Python dataclasses.is_dataclass方法代码示例

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def coerce_response(response_data):
    """Coerce response data to JSON serializable object with camelCase keys

    Recursively walk through the response object in case there are things
    like nested dataclasses that need to be reformatted

    :param response_data: data returned from the API request
    :returns: the same data but with keys in camelCase
    """
    if is_dataclass(response_data):
        coerced = {
            snake_to_camel(key): coerce_response(value)
            for key, value in asdict(response_data).items()
        }
    elif isinstance(response_data, dict):
        coerced = {
            snake_to_camel(key): coerce_response(value)
            for key, value in response_data.items()
        }
    elif isinstance(response_data, list):
        coerced = [coerce_response(item) for item in response_data]
    else:
        coerced = response_data

    return coerced 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def export_value(obj, key, value):
    # export and _asdict are not classmethods
    if hasattr(value, "ENTRY_POINT_ORIG_LABEL") and hasattr(value, "config"):
        obj[key] = {"plugin": value.ENTRY_POINT_ORIG_LABEL}
        export_value(obj[key], "config", value.config)
    elif inspect.isclass(value):
        obj[key] = value.__qualname__
    elif isinstance(value, (pathlib.Path, uuid.UUID)):
        obj[key] = str(value)
    elif hasattr(value, "export"):
        obj[key] = value.export()
    elif hasattr(value, "_asdict"):
        obj[key] = value._asdict()
    elif getattr(type(value), "__module__", None) == "numpy" and isinstance(
        getattr(value, "flatten", None), collections.Callable
    ):
        obj[key] = tuple(value.flatten())
    elif dataclasses.is_dataclass(value):
        obj[key] = export_dict(**dataclasses.asdict(value))
    elif getattr(value, "__module__", None) == "typing":
        obj[key] = STANDARD_TYPES.get(
            str(value).replace("typing.", ""), "generic"
        ) 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def gen_variants(self, obj: object, gen, path):
    if is_dataclass(obj):
        fields = list(obj.__dataclass_fields__.keys())
        for field in fields:
            x = getattr(obj, field)
            for variants, p in self(x, gen, path + (field,)):
                new_variants = []
                for variant in variants:
                    d = {
                        f2: (variant if f2 == field else getattr(obj, f2))
                        for f2 in fields
                    }
                    new_variants.append(type(obj)(**d))
                yield (new_variants, p)
    else:
        yield (gen(obj), path) 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [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 is_dataclass [as 别名]
def _serialize(self, value, attr, obj, **kwargs):
        if self.allow_none and value is None:
            return None
        for type_, schema_ in self.desc.items():
            if _issubclass_safe(type(value), type_):
                if is_dataclass(value):
                    res = schema_._serialize(value, attr, obj, **kwargs)
                    res['__type'] = str(type_.__name__)
                    return res
                break
            elif isinstance(value, _get_type_origin(type_)):
                return schema_._serialize(value, attr, obj, **kwargs)
        else:
            warnings.warn(
                f'The type "{type(value).__name__}" (value: "{value}") '
                f'is not in the list of possible types of typing.Union '
                f'(dataclass: {self.cls.__name__}, field: {self.field.name}). '
                f'Value cannot be serialized properly.')
        return super()._serialize(value, attr, obj, **kwargs) 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def _deserialize(self, value, attr, data, **kwargs):
        tmp_value = deepcopy(value)
        if isinstance(tmp_value, dict) and '__type' in tmp_value:
            dc_name = tmp_value['__type']
            for type_, schema_ in self.desc.items():
                if is_dataclass(type_) and type_.__name__ == dc_name:
                    del tmp_value['__type']
                    return schema_._deserialize(tmp_value, attr, data, **kwargs)
        for type_, schema_ in self.desc.items():
            if isinstance(tmp_value, _get_type_origin(type_)):
                return schema_._deserialize(tmp_value, attr, data, **kwargs)
        else:
            warnings.warn(
                f'The type "{type(tmp_value).__name__}" (value: "{tmp_value}") '
                f'is not in the list of possible types of typing.Union '
                f'(dataclass: {self.cls.__name__}, field: {self.field.name}). '
                f'Value cannot be deserialized properly.')
        return super()._deserialize(tmp_value, attr, data, **kwargs) 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def pydantic_encoder(obj: Any) -> Any:
    from dataclasses import asdict, is_dataclass
    from .main import BaseModel

    if isinstance(obj, BaseModel):
        return obj.dict()
    elif is_dataclass(obj):
        return asdict(obj)

    # Check the class type and its superclasses for a matching encoder
    for base in obj.__class__.__mro__[:-1]:
        try:
            encoder = ENCODERS_BY_TYPE[base]
        except KeyError:
            continue
        return encoder(obj)
    else:  # We have exited the for loop without finding a suitable encoder
        raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable") 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def validate(self, *, prefix=''):
        assert is_dataclass(self), f"You forgot to annotate {type(self)} with @dataclass"
        for f in fields(self):
            fieldval = getattr(self, f.name)
            check_type(prefix + f.name, fieldval, f.type)
            if isinstance(fieldval, HParams):
                fieldval.validate(prefix=prefix + f.name + '.') 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def is_hparam_type(ty):
    if isinstance(ty, type) and issubclass(ty, HParams):
        assert is_dataclass(ty)
        return True
    else:
        return False 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def force_type(cls, rv, environ=None):
        if isinstance(rv, dict) or isinstance(rv, list) or is_dataclass(rv):
            reformat = coerce_response(rv)
            rv = jsonify(reformat)
        return super(SeleneResponse, cls).force_type(rv, environ) 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def default(self, obj):
        if dataclasses.is_dataclass(obj) and hasattr(obj, "json"):
            return obj.json()
        return super().default(obj) 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def __init_subclass__(
            cls,
            discriminator: Optional[Union[str, bool]] = None,
            allow_additional_props: bool = True,
            serialise_properties: Union[Tuple[str, ...], bool] = False,
    ):
        # Initialise caches
        cls.__schema = {}
        cls.__compiled_schema = {}
        cls.__definitions = {}
        cls.__encode_cache = {}
        cls.__decode_cache = {}
        cls.__mapped_fields = []
        cls.__discriminator_inherited = False
        cls.__serialise_properties = serialise_properties
        if discriminator is not None:
            cls.__discriminator_name = discriminator if isinstance(discriminator, str) else f"{cls.__name__}Type"
        else:
            dataclass_bases = [
                klass for klass in cls.__bases__ if is_dataclass(klass) and issubclass(klass, JsonSchemaMixin)
            ]
            if len(dataclass_bases) > 0:
                if not allow_additional_props:
                    raise TypeError("Dataclass inheritance and additional_props_false=False not currently supported")
                base_discriminators = [
                    base._discriminator() for base in dataclass_bases if base._discriminator() is not None
                ]
                if len(base_discriminators):
                    if len(base_discriminators) > 1:
                        raise TypeError("Multiple bases with discriminators is unsupported")
                    cls.__discriminator_name = base_discriminators[0]
                    cls.__discriminator_inherited = True
            else:
                cls.__discriminator_name = None
        cls.__allow_additional_props = allow_additional_props 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [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 is_dataclass [as 别名]
def all_json_schemas(
            cls: Type[T], schema_type: SchemaType = DEFAULT_SCHEMA_TYPE, validate_enums: bool = True
    ) -> JsonDict:
        """Returns JSON schemas for all subclasses"""
        definitions = {}
        for subclass in cls.__subclasses__():
            if is_dataclass(subclass):
                definitions.update(
                    subclass.json_schema(embeddable=True, schema_type=schema_type, validate_enums=validate_enums)
                )
            else:
                definitions.update(subclass.all_json_schemas(schema_type=schema_type, validate_enums=validate_enums))
        return definitions 

# 需要导入模块: import dataclasses [as 别名]
# 或者: from dataclasses import is_dataclass [as 别名]
def export_list(iterable):
    for i, value in enumerate(iterable):
        export_value(iterable, i, value)
        if isinstance(value, (dict, types.MappingProxyType)):
            iterable[i] = export_dict(**iterable[i])
        elif dataclasses.is_dataclass(value):
            iterable[i] = export_dict(**dataclasses.asdict(value))
        elif isinstance(value, list):
            iterable[i] = export_list(iterable[i])
    return iterable