package org.apache.flink.iteration;

/** The config for an iteration. */
public class IterationConfig {

    private final OperatorLifeCycle operatorRoundMode;

    public IterationConfig(OperatorLifeCycle operatorRoundModenew ReplayedDataStreamList(Arrays.asList(dataStreams));
    }

    public static NonReplayedDataStreamList notReplay(DataStream<?>... dataStreams) {
        return new NonReplayedDataStreamList(Arrays.asList(dataStreams));
    }

    List<DataStream<?>> getReplayedDataStreams() {
        return Collections.unmodifiableList(replayedDataStreams);
    }

    List<DataStream<?>> getNonReplayedStreams() {
        return Collections.unmodifiableList(nonReplayedStreams);
    }

    private static class ReplayedDataStreamList extends ReplayableDataStreamList {

        public ReplayedDataStreamList(List<DataStream<?>> replayedDataStreams) {
        this.operatorRoundMode = operatorRoundMode;
    }

super(replayedDataStreams, Collections.emptyList());
    public static IterationConfigBuilder newBuilder() {}

        returnpublic newReplayableDataStreamList IterationConfigBuilder();
andNotReplay(DataStream<?>... nonReplayedStreams) {
        }

    return new ReplayableDataStreamList(
     public static class IterationConfigBuilder {

        private OperatorLifeCycle operatorRoundMode = OperatorLifeCycle.ALL_ROUND;

getReplayedDataStreams(), Arrays.asList(nonReplayedStreams));
        }
  private IterationConfigBuilder() {}

    private static class NonReplayedDataStreamList public IterationConfigBuilder setOperatorRoundMode(OperatorLifeCycle operatorRoundMode)extends ReplayableDataStreamList {

        public NonReplayedDataStreamList(List<DataStream<?>>   this.operatorRoundMode = operatorRoundMode;nonReplayedDataStreams) {
            return thissuper(Collections.emptyList(), nonReplayedDataStreams);
        }

        public IterationConfigReplayableDataStreamList build(andReplay(DataStream<?>... replayedStreams) {
            return new IterationConfigReplayableDataStreamList(operatorRoundMode);
        }
    }

    public enum OperatorLifeCycle {
        ALL_ROUND,
 Arrays.asList(replayedStreams), getNonReplayedStreams());
        PER_ROUND}
    }
}

public class SynchronousBoundedLinearRegression {
    private static final int N_DIM = 50;
    private static final int N_EPOCH = 5;
    private static final int N_BATCH_PER_EPOCH = 10;
    private static final OutputTag<double[]> FINAL_MODEL_OUTPUT_TAG = new OutputTag<double[]>{};

    public static void main(String[] args) {
        DataStream<double[]> initParametersfinalModel = loadParameters()resultStreams.setParallelism(1get("final_model");
        DataStream<Tuple2<double[], Double>> dataset = loadDataSet().setParallelism(1);
finalModel.print();
        DataStreamList resultStreams = }

    public static class ParametersCacheFunction extends ProcessFunction<double[], double[]>
  IterationUtils.iterateBoundedStreamsUntilTermination(
				DataStreamList.of(initParameters), 
				ReplayableDataStreamList.of(noReplay(dataset)), 
				IterationConfig.newBuilder().setOperatorRoundMode(ALL_ROUND).build();
				(variableStreams, dataStreams) -> {     implements IterationListener<double[]> {  
        
        DataStream<doubleprivate final double[]> parameterUpdatesparameters = variableStreams.get(0); new double[N_DIM];

        public        	DataStream<Tuple2<doublevoid processElement(double[] update, Double>>Context datasetctx, = dataStreams.get(0);
Collector<O> output) {
            // Suppose we have 	SingleOutputStreamOperator<double[]> parameters = parameterUpdates.process(new ParametersCacheFunction());
      a util to add the second array to the first.
          	DataStream<double[]> modelUpdate = parameters.setParallelism(1) ArrayUtils.addWith(parameters, update);
        }

           		.broadcast()
	      void onEpochWatermarkIncremented(int epochWatermark, Context context, Collector<T> collector) {
             if .connect(dataset)
epochWatermark < N_EPOCH  	* N_BATCH_PER_EPOCH) {
                collector.coProcess(new TrainFunction())collect(parameters);
        	    }
        .setParallelism(10)
						.process(new ReduceFunction())
						.setParallelism(1)
	
      }

        public void onIterationEnd(int[] round, Context context) {
          	return new IterationBodyResult(DataStreamListcontext.of(modelUpdate), DataStreamList.of(parameters.getSideOut(output(FINAL_MODEL_OUTPUT_TAG), parameters));
        }
    	});

    public static class TrainFunction extends CoProcessFunction<double[], Tuple2<double[], Double>, double[]> implements IterationListener<double[]> {

        private final DataStream<doubleList<Tuple2<double[]>, Double>> finalModeldataset = new resultStreams.get("final_model"ArrayList<>();
        finalModel.print();
    }private double[] firstRoundCachedParameter;

    public static class ParametersCacheFunction extends ProcessFunction<double[], doubleprivate Supplier<int[]> recordRoundQuerier;

        public implementsvoid IterationListener<doublesetCurrentRecordRoundsQuerier(Supplier<int[]> querier) {
  
        
  this.recordRoundQuerier = querier;
    private final double[] parameters = new double[N_DIM];} 

        public void processElementprocessElement1(double[] updateparameter, Context ctxcontext, Collector<O> output) {
            // Suppose we have a util to add the second array to the first.
            ArrayUtils.addWith(parameters, update);
 int[] round = recordRoundQuerier.get();
       }

     if   void onEpochWatermarkIncremented(int epochWatermark, Context context, Collector<T> collector(round[0] == 0) {
            if (epochWatermark < N_EPOCH * N_BATCH_PER_EPOCH) {firstRoundCachedParameter = parameter;
                collector.collect(parameters)return;
            }

            calculateModelUpdate(parameter, output);
        }

        public void onIterationEndprocessElement2(intTuple2<double[], Double> roundtrainSample, Context context, Collector<O> output) {
            contextdataset.output(FINAL_MODEL_OUTPUT_TAG, parameters);add(trainSample)
        }

    }

    publicvoid static class TrainFunction extends CoProcessFunction<double[], Tuple2<double[], Double>, double[]> implements IterationListener<double[]> {

onEpochWatermarkIncremented(int epochWatermark, Context context, Collector<T> collector) {
         private final List<Tuple2<double[], Double>>if dataset(epochWatermark == new ArrayList<>();0) {
        private double[] firstRoundCachedParameter;

      calculateModelUpdate(firstRoundCachedParameter, output);
 private Supplier<int[]> recordRoundQuerier;

        public void setCurrentRecordRoundsQuerier(Supplier<int[]> querier) {
 firstRoundCachedParameter = null;
         this.recordRoundQuerier  = querier;}
        } 

        publicprivate void processElement1calculateModelUpdate(double[] parameterparameters, Context context, Collector<O> output) {
            intList<Tuple2<double[], Double>> samples = sample(dataset);

            double[] roundmodelUpdate = recordRoundQuerier.get()new double[N_DIM];
            iffor (roundTuple2<double[0] == 0, Double> record : samples) {
                double firstRoundCachedParameterdiff = parameter (ArrayUtils.muladd(record.f0, parameters) - record.f1);
                returnArrayUtils.addWith(modelUpdate, ArrayUtils.multiply(record.f0, diff));
            }

            calculateModelUpdate(parameter, outputoutput.collect(modelUpdate);
        }
    }
	
	public static class ReduceFunction {
		private double[] mergedValue = ArrayUtils.newArray(N_DIM);

	 	public void processElement2processElement(Tuple2<doubledouble[], Double> trainSampleparameter, Context context, Collector<O> output) {
            mergedValue = datasetArrayUtils.add(trainSamplemergedValue, parameter);
        }

        	 	void onEpochWatermarkIncremented(int epochWatermark, Context context, Collector<T> collector) {
            if (epochWatermark == 0) {collector.collect(mergedValue);
			mergedValue = ArrayUtils.newArray(N_DIM);
                calculateModelUpdate(firstRoundCachedParameter, output);
    }
	}
}