Lightstar/venv/Lib/site-packages/pydantic/_internal/_fields.py

"""Private logic related to fields (the `Field()` function and `FieldInfo` class), and arguments to `Annotated`."""

from __future__ import annotations as _annotations

import dataclasses
import warnings
from copy import copy
from functools import lru_cache
from inspect import Parameter, ismethoddescriptor, signature
from typing import TYPE_CHECKING, Any, Callable, Pattern

from pydantic_core import PydanticUndefined
from typing_extensions import TypeIs

from pydantic.errors import PydanticUserError

from . import _typing_extra
from ._config import ConfigWrapper
from ._docs_extraction import extract_docstrings_from_cls
from ._import_utils import import_cached_base_model, import_cached_field_info
from ._namespace_utils import NsResolver
from ._repr import Representation
from ._utils import can_be_positional

if TYPE_CHECKING:
    from annotated_types import BaseMetadata

    from ..fields import FieldInfo
    from ..main import BaseModel
    from ._dataclasses import StandardDataclass
    from ._decorators import DecoratorInfos


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 _update_fields_from_docstrings(cls: type[Any], fields: dict[str, FieldInfo], config_wrapper: ConfigWrapper) -> None:
    if config_wrapper.use_attribute_docstrings:
        fields_docs = extract_docstrings_from_cls(cls)
        for ann_name, field_info in fields.items():
            if field_info.description is None and ann_name in fields_docs:
                field_info.description = fields_docs[ann_name]


def collect_model_fields(  # noqa: C901
    cls: type[BaseModel],
    bases: tuple[type[Any], ...],
    config_wrapper: ConfigWrapper,
    ns_resolver: NsResolver | 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.
        ns_resolver: Namespace resolver to use when getting model annotations.
        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.
    """
    BaseModel = import_cached_base_model()
    FieldInfo_ = import_cached_field_info()

    parent_fields_lookup: dict[str, FieldInfo] = {}
    for base in reversed(bases):
        if model_fields := getattr(base, '__pydantic_fields__', None):
            parent_fields_lookup.update(model_fields)

    type_hints = _typing_extra.get_model_type_hints(cls, ns_resolver=ns_resolver)

    # 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, evaluated) 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:
            ns_violation: bool = False
            if isinstance(protected_namespace, Pattern):
                ns_violation = protected_namespace.match(ann_name) is not None
            elif isinstance(protected_namespace, str):
                ns_violation = ann_name.startswith(protected_namespace)

            if ns_violation:
                for b in bases:
                    if hasattr(b, ann_name):
                        if not (issubclass(b, BaseModel) and ann_name in getattr(b, '__pydantic_fields__', {})):
                            raise NameError(
                                f'Field "{ann_name}" conflicts with member {getattr(b, ann_name)}'
                                f' of protected namespace "{protected_namespace}".'
                            )
                else:
                    valid_namespaces = ()
                    for pn in config_wrapper.protected_namespaces:
                        if isinstance(pn, Pattern):
                            if not pn.match(ann_name):
                                valid_namespaces += (f're.compile({pn.pattern})',)
                        else:
                            if not ann_name.startswith(pn):
                                valid_namespaces += (pn,)

                    warnings.warn(
                        f'Field "{ann_name}" in {cls.__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 _typing_extra.is_classvar_annotation(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" warnings
        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 warn when "shadowing" of attributes in parametrized generics
                    continue

                if ann_name in dataclass_fields:
                    # Don't warn when inheriting stdlib dataclasses whose fields are "shadowed" by defaults being set
                    # on the class instance.
                    continue

                if ann_name not in annotations:
                    # Don't warn when a field exists in a parent class but has not been defined in the current class
                    continue

                warnings.warn(
                    f'Field name "{ann_name}" in "{cls.__qualname__}" shadows an attribute in parent '
                    f'"{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)
                field_info.evaluated = evaluated
            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
                if ann_name in parent_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(parent_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)
                    field_info.evaluated = evaluated
        else:
            _warn_on_nested_alias_in_annotation(ann_type, ann_name)
            if isinstance(default, FieldInfo_) and ismethoddescriptor(default.default):
                # the `getattr` call above triggers a call to `__get__` for descriptors, so we do
                # the same if the `= field(default=...)` form is used. Note that we only do this
                # for method descriptors for now, we might want to extend this to any descriptor
                # in the future (by simply checking for `hasattr(default.default, '__get__')`).
                default.default = default.default.__get__(None, cls)

            field_info = FieldInfo_.from_annotated_attribute(ann_type, default)
            field_info.evaluated = evaluated
            # 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)

    _update_fields_from_docstrings(cls, fields, config_wrapper)
    return fields, class_vars


def _warn_on_nested_alias_in_annotation(ann_type: type[Any], ann_name: str) -> None:
    FieldInfo = import_cached_field_info()

    args = getattr(ann_type, '__args__', None)
    if args:
        for anno_arg in args:
            if _typing_extra.is_annotated(anno_arg):
                for anno_type_arg in _typing_extra.get_args(anno_arg):
                    if isinstance(anno_type_arg, FieldInfo) and anno_type_arg.alias is not None:
                        warnings.warn(
                            f'`alias` specification on field "{ann_name}" must be set on outermost annotation to take effect.',
                            UserWarning,
                        )
                        return


def _is_finalvar_with_default_val(type_: type[Any], val: Any) -> bool:
    FieldInfo = import_cached_field_info()

    if not _typing_extra.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],
    *,
    ns_resolver: NsResolver | None = None,
    typevars_map: dict[Any, Any] | None = None,
    config_wrapper: ConfigWrapper | None = None,
) -> dict[str, FieldInfo]:
    """Collect the fields of a dataclass.

    Args:
        cls: dataclass.
        ns_resolver: Namespace resolver to use when getting dataclass annotations.
            Defaults to an empty instance.
        typevars_map: A dictionary mapping type variables to their concrete types.
        config_wrapper: The config wrapper instance.

    Returns:
        The dataclass fields.
    """
    FieldInfo_ = import_cached_field_info()

    fields: dict[str, FieldInfo] = {}
    ns_resolver = ns_resolver or NsResolver()
    dataclass_fields = cls.__dataclass_fields__

    # The logic here is similar to `_typing_extra.get_cls_type_hints`,
    # although we do it manually as stdlib dataclasses already have annotations
    # collected in each class:
    for base in reversed(cls.__mro__):
        if not dataclasses.is_dataclass(base):
            continue

        with ns_resolver.push(base):
            for ann_name, dataclass_field in dataclass_fields.items():
                if ann_name not in base.__dict__.get('__annotations__', {}):
                    # `__dataclass_fields__`contains every field, even the ones from base classes.
                    # Only collect the ones defined on `base`.
                    continue

                globalns, localns = ns_resolver.types_namespace
                ann_type, _ = _typing_extra.try_eval_type(dataclass_field.type, globalns, localns)

                if _typing_extra.is_classvar_annotation(ann_type):
                    continue

                if (
                    not dataclass_field.init
                    and dataclass_field.default is dataclasses.MISSING
                    and dataclass_field.default_factory is 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():
            # We don't pass any ns, as `field.annotation`
            # was already evaluated. TODO: is this method relevant?
            # Can't we juste use `_generics.replace_types`?
            field.apply_typevars_map(typevars_map)

    if config_wrapper is not None:
        _update_fields_from_docstrings(cls, fields, config_wrapper)

    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('__')


def takes_validated_data_argument(
    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any],
) -> TypeIs[Callable[[dict[str, Any]], Any]]:
    """Whether the provided default factory callable has a validated data parameter."""
    try:
        sig = signature(default_factory)
    except (ValueError, TypeError):
        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
        # In this case, we assume no data argument is present:
        return False

    parameters = list(sig.parameters.values())

    return len(parameters) == 1 and can_be_positional(parameters[0]) and parameters[0].default is Parameter.empty