import pydantic
import typing
from typing_extensions import Annotated
from pydantic import BaseModel, ValidationInfo, field_validator
from pydantic.dataclasses import dataclass


class Base(BaseModel):
    raw: str
    norm: typing.Any | None = None

    @field_validator('norm')
    def normalize_raw_if_no_norm(cls, norm: str | None, info: ValidationInfo):
        if norm is None:
            norm = cls.normalize_raw(info['model'].raw)
        return norm

@dataclass
class Weight:
    raw: str | None = None
    norm: Annotated[typing.Any | None, pydantic.Field(validate_default=True)] = None

    @pydantic.field_validator("norm", mode="before")
    def normalize_raw_if_no_norm(cls, norm: str | None, info: ValidationInfo):
        print("normalize_raw_if_no_norm")
        if norm is None:
            if (raw := info.data.get("raw")):
                norm = cls.normalize_raw(info.data["raw"])
            else:
                raise ValueError("Either raw or norm has to be provided")
        return norm

    @pydantic.field_validator("norm")
    def check_positive(cls, norm):
        print(norm)
        assert norm >= 0, "Weight has to be positive"
        return norm

    @classmethod
    def normalize_raw(cls, raw: str):
        return float(raw)

Weight(raw='-32')