Model

The Model provides the root-node for the Yace Interface Definition, it provides helper-functions for loading Yace Interface Definition Language files from their .yaml format on the file-system, into the representation offered by the Yace Interface Definition Language-entities.

• Meta

• Model

• ModelWalker

Their Yace Interface Definition Language representation follows below.

class yace.model.Meta(*, lic: str, version: str = '0.0.1', author: str = 'Foo Bar <foo@example.com>', project: str = 'Example', prefix: str = 'ex', brief: str = 'yace example', full: str = 'This description is provided with the default IR-meta, to change it, then make sure you have a meta-lbl in your yace-file')

Meta data describing the FFI, such as license, version, and documentation

author: str

brief: str

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

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.

dict(*, include: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

classmethod from_orm(obj: Any) → Self

full: str

json(*, include: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

lic: str

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property model_extra: 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”.

model_fields = {'author': FieldInfo(annotation=str, required=False, default='Foo Bar <foo@example.com>'), 'brief': FieldInfo(annotation=str, required=False, default='yace example'), 'full': FieldInfo(annotation=str, required=False, default='This description is provided with the default IR-meta, to change it, then make sure you have a meta-lbl in your yace-file'), 'lic': FieldInfo(annotation=str, required=True), 'prefix': FieldInfo(annotation=str, required=False, default='ex'), 'project': FieldInfo(annotation=str, required=False, default='Example'), 'version': FieldInfo(annotation=str, required=False, default='0.0.1')}

property model_fields_set: 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.

classmethod model_parametrized_name(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.

model_post_init(_BaseModel__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.

classmethod model_rebuild(*, 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.

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: Any | 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.

Raises:

ValidationError: If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: Any | None = None) → Self

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

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: Any, *, strict: bool | None = None, context: Any | 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.

Returns:

The validated Pydantic model.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

prefix: str

project: str

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

classmethod update_forward_refs(**localns: Any) → None

classmethod validate(value: Any) → Self

version: str

class yace.model.Model(*, meta: ~yace.model.Meta, entities: ~typing.List[~yace.ir.constants.Define | ~yace.ir.constants.Enum | ~yace.ir.derivedtypes.Struct | ~yace.ir.derivedtypes.Union | ~yace.ir.functiontypes.Function | ~yace.ir.functiontypes.FunctionPointer | ~yace.ir.directives.IncludeDirective] = <factory>)

The Model serves as the root of the “tree” describing the interface to generate C API and bindings for.

MAPPING: ClassVar = {'array_tspec': <class 'yace.ir.datatypes.Array'>, 'bitfield_decl': <class 'yace.ir.derivedtypes.Bitfield'>, 'bitfield_struct_decl': <class 'yace.ir.derivedtypes.BitfieldStruct'>, 'bool_tspec': <class 'yace.ir.datatypes.Bool'>, 'char_tspec': <class 'yace.ir.datatypes.Char'>, 'dec': <class 'yace.ir.constants.Dec'>, 'define': <class 'yace.ir.constants.Define'>, 'enum': <class 'yace.ir.constants.Enum'>, 'enum_tspec': <class 'yace.ir.datatypes.Enumeration'>, 'enum_value': <class 'yace.ir.constants.EnumValue'>, 'f32_tspec': <class 'yace.ir.datatypes.F32'>, 'f64_tspec': <class 'yace.ir.datatypes.F64'>, 'field_decl': <class 'yace.ir.derivedtypes.Field'>, 'function_decl': <class 'yace.ir.functiontypes.Function'>, 'function_pointer_decl': <class 'yace.ir.functiontypes.FunctionPointer'>, 'function_pointer_tspec': <class 'yace.ir.datatypes.FunctionPointer'>, 'hex': <class 'yace.ir.constants.Hex'>, 'i16_tspec': <class 'yace.ir.datatypes.I16'>, 'i32_tspec': <class 'yace.ir.datatypes.I32'>, 'i64_tspec': <class 'yace.ir.datatypes.I64'>, 'i8_tspec': <class 'yace.ir.datatypes.I8'>, 'i_tspec': <class 'yace.ir.datatypes.I'>, 'ih_tspec': <class 'yace.ir.datatypes.IShort'>, 'il_tspec': <class 'yace.ir.datatypes.ILong'>, 'ill_tspec': <class 'yace.ir.datatypes.ILongLong'>, 'include_stmt': <class 'yace.ir.directives.IncludeDirective'>, 'isize_tspec': <class 'yace.ir.datatypes.ISize'>, 'parameter_decl': <class 'yace.ir.functiontypes.Parameter'>, 'pointer_tspec': <class 'yace.ir.datatypes.Pointer'>, 'record_tspec': <class 'yace.ir.datatypes.Record'>, 'str': <class 'yace.ir.constants.String'>, 'string_tspec': <class 'yace.ir.datatypes.CString'>, 'struct_decl': <class 'yace.ir.derivedtypes.Struct'>, 'u16_tspec': <class 'yace.ir.datatypes.U16'>, 'u32_tspec': <class 'yace.ir.datatypes.U32'>, 'u64_tspec': <class 'yace.ir.datatypes.U64'>, 'u8_tspec': <class 'yace.ir.datatypes.U8'>, 'u_tspec': <class 'yace.ir.datatypes.U'>, 'ul_tspec': <class 'yace.ir.datatypes.ULong'>, 'ull_tspec': <class 'yace.ir.datatypes.ULongLong'>, 'union_decl': <class 'yace.ir.derivedtypes.Union'>, 'us_tspec': <class 'yace.ir.datatypes.UShort'>, 'usize_tspec': <class 'yace.ir.datatypes.USize'>, 'void_pointer_tspec': <class 'yace.ir.datatypes.VoidPtr'>, 'void_tspec': <class 'yace.ir.datatypes.Void'>}

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

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.

dict(*, include: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

entities: List[Define | Enum | Struct | Union | Function | FunctionPointer | IncludeDirective]

classmethod entity_from_data(cur: dict, parent=None, depth=0)

Walk ‘cur’ recursively with depth-count and “short-hand” expansion

A tree-walk is needed as just invoking the dataclass-constructor:

entity = Model.MAPPING["struct"](data)

Would not instantiate members, parameters, typ, etc. additionally, the short-hands are expanded:

• Integer literals

  • {typ: 10} instead of {typ: {cls: dec, val: 10}}

• Data Types

  • {typ: u16} instead of {typ: {cls: u16}}

Foo…

classmethod from_data(meta: dict, entities: list)

Construct a Model using the given ‘meta’ and ‘entities’

classmethod from_orm(obj: Any) → Self

classmethod from_path(path: Path)

Returns a dict composed of the merged content of all yaml-files in the given ‘path’

json(*, include: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

meta: Meta

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property model_extra: 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”.

model_fields = {'entities': FieldInfo(annotation=List[Union[Define, Enum, Struct, Union, Function, FunctionPointer, IncludeDirective]], required=False, default_factory=list), 'meta': FieldInfo(annotation=Meta, required=True)}

property model_fields_set: 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.

classmethod model_parametrized_name(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.

model_post_init(_BaseModel__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.

classmethod model_rebuild(*, 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.

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: Any | 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.

Raises:

ValidationError: If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: Any | None = None) → Self

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

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: Any, *, strict: bool | None = None, context: Any | 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.

Returns:

The validated Pydantic model.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

to_file(path)

Write to file

classmethod update_forward_refs(**localns: Any) → None

classmethod validate(value: Any) → Self

class yace.model.ModelWalker(model)

Base-class for walking the Model

This is useful for inspecting properties of the model.

HOWTO: sub-class this and implement the visit() method

visit(current, ancestors, depth)

This is the thing which class should implement

walk()

Walks the Model invoking visit()