two version of R2R are here HEAD master

1 files changed, 1253 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/fields.py b/.venv/lib/python3.12/site-packages/pydantic/v1/fields.py
new file mode 100644
index 00000000..002b60cd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/fields.py
@@ -0,0 +1,1253 @@
+import copy
+import re
+from collections import Counter as CollectionCounter, defaultdict, deque
+from collections.abc import Callable, Hashable as CollectionsHashable, Iterable as CollectionsIterable
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Counter,
+    DefaultDict,
+    Deque,
+    Dict,
+    ForwardRef,
+    FrozenSet,
+    Generator,
+    Iterable,
+    Iterator,
+    List,
+    Mapping,
+    Optional,
+    Pattern,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+
+from typing_extensions import Annotated, Final
+
+from pydantic.v1 import errors as errors_
+from pydantic.v1.class_validators import Validator, make_generic_validator, prep_validators
+from pydantic.v1.error_wrappers import ErrorWrapper
+from pydantic.v1.errors import ConfigError, InvalidDiscriminator, MissingDiscriminator, NoneIsNotAllowedError
+from pydantic.v1.types import Json, JsonWrapper
+from pydantic.v1.typing import (
+    NoArgAnyCallable,
+    convert_generics,
+    display_as_type,
+    get_args,
+    get_origin,
+    is_finalvar,
+    is_literal_type,
+    is_new_type,
+    is_none_type,
+    is_typeddict,
+    is_typeddict_special,
+    is_union,
+    new_type_supertype,
+)
+from pydantic.v1.utils import (
+    PyObjectStr,
+    Representation,
+    ValueItems,
+    get_discriminator_alias_and_values,
+    get_unique_discriminator_alias,
+    lenient_isinstance,
+    lenient_issubclass,
+    sequence_like,
+    smart_deepcopy,
+)
+from pydantic.v1.validators import constant_validator, dict_validator, find_validators, validate_json
+
+Required: Any = Ellipsis
+
+T = TypeVar('T')
+
+
+class UndefinedType:
+    def __repr__(self) -> str:
+        return 'PydanticUndefined'
+
+    def __copy__(self: T) -> T:
+        return self
+
+    def __reduce__(self) -> str:
+        return 'Undefined'
+
+    def __deepcopy__(self: T, _: Any) -> T:
+        return self
+
+
+Undefined = UndefinedType()
+
+if TYPE_CHECKING:
+    from pydantic.v1.class_validators import ValidatorsList
+    from pydantic.v1.config import BaseConfig
+    from pydantic.v1.error_wrappers import ErrorList
+    from pydantic.v1.types import ModelOrDc
+    from pydantic.v1.typing import AbstractSetIntStr, MappingIntStrAny, ReprArgs
+
+    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',
+        'exclude',
+        'include',
+        'const',
+        'gt',
+        'ge',
+        'lt',
+        'le',
+        'multiple_of',
+        'allow_inf_nan',
+        'max_digits',
+        'decimal_places',
+        'min_items',
+        'max_items',
+        'unique_items',
+        'min_length',
+        'max_length',
+        'allow_mutation',
+        'repr',
+        'regex',
+        'discriminator',
+        'extra',
+    )
+
+    # field constraints with the default value, it's also used in update_from_config below
+    __field_constraints__ = {
+        'min_length': None,
+        'max_length': None,
+        'regex': None,
+        'gt': None,
+        'lt': None,
+        'ge': None,
+        'le': None,
+        'multiple_of': None,
+        'allow_inf_nan': None,
+        'max_digits': None,
+        'decimal_places': None,
+        'min_items': None,
+        'max_items': None,
+        'unique_items': None,
+        'allow_mutation': True,
+    }
+
+    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 is not None else None)
+        self.title = kwargs.pop('title', None)
+        self.description = kwargs.pop('description', None)
+        self.exclude = kwargs.pop('exclude', None)
+        self.include = kwargs.pop('include', 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.allow_inf_nan = kwargs.pop('allow_inf_nan', None)
+        self.max_digits = kwargs.pop('max_digits', None)
+        self.decimal_places = kwargs.pop('decimal_places', None)
+        self.min_items = kwargs.pop('min_items', None)
+        self.max_items = kwargs.pop('max_items', None)
+        self.unique_items = kwargs.pop('unique_items', None)
+        self.min_length = kwargs.pop('min_length', None)
+        self.max_length = kwargs.pop('max_length', None)
+        self.allow_mutation = kwargs.pop('allow_mutation', True)
+        self.regex = kwargs.pop('regex', None)
+        self.discriminator = kwargs.pop('discriminator', None)
+        self.repr = kwargs.pop('repr', True)
+        self.extra = kwargs
+
+    def __repr_args__(self) -> 'ReprArgs':
+        field_defaults_to_hide: Dict[str, Any] = {
+            'repr': True,
+            **self.__field_constraints__,
+        }
+
+        attrs = ((s, getattr(self, s)) for s in self.__slots__)
+        return [(a, v) for a, v in attrs if v != field_defaults_to_hide.get(a, None)]
+
+    def get_constraints(self) -> Set[str]:
+        """
+        Gets the constraints set on the field by comparing the constraint value with its default value
+
+        :return: the constraints set on field_info
+        """
+        return {attr for attr, default in self.__field_constraints__.items() if getattr(self, attr) != default}
+
+    def update_from_config(self, from_config: Dict[str, Any]) -> None:
+        """
+        Update this FieldInfo based on a dict from get_field_info, only fields which have not been set are dated.
+        """
+        for attr_name, value in from_config.items():
+            try:
+                current_value = getattr(self, attr_name)
+            except AttributeError:
+                # attr_name is not an attribute of FieldInfo, it should therefore be added to extra
+                # (except if extra already has this value!)
+                self.extra.setdefault(attr_name, value)
+            else:
+                if current_value is self.__field_constraints__.get(attr_name, None):
+                    setattr(self, attr_name, value)
+                elif attr_name == 'exclude':
+                    self.exclude = ValueItems.merge(value, current_value)
+                elif attr_name == 'include':
+                    self.include = ValueItems.merge(value, current_value, intersect=True)
+
+    def _validate(self) -> None:
+        if self.default is not Undefined and self.default_factory is not None:
+            raise ValueError('cannot specify both default and default_factory')
+
+
+def Field(
+    default: Any = Undefined,
+    *,
+    default_factory: Optional[NoArgAnyCallable] = None,
+    alias: Optional[str] = None,
+    title: Optional[str] = None,
+    description: Optional[str] = None,
+    exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny', Any]] = None,
+    include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny', Any]] = None,
+    const: Optional[bool] = None,
+    gt: Optional[float] = None,
+    ge: Optional[float] = None,
+    lt: Optional[float] = None,
+    le: Optional[float] = None,
+    multiple_of: Optional[float] = None,
+    allow_inf_nan: Optional[bool] = None,
+    max_digits: Optional[int] = None,
+    decimal_places: Optional[int] = None,
+    min_items: Optional[int] = None,
+    max_items: Optional[int] = None,
+    unique_items: Optional[bool] = None,
+    min_length: Optional[int] = None,
+    max_length: Optional[int] = None,
+    allow_mutation: bool = True,
+    regex: Optional[str] = None,
+    discriminator: Optional[str] = None,
+    repr: bool = True,
+    **extra: Any,
+) -> Any:
+    """
+    Used to provide extra information about a field, either for the model schema or complex validation. 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 exclude: exclude this field while dumping.
+      Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method.
+    :param include: include this field while dumping.
+      Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method.
+    :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 allow_inf_nan: only applies to numbers, allows the field to be NaN or infinity (+inf or -inf),
+        which is a valid Python float. Default True, set to False for compatibility with JSON.
+    :param max_digits: only applies to Decimals, requires the field to have a maximum number
+      of digits within the decimal. It does not include a zero before the decimal point or trailing decimal zeroes.
+    :param decimal_places: only applies to Decimals, requires the field to have at most a number of decimal places
+      allowed. It does not include trailing decimal zeroes.
+    :param min_items: only applies to lists, requires the field to have a minimum number of
+      elements. The schema will have a ``minItems`` validation keyword
+    :param max_items: only applies to lists, requires the field to have a maximum number of
+      elements. The schema will have a ``maxItems`` validation keyword
+    :param unique_items: only applies to lists, requires the field not to have duplicated
+      elements. The schema will have a ``uniqueItems`` validation keyword
+    :param min_length: only applies to strings, requires the field to have a minimum length. The
+      schema will have a ``minLength`` 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 allow_mutation: a boolean which defaults to True. When False, the field raises a TypeError if the field is
+      assigned on an instance.  The BaseModel Config must set validate_assignment to True
+    :param regex: only applies to strings, requires the field match against a regular expression
+      pattern string. The schema will have a ``pattern`` validation keyword
+    :param discriminator: only useful with a (discriminated a.k.a. tagged) `Union` of sub models with a common field.
+      The `discriminator` is the name of this common field to shorten validation and improve generated schema
+    :param repr: show this field in the representation
+    :param **extra: any additional keyword arguments will be added as is to the schema
+    """
+    field_info = FieldInfo(
+        default,
+        default_factory=default_factory,
+        alias=alias,
+        title=title,
+        description=description,
+        exclude=exclude,
+        include=include,
+        const=const,
+        gt=gt,
+        ge=ge,
+        lt=lt,
+        le=le,
+        multiple_of=multiple_of,
+        allow_inf_nan=allow_inf_nan,
+        max_digits=max_digits,
+        decimal_places=decimal_places,
+        min_items=min_items,
+        max_items=max_items,
+        unique_items=unique_items,
+        min_length=min_length,
+        max_length=max_length,
+        allow_mutation=allow_mutation,
+        regex=regex,
+        discriminator=discriminator,
+        repr=repr,
+        **extra,
+    )
+    field_info._validate()
+    return field_info
+
+
+# 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_DEQUE = 11
+SHAPE_DICT = 12
+SHAPE_DEFAULTDICT = 13
+SHAPE_COUNTER = 14
+SHAPE_NAME_LOOKUP = {
+    SHAPE_LIST: 'List[{}]',
+    SHAPE_SET: 'Set[{}]',
+    SHAPE_TUPLE_ELLIPSIS: 'Tuple[{}, ...]',
+    SHAPE_SEQUENCE: 'Sequence[{}]',
+    SHAPE_FROZENSET: 'FrozenSet[{}]',
+    SHAPE_ITERABLE: 'Iterable[{}]',
+    SHAPE_DEQUE: 'Deque[{}]',
+    SHAPE_DICT: 'Dict[{}]',
+    SHAPE_DEFAULTDICT: 'DefaultDict[{}]',
+    SHAPE_COUNTER: 'Counter[{}]',
+}
+
+MAPPING_LIKE_SHAPES: Set[int] = {SHAPE_DEFAULTDICT, SHAPE_DICT, SHAPE_MAPPING, SHAPE_COUNTER}
+
+
+class ModelField(Representation):
+    __slots__ = (
+        'type_',
+        'outer_type_',
+        'annotation',
+        'sub_fields',
+        'sub_fields_mapping',
+        'key_field',
+        'validators',
+        'pre_validators',
+        'post_validators',
+        'default',
+        'default_factory',
+        'required',
+        'final',
+        'model_config',
+        'name',
+        'alias',
+        'has_alias',
+        'field_info',
+        'discriminator_key',
+        'discriminator_alias',
+        '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,
+        final: bool = False,
+        alias: Optional[str] = None,
+        field_info: Optional[FieldInfo] = None,
+    ) -> None:
+        self.name: str = name
+        self.has_alias: bool = alias is not None
+        self.alias: str = alias if alias is not None else name
+        self.annotation = type_
+        self.type_: Any = convert_generics(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.final: bool = final
+        self.model_config = model_config
+        self.field_info: FieldInfo = field_info or FieldInfo(default)
+        self.discriminator_key: Optional[str] = self.field_info.discriminator
+        self.discriminator_alias: Optional[str] = self.discriminator_key
+
+        self.allow_none: bool = False
+        self.validate_always: bool = False
+        self.sub_fields: Optional[List[ModelField]] = None
+        self.sub_fields_mapping: Optional[Dict[str, 'ModelField']] = None  # used for discriminated union
+        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:
+        return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
+
+    @staticmethod
+    def _get_field_info(
+        field_name: str, annotation: Any, value: Any, config: Type['BaseConfig']
+    ) -> Tuple[FieldInfo, Any]:
+        """
+        Get a FieldInfo from a root typing.Annotated annotation, value, or config default.
+
+        The FieldInfo may be set in typing.Annotated or the value, but not both. If neither contain
+        a FieldInfo, a new one will be created using the config.
+
+        :param field_name: name of the field for use in error messages
+        :param annotation: a type hint such as `str` or `Annotated[str, Field(..., min_length=5)]`
+        :param value: the field's assigned value
+        :param config: the model's config object
+        :return: the FieldInfo contained in the `annotation`, the value, or a new one from the config.
+        """
+        field_info_from_config = config.get_field_info(field_name)
+
+        field_info = None
+        if get_origin(annotation) is Annotated:
+            field_infos = [arg for arg in get_args(annotation)[1:] if isinstance(arg, FieldInfo)]
+            if len(field_infos) > 1:
+                raise ValueError(f'cannot specify multiple `Annotated` `Field`s for {field_name!r}')
+            field_info = next(iter(field_infos), None)
+            if field_info is not None:
+                field_info = copy.copy(field_info)
+                field_info.update_from_config(field_info_from_config)
+                if field_info.default not in (Undefined, Required):
+                    raise ValueError(f'`Field` default cannot be set in `Annotated` for {field_name!r}')
+                if value is not Undefined and value is not Required:
+                    # check also `Required` because of `validate_arguments` that sets `...` as default value
+                    field_info.default = value
+
+        if isinstance(value, FieldInfo):
+            if field_info is not None:
+                raise ValueError(f'cannot specify `Annotated` and value `Field`s together for {field_name!r}')
+            field_info = value
+            field_info.update_from_config(field_info_from_config)
+        elif field_info is None:
+            field_info = FieldInfo(value, **field_info_from_config)
+        value = None if field_info.default_factory is not None else field_info.default
+        field_info._validate()
+        return field_info, value
+
+    @classmethod
+    def infer(
+        cls,
+        *,
+        name: str,
+        value: Any,
+        annotation: Any,
+        class_validators: Optional[Dict[str, Validator]],
+        config: Type['BaseConfig'],
+    ) -> 'ModelField':
+        from pydantic.v1.schema import get_annotation_from_field_info
+
+        field_info, value = cls._get_field_info(name, annotation, value, config)
+        required: 'BoolUndefined' = Undefined
+        if value is Required:
+            required = True
+            value = None
+        elif value is not Undefined:
+            required = False
+        annotation = get_annotation_from_field_info(annotation, field_info, name, config.validate_assignment)
+
+        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
+        new_exclude = info_from_config.get('exclude')
+        if new_exclude is not None:
+            self.field_info.exclude = ValueItems.merge(self.field_info.exclude, new_exclude)
+        new_include = info_from_config.get('include')
+        if new_include is not None:
+            self.field_info.include = ValueItems.merge(self.field_info.include, new_include, intersect=True)
+
+    @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.
+        """
+        self._set_default_and_type()
+        if self.type_.__class__ is ForwardRef or self.type_.__class__ is DeferredType:
+            # 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._type_analysis()
+        if self.required is Undefined:
+            self.required = True
+        if self.default is Undefined and self.default_factory is None:
+            self.default = None
+        self.populate_validators()
+
+    def _set_default_and_type(self) -> None:
+        """
+        Set the default value, infer the type if needed and check if `None` value is valid.
+        """
+        if self.default_factory is not None:
+            if self.type_ is Undefined:
+                raise errors_.ConfigError(
+                    f'you need to set the type of field {self.name!r} when using `default_factory`'
+                )
+            return
+
+        default_value = self.get_default()
+
+        if default_value is not None and self.type_ is Undefined:
+            self.type_ = default_value.__class__
+            self.outer_type_ = self.type_
+            self.annotation = self.type_
+
+        if self.type_ is Undefined:
+            raise errors_.ConfigError(f'unable to infer type for attribute "{self.name}"')
+
+        if self.required is False and default_value is None:
+            self.allow_none = True
+
+    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):
+            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 or self.type_ is object:
+            if self.required is Undefined:
+                self.required = False
+            self.allow_none = True
+            return
+        elif self.type_ is Pattern or self.type_ is re.Pattern:
+            # python 3.7 only, Pattern is a typing object but without sub fields
+            return
+        elif is_literal_type(self.type_):
+            return
+        elif is_typeddict(self.type_):
+            return
+
+        if is_finalvar(self.type_):
+            self.final = True
+
+            if self.type_ is Final:
+                self.type_ = Any
+            else:
+                self.type_ = get_args(self.type_)[0]
+
+            self._type_analysis()
+            return
+
+        origin = get_origin(self.type_)
+
+        if origin is Annotated or is_typeddict_special(origin):
+            self.type_ = get_args(self.type_)[0]
+            self._type_analysis()
+            return
+
+        if self.discriminator_key is not None and not is_union(origin):
+            raise TypeError('`discriminator` can only be used with `Union` type with more than one variant')
+
+        # add extra check for `collections.abc.Hashable` for python 3.10+ where origin is not `None`
+        if origin is None or origin is CollectionsHashable:
+            # 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
+        elif origin is Callable:
+            return
+        elif is_union(origin):
+            types_ = []
+            for type_ in get_args(self.type_):
+                if is_none_type(type_) or type_ is Any or type_ is object:
+                    if self.required is Undefined:
+                        self.required = False
+                    self.allow_none = True
+                if is_none_type(type_):
+                    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_]
+
+                if self.discriminator_key is not None:
+                    self.prepare_discriminated_union_sub_fields()
+            return
+        elif issubclass(origin, Tuple):  # type: ignore
+            # origin == Tuple without item type
+            args = get_args(self.type_)
+            if not args:  # plain tuple
+                self.type_ = Any
+                self.shape = SHAPE_TUPLE_ELLIPSIS
+            elif len(args) == 2 and args[1] is Ellipsis:  # e.g. Tuple[int, ...]
+                self.type_ = args[0]
+                self.shape = SHAPE_TUPLE_ELLIPSIS
+                self.sub_fields = [self._create_sub_type(args[0], f'{self.name}_0')]
+            elif args == ((),):  # Tuple[()] means empty tuple
+                self.shape = SHAPE_TUPLE
+                self.type_ = Any
+                self.sub_fields = []
+            else:
+                self.shape = SHAPE_TUPLE
+                self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(args)]
+            return
+        elif 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) for i, validator in enumerate(get_validators())}
+                )
+
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_LIST
+        elif issubclass(origin, Set):
+            # Create self validators
+            get_validators = getattr(self.type_, '__get_validators__', None)
+            if get_validators:
+                self.class_validators.update(
+                    {f'set_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
+                )
+
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_SET
+        elif issubclass(origin, FrozenSet):
+            # Create self validators
+            get_validators = getattr(self.type_, '__get_validators__', None)
+            if get_validators:
+                self.class_validators.update(
+                    {f'frozenset_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
+                )
+
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_FROZENSET
+        elif issubclass(origin, Deque):
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_DEQUE
+        elif issubclass(origin, Sequence):
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_SEQUENCE
+        # priority to most common mapping: dict
+        elif origin is dict or origin is Dict:
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
+            self.type_ = get_args(self.type_)[1]
+            self.shape = SHAPE_DICT
+        elif issubclass(origin, DefaultDict):
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
+            self.type_ = get_args(self.type_)[1]
+            self.shape = SHAPE_DEFAULTDICT
+        elif issubclass(origin, Counter):
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
+            self.type_ = int
+            self.shape = SHAPE_COUNTER
+        elif issubclass(origin, Mapping):
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
+            self.type_ = get_args(self.type_)[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_ = get_args(self.type_)[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(get_args(self.type_))]
+            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 prepare_discriminated_union_sub_fields(self) -> None:
+        """
+        Prepare the mapping <discriminator key> -> <ModelField> and update `sub_fields`
+        Note that this process can be aborted if a `ForwardRef` is encountered
+        """
+        assert self.discriminator_key is not None
+
+        if self.type_.__class__ is DeferredType:
+            return
+
+        assert self.sub_fields is not None
+        sub_fields_mapping: Dict[str, 'ModelField'] = {}
+        all_aliases: Set[str] = set()
+
+        for sub_field in self.sub_fields:
+            t = sub_field.type_
+            if t.__class__ is ForwardRef:
+                # Stopping everything...will need to call `update_forward_refs`
+                return
+
+            alias, discriminator_values = get_discriminator_alias_and_values(t, self.discriminator_key)
+            all_aliases.add(alias)
+            for discriminator_value in discriminator_values:
+                sub_fields_mapping[discriminator_value] = sub_field
+
+        self.sub_fields_mapping = sub_fields_mapping
+        self.discriminator_alias = get_unique_discriminator_alias(all_aliases, self.discriminator_key)
+
+    def _create_sub_type(self, type_: Type[Any], name: str, *, for_keys: bool = False) -> 'ModelField':
+        if for_keys:
+            class_validators = None
+        else:
+            # validators for sub items should not have `each_item` as we want to check only the first sublevel
+            class_validators = {
+                k: Validator(
+                    func=v.func,
+                    pre=v.pre,
+                    each_item=False,
+                    always=v.always,
+                    check_fields=v.check_fields,
+                    skip_on_failure=v.skip_on_failure,
+                )
+                for k, v in self.class_validators.items()
+                if v.each_item
+            }
+
+        field_info, _ = self._get_field_info(name, type_, None, self.model_config)
+
+        return self.__class__(
+            type_=type_,
+            name=name,
+            class_validators=class_validators,
+            model_config=self.model_config,
+            field_info=field_info,
+        )
+
+    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.
+        """
+        self.validate_always = getattr(self.type_, 'validate_always', False) or any(
+            v.always for v in self.class_validators.values()
+        )
+
+        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':
+        assert self.type_.__class__ is not DeferredType
+
+        if self.type_.__class__ is ForwardRef:
+            assert cls is not None
+            raise ConfigError(
+                f'field "{self.name}" not yet prepared so type is still a ForwardRef, '
+                f'you might need to call {cls.__name__}.update_forward_refs().'
+            )
+
+        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 is_none_type(self.type_):
+                # keep validating
+                pass
+            elif 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 in MAPPING_LIKE_SHAPES:
+            v, errors = self._validate_mapping_like(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 in (SHAPE_TUPLE, SHAPE_TUPLE_ELLIPSIS):
+                e = errors_.TupleError()
+            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], Deque[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_DEQUE:
+            converted = deque(result, maxlen=getattr(v, 'maxlen', None))
+        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)
+            elif isinstance(v, deque):
+                converted = deque(result, maxlen=getattr(v, 'maxlen', None))
+        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_like(
+        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
+        elif self.shape == SHAPE_DICT:
+            return result, None
+        elif self.shape == SHAPE_DEFAULTDICT:
+            return defaultdict(self.type_, result), None
+        elif self.shape == SHAPE_COUNTER:
+            return CollectionCounter(result), None
+        else:
+            return self._get_mapping_value(v, result), None
+
+    def _get_mapping_value(self, original: T, converted: Dict[Any, Any]) -> Union[T, Dict[Any, Any]]:
+        """
+        When type is `Mapping[KT, KV]` (or another unsupported mapping), we try to avoid
+        coercing to `dict` unwillingly.
+        """
+        original_cls = original.__class__
+
+        if original_cls == dict or original_cls == Dict:
+            return converted
+        elif original_cls in {defaultdict, DefaultDict}:
+            return defaultdict(self.type_, converted)
+        else:
+            try:
+                # Counter, OrderedDict, UserDict, ...
+                return original_cls(converted)  # type: ignore
+            except TypeError:
+                raise RuntimeError(f'Could not convert dictionary to {original_cls.__name__!r}') from None
+
+    def _validate_singleton(
+        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
+    ) -> 'ValidateReturn':
+        if self.sub_fields:
+            if self.discriminator_key is not None:
+                return self._validate_discriminated_union(v, values, loc, cls)
+
+            errors = []
+
+            if self.model_config.smart_union and is_union(get_origin(self.type_)):
+                # 1st pass: check if the value is an exact instance of one of the Union types
+                # (e.g. to avoid coercing a bool into an int)
+                for field in self.sub_fields:
+                    if v.__class__ is field.outer_type_:
+                        return v, None
+
+                # 2nd pass: check if the value is an instance of any subclass of the Union types
+                for field in self.sub_fields:
+                    # This whole logic will be improved later on to support more complex `isinstance` checks
+                    # It will probably be done once a strict mode is added and be something like:
+                    # ```
+                    #     value, error = field.validate(v, values, strict=True)
+                    #     if error is None:
+                    #         return value, None
+                    # ```
+                    try:
+                        if isinstance(v, field.outer_type_):
+                            return v, None
+                    except TypeError:
+                        # compound type
+                        if lenient_isinstance(v, get_origin(field.outer_type_)):
+                            value, error = field.validate(v, values, loc=loc, cls=cls)
+                            if not error:
+                                return value, None
+
+            # 1st pass by default or 3rd pass with `smart_union` enabled:
+            # check if the value can be coerced into one of the Union types
+            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 _validate_discriminated_union(
+        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
+    ) -> 'ValidateReturn':
+        assert self.discriminator_key is not None
+        assert self.discriminator_alias is not None
+
+        try:
+            try:
+                discriminator_value = v[self.discriminator_alias]
+            except KeyError:
+                if self.model_config.allow_population_by_field_name:
+                    discriminator_value = v[self.discriminator_key]
+                else:
+                    raise
+        except KeyError:
+            return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc)
+        except TypeError:
+            try:
+                # BaseModel or dataclass
+                discriminator_value = getattr(v, self.discriminator_key)
+            except (AttributeError, TypeError):
+                return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc)
+
+        if self.sub_fields_mapping is None:
+            assert cls is not None
+            raise ConfigError(
+                f'field "{self.name}" not yet prepared so type is still a ForwardRef, '
+                f'you might need to call {cls.__name__}.update_forward_refs().'
+            )
+
+        try:
+            sub_field = self.sub_fields_mapping[discriminator_value]
+        except (KeyError, TypeError):
+            # KeyError: `discriminator_value` is not in the dictionary.
+            # TypeError: `discriminator_value` is unhashable.
+            assert self.sub_fields_mapping is not None
+            return v, ErrorWrapper(
+                InvalidDiscriminator(
+                    discriminator_key=self.discriminator_key,
+                    discriminator_value=discriminator_value,
+                    allowed_values=list(self.sub_fields_mapping),
+                ),
+                loc,
+            )
+        else:
+            if not isinstance(loc, tuple):
+                loc = (loc,)
+            return sub_field.validate(v, values, loc=(*loc, display_as_type(sub_field.type_)), cls=cls)
+
+    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 is_complex(self) -> bool:
+        """
+        Whether the field is "complex" eg. env variables should be parsed as JSON.
+        """
+        from pydantic.v1.main import BaseModel
+
+        return (
+            self.shape != SHAPE_SINGLETON
+            or hasattr(self.type_, '__pydantic_model__')
+            or lenient_issubclass(self.type_, (BaseModel, list, set, frozenset, dict))
+        )
+
+    def _type_display(self) -> PyObjectStr:
+        t = display_as_type(self.type_)
+
+        if self.shape in MAPPING_LIKE_SHAPES:
+            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
+
+
+class ModelPrivateAttr(Representation):
+    __slots__ = ('default', 'default_factory')
+
+    def __init__(self, default: Any = Undefined, *, default_factory: Optional[NoArgAnyCallable] = None) -> None:
+        self.default = default
+        self.default_factory = default_factory
+
+    def get_default(self) -> Any:
+        return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, self.__class__) and (self.default, self.default_factory) == (
+            other.default,
+            other.default_factory,
+        )
+
+
+def PrivateAttr(
+    default: Any = Undefined,
+    *,
+    default_factory: Optional[NoArgAnyCallable] = None,
+) -> Any:
+    """
+    Indicates that attribute is only used internally and never mixed with regular fields.
+
+    Types or values of private attrs are not checked by pydantic and it's up to you to keep them relevant.
+
+    Private attrs are stored in model __slots__.
+
+    :param default: the attribute’s default value
+    :param default_factory: callable that will be called when a default value is needed for this attribute
+      If both `default` and `default_factory` are set, an error is raised.
+    """
+    if default is not Undefined and default_factory is not None:
+        raise ValueError('cannot specify both default and default_factory')
+
+    return ModelPrivateAttr(
+        default,
+        default_factory=default_factory,
+    )
+
+
+class DeferredType:
+    """
+    Used to postpone field preparation, while creating recursive generic models.
+    """
+
+
+def is_finalvar_with_default_val(type_: Type[Any], val: Any) -> bool:
+    return is_finalvar(type_) and val is not Undefined and not isinstance(val, FieldInfo)