This section explains how you can define your own generic classes that take
one or more type parameters, similar to built-in types such as List[X]
.
User-defined generics are a moderately advanced feature and you can get far
without ever using them -- feel free to skip this section and come back later.
The built-in collection classes are generic classes. Generic types
have one or more type parameters, which can be arbitrary types. For
example, Dict[int, str]
has the type parameters int
and
str
, and List[int]
has a type parameter int
.
Programs can also define new generic classes. Here is a very simple generic class that represents a stack:
from typing import TypeVar, Generic
T = TypeVar('T')
class Stack(Generic[T]):
def __init__(self) -> None:
# Create an empty list with items of type T
self.items: List[T] = []
def push(self, item: T) -> None:
self.items.append(item)
def pop(self) -> T:
return self.items.pop()
def empty(self) -> bool:
return not self.items
The Stack
class can be used to represent a stack of any type:
Stack[int]
, Stack[Tuple[int, str]]
, etc.
Using Stack
is similar to built-in container types:
# Construct an empty Stack[int] instance
stack = Stack[int]()
stack.push(2)
stack.pop()
stack.push('x') # Type error
Type inference works for user-defined generic types as well:
def process(stack: Stack[int]) -> None: ...
process(Stack()) # Argument has inferred type Stack[int]
Construction of instances of generic types is also type checked:
class Box(Generic[T]):
def __init__(self, content: T) -> None:
self.content = content
Box(1) # OK, inferred type is Box[int]
Box[int](1) # Also OK
s = 'some string'
Box[int](s) # Type error
You may wonder what happens at runtime when you index
Stack
. Actually, indexing Stack
returns essentially a copy
of Stack
that returns instances of the original class on
instantiation:
>>> print(Stack)
__main__.Stack
>>> print(Stack[int])
__main__.Stack[int]
>>> print(Stack[int]().__class__)
__main__.Stack
Note that built-in types :py:class:`list`, :py:class:`dict` and so on do not support indexing in Python. This is why we have the aliases :py:class:`~typing.List`, :py:class:`~typing.Dict` and so on in the :py:mod:`typing` module. Indexing these aliases gives you a class that directly inherits from the target class in Python:
>>> from typing import List
>>> List[int]
typing.List[int]
>>> List[int].__bases__
(<class 'list'>, typing.MutableSequence)
Generic types could be instantiated or subclassed as usual classes,
but the above examples illustrate that type variables are erased at
runtime. Generic Stack
instances are just ordinary
Python objects, and they have no extra runtime overhead or magic due
to being generic, other than a metaclass that overloads the indexing
operator.
User-defined generic classes and generic classes defined in :py:mod:`typing` can be used as base classes for another classes, both generic and non-generic. For example:
from typing import Generic, TypeVar, Mapping, Iterator, Dict
KT = TypeVar('KT')
VT = TypeVar('VT')
class MyMap(Mapping[KT, VT]): # This is a generic subclass of Mapping
def __getitem__(self, k: KT) -> VT:
... # Implementations omitted
def __iter__(self) -> Iterator[KT]:
...
def __len__(self) -> int:
...
items: MyMap[str, int] # Okay
class StrDict(Dict[str, str]): # This is a non-generic subclass of Dict
def __str__(self) -> str:
return 'StrDict({})'.format(super().__str__())
data: StrDict[int, int] # Error! StrDict is not generic
data2: StrDict # OK
class Receiver(Generic[T]):
def accept(self, value: T) -> None:
...
class AdvancedReceiver(Receiver[T]):
...
Note
You have to add an explicit :py:class:`~typing.Mapping` base class if you want mypy to consider a user-defined class as a mapping (and :py:class:`~typing.Sequence` for sequences, etc.). This is because mypy doesn't use structural subtyping for these ABCs, unlike simpler protocols like :py:class:`~typing.Iterable`, which use :ref:`structural subtyping <protocol-types>`.
:py:class:`Generic <typing.Generic>` can be omitted from bases if there are
other base classes that include type variables, such as Mapping[KT, VT]
in the above example. If you include Generic[...]
in bases, then
it should list all type variables present in other bases (or more,
if needed). The order of type variables is defined by the following
rules:
- If
Generic[...]
is present, then the order of variables is always determined by their order inGeneric[...]
. - If there are no
Generic[...]
in bases, then all type variables are collected in the lexicographic order (i.e. by first appearance).
For example:
from typing import Generic, TypeVar, Any
T = TypeVar('T')
S = TypeVar('S')
U = TypeVar('U')
class One(Generic[T]): ...
class Another(Generic[T]): ...
class First(One[T], Another[S]): ...
class Second(One[T], Another[S], Generic[S, U, T]): ...
x: First[int, str] # Here T is bound to int, S is bound to str
y: Second[int, str, Any] # Here T is Any, S is int, and U is str
Generic type variables can also be used to define generic functions:
from typing import TypeVar, Sequence
T = TypeVar('T') # Declare type variable
def first(seq: Sequence[T]) -> T: # Generic function
return seq[0]
As with generic classes, the type variable can be replaced with any
type. That means first
can be used with any sequence type, and the
return type is derived from the sequence item type. For example:
# Assume first defined as above.
s = first('foo') # s has type str.
n = first([1, 2, 3]) # n has type int.
Note also that a single definition of a type variable (such as T
above) can be used in multiple generic functions or classes. In this
example we use the same type variable in two generic functions:
from typing import TypeVar, Sequence
T = TypeVar('T') # Declare type variable
def first(seq: Sequence[T]) -> T:
return seq[0]
def last(seq: Sequence[T]) -> T:
return seq[-1]
A variable cannot have a type variable in its type unless the type variable is bound in a containing generic class or function.
You can also define generic methods — just use a type variable in the
method signature that is different from class type variables. In particular,
self
may also be generic, allowing a method to return the most precise
type known at the point of access.
Note
This feature is experimental. Checking code with type annotations for self arguments is still not fully implemented. Mypy may disallow valid code or allow unsafe code.
In this way, for example, you can typecheck chaining of setter methods:
from typing import TypeVar
T = TypeVar('T', bound='Shape')
class Shape:
def set_scale(self: T, scale: float) -> T:
self.scale = scale
return self
class Circle(Shape):
def set_radius(self, r: float) -> 'Circle':
self.radius = r
return self
class Square(Shape):
def set_width(self, w: float) -> 'Square':
self.width = w
return self
circle = Circle().set_scale(0.5).set_radius(2.7) # type: Circle
square = Square().set_scale(0.5).set_width(3.2) # type: Square
Without using generic self
, the last two lines could not be type-checked properly.
Other uses are factory methods, such as copy and deserialization.
For class methods, you can also define generic cls
, using :py:class:`Type[T] <typing.Type>`:
from typing import TypeVar, Tuple, Type
T = TypeVar('T', bound='Friend')
class Friend:
other = None # type: Friend
@classmethod
def make_pair(cls: Type[T]) -> Tuple[T, T]:
a, b = cls(), cls()
a.other = b
b.other = a
return a, b
class SuperFriend(Friend):
pass
a, b = SuperFriend.make_pair()
Note that when overriding a method with generic self
, you must either
return a generic self
too, or return an instance of the current class.
In the latter case, you must implement this method in all future subclasses.
Note also that mypy cannot always verify that the implementation of a copy
or a deserialization method returns the actual type of self. Therefore
you may need to silence mypy inside these methods (but not at the call site),
possibly by making use of the Any
type.
For some advanced uses of self-types see :ref:`additional examples <advanced_self>`.
There are three main kinds of generic types with respect to subtype
relations between them: invariant, covariant, and contravariant.
Assuming that we have a pair of types A
and B
, and B
is
a subtype of A
, these are defined as follows:
- A generic class
MyCovGen[T, ...]
is called covariant in type variableT
ifMyCovGen[B, ...]
is always a subtype ofMyCovGen[A, ...]
. - A generic class
MyContraGen[T, ...]
is called contravariant in type variableT
ifMyContraGen[A, ...]
is always a subtype ofMyContraGen[B, ...]
. - A generic class
MyInvGen[T, ...]
is called invariant inT
if neither of the above is true.
Let us illustrate this by few simple examples:
:py:data:`~typing.Union` is covariant in all variables:
Union[Cat, int]
is a subtype ofUnion[Animal, int]
,Union[Dog, int]
is also a subtype ofUnion[Animal, int]
, etc. Most immutable containers such as :py:class:`~typing.Sequence` and :py:class:`~typing.FrozenSet` are also covariant.:py:data:`~typing.Callable` is an example of type that behaves contravariant in types of arguments, namely
Callable[[Employee], int]
is a subtype ofCallable[[Manager], int]
. To understand this, consider a function:def salaries(staff: List[Manager], accountant: Callable[[Manager], int]) -> List[int]: ...
This function needs a callable that can calculate a salary for managers, and if we give it a callable that can calculate a salary for an arbitrary employee, it's still safe.
:py:class:`~typing.List` is an invariant generic type. Naively, one would think that it is covariant, but let us consider this code:
class Shape: pass class Circle(Shape): def rotate(self): ... def add_one(things: List[Shape]) -> None: things.append(Shape()) my_things: List[Circle] = [] add_one(my_things) # This may appear safe, but... my_things[0].rotate() # ...this will fail
Another example of invariant type is :py:class:`~typing.Dict`. Most mutable containers are invariant.
By default, mypy assumes that all user-defined generics are invariant.
To declare a given generic class as covariant or contravariant use
type variables defined with special keyword arguments covariant
or
contravariant
. For example:
from typing import Generic, TypeVar
T_co = TypeVar('T_co', covariant=True)
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
def look_into(box: Box[Animal]): ...
my_box = Box(Cat())
look_into(my_box) # OK, but mypy would complain here for an invariant type
By default, a type variable can be replaced with any type. However, sometimes
it's useful to have a type variable that can only have some specific types
as its value. A typical example is a type variable that can only have values
str
and bytes
:
from typing import TypeVar
AnyStr = TypeVar('AnyStr', str, bytes)
This is actually such a common type variable that :py:data:`~typing.AnyStr` is defined in :py:mod:`typing` and we don't need to define it ourselves.
We can use :py:data:`~typing.AnyStr` to define a function that can concatenate two strings or bytes objects, but it can't be called with other argument types:
from typing import AnyStr
def concat(x: AnyStr, y: AnyStr) -> AnyStr:
return x + y
concat('a', 'b') # Okay
concat(b'a', b'b') # Okay
concat(1, 2) # Error!
Note that this is different from a union type, since combinations
of str
and bytes
are not accepted:
concat('string', b'bytes') # Error!
In this case, this is exactly what we want, since it's not possible to concatenate a string and a bytes object! The type checker will reject this function:
def union_concat(x: Union[str, bytes], y: Union[str, bytes]) -> Union[str, bytes]:
return x + y # Error: can't concatenate str and bytes
Another interesting special case is calling concat()
with a
subtype of str
:
class S(str): pass
ss = concat(S('foo'), S('bar'))
You may expect that the type of ss
is S
, but the type is
actually str
: a subtype gets promoted to one of the valid values
for the type variable, which in this case is str
. This is thus
subtly different from bounded quantification in languages such as
Java, where the return type would be S
. The way mypy implements
this is correct for concat
, since concat
actually returns a
str
instance in the above example:
>>> print(type(ss))
<class 'str'>
You can also use a :py:class:`~typing.TypeVar` with a restricted set of possible values when defining a generic class. For example, mypy uses the type :py:class:`Pattern[AnyStr] <typing.Pattern>` for the return value of :py:func:`re.compile`, since regular expressions can be based on a string or a bytes pattern.
A type variable can also be restricted to having values that are
subtypes of a specific type. This type is called the upper bound of
the type variable, and is specified with the bound=...
keyword
argument to :py:class:`~typing.TypeVar`.
from typing import TypeVar, SupportsAbs
T = TypeVar('T', bound=SupportsAbs[float])
In the definition of a generic function that uses such a type variable
T
, the type represented by T
is assumed to be a subtype of
its upper bound, so the function can use methods of the upper bound on
values of type T
.
def largest_in_absolute_value(*xs: T) -> T:
return max(xs, key=abs) # Okay, because T is a subtype of SupportsAbs[float].
In a call to such a function, the type T
must be replaced by a
type that is a subtype of its upper bound. Continuing the example
above,
largest_in_absolute_value(-3.5, 2) # Okay, has type float.
largest_in_absolute_value(5+6j, 7) # Okay, has type complex.
largest_in_absolute_value('a', 'b') # Error: 'str' is not a subtype of SupportsAbs[float].
Type parameters of generic classes may also have upper bounds, which restrict the valid values for the type parameter in the same way.
A type variable may not have both a value restriction (see :ref:`type-variable-value-restriction`) and an upper bound.
One common application of type variable upper bounds is in declaring a decorator that preserves the signature of the function it decorates, regardless of that signature.
Note that class decorators are handled differently than function decorators in mypy: decorating a class does not erase its type, even if the decorator has incomplete type annotations.
Here's a complete example of a function decorator:
from typing import Any, Callable, TypeVar, Tuple, cast
F = TypeVar('F', bound=Callable[..., Any])
# A decorator that preserves the signature.
def my_decorator(func: F) -> F:
def wrapper(*args, **kwds):
print("Calling", func)
return func(*args, **kwds)
return cast(F, wrapper)
# A decorated function.
@my_decorator
def foo(a: int) -> str:
return str(a)
a = foo(12)
reveal_type(a) # str
foo('x') # Type check error: incompatible type "str"; expected "int"
From the final block we see that the signatures of the decorated
functions foo()
and bar()
are the same as those of the original
functions (before the decorator is applied).
The bound on F
is used so that calling the decorator on a
non-function (e.g. my_decorator(1)
) will be rejected.
Also note that the wrapper()
function is not type-checked. Wrapper
functions are typically small enough that this is not a big
problem. This is also the reason for the :py:func:`~typing.cast` call in the
return
statement in my_decorator()
. See :ref:`casts`.
Functions that take arguments and return a decorator (also called second-order decorators), are similarly supported via generics:
from typing import Any, Callable, TypeVar
F = TypeVar('F', bound=Callable[..., Any])
def route(url: str) -> Callable[[F], F]:
...
@route(url='/')
def index(request: Any) -> str:
return 'Hello world'
Sometimes the same decorator supports both bare calls and calls with arguments. This can be achieved by combining with :py:func:`@overload <typing.overload>`:
from typing import Any, Callable, TypeVar, overload
F = TypeVar('F', bound=Callable[..., Any])
# Bare decorator usage
@overload
def atomic(__func: F) -> F: ...
# Decorator with arguments
@overload
def atomic(*, savepoint: bool = True) -> Callable[[F], F]: ...
# Implementation
def atomic(__func: Callable[..., Any] = None, *, savepoint: bool = True):
def decorator(func: Callable[..., Any]):
... # Code goes here
if __func is not None:
return decorator(__func)
else:
return decorator
# Usage
@atomic
def func1() -> None: ...
@atomic(savepoint=False)
def func2() -> None: ...
Mypy supports generic protocols (see also :ref:`protocol-types`). Several :ref:`predefined protocols <predefined_protocols>` are generic, such as :py:class:`Iterable[T] <typing.Iterable>`, and you can define additional generic protocols. Generic protocols mostly follow the normal rules for generic classes. Example:
from typing import TypeVar
from typing_extensions import Protocol
T = TypeVar('T')
class Box(Protocol[T]):
content: T
def do_stuff(one: Box[str], other: Box[bytes]) -> None:
...
class StringWrapper:
def __init__(self, content: str) -> None:
self.content = content
class BytesWrapper:
def __init__(self, content: bytes) -> None:
self.content = content
do_stuff(StringWrapper('one'), BytesWrapper(b'other')) # OK
x: Box[float] = ...
y: Box[int] = ...
x = y # Error -- Box is invariant
The main difference between generic protocols and ordinary generic
classes is that mypy checks that the declared variances of generic
type variables in a protocol match how they are used in the protocol
definition. The protocol in this example is rejected, since the type
variable T
is used covariantly as a return type, but the type
variable is invariant:
from typing import TypeVar
from typing_extensions import Protocol
T = TypeVar('T')
class ReadOnlyBox(Protocol[T]): # Error: covariant type variable expected
def content(self) -> T: ...
This example correctly uses a covariant type variable:
from typing import TypeVar
from typing_extensions import Protocol
T_co = TypeVar('T_co', covariant=True)
class ReadOnlyBox(Protocol[T_co]): # OK
def content(self) -> T_co: ...
ax: ReadOnlyBox[float] = ...
ay: ReadOnlyBox[int] = ...
ax = ay # OK -- ReadOnlyBox is covariant
See :ref:`variance-of-generics` for more about variance.
Generic protocols can also be recursive. Example:
T = TypeVar('T')
class Linked(Protocol[T]):
val: T
def next(self) -> 'Linked[T]': ...
class L:
val: int
... # details omitted
def next(self) -> 'L':
... # details omitted
def last(seq: Linked[T]) -> T:
... # implementation omitted
result = last(L()) # Inferred type of 'result' is 'int'
Type aliases can be generic. In this case they can be used in two ways:
Subscripted aliases are equivalent to original types with substituted type
variables, so the number of type arguments must match the number of free type variables
in the generic type alias. Unsubscripted aliases are treated as original types with free
variables replaced with Any
. Examples (following :pep:`PEP 484: Type aliases
<484#type-aliases>`):
from typing import TypeVar, Iterable, Tuple, Union, Callable
S = TypeVar('S')
TInt = Tuple[int, S]
UInt = Union[S, int]
CBack = Callable[..., S]
def response(query: str) -> UInt[str]: # Same as Union[str, int]
...
def activate(cb: CBack[S]) -> S: # Same as Callable[..., S]
...
table_entry: TInt # Same as Tuple[int, Any]
T = TypeVar('T', int, float, complex)
Vec = Iterable[Tuple[T, T]]
def inproduct(v: Vec[T]) -> T:
return sum(x*y for x, y in v)
def dilate(v: Vec[T], scale: T) -> Vec[T]:
return ((x * scale, y * scale) for x, y in v)
v1: Vec[int] = [] # Same as Iterable[Tuple[int, int]]
v2: Vec = [] # Same as Iterable[Tuple[Any, Any]]
v3: Vec[int, int] = [] # Error: Invalid alias, too many type arguments!
Type aliases can be imported from modules just like other names. An alias can also target another alias, although building complex chains of aliases is not recommended -- this impedes code readability, thus defeating the purpose of using aliases. Example:
from typing import TypeVar, Generic, Optional
from example1 import AliasType
from example2 import Vec
# AliasType and Vec are type aliases (Vec as defined above)
def fun() -> AliasType:
...
T = TypeVar('T')
class NewVec(Vec[T]):
...
for i, j in NewVec[int]():
...
OIntVec = Optional[Vec[int]]
Note
A type alias does not define a new type. For generic type aliases this means that variance of type variables used for alias definition does not apply to aliases. A parameterized generic alias is treated simply as an original type with the corresponding type variables substituted.