관리-도구
편집 파일: dataclasses.cpython-38.pyc
U /?�f�F � @ s� d Z ddlZddlZddlZddlmZ ddlmZ zddlmZ W n e k rX Y nX ddl m Z m Z m Z mZmZmZmZmZmZmZmZ ddlmZ ddlmZ dd lmZm Z m Z m Z dd l m Z dd l!m"Z" dd l#m$Z$m%Z%m&Z&m'Z' dd l(m)Z)m*Z* ddl+m,Z, e �rVddl(m-Z- ddl.m/Z/m0Z0 eddd�Z1eed df Z2G dd� d�Z3dddddgZ4ed�Z5ej6d k�rVeej7e$fd �ed d d d d d dddd d!� e8e8e8e8e8e8ee ee9 df ee8 ee8 e8e ee5 gd"f d#� d$d���Z:eej7e$fd �ed d d d d d dddd d!� ee5 e8e8e8e8e8e8ee ee9 df ee8 ee8 e8d"d%� d&d���Z:n�eej7e$fd �ed d d d d d dddd'� e8e8e8e8e8e8ee ee9 df ee8 ee8 e ee5 gd"f d(� d)d���Z:eej7e$fd �ed d d d d d dddd'� ee5 e8e8e8e8e8e8ee ee9 df ee8 ee8 d"d*� d+d���Z:eej7e$fd �dLd d d d d d dddd d!� eee5 e8e8e8e8e8e8ee ee9 df ee8 ee8 e8ee ee5 gd"f d"f d%� d,d��Z:eed e8eed ddf d-�d.d��Z;G d/d� d�Z<ed ee e8e=dd0�d1d2�Z>d"d3d4�d5d6�Z?ed e dd7�d8d9�Z@edfed ee ee= ed: d;�d<d�ZAej6d=k�rpde=e8d>�d?d@�ZBnde=e8d>�dAd@�ZBdddB�dCdD�ZCde=e ddE�dFdG�ZDee e8dH�dId�ZEed ee d3dJ�dKd�ZFdS )MaX The main purpose is to enhance stdlib dataclasses by adding validation A pydantic dataclass can be generated from scratch or from a stdlib one. Behind the scene, a pydantic dataclass is just like a regular one on which we attach a `BaseModel` and magic methods to trigger the validation of the data. `__init__` and `__post_init__` are hence overridden and have extra logic to be able to validate input data. When a pydantic dataclass is generated from scratch, it's just a plain dataclass with validation triggered at initialization The tricky part if for stdlib dataclasses that are converted after into pydantic ones e.g. ```py @dataclasses.dataclass class M: x: int ValidatedM = pydantic.dataclasses.dataclass(M) ``` We indeed still want to support equality, hashing, repr, ... as if it was the stdlib one! ```py assert isinstance(ValidatedM(x=1), M) assert ValidatedM(x=1) == M(x=1) ``` This means we **don't want to create a new dataclass that inherits from it** The trick is to create a wrapper around `M` that will act as a proxy to trigger validation without altering default `M` behaviour. � N)�contextmanager)�wraps)�cached_property) � TYPE_CHECKING�Any�Callable�ClassVar�Dict� Generator�Optional�Type�TypeVar�Union�overload)�dataclass_transform)�gather_all_validators)� BaseConfig� ConfigDict�Extra� get_config)�ValidationError)�DataclassTypeError)�Field� FieldInfo�Required� Undefined)� create_model�validate_model)�ClassAttribute)� BaseModel)�CallableGenerator�NoArgAnyCallable� DataclassT� Dataclass)�bound�DataclassProxyc @ s� e Zd ZU eeeef ed< ee ed< eed ed< ee ed< eed ed< ee ed< ee e ed< eed gd f ed < ee ed < e e d d �d d�Z ee d dd�dd��Zee d edd�dd��Zd S )r# �__dataclass_fields__�__dataclass_params__).N� __post_init__�__pydantic_run_validation__�__post_init_post_parse__�__pydantic_initialised__�__pydantic_model__N� __pydantic_validate_values__�#__pydantic_has_field_info_default__��args�kwargs�returnc O s d S �N� ��selfr0 r1 r4 r4 �E/opt/hc_python/lib/python3.8/site-packages/pydantic/v1/dataclasses.py�__init__P s zDataclass.__init__r ��clsr2 c C s d S r3 r4 �r: r4 r4 r7 �__get_validators__S s z Dataclass.__get_validators__r" �r: �vr2 c C s d S r3 r4 �r: r> r4 r4 r7 � __validate__W s zDataclass.__validate__)�__name__� __module__� __qualname__r r �strr �__annotations__r �boolr r �objectr8 � classmethodr< r@ r4 r4 r4 r7 r# B s � dataclass�set_validation�$create_pydantic_model_from_dataclass�is_builtin_dataclass�make_dataclass_validator�_T�� � )�field_specifiersTF.� �init�repr�eq�order� unsafe_hash�frozen�config�validate_on_init� use_proxy�kw_only�DataclassClassOrWrapper) rT rU rV rW rX rY rZ r[ r\ r] r2 c C s d S r3 r4 rS r4 r4 r7 rI h s ) �_clsrT rU rV rW rX rY rZ r[ r\ r] r2 c C s d S r3 r4 ) r_ rT rU rV rW rX rY rZ r[ r\ r] r4 r4 r7 rI y s � rT rU rV rW rX rY rZ r[ r\ ) rT rU rV rW rX rY rZ r[ r\ r2 c C s d S r3 r4 r` r4 r4 r7 rI � s ) r_ rT rU rV rW rX rY rZ r[ r\ r2 c C s d S r3 r4 ) r_ rT rU rV rW rX rY rZ r[ r\ r4 r4 r7 rI � s c sF t |��tt dd�� ��������� f dd� } | dkr>| S | | �S )a Like the python standard lib dataclasses but with type validation. 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