text: /*******************************************************************************
   * Copyright (c) 2015-2018 Skymind, Inc.
   *
   * 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.
   *
   * 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 raver119@gmail.com
  //
  
  #include <op_boilerplate.h>
  #if NOT_EXCLUDED(OP_cumprod)
  
  #include <ops/declarable/helpers/prefix.h>
  #include <ops/declarable/CustomOperations.h>
  
  namespace nd4j {
      namespace ops {
          CONFIGURABLE_OP_IMPL(cumprod, 1, 1, true, 0, 2) {
              auto input = INPUT_VARIABLE(0);
              auto output = OUTPUT_VARIABLE(0);
  
              const bool exclusive = INT_ARG(0) == 1;
              const bool reverse = INT_ARG(1) == 1;
  
              if (block.getIArguments()->size() == 2 && block.width() == 1) {
                  // all at once case
                  nd4j::ops::helpers::_prefix<T, simdOps::Multiply<T>>(input->buffer(), input->shapeInfo(), output->buffer(), output->shapeInfo(), exclusive, reverse);
              } else {
                  std::vector<int> dims(block.numI() - 2);
  
                  if (block.width() == 1) {
  
                      for (int e = 0; e < block.numI() - 2; e++)
                          dims[e] = INT_ARG(e + 2);
                  } else {
                      auto ax = INPUT_VARIABLE(1);
                      dims = ax->template asVectorT<int>();
                  }
  
                  for (int e = 0; e < dims.size(); e++)
                      if (dims[e] < 0)
                          dims[e] += input->rankOf();
  
                  nd4j::ops::helpers::_prefix<T, simdOps::Multiply<T>>(input, output, dims, exclusive, reverse);
              }
  
              return ND4J_STATUS_OK;
          }
  
          CUSTOM_OP_IMPL(cumprod_bp, 2, -1, false, 0, 2) {
              auto input = INPUT_VARIABLE(0);
              auto epsilon = INPUT_VARIABLE(1);
              auto output = OUTPUT_VARIABLE(0);
  
              const bool exclusive = INT_ARG(0) == 1;
              const bool reverse = INT_ARG(1) == 1;
  //            output->assign(epsilon);
              input->template applyPairwiseTransform<simdOps::Multiply<T>>(epsilon, output, nullptr);
              output->putScalar(0, epsilon->getScalar(0));
              // 
              return ND4J_STATUS_OK;
          }
  
          DECLARE_SHAPE_FN(cumprod_bp) {
              auto inp = inputShape->at(0);
              Nd4jLong *newShapeX = nullptr;
              COPY_SHAPE(inp, newShapeX);
  
              if (block.width() == 2) {
                  return SHAPELIST(newShapeX);
              } else {
                  Nd4jLong *newShapeA = nullptr;
                  COPY_SHAPE(inputShape->at(1), newShapeA);
  
                  return SHAPELIST(newShapeX, newShapeA);
              }
          }
      }
  }
  
  #endif