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Update cherrypy==18.9.0

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JonnyWong16 2024-03-24 17:55:12 -07:00
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0
lib/pydantic/deprecated/__init__.py Normal file
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253
lib/pydantic/deprecated/class_validators.py Normal file
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"""Old `@validator` and `@root_validator` function validators from V1."""
from __future__ import annotations as _annotations
from functools import partial, partialmethod
from types import FunctionType
from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union, overload
from warnings import warn
from typing_extensions import Literal, Protocol, TypeAlias
from .._internal import _decorators, _decorators_v1
from ..errors import PydanticUserError
from ..warnings import PydanticDeprecatedSince20
_ALLOW_REUSE_WARNING_MESSAGE = '`allow_reuse` is deprecated and will be ignored; it should no longer be necessary'
if TYPE_CHECKING:
class _OnlyValueValidatorClsMethod(Protocol):
def __call__(self, __cls: Any, __value: Any) -> Any:
...
class _V1ValidatorWithValuesClsMethod(Protocol):
def __call__(self, __cls: Any, __value: Any, values: dict[str, Any]) -> Any:
...
class _V1ValidatorWithValuesKwOnlyClsMethod(Protocol):
def __call__(self, __cls: Any, __value: Any, *, values: dict[str, Any]) -> Any:
...
class _V1ValidatorWithKwargsClsMethod(Protocol):
def __call__(self, __cls: Any, **kwargs: Any) -> Any:
...
class _V1ValidatorWithValuesAndKwargsClsMethod(Protocol):
def __call__(self, __cls: Any, values: dict[str, Any], **kwargs: Any) -> Any:
...
class _V1RootValidatorClsMethod(Protocol):
def __call__(
self, __cls: Any, __values: _decorators_v1.RootValidatorValues
) -> _decorators_v1.RootValidatorValues:
...
V1Validator = Union[
_OnlyValueValidatorClsMethod,
_V1ValidatorWithValuesClsMethod,
_V1ValidatorWithValuesKwOnlyClsMethod,
_V1ValidatorWithKwargsClsMethod,
_V1ValidatorWithValuesAndKwargsClsMethod,
_decorators_v1.V1ValidatorWithValues,
_decorators_v1.V1ValidatorWithValuesKwOnly,
_decorators_v1.V1ValidatorWithKwargs,
_decorators_v1.V1ValidatorWithValuesAndKwargs,
]
V1RootValidator = Union[
_V1RootValidatorClsMethod,
_decorators_v1.V1RootValidatorFunction,
]
_PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]]
# Allow both a V1 (assumed pre=False) or V2 (assumed mode='after') validator
# We lie to type checkers and say we return the same thing we get
# but in reality we return a proxy object that _mostly_ behaves like the wrapped thing
_V1ValidatorType = TypeVar('_V1ValidatorType', V1Validator, _PartialClsOrStaticMethod)
_V1RootValidatorFunctionType = TypeVar(
'_V1RootValidatorFunctionType',
_decorators_v1.V1RootValidatorFunction,
_V1RootValidatorClsMethod,
_PartialClsOrStaticMethod,
)
else:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
def validator(
__field: str,
*fields: str,
pre: bool = False,
each_item: bool = False,
always: bool = False,
check_fields: bool | None = None,
allow_reuse: bool = False,
) -> Callable[[_V1ValidatorType], _V1ValidatorType]:
"""Decorate methods on the class indicating that they should be used to validate fields.
Args:
__field (str): The first field the validator should be called on; this is separate
from `fields` to ensure an error is raised if you don't pass at least one.
*fields (str): Additional field(s) the validator should be called on.
pre (bool, optional): Whether this validator should be called before the standard
validators (else after). Defaults to False.
each_item (bool, optional): For complex objects (sets, lists etc.) whether to validate
individual elements rather than the whole object. Defaults to False.
always (bool, optional): Whether this method and other validators should be called even if
the value is missing. Defaults to False.
check_fields (bool | None, optional): Whether to check that the fields actually exist on the model.
Defaults to None.
allow_reuse (bool, optional): Whether to track and raise an error if another validator refers to
the decorated function. Defaults to False.
Returns:
Callable: A decorator that can be used to decorate a
function to be used as a validator.
"""
if allow_reuse is True: # pragma: no cover
warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning)
fields = tuple((__field, *fields))
if isinstance(fields[0], FunctionType):
raise PydanticUserError(
'`@validator` should be used with fields and keyword arguments, not bare. '
"E.g. usage should be `@validator('<field_name>', ...)`",
code='validator-no-fields',
)
elif not all(isinstance(field, str) for field in fields):
raise PydanticUserError(
'`@validator` fields should be passed as separate string args. '
"E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`",
code='validator-invalid-fields',
)
warn(
'Pydantic V1 style `@validator` validators are deprecated.'
' You should migrate to Pydantic V2 style `@field_validator` validators,'
' see the migration guide for more details',
DeprecationWarning,
stacklevel=2,
)
mode: Literal['before', 'after'] = 'before' if pre is True else 'after'
def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]:
if _decorators.is_instance_method_from_sig(f):
raise PydanticUserError(
'`@validator` cannot be applied to instance methods', code='validator-instance-method'
)
# auto apply the @classmethod decorator
f = _decorators.ensure_classmethod_based_on_signature(f)
wrap = _decorators_v1.make_generic_v1_field_validator
validator_wrapper_info = _decorators.ValidatorDecoratorInfo(
fields=fields,
mode=mode,
each_item=each_item,
always=always,
check_fields=check_fields,
)
return _decorators.PydanticDescriptorProxy(f, validator_wrapper_info, shim=wrap)
return dec # type: ignore[return-value]
@overload
def root_validator(
*,
# if you don't specify `pre` the default is `pre=False`
# which means you need to specify `skip_on_failure=True`
skip_on_failure: Literal[True],
allow_reuse: bool = ...,
) -> Callable[
[_V1RootValidatorFunctionType],
_V1RootValidatorFunctionType,
]:
...
@overload
def root_validator(
*,
# if you specify `pre=True` then you don't need to specify
# `skip_on_failure`, in fact it is not allowed as an argument!
pre: Literal[True],
allow_reuse: bool = ...,
) -> Callable[
[_V1RootValidatorFunctionType],
_V1RootValidatorFunctionType,
]:
...
@overload
def root_validator(
*,
# if you explicitly specify `pre=False` then you
# MUST specify `skip_on_failure=True`
pre: Literal[False],
skip_on_failure: Literal[True],
allow_reuse: bool = ...,
) -> Callable[
[_V1RootValidatorFunctionType],
_V1RootValidatorFunctionType,
]:
...
def root_validator(
*__args,
pre: bool = False,
skip_on_failure: bool = False,
allow_reuse: bool = False,
) -> Any:
"""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.
Args:
pre (bool, optional): Whether this validator should be called before the standard
validators (else after). Defaults to False.
skip_on_failure (bool, optional): Whether to stop validation and return as soon as a
failure is encountered. Defaults to False.
allow_reuse (bool, optional): Whether to track and raise an error if another validator
refers to the decorated function. Defaults to False.
Returns:
Any: A decorator that can be used to decorate a function to be used as a root_validator.
"""
warn(
'Pydantic V1 style `@root_validator` validators are deprecated.'
' You should migrate to Pydantic V2 style `@model_validator` validators,'
' see the migration guide for more details',
DeprecationWarning,
stacklevel=2,
)
if __args:
# Ensure a nice error is raised if someone attempts to use the bare decorator
return root_validator()(*__args) # type: ignore
if allow_reuse is True: # pragma: no cover
warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning)
mode: Literal['before', 'after'] = 'before' if pre is True else 'after'
if pre is False and skip_on_failure is not True:
raise PydanticUserError(
'If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`.'
' Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.',
code='root-validator-pre-skip',
)
wrap = partial(_decorators_v1.make_v1_generic_root_validator, pre=pre)
def dec(f: Callable[..., Any] | classmethod[Any, Any, Any] | staticmethod[Any, Any]) -> Any:
if _decorators.is_instance_method_from_sig(f):
raise TypeError('`@root_validator` cannot be applied to instance methods')
# auto apply the @classmethod decorator
res = _decorators.ensure_classmethod_based_on_signature(f)
dec_info = _decorators.RootValidatorDecoratorInfo(mode=mode)
return _decorators.PydanticDescriptorProxy(res, dec_info, shim=wrap)
return dec

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from __future__ import annotations as _annotations
import warnings
from typing import TYPE_CHECKING, Any
from typing_extensions import Literal, deprecated
from .._internal import _config
from ..warnings import PydanticDeprecatedSince20
if not TYPE_CHECKING:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
__all__ = 'BaseConfig', 'Extra'
class _ConfigMetaclass(type):
def __getattr__(self, item: str) -> Any:
try:
obj = _config.config_defaults[item]
warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
return obj
except KeyError as exc:
raise AttributeError(f"type object '{self.__name__}' has no attribute {exc}") from exc
@deprecated('BaseConfig is deprecated. Use the `pydantic.ConfigDict` instead.', category=PydanticDeprecatedSince20)
class BaseConfig(metaclass=_ConfigMetaclass):
"""This class is only retained for backwards compatibility.
!!! Warning "Deprecated"
BaseConfig is deprecated. Use the [`pydantic.ConfigDict`][pydantic.ConfigDict] instead.
"""
def __getattr__(self, item: str) -> Any:
try:
obj = super().__getattribute__(item)
warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
return obj
except AttributeError as exc:
try:
return getattr(type(self), item)
except AttributeError:
# re-raising changes the displayed text to reflect that `self` is not a type
raise AttributeError(str(exc)) from exc
def __init_subclass__(cls, **kwargs: Any) -> None:
warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
return super().__init_subclass__(**kwargs)
class _ExtraMeta(type):
def __getattribute__(self, __name: str) -> Any:
# The @deprecated decorator accesses other attributes, so we only emit a warning for the expected ones
if __name in {'allow', 'ignore', 'forbid'}:
warnings.warn(
"`pydantic.config.Extra` is deprecated, use literal values instead (e.g. `extra='allow'`)",
DeprecationWarning,
stacklevel=2,
)
return super().__getattribute__(__name)
@deprecated(
"Extra is deprecated. Use literal values instead (e.g. `extra='allow'`)", category=PydanticDeprecatedSince20
)
class Extra(metaclass=_ExtraMeta):
allow: Literal['allow'] = 'allow'
ignore: Literal['ignore'] = 'ignore'
forbid: Literal['forbid'] = 'forbid'

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lib/pydantic/deprecated/copy_internals.py Normal file
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from __future__ import annotations as _annotations
import typing
from copy import deepcopy
from enum import Enum
from typing import Any, Tuple
import typing_extensions
from .._internal import (
_model_construction,
_typing_extra,
_utils,
)
if typing.TYPE_CHECKING:
from .. import BaseModel
from .._internal._utils import AbstractSetIntStr, MappingIntStrAny
AnyClassMethod = classmethod[Any, Any, Any]
TupleGenerator = typing.Generator[Tuple[str, Any], None, None]
Model = typing.TypeVar('Model', bound='BaseModel')
# should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
IncEx: typing_extensions.TypeAlias = 'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None'
_object_setattr = _model_construction.object_setattr
def _iter(
self: BaseModel,
to_dict: bool = False,
by_alias: bool = False,
include: AbstractSetIntStr | MappingIntStrAny | None = None,
exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
) -> TupleGenerator:
# Merge field set excludes with explicit exclude parameter with explicit overriding field set options.
# The extra "is not None" guards are not logically necessary but optimizes performance for the simple case.
if exclude is not None:
exclude = _utils.ValueItems.merge(
{k: v.exclude for k, v in self.model_fields.items() if v.exclude is not None}, exclude
)
if include is not None:
include = _utils.ValueItems.merge({k: True for k in self.model_fields}, include, intersect=True)
allowed_keys = _calculate_keys(self, include=include, exclude=exclude, exclude_unset=exclude_unset) # type: ignore
if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none):
# huge boost for plain _iter()
yield from self.__dict__.items()
if self.__pydantic_extra__:
yield from self.__pydantic_extra__.items()
return
value_exclude = _utils.ValueItems(self, exclude) if exclude is not None else None
value_include = _utils.ValueItems(self, include) if include is not None else None
if self.__pydantic_extra__ is None:
items = self.__dict__.items()
else:
items = list(self.__dict__.items()) + list(self.__pydantic_extra__.items())
for field_key, v in items:
if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None):
continue
if exclude_defaults:
try:
field = self.model_fields[field_key]
except KeyError:
pass
else:
if not field.is_required() and field.default == v:
continue
if by_alias and field_key in self.model_fields:
dict_key = self.model_fields[field_key].alias or field_key
else:
dict_key = field_key
if to_dict or value_include or value_exclude:
v = _get_value(
type(self),
v,
to_dict=to_dict,
by_alias=by_alias,
include=value_include and value_include.for_element(field_key),
exclude=value_exclude and value_exclude.for_element(field_key),
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
yield dict_key, v
def _copy_and_set_values(
self: Model,
values: dict[str, Any],
fields_set: set[str],
extra: dict[str, Any] | None = None,
private: dict[str, Any] | None = None,
*,
deep: bool, # UP006
) -> Model:
if deep:
# chances of having empty dict here are quite low for using smart_deepcopy
values = deepcopy(values)
extra = deepcopy(extra)
private = deepcopy(private)
cls = self.__class__
m = cls.__new__(cls)
_object_setattr(m, '__dict__', values)
_object_setattr(m, '__pydantic_extra__', extra)
_object_setattr(m, '__pydantic_fields_set__', fields_set)
_object_setattr(m, '__pydantic_private__', private)
return m
@typing.no_type_check
def _get_value(
cls: type[BaseModel],
v: Any,
to_dict: bool,
by_alias: bool,
include: AbstractSetIntStr | MappingIntStrAny | None,
exclude: AbstractSetIntStr | MappingIntStrAny | None,
exclude_unset: bool,
exclude_defaults: bool,
exclude_none: bool,
) -> Any:
from .. import BaseModel
if isinstance(v, BaseModel):
if to_dict:
return v.model_dump(
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
include=include, # type: ignore
exclude=exclude, # type: ignore
exclude_none=exclude_none,
)
else:
return v.copy(include=include, exclude=exclude)
value_exclude = _utils.ValueItems(v, exclude) if exclude else None
value_include = _utils.ValueItems(v, include) if include else None
if isinstance(v, dict):
return {
k_: _get_value(
cls,
v_,
to_dict=to_dict,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
include=value_include and value_include.for_element(k_),
exclude=value_exclude and value_exclude.for_element(k_),
exclude_none=exclude_none,
)
for k_, v_ in v.items()
if (not value_exclude or not value_exclude.is_excluded(k_))
and (not value_include or value_include.is_included(k_))
}
elif _utils.sequence_like(v):
seq_args = (
_get_value(
cls,
v_,
to_dict=to_dict,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
include=value_include and value_include.for_element(i),
exclude=value_exclude and value_exclude.for_element(i),
exclude_none=exclude_none,
)
for i, v_ in enumerate(v)
if (not value_exclude or not value_exclude.is_excluded(i))
and (not value_include or value_include.is_included(i))
)
return v.__class__(*seq_args) if _typing_extra.is_namedtuple(v.__class__) else v.__class__(seq_args)
elif isinstance(v, Enum) and getattr(cls.model_config, 'use_enum_values', False):
return v.value
else:
return v
def _calculate_keys(
self: BaseModel,
include: MappingIntStrAny | None,
exclude: MappingIntStrAny | None,
exclude_unset: bool,
update: typing.Dict[str, Any] | None = None, # noqa UP006
) -> typing.AbstractSet[str] | None:
if include is None and exclude is None and exclude_unset is False:
return None
keys: typing.AbstractSet[str]
if exclude_unset:
keys = self.__pydantic_fields_set__.copy()
else:
keys = set(self.__dict__.keys())
keys = keys | (self.__pydantic_extra__ or {}).keys()
if include is not None:
keys &= include.keys()
if update:
keys -= update.keys()
if exclude:
keys -= {k for k, v in exclude.items() if _utils.ValueItems.is_true(v)}
return keys

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import warnings
from functools import wraps
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional, Tuple, Type, TypeVar, Union, overload
from typing_extensions import deprecated
from .._internal import _config, _typing_extra
from ..alias_generators import to_pascal
from ..errors import PydanticUserError
from ..functional_validators import field_validator
from ..main import BaseModel, create_model
from ..warnings import PydanticDeprecatedSince20
if not TYPE_CHECKING:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
__all__ = ('validate_arguments',)
if TYPE_CHECKING:
AnyCallable = Callable[..., Any]
AnyCallableT = TypeVar('AnyCallableT', bound=AnyCallable)
ConfigType = Union[None, Type[Any], Dict[str, Any]]
@overload
def validate_arguments(func: None = None, *, config: 'ConfigType' = None) -> Callable[['AnyCallableT'], 'AnyCallableT']:
...
@overload
def validate_arguments(func: 'AnyCallableT') -> 'AnyCallableT':
...
@deprecated(
'The `validate_arguments` method is deprecated; use `validate_call` instead.',
category=None,
)
def validate_arguments(func: Optional['AnyCallableT'] = None, *, config: 'ConfigType' = None) -> Any:
"""Decorator to validate the arguments passed to a function."""
warnings.warn(
'The `validate_arguments` method is deprecated; use `validate_call` instead.',
PydanticDeprecatedSince20,
stacklevel=2,
)
def validate(_func: 'AnyCallable') -> 'AnyCallable':
vd = ValidatedFunction(_func, config)
@wraps(_func)
def wrapper_function(*args: Any, **kwargs: Any) -> Any:
return vd.call(*args, **kwargs)
wrapper_function.vd = vd # type: ignore
wrapper_function.validate = vd.init_model_instance # type: ignore
wrapper_function.raw_function = vd.raw_function # type: ignore
wrapper_function.model = vd.model # type: ignore
return wrapper_function
if func:
return validate(func)
else:
return validate
ALT_V_ARGS = 'v__args'
ALT_V_KWARGS = 'v__kwargs'
V_POSITIONAL_ONLY_NAME = 'v__positional_only'
V_DUPLICATE_KWARGS = 'v__duplicate_kwargs'
class ValidatedFunction:
def __init__(self, function: 'AnyCallable', config: 'ConfigType'):
from inspect import Parameter, signature
parameters: Mapping[str, Parameter] = signature(function).parameters
if parameters.keys() & {ALT_V_ARGS, ALT_V_KWARGS, V_POSITIONAL_ONLY_NAME, V_DUPLICATE_KWARGS}:
raise PydanticUserError(
f'"{ALT_V_ARGS}", "{ALT_V_KWARGS}", "{V_POSITIONAL_ONLY_NAME}" and "{V_DUPLICATE_KWARGS}" '
f'are not permitted as argument names when using the "{validate_arguments.__name__}" decorator',
code=None,
)
self.raw_function = function
self.arg_mapping: Dict[int, str] = {}
self.positional_only_args: set[str] = set()
self.v_args_name = 'args'
self.v_kwargs_name = 'kwargs'
type_hints = _typing_extra.get_type_hints(function, include_extras=True)
takes_args = False
takes_kwargs = False
fields: Dict[str, Tuple[Any, Any]] = {}
for i, (name, p) in enumerate(parameters.items()):
if p.annotation is p.empty:
annotation = Any
else:
annotation = type_hints[name]
default = ... if p.default is p.empty else p.default
if p.kind == Parameter.POSITIONAL_ONLY:
self.arg_mapping[i] = name
fields[name] = annotation, default
fields[V_POSITIONAL_ONLY_NAME] = List[str], None
self.positional_only_args.add(name)
elif p.kind == Parameter.POSITIONAL_OR_KEYWORD:
self.arg_mapping[i] = name
fields[name] = annotation, default
fields[V_DUPLICATE_KWARGS] = List[str], None
elif p.kind == Parameter.KEYWORD_ONLY:
fields[name] = annotation, default
elif p.kind == Parameter.VAR_POSITIONAL:
self.v_args_name = name
fields[name] = Tuple[annotation, ...], None
takes_args = True
else:
assert p.kind == Parameter.VAR_KEYWORD, p.kind
self.v_kwargs_name = name
fields[name] = Dict[str, annotation], None
takes_kwargs = True
# these checks avoid a clash between "args" and a field with that name
if not takes_args and self.v_args_name in fields:
self.v_args_name = ALT_V_ARGS
# same with "kwargs"
if not takes_kwargs and self.v_kwargs_name in fields:
self.v_kwargs_name = ALT_V_KWARGS
if not takes_args:
# we add the field so validation below can raise the correct exception
fields[self.v_args_name] = List[Any], None
if not takes_kwargs:
# same with kwargs
fields[self.v_kwargs_name] = Dict[Any, Any], None
self.create_model(fields, takes_args, takes_kwargs, config)
def init_model_instance(self, *args: Any, **kwargs: Any) -> BaseModel:
values = self.build_values(args, kwargs)
return self.model(**values)
def call(self, *args: Any, **kwargs: Any) -> Any:
m = self.init_model_instance(*args, **kwargs)
return self.execute(m)
def build_values(self, args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> Dict[str, Any]:
values: Dict[str, Any] = {}
if args:
arg_iter = enumerate(args)
while True:
try:
i, a = next(arg_iter)
except StopIteration:
break
arg_name = self.arg_mapping.get(i)
if arg_name is not None:
values[arg_name] = a
else:
values[self.v_args_name] = [a] + [a for _, a in arg_iter]
break
var_kwargs: Dict[str, Any] = {}
wrong_positional_args = []
duplicate_kwargs = []
fields_alias = [
field.alias
for name, field in self.model.model_fields.items()
if name not in (self.v_args_name, self.v_kwargs_name)
]
non_var_fields = set(self.model.model_fields) - {self.v_args_name, self.v_kwargs_name}
for k, v in kwargs.items():
if k in non_var_fields or k in fields_alias:
if k in self.positional_only_args:
wrong_positional_args.append(k)
if k in values:
duplicate_kwargs.append(k)
values[k] = v
else:
var_kwargs[k] = v
if var_kwargs:
values[self.v_kwargs_name] = var_kwargs
if wrong_positional_args:
values[V_POSITIONAL_ONLY_NAME] = wrong_positional_args
if duplicate_kwargs:
values[V_DUPLICATE_KWARGS] = duplicate_kwargs
return values
def execute(self, m: BaseModel) -> Any:
d = {k: v for k, v in m.__dict__.items() if k in m.__pydantic_fields_set__ or m.model_fields[k].default_factory}
var_kwargs = d.pop(self.v_kwargs_name, {})
if self.v_args_name in d:
args_: List[Any] = []
in_kwargs = False
kwargs = {}
for name, value in d.items():
if in_kwargs:
kwargs[name] = value
elif name == self.v_args_name:
args_ += value
in_kwargs = True
else:
args_.append(value)
return self.raw_function(*args_, **kwargs, **var_kwargs)
elif self.positional_only_args:
args_ = []
kwargs = {}
for name, value in d.items():
if name in self.positional_only_args:
args_.append(value)
else:
kwargs[name] = value
return self.raw_function(*args_, **kwargs, **var_kwargs)
else:
return self.raw_function(**d, **var_kwargs)
def create_model(self, fields: Dict[str, Any], takes_args: bool, takes_kwargs: bool, config: 'ConfigType') -> None:
pos_args = len(self.arg_mapping)
config_wrapper = _config.ConfigWrapper(config)
if config_wrapper.alias_generator:
raise PydanticUserError(
'Setting the "alias_generator" property on custom Config for '
'@validate_arguments is not yet supported, please remove.',
code=None,
)
if config_wrapper.extra is None:
config_wrapper.config_dict['extra'] = 'forbid'
class DecoratorBaseModel(BaseModel):
@field_validator(self.v_args_name, check_fields=False)
@classmethod
def check_args(cls, v: Optional[List[Any]]) -> Optional[List[Any]]:
if takes_args or v is None:
return v
raise TypeError(f'{pos_args} positional arguments expected but {pos_args + len(v)} given')
@field_validator(self.v_kwargs_name, check_fields=False)
@classmethod
def check_kwargs(cls, v: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
if takes_kwargs or v is None:
return v
plural = '' if len(v) == 1 else 's'
keys = ', '.join(map(repr, v.keys()))
raise TypeError(f'unexpected keyword argument{plural}: {keys}')
@field_validator(V_POSITIONAL_ONLY_NAME, check_fields=False)
@classmethod
def check_positional_only(cls, v: Optional[List[str]]) -> None:
if v is None:
return
plural = '' if len(v) == 1 else 's'
keys = ', '.join(map(repr, v))
raise TypeError(f'positional-only argument{plural} passed as keyword argument{plural}: {keys}')
@field_validator(V_DUPLICATE_KWARGS, check_fields=False)
@classmethod
def check_duplicate_kwargs(cls, v: Optional[List[str]]) -> None:
if v is None:
return
plural = '' if len(v) == 1 else 's'
keys = ', '.join(map(repr, v))
raise TypeError(f'multiple values for argument{plural}: {keys}')
model_config = config_wrapper.config_dict
self.model = create_model(to_pascal(self.raw_function.__name__), __base__=DecoratorBaseModel, **fields)

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import datetime
import warnings
from collections import deque
from decimal import Decimal
from enum import Enum
from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
from pathlib import Path
from re import Pattern
from types import GeneratorType
from typing import TYPE_CHECKING, Any, Callable, Dict, Type, Union
from uuid import UUID
from typing_extensions import deprecated
from ..color import Color
from ..networks import NameEmail
from ..types import SecretBytes, SecretStr
from ..warnings import PydanticDeprecatedSince20
if not TYPE_CHECKING:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
__all__ = 'pydantic_encoder', 'custom_pydantic_encoder', 'timedelta_isoformat'
def isoformat(o: Union[datetime.date, datetime.time]) -> str:
return o.isoformat()
def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
"""Encodes a Decimal as int of there's no exponent, otherwise float.
This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
where a integer (but not int typed) is used. Encoding this as a float
results in failed round-tripping between encode and parse.
Our Id type is a prime example of this.
>>> decimal_encoder(Decimal("1.0"))
1.0
>>> decimal_encoder(Decimal("1"))
1
"""
exponent = dec_value.as_tuple().exponent
if isinstance(exponent, int) and exponent >= 0:
return int(dec_value)
else:
return float(dec_value)
ENCODERS_BY_TYPE: Dict[Type[Any], Callable[[Any], Any]] = {
bytes: lambda o: o.decode(),
Color: str,
datetime.date: isoformat,
datetime.datetime: isoformat,
datetime.time: isoformat,
datetime.timedelta: lambda td: td.total_seconds(),
Decimal: decimal_encoder,
Enum: lambda o: o.value,
frozenset: list,
deque: list,
GeneratorType: list,
IPv4Address: str,
IPv4Interface: str,
IPv4Network: str,
IPv6Address: str,
IPv6Interface: str,
IPv6Network: str,
NameEmail: str,
Path: str,
Pattern: lambda o: o.pattern,
SecretBytes: str,
SecretStr: str,
set: list,
UUID: str,
}
@deprecated(
'`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.',
category=None,
)
def pydantic_encoder(obj: Any) -> Any:
warnings.warn(
'`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.',
category=PydanticDeprecatedSince20,
stacklevel=2,
)
from dataclasses import asdict, is_dataclass
from ..main import BaseModel
if isinstance(obj, BaseModel):
return obj.model_dump()
elif is_dataclass(obj):
return asdict(obj)
# Check the class type and its superclasses for a matching encoder
for base in obj.__class__.__mro__[:-1]:
try:
encoder = ENCODERS_BY_TYPE[base]
except KeyError:
continue
return encoder(obj)
else: # We have exited the for loop without finding a suitable encoder
raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable")
# TODO: Add a suggested migration path once there is a way to use custom encoders
@deprecated(
'`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.',
category=None,
)
def custom_pydantic_encoder(type_encoders: Dict[Any, Callable[[Type[Any]], Any]], obj: Any) -> Any:
warnings.warn(
'`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.',
category=PydanticDeprecatedSince20,
stacklevel=2,
)
# Check the class type and its superclasses for a matching encoder
for base in obj.__class__.__mro__[:-1]:
try:
encoder = type_encoders[base]
except KeyError:
continue
return encoder(obj)
else: # We have exited the for loop without finding a suitable encoder
return pydantic_encoder(obj)
@deprecated('`timedelta_isoformat` is deprecated.', category=None)
def timedelta_isoformat(td: datetime.timedelta) -> str:
"""ISO 8601 encoding for Python timedelta object."""
warnings.warn('`timedelta_isoformat` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
minutes, seconds = divmod(td.seconds, 60)
hours, minutes = divmod(minutes, 60)
return f'{"-" if td.days < 0 else ""}P{abs(td.days)}DT{hours:d}H{minutes:d}M{seconds:d}.{td.microseconds:06d}S'

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from __future__ import annotations
import json
import pickle
import warnings
from enum import Enum
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable
from typing_extensions import deprecated
from ..warnings import PydanticDeprecatedSince20
if not TYPE_CHECKING:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
class Protocol(str, Enum):
json = 'json'
pickle = 'pickle'
@deprecated('`load_str_bytes` is deprecated.', category=None)
def load_str_bytes(
b: str | bytes,
*,
content_type: str | None = None,
encoding: str = 'utf8',
proto: Protocol | None = None,
allow_pickle: bool = False,
json_loads: Callable[[str], Any] = json.loads,
) -> Any:
warnings.warn('`load_str_bytes` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
if proto is None and content_type:
if content_type.endswith(('json', 'javascript')):
pass
elif allow_pickle and content_type.endswith('pickle'):
proto = Protocol.pickle
else:
raise TypeError(f'Unknown content-type: {content_type}')
proto = proto or Protocol.json
if proto == Protocol.json:
if isinstance(b, bytes):
b = b.decode(encoding)
return json_loads(b) # type: ignore
elif proto == Protocol.pickle:
if not allow_pickle:
raise RuntimeError('Trying to decode with pickle with allow_pickle=False')
bb = b if isinstance(b, bytes) else b.encode() # type: ignore
return pickle.loads(bb)
else:
raise TypeError(f'Unknown protocol: {proto}')
@deprecated('`load_file` is deprecated.', category=None)
def load_file(
path: str | Path,
*,
content_type: str | None = None,
encoding: str = 'utf8',
proto: Protocol | None = None,
allow_pickle: bool = False,
json_loads: Callable[[str], Any] = json.loads,
) -> Any:
warnings.warn('`load_file` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
path = Path(path)
b = path.read_bytes()
if content_type is None:
if path.suffix in ('.js', '.json'):
proto = Protocol.json
elif path.suffix == '.pkl':
proto = Protocol.pickle
return load_str_bytes(
b, proto=proto, content_type=content_type, encoding=encoding, allow_pickle=allow_pickle, json_loads=json_loads
)

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from __future__ import annotations
import json
import warnings
from typing import TYPE_CHECKING, Any, Callable, Type, TypeVar, Union
from typing_extensions import deprecated
from ..json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema
from ..type_adapter import TypeAdapter
from ..warnings import PydanticDeprecatedSince20
if not TYPE_CHECKING:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
__all__ = 'parse_obj_as', 'schema_of', 'schema_json_of'
NameFactory = Union[str, Callable[[Type[Any]], str]]
T = TypeVar('T')
@deprecated(
'`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.',
category=None,
)
def parse_obj_as(type_: type[T], obj: Any, type_name: NameFactory | None = None) -> T:
warnings.warn(
'`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.',
category=PydanticDeprecatedSince20,
stacklevel=2,
)
if type_name is not None: # pragma: no cover
warnings.warn(
'The type_name parameter is deprecated. parse_obj_as no longer creates temporary models',
DeprecationWarning,
stacklevel=2,
)
return TypeAdapter(type_).validate_python(obj)
@deprecated(
'`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
category=None,
)
def schema_of(
type_: Any,
*,
title: NameFactory | None = None,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
) -> dict[str, Any]:
"""Generate a JSON schema (as dict) for the passed model or dynamically generated one."""
warnings.warn(
'`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
category=PydanticDeprecatedSince20,
stacklevel=2,
)
res = TypeAdapter(type_).json_schema(
by_alias=by_alias,
schema_generator=schema_generator,
ref_template=ref_template,
)
if title is not None:
if isinstance(title, str):
res['title'] = title
else:
warnings.warn(
'Passing a callable for the `title` parameter is deprecated and no longer supported',
DeprecationWarning,
stacklevel=2,
)
res['title'] = title(type_)
return res
@deprecated(
'`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
category=None,
)
def schema_json_of(
type_: Any,
*,
title: NameFactory | None = None,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
**dumps_kwargs: Any,
) -> str:
"""Generate a JSON schema (as JSON) for the passed model or dynamically generated one."""
warnings.warn(
'`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
category=PydanticDeprecatedSince20,
stacklevel=2,
)
return json.dumps(
schema_of(type_, title=title, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator),
**dumps_kwargs,
)