/* ******************************************************************************
 *
 *
 * 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
 ******************************************************************************/

//
// Created by Yurii Shyrma on 20.11.2017.
//

#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_absolute_difference_loss)
#include <ops/declarable/CustomOperations.h>

namespace sd {
namespace ops {

//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(absolute_difference_loss, 3, 1, false, 0, 1) {
  auto predictions = INPUT_VARIABLE(0);
  auto weights = INPUT_VARIABLE(1);
  auto labels = INPUT_VARIABLE(2);

  auto output = OUTPUT_VARIABLE(0);

  int reductionMode =
      INT_ARG(0);  // 0 - "none"; 1 - "weighted_sum";  2 - "weighted_mean";  3 - "weighted_sum_by_nonzero_weights"

  // input validation
  REQUIRE_TRUE(labels->isSameShape(predictions), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and "
               "%s correspondingly !",
               ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(predictions).c_str());
  // weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
  REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: weights array should be scalar or have the same rank as labels array, but "
               "got %i and %i correspondingly!",
               weights->rankOf(), labels->rankOf());
  // check whether broadcast operation is possible for weights array
  REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got "
               "weights = %s and labels = %s instead!",
               ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
  // only 4 possible reduction modes exist
  REQUIRE_TRUE(reductionMode == 0 || reductionMode == 1 || reductionMode == 2 || reductionMode == 3, 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, "
               "but got %i instead!",
               reductionMode);

  // perform weights broadcasting/tile to labels if needed
  auto weightsBroad = weights;
  if (!weights->isScalar() && !weights->isSameShape(predictions))
    weightsBroad = new NDArray(weights->tileToShape(predictions->shapeInfo()));

  NDArray* diff = (*predictions) - (*labels);
  NDArray* E = diff->transform(transform::Abs);
  delete diff;
  
  NDArray* EWeighted = (*E) * (*weightsBroad);
  delete E;

  switch (reductionMode) {
    case 0:  // 0 - "none", un-reduced weighted losses with the same shape as labels.
      output->assign(EWeighted);
      break;

    case 1: {  // 1 - "weighted_sum", output is scalar and equal to sum of all elements of E array
      auto* sumResult = EWeighted->reduceNumber(reduce::Sum);
      output->assign(sumResult);
      delete sumResult;
      break;
    }
    case 2: {  // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of
      // all elements of weightsBroad array
      NDArray* sum;
      if (weights->isScalar()) {
        sum = (*weights) * EWeighted->lengthOf();
      } else {
        sum = weightsBroad->reduceNumber(reduce::Sum);
      }

      if (sum->e<double>(0) == 0.) {
        (*output) = 0.;
      } else {
        NDArray* sumE = EWeighted->reduceNumber(reduce::Sum);
        NDArray* result = (*sumE) / (*sum);
        output->assign(result);
        delete result;
        delete sumE;
      }
      delete sum;
      break;
    }
    case 3: {  // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E
      // array divided by number of non-zero weights
      LongType numOfNonZeroWeights = 0;
      if (weights->isScalar()) {
        if (weights->e<double>(0) != 0.) numOfNonZeroWeights = EWeighted->lengthOf();
      } else {
        auto* countResult = weightsBroad->reduceNumber(reduce::CountNonZero);
        numOfNonZeroWeights = countResult->e<LongType>(0);
        delete countResult;
      }

      if (numOfNonZeroWeights == 0) {
        (*output) = 0.;
      } else {
        NDArray* sumE = EWeighted->reduceNumber(reduce::Sum);
        NDArray* result = (*sumE) / double(numOfNonZeroWeights);
        output->assign(result);
        delete result;
        delete sumE;
      }
      break;
    }
  }

  delete EWeighted;
  if (weightsBroad != weights) delete weightsBroad;

  return Status::OK;
}

DECLARE_TYPES(absolute_difference_loss) {
  getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

DECLARE_SHAPE_FN(absolute_difference_loss) {
  auto predictionsShapeInfo = inputShape->at(0);
  auto weightsShapeInfo = inputShape->at(1);
  auto labelsShapeInfo = inputShape->at(2);



  // labels and predictions must have the same shapes
  REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and "
               "%s correspondingly !",
               ShapeUtils::shapeAsString(labelsShapeInfo).c_str(),
               ShapeUtils::shapeAsString(predictionsShapeInfo).c_str());
  // weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
  REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: weights array should be scalar or have the same rank as labels array, but "
               "got %i and %i correspondingly!",
               shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
  // check whether broadcast operation is possible for weights array
  REQUIRE_TRUE(
      shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0,
      "ABSOLUTE_DIFFERENCE_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s "
      "and labels = %s instead!",
      ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());


  DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(predictionsShapeInfo));
  LongType * outShapeInfo = nullptr;

  if (INT_ARG(0) != 0) {  // in this case output is scalar
    outShapeInfo = ConstantShapeHelper::getInstance().scalarShapeInfo(outType);
  } else {  // in this case output has the same shape as labels and predictions
    outShapeInfo = ConstantShapeHelper::getInstance().bufferForShapeInfo(outType, shape::order(labelsShapeInfo),
                                                                         shape::rank(labelsShapeInfo),
                                                                         shape::shapeOf(labelsShapeInfo))->primary();
  }

  return SHAPELIST(outShapeInfo);
}

//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(absolute_difference_loss_grad, 3, 3, false, 0, 1) {
  auto predictions = INPUT_VARIABLE(0);
  auto weights = INPUT_VARIABLE(1);
  auto labels = INPUT_VARIABLE(2);

  auto dLdp = OUTPUT_VARIABLE(0);  // dL/dpredictions
  auto dLdw = OUTPUT_VARIABLE(1);  // dL/dweights
  auto dLdl = OUTPUT_VARIABLE(2);  // dL/dlabels

  int reductionMode =
      INT_ARG(0);  // 0 - "none"; 1 - "weighted_sum";  2 - "weighted_mean";  3 - "weighted_sum_by_nonzero_weights"
  // take into account Alex's proposition to treat "none" the same as "weighted_sum" mode when calculating gradients
  if (reductionMode == 0) reductionMode = 1;

  // inputs validation
  REQUIRE_TRUE(labels->isSameShape(predictions), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and "
               "%s correspondingly !",
               ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(predictions).c_str());
  // weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
  REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: weights array should be scalar or have the same rank as labels array, but "
               "got %i and %i correspondingly!",
               weights->rankOf(), labels->rankOf());
  // check whether broadcast operation is possible for weights array
  REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got "
               "weights = %s and labels = %s instead!",
               ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
  // only 4 possible reduction modes exist
  REQUIRE_TRUE(reductionMode == 0 || reductionMode == 1 || reductionMode == 2 || reductionMode == 3, 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, "
               "but got %i instead!",
               reductionMode);

  // perform weights broadcasting/tile to labels if needed
  auto weightsBroad = weights;
  if (!weights->isScalar() && !weights->isSameShape(predictions))
    weightsBroad = new NDArray(weights->tileToShape(predictions->shapeInfo()));

  NDArray* E = (*predictions) - (*labels);

  // dE_i/dp_i = sign(p_i - y_i)
  E->applyTransform(transform::Sign, dLdp);  // dE/dp
  // dE_i/dy_i = -sign(p_i - y_i)

  E->applyTransform(transform::Abs, E);

  switch (reductionMode) {
    case 1: {  // 1 - "none" and "weighted_sum", output is scalar and equal to sum of all elements of E array

      NDArray* dLdpWeighted = (*dLdp) * (*weightsBroad);
      dLdp->assign(dLdpWeighted);
      delete dLdpWeighted;

      if (weights->isScalar()) {
        auto* sumE = E->reduceNumber(reduce::Sum);
        dLdw->assign(sumE);
        delete sumE;
      }
      else if (weights != weightsBroad) {
        std::vector<LongType> axesToReduceAlong =
            ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
        E->reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
      } else
        dLdw->assign(E);
      break;
    }
    case 2: {  // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of
      // all elements of weightsBroad array

      NDArray* sum;
      if (weights->isScalar()) {
        sum = (*weights) * E->lengthOf();
      } else {
        sum = weightsBroad->reduceNumber(reduce::Sum);
      }

      if (sum->e<double>(0) == 0.) {
        *dLdp = 0.;
        *dLdw = 0.;
      } else {
        NDArray* dLdpWeighted = (*dLdp) * (*weightsBroad);
        NDArray* dLdpResult = (*dLdpWeighted) / (*sum);
        dLdp->assign(dLdpResult);
        delete dLdpResult;
        delete dLdpWeighted;

        if (weights->isScalar()) {
          *dLdw = 0.;
        } else if (weights != weightsBroad) {
          std::vector<LongType> axesToReduceAlong =
              ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
          NDArray* EWeighted = (*E) * (*weightsBroad);
          NDArray* EWeightedSum = EWeighted->reduceNumber(reduce::Sum);
          delete EWeighted;
          NDArray* ESum = (*E) * (*sum);
          NDArray* numerator = (*ESum) - (*EWeightedSum);
          delete ESum;
          delete EWeightedSum;
          NDArray* sumSquared = (*sum) * (*sum);
          NDArray* gradTemp = (*numerator) / (*sumSquared);
          delete numerator;
          delete sumSquared;
          gradTemp->reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
          delete gradTemp;
        } else {
          NDArray* EWeighted = (*E) * (*weightsBroad);
          NDArray* EWeightedSum = EWeighted->reduceNumber(reduce::Sum);
          delete EWeighted;
          NDArray* ESum = (*E) * (*sum);
          NDArray* numerator = (*ESum) - (*EWeightedSum);
          delete ESum;
          delete EWeightedSum;
          NDArray* sumSquared = (*sum) * (*sum);
          NDArray* gradTemp = (*numerator) / (*sumSquared);
          delete numerator;
          delete sumSquared;
          dLdw->assign(gradTemp);
          delete gradTemp;
        }
      }
      delete sum;
      break;
    }
    case 3: {  // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E
      // array divided by number of non-zero weights

      LongType numOfNonZeroWeights = 0;
      if (weights->isScalar()) {
        if (weights->e<double>(0) != 0.) numOfNonZeroWeights = E->lengthOf();
      } else {
        auto* countResult = weightsBroad->reduceNumber(reduce::CountNonZero);
        numOfNonZeroWeights = countResult->e<LongType>(0);
        delete countResult;
      }

      if (numOfNonZeroWeights == 0) {
        *dLdp = 0.;
        *dLdw = 0.;
      } else {
        auto numOfNonZeroWeightsScalar =
            NDArrayFactory::create(dLdw->dataType(), numOfNonZeroWeights, block.launchContext());

        if (weights->isScalar()) {
          auto* sumE = E->reduceNumber(reduce::Sum);
          auto* result = (*sumE) / double(numOfNonZeroWeights);
          dLdw->assign(result);
          delete result;
          delete sumE;
        } else if (weights != weightsBroad) {
          std::vector<LongType> axesToReduceAlong =
              ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
          E->reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
          NDArray* dLdwResult = (*dLdw) / (*numOfNonZeroWeightsScalar);
          dLdw->assign(dLdwResult);
          delete dLdwResult;
        } else {
          auto* result = (*E) / (*numOfNonZeroWeightsScalar);
          dLdw->assign(result);
          delete result;
        }

        NDArray* temp = (*weightsBroad) / (*numOfNonZeroWeightsScalar);
        NDArray* dLdpResult = (*dLdp) * (*temp);
        dLdp->assign(dLdpResult);
        delete dLdpResult;
        delete temp;

        delete numOfNonZeroWeightsScalar;
      }

      break;
    }
  }
  
  NDArray neg = -*dLdp;
  dLdl->assign(&neg);

  delete E;
  if (weightsBroad != weights) delete weightsBroad;

  return Status::OK;
}

DECLARE_TYPES(absolute_difference_loss_grad) {
  getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

DECLARE_SHAPE_FN(absolute_difference_loss_grad) {
  auto predictionsShapeInfo = inputShape->at(0);
  auto weightsShapeInfo = inputShape->at(1);
  auto labelsShapeInfo = inputShape->at(2);

  // labels and predictions must have the same shapes
  REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and "
               "%s correspondingly !",
               ShapeUtils::shapeAsString(labelsShapeInfo).c_str(),
               ShapeUtils::shapeAsString(predictionsShapeInfo).c_str());
  // weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
  REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0,
               "ABSOLUTE_DIFFERENCE_LOSS OP: weights array should be scalar or have the same rank as labels array, but "
               "got %i and %i correspondingly!",
               shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
  // check whether broadcast operation is possible for weights array
  REQUIRE_TRUE(
      shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0,
      "ABSOLUTE_DIFFERENCE_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s "
      "and labels = %s instead!",
      ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());

  DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(predictionsShapeInfo));

  auto dLdpShapeInfo = ShapeBuilders::copyShapeInfoAndType(predictionsShapeInfo, outType, false, block.getWorkspace());
  auto dLdwShapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, outType, false, block.getWorkspace());
  auto dLdlShapeInfo = ShapeBuilders::copyShapeInfoAndType(labelsShapeInfo, outType, false, block.getWorkspace());

  return SHAPELIST(CONSTANT(dLdpShapeInfo), CONSTANT(dLdwShapeInfo), CONSTANT(dLdlShapeInfo));
}

}  // namespace ops
}  // namespace sd

#endif