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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.clip;

import onnx.Onnx;
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.DynamicCustomOp;
import org.nd4j.shade.guava.primitives.Ints;
import org.nd4j.shade.guava.primitives.Longs;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

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

public class ClipByNorm extends DynamicCustomOp {

    private double clipValue;

    public ClipByNorm() {

    }

    public ClipByNorm(SameDiff sameDiff, SDVariable x, double clipValue, long... dimensions) {
        super(null, sameDiff, new SDVariable[]{x});
        this.clipValue = clipValue;
        this.dimensions = dimensions;
        addIArgument(dimensions);
        addTArgument(clipValue);
    }

    public ClipByNorm(INDArray in, double clipValue, long... dimensions){
        this(in, null, clipValue, dimensions);
    }

    public ClipByNorm(INDArray in, INDArray out, double clipValue, long... dimensions){
        super(null, new INDArray[]{in}, wrapOrNull(out), Collections.singletonList(clipValue), dimensions);
    }

    public ClipByNorm(SameDiff sd, SDVariable x, SDVariable clipValue, SDVariable dimensions) {
        super(null,sd,new SDVariable[]{x,clipValue,dimensions});
    }

    public ClipByNorm(INDArray x, INDArray clipValue, INDArray dimensions) {
        super(null,new INDArray[]{x,clipValue,dimensions},null);
    }

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

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
        throw new UnsupportedOperationException("Not yet implemented");
    }

    @Override
    public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map<String, Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph) {
        throw new UnsupportedOperationException("Not yet implemented");
    }

    @Override
    public void setPropertiesForFunction(Map<String, Object> properties) {
        if(properties.containsKey("clipValue")) {
            Double clip = getDoubleValueFromProperty("clipValue",properties);
            this.clipValue = clip;
        }

        if(properties.containsKey("dimensions")) {
            Long dimension = getLongValueFromProperty("dimensions",properties);
            this.dimensions = new long[]{dimension.longValue()};
        }

    }

    @Override
    public void configureFromArguments() {
        super.configureFromArguments();
        if(!iArguments.isEmpty()) {
            this.dimensions = Longs.toArray(iArguments);
        }

        if(!tArguments.isEmpty()) {
            this.clipValue = tArguments.get(0);
        }

    }

    @Override
    public List<SDVariable> doDiff(List<SDVariable> grad) {
        return new ClipByNormBp(sameDiff, arg(), grad.get(0), clipValue, dimensions).outputs();
    }

    @Override
    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), inputDataTypes);
        return inputDataTypes;
    }
}
