//
// Copyright © 2020, 2023-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#pragma once

#include "TestUtils.hpp"

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

#include <tensorflow/lite/version.h>

namespace
{

std::vector<char> CreateLogicalBinaryTfLiteModel(tflite::BuiltinOperator logicalOperatorCode,
                                                 tflite::TensorType tensorType,
                                                 const std::vector <int32_t>& input0TensorShape,
                                                 const std::vector <int32_t>& input1TensorShape,
                                                 const std::vector <int32_t>& outputTensorShape,
                                                 float quantScale = 1.0f,
                                                 int quantOffset  = 0)
{
    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));
    buffers.push_back(CreateBuffer(flatBufferBuilder));

    auto quantizationParameters =
        CreateQuantizationParameters(flatBufferBuilder,
                                     0,
                                     0,
                                     flatBufferBuilder.CreateVector<float>({ quantScale }),
                                     flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));


    std::array<flatbuffers::Offset<Tensor>, 3> tensors;
    tensors[0] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(),
                                                                      input0TensorShape.size()),
                              tensorType,
                              1,
                              flatBufferBuilder.CreateString("input_0"),
                              quantizationParameters);
    tensors[1] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
                                                                      input1TensorShape.size()),
                              tensorType,
                              2,
                              flatBufferBuilder.CreateString("input_1"),
                              quantizationParameters);
    tensors[2] = CreateTensor(flatBufferBuilder,
                              flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
                                                                      outputTensorShape.size()),
                              tensorType,
                              3,
                              flatBufferBuilder.CreateString("output"),
                              quantizationParameters);

    // create operator
    tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
    flatbuffers::Offset<void> operatorBuiltinOptions = 0;
    switch (logicalOperatorCode)
    {
        case BuiltinOperator_LOGICAL_AND:
        {
            operatorBuiltinOptionsType = BuiltinOptions_LogicalAndOptions;
            operatorBuiltinOptions = CreateLogicalAndOptions(flatBufferBuilder).Union();
            break;
        }
        case BuiltinOperator_LOGICAL_OR:
        {
            operatorBuiltinOptionsType = BuiltinOptions_LogicalOrOptions;
            operatorBuiltinOptions = CreateLogicalOrOptions(flatBufferBuilder).Union();
            break;
        }
        default:
            break;
    }
    const std::vector<int32_t> operatorInputs{ {0, 1} };
    const std::vector<int32_t> operatorOutputs{ 2 };
    flatbuffers::Offset <Operator> logicalBinaryOperator =
        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} };
    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(&logicalBinaryOperator, 1));

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

    flatbuffers::Offset <Model> flatbufferModel =
        CreateModel(flatBufferBuilder,
                    TFLITE_SCHEMA_VERSION,
                    flatBufferBuilder.CreateVector(&operatorCode, 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 LogicalBinaryTest(tflite::BuiltinOperator logicalOperatorCode,
                       tflite::TensorType tensorType,
                       std::vector<int32_t>& input0Shape,
                       std::vector<int32_t>& input1Shape,
                       std::vector<int32_t>& expectedOutputShape,
                       std::vector<bool>& input0Values,
                       std::vector<bool>& input1Values,
                       std::vector<bool>& expectedOutputValues,
                       float quantScale = 1.0f,
                       int quantOffset  = 0,
                       const std::vector<armnn::BackendId>& backends = {})
{
    using namespace delegateTestInterpreter;
    std::vector<char> modelBuffer = CreateLogicalBinaryTfLiteModel(logicalOperatorCode,
                                                                   tensorType,
                                                                   input0Shape,
                                                                   input1Shape,
                                                                   expectedOutputShape,
                                                                   quantScale,
                                                                   quantOffset);

    // Setup interpreter with just TFLite Runtime.
    auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
    CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
    CHECK(tfLiteInterpreter.FillInputTensor(input0Values, 0) == kTfLiteOk);
    CHECK(tfLiteInterpreter.FillInputTensor(input1Values, 1) == kTfLiteOk);
    CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
    std::vector<bool>    tfLiteOutputValues = tfLiteInterpreter.GetOutputResult(0);
    std::vector<int32_t> tfLiteOutputShape  = tfLiteInterpreter.GetOutputShape(0);

    // Setup interpreter with Arm NN Delegate applied.
    auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends));
    CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
    CHECK(armnnInterpreter.FillInputTensor(input0Values, 0) == kTfLiteOk);
    CHECK(armnnInterpreter.FillInputTensor(input1Values, 1) == kTfLiteOk);
    CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
    std::vector<bool>    armnnOutputValues = armnnInterpreter.GetOutputResult(0);
    std::vector<int32_t> armnnOutputShape  = armnnInterpreter.GetOutputShape(0);

    armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape);

    armnnDelegate::CompareData(expectedOutputValues, armnnOutputValues, expectedOutputValues.size());
    armnnDelegate::CompareData(expectedOutputValues, tfLiteOutputValues, expectedOutputValues.size());
    armnnDelegate::CompareData(tfLiteOutputValues, armnnOutputValues, expectedOutputValues.size());

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

} // anonymous namespace