<commit_before># replacement of CoroutinePool implemented with proc module
from eventlet import coros, proc, api

class Pool(object):

    def __init__(self, min_size=0, max_size=4, track_events=False):
        if min_size > max_size:
            raise ValueError('min_size cannot be bigger than max_size')
        self.sem = coros.Semaphore(max_size)
        for _ in xrange(min_size):
            self.sem.acquire()
        self.procs = proc.RunningProcSet()
        if track_events:
            self.results = coros.queue()
        else:
            self.results = None

    def free(self):
        return self.sem.counter

    def execute(self, func, *args, **kwargs):
        """Execute func in one of the coroutines maintained
        by the pool, when one is free.

        Immediately returns a Proc object which can be queried
        for the func's result.

        >>> pool = Pool()
        >>> task = pool.execute(lambda a: ('foo', a), 1)
        >>> task.wait()
        ('foo', 1)
        """
        # if reentering an empty pool, don't try to wait on a coroutine freeing
        # itself -- instead, just execute in the current coroutine
        if self.sem.locked() and api.getcurrent() in self.procs:
            p = proc.spawn(func, *args, **kwargs)
            try:
                p.wait()
            except:
                pass
        else:
            self.sem.acquire()
            p = self.procs.spawn(func, *args, **kwargs)
            # assuming the above line cannot raise
            p.link(lambda p: self.sem.release())
        if self.results is not None:
            p.link(self.results)
        return p

    execute_async = execute

    def _execute(self, evt, func, args, kw):
        p = self.execute(func, args, kw)
        p.link(evt)
        return p

    def waitall(self):
        return self.procs.waitall()

    wait_all = waitall

    def wait(self):
        """Wait for the next execute in the pool to complete,
        and return the result."""
        return self.results.wait()

    def killall(self):
        return self.procs.killall()

    def launch_all(self, function, iterable):
        """For each tuple (sequence) in iterable, launch function(*tuple) in
        its own coroutine -- like itertools.starmap(), but in parallel.
        Discard values returned by function(). You should call wait_all() to
        wait for all coroutines, newly-launched plus any previously-submitted
        execute() or execute_async() calls, to complete.

        >>> pool = Pool()
        >>> def saw(x):
        ...     print "I saw %s!" % x
        ...
        >>> pool.launch_all(saw, "ABC")
        >>> pool.wait_all()
        I saw A!
        I saw B!
        I saw C!
        """
        for tup in iterable:
            self.execute(function, *tup)

    def process_all(self, function, iterable):
        """For each tuple (sequence) in iterable, launch function(*tuple) in
        its own coroutine -- like itertools.starmap(), but in parallel.
        Discard values returned by function(). Don't return until all
        coroutines, newly-launched plus any previously-submitted execute() or
        execute_async() calls, have completed.

        >>> from eventlet import coros
        >>> pool = coros.CoroutinePool()
        >>> def saw(x): print "I saw %s!" % x
        ...
        >>> pool.process_all(saw, "DEF")
        I saw D!
        I saw E!
        I saw F!
        """
        self.launch_all(function, iterable)
        self.wait_all()

    def generate_results(self, function, iterable, qsize=None):
        """For each tuple (sequence) in iterable, launch function(*tuple) in
        its own coroutine -- like itertools.starmap(), but in parallel.
        Yield each of the values returned by function(), in the order they're
        completed rather than the order the coroutines were launched.

        Iteration stops when we've yielded results for each arguments tuple in
        iterable. Unlike wait_all() and process_all(), this function does not
        wait for any previously-submitted execute() or execute_async() calls.

        Results are temporarily buffered in a queue. If you pass qsize=, this
        value is used to limit the max size of the queue: an attempt to buffer
        too many results will suspend the completed CoroutinePool coroutine
        until the requesting coroutine (the caller of generate_results()) has
        retrieved one or more results by calling this generator-iterator's
        next().

        If any coroutine raises an uncaught exception, that exception will
        propagate to the requesting coroutine via the corresponding next() call.

        What I particularly want these tests to illustrate is that using this
        generator function:

        for result in generate_results(function, iterable):
            # ... do something with result ...

        executes coroutines at least as aggressively as the classic eventlet
        idiom:

        events = [pool.execute(function, *args) for args in iterable]
        for event in events:
            result = event.wait()
            # ... do something with result ...

        even without a distinct event object for every arg tuple in iterable,
        and despite the funny flow control from interleaving launches of new
        coroutines with yields of completed coroutines' results.

        (The use case that makes this function preferable to the classic idiom
        above is when the iterable, which may itself be a generator, produces
        millions of items.)

        >>> from eventlet import coros
        >>> import string
        >>> pool = coros.CoroutinePool(max_size=5)
        >>> pausers = [coros.event() for x in xrange(2)]
        >>> def longtask(evt, desc):
        ...     print "%s woke up with %s" % (desc, evt.wait())
        ...
        >>> pool.launch_all(longtask, zip(pausers, "AB"))
        >>> def quicktask(desc):
        ...     print "returning %s" % desc
        ...     return desc
        ...

        (Instead of using a for loop, step through generate_results()
        items individually to illustrate timing)

        >>> step = iter(pool.generate_results(quicktask, string.ascii_lowercase))
        >>> print step.next()
        returning a
        returning b
        returning c
        a
        >>> print step.next()
        b
        >>> print step.next()
        c
        >>> print step.next()
        returning d
        returning e
        returning f
        d
        >>> pausers[0].send("A")
        >>> print step.next()
        e
        >>> print step.next()
        f
        >>> print step.next()
        A woke up with A
        returning g
        returning h
        returning i
        g
        >>> print "".join([step.next() for x in xrange(3)])
        returning j
        returning k
        returning l
        returning m
        hij
        >>> pausers[1].send("B")
        >>> print "".join([step.next() for x in xrange(4)])
        B woke up with B
        returning n
        returning o
        returning p
        returning q
        klmn
        """
        # Get an iterator because of our funny nested loop below. Wrap the
        # iterable in enumerate() so we count items that come through.
        tuples = iter(enumerate(iterable))
        # If the iterable is empty, this whole function is a no-op, and we can
        # save ourselves some grief by just quitting out. In particular, once
        # we enter the outer loop below, we're going to wait on the queue --
        # but if we launched no coroutines with that queue as the destination,
        # we could end up waiting a very long time.
        try:
            index, args = tuples.next()
        except StopIteration:
            return
        # From this point forward, 'args' is the current arguments tuple and
        # 'index+1' counts how many such tuples we've seen.
        # This implementation relies on the fact that _execute() accepts an
        # event-like object, and -- unless it's None -- the completed
        # coroutine calls send(result). We slyly pass a queue rather than an
        # event -- the same queue instance for all coroutines. This is why our
        # queue interface intentionally resembles the event interface.
        q = coros.queue(max_size=qsize)
        # How many results have we yielded so far?
        finished = 0
        # This first loop is only until we've launched all the coroutines. Its
        # complexity is because if iterable contains more args tuples than the
        # size of our pool, attempting to _execute() the (poolsize+1)th
        # coroutine would suspend until something completes and send()s its
        # result to our queue. But to keep down queue overhead and to maximize
        # responsiveness to our caller, we'd rather suspend on reading the
        # queue. So we stuff the pool as full as we can, then wait for
        # something to finish, then stuff more coroutines into the pool.
        try:
            while True:
                # Before each yield, start as many new coroutines as we can fit.
                # (The self.free() test isn't 100% accurate: if we happen to be
                # executing in one of the pool's coroutines, we could _execute()
                # without waiting even if self.free() reports 0. See _execute().)
                # The point is that we don't want to wait in the _execute() call,
                # we want to wait in the q.wait() call.
                # IMPORTANT: at start, and whenever we've caught up with all
                # coroutines we've launched so far, we MUST iterate this inner
                # loop at least once, regardless of self.free() -- otherwise the
                # q.wait() call below will deadlock!
                # Recall that index is the index of the NEXT args tuple that we
                # haven't yet launched. Therefore it counts how many args tuples
                # we've launched so far.
                while self.free() > 0 or finished == index:
                    # Just like the implementation of execute_async(), save that
                    # we're passing our queue instead of None as the "event" to
                    # which to send() the result.
                    self._execute(q, function, args, {})
                    # We've consumed that args tuple, advance to next.
                    index, args = tuples.next()
                # Okay, we've filled up the pool again, yield a result -- which
                # will probably wait for a coroutine to complete. Although we do
                # have q.ready(), so we could iterate without waiting, we avoid
                # that because every yield could involve considerable real time.
                # We don't know how long it takes to return from yield, so every
                # time we do, take the opportunity to stuff more requests into the
                # pool before yielding again.
                yield q.wait()
                # Be sure to count results so we know when to stop!
                finished += 1
        except StopIteration:
            pass
        # Here we've exhausted the input iterable. index+1 is the total number
        # of coroutines we've launched. We probably haven't yielded that many
        # results yet. Wait for the rest of the results, yielding them as they
        # arrive.
        while finished < index + 1:
            yield q.wait()
            finished += 1


if __name__=='__main__':
    import doctest
    doctest.testmod()
<commit_msg>add current_size property to pool.Pool, akin to what CoroutinePool had<commit_after># replacement of CoroutinePool implemented with proc module
from eventlet import coros, proc, api

class Pool(object):

    def __init__(self, min_size=0, max_size=4, track_events=False):
        if min_size > max_size:
            raise ValueError('min_size cannot be bigger than max_size')
        self.sem = coros.Semaphore(max_size)
        for _ in xrange(min_size):
            self.sem.acquire()
        self.procs = proc.RunningProcSet()
        if track_events:
            self.results = coros.queue()
        else:
            self.results = None

    @property
    def current_size(self):
        return len(self.procs)

    def free(self):
        return self.sem.counter

    def execute(self, func, *args, **kwargs):
        """Execute func in one of the coroutines maintained
        by the pool, when one is free.

        Immediately returns a Proc object which can be queried
        for the func's result.

        >>> pool = Pool()
        >>> task = pool.execute(lambda a: ('foo', a), 1)
        >>> task.wait()
        ('foo', 1)
        """
        # if reentering an empty pool, don't try to wait on a coroutine freeing
        # itself -- instead, just execute in the current coroutine
        if self.sem.locked() and api.getcurrent() in self.procs:
            p = proc.spawn(func, *args, **kwargs)
            try:
                p.wait()
            except:
                pass
        else:
            self.sem.acquire()
            p = self.procs.spawn(func, *args, **kwargs)
            # assuming the above line cannot raise
            p.link(lambda p: self.sem.release())
        if self.results is not None:
            p.link(self.results)
        return p

    execute_async = execute

    def _execute(self, evt, func, args, kw):
        p = self.execute(func, args, kw)
        p.link(evt)
        return p

    def waitall(self):
        return self.procs.waitall()

    wait_all = waitall

    def wait(self):
        """Wait for the next execute in the pool to complete,
        and return the result."""
        return self.results.wait()

    def killall(self):
        return self.procs.killall()

    def launch_all(self, function, iterable):
        """For each tuple (sequence) in iterable, launch function(*tuple) in
        its own coroutine -- like itertools.starmap(), but in parallel.
        Discard values returned by function(). You should call wait_all() to
        wait for all coroutines, newly-launched plus any previously-submitted
        execute() or execute_async() calls, to complete.

        >>> pool = Pool()
        >>> def saw(x):
        ...     print "I saw %s!" % x
        ...
        >>> pool.launch_all(saw, "ABC")
        >>> pool.wait_all()
        I saw A!
        I saw B!
        I saw C!
        """
        for tup in iterable:
            self.execute(function, *tup)

    def process_all(self, function, iterable):
        """For each tuple (sequence) in iterable, launch function(*tuple) in
        its own coroutine -- like itertools.starmap(), but in parallel.
        Discard values returned by function(). Don't return until all
        coroutines, newly-launched plus any previously-submitted execute() or
        execute_async() calls, have completed.

        >>> from eventlet import coros
        >>> pool = coros.CoroutinePool()
        >>> def saw(x): print "I saw %s!" % x
        ...
        >>> pool.process_all(saw, "DEF")
        I saw D!
        I saw E!
        I saw F!
        """
        self.launch_all(function, iterable)
        self.wait_all()

    def generate_results(self, function, iterable, qsize=None):
        """For each tuple (sequence) in iterable, launch function(*tuple) in
        its own coroutine -- like itertools.starmap(), but in parallel.
        Yield each of the values returned by function(), in the order they're
        completed rather than the order the coroutines were launched.

        Iteration stops when we've yielded results for each arguments tuple in
        iterable. Unlike wait_all() and process_all(), this function does not
        wait for any previously-submitted execute() or execute_async() calls.

        Results are temporarily buffered in a queue. If you pass qsize=, this
        value is used to limit the max size of the queue: an attempt to buffer
        too many results will suspend the completed CoroutinePool coroutine
        until the requesting coroutine (the caller of generate_results()) has
        retrieved one or more results by calling this generator-iterator's
        next().

        If any coroutine raises an uncaught exception, that exception will
        propagate to the requesting coroutine via the corresponding next() call.

        What I particularly want these tests to illustrate is that using this
        generator function:

        for result in generate_results(function, iterable):
            # ... do something with result ...

        executes coroutines at least as aggressively as the classic eventlet
        idiom:

        events = [pool.execute(function, *args) for args in iterable]
        for event in events:
            result = event.wait()
            # ... do something with result ...

        even without a distinct event object for every arg tuple in iterable,
        and despite the funny flow control from interleaving launches of new
        coroutines with yields of completed coroutines' results.

        (The use case that makes this function preferable to the classic idiom
        above is when the iterable, which may itself be a generator, produces
        millions of items.)

        >>> from eventlet import coros
        >>> import string
        >>> pool = coros.CoroutinePool(max_size=5)
        >>> pausers = [coros.event() for x in xrange(2)]
        >>> def longtask(evt, desc):
        ...     print "%s woke up with %s" % (desc, evt.wait())
        ...
        >>> pool.launch_all(longtask, zip(pausers, "AB"))
        >>> def quicktask(desc):
        ...     print "returning %s" % desc
        ...     return desc
        ...

        (Instead of using a for loop, step through generate_results()
        items individually to illustrate timing)

        >>> step = iter(pool.generate_results(quicktask, string.ascii_lowercase))
        >>> print step.next()
        returning a
        returning b
        returning c
        a
        >>> print step.next()
        b
        >>> print step.next()
        c
        >>> print step.next()
        returning d
        returning e
        returning f
        d
        >>> pausers[0].send("A")
        >>> print step.next()
        e
        >>> print step.next()
        f
        >>> print step.next()
        A woke up with A
        returning g
        returning h
        returning i
        g
        >>> print "".join([step.next() for x in xrange(3)])
        returning j
        returning k
        returning l
        returning m
        hij
        >>> pausers[1].send("B")
        >>> print "".join([step.next() for x in xrange(4)])
        B woke up with B
        returning n
        returning o
        returning p
        returning q
        klmn
        """
        # Get an iterator because of our funny nested loop below. Wrap the
        # iterable in enumerate() so we count items that come through.
        tuples = iter(enumerate(iterable))
        # If the iterable is empty, this whole function is a no-op, and we can
        # save ourselves some grief by just quitting out. In particular, once
        # we enter the outer loop below, we're going to wait on the queue --
        # but if we launched no coroutines with that queue as the destination,
        # we could end up waiting a very long time.
        try:
            index, args = tuples.next()
        except StopIteration:
            return
        # From this point forward, 'args' is the current arguments tuple and
        # 'index+1' counts how many such tuples we've seen.
        # This implementation relies on the fact that _execute() accepts an
        # event-like object, and -- unless it's None -- the completed
        # coroutine calls send(result). We slyly pass a queue rather than an
        # event -- the same queue instance for all coroutines. This is why our
        # queue interface intentionally resembles the event interface.
        q = coros.queue(max_size=qsize)
        # How many results have we yielded so far?
        finished = 0
        # This first loop is only until we've launched all the coroutines. Its
        # complexity is because if iterable contains more args tuples than the
        # size of our pool, attempting to _execute() the (poolsize+1)th
        # coroutine would suspend until something completes and send()s its
        # result to our queue. But to keep down queue overhead and to maximize
        # responsiveness to our caller, we'd rather suspend on reading the
        # queue. So we stuff the pool as full as we can, then wait for
        # something to finish, then stuff more coroutines into the pool.
        try:
            while True:
                # Before each yield, start as many new coroutines as we can fit.
                # (The self.free() test isn't 100% accurate: if we happen to be
                # executing in one of the pool's coroutines, we could _execute()
                # without waiting even if self.free() reports 0. See _execute().)
                # The point is that we don't want to wait in the _execute() call,
                # we want to wait in the q.wait() call.
                # IMPORTANT: at start, and whenever we've caught up with all
                # coroutines we've launched so far, we MUST iterate this inner
                # loop at least once, regardless of self.free() -- otherwise the
                # q.wait() call below will deadlock!
                # Recall that index is the index of the NEXT args tuple that we
                # haven't yet launched. Therefore it counts how many args tuples
                # we've launched so far.
                while self.free() > 0 or finished == index:
                    # Just like the implementation of execute_async(), save that
                    # we're passing our queue instead of None as the "event" to
                    # which to send() the result.
                    self._execute(q, function, args, {})
                    # We've consumed that args tuple, advance to next.
                    index, args = tuples.next()
                # Okay, we've filled up the pool again, yield a result -- which
                # will probably wait for a coroutine to complete. Although we do
                # have q.ready(), so we could iterate without waiting, we avoid
                # that because every yield could involve considerable real time.
                # We don't know how long it takes to return from yield, so every
                # time we do, take the opportunity to stuff more requests into the
                # pool before yielding again.
                yield q.wait()
                # Be sure to count results so we know when to stop!
                finished += 1
        except StopIteration:
            pass
        # Here we've exhausted the input iterable. index+1 is the total number
        # of coroutines we've launched. We probably haven't yielded that many
        # results yet. Wait for the rest of the results, yielding them as they
        # arrive.
        while finished < index + 1:
            yield q.wait()
            finished += 1


if __name__=='__main__':
    import doctest
    doctest.testmod()

{'fraction_non_alphanumeric': 0.07984006191151813, 'fraction_numerical': 0.0018917408315060837, 'mean_word_length': 1.9169801856032105, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 187, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '4070719', 'n_tokens_mistral': 6054, 'n_tokens_neox': 5876, 'n_words': 2592}