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

import lombok.Getter;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.api.ops.Op.Type;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

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

public class Switch extends BaseCompatOp {

    @Getter
    private SDVariable predicate;

    public Switch(SameDiff sameDiff, SDVariable input, SDVariable predicate){
        super(sameDiff, new SDVariable[]{input, predicate});
        this.predicate = predicate;
    }

    public Switch(INDArray input, INDArray predicate) {
        addInputArgument(input, predicate);
    }

    public Switch(){
    }

    /**
     * WARNING: do not change without changing serialization methods
     * See {@link org.nd4j.autodiff.samediff.serde.FlatBuffersMapper#getOpNum(String, Type)}
     *  and {@link org.nd4j.imports.converters.DifferentialFunctionClassHolder#customOpClassForHashAndName(long, String)}
     */
    public static final String OP_NAME = "switch";
    public static final int OP_NUM = 30;

    @Override
    public String opName() {
        return OP_NAME;
    }

    @Override
    public SDVariable[] outputVariables() {
        return super.outputVariables();
    }

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

    @Override
    public Type opType() {
        return Type.LOGIC;
    }



    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
        super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
    }

    @Override
    public void configureFromArguments() {
        super.configureFromArguments();
    }

    @Override
    public void setPropertiesForFunction(Map<String, Object> properties) {
        super.setPropertiesForFunction(properties);
    }


    @Override
    public void configureWithSameDiff(SameDiff sameDiff) {
        super.configureWithSameDiff(sameDiff);
        //samediff instance should already be set at this point
        if(args() != null && args().length > 1)
            this.predicate = arg(1);
    }


    @Override
    public int getNumOutputs(){
        return 2;   //2 outputs - 2 branches
    }

    @Override
    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes) {
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 2, "Expected 2 input dataypes for %s, got %s", getClass(), inputDataTypes);
        return Arrays.asList(inputDataTypes.get(0), inputDataTypes.get(0));
    }
}
