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2026-07-02 20:04:38 +00:00
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131
venv/Lib/site-packages/pydantic/v1/__init__.py Normal file
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# flake8: noqa
from pydantic.v1 import dataclasses
from pydantic.v1.annotated_types import create_model_from_namedtuple, create_model_from_typeddict
from pydantic.v1.class_validators import root_validator, validator
from pydantic.v1.config import BaseConfig, ConfigDict, Extra
from pydantic.v1.decorator import validate_arguments
from pydantic.v1.env_settings import BaseSettings
from pydantic.v1.error_wrappers import ValidationError
from pydantic.v1.errors import *
from pydantic.v1.fields import Field, PrivateAttr, Required
from pydantic.v1.main import *
from pydantic.v1.networks import *
from pydantic.v1.parse import Protocol
from pydantic.v1.tools import *
from pydantic.v1.types import *
from pydantic.v1.version import VERSION, compiled
__version__ = VERSION
# WARNING __all__ from pydantic.errors is not included here, it will be removed as an export here in v2
# please use "from pydantic.v1.errors import ..." instead
__all__ = [
# annotated types utils
'create_model_from_namedtuple',
'create_model_from_typeddict',
# dataclasses
'dataclasses',
# class_validators
'root_validator',
'validator',
# config
'BaseConfig',
'ConfigDict',
'Extra',
# decorator
'validate_arguments',
# env_settings
'BaseSettings',
# error_wrappers
'ValidationError',
# fields
'Field',
'Required',
# main
'BaseModel',
'create_model',
'validate_model',
# network
'AnyUrl',
'AnyHttpUrl',
'FileUrl',
'HttpUrl',
'stricturl',
'EmailStr',
'NameEmail',
'IPvAnyAddress',
'IPvAnyInterface',
'IPvAnyNetwork',
'PostgresDsn',
'CockroachDsn',
'AmqpDsn',
'RedisDsn',
'MongoDsn',
'KafkaDsn',
'validate_email',
# parse
'Protocol',
# tools
'parse_file_as',
'parse_obj_as',
'parse_raw_as',
'schema_of',
'schema_json_of',
# types
'NoneStr',
'NoneBytes',
'StrBytes',
'NoneStrBytes',
'StrictStr',
'ConstrainedBytes',
'conbytes',
'ConstrainedList',
'conlist',
'ConstrainedSet',
'conset',
'ConstrainedFrozenSet',
'confrozenset',
'ConstrainedStr',
'constr',
'PyObject',
'ConstrainedInt',
'conint',
'PositiveInt',
'NegativeInt',
'NonNegativeInt',
'NonPositiveInt',
'ConstrainedFloat',
'confloat',
'PositiveFloat',
'NegativeFloat',
'NonNegativeFloat',
'NonPositiveFloat',
'FiniteFloat',
'ConstrainedDecimal',
'condecimal',
'ConstrainedDate',
'condate',
'UUID1',
'UUID3',
'UUID4',
'UUID5',
'FilePath',
'DirectoryPath',
'Json',
'JsonWrapper',
'SecretField',
'SecretStr',
'SecretBytes',
'StrictBool',
'StrictBytes',
'StrictInt',
'StrictFloat',
'PaymentCardNumber',
'PrivateAttr',
'ByteSize',
'PastDate',
'FutureDate',
# version
'compiled',
'VERSION',
]

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venv/Lib/site-packages/pydantic/v1/_hypothesis_plugin.py Normal file
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"""
Register Hypothesis strategies for Pydantic custom types.
This enables fully-automatic generation of test data for most Pydantic classes.
Note that this module has *no* runtime impact on Pydantic itself; instead it
is registered as a setuptools entry point and Hypothesis will import it if
Pydantic is installed. See also:
https://hypothesis.readthedocs.io/en/latest/strategies.html#registering-strategies-via-setuptools-entry-points
https://hypothesis.readthedocs.io/en/latest/data.html#hypothesis.strategies.register_type_strategy
https://hypothesis.readthedocs.io/en/latest/strategies.html#interaction-with-pytest-cov
https://docs.pydantic.dev/usage/types/#pydantic-types
Note that because our motivation is to *improve user experience*, the strategies
are always sound (never generate invalid data) but sacrifice completeness for
maintainability (ie may be unable to generate some tricky but valid data).
Finally, this module makes liberal use of `# type: ignore[<code>]` pragmas.
This is because Hypothesis annotates `register_type_strategy()` with
`(T, SearchStrategy[T])`, but in most cases we register e.g. `ConstrainedInt`
to generate instances of the builtin `int` type which match the constraints.
"""
import contextlib
import datetime
import ipaddress
import json
import math
from fractions import Fraction
from typing import Callable, Dict, Type, Union, cast, overload
import hypothesis.strategies as st
import pydantic
import pydantic.color
import pydantic.types
from pydantic.v1.utils import lenient_issubclass
# FilePath and DirectoryPath are explicitly unsupported, as we'd have to create
# them on-disk, and that's unsafe in general without being told *where* to do so.
#
# URLs are unsupported because it's easy for users to define their own strategy for
# "normal" URLs, and hard for us to define a general strategy which includes "weird"
# URLs but doesn't also have unpredictable performance problems.
#
# conlist() and conset() are unsupported for now, because the workarounds for
# Cython and Hypothesis to handle parametrized generic types are incompatible.
# We are rethinking Hypothesis compatibility in Pydantic v2.
# Emails
try:
import email_validator
except ImportError: # pragma: no cover
pass
else:
def is_valid_email(s: str) -> bool:
# Hypothesis' st.emails() occasionally generates emails like 0@A0--0.ac
# that are invalid according to email-validator, so we filter those out.
try:
email_validator.validate_email(s, check_deliverability=False)
return True
except email_validator.EmailNotValidError: # pragma: no cover
return False
# Note that these strategies deliberately stay away from any tricky Unicode
# or other encoding issues; we're just trying to generate *something* valid.
st.register_type_strategy(pydantic.EmailStr, st.emails().filter(is_valid_email)) # type: ignore[arg-type]
st.register_type_strategy(
pydantic.NameEmail,
st.builds(
'{} <{}>'.format, # type: ignore[arg-type]
st.from_regex('[A-Za-z0-9_]+( [A-Za-z0-9_]+){0,5}', fullmatch=True),
st.emails().filter(is_valid_email),
),
)
# PyObject - dotted names, in this case taken from the math module.
st.register_type_strategy(
pydantic.PyObject, # type: ignore[arg-type]
st.sampled_from(
[cast(pydantic.PyObject, f'math.{name}') for name in sorted(vars(math)) if not name.startswith('_')]
),
)
# CSS3 Colors; as name, hex, rgb(a) tuples or strings, or hsl strings
_color_regexes = (
'|'.join(
(
pydantic.color.r_hex_short,
pydantic.color.r_hex_long,
pydantic.color.r_rgb,
pydantic.color.r_rgba,
pydantic.color.r_hsl,
pydantic.color.r_hsla,
)
)
# Use more precise regex patterns to avoid value-out-of-range errors
.replace(pydantic.color._r_sl, r'(?:(\d\d?(?:\.\d+)?|100(?:\.0+)?)%)')
.replace(pydantic.color._r_alpha, r'(?:(0(?:\.\d+)?|1(?:\.0+)?|\.\d+|\d{1,2}%))')
.replace(pydantic.color._r_255, r'(?:((?:\d|\d\d|[01]\d\d|2[0-4]\d|25[0-4])(?:\.\d+)?|255(?:\.0+)?))')
)
st.register_type_strategy(
pydantic.color.Color,
st.one_of(
st.sampled_from(sorted(pydantic.color.COLORS_BY_NAME)),
st.tuples(
st.integers(0, 255),
st.integers(0, 255),
st.integers(0, 255),
st.none() | st.floats(0, 1) | st.floats(0, 100).map('{}%'.format),
),
st.from_regex(_color_regexes, fullmatch=True),
),
)
# Card numbers, valid according to the Luhn algorithm
def add_luhn_digit(card_number: str) -> str:
# See https://en.wikipedia.org/wiki/Luhn_algorithm
for digit in '0123456789':
with contextlib.suppress(Exception):
pydantic.PaymentCardNumber.validate_luhn_check_digit(card_number + digit)
return card_number + digit
raise AssertionError('Unreachable') # pragma: no cover
card_patterns = (
# Note that these patterns omit the Luhn check digit; that's added by the function above
'4[0-9]{14}', # Visa
'5[12345][0-9]{13}', # Mastercard
'3[47][0-9]{12}', # American Express
'[0-26-9][0-9]{10,17}', # other (incomplete to avoid overlap)
)
st.register_type_strategy(
pydantic.PaymentCardNumber,
st.from_regex('|'.join(card_patterns), fullmatch=True).map(add_luhn_digit), # type: ignore[arg-type]
)
# UUIDs
st.register_type_strategy(pydantic.UUID1, st.uuids(version=1))
st.register_type_strategy(pydantic.UUID3, st.uuids(version=3))
st.register_type_strategy(pydantic.UUID4, st.uuids(version=4))
st.register_type_strategy(pydantic.UUID5, st.uuids(version=5))
# Secrets
st.register_type_strategy(pydantic.SecretBytes, st.binary().map(pydantic.SecretBytes))
st.register_type_strategy(pydantic.SecretStr, st.text().map(pydantic.SecretStr))
# IP addresses, networks, and interfaces
st.register_type_strategy(pydantic.IPvAnyAddress, st.ip_addresses()) # type: ignore[arg-type]
st.register_type_strategy(
pydantic.IPvAnyInterface,
st.from_type(ipaddress.IPv4Interface) | st.from_type(ipaddress.IPv6Interface), # type: ignore[arg-type]
)
st.register_type_strategy(
pydantic.IPvAnyNetwork,
st.from_type(ipaddress.IPv4Network) | st.from_type(ipaddress.IPv6Network), # type: ignore[arg-type]
)
# We hook into the con***() functions and the ConstrainedNumberMeta metaclass,
# so here we only have to register subclasses for other constrained types which
# don't go via those mechanisms. Then there are the registration hooks below.
st.register_type_strategy(pydantic.StrictBool, st.booleans())
st.register_type_strategy(pydantic.StrictStr, st.text())
# FutureDate, PastDate
st.register_type_strategy(pydantic.FutureDate, st.dates(min_value=datetime.date.today() + datetime.timedelta(days=1)))
st.register_type_strategy(pydantic.PastDate, st.dates(max_value=datetime.date.today() - datetime.timedelta(days=1)))
# Constrained-type resolver functions
#
# For these ones, we actually want to inspect the type in order to work out a
# satisfying strategy. First up, the machinery for tracking resolver functions:
RESOLVERS: Dict[type, Callable[[type], st.SearchStrategy]] = {} # type: ignore[type-arg]
@overload
def _registered(typ: Type[pydantic.types.T]) -> Type[pydantic.types.T]:
pass
@overload
def _registered(typ: pydantic.types.ConstrainedNumberMeta) -> pydantic.types.ConstrainedNumberMeta:
pass
def _registered(
typ: Union[Type[pydantic.types.T], pydantic.types.ConstrainedNumberMeta]
) -> Union[Type[pydantic.types.T], pydantic.types.ConstrainedNumberMeta]:
# This function replaces the version in `pydantic.types`, in order to
# effect the registration of new constrained types so that Hypothesis
# can generate valid examples.
pydantic.types._DEFINED_TYPES.add(typ)
for supertype, resolver in RESOLVERS.items():
if issubclass(typ, supertype):
st.register_type_strategy(typ, resolver(typ)) # type: ignore
return typ
raise NotImplementedError(f'Unknown type {typ!r} has no resolver to register') # pragma: no cover
def resolves(
typ: Union[type, pydantic.types.ConstrainedNumberMeta]
) -> Callable[[Callable[..., st.SearchStrategy]], Callable[..., st.SearchStrategy]]: # type: ignore[type-arg]
def inner(f): # type: ignore
assert f not in RESOLVERS
RESOLVERS[typ] = f
return f
return inner
# Type-to-strategy resolver functions
@resolves(pydantic.JsonWrapper)
def resolve_json(cls): # type: ignore[no-untyped-def]
try:
inner = st.none() if cls.inner_type is None else st.from_type(cls.inner_type)
except Exception: # pragma: no cover
finite = st.floats(allow_infinity=False, allow_nan=False)
inner = st.recursive(
base=st.one_of(st.none(), st.booleans(), st.integers(), finite, st.text()),
extend=lambda x: st.lists(x) | st.dictionaries(st.text(), x), # type: ignore
)
inner_type = getattr(cls, 'inner_type', None)
return st.builds(
cls.inner_type.json if lenient_issubclass(inner_type, pydantic.BaseModel) else json.dumps,
inner,
ensure_ascii=st.booleans(),
indent=st.none() | st.integers(0, 16),
sort_keys=st.booleans(),
)
@resolves(pydantic.ConstrainedBytes)
def resolve_conbytes(cls): # type: ignore[no-untyped-def] # pragma: no cover
min_size = cls.min_length or 0
max_size = cls.max_length
if not cls.strip_whitespace:
return st.binary(min_size=min_size, max_size=max_size)
# Fun with regex to ensure we neither start nor end with whitespace
repeats = '{{{},{}}}'.format(
min_size - 2 if min_size > 2 else 0,
max_size - 2 if (max_size or 0) > 2 else '',
)
if min_size >= 2:
pattern = rf'\W.{repeats}\W'
elif min_size == 1:
pattern = rf'\W(.{repeats}\W)?'
else:
assert min_size == 0
pattern = rf'(\W(.{repeats}\W)?)?'
return st.from_regex(pattern.encode(), fullmatch=True)
@resolves(pydantic.ConstrainedDecimal)
def resolve_condecimal(cls): # type: ignore[no-untyped-def]
min_value = cls.ge
max_value = cls.le
if cls.gt is not None:
assert min_value is None, 'Set `gt` or `ge`, but not both'
min_value = cls.gt
if cls.lt is not None:
assert max_value is None, 'Set `lt` or `le`, but not both'
max_value = cls.lt
s = st.decimals(min_value, max_value, allow_nan=False, places=cls.decimal_places)
if cls.lt is not None:
s = s.filter(lambda d: d < cls.lt)
if cls.gt is not None:
s = s.filter(lambda d: cls.gt < d)
return s
@resolves(pydantic.ConstrainedFloat)
def resolve_confloat(cls): # type: ignore[no-untyped-def]
min_value = cls.ge
max_value = cls.le
exclude_min = False
exclude_max = False
if cls.gt is not None:
assert min_value is None, 'Set `gt` or `ge`, but not both'
min_value = cls.gt
exclude_min = True
if cls.lt is not None:
assert max_value is None, 'Set `lt` or `le`, but not both'
max_value = cls.lt
exclude_max = True
if cls.multiple_of is None:
return st.floats(min_value, max_value, exclude_min=exclude_min, exclude_max=exclude_max, allow_nan=False)
if min_value is not None:
min_value = math.ceil(min_value / cls.multiple_of)
if exclude_min:
min_value = min_value + 1
if max_value is not None:
assert max_value >= cls.multiple_of, 'Cannot build model with max value smaller than multiple of'
max_value = math.floor(max_value / cls.multiple_of)
if exclude_max:
max_value = max_value - 1
return st.integers(min_value, max_value).map(lambda x: x * cls.multiple_of)
@resolves(pydantic.ConstrainedInt)
def resolve_conint(cls): # type: ignore[no-untyped-def]
min_value = cls.ge
max_value = cls.le
if cls.gt is not None:
assert min_value is None, 'Set `gt` or `ge`, but not both'
min_value = cls.gt + 1
if cls.lt is not None:
assert max_value is None, 'Set `lt` or `le`, but not both'
max_value = cls.lt - 1
if cls.multiple_of is None or cls.multiple_of == 1:
return st.integers(min_value, max_value)
# These adjustments and the .map handle integer-valued multiples, while the
# .filter handles trickier cases as for confloat.
if min_value is not None:
min_value = math.ceil(Fraction(min_value) / Fraction(cls.multiple_of))
if max_value is not None:
max_value = math.floor(Fraction(max_value) / Fraction(cls.multiple_of))
return st.integers(min_value, max_value).map(lambda x: x * cls.multiple_of)
@resolves(pydantic.ConstrainedDate)
def resolve_condate(cls): # type: ignore[no-untyped-def]
if cls.ge is not None:
assert cls.gt is None, 'Set `gt` or `ge`, but not both'
min_value = cls.ge
elif cls.gt is not None:
min_value = cls.gt + datetime.timedelta(days=1)
else:
min_value = datetime.date.min
if cls.le is not None:
assert cls.lt is None, 'Set `lt` or `le`, but not both'
max_value = cls.le
elif cls.lt is not None:
max_value = cls.lt - datetime.timedelta(days=1)
else:
max_value = datetime.date.max
return st.dates(min_value, max_value)
@resolves(pydantic.ConstrainedStr)
def resolve_constr(cls): # type: ignore[no-untyped-def] # pragma: no cover
min_size = cls.min_length or 0
max_size = cls.max_length
if cls.regex is None and not cls.strip_whitespace:
return st.text(min_size=min_size, max_size=max_size)
if cls.regex is not None:
strategy = st.from_regex(cls.regex)
if cls.strip_whitespace:
strategy = strategy.filter(lambda s: s == s.strip())
elif cls.strip_whitespace:
repeats = '{{{},{}}}'.format(
min_size - 2 if min_size > 2 else 0,
max_size - 2 if (max_size or 0) > 2 else '',
)
if min_size >= 2:
strategy = st.from_regex(rf'\W.{repeats}\W')
elif min_size == 1:
strategy = st.from_regex(rf'\W(.{repeats}\W)?')
else:
assert min_size == 0
strategy = st.from_regex(rf'(\W(.{repeats}\W)?)?')
if min_size == 0 and max_size is None:
return strategy
elif max_size is None:
return strategy.filter(lambda s: min_size <= len(s))
return strategy.filter(lambda s: min_size <= len(s) <= max_size)
# Finally, register all previously-defined types, and patch in our new function
for typ in list(pydantic.types._DEFINED_TYPES):
_registered(typ)
pydantic.types._registered = _registered
st.register_type_strategy(pydantic.Json, resolve_json)

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venv/Lib/site-packages/pydantic/v1/annotated_types.py Normal file
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import sys
from typing import TYPE_CHECKING, Any, Dict, FrozenSet, NamedTuple, Type
from pydantic.v1.fields import Required
from pydantic.v1.main import BaseModel, create_model
from pydantic.v1.typing import is_typeddict, is_typeddict_special
if TYPE_CHECKING:
from typing_extensions import TypedDict
if sys.version_info < (3, 11):
def is_legacy_typeddict(typeddict_cls: Type['TypedDict']) -> bool: # type: ignore[valid-type]
return is_typeddict(typeddict_cls) and type(typeddict_cls).__module__ == 'typing'
else:
def is_legacy_typeddict(_: Any) -> Any:
return False
def create_model_from_typeddict(
# Mypy bug: `Type[TypedDict]` is resolved as `Any` https://github.com/python/mypy/issues/11030
typeddict_cls: Type['TypedDict'], # type: ignore[valid-type]
**kwargs: Any,
) -> Type['BaseModel']:
"""
Create a `BaseModel` based on the fields of a `TypedDict`.
Since `typing.TypedDict` in Python 3.8 does not store runtime information about optional keys,
we raise an error if this happens (see https://bugs.python.org/issue38834).
"""
field_definitions: Dict[str, Any]
# Best case scenario: with python 3.9+ or when `TypedDict` is imported from `typing_extensions`
if not hasattr(typeddict_cls, '__required_keys__'):
raise TypeError(
'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.9.2. '
'Without it, there is no way to differentiate required and optional fields when subclassed.'
)
if is_legacy_typeddict(typeddict_cls) and any(
is_typeddict_special(t) for t in typeddict_cls.__annotations__.values()
):
raise TypeError(
'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.11. '
'Without it, there is no way to reflect Required/NotRequired keys.'
)
required_keys: FrozenSet[str] = typeddict_cls.__required_keys__ # type: ignore[attr-defined]
field_definitions = {
field_name: (field_type, Required if field_name in required_keys else None)
for field_name, field_type in typeddict_cls.__annotations__.items()
}
return create_model(typeddict_cls.__name__, **kwargs, **field_definitions)
def create_model_from_namedtuple(namedtuple_cls: Type['NamedTuple'], **kwargs: Any) -> Type['BaseModel']:
"""
Create a `BaseModel` based on the fields of a named tuple.
A named tuple can be created with `typing.NamedTuple` and declared annotations
but also with `collections.namedtuple`, in this case we consider all fields
to have type `Any`.
"""
# With python 3.10+, `__annotations__` always exists but can be empty hence the `getattr... or...` logic
namedtuple_annotations: Dict[str, Type[Any]] = getattr(namedtuple_cls, '__annotations__', None) or {
k: Any for k in namedtuple_cls._fields
}
field_definitions: Dict[str, Any] = {
field_name: (field_type, Required) for field_name, field_type in namedtuple_annotations.items()
}
return create_model(namedtuple_cls.__name__, **kwargs, **field_definitions)

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venv/Lib/site-packages/pydantic/v1/class_validators.py Normal file
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import warnings
from collections import ChainMap
from functools import partial, partialmethod, wraps
from itertools import chain
from types import FunctionType
from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, overload
from pydantic.v1.errors import ConfigError
from pydantic.v1.typing import AnyCallable
from pydantic.v1.utils import ROOT_KEY, in_ipython
if TYPE_CHECKING:
from pydantic.v1.typing import AnyClassMethod
class Validator:
__slots__ = 'func', 'pre', 'each_item', 'always', 'check_fields', 'skip_on_failure'
def __init__(
self,
func: AnyCallable,
pre: bool = False,
each_item: bool = False,
always: bool = False,
check_fields: bool = False,
skip_on_failure: bool = False,
):
self.func = func
self.pre = pre
self.each_item = each_item
self.always = always
self.check_fields = check_fields
self.skip_on_failure = skip_on_failure
if TYPE_CHECKING:
from inspect import Signature
from pydantic.v1.config import BaseConfig
from pydantic.v1.fields import ModelField
from pydantic.v1.types import ModelOrDc
ValidatorCallable = Callable[[Optional[ModelOrDc], Any, Dict[str, Any], ModelField, Type[BaseConfig]], Any]
ValidatorsList = List[ValidatorCallable]
ValidatorListDict = Dict[str, List[Validator]]
_FUNCS: Set[str] = set()
VALIDATOR_CONFIG_KEY = '__validator_config__'
ROOT_VALIDATOR_CONFIG_KEY = '__root_validator_config__'
def validator(
*fields: str,
pre: bool = False,
each_item: bool = False,
always: bool = False,
check_fields: bool = True,
whole: Optional[bool] = None,
allow_reuse: bool = False,
) -> Callable[[AnyCallable], 'AnyClassMethod']:
"""
Decorate methods on the class indicating that they should be used to validate fields
:param fields: which field(s) the method should be called on
:param pre: whether or not this validator should be called before the standard validators (else after)
:param each_item: for complex objects (sets, lists etc.) whether to validate individual elements rather than the
whole object
:param always: whether this method and other validators should be called even if the value is missing
:param check_fields: whether to check that the fields actually exist on the model
:param allow_reuse: whether to track and raise an error if another validator refers to the decorated function
"""
if not fields:
raise ConfigError('validator with no fields specified')
elif isinstance(fields[0], FunctionType):
raise ConfigError(
"validators should be used with fields and keyword arguments, not bare. " # noqa: Q000
"E.g. usage should be `@validator('<field_name>', ...)`"
)
elif not all(isinstance(field, str) for field in fields):
raise ConfigError(
"validator fields should be passed as separate string args. " # noqa: Q000
"E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`"
)
if whole is not None:
warnings.warn(
'The "whole" keyword argument is deprecated, use "each_item" (inverse meaning, default False) instead',
DeprecationWarning,
)
assert each_item is False, '"each_item" and "whole" conflict, remove "whole"'
each_item = not whole
def dec(f: AnyCallable) -> 'AnyClassMethod':
f_cls = _prepare_validator(f, allow_reuse)
setattr(
f_cls,
VALIDATOR_CONFIG_KEY,
(
fields,
Validator(func=f_cls.__func__, pre=pre, each_item=each_item, always=always, check_fields=check_fields),
),
)
return f_cls
return dec
@overload
def root_validator(_func: AnyCallable) -> 'AnyClassMethod':
...
@overload
def root_validator(
*, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False
) -> Callable[[AnyCallable], 'AnyClassMethod']:
...
def root_validator(
_func: Optional[AnyCallable] = None, *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False
) -> Union['AnyClassMethod', Callable[[AnyCallable], 'AnyClassMethod']]:
"""
Decorate methods on a model indicating that they should be used to validate (and perhaps modify) data either
before or after standard model parsing/validation is performed.
"""
if _func:
f_cls = _prepare_validator(_func, allow_reuse)
setattr(
f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)
)
return f_cls
def dec(f: AnyCallable) -> 'AnyClassMethod':
f_cls = _prepare_validator(f, allow_reuse)
setattr(
f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)
)
return f_cls
return dec
def _prepare_validator(function: AnyCallable, allow_reuse: bool) -> 'AnyClassMethod':
"""
Avoid validators with duplicated names since without this, validators can be overwritten silently
which generally isn't the intended behaviour, don't run in ipython (see #312) or if allow_reuse is False.
"""
f_cls = function if isinstance(function, classmethod) else classmethod(function)
if not in_ipython() and not allow_reuse:
ref = (
getattr(f_cls.__func__, '__module__', '<No __module__>')
+ '.'
+ getattr(f_cls.__func__, '__qualname__', f'<No __qualname__: id:{id(f_cls.__func__)}>')
)
if ref in _FUNCS:
raise ConfigError(f'duplicate validator function "{ref}"; if this is intended, set `allow_reuse=True`')
_FUNCS.add(ref)
return f_cls
class ValidatorGroup:
def __init__(self, validators: 'ValidatorListDict') -> None:
self.validators = validators
self.used_validators = {'*'}
def get_validators(self, name: str) -> Optional[Dict[str, Validator]]:
self.used_validators.add(name)
validators = self.validators.get(name, [])
if name != ROOT_KEY:
validators += self.validators.get('*', [])
if validators:
return {getattr(v.func, '__name__', f'<No __name__: id:{id(v.func)}>'): v for v in validators}
else:
return None
def check_for_unused(self) -> None:
unused_validators = set(
chain.from_iterable(
(
getattr(v.func, '__name__', f'<No __name__: id:{id(v.func)}>')
for v in self.validators[f]
if v.check_fields
)
for f in (self.validators.keys() - self.used_validators)
)
)
if unused_validators:
fn = ', '.join(unused_validators)
raise ConfigError(
f"Validators defined with incorrect fields: {fn} " # noqa: Q000
f"(use check_fields=False if you're inheriting from the model and intended this)"
)
def extract_validators(namespace: Dict[str, Any]) -> Dict[str, List[Validator]]:
validators: Dict[str, List[Validator]] = {}
for var_name, value in namespace.items():
validator_config = getattr(value, VALIDATOR_CONFIG_KEY, None)
if validator_config:
fields, v = validator_config
for field in fields:
if field in validators:
validators[field].append(v)
else:
validators[field] = [v]
return validators
def extract_root_validators(namespace: Dict[str, Any]) -> Tuple[List[AnyCallable], List[Tuple[bool, AnyCallable]]]:
from inspect import signature
pre_validators: List[AnyCallable] = []
post_validators: List[Tuple[bool, AnyCallable]] = []
for name, value in namespace.items():
validator_config: Optional[Validator] = getattr(value, ROOT_VALIDATOR_CONFIG_KEY, None)
if validator_config:
sig = signature(validator_config.func)
args = list(sig.parameters.keys())
if args[0] == 'self':
raise ConfigError(
f'Invalid signature for root validator {name}: {sig}, "self" not permitted as first argument, '
f'should be: (cls, values).'
)
if len(args) != 2:
raise ConfigError(f'Invalid signature for root validator {name}: {sig}, should be: (cls, values).')
# check function signature
if validator_config.pre:
pre_validators.append(validator_config.func)
else:
post_validators.append((validator_config.skip_on_failure, validator_config.func))
return pre_validators, post_validators
def inherit_validators(base_validators: 'ValidatorListDict', validators: 'ValidatorListDict') -> 'ValidatorListDict':
for field, field_validators in base_validators.items():
if field not in validators:
validators[field] = []
validators[field] += field_validators
return validators
def make_generic_validator(validator: AnyCallable) -> 'ValidatorCallable':
"""
Make a generic function which calls a validator with the right arguments.
Unfortunately other approaches (eg. return a partial of a function that builds the arguments) is slow,
hence this laborious way of doing things.
It's done like this so validators don't all need **kwargs in their signature, eg. any combination of
the arguments "values", "fields" and/or "config" are permitted.
"""
from inspect import signature
if not isinstance(validator, (partial, partialmethod)):
# This should be the default case, so overhead is reduced
sig = signature(validator)
args = list(sig.parameters.keys())
else:
# Fix the generated argument lists of partial methods
sig = signature(validator.func)
args = [
k
for k in signature(validator.func).parameters.keys()
if k not in validator.args | validator.keywords.keys()
]
first_arg = args.pop(0)
if first_arg == 'self':
raise ConfigError(
f'Invalid signature for validator {validator}: {sig}, "self" not permitted as first argument, '
f'should be: (cls, value, values, config, field), "values", "config" and "field" are all optional.'
)
elif first_arg == 'cls':
# assume the second argument is value
return wraps(validator)(_generic_validator_cls(validator, sig, set(args[1:])))
else:
# assume the first argument was value which has already been removed
return wraps(validator)(_generic_validator_basic(validator, sig, set(args)))
def prep_validators(v_funcs: Iterable[AnyCallable]) -> 'ValidatorsList':
return [make_generic_validator(f) for f in v_funcs if f]
all_kwargs = {'values', 'field', 'config'}
def _generic_validator_cls(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable':
# assume the first argument is value
has_kwargs = False
if 'kwargs' in args:
has_kwargs = True
args -= {'kwargs'}
if not args.issubset(all_kwargs):
raise ConfigError(
f'Invalid signature for validator {validator}: {sig}, should be: '
f'(cls, value, values, config, field), "values", "config" and "field" are all optional.'
)
if has_kwargs:
return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)
elif args == set():
return lambda cls, v, values, field, config: validator(cls, v)
elif args == {'values'}:
return lambda cls, v, values, field, config: validator(cls, v, values=values)
elif args == {'field'}:
return lambda cls, v, values, field, config: validator(cls, v, field=field)
elif args == {'config'}:
return lambda cls, v, values, field, config: validator(cls, v, config=config)
elif args == {'values', 'field'}:
return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field)
elif args == {'values', 'config'}:
return lambda cls, v, values, field, config: validator(cls, v, values=values, config=config)
elif args == {'field', 'config'}:
return lambda cls, v, values, field, config: validator(cls, v, field=field, config=config)
else:
# args == {'values', 'field', 'config'}
return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)
def _generic_validator_basic(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable':
has_kwargs = False
if 'kwargs' in args:
has_kwargs = True
args -= {'kwargs'}
if not args.issubset(all_kwargs):
raise ConfigError(
f'Invalid signature for validator {validator}: {sig}, should be: '
f'(value, values, config, field), "values", "config" and "field" are all optional.'
)
if has_kwargs:
return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)
elif args == set():
return lambda cls, v, values, field, config: validator(v)
elif args == {'values'}:
return lambda cls, v, values, field, config: validator(v, values=values)
elif args == {'field'}:
return lambda cls, v, values, field, config: validator(v, field=field)
elif args == {'config'}:
return lambda cls, v, values, field, config: validator(v, config=config)
elif args == {'values', 'field'}:
return lambda cls, v, values, field, config: validator(v, values=values, field=field)
elif args == {'values', 'config'}:
return lambda cls, v, values, field, config: validator(v, values=values, config=config)
elif args == {'field', 'config'}:
return lambda cls, v, values, field, config: validator(v, field=field, config=config)
else:
# args == {'values', 'field', 'config'}
return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)
def gather_all_validators(type_: 'ModelOrDc') -> Dict[str, 'AnyClassMethod']:
all_attributes = ChainMap(*[cls.__dict__ for cls in type_.__mro__]) # type: ignore[arg-type,var-annotated]
return {
k: v
for k, v in all_attributes.items()
if hasattr(v, VALIDATOR_CONFIG_KEY) or hasattr(v, ROOT_VALIDATOR_CONFIG_KEY)
}

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venv/Lib/site-packages/pydantic/v1/color.py Normal file
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"""
Color definitions are used as per CSS3 specification:
http://www.w3.org/TR/css3-color/#svg-color
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".
"""
import math
import re
from colorsys import hls_to_rgb, rgb_to_hls
from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union, cast
from pydantic.v1.errors import ColorError
from pydantic.v1.utils import Representation, almost_equal_floats
if TYPE_CHECKING:
from pydantic.v1.typing import CallableGenerator, ReprArgs
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_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*'
_r_255 = r'(\d{1,3}(?:\.\d+)?)'
_r_comma = r'\s*,\s*'
r_rgb = fr'\s*rgb\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}\)\s*'
_r_alpha = r'(\d(?:\.\d+)?|\.\d+|\d{1,2}%)'
r_rgba = fr'\s*rgba\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_alpha}\s*\)\s*'
_r_h = r'(-?\d+(?:\.\d+)?|-?\.\d+)(deg|rad|turn)?'
_r_sl = r'(\d{1,3}(?:\.\d+)?)%'
r_hsl = fr'\s*hsl\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}\s*\)\s*'
r_hsla = fr'\s*hsl\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}{_r_comma}{_r_alpha}\s*\)\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
class Color(Representation):
__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 ColorError(reason='value must be a tuple, list or string')
# if we've got here value must be a valid color
self._original = value
@classmethod
def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
field_schema.update(type='string', format='color')
def original(self) -> ColorType:
"""
Original value passed to Color
"""
return self._original
def as_named(self, *, fallback: bool = False) -> str:
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:
"""
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.
"""
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:
"""
Color as an RGB or RGBA tuple; red, green and blue are in the range 0 to 255, alpha if included is
in the range 0 to 1.
:param alpha: whether to include the alpha channel, options are
None - (default) include alpha only if it's set (e.g. not None)
True - always include alpha,
False - always omit alpha,
"""
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:
"""
Color as an HSL or HSLA tuple, e.g. hue, saturation, lightness and optionally alpha; 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.
:param alpha: whether to include the alpha channel, options are
None - (default) include alpha only if it's set (e.g. not None)
True - always include alpha,
False - always omit alpha,
"""
h, l, s = rgb_to_hls(self._rgba.r, self._rgba.g, self._rgba.b)
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_validators__(cls) -> 'CallableGenerator':
yield cls
def __str__(self) -> str:
return self.as_named(fallback=True)
def __repr_args__(self) -> 'ReprArgs':
return [(None, self.as_named(fallback=True))] + [('rgb', self.as_rgb_tuple())] # type: ignore
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 as a color.
"""
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 ColorError(reason='tuples must have length 3 or 4')
def parse_str(value: str) -> RGBA:
"""
Parse a string to an RGBA tuple, trying the following formats (in this order):
* named color, see COLORS_BY_NAME below
* 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>)`
"""
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)
if m:
return ints_to_rgba(*m.groups(), None) # type: ignore
m = re.fullmatch(r_rgba, value_lower)
if m:
return ints_to_rgba(*m.groups()) # type: ignore
m = re.fullmatch(r_hsl, value_lower)
if m:
h, h_units, s, l_ = m.groups()
return parse_hsl(h, h_units, s, l_)
m = re.fullmatch(r_hsla, value_lower)
if m:
h, h_units, s, l_, a = m.groups()
return parse_hsl(h, h_units, s, l_, parse_float_alpha(a))
raise ColorError(reason='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]) -> RGBA:
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 a value checking it's a valid int in the range 0 to max_val and divide by max_val to give a number
in the range 0 to 1
"""
try:
color = float(value)
except ValueError:
raise ColorError(reason='color values must be a valid number')
if 0 <= color <= max_val:
return color / max_val
else:
raise ColorError(reason=f'color values must be in the range 0 to {max_val}')
def parse_float_alpha(value: Union[None, str, float, int]) -> Optional[float]:
"""
Parse a value checking it's a valid float in the range 0 to 1
"""
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 ColorError(reason='alpha values must be a valid float')
if almost_equal_floats(alpha, 1):
return None
elif 0 <= alpha <= 1:
return alpha
else:
raise ColorError(reason='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.
"""
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, alpha)
def float_to_255(c: float) -> int:
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()}