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defrebuild(self,*,force:bool=False,raise_errors:bool=True,_parent_namespace_depth:int=2,_types_namespace:_namespace_utils.MappingNamespace|None=None,)->bool|None:"""Try to rebuild the pydantic-core schema for the adapter's type.    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 type adapter's schema, defaults to `False`.        raise_errors: Whether to raise errors, defaults to `True`.        _parent_namespace_depth: Depth at which to search for the [parent frame][frame-objects]. This            frame is used when resolving forward annotations during schema rebuilding, by looking for            the locals of this frame. Defaults to 2, which will result in the frame where the method            was called.        _types_namespace: An explicit types namespace to use, instead of using the local namespace            from the parent frame. 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`.    """ifnotforceandself.pydantic_complete:returnNoneif_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={}# we have to manually fetch globals here because there's no type on the stack of the NsResolver# and so we skip the globalns = get_module_ns_of(typ) call that would normally happenglobalns=sys._getframe(max(_parent_namespace_depth-1,1)).f_globalsns_resolver=_namespace_utils.NsResolver(namespaces_tuple=_namespace_utils.NamespacesTuple(locals=rebuild_ns,globals=globalns),parent_namespace=rebuild_ns,)returnself._init_core_attrs(ns_resolver=ns_resolver,force=True,raise_errors=raise_errors)

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defvalidate_python(self,object:Any,/,*,strict:bool|None=None,from_attributes:bool|None=None,context:dict[str,Any]|None=None,experimental_allow_partial:bool|Literal['off','on','trailing-strings']=False,by_alias:bool|None=None,by_name:bool|None=None,)->T:"""Validate a Python object against the model.    Args:        object: The Python object to validate against the model.        strict: Whether to strictly check types.        from_attributes: Whether to extract data from object attributes.        context: Additional context to pass to the validator.        experimental_allow_partial: **Experimental** whether to enable            [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.            * False / 'off': Default behavior, no partial validation.            * True / 'on': Enable partial validation.            * 'trailing-strings': Enable partial validation and allow trailing strings in the input.        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.    !!! note        When using `TypeAdapter` with a Pydantic `dataclass`, the use of the `from_attributes`        argument is not supported.    Returns:        The validated object.    """ifby_aliasisFalseandby_nameisnotTrue:raisePydanticUserError('At least one of `by_alias` or `by_name` must be set to True.',code='validate-by-alias-and-name-false',)returnself.validator.validate_python(object,strict=strict,from_attributes=from_attributes,context=context,allow_partial=experimental_allow_partial,by_alias=by_alias,by_name=by_name,)

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defvalidate_json(self,data:str|bytes|bytearray,/,*,strict:bool|None=None,context:dict[str,Any]|None=None,experimental_allow_partial:bool|Literal['off','on','trailing-strings']=False,by_alias:bool|None=None,by_name:bool|None=None,)->T:"""!!! abstract "Usage Documentation"        [JSON Parsing](../concepts/json.md#json-parsing)    Validate a JSON string or bytes against the model.    Args:        data: The JSON data to validate against the model.        strict: Whether to strictly check types.        context: Additional context to use during validation.        experimental_allow_partial: **Experimental** whether to enable            [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.            * False / 'off': Default behavior, no partial validation.            * True / 'on': Enable partial validation.            * 'trailing-strings': Enable partial validation and allow trailing strings in the input.        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 object.    """ifby_aliasisFalseandby_nameisnotTrue:raisePydanticUserError('At least one of `by_alias` or `by_name` must be set to True.',code='validate-by-alias-and-name-false',)returnself.validator.validate_json(data,strict=strict,context=context,allow_partial=experimental_allow_partial,by_alias=by_alias,by_name=by_name,)

477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518

defvalidate_strings(self,obj:Any,/,*,strict:bool|None=None,context:dict[str,Any]|None=None,experimental_allow_partial:bool|Literal['off','on','trailing-strings']=False,by_alias:bool|None=None,by_name:bool|None=None,)->T:"""Validate object contains string data against the model.    Args:        obj: The object contains string data to validate.        strict: Whether to strictly check types.        context: Additional context to use during validation.        experimental_allow_partial: **Experimental** whether to enable            [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.            * False / 'off': Default behavior, no partial validation.            * True / 'on': Enable partial validation.            * 'trailing-strings': Enable partial validation and allow trailing strings in the input.        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 object.    """ifby_aliasisFalseandby_nameisnotTrue:raisePydanticUserError('At least one of `by_alias` or `by_name` must be set to True.',code='validate-by-alias-and-name-false',)returnself.validator.validate_strings(obj,strict=strict,context=context,allow_partial=experimental_allow_partial,by_alias=by_alias,by_name=by_name,)

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defget_default_value(self,*,strict:bool|None=None,context:dict[str,Any]|None=None)->Some[T]|None:"""Get the default value for the wrapped type.    Args:        strict: Whether to strictly check types.        context: Additional context to pass to the validator.    Returns:        The default value wrapped in a `Some` if there is one or None if not.    """returnself.validator.get_default_value(strict=strict,context=context)

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defdump_python(self,instance:T,/,*,mode:Literal['json','python']='python',include:IncEx|None=None,exclude:IncEx|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,context:dict[str,Any]|None=None,)->Any:"""Dump an instance of the adapted type to a Python object.    Args:        instance: The Python object to serialize.        mode: The output format.        include: Fields to include in the output.        exclude: Fields to exclude from the output.        by_alias: Whether to use alias names for field names.        exclude_unset: Whether to exclude unset fields.        exclude_defaults: Whether to exclude fields with default values.        exclude_none: Whether to exclude fields with None values.        round_trip: Whether to output the serialized data in a way that is compatible with deserialization.        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.        context: Additional context to pass to the serializer.    Returns:        The serialized object.    """returnself.serializer.to_python(instance,mode=mode,by_alias=by_alias,include=include,exclude=exclude,exclude_unset=exclude_unset,exclude_defaults=exclude_defaults,exclude_none=exclude_none,round_trip=round_trip,warnings=warnings,fallback=fallback,serialize_as_any=serialize_as_any,context=context,)

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defdump_json(self,instance:T,/,*,indent:int|None=None,include:IncEx|None=None,exclude:IncEx|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,context:dict[str,Any]|None=None,)->bytes:"""!!! abstract "Usage Documentation"        [JSON Serialization](../concepts/json.md#json-serialization)    Serialize an instance of the adapted type to JSON.    Args:        instance: The instance to be serialized.        indent: Number of spaces for JSON indentation.        include: Fields to include.        exclude: Fields to exclude.        by_alias: Whether to use alias names for field names.        exclude_unset: Whether to exclude unset fields.        exclude_defaults: Whether to exclude fields with default values.        exclude_none: Whether to exclude fields with a value of `None`.        round_trip: Whether to serialize and deserialize the instance to ensure round-tripping.        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.        context: Additional context to pass to the serializer.    Returns:        The JSON representation of the given instance as bytes.    """returnself.serializer.to_json(instance,indent=indent,include=include,exclude=exclude,by_alias=by_alias,exclude_unset=exclude_unset,exclude_defaults=exclude_defaults,exclude_none=exclude_none,round_trip=round_trip,warnings=warnings,fallback=fallback,serialize_as_any=serialize_as_any,context=context,)

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defjson_schema(self,*,by_alias:bool=True,ref_template:str=DEFAULT_REF_TEMPLATE,schema_generator:type[GenerateJsonSchema]=GenerateJsonSchema,mode:JsonSchemaMode='validation',)->dict[str,Any]:"""Generate a JSON schema for the adapted type.    Args:        by_alias: Whether to use alias names for field names.        ref_template: The format string used for generating $ref strings.        schema_generator: The generator class used for creating the schema.        mode: The mode to use for schema generation.    Returns:        The JSON schema for the model as a dictionary.    """schema_generator_instance=schema_generator(by_alias=by_alias,ref_template=ref_template)ifisinstance(self.core_schema,_mock_val_ser.MockCoreSchema):self.core_schema.rebuild()assertnotisinstance(self.core_schema,_mock_val_ser.MockCoreSchema),'this is a bug! please report it'returnschema_generator_instance.generate(self.core_schema,mode=mode)

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@staticmethoddefjson_schemas(inputs:Iterable[tuple[JsonSchemaKeyT,JsonSchemaMode,TypeAdapter[Any]]],/,*,by_alias:bool=True,title:str|None=None,description:str|None=None,ref_template:str=DEFAULT_REF_TEMPLATE,schema_generator:type[GenerateJsonSchema]=GenerateJsonSchema,)->tuple[dict[tuple[JsonSchemaKeyT,JsonSchemaMode],JsonSchemaValue],JsonSchemaValue]:"""Generate a JSON schema including definitions from multiple type adapters.    Args:        inputs: Inputs to schema generation. The first two items will form the keys of the (first)            output mapping; the type adapters will provide the core schemas that get converted into            definitions in the output JSON schema.        by_alias: Whether to use alias names.        title: The title for the schema.        description: The description for the schema.        ref_template: The format string used for generating $ref strings.        schema_generator: The generator class used for creating the schema.    Returns:        A tuple where:            - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and                whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have                JsonRef references to definitions that are defined in the second returned element.)            - The second element is a JSON schema containing all definitions referenced in the first returned                element, along with the optional title and description keys.    """schema_generator_instance=schema_generator(by_alias=by_alias,ref_template=ref_template)inputs_=[]forkey,mode,adapterininputs:# This is the same pattern we follow for model json schemas - we attempt a core schema rebuild if we detect a mockifisinstance(adapter.core_schema,_mock_val_ser.MockCoreSchema):adapter.core_schema.rebuild()assertnotisinstance(adapter.core_schema,_mock_val_ser.MockCoreSchema),('this is a bug! please report it')inputs_.append((key,mode,adapter.core_schema))json_schemas_map,definitions=schema_generator_instance.generate_definitions(inputs_)json_schema:dict[str,Any]={}ifdefinitions:json_schema['$defs']=definitionsiftitle:json_schema['title']=titleifdescription:json_schema['description']=descriptionreturnjson_schemas_map,json_schema