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/*
 *  ******************************************************************************
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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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 *  * SPDX-License-Identifier: Apache-2.0
 *  *****************************************************************************
 */

package org.nd4j.linalg.api.ops.random.custom;

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.ops.DynamicCustomOp;

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

public class RandomNormal extends DynamicCustomOp {

    private double mean;
    private double stdev;

    public RandomNormal() {

    }

    public RandomNormal(SameDiff sameDiff, SDVariable shape, double mean, double stdev) {
        super(null, sameDiff, new SDVariable[]{shape});
        this.mean = mean;
        this.stdev = stdev;

        addTArgument(mean, stdev);
    }

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

    @Override
    public String tensorflowName() {
        throw new NoOpNameFoundException("Not TF op name set for " + getClass().getSimpleName());
    }

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

    @Override
    public List calculateOutputDataTypes(List inputDataTypes){
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), inputDataTypes);
        //Input data type specifies the shape; output data type should be any float
        //TODO MAKE CONFIGUREABLE - https://github.com/eclipse/deeplearning4j/issues/6854
        return Collections.singletonList(DataType.FLOAT);
    }
}