pekko/akka-stream/src/main/scala/akka/stream/javadsl/SubSource.scala

/**
 * Copyright (C) 2015-2017 Lightbend Inc. <http://www.lightbend.com>
 */
package akka.stream.javadsl

import akka.NotUsed
import akka.event.LoggingAdapter
import akka.japi.function
import akka.stream._
import akka.util.ConstantFun

import scala.collection.JavaConverters._
import scala.annotation.unchecked.uncheckedVariance
import scala.concurrent.duration.FiniteDuration
import akka.japi.Util
import java.util.Comparator
import java.util.concurrent.CompletionStage

import akka.stream.impl.fusing.MapError

import scala.compat.java8.FutureConverters._

/**
 * A “stream of streams” sub-flow of data elements, e.g. produced by `groupBy`.
 * SubFlows cannot contribute to the super-flow’s materialized value since they
 * are materialized later, during the runtime of the flow graph processing.
 */
class SubSource[+Out, +Mat](delegate: scaladsl.SubFlow[Out, Mat, scaladsl.Source[Out, Mat]#Repr, scaladsl.RunnableGraph[Mat]]) {

  /** Converts this Flow to its Scala DSL counterpart */
  def asScala: scaladsl.SubFlow[Out, Mat, scaladsl.Source[Out, Mat]#Repr, scaladsl.RunnableGraph[Mat]] @uncheckedVariance = delegate

  /**
   * Flatten the sub-flows back into the super-source by performing a merge
   * without parallelism limit (i.e. having an unbounded number of sub-flows
   * active concurrently).
   *
   * This is identical in effect to `mergeSubstreamsWithParallelism(Integer.MAX_VALUE)`.
   */
  def mergeSubstreams(): Source[Out, Mat] =
    new Source(delegate.mergeSubstreams)

  /**
   * Flatten the sub-flows back into the super-source by performing a merge
   * with the given parallelism limit. This means that only up to `parallelism`
   * substreams will be executed at any given time. Substreams that are not
   * yet executed are also not materialized, meaning that back-pressure will
   * be exerted at the operator that creates the substreams when the parallelism
   * limit is reached.
   */
  def mergeSubstreamsWithParallelism(parallelism: Int): Source[Out, Mat] =
    new Source(delegate.mergeSubstreamsWithParallelism(parallelism))

  /**
   * Flatten the sub-flows back into the super-source by concatenating them.
   * This is usually a bad idea when combined with `groupBy` since it can
   * easily lead to deadlock—the concatenation does not consume from the second
   * substream until the first has finished and the `groupBy` stage will get
   * back-pressure from the second stream.
   *
   * This is identical in effect to `mergeSubstreamsWithParallelism(1)`.
   */
  def concatSubstreams(): Source[Out, Mat] =
    new Source(delegate.concatSubstreams)

  /**
   * Transform this [[SubSource]] by appending the given processing steps.
   * {{{
   *     +----------------------------+
   *     | Resulting Source           |
   *     |                            |
   *     |  +------+        +------+  |
   *     |  |      |        |      |  |
   *     |  | this | ~Out~> | flow | ~~> T
   *     |  |      |        |      |  |
   *     |  +------+        +------+  |
   *     +----------------------------+
   * }}}
   * The materialized value of the combined [[Flow]] will be the materialized
   * value of the current flow (ignoring the other Flow’s value), use
   * [[Flow#viaMat viaMat]] if a different strategy is needed.
   */
  def via[T, M](flow: Graph[FlowShape[Out, T], M]): SubSource[T, Mat] =
    new SubSource(delegate.via(flow))

  /**
   * Connect this [[SubSource]] to a [[Sink]], concatenating the processing steps of both.
   * This means that all sub-flows that result from the previous sub-stream operator
   * will be attached to the given sink.
   * {{{
   *     +----------------------------+
   *     | Resulting RunnableGraph    |
   *     |                            |
   *     |  +------+        +------+  |
   *     |  |      |        |      |  |
   *     |  | this | ~Out~> | sink |  |
   *     |  |      |        |      |  |
   *     |  +------+        +------+  |
   *     +----------------------------+
   * }}}
   */
  def to(sink: Graph[SinkShape[Out], _]): RunnableGraph[Mat] =
    RunnableGraph.fromGraph(delegate.to(sink))

  /**
   * Transform this stream by applying the given function to each of the elements
   * as they pass through this processing step.
   *
   * '''Emits when''' the mapping function returns an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def map[T](f: function.Function[Out, T]): SubSource[T, Mat] =
    new SubSource(delegate.map(f.apply))

  /**
   * Transform each input element into an `Iterable` of output elements that is
   * then flattened into the output stream.
   *
   * Make sure that the `Iterable` is immutable or at least not modified after
   * being used as an output sequence. Otherwise the stream may fail with
   * `ConcurrentModificationException` or other more subtle errors may occur.
   *
   * The returned `Iterable` MUST NOT contain `null` values,
   * as they are illegal as stream elements - according to the Reactive Streams specification.
   *
   * '''Emits when''' the mapping function returns an element or there are still remaining elements
   * from the previously calculated collection
   *
   * '''Backpressures when''' downstream backpressures or there are still remaining elements from the
   * previously calculated collection
   *
   * '''Completes when''' upstream completes and all remaining elements has been emitted
   *
   * '''Cancels when''' downstream cancels
   */
  def mapConcat[T](f: function.Function[Out, java.lang.Iterable[T]]): SubSource[T, Mat] =
    new SubSource(delegate.mapConcat { elem ⇒ Util.immutableSeq(f(elem)) })

  /**
   * Transform each input element into an `Iterable` of output elements that is
   * then flattened into the output stream. The transformation is meant to be stateful,
   * which is enabled by creating the transformation function anew for every materialization —
   * the returned function will typically close over mutable objects to store state between
   * invocations. For the stateless variant see [[#mapConcat]].
   *
   * Make sure that the `Iterable` is immutable or at least not modified after
   * being used as an output sequence. Otherwise the stream may fail with
   * `ConcurrentModificationException` or other more subtle errors may occur.
   *
   * The returned `Iterable` MUST NOT contain `null` values,
   * as they are illegal as stream elements - according to the Reactive Streams specification.
   *
   * '''Emits when''' the mapping function returns an element or there are still remaining elements
   * from the previously calculated collection
   *
   * '''Backpressures when''' downstream backpressures or there are still remaining elements from the
   * previously calculated collection
   *
   * '''Completes when''' upstream completes and all remaining elements has been emitted
   *
   * '''Cancels when''' downstream cancels
   */
  def statefulMapConcat[T](f: function.Creator[function.Function[Out, java.lang.Iterable[T]]]): SubSource[T, Mat] =
    new SubSource(delegate.statefulMapConcat { () ⇒
      val fun = f.create()
      elem ⇒ Util.immutableSeq(fun(elem))
    })

  /**
   * Transform this stream by applying the given function to each of the elements
   * as they pass through this processing step. The function returns a `CompletionStage` and the
   * value of that future will be emitted downstream. The number of CompletionStages
   * that shall run in parallel is given as the first argument to ``mapAsync``.
   * These CompletionStages may complete in any order, but the elements that
   * are emitted downstream are in the same order as received from upstream.
   *
   * If the function `f` throws an exception or if the `CompletionStage` is completed
   * with failure and the supervision decision is [[akka.stream.Supervision#stop]]
   * the stream will be completed with failure.
   *
   * If the function `f` throws an exception or if the `CompletionStage` is completed
   * with failure and the supervision decision is [[akka.stream.Supervision#resume]] or
   * [[akka.stream.Supervision#restart]] the element is dropped and the stream continues.
   *
   * The function `f` is always invoked on the elements in the order they arrive.
   *
   * '''Emits when''' the CompletionStage returned by the provided function finishes for the next element in sequence
   *
   * '''Backpressures when''' the number of CompletionStages reaches the configured parallelism and the downstream
   * backpressures or the first CompletionStage is not completed
   *
   * '''Completes when''' upstream completes and all CompletionStages has been completed and all elements has been emitted
   *
   * '''Cancels when''' downstream cancels
   *
   * @see [[#mapAsyncUnordered]]
   */
  def mapAsync[T](parallelism: Int, f: function.Function[Out, CompletionStage[T]]): SubSource[T, Mat] =
    new SubSource(delegate.mapAsync(parallelism)(x ⇒ f(x).toScala))

  /**
   * Transform this stream by applying the given function to each of the elements
   * as they pass through this processing step. The function returns a `CompletionStage` and the
   * value of that future will be emitted downstream. The number of CompletionStages
   * that shall run in parallel is given as the first argument to ``mapAsyncUnordered``.
   * Each processed element will be emitted downstream as soon as it is ready, i.e. it is possible
   * that the elements are not emitted downstream in the same order as received from upstream.
   *
   * If the function `f` throws an exception or if the `CompletionStage` is completed
   * with failure and the supervision decision is [[akka.stream.Supervision#stop]]
   * the stream will be completed with failure.
   *
   * If the function `f` throws an exception or if the `CompletionStage` is completed
   * with failure and the supervision decision is [[akka.stream.Supervision#resume]] or
   * [[akka.stream.Supervision#restart]] the element is dropped and the stream continues.
   *
   * The function `f` is always invoked on the elements in the order they arrive (even though the result of the futures
   * returned by `f` might be emitted in a different order).
   *
   * '''Emits when''' any of the CompletionStage returned by the provided function complete
   *
   * '''Backpressures when''' the number of CompletionStage reaches the configured parallelism and the downstream backpressures
   *
   * '''Completes when''' upstream completes and all CompletionStage has been completed and all elements has been emitted
   *
   * '''Cancels when''' downstream cancels
   *
   * @see [[#mapAsync]]
   */
  def mapAsyncUnordered[T](parallelism: Int, f: function.Function[Out, CompletionStage[T]]): SubSource[T, Mat] =
    new SubSource(delegate.mapAsyncUnordered(parallelism)(x ⇒ f(x).toScala))

  /**
   * Only pass on those elements that satisfy the given predicate.
   *
   * '''Emits when''' the given predicate returns true for the element
   *
   * '''Backpressures when''' the given predicate returns true for the element and downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   *
   */
  def filter(p: function.Predicate[Out]): SubSource[Out, Mat] =
    new SubSource(delegate.filter(p.test))

  /**
   * Only pass on those elements that NOT satisfy the given predicate.
   *
   * '''Emits when''' the given predicate returns false for the element
   *
   * '''Backpressures when''' the given predicate returns false for the element and downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def filterNot(p: function.Predicate[Out]): SubSource[Out, Mat] =
    new SubSource(delegate.filterNot(p.test))

  /**
   * Transform this stream by applying the given partial function to each of the elements
   * on which the function is defined as they pass through this processing step.
   * Non-matching elements are filtered out.
   *
   * '''Emits when''' the provided partial function is defined for the element
   *
   * '''Backpressures when''' the partial function is defined for the element and downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def collect[T](pf: PartialFunction[Out, T]): SubSource[T, Mat] =
    new SubSource(delegate.collect(pf))

  /**
   * Chunk up this stream into groups of the given size, with the last group
   * possibly smaller than requested due to end-of-stream.
   *
   * `n` must be positive, otherwise IllegalArgumentException is thrown.
   *
   * '''Emits when''' the specified number of elements has been accumulated or upstream completed
   *
   * '''Backpressures when''' a group has been assembled and downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def grouped(n: Int): SubSource[java.util.List[Out @uncheckedVariance], Mat] =
    new SubSource(delegate.grouped(n).map(_.asJava)) // TODO optimize to one step

  /**
   * Apply a sliding window over the stream and return the windows as groups of elements, with the last group
   * possibly smaller than requested due to end-of-stream.
   *
   * `n` must be positive, otherwise IllegalArgumentException is thrown.
   * `step` must be positive, otherwise IllegalArgumentException is thrown.
   *
   * '''Emits when''' enough elements have been collected within the window or upstream completed
   *
   * '''Backpressures when''' a window has been assembled and downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */

  /**
   * Ensure stream boundedness by limiting the number of elements from upstream.
   * If the number of incoming elements exceeds max, it will signal
   * upstream failure `StreamLimitException` downstream.
   *
   * Due to input buffering some elements may have been
   * requested from upstream publishers that will then not be processed downstream
   * of this step.
   *
   * The stream will be completed without producing any elements if `n` is zero
   * or negative.
   *
   * '''Emits when''' the specified number of elements to take has not yet been reached
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' the defined number of elements has been taken or upstream completes
   *
   * '''Cancels when''' the defined number of elements has been taken or downstream cancels
   *
   * See also [[Flow.take]], [[Flow.takeWithin]], [[Flow.takeWhile]]
   */
  def limit(n: Int): javadsl.SubSource[Out, Mat] = new SubSource(delegate.limit(n))

  /**
   * Ensure stream boundedness by evaluating the cost of incoming elements
   * using a cost function. Exactly how many elements will be allowed to travel downstream depends on the
   * evaluated cost of each element. If the accumulated cost exceeds max, it will signal
   * upstream failure `StreamLimitException` downstream.
   *
   * Due to input buffering some elements may have been
   * requested from upstream publishers that will then not be processed downstream
   * of this step.
   *
   * The stream will be completed without producing any elements if `n` is zero
   * or negative.
   *
   * '''Emits when''' the specified number of elements to take has not yet been reached
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' the defined number of elements has been taken or upstream completes
   *
   * '''Cancels when''' the defined number of elements has been taken or downstream cancels
   *
   * See also [[Flow.take]], [[Flow.takeWithin]], [[Flow.takeWhile]]
   */
  def limitWeighted(n: Long)(costFn: function.Function[Out, Long]): javadsl.SubSource[Out, Mat] = {
    new SubSource(delegate.limitWeighted(n)(costFn.apply))
  }

  def sliding(n: Int, step: Int = 1): SubSource[java.util.List[Out @uncheckedVariance], Mat] =
    new SubSource(delegate.sliding(n, step).map(_.asJava)) // TODO optimize to one step

  /**
   * Similar to `fold` but is not a terminal operation,
   * emits its current value which starts at `zero` and then
   * applies the current and next value to the given function `f`,
   * emitting the next current value.
   *
   * If the function `f` throws an exception and the supervision decision is
   * [[akka.stream.Supervision#restart]] current value starts at `zero` again
   * the stream will continue.
   *
   * '''Emits when''' the function scanning the element returns a new element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def scan[T](zero: T)(f: function.Function2[T, Out, T]): SubSource[T, Mat] =
    new SubSource(delegate.scan(zero)(f.apply))

  /**
   * Similar to `scan` but with a asynchronous function,
   * emits its current value which starts at `zero` and then
   * applies the current and next value to the given function `f`,
   * emitting a `Future` that resolves to the next current value.
   *
   * If the function `f` throws an exception and the supervision decision is
   * [[akka.stream.Supervision.Restart]] current value starts at `zero` again
   * the stream will continue.
   *
   * If the function `f` throws an exception and the supervision decision is
   * [[akka.stream.Supervision.Resume]] current value starts at the previous
   * current value, or zero when it doesn't have one, and the stream will continue.
   *
   * '''Emits when''' the future returned by f` completes
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes and the last future returned by `f` completes
   *
   * '''Cancels when''' downstream cancels
   *
   * See also [[FlowOps.scan]]
   */
  def scanAsync[T](zero: T)(f: function.Function2[T, Out, CompletionStage[T]]): SubSource[T, Mat] =
    new SubSource(delegate.scanAsync(zero) { (out, in) ⇒ f(out, in).toScala })

  /**
   * Similar to `scan` but only emits its result when the upstream completes,
   * after which it also completes. Applies the given function `f` towards its current and next value,
   * yielding the next current value.
   *
   * If the function `f` throws an exception and the supervision decision is
   * [[akka.stream.Supervision#restart]] current value starts at `zero` again
   * the stream will continue.
   *
   * '''Emits when''' upstream completes
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def fold[T](zero: T)(f: function.Function2[T, Out, T]): SubSource[T, Mat] =
    new SubSource(delegate.fold(zero)(f.apply))

  /**
   * Similar to `fold` but with an asynchronous function.
   * Applies the given function towards its current and next value,
   * yielding the next current value.
   *
   * If the function `f` returns a failure and the supervision decision is
   * [[akka.stream.Supervision.Restart]] current value starts at `zero` again
   * the stream will continue.
   *
   * '''Emits when''' upstream completes
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def foldAsync[T](zero: T)(f: function.Function2[T, Out, CompletionStage[T]]): SubSource[T, Mat] = new SubSource(delegate.foldAsync(zero) { (out, in) ⇒ f(out, in).toScala })

  /**
   * Similar to `fold` but uses first element as zero element.
   * Applies the given function towards its current and next value,
   * yielding the next current value.
   *
   * '''Emits when''' upstream completes
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def reduce(f: function.Function2[Out, Out, Out @uncheckedVariance]): SubSource[Out, Mat] =
    new SubSource(delegate.reduce(f.apply))

  /**
   * Intersperses stream with provided element, similar to how [[scala.collection.immutable.List.mkString]]
   * injects a separator between a List's elements.
   *
   * Additionally can inject start and end marker elements to stream.
   *
   * Examples:
   *
   * {{{
   * Source<Integer, ?> nums = Source.from(Arrays.asList(0, 1, 2, 3));
   * nums.intersperse(",");            //   1 , 2 , 3
   * nums.intersperse("[", ",", "]");  // [ 1 , 2 , 3 ]
   * }}}
   *
   * In case you want to only prepend or only append an element (yet still use the `intercept` feature
   * to inject a separator between elements, you may want to use the following pattern instead of the 3-argument
   * version of intersperse (See [[Source.concat]] for semantics details):
   *
   * {{{
   * Source.single(">> ").concat(flow.intersperse(","))
   * flow.intersperse(",").concat(Source.single("END"))
   * }}}
   *
   * '''Emits when''' upstream emits (or before with the `start` element if provided)
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def intersperse[T >: Out](start: T, inject: T, end: T): SubSource[T, Mat] =
    new SubSource(delegate.intersperse(start, inject, end))

  /**
   * Intersperses stream with provided element, similar to how [[scala.collection.immutable.List.mkString]]
   * injects a separator between a List's elements.
   *
   * Additionally can inject start and end marker elements to stream.
   *
   * Examples:
   *
   * {{{
   * Source<Integer, ?> nums = Source.from(Arrays.asList(0, 1, 2, 3));
   * nums.intersperse(",");            //   1 , 2 , 3
   * nums.intersperse("[", ",", "]");  // [ 1 , 2 , 3 ]
   * }}}
   *
   * '''Emits when''' upstream emits (or before with the `start` element if provided)
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def intersperse[T >: Out](inject: T): SubSource[T, Mat] =
    new SubSource(delegate.intersperse(inject))

  /**
   * Chunk up this stream into groups of elements received within a time window,
   * or limited by the given number of elements, whatever happens first.
   * Empty groups will not be emitted if no elements are received from upstream.
   * The last group before end-of-stream will contain the buffered elements
   * since the previously emitted group.
   *
   * '''Emits when''' the configured time elapses since the last group has been emitted or `n` elements is buffered
   *
   * '''Backpressures when''' downstream backpressures, and there are `n+1` buffered elements
   *
   * '''Completes when''' upstream completes (emits last group)
   *
   * '''Cancels when''' downstream completes
   *
   * `n` must be positive, and `d` must be greater than 0 seconds, otherwise
   * IllegalArgumentException is thrown.
   */
  def groupedWithin(n: Int, d: FiniteDuration): SubSource[java.util.List[Out @uncheckedVariance], Mat] =
    new SubSource(delegate.groupedWithin(n, d).map(_.asJava)) // TODO optimize to one step

  /**
   * Chunk up this stream into groups of elements received within a time window,
   * or limited by the weight of the elements, whatever happens first.
   * Empty groups will not be emitted if no elements are received from upstream.
   * The last group before end-of-stream will contain the buffered elements
   * since the previously emitted group.
   *
   * '''Emits when''' the configured time elapses since the last group has been emitted or weight limit reached
   *
   * '''Backpressures when''' downstream backpressures, and buffered group (+ pending element) weighs more than `maxWeight`
   *
   * '''Completes when''' upstream completes (emits last group)
   *
   * '''Cancels when''' downstream completes
   *
   * `maxWeight` must be positive, and `d` must be greater than 0 seconds, otherwise
   * IllegalArgumentException is thrown.
   */
  def groupedWeightedWithin(maxWeight: Long, costFn: function.Function[Out, Long], d: FiniteDuration): javadsl.SubSource[java.util.List[Out @uncheckedVariance], Mat] =
    new SubSource(delegate.groupedWeightedWithin(maxWeight, d)(costFn.apply).map(_.asJava))

  /**
   * Discard the given number of elements at the beginning of the stream.
   * No elements will be dropped if `n` is zero or negative.
   *
   * '''Emits when''' the specified number of elements has been dropped already
   *
   * '''Backpressures when''' the specified number of elements has been dropped and downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def drop(n: Long): SubSource[Out, Mat] =
    new SubSource(delegate.drop(n))

  /**
   * Discard the elements received within the given duration at beginning of the stream.
   *
   * '''Emits when''' the specified time elapsed and a new upstream element arrives
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def dropWithin(d: FiniteDuration): SubSource[Out, Mat] =
    new SubSource(delegate.dropWithin(d))

  /**
   * Terminate processing (and cancel the upstream publisher) after predicate
   * returns false for the first time,
   * Due to input buffering some elements may have been requested from upstream publishers
   * that will then not be processed downstream of this step.
   *
   * The stream will be completed without producing any elements if predicate is false for
   * the first stream element.
   *
   * '''Emits when''' the predicate is true
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' predicate returned false (or 1 after predicate returns false if `inclusive` or upstream completes
   *
   * '''Cancels when''' predicate returned false or downstream cancels
   */
  def takeWhile(p: function.Predicate[Out]): SubSource[Out, Mat] = takeWhile(p, false)

  /**
   * Terminate processing (and cancel the upstream publisher) after predicate
   * returns false for the first time, including the first failed element iff inclusive is true
   * Due to input buffering some elements may have been requested from upstream publishers
   * that will then not be processed downstream of this step.
   *
   * The stream will be completed without producing any elements if predicate is false for
   * the first stream element.
   *
   * '''Emits when''' the predicate is true
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' predicate returned false (or 1 after predicate returns false if `inclusive` or upstream completes
   *
   * '''Cancels when''' predicate returned false or downstream cancels
   */
  def takeWhile(p: function.Predicate[Out], inclusive: Boolean): SubSource[Out, Mat] =
    new SubSource(delegate.takeWhile(p.test, inclusive))

  /**
   * Discard elements at the beginning of the stream while predicate is true.
   * All elements will be taken after predicate returns false first time.
   *
   * '''Emits when''' predicate returned false and for all following stream elements
   *
   * '''Backpressures when''' predicate returned false and downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def dropWhile(p: function.Predicate[Out]): SubSource[Out, Mat] =
    new SubSource(delegate.dropWhile(p.test))

  /**
   * Shifts elements emission in time by a specified amount. It allows to store elements
   * in internal buffer while waiting for next element to be emitted. Depending on the defined
   * [[akka.stream.DelayOverflowStrategy]] it might drop elements or backpressure the upstream if
   * there is no space available in the buffer.
   *
   * Delay precision is 10ms to avoid unnecessary timer scheduling cycles
   *
   * Internal buffer has default capacity 16. You can set buffer size by calling `withAttributes(inputBuffer)`
   *
   * '''Emits when''' there is a pending element in the buffer and configured time for this element elapsed
   *  * EmitEarly - strategy do not wait to emit element if buffer is full
   *
   * '''Backpressures when''' depending on OverflowStrategy
   *  * Backpressure - backpressures when buffer is full
   *  * DropHead, DropTail, DropBuffer - never backpressures
   *  * Fail - fails the stream if buffer gets full
   *
   * '''Completes when''' upstream completes and buffered elements has been drained
   *
   * '''Cancels when''' downstream cancels
   *
   * @param of time to shift all messages
   * @param strategy Strategy that is used when incoming elements cannot fit inside the buffer
   */
  def delay(of: FiniteDuration, strategy: DelayOverflowStrategy): SubSource[Out, Mat] =
    new SubSource(delegate.delay(of, strategy))

  /**
   * Recover allows to send last element on failure and gracefully complete the stream
   * Since the underlying failure signal onError arrives out-of-band, it might jump over existing elements.
   * This stage can recover the failure signal, but not the skipped elements, which will be dropped.
   *
   * '''Emits when''' element is available from the upstream or upstream is failed and pf returns an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes or upstream failed with exception pf can handle
   *
   * '''Cancels when''' downstream cancels
   *
   */
  def recover[T >: Out](pf: PartialFunction[Throwable, T]): SubSource[T, Mat] =
    new SubSource(delegate.recover(pf))

  /**
   * RecoverWith allows to switch to alternative Source on flow failure. It will stay in effect after
   * a failure has been recovered so that each time there is a failure it is fed into the `pf` and a new
   * Source may be materialized.
   *
   * Since the underlying failure signal onError arrives out-of-band, it might jump over existing elements.
   * This stage can recover the failure signal, but not the skipped elements, which will be dropped.
   *
   * '''Emits when''' element is available from the upstream or upstream is failed and element is available
   * from alternative Source
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes or upstream failed with exception pf can handle
   *
   * '''Cancels when''' downstream cancels
   *
   */
  @deprecated("Use recoverWithRetries instead.", "2.4.4")
  def recoverWith[T >: Out](pf: PartialFunction[Throwable, _ <: Graph[SourceShape[T], NotUsed]]): SubSource[T, Mat @uncheckedVariance] =
    new SubSource(delegate.recoverWith(pf))

  /**
   * RecoverWithRetries allows to switch to alternative Source on flow failure. It will stay in effect after
   * a failure has been recovered up to `attempts` number of times so that each time there is a failure
   * it is fed into the `pf` and a new Source may be materialized. Note that if you pass in 0, this won't
   * attempt to recover at all.
   *
   * A negative `attempts` number is interpreted as "infinite", which results in the exact same behavior as `recoverWith`.
   *
   * Since the underlying failure signal onError arrives out-of-band, it might jump over existing elements.
   * This stage can recover the failure signal, but not the skipped elements, which will be dropped.
   *
   * '''Emits when''' element is available from the upstream or upstream is failed and element is available
   * from alternative Source
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes or upstream failed with exception pf can handle
   *
   * '''Cancels when''' downstream cancels
   *
   */
  def recoverWithRetries[T >: Out](attempts: Int, pf: PartialFunction[Throwable, _ <: Graph[SourceShape[T], NotUsed]]): SubSource[T, Mat @uncheckedVariance] =
    new SubSource(delegate.recoverWithRetries(attempts, pf))

  /**
   * While similar to [[recover]] this stage can be used to transform an error signal to a different one *without* logging
   * it as an error in the process. So in that sense it is NOT exactly equivalent to `recover(t => throw t2)` since recover
   * would log the `t2` error.
   *
   * Since the underlying failure signal onError arrives out-of-band, it might jump over existing elements.
   * This stage can recover the failure signal, but not the skipped elements, which will be dropped.
   *
   * Similarily to [[recover]] throwing an exception inside `mapError` _will_ be logged.
   *
   * '''Emits when''' element is available from the upstream or upstream is failed and pf returns an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes or upstream failed with exception pf can handle
   *
   * '''Cancels when''' downstream cancels
   *
   */
  def mapError(pf: PartialFunction[Throwable, Throwable]): SubSource[Out, Mat] =
    new SubSource(delegate.mapError(pf))

  /**
   * Terminate processing (and cancel the upstream publisher) after the given
   * number of elements. Due to input buffering some elements may have been
   * requested from upstream publishers that will then not be processed downstream
   * of this step.
   *
   * The stream will be completed without producing any elements if `n` is zero
   * or negative.
   *
   * '''Emits when''' the specified number of elements to take has not yet been reached
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' the defined number of elements has been taken or upstream completes
   *
   * '''Cancels when''' the defined number of elements has been taken or downstream cancels
   */
  def take(n: Long): SubSource[Out, Mat] =
    new SubSource(delegate.take(n))

  /**
   * Terminate processing (and cancel the upstream publisher) after the given
   * duration. Due to input buffering some elements may have been
   * requested from upstream publishers that will then not be processed downstream
   * of this step.
   *
   * Note that this can be combined with [[#take]] to limit the number of elements
   * within the duration.
   *
   * '''Emits when''' an upstream element arrives
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes or timer fires
   *
   * '''Cancels when''' downstream cancels or timer fires
   */
  def takeWithin(d: FiniteDuration): SubSource[Out, Mat] =
    new SubSource(delegate.takeWithin(d))

  /**
   * Allows a faster upstream to progress independently of a slower subscriber by conflating elements into a summary
   * until the subscriber is ready to accept them. For example a conflate step might average incoming numbers if the
   * upstream publisher is faster.
   *
   * This version of conflate allows to derive a seed from the first element and change the aggregated type to be
   * different than the input type. See [[Flow.conflate]] for a simpler version that does not change types.
   *
   * This element only rolls up elements if the upstream is faster, but if the downstream is faster it will not
   * duplicate elements.
   *
   * '''Emits when''' downstream stops backpressuring and there is a conflated element available
   *
   * '''Backpressures when''' never
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   *
   * see also [[SubSource.conflate]] [[SubSource.batch]] [[SubSource.batchWeighted]]
   *
   * @param seed Provides the first state for a conflated value using the first unconsumed element as a start
   * @param aggregate Takes the currently aggregated value and the current pending element to produce a new aggregate
   *
   */
  def conflateWithSeed[S](seed: function.Function[Out, S], aggregate: function.Function2[S, Out, S]): SubSource[S, Mat] =
    new SubSource(delegate.conflateWithSeed(seed.apply)(aggregate.apply))

  /**
   * Allows a faster upstream to progress independently of a slower subscriber by conflating elements into a summary
   * until the subscriber is ready to accept them. For example a conflate step might average incoming numbers if the
   * upstream publisher is faster.
   *
   * This version of conflate does not change the output type of the stream. See [[SubSource.conflateWithSeed]] for a
   * more flexible version that can take a seed function and transform elements while rolling up.
   *
   * This element only rolls up elements if the upstream is faster, but if the downstream is faster it will not
   * duplicate elements.
   *
   * '''Emits when''' downstream stops backpressuring and there is a conflated element available
   *
   * '''Backpressures when''' never
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   *
   * see also [[SubSource.conflateWithSeed]] [[SubSource.batch]] [[SubSource.batchWeighted]]
   *
   * @param aggregate Takes the currently aggregated value and the current pending element to produce a new aggregate
   *
   */
  def conflate[O2 >: Out](aggregate: function.Function2[O2, O2, O2]): SubSource[O2, Mat] =
    new SubSource(delegate.conflate(aggregate.apply))

  /**
   * Allows a faster upstream to progress independently of a slower subscriber by aggregating elements into batches
   * until the subscriber is ready to accept them. For example a batch step might store received elements in
   * an array up to the allowed max limit if the upstream publisher is faster.
   *
   * This element only rolls up elements if the upstream is faster, but if the downstream is faster it will not
   * duplicate elements.
   *
   * '''Emits when''' downstream stops backpressuring and there is an aggregated element available
   *
   * '''Backpressures when''' there are `max` batched elements and 1 pending element and downstream backpressures
   *
   * '''Completes when''' upstream completes and there is no batched/pending element waiting
   *
   * '''Cancels when''' downstream cancels
   *
   * See also [[SubSource.conflate]], [[SubSource.batchWeighted]]
   *
   * @param max maximum number of elements to batch before backpressuring upstream (must be positive non-zero)
   * @param seed Provides the first state for a batched value using the first unconsumed element as a start
   * @param aggregate Takes the currently batched value and the current pending element to produce a new aggregate
   */
  def batch[S](max: Long, seed: function.Function[Out, S], aggregate: function.Function2[S, Out, S]): SubSource[S, Mat] =
    new SubSource(delegate.batch(max, seed.apply)(aggregate.apply))

  /**
   * Allows a faster upstream to progress independently of a slower subscriber by aggregating elements into batches
   * until the subscriber is ready to accept them. For example a batch step might concatenate `ByteString`
   * elements up to the allowed max limit if the upstream publisher is faster.
   *
   * This element only rolls up elements if the upstream is faster, but if the downstream is faster it will not
   * duplicate elements.
   *
   * Batching will apply for all elements, even if a single element cost is greater than the total allowed limit.
   * In this case, previous batched elements will be emitted, then the "heavy" element will be emitted (after
   * being applied with the `seed` function) without batching further elements with it, and then the rest of the
   * incoming elements are batched.
   *
   * '''Emits when''' downstream stops backpressuring and there is a batched element available
   *
   * '''Backpressures when''' there are `max` weighted batched elements + 1 pending element and downstream backpressures
   *
   * '''Completes when''' upstream completes and there is no batched/pending element waiting
   *
   * '''Cancels when''' downstream cancels
   *
   * See also [[SubSource.conflate]], [[SubSource.batch]]
   *
   * @param max maximum weight of elements to batch before backpressuring upstream (must be positive non-zero)
   * @param costFn a function to compute a single element weight
   * @param seed Provides the first state for a batched value using the first unconsumed element as a start
   * @param aggregate Takes the currently batched value and the current pending element to produce a new batch
   */
  def batchWeighted[S](max: Long, costFn: function.Function[Out, Long], seed: function.Function[Out, S], aggregate: function.Function2[S, Out, S]): SubSource[S, Mat] =
    new SubSource(delegate.batchWeighted(max, costFn.apply, seed.apply)(aggregate.apply))

  /**
   * Allows a faster downstream to progress independently of a slower publisher by extrapolating elements from an older
   * element until new element comes from the upstream. For example an expand step might repeat the last element for
   * the subscriber until it receives an update from upstream.
   *
   * This element will never "drop" upstream elements as all elements go through at least one extrapolation step.
   * This means that if the upstream is actually faster than the upstream it will be backpressured by the downstream
   * subscriber.
   *
   * Expand does not support [[akka.stream.Supervision#restart]] and [[akka.stream.Supervision#resume]].
   * Exceptions from the `seed` or `extrapolate` functions will complete the stream with failure.
   *
   * '''Emits when''' downstream stops backpressuring
   *
   * '''Backpressures when''' downstream backpressures or iterator runs empty
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   *
   * @param seed Provides the first state for extrapolation using the first unconsumed element
   * @param extrapolate Takes the current extrapolation state to produce an output element and the next extrapolation
   *                    state.
   */
  def expand[U](extrapolate: function.Function[Out, java.util.Iterator[U]]): SubSource[U, Mat] =
    new SubSource(delegate.expand(in ⇒ extrapolate(in).asScala))

  /**
   * Adds a fixed size buffer in the flow that allows to store elements from a faster upstream until it becomes full.
   * Depending on the defined [[akka.stream.OverflowStrategy]] it might drop elements or backpressure the upstream if
   * there is no space available
   *
   * '''Emits when''' downstream stops backpressuring and there is a pending element in the buffer
   *
   * '''Backpressures when''' depending on OverflowStrategy
   *  * Backpressure - backpressures when buffer is full
   *  * DropHead, DropTail, DropBuffer - never backpressures
   *  * Fail - fails the stream if buffer gets full
   *
   * '''Completes when''' upstream completes and buffered elements has been drained
   *
   * '''Cancels when''' downstream cancels
   *
   * @param size The size of the buffer in element count
   * @param overflowStrategy Strategy that is used when incoming elements cannot fit inside the buffer
   */
  def buffer(size: Int, overflowStrategy: OverflowStrategy): SubSource[Out, Mat] =
    new SubSource(delegate.buffer(size, overflowStrategy))

  /**
   * Takes up to `n` elements from the stream (less than `n` only if the upstream completes before emitting `n` elements)
   * and returns a pair containing a strict sequence of the taken element
   * and a stream representing the remaining elements. If ''n'' is zero or negative, then this will return a pair
   * of an empty collection and a stream containing the whole upstream unchanged.
   *
   * In case of an upstream error, depending on the current state
   *  - the master stream signals the error if less than `n` elements has been seen, and therefore the substream
   *    has not yet been emitted
   *  - the tail substream signals the error after the prefix and tail has been emitted by the main stream
   *    (at that point the main stream has already completed)
   *
   * '''Emits when''' the configured number of prefix elements are available. Emits this prefix, and the rest
   * as a substream
   *
   * '''Backpressures when''' downstream backpressures or substream backpressures
   *
   * '''Completes when''' prefix elements has been consumed and substream has been consumed
   *
   * '''Cancels when''' downstream cancels or substream cancels
   */
  def prefixAndTail(n: Int): SubSource[akka.japi.Pair[java.util.List[Out @uncheckedVariance], javadsl.Source[Out @uncheckedVariance, NotUsed]], Mat] =
    new SubSource(delegate.prefixAndTail(n).map { case (taken, tail) ⇒ akka.japi.Pair(taken.asJava, tail.asJava) })

  /**
   * Transform each input element into a `Source` of output elements that is
   * then flattened into the output stream by concatenation,
   * fully consuming one Source after the other.
   *
   * '''Emits when''' a currently consumed substream has an element available
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes and all consumed substreams complete
   *
   * '''Cancels when''' downstream cancels
   *
   */
  def flatMapConcat[T, M](f: function.Function[Out, _ <: Graph[SourceShape[T], M]]): SubSource[T, Mat] =
    new SubSource(delegate.flatMapConcat(x ⇒ f(x)))

  /**
   * Transform each input element into a `Source` of output elements that is
   * then flattened into the output stream by merging, where at most `breadth`
   * substreams are being consumed at any given time.
   *
   * '''Emits when''' a currently consumed substream has an element available
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes and all consumed substreams complete
   *
   * '''Cancels when''' downstream cancels
   */
  def flatMapMerge[T, M](breadth: Int, f: function.Function[Out, _ <: Graph[SourceShape[T], M]]): SubSource[T, Mat] =
    new SubSource(delegate.flatMapMerge(breadth, o ⇒ f(o)))

  /**
   * Concatenate the given [[Source]] to this [[Flow]], meaning that once this
   * Flow’s input is exhausted and all result elements have been generated,
   * the Source’s elements will be produced.
   *
   * Note that the [[Source]] is materialized together with this Flow and just kept
   * from producing elements by asserting back-pressure until its time comes.
   *
   * If this [[Flow]] gets upstream error - no elements from the given [[Source]] will be pulled.
   *
   * '''Emits when''' element is available from current stream or from the given [[Source]] when current is completed
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' given [[Source]] completes
   *
   * '''Cancels when''' downstream cancels
   */
  def concat[T >: Out, M](that: Graph[SourceShape[T], M]): SubSource[T, Mat] =
    new SubSource(delegate.concat(that))

  /**
   * Prepend the given [[Source]] to this [[Flow]], meaning that before elements
   * are generated from this Flow, the Source's elements will be produced until it
   * is exhausted, at which point Flow elements will start being produced.
   *
   * Note that this Flow will be materialized together with the [[Source]] and just kept
   * from producing elements by asserting back-pressure until its time comes.
   *
   * If the given [[Source]] gets upstream error - no elements from this [[Flow]] will be pulled.
   *
   * '''Emits when''' element is available from the given [[Source]] or from current stream when the [[Source]] is completed
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' this [[Flow]] completes
   *
   * '''Cancels when''' downstream cancels
   */
  def prepend[T >: Out, M](that: Graph[SourceShape[T], M]): SubSource[T, Mat] =
    new SubSource(delegate.prepend(that))

  /**
   * Provides a secondary source that will be consumed if this source completes without any
   * elements passing by. As soon as the first element comes through this stream, the alternative
   * will be cancelled.
   *
   * Note that this Flow will be materialized together with the [[Source]] and just kept
   * from producing elements by asserting back-pressure until its time comes or it gets
   * cancelled.
   *
   * On errors the stage is failed regardless of source of the error.
   *
   * '''Emits when''' element is available from first stream or first stream closed without emitting any elements and an element
   *                  is available from the second stream
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' the primary stream completes after emitting at least one element, when the primary stream completes
   *                      without emitting and the secondary stream already has completed or when the secondary stream completes
   *
   * '''Cancels when''' downstream cancels and additionally the alternative is cancelled as soon as an element passes
   *                    by from this stream.
   */
  def orElse[T >: Out, M](secondary: Graph[SourceShape[T], M]): javadsl.SubSource[T, Mat] =
    new SubSource(delegate.orElse(secondary))

  /**
   * Attaches the given [[Sink]] to this [[Flow]], meaning that elements that passes
   * through will also be sent to the [[Sink]].
   *
   * '''Emits when''' element is available and demand exists both from the Sink and the downstream.
   *
   * '''Backpressures when''' downstream or Sink backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def alsoTo(that: Graph[SinkShape[Out], _]): SubSource[Out, Mat] =
    new SubSource(delegate.alsoTo(that))

  /**
   * Merge the given [[Source]] to this [[Flow]], taking elements as they arrive from input streams,
   * picking randomly when several elements ready.
   *
   * '''Emits when''' one of the inputs has an element available
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' all upstreams complete
   *
   * '''Cancels when''' downstream cancels
   */
  def merge[T >: Out](that: Graph[SourceShape[T], _]): SubSource[T, Mat] =
    new SubSource(delegate.merge(that))

  /**
   * Interleave is a deterministic merge of the given [[Source]] with elements of this [[Source]].
   * It first emits `segmentSize` number of elements from this flow to downstream, then - same amount for `that` source,
   * then repeat process.
   *
   * Example:
   * {{{
   * Source.from(Arrays.asList(1, 2, 3)).interleave(Source.from(Arrays.asList(4, 5, 6, 7), 2)
   * // 1, 2, 4, 5, 3, 6, 7
   * }}}
   *
   * After one of sources is complete than all the rest elements will be emitted from the second one
   *
   * If one of sources gets upstream error - stream completes with failure.
   *
   * '''Emits when''' element is available from the currently consumed upstream
   *
   * '''Backpressures when''' downstream backpressures. Signal to current
   * upstream, switch to next upstream when received `segmentSize` elements
   *
   * '''Completes when''' this [[Source]] and given one completes
   *
   * '''Cancels when''' downstream cancels
   */
  def interleave[T >: Out](that: Graph[SourceShape[T], _], segmentSize: Int): SubSource[T, Mat] =
    new SubSource(delegate.interleave(that, segmentSize))

  /**
   * Merge the given [[Source]] to this [[Source]], taking elements as they arrive from input streams,
   * picking always the smallest of the available elements (waiting for one element from each side
   * to be available). This means that possible contiguity of the input streams is not exploited to avoid
   * waiting for elements, this merge will block when one of the inputs does not have more elements (and
   * does not complete).
   *
   * '''Emits when''' all of the inputs have an element available
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' all upstreams complete
   *
   * '''Cancels when''' downstream cancels
   */
  def mergeSorted[U >: Out, M](that: Graph[SourceShape[U], M], comp: Comparator[U]): javadsl.SubSource[U, Mat] =
    new SubSource(delegate.mergeSorted(that)(Ordering.comparatorToOrdering(comp)))

  /**
   * Combine the elements of current [[Flow]] and the given [[Source]] into a stream of tuples.
   *
   * '''Emits when''' all of the inputs has an element available
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' any upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def zip[T](source: Graph[SourceShape[T], _]): SubSource[akka.japi.Pair[Out @uncheckedVariance, T], Mat] =
    new SubSource(delegate.zip(source).map { case (o, t) ⇒ akka.japi.Pair.create(o, t) })

  /**
   * Put together the elements of current [[Flow]] and the given [[Source]]
   * into a stream of combined elements using a combiner function.
   *
   * '''Emits when''' all of the inputs has an element available
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' any upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def zipWith[Out2, Out3](
    that:    Graph[SourceShape[Out2], _],
    combine: function.Function2[Out, Out2, Out3]): SubSource[Out3, Mat] =
    new SubSource(delegate.zipWith[Out2, Out3](that)(combinerToScala(combine)))

  /**
   * Combine the elements of current [[Source]] into a stream of tuples consisting
   * of all elements paired with their index. Indices start at 0.
   *
   * '''Emits when''' upstream emits an element and is paired with their index
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def zipWithIndex: javadsl.SubSource[akka.japi.Pair[Out @uncheckedVariance, Long], Mat] =
    new SubSource(delegate.zipWithIndex.map { case (elem, index) ⇒ akka.japi.Pair(elem, index) })

  /**
   * If the first element has not passed through this stage before the provided timeout, the stream is failed
   * with a [[java.util.concurrent.TimeoutException]].
   *
   * '''Emits when''' upstream emits an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes or fails if timeout elapses before first element arrives
   *
   * '''Cancels when''' downstream cancels
   */
  def initialTimeout(timeout: FiniteDuration): SubSource[Out, Mat] =
    new SubSource(delegate.initialTimeout(timeout))

  /**
   * If the completion of the stream does not happen until the provided timeout, the stream is failed
   * with a [[java.util.concurrent.TimeoutException]].
   *
   * '''Emits when''' upstream emits an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes or fails if timeout elapses before upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def completionTimeout(timeout: FiniteDuration): SubSource[Out, Mat] =
    new SubSource(delegate.completionTimeout(timeout))

  /**
   * If the time between two processed elements exceeds the provided timeout, the stream is failed
   * with a [[java.util.concurrent.TimeoutException]]. The timeout is checked periodically,
   * so the resolution of the check is one period (equals to timeout value).
   *
   * '''Emits when''' upstream emits an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes or fails if timeout elapses between two emitted elements
   *
   * '''Cancels when''' downstream cancels
   */
  def idleTimeout(timeout: FiniteDuration): SubSource[Out, Mat] =
    new SubSource(delegate.idleTimeout(timeout))

  /**
   * If the time between the emission of an element and the following downstream demand exceeds the provided timeout,
   * the stream is failed with a [[java.util.concurrent.TimeoutException]]. The timeout is checked periodically,
   * so the resolution of the check is one period (equals to timeout value).
   *
   * '''Emits when''' upstream emits an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes or fails if timeout elapses between element emission and downstream demand.
   *
   * '''Cancels when''' downstream cancels
   */
  def backpressureTimeout(timeout: FiniteDuration): SubSource[Out, Mat] =
    new SubSource(delegate.backpressureTimeout(timeout))

  /**
   * Injects additional elements if upstream does not emit for a configured amount of time. In other words, this
   * stage attempts to maintains a base rate of emitted elements towards the downstream.
   *
   * If the downstream backpressures then no element is injected until downstream demand arrives. Injected elements
   * do not accumulate during this period.
   *
   * Upstream elements are always preferred over injected elements.
   *
   * '''Emits when''' upstream emits an element or if the upstream was idle for the configured period
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def keepAlive[U >: Out](maxIdle: FiniteDuration, injectedElem: function.Creator[U]): SubSource[U, Mat] =
    new SubSource(delegate.keepAlive(maxIdle, () ⇒ injectedElem.create()))

  /**
   * Sends elements downstream with speed limited to `elements/per`. In other words, this stage set the maximum rate
   * for emitting messages. This combinator works for streams where all elements have the same cost or length.
   *
   * Throttle implements the token bucket model. There is a bucket with a given token capacity (burst size or maximumBurst).
   * Tokens drops into the bucket at a given rate and can be `spared` for later use up to bucket capacity
   * to allow some burstiness. Whenever stream wants to send an element, it takes as many
   * tokens from the bucket as number of elements. If there isn't any, throttle waits until the
   * bucket accumulates enough tokens. Bucket is full when stream just materialized and started.
   *
   * Parameter `mode` manages behaviour when upstream is faster than throttle rate:
   *  - [[akka.stream.ThrottleMode.Shaping]] makes pauses before emitting messages to meet throttle rate
   *  - [[akka.stream.ThrottleMode.Enforcing]] fails with exception when upstream is faster than throttle rate
   *
   * '''Emits when''' upstream emits an element and configured time per each element elapsed
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def throttle(elements: Int, per: FiniteDuration, maximumBurst: Int,
               mode: ThrottleMode): javadsl.SubSource[Out, Mat] =
    new SubSource(delegate.throttle(elements, per, maximumBurst, mode))

  /**
   * Sends elements downstream with speed limited to `cost/per`. Cost is
   * calculating for each element individually by calling `calculateCost` function.
   * This combinator works for streams when elements have different cost(length).
   * Streams of `ByteString` for example.
   *
   * Throttle implements the token bucket model. There is a bucket with a given token capacity (burst size or maximumBurst).
   * Tokens drops into the bucket at a given rate and can be `spared` for later use up to bucket capacity
   * to allow some burstiness. Whenever stream wants to send an element, it takes as many
   * tokens from the bucket as element cost. If there isn't any, throttle waits until the
   * bucket accumulates enough tokens. Elements that costs more than the allowed burst will be delayed proportionally
   * to their cost minus available tokens, meeting the target rate.
   *
   * Parameter `mode` manages behaviour when upstream is faster than throttle rate:
   *  - [[akka.stream.ThrottleMode.Shaping]] makes pauses before emitting messages to meet throttle rate
   *  - [[akka.stream.ThrottleMode.Enforcing]] fails with exception when upstream is faster than throttle rate. Enforcing
   *  cannot emit elements that cost more than the maximumBurst
   *
   * '''Emits when''' upstream emits an element and configured time per each element elapsed
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def throttle(cost: Int, per: FiniteDuration, maximumBurst: Int,
               costCalculation: function.Function[Out, Integer], mode: ThrottleMode): javadsl.SubSource[Out, Mat] =
    new SubSource(delegate.throttle(cost, per, maximumBurst, costCalculation.apply _, mode))

  /**
   * Detaches upstream demand from downstream demand without detaching the
   * stream rates; in other words acts like a buffer of size 1.
   *
   * '''Emits when''' upstream emits an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def detach: javadsl.SubSource[Out, Mat] = new SubSource(delegate.detach)

  /**
   * Delays the initial element by the specified duration.
   *
   * '''Emits when''' upstream emits an element if the initial delay is already elapsed
   *
   * '''Backpressures when''' downstream backpressures or initial delay is not yet elapsed
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def initialDelay(delay: FiniteDuration): SubSource[Out, Mat] =
    new SubSource(delegate.initialDelay(delay))

  /**
   * Change the attributes of this [[Source]] to the given ones and seal the list
   * of attributes. This means that further calls will not be able to remove these
   * attributes, but instead add new ones. Note that this
   * operation has no effect on an empty Flow (because the attributes apply
   * only to the contained processing stages).
   */
  def withAttributes(attr: Attributes): SubSource[Out, Mat] =
    new SubSource(delegate.withAttributes(attr))

  /**
   * Add the given attributes to this Source. Further calls to `withAttributes`
   * will not remove these attributes. Note that this
   * operation has no effect on an empty Flow (because the attributes apply
   * only to the contained processing stages).
   */
  def addAttributes(attr: Attributes): SubSource[Out, Mat] =
    new SubSource(delegate.addAttributes(attr))

  /**
   * Add a ``name`` attribute to this Flow.
   */
  def named(name: String): SubSource[Out, Mat] =
    new SubSource(delegate.named(name))

  /**
   * Put an asynchronous boundary around this `SubSource`
   */
  def async: SubSource[Out, Mat] =
    new SubSource(delegate.async)

  /**
   * Logs elements flowing through the stream as well as completion and erroring.
   *
   * By default element and completion signals are logged on debug level, and errors are logged on Error level.
   * This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow:
   *
   * The `extract` function will be applied to each element before logging, so it is possible to log only those fields
   * of a complex object flowing through this element.
   *
   * Uses the given [[LoggingAdapter]] for logging.
   *
   * '''Emits when''' the mapping function returns an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def log(name: String, extract: function.Function[Out, Any], log: LoggingAdapter): SubSource[Out, Mat] =
    new SubSource(delegate.log(name, e ⇒ extract.apply(e))(log))

  /**
   * Logs elements flowing through the stream as well as completion and erroring.
   *
   * By default element and completion signals are logged on debug level, and errors are logged on Error level.
   * This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow:
   *
   * The `extract` function will be applied to each element before logging, so it is possible to log only those fields
   * of a complex object flowing through this element.
   *
   * Uses an internally created [[LoggingAdapter]] which uses `akka.stream.Log` as it's source (use this class to configure slf4j loggers).
   *
   * '''Emits when''' the mapping function returns an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def log(name: String, extract: function.Function[Out, Any]): SubSource[Out, Mat] =
    this.log(name, extract, null)

  /**
   * Logs elements flowing through the stream as well as completion and erroring.
   *
   * By default element and completion signals are logged on debug level, and errors are logged on Error level.
   * This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow:
   *
   * Uses the given [[LoggingAdapter]] for logging.
   *
   * '''Emits when''' the mapping function returns an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def log(name: String, log: LoggingAdapter): SubSource[Out, Mat] =
    this.log(name, ConstantFun.javaIdentityFunction[Out], log)

  /**
   * Logs elements flowing through the stream as well as completion and erroring.
   *
   * By default element and completion signals are logged on debug level, and errors are logged on Error level.
   * This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow.
   *
   * Uses an internally created [[LoggingAdapter]] which uses `akka.stream.Log` as it's source (use this class to configure slf4j loggers).
   *
   * '''Emits when''' the mapping function returns an element
   *
   * '''Backpressures when''' downstream backpressures
   *
   * '''Completes when''' upstream completes
   *
   * '''Cancels when''' downstream cancels
   */
  def log(name: String): SubSource[Out, Mat] =
    this.log(name, ConstantFun.javaIdentityFunction[Out], null)

}