我想我明白你想要什么，这里有一种动态生成类的方法。正如注释中所指出的，这永远不会通过静态类型检查器。如果你想运行类似或的东西，你将不得不退出修改选项。这是 Python 中非常动态的反映。A_ModificationOptionsmypypyrightAny

现在，几点说明。 是一个装饰器，就像任何装饰器一样，它只是在事后应用于类。那是dataclass

@dataclass
class X:
    ...

只是

class X:
    ...
X = dataclass(X)

因此，如果我们愿意，我们可以像普通的 Python 函数一样在我们组成的类上调用。当我们讨论这个话题时，我们也可以使用普通的 Python 制作类。type 有一个三参数形式，它充当新类的构造函数。dataclass

 class type(name, bases, dict, **kwds)

因此，让我们看看我们实际上是如何做到这一点的。我们需要和.我还导入以获得技术上正确的注释，尽管它不影响语义。dataclassfieldsOptional

from dataclasses import dataclass, fields
from typing import Optional

现在魔术酱，为方便起见评论。

def make_modification_dataclass(original_dataclass, new_class_name=None):
    # Provide a default name if the caller doesn't supply a custom
    # name for the new class.
    if new_class_name is None:
        new_class_name = original_dataclass.__name__ + "_ModificationOptions"
    # This actually creates the class. @dataclass is going to look at
    # the __annotations__ field on the class, which is normally
    # generated by writing type annotations in Python code. But it's
    # explicitly defined to be a mutable dictionary, so we're well
    # within our rights to create and mutate it ourselves.
    new_class = type(new_class_name, original_dataclass.__bases__, {
        "__annotations__": {}
    })
    # Iterate over all of the fields of the original dataclass.
    for field in fields(original_dataclass):
        # For each field, put a type in __annotations__. The type
        # could be anything as far as @dataclass is concerned, but we
        # make it Optional[whatever], which is actually the correct
        # type. No static type checker will ever see this, but other
        # tools that analyze __annotations__ at runtime will see a
        # correct type annotation.
        new_class.__annotations__[field.name] = Optional[field.type]
        # We also need to set the attribute itself on the class. This
        # is the "= None" part of the A_ModificationOptions class you
        # wrote, and it will show @dataclass what the default value of
        # the field should be.
        setattr(new_class, field.name, None)
    # Apply the decorator and return our brand new class.
    return dataclass(new_class)

要使用它，我们只需传递原始类并将结果分配给一个名称。

@dataclass
class A:
    x: int
    y: int

# This is making a class. A real, genuine dataclass.
A_ModificationOptions = make_modification_dataclass(A)

您的函数有点接近 dataclasses.replace，但后者 （a） 接受字典，（b） 返回一个新实例而不是就地更改。幸运的是，编写我们自己的代码相当简单。modifyA

这基本上是 wjandrea 在评论中建议的。我只是更喜欢使用而不是，因为它可以保证只获取数据类字段，而不是从非数据类超类或四处闲逛并做有趣业务的人那里获得任何额外的内容。dataclasses.fieldsvars

def modify(original, modification):
    for field in fields(modification):
        value = getattr(modification, field.name)
        if value is not None:
            setattr(original, field.name, value)

您的代码将按建议工作。

original_A = A(x=1, y=2)
print("A before modification: ", original_A) # A(x=1, y=2)

modification_A = A_ModificationOptions(y=7)
modify(original_A, modification_A)
print("A after modification: ", original_A) # A(x=1, y=7)

我重命名了该函数，因为它实际上从未执行任何特定于 .这个函数将适用于任何和相应的类。无需重写它，即使是表面上的。modifymodifyAA@dataclass_ModificationOptions

在线试用！