Supported Models

This library works with any class that inherits the pydantic BaseModel class, including GenericModel and classes from 3rd party libraries, and also with dataclasses - both those from the python standard library and pydantic's dataclasses. Finally, it also supports TypedDict classes. In fact, you can use them interchangeably as you like:

import dataclasses
from typing import Dict, List

import pydantic

from pyfactories import ModelFactory


@pydantic.dataclasses.dataclass
class MyPydanticDataClass:
    name: str


class MyFirstModel(pydantic.BaseModel):
    dataclass: MyPydanticDataClass


@dataclasses.dataclass()
class MyPythonDataClass:
    id: str
    complex_type: Dict[str, Dict[int, List[MyFirstModel]]]


class MySecondModel(pydantic.BaseModel):
    dataclasses: List[MyPythonDataClass]


class MyFactory(ModelFactory):
    __model__ = MySecondModel


result = MyFactory.build()

The above example will build correctly.

Note Regarding Nested Optional Types in Dataclasses

When generating mock values for fields typed as Optional, if the factory is defined with __allow_none_optionals__ = True, the field value will be either a value or None - depending on a random decision. This works even when the Optional typing is deeply nested, except for dataclasses - typing is only shallowly evaluated for dataclasses, and as such they are always assumed to require a value. If you wish to have a None value, in this particular case, you should do so manually by configured a Use callback for the particular field.