"dataclasses" --- 데이터 클래스
*******************************

**소스 코드:** 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**.

생성된 메서드에서 사용할 멤버 변수는 **PEP 526** 형 어노테이션을 사용
하여 정의됩니다. 예를 들어, 이 코드는:

   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

다른 것 중에서도, 다음과 같은 "__init__()" 를 추가합니다:

   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.

버전 3.7에 추가.


Module contents
===============

@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)

   이 함수는 (아래에서 설명하는) 생성된 *특수 메서드*를 클래스에 추가
   하는데 사용되는 *데코레이터* 입니다.

   "@dataclass" 데코레이터는 클래스를 검사하여 필드를 찾습니다. 필드는
   *형 어노테이션*을 가진 클래스 변수로 정의됩니다. 아래에 설명된 두
   가지 예외를 제외하고는, "@dataclass" 는 변수 어노테이션에 지정된 형
   을 검사하지 않습니다.

   생성된 모든 메서드의 필드 순서는 클래스 정의에 나타나는 순서입니다.

   "@dataclass" 데코레이터는 여러 "던더(dunder)" 메서드들을 클래스에
   추가하는데, 아래에서 설명합니다. 추가할 메서드가 클래스에 이미 존재
   하면, 동작은 아래에 설명된 대로 매개변수에 따라 다릅니다. 데코레이
   터는 호출된 클래스와 같은 클래스를 반환합니다; 새 클래스가 만들어지
   지 않습니다.

   "@dataclass" 가 매개변수 없는 단순한 데코레이터로 사용되면, 이 서명
   에 문서화 된 기본값들이 제공된 것처럼 행동합니다. 즉, 다음
   "@dataclass" 의 세 가지 용법은 동등합니다:

      @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:
          ...

   "@dataclass" 의 매개변수는 다음과 같습니다:

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

     클래스가 이미 "__init__()" 를 정의했으면, 이 매개변수는 무시됩니
     다.

   * "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)".

     클래스가 이미 "__repr__()" 을 정의했으면, 이 매개변수는 무시됩니
     다.

   * "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.

     클래스가 이미 "__eq__()" 를 정의했으면, 이 매개변수는 무시됩니다.

   * "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.

     클래스가 이미 "__lt__()", "__le__()", "__gt__()", "__ge__()" 중
     하나를 정의하고 있다면 "TypeError" 가 발생합니다.

   * "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.

     기본적으로, "@dataclass" 는 안전하지 않다면 "__hash__()" 메서드를
     묵시적으로 추가하지 않습니다. 기존에 명시적으로 정의된
     "__hash__()" 메서드를 추가하거나 변경하지도 않습니다.
     "__hash__()" 문서에서 설명된 대로, 클래스 어트리뷰트를 "__hash__
     = None" 로 설정하는 것은 파이썬에 특별한 의미가 있습니다.

     "__hash__()" 가 명시적으로 정의되어 있지 않거나 "None" 으로 설정
     된 경우, "@dataclass" 는 묵시적 "__hash__()" 메서드를 *추가할 수
     있습니다*. 권장하지는 않지만, "unsafe_hash=True" 로 "@dataclass"
     가 "__hash__()" 메서드를 만들도록 강제할 수 있습니다. 이것은 당신
     의 클래스가 논리적으로 불변이지만, 여전히 변경될 수 있는 경우 일
     수 있습니다. 이는 특수한 사용 사례이므로 신중하게 고려해야 합니다
     .

     다음은 "__hash__()" 메서드의 묵시적 생성을 관장하는 규칙입니다.
     데이터 클래스에 명시적 "__hash__()" 메서드를 가지면서
     "unsafe_hash=True" 를 설정할 수는 없습니다; 그러면 "TypeError" 가
     발생합니다.

     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.

      버전 3.10에 추가.

   * "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.

      버전 3.10에 추가.

   * "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.

      버전 3.10에 추가.

      버전 3.11에서 변경: 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".

      버전 3.11에 추가.

   필드는 선택적으로 일반적인 파이썬 문법을 사용하여 기본값을 지정할
   수 있습니다:

      @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):

   기본값이 없는 필드가 기본값이 있는 필드 뒤에 오는 경우 "TypeError"
   가 발생합니다. 이것은 단일 클래스 내에서 발생하든, 클래스 상속의 결
   과로 발생하든 마찬가지입니다.

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

   일반적이고 간단한 사용 사례의 경우 다른 기능은 필요하지 않습니다.
   그러나 필드별로 추가 정보가 필요한 일부 데이터 클래스 기능이 있습니
   다. 추가 정보에 대한 필요성을 충족시키기 위해, 기본 필드 값을 제공
   된 "field()" 함수 호출로 바꿀 수 있습니다. 예를 들면:

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

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

   위에서 보인 것처럼, "MISSING" 값은 사용자가 일부 매개변수를 제공했
   는지를 탐지하는데 사용되는 표지 객체입니다. 일부 매개변수에 대해서
   는 "None" 이 별개의 의미를 갖는 유효한 값이기 때문에 이 표지가 사용
   됩니다. 어떤 코드도 "MISSING" 값을 직접 사용해서는 안 됩니다.

   "field()" 의 매개변수는 다음과 같습니다:

   * "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 "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.

     "hash=False" 이지만 "compare=True" 로 설정하는 한 가지 가능한 이
     유는, 동등 비교에 포함되는 필드가 해시값을 계산하는 데 비용이 많
     이 들고, 형의 해시값에 이바지하는 다른 필드가 있는 경우입니다. 필
     드가 해시에서 제외된 경우에도 비교에는 계속 사용됩니다.

   * "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.

      버전 3.10에 추가.

   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

   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" 객체는 정의된 각 필드를 설명합니다. 이 객체는 내부적으로 생
   성되며 "fields()" 모듈 수준 메서드(아래 참조)가 돌려줍니다. 사용자
   는 직접 "Field" 인스턴스 객체를 만들어서는 안 됩니다. 문서화 된 어
   트리뷰트는 다음과 같습니다:

   * "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.

   다른 어트리뷰트도 있을 수 있지만, 내부적인 것이므로 검사하거나 의존
   해서는 안 됩니다.

dataclasses.fields(class_or_instance)

   데이터 클래스의 필드들을 정의하는 "Field" 객체들의 튜플을 돌려줍니
   다. 데이터 클래스나 데이터 클래스의 인스턴스를 받아들입니다. 데이터
   클래스 나 데이터 클래스의 인스턴스를 전달하지 않으면 "TypeError" 를
   돌려줍니다. "ClassVar" 또는 "InitVar" 인 의사 필드는 반환하지 않습
   니다.

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}]}

   To create a shallow copy, the following workaround may be used:

      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)

   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()".

   이전 예에서 계속하면:

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

   To create a shallow copy, the following workaround may be used:

      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})

   는 다음과 동등합니다:

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

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

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 values in "changes" do not specify fields, 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.

   기본값을 가지지 않는 초기화 전용 변수가 존재한다면, "replace()" 호
   출에 반드시 지정해서 "__init__()" 와 "__post_init__()" 에 전달 될
   수 있도록 해야 합니다.

   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.

dataclasses.is_dataclass(obj)

   Return "True" if its parameter is a dataclass or an instance of
   one, otherwise return "False".

   (데이터 클래스 자체가 아니라) 데이터 클래스의 인스턴스인지 알아야
   한다면 "not isinstance(obj, type)" 검사를 추가하십시오:

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

dataclasses.MISSING

   A sentinel value signifying a missing default or 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.

   In this example, the fields "y" and "z" will be marked as keyword-
   only fields:

      @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".

   버전 3.10에 추가.

exception dataclasses.FrozenInstanceError

   "frozen=True" 로 정의된 데이터 클래스에서 묵시적으로 정의된
   "__setattr__()" 또는 "__delattr__()" 이 호출 될 때 발생합니다.
   "AttributeError"의 서브 클래스입니다.


초기화 후처리
=============

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.

매개변수를 "__post_init__()" 에 전달하는 방법은 초기화 전용 변수에 대
한 아래 섹션을 참조하십시오. 또한 "replace()" 가 "init=False" 필드를
처리하는 방식에 관한 경고를 보십시오.


클래스 변수
===========

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.


초기화 전용 변수
================

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.

예를 들어, 클래스를 만들 때 값이 제공되지 않으면, 필드가 데이터베이스
로부터 초기화된다고 가정합시다:

   @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".


고정 인스턴스
=============

정말로 불변인 파이썬 객체를 만드는 것은 불가능합니다. 그러나,
"frozen=True" 를 "@dataclass" 데코레이터에 전달함으로써 불변성을 흉내
낼 수 있습니다. 이 경우, 데이터 클래스는 "__setattr__()" 과
"__delattr__()" 메서드를 클래스에 추가합니다. 이 메서드는 호출될 때
"FrozenInstanceError" 를 발생시킵니다.

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


계승
====

데이터 클래스가 "@dataclass" 데코레이터에 의해 생성될 때, 클래스의 모
든 베이스 클래스들을 MRO 역순(즉, "object" 에서 시작해서)으로 조사하고
, 발견되는 데이터 클래스마다 그 베이스 클래스의 필드들을 순서 있는 필
드 매핑에 추가합니다. 모든 생성된 메서드들은 이 합쳐지고 계산된 순서
있는 필드 매핑을 사용합니다. 필드들이 삽입 순서이기 때문에, 파생 클래
스는 베이스 클래스를 재정의합니다. 예:

   @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):

Note that the parameters have been re-ordered from how they appear in
the list of fields: parameters derived from regular fields are
followed by parameters derived from keyword-only fields.

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


기본 팩토리 함수
================

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.


가변 기본값
===========

파이썬은 기본 멤버 변숫값을 클래스 어트리뷰트에 저장합니다. 데이터 클
래스를 사용하지 않는 이 예제를 생각해보세요:

   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.

데이터 클래스를 사용해서, *만약* 이 코드가 올바르다면:

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

비슷한 코드를 생성합니다:

   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.

기본 팩토리 함수를 사용하면 필드의 기본값으로 가변형의 새 인스턴스를
만들 수 있습니다:

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

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

버전 3.11에서 변경: 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.
