/* ******************************************************************************
*
*
* 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 sgazeos@gmail.com
//
#include <array/NDArray.h>
#include <helpers/ShapeUtils.h>
#include <ops/declarable/helpers/minimax.h>
#include <system/op_boilerplate.h>

namespace sd {
namespace ops {
namespace helpers {

template <typename T>
static void minimumBPFunctor_(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) {
 auto lambdaX = LAMBDA_TTT(_e, _x, _y) { return _x <= _y ? _e : (T)0.; });

 auto lambdaY = LAMBDA_TTT(_e, _x, _y) { return _x >= _y ? _e : (T)0.; });

 if (x->isSameShape(y)) {
   // PWT case case

   // X gradient
   epsNext->applyTriplewiseLambda<T>(x, y, lambdaX, gradX);

   // Y gradient
   epsNext->applyTriplewiseLambda<T>(x, y, lambdaY, gradY);

 } else if (y->isScalar()) {
   T s = y->e<T>(0);
   auto lambdaS = LAMBDA_TT(_e, _x, s) { return _x <= s ? _e : (T)0.; });
   float zero = 0.0f;
   // scalar case
   auto tmp = epsNext->reduceNumber(reduce::Sum);
   if (x <= y)
     gradY->assign(tmp);
   else
     gradY->assign(zero);

   epsNext->applyPairwiseLambda<T>(x, lambdaS, gradX);
   delete tmp;
 } else {
   // broadcast case

   // in this case we want to boost our X and Y shapes to the size of FF pass output (or epsNext, which has the same
   // shape)
   auto preX = x->dup();
   auto preY = y->dup();

   auto targetShape = epsNext->getShapeAsVector();

   preX->tileToShape(*targetShape, *preX);
   preY->tileToShape(*targetShape, *preY);

   epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaX, preX);
   epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaY, preY);

   auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
   auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());

   if (axisX.size() > 0) {
     auto sum = preX->reduceAlongDimension(reduce::Sum, &axisX);
     gradX->assign(sum);
   } else
     gradX->assign(preX);

   if (axisY.size() > 0) {
     auto sum = preY->reduceAlongDimension(reduce::Sum, &axisY);
     gradY->assign(sum);
     delete sum;
   } else
     gradY->assign(preY);

   delete targetShape;
   delete preX;  // Clean up duped array
   delete preY;  // Clean up duped array
 }

}

template <typename T>
void maximumBPFunctor_(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) {
 auto lambdaX = LAMBDA_TTT(_e, _x, _y) { return _x >= _y ? _e : (T)0.; });

 auto lambdaY = LAMBDA_TTT(_e, _x, _y) { return _x <= _y ? _e : (T)0.; });

 if (x->isSameShape(y)) {
   // PWT case case

   // X gradient
   epsNext->applyTriplewiseLambda<T>(x, y, lambdaX, gradX);

   // Y gradient
   epsNext->applyTriplewiseLambda<T>(x, y, lambdaY, gradY);

 } else if (y->isScalar()) {
   T s = y->e<T>(0);
   auto lambdaS = LAMBDA_TT(_e, _x, s) { return _x >= s ? _e : (T)0.; });

   // scalar case
   auto tmp = epsNext->reduceNumber(reduce::Sum);
   float zero = 0.0f;
   if (x <= y)
     gradY->assign(tmp);
   else
     gradY->assign(zero);

   delete tmp;
   epsNext->applyPairwiseLambda<T>(x, lambdaS, gradX);
 } else {
   // broadcast case

   // in this case we want to boost our X and Y shapes to the size of FF pass output (or epsNext, which has the same
   // shape)
   auto preX = x->dup();
   auto preY = y->dup();

   auto targetShape = epsNext->getShapeAsVector();

   preX->tileToShape(*targetShape, *preX);
   preY->tileToShape(*targetShape, *preY);

   epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaX, preX);
   epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaY, preY);

   auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
   auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());

   if (axisX.size() > 0) {
     auto sum = preX->reduceAlongDimension(reduce::Sum, &axisX);
     gradX->assign(sum);
     delete sum;
   } else
     gradX->assign(preX);

   if (axisY.size() > 0) {
     auto sum = preY->reduceAlongDimension(reduce::Sum, &axisY);
     gradY->assign(sum);
     delete sum;
   } else
     gradY->assign(preY);

   delete preX;  // Clean up duped array
   delete preY;  // Clean up duped array
 }

}

void minimumBPFunctor(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX,
                     NDArray* gradY) {
 BUILD_SINGLE_SELECTOR(x->dataType(), minimumBPFunctor_, (context, x, y, epsNext, gradX, gradY), SD_NUMERIC_TYPES);
}

void maximumBPFunctor(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX,
                     NDArray* gradY) {
 BUILD_SINGLE_SELECTOR(x->dataType(), maximumBPFunctor_, (context, x, y, epsNext, gradX, gradY), SD_NUMERIC_TYPES);
}
BUILD_SINGLE_TEMPLATE( void minimumBPFunctor_,
                     (LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY), SD_NUMERIC_TYPES);
BUILD_SINGLE_TEMPLATE( void maximumBPFunctor_,
                     (LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY), SD_NUMERIC_TYPES);

}  // namespace helpers
}  // namespace ops
}  // namespace sd