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

#include <armnn/Tensor.hpp>
#include <armnn/Types.hpp>

#include "Network.hpp"
#include "LayerFwd.hpp"
#include "Profiling.hpp"

#include <backends/reference/RefWorkloadFactory.hpp>
#include <backends/neon/NeonWorkloadFactory.hpp>
#include <backends/cl/ClWorkloadFactory.hpp>
#include <backends/Workload.hpp>
#include <backends/WorkloadFactory.hpp>

namespace cl
{
    class Context;
    class CommandQueue;
    class Device;
}

namespace armnn
{

class LoadedNetwork
{
public:
    TensorInfo GetInputTensorInfo(LayerBindingId layerId) const;
    TensorInfo GetOutputTensorInfo(LayerBindingId layerId) const;

    Status EnqueueWorkload(const InputTensors& inputTensors, const OutputTensors& outputTensors);

    static std::unique_ptr<LoadedNetwork> MakeLoadedNetwork(std::unique_ptr<OptimizedNetwork> net,
                                                            std::string & errorMessage);

    // NOTE we return by reference as the purpose of this method is only to provide
    // access to the private m_Profiler and in theory we should not need to increment
    // the shared_ptr's reference counter
    const std::shared_ptr<Profiler>& GetProfiler() const { return m_Profiler; }

private:
    LoadedNetwork(std::unique_ptr<OptimizedNetwork> net);

    void EnqueueInput(const BindableLayer& layer, ITensorHandle* tensorHandle, const TensorInfo& tensorInfo);

    void EnqueueOutput(const BindableLayer& layer, ITensorHandle* tensorHandle, const TensorInfo& tensorInfo);

    bool Execute();

    void TidyWorkloadQueue(size_t numInputs, size_t numOutputs);

    const IWorkloadFactory& GetWorkloadFactory(const Layer& layer) const;

    RefWorkloadFactory  m_CpuRef;
    NeonWorkloadFactory m_CpuAcc;
    ClWorkloadFactory   m_GpuAcc;

    std::unique_ptr<OptimizedNetwork> m_OptimizedNetwork;
    std::vector< std::unique_ptr<IWorkload> > m_WorkloadQueue;
    std::shared_ptr<Profiler> m_Profiler;
};

}