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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 org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
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.*;

/**
 *
 */
public class Squeeze extends DynamicCustomOp {

    private int[] squeezeDims;

    public Squeeze() {
    }

    public Squeeze(SameDiff sameDiff, SDVariable arg, int[] squeezeDims) {
        super(null, sameDiff, new SDVariable[]{arg});
        this.squeezeDims = squeezeDims;
    }

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
        super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
        if (squeezeDims != null)
            addIArgument(squeezeDims);
    }

    @Override
    public void resolvePropertiesFromSameDiffBeforeExecution() {
        super.resolvePropertiesFromSameDiffBeforeExecution();
        if (squeezeDims != null && numIArguments() < squeezeDims.length) {
            addIArgument(squeezeDims);
        }
    }

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

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

    @Override
    public Map> mappingsForFunction() {
        Map> ret = new HashMap<>();
        Map mapping = new LinkedHashMap<>();
        val squeezeDims = PropertyMapping.builder()
                .tfAttrName("squeeze_dims")
                .propertyNames(new String[]{"squeezeDims"})
                .build();
        mapping.put("squeezeDims", squeezeDims);
        ret.put(tensorflowName(), mapping);
        return ret;
    }

    @Override
    public List doDiff(List i_v) {
        if (squeezeDims == null) {
            //TODO Strictly speaking this *is* possible by inspecting the input array
            throw new IllegalStateException("Cannot do Squeeze backprop with no dimensions");
        }
        SDVariable ret = i_v.get(0);
        for (int d : squeezeDims) {
            ret = sameDiff.expandDims(ret, d);
        }
        ;
        return Arrays.asList(ret);
    }
}




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