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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.api.ops.impl.transforms.custom;

import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.common.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

import java.util.Arrays;
import java.util.List;
import java.util.Map;

public class TopK extends DynamicCustomOp {

    private boolean sorted;
    private int k;

    public TopK(){ }

    public TopK(SameDiff sd, SDVariable in, int k, boolean sorted) {
        super(sd, new SDVariable[]{in}, false);
        this.k = k;
        this.sorted = sorted;
        addIArgument(ArrayUtil.fromBoolean(sorted), k);
    }


    public TopK(INDArray input, double k, boolean sorted) {
        super(null,new INDArray[]{input},null);
        this.k = (int) k;
        this.sorted = sorted;
        addIArgument(ArrayUtil.fromBoolean(sorted),this.k);
    }

    public TopK(SameDiff sd, SDVariable input, double k, boolean sorted) {
        this(sd,input,(int) k,sorted);
    }

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

    @Override
    public String tensorflowName() {
        return "TopKV2";
    }

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {

        String thisName = nodeDef.getName();

        // FIXME: ????
        String inputName = thisName + "/k";
        NodeDef kNode = null;
        for(int i = 0; i < graph.getNodeCount(); i++) {
            if(graph.getNode(i).getName().equals(inputName)){
                kNode = graph.getNode(i);
                break;
            }
        }

        this.sorted = nodeDef.getAttrOrThrow("sorted").getB();

        if (kNode != null) {
            Preconditions.checkState(kNode != null, "Could not find 'k' parameter node for op: %s", thisName);

            INDArray arr = TFGraphMapper.getNDArrayFromTensor(kNode);
            this.k = arr.getInt(0);

            addIArgument(ArrayUtil.fromBoolean(sorted), k);
        } else
            addIArgument(ArrayUtil.fromBoolean(sorted));
    }

    @Override
    public List doDiff(List i_v) {
        throw new UnsupportedOperationException("Not implemented yet");
    }

    @Override
    public void setPropertiesForFunction(Map properties) {
        if(properties.containsKey("sorted")) {
            this.sorted = getBooleanFromProperty("sorted",properties);
        }

        if(properties.containsKey("k")) {
            this.k = getIntValueFromProperty("k",properties);
        }

    }

    @Override
    public List calculateOutputDataTypes(List dataTypes) {
        //2 outputs: values and indices
        //TODO make this configurable
        return Arrays.asList(dataTypes.get(0), DataType.INT);
    }
}