"""Logic for creating models."""
from __future__ import annotations as _annotations
import types
import typing
import warnings
from copy import copy, deepcopy
from typing import Any, ClassVar
import pydantic_core
import typing_extensions
from pydantic_core import PydanticUndefined
from ._internal import (
_annotated_handlers,
_config,
_decorators,
_fields,
_forward_ref,
_generics,
_mock_validator,
_model_construction,
_repr,
_typing_extra,
_utils,
)
from ._migration import getattr_migration
from .config import ConfigDict
from .deprecated import copy_internals as _deprecated_copy_internals
from .deprecated import parse as _deprecated_parse
from .errors import PydanticUndefinedAnnotation, PydanticUserError
from .fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr
from .json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema, JsonSchemaMode, JsonSchemaValue, model_json_schema
from .warnings import PydanticDeprecatedSince20
if typing.TYPE_CHECKING:
from inspect import Signature
from pathlib import Path
from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator
from typing_extensions import Literal, Unpack
from ._internal._utils import AbstractSetIntStr, MappingIntStrAny
from .fields import Field as _Field
AnyClassMethod = classmethod[Any, Any, Any]
TupleGenerator = typing.Generator[typing.Tuple[str, Any], None, None]
Model = typing.TypeVar('Model', bound='BaseModel')
# should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
IncEx: typing_extensions.TypeAlias = 'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None'
else:
# See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
# and https://youtrack.jetbrains.com/issue/PY-51428
DeprecationWarning = PydanticDeprecatedSince20
__all__ = 'BaseModel', 'create_model'
_object_setattr = _model_construction.object_setattr
class BaseModel(metaclass=_model_construction.ModelMetaclass):
"""A base model class for creating Pydantic models.
Attributes:
model_config: Configuration settings for the model.
model_fields: Metadata about the fields defined on the model.
This replaces `Model.__fields__` from Pydantic V1.
__class_vars__: The names of classvars defined on the model.
__private_attributes__: Metadata about the private attributes of the model.
__signature__: The signature for instantiating the model.
__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
__pydantic_extra__: An instance attribute with the values of extra fields from validation when
`model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly specified during validation.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.
"""
if typing.TYPE_CHECKING:
# Here we provide annotations for the attributes of BaseModel.
# Many of these are populated by the metaclass, which is why this section is in a `TYPE_CHECKING` block.
# However, for the sake of easy review, we have included type annotations of all class and instance attributes
# of `BaseModel` here:
# Class attributes
model_config: ClassVar[ConfigDict]
model_fields: ClassVar[dict[str, FieldInfo]]
__class_vars__: ClassVar[set[str]]
__private_attributes__: ClassVar[dict[str, ModelPrivateAttr]]
__signature__: ClassVar[Signature]
__pydantic_complete__: ClassVar[bool]
__pydantic_core_schema__: ClassVar[CoreSchema]
__pydantic_custom_init__: ClassVar[bool]
__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos]
__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata]
__pydantic_parent_namespace__: ClassVar[dict[str, Any] | None]
__pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
__pydantic_root_model__: ClassVar[bool]
__pydantic_serializer__: ClassVar[SchemaSerializer]
__pydantic_validator__: ClassVar[SchemaValidator]
# Instance attributes
# Note: we use the non-existent kwarg `init=False` in pydantic.fields.Field below so that @dataclass_transform
# doesn't think these are valid as keyword arguments to the class initializer.
__pydantic_extra__: dict[str, Any] | None = _Field(init=False) # type: ignore
__pydantic_fields_set__: set[str] = _Field(init=False) # type: ignore
__pydantic_private__: dict[str, Any] | None = _Field(init=False) # type: ignore
else:
# `model_fields` and `__pydantic_decorators__` must be set for
# pydantic._internal._generate_schema.GenerateSchema.model_schema to work for a plain BaseModel annotation
model_fields = {}
__pydantic_decorators__ = _decorators.DecoratorInfos()
# Prevent `BaseModel` from being instantiated directly:
__pydantic_validator__ = _mock_validator.MockValidator(
'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
code='base-model-instantiated',
)
__slots__ = '__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__'
model_config = ConfigDict()
__pydantic_complete__ = False
__pydantic_root_model__ = False
def __init__(__pydantic_self__, **data: Any) -> None: # type: ignore
"""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 `__pydantic_self__` instead of the more common `self` for the first arg to
allow `self` as a field name.
"""
# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
__tracebackhide__ = True
__pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
# The following line sets a flag that we use to determine when `__init__` gets overridden by the user
__init__.__pydantic_base_init__ = True # type: ignore
@property
def model_computed_fields(self) -> dict[str, ComputedFieldInfo]:
"""Get the computed fields of this model instance.
Returns:
A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects.
"""
return {k: v.info for k, v in self.__pydantic_decorators__.computed_fields.items()}
@property
def model_extra(self) -> dict[str, Any] | None:
"""Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
"""
return self.__pydantic_extra__
@property
def model_fields_set(self) -> set[str]:
"""Returns the set of fields that have been set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
"""
return self.__pydantic_fields_set__
@classmethod
def model_construct(cls: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model:
"""Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_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
Args:
_fields_set: The set of field names accepted for the Model instance.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
"""
m = cls.__new__(cls)
fields_values: dict[str, Any] = {}
defaults: dict[str, Any] = {} # keeping this separate from `fields_values` helps us compute `_fields_set`
for name, field in cls.model_fields.items():
if field.alias and field.alias in values:
fields_values[name] = values.pop(field.alias)
elif name in values:
fields_values[name] = values.pop(name)
elif not field.is_required():
defaults[name] = field.get_default(call_default_factory=True)
if _fields_set is None:
_fields_set = set(fields_values.keys())
fields_values.update(defaults)
_extra: dict[str, Any] | None = None
if cls.model_config.get('extra') == 'allow':
_extra = {}
for k, v in values.items():
_extra[k] = v
else:
fields_values.update(values)
_object_setattr(m, '__dict__', fields_values)
_object_setattr(m, '__pydantic_fields_set__', _fields_set)
if not cls.__pydantic_root_model__:
_object_setattr(m, '__pydantic_extra__', _extra)
if cls.__pydantic_post_init__:
m.model_post_init(None)
elif not cls.__pydantic_root_model__:
# Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
# Since it doesn't, that means that `__pydantic_private__` should be set to None
_object_setattr(m, '__pydantic_private__', None)
return m
def model_copy(self: Model, *, update: dict[str, Any] | None = None, deep: bool = False) -> Model:
"""Returns a copy of the model.
Args:
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.
deep: Set to `True` to make a deep copy of the model.
Returns:
New model instance.
"""
copied = self.__deepcopy__() if deep else self.__copy__()
if update:
if self.model_config.get('extra') == 'allow':
for k, v in update.items():
if k in self.model_fields:
copied.__dict__[k] = v
else:
if copied.__pydantic_extra__ is None:
copied.__pydantic_extra__ = {}
copied.__pydantic_extra__[k] = v
else:
copied.__dict__.update(update)
copied.__pydantic_fields_set__.update(update.keys())
return copied
def model_dump(
self,
*,
mode: Literal['json', 'python'] | str = 'python',
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool = True,
) -> dict[str, Any]:
"""Usage docs: https://docs.pydantic.dev/dev-v2/usage/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the dictionary will only contain JSON serializable types.
If mode is 'python', the dictionary may contain any Python objects.
include: A list of fields to include in the output.
exclude: A list of fields to exclude from the output.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that are unset or None from the output.
exclude_defaults: Whether to exclude fields that are set to their default value from the output.
exclude_none: Whether to exclude fields that have a value of `None` from the output.
round_trip: Whether to enable serialization and deserialization round-trip support.
warnings: Whether to log warnings when invalid fields are encountered.
Returns:
A dictionary representation of the model.
"""
return self.__pydantic_serializer__.to_python(
self,
mode=mode,
by_alias=by_alias,
include=include,
exclude=exclude,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
)
def model_dump_json(
self,
*,
indent: int | None = None,
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool = True,
) -> str:
"""Usage docs: https://docs.pydantic.dev/dev-v2/usage/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output. Can take either a string or set of strings.
exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that have the default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: Whether to use serialization/deserialization between JSON and class instance.
warnings: Whether to show any warnings that occurred during serialization.
Returns:
A JSON string representation of the model.
"""
return self.__pydantic_serializer__.to_json(
self,
indent=indent,
include=include,
exclude=exclude,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
).decode()
@classmethod
def model_json_schema(
cls,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
mode: JsonSchemaMode = 'validation',
) -> dict[str, Any]:
"""Generates a JSON schema for a model class.
To override the logic used to generate the JSON schema, you can create a subclass of `GenerateJsonSchema`
with your desired modifications, then override this method on a custom base class and set the default
value of `schema_generator` to be your subclass.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
schema_generator: The JSON schema generator.
mode: The mode in which to generate the schema.
Returns:
The JSON schema for the given model class.
"""
return model_json_schema(
cls, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator, mode=mode
)
@classmethod
def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str:
"""Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
"""
if not issubclass(cls, typing.Generic): # type: ignore[arg-type]
raise TypeError('Concrete names should only be generated for generic models.')
# Any strings received should represent forward references, so we handle them specially below.
# If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
# we may be able to remove this special case.
param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
params_component = ', '.join(param_names)
return f'{cls.__name__}[{params_component}]'
def model_post_init(self, __context: Any) -> None:
"""Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
"""
pass
@classmethod
def model_rebuild(
cls,
*,
force: bool = False,
raise_errors: bool = True,
_parent_namespace_depth: int = 2,
_types_namespace: dict[str, Any] | None = None,
) -> bool | None:
"""Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
"""
if not force and cls.__pydantic_complete__:
return None
else:
if _types_namespace is not None:
types_namespace: dict[str, Any] | None = _types_namespace.copy()
else:
if _parent_namespace_depth > 0:
frame_parent_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth) or {}
cls_parent_ns = cls.__pydantic_parent_namespace__ or {}
cls.__pydantic_parent_namespace__ = {**cls_parent_ns, **frame_parent_ns}
types_namespace = cls.__pydantic_parent_namespace__
types_namespace = _typing_extra.get_cls_types_namespace(cls, types_namespace)
return _model_construction.complete_model_class(
cls,
cls.__name__,
_config.ConfigWrapper(cls.model_config, check=False),
raise_errors=raise_errors,
types_namespace=types_namespace,
)
@classmethod
def model_validate(
cls: type[Model],
obj: Any,
*,
strict: bool | None = None,
from_attributes: bool | None = None,
context: dict[str, Any] | None = None,
) -> Model:
"""Validate a pydantic model instance.
Args:
obj: The object to validate.
strict: Whether to raise an exception on invalid fields.
from_attributes: Whether to extract data from object attributes.
context: Additional context to pass to the validator.
Raises:
ValidationError: If the object could not be validated.
Returns:
The validated model instance.
"""
# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
__tracebackhide__ = True
return cls.__pydantic_validator__.validate_python(
obj, strict=strict, from_attributes=from_attributes, context=context
)
@classmethod
def model_validate_json(
cls: type[Model],
json_data: str | bytes | bytearray,
*,
strict: bool | None = None,
context: dict[str, Any] | None = None,
) -> Model:
"""Validate the given JSON data against the Pydantic model.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
Returns:
The validated Pydantic model.
Raises:
ValueError: If `json_data` is not a JSON string.
"""
# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
__tracebackhide__ = True
return cls.__pydantic_validator__.validate_json(json_data, strict=strict, context=context)
@classmethod
def __get_pydantic_core_schema__(
cls, __source: type[BaseModel], __handler: _annotated_handlers.GetCoreSchemaHandler
) -> CoreSchema:
"""Hook into generating the model's CoreSchema.
Args:
__source: The class we are generating a schema for.
This will generally be the same as the `cls` argument if this is a classmethod.
__handler: Call into Pydantic's internal JSON schema generation.
A callable that calls into Pydantic's internal CoreSchema generation logic.
Returns:
A `pydantic-core` `CoreSchema`.
"""
# Only use the cached value from this _exact_ class; we don't want one from a parent class
# This is why we check `cls.__dict__` and don't use `cls.__pydantic_core_schema__` or similar.
if '__pydantic_core_schema__' in cls.__dict__:
# Due to the way generic classes are built, it's possible that an invalid schema may be temporarily
# set on generic classes. I think we could resolve this to ensure that we get proper schema caching
# for generics, but for simplicity for now, we just always rebuild if the class has a generic origin.
if not cls.__pydantic_generic_metadata__['origin']:
return cls.__pydantic_core_schema__
return __handler(__source)
@classmethod
def __get_pydantic_json_schema__(
cls,
__core_schema: CoreSchema,
__handler: _annotated_handlers.GetJsonSchemaHandler,
) -> JsonSchemaValue:
"""Hook into generating the model's JSON schema.
Args:
__core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
__handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
"""
return __handler(__core_schema)
@classmethod
def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:
"""This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
"""
pass
def __class_getitem__(
cls, typevar_values: type[Any] | tuple[type[Any], ...]
) -> type[BaseModel] | _forward_ref.PydanticRecursiveRef:
cached = _generics.get_cached_generic_type_early(cls, typevar_values)
if cached is not None:
return cached
if cls is BaseModel:
raise TypeError('Type parameters should be placed on typing.Generic, not BaseModel')
if not hasattr(cls, '__parameters__'):
raise TypeError(f'{cls} cannot be parametrized because it does not inherit from typing.Generic')
if not cls.__pydantic_generic_metadata__['parameters'] and typing.Generic not in cls.__bases__:
raise TypeError(f'{cls} is not a generic class')
if not isinstance(typevar_values, tuple):
typevar_values = (typevar_values,)
_generics.check_parameters_count(cls, typevar_values)
# Build map from generic typevars to passed params
typevars_map: dict[_typing_extra.TypeVarType, type[Any]] = dict(
zip(cls.__pydantic_generic_metadata__['parameters'], typevar_values)
)
if _utils.all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map:
submodel = cls # if arguments are equal to parameters it's the same object
_generics.set_cached_generic_type(cls, typevar_values, submodel)
else:
parent_args = cls.__pydantic_generic_metadata__['args']
if not parent_args:
args = typevar_values
else:
args = tuple(_generics.replace_types(arg, typevars_map) for arg in parent_args)
origin = cls.__pydantic_generic_metadata__['origin'] or cls
model_name = origin.model_parametrized_name(args)
params = tuple(
{param: None for param in _generics.iter_contained_typevars(typevars_map.values())}
) # use dict as ordered set
with _generics.generic_recursion_self_type(origin, args) as maybe_self_type:
if maybe_self_type is not None:
return maybe_self_type
cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args)
if cached is not None:
return cached
# Attempt to rebuild the origin in case new types have been defined
try:
# depth 3 gets you above this __class_getitem__ call
origin.model_rebuild(_parent_namespace_depth=3)
except PydanticUndefinedAnnotation:
# It's okay if it fails, it just means there are still undefined types
# that could be evaluated later.
# TODO: Make sure validation fails if there are still undefined types, perhaps using MockValidator
pass
submodel = _generics.create_generic_submodel(model_name, origin, args, params)
# Update cache
_generics.set_cached_generic_type(cls, typevar_values, submodel, origin, args)
return submodel
def __copy__(self: Model) -> Model:
"""Returns a shallow copy of the model."""
cls = type(self)
m = cls.__new__(cls)
_object_setattr(m, '__dict__', copy(self.__dict__))
_object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__))
_object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
if self.__pydantic_private__ is None:
_object_setattr(m, '__pydantic_private__', None)
else:
_object_setattr(
m,
'__pydantic_private__',
{k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined},
)
return m
def __deepcopy__(self: Model, memo: dict[int, Any] | None = None) -> Model:
"""Returns a deep copy of the model."""
cls = type(self)
m = cls.__new__(cls)
_object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
_object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo))
# This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
# and attempting a deepcopy would be marginally slower.
_object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
if self.__pydantic_private__ is None:
_object_setattr(m, '__pydantic_private__', None)
else:
_object_setattr(
m,
'__pydantic_private__',
deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo),
)
return m
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:
private_attributes = object.__getattribute__(self, '__private_attributes__')
if item in private_attributes:
attribute = private_attributes[item]
if hasattr(attribute, '__get__'):
return attribute.__get__(self, type(self)) # type: ignore
try:
# Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
return self.__pydantic_private__[item] # type: ignore
except KeyError as exc:
raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
else:
pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
if pydantic_extra is not None:
try:
return pydantic_extra[item]
except KeyError as exc:
raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
else:
if hasattr(self.__class__, item):
return super().__getattribute__(item) # Raises AttributeError if appropriate
else:
# this is the current error
raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
def __setattr__(self, name: str, value: Any) -> None:
if name in self.__class_vars__:
raise AttributeError(
f'{name!r} is a ClassVar of `{self.__class__.__name__}` and cannot be set on an instance. '
f'If you want to set a value on the class, use `{self.__class__.__name__}.{name} = value`.'
)
elif not _fields.is_valid_field_name(name):
if self.__pydantic_private__ is None or name not in self.__private_attributes__:
_object_setattr(self, name, value)
else:
attribute = self.__private_attributes__[name]
if hasattr(attribute, '__set__'):
attribute.__set__(self, value) # type: ignore
else:
self.__pydantic_private__[name] = value
return
elif self.model_config.get('frozen', None):
error: pydantic_core.InitErrorDetails = {
'type': 'frozen_instance',
'loc': (name,),
'input': value,
}
raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])
attr = getattr(self.__class__, name, None)
if isinstance(attr, property):
attr.__set__(self, value)
elif self.model_config.get('validate_assignment', None):
self.__pydantic_validator__.validate_assignment(self, name, value)
elif self.model_config.get('extra') != 'allow' and name not in self.model_fields:
# TODO - matching error
raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
elif self.model_config.get('extra') == 'allow' and name not in self.model_fields:
# SAFETY: __pydantic_extra__ is not None when extra = 'allow'
self.__pydantic_extra__[name] = value # type: ignore
else:
self.__dict__[name] = value
self.__pydantic_fields_set__.add(name)
def __delattr__(self, item: str) -> Any:
if item in self.__private_attributes__:
attribute = self.__private_attributes__[item]
if hasattr(attribute, '__delete__'):
attribute.__delete__(self) # type: ignore
return
try:
# Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
del self.__pydantic_private__[item] # type: ignore
except KeyError as exc:
raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
elif item in self.model_fields:
object.__delattr__(self, item)
elif self.__pydantic_extra__ is not None and item in self.__pydantic_extra__:
del self.__pydantic_extra__[item]
else:
try:
object.__delattr__(self, item)
except AttributeError:
raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
def __getstate__(self) -> dict[Any, Any]:
private = self.__pydantic_private__
if private:
private = {k: v for k, v in private.items() if v is not PydanticUndefined}
return {
'__dict__': self.__dict__,
'__pydantic_extra__': self.__pydantic_extra__,
'__pydantic_fields_set__': self.__pydantic_fields_set__,
'__pydantic_private__': private,
}
def __setstate__(self, state: dict[Any, Any]) -> None:
_object_setattr(self, '__pydantic_fields_set__', state['__pydantic_fields_set__'])
_object_setattr(self, '__pydantic_extra__', state['__pydantic_extra__'])
_object_setattr(self, '__pydantic_private__', state['__pydantic_private__'])
_object_setattr(self, '__dict__', state['__dict__'])
def __eq__(self, other: Any) -> bool:
if isinstance(other, BaseModel):
# When comparing instances of generic types for equality, as long as all field values are equal,
# only require their generic origin types to be equal, rather than exact type equality.
# This prevents headaches like MyGeneric(x=1) != MyGeneric[Any](x=1).
self_type = self.__pydantic_generic_metadata__['origin'] or self.__class__
other_type = other.__pydantic_generic_metadata__['origin'] or other.__class__
return (
self_type == other_type
and self.__dict__ == other.__dict__
and self.__pydantic_private__ == other.__pydantic_private__
and self.__pydantic_extra__ == other.__pydantic_extra__
)
else:
return NotImplemented # delegate to the other item in the comparison
if typing.TYPE_CHECKING:
# We put `__init_subclass__` in a TYPE_CHECKING block because, even though we want the type-checking benefits
# described in the signature of `__init_subclass__` below, we don't want to modify the default behavior of
# subclass initialization.
def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]):
"""This signature is included purely to help type-checkers check arguments to class declaration, which
provides a way to conveniently set model_config key/value pairs.
```py
from pydantic import BaseModel
class MyModel(BaseModel, extra='allow'):
...
```
However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any
of the config arguments, and will only receive any keyword arguments passed during class initialization
that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.)
Args:
**kwargs: Keyword arguments passed to the class definition, which set model_config
Note:
You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called
*after* the class is fully initialized.
"""
def __iter__(self) -> TupleGenerator:
"""So `dict(model)` works."""
yield from self.__dict__.items()
extra = self.__pydantic_extra__
if extra:
yield from extra.items()
def __repr__(self) -> str:
return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
def __repr_args__(self) -> _repr.ReprArgs:
for k, v in self.__dict__.items():
field = self.model_fields.get(k)
if field and field.repr:
yield k, v
pydantic_extra = self.__pydantic_extra__
if pydantic_extra is not None:
yield from ((k, v) for k, v in pydantic_extra.items())
yield from ((k, getattr(self, k)) for k, v in self.model_computed_fields.items() if v.repr)
# take logic from `_repr.Representation` without the side effects of inheritance, see #5740
__repr_name__ = _repr.Representation.__repr_name__
__repr_str__ = _repr.Representation.__repr_str__
__pretty__ = _repr.Representation.__pretty__
__rich_repr__ = _repr.Representation.__rich_repr__
def __str__(self) -> str:
return self.__repr_str__(' ')
# ##### Deprecated methods from v1 #####
@property
@typing_extensions.deprecated(
'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=PydanticDeprecatedSince20
)
def __fields__(self) -> dict[str, FieldInfo]:
warnings.warn('The `__fields__` attribute is deprecated, use `model_fields` instead.', DeprecationWarning)
return self.model_fields
@property
@typing_extensions.deprecated(
'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
category=PydanticDeprecatedSince20,
)
def __fields_set__(self) -> set[str]:
warnings.warn(
'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.', DeprecationWarning
)
return self.__pydantic_fields_set__
@typing_extensions.deprecated(
'The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20
)
def dict( # noqa: D102
self,
*,
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
) -> typing.Dict[str, Any]: # noqa UP006
warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', DeprecationWarning)
return self.model_dump(
include=include,
exclude=exclude,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
@typing_extensions.deprecated(
'The `json` method is deprecated; use `model_dump_json` instead.', category=PydanticDeprecatedSince20
)
def json( # noqa: D102
self,
*,
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
encoder: typing.Callable[[Any], Any] | None = PydanticUndefined, # type: ignore[assignment]
models_as_dict: bool = PydanticUndefined, # type: ignore[assignment]
**dumps_kwargs: Any,
) -> str:
warnings.warn('The `json` method is deprecated; use `model_dump_json` instead.', DeprecationWarning)
if encoder is not PydanticUndefined:
raise TypeError('The `encoder` argument is no longer supported; use field serializers instead.')
if models_as_dict is not PydanticUndefined:
raise TypeError('The `models_as_dict` argument is no longer supported; use a model serializer instead.')
if dumps_kwargs:
raise TypeError('`dumps_kwargs` keyword arguments are no longer supported.')
return self.model_dump_json(
include=include,
exclude=exclude,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
@classmethod
@typing_extensions.deprecated(
'The `parse_obj` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
)
def parse_obj(cls: type[Model], obj: Any) -> Model: # noqa: D102
warnings.warn('The `parse_obj` method is deprecated; use `model_validate` instead.', DeprecationWarning)
return cls.model_validate(obj)
@classmethod
@typing_extensions.deprecated(
'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
'otherwise load the data then use `model_validate` instead.',
category=PydanticDeprecatedSince20,
)
def parse_raw( # noqa: D102
cls: type[Model],
b: str | bytes,
*,
content_type: str | None = None,
encoding: str = 'utf8',
proto: _deprecated_parse.Protocol | None = None,
allow_pickle: bool = False,
) -> Model: # pragma: no cover
warnings.warn(
'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
'otherwise load the data then use `model_validate` instead.',
DeprecationWarning,
)
try:
obj = _deprecated_parse.load_str_bytes(
b,
proto=proto,
content_type=content_type,
encoding=encoding,
allow_pickle=allow_pickle,
)
except (ValueError, TypeError) as exc:
import json
# try to match V1
if isinstance(exc, UnicodeDecodeError):
type_str = 'value_error.unicodedecode'
elif isinstance(exc, json.JSONDecodeError):
type_str = 'value_error.jsondecode'
elif isinstance(exc, ValueError):
type_str = 'value_error'
else:
type_str = 'type_error'
# ctx is missing here, but since we've added `input` to the error, we're not pretending it's the same
error: pydantic_core.InitErrorDetails = {
# The type: ignore on the next line is to ignore the requirement of LiteralString
'type': pydantic_core.PydanticCustomError(type_str, str(exc)), # type: ignore
'loc': ('__root__',),
'input': b,
}
raise pydantic_core.ValidationError.from_exception_data(cls.__name__, [error])
return cls.model_validate(obj)
@classmethod
@typing_extensions.deprecated(
'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
'use `model_validate_json`, otherwise `model_validate` instead.',
category=PydanticDeprecatedSince20,
)
def parse_file( # noqa: D102
cls: type[Model],
path: str | Path,
*,
content_type: str | None = None,
encoding: str = 'utf8',
proto: _deprecated_parse.Protocol | None = None,
allow_pickle: bool = False,
) -> Model:
warnings.warn(
'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
'use `model_validate_json` otherwise `model_validate` instead.',
DeprecationWarning,
)
obj = _deprecated_parse.load_file(
path,
proto=proto,
content_type=content_type,
encoding=encoding,
allow_pickle=allow_pickle,
)
return cls.parse_obj(obj)
@classmethod
@typing_extensions.deprecated(
"The `from_orm` method is deprecated; set "
"`model_config['from_attributes']=True` and use `model_validate` instead.",
category=PydanticDeprecatedSince20,
)
def from_orm(cls: type[Model], obj: Any) -> Model: # noqa: D102
warnings.warn(
'The `from_orm` method is deprecated; set `model_config["from_attributes"]=True` '
'and use `model_validate` instead.',
DeprecationWarning,
)
if not cls.model_config.get('from_attributes', None):
raise PydanticUserError(
'You must set the config attribute `from_attributes=True` to use from_orm', code=None
)
return cls.model_validate(obj)
@classmethod
@typing_extensions.deprecated(
'The `construct` method is deprecated; use `model_construct` instead.', category=PydanticDeprecatedSince20
)
def construct(cls: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model: # noqa: D102
warnings.warn('The `construct` method is deprecated; use `model_construct` instead.', DeprecationWarning)
return cls.model_construct(_fields_set=_fields_set, **values)
@typing_extensions.deprecated(
'The copy method is deprecated; use `model_copy` instead.', category=PydanticDeprecatedSince20
)
def copy(
self: Model,
*,
include: AbstractSetIntStr | MappingIntStrAny | None = None,
exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
update: typing.Dict[str, Any] | None = None, # noqa UP006
deep: bool = False,
) -> Model: # pragma: no cover
"""Returns a copy of the model.
This method is now deprecated; use `model_copy` instead. If you need `include` or `exclude`, use:
```py
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping
specifying which fields to include in the copied model.
exclude: Optional set or mapping
specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values
in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
"""
warnings.warn(
'The `copy` method is deprecated; use `model_copy` instead. '
'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
DeprecationWarning,
)
values = dict(
_deprecated_copy_internals._iter( # type: ignore
self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
),
**(update or {}),
)
if self.__pydantic_private__ is None:
private = None
else:
private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}
if self.__pydantic_extra__ is None:
extra: dict[str, Any] | None = None
else:
extra = self.__pydantic_extra__.copy()
for k in list(self.__pydantic_extra__):
if k not in values: # k was in the exclude
extra.pop(k)
for k in list(values):
if k in self.__pydantic_extra__: # k must have come from extra
extra[k] = values.pop(k)
# new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
if update:
fields_set = self.__pydantic_fields_set__ | update.keys()
else:
fields_set = set(self.__pydantic_fields_set__)
# removing excluded fields from `__pydantic_fields_set__`
if exclude:
fields_set -= set(exclude)
return _deprecated_copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)
@classmethod
@typing_extensions.deprecated(
'The `schema` method is deprecated; use `model_json_schema` instead.', category=PydanticDeprecatedSince20
)
def schema( # noqa: D102
cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
) -> typing.Dict[str, Any]: # noqa UP006
warnings.warn('The `schema` method is deprecated; use `model_json_schema` instead.', DeprecationWarning)
return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template)
@classmethod
@typing_extensions.deprecated(
'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
category=PydanticDeprecatedSince20,
)
def schema_json( # noqa: D102
cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
) -> str: # pragma: no cover
import json
warnings.warn(
'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
DeprecationWarning,
)
from .deprecated.json import pydantic_encoder
return json.dumps(
cls.model_json_schema(by_alias=by_alias, ref_template=ref_template),
default=pydantic_encoder,
**dumps_kwargs,
)
@classmethod
@typing_extensions.deprecated(
'The `validate` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
)
def validate(cls: type[Model], value: Any) -> Model: # noqa: D102
warnings.warn('The `validate` method is deprecated; use `model_validate` instead.', DeprecationWarning)
return cls.model_validate(value)
@classmethod
@typing_extensions.deprecated(
'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
category=PydanticDeprecatedSince20,
)
def update_forward_refs(cls, **localns: Any) -> None: # noqa: D102
warnings.warn(
'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.', DeprecationWarning
)
if localns: # pragma: no cover
raise TypeError('`localns` arguments are not longer accepted.')
cls.model_rebuild(force=True)
@typing_extensions.deprecated(
'The private method `_iter` will be removed and should no longer be used.', category=PydanticDeprecatedSince20
)
def _iter(self, *args: Any, **kwargs: Any) -> Any:
warnings.warn('The private method `_iter` will be removed and should no longer be used.', DeprecationWarning)
return _deprecated_copy_internals._iter(self, *args, **kwargs) # type: ignore
@typing_extensions.deprecated(
'The private method `_copy_and_set_values` will be removed and should no longer be used.',
category=PydanticDeprecatedSince20,
)
def _copy_and_set_values(self, *args: Any, **kwargs: Any) -> Any:
warnings.warn(
'The private method `_copy_and_set_values` will be removed and should no longer be used.',
DeprecationWarning,
)
return _deprecated_copy_internals._copy_and_set_values(self, *args, **kwargs) # type: ignore
@classmethod
@typing_extensions.deprecated(
'The private method `_get_value` will be removed and should no longer be used.',
category=PydanticDeprecatedSince20,
)
def _get_value(cls, *args: Any, **kwargs: Any) -> Any:
warnings.warn(
'The private method `_get_value` will be removed and should no longer be used.', DeprecationWarning
)
return _deprecated_copy_internals._get_value(cls, *args, **kwargs) # type: ignore
@typing_extensions.deprecated(
'The private method `_calculate_keys` will be removed and should no longer be used.',
category=PydanticDeprecatedSince20,
)
def _calculate_keys(self, *args: Any, **kwargs: Any) -> Any:
warnings.warn(
'The private method `_calculate_keys` will be removed and should no longer be used.', DeprecationWarning
)
return _deprecated_copy_internals._calculate_keys(self, *args, **kwargs) # type: ignore
@typing.overload
def create_model(
__model_name: str,
*,
__config__: ConfigDict | None = None,
__base__: None = None,
__module__: str = __name__,
__validators__: dict[str, AnyClassMethod] | None = None,
__cls_kwargs__: dict[str, Any] | None = None,
**field_definitions: Any,
) -> type[BaseModel]:
...
@typing.overload
def create_model(
__model_name: str,
*,
__config__: ConfigDict | None = None,
__base__: type[Model] | tuple[type[Model], ...],
__module__: str = __name__,
__validators__: dict[str, AnyClassMethod] | None = None,
__cls_kwargs__: dict[str, Any] | None = None,
**field_definitions: Any,
) -> type[Model]:
...
def create_model(
__model_name: str,
*,
__config__: ConfigDict | None = None,
__base__: type[Model] | tuple[type[Model], ...] | None = None,
__module__: str = __name__,
__validators__: dict[str, AnyClassMethod] | None = None,
__cls_kwargs__: dict[str, Any] | None = None,
__slots__: tuple[str, ...] | None = None,
**field_definitions: Any,
) -> type[Model]:
"""Dynamically creates and returns a new Pydantic model.
Args:
__model_name: The name of the newly created model.
__config__: The configuration of the new model.
__base__: The base class for the new model.
__module__: The name of the module that the model belongs to.
__validators__: A dictionary of methods that validate
fields.
__cls_kwargs__: A dictionary of keyword arguments for class creation.
__slots__: Deprecated. Should not be passed to `create_model`.
**field_definitions: Attributes of the new model. They should be passed in the format:
`<name>=(<type>, <default value>)` or `<name>=<default value>`. For more complex cases, they can be
passed in the format: `<name>=<Field>` or `<name>=(<type>, <FieldInfo>)`.
Returns:
The newly created model.
Raises:
PydanticUserError: If `__base__` and `__config__` are both passed.
"""
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 PydanticUserError(
'to avoid confusion `__config__` and `__base__` cannot be used together',
code='create-model-config-base',
)
if not isinstance(__base__, tuple):
__base__ = (__base__,)
else:
__base__ = (typing.cast(typing.Type['Model'], BaseModel),)
__cls_kwargs__ = __cls_kwargs__ or {}
fields = {}
annotations = {}
for f_name, f_def in field_definitions.items():
if not _fields.is_valid_field_name(f_name):
warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning)
if isinstance(f_def, tuple):
f_def = typing.cast('tuple[str, Any]', f_def)
try:
f_annotation, f_value = f_def
except ValueError as e:
raise PydanticUserError(
'Field definitions should be a `(<type>, <default>)`.',
code='create-model-field-definitions',
) from e
else:
f_annotation, f_value = None, f_def
if f_annotation:
annotations[f_name] = f_annotation
fields[f_name] = f_value
namespace: dict[str, Any] = {'__annotations__': annotations, '__module__': __module__}
if __validators__:
namespace.update(__validators__)
namespace.update(fields)
if __config__:
namespace['model_config'] = _config.ConfigWrapper(__config__).config_dict
resolved_bases = types.resolve_bases(__base__)
meta, ns, kwds = types.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, __pydantic_reset_parent_namespace__=False, **kwds)
__getattr__ = getattr_migration(__name__)