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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.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
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.*;

/**
 * Stack operation. Stacks n input tensors along provided axis.
 *
 * @author [email protected]
 */
public class Stack extends DynamicCustomOp {
    protected int jaxis;

    public Stack() {
    }

    public Stack(INDArray[] inputs, INDArray output, int axis){
        super(null, inputs, output == null ? null : new INDArray[]{output}, null, (List)null);
        this.jaxis = axis;
        addArgs();
    }

    public Stack(SameDiff sameDiff, SDVariable[] values, int axis) {
        super(null, sameDiff, values, false);
        this.jaxis = axis;
        addArgs();
    }

    public void addArgs() {
        addIArgument(jaxis);
    }

    @Override
    public String onnxName() {
        throw new NoOpNameFoundException("No onnx opName found for " + opName());
    }

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


    @Override
    public String toString() {
        return opName();
    }

    @Override
    public String[] tensorflowNames() {
        return new String[]{"Pack", "Stack"};
    }

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

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
        TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
        addArgs();
    }

    @Override
    public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
        throw new UnsupportedOperationException("No analog found for onnx for " + opName());
    }


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

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

        map.put("jaxis", axisMapping);

        for (val name : tensorflowNames())
            ret.put(name, map);

        return ret;
    }

    @Override
    public List doDiff(List f1) {
        return Arrays.asList(f().unstack(f1.get(0), jaxis, args().length));
    }

    @Override
    public List calculateOutputDataTypes(List dataTypes){
        DataType first = dataTypes.get(0);
        for( int i=1; i