/* ******************************************************************************
 *
 *
 * 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 04.06.2018
//

#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/axis.h>
#include <ops/declarable/helpers/reductions.h>
#if NOT_EXCLUDED(OP_reduce_variance)
namespace sd {
namespace ops {

//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(reduce_variance, -1, 1, false, 0, 0) {
  auto input = INPUT_VARIABLE(0);
  auto output = OUTPUT_VARIABLE(0);

  bool biasCorrected = false;  // block.getTArguments()->size() > 1 ? (bool)T_ARG(1) : false;

  auto dimensions = *block.getIArguments();
  if (block.width() > 1) {
    auto axesVector = INPUT_VARIABLE(1);
    helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
  }

  if (block.getBArguments()->size()) {
    if (block.getBArguments()->size() > 1) biasCorrected = B_ARG(1);
  } else if (block.getTArguments()->size()) {
    if (block.getTArguments()->size() > 1) biasCorrected = (bool)T_ARG(1);
  }

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
      "REDUCE_VARIANCE OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
      dimensions.size());

  for (const auto& item : dimensions)
  REQUIRE_TRUE(
      item >= -input->rankOf() && item < input->rankOf(), 0,
      "REDUCE_VARIANCE OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
      input->rankOf(), input->rankOf(), item);

  sd::ops::helpers::variance(*input, *output, dimensions, biasCorrected);

  return sd::Status::OK;
}

DECLARE_SHAPE_FN(reduce_variance) {
  bool keepDims = false;  // block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
  auto dimensions = *block.getIArguments();
  if (block.width() > 1) {
    auto axesVector = INPUT_VARIABLE(1);
    helpers::adjustAxis(INPUT_VARIABLE(0)->rankOf(), axesVector, dimensions);
  }

  if (block.getBArguments()->size()) {
    keepDims = B_ARG(0);
  } else if (block.getTArguments()->size()) {
    keepDims = (bool)T_ARG(0);
  }

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(INPUT_VARIABLE(0)->rankOf()), 0,
      "REDUCE_VARIANCE OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
      dimensions.size());

  for (const auto& item : dimensions)
  REQUIRE_TRUE(
      item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0,
      "REDUCE_VARIANCE OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
      inputShape->at(0)[0], inputShape->at(0)[0], item);

  auto outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(inputShape->at(0)), &dimensions, inputShape->at(0),
                                                      keepDims, false, block.getWorkspace());

  return SHAPELIST(outShapeInfo);
}

DECLARE_TYPES(reduce_variance) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(reduce_variance_bp, -1, 1, false, 0, 0) {
  auto input = INPUT_VARIABLE(0);
  auto gradO = INPUT_VARIABLE(1);

  auto gradI = OUTPUT_VARIABLE(0);
  bool keepDims = true;
  bool biasCorrected = false;


  auto dimensions = *block.getIArguments();
  if (block.width() > 2) {
    auto axesVector = INPUT_VARIABLE(2);
    helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
  }

  if (block.getBArguments()->size()) {
    keepDims = B_ARG(0);
    if (block.getBArguments()->size() > 1) biasCorrected = B_ARG(1);
  } else if (block.getTArguments()->size()) {
    keepDims = (bool)T_ARG(0);
    if (block.getTArguments()->size() > 1) biasCorrected = (bool)T_ARG(1);
  }

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
      "REDUCE_VARIANCE_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
      dimensions.size());

  for (const auto& item : dimensions) {
    REQUIRE_TRUE(
        item >= -input->rankOf() && item < input->rankOf(), 0,
        "REDUCE_VARIANCE_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
        input->rankOf(), input->rankOf(), item);
    sd_debug("Dimension item is %d\n", item);
  }

  auto inputLen = input->lengthOf();
  //avoid divide by zero
  auto grad0Length = gradO->isScalar() || gradO->lengthOf() < 1 ? 1  :  gradO->lengthOf();
  const sd::LongType N = inputLen / grad0Length;
  const sd::LongType NminusOne = biasCorrected ? N - 1 : N;
  
  // Break down: (*input - mean) * (2.0f / NminusOne)
  auto* mean = input->reduceAlongDimension(reduce::Mean, &dimensions, true);
  auto* inputMinusMean = (*input) - (*mean);
  delete mean;
  auto* assign = (*inputMinusMean) * (2.0f / NminusOne);
  delete inputMinusMean;
  
  gradI->assign(assign);  // automatic broadcasting happens here
  delete assign;
  
  if (!keepDims) {
    auto gradOShapeKeepDims = ShapeUtils::evalReduceShapeInfo(gradO->ordering(), &dimensions, *input, true, false, block.getWorkspace());
    auto grad0Shape = ShapeUtils::pullShapeFromShapeInfo(gradOShapeKeepDims);
    auto* reshaped = !gradO->isScalar() ? gradO->reshape(gradO->ordering(), grad0Shape) : gradO;  // for example could be something like [a,b] -> [1,a,1,b];
    *gradI *= (*reshaped);  // for example could be something like [a,b] -> [1,a,1,b]
    //reshape can vary and may have the same buffer as the original
    if(reshaped != gradO && reshaped->buffer() != gradO->buffer() && reshaped->specialBuffer() != gradI->specialBuffer())
      delete reshaped;

  } else {
    *gradI *= (*gradO);  // automatic broadcasting happens here
  }
  return sd::Status::OK;
}

DECLARE_SHAPE_FN(reduce_variance_bp) {
  auto in = inputShape->at(0);
  auto rank = shape::rank(in);
  auto dimensions = *block.getIArguments();
  if (block.width() > 2) {
    auto axesVector = INPUT_VARIABLE(2);
    helpers::adjustAxis(rank, axesVector, dimensions);
  }

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(rank), 0,
      "REDUCE_VARIANCE_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
      dimensions.size());

  for (const auto& item : dimensions)
  REQUIRE_TRUE(
      item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0,
      "REDUCE_VARIANCE_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
      inputShape->at(0)[0], inputShape->at(0)[0], item);
  return SHAPELIST(CONSTANT(in));
}

DECLARE_TYPES(reduce_variance_bp) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

}  // namespace ops
}  // namespace sd
#endif