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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.
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 *  *  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
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package org.nd4j.linalg.api.ops.impl.transforms.custom;


import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
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.factory.Nd4j;

import java.util.*;

public class BatchToSpace extends DynamicCustomOp {

    private int[] blocks;
    private int[][] crops;

    public BatchToSpace() {
    }

    public BatchToSpace(SameDiff sameDiff, SDVariable x, int[] blocks, int[] croppingTop, int... croppingBottom) {
        this(sameDiff, x, blocks, new int[][]{croppingTop, croppingBottom}, false);
    }

    public BatchToSpace(SameDiff sameDiff, SDVariable x, int[] blocks, int[][] crops, boolean inPlace) {
        this(sameDiff, new SDVariable[]{x}, blocks, crops, inPlace);
    }

    public BatchToSpace(SameDiff sameDiff, SDVariable[] args, int[] blocks, int[][] crops, boolean inPlace) {
        super(null, sameDiff, new SDVariable[]{args[0], sameDiff.constant(Nd4j.createFromArray(crops))}, inPlace);

        this.blocks = blocks;
        this.crops = crops;

        for (val b : blocks)
            addIArgument(b);
    }

    public BatchToSpace(INDArray x, int[] blocks, int[] croppingTop, int... croppingBottom) {
        addInputArgument(x);
        int[][] crops = new int[][]{croppingTop, croppingBottom};
        this.blocks = blocks;
        this.crops = crops;

        for (val b : blocks)
            addIArgument(b);
    }


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

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

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



    @Override
    public void configureFromArguments() {
        super.configureFromArguments();
    }


    @Override
    public Map propertiesForFunction() {
        Map ret = new HashMap<>();
        if(blocks != null)
            ret.put("blocks",blocks);
        if(crops != null)
            ret.put("crops",crops);
        return ret;
    }

    @Override
    public void setPropertiesForFunction(Map properties) {
        if(properties.containsKey("crops")) {
            int[][] crops = (int[][]) properties.get("crops");
            this.crops =  crops;
        }
        if(properties.containsKey("blocks")) {
            int[] blocks = (int[]) properties.get("blocks");
            this.blocks = blocks;
        }
    }


    @Override
    public List doDiff(List i_v) {
        // Inverse of batch to space is space to batch with same blocks and padding as crops
        SDVariable gradient = sameDiff.setupFunction(i_v.get(0));
        return Arrays.asList(sameDiff.cnn().spaceToBatch(gradient, blocks, crops[0], crops[1]));
    }

    @Override
    public List calculateOutputDataTypes(List dataTypes){
        return Collections.singletonList(dataTypes.get(0));
    }
}