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

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

import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.OpContext;
import org.nd4j.linalg.api.ops.random.BaseRandomOp;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.factory.Nd4j;

import java.util.Arrays;
import java.util.List;

public class BinomialDistributionEx extends BaseRandomOp {
    private long trials;
    private double probability;

    public BinomialDistributionEx() {
        super();
    }

    /**
     * This op fills Z with binomial distribution over given trials with single given probability for all trials
     * @param z
     * @param trials
     * @param probability
     */
    public BinomialDistributionEx(@NonNull INDArray z, long trials, double probability) {
        super(z, z, z);
        this.trials = trials;
        this.probability = probability;
        this.extraArgs = new Object[] {(double) this.trials, this.probability};
    }

    /**
     * This op fills Z with binomial distribution over given trials with probability for each trial given as probabilities INDArray
     * @param z
     * @param trials
     * @param probabilities array with probability value for each trial
     */
    public BinomialDistributionEx(@NonNull INDArray z, long trials, @NonNull INDArray probabilities) {
        super(z, probabilities, z);
        if (z.length() != probabilities.length())
            throw new IllegalStateException("Length of probabilities array should match length of target array");

        if (probabilities.elementWiseStride() < 1)
            throw new IllegalStateException("Probabilities array shouldn't have negative elementWiseStride");

        Preconditions.checkArgument(probabilities.dataType() == z.dataType(), "Probabilities and Z operand should have same data type");

        this.trials = trials;
        this.probability = 0.0;
        this.extraArgs = new Object[] {(double) this.trials, this.probability};
    }


    /**
     * This op fills Z with binomial distribution over given trials with probability for each trial given as probabilities INDArray
     *
     * @param z
     * @param probabilities
     */
    public BinomialDistributionEx(@NonNull INDArray z, @NonNull INDArray probabilities) {
        this(z, probabilities.length(), probabilities);
    }


    @Override
    public int opNum() {
        return 9;
    }

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

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

    @Override
    public String tensorflowName() {
        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
    }

    @Override
    public List<SDVariable> doDiff(List<SDVariable> f1) {
        throw new UnsupportedOperationException("BinomialDistributionEx does not have a derivative.");
    }

    @Override
    public List<DataBuffer> calculateOutputShape(OpContext oc) {
        return calculateOutputShape();
    }

    @Override
    public List<DataBuffer> calculateOutputShape() {
        LongShapeDescriptor longShapeDescriptor = LongShapeDescriptor.fromShape(shape,dataType);
        return Arrays.asList(Nd4j.createBuffer(longShapeDescriptor.toShapeInfo()));
    }
}
