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

New in version 3.5.

**Source code:** Lib/typing.py

Note:

  The Python runtime does not enforce function and variable type
  annotations. They can be used by third party tools such as type
  checkers, IDEs, linters, etc.

======================================================================

This module provides runtime support for type hints. The most
fundamental support consists of the types "Any", "Union", "Callable",
"TypeVar", and "Generic". For a full specification, please see **PEP
484**. For a simplified introduction to type hints, see **PEP 483**.

The function below takes and returns a string and is annotated as
follows:

   def greeting(name: str) -> str:
       return 'Hello ' + name

In the function "greeting", the argument "name" is expected to be of
type "str" and the return type "str". Subtypes are accepted as
arguments.

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


Relevant PEPs
=============

Since the initial introduction of type hints in **PEP 484** and **PEP
483**, a number of PEPs have modified and enhanced Python's framework
for type annotations. These include:

* **PEP 526**: Syntax for Variable Annotations
     *Introducing* syntax for annotating variables outside of function
     definitions, and "ClassVar"

* **PEP 544**: Protocols: Structural subtyping (static duck typing)
     *Introducing* "Protocol" and the "@runtime_checkable" decorator

* **PEP 585**: Type Hinting Generics In Standard Collections
     *Introducing* "types.GenericAlias" and the ability to use
     standard library classes as generic types

* **PEP 586**: Literal Types
     *Introducing* "Literal"

* **PEP 589**: TypedDict: Type Hints for Dictionaries with a Fixed Set
  of Keys
     *Introducing* "TypedDict"

* **PEP 591**: Adding a final qualifier to typing
     *Introducing* "Final" and the "@final" decorator

* **PEP 593**: Flexible function and variable annotations
     *Introducing* "Annotated"

* **PEP 604**: Allow writing union types as "X | Y"
     *Introducing* "types.UnionType" and the ability to use the
     binary-or operator "|" to signify a union of types

* **PEP 612**: Parameter Specification Variables
     *Introducing* "ParamSpec" and "Concatenate"

* **PEP 613**: Explicit Type Aliases
     *Introducing* "TypeAlias"

* **PEP 646**: Variadic Generics
     *Introducing* "TypeVarTuple"

* **PEP 647**: User-Defined Type Guards
     *Introducing* "TypeGuard"

* **PEP 673**: Self type
     *Introducing* "Self"

* **PEP 675**: Arbitrary Literal String Type
     *Introducing* "LiteralString"


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

A type alias is defined by assigning the type to the alias. In this
example, "Vector" and "list[float]" will be treated as interchangeable
synonyms:

   Vector = list[float]

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

   # typechecks; a list of floats qualifies as a Vector.
   new_vector = scale(2.0, [1.0, -4.2, 5.4])

Type aliases are useful for simplifying complex type signatures. For
example:

   from collections.abc import Sequence

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

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

   # The static type checker will treat the previous type signature as
   # being exactly equivalent to this one.
   def broadcast_message(
           message: str,
           servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
       ...

Note that "None" as a type hint is a special case and is replaced by
"type(None)".


NewType
=======

Use the "NewType" helper class to create distinct types:

   from typing import NewType

   UserId = NewType('UserId', int)
   some_id = UserId(524313)

The static type checker will treat the new type as if it were a
subclass of the original type. This is useful in helping catch logical
errors:

   def get_user_name(user_id: UserId) -> str:
       ...

   # typechecks
   user_a = get_user_name(UserId(42351))

   # does not typecheck; an int is not a UserId
   user_b = get_user_name(-1)

You may still perform all "int" operations on a variable of type
"UserId", but the result will always be of type "int". This lets you
pass in a "UserId" wherever an "int" might be expected, but will
prevent you from accidentally creating a "UserId" in an invalid way:

   # 'output' is of type 'int', not 'UserId'
   output = UserId(23413) + UserId(54341)

Note that these checks are enforced only by the static type checker.
At runtime, the statement "Derived = NewType('Derived', Base)" will
make "Derived" a class 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 typecheck
   class AdminUserId(UserId): pass

However, it is possible to create a "NewType" based on a 'derived'
"NewType":

   from typing import NewType

   UserId = NewType('UserId', int)

   ProUserId = NewType('ProUserId', UserId)

and typechecking for "ProUserId" will work as expected.

See **PEP 484** for more details.

Note:

  Recall that the use of a type alias declares two types to be
  *equivalent* to one another. Doing "Alias = Original" will make the
  static type checker treat "Alias" as being *exactly equivalent* to
  "Original" in all cases. This is useful when you want to simplify
  complex type signatures.In contrast, "NewType" declares one type to
  be a *subtype* of another. Doing "Derived = NewType('Derived',
  Original)" will make the static type checker treat "Derived" as a
  *subclass* of "Original", which means a value of type "Original"
  cannot be used in places where a value of type "Derived" is
  expected. This is useful when you want to prevent logic errors with
  minimal runtime cost.

New in version 3.5.2.

Changed in version 3.10: "NewType" is now a class rather than a
function.  There is some additional runtime cost when calling
"NewType" over a regular function.  However, this cost will be reduced
in 3.11.0.


Callable
========

Frameworks expecting callback functions of specific signatures might
be type hinted using "Callable[[Arg1Type, Arg2Type], ReturnType]".

For example:

   from collections.abc import Callable

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

   def async_query(on_success: Callable[[int], None],
                   on_error: Callable[[int, Exception], None]) -> None:
       # Body

It is possible to declare the return type of a callable without
specifying the call signature by substituting a literal ellipsis for
the list of arguments in the type hint: "Callable[..., ReturnType]".

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 information.

See also:

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


Generics
========

Since type information about objects kept in containers cannot be
statically inferred in a generic way, abstract base classes have been
extended to support subscription to denote expected types for
container elements.

   from collections.abc import Mapping, Sequence

   def notify_by_email(employees: Sequence[Employee],
                       overrides: Mapping[str, str]) -> None: ...

Generics can be parameterized by using a factory available in typing
called "TypeVar".

   from collections.abc import Sequence
   from typing import TypeVar

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

   def first(l: Sequence[T]) -> T:   # Generic function
       return l[0]


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

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

   from typing import TypeVar, Generic
   from logging import Logger

   T = TypeVar('T')

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

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

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

       def log(self, message: str) -> None:
           self.logger.info('%s: %s', self.name, message)

"Generic[T]" as a base class defines that the class "LoggedVar" takes
a single type parameter "T" . This also makes "T" valid as a type
within the class body.

The "Generic" base class defines "__class_getitem__()" so that
"LoggedVar[t]" is valid as a type:

   from collections.abc import Iterable

   def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
       for var in vars:
           var.set(0)

A generic type can have any number of type variables. All varieties of
"TypeVar" are permissible as parameters for a generic type:

   from typing import TypeVar, Generic, Sequence

   T = TypeVar('T', contravariant=True)
   B = TypeVar('B', bound=Sequence[bytes], covariant=True)
   S = TypeVar('S', int, str)

   class WeirdTrio(Generic[T, B, S]):
       ...

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

   from typing import TypeVar, Generic
   ...

   T = TypeVar('T')

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

You can use multiple inheritance with "Generic":

   from collections.abc import Sized
   from typing import TypeVar, Generic

   T = TypeVar('T')

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

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

   from collections.abc import Mapping
   from typing import TypeVar

   T = TypeVar('T')

   class MyDict(Mapping[str, T]):
       ...

In this case "MyDict" has a single parameter, "T".

Using a generic class without specifying type parameters assumes "Any"
for each position. In the following example, "MyIterable" is not
generic but implicitly inherits from "Iterable[Any]":

   from collections.abc import Iterable

   class MyIterable(Iterable): # Same as Iterable[Any]

User defined generic type aliases are also supported. Examples:

   from collections.abc import Iterable
   from typing import TypeVar
   S = TypeVar('S')
   Response = Iterable[S] | int

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

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

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

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

User-defined generics for parameter expressions are also supported via
parameter specification variables in the form "Generic[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":

   >>> from typing import Generic, ParamSpec, TypeVar

   >>> T = TypeVar('T')
   >>> P = ParamSpec('P')

   >>> class Z(Generic[T, P]): ...
   ...
   >>> Z[int, [dict, float]]
   __main__.Z[int, (<class 'dict'>, <class 'float'>)]

Furthermore, 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 and are thus equivalent:

   >>> class X(Generic[P]): ...
   ...
   >>> X[int, str]
   __main__.X[(<class 'int'>, <class 'str'>)]
   >>> X[[int, str]]
   __main__.X[(<class 'int'>, <class 'str'>)]

Do 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:
       # Typechecks; 'item' could be any type,
       # and that type might have a 'bar' method
       item.bar()
       ...

Notice that no typechecking is performed when assigning a value of
type "Any" to a more precise type. For example, the static type
checker did not report an error when assigning "a" to "s" even though
"s" was declared to be of type "str" and receives an "int" value at
runtime!

Furthermore, all functions without a return type or parameter types
will implicitly default to using "Any":

   def legacy_parser(text):
       ...
       return data

   # A static type checker will treat the above
   # as having the same signature as:
   def legacy_parser(text: Any) -> Any:
       ...
       return data

This behavior allows "Any" to be used as an *escape hatch* when you
need to mix dynamically and statically typed code.

Contrast the behavior of "Any" with the behavior of "object". Similar
to "Any", every type is a subtype of "object". However, unlike "Any",
the reverse is not true: "object" is *not* a subtype of every other
type.

That means when the type of a value is "object", a type checker will
reject almost all operations on it, and assigning it to a variable (or
using it as a return value) of a more specialized type is a type
error. For example:

   def hash_a(item: object) -> int:
       # Fails; an object does not have a 'magic' method.
       item.magic()
       ...

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

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

   # Typechecks, since Any is compatible with all types
   hash_b(42)
   hash_b("foo")

Use "object" to indicate that a value could be any type in a typesafe
manner. Use "Any" to indicate that a value is dynamically typed.


Nominal vs structural subtyping
===============================

Initially **PEP 484** defined Python static type system as using
*nominal subtyping*. This means that a class "A" is allowed where a
class "B" is expected if and only if "A" is a subclass of "B".

This requirement previously also applied to abstract base classes,
such as "Iterable". The problem with this approach is that a class had
to be explicitly marked to support them, which is unpythonic and
unlike what one would normally do in idiomatic dynamically typed
Python code. For example, this conforms to **PEP 484**:

   from collections.abc import Sized, Iterable, Iterator

   class Bucket(Sized, Iterable[int]):
       ...
       def __len__(self) -> int: ...
       def __iter__(self) -> Iterator[int]: ...

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

   from collections.abc import Iterator, Iterable

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

   def collect(items: Iterable[int]) -> int: ...
   result = collect(Bucket())  # Passes type check

Moreover, by subclassing a special class "Protocol", a user can define
new custom protocols to fully enjoy structural subtyping (see examples
below).


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

The module defines the following classes, functions and decorators.

Note:

  This module defines several types that are subclasses of pre-
  existing standard library classes which also extend "Generic" to
  support type variables inside "[]". These types became redundant in
  Python 3.9 when the corresponding pre-existing classes were enhanced
  to support "[]".The redundant types are deprecated as of Python 3.9
  but no deprecation warnings will be issued by the interpreter. It is
  expected that type checkers will flag the deprecated types when the
  checked program targets Python 3.9 or newer.The deprecated types
  will be removed from the "typing" module in the first Python version
  released 5 years after the release of Python 3.9.0. See details in
  **PEP 585**—*Type Hinting Generics In Standard Collections*.


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


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

These can be used as types in annotations and do not support "[]".

typing.Any

   Special type indicating an unconstrained type.

   * Every type is compatible with "Any".

   * "Any" is compatible with every type.

      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.LiteralString

   Special type that includes only literal strings. A string literal
   is compatible with "LiteralString", as is another "LiteralString",
   but 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) -> ...
          ...

      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}"
          )

   This 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.

   New in version 3.11.

typing.Never

   The bottom type, a type that has no members.

   This can be used to define a function that should never be called,
   or a function that never returns:

      from typing import Never

      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

   New in version 3.11: On older Python versions, "NoReturn" may be
   used to express the same concept. "Never" was added to make the
   intended meaning more explicit.

typing.NoReturn

   Special type indicating that a function never returns. For example:

      from typing import NoReturn

      def stop() -> NoReturn:
          raise RuntimeError('no way')

   "NoReturn" can also be used as a bottom type, a type that has no
   values. Starting in Python 3.11, the "Never" type should be used
   for this concept instead. Type checkers should treat the two
   equivalently.

   New in version 3.5.4.

   New in version 3.6.2.

typing.Self

   Special type to represent the current enclosed class. For example:

      from typing import Self

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

   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 returns_self(self: Self) -> Self:
            ...
            return self

   In general if something currently follows the pattern of:

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

   You should use use "Self" as calls to "SubclassOfFoo.returns_self"
   would have "Foo" as the return type and not "SubclassOfFoo".

   Other common use cases include:

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

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

   For more information, see **PEP 673**.

   New in version 3.11.

typing.TypeAlias

   Special annotation for explicitly declaring a type alias. For
   example:

      from typing import TypeAlias

      Factors: TypeAlias = list[int]

   See **PEP 613** for more details about explicit type aliases.

   New in version 3.10.


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

These can be used as types in annotations using "[]", each having a
unique syntax.

typing.Tuple

   Tuple type; "Tuple[X, Y]" is the type of a tuple of two items with
   the first item of type X and the second of type Y. The type of the
   empty tuple can be written as "Tuple[()]".

   Example: "Tuple[T1, T2]" is a tuple of two elements corresponding
   to type variables T1 and T2.  "Tuple[int, float, str]" is a tuple
   of an int, a float and a string.

   To specify a variable-length tuple of homogeneous type, use literal
   ellipsis, e.g. "Tuple[int, ...]". A plain "Tuple" is equivalent to
   "Tuple[Any, ...]", and in turn to "tuple".

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

typing.Union

   Union type; "Union[X, Y]" 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 type.

   "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.Callable

   Callable type; "Callable[[int], str]" is a function of (int) ->
   str.

   The subscription syntax must always be used with exactly two
   values: the argument list and the return type.  The argument list
   must be a list of types or an ellipsis; the return type must be a
   single type.

   There is no syntax to indicate optional or keyword arguments; such
   function types are rarely used as callback types. "Callable[...,
   ReturnType]" (literal ellipsis) can be used to type hint a callable
   taking any number of arguments and returning "ReturnType".  A plain
   "Callable" is equivalent to "Callable[..., Any]", and in turn to
   "collections.abc.Callable".

   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.

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

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

   See also:

     The documentation for "ParamSpec" and "Concatenate" provide
     examples of usage with "Callable".

typing.Concatenate

   Used with "Callable" and "ParamSpec" to type 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".

   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, ParamSpec, TypeVar

      P = ParamSpec('P')
      R = TypeVar('R')

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

      def with_lock(f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]:
          '''A type-safe decorator which provides a lock.'''
          global my_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])

New in version 3.10.

See also:

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

  * "ParamSpec" and "Callable".

class typing.Type(Generic[CT_co])

   A variable annotated with "C" may accept a value of type "C". In
   contrast, a variable annotated with "Type[C]" may accept values
   that are classes themselves -- specifically, it will accept the
   *class object* of "C". For example:

      a = 3         # Has type 'int'
      b = int       # Has type 'Type[int]'
      c = type(a)   # Also has type 'Type[int]'

   Note that "Type[C]" is covariant:

      class User: ...
      class BasicUser(User): ...
      class ProUser(User): ...
      class TeamUser(User): ...

      # Accepts User, BasicUser, ProUser, TeamUser, ...
      def make_new_user(user_class: Type[User]) -> User:
          # ...
          return user_class()

   The fact that "Type[C]" is covariant implies that all subclasses of
   "C" should implement the same constructor signature and class
   method signatures as "C". The type checker should flag violations
   of this, but should also allow constructor calls in subclasses that
   match the constructor calls in the indicated base class. How the
   type checker is required to handle this particular case may change
   in future revisions of **PEP 484**.

   The only legal parameters for "Type" are classes, "Any", type
   variables, and unions of any of these types. For example:

      def new_non_team_user(user_class: Type[BasicUser | ProUser]): ...

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

   New in version 3.5.2.

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

typing.Literal

   A type that can be used to indicate to type checkers that the
   corresponding variable or function parameter has a value equivalent
   to the provided literal (or one of several literals). For example:

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

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

      open_helper('/some/path', 'r')  # Passes type check
      open_helper('/other/path', 'typo')  # Error in type checker

   "Literal[...]" cannot be subclassed. At runtime, an arbitrary value
   is allowed as type argument to "Literal[...]", but type checkers
   may impose restrictions. See **PEP 586** for more details about
   literal types.

   New in version 3.8.

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

   New in version 3.5.3.

typing.Final

   A special typing construct to indicate to type checkers that a name
   cannot be re-assigned or overridden in a subclass. For example:

      MAX_SIZE: Final = 9000
      MAX_SIZE += 1  # Error reported by type checker

      class Connection:
          TIMEOUT: Final[int] = 10

      class FastConnector(Connection):
          TIMEOUT = 1  # Error reported by type checker

   There is no runtime checking of these properties. See **PEP 591**
   for more details.

   New in version 3.8.

typing.Annotated

   A type, introduced in **PEP 593** ("Flexible function and variable
   annotations"), to decorate existing types with context-specific
   metadata (possibly multiple pieces of it, as "Annotated" is
   variadic). Specifically, a type "T" can be annotated with metadata
   "x" via the typehint "Annotated[T, x]". This metadata can be used
   for either static analysis or at runtime. If a library (or tool)
   encounters a typehint "Annotated[T, x]" and has no special logic
   for metadata "x", it should ignore it and simply treat the type as
   "T". Unlike the "no_type_check" functionality that currently exists
   in the "typing" module which completely disables typechecking
   annotations on a function or a class, the "Annotated" type allows
   for both static typechecking of "T" (e.g., via mypy or Pyre, which
   can safely ignore "x") together with runtime access to "x" within a
   specific application.

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

   When a tool or a library does not support annotations or encounters
   an unknown annotation it should just ignore it and treat annotated
   type as the underlying type.

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

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

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

   Passing "include_extras=True" to "get_type_hints()" lets one access
   the extra annotations at runtime.

   The details of the syntax:

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

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

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

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

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

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

   * Nested "Annotated" types are flattened, with metadata ordered
     starting with the innermost annotation:

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

   * Duplicated annotations are not removed:

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

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

        T = TypeVar('T')
        Vec = Annotated[list[tuple[T, T]], MaxLen(10)]
        V = Vec[int]

        V == Annotated[list[tuple[int, int]], MaxLen(10)]

   New in version 3.9.

typing.TypeGuard

   Special typing form used to annotate the return type of a user-
   defined type guard function.  "TypeGuard" only accepts a single
   type argument. At runtime, functions marked this way should return
   a boolean.

   "TypeGuard" 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 guard":

      def is_str(val: str | float):
          # "isinstance" type guard
          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 guard.  Such a function should use
   "TypeGuard[...]" as its return type to alert static type checkers
   to this intention.

   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".

      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!")

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

   In short, the form "def foo(arg: TypeA) -> TypeGuard[TypeB]: ...",
   means that if "foo(arg)" returns "True", then "arg" narrows from
   "TypeA" to "TypeB".

   Note:

     "TypeB" need not be a narrower form of "TypeA" -- it can even be
     a wider form. 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. The
     responsibility of writing type-safe type guards is left to the
     user.

   "TypeGuard" also works with type variables.  For more information,
   see **PEP 647** (User-Defined Type Guards).

   New in version 3.10.


Building generic types
~~~~~~~~~~~~~~~~~~~~~~

These are not used in annotations. They are building blocks for
creating generic types.

class typing.Generic

   Abstract base class for generic types.

   A generic type is typically declared by inheriting from an
   instantiation of this class with one or more type variables. For
   example, a generic mapping type might be defined as:

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

   This class can then be used as follows:

      X = TypeVar('X')
      Y = TypeVar('Y')

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

class typing.TypeVar

   Type variable.

   Usage:

      T = TypeVar('T')  # Can be anything
      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 definitions.  See "Generic" for more
   information on generic types.  Generic functions work as follows:

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


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


      def concatenate(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.

   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:

      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".  In general,
   "isinstance()" and "issubclass()" should not be used with types.

   Type variables may be marked covariant or contravariant by passing
   "covariant=True" or "contravariant=True".  See **PEP 484** for more
   details.  By default, type variables are invariant.

class typing.TypeVarTuple

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

   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 = TypeVar('T')
      Ts = TypeVarTuple('Ts')

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

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

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

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

   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 types from normal type variables:

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

      Shape = TypeVarTuple('Shape')
      class Array(Generic[*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:

      DType = TypeVar('DType')

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

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

      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(Generic[*Shape, *Shape]):  # Not valid
          pass

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

      def call_soon(
              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".

   For more details on type variable tuples, see **PEP 646**.

   New in version 3.11.

typing.Unpack

      A typing operator that conceptually marks 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 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

   New in version 3.11.

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

   Parameter specification variable.  A specialized version of "type
   variables".

   Usage:

      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
      from typing import TypeVar, ParamSpec
      import logging

      T = TypeVar('T')
      P = ParamSpec('P')

      def add_logging(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".

   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.

   New in version 3.10.

   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").

     * "Callable" and "Concatenate".

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":

      P = ParamSpec("P")
      get_origin(P.args)  # returns P
      get_origin(P.kwargs)  # returns P

   New in version 3.10.

typing.AnyStr

   "AnyStr" is a "constrained type variable" defined as "AnyStr =
   TypeVar('AnyStr', str, bytes)".

   It is meant to be used for functions that may accept any kind of
   string without allowing different kinds of strings to mix. For
   example:

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

      concat(u"foo", u"bar")  # Ok, output has type 'unicode'
      concat(b"foo", b"bar")  # Ok, output has type 'bytes'
      concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes

class typing.Protocol(Generic)

   Base class for protocol classes. Protocol classes are defined like
   this:

      class Proto(Protocol):
          def meth(self) -> int:
              ...

   Such classes are primarily used with static type checkers that
   recognize structural subtyping (static duck-typing), for example:

      class C:
          def meth(self) -> int:
              return 0

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

      func(C())  # Passes static type check

   See **PEP 544** for details. Protocol classes decorated with
   "runtime_checkable()" (described later) act as simple-minded
   runtime protocols that check only the presence of given attributes,
   ignoring their type signatures.

   Protocol classes can be generic, for example:

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

   New in version 3.8.

@typing.runtime_checkable

   Mark a protocol class as a runtime protocol.

   Such a protocol can be used with "isinstance()" and "issubclass()".
   This raises "TypeError" when applied to a non-protocol class.  This
   allows a simple-minded structural check, very similar to "one trick
   ponies" in "collections.abc" such as "Iterable".  For example:

      @runtime_checkable
      class Closable(Protocol):
          def close(self): ...

      assert isinstance(open('/some/file'), Closable)

   Note:

     "runtime_checkable()" will check only the presence of the
     required methods, not their type signatures. For example,
     "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".

   New in version 3.8.


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

These are not used in annotations. They are building blocks for
declaring types.

class typing.NamedTuple

   Typed version of "collections.namedtuple()".

   Usage:

      class Employee(NamedTuple):
          name: str
          id: int

   This is equivalent to:

      Employee = collections.namedtuple('Employee', ['name', 'id'])

   To give a field a default value, you can assign to it in the class
   body:

      class Employee(NamedTuple):
          name: str
          id: int = 3

      employee = Employee('Guido')
      assert employee.id == 3

   Fields with a default value must come after any fields without a
   default.

   The resulting class has an extra attribute "__annotations__" giving
   a dict that maps the field names to the field types.  (The field
   names are in the "_fields" attribute and the default values are in
   the "_field_defaults" attribute both of which are part of the
   namedtuple API.)

   "NamedTuple" subclasses can also have docstrings and methods:

      class Employee(NamedTuple):
          """Represents an employee."""
          name: str
          id: int = 3

          def __repr__(self) -> str:
              return f'<Employee {self.name}, id={self.id}>'

   Backward-compatible usage:

      Employee = NamedTuple('Employee', [('name', str), ('id', int)])

   Changed in version 3.6: Added support for **PEP 526** variable
   annotation syntax.

   Changed in version 3.6.1: Added support for default values,
   methods, and docstrings.

   Changed in version 3.8: The "_field_types" and "__annotations__"
   attributes are now regular dictionaries instead of instances of
   "OrderedDict".

   Changed in version 3.9: Removed the "_field_types" attribute in
   favor of the more standard "__annotations__" attribute which has
   the same information.

class typing.NewType(name, tp)

   A helper class to indicate a distinct type to a typechecker, see
   NewType. At runtime it returns an object that returns its argument
   when called. Usage:

      UserId = NewType('UserId', int)
      first_user = UserId(1)

   New in version 3.5.2.

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

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')

   To allow using this feature with older versions of Python that do
   not support **PEP 526**, "TypedDict" supports two additional
   equivalent syntactic forms:

   * Using a literal "dict" as the second argument:

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

   * Using keyword arguments:

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

   Deprecated since version 3.11, will be removed in version 3.13: The
   keyword-argument syntax is deprecated in 3.11 and will be removed
   in 3.13. It may also be unsupported by static type checkers.

   The functional syntax should also be used when any of the keys are
   not valid Identifiers and keywords, for example because they are
   keywords or contain hyphens. Example:

      # 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 override this by specifying totality. Usage:

      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.

   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, notably
   including "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

      T = TypeVar('T')
      class XT(X, Generic[T]): pass  # raises TypeError

   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

   __required_keys__

   __optional_keys__

      "Point2D.__required_keys__" and "Point2D.__optional_keys__"
      return "frozenset" objects containing required and non-required
      keys, respectively. Currently the only way to declare both
      required and non-required keys in the same "TypedDict" is mixed
      inheritance, declaring a "TypedDict" with one value for the
      "total" argument and then inheriting it from another "TypedDict"
      with a different value for "total". Usage:

         >>> 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

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

   New in version 3.8.


Generic concrete collections
----------------------------


Corresponding to built-in types
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

   A generic version of "dict". Useful for annotating return types. To
   annotate arguments it is preferred to use an abstract collection
   type such as "Mapping".

   This type can be used as follows:

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

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

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

   Generic version of "list". Useful for annotating return types. To
   annotate arguments it is preferred to use an abstract collection
   type such as "Sequence" or "Iterable".

   This type may be used as follows:

      T = TypeVar('T', int, float)

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

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

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

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

   A generic version of "builtins.set". Useful for annotating return
   types. To annotate arguments it is preferred to use an abstract
   collection type such as "AbstractSet".

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

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

   A generic version of "builtins.frozenset".

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

Note:

  "Tuple" is a special form.


Corresponding to types in "collections"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

   A generic version of "collections.defaultdict".

   New in version 3.5.2.

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

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

   A generic version of "collections.OrderedDict".

   New in version 3.7.2.

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

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

   A generic version of "collections.ChainMap".

   New in version 3.5.4.

   New in version 3.6.1.

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

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

   A generic version of "collections.Counter".

   New in version 3.5.4.

   New in version 3.6.1.

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

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

   A generic version of "collections.deque".

   New in version 3.5.4.

   New in version 3.6.1.

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


Other concrete types
~~~~~~~~~~~~~~~~~~~~

class typing.IO
class typing.TextIO
class typing.BinaryIO

   Generic type "IO[AnyStr]" and its subclasses "TextIO(IO[str])" and
   "BinaryIO(IO[bytes])" represent the types of I/O streams such as
   returned by "open()".

   Deprecated since version 3.8, will be removed in version 3.12: The
   "typing.io" namespace is deprecated and will be removed. These
   types should be directly imported from "typing" instead.

class typing.Pattern
class typing.Match

   These type aliases correspond to the return types from
   "re.compile()" and "re.match()".  These types (and the
   corresponding functions) are generic in "AnyStr" and can be made
   specific by writing "Pattern[str]", "Pattern[bytes]", "Match[str]",
   or "Match[bytes]".

   Deprecated since version 3.8, will be removed in version 3.12: The
   "typing.re" namespace is deprecated and will be removed. These
   types should be directly imported from "typing" instead.

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

class typing.Text

   "Text" is an alias for "str". It is provided to supply a forward
   compatible path for Python 2 code: in Python 2, "Text" is an alias
   for "unicode".

   Use "Text" to indicate that a value must contain a unicode string
   in a manner that is compatible with both Python 2 and Python 3:

      def add_unicode_checkmark(text: Text) -> Text:
          return text + u' \u2713'

   New in version 3.5.2.


Abstract Base Classes
---------------------


Corresponding to collections in "collections.abc"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.AbstractSet(Sized, Collection[T_co])

   A generic version of "collections.abc.Set".

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

class typing.ByteString(Sequence[int])

   A generic version of "collections.abc.ByteString".

   This type represents the types "bytes", "bytearray", and
   "memoryview" of byte sequences.

   As a shorthand for this type, "bytes" can be used to annotate
   arguments of any of the types mentioned above.

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

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

   A generic version of "collections.abc.Collection"

   New in version 3.6.0.

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

class typing.Container(Generic[T_co])

   A generic version of "collections.abc.Container".

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

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

   A generic version of "collections.abc.ItemsView".

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

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

   A generic version of "collections.abc.KeysView".

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

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

   A generic version of "collections.abc.Mapping". This type can be
   used as follows:

      def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
          return word_list[word]

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

class typing.MappingView(Sized, Iterable[T_co])

   A generic version of "collections.abc.MappingView".

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

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

   A generic version of "collections.abc.MutableMapping".

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

class typing.MutableSequence(Sequence[T])

   A generic version of "collections.abc.MutableSequence".

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

class typing.MutableSet(AbstractSet[T])

   A generic version of "collections.abc.MutableSet".

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

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

   A generic version of "collections.abc.Sequence".

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

class typing.ValuesView(MappingView[VT_co])

   A generic version of "collections.abc.ValuesView".

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


Corresponding to other types in "collections.abc"
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

class typing.Iterable(Generic[T_co])

   A generic version of "collections.abc.Iterable".

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

class typing.Iterator(Iterable[T_co])

   A generic version of "collections.abc.Iterator".

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

class typing.Generator(Iterator[T_co], Generic[T_co, T_contra, V_co])

   A generator can be annotated by the generic type
   "Generator[YieldType, SendType, ReturnType]". For example:

      def echo_round() -> Generator[int, float, str]:
          sent = yield 0
          while sent >= 0:
              sent = yield round(sent)
          return 'Done'

   Note that unlike many other generics in the typing module, the
   "SendType" of "Generator" behaves contravariantly, not covariantly
   or invariantly.

   If your generator will only yield values, set the "SendType" and
   "ReturnType" to "None":

      def infinite_stream(start: int) -> Generator[int, None, None]:
          while True:
              yield start
              start += 1

   Alternatively, annotate your generator as having a return type of
   either "Iterable[YieldType]" or "Iterator[YieldType]":

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

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

class typing.Hashable

   An alias to "collections.abc.Hashable"

class typing.Reversible(Iterable[T_co])

   A generic version of "collections.abc.Reversible".

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

class typing.Sized

   An alias to "collections.abc.Sized"


Asynchronous programming
~~~~~~~~~~~~~~~~~~~~~~~~

class typing.Coroutine(Awaitable[V_co], Generic[T_co, T_contra, V_co])

   A generic version of "collections.abc.Coroutine". The variance and
   order of type variables correspond to those of "Generator", for
   example:

      from collections.abc import Coroutine
      c: 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

   New in version 3.5.3.

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

class typing.AsyncGenerator(AsyncIterator[T_co], Generic[T_co, T_contra])

   An async generator can be annotated by the generic type
   "AsyncGenerator[YieldType, SendType]". For example:

      async def echo_round() -> AsyncGenerator[int, float]:
          sent = yield 0
          while sent >= 0.0:
              rounded = await round(sent)
              sent = yield rounded

   Unlike normal generators, async generators cannot return a value,
   so there is no "ReturnType" type parameter. As with "Generator",
   the "SendType" behaves contravariantly.

   If your generator will only yield values, set the "SendType" to
   "None":

      async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
          while True:
              yield start
              start = await increment(start)

   Alternatively, annotate your generator as having a return type of
   either "AsyncIterable[YieldType]" or "AsyncIterator[YieldType]":

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

   New in version 3.6.1.

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

class typing.AsyncIterable(Generic[T_co])

   A generic version of "collections.abc.AsyncIterable".

   New in version 3.5.2.

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

class typing.AsyncIterator(AsyncIterable[T_co])

   A generic version of "collections.abc.AsyncIterator".

   New in version 3.5.2.

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

class typing.Awaitable(Generic[T_co])

   A generic version of "collections.abc.Awaitable".

   New in version 3.5.2.

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


Context manager types
~~~~~~~~~~~~~~~~~~~~~

class typing.ContextManager(Generic[T_co])

   A generic version of "contextlib.AbstractContextManager".

   New in version 3.5.4.

   New in version 3.6.0.

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

class typing.AsyncContextManager(Generic[T_co])

   A generic version of "contextlib.AbstractAsyncContextManager".

   New in version 3.5.4.

   New in version 3.6.2.

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


Protocols
---------

These protocols are decorated with "runtime_checkable()".

class typing.SupportsAbs

   An ABC with one abstract method "__abs__" that is covariant in its
   return type.

class typing.SupportsBytes

   An ABC with one abstract method "__bytes__".

class typing.SupportsComplex

   An ABC with one abstract method "__complex__".

class typing.SupportsFloat

   An ABC with one abstract method "__float__".

class typing.SupportsIndex

   An ABC with one abstract method "__index__".

   New in version 3.8.

class typing.SupportsInt

   An ABC with one abstract method "__int__".

class typing.SupportsRound

   An ABC with one abstract method "__round__" that is covariant in
   its return type.


Functions and decorators
------------------------

typing.cast(typ, val)

   Cast a value to a type.

   This returns the value unchanged.  To the type checker this signals
   that the return value has the designated type, but at runtime we
   intentionally don't check anything (we want this to be as fast as
   possible).

typing.assert_type(val, typ, /)

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

   When the 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

   At runtime this returns the first argument unchanged with no side
   effects.

   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)

   New in version 3.11.

typing.assert_never(arg, /)

   Assert to the type checker 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 succeed, 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 <https://typing.re
     adthedocs.io/en/latest/source/unreachable.html>_* has more
     information about exhaustiveness checking with static typing.

   New in version 3.11.

typing.reveal_type(obj)

   Reveal the inferred static type of an expression.

   When a static type checker encounters a call to this function, it
   emits a diagnostic with the 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.

   The function returns its argument unchanged, which allows using it
   within an expression:

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

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

   At runtime, this function prints the runtime type of its argument
   to stderr and returns it unchanged:

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

   New in version 3.11.

@typing.overload

   The "@overload" decorator allows describing functions and methods
   that support multiple different combinations of argument types. A
   series of "@overload"-decorated definitions must be followed by
   exactly one non-"@overload"-decorated definition (for the same
   function/method). The "@overload"-decorated definitions are for the
   benefit of the type checker only, since they will be overwritten by
   the non-"@overload"-decorated definition, while the latter is used
   at runtime but should be ignored by a type checker.  At runtime,
   calling a "@overload"-decorated function directly will raise
   "NotImplementedError". An example of overload that gives a more
   precise type than can be expressed using a union or a type
   variable:

      @overload
      def process(response: None) -> None:
          ...
      @overload
      def process(response: int) -> tuple[int, str]:
          ...
      @overload
      def process(response: bytes) -> str:
          ...
      def process(response):
          <actual implementation>

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

   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.

   New 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.

   New in version 3.11.

@typing.final

   A decorator to indicate to type checkers that the decorated method
   cannot be overridden, and the decorated class cannot be subclassed.
   For example:

      class Base:
          @final
          def done(self) -> None:
              ...
      class Sub(Base):
          def done(self) -> None:  # Error reported by type checker
                ...

      @final
      class Leaf:
          ...
      class Other(Leaf):  # Error reported by type checker
          ...

   There is no runtime checking of these properties. See **PEP 591**
   for more details.

   New in version 3.8.

   Changed in version 3.11: The decorator will now set the "__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 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).

   This mutates the function(s) in place.

@typing.no_type_check_decorator

   Decorator to give another decorator the "no_type_check()" effect.

   This wraps the decorator with something that wraps the decorated
   function in "no_type_check()".

@typing.type_check_only

   Decorator to mark a class or function to be unavailable at runtime.

   This decorator is itself not available at runtime. It is mainly
   intended to mark classes that are defined in type stub files if an
   implementation returns an instance of a private class:

      @type_check_only
      class Response:  # private or not available at runtime
          code: int
          def get_header(self, name: str) -> str: ...

      def fetch_response() -> Response: ...

   Note that returning instances of private classes is not
   recommended. It is usually preferable to make such classes public.


Introspection helpers
---------------------

typing.get_type_hints(obj, globalns=None, localns=None, include_extras=False)

   Return a dictionary containing type hints for a function, method,
   module or class object.

   This is often the same as "obj.__annotations__". In addition,
   forward references encoded as string literals are handled by
   evaluating them in "globals" and "locals" namespaces. For a class
   "C", return a dictionary constructed by merging all the
   "__annotations__" along "C.__mro__" in reverse order.

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

      class Student(NamedTuple):
          name: Annotated[str, 'some marker']

      get_type_hints(Student) == {'name': str}
      get_type_hints(Student, include_extras=False) == {'name': str}
      get_type_hints(Student, include_extras=True) == {
          'name': Annotated[str, 'some marker']
      }

   Note:

     "get_type_hints()" does not work with imported type aliases that
     include forward references. Enabling postponed evaluation of
     annotations (**PEP 563**) may remove the need for most forward
     references.

   Changed in version 3.9: Added "include_extras" parameter as part of
   **PEP 593**.

   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_args(tp)

typing.get_origin(tp)

   Provide basic introspection for generic types and special typing
   forms.

   For a typing object of the form "X[Y, Z, ...]" these functions
   return "X" and "(Y, Z, ...)". If "X" is a generic alias for a
   builtin or "collections" class, it gets normalized to the original
   class. If "X" is a union or "Literal" contained in another generic
   type, the order of "(Y, Z, ...)" may be different from the order of
   the original arguments "[Y, Z, ...]" due to type caching. For
   unsupported objects return "None" and "()" correspondingly.
   Examples:

      assert get_origin(Dict[str, int]) is dict
      assert get_args(Dict[int, str]) == (int, str)

      assert get_origin(Union[int, str]) is Union
      assert get_args(Union[int, str]) == (int, str)

   New in version 3.8.

typing.is_typeddict(tp)

   Check if a type is a "TypedDict".

   For example:

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

      is_typeddict(Film)  # => True
      is_typeddict(list | str)  # => False

   New in version 3.10.

class typing.ForwardRef

   A class used for internal typing representation of string forward
   references. For example, "list["SomeClass"]" is implicitly
   transformed into "list[ForwardRef("SomeClass")]".  This class
   should not be instantiated by a user, but may be used by
   introspection tools.

   Note:

     **PEP 585** generic types such as "list["SomeClass"]" will not be
     implicitly transformed into "list[ForwardRef("SomeClass")]" and
     thus will not automatically resolve to "list[SomeClass]".

   New in version 3.7.4.


Constant
--------

typing.TYPE_CHECKING

   A special constant that is assumed to be "True" by 3rd party static
   type checkers. It is "False" at runtime. Usage:

      if TYPE_CHECKING:
          import expensive_mod

      def fun(arg: 'expensive_mod.SomeType') -> None:
          local_var: expensive_mod.AnotherType = other_fun()

   The first type annotation must be enclosed in quotes, making it a
   "forward reference", to hide the "expensive_mod" reference from the
   interpreter runtime.  Type annotations for local variables are not
   evaluated, so the second annotation does not need to be enclosed in
   quotes.

   Note:

     If "from __future__ import annotations" is used in Python 3.7 or
     later, annotations are not evaluated at function definition time.
     Instead, they are stored as strings in "__annotations__". This
     makes it unnecessary to use quotes around the annotation. (see
     **PEP 563**).

   New in version 3.5.2.
