Python dataclasses.MISSING属性代码示例

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def _contains_non_default_init_vars(cls, previous_classes=None):
        """Check whether this dataclass contains non-default init-only vars.

        Performs a recursive check through all fields that are declared as
        dataclasses to ensure that no nested dataclasses contain init-only
        variables. The ``previous_classes`` argument is a set of previously
        checked classes to prevent infinite recursion on recursive structures.

        :param previous_classes: The set of previously checked classes.
        """
        try:
            previous_classes.add(cls)
        except AttributeError:  # NoneType
            previous_classes = {cls}

        # The identify check (.. is MISSING) is fine, MISSING is a singleton
        has_init_vars = any(field.type == InitVar and field.default is MISSING
                            for field in cls.__dataclass_fields__.values())
        children_have_init_vars = any(
                child.type._contains_non_default_init_vars(previous_classes)
                for child in fields(cls)
                if (is_dataclass(child.type)
                    and child.type not in previous_classes))
        return has_init_vars or children_have_init_vars 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def _get_fields(cls, base_fields=True) -> List[JsonSchemaField]:

        def _get_fields_uncached():
            dataclass_bases = [
                klass for klass in cls.__bases__ if is_dataclass(klass) and issubclass(klass, JsonSchemaMixin)
            ]
            base_fields_types = set()
            for base in dataclass_bases:
                base_fields_types |= {(f.name, f.type) for f in fields(base)}

            mapped_fields = []
            type_hints = get_type_hints(cls)
            for f in fields(cls):
                # Skip internal fields
                if f.name.startswith("__") or (not base_fields and (f.name, f.type) in base_fields_types):
                    continue
                # Note fields() doesn't resolve forward refs
                f.type = type_hints[f.name]
                mapped_fields.append(JsonSchemaField(f, cls.field_mapping().get(f.name, f.name)))

            if cls.__serialise_properties:
                include_properties = None
                if isinstance(cls.__serialise_properties, tuple):
                    include_properties = set(cls.__serialise_properties)

                members = inspect.getmembers(cls, inspect.isdatadescriptor)
                for name, member in members:
                    if name != "__weakref__" and (include_properties is None or name in include_properties):
                        f = Field(MISSING, None, None, None, None, None, None)
                        f.name = name
                        f.type = member.fget.__annotations__['return']
                        mapped_fields.append(JsonSchemaField(f, name, is_property=True))

            return mapped_fields

        if not base_fields:
            return _get_fields_uncached()

        if not cls.__mapped_fields:
            cls.__mapped_fields = _get_fields_uncached()
        return cls.__mapped_fields  # type: ignore 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def from_dict(cls: Type[T], data: JsonDict, validate=True, validate_enums: bool = True) -> T:
        """Returns a dataclass instance with all nested classes converted from the dict given"""
        if cls is JsonSchemaMixin:
            raise NotImplementedError

        if cls.__discriminator_name is not None and cls.__discriminator_name in data:
            if data[cls.__discriminator_name] != cls.__name__:
                for subclass in cls.__subclasses__():
                    if subclass.__name__ == data[cls.__discriminator_name]:
                        return subclass.from_dict(data, validate)

        init_values: Dict[str, Any] = {}
        non_init_values: Dict[str, Any] = {}
        if validate:
            cls._validate(data, validate_enums)

        for f in cls._get_fields():
            values = init_values if f.field.init else non_init_values
            if f.mapped_name in data or (
                    f.field.default == MISSING and f.field.default_factory == MISSING):  # type: ignore
                try:
                    values[f.field.name] = cls._decode_field(f.field.name, f.field.type, data.get(f.mapped_name))
                except ValueError:
                    if is_enum(f.field.type):
                        values[f.field.name] = data.get(f.mapped_name)
                    else:
                        raise

        # Need to ignore the type error here, since mypy doesn't know that subclasses are dataclasses
        instance = cls(**init_values)  # type: ignore
        for field_name, value in non_init_values.items():
            setattr(instance, field_name, value)
        return instance 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def _get_field_meta(cls, field: Field, schema_type: SchemaType) -> Tuple[FieldMeta, bool]:
        required = True
        field_meta = FieldMeta(schema_type=schema_type)
        default_value = MISSING
        if field.default is not MISSING:
            # In case of default value given
            default_value = field.default
        elif field.default_factory is not MISSING and field.default_factory is not None:  # type: ignore
            # In case of a default factory given, we call it
            default_value = field.default_factory()  # type: ignore

        if default_value is not MISSING:
            field_meta.default = cls._encode_field(field.type, default_value, omit_none=False)
            required = False
        if field.metadata is not None:
            if "examples" in field.metadata:
                field_meta.examples = [
                    cls._encode_field(field.type, example, omit_none=False) for example in field.metadata["examples"]
                ]
            if "extensions" in field.metadata:
                field_meta.extensions = field.metadata["extensions"]
            if "description" in field.metadata:
                field_meta.description = field.metadata["description"]
            if "title" in field.metadata:
                field_meta.title = field.metadata["title"]
            if schema_type == SchemaType.OPENAPI_3:
                field_meta.read_only = field.metadata.get("read_only")
                if field_meta.read_only and default_value is MISSING:
                    warnings.warn(f"Read-only fields should have a default value")
                field_meta.write_only = field.metadata.get("write_only")
        return field_meta, required 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def tensorflow_get_default(type_str):
    if not "default" in type_str:
        return dataclasses.MISSING
    type_str = type_str[type_str.index("default") :]
    type_str = type_str.replace("default", "")
    type_str = type_str.replace(")", "")
    type_str = type_str.replace("=", "")
    type_str = type_str.replace('"', "")
    type_str = type_str.replace("'", "")
    type_str = type_str.strip()
    if type_str == "None":
        return None
    return type_str 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def make_config(cls_name: str, fields, *args, namespace=None, **kwargs):
    """
    Function to create a dataclass
    """
    if namespace is None:
        namespace = {}
    namespace.setdefault("_fromdict", classmethod(_fromdict))
    namespace.setdefault(
        "_replace",
        lambda self, *args, **kwargs: dataclasses.replace(
            self, *args, **kwargs
        ),
    )
    namespace.setdefault("_asdict", config_asdict)
    kwargs["eq"] = True
    kwargs["init"] = True
    # Ensure non-default arguments always come before default arguments
    fields_non_default = []
    fields_default = []
    for name, cls, field in fields:
        if (
            field.default is not dataclasses.MISSING
            or field.default_factory is not dataclasses.MISSING
        ):
            fields_default.append((name, cls, field))
        else:
            fields_non_default.append((name, cls, field))
    fields = fields_non_default + fields_default
    # Create dataclass
    return dataclasses.make_dataclass(
        cls_name, fields, *args, namespace=namespace, **kwargs
    ) 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def numpy_get_default(type_str):
    if not "default" in type_str:
        return dataclasses.MISSING
    type_str = type_str[type_str.index("default") :]
    type_str = type_str.replace("default", "")
    type_str = type_str.replace(")", "")
    type_str = type_str.replace("=", "")
    type_str = type_str.replace('"', "")
    type_str = type_str.replace("'", "")
    type_str = type_str.strip()
    if type_str == "None":
        return None
    return type_str 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def _raise_missing_error(obj: Any, name: str) -> None:
    raise ValueError(
        f"Missing default value for {get_type_of(obj).__name__}.{name}, to indicate "
        "default must be populated later use OmegaConf.MISSING"
    ) 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def get_dataclass_data(obj: Any) -> Dict[str, Any]:
    from omegaconf.omegaconf import _maybe_wrap

    d = {}
    for field in dataclasses.fields(obj):
        name = field.name
        is_optional, type_ = _resolve_optional(field.type)
        type_ = _resolve_forward(type_, obj.__module__)
        is_nested = is_structured_config(type_)

        if hasattr(obj, name):
            value = getattr(obj, name)
            if value == dataclasses.MISSING:
                _raise_missing_error(obj, name)
                assert False
        else:
            if field.default_factory == dataclasses.MISSING:  # type: ignore
                if is_nested:
                    value = type_
                else:
                    _raise_missing_error(obj, name)
                    assert False
            else:
                value = field.default_factory()  # type: ignore

        if _is_union(type_):
            e = ConfigValueError(
                f"Union types are not supported:\n{name}: {type_str(type_)}"
            )
            format_and_raise(node=None, key=None, value=value, cause=e, msg=str(e))
        d[name] = _maybe_wrap(
            ref_type=type_, is_optional=is_optional, key=name, value=value, parent=None,
        )
    return d 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def _resolve_default(val):
    if not isinstance(val, Field):
        return val
    if val.default != MISSING:
        return val.default
    if val.default_factory != MISSING:
        return val.default_factory()
    raise ValueError("No default value") 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def _get_field_default(field: dataclasses.Field):
    """
    Return a marshmallow default value given a dataclass default value

    >>> _get_field_default(dataclasses.field())
    <marshmallow.missing>
    """
    # Remove `type: ignore` when https://github.com/python/mypy/issues/6910 is fixed
    default_factory = field.default_factory  # type: ignore
    if default_factory is not dataclasses.MISSING:
        return default_factory
    elif field.default is dataclasses.MISSING:
        return marshmallow.missing
    return field.default 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def schema(cls, mixin, infer_missing):
    schema = {}
    overrides = _user_overrides_or_exts(cls)
    # TODO check the undefined parameters and add the proper schema action
    #  https://marshmallow.readthedocs.io/en/stable/quickstart.html
    for field in dc_fields(cls):
        metadata = (field.metadata or {}).get('dataclasses_json', {})
        metadata = overrides[field.name]
        if metadata.mm_field is not None:
            schema[field.name] = metadata.mm_field
        else:
            type_ = field.type
            options = {}
            missing_key = 'missing' if infer_missing else 'default'
            if field.default is not MISSING:
                options[missing_key] = field.default
            elif field.default_factory is not MISSING:
                options[missing_key] = field.default_factory

            if options.get(missing_key, ...) is None:
                options['allow_none'] = True

            if _is_optional(type_):
                options.setdefault(missing_key, None)
                options['allow_none'] = True
                if len(type_.__args__) == 2:
                    # Union[str, int, None] is optional too, but it has more than 1 typed field.
                    type_ = type_.__args__[0]

            if metadata.letter_case is not None:
                options['data_key'] = metadata.letter_case(field.name)

            t = build_type(type_, options, mixin, field, cls)
            # if type(t) is not fields.Field:  # If we use `isinstance` we would return nothing.
            if field.type != typing.Optional[CatchAllVar]:
                schema[field.name] = t

    return schema 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def defaults(self):
        d = {}
        for ancestor in self.cls.__mro__[-1:0:-1]:
            if is_dataclass(ancestor):
                for field in getattr(ancestor, _FIELDS).values():
                    d[field.name] = field.default
        for name in self.__get_fields(recursive=False):
            d[name] = self.namespace.get(name, MISSING)
        return d 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def from_object(cls: Type[T], obj: Any, exclude: FieldExcludeList = tuple()) -> T:
        """Returns a dataclass instance from another object (typically an ORM model).
        The `exclude` parameter is a tuple of field names or (field.name, nested_exclude)
        to exclude from the conversion. For example `exclude=('artist_name', ('albums', ('tracks',))` will exclude
        the `artist_name` and `tracks` from related albums
        """
        exclude_dict = dict([(f[0], f[1]) if isinstance(f, tuple) else (f, None) for f in exclude])
        init_values: Dict[str, Any] = {}
        non_init_values: Dict[str, Any] = {}
        for f in cls._get_fields():
            sub_exclude: FieldExcludeList = tuple()
            if f.field.name in exclude_dict:
                if exclude_dict[f.field.name] is None:
                    if f.field.default == MISSING and f.field.default == MISSING:
                        raise ValueError("Excluded fields must have a default value")
                    continue
                else:
                    sub_exclude = exclude_dict[f.field.name]  # type: ignore
            values = init_values if f.field.init else non_init_values
            ft = f.field.type
            if is_optional(ft):
                ft = unwrap_optional(ft)
            field_type_name = cls._get_field_type_name(ft)

            from_value = getattr(obj, f.field.name)
            if from_value is None:
                values[f.field.name] = from_value
            elif cls._is_json_schema_subclass(ft):
                values[f.field.name] = ft.from_object(from_value, exclude=sub_exclude)
            elif is_enum(ft):
                values[f.field.name] = ft(from_value)
            elif field_type_name == "List" and cls._is_json_schema_subclass(ft.__args__[0]):
                values[f.field.name] = [
                    ft.__args__[0].from_object(v, exclude=sub_exclude) for v in from_value
                ]
            else:
                values[f.field.name] = from_value

        instance = cls(**init_values)  # type: ignore
        for field_name, value in non_init_values.items():
            setattr(instance, field_name, value)
        return instance 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import MISSING [as 别名]
def wrap(cls, func):
        """
        If a subclass of BaseConfigurable is passed keyword arguments, convert
        them into the instance of the CONFIG class.
        """

        @functools.wraps(func)
        def wrapper(self, config: Optional[BaseConfig] = None, **kwargs):
            if config is not None and len(kwargs):
                raise ConfigAndKWArgsMutuallyExclusive
            elif config is None and hasattr(self, "CONFIG"):
                if kwargs:
                    try:
                        config = self.CONFIG(**kwargs)
                    except TypeError as error:
                        error.args = (
                            error.args[0].replace(
                                "__init__", f"{self.CONFIG.__qualname__}"
                            ),
                        )
                        raise
                else:
                    use_CONFIG = True
                    for field in dataclasses.fields(self.CONFIG):
                        if (
                            field.default is dataclasses.MISSING
                            and field.default_factory is dataclasses.MISSING
                        ):
                            use_CONFIG = False
                            break
                    if use_CONFIG:
                        config = self.CONFIG()
                    else:
                        raise TypeError(
                            "__init__() missing 1 required positional argument: 'config'"
                        )
            elif config is None:
                raise TypeError(
                    "__init__() missing 1 required positional argument: 'config'"
                )
            return func(self, config)

        return wrapper