text: import warnings
  from collections import deque
  from collections.abc import Iterable as CollectionsIterable
  from typing import (
      TYPE_CHECKING,
      Any,
      Deque,
      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 ConfigError, NoneIsNotAllowedError
  from .types import Json, JsonWrapper
  from .typing import (
      NONE_TYPES,
      Callable,
      ForwardRef,
      NoArgAnyCallable,
      NoneType,
      display_as_type,
      get_args,
      get_origin,
      is_literal_type,
      is_new_type,
      is_typeddict,
      new_type_supertype,
  )
  from .utils import PyObjectStr, Representation, lenient_issubclass, sequence_like, smart_deepcopy
  from .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 __deepcopy__(self: T, _: Any) -> T:
          return self
  
  
  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 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 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_DEQUE = 11
  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[{}]',
  }
  
  
  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:
          return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
  
      @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 = None 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.
          """
          self._set_default_and_type()
          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._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 _set_default_and_type(self) -> None:
          """
          Set the default value, infer the type if needed and check if `None` value is valid.
  
          Note: to prevent side effects by calling the `default_factory` for nothing, we only call it
          when we want to validate the default value i.e. when `validate_all` is set to True.
          """
          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`'
                  )
              if not self.model_config.validate_all:
                  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_
  
          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:
              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
          elif is_typeddict(self.type_):
              return
  
          origin = get_origin(self.type_)
          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 get_args(self.type_):
                  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
              # 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
              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
  
          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) 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):
              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
          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 _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
              }
          return self.__class__(
              type_=type_,
              name=name,
              class_validators=class_validators,
              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.
          """
          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':
  
          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 self.type_ in NONE_TYPES:
                  # 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 == 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 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)
          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)
          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 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
  
  
  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,
      )
  