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

#include <Graph.hpp>

#include <backendsCommon/WorkloadFactory.hpp>

#include <armnn/utility/IgnoreUnused.hpp>

namespace
{
armnn::Graph dummyGraph;

// Make a dummy TensorInfo object.
template<armnn::DataType DataType>
armnn::TensorInfo MakeDummyTensorInfo()
{
    return armnn::TensorInfo({2,2,2,2}, DataType, 1.0, 0);
}


// Make a dummy WorkloadInfo using a dummy TensorInfo.
template<armnn::DataType DataType>
armnn::WorkloadInfo MakeDummyWorkloadInfo(unsigned int numInputs, unsigned int numOutputs)
{
    armnn::WorkloadInfo info;

    for (unsigned int i=0; i < numInputs; i++)
    {
        info.m_InputTensorInfos.push_back(MakeDummyTensorInfo<DataType>());
    }

    for (unsigned int o=0; o < numOutputs; o++)
    {
        info.m_OutputTensorInfos.push_back(MakeDummyTensorInfo<DataType>());
    }

    return info;
}

// Template class to create a dummy layer (2 parameters).
template<typename LayerType, typename DescType = typename LayerType::DescriptorType>
struct DummyLayer
{
    DummyLayer()
    {
        m_Layer = dummyGraph.AddLayer<LayerType>(DescType(), "");
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    LayerType* m_Layer;
};

// Template class to create a dummy layer (1 parameter).
template<typename LayerType>
struct DummyLayer<LayerType, void>
{
    DummyLayer()
    {
        m_Layer = dummyGraph.AddLayer<LayerType>("");
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    LayerType* m_Layer;
};

template<>
struct DummyLayer<armnn::BatchNormalizationLayer>
{
    DummyLayer()
    {
        m_Layer = dummyGraph.AddLayer<armnn::BatchNormalizationLayer>(armnn::BatchNormalizationDescriptor(), "");
        m_Layer->m_Mean = std::make_unique<armnn::ScopedCpuTensorHandle>(
            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_Variance = std::make_unique<armnn::ScopedCpuTensorHandle>(
            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_Beta = std::make_unique<armnn::ScopedCpuTensorHandle>(
            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_Gamma = std::make_unique<armnn::ScopedCpuTensorHandle>(
            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::BatchNormalizationLayer* m_Layer;
};

template<>
struct DummyLayer<armnn::BatchToSpaceNdLayer>
{
    DummyLayer()
    {
        m_Layer = dummyGraph.AddLayer<armnn::BatchToSpaceNdLayer>(armnn::BatchToSpaceNdDescriptor(), "");
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::BatchToSpaceNdLayer* m_Layer;
};

template<>
struct DummyLayer<armnn::ConstantLayer, void>
{
    DummyLayer()
    {
        m_Layer = dummyGraph.AddLayer<armnn::ConstantLayer>("");
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::ConstantLayer* m_Layer;
};

template<>
struct DummyLayer<armnn::InputLayer, armnn::LayerBindingId>
{
    DummyLayer()
    {
        m_Layer = dummyGraph.AddLayer<armnn::InputLayer>(armnn::LayerBindingId(), "");
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::InputLayer* m_Layer;
};

template<>
struct DummyLayer<armnn::ConcatLayer>
{
    DummyLayer()
    {
        armnn::OriginsDescriptor desc(2);
        m_Layer = dummyGraph.AddLayer<armnn::ConcatLayer>(desc, "");
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::ConcatLayer* m_Layer;
};

template<>
struct DummyLayer<armnn::OutputLayer, armnn::LayerBindingId>
{
    DummyLayer()
    {
        m_Layer = dummyGraph.AddLayer<armnn::OutputLayer>(armnn::LayerBindingId(), "");
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::OutputLayer* m_Layer;
};

template<>
struct DummyLayer<armnn::SplitterLayer>
{
    DummyLayer()
    {
        armnn::ViewsDescriptor desc(1);
        m_Layer = dummyGraph.AddLayer<armnn::SplitterLayer>(desc, "");
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::SplitterLayer* m_Layer;
};

template <typename ConvolutionLayerType>
struct DummyConvolutionLayer
{
    DummyConvolutionLayer()
    {
        typename ConvolutionLayerType::DescriptorType desc;
        desc.m_StrideX = 1;
        desc.m_StrideY = 1;
        m_Layer = dummyGraph.AddLayer<ConvolutionLayerType>(desc, "");
        m_Layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(
            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_Bias = std::make_unique<armnn::ScopedCpuTensorHandle>(
            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
    }

    ~DummyConvolutionLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    ConvolutionLayerType* m_Layer;
};

template<>
struct DummyLayer<armnn::Convolution2dLayer>
    : public DummyConvolutionLayer<armnn::Convolution2dLayer>
{
};

template<>
struct DummyLayer<armnn::DepthwiseConvolution2dLayer>
    : public DummyConvolutionLayer<armnn::DepthwiseConvolution2dLayer>
{
};

template<>
struct DummyLayer<armnn::TransposeConvolution2dLayer>
    : public DummyConvolutionLayer<armnn::TransposeConvolution2dLayer>
{
};

template<>
struct DummyLayer<armnn::DetectionPostProcessLayer>
{
    DummyLayer()
    {
        m_Layer = dummyGraph.AddLayer<armnn::DetectionPostProcessLayer>(armnn::DetectionPostProcessDescriptor(), "");
        m_Layer->m_Anchors = std::make_unique<armnn::ScopedCpuTensorHandle>(
            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::DetectionPostProcessLayer* m_Layer;
};

template <typename LstmLayerType>
struct DummyLstmLayer
{
    DummyLstmLayer()
    {
        typename LstmLayerType::DescriptorType desc;
        desc.m_CifgEnabled = false;

        m_Layer = dummyGraph.AddLayer<LstmLayerType>(armnn::LstmDescriptor(), "");
        m_Layer->m_BasicParameters.m_InputToForgetWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_BasicParameters.m_InputToCellWeights       = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_BasicParameters.m_InputToOutputWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_BasicParameters.m_RecurrentToCellWeights   = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_BasicParameters.m_ForgetGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_BasicParameters.m_CellBias                 = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_BasicParameters.m_OutputGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));

        m_Layer->m_CifgParameters.m_InputToInputWeights        = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_CifgParameters.m_RecurrentToInputWeights    = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
        m_Layer->m_CifgParameters.m_InputGateBias              = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
    }

    ~DummyLstmLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::LstmLayer* m_Layer;
};

template<>
struct DummyLayer<armnn::LstmLayer>
        : public DummyLstmLayer<armnn::LstmLayer>
{
};

template <typename QLstmLayerType>
struct DummyQLstmLayer
{
    DummyQLstmLayer()
    {
        typename QLstmLayerType::DescriptorType desc;
        desc.m_CifgEnabled = false;
        desc.m_PeepholeEnabled = true;
        desc.m_ProjectionEnabled = true;
        desc.m_LayerNormEnabled = true;

        m_Layer = dummyGraph.AddLayer<QLstmLayerType>(armnn::QLstmDescriptor(), "qLstm");

        // Basic params
        m_Layer->m_BasicParameters.m_InputToForgetWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
        m_Layer->m_BasicParameters.m_InputToCellWeights       = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
        m_Layer->m_BasicParameters.m_InputToOutputWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));

        m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
        m_Layer->m_BasicParameters.m_RecurrentToCellWeights   = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
        m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));

        m_Layer->m_BasicParameters.m_ForgetGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
        m_Layer->m_BasicParameters.m_CellBias                 = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
        m_Layer->m_BasicParameters.m_OutputGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));

        // CIFG optional params
        m_Layer->m_CifgParameters.m_InputToInputWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
        m_Layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
        m_Layer->m_CifgParameters.m_InputGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));

        // Projection optional params
        m_Layer->m_ProjectionParameters.m_ProjectionWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
        m_Layer->m_ProjectionParameters.m_ProjectionBias    = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));

        // Peephole optional params
        m_Layer->m_PeepholeParameters.m_CellToInputWeights  = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
        m_Layer->m_PeepholeParameters.m_CellToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
        m_Layer->m_PeepholeParameters.m_CellToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));

        // Layer normalization optional params
        m_Layer->m_LayerNormParameters.m_InputLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
        m_Layer->m_LayerNormParameters.m_ForgetLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
        m_Layer->m_LayerNormParameters.m_CellLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
        m_Layer->m_LayerNormParameters.m_OutputLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
    }

    ~DummyQLstmLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::QLstmLayer* m_Layer;
};

template<>
struct DummyLayer<armnn::QuantizedLstmLayer, void>
{
    DummyLayer()
    {
        m_Layer = dummyGraph.AddLayer<armnn::QuantizedLstmLayer>("");

        m_Layer->m_QuantizedLstmParameters.m_InputToInputWeights  = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
        m_Layer->m_QuantizedLstmParameters.m_InputToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
        m_Layer->m_QuantizedLstmParameters.m_InputToCellWeights   = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
        m_Layer->m_QuantizedLstmParameters.m_InputToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));

        m_Layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights  = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
        m_Layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
        m_Layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights   = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
        m_Layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));

        m_Layer->m_QuantizedLstmParameters.m_InputGateBias  = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
        m_Layer->m_QuantizedLstmParameters.m_ForgetGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
        m_Layer->m_QuantizedLstmParameters.m_CellBias       = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
        m_Layer->m_QuantizedLstmParameters.m_OutputGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::QuantizedLstmLayer* m_Layer;
};

template<>
struct DummyLayer<armnn::FullyConnectedLayer>
{
    DummyLayer()
    {
        armnn::FullyConnectedLayer::DescriptorType desc;
        m_Layer = dummyGraph.AddLayer<armnn::FullyConnectedLayer>(desc, "");
        m_Layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(
            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
    }

    ~DummyLayer()
    {
        dummyGraph.EraseLayer(m_Layer);
    }

    armnn::FullyConnectedLayer* m_Layer;
};

// Tag for giving LayerType entries a unique strong type each.
template<armnn::LayerType>
struct Tag{};

#define DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, descType) \
template<armnn::DataType DataType> \
struct LayerTypePolicy<armnn::LayerType::name, DataType> \
{ \
    using Type = armnn::name##Layer; \
    using Desc = descType; \
    using QueueDesc = armnn::name##QueueDescriptor; \
    constexpr static const char* NameStr = #name; \
    constexpr static const bool IsException = false; \
    \
    static std::unique_ptr<armnn::IWorkload> MakeDummyWorkload(armnn::IWorkloadFactory *factory, \
        unsigned int nIn, unsigned int nOut) \
    { \
        QueueDesc desc; \
        armnn::WorkloadInfo info = MakeDummyWorkloadInfo<DataType>(nIn, nOut); \
        return factory->Create##name(desc, info); \
    } \
};

// Define a layer policy specialization for use with the IsLayerSupported tests.
// Use this version for layers whose constructor takes 1 parameter(name).
#define DECLARE_LAYER_POLICY_1_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, void)

// Define a layer policy specialization for use with the IsLayerSupported tests.
// Use this version for layers whose constructor takes 2 parameters(descriptor and name).
#define DECLARE_LAYER_POLICY_2_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, armnn::name##Descriptor)


#define DECLARE_LAYER_POLICY_EXCEPTION(name, descType) \
template<armnn::DataType DataType> \
struct LayerTypePolicy<armnn::LayerType::name, DataType> \
{ \
    using Type = armnn::name##Layer; \
    using Desc = descType; \
    constexpr static const char* NameStr = #name; \
    constexpr static const bool IsException = true; \
    \
    static std::unique_ptr<armnn::IWorkload> MakeDummyWorkload(armnn::IWorkloadFactory *factory, \
        unsigned int nIn, unsigned int nOut) \
    { \
        IgnoreUnused(factory, nIn, nOut); \
        return std::unique_ptr<armnn::IWorkload>(); \
    } \
};

#define DECLARE_LAYER_POLICY_EXCEPTION_1_PARAM(name) DECLARE_LAYER_POLICY_EXCEPTION(name, void)
#define DECLARE_LAYER_POLICY_EXCEPTION_2_PARAM(name) DECLARE_LAYER_POLICY_EXCEPTION(name, armnn::name##Descriptor)

// Layer policy template.
template<armnn::LayerType Type, armnn::DataType DataType>
struct LayerTypePolicy;

// Every entry in the armnn::LayerType enum must be accounted for below.
DECLARE_LAYER_POLICY_2_PARAM(Activation)

DECLARE_LAYER_POLICY_1_PARAM(Addition)

DECLARE_LAYER_POLICY_2_PARAM(ArgMinMax)

DECLARE_LAYER_POLICY_2_PARAM(BatchNormalization)

DECLARE_LAYER_POLICY_2_PARAM(BatchToSpaceNd)

DECLARE_LAYER_POLICY_2_PARAM(Comparison)

DECLARE_LAYER_POLICY_2_PARAM(Concat)

DECLARE_LAYER_POLICY_1_PARAM(Constant)

DECLARE_LAYER_POLICY_1_PARAM(ConvertBf16ToFp32)

DECLARE_LAYER_POLICY_1_PARAM(ConvertFp16ToFp32)

DECLARE_LAYER_POLICY_1_PARAM(ConvertFp32ToBf16)

DECLARE_LAYER_POLICY_1_PARAM(ConvertFp32ToFp16)

DECLARE_LAYER_POLICY_2_PARAM(Convolution2d)

DECLARE_LAYER_POLICY_1_PARAM(MemCopy)

DECLARE_LAYER_POLICY_1_PARAM(MemImport)

DECLARE_LAYER_POLICY_1_PARAM(Debug)

DECLARE_LAYER_POLICY_2_PARAM(DepthToSpace)

DECLARE_LAYER_POLICY_2_PARAM(DepthwiseConvolution2d)

DECLARE_LAYER_POLICY_1_PARAM(Dequantize)

DECLARE_LAYER_POLICY_2_PARAM(DetectionPostProcess)

DECLARE_LAYER_POLICY_2_PARAM(ElementwiseUnary)

DECLARE_LAYER_POLICY_2_PARAM(FakeQuantization)

DECLARE_LAYER_POLICY_2_PARAM(Fill)

DECLARE_LAYER_POLICY_1_PARAM(Floor)

DECLARE_LAYER_POLICY_2_PARAM(FullyConnected)

DECLARE_LAYER_POLICY_1_PARAM(Gather)

DECLARE_LAYER_POLICY_CUSTOM_PARAM(Input, armnn::LayerBindingId)

DECLARE_LAYER_POLICY_2_PARAM(InstanceNormalization)

DECLARE_LAYER_POLICY_2_PARAM(L2Normalization)

DECLARE_LAYER_POLICY_2_PARAM(LogSoftmax)

DECLARE_LAYER_POLICY_2_PARAM(Lstm)

DECLARE_LAYER_POLICY_1_PARAM(Maximum)

DECLARE_LAYER_POLICY_2_PARAM(Mean)

DECLARE_LAYER_POLICY_1_PARAM(Merge)

DECLARE_LAYER_POLICY_1_PARAM(Minimum)

DECLARE_LAYER_POLICY_1_PARAM(Multiplication)

DECLARE_LAYER_POLICY_2_PARAM(Normalization)

DECLARE_LAYER_POLICY_CUSTOM_PARAM(Output, armnn::LayerBindingId)

DECLARE_LAYER_POLICY_2_PARAM(Pad)

DECLARE_LAYER_POLICY_1_PARAM(Quantize)

DECLARE_LAYER_POLICY_2_PARAM(Permute)

DECLARE_LAYER_POLICY_2_PARAM(Pooling2d)

DECLARE_LAYER_POLICY_2_PARAM(PreCompiled)

DECLARE_LAYER_POLICY_1_PARAM(Prelu)

DECLARE_LAYER_POLICY_2_PARAM(QLstm)

DECLARE_LAYER_POLICY_1_PARAM(QuantizedLstm)

DECLARE_LAYER_POLICY_1_PARAM(Division)

DECLARE_LAYER_POLICY_2_PARAM(Resize)

DECLARE_LAYER_POLICY_2_PARAM(Reshape)

DECLARE_LAYER_POLICY_2_PARAM(Slice)

DECLARE_LAYER_POLICY_2_PARAM(Softmax)

DECLARE_LAYER_POLICY_2_PARAM(SpaceToBatchNd)

DECLARE_LAYER_POLICY_2_PARAM(SpaceToDepth)

DECLARE_LAYER_POLICY_2_PARAM(Splitter)

DECLARE_LAYER_POLICY_2_PARAM(Stack)

DECLARE_LAYER_POLICY_EXCEPTION_2_PARAM(StandIn)

DECLARE_LAYER_POLICY_2_PARAM(StridedSlice)

DECLARE_LAYER_POLICY_1_PARAM(Subtraction)

DECLARE_LAYER_POLICY_1_PARAM(Switch)

DECLARE_LAYER_POLICY_2_PARAM(Transpose)

DECLARE_LAYER_POLICY_2_PARAM(TransposeConvolution2d)


// Generic implementation to get the number of input slots for a given layer type;
template<armnn::LayerType Type>
unsigned int GetNumInputs(const armnn::Layer& layer)
{
    return layer.GetNumInputSlots();
}

// Generic implementation to get the number of output slots for a given layer type;
template<armnn::LayerType Type>
unsigned int GetNumOutputs(const armnn::Layer& layer)
{
    return layer.GetNumOutputSlots();
}

template<>
unsigned int GetNumInputs<armnn::LayerType::Concat>(const armnn::Layer& layer)
{
    IgnoreUnused(layer);
    return 2;
}

// Tests that the IsLayerSupported() function returns the correct value.
// We determined the correct value by *trying* to create the relevant workload and seeing if it matches what we expect.
// Returns true if expectations are met, otherwise returns false.
template<typename FactoryType, armnn::DataType DataType, armnn::LayerType Type>
bool IsLayerSupportedTest(FactoryType *factory, Tag<Type>)
{
    using LayerPolicy = LayerTypePolicy<Type, DataType>;
    using LayerType = typename LayerPolicy::Type;
    using LayerDesc = typename LayerPolicy::Desc;
    DummyLayer<LayerType, LayerDesc> layer;

    if (LayerPolicy::IsException) //Don't test exceptions to the rule.
    {
        return true;
    }

    unsigned int numIn = GetNumInputs<Type>(*layer.m_Layer);
    unsigned int numOut = GetNumOutputs<Type>(*layer.m_Layer);

    // Make another dummy layer just to make IsLayerSupported have valid inputs.
    DummyLayer<armnn::ConstantLayer, void> previousLayer;
    // Set output of the previous layer to a dummy tensor.
    armnn::TensorInfo output = MakeDummyTensorInfo<DataType>();
    previousLayer.m_Layer->GetOutputSlot(0).SetTensorInfo(output);
    // Connect all outputs of the previous layer to inputs of tested layer.
    for (unsigned int i = 0; i < numIn; i++)
    {
        armnn::IOutputSlot& previousLayerOutputSlot = previousLayer.m_Layer->GetOutputSlot(0);
        armnn::IInputSlot& layerInputSlot = layer.m_Layer->GetInputSlot(i);
        previousLayerOutputSlot.Connect(layerInputSlot);
    }
    // Set outputs of tested layer to a dummy tensor.
    for (unsigned int i = 0; i < numOut; i++)
    {
        layer.m_Layer->GetOutputSlot(0).SetTensorInfo(output);
    }

    std::string layerName = LayerPolicy::NameStr;
    std::string reasonIfUnsupported;
    if (FactoryType::IsLayerSupported(*layer.m_Layer, DataType, reasonIfUnsupported))
    {
        std::string errorMsg = " layer expected support but found none.";
        try
        {
            bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() != nullptr;
            BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg);
            return retVal;
        }
        catch(const armnn::InvalidArgumentException& e)
        {
            IgnoreUnused(e);
            // This is ok since we throw InvalidArgumentException when creating the dummy workload.
            return true;
        }
        catch(const std::exception& e)
        {
            errorMsg = e.what();
            BOOST_TEST_ERROR(layerName << ": " << errorMsg);
            return false;
        }
        catch(...)
        {
            errorMsg = "Unexpected error while testing support for ";
            BOOST_TEST_ERROR(errorMsg << layerName);
            return false;
        }
    }
    else
    {
        std::string errorMsg = "layer expected no support (giving reason: " + reasonIfUnsupported + ") but found some.";
        try
        {
            bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() == nullptr;
            BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg);
            return retVal;
        }
        // These two exceptions are ok: For workloads that are partially supported, attempting to instantiate them
        // using parameters that make IsLayerSupported() return false should throw an
        // InvalidArgumentException or UnimplementedException.
        catch(const armnn::InvalidArgumentException& e)
        {
            IgnoreUnused(e);
            return true;
        }
        catch(const armnn::UnimplementedException& e)
        {
            IgnoreUnused(e);
            return true;
        }
        catch(const std::exception& e)
        {
            errorMsg = e.what();
            BOOST_TEST_ERROR(layerName << ": " << errorMsg);
            return false;
        }
        catch(...)
        {
            errorMsg = "Unexpected error while testing support for ";
            BOOST_TEST_ERROR(errorMsg << layerName);
            return false;
        }
    }
}

// Helper function to compute the next type in the LayerType enum.
constexpr armnn::LayerType NextType(armnn::LayerType type)
{
    return static_cast<armnn::LayerType>(static_cast<int>(type)+1);
}

// Termination function for determining the end of the LayerType enumeration.
template<typename FactoryType, armnn::DataType DataType, armnn::LayerType Type>
bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag<armnn::LayerType::LastLayer>)
{
    return IsLayerSupportedTest<FactoryType, DataType, Type>(factory, Tag<Type>());
}

// Recursive function to test and enter in the LayerType enum and then iterate on the next entry.
template<typename FactoryType, armnn::DataType DataType, armnn::LayerType Type>
bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag<Type>)
{
    bool v = IsLayerSupportedTest<FactoryType, DataType, Type>(factory, Tag<Type>());

    return v &&
    IsLayerSupportedTestsImpl<FactoryType, DataType, NextType(Type)>
        (factory, Tag<NextType(Type)>());
}

// Helper function to pass through to the test framework.
template<typename FactoryType, armnn::DataType DataType>
bool IsLayerSupportedTests(FactoryType *factory)
{
    return IsLayerSupportedTestsImpl<FactoryType, DataType>(factory, Tag<armnn::LayerType::FirstLayer>());
}

template<armnn::LayerType Type>
bool TestLayerTypeMatches()
{
    using LayerPolicy = LayerTypePolicy<Type, armnn::DataType::Float32>;
    using LayerType = typename LayerPolicy::Type;
    using LayerDesc = typename LayerPolicy::Desc;
    DummyLayer<LayerType, LayerDesc> layer;

    std::stringstream ss;
    ss << LayerPolicy::NameStr << " layer type mismatches expected layer type value.";
    bool v = Type == layer.m_Layer->GetType();
    BOOST_CHECK_MESSAGE(v, ss.str());
    return v;
}

template<armnn::LayerType Type>
bool LayerTypeMatchesTestImpl(Tag<armnn::LayerType::LastLayer>)
{
    return TestLayerTypeMatches<Type>();
}

template<armnn::LayerType Type>
bool LayerTypeMatchesTestImpl(Tag<Type>)
{
    return TestLayerTypeMatches<Type>() &&
        LayerTypeMatchesTestImpl<NextType(Type)>(Tag<NextType(Type)>());
}

template<typename FactoryType, typename LayerType, armnn::DataType InputDataType , armnn::DataType OutputDataType>
bool IsConvertLayerSupportedTests(std::string& reasonIfUnsupported)
{
    armnn::Graph graph;
    LayerType* const layer = graph.AddLayer<LayerType>("LayerName");

    armnn::Layer* const input = graph.AddLayer<armnn::InputLayer>(0, "input");
    armnn::Layer* const output = graph.AddLayer<armnn::OutputLayer>(0, "output");

    armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, InputDataType);
    armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, OutputDataType);

    input->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
    input->GetOutputHandler(0).SetTensorInfo(inputTensorInfo);
    layer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
    layer->GetOutputHandler(0).SetTensorInfo(outputTensorInfo);

    bool result = FactoryType::IsLayerSupported(*layer, InputDataType, reasonIfUnsupported);

    return result;
}

template<typename FactoryType, armnn::DataType InputDataType , armnn::DataType OutputDataType>
bool IsMeanLayerSupportedTests(std::string& reasonIfUnsupported)
{
    armnn::Graph graph;
    static const std::vector<unsigned> axes = {1, 0};
    armnn::MeanDescriptor desc(axes, false);

    armnn::Layer* const layer = graph.AddLayer<armnn::MeanLayer>(desc, "LayerName");

    armnn::Layer* const input = graph.AddLayer<armnn::InputLayer>(0, "input");
    armnn::Layer* const output = graph.AddLayer<armnn::OutputLayer>(0, "output");

    armnn::TensorInfo inputTensorInfo({4, 3, 2}, InputDataType);
    armnn::TensorInfo outputTensorInfo({2}, OutputDataType);

    input->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
    input->GetOutputHandler(0).SetTensorInfo(inputTensorInfo);
    layer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
    layer->GetOutputHandler(0).SetTensorInfo(outputTensorInfo);

    bool result = FactoryType::IsLayerSupported(*layer, InputDataType, reasonIfUnsupported);

    return result;
}

// Tests that IsMeanSupported fails when input tensor dimensions
// do not match output tensor dimensions when keepDims == true
template<typename FactoryType, armnn::DataType InputDataType , armnn::DataType OutputDataType>
bool IsMeanLayerNotSupportedTests(std::string& reasonIfUnsupported)
{
    armnn::Graph graph;
    static const std::vector<unsigned> axes = {};
    // Set keepDims == true
    armnn::MeanDescriptor desc(axes, true);

    armnn::Layer* const layer = graph.AddLayer<armnn::MeanLayer>(desc, "LayerName");

    armnn::Layer* const input = graph.AddLayer<armnn::InputLayer>(0, "input");
    armnn::Layer* const output = graph.AddLayer<armnn::OutputLayer>(0, "output");

    // Mismatching number of tensor dimensions
    armnn::TensorInfo inputTensorInfo({1, 1, 1, 1}, InputDataType);
    armnn::TensorInfo outputTensorInfo({1, 1}, OutputDataType);

    input->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
    input->GetOutputHandler(0).SetTensorInfo(inputTensorInfo);
    layer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
    layer->GetOutputHandler(0).SetTensorInfo(outputTensorInfo);

    bool result = FactoryType::IsLayerSupported(*layer, InputDataType, reasonIfUnsupported);

    return result;
}

template<typename FactoryType, armnn::DataType OutputDataType>
bool IsConstantLayerSupportedTests(std::string& reasonIfUnsupported)
{
    armnn::Graph graph;

    armnn::Layer* const layer = graph.AddLayer<armnn::ConstantLayer>("ConstantLayerName");
    armnn::Layer* const output = graph.AddLayer<armnn::OutputLayer>(0, "OutputLayerName");

    armnn::TensorInfo outputTensorInfo({1, 1}, OutputDataType);

    layer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
    layer->GetOutputHandler(0).SetTensorInfo(outputTensorInfo);

    bool result = FactoryType::IsLayerSupported(*layer, OutputDataType, reasonIfUnsupported);

    return result;
}

} //namespace