"threading" --- Thread-based parallelism
****************************************

**Code source :** Lib/threading.py

======================================================================

This module constructs higher-level threading interfaces on top of the
lower level "_thread" module.

Availability: not WASI.

This module does not work or is not available on WebAssembly. See
Plateformes WebAssembly for more information.


Introduction
============

The "threading" module provides a way to run multiple threads (smaller
units of a process) concurrently within a single process. It allows
for the creation and management of threads, making it possible to
execute tasks in parallel, sharing memory space. Threads are
particularly useful when tasks are I/O bound, such as file operations
or making network requests, where much of the time is spent waiting
for external resources.

A typical use case for "threading" includes managing a pool of worker
threads that can process multiple tasks concurrently.  Here's a basic
example of creating and starting threads using "Thread":

   import threading
   import time

   def crawl(link, delay=3):
       print(f"crawl started for {link}")
       time.sleep(delay)  # Blocking I/O (simulating a network request)
       print(f"crawl ended for {link}")

   links = [
       "https://python.org",
       "https://docs.python.org",
       "https://peps.python.org",
   ]

   # Start threads for each link
   threads = []
   for link in links:
       # Using `args` to pass positional arguments and `kwargs` for keyword arguments
       t = threading.Thread(target=crawl, args=(link,), kwargs={"delay": 2})
       threads.append(t)

   # Start each thread
   for t in threads:
       t.start()

   # Wait for all threads to finish
   for t in threads:
       t.join()

Modifié dans la version 3.7: Ce module était auparavant optionnel, il
est maintenant toujours disponible.

Voir aussi:

  "concurrent.futures.ThreadPoolExecutor" offers a higher level
  interface to push tasks to a background thread without blocking
  execution of the calling thread, while still being able to retrieve
  their results when needed.

  "queue" provides a thread-safe interface for exchanging data between
  running threads.

  "asyncio" offers an alternative approach to achieving task level
  concurrency without requiring the use of multiple operating system
  threads.

Note:

  In the Python 2.x series, this module contained "camelCase" names
  for some methods and functions. These are deprecated as of Python
  3.10, but they are still supported for compatibility with Python 2.5
  and lower.

**Particularité de l'implémentation CPython :** In CPython, due to the
*Global Interpreter Lock*, only one thread can execute Python code at
once (even though certain performance-oriented libraries might
overcome this limitation). If you want your application to make better
use of the computational resources of multi-core machines, you are
advised to use "multiprocessing" or
"concurrent.futures.ProcessPoolExecutor". However, threading is still
an appropriate model if you want to run multiple I/O-bound tasks
simultaneously.


GIL and performance considerations
==================================

Unlike the "multiprocessing" module, which uses separate processes to
bypass the *global interpreter lock* (GIL), the threading module
operates within a single process, meaning that all threads share the
same memory space. However, the GIL limits the performance gains of
threading when it comes to CPU-bound tasks, as only one thread can
execute Python bytecode at a time. Despite this, threads remain a
useful tool for achieving concurrency in many scenarios.

As of Python 3.13, experimental *free-threaded* builds can disable the
GIL, enabling true parallel execution of threads, but this feature is
not available by default (see **PEP 703**).


Reference
=========

Ce module définit les fonctions suivantes :

threading.active_count()

   Renvoie le nombre d'objets "Thread" actuellement vivants. Le compte
   renvoyé est égal à la longueur de la liste renvoyée par
   "enumerate()".

   The function "activeCount" is a deprecated alias for this function.

threading.current_thread()

   Return the current "Thread" object, corresponding to the caller's
   thread of control.  If the caller's thread of control was not
   created through the "threading" module, a dummy thread object with
   limited functionality is returned.

   The function "currentThread" is a deprecated alias for this
   function.

threading.excepthook(args, /)

   Gère les exceptions non-attrapées levées par "Thread.run()".

   L'argument *arg* a les attributs suivants :

   * *exc_type* : le type de l'exception ;

   * *exc_value*: la valeur de l'exception, peut être "None" ;

   * *exc_traceback* : la pile d'appels pour cette exception, peut
     être "None" ;

   * *thread*: le fil d'exécution ayant levé l'exception, peut être
     "None".

   Si *exc_type* est "SystemExit", l'exception est ignorée
   silencieusement. Toutes les autres sont affichées sur "sys.stderr".

   Si cette fonction lève une exception, "sys.excepthook()" est
   appelée pour la gérer.

   La fonction "threading.excepthook()" peut être surchargée afin de
   contrôler comment les exceptions non-attrapées levées par
   "Thread.run()" sont gérées.

   Stocker *exc_value* en utilisant une fonction de rappel
   personnalisée peut créer un cycle de références. *exc_value* doit
   être nettoyée explicitement pour casser ce cycle lorsque
   l'exception n'est plus nécessaire.

   Stocker *thread* en utilisant une fonction de rappel personnalisée
   peut le ressusciter, si c'est un objet en cours de finalisation.
   Évitez de stocker *thread* après la fin de la fonction de rappel,
   pour éviter de ressusciter des objets.

   Voir aussi:

     "sys.excepthook()" gère les exceptions qui n'ont pas été
     attrapées.

   Ajouté dans la version 3.8.

threading.__excepthook__

   Holds the original value of "threading.excepthook()". It is saved
   so that the original value can be restored in case they happen to
   get replaced with broken or alternative objects.

   Ajouté dans la version 3.10.

threading.get_ident()

   Renvoie l'« identifiant de fil »  du fil d'exécution courant. C'est
   un entier non nul. Sa valeur n'a pas de signification directe ; il
   est destiné à être utilisé comme valeur magique opaque, par exemple
   comme clef de dictionnaire de données pour chaque fil. Les
   identificateurs de fils peuvent être recyclés lorsqu'un fil se
   termine et qu'un autre fil est créé.

   Ajouté dans la version 3.3.

threading.get_native_id()

   Renvoie l'identifiant natif complet assigné par le noyau du fil
   d'exécution actuel. C'est un entier non négatif. Sa valeur peut
   uniquement être utilisée pour identifier ce fil d'exécution à
   l'échelle du système (jusqu'à ce que le fil d'exécution se termine,
   après quoi la valeur peut être recyclée par le système
   d'exploitation).

   Availability: Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD, AIX,
   DragonFlyBSD, GNU/kFreeBSD.

   Ajouté dans la version 3.8.

   Modifié dans la version 3.13: Added support for GNU/kFreeBSD.

threading.enumerate()

   Return a list of all "Thread" objects currently active.  The list
   includes daemonic threads and dummy thread objects created by
   "current_thread()".  It excludes terminated threads and threads
   that have not yet been started.  However, the main thread is always
   part of the result, even when terminated.

threading.main_thread()

   Renvoie l'objet fil d'exécution "Thread" principal. Dans des
   conditions normales, le fil principal est le fil à partir duquel
   l'interpréteur Python a été lancé.

   Ajouté dans la version 3.4.

threading.settrace(func)

   Set a trace function for all threads started from the "threading"
   module. The *func* will be passed to  "sys.settrace()" for each
   thread, before its "run()" method is called.

threading.settrace_all_threads(func)

   Set a trace function for all threads started from the "threading"
   module and all Python threads that are currently executing.

   The *func* will be passed to  "sys.settrace()" for each thread,
   before its "run()" method is called.

   Ajouté dans la version 3.12.

threading.gettrace()

   Renvoie la fonction de traçage tel que définie par "settrace()".

   Ajouté dans la version 3.10.

threading.setprofile(func)

   Set a profile function for all threads started from the "threading"
   module. The *func* will be passed to  "sys.setprofile()" for each
   thread, before its "run()" method is called.

threading.setprofile_all_threads(func)

   Set a profile function for all threads started from the "threading"
   module and all Python threads that are currently executing.

   The *func* will be passed to  "sys.setprofile()" for each thread,
   before its "run()" method is called.

   Ajouté dans la version 3.12.

threading.getprofile()

   Renvoie la fonction de profilage tel que défini par "setprofile()".

   Ajouté dans la version 3.10.

threading.stack_size([size])

   Renvoie la taille de la pile d'exécution utilisée lors de la
   création de nouveaux fils d'exécution. L'argument optionnel *size*
   spécifie la taille de pile à utiliser pour les fils créés
   ultérieurement, et doit être 0 (pour utiliser la taille de la
   plate-forme ou la valeur configurée par défaut) ou un entier
   positif supérieur ou égal à 32 768 (32 Kio). Si *size* n'est pas
   spécifié, 0 est utilisé. Si la modification de la taille de la pile
   de fils n'est pas prise en charge, une "RuntimeError" est levée. Si
   la taille de pile spécifiée n'est pas valide, une "ValueError" est
   levée et la taille de pile n'est pas modifiée. 32 Kio est
   actuellement la valeur minimale de taille de pile prise en charge
   pour garantir un espace de pile suffisant pour l'interpréteur lui-
   même. Notez que certaines plates-formes peuvent avoir des
   restrictions particulières sur les valeurs de taille de la pile,
   telles que l'exigence d'une taille de pile minimale > 32 Kio ou
   d'une allocation en multiples de la taille de page de la mémoire du
   système – la documentation de la plate-forme devrait être consultée
   pour plus d'informations (4 Kio sont courantes ; en l'absence de
   renseignements plus spécifiques, l'approche proposée est
   l'utilisation de multiples de 4 096 pour la taille de la pile).

   Availability: Windows, pthreads.

   Unix platforms with POSIX threads support.

Ce module définit également la constante suivante :

threading.TIMEOUT_MAX

   La valeur maximale autorisée pour le paramètre *timeout* des
   fonctions bloquantes ("Lock.acquire()", "RLock.acquire()",
   "Condition.wait()", etc.). Spécifier un délai d'attente supérieur à
   cette valeur lève une "OverflowError".

   Ajouté dans la version 3.2.

Ce module définit un certain nombre de classes, qui sont détaillées
dans les sections ci-dessous.

La conception de ce module est librement basée sur le modèle des fils
d'exécution de Java. Cependant, là où Java fait des verrous et des
variables de condition le comportement de base de chaque objet, ils
sont des objets séparés en Python. La classe Python "Thread" prend en
charge un sous-ensemble du comportement de la classe *Thread* de Java
; actuellement, il n'y a aucune priorité, aucun groupe de fils
d'exécution, et les fils ne peuvent être détruits, arrêtés, suspendus,
repris ni interrompus. Les méthodes statiques de la classe *Thread* de
Java, lorsqu'elles sont implémentées, correspondent à des fonctions au
niveau du module.

Toutes les méthodes décrites ci-dessous sont exécutées de manière
atomique.


Thread-local data
-----------------

Thread-local data is data whose values are thread specific. If you
have data that you want to be local to a thread, create a "local"
object and use its attributes:

   >>> mydata = local()
   >>> mydata.number = 42
   >>> mydata.number
   42

You can also access the "local"-object's dictionary:

   >>> mydata.__dict__
   {'number': 42}
   >>> mydata.__dict__.setdefault('widgets', [])
   []
   >>> mydata.widgets
   []

If we access the data in a different thread:

   >>> log = []
   >>> def f():
   ...     items = sorted(mydata.__dict__.items())
   ...     log.append(items)
   ...     mydata.number = 11
   ...     log.append(mydata.number)

   >>> import threading
   >>> thread = threading.Thread(target=f)
   >>> thread.start()
   >>> thread.join()
   >>> log
   [[], 11]

we get different data.  Furthermore, changes made in the other thread
don't affect data seen in this thread:

   >>> mydata.number
   42

Of course, values you get from a "local" object, including their
"__dict__" attribute, are for whatever thread was current at the time
the attribute was read.  For that reason, you generally don't want to
save these values across threads, as they apply only to the thread
they came from.

You can create custom "local" objects by subclassing the "local"
class:

   >>> class MyLocal(local):
   ...     number = 2
   ...     def __init__(self, /, **kw):
   ...         self.__dict__.update(kw)
   ...     def squared(self):
   ...         return self.number ** 2

This can be useful to support default values, methods and
initialization.  Note that if you define an "__init__()" method, it
will be called each time the "local" object is used in a separate
thread.  This is necessary to initialize each thread's dictionary.

Now if we create a "local" object:

   >>> mydata = MyLocal(color='red')

we have a default number:

   >>> mydata.number
   2

an initial color:

   >>> mydata.color
   'red'
   >>> del mydata.color

And a method that operates on the data:

   >>> mydata.squared()
   4

As before, we can access the data in a separate thread:

   >>> log = []
   >>> thread = threading.Thread(target=f)
   >>> thread.start()
   >>> thread.join()
   >>> log
   [[('color', 'red')], 11]

without affecting this thread's data:

   >>> mydata.number
   2
   >>> mydata.color
   Traceback (most recent call last):
   ...
   AttributeError: 'MyLocal' object has no attribute 'color'

Note that subclasses can define *__slots__*, but they are not thread
local. They are shared across threads:

   >>> class MyLocal(local):
   ...     __slots__ = 'number'

   >>> mydata = MyLocal()
   >>> mydata.number = 42
   >>> mydata.color = 'red'

So, the separate thread:

   >>> thread = threading.Thread(target=f)
   >>> thread.start()
   >>> thread.join()

affects what we see:

   >>> mydata.number
   11

class threading.local

   Classe qui représente les données locales au fil d'exécution.


Thread objects
--------------

The "Thread" class represents an activity that is run in a separate
thread of control.  There are two ways to specify the activity: by
passing a callable object to the constructor, or by overriding the
"run()" method in a subclass.  No other methods (except for the
constructor) should be overridden in a subclass.  In other words,
*only*  override the "__init__()" and "run()" methods of this class.

Une fois qu'un objet fil d'exécution est créé, son activité doit être
lancée en appelant la méthode "start()" du fil. Ceci invoque la
méthode "run()" dans un fil d'exécution séparé.

Une fois que l'activité du fil d'exécution est lancée, le fil est
considéré comme « vivant ». Il cesse d'être vivant lorsque sa méthode
"run()" se termine – soit normalement, soit en levant une exception
non gérée. La méthode "is_alive()" teste si le fil est vivant.

D'autres fils d'exécution peuvent appeler la méthode "join()" d'un
fil. Ceci bloque le fil appelant jusqu'à ce que le fil dont la méthode
"join()" est appelée soit terminé.

Un fil d'exécution a un nom. Le nom peut être passé au constructeur,
et lu ou modifié via l'attribut "name".

Si la méthode "run()" lève une exception, "threading.excepthook()" est
appelée pour s'en occuper. Par défaut, "threading.excepthook()" ignore
silencieusement "SystemExit".

Un fil d'exécution peut être marqué comme « fil démon ». Un programme
Python se termine quand il ne reste plus que des fils démons. La
valeur initiale est héritée du fil d'exécution qui l'a créé. Cette
option peut être définie par la propriété "daemon" ou par l'argument
*daemon* du constructeur.

Note:

  Les fils d'exécution démons sont brusquement terminés à l'arrêt du
  programme Python. Leurs ressources (fichiers ouverts, transactions
  de base de données, etc.) peuvent ne pas être libérées correctement.
  Si vous voulez que vos fils s'arrêtent proprement, faites en sorte
  qu'ils ne soient pas démoniques et utilisez un mécanisme de
  signalisation approprié tel qu'un objet évènement "Event".

Il y a un objet "fil principal", qui correspond au fil de contrôle
initial dans le programme Python. Ce n'est pas un fil démon.

There is the possibility that "dummy thread objects" are created.
These are thread objects corresponding to "alien threads", which are
threads of control started outside the threading module, such as
directly from C code.  Dummy thread objects have limited
functionality; they are always considered alive and daemonic, and
cannot be joined.  They are never deleted, since it is impossible to
detect the termination of alien threads.

class threading.Thread(group=None, target=None, name=None, args=(), kwargs={}, *, daemon=None)

   Ce constructeur doit toujours être appelé avec des arguments
   nommés. Les arguments sont :

   *group* should be "None"; reserved for future extension when a
   "ThreadGroup" class is implemented.

   *target* est l'objet appelable qui doit être invoqué par la méthode
   "run()". La valeur par défaut est "None", ce qui signifie que rien
   n'est appelé.

   *name* is the thread name. By default, a unique name is constructed
   of the form "Thread-*N*" where *N* is a small decimal number, or
   "Thread-*N* (target)" where "target" is "target.__name__" if the
   *target* argument is specified.

   *args* is a list or tuple of arguments for the target invocation.
   Defaults to "()".

   *kwargs* est un dictionnaire d'arguments nommés pour l'invocation
   de l'objet appelable. La valeur par défaut est "{}".

   S'il ne vaut pas "None", *daemon* définit explicitement si le fil
   d'exécution est démonique ou pas. S'il vaut "None" (par défaut), la
   valeur est héritée du fil courant.

   Si la sous-classe réimplémente le constructeur, elle doit s'assurer
   d'appeler le constructeur de la classe de base
   ("Thread.__init__()") avant de faire autre chose au fil
   d'exécution.

   Modifié dans la version 3.3: Added the *daemon* parameter.

   Modifié dans la version 3.10: Use the *target* name if *name*
   argument is omitted.

   start()

      Lance l'activité du fil d'exécution.

      Elle ne doit être appelée qu'une fois par objet de fil. Elle
      fait en sorte que la méthode "run()" de l'objet soit invoquée
      dans un fil d'exécution.

      Cette méthode lève une "RuntimeError" si elle est appelée plus
      d'une fois sur le même objet fil d'exécution.

   run()

      Méthode représentant l'activité du fil d'exécution.

      Vous pouvez remplacer cette méthode dans une sous-classe.  La
      méthode standard "run()" invoque l'objet appelable passé au
      constructeur de l'objet en tant qu'argument *target*, le cas
      échéant, avec des arguments positionnels et des arguments nommés
      tirés respectivement des arguments *args* et *kwargs*.

      Using list or tuple as the *args* argument which passed to the
      "Thread" could achieve the same effect.

      Example:

         >>> from threading import Thread
         >>> t = Thread(target=print, args=[1])
         >>> t.run()
         1
         >>> t = Thread(target=print, args=(1,))
         >>> t.run()
         1

   join(timeout=None)

      Attend que le fil d'exécution se termine. Ceci bloque le fil
      appelant jusqu'à ce que le fil dont la méthode "join()" est
      appelée se termine – soit normalement, soit par une exception
      non gérée – ou jusqu'à ce que le délai optionnel *timeout* soit
      atteint.

      When the *timeout* argument is present and not "None", it should
      be a floating-point number specifying a timeout for the
      operation in seconds (or fractions thereof). As "join()" always
      returns "None", you must call "is_alive()" after "join()" to
      decide whether a timeout happened -- if the thread is still
      alive, the "join()" call timed out.

      Lorsque l'argument *timeout* n'est pas présent ou vaut "None",
      l'opération se bloque jusqu'à ce que le fil d'exécution se
      termine.

      A thread can be joined many times.

      "join()" lève une "RuntimeError" si une tentative est faite pour
      attendre le fil d'exécution courant car cela conduirait à un
      interblocage (*deadlock* en anglais). Attendre via "join()" un
      fil d'exécution avant son lancement est aussi une erreur et, si
      vous tentez de le faire, lève la même exception.

   name

      Une chaîne de caractères utilisée à des fins d'identification
      seulement. Elle n'a pas de sémantique. Plusieurs fils
      d'exécution peuvent porter le même nom.  Le nom initial est
      défini par le constructeur.

   getName()
   setName()

      Deprecated getter/setter API for "name"; use it directly as a
      property instead.

      Obsolète depuis la version 3.10.

   ident

      « L'identificateur de fil d'exécution » de ce fil ou "None" si
      le fil n'a pas été lancé. C'est un entier non nul. Voyez
      également la fonction "get_ident()". Les identificateurs de fils
      peuvent être recyclés lorsqu'un fil se termine et qu'un autre
      fil est créé. L'identifiant est disponible même après que le fil
      ait terminé.

   native_id

      The Thread ID ("TID") of this thread, as assigned by the OS
      (kernel). This is a non-negative integer, or "None" if the
      thread has not been started. See the "get_native_id()" function.
      This value may be used to uniquely identify this particular
      thread system-wide (until the thread terminates, after which the
      value may be recycled by the OS).

      Note:

        Tout comme pour les *Process IDs*, les *Thread IDs* ne sont
        valides (garantis uniques sur le système) uniquement du
        démarrage du fil à sa fin.

      Availability: Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD,
      AIX, DragonFlyBSD.

      Ajouté dans la version 3.8.

   is_alive()

      Renvoie si le fil d'exécution est vivant ou pas.

      Cette méthode renvoie "True" depuis juste avant le démarrage de
      la méthode "run()" et jusqu'à juste après la terminaison de la
      méthode "run()". La fonction "enumerate()" du module renvoie une
      liste de tous les fils d'exécution vivants.

   daemon

      A boolean value indicating whether this thread is a daemon
      thread ("True") or not ("False").  This must be set before
      "start()" is called, otherwise "RuntimeError" is raised.  Its
      initial value is inherited from the creating thread; the main
      thread is not a daemon thread and therefore all threads created
      in the main thread default to "daemon" = "False".

      Le programme Python se termine lorsqu'il ne reste plus de fils
      d'exécution non-démons vivants.

   isDaemon()
   setDaemon()

      Deprecated getter/setter API for "daemon"; use it directly as a
      property instead.

      Obsolète depuis la version 3.10.


Lock objects
------------

Un verrou primitif n'appartient pas à un fil d'exécution lorsqu'il est
verrouillé. En Python, c'est actuellement la méthode de
synchronisation la plus bas-niveau qui soit disponible, implémentée
directement par le module d'extension "_thread".

Un verrou primitif est soit « verrouillé » soit « déverrouillé ». Il
est créé dans un état déverrouillé. Il a deux méthodes, "acquire()" et
"release()". Lorsque l'état est déverrouillé, "acquire()" verrouille
et se termine immédiatement. Lorsque l'état est verrouillé,
"acquire()" bloque jusqu'à ce qu'un appel à "release()" provenant d'un
autre fil d'exécution le déverrouille. À ce moment "acquire()" le
verrouille à nouveau et rend la main. La méthode "release()" ne doit
être appelée que si le verrou est verrouillé, elle le déverrouille
alors et se termine immédiatement. Déverrouiller un verrou qui n'est
pas verrouillé provoque une "RuntimeError".

Locks also support the context management protocol.

When more than one thread is blocked in "acquire()" waiting for the
state to turn to unlocked, only one thread proceeds when a "release()"
call resets the state to unlocked; which one of the waiting threads
proceeds is not defined, and may vary across implementations.

All methods are executed atomically.

class threading.Lock

   The class implementing primitive lock objects.  Once a thread has
   acquired a lock, subsequent attempts to acquire it block, until it
   is released; any thread may release it.

   Modifié dans la version 3.13: "Lock" is now a class. In earlier
   Pythons, "Lock" was a factory function which returned an instance
   of the underlying private lock type.

   acquire(blocking=True, timeout=-1)

      Acquiert un verrou, bloquant ou non bloquant.

      When invoked with the *blocking* argument set to "True" (the
      default), block until the lock is unlocked, then set it to
      locked and return "True".

      When invoked with the *blocking* argument set to "False", do not
      block. If a call with *blocking* set to "True" would block,
      return "False" immediately; otherwise, set the lock to locked
      and return "True".

      When invoked with the floating-point *timeout* argument set to a
      positive value, block for at most the number of seconds
      specified by *timeout* and as long as the lock cannot be
      acquired.  A *timeout* argument of "-1" specifies an unbounded
      wait.  It is forbidden to specify a *timeout* when *blocking* is
      "False".

      The return value is "True" if the lock is acquired successfully,
      "False" if not (for example if the *timeout* expired).

      Modifié dans la version 3.2: Le paramètre *timeout* est nouveau.

      Modifié dans la version 3.2: Lock acquisition can now be
      interrupted by signals on POSIX if the underlying threading
      implementation supports it.

   release()

      Release a lock.  This can be called from any thread, not only
      the thread which has acquired the lock.

      When the lock is locked, reset it to unlocked, and return.  If
      any other threads are blocked waiting for the lock to become
      unlocked, allow exactly one of them to proceed.

      Lorsqu'elle est invoquée sur un verrou déverrouillé, une
      "RuntimeError" est levée.

      Il n'y a pas de valeur de retour.

   locked()

      Return "True" if the lock is acquired.


RLock objects
-------------

A reentrant lock is a synchronization primitive that may be acquired
multiple times by the same thread.  Internally, it uses the concepts
of "owning thread" and "recursion level" in addition to the
locked/unlocked state used by primitive locks.  In the locked state,
some thread owns the lock; in the unlocked state, no thread owns it.

Threads call a lock's "acquire()" method to lock it, and its
"release()" method to unlock it.

Note:

  Reentrant locks support the context management protocol, so it is
  recommended to use "with" instead of manually calling "acquire()"
  and "release()" to handle acquiring and releasing the lock for a
  block of code.

RLock's "acquire()"/"release()" call pairs may be nested, unlike
Lock's "acquire()"/"release()". Only the final "release()" (the
"release()" of the outermost pair) resets the lock to an unlocked
state and allows another thread blocked in "acquire()" to proceed.

"acquire()"/"release()" must be used in pairs: each acquire must have
a release in the thread that has acquired the lock. Failing to call
release as many times the lock has been acquired can lead to deadlock.

class threading.RLock

   This class implements reentrant lock objects.  A reentrant lock
   must be released by the thread that acquired it.  Once a thread has
   acquired a reentrant lock, the same thread may acquire it again
   without blocking; the thread must release it once for each time it
   has acquired it.

   Note that "RLock" is actually a factory function which returns an
   instance of the most efficient version of the concrete RLock class
   that is supported by the platform.

   acquire(blocking=True, timeout=-1)

      Acquiert un verrou, bloquant ou non bloquant.

      Voir aussi:

        Using RLock as a context manager
           Recommended over manual "acquire()" and "release()" calls
           whenever practical.

      When invoked with the *blocking* argument set to "True" (the
      default):

         * If no thread owns the lock, acquire the lock and return
           immediately.

         * If another thread owns the lock, block until we are able to
           acquire lock, or *timeout*, if set to a positive float
           value.

         * If the same thread owns the lock, acquire the lock again,
           and return immediately. This is the difference between
           "Lock" and "RLock"; "Lock" handles this case the same as
           the previous, blocking until the lock can be acquired.

      When invoked with the *blocking* argument set to "False":

         * If no thread owns the lock, acquire the lock and return
           immediately.

         * If another thread owns the lock, return immediately.

         * If the same thread owns the lock, acquire the lock again
           and return immediately.

      In all cases, if the thread was able to acquire the lock, return
      "True". If the thread was unable to acquire the lock (i.e. if
      not blocking or the timeout was reached) return "False".

      If called multiple times, failing to call "release()" as many
      times may lead to deadlock. Consider using "RLock" as a context
      manager rather than calling acquire/release directly.

      Modifié dans la version 3.2: Le paramètre *timeout* est nouveau.

   release()

      Release a lock, decrementing the recursion level.  If after the
      decrement it is zero, reset the lock to unlocked (not owned by
      any thread), and if any other threads are blocked waiting for
      the lock to become unlocked, allow exactly one of them to
      proceed.  If after the decrement the recursion level is still
      nonzero, the lock remains locked and owned by the calling
      thread.

      Only call this method when the calling thread owns the lock. A
      "RuntimeError" is raised if this method is called when the lock
      is not acquired.

      Il n'y a pas de valeur de retour.


Condition objects
-----------------

A condition variable is always associated with some kind of lock; this
can be passed in or one will be created by default.  Passing one in is
useful when several condition variables must share the same lock.  The
lock is part of the condition object: you don't have to track it
separately.

A condition variable obeys the context management protocol: using the
"with" statement acquires the associated lock for the duration of the
enclosed block.  The "acquire()" and "release()" methods also call the
corresponding methods of the associated lock.

Other methods must be called with the associated lock held.  The
"wait()" method releases the lock, and then blocks until another
thread awakens it by calling "notify()" or "notify_all()".  Once
awakened, "wait()" re-acquires the lock and returns.  It is also
possible to specify a timeout.

The "notify()" method wakes up one of the threads waiting for the
condition variable, if any are waiting.  The "notify_all()" method
wakes up all threads waiting for the condition variable.

Note: the "notify()" and "notify_all()" methods don't release the
lock; this means that the thread or threads awakened will not return
from their "wait()" call immediately, but only when the thread that
called "notify()" or "notify_all()" finally relinquishes ownership of
the lock.

The typical programming style using condition variables uses the lock
to synchronize access to some shared state; threads that are
interested in a particular change of state call "wait()" repeatedly
until they see the desired state, while threads that modify the state
call "notify()" or "notify_all()" when they change the state in such a
way that it could possibly be a desired state for one of the waiters.
For example, the following code is a generic producer-consumer
situation with unlimited buffer capacity:

   # Consume one item
   with cv:
       while not an_item_is_available():
           cv.wait()
       get_an_available_item()

   # Produce one item
   with cv:
       make_an_item_available()
       cv.notify()

The "while" loop checking for the application's condition is necessary
because "wait()" can return after an arbitrary long time, and the
condition which prompted the "notify()" call may no longer hold true.
This is inherent to multi-threaded programming.  The "wait_for()"
method can be used to automate the condition checking, and eases the
computation of timeouts:

   # Consume an item
   with cv:
       cv.wait_for(an_item_is_available)
       get_an_available_item()

To choose between "notify()" and "notify_all()", consider whether one
state change can be interesting for only one or several waiting
threads.  E.g. in a typical producer-consumer situation, adding one
item to the buffer only needs to wake up one consumer thread.

class threading.Condition(lock=None)

   This class implements condition variable objects.  A condition
   variable allows one or more threads to wait until they are notified
   by another thread.

   If the *lock* argument is given and not "None", it must be a "Lock"
   or "RLock" object, and it is used as the underlying lock.
   Otherwise, a new "RLock" object is created and used as the
   underlying lock.

   Modifié dans la version 3.3: changed from a factory function to a
   class.

   acquire(*args)

      Acquire the underlying lock. This method calls the corresponding
      method on the underlying lock; the return value is whatever that
      method returns.

   release()

      Release the underlying lock. This method calls the corresponding
      method on the underlying lock; there is no return value.

   wait(timeout=None)

      Wait until notified or until a timeout occurs. If the calling
      thread has not acquired the lock when this method is called, a
      "RuntimeError" is raised.

      This method releases the underlying lock, and then blocks until
      it is awakened by a "notify()" or "notify_all()" call for the
      same condition variable in another thread, or until the optional
      timeout occurs.  Once awakened or timed out, it re-acquires the
      lock and returns.

      When the *timeout* argument is present and not "None", it should
      be a floating-point number specifying a timeout for the
      operation in seconds (or fractions thereof).

      When the underlying lock is an "RLock", it is not released using
      its "release()" method, since this may not actually unlock the
      lock when it was acquired multiple times recursively.  Instead,
      an internal interface of the "RLock" class is used, which really
      unlocks it even when it has been recursively acquired several
      times. Another internal interface is then used to restore the
      recursion level when the lock is reacquired.

      The return value is "True" unless a given *timeout* expired, in
      which case it is "False".

      Modifié dans la version 3.2: Previously, the method always
      returned "None".

   wait_for(predicate, timeout=None)

      Wait until a condition evaluates to true.  *predicate* should be
      a callable which result will be interpreted as a boolean value.
      A *timeout* may be provided giving the maximum time to wait.

      This utility method may call "wait()" repeatedly until the
      predicate is satisfied, or until a timeout occurs. The return
      value is the last return value of the predicate and will
      evaluate to "False" if the method timed out.

      Ignoring the timeout feature, calling this method is roughly
      equivalent to writing:

         while not predicate():
             cv.wait()

      Therefore, the same rules apply as with "wait()": The lock must
      be held when called and is re-acquired on return.  The predicate
      is evaluated with the lock held.

      Ajouté dans la version 3.2.

   notify(n=1)

      By default, wake up one thread waiting on this condition, if
      any.  If the calling thread has not acquired the lock when this
      method is called, a "RuntimeError" is raised.

      This method wakes up at most *n* of the threads waiting for the
      condition variable; it is a no-op if no threads are waiting.

      The current implementation wakes up exactly *n* threads, if at
      least *n* threads are waiting.  However, it's not safe to rely
      on this behavior. A future, optimized implementation may
      occasionally wake up more than *n* threads.

      Note: an awakened thread does not actually return from its
      "wait()" call until it can reacquire the lock.  Since "notify()"
      does not release the lock, its caller should.

   notify_all()

      Wake up all threads waiting on this condition.  This method acts
      like "notify()", but wakes up all waiting threads instead of
      one. If the calling thread has not acquired the lock when this
      method is called, a "RuntimeError" is raised.

      The method "notifyAll" is a deprecated alias for this method.


Semaphore objects
-----------------

This is one of the oldest synchronization primitives in the history of
computer science, invented by the early Dutch computer scientist
Edsger W. Dijkstra (he used the names "P()" and "V()" instead of
"acquire()" and "release()").

A semaphore manages an internal counter which is decremented by each
"acquire()" call and incremented by each "release()" call.  The
counter can never go below zero; when "acquire()" finds that it is
zero, it blocks, waiting until some other thread calls "release()".

Semaphores also support the context management protocol.

class threading.Semaphore(value=1)

   This class implements semaphore objects.  A semaphore manages an
   atomic counter representing the number of "release()" calls minus
   the number of "acquire()" calls, plus an initial value.  The
   "acquire()" method blocks if necessary until it can return without
   making the counter negative. If not given, *value* defaults to 1.

   The optional argument gives the initial *value* for the internal
   counter; it defaults to "1". If the *value* given is less than 0,
   "ValueError" is raised.

   Modifié dans la version 3.3: changed from a factory function to a
   class.

   acquire(blocking=True, timeout=None)

      Acquiert un sémaphore.

      When invoked without arguments:

      * If the internal counter is larger than zero on entry,
        decrement it by one and return "True" immediately.

      * If the internal counter is zero on entry, block until awoken
        by a call to "release()".  Once awoken (and the counter is
        greater than 0), decrement the counter by 1 and return "True".
        Exactly one thread will be awoken by each call to "release()".
        The order in which threads are awoken should not be relied on.

      When invoked with *blocking* set to "False", do not block.  If a
      call without an argument would block, return "False"
      immediately; otherwise, do the same thing as when called without
      arguments, and return "True".

      When invoked with a *timeout* other than "None", it will block
      for at most *timeout* seconds.  If acquire does not complete
      successfully in that interval, return "False".  Return "True"
      otherwise.

      Modifié dans la version 3.2: Le paramètre *timeout* est nouveau.

   release(n=1)

      Release a semaphore, incrementing the internal counter by *n*.
      When it was zero on entry and other threads are waiting for it
      to become larger than zero again, wake up *n* of those threads.

      Modifié dans la version 3.9: Added the *n* parameter to release
      multiple waiting threads at once.

class threading.BoundedSemaphore(value=1)

   Class implementing bounded semaphore objects.  A bounded semaphore
   checks to make sure its current value doesn't exceed its initial
   value.  If it does, "ValueError" is raised. In most situations
   semaphores are used to guard resources with limited capacity.  If
   the semaphore is released too many times it's a sign of a bug.  If
   not given, *value* defaults to 1.

   Modifié dans la version 3.3: changed from a factory function to a
   class.


"Semaphore" example
-------------------

Semaphores are often used to guard resources with limited capacity,
for example, a database server.  In any situation where the size of
the resource is fixed, you should use a bounded semaphore.  Before
spawning any worker threads, your main thread would initialize the
semaphore:

   maxconnections = 5
   # ...
   pool_sema = BoundedSemaphore(value=maxconnections)

Once spawned, worker threads call the semaphore's acquire and release
methods when they need to connect to the server:

   with pool_sema:
       conn = connectdb()
       try:
           # ... use connection ...
       finally:
           conn.close()

The use of a bounded semaphore reduces the chance that a programming
error which causes the semaphore to be released more than it's
acquired will go undetected.


Event objects
-------------

This is one of the simplest mechanisms for communication between
threads: one thread signals an event and other threads wait for it.

An event object manages an internal flag that can be set to true with
the "set()" method and reset to false with the "clear()" method.  The
"wait()" method blocks until the flag is true.

class threading.Event

   Class implementing event objects.  An event manages a flag that can
   be set to true with the "set()" method and reset to false with the
   "clear()" method.  The "wait()" method blocks until the flag is
   true. The flag is initially false.

   Modifié dans la version 3.3: changed from a factory function to a
   class.

   is_set()

      Return "True" if and only if the internal flag is true.

      The method "isSet" is a deprecated alias for this method.

   set()

      Set the internal flag to true. All threads waiting for it to
      become true are awakened. Threads that call "wait()" once the
      flag is true will not block at all.

   clear()

      Reset the internal flag to false. Subsequently, threads calling
      "wait()" will block until "set()" is called to set the internal
      flag to true again.

   wait(timeout=None)

      Block as long as the internal flag is false and the timeout, if
      given, has not expired. The return value represents the reason
      that this blocking method returned; "True" if returning because
      the internal flag is set to true, or "False" if a timeout is
      given and the internal flag did not become true within the given
      wait time.

      When the timeout argument is present and not "None", it should
      be a floating-point number specifying a timeout for the
      operation in seconds, or fractions thereof.

      Modifié dans la version 3.1: Previously, the method always
      returned "None".


Timer objects
-------------

This class represents an action that should be run only after a
certain amount of time has passed --- a timer.  "Timer" is a subclass
of "Thread" and as such also functions as an example of creating
custom threads.

Timers are started, as with threads, by calling their "Timer.start"
method.  The timer can be stopped (before its action has begun) by
calling the "cancel()" method.  The interval the timer will wait
before executing its action may not be exactly the same as the
interval specified by the user.

Par exemple :

   def hello():
       print("hello, world")

   t = Timer(30.0, hello)
   t.start()  # after 30 seconds, "hello, world" will be printed

class threading.Timer(interval, function, args=None, kwargs=None)

   Create a timer that will run *function* with arguments *args* and
   keyword arguments *kwargs*, after *interval* seconds have passed.
   If *args* is "None" (the default) then an empty list will be used.
   If *kwargs* is "None" (the default) then an empty dict will be
   used.

   Modifié dans la version 3.3: changed from a factory function to a
   class.

   cancel()

      Stop the timer, and cancel the execution of the timer's action.
      This will only work if the timer is still in its waiting stage.


Barrier objects
---------------

Ajouté dans la version 3.2.

This class provides a simple synchronization primitive for use by a
fixed number of threads that need to wait for each other.  Each of the
threads tries to pass the barrier by calling the "wait()" method and
will block until all of the threads have made their "wait()" calls. At
this point, the threads are released simultaneously.

The barrier can be reused any number of times for the same number of
threads.

As an example, here is a simple way to synchronize a client and server
thread:

   b = Barrier(2, timeout=5)

   def server():
       start_server()
       b.wait()
       while True:
           connection = accept_connection()
           process_server_connection(connection)

   def client():
       b.wait()
       while True:
           connection = make_connection()
           process_client_connection(connection)

class threading.Barrier(parties, action=None, timeout=None)

   Create a barrier object for *parties* number of threads.  An
   *action*, when provided, is a callable to be called by one of the
   threads when they are released.  *timeout* is the default timeout
   value if none is specified for the "wait()" method.

   wait(timeout=None)

      Pass the barrier.  When all the threads party to the barrier
      have called this function, they are all released simultaneously.
      If a *timeout* is provided, it is used in preference to any that
      was supplied to the class constructor.

      The return value is an integer in the range 0 to *parties* -- 1,
      different for each thread.  This can be used to select a thread
      to do some special housekeeping, e.g.:

         i = barrier.wait()
         if i == 0:
             # Only one thread needs to print this
             print("passed the barrier")

      If an *action* was provided to the constructor, one of the
      threads will have called it prior to being released.  Should
      this call raise an error, the barrier is put into the broken
      state.

      If the call times out, the barrier is put into the broken state.

      This method may raise a "BrokenBarrierError" exception if the
      barrier is broken or reset while a thread is waiting.

   reset()

      Return the barrier to the default, empty state.  Any threads
      waiting on it will receive the "BrokenBarrierError" exception.

      Note that using this function may require some external
      synchronization if there are other threads whose state is
      unknown.  If a barrier is broken it may be better to just leave
      it and create a new one.

   abort()

      Put the barrier into a broken state.  This causes any active or
      future calls to "wait()" to fail with the "BrokenBarrierError".
      Use this for example if one of the threads needs to abort, to
      avoid deadlocking the application.

      It may be preferable to simply create the barrier with a
      sensible *timeout* value to automatically guard against one of
      the threads going awry.

   parties

      The number of threads required to pass the barrier.

   n_waiting

      The number of threads currently waiting in the barrier.

   broken

      Booléen qui vaut "True" si la barrière est rompue.

exception threading.BrokenBarrierError

   Cette exception, une sous-classe de "RuntimeError", est déclenchée
   lorsque l'objet "Barrier" est réinitialisé ou cassé.


Using locks, conditions, and semaphores in the "with" statement
===============================================================

All of the objects provided by this module that have "acquire" and
"release" methods can be used as context managers for a "with"
statement.  The "acquire" method will be called when the block is
entered, and "release" will be called when the block is exited.
Hence, the following snippet:

   with some_lock:
       # do something...

est équivalente à :

   some_lock.acquire()
   try:
       # do something...
   finally:
       some_lock.release()

Currently, "Lock", "RLock", "Condition", "Semaphore", and
"BoundedSemaphore" objects may be used as "with" statement context
managers.
