kittycad.models.jetstream.Jetstream

class kittycad.models.jetstream.Jetstream(**data)[source][source]

Bases: BaseModel

Jetstream information.

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.

__init__(**data)[source]

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.

Methods

`__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`from_orm`(obj)
`json`(*[, include, exclude, by_alias, ...])
`model_construct`([_fields_set])	Creates a new instance of the `Model` class with validated data.
`model_copy`(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
`model_dump`(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
`model_dump_json`(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(_BaseModel__context)	Override this method to perform additional initialization after `__init__` and `model_construct`.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
`model_validate_strings`(obj, *[, strict, context])	Validate the given object with string data against the Pydantic model.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`update_forward_refs`(**localns)
`validate`(value)

Attributes

`model_computed_fields`
`model_config`	Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.
`config`
`meta`
`stats`

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'config': <class 'kittycad.models.jetstream_config.JetstreamConfig'>, 'meta': <class 'kittycad.models.meta_cluster_info.MetaClusterInfo'>, 'model_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', 'stats': <class 'kittycad.models.jetstream_stats.JetstreamStats'>}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – 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 (GetJsonSchemaHandler) – 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.

Return type:

Dict[str, Any]

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

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.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.jetstream'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.jetstream.Jetstream'>, 'config': {'title': 'Jetstream'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.jetstream.Jetstream'>>]}, 'ref': 'kittycad.models.jetstream.Jetstream:94250663979344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'config': {'metadata': {}, 'schema': {'default': {'domain': '', 'max_memory': 0, 'max_storage': 0, 'store_dir': ''}, 'schema': {'cls': <class 'kittycad.models.jetstream_config.JetstreamConfig'>, 'config': {'title': 'JetstreamConfig'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.jetstream_config.JetstreamConfig'>>]}, 'ref': 'kittycad.models.jetstream_config.JetstreamConfig:94250663971264', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'domain': {'metadata': {}, 'schema': {'default': '', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'max_memory': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'max_storage': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'store_dir': {'metadata': {}, 'schema': {'default': '', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'JetstreamConfig', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'default'}, 'type': 'model-field'}, 'meta': {'metadata': {}, 'schema': {'default': {'cluster_size': 0, 'leader': '', 'name': ''}, 'schema': {'cls': <class 'kittycad.models.meta_cluster_info.MetaClusterInfo'>, 'config': {'title': 'MetaClusterInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.meta_cluster_info.MetaClusterInfo'>>]}, 'ref': 'kittycad.models.meta_cluster_info.MetaClusterInfo:94250663947424', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'cluster_size': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'leader': {'metadata': {}, 'schema': {'default': '', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'name': {'metadata': {}, 'schema': {'default': '', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MetaClusterInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'default'}, 'type': 'model-field'}, 'stats': {'metadata': {}, 'schema': {'default': {'accounts': 0, 'api': {'errors': 0, 'inflight': 0, 'total': 0}, 'ha_assets': 0, 'memory': 0, 'reserved_memory': 0, 'reserved_store': 0, 'store': 0}, 'schema': {'cls': <class 'kittycad.models.jetstream_stats.JetstreamStats'>, 'config': {'title': 'JetstreamStats'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.jetstream_stats.JetstreamStats'>>]}, 'ref': 'kittycad.models.jetstream_stats.JetstreamStats:94250663913200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'accounts': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'api': {'metadata': {}, 'schema': {'default': {'errors': 0, 'inflight': 0, 'total': 0}, 'schema': {'cls': <class 'kittycad.models.jetstream_api_stats.JetstreamApiStats'>, 'config': {'title': 'JetstreamApiStats'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.jetstream_api_stats.JetstreamApiStats'>>]}, 'ref': 'kittycad.models.jetstream_api_stats.JetstreamApiStats:94250663903168', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'errors': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'inflight': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'total': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'JetstreamApiStats', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'default'}, 'type': 'model-field'}, 'ha_assets': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'memory': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'reserved_memory': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'reserved_store': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'store': {'metadata': {}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'JetstreamStats', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Jetstream', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'config': FieldInfo(annotation=JetstreamConfig, required=False, default={'domain': '', 'max_memory': 0, 'max_storage': 0, 'store_dir': ''}), 'meta': FieldInfo(annotation=MetaClusterInfo, required=False, default={'cluster_size': 0, 'leader': '', 'name': ''}), 'stats': FieldInfo(annotation=JetstreamStats, required=False, default={'accounts': 0, 'api': {'errors': 0, 'inflight': 0, 'total': 0}, 'ha_assets': 0, 'memory': 0, 'reserved_memory': 0, 'reserved_store': 0, 'store': 0})}[source]: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: 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.

classmethod __pydantic_init_subclass__(**kwargs)[source]

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.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055b87158a950, ), serializer: Fields( GeneralFieldsSerializer { fields: { "config": SerField { key_py: Py( 0x00007fc70fb977b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc70bc19840, ), ), serializer: Model( ModelSerializer { class: Py( 0x000055b8715889c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "max_storage": SerField { key_py: Py( 0x00007fc70bc1bf30, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007fc70f7eaa90, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710619e30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "max_memory": SerField { key_py: Py( 0x00007fc70bc1bef0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "store_dir": SerField { key_py: Py( 0x00007fc70bc1bf70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710619e30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "JetstreamConfig", }, ), }, ), ), required: true, }, "meta": SerField { key_py: Py( 0x00007fc70f730d80, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc70bc25500, ), ), serializer: Model( ModelSerializer { class: Py( 0x000055b871582ca0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "cluster_size": SerField { key_py: Py( 0x00007fc70bc1bfb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "leader": SerField { key_py: Py( 0x00007fc70bd444b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710619e30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "name": SerField { key_py: Py( 0x00007fc710620668, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710619e30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "MetaClusterInfo", }, ), }, ), ), required: true, }, "stats": SerField { key_py: Py( 0x00007fc70d56af10, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc70bc254c0, ), ), serializer: Model( ModelSerializer { class: Py( 0x000055b87157a6f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "reserved_memory": SerField { key_py: Py( 0x00007fc70bc24170, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "store": SerField { key_py: Py( 0x00007fc70f7e87b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "ha_assets": SerField { key_py: Py( 0x00007fc70bc240f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "api": SerField { key_py: Py( 0x00007fc70d59dfe0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc70bc3c100, ), ), serializer: Model( ModelSerializer { class: Py( 0x000055b871577fc0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "errors": SerField { key_py: Py( 0x00007fc71061e780, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "total": SerField { key_py: Py( 0x00007fc70f7666d0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "inflight": SerField { key_py: Py( 0x00007fc70bc240b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "JetstreamApiStats", }, ), }, ), ), required: true, }, "memory": SerField { key_py: Py( 0x00007fc70bd447e0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "accounts": SerField { key_py: Py( 0x00007fc70bc24070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "reserved_store": SerField { key_py: Py( 0x00007fc70bc241f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007fc710614b28, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "JetstreamStats", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Jetstream", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="Jetstream", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "config", lookup_key: Simple { key: "config", py_key: Py( 0x00007fc70c715c50, ), path: LookupPath( [ S( "config", Py( 0x00007fc70c716cd0, ), ), ], ), }, name_py: Py( 0x00007fc70fb977b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc70bc19840, ), ), on_error: Raise, validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007fc70c716f10, ), path: LookupPath( [ S( "domain", Py( 0x00007fc70c716c10, ), ), ], ), }, name_py: Py( 0x00007fc70f7eaa90, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710619e30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "max_memory", lookup_key: Simple { key: "max_memory", py_key: Py( 0x00007fc70bc266b0, ), path: LookupPath( [ S( "max_memory", Py( 0x00007fc70bc26ef0, ), ), ], ), }, name_py: Py( 0x00007fc70bc1bef0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "max_storage", lookup_key: Simple { key: "max_storage", py_key: Py( 0x00007fc70bc27130, ), path: LookupPath( [ S( "max_storage", Py( 0x00007fc70bc25d30, ), ), ], ), }, name_py: Py( 0x00007fc70bc1bf30, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "store_dir", lookup_key: Simple { key: "store_dir", py_key: Py( 0x00007fc70bc27270, ), path: LookupPath( [ S( "store_dir", Py( 0x00007fc70bc265f0, ), ), ], ), }, name_py: Py( 0x00007fc70bc1bf70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710619e30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, ], model_name: "JetstreamConfig", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055b8715889c0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fc70e4ea3d0, ), name: "JetstreamConfig", }, ), validate_default: false, copy_default: true, name: "default[JetstreamConfig]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "meta", lookup_key: Simple { key: "meta", py_key: Py( 0x00007fc70c714690, ), path: LookupPath( [ S( "meta", Py( 0x00007fc70c714750, ), ), ], ), }, name_py: Py( 0x00007fc70f730d80, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc70bc25500, ), ), on_error: Raise, validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "cluster_size", lookup_key: Simple { key: "cluster_size", py_key: Py( 0x00007fc70bc263b0, ), path: LookupPath( [ S( "cluster_size", Py( 0x00007fc70bc258f0, ), ), ], ), }, name_py: Py( 0x00007fc70bc1bfb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "leader", lookup_key: Simple { key: "leader", py_key: Py( 0x00007fc70c716ca0, ), path: LookupPath( [ S( "leader", Py( 0x00007fc70c716be0, ), ), ], ), }, name_py: Py( 0x00007fc70bd444b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710619e30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "name", lookup_key: Simple { key: "name", py_key: Py( 0x00007fc70c716c40, ), path: LookupPath( [ S( "name", Py( 0x00007fc70c716ee0, ), ), ], ), }, name_py: Py( 0x00007fc710620668, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710619e30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, ], model_name: "MetaClusterInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055b871582ca0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fc70e4ea3d0, ), name: "MetaClusterInfo", }, ), validate_default: false, copy_default: true, name: "default[MetaClusterInfo]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "stats", lookup_key: Simple { key: "stats", py_key: Py( 0x00007fc70c712160, ), path: LookupPath( [ S( "stats", Py( 0x00007fc70c7122e0, ), ), ], ), }, name_py: Py( 0x00007fc70d56af10, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc70bc254c0, ), ), on_error: Raise, validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "accounts", lookup_key: Simple { key: "accounts", py_key: Py( 0x00007fc70bc26e30, ), path: LookupPath( [ S( "accounts", Py( 0x00007fc70bc26f70, ), ), ], ), }, name_py: Py( 0x00007fc70bc24070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "api", lookup_key: Simple { key: "api", py_key: Py( 0x00007fc70c715ec0, ), path: LookupPath( [ S( "api", Py( 0x00007fc70c715d40, ), ), ], ), }, name_py: Py( 0x00007fc70d59dfe0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc70bc3c100, ), ), on_error: Raise, validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "errors", lookup_key: Simple { key: "errors", py_key: Py( 0x00007fc70c715e30, ), path: LookupPath( [ S( "errors", Py( 0x00007fc70c715d70, ), ), ], ), }, name_py: Py( 0x00007fc71061e780, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "inflight", lookup_key: Simple { key: "inflight", py_key: Py( 0x00007fc70bc26ff0, ), path: LookupPath( [ S( "inflight", Py( 0x00007fc70bc26d70, ), ), ], ), }, name_py: Py( 0x00007fc70bc240b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "total", lookup_key: Simple { key: "total", py_key: Py( 0x00007fc70c715e00, ), path: LookupPath( [ S( "total", Py( 0x00007fc70c715e60, ), ), ], ), }, name_py: Py( 0x00007fc70f7666d0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, ], model_name: "JetstreamApiStats", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055b871577fc0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fc70e4ea3d0, ), name: "JetstreamApiStats", }, ), validate_default: false, copy_default: true, name: "default[JetstreamApiStats]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "ha_assets", lookup_key: Simple { key: "ha_assets", py_key: Py( 0x00007fc70bc26eb0, ), path: LookupPath( [ S( "ha_assets", Py( 0x00007fc70bc26f30, ), ), ], ), }, name_py: Py( 0x00007fc70bc240f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "memory", lookup_key: Simple { key: "memory", py_key: Py( 0x00007fc70c715ef0, ), path: LookupPath( [ S( "memory", Py( 0x00007fc70c715e90, ), ), ], ), }, name_py: Py( 0x00007fc70bd447e0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "reserved_memory", lookup_key: Simple { key: "reserved_memory", py_key: Py( 0x00007fc70bc26c70, ), path: LookupPath( [ S( "reserved_memory", Py( 0x00007fc70bc26df0, ), ), ], ), }, name_py: Py( 0x00007fc70bc24170, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "reserved_store", lookup_key: Simple { key: "reserved_store", py_key: Py( 0x00007fc70bc26e70, ), path: LookupPath( [ S( "reserved_store", Py( 0x00007fc70bc26830, ), ), ], ), }, name_py: Py( 0x00007fc70bc241f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, Field { name: "store", lookup_key: Simple { key: "store", py_key: Py( 0x00007fc70c7d7060, ), path: LookupPath( [ S( "store", Py( 0x00007fc70c7120d0, ), ), ], ), }, name_py: Py( 0x00007fc70f7e87b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007fc710614b28, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, ], model_name: "JetstreamStats", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055b87157a6f0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fc70e4ea3d0, ), name: "JetstreamStats", }, ), validate_default: false, copy_default: true, name: "default[JetstreamStats]", undefined: Py( 0x00007fc70e4ea3d0, ), }, ), frozen: false, }, ], model_name: "Jetstream", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055b87158a950, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007fc70e4ea3d0, ), name: "Jetstream", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__replace__(**changes)[source]

Return type:: Self

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[str | None, Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_recursion__(object)[source]

Returns the string representation of a recursive object.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, config: kittycad.models.jetstream_config.JetstreamConfig = {'domain': '', 'max_memory': 0, 'max_storage': 0, 'store_dir': ''}, meta: kittycad.models.meta_cluster_info.MetaClusterInfo = {'cluster_size': 0, 'leader': '', 'name': ''}, stats: kittycad.models.jetstream_stats.JetstreamStats = {'accounts': 0, 'api': {'errors': 0, 'inflight': 0, 'total': 0}, 'ha_assets': 0, 'memory': 0, 'reserved_memory': 0, 'reserved_store': 0, 'store': 0}) -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

config: JetstreamConfig[source]

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

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) `

Parameters:

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.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

meta: MetaClusterInfo[source]

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}[source]

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

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.

Parameters:

_fields_set (set[str] | None) – 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 (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[Mapping[str, Any]]) – 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 (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – 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 (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[dict[str, FieldInfo]] = {'config': FieldInfo(annotation=JetstreamConfig, required=False, default={'domain': '', 'max_memory': 0, 'max_storage': 0, 'store_dir': ''}), 'meta': FieldInfo(annotation=MetaClusterInfo, required=False, default={'cluster_size': 0, 'leader': '', 'name': ''}), 'stats': FieldInfo(annotation=JetstreamStats, required=False, default={'accounts': 0, 'api': {'errors': 0, 'inflight': 0, 'total': 0}, 'ha_assets': 0, 'memory': 0, 'reserved_memory': 0, 'reserved_store': 0, 'store': 0})}[source]

property model_fields_set: set[str][source]

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.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – 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.
Return type:: str
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.

model_post_init(_BaseModel__context)[source]

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.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

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.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[Mapping[str, Any]]) – The types namespace, defaults to None.

Return type:

bool | 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.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

stats: JetstreamStats[source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self