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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.shape;

import lombok.NonNull;
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.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
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;


/**
 * Computes a diagonal matrix of shape (n, n) from a vector of length n.
 * More generally puts a an n-dimensional tensor on the diagonal part
 * of a tensor of 2*n dimensions.
 *
 * @author Max Pumperla
 */
public class Diag extends DynamicCustomOp {

    public Diag() {
    }

    public Diag(@NonNull INDArray input) {
        this(input, null);
    }

    public Diag(@NonNull INDArray input, @NonNull INDArray output){
        super(null, new INDArray[]{input}, wrapOrNull(output));
    }

    public Diag(SameDiff sameDiff, SDVariable[] args, boolean inPlace) {
        super(null, sameDiff, args, inPlace);

    }

    @Override
    public List doDiff(List i_v) {
        SDVariable grad = i_v.get(0);
        SDVariable ret = sameDiff.math().diagPart(grad);
        return Collections.singletonList(ret);
    }


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

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

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

    @Override
    public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
        super.initFromOnnx(node, initWith, attributesForNode, graph);
    }

    @Override
    public List calculateOutputDataTypes(List dataTypes){
        Preconditions.checkState(dataTypes.size() == 1, "Expected list with exactly 1 datatype for %s, got %s", getClass(), dataTypes);
        //Output type is same as input type
        return dataTypes;
    }
}