Current File : //opt/cloudlinux/venv/lib64/python3.11/site-packages/pydantic/_internal/_dataclasses.py
"""Private logic for creating pydantic dataclasses."""
from __future__ import annotations as _annotations
import dataclasses
import inspect
import typing
import warnings
from functools import partial, wraps
from inspect import Parameter, Signature, signature
from typing import Any, Callable, ClassVar
from pydantic_core import (
ArgsKwargs,
PydanticUndefined,
SchemaSerializer,
SchemaValidator,
core_schema,
)
from typing_extensions import TypeGuard
from ..errors import PydanticUndefinedAnnotation
from ..fields import FieldInfo
from ..plugin._schema_validator import create_schema_validator
from ..warnings import PydanticDeprecatedSince20
from . import _config, _decorators, _discriminated_union, _typing_extra
from ._core_utils import collect_invalid_schemas, simplify_schema_references, validate_core_schema
from ._fields import collect_dataclass_fields
from ._generate_schema import GenerateSchema
from ._generics import get_standard_typevars_map
from ._mock_val_ser import set_dataclass_mock_validator
from ._schema_generation_shared import CallbackGetCoreSchemaHandler
from ._utils import is_valid_identifier
if typing.TYPE_CHECKING:
from ..config import ConfigDict
class StandardDataclass(typing.Protocol):
__dataclass_fields__: ClassVar[dict[str, Any]]
__dataclass_params__: ClassVar[Any] # in reality `dataclasses._DataclassParams`
__post_init__: ClassVar[Callable[..., None]]
def __init__(self, *args: object, **kwargs: object) -> None:
pass
class PydanticDataclass(StandardDataclass, typing.Protocol):
"""A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass.
Attributes:
__pydantic_config__: Pydantic-specific configuration settings for the dataclass.
__pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_decorators__: Metadata containing the decorators defined on the dataclass.
__pydantic_fields__: Metadata about the fields defined on the dataclass.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass.
"""
__pydantic_config__: ClassVar[ConfigDict]
__pydantic_complete__: ClassVar[bool]
__pydantic_core_schema__: ClassVar[core_schema.CoreSchema]
__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos]
__pydantic_fields__: ClassVar[dict[str, FieldInfo]]
__pydantic_serializer__: ClassVar[SchemaSerializer]
__pydantic_validator__: ClassVar[SchemaValidator]
else:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
def set_dataclass_fields(cls: type[StandardDataclass], types_namespace: dict[str, Any] | None = None) -> None:
"""Collect and set `cls.__pydantic_fields__`.
Args:
cls: The class.
types_namespace: The types namespace, defaults to `None`.
"""
typevars_map = get_standard_typevars_map(cls)
fields = collect_dataclass_fields(cls, types_namespace, typevars_map=typevars_map)
cls.__pydantic_fields__ = fields # type: ignore
def complete_dataclass(
cls: type[Any],
config_wrapper: _config.ConfigWrapper,
*,
raise_errors: bool = True,
types_namespace: dict[str, Any] | None,
) -> bool:
"""Finish building a pydantic dataclass.
This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`.
This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`.
Args:
cls: The class.
config_wrapper: The config wrapper instance.
raise_errors: Whether to raise errors, defaults to `True`.
types_namespace: The types namespace.
Returns:
`True` if building a pydantic dataclass is successfully completed, `False` otherwise.
Raises:
PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations.
"""
if hasattr(cls, '__post_init_post_parse__'):
warnings.warn(
'Support for `__post_init_post_parse__` has been dropped, the method will not be called', DeprecationWarning
)
if types_namespace is None:
types_namespace = _typing_extra.get_cls_types_namespace(cls)
set_dataclass_fields(cls, types_namespace)
typevars_map = get_standard_typevars_map(cls)
gen_schema = GenerateSchema(
config_wrapper,
types_namespace,
typevars_map,
)
# dataclass.__init__ must be defined here so its `__qualname__` can be changed since functions can't be copied.
def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) -> None:
__tracebackhide__ = True
s = __dataclass_self__
s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
__init__.__qualname__ = f'{cls.__qualname__}.__init__'
sig = generate_dataclass_signature(cls)
cls.__init__ = __init__ # type: ignore
cls.__signature__ = sig # type: ignore
cls.__pydantic_config__ = config_wrapper.config_dict # type: ignore
get_core_schema = getattr(cls, '__get_pydantic_core_schema__', None)
try:
if get_core_schema:
schema = get_core_schema(
cls,
CallbackGetCoreSchemaHandler(
partial(gen_schema.generate_schema, from_dunder_get_core_schema=False),
gen_schema,
ref_mode='unpack',
),
)
else:
schema = gen_schema.generate_schema(cls, from_dunder_get_core_schema=False)
except PydanticUndefinedAnnotation as e:
if raise_errors:
raise
set_dataclass_mock_validator(cls, cls.__name__, f'`{e.name}`')
return False
core_config = config_wrapper.core_config(cls)
schema = gen_schema.collect_definitions(schema)
if collect_invalid_schemas(schema):
set_dataclass_mock_validator(cls, cls.__name__, 'all referenced types')
return False
schema = _discriminated_union.apply_discriminators(simplify_schema_references(schema))
# We are about to set all the remaining required properties expected for this cast;
# __pydantic_decorators__ and __pydantic_fields__ should already be set
cls = typing.cast('type[PydanticDataclass]', cls)
# debug(schema)
cls.__pydantic_core_schema__ = schema = validate_core_schema(schema)
cls.__pydantic_validator__ = validator = create_schema_validator(
schema, core_config, config_wrapper.plugin_settings
)
cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config)
if config_wrapper.validate_assignment:
@wraps(cls.__setattr__)
def validated_setattr(instance: Any, __field: str, __value: str) -> None:
validator.validate_assignment(instance, __field, __value)
cls.__setattr__ = validated_setattr.__get__(None, cls) # type: ignore
return True
def generate_dataclass_signature(cls: type[StandardDataclass]) -> Signature:
"""Generate signature for a pydantic dataclass.
This implementation assumes we do not support custom `__init__`, which is currently true for pydantic dataclasses.
If we change this eventually, we should make this function's logic more closely mirror that from
`pydantic._internal._model_construction.generate_model_signature`.
Args:
cls: The dataclass.
Returns:
The signature.
"""
sig = signature(cls)
final_params: dict[str, Parameter] = {}
for param in sig.parameters.values():
param_default = param.default
if isinstance(param_default, FieldInfo):
annotation = param.annotation
# Replace the annotation if appropriate
# inspect does "clever" things to show annotations as strings because we have
# `from __future__ import annotations` in main, we don't want that
if annotation == 'Any':
annotation = Any
# Replace the field name with the alias if present
name = param.name
alias = param_default.alias
validation_alias = param_default.validation_alias
if validation_alias is None and isinstance(alias, str) and is_valid_identifier(alias):
name = alias
elif isinstance(validation_alias, str) and is_valid_identifier(validation_alias):
name = validation_alias
# Replace the field default
default = param_default.default
if default is PydanticUndefined:
if param_default.default_factory is PydanticUndefined:
default = inspect.Signature.empty
else:
# this is used by dataclasses to indicate a factory exists:
default = dataclasses._HAS_DEFAULT_FACTORY # type: ignore
param = param.replace(annotation=annotation, name=name, default=default)
final_params[param.name] = param
return Signature(parameters=list(final_params.values()), return_annotation=None)
def is_builtin_dataclass(_cls: type[Any]) -> TypeGuard[type[StandardDataclass]]:
"""Returns True if a class is a stdlib dataclass and *not* a pydantic dataclass.
We check that
- `_cls` is a dataclass
- `_cls` does not inherit from a processed pydantic dataclass (and thus have a `__pydantic_validator__`)
- `_cls` does not have any annotations that are not dataclass fields
e.g.
```py
import dataclasses
import pydantic.dataclasses
@dataclasses.dataclass
class A:
x: int
@pydantic.dataclasses.dataclass
class B(A):
y: int
```
In this case, when we first check `B`, we make an extra check and look at the annotations ('y'),
which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x')
Args:
cls: The class.
Returns:
`True` if the class is a stdlib dataclass, `False` otherwise.
"""
return (
dataclasses.is_dataclass(_cls)
and not hasattr(_cls, '__pydantic_validator__')
and set(_cls.__dataclass_fields__).issuperset(set(getattr(_cls, '__annotations__', {})))
)
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