// Copyright (C) 2018-2020 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#include <gtest/gtest.h>
#include <gmock/gmock-spec-builders.h>
#include "mkldnn_graph.h"

#include "test_graph.hpp"

#include "single_layer_common.hpp"
#include <mkldnn_extension_utils.h>
#include <cnn_network_impl.hpp>
#include "tests_common.hpp"

#include <cpp/ie_cnn_net_reader.h>
#include <ie_plugin_config.hpp>

using namespace ::testing;
using namespace std;
using namespace mkldnn;


struct power_test_params {
    struct {
        size_t n;
        size_t c;
        size_t h;
        size_t w;
    } in;

    float power;
    float scale;
    float shift;

    size_t num_prim_desc;

    MKLDNNPlugin::impl_desc_type selectedType;

    std::vector<std::function<void(MKLDNNPlugin::PrimitiveDescInfo)>> comp;
};

template <typename data_t>
void ref_power(const InferenceEngine::TBlob<data_t> &src, InferenceEngine::TBlob<data_t> &dst, power_test_params prm) {
    const data_t *src_data = src.readOnly();
    data_t *dst_data = dst.data();

    for (int i=0; i < src.size(); i++)
        dst_data[i] = pow(src_data[i]*prm.scale + prm.shift, prm.power);
}

class MKLDNNGraphPowerTests: public TestsCommon,
                                     public WithParamInterface<power_test_params> {
    std::string model_t = R"V0G0N(
<Net Name="Power_Only" version="2" precision="FP32" batch="1">
    <layers>
        <layer name="in1" type="Input" precision="FP32" id="0">
            <output>
                <port id="0">
                    <dim>_IN_</dim>
                    <dim>_IC_</dim>
                    <dim>_IH_</dim>
                    <dim>_IW_</dim>
                </port>
            </output>
        </layer>
        <layer name="power" id="1" type="Power" precision="FP32">
            <power_data power="_POWER_" scale="_SCALE_" shift="_SHIFT_"/>
            <input>
                <port id="1">
                    <dim>_IN_</dim>
                    <dim>_IC_</dim>
                    <dim>_IH_</dim>
                    <dim>_IW_</dim>
                </port>
            </input>
            <output>
                <port id="2">
                    <dim>_IN_</dim>
                    <dim>_IC_</dim>
                    <dim>_IH_</dim>
                    <dim>_IW_</dim>
                </port>
            </output>
        </layer>
    </layers>
    <edges>
        <edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
    </edges>
</Net>
)V0G0N";

protected:
    std::string getModel(power_test_params p) {
        std::string model = model_t;

        REPLACE_WITH_NUM(model, "_IW_", p.in.w);
        REPLACE_WITH_NUM(model, "_IH_", p.in.h);
        REPLACE_WITH_NUM(model, "_IC_", p.in.c);
        REPLACE_WITH_NUM(model, "_IN_", p.in.n);
        REPLACE_WITH_NUM(model, "_POWER_", p.power);
        REPLACE_WITH_NUM(model, "_SCALE_", p.scale);
        REPLACE_WITH_NUM(model, "_SHIFT_", p.shift);

        return model;
    }

    virtual void TearDown() {
    }

    virtual void SetUp() {
        try {
            TestsCommon::SetUp();
            power_test_params p = ::testing::WithParamInterface<power_test_params>::GetParam();
            std::string model = getModel(p);

            InferenceEngine::CNNNetReader net_reader;
            ASSERT_NO_THROW(net_reader.ReadNetwork(model.data(), model.length()));

            MKLDNNGraphTestClass graph;
            graph.CreateGraph(net_reader.getNetwork());
            auto& nodes = graph.getNodes();
            for (int i = 0; i < nodes.size(); i++) {
                if (nodes[i]->getType() == MKLDNNPlugin::Power) {
                    ASSERT_EQ(p.num_prim_desc, nodes[i]->getSupportedPrimitiveDescriptors().size());
                    for (size_t j = 0; j < p.num_prim_desc && j < p.comp.size(); j++) {
                        p.comp.at(j)(nodes[i]->getSupportedPrimitiveDescriptors().at(j));
                    }
                    ASSERT_NE(nullptr, nodes[i]->getSelectedPrimitiveDescriptor());
                    ASSERT_EQ(p.selectedType, nodes[i]->getSelectedPrimitiveDescriptor()->getImplementationType());
                }
            }

            InferenceEngine::SizeVector dims_src = {p.in.n, p.in.c, p.in.h, p.in.w};

            InferenceEngine::Blob::Ptr src = InferenceEngine::make_shared_blob<float>({InferenceEngine::Precision::FP32, dims_src, InferenceEngine::NCHW});
            src->allocate();
            fill_data(src->buffer(), src->size());

            InferenceEngine::TBlob<float>* srcPtr = dynamic_cast<InferenceEngine::TBlob<float>*>(src.get());

            if (srcPtr == nullptr)
                FAIL() << "Cannot cast blob to TBlob<float>.";

            InferenceEngine::BlobMap srcs;
            srcs.insert(std::pair<std::string, InferenceEngine::Blob::Ptr>("in1", src));

            InferenceEngine::OutputsDataMap out;
            out = net_reader.getNetwork().getOutputsInfo();
            InferenceEngine::BlobMap outputBlobs;

            std::pair<std::string, InferenceEngine::DataPtr> item = *out.begin();

            InferenceEngine::TBlob<float>::Ptr output;
            output = InferenceEngine::make_shared_blob<float>(item.second->getTensorDesc());
            output->allocate();
            outputBlobs[item.first] = output;

            graph.Infer(srcs, outputBlobs);

            InferenceEngine::TBlob<float> dst_ref(item.second->getTensorDesc());
            dst_ref.allocate();

            ref_power(*srcPtr, dst_ref, p);

            compare(*output, dst_ref);
        } catch (const InferenceEngine::details::InferenceEngineException &e) {
            FAIL() << e.what();
        }
    }
};

TEST_P(MKLDNNGraphPowerTests, TestsPower) {}


INSTANTIATE_TEST_CASE_P(
        TestsPower, MKLDNNGraphPowerTests,
        ::testing::Values(
                power_test_params{
                        {1, 3, 13, 13}, 1, 2, 0.5f, 3, MKLDNNPlugin::impl_desc_type::unknown, {
                                [](MKLDNNPlugin::PrimitiveDescInfo impl) {
                                    ASSERT_EQ(MKLDNNPlugin::impl_desc_type::unknown, impl.getImplementationType());
                                    ASSERT_EQ(1, impl.getConfig().inConfs.size());
                                    ASSERT_EQ(1, impl.getConfig().outConfs.size());
                                    ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().inConfs.at(0).desc.getLayout());
                                    ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().outConfs.at(0).desc.getLayout());
                                },
                                [](MKLDNNPlugin::PrimitiveDescInfo impl) {
                                    ASSERT_EQ(MKLDNNPlugin::impl_desc_type::unknown, impl.getImplementationType());
                                    ASSERT_EQ(1, impl.getConfig().inConfs.size());
                                    ASSERT_EQ(1, impl.getConfig().outConfs.size());
                                    ASSERT_EQ(InferenceEngine::Layout::BLOCKED, impl.getConfig().inConfs.at(0).desc.getLayout());
                                    ASSERT_EQ(InferenceEngine::Layout::BLOCKED, impl.getConfig().outConfs.at(0).desc.getLayout());
                                },
                                [](MKLDNNPlugin::PrimitiveDescInfo impl) {
                                    ASSERT_EQ(MKLDNNPlugin::impl_desc_type::unknown, impl.getImplementationType());
                                    ASSERT_EQ(1, impl.getConfig().inConfs.size());
                                    ASSERT_EQ(1, impl.getConfig().outConfs.size());
                                    ASSERT_EQ(InferenceEngine::Layout::BLOCKED, impl.getConfig().inConfs.at(0).desc.getLayout());
                                    ASSERT_EQ(InferenceEngine::Layout::BLOCKED, impl.getConfig().outConfs.at(0).desc.getLayout());
                                }}},
                power_test_params{{1, 1, 23, 23}, 3, 8, 2, 3 },
                power_test_params{{1, 8, 23, 23}, 8, 2, 1, 3 },
                power_test_params{{1, 8, 23, 23}, 2, 2, 4, 3 }
        ));

class MKLDNNGraphDynBatchPowerTests: public MKLDNNGraphPowerTests {
    std::string model_t = R"V0G0N(
<Net Name="Power_Only" version="2" precision="FP32" batch="1">
    <layers>
        <layer name="in1" type="Input" precision="FP32" id="0">
            <output>
                <port id="0">
                    <dim>_IN_</dim>
                    <dim>_IC_</dim>
                    <dim>_IH_</dim>
                    <dim>_IW_</dim>
                </port>
            </output>
        </layer>
        <layer name="power" id="1" type="Power" precision="FP32">
            <power_data power="_POWER_" scale="_SCALE_" shift="_SHIFT_"/>
            <input>
                <port id="1">
                    <dim>_IN_</dim>
                    <dim>_IC_</dim>
                    <dim>_IH_</dim>
                    <dim>_IW_</dim>
                </port>
            </input>
            <output>
                <port id="2">
                    <dim>_IN_</dim>
                    <dim>_IC_</dim>
                    <dim>_IH_</dim>
                    <dim>_IW_</dim>
                </port>
            </output>
        </layer>
    </layers>
    <edges>
        <edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
    </edges>
</Net>
)V0G0N";

    std::string getModel(power_test_params p) {
        std::string model = model_t;

        REPLACE_WITH_NUM(model, "_IW_", p.in.w);
        REPLACE_WITH_NUM(model, "_IH_", p.in.h);
        REPLACE_WITH_NUM(model, "_IC_", p.in.c);
        REPLACE_WITH_NUM(model, "_IN_", p.in.n);
        REPLACE_WITH_NUM(model, "_POWER_", p.power);
        REPLACE_WITH_NUM(model, "_SCALE_", p.scale);
        REPLACE_WITH_NUM(model, "_SHIFT_", p.shift);

        return model;
    }

protected:
    virtual void TearDown() {
    }

    virtual void SetUp() {
        try {
            TestsCommon::SetUp();
            power_test_params p = ::testing::WithParamInterface<power_test_params>::GetParam();
            std::string model = getModel(p);
            size_t MB = p.in.n;
            if (MB < 2)
                MB = 2;

            InferenceEngine::CNNNetReader net_reader;
            ASSERT_NO_THROW(net_reader.ReadNetwork(model.data(), model.length()));
            InferenceEngine::CNNNetwork network = net_reader.getNetwork();
            auto implNet = dynamic_cast<InferenceEngine::details::CNNNetworkImpl *>(&((InferenceEngine::ICNNNetwork&)network));
            ASSERT_NE(nullptr, implNet) << "Failed to cast ICNNNetwork to CNNNetworkImpl";
            InferenceEngine::ResponseDesc resp;
            InferenceEngine::StatusCode sts  = implNet->setBatchSizeReshape(MB, &resp);
            ASSERT_EQ((int)InferenceEngine::StatusCode::OK, sts) << resp.msg;

            MKLDNNGraphTestClass graph;
            graph.setProperty({{InferenceEngine::PluginConfigParams::KEY_DYN_BATCH_ENABLED, InferenceEngine::PluginConfigParams::YES}});
            graph.CreateGraph(net_reader.getNetwork());

            InferenceEngine::SizeVector dims_src = {MB, p.in.c, p.in.h, p.in.w};

            InferenceEngine::Blob::Ptr src = InferenceEngine::make_shared_blob<float>({InferenceEngine::Precision::FP32, dims_src, InferenceEngine::NCHW});
            src->allocate();
            fill_data(src->buffer(), src->size());

            InferenceEngine::TBlob<float>* srcPtr = dynamic_cast<InferenceEngine::TBlob<float>*>(src.get());

            if (srcPtr == nullptr)
                FAIL() << "Cannot cast blob to TBlob<float>.";

            InferenceEngine::BlobMap srcs;
            srcs.insert(std::pair<std::string, InferenceEngine::Blob::Ptr>("in1", src));

            InferenceEngine::OutputsDataMap out;
            out = net_reader.getNetwork().getOutputsInfo();
            InferenceEngine::BlobMap outputBlobs;

            std::pair<std::string, InferenceEngine::DataPtr> item = *out.begin();

            InferenceEngine::TBlob<float>::Ptr output;
            output = InferenceEngine::make_shared_blob<float>(item.second->getTensorDesc());
            output->allocate();
            outputBlobs[item.first] = output;

            auto checkPower = [](const MKLDNNPlugin::MKLDNNNodePtr& node) {
                return node->getType() == MKLDNNPlugin::Power;
            };
            graph.checkDynBatch(srcs, outputBlobs, MB, MB, checkPower);
            graph.checkDynBatch(srcs, outputBlobs, 1, MB, checkPower);
        } catch (const InferenceEngine::details::InferenceEngineException &e) {
            FAIL() << e.what();
        }
    }
};

TEST_P(MKLDNNGraphDynBatchPowerTests, TestsDynBatchPower) {}

INSTANTIATE_TEST_CASE_P(
        TestsDynBatchPower, MKLDNNGraphDynBatchPowerTests,
        ::testing::Values(
                power_test_params{
                        {1, 3, 13, 13}, 1, 2, 0.5f, 3, MKLDNNPlugin::impl_desc_type::unknown, {
                                [](MKLDNNPlugin::PrimitiveDescInfo impl) {
                                    ASSERT_EQ(MKLDNNPlugin::impl_desc_type::unknown, impl.getImplementationType());
                                    ASSERT_EQ(1, impl.getConfig().inConfs.size());
                                    ASSERT_EQ(1, impl.getConfig().outConfs.size());
                                    ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().inConfs.at(0).desc.getLayout());
                                    ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().outConfs.at(0).desc.getLayout());
                                },
                                [](MKLDNNPlugin::PrimitiveDescInfo impl) {
                                    ASSERT_EQ(MKLDNNPlugin::impl_desc_type::unknown, impl.getImplementationType());
                                    ASSERT_EQ(1, impl.getConfig().inConfs.size());
                                    ASSERT_EQ(1, impl.getConfig().outConfs.size());
                                    ASSERT_EQ(InferenceEngine::Layout::BLOCKED, impl.getConfig().inConfs.at(0).desc.getLayout());
                                    ASSERT_EQ(InferenceEngine::Layout::BLOCKED, impl.getConfig().outConfs.at(0).desc.getLayout());
                                },
                                [](MKLDNNPlugin::PrimitiveDescInfo impl) {
                                    ASSERT_EQ(MKLDNNPlugin::impl_desc_type::unknown, impl.getImplementationType());
                                    ASSERT_EQ(1, impl.getConfig().inConfs.size());
                                    ASSERT_EQ(1, impl.getConfig().outConfs.size());
                                    ASSERT_EQ(InferenceEngine::Layout::BLOCKED, impl.getConfig().inConfs.at(0).desc.getLayout());
                                    ASSERT_EQ(InferenceEngine::Layout::BLOCKED, impl.getConfig().outConfs.at(0).desc.getLayout());
                                }}},
                power_test_params{{1, 1, 23, 23}, 3, 8, 2, 3 },
                power_test_params{{1, 8, 23, 23}, 8, 2, 1, 3 },
                power_test_params{{1, 8, 23, 23}, 2, 2, 4, 3 }
        ));
