ClickUp Operator
by noahvanhart
- .venv
- Lib
- site-packages
- pydantic
- _internal
"""Private logic for creating models."""
from __future__ import annotations as _annotations
import builtins
import operator
import sys
import typing
import warnings
import weakref
from abc import ABCMeta
from functools import lru_cache, partial
from types import FunctionType
from typing import Any, Callable, Generic, Literal, NoReturn, TypeVar, cast
from pydantic_core import PydanticUndefined, SchemaSerializer
from typing_extensions import TypeAliasType, dataclass_transform, deprecated, get_args
from ..errors import PydanticUndefinedAnnotation, PydanticUserError
from ..plugin._schema_validator import create_schema_validator
from ..warnings import GenericBeforeBaseModelWarning, PydanticDeprecatedSince20
from ._config import ConfigWrapper
from ._decorators import DecoratorInfos, PydanticDescriptorProxy, get_attribute_from_bases, unwrap_wrapped_function
from ._fields import collect_model_fields, is_valid_field_name, is_valid_privateattr_name
from ._generate_schema import GenerateSchema
from ._generics import PydanticGenericMetadata, get_model_typevars_map
from ._import_utils import import_cached_base_model, import_cached_field_info
from ._mock_val_ser import set_model_mocks
from ._namespace_utils import NsResolver
from ._schema_generation_shared import CallbackGetCoreSchemaHandler
from ._signature import generate_pydantic_signature
from ._typing_extra import (
_make_forward_ref,
eval_type_backport,
is_annotated,
is_classvar_annotation,
parent_frame_namespace,
)
from ._utils import LazyClassAttribute, SafeGetItemProxy
if typing.TYPE_CHECKING:
from ..fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr
from ..fields import Field as PydanticModelField
from ..fields import PrivateAttr as PydanticModelPrivateAttr
from ..main import BaseModel
else:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
PydanticModelField = object()
PydanticModelPrivateAttr = object()
object_setattr = object.__setattr__
class _ModelNamespaceDict(dict):
"""A dictionary subclass that intercepts attribute setting on model classes and
warns about overriding of decorators.
"""
def __setitem__(self, k: str, v: object) -> None:
existing: Any = self.get(k, None)
if existing and v is not existing and isinstance(existing, PydanticDescriptorProxy):
warnings.warn(f'`{k}` overrides an existing Pydantic `{existing.decorator_info.decorator_repr}` decorator')
return super().__setitem__(k, v)
def NoInitField(
*,
init: Literal[False] = False,
) -> Any:
"""Only for typing purposes. Used as default value of `__pydantic_fields_set__`,
`__pydantic_extra__`, `__pydantic_private__`, so they could be ignored when
synthesizing the `__init__` signature.
"""
@dataclass_transform(kw_only_default=True, field_specifiers=(PydanticModelField, PydanticModelPrivateAttr, NoInitField))
class ModelMetaclass(ABCMeta):
def __new__(
mcs,
cls_name: str,
bases: tuple[type[Any], ...],
namespace: dict[str, Any],
__pydantic_generic_metadata__: PydanticGenericMetadata | None = None,
__pydantic_reset_parent_namespace__: bool = True,
_create_model_module: str | None = None,
**kwargs: Any,
) -> type:
"""Metaclass for creating Pydantic models.
Args:
cls_name: The name of the class to be created.
bases: The base classes of the class to be created.
namespace: The attribute dictionary of the class to be created.
__pydantic_generic_metadata__: Metadata for generic models.
__pydantic_reset_parent_namespace__: Reset parent namespace.
_create_model_module: The module of the class to be created, if created by `create_model`.
**kwargs: Catch-all for any other keyword arguments.
Returns:
The new class created by the metaclass.
"""
# Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we rely on the fact
# that `BaseModel` itself won't have any bases, but any subclass of it will, to determine whether the `__new__`
# call we're in the middle of is for the `BaseModel` class.
if bases:
base_field_names, class_vars, base_private_attributes = mcs._collect_bases_data(bases)
config_wrapper = ConfigWrapper.for_model(bases, namespace, kwargs)
namespace['model_config'] = config_wrapper.config_dict
private_attributes = inspect_namespace(
namespace, config_wrapper.ignored_types, class_vars, base_field_names
)
if private_attributes or base_private_attributes:
original_model_post_init = get_model_post_init(namespace, bases)
if original_model_post_init is not None:
# if there are private_attributes and a model_post_init function, we handle both
def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None:
"""We need to both initialize private attributes and call the user-defined model_post_init
method.
"""
init_private_attributes(self, context)
original_model_post_init(self, context)
namespace['model_post_init'] = wrapped_model_post_init
else:
namespace['model_post_init'] = init_private_attributes
namespace['__class_vars__'] = class_vars
namespace['__private_attributes__'] = {**base_private_attributes, **private_attributes}
if __pydantic_generic_metadata__:
namespace['__pydantic_generic_metadata__'] = __pydantic_generic_metadata__
cls = cast('type[BaseModel]', super().__new__(mcs, cls_name, bases, namespace, **kwargs))
BaseModel_ = import_cached_base_model()
mro = cls.__mro__
if Generic in mro and mro.index(Generic) < mro.index(BaseModel_):
warnings.warn(
GenericBeforeBaseModelWarning(
'Classes should inherit from `BaseModel` before generic classes (e.g. `typing.Generic[T]`) '
'for pydantic generics to work properly.'
),
stacklevel=2,
)
cls.__pydantic_custom_init__ = not getattr(cls.__init__, '__pydantic_base_init__', False)
cls.__pydantic_post_init__ = (
None if cls.model_post_init is BaseModel_.model_post_init else 'model_post_init'
)
cls.__pydantic_decorators__ = DecoratorInfos.build(cls)
# Use the getattr below to grab the __parameters__ from the `typing.Generic` parent class
if __pydantic_generic_metadata__:
cls.__pydantic_generic_metadata__ = __pydantic_generic_metadata__
else:
parent_parameters = getattr(cls, '__pydantic_generic_metadata__', {}).get('parameters', ())
parameters = getattr(cls, '__parameters__', None) or parent_parameters
if parameters and parent_parameters and not all(x in parameters for x in parent_parameters):
from ..root_model import RootModelRootType
missing_parameters = tuple(x for x in parameters if x not in parent_parameters)
if RootModelRootType in parent_parameters and RootModelRootType not in parameters:
# This is a special case where the user has subclassed `RootModel`, but has not parametrized
# RootModel with the generic type identifiers being used. Ex:
# class MyModel(RootModel, Generic[T]):
# root: T
# Should instead just be:
# class MyModel(RootModel[T]):
# root: T
parameters_str = ', '.join([x.__name__ for x in missing_parameters])
error_message = (
f'{cls.__name__} is a subclass of `RootModel`, but does not include the generic type identifier(s) '
f'{parameters_str} in its parameters. '
f'You should parametrize RootModel directly, e.g., `class {cls.__name__}(RootModel[{parameters_str}]): ...`.'
)
else:
combined_parameters = parent_parameters + missing_parameters
parameters_str = ', '.join([str(x) for x in combined_parameters])
generic_type_label = f'typing.Generic[{parameters_str}]'
error_message = (
f'All parameters must be present on typing.Generic;'
f' you should inherit from {generic_type_label}.'
)
if Generic not in bases: # pragma: no cover
# We raise an error here not because it is desirable, but because some cases are mishandled.
# It would be nice to remove this error and still have things behave as expected, it's just
# challenging because we are using a custom `__class_getitem__` to parametrize generic models,
# and not returning a typing._GenericAlias from it.
bases_str = ', '.join([x.__name__ for x in bases] + [generic_type_label])
error_message += (
f' Note: `typing.Generic` must go last: `class {cls.__name__}({bases_str}): ...`)'
)
raise TypeError(error_message)
cls.__pydantic_generic_metadata__ = {
'origin': None,
'args': (),
'parameters': parameters,
}
cls.__pydantic_complete__ = False # Ensure this specific class gets completed
# preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487
# for attributes not in `new_namespace` (e.g. private attributes)
for name, obj in private_attributes.items():
obj.__set_name__(cls, name)
if __pydantic_reset_parent_namespace__:
cls.__pydantic_parent_namespace__ = build_lenient_weakvaluedict(parent_frame_namespace())
parent_namespace: dict[str, Any] | None = getattr(cls, '__pydantic_parent_namespace__', None)
if isinstance(parent_namespace, dict):
parent_namespace = unpack_lenient_weakvaluedict(parent_namespace)
ns_resolver = NsResolver(parent_namespace=parent_namespace)
set_model_fields(cls, bases, config_wrapper, ns_resolver)
if config_wrapper.frozen and '__hash__' not in namespace:
set_default_hash_func(cls, bases)
complete_model_class(
cls,
cls_name,
config_wrapper,
raise_errors=False,
ns_resolver=ns_resolver,
create_model_module=_create_model_module,
)
# If this is placed before the complete_model_class call above,
# the generic computed fields return type is set to PydanticUndefined
cls.__pydantic_computed_fields__ = {
k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items()
}
set_deprecated_descriptors(cls)
# using super(cls, cls) on the next line ensures we only call the parent class's __pydantic_init_subclass__
# I believe the `type: ignore` is only necessary because mypy doesn't realize that this code branch is
# only hit for _proper_ subclasses of BaseModel
super(cls, cls).__pydantic_init_subclass__(**kwargs) # type: ignore[misc]
return cls
else:
# These are instance variables, but have been assigned to `NoInitField` to trick the type checker.
for instance_slot in '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__':
namespace.pop(
instance_slot,
None, # In case the metaclass is used with a class other than `BaseModel`.
)
namespace.get('__annotations__', {}).clear()
return super().__new__(mcs, cls_name, bases, namespace, **kwargs)
def mro(cls) -> list[type[Any]]:
original_mro = super().mro()
if cls.__bases__ == (object,):
return original_mro
generic_metadata: PydanticGenericMetadata | None = cls.__dict__.get('__pydantic_generic_metadata__')
if not generic_metadata:
return original_mro
assert_err_msg = 'Unexpected error occurred when generating MRO of generic subclass. Please report this issue on GitHub: https://github.com/pydantic/pydantic/issues.'
origin: type[BaseModel] | None
origin, args = (
generic_metadata['origin'],
generic_metadata['args'],
)
if not origin:
return original_mro
target_params = origin.__pydantic_generic_metadata__['parameters']
param_dict = dict(zip(target_params, args))
indexed_origins = {origin}
new_mro: list[type[Any]] = [cls]
for base in original_mro[1:]:
base_origin: type[BaseModel] | None = getattr(base, '__pydantic_generic_metadata__', {}).get('origin')
base_params: tuple[TypeVar, ...] = getattr(base, '__pydantic_generic_metadata__', {}).get('parameters', ())
if base_origin in indexed_origins:
continue
elif base not in indexed_origins and base_params:
assert set(base_params) <= param_dict.keys(), assert_err_msg
new_base_args = tuple(param_dict[param] for param in base_params)
new_base = base[new_base_args] # type: ignore
new_mro.append(new_base)
indexed_origins.add(base_origin or base)
if base_origin is not None:
# dropped previous indexed origins
continue
else:
indexed_origins.add(base_origin or base)
# Avoid redundunt case such as
# class A(BaseModel, Generic[T]): ...
# A[T] is A # True
if base is not new_mro[-1]:
new_mro.append(base)
return new_mro
if not typing.TYPE_CHECKING: # pragma: no branch
# We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
def __getattr__(self, item: str) -> Any:
"""This is necessary to keep attribute access working for class attribute access."""
private_attributes = self.__dict__.get('__private_attributes__')
if private_attributes and item in private_attributes:
return private_attributes[item]
raise AttributeError(item)
@classmethod
def __prepare__(cls, *args: Any, **kwargs: Any) -> dict[str, object]:
return _ModelNamespaceDict()
def __instancecheck__(self, instance: Any) -> bool:
"""Avoid calling ABC _abc_subclasscheck unless we're pretty sure.
See #3829 and python/cpython#92810
"""
return hasattr(instance, '__pydantic_validator__') and super().__instancecheck__(instance)
@staticmethod
def _collect_bases_data(bases: tuple[type[Any], ...]) -> tuple[set[str], set[str], dict[str, ModelPrivateAttr]]:
BaseModel = import_cached_base_model()
field_names: set[str] = set()
class_vars: set[str] = set()
private_attributes: dict[str, ModelPrivateAttr] = {}
for base in bases:
if issubclass(base, BaseModel) and base is not BaseModel:
# model_fields might not be defined yet in the case of generics, so we use getattr here:
field_names.update(getattr(base, '__pydantic_fields__', {}).keys())
class_vars.update(base.__class_vars__)
private_attributes.update(base.__private_attributes__)
return field_names, class_vars, private_attributes
@property
@deprecated('The `__fields__` attribute is deprecated, use `model_fields` instead.', category=None)
def __fields__(self) -> dict[str, FieldInfo]:
warnings.warn(
'The `__fields__` attribute is deprecated, use `model_fields` instead.',
PydanticDeprecatedSince20,
stacklevel=2,
)
return self.model_fields
@property
def model_fields(self) -> dict[str, FieldInfo]:
"""Get metadata about the fields defined on the model.
Returns:
A mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects.
"""
return getattr(self, '__pydantic_fields__', {})
@property
def model_computed_fields(self) -> dict[str, ComputedFieldInfo]:
"""Get metadata about the computed fields defined on the model.
Returns:
A mapping of computed field names to [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.
"""
return getattr(self, '__pydantic_computed_fields__', {})
def __dir__(self) -> list[str]:
attributes = list(super().__dir__())
if '__fields__' in attributes:
attributes.remove('__fields__')
return attributes
def init_private_attributes(self: BaseModel, context: Any, /) -> None:
"""This function is meant to behave like a BaseModel method to initialise private attributes.
It takes context as an argument since that's what pydantic-core passes when calling it.
Args:
self: The BaseModel instance.
context: The context.
"""
if getattr(self, '__pydantic_private__', None) is None:
pydantic_private = {}
for name, private_attr in self.__private_attributes__.items():
default = private_attr.get_default()
if default is not PydanticUndefined:
pydantic_private[name] = default
object_setattr(self, '__pydantic_private__', pydantic_private)
def get_model_post_init(namespace: dict[str, Any], bases: tuple[type[Any], ...]) -> Callable[..., Any] | None:
"""Get the `model_post_init` method from the namespace or the class bases, or `None` if not defined."""
if 'model_post_init' in namespace:
return namespace['model_post_init']
BaseModel = import_cached_base_model()
model_post_init = get_attribute_from_bases(bases, 'model_post_init')
if model_post_init is not BaseModel.model_post_init:
return model_post_init
def inspect_namespace( # noqa C901
namespace: dict[str, Any],
ignored_types: tuple[type[Any], ...],
base_class_vars: set[str],
base_class_fields: set[str],
) -> dict[str, ModelPrivateAttr]:
"""Iterate over the namespace and:
* gather private attributes
* check for items which look like fields but are not (e.g. have no annotation) and warn.
Args:
namespace: The attribute dictionary of the class to be created.
ignored_types: A tuple of ignore types.
base_class_vars: A set of base class class variables.
base_class_fields: A set of base class fields.
Returns:
A dict contains private attributes info.
Raises:
TypeError: If there is a `__root__` field in model.
NameError: If private attribute name is invalid.
PydanticUserError:
- If a field does not have a type annotation.
- If a field on base class was overridden by a non-annotated attribute.
"""
from ..fields import ModelPrivateAttr, PrivateAttr
FieldInfo = import_cached_field_info()
all_ignored_types = ignored_types + default_ignored_types()
private_attributes: dict[str, ModelPrivateAttr] = {}
raw_annotations = namespace.get('__annotations__', {})
if '__root__' in raw_annotations or '__root__' in namespace:
raise TypeError("To define root models, use `pydantic.RootModel` rather than a field called '__root__'")
ignored_names: set[str] = set()
for var_name, value in list(namespace.items()):
if var_name == 'model_config' or var_name == '__pydantic_extra__':
continue
elif (
isinstance(value, type)
and value.__module__ == namespace['__module__']
and '__qualname__' in namespace
and value.__qualname__.startswith(namespace['__qualname__'])
):
# `value` is a nested type defined in this namespace; don't error
continue
elif isinstance(value, all_ignored_types) or value.__class__.__module__ == 'functools':
ignored_names.add(var_name)
continue
elif isinstance(value, ModelPrivateAttr):
if var_name.startswith('__'):
raise NameError(
'Private attributes must not use dunder names;'
f' use a single underscore prefix instead of {var_name!r}.'
)
elif is_valid_field_name(var_name):
raise NameError(
'Private attributes must not use valid field names;'
f' use sunder names, e.g. {"_" + var_name!r} instead of {var_name!r}.'
)
private_attributes[var_name] = value
del namespace[var_name]
elif isinstance(value, FieldInfo) and not is_valid_field_name(var_name):
suggested_name = var_name.lstrip('_') or 'my_field' # don't suggest '' for all-underscore name
raise NameError(
f'Fields must not use names with leading underscores;'
f' e.g., use {suggested_name!r} instead of {var_name!r}.'
)
elif var_name.startswith('__'):
continue
elif is_valid_privateattr_name(var_name):
if var_name not in raw_annotations or not is_classvar_annotation(raw_annotations[var_name]):
private_attributes[var_name] = cast(ModelPrivateAttr, PrivateAttr(default=value))
del namespace[var_name]
elif var_name in base_class_vars:
continue
elif var_name not in raw_annotations:
if var_name in base_class_fields:
raise PydanticUserError(
f'Field {var_name!r} defined on a base class was overridden by a non-annotated attribute. '
f'All field definitions, including overrides, require a type annotation.',
code='model-field-overridden',
)
elif isinstance(value, FieldInfo):
raise PydanticUserError(
f'Field {var_name!r} requires a type annotation', code='model-field-missing-annotation'
)
else:
raise PydanticUserError(
f'A non-annotated attribute was detected: `{var_name} = {value!r}`. All model fields require a '
f'type annotation; if `{var_name}` is not meant to be a field, you may be able to resolve this '
f"error by annotating it as a `ClassVar` or updating `model_config['ignored_types']`.",
code='model-field-missing-annotation',
)
for ann_name, ann_type in raw_annotations.items():
if (
is_valid_privateattr_name(ann_name)
and ann_name not in private_attributes
and ann_name not in ignored_names
# This condition can be a false negative when `ann_type` is stringified,
# but it is handled in most cases in `set_model_fields`:
and not is_classvar_annotation(ann_type)
and ann_type not in all_ignored_types
and getattr(ann_type, '__module__', None) != 'functools'
):
if isinstance(ann_type, str):
# Walking up the frames to get the module namespace where the model is defined
# (as the model class wasn't created yet, we unfortunately can't use `cls.__module__`):
frame = sys._getframe(2)
if frame is not None:
try:
ann_type = eval_type_backport(
_make_forward_ref(ann_type, is_argument=False, is_class=True),
globalns=frame.f_globals,
localns=frame.f_locals,
)
except (NameError, TypeError):
pass
if is_annotated(ann_type):
_, *metadata = get_args(ann_type)
private_attr = next((v for v in metadata if isinstance(v, ModelPrivateAttr)), None)
if private_attr is not None:
private_attributes[ann_name] = private_attr
continue
private_attributes[ann_name] = PrivateAttr()
return private_attributes
def set_default_hash_func(cls: type[BaseModel], bases: tuple[type[Any], ...]) -> None:
base_hash_func = get_attribute_from_bases(bases, '__hash__')
new_hash_func = make_hash_func(cls)
if base_hash_func in {None, object.__hash__} or getattr(base_hash_func, '__code__', None) == new_hash_func.__code__:
# If `__hash__` is some default, we generate a hash function.
# It will be `None` if not overridden from BaseModel.
# It may be `object.__hash__` if there is another
# parent class earlier in the bases which doesn't override `__hash__` (e.g. `typing.Generic`).
# It may be a value set by `set_default_hash_func` if `cls` is a subclass of another frozen model.
# In the last case we still need a new hash function to account for new `model_fields`.
cls.__hash__ = new_hash_func
def make_hash_func(cls: type[BaseModel]) -> Any:
getter = operator.itemgetter(*cls.__pydantic_fields__.keys()) if cls.__pydantic_fields__ else lambda _: 0
def hash_func(self: Any) -> int:
try:
return hash(getter(self.__dict__))
except KeyError:
# In rare cases (such as when using the deprecated copy method), the __dict__ may not contain
# all model fields, which is how we can get here.
# getter(self.__dict__) is much faster than any 'safe' method that accounts for missing keys,
# and wrapping it in a `try` doesn't slow things down much in the common case.
return hash(getter(SafeGetItemProxy(self.__dict__)))
return hash_func
def set_model_fields(
cls: type[BaseModel],
bases: tuple[type[Any], ...],
config_wrapper: ConfigWrapper,
ns_resolver: NsResolver | None,
) -> None:
"""Collect and set `cls.__pydantic_fields__` and `cls.__class_vars__`.
Args:
cls: BaseModel or dataclass.
bases: Parents of the class, generally `cls.__bases__`.
config_wrapper: The config wrapper instance.
ns_resolver: Namespace resolver to use when getting model annotations.
"""
typevars_map = get_model_typevars_map(cls)
fields, class_vars = collect_model_fields(cls, bases, config_wrapper, ns_resolver, typevars_map=typevars_map)
cls.__pydantic_fields__ = fields
cls.__class_vars__.update(class_vars)
for k in class_vars:
# Class vars should not be private attributes
# We remove them _here_ and not earlier because we rely on inspecting the class to determine its classvars,
# but private attributes are determined by inspecting the namespace _prior_ to class creation.
# In the case that a classvar with a leading-'_' is defined via a ForwardRef (e.g., when using
# `__future__.annotations`), we want to remove the private attribute which was detected _before_ we knew it
# evaluated to a classvar
value = cls.__private_attributes__.pop(k, None)
if value is not None and value.default is not PydanticUndefined:
setattr(cls, k, value.default)
def complete_model_class(
cls: type[BaseModel],
cls_name: str,
config_wrapper: ConfigWrapper,
*,
raise_errors: bool = True,
ns_resolver: NsResolver | None = None,
create_model_module: str | None = None,
) -> bool:
"""Finish building a model class.
This logic must be called after class has been created since validation functions must be bound
and `get_type_hints` requires a class object.
Args:
cls: BaseModel or dataclass.
cls_name: The model or dataclass name.
config_wrapper: The config wrapper instance.
raise_errors: Whether to raise errors.
ns_resolver: The namespace resolver instance to use during schema building.
create_model_module: The module of the class to be created, if created by `create_model`.
Returns:
`True` if the model is successfully completed, else `False`.
Raises:
PydanticUndefinedAnnotation: If `PydanticUndefinedAnnotation` occurs in`__get_pydantic_core_schema__`
and `raise_errors=True`.
"""
if config_wrapper.defer_build:
set_model_mocks(cls, cls_name)
return False
typevars_map = get_model_typevars_map(cls)
gen_schema = GenerateSchema(
config_wrapper,
ns_resolver,
typevars_map,
)
handler = CallbackGetCoreSchemaHandler(
partial(gen_schema.generate_schema, from_dunder_get_core_schema=False),
gen_schema,
ref_mode='unpack',
)
try:
schema = cls.__get_pydantic_core_schema__(cls, handler)
except PydanticUndefinedAnnotation as e:
if raise_errors:
raise
set_model_mocks(cls, cls_name, f'`{e.name}`')
return False
core_config = config_wrapper.core_config(title=cls.__name__)
try:
schema = gen_schema.clean_schema(schema)
except gen_schema.CollectedInvalid:
set_model_mocks(cls, cls_name)
return False
# debug(schema)
cls.__pydantic_core_schema__ = schema
cls.__pydantic_validator__ = create_schema_validator(
schema,
cls,
create_model_module or cls.__module__,
cls.__qualname__,
'create_model' if create_model_module else 'BaseModel',
core_config,
config_wrapper.plugin_settings,
)
cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config)
cls.__pydantic_complete__ = True
# set __signature__ attr only for model class, but not for its instances
# (because instances can define `__call__`, and `inspect.signature` shouldn't
# use the `__signature__` attribute and instead generate from `__call__`).
cls.__signature__ = LazyClassAttribute(
'__signature__',
partial(
generate_pydantic_signature,
init=cls.__init__,
fields=cls.__pydantic_fields__,
populate_by_name=config_wrapper.populate_by_name,
extra=config_wrapper.extra,
),
)
return True
def set_deprecated_descriptors(cls: type[BaseModel]) -> None:
"""Set data descriptors on the class for deprecated fields."""
for field, field_info in cls.__pydantic_fields__.items():
if (msg := field_info.deprecation_message) is not None:
desc = _DeprecatedFieldDescriptor(msg)
desc.__set_name__(cls, field)
setattr(cls, field, desc)
for field, computed_field_info in cls.__pydantic_computed_fields__.items():
if (
(msg := computed_field_info.deprecation_message) is not None
# Avoid having two warnings emitted:
and not hasattr(unwrap_wrapped_function(computed_field_info.wrapped_property), '__deprecated__')
):
desc = _DeprecatedFieldDescriptor(msg, computed_field_info.wrapped_property)
desc.__set_name__(cls, field)
setattr(cls, field, desc)
class _DeprecatedFieldDescriptor:
"""Read-only data descriptor used to emit a runtime deprecation warning before accessing a deprecated field.
Attributes:
msg: The deprecation message to be emitted.
wrapped_property: The property instance if the deprecated field is a computed field, or `None`.
field_name: The name of the field being deprecated.
"""
field_name: str
def __init__(self, msg: str, wrapped_property: property | None = None) -> None:
self.msg = msg
self.wrapped_property = wrapped_property
def __set_name__(self, cls: type[BaseModel], name: str) -> None:
self.field_name = name
def __get__(self, obj: BaseModel | None, obj_type: type[BaseModel] | None = None) -> Any:
if obj is None:
if self.wrapped_property is not None:
return self.wrapped_property.__get__(None, obj_type)
raise AttributeError(self.field_name)
warnings.warn(self.msg, builtins.DeprecationWarning, stacklevel=2)
if self.wrapped_property is not None:
return self.wrapped_property.__get__(obj, obj_type)
return obj.__dict__[self.field_name]
# Defined to make it a data descriptor and take precedence over the instance's dictionary.
# Note that it will not be called when setting a value on a model instance
# as `BaseModel.__setattr__` is defined and takes priority.
def __set__(self, obj: Any, value: Any) -> NoReturn:
raise AttributeError(self.field_name)
class _PydanticWeakRef:
"""Wrapper for `weakref.ref` that enables `pickle` serialization.
Cloudpickle fails to serialize `weakref.ref` objects due to an arcane error related
to abstract base classes (`abc.ABC`). This class works around the issue by wrapping
`weakref.ref` instead of subclassing it.
See https://github.com/pydantic/pydantic/issues/6763 for context.
Semantics:
- If not pickled, behaves the same as a `weakref.ref`.
- If pickled along with the referenced object, the same `weakref.ref` behavior
will be maintained between them after unpickling.
- If pickled without the referenced object, after unpickling the underlying
reference will be cleared (`__call__` will always return `None`).
"""
def __init__(self, obj: Any):
if obj is None:
# The object will be `None` upon deserialization if the serialized weakref
# had lost its underlying object.
self._wr = None
else:
self._wr = weakref.ref(obj)
def __call__(self) -> Any:
if self._wr is None:
return None
else:
return self._wr()
def __reduce__(self) -> tuple[Callable, tuple[weakref.ReferenceType | None]]:
return _PydanticWeakRef, (self(),)
def build_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None:
"""Takes an input dictionary, and produces a new value that (invertibly) replaces the values with weakrefs.
We can't just use a WeakValueDictionary because many types (including int, str, etc.) can't be stored as values
in a WeakValueDictionary.
The `unpack_lenient_weakvaluedict` function can be used to reverse this operation.
"""
if d is None:
return None
result = {}
for k, v in d.items():
try:
proxy = _PydanticWeakRef(v)
except TypeError:
proxy = v
result[k] = proxy
return result
def unpack_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None:
"""Inverts the transform performed by `build_lenient_weakvaluedict`."""
if d is None:
return None
result = {}
for k, v in d.items():
if isinstance(v, _PydanticWeakRef):
v = v()
if v is not None:
result[k] = v
else:
result[k] = v
return result
@lru_cache(maxsize=None)
def default_ignored_types() -> tuple[type[Any], ...]:
from ..fields import ComputedFieldInfo
ignored_types = [
FunctionType,
property,
classmethod,
staticmethod,
PydanticDescriptorProxy,
ComputedFieldInfo,
TypeAliasType, # from `typing_extensions`
]
if sys.version_info >= (3, 12):
ignored_types.append(typing.TypeAliasType)
return tuple(ignored_types)