//
//  TensorUtils.cpp
//  MNN
//
//  Created by MNN on 2018/08/11.
//  Copyright © 2018, Alibaba Group Holding Limited
//

#include "core/TensorUtils.hpp"
#include <float.h>
#include <math.h>
#include <stdio.h>
#include <cmath>
#include <cstring>
#include "core/Backend.hpp"
#include "core/Macro.h"
namespace MNN {
Tensor::InsideDescribe* TensorUtils::getDescribe(const Tensor* tensor) {
    return tensor->mDescribe;
}
bool TensorUtils::regionIsFull(Tensor* input) {
    auto des = TensorUtils::getDescribe(input);
    if (des->memoryType != Tensor::InsideDescribe::MEMORY_VIRTUAL) {
        return true;
    }
    int size = 1;
    for (int i = 0; i < input->dimensions(); ++i) {
        size *= input->length(i);
    }
    int regionSize = 0;
    for (auto& region : des->regions) {
        regionSize += region.size[1] * region.size[0] * region.size[2];
    }
    return regionSize == size;
}

Tensor::InsideDescribe::Region TensorUtils::makeFullSlice(Tensor* input) {
    Tensor::InsideDescribe::Region totalSlice;
    totalSlice.src.offset = 0;
    totalSlice.dst.offset = 0;
    totalSlice.origin     = input;
    for (int i = 0; i < input->dimensions(); ++i) {
        totalSlice.size[2] *= input->length(i);
    }
    totalSlice.dst.stride[1] = totalSlice.size[2];
    totalSlice.dst.stride[0] = totalSlice.size[2];
    totalSlice.src.stride[1] = totalSlice.size[2];
    totalSlice.src.stride[0] = totalSlice.size[2];
    return totalSlice;
}
bool TensorUtils::reshapeSlice(Tensor::InsideDescribe::Region& slice, int outside, int inside, int axis) {
    if (slice.size[1] == 1 && slice.size[0] == 1 && slice.size[2] == outside * inside * axis) {
        slice.size[0]       = outside;
        slice.size[2]       = inside;
        slice.size[1]       = axis;
        slice.dst.stride[0] = inside * axis;
        slice.dst.stride[1] = inside;

        auto originStride   = slice.src.stride[2];
        slice.src.stride[0] = originStride * inside * axis;
        slice.src.stride[1] = originStride * inside;
        return true;
    }
    if (slice.size[0] == outside && slice.size[1] == axis && slice.size[2] == inside) {
        return true;
    }
    return false;
}

void TensorUtils::setupTensorInfo(const Tensor* tensor, Tensor* wrapTensor, MNN_DATA_FORMAT mMidFormat) {
    TensorUtils::getDescribe(wrapTensor)->dimensionFormat = mMidFormat;
    auto tensorFormat                                     = TensorUtils::getDescribe(tensor)->dimensionFormat;
    bool originCaffeFormat = (tensorFormat == MNN_DATA_FORMAT_NCHW || tensorFormat == MNN_DATA_FORMAT_NC4HW4);
    bool wrapCaffeFormat   = (mMidFormat == MNN_DATA_FORMAT_NCHW || mMidFormat == MNN_DATA_FORMAT_NC4HW4);
    bool originTfFormat    = (tensorFormat == MNN_DATA_FORMAT_NHWC || tensorFormat == MNN_DATA_FORMAT_NHWC4);
    bool wrapTfFormat      = (mMidFormat == MNN_DATA_FORMAT_NHWC || mMidFormat == MNN_DATA_FORMAT_NHWC4);
    if ((originCaffeFormat && wrapCaffeFormat) || (originTfFormat && wrapTfFormat)) {
        TensorUtils::copyShape(tensor, wrapTensor);
    } else if (originCaffeFormat && wrapTfFormat) {
        for (int i = 1; i < wrapTensor->dimensions() - 1; ++i) {
            wrapTensor->setLength(i, tensor->length(i + 1));
        }
        wrapTensor->setLength(0, tensor->length(0));
        wrapTensor->setLength(wrapTensor->dimensions() - 1, tensor->length(1));
    } else if (originTfFormat && wrapCaffeFormat) {
        for (int i = 2; i < wrapTensor->dimensions(); ++i) {
            wrapTensor->setLength(i, tensor->length(i - 1));
        }
        wrapTensor->setLength(0, tensor->length(0));
        wrapTensor->setLength(1, tensor->length(tensor->dimensions() - 1));
    } else {
        // will not reach here
        MNN_ASSERT(false);
    }
    TensorUtils::setLinearLayout(wrapTensor);
    wrapTensor->buffer().type = tensor->getType();
}

void TensorUtils::copyShape(const Tensor* source, Tensor* dest, bool copyFormat) {
    auto& ob      = dest->buffer();
    auto& ib      = source->buffer();
    ob.dimensions = ib.dimensions;
    ::memcpy(ob.dim, ib.dim, ib.dimensions * sizeof(halide_dimension_t));
    if (copyFormat) {
        getDescribe(dest)->dimensionFormat = getDescribe(source)->dimensionFormat;
    }
}

void TensorUtils::setShape(Tensor* dest, const std::vector<int>& alldims) {
    auto& ob      = dest->buffer();
    ob.dimensions = alldims.size();
    int stride = 1;
    for (int i = alldims.size() - 1; i >= 0; --i) {
        ob.dim[i].stride = stride;
        ob.dim[i].extent = alldims[i];
        stride *= alldims[i];
    }
    return;
}

void TensorUtils::setLinearLayout(Tensor* tensor) {
    auto& buffer = tensor->buffer();
    int size     = 1;
    for (int i = 0; i < buffer.dimensions; ++i) {
        auto index  = buffer.dimensions - i - 1;
        auto extent = buffer.dim[index].extent;
        if (1 == index && tensor->mDescribe->dimensionFormat == MNN_DATA_FORMAT_NC4HW4) {
            extent = ROUND_UP(extent, 4);
        }
        buffer.dim[index].stride = size;
        size *= extent;
    }
}

void TensorUtils::clearHandleData(Tensor* tensor) {
    if (tensor->buffer().type.code != halide_type_handle) {
        return;
    }
    auto handle = tensor->host<void*>();
    if (nullptr == handle) {
        return;
    }

    MNN_ASSERT(tensor->mDescribe->extra.handleFreeFunction != nullptr);
    for (int i = 0; i < tensor->elementSize(); ++i) {
        if (nullptr != handle[i]) {
            tensor->mDescribe->extra.handleFreeFunction(handle[i]);
            handle[i] = nullptr;
        }
    }
}

static const Tensor* createHostPlanar(const Tensor* source) {
    // check
    auto bnType        = MNN_FORWARD_CPU;
    auto tensorBackend = TensorUtils::getDescribe(source)->backend;
    if (tensorBackend) {
        bnType = tensorBackend->type();
    }
    bool device = bnType != MNN_FORWARD_CPU;
    bool chunky = TensorUtils::getDescribe(source)->dimensionFormat == MNN_DATA_FORMAT_NC4HW4;

    // no convert needed
    if (!device && !chunky) {
        return source;
    }

    // convert
    if (chunky) {
        Tensor* result = source->createHostTensorFromDevice(source, false);
        if (result->getDimensionType() == MNN::Tensor::TENSORFLOW) {
            TensorUtils::getDescribe(result)->dimensionFormat = MNN_DATA_FORMAT_NHWC;
        } else {
            TensorUtils::getDescribe(result)->dimensionFormat = MNN_DATA_FORMAT_NCHW;
        }
        TensorUtils::setLinearLayout(result);

        if (device) {
            void *host = ((Tensor *)source)->map(MNN::Tensor::MAP_TENSOR_READ, result->getDimensionType());
            if(host != nullptr) {
                ::memcpy(result->buffer().host, host, result->size());
            }
            ((Tensor *)source)->unmap(MNN::Tensor::MAP_TENSOR_READ,  result->getDimensionType(), host);
        } else {
            Backend::Info info;
            info.type = MNN_FORWARD_CPU;
            std::shared_ptr<Runtime> runtime(MNNGetExtraRuntimeCreator(MNN_FORWARD_CPU)->onCreate(info));
            auto backend = runtime->onCreate();
            backend->onCopyBuffer(source, result);
            delete backend;
        }
        return result;
    } else {
        return source->createHostTensorFromDevice(source, true);
    }
}

template <typename T>
static void copyTensorToFloat(const Tensor* source, double* dest) {
    auto srcData = source->host<T>();
    auto size    = source->elementSize();
    for (int i = 0; i < size; ++i) {
        dest[i] = srcData[i];
    }
}

static bool equals(const double* pa, const double* pb, size_t size, double tolerance, double epsilon, bool overall,
                   bool prints) {
    // get max if using overall torelance
    double max = fabs(pb[0]);
    if (overall) {
        for (int i = 1; i < size; i++) {
            max = std::max(max, fabs(pb[i]));
        }
    }

    // compare
    for (int i = 0; i < size; i++) {
        float va = pa[i], vb = pb[i];
        if (std::isinf(va) && std::isinf(vb)) {
            continue;
        }
        if (fabs(va) < epsilon && fabs(vb) < epsilon) {
            continue;
        }
        float div = overall ? max : fabsf(vb);
        if (fabsf(va - vb) / div > tolerance) {
            if (prints) {
                MNN_PRINT("%d: %f != %f\n", i, va, vb);
            }
            return false;
        }
    }
    return true;
}

bool TensorUtils::compareTensors(const Tensor* compare, const Tensor* expect, float tolerance, bool overall,
                                 bool printsErrors, bool printsTensors) {
    // type
    if (compare->getType().code != expect->getType().code || compare->getType().bits != expect->getType().bits) {
        if (printsErrors) {
            MNN_PRINT("NOT equal in type: %d/%d - %d/%d.\n", compare->getType().code, compare->getType().bits,
                      expect->getType().code, expect->getType().bits);
        }
        return false;
    }

    // dimensions
    if (compare->dimensions() != expect->dimensions()) {
        if (printsErrors) {
            MNN_PRINT("NOT equal in dimensions: %d - %d.\n", compare->dimensions(), expect->dimensions());
        }
        return false;
    }
    for (int i = 0; i < compare->dimensions(); i++) {
        if (compare->length(i) == expect->length(i)) {
            continue;
        }
        if (printsErrors) {
            MNN_PRINT("NOT equal in dimensions[%d]: %d - %d.\n", i, compare->length(i), expect->length(i));
        }
        return false;
    }

    // convert to host if needed
    auto a = createHostPlanar(compare), b = createHostPlanar(expect);

    // get value as double
    auto size = expect->elementSize();
    std::vector<double> expectValue(expect->elementSize(), 0.0f);
    std::vector<double> compareValue(compare->elementSize(), 0.0f);

    auto result = false;
    if (b->buffer().type.code == halide_type_uint) {
        switch (b->buffer().type.bits) {
            case 8:
                copyTensorToFloat<uint8_t>(a, compareValue.data());
                copyTensorToFloat<uint8_t>(b, expectValue.data());
                break;
            case 16:
                copyTensorToFloat<uint16_t>(a, compareValue.data());
                copyTensorToFloat<uint16_t>(b, expectValue.data());
                break;
            case 32:
                copyTensorToFloat<uint32_t>(a, compareValue.data());
                copyTensorToFloat<uint32_t>(b, expectValue.data());
                break;
            case 64:
                copyTensorToFloat<uint64_t>(a, compareValue.data());
                copyTensorToFloat<uint64_t>(b, expectValue.data());
                break;
            default:
                break;
        }
    } else if (b->buffer().type.code == halide_type_int) {
        switch (b->buffer().type.bits) {
            case 8:
                copyTensorToFloat<int8_t>(a, compareValue.data());
                copyTensorToFloat<int8_t>(b, expectValue.data());
                break;
            case 16:
                copyTensorToFloat<int16_t>(a, compareValue.data());
                copyTensorToFloat<int16_t>(b, expectValue.data());
                break;
            case 32:
                copyTensorToFloat<int32_t>(a, compareValue.data());
                copyTensorToFloat<int32_t>(b, expectValue.data());
                break;
            case 64:
                copyTensorToFloat<int64_t>(a, compareValue.data());
                copyTensorToFloat<int64_t>(b, expectValue.data());
                break;
            default:
                break;
        }
    } else if (b->buffer().type.code == halide_type_float) {
        switch (b->buffer().type.bits) {
            case 32:
                copyTensorToFloat<float>(a, compareValue.data());
                copyTensorToFloat<float>(b, expectValue.data());
                break;
            default:
                break;
        }
    } else {
        if (printsErrors) {
            MNN_PRINT("unsupported data type.");
        }
    }
    auto epsilon = FLT_EPSILON;
    if ((NULL != compareValue.data()) && (NULL != expectValue.data())) {
        result = equals(compareValue.data(), expectValue.data(), size, tolerance, epsilon, overall, printsErrors);
    }

    // clean up
    if (a != compare) {
        delete a;
    }
    if (b != expect) {
        delete b;
    }
    return result;
}

// is copy only region
bool TensorUtils::isCopyRegion(const Tensor::InsideDescribe::Region& region) {
    bool eq = true;
    for (int i = 0; i < 3; i++) {
        eq &= ((region.src.stride[i] == region.dst.stride[i]) || (region.size[i] <= 1));
    }
    return eq;
}

// compute offset through region
static inline int offsetCompute(Tensor::InsideDescribe::Region reg, int offset, bool backward) {
    if (backward) {
        auto tmp = reg.src;
        reg.src = reg.dst;
        reg.dst = tmp;
    }
    int res = 0;
    for (int i = 0; i < 3; i++) {
        if (reg.size[i] > 1) {
            res += offset / reg.src.stride[i] * reg.dst.stride[i];
            offset %= reg.src.stride[i];
        }
    }
    return res;
}

// expand src stride with expand value
static inline bool expandSrc(std::vector<int>& src, std::vector<int>& dst, std::vector<int>& size, int expandValue) {
    if (expandValue <= 0) {
        return false;
    }
    for (int i = size.size()-1; i >= 0; i--) {
        int splitSize = expandValue / src[i];
        if (!(expandValue % src[i] || size[i] % splitSize)) {
            src.insert(src.begin()+i, expandValue);
            dst.insert(dst.begin()+i, splitSize * dst[i]);
            size[i] /= splitSize;
            size.insert(size.begin()+i+1, splitSize);
            return true;
        }
    }
    return false;
}
// expand stride and size with expand value
static inline bool expandStrideSize(int* src, int* dst, int* size, int& num, int expandValue) {
#define MNN_3_INT_INSERT(x, i, y) if (i == 2) { x[2] = y; } else if (i == 1) { x[2] = x[1]; x[1] = y; } else if (i == 0) { x[2] = x[1]; x[1] = x[0]; x[0] = y; } else { return false; }
    for (int i = num-1; i >= 0; i--) {
        int splitSize = expandValue / src[i];
        if (!(expandValue % src[i] || size[i] % splitSize)) {
            MNN_3_INT_INSERT(src, i, expandValue)
            MNN_3_INT_INSERT(dst, i, (splitSize * dst[i]))
            size[i] /= splitSize;
            MNN_3_INT_INSERT(size, (i+1), splitSize)
            if (++num > 3) return false;
            return true;
        }
    }
    return false;
#undef MNN_3_INT_INSERT
}

// fuse srcRegion and dstRegion to dstRegion if return true
bool TensorUtils::fuseRegion(Tensor::InsideDescribe::Region& srcReg, Tensor::InsideDescribe::Region& dstReg) {
    // src data isnot full data of dst
    if (srcReg.dst.offset > dstReg.src.offset ||
        srcReg.dst.stride[1] > srcReg.size[2] ||
        srcReg.dst.stride[2] > srcReg.size[1] * srcReg.size[2]) {
        return false;
    }
    int dstTotalSize = 1, srcTotalSize = 1;
    for (int i = 0; i < 3; i++) {
        if (dstReg.size[i] > 1) {
            dstTotalSize *= dstReg.size[i];
        }
        if (srcReg.size[i] > 1) {
            srcTotalSize *= srcReg.size[i];
        }
    }
    // src data is not full data of dst
    if (dstTotalSize > srcTotalSize) {
        return false;
    }
    // dont deal size > 1 && stride <= 0
    for (int i = 0; i < 3; i++) {
        if (srcReg.size[i] > 1 && (srcReg.src.stride[i] <= 0 || srcReg.dst.stride[i] <= 0)) {
            return false;
        }
        if (dstReg.size[i] > 1 && (dstReg.src.stride[i] <= 0 || dstReg.dst.stride[i] <= 0)) {
            return false;
        }
    }
    // src copy fuse
    if (isCopyRegion(srcReg)) {
        dstReg.origin = srcReg.origin;
        dstReg.src.offset += srcReg.src.offset - srcReg.dst.offset;
        return true;
    }
    // dst copy fuse
    if (isCopyRegion(dstReg) && dstTotalSize == srcTotalSize) {
        int srcOff = dstReg.src.offset - srcReg.dst.offset;
        int dstOff = dstReg.dst.offset;
        srcOff = offsetCompute(srcReg, srcOff, true) + srcReg.src.offset;
        if (srcReg.src.stride[2] > 0 && srcOff % srcReg.src.stride[2] != 0) {
            // when transpose + slice, offset is not align can't fuse
            return false;
        }
        dstReg.origin = srcReg.origin;
        dstReg.dst = srcReg.dst;
        dstReg.src = srcReg.src;
        dstReg.src.offset = srcOff;
        dstReg.dst.offset = dstOff;
        dstReg.size[0] = srcReg.size[0];
        dstReg.size[1] = srcReg.size[1];
        dstReg.size[2] = srcReg.size[2];
        return true;
    }
#define MNN_FAST_FUSE_WITHOUT_STL
#ifdef MNN_FAST_FUSE_WITHOUT_STL
    // general fuse
    int srcDst[3], srcSrc[3], dstSrc[3], dstDst[3], srcSize[3], dstSize[3], newSrc[3], dstStride[3], srcStride[3];
#define MNN_3_INT_INIT(x, y) { x[0] = y; x[1] = y; x[2] = y; }
    MNN_3_INT_INIT(dstStride, -1)
    MNN_3_INT_INIT(srcStride, -1)
#undef MNN_3_INT_INIT
    int srcNum = 0, dstNum = 0, sizeNum = 0;
    for (int i = 0; i < 3; i++) {
        if (srcReg.size[i] > 1) {
            srcStride[srcNum] = srcReg.dst.stride[i];
            srcDst[srcNum]    = srcReg.dst.stride[i];
            srcSrc[srcNum]    = srcReg.src.stride[i];
            srcSize[srcNum]   = srcReg.size[i];
            srcNum++;
        }
        if (dstReg.size[i] > 1) {
            dstStride[dstNum] = dstReg.src.stride[i];
            dstDst[dstNum]    = dstReg.dst.stride[i];
            dstSrc[dstNum]    = dstReg.src.stride[i];
            dstSize[dstNum]   = dstReg.size[i];
            dstNum++;
        }
    }
    sizeNum = dstNum;
#define MNN_3_INT_DIFF(r, x, y, i) if ((x[i] != y[0]) && (x[i] != y[1]) && (x[i] != y[2])) { if (r > 0) { return false; } else { r = x[i]; } }
    int srcExtra = -1, dstExtra = -1;
    MNN_3_INT_DIFF(srcExtra, srcStride, dstStride, 0)
    MNN_3_INT_DIFF(srcExtra, srcStride, dstStride, 1)
    MNN_3_INT_DIFF(srcExtra, srcStride, dstStride, 2)
    MNN_3_INT_DIFF(dstExtra, dstStride, srcStride, 0)
    MNN_3_INT_DIFF(dstExtra, dstStride, srcStride, 1)
    MNN_3_INT_DIFF(dstExtra, dstStride, srcStride, 2)
#undef MNN_3_INT_DIFF
    if (dstExtra > 0) {
        if (!expandStrideSize(srcDst, srcSrc, srcSize, srcNum, dstExtra)) {
            return false;
        }
    }
    if (srcExtra > 0) {
        if (!expandStrideSize(dstSrc, dstDst, dstSize, dstNum, srcExtra)) {
            return false;
        }
    }
    // reorder srcSrc to newSrc by align srcDst and dstSrc
    for (int i = 0; i < dstNum; i++) {
        int index = 0;
        for (int j = 0; j < srcNum; j++) {
            if (dstSrc[j] == srcDst[i]) {
                index = j;
            }
        }
        newSrc[index] = srcSrc[i];
    }
    // set final size and set expandIdx if expand val is 1
    int expandIdx = -1;
    if (dstNum > sizeNum) {
        for (int i = 2; i >= 0; i--) {
            if (i < dstNum) {
                if (dstSize[i] == 1) {
                    expandIdx = i;
                }
                dstReg.size[i] = dstSize[i];
            } else {
                dstReg.size[i] = 1;
            }
        }
    }
#else
    // general fuse
    std::set<int> dstStride, srcStride, dstDiff, srcDiff;
    std::vector<int> dstDst, dstSrc, srcDst, srcSrc, newSrc, dstSize, srcSize;
    for (int i = 0; i < 3; i++) {
        if (srcReg.size[i] > 1) {
            srcStride.insert(srcReg.dst.stride[i]);
            srcDst.push_back(srcReg.dst.stride[i]);
            srcSrc.push_back(srcReg.src.stride[i]);
            srcSize.push_back(srcReg.size[i]);
        }
        if (dstReg.size[i] > 1) {
            dstStride.insert(dstReg.src.stride[i]);
            dstDst.push_back(dstReg.dst.stride[i]);
            dstSrc.push_back(dstReg.src.stride[i]);
            dstSize.push_back(dstReg.size[i]);
        }
    }
    int sizeNum = dstSize.size();
    std::set_difference(dstStride.begin(), dstStride.end(), srcStride.begin(), srcStride.end(), std::inserter(dstDiff, dstDiff.begin()));
    std::set_difference(srcStride.begin(), srcStride.end(), dstStride.begin(), dstStride.end(), std::inserter(srcDiff, srcDiff.begin()));
    if (dstDiff.size() > 1 || srcDiff.size() > 1) {
        // many diff stride, now dont deal
        return false;
    }
    // expand stride when middle tensor's stride diff
    if (!dstDiff.empty()) {
        if (!expandSrc(srcDst, srcSrc, srcSize, *dstDiff.begin())) {
            return false;
        }
    }
    if (!srcDiff.empty()) {
        if (!expandSrc(dstSrc, dstDst, dstSize, *srcDiff.begin())) {
            return false;
        }
    }
    if (dstSize.size() > 3) {
        // need splite region, dont deal
        return false;
    }
    // reorder srcSrc to newSrc by align srcDst and dstSrc
    newSrc.resize(srcSrc.size());
    for (int i = 0; i < dstSrc.size(); i++) {
        int index = std::distance(dstSrc.begin(), std::find(dstSrc.begin(), dstSrc.end(), srcDst[i]));
        newSrc[index] = srcSrc[i];
    }
    // set final size and set expandIdx if expand val is 1
    int expandIdx = -1;
    if (dstSize.size() > sizeNum) {
        for (int i = 2; i >= 0; i--) {
            if (i < dstSize.size()) {
                if (dstSize[i] == 1) {
                    expandIdx = i;
                }
                dstReg.size[i] = dstSize[i];
            } else {
                dstReg.size[i] = 1;
            }
        }
    }
#endif
    int idx = 0;
    for (int i = 0; i < 3; i++) {
        if (dstReg.size[i] > 1 || i == expandIdx) {
            dstReg.src.stride[i] = newSrc[idx];
            dstReg.dst.stride[i] = dstDst[idx++];
        }
    }
    dstReg.origin = srcReg.origin;
    dstReg.src.offset = offsetCompute(srcReg, dstReg.src.offset - srcReg.dst.offset, true) + srcReg.src.offset;
    return true;
}
void TensorUtils::adjustTensorForCompability(Tensor* newTensor) {
    if (newTensor->dimensions() < 4) {
        for (int n = newTensor->dimensions(); n < 4; ++n) {
            newTensor->setLength(n, 1);
        }
    }
}

Tensor::DimensionType TensorUtils::getDimType(const Tensor* t) {
    auto format = TensorUtils::getDescribe(t)->dimensionFormat;
    switch (format) {
        case MNN_DATA_FORMAT_NCHW:
            return Tensor::CAFFE;
        case MNN_DATA_FORMAT_NC4HW4:
            return Tensor::CAFFE_C4;
        case MNN_DATA_FORMAT_NHWC:
            return Tensor::TENSORFLOW;
        default:
            break;
    }
    return Tensor::TENSORFLOW;
}

halide_type_t TensorUtils::DataTypeToHalideType(DataType t) {
    switch (t) {
        case DataType_DT_DOUBLE:
        case DataType_DT_FLOAT:
            return halide_type_of<float>();
        case DataType_DT_BFLOAT16:
            return halide_type_t(halide_type_float, 16);
        case DataType_DT_QINT32:
        case DataType_DT_INT32:
        case DataType_DT_BOOL:
        case DataType_DT_INT64:
            return halide_type_of<int32_t>();
        case DataType_DT_QINT8:
        case DataType_DT_INT8:
            return halide_type_of<int8_t>();
        case DataType_DT_QUINT8:
        case DataType_DT_UINT8:
            return halide_type_of<uint8_t>();
        case DataType_DT_QUINT16:
        case DataType_DT_UINT16:
            return halide_type_of<uint16_t>();
        case DataType_DT_QINT16:
        case DataType_DT_INT16:
            return halide_type_of<int16_t>();
        case DataType_DT_STRING:
        default:
            MNN_PRINT("Unsupported data type!");
            MNN_ASSERT(false);
            return halide_type_of<float>();
    }
}

DataType TensorUtils::HaildeTypeToDataType(halide_type_t t) {
    if (t == halide_type_of<int8_t>()) {
        return DataType_DT_INT8;
    }
    if (t == halide_type_of<int16_t>()) {
        return DataType_DT_INT16;
    }
    if (t == halide_type_of<int32_t>()) {
        return DataType_DT_INT32;
    }
    if (t == halide_type_of<int64_t>()) {
        return DataType_DT_INT64;
    }
    if (t == halide_type_of<uint8_t>()) {
        return DataType_DT_UINT8;
    }
    if (t == halide_type_of<uint16_t>()) {
        return DataType_DT_UINT16;
    }
    if (t == halide_type_t(halide_type_float, 16)) {
        return DataType_DT_BFLOAT16;
    }
    if (t == halide_type_of<float>()) {
        return DataType_DT_FLOAT;
    }
    if (t == halide_type_of<double>()) {
        return DataType_DT_DOUBLE;
    }
    MNN_PRINT("Unsupported data type!");
    MNN_ASSERT(false);
    return DataType_DT_INVALID;
}
std::vector<float> TensorUtils::getQuantInfo(const Tensor* t) {
    float scale = getDescribe(t)->quantAttr ? getDescribe(t)->quantAttr->scale : 0.0f;
    float zero = getDescribe(t)->quantAttr ? getDescribe(t)->quantAttr->zero : 0.0f;
    float min = getDescribe(t)->quantAttr ? getDescribe(t)->quantAttr->min : -127.0f;
    float max = getDescribe(t)->quantAttr ? getDescribe(t)->quantAttr->max : 127.0f;
    return {scale, zero, min, max};
}
} // namespace MNN
