//
// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#pragma once

#include "TestUtils.hpp"

#include <armnn_delegate.hpp>
#include <DelegateTestInterpreter.hpp>

#include <flatbuffers/flatbuffers.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/version.h>

#include <doctest/doctest.h>

namespace
{

std::vector<char> CreateSliceTfLiteModel(tflite::TensorType tensorType,
                                         const std::vector<int32_t>& inputTensorShape,
                                         const std::vector<int32_t>& beginTensorData,
                                         const std::vector<int32_t>& sizeTensorData,
                                         const std::vector<int32_t>& beginTensorShape,
                                         const std::vector<int32_t>& sizeTensorShape,
                                         const std::vector<int32_t>& outputTensorShape)
{
    using namespace tflite;
    flatbuffers::FlatBufferBuilder flatBufferBuilder;

    flatbuffers::Offset<tflite::Buffer> buffers[5] = {
            CreateBuffer(flatBufferBuilder),
            CreateBuffer(flatBufferBuilder),
            CreateBuffer(flatBufferBuilder,
            flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()),
            sizeof(int32_t) * beginTensorData.size())),
            CreateBuffer(flatBufferBuilder,
            flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(sizeTensorData.data()),
            sizeof(int32_t) * sizeTensorData.size())),
            CreateBuffer(flatBufferBuilder)
    };

    std::array<flatbuffers::Offset<Tensor>, 4> tensors;
    tensors[0] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
                                                                      inputTensorShape.size()),
                              tensorType,
                              1,
                              flatBufferBuilder.CreateString("input"));
    tensors[1] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(beginTensorShape.data(),
                                                                      beginTensorShape.size()),
                              ::tflite::TensorType_INT32,
                              2,
                              flatBufferBuilder.CreateString("begin_tensor"));
    tensors[2] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(),
                                                                      sizeTensorShape.size()),
                              ::tflite::TensorType_INT32,
                              3,
                              flatBufferBuilder.CreateString("size_tensor"));
    tensors[3] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
                                                                      outputTensorShape.size()),
                              tensorType,
                              4,
                              flatBufferBuilder.CreateString("output"));


    // create operator
    tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SliceOptions;
    flatbuffers::Offset<void> operatorBuiltinOptions = CreateSliceOptions(flatBufferBuilder).Union();

    const std::vector<int> operatorInputs{ 0, 1, 2 };
    const std::vector<int> operatorOutputs{ 3 };
    flatbuffers::Offset <Operator> sliceOperator =
        CreateOperator(flatBufferBuilder,
                       0,
                       flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
                       flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
                       operatorBuiltinOptionsType,
                       operatorBuiltinOptions);

    const std::vector<int> subgraphInputs{ 0, 1, 2 };
    const std::vector<int> subgraphOutputs{ 3 };
    flatbuffers::Offset <SubGraph> subgraph =
        CreateSubGraph(flatBufferBuilder,
                       flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
                       flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
                       flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
                       flatBufferBuilder.CreateVector(&sliceOperator, 1));

    flatbuffers::Offset <flatbuffers::String> modelDescription =
        flatBufferBuilder.CreateString("ArmnnDelegate: Slice Operator Model");
    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
                                                                         BuiltinOperator_SLICE);

    flatbuffers::Offset <Model> flatbufferModel =
        CreateModel(flatBufferBuilder,
                    TFLITE_SCHEMA_VERSION,
                    flatBufferBuilder.CreateVector(&operatorCode, 1),
                    flatBufferBuilder.CreateVector(&subgraph, 1),
                    modelDescription,
                    flatBufferBuilder.CreateVector(buffers, 5));

    flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);

    return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
                             flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}

template <typename T>
void SliceTestImpl(std::vector<armnn::BackendId>& backends,
                   std::vector<T>& inputValues,
                   std::vector<T>& expectedOutputValues,
                   std::vector<int32_t>& beginTensorData,
                   std::vector<int32_t>& sizeTensorData,
                   std::vector<int32_t>& inputTensorShape,
                   std::vector<int32_t>& beginTensorShape,
                   std::vector<int32_t>& sizeTensorShape,
                   std::vector<int32_t>& outputTensorShape)
{
    using namespace delegateTestInterpreter;
    std::vector<char> modelBuffer = CreateSliceTfLiteModel(
        ::tflite::TensorType_FLOAT32,
        inputTensorShape,
        beginTensorData,
        sizeTensorData,
        beginTensorShape,
        sizeTensorShape,
        outputTensorShape);

    // Setup interpreter with just TFLite Runtime.
    auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
    CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
    CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
    CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
    std::vector<T>       tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
    std::vector<int32_t> tfLiteOutputShape  = tfLiteInterpreter.GetOutputShape(0);

    // Setup interpreter with Arm NN Delegate applied.
    auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
    CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
    CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
    CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
    std::vector<T>       armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
    std::vector<int32_t> armnnOutputShape  = armnnInterpreter.GetOutputShape(0);

    armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
    armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShape);

    tfLiteInterpreter.Cleanup();
    armnnInterpreter.Cleanup();
} // End of Slice Test

} // anonymous namespace