dataclass_to_pydantic

dataclass_to_pydantic.py

# Extend https://yeonwoosung.github.io/posts/pydantic-vs-dataclass/
# to convert nested dataclasses.

from functools import lru_cache

from dataclasses import fields, _MISSING_TYPE, is_dataclass
from typing import Any, Optional

import pydantic


@lru_cache(maxsize=None)
def convert_dataclass_to_pydantic(
    dcls: type, name: Optional[str] = None
) -> type[pydantic.BaseModel]:
    if name is None:
        name_ = f"{dcls.__name__}Model"
    else:
        name_ = name
    return pydantic.create_model(  # type: ignore
        name_,
        **_get_pydantic_field_kwargs(dcls),
    )


def _get_pydantic_field_kwargs(dcls: type) -> dict[str, tuple[type, Any]]:
    # get attribute names and types from dataclass into pydantic format
    pydantic_field_kwargs = dict()
    for _field in fields(dcls):
        # check is field has default value
        field_type = _field.type
        
        if isinstance(_field.default, _MISSING_TYPE):
            # no default
            default = ...
        else:
            default = _field.default
            # TODO: Convert default if it's a dataclass
        
        if(is_dataclass(field_type)):
            # Convert fields that are dataclasses.
            field_type = convert_dataclass_to_pydantic(field_type)

        pydantic_field_kwargs[_field.name] = (field_type, default)
    return pydantic_field_kwargs


# Example:
# Create nested dataclass
from typing import Literal
from dataclasses import dataclass

@dataclass
class Inner:
    a: str
    b: str

@dataclass
class Outer:
    inner: Inner

# Create pydantic schemas from dataclasses
InnerModel = convert_dataclass_to_pydantic(Inner)
OuterModel = convert_dataclass_to_pydantic(Outer)


# Example roundtrip data:

# pydantic model data->OuterModel-> raw data
model_data = OuterModel(inner=InnerModel(a="hello", b="world"))
print(model_data.model_dump())

# raw data -> OuterModel
print(OuterModel(**{'inner': {'a': 'hello', 'b': 'world'}}))