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classBaseModel(metaclass=_model_construction.ModelMetaclass):"""!!! abstract "Usage Documentation"        [Models](../concepts/models.md)    A base class for creating Pydantic models.    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."""ifnotTYPE_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__=Truevalidated_self=self.__pydantic_validator__.validate_python(data,self_instance=self)ifselfisnotvalidated_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@classmethoddefmodel_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.        """returngetattr(cls,'__pydantic_fields__',{})@_utils.deprecated_instance_property@classmethoddefmodel_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.        """returngetattr(cls,'__pydantic_computed_fields__',{})@propertydefmodel_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"`.        """returnself.__pydantic_extra__@propertydefmodel_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.        """returnself.__pydantic_fields_set__@classmethoddefmodel_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()forname,fieldincls.__pydantic_fields__.items():iffield.aliasisnotNoneandfield.aliasinvalues:fields_values[name]=values.pop(field.alias)fields_set.add(name)if(namenotinfields_set)and(field.validation_aliasisnotNone):validation_aliases:list[str|AliasPath]=(field.validation_alias.choicesifisinstance(field.validation_alias,AliasChoices)else[field.validation_alias])foraliasinvalidation_aliases:ifisinstance(alias,str)andaliasinvalues:fields_values[name]=values.pop(alias)fields_set.add(name)breakelifisinstance(alias,AliasPath):value=alias.search_dict_for_path(values)ifvalueisnotPydanticUndefined:fields_values[name]=valuefields_set.add(name)breakifnamenotinfields_set:ifnameinvalues:fields_values[name]=values.pop(name)fields_set.add(name)elifnotfield.is_required():fields_values[name]=field.get_default(call_default_factory=True,validated_data=fields_values)if_fields_setisNone:_fields_set=fields_set_extra:dict[str,Any]|None=valuesifcls.model_config.get('extra')=='allow'elseNone_object_setattr(m,'__dict__',fields_values)_object_setattr(m,'__pydantic_fields_set__',_fields_set)ifnotcls.__pydantic_root_model__:_object_setattr(m,'__pydantic_extra__',_extra)ifcls.__pydantic_post_init__:m.model_post_init(None)# update private attributes with values setifhasattr(m,'__pydantic_private__')andm.__pydantic_private__isnotNone:fork,vinvalues.items():ifkinm.__private_attributes__:m.__pydantic_private__[k]=velifnotcls.__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)returnmdefmodel_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__()ifdeepelseself.__copy__()ifupdate:ifself.model_config.get('extra')=='allow':fork,vinupdate.items():ifkinself.__pydantic_fields__:copied.__dict__[k]=velse:ifcopied.__pydantic_extra__isNone:copied.__pydantic_extra__={}copied.__pydantic_extra__[k]=velse:copied.__dict__.update(update)copied.__pydantic_fields_set__.update(update.keys())returncopieddefmodel_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.        """returnself.__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,)defmodel_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.        """returnself.__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()@classmethoddefmodel_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.        """returnmodel_json_schema(cls,by_alias=by_alias,ref_template=ref_template,schema_generator=schema_generator,mode=mode)@classmethoddefmodel_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.        """ifnotissubclass(cls,typing.Generic):raiseTypeError('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=[paramifisinstance(param,str)else_repr.display_as_type(param)forparaminparams]params_component=', '.join(param_names)returnf'{cls.__name__}[{params_component}]'defmodel_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@classmethoddefmodel_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`.        """ifnotforceandcls.__pydantic_complete__:returnNoneforattrin('__pydantic_core_schema__','__pydantic_validator__','__pydantic_serializer__'):ifattrincls.__dict__andnotisinstance(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__=Falseif_types_namespaceisnotNone:rebuild_ns=_types_namespaceelif_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},)ifnotcls.__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,)exceptNameErrorase:exc=PydanticUndefinedAnnotation.from_name_error(e)_mock_val_ser.set_model_mocks(cls,f'`{exc.name}`')ifraise_errors:raiseexcfromeifnotraise_errorsandnotcls.__pydantic_fields_complete__:# No need to continue with schema gen, it is guaranteed to failreturnFalseassertcls.__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,)@classmethoddefmodel_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__=Trueifby_aliasisFalseandby_nameisnotTrue:raisePydanticUserError('At least one of `by_alias` or `by_name` must be set to True.',code='validate-by-alias-and-name-false',)returncls.__pydantic_validator__.validate_python(obj,strict=strict,from_attributes=from_attributes,context=context,by_alias=by_alias,by_name=by_name)@classmethoddefmodel_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__=Trueifby_aliasisFalseandby_nameisnotTrue:raisePydanticUserError('At least one of `by_alias` or `by_name` must be set to True.',code='validate-by-alias-and-name-false',)returncls.__pydantic_validator__.validate_json(json_data,strict=strict,context=context,by_alias=by_alias,by_name=by_name)@classmethoddefmodel_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__=Trueifby_aliasisFalseandby_nameisnotTrue:raisePydanticUserError('At least one of `by_alias` or `by_name` must be set to True.',code='validate-by-alias-and-name-false',)returncls.__pydantic_validator__.validate_strings(obj,strict=strict,context=context,by_alias=by_alias,by_name=by_name)@classmethoddef__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__')ifschemaisnotNoneandnotisinstance(schema,_mock_val_ser.MockCoreSchema):returncls.__pydantic_core_schema__returnhandler(source)@classmethoddef__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.        """returnhandler(core_schema)@classmethoddef__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.        """passdef__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)ifcachedisnotNone:returncachedifclsisBaseModel:raiseTypeError('Type parameters should be placed on typing.Generic, not BaseModel')ifnothasattr(cls,'__parameters__'):raiseTypeError(f'{cls} cannot be parametrized because it does not inherit from typing.Generic')ifnotcls.__pydantic_generic_metadata__['parameters']andtyping.Genericnotincls.__bases__:raiseTypeError(f'{cls} is not a generic class')ifnotisinstance(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(vforvintypevars_map.values())if_utils.all_identical(typevars_map.keys(),typevars_map.values())andtypevars_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']ifnotparent_args:args=typevar_valueselse:args=tuple(_generics.replace_types(arg,typevars_map)forarginparent_args)origin=cls.__pydantic_generic_metadata__['origin']orclsmodel_name=origin.model_parametrized_name(args)params=tuple({param:Noneforparamin_generics.iter_contained_typevars(typevars_map.values())})# use dict as ordered setwith_generics.generic_recursion_self_type(origin,args)asmaybe_self_type:cached=_generics.get_cached_generic_type_late(cls,typevar_values,origin,args)ifcachedisnotNone:returncachedifmaybe_self_typeisnotNone:returnmaybe_self_type# Attempt to rebuild the origin in case new types have been definedtry:# 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)exceptPydanticUndefinedAnnotation:# It's okay if it fails, it just means there are still undefined types# that could be evaluated later.passsubmodel=_generics.create_generic_submodel(model_name,origin,args,params)_generics.set_cached_generic_type(cls,typevar_values,submodel,origin,args)returnsubmodeldef__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__))ifnothasattr(self,'__pydantic_private__')orself.__pydantic_private__isNone:_object_setattr(m,'__pydantic_private__',None)else:_object_setattr(m,'__pydantic_private__',{k:vfork,vinself.__pydantic_private__.items()ifvisnotPydanticUndefined},)returnmdef__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__))ifnothasattr(self,'__pydantic_private__')orself.__pydantic_private__isNone:_object_setattr(m,'__pydantic_private__',None)else:_object_setattr(m,'__pydantic_private__',deepcopy({k:vfork,vinself.__pydantic_private__.items()ifvisnotPydanticUndefined},memo=memo),)returnmifnotTYPE_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/8643def__getattr__(self,item:str)->Any:private_attributes=object.__getattribute__(self,'__private_attributes__')ifiteminprivate_attributes:attribute=private_attributes[item]ifhasattr(attribute,'__get__'):returnattribute.__get__(self,type(self))# type: ignoretry:# Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has itemsreturnself.__pydantic_private__[item]# type: ignoreexceptKeyErrorasexc:raiseAttributeError(f'{type(self).__name__!r} object has no attribute{item!r}')fromexcelse:# `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.# See `BaseModel.__repr_args__` for more detailstry:pydantic_extra=object.__getattribute__(self,'__pydantic_extra__')exceptAttributeError:pydantic_extra=Noneifpydantic_extra:try:returnpydantic_extra[item]exceptKeyErrorasexc:raiseAttributeError(f'{type(self).__name__!r} object has no attribute{item!r}')fromexcelse:ifhasattr(self.__class__,item):returnsuper().__getattribute__(item)# Raises AttributeError if appropriateelse:# this is the current errorraiseAttributeError(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))isnotNone:setattr_handler(self,name,value)# if None is returned from _setattr_handler, the attribute was set directlyelif(setattr_handler:=self._setattr_handler(name,value))isnotNone:setattr_handler(self,name,value)# call here to not memo on possibly unknown fieldsself.__pydantic_setattr_handlers__[name]=setattr_handler# memoize the handler for faster accessdef_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__ifnameincls.__class_vars__:raiseAttributeError(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`.')elifnot_fields.is_valid_field_name(name):if(attribute:=cls.__private_attributes__.get(name))isnotNone:ifhasattr(attribute,'__set__'):returnlambdamodel,_name,val:attribute.__set__(model,val)else:return_SIMPLE_SETATTR_HANDLERS['private']else:_object_setattr(self,name,value)returnNone# Can not return memoized handler with possibly freeform attr namesattr=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.ifisinstance(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.ifisinstance(attr,property):returnlambdamodel,_name,val:attr.__set__(model,val)elifcls.model_config.get('validate_assignment'):return_SIMPLE_SETATTR_HANDLERS['validate_assignment']elifnamenotincls.__pydantic_fields__:ifcls.model_config.get('extra')!='allow':# TODO - matching errorraiseValueError(f'"{cls.__name__}" object has no field "{name}"')elifattrisNone:# attribute does not exist, so put it in extraself.__pydantic_extra__[name]=valuereturnNone# Can not return memoized handler with possibly freeform attr nameselse:# attribute _does_ exist, and was not in extra, so update itreturn_SIMPLE_SETATTR_HANDLERS['extra_known']else:return_SIMPLE_SETATTR_HANDLERS['model_field']def__delattr__(self,item:str)->Any:cls=self.__class__ifiteminself.__private_attributes__:attribute=self.__private_attributes__[item]ifhasattr(attribute,'__delete__'):attribute.__delete__(self)# type: ignorereturntry:# Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has itemsdelself.__pydantic_private__[item]# type: ignorereturnexceptKeyErrorasexc:raiseAttributeError(f'{cls.__name__!r} object has no attribute{item!r}')fromexc# Allow cached properties to be deleted (even if the class is frozen):attr=getattr(cls,item,None)ifisinstance(attr,cached_property):returnobject.__delattr__(self,item)_check_frozen(cls,name=item,value=None)ifiteminself.__pydantic_fields__:object.__delattr__(self,item)elifself.__pydantic_extra__isnotNoneanditeminself.__pydantic_extra__:delself.__pydantic_extra__[item]else:try:object.__delattr__(self,item)exceptAttributeError:raiseAttributeError(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:returnself.model_copy(update=changes)def__getstate__(self)->dict[Any,Any]:private=self.__pydantic_private__ifprivate:private={k:vfork,vinprivate.items()ifvisnotPydanticUndefined}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__',{}))ifnotTYPE_CHECKING:def__eq__(self,other:Any)->bool:ifisinstance(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']orself.__class__other_type=other.__pydantic_generic_metadata__['origin']orother.__class__# Perform common checks firstifnot(self_type==other_typeandgetattr(self,'__pydantic_private__',None)==getattr(other,'__pydantic_private__',None)andself.__pydantic_extra__==other.__pydantic_extra__):returnFalse# 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__ comparisonifself.__dict__==other.__dict__:# If the check above passes, then pydantic fields are equal, we can return earlyreturnTrue# 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()ifself.__dict__.keys()<=model_fieldsandother.__dict__.keys()<=model_fields:returnFalse# 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)ifmodel_fieldselselambda_:_utils._SENTINELtry:returngetter(self.__dict__)==getter(other.__dict__)exceptKeyError:# 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__)returngetter(self_fields_proxy)==getter(other_fields_proxy)# other instance is not a BaseModelelse:returnNotImplemented# delegate to the other item in the comparisonifTYPE_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)inself.__dict__.items()ifnotk.startswith('_')]extra=self.__pydantic_extra__ifextra:yield fromextra.items()def__repr__(self)->str:returnf'{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))fork,vinself.__pydantic_computed_fields__.items()ifv.repr]fork,vinself.__dict__.items():field=self.__pydantic_fields__.get(k)iffieldandfield.repr:ifvisnotself:yieldk,velse:yieldk,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__')exceptAttributeError:pydantic_extra=Noneifpydantic_extraisnotNone:yield from((k,v)fork,vinpydantic_extra.items())yield fromcomputed_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:returnself.__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,)returngetattr(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,)returnself.__pydantic_fields_set__@typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.',category=None)defdict(# noqa: D102self,*,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 UP006warnings.warn('The `dict` method is deprecated; use `model_dump` instead.',category=PydanticDeprecatedSince20,stacklevel=2,)returnself.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)defjson(# noqa: D102self,*,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,)ifencoderisnotPydanticUndefined:raiseTypeError('The `encoder` argument is no longer supported; use field serializers instead.')ifmodels_as_dictisnotPydanticUndefined:raiseTypeError('The `models_as_dict` argument is no longer supported; use a model serializer instead.')ifdumps_kwargs:raiseTypeError('`dumps_kwargs` keyword arguments are no longer supported.')returnself.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)defparse_obj(cls,obj:Any)->Self:# noqa: D102warnings.warn('The `parse_obj` method is deprecated; use `model_validate` instead.',category=PydanticDeprecatedSince20,stacklevel=2,)returncls.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,)defparse_raw(# noqa: D102cls,b:str|bytes,*,content_type:str|None=None,encoding:str='utf8',proto:DeprecatedParseProtocol|None=None,allow_pickle:bool=False,)->Self:# pragma: no coverwarnings.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.deprecatedimportparsetry:obj=parse.load_str_bytes(b,proto=proto,content_type=content_type,encoding=encoding,allow_pickle=allow_pickle,)except(ValueError,TypeError)asexc:importjson# try to match V1ifisinstance(exc,UnicodeDecodeError):type_str='value_error.unicodedecode'elifisinstance(exc,json.JSONDecodeError):type_str='value_error.jsondecode'elifisinstance(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 sameerror: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,}raisepydantic_core.ValidationError.from_exception_data(cls.__name__,[error])returncls.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,)defparse_file(# noqa: D102cls,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.deprecatedimportparseobj=parse.load_file(path,proto=proto,content_type=content_type,encoding=encoding,allow_pickle=allow_pickle,)returncls.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,)deffrom_orm(cls,obj:Any)->Self:# noqa: D102warnings.warn('The `from_orm` method is deprecated; set '"`model_config['from_attributes']=True` and use `model_validate` instead.",category=PydanticDeprecatedSince20,stacklevel=2,)ifnotcls.model_config.get('from_attributes',None):raisePydanticUserError('You must set the config attribute `from_attributes=True` to use from_orm',code=None)returncls.model_validate(obj)@classmethod@typing_extensions.deprecated('The `construct` method is deprecated; use `model_construct` instead.',category=None)defconstruct(cls,_fields_set:set[str]|None=None,**values:Any)->Self:# noqa: D102warnings.warn('The `construct` method is deprecated; use `model_construct` instead.',category=PydanticDeprecatedSince20,stacklevel=2,)returncls.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,)defcopy(self,*,include:AbstractSetIntStr|MappingIntStrAny|None=None,exclude:AbstractSetIntStr|MappingIntStrAny|None=None,update:Dict[str,Any]|None=None,# noqa UP006deep: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.deprecatedimportcopy_internalsvalues=dict(copy_internals._iter(self,to_dict=False,by_alias=False,include=include,exclude=exclude,exclude_unset=False),**(updateor{}),)ifself.__pydantic_private__isNone:private=Noneelse:private={k:vfork,vinself.__pydantic_private__.items()ifvisnotPydanticUndefined}ifself.__pydantic_extra__isNone:extra:dict[str,Any]|None=Noneelse:extra=self.__pydantic_extra__.copy()forkinlist(self.__pydantic_extra__):ifknotinvalues:# k was in the excludeextra.pop(k)forkinlist(values):ifkinself.__pydantic_extra__:# k must have come from extraextra[k]=values.pop(k)# new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwargifupdate:fields_set=self.__pydantic_fields_set__|update.keys()else:fields_set=set(self.__pydantic_fields_set__)# removing excluded fields from `__pydantic_fields_set__`ifexclude:fields_set-=set(exclude)returncopy_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)defschema(# noqa: D102cls,by_alias:bool=True,ref_template:str=DEFAULT_REF_TEMPLATE)->Dict[str,Any]:# noqa UP006warnings.warn('The `schema` method is deprecated; use `model_json_schema` instead.',category=PydanticDeprecatedSince20,stacklevel=2,)returncls.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,)defschema_json(# noqa: D102cls,*,by_alias:bool=True,ref_template:str=DEFAULT_REF_TEMPLATE,**dumps_kwargs:Any)->str:# pragma: no coverwarnings.warn('The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',category=PydanticDeprecatedSince20,stacklevel=2,)importjsonfrom.deprecated.jsonimportpydantic_encoderreturnjson.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)defvalidate(cls,value:Any)->Self:# noqa: D102warnings.warn('The `validate` method is deprecated; use `model_validate` instead.',category=PydanticDeprecatedSince20,stacklevel=2,)returncls.model_validate(value)@classmethod@typing_extensions.deprecated('The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',category=None,)defupdate_forward_refs(cls,**localns:Any)->None:# noqa: D102warnings.warn('The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',category=PydanticDeprecatedSince20,stacklevel=2,)iflocalns:# pragma: no coverraiseTypeError('`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.deprecatedimportcopy_internalsreturncopy_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.deprecatedimportcopy_internalsreturncopy_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.deprecatedimportcopy_internalsreturncopy_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.deprecatedimportcopy_internalsreturncopy_internals._calculate_keys(self,*args,**kwargs)