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"""Defining fields on models."""
from __future__ import annotations as _annotations
import dataclasses
import inspect
import typing
from copy import copy
from dataclasses import Field as DataclassField
from functools import cached_property
from typing import Any, ClassVar
from warnings import warn
import annotated_types
import typing_extensions
from pydantic_core import PydanticUndefined
from typing_extensions import Literal, Unpack
from . import types
from ._internal import _decorators, _fields, _generics, _internal_dataclass, _repr, _typing_extra, _utils
from .aliases import AliasChoices, AliasPath
from .config import JsonDict
from .errors import PydanticUserError
from .warnings import PydanticDeprecatedSince20
if typing.TYPE_CHECKING:
from ._internal._repr import ReprArgs
else:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
_Unset: Any = PydanticUndefined
class _FromFieldInfoInputs(typing_extensions.TypedDict, total=False):
"""This class exists solely to add type checking for the `**kwargs` in `FieldInfo.from_field`."""
annotation: type[Any] | None
default_factory: typing.Callable[[], Any] | None
alias: str | None
alias_priority: int | None
validation_alias: str | AliasPath | AliasChoices | None
serialization_alias: str | None
title: str | None
description: str | None
examples: list[Any] | None
exclude: bool | None
gt: float | None
ge: float | None
lt: float | None
le: float | None
multiple_of: float | None
strict: bool | None
min_length: int | None
max_length: int | None
pattern: str | None
allow_inf_nan: bool | None
max_digits: int | None
decimal_places: int | None
union_mode: Literal['smart', 'left_to_right'] | None
discriminator: str | types.Discriminator | None
json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None
frozen: bool | None
validate_default: bool | None
repr: bool
init: bool | None
init_var: bool | None
kw_only: bool | None
class _FieldInfoInputs(_FromFieldInfoInputs, total=False):
"""This class exists solely to add type checking for the `**kwargs` in `FieldInfo.__init__`."""
default: Any
class FieldInfo(_repr.Representation):
"""This class holds information about a field.
`FieldInfo` is used for any field definition regardless of whether the [`Field()`][pydantic.fields.Field]
function is explicitly used.
!!! warning
You generally shouldn't be creating `FieldInfo` directly, you'll only need to use it when accessing
[`BaseModel`][pydantic.main.BaseModel] `.model_fields` internals.
Attributes:
annotation: The type annotation of the field.
default: The default value of the field.
default_factory: The factory function used to construct the default for the field.
alias: The alias name of the field.
alias_priority: The priority of the field's alias.
validation_alias: The validation alias of the field.
serialization_alias: The serialization alias of the field.
title: The title of the field.
description: The description of the field.
examples: List of examples of the field.
exclude: Whether to exclude the field from the model serialization.
discriminator: Field name or Discriminator for discriminating the type in a tagged union.
json_schema_extra: A dict or callable to provide extra JSON schema properties.
frozen: Whether the field is frozen.
validate_default: Whether to validate the default value of the field.
repr: Whether to include the field in representation of the model.
init: Whether the field should be included in the constructor of the dataclass.
init_var: Whether the field should _only_ be included in the constructor of the dataclass, and not stored.
kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass.
metadata: List of metadata constraints.
"""
annotation: type[Any] | None
default: Any
default_factory: typing.Callable[[], Any] | None
alias: str | None
alias_priority: int | None
validation_alias: str | AliasPath | AliasChoices | None
serialization_alias: str | None
title: str | None
description: str | None
examples: list[Any] | None
exclude: bool | None
discriminator: str | types.Discriminator | None
json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None
frozen: bool | None
validate_default: bool | None
repr: bool
init: bool | None
init_var: bool | None
kw_only: bool | None
metadata: list[Any]
__slots__ = (
'annotation',
'default',
'default_factory',
'alias',
'alias_priority',
'validation_alias',
'serialization_alias',
'title',
'description',
'examples',
'exclude',
'discriminator',
'json_schema_extra',
'frozen',
'validate_default',
'repr',
'init',
'init_var',
'kw_only',
'metadata',
'_attributes_set',
)
# used to convert kwargs to metadata/constraints,
# None has a special meaning - these items are collected into a `PydanticGeneralMetadata`
metadata_lookup: ClassVar[dict[str, typing.Callable[[Any], Any] | None]] = {
'strict': types.Strict,
'gt': annotated_types.Gt,
'ge': annotated_types.Ge,
'lt': annotated_types.Lt,
'le': annotated_types.Le,
'multiple_of': annotated_types.MultipleOf,
'min_length': annotated_types.MinLen,
'max_length': annotated_types.MaxLen,
'pattern': None,
'allow_inf_nan': None,
'max_digits': None,
'decimal_places': None,
'union_mode': None,
}
def __init__(self, **kwargs: Unpack[_FieldInfoInputs]) -> None:
"""This class should generally not be initialized directly; instead, use the `pydantic.fields.Field` function
or one of the constructor classmethods.
See the signature of `pydantic.fields.Field` for more details about the expected arguments.
"""
self._attributes_set = {k: v for k, v in kwargs.items() if v is not _Unset}
kwargs = {k: _DefaultValues.get(k) if v is _Unset else v for k, v in kwargs.items()} # type: ignore
self.annotation, annotation_metadata = self._extract_metadata(kwargs.get('annotation'))
default = kwargs.pop('default', PydanticUndefined)
if default is Ellipsis:
self.default = PydanticUndefined
else:
self.default = default
self.default_factory = kwargs.pop('default_factory', None)
if self.default is not PydanticUndefined and self.default_factory is not None:
raise TypeError('cannot specify both default and default_factory')
self.title = kwargs.pop('title', None)
self.alias = kwargs.pop('alias', None)
self.validation_alias = kwargs.pop('validation_alias', None)
self.serialization_alias = kwargs.pop('serialization_alias', None)
alias_is_set = any(alias is not None for alias in (self.alias, self.validation_alias, self.serialization_alias))
self.alias_priority = kwargs.pop('alias_priority', None) or 2 if alias_is_set else None
self.description = kwargs.pop('description', None)
self.examples = kwargs.pop('examples', None)
self.exclude = kwargs.pop('exclude', None)
self.discriminator = kwargs.pop('discriminator', None)
self.repr = kwargs.pop('repr', True)
self.json_schema_extra = kwargs.pop('json_schema_extra', None)
self.validate_default = kwargs.pop('validate_default', None)
self.frozen = kwargs.pop('frozen', None)
# currently only used on dataclasses
self.init = kwargs.pop('init', None)
self.init_var = kwargs.pop('init_var', None)
self.kw_only = kwargs.pop('kw_only', None)
self.metadata = self._collect_metadata(kwargs) + annotation_metadata # type: ignore
@staticmethod
def from_field(default: Any = PydanticUndefined, **kwargs: Unpack[_FromFieldInfoInputs]) -> FieldInfo:
"""Create a new `FieldInfo` object with the `Field` function.
Args:
default: The default value for the field. Defaults to Undefined.
**kwargs: Additional arguments dictionary.
Raises:
TypeError: If 'annotation' is passed as a keyword argument.
Returns:
A new FieldInfo object with the given parameters.
Example:
This is how you can create a field with default value like this:
```python
import pydantic
class MyModel(pydantic.BaseModel):
foo: int = pydantic.Field(4)
```
"""
if 'annotation' in kwargs:
raise TypeError('"annotation" is not permitted as a Field keyword argument')
return FieldInfo(default=default, **kwargs)
@staticmethod
def from_annotation(annotation: type[Any]) -> FieldInfo:
"""Creates a `FieldInfo` instance from a bare annotation.
This function is used internally to create a `FieldInfo` from a bare annotation like this:
```python
import pydantic
class MyModel(pydantic.BaseModel):
foo: int # <-- like this
```
We also account for the case where the annotation can be an instance of `Annotated` and where
one of the (not first) arguments in `Annotated` is an instance of `FieldInfo`, e.g.:
```python
import annotated_types
from typing_extensions import Annotated
import pydantic
class MyModel(pydantic.BaseModel):
foo: Annotated[int, annotated_types.Gt(42)]
bar: Annotated[int, pydantic.Field(gt=42)]
```
Args:
annotation: An annotation object.
Returns:
An instance of the field metadata.
"""
final = False
if _typing_extra.is_finalvar(annotation):
final = True
if annotation is not typing_extensions.Final:
annotation = typing_extensions.get_args(annotation)[0]
if _typing_extra.is_annotated(annotation):
first_arg, *extra_args = typing_extensions.get_args(annotation)
if _typing_extra.is_finalvar(first_arg):
final = True
field_info_annotations = [a for a in extra_args if isinstance(a, FieldInfo)]
field_info = FieldInfo.merge_field_infos(*field_info_annotations, annotation=first_arg)
if field_info:
new_field_info = copy(field_info)
new_field_info.annotation = first_arg
new_field_info.frozen = final or field_info.frozen
metadata: list[Any] = []
for a in extra_args:
if not isinstance(a, FieldInfo):
metadata.append(a)
else:
metadata.extend(a.metadata)
new_field_info.metadata = metadata
return new_field_info
return FieldInfo(annotation=annotation, frozen=final or None)
@staticmethod
def from_annotated_attribute(annotation: type[Any], default: Any) -> FieldInfo:
"""Create `FieldInfo` from an annotation with a default value.
This is used in cases like the following:
```python
import annotated_types
from typing_extensions import Annotated
import pydantic
class MyModel(pydantic.BaseModel):
foo: int = 4 # <-- like this
bar: Annotated[int, annotated_types.Gt(4)] = 4 # <-- or this
spam: Annotated[int, pydantic.Field(gt=4)] = 4 # <-- or this
```
Args:
annotation: The type annotation of the field.
default: The default value of the field.
Returns:
A field object with the passed values.
"""
if annotation is default:
raise PydanticUserError(
'Error when building FieldInfo from annotated attribute. '
"Make sure you don't have any field name clashing with a type annotation ",
code='unevaluable-type-annotation',
)
final = False
if _typing_extra.is_finalvar(annotation):
final = True
if annotation is not typing_extensions.Final:
annotation = typing_extensions.get_args(annotation)[0]
if isinstance(default, FieldInfo):
default.annotation, annotation_metadata = FieldInfo._extract_metadata(annotation)
default.metadata += annotation_metadata
default = default.merge_field_infos(
*[x for x in annotation_metadata if isinstance(x, FieldInfo)], default, annotation=default.annotation
)
default.frozen = final or default.frozen
return default
elif isinstance(default, dataclasses.Field):
init_var = False
if annotation is dataclasses.InitVar:
init_var = True
annotation = Any
elif isinstance(annotation, dataclasses.InitVar):
init_var = True
annotation = annotation.type
pydantic_field = FieldInfo._from_dataclass_field(default)
pydantic_field.annotation, annotation_metadata = FieldInfo._extract_metadata(annotation)
pydantic_field.metadata += annotation_metadata
pydantic_field = pydantic_field.merge_field_infos(
*[x for x in annotation_metadata if isinstance(x, FieldInfo)],
pydantic_field,
annotation=pydantic_field.annotation,
)
pydantic_field.frozen = final or pydantic_field.frozen
pydantic_field.init_var = init_var
pydantic_field.init = getattr(default, 'init', None)
pydantic_field.kw_only = getattr(default, 'kw_only', None)
return pydantic_field
else:
if _typing_extra.is_annotated(annotation):
first_arg, *extra_args = typing_extensions.get_args(annotation)
field_infos = [a for a in extra_args if isinstance(a, FieldInfo)]
field_info = FieldInfo.merge_field_infos(*field_infos, annotation=first_arg, default=default)
metadata: list[Any] = []
for a in extra_args:
if not isinstance(a, FieldInfo):
metadata.append(a)
else:
metadata.extend(a.metadata)
field_info.metadata = metadata
return field_info
return FieldInfo(annotation=annotation, default=default, frozen=final or None)
@staticmethod
def merge_field_infos(*field_infos: FieldInfo, **overrides: Any) -> FieldInfo:
"""Merge `FieldInfo` instances keeping only explicitly set attributes.
Later `FieldInfo` instances override earlier ones.
Returns:
FieldInfo: A merged FieldInfo instance.
"""
flattened_field_infos: list[FieldInfo] = []
for field_info in field_infos:
flattened_field_infos.extend(x for x in field_info.metadata if isinstance(x, FieldInfo))
flattened_field_infos.append(field_info)
field_infos = tuple(flattened_field_infos)
if len(field_infos) == 1:
# No merging necessary, but we still need to make a copy and apply the overrides
field_info = copy(field_infos[0])
field_info._attributes_set.update(overrides)
for k, v in overrides.items():
setattr(field_info, k, v)
return field_info # type: ignore
new_kwargs: dict[str, Any] = {}
metadata = {}
for field_info in field_infos:
new_kwargs.update(field_info._attributes_set)
for x in field_info.metadata:
if not isinstance(x, FieldInfo):
metadata[type(x)] = x
new_kwargs.update(overrides)
field_info = FieldInfo(**new_kwargs)
field_info.metadata = list(metadata.values())
return field_info
@staticmethod
def _from_dataclass_field(dc_field: DataclassField[Any]) -> FieldInfo:
"""Return a new `FieldInfo` instance from a `dataclasses.Field` instance.
Args:
dc_field: The `dataclasses.Field` instance to convert.
Returns:
The corresponding `FieldInfo` instance.
Raises:
TypeError: If any of the `FieldInfo` kwargs does not match the `dataclass.Field` kwargs.
"""
default = dc_field.default
if default is dataclasses.MISSING:
default = PydanticUndefined
if dc_field.default_factory is dataclasses.MISSING:
default_factory: typing.Callable[[], Any] | None = None
else:
default_factory = dc_field.default_factory
# use the `Field` function so in correct kwargs raise the correct `TypeError`
dc_field_metadata = {k: v for k, v in dc_field.metadata.items() if k in _FIELD_ARG_NAMES}
return Field(default=default, default_factory=default_factory, repr=dc_field.repr, **dc_field_metadata)
@staticmethod
def _extract_metadata(annotation: type[Any] | None) -> tuple[type[Any] | None, list[Any]]:
"""Tries to extract metadata/constraints from an annotation if it uses `Annotated`.
Args:
annotation: The type hint annotation for which metadata has to be extracted.
Returns:
A tuple containing the extracted metadata type and the list of extra arguments.
"""
if annotation is not None:
if _typing_extra.is_annotated(annotation):
first_arg, *extra_args = typing_extensions.get_args(annotation)
return first_arg, list(extra_args)
return annotation, []
@staticmethod
def _collect_metadata(kwargs: dict[str, Any]) -> list[Any]:
"""Collect annotations from kwargs.
Args:
kwargs: Keyword arguments passed to the function.
Returns:
A list of metadata objects - a combination of `annotated_types.BaseMetadata` and
`PydanticMetadata`.
"""
metadata: list[Any] = []
general_metadata = {}
for key, value in list(kwargs.items()):
try:
marker = FieldInfo.metadata_lookup[key]
except KeyError:
continue
del kwargs[key]
if value is not None:
if marker is None:
general_metadata[key] = value
else:
metadata.append(marker(value))
if general_metadata:
metadata.append(_fields.pydantic_general_metadata(**general_metadata))
return metadata
def get_default(self, *, call_default_factory: bool = False) -> Any:
"""Get the default value.
We expose an option for whether to call the default_factory (if present), as calling it may
result in side effects that we want to avoid. However, there are times when it really should
be called (namely, when instantiating a model via `model_construct`).
Args:
call_default_factory: Whether to call the default_factory or not. Defaults to `False`.
Returns:
The default value, calling the default factory if requested or `None` if not set.
"""
if self.default_factory is None:
return _utils.smart_deepcopy(self.default)
elif call_default_factory:
return self.default_factory()
else:
return None
def is_required(self) -> bool:
"""Check if the field is required (i.e., does not have a default value or factory).
Returns:
`True` if the field is required, `False` otherwise.
"""
return self.default is PydanticUndefined and self.default_factory is None
def rebuild_annotation(self) -> Any:
"""Attempts to rebuild the original annotation for use in function signatures.
If metadata is present, it adds it to the original annotation using
`Annotated`. Otherwise, it returns the original annotation as-is.
Note that because the metadata has been flattened, the original annotation
may not be reconstructed exactly as originally provided, e.g. if the original
type had unrecognized annotations, or was annotated with a call to `pydantic.Field`.
Returns:
The rebuilt annotation.
"""
if not self.metadata:
return self.annotation
else:
# Annotated arguments must be a tuple
return typing_extensions.Annotated[(self.annotation, *self.metadata)] # type: ignore
def apply_typevars_map(self, typevars_map: dict[Any, Any] | None, types_namespace: dict[str, Any] | None) -> None:
"""Apply a `typevars_map` to the annotation.
This method is used when analyzing parametrized generic types to replace typevars with their concrete types.
This method applies the `typevars_map` to the annotation in place.
Args:
typevars_map: A dictionary mapping type variables to their concrete types.
types_namespace (dict | None): A dictionary containing related types to the annotated type.
See Also:
pydantic._internal._generics.replace_types is used for replacing the typevars with
their concrete types.
"""
annotation = _typing_extra.eval_type_lenient(self.annotation, types_namespace)
self.annotation = _generics.replace_types(annotation, typevars_map)
def __repr_args__(self) -> ReprArgs:
yield 'annotation', _repr.PlainRepr(_repr.display_as_type(self.annotation))
yield 'required', self.is_required()
for s in self.__slots__:
if s == '_attributes_set':
continue
if s == 'annotation':
continue
elif s == 'metadata' and not self.metadata:
continue
elif s == 'repr' and self.repr is True:
continue
if s == 'frozen' and self.frozen is False:
continue
if s == 'validation_alias' and self.validation_alias == self.alias:
continue
if s == 'serialization_alias' and self.serialization_alias == self.alias:
continue
if s == 'default_factory' and self.default_factory is not None:
yield 'default_factory', _repr.PlainRepr(_repr.display_as_type(self.default_factory))
else:
value = getattr(self, s)
if value is not None and value is not PydanticUndefined:
yield s, value
class _EmptyKwargs(typing_extensions.TypedDict):
"""This class exists solely to ensure that type checking warns about passing `**extra` in `Field`."""
_DefaultValues = dict(
default=...,
default_factory=None,
alias=None,
alias_priority=None,
validation_alias=None,
serialization_alias=None,
title=None,
description=None,
examples=None,
exclude=None,
discriminator=None,
json_schema_extra=None,
frozen=None,
validate_default=None,
repr=True,
init=None,
init_var=None,
kw_only=None,
pattern=None,
strict=None,
gt=None,
ge=None,
lt=None,
le=None,
multiple_of=None,
allow_inf_nan=None,
max_digits=None,
decimal_places=None,
min_length=None,
max_length=None,
)
def Field( # noqa: C901
default: Any = PydanticUndefined,
*,
default_factory: typing.Callable[[], Any] | None = _Unset,
alias: str | None = _Unset,
alias_priority: int | None = _Unset,
validation_alias: str | AliasPath | AliasChoices | None = _Unset,
serialization_alias: str | None = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
examples: list[Any] | None = _Unset,
exclude: bool | None = _Unset,
discriminator: str | types.Discriminator | None = _Unset,
json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None = _Unset,
frozen: bool | None = _Unset,
validate_default: bool | None = _Unset,
repr: bool = _Unset,
init: bool | None = _Unset,
init_var: bool | None = _Unset,
kw_only: bool | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
union_mode: Literal['smart', 'left_to_right'] = _Unset,
**extra: Unpack[_EmptyKwargs],
) -> Any:
"""Usage docs: https://docs.pydantic.dev/2.6/concepts/fields
Create a field for objects that can be configured.
Used to provide extra information about a field, either for the model schema or complex validation. Some arguments
apply only to number fields (`int`, `float`, `Decimal`) and some apply only to `str`.
Note:
- Any `_Unset` objects will be replaced by the corresponding value defined in the `_DefaultValues` dictionary. If a key for the `_Unset` object is not found in the `_DefaultValues` dictionary, it will default to `None`
Args:
default: Default value if the field is not set.
default_factory: A callable to generate the default value, such as :func:`~datetime.utcnow`.
alias: The name to use for the attribute when validating or serializing by alias.
This is often used for things like converting between snake and camel case.
alias_priority: Priority of the alias. This affects whether an alias generator is used.
validation_alias: Like `alias`, but only affects validation, not serialization.
serialization_alias: Like `alias`, but only affects serialization, not validation.
title: Human-readable title.
description: Human-readable description.
examples: Example values for this field.
exclude: Whether to exclude the field from the model serialization.
discriminator: Field name or Discriminator for discriminating the type in a tagged union.
json_schema_extra: A dict or callable to provide extra JSON schema properties.
frozen: Whether the field is frozen. If true, attempts to change the value on an instance will raise an error.
validate_default: If `True`, apply validation to the default value every time you create an instance.
Otherwise, for performance reasons, the default value of the field is trusted and not validated.
repr: A boolean indicating whether to include the field in the `__repr__` output.
init: Whether the field should be included in the constructor of the dataclass.
(Only applies to dataclasses.)
init_var: Whether the field should _only_ be included in the constructor of the dataclass.
(Only applies to dataclasses.)
kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass.
(Only applies to dataclasses.)
strict: If `True`, strict validation is applied to the field.
See [Strict Mode](../concepts/strict_mode.md) for details.
gt: Greater than. If set, value must be greater than this. Only applicable to numbers.
ge: Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers.
lt: Less than. If set, value must be less than this. Only applicable to numbers.
le: Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers.
multiple_of: Value must be a multiple of this. Only applicable to numbers.
min_length: Minimum length for strings.
max_length: Maximum length for strings.
pattern: Pattern for strings (a regular expression).
allow_inf_nan: Allow `inf`, `-inf`, `nan`. Only applicable to numbers.
max_digits: Maximum number of allow digits for strings.
decimal_places: Maximum number of decimal places allowed for numbers.
union_mode: The strategy to apply when validating a union. Can be `smart` (the default), or `left_to_right`.
See [Union Mode](standard_library_types.md#union-mode) for details.
extra: (Deprecated) Extra fields that will be included in the JSON schema.
!!! warning Deprecated
The `extra` kwargs is deprecated. Use `json_schema_extra` instead.
Returns:
A new [`FieldInfo`][pydantic.fields.FieldInfo]. The return annotation is `Any` so `Field` can be used on
type-annotated fields without causing a type error.
"""
# Check deprecated and removed params from V1. This logic should eventually be removed.
const = extra.pop('const', None) # type: ignore
if const is not None:
raise PydanticUserError('`const` is removed, use `Literal` instead', code='removed-kwargs')
min_items = extra.pop('min_items', None) # type: ignore
if min_items is not None:
warn('`min_items` is deprecated and will be removed, use `min_length` instead', DeprecationWarning)
if min_length in (None, _Unset):
min_length = min_items # type: ignore
max_items = extra.pop('max_items', None) # type: ignore
if max_items is not None:
warn('`max_items` is deprecated and will be removed, use `max_length` instead', DeprecationWarning)
if max_length in (None, _Unset):
max_length = max_items # type: ignore
unique_items = extra.pop('unique_items', None) # type: ignore
if unique_items is not None:
raise PydanticUserError(
(
'`unique_items` is removed, use `Set` instead'
'(this feature is discussed in https://github.com/pydantic/pydantic-core/issues/296)'
),
code='removed-kwargs',
)
allow_mutation = extra.pop('allow_mutation', None) # type: ignore
if allow_mutation is not None:
warn('`allow_mutation` is deprecated and will be removed. use `frozen` instead', DeprecationWarning)
if allow_mutation is False:
frozen = True
regex = extra.pop('regex', None) # type: ignore
if regex is not None:
raise PydanticUserError('`regex` is removed. use `pattern` instead', code='removed-kwargs')
if extra:
warn(
'Using extra keyword arguments on `Field` is deprecated and will be removed.'
' Use `json_schema_extra` instead.'
f' (Extra keys: {", ".join(k.__repr__() for k in extra.keys())})',
DeprecationWarning,
)
if not json_schema_extra or json_schema_extra is _Unset:
json_schema_extra = extra # type: ignore
if (
validation_alias
and validation_alias is not _Unset
and not isinstance(validation_alias, (str, AliasChoices, AliasPath))
):
raise TypeError('Invalid `validation_alias` type. it should be `str`, `AliasChoices`, or `AliasPath`')
if serialization_alias in (_Unset, None) and isinstance(alias, str):
serialization_alias = alias
if validation_alias in (_Unset, None):
validation_alias = alias
include = extra.pop('include', None) # type: ignore
if include is not None:
warn('`include` is deprecated and does nothing. It will be removed, use `exclude` instead', DeprecationWarning)
return FieldInfo.from_field(
default,
default_factory=default_factory,
alias=alias,
alias_priority=alias_priority,
validation_alias=validation_alias,
serialization_alias=serialization_alias,
title=title,
description=description,
examples=examples,
exclude=exclude,
discriminator=discriminator,
json_schema_extra=json_schema_extra,
frozen=frozen,
pattern=pattern,
validate_default=validate_default,
repr=repr,
init=init,
init_var=init_var,
kw_only=kw_only,
strict=strict,
gt=gt,
ge=ge,
lt=lt,
le=le,
multiple_of=multiple_of,
min_length=min_length,
max_length=max_length,
allow_inf_nan=allow_inf_nan,
max_digits=max_digits,
decimal_places=decimal_places,
union_mode=union_mode,
)
_FIELD_ARG_NAMES = set(inspect.signature(Field).parameters)
_FIELD_ARG_NAMES.remove('extra') # do not include the varkwargs parameter
class ModelPrivateAttr(_repr.Representation):
"""A descriptor for private attributes in class models.
!!! warning
You generally shouldn't be creating `ModelPrivateAttr` instances directly, instead use
`pydantic.fields.PrivateAttr`. (This is similar to `FieldInfo` vs. `Field`.)
Attributes:
default: The default value of the attribute if not provided.
default_factory: A callable function that generates the default value of the
attribute if not provided.
"""
__slots__ = 'default', 'default_factory'
def __init__(
self, default: Any = PydanticUndefined, *, default_factory: typing.Callable[[], Any] | None = None
) -> None:
self.default = default
self.default_factory = default_factory
if not typing.TYPE_CHECKING:
# We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
def __getattr__(self, item: str) -> Any:
"""This function improves compatibility with custom descriptors by ensuring delegation happens
as expected when the default value of a private attribute is a descriptor.
"""
if item in {'__get__', '__set__', '__delete__'}:
if hasattr(self.default, item):
return getattr(self.default, item)
raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
def __set_name__(self, cls: type[Any], name: str) -> None:
"""Preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487."""
if self.default is PydanticUndefined:
return
if not hasattr(self.default, '__set_name__'):
return
set_name = self.default.__set_name__
if callable(set_name):
set_name(cls, name)
def get_default(self) -> Any:
"""Retrieve the default value of the object.
If `self.default_factory` is `None`, the method will return a deep copy of the `self.default` object.
If `self.default_factory` is not `None`, it will call `self.default_factory` and return the value returned.
Returns:
The default value of the object.
"""
return _utils.smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
def __eq__(self, other: Any) -> bool:
return isinstance(other, self.__class__) and (self.default, self.default_factory) == (
other.default,
other.default_factory,
)
def PrivateAttr(
default: Any = PydanticUndefined,
*,
default_factory: typing.Callable[[], Any] | None = None,
) -> Any:
"""Usage docs: https://docs.pydantic.dev/2.6/concepts/models/#private-model-attributes
Indicates that an attribute is intended for private use and not handled during normal validation/serialization.
Private attributes are not validated by Pydantic, so it's up to you to ensure they are used in a type-safe manner.
Private attributes are stored in `__private_attributes__` on the model.
Args:
default: The attribute's default value. Defaults to Undefined.
default_factory: Callable that will be
called when a default value is needed for this attribute.
If both `default` and `default_factory` are set, an error will be raised.
Returns:
An instance of [`ModelPrivateAttr`][pydantic.fields.ModelPrivateAttr] class.
Raises:
ValueError: If both `default` and `default_factory` are set.
"""
if default is not PydanticUndefined and default_factory is not None:
raise TypeError('cannot specify both default and default_factory')
return ModelPrivateAttr(
default,
default_factory=default_factory,
)
@dataclasses.dataclass(**_internal_dataclass.slots_true)
class ComputedFieldInfo:
"""A container for data from `@computed_field` so that we can access it while building the pydantic-core schema.
Attributes:
decorator_repr: A class variable representing the decorator string, '@computed_field'.
wrapped_property: The wrapped computed field property.
return_type: The type of the computed field property's return value.
alias: The alias of the property to be used during serialization.
alias_priority: The priority of the alias. This affects whether an alias generator is used.
title: Title of the computed field to include in the serialization JSON schema.
description: Description of the computed field to include in the serialization JSON schema.
examples: Example values of the computed field to include in the serialization JSON schema.
json_schema_extra: A dict or callable to provide extra JSON schema properties.
repr: A boolean indicating whether to include the field in the __repr__ output.
"""
decorator_repr: ClassVar[str] = '@computed_field'
wrapped_property: property
return_type: Any
alias: str | None
alias_priority: int | None
title: str | None
description: str | None
examples: list[Any] | None
json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None
repr: bool
def _wrapped_property_is_private(property_: cached_property | property) -> bool: # type: ignore
"""Returns true if provided property is private, False otherwise."""
wrapped_name: str = ''
if isinstance(property_, property):
wrapped_name = getattr(property_.fget, '__name__', '')
elif isinstance(property_, cached_property): # type: ignore
wrapped_name = getattr(property_.func, '__name__', '') # type: ignore
return wrapped_name.startswith('_') and not wrapped_name.startswith('__')
# this should really be `property[T], cached_property[T]` but property is not generic unlike cached_property
# See https://github.com/python/typing/issues/985 and linked issues
PropertyT = typing.TypeVar('PropertyT')
@typing.overload
def computed_field(
*,
alias: str | None = None,
alias_priority: int | None = None,
title: str | None = None,
description: str | None = None,
examples: list[Any] | None = None,
json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None = None,
repr: bool = True,
return_type: Any = PydanticUndefined,
) -> typing.Callable[[PropertyT], PropertyT]:
...
@typing.overload
def computed_field(__func: PropertyT) -> PropertyT:
...
def computed_field(
__f: PropertyT | None = None,
*,
alias: str | None = None,
alias_priority: int | None = None,
title: str | None = None,
description: str | None = None,
examples: list[Any] | None = None,
json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None = None,
repr: bool | None = None,
return_type: Any = PydanticUndefined,
) -> PropertyT | typing.Callable[[PropertyT], PropertyT]:
"""Usage docs: https://docs.pydantic.dev/2.6/concepts/fields#the-computed_field-decorator
Decorator to include `property` and `cached_property` when serializing models or dataclasses.
This is useful for fields that are computed from other fields, or for fields that are expensive to compute and should be cached.
```py
from pydantic import BaseModel, computed_field
class Rectangle(BaseModel):
width: int
length: int
@computed_field
@property
def area(self) -> int:
return self.width * self.length
print(Rectangle(width=3, length=2).model_dump())
#> {'width': 3, 'length': 2, 'area': 6}
```
If applied to functions not yet decorated with `@property` or `@cached_property`, the function is
automatically wrapped with `property`. Although this is more concise, you will lose IntelliSense in your IDE,
and confuse static type checkers, thus explicit use of `@property` is recommended.
!!! warning "Mypy Warning"
Even with the `@property` or `@cached_property` applied to your function before `@computed_field`,
mypy may throw a `Decorated property not supported` error.
See [mypy issue #1362](https://github.com/python/mypy/issues/1362), for more information.
To avoid this error message, add `# type: ignore[misc]` to the `@computed_field` line.
[pyright](https://github.com/microsoft/pyright) supports `@computed_field` without error.
```py
import random
from pydantic import BaseModel, computed_field
class Square(BaseModel):
width: float
@computed_field
def area(self) -> float: # converted to a `property` by `computed_field`
return round(self.width**2, 2)
@area.setter
def area(self, new_area: float) -> None:
self.width = new_area**0.5
@computed_field(alias='the magic number', repr=False)
def random_number(self) -> int:
return random.randint(0, 1_000)
square = Square(width=1.3)
# `random_number` does not appear in representation
print(repr(square))
#> Square(width=1.3, area=1.69)
print(square.random_number)
#> 3
square.area = 4
print(square.model_dump_json(by_alias=True))
#> {"width":2.0,"area":4.0,"the magic number":3}
```
!!! warning "Overriding with `computed_field`"
You can't override a field from a parent class with a `computed_field` in the child class.
`mypy` complains about this behavior if allowed, and `dataclasses` doesn't allow this pattern either.
See the example below:
```py
from pydantic import BaseModel, computed_field
class Parent(BaseModel):
a: str
try:
class Child(Parent):
@computed_field
@property
def a(self) -> str:
return 'new a'
except ValueError as e:
print(repr(e))
#> ValueError("you can't override a field with a computed field")
```
Private properties decorated with `@computed_field` have `repr=False` by default.
```py
from functools import cached_property
from pydantic import BaseModel, computed_field
class Model(BaseModel):
foo: int
@computed_field
@cached_property
def _private_cached_property(self) -> int:
return -self.foo
@computed_field
@property
def _private_property(self) -> int:
return -self.foo
m = Model(foo=1)
print(repr(m))
#> M(foo=1)
```
Args:
__f: the function to wrap.
alias: alias to use when serializing this computed field, only used when `by_alias=True`
alias_priority: priority of the alias. This affects whether an alias generator is used
title: Title to use when including this computed field in JSON Schema
description: Description to use when including this computed field in JSON Schema, defaults to the function's
docstring
examples: Example values to use when including this computed field in JSON Schema
json_schema_extra: A dict or callable to provide extra JSON schema properties.
repr: whether to include this computed field in model repr.
Default is `False` for private properties and `True` for public properties.
return_type: optional return for serialization logic to expect when serializing to JSON, if included
this must be correct, otherwise a `TypeError` is raised.
If you don't include a return type Any is used, which does runtime introspection to handle arbitrary
objects.
Returns:
A proxy wrapper for the property.
"""
def dec(f: Any) -> Any:
nonlocal description, return_type, alias_priority
unwrapped = _decorators.unwrap_wrapped_function(f)
if description is None and unwrapped.__doc__:
description = inspect.cleandoc(unwrapped.__doc__)
# if the function isn't already decorated with `@property` (or another descriptor), then we wrap it now
f = _decorators.ensure_property(f)
alias_priority = (alias_priority or 2) if alias is not None else None
if repr is None:
repr_: bool = False if _wrapped_property_is_private(property_=f) else True
else:
repr_ = repr
dec_info = ComputedFieldInfo(
f, return_type, alias, alias_priority, title, description, examples, json_schema_extra, repr_
)
return _decorators.PydanticDescriptorProxy(f, dec_info)
if __f is None:
return dec
else:
return dec(__f)