python-peps/pep-0483.txt

PEP: 483
Title: The Theory of Type Hints
Version: $Revision$
Last-Modified: $Date$
Author: Guido van Rossum <guido@python.org>, Ivan Levkivskyi <levkivskyi@gmail.com>
Discussions-To: Python-Ideas <python-ideas@python.org>
Status: Draft
Type: Informational
Content-Type: text/x-rst
Created: 19-Dec-2014
Post-History:
Resolution:

Abstract
========

This PEP lays out the theory referenced by PEP 484.


Introduction
============

This document lays out the theory of the new type hinting proposal for
Python 3.5. It's not quite a full proposal or specification because
there are many details that need to be worked out, but it lays out the
theory without which it is hard to discuss more detailed specifications.
We start by recalling basic concepts of type theory; then we explain
gradual typing; then we state some general rules and
define the new special types (such as ``Union``) that can be used
in annotations; and finally we define the approach to generic types
and pragmatic aspects of type hinting.


Notational conventions
----------------------

-  ``t1``, ``t2``, etc. and ``u1``, ``u2``, etc. are types. Sometimes we write
   ``ti`` or ``tj`` to refer to "any of ``t1``, ``t2``, etc."
-  ``T``, ``U`` etc. are type variables (defined with ``TypeVar()``, see below).
-  Objects, classes defined with a class statement, and instances are
   denoted using standard PEP 8 conventions.
-  the symbol ``==`` applied to types in the context of this PEP means that
   two expressions represent the same type.
-  Note that PEP 484 makes a distinction between types and classes
   (a type is a concept for the type checker,
   while a class is a runtime concept).  In this PEP we clarify
   this distinction but avoid unnecessary strictness to allow more
   flexibility in the implementation of type checkers.


Background
==========

There are many definitions of the concept of type in the literature.
Here we assume that type is a set of values and a set of functions that
one can apply to these values.

There are several ways to define a particular type:

- By explicitly listing all values.  E.g., ``True`` and ``False``
  form the type ``bool``.
- By specifying functions which can be used with variables of
  a type.  E.g. all objects that have a ``__len__`` method form
  the type ``Sized``.  Both ``[1, 2, 3]`` and ``'abc'`` belong to
  this type, since one can call ``len`` on them::

    len([1, 2, 3])  # OK
    len('abc')      # also OK
    len(42)         # not a member of Sized

- By a simple class definition, for example if one defines a class::

    class UserID(int):
        pass

  then all instances of this class also form a type.
- There are also more complex types.  E.g., one can define the type
  ``FancyList`` as all lists containing only instances of ``int``, ``str``
  or their subclasses.  The value ``[1, 'abc', UserID(42)]`` has this type.

It is important for the user to be able to define types in a form
that can be understood by type checkers.
The goal of this PEP is to propose such a systematic way of defining types
for type annotations of variables and functions using PEP 3107 syntax.
These annotations can be used to avoid many kind of bugs, for documentation
purposes, or maybe even to increase speed of program execution.
Here we only focus on avoiding bugs by using a static type checker.


Subtype relationships
---------------------

A crucial notion for static type checker is the subtype relationship.
It arises from the question: If ``first_var`` has type ``first_type``, and
``second_var`` has type ``second_type``, is it safe to assign
``first_var = second_var``?

A strong criterion for when it *should* be safe is:

- every value from ``second_type`` is also in the set of values
  of ``first_type``; and
- every function from ``first_type`` is also in the set of functions
  of ``second_type``.

The relation defined thus is called a subtype relation.

By this definition:

-  Every type is a subtype of itself.
-  The set of values becomes smaller in the process of subtyping,
   while the set of functions becomes larger.

An intuitive example: Every ``Dog`` is an ``Animal``, also ``Dog``
has more functions, for example it can bark, therefore ``Dog``
is a subtype of ``Animal``.  Conversely, ``Animal`` is not a subtype of ``Dog``.

A more formal example: Integers are subtype of real numbers.
Indeed, every integer is of course also a real number, and integers
support more operations, such as, e.g., bitwise shifts ``<<`` and ``>>``::

  lucky_number = 3.14    # type: float
  lucky_number = 42      # Safe
  lucky_number * 2       # This works
  lucky_number << 5      # Fails

  unlucky_number = 13    # type: int
  unlucky_number << 5    # This works
  unlucky_number = 2.72  # Unsafe

Let us also consider a tricky example: If ``List[int]`` denotes the type
formed by all lists containing only integer numbers,
then it is *not* a subtype of ``List[float]``, formed by all lists that contain
only real numbers. The first condition of subtyping holds,
but appending a real number only works with ``List[float]`` so that
the second condition fails::

  def append_pi(lst: List[float]) -> None:
      lst += [3.14]

  my_list = [1, 3, 5]  # type: List[int]

  append_pi(my_list)   # Naively, this should be safe...

  my_list[-1] << 5     # ... but this fails

There are two widespread approaches to *declare* subtype information
to type checker.

In nominal subtyping, the type tree is based on the class tree,
i.e., ``UserID`` is considered a subtype of ``int``.
This approach should be used under control of the type checker,
because in Python one can override attributes in an incompatible way::

  class Base:
      answer = '42' # type: str

  class Derived(Base):
      answer = 5 # should be marked as error by type checker

In structural subtyping the subtype relation is deduced from the
declared methods, i.e., ``UserID`` and ``int`` would be considered the same type.
While this may occasionally cause confusion,
structural subtyping is considered more flexible.
We strive to provide support for both approaches, so that
structural information can be used in addition to nominal subtyping.


Summary of gradual typing
=========================

Gradual typing allows one to annotate only part of a program,
thus leverage desirable aspects of both dynamic and static typing.

We define a new relationship, is-consistent-with, which is similar to
is-subtype-of, except it is not transitive when the new type ``Any`` is
involved. (Neither relationship is symmetric.) Assigning ``a_value``
to ``a_variable`` is OK if the type of ``a_value`` is consistent with
the type of ``a_variable``. (Compare this to "... if the type of ``a_value``
is a subtype of the type of ``a_variable``", which states one of the
fundamentals of OO programming.) The is-consistent-with relationship is
defined by three rules:

-  A type ``t1`` is consistent with a type ``t2`` if ``t1`` is a
   subtype of ``t2``. (But not the other way around.)
-  ``Any`` is consistent with every type. (But ``Any`` is not a subtype
   of every type.)
-  Every type is a subtype of ``Any``. (Which also makes every type
   consistent with ``Any``, via rule 1.)

That's all! See Jeremy Siek's blog post `What is Gradual
Typing <http://wphomes.soic.indiana.edu/jsiek/what-is-gradual-typing/>`_
for a longer explanation and motivation. Note that rule 3 places ``Any``
at the root of the type graph. This makes it very similar to
``object``. The difference is that ``object`` is not consistent with
most types (e.g. you can't use an ``object()`` instance where an
``int`` is expected). IOW both ``Any`` and ``object`` mean
"any type is allowed" when used to annotate an argument, but only ``Any``
can be passed no matter what type is expected (in essence, ``Any``
declares a fallback to dynamic typing and shuts up complaints
from the static checker).

Here's an example showing how these rules work out in practice:

Say we have an ``Employee`` class, and a subclass ``Manager``::

  class Employee: ...
  class Manager(Employee): ...

Let's say variable ``worker`` is declared with type ``Employee``::

  worker = Employee()  # type: Employee

Now it's okay to assign a ``Manager`` instance to ``worker`` (rule 1)::

  worker = Manager()

It's not okay to assign an ``Employee`` instance to a variable declared with
type ``Manager``::

  boss = Manager()  # type: Manager
  boss = Employee()  # Fails static check

However, suppose we have a variable whose type is ``Any``::

  something = some_func()  # type: Any

Now it's okay to assign ``something`` to ``worker`` (rule 2)::

  worker = something  # OK

Of course it's also okay to assign ``worker`` to ``something`` (rule 3),
but we didn't need the concept of consistency for that::

  something = worker  # OK


Types vs. Classes
-----------------

In Python, classes are object factories defined by the ``class`` statement,
and returned by the ``type(obj)`` built-in function. Class is a dynamic,
runtime concept.

Type concept is described above, types appear in variable
and function type annotations, can be constructed
from building blocks described below, and are used by static type checkers.

Every class is a type as discussed above.
But it is tricky and error prone to implement a class that exactly represents
semantics of a given type, and it is not a goal of PEP 484.
*The static types described in PEP 484, should not be confused with
the runtime classes.* Examples:

- ``int`` is a class and a type.
- ``UserID`` is a class and a type.
- ``Union[str, int]`` is a type but not a proper class::

      class MyUnion(Union[str, int]): ...  # raises TypeError

      Union[str, int]()  # raises TypeError

Typing interface is implemented with classes, i.e., at runtime it is possible
to evaluate, e.g., ``Generic[T].__bases__``. But to emphasize the distinction
between classes and types the following general rules apply:

-  No types defined below (i.e. ``Any``, ``Union``, etc.) can be instantiated,
   an attempt to do so will raise ``TypeError``.
   (But non-abstract subclasses of ``Generic`` can be.)
-  No types defined below can be subclassed, except for ``Generic`` and
   classes derived from it.
-  All of these will raise ``TypeError`` if they appear
   in ``isinstance`` or ``issubclass``.


Fundamental building blocks
---------------------------

-  **Any**. Every type is a subtype of ``Any``; however, to the static
   type checker it is also consistent with every type (see above).
-  **Union[t1, t2, ...]**. Types that are subtype of at least one of
   ``t1`` etc. are subtypes of this.

   *  Unions whose components are all subtypes of ``t1`` etc. are subtypes
      of this.
      Example: ``Union[int, str]`` is a subtype of ``Union[int, float, str]``.
   *  The order of the arguments doesn't matter.
      Example: ``Union[int, str] == Union[str, int]``.
   *  If ``ti`` is itself a ``Union`` the result is flattened.
      Example: ``Union[int, Union[float, str]] == Union[int, float, str]``.
   *  If ``ti`` and ``tj`` have a subtype relationship,
      the less specific type survives.
      Example: ``Union[Employee, Manager] == Union[Employee]``.
   *  ``Union[t1]`` returns just ``t1``. ``Union[]`` is illegal,
      so is ``Union[()]``
   *  Corollary: ``Union[..., Any, ...]`` returns ``Any``;
      ``Union[..., object, ...]`` returns ``object``; to cut a
      tie, ``Union[Any, object] == Union[object, Any] == Any``.

-  **Optional[t1]**. Alias for ``Union[t1, None]``, i.e. ``Union[t1,
   type(None)]``.
-  **Tuple[t1, t2, ..., tn]**. A tuple whose items are instances of ``t1``,
   etc. Example: ``Tuple[int, float]`` means a tuple of two items, the
   first is an ``int``, the second is a ``float``; e.g., ``(42, 3.14)``.

   *  ``Tuple[u1, u2, ..., um]`` is a subtype of ``Tuple[t1, t2, ..., tn]``
      if they have the same length ``n==m`` and each ``ui``
      is a subtype of ``ti``.
   *  To spell the type of the empty tuple, use ``Tuple[()]``.
   *  A variadic homogeneous tuple type can be written ``Tuple[t1, ...]``.
      (That's three dots, a literal ellipsis;
      and yes, that's a valid token in Python's syntax.)

-  **Callable[[t1, t2, ..., tn], tr]**. A function with positional
   argument types ``t1`` etc., and return type ``tr``. The argument list may be
   empty ``n==0``. There is no way to indicate optional or keyword
   arguments, nor varargs, but you can say the argument list is entirely
   unchecked by writing ``Callable[..., tr]`` (again, a literal ellipsis).

We might add:

-  **Intersection[t1, t2, ...]**. Types that are subtype of *each* of
   ``t1``, etc are subtypes of this. (Compare to ``Union``, which has *at
   least one* instead of *each* in its definition.)

   *  The order of the arguments doesn't matter. Nested intersections
      are flattened, e.g. ``Intersection[int, Intersection[float, str]]
      == Intersection[int, float, str]``.
   *  An intersection of fewer types is a supertype of an intersection of
      more types, e.g. ``Intersection[int, str]`` is a supertype
      of ``Intersection[int, float, str]``.
   *  An intersection of one argument is just that argument,
      e.g. ``Intersection[int]`` is ``int``.
   *  When argument have a subtype relationship, the more specific type
      survives, e.g. ``Intersection[str, Employee, Manager]`` is
      ``Intersection[str, Manager]``.
   *  ``Intersection[]`` is illegal, so is ``Intersection[()]``.
   *  Corollary: ``Any`` disappears from the argument list, e.g.
      ``Intersection[int, str, Any] == Intersection[int, str]``.
      ``Intersection[Any, object]`` is ``object``.
   *  The interaction between ``Intersection`` and ``Union`` is complex but
      should be no surprise if you understand the interaction between
      intersections and unions of regular sets (note that sets of types can be
      infinite in size, since there is no limit on the number
      of new subclasses).


Generic types
=============

The fundamental building blocks defined above allow to construct new types
in a generic manner. For example, ``Tuple`` can take a concrete type ``float``
and make a concrete type ``Vector = Tuple[float, ...]``, or it can take
another type ``UserID`` and make another concrete type
``Registry = Tuple[UserID, ...]``. Such semantics is known as generic type
constructor, it is similar to semantics of functions, but a function takes
a value and returns a value, while generic type constructor takes a type and
"returns" a type.

It is common when a particular class or a function behaves in such a type
generic manner. Consider two examples:

- Container classes, such as ``list`` or ``dict``, typically contain only
  values of a particular type. Therefore, a user might want to type annotate
  them as such::

    users = [] # type: List[UserID]
    users.append(UserID(42)) # OK
    users.append('Some guy') # Should be rejected by the type checker

    examples = {} # type: Dict[str, Any]
    examples['first example'] = object() # OK
    examples[2] = None                   # rejected by the type checker

- The following function can take two arguments of type ``int`` and return
  an ``int``, of take two arguments of type ``float`` and return
  a ``float``, etc.::

    def add(x, y):
        return x + y

    add(1, 2) == 3
    add('1', '2') == '12'
    add(2.7, 3.5) == 6.2

To allow type annotations in situations from the first example, built-in
containers and container abstract base classes are extended with type
parameters, so that they behave as generic type constructors.
Classes, that behave as generic type constructors are called *generic types*.
Example::

  from typing import Iterable

  class Task:
      ...

  def work(todo_list: Iterable[Task]) -> None:
      ...

Here ``Iterable`` is a generic type that takes a concrete type ``Task``
and returns a concrete type ``Iterable[Task]``.

Functions that behave in the type generic manner (as in second example)
are called *generic functions*.
Type annotations of generic functions are allowed by *type variables*.
Their semantics with respect to generic types is somewhat similar
to semantics of parameters in functions. But one does not assign
concrete types to type variables, it is the task of a static type checker
to find their possible values and warn the user if it cannot find.
Example::

  def take_first(seq: Sequence[T]) -> T: # a generic function
      return seq[0]

  accumulator = 0 # type: int

  accumulator += take_first([1, 2, 3])   # Safe, T deduced to be int
  accumulator += take_first((2.7, 3.5))  # Unsafe

Type variables are used extensively in type annotations, also internal
machinery of the type inference in type checkers is typically build on
type variables. Therefore, let us consider them in detail.


Type variables
--------------

``X = TypeVar('X')`` declares a unique type variable. The name must match
the variable name. By default, a type variable ranges
over all possible types. Example::

  def do_nothing(one_arg: T, other_arg: T) -> None:
      pass

  do_nothing(1, 2)               # OK, T is int
  do_nothing('abc', UserID(42))  # also OK, T is object

``Y = TypeVar('Y', t1, t2, ...)``. Ditto, constrained to ``t1``, etc. Behaves
similar to ``Union[t1, t2, ...]``. A constrained type variable ranges only
over constrains ``t1``, etc. *exactly*; subclasses of the constrains are
replaced by the most-derived base class among ``t1``, etc. Examples:

- Function type annotation with a constrained type variable::

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

    def longest(first: S, second: S) -> S:
        return first if len(first) >= len(second) else second

    result = longest('a', 'abc')  # The inferred type for result is str

    result = longest('a', b'abc')  # Fails static type check

  In this example, both arguments to ``longest()`` must have the same type
  (``str`` or ``bytes``), and moreover, even if the arguments are instances
  of a common ``str`` subclass, the return type is still ``str``, not that
  subclass (see next example).

- For comparison, if the type variable was unconstrained, the common
  subclass would be chosen as the return type, e.g.::

    S = TypeVar('S')

    def longest(first: S, second: S) -> S:
        return first if len(first) >= len(second) else second

    class MyStr(str): ...

    result = longest(MyStr('a'), MyStr('abc'))

  The inferred type of ``result`` is ``MyStr`` (whereas in the ``AnyStr`` example
  it would be ``str``).

- Also for comparison, if a ``Union`` is used, the return type also has to be
  a ``Union``::

    U = Union[str, bytes]

    def longest(first: U, second: U) -> U:
        return first if len(first) >= len(second) else second

    result = longest('a', 'abc')

  The inferred type of ``result`` is still ``Union[str, bytes]``, even though
  both arguments are ``str``.

  Note that the type checker will reject this function::

    def concat(first: U, second: U) -> U:
        return x + y  # Error: can't concatenate str and bytes

  For such cases where parameters could change their types only simultaneously
  one should use constrained type variables.


Defining and using generic types
--------------------------------

Users can declare their classes as generic types using
the special building block ``Generic``. The definition
``class MyGeneric(Generic[X, Y, ...]): ...`` defines a generic type
``MyGeneric`` over type variables ``X``, etc. ``MyGeneric`` itself becomes
parameterizable, e.g. ``MyGeneric[int, str, ...]`` is a specific type with
substitutions ``X -> int``, etc. Example::

  class CustomQueue(Generic[T]):

      def put(self, task: T) -> None:
          ...
      def get(self) -> T:
          ...

  def communicate(queue: CustomQueue[str]) -> Optional[str]:
      ...

Classes that derive from generic types become generic.
A class can subclass multiple generic types. However,
classes derived from specific types returned by generics are
not generic. Examples::

  class TodoList(Iterable[T], Container[T]):
      def check(self, item: T) -> None:
          ...

  def check_all(todo: TodoList[T]) -> None:  # TodoList is generic
      ...

  class URLList(Iterable[bytes]):
      def scrape_all(self) -> None:
          ...

  def search(urls: URLList) -> Optional[bytes]  # URLList is not generic
      ...

Subclassing a generic type imposes the subtype relation on the corresponding
specific types, so that ``TodoList[t1]`` is a subtype of ``Iterable[t1]``
in the above example.

Generic types can be specialized (indexed) in several steps.
Every type variable could be substituted by a specific type
or by another generic type. If ``Generic`` appears in the base class list,
then it should contain all type variables, and the order of type parameters is
determined by the order in which they appear in ``Generic``. Examples::

  Table = Dict[int, T]     # Table is generic
  Messages = Table[bytes]  # Same as Dict[int, bytes]

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

  class DerivedGeneric(BaseGeneric[int, T]): # DerivedGeneric has one parameter
      ...

  SpecificType = DerivedGeneric[int]         # OK

  class MyDictView(Generic[S, T, U], Iterable[Tuple[U, T]]):
      ...

  Example = MyDictView[list, int, str]       # S -> list, T -> int, U -> str

If a generic type appears in a type annotation with a type variable omitted,
it is assumed to be ``Any``. Such form could be used as a fallback
to dynamic typing and is allowed for use with ``issubclass``
and ``isinstance``. All type information in instances is erased at runtime.
Examples::

  def count(seq: Sequence) -> int:      # Same as Sequence[Any]
      ...

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

  class UserClass:
      ...

  issubclass(UserClass, FrameworkBase)  # This is OK

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

  IntNode = Node[int]
  my_node = IntNode()  # at runtime my_node.__class__ is Node
                       # inferred static type of my_node is Node[int]


Covariance and Contravariance
-----------------------------

If ``t2`` is a subtype of ``t1``, then a generic
type constructor ``GenType`` is called:

- Covariant, if ``GenType[t2]`` is a subtype of ``GenType[t1]``
  for all such ``t1`` and ``t2``.
- Contravariant,  if ``GenType[t1]`` is a subtype of ``GenType[t2]``
  for all such ``t1`` and ``t2``.
- Invariant, if neither of the above is true.

To better understand this definition, let us make an analogy with
ordinary functions. Assume that we have::

  def cov(x: float) -> float:
      return 2*x

  def contra(x: float) -> float:
      return -x

  def inv(x: float) -> float:
      return x*x

If ``x1 < x2``, then *always* ``cov(x1) < cov(x2)``, and
``contra(x2) < contra(x1)``, while nothing could be said about ``inv``.
Replacing ``<`` with is-subtype-of, and functions with generic type
constructor we get examples of covariant, contravariant,
and invariant behavior. Let us now consider practical examples:

- ``Union`` behaves covariantly in all its arguments.
  Indeed, as discussed above, ``Union[t1, t2, ...]`` is a subtype of
  ``Union[u1, u2, ...]``, if ``t1`` is a subtype of ``u1``, etc.
- ``FrozenSet[T]`` is also covariant. Let us consider ``int`` and
  ``float`` in place of ``T``. First, ``int`` is a subtype of ``float``.
  Second, set of values of ``FrozenSet[int]`` is
  clearly a subset of values of ``FrozenSet[float]``, while set of functions
  from ``FrozenSet[float]`` is a subset of set of functions
  from ``FrozenSet[int]``. Therefore, by definition ``FrozenSet[int]``
  is a subtype of ``FrozenSet[float]``.
- ``List[T]`` is invariant. Indeed, although set of values of ``List[int]``
  is a subset of values of ``List[float]``, only ``int`` could be appended
  to a ``List[int]``, as discussed in section "Background". Therefore,
  ``List[int]`` is not a subtype of ``List[float]``. This is a typical
  situation with mutable types, they are typically invariant.

One of the best examples to illustrate (somewhat counterintuitive)
contravariant behavior is the callable type.
It is covariant in the return type, but contravariant in the
arguments. For two callable types that
differ only in the return type, the subtype relationship for the
callable types follows that of the return types. Examples:

- ``Callable[[], int]`` is a subtype of ``Callable[[], float]``.
- ``Callable[[], Manager]`` is a subtype of ``Callable[[], Employee]``.

While for two callable types that differ
only in the type of one argument, the subtype relationship for the
callable types goes *in the opposite direction* as for the argument
types. Examples:

- ``Callable[[float], None]`` is a subtype of ``Callable[[int], None]``.
- ``Callable[[Employee], None]`` is a subtype of ``Callable[[Manager], None]``.

Yes, you read that right. Indeed, if
a function that can calculate the salary for a manager is expected::

  def calculate_all(lst: List[Manager], salary: Callable[[Manager], Decimal]):
      ...

then ``Callable[[Employee], Decimal]`` that can calculate a salary for any
employee is also acceptable.

The example with ``Callable`` shows how to make more precise type annotations
for functions: choose the most general type for every argument,
and the most specific type for the return value.

It is possible to *declare* the variance for user defined generic types by
using special keywords ``covariant`` and ``contravariant`` in the
definition of type variables used as parameters.
Types are invariant by default. Examples::

  T = TypeVar('T')
  T_co = TypeVar('T_co', covariant=True)
  T_contra = TypeVar('T_contra', contravariant=True)

  class LinkedList(Generic[T]):  # invariant by default
      ...
      def append(self, element: T) -> None:
          ...

  class Box(Generic[T_co]):      #  this type is declared covariant
      def __init__(self, content: T_co) -> None:
          self._content = content
      def get_content(self) -> T_co:
          return self._content

  class Sink(Generic[T_contra]): # this type is declared contravariant
      def send_to_nowhere(self, data: T_contra) -> None:
          with open(os.devnull, 'w') as devnull:
              print(data, file=devnull)

Note, that although the variance is defined via type variables, it is not
a property of type variables, but a property of generic types.
In complex definitions of derived generics, variance *only*
determined from type variables used. A complex example::

  T_co = TypeVar('T_co', Employee, Manager, covariant=True)
  T_contra = TypeVar('T_contra', Employee, Manager, contravariant=True)

  class Base(Generic[T_contra]):
      ...

  class Derived(Base[T_co]):
      ...

A type checker finds from the second declaration that ``Derived[Manager]``
is a subtype of ``Derived[Employee]``, and ``Derived[t1]``
is a subtype of ``Base[t1]``.
If we denote the is-subtype-of relationship with ``<``, then the
full diagram of subtyping for this case will be::

  Base[Manager]    >  Base[Employee]
      v                   v
  Derived[Manager] <  Derived[Employee]

so that a type checker will also find that, e.g., ``Derived[Manager]`` is
a subtype of ``Base[Employee]``.

For more information on type variables, generic types, and variance,
see PEP 484, the `mypy docs on
generics <http://mypy.readthedocs.io/en/latest/generics.html>`_,
and `Wikipedia <http://en.wikipedia.org/wiki/
Covariance_and_contravariance_%28computer_science%29>`_.


Pragmatics
==========

Some things are irrelevant to the theory but make practical use more
convenient. (This is not a full list; I probably missed a few and some
are still controversial or not fully specified.)

-  Where a type is expected, ``None`` can be substituted for ``type(None)``;
   e.g. ``Union[t1, None] == Union[t1, type(None)]``.

- Type aliases, e.g.::

    Point = Tuple[float, float]
    def distance(point: Point) -> float: ...

- Forward references via strings, e.g.::

    class MyComparable:
        def compare(self, other: 'MyComparable') -> int: ...

- If a default of ``None`` is specified, the type is implicitly
  ``Optional``, e.g.::

    def get(key: KT, default: VT = None) -> VT: ...

- Type variables can be declared in unconstrained, constrained,
  or bounded form. The variance of a generic type can also
  be indicated using a type variable declared with special keyword
  arguments, thus avoiding any special syntax, e.g.::

    T = TypeVar('T', bound=complex)

    def add(x: T, y: T) -> T:
        return x + y

    T_co = TypeVar('T_co', covariant=True)

    class ImmutableList(Generic[T_co]): ...

- Type declaration in comments, e.g.::

    lst = []  # type: Sequence[int]

- Casts using ``cast(T, obj)``, e.g.::

    zork = cast(Any, frobozz())

- Other things, e.g. overloading and stub modules, see PEP 484.


Predefined generic types and Protocols in typing.py
---------------------------------------------------

(See also the `typing.py module
<https://github.com/python/typing/blob/master/src/typing.py>`_.)

-  Everything from ``collections.abc`` (but ``Set`` renamed to ``AbstractSet``).
-  ``Dict``, ``List``, ``Set``, ``FrozenSet``, a few more.
-  ``re.Pattern[AnyStr]``, ``re.Match[AnyStr]``.
-  ``io.IO[AnyStr]``, ``io.TextIO ~ io.IO[str]``, ``io.BinaryIO ~ io.IO[bytes]``.


Copyright
=========

This document is licensed under the `Open Publication License`_.


References and Footnotes
========================

.. _Open Publication License: http://www.opencontent.org/openpub/



..
   Local Variables:
   mode: indented-text
   indent-tabs-mode: nil
   sentence-end-double-space: t
   fill-column: 70
   coding: utf-8
   End: