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

import lombok.NoArgsConstructor;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
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.*;

@NoArgsConstructor
public class Repeat extends DynamicCustomOp {
    private int jaxis;

    public Repeat(int axis) {
        this.jaxis = axis;
    }

    public Repeat(SameDiff sameDiff, SDVariable[] args, int axis) {
        super(null, sameDiff, args);
        this.jaxis = axis;
    }

    public Repeat(INDArray[] inputs, INDArray[] outputs, List<Double> tArguments, List<Long> iArguments, int axis) {
        super(null, inputs, outputs, tArguments, iArguments);
        this.jaxis = axis;
    }

    public Repeat(INDArray[] inputs, INDArray[] outputs, int axis) {
        super(null, inputs, outputs);
        this.jaxis = axis;
    }

    public Repeat(SameDiff sameDiff, SDVariable[] args, boolean inPlace, int axis) {
        super(null, sameDiff, args, inPlace);
        this.jaxis = axis;
    }

    public Repeat(SameDiff sd, SDVariable input, SDVariable repeats, int axis) {
        this(sd,new SDVariable[]{input,repeats},axis);
    }

    public Repeat(INDArray input, INDArray repeats, int axis) {
        this(new INDArray[]{input,repeats},null,axis);
    }


    @Override
    public Map<String, Object> propertiesForFunction() {
        return Collections.<String,Object>singletonMap("axis", axis);
    }


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


    @Override
    public Map<String, Map<String, PropertyMapping>> mappingsForFunction() {
        Map<String, Map<String, PropertyMapping>> ret = new HashMap<>();
        Map<String, PropertyMapping> map = new HashMap<>();

        val axisMapping = PropertyMapping.builder()
                .onnxAttrName("axis")
                .tfInputPosition(-1)
                .propertyNames(new String[]{"axis"})
                .build();

        map.put("axis", axisMapping);

        ret.put(tensorflowName(), map);
        ret.put(onnxName(), map);

        return ret;
    }


    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
        TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
        addIArgument(jaxis);
    }

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

    @Override
    public String onnxName() {
        return "Repeat";
    }

    @Override
    public List<SDVariable> doDiff(List<SDVariable> i_v) {
        SDVariable ret = outputVariables()[0];
        return Collections.singletonList(ret);
    }

    @Override
    public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
        //Output type is always same as input type
        return Collections.singletonList(dataTypes.get(0));
    }

}
