Python Descriptors: Highlighting Bug In Pyright

Nov 4, 2025 by Admin 48 views

Hey guys, let's dive into a peculiar issue I stumbled upon in the world of Python, specifically concerning descriptors and how they're handled by the type checker, Pyright. Descriptors are a powerful feature in Python that allow you to customize attribute access, essentially giving you control over what happens when you get, set, or delete an object's attribute. The crux of the problem lies in how Pyright, a static type checker for Python, misidentifies a subtype of a descriptor when its __get__ method doesn't return a callable. This can lead to incorrect highlighting and, potentially, confusing errors in your code. Let's break down this issue, why it's happening, and what it means for your projects.

The Descriptor Dilemma: Understanding the Basics

First off, let's get on the same page about descriptors. In Python, a descriptor is any object that defines the methods __get__, __set__, or __delete__. When you access an attribute that's managed by a descriptor, Python calls the appropriate descriptor method instead of directly accessing the attribute. This is super useful for things like computed properties, managing access to underlying data, or even implementing things like the @property decorator. The magic happens because Python checks if an object's attribute is a descriptor before it tries to access it directly. If it is, then the descriptor's methods are called; otherwise, the attribute is accessed normally. Think of descriptors as attribute gatekeepers.

Now, the __get__ method is particularly important. It's called when you try to retrieve the attribute value. This method typically takes the instance of the class (if it exists) and the owner class as arguments, allowing the descriptor to return a value based on the instance or class context. The value returned by __get__ can be anything – a simple value, an object, or even a callable function. This is where the issue comes in. If __get__ doesn't return a callable, but the type checker thinks it should, you can run into problems. Descriptors are really the secret sauce behind many Python features, but they can be a bit tricky to get right, especially when you start mixing them with type checking.

Code Example: Illustrating the Problem

To make this clearer, let's look at the code example from the original report. This is where the bug shows itself. You've got two classes, A and B, that act as descriptors. Class A takes a function as an argument in its initializer and, crucially, its __get__ method does not return a callable. Class B inherits from A. When you apply these descriptors to methods within a class (like Foo), Pyright seems to get confused. Specifically, methods decorated with the descriptor B are incorrectly highlighted as methods instead of properties. This is not just a cosmetic issue; it's a real problem because the type checker is not accurately reflecting how your code works. This can lead to unexpected type errors and make debugging a lot harder. This is why it's so important to understand the details.

from typing import Callable


class A[T, U]:
    def __init__(self, func: Callable[[T], U]) -> None: ...
    def __get__(self, instance: T, owner: type[T] | None = None) -> U: ...
    def __set__(self, instance: T, value: U) -> None: ...


class B[T, U](A[T, U]): ...


class Foo:
    @A
    def foo(self) -> int: ...  # highlighted as property (correct)

    @B
    def bar(self) -> int: ...  # highlighted as method (incorrect)


Foo().foo  # highlighted as property (correct)
Foo().bar  # highlighted as method (incorrect)

In this example, the foo method, decorated with A, is correctly identified as a property. However, bar, decorated with B (which inherits from A), is wrongly labeled as a method. This difference is subtle but significant.

Unpacking the Error: Why Pyright Gets It Wrong

So, why is Pyright making this mistake? The problem likely stems from how Pyright infers the type of the attribute when it's accessed. When __get__ doesn't return a callable, Pyright should recognize that the attribute should behave like a property (i.e., you get a value when you access it directly). However, it seems like there's a glitch in the logic. When a class inherits from a descriptor class (like B from A), Pyright might not be correctly identifying that __get__ doesn't return a callable, leading it to assume the attribute is a regular method.

This can happen for a few reasons. Type checkers like Pyright work by analyzing your code and trying to figure out the types of variables and expressions. It does this by following the rules of Python and using any type hints you've provided. In this case, it might be that the type inference for derived classes of descriptors isn't working correctly. Or, perhaps the type checker isn't properly handling the case where __get__ doesn't return a callable. This could be due to a misunderstanding of how descriptors work or an issue with how the inheritance is handled. Remember, type checkers are complex tools, and there are always edge cases that they might not handle perfectly.

Impact of the Misidentification

The consequences of this incorrect highlighting are twofold. First, it can lead to confusion. When you're reading code, you might expect bar to behave like a regular method, but in reality, it's acting like a property. Second, and more importantly, it can lead to incorrect type checking. Pyright might allow operations on bar that wouldn't be valid for a property, potentially leading to runtime errors. This can be particularly troublesome if you rely heavily on type checking to catch errors early. Misidentifying the type of an attribute can cause all sorts of problems down the line.

Potential Solutions and Workarounds

So, what can you do if you encounter this issue? Well, until Pyright is updated to fix this bug, you have a few options to mitigate the problem.

Be Aware: The first and most important thing is to be aware of the issue. Knowing that Pyright might misidentify certain descriptors will help you avoid making incorrect assumptions about your code. Keep an eye on the highlighting, but don't blindly trust it, especially if you're using descriptors in a complex way.
Explicit Typing: You can try to be more explicit with your typing to help Pyright understand your code better. This might involve adding more type hints to your descriptor's methods or using type aliases to clarify the expected types. The more information you give the type checker, the better it can understand your code.
Refactoring: In some cases, you might be able to refactor your code to avoid the issue altogether. This could involve restructuring your descriptors or changing how you use them. For example, if you can avoid inheriting from descriptor classes, you might be able to sidestep the bug.
Issue Tracking: Keep an eye on the issue tracker for Pyright (and other type checkers). The original issue report is a great place to start. You can follow the progress of the bug fix and get updates on when a solution might be available.

These workarounds aren't ideal, but they can help you manage the problem in the short term while the type checker is updated. The best solution, of course, is a fix in Pyright itself, which will hopefully come in a future update.

Conclusion: The Path Forward

In a nutshell, this is a tricky problem that underscores the complexity of type checking and the subtleties of descriptors in Python. The bug in Pyright, where subtypes of descriptors are incorrectly highlighted as methods, can lead to confusion and potential errors. While we wait for a fix, being aware of the issue, using explicit typing, and exploring alternative code structures can help you work around the problem. Remember, the goal is always to write clean, maintainable code that's easy to understand and debug. This is what makes a huge difference.

As the Python community continues to refine its tools and practices, we can expect to see improvements in type checkers like Pyright. Until then, stay vigilant, keep an eye out for these types of issues, and always test your code thoroughly. Type checking is a great tool, but it's not foolproof, so it's always important to use it in conjunction with other testing methods. That's all for now, folks! Thanks for sticking with me as we explored this descriptor dilemma. Hopefully, this helps you understand the problem better and navigate your Python projects with a little more confidence.