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"""Private logic related to fields (the `Field()` function and `FieldInfo` class), and arguments to `Annotated`."""
from __future__ import annotations as _annotations
import dataclasses
import sys
import warnings
from copy import copy
from functools import lru_cache
from typing import TYPE_CHECKING, Any
from pydantic_core import PydanticUndefined
from pydantic.errors import PydanticUserError
from . import _typing_extra
from ._config import ConfigWrapper
from ._repr import Representation
from ._typing_extra import get_cls_type_hints_lenient, get_type_hints, is_classvar, is_finalvar
if TYPE_CHECKING:
from annotated_types import BaseMetadata
from ..fields import FieldInfo
from ..main import BaseModel
from ._dataclasses import StandardDataclass
from ._decorators import DecoratorInfos
def get_type_hints_infer_globalns(
obj: Any,
localns: dict[str, Any] | None = None,
include_extras: bool = False,
) -> dict[str, Any]:
"""Gets type hints for an object by inferring the global namespace.
It uses the `typing.get_type_hints`, The only thing that we do here is fetching
global namespace from `obj.__module__` if it is not `None`.
Args:
obj: The object to get its type hints.
localns: The local namespaces.
include_extras: Whether to recursively include annotation metadata.
Returns:
The object type hints.
"""
module_name = getattr(obj, '__module__', None)
globalns: dict[str, Any] | None = None
if module_name:
try:
globalns = sys.modules[module_name].__dict__
except KeyError:
# happens occasionally, see https://github.com/pydantic/pydantic/issues/2363
pass
return get_type_hints(obj, globalns=globalns, localns=localns, include_extras=include_extras)
class PydanticMetadata(Representation):
"""Base class for annotation markers like `Strict`."""
__slots__ = ()
def pydantic_general_metadata(**metadata: Any) -> BaseMetadata:
"""Create a new `_PydanticGeneralMetadata` class with the given metadata.
Args:
**metadata: The metadata to add.
Returns:
The new `_PydanticGeneralMetadata` class.
"""
return _general_metadata_cls()(metadata) # type: ignore
@lru_cache(maxsize=None)
def _general_metadata_cls() -> type[BaseMetadata]:
"""Do it this way to avoid importing `annotated_types` at import time."""
from annotated_types import BaseMetadata
class _PydanticGeneralMetadata(PydanticMetadata, BaseMetadata):
"""Pydantic general metadata like `max_digits`."""
def __init__(self, metadata: Any):
self.__dict__ = metadata
return _PydanticGeneralMetadata # type: ignore
def collect_model_fields( # noqa: C901
cls: type[BaseModel],
bases: tuple[type[Any], ...],
config_wrapper: ConfigWrapper,
types_namespace: dict[str, Any] | None,
*,
typevars_map: dict[Any, Any] | None = None,
) -> tuple[dict[str, FieldInfo], set[str]]:
"""Collect the fields of a nascent pydantic model.
Also collect the names of any ClassVars present in the type hints.
The returned value is a tuple of two items: the fields dict, and the set of ClassVar names.
Args:
cls: BaseModel or dataclass.
bases: Parents of the class, generally `cls.__bases__`.
config_wrapper: The config wrapper instance.
types_namespace: Optional extra namespace to look for types in.
typevars_map: A dictionary mapping type variables to their concrete types.
Returns:
A tuple contains fields and class variables.
Raises:
NameError:
- If there is a conflict between a field name and protected namespaces.
- If there is a field other than `root` in `RootModel`.
- If a field shadows an attribute in the parent model.
"""
from ..fields import FieldInfo
type_hints = get_cls_type_hints_lenient(cls, types_namespace)
# https://docs.python.org/3/howto/annotations.html#accessing-the-annotations-dict-of-an-object-in-python-3-9-and-older
# annotations is only used for finding fields in parent classes
annotations = cls.__dict__.get('__annotations__', {})
fields: dict[str, FieldInfo] = {}
class_vars: set[str] = set()
for ann_name, ann_type in type_hints.items():
if ann_name == 'model_config':
# We never want to treat `model_config` as a field
# Note: we may need to change this logic if/when we introduce a `BareModel` class with no
# protected namespaces (where `model_config` might be allowed as a field name)
continue
for protected_namespace in config_wrapper.protected_namespaces:
if ann_name.startswith(protected_namespace):
for b in bases:
if hasattr(b, ann_name):
from ..main import BaseModel
if not (issubclass(b, BaseModel) and ann_name in b.model_fields):
raise NameError(
f'Field "{ann_name}" conflicts with member {getattr(b, ann_name)}'
f' of protected namespace "{protected_namespace}".'
)
else:
valid_namespaces = tuple(
x for x in config_wrapper.protected_namespaces if not ann_name.startswith(x)
)
warnings.warn(
f'Field "{ann_name}" has conflict with protected namespace "{protected_namespace}".'
'\n\nYou may be able to resolve this warning by setting'
f" `model_config['protected_namespaces'] = {valid_namespaces}`.",
UserWarning,
)
if is_classvar(ann_type):
class_vars.add(ann_name)
continue
if _is_finalvar_with_default_val(ann_type, getattr(cls, ann_name, PydanticUndefined)):
class_vars.add(ann_name)
continue
if not is_valid_field_name(ann_name):
continue
if cls.__pydantic_root_model__ and ann_name != 'root':
raise NameError(
f"Unexpected field with name {ann_name!r}; only 'root' is allowed as a field of a `RootModel`"
)
# when building a generic model with `MyModel[int]`, the generic_origin check makes sure we don't get
# "... shadows an attribute" errors
generic_origin = getattr(cls, '__pydantic_generic_metadata__', {}).get('origin')
for base in bases:
dataclass_fields = {
field.name for field in (dataclasses.fields(base) if dataclasses.is_dataclass(base) else ())
}
if hasattr(base, ann_name):
if base is generic_origin:
# Don't error when "shadowing" of attributes in parametrized generics
continue
if ann_name in dataclass_fields:
# Don't error when inheriting stdlib dataclasses whose fields are "shadowed" by defaults being set
# on the class instance.
continue
warnings.warn(
f'Field name "{ann_name}" shadows an attribute in parent "{base.__qualname__}"; ',
UserWarning,
)
try:
default = getattr(cls, ann_name, PydanticUndefined)
if default is PydanticUndefined:
raise AttributeError
except AttributeError:
if ann_name in annotations:
field_info = FieldInfo.from_annotation(ann_type)
else:
# if field has no default value and is not in __annotations__ this means that it is
# defined in a base class and we can take it from there
model_fields_lookup: dict[str, FieldInfo] = {}
for x in cls.__bases__[::-1]:
model_fields_lookup.update(getattr(x, 'model_fields', {}))
if ann_name in model_fields_lookup:
# The field was present on one of the (possibly multiple) base classes
# copy the field to make sure typevar substitutions don't cause issues with the base classes
field_info = copy(model_fields_lookup[ann_name])
else:
# The field was not found on any base classes; this seems to be caused by fields not getting
# generated thanks to models not being fully defined while initializing recursive models.
# Nothing stops us from just creating a new FieldInfo for this type hint, so we do this.
field_info = FieldInfo.from_annotation(ann_type)
else:
field_info = FieldInfo.from_annotated_attribute(ann_type, default)
# attributes which are fields are removed from the class namespace:
# 1. To match the behaviour of annotation-only fields
# 2. To avoid false positives in the NameError check above
try:
delattr(cls, ann_name)
except AttributeError:
pass # indicates the attribute was on a parent class
# Use cls.__dict__['__pydantic_decorators__'] instead of cls.__pydantic_decorators__
# to make sure the decorators have already been built for this exact class
decorators: DecoratorInfos = cls.__dict__['__pydantic_decorators__']
if ann_name in decorators.computed_fields:
raise ValueError("you can't override a field with a computed field")
fields[ann_name] = field_info
if typevars_map:
for field in fields.values():
field.apply_typevars_map(typevars_map, types_namespace)
return fields, class_vars
def _is_finalvar_with_default_val(type_: type[Any], val: Any) -> bool:
from ..fields import FieldInfo
if not is_finalvar(type_):
return False
elif val is PydanticUndefined:
return False
elif isinstance(val, FieldInfo) and (val.default is PydanticUndefined and val.default_factory is None):
return False
else:
return True
def collect_dataclass_fields(
cls: type[StandardDataclass], types_namespace: dict[str, Any] | None, *, typevars_map: dict[Any, Any] | None = None
) -> dict[str, FieldInfo]:
"""Collect the fields of a dataclass.
Args:
cls: dataclass.
types_namespace: Optional extra namespace to look for types in.
typevars_map: A dictionary mapping type variables to their concrete types.
Returns:
The dataclass fields.
"""
from ..fields import FieldInfo
fields: dict[str, FieldInfo] = {}
dataclass_fields: dict[str, dataclasses.Field] = cls.__dataclass_fields__
cls_localns = dict(vars(cls)) # this matches get_cls_type_hints_lenient, but all tests pass with `= None` instead
source_module = sys.modules.get(cls.__module__)
if source_module is not None:
types_namespace = {**source_module.__dict__, **(types_namespace or {})}
for ann_name, dataclass_field in dataclass_fields.items():
ann_type = _typing_extra.eval_type_lenient(dataclass_field.type, types_namespace, cls_localns)
if is_classvar(ann_type):
continue
if (
not dataclass_field.init
and dataclass_field.default == dataclasses.MISSING
and dataclass_field.default_factory == dataclasses.MISSING
):
# TODO: We should probably do something with this so that validate_assignment behaves properly
# Issue: https://github.com/pydantic/pydantic/issues/5470
continue
if isinstance(dataclass_field.default, FieldInfo):
if dataclass_field.default.init_var:
if dataclass_field.default.init is False:
raise PydanticUserError(
f'Dataclass field {ann_name} has init=False and init_var=True, but these are mutually exclusive.',
code='clashing-init-and-init-var',
)
# TODO: same note as above re validate_assignment
continue
field_info = FieldInfo.from_annotated_attribute(ann_type, dataclass_field.default)
else:
field_info = FieldInfo.from_annotated_attribute(ann_type, dataclass_field)
fields[ann_name] = field_info
if field_info.default is not PydanticUndefined and isinstance(getattr(cls, ann_name, field_info), FieldInfo):
# We need this to fix the default when the "default" from __dataclass_fields__ is a pydantic.FieldInfo
setattr(cls, ann_name, field_info.default)
if typevars_map:
for field in fields.values():
field.apply_typevars_map(typevars_map, types_namespace)
return fields
def is_valid_field_name(name: str) -> bool:
return not name.startswith('_')
def is_valid_privateattr_name(name: str) -> bool:
return name.startswith('_') and not name.startswith('__')