//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "DriverTestHelpers.hpp"

#include "../1.0/HalPolicy.hpp"

#include <boost/test/unit_test.hpp>

#include <log/log.h>

BOOST_AUTO_TEST_SUITE(FullyConnectedTests)

using namespace android::hardware;
using namespace driverTestHelpers;
using namespace armnn_driver;

using HalPolicy = hal_1_0::HalPolicy;

// Add our own test here since we fail the fc tests which Google supplies (because of non-const weights)
BOOST_AUTO_TEST_CASE(FullyConnected)
{
    // this should ideally replicate fully_connected_float.model.cpp
    // but that uses slightly weird dimensions which I don't think we need to support for now

    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
    HalPolicy::Model model = {};

    // add operands
    int32_t actValue      = 0;
    float   weightValue[] = {2, 4, 1};
    float   biasValue[]   = {4};

    AddInputOperand<HalPolicy>(model, hidl_vec<uint32_t>{1, 3});
    AddTensorOperand<HalPolicy>(model, hidl_vec<uint32_t>{1, 3}, weightValue);
    AddTensorOperand<HalPolicy>(model, hidl_vec<uint32_t>{1}, biasValue);
    AddIntOperand<HalPolicy>(model, actValue);
    AddOutputOperand<HalPolicy>(model, hidl_vec<uint32_t>{1, 1});

    // make the fully connected operation
    model.operations.resize(1);
    model.operations[0].type = HalPolicy::OperationType::FULLY_CONNECTED;
    model.operations[0].inputs  = hidl_vec<uint32_t>{0, 1, 2, 3};
    model.operations[0].outputs = hidl_vec<uint32_t>{4};

    // make the prepared model
    android::sp<V1_0::IPreparedModel> preparedModel = PrepareModel(model, *driver);

    // construct the request
    DataLocation inloc = {};
    inloc.poolIndex = 0;
    inloc.offset    = 0;
    inloc.length    = 3 * sizeof(float);
    RequestArgument input = {};
    input.location = inloc;
    input.dimensions = hidl_vec<uint32_t>{};

    DataLocation outloc = {};
    outloc.poolIndex = 1;
    outloc.offset    = 0;
    outloc.length    = 1 * sizeof(float);
    RequestArgument output = {};
    output.location  = outloc;
    output.dimensions = hidl_vec<uint32_t>{};

    Request request = {};
    request.inputs  = hidl_vec<RequestArgument>{input};
    request.outputs = hidl_vec<RequestArgument>{output};

    // set the input data (matching source test)
    float indata[] = {2, 32, 16};
    AddPoolAndSetData<float>(3, request, indata);

    // add memory for the output
    android::sp<IMemory> outMemory = AddPoolAndGetData<float>(1, request);
    float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));

    // run the execution
    Execute(preparedModel, request);

    // check the result
    BOOST_TEST(outdata[0] == 152);
}

BOOST_AUTO_TEST_CASE(TestFullyConnected4dInput)
{
    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));

    ErrorStatus error;
    std::vector<bool> sup;

    ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
        {
            error = status;
            sup = supported;
        };

    HalPolicy::Model model = {};

    // operands
    int32_t actValue      = 0;
    float   weightValue[] = {1, 0, 0, 0, 0, 0, 0, 0,
                             0, 1, 0, 0, 0, 0, 0, 0,
                             0, 0, 1, 0, 0, 0, 0, 0,
                             0, 0, 0, 1, 0, 0, 0, 0,
                             0, 0, 0, 0, 1, 0, 0, 0,
                             0, 0, 0, 0, 0, 1, 0, 0,
                             0, 0, 0, 0, 0, 0, 1, 0,
                             0, 0, 0, 0, 0, 0, 0, 1}; //identity
    float   biasValue[]   = {0, 0, 0, 0, 0, 0, 0, 0};

    // fully connected operation
    AddInputOperand<HalPolicy>(model, hidl_vec<uint32_t>{1, 1, 1, 8});
    AddTensorOperand<HalPolicy>(model, hidl_vec<uint32_t>{8, 8}, weightValue);
    AddTensorOperand<HalPolicy>(model, hidl_vec<uint32_t>{8}, biasValue);
    AddIntOperand<HalPolicy>(model, actValue);
    AddOutputOperand<HalPolicy>(model, hidl_vec<uint32_t>{1, 8});

    model.operations.resize(1);

    model.operations[0].type = HalPolicy::OperationType::FULLY_CONNECTED;
    model.operations[0].inputs  = hidl_vec<uint32_t>{0,1,2,3};
    model.operations[0].outputs = hidl_vec<uint32_t>{4};

    // make the prepared model
    android::sp<V1_0::IPreparedModel> preparedModel = PrepareModel(model, *driver);

    // construct the request
    DataLocation inloc = {};
    inloc.poolIndex = 0;
    inloc.offset    = 0;
    inloc.length    = 8 * sizeof(float);
    RequestArgument input = {};
    input.location = inloc;
    input.dimensions = hidl_vec<uint32_t>{};

    DataLocation outloc = {};
    outloc.poolIndex = 1;
    outloc.offset    = 0;
    outloc.length    = 8 * sizeof(float);
    RequestArgument output = {};
    output.location  = outloc;
    output.dimensions = hidl_vec<uint32_t>{};

    Request request = {};
    request.inputs  = hidl_vec<RequestArgument>{input};
    request.outputs = hidl_vec<RequestArgument>{output};

    // set the input data
    float indata[] = {1,2,3,4,5,6,7,8};
    AddPoolAndSetData(8, request, indata);

    // add memory for the output
    android::sp<IMemory> outMemory = AddPoolAndGetData<float>(8, request);
    float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));

    // run the execution
    Execute(preparedModel, request);

    // check the result
    BOOST_TEST(outdata[0] == 1);
    BOOST_TEST(outdata[1] == 2);
    BOOST_TEST(outdata[2] == 3);
    BOOST_TEST(outdata[3] == 4);
    BOOST_TEST(outdata[4] == 5);
    BOOST_TEST(outdata[5] == 6);
    BOOST_TEST(outdata[6] == 7);
    BOOST_TEST(outdata[7] == 8);
}

BOOST_AUTO_TEST_CASE(TestFullyConnected4dInputReshape)
{
    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));

    ErrorStatus error;
    std::vector<bool> sup;

    ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector<bool>& supported)
        {
            error = status;
            sup = supported;
        };

    HalPolicy::Model model = {};

    // operands
    int32_t actValue      = 0;
    float   weightValue[] = {1, 0, 0, 0, 0, 0, 0, 0,
                             0, 1, 0, 0, 0, 0, 0, 0,
                             0, 0, 1, 0, 0, 0, 0, 0,
                             0, 0, 0, 1, 0, 0, 0, 0,
                             0, 0, 0, 0, 1, 0, 0, 0,
                             0, 0, 0, 0, 0, 1, 0, 0,
                             0, 0, 0, 0, 0, 0, 1, 0,
                             0, 0, 0, 0, 0, 0, 0, 1}; //identity
    float   biasValue[]   = {0, 0, 0, 0, 0, 0, 0, 0};

    // fully connected operation
    AddInputOperand<HalPolicy>(model, hidl_vec<uint32_t>{1, 2, 2, 2});
    AddTensorOperand<HalPolicy>(model, hidl_vec<uint32_t>{8, 8}, weightValue);
    AddTensorOperand<HalPolicy>(model, hidl_vec<uint32_t>{8}, biasValue);
    AddIntOperand<HalPolicy>(model, actValue);
    AddOutputOperand<HalPolicy>(model, hidl_vec<uint32_t>{1, 8});

    model.operations.resize(1);

    model.operations[0].type = HalPolicy::OperationType::FULLY_CONNECTED;
    model.operations[0].inputs  = hidl_vec<uint32_t>{0,1,2,3};
    model.operations[0].outputs = hidl_vec<uint32_t>{4};

    // make the prepared model
    android::sp<V1_0::IPreparedModel> preparedModel = PrepareModel(model, *driver);

    // construct the request
    DataLocation inloc = {};
    inloc.poolIndex = 0;
    inloc.offset    = 0;
    inloc.length    = 8 * sizeof(float);
    RequestArgument input = {};
    input.location = inloc;
    input.dimensions = hidl_vec<uint32_t>{};

    DataLocation outloc = {};
    outloc.poolIndex = 1;
    outloc.offset    = 0;
    outloc.length    = 8 * sizeof(float);
    RequestArgument output = {};
    output.location  = outloc;
    output.dimensions = hidl_vec<uint32_t>{};

    Request request = {};
    request.inputs  = hidl_vec<RequestArgument>{input};
    request.outputs = hidl_vec<RequestArgument>{output};

    // set the input data
    float indata[] = {1,2,3,4,5,6,7,8};
    AddPoolAndSetData(8, request, indata);

    // add memory for the output
    android::sp<IMemory> outMemory = AddPoolAndGetData<float>(8, request);
    float* outdata = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));

    // run the execution
    Execute(preparedModel, request);

    // check the result
    BOOST_TEST(outdata[0] == 1);
    BOOST_TEST(outdata[1] == 2);
    BOOST_TEST(outdata[2] == 3);
    BOOST_TEST(outdata[3] == 4);
    BOOST_TEST(outdata[4] == 5);
    BOOST_TEST(outdata[5] == 6);
    BOOST_TEST(outdata[6] == 7);
    BOOST_TEST(outdata[7] == 8);
}

BOOST_AUTO_TEST_SUITE_END()