/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * 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.
 *
 * 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
 ******************************************************************************/

package org.nd4j.linalg.api.ops.impl.controlflow.compat;

import com.google.common.collect.Lists;
import lombok.Getter;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.api.ops.Op.Type;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

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

/**
 * Switch op forwards input to one of two outputs based on the value of a predicate
 */
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(){ }

    /**
     * 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 List<LongShapeDescriptor> calculateOutputShape() {
        if(args()[0].getArr() != null) {
            val arg0 = args()[0];
            val arr0 = arg0.getArr();
            val dtype = arr0.dataType();
            return Arrays.asList(LongShapeDescriptor.fromShape(arg0.getShape(), dtype),LongShapeDescriptor.fromShape(arg0.getShape(), dtype));
        }
        else
            return Collections.emptyList();
    }

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

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

    @Override
    public Op.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 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);
        Preconditions.checkState(inputDataTypes.get(1) == DataType.BOOL, "Input datatype 1 (predicate) should be bool for %s, got %s", getClass(), inputDataTypes);
        return Arrays.asList(inputDataTypes.get(0), inputDataTypes.get(0));
    }
}
