## Summary * Attributes/method are now properly looked up on metaclasses, when called on class objects * We properly distinguish between data descriptors and non-data descriptors (but we do not yet support them in store-context, i.e. `obj.data_descr = …`) * The descriptor protocol is now implemented in a single unified place for instances, classes and dunder-calls. Unions and possibly-unbound symbols are supported in all possible stages of the process by creating union types as results. * In general, the handling of "possibly-unbound" symbols has been improved in a lot of places: meta-class attributes, attributes, descriptors with possibly-unbound `__get__` methods, instance attributes, … * We keep track of type qualifiers in a lot more places. I anticipate that this will be useful if we import e.g. `Final` symbols from other modules (see relevant change to typing spec: https://github.com/python/typing/pull/1937). * Detection and special-casing of the `typing.Protocol` special form in order to avoid lots of changes in the test suite due to new `@Todo` types when looking up attributes on builtin types which have `Protocol` in their MRO. We previously looked up attributes in a wrong way, which is why this didn't come up before. closes #16367 closes #15966 ## Context The way attribute lookup in `Type::member` worked before was simply wrong (mostly my own fault). The whole instance-attribute lookup should probably never have been integrated into `Type::member`. And the `Type::static_member` function that I introduced in my last descriptor PR was the wrong abstraction. It's kind of fascinating how far this approach took us, but I am pretty confident that the new approach proposed here is what we need to model this correctly. There are three key pieces that are required to implement attribute lookups: - **`Type::class_member`**/**`Type::find_in_mro`**: The `Type::find_in_mro` method that can look up attributes on class bodies (and corresponding bases). This is a partial function on types, as it can not be called on instance types like`Type::Instance(…)` or `Type::IntLiteral(…)`. For this reason, we usually call it through `Type::class_member`, which is essentially just `type.to_meta_type().find_in_mro(…)` plus union/intersection handling. - **`Type::instance_member`**: This new function is basically the type-level equivalent to `obj.__dict__[name]` when called on `Type::Instance(…)`. We use this to discover instance attributes such as those that we see as declarations on class bodies or as (annotated) assignments to `self.attr` in methods of a class. - The implementation of the descriptor protocol. It works slightly different for instances and for class objects, but it can be described by the general framework: - Call `type.class_member("attribute")` to look up "attribute" in the MRO of the meta type of `type`. Call the resulting `Symbol` `meta_attr` (even if it's unbound). - Use `meta_attr.class_member("__get__")` to look up `__get__` on the *meta type* of `meta_attr`. Call it with `__get__(meta_attr, self, self.to_meta_type())`. If this fails (either the lookup or the call), just proceed with `meta_attr`. Otherwise, replace `meta_attr` in the following with the return type of `__get__`. In this step, we also probe if a `__set__` or `__delete__` method exists and store it in `meta_attr_kind` (can be either "data descriptor" or "normal attribute or non-data descriptor"). - Compute a `fallback` type. - For instances, we use `self.instance_member("attribute")` - For class objects, we use `class_attr = self.find_in_mro("attribute")`, and then try to invoke the descriptor protocol on `class_attr`, i.e. we look up `__get__` on the meta type of `class_attr` and call it with `__get__(class_attr, None, self)`. This additional invocation of the descriptor protocol on the fallback type is one major asymmetry in the otherwise universal descriptor protocol implementation. - Finally, we look at `meta_attr`, `meta_attr_kind` and `fallback`, and handle various cases of (possible) unboundness of these symbols. - If `meta_attr` is bound and a data descriptor, just return `meta_attr` - If `meta_attr` is not a data descriptor, and `fallback` is bound, just return `fallback` - If `meta_attr` is not a data descriptor, and `fallback` is unbound, return `meta_attr` - Return unions of these three possibilities for partially-bound symbols. This allows us to handle class objects and instances within the same framework. There is a minor additional detail where for instances, we do not allow the fallback type (the instance attribute) to completely shadow the non-data descriptor. We do this because we (currently) don't want to pretend that we can statically infer that an instance attribute is always set. Dunder method calls can also be embedded into this framework. The only thing that changes is that *there is no fallback type*. If a dunder method is called on an instance, we do not fall back to instance variables. If a dunder method is called on a class object, we only look it up on the meta class, never on the class itself. ## Test Plan New Markdown tests.
5.7 KiB
Scoping rules for type variables
Most of these tests come from the Scoping rules for type variables section of the typing spec.
Typevar used outside of generic function or class
Typevars may only be used in generic function or class definitions.
from typing import TypeVar
T = TypeVar("T")
# TODO: error
x: T
class C:
# TODO: error
x: T
def f() -> None:
# TODO: error
x: T
Legacy typevar used multiple times
A type variable used in a generic function could be inferred to represent different types in the same code block.
This only applies to typevars defined using the legacy syntax, since the PEP 695 syntax creates a new distinct typevar for each occurrence.
from typing import TypeVar
T = TypeVar("T")
def f1(x: T) -> T: ...
def f2(x: T) -> T: ...
f1(1)
f2("a")
Typevar inferred multiple times
A type variable used in a generic function could be inferred to represent different types in the same code block.
This also applies to a single generic function being used multiple times, instantiating the typevar to a different type each time.
def f[T](x: T) -> T: ...
# TODO: no error
# TODO: revealed: int or Literal[1]
# error: [invalid-argument-type]
reveal_type(f(1)) # revealed: T
# TODO: no error
# TODO: revealed: str or Literal["a"]
# error: [invalid-argument-type]
reveal_type(f("a")) # revealed: T
Methods can mention class typevars
A type variable used in a method of a generic class that coincides with one of the variables that parameterize this class is always bound to that variable.
class C[T]:
def m1(self, x: T) -> T: ...
def m2(self, x: T) -> T: ...
c: C[int] = C()
# TODO: no error
# error: [invalid-argument-type]
c.m1(1)
# TODO: no error
# error: [invalid-argument-type]
c.m2(1)
# TODO: expected type `int`
# error: [invalid-argument-type] "Object of type `Literal["string"]` cannot be assigned to parameter 2 (`x`) of bound method `m2`; expected type `T`"
c.m2("string")
Methods can mention other typevars
A type variable used in a method that does not match any of the variables that parameterize the class makes this method a generic function in that variable.
from typing import TypeVar, Generic
T = TypeVar("T")
S = TypeVar("S")
# TODO: no error
# error: [invalid-base]
class Legacy(Generic[T]):
def m(self, x: T, y: S) -> S: ...
legacy: Legacy[int] = Legacy()
# TODO: revealed: str
reveal_type(legacy.m(1, "string")) # revealed: @Todo(Invalid or unsupported `Instance` in `Type::to_type_expression`)
With PEP 695 syntax, it is clearer that the method uses a separate typevar:
class C[T]:
def m[S](self, x: T, y: S) -> S: ...
c: C[int] = C()
# TODO: no errors
# TODO: revealed: str
# error: [invalid-argument-type]
# error: [invalid-argument-type]
reveal_type(c.m(1, "string")) # revealed: S
Unbound typevars
Unbound type variables should not appear in the bodies of generic functions, or in the class bodies apart from method definitions.
This is true with the legacy syntax:
from typing import TypeVar, Generic
T = TypeVar("T")
S = TypeVar("S")
def f(x: T) -> None:
x: list[T] = []
# TODO: error
y: list[S] = []
# TODO: no error
# error: [invalid-base]
class C(Generic[T]):
# TODO: error
x: list[S] = []
# This is not an error, as shown in the previous test
def m(self, x: S) -> S: ...
This is true with PEP 695 syntax, as well, though we must use the legacy syntax to define the unbound typevars:
pep695.py:
from typing import TypeVar
S = TypeVar("S")
def f[T](x: T) -> None:
x: list[T] = []
# TODO: error
y: list[S] = []
class C[T]:
# TODO: error
x: list[S] = []
def m1(self, x: S) -> S: ...
def m2[S](self, x: S) -> S: ...
Nested formal typevars must be distinct
Generic functions and classes can be nested in each other, but it is an error for the same typevar to be used in nested generic definitions.
Note that the typing spec only mentions two specific versions of this rule:
A generic class definition that appears inside a generic function should not use type variables that parameterize the generic function.
and
A generic class nested in another generic class cannot use the same type variables.
We assume that the more general form holds.
Generic function within generic function
def f[T](x: T, y: T) -> None:
def ok[S](a: S, b: S) -> None: ...
# TODO: error
def bad[T](a: T, b: T) -> None: ...
Generic method within generic class
class C[T]:
def ok[S](self, a: S, b: S) -> None: ...
# TODO: error
def bad[T](self, a: T, b: T) -> None: ...
Generic class within generic function
from typing import Iterable
def f[T](x: T, y: T) -> None:
class Ok[S]: ...
# TODO: error for reuse of typevar
class Bad1[T]: ...
# TODO: no non-subscriptable error, error for reuse of typevar
# error: [non-subscriptable]
class Bad2(Iterable[T]): ...
Generic class within generic class
from typing import Iterable
class C[T]:
class Ok1[S]: ...
# TODO: error for reuse of typevar
class Bad1[T]: ...
# TODO: no non-subscriptable error, error for reuse of typevar
# error: [non-subscriptable]
class Bad2(Iterable[T]): ...
Class scopes do not cover inner scopes
Just like regular symbols, the typevars of a generic class are only available in that class's scope, and are not available in nested scopes.
class C[T]:
ok1: list[T] = []
class Bad:
# TODO: error
bad: list[T] = []
class Inner[S]: ...
ok2: Inner[T]