typing — Support for type hints  New in version 3.5.  Source code: Lib/typing.py  Note The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.  This module provides runtime support for type hints as specified by PEP 484, PEP 526, PEP 544, PEP 586, PEP 589, and PEP 591. The most fundamental support consists of the types Any, Union, Tuple, Callable, TypeVar, and Generic. For full specification please see PEP 484. For a simplified introduction to type hints see PEP 483. The function below takes and returns a string and is annotated as follows: def greeting(name: str) -> str:
    return 'Hello ' + name
 In the function greeting, the argument name is expected to be of type str and the return type str. Subtypes are accepted as arguments. Type aliases A type alias is defined by assigning the type to the alias. In this example, Vector and list[float] will be treated as interchangeable synonyms: Vector = list[float]

def scale(scalar: float, vector: Vector) -> Vector:
    return [scalar * num for num in vector]

# typechecks; a list of floats qualifies as a Vector.
new_vector = scale(2.0, [1.0, -4.2, 5.4])
 Type aliases are useful for simplifying complex type signatures. For example: from collections.abc import Sequence

ConnectionOptions = dict[str, str]
Address = tuple[str, int]
Server = tuple[Address, ConnectionOptions]

def broadcast_message(message: str, servers: Sequence[Server]) -> None:
    ...

# The static type checker will treat the previous type signature as
# being exactly equivalent to this one.
def broadcast_message(
        message: str,
        servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
    ...
 Note that None as a type hint is a special case and is replaced by type(None). NewType Use the NewType() helper function to create distinct types: from typing import NewType

UserId = NewType('UserId', int)
some_id = UserId(524313)
 The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors: def get_user_name(user_id: UserId) -> str:
    ...

# typechecks
user_a = get_user_name(UserId(42351))

# does not typecheck; an int is not a UserId
user_b = get_user_name(-1)
 You may still perform all int operations on a variable of type UserId, but the result will always be of type int. This lets you pass in a UserId wherever an int might be expected, but will prevent you from accidentally creating a UserId in an invalid way: # 'output' is of type 'int', not 'UserId'
output = UserId(23413) + UserId(54341)
 Note that these checks are enforced only by the static type checker. At runtime, the statement Derived = NewType('Derived', Base) will make Derived a function that immediately returns whatever parameter you pass it. That means the expression Derived(some_value) does not create a new class or introduce any overhead beyond that of a regular function call. More precisely, the expression some_value is Derived(some_value) is always true at runtime. This also means that it is not possible to create a subtype of Derived since it is an identity function at runtime, not an actual type: from typing import NewType

UserId = NewType('UserId', int)

# Fails at runtime and does not typecheck
class AdminUserId(UserId): pass
 However, it is possible to create a NewType() based on a ‘derived’ NewType: from typing import NewType

UserId = NewType('UserId', int)

ProUserId = NewType('ProUserId', UserId)
 and typechecking for ProUserId will work as expected. See PEP 484 for more details.  Note Recall that the use of a type alias declares two types to be equivalent to one another. Doing Alias = Original will make the static type checker treat Alias as being exactly equivalent to Original in all cases. This is useful when you want to simplify complex type signatures. In contrast, NewType declares one type to be a subtype of another. Doing Derived = NewType('Derived', Original) will make the static type checker treat Derived as a subclass of Original, which means a value of type Original cannot be used in places where a value of type Derived is expected. This is useful when you want to prevent logic errors with minimal runtime cost.   New in version 3.5.2.  Callable Frameworks expecting callback functions of specific signatures might be type hinted using Callable[[Arg1Type, Arg2Type], ReturnType]. For example: from collections.abc import Callable

def feeder(get_next_item: Callable[[], str]) -> None:
    # Body

def async_query(on_success: Callable[[int], None],
                on_error: Callable[[int, Exception], None]) -> None:
    # Body
 It is possible to declare the return type of a callable without specifying the call signature by substituting a literal ellipsis for the list of arguments in the type hint: Callable[..., ReturnType]. Generics Since type information about objects kept in containers cannot be statically inferred in a generic way, abstract base classes have been extended to support subscription to denote expected types for container elements. from collections.abc import Mapping, Sequence

def notify_by_email(employees: Sequence[Employee],
                    overrides: Mapping[str, str]) -> None: ...
 Generics can be parameterized by using a new factory available in typing called TypeVar. from collections.abc import Sequence
from typing import TypeVar

T = TypeVar('T')      # Declare type variable

def first(l: Sequence[T]) -> T:   # Generic function
    return l[0]
 User-defined generic types A user-defined class can be defined as a generic class. from typing import TypeVar, Generic
from logging import Logger

T = TypeVar('T')

class LoggedVar(Generic[T]):
    def __init__(self, value: T, name: str, logger: Logger) -> None:
        self.name = name
        self.logger = logger
        self.value = value

    def set(self, new: T) -> None:
        self.log('Set ' + repr(self.value))
        self.value = new

    def get(self) -> T:
        self.log('Get ' + repr(self.value))
        return self.value

    def log(self, message: str) -> None:
        self.logger.info('%s: %s', self.name, message)
 Generic[T] as a base class defines that the class LoggedVar takes a single type parameter T . This also makes T valid as a type within the class body. The Generic base class defines __class_getitem__() so that LoggedVar[t] is valid as a type: from collections.abc import Iterable

def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
    for var in vars:
        var.set(0)
 A generic type can have any number of type variables, and type variables may be constrained: from typing import TypeVar, Generic
...

T = TypeVar('T')
S = TypeVar('S', int, str)

class StrangePair(Generic[T, S]):
    ...
 Each type variable argument to Generic must be distinct. This is thus invalid: from typing import TypeVar, Generic
...

T = TypeVar('T')

class Pair(Generic[T, T]):   # INVALID
    ...
 You can use multiple inheritance with Generic: from collections.abc import Sized
from typing import TypeVar, Generic

T = TypeVar('T')

class LinkedList(Sized, Generic[T]):
    ...
 When inheriting from generic classes, some type variables could be fixed: from collections.abc import Mapping
from typing import TypeVar

T = TypeVar('T')

class MyDict(Mapping[str, T]):
    ...
 In this case MyDict has a single parameter, T. Using a generic class without specifying type parameters assumes Any for each position. In the following example, MyIterable is not generic but implicitly inherits from Iterable[Any]: from collections.abc import Iterable

class MyIterable(Iterable): # Same as Iterable[Any]
 User defined generic type aliases are also supported. Examples: from collections.abc import Iterable
from typing import TypeVar, Union
S = TypeVar('S')
Response = Union[Iterable[S], int]

# Return type here is same as Union[Iterable[str], int]
def response(query: str) -> Response[str]:
    ...

T = TypeVar('T', int, float, complex)
Vec = Iterable[tuple[T, T]]

def inproduct(v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
    return sum(x*y for x, y in v)
  Changed in version 3.7: Generic no longer has a custom metaclass.  A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the typing module are hashable and comparable for equality. The Any type A special kind of type is Any. A static type checker will treat every type as being compatible with Any and Any as being compatible with every type. This means that it is possible to perform any operation or method call on a value of type Any and assign it to any variable: from typing import Any

a = None    # type: Any
a = []      # OK
a = 2       # OK

s = ''      # type: str
s = a       # OK

def foo(item: Any) -> int:
    # Typechecks; 'item' could be any type,
    # and that type might have a 'bar' method
    item.bar()
    ...
 Notice that no typechecking is performed when assigning a value of type Any to a more precise type. For example, the static type checker did not report an error when assigning a to s even though s was declared to be of type str and receives an int value at runtime! Furthermore, all functions without a return type or parameter types will implicitly default to using Any: def legacy_parser(text):
    ...
    return data

# A static type checker will treat the above
# as having the same signature as:
def legacy_parser(text: Any) -> Any:
    ...
    return data
 This behavior allows Any to be used as an escape hatch when you need to mix dynamically and statically typed code. Contrast the behavior of Any with the behavior of object. Similar to Any, every type is a subtype of object. However, unlike Any, the reverse is not true: object is not a subtype of every other type. That means when the type of a value is object, a type checker will reject almost all operations on it, and assigning it to a variable (or using it as a return value) of a more specialized type is a type error. For example: def hash_a(item: object) -> int:
    # Fails; an object does not have a 'magic' method.
    item.magic()
    ...

def hash_b(item: Any) -> int:
    # Typechecks
    item.magic()
    ...

# Typechecks, since ints and strs are subclasses of object
hash_a(42)
hash_a("foo")

# Typechecks, since Any is compatible with all types
hash_b(42)
hash_b("foo")
 Use object to indicate that a value could be any type in a typesafe manner. Use Any to indicate that a value is dynamically typed. Nominal vs structural subtyping Initially PEP 484 defined Python static type system as using nominal subtyping. This means that a class A is allowed where a class B is expected if and only if A is a subclass of B. This requirement previously also applied to abstract base classes, such as Iterable. The problem with this approach is that a class had to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to PEP 484: from collections.abc import Sized, Iterable, Iterator

class Bucket(Sized, Iterable[int]):
    ...
    def __len__(self) -> int: ...
    def __iter__(self) -> Iterator[int]: ...
 PEP 544 allows to solve this problem by allowing users to write the above code without explicit base classes in the class definition, allowing Bucket to be implicitly considered a subtype of both Sized and Iterable[int] by static type checkers. This is known as structural subtyping (or static duck-typing): from collections.abc import Iterator, Iterable

class Bucket:  # Note: no base classes
    ...
    def __len__(self) -> int: ...
    def __iter__(self) -> Iterator[int]: ...

def collect(items: Iterable[int]) -> int: ...
result = collect(Bucket())  # Passes type check
 Moreover, by subclassing a special class Protocol, a user can define new custom protocols to fully enjoy structural subtyping (see examples below). Module contents The module defines the following classes, functions and decorators.  Note This module defines several types that are subclasses of pre-existing standard library classes which also extend Generic to support type variables inside []. These types became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support []. The redundant types are deprecated as of Python 3.9 but no deprecation warnings will be issued by the interpreter. It is expected that type checkers will flag the deprecated types when the checked program targets Python 3.9 or newer. The deprecated types will be removed from the typing module in the first Python version released 5 years after the release of Python 3.9.0. See details in PEP 585—Type Hinting Generics In Standard Collections.  Special typing primitives Special types These can be used as types in annotations and do not support [].  
typing.Any  
Special type indicating an unconstrained type.  Every type is compatible with Any. 
Any is compatible with every type.  
  
typing.NoReturn  
Special type indicating that a function never returns. For example: from typing import NoReturn

def stop() -> NoReturn:
    raise RuntimeError('no way')
  New in version 3.5.4.   New in version 3.6.2.  
 Special forms These can be used as types in annotations using [], each having a unique syntax.  
typing.Tuple  
Tuple type; Tuple[X, Y] is the type of a tuple of two items with the first item of type X and the second of type Y. The type of the empty tuple can be written as Tuple[()]. Example: Tuple[T1, T2] is a tuple of two elements corresponding to type variables T1 and T2. Tuple[int, float, str] is a tuple of an int, a float and a string. To specify a variable-length tuple of homogeneous type, use literal ellipsis, e.g. Tuple[int, ...]. A plain Tuple is equivalent to Tuple[Any, ...], and in turn to tuple.  Deprecated since version 3.9: builtins.tuple now supports []. See PEP 585 and Generic Alias Type.  
  
typing.Union  
Union type; Union[X, Y] means either X or Y. To define a union, use e.g. Union[int, str]. Details:  The arguments must be types and there must be at least one. 
Unions of unions are flattened, e.g.: Union[Union[int, str], float] == Union[int, str, float]
  
Unions of a single argument vanish, e.g.: Union[int] == int  # The constructor actually returns int
  
Redundant arguments are skipped, e.g.: Union[int, str, int] == Union[int, str]
  
When comparing unions, the argument order is ignored, e.g.: Union[int, str] == Union[str, int]
  You cannot subclass or instantiate a union. You cannot write Union[X][Y]. You can use Optional[X] as a shorthand for Union[X, None].   Changed in version 3.7: Don’t remove explicit subclasses from unions at runtime.  
  
typing.Optional  
Optional type. Optional[X] is equivalent to Union[X, None]. Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the Optional qualifier on its type annotation just because it is optional. For example: def foo(arg: int = 0) -> None:
    ...
 On the other hand, if an explicit value of None is allowed, the use of Optional is appropriate, whether the argument is optional or not. For example: def foo(arg: Optional[int] = None) -> None:
    ...
 
  
typing.Callable  
Callable type; Callable[[int], str] is a function of (int) -> str. The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types or an ellipsis; the return type must be a single type. There is no syntax to indicate optional or keyword arguments; such function types are rarely used as callback types. Callable[..., ReturnType] (literal ellipsis) can be used to type hint a callable taking any number of arguments and returning ReturnType. A plain Callable is equivalent to Callable[..., Any], and in turn to collections.abc.Callable.  Deprecated since version 3.9: collections.abc.Callable now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Type(Generic[CT_co])  
A variable annotated with C may accept a value of type C. In contrast, a variable annotated with Type[C] may accept values that are classes themselves – specifically, it will accept the class object of C. For example: a = 3         # Has type 'int'
b = int       # Has type 'Type[int]'
c = type(a)   # Also has type 'Type[int]'
 Note that Type[C] is covariant: class User: ...
class BasicUser(User): ...
class ProUser(User): ...
class TeamUser(User): ...

# Accepts User, BasicUser, ProUser, TeamUser, ...
def make_new_user(user_class: Type[User]) -> User:
    # ...
    return user_class()
 The fact that Type[C] is covariant implies that all subclasses of C should implement the same constructor signature and class method signatures as C. The type checker should flag violations of this, but should also allow constructor calls in subclasses that match the constructor calls in the indicated base class. How the type checker is required to handle this particular case may change in future revisions of PEP 484. The only legal parameters for Type are classes, Any, type variables, and unions of any of these types. For example: def new_non_team_user(user_class: Type[Union[BasicUser, ProUser]]): ...
 Type[Any] is equivalent to Type which in turn is equivalent to type, which is the root of Python’s metaclass hierarchy.  New in version 3.5.2.   Deprecated since version 3.9: builtins.type now supports []. See PEP 585 and Generic Alias Type.  
  
typing.Literal  
A type that can be used to indicate to type checkers that the corresponding variable or function parameter has a value equivalent to the provided literal (or one of several literals). For example: def validate_simple(data: Any) -> Literal[True]:  # always returns True
    ...

MODE = Literal['r', 'rb', 'w', 'wb']
def open_helper(file: str, mode: MODE) -> str:
    ...

open_helper('/some/path', 'r')  # Passes type check
open_helper('/other/path', 'typo')  # Error in type checker
 Literal[...] cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to Literal[...], but type checkers may impose restrictions. See PEP 586 for more details about literal types.  New in version 3.8.   Changed in version 3.9.1: Literal now de-duplicates parameters. Equality comparison of Literal objects are no longer order dependent. Literal objects will now raise a TypeError exception during equality comparisons if one of their parameters are not immutable.  
  
typing.ClassVar  
Special type construct to mark class variables. As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage: class Starship:
    stats: ClassVar[dict[str, int]] = {} # class variable
    damage: int = 10                     # instance variable
 ClassVar accepts only types and cannot be further subscribed. ClassVar is not a class itself, and should not be used with isinstance() or issubclass(). ClassVar does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error: enterprise_d = Starship(3000)
enterprise_d.stats = {} # Error, setting class variable on instance
Starship.stats = {}     # This is OK
  New in version 3.5.3.  
  
typing.Final  
A special typing construct to indicate to type checkers that a name cannot be re-assigned or overridden in a subclass. For example: MAX_SIZE: Final = 9000
MAX_SIZE += 1  # Error reported by type checker

class Connection:
    TIMEOUT: Final[int] = 10

class FastConnector(Connection):
    TIMEOUT = 1  # Error reported by type checker
 There is no runtime checking of these properties. See PEP 591 for more details.  New in version 3.8.  
  
typing.Annotated  
A type, introduced in PEP 593 (Flexible function and variable
annotations), to decorate existing types with context-specific metadata (possibly multiple pieces of it, as Annotated is variadic). Specifically, a type T can be annotated with metadata x via the typehint Annotated[T, x]. This metadata can be used for either static analysis or at runtime. If a library (or tool) encounters a typehint Annotated[T, x] and has no special logic for metadata x, it should ignore it and simply treat the type as T. Unlike the no_type_check functionality that currently exists in the typing module which completely disables typechecking annotations on a function or a class, the Annotated type allows for both static typechecking of T (e.g., via mypy or Pyre, which can safely ignore x) together with runtime access to x within a specific application. 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()). 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. 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)]
 Passing include_extras=True to get_type_hints() lets one access the extra annotations at runtime. The 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)]
    New in version 3.9.  
 Building generic types These are not used in annotations. They are building blocks for creating generic types.  
class typing.Generic  
Abstract base class for generic types. A generic type is typically declared by inheriting from an instantiation of this class with one or more type variables. For example, a generic mapping type might be defined as: class Mapping(Generic[KT, VT]):
    def __getitem__(self, key: KT) -> VT:
        ...
        # Etc.
 This class can then be used as follows: X = TypeVar('X')
Y = TypeVar('Y')

def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
    try:
        return mapping[key]
    except KeyError:
        return default
 
  
class typing.TypeVar  
Type variable. Usage: T = TypeVar('T')  # Can be anything
A = TypeVar('A', str, bytes)  # Must be str or bytes
 Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See Generic for more information on generic types. Generic functions work as follows: def repeat(x: T, n: int) -> Sequence[T]:
    """Return a list containing n references to x."""
    return [x]*n

def longest(x: A, y: A) -> A:
    """Return the longest of two strings."""
    return x if len(x) >= len(y) else y
 The latter example’s signature is essentially the overloading of (str, str) -> str and (bytes, bytes) -> bytes. Also note that if the arguments are instances of some subclass of str, the return type is still plain str. At runtime, isinstance(x, T) will raise TypeError. In general, isinstance() and issubclass() should not be used with types. Type variables may be marked covariant or contravariant by passing covariant=True or contravariant=True. See PEP 484 for more details. By default type variables are invariant. Alternatively, a type variable may specify an upper bound using bound=<type>. This means that an actual type substituted (explicitly or implicitly) for the type variable must be a subclass of the boundary type, see PEP 484. 
  
typing.AnyStr  
AnyStr is a type variable defined as AnyStr = TypeVar('AnyStr', str, bytes). It is meant to be used for functions that may accept any kind of string without allowing different kinds of strings to mix. For example: def concat(a: AnyStr, b: AnyStr) -> AnyStr:
    return a + b

concat(u"foo", u"bar")  # Ok, output has type 'unicode'
concat(b"foo", b"bar")  # Ok, output has type 'bytes'
concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes
 
  
class typing.Protocol(Generic)  
Base class for protocol classes. Protocol classes are defined like this: class Proto(Protocol):
    def meth(self) -> int:
        ...
 Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example: class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check
 See PEP 544 for details. Protocol classes decorated with runtime_checkable() (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, for example: class GenProto(Protocol[T]):
    def meth(self) -> T:
        ...
  New in version 3.8.  
  
@typing.runtime_checkable  
Mark a protocol class as a runtime protocol. Such a protocol can be used with isinstance() and issubclass(). This raises TypeError when applied to a non-protocol class. This allows a simple-minded structural check, very similar to “one trick ponies” in collections.abc such as Iterable. For example: @runtime_checkable
class Closable(Protocol):
    def close(self): ...

assert isinstance(open('/some/file'), Closable)
  Note runtime_checkable() will check only the presence of the required methods, not their type signatures! For example, builtins.complex implements __float__(), therefore it passes an issubclass() check against SupportsFloat. However, the complex.__float__ method exists only to raise a TypeError with a more informative message.   New in version 3.8.  
 Other special directives These are not used in annotations. They are building blocks for declaring types.  
class typing.NamedTuple  
Typed version of collections.namedtuple(). Usage: class Employee(NamedTuple):
    name: str
    id: int
 This is equivalent to: Employee = collections.namedtuple('Employee', ['name', 'id'])
 To give a field a default value, you can assign to it in the class body: class Employee(NamedTuple):
    name: str
    id: int = 3

employee = Employee('Guido')
assert employee.id == 3
 Fields with a default value must come after any fields without a default. The resulting class has an extra attribute __annotations__ giving a dict that maps the field names to the field types. (The field names are in the _fields attribute and the default values are in the _field_defaults attribute both of which are part of the namedtuple API.) NamedTuple subclasses can also have docstrings and methods: class Employee(NamedTuple):
    """Represents an employee."""
    name: str
    id: int = 3

    def __repr__(self) -> str:
        return f'<Employee {self.name}, id={self.id}>'
 Backward-compatible usage: Employee = NamedTuple('Employee', [('name', str), ('id', int)])
  Changed in version 3.6: Added support for PEP 526 variable annotation syntax.   Changed in version 3.6.1: Added support for default values, methods, and docstrings.   Changed in version 3.8: The _field_types and __annotations__ attributes are now regular dictionaries instead of instances of OrderedDict.   Changed in version 3.9: Removed the _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.  
  
typing.NewType(name, tp)  
A helper function to indicate a distinct type to a typechecker, see NewType. At runtime it returns a function that returns its argument. Usage: UserId = NewType('UserId', int)
first_user = UserId(1)
  New in version 3.5.2.  
  
class typing.TypedDict(dict)  
Special construct to add type hints to a dictionary. At runtime it is a plain dict. TypedDict declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage: class Point2D(TypedDict):
    x: int
    y: int
    label: str

a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check

assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
 The type info for introspection can be accessed via Point2D.__annotations__ and Point2D.__total__. To allow using this feature with older versions of Python that do not support PEP 526, TypedDict supports two additional equivalent syntactic forms: Point2D = TypedDict('Point2D', x=int, y=int, label=str)
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
 By default, all keys must be present in a TypedDict. It is possible to override this by specifying totality. Usage: class point2D(TypedDict, total=False):
    x: int
    y: int
 This means that a point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body be required. See PEP 589 for more examples and detailed rules of using TypedDict.  New in version 3.8.  
 Generic concrete collections Corresponding to built-in types  
class typing.Dict(dict, MutableMapping[KT, VT])  
A generic version of dict. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Mapping. This type can be used as follows: def count_words(text: str) -> Dict[str, int]:
    ...
  Deprecated since version 3.9: builtins.dict now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.List(list, MutableSequence[T])  
Generic version of list. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as Sequence or Iterable. This type may be used as follows: T = TypeVar('T', int, float)

def vec2(x: T, y: T) -> List[T]:
    return [x, y]

def keep_positives(vector: Sequence[T]) -> List[T]:
    return [item for item in vector if item > 0]
  Deprecated since version 3.9: builtins.list now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Set(set, MutableSet[T])  
A generic version of builtins.set. Useful for annotating return types. To annotate arguments it is preferred to use an abstract collection type such as AbstractSet.  Deprecated since version 3.9: builtins.set now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.FrozenSet(frozenset, AbstractSet[T_co])  
A generic version of builtins.frozenset.  Deprecated since version 3.9: builtins.frozenset now supports []. See PEP 585 and Generic Alias Type.  
  Note Tuple is a special form.  Corresponding to types in collections
  
class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])  
A generic version of collections.defaultdict.  New in version 3.5.2.   Deprecated since version 3.9: collections.defaultdict now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])  
A generic version of collections.OrderedDict.  New in version 3.7.2.   Deprecated since version 3.9: collections.OrderedDict now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])  
A generic version of collections.ChainMap.  New in version 3.5.4.   New in version 3.6.1.   Deprecated since version 3.9: collections.ChainMap now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Counter(collections.Counter, Dict[T, int])  
A generic version of collections.Counter.  New in version 3.5.4.   New in version 3.6.1.   Deprecated since version 3.9: collections.Counter now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Deque(deque, MutableSequence[T])  
A generic version of collections.deque.  New in version 3.5.4.   New in version 3.6.1.   Deprecated since version 3.9: collections.deque now supports []. See PEP 585 and Generic Alias Type.  
 Other concrete types  
class typing.IO  
class typing.TextIO  
class typing.BinaryIO  
Generic type IO[AnyStr] and its subclasses TextIO(IO[str]) and BinaryIO(IO[bytes]) represent the types of I/O streams such as returned by open(). These types are also in the typing.io namespace. 
  
class typing.Pattern  
class typing.Match  
These type aliases correspond to the return types from re.compile() and re.match(). These types (and the corresponding functions) are generic in AnyStr and can be made specific by writing Pattern[str], Pattern[bytes], Match[str], or Match[bytes]. These types are also in the typing.re namespace.  Deprecated since version 3.9: Classes Pattern and Match from re now support []. See PEP 585 and Generic Alias Type.  
  
class typing.Text  
Text is an alias for str. It is provided to supply a forward compatible path for Python 2 code: in Python 2, Text is an alias for unicode. Use Text to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3: def add_unicode_checkmark(text: Text) -> Text:
    return text + u' \u2713'
  New in version 3.5.2.  
 Abstract Base Classes Corresponding to collections in collections.abc
  
class typing.AbstractSet(Sized, Collection[T_co])  
A generic version of collections.abc.Set.  Deprecated since version 3.9: collections.abc.Set now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.ByteString(Sequence[int])  
A generic version of collections.abc.ByteString. This type represents the types bytes, bytearray, and memoryview of byte sequences. As a shorthand for this type, bytes can be used to annotate arguments of any of the types mentioned above.  Deprecated since version 3.9: collections.abc.ByteString now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Collection(Sized, Iterable[T_co], Container[T_co])  
A generic version of collections.abc.Collection  New in version 3.6.0.   Deprecated since version 3.9: collections.abc.Collection now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Container(Generic[T_co])  
A generic version of collections.abc.Container.  Deprecated since version 3.9: collections.abc.Container now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.ItemsView(MappingView, Generic[KT_co, VT_co])  
A generic version of collections.abc.ItemsView.  Deprecated since version 3.9: collections.abc.ItemsView now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.KeysView(MappingView[KT_co], AbstractSet[KT_co])  
A generic version of collections.abc.KeysView.  Deprecated since version 3.9: collections.abc.KeysView now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Mapping(Sized, Collection[KT], Generic[VT_co])  
A generic version of collections.abc.Mapping. This type can be used as follows: def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
    return word_list[word]
  Deprecated since version 3.9: collections.abc.Mapping now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.MappingView(Sized, Iterable[T_co])  
A generic version of collections.abc.MappingView.  Deprecated since version 3.9: collections.abc.MappingView now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.MutableMapping(Mapping[KT, VT])  
A generic version of collections.abc.MutableMapping.  Deprecated since version 3.9: collections.abc.MutableMapping now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.MutableSequence(Sequence[T])  
A generic version of collections.abc.MutableSequence.  Deprecated since version 3.9: collections.abc.MutableSequence now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.MutableSet(AbstractSet[T])  
A generic version of collections.abc.MutableSet.  Deprecated since version 3.9: collections.abc.MutableSet now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Sequence(Reversible[T_co], Collection[T_co])  
A generic version of collections.abc.Sequence.  Deprecated since version 3.9: collections.abc.Sequence now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.ValuesView(MappingView[VT_co])  
A generic version of collections.abc.ValuesView.  Deprecated since version 3.9: collections.abc.ValuesView now supports []. See PEP 585 and Generic Alias Type.  
 Corresponding to other types in collections.abc
  
class typing.Iterable(Generic[T_co])  
A generic version of collections.abc.Iterable.  Deprecated since version 3.9: collections.abc.Iterable now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Iterator(Iterable[T_co])  
A generic version of collections.abc.Iterator.  Deprecated since version 3.9: collections.abc.Iterator now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Generator(Iterator[T_co], Generic[T_co, T_contra, V_co])  
A generator can be annotated by the generic type Generator[YieldType, SendType, ReturnType]. For example: def echo_round() -> Generator[int, float, str]:
    sent = yield 0
    while sent >= 0:
        sent = yield round(sent)
    return 'Done'
 Note that unlike many other generics in the typing module, the SendType of Generator behaves contravariantly, not covariantly or invariantly. If your generator will only yield values, set the SendType and ReturnType to None: def infinite_stream(start: int) -> Generator[int, None, None]:
    while True:
        yield start
        start += 1
 Alternatively, annotate your generator as having a return type of either Iterable[YieldType] or Iterator[YieldType]: def infinite_stream(start: int) -> Iterator[int]:
    while True:
        yield start
        start += 1
  Deprecated since version 3.9: collections.abc.Generator now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Hashable  
An alias to collections.abc.Hashable 
  
class typing.Reversible(Iterable[T_co])  
A generic version of collections.abc.Reversible.  Deprecated since version 3.9: collections.abc.Reversible now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Sized  
An alias to collections.abc.Sized 
 Asynchronous programming  
class typing.Coroutine(Awaitable[V_co], Generic[T_co, T_contra, V_co])  
A generic version of collections.abc.Coroutine. The variance and order of type variables correspond to those of Generator, for example: from collections.abc import Coroutine
c = None # type: Coroutine[list[str], str, int]
...
x = c.send('hi') # type: list[str]
async def bar() -> None:
    x = await c # type: int
  New in version 3.5.3.   Deprecated since version 3.9: collections.abc.Coroutine now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.AsyncGenerator(AsyncIterator[T_co], Generic[T_co, T_contra])  
An async generator can be annotated by the generic type AsyncGenerator[YieldType, SendType]. For example: async def echo_round() -> AsyncGenerator[int, float]:
    sent = yield 0
    while sent >= 0.0:
        rounded = await round(sent)
        sent = yield rounded
 Unlike normal generators, async generators cannot return a value, so there is no ReturnType type parameter. As with Generator, the SendType behaves contravariantly. If your generator will only yield values, set the SendType to None: async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
    while True:
        yield start
        start = await increment(start)
 Alternatively, annotate your generator as having a return type of either AsyncIterable[YieldType] or AsyncIterator[YieldType]: async def infinite_stream(start: int) -> AsyncIterator[int]:
    while True:
        yield start
        start = await increment(start)
  New in version 3.6.1.   Deprecated since version 3.9: collections.abc.AsyncGenerator now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.AsyncIterable(Generic[T_co])  
A generic version of collections.abc.AsyncIterable.  New in version 3.5.2.   Deprecated since version 3.9: collections.abc.AsyncIterable now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.AsyncIterator(AsyncIterable[T_co])  
A generic version of collections.abc.AsyncIterator.  New in version 3.5.2.   Deprecated since version 3.9: collections.abc.AsyncIterator now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.Awaitable(Generic[T_co])  
A generic version of collections.abc.Awaitable.  New in version 3.5.2.   Deprecated since version 3.9: collections.abc.Awaitable now supports []. See PEP 585 and Generic Alias Type.  
 Context manager types  
class typing.ContextManager(Generic[T_co])  
A generic version of contextlib.AbstractContextManager.  New in version 3.5.4.   New in version 3.6.0.   Deprecated since version 3.9: contextlib.AbstractContextManager now supports []. See PEP 585 and Generic Alias Type.  
  
class typing.AsyncContextManager(Generic[T_co])  
A generic version of contextlib.AbstractAsyncContextManager.  New in version 3.5.4.   New in version 3.6.2.   Deprecated since version 3.9: contextlib.AbstractAsyncContextManager now supports []. See PEP 585 and Generic Alias Type.  
 Protocols These protocols are decorated with runtime_checkable().  
class typing.SupportsAbs  
An ABC with one abstract method __abs__ that is covariant in its return type. 
  
class typing.SupportsBytes  
An ABC with one abstract method __bytes__. 
  
class typing.SupportsComplex  
An ABC with one abstract method __complex__. 
  
class typing.SupportsFloat  
An ABC with one abstract method __float__. 
  
class typing.SupportsIndex  
An ABC with one abstract method __index__.  New in version 3.8.  
  
class typing.SupportsInt  
An ABC with one abstract method __int__. 
  
class typing.SupportsRound  
An ABC with one abstract method __round__ that is covariant in its return type. 
 Functions and decorators  
typing.cast(typ, val)  
Cast a value to a type. This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible). 
  
@typing.overload  
The @overload decorator allows describing functions and methods that support multiple different combinations of argument types. A series of @overload-decorated definitions must be followed by exactly one non-@overload-decorated definition (for the same function/method). The @overload-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a @overload-decorated function directly will raise NotImplementedError. An example of overload that gives a more precise type than can be expressed using a union or a type variable: @overload
def process(response: None) -> None:
    ...
@overload
def process(response: int) -> tuple[int, str]:
    ...
@overload
def process(response: bytes) -> str:
    ...
def process(response):
    <actual implementation>
 See PEP 484 for details and comparison with other typing semantics. 
  
@typing.final  
A decorator to indicate to type checkers that the decorated method cannot be overridden, and the decorated class cannot be subclassed. For example: class Base:
    @final
    def done(self) -> None:
        ...
class Sub(Base):
    def done(self) -> None:  # Error reported by type checker
          ...

@final
class Leaf:
    ...
class Other(Leaf):  # Error reported by type checker
    ...
 There is no runtime checking of these properties. See PEP 591 for more details.  New in version 3.8.  
  
@typing.no_type_check  
Decorator to indicate that annotations are not type hints. This works as class or function decorator. With a class, it applies recursively to all methods defined in that class (but not to methods defined in its superclasses or subclasses). This mutates the function(s) in place. 
  
@typing.no_type_check_decorator  
Decorator to give another decorator the no_type_check() effect. This wraps the decorator with something that wraps the decorated function in no_type_check(). 
  
@typing.type_check_only  
Decorator to mark a class or function to be unavailable at runtime. This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class: @type_check_only
class Response:  # private or not available at runtime
    code: int
    def get_header(self, name: str) -> str: ...

def fetch_response() -> Response: ...
 Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public. 
 Introspection helpers  
typing.get_type_hints(obj, globalns=None, localns=None, include_extras=False)  
Return a dictionary containing type hints for a function, method, module or class object. This is often the same as obj.__annotations__. In addition, forward references encoded as string literals are handled by evaluating them in globals and locals namespaces. If necessary, Optional[t] is added for function and method annotations if a default value equal to None is set. For a class C, return a dictionary constructed by merging all the __annotations__ along C.__mro__ in reverse order. The function recursively replaces all Annotated[T, ...] with T, unless include_extras is set to True (see Annotated for more information). For example: class Student(NamedTuple):
    name: Annotated[str, 'some marker']

get_type_hints(Student) == {'name': str}
get_type_hints(Student, include_extras=False) == {'name': str}
get_type_hints(Student, include_extras=True) == {
    'name': Annotated[str, 'some marker']
}
  Changed in version 3.9: Added include_extras parameter as part of PEP 593.  
  
typing.get_args(tp) 
  
typing.get_origin(tp)  
Provide basic introspection for generic types and special typing forms. For a typing object of the form X[Y, Z, ...] these functions return X and (Y, Z, ...). If X is a generic alias for a builtin or collections class, it gets normalized to the original class. If X is a Union or Literal contained in another generic type, the order of (Y, Z, ...) may be different from the order of the original arguments [Y, Z, ...] due to type caching. For unsupported objects return None and () correspondingly. Examples: assert get_origin(Dict[str, int]) is dict
assert get_args(Dict[int, str]) == (int, str)

assert get_origin(Union[int, str]) is Union
assert get_args(Union[int, str]) == (int, str)
  New in version 3.8.  
  
class typing.ForwardRef  
A class used for internal typing representation of string forward references. For example, List["SomeClass"] is implicitly transformed into List[ForwardRef("SomeClass")]. This class should not be instantiated by a user, but may be used by introspection tools.  Note PEP 585 generic types such as list["SomeClass"] will not be implicitly transformed into list[ForwardRef("SomeClass")] and thus will not automatically resolve to list[SomeClass].   New in version 3.7.4.  
 Constant  
typing.TYPE_CHECKING  
A special constant that is assumed to be True by 3rd party static type checkers. It is False at runtime. Usage: if TYPE_CHECKING:
    import expensive_mod

def fun(arg: 'expensive_mod.SomeType') -> None:
    local_var: expensive_mod.AnotherType = other_fun()
 The first type annotation must be enclosed in quotes, making it a “forward reference”, to hide the expensive_mod reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.  Note If from __future__ import annotations is used in Python 3.7 or later, annotations are not evaluated at function definition time. Instead, they are stored as strings in __annotations__, This makes it unnecessary to use quotes around the annotation. (see PEP 563).   New in version 3.5.2.