Pylance And Pydantic Models Hashable Issue With Inheritance

by StackCamp Team 60 views

Hey everyone! Let's dive into a quirky issue I stumbled upon while working with Pydantic models and Pylance. It's all about how Pylance, the language server for Python in VS Code, infers the __hash__ method when using inheritance with a BaseModel mixin. So, grab your favorite beverage, and let's get started!

The Problem Pylance's Misunderstanding of __hash__ Inference

In this particular scenario, Pylance doesn't seem to correctly infer the __hash__ method from a BaseModel mixin in a Pydantic model. This can lead to some head-scratching moments when you're trying to use your models in sets or as dictionary keys, which require hashable objects. Specifically, when you define a mixin class that provides the __hash__ and __eq__ methods, and a Pydantic model inherits from this mixin, Pylance sometimes fails to recognize that the model instances are indeed hashable. This issue arises even when the model_config = {'frozen': True} setting is used, which should make the Pydantic model immutable and thus hashable. The core issue revolves around how Pylance analyzes the inheritance structure and the implementation of __hash__ within the mixin class. When a Pydantic model inherits from a mixin that defines __hash__, Pylance is expected to recognize this inherited method and treat instances of the model as hashable. However, in certain cases, Pylance overlooks this inheritance, leading to false positives in its static analysis. This can be particularly confusing for developers who rely on Pylance to catch potential issues early in the development process. Imagine you're building a system that relies heavily on sets or dictionaries to store and retrieve model instances. If Pylance incorrectly flags your models as unhashable, you might waste valuable time trying to debug a problem that doesn't actually exist. Furthermore, this issue highlights the importance of understanding how language servers like Pylance interpret Python code, especially when dealing with advanced features like inheritance and mixins. While Pylance is generally very accurate, it's not perfect, and there are situations where its analysis might not align with the actual behavior of the code. To better understand the scope of this issue, it's essential to consider the specific conditions under which it occurs. For instance, the presence of abstract methods in the mixin class, the way the _identification method is implemented, and the overall structure of the inheritance hierarchy can all play a role in whether Pylance correctly infers the __hash__ method. Additionally, the configuration of Pylance itself, including any custom settings or extensions, might influence its behavior. By pinpointing the exact circumstances that trigger this issue, we can provide more targeted feedback to the Pylance team and help them improve the accuracy of their analysis. This, in turn, will benefit the broader Python development community by reducing the number of false positives and making it easier to write robust and reliable code.

Environment Details

Before we dive into the code, here’s a quick rundown of my setup:

  • Python: 3.13.5
  • Pylance in VS Code: 2025.7.1
  • Pydantic: 2.11.7
  • OS: Windows 10

These details are crucial because the behavior might vary across different versions and environments. So, if you're trying to reproduce this, make sure your setup is similar.

The Code Snippet Unveiling the Hashable Mystery

Let's look at the code that triggers this behavior:

from abc import ABC, abstractmethod
from typing import Any, Hashable

from pydantic import BaseModel, Field


class WithHashable(ABC):
    """Add __hash__ and __eq__ for subclass"""
    model_config = {'frozen': True}

    @abstractmethod
    def _identification(self) -> Hashable:
        """Returning a hashable value for __eq__ and __hash__"""

    def __hash__(self) -> int:
        return hash(self._identification())

    def __eq__(self, o: Any) -> bool:
        return isinstance(o, type(self)) and self._identification() == o._identification()


class Person(WithHashable, BaseModel):
    id: int = Field(ge=0)
    name: str = Field(max_length=100)

    def _identification(self) -> Hashable:
        return (self.id,)

    # Removing this comment from Pylance correctly identifies Person as Hashable
    # def __hash__(self) -> int:
    #     return WithHashable.__hash__(self)


if __name__ == '__main__':
    p1 = Person(id=1, name='p1')
    p12 = Person(id=1, name='p1')
    p2 = Person(id=2, name='p2')
    p3 = Person(id=3, name='p3')

    print(p1 == p1, p1 == p12, p1 == p2, p1 == p3)  # -> True True False False
    print(hash(p1), hash(p12), hash(p2), hash(p3))

    # FIXME: Pylance reports: “Type 'Person' is unhashable”
    arrs = {p1, p12, p2, p3}
    for a in arrs:
        print(a)

In this code, we define a mixin class WithHashable that provides __hash__ and __eq__ methods. This mixin is designed to be inherited by Pydantic models, making them hashable. The Person class inherits from both WithHashable and BaseModel. Everything seems fine, right? Well, Pylance throws a wrench in the gears.

Walking Through the Code A Deep Dive

Let's break this down. We start with the WithHashable class, which is an abstract base class (ABC). This class includes:

  • model_config = {'frozen': True}: This makes instances immutable, a requirement for hashability.
  • _identification(): An abstract method that subclasses must implement to provide a hashable representation of the object.
  • __hash__(): Returns the hash of the result from _identification().
  • __eq__(): Checks equality based on the result from _identification().

Next, we have the Person class, which inherits from WithHashable and BaseModel. It defines:

  • id and name fields.
  • _identification(): Returns a tuple containing the id, making a Person instance identifiable and hashable based on its ID.

The interesting part is the commented-out __hash__() method in the Person class. If you uncomment this, Pylance correctly identifies Person as hashable. But why does it fail when relying on inheritance?

Reproducing the Issue Steps to See the Warning

To see this in action:

  1. Open the above code in VS Code with the Pylance extension enabled.

  2. Look for the red squiggly line and lint warning on this line:

    arrs = {p1, p12, p2, p3}

  3. Hover over the set literal, and Pylance will report: “Type ‘Person’ is unhashable”.

This is the crux of the issue. Pylance isn’t picking up the inherited __hash__ method.

Expected Behavior What Should Happen

Here’s what I expect to happen:

Since WithHashable provides both __hash__ and __eq__, and model_config = {'frozen': True} makes the Pydantic model immutable, Pylance should recognize that Person implements __hash__ and therefore is hashable. No lint warning should be emitted when using instances of Person in a set or as dict keys. Essentially, Pylance should understand that the inheritance of __hash__ from WithHashable makes Person instances naturally hashable, without needing an explicit __hash__ method in the Person class itself. This expectation is rooted in the principles of object-oriented programming, where inheritance allows subclasses to inherit methods and behaviors from their parent classes. In this case, the Person class inherits the __hash__ method from the WithHashable mixin, which should be sufficient for Pylance to recognize its hashability. However, as the issue demonstrates, Pylance sometimes fails to correctly interpret this inheritance, leading to the erroneous warning. To further clarify the expected behavior, consider the implications of marking a Pydantic model as frozen. When model_config = {'frozen': True} is set, Pydantic ensures that instances of the model are immutable, meaning their attributes cannot be changed after creation. This immutability is a key requirement for hashability, as the hash value of an object should not change over its lifetime. Given that the Person class inherits from WithHashable, which enforces immutability through model_config, Pylance should confidently recognize that instances of Person are both immutable and have a valid __hash__ method. Therefore, the absence of a lint warning when using Person instances in sets or dictionaries is the logical and expected outcome. In essence, the core expectation is that Pylance should accurately reflect the hashability of Pydantic models based on their inheritance and configuration. By correctly interpreting the inheritance of __hash__ and the immutability enforced by model_config, Pylance can provide more reliable and less noisy static analysis, ultimately improving the developer experience. This not only reduces the frustration of dealing with false positives but also ensures that developers can trust Pylance's guidance in identifying genuine issues in their code.

Why This Matters The Real-World Impact

This isn't just a theoretical issue. It affects real-world code where you might want to use Pydantic models as keys in dictionaries or elements in sets. Imagine you're building a caching system or trying to ensure uniqueness in a collection of objects. This false warning can lead to unnecessary workarounds or, worse, overlooking potential issues in your code. Moreover, it undermines the trust in Pylance's static analysis, making developers second-guess its warnings. The practical implications of this issue extend beyond simple code examples. In complex applications, where Pydantic models are used extensively, the incorrect flagging of unhashable types can create significant challenges. For instance, consider a scenario where you're building a data processing pipeline that relies on sets to eliminate duplicate records. If Pylance falsely identifies your Pydantic models as unhashable, you might be forced to implement alternative de-duplication strategies, which could be less efficient or more error-prone. Similarly, in web applications, Pydantic models are often used to represent request and response payloads. If you need to use these models as keys in a cache or as elements in a set for tracking user sessions, Pylance's incorrect warning can lead to unnecessary complexity and potential performance bottlenecks. Furthermore, this issue can impact the maintainability and readability of your code. When developers encounter false positives from static analysis tools, they might be tempted to add workarounds or suppress the warnings altogether. This can clutter the codebase and make it harder to understand the intended logic. In the long run, it's crucial for static analysis tools like Pylance to provide accurate and reliable feedback to developers. This not only improves the developer experience but also ensures that code is robust and free from potential errors. By addressing this specific issue with __hash__ inference, the Pylance team can enhance the overall quality and trustworthiness of their tool, benefiting the entire Python development community. This ultimately leads to more efficient development workflows and higher-quality software.

Possible Solutions and Workarounds Taming the Hashability Beast

While we wait for a fix, there are a couple of ways to work around this:

  1. Uncomment the __hash__() method in the Person class: This explicitly tells Pylance that Person is hashable.
  2. Ignore the warning: If you're confident your code is correct, you can add a # type: ignore comment to suppress the warning.

However, these are just temporary fixes. The ideal solution is for Pylance to correctly infer hashability from the mixin.

Diving Deeper into Solutions

To truly address this issue, the Pylance team needs to investigate how it handles inheritance and method resolution, especially in the context of Pydantic models. Here are a few potential areas to explore:

  • Mixin handling: Pylance might not be fully recognizing mixin classes and their contributions to the class hierarchy.
  • Method resolution order (MRO): The way Pylance resolves methods in complex inheritance scenarios could be a factor.
  • Pydantic integration: There might be specific interactions between Pylance and Pydantic that need tweaking.

Final Thoughts A Call to Action

This issue highlights the importance of accurate static analysis in modern Python development. Pylance is a fantastic tool, and I’m confident the team will address this. In the meantime, I hope this write-up helps others facing the same problem. If you've encountered this, consider adding your voice to the discussion on the Pylance GitHub repository! Let's work together to make our tools even better.

That's all for now, folks. Happy coding, and may your hashes always be unique!

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