"dataclasses" --- Data Classes
******************************

**Code source :** Lib/dataclasses.py

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

This module provides a decorator and functions for automatically
adding generated *special method*s such as "__init__()" and
"__repr__()" to user-defined classes.  It was originally described in
**PEP 557**.

Les variables membres à utiliser dans ces méthodes générées sont
définies en utilisant les annotations de type **PEP 526**.  Par
exemple :

   from dataclasses import dataclass

   @dataclass
   class InventoryItem:
       """Class for keeping track of an item in inventory."""
       name: str
       unit_price: float
       quantity_on_hand: int = 0

       def total_cost(self) -> float:
           return self.unit_price * self.quantity_on_hand

will add, among other things, a "__init__()" that looks like:

   def __init__(self, name: str, unit_price: float, quantity_on_hand: int = 0):
       self.name = name
       self.unit_price = unit_price
       self.quantity_on_hand = quantity_on_hand

Il est important de noter que cette méthode est ajoutée
automatiquement dans la classe. Elle n’est jamais écrite dans la
définition de "InventoryItem".

Nouveau dans la version 3.7.


Classe de données
=================

@dataclasses.dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False)

   This function is a *decorator* that is used to add generated
   *special methods* to classes, as described below.

   The "@dataclass" decorator examines the class to find "field"s.  A
   "field" is defined as a class variable that has a *type
   annotation*.  With two exceptions described below, nothing in
   "@dataclass" examines the type specified in the variable
   annotation.

   L’ordre des paramètres des méthodes générées est celui d’apparition
   des champs dans la définition de la classe.

   The "@dataclass" decorator will add various "dunder" methods to the
   class, described below.  If any of the added methods already exist
   in the class, the behavior depends on the parameter, as documented
   below. The decorator returns the same class that it is called on;
   no new class is created.

   If "@dataclass" is used just as a simple decorator with no
   parameters, it acts as if it has the default values documented in
   this signature.  That is, these three uses of "@dataclass" are
   equivalent:

      @dataclass
      class C:
          ...

      @dataclass()
      class C:
          ...

      @dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False,
                 match_args=True, kw_only=False, slots=False, weakref_slot=False)
      class C:
          ...

   The parameters to "@dataclass" are:

   * "init": If true (the default), a "__init__()" method will be
     generated.

     If the class already defines "__init__()", this parameter is
     ignored.

   * "repr": If true (the default), a "__repr__()" method will be
     generated.  The generated repr string will have the class name
     and the name and repr of each field, in the order they are
     defined in the class.  Fields that are marked as being excluded
     from the repr are not included.  For example:
     "InventoryItem(name='widget', unit_price=3.0,
     quantity_on_hand=10)".

     If the class already defines "__repr__()", this parameter is
     ignored.

   * "eq": If true (the default), an "__eq__()" method will be
     generated.  This method compares the class as if it were a tuple
     of its fields, in order.  Both instances in the comparison must
     be of the identical type.

     If the class already defines "__eq__()", this parameter is
     ignored.

   * "order": If true (the default is "False"), "__lt__()",
     "__le__()", "__gt__()", and "__ge__()" methods will be generated.
     These compare the class as if it were a tuple of its fields, in
     order.  Both instances in the comparison must be of the identical
     type.  If "order" is true and "eq" is false, a "ValueError" is
     raised.

     If the class already defines any of "__lt__()", "__le__()",
     "__gt__()", or "__ge__()", then "TypeError" is raised.

   * "unsafe_hash": If "False" (the default), a "__hash__()" method is
     generated according to how "eq" and "frozen" are set.

     "__hash__()" is used by built-in "hash()", and when objects are
     added to hashed collections such as dictionaries and sets.
     Having a "__hash__()" implies that instances of the class are
     immutable. Mutability is a complicated property that depends on
     the programmer's intent, the existence and behavior of
     "__eq__()", and the values of the "eq" and "frozen" flags in the
     "@dataclass" decorator.

     By default, "@dataclass" will not implicitly add a "__hash__()"
     method unless it is safe to do so.  Neither will it add or change
     an existing explicitly defined "__hash__()" method.  Setting the
     class attribute "__hash__ = None" has a specific meaning to
     Python, as described in the "__hash__()" documentation.

     If "__hash__()" is not explicitly defined, or if it is set to
     "None", then "@dataclass" *may* add an implicit "__hash__()"
     method. Although not recommended, you can force "@dataclass" to
     create a "__hash__()" method with "unsafe_hash=True". This might
     be the case if your class is logically immutable but can still be
     mutated. This is a specialized use case and should be considered
     carefully.

     Here are the rules governing implicit creation of a "__hash__()"
     method.  Note that you cannot both have an explicit "__hash__()"
     method in your dataclass and set "unsafe_hash=True"; this will
     result in a "TypeError".

     If "eq" and "frozen" are both true, by default "@dataclass" will
     generate a "__hash__()" method for you.  If "eq" is true and
     "frozen" is false, "__hash__()" will be set to "None", marking it
     unhashable (which it is, since it is mutable).  If "eq" is false,
     "__hash__()" will be left untouched meaning the "__hash__()"
     method of the superclass will be used (if the superclass is
     "object", this means it will fall back to id-based hashing).

   * "frozen": If true (the default is "False"), assigning to fields
     will generate an exception.  This emulates read-only frozen
     instances.  If "__setattr__()" or "__delattr__()" is defined in
     the class, then "TypeError" is raised.  See the discussion below.

   * "match_args": If true (the default is "True"), the
     "__match_args__" tuple will be created from the list of
     parameters to the generated "__init__()" method (even if
     "__init__()" is not generated, see above).  If false, or if
     "__match_args__" is already defined in the class, then
     "__match_args__" will not be generated.

      Nouveau dans la version 3.10.

   * "kw_only": If true (the default value is "False"), then all
     fields will be marked as keyword-only.  If a field is marked as
     keyword-only, then the only effect is that the "__init__()"
     parameter generated from a keyword-only field must be specified
     with a keyword when "__init__()" is called.  There is no effect
     on any other aspect of dataclasses.  See the *parameter* glossary
     entry for details.  Also see the "KW_ONLY" section.

      Nouveau dans la version 3.10.

   * "slots": If true (the default is "False"), "__slots__" attribute
     will be generated and new class will be returned instead of the
     original one. If "__slots__" is already defined in the class,
     then "TypeError" is raised.

      Nouveau dans la version 3.10.

      Modifié dans la version 3.11: If a field name is already
      included in the "__slots__" of a base class, it will not be
      included in the generated "__slots__" to prevent overriding
      them. Therefore, do not use "__slots__" to retrieve the field
      names of a dataclass. Use "fields()" instead. To be able to
      determine inherited slots, base class "__slots__" may be any
      iterable, but *not* an iterator.

   * "weakref_slot" : s'il est vrai (la valeur par défaut est
     "False"), ajoute un *slot* nommé ""__weakref__"", ce qui est
     nécessaire pour pouvoir référencer faiblement une instance. C'est
     une erreur de spécifier "weakref_slot=True" sans spécifier
     également "slots=True".

      Nouveau dans la version 3.11.

   Les champs peuvent éventuellement préciser une valeur par défaut,
   en utilisant la syntaxe Python normale :

      @dataclass
      class C:
          a: int       # 'a' has no default value
          b: int = 0   # assign a default value for 'b'

   In this example, both "a" and "b" will be included in the added
   "__init__()" method, which will be defined as:

      def __init__(self, a: int, b: int = 0):

   Une "TypeError" est levée si un champ sans valeur par défaut est
   défini après un champ avec une valeur par défaut.  C’est le cas que
   ce soit dans une seule classe ou si c’est le résultat d’un héritage
   de classes.

dataclasses.field(*, default=MISSING, default_factory=MISSING, init=True, repr=True, hash=None, compare=True, metadata=None, kw_only=MISSING)

   For common and simple use cases, no other functionality is
   required.  There are, however, some dataclass features that require
   additional per-field information.  To satisfy this need for
   additional information, you can replace the default field value
   with a call to the provided "field()" function.  For example:

      @dataclass
      class C:
          mylist: list[int] = field(default_factory=list)

      c = C()
      c.mylist += [1, 2, 3]

   Comme le montre la signature, la constante "MISSING" est une valeur
   sentinelle pour déterminer si des paramètres ont été fournis par
   l'utilisateur. "None" ne conviendrait pas puisque c'est une valeur
   avec un sens qui peut être différent pour certains paramètres. La
   sentinelle "MISSING" est interne au module et ne doit pas être
   utilisée dans vos programmes.

   The parameters to "field()" are:

   * "default": If provided, this will be the default value for this
     field.  This is needed because the "field()" call itself replaces
     the normal position of the default value.

   * *default_factory* : s'il est fourni, ce doit être un objet
     appelable sans argument. Il est alors appelé à chaque fois qu'il
     faut une valeur par défaut pour le champ. Ceci permet, entre
     autres choses, de définir des champs dont les valeurs par défaut
     sont mutables. Une erreur se produit si *default* et
     *default_factory* sont donnés tous les deux.

   * "init": If true (the default), this field is included as a
     parameter to the generated "__init__()" method.

   * "repr": If true (the default), this field is included in the
     string returned by the generated "__repr__()" method.

   * "hash": This can be a bool or "None".  If true, this field is
     included in the generated "__hash__()" method.  If "None" (the
     default), use the value of "compare": this would normally be the
     expected behavior.  A field should be considered in the hash if
     it's used for comparisons.  Setting this value to anything other
     than "None" is discouraged.

     Cependant, une raison légitime de mettre *hash* à "False" alors
     que *compare* est à "True" est la concourance de trois facteurs :
     le champ est coûteux à hacher ; il est nécessaire pour les
     comparaisons d'égalité ; et il y a déjà d'autres champs qui
     participent au hachage des instances. À ce moment, on peut alors
     se passer du champ dans le hachage tout en le faisant participer
     aux comparaisons.

   * "compare": If true (the default), this field is included in the
     generated equality and comparison methods ("__eq__()",
     "__gt__()", et al.).

   * *metadata* : ce paramètre est un tableau associatif (*mapping* en
     anglais). La valeur par défaut de "None" est prise comme un
     dictionnaire vide. Le tableau associatif devient accessible sur
     l'objet "Field", sous la forme d'un "MappingProxyType()" afin
     qu'il soit en lecture seule.

   * "kw_only": If true, this field will be marked as keyword-only.
     This is used when the generated "__init__()" method's parameters
     are computed.

      Nouveau dans la version 3.10.

   If the default value of a field is specified by a call to
   "field()", then the class attribute for this field will be replaced
   by the specified "default" value.  If no "default" is provided,
   then the class attribute will be deleted.  The intent is that after
   the "@dataclass" decorator runs, the class attributes will all
   contain the default values for the fields, just as if the default
   value itself were specified.  For example, after:

      @dataclass
      class C:
          x: int
          y: int = field(repr=False)
          z: int = field(repr=False, default=10)
          t: int = 20

   Après l'exécution de ce code, l'attribut de classe "C.z" vaut "10"
   et l'attribut "C.t" vaut "20", alors que les attributs "C.x" et
   "C.y" n'existent pas.

class dataclasses.Field

   "Field" objects describe each defined field. These objects are
   created internally, and are returned by the "fields()" module-level
   method (see below).  Users should never instantiate a "Field"
   object directly.  Its documented attributes are:

   * *name* : le nom du champ ;

   * *type* : le type associé au champ par l'annotation ;

   * "default", "default_factory", "init", "repr", "hash", "compare",
     "metadata" et "kw_only" qui correspondent aux paramètres de
     "field()" et en prennent les valeurs.

   D'autres attributs peuvent exister, mais ils sont privés et ne sont
   pas censés être inspectés. Le code ne doit jamais reposer sur eux.

dataclasses.fields(class_or_instance)

   Renvoie un *n*-uplet d'objets "Field" correspondant aux champs de
   l'argument, à l'exclusion des pseudo-champs "ClassVar" ou
   "InitVar". L'argument peut être soit une classe de données, soit
   une instance d'une telle classe ; si ce n'est pas le cas, une
   exception "TypeError" est levée.

dataclasses.asdict(obj, *, dict_factory=dict)

   Convertit la classe de données "obj" en un dictionnaire (en
   utilisant la fonction "dict_factory"). Les clés et valeurs
   proviennent directement des champs. Les dictionnaires, listes,
   *n*-uplets et instances de classes de données sont parcourus
   récursivement. Les autres objets sont copiés avec
   "copy.deepcopy()".

   Example of using "asdict()" on nested dataclasses:

      @dataclass
      class Point:
           x: int
           y: int

      @dataclass
      class C:
           mylist: list[Point]

      p = Point(10, 20)
      assert asdict(p) == {'x': 10, 'y': 20}

      c = C([Point(0, 0), Point(10, 4)])
      assert asdict(c) == {'mylist': [{'x': 0, 'y': 0}, {'x': 10, 'y': 4}]}

   Pour créer une copie superficielle, la solution de contournement
   suivante peut être utilisée :

      dict((field.name, getattr(obj, field.name)) for field in fields(obj))

   "asdict()" raises "TypeError" if "obj" is not a dataclass instance.

dataclasses.astuple(obj, *, tuple_factory=tuple)

   Convertit l'instance d'une classe de données "obj" en un *n*-uplet
   (en utilisant la fonction "tuple_factory"). Chaque classe de
   données est convertie vers un *n*-uplet des valeurs de ses champs.
   Cette fonction agit récursivement sur les dictionnaires, listes et
   *n*-uplets. Les autres objets sont copiés avec "copy.deepcopy()".

   Pour continuer l'exemple précédent :

      assert astuple(p) == (10, 20)
      assert astuple(c) == ([(0, 0), (10, 4)],)

   Pour créer une copie superficielle, la solution de contournement
   suivante peut être utilisée :

      tuple(getattr(obj, field.name) for field in dataclasses.fields(obj))

   "astuple()" raises "TypeError" if "obj" is not a dataclass
   instance.

dataclasses.make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False)

   Creates a new dataclass with name "cls_name", fields as defined in
   "fields", base classes as given in "bases", and initialized with a
   namespace as given in "namespace".  "fields" is an iterable whose
   elements are each either "name", "(name, type)", or "(name, type,
   Field)".  If just "name" is supplied, "typing.Any" is used for
   "type".  The values of "init", "repr", "eq", "order",
   "unsafe_hash", "frozen", "match_args", "kw_only", "slots", and
   "weakref_slot" have the same meaning as they do in "@dataclass".

   This function is not strictly required, because any Python
   mechanism for creating a new class with "__annotations__" can then
   apply the "@dataclass" function to convert that class to a
   dataclass.  This function is provided as a convenience.  For
   example:

      C = make_dataclass('C',
                         [('x', int),
                           'y',
                          ('z', int, field(default=5))],
                         namespace={'add_one': lambda self: self.x + 1})

   est équivalent à :

      @dataclass
      class C:
          x: int
          y: 'typing.Any'
          z: int = 5

          def add_one(self):
              return self.x + 1

dataclasses.replace(obj, /, **changes)

   Crée un nouvel objet du même type que "obj" en affectant aux champs
   les valeurs données par "changes". Si "obj" n'est pas une classe de
   données, "TypeError" est levée. Si une clé dans "changes" ne
   correspond à aucun champ de l'instance, "TypeError" est levée.

   The newly returned object is created by calling the "__init__()"
   method of the dataclass.  This ensures that __post_init__, if
   present, is also called.

   Init-only variables without default values, if any exist, must be
   specified on the call to "replace()" so that they can be passed to
   "__init__()" and "__post_init__()".

   Si une clé de *changes* correspond à un champ défini avec
   "init=False", "ValueError" est levée.

   Be forewarned about how "init=False" fields work during a call to
   "replace()".  They are not copied from the source object, but
   rather are initialized in "__post_init__()", if they're initialized
   at all.  It is expected that "init=False" fields will be rarely and
   judiciously used.  If they are used, it might be wise to have
   alternate class constructors, or perhaps a custom "replace()" (or
   similarly named) method which handles instance copying.

dataclasses.is_dataclass(obj)

   Renvoie "True" si l'argument est soit une classe de données, soit
   une instance d'une telle classe. Sinon, renvoie "False".

   Pour vérifier qu'un objet *obj* est une instance d'une classe de
   données, et non pas lui-même une classe de données, ajoutez le test
   "not isinstance(obj, type)" :

      def is_dataclass_instance(obj):
          return is_dataclass(obj) and not isinstance(obj, type)

dataclasses.MISSING

   Une valeur sentinelle pour dénoter l'absence de *default* ou
   *default_factory*.

dataclasses.KW_ONLY

   A sentinel value used as a type annotation.  Any fields after a
   pseudo-field with the type of "KW_ONLY" are marked as keyword-only
   fields.  Note that a pseudo-field of type "KW_ONLY" is otherwise
   completely ignored.  This includes the name of such a field.  By
   convention, a name of "_" is used for a "KW_ONLY" field.  Keyword-
   only fields signify "__init__()" parameters that must be specified
   as keywords when the class is instantiated.

   Dans cet exemple "y" et "z" sont marqués comme exclusivement nommés
   :

      @dataclass
      class Point:
          x: float
          _: KW_ONLY
          y: float
          z: float

      p = Point(0, y=1.5, z=2.0)

   In a single dataclass, it is an error to specify more than one
   field whose type is "KW_ONLY".

   Nouveau dans la version 3.10.

exception dataclasses.FrozenInstanceError

   Raised when an implicitly defined "__setattr__()" or
   "__delattr__()" is called on a dataclass which was defined with
   "frozen=True". It is a subclass of "AttributeError".


Post-initialisation
===================

The generated "__init__()" code will call a method named
"__post_init__()", if "__post_init__()" is defined on the class.  It
will normally be called as "self.__post_init__()". However, if any
"InitVar" fields are defined, they will also be passed to
"__post_init__()" in the order they were defined in the class.  If no
"__init__()" method is generated, then "__post_init__()" will not
automatically be called.

   When defined on the class, it will be called by the generated
   "__init__()", normally as "self.__post_init__()". However, if any
   "InitVar" fields are defined, they will also be passed to
   "__post_init__()" in the order they were defined in the class.  If
   no "__init__()" method is generated, then "__post_init__()" will
   not automatically be called.

      @dataclass class C:

         a: float b: float c: float = field(init=False)

         def __post_init__(self):
            self.c = self.a + self.b

The "__init__()" method generated by "@dataclass" does not call base
class "__init__()" methods. If the base class has an "__init__()"
method that has to be called, it is common to call this method in a
"__post_init__()" method:

   class Rectangle:
       def __init__(self, height, width):
         self.height = height
         self.width = width

   @dataclass
   class Square(Rectangle):
       side: float

       def __post_init__(self):
           super().__init__(self.side, self.side)

Note, however, that in general the dataclass-generated "__init__()"
methods don't need to be called, since the derived dataclass will take
care of initializing all fields of any base class that is a dataclass
itself.

See the section below on init-only variables for ways to pass
parameters to "__post_init__()".  Also see the warning about how
"replace()" handles "init=False" fields.


Variables de classe
===================

One of the few places where "@dataclass" actually inspects the type of
a field is to determine if a field is a class variable as defined in
**PEP 526**.  It does this by checking if the type of the field is
"typing.ClassVar".  If a field is a "ClassVar", it is excluded from
consideration as a field and is ignored by the dataclass mechanisms.
Such "ClassVar" pseudo-fields are not returned by the module-level
"fields()" function.


Variables d'initialisation
==========================

Another place where "@dataclass" inspects a type annotation is to
determine if a field is an init-only variable.  It does this by seeing
if the type of a field is of type "dataclasses.InitVar".  If a field
is an "InitVar", it is considered a pseudo-field called an init-only
field.  As it is not a true field, it is not returned by the module-
level "fields()" function.  Init-only fields are added as parameters
to the generated "__init__()" method, and are passed to the optional
__post_init__ method.  They are not otherwise used by dataclasses.

On peut par exemple imaginer un champ initialisé à partir d'une base
de données s'il n'a pas reçu de valeur explicite :

   @dataclass
   class C:
       i: int
       j: int | None = None
       database: InitVar[DatabaseType | None] = None

       def __post_init__(self, database):
           if self.j is None and database is not None:
               self.j = database.lookup('j')

   c = C(10, database=my_database)

Ici, "fields()" renvoie des objets "Field" correspondant à "i" et à
"j", mais pas à "database".


Instances figées
================

It is not possible to create truly immutable Python objects.  However,
by passing "frozen=True" to the "@dataclass" decorator you can emulate
immutability.  In that case, dataclasses will add "__setattr__()" and
"__delattr__()" methods to the class.  These methods will raise a
"FrozenInstanceError" when invoked.

There is a tiny performance penalty when using "frozen=True":
"__init__()" cannot use simple assignment to initialize fields, and
must use "__setattr__()".


Héritage
========

When the dataclass is being created by the "@dataclass" decorator, it
looks through all of the class's base classes in reverse MRO (that is,
starting at "object") and, for each dataclass that it finds, adds the
fields from that base class to an ordered mapping of fields. After all
of the base class fields are added, it adds its own fields to the
ordered mapping.  All of the generated methods will use this combined,
calculated ordered mapping of fields.  Because the fields are in
insertion order, derived classes override base classes.  An example:

   @dataclass
   class Base:
       x: Any = 15.0
       y: int = 0

   @dataclass
   class C(Base):
       z: int = 10
       x: int = 15

La liste finale des champs contient, dans l'ordre, "x", "y", "z". Le
type de "x" est "int", comme déclaré dans "C".

The generated "__init__()" method for "C" will look like:

   def __init__(self, x: int = 15, y: int = 0, z: int = 10):


Re-ordering of keyword-only parameters in "__init__()"
======================================================

After the parameters needed for "__init__()" are computed, any
keyword-only parameters are moved to come after all regular (non-
keyword-only) parameters.  This is a requirement of how keyword-only
parameters are implemented in Python: they must come after non-
keyword-only parameters.

Dans cet exemple, "Base.y", "Base.w", et "D.t" sont des champs
exclusivement nommés alors que "Base.x" et "D.z" sont des champs
normaux :

   @dataclass
   class Base:
       x: Any = 15.0
       _: KW_ONLY
       y: int = 0
       w: int = 1

   @dataclass
   class D(Base):
       z: int = 10
       t: int = field(kw_only=True, default=0)

The generated "__init__()" method for "D" will look like:

   def __init__(self, x: Any = 15.0, z: int = 10, *, y: int = 0, w: int = 1, t: int = 0):

Les paramètres ont été réarrangés par rapport à leur ordre
d'apparition dans la liste des champs : les paramètres provenant des
attributs normaux sont suivis par les paramètres qui proviennent des
attributs exclusivement nommés.

The relative ordering of keyword-only parameters is maintained in the
re-ordered "__init__()" parameter list.


Fabriques de valeurs par défaut
===============================

Le paramètre facultatif *default_factory* de "field()" est une
fonction qui est appelée sans argument pour fournir des valeurs par
défaut. Par exemple, voici comment donner la valeur par défaut d'une
liste vide :

   mylist: list = field(default_factory=list)

If a field is excluded from "__init__()" (using "init=False") and the
field also specifies "default_factory", then the default factory
function will always be called from the generated "__init__()"
function.  This happens because there is no other way to give the
field an initial value.


Valeurs par défaut mutables
===========================

En Python, les valeurs par défaut des attributs sont stockées dans des
attributs de la classe. Observez cet exemple, sans classe de données :

   class C:
       x = []
       def add(self, element):
           self.x.append(element)

   o1 = C()
   o2 = C()
   o1.add(1)
   o2.add(2)
   assert o1.x == [1, 2]
   assert o1.x is o2.x

Comme attendu, les deux instances de "C" partagent le même objet pour
l'attribut "x".

Avec les classes de données, si ce code était valide :

   @dataclass
   class D:
       x: list = []      # This code raises ValueError
       def add(self, element):
           self.x.append(element)

il générerait un code équivalent à :

   class D:
       x = []
       def __init__(self, x=x):
           self.x = x
       def add(self, element):
           self.x.append(element)

   assert D().x is D().x

This has the same issue as the original example using class "C". That
is, two instances of class "D" that do not specify a value for "x"
when creating a class instance will share the same copy of "x".
Because dataclasses just use normal Python class creation they also
share this behavior.  There is no general way for Data Classes to
detect this condition.  Instead, the "@dataclass" decorator will raise
a "ValueError" if it detects an unhashable default parameter.  The
assumption is that if a value is unhashable, it is mutable.  This is a
partial solution, but it does protect against many common errors.

Pour qu'un champ d'un type mutable soit par défaut initialisé à un
nouvel objet pour chaque instance, utilisez une fonction de fabrique :

   @dataclass
   class D:
       x: list = field(default_factory=list)

   assert D().x is not D().x

Modifié dans la version 3.11: au lieu de rechercher et d'interdire les
objets de type "list", "dict" ou "set", les objets non hachables ne
sont plus autorisés comme valeurs par défaut. Le caractère non-
hachable est utilisé pour approximer la muabilité.


Descriptor-typed fields
=======================

Fields that are assigned descriptor objects as their default value
have the following special behaviors:

* The value for the field passed to the dataclass's "__init__()"
  method is passed to the descriptor's "__set__()" method rather than
  overwriting the descriptor object.

* Similarly, when getting or setting the field, the descriptor's
  "__get__()" or "__set__()" method is called rather than returning or
  overwriting the descriptor object.

* To determine whether a field contains a default value, "@dataclass"
  will call the descriptor's "__get__()" method using its class access
  form: "descriptor.__get__(obj=None, type=cls)".  If the descriptor
  returns a value in this case, it will be used as the field's
  default. On the other hand, if the descriptor raises
  "AttributeError" in this situation, no default value will be
  provided for the field.

   class IntConversionDescriptor:
       def __init__(self, *, default):
           self._default = default

       def __set_name__(self, owner, name):
           self._name = "_" + name

       def __get__(self, obj, type):
           if obj is None:
               return self._default

           return getattr(obj, self._name, self._default)

       def __set__(self, obj, value):
           setattr(obj, self._name, int(value))

   @dataclass
   class InventoryItem:
       quantity_on_hand: IntConversionDescriptor = IntConversionDescriptor(default=100)

   i = InventoryItem()
   print(i.quantity_on_hand)   # 100
   i.quantity_on_hand = 2.5    # calls __set__ with 2.5
   print(i.quantity_on_hand)   # 2

Note that if a field is annotated with a descriptor type, but is not
assigned a descriptor object as its default value, the field will act
like a normal field.
