/*
 *  ******************************************************************************
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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.
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package org.nd4j.linalg.api.ops.impl.transforms.gradient;

import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;

import java.util.Collections;
import java.util.List;

public class SoftmaxBp extends DynamicCustomOp {

    public SoftmaxBp(){ }

    public SoftmaxBp(SameDiff sd, SDVariable input, SDVariable grad, SDVariable softmaxOut, Integer dimension) {
        super(null, sd, new SDVariable[]{input, grad,softmaxOut});
        if(dimension != null)
            addIArgument(dimension);
    }

    public SoftmaxBp(@NonNull INDArray input, @NonNull INDArray grad, INDArray softmaxOut, Integer dimension) {
        this(input, grad, softmaxOut,null, dimension);
    }

    public SoftmaxBp(@NonNull INDArray input, @NonNull INDArray grad, INDArray output, INDArray softmaxOut, Integer dimension) {
        super(new INDArray[]{input, grad,softmaxOut}, wrapOrNull(output));
        if(dimension != null)
            addIArgument(dimension);
    }



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

    @Override
    public List<SDVariable> doDiff(List<SDVariable> grad){
        throw new UnsupportedOperationException("Differentiating op softmax_bp not supported");
    }

    @Override
    public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes) {
        Preconditions.checkState(dataTypes != null && dataTypes.size() == 3, "Expected exactly 2 inputs datatype for %s, got %s", getClass(), dataTypes);
        Preconditions.checkState(dataTypes.get(0).isFPType(), "Input 0 must be a floating point type, got %s", dataTypes.get(0));
        Preconditions.checkState(dataTypes.get(1).isFPType(), "Input 1 must be a floating point type, got %s", dataTypes.get(1));
        Preconditions.checkState(dataTypes.get(0) == dataTypes.get(1), "Both input must be same type: got %s", dataTypes);
        return Collections.singletonList(dataTypes.get(0));
    }
}
