/*
 *  ******************************************************************************
 *  *
 *  *
 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
 *  *
 *  * See the NOTICE file distributed with this work for additional
 *  * information regarding copyright ownership.
 *  * Unless required by applicable law or agreed to in writing, software
 *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 *  * License for the specific language governing permissions and limitations
 *  * under the License.
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 *  * SPDX-License-Identifier: Apache-2.0
 *  *****************************************************************************
 */

//
// @author Yurii Shyrma (iuriish@yahoo.com)
//

#include <ops/declarable/helpers/convolutions.h>
#include <ops/declarable/helpers/im2col.h>
#include <ops/declarable/helpers/col2im.h>
#include<ops/declarable/helpers/addBias.h>
#include <helpers/MmulHelper.h>
#include <helpers/PointersManager.h>

namespace sd {
namespace ops  {

//////////////////////////////////////////////////////////////////////////
template <typename X, typename Y>
static void conv2d_(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW, const int wFormat) {

    // input   [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
    // weights [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
    // bias    [oC]
    // output  [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)

    // kH  filter(kernel) height
    // kW  filter(kernel) width
    // sH  strides height
    // sW  strides width
    // pH  paddings height
    // pW  paddings width
    // dH  dilations height
    // dW  dilations width
    // paddingMode 0-VALID, 1-SAME
    // isNCHW     1-NCHW,  0-NHWC

    int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
    int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
    ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);

    ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);

    std::vector<int> permutForOutput;

    if(isNCHW)
        permutForOutput = {0, 3, 1, 2};                                             // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
    else
        input = new NDArray(input->permute({0, 3, 1, 2}));                         // [bS, iH, iW, iC] -> [bS, iC, iH, iW] if NHWC

    std::vector<int> wAxes;
    if(0 == wFormat)
        wAxes = {0, 1, 2};
    else if(1 == wFormat)
        wAxes = {2, 3, 1};
    else
        wAxes = {1, 2, 3};

    NDArray col('c', {bS, oH, oW, kH, kW, iC}, input->dataType(), input->getContext());
    NDArray colP = col.permute({0, 5, 3, 4, 1, 2});            // {bS, iC, kH, kW, oH, oW}
    NDArray mmulResult('f', {bS*oH*oW, oC}, output->dataType(), output->getContext());

    //----- calculation of output -----//
    auto ctx = block.launchContext();
    helpers::im2col(*ctx, *input, colP, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext()));  // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW]
    MmulHelper::tensorDot(&col, weights, &mmulResult, {3,4,5}, wAxes, {}); // [bS, oH, oW, kH, kW, iC] x [kH, kW, iC, oC] = [bS, oH, oW, oC]

    //----- assign outTemp to output  -----//
    if(isNCHW) {
        mmulResult.reshapei({bS, oH, oW, oC});
        mmulResult.permutei(permutForOutput);
    }
    output->assign(mmulResult);

    //----- add biases if required -----//
    if(bias)
        // output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
        helpers::addBias(block, *output, *bias, *output, isNCHW);

    if(!isNCHW)
        delete input;

}

//////////////////////////////////////////////////////////////////////////
void ConvolutionUtils::conv2d(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW, const int wFormat) {
    BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW, wFormat), FLOAT_TYPES);
}

}
}