﻿//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//

#pragma once

#include <armnn/IRuntime.hpp>
#include <test/TensorHelpers.hpp>

#include <armnnOnnxParser/IOnnxParser.hpp>

#include <VerificationHelpers.hpp>

#include <backendsCommon/BackendRegistry.hpp>

#include <boost/format.hpp>

#include <string>

namespace armnnUtils
{

template<typename TParser>
struct ParserPrototxtFixture
{
    ParserPrototxtFixture()
        : m_Parser(TParser::Create())
        , m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions()))
        , m_NetworkIdentifier(-1)
    {
    }

    /// Parses and loads the network defined by the m_Prototext string.
    /// @{
    void SetupSingleInputSingleOutput(const std::string& inputName, const std::string& outputName);
    void SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape,
        const std::string& inputName,
        const std::string& outputName);
    void Setup(const std::map<std::string, armnn::TensorShape>& inputShapes,
        const std::vector<std::string>& requestedOutputs);
    void Setup();
    /// @}

    /// Executes the network with the given input tensor and checks the result against the given output tensor.
    /// This overload assumes that the network has a single input and a single output.
    template <std::size_t NumOutputDimensions>
    void RunTest(const std::vector<float>& inputData, const std::vector<float>& expectedOutputData);

    /// Executes the network with the given input tensors and checks the results against the given output tensors.
    /// This overload supports multiple inputs and multiple outputs, identified by name.
    template <std::size_t NumOutputDimensions>
    void RunTest(const std::map<std::string, std::vector<float>>& inputData,
        const std::map<std::string, std::vector<float>>& expectedOutputData);

    std::string                                         m_Prototext;
    std::unique_ptr<TParser, void(*)(TParser* parser)>  m_Parser;
    armnn::IRuntimePtr                                  m_Runtime;
    armnn::NetworkId                                    m_NetworkIdentifier;

    /// If the single-input-single-output overload of Setup() is called, these will store the input and output name
    /// so they don't need to be passed to the single-input-single-output overload of RunTest().
    /// @{
    std::string m_SingleInputName;
    std::string m_SingleOutputName;
    /// @}
};

template<typename TParser>
void ParserPrototxtFixture<TParser>::SetupSingleInputSingleOutput(const std::string& inputName,
    const std::string& outputName)
{
    // Stores the input and output name so they don't need to be passed to the single-input-single-output RunTest().
    m_SingleInputName = inputName;
    m_SingleOutputName = outputName;
    Setup({ }, { outputName });
}

template<typename TParser>
void ParserPrototxtFixture<TParser>::SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape,
    const std::string& inputName,
    const std::string& outputName)
{
    // Stores the input and output name so they don't need to be passed to the single-input-single-output RunTest().
    m_SingleInputName = inputName;
    m_SingleOutputName = outputName;
    Setup({ { inputName, inputTensorShape } }, { outputName });
}

template<typename TParser>
void ParserPrototxtFixture<TParser>::Setup(const std::map<std::string, armnn::TensorShape>& inputShapes,
    const std::vector<std::string>& requestedOutputs)
{
    std::string errorMessage;

    armnn::INetworkPtr network =
        m_Parser->CreateNetworkFromString(m_Prototext.c_str(), inputShapes, requestedOutputs);
    auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec());
    armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage);
    if (ret != armnn::Status::Success)
    {
        throw armnn::Exception(boost::str(
            boost::format("LoadNetwork failed with error: '%1%' %2%")
                            % errorMessage
                            % CHECK_LOCATION().AsString()));
    }
}

template<typename TParser>
void ParserPrototxtFixture<TParser>::Setup()
{
    std::string errorMessage;

    armnn::INetworkPtr network =
        m_Parser->CreateNetworkFromString(m_Prototext.c_str());
    auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec());
    armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage);
    if (ret != armnn::Status::Success)
    {
        throw armnn::Exception(boost::str(
            boost::format("LoadNetwork failed with error: '%1%' %2%")
                            % errorMessage
                            % CHECK_LOCATION().AsString()));
    }
}

template<typename TParser>
template <std::size_t NumOutputDimensions>
void ParserPrototxtFixture<TParser>::RunTest(const std::vector<float>& inputData,
    const std::vector<float>& expectedOutputData)
{
    RunTest<NumOutputDimensions>({ { m_SingleInputName, inputData } }, { { m_SingleOutputName, expectedOutputData } });
}

template<typename TParser>
template <std::size_t NumOutputDimensions>
void ParserPrototxtFixture<TParser>::RunTest(const std::map<std::string, std::vector<float>>& inputData,
    const std::map<std::string, std::vector<float>>& expectedOutputData)
{
    using BindingPointInfo = std::pair<armnn::LayerBindingId, armnn::TensorInfo>;

    // Sets up the armnn input tensors from the given vectors.
    armnn::InputTensors inputTensors;
    for (auto&& it : inputData)
    {
        BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(it.first);
        inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
    }

    // Allocates storage for the output tensors to be written to and sets up the armnn output tensors.
    std::map<std::string, boost::multi_array<float, NumOutputDimensions>> outputStorage;
    armnn::OutputTensors outputTensors;
    for (auto&& it : expectedOutputData)
    {
        BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(it.first);
        outputStorage.emplace(it.first, MakeTensor<float, NumOutputDimensions>(bindingInfo.second));
        outputTensors.push_back(
            { bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) });
    }

    m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);

    // Compares each output tensor to the expected values.
    for (auto&& it : expectedOutputData)
    {
        BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(it.first);
        if (bindingInfo.second.GetNumElements() != it.second.size())
        {
            throw armnn::Exception(
                boost::str(
                    boost::format("Output tensor %1% is expected to have %2% elements. "
                                    "%3% elements supplied. %4%") %
                                    it.first %
                                    bindingInfo.second.GetNumElements() %
                                    it.second.size() %
                                    CHECK_LOCATION().AsString()));
        }
        auto outputExpected = MakeTensor<float, NumOutputDimensions>(bindingInfo.second, it.second);
        BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first]));
    }
}

} // namespace armnnUtils