, 3, 4, -5, 6, 7, 8, 9}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 1}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {-6, -2, 600, -120}),
                                      make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::i32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<int32_t>(result_tensor);
    const vector<int32_t> out{-3, 2, 3, 4, -43, 6, 303, 8, 9};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_min) {
    const Shape data_shape{9};
    const Shape indices_shape{9};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::i32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MIN,
                                                        true);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::i32>(data_shape, {-1000, 2, 3, 4, -5, 6, 7, -2, 8}),
                         make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 3, 4, 5, 6, 7, 0}),
                         make_tensor<element::Type_t::i32>(indices_shape, {-999, 1, 3, 5, -4, 6, 8, 9, -1001}),
                         make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::i32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<int32_t>(result_tensor);
    const vector<int32_t> out{-1001, 1, 3, 4, -5, 6, 7, -2, 8};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_max) {
    const Shape data_shape{9};
    const Shape indices_shape{9};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::i32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MAX,
                                                        true);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::i32>(data_shape, {-1000, 2, 3, 4, -5, 6, 7, -2, 8}),
                         make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 3, 4, 5, 6, 7, 0}),
                         make_tensor<element::Type_t::i32>(indices_shape, {-999, 1, 3, 5, -4, 6, 8, 9, -1001}),
                         make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::i32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<int32_t>(result_tensor);
    const vector<int32_t> out{-999, 2, 3, 5, -4, 6, 8, 9, 8};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_max_exclusive) {
    const Shape data_shape{9};
    const Shape indices_shape{9};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::i32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MAX,
                                                        false);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::i32>(data_shape, {1000, 2, 3, 4, -5, 6, 7, -2, 8}),
                         make_tensor<element::Type_t::i32>(indices_shape, {0, 2, 1, 3, 7, 5, 6, 7, 0}),
                         make_tensor<element::Type_t::i32>(indices_shape, {999, 10, 20, 30, -40, 6, 8, 9, 555}),
                         make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::i32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<int32_t>(result_tensor);
    const vector<int32_t> out{999, 20, 10, 30, -5, 6, 8, 9, 8};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_max_exclusive_float) {
    using namespace ov::op;
    const Shape data_shape{9};
    const Shape indices_shape{9};
    const auto arg1 = make_shared<v0::Parameter>(element::f32, data_shape);
    const auto arg2 = make_shared<v0::Parameter>(element::i32, indices_shape);
    const auto arg3 = make_shared<v0::Parameter>(element::f32, indices_shape);
    const auto arg4 = make_shared<v0::Parameter>(element::i64, Shape{});
    const auto scatter_elements_update =
        make_shared<v12::ScatterElementsUpdate>(arg1,
                                                arg2,
                                                arg3,
                                                arg4,
                                                v12::ScatterElementsUpdate::Reduction::MAX,
                                                false);
    const auto model =
        make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = Tensor{};
    auto out_vector = TensorVector{result_tensor};
    auto in_vector =
        TensorVector{make_tensor<element::Type_t::f32>(data_shape, {1000, 2, 3, 4, -5, 6, 7, -2, 8}),
                     make_tensor<element::Type_t::i32>(indices_shape, {0, 2, 1, 3, 7, 4, 6, 7, 0}),
                     make_tensor<element::Type_t::f32>(indices_shape, {999, 10, 20, 30, -40, -6, 8, -9, 555}),
                     make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<float>(result_tensor);
    const vector<float> out{999, 20, 10, 30, -6, 6, 8, -9, 8};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_boolean_sum) {
    const Shape data_shape{5};
    const Shape indices_shape{6};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::boolean, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::boolean, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::SUM,
                                                        true);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(data_shape, {1, 0, 0, 1, 0}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 3, 4, 1}),
                                      make_tensor<element::Type_t::boolean>(indices_shape, {0, 0, 0, 1, 1, 1}),
                                      make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::boolean);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<char>(result_tensor);
    const vector<char> out{1, 1, 0, 1, 1};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_boolean_sum_exclusive) {
    const Shape data_shape{5};
    const Shape indices_shape{6};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::boolean, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::boolean, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::SUM,
                                                        false);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(data_shape, {1, 0, 1, 1, 0}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 4, 4, 0}),
                                      make_tensor<element::Type_t::boolean>(indices_shape, {0, 1, 0, 1, 1, 1}),
                                      make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::boolean);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<char>(result_tensor);
    const vector<char> out{1, 1, 0, 1, 1};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_boolean_prod) {
    const Shape data_shape{5};
    const Shape indices_shape{6};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::boolean, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::boolean, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::PROD,
                                                        true);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(data_shape, {1, 0, 0, 1, 1}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 3, 4, 1}),
                                      make_tensor<element::Type_t::boolean>(indices_shape, {0, 0, 1, 1, 0, 1}),
                                      make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::boolean);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<char>(result_tensor);
    const vector<char> out{0, 0, 0, 1, 0};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_boolean_prod_exclusive) {
    const Shape data_shape{5};
    const Shape indices_shape{6};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::boolean, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::boolean, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::PROD,
                                                        false);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(data_shape, {1, 0, 1, 1, 0}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 4, 4, 0}),
                                      make_tensor<element::Type_t::boolean>(indices_shape, {0, 0, 1, 1, 1, 1}),
                                      make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::boolean);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<char>(result_tensor);
    const vector<char> out{0, 0, 1, 1, 1};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_boolean_min) {
    const Shape data_shape{6};
    const Shape indices_shape{8};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::boolean, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::boolean, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MIN,
                                                        true);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(data_shape, {1, 0, 0, 1, 1, 0}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 3, 4, 4, 5, 5}),
                                      make_tensor<element::Type_t::boolean>(indices_shape, {0, 0, 0, 1, 0, 1, 1, 0}),
                                      make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::boolean);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<char>(result_tensor);
    const vector<char> out{0, 0, 0, 1, 0, 0};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_boolean_min_exclusive) {
    const Shape data_shape{6};
    const Shape indices_shape{8};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::boolean, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::boolean, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MIN,
                                                        false);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(data_shape, {1, 0, 1, 0, 1, 0}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 3, 4, 4, 5, 5}),
                                      make_tensor<element::Type_t::boolean>(indices_shape, {0, 0, 1, 1, 0, 1, 1, 1}),
                                      make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::boolean);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<char>(result_tensor);
    const vector<char> out{0, 0, 1, 1, 0, 1};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_boolean_max) {
    const Shape data_shape{6};
    const Shape indices_shape{8};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::boolean, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::boolean, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MAX,
                                                        true);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(data_shape, {1, 0, 0, 1, 1, 0}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 3, 4, 4, 5, 5}),
                                      make_tensor<element::Type_t::boolean>(indices_shape, {0, 1, 0, 1, 0, 1, 0, 0}),
                                      make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::boolean);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<char>(result_tensor);
    const vector<char> out{1, 1, 0, 1, 1, 0};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_boolean_max_exclusive) {
    const Shape data_shape{6};
    const Shape indices_shape{8};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::boolean, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::boolean, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::MAX,
                                                        false);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::boolean>(data_shape, {1, 0, 1, 0, 1, 0}),
                                      make_tensor<element::Type_t::i32>(indices_shape, {0, 1, 2, 3, 4, 4, 5, 5}),
                                      make_tensor<element::Type_t::boolean>(indices_shape, {0, 1, 1, 0, 0, 1, 0, 0}),
                                      make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::boolean);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<char>(result_tensor);
    const vector<char> out{0, 1, 1, 0, 1, 0};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_sum_negative_idx) {
    const Shape data_shape{10};
    const Shape indices_shape{4};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::SUM);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(data_shape, {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f}),
        make_tensor<element::Type_t::i32>(indices_shape, {-5, 0, -3, -5}),
        make_tensor<element::Type_t::f32>(indices_shape, {5.0f, 6.0f, 1.5f, -5.0f}),
        make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<float>(result_tensor);
    const vector<float> out{6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 8.5f, 8.0f, 9.0f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_elements_update_reduction_none_negative_idx) {
    const Shape data_shape{2, 5};
    const Shape indices_shape{2, 2};
    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, indices_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_elements_update =
        make_shared<ov::op::v12::ScatterElementsUpdate>(arg1,
                                                        arg2,
                                                        arg3,
                                                        arg4,
                                                        ov::op::v12::ScatterElementsUpdate::Reduction::NONE);
    auto model = make_shared<Model>(OutputVector{scatter_elements_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{
        make_tensor<element::Type_t::f32>(data_shape, {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f}),
        make_tensor<element::Type_t::i32>(indices_shape, {-5, -4, -3, -1}),
        make_tensor<element::Type_t::f32>(indices_shape, {11.5f, 12.5f, 13.5f, 14.5f}),
        make_tensor<element::Type_t::i64>({}, {1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), data_shape);
    const auto cval = read_vector<float>(result_tensor);
    const vector<float> out{11.5f, 12.5f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 13.5f, 8.0f, 14.5f};
    ASSERT_EQ(cval, out);
}

TEST(eval, topk_v1) {
    Shape shape{2, 3, 2};
    Shape rshape{2, 2, 2};

    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape);
    const auto k = ov::op::v0::Constant::create(element::i32, Shape{}, {2});
    auto B = make_shared<op::v1::TopK>(A, k, 1, "max", "index", element::i32);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 2, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 2, 2}));
    auto result0_val = read_vector<float>(result0);

    auto result1_val = read_vector<int32_t>(result1);

    vector<float> expec0{12, 9, 10, 4, 6, 3, 11, 7};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 0, 1, 2, 2};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, topk_v1_dyn) {
    Shape shape{2, 3, 2};

    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape);
    auto k = make_shared<ov::op::v0::Parameter>(element::i32, Shape{});
    auto B = make_shared<op::v1::TopK>(A, k, 1, "max", "index", element::i32);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A, k});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}),
                         make_tensor<element::Type_t::i32>(Shape{}, {2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 2, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 2, 2}));
    auto result0_val = read_vector<float>(result0);
    auto result1_val = read_vector<int32_t>(result1);
    vector<float> expec0{12, 9, 10, 4, 6, 3, 11, 7};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 0, 1, 2, 2};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, topk_v3_dyn) {
    Shape shape{2, 3, 2};

    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape);
    auto k = make_shared<ov::op::v0::Parameter>(element::u32, Shape{});
    auto B = make_shared<op::v3::TopK>(A, k, 1, "max", "index", element::i32);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A, k});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}),
                         make_tensor<element::Type_t::i32>(Shape{}, {2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 2, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 2, 2}));
    auto result0_val = read_vector<float>(result0);
    auto result1_val = read_vector<int32_t>(result1);
    vector<float> expec0{12, 9, 10, 4, 6, 3, 11, 7};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 0, 1, 2, 2};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, topk_v3_dyn_values) {
    Shape shape{2, 3, 2};

    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape);
    auto k = make_shared<ov::op::v0::Parameter>(element::u32, Shape{});
    auto B = make_shared<op::v3::TopK>(A, k, 1, "max", "value", element::i32);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A, k});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}),
                         make_tensor<element::Type_t::i32>(Shape{}, {2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 2, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 2, 2}));
    auto result0_val = read_vector<float>(result0);
    auto result1_val = read_vector<int32_t>(result1);
    vector<float> expec0{12, 9, 10, 4, 11, 7, 6, 3};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 2, 2, 0, 1};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, topk_v3_dyn_values_k0) {
    Shape shape{2, 3, 2};

    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape);
    auto k = make_shared<ov::op::v0::Parameter>(element::u32, Shape{});
    auto B = make_shared<op::v3::TopK>(A, k, 1, "max", "value", element::i32);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A, k});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}),
                         make_tensor<element::Type_t::i32>(Shape{}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 3, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 3, 2}));
    auto result0_val = read_vector<float>(result0);
    auto result1_val = read_vector<int32_t>(result1);
    vector<float> expec0{12, 9, 10, 4, 8, 2, 11, 7, 6, 3, 5, 1};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 2, 0, 2, 2, 0, 1, 1, 0};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, topk_v1_dyn_k0) {
    Shape shape{2, 3, 2};

    auto A = make_shared<ov::op::v0::Parameter>(element::f32, shape);
    auto k = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});

    element::Type result_et{element::i32};
    auto B =
        make_shared<op::v1::TopK>(A, k, 1, op::v1::TopK::Mode::MAX, op::v1::TopK::SortType::SORT_VALUES, result_et);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A, k});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}),
                         make_tensor<element::Type_t::i64>(Shape{}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 3, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 3, 2}));
    auto result0_val = read_vector<float>(result0);
    auto result1_val = read_vector<int32_t>(result1);

    vector<float> expec0{12, 9, 10, 4, 8, 2, 11, 7, 6, 3, 5, 1};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 2, 0, 2, 2, 0, 1, 1, 0};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, topk_v3_param_dyn_values_k0) {
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto k = make_shared<ov::op::v0::Parameter>(element::u32, Shape{});
    auto B = make_shared<op::v3::TopK>(A, k, 1, "max", "value", element::i32);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A, k});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}),
                         make_tensor<element::Type_t::i32>(Shape{}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 3, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 3, 2}));
    auto result0_val = read_vector<float>(result0);
    auto result1_val = read_vector<int32_t>(result1);
    vector<float> expec0{12, 9, 10, 4, 8, 2, 11, 7, 6, 3, 5, 1};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 2, 0, 2, 2, 0, 1, 1, 0};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, topk_v3_param_dyn_values_k2) {
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto k = make_shared<ov::op::v0::Parameter>(element::u32, Shape{});
    auto B = make_shared<op::v3::TopK>(A, k, 1, "max", "value", element::i32);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A, k});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}),
                         make_tensor<element::Type_t::i32>(Shape{}, {2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 2, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 2, 2}));
    auto result0_val = read_vector<float>(result0);
    auto result1_val = read_vector<int32_t>(result1);
    vector<float> expec0{12, 9, 10, 4, 11, 7, 6, 3};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 2, 2, 0, 1};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, topk_v1_param_dyn_k2) {
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto k = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto axis = 1;

    element::Type result_et{element::i32};
    auto B =
        make_shared<op::v1::TopK>(A, k, axis, op::v1::TopK::Mode::MAX, op::v1::TopK::SortType::SORT_VALUES, result_et);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A, k});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}),
                         make_tensor<element::Type_t::i64>(Shape{}, {2})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 2, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 2, 2}));
    auto result0_val = read_vector<float>(result0);
    auto result1_val = read_vector<int32_t>(result1);

    vector<float> expec0{12, 9, 10, 4, 11, 7, 6, 3};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 2, 2, 0, 1};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, topk_v1_param_dyn_k0) {
    auto A = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto k = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});

    element::Type result_et{element::i32};

    auto B =
        make_shared<op::v1::TopK>(A, k, 1, op::v1::TopK::Mode::MAX, op::v1::TopK::SortType::SORT_VALUES, result_et);

    auto model = make_shared<Model>(OutputVector{B->output(0), B->output(1)}, ParameterVector{A, k});

    auto result0 = ov::Tensor();
    auto result1 = ov::Tensor();
    auto out_vector = ov::TensorVector{result0, result1};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(Shape{2, 3, 2}, {12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}),
                         make_tensor<element::Type_t::i64>(Shape{}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result0 = out_vector.at(0);
    result1 = out_vector.at(1);
    EXPECT_EQ(result0.get_element_type(), element::f32);
    EXPECT_EQ(result0.get_shape(), (Shape{2, 3, 2}));
    EXPECT_EQ(result1.get_element_type(), element::i32);
    EXPECT_EQ(result1.get_shape(), (Shape{2, 3, 2}));
    auto result0_val = read_vector<float>(result0);
    auto result1_val = read_vector<int32_t>(result1);

    vector<float> expec0{12, 9, 10, 4, 8, 2, 11, 7, 6, 3, 5, 1};
    ASSERT_EQ(result0_val, expec0);

    vector<int32_t> expec1{0, 1, 1, 2, 2, 0, 2, 2, 0, 1, 1, 0};
    ASSERT_EQ(result1_val, expec1);
}

TEST(eval, evaluate_static_scatter_update_basic_axes_indices_i32) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{1, 2};
    const Shape updates_shape{1, 2, 3};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, updates_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i32, Shape{});
    auto scatter_update = make_shared<op::v3::ScatterUpdate>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, std::vector<float>(shape_size(data_shape))),
                         make_tensor<element::Type_t::i32>(indices_shape, {1, 2}),
                         make_tensor<element::Type_t::f32>(updates_shape, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f}),
                         make_tensor<element::Type_t::i32>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{0.f, 0.f, 0.f, 1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_static_scatter_update_basic_axes_indices_i64) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{1, 2};
    const Shape updates_shape{1, 2, 3};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, data_shape);
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i64, indices_shape);
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, updates_shape);
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, Shape{});
    auto scatter_update = make_shared<op::v3::ScatterUpdate>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, std::vector<float>(shape_size(data_shape))),
                         make_tensor<element::Type_t::i64>(indices_shape, {1, 2}),
                         make_tensor<element::Type_t::f32>(updates_shape, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f}),
                         make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);
    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{0.f, 0.f, 0.f, 1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_dynamic_scatter_update_basic) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{1, 2};
    const Shape updates_shape{1, 2, 3};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());

    auto scatter_update = make_shared<op::v3::ScatterUpdate>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, std::vector<float>(shape_size(data_shape))),
                         make_tensor<element::Type_t::i32>(indices_shape, {1, 2}),
                         make_tensor<element::Type_t::f32>(updates_shape, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f}),
                         make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{0.f, 0.f, 0.f, 1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_dynamic_scatter_update_negative_axis) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{1, 2};
    const Shape updates_shape{3, 1, 2};
    const Shape axis_shape{};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());

    auto scatter_update = make_shared<op::v3::ScatterUpdate>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, std::vector<float>(shape_size(data_shape))),
                         make_tensor<element::Type_t::i32>(indices_shape, {1, 2}),
                         make_tensor<element::Type_t::f32>(updates_shape, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f}),
                         make_tensor<element::Type_t::i64>(axis_shape, {-1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{0.f, 1.0f, 1.1f, 0.0f, 1.2f, 2.0f, 0.0f, 2.1f, 2.2f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_dynamic_scatter_update_1d_axis) {
    const Shape data_shape{3, 3};
    const Shape indices_shape{1, 2};
    const Shape updates_shape{3, 1, 2};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());

    auto scatter_update = make_shared<op::v3::ScatterUpdate>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, std::vector<float>(shape_size(data_shape))),
                         make_tensor<element::Type_t::i32>(indices_shape, {1, 2}),
                         make_tensor<element::Type_t::f32>(updates_shape, {1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f}),
                         make_tensor<element::Type_t::i64>({1}, {1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3}));
    auto cval = read_vector<float>(result_tensor);
    vector<float> out{0.f, 1.0f, 1.1f, 0.0f, 1.2f, 2.0f, 0.0f, 2.1f, 2.2f};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_dynamic_scatter_update_one_elem_i32) {
    const Shape data_shape{3, 3, 2};
    const Shape indices_shape{1, 1};
    const Shape updates_shape{1, 1, 3, 2};

    auto arg1 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg2 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg3 = make_shared<ov::op::v0::Parameter>(element::i32, PartialShape::dynamic());
    auto arg4 = make_shared<ov::op::v0::Parameter>(element::i64, PartialShape::dynamic());

    auto scatter_update = make_shared<op::v3::ScatterUpdate>(arg1, arg2, arg3, arg4);
    auto model = make_shared<Model>(OutputVector{scatter_update}, ParameterVector{arg1, arg2, arg3, arg4});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector =
        ov::TensorVector{make_tensor<element::Type_t::i32>(data_shape, std::vector<int32_t>(shape_size(data_shape))),
                         make_tensor<element::Type_t::i32>(indices_shape, {1}),
                         make_tensor<element::Type_t::i32>(updates_shape, {1, 2, 3, 4, 5, 6}),
                         make_tensor<element::Type_t::i64>({}, {0})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::i32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{3, 3, 2}));
    auto cval = read_vector<int32_t>(result_tensor);
    vector<int32_t> out{0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0};
    ASSERT_EQ(cval, out);
}

TEST(eval, evaluate_softmax_8) {
    const Shape data_shape{1, 2};
    auto arg = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto softmax = std::make_shared<op::v8::Softmax>(arg, -1);
    auto model = std::make_shared<Model>(OutputVector{softmax}, ParameterVector{arg});
    auto result_tensor = ov::Tensor();
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>(data_shape, {1, 1})};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result_tensor = out_vector.at(0);

    EXPECT_EQ(result_tensor.get_element_type(), element::f32);
    EXPECT_EQ(result_tensor.get_shape(), (Shape{1, 2}));
    auto val = read_vector<float>(result_tensor);
    vector<float> out{0.5, 0.5};
    ASSERT_EQ(val, out);
}

TEST(eval, evaluate_softsign_9) {
    auto arg = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape::dynamic());
    auto softsign = std::make_shared<op::v9::SoftSign>(arg);
    auto model = std::make_shared<Model>(OutputVector{softsign}, ParameterVector{arg});
    ov::TensorVector result_tensor(1);
    float input_vector[] = {1, -1, 2.5, -3.5};
    ov::Tensor input{ov::element::f32, ov::Shape{4}, input_vector};

    ASSERT_TRUE(model->evaluate(result_tensor, ov::TensorVector{input}));
    EXPECT_EQ(result_tensor[0].get_element_type(), ov::element::f32);
    EXPECT_EQ(result_tensor[0].get_shape(), ov::Shape{4});

    vector<float> out{0.5f, -0.5f, 0.714285f, -0.777777f};
    auto result_data = result_tensor[0].data<float>();
    for (size_t i = 0; i < result_tensor[0].get_size(); ++i)
        EXPECT_NEAR(result_data[i], out[i], 1e-6F);
}

TEST(eval, evaluate_fake_quantize_dynamic_input) {
    using namespace testing;
    constexpr auto et = element::f32;

    auto param = make_shared<ov::op::v0::Parameter>(et, PartialShape::dynamic());
    auto in_low = op::v0::Constant::create(et, Shape{}, {0.f});
    auto in_high = op::v0::Constant::create(et, Shape{}, {5.f});
    auto out_low = op::v0::Constant::create(et, Shape{}, {2.f});
    auto out_high = op::v0::Constant::create(et, Shape{}, {4.f});

    auto op = make_shared<op::v0::FakeQuantize>(param, in_low, in_high, out_low, out_high, 4);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{param});

    const auto exp_shape = Shape{1, 3, 2};
    std::vector<float> input_data;
    std::generate_n(std::back_inserter(input_data), shape_size(exp_shape), ov::SeqGen<float>(0.f));

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(exp_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), exp_shape);
    EXPECT_THAT(read_vector<float>(result),
                Pointwise(FloatEq(), std::vector<float>{2.f, 2.6666667f, 2.6666667f, 3.3333333f, 3.3333333f, 4.f}));
}

///////////////////////////////////// FakeConvert (FP8)

namespace testing {
namespace fp8 {
constexpr float MAX_F8E4M3 = 448.f;
constexpr float MIN_F8E4M3 = 0.001953125f;

constexpr float MAX_F8E5M2 = 57344.f;
constexpr float MIN_F8E5M2 = 0.0000152587890625f;
}  // namespace fp8
}  // namespace testing

///////////////////////////////////// FakeConvert f8e4m3

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_scale_small) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MIN_F8E4M3, fp8::MIN_F8E4M3 / 2.f, fp8::MIN_F8E4M3 / 4.f};

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));

    auto scale = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E4M3 / max_input_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e4m3);

    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result),
                Pointwise(FloatEq(), std::vector<float>{0.001953125f, 0.0009765625f, 0.00048828125f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_scale_1_small) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MIN_F8E4M3, fp8::MIN_F8E4M3 / 2.f, fp8::MIN_F8E4M3 / 4.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), std::vector<float>{0.001953125f, 0.f, 0.f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_scale_big) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E4M3 / 2.f, fp8::MAX_F8E4M3, fp8::MAX_F8E4M3 * 2.f, fp8::MAX_F8E4M3 * 4.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    auto scale = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E4M3 / max_input_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), std::vector<float>{224.f, 448.f, 896.f, 1792.f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_4x3_scale_big) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E4M3 / 4.f,
                                  fp8::MAX_F8E4M3 / 3.f,
                                  fp8::MAX_F8E4M3 / 2.f,
                                  fp8::MAX_F8E4M3,
                                  fp8::MAX_F8E4M3,
                                  fp8::MAX_F8E4M3,
                                  fp8::MAX_F8E4M3 * 1.2f,
                                  fp8::MAX_F8E4M3 * 2.3f,
                                  fp8::MAX_F8E4M3 * 3.4f,
                                  fp8::MAX_F8E4M3 * 2.f,
                                  fp8::MAX_F8E4M3 * 3.f,
                                  fp8::MAX_F8E4M3 * 4.f};

    std::vector<float> output_data{112, 144, 224, 448, 448, 448, 560, 1008, 1568, 896, 1280, 1792};

    const auto data_shape = Shape{4, 3};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et,
                                          Shape{4, 1},
                                          {fp8::MAX_F8E4M3 / (fp8::MAX_F8E4M3 / 2.f),
                                           1.0f,
                                           fp8::MAX_F8E4M3 / (fp8::MAX_F8E4M3 * 3.5f),
                                           fp8::MAX_F8E4M3 / (fp8::MAX_F8E4M3 * 4.f)});
    auto shift = op::v0::Constant::create(et, Shape{4, 1}, {0.f, 0.f, 0.f, 0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_3x4_scale_big) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E4M3 / 4.f,
                                  fp8::MAX_F8E4M3 / 3.f,
                                  fp8::MAX_F8E4M3 / 2.f,
                                  fp8::MAX_F8E4M3,
                                  fp8::MAX_F8E4M3,
                                  fp8::MAX_F8E4M3,
                                  fp8::MAX_F8E4M3 * 1.2f,
                                  fp8::MAX_F8E4M3 * 2.3f,
                                  fp8::MAX_F8E4M3 * 3.4f,
                                  fp8::MAX_F8E4M3 * 2.f,
                                  fp8::MAX_F8E4M3 * 3.f,
                                  fp8::MAX_F8E4M3 * 4.f};

    std::vector<float> output_data{112, 448, 560, 896, 144, 448, 1008, 1280, 224, 448, 1568, 1792};

    const auto data_shape = Shape{4, 3};  // To be transposed to 3x4
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    std::vector<int32_t> order{1, 0};
    auto transpose_order = make_shared<ov::op::v0::Constant>(element::i32, Shape{order.size()}, order);
    auto transposed_data = make_shared<ov::op::v1::Transpose>(data, transpose_order);
    const auto transposed_data_shape = Shape{3, 4};

    auto scale = op::v0::Constant::create(et,
                                          Shape{1, 4},
                                          {fp8::MAX_F8E4M3 / (fp8::MAX_F8E4M3 / 2.f),
                                           1.0f,
                                           fp8::MAX_F8E4M3 / (fp8::MAX_F8E4M3 * 3.5f),
                                           fp8::MAX_F8E4M3 / (fp8::MAX_F8E4M3 * 4.f)});
    auto shift = op::v0::Constant::create(et, Shape{1, 4}, {0.f, 0.f, 0.f, 0.f});

    auto op = make_shared<op::v13::FakeConvert>(transposed_data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), transposed_data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_scale_shift_big) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E4M3 / 2.f, fp8::MAX_F8E4M3, fp8::MAX_F8E4M3 * 2.f, fp8::MAX_F8E4M3 * 4.f};
    std::vector<float> output_data{224.f, 448.f, 896.f, 1728.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    const auto scale_val = fp8::MAX_F8E4M3 / max_input_val;
    auto scale = op::v0::Constant::create(et, Shape{1}, {scale_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E4M3 * scale_val});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_big_scale_1) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E4M3 / 2.f, fp8::MAX_F8E4M3, fp8::MAX_F8E4M3 * 2.f, fp8::MAX_F8E4M3 * 4.f};
    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), std::vector<float>{224.f, 448.f, 448.f, 448.f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_scale_1) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{0.0f, 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 1.f};
    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(
        read_vector<float>(result),
        Pointwise(
            FloatEq(),
            std::vector<
                float>{0.f, 0.1015625f, 0.203125f, 0.3125f, 0.40625f, 0.5f, 0.625f, 0.6875f, 0.8125f, 0.875f, 1.f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_no_scale_no_shift) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{0.0f,  0.1f,  0.2f,  0.3f,  0.4f,  0.5f,  0.6f,  0.7f,  0.8f,  0.9f,  1.f,
                                  -0.0f, -0.1f, -0.2f, -0.3f, -0.4f, -0.5f, -0.6f, -0.7f, -0.8f, -0.9f, -1.f};
    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.0f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(
        read_vector<float>(result),
        Pointwise(FloatEq(), std::vector<float>{0.f,         0.1015625f, 0.203125f, 0.3125f,   0.40625f, 0.5f,
                                                0.625f,      0.6875f,    0.8125f,   0.875f,    1.f,      -0.f,
                                                -0.1015625f, -0.203125f, -0.3125f,  -0.40625f, -0.5f,    -0.625f,
                                                -0.6875f,    -0.8125f,   -0.875f,   -1.f}));
}

TEST(eval, evaluate_fake_convert_f32_seq_to_f8e4m3_scale_1) {
    using namespace testing;
    constexpr auto et = element::f32;

    const auto data_shape = Shape{8};

    std::vector<float> input_data;
    std::generate_n(std::back_inserter(input_data), shape_size(data_shape), ov::SeqGen<float>(0.143f));

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result),
                Pointwise(FloatEq(), std::vector<float>{0.140625, 1.125, 2.25, 3.25, 4, 5, 6, 7}));
}

TEST(eval, evaluate_fake_convert_f32_seq_to_f8e4m3_scale) {
    using namespace testing;
    constexpr auto et = element::f32;

    const auto data_shape = Shape{8};
    std::vector<float> input_data;
    std::generate_n(std::back_inserter(input_data), shape_size(data_shape), ov::SeqGen<float>(0.143f));

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    auto scale = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E4M3 / max_input_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);

    EXPECT_THAT(read_vector<float>(result),
                Pointwise(FloatEq(),
                          std::vector<float>{0.14349776506424f,
                                             1.14798212051392f,
                                             2.0408570766449f,
                                             3.06128573417664f,
                                             4.08171415328979f,
                                             5.10214281082153f,
                                             6.12257146835327f,
                                             7.14300012588501f}));
}

TEST(eval, evaluate_fake_convert_f32_seq_to_f8e4m3_scale_shift) {
    using namespace testing;
    constexpr auto et = element::f32;

    const auto data_shape = Shape{8};
    std::vector<float> input_data;
    std::generate_n(std::back_inserter(input_data), shape_size(data_shape), ov::SeqGen<float>(0.143f));

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    auto scale = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E4M3 / max_input_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {5.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result),
                Pointwise(FloatEq(),
                          std::vector<float>{0.14349776506424f,
                                             1.10014951229095f,
                                             2.1205780506134f,
                                             3.14100670814514f,
                                             4.1614351272583f,
                                             5.18186378479004f,
                                             6.20229244232178f,
                                             7.22272109985352f}));
}

TEST(eval, evaluate_fake_convert_f32_matching_f8_to_f8e4m3_scale_1) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{
        0.f,       0.001953125f, 0.00390625f, 0.005859375f, 0.0078125f, 0.009765625f, 0.01171875f, 0.013671875f,
        0.015625f, 0.017578125f, 0.01953125f, 0.021484375f, 0.0234375f, 0.025390625f, 0.02734375f, 0.029296875f,
        0.03125f,  0.03515625f,  0.0390625f,  0.04296875f,  0.046875f,  0.05078125f,  0.0546875f,  0.05859375f,
        0.0625f,   0.0703125f,   0.078125f,   0.0859375f,   0.09375f,   0.1015625f,   0.109375f,   0.1171875f,
        0.125f,    0.140625f,    0.15625f,    0.171875f,    0.1875f,    0.203125f,    0.21875f,    0.234375f,
        0.25f,     0.28125f,     0.3125f,     0.34375f,     0.375f,     0.40625f,     0.4375f,     0.46875f,
        0.5f,      0.5625f,      0.625f,      0.6875f,      0.75f,      0.8125f,      0.875f,      0.9375f,
        1.f,       1.125f,       1.25f,       1.375f,       1.5f,       1.625f,       1.75f,       1.875f,
        2.f,       2.25f,        2.5f,        2.75f,        3.f,        3.25f,        3.5f,        3.75f,
        4.f,       4.5f,         5.f,         5.5f,         6.f,        6.5f,         7.f,         7.5f,
        8.f,       9.f,          10.f,        11.f,         12.f,       13.f,         14.f,        15.f,
        16.f,      18.f,         20.f,        22.f,         24.f,       26.f,         28.f,        30.f,
        32.f,      36.f,         40.f,        44.f,         48.f,       52.f,         56.f,        60.f,
        64.f,      72.f,         80.f,        88.f,         96.f,       104.f,        112.f,       120.f,
        128.f,     144.f,        160.f,       176.f,        192.f,      208.f,        224.f,       240.f,
        256.f,     288.f,        320.f,       352.f,        384.f,      416.f,        448.f};

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);

    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");

    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), input_data));
}

TEST(eval, evaluate_fake_convert_f16_matching_f8_to_f8e4m3_scale_1) {
    using namespace testing;
    constexpr auto et = element::f16;

    std::vector<float16> input_data{
        0.f,       0.001953125f, 0.00390625f, 0.005859375f, 0.0078125f, 0.009765625f, 0.01171875f, 0.013671875f,
        0.015625f, 0.017578125f, 0.01953125f, 0.021484375f, 0.0234375f, 0.025390625f, 0.02734375f, 0.029296875f,
        0.03125f,  0.03515625f,  0.0390625f,  0.04296875f,  0.046875f,  0.05078125f,  0.0546875f,  0.05859375f,
        0.0625f,   0.0703125f,   0.078125f,   0.0859375f,   0.09375f,   0.1015625f,   0.109375f,   0.1171875f,
        0.125f,    0.140625f,    0.15625f,    0.171875f,    0.1875f,    0.203125f,    0.21875f,    0.234375f,
        0.25f,     0.28125f,     0.3125f,     0.34375f,     0.375f,     0.40625f,     0.4375f,     0.46875f,
        0.5f,      0.5625f,      0.625f,      0.6875f,      0.75f,      0.8125f,      0.875f,      0.9375f,
        1.f,       1.125f,       1.25f,       1.375f,       1.5f,       1.625f,       1.75f,       1.875f,
        2.f,       2.25f,        2.5f,        2.75f,        3.f,        3.25f,        3.5f,        3.75f,
        4.f,       4.5f,         5.f,         5.5f,         6.f,        6.5f,         7.f,         7.5f,
        8.f,       9.f,          10.f,        11.f,         12.f,       13.f,         14.f,        15.f,
        16.f,      18.f,         20.f,        22.f,         24.f,       26.f,         28.f,        30.f,
        32.f,      36.f,         40.f,        44.f,         48.f,       52.f,         56.f,        60.f,
        64.f,      72.f,         80.f,        88.f,         96.f,       104.f,        112.f,       120.f,
        128.f,     144.f,        160.f,       176.f,        192.f,      208.f,        224.f,       240.f,
        256.f,     288.f,        320.f,       352.f,        384.f,      416.f,        448.f};

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, std::vector<float16>{1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, std::vector<float16>{0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float16>(result), Pointwise(FloatEq(), input_data));
}

TEST(eval, evaluate_fake_convert_bf16_matching_f8_to_f8e4m3_scale_1) {
    using namespace testing;
    constexpr auto et = element::bf16;

    std::vector<bfloat16> input_data{
        0.f,       0.001953125f, 0.00390625f, 0.005859375f, 0.0078125f, 0.009765625f, 0.01171875f, 0.013671875f,
        0.015625f, 0.017578125f, 0.01953125f, 0.021484375f, 0.0234375f, 0.025390625f, 0.02734375f, 0.029296875f,
        0.03125f,  0.03515625f,  0.0390625f,  0.04296875f,  0.046875f,  0.05078125f,  0.0546875f,  0.05859375f,
        0.0625f,   0.0703125f,   0.078125f,   0.0859375f,   0.09375f,   0.1015625f,   0.109375f,   0.1171875f,
        0.125f,    0.140625f,    0.15625f,    0.171875f,    0.1875f,    0.203125f,    0.21875f,    0.234375f,
        0.25f,     0.28125f,     0.3125f,     0.34375f,     0.375f,     0.40625f,     0.4375f,     0.46875f,
        0.5f,      0.5625f,      0.625f,      0.6875f,      0.75f,      0.8125f,      0.875f,      0.9375f,
        1.f,       1.125f,       1.25f,       1.375f,       1.5f,       1.625f,       1.75f,       1.875f,
        2.f,       2.25f,        2.5f,        2.75f,        3.f,        3.25f,        3.5f,        3.75f,
        4.f,       4.5f,         5.f,         5.5f,         6.f,        6.5f,         7.f,         7.5f,
        8.f,       9.f,          10.f,        11.f,         12.f,       13.f,         14.f,        15.f,
        16.f,      18.f,         20.f,        22.f,         24.f,       26.f,         28.f,        30.f,
        32.f,      36.f,         40.f,        44.f,         48.f,       52.f,         56.f,        60.f,
        64.f,      72.f,         80.f,        88.f,         96.f,       104.f,        112.f,       120.f,
        128.f,     144.f,        160.f,       176.f,        192.f,      208.f,        224.f,       240.f,
        256.f,     288.f,        320.f,       352.f,        384.f,      416.f,        448.f};

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, std::vector<bfloat16>{1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, std::vector<bfloat16>{0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<bfloat16>(result), Pointwise(FloatEq(), input_data));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_no_scale_no_shift_str_ctor) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{0.0f,  0.1f,  0.2f,  0.3f,  0.4f,  0.5f,  0.6f,  0.7f,  0.8f,  0.9f,  1.f,
                                  -0.0f, -0.1f, -0.2f, -0.3f, -0.4f, -0.5f, -0.6f, -0.7f, -0.8f, -0.9f, -1.f};
    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.0f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e4m3");
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(
        read_vector<float>(result),
        Pointwise(FloatEq(), std::vector<float>{0.f,         0.1015625f, 0.203125f, 0.3125f,   0.40625f, 0.5f,
                                                0.625f,      0.6875f,    0.8125f,   0.875f,    1.f,      -0.f,
                                                -0.1015625f, -0.203125f, -0.3125f,  -0.40625f, -0.5f,    -0.625f,
                                                -0.6875f,    -0.8125f,   -0.875f,   -1.f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e4m3_no_scale_no_shift_default_type_ctor) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{0.0f,  0.1f,  0.2f,  0.3f,  0.4f,  0.5f,  0.6f,  0.7f,  0.8f,  0.9f,  1.f,
                                  -0.0f, -0.1f, -0.2f, -0.3f, -0.4f, -0.5f, -0.6f, -0.7f, -0.8f, -0.9f, -1.f};
    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.0f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(
        read_vector<float>(result),
        Pointwise(FloatEq(), std::vector<float>{0.f,         0.1015625f, 0.203125f, 0.3125f,   0.40625f, 0.5f,
                                                0.625f,      0.6875f,    0.8125f,   0.875f,    1.f,      -0.f,
                                                -0.1015625f, -0.203125f, -0.3125f,  -0.40625f, -0.5f,    -0.625f,
                                                -0.6875f,    -0.8125f,   -0.875f,   -1.f}));
}

///////////////////////////////////// FakeConvert f8e5m2
TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_scale_1) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{0.0f,  0.1f,  0.2f,  0.3f,  0.4f,  0.5f,  0.6f,  0.7f,  0.8f,  0.9f,  1.f,
                                  -0.0f, -0.1f, -0.2f, -0.3f, -0.4f, -0.5f, -0.6f, -0.7f, -0.8f, -0.9f, -1.f};

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);

    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);

    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result),
                Pointwise(FloatEq(),
                          std::vector<float>{0.f,   0.09375f, 0.1875f, 0.3125f, 0.375f,    0.5f,     0.625f,   0.75f,
                                             0.75f, 0.875f,   1.f,     -0.f,    -0.09375f, -0.1875f, -0.3125f, -0.375f,
                                             -0.5f, -0.625f,  -0.75f,  -0.75f,  -0.875f,   -1.f}));
}

TEST(eval, evaluate_fake_convert_f16_to_f8e5m2_scale_1) {
    using namespace testing;
    constexpr auto et = element::f16;

    std::vector<float16> input_data{0.0f, 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 1.f};

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(
        read_vector<float16>(result),
        Pointwise(
            FloatEq(),
            std::vector<float16>{0.f, 0.09375f, 0.1875f, 0.3125f, 0.375f, 0.5f, 0.625f, 0.75f, 0.75f, 0.875f, 1.f}));
}

TEST(eval, evaluate_fake_convert_bf16_to_f8e5m2_scale_1) {
    using namespace testing;
    constexpr auto et = element::bf16;

    std::vector<bfloat16> input_data{0.0f, 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 1.f};

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(
        read_vector<bfloat16>(result),
        Pointwise(
            FloatEq(),
            std::vector<bfloat16>{0.f, 0.09375f, 0.1875f, 0.3125f, 0.375f, 0.5f, 0.625f, 0.75f, 0.75f, 0.875f, 1.f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_scale_small) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MIN_F8E5M2, fp8::MIN_F8E5M2 / 2.f, fp8::MIN_F8E5M2 / 4.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    auto scale = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E5M2 / max_input_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result),
                Pointwise(FloatEq(), std::vector<float>{1.52587890625e-05, 7.62939453125e-06, 3.814697265625e-06}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_scale_1_small) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MIN_F8E5M2, fp8::MIN_F8E5M2 / 2.f, fp8::MIN_F8E5M2 / 4.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);

    auto scale = op::v0::Constant::create(et, Shape{1}, {1.0f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);

    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), std::vector<float>{1.52587890625e-05, 0.f, 0.f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_small_scale_1) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MIN_F8E5M2, fp8::MIN_F8E5M2 / 2.f, fp8::MIN_F8E5M2 / 4.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);

    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);

    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), std::vector<float>{1.52587890625e-05, 0.f, 0.f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_scale_big) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E5M2 / 2.f, fp8::MAX_F8E5M2, fp8::MAX_F8E5M2 * 2.f, fp8::MAX_F8E5M2 * 4.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    auto scale = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E5M2 / max_input_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), input_data));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_scale_shift_big) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E5M2 / 2.f, fp8::MAX_F8E5M2, fp8::MAX_F8E5M2 * 2.f, fp8::MAX_F8E5M2 * 4.f};
    std::vector<float> output_data{28672.f, 57344.f, 114688.f, 221184.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    const auto scale_val = fp8::MAX_F8E5M2 / max_input_val;
    auto scale = op::v0::Constant::create(et, Shape{1}, {scale_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E5M2 * scale_val});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_fake_convert_f16_to_f8e5m2_scale_shift_big) {
    using namespace testing;
    constexpr auto et = element::f16;

    std::vector<float16> input_data{fp8::MAX_F8E5M2 / 2.f,
                                    fp8::MAX_F8E5M2,
                                    fp8::MAX_F8E5M2 + 500.f,
                                    fp8::MAX_F8E5M2 + 1000.f};
    std::vector<float16> output_data{28192.f, 57344.f, 57888.f, 58400.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    const auto scale_val = fp8::MAX_F8E5M2 / max_input_val;
    auto scale = op::v0::Constant::create(et, Shape{1}, {scale_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E5M2 * scale_val});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float16>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_fake_convert_bf16_to_f8e5m2_scale_shift_big) {
    using namespace testing;
    constexpr auto et = element::bf16;

    std::vector<bfloat16> input_data{fp8::MAX_F8E5M2 / 2.f,
                                     fp8::MAX_F8E5M2,
                                     fp8::MAX_F8E5M2 + 500.f,
                                     fp8::MAX_F8E5M2 + 1000.f};
    std::vector<bfloat16> output_data{28032.f, 57088.f, 57856.f, 58368.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    const auto scale_val = fp8::MAX_F8E5M2 / max_input_val;
    auto scale = op::v0::Constant::create(et, Shape{1}, {scale_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E5M2 * scale_val});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<bfloat16>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_big_scale_1) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E5M2 / 2.f,
                                  fp8::MAX_F8E5M2,
                                  fp8::MAX_F8E5M2 + 1,
                                  fp8::MAX_F8E5M2 * 2.f,
                                  fp8::MAX_F8E5M2 * 4.f};
    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);

    constexpr auto inf = std::numeric_limits<float>::infinity();
    EXPECT_THAT(
        read_vector<float>(result),
        Pointwise(FloatEq(), std::vector<float>{fp8::MAX_F8E5M2 / 2.f, fp8::MAX_F8E5M2, fp8::MAX_F8E5M2, inf, inf}));
}

TEST(eval, evaluate_fake_convert_f32_matching_f8_to_f8e5m2_scale_1) {
    using namespace testing;
    constexpr auto et = element::f32;

    // clang-format off
    std::vector<float> input_data{
        0.f, 0.0000152587890625f, 0.00003051758125f, 0.0000457763671875f,
        0.00006103515625f, 0.0000762939453125f, 0.000091552734375, 0.0001068115234375,
        0.0001220703125f, 0.000152587890625f, 0.00018310546875f, 0.000213623046875f,
        0.000244140625f, 0.00030517578125, 0.0003662109375f, 0.00042724609375f,
        0.00048828125f, 0.0006103515625f, 0.000732421875f, 0.0008544921875,
        0.0009765625f, 0.001220703125, 0.00146484375f, 0.001708984375f,
        0.001953125f, 0.00244140625f, 0.0029296875f, 0.00341796875f,
        0.00390625f, 0.0048828125f, 0.005859375f, 0.0068359375f,
        0.0078125f, 0.009765625f, 0.01171875f, 0.013671875f,

        0.015625f, /*0.017578125f,*/ 0.01953125f, /*0.021484375f,*/ 0.0234375f, /*0.025390625f,*/ 0.02734375f, /*0.029296875f,*/
        0.03125f,  /*0.03515625f,*/  0.0390625f,  /*0.04296875f,*/  0.046875f,  /*0.05078125f,*/  0.0546875f,  /*0.05859375f,*/
        0.0625f,   /*0.0703125f,*/   0.078125f,   /*0.0859375f,*/   0.09375f,   /*0.1015625f,*/   0.109375f,   /*0.1171875f,*/
        0.125f,    /*0.140625f,*/    0.15625f,    /*0.171875f,*/    0.1875f,    /*0.203125f,*/    0.21875f,    /*0.234375f,*/
        0.25f,     /*0.28125f,*/     0.3125f,     /*0.34375f,*/     0.375f,     /*0.40625f,*/     0.4375f,     /*0.46875f,*/
        0.5f,      /*0.5625f,*/      0.625f,      /*0.6875f,*/      0.75f,      /*0.8125f,*/      0.875f,      /*0.9375f,*/
        1.f,        /*1.125f,*/      1.25f,       /*1.375f,*/       1.5f,       /*1.625f,*/       1.75f,       /*1.875f,*/
        2.f,        /*2.25f,*/       2.5f,        /*2.75f,*/        3.f,         /*3.25f,*/       3.5f,        /*3.75f,*/
        4.f,        /*4.5f,*/        5.f,          /*5.5f,*/        6.f,         /*6.5f,*/        7.f,          /*7.5f,*/
        8.f,        /*9.f,*/         10.f,         /*11.f,*/        12.f,        /*13.f,*/        14.f,         /*15.f,*/
        16.f,       /*18.f,*/        20.f,         /*22.f,*/        24.f,        /*26.f,*/        28.f,         /*30.f,*/
        32.f,       /*36.f*,*/       40.f,         /*44.f,*/        48.f,        /*52.f,*/        56.f,         /*60.f,*/
        64.f,       /*72.f,*/        80.f,         /*88.f,*/        96.f,        /*104.f,*/       112.f,        /*120.f,*/
        128.f,      /*144.f,*/       160.f,        /*176.f,*/       192.f,       /*208.f,*/       224.f,        /*240.f,*/
        256.f,      /*288.f,*/       320.f,        /*352.f,*/       384.f,       /*416.f,*/       448.f,

        512.f, 640.f, 768.f, 896.f,
        1024.f, 1280.f, 1536.f, 1792.f,
        2048.f, 2560.f, 3072.f, 3584.f,
        4096.f, 5120.f, 6144.f, 7168.f,
        8192.f, 10240.f, 12288.f, 14336.f,
        16384.f, 20480.f, 24576.f, 28672.f,
        32768.f, 40960.f, 49152.f, 57344.0
    };
    // clang-format on

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);

    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);

    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), input_data));
}

TEST(eval, evaluate_fake_convert_f16_matching_f8_to_f8e5m2_scale_1) {
    using namespace testing;
    constexpr auto et = element::f16;

    // clang-format off
    std::vector<float16> input_data{
        0.f, 0.0000152587890625f, 0.00003051758125f, 0.0000457763671875f,
        0.00006103515625f, 0.0000762939453125f, 0.000091552734375, 0.0001068115234375,
        0.0001220703125f, 0.000152587890625f, 0.00018310546875f, 0.000213623046875f,
        0.000244140625f, 0.00030517578125, 0.0003662109375f, 0.00042724609375f,
        0.00048828125f, 0.0006103515625f, 0.000732421875f, 0.0008544921875,
        0.0009765625f, 0.001220703125, 0.00146484375f, 0.001708984375f,
        0.001953125f, 0.00244140625f, 0.0029296875f, 0.00341796875f,
        0.00390625f, 0.0048828125f, 0.005859375f, 0.0068359375f,
        0.0078125f, 0.009765625f, 0.01171875f, 0.013671875f,

        0.015625f, /*0.017578125f,*/ 0.01953125f, /*0.021484375f,*/ 0.0234375f, /*0.025390625f,*/ 0.02734375f, /*0.029296875f,*/
        0.03125f,  /*0.03515625f,*/  0.0390625f,  /*0.04296875f,*/  0.046875f,  /*0.05078125f,*/  0.0546875f,  /*0.05859375f,*/
        0.0625f,   /*0.0703125f,*/   0.078125f,   /*0.0859375f,*/   0.09375f,   /*0.1015625f,*/   0.109375f,   /*0.1171875f,*/
        0.125f,    /*0.140625f,*/    0.15625f,    /*0.171875f,*/    0.1875f,    /*0.203125f,*/    0.21875f,    /*0.234375f,*/
        0.25f,     /*0.28125f,*/     0.3125f,     /*0.34375f,*/     0.375f,     /*0.40625f,*/     0.4375f,     /*0.46875f,*/
        0.5f,      /*0.5625f,*/      0.625f,      /*0.6875f,*/      0.75f,      /*0.8125f,*/      0.875f,      /*0.9375f,*/
        1.f,        /*1.125f,*/      1.25f,       /*1.375f,*/       1.5f,       /*1.625f,*/       1.75f,       /*1.875f,*/
        2.f,        /*2.25f,*/       2.5f,        /*2.75f,*/        3.f,         /*3.25f,*/       3.5f,        /*3.75f,*/
        4.f,        /*4.5f,*/        5.f,          /*5.5f,*/        6.f,         /*6.5f,*/        7.f,          /*7.5f,*/
        8.f,        /*9.f,*/         10.f,         /*11.f,*/        12.f,        /*13.f,*/        14.f,         /*15.f,*/
        16.f,       /*18.f,*/        20.f,         /*22.f,*/        24.f,        /*26.f,*/        28.f,         /*30.f,*/
        32.f,       /*36.f*,*/       40.f,         /*44.f,*/        48.f,        /*52.f,*/        56.f,         /*60.f,*/
        64.f,       /*72.f,*/        80.f,         /*88.f,*/        96.f,        /*104.f,*/       112.f,        /*120.f,*/
        128.f,      /*144.f,*/       160.f,        /*176.f,*/       192.f,       /*208.f,*/       224.f,        /*240.f,*/
        256.f,      /*288.f,*/       320.f,        /*352.f,*/       384.f,       /*416.f,*/       448.f,

        512.f, 640.f, 768.f, 896.f,
        1024.f, 1280.f, 1536.f, 1792.f,
        2048.f, 2560.f, 3072.f, 3584.f,
        4096.f, 5120.f, 6144.f, 7168.f,
        8192.f, 10240.f, 12288.f, 14336.f,
        16384.f, 20480.f, 24576.f, 28672.f,
        32768.f, 40960.f, 49152.f, 57344.0
    };
    // clang-format on

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);

    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);

    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float16>(result), Pointwise(FloatEq(), input_data));
}

TEST(eval, evaluate_fake_convert_bf16_matching_f8_to_f8e5m2_scale_1) {
    using namespace testing;
    constexpr auto et = element::bf16;

    // clang-format off
    std::vector<bfloat16> input_data{
        0.f, 0.0000152587890625f, 0.00003051758125f, 0.0000457763671875f,
        0.00006103515625f, 0.0000762939453125f, 0.000091552734375, 0.0001068115234375,
        0.0001220703125f, 0.000152587890625f, 0.00018310546875f, 0.000213623046875f,
        0.000244140625f, 0.00030517578125, 0.0003662109375f, 0.00042724609375f,
        0.00048828125f, 0.0006103515625f, 0.000732421875f, 0.0008544921875,
        0.0009765625f, 0.001220703125, 0.00146484375f, 0.001708984375f,
        0.001953125f, 0.00244140625f, 0.0029296875f, 0.00341796875f,
        0.00390625f, 0.0048828125f, 0.005859375f, 0.0068359375f,
        0.0078125f, 0.009765625f, 0.01171875f, 0.013671875f,

        0.015625f, /*0.017578125f,*/ 0.01953125f, /*0.021484375f,*/ 0.0234375f, /*0.025390625f,*/ 0.02734375f, /*0.029296875f,*/
        0.03125f,  /*0.03515625f,*/  0.0390625f,  /*0.04296875f,*/  0.046875f,  /*0.05078125f,*/  0.0546875f,  /*0.05859375f,*/
        0.0625f,   /*0.0703125f,*/   0.078125f,   /*0.0859375f,*/   0.09375f,   /*0.1015625f,*/   0.109375f,   /*0.1171875f,*/
        0.125f,    /*0.140625f,*/    0.15625f,    /*0.171875f,*/    0.1875f,    /*0.203125f,*/    0.21875f,    /*0.234375f,*/
        0.25f,     /*0.28125f,*/     0.3125f,     /*0.34375f,*/     0.375f,     /*0.40625f,*/     0.4375f,     /*0.46875f,*/
        0.5f,      /*0.5625f,*/      0.625f,      /*0.6875f,*/      0.75f,      /*0.8125f,*/      0.875f,      /*0.9375f,*/
        1.f,        /*1.125f,*/      1.25f,       /*1.375f,*/       1.5f,       /*1.625f,*/       1.75f,       /*1.875f,*/
        2.f,        /*2.25f,*/       2.5f,        /*2.75f,*/        3.f,         /*3.25f,*/       3.5f,        /*3.75f,*/
        4.f,        /*4.5f,*/        5.f,          /*5.5f,*/        6.f,         /*6.5f,*/        7.f,          /*7.5f,*/
        8.f,        /*9.f,*/         10.f,         /*11.f,*/        12.f,        /*13.f,*/        14.f,         /*15.f,*/
        16.f,       /*18.f,*/        20.f,         /*22.f,*/        24.f,        /*26.f,*/        28.f,         /*30.f,*/
        32.f,       /*36.f*,*/       40.f,         /*44.f,*/        48.f,        /*52.f,*/        56.f,         /*60.f,*/
        64.f,       /*72.f,*/        80.f,         /*88.f,*/        96.f,        /*104.f,*/       112.f,        /*120.f,*/
        128.f,      /*144.f,*/       160.f,        /*176.f,*/       192.f,       /*208.f,*/       224.f,        /*240.f,*/
        256.f,      /*288.f,*/       320.f,        /*352.f,*/       384.f,       /*416.f,*/       448.f,

        512.f, 640.f, 768.f, 896.f,
        1024.f, 1280.f, 1536.f, 1792.f,
        2048.f, 2560.f, 3072.f, 3584.f,
        4096.f, 5120.f, 6144.f, 7168.f,
        8192.f, 10240.f, 12288.f, 14336.f,
        16384.f, 20480.f, 24576.f, 28672.f,
        32768.f, 40960.f, 49152.f, 57344.f
    };
    // clang-format on

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);

    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);

    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<bfloat16>(result), Pointwise(FloatEq(), input_data));
}

TEST(eval, evaluate_fake_convert_f32_matching_f8e4m3_to_f8e5m2_scale_1) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{
        0.017578125f, 0.021484375f, 0.025390625f, 0.029296875f, 0.03515625f, 0.0703125f, 0.140625f,
        0.28125f,     0.5625f,      1.125f,       1.625f,       1.875f,      2.25f,      3.75f,
        4.5f,         9.f,          18.f,         36.f,         72.f,        144.f,      288.f,
    };
    /* Rounded to f8e5m2 vals */
    std::vector<float> output_data{0.015625f, 0.0234375f, 0.0234375f, 0.03125f, 0.03125f, 0.0625f, 0.125f,
                                   0.25f,     0.5f,       1.f,        1.5,      2.f,      2.f,     4.f,
                                   4.f,       8.f,        16.f,       32.f,     64.f,     128.f,   256.f};

    const auto data_shape = Shape{input_data.size()};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_scale_1_str_ctor) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{0.0f,  0.1f,  0.2f,  0.3f,  0.4f,  0.5f,  0.6f,  0.7f,  0.8f,  0.9f,  1.f,
                                  -0.0f, -0.1f, -0.2f, -0.3f, -0.4f, -0.5f, -0.6f, -0.7f, -0.8f, -0.9f, -1.f};

    const auto data_shape = Shape{input_data.size()};

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);

    auto scale = op::v0::Constant::create(et, Shape{1}, {1.f});
    auto shift = op::v0::Constant::create(et, Shape{1}, {0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, "f8e5m2");

    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result),
                Pointwise(FloatEq(),
                          std::vector<float>{0.f,   0.09375f, 0.1875f, 0.3125f, 0.375f,    0.5f,     0.625f,   0.75f,
                                             0.75f, 0.875f,   1.f,     -0.f,    -0.09375f, -0.1875f, -0.3125f, -0.375f,
                                             -0.5f, -0.625f,  -0.75f,  -0.75f,  -0.875f,   -1.f}));
}

TEST(eval, evaluate_f8e5m2_const_from_f32) {
    using namespace testing;
    constexpr auto et = element::f8e5m2;

    std::vector<float> input_data{
        0.017578125f, 0.021484375f, 0.025390625f, 0.029296875f, 0.03515625f, 0.0703125f, 0.140625f,
        0.28125f,     0.5625f,      1.125f,       1.625f,       1.875f,      2.25f,      3.75f,
        4.5f,         9.f,          18.f,         36.f,         72.f,        144.f,      288.f,
    };
    /* Rounded to f8e5m2 vals */
    std::vector<ov::float8_e5m2> output_data{0.015625f, 0.0234375f, 0.0234375f, 0.03125f, 0.03125f, 0.0625f, 0.125f,
                                             0.25f,     0.5f,       1.f,        1.5,      2.f,      2.f,     4.f,
                                             4.f,       8.f,        16.f,       32.f,     64.f,     128.f,   256.f};

    const auto data_shape = Shape{input_data.size()};

    auto op = make_shared<op::v0::Constant>(et, data_shape, input_data);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<ov::float8_e5m2>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_f8e5m2_const_seq_from_f32) {
    using namespace testing;
    constexpr auto et = element::f8e5m2;

    std::vector<float> input_data{0.0f,  0.1f,  0.2f,  0.3f,  0.4f,  0.5f,  0.6f,  0.7f,  0.8f,  0.9f,  1.f,
                                  -0.0f, -0.1f, -0.2f, -0.3f, -0.4f, -0.5f, -0.6f, -0.7f, -0.8f, -0.9f, -1.f};

    /* Rounded to f8e5m2 vals */
    std::vector<ov::float8_e5m2> output_data{0.f,   0.09375f, 0.1875f, 0.3125f, 0.375f,    0.5f,     0.625f,   0.75f,
                                             0.75f, 0.875f,   1.f,     -0.f,    -0.09375f, -0.1875f, -0.3125f, -0.375f,
                                             -0.5f, -0.625f,  -0.75f,  -0.75f,  -0.875f,   -1.f};

    const auto data_shape = Shape{input_data.size()};

    auto op = make_shared<op::v0::Constant>(et, data_shape, input_data);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<ov::float8_e5m2>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_f8e4m3_const_seq_from_f32) {
    using namespace testing;
    constexpr auto et = element::f8e4m3;

    std::vector<float> input_data{0.0f,  0.1f,  0.2f,  0.3f,  0.4f,  0.5f,  0.6f,  0.7f,  0.8f,  0.9f,  1.f,
                                  -0.0f, -0.1f, -0.2f, -0.3f, -0.4f, -0.5f, -0.6f, -0.7f, -0.8f, -0.9f, -1.f};

    /* Rounded to f8e4m3 vals */
    std::vector<ov::float8_e4m3> output_data{
        0.f,  0.1015625f,  0.203125f,  0.3125f,  0.40625f,  0.5f,  0.625f,  0.6875f,  0.8125f,  0.875f,  1.f,
        -0.f, -0.1015625f, -0.203125f, -0.3125f, -0.40625f, -0.5f, -0.625f, -0.6875f, -0.8125f, -0.875f, -1.f};

    const auto data_shape = Shape{input_data.size()};

    auto op = make_shared<op::v0::Constant>(et, data_shape, input_data);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<ov::float8_e4m3>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_fake_convert_f32_seq_to_f8e5m2_scale_shift) {
    using namespace testing;
    constexpr auto et = element::f32;

    const auto data_shape = Shape{8};
    std::vector<float> input_data;
    std::generate_n(std::back_inserter(input_data), shape_size(data_shape), ov::SeqGen<float>(0.143f));

    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto max_input_val = std::abs(*std::max_element(input_data.begin(), input_data.end()));
    auto scale = op::v0::Constant::create(et, Shape{1}, {fp8::MAX_F8E5M2 / max_input_val});
    auto shift = op::v0::Constant::create(et, Shape{1}, {5.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result),
                Pointwise(FloatEq(),
                          std::vector<float>{0.128176391124725f,
                                             1.02105140686035f,
                                             2.04147982597351f,
                                             3.06190848350525f,
                                             4.08233690261841f,
                                             5.10276556015015f,
                                             6.12319421768188f,
                                             7.14362287521362f}));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_4x3_scale_big) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E5M2 / 4.f,
                                  fp8::MAX_F8E5M2 / 3.f,
                                  fp8::MAX_F8E5M2 / 2.f,
                                  fp8::MAX_F8E5M2,
                                  fp8::MAX_F8E5M2,
                                  fp8::MAX_F8E5M2,
                                  fp8::MAX_F8E5M2 * 1.2f,
                                  fp8::MAX_F8E5M2 * 2.3f,
                                  fp8::MAX_F8E5M2 * 3.4f,
                                  fp8::MAX_F8E5M2 * 2.f,
                                  fp8::MAX_F8E5M2 * 3.f,
                                  fp8::MAX_F8E5M2 * 4.f};

    std::vector<float> output_data{14336.f,
                                   20480.f,
                                   28672.f,
                                   57344.f,
                                   57344.f,
                                   57344.f,
                                   71680.f,
                                   143360.f,
                                   200704.f,
                                   114688.f,
                                   163840.f,
                                   229376.f};

    const auto data_shape = Shape{4, 3};
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    auto scale = op::v0::Constant::create(et,
                                          Shape{4, 1},
                                          {fp8::MAX_F8E5M2 / (fp8::MAX_F8E5M2 / 2.f),
                                           1.0f,
                                           fp8::MAX_F8E5M2 / (fp8::MAX_F8E5M2 * 3.5f),
                                           fp8::MAX_F8E5M2 / (fp8::MAX_F8E5M2 * 4.f)});
    auto shift = op::v0::Constant::create(et, Shape{4, 1}, {0.f, 0.f, 0.f, 0.f});

    auto op = make_shared<op::v13::FakeConvert>(data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_fake_convert_f32_to_f8e5m2_3x4_scale_big) {
    using namespace testing;
    constexpr auto et = element::f32;

    std::vector<float> input_data{fp8::MAX_F8E5M2 / 4.f,
                                  fp8::MAX_F8E5M2 / 3.f,
                                  fp8::MAX_F8E5M2 / 2.f,
                                  fp8::MAX_F8E5M2,
                                  fp8::MAX_F8E5M2,
                                  fp8::MAX_F8E5M2,
                                  fp8::MAX_F8E5M2 * 1.2f,
                                  fp8::MAX_F8E5M2 * 2.3f,
                                  fp8::MAX_F8E5M2 * 3.4f,
                                  fp8::MAX_F8E5M2 * 2.f,
                                  fp8::MAX_F8E5M2 * 3.f,
                                  fp8::MAX_F8E5M2 * 4.f};

    std::vector<float> output_data{14336.f,
                                   57344.f,
                                   71680.f,
                                   114688.f,
                                   20480.f,
                                   57344.f,
                                   143360.f,
                                   163840.f,
                                   28672.f,
                                   57344.f,
                                   200704.f,
                                   229376.f};

    const auto data_shape = Shape{4, 3};  // To be transposed to 3x4
    auto data = make_shared<ov::op::v0::Parameter>(et, data_shape);
    std::vector<int32_t> order{1, 0};
    auto transpose_order = make_shared<ov::op::v0::Constant>(element::i32, Shape{order.size()}, order);
    auto transposed_data = make_shared<ov::op::v1::Transpose>(data, transpose_order);
    const auto transposed_data_shape = Shape{3, 4};

    auto scale = op::v0::Constant::create(et,
                                          Shape{1, 4},
                                          {fp8::MAX_F8E5M2 / (fp8::MAX_F8E5M2 / 2.f),
                                           1.0f,
                                           fp8::MAX_F8E5M2 / (fp8::MAX_F8E5M2 * 3.5f),
                                           fp8::MAX_F8E5M2 / (fp8::MAX_F8E5M2 * 4.f)});
    auto shift = op::v0::Constant::create(et, Shape{1, 4}, {0.f, 0.f, 0.f, 0.f});

    auto op = make_shared<op::v13::FakeConvert>(transposed_data, scale, shift, ov::element::f8e5m2);
    auto model = make_shared<Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor();
    auto out_vector = ov::TensorVector{result};
    auto in_vector = ov::TensorVector{make_tensor<et>(data_shape, input_data)};
    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    result = out_vector.at(0);

    EXPECT_EQ(result.get_element_type(), et);
    EXPECT_EQ(result.get_shape(), transposed_data_shape);
    EXPECT_THAT(read_vector<float>(result), Pointwise(FloatEq(), output_data));
}

TEST(eval, evaluate_cum_sum_v0) {
    auto data = make_shared<ov::op::v0::Parameter>(element::f32, Shape{2, 3});
    auto axis = ov::op::v0::Constant::create<int32_t>(element::i32, Shape{1}, {1});
    auto cs = make_shared<op::v0::CumSum>(data, axis);
    auto m = make_shared<ov::Model>(OutputVector{cs}, ParameterVector{data});

    float input_values[6] = {1.f, 2.f, 3.f, 4.f, 5.f, 6.f};
    float out_expected[6] = {1.f, 3.f, 6.f, 4.f, 9.f, 15.f};

    auto outputs = ov::TensorVector(1);
    ASSERT_TRUE(m->evaluate(outputs, {{ov::element::f32, {2, 3}, input_values}}));
    EXPECT_EQ(outputs[0].get_element_type(), data->get_element_type());
    EXPECT_EQ(outputs[0].get_shape(), data->get_shape());
    EXPECT_EQ(memcmp(outputs[0].data(), out_expected, sizeof(out_expected)), 0);
}

TEST(eval, evaluate_cum_sum_v0_exclusive_reversed) {
    auto data = make_shared<ov::op::v0::Parameter>(element::f32, Shape{5});
    auto axis = ov::op::v0::Constant::create<int32_t>(element::i32, Shape{1}, {0});
    auto cs = make_shared<op::v0::CumSum>(data, axis, true, true);
    auto m = make_shared<ov::Model>(OutputVector{cs}, ParameterVector{data});

    float input_values[5] = {1.f, 2.f, 3.f, 4.f, 5.f};
    float out_expected[5] = {14.f, 12.f, 9.f, 5.f, 0.f};

    auto outputs = ov::TensorVector(1);
    ASSERT_TRUE(m->evaluate(outputs, {{ov::element::f32, {5}, input_values}}));
    EXPECT_EQ(outputs[0].get_element_type(), data->get_element_type());
    EXPECT_EQ(outputs[0].get_shape(), data->get_shape());
    EXPECT_EQ(memcmp(outputs[0].data(), out_expected, sizeof(out_expected)), 0);
}

TEST(eval, invalid_shape) {
    auto p1 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{1, 2});
    auto p2 = make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{1, 2});
    auto add = make_shared<op::v1::Add>(p1, p2);
    auto model = make_shared<Model>(OutputVector{add}, ParameterVector{p1, p2});
    auto result_tensor = ov::Tensor(element::f32, {1, 2});
    auto out_vector = ov::TensorVector{result_tensor};
    auto in_vector = ov::TensorVector{make_tensor<element::Type_t::f32>({1, 3}, {1.0f, 1.0f, 1.0f}),
                                      make_tensor<element::Type_t::f32>({1, 3}, {7.0f, 6.0f, 1.0f})};
    ASSERT_THROW(model->evaluate(out_vector, in_vector), ov::Exception);
}

TEST(eval, evaluate_gather_string_basic) {
    std::vector<std::string> input_values = {"Abc", "x", "1234", "...."};
    std::vector<std::string> out_expected{"x", "...."};
    std::vector<int32_t> indices_val{1, 3};

    const auto data_shape = Shape{input_values.size()};
    const auto exp_out_shape = Shape{out_expected.size()};
    auto data = make_shared<ov::op::v0::Parameter>(element::string, data_shape);
    auto indices = ov::op::v0::Constant::create<int32_t>(element::i32, Shape{indices_val.size()}, indices_val);
    auto axis = ov::op::v0::Constant::create<int32_t>(element::i32, Shape{1}, {0});
    auto op = make_shared<op::v8::Gather>(data, indices, axis, 0);
    auto model = make_shared<ov::Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor(element::string, exp_out_shape);
    auto out_vector = ov::TensorVector{result};
    auto in_tensor = ov::Tensor(element::string, data_shape, input_values.data());
    auto in_vector = ov::TensorVector{in_tensor};

    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    EXPECT_EQ(result.get_element_type(), element::string);
    EXPECT_EQ(result.get_shape(), exp_out_shape);

    const auto result_const = ov::op::v0::Constant(out_vector.at(0));
    EXPECT_EQ(out_expected, result_const.get_value_strings());
}

TEST(eval, evaluate_reshape_string_1D_to_2D) {
    std::vector<std::string> input_values = {"A", "B c", "d.Ef", " G h,i;", "JK ", "l,m,n,", " \0", " "};
    std::vector<size_t> target_shape_val{2, 4};
    auto target_shape = Shape(target_shape_val);
    bool has_special_zero = false;

    const auto data_shape = Shape{input_values.size()};
    auto data = make_shared<ov::op::v0::Parameter>(element::string, data_shape);
    auto target_shape_node =
        ov::op::v0::Constant::create(element::i64, Shape{target_shape_val.size()}, target_shape_val);
    auto op = make_shared<op::v1::Reshape>(data, target_shape_node, has_special_zero);
    auto model = make_shared<ov::Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor(element::string, target_shape);
    auto out_vector = ov::TensorVector{result};
    auto in_tensor = ov::Tensor(element::string, data_shape, input_values.data());
    auto in_vector = ov::TensorVector{in_tensor};

    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    EXPECT_EQ(result.get_element_type(), element::string);
    EXPECT_EQ(result.get_shape(), target_shape);

    const auto result_const = ov::op::v0::Constant(out_vector.at(0));
    EXPECT_EQ(input_values, result_const.get_value_strings());
}

TEST(eval, evaluate_reshape_string_2D_to_1D) {
    std::vector<std::string> input_values = {"A", "B c", "d.Ef", " G h,i;", "JK ", "l,m,n,", " \0", " "};
    std::vector<size_t> target_shape_val{input_values.size()};
    const auto target_shape = Shape(target_shape_val);
    bool has_special_zero = false;

    const auto data_shape = Shape{4, 2};
    auto data = make_shared<ov::op::v0::Parameter>(element::string, data_shape);
    auto target_shape_node =
        ov::op::v0::Constant::create(element::i64, Shape{target_shape_val.size()}, target_shape_val);
    auto op = make_shared<op::v1::Reshape>(data, target_shape_node, has_special_zero);
    auto model = make_shared<ov::Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor(element::string, target_shape);
    auto out_vector = ov::TensorVector{result};
    auto in_tensor = ov::Tensor(element::string, data_shape, input_values.data());
    auto in_vector = ov::TensorVector{in_tensor};

    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    EXPECT_EQ(result.get_element_type(), element::string);
    EXPECT_EQ(result.get_shape(), target_shape);

    const auto result_const = ov::op::v0::Constant(out_vector.at(0));
    EXPECT_EQ(input_values, result_const.get_value_strings());
}

TEST(eval, evaluate_reshape_string_3D_to_2D) {
    std::vector<std::string> input_values = {"A", "B c", "d.Ef", " G h,i;", "JK ", "l,m,n,", " \0", " "};
    std::vector<size_t> target_shape_val{2, 4};
    auto target_shape = Shape(target_shape_val);
    bool has_special_zero = false;

    const auto data_shape = Shape{2, 2, 2};
    auto data = make_shared<ov::op::v0::Parameter>(element::string, data_shape);
    auto target_shape_node =
        ov::op::v0::Constant::create(element::i64, Shape{target_shape_val.size()}, target_shape_val);
    auto op = make_shared<op::v1::Reshape>(data, target_shape_node, has_special_zero);
    auto model = make_shared<ov::Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor(element::string, target_shape);
    auto out_vector = ov::TensorVector{result};
    auto in_tensor = ov::Tensor(element::string, data_shape, input_values.data());
    auto in_vector = ov::TensorVector{in_tensor};

    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    EXPECT_EQ(result.get_element_type(), element::string);
    EXPECT_EQ(result.get_shape(), target_shape);

    const auto result_const = ov::op::v0::Constant(out_vector.at(0));
    EXPECT_EQ(input_values, result_const.get_value_strings());
}

TEST(eval, evaluate_reshape_string_2D_to_4D) {
    std::vector<std::string> input_values = {"A", "B c", "d.Ef", " G h,i;", "JK ", "l,m,n,", " \0", " "};
    std::vector<size_t> target_shape_val{2, 1, 2, 2};
    auto target_shape = Shape(target_shape_val);
    bool has_special_zero = false;

    const auto data_shape = Shape{4, 2};
    auto data = make_shared<ov::op::v0::Parameter>(element::string, data_shape);
    auto target_shape_node =
        ov::op::v0::Constant::create(element::i64, Shape{target_shape_val.size()}, target_shape_val);
    auto op = make_shared<op::v1::Reshape>(data, target_shape_node, has_special_zero);
    auto model = make_shared<ov::Model>(OutputVector{op}, ParameterVector{data});

    auto result = ov::Tensor(element::string, target_shape);
    auto out_vector = ov::TensorVector{result};
    auto in_tensor = ov::Tensor(element::string, data_shape, input_values.data());
    auto in_vector = ov::TensorVector{in_tensor};

    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    EXPECT_EQ(result.get_element_type(), element::string);
    EXPECT_EQ(result.get_shape(), target_shape);

    const auto result_const = ov::op::v0::Constant(out_vector.at(0));
    EXPECT_EQ(input_values, result_const.get_value_strings());
}

TEST(eval, evaluate_concat_string_basic) {
    std::vector<std::string> input_values_a = {"Abc", "x"};
    std::vector<std::string> input_values_b = {"1234", "...."};

    std::vector<std::string> out_expected{"Abc", "x", "1234", "...."};

    const auto data_shape = Shape{1, 2};
    const auto exp_out_shape = Shape{2, 2};
    auto data_a = make_shared<ov::op::v0::Parameter>(element::string, data_shape);
    auto data_b = make_shared<ov::op::v0::Parameter>(element::string, data_shape);

    auto axis = 0;
    auto op = make_shared<op::v0::Concat>(OutputVector{data_a, data_b}, axis);
    auto model = make_shared<ov::Model>(OutputVector{op}, ParameterVector{data_a, data_b});

    auto result = ov::Tensor(element::string, exp_out_shape);
    auto out_vector = ov::TensorVector{result};
    auto in_tensor_a = ov::Tensor(element::string, data_shape, input_values_a.data());
    auto in_tensor_b = ov::Tensor(element::string, data_shape, input_values_b.data());

    auto in_vector = ov::TensorVector{in_tensor_a, in_tensor_b};

    ASSERT_TRUE(model->evaluate(out_vector, in_vector));
    EXPECT_EQ(result.get_element_type(), element::string);
    EXPECT_EQ(result.get_shape(), exp_out_shape);

    const auto result_const = ov::op::v0::Constant(out_vector.at(0));
    EXPECT_EQ(out_expected, result_const.get_value_strings());
}