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#include "../precomp.hpp"
#include "layers_common.hpp"
#include <opencv2/dnn/shape_utils.hpp>

namespace cv
{
namespace dnn
{

static void computeShapeByReshapeMask(const MatShape &srcShape,
                                      const MatShape &maskShape,
                                      Range srcRange /*= Range::all()*/,
                                      MatShape& dstShape)
{
    int srcShapeSize = (int)srcShape.size();
    int maskShapeSize = (int)maskShape.size();

    if (srcRange == Range::all())
        srcRange = Range(0, srcShapeSize);
    else
    {
        int sz = srcRange.size();
        srcRange.start = clamp(srcRange.start, srcShapeSize);
        srcRange.end = srcRange.end == INT_MAX ? srcShapeSize : srcRange.start + sz;
    }

    bool explicitMask = !maskShape.empty();  // All mask values are positive.
    for (int i = 0, n = maskShape.size(); i < n && explicitMask; ++i)
    {
        explicitMask = maskShape[i] > 0;
    }
    // Working range of source shape is a range where area(src) == area(mask).
    if (explicitMask)
    {
        int maskTotal = total(maskShape);
        for (int i = srcRange.start + 1; i < srcRange.end; ++i)
        {
            if (total(srcShape, i, srcRange.end) != maskTotal)
            {
                srcRange.start = i - 1;
                break;
            }
        }
        CV_Assert(total(srcShape, srcRange.start, srcRange.end) == maskTotal);
    }

    CV_Assert(0 <= srcRange.start && srcRange.start <= srcRange.end && srcRange.end <= srcShapeSize);
    int dstShapeSize = srcShapeSize - srcRange.size() + maskShapeSize;
    dstShape.resize(dstShapeSize);

    std::copy(srcShape.begin(), srcShape.begin() + srcRange.start, dstShape.begin());
    std::copy(srcShape.begin() + srcRange.end, srcShape.begin() + srcShapeSize, dstShape.begin() + srcRange.start + maskShapeSize);

    int inferDim = -1;
    for (int i = 0; i < maskShapeSize; i++)
    {
        if (maskShape[i] > 0)
        {
            dstShape[srcRange.start + i] = maskShape[i];
        }
        else if (maskShape[i] == 0)
        {
            if (srcRange.start + i >= srcShapeSize)
                CV_Error(Error::StsBadArg, format("Copy dim[%d] (which has zero size) is out of the source shape bounds", srcRange.start + i));
            dstShape[srcRange.start + i] = srcShape[srcRange.start + i];
        }
        else if (maskShape[i] == -1)
        {
            if (inferDim != -1)
                CV_Error(Error::StsAssert, "Duplicate of inferred dim (which is denoted by -1)");
            inferDim = srcRange.start + i;
            dstShape[inferDim] = 1;
        }
        else
            CV_Error(Error::StsBadArg, "maskShape[i] >= -1");
    }

    size_t srcTotal = total(srcShape);
    size_t dstTotal = total(dstShape);

    if (inferDim != -1)
    {
        if (srcTotal % dstTotal != 0)
            CV_Error(Error::StsBackTrace, "Can't infer a dim denoted by -1");

        dstShape[inferDim] = (int)(srcTotal / dstTotal);
    }
    else
    {
        CV_Assert(srcTotal == dstTotal);
    }
}


class ReshapeLayerImpl : public ReshapeLayer
{
public:
    ReshapeLayerImpl(const LayerParams& params):
        performReordering(false)
    {
        setParamsFrom(params);
        int axis = params.get<int>("axis", 0);
        int numAxes = params.get<int>("num_axes", -1);
        enableReordering = params.get<bool>("reorder_dims", false);
        CV_Assert(numAxes >= -1);
        newShapeRange = (numAxes == -1) ? Range(axis, INT_MAX) : Range(axis, axis + numAxes);

        newShapeDesc.clear();
        if (params.has("dim"))
        {
            const DictValue &paramShape = params.get("dim");
            int i, dims = paramShape.size();
            newShapeDesc.resize(dims);
            for (i = 0; i < dims; i++)
                newShapeDesc[i] = paramShape.get<int>(i);
        }
    }

    bool getMemoryShapes(const std::vector<MatShape> &inputs,
                         const int requiredOutputs,
                         std::vector<MatShape> &outputs,
                         std::vector<MatShape> &internals) const
    {
        outputs.clear();

        for (size_t i = 0; i < inputs.size(); i++)
        {
            outputs.push_back(MatShape());
            computeShapeByReshapeMask(inputs[i], newShapeDesc, newShapeRange, outputs.back());
        }
        internals = outputs;

        return true;
    }

    void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
    {
        CV_Assert(inputs.size());
        CV_Assert(outputs.size());
        Mat srcBlob = *inputs[0];
        int dims = srcBlob.dims;
        MatShape inputShape = shape(srcBlob), outShape = shape(outputs[0]);

        // input.total() == output.total(). So if reordering is require,
        // one of the sizes will be are not equal.
        // Example where reordering is require: from 1x128x4x4 to 1x2048
        // Example where reordering is NOT require: from 1x1024x1x1 to 1x1024.
        bool reorderingRequire = false;
        const int minDims = min(dims, (int)outShape.size());
        for (int i = 0; !reorderingRequire && i < minDims; ++i)
            reorderingRequire = inputShape[i] != outShape[i];
        performReordering = enableReordering && reorderingRequire;
    }

    void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
    {
        for (size_t i = 0; i < inputs.size(); i++)
        {
            Mat srcBlob = *inputs[i];
            MatShape inputShape = shape(srcBlob), outShape = shape(outputs[i]);

            if (performReordering)
            {
                float *dstData = internals[i].ptr<float>();
                const float *srcData = srcBlob.ptr<float>();

                int num = inputShape[0], channels = inputShape[1], height = inputShape[2], width = inputShape[3];
                int total = num*channels*height*width;
                for(int i_n = 0; i_n < num; i_n++) {
                    for(int i_c = 0; i_c < channels; i_c++) {
                        for(int i_h = 0; i_h < height; i_h++) {
                            for(int i_w = 0; i_w < width; i_w++) {
                                int src_i = channels*height*width*i_n + height*width*i_c + width*i_h + i_w;
                                int dst_i = channels*height*width*i_n + i_c + channels*width*i_h + channels*i_w;

                                CV_Assert(dst_i < total);
                                CV_Assert(src_i < total);

                                dstData[dst_i] = srcData[src_i];
                            }
                        }
                    }
                }
                internals[i].copyTo(outputs[i]);
            }
            else
            {
                if (outputs[i].data != srcBlob.data)
                    srcBlob.reshape(1, outShape).copyTo(outputs[i]);
            }
        }
    }

private:
    std::vector<std::vector<int> > outShapes;
    bool enableReordering, performReordering;
};

Ptr<ReshapeLayer> ReshapeLayer::create(const LayerParams& params)
{
    return Ptr<ReshapeLayer>(new ReshapeLayerImpl(params));
}


}
}
