/* ******************************************************************************
 *
 *
 * 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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

//
// @author Yurii Shyrma (iuriish@yahoo.com), created on 18.09.2018
//
#include <execution/Threads.h>
#include <ops/declarable/helpers/convolutions.h>

namespace sd {
namespace ops {

//////////////////////////////////////////////////////////////////////////
template <typename T>
static void upsampling3d_(NDArray& input, NDArray& output, const LongType factorD, const LongType factorH,
                          const LongType factorW, const bool isNCDHW) {
  // input  has shape [bS, iC, iD, iH, iW] (NCDHW) or [bS, iD, iH, iW, iC] (NDHWC)
  // output has shape [bS, iC, factorD*iD, factorH*iH, factorW*iW ] (NCDHW) or [bS, factorD*iD, factorH*iH, factorW*iW,
  // iC] (NDHWC)

  const T* x = input.bufferAsT<T>();
  T* z = output.bufferAsT<T>();

  const sd::LongType dimID = isNCDHW ? 2 : 1;
  const sd::LongType dimIC = isNCDHW ? 1 : 4;

  const sd::LongType bS = input.sizeAt(0);
  const sd::LongType iC = input.sizeAt(dimIC);
  const sd::LongType oD = output.sizeAt(dimID);
  const sd::LongType oH = output.sizeAt(dimID + 1);
  const sd::LongType oW = output.sizeAt(dimID + 2);

  const sd::LongType xStride0 = input.stridesOf()[0];
  const sd::LongType xStride1 = input.stridesOf()[dimIC];
  const sd::LongType xStride2 = input.stridesOf()[dimID];
  const sd::LongType xStride3 = input.stridesOf()[dimID + 1];
  const sd::LongType xStride4 = input.stridesOf()[dimID + 2];

  const sd::LongType zStride0 = output.stridesOf()[0];
  const sd::LongType zStride1 = output.stridesOf()[dimIC];
  const sd::LongType zStride2 = output.stridesOf()[dimID];
  const sd::LongType zStride3 = output.stridesOf()[dimID + 1];
  const sd::LongType zStride4 = output.stridesOf()[dimID + 2];

  // loop through output array
  auto func = PRAGMA_THREADS_FOR_3D {
    sd::LongType xCoord2, xCoord3, xCoord4;

    for (sd::LongType b = start_x; b < stop_x; b += inc_x) {
      for (sd::LongType c = start_y; c < stop_y; c += inc_y) {
        for (sd::LongType d = start_z; d < stop_z; d += inc_z) {
          for (sd::LongType h = 0; h < oH; ++h) {
            for (sd::LongType w = 0; w < oW; ++w) {
              xCoord2 = d / factorD;
              xCoord3 = h / factorH;
              xCoord4 = w / factorW;

              z[b * zStride0 + c * zStride1 + d * zStride2 + h * zStride3 + w * zStride4] =
                  x[b * xStride0 + c * xStride1 + xCoord2 * xStride2 + xCoord3 * xStride3 + xCoord4 * xStride4];
            }
          }
        }
      }
    }
  };

  samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1, 0, oD, 1);
}

void ConvolutionUtils::upsampling3d(sd::graph::Context& block, NDArray& input, NDArray& output, const LongType factorD,
                                    const LongType factorH, const LongType factorW, const bool isNCDHW) {
  BUILD_SINGLE_SELECTOR(input.dataType(), upsampling3d_, (input, output, factorD, factorH, factorW, isNCDHW),
                        SD_FLOAT_TYPES);
}

}  // namespace ops
}  // namespace sd