/*
 *  ******************************************************************************
 *  *
 *  *
 *  * 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.NonNull;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
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.impl.shape.bp.SliceBp;

import java.util.*;

@Slf4j
public class Slice extends DynamicCustomOp {

    private int[] begin;
    private int[] size;

    public Slice() {
    }

    public Slice(SameDiff sameDiff, @NonNull SDVariable input, @NonNull int[] begin, @NonNull int[] size) {
        super(null, sameDiff, new SDVariable[]{input});
        this.begin = begin;
        this.size = size;
        addIArgument(begin);
        addIArgument(size);
    }

    public Slice(SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable begin, @NonNull SDVariable end) {
        super(null, sameDiff, new SDVariable[]{input, begin, end});
    }

    public Slice(INDArray input, int[] begin, int... size) {
        super(new INDArray[] {input}, null);
        this.begin = begin;
        this.size = size;
        addIArgument(begin);
        addIArgument(size);
    }

    public Slice(@NonNull INDArray input, @NonNull INDArray begin, @NonNull INDArray end) {
        super(new INDArray[]{input, begin, end}, null);
    }

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


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

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


    @Override
    public void configureFromArguments() {
        super.configureFromArguments();
        if(!iArguments.isEmpty()) {
            int indicesSize = iArguments.size() / 2;
            this.begin = new int[indicesSize];
            this.size = new int[indicesSize];
            for(int i = 0; i < indicesSize; i++) {
                begin[i] = iArguments.get(i).intValue();
                size[i] = iArguments.get(i + indicesSize).intValue();
            }
        }

    }

    @Override
    public void setPropertiesForFunction(Map<String, Object> properties) {
             //just use configure here
    }

    @Override
    public List<SDVariable> doDiff(List<SDVariable> grad) {
        if(args().length == 1) {
            return new SliceBp(sameDiff, arg(), grad.get(0), begin, size).outputs();
        } else {
            //Dynamic begin/size
            return new SliceBp(sameDiff, arg(0), grad.get(0), arg(1), arg(2)).outputs();
        }
    }

    @Override
    public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
        Preconditions.checkState(dataTypes != null & (dataTypes.size() == 1 || dataTypes.size() == 3),
                "Expected list with 1 or 3 datatypes for %s, got %s", getClass(), dataTypes);
        //Output type is same as input type. 3 inputs for import case
        return Collections.singletonList(dataTypes.get(0));
    }
}
