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/*
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 *  * 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.
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package org.nd4j.linalg.lossfunctions.impl;

import lombok.EqualsAndHashCode;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossUtil;
import org.nd4j.common.primitives.Pair;

@EqualsAndHashCode(callSuper = false)
public class LossWasserstein implements ILossFunction {

    private INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask){
        if(!labels.equalShapes(preOutput)){
            Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
        }
        labels = labels.castTo(preOutput.dataType());   //No-op if already correct dtype

        INDArray output = activationFn.getActivation(preOutput.dup(), true);

        INDArray scoreArr = labels.mul(output);
        if (mask != null) {
            LossUtil.applyMask(scoreArr, mask);
        }
        return scoreArr;
    }

    @Override
    public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask,
            boolean average) {
        INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);

        double score = scoreArr.mean(1).sumNumber().doubleValue();

        if (average) {
            score /= scoreArr.size(0);
        }

        return score;
    }

    @Override
    public INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
        INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);
        return Nd4j.expandDims(scoreArr.mean(1), 1);
    }

    @Override
    public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
        if(!labels.equalShapes(preOutput)){
            Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
        }
        labels = labels.castTo(preOutput.dataType());   //No-op if already correct dtype
        INDArray dLda = labels.div(labels.size(1));

        if (mask != null && LossUtil.isPerOutputMasking(dLda, mask)) {
            LossUtil.applyMask(labels, mask);
        }

        INDArray grad = activationFn.backprop(preOutput, dLda).getFirst();

        if (mask != null) {
            LossUtil.applyMask(grad, mask);
        }

        return grad;
    }

    @Override
    public Pair computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn,
            INDArray mask, boolean average) {
        return new Pair<>(computeScore(labels, preOutput, activationFn, mask, average),
                computeGradient(labels, preOutput, activationFn, mask));
    }
    @Override
    public String name() {
        return toString();
    }

    @Override
    public String toString() {
        return "LossWasserstein()";
    }
}