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/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * 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.
 *
 * 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.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.random.BaseRandomOp;
import org.nd4j.linalg.factory.Nd4j;

import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

/**
 * This Op generates log-normal distribution over provided mean and stddev
 *
 * @author [email protected]
 */
public class LogNormalDistribution extends BaseRandomOp {
    private double mean;
    private double stddev;

    public LogNormalDistribution() {
        super();
    }

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

    public LogNormalDistribution(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 LogNormalDistribution(@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 LogNormalDistribution(@NonNull INDArray z, @NonNull INDArray means, double stddev) {
        super(z,means,z);
        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.extraArgs = new Object[] {this.mean, this.stddev};
    }

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

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

    @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 int opNum() {
        return 10;
    }

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

    @Override
    public void setZ(INDArray z){
        //We want all 3 args set to z for this op
        this.x = z;
        this.y = z;
        this.z = z;
    }

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

    @Override
    public List calculateOutputDataTypes(List inputDataTypes){
        Preconditions.checkState(inputDataTypes == null || inputDataTypes.isEmpty(), "Expected no input datatypes (no args) 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/deeplearning4j/deeplearning4j/issues/6854
        return Collections.singletonList(DataType.DOUBLE);
    }
}