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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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 *  * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.scatter;

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.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Map;


public class ScatterUpdate extends DynamicCustomOp {
    public static enum UpdateOp {
        ADD,
        SUBTRACT,
        MULTIPLY,
        DIVIDE,
        REVERSE_SUBTRACT,
        REVERSE_DIVIDE,
        ASSIGN
    }

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

    public ScatterUpdate(){}

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

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

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

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

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
        if (nodeDef.containsAttr("use_locking")) {
            if (nodeDef.getAttrOrThrow("use_locking").getB() == true) {
                bArguments.add(true);
            } else {
                bArguments.add(false);
            }
        } else
            bArguments.add(false);
    }

    @Override
    public List doDiff(List 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 * dOut/dRef = dL/dOut * d(update)/dRef = 0
        //And for updates, dL/du = dL/dOut * dOut/du = dL/dOut * d(update)/du = dL/dOut -> gather op

        SDVariable indices = arg(1);
        SDVariable updates = arg(2);

        List ret = new ArrayList<>(3);
        SDVariable zerosUpdate = sameDiff.zerosLike(updates);
        SDVariable gradRef = sameDiff.scatterMul(gradOut.get(0), indices, zerosUpdate);  //TODO optimize
        ret.add(gradRef);            //Reference array gradient
        ret.add(sameDiff.zerosLike(arg(1)));  //Indices

        SDVariable gather = sameDiff.gather(gradOut.get(0), indices, 0);       //Updates
        ret.add(gather);

        return ret;
    }

    @Override
    public List calculateOutputDataTypes(List 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));
    }

}