//
//  DepthwiseConv2DTflite.cpp
//  MNNConverter
//
//  Created by MNN on 2019/01/31.
//  Copyright © 2018, Alibaba Group Holding Limited
//

#include <stdio.h>

#include "TfliteUtils.hpp"
#include "liteOpConverter.hpp"
#include "core/IDSTEncoder.hpp"

DECLARE_OP_COVERTER(DepthwiseConv2DTflite);

MNN::OpType DepthwiseConv2DTflite::opType(int quantizedModel) {
    if (quantizedModel)
        return MNN::OpType_QuantizedDepthwiseConv2D;
    return MNN::OpType_ConvolutionDepthwise;
}

MNN::OpParameter DepthwiseConv2DTflite::type(int quantizedModel) {
    if (quantizedModel)
        return MNN::OpParameter_TfQuantizedConv2D;
    return MNN::OpParameter_Convolution2D;
}
static void _writeCommon(MNN::OpT* dstOp, Convolution2DCommonT* common, tflite::OperatorT* tfliteOp, int ci, int kw, int kh) {
    common->relu       = false;
    common->relu6      = false;
    const auto& tfliteConvOption = tfliteOp->builtin_options.AsDepthwiseConv2DOptions();
    auto acticationFun = tfliteConvOption->fused_activation_function;
    if (acticationFun == tflite::ActivationFunctionType_RELU) {
        common->relu = true;
    } else if (acticationFun == tflite::ActivationFunctionType_RELU6) {
        common->relu6 = true;
    } else if (acticationFun > tflite::ActivationFunctionType_NONE) {
        DLOG(ERROR) << "MNN Convolution do not Support fused_activation_function: " << acticationFun;
    }

    common->group       = ci;
    common->outputCount = ci;
    common->inputCount  = ci;
    common->kernelX     = kw;
    common->kernelY     = kh;
    common->dilateX     = tfliteConvOption->dilation_w_factor;
    common->dilateY     = tfliteConvOption->dilation_h_factor;
    common->strideX     = tfliteConvOption->stride_w;
    common->strideY     = tfliteConvOption->stride_h;
    common->padMode     = MNN::PadMode_SAME;
    if (tfliteConvOption->depth_multiplier > 1) {
        if (ci == tfliteConvOption->depth_multiplier) {
            // Special case, turn to convolution
            dstOp->type = MNN::OpType_Convolution;
            common->outputCount = tfliteConvOption->depth_multiplier;
            common->inputCount = 1;
            common->group = 1;
        } else {
            DLOG(ERROR) << "MNN don't support tflite's depth_multiplier, please turn to pb or onnx";
        }
    }

    if (tfliteConvOption->padding == tflite::Padding_VALID) {
        common->padMode = MNN::PadMode_VALID;
    }
}

void DepthwiseConv2DTflite::run(MNN::OpT* dstOp, const std::unique_ptr<tflite::OperatorT>& tfliteOp,
                                const std::vector<std::unique_ptr<tflite::TensorT>>& tfliteTensors,
                                const std::vector<std::unique_ptr<tflite::BufferT>>& tfliteModelBuffer,
                                const std::vector<std::unique_ptr<tflite::OperatorCodeT>>& tfliteOpSet,
                                int quantizedModel) {
    // 3|2 inputs: input tensor, weight, (bias)
    const int inputSize = tfliteOp->inputs.size();
    DCHECK(inputSize == 2 || inputSize == 3) << "tflite DepthiwiseConv2D input ERROR! ";
    // weight index
    const int weightIndex    = tfliteOp->inputs[1];
    const auto& weightTensor = tfliteTensors[weightIndex];
    // co kh kw ci
    const auto& weightShape = weightTensor->shape;
    DCHECK(weightShape.size() == 4) << "Conv2D weight ERROR!";
    // const int co = weightShape[0];
    const int kh                 = weightShape[1];
    const int kw                 = weightShape[2];
    const int ci                 = weightShape[3];
    const int weightSize         = kh * kw * ci;
    const auto& tfliteConvOption = tfliteOp->builtin_options.AsDepthwiseConv2DOptions();
    if (weightTensor->type == tflite::TensorType_INT8) {
        quantizedModel = 2;
        dstOp->type = MNN::OpType_ConvolutionDepthwise;
        dstOp->main.type = MNN::OpParameter_Convolution2D;
    } else if (weightTensor->type == tflite::TensorType_UINT8) {
        quantizedModel = 1;
        dstOp->type = MNN::OpType_DepthwiseConvInt8;
        dstOp->main.type = MNN::OpParameter_TfQuantizedConv2D;
    } else {
        MNN_ASSERT(weightTensor->type == tflite::TensorType_FLOAT32);
        quantizedModel = 0;
        dstOp->type = MNN::OpType_ConvolutionDepthwise;
        dstOp->main.type = MNN::OpParameter_Convolution2D;
    }

    std::unique_ptr<MNN::Convolution2DCommonT> dstCommon(new MNN::Convolution2DCommonT);
    _writeCommon(dstOp, dstCommon.get(), tfliteOp.get(), ci, kw, kh);
    if (quantizedModel) {
        if (weightTensor->type == tflite::TensorType_INT8) {
            dstOp->type = OpType_ConvolutionDepthwise;
            dstOp->main.type = OpParameter_Convolution2D;
            auto depthwiseConv2dParamFloat = new MNN::Convolution2DT;
            depthwiseConv2dParamFloat->common = std::move(dstCommon);
            dstOp->main.value = depthwiseConv2dParamFloat;
            // Bias Turn to float
            auto outputCount = depthwiseConv2dParamFloat->common->outputCount;
            depthwiseConv2dParamFloat->bias.resize(ci);
            ::memset(depthwiseConv2dParamFloat->bias.data(), 0, outputCount * sizeof(float));
            if (inputSize == 3) {
                const auto& biasTensor = tfliteTensors[tfliteOp->inputs[2]];
                if (biasTensor->quantization->scale.size() == 1) {
                    auto scale = biasTensor->quantization->scale[0];
                    auto zero = biasTensor->quantization->zero_point[0];;
                    const auto& biasData = tfliteModelBuffer[biasTensor->buffer]->data;
                    auto biasDataPtr = biasData.data();
                    const int32_t* realBiasDataPtr = (int32_t*)biasDataPtr;
                    for (int i=0; i<outputCount; ++i) {
                        depthwiseConv2dParamFloat->bias[i] = (float)(realBiasDataPtr[i] - zero) * scale;
                    }
                } else {
                    const auto& biasData = tfliteModelBuffer[biasTensor->buffer]->data;
                    auto biasDataPtr = biasData.data();
                    const int32_t* realBiasDataPtr = (int32_t*)biasDataPtr;
                    for (int i=0; i<outputCount; ++i) {
                        depthwiseConv2dParamFloat->bias[i] = (float)(realBiasDataPtr[i] - biasTensor->quantization->zero_point[i]) * biasTensor->quantization->scale[i];
                    }
                }
            }
            // Weight
            // Transpose first
            std::vector<int8_t> transposeWeight(kw * kh * ci);
            const auto& weightData = tfliteModelBuffer[weightTensor->buffer]->data;
            auto weightDataPtr = (int8_t*)weightData.data();

            for (int i=0; i<ci; ++i) {
                for (int j=0; j<kw*kh; ++j) {
                    transposeWeight[i*kw*kh+j] = weightDataPtr[i+j*ci];
                }
            }
            auto quan = IDSTEncoder::encode(nullptr, weightTensor->quantization->scale, kw * kh, ci, false, transposeWeight.data(), -128);
            depthwiseConv2dParamFloat->quanParameter = std::move(quan);
        } else {
            // For old uint8 model
            auto depthwiseConv2dParamQuan         = new MNN::TfQuantizedConv2DT;
            depthwiseConv2dParamQuan->modelFormat = MNN::ModeFormat_TFLITE;
            depthwiseConv2dParamQuan->common = std::move(dstCommon);

            // filterOffset
            depthwiseConv2dParamQuan->filterQuantizedParam =
                std::unique_ptr<MNN::QuantizedParamT>(new MNN::QuantizedParamT);
            depthwiseConv2dParamQuan->filterQuantizedParam->zeroPoint = weightTensor->quantization->zero_point[0];
            depthwiseConv2dParamQuan->filterQuantizedParam->scale     = weightTensor->quantization->scale[0];

            // input
            const int inputIndex                          = tfliteOp->inputs[0];
            const auto& inputTensor                       = tfliteTensors[inputIndex];
            depthwiseConv2dParamQuan->inputQuantizedParam = std::unique_ptr<MNN::QuantizedParamT>(new MNN::QuantizedParamT);
            depthwiseConv2dParamQuan->inputQuantizedParam->zeroPoint = inputTensor->quantization->zero_point[0];
            depthwiseConv2dParamQuan->inputQuantizedParam->scale     = inputTensor->quantization->scale[0];

            // output
            const int outputIndex    = tfliteOp->outputs[0];
            const auto& outputTensor = tfliteTensors[outputIndex];
            depthwiseConv2dParamQuan->outputQuantizedParam =
                std::unique_ptr<MNN::QuantizedParamT>(new MNN::QuantizedParamT);
            depthwiseConv2dParamQuan->outputQuantizedParam->zeroPoint = outputTensor->quantization->zero_point[0];
            depthwiseConv2dParamQuan->outputQuantizedParam->scale     = outputTensor->quantization->scale[0];


            depthwiseConv2dParamQuan->depthMultiplier = tfliteConvOption->depth_multiplier;

            // weight
            DCHECK(weightTensor->type == tflite::TensorType_UINT8) << "Data type ERROR";
            depthwiseConv2dParamQuan->weight   = tfliteModelBuffer[weightTensor->buffer]->data;
            depthwiseConv2dParamQuan->biasflag = inputSize == 3;
            // have bias
            if (inputSize == 3) {
                const auto& biasTensor = tfliteTensors[tfliteOp->inputs[2]];
                DCHECK(biasTensor->type == tflite::TensorType_INT32) << "Bias Type ERROR";

                const auto& biasData = tfliteModelBuffer[biasTensor->buffer]->data;
                depthwiseConv2dParamQuan->biasQuantizedParam =
                    std::unique_ptr<MNN::QuantizedParamT>(new MNN::QuantizedParamT);
                depthwiseConv2dParamQuan->biasQuantizedParam->zeroPoint = biasTensor->quantization->zero_point[0];
                depthwiseConv2dParamQuan->biasQuantizedParam->scale     = biasTensor->quantization->scale[0];

                auto shape = biasTensor->shape;

                DCHECK(biasData.size() / 4 == ci) << "Bias Data ERROR";
                auto biasDataPtr               = biasData.data();
                const int32_t* realBiasDataPtr = (int32_t*)biasDataPtr;
                std::vector<int32_t> biasInt32Vec(realBiasDataPtr, realBiasDataPtr + ci);
                depthwiseConv2dParamQuan->bias = biasInt32Vec;
            }
            depthwiseConv2dParamQuan->activationType =
                static_cast<MNN::FusedActivation>(tfliteConvOption->fused_activation_function);
            dstOp->main.value = depthwiseConv2dParamQuan;
        }
    } else {
        auto depthwiseConv2dParamFloat = new MNN::Convolution2DT;
        std::vector<float> weightData;
        weightData.resize(weightSize);
        auto originalWeightPtr = reinterpret_cast<const float*>(tfliteModelBuffer[weightTensor->buffer]->data.data());
        
        if(originalWeightPtr){
            convertDataFormatTflite(originalWeightPtr, weightData.data(), kh, kw, ci, 1);
            depthwiseConv2dParamFloat->weight = weightData;
        }
        // bias
        if (inputSize == 3) {
            const auto& biasTensor = tfliteTensors[tfliteOp->inputs[2]];
            auto originalBiasPtr = reinterpret_cast<const float*>(tfliteModelBuffer[biasTensor->buffer]->data.data());
            if (originalBiasPtr) {
                std::vector<float> biasData(ci, 0.0f);
                ::memcpy(biasData.data(), originalBiasPtr, sizeof(float) * ci);
                depthwiseConv2dParamFloat->bias   = biasData;
            }
        }
        depthwiseConv2dParamFloat->common = std::move(dstCommon);
        dstOp->main.value = depthwiseConv2dParamFloat;
    }
    
    // set input output index
    {
        auto originalWeightPtr = reinterpret_cast<const float*>(tfliteModelBuffer[weightTensor->buffer]->data.data());
        if(originalWeightPtr){
            dstOp->inputIndexes.resize(1);
            dstOp->outputIndexes.resize(1);
            dstOp->inputIndexes[0]  = tfliteOp->inputs[0];
            dstOp->outputIndexes[0] = tfliteOp->outputs[0];
        } else if (inputSize == 3 && tfliteModelBuffer[tfliteTensors[tfliteOp->inputs[2]]->buffer]->data.data() != nullptr) {
            dstOp->inputIndexes.resize(2);
            dstOp->outputIndexes.resize(1);
            dstOp->inputIndexes[0]  = tfliteOp->inputs[0];
            dstOp->inputIndexes[1]  = tfliteOp->inputs[1];
            dstOp->outputIndexes[0] = tfliteOp->outputs[0];
        } else {
            dstOp->inputIndexes.resize(inputSize);
            dstOp->outputIndexes.resize(1);
            dstOp->outputIndexes[0] = tfliteOp->outputs[0];
            for(int i = 0; i < inputSize; ++i){
                dstOp->inputIndexes[i] = tfliteOp->inputs[i];
            }
        }
    }
}

using namespace tflite;
REGISTER_CONVERTER(DepthwiseConv2DTflite, BuiltinOperator_DEPTHWISE_CONV_2D);