//
// Copyright © 2020 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//

#pragma once

#include "ArmnnDriver.hpp"
#include "ArmnnDriverImpl.hpp"
#include "RequestThread_1_3.hpp"
#include "ModelToINetworkConverter.hpp"

#include <NeuralNetworks.h>
#include <armnn/ArmNN.hpp>
#include <armnn/Threadpool.hpp>


#include <string>
#include <vector>

namespace armnn_driver
{
using CallbackAsync_1_3 = std::function<
                                void(V1_3::ErrorStatus errorStatus,
                                std::vector<::android::hardware::neuralnetworks::V1_2::OutputShape> outputShapes,
                                const ::android::hardware::neuralnetworks::V1_2::Timing& timing,
                                std::string callingFunction)>;

struct ExecutionContext_1_3
{
    ::android::hardware::neuralnetworks::V1_2::MeasureTiming    measureTimings =
        ::android::hardware::neuralnetworks::V1_2::MeasureTiming::NO;
    TimePoint driverStart;
    TimePoint driverEnd;
    TimePoint deviceStart;
    TimePoint deviceEnd;
};

using CallbackContext_1_3 = CallbackContext<CallbackAsync_1_3, ExecutionContext_1_3>;

using executeFenced_cb = std::function<void(::android::hardware::neuralnetworks::V1_3::ErrorStatus status,
    const ::android::hardware::hidl_handle& syncFence,
    const ::android::sp<::android::hardware::neuralnetworks::V1_3::IFencedExecutionCallback>& callback)>;

template <typename HalVersion>
class ArmnnPreparedModel_1_3 : public V1_3::IPreparedModel
{
public:
    using HalModel = typename V1_3::Model;

    ArmnnPreparedModel_1_3(armnn::NetworkId networkId,
                           armnn::IRuntime* runtime,
                           const HalModel& model,
                           const std::string& requestInputsAndOutputsDumpDir,
                           const bool gpuProfilingEnabled,
                           V1_3::Priority priority = V1_3::Priority::MEDIUM,
                           const bool asyncModelExecutionEnabled = false,
                           const unsigned int numberOfThreads = 1);

    ArmnnPreparedModel_1_3(armnn::NetworkId networkId,
                           armnn::IRuntime* runtime,
                           const std::string& requestInputsAndOutputsDumpDir,
                           const bool gpuProfilingEnabled,
                           V1_3::Priority priority = V1_3::Priority::MEDIUM,
                           const bool asyncModelExecutionEnabled = false,
                           const unsigned int numberOfThreads = 1,
                           const bool preparedFromCache = false);

    virtual ~ArmnnPreparedModel_1_3();

    Return<V1_0::ErrorStatus> execute(const V1_0::Request& request,
                                      const ::android::sp<V1_0::IExecutionCallback>& callback) override;

    Return<V1_0::ErrorStatus> execute_1_2(const V1_0::Request& request, V1_2::MeasureTiming measure,
                                          const ::android::sp<V1_2::IExecutionCallback>& callback) override;

    Return<V1_3::ErrorStatus> execute_1_3(const V1_3::Request& request,
                                          V1_2::MeasureTiming measure,
                                          const V1_3::OptionalTimePoint&,
                                          const V1_3::OptionalTimeoutDuration&,
                                          const ::android::sp<V1_3::IExecutionCallback>& callback) override;

    Return<void> executeSynchronously(const V1_0::Request &request,
                                      V1_2::MeasureTiming measure,
                                      V1_3::IPreparedModel::executeSynchronously_cb cb) override;

    Return<void> executeSynchronously_1_3(const V1_3::Request &request,
                                          V1_2::MeasureTiming measure,
                                          const V1_3::OptionalTimePoint& deadline,
                                          const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
                                          V1_3::IPreparedModel::executeSynchronously_1_3_cb cb) override;

    Return<void> executeFenced(const V1_3::Request& request,
                               const android::hardware::hidl_vec<android::hardware::hidl_handle>& fenceWaitFor,
                               V1_2::MeasureTiming measure,
                               const V1_3::OptionalTimePoint& deadline,
                               const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
                               const V1_3::OptionalTimeoutDuration& duration,
                               executeFenced_cb callback) override;

    Return<void> configureExecutionBurst(
            const ::android::sp<V1_2::IBurstCallback>& callback,
            const android::hardware::MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel,
            const android::hardware::MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel,
            configureExecutionBurst_cb cb) override;

    template<typename CallbackContext>
    Return<void> ExecuteSynchronously(const V1_3::Request& request, CallbackContext cbCtx);

    /// execute the graph prepared from the request
    template<typename CallbackContext>
    Return <V1_3::ErrorStatus> ExecuteGraph(
              std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
              armnn::InputTensors& inputTensors,
              armnn::OutputTensors& outputTensors,
              CallbackContext callback);

    /// Executes this model with dummy inputs (e.g. all zeroes).
    /// \return false on failure, otherwise true
    bool ExecuteWithDummyInputs(unsigned int numInputs, unsigned int numOutputs);

    V1_3::Priority GetModelPriority();

private:

    template<typename CallbackContext>
    class ArmnnThreadPoolCallback_1_3 : public armnn::IAsyncExecutionCallback
    {
    public:
        ArmnnThreadPoolCallback_1_3(ArmnnPreparedModel_1_3<HalVersion>* model,
                                    std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
                                    std::vector<V1_2::OutputShape> outputShapes,
                                    std::shared_ptr<armnn::InputTensors>& inputTensors,
                                    std::shared_ptr<armnn::OutputTensors>& outputTensors,
                                    CallbackContext callbackContext) :
                m_Model(model),
                m_MemPools(pMemPools),
                m_OutputShapes(outputShapes),
                m_InputTensors(inputTensors),
                m_OutputTensors(outputTensors),
                m_CallbackContext(callbackContext)
        {}

        void Notify(armnn::Status status, armnn::InferenceTimingPair timeTaken) override;

        ArmnnPreparedModel_1_3<HalVersion>* m_Model;
        std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>> m_MemPools;
        std::vector<V1_2::OutputShape> m_OutputShapes;
        std::shared_ptr<armnn::InputTensors> m_InputTensors;
        std::shared_ptr<armnn::OutputTensors> m_OutputTensors;
        CallbackContext m_CallbackContext;
    };

    Return <V1_3::ErrorStatus> Execute(const V1_3::Request& request,
                                       V1_2::MeasureTiming measureTiming,
                                       CallbackAsync_1_3 callback);

    Return<V1_3::ErrorStatus> PrepareMemoryForInputs(
        armnn::InputTensors& inputs,
        const V1_3::Request& request,
        const std::vector<android::nn::RunTimePoolInfo>& memPools);

    Return<V1_3::ErrorStatus> PrepareMemoryForOutputs(
        armnn::OutputTensors& outputs,
        std::vector<V1_2::OutputShape> &outputShapes,
        const V1_3::Request& request,
        const std::vector<android::nn::RunTimePoolInfo>& memPools);

    std::tuple<V1_3::ErrorStatus, hidl_vec<V1_2::OutputShape>, V1_2::Timing, std::string> PrepareMemoryForIO(
        armnn::InputTensors& inputs,
        armnn::OutputTensors& outputs,
        std::vector<android::nn::RunTimePoolInfo>& memPools,
        const V1_3::Request& request);

    template <typename TensorBindingCollection>
    void DumpTensorsIfRequired(char const* tensorNamePrefix, const TensorBindingCollection& tensorBindings);

    /// schedule the graph prepared from the request for execution
    template<typename CallbackContext>
    void ScheduleGraphForExecution(
            std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
            std::shared_ptr<armnn::InputTensors>& inputTensors,
            std::shared_ptr<armnn::OutputTensors>& outputTensors,
            CallbackContext m_CallbackContext,
            armnn::QosExecPriority priority);

    armnn::NetworkId                               m_NetworkId;
    armnn::IRuntime*                               m_Runtime;
    V1_3::Model                                    m_Model;
    // There must be a single RequestThread for all ArmnnPreparedModel objects to ensure serial execution of workloads
    // It is specific to this class, so it is declared as static here
    static RequestThread_1_3<ArmnnPreparedModel_1_3,
                             HalVersion,
                             CallbackContext_1_3>  m_RequestThread;
    uint32_t                                       m_RequestCount;
    const std::string&                             m_RequestInputsAndOutputsDumpDir;
    const bool                                     m_GpuProfilingEnabled;
    V1_3::Priority                                 m_ModelPriority;

    // Static to allow sharing of threadpool between ArmnnPreparedModel instances
    static std::unique_ptr<armnn::Threadpool>      m_Threadpool;
    std::shared_ptr<IWorkingMemHandle>             m_WorkingMemHandle;
    const bool                                     m_AsyncModelExecutionEnabled;
    const bool                                     m_PreparedFromCache;
};

}