//
// Copyright © 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> CreateTileTfLiteModel(tflite::BuiltinOperator operatorCode,
                                        tflite::TensorType inputTensorType,
                                        const std::vector<int32_t>& inputTensorShape,
                                        const std::vector<int32_t>& multiplesTensorData,
                                        const std::vector<int32_t>& multiplesTensorShape,
                                        const std::vector<int32_t>& outputTensorShape)
{
    using namespace tflite;
    flatbuffers::FlatBufferBuilder flatBufferBuilder;

    std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
    buffers.push_back(CreateBuffer(flatBufferBuilder));
    buffers.push_back(CreateBuffer(flatBufferBuilder));
    buffers.push_back(CreateBuffer(flatBufferBuilder,
                                   flatBufferBuilder.CreateVector(
                                           reinterpret_cast<const uint8_t*>(multiplesTensorData.data()),
                                           sizeof(int32_t) * multiplesTensorData.size())));
    buffers.push_back(CreateBuffer(flatBufferBuilder));

    std::array<flatbuffers::Offset<Tensor>, 3> tensors;
    tensors[0] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
                                                                      inputTensorShape.size()),
                              inputTensorType,
                              1,
                              flatBufferBuilder.CreateString("input_tensor"));

    tensors[1] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(multiplesTensorShape.data(),
                                                                      multiplesTensorShape.size()),
                              TensorType_INT32,
                              2,
                              flatBufferBuilder.CreateString("axis_input_tensor"));

    tensors[2] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
                                                                      outputTensorShape.size()),
                              inputTensorType,
                              3,
                              flatBufferBuilder.CreateString("output_tensor"));

    // Create Operator
    tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
    flatbuffers::Offset<void> operatorBuiltinOption = 0;

    const std::vector<int> operatorInputs {0, 1};
    const std::vector<int> operatorOutputs {2};

    flatbuffers::Offset<Operator> tileOperator =
            CreateOperator(flatBufferBuilder,
                           0,
                           flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
                           flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
                           operatorBuiltinOptionsType,
                           operatorBuiltinOption);

    const std::vector<int> subgraphInputs{0, 1};
    const std::vector<int> subgraphOutputs{2};
    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(&tileOperator, 1));

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

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

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

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

void TileFP32TestImpl(tflite::BuiltinOperator operatorCode,
                      std::vector<armnn::BackendId>& backends,
                      std::vector<float>& inputValues,
                      std::vector<int32_t> inputShape,
                      std::vector<int32_t> multiplesValues,
                      std::vector<int32_t> multiplesShapes,
                      std::vector<float>& expectedOutputValues,
                      std::vector<int32_t> expectedOutputShape)
{
    using namespace delegateTestInterpreter;

    std::vector<char> modelBuffer = CreateTileTfLiteModel(operatorCode,
                                                          ::tflite::TensorType::TensorType_FLOAT32,
                                                          inputShape,
                                                          multiplesValues,
                                                          multiplesShapes,
                                                          expectedOutputShape);

    // Setup interpreter with just TFLite Runtime.
    auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
    CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
    CHECK(tfLiteInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk);
    CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(multiplesValues, 1) == kTfLiteOk);
    CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
    std::vector<float>   tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(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<float>(inputValues, 0) == kTfLiteOk);
    CHECK(armnnInterpreter.FillInputTensor<int32_t>(multiplesValues, 1) == kTfLiteOk);
    CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
    std::vector<float>   armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0);
    std::vector<int32_t> armnnOutputShape  = armnnInterpreter.GetOutputShape(0);

    armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
    armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape);

    tfLiteInterpreter.Cleanup();
    armnnInterpreter.Cleanup();
}

} // anonymous namespace