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 * Copyright (c) 2022-2024 Arm Limited.
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 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
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#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_ELEMENTWISEBINARYFIXTURE_H
#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_ELEMENTWISEBINARYFIXTURE_H

#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"

#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
#include "tests/validation/reference/ElementwiseOperations.h"

using namespace arm_compute::experimental::dynamic_fusion;

namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionGpuElementwiseBinaryValidationGenericFixture : public framework::Fixture
{
public:
    void setup(ArithmeticOperation ref_op,
               const TensorShape  &shape0,
               const TensorShape  &shape1,
               const TensorShape  &shape2,
               DataType            data_type,
               bool                is_inplace,
               bool                fuse_two_ops = false)
    {
        _ref_op     = ref_op;
        _is_inplace = is_inplace;
        _data_type  = data_type;
        _fuse       = fuse_two_ops;
        ARM_COMPUTE_ERROR_ON_MSG(_fuse && shape2.total_size() == 0, "No shape2 provided for fusion of two ops.");
        ARM_COMPUTE_ERROR_ON_MSG(_fuse && _is_inplace, "In place for fusing case not supported yet.");
        _target    = compute_target(shape0, shape1, shape2);
        _reference = compute_reference(shape0, shape1, shape2);
    }

protected:
    template <typename U>
    void fill(U &&tensor, int i)
    {
        if (is_data_type_float(tensor.data_type()))
        {
            switch (_ref_op)
            {
                case ArithmeticOperation::DIV:
                    library->fill_tensor_uniform_ranged(tensor, i, {std::pair<float, float>(-0.001f, 0.001f)});
                    break;
                case ArithmeticOperation::POWER:
                    library->fill_tensor_uniform(tensor, i, 0.0f, 5.0f);
                    break;
                default:
                    library->fill_tensor_uniform(tensor, i);
            }
        }
        else if (tensor.data_type() == DataType::S32)
        {
            switch (_ref_op)
            {
                case ArithmeticOperation::DIV:
                    library->fill_tensor_uniform_ranged(tensor, i, {std::pair<int32_t, int32_t>(-1U, 1U)});
                    break;
                default:
                    library->fill_tensor_uniform(tensor, i);
            }
        }
        else
        {
            library->fill_tensor_uniform(tensor, i);
        }
    }

    TensorType compute_target(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2)
    {
        // Create a new workload sketch
        auto              cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
        auto              context        = GpuWorkloadContext{&cl_compile_ctx};
        GpuWorkloadSketch sketch{&context};

        // Fuse first element wise binary Op
        ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(shape0, 1, _data_type));
        ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(shape1, 1, _data_type));
        ITensorInfo *dst_info = context.create_tensor_info();

        ITensorInfo *rhs_info_fuse = nullptr;

        ITensorInfo *ans_info = FunctionType::create_op(sketch, lhs_info, rhs_info);

        if (_fuse)
        {
            rhs_info_fuse          = context.create_tensor_info(TensorInfo(shape2, 1, _data_type));
            ITensorInfo *ans2_info = FunctionType::create_op(sketch, ans_info, rhs_info_fuse);
            GpuOutput::create_op(sketch, ans2_info, dst_info);
        }
        else
        {
            GpuOutput::create_op(sketch, ans_info, dst_info);
        }

        // Configure runtime
        ClWorkloadRuntime runtime;
        runtime.configure(sketch);

        // (Important) Allocate auxiliary tensor memory if there are any
        for (auto &data : runtime.get_auxiliary_tensors())
        {
            CLTensor     *tensor      = std::get<0>(data);
            TensorInfo    info        = std::get<1>(data);
            AuxMemoryInfo aux_mem_req = std::get<2>(data);
            tensor->allocator()->init(info, aux_mem_req.alignment);
            tensor->allocator()->allocate(); // Use ACL allocated memory
        }

        // Construct user tensors
        TensorType t_lhs{};
        TensorType t_rhs{};
        TensorType t_rhs_fuse{};
        TensorType t_dst{};

        // Initialize user tensors
        t_lhs.allocator()->init(*lhs_info);
        t_rhs.allocator()->init(*rhs_info);
        t_dst.allocator()->init(*dst_info);
        if (_fuse)
        {
            t_rhs_fuse.allocator()->init(*rhs_info_fuse);
        }

        // Allocate and fill user tensors
        // Instead of using ACL allocator, the user can choose to import memory into the tensors
        t_lhs.allocator()->allocate();
        t_rhs.allocator()->allocate();
        t_dst.allocator()->allocate();
        if (_fuse)
        {
            t_rhs_fuse.allocator()->allocate();
        }

        fill(AccessorType(t_lhs), 0);
        fill(AccessorType(t_rhs), 1);
        if (_fuse)
        {
            fill(AccessorType(t_rhs_fuse), 2);
        }

        // Run runtime
        if (_fuse)
        {
            runtime.run({&t_lhs, &t_rhs, &t_rhs_fuse, &t_dst});
        }
        else
        {
            runtime.run({&t_lhs, &t_rhs, &t_dst});
        }

        return t_dst;
    }

    SimpleTensor<T> compute_reference(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2)
    {
        const TensorShape out_shape      = TensorShape::broadcast_shape(shape0, shape1);
        const TensorShape out_shape_fuse = TensorShape::broadcast_shape(out_shape, shape1);

        // Create reference
        SimpleTensor<T> ref_lhs{shape0, _data_type, 1, QuantizationInfo()};
        SimpleTensor<T> ref_rhs{shape1, _data_type, 1, QuantizationInfo()};
        SimpleTensor<T> ref_rhs_fuse{shape2, _data_type, 1, QuantizationInfo()};
        SimpleTensor<T> ref_dst{out_shape, _data_type, 1, QuantizationInfo()};
        SimpleTensor<T> ref_dst_fuse{out_shape_fuse, _data_type, 1, QuantizationInfo()};

        // Fill reference
        fill(ref_lhs, 0);
        fill(ref_rhs, 1);

        reference::arithmetic_operation<T>(_ref_op, ref_lhs, ref_rhs, ref_dst, ConvertPolicy::WRAP);
        if (_fuse)
        {
            fill(ref_rhs_fuse, 2);
            reference::arithmetic_operation<T>(_ref_op, ref_dst, ref_rhs_fuse, ref_dst_fuse, ConvertPolicy::WRAP);
        }
        SimpleTensor<T> *ret = _fuse ? &ref_dst_fuse : &ref_dst;
        return *ret;
    }

    ArithmeticOperation _ref_op{ArithmeticOperation::ADD};
    TensorType          _target{};
    SimpleTensor<T>     _reference{};
    DataType            _data_type{};
    DataLayout          _data_layout{};
    bool                _is_inplace{false};
    bool                _fuse{false};
};

template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionGpuElementwiseBinaryOneOpValidationFixture
    : public DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
    void setup(ArithmeticOperation ref_op, const TensorShape &shape0, DataType data_type, bool is_inplace)
    {
        DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
            ref_op, shape0, shape0, TensorShape(), data_type, is_inplace);
    }
};

template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionGpuElementwiseBinaryBroadcastOneOpValidationFixture
    : public DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
    void setup(ArithmeticOperation ref_op,
               const TensorShape  &shape0,
               const TensorShape  &shape1,
               DataType            data_type,
               bool                is_inplace)
    {
        DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
            ref_op, shape0, shape1, TensorShape(), data_type, is_inplace);
    }
};

template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionGpuElementwiseBinaryTwoOpsValidationFixture
    : public DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
    void setup(ArithmeticOperation ref_op,
               const TensorShape  &shape0,
               const TensorShape  &shape1,
               const TensorShape  &shape2,
               DataType            data_type,
               bool                is_inplace,
               bool                fuse_two_ops)
    {
        DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
            ref_op, shape0, shape1, shape2, data_type, is_inplace, fuse_two_ops);
    }
};

} // namespace validation
} // namespace test
} // namespace arm_compute
#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_ELEMENTWISEBINARYFIXTURE_H