.. module:: typing
   :synopsis: Support for type hints (see PEP 484).

.. versionadded:: 3.5

Source code: :source:`Lib/typing.py`

This module supports type hints as specified by PEP 484. The most fundamental support consists of the types :data:`Any`, :data:`Union`, :data:`Tuple`, :data:`Callable`, :class:`TypeVar`, and :class:`Generic`. For full specification please see PEP 484. For a simplified introduction to type hints see PEP 483.

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

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

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

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:

from typing import List
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 typing import Dict, Tuple, List

ConnectionOptions = Dict[str, str]
Address = Tuple[str, int]
Server = Tuple[Address, ConnectionOptions]

def broadcast_message(message: str, servers: List[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: List[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).

Use the :func:`NewType` helper function to create distinct types:

from typing import NewType

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

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

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

# typechecks
user_a = get_user_name(UserId(42351))

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

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

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

Note that these checks are enforced only by the static type checker. At runtime the statement Derived = NewType('Derived', Base) will make Derived a function that immediately returns whatever parameter you pass it. That means the expression Derived(some_value) does not create a new class or introduce any overhead beyond that of a regular function call.

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

This also means that it is not possible to create a subtype of Derived since it is an identity function at runtime, not an actual type. Similarly, it is not possible to create another :func:`NewType` based on a Derived type:

from typing import NewType

UserId = NewType('UserId', int)

# Fails at runtime and does not typecheck
class AdminUserId(UserId): pass

# Also does not typecheck
ProUserId = NewType('ProUserId', UserId)

See PEP 484 for more details.

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

For example:

from typing 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].

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 typing import Mapping, Sequence

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

Generics can be parametrized by using a new factory available in typing called :class:`TypeVar`.

from typing import Sequence, TypeVar

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

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

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 :class:`Generic` base class uses a metaclass that defines :meth:`__getitem__` so that LoggedVar[t] is valid as a type:

from typing import Iterable

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

A generic type can have any number of type variables, and type variables may be constrained:

from typing import TypeVar, Generic
...

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

class StrangePair(Generic[T, S]):
    ...

Each type variable argument to :class:`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 :class:`Generic`:

from typing import TypeVar, Generic, Sized

T = TypeVar('T')

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

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

from typing import TypeVar, Mapping

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 :data:`Any` for each position. In the following example, MyIterable is not generic but implicitly inherits from Iterable[Any]:

from typing import Iterable

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

User defined generic type aliases are also supported. Examples:

from typing import TypeVar, Iterable, Tuple, Union
S = TypeVar('S')
Response = Union[Iterable[S], int]

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

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

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

The metaclass used by :class:`Generic` is a subclass of :class:`abc.ABCMeta`. A generic class can be an ABC by including abstract methods or properties, and generic classes can also 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.

A special kind of type is :data:`Any`. A static type checker will treat every type as being compatible with :data:`Any` and :data:`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 on :data:`Any` and assign it to any variable:

from typing import Any

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

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

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

Notice that no typechecking is performed when assigning a value of type :data:`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 :class:`str` and receives an :class:`int` value at runtime!

Furthermore, all functions without a return type or parameter types will implicitly default to using :data:`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 :data:`Any` to be used as an escape hatch when you need to mix dynamically and statically typed code.

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

That means when the type of a value is :class:`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 :class:`object` to indicate that a value could be any type in a typesafe manner. Use :data:`Any` to indicate that a value is dynamically typed.

The module defines the following classes, functions and decorators:

Type variable.

Usage:

T = TypeVar('T')  # Can be anything
A = TypeVar('A', str, bytes)  # Must be str or bytes

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See class Generic for more information on generic types. Generic functions work as follows:

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

def longest(x: A, y: A) -> A:
    """Return the longest of two strings."""
    return x if len(x) >= len(y) else y

The latter example's signature is essentially the overloading of (str, str) -> str and (bytes, bytes) -> bytes. Also note that if the arguments are instances of some subclass of :class:`str`, the return type is still plain :class:`str`.

At runtime, isinstance(x, T) will raise :exc:`TypeError`. In general, :func:`isinstance` and :func:`issubclass` should not be used with types.

Type variables may be marked covariant or contravariant by passing covariant=True or contravariant=True. See PEP 484 for more details. By default type variables are invariant. Alternatively, a type variable may specify an upper bound using bound=<type>. This means that an actual type substituted (explicitly or implicitly) for the type variable must be a subclass of the boundary type, see PEP 484.

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

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 :class:`Type` are classes, unions of classes, and :data:`Any`. For example:

def new_non_team_user(user_class: Type[Union[BaseUser, ProUser]]): ...

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

A generic version of :class:`collections.abc.Iterable`.

A generic version of :class:`collections.abc.Iterator`.

A generic version of :class:`collections.abc.Reversible`.

An ABC with one abstract method __int__.

An ABC with one abstract method __float__.

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

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

A generic version of :class:`collections.abc.Container`.

An alias to :class:`collections.abc.Hashable`

An alias to :class:`collections.abc.Sized`

A generic version of :class:`collections.abc.Set`.

A generic version of :class:`collections.abc.MutableSet`.

A generic version of :class:`collections.abc.Mapping`.

A generic version of :class:`collections.abc.MutableMapping`.

A generic version of :class:`collections.abc.Sequence`.

A generic version of :class:`collections.abc.MutableSequence`.

A generic version of :class:`collections.abc.ByteString`.

This type represents the types :class:`bytes`, :class:`bytearray`, and :class:`memoryview`.

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

A generic version of :class:`collections.deque`.

.. versionadded:: 3.5.4

Generic version of :class:`list`. Useful for annotating return types. To annotate arguments it is preferred to use abstract collection types such as :class:`Mapping`, :class:`Sequence`, or :class:`AbstractSet`.

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]

A generic version of :class:`builtins.set <set>`.

A generic version of :class:`builtins.frozenset <frozenset>`.

A generic version of :class:`collections.abc.MappingView`.

A generic version of :class:`collections.abc.KeysView`.

A generic version of :class:`collections.abc.ItemsView`.

A generic version of :class:`collections.abc.ValuesView`.

A generic version of :class:`collections.abc.Awaitable`.

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

from typing import List, Coroutine
c = None # type: Coroutine[List[str], str, int]
...
x = c.send('hi') # type: List[str]
async def bar() -> None:
    x = await c # type: int

A generic version of :class:`collections.abc.AsyncIterable`.

A generic version of :class:`collections.abc.AsyncIterator`.

A generic version of :class:`dict`. The usage of this type is as follows:

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

A generic version of :class:`collections.defaultdict`

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 :class:`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

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 :class:`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)

.. versionadded:: 3.5.4

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'

Wrapper namespace for I/O stream types.

This defines the generic type IO[AnyStr] and aliases TextIO and BinaryIO for respectively IO[str] and IO[bytes]. These representing the types of I/O streams such as returned by :func:`open`.

Wrapper namespace for regular expression matching types.

This defines the type aliases Pattern and Match which correspond to the return types from :func:`re.compile` and :func:`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].

.. function:: NamedTuple(typename, fields)

   Typed version of namedtuple.

   Usage::

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

   This is equivalent to::

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

   The resulting class has one extra attribute: _field_types,
   giving a dict mapping field names to types.  (The field names
   are in the _fields attribute, which is part of the namedtuple
   API.)

.. function:: NewType(typ)

   A helper function to indicate a distinct types to a typechecker,
   see :ref:`distinct`. At runtime it returns a function that returns
   its argument. Usage::

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

.. function:: 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).

.. function:: get_type_hints(obj[, globals[, locals]])

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

   This is often the same as ``obj.__annotations__``. In addition,
   forward references encoded as string literals are handled by evaluating
   them in ``globals`` and ``locals`` namespaces. If necessary,
   ``Optional[t]`` is added for function and method annotations if a default
   value equal to ``None`` is set. For a class ``C``, return
   a dictionary constructed by merging all the ``__annotations__`` along
   ``C.__mro__`` in reverse order.

.. decorator:: 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.

.. decorator:: no_type_check(arg)

   Decorator to indicate that annotations are not type hints.

   The argument must be a class or function; if it is a class, it
   applies recursively to all methods defined in that class (but not
   to methods defined in its superclasses or subclasses).

   This mutates the function(s) in place.

.. decorator:: no_type_check_decorator(decorator)

   Decorator to give another decorator the :func:`no_type_check` effect.

   This wraps the decorator with something that wraps the decorated
   function in :func:`no_type_check`.

.. data:: Any

   Special type indicating an unconstrained type.

   * Every type is compatible with :data:`Any`.
   * :data:`Any` is compatible with every type.

.. data:: Union

   Union type; ``Union[X, Y]`` means either X or Y.

   To define a union, use e.g. ``Union[int, str]``.  Details:

   * The arguments must be types and there must be at least one.

   * Unions of unions are flattened, e.g.::

       Union[Union[int, str], float] == Union[int, str, float]

   * Unions of a single argument vanish, e.g.::

       Union[int] == int  # The constructor actually returns int

   * Redundant arguments are skipped, e.g.::

       Union[int, str, int] == Union[int, str]

   * When comparing unions, the argument order is ignored, e.g.::

       Union[int, str] == Union[str, int]

   * When a class and its subclass are present, the latter is skipped, e.g.::

       Union[int, object] == object

   * You cannot subclass or instantiate a union.

   * You cannot write ``Union[X][Y]``.

   * You can use ``Optional[X]`` as a shorthand for ``Union[X, None]``.

.. data:: Optional

   Optional type.

   ``Optional[X]`` is equivalent to ``Union[X, None]``.

   Note that this is not the same concept as an optional argument,
   which is one that has a default.  An optional argument with a
   default needn't use the ``Optional`` qualifier on its type
   annotation (although it is inferred if the default is ``None``).
   A mandatory argument may still have an ``Optional`` type if an
   explicit value of ``None`` is allowed.

.. data:: 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.

   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 :data:`Tuple`
   is equivalent to ``Tuple[Any, ...]``, and in turn to :class:`tuple`.

.. data:: 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 :data:`Callable` is equivalent to
   ``Callable[..., Any]``, and in turn to
   :class:`collections.abc.Callable`.

.. data:: 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 = {}  # type: ClassVar[Dict[str, int]] # class variable
          damage = 10 # type: int                      # instance variable

   :data:`ClassVar` accepts only types and cannot be further subscribed.

   :data:`ClassVar` is not a class itself, and should not
   be used with :func:`isinstance` or :func:`issubclass`.
   Note that :data:`ClassVar` does not change Python runtime behavior;
   it can be used by 3rd party type checkers, so that the following
   code might flagged as an error by those::

      enterprise_d = Starship(3000)
      enterprise_d.stats = {} # Error, setting class variable on instance
      Starship.stats = {}     # This is OK

   .. versionadded:: 3.5.3

.. data:: AnyStr

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

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

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

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

.. data:: 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()

   Note that 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.