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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 lombok.NonNull;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
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.linalg.api.ops.impl.transforms.gradient.DynamicPartitionBp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

import java.util.*;


public class DynamicPartition extends DynamicCustomOp {

    private int numPartitions;
    private SDVariable partitions;

    public DynamicPartition() {
    }

    public DynamicPartition(SameDiff sameDiff, SDVariable input,  SDVariable[] partitions, int numPartitions) {
        this(sameDiff, input, partitions[0], numPartitions);
    }

    public DynamicPartition(SameDiff sameDiff, SDVariable input,  SDVariable partitions, int numPartitions) {
        super(null, sameDiff,  new SDVariable[] {input, partitions}, false);

        this.partitions = partitions;
        this.numPartitions = numPartitions;
        addArgs();
    }

    public DynamicPartition(@NonNull INDArray input, @NonNull INDArray partitions, int numPartitions) {
        super(new INDArray[]{input, partitions}, null);
        this.numPartitions = numPartitions;
        addArgs();
    }

    public DynamicPartition(INDArray x, INDArray [] partitions, int numPartitions){
        //TODO; This needs fixing.
        super(new INDArray[]{x}, null);
        // this.partitions = partitions;
        this.numPartitions = numPartitions;
        addArgs();
    }

    @Override
    public List doDiff(List i_v) {
        return new DynamicPartitionBp(sameDiff, arg(0), arg(1), i_v.toArray(new SDVariable[i_v.size()]), numPartitions).outputs();
    }

    protected void addArgs() {
        addIArgument(numPartitions);
    }

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
        TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
        addArgs();
    }


    @Override
    public Map> mappingsForFunction() {
        Map> ret = new HashMap<>();
        Map attrs = new LinkedHashMap<>();

        val numPartitions = PropertyMapping.builder()
                .tfAttrName("num_partitions")
                .propertyNames(new String[]{"numPartitions"})
                .build();
        attrs.put("numPartitions", numPartitions);

        ret.put(tensorflowName(),attrs);
        return ret;
    }


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


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

    @Override
    public String onnxName() {
        return "Dynamic partitioning currently not supported by ONNX";
    }

    @Override
    public int getNumOutputs(){
        return numPartitions;
    }

    @Override
    public List calculateOutputDataTypes(List dataTypes){
        //Output type: same as (data) input type
        List out = new ArrayList<>(numPartitions);
        for( int i=0; i