Mailman 3 July 2020 - Typing-sig

Any way to express that a dict is expected to have a key(s) but don't care about other keys?
by Brett Cannon 20 Jun '23

20 Jun '23

Looking at TypedDict it seems that it does not like the idea of superfluous keys existing in a dict. In my case I have a REST call returning JSON for an error and all I'm guaranteed is to get an "error" key. But there may be extra keys that provide more details about the specific error. Unfortunately I don't know the exhaustive list of keys so I can't use a TypedDict with `totality == False` to try to be complete. I can't think of any way to somehow say "require/expect this one key, but other keys are OK". Am I missing anything that would let me do this?

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Runtime Access to Type Parameters
by Saul Shanabrook 07 Jun '23

07 Jun '23

I would like to propose being able to access, at runtime, the type parameters in classmethods and in constructors. I wrote up a draft PEP on this to capture the thinking here: https://github.com/saulshanabrook/peps/blob/master/pep-9999.md It seems easier to add them to classmethods, so even that addition alone would be very welcome! It could be added to Guido's existing draft PR for pep 585, by making a small change in the getattr function (https://github.com/python/cpython/pull/18239/files#diff-a34ae826f869897e56e…), to first do a `__getattribute__` and check if the result is a descriptor (has a `__get__` attribute), and if so passing the generic class itself in as the class, instead of the origin class. I wrote up a little example of that in the PEP. To make it available in the constructor, I proposed adding a global function `get_parameters` to `typing` which returns the parameters in the constructor. I also provide a little mock implementation of that. Note that work is only for custom classes which inherit from `typing.Generic` not for any built in collections.

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Explicit Type Aliases
by Shannon Zhu 17 Aug '22

17 Aug '22

Hi everyone, I’d like to continue the discussion from our last typing summit on explicit type aliases. A quick summary of the current state and the proposal -- Current state: ``` from typing import List x = List[int] # considered a type alias y : Type[List[int]] = List[int] # considered an expression z : Any # considered a type alias z : x = [] # fine z : y = [] # invalid type error ``` Proposal (with explicit aliases): ``` from typing import List, TypeAlias x = TypeAlias[List[int]] # considered a type alias y = List[int] # considered an expression z : Any # considered an expression z = TypeAlias[Any] # considered a type alias reveal_type(x) # Type[List[int]] reveal_type(y) # Any (return type of __getitem__) z : x = [] # fine z : y = [] # invalid type error ``` Some of the benefits we’re hoping to gain from explicit type aliases: * Clearly distinguish between an unannotated global assignment and a type alias, especially when parsing forward-referencing string annotations. * Avoid the confusing case of type aliases in which some part of the type is invalid or undefined. This would facilitate warnings on invalid types at the alias definition rather than later on when the alias is used. * Make valid and invalid types more intuitive to Python programmers by shifting from delineation of non-aliases with a meta annotation toward delineation of aliases with `TypeAlias`. * Remove special treatment of values annotated as `Any`, which currently break all type aliasing rules. This will also allow us to clean up some of the distinctions we maintain between unannotated values and explicit Anys. Note: We also considered denoting the alias as an annotation (e.g. `x: TypeAlias = int`) as an alternative to the syntax above. I’m interested to hear your thoughts and suggestions on making type aliasing explicit going forward – we’d like to start implementing a version of this in Pyre soon! Shannon

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PEP 612: Parameter Specification Variables Discussion Pt. II
by Mark Mendoza 11 Aug '20

11 Aug '20

Hi All, I want to start off by thanking everyone that gave feedback on the first round of review of PEP 612. Your feedback illuminated a lot of things that I think have made this PEP a lot better. Over the past few weeks I have had the time to rewrite much of that document to integrate all of that feedback. Notably, this included integrating `typing.type_variable_operators.Concatenate` to support decorators that modify a finite number of positional parameters. With that being said, I'd appreciate another round of feedback from all of you to determine whether this form of the PEP is ready to move on to the Steering Council. Thanks again! Mark Mendoza

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Tensor typing - next group call
by Matthew Rahtz 11 Aug '20

11 Aug '20

Hi everyone, Next week we'll be holding our next tensor typing meeting. WHEN? Monday 3rd August, 9:30am SF time. GCal link: https://calendar.google.com/event?action=TEMPLATE&tmeid=NzY5cXM1am5uNDNkMW1… WHERE? Google Meet. We'll post the link on the day itself. WHAT? 1. Status updates. What have folks been working on over the past month? 2. What should symbols mean? Jörg and I would like to make a second attempt at making the case that symbols should by default refer to semantic axis types (e.g. `Batch` => a batch-like axis) rather than actual shapes (`Batch` => a concrete value of '32'). We'll have a short presentation and then we'll open it up for discussion. 3. Other. Is there anything else folks would like to talk about? Cheers! Matthew

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What is the intended use case for the typing.SuportsXyz protocols?
by Luciano Ramalho 02 Aug '20

02 Aug '20

I was excited to learn about SupportsInt, SupportsFloat, and SupportsComplex protocols decorated with @runtime_checkable because I thought they bridged the gap between ABCs and typing protocol classes, allowing consistent type checks by static tools and at runtime. However, I am unable to see how to use those classes in practice. For example, SupportsComplex: I understand at runtime the @runtime_checkable decorator merely checks whether __complex__ exists in the type, but that is not a good predictor of how to use a numeric value. Types that actually can be converted to complex fail the insinstance check with SupportsComplex. That's the case of built-in int and float, as well as NumPy integer and float types like float16 and uint8. On the other hand, SupportsInt suggests that I can convert a complex to int, but in fact the int and float types implement __complex__ only to provide a better error message, according to Guido van Rossum in an earlier thread here. >>> isinstance(1+0j, SupportsInt) True >>> int(1+0j) Traceback (most recent call last): ... TypeError: can't convert complex to int The issue with SupportsInt also happens with SupportsFloat. In addition, the way Mypy interprets these protocol classes is not the same as what we can see at runtime. Given the above, what are recommended use cases for the runtime checkable SupportsInt, SupportsFloat, and SupportsComplex? Cheers, Luciano -- Luciano Ramalho | Author of Fluent Python (O'Reilly, 2015) | http://shop.oreilly.com/product/0636920032519.do | Technical Principal at ThoughtWorks | Twitter: @ramalhoorg

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assigning a NoReturn function call
by Martin DeMello 01 Aug '20

01 Aug '20

Should the following be a type error? ``` def f() -> NoReturn while True: ... x = f() ``` Neither pytype nor mypy currently catch it. martin

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PEP 604 status update
by Guido van Rossum 30 Jul '20

30 Jul '20

Here's a status update for PEP 604: https://www.python.org/dev/peps/pep-0604/ - Ivan has asked the SC to find another PEP delegate for personal reasons, and the SC has asked me to step up in his place. Philippe, the PEP's author, has agreed. - Maggie Moss has volunteered to write an implementation which is currently in progress at https://github.com/python/cpython/pull/21515. - I am quite positive regarding the PEP, I just think we need to tighten the specification some more, especially in corner cases and for interoperability with types defined in typing.py. I will work with Maggie and Philippe on this. I see no problem getting this into 3.10. It will make a nice complement to the list[int] notation introduced in 3.9 by PEP 585. Here's a summary of the key points from the PEP: - `int | str` represents the union of `int` and `str` - There will be a new extension type, `types.Union` (1), to represent unions created using `|` - Such types will be acceptable in `isinstance(x, t)` and `issubclass(C, t)` The rest is elaboration; most semantics are derived from existing semantics of `typing.Union[]`. We also strive for decent interoperability with the `typing` module. (1) Beware! The existing union notation uses `typing.Union`; the new notation uses `types.Union`. -- --Guido van Rossum (python.org/~guido) *Pronouns: he/him **(why is my pronoun here?)* <http://feministing.com/2015/02/03/how-using-they-as-a-singular-pronoun-can-…>

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[PEP 604] Clarify interaction with ForwardRef, and None
by Richard Eames 29 Jul '20

29 Jul '20

Hi list! I was asked to bring this up here. When PEP604 was first introduced last year I noticed a few under-specified parts, namely how it interacts with ForwardRef and the edge case of `None | None` eg: >>> from typing import Union >>> Union[int, 'str'] typing.Union[int, ForwardRef('str')] >>> int | 'str' # should this raise an error, or return the same as above? >>> Union[None, "ref1"] typing.Optional[ForwardRef('ref1')] >>> None | "ref1" # should this construct (None first) be forbidden, or does str.__or__ or NoneType.__or__ need to be implemented? >>> Union[None, None] <class 'NoneType'> >>> None | None # should be forbidden? It seems very unlikely to come up in practise even if typing.Union supports it >>> Union["ref1","ref2"] typing.Union[ForwardRef('ref1'), ForwardRef('ref2')] >>> "ref1" | "ref2" # would this be str.__or__ returning types.Union? In the discussion last year, it was suggested that there could be a `typing.Forward` that could be used instead of the bare string "ref" cases, or even requiring the union to be fully quoted eg `T = "Ref | OtherRef"`.

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Creating custom types with metadata (similar to Annotated)
by Patrick Arminio 24 Jul '20

24 Jul '20

Hello again, I'm working on a library that uses the return type of methods at runtime to generate GraphQL types and I'm trying to deal with the case of circular imports and classes referencing each others. I was thinking of create a LazyType where you can pass the type name and the module, so that we are able to resolve the actual type at runtime without having to guess where it came from, like this: ``` import typing import strawberry if typing.TYPE_CHECKING: from .type_a import TypeA @strawberry.type class TypeB: @strawberry.field() def type_a(self, info) -> strawberry.LazyType["TypeA", ".type_a"]: from .type_a import TypeA return TypeA() ``` As previous post unfortunately I can't relay on sys.module, since the type is only imported when TYPE_CHECKING is True or inside the method. Now, I managed to make it work with a custom LazyType class, so the annotation above works; ``` strawberry.LazyType["TypeA", ".type_a"] ``` I don't dislike this to be honest, but looks like mypy isn't really happy, as you can see from the errors here: ``` tests/test_cyclic/type_b.py:13: error: "LazyType" expects no type arguments, but 2 given tests/test_cyclic/type_b.py:13: error: Invalid type comment or annotation tests/test_cyclic/type_b.py:16: error: Incompatible return value type (got "TypeA", expected "LazyType") ``` The first one could be resolve by using generics, but I'm not sure that's a good idea. The last one could be solved by creating a plugin, hopefully it won't be too difficult. But I have no clue about `Invalid type comment or annotation`. Why would that be the case? I noticed that the error disappears when removing the dot from the module name, but I'd like to be able to pass a dot there, so users of the library don't have to type the full module, since we can infer that. Any ideas? Or do I need to change the approach for this? Maybe doing a less typehint-y way: ``` def x() -> strawberry.LazyType("TypeA", ".type_a"): ``` Let me know! :)

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