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#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H
#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_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/attributes/ResizeAttributes.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"

#include "tests/CL/CLAccessor.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
#include "tests/SimpleTensor.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/Scale.h"
#include "tests/validation/Validation.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 DynamicFusionResizeGenericValidationFixture : public framework::Fixture
{
public:
    void setup(TensorShape         shape,
               DataType            data_type,
               QuantizationInfo    quantization_info,
               DataLayout          data_layout,
               InterpolationPolicy interpolation_policy,
               SamplingPolicy      sampling_policy,
               bool                align_corners,
               QuantizationInfo    output_quantization_info)
    {
        _shape                    = shape;
        _interpolation_policy     = interpolation_policy;
        _sampling_policy          = sampling_policy;
        _data_type                = data_type;
        _input_quantization_info  = quantization_info;
        _output_quantization_info = output_quantization_info;
        _align_corners            = align_corners;
        _data_layout              = data_layout;

        ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion resize supports only NHWC layout

        generate_scale(shape);

        std::mt19937                            generator(library->seed());
        std::uniform_int_distribution<uint32_t> distribution_u8(0, 255);

        _target    = compute_target(shape);
        _reference = compute_reference(shape);
    }

protected:
    void generate_scale(const TensorShape &shape)
    {
        static constexpr float _min_scale{0.25f};
        static constexpr float _max_scale{3.f};

        constexpr float max_width{8192.0f};
        constexpr float max_height{6384.0f};
        constexpr float min_width{1.f};
        constexpr float min_height{1.f};

        std::mt19937                          generator(library->seed());
        std::uniform_real_distribution<float> distribution_float(_min_scale, _max_scale);

        auto generate = [&](size_t input_size, float min_output, float max_output) -> int
        {
            const float generated_scale = distribution_float(generator);
            const int   output_size     = static_cast<int>(
                utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output));
            return output_size;
        };

        // Input shape is always given in NCHW layout. NHWC is dealt by permute in compute_target()
        const int idx_width  = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
        const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);

        _output_width  = generate(shape[idx_width], min_width, max_width);
        _output_height = generate(shape[idx_height], min_height, max_height);
    }

    template <typename U>
    void fill(U &&tensor)
    {
        if (tensor.data_type() == DataType::F32)
        {
            std::uniform_real_distribution<float> distribution(-5.0f, 5.0f);
            library->fill(tensor, distribution, 0);
        }
        else if (tensor.data_type() == DataType::F16)
        {
            arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-5.0f, 5.0f};
            library->fill(tensor, distribution, 0);
        }
        else if (is_data_type_quantized(tensor.data_type()))
        {
            std::uniform_int_distribution<> distribution(0, 100);
            library->fill(tensor, distribution, 0);
        }
        else
        {
            library->fill_tensor_uniform(tensor, 0);
        }
    }

    TensorType compute_target(TensorShape shape)
    {
        // Our test shapes are assumed in NCHW data layout, thus the permutation
        permute(shape, PermutationVector(2U, 0U, 1U));

        // Create a new workload sketch
        CLCompileContext   cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
        GpuWorkloadContext context        = GpuWorkloadContext{&cl_compile_ctx};
        GpuWorkloadSketch  sketch{&context};

        // Create sketch tensors
        ITensorInfo *src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type, _data_layout));
        src_info->set_quantization_info(_input_quantization_info);
        ITensorInfo *dst_info = context.create_tensor_info();

        ResizeAttributes attributes;
        attributes.align_corners(_align_corners)
            .sampling_policy(_sampling_policy)
            .interpolation_policy(_interpolation_policy)
            .output_width(_output_width)
            .output_height(_output_height);

        ITensorInfo *scale_result_info = FunctionType::create_op(sketch, src_info, attributes);
        GpuOutput::create_op(sketch, scale_result_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_src{};
        TensorType t_dst{};

        // Initialize user tensors
        t_src.allocator()->init(*src_info);
        t_dst.allocator()->init(*dst_info);

        // Allocate and fill user tensors
        t_src.allocator()->allocate();
        t_dst.allocator()->allocate();

        fill(AccessorType(t_src));

        // Run runtime
        runtime.run({&t_src, &t_dst});

        return t_dst;
    }

    SimpleTensor<T> compute_reference(const TensorShape &shape)
    {
        // Create reference
        SimpleTensor<T> src{shape, _data_type, 1, _input_quantization_info};

        // Reference code is NCHW, so the input shapes are NCHW
        const int idx_width  = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
        const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);

        const float scale_x = static_cast<float>(_output_width) / shape[idx_width];
        const float scale_y = static_cast<float>(_output_height) / shape[idx_height];

        // Fill reference
        fill(src);

        return reference::scale<T>(src, scale_x, scale_y, _interpolation_policy, BorderMode::REPLICATE,
                                   static_cast<T>(0), _sampling_policy, /* ceil_policy_scale */ false, _align_corners,
                                   _output_quantization_info);
    }

    TensorType          _target{};
    SimpleTensor<T>     _reference{};
    TensorShape         _shape{};
    InterpolationPolicy _interpolation_policy{};
    SamplingPolicy      _sampling_policy{};
    DataType            _data_type{};
    DataLayout          _data_layout{};
    QuantizationInfo    _input_quantization_info{};
    QuantizationInfo    _output_quantization_info{};
    bool                _align_corners{false};
    int                 _output_width{0};
    int                 _output_height{0};
};

template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionResizeValidationFixture
    : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
    void setup(TensorShape         shape,
               DataType            data_type,
               DataLayout          data_layout,
               InterpolationPolicy policy,
               SamplingPolicy      sampling_policy,
               bool                align_corners)
    {
        DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
            shape, data_type, QuantizationInfo(), data_layout, policy, sampling_policy, align_corners,
            QuantizationInfo());
    }
};

template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
class DynamicFusionResizeQuantizedValidationFixture
    : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
    void setup(TensorShape         shape,
               DataType            data_type,
               QuantizationInfo    quantization_info,
               DataLayout          data_layout,
               InterpolationPolicy policy,
               SamplingPolicy      sampling_policy,
               bool                align_corners)
    {
        DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
            shape, data_type, quantization_info, data_layout, policy, sampling_policy, align_corners,
            quantization_info);
    }
};

} // namespace validation
} // namespace test
} // namespace arm_compute

#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H