/*
 *  ******************************************************************************
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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.api.ops.impl.loss;

import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.loss.bp.LogPoissonLossBp;

import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class LogPoissonLoss extends BaseLoss {
    private boolean full;

    public LogPoissonLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels){
        this(sameDiff, lossReduce, predictions, weights, labels, false);
    }

    public LogPoissonLoss(SameDiff sameDiff, SDVariable labels, SDVariable predictions, SDVariable weights,
                          LossReduce lossReduce, boolean full) {
        this(sameDiff, lossReduce, predictions, weights, labels, full);
    }

    public LogPoissonLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, boolean full){
        super(sameDiff, lossReduce, predictions, weights, labels);
        this.full = full;
        addArgs();
    }

    public LogPoissonLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce, boolean full){
        super(lossReduce, predictions, weights, labels);
        this.full = full;
        addArgs();
    }

    public LogPoissonLoss(){ }

    protected void addArgs(){
        super.addArgs();
        if(full){
            iArguments.add((long) 1);
        }
    }

    @Override
    public void configureFromArguments() {
        if(!iArguments.isEmpty()) {
            this.lossReduce = LossReduce.values()[iArguments.get(0).intValue()];
        }
    }

    @Override
    public Map<String, Object> propertiesForFunction() {
        Map<String,Object> ret = new HashMap<>();
        ret.put("full",full);
        ret.put("reductionMode",lossReduce.ordinal());
        return ret;
    }

    @Override
    public void setPropertiesForFunction(Map<String, Object> properties) {
        if(properties.containsKey("reductionMode")) {
            Integer reductionMode = getIntValueFromProperty("reductionMode",properties);
            this.lossReduce = LossReduce.values()[reductionMode];
        }

        if(properties.containsKey("full")) {
            Boolean full = getBooleanFromProperty("full",properties);
            this.full = full;
        }

    }

    @Override
    public String opName() {
        return "log_poisson_loss";
    }

    @Override
    public List<SDVariable> doDiff(List<SDVariable> grad){
        //No external gradient
        //Args are: predictions, weights, label
        return new LogPoissonLossBp(sameDiff, lossReduce, arg(0), arg(1), arg(2), full).outputs();
    }

}
