Parameters to generics are available at runtime

Preserving the generic type at runtime enables introspection of the type which can be used for API generation or runtime type checking. Such usage is already present in the wild.

Just like with the typing module today, the parameterized generic types listed in the previous section all preserve their type parameters at runtime:

>>> list[str]
list[str]
>>> tuple[int, ...]
tuple[int, ...]
>>> ChainMap[str, list[str]]
collections.ChainMap[str, list[str]]

This is implemented using a thin proxy type that forwards all method calls and attribute accesses to the bare origin type with the following exceptions:

• the __repr__ shows the parameterized type;
• the __origin__ attribute points at the non-parameterized generic class;
• the __args__ attribute is a tuple (possibly of length 1) of generic types passed to the original __class_getitem__;
• the __parameters__ attribute is a lazily computed tuple (possibly empty) of unique type variables found in __args__;
• the __getitem__ raises an exception to disallow mistakes like dict[str][str]. However it allows e.g. dict[str, T][int] and in that case returns dict[str, int].

This design means that it is possible to create instances of parameterized collections, like:

>>> l = list[str]()
[]
>>> list is list[str]
False
>>> list == list[str]
False
>>> list[str] == list[str]
True
>>> list[str] == list[int]
False
>>> isinstance([1, 2, 3], list[str])
TypeError: isinstance() arg 2 cannot be a parameterized generic
>>> issubclass(list, list[str])
TypeError: issubclass() arg 2 cannot be a parameterized generic
>>> isinstance(list[str], types.GenericAlias)
True

Objects created with bare types and parameterized types are exactly the same. The generic parameters are not preserved in instances created with parameterized types, in other words generic types erase type parameters during object creation.

One important consequence of this is that the interpreter does not attempt to type check operations on the collection created with a parameterized type. This provides symmetry between:

l: list[str] = []

and:

l = list[str]()

For accessing the proxy type from Python code, it will be exported from the types module as GenericAlias.

Pickling or (shallow- or deep-) copying a GenericAlias instance will preserve the type, origin, attributes and parameters.

Forward compatibility

Future standard collections must implement the same behavior.

Do nothing

Keeping the status quo forces Python programmers to perform book-keeping of imports from the typing module for standard collections, making all but the simplest annotations cumbersome to maintain. The existence of parallel types is confusing to newcomers (why is there both list and List?).

The above problems also don’t exist in user-built generic classes which share runtime functionality and the ability to use them as generic type annotations. Making standard collections harder to use in type hinting from user classes hindered typing adoption and usability.

Generics erasure

It would be easier to implement __class_getitem__ on the listed standard collections in a way that doesn’t preserve the generic type, in other words:

>>> list[str]
<class 'list'>
>>> tuple[int, ...]
<class 'tuple'>
>>> collections.ChainMap[str, list[str]]
<class 'collections.ChainMap'>

This is problematic as it breaks backwards compatibility: current equivalents of those types in the typing module do preserve the generic type:

>>> from typing import List, Tuple, ChainMap
>>> List[str]
typing.List[str]
>>> Tuple[int, ...]
typing.Tuple[int, ...]
>>> ChainMap[str, List[str]]
typing.ChainMap[str, typing.List[str]]

As mentioned in the “Implementation” section, preserving the generic type at runtime enables runtime introspection of the type which can be used for API generation or runtime type checking. Such usage is already present in the wild.

Additionally, implementing subscripts as identity functions would make Python less friendly to beginners. Say, if a user is mistakenly passing a list type instead of a list object to a function, and that function is indexing the received object, the code would no longer raise an error.

Today:

>>> l = list
>>> l[-1]
TypeError: 'type' object is not subscriptable

With __class_getitem__ as an identity function:

>>> l = list
>>> l[-1]
list

The indexing being successful here would likely end up raising an exception at a distance, confusing the user.

Disallowing instantiation of parameterized types

Given that the proxy type which preserves __origin__ and __args__ is mostly useful for runtime introspection purposes, we might have disallowed instantiation of parameterized types.

In fact, forbidding instantiation of parameterized types is what the typing module does today for types which parallel builtin collections (instantiation of other parameterized types is allowed).

The original reason for this decision was to discourage spurious parameterization which made object creation up to two orders of magnitude slower compared to the special syntax available for those builtin collections.

This rationale is not strong enough to allow the exceptional treatment of builtins. All other parameterized types can be instantiated, including parallels of collections in the standard library. Moreover, Python allows for instantiation of lists using list() and some builtin collections don’t provide special syntax for instantiation.

Making isinstance(obj, list[str]) perform a check ignoring generics

An earlier version of this PEP suggested treating parameterized generics like list[str] as equivalent to their non-parameterized variants like list for purposes of isinstance() and issubclass(). This would be symmetrical to how list[str]() creates a regular list.

This design was rejected because isinstance() and issubclass() checks with parameterized generics would read like element-by-element runtime type checks. The result of those checks would be surprising, for example:

>>> isinstance([1, 2, 3], list[str])
True

Note the object doesn’t match the provided generic type but isinstance() still returns True because it only checks whether the object is a list.

If a library is faced with a parameterized generic and would like to perform an isinstance() check using the base type, that type can be retrieved using the __origin__ attribute on the parameterized generic.

Making isinstance(obj, list[str]) perform a runtime type check

This functionality requires iterating over the collection which is a destructive operation in some of them. This functionality would have been useful, however implementing the type checker within Python that would deal with complex types, nested type checking, type variables, string forward references, and so on is out of scope for this PEP.

Naming the type GenericType instead of GenericAlias

We considered a different name for this type, but decided GenericAlias is better – these aren’t real types, they are aliases for the corresponding container type with some extra metadata attached.