"typing" --- Support for type hints
***********************************

Added 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.

Consider the function below:

   def moon_weight(earth_weight: float) -> str:
       return f'On the moon, you would weigh {earth_weight * 0.166} kilograms.'

The function "moon_weight" takes an argument expected to be an
instance of "float", as indicated by the *type hint* "earth_weight:
float". The function is expected to return an instance of "str", as
indicated by the "-> str" hint.

While type hints can be simple classes like "float" or "str", they can
also be more complex. The "typing" module provides a vocabulary of
more advanced type hints.

New features are frequently added to the "typing" module. The
typing_extensions package provides backports of these new features to
older versions of Python.

See also:

  "Typing cheat sheet"
     A quick overview of type hints (hosted at the mypy docs)

  "Type System Reference" section of the mypy docs
     The Python typing system is standardised via PEPs, so this
     reference should broadly apply to most Python type checkers.
     (Some parts may still be specific to mypy.)

  "Static Typing with Python"
     Type-checker-agnostic documentation written by the community
     detailing type system features, useful typing related tools and
     typing best practices.


Specification for the Python Type System
========================================

The canonical, up-to-date specification of the Python type system can
be found at "Specification for the Python type system".


Type aliases
============

A type alias is defined using the "type" statement, which creates an
instance of "TypeAliasType". In this example, "Vector" and
"list[float]" will be treated equivalently by static type checkers:

   type Vector = list[float]

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

   # passes type checking; 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

   type ConnectionOptions = dict[str, str]
   type Address = tuple[str, int]
   type 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:
       ...

The "type" statement is new in Python 3.12. For backwards
compatibility, type aliases can also be created through simple
assignment:

   Vector = list[float]

Or marked with "TypeAlias" to make it explicit that this is a type
alias, not a normal variable assignment:

   from typing import TypeAlias

   Vector: TypeAlias = list[float]


NewType
=======

Use the "NewType" helper 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:
       ...

   # passes type checking
   user_a = get_user_name(UserId(42351))

   # fails type checking; 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 callable that immediately returns whatever parameter
you pass it. That means the expression "Derived(some_value)" does not
create a new class or introduce much overhead beyond that of a regular
function call.

More precisely, the expression "some_value is Derived(some_value)" is
always true at runtime.

It is invalid to create a subtype of "Derived":

   from typing import NewType

   UserId = NewType('UserId', int)

   # Fails at runtime and does not pass type checking
   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 "type 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.

Added in version 3.5.2.

Changed in version 3.10: "NewType" is now a class rather than a
function.  As a result, there is some additional runtime cost when
calling "NewType" over a regular function.

Changed in version 3.11: The performance of calling "NewType" has been
restored to its level in Python 3.9.


Annotating callable objects
===========================

Functions -- or other *callable* objects -- can be annotated using
"collections.abc.Callable" or "typing.Callable". "Callable[[int],
str]" signifies a function that takes a single parameter of type "int"
and returns a "str".

For example:

   from collections.abc import Callable, Awaitable

   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

   async def on_update(value: str) -> None:
       ...  # Body

   callback: Callable[[str], Awaitable[None]] = on_update

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, a "ParamSpec", "Concatenate", or an ellipsis. The
return type must be a single type.

If a literal ellipsis "..." is given as the argument list, it
indicates that a callable with any arbitrary parameter list would be
acceptable:

   def concat(x: str, y: str) -> str:
       return x + y

   x: Callable[..., str]
   x = str     # OK
   x = concat  # Also OK

"Callable" cannot express complex signatures such as functions that
take a variadic number of arguments, overloaded functions, or
functions that have keyword-only parameters. However, these signatures
can be expressed by defining a "Protocol" class with a "__call__()"
method:

   from collections.abc import Iterable
   from typing import Protocol

   class Combiner(Protocol):
       def __call__(self, *vals: bytes, maxlen: int | None = None) -> list[bytes]: ...

   def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes:
       for item in data:
           ...

   def good_cb(*vals: bytes, maxlen: int | None = None) -> list[bytes]:
       ...
   def bad_cb(*vals: bytes, maxitems: int | None) -> list[bytes]:
       ...

   batch_proc([], good_cb)  # OK
   batch_proc([], bad_cb)   # Error! Argument 2 has incompatible type because of
                            # different name and kind in the callback

Callables which take other callables as arguments may indicate that
their parameter types are dependent on each other using "ParamSpec".
Additionally, if that callable adds or removes arguments from other
callables, the "Concatenate" operator may be used.  They take the form
"Callable[ParamSpecVariable, ReturnType]" and
"Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable],
ReturnType]" respectively.

Changed in version 3.10: "Callable" now supports "ParamSpec" and
"Concatenate". See **PEP 612** for more details.

See also:

  The documentation for "ParamSpec" and "Concatenate" provides
  examples of usage in "Callable".


Generics
========

Since type information about objects kept in containers cannot be
statically inferred in a generic way, many container classes in the
standard library support subscription to denote the expected types of
container elements.

   from collections.abc import Mapping, Sequence

   class Employee: ...

   # Sequence[Employee] indicates that all elements in the sequence
   # must be instances of "Employee".
   # Mapping[str, str] indicates that all keys and all values in the mapping
   # must be strings.
   def notify_by_email(employees: Sequence[Employee],
                       overrides: Mapping[str, str]) -> None: ...

Generic functions and classes can be parameterized by using type
parameter syntax:

   from collections.abc import Sequence

   def first[T](l: Sequence[T]) -> T:  # Function is generic over the TypeVar "T"
       return l[0]

Or by using the "TypeVar" factory directly:

   from collections.abc import Sequence
   from typing import TypeVar

   U = TypeVar('U')                  # Declare type variable "U"

   def second(l: Sequence[U]) -> U:  # Function is generic over the TypeVar "U"
       return l[1]

Changed in version 3.12: Syntactic support for generics is new in
Python 3.12.


Annotating tuples
=================

For most containers in Python, the typing system assumes that all
elements in the container will be of the same type. For example:

   from collections.abc import Mapping

   # Type checker will infer that all elements in ``x`` are meant to be ints
   x: list[int] = []

   # Type checker error: ``list`` only accepts a single type argument:
   y: list[int, str] = [1, 'foo']

   # Type checker will infer that all keys in ``z`` are meant to be strings,
   # and that all values in ``z`` are meant to be either strings or ints
   z: Mapping[str, str | int] = {}

"list" only accepts one type argument, so a type checker would emit an
error on the "y" assignment above. Similarly, "Mapping" only accepts
two type arguments: the first indicates the type of the keys, and the
second indicates the type of the values.

Unlike most other Python containers, however, it is common in
idiomatic Python code for tuples to have elements which are not all of
the same type. For this reason, tuples are special-cased in Python's
typing system. "tuple" accepts *any number* of type arguments:

   # OK: ``x`` is assigned to a tuple of length 1 where the sole element is an int
   x: tuple[int] = (5,)

   # OK: ``y`` is assigned to a tuple of length 2;
   # element 1 is an int, element 2 is a str
   y: tuple[int, str] = (5, "foo")

   # Error: the type annotation indicates a tuple of length 1,
   # but ``z`` has been assigned to a tuple of length 3
   z: tuple[int] = (1, 2, 3)

To denote a tuple which could be of *any* length, and in which all
elements are of the same type "T", use "tuple[T, ...]". To denote an
empty tuple, use "tuple[()]". Using plain "tuple" as an annotation is
equivalent to using "tuple[Any, ...]":

   x: tuple[int, ...] = (1, 2)
   # These reassignments are OK: ``tuple[int, ...]`` indicates x can be of any length
   x = (1, 2, 3)
   x = ()
   # This reassignment is an error: all elements in ``x`` must be ints
   x = ("foo", "bar")

   # ``y`` can only ever be assigned to an empty tuple
   y: tuple[()] = ()

   z: tuple = ("foo", "bar")
   # These reassignments are OK: plain ``tuple`` is equivalent to ``tuple[Any, ...]``
   z = (1, 2, 3)
   z = ()


The type of class objects
=========================

A variable annotated with "C" may accept a value of type "C". In
contrast, a variable annotated with "type[C]" (or "typing.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 ProUser(User): ...
   class TeamUser(User): ...

   def make_new_user(user_class: type[User]) -> User:
       # ...
       return user_class()

   make_new_user(User)      # OK
   make_new_user(ProUser)   # Also OK: ``type[ProUser]`` is a subtype of ``type[User]``
   make_new_user(TeamUser)  # Still fine
   make_new_user(User())    # Error: expected ``type[User]`` but got ``User``
   make_new_user(int)       # Error: ``type[int]`` is not a subtype of ``type[User]``

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[BasicUser | ProUser]): ...

   new_non_team_user(BasicUser)  # OK
   new_non_team_user(ProUser)    # OK
   new_non_team_user(TeamUser)   # Error: ``type[TeamUser]`` is not a subtype
                                 # of ``type[BasicUser | ProUser]``
   new_non_team_user(User)       # Also an error

"type[Any]" is equivalent to "type", which is the root of Python's
metaclass hierarchy.


User-defined generic types
==========================

A user-defined class can be defined as a generic class.

   from logging import Logger

   class LoggedVar[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)

This syntax indicates that the class "LoggedVar" is parameterised
around a single type variable "T" . This also makes "T" valid as a
type within the class body.

Generic classes implicitly inherit from "Generic". For compatibility
with Python 3.11 and lower, it is also possible to inherit explicitly
from "Generic" to indicate a generic class:

   from typing import TypeVar, Generic

   T = TypeVar('T')

   class LoggedVar(Generic[T]):
       ...

Generic classes have "__class_getitem__()" methods, meaning they can
be parameterised at runtime (e.g. "LoggedVar[int]" below):

   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. All varieties of
"TypeVar" are permissible as parameters for a generic type:

   from typing import TypeVar, Generic, Sequence

   class WeirdTrio[T, B: Sequence[bytes], S: (int, str)]:
       ...

   OldT = TypeVar('OldT', contravariant=True)
   OldB = TypeVar('OldB', bound=Sequence[bytes], covariant=True)
   OldS = TypeVar('OldS', int, str)

   class OldWeirdTrio(Generic[OldT, OldB, OldS]):
       ...

Each type variable argument to "Generic" must be distinct. This is
thus invalid:

   from typing import TypeVar, Generic
   ...

   class Pair[M, M]:  # SyntaxError
       ...

   T = TypeVar('T')

   class Pair(Generic[T, T]):   # INVALID
       ...

Generic classes can also inherit from other classes:

   from collections.abc import Sized

   class LinkedList[T](Sized):
       ...

When inheriting from generic classes, some type parameters could be
fixed:

   from collections.abc import Mapping

   class MyDict[T](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

   type Response[S] = Iterable[S] | int

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

   type Vec[T] = Iterable[tuple[T, T]]

   def inproduct[T: (int, float, complex)](v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
       return sum(x*y for x, y in v)

For backward compatibility, generic type aliases can also be created
through a simple assignment:

   from collections.abc import Iterable
   from typing import TypeVar

   S = TypeVar("S")
   Response = Iterable[S] | int

Changed in version 3.7: "Generic" no longer has a custom metaclass.

Changed in version 3.12: Syntactic support for generics and type
aliases is new in version 3.12. Previously, generic classes had to
explicitly inherit from "Generic" or contain a type variable in one of
their bases.

User-defined generics for parameter expressions are also supported via
parameter specification variables in the form "[**P]".  The behavior
is consistent with type variables' described above as parameter
specification variables are treated by the typing module as a
specialized type variable.  The one exception to this is that a list
of types can be used to substitute a "ParamSpec":

   >>> class Z[T, **P]: ...  # T is a TypeVar; P is a ParamSpec
   ...
   >>> Z[int, [dict, float]]
   __main__.Z[int, [dict, float]]

Classes generic over a "ParamSpec" can also be created using explicit
inheritance from "Generic". In this case, "**" is not used:

   from typing import ParamSpec, Generic

   P = ParamSpec('P')

   class Z(Generic[P]):
       ...

Another difference between "TypeVar" and "ParamSpec" is that a generic
with only one parameter specification variable will accept parameter
lists in the forms "X[[Type1, Type2, ...]]" and also "X[Type1, Type2,
...]" for aesthetic reasons.  Internally, the latter is converted to
the former, so the following are equivalent:

   >>> class X[**P]: ...
   ...
   >>> X[int, str]
   __main__.X[[int, str]]
   >>> X[[int, str]]
   __main__.X[[int, str]]

Note that generics with "ParamSpec" may not have correct
"__parameters__" after substitution in some cases because they are
intended primarily for static type checking.

Changed in version 3.10: "Generic" can now be parameterized over
parameter expressions. See "ParamSpec" and **PEP 612** for more
details.

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

   s: str = ''
   s = a           # OK

   def foo(item: Any) -> int:
       # Passes type checking; 'item' could be any type,
       # and that type might have a 'bar' method
       item.bar()
       ...

Notice that no type checking 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 type checking; an object does not have a 'magic' method.
       item.magic()
       ...

   def hash_b(item: Any) -> int:
       # Passes type checking
       item.magic()
       ...

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

   # Passes type checking, 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 the 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 "typing" module defines the following classes, functions and
decorators.


Special typing primitives
-------------------------


Special types
~~~~~~~~~~~~~

These can be used as types in annotations. They do not support
subscription using "[]".

typing.Any

   Special type indicating an unconstrained type.

   * Every type is compatible with "Any".

   * "Any" is compatible with every type.

   Changed in version 3.11: "Any" can now be used as a base class.
   This can be useful for avoiding type checker errors with classes
   that can duck type anywhere or are highly dynamic.

typing.AnyStr

   A constrained type variable.

   Definition:

      AnyStr = TypeVar('AnyStr', str, bytes)

   "AnyStr" is meant to be used for functions that may accept "str" or
   "bytes" arguments but cannot allow the two to mix.

   For example:

      def concat(a: AnyStr, b: AnyStr) -> AnyStr:
          return a + b

      concat("foo", "bar")    # OK, output has type 'str'
      concat(b"foo", b"bar")  # OK, output has type 'bytes'
      concat("foo", b"bar")   # Error, cannot mix str and bytes

   Note that, despite its name, "AnyStr" has nothing to do with the
   "Any" type, nor does it mean "any string". In particular, "AnyStr"
   and "str | bytes" are different from each other and have different
   use cases:

      # Invalid use of AnyStr:
      # The type variable is used only once in the function signature,
      # so cannot be "solved" by the type checker
      def greet_bad(cond: bool) -> AnyStr:
          return "hi there!" if cond else b"greetings!"

      # The better way of annotating this function:
      def greet_proper(cond: bool) -> str | bytes:
          return "hi there!" if cond else b"greetings!"

   Deprecated since version 3.13, will be removed in version 3.18:
   Deprecated in favor of the new type parameter syntax. Use "class
   A[T: (str, bytes)]: ..." instead of importing "AnyStr". See **PEP
   695** for more details.In Python 3.16, "AnyStr" will be removed
   from "typing.__all__", and deprecation warnings will be emitted at
   runtime when it is accessed or imported from "typing". "AnyStr"
   will be removed from "typing" in Python 3.18.

typing.LiteralString

   Special type that includes only literal strings.

   Any string literal is compatible with "LiteralString", as is
   another "LiteralString". However, an object typed as just "str" is
   not. A string created by composing "LiteralString"-typed objects is
   also acceptable as a "LiteralString".

   Example:

      def run_query(sql: LiteralString) -> None:
          ...

      def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
          run_query("SELECT * FROM students")  # OK
          run_query(literal_string)  # OK
          run_query("SELECT * FROM " + literal_string)  # OK
          run_query(arbitrary_string)  # type checker error
          run_query(  # type checker error
              f"SELECT * FROM students WHERE name = {arbitrary_string}"
          )

   "LiteralString" is useful for sensitive APIs where arbitrary user-
   generated strings could generate problems. For example, the two
   cases above that generate type checker errors could be vulnerable
   to an SQL injection attack.

   See **PEP 675** for more details.

   Added in version 3.11.

typing.Never
typing.NoReturn

   "Never" and "NoReturn" represent the bottom type, a type that has
   no members.

   They can be used to indicate that a function never returns, such as
   "sys.exit()":

      from typing import Never  # or NoReturn

      def stop() -> Never:
          raise RuntimeError('no way')

   Or to define a function that should never be called, as there are
   no valid arguments, such as "assert_never()":

      from typing import Never  # or NoReturn

      def never_call_me(arg: Never) -> None:
          pass

      def int_or_str(arg: int | str) -> None:
          never_call_me(arg)  # type checker error
          match arg:
              case int():
                  print("It's an int")
              case str():
                  print("It's a str")
              case _:
                  never_call_me(arg)  # OK, arg is of type Never (or NoReturn)

   "Never" and "NoReturn" have the same meaning in the type system and
   static type checkers treat both equivalently.

   Added in version 3.6.2: Added "NoReturn".

   Added in version 3.11: Added "Never".

typing.Self

   Special type to represent the current enclosed class.

   For example:

      from typing import Self, reveal_type

      class Foo:
          def return_self(self) -> Self:
              ...
              return self

      class SubclassOfFoo(Foo): pass

      reveal_type(Foo().return_self())  # Revealed type is "Foo"
      reveal_type(SubclassOfFoo().return_self())  # Revealed type is "SubclassOfFoo"

   This annotation is semantically equivalent to the following, albeit
   in a more succinct fashion:

      from typing import TypeVar

      Self = TypeVar("Self", bound="Foo")

      class Foo:
          def return_self(self: Self) -> Self:
              ...
              return self

   In general, if something returns "self", as in the above examples,
   you should use "Self" as the return annotation. If
   "Foo.return_self" was annotated as returning ""Foo"", then the type
   checker would infer the object returned from
   "SubclassOfFoo.return_self" as being of type "Foo" rather than
   "SubclassOfFoo".

   Other common use cases include:

   * "classmethod"s that are used as alternative constructors and
     return instances of the "cls" parameter.

   * Annotating an "__enter__()" method which returns self.

   You should not use "Self" as the return annotation if the method is
   not guaranteed to return an instance of a subclass when the class
   is subclassed:

      class Eggs:
          # Self would be an incorrect return annotation here,
          # as the object returned is always an instance of Eggs,
          # even in subclasses
          def returns_eggs(self) -> "Eggs":
              return Eggs()

   See **PEP 673** for more details.

   Added in version 3.11.

typing.TypeAlias

   Special annotation for explicitly declaring a type alias.

   For example:

      from typing import TypeAlias

      Factors: TypeAlias = list[int]

   "TypeAlias" is particularly useful on older Python versions for
   annotating aliases that make use of forward references, as it can
   be hard for type checkers to distinguish these from normal variable
   assignments:

      from typing import Generic, TypeAlias, TypeVar

      T = TypeVar("T")

      # "Box" does not exist yet,
      # so we have to use quotes for the forward reference on Python <3.12.
      # Using ``TypeAlias`` tells the type checker that this is a type alias declaration,
      # not a variable assignment to a string.
      BoxOfStrings: TypeAlias = "Box[str]"

      class Box(Generic[T]):
          @classmethod
          def make_box_of_strings(cls) -> BoxOfStrings: ...

   See **PEP 613** for more details.

   Added in version 3.10.

   Deprecated since version 3.12: "TypeAlias" is deprecated in favor
   of the "type" statement, which creates instances of "TypeAliasType"
   and which natively supports forward references. Note that while
   "TypeAlias" and "TypeAliasType" serve similar purposes and have
   similar names, they are distinct and the latter is not the type of
   the former. Removal of "TypeAlias" is not currently planned, but
   users are encouraged to migrate to "type" statements.


Special forms
~~~~~~~~~~~~~

These can be used as types in annotations. They all support
subscription using "[]", but each has a unique syntax.

typing.Union

   Union type; "Union[X, Y]" is equivalent to "X | Y" and means either
   X or Y.

   To define a union, use e.g. "Union[int, str]" or the shorthand "int
   | str". Using that shorthand is recommended. 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] == 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]".

   Changed in version 3.7: Don't remove explicit subclasses from
   unions at runtime.

   Changed in version 3.10: Unions can now be written as "X | Y". See
   union type expressions.

typing.Optional

   "Optional[X]" is equivalent to "X | None" (or "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:
          ...

   Changed in version 3.10: Optional can now be written as "X | None".
   See union type expressions.

typing.Concatenate

   Special form for annotating higher-order functions.

   "Concatenate" can be used in conjunction with Callable and
   "ParamSpec" to annotate a higher-order callable which adds,
   removes, or transforms parameters of another callable.  Usage is in
   the form "Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]".
   "Concatenate" is currently only valid when used as the first
   argument to a Callable. The last parameter to "Concatenate" must be
   a "ParamSpec" or ellipsis ("...").

   For example, to annotate a decorator "with_lock" which provides a
   "threading.Lock" to the decorated function,  "Concatenate" can be
   used to indicate that "with_lock" expects a callable which takes in
   a "Lock" as the first argument, and returns a callable with a
   different type signature.  In this case, the "ParamSpec" indicates
   that the returned callable's parameter types are dependent on the
   parameter types of the callable being passed in:

      from collections.abc import Callable
      from threading import Lock
      from typing import Concatenate

      # Use this lock to ensure that only one thread is executing a function
      # at any time.
      my_lock = Lock()

      def with_lock[**P, R](f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]:
          '''A type-safe decorator which provides a lock.'''
          def inner(*args: P.args, **kwargs: P.kwargs) -> R:
              # Provide the lock as the first argument.
              return f(my_lock, *args, **kwargs)
          return inner

      @with_lock
      def sum_threadsafe(lock: Lock, numbers: list[float]) -> float:
          '''Add a list of numbers together in a thread-safe manner.'''
          with lock:
              return sum(numbers)

      # We don't need to pass in the lock ourselves thanks to the decorator.
      sum_threadsafe([1.1, 2.2, 3.3])

   Added in version 3.10.

   See also:

     * **PEP 612** -- Parameter Specification Variables (the PEP which
       introduced "ParamSpec" and "Concatenate")

     * "ParamSpec"

     * Annotating callable objects

typing.Literal

   Special typing form to define "literal types".

   "Literal" can be used to indicate to type checkers that the
   annotated object has a value equivalent to one of the provided
   literals.

   For example:

      def validate_simple(data: Any) -> Literal[True]:  # always returns True
          ...

      type 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.

   Added in version 3.8.

   Changed in version 3.9.1: "Literal" now de-duplicates parameters.
   Equality comparisons 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
   *hashable*.

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

   Added in version 3.5.3.

   Changed in version 3.13: "ClassVar" can now be nested in "Final"
   and vice versa.

typing.Final

   Special typing construct to indicate final names to type checkers.

   Final names cannot be reassigned in any scope. Final names declared
   in class scopes cannot be overridden in subclasses.

   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.

   Added in version 3.8.

   Changed in version 3.13: "Final" can now be nested in "ClassVar"
   and vice versa.

typing.Required

   Special typing construct to mark a "TypedDict" key as required.

   This is mainly useful for "total=False" TypedDicts. See "TypedDict"
   and **PEP 655** for more details.

   Added in version 3.11.

typing.NotRequired

   Special typing construct to mark a "TypedDict" key as potentially
   missing.

   See "TypedDict" and **PEP 655** for more details.

   Added in version 3.11.

typing.ReadOnly

   A special typing construct to mark an item of a "TypedDict" as
   read-only.

   For example:

      class Movie(TypedDict):
         title: ReadOnly[str]
         year: int

      def mutate_movie(m: Movie) -> None:
         m["year"] = 1992  # allowed
         m["title"] = "The Matrix"  # typechecker error

   There is no runtime checking for this property.

   See "TypedDict" and **PEP 705** for more details.

   Added in version 3.13.

typing.Annotated

   Special typing form to add context-specific metadata to an
   annotation.

   Add metadata "x" to a given type "T" by using the annotation
   "Annotated[T, x]". Metadata added using "Annotated" can be used by
   static analysis tools or at runtime. At runtime, the metadata is
   stored in a "__metadata__" attribute.

   If a library or tool encounters an annotation "Annotated[T, x]" and
   has no special logic for the metadata, it should ignore the
   metadata and simply treat the annotation as "T". As such,
   "Annotated" can be useful for code that wants to use annotations
   for purposes outside Python's static typing system.

   Using "Annotated[T, x]" as an annotation still allows for static
   typechecking of "T", as type checkers will simply ignore the
   metadata "x". In this way, "Annotated" differs from the
   "@no_type_check" decorator, which can also be used for adding
   annotations outside the scope of the typing system, but completely
   disables typechecking for a function or class.

   The responsibility of how to interpret the metadata lies with the
   tool or library encountering an "Annotated" annotation. A tool or
   library encountering an "Annotated" type can scan through the
   metadata elements to determine if they are of interest (e.g., using
   "isinstance()").

   Annotated[<type>, <metadata>]

   Here is an example of how you might use "Annotated" to add metadata
   to type annotations if you were doing range analysis:

      @dataclass
      class ValueRange:
          lo: int
          hi: int

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

   Details of the syntax:

   * The first argument to "Annotated" must be a valid type

   * Multiple metadata elements can be supplied ("Annotated" supports
     variadic arguments):

        @dataclass
        class ctype:
            kind: str

        Annotated[int, ValueRange(3, 10), ctype("char")]

     It is up to the tool consuming the annotations to decide whether
     the client is allowed to add multiple metadata elements to one
     annotation and how to merge those annotations.

   * "Annotated" must be subscripted with at least two arguments (
     "Annotated[int]" is not valid)

   * The order of the metadata elements is preserved and matters for
     equality checks:

        assert Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
            int, ctype("char"), ValueRange(3, 10)
        ]

   * Nested "Annotated" types are flattened. The order of the metadata
     elements starts with the innermost annotation:

        assert Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
            int, ValueRange(3, 10), ctype("char")
        ]

   * Duplicated metadata elements are not removed:

        assert Annotated[int, ValueRange(3, 10)] != Annotated[
            int, ValueRange(3, 10), ValueRange(3, 10)
        ]

   * "Annotated" can be used with nested and generic aliases:

        @dataclass
        class MaxLen:
            value: int

        type Vec[T] = Annotated[list[tuple[T, T]], MaxLen(10)]

        # When used in a type annotation, a type checker will treat "V" the same as
        # ``Annotated[list[tuple[int, int]], MaxLen(10)]``:
        type V = Vec[int]

   * "Annotated" cannot be used with an unpacked "TypeVarTuple":

        type Variadic[*Ts] = Annotated[*Ts, Ann1]  # NOT valid

     This would be equivalent to:

        Annotated[T1, T2, T3, ..., Ann1]

     where "T1", "T2", etc. are "TypeVars". This would be invalid:
     only one type should be passed to Annotated.

   * By default, "get_type_hints()" strips the metadata from
     annotations. Pass "include_extras=True" to have the metadata
     preserved:

        >>> from typing import Annotated, get_type_hints
        >>> def func(x: Annotated[int, "metadata"]) -> None: pass
        ...
        >>> get_type_hints(func)
        {'x': <class 'int'>, 'return': <class 'NoneType'>}
        >>> get_type_hints(func, include_extras=True)
        {'x': typing.Annotated[int, 'metadata'], 'return': <class 'NoneType'>}

   * At runtime, the metadata associated with an "Annotated" type can
     be retrieved via the "__metadata__" attribute:

        >>> from typing import Annotated
        >>> X = Annotated[int, "very", "important", "metadata"]
        >>> X
        typing.Annotated[int, 'very', 'important', 'metadata']
        >>> X.__metadata__
        ('very', 'important', 'metadata')

   See also:

     **PEP 593** - Flexible function and variable annotations
        The PEP introducing "Annotated" to the standard library.

   Added in version 3.9.

typing.TypeIs

   Special typing construct for marking user-defined type predicate
   functions.

   "TypeIs" can be used to annotate the return type of a user-defined
   type predicate function.  "TypeIs" only accepts a single type
   argument. At runtime, functions marked this way should return a
   boolean and take at least one positional argument.

   "TypeIs" aims to benefit *type narrowing* -- a technique used by
   static type checkers to determine a more precise type of an
   expression within a program's code flow.  Usually type narrowing is
   done by analyzing conditional code flow and applying the narrowing
   to a block of code.  The conditional expression here is sometimes
   referred to as a "type predicate":

      def is_str(val: str | float):
          # "isinstance" type predicate
          if isinstance(val, str):
              # Type of ``val`` is narrowed to ``str``
              ...
          else:
              # Else, type of ``val`` is narrowed to ``float``.
              ...

   Sometimes it would be convenient to use a user-defined boolean
   function as a type predicate.  Such a function should use
   "TypeIs[...]" or "TypeGuard" as its return type to alert static
   type checkers to this intention.  "TypeIs" usually has more
   intuitive behavior than "TypeGuard", but it cannot be used when the
   input and output types are incompatible (e.g., "list[object]" to
   "list[int]") or when the function does not return "True" for all
   instances of the narrowed type.

   Using  "-> TypeIs[NarrowedType]" tells the static type checker that
   for a given function:

   1. The return value is a boolean.

   2. If the return value is "True", the type of its argument is the
      intersection of the argument's original type and "NarrowedType".

   3. If the return value is "False", the type of its argument is
      narrowed to exclude "NarrowedType".

   For example:

      from typing import assert_type, final, TypeIs

      class Parent: pass
      class Child(Parent): pass
      @final
      class Unrelated: pass

      def is_parent(val: object) -> TypeIs[Parent]:
          return isinstance(val, Parent)

      def run(arg: Child | Unrelated):
          if is_parent(arg):
              # Type of ``arg`` is narrowed to the intersection
              # of ``Parent`` and ``Child``, which is equivalent to
              # ``Child``.
              assert_type(arg, Child)
          else:
              # Type of ``arg`` is narrowed to exclude ``Parent``,
              # so only ``Unrelated`` is left.
              assert_type(arg, Unrelated)

   The type inside "TypeIs" must be consistent with the type of the
   function's argument; if it is not, static type checkers will raise
   an error.  An incorrectly written "TypeIs" function can lead to
   unsound behavior in the type system; it is the user's
   responsibility to write such functions in a type-safe manner.

   If a "TypeIs" function is a class or instance method, then the type
   in "TypeIs" maps to the type of the second parameter after "cls" or
   "self".

   In short, the form "def foo(arg: TypeA) -> TypeIs[TypeB]: ...",
   means that if "foo(arg)" returns "True", then "arg" is an instance
   of "TypeB", and if it returns "False", it is not an instance of
   "TypeB".

   "TypeIs" also works with type variables.  For more information, see
   **PEP 742** (Narrowing types with "TypeIs").

   Added in version 3.13.

typing.TypeGuard

   Special typing construct for marking user-defined type predicate
   functions.

   Type predicate functions are user-defined functions that return
   whether their argument is an instance of a particular type.
   "TypeGuard" works similarly to "TypeIs", but has subtly different
   effects on type checking behavior (see below).

   Using  "-> TypeGuard" tells the static type checker that for a
   given function:

   1. The return value is a boolean.

   2. If the return value is "True", the type of its argument is the
      type inside "TypeGuard".

   "TypeGuard" also works with type variables.  See **PEP 647** for
   more details.

   For example:

      def is_str_list(val: list[object]) -> TypeGuard[list[str]]:
          '''Determines whether all objects in the list are strings'''
          return all(isinstance(x, str) for x in val)

      def func1(val: list[object]):
          if is_str_list(val):
              # Type of ``val`` is narrowed to ``list[str]``.
              print(" ".join(val))
          else:
              # Type of ``val`` remains as ``list[object]``.
              print("Not a list of strings!")

   "TypeIs" and "TypeGuard" differ in the following ways:

   * "TypeIs" requires the narrowed type to be a subtype of the input
     type, while "TypeGuard" does not.  The main reason is to allow
     for things like narrowing "list[object]" to "list[str]" even
     though the latter is not a subtype of the former, since "list" is
     invariant.

   * When a "TypeGuard" function returns "True", type checkers narrow
     the type of the variable to exactly the "TypeGuard" type. When a
     "TypeIs" function returns "True", type checkers can infer a more
     precise type combining the previously known type of the variable
     with the "TypeIs" type. (Technically, this is known as an
     intersection type.)

   * When a "TypeGuard" function returns "False", type checkers cannot
     narrow the type of the variable at all. When a "TypeIs" function
     returns "False", type checkers can narrow the type of the
     variable to exclude the "TypeIs" type.

   Added in version 3.10.

typing.Unpack

   Typing operator to conceptually mark an object as having been
   unpacked.

   For example, using the unpack operator "*" on a type variable tuple
   is equivalent to using "Unpack" to mark the type variable tuple as
   having been unpacked:

      Ts = TypeVarTuple('Ts')
      tup: tuple[*Ts]
      # Effectively does:
      tup: tuple[Unpack[Ts]]

   In fact, "Unpack" can be used interchangeably with "*" in the
   context of "typing.TypeVarTuple" and "builtins.tuple" types. You
   might see "Unpack" being used explicitly in older versions of
   Python, where "*" couldn't be used in certain places:

      # In older versions of Python, TypeVarTuple and Unpack
      # are located in the `typing_extensions` backports package.
      from typing_extensions import TypeVarTuple, Unpack

      Ts = TypeVarTuple('Ts')
      tup: tuple[*Ts]         # Syntax error on Python <= 3.10!
      tup: tuple[Unpack[Ts]]  # Semantically equivalent, and backwards-compatible

   "Unpack" can also be used along with "typing.TypedDict" for typing
   "**kwargs" in a function signature:

      from typing import TypedDict, Unpack

      class Movie(TypedDict):
          name: str
          year: int

      # This function expects two keyword arguments - `name` of type `str`
      # and `year` of type `int`.
      def foo(**kwargs: Unpack[Movie]): ...

   See **PEP 692** for more details on using "Unpack" for "**kwargs"
   typing.

   Added in version 3.11.


Building generic types and type aliases
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The following classes should not be used directly as annotations.
Their intended purpose is to be building blocks for creating generic
types and type aliases.

These objects can be created through special syntax (type parameter
lists and the "type" statement). For compatibility with Python 3.11
and earlier, they can also be created without the dedicated syntax, as
documented below.

class typing.Generic

   Abstract base class for generic types.

   A generic type is typically declared by adding a list of type
   parameters after the class name:

      class Mapping[KT, VT]:
          def __getitem__(self, key: KT) -> VT:
              ...
              # Etc.

   Such a class implicitly inherits from "Generic". The runtime
   semantics of this syntax are discussed in the Language Reference.

   This class can then be used as follows:

      def lookup_name[X, Y](mapping: Mapping[X, Y], key: X, default: Y) -> Y:
          try:
              return mapping[key]
          except KeyError:
              return default

   Here the brackets after the function name indicate a generic
   function.

   For backwards compatibility, generic classes can also be declared
   by explicitly inheriting from "Generic". In this case, the type
   parameters must be declared separately:

      KT = TypeVar('KT')
      VT = TypeVar('VT')

      class Mapping(Generic[KT, VT]):
          def __getitem__(self, key: KT) -> VT:
              ...
              # Etc.

class typing.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False, infer_variance=False, default=typing.NoDefault)

   Type variable.

   The preferred way to construct a type variable is via the dedicated
   syntax for generic functions, generic classes, and generic type
   aliases:

      class Sequence[T]:  # T is a TypeVar
          ...

   This syntax can also be used to create bound and constrained type
   variables:

      class StrSequence[S: str]:  # S is a TypeVar bound to str
          ...


      class StrOrBytesSequence[A: (str, bytes)]:  # A is a TypeVar constrained to str or bytes
          ...

   However, if desired, reusable type variables can also be
   constructed manually, like so:

      T = TypeVar('T')  # Can be anything
      S = TypeVar('S', bound=str)  # Can be any subtype of str
      A = TypeVar('A', str, bytes)  # Must be exactly 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 and type alias definitions. See "Generic"
   for more information on generic types.  Generic functions work as
   follows:

      def repeat[T](x: T, n: int) -> Sequence[T]:
          """Return a list containing n references to x."""
          return [x]*n


      def print_capitalized[S: str](x: S) -> S:
          """Print x capitalized, and return x."""
          print(x.capitalize())
          return x


      def concatenate[A: (str, bytes)](x: A, y: A) -> A:
          """Add two strings or bytes objects together."""
          return x + y

   Note that type variables can be *bound*, *constrained*, or neither,
   but cannot be both bound *and* constrained.

   The variance of type variables is inferred by type checkers when
   they are created through the type parameter syntax or when
   "infer_variance=True" is passed. Manually created type variables
   may be explicitly marked covariant or contravariant by passing
   "covariant=True" or "contravariant=True". By default, manually
   created type variables are invariant. See **PEP 484** and **PEP
   695** for more details.

   Bound type variables and constrained type variables have different
   semantics in several important ways. Using a *bound* type variable
   means that the "TypeVar" will be solved using the most specific
   type possible:

      x = print_capitalized('a string')
      reveal_type(x)  # revealed type is str

      class StringSubclass(str):
          pass

      y = print_capitalized(StringSubclass('another string'))
      reveal_type(y)  # revealed type is StringSubclass

      z = print_capitalized(45)  # error: int is not a subtype of str

   Type variables can be bound to concrete types, abstract types (ABCs
   or protocols), and even unions of types:

      # Can be anything with an __abs__ method
      def print_abs[T: SupportsAbs](arg: T) -> None:
          print("Absolute value:", abs(arg))

      U = TypeVar('U', bound=str|bytes)  # Can be any subtype of the union str|bytes
      V = TypeVar('V', bound=SupportsAbs)  # Can be anything with an __abs__ method

   Using a *constrained* type variable, however, means that the
   "TypeVar" can only ever be solved as being exactly one of the
   constraints given:

      a = concatenate('one', 'two')
      reveal_type(a)  # revealed type is str

      b = concatenate(StringSubclass('one'), StringSubclass('two'))
      reveal_type(b)  # revealed type is str, despite StringSubclass being passed in

      c = concatenate('one', b'two')  # error: type variable 'A' can be either str or bytes in a function call, but not both

   At runtime, "isinstance(x, T)" will raise "TypeError".

   __name__

      The name of the type variable.

   __covariant__

      Whether the type var has been explicitly marked as covariant.

   __contravariant__

      Whether the type var has been explicitly marked as
      contravariant.

   __infer_variance__

      Whether the type variable's variance should be inferred by type
      checkers.

      Added in version 3.12.

   __bound__

      The bound of the type variable, if any.

      Changed in version 3.12: For type variables created through type
      parameter syntax, the bound is evaluated only when the attribute
      is accessed, not when the type variable is created (see Lazy
      evaluation).

   __constraints__

      A tuple containing the constraints of the type variable, if any.

      Changed in version 3.12: For type variables created through type
      parameter syntax, the constraints are evaluated only when the
      attribute is accessed, not when the type variable is created
      (see Lazy evaluation).

   __default__

      The default value of the type variable, or "typing.NoDefault" if
      it has no default.

      Added in version 3.13.

   has_default()

      Return whether or not the type variable has a default value.
      This is equivalent to checking whether "__default__" is not the
      "typing.NoDefault" singleton, except that it does not force
      evaluation of the lazily evaluated default value.

      Added in version 3.13.

   Changed in version 3.12: Type variables can now be declared using
   the type parameter syntax introduced by **PEP 695**. The
   "infer_variance" parameter was added.

   Changed in version 3.13: Support for default values was added.

class typing.TypeVarTuple(name, *, default=typing.NoDefault)

   Type variable tuple. A specialized form of type variable that
   enables *variadic* generics.

   Type variable tuples can be declared in type parameter lists using
   a single asterisk ("*") before the name:

      def move_first_element_to_last[T, *Ts](tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
          return (*tup[1:], tup[0])

   Or by explicitly invoking the "TypeVarTuple" constructor:

      T = TypeVar("T")
      Ts = TypeVarTuple("Ts")

      def move_first_element_to_last(tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
          return (*tup[1:], tup[0])

   A normal type variable enables parameterization with a single type.
   A type variable tuple, in contrast, allows parameterization with an
   *arbitrary* number of types by acting like an *arbitrary* number of
   type variables wrapped in a tuple. For example:

      # T is bound to int, Ts is bound to ()
      # Return value is (1,), which has type tuple[int]
      move_first_element_to_last(tup=(1,))

      # T is bound to int, Ts is bound to (str,)
      # Return value is ('spam', 1), which has type tuple[str, int]
      move_first_element_to_last(tup=(1, 'spam'))

      # T is bound to int, Ts is bound to (str, float)
      # Return value is ('spam', 3.0, 1), which has type tuple[str, float, int]
      move_first_element_to_last(tup=(1, 'spam', 3.0))

      # This fails to type check (and fails at runtime)
      # because tuple[()] is not compatible with tuple[T, *Ts]
      # (at least one element is required)
      move_first_element_to_last(tup=())

   Note the use of the unpacking operator "*" in "tuple[T, *Ts]".
   Conceptually, you can think of "Ts" as a tuple of type variables
   "(T1, T2, ...)". "tuple[T, *Ts]" would then become "tuple[T, *(T1,
   T2, ...)]", which is equivalent to "tuple[T, T1, T2, ...]". (Note
   that in older versions of Python, you might see this written using
   "Unpack" instead, as "Unpack[Ts]".)

   Type variable tuples must *always* be unpacked. This helps
   distinguish type variable tuples from normal type variables:

      x: Ts          # Not valid
      x: tuple[Ts]   # Not valid
      x: tuple[*Ts]  # The correct way to do it

   Type variable tuples can be used in the same contexts as normal
   type variables. For example, in class definitions, arguments, and
   return types:

      class Array[*Shape]:
          def __getitem__(self, key: tuple[*Shape]) -> float: ...
          def __abs__(self) -> "Array[*Shape]": ...
          def get_shape(self) -> tuple[*Shape]: ...

   Type variable tuples can be happily combined with normal type
   variables:

      class Array[DType, *Shape]:  # This is fine
          pass

      class Array2[*Shape, DType]:  # This would also be fine
          pass

      class Height: ...
      class Width: ...

      float_array_1d: Array[float, Height] = Array()     # Totally fine
      int_array_2d: Array[int, Height, Width] = Array()  # Yup, fine too

   However, note that at most one type variable tuple may appear in a
   single list of type arguments or type parameters:

      x: tuple[*Ts, *Ts]            # Not valid
      class Array[*Shape, *Shape]:  # Not valid
          pass

   Finally, an unpacked type variable tuple can be used as the type
   annotation of "*args":

      def call_soon[*Ts](
               callback: Callable[[*Ts], None],
               *args: *Ts
      ) -> None:
          ...
          callback(*args)

   In contrast to non-unpacked annotations of "*args" - e.g. "*args:
   int", which would specify that *all* arguments are "int" - "*args:
   *Ts" enables reference to the types of the *individual* arguments
   in "*args". Here, this allows us to ensure the types of the "*args"
   passed to "call_soon" match the types of the (positional) arguments
   of "callback".

   See **PEP 646** for more details on type variable tuples.

   __name__

      The name of the type variable tuple.

   __default__

      The default value of the type variable tuple, or
      "typing.NoDefault" if it has no default.

      Added in version 3.13.

   has_default()

      Return whether or not the type variable tuple has a default
      value. This is equivalent to checking whether "__default__" is
      not the "typing.NoDefault" singleton, except that it does not
      force evaluation of the lazily evaluated default value.

      Added in version 3.13.

   Added in version 3.11.

   Changed in version 3.12: Type variable tuples can now be declared
   using the type parameter syntax introduced by **PEP 695**.

   Changed in version 3.13: Support for default values was added.

class typing.ParamSpec(name, *, bound=None, covariant=False, contravariant=False, default=typing.NoDefault)

   Parameter specification variable.  A specialized version of type
   variables.

   In type parameter lists, parameter specifications can be declared
   with two asterisks ("**"):

      type IntFunc[**P] = Callable[P, int]

   For compatibility with Python 3.11 and earlier, "ParamSpec" objects
   can also be created as follows:

      P = ParamSpec('P')

   Parameter specification variables exist primarily for the benefit
   of static type checkers.  They are used to forward the parameter
   types of one callable to another callable -- a pattern commonly
   found in higher order functions and decorators.  They are only
   valid when used in "Concatenate", or as the first argument to
   "Callable", or as parameters for user-defined Generics.  See
   "Generic" for more information on generic types.

   For example, to add basic logging to a function, one can create a
   decorator "add_logging" to log function calls.  The parameter
   specification variable tells the type checker that the callable
   passed into the decorator and the new callable returned by it have
   inter-dependent type parameters:

      from collections.abc import Callable
      import logging

      def add_logging[T, **P](f: Callable[P, T]) -> Callable[P, T]:
          '''A type-safe decorator to add logging to a function.'''
          def inner(*args: P.args, **kwargs: P.kwargs) -> T:
              logging.info(f'{f.__name__} was called')
              return f(*args, **kwargs)
          return inner

      @add_logging
      def add_two(x: float, y: float) -> float:
          '''Add two numbers together.'''
          return x + y

   Without "ParamSpec", the simplest way to annotate this previously
   was to use a "TypeVar" with bound "Callable[..., Any]".  However
   this causes two problems:

   1. The type checker can't type check the "inner" function because
      "*args" and "**kwargs" have to be typed "Any".

   2. "cast()" may be required in the body of the "add_logging"
      decorator when returning the "inner" function, or the static
      type checker must be told to ignore the "return inner".

   args

   kwargs

      Since "ParamSpec" captures both positional and keyword
      parameters, "P.args" and "P.kwargs" can be used to split a
      "ParamSpec" into its components.  "P.args" represents the tuple
      of positional parameters in a given call and should only be used
      to annotate "*args".  "P.kwargs" represents the mapping of
      keyword parameters to their values in a given call, and should
      be only be used to annotate "**kwargs".  Both attributes require
      the annotated parameter to be in scope. At runtime, "P.args" and
      "P.kwargs" are instances respectively of "ParamSpecArgs" and
      "ParamSpecKwargs".

   __name__

      The name of the parameter specification.

   __default__

      The default value of the parameter specification, or
      "typing.NoDefault" if it has no default.

      Added in version 3.13.

   has_default()

      Return whether or not the parameter specification has a default
      value. This is equivalent to checking whether "__default__" is
      not the "typing.NoDefault" singleton, except that it does not
      force evaluation of the lazily evaluated default value.

      Added in version 3.13.

   Parameter specification variables created with "covariant=True" or
   "contravariant=True" can be used to declare covariant or
   contravariant generic types.  The "bound" argument is also
   accepted, similar to "TypeVar".  However the actual semantics of
   these keywords are yet to be decided.

   Added in version 3.10.

   Changed in version 3.12: Parameter specifications can now be
   declared using the type parameter syntax introduced by **PEP 695**.

   Changed in version 3.13: Support for default values was added.

   Note:

     Only parameter specification variables defined in global scope
     can be pickled.

   See also:

     * **PEP 612** -- Parameter Specification Variables (the PEP which
       introduced "ParamSpec" and "Concatenate")

     * "Concatenate"

     * Annotating callable objects

typing.ParamSpecArgs
typing.ParamSpecKwargs

   Arguments and keyword arguments attributes of a "ParamSpec". The
   "P.args" attribute of a "ParamSpec" is an instance of
   "ParamSpecArgs", and "P.kwargs" is an instance of
   "ParamSpecKwargs". They are intended for runtime introspection and
   have no special meaning to static type checkers.

   Calling "get_origin()" on either of these objects will return the
   original "ParamSpec":

      >>> from typing import ParamSpec, get_origin
      >>> P = ParamSpec("P")
      >>> get_origin(P.args) is P
      True
      >>> get_origin(P.kwargs) is P
      True

   Added in version 3.10.

class typing.TypeAliasType(name, value, *, type_params=())

   The type of type aliases created through the "type" statement.

   Example:

      >>> type Alias = int
      >>> type(Alias)
      <class 'typing.TypeAliasType'>

   Added in version 3.12.

   __name__

      The name of the type alias:

         >>> type Alias = int
         >>> Alias.__name__
         'Alias'

   __module__

      The module in which the type alias was defined:

         >>> type Alias = int
         >>> Alias.__module__
         '__main__'

   __type_params__

      The type parameters of the type alias, or an empty tuple if the
      alias is not generic:

         >>> type ListOrSet[T] = list[T] | set[T]
         >>> ListOrSet.__type_params__
         (T,)
         >>> type NotGeneric = int
         >>> NotGeneric.__type_params__
         ()

   __value__

      The type alias's value. This is lazily evaluated, so names used
      in the definition of the alias are not resolved until the
      "__value__" attribute is accessed:

         >>> type Mutually = Recursive
         >>> type Recursive = Mutually
         >>> Mutually
         Mutually
         >>> Recursive
         Recursive
         >>> Mutually.__value__
         Recursive
         >>> Recursive.__value__
         Mutually


Other special directives
~~~~~~~~~~~~~~~~~~~~~~~~

These functions and classes should not be used directly as
annotations. Their intended purpose is to be building blocks for
creating and 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}>'

   "NamedTuple" subclasses can be generic:

      class Group[T](NamedTuple):
          key: T
          group: list[T]

   Backward-compatible usage:

      # For creating a generic NamedTuple on Python 3.11 or lower
      class Group(NamedTuple, Generic[T]):
          key: T
          group: list[T]

      # A functional syntax is also supported
      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.

   Changed in version 3.11: Added support for generic namedtuples.

   Deprecated since version 3.13, will be removed in version 3.15: The
   undocumented keyword argument syntax for creating NamedTuple
   classes ("NT = NamedTuple("NT", x=int)") is deprecated, and will be
   disallowed in 3.15. Use the class-based syntax or the functional
   syntax instead.

   Deprecated since version 3.13, will be removed in version 3.15:
   When using the functional syntax to create a NamedTuple class,
   failing to pass a value to the 'fields' parameter ("NT =
   NamedTuple("NT")") is deprecated. Passing "None" to the 'fields'
   parameter ("NT = NamedTuple("NT", None)") is also deprecated. Both
   will be disallowed in Python 3.15. To create a NamedTuple class
   with 0 fields, use "class NT(NamedTuple): pass" or "NT =
   NamedTuple("NT", [])".

class typing.NewType(name, tp)

   Helper class to create low-overhead distinct types.

   A "NewType" is considered a distinct type by a typechecker. At
   runtime, however, calling a "NewType" returns its argument
   unchanged.

   Usage:

      UserId = NewType('UserId', int)  # Declare the NewType "UserId"
      first_user = UserId(1)  # "UserId" returns the argument unchanged at runtime

   __module__

      The module in which the new type is defined.

   __name__

      The name of the new type.

   __supertype__

      The type that the new type is based on.

   Added in version 3.5.2.

   Changed in version 3.10: "NewType" is now a class rather than a
   function.

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 more 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[T](Protocol):
          def meth(self) -> T:
              ...

   In code that needs to be compatible with Python 3.11 or older,
   generic Protocols can be written as follows:

      T = TypeVar("T")

      class GenProto(Protocol[T]):
          def meth(self) -> T:
              ...

   Added 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)

      @runtime_checkable
      class Named(Protocol):
          name: str

      import threading
      assert isinstance(threading.Thread(name='Bob'), Named)

   Note:

     "runtime_checkable()" will check only the presence of the
     required methods or attributes, not their type signatures or
     types. For example, "ssl.SSLObject" is a class, therefore it
     passes an "issubclass()" check against Callable. However, the
     "ssl.SSLObject.__init__" method exists only to raise a
     "TypeError" with a more informative message, therefore making it
     impossible to call (instantiate) "ssl.SSLObject".

   Note:

     An "isinstance()" check against a runtime-checkable protocol can
     be surprisingly slow compared to an "isinstance()" check against
     a non-protocol class. Consider using alternative idioms such as
     "hasattr()" calls for structural checks in performance-sensitive
     code.

   Added in version 3.8.

   Changed in version 3.12: The internal implementation of
   "isinstance()" checks against runtime-checkable protocols now uses
   "inspect.getattr_static()" to look up attributes (previously,
   "hasattr()" was used). As a result, some objects which used to be
   considered instances of a runtime-checkable protocol may no longer
   be considered instances of that protocol on Python 3.12+, and vice
   versa. Most users are unlikely to be affected by this change.

   Changed in version 3.12: The members of a runtime-checkable
   protocol are now considered "frozen" at runtime as soon as the
   class has been created. Monkey-patching attributes onto a runtime-
   checkable protocol will still work, but will have no impact on
   "isinstance()" checks comparing objects to the protocol. See
   "What's new in Python 3.12" for more details.

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')

   An alternative way to create a "TypedDict" is by using function-
   call syntax. The second argument must be a literal "dict":

      Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})

   This functional syntax allows defining keys which are not valid
   identifiers, for example because they are keywords or contain
   hyphens:

      # raises SyntaxError
      class Point2D(TypedDict):
          in: int  # 'in' is a keyword
          x-y: int  # name with hyphens

      # OK, functional syntax
      Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})

   By default, all keys must be present in a "TypedDict". It is
   possible to mark individual keys as non-required using
   "NotRequired":

      class Point2D(TypedDict):
          x: int
          y: int
          label: NotRequired[str]

      # Alternative syntax
      Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': NotRequired[str]})

   This means that a "Point2D" "TypedDict" can have the "label" key
   omitted.

   It is also possible to mark all keys as non-required by default by
   specifying a totality of "False":

      class Point2D(TypedDict, total=False):
          x: int
          y: int

      # Alternative syntax
      Point2D = TypedDict('Point2D', {'x': int, 'y': int}, total=False)

   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
   required.

   Individual keys of a "total=False" "TypedDict" can be marked as
   required using "Required":

      class Point2D(TypedDict, total=False):
          x: Required[int]
          y: Required[int]
          label: str

      # Alternative syntax
      Point2D = TypedDict('Point2D', {
          'x': Required[int],
          'y': Required[int],
          'label': str
      }, total=False)

   It is possible for a "TypedDict" type to inherit from one or more
   other "TypedDict" types using the class-based syntax. Usage:

      class Point3D(Point2D):
          z: int

   "Point3D" has three items: "x", "y" and "z". It is equivalent to
   this definition:

      class Point3D(TypedDict):
          x: int
          y: int
          z: int

   A "TypedDict" cannot inherit from a non-"TypedDict" class, except
   for "Generic". For example:

      class X(TypedDict):
          x: int

      class Y(TypedDict):
          y: int

      class Z(object): pass  # A non-TypedDict class

      class XY(X, Y): pass  # OK

      class XZ(X, Z): pass  # raises TypeError

   A "TypedDict" can be generic:

      class Group[T](TypedDict):
          key: T
          group: list[T]

   To create a generic "TypedDict" that is compatible with Python 3.11
   or lower, inherit from "Generic" explicitly:

      T = TypeVar("T")

      class Group(TypedDict, Generic[T]):
          key: T
          group: list[T]

   A "TypedDict" can be introspected via annotations dicts (see
   Annotations Best Practices for more information on annotations best
   practices), "__total__", "__required_keys__", and
   "__optional_keys__".

   __total__

      "Point2D.__total__" gives the value of the "total" argument.
      Example:

         >>> from typing import TypedDict
         >>> class Point2D(TypedDict): pass
         >>> Point2D.__total__
         True
         >>> class Point2D(TypedDict, total=False): pass
         >>> Point2D.__total__
         False
         >>> class Point3D(Point2D): pass
         >>> Point3D.__total__
         True

      This attribute reflects *only* the value of the "total" argument
      to the current "TypedDict" class, not whether the class is
      semantically total. For example, a "TypedDict" with "__total__"
      set to "True" may have keys marked with "NotRequired", or it may
      inherit from another "TypedDict" with "total=False". Therefore,
      it is generally better to use "__required_keys__" and
      "__optional_keys__" for introspection.

   __required_keys__

      Added in version 3.9.

   __optional_keys__

      "Point2D.__required_keys__" and "Point2D.__optional_keys__"
      return "frozenset" objects containing required and non-required
      keys, respectively.

      Keys marked with "Required" will always appear in
      "__required_keys__" and keys marked with "NotRequired" will
      always appear in "__optional_keys__".

      For backwards compatibility with Python 3.10 and below, it is
      also possible to use inheritance to declare both required and
      non-required keys in the same "TypedDict" . This is done by
      declaring a "TypedDict" with one value for the "total" argument
      and then inheriting from it in another "TypedDict" with a
      different value for "total":

         >>> class Point2D(TypedDict, total=False):
         ...     x: int
         ...     y: int
         ...
         >>> class Point3D(Point2D):
         ...     z: int
         ...
         >>> Point3D.__required_keys__ == frozenset({'z'})
         True
         >>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
         True

      Added in version 3.9.

      Note:

        If "from __future__ import annotations" is used or if
        annotations are given as strings, annotations are not
        evaluated when the "TypedDict" is defined. Therefore, the
        runtime introspection that "__required_keys__" and
        "__optional_keys__" rely on may not work properly, and the
        values of the attributes may be incorrect.

   Support for "ReadOnly" is reflected in the following attributes:

   __readonly_keys__

      A "frozenset" containing the names of all read-only keys. Keys
      are read-only if they carry the "ReadOnly" qualifier.

      Added in version 3.13.

   __mutable_keys__

      A "frozenset" containing the names of all mutable keys. Keys are
      mutable if they do not carry the "ReadOnly" qualifier.

      Added in version 3.13.

   See **PEP 589** for more examples and detailed rules of using
   "TypedDict".

   Added in version 3.8.

   Changed in version 3.11: Added support for marking individual keys
   as "Required" or "NotRequired". See **PEP 655**.

   Changed in version 3.11: Added support for generic "TypedDict"s.

   Changed in version 3.13: Removed support for the keyword-argument
   method of creating "TypedDict"s.

   Changed in version 3.13: Support for the "ReadOnly" qualifier was
   added.

   Deprecated since version 3.13, will be removed in version 3.15:
   When using the functional syntax to create a TypedDict class,
   failing to pass a value to the 'fields' parameter ("TD =
   TypedDict("TD")") is deprecated. Passing "None" to the 'fields'
   parameter ("TD = TypedDict("TD", None)") is also deprecated. Both
   will be disallowed in Python 3.15. To create a TypedDict class with
   0 fields, use "class TD(TypedDict): pass" or "TD = TypedDict("TD",
   {})".


Protocols
---------

The following protocols are provided by the typing module. All 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__".

   Added 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.


ABCs for working with IO
------------------------

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()".


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.assert_type(val, typ, /)

   Ask a static type checker to confirm that *val* has an inferred
   type of *typ*.

   At runtime this does nothing: it returns the first argument
   unchanged with no checks or side effects, no matter the actual type
   of the argument.

   When a static type checker encounters a call to "assert_type()", it
   emits an error if the value is not of the specified type:

      def greet(name: str) -> None:
          assert_type(name, str)  # OK, inferred type of `name` is `str`
          assert_type(name, int)  # type checker error

   This function is useful for ensuring the type checker's
   understanding of a script is in line with the developer's
   intentions:

      def complex_function(arg: object):
          # Do some complex type-narrowing logic,
          # after which we hope the inferred type will be `int`
          ...
          # Test whether the type checker correctly understands our function
          assert_type(arg, int)

   Added in version 3.11.

typing.assert_never(arg, /)

   Ask a static type checker to confirm that a line of code is
   unreachable.

   Example:

      def int_or_str(arg: int | str) -> None:
          match arg:
              case int():
                  print("It's an int")
              case str():
                  print("It's a str")
              case _ as unreachable:
                  assert_never(unreachable)

   Here, the annotations allow the type checker to infer that the last
   case can never execute, because "arg" is either an "int" or a
   "str", and both options are covered by earlier cases.

   If a type checker finds that a call to "assert_never()" is
   reachable, it will emit an error. For example, if the type
   annotation for "arg" was instead "int | str | float", the type
   checker would emit an error pointing out that "unreachable" is of
   type "float". For a call to "assert_never" to pass type checking,
   the inferred type of the argument passed in must be the bottom
   type, "Never", and nothing else.

   At runtime, this throws an exception when called.

   See also:

     Unreachable Code and Exhaustiveness Checking has more information
     about exhaustiveness checking with static typing.

   Added in version 3.11.

typing.reveal_type(obj, /)

   Ask a static type checker to reveal the inferred type of an
   expression.

   When a static type checker encounters a call to this function, it
   emits a diagnostic with the inferred type of the argument. For
   example:

      x: int = 1
      reveal_type(x)  # Revealed type is "builtins.int"

   This can be useful when you want to debug how your type checker
   handles a particular piece of code.

   At runtime, this function prints the runtime type of its argument
   to "sys.stderr" and returns the argument unchanged (allowing the
   call to be used within an expression):

      x = reveal_type(1)  # prints "Runtime type is int"
      print(x)  # prints "1"

   Note that the runtime type may be different from (more or less
   specific than) the type statically inferred by a type checker.

   Most type checkers support "reveal_type()" anywhere, even if the
   name is not imported from "typing". Importing the name from
   "typing", however, allows your code to run without runtime errors
   and communicates intent more clearly.

   Added in version 3.11.

@typing.dataclass_transform(*, eq_default=True, order_default=False, kw_only_default=False, frozen_default=False, field_specifiers=(), **kwargs)

   Decorator to mark an object as providing "dataclass"-like behavior.

   "dataclass_transform" may be used to decorate a class, metaclass,
   or a function that is itself a decorator. The presence of
   "@dataclass_transform()" tells a static type checker that the
   decorated object performs runtime "magic" that transforms a class
   in a similar way to "@dataclasses.dataclass".

   Example usage with a decorator function:

      @dataclass_transform()
      def create_model[T](cls: type[T]) -> type[T]:
          ...
          return cls

      @create_model
      class CustomerModel:
          id: int
          name: str

   On a base class:

      @dataclass_transform()
      class ModelBase: ...

      class CustomerModel(ModelBase):
          id: int
          name: str

   On a metaclass:

      @dataclass_transform()
      class ModelMeta(type): ...

      class ModelBase(metaclass=ModelMeta): ...

      class CustomerModel(ModelBase):
          id: int
          name: str

   The "CustomerModel" classes defined above will be treated by type
   checkers similarly to classes created with
   "@dataclasses.dataclass". For example, type checkers will assume
   these classes have "__init__" methods that accept "id" and "name".

   The decorated class, metaclass, or function may accept the
   following bool arguments which type checkers will assume have the
   same effect as they would have on the "@dataclasses.dataclass"
   decorator: "init", "eq", "order", "unsafe_hash", "frozen",
   "match_args", "kw_only", and "slots". It must be possible for the
   value of these arguments ("True" or "False") to be statically
   evaluated.

   The arguments to the "dataclass_transform" decorator can be used to
   customize the default behaviors of the decorated class, metaclass,
   or function:

   Parameters:
      * **eq_default** (*bool*) -- Indicates whether the "eq"
        parameter is assumed to be "True" or "False" if it is omitted
        by the caller. Defaults to "True".

      * **order_default** (*bool*) -- Indicates whether the "order"
        parameter is assumed to be "True" or "False" if it is omitted
        by the caller. Defaults to "False".

      * **kw_only_default** (*bool*) -- Indicates whether the
        "kw_only" parameter is assumed to be "True" or "False" if it
        is omitted by the caller. Defaults to "False".

      * **frozen_default** (*bool*) --

        Indicates whether the "frozen" parameter is assumed to be
        "True" or "False" if it is omitted by the caller. Defaults to
        "False".

        Added in version 3.12.

      * **field_specifiers** (*tuple**[**Callable**[**...**,
        **Any**]**, **...**]*) -- Specifies a static list of supported
        classes or functions that describe fields, similar to
        "dataclasses.field()". Defaults to "()".

      * ****kwargs** (*Any*) -- Arbitrary other keyword arguments are
        accepted in order to allow for possible future extensions.

   Type checkers recognize the following optional parameters on field
   specifiers:


   **Recognised parameters for field specifiers**
   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

   +----------------------+----------------------------------------------------------------------------------+
   | Parameter name       | Description                                                                      |
   |======================|==================================================================================|
   | "init"               | Indicates whether the field should be included in the synthesized "__init__"     |
   |                      | method. If unspecified, "init" defaults to "True".                               |
   +----------------------+----------------------------------------------------------------------------------+
   | "default"            | Provides the default value for the field.                                        |
   +----------------------+----------------------------------------------------------------------------------+
   | "default_factory"    | Provides a runtime callback that returns the default value for the field. If     |
   |                      | neither "default" nor "default_factory" are specified, the field is assumed to   |
   |                      | have no default value and must be provided a value when the class is             |
   |                      | instantiated.                                                                    |
   +----------------------+----------------------------------------------------------------------------------+
   | "factory"            | An alias for the "default_factory" parameter on field specifiers.                |
   +----------------------+----------------------------------------------------------------------------------+
   | "kw_only"            | Indicates whether the field should be marked as keyword-only. If "True", the     |
   |                      | field will be keyword-only. If "False", it will not be keyword-only. If          |
   |                      | unspecified, the value of the "kw_only" parameter on the object decorated with   |
   |                      | "dataclass_transform" will be used, or if that is unspecified, the value of      |
   |                      | "kw_only_default" on "dataclass_transform" will be used.                         |
   +----------------------+----------------------------------------------------------------------------------+
   | "alias"              | Provides an alternative name for the field. This alternative name is used in the |
   |                      | synthesized "__init__" method.                                                   |
   +----------------------+----------------------------------------------------------------------------------+

   At runtime, this decorator records its arguments in the
   "__dataclass_transform__" attribute on the decorated object. It has
   no other runtime effect.

   See **PEP 681** for more details.

   Added in version 3.11.

@typing.overload

   Decorator for creating overloaded functions and methods.

   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).

   "@overload"-decorated definitions are for the benefit of the type
   checker only, since they will be overwritten by the
   non-"@overload"-decorated definition. The non-"@overload"-decorated
   definition, meanwhile, will be used at runtime but should be
   ignored by a type checker.  At runtime, calling an
   "@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 goes here

   See **PEP 484** for more details and comparison with other typing
   semantics.

   Changed in version 3.11: Overloaded functions can now be
   introspected at runtime using "get_overloads()".

typing.get_overloads(func)

   Return a sequence of "@overload"-decorated definitions for *func*.

   *func* is the function object for the implementation of the
   overloaded function. For example, given the definition of "process"
   in the documentation for "@overload", "get_overloads(process)" will
   return a sequence of three function objects for the three defined
   overloads. If called on a function with no overloads,
   "get_overloads()" returns an empty sequence.

   "get_overloads()" can be used for introspecting an overloaded
   function at runtime.

   Added in version 3.11.

typing.clear_overloads()

   Clear all registered overloads in the internal registry.

   This can be used to reclaim the memory used by the registry.

   Added in version 3.11.

@typing.final

   Decorator to indicate final methods and final classes.

   Decorating a method with "@final" indicates to a type checker that
   the method cannot be overridden in a subclass. Decorating a class
   with "@final" indicates that it 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.

   Added in version 3.8.

   Changed in version 3.11: The decorator will now attempt to set a
   "__final__" attribute to "True" on the decorated object. Thus, a
   check like "if getattr(obj, "__final__", False)" can be used at
   runtime to determine whether an object "obj" has been marked as
   final. If the decorated object does not support setting attributes,
   the decorator returns the object unchanged without raising an
   exception.

@typing.no_type_check

   Decorator to indicate that annotations are not type hints.

   This works as a class or function *decorator*.  With a class, it
   applies recursively to all methods and classes defined in that
   class (but not to methods defined in its superclasses or
   subclasses). Type checkers will ignore all annotations in a
   function or class with this decorator.

   "@no_type_check" mutates the decorated object 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()".

   Deprecated since version 3.13, will be removed in version 3.15: No
   type checker ever added support for "@no_type_check_decorator". It
   is therefore deprecated, and will be removed in Python 3.15.

@typing.override

   Decorator to indicate that a method in a subclass is intended to
   override a method or attribute in a superclass.

   Type checkers should emit an error if a method decorated with
   "@override" does not, in fact, override anything. This helps
   prevent bugs that may occur when a base class is changed without an
   equivalent change to a child class.

   For example:

      class Base:
          def log_status(self) -> None:
              ...

      class Sub(Base):
          @override
          def log_status(self) -> None:  # Okay: overrides Base.log_status
              ...

          @override
          def done(self) -> None:  # Error reported by type checker
              ...

   There is no runtime checking of this property.

   The decorator will attempt to set an "__override__" attribute to
   "True" on the decorated object. Thus, a check like "if getattr(obj,
   "__override__", False)" can be used at runtime to determine whether
   an object "obj" has been marked as an override.  If the decorated
   object does not support setting attributes, the decorator returns
   the object unchanged without raising an exception.

   See **PEP 698** for more details.

   Added in version 3.12.

@typing.type_check_only

   Decorator to mark a class or function as 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__", but this function
   makes the following changes to the annotations dictionary:

   * Forward references encoded as string literals or "ForwardRef"
     objects are handled by evaluating them in *globalns*, *localns*,
     and (where applicable) *obj*'s type parameter namespace. If
     *globalns* or *localns* is not given, appropriate namespace
     dictionaries are inferred from *obj*.

   * "None" is replaced with "types.NoneType".

   * If "@no_type_check" has been applied to *obj*, an empty
     dictionary is returned.

   * If *obj* is a class "C", the function returns a dictionary that
     merges annotations from "C"'s base classes with those on "C"
     directly. This is done by traversing "C.__mro__" and iteratively
     combining "__annotations__" dictionaries. Annotations on classes
     appearing earlier in the *method resolution order* always take
     precedence over annotations on classes appearing later in the
     method resolution order.

   * The function recursively replaces all occurrences of
     "Annotated[T, ...]" with "T", unless *include_extras* is set to
     "True" (see "Annotated" for more information).

   See also "inspect.get_annotations()", a lower-level function that
   returns annotations more directly.

   Note:

     If any forward references in the annotations of *obj* are not
     resolvable or are not valid Python code, this function will raise
     an exception such as "NameError". For example, this can happen
     with imported type aliases that include forward references, or
     with names imported under "if TYPE_CHECKING".

   Changed in version 3.9: Added "include_extras" parameter as part of
   **PEP 593**. See the documentation on "Annotated" for more
   information.

   Changed in version 3.11: Previously, "Optional[t]" was added for
   function and method annotations if a default value equal to "None"
   was set. Now the annotation is returned unchanged.

typing.get_origin(tp)

   Get the unsubscripted version of a type: for a typing object of the
   form "X[Y, Z, ...]" return "X".

   If "X" is a typing-module alias for a builtin or "collections"
   class, it will be normalized to the original class. If "X" is an
   instance of "ParamSpecArgs" or "ParamSpecKwargs", return the
   underlying "ParamSpec". Return "None" for unsupported objects.

   Examples:

      assert get_origin(str) is None
      assert get_origin(Dict[str, int]) is dict
      assert get_origin(Union[int, str]) is Union
      P = ParamSpec('P')
      assert get_origin(P.args) is P
      assert get_origin(P.kwargs) is P

   Added in version 3.8.

typing.get_args(tp)

   Get type arguments with all substitutions performed: for a typing
   object of the form "X[Y, Z, ...]" return "(Y, Z, ...)".

   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. Return "()"
   for unsupported objects.

   Examples:

      assert get_args(int) == ()
      assert get_args(Dict[int, str]) == (int, str)
      assert get_args(Union[int, str]) == (int, str)

   Added in version 3.8.

typing.get_protocol_members(tp)

   Return the set of members defined in a "Protocol".

      >>> from typing import Protocol, get_protocol_members
      >>> class P(Protocol):
      ...     def a(self) -> str: ...
      ...     b: int
      >>> get_protocol_members(P) == frozenset({'a', 'b'})
      True

   Raise "TypeError" for arguments that are not Protocols.

   Added in version 3.13.

typing.is_protocol(tp)

   Determine if a type is a "Protocol".

   For example:

      class P(Protocol):
          def a(self) -> str: ...
          b: int

      is_protocol(P)    # => True
      is_protocol(int)  # => False

   Added in version 3.13.

typing.is_typeddict(tp)

   Check if a type is a "TypedDict".

   For example:

      class Film(TypedDict):
          title: str
          year: int

      assert is_typeddict(Film)
      assert not is_typeddict(list | str)

      # TypedDict is a factory for creating typed dicts,
      # not a typed dict itself
      assert not is_typeddict(TypedDict)

   Added in version 3.10.

class typing.ForwardRef

   Class used for internal typing representation of string forward
   references.

   For example, "List["SomeClass"]" is implicitly transformed into
   "List[ForwardRef("SomeClass")]".  "ForwardRef" 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]".

   Added in version 3.7.4.

typing.NoDefault

   A sentinel object used to indicate that a type parameter has no
   default value. For example:

      >>> T = TypeVar("T")
      >>> T.__default__ is typing.NoDefault
      True
      >>> S = TypeVar("S", default=None)
      >>> S.__default__ is None
      True

   Added in version 3.13.


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, 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**).

   Added in version 3.5.2.


Deprecated aliases
------------------

This module defines several deprecated aliases to pre-existing
standard library classes. These were originally included in the typing
module in order to support parameterizing these generic classes using
"[]". However, the aliases became redundant in Python 3.9 when the
corresponding pre-existing classes were enhanced to support "[]" (see
**PEP 585**).

The redundant types are deprecated as of Python 3.9. However, while
the aliases may be removed at some point, removal of these aliases is
not currently planned. As such, no deprecation warnings are currently
issued by the interpreter for these aliases.

If at some point it is decided to remove these deprecated aliases, a
deprecation warning will be issued by the interpreter for at least two
releases prior to removal. The aliases are guaranteed to remain in the
typing module without deprecation warnings until at least Python 3.14.

Type checkers are encouraged to flag uses of the deprecated types if
the program they are checking targets a minimum Python version of 3.9
or newer.


Aliases to built-in types
~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.Dict(dict, MutableMapping[KT, VT])

   Deprecated alias to "dict".

   Note that to annotate arguments, it is preferred to use an abstract
   collection type such as "Mapping" rather than to use "dict" or
   "typing.Dict".

   This type can be used as follows:

      def count_words(text: str) -> Dict[str, int]:
          ...

   Deprecated since version 3.9: "builtins.dict" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.

class typing.List(list, MutableSequence[T])

   Deprecated alias to "list".

   Note that to annotate arguments, it is preferred to use an abstract
   collection type such as "Sequence" or "Iterable" rather than to use
   "list" or "typing.List".

   This type may be used as follows:

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

      def keep_positives[T: (int, float)](vector: Sequence[T]) -> List[T]:
          return [item for item in vector if item > 0]

   Deprecated since version 3.9: "builtins.list" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.

class typing.Set(set, MutableSet[T])

   Deprecated alias to "builtins.set".

   Note that to annotate arguments, it is preferred to use an abstract
   collection type such as "AbstractSet" rather than to use "set" or
   "typing.Set".

   Deprecated since version 3.9: "builtins.set" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.

class typing.FrozenSet(frozenset, AbstractSet[T_co])

   Deprecated alias to "builtins.frozenset".

   Deprecated since version 3.9: "builtins.frozenset" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.

typing.Tuple

   Deprecated alias for "tuple".

   "tuple" and "Tuple" are special-cased in the type system; see
   Annotating tuples for more details.

   Deprecated since version 3.9: "builtins.tuple" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.

class typing.Type(Generic[CT_co])

   Deprecated alias to "type".

   See The type of class objects for details on using "type" or
   "typing.Type" in type annotations.

   Added in version 3.5.2.

   Deprecated since version 3.9: "builtins.type" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.


Aliases to types in "collections"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])

   Deprecated alias to "collections.defaultdict".

   Added in version 3.5.2.

   Deprecated since version 3.9: "collections.defaultdict" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])

   Deprecated alias to "collections.OrderedDict".

   Added in version 3.7.2.

   Deprecated since version 3.9: "collections.OrderedDict" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])

   Deprecated alias to "collections.ChainMap".

   Added in version 3.6.1.

   Deprecated since version 3.9: "collections.ChainMap" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.

class typing.Counter(collections.Counter, Dict[T, int])

   Deprecated alias to "collections.Counter".

   Added in version 3.6.1.

   Deprecated since version 3.9: "collections.Counter" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.

class typing.Deque(deque, MutableSequence[T])

   Deprecated alias to "collections.deque".

   Added in version 3.6.1.

   Deprecated since version 3.9: "collections.deque" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.


Aliases to other concrete types
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.Pattern
class typing.Match

   Deprecated aliases corresponding to the return types from
   "re.compile()" and "re.match()".

   These types (and the corresponding functions) are generic over
   "AnyStr". "Pattern" can be specialised as "Pattern[str]" or
   "Pattern[bytes]"; "Match" can be specialised as "Match[str]" or
   "Match[bytes]".

   Deprecated since version 3.9: Classes "Pattern" and "Match" from
   "re" now support "[]". See **PEP 585** and Generic Alias Type.

class typing.Text

   Deprecated alias for "str".

   "Text" 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'

   Added in version 3.5.2.

   Deprecated since version 3.11: Python 2 is no longer supported, and
   most type checkers also no longer support type checking Python 2
   code. Removal of the alias is not currently planned, but users are
   encouraged to use "str" instead of "Text".


Aliases to container ABCs in "collections.abc"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.AbstractSet(Collection[T_co])

   Deprecated alias to "collections.abc.Set".

   Deprecated since version 3.9: "collections.abc.Set" now supports
   subscripting ("[]"). See **PEP 585** and Generic Alias Type.

class typing.Collection(Sized, Iterable[T_co], Container[T_co])

   Deprecated alias to "collections.abc.Collection".

   Added in version 3.6.

   Deprecated since version 3.9: "collections.abc.Collection" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.Container(Generic[T_co])

   Deprecated alias to "collections.abc.Container".

   Deprecated since version 3.9: "collections.abc.Container" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.ItemsView(MappingView, AbstractSet[tuple[KT_co, VT_co]])

   Deprecated alias to "collections.abc.ItemsView".

   Deprecated since version 3.9: "collections.abc.ItemsView" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.KeysView(MappingView, AbstractSet[KT_co])

   Deprecated alias to "collections.abc.KeysView".

   Deprecated since version 3.9: "collections.abc.KeysView" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.Mapping(Collection[KT], Generic[KT, VT_co])

   Deprecated alias to "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 subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.MappingView(Sized)

   Deprecated alias to "collections.abc.MappingView".

   Deprecated since version 3.9: "collections.abc.MappingView" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.MutableMapping(Mapping[KT, VT])

   Deprecated alias to "collections.abc.MutableMapping".

   Deprecated since version 3.9: "collections.abc.MutableMapping" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.MutableSequence(Sequence[T])

   Deprecated alias to "collections.abc.MutableSequence".

   Deprecated since version 3.9: "collections.abc.MutableSequence" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.MutableSet(AbstractSet[T])

   Deprecated alias to "collections.abc.MutableSet".

   Deprecated since version 3.9: "collections.abc.MutableSet" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.Sequence(Reversible[T_co], Collection[T_co])

   Deprecated alias to "collections.abc.Sequence".

   Deprecated since version 3.9: "collections.abc.Sequence" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.ValuesView(MappingView, Collection[_VT_co])

   Deprecated alias to "collections.abc.ValuesView".

   Deprecated since version 3.9: "collections.abc.ValuesView" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.


Aliases to asynchronous ABCs in "collections.abc"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.Coroutine(Awaitable[ReturnType], Generic[YieldType, SendType, ReturnType])

   Deprecated alias to "collections.abc.Coroutine".

   The variance and order of type variables correspond to those of
   "Generator", for example:

      from collections.abc import Coroutine
      c: Coroutine[list[str], str, int]  # Some coroutine defined elsewhere
      x = c.send('hi')                   # Inferred type of 'x' is list[str]
      async def bar() -> None:
          y = await c                    # Inferred type of 'y' is int

   Added in version 3.5.3.

   Deprecated since version 3.9: "collections.abc.Coroutine" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.AsyncGenerator(AsyncIterator[YieldType], Generic[YieldType, SendType])

   Deprecated alias to "collections.abc.AsyncGenerator".

   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.

   The "SendType" defaults to "None":

      async def infinite_stream(start: int) -> AsyncGenerator[int]:
          while True:
              yield start
              start = await increment(start)

   It is also possible to set this type explicitly:

      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)

   Added in version 3.6.1.

   Deprecated since version 3.9: "collections.abc.AsyncGenerator" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

   Changed in version 3.13: The "SendType" parameter now has a
   default.

class typing.AsyncIterable(Generic[T_co])

   Deprecated alias to "collections.abc.AsyncIterable".

   Added in version 3.5.2.

   Deprecated since version 3.9: "collections.abc.AsyncIterable" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.AsyncIterator(AsyncIterable[T_co])

   Deprecated alias to "collections.abc.AsyncIterator".

   Added in version 3.5.2.

   Deprecated since version 3.9: "collections.abc.AsyncIterator" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.Awaitable(Generic[T_co])

   Deprecated alias to "collections.abc.Awaitable".

   Added in version 3.5.2.

   Deprecated since version 3.9: "collections.abc.Awaitable" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.


Aliases to other ABCs in "collections.abc"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.Iterable(Generic[T_co])

   Deprecated alias to "collections.abc.Iterable".

   Deprecated since version 3.9: "collections.abc.Iterable" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.Iterator(Iterable[T_co])

   Deprecated alias to "collections.abc.Iterator".

   Deprecated since version 3.9: "collections.abc.Iterator" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

typing.Callable

   Deprecated alias to "collections.abc.Callable".

   See Annotating callable objects for details on how to use
   "collections.abc.Callable" and "typing.Callable" in type
   annotations.

   Deprecated since version 3.9: "collections.abc.Callable" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

   Changed in version 3.10: "Callable" now supports "ParamSpec" and
   "Concatenate". See **PEP 612** for more details.

class typing.Generator(Iterator[YieldType], Generic[YieldType, SendType, ReturnType])

   Deprecated alias to "collections.abc.Generator".

   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.

   The "SendType" and "ReturnType" parameters default to "None":

      def infinite_stream(start: int) -> Generator[int]:
          while True:
              yield start
              start += 1

   It is also possible to set these types explicitly:

      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 subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

   Changed in version 3.13: Default values for the send and return
   types were added.

class typing.Hashable

   Deprecated alias to "collections.abc.Hashable".

   Deprecated since version 3.12: Use "collections.abc.Hashable"
   directly instead.

class typing.Reversible(Iterable[T_co])

   Deprecated alias to "collections.abc.Reversible".

   Deprecated since version 3.9: "collections.abc.Reversible" now
   supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

class typing.Sized

   Deprecated alias to "collections.abc.Sized".

   Deprecated since version 3.12: Use "collections.abc.Sized" directly
   instead.


Aliases to "contextlib" ABCs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.ContextManager(Generic[T_co, ExitT_co])

   Deprecated alias to "contextlib.AbstractContextManager".

   The first type parameter, "T_co", represents the type returned by
   the "__enter__()" method. The optional second type parameter,
   "ExitT_co", which defaults to "bool | None", represents the type
   returned by the "__exit__()" method.

   Added in version 3.5.4.

   Deprecated since version 3.9: "contextlib.AbstractContextManager"
   now supports subscripting ("[]"). See **PEP 585** and Generic Alias
   Type.

   Changed in version 3.13: Added the optional second type parameter,
   "ExitT_co".

class typing.AsyncContextManager(Generic[T_co, AExitT_co])

   Deprecated alias to "contextlib.AbstractAsyncContextManager".

   The first type parameter, "T_co", represents the type returned by
   the "__aenter__()" method. The optional second type parameter,
   "AExitT_co", which defaults to "bool | None", represents the type
   returned by the "__aexit__()" method.

   Added in version 3.6.2.

   Deprecated since version 3.9:
   "contextlib.AbstractAsyncContextManager" now supports subscripting
   ("[]"). See **PEP 585** and Generic Alias Type.

   Changed in version 3.13: Added the optional second type parameter,
   "AExitT_co".


Deprecation Timeline of Major Features
======================================

Certain features in "typing" are deprecated and may be removed in a
future version of Python. The following table summarizes major
deprecations for your convenience. This is subject to change, and not
all deprecations are listed.

+---------------------------+---------------------------+---------------------------+---------------------------+
| Feature                   | Deprecated in             | Projected removal         | PEP/issue                 |
|===========================|===========================|===========================|===========================|
| "typing" versions of      | 3.9                       | Undecided (see Deprecated | **PEP 585**               |
| standard collections      |                           | aliases for more          |                           |
|                           |                           | information)              |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.Text"             | 3.11                      | Undecided                 | gh-92332                  |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.Hashable" and     | 3.12                      | Undecided                 | gh-94309                  |
| "typing.Sized"            |                           |                           |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.TypeAlias"        | 3.12                      | Undecided                 | **PEP 695**               |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "@typing.no_type_check_d  | 3.13                      | 3.15                      | gh-106309                 |
| ecorator"                 |                           |                           |                           |
+---------------------------+---------------------------+---------------------------+---------------------------+
| "typing.AnyStr"           | 3.13                      | 3.18                      | gh-105578                 |
+---------------------------+---------------------------+---------------------------+---------------------------+
