Type qualifiers

@final

(Originally specified in PEP 591.)

The typing.final decorator is used to restrict the use of inheritance and overriding.

A type checker should prohibit any class decorated with @final from being subclassed and any method decorated with @final from being overridden in a subclass. The method decorator version may be used with all of instance methods, class methods, static methods, and properties.

For example:

from typing import final

@final
class Base:
    ...

class Derived(Base):  # Error: Cannot inherit from final class "Base"
    ...

and:

from typing import final

class Base:
    @final
    def foo(self) -> None:
        ...

class Derived(Base):
    def foo(self) -> None:  # Error: Cannot override final attribute "foo"
                            # (previously declared in base class "Base")
        ...

For overloaded methods, @final should be placed on the implementation (or on the first overload, for stubs):

from typing import Any, overload

class Base:
    @overload
    def method(self) -> None: ...
    @overload
    def method(self, arg: int) -> int: ...
    @final
    def method(self, x=None):
        ...

It is an error to use @final on a non-method function.

Final

(Originally specified in PEP 591.)

The typing.Final type qualifier is used to indicate that a variable or attribute should not be reassigned, redefined, or overridden.

Syntax

Final may be used in one of several forms:

  • With an explicit type, using the syntax Final[<type>]. Example:

    ID: Final[float] = 1
    
  • With no type annotation. Example:

    ID: Final = 1
    

    The typechecker should apply its usual type inference mechanisms to determine the type of ID (here, likely, int). Note that unlike for generic classes this is not the same as Final[Any].

  • In class bodies and stub files you can omit the right hand side and just write ID: Final[float]. If the right hand side is omitted, there must be an explicit type argument to Final.

  • Finally, as self.id: Final = 1 (also optionally with a type in square brackets). This is allowed only in __init__ methods, so that the final instance attribute is assigned only once when an instance is created.

Semantics and examples

The two main rules for defining a final name are:

  • There can be at most one final declaration per module or class for a given attribute. There can’t be separate class-level and instance-level constants with the same name.

  • There must be exactly one assignment to a final name.

This means a type checker should prevent further assignments to final names in type-checked code:

from typing import Final

RATE: Final = 3000

class Base:
    DEFAULT_ID: Final = 0

RATE = 300  # Error: can't assign to final attribute
Base.DEFAULT_ID = 1  # Error: can't override a final attribute

Note that a type checker need not allow Final declarations inside loops since the runtime will see multiple assignments to the same variable in subsequent iterations.

Additionally, a type checker should prevent final attributes from being overridden in a subclass:

from typing import Final

class Window:
    BORDER_WIDTH: Final = 2.5
    ...

class ListView(Window):
    BORDER_WIDTH = 3  # Error: can't override a final attribute

A final attribute declared in a class body without an initializer must be initialized in the __init__ method (except in stub files):

class ImmutablePoint:
    x: Final[int]
    y: Final[int]  # Error: final attribute without an initializer

    def __init__(self) -> None:
        self.x = 1  # Good

The generated __init__ method of Dataclasses qualifies for this requirement: a bare x: Final[int] is permitted in a dataclass body, because the generated __init__ will initialize x.

Type checkers should infer a final attribute that is initialized in a class body as being a class variable, except in the case of Dataclasses, where x: Final[int] = 3 creates a dataclass field and instance-level final attribute x with default value 3; x: ClassVar[Final[int]] = 3 is necessary to create a final class variable with value 3. In non-dataclasses, combining ClassVar and Final is redundant, and type checkers may choose to warn or error on the redundancy.

Final may only be used in assignments or variable annotations. Using it in any other position is an error. In particular, Final can’t be used in annotations for function arguments:

x: list[Final[int]] = []  # Error!

def fun(x: Final[List[int]]) ->  None:  # Error!
    ...

Final may be wrapped only by other type qualifiers (e.g. ClassVar or Annotation). It cannot be used in a type parameter (e.g. list[Final[int]] is not permitted.)

Note that declaring a name as final only guarantees that the name will not be re-bound to another value, but does not make the value immutable. Immutable ABCs and containers may be used in combination with Final to prevent mutating such values:

x: Final = ['a', 'b']
x.append('c')  # OK

y: Final[Sequence[str]] = ['a', 'b']
y.append('x')  # Error: "Sequence[str]" has no attribute "append"
z: Final = ('a', 'b')  # Also works

Type checkers should treat uses of a final name that was initialized with a literal as if it was replaced by the literal. For example, the following should be allowed:

from typing import NamedTuple, Final

X: Final = "x"
Y: Final = "y"
N = NamedTuple("N", [(X, int), (Y, int)])

Annotated

(Originally specified by PEP 593.)

Syntax

Annotated is parameterized with a type and an arbitrary list of Python values that represent the annotations. Here are the specific details of the syntax:

  • The first argument to Annotated must be a valid type

  • Multiple type annotations are supported (Annotated supports variadic arguments):

    Annotated[int, ValueRange(3, 10), ctype("char")]
    
  • Annotated must be called with at least two arguments ( Annotated[int] is not valid)

  • The order of the annotations is preserved and matters for equality checks:

    Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
        int, ctype("char"), ValueRange(3, 10)
    ]
    
  • Nested Annotated types are flattened, with metadata ordered starting with the innermost annotation:

    Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
        int, ValueRange(3, 10), ctype("char")
    ]
    
  • Duplicated annotations are not removed:

    Annotated[int, ValueRange(3, 10)] != Annotated[
        int, ValueRange(3, 10), ValueRange(3, 10)
    ]
    
  • Annotated can be used with nested and generic aliases:

    T = TypeVar("T")
    Vec = Annotated[list[tuple[T, T]], MaxLen(10)]
    V = Vec[int]
    
    V == Annotated[list[tuple[int, int]], MaxLen(10)]
    
  • As with most special forms, Annotated is not type compatible with type or type[T]:

    v1: type[int] = Annotated[int, ""]  # Type error
    
    SmallInt: TypeAlias = Annotated[int, ValueRange(0, 100)]
    v2: type[Any] = SmallInt  # Type error
    
  • An attempt to call Annotated (whether parameterized or not) should be treated as a type error by type checkers:

    Annotated()  # Type error
    Annotated[int, ""](0)  # Type error
    
    SmallInt = Annotated[int, ValueRange(0, 100)]
    SmallInt(1)  # Type error
    

Consuming annotations

Ultimately, the responsibility of how to interpret the annotations (if at all) is the responsibility of the tool or library encountering the Annotated type. A tool or library encountering an Annotated type can scan through the annotations to determine if they are of interest (e.g., using isinstance()).

Unknown annotations: When a tool or a library does not support annotations or encounters an unknown annotation it should just ignore it and treat annotated type as the underlying type. For example, when encountering an annotation that is not an instance of struct2.ctype to the annotations for name (e.g., Annotated[str, 'foo', struct2.ctype("<10s")]), the unpack method should ignore it.

Namespacing annotations: Namespaces are not needed for annotations since the class used by the annotations acts as a namespace.

Multiple annotations: It’s up to the tool consuming the annotations to decide whether the client is allowed to have several annotations on one type and how to merge those annotations.

Since the Annotated type allows you to put several annotations of the same (or different) type(s) on any node, the tools or libraries consuming those annotations are in charge of dealing with potential duplicates. For example, if you are doing value range analysis you might allow this:

T1 = Annotated[int, ValueRange(-10, 5)]
T2 = Annotated[T1, ValueRange(-20, 3)]

Flattening nested annotations, this translates to:

T2 = Annotated[int, ValueRange(-10, 5), ValueRange(-20, 3)]

Aliases & Concerns over verbosity

Writing typing.Annotated everywhere can be quite verbose; fortunately, the ability to alias annotations means that in practice we don’t expect clients to have to write lots of boilerplate code:

T = TypeVar('T')
Const = Annotated[T, my_annotations.CONST]

class C:
    def const_method(self: Const[List[int]]) -> int:
        ...