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Attributes: __class_vars__: The names of the class variables defined on the model. __private_attributes__: Metadata about the private attributes of the model. __signature__: The synthesized `__init__` [`Signature`][inspect.Signature] of the model. __pydantic_complete__: Whether model building is completed, or if there are still undefined fields. __pydantic_core_schema__: The core schema of the model. __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.root_model.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_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects. __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects. __pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`. __pydantic_fields_set__: The names of fields explicitly set during instantiation. __pydantic_private__: Values of private attributes set on the model instance. """ # Note: Many of the below class vars are defined in the metaclass, but we define them here for type checking purposes. model_config: ClassVar[ConfigDict] = ConfigDict() """ Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. """ __class_vars__: ClassVar[set[str]] """The names of the class variables defined on the model.""" __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] # noqa: UP006 """Metadata about the private attributes of the model.""" __signature__: ClassVar[Signature] """The synthesized `__init__` [`Signature`][inspect.Signature] of the model.""" __pydantic_complete__: ClassVar[bool] = False """Whether model building is completed, or if there are still undefined fields.""" __pydantic_core_schema__: ClassVar[CoreSchema] """The core schema of the model.""" __pydantic_custom_init__: ClassVar[bool] """Whether the model has a custom `__init__` method.""" # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation. __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = _decorators.DecoratorInfos() """Metadata containing the decorators defined on the model. This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.""" __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] """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__: ClassVar[Dict[str, Any] | None] = None # noqa: UP006 """Parent namespace of the model, used for automatic rebuilding of models.""" __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] """The name of the post-init method for the model, if defined.""" __pydantic_root_model__: ClassVar[bool] = False """Whether the model is a [`RootModel`][pydantic.root_model.RootModel].""" __pydantic_serializer__: ClassVar[SchemaSerializer] """The `pydantic-core` `SchemaSerializer` used to dump instances of the model.""" __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] """The `pydantic-core` `SchemaValidator` used to validate instances of the model.""" __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] # noqa: UP006 """A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects. This replaces `Model.__fields__` from Pydantic V1. """ __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] # noqa: UP006 """`__setattr__` handlers. Memoizing the handlers leads to a dramatic performance improvement in `__setattr__`""" __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] # noqa: UP006 """A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.""" __pydantic_extra__: dict[str, Any] | None = _model_construction.NoInitField(init=False) """A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`.""" __pydantic_fields_set__: set[str] = _model_construction.NoInitField(init=False) """The names of fields explicitly set during instantiation.""" __pydantic_private__: dict[str, Any] | None = _model_construction.NoInitField(init=False) """Values of private attributes set on the model instance.""" if not TYPE_CHECKING: # Prevent `BaseModel` from being instantiated directly # (defined in an `if not TYPE_CHECKING` block for clarity and to avoid type checking errors): __pydantic_core_schema__ = _mock_val_ser.MockCoreSchema( 'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly', code='base-model-instantiated', ) __pydantic_validator__ = _mock_val_ser.MockValSer( 'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly', val_or_ser='validator', code='base-model-instantiated', ) __pydantic_serializer__ = _mock_val_ser.MockValSer( 'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly', val_or_ser='serializer', code='base-model-instantiated', ) __slots__ = '__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__' def __init__(self, /, **data: Any) -> None: """Create a new model by parsing and validating input data from keyword arguments. Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model. `self` is explicitly positional-only to allow `self` as a field name. """ # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks __tracebackhide__ = True validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self) if self is not validated_self: warnings.warn( 'A custom validator is returning a value other than `self`.\n' "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n" 'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.', stacklevel=2, ) # The following line sets a flag that we use to determine when `__init__` gets overridden by the user __init__.__pydantic_base_init__ = True # pyright: ignore[reportFunctionMemberAccess] @_utils.deprecated_instance_property @classmethod def model_fields(cls) -> dict[str, FieldInfo]: """A mapping of field names to their respective [`FieldInfo`][pydantic.fields.FieldInfo] instances. !!! warning Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class. """ return getattr(cls, '__pydantic_fields__', {}) @_utils.deprecated_instance_property @classmethod def model_computed_fields(cls) -> dict[str, ComputedFieldInfo]: """A mapping of computed field names to their respective [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] instances. !!! warning Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class. """ return getattr(cls, '__pydantic_computed_fields__', {}) @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 explicitly 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, _fields_set: set[str] | None = None, **values: Any) -> Self: # noqa: C901 """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. !!! note `model_construct()` generally respects the `model_config.extra` setting on the provided model. That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__` and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored. Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in an error if extra values are passed, but they will be ignored. Args: _fields_set: A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the `values` argument will be used. 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] = {} fields_set = set() for name, field in cls.__pydantic_fields__.items(): if field.alias is not None and field.alias in values: fields_values[name] = values.pop(field.alias) fields_set.add(name) if (name not in fields_set) and (field.validation_alias is not None): validation_aliases: list[str | AliasPath] = ( field.validation_alias.choices if isinstance(field.validation_alias, AliasChoices) else [field.validation_alias] ) for alias in validation_aliases: if isinstance(alias, str) and alias in values: fields_values[name] = values.pop(alias) fields_set.add(name) break elif isinstance(alias, AliasPath): value = alias.search_dict_for_path(values) if value is not PydanticUndefined: fields_values[name] = value fields_set.add(name) break if name not in fields_set: if name in values: fields_values[name] = values.pop(name) fields_set.add(name) elif not field.is_required(): fields_values[name] = field.get_default(call_default_factory=True, validated_data=fields_values) if _fields_set is None: _fields_set = fields_set _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None _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) # update private attributes with values set if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None: for k, v in values.items(): if k in m.__private_attributes__: m.__pydantic_private__[k] = v 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, *, update: Mapping[str, Any] | None = None, deep: bool = False) -> Self: """!!! abstract "Usage Documentation" [`model_copy`](../concepts/serialization.md#model_copy) Returns a copy of the model. !!! note The underlying instance's [`__dict__`][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.__pydantic_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 = None, exclude: IncEx | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, ) -> dict[str, Any]: """!!! abstract "Usage Documentation" [`model_dump`](../concepts/serialization.md#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 output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. include: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of `None`. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. fallback: A function to call when an unknown value is encountered. If not provided, a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model. """ return self.__pydantic_serializer__.to_python( self, mode=mode, by_alias=by_alias, include=include, exclude=exclude, context=context, exclude_unset=exclude_unset, exclude_defaults=exclude_defaults, exclude_none=exclude_none, round_trip=round_trip, warnings=warnings, fallback=fallback, serialize_as_any=serialize_as_any, ) def model_dump_json( self, *, indent: int | None = None, include: IncEx | None = None, exclude: IncEx | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, ) -> str: """!!! abstract "Usage Documentation" [`model_dump_json`](../concepts/serialization.md#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. exclude: Field(s) to exclude from the JSON output. context: Additional context to pass to the serializer. 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 are set to their default value. exclude_none: Whether to exclude fields that have a value of `None`. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. fallback: A function to call when an unknown value is encountered. If not provided, a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model. """ return self.__pydantic_serializer__.to_json( self, indent=indent, include=include, exclude=exclude, context=context, by_alias=by_alias, exclude_unset=exclude_unset, exclude_defaults=exclude_defaults, exclude_none=exclude_none, round_trip=round_trip, warnings=warnings, fallback=fallback, serialize_as_any=serialize_as_any, ).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. Args: by_alias: Whether to use attribute aliases or not. ref_template: The reference template. schema_generator: To override the logic used to generate the JSON schema, as a subclass of `GenerateJsonSchema` with your desired modifications 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): 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: MappingNamespace | 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 for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'): if attr in cls.__dict__ and not isinstance(getattr(cls, attr), _mock_val_ser.MockValSer): # Deleting the validator/serializer is necessary as otherwise they can get reused in # pydantic-core. We do so only if they aren't mock instances, otherwise — as `model_rebuild()` # isn't thread-safe — concurrent model instantiations can lead to the parent validator being used. # Same applies for the core schema that can be reused in schema generation. delattr(cls, attr) cls.__pydantic_complete__ = False if _types_namespace is not None: rebuild_ns = _types_namespace elif _parent_namespace_depth > 0: rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} else: rebuild_ns = {} parent_ns = _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {} ns_resolver = _namespace_utils.NsResolver( parent_namespace={**rebuild_ns, **parent_ns}, ) if not cls.__pydantic_fields_complete__: typevars_map = _generics.get_model_typevars_map(cls) try: cls.__pydantic_fields__ = _fields.rebuild_model_fields( cls, ns_resolver=ns_resolver, typevars_map=typevars_map, ) except NameError as e: exc = PydanticUndefinedAnnotation.from_name_error(e) _mock_val_ser.set_model_mocks(cls, f'`{exc.name}`') if raise_errors: raise exc from e if not raise_errors and not cls.__pydantic_fields_complete__: # No need to continue with schema gen, it is guaranteed to fail return False assert cls.__pydantic_fields_complete__ return _model_construction.complete_model_class( cls, _config.ConfigWrapper(cls.model_config, check=False), raise_errors=raise_errors, ns_resolver=ns_resolver, ) @classmethod def model_validate( cls, obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None, ) -> Self: """Validate a pydantic model instance. Args: obj: The object to validate. strict: Whether to enforce types strictly. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator. by_alias: Whether to use the field's alias when validating against the provided input data. by_name: Whether to use the field's name when validating against the provided input data. 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 if by_alias is False and by_name is not True: raise PydanticUserError( 'At least one of `by_alias` or `by_name` must be set to True.', code='validate-by-alias-and-name-false', ) return cls.__pydantic_validator__.validate_python( obj, strict=strict, from_attributes=from_attributes, context=context, by_alias=by_alias, by_name=by_name ) @classmethod def model_validate_json( cls, json_data: str | bytes | bytearray, *, strict: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None, ) -> Self: """!!! abstract "Usage Documentation" [JSON Parsing](../concepts/json.md#json-parsing) 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. by_alias: Whether to use the field's alias when validating against the provided input data. by_name: Whether to use the field's name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If `json_data` is not a JSON string or the object could not be validated. """ # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks __tracebackhide__ = True if by_alias is False and by_name is not True: raise PydanticUserError( 'At least one of `by_alias` or `by_name` must be set to True.', code='validate-by-alias-and-name-false', ) return cls.__pydantic_validator__.validate_json( json_data, strict=strict, context=context, by_alias=by_alias, by_name=by_name ) @classmethod def model_validate_strings( cls, obj: Any, *, strict: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None, ) -> Self: """Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field's alias when validating against the provided input data. by_name: Whether to use the field's name when validating against the provided input data. Returns: The validated Pydantic model. """ # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks __tracebackhide__ = True if by_alias is False and by_name is not True: raise PydanticUserError( 'At least one of `by_alias` or `by_name` must be set to True.', code='validate-by-alias-and-name-false', ) return cls.__pydantic_validator__.validate_strings( obj, strict=strict, context=context, by_alias=by_alias, by_name=by_name ) @classmethod def __get_pydantic_core_schema__(cls, source: type[BaseModel], handler: GetCoreSchemaHandler, /) -> CoreSchema: # This warning is only emitted when calling `super().__get_pydantic_core_schema__` from a model subclass. # In the generate schema logic, this method (`BaseModel.__get_pydantic_core_schema__`) is special cased to # *not* be called if not overridden. warnings.warn( 'The `__get_pydantic_core_schema__` method of the `BaseModel` class is deprecated. If you are calling ' '`super().__get_pydantic_core_schema__` when overriding the method on a Pydantic model, consider using ' '`handler(source)` instead. However, note that overriding this method on models can lead to unexpected ' 'side effects.', PydanticDeprecatedSince211, stacklevel=2, ) # Logic copied over from `GenerateSchema._model_schema`: schema = cls.__dict__.get('__pydantic_core_schema__') if schema is not None and not isinstance(schema, _mock_val_ser.MockCoreSchema): return cls.__pydantic_core_schema__ return handler(source) @classmethod def __get_pydantic_json_schema__( cls, core_schema: CoreSchema, handler: 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,) # For a model `class Model[T, U, V = int](BaseModel): ...` parametrized with `(str, bool)`, # this gives us `{T: str, U: bool, V: int}`: typevars_map = _generics.map_generic_model_arguments(cls, typevar_values) # We also update the provided args to use defaults values (`(str, bool)` becomes `(str, bool, int)`): typevar_values = tuple(v for v in typevars_map.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: cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args) if cached is not None: return cached if maybe_self_type is not None: return maybe_self_type # Attempt to rebuild the origin in case new types have been defined try: # depth 2 gets you above this __class_getitem__ call. # Note that we explicitly provide the parent ns, otherwise # `model_rebuild` will use the parent ns no matter if it is the ns of a module. # We don't want this here, as this has unexpected effects when a model # is being parametrized during a forward annotation evaluation. parent_ns = _typing_extra.parent_frame_namespace(parent_depth=2) or {} origin.model_rebuild(_types_namespace=parent_ns) except PydanticUndefinedAnnotation: # It's okay if it fails, it just means there are still undefined types # that could be evaluated later. pass submodel = _generics.create_generic_submodel(model_name, origin, args, params) _generics.set_cached_generic_type(cls, typevar_values, submodel, origin, args) return submodel def __copy__(self) -> Self: """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 not hasattr(self, '__pydantic_private__') or 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, memo: dict[int, Any] | None = None) -> Self: """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 not hasattr(self, '__pydantic_private__') or 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 TYPE_CHECKING: # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access # The same goes for __setattr__ and __delattr__, see: https://github.com/pydantic/pydantic/issues/8643 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__` can fail to be set if the model is not yet fully initialized. # See `BaseModel.__repr_args__` for more details try: pydantic_extra = object.__getattribute__(self, '__pydantic_extra__') except AttributeError: pydantic_extra = None if pydantic_extra: 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 (setattr_handler := self.__pydantic_setattr_handlers__.get(name)) is not None: setattr_handler(self, name, value) # if None is returned from _setattr_handler, the attribute was set directly elif (setattr_handler := self._setattr_handler(name, value)) is not None: setattr_handler(self, name, value) # call here to not memo on possibly unknown fields self.__pydantic_setattr_handlers__[name] = setattr_handler # memoize the handler for faster access def _setattr_handler(self, name: str, value: Any) -> Callable[[BaseModel, str, Any], None] | None: """Get a handler for setting an attribute on the model instance. Returns: A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in `__setattr__` Returns `None` when memoization is not safe, then the attribute is set directly. """ cls = self.__class__ if name in cls.__class_vars__: raise AttributeError( f'{name!r} is a ClassVar of `{cls.__name__}` and cannot be set on an instance. ' f'If you want to set a value on the class, use `{cls.__name__}.{name} = value`.' ) elif not _fields.is_valid_field_name(name): if (attribute := cls.__private_attributes__.get(name)) is not None: if hasattr(attribute, '__set__'): return lambda model, _name, val: attribute.__set__(model, val) else: return _SIMPLE_SETATTR_HANDLERS['private'] else: _object_setattr(self, name, value) return None # Can not return memoized handler with possibly freeform attr names attr = getattr(cls, name, None) # NOTE: We currently special case properties and `cached_property`, but we might need # to generalize this to all data/non-data descriptors at some point. For non-data descriptors # (such as `cached_property`), it isn't obvious though. `cached_property` caches the value # to the instance's `__dict__`, but other non-data descriptors might do things differently. if isinstance(attr, cached_property): return _SIMPLE_SETATTR_HANDLERS['cached_property'] _check_frozen(cls, name, value) # We allow properties to be set only on non frozen models for now (to match dataclasses). # This can be changed if it ever gets requested. if isinstance(attr, property): return lambda model, _name, val: attr.__set__(model, val) elif cls.model_config.get('validate_assignment'): return _SIMPLE_SETATTR_HANDLERS['validate_assignment'] elif name not in cls.__pydantic_fields__: if cls.model_config.get('extra') != 'allow': # TODO - matching error raise ValueError(f'"{cls.__name__}" object has no field "{name}"') elif attr is None: # attribute does not exist, so put it in extra self.__pydantic_extra__[name] = value return None # Can not return memoized handler with possibly freeform attr names else: # attribute _does_ exist, and was not in extra, so update it return _SIMPLE_SETATTR_HANDLERS['extra_known'] else: return _SIMPLE_SETATTR_HANDLERS['model_field'] def __delattr__(self, item: str) -> Any: cls = self.__class__ 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 return except KeyError as exc: raise AttributeError(f'{cls.__name__!r} object has no attribute {item!r}') from exc # Allow cached properties to be deleted (even if the class is frozen): attr = getattr(cls, item, None) if isinstance(attr, cached_property): return object.__delattr__(self, item) _check_frozen(cls, name=item, value=None) if item in self.__pydantic_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}') # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by # type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block: def __replace__(self, **changes: Any) -> Self: return self.model_copy(update=changes) 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.get('__pydantic_fields_set__', {})) _object_setattr(self, '__pydantic_extra__', state.get('__pydantic_extra__', {})) _object_setattr(self, '__pydantic_private__', state.get('__pydantic_private__', {})) _object_setattr(self, '__dict__', state.get('__dict__', {})) if not TYPE_CHECKING: 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__ # Perform common checks first if not ( self_type == other_type and getattr(self, '__pydantic_private__', None) == getattr(other, '__pydantic_private__', None) and self.__pydantic_extra__ == other.__pydantic_extra__ ): return False # We only want to compare pydantic fields but ignoring fields is costly. # We'll perform a fast check first, and fallback only when needed # See GH-7444 and GH-7825 for rationale and a performance benchmark # First, do the fast (and sometimes faulty) __dict__ comparison if self.__dict__ == other.__dict__: # If the check above passes, then pydantic fields are equal, we can return early return True # We don't want to trigger unnecessary costly filtering of __dict__ on all unequal objects, so we return # early if there are no keys to ignore (we would just return False later on anyway) model_fields = type(self).__pydantic_fields__.keys() if self.__dict__.keys() <= model_fields and other.__dict__.keys() <= model_fields: return False # If we reach here, there are non-pydantic-fields keys, mapped to unequal values, that we need to ignore # Resort to costly filtering of the __dict__ objects # We use operator.itemgetter because it is much faster than dict comprehensions # NOTE: Contrary to standard python class and instances, when the Model class has a default value for an # attribute and the model instance doesn't have a corresponding attribute, accessing the missing attribute # raises an error in BaseModel.__getattr__ instead of returning the class attribute # So we can use operator.itemgetter() instead of operator.attrgetter() getter = operator.itemgetter(*model_fields) if model_fields else lambda _: _utils._SENTINEL try: return getter(self.__dict__) == getter(other.__dict__) except KeyError: # In rare cases (such as when using the deprecated BaseModel.copy() method), # the __dict__ may not contain all model fields, which is how we can get here. # getter(self.__dict__) is much faster than any 'safe' method that accounts # for missing keys, and wrapping it in a `try` doesn't slow things down much # in the common case. self_fields_proxy = _utils.SafeGetItemProxy(self.__dict__) other_fields_proxy = _utils.SafeGetItemProxy(other.__dict__) return getter(self_fields_proxy) == getter(other_fields_proxy) # other instance is not a BaseModel else: return NotImplemented # delegate to the other item in the comparison if 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. ```python 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 [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')] 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: # Eagerly create the repr of computed fields, as this may trigger access of cached properties and as such # modify the instance's `__dict__`. If we don't do it now, it could happen when iterating over the `__dict__` # below if the instance happens to be referenced in a field, and would modify the `__dict__` size *during* iteration. computed_fields_repr_args = [ (k, getattr(self, k)) for k, v in self.__pydantic_computed_fields__.items() if v.repr ] for k, v in self.__dict__.items(): field = self.__pydantic_fields__.get(k) if field and field.repr: if v is not self: yield k, v else: yield k, self.__repr_recursion__(v) # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized. # This can happen if a `ValidationError` is raised during initialization and the instance's # repr is generated as part of the exception handling. Therefore, we use `getattr` here # with a fallback, even though the type hints indicate the attribute will always be present. try: pydantic_extra = object.__getattribute__(self, '__pydantic_extra__') except AttributeError: pydantic_extra = None if pydantic_extra is not None: yield from ((k, v) for k, v in pydantic_extra.items()) yield from computed_fields_repr_args # take logic from `_repr.Representation` without the side effects of inheritance, see #5740 __repr_name__ = _repr.Representation.__repr_name__ __repr_recursion__ = _repr.Representation.__repr_recursion__ __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=None ) def __fields__(self) -> dict[str, FieldInfo]: warnings.warn( 'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=PydanticDeprecatedSince20, stacklevel=2, ) return getattr(type(self), '__pydantic_fields__', {}) @property @typing_extensions.deprecated( 'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.', category=None, ) def __fields_set__(self) -> set[str]: warnings.warn( 'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.', category=PydanticDeprecatedSince20, stacklevel=2, ) return self.__pydantic_fields_set__ @typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.', category=None) def dict( # noqa: D102 self, *, include: IncEx | None = None, exclude: IncEx | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, ) -> Dict[str, Any]: # noqa UP006 warnings.warn( 'The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20, stacklevel=2, ) 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=None) def json( # noqa: D102 self, *, include: IncEx | None = None, exclude: IncEx | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: 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.', category=PydanticDeprecatedSince20, stacklevel=2, ) 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=None) def parse_obj(cls, obj: Any) -> Self: # noqa: D102 warnings.warn( 'The `parse_obj` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20, stacklevel=2, ) 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=None, ) def parse_raw( # noqa: D102 cls, b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False, ) -> Self: # 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.', category=PydanticDeprecatedSince20, stacklevel=2, ) from .deprecated import parse try: obj = 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=None, ) def parse_file( # noqa: D102 cls, path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False, ) -> Self: 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.', category=PydanticDeprecatedSince20, stacklevel=2, ) from .deprecated import parse obj = 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=None, ) def from_orm(cls, obj: Any) -> Self: # noqa: D102 warnings.warn( 'The `from_orm` method is deprecated; set ' "`model_config['from_attributes']=True` and use `model_validate` instead.", category=PydanticDeprecatedSince20, stacklevel=2, ) 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=None) def construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self: # noqa: D102 warnings.warn( 'The `construct` method is deprecated; use `model_construct` instead.', category=PydanticDeprecatedSince20, stacklevel=2, ) return cls.model_construct(_fields_set=_fields_set, **values) @typing_extensions.deprecated( 'The `copy` method is deprecated; use `model_copy` instead. ' 'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.', category=None, ) def copy( self, *, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, # noqa UP006 deep: bool = False, ) -> Self: # pragma: no cover """Returns a copy of the model. !!! warning "Deprecated" This method is now deprecated; use `model_copy` instead. If you need `include` or `exclude`, use: ```python {test="skip" lint="skip"} 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`.', category=PydanticDeprecatedSince20, stacklevel=2, ) from .deprecated import copy_internals values = dict( copy_internals._iter( 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 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=None) def schema( # noqa: D102 cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE ) -> Dict[str, Any]: # noqa UP006 warnings.warn( 'The `schema` method is deprecated; use `model_json_schema` instead.', category=PydanticDeprecatedSince20, stacklevel=2, ) 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=None, ) def schema_json( # noqa: D102 cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any ) -> str: # pragma: no cover warnings.warn( 'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.', category=PydanticDeprecatedSince20, stacklevel=2, ) import json 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=None) def validate(cls, value: Any) -> Self: # noqa: D102 warnings.warn( 'The `validate` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20, stacklevel=2, ) return cls.model_validate(value) @classmethod @typing_extensions.deprecated( 'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.', category=None, ) def update_forward_refs(cls, **localns: Any) -> None: # noqa: D102 warnings.warn( 'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.', category=PydanticDeprecatedSince20, stacklevel=2, ) 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=None ) def _iter(self, *args: Any, **kwargs: Any) -> Any: warnings.warn( 'The private method `_iter` will be removed and should no longer be used.', category=PydanticDeprecatedSince20, stacklevel=2, ) from .deprecated import copy_internals return copy_internals._iter(self, *args, **kwargs) @typing_extensions.deprecated( 'The private method `_copy_and_set_values` will be removed and should no longer be used.', category=None, ) 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.', category=PydanticDeprecatedSince20, stacklevel=2, ) from .deprecated import copy_internals return copy_internals._copy_and_set_values(self, *args, **kwargs) @classmethod @typing_extensions.deprecated( 'The private method `_get_value` will be removed and should no longer be used.', category=None, ) 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.', category=PydanticDeprecatedSince20, stacklevel=2, ) from .deprecated import copy_internals return copy_internals._get_value(cls, *args, **kwargs) @typing_extensions.deprecated( 'The private method `_calculate_keys` will be removed and should no longer be used.', category=None, ) 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.', category=PydanticDeprecatedSince20, stacklevel=2, ) from .deprecated import copy_internals return copy_internals._calculate_keys(self, *args, **kwargs)
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