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

public class GaussianDistribution extends BaseRandomOp {
    private double mean;
    private double stddev;    

    public GaussianDistribution(SameDiff sd, double mean, double stddev, long[] shape){
        super(sd, shape);
        this.mean = mean;
        this.stddev = stddev;
        this.extraArgs = new Object[] {this.mean, this.stddev};
    }

    public GaussianDistribution(SameDiff sd, double mean, double stddev, DataType dataType, long[] shape){
        super(sd, shape);
        this.mean = mean;
        this.stddev = stddev;
        this.dataType = dataType;
        this.extraArgs = new Object[] {this.mean, this.stddev};
    }

    public GaussianDistribution() {
        super();
    }

    public GaussianDistribution(double mean, double stddev, DataType datatype, long... shape) {
        this(Nd4j.createUninitialized(datatype, shape), mean, stddev);
    }

    /**
     * This op fills Z with random values within stddev..mean..stddev boundaries
     * @param z
     * @param mean
     * @param stddev
     */
    public GaussianDistribution(@NonNull INDArray z, double mean, double stddev) {
        super(z, z, z);
        this.mean = mean;
        this.stddev = stddev;
        this.extraArgs = new Object[] {this.mean, this.stddev};
    }


    public GaussianDistribution(@NonNull INDArray z, @NonNull INDArray means, double stddev) {
        if (z.length() != means.length())
            throw new IllegalStateException("Result length should be equal to provided Means length");

        if (means.elementWiseStride() < 1)
            throw new IllegalStateException("Means array can't have negative EWS");

        this.mean = 0.0;
        this.stddev = stddev;
        this.z = z;
        this.extraArgs = new Object[] {this.mean, this.stddev};
    }

    /**
     * This op fills Z with random values within -1.0..0..1.0
     * @param z
     */
    public GaussianDistribution(@NonNull INDArray z) {
        this(z, 0.0, 1.0);
    }

    /**
     * This op fills Z with random values within stddev..0..stddev
     * @param z
     */
    public GaussianDistribution(@NonNull INDArray z, double stddev) {
        this(z, 0.0, stddev);
    }

    @Override
    public int opNum() {
        return 6;
    }


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

    @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 doDiff(List f1) {
        return Collections.emptyList();
    }


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

    @Override
    public List calculateOutputShape() {
        LongShapeDescriptor longShapeDescriptor = LongShapeDescriptor.fromShape(shape,dataType);
        return Arrays.asList(longShapeDescriptor);
    }

    @Override
    public List calculateOutputDataTypes(List inputDataTypes){
        Preconditions.checkState(inputDataTypes == null || inputDataTypes.isEmpty(), "Expected no input datatypes (no args) for %s, got %s", getClass(), inputDataTypes);
        return Collections.singletonList(dataType);
    }

    @Override
    public boolean isTripleArgRngOp() {
        return true;
    }
}