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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.
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 *  *  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.scatter;

import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

import java.util.*;


public class ScatterMin extends DynamicCustomOp {

    public ScatterMin(SameDiff sameDiff, SDVariable ref, SDVariable indices, SDVariable updates) {
        super(null, sameDiff, new SDVariable[]{ref, indices, updates}, false);
    }

    public ScatterMin() {}

    public ScatterMin(@NonNull INDArray ref, @NonNull INDArray indices, @NonNull INDArray update){
        super(new INDArray[]{ref, indices, update}, null);
    }

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

    @Override
    public String onnxName() {
        throw new NoOpNameFoundException("No onnx op opName found for " + opName());
    }

    @Override
    public String tensorflowName() {
        return "ScatterMin";
    }

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
        throw new UnsupportedOperationException("Use the new Tensorflow Importer instead. This method is now removed.");

    }

    @Override
    public List<SDVariable> doDiff(List<SDVariable> gradOut) {
        //3 args: ref, indices, updates
        //For non-modified indices, input gradient (reference) is same as output gradient
        //For modified indices, dL/dref = dL/dOut if(ref[index[i],j] == min) or 0 otherwise
        //And for updates, dL/du = dL/dOut if(update[i,j]==min) or 0 otherwise

        List<SDVariable> ret = new ArrayList<>(3);
        SDVariable notModified = arg(0).eq(outputVariable()).castTo(arg(0).dataType());   //0 if modified, 1 otherwise
        SDVariable refGrad = gradOut.get(0).mul(notModified);

        SDVariable gatherOut = sameDiff.gather(outputVariable(), arg(1), 0);
        SDVariable gatherGrad = sameDiff.gather(gradOut.get(0), arg(1), 0);
        SDVariable outIsUpdate = gatherOut.eq(arg(2)).castTo(arg(2).dataType());
        SDVariable updateGrad = gatherGrad.mul(outIsUpdate);

        return Arrays.asList(refGrad, sameDiff.zerosLike(arg(1)), updateGrad);
    }

    @Override
    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 3, "Expected exactly 3 input datatypes for %s, got %s", getClass(), inputDataTypes);
        Preconditions.checkState(inputDataTypes.get(0) == inputDataTypes.get(2), "Reference (input 0) and updates (input 2) must have exactly same data types, got %s and %s",
                inputDataTypes.get(0), inputDataTypes.get(2));
        return Collections.singletonList(inputDataTypes.get(0));
    }

}
