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/*******************************************************************************
 * 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.transforms.clip;

import onnx.Onnx;
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.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.shape.Shape;
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;

public class ClipByNorm extends DynamicCustomOp {

    private double clipValue;

    public ClipByNorm() {

    }

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

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

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

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

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

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


    @Override
    public List doDiff(List grad) {
        return Collections.singletonList(new ClipByNormBp(f().sameDiff(), arg(), grad.get(0), clipValue, dimensions).outputVariable());
    }

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