/* ******************************************************************************
 *
 *
 * 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 raver119@gmail.com
//

#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_split_string)

#include <helpers/StringUtils.h>
#include <ops/declarable/CustomOperations.h>

namespace sd {
namespace ops {
CUSTOM_OP_IMPL(compat_string_split, 2, 2, false, 0, 0) {
  auto input = INPUT_VARIABLE(0);
  auto delim = INPUT_VARIABLE(1);

  auto indices = OUTPUT_VARIABLE(0);
  auto values = OUTPUT_VARIABLE(1);

  auto d = delim->e<std::string>(0);

  NDArray::preparePrimaryUse({values},{indices});

  // output rank N+1 wrt input rank
  std::vector<LongType> icoords(input->rankOf());

  // getting buffer lengths
  auto outputLength = StringUtils::byteLength(*input);
  LongType ic = 0L;
  int len = input->isScalar() ? 1 : input->lengthOf();

  sd::LongType inputRank = input->rankOf();
  sd::LongType *inputShape = shape::shapeOf(input->shapeInfo());

  // loop through each string within tensor
  for (LongType e = 0L; e < len; e++) {
    // now we should map substring to indices
    auto s = input->e<std::string>(e);

    // getting base index
    INDEX2COORDS(e, inputRank, inputShape, icoords.data());

    // getting number of substrings
    auto cnt = StringUtils::countSubarrays(s.c_str(), s.length(), d.c_str(), d.length());
    // filling output indices
    for (LongType f = 0; f < cnt; f++) {
      for (auto v : icoords) {
        indices->p(ic++, v);
      }

      // last index
      indices->p(ic++, f);
    }

  }

  // process strings now
  std::vector<std::string> strings;
  for (auto e = 0L; e < input->lengthOf(); e++) {
    auto split = StringUtils::split(input->e<std::string>(e), d);

    for (const auto& s : split) strings.emplace_back(s);
  }

  // now once we have all strings in single vector time to fill
  auto nonConst = const_cast<NDArray*>(values);
  auto* valuesShapeVec = nonConst->getShapeAsVector();
  auto tmp = NDArrayFactory::string(*valuesShapeVec, strings);
  delete valuesShapeVec;
  auto blen = StringUtils::byteLength(*tmp) + ShapeUtils::stringBufferHeaderRequirements(strings.size());
  values->dataBuffer()->expand(blen);
  memcpy(values->buffer(), tmp->buffer(), blen);
  values->tickWriteHost();

  // special case, for future use
  indices->syncToDevice();
  values->syncToDevice();

  NDArray::registerPrimaryUse({values});
  // we have to tick buffers
  values->dataBuffer()->writePrimary();
  values->dataBuffer()->readSpecial();

  delete tmp;

  return Status::OK;
};

DECLARE_SHAPE_FN(compat_string_split) {
  auto input = INPUT_VARIABLE(0);
  auto delim = INPUT_VARIABLE(1);


  auto d = delim->e<std::string>(0);

  // count number of delimiter substrings in all strings within input tensor
  LongType cnt = 0;
  int len = input->isScalar() ? 1 : input->lengthOf();
  for (auto e = 0L; e < len; e++) {
    auto s = input->e<std::string>(e);

    // each substring we see in haystack, splits string in two parts. so we should add 1 to the number of subarrays
    cnt += StringUtils::countSubarrays(s.c_str(), s.length(), d.c_str(), d.length());
  }
  cnt++;

  // shape calculations
  // virtual tensor rank will be N+1, for N rank input array, where data will be located at the biggest dimension
  // values tensor is going to be vector always
  // indices tensor is going to be vector with length equal to values.length * output rank

  sd_printf("compat_string_split: Assigning number of values: %d\n",cnt);

  auto valuesShape = ConstantShapeHelper::getInstance().vectorShapeInfo(cnt, UTF8);
  auto indicesShape =
      ConstantShapeHelper::getInstance().vectorShapeInfo(cnt * (input->rankOf() + 1), INT64);

  return SHAPELIST(indicesShape, valuesShape);
}

DECLARE_TYPES(compat_string_split) {
  getOpDescriptor()
      ->setAllowedInputTypes({ALL_STRINGS})
      ->setAllowedOutputTypes(0, {ALL_INDICES})
      ->setAllowedOutputTypes(1, {ALL_STRINGS});
}
}  // namespace ops
}  // namespace sd

#endif