Unpack dict based on pydantic BaseModel into model

deserialize_pydantic.py

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from pydantic import BaseModel
from typing import Optional, Type, TypeVar


Model = TypeVar("Model", bound=BaseModel)


def _load_model(t: Type[Model], o: dict) -> Model:
    populated_keys = o.keys()
    required_keys = set(t.schema()['required'])
    missing_keys = required_keys.difference(populated_keys)
    if missing_keys:
        raise ValueError(f'Required keys missing: {missing_keys}')
    all_definition_keys = t.schema()['properties'].keys()
    return t(**{k: v for k, v in o.items() if k in all_definition_keys})

if __name__ == '__main__':
    class Car(BaseModel):
        year: Optional[int]
        name: str
        company: str
        
    car_dict = {'name': 'Uncivic', 'company': 'Fonda'}
    car = _load_model(Car, car_dict)
    print(car)