fields.py

import warnings
from collections.abc import Iterable as CollectionsIterable
from copy import deepcopy
from typing import (
    TYPE_CHECKING,
    Any,
    Dict,
    FrozenSet,
    Generator,
    Iterable,
    Iterator,
    List,
    Mapping,
    Optional,
    Pattern,
    Sequence,
    Set,
    Tuple,
    Type,
    TypeVar,
    Union,
)

from . import errors as errors_
from .class_validators import Validator, make_generic_validator, prep_validators
from .error_wrappers import ErrorWrapper
from .errors import NoneIsNotAllowedError
from .types import Json, JsonWrapper
from .typing import (
    Callable,
    ForwardRef,
    NoArgAnyCallable,
    NoneType,
    display_as_type,
    is_literal_type,
    is_new_type,
    new_type_supertype,
)
from .utils import PyObjectStr, Representation, lenient_issubclass, sequence_like
from .validators import constant_validator, dict_validator, find_validators, validate_json

Required: Any = Ellipsis


class UndefinedType:
    def __repr__(self) -> str:
        return 'PydanticUndefined'


Undefined = UndefinedType()

if TYPE_CHECKING:
    from .class_validators import ValidatorsList  # noqa: F401
    from .error_wrappers import ErrorList
    from .main import BaseConfig, BaseModel  # noqa: F401
    from .types import ModelOrDc  # noqa: F401
    from .typing import ReprArgs  # noqa: F401

    ValidateReturn = Tuple[Optional[Any], Optional[ErrorList]]
    LocStr = Union[Tuple[Union[int, str], ...], str]
    BoolUndefined = Union[bool, UndefinedType]


class FieldInfo(Representation):
    """
    Captures extra information about a field.
    """

    __slots__ = (
        'default',
        'default_factory',
        'alias',
        'alias_priority',
        'title',
        'description',
        'const',
        'gt',
        'ge',
        'lt',
        'le',
        'multiple_of',
        'min_items',
        'max_items',
        'min_length',
        'max_length',
        'regex',
        'extra',
    )

    def __init__(self, default: Any = Undefined, **kwargs: Any) -> None:
        self.default = default
        self.default_factory = kwargs.pop('default_factory', None)
        self.alias = kwargs.pop('alias', None)
        self.alias_priority = kwargs.pop('alias_priority', 2 if self.alias else None)
        self.title = kwargs.pop('title', None)
        self.description = kwargs.pop('description', None)
        self.const = kwargs.pop('const', None)
        self.gt = kwargs.pop('gt', None)
        self.ge = kwargs.pop('ge', None)
        self.lt = kwargs.pop('lt', None)
        self.le = kwargs.pop('le', None)
        self.multiple_of = kwargs.pop('multiple_of', None)
        self.min_items = kwargs.pop('min_items', None)
        self.max_items = kwargs.pop('max_items', None)
        self.min_length = kwargs.pop('min_length', None)
        self.max_length = kwargs.pop('max_length', None)
        self.regex = kwargs.pop('regex', None)
        self.extra = kwargs


def Field(
    default: Any = Undefined,
    *,
    default_factory: Optional[NoArgAnyCallable] = None,
    alias: str = None,
    title: str = None,
    description: str = None,
    const: bool = None,
    gt: float = None,
    ge: float = None,
    lt: float = None,
    le: float = None,
    multiple_of: float = None,
    min_items: int = None,
    max_items: int = None,
    min_length: int = None,
    max_length: int = None,
    regex: str = None,
    **extra: Any,
) -> Any:
    """
    Used to provide extra information about a field, either for the model schema or complex valiation. Some arguments
    apply only to number fields (``int``, ``float``, ``Decimal``) and some apply only to ``str``.

    :param default: since this is replacing the field’s default, its first argument is used
      to set the default, use ellipsis (``...``) to indicate the field is required
    :param default_factory: callable that will be called when a default value is needed for this field
      If both `default` and `default_factory` are set, an error is raised.
    :param alias: the public name of the field
    :param title: can be any string, used in the schema
    :param description: can be any string, used in the schema
    :param const: this field is required and *must* take it's default value
    :param gt: only applies to numbers, requires the field to be "greater than". The schema
      will have an ``exclusiveMinimum`` validation keyword
    :param ge: only applies to numbers, requires the field to be "greater than or equal to". The
      schema will have a ``minimum`` validation keyword
    :param lt: only applies to numbers, requires the field to be "less than". The schema
      will have an ``exclusiveMaximum`` validation keyword
    :param le: only applies to numbers, requires the field to be "less than or equal to". The
      schema will have a ``maximum`` validation keyword
    :param multiple_of: only applies to numbers, requires the field to be "a multiple of". The
      schema will have a ``multipleOf`` validation keyword
    :param min_length: only applies to strings, requires the field to have a minimum length. The
      schema will have a ``maximum`` validation keyword
    :param max_length: only applies to strings, requires the field to have a maximum length. The
      schema will have a ``maxLength`` validation keyword
    :param regex: only applies to strings, requires the field match agains a regular expression
      pattern string. The schema will have a ``pattern`` validation keyword
    :param **extra: any additional keyword arguments will be added as is to the schema
    """
    if default is not Undefined and default_factory is not None:
        raise ValueError('cannot specify both default and default_factory')

    return FieldInfo(
        default,
        default_factory=default_factory,
        alias=alias,
        title=title,
        description=description,
        const=const,
        gt=gt,
        ge=ge,
        lt=lt,
        le=le,
        multiple_of=multiple_of,
        min_items=min_items,
        max_items=max_items,
        min_length=min_length,
        max_length=max_length,
        regex=regex,
        **extra,
    )


def Schema(default: Any, **kwargs: Any) -> Any:
    warnings.warn('`Schema` is deprecated, use `Field` instead', DeprecationWarning)
    return Field(default, **kwargs)


# used to be an enum but changed to int's for small performance improvement as less access overhead
SHAPE_SINGLETON = 1
SHAPE_LIST = 2
SHAPE_SET = 3
SHAPE_MAPPING = 4
SHAPE_TUPLE = 5
SHAPE_TUPLE_ELLIPSIS = 6
SHAPE_SEQUENCE = 7
SHAPE_FROZENSET = 8
SHAPE_ITERABLE = 9
SHAPE_GENERIC = 10
SHAPE_NAME_LOOKUP = {
    SHAPE_LIST: 'List[{}]',
    SHAPE_SET: 'Set[{}]',
    SHAPE_TUPLE_ELLIPSIS: 'Tuple[{}, ...]',
    SHAPE_SEQUENCE: 'Sequence[{}]',
    SHAPE_FROZENSET: 'FrozenSet[{}]',
    SHAPE_ITERABLE: 'Iterable[{}]',
}


class ModelField(Representation):
    __slots__ = (
        'type_',
        'outer_type_',
        'sub_fields',
        'key_field',
        'validators',
        'pre_validators',
        'post_validators',
        'default',
        'default_factory',
        'required',
        'model_config',
        'name',
        'alias',
        'has_alias',
        'field_info',
        'validate_always',
        'allow_none',
        'shape',
        'class_validators',
        'parse_json',
    )

    def __init__(
        self,
        *,
        name: str,
        type_: Type[Any],
        class_validators: Optional[Dict[str, Validator]],
        model_config: Type['BaseConfig'],
        default: Any = None,
        default_factory: Optional[NoArgAnyCallable] = None,
        required: 'BoolUndefined' = Undefined,
        alias: str = None,
        field_info: Optional[FieldInfo] = None,
    ) -> None:

        self.name: str = name
        self.has_alias: bool = bool(alias)
        self.alias: str = alias or name
        self.type_: Any = type_
        self.outer_type_: Any = type_
        self.class_validators = class_validators or {}
        self.default: Any = default
        self.default_factory: Optional[NoArgAnyCallable] = default_factory
        self.required: 'BoolUndefined' = required
        self.model_config = model_config
        self.field_info: FieldInfo = field_info or FieldInfo(default)

        self.allow_none: bool = False
        self.validate_always: bool = False
        self.sub_fields: Optional[List[ModelField]] = None
        self.key_field: Optional[ModelField] = None
        self.validators: 'ValidatorsList' = []
        self.pre_validators: Optional['ValidatorsList'] = None
        self.post_validators: Optional['ValidatorsList'] = None
        self.parse_json: bool = False
        self.shape: int = SHAPE_SINGLETON
        self.model_config.prepare_field(self)
        self.prepare()

    def get_default(self) -> Any:
        if self.default_factory is not None:
            value = self.default_factory()
        elif self.default is None:
            # deepcopy is quite slow on None
            value = None
        else:
            value = deepcopy(self.default)
        return value

    @classmethod
    def infer(
        cls,
        *,
        name: str,
        value: Any,
        annotation: Any,
        class_validators: Optional[Dict[str, Validator]],
        config: Type['BaseConfig'],
    ) -> 'ModelField':
        field_info_from_config = config.get_field_info(name)
        from .schema import get_annotation_from_field_info

        if isinstance(value, FieldInfo):
            field_info = value
            value = field_info.default_factory() if field_info.default_factory is not None else field_info.default
        else:
            field_info = FieldInfo(value, **field_info_from_config)
        required: 'BoolUndefined' = Undefined
        if value is Required:
            required = True
            value = None
        elif value is not Undefined:
            required = False
        field_info.alias = field_info.alias or field_info_from_config.get('alias')
        annotation = get_annotation_from_field_info(annotation, field_info, name)
        return cls(
            name=name,
            type_=annotation,
            alias=field_info.alias,
            class_validators=class_validators,
            default=value,
            default_factory=field_info.default_factory,
            required=required,
            model_config=config,
            field_info=field_info,
        )

    def set_config(self, config: Type['BaseConfig']) -> None:
        self.model_config = config
        info_from_config = config.get_field_info(self.name)
        config.prepare_field(self)
        new_alias = info_from_config.get('alias')
        new_alias_priority = info_from_config.get('alias_priority') or 0
        if new_alias and new_alias_priority >= (self.field_info.alias_priority or 0):
            self.field_info.alias = new_alias
            self.field_info.alias_priority = new_alias_priority
            self.alias = new_alias

    @property
    def alt_alias(self) -> bool:
        return self.name != self.alias

    def prepare(self) -> None:
        """
        Prepare the field but inspecting self.default, self.type_ etc.

        Note: this method is **not** idempotent (because _type_analysis is not idempotent),
        e.g. calling it it multiple times may modify the field and configure it incorrectly.
        """
        default_value = self.get_default()
        if default_value is not None and self.type_ is None:
            self.type_ = default_value.__class__
            self.outer_type_ = self.type_

        if self.type_ is None:
            raise errors_.ConfigError(f'unable to infer type for attribute "{self.name}"')

        if self.type_.__class__ == ForwardRef:
            # self.type_ is currently a ForwardRef and there's nothing we can do now,
            # user will need to call model.update_forward_refs()
            return

        self.validate_always = getattr(self.type_, 'validate_always', False) or any(
            v.always for v in self.class_validators.values()
        )

        if self.required is False and default_value is None:
            self.allow_none = True

        self._type_analysis()
        if self.required is Undefined:
            self.required = True
            self.field_info.default = Required
        if self.default is Undefined and self.default_factory is None:
            self.default = None
        self.populate_validators()

    def _type_analysis(self) -> None:  # noqa: C901 (ignore complexity)
        # typing interface is horrible, we have to do some ugly checks
        if lenient_issubclass(self.type_, JsonWrapper):
            self.type_ = self.type_.inner_type
            self.parse_json = True
        elif lenient_issubclass(self.type_, Json):
            self.type_ = Any
            self.parse_json = True
        elif isinstance(self.type_, TypeVar):  # type: ignore
            if self.type_.__bound__:
                self.type_ = self.type_.__bound__
            elif self.type_.__constraints__:
                self.type_ = Union[self.type_.__constraints__]
            else:
                self.type_ = Any
        elif is_new_type(self.type_):
            self.type_ = new_type_supertype(self.type_)

        if self.type_ is Any:
            if self.required is Undefined:
                self.required = False
            self.allow_none = True
            return
        elif self.type_ is Pattern:
            # python 3.7 only, Pattern is a typing object but without sub fields
            return
        elif is_literal_type(self.type_):
            return

        origin = getattr(self.type_, '__origin__', None)
        if origin is None:
            # field is not "typing" object eg. Union, Dict, List etc.
            # allow None for virtual superclasses of NoneType, e.g. Hashable
            if isinstance(self.type_, type) and isinstance(None, self.type_):
                self.allow_none = True
            return
        if origin is Callable:
            return
        if origin is Union:
            types_ = []
            for type_ in self.type_.__args__:
                if type_ is NoneType:
                    if self.required is Undefined:
                        self.required = False
                    self.allow_none = True
                    continue
                types_.append(type_)

            if len(types_) == 1:
                # Optional[]
                self.type_ = types_[0]
                # this is the one case where the "outer type" isn't just the original type
                self.outer_type_ = self.type_
                # re-run to correctly interpret the new self.type_
                self._type_analysis()
            else:
                self.sub_fields = [self._create_sub_type(t, f'{self.name}_{display_as_type(t)}') for t in types_]
            return

        if issubclass(origin, Tuple):  # type: ignore
            self.shape = SHAPE_TUPLE
            self.sub_fields = []
            for i, t in enumerate(self.type_.__args__):
                if t is Ellipsis:
                    self.type_ = self.type_.__args__[0]
                    self.shape = SHAPE_TUPLE_ELLIPSIS
                    return
                self.sub_fields.append(self._create_sub_type(t, f'{self.name}_{i}'))
            return

        if issubclass(origin, List):
            # Create self validators
            get_validators = getattr(self.type_, '__get_validators__', None)
            if get_validators:
                self.class_validators.update(
                    {
                        f'list_{i}': Validator(validator, pre=True, always=True)
                        for i, validator in enumerate(get_validators())
                    }
                )

            self.type_ = self.type_.__args__[0]
            self.shape = SHAPE_LIST
        elif issubclass(origin, Set):
            self.type_ = self.type_.__args__[0]
            self.shape = SHAPE_SET
        elif issubclass(origin, FrozenSet):
            self.type_ = self.type_.__args__[0]
            self.shape = SHAPE_FROZENSET
        elif issubclass(origin, Sequence):
            self.type_ = self.type_.__args__[0]
            self.shape = SHAPE_SEQUENCE
        elif issubclass(origin, Mapping):
            self.key_field = self._create_sub_type(self.type_.__args__[0], 'key_' + self.name, for_keys=True)
            self.type_ = self.type_.__args__[1]
            self.shape = SHAPE_MAPPING
        # Equality check as almost everything inherits form Iterable, including str
        # check for Iterable and CollectionsIterable, as it could receive one even when declared with the other
        elif origin in {Iterable, CollectionsIterable}:
            self.type_ = self.type_.__args__[0]
            self.shape = SHAPE_ITERABLE
            self.sub_fields = [self._create_sub_type(self.type_, f'{self.name}_type')]
        elif issubclass(origin, Type):  # type: ignore
            return
        elif hasattr(origin, '__get_validators__') or self.model_config.arbitrary_types_allowed:
            # Is a Pydantic-compatible generic that handles itself
            # or we have arbitrary_types_allowed = True
            self.shape = SHAPE_GENERIC
            self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(self.type_.__args__)]
            self.type_ = origin
            return
        else:
            raise TypeError(f'Fields of type "{origin}" are not supported.')

        # type_ has been refined eg. as the type of a List and sub_fields needs to be populated
        self.sub_fields = [self._create_sub_type(self.type_, '_' + self.name)]

    def _create_sub_type(self, type_: Type[Any], name: str, *, for_keys: bool = False) -> 'ModelField':
        return self.__class__(
            type_=type_,
            name=name,
            class_validators=None if for_keys else {k: v for k, v in self.class_validators.items() if v.each_item},
            model_config=self.model_config,
        )

    def populate_validators(self) -> None:
        """
        Prepare self.pre_validators, self.validators, and self.post_validators based on self.type_'s  __get_validators__
        and class validators. This method should be idempotent, e.g. it should be safe to call multiple times
        without mis-configuring the field.
        """
        class_validators_ = self.class_validators.values()
        if not self.sub_fields or self.shape == SHAPE_GENERIC:
            get_validators = getattr(self.type_, '__get_validators__', None)
            v_funcs = (
                *[v.func for v in class_validators_ if v.each_item and v.pre],
                *(get_validators() if get_validators else list(find_validators(self.type_, self.model_config))),
                *[v.func for v in class_validators_ if v.each_item and not v.pre],
            )
            self.validators = prep_validators(v_funcs)

        self.pre_validators = []
        self.post_validators = []

        if self.field_info and self.field_info.const:
            self.post_validators.append(make_generic_validator(constant_validator))

        if class_validators_:
            self.pre_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and v.pre)
            self.post_validators = prep_validators(v.func for v in class_validators_ if not v.each_item and not v.pre)

        if self.parse_json:
            self.pre_validators.append(make_generic_validator(validate_json))

        self.pre_validators = self.pre_validators or None
        self.post_validators = self.post_validators or None

    def validate(
        self, v: Any, values: Dict[str, Any], *, loc: 'LocStr', cls: Optional['ModelOrDc'] = None
    ) -> 'ValidateReturn':

        errors: Optional['ErrorList']
        if self.pre_validators:
            v, errors = self._apply_validators(v, values, loc, cls, self.pre_validators)
            if errors:
                return v, errors

        if v is None:
            if self.allow_none:
                if self.post_validators:
                    return self._apply_validators(v, values, loc, cls, self.post_validators)
                else:
                    return None, None
            else:
                return v, ErrorWrapper(NoneIsNotAllowedError(), loc)

        if self.shape == SHAPE_SINGLETON:
            v, errors = self._validate_singleton(v, values, loc, cls)
        elif self.shape == SHAPE_MAPPING:
            v, errors = self._validate_mapping(v, values, loc, cls)
        elif self.shape == SHAPE_TUPLE:
            v, errors = self._validate_tuple(v, values, loc, cls)
        elif self.shape == SHAPE_ITERABLE:
            v, errors = self._validate_iterable(v, values, loc, cls)
        elif self.shape == SHAPE_GENERIC:
            v, errors = self._apply_validators(v, values, loc, cls, self.validators)
        else:
            #  sequence, list, set, generator, tuple with ellipsis, frozen set
            v, errors = self._validate_sequence_like(v, values, loc, cls)

        if not errors and self.post_validators:
            v, errors = self._apply_validators(v, values, loc, cls, self.post_validators)
        return v, errors

    def _validate_sequence_like(  # noqa: C901 (ignore complexity)
        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
    ) -> 'ValidateReturn':
        """
        Validate sequence-like containers: lists, tuples, sets and generators
        Note that large if-else blocks are necessary to enable Cython
        optimization, which is why we disable the complexity check above.
        """
        if not sequence_like(v):
            e: errors_.PydanticTypeError
            if self.shape == SHAPE_LIST:
                e = errors_.ListError()
            elif self.shape == SHAPE_SET:
                e = errors_.SetError()
            elif self.shape == SHAPE_FROZENSET:
                e = errors_.FrozenSetError()
            else:
                e = errors_.SequenceError()
            return v, ErrorWrapper(e, loc)

        loc = loc if isinstance(loc, tuple) else (loc,)
        result = []
        errors: List[ErrorList] = []
        for i, v_ in enumerate(v):
            v_loc = *loc, i
            r, ee = self._validate_singleton(v_, values, v_loc, cls)
            if ee:
                errors.append(ee)
            else:
                result.append(r)

        if errors:
            return v, errors

        converted: Union[List[Any], Set[Any], FrozenSet[Any], Tuple[Any, ...], Iterator[Any]] = result

        if self.shape == SHAPE_SET:
            converted = set(result)
        elif self.shape == SHAPE_FROZENSET:
            converted = frozenset(result)
        elif self.shape == SHAPE_TUPLE_ELLIPSIS:
            converted = tuple(result)
        elif self.shape == SHAPE_SEQUENCE:
            if isinstance(v, tuple):
                converted = tuple(result)
            elif isinstance(v, set):
                converted = set(result)
            elif isinstance(v, Generator):
                converted = iter(result)
        return converted, None

    def _validate_iterable(
        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
    ) -> 'ValidateReturn':
        """
        Validate Iterables.

        This intentionally doesn't validate values to allow infinite generators.
        """

        try:
            iterable = iter(v)
        except TypeError:
            return v, ErrorWrapper(errors_.IterableError(), loc)
        return iterable, None

    def _validate_tuple(
        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
    ) -> 'ValidateReturn':
        e: Optional[Exception] = None
        if not sequence_like(v):
            e = errors_.TupleError()
        else:
            actual_length, expected_length = len(v), len(self.sub_fields)  # type: ignore
            if actual_length != expected_length:
                e = errors_.TupleLengthError(actual_length=actual_length, expected_length=expected_length)

        if e:
            return v, ErrorWrapper(e, loc)

        loc = loc if isinstance(loc, tuple) else (loc,)
        result = []
        errors: List[ErrorList] = []
        for i, (v_, field) in enumerate(zip(v, self.sub_fields)):  # type: ignore
            v_loc = *loc, i
            r, ee = field.validate(v_, values, loc=v_loc, cls=cls)
            if ee:
                errors.append(ee)
            else:
                result.append(r)

        if errors:
            return v, errors
        else:
            return tuple(result), None

    def _validate_mapping(
        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
    ) -> 'ValidateReturn':
        try:
            v_iter = dict_validator(v)
        except TypeError as exc:
            return v, ErrorWrapper(exc, loc)

        loc = loc if isinstance(loc, tuple) else (loc,)
        result, errors = {}, []
        for k, v_ in v_iter.items():
            v_loc = *loc, '__key__'
            key_result, key_errors = self.key_field.validate(k, values, loc=v_loc, cls=cls)  # type: ignore
            if key_errors:
                errors.append(key_errors)
                continue

            v_loc = *loc, k
            value_result, value_errors = self._validate_singleton(v_, values, v_loc, cls)
            if value_errors:
                errors.append(value_errors)
                continue

            result[key_result] = value_result
        if errors:
            return v, errors
        else:
            return result, None

    def _validate_singleton(
        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
    ) -> 'ValidateReturn':
        if self.sub_fields:
            errors = []
            for field in self.sub_fields:
                value, error = field.validate(v, values, loc=loc, cls=cls)
                if error:
                    errors.append(error)
                else:
                    return value, None
            return v, errors
        else:
            return self._apply_validators(v, values, loc, cls, self.validators)

    def _apply_validators(
        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'], validators: 'ValidatorsList'
    ) -> 'ValidateReturn':
        for validator in validators:
            try:
                v = validator(cls, v, values, self, self.model_config)
            except (ValueError, TypeError, AssertionError) as exc:
                return v, ErrorWrapper(exc, loc)
        return v, None

    def include_in_schema(self) -> bool:
        """
        False if this is a simple field just allowing None as used in Unions/Optional.
        """
        return self.type_ != NoneType

    def is_complex(self) -> bool:
        """
        Whether the field is "complex" eg. env variables should be parsed as JSON.
        """
        from .main import BaseModel  # noqa: F811

        return (
            self.shape != SHAPE_SINGLETON
            or lenient_issubclass(self.type_, (BaseModel, list, set, frozenset, dict))
            or hasattr(self.type_, '__pydantic_model__')  # pydantic dataclass
        )

    def _type_display(self) -> PyObjectStr:
        t = display_as_type(self.type_)

        # have to do this since display_as_type(self.outer_type_) is different (and wrong) on python 3.6
        if self.shape == SHAPE_MAPPING:
            t = f'Mapping[{display_as_type(self.key_field.type_)}, {t}]'  # type: ignore
        elif self.shape == SHAPE_TUPLE:
            t = 'Tuple[{}]'.format(', '.join(display_as_type(f.type_) for f in self.sub_fields))  # type: ignore
        elif self.shape == SHAPE_GENERIC:
            assert self.sub_fields
            t = '{}[{}]'.format(
                display_as_type(self.type_), ', '.join(display_as_type(f.type_) for f in self.sub_fields)
            )
        elif self.shape != SHAPE_SINGLETON:
            t = SHAPE_NAME_LOOKUP[self.shape].format(t)

        if self.allow_none and (self.shape != SHAPE_SINGLETON or not self.sub_fields):
            t = f'Optional[{t}]'
        return PyObjectStr(t)

    def __repr_args__(self) -> 'ReprArgs':
        args = [('name', self.name), ('type', self._type_display()), ('required', self.required)]

        if not self.required:
            if self.default_factory is not None:
                args.append(('default_factory', f'<function {self.default_factory.__name__}>'))
            else:
                args.append(('default', self.default))

        if self.alt_alias:
            args.append(('alias', self.alias))
        return args