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ORPA-pyOpenRPA/Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/pydantic/fields.py

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Start to go to fast api (with backward compatibility)
2 years ago
import copy
import re
from collections import Counter as CollectionCounter, defaultdict, deque
from collections.abc import Callable, Hashable as CollectionsHashable, Iterable as CollectionsIterable
from typing import (
TYPE_CHECKING,
Any,
Counter,
DefaultDict,
Deque,
Dict,
ForwardRef,
FrozenSet,
Generator,
Iterable,
Iterator,
List,
Mapping,
Optional,
Pattern,
Sequence,
Set,
Tuple,
Type,
TypeVar,
Union,
)
from typing_extensions import Annotated, Final
from . import errors as errors_
from .class_validators import Validator, make_generic_validator, prep_validators
from .error_wrappers import ErrorWrapper
from .errors import ConfigError, InvalidDiscriminator, MissingDiscriminator, NoneIsNotAllowedError
from .types import Json, JsonWrapper
from .typing import (
NoArgAnyCallable,
convert_generics,
display_as_type,
get_args,
get_origin,
is_finalvar,
is_literal_type,
is_new_type,
is_none_type,
is_typeddict,
is_typeddict_special,
is_union,
new_type_supertype,
)
from .utils import (
PyObjectStr,
Representation,
ValueItems,
get_discriminator_alias_and_values,
get_unique_discriminator_alias,
lenient_isinstance,
lenient_issubclass,
sequence_like,
smart_deepcopy,
)
from .validators import constant_validator, dict_validator, find_validators, validate_json
Required: Any = Ellipsis
T = TypeVar('T')
class UndefinedType:
def __repr__(self) -> str:
return 'PydanticUndefined'
def __copy__(self: T) -> T:
return self
def __reduce__(self) -> str:
return 'Undefined'
def __deepcopy__(self: T, _: Any) -> T:
return self
Undefined = UndefinedType()
if TYPE_CHECKING:
from .class_validators import ValidatorsList
from .config import BaseConfig
from .error_wrappers import ErrorList
from .types import ModelOrDc
from .typing import AbstractSetIntStr, MappingIntStrAny, ReprArgs
ValidateReturn = Tuple[Optional[Any], Optional[ErrorList]]
LocStr = Union[Tuple[Union[int, str], ...], str]
BoolUndefined = Union[bool, UndefinedType]
class FieldInfo(Representation):
"""
Captures extra information about a field.
"""
__slots__ = (
'default',
'default_factory',
'alias',
'alias_priority',
'title',
'description',
'exclude',
'include',
'const',
'gt',
'ge',
'lt',
'le',
'multiple_of',
'allow_inf_nan',
'max_digits',
'decimal_places',
'min_items',
'max_items',
'unique_items',
'min_length',
'max_length',
'allow_mutation',
'repr',
'regex',
'discriminator',
'extra',
)
# field constraints with the default value, it's also used in update_from_config below
__field_constraints__ = {
'min_length': None,
'max_length': None,
'regex': None,
'gt': None,
'lt': None,
'ge': None,
'le': None,
'multiple_of': None,
'allow_inf_nan': None,
'max_digits': None,
'decimal_places': None,
'min_items': None,
'max_items': None,
'unique_items': None,
'allow_mutation': True,
}
def __init__(self, default: Any = Undefined, **kwargs: Any) -> None:
self.default = default
self.default_factory = kwargs.pop('default_factory', None)
self.alias = kwargs.pop('alias', None)
self.alias_priority = kwargs.pop('alias_priority', 2 if self.alias is not None else None)
self.title = kwargs.pop('title', None)
self.description = kwargs.pop('description', None)
self.exclude = kwargs.pop('exclude', None)
self.include = kwargs.pop('include', None)
self.const = kwargs.pop('const', None)
self.gt = kwargs.pop('gt', None)
self.ge = kwargs.pop('ge', None)
self.lt = kwargs.pop('lt', None)
self.le = kwargs.pop('le', None)
self.multiple_of = kwargs.pop('multiple_of', None)
self.allow_inf_nan = kwargs.pop('allow_inf_nan', None)
self.max_digits = kwargs.pop('max_digits', None)
self.decimal_places = kwargs.pop('decimal_places', None)
self.min_items = kwargs.pop('min_items', None)
self.max_items = kwargs.pop('max_items', None)
self.unique_items = kwargs.pop('unique_items', None)
self.min_length = kwargs.pop('min_length', None)
self.max_length = kwargs.pop('max_length', None)
self.allow_mutation = kwargs.pop('allow_mutation', True)
self.regex = kwargs.pop('regex', None)
self.discriminator = kwargs.pop('discriminator', None)
self.repr = kwargs.pop('repr', True)
self.extra = kwargs
def __repr_args__(self) -> 'ReprArgs':
field_defaults_to_hide: Dict[str, Any] = {
'repr': True,
**self.__field_constraints__,
}
attrs = ((s, getattr(self, s)) for s in self.__slots__)
return [(a, v) for a, v in attrs if v != field_defaults_to_hide.get(a, None)]
def get_constraints(self) -> Set[str]:
"""
Gets the constraints set on the field by comparing the constraint value with its default value
:return: the constraints set on field_info
"""
return {attr for attr, default in self.__field_constraints__.items() if getattr(self, attr) != default}
def update_from_config(self, from_config: Dict[str, Any]) -> None:
"""
Update this FieldInfo based on a dict from get_field_info, only fields which have not been set are dated.
"""
for attr_name, value in from_config.items():
try:
current_value = getattr(self, attr_name)
except AttributeError:
# attr_name is not an attribute of FieldInfo, it should therefore be added to extra
# (except if extra already has this value!)
self.extra.setdefault(attr_name, value)
else:
if current_value is self.__field_constraints__.get(attr_name, None):
setattr(self, attr_name, value)
elif attr_name == 'exclude':
self.exclude = ValueItems.merge(value, current_value)
elif attr_name == 'include':
self.include = ValueItems.merge(value, current_value, intersect=True)
def _validate(self) -> None:
if self.default is not Undefined and self.default_factory is not None:
raise ValueError('cannot specify both default and default_factory')
def Field(
default: Any = Undefined,
*,
default_factory: Optional[NoArgAnyCallable] = None,
alias: str = None,
title: str = None,
description: str = None,
exclude: Union['AbstractSetIntStr', 'MappingIntStrAny', Any] = None,
include: Union['AbstractSetIntStr', 'MappingIntStrAny', Any] = None,
const: bool = None,
gt: float = None,
ge: float = None,
lt: float = None,
le: float = None,
multiple_of: float = None,
allow_inf_nan: bool = None,
max_digits: int = None,
decimal_places: int = None,
min_items: int = None,
max_items: int = None,
unique_items: bool = None,
min_length: int = None,
max_length: int = None,
allow_mutation: bool = True,
regex: str = None,
discriminator: str = None,
repr: bool = True,
**extra: Any,
) -> Any:
"""
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``.
:param default: since this is replacing the field’s default, its first argument is used
to set the default, use ellipsis (``...``) to indicate the field is required
:param default_factory: callable that will be called when a default value is needed for this field
If both `default` and `default_factory` are set, an error is raised.
:param alias: the public name of the field
:param title: can be any string, used in the schema
:param description: can be any string, used in the schema
:param exclude: exclude this field while dumping.
Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method.
:param include: include this field while dumping.
Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method.
:param const: this field is required and *must* take it's default value
:param gt: only applies to numbers, requires the field to be "greater than". The schema
will have an ``exclusiveMinimum`` validation keyword
:param ge: only applies to numbers, requires the field to be "greater than or equal to". The
schema will have a ``minimum`` validation keyword
:param lt: only applies to numbers, requires the field to be "less than". The schema
will have an ``exclusiveMaximum`` validation keyword
:param le: only applies to numbers, requires the field to be "less than or equal to". The
schema will have a ``maximum`` validation keyword
:param multiple_of: only applies to numbers, requires the field to be "a multiple of". The
schema will have a ``multipleOf`` validation keyword
:param allow_inf_nan: only applies to numbers, allows the field to be NaN or infinity (+inf or -inf),
which is a valid Python float. Default True, set to False for compatibility with JSON.
:param max_digits: only applies to Decimals, requires the field to have a maximum number
of digits within the decimal. It does not include a zero before the decimal point or trailing decimal zeroes.
:param decimal_places: only applies to Decimals, requires the field to have at most a number of decimal places
allowed. It does not include trailing decimal zeroes.
:param min_items: only applies to lists, requires the field to have a minimum number of
elements. The schema will have a ``minItems`` validation keyword
:param max_items: only applies to lists, requires the field to have a maximum number of
elements. The schema will have a ``maxItems`` validation keyword
:param unique_items: only applies to lists, requires the field not to have duplicated
elements. The schema will have a ``uniqueItems`` validation keyword
:param min_length: only applies to strings, requires the field to have a minimum length. The
schema will have a ``maximum`` validation keyword
:param max_length: only applies to strings, requires the field to have a maximum length. The
schema will have a ``maxLength`` validation keyword
:param allow_mutation: a boolean which defaults to True. When False, the field raises a TypeError if the field is
assigned on an instance. The BaseModel Config must set validate_assignment to True
:param regex: only applies to strings, requires the field match against a regular expression
pattern string. The schema will have a ``pattern`` validation keyword
:param discriminator: only useful with a (discriminated a.k.a. tagged) `Union` of sub models with a common field.
The `discriminator` is the name of this common field to shorten validation and improve generated schema
:param repr: show this field in the representation
:param **extra: any additional keyword arguments will be added as is to the schema
"""
field_info = FieldInfo(
default,
default_factory=default_factory,
alias=alias,
title=title,
description=description,
exclude=exclude,
include=include,
const=const,
gt=gt,
ge=ge,
lt=lt,
le=le,
multiple_of=multiple_of,
allow_inf_nan=allow_inf_nan,
max_digits=max_digits,
decimal_places=decimal_places,
min_items=min_items,
max_items=max_items,
unique_items=unique_items,
min_length=min_length,
max_length=max_length,
allow_mutation=allow_mutation,
regex=regex,
discriminator=discriminator,
repr=repr,
**extra,
)
field_info._validate()
return field_info
# used to be an enum but changed to int's for small performance improvement as less access overhead
SHAPE_SINGLETON = 1
SHAPE_LIST = 2
SHAPE_SET = 3
SHAPE_MAPPING = 4
SHAPE_TUPLE = 5
SHAPE_TUPLE_ELLIPSIS = 6
SHAPE_SEQUENCE = 7
SHAPE_FROZENSET = 8
SHAPE_ITERABLE = 9
SHAPE_GENERIC = 10
SHAPE_DEQUE = 11
SHAPE_DICT = 12
SHAPE_DEFAULTDICT = 13
SHAPE_COUNTER = 14
SHAPE_NAME_LOOKUP = {
SHAPE_LIST: 'List[{}]',
SHAPE_SET: 'Set[{}]',
SHAPE_TUPLE_ELLIPSIS: 'Tuple[{}, ...]',
SHAPE_SEQUENCE: 'Sequence[{}]',
SHAPE_FROZENSET: 'FrozenSet[{}]',
SHAPE_ITERABLE: 'Iterable[{}]',
SHAPE_DEQUE: 'Deque[{}]',
SHAPE_DICT: 'Dict[{}]',
SHAPE_DEFAULTDICT: 'DefaultDict[{}]',
SHAPE_COUNTER: 'Counter[{}]',
}
MAPPING_LIKE_SHAPES: Set[int] = {SHAPE_DEFAULTDICT, SHAPE_DICT, SHAPE_MAPPING, SHAPE_COUNTER}
class ModelField(Representation):
__slots__ = (
'type_',
'outer_type_',
'annotation',
'sub_fields',
'sub_fields_mapping',
'key_field',
'validators',
'pre_validators',
'post_validators',
'default',
'default_factory',
'required',
'final',
'model_config',
'name',
'alias',
'has_alias',
'field_info',
'discriminator_key',
'discriminator_alias',
'validate_always',
'allow_none',
'shape',
'class_validators',
'parse_json',
)
def __init__(
self,
*,
name: str,
type_: Type[Any],
class_validators: Optional[Dict[str, Validator]],
model_config: Type['BaseConfig'],
default: Any = None,
default_factory: Optional[NoArgAnyCallable] = None,
required: 'BoolUndefined' = Undefined,
final: bool = False,
alias: str = None,
field_info: Optional[FieldInfo] = None,
) -> None:
self.name: str = name
self.has_alias: bool = alias is not None
self.alias: str = alias if alias is not None else name
self.annotation = type_
self.type_: Any = convert_generics(type_)
self.outer_type_: Any = type_
self.class_validators = class_validators or {}
self.default: Any = default
self.default_factory: Optional[NoArgAnyCallable] = default_factory
self.required: 'BoolUndefined' = required
self.final: bool = final
self.model_config = model_config
self.field_info: FieldInfo = field_info or FieldInfo(default)
self.discriminator_key: Optional[str] = self.field_info.discriminator
self.discriminator_alias: Optional[str] = self.discriminator_key
self.allow_none: bool = False
self.validate_always: bool = False
self.sub_fields: Optional[List[ModelField]] = None
self.sub_fields_mapping: Optional[Dict[str, 'ModelField']] = None # used for discriminated union
self.key_field: Optional[ModelField] = None
self.validators: 'ValidatorsList' = []
self.pre_validators: Optional['ValidatorsList'] = None
self.post_validators: Optional['ValidatorsList'] = None
self.parse_json: bool = False
self.shape: int = SHAPE_SINGLETON
self.model_config.prepare_field(self)
self.prepare()
def get_default(self) -> Any:
return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
@staticmethod
def _get_field_info(
field_name: str, annotation: Any, value: Any, config: Type['BaseConfig']
) -> Tuple[FieldInfo, Any]:
"""
Get a FieldInfo from a root typing.Annotated annotation, value, or config default.
The FieldInfo may be set in typing.Annotated or the value, but not both. If neither contain
a FieldInfo, a new one will be created using the config.
:param field_name: name of the field for use in error messages
:param annotation: a type hint such as `str` or `Annotated[str, Field(..., min_length=5)]`
:param value: the field's assigned value
:param config: the model's config object
:return: the FieldInfo contained in the `annotation`, the value, or a new one from the config.
"""
field_info_from_config = config.get_field_info(field_name)
field_info = None
if get_origin(annotation) is Annotated:
field_infos = [arg for arg in get_args(annotation)[1:] if isinstance(arg, FieldInfo)]
if len(field_infos) > 1:
raise ValueError(f'cannot specify multiple `Annotated` `Field`s for {field_name!r}')
field_info = next(iter(field_infos), None)
if field_info is not None:
field_info = copy.copy(field_info)
field_info.update_from_config(field_info_from_config)
if field_info.default not in (Undefined, Required):
raise ValueError(f'`Field` default cannot be set in `Annotated` for {field_name!r}')
if value is not Undefined and value is not Required:
# check also `Required` because of `validate_arguments` that sets `...` as default value
field_info.default = value
if isinstance(value, FieldInfo):
if field_info is not None:
raise ValueError(f'cannot specify `Annotated` and value `Field`s together for {field_name!r}')
field_info = value
field_info.update_from_config(field_info_from_config)
elif field_info is None:
field_info = FieldInfo(value, **field_info_from_config)
value = None if field_info.default_factory is not None else field_info.default
field_info._validate()
return field_info, value
@classmethod
def infer(
cls,
*,
name: str,
value: Any,
annotation: Any,
class_validators: Optional[Dict[str, Validator]],
config: Type['BaseConfig'],
) -> 'ModelField':
from .schema import get_annotation_from_field_info
field_info, value = cls._get_field_info(name, annotation, value, config)
required: 'BoolUndefined' = Undefined
if value is Required:
required = True
value = None
elif value is not Undefined:
required = False
annotation = get_annotation_from_field_info(annotation, field_info, name, config.validate_assignment)
return cls(
name=name,
type_=annotation,
alias=field_info.alias,
class_validators=class_validators,
default=value,
default_factory=field_info.default_factory,
required=required,
model_config=config,
field_info=field_info,
)
def set_config(self, config: Type['BaseConfig']) -> None:
self.model_config = config
info_from_config = config.get_field_info(self.name)
config.prepare_field(self)
new_alias = info_from_config.get('alias')
new_alias_priority = info_from_config.get('alias_priority') or 0
if new_alias and new_alias_priority >= (self.field_info.alias_priority or 0):
self.field_info.alias = new_alias
self.field_info.alias_priority = new_alias_priority
self.alias = new_alias
new_exclude = info_from_config.get('exclude')
if new_exclude is not None:
self.field_info.exclude = ValueItems.merge(self.field_info.exclude, new_exclude)
new_include = info_from_config.get('include')
if new_include is not None:
self.field_info.include = ValueItems.merge(self.field_info.include, new_include, intersect=True)
@property
def alt_alias(self) -> bool:
return self.name != self.alias
def prepare(self) -> None:
"""
Prepare the field but inspecting self.default, self.type_ etc.
Note: this method is **not** idempotent (because _type_analysis is not idempotent),
e.g. calling it it multiple times may modify the field and configure it incorrectly.
"""
self._set_default_and_type()
if self.type_.__class__ is ForwardRef or self.type_.__class__ is DeferredType:
# self.type_ is currently a ForwardRef and there's nothing we can do now,
# user will need to call model.update_forward_refs()
return
self._type_analysis()
if self.required is Undefined:
self.required = True
if self.default is Undefined and self.default_factory is None:
self.default = None
self.populate_validators()
def _set_default_and_type(self) -> None:
"""
Set the default value, infer the type if needed and check if `None` value is valid.
"""
if self.default_factory is not None:
if self.type_ is Undefined:
raise errors_.ConfigError(
f'you need to set the type of field {self.name!r} when using `default_factory`'
)
return
default_value = self.get_default()
if default_value is not None and self.type_ is Undefined:
self.type_ = default_value.__class__
self.outer_type_ = self.type_
self.annotation = self.type_
if self.type_ is Undefined:
raise errors_.ConfigError(f'unable to infer type for attribute "{self.name}"')
if self.required is False and default_value is None:
self.allow_none = True
def _type_analysis(self) -> None: # noqa: C901 (ignore complexity)
# typing interface is horrible, we have to do some ugly checks
if lenient_issubclass(self.type_, JsonWrapper):
self.type_ = self.type_.inner_type
self.parse_json = True
elif lenient_issubclass(self.type_, Json):
self.type_ = Any
self.parse_json = True
elif isinstance(self.type_, TypeVar):
if self.type_.__bound__:
self.type_ = self.type_.__bound__
elif self.type_.__constraints__:
self.type_ = Union[self.type_.__constraints__]
else:
self.type_ = Any
elif is_new_type(self.type_):
self.type_ = new_type_supertype(self.type_)
if self.type_ is Any or self.type_ is object:
if self.required is Undefined:
self.required = False
self.allow_none = True
return
elif self.type_ is Pattern or self.type_ is re.Pattern:
# python 3.7 only, Pattern is a typing object but without sub fields
return
elif is_literal_type(self.type_):
return
elif is_typeddict(self.type_):
return
if is_finalvar(self.type_):
self.final = True
if self.type_ is Final:
self.type_ = Any
else:
self.type_ = get_args(self.type_)[0]
self._type_analysis()
return
origin = get_origin(self.type_)
if origin is Annotated or is_typeddict_special(origin):
self.type_ = get_args(self.type_)[0]
self._type_analysis()
return
if self.discriminator_key is not None and not is_union(origin):
raise TypeError('`discriminator` can only be used with `Union` type with more than one variant')
# add extra check for `collections.abc.Hashable` for python 3.10+ where origin is not `None`
if origin is None or origin is CollectionsHashable:
# field is not "typing" object eg. Union, Dict, List etc.
# allow None for virtual superclasses of NoneType, e.g. Hashable
if isinstance(self.type_, type) and isinstance(None, self.type_):
self.allow_none = True
return
elif origin is Callable:
return
elif is_union(origin):
types_ = []
for type_ in get_args(self.type_):
if is_none_type(type_) or type_ is Any or type_ is object:
if self.required is Undefined:
self.required = False
self.allow_none = True
if is_none_type(type_):
continue
types_.append(type_)
if len(types_) == 1:
# Optional[]
self.type_ = types_[0]
# this is the one case where the "outer type" isn't just the original type
self.outer_type_ = self.type_
# re-run to correctly interpret the new self.type_
self._type_analysis()
else:
self.sub_fields = [self._create_sub_type(t, f'{self.name}_{display_as_type(t)}') for t in types_]
if self.discriminator_key is not None:
self.prepare_discriminated_union_sub_fields()
return
elif issubclass(origin, Tuple): # type: ignore
# origin == Tuple without item type
args = get_args(self.type_)
if not args: # plain tuple
self.type_ = Any
self.shape = SHAPE_TUPLE_ELLIPSIS
elif len(args) == 2 and args[1] is Ellipsis: # e.g. Tuple[int, ...]
self.type_ = args[0]
self.shape = SHAPE_TUPLE_ELLIPSIS
self.sub_fields = [self._create_sub_type(args[0], f'{self.name}_0')]
elif args == ((),): # Tuple[()] means empty tuple
self.shape = SHAPE_TUPLE
self.type_ = Any
self.sub_fields = []
else:
self.shape = SHAPE_TUPLE
self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(args)]
return
elif issubclass(origin, List):
# Create self validators
get_validators = getattr(self.type_, '__get_validators__', None)
if get_validators:
self.class_validators.update(
{f'list_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
)
self.type_ = get_args(self.type_)[0]
self.shape = SHAPE_LIST
elif issubclass(origin, Set):
# Create self validators
get_validators = getattr(self.type_, '__get_validators__', None)
if get_validators:
self.class_validators.update(
{f'set_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
)
self.type_ = get_args(self.type_)[0]
self.shape = SHAPE_SET
elif issubclass(origin, FrozenSet):
# Create self validators
get_validators = getattr(self.type_, '__get_validators__', None)
if get_validators:
self.class_validators.update(
{f'frozenset_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
)
self.type_ = get_args(self.type_)[0]
self.shape = SHAPE_FROZENSET
elif issubclass(origin, Deque):
self.type_ = get_args(self.type_)[0]
self.shape = SHAPE_DEQUE
elif issubclass(origin, Sequence):
self.type_ = get_args(self.type_)[0]
self.shape = SHAPE_SEQUENCE
# priority to most common mapping: dict
elif origin is dict or origin is Dict:
self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
self.type_ = get_args(self.type_)[1]
self.shape = SHAPE_DICT
elif issubclass(origin, DefaultDict):
self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
self.type_ = get_args(self.type_)[1]
self.shape = SHAPE_DEFAULTDICT
elif issubclass(origin, Counter):
self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
self.type_ = int
self.shape = SHAPE_COUNTER
elif issubclass(origin, Mapping):
self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
self.type_ = get_args(self.type_)[1]
self.shape = SHAPE_MAPPING
# Equality check as almost everything inherits form Iterable, including str
# check for Iterable and CollectionsIterable, as it could receive one even when declared with the other
elif origin in {Iterable, CollectionsIterable}:
self.type_ = get_args(self.type_)[0]
self.shape = SHAPE_ITERABLE
self.sub_fields = [self._create_sub_type(self.type_, f'{self.name}_type')]
elif issubclass(origin, Type): # type: ignore
return
elif hasattr(origin, '__get_validators__') or self.model_config.arbitrary_types_allowed:
# Is a Pydantic-compatible generic that handles itself
# or we have arbitrary_types_allowed = True
self.shape = SHAPE_GENERIC
self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(get_args(self.type_))]
self.type_ = origin
return
else:
raise TypeError(f'Fields of type "{origin}" are not supported.')
# type_ has been refined eg. as the type of a List and sub_fields needs to be populated
self.sub_fields = [self._create_sub_type(self.type_, '_' + self.name)]
def prepare_discriminated_union_sub_fields(self) -> None:
"""
Prepare the mapping <discriminator key> -> <ModelField> and update `sub_fields`
Note that this process can be aborted if a `ForwardRef` is encountered
"""
assert self.discriminator_key is not None
if self.type_.__class__ is DeferredType:
return
assert self.sub_fields is not None
sub_fields_mapping: Dict[str, 'ModelField'] = {}
all_aliases: Set[str] = set()
for sub_field in self.sub_fields:
t = sub_field.type_
if t.__class__ is ForwardRef:
# Stopping everything...will need to call `update_forward_refs`
return
alias, discriminator_values = get_discriminator_alias_and_values(t, self.discriminator_key)
all_aliases.add(alias)
for discriminator_value in discriminator_values:
sub_fields_mapping[discriminator_value] = sub_field
self.sub_fields_mapping = sub_fields_mapping
self.discriminator_alias = get_unique_discriminator_alias(all_aliases, self.discriminator_key)
def _create_sub_type(self, type_: Type[Any], name: str, *, for_keys: bool = False) -> 'ModelField':
if for_keys:
class_validators = None
else:
# validators for sub items should not have `each_item` as we want to check only the first sublevel
class_validators = {
k: Validator(
func=v.func,
pre=v.pre,
each_item=False,
always=v.always,
check_fields=v.check_fields,
skip_on_failure=v.skip_on_failure,
)
for k, v in self.class_validators.items()
if v.each_item
}
field_info, _ = self._get_field_info(name, type_, None, self.model_config)
return self.__class__(
type_=type_,
name=name,
class_validators=class_validators,
model_config=self.model_config,
field_info=field_info,
)
def populate_validators(self) -> None:
"""
Prepare self.pre_validators, self.validators, and self.post_validators based on self.type_'s __get_validators__
and class validators. This method should be idempotent, e.g. it should be safe to call multiple times
without mis-configuring the field.
"""
self.validate_always = getattr(self.type_, 'validate_always', False) or any(
v.always for v in self.class_validators.values()
)
class_validators_ = self.class_validators.values()
if not self.sub_fields or self.shape == SHAPE_GENERIC:
get_validators = getattr(self.type_, '__get_validators__', None)
v_funcs = (
*[v.func for v in class_validators_ if v.each_item and v.pre],
*(get_validators() if get_validators else list(find_validators(self.type_, self.model_config))),
*[v.func for v in class_validators_ if v.each_item and not v.pre],
)
self.validators = prep_validators(v_funcs)
self.pre_validators = []
self.post_validators = []
if self.field_info and self.field_info.const:
self.post_validators.append(make_generic_validator(constant_validator))
if class_validators_:
self.pre_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and v.pre)
self.post_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and not v.pre)
if self.parse_json:
self.pre_validators.append(make_generic_validator(validate_json))
self.pre_validators = self.pre_validators or None
self.post_validators = self.post_validators or None
def validate(
self, v: Any, values: Dict[str, Any], *, loc: 'LocStr', cls: Optional['ModelOrDc'] = None
) -> 'ValidateReturn':
assert self.type_.__class__ is not DeferredType
if self.type_.__class__ is ForwardRef:
assert cls is not None
raise ConfigError(
f'field "{self.name}" not yet prepared so type is still a ForwardRef, '
f'you might need to call {cls.__name__}.update_forward_refs().'
)
errors: Optional['ErrorList']
if self.pre_validators:
v, errors = self._apply_validators(v, values, loc, cls, self.pre_validators)
if errors:
return v, errors
if v is None:
if is_none_type(self.type_):
# keep validating
pass
elif self.allow_none:
if self.post_validators:
return self._apply_validators(v, values, loc, cls, self.post_validators)
else:
return None, None
else:
return v, ErrorWrapper(NoneIsNotAllowedError(), loc)
if self.shape == SHAPE_SINGLETON:
v, errors = self._validate_singleton(v, values, loc, cls)
elif self.shape in MAPPING_LIKE_SHAPES:
v, errors = self._validate_mapping_like(v, values, loc, cls)
elif self.shape == SHAPE_TUPLE:
v, errors = self._validate_tuple(v, values, loc, cls)
elif self.shape == SHAPE_ITERABLE:
v, errors = self._validate_iterable(v, values, loc, cls)
elif self.shape == SHAPE_GENERIC:
v, errors = self._apply_validators(v, values, loc, cls, self.validators)
else:
# sequence, list, set, generator, tuple with ellipsis, frozen set
v, errors = self._validate_sequence_like(v, values, loc, cls)
if not errors and self.post_validators:
v, errors = self._apply_validators(v, values, loc, cls, self.post_validators)
return v, errors
def _validate_sequence_like( # noqa: C901 (ignore complexity)
self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
) -> 'ValidateReturn':
"""
Validate sequence-like containers: lists, tuples, sets and generators
Note that large if-else blocks are necessary to enable Cython
optimization, which is why we disable the complexity check above.
"""
if not sequence_like(v):
e: errors_.PydanticTypeError
if self.shape == SHAPE_LIST:
e = errors_.ListError()
elif self.shape in (SHAPE_TUPLE, SHAPE_TUPLE_ELLIPSIS):
e = errors_.TupleError()
elif self.shape == SHAPE_SET:
e = errors_.SetError()
elif self.shape == SHAPE_FROZENSET:
e = errors_.FrozenSetError()
else:
e = errors_.SequenceError()
return v, ErrorWrapper(e, loc)
loc = loc if isinstance(loc, tuple) else (loc,)
result = []
errors: List[ErrorList] = []
for i, v_ in enumerate(v):
v_loc = *loc, i
r, ee = self._validate_singleton(v_, values, v_loc, cls)
if ee:
errors.append(ee)
else:
result.append(r)
if errors:
return v, errors
converted: Union[List[Any], Set[Any], FrozenSet[Any], Tuple[Any, ...], Iterator[Any], Deque[Any]] = result
if self.shape == SHAPE_SET:
converted = set(result)
elif self.shape == SHAPE_FROZENSET:
converted = frozenset(result)
elif self.shape == SHAPE_TUPLE_ELLIPSIS:
converted = tuple(result)
elif self.shape == SHAPE_DEQUE:
converted = deque(result)
elif self.shape == SHAPE_SEQUENCE:
if isinstance(v, tuple):
converted = tuple(result)
elif isinstance(v, set):
converted = set(result)
elif isinstance(v, Generator):
converted = iter(result)
elif isinstance(v, deque):
converted = deque(result)
return converted, None
def _validate_iterable(
self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
) -> 'ValidateReturn':
"""
Validate Iterables.
This intentionally doesn't validate values to allow infinite generators.
"""
try:
iterable = iter(v)
except TypeError:
return v, ErrorWrapper(errors_.IterableError(), loc)
return iterable, None
def _validate_tuple(
self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
) -> 'ValidateReturn':
e: Optional[Exception] = None
if not sequence_like(v):
e = errors_.TupleError()
else:
actual_length, expected_length = len(v), len(self.sub_fields) # type: ignore
if actual_length != expected_length:
e = errors_.TupleLengthError(actual_length=actual_length, expected_length=expected_length)
if e:
return v, ErrorWrapper(e, loc)
loc = loc if isinstance(loc, tuple) else (loc,)
result = []
errors: List[ErrorList] = []
for i, (v_, field) in enumerate(zip(v, self.sub_fields)): # type: ignore
v_loc = *loc, i
r, ee = field.validate(v_, values, loc=v_loc, cls=cls)
if ee:
errors.append(ee)
else:
result.append(r)
if errors:
return v, errors
else:
return tuple(result), None
def _validate_mapping_like(
self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
) -> 'ValidateReturn':
try:
v_iter = dict_validator(v)
except TypeError as exc:
return v, ErrorWrapper(exc, loc)
loc = loc if isinstance(loc, tuple) else (loc,)
result, errors = {}, []
for k, v_ in v_iter.items():
v_loc = *loc, '__key__'
key_result, key_errors = self.key_field.validate(k, values, loc=v_loc, cls=cls) # type: ignore
if key_errors:
errors.append(key_errors)
continue
v_loc = *loc, k
value_result, value_errors = self._validate_singleton(v_, values, v_loc, cls)
if value_errors:
errors.append(value_errors)
continue
result[key_result] = value_result
if errors:
return v, errors
elif self.shape == SHAPE_DICT:
return result, None
elif self.shape == SHAPE_DEFAULTDICT:
return defaultdict(self.type_, result), None
elif self.shape == SHAPE_COUNTER:
return CollectionCounter(result), None
else:
return self._get_mapping_value(v, result), None
def _get_mapping_value(self, original: T, converted: Dict[Any, Any]) -> Union[T, Dict[Any, Any]]:
"""
When type is `Mapping[KT, KV]` (or another unsupported mapping), we try to avoid
coercing to `dict` unwillingly.
"""
original_cls = original.__class__
if original_cls == dict or original_cls == Dict:
return converted
elif original_cls in {defaultdict, DefaultDict}:
return defaultdict(self.type_, converted)
else:
try:
# Counter, OrderedDict, UserDict, ...
return original_cls(converted) # type: ignore
except TypeError:
raise RuntimeError(f'Could not convert dictionary to {original_cls.__name__!r}') from None
def _validate_singleton(
self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
) -> 'ValidateReturn':
if self.sub_fields:
if self.discriminator_key is not None:
return self._validate_discriminated_union(v, values, loc, cls)
errors = []
if self.model_config.smart_union and is_union(get_origin(self.type_)):
# 1st pass: check if the value is an exact instance of one of the Union types
# (e.g. to avoid coercing a bool into an int)
for field in self.sub_fields:
if v.__class__ is field.outer_type_:
return v, None
# 2nd pass: check if the value is an instance of any subclass of the Union types
for field in self.sub_fields:
# This whole logic will be improved later on to support more complex `isinstance` checks
# It will probably be done once a strict mode is added and be something like:
# ```
# value, error = field.validate(v, values, strict=True)
# if error is None:
# return value, None
# ```
try:
if isinstance(v, field.outer_type_):
return v, None
except TypeError:
# compound type
if lenient_isinstance(v, get_origin(field.outer_type_)):
value, error = field.validate(v, values, loc=loc, cls=cls)
if not error:
return value, None
# 1st pass by default or 3rd pass with `smart_union` enabled:
# check if the value can be coerced into one of the Union types
for field in self.sub_fields:
value, error = field.validate(v, values, loc=loc, cls=cls)
if error:
errors.append(error)
else:
return value, None
return v, errors
else:
return self._apply_validators(v, values, loc, cls, self.validators)
def _validate_discriminated_union(
self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
) -> 'ValidateReturn':
assert self.discriminator_key is not None
assert self.discriminator_alias is not None
try:
discriminator_value = v[self.discriminator_alias]
except KeyError:
return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc)
except TypeError:
try:
# BaseModel or dataclass
discriminator_value = getattr(v, self.discriminator_key)
except (AttributeError, TypeError):
return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc)
try:
sub_field = self.sub_fields_mapping[discriminator_value] # type: ignore[index]
except TypeError:
assert cls is not None
raise ConfigError(
f'field "{self.name}" not yet prepared so type is still a ForwardRef, '
f'you might need to call {cls.__name__}.update_forward_refs().'
)
except KeyError:
assert self.sub_fields_mapping is not None
return v, ErrorWrapper(
InvalidDiscriminator(
discriminator_key=self.discriminator_key,
discriminator_value=discriminator_value,
allowed_values=list(self.sub_fields_mapping),
),
loc,
)
else:
if not isinstance(loc, tuple):
loc = (loc,)
return sub_field.validate(v, values, loc=(*loc, display_as_type(sub_field.type_)), cls=cls)
def _apply_validators(
self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'], validators: 'ValidatorsList'
) -> 'ValidateReturn':
for validator in validators:
try:
v = validator(cls, v, values, self, self.model_config)
except (ValueError, TypeError, AssertionError) as exc:
return v, ErrorWrapper(exc, loc)
return v, None
def is_complex(self) -> bool:
"""
Whether the field is "complex" eg. env variables should be parsed as JSON.
"""
from .main import BaseModel
return (
self.shape != SHAPE_SINGLETON
or hasattr(self.type_, '__pydantic_model__')
or lenient_issubclass(self.type_, (BaseModel, list, set, frozenset, dict))
)
def _type_display(self) -> PyObjectStr:
t = display_as_type(self.type_)
if self.shape in MAPPING_LIKE_SHAPES:
t = f'Mapping[{display_as_type(self.key_field.type_)}, {t}]' # type: ignore
elif self.shape == SHAPE_TUPLE:
t = 'Tuple[{}]'.format(', '.join(display_as_type(f.type_) for f in self.sub_fields)) # type: ignore
elif self.shape == SHAPE_GENERIC:
assert self.sub_fields
t = '{}[{}]'.format(
display_as_type(self.type_), ', '.join(display_as_type(f.type_) for f in self.sub_fields)
)
elif self.shape != SHAPE_SINGLETON:
t = SHAPE_NAME_LOOKUP[self.shape].format(t)
if self.allow_none and (self.shape != SHAPE_SINGLETON or not self.sub_fields):
t = f'Optional[{t}]'
return PyObjectStr(t)
def __repr_args__(self) -> 'ReprArgs':
args = [('name', self.name), ('type', self._type_display()), ('required', self.required)]
if not self.required:
if self.default_factory is not None:
args.append(('default_factory', f'<function {self.default_factory.__name__}>'))
else:
args.append(('default', self.default))
if self.alt_alias:
args.append(('alias', self.alias))
return args
class ModelPrivateAttr(Representation):
__slots__ = ('default', 'default_factory')
def __init__(self, default: Any = Undefined, *, default_factory: Optional[NoArgAnyCallable] = None) -> None:
self.default = default
self.default_factory = default_factory
def get_default(self) -> Any:
return 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 = Undefined,
*,
default_factory: Optional[NoArgAnyCallable] = None,
) -> Any:
"""
Indicates that attribute is only used internally and never mixed with regular fields.
Types or values of private attrs are not checked by pydantic and it's up to you to keep them relevant.
Private attrs are stored in model __slots__.
:param default: the attribute’s default value
:param 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 is raised.
"""
if default is not Undefined and default_factory is not None:
raise ValueError('cannot specify both default and default_factory')
return ModelPrivateAttr(
default,
default_factory=default_factory,
)
class DeferredType:
"""
Used to postpone field preparation, while creating recursive generic models.
"""
def is_finalvar_with_default_val(type_: Type[Any], val: Any) -> bool:
return is_finalvar(type_) and val is not Undefined and not isinstance(val, FieldInfo)