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 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
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 *  *  information regarding copyright ownership.
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package org.deeplearning4j.spark.api.worker;

import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.broadcast.Broadcast;
import org.datavec.spark.util.SerializableHadoopConfig;
import org.deeplearning4j.core.loader.DataSetLoader;
import org.deeplearning4j.spark.api.TrainingResult;
import org.deeplearning4j.spark.api.TrainingWorker;
import org.deeplearning4j.spark.api.WorkerConfiguration;
import org.deeplearning4j.spark.iterator.PathSparkDataSetIterator;
import org.nd4j.linalg.dataset.DataSet;

import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;

public class ExecuteWorkerPathFlatMap<R extends TrainingResult> implements FlatMapFunction<Iterator<String>, R> {

    private final FlatMapFunction<Iterator<DataSet>, R> workerFlatMap;
    private final DataSetLoader dataSetLoader;
    private final int maxDataSetObjects;
    private final Broadcast<SerializableHadoopConfig> hadoopConfig;

    public ExecuteWorkerPathFlatMap(TrainingWorker<R> worker, DataSetLoader dataSetLoader, Broadcast<SerializableHadoopConfig> hadoopConfig) {
        this.workerFlatMap = new ExecuteWorkerFlatMap<>(worker);
        this.dataSetLoader = dataSetLoader;
        this.hadoopConfig = hadoopConfig;

        //How many dataset objects of size 'dataSetObjectNumExamples' should we load?
        //Only pass on the required number, not all of them (to avoid async preloading data that won't be used)
        //Most of the time we'll get exactly the number we want, but this isn't guaranteed all the time for all
        // splitting strategies
        WorkerConfiguration conf = worker.getDataConfiguration();
        int dataSetObjectNumExamples = conf.getDataSetObjectSizeExamples();
        int workerMinibatchSize = conf.getBatchSizePerWorker();
        int maxMinibatches = (conf.getMaxBatchesPerWorker() > 0 ? conf.getMaxBatchesPerWorker() : Integer.MAX_VALUE);

        if (maxMinibatches == Integer.MAX_VALUE) {
            maxDataSetObjects = Integer.MAX_VALUE;
        } else {
            //Required: total number of examples / examples per dataset object
            maxDataSetObjects =
                            (int) Math.ceil(maxMinibatches * workerMinibatchSize / ((double) dataSetObjectNumExamples));
        }
    }

    @Override
    public Iterator<R> call(Iterator<String> iter) throws Exception {
        List<String> list = new ArrayList<>();
        int count = 0;
        while (iter.hasNext() && count++ < maxDataSetObjects) {
            list.add(iter.next());
        }

        return workerFlatMap.call(new PathSparkDataSetIterator(list.iterator(), dataSetLoader, hadoopConfig));
    }
}
