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2026-07-02 20:05:39 +00:00
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venv/Lib/site-packages/pydantic/class_validators.py Normal file
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"""`class_validators` module is a backport module from V1."""
from ._migration import getattr_migration
__getattr__ = getattr_migration(__name__)

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venv/Lib/site-packages/pydantic/color.py Normal file
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"""Color definitions are used as per the CSS3
[CSS Color Module Level 3](http://www.w3.org/TR/css3-color/#svg-color) specification.
A few colors have multiple names referring to the sames colors, eg. `grey` and `gray` or `aqua` and `cyan`.
In these cases the _last_ color when sorted alphabetically takes preferences,
eg. `Color((0, 255, 255)).as_named() == 'cyan'` because "cyan" comes after "aqua".
Warning: Deprecated
The `Color` class is deprecated, use `pydantic_extra_types` instead.
See [`pydantic-extra-types.Color`](../usage/types/extra_types/color_types.md)
for more information.
"""
import math
import re
from colorsys import hls_to_rgb, rgb_to_hls
from typing import Any, Callable, Optional, Union, cast
from pydantic_core import CoreSchema, PydanticCustomError, core_schema
from typing_extensions import deprecated
from ._internal import _repr
from ._internal._schema_generation_shared import GetJsonSchemaHandler as _GetJsonSchemaHandler
from .json_schema import JsonSchemaValue
from .warnings import PydanticDeprecatedSince20
ColorTuple = Union[tuple[int, int, int], tuple[int, int, int, float]]
ColorType = Union[ColorTuple, str]
HslColorTuple = Union[tuple[float, float, float], tuple[float, float, float, float]]
class RGBA:
"""Internal use only as a representation of a color."""
__slots__ = 'r', 'g', 'b', 'alpha', '_tuple'
def __init__(self, r: float, g: float, b: float, alpha: Optional[float]):
self.r = r
self.g = g
self.b = b
self.alpha = alpha
self._tuple: tuple[float, float, float, Optional[float]] = (r, g, b, alpha)
def __getitem__(self, item: Any) -> Any:
return self._tuple[item]
# these are not compiled here to avoid import slowdown, they'll be compiled the first time they're used, then cached
_r_255 = r'(\d{1,3}(?:\.\d+)?)'
_r_comma = r'\s*,\s*'
_r_alpha = r'(\d(?:\.\d+)?|\.\d+|\d{1,2}%)'
_r_h = r'(-?\d+(?:\.\d+)?|-?\.\d+)(deg|rad|turn)?'
_r_sl = r'(\d{1,3}(?:\.\d+)?)%'
r_hex_short = r'\s*(?:#|0x)?([0-9a-f])([0-9a-f])([0-9a-f])([0-9a-f])?\s*'
r_hex_long = r'\s*(?:#|0x)?([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})?\s*'
# CSS3 RGB examples: rgb(0, 0, 0), rgba(0, 0, 0, 0.5), rgba(0, 0, 0, 50%)
r_rgb = rf'\s*rgba?\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}(?:{_r_comma}{_r_alpha})?\s*\)\s*'
# CSS3 HSL examples: hsl(270, 60%, 50%), hsla(270, 60%, 50%, 0.5), hsla(270, 60%, 50%, 50%)
r_hsl = rf'\s*hsla?\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}(?:{_r_comma}{_r_alpha})?\s*\)\s*'
# CSS4 RGB examples: rgb(0 0 0), rgb(0 0 0 / 0.5), rgb(0 0 0 / 50%), rgba(0 0 0 / 50%)
r_rgb_v4_style = rf'\s*rgba?\(\s*{_r_255}\s+{_r_255}\s+{_r_255}(?:\s*/\s*{_r_alpha})?\s*\)\s*'
# CSS4 HSL examples: hsl(270 60% 50%), hsl(270 60% 50% / 0.5), hsl(270 60% 50% / 50%), hsla(270 60% 50% / 50%)
r_hsl_v4_style = rf'\s*hsla?\(\s*{_r_h}\s+{_r_sl}\s+{_r_sl}(?:\s*/\s*{_r_alpha})?\s*\)\s*'
# colors where the two hex characters are the same, if all colors match this the short version of hex colors can be used
repeat_colors = {int(c * 2, 16) for c in '0123456789abcdef'}
rads = 2 * math.pi
@deprecated(
'The `Color` class is deprecated, use `pydantic_extra_types` instead. '
'See https://docs.pydantic.dev/latest/api/pydantic_extra_types_color/.',
category=PydanticDeprecatedSince20,
)
class Color(_repr.Representation):
"""Represents a color."""
__slots__ = '_original', '_rgba'
def __init__(self, value: ColorType) -> None:
self._rgba: RGBA
self._original: ColorType
if isinstance(value, (tuple, list)):
self._rgba = parse_tuple(value)
elif isinstance(value, str):
self._rgba = parse_str(value)
elif isinstance(value, Color):
self._rgba = value._rgba
value = value._original
else:
raise PydanticCustomError(
'color_error', 'value is not a valid color: value must be a tuple, list or string'
)
# if we've got here value must be a valid color
self._original = value
@classmethod
def __get_pydantic_json_schema__(
cls, core_schema: core_schema.CoreSchema, handler: _GetJsonSchemaHandler
) -> JsonSchemaValue:
field_schema = {}
field_schema.update(type='string', format='color')
return field_schema
def original(self) -> ColorType:
"""Original value passed to `Color`."""
return self._original
def as_named(self, *, fallback: bool = False) -> str:
"""Returns the name of the color if it can be found in `COLORS_BY_VALUE` dictionary,
otherwise returns the hexadecimal representation of the color or raises `ValueError`.
Args:
fallback: If True, falls back to returning the hexadecimal representation of
the color instead of raising a ValueError when no named color is found.
Returns:
The name of the color, or the hexadecimal representation of the color.
Raises:
ValueError: When no named color is found and fallback is `False`.
"""
if self._rgba.alpha is None:
rgb = cast(tuple[int, int, int], self.as_rgb_tuple())
try:
return COLORS_BY_VALUE[rgb]
except KeyError as e:
if fallback:
return self.as_hex()
else:
raise ValueError('no named color found, use fallback=True, as_hex() or as_rgb()') from e
else:
return self.as_hex()
def as_hex(self) -> str:
"""Returns the hexadecimal representation of the color.
Hex string representing the color can be 3, 4, 6, or 8 characters depending on whether the string
a "short" representation of the color is possible and whether there's an alpha channel.
Returns:
The hexadecimal representation of the color.
"""
values = [float_to_255(c) for c in self._rgba[:3]]
if self._rgba.alpha is not None:
values.append(float_to_255(self._rgba.alpha))
as_hex = ''.join(f'{v:02x}' for v in values)
if all(c in repeat_colors for c in values):
as_hex = ''.join(as_hex[c] for c in range(0, len(as_hex), 2))
return '#' + as_hex
def as_rgb(self) -> str:
"""Color as an `rgb(<r>, <g>, <b>)` or `rgba(<r>, <g>, <b>, <a>)` string."""
if self._rgba.alpha is None:
return f'rgb({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)})'
else:
return (
f'rgba({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)}, '
f'{round(self._alpha_float(), 2)})'
)
def as_rgb_tuple(self, *, alpha: Optional[bool] = None) -> ColorTuple:
"""Returns the color as an RGB or RGBA tuple.
Args:
alpha: Whether to include the alpha channel. There are three options for this input:
- `None` (default): Include alpha only if it's set. (e.g. not `None`)
- `True`: Always include alpha.
- `False`: Always omit alpha.
Returns:
A tuple that contains the values of the red, green, and blue channels in the range 0 to 255.
If alpha is included, it is in the range 0 to 1.
"""
r, g, b = (float_to_255(c) for c in self._rgba[:3])
if alpha is None:
if self._rgba.alpha is None:
return r, g, b
else:
return r, g, b, self._alpha_float()
elif alpha:
return r, g, b, self._alpha_float()
else:
# alpha is False
return r, g, b
def as_hsl(self) -> str:
"""Color as an `hsl(<h>, <s>, <l>)` or `hsl(<h>, <s>, <l>, <a>)` string."""
if self._rgba.alpha is None:
h, s, li = self.as_hsl_tuple(alpha=False) # type: ignore
return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%})'
else:
h, s, li, a = self.as_hsl_tuple(alpha=True) # type: ignore
return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%}, {round(a, 2)})'
def as_hsl_tuple(self, *, alpha: Optional[bool] = None) -> HslColorTuple:
"""Returns the color as an HSL or HSLA tuple.
Args:
alpha: Whether to include the alpha channel.
- `None` (default): Include the alpha channel only if it's set (e.g. not `None`).
- `True`: Always include alpha.
- `False`: Always omit alpha.
Returns:
The color as a tuple of hue, saturation, lightness, and alpha (if included).
All elements are in the range 0 to 1.
Note:
This is HSL as used in HTML and most other places, not HLS as used in Python's `colorsys`.
"""
h, l, s = rgb_to_hls(self._rgba.r, self._rgba.g, self._rgba.b) # noqa: E741
if alpha is None:
if self._rgba.alpha is None:
return h, s, l
else:
return h, s, l, self._alpha_float()
if alpha:
return h, s, l, self._alpha_float()
else:
# alpha is False
return h, s, l
def _alpha_float(self) -> float:
return 1 if self._rgba.alpha is None else self._rgba.alpha
@classmethod
def __get_pydantic_core_schema__(
cls, source: type[Any], handler: Callable[[Any], CoreSchema]
) -> core_schema.CoreSchema:
return core_schema.with_info_plain_validator_function(
cls._validate, serialization=core_schema.to_string_ser_schema()
)
@classmethod
def _validate(cls, __input_value: Any, _: Any) -> 'Color':
return cls(__input_value)
def __str__(self) -> str:
return self.as_named(fallback=True)
def __repr_args__(self) -> '_repr.ReprArgs':
return [(None, self.as_named(fallback=True))] + [('rgb', self.as_rgb_tuple())]
def __eq__(self, other: Any) -> bool:
return isinstance(other, Color) and self.as_rgb_tuple() == other.as_rgb_tuple()
def __hash__(self) -> int:
return hash(self.as_rgb_tuple())
def parse_tuple(value: tuple[Any, ...]) -> RGBA:
"""Parse a tuple or list to get RGBA values.
Args:
value: A tuple or list.
Returns:
An `RGBA` tuple parsed from the input tuple.
Raises:
PydanticCustomError: If tuple is not valid.
"""
if len(value) == 3:
r, g, b = (parse_color_value(v) for v in value)
return RGBA(r, g, b, None)
elif len(value) == 4:
r, g, b = (parse_color_value(v) for v in value[:3])
return RGBA(r, g, b, parse_float_alpha(value[3]))
else:
raise PydanticCustomError('color_error', 'value is not a valid color: tuples must have length 3 or 4')
def parse_str(value: str) -> RGBA:
"""Parse a string representing a color to an RGBA tuple.
Possible formats for the input string include:
* named color, see `COLORS_BY_NAME`
* hex short eg. `<prefix>fff` (prefix can be `#`, `0x` or nothing)
* hex long eg. `<prefix>ffffff` (prefix can be `#`, `0x` or nothing)
* `rgb(<r>, <g>, <b>)`
* `rgba(<r>, <g>, <b>, <a>)`
Args:
value: A string representing a color.
Returns:
An `RGBA` tuple parsed from the input string.
Raises:
ValueError: If the input string cannot be parsed to an RGBA tuple.
"""
value_lower = value.lower()
try:
r, g, b = COLORS_BY_NAME[value_lower]
except KeyError:
pass
else:
return ints_to_rgba(r, g, b, None)
m = re.fullmatch(r_hex_short, value_lower)
if m:
*rgb, a = m.groups()
r, g, b = (int(v * 2, 16) for v in rgb)
if a:
alpha: Optional[float] = int(a * 2, 16) / 255
else:
alpha = None
return ints_to_rgba(r, g, b, alpha)
m = re.fullmatch(r_hex_long, value_lower)
if m:
*rgb, a = m.groups()
r, g, b = (int(v, 16) for v in rgb)
if a:
alpha = int(a, 16) / 255
else:
alpha = None
return ints_to_rgba(r, g, b, alpha)
m = re.fullmatch(r_rgb, value_lower) or re.fullmatch(r_rgb_v4_style, value_lower)
if m:
return ints_to_rgba(*m.groups()) # type: ignore
m = re.fullmatch(r_hsl, value_lower) or re.fullmatch(r_hsl_v4_style, value_lower)
if m:
return parse_hsl(*m.groups()) # type: ignore
raise PydanticCustomError('color_error', 'value is not a valid color: string not recognised as a valid color')
def ints_to_rgba(r: Union[int, str], g: Union[int, str], b: Union[int, str], alpha: Optional[float] = None) -> RGBA:
"""Converts integer or string values for RGB color and an optional alpha value to an `RGBA` object.
Args:
r: An integer or string representing the red color value.
g: An integer or string representing the green color value.
b: An integer or string representing the blue color value.
alpha: A float representing the alpha value. Defaults to None.
Returns:
An instance of the `RGBA` class with the corresponding color and alpha values.
"""
return RGBA(parse_color_value(r), parse_color_value(g), parse_color_value(b), parse_float_alpha(alpha))
def parse_color_value(value: Union[int, str], max_val: int = 255) -> float:
"""Parse the color value provided and return a number between 0 and 1.
Args:
value: An integer or string color value.
max_val: Maximum range value. Defaults to 255.
Raises:
PydanticCustomError: If the value is not a valid color.
Returns:
A number between 0 and 1.
"""
try:
color = float(value)
except ValueError:
raise PydanticCustomError('color_error', 'value is not a valid color: color values must be a valid number')
if 0 <= color <= max_val:
return color / max_val
else:
raise PydanticCustomError(
'color_error',
'value is not a valid color: color values must be in the range 0 to {max_val}',
{'max_val': max_val},
)
def parse_float_alpha(value: Union[None, str, float, int]) -> Optional[float]:
"""Parse an alpha value checking it's a valid float in the range 0 to 1.
Args:
value: The input value to parse.
Returns:
The parsed value as a float, or `None` if the value was None or equal 1.
Raises:
PydanticCustomError: If the input value cannot be successfully parsed as a float in the expected range.
"""
if value is None:
return None
try:
if isinstance(value, str) and value.endswith('%'):
alpha = float(value[:-1]) / 100
else:
alpha = float(value)
except ValueError:
raise PydanticCustomError('color_error', 'value is not a valid color: alpha values must be a valid float')
if math.isclose(alpha, 1):
return None
elif 0 <= alpha <= 1:
return alpha
else:
raise PydanticCustomError('color_error', 'value is not a valid color: alpha values must be in the range 0 to 1')
def parse_hsl(h: str, h_units: str, sat: str, light: str, alpha: Optional[float] = None) -> RGBA:
"""Parse raw hue, saturation, lightness, and alpha values and convert to RGBA.
Args:
h: The hue value.
h_units: The unit for hue value.
sat: The saturation value.
light: The lightness value.
alpha: Alpha value.
Returns:
An instance of `RGBA`.
"""
s_value, l_value = parse_color_value(sat, 100), parse_color_value(light, 100)
h_value = float(h)
if h_units in {None, 'deg'}:
h_value = h_value % 360 / 360
elif h_units == 'rad':
h_value = h_value % rads / rads
else:
# turns
h_value = h_value % 1
r, g, b = hls_to_rgb(h_value, l_value, s_value)
return RGBA(r, g, b, parse_float_alpha(alpha))
def float_to_255(c: float) -> int:
"""Converts a float value between 0 and 1 (inclusive) to an integer between 0 and 255 (inclusive).
Args:
c: The float value to be converted. Must be between 0 and 1 (inclusive).
Returns:
The integer equivalent of the given float value rounded to the nearest whole number.
Raises:
ValueError: If the given float value is outside the acceptable range of 0 to 1 (inclusive).
"""
return int(round(c * 255))
COLORS_BY_NAME = {
'aliceblue': (240, 248, 255),
'antiquewhite': (250, 235, 215),
'aqua': (0, 255, 255),
'aquamarine': (127, 255, 212),
'azure': (240, 255, 255),
'beige': (245, 245, 220),
'bisque': (255, 228, 196),
'black': (0, 0, 0),
'blanchedalmond': (255, 235, 205),
'blue': (0, 0, 255),
'blueviolet': (138, 43, 226),
'brown': (165, 42, 42),
'burlywood': (222, 184, 135),
'cadetblue': (95, 158, 160),
'chartreuse': (127, 255, 0),
'chocolate': (210, 105, 30),
'coral': (255, 127, 80),
'cornflowerblue': (100, 149, 237),
'cornsilk': (255, 248, 220),
'crimson': (220, 20, 60),
'cyan': (0, 255, 255),
'darkblue': (0, 0, 139),
'darkcyan': (0, 139, 139),
'darkgoldenrod': (184, 134, 11),
'darkgray': (169, 169, 169),
'darkgreen': (0, 100, 0),
'darkgrey': (169, 169, 169),
'darkkhaki': (189, 183, 107),
'darkmagenta': (139, 0, 139),
'darkolivegreen': (85, 107, 47),
'darkorange': (255, 140, 0),
'darkorchid': (153, 50, 204),
'darkred': (139, 0, 0),
'darksalmon': (233, 150, 122),
'darkseagreen': (143, 188, 143),
'darkslateblue': (72, 61, 139),
'darkslategray': (47, 79, 79),
'darkslategrey': (47, 79, 79),
'darkturquoise': (0, 206, 209),
'darkviolet': (148, 0, 211),
'deeppink': (255, 20, 147),
'deepskyblue': (0, 191, 255),
'dimgray': (105, 105, 105),
'dimgrey': (105, 105, 105),
'dodgerblue': (30, 144, 255),
'firebrick': (178, 34, 34),
'floralwhite': (255, 250, 240),
'forestgreen': (34, 139, 34),
'fuchsia': (255, 0, 255),
'gainsboro': (220, 220, 220),
'ghostwhite': (248, 248, 255),
'gold': (255, 215, 0),
'goldenrod': (218, 165, 32),
'gray': (128, 128, 128),
'green': (0, 128, 0),
'greenyellow': (173, 255, 47),
'grey': (128, 128, 128),
'honeydew': (240, 255, 240),
'hotpink': (255, 105, 180),
'indianred': (205, 92, 92),
'indigo': (75, 0, 130),
'ivory': (255, 255, 240),
'khaki': (240, 230, 140),
'lavender': (230, 230, 250),
'lavenderblush': (255, 240, 245),
'lawngreen': (124, 252, 0),
'lemonchiffon': (255, 250, 205),
'lightblue': (173, 216, 230),
'lightcoral': (240, 128, 128),
'lightcyan': (224, 255, 255),
'lightgoldenrodyellow': (250, 250, 210),
'lightgray': (211, 211, 211),
'lightgreen': (144, 238, 144),
'lightgrey': (211, 211, 211),
'lightpink': (255, 182, 193),
'lightsalmon': (255, 160, 122),
'lightseagreen': (32, 178, 170),
'lightskyblue': (135, 206, 250),
'lightslategray': (119, 136, 153),
'lightslategrey': (119, 136, 153),
'lightsteelblue': (176, 196, 222),
'lightyellow': (255, 255, 224),
'lime': (0, 255, 0),
'limegreen': (50, 205, 50),
'linen': (250, 240, 230),
'magenta': (255, 0, 255),
'maroon': (128, 0, 0),
'mediumaquamarine': (102, 205, 170),
'mediumblue': (0, 0, 205),
'mediumorchid': (186, 85, 211),
'mediumpurple': (147, 112, 219),
'mediumseagreen': (60, 179, 113),
'mediumslateblue': (123, 104, 238),
'mediumspringgreen': (0, 250, 154),
'mediumturquoise': (72, 209, 204),
'mediumvioletred': (199, 21, 133),
'midnightblue': (25, 25, 112),
'mintcream': (245, 255, 250),
'mistyrose': (255, 228, 225),
'moccasin': (255, 228, 181),
'navajowhite': (255, 222, 173),
'navy': (0, 0, 128),
'oldlace': (253, 245, 230),
'olive': (128, 128, 0),
'olivedrab': (107, 142, 35),
'orange': (255, 165, 0),
'orangered': (255, 69, 0),
'orchid': (218, 112, 214),
'palegoldenrod': (238, 232, 170),
'palegreen': (152, 251, 152),
'paleturquoise': (175, 238, 238),
'palevioletred': (219, 112, 147),
'papayawhip': (255, 239, 213),
'peachpuff': (255, 218, 185),
'peru': (205, 133, 63),
'pink': (255, 192, 203),
'plum': (221, 160, 221),
'powderblue': (176, 224, 230),
'purple': (128, 0, 128),
'red': (255, 0, 0),
'rosybrown': (188, 143, 143),
'royalblue': (65, 105, 225),
'saddlebrown': (139, 69, 19),
'salmon': (250, 128, 114),
'sandybrown': (244, 164, 96),
'seagreen': (46, 139, 87),
'seashell': (255, 245, 238),
'sienna': (160, 82, 45),
'silver': (192, 192, 192),
'skyblue': (135, 206, 235),
'slateblue': (106, 90, 205),
'slategray': (112, 128, 144),
'slategrey': (112, 128, 144),
'snow': (255, 250, 250),
'springgreen': (0, 255, 127),
'steelblue': (70, 130, 180),
'tan': (210, 180, 140),
'teal': (0, 128, 128),
'thistle': (216, 191, 216),
'tomato': (255, 99, 71),
'turquoise': (64, 224, 208),
'violet': (238, 130, 238),
'wheat': (245, 222, 179),
'white': (255, 255, 255),
'whitesmoke': (245, 245, 245),
'yellow': (255, 255, 0),
'yellowgreen': (154, 205, 50),
}
COLORS_BY_VALUE = {v: k for k, v in COLORS_BY_NAME.items()}

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"""Provide an enhanced dataclass that performs validation."""
from __future__ import annotations as _annotations
import dataclasses
import functools
import sys
import types
from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, NoReturn, TypeVar, overload
from warnings import warn
from typing_extensions import TypeGuard, dataclass_transform
from ._internal import _config, _decorators, _mock_val_ser, _namespace_utils, _typing_extra
from ._internal import _dataclasses as _pydantic_dataclasses
from ._migration import getattr_migration
from .config import ConfigDict
from .errors import PydanticUserError
from .fields import Field, FieldInfo, PrivateAttr
if TYPE_CHECKING:
from ._internal._dataclasses import PydanticDataclass
from ._internal._namespace_utils import MappingNamespace
__all__ = 'dataclass', 'rebuild_dataclass'
_T = TypeVar('_T')
if sys.version_info >= (3, 10):
@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
@overload
def dataclass(
*,
init: Literal[False] = False,
repr: bool = True,
eq: bool = True,
order: bool = False,
unsafe_hash: bool = False,
frozen: bool = False,
config: ConfigDict | type[object] | None = None,
validate_on_init: bool | None = None,
kw_only: bool = ...,
slots: bool = ...,
) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore
...
@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
@overload
def dataclass(
_cls: type[_T], # type: ignore
*,
init: Literal[False] = False,
repr: bool = True,
eq: bool = True,
order: bool = False,
unsafe_hash: bool = False,
frozen: bool | None = None,
config: ConfigDict | type[object] | None = None,
validate_on_init: bool | None = None,
kw_only: bool = ...,
slots: bool = ...,
) -> type[PydanticDataclass]: ...
else:
@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
@overload
def dataclass(
*,
init: Literal[False] = False,
repr: bool = True,
eq: bool = True,
order: bool = False,
unsafe_hash: bool = False,
frozen: bool | None = None,
config: ConfigDict | type[object] | None = None,
validate_on_init: bool | None = None,
) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore
...
@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
@overload
def dataclass(
_cls: type[_T], # type: ignore
*,
init: Literal[False] = False,
repr: bool = True,
eq: bool = True,
order: bool = False,
unsafe_hash: bool = False,
frozen: bool | None = None,
config: ConfigDict | type[object] | None = None,
validate_on_init: bool | None = None,
) -> type[PydanticDataclass]: ...
@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
def dataclass(
_cls: type[_T] | None = None,
*,
init: Literal[False] = False,
repr: bool = True,
eq: bool = True,
order: bool = False,
unsafe_hash: bool = False,
frozen: bool | None = None,
config: ConfigDict | type[object] | None = None,
validate_on_init: bool | None = None,
kw_only: bool = False,
slots: bool = False,
) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]:
"""!!! abstract "Usage Documentation"
[`dataclasses`](../concepts/dataclasses.md)
A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`,
but with added validation.
This function should be used similarly to `dataclasses.dataclass`.
Args:
_cls: The target `dataclass`.
init: Included for signature compatibility with `dataclasses.dataclass`, and is passed through to
`dataclasses.dataclass` when appropriate. If specified, must be set to `False`, as pydantic inserts its
own `__init__` function.
repr: A boolean indicating whether to include the field in the `__repr__` output.
eq: Determines if a `__eq__` method should be generated for the class.
order: Determines if comparison magic methods should be generated, such as `__lt__`, but not `__eq__`.
unsafe_hash: Determines if a `__hash__` method should be included in the class, as in `dataclasses.dataclass`.
frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its
attributes to be modified after it has been initialized. If not set, the value from the provided `config` argument will be used (and will default to `False` otherwise).
config: The Pydantic config to use for the `dataclass`.
validate_on_init: A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses
are validated on init.
kw_only: Determines if `__init__` method parameters must be specified by keyword only. Defaults to `False`.
slots: Determines if the generated class should be a 'slots' `dataclass`, which does not allow the addition of
new attributes after instantiation.
Returns:
A decorator that accepts a class as its argument and returns a Pydantic `dataclass`.
Raises:
AssertionError: Raised if `init` is not `False` or `validate_on_init` is `False`.
"""
assert init is False, 'pydantic.dataclasses.dataclass only supports init=False'
assert validate_on_init is not False, 'validate_on_init=False is no longer supported'
if sys.version_info >= (3, 10):
kwargs = {'kw_only': kw_only, 'slots': slots}
else:
kwargs = {}
def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]:
"""Create a Pydantic dataclass from a regular dataclass.
Args:
cls: The class to create the Pydantic dataclass from.
Returns:
A Pydantic dataclass.
"""
from ._internal._utils import is_model_class
if is_model_class(cls):
raise PydanticUserError(
f'Cannot create a Pydantic dataclass from {cls.__name__} as it is already a Pydantic model',
code='dataclass-on-model',
)
original_cls = cls
# we warn on conflicting config specifications, but only if the class doesn't have a dataclass base
# because a dataclass base might provide a __pydantic_config__ attribute that we don't want to warn about
has_dataclass_base = any(dataclasses.is_dataclass(base) for base in cls.__bases__)
if not has_dataclass_base and config is not None and hasattr(cls, '__pydantic_config__'):
warn(
f'`config` is set via both the `dataclass` decorator and `__pydantic_config__` for dataclass {cls.__name__}. '
f'The `config` specification from `dataclass` decorator will take priority.',
category=UserWarning,
stacklevel=2,
)
# if config is not explicitly provided, try to read it from the type
config_dict = config if config is not None else getattr(cls, '__pydantic_config__', None)
config_wrapper = _config.ConfigWrapper(config_dict)
decorators = _decorators.DecoratorInfos.build(cls, replace_wrapped_methods=True)
decorators.update_from_config(config_wrapper)
# Keep track of the original __doc__ so that we can restore it after applying the dataclasses decorator
# Otherwise, classes with no __doc__ will have their signature added into the JSON schema description,
# since dataclasses.dataclass will set this as the __doc__
original_doc = cls.__doc__
if _pydantic_dataclasses.is_stdlib_dataclass(cls):
# Vanilla dataclasses include a default docstring (representing the class signature),
# which we don't want to preserve.
original_doc = None
# We don't want to add validation to the existing std lib dataclass, so we will subclass it
# If the class is generic, we need to make sure the subclass also inherits from Generic
# with all the same parameters.
bases = (cls,)
if issubclass(cls, Generic):
generic_base = Generic[cls.__parameters__] # type: ignore
bases = bases + (generic_base,)
cls = types.new_class(cls.__name__, bases)
# Respect frozen setting from dataclass constructor and fallback to config setting if not provided
if frozen is not None:
frozen_ = frozen
if config_wrapper.frozen:
# It's not recommended to define both, as the setting from the dataclass decorator will take priority.
warn(
f'`frozen` is set via both the `dataclass` decorator and `config` for dataclass {cls.__name__!r}.'
'This is not recommended. The `frozen` specification on `dataclass` will take priority.',
category=UserWarning,
stacklevel=2,
)
else:
frozen_ = config_wrapper.frozen or False
# Make Pydantic's `Field()` function compatible with stdlib dataclasses. As we'll decorate
# `cls` with the stdlib `@dataclass` decorator first, there are two attributes, `kw_only` and
# `repr` that need to be understood *during* the stdlib creation. We do so in two steps:
# 1. On the decorated class, wrap `Field()` assignment with `dataclass.field()`, with the
# two attributes set (done in `as_dataclass_field()`)
cls_anns = _typing_extra.safe_get_annotations(cls)
for field_name in cls_anns:
# We should look for assignments in `__dict__` instead, but for now we follow
# the same behavior as stdlib dataclasses (see https://github.com/python/cpython/issues/88609)
field_value = getattr(cls, field_name, None)
if isinstance(field_value, FieldInfo):
setattr(cls, field_name, _pydantic_dataclasses.as_dataclass_field(field_value))
# 2. For bases of `cls` that are stdlib dataclasses, we temporarily patch their fields
# (see the docstring of the context manager):
with _pydantic_dataclasses.patch_base_fields(cls):
cls = dataclasses.dataclass( # pyright: ignore[reportCallIssue]
cls,
# the value of init here doesn't affect anything except that it makes it easier to generate a signature
init=True,
repr=repr,
eq=eq,
order=order,
unsafe_hash=unsafe_hash,
frozen=frozen_,
**kwargs,
)
if config_wrapper.validate_assignment:
original_setattr = cls.__setattr__
@functools.wraps(cls.__setattr__)
def validated_setattr(instance: PydanticDataclass, name: str, value: Any, /) -> None:
if frozen_:
return original_setattr(instance, name, value) # pyright: ignore[reportCallIssue]
inst_cls = type(instance)
attr = getattr(inst_cls, name, None)
if isinstance(attr, property):
attr.__set__(instance, value)
elif isinstance(attr, functools.cached_property):
instance.__dict__.__setitem__(name, value)
else:
inst_cls.__pydantic_validator__.validate_assignment(instance, name, value)
cls.__setattr__ = validated_setattr.__get__(None, cls) # type: ignore
if slots and not hasattr(cls, '__setstate__'):
# If slots is set, `pickle` (relied on by `copy.copy()`) will use
# `__setattr__()` to reconstruct the dataclass. However, the custom
# `__setattr__()` set above relies on `validate_assignment()`, which
# in turn expects all the field values to be already present on the
# instance, resulting in attribute errors.
# As such, we make use of `object.__setattr__()` instead.
# Note that we do so only if `__setstate__()` isn't already set (this is the
# case if on top of `slots`, `frozen` is used).
# Taken from `dataclasses._dataclass_get/setstate()`:
def _dataclass_getstate(self: Any) -> list[Any]:
return [getattr(self, f.name) for f in dataclasses.fields(self)]
def _dataclass_setstate(self: Any, state: list[Any]) -> None:
for field, value in zip(dataclasses.fields(self), state):
object.__setattr__(self, field.name, value)
cls.__getstate__ = _dataclass_getstate # pyright: ignore[reportAttributeAccessIssue]
cls.__setstate__ = _dataclass_setstate # pyright: ignore[reportAttributeAccessIssue]
# This is an undocumented attribute to distinguish stdlib/Pydantic dataclasses.
# It should be set as early as possible:
cls.__is_pydantic_dataclass__ = True
cls.__pydantic_decorators__ = decorators # type: ignore
cls.__doc__ = original_doc
# Can be non-existent for dynamically created classes:
firstlineno = getattr(original_cls, '__firstlineno__', None)
cls.__module__ = original_cls.__module__
if sys.version_info >= (3, 13) and firstlineno is not None:
# As per https://docs.python.org/3/reference/datamodel.html#type.__firstlineno__:
# Setting the `__module__` attribute removes the `__firstlineno__` item from the type’s dictionary.
original_cls.__firstlineno__ = firstlineno
cls.__firstlineno__ = firstlineno
cls.__qualname__ = original_cls.__qualname__
cls.__pydantic_fields_complete__ = classmethod(_pydantic_fields_complete)
cls.__pydantic_complete__ = False # `complete_dataclass` will set it to `True` if successful.
# TODO `parent_namespace` is currently None, but we could do the same thing as Pydantic models:
# fetch the parent ns using `parent_frame_namespace` (if the dataclass was defined in a function),
# and possibly cache it (see the `__pydantic_parent_namespace__` logic for models).
_pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False)
return cls
return create_dataclass if _cls is None else create_dataclass(_cls)
def _pydantic_fields_complete(cls: type[PydanticDataclass]) -> bool:
"""Return whether the fields were successfully collected (i.e. type hints were successfully resolved).
This is a private helper, not meant to be used outside Pydantic.
"""
return all(field_info._complete for field_info in cls.__pydantic_fields__.values())
__getattr__ = getattr_migration(__name__)
if sys.version_info < (3, 11):
# Monkeypatch dataclasses.InitVar so that typing doesn't error if it occurs as a type when evaluating type hints
# Starting in 3.11, typing.get_type_hints will not raise an error if the retrieved type hints are not callable.
def _call_initvar(*args: Any, **kwargs: Any) -> NoReturn:
"""This function does nothing but raise an error that is as similar as possible to what you'd get
if you were to try calling `InitVar[int]()` without this monkeypatch. The whole purpose is just
to ensure typing._type_check does not error if the type hint evaluates to `InitVar[<parameter>]`.
"""
raise TypeError("'InitVar' object is not callable")
dataclasses.InitVar.__call__ = _call_initvar
def rebuild_dataclass(
cls: type[PydanticDataclass],
*,
force: bool = False,
raise_errors: bool = True,
_parent_namespace_depth: int = 2,
_types_namespace: MappingNamespace | None = None,
) -> bool | None:
"""Try to rebuild the pydantic-core schema for the dataclass.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
This is analogous to `BaseModel.model_rebuild`.
Args:
cls: The class to rebuild the pydantic-core schema for.
force: Whether to force the rebuilding of the schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
"""
if not force and cls.__pydantic_complete__:
return None
for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'):
if attr in cls.__dict__ and not isinstance(getattr(cls, attr), _mock_val_ser.MockValSer):
# Deleting the validator/serializer is necessary as otherwise they can get reused in
# pydantic-core. Same applies for the core schema that can be reused in schema generation.
delattr(cls, attr)
cls.__pydantic_complete__ = False
if _types_namespace is not None:
rebuild_ns = _types_namespace
elif _parent_namespace_depth > 0:
rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
else:
rebuild_ns = {}
ns_resolver = _namespace_utils.NsResolver(
parent_namespace=rebuild_ns,
)
return _pydantic_dataclasses.complete_dataclass(
cls,
_config.ConfigWrapper(cls.__pydantic_config__, check=False),
raise_errors=raise_errors,
ns_resolver=ns_resolver,
# We could provide a different config instead (with `'defer_build'` set to `True`)
# of this explicit `_force_build` argument, but because config can come from the
# decorator parameter or the `__pydantic_config__` attribute, `complete_dataclass`
# will overwrite `__pydantic_config__` with the provided config above:
_force_build=True,
)
def is_pydantic_dataclass(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]:
"""Whether a class is a pydantic dataclass.
Args:
class_: The class.
Returns:
`True` if the class is a pydantic dataclass, `False` otherwise.
"""
try:
return '__is_pydantic_dataclass__' in class_.__dict__ and dataclasses.is_dataclass(class_)
except AttributeError:
return False

5
venv/Lib/site-packages/pydantic/datetime_parse.py Normal file
View File

@@ -0,0 +1,5 @@
"""The `datetime_parse` module is a backport module from V1."""
from ._migration import getattr_migration
__getattr__ = getattr_migration(__name__)