import warnings
from abc import ABCMeta
from copy import deepcopy
from enum import Enum
from functools import partial
from pathlib import Path
from types import FunctionType, prepare_class, resolve_bases
from typing import (
TYPE_CHECKING,
AbstractSet,
Any,
Callable,
ClassVar,
Dict,
List,
Mapping,
Optional,
Tuple,
Type,
TypeVar,
Union,
cast,
no_type_check,
overload,
)
from typing_extensions import dataclass_transform
from pydantic.v1.class_validators import ValidatorGroup, extract_root_validators, extract_validators, inherit_validators
from pydantic.v1.config import BaseConfig, Extra, inherit_config, prepare_config
from pydantic.v1.error_wrappers import ErrorWrapper, ValidationError
from pydantic.v1.errors import ConfigError, DictError, ExtraError, MissingError
from pydantic.v1.fields import (
MAPPING_LIKE_SHAPES,
Field,
ModelField,
ModelPrivateAttr,
PrivateAttr,
Undefined,
is_finalvar_with_default_val,
)
from pydantic.v1.json import custom_pydantic_encoder, pydantic_encoder
from pydantic.v1.parse import Protocol, load_file, load_str_bytes
from pydantic.v1.schema import default_ref_template, model_schema
from pydantic.v1.types import PyObject, StrBytes
from pydantic.v1.typing import (
AnyCallable,
get_args,
get_origin,
is_classvar,
is_namedtuple,
is_union,
resolve_annotations,
update_model_forward_refs,
)
from pydantic.v1.utils import (
DUNDER_ATTRIBUTES,
ROOT_KEY,
ClassAttribute,
GetterDict,
Representation,
ValueItems,
generate_model_signature,
is_valid_field,
is_valid_private_name,
lenient_issubclass,
sequence_like,
smart_deepcopy,
unique_list,
validate_field_name,
)
if TYPE_CHECKING:
from inspect import Signature
from pydantic.v1.class_validators import ValidatorListDict
from pydantic.v1.types import ModelOrDc
from pydantic.v1.typing import (
AbstractSetIntStr,
AnyClassMethod,
CallableGenerator,
DictAny,
DictStrAny,
MappingIntStrAny,
ReprArgs,
SetStr,
TupleGenerator,
)
Model = TypeVar('Model', bound='BaseModel')
__all__ = 'BaseModel', 'create_model', 'validate_model'
_T = TypeVar('_T')
def validate_custom_root_type(fields: Dict[str, ModelField]) -> None:
if len(fields) > 1:
raise ValueError(f'{ROOT_KEY} cannot be mixed with other fields')
def generate_hash_function(frozen: bool) -> Optional[Callable[[Any], int]]:
def hash_function(self_: Any) -> int:
return hash(self_.__class__) + hash(tuple(self_.__dict__.values()))
return hash_function if frozen else None
# If a field is of type `Callable`, its default value should be a function and cannot to ignored.
ANNOTATED_FIELD_UNTOUCHED_TYPES: Tuple[Any, ...] = (property, type, classmethod, staticmethod)
# When creating a `BaseModel` instance, we bypass all the methods, properties... added to the model
UNTOUCHED_TYPES: Tuple[Any, ...] = (FunctionType,) + ANNOTATED_FIELD_UNTOUCHED_TYPES
# Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we need to add this extra
# (somewhat hacky) boolean to keep track of whether we've created the `BaseModel` class yet, and therefore whether it's
# safe to refer to it. If it *hasn't* been created, we assume that the `__new__` call we're in the middle of is for
# the `BaseModel` class, since that's defined immediately after the metaclass.
_is_base_model_class_defined = False
@dataclass_transform(kw_only_default=True, field_specifiers=(Field,))
class ModelMetaclass(ABCMeta):
@no_type_check # noqa C901
def __new__(mcs, name, bases, namespace, **kwargs): # noqa C901
fields: Dict[str, ModelField] = {}
config = BaseConfig
validators: 'ValidatorListDict' = {}
pre_root_validators, post_root_validators = [], []
private_attributes: Dict[str, ModelPrivateAttr] = {}
base_private_attributes: Dict[str, ModelPrivateAttr] = {}
slots: SetStr = namespace.get('__slots__', ())
slots = {slots} if isinstance(slots, str) else set(slots)
class_vars: SetStr = set()
hash_func: Optional[Callable[[Any], int]] = None
for base in reversed(bases):
if _is_base_model_class_defined and issubclass(base, BaseModel) and base != BaseModel:
fields.update(smart_deepcopy(base.__fields__))
config = inherit_config(base.__config__, config)
validators = inherit_validators(base.__validators__, validators)
pre_root_validators += base.__pre_root_validators__
post_root_validators += base.__post_root_validators__
base_private_attributes.update(base.__private_attributes__)
class_vars.update(base.__class_vars__)
hash_func = base.__hash__
resolve_forward_refs = kwargs.pop('__resolve_forward_refs__', True)
allowed_config_kwargs: SetStr = {
key
for key in dir(config)
if not (key.startswith('__') and key.endswith('__')) # skip dunder methods and attributes
}
config_kwargs = {key: kwargs.pop(key) for key in kwargs.keys() & allowed_config_kwargs}
config_from_namespace = namespace.get('Config')
if config_kwargs and config_from_namespace:
raise TypeError('Specifying config in two places is ambiguous, use either Config attribute or class kwargs')
config = inherit_config(config_from_namespace, config, **config_kwargs)
validators = inherit_validators(extract_validators(namespace), validators)
vg = ValidatorGroup(validators)
for f in fields.values():
f.set_config(config)
extra_validators = vg.get_validators(f.name)
if extra_validators:
f.class_validators.update(extra_validators)
# re-run prepare to add extra validators
f.populate_validators()
prepare_config(config, name)
untouched_types = ANNOTATED_FIELD_UNTOUCHED_TYPES
def is_untouched(v: Any) -> bool:
return isinstance(v, untouched_types) or v.__class__.__name__ == 'cython_function_or_method'
if (namespace.get('__module__'), namespace.get('__qualname__')) != ('pydantic.main', 'BaseModel'):
annotations = resolve_annotations(namespace.get('__annotations__', {}), namespace.get('__module__', None))
# annotation only fields need to come first in fields
for ann_name, ann_type in annotations.items():
if is_classvar(ann_type):
class_vars.add(ann_name)
elif is_finalvar_with_default_val(ann_type, namespace.get(ann_name, Undefined)):
class_vars.add(ann_name)
elif is_valid_field(ann_name):
validate_field_name(bases, ann_name)
value = namespace.get(ann_name, Undefined)
allowed_types = get_args(ann_type) if is_union(get_origin(ann_type)) else (ann_type,)
if (
is_untouched(value)
and ann_type != PyObject
and not any(
lenient_issubclass(get_origin(allowed_type), Type) for allowed_type in allowed_types
)
):
continue
fields[ann_name] = ModelField.infer(
name=ann_name,
value=value,
annotation=ann_type,
class_validators=vg.get_validators(ann_name),
config=config,
)
elif ann_name not in namespace and config.underscore_attrs_are_private:
private_attributes[ann_name] = PrivateAttr()
untouched_types = UNTOUCHED_TYPES + config.keep_untouched
for var_name, value in namespace.items():
can_be_changed = var_name not in class_vars and not is_untouched(value)
if isinstance(value, ModelPrivateAttr):
if not is_valid_private_name(var_name):
raise NameError(
f'Private attributes "{var_name}" must not be a valid field name; '
f'Use sunder or dunder names, e. g. "_{var_name}" or "__{var_name}__"'
)
private_attributes[var_name] = value
elif config.underscore_attrs_are_private and is_valid_private_name(var_name) and can_be_changed:
private_attributes[var_name] = PrivateAttr(default=value)
elif is_valid_field(var_name) and var_name not in annotations and can_be_changed:
validate_field_name(bases, var_name)
inferred = ModelField.infer(
name=var_name,
value=value,
annotation=annotations.get(var_name, Undefined),
class_validators=vg.get_validators(var_name),
config=config,
)
if var_name in fields:
if lenient_issubclass(inferred.type_, fields[var_name].type_):
inferred.type_ = fields[var_name].type_
else:
raise TypeError(
f'The type of {name}.{var_name} differs from the new default value; '
f'if you wish to change the type of this field, please use a type annotation'
)
fields[var_name] = inferred
_custom_root_type = ROOT_KEY in fields
if _custom_root_type:
validate_custom_root_type(fields)
vg.check_for_unused()
if config.json_encoders:
json_encoder = partial(custom_pydantic_encoder, config.json_encoders)
else:
json_encoder = pydantic_encoder
pre_rv_new, post_rv_new = extract_root_validators(namespace)
if hash_func is None:
hash_func = generate_hash_function(config.frozen)
exclude_from_namespace = fields | private_attributes.keys() | {'__slots__'}
new_namespace = {
'__config__': config,
'__fields__': fields,
'__exclude_fields__': {
name: field.field_info.exclude for name, field in fields.items() if field.field_info.exclude is not None
}
or None,
'__include_fields__': {
name: field.field_info.include for name, field in fields.items() if field.field_info.include is not None
}
or None,
'__validators__': vg.validators,
'__pre_root_validators__': unique_list(
pre_root_validators + pre_rv_new,
name_factory=lambda v: v.__name__,
),
'__post_root_validators__': unique_list(
post_root_validators + post_rv_new,
name_factory=lambda skip_on_failure_and_v: skip_on_failure_and_v[1].__name__,
),
'__schema_cache__': {},
'__json_encoder__': staticmethod(json_encoder),
'__custom_root_type__': _custom_root_type,
'__private_attributes__': {**base_private_attributes, **private_attributes},
'__slots__': slots | private_attributes.keys(),
'__hash__': hash_func,
'__class_vars__': class_vars,
**{n: v for n, v in namespace.items() if n not in exclude_from_namespace},
}
cls = super().__new__(mcs, name, bases, new_namespace, **kwargs)
# set __signature__ attr only for model class, but not for its instances
cls.__signature__ = ClassAttribute('__signature__', generate_model_signature(cls.__init__, fields, config))
if resolve_forward_refs:
cls.__try_update_forward_refs__()
# preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487
# for attributes not in `new_namespace` (e.g. private attributes)
for name, obj in namespace.items():
if name not in new_namespace:
set_name = getattr(obj, '__set_name__', None)
if callable(set_name):
set_name(cls, name)
return cls
def __instancecheck__(self, instance: Any) -> bool:
"""
Avoid calling ABC _abc_subclasscheck unless we're pretty sure.
See #3829 and python/cpython#92810
"""
return hasattr(instance, '__fields__') and super().__instancecheck__(instance)
object_setattr = object.__setattr__
class BaseModel(Representation, metaclass=ModelMetaclass):
if TYPE_CHECKING:
# populated by the metaclass, defined here to help IDEs only
__fields__: ClassVar[Dict[str, ModelField]] = {}
__include_fields__: ClassVar[Optional[Mapping[str, Any]]] = None
__exclude_fields__: ClassVar[Optional[Mapping[str, Any]]] = None
__validators__: ClassVar[Dict[str, AnyCallable]] = {}
__pre_root_validators__: ClassVar[List[AnyCallable]]
__post_root_validators__: ClassVar[List[Tuple[bool, AnyCallable]]]
__config__: ClassVar[Type[BaseConfig]] = BaseConfig
__json_encoder__: ClassVar[Callable[[Any], Any]] = lambda x: x
__schema_cache__: ClassVar['DictAny'] = {}
__custom_root_type__: ClassVar[bool] = False
__signature__: ClassVar['Signature']
__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]]
__class_vars__: ClassVar[SetStr]
__fields_set__: ClassVar[SetStr] = set()
Config = BaseConfig
__slots__ = ('__dict__', '__fields_set__')
__doc__ = '' # Null out the Representation docstring
def __init__(__pydantic_self__, **data: Any) -> None:
"""
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
"""
# Uses something other than `self` the first arg to allow "self" as a settable attribute
values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
if validation_error:
raise validation_error
try:
object_setattr(__pydantic_self__, '__dict__', values)
except TypeError as e:
raise TypeError(
'Model values must be a dict; you may not have returned a dictionary from a root validator'
) from e
object_setattr(__pydantic_self__, '__fields_set__', fields_set)
__pydantic_self__._init_private_attributes()
@no_type_check
def __setattr__(self, name, value): # noqa: C901 (ignore complexity)
if name in self.__private_attributes__ or name in DUNDER_ATTRIBUTES:
return object_setattr(self, name, value)
if self.__config__.extra is not Extra.allow and name not in self.__fields__:
raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
elif not self.__config__.allow_mutation or self.__config__.frozen:
raise TypeError(f'"{self.__class__.__name__}" is immutable and does not support item assignment')
elif name in self.__fields__ and self.__fields__[name].final:
raise TypeError(
f'"{self.__class__.__name__}" object "{name}" field is final and does not support reassignment'
)
elif self.__config__.validate_assignment:
new_values = {**self.__dict__, name: value}
for validator in self.__pre_root_validators__:
try:
new_values = validator(self.__class__, new_values)
except (ValueError, TypeError, AssertionError) as exc:
raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], self.__class__)
known_field = self.__fields__.get(name, None)
if known_field:
# We want to
# - make sure validators are called without the current value for this field inside `values`
# - keep other values (e.g. submodels) untouched (using `BaseModel.dict()` will change them into dicts)
# - keep the order of the fields
if not known_field.field_info.allow_mutation:
raise TypeError(f'"{known_field.name}" has allow_mutation set to False and cannot be assigned')
dict_without_original_value = {k: v for k, v in self.__dict__.items() if k != name}
value, error_ = known_field.validate(value, dict_without_original_value, loc=name, cls=self.__class__)
if error_:
raise ValidationError([error_], self.__class__)
else:
new_values[name] = value
errors = []
for skip_on_failure, validator in self.__post_root_validators__:
if skip_on_failure and errors:
continue
try:
new_values = validator(self.__class__, new_values)
except (ValueError, TypeError, AssertionError) as exc:
errors.append(ErrorWrapper(exc, loc=ROOT_KEY))
if errors:
raise ValidationError(errors, self.__class__)
# update the whole __dict__ as other values than just `value`
# may be changed (e.g. with `root_validator`)
object_setattr(self, '__dict__', new_values)
else:
self.__dict__[name] = value
self.__fields_set__.add(name)
def __getstate__(self) -> 'DictAny':
private_attrs = ((k, getattr(self, k, Undefined)) for k in self.__private_attributes__)
return {
'__dict__': self.__dict__,
'__fields_set__': self.__fields_set__,
'__private_attribute_values__': {k: v for k, v in private_attrs if v is not Undefined},
}
def __setstate__(self, state: 'DictAny') -> None:
object_setattr(self, '__dict__', state['__dict__'])
object_setattr(self, '__fields_set__', state['__fields_set__'])
for name, value in state.get('__private_attribute_values__', {}).items():
object_setattr(self, name, value)
def _init_private_attributes(self) -> None:
for name, private_attr in self.__private_attributes__.items():
default = private_attr.get_default()
if default is not Undefined:
object_setattr(self, name, default)
def dict(
self,
*,
include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
by_alias: bool = False,
skip_defaults: Optional[bool] = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
) -> 'DictStrAny':
"""
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
"""
if skip_defaults is not None:
warnings.warn(
f'{self.__class__.__name__}.dict(): "skip_defaults" is deprecated and replaced by "exclude_unset"',
DeprecationWarning,
)
exclude_unset = skip_defaults
return dict(
self._iter(
to_dict=True,
by_alias=by_alias,
include=include,
exclude=exclude,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
)
def json(
self,
*,
include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
by_alias: bool = False,
skip_defaults: Optional[bool] = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
encoder: Optional[Callable[[Any], Any]] = None,
models_as_dict: bool = True,
**dumps_kwargs: Any,
) -> str:
"""
Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`.
`encoder` is an optional function to supply as `default` to json.dumps(), other arguments as per `json.dumps()`.
"""
if skip_defaults is not None:
warnings.warn(
f'{self.__class__.__name__}.json(): "skip_defaults" is deprecated and replaced by "exclude_unset"',
DeprecationWarning,
)
exclude_unset = skip_defaults
encoder = cast(Callable[[Any], Any], encoder or self.__json_encoder__)
# We don't directly call `self.dict()`, which does exactly this with `to_dict=True`
# because we want to be able to keep raw `BaseModel` instances and not as `dict`.
# This allows users to write custom JSON encoders for given `BaseModel` classes.
data = dict(
self._iter(
to_dict=models_as_dict,
by_alias=by_alias,
include=include,
exclude=exclude,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
)
if self.__custom_root_type__:
data = data[ROOT_KEY]
return self.__config__.json_dumps(data, default=encoder, **dumps_kwargs)
@classmethod
def _enforce_dict_if_root(cls, obj: Any) -> Any:
if cls.__custom_root_type__ and (
not (isinstance(obj, dict) and obj.keys() == {ROOT_KEY})
and not (isinstance(obj, BaseModel) and obj.__fields__.keys() == {ROOT_KEY})
or cls.__fields__[ROOT_KEY].shape in MAPPING_LIKE_SHAPES
):
return {ROOT_KEY: obj}
else:
return obj
@classmethod
def parse_obj(cls: Type['Model'], obj: Any) -> 'Model':
obj = cls._enforce_dict_if_root(obj)
if not isinstance(obj, dict):
try:
obj = dict(obj)
except (TypeError, ValueError) as e:
exc = TypeError(f'{cls.__name__} expected dict not {obj.__class__.__name__}')
raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls) from e
return cls(**obj)
@classmethod
def parse_raw(
cls: Type['Model'],
b: StrBytes,
*,
content_type: str = None,
encoding: str = 'utf8',
proto: Protocol = None,
allow_pickle: bool = False,
) -> 'Model':
try:
obj = load_str_bytes(
b,
proto=proto,
content_type=content_type,
encoding=encoding,
allow_pickle=allow_pickle,
json_loads=cls.__config__.json_loads,
)
except (ValueError, TypeError, UnicodeDecodeError) as e:
raise ValidationError([ErrorWrapper(e, loc=ROOT_KEY)], cls)
return cls.parse_obj(obj)
@classmethod
def parse_file(
cls: Type['Model'],
path: Union[str, Path],
*,
content_type: str = None,
encoding: str = 'utf8',
proto: Protocol = None,
allow_pickle: bool = False,
) -> 'Model':
obj = load_file(
path,
proto=proto,
content_type=content_type,
encoding=encoding,
allow_pickle=allow_pickle,
json_loads=cls.__config__.json_loads,
)
return cls.parse_obj(obj)
@classmethod
def from_orm(cls: Type['Model'], obj: Any) -> 'Model':
if not cls.__config__.orm_mode:
raise ConfigError('You must have the config attribute orm_mode=True to use from_orm')
obj = {ROOT_KEY: obj} if cls.__custom_root_type__ else cls._decompose_class(obj)
m = cls.__new__(cls)
values, fields_set, validation_error = validate_model(cls, obj)
if validation_error:
raise validation_error
object_setattr(m, '__dict__', values)
object_setattr(m, '__fields_set__', fields_set)
m._init_private_attributes()
return m
@classmethod
def construct(cls: Type['Model'], _fields_set: Optional['SetStr'] = None, **values: Any) -> 'Model':
"""
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if `Config.extra = 'allow'` was set since it adds all passed values
"""
m = cls.__new__(cls)
fields_values: Dict[str, Any] = {}
for name, field in cls.__fields__.items():
if field.alt_alias and field.alias in values:
fields_values[name] = values[field.alias]
elif name in values:
fields_values[name] = values[name]
elif not field.required:
fields_values[name] = field.get_default()
fields_values.update(values)
object_setattr(m, '__dict__', fields_values)
if _fields_set is None:
_fields_set = set(values.keys())
object_setattr(m, '__fields_set__', _fields_set)
m._init_private_attributes()
return m
def _copy_and_set_values(self: 'Model', values: 'DictStrAny', fields_set: 'SetStr', *, deep: bool) -> 'Model':
if deep:
# chances of having empty dict here are quite low for using smart_deepcopy
values = deepcopy(values)
cls = self.__class__
m = cls.__new__(cls)
object_setattr(m, '__dict__', values)
object_setattr(m, '__fields_set__', fields_set)
for name in self.__private_attributes__:
value = getattr(self, name, Undefined)
if value is not Undefined:
if deep:
value = deepcopy(value)
object_setattr(m, name, value)
return m
def copy(
self: 'Model',
*,
include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
update: Optional['DictStrAny'] = None,
deep: bool = False,
) -> 'Model':
"""
Duplicate a model, optionally choose which fields to include, exclude and change.
:param include: fields to include in new model
:param exclude: fields to exclude from new model, as with values this takes precedence over include
:param update: values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
:param deep: set to `True` to make a deep copy of the model
:return: new model instance
"""
values = dict(
self._iter(to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False),
**(update or {}),
)
# new `__fields_set__` can have unset optional fields with a set value in `update` kwarg
if update:
fields_set = self.__fields_set__ | update.keys()
else:
fields_set = set(self.__fields_set__)
return self._copy_and_set_values(values, fields_set, deep=deep)
@classmethod
def schema(cls, by_alias: bool = True, ref_template: str = default_ref_template) -> 'DictStrAny':
cached = cls.__schema_cache__.get((by_alias, ref_template))
if cached is not None:
return cached
s = model_schema(cls, by_alias=by_alias, ref_template=ref_template)
cls.__schema_cache__[(by_alias, ref_template)] = s
return s
@classmethod
def schema_json(
cls, *, by_alias: bool = True, ref_template: str = default_ref_template, **dumps_kwargs: Any
) -> str:
from pydantic.v1.json import pydantic_encoder
return cls.__config__.json_dumps(
cls.schema(by_alias=by_alias, ref_template=ref_template), default=pydantic_encoder, **dumps_kwargs
)
@classmethod
def __get_validators__(cls) -> 'CallableGenerator':
yield cls.validate
@classmethod
def validate(cls: Type['Model'], value: Any) -> 'Model':
if isinstance(value, cls):
copy_on_model_validation = cls.__config__.copy_on_model_validation
# whether to deep or shallow copy the model on validation, None means do not copy
deep_copy: Optional[bool] = None
if copy_on_model_validation not in {'deep', 'shallow', 'none'}:
# Warn about deprecated behavior
warnings.warn(
"`copy_on_model_validation` should be a string: 'deep', 'shallow' or 'none'", DeprecationWarning
)
if copy_on_model_validation:
deep_copy = False
if copy_on_model_validation == 'shallow':
# shallow copy
deep_copy = False
elif copy_on_model_validation == 'deep':
# deep copy
deep_copy = True
if deep_copy is None:
return value
else:
return value._copy_and_set_values(value.__dict__, value.__fields_set__, deep=deep_copy)
value = cls._enforce_dict_if_root(value)
if isinstance(value, dict):
return cls(**value)
elif cls.__config__.orm_mode:
return cls.from_orm(value)
else:
try:
value_as_dict = dict(value)
except (TypeError, ValueError) as e:
raise DictError() from e
return cls(**value_as_dict)
@classmethod
def _decompose_class(cls: Type['Model'], obj: Any) -> GetterDict:
if isinstance(obj, GetterDict):
return obj
return cls.__config__.getter_dict(obj)
@classmethod
@no_type_check
def _get_value(
cls,
v: Any,
to_dict: bool,
by_alias: bool,
include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']],
exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']],
exclude_unset: bool,
exclude_defaults: bool,
exclude_none: bool,
) -> Any:
if isinstance(v, BaseModel):
if to_dict:
v_dict = v.dict(
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
include=include,
exclude=exclude,
exclude_none=exclude_none,
)
if ROOT_KEY in v_dict:
return v_dict[ROOT_KEY]
return v_dict
else:
return v.copy(include=include, exclude=exclude)
value_exclude = ValueItems(v, exclude) if exclude else None
value_include = ValueItems(v, include) if include else None
if isinstance(v, dict):
return {
k_: cls._get_value(
v_,
to_dict=to_dict,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
include=value_include and value_include.for_element(k_),
exclude=value_exclude and value_exclude.for_element(k_),
exclude_none=exclude_none,
)
for k_, v_ in v.items()
if (not value_exclude or not value_exclude.is_excluded(k_))
and (not value_include or value_include.is_included(k_))
}
elif sequence_like(v):
seq_args = (
cls._get_value(
v_,
to_dict=to_dict,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
include=value_include and value_include.for_element(i),
exclude=value_exclude and value_exclude.for_element(i),
exclude_none=exclude_none,
)
for i, v_ in enumerate(v)
if (not value_exclude or not value_exclude.is_excluded(i))
and (not value_include or value_include.is_included(i))
)
return v.__class__(*seq_args) if is_namedtuple(v.__class__) else v.__class__(seq_args)
elif isinstance(v, Enum) and getattr(cls.Config, 'use_enum_values', False):
return v.value
else:
return v
@classmethod
def __try_update_forward_refs__(cls, **localns: Any) -> None:
"""
Same as update_forward_refs but will not raise exception
when forward references are not defined.
"""
update_model_forward_refs(cls, cls.__fields__.values(), cls.__config__.json_encoders, localns, (NameError,))
@classmethod
def update_forward_refs(cls, **localns: Any) -> None:
"""
Try to update ForwardRefs on fields based on this Model, globalns and localns.
"""
update_model_forward_refs(cls, cls.__fields__.values(), cls.__config__.json_encoders, localns)
def __iter__(self) -> 'TupleGenerator':
"""
so `dict(model)` works
"""
yield from self.__dict__.items()
def _iter(
self,
to_dict: bool = False,
by_alias: bool = False,
include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
) -> 'TupleGenerator':
# Merge field set excludes with explicit exclude parameter with explicit overriding field set options.
# The extra "is not None" guards are not logically necessary but optimizes performance for the simple case.
if exclude is not None or self.__exclude_fields__ is not None:
exclude = ValueItems.merge(self.__exclude_fields__, exclude)
if include is not None or self.__include_fields__ is not None:
include = ValueItems.merge(self.__include_fields__, include, intersect=True)
allowed_keys = self._calculate_keys(
include=include, exclude=exclude, exclude_unset=exclude_unset # type: ignore
)
if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none):
# huge boost for plain _iter()
yield from self.__dict__.items()
return
value_exclude = ValueItems(self, exclude) if exclude is not None else None
value_include = ValueItems(self, include) if include is not None else None
for field_key, v in self.__dict__.items():
if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None):
continue
if exclude_defaults:
model_field = self.__fields__.get(field_key)
if not getattr(model_field, 'required', True) and getattr(model_field, 'default', _missing) == v:
continue
if by_alias and field_key in self.__fields__:
dict_key = self.__fields__[field_key].alias
else:
dict_key = field_key
if to_dict or value_include or value_exclude:
v = self._get_value(
v,
to_dict=to_dict,
by_alias=by_alias,
include=value_include and value_include.for_element(field_key),
exclude=value_exclude and value_exclude.for_element(field_key),
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
yield dict_key, v
def _calculate_keys(
self,
include: Optional['MappingIntStrAny'],
exclude: Optional['MappingIntStrAny'],
exclude_unset: bool,
update: Optional['DictStrAny'] = None,
) -> Optional[AbstractSet[str]]:
if include is None and exclude is None and exclude_unset is False:
return None
keys: AbstractSet[str]
if exclude_unset:
keys = self.__fields_set__.copy()
else:
keys = self.__dict__.keys()
if include is not None:
keys &= include.keys()
if update:
keys -= update.keys()
if exclude:
keys -= {k for k, v in exclude.items() if ValueItems.is_true(v)}
return keys
def __eq__(self, other: Any) -> bool:
if isinstance(other, BaseModel):
return self.dict() == other.dict()
else:
return self.dict() == other
def __repr_args__(self) -> 'ReprArgs':
return [
(k, v)
for k, v in self.__dict__.items()
if k not in DUNDER_ATTRIBUTES and (k not in self.__fields__ or self.__fields__[k].field_info.repr)
]
_is_base_model_class_defined = True
@overload
def create_model(
__model_name: str,
*,
__config__: Optional[Type[BaseConfig]] = None,
__base__: None = None,
__module__: str = __name__,
__validators__: Dict[str, 'AnyClassMethod'] = None,
__cls_kwargs__: Dict[str, Any] = None,
**field_definitions: Any,
) -> Type['BaseModel']:
...
@overload
def create_model(
__model_name: str,
*,
__config__: Optional[Type[BaseConfig]] = None,
__base__: Union[Type['Model'], Tuple[Type['Model'], ...]],
__module__: str = __name__,
__validators__: Dict[str, 'AnyClassMethod'] = None,
__cls_kwargs__: Dict[str, Any] = None,
**field_definitions: Any,
) -> Type['Model']:
...
def create_model(
__model_name: str,
*,
__config__: Optional[Type[BaseConfig]] = None,
__base__: Union[None, Type['Model'], Tuple[Type['Model'], ...]] = None,
__module__: str = __name__,
__validators__: Dict[str, 'AnyClassMethod'] = None,
__cls_kwargs__: Dict[str, Any] = None,
__slots__: Optional[Tuple[str, ...]] = None,
**field_definitions: Any,
) -> Type['Model']:
"""
Dynamically create a model.
:param __model_name: name of the created model
:param __config__: config class to use for the new model
:param __base__: base class for the new model to inherit from
:param __module__: module of the created model
:param __validators__: a dict of method names and @validator class methods
:param __cls_kwargs__: a dict for class creation
:param __slots__: Deprecated, `__slots__` should not be passed to `create_model`
:param field_definitions: fields of the model (or extra fields if a base is supplied)
in the format `<name>=(<type>, <default default>)` or `<name>=<default value>, e.g.
`foobar=(str, ...)` or `foobar=123`, or, for complex use-cases, in the format
`<name>=<Field>` or `<name>=(<type>, <FieldInfo>)`, e.g.
`foo=Field(datetime, default_factory=datetime.utcnow, alias='bar')` or
`foo=(str, FieldInfo(title='Foo'))`
"""
if __slots__ is not None:
# __slots__ will be ignored from here on
warnings.warn('__slots__ should not be passed to create_model', RuntimeWarning)
if __base__ is not None:
if __config__ is not None:
raise ConfigError('to avoid confusion __config__ and __base__ cannot be used together')
if not isinstance(__base__, tuple):
__base__ = (__base__,)
else:
__base__ = (cast(Type['Model'], BaseModel),)
__cls_kwargs__ = __cls_kwargs__ or {}
fields = {}
annotations = {}
for f_name, f_def in field_definitions.items():
if not is_valid_field(f_name):
warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning)
if isinstance(f_def, tuple):
try:
f_annotation, f_value = f_def
except ValueError as e:
raise ConfigError(
'field definitions should either be a tuple of (<type>, <default>) or just a '
'default value, unfortunately this means tuples as '
'default values are not allowed'
) from e
else:
f_annotation, f_value = None, f_def
if f_annotation:
annotations[f_name] = f_annotation
fields[f_name] = f_value
namespace: 'DictStrAny' = {'__annotations__': annotations, '__module__': __module__}
if __validators__:
namespace.update(__validators__)
namespace.update(fields)
if __config__:
namespace['Config'] = inherit_config(__config__, BaseConfig)
resolved_bases = resolve_bases(__base__)
meta, ns, kwds = prepare_class(__model_name, resolved_bases, kwds=__cls_kwargs__)
if resolved_bases is not __base__:
ns['__orig_bases__'] = __base__
namespace.update(ns)
return meta(__model_name, resolved_bases, namespace, **kwds)
_missing = object()
def validate_model( # noqa: C901 (ignore complexity)
model: Type[BaseModel], input_data: 'DictStrAny', cls: 'ModelOrDc' = None
) -> Tuple['DictStrAny', 'SetStr', Optional[ValidationError]]:
"""
validate data against a model.
"""
values = {}
errors = []
# input_data names, possibly alias
names_used = set()
# field names, never aliases
fields_set = set()
config = model.__config__
check_extra = config.extra is not Extra.ignore
cls_ = cls or model
for validator in model.__pre_root_validators__:
try:
input_data = validator(cls_, input_data)
except (ValueError, TypeError, AssertionError) as exc:
return {}, set(), ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls_)
for name, field in model.__fields__.items():
value = input_data.get(field.alias, _missing)
using_name = False
if value is _missing and config.allow_population_by_field_name and field.alt_alias:
value = input_data.get(field.name, _missing)
using_name = True
if value is _missing:
if field.required:
errors.append(ErrorWrapper(MissingError(), loc=field.alias))
continue
value = field.get_default()
if not config.validate_all and not field.validate_always:
values[name] = value
continue
else:
fields_set.add(name)
if check_extra:
names_used.add(field.name if using_name else field.alias)
v_, errors_ = field.validate(value, values, loc=field.alias, cls=cls_)
if isinstance(errors_, ErrorWrapper):
errors.append(errors_)
elif isinstance(errors_, list):
errors.extend(errors_)
else:
values[name] = v_
if check_extra:
if isinstance(input_data, GetterDict):
extra = input_data.extra_keys() - names_used
else:
extra = input_data.keys() - names_used
if extra:
fields_set |= extra
if config.extra is Extra.allow:
for f in extra:
values[f] = input_data[f]
else:
for f in sorted(extra):
errors.append(ErrorWrapper(ExtraError(), loc=f))
for skip_on_failure, validator in model.__post_root_validators__:
if skip_on_failure and errors:
continue
try:
values = validator(cls_, values)
except (ValueError, TypeError, AssertionError) as exc:
errors.append(ErrorWrapper(exc, loc=ROOT_KEY))
if errors:
return values, fields_set, ValidationError(errors, cls_)
else:
return values, fields_set, None
