main.py

"""
Logic for creating models, could perhaps be renamed to `models.py`.
"""
from __future__ import annotations as _annotations

import sys
import typing
import warnings
from abc import ABCMeta
from copy import copy, deepcopy
from enum import Enum
from functools import partial
from inspect import getdoc
from types import prepare_class, resolve_bases
from typing import Any, Generic, TypeVar, overload

import typing_extensions
from pydantic_core import CoreConfig, SchemaValidator

from ._internal import (
    _decorators,
    _forward_ref,
    _generate_schema,
    _generics,
    _model_construction,
    _repr,
    _typing_extra,
    _utils,
)
from ._internal._fields import Undefined
from .config import BaseConfig, ConfigDict, Extra, build_config, get_config
from .errors import PydanticUserError
from .fields import Field, FieldInfo, ModelPrivateAttr
from .json import custom_pydantic_encoder, pydantic_encoder
from .json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema, JsonSchemaMetadata

if typing.TYPE_CHECKING:
    from inspect import Signature

    from pydantic_core import CoreSchema, SchemaSerializer

    from ._internal._utils import AbstractSetIntStr, MappingIntStrAny

    AnyClassMethod = classmethod[Any]
    TupleGenerator = typing.Generator[tuple[str, Any], None, None]
    Model = typing.TypeVar('Model', bound='BaseModel')
    # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
    IncEx = set[int] | set[str] | dict[int, Any] | dict[str, Any] | None

__all__ = 'BaseModel', 'create_model', 'Validator'

_object_setattr = _model_construction.object_setattr
# Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we need to add this extra
# (somewhat hacky) boolean to keep track of whether we've created the `BaseModel` class yet, and therefore whether it's
# safe to refer to it. If it *hasn't* been created, we assume that the `__new__` call we're in the middle of is for
# the `BaseModel` class, since that's defined immediately after the metaclass.
_base_class_defined = False


@typing_extensions.dataclass_transform(kw_only_default=True, field_specifiers=(Field,))
class ModelMetaclass(ABCMeta):
    def __new__(
        mcs,
        cls_name: str,
        bases: tuple[type[Any], ...],
        namespace: dict[str, Any],
        __pydantic_generic_origin__: type[BaseModel] | None = None,
        __pydantic_generic_args__: tuple[Any, ...] | None = None,
        __pydantic_generic_parameters__: tuple[Any, ...] | None = None,
        **kwargs: Any,
    ) -> type:
        if _base_class_defined:
            base_field_names, class_vars, base_private_attributes = _collect_bases_data(bases)

            config_new = build_config(cls_name, bases, namespace, kwargs)
            namespace['model_config'] = config_new
            private_attributes = _model_construction.inspect_namespace(
                namespace, config_new.get('ignored_types', ()), class_vars, base_field_names
            )
            if private_attributes:
                slots: set[str] = set(namespace.get('__slots__', ()))
                namespace['__slots__'] = slots | private_attributes.keys()

                if 'model_post_init' in namespace:
                    # if there are private_attributes and a model_post_init function, we wrap them both
                    # in a single function
                    namespace['_init_private_attributes'] = _model_construction.init_private_attributes

                    def __pydantic_post_init__(self_: Any, context: Any) -> None:
                        self_._init_private_attributes(context)
                        self_.model_post_init(context)

                    namespace['__pydantic_post_init__'] = __pydantic_post_init__
                else:
                    namespace['__pydantic_post_init__'] = _model_construction.init_private_attributes
            elif 'model_post_init' in namespace:
                namespace['__pydantic_post_init__'] = namespace['model_post_init']

            namespace['__class_vars__'] = class_vars
            namespace['__private_attributes__'] = {**base_private_attributes, **private_attributes}

            if config_new['json_encoders']:
                json_encoder = partial(custom_pydantic_encoder, config_new['json_encoders'])
            else:
                json_encoder = pydantic_encoder  # type: ignore[assignment]
            namespace['__json_encoder__'] = staticmethod(json_encoder)
            namespace['__schema_cache__'] = {}

            if '__hash__' not in namespace and config_new['frozen']:

                def hash_func(self_: Any) -> int:
                    return hash(self_.__class__) + hash(tuple(self_.__dict__.values()))

                namespace['__hash__'] = hash_func

            cls: type[BaseModel] = super().__new__(mcs, cls_name, bases, namespace, **kwargs)  # type: ignore

            cls.__pydantic_decorators__ = _decorators.gather_decorator_functions(cls)

            cls.__pydantic_generic_args__ = __pydantic_generic_args__
            cls.__pydantic_generic_origin__ = __pydantic_generic_origin__
            cls.__pydantic_generic_parameters__ = __pydantic_generic_parameters__ or getattr(
                cls, '__parameters__', None
            )
            cls.__pydantic_generic_defaults__ = None if not cls.__pydantic_generic_parameters__ else {}
            cls.__pydantic_generic_typevars_map__ = (
                None
                if __pydantic_generic_origin__ is None
                else dict(
                    zip(_generics.iter_contained_typevars(__pydantic_generic_origin__), __pydantic_generic_args__ or ())
                )
            )

            cls.__pydantic_model_complete__ = False  # Ensure this specific class gets completed

            # preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487
            # for attributes not in `new_namespace` (e.g. private attributes)
            for name, obj in private_attributes.items():
                set_name = getattr(obj, '__set_name__', None)
                if callable(set_name):
                    set_name(cls, name)

            _model_construction.complete_model_class(
                cls,
                cls_name,
                bases,
                types_namespace=_typing_extra.parent_frame_namespace(),
                raise_errors=False,
            )
            return cls
        else:
            # this is the BaseModel class itself being created, no logic required
            return super().__new__(mcs, cls_name, bases, namespace, **kwargs)

    def __instancecheck__(self, instance: Any) -> bool:
        """
        Avoid calling ABC _abc_subclasscheck unless we're pretty sure.

        See #3829 and python/cpython#92810
        """
        return hasattr(instance, '__pydantic_validator__') and super().__instancecheck__(instance)


class BaseModel(_repr.Representation, metaclass=ModelMetaclass):
    if typing.TYPE_CHECKING:
        # populated by the metaclass, defined here to help IDEs only
        __pydantic_validator__: typing.ClassVar[SchemaValidator]
        __pydantic_core_schema__: typing.ClassVar[CoreSchema]
        __pydantic_serializer__: typing.ClassVar[SchemaSerializer]
        __pydantic_decorators__: typing.ClassVar[_decorators.DecoratorInfos]
        """metadata for `@validator`, `@root_validator` and `@serializer` decorators"""
        model_fields: typing.ClassVar[dict[str, FieldInfo]] = {}
        __json_encoder__: typing.ClassVar[typing.Callable[[Any], Any]] = lambda x: x  # noqa: E731
        __schema_cache__: typing.ClassVar[dict[Any, Any]] = {}
        __signature__: typing.ClassVar[Signature]
        __private_attributes__: typing.ClassVar[dict[str, ModelPrivateAttr]]
        __class_vars__: typing.ClassVar[set[str]]
        __fields_set__: set[str] = set()
        __pydantic_generic_args__: typing.ClassVar[tuple[Any, ...] | None]
        __pydantic_generic_defaults__: typing.ClassVar[dict[str, Any] | None]
        __pydantic_generic_origin__: typing.ClassVar[type[BaseModel] | None]
        __pydantic_generic_parameters__: typing.ClassVar[tuple[_typing_extra.TypeVarType, ...] | None]
        __pydantic_generic_typevars_map__: typing.ClassVar[dict[_typing_extra.TypeVarType, Any] | None]
    else:
        __pydantic_validator__ = _model_construction.MockValidator(
            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly'
        )

    model_config = ConfigDict()
    __slots__ = '__dict__', '__fields_set__'
    __doc__ = ''  # Null out the Representation docstring
    __pydantic_model_complete__ = False

    def __init__(__pydantic_self__, **data: Any) -> None:
        """
        Create a new model by parsing and validating input data from keyword arguments.

        Raises ValidationError if the input data cannot be parsed to form a valid model.

        Uses something other than `self` for the first arg to allow "self" as a field name.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)

    @classmethod
    def model_validate(cls: type[Model], obj: Any) -> Model:
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        return cls.__pydantic_validator__.validate_python(obj)

    @classmethod
    def model_validate_json(cls: type[Model], json_data: str | bytes | bytearray) -> Model:
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        return cls.__pydantic_validator__.validate_json(json_data)

    if typing.TYPE_CHECKING:
        # model_after_init is called after at the end of `__init__` if it's defined
        def model_post_init(self, _context: Any) -> None:
            pass

    @typing.no_type_check
    def __setattr__(self, name, value):
        if name in self.__class_vars__:
            raise AttributeError(
                f'"{name}" is a ClassVar of `{self.__class__.__name__}` and cannot be set on an instance. '
                f'If you want to set a value on the class, use `{self.__class__.__name__}.{name} = value`.'
            )
        if name.startswith('_'):
            _object_setattr(self, name, value)
        elif self.model_config['frozen']:
            raise TypeError(f'"{self.__class__.__name__}" is frozen and does not support item assignment')
        elif self.model_config['validate_assignment']:
            self.__pydantic_validator__.validate_assignment(self, name, value)
        elif self.model_config['extra'] is not Extra.allow and name not in self.model_fields:
            # TODO - matching error
            raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
        else:
            self.__dict__[name] = value
            self.__fields_set__.add(name)

    def __getstate__(self) -> dict[Any, Any]:
        private_attrs = ((k, getattr(self, k, Undefined)) for k in self.__private_attributes__)
        return {
            '__dict__': self.__dict__,
            '__fields_set__': self.__fields_set__,
            '__private_attribute_values__': {k: v for k, v in private_attrs if v is not Undefined},
        }

    def __setstate__(self, state: dict[Any, Any]) -> None:
        _object_setattr(self, '__dict__', state['__dict__'])
        _object_setattr(self, '__fields_set__', state['__fields_set__'])
        for name, value in state.get('__private_attribute_values__', {}).items():
            _object_setattr(self, name, value)

    def model_dump(
        self,
        *,
        mode: typing_extensions.Literal['json', 'python'] | str = 'python',
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> dict[str, Any]:
        """
        Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
        """
        return self.__pydantic_serializer__.to_python(
            self,
            mode=mode,
            by_alias=by_alias,
            include=include,
            exclude=exclude,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
        )

    def model_dump_json(
        self,
        *,
        indent: int | None = None,
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> bytes:
        """
        Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`.
        """
        return self.__pydantic_serializer__.to_json(
            self,
            indent=indent,
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
        )

    @classmethod
    def from_orm(cls: type[Model], obj: Any) -> Model:
        # TODO remove
        return cls.model_validate(obj)

    @classmethod
    def model_construct(cls: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model:
        """
        Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
        Default values are respected, but no other validation is performed.
        Behaves as if `Config.extra = 'allow'` was set since it adds all passed values
        """
        m = cls.__new__(cls)
        fields_values: dict[str, Any] = {}
        for name, field in cls.model_fields.items():
            if field.alias and field.alias in values:
                fields_values[name] = values[field.alias]
            elif name in values:
                fields_values[name] = values[name]
            elif not field.is_required():
                fields_values[name] = field.get_default()
        fields_values.update(values)
        _object_setattr(m, '__dict__', fields_values)
        if _fields_set is None:
            _fields_set = set(values.keys())
        _object_setattr(m, '__fields_set__', _fields_set)
        if hasattr(m, '__pydantic_post_init__'):
            m.__pydantic_post_init__(context=None)
        return m

    def _copy_and_set_values(self: Model, values: dict[str, Any], fields_set: set[str], *, deep: bool) -> Model:
        if deep:
            # chances of having empty dict here are quite low for using smart_deepcopy
            values = deepcopy(values)

        cls = self.__class__
        m = cls.__new__(cls)
        _object_setattr(m, '__dict__', values)
        _object_setattr(m, '__fields_set__', fields_set)
        for name in self.__private_attributes__:
            value = getattr(self, name, Undefined)
            if value is not Undefined:
                if deep:
                    value = deepcopy(value)
                _object_setattr(m, name, value)

        return m

    # @typing_extensions.deprecated('This method is now deprecated; use `model_copy` instead')
    def copy(
        self: Model,
        *,
        include: AbstractSetIntStr | MappingIntStrAny | None = None,
        exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
        update: dict[str, Any] | None = None,
        deep: bool = False,
    ) -> Model:
        """
        This method is now deprecated; use `model_copy` instead. If you need include / exclude, use:

            data = self.model_dump(include=include, exclude=exclude, round_trip=True)
            data = {**data, **(update or {})}
            copied = self.model_validate(data)

        Duplicate a model, optionally choose which fields to include, exclude and change.

        :param include: fields to include in new model
        :param exclude: fields to exclude from new model, as with values this takes precedence over include
        :param update: values to change/add in the new model. Note: the data is not validated before creating
            the new model: you should trust this data
        :param deep: set to `True` to make a deep copy of the model
        :return: new model instance
        """
        warnings.warn(
            'The `copy` method is deprecated; use `model_copy` instead. '
            'See the docstring of `BaseModel.copy` for details about how to handle include / exclude / update.',
            DeprecationWarning,
        )

        values = dict(
            self._iter(to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False),
            **(update or {}),
        )

        # new `__fields_set__` can have unset optional fields with a set value in `update` kwarg
        if update:
            fields_set = self.__fields_set__ | update.keys()
        else:
            fields_set = set(self.__fields_set__)

        # removing excluded fields from `__fields_set__`
        if exclude:
            fields_set -= set(exclude)

        return self._copy_and_set_values(values, fields_set, deep=deep)

    @classmethod
    def model_json_schema(
        cls,
        by_alias: bool = True,
        ref_template: str = DEFAULT_REF_TEMPLATE,
        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
    ) -> dict[str, Any]:
        """
        To override the logic used to generate the JSON schema, you can create a subclass of GenerateJsonSchema
        with your desired modifications, then override this method on a custom base class and set the default
        value of `schema_generator` to be your subclass.
        """
        cached = cls.__schema_cache__.get((by_alias, ref_template))
        if cached is not None:
            return cached
        s = schema_generator(by_alias=by_alias, ref_template=ref_template).generate(cls.__pydantic_core_schema__)
        cls.__schema_cache__[(by_alias, ref_template)] = s
        return s

    @classmethod
    def model_json_schema_metadata(cls) -> JsonSchemaMetadata | None:
        """
        Overriding this method provides a simple way to modify certain aspects of the JSON schema generation.

        This is a convenience method primarily intended to control how the "generic" properties
        of the JSON schema are populated, or apply minor transformations through `extra_updates` or
        `modify_js_function`. See https://json-schema.org/understanding-json-schema/reference/generic.html
        and the comments surrounding the definition of `JsonSchemaMetadata` for more details.

        If you want to make more sweeping changes to how the JSON schema is generated, you will probably
        want to subclass `GenerateJsonSchema` and pass your subclass in the `schema_generator` argument to the
        `model_json_schema` method.
        """
        title = cls.model_config['title'] or cls.__name__
        description = getdoc(cls) or None
        return {'title': title, 'description': description}

    @classmethod
    def schema_json(
        cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
    ) -> str:
        from .json import pydantic_encoder

        return cls.model_config['json_dumps'](
            cls.model_json_schema(by_alias=by_alias, ref_template=ref_template),
            default=pydantic_encoder,
            **dumps_kwargs,
        )

    @classmethod
    # @typing_extensions.deprecated('This method is only used for _iter, which is deprecated')
    @typing.no_type_check
    def _get_value(
        cls,
        v: Any,
        to_dict: bool,
        by_alias: bool,
        include: AbstractSetIntStr | MappingIntStrAny | None,
        exclude: AbstractSetIntStr | MappingIntStrAny | None,
        exclude_unset: bool,
        exclude_defaults: bool,
        exclude_none: bool,
    ) -> Any:
        if isinstance(v, BaseModel):
            if to_dict:
                return v.model_dump(
                    by_alias=by_alias,
                    exclude_unset=exclude_unset,
                    exclude_defaults=exclude_defaults,
                    include=include,
                    exclude=exclude,
                    exclude_none=exclude_none,
                )
            else:
                return v.copy(include=include, exclude=exclude)

        value_exclude = _utils.ValueItems(v, exclude) if exclude else None
        value_include = _utils.ValueItems(v, include) if include else None

        if isinstance(v, dict):
            return {
                k_: cls._get_value(
                    v_,
                    to_dict=to_dict,
                    by_alias=by_alias,
                    exclude_unset=exclude_unset,
                    exclude_defaults=exclude_defaults,
                    include=value_include and value_include.for_element(k_),
                    exclude=value_exclude and value_exclude.for_element(k_),
                    exclude_none=exclude_none,
                )
                for k_, v_ in v.items()
                if (not value_exclude or not value_exclude.is_excluded(k_))
                and (not value_include or value_include.is_included(k_))
            }

        elif _utils.sequence_like(v):
            seq_args = (
                cls._get_value(
                    v_,
                    to_dict=to_dict,
                    by_alias=by_alias,
                    exclude_unset=exclude_unset,
                    exclude_defaults=exclude_defaults,
                    include=value_include and value_include.for_element(i),
                    exclude=value_exclude and value_exclude.for_element(i),
                    exclude_none=exclude_none,
                )
                for i, v_ in enumerate(v)
                if (not value_exclude or not value_exclude.is_excluded(i))
                and (not value_include or value_include.is_included(i))
            )

            return v.__class__(*seq_args) if _typing_extra.is_namedtuple(v.__class__) else v.__class__(seq_args)

        elif isinstance(v, Enum) and getattr(cls.model_config, 'use_enum_values', False):
            return v.value

        else:
            return v

    @classmethod
    def model_rebuild(
        cls,
        *,
        force: bool = False,
        raise_errors: bool = True,
        types_namespace: dict[str, Any] | None = None,
        typevars_map: dict[str, Any] | None = None,
    ) -> bool | None:
        """
        Try to (Re)construct the model schema.
        """
        if not force and cls.__pydantic_model_complete__:
            return None
        else:
            parents_namespace = _typing_extra.parent_frame_namespace()
            if types_namespace and parents_namespace:
                types_namespace = {**parents_namespace, **types_namespace}
            elif parents_namespace:
                types_namespace = parents_namespace

            return _model_construction.complete_model_class(
                cls,
                cls.__name__,
                cls.__bases__,
                raise_errors=raise_errors,
                types_namespace=types_namespace,
                typevars_map=typevars_map,
            )

    def __iter__(self) -> TupleGenerator:
        """
        so `dict(model)` works
        """
        yield from self.__dict__.items()

    # @typing_extensions.deprecated('This private method is only used for `BaseModel.copy`, which is deprecated')
    def _iter(
        self,
        to_dict: bool = False,
        by_alias: bool = False,
        include: AbstractSetIntStr | MappingIntStrAny | None = None,
        exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
    ) -> TupleGenerator:
        # Merge field set excludes with explicit exclude parameter with explicit overriding field set options.
        # The extra "is not None" guards are not logically necessary but optimizes performance for the simple case.
        if exclude is not None:
            exclude = _utils.ValueItems.merge(
                {k: v.exclude for k, v in self.model_fields.items() if v.exclude is not None}, exclude
            )

        if include is not None:
            include = _utils.ValueItems.merge(
                {k: v.include for k, v in self.model_fields.items()}, include, intersect=True
            )

        allowed_keys = self._calculate_keys(
            include=include, exclude=exclude, exclude_unset=exclude_unset  # type: ignore
        )
        if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none):
            # huge boost for plain _iter()
            yield from self.__dict__.items()
            return

        value_exclude = _utils.ValueItems(self, exclude) if exclude is not None else None
        value_include = _utils.ValueItems(self, include) if include is not None else None

        for field_key, v in self.__dict__.items():
            if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None):
                continue

            if exclude_defaults:
                try:
                    field = self.model_fields[field_key]
                except KeyError:
                    pass
                else:
                    if not field.is_required() and field.default == v:
                        continue

            if by_alias and field_key in self.model_fields:
                dict_key = self.model_fields[field_key].alias or field_key
            else:
                dict_key = field_key

            if to_dict or value_include or value_exclude:
                v = self._get_value(
                    v,
                    to_dict=to_dict,
                    by_alias=by_alias,
                    include=value_include and value_include.for_element(field_key),
                    exclude=value_exclude and value_exclude.for_element(field_key),
                    exclude_unset=exclude_unset,
                    exclude_defaults=exclude_defaults,
                    exclude_none=exclude_none,
                )
            yield dict_key, v

    def _calculate_keys(
        self,
        include: MappingIntStrAny | None,
        exclude: MappingIntStrAny | None,
        exclude_unset: bool,
        update: dict[str, Any] | None = None,
    ) -> typing.AbstractSet[str] | None:
        if include is None and exclude is None and exclude_unset is False:
            return None

        keys: typing.AbstractSet[str]
        if exclude_unset:
            keys = self.__fields_set__.copy()
        else:
            keys = self.__dict__.keys()

        if include is not None:
            keys &= include.keys()

        if update:
            keys -= update.keys()

        if exclude:
            keys -= {k for k, v in exclude.items() if _utils.ValueItems.is_true(v)}

        return keys

    def __eq__(self, other: Any) -> bool:
        if not isinstance(other, BaseModel):
            return False

        # When comparing instances of generic types for equality, as long as all field values are equal,
        # only require their generic origin types to be equal, rather than exact type equality.
        # This prevents headaches like MyGeneric(x=1) != MyGeneric[Any](x=1).
        self_type = getattr(self, '__pydantic_generic_origin__', None) or self.__class__
        other_type = getattr(other, '__pydantic_generic_origin__', None) or other.__class__

        if self_type != other_type:
            return False

        if self.__dict__ != other.__dict__:
            return False

        # If the types and field values match, check for equality of private attributes
        for k in self.__private_attributes__:
            if getattr(self, k, Undefined) != getattr(other, k, Undefined):
                return False

        return True

    def model_copy(self: Model, *, update: dict[str, Any] | None = None, deep: bool = False) -> Model:
        """
        Returns a copy of the model.

        :param update: values to change/add in the new model. Note: the data is not validated before creating
            the new model: you should trust this data
        :param deep: set to `True` to make a deep copy of the model
        :return: new model instance
        """
        copied = self.__deepcopy__() if deep else self.__copy__()
        if update:
            copied.__dict__.update(update)
            copied.__fields_set__.update(update.keys())
        return copied

    def __copy__(self: Model) -> Model:
        """
        Returns a shallow copy of the model
        """
        cls = type(self)
        m = cls.__new__(cls)
        _object_setattr(m, '__dict__', copy(self.__dict__))
        _object_setattr(m, '__fields_set__', copy(self.__fields_set__))
        for name in self.__private_attributes__:
            value = getattr(self, name, Undefined)
            if value is not Undefined:
                _object_setattr(m, name, value)
        return m

    def __deepcopy__(self: Model, memo: dict[int, Any] | None = None) -> Model:
        """
        Returns a deep copy of the model
        """
        cls = type(self)
        m = cls.__new__(cls)
        _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
        # This next line doesn't need a deepcopy because __fields_set__ is a set[str],
        # and attempting a deepcopy would be marginally slower.
        _object_setattr(m, '__fields_set__', copy(self.__fields_set__))
        for name in self.__private_attributes__:
            value = getattr(self, name, Undefined)
            if value is not Undefined:
                _object_setattr(m, name, deepcopy(value, memo=memo))
        return m

    def __repr_args__(self) -> _repr.ReprArgs:
        return [
            (k, v)
            for k, v in self.__dict__.items()
            if not k.startswith('_') and (k not in self.model_fields or self.model_fields[k].repr)
        ]

    def __class_getitem__(
        cls, typevar_values: type[Any] | tuple[type[Any], ...]
    ) -> type[BaseModel] | _forward_ref.PydanticForwardRef:
        cached = _generics.get_cached_generic_type_early(cls, typevar_values)
        if cached is not None:
            return cached

        if cls is BaseModel:
            raise TypeError('Type parameters should be placed on typing.Generic, not BaseModel')
        if not hasattr(cls, '__parameters__'):
            raise TypeError(f'{cls} cannot be parametrized because it does not inherit from typing.Generic')
        if not cls.__pydantic_generic_parameters__ and Generic not in cls.__bases__:
            raise TypeError(f'{cls} is not a generic class')

        if not isinstance(typevar_values, tuple):
            typevar_values = (typevar_values,)
        _generics.check_parameters_count(cls, typevar_values)

        # Build map from generic typevars to passed params
        typevars_map: dict[_typing_extra.TypeVarType, type[Any]] = dict(
            zip(cls.__pydantic_generic_parameters__ or (), typevar_values)
        )
        if _utils.all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map:
            submodel = cls  # if arguments are equal to parameters it's the same object
            _generics.set_cached_generic_type(cls, typevar_values, submodel)
        else:
            parent_args = cls.__pydantic_generic_args__
            if not parent_args:
                args = typevar_values
            else:
                args = tuple(_generics.replace_types(arg, typevars_map) for arg in parent_args)

            origin = cls.__pydantic_generic_origin__ or cls
            model_name = origin.model_parametrized_name(args)
            params = tuple(
                {param: None for param in _generics.iter_contained_typevars(typevars_map.values())}
            )  # use dict as ordered set

            with _generics.generic_recursion_self_type(origin, args) as maybe_self_type:
                if maybe_self_type is not None:
                    return maybe_self_type

                cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args)
                if cached is not None:
                    return cached
                submodel = _generics.create_generic_submodel(model_name, origin, args, params)

                # Update cache
                _generics.set_cached_generic_type(cls, typevar_values, submodel, origin, args)

                # Doing the rebuild _after_ populating the cache prevents infinite recursion
                submodel.model_rebuild(force=True, raise_errors=False, typevars_map=typevars_map)

        return submodel

    @classmethod
    def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str:
        """
        Compute class name for parametrizations of generic classes.

        :param params: Tuple of types of the class . Given a generic class
            `Model` with 2 type variables and a concrete model `Model[str, int]`,
            the value `(str, int)` would be passed to `params`.
        :return: String representing the new class where `params` are
            passed to `cls` as type variables.

        This method can be overridden to achieve a custom naming scheme for generic BaseModels.
        """
        if not issubclass(cls, Generic):  # type: ignore[arg-type]
            raise TypeError('Concrete names should only be generated for generic models.')

        # Any strings received should represent forward references, so we handle them specially below.
        # If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
        # we may be able to remove this special case.
        param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
        params_component = ', '.join(param_names)
        return f'{cls.__name__}[{params_component}]'


_base_class_defined = True


@typing.overload
def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | type[BaseConfig] | None = None,
    __base__: None = None,
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] = None,
    __cls_kwargs__: dict[str, Any] = None,
    **field_definitions: Any,
) -> type[Model]:
    ...


@typing.overload
def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | type[BaseConfig] | None = None,
    __base__: type[Model] | tuple[type[Model], ...],
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] = None,
    __cls_kwargs__: dict[str, Any] = None,
    **field_definitions: Any,
) -> type[Model]:
    ...


def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | type[BaseConfig] | None = None,
    __base__: type[Model] | tuple[type[Model], ...] | None = None,
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] = None,
    __cls_kwargs__: dict[str, Any] = None,
    __slots__: tuple[str, ...] | None = None,
    **field_definitions: Any,
) -> type[Model]:
    """
    Dynamically create a model.
    :param __model_name: name of the created model
    :param __config__: config dict/class to use for the new model
    :param __base__: base class for the new model to inherit from
    :param __module__: module of the created model
    :param __validators__: a dict of method names and @validator class methods
    :param __cls_kwargs__: a dict for class creation
    :param __slots__: Deprecated, `__slots__` should not be passed to `create_model`
    :param field_definitions: fields of the model (or extra fields if a base is supplied)
        in the format `<name>=(<type>, <default value>)` or `<name>=<default value>, e.g.
        `foobar=(str, ...)` or `foobar=123`, or, for complex use-cases, in the format
        `<name>=<Field>` or `<name>=(<type>, <FieldInfo>)`, e.g.
        `foo=Field(datetime, default_factory=datetime.utcnow, alias='bar')` or
        `foo=(str, FieldInfo(title='Foo'))`
    """
    if __slots__ is not None:
        # __slots__ will be ignored from here on
        warnings.warn('__slots__ should not be passed to create_model', RuntimeWarning)

    if __base__ is not None:
        if __config__ is not None:
            raise PydanticUserError('to avoid confusion __config__ and __base__ cannot be used together')
        if not isinstance(__base__, tuple):
            __base__ = (__base__,)
    else:
        __base__ = (typing.cast(typing.Type['Model'], BaseModel),)

    __cls_kwargs__ = __cls_kwargs__ or {}

    fields = {}
    annotations = {}

    for f_name, f_def in field_definitions.items():
        if f_name.startswith('_'):
            warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning)
        if isinstance(f_def, tuple):
            try:
                f_annotation, f_value = f_def
            except ValueError as e:
                raise PydanticUserError(
                    'field definitions should either be a tuple of (<type>, <default>) or just a '
                    'default value, unfortunately this means tuples as '
                    'default values are not allowed'
                ) from e
        else:
            f_annotation, f_value = None, f_def

        if f_annotation:
            annotations[f_name] = f_annotation
        fields[f_name] = f_value

    namespace: dict[str, Any] = {'__annotations__': annotations, '__module__': __module__}
    if __validators__:
        namespace.update(__validators__)
    namespace.update(fields)
    if __config__:
        namespace['model_config'] = get_config(__config__)
    resolved_bases = resolve_bases(__base__)
    meta, ns, kwds = prepare_class(__model_name, resolved_bases, kwds=__cls_kwargs__)
    if resolved_bases is not __base__:
        ns['__orig_bases__'] = __base__
    namespace.update(ns)
    return meta(__model_name, resolved_bases, namespace, **kwds)


T = TypeVar('T')


class Validator(Generic[T]):
    @overload
    def __init__(self, __type: type[T], *, config: CoreConfig | None = None) -> None:
        ...

    # Adding this overload ensures you can use special forms without getting mypy errors.
    # For example:
    #   v: Validator[int | str] = Validator(int | str)
    # Type checkers don't consider special forms like `int | str` (or `Union[int, str]`) to satisfy a Type[T]
    @overload
    def __init__(self, __type: Any, *, config: CoreConfig | None = None) -> None:
        ...

    def __init__(self, __type: Any, *, config: CoreConfig | None = None) -> None:
        self._type = __type
        merged_config: CoreConfig = {
            **(config or {}),  # type: ignore[misc]
            **getattr(__type, 'model_config', {}),
        }
        arbitrary_types = bool((config or {}).get('arbitrary_types_allowed', False))
        local_ns = _typing_extra.parent_frame_namespace(parent_depth=2)
        # BaseModel uses it's own __module__ to find out where it was defined
        # and then look for symbols to resolve forward references in those globals
        # On the other hand Validator() can be called with arbitrary objects,
        # including type aliases where __module__ (always `typing.py`) is not useful
        # So instead we look at the globals in our parent stack frame
        # This works for the case where Validator() is called in a module that
        # has the target of forward references in its scope but
        # does not work for more complex cases
        # for example, take the following:
        #
        # a.py
        # ```python
        # from typing import List, Dict
        # IntList = List[int]
        # OuterDict = Dict[str, 'IntList']
        # ```
        #
        # b.py
        # ```python
        # from pydantic import Validator
        # from a import OuterDict
        # IntList = int  # replaces the symbol the forward reference is looking for
        # v = Validator(OuterDict)
        # v({"x": 1})  # should fail but doesn't
        # ```
        #
        # If OuterDict were a BaseModel this would work because it would resolve
        # the forward reference within the `a.py` namespace.
        # But Validator(OuterDict) can't know what module OuterDict came from.
        # In other words, the assumption that _all_ forward references exist in the
        # module we are being called from is not technically always true
        # Although most of the time it is and it works fine for recursive models and such/
        # BaseModel's behavior isn't perfect either and _can_ break in similar ways,
        # so there is no right or wrong between the two.
        # But at the very least this behavior is _subtly_ different from BaseModel's.
        global_ns = sys._getframe(1).f_globals.copy()
        global_ns.update(local_ns or {})
        gen = _generate_schema.GenerateSchema(
            arbitrary_types=arbitrary_types, types_namespace=global_ns, typevars_map={}
        )
        schema = gen.generate_schema(__type)
        self._validator = SchemaValidator(schema, config=merged_config)

    def __call__(self, __input: Any) -> T:
        return self._validator.validate_python(__input)


def _collect_bases_data(bases: tuple[type[Any], ...]) -> tuple[set[str], set[str], dict[str, ModelPrivateAttr]]:
    field_names: set[str] = set()
    class_vars: set[str] = set()
    private_attributes: dict[str, ModelPrivateAttr] = {}
    for base in bases:
        if _base_class_defined and issubclass(base, BaseModel) and base != BaseModel:
            # model_fields might not be defined yet in the case of generics, so we use getattr here:
            field_names.update(getattr(base, 'model_fields', {}).keys())
            class_vars.update(base.__class_vars__)
            private_attributes.update(base.__private_attributes__)
    return field_names, class_vars, private_attributes