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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.
 *  *
 *  * 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.autodiff.samediff.serde.FlatBuffersMapper;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.imports.descriptors.properties.adapters.DataTypeAdapter;
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.nd4j.linalg.api.ops.Op;
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;

/**
 * Returns the shape of the input array.
 *
 * @author Adam Gibson
 */
public class Shape extends DynamicCustomOp {

    protected DataType dataType;

    public Shape() {}

    public Shape(SameDiff sameDiff, SDVariable input) {
        this(sameDiff, input, false);
    }

    public Shape(SameDiff sameDiff, SDVariable input, boolean inPlace) {
        super(null, sameDiff, new SDVariable[] {input}, inPlace);
    }

    public Shape(INDArray in, INDArray out){
        super(null, in, out, null, null);
    }

    public Shape(INDArray in){
        this(in, null);
    }

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


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

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

    @Override
    public Op.Type opType() {
        return Op.Type.CUSTOM;
    }

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

        dataType = TFGraphMapper.convertType(nodeDef.getAttrOrThrow("out_type").getType());
        val dtype = DataTypeAdapter.dtypeConv(nodeDef.getAttrOrThrow("out_type").getType());
        iArguments.add((long) FlatBuffersMapper.getDataTypeAsByte(dtype));
    }

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

    @Override
    public List doDiff(List i_v) {
        return Collections.singletonList(sameDiff.zerosLike(arg()));
    }

    @Override
    public List calculateOutputDataTypes(List dataTypes){
        Preconditions.checkState(dataTypes.size() == 1, "Expected list with exactly 1 datatype for %s, got %s", getClass(), dataTypes);
        return Collections.singletonList(dataType == null ? DataType.LONG : dataType);
    }
}