"dataclasses" — Classes de données
**********************************

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

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

Ce module fournit un décorateur et des fonctions pour générer
automatiquement les *méthodes spéciales* comme "__init__()" et
"__repr__()" dans les classes définies par l’utilisateur.  Ce
mécanisme a été décrit dans la **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

Note that this method is automatically added to the class: it is not
directly specified in the "InventoryItem" definition shown above.

Ajouté 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 true, force "dataclasses" to create a
     "__hash__()" method, even though it may not be safe to do so.
     Otherwise, generate a "__hash__()" method according to how *eq*
     and *frozen* are set. The default value is "False".

     "__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. See the discussion below.

     If "__setattr__()" or "__delattr__()" is defined in the class and
     *frozen* is true, then "TypeError" is raised.

   * *match_args*: If true (the default is "True"), the
     "__match_args__" tuple will be created from the list of non
     keyword-only 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.

      Ajouté 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. See the *parameter*
     glossary entry for details.  Also see the "KW_ONLY" section.

     Keyword-only fields are not included in "__match_args__".

      Ajouté 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.

      Avertissement:

        Passing parameters to a base class "__init_subclass__()" when
        using "slots=True" will result in a "TypeError". Either use
        "__init_subclass__" with no parameters or use default values
        as a workaround. See gh-91126 for full details.

      Ajouté 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*: If true (the default is "False"), add a slot
     named "__weakref__", which is required to make an instance
     "weakref-able". It is an error to specify "weakref_slot=True"
     without also specifying "slots=True".

      Ajouté 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, doc=None)

   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*: If provided, it must be a zero-argument
     callable that will be called when a default value is needed for
     this field.  Among other purposes, this can be used to specify
     fields with mutable default values, as discussed below.  It is an
     error to specify both *default* and *default_factory*.

   * *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 false, this
     field is excluded from the generated "__hash__()". If "None" (the
     default), use the value of *compare*: this would normally be the
     expected behavior, since a field should be included 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*: This can be a mapping or "None". "None" is treated as
     an empty dict.  This value is wrapped in "MappingProxyType()" to
     make it read-only, and exposed on the "Field" object. It is not
     used at all by Data Classes, and is provided as a third-party
     extension mechanism. Multiple third-parties can each have their
     own key, to use as a namespace in the metadata.

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

     Keyword-only fields are also not included in "__match_args__".

      Ajouté dans la version 3.10.

   * *doc*: optional docstring for this field.

      Ajouté dans la version 3.14.

   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 *default* is not 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

   The class attribute "C.z" will be "10", the class attribute "C.t"
   will be "20", and the class attributes "C.x" and "C.y" will not be
   set.

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": The name of the field.

   * "type": The type of the field.

   * "default", "default_factory", "init", "repr", "hash", "compare",
     "metadata", and "kw_only" have the identical meaning and values
     as they do in the "field()" function.

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

class dataclasses.InitVar

   "InitVar[T]" type annotations describe variables that are init-
   only. Fields annotated with "InitVar" are considered pseudo-fields,
   and thus are neither returned by the "fields()" function nor used
   in any way except adding them as parameters to "__init__()" and an
   optional "__post_init__()".

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)

   Converts the dataclass *obj* to a dict (by using the factory
   function *dict_factory*).  Each dataclass is converted to a dict of
   its fields, as "name: value" pairs.  dataclasses, dicts, lists, and
   tuples are recursed into.  Other objects are copied with
   "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 :

      {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)

   Converts the dataclass *obj* to a tuple (by using the factory
   function *tuple_factory*).  Each dataclass is converted to a tuple
   of its field values.  dataclasses, dicts, lists, and tuples are
   recursed into. Other objects are copied with "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, module=None, decorator=dataclass)

   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".

   If *module* is defined, the "__module__" attribute of the dataclass
   is set to that value. By default, it is set to the module name of
   the caller.

   The *decorator* parameter is a callable that will be used to create
   the dataclass. It should take the class object as a first argument
   and the same keyword arguments as "@dataclass". By default, the
   "@dataclass" function is used.

   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

   Ajouté dans la version 3.14: Added the *decorator* parameter.

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

   Creates a new object of the same type as *obj*, replacing fields
   with values from *changes*.  If *obj* is not a Data Class, raises
   "TypeError".  If keys in *changes* are not field names of the given
   dataclass, raises "TypeError".

   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__()".

   It is an error for *changes* to contain any fields that are defined
   as having "init=False".  A "ValueError" will be raised in this
   case.

   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.

   Dataclass instances are also supported by generic function
   "copy.replace()".

dataclasses.is_dataclass(obj)

   Return "True" if its parameter is a dataclass (including subclasses
   of a dataclass) or an instance of one, otherwise return "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".

   Ajouté 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
===================

dataclasses.__post_init__()

   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.

   Cette méthode permet, entre autres, d'initialiser des champs qui
   dépendent d'autres champs. Par exemple :

      @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 "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)

In this case, "fields()" will return "Field" objects for "i" and "j",
but not for "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 "object.__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

The final list of fields is, in order, "x", "y", "z".  The final type
of "x" is "int", as specified in class "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.

In this example, "Base.y", "Base.w", and "D.t" are keyword-only
fields, and "Base.x" and "D.z" are regular fields:

   @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
===============================

If a "field()" specifies a *default_factory*, it is called with zero
arguments when a default value for the field is needed.  For example,
to create a new instance of a list, use:

   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

Note that the two instances of class "C" share the same class variable
"x", as expected.

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: Instead of looking for and disallowing
objects of type "list", "dict", or "set", unhashable objects are now
not allowed as default values.  Unhashability is used to approximate
mutability.


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.
