Module aws_lambda_powertools.utilities.parser

Advanced event_parser utility

Sub-modules

aws_lambda_powertools.utilities.parser.envelopes

aws_lambda_powertools.utilities.parser.exceptions

aws_lambda_powertools.utilities.parser.functions

aws_lambda_powertools.utilities.parser.models

aws_lambda_powertools.utilities.parser.parser

aws_lambda_powertools.utilities.parser.types

Generics and other shared types used across parser

Functions

def Field(default: Any = PydanticUndefined, *, default_factory: typing.Callable[[], Any] | None = PydanticUndefined, alias: str | None = PydanticUndefined, alias_priority: int | None = PydanticUndefined, validation_alias: str | AliasPath | AliasChoices | None = PydanticUndefined, serialization_alias: str | None = PydanticUndefined, title: str | None = PydanticUndefined, field_title_generator: typing_extensions.Callable[[str, FieldInfo], str] | None = PydanticUndefined, description: str | None = PydanticUndefined, examples: list[Any] | None = PydanticUndefined, exclude: bool | None = PydanticUndefined, discriminator: str | types.Discriminator | None = PydanticUndefined, deprecated: Deprecated | str | bool | None = PydanticUndefined, json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None = PydanticUndefined, frozen: bool | None = PydanticUndefined, validate_default: bool | None = PydanticUndefined, repr: bool = PydanticUndefined, init: bool | None = PydanticUndefined, init_var: bool | None = PydanticUndefined, kw_only: bool | None = PydanticUndefined, pattern: str | typing.Pattern[str] | None = PydanticUndefined, strict: bool | None = PydanticUndefined, coerce_numbers_to_str: bool | None = PydanticUndefined, gt: annotated_types.SupportsGt | None = PydanticUndefined, ge: annotated_types.SupportsGe | None = PydanticUndefined, lt: annotated_types.SupportsLt | None = PydanticUndefined, le: annotated_types.SupportsLe | None = PydanticUndefined, multiple_of: float | None = PydanticUndefined, allow_inf_nan: bool | None = PydanticUndefined, max_digits: int | None = PydanticUndefined, decimal_places: int | None = PydanticUndefined, min_length: int | None = PydanticUndefined, max_length: int | None = PydanticUndefined, union_mode: "Literal['smart', 'left_to_right']" = PydanticUndefined, fail_fast: bool | None = PydanticUndefined, **extra: Unpack[_EmptyKwargs])

Usage docs: https://docs.pydantic.dev/2.9/concepts/fields

Create a field for objects that can be configured.

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.

Note

• Any _Unset objects will be replaced by the corresponding value defined in the _DefaultValues dictionary. If a key for the _Unset object is not found in the _DefaultValues dictionary, it will default to None

Args

default

Default value if the field is not set.

default_factory

A callable to generate the default value, such as :func:~datetime.utcnow.

alias

The name to use for the attribute when validating or serializing by alias. This is often used for things like converting between snake and camel case.

alias_priority

Priority of the alias. This affects whether an alias generator is used.

validation_alias

Like alias, but only affects validation, not serialization.

serialization_alias

Like alias, but only affects serialization, not validation.

title

Human-readable title.

field_title_generator

A callable that takes a field name and returns title for it.

description

Human-readable description.

examples

Example values for this field.

exclude

Whether to exclude the field from the model serialization.

discriminator

Field name or Discriminator for discriminating the type in a tagged union.

deprecated

A deprecation message, an instance of warnings.deprecated or the typing_extensions.deprecated backport, or a boolean. If True, a default deprecation message will be emitted when accessing the field.

json_schema_extra

A dict or callable to provide extra JSON schema properties.

frozen

Whether the field is frozen. If true, attempts to change the value on an instance will raise an error.

validate_default

If True, apply validation to the default value every time you create an instance. Otherwise, for performance reasons, the default value of the field is trusted and not validated.

repr

A boolean indicating whether to include the field in the __repr__ output.

init

Whether the field should be included in the constructor of the dataclass. (Only applies to dataclasses.)

init_var

Whether the field should only be included in the constructor of the dataclass. (Only applies to dataclasses.)

kw_only

Whether the field should be a keyword-only argument in the constructor of the dataclass. (Only applies to dataclasses.)

coerce_numbers_to_str

Whether to enable coercion of any Number type to str (not applicable in strict mode).

strict

If True, strict validation is applied to the field. See Strict Mode for details.

gt

Greater than. If set, value must be greater than this. Only applicable to numbers.

ge

Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers.

lt

Less than. If set, value must be less than this. Only applicable to numbers.

le

Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers.

multiple_of

Value must be a multiple of this. Only applicable to numbers.

min_length

Minimum length for iterables.

max_length

Maximum length for iterables.

pattern

Pattern for strings (a regular expression).

allow_inf_nan

Allow inf, -inf, nan. Only applicable to numbers.

max_digits

Maximum number of allow digits for strings.

decimal_places

Maximum number of decimal places allowed for numbers.

union_mode

The strategy to apply when validating a union. Can be smart (the default), or left_to_right. See Union Mode for details.

fail_fast

If True, validation will stop on the first error. If False, all validation errors will be collected. This option can be applied only to iterable types (list, tuple, set, and frozenset).

extra

(Deprecated) Extra fields that will be included in the JSON schema.

Warning

The extra kwargs is deprecated. Use json_schema_extra instead.

Returns

A new [FieldInfo][pydantic.fields.FieldInfo]. The return annotation is Any so Field() can be used on type-annotated fields without causing a type error.

def event_parser(handler: Callable[..., EventParserReturnType], event: dict[str, Any], context: LambdaContext, model: type[T] | None = None, envelope: type[Envelope] | None = None, **kwargs: Any)

Lambda handler decorator to parse & validate events using Pydantic models

It requires a model that implements Pydantic BaseModel to parse & validate the event.

When an envelope is given, it'll use the following logic:

1. Parse the event against the envelope model first e.g. EnvelopeModel(**event)
2. Envelope will extract a given key to be parsed against the model e.g. event.detail

This is useful when you need to confirm event wrapper structure, and b) selectively extract a portion of your payload for parsing & validation.

NOTE: If envelope is omitted, the complete event is parsed to match the model parameter definition.

Example

Lambda handler decorator to parse & validate event

class Order(BaseModel):
    id: int
    description: str
    ...

@event_parser(model=Order)
def handler(event: Order, context: LambdaContext):
    ...

Lambda handler decorator to parse & validate event - using built-in envelope

class Order(BaseModel):
    id: int
    description: str
    ...

@event_parser(model=Order, envelope=envelopes.EVENTBRIDGE)
def handler(event: Order, context: LambdaContext):
    ...

Parameters

handler :  Callable

Method to annotate on

event :  dict

Lambda event to be parsed & validated

context :  LambdaContext

Lambda context object

model : type[T] | None

Your data model that will replace the event.

envelope : Envelope

Optional envelope to extract the model from

Raises

ValidationError

When input event does not conform with model provided

InvalidModelTypeError

When model given does not implement BaseModel or is not provided

InvalidEnvelopeError

When envelope given does not implement BaseEnvelope

def field_validator(field: str, /, *fields: str, mode: FieldValidatorModes = 'after', check_fields: bool | None = None, json_schema_input_type: Any = PydanticUndefined) ‑> Callable[[Any], Any]

Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#field-validators

Decorate methods on the class indicating that they should be used to validate fields.

Example usage:

from typing import Any

from pydantic import (
    BaseModel,
    ValidationError,
    field_validator,
)

class Model(BaseModel):
    a: str

    @field_validator('a')
    @classmethod
    def ensure_foobar(cls, v: Any):
        if 'foobar' not in v:
            raise ValueError('"foobar" not found in a')
        return v

print(repr(Model(a='this is foobar good')))
#> Model(a='this is foobar good')

try:
    Model(a='snap')
except ValidationError as exc_info:
    print(exc_info)
    '''
    1 validation error for Model
    a
      Value error, "foobar" not found in a [type=value_error, input_value='snap', input_type=str]
    '''

For more in depth examples, see Field Validators.

Args

field

The first field the field_validator() should be called on; this is separate from fields to ensure an error is raised if you don't pass at least one.

*fields

Additional field(s) the field_validator() should be called on.

mode

Specifies whether to validate the fields before or after validation.

check_fields

Whether to check that the fields actually exist on the model.

json_schema_input_type

The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode) and can only specified when mode is either 'before', 'plain' or 'wrap'.

Returns

A decorator that can be used to decorate a function to be used as a field_validator.

Raises

PydanticUserError: - If @field_validator is used bare (with no fields). - If the args passed to @field_validator as fields are not strings. - If @field_validator applied to instance methods.

def model_validator(*, mode: "Literal['wrap', 'before', 'after']") ‑> Any

Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#model-validators

Decorate model methods for validation purposes.

Example usage:

from typing_extensions import Self

from pydantic import BaseModel, ValidationError, model_validator

class Square(BaseModel):
    width: float
    height: float

    @model_validator(mode='after')
    def verify_square(self) -> Self:
        if self.width != self.height:
            raise ValueError('width and height do not match')
        return self

s = Square(width=1, height=1)
print(repr(s))
#> Square(width=1.0, height=1.0)

try:
    Square(width=1, height=2)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Square
      Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict]
    '''

For more in depth examples, see Model Validators.

Args

mode

A required string literal that specifies the validation mode. It can be one of the following: 'wrap', 'before', or 'after'.

Returns

A decorator that can be used to decorate a function to be used as a model validator.

def parse(event: dict[str, Any], model: type[T], envelope: type[Envelope] | None = None)

Standalone function to parse & validate events using Pydantic models

Typically used when you need fine-grained control over error handling compared to event_parser decorator.

Example

Lambda handler decorator to parse & validate event

from aws_lambda_powertools.utilities.parser import ValidationError

class Order(BaseModel):
    id: int
    description: str
    ...

def handler(event: Order, context: LambdaContext):
    try:
        parse(model=Order)
    except ValidationError:
        ...

Lambda handler decorator to parse & validate event - using built-in envelope

class Order(BaseModel):
    id: int
    description: str
    ...

def handler(event: Order, context: LambdaContext):
    try:
        parse(model=Order, envelope=envelopes.EVENTBRIDGE)
    except ValidationError:
        ...

Parameters

event :  dict

Lambda event to be parsed & validated

model :  Model

Your data model that will replace the event

envelope : Envelope

Optional envelope to extract the model from

Raises

ValidationError

When input event does not conform with model provided

InvalidModelTypeError

When model given does not implement BaseModel

InvalidEnvelopeError

When envelope given does not implement BaseEnvelope

Classes

class BaseEnvelope

ABC implementation for creating a supported Envelope

Expand source code

class BaseEnvelope(ABC):
    """ABC implementation for creating a supported Envelope"""

    @staticmethod
    def _parse(data: dict[str, Any] | Any | None, model: type[T]) -> T | None:
        """Parses envelope data against model provided

        Parameters
        ----------
        data : dict
            Data to be parsed and validated
        model : type[T]
            Data model to parse and validate data against

        Returns
        -------
        Any
            Parsed data
        """
        if data is None:
            logger.debug("Skipping parsing as event is None")
            return data

        adapter = _retrieve_or_set_model_from_cache(model=model)

        logger.debug("parsing event against model")
        return _parse_and_validate_event(data=data, adapter=adapter)

    @abstractmethod
    def parse(self, data: dict[str, Any] | Any | None, model: type[T]):
        """Implementation to parse data against envelope model, then against the data model

        NOTE: Call `_parse` method to fully parse data with model provided.

        Example
        -------

        **EventBridge envelope implementation example**

        def parse(...):
            # 1. parses data against envelope model
            parsed_envelope = EventBridgeModel(**data)

            # 2. parses portion of data within the envelope against model
            return self._parse(data=parsed_envelope.detail, model=data_model)
        """
        return NotImplemented  # pragma: no cover

Ancestors

• abc.ABC

Subclasses

• ApiGatewayEnvelope
• ApiGatewayV2Envelope
• BedrockAgentEnvelope
• CloudWatchLogsEnvelope
• DynamoDBStreamEnvelope
• EventBridgeEnvelope
• KafkaEnvelope
• KinesisDataStreamEnvelope
• KinesisFirehoseEnvelope
• LambdaFunctionUrlEnvelope
• SnsEnvelope
• SnsSqsEnvelope
• SqsEnvelope
• VpcLatticeEnvelope
• VpcLatticeV2Envelope

Methods

def parse(self, data: dict[str, Any] | Any | None, model: type[T])

Implementation to parse data against envelope model, then against the data model

NOTE: Call _parse method to fully parse data with model provided.

Example

EventBridge envelope implementation example

def parse(…): # 1. parses data against envelope model parsed_envelope = EventBridgeModel(**data)

# 2. parses portion of data within the envelope against model
return self._parse(data=parsed_envelope.detail, model=data_model)

class BaseModel (**data: Any)

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

A base class for creating Pydantic models.

Attributes

__class_vars__

The names of the class variables defined on the model.

__private_attributes__

Metadata about the private attributes of the model.

__signature__

The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__

The core schema of the model.

__pydantic_custom_init__

Whether the model has a custom __init__ function.

__pydantic_decorators__

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__

The name of the post-init method for the model, if defined.

__pydantic_root_model__

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__

The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__

The names of fields explicitly set during instantiation.

__pydantic_private__

Values of private attributes set on the model instance.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Expand source code

class BaseModel(metaclass=_model_construction.ModelMetaclass):
    """Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

    A base class for creating Pydantic models.

    Attributes:
        __class_vars__: The names of the class variables defined on the model.
        __private_attributes__: Metadata about the private attributes of the model.
        __signature__: The synthesized `__init__` [`Signature`][inspect.Signature] of the model.

        __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
        __pydantic_core_schema__: The core schema of the model.
        __pydantic_custom_init__: Whether the model has a custom `__init__` function.
        __pydantic_decorators__: Metadata containing the decorators defined on the model.
            This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
        __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
            __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
        __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
        __pydantic_post_init__: The name of the post-init method for the model, if defined.
        __pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].
        __pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.
        __pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.

        __pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
            is set to `'allow'`.
        __pydantic_fields_set__: The names of fields explicitly set during instantiation.
        __pydantic_private__: Values of private attributes set on the model instance.
    """

    # Class attributes:
    # `model_fields` and `__pydantic_decorators__` must be set for
    # `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation.

    model_config: ClassVar[ConfigDict] = ConfigDict()
    """
    Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
    """

    # Because `dict` is in the local namespace of the `BaseModel` class, we use `Dict` for annotations.
    # TODO v3 fallback to `dict` when the deprecated `dict` method gets removed.
    model_fields: ClassVar[Dict[str, FieldInfo]] = {}  # noqa: UP006
    """
    Metadata about the fields defined on the model,
    mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects.

    This replaces `Model.__fields__` from Pydantic V1.
    """

    model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}  # noqa: UP006
    """A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects."""

    __class_vars__: ClassVar[set[str]]
    """The names of the class variables defined on the model."""

    __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]]  # noqa: UP006
    """Metadata about the private attributes of the model."""

    __signature__: ClassVar[Signature]
    """The synthesized `__init__` [`Signature`][inspect.Signature] of the model."""

    __pydantic_complete__: ClassVar[bool] = False
    """Whether model building is completed, or if there are still undefined fields."""

    __pydantic_core_schema__: ClassVar[CoreSchema]
    """The core schema of the model."""

    __pydantic_custom_init__: ClassVar[bool]
    """Whether the model has a custom `__init__` method."""

    __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = _decorators.DecoratorInfos()
    """Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1."""

    __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata]
    """Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these."""

    __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None  # noqa: UP006
    """Parent namespace of the model, used for automatic rebuilding of models."""

    __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
    """The name of the post-init method for the model, if defined."""

    __pydantic_root_model__: ClassVar[bool] = False
    """Whether the model is a [`RootModel`][pydantic.root_model.RootModel]."""

    __pydantic_serializer__: ClassVar[SchemaSerializer]
    """The `pydantic-core` `SchemaSerializer` used to dump instances of the model."""

    __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator]
    """The `pydantic-core` `SchemaValidator` used to validate instances of the model."""

    __pydantic_extra__: dict[str, Any] | None = _model_construction.NoInitField(init=False)
    """A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`."""

    __pydantic_fields_set__: set[str] = _model_construction.NoInitField(init=False)
    """The names of fields explicitly set during instantiation."""

    __pydantic_private__: dict[str, Any] | None = _model_construction.NoInitField(init=False)
    """Values of private attributes set on the model instance."""

    if not TYPE_CHECKING:
        # Prevent `BaseModel` from being instantiated directly
        # (defined in an `if not TYPE_CHECKING` block for clarity and to avoid type checking errors):
        __pydantic_core_schema__ = _mock_val_ser.MockCoreSchema(
            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
            code='base-model-instantiated',
        )
        __pydantic_validator__ = _mock_val_ser.MockValSer(
            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
            val_or_ser='validator',
            code='base-model-instantiated',
        )
        __pydantic_serializer__ = _mock_val_ser.MockValSer(
            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
            val_or_ser='serializer',
            code='base-model-instantiated',
        )

    __slots__ = '__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__'

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

        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
        validated to form a valid model.

        `self` is explicitly positional-only to allow `self` as a field name.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
        if self is not validated_self:
            warnings.warn(
                'A custom validator is returning a value other than `self`.\n'
                "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
                'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
                category=None,
            )

    # The following line sets a flag that we use to determine when `__init__` gets overridden by the user
    __init__.__pydantic_base_init__ = True  # pyright: ignore[reportFunctionMemberAccess]

    @property
    def model_extra(self) -> dict[str, Any] | None:
        """Get extra fields set during validation.

        Returns:
            A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
        """
        return self.__pydantic_extra__

    @property
    def model_fields_set(self) -> set[str]:
        """Returns the set of fields that have been explicitly set on this model instance.

        Returns:
            A set of strings representing the fields that have been set,
                i.e. that were not filled from defaults.
        """
        return self.__pydantic_fields_set__

    @classmethod
    def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: C901
        """Creates a new instance of the `Model` class with validated data.

        Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
        Default values are respected, but no other validation is performed.

        !!! note
            `model_construct()` generally respects the `model_config.extra` setting on the provided model.
            That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
            and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
            Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
            an error if extra values are passed, but they will be ignored.

        Args:
            _fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
                this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
                Otherwise, the field names from the `values` argument will be used.
            values: Trusted or pre-validated data dictionary.

        Returns:
            A new instance of the `Model` class with validated data.
        """
        m = cls.__new__(cls)
        fields_values: dict[str, Any] = {}
        fields_set = set()

        for name, field in cls.model_fields.items():
            if field.alias is not None and field.alias in values:
                fields_values[name] = values.pop(field.alias)
                fields_set.add(name)

            if (name not in fields_set) and (field.validation_alias is not None):
                validation_aliases: list[str | AliasPath] = (
                    field.validation_alias.choices
                    if isinstance(field.validation_alias, AliasChoices)
                    else [field.validation_alias]
                )

                for alias in validation_aliases:
                    if isinstance(alias, str) and alias in values:
                        fields_values[name] = values.pop(alias)
                        fields_set.add(name)
                        break
                    elif isinstance(alias, AliasPath):
                        value = alias.search_dict_for_path(values)
                        if value is not PydanticUndefined:
                            fields_values[name] = value
                            fields_set.add(name)
                            break

            if name not in fields_set:
                if name in values:
                    fields_values[name] = values.pop(name)
                    fields_set.add(name)
                elif not field.is_required():
                    fields_values[name] = field.get_default(call_default_factory=True)
        if _fields_set is None:
            _fields_set = fields_set

        _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None
        _object_setattr(m, '__dict__', fields_values)
        _object_setattr(m, '__pydantic_fields_set__', _fields_set)
        if not cls.__pydantic_root_model__:
            _object_setattr(m, '__pydantic_extra__', _extra)

        if cls.__pydantic_post_init__:
            m.model_post_init(None)
            # update private attributes with values set
            if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
                for k, v in values.items():
                    if k in m.__private_attributes__:
                        m.__pydantic_private__[k] = v

        elif not cls.__pydantic_root_model__:
            # Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
            # Since it doesn't, that means that `__pydantic_private__` should be set to None
            _object_setattr(m, '__pydantic_private__', None)

        return m

    def model_copy(self, *, update: dict[str, Any] | None = None, deep: bool = False) -> Self:
        """Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

        Returns a copy of the model.

        Args:
            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.
            deep: Set to `True` to make a deep copy of the model.

        Returns:
            New model instance.
        """
        copied = self.__deepcopy__() if deep else self.__copy__()
        if update:
            if self.model_config.get('extra') == 'allow':
                for k, v in update.items():
                    if k in self.model_fields:
                        copied.__dict__[k] = v
                    else:
                        if copied.__pydantic_extra__ is None:
                            copied.__pydantic_extra__ = {}
                        copied.__pydantic_extra__[k] = v
            else:
                copied.__dict__.update(update)
            copied.__pydantic_fields_set__.update(update.keys())
        return copied

    def model_dump(
        self,
        *,
        mode: Literal['json', 'python'] | str = 'python',
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        context: Any | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool | Literal['none', 'warn', 'error'] = True,
        serialize_as_any: bool = False,
    ) -> dict[str, Any]:
        """Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

        Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

        Args:
            mode: The mode in which `to_python` should run.
                If mode is 'json', the output will only contain JSON serializable types.
                If mode is 'python', the output may contain non-JSON-serializable Python objects.
            include: A set of fields to include in the output.
            exclude: A set of fields to exclude from the output.
            context: Additional context to pass to the serializer.
            by_alias: Whether to use the field's alias in the dictionary key if defined.
            exclude_unset: Whether to exclude fields that have not been explicitly set.
            exclude_defaults: Whether to exclude fields that are set to their default value.
            exclude_none: Whether to exclude fields that have a value of `None`.
            round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

        Returns:
            A dictionary representation of the model.
        """
        return self.__pydantic_serializer__.to_python(
            self,
            mode=mode,
            by_alias=by_alias,
            include=include,
            exclude=exclude,
            context=context,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
            serialize_as_any=serialize_as_any,
        )

    def model_dump_json(
        self,
        *,
        indent: int | None = None,
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        context: Any | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool | Literal['none', 'warn', 'error'] = True,
        serialize_as_any: bool = False,
    ) -> str:
        """Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

        Generates a JSON representation of the model using Pydantic's `to_json` method.

        Args:
            indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
            include: Field(s) to include in the JSON output.
            exclude: Field(s) to exclude from the JSON output.
            context: Additional context to pass to the serializer.
            by_alias: Whether to serialize using field aliases.
            exclude_unset: Whether to exclude fields that have not been explicitly set.
            exclude_defaults: Whether to exclude fields that are set to their default value.
            exclude_none: Whether to exclude fields that have a value of `None`.
            round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

        Returns:
            A JSON string representation of the model.
        """
        return self.__pydantic_serializer__.to_json(
            self,
            indent=indent,
            include=include,
            exclude=exclude,
            context=context,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
            serialize_as_any=serialize_as_any,
        ).decode()

    @classmethod
    def model_json_schema(
        cls,
        by_alias: bool = True,
        ref_template: str = DEFAULT_REF_TEMPLATE,
        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
        mode: JsonSchemaMode = 'validation',
    ) -> dict[str, Any]:
        """Generates a JSON schema for a model class.

        Args:
            by_alias: Whether to use attribute aliases or not.
            ref_template: The reference template.
            schema_generator: To override the logic used to generate the JSON schema, as a subclass of
                `GenerateJsonSchema` with your desired modifications
            mode: The mode in which to generate the schema.

        Returns:
            The JSON schema for the given model class.
        """
        return model_json_schema(
            cls, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator, mode=mode
        )

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

        This method can be overridden to achieve a custom naming scheme for generic BaseModels.

        Args:
            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`.

        Returns:
            String representing the new class where `params` are passed to `cls` as type variables.

        Raises:
            TypeError: Raised when trying to generate concrete names for non-generic models.
        """
        if not issubclass(cls, typing.Generic):
            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}]'

    def model_post_init(self, __context: Any) -> None:
        """Override this method to perform additional initialization after `__init__` and `model_construct`.
        This is useful if you want to do some validation that requires the entire model to be initialized.
        """
        pass

    @classmethod
    def model_rebuild(
        cls,
        *,
        force: bool = False,
        raise_errors: bool = True,
        _parent_namespace_depth: int = 2,
        _types_namespace: dict[str, Any] | None = None,
    ) -> bool | None:
        """Try to rebuild the pydantic-core schema for the model.

        This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
        the initial attempt to build the schema, and automatic rebuilding fails.

        Args:
            force: Whether to force the rebuilding of the model schema, defaults to `False`.
            raise_errors: Whether to raise errors, defaults to `True`.
            _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
            _types_namespace: The types namespace, defaults to `None`.

        Returns:
            Returns `None` if the schema is already "complete" and rebuilding was not required.
            If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
        """
        if not force and cls.__pydantic_complete__:
            return None
        else:
            if '__pydantic_core_schema__' in cls.__dict__:
                delattr(cls, '__pydantic_core_schema__')  # delete cached value to ensure full rebuild happens
            if _types_namespace is not None:
                types_namespace: dict[str, Any] | None = _types_namespace.copy()
            else:
                if _parent_namespace_depth > 0:
                    frame_parent_ns = (
                        _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
                    )
                    cls_parent_ns = (
                        _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}
                    )
                    types_namespace = {**cls_parent_ns, **frame_parent_ns}
                    cls.__pydantic_parent_namespace__ = _model_construction.build_lenient_weakvaluedict(types_namespace)
                else:
                    types_namespace = _model_construction.unpack_lenient_weakvaluedict(
                        cls.__pydantic_parent_namespace__
                    )

                types_namespace = _typing_extra.merge_cls_and_parent_ns(cls, types_namespace)

            # manually override defer_build so complete_model_class doesn't skip building the model again
            config = {**cls.model_config, 'defer_build': False}
            return _model_construction.complete_model_class(
                cls,
                cls.__name__,
                _config.ConfigWrapper(config, check=False),
                raise_errors=raise_errors,
                types_namespace=types_namespace,
            )

    @classmethod
    def model_validate(
        cls,
        obj: Any,
        *,
        strict: bool | None = None,
        from_attributes: bool | None = None,
        context: Any | None = None,
    ) -> Self:
        """Validate a pydantic model instance.

        Args:
            obj: The object to validate.
            strict: Whether to enforce types strictly.
            from_attributes: Whether to extract data from object attributes.
            context: Additional context to pass to the validator.

        Raises:
            ValidationError: If the object could not be validated.

        Returns:
            The validated model instance.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        return cls.__pydantic_validator__.validate_python(
            obj, strict=strict, from_attributes=from_attributes, context=context
        )

    @classmethod
    def model_validate_json(
        cls,
        json_data: str | bytes | bytearray,
        *,
        strict: bool | None = None,
        context: Any | None = None,
    ) -> Self:
        """Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

        Validate the given JSON data against the Pydantic model.

        Args:
            json_data: The JSON data to validate.
            strict: Whether to enforce types strictly.
            context: Extra variables to pass to the validator.

        Returns:
            The validated Pydantic model.

        Raises:
            ValidationError: If `json_data` is not a JSON string or the object could not be validated.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        return cls.__pydantic_validator__.validate_json(json_data, strict=strict, context=context)

    @classmethod
    def model_validate_strings(
        cls,
        obj: Any,
        *,
        strict: bool | None = None,
        context: Any | None = None,
    ) -> Self:
        """Validate the given object with string data against the Pydantic model.

        Args:
            obj: The object containing string data to validate.
            strict: Whether to enforce types strictly.
            context: Extra variables to pass to the validator.

        Returns:
            The validated Pydantic model.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        return cls.__pydantic_validator__.validate_strings(obj, strict=strict, context=context)

    @classmethod
    def __get_pydantic_core_schema__(cls, source: type[BaseModel], handler: GetCoreSchemaHandler, /) -> CoreSchema:
        """Hook into generating the model's CoreSchema.

        Args:
            source: The class we are generating a schema for.
                This will generally be the same as the `cls` argument if this is a classmethod.
            handler: A callable that calls into Pydantic's internal CoreSchema generation logic.

        Returns:
            A `pydantic-core` `CoreSchema`.
        """
        # Only use the cached value from this _exact_ class; we don't want one from a parent class
        # This is why we check `cls.__dict__` and don't use `cls.__pydantic_core_schema__` or similar.
        schema = cls.__dict__.get('__pydantic_core_schema__')
        if schema is not None and not isinstance(schema, _mock_val_ser.MockCoreSchema):
            # Due to the way generic classes are built, it's possible that an invalid schema may be temporarily
            # set on generic classes. I think we could resolve this to ensure that we get proper schema caching
            # for generics, but for simplicity for now, we just always rebuild if the class has a generic origin.
            if not cls.__pydantic_generic_metadata__['origin']:
                return cls.__pydantic_core_schema__

        return handler(source)

    @classmethod
    def __get_pydantic_json_schema__(
        cls,
        core_schema: CoreSchema,
        handler: GetJsonSchemaHandler,
        /,
    ) -> JsonSchemaValue:
        """Hook into generating the model's JSON schema.

        Args:
            core_schema: A `pydantic-core` CoreSchema.
                You can ignore this argument and call the handler with a new CoreSchema,
                wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
                or just call the handler with the original schema.
            handler: Call into Pydantic's internal JSON schema generation.
                This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
                generation fails.
                Since this gets called by `BaseModel.model_json_schema` you can override the
                `schema_generator` argument to that function to change JSON schema generation globally
                for a type.

        Returns:
            A JSON schema, as a Python object.
        """
        return handler(core_schema)

    @classmethod
    def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:
        """This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
        only after the class is actually fully initialized. In particular, attributes like `model_fields` will
        be present when this is called.

        This is necessary because `__init_subclass__` will always be called by `type.__new__`,
        and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
        `type.__new__` was called in such a manner that the class would already be sufficiently initialized.

        This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
        any kwargs passed to the class definition that aren't used internally by pydantic.

        Args:
            **kwargs: Any keyword arguments passed to the class definition that aren't used internally
                by pydantic.
        """
        pass

    def __class_getitem__(
        cls, typevar_values: type[Any] | tuple[type[Any], ...]
    ) -> type[BaseModel] | _forward_ref.PydanticRecursiveRef:
        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_metadata__['parameters'] and typing.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_metadata__['parameters'], 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_metadata__['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_metadata__['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

                # Attempt to rebuild the origin in case new types have been defined
                try:
                    # depth 3 gets you above this __class_getitem__ call
                    origin.model_rebuild(_parent_namespace_depth=3)
                except PydanticUndefinedAnnotation:
                    # It's okay if it fails, it just means there are still undefined types
                    # that could be evaluated later.
                    # TODO: Make sure validation fails if there are still undefined types, perhaps using MockValidator
                    pass

                submodel = _generics.create_generic_submodel(model_name, origin, args, params)

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

        return submodel

    def __copy__(self) -> Self:
        """Returns a shallow copy of the model."""
        cls = type(self)
        m = cls.__new__(cls)
        _object_setattr(m, '__dict__', copy(self.__dict__))
        _object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__))
        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

        if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
            _object_setattr(m, '__pydantic_private__', None)
        else:
            _object_setattr(
                m,
                '__pydantic_private__',
                {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined},
            )

        return m

    def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
        """Returns a deep copy of the model."""
        cls = type(self)
        m = cls.__new__(cls)
        _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
        _object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo))
        # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
        # and attempting a deepcopy would be marginally slower.
        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

        if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
            _object_setattr(m, '__pydantic_private__', None)
        else:
            _object_setattr(
                m,
                '__pydantic_private__',
                deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo),
            )

        return m

    if not TYPE_CHECKING:
        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
        # The same goes for __setattr__ and __delattr__, see: https://github.com/pydantic/pydantic/issues/8643

        def __getattr__(self, item: str) -> Any:
            private_attributes = object.__getattribute__(self, '__private_attributes__')
            if item in private_attributes:
                attribute = private_attributes[item]
                if hasattr(attribute, '__get__'):
                    return attribute.__get__(self, type(self))  # type: ignore

                try:
                    # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
                    return self.__pydantic_private__[item]  # type: ignore
                except KeyError as exc:
                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
            else:
                # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.
                # See `BaseModel.__repr_args__` for more details
                try:
                    pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
                except AttributeError:
                    pydantic_extra = None

                if pydantic_extra:
                    try:
                        return pydantic_extra[item]
                    except KeyError as exc:
                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
                else:
                    if hasattr(self.__class__, item):
                        return super().__getattribute__(item)  # Raises AttributeError if appropriate
                    else:
                        # this is the current error
                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')

        def __setattr__(self, name: str, value: Any) -> None:
            if name in self.__class_vars__:
                raise AttributeError(
                    f'{name!r} 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`.'
                )
            elif not _fields.is_valid_field_name(name):
                if self.__pydantic_private__ is None or name not in self.__private_attributes__:
                    _object_setattr(self, name, value)
                else:
                    attribute = self.__private_attributes__[name]
                    if hasattr(attribute, '__set__'):
                        attribute.__set__(self, value)  # type: ignore
                    else:
                        self.__pydantic_private__[name] = value
                return

            self._check_frozen(name, value)

            attr = getattr(self.__class__, name, None)
            if isinstance(attr, property):
                attr.__set__(self, value)
            elif self.model_config.get('validate_assignment', None):
                self.__pydantic_validator__.validate_assignment(self, name, value)
            elif self.model_config.get('extra') != 'allow' and name not in self.model_fields:
                # TODO - matching error
                raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
            elif self.model_config.get('extra') == 'allow' and name not in self.model_fields:
                if self.model_extra and name in self.model_extra:
                    self.__pydantic_extra__[name] = value  # type: ignore
                else:
                    try:
                        getattr(self, name)
                    except AttributeError:
                        # attribute does not already exist on instance, so put it in extra
                        self.__pydantic_extra__[name] = value  # type: ignore
                    else:
                        # attribute _does_ already exist on instance, and was not in extra, so update it
                        _object_setattr(self, name, value)
            else:
                self.__dict__[name] = value
                self.__pydantic_fields_set__.add(name)

        def __delattr__(self, item: str) -> Any:
            if item in self.__private_attributes__:
                attribute = self.__private_attributes__[item]
                if hasattr(attribute, '__delete__'):
                    attribute.__delete__(self)  # type: ignore
                    return

                try:
                    # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
                    del self.__pydantic_private__[item]  # type: ignore
                    return
                except KeyError as exc:
                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc

            self._check_frozen(item, None)

            if item in self.model_fields:
                object.__delattr__(self, item)
            elif self.__pydantic_extra__ is not None and item in self.__pydantic_extra__:
                del self.__pydantic_extra__[item]
            else:
                try:
                    object.__delattr__(self, item)
                except AttributeError:
                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')

    def _check_frozen(self, name: str, value: Any) -> None:
        if self.model_config.get('frozen', None):
            typ = 'frozen_instance'
        elif getattr(self.model_fields.get(name), 'frozen', False):
            typ = 'frozen_field'
        else:
            return
        error: pydantic_core.InitErrorDetails = {
            'type': typ,
            'loc': (name,),
            'input': value,
        }
        raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])

    def __getstate__(self) -> dict[Any, Any]:
        private = self.__pydantic_private__
        if private:
            private = {k: v for k, v in private.items() if v is not PydanticUndefined}
        return {
            '__dict__': self.__dict__,
            '__pydantic_extra__': self.__pydantic_extra__,
            '__pydantic_fields_set__': self.__pydantic_fields_set__,
            '__pydantic_private__': private,
        }

    def __setstate__(self, state: dict[Any, Any]) -> None:
        _object_setattr(self, '__pydantic_fields_set__', state.get('__pydantic_fields_set__', {}))
        _object_setattr(self, '__pydantic_extra__', state.get('__pydantic_extra__', {}))
        _object_setattr(self, '__pydantic_private__', state.get('__pydantic_private__', {}))
        _object_setattr(self, '__dict__', state.get('__dict__', {}))

    if not TYPE_CHECKING:

        def __eq__(self, other: Any) -> bool:
            if isinstance(other, BaseModel):
                # 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 = self.__pydantic_generic_metadata__['origin'] or self.__class__
                other_type = other.__pydantic_generic_metadata__['origin'] or other.__class__

                # Perform common checks first
                if not (
                    self_type == other_type
                    and getattr(self, '__pydantic_private__', None) == getattr(other, '__pydantic_private__', None)
                    and self.__pydantic_extra__ == other.__pydantic_extra__
                ):
                    return False

                # We only want to compare pydantic fields but ignoring fields is costly.
                # We'll perform a fast check first, and fallback only when needed
                # See GH-7444 and GH-7825 for rationale and a performance benchmark

                # First, do the fast (and sometimes faulty) __dict__ comparison
                if self.__dict__ == other.__dict__:
                    # If the check above passes, then pydantic fields are equal, we can return early
                    return True

                # We don't want to trigger unnecessary costly filtering of __dict__ on all unequal objects, so we return
                # early if there are no keys to ignore (we would just return False later on anyway)
                model_fields = type(self).model_fields.keys()
                if self.__dict__.keys() <= model_fields and other.__dict__.keys() <= model_fields:
                    return False

                # If we reach here, there are non-pydantic-fields keys, mapped to unequal values, that we need to ignore
                # Resort to costly filtering of the __dict__ objects
                # We use operator.itemgetter because it is much faster than dict comprehensions
                # NOTE: Contrary to standard python class and instances, when the Model class has a default value for an
                # attribute and the model instance doesn't have a corresponding attribute, accessing the missing attribute
                # raises an error in BaseModel.__getattr__ instead of returning the class attribute
                # So we can use operator.itemgetter() instead of operator.attrgetter()
                getter = operator.itemgetter(*model_fields) if model_fields else lambda _: _utils._SENTINEL
                try:
                    return getter(self.__dict__) == getter(other.__dict__)
                except KeyError:
                    # In rare cases (such as when using the deprecated BaseModel.copy() method),
                    # the __dict__ may not contain all model fields, which is how we can get here.
                    # getter(self.__dict__) is much faster than any 'safe' method that accounts
                    # for missing keys, and wrapping it in a `try` doesn't slow things down much
                    # in the common case.
                    self_fields_proxy = _utils.SafeGetItemProxy(self.__dict__)
                    other_fields_proxy = _utils.SafeGetItemProxy(other.__dict__)
                    return getter(self_fields_proxy) == getter(other_fields_proxy)

            # other instance is not a BaseModel
            else:
                return NotImplemented  # delegate to the other item in the comparison

    if TYPE_CHECKING:
        # We put `__init_subclass__` in a TYPE_CHECKING block because, even though we want the type-checking benefits
        # described in the signature of `__init_subclass__` below, we don't want to modify the default behavior of
        # subclass initialization.

        def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]):
            """This signature is included purely to help type-checkers check arguments to class declaration, which
            provides a way to conveniently set model_config key/value pairs.

            ```py
            from pydantic import BaseModel

            class MyModel(BaseModel, extra='allow'): ...
            ```

            However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any
            of the config arguments, and will only receive any keyword arguments passed during class initialization
            that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.)

            Args:
                **kwargs: Keyword arguments passed to the class definition, which set model_config

            Note:
                You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called
                *after* the class is fully initialized.
            """

    def __iter__(self) -> TupleGenerator:
        """So `dict(model)` works."""
        yield from [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')]
        extra = self.__pydantic_extra__
        if extra:
            yield from extra.items()

    def __repr__(self) -> str:
        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'

    def __repr_args__(self) -> _repr.ReprArgs:
        for k, v in self.__dict__.items():
            field = self.model_fields.get(k)
            if field and field.repr:
                yield k, v

        # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.
        # This can happen if a `ValidationError` is raised during initialization and the instance's
        # repr is generated as part of the exception handling. Therefore, we use `getattr` here
        # with a fallback, even though the type hints indicate the attribute will always be present.
        try:
            pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
        except AttributeError:
            pydantic_extra = None

        if pydantic_extra is not None:
            yield from ((k, v) for k, v in pydantic_extra.items())
        yield from ((k, getattr(self, k)) for k, v in self.model_computed_fields.items() if v.repr)

    # take logic from `_repr.Representation` without the side effects of inheritance, see #5740
    __repr_name__ = _repr.Representation.__repr_name__
    __repr_str__ = _repr.Representation.__repr_str__
    __pretty__ = _repr.Representation.__pretty__
    __rich_repr__ = _repr.Representation.__rich_repr__

    def __str__(self) -> str:
        return self.__repr_str__(' ')

    # ##### Deprecated methods from v1 #####
    @property
    @typing_extensions.deprecated(
        'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=None
    )
    def __fields__(self) -> dict[str, FieldInfo]:
        warnings.warn(
            'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=PydanticDeprecatedSince20
        )
        return self.model_fields

    @property
    @typing_extensions.deprecated(
        'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
        category=None,
    )
    def __fields_set__(self) -> set[str]:
        warnings.warn(
            'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
            category=PydanticDeprecatedSince20,
        )
        return self.__pydantic_fields_set__

    @typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.', category=None)
    def dict(  # noqa: D102
        self,
        *,
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
    ) -> Dict[str, Any]:  # noqa UP006
        warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)
        return self.model_dump(
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )

    @typing_extensions.deprecated('The `json` method is deprecated; use `model_dump_json` instead.', category=None)
    def json(  # noqa: D102
        self,
        *,
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        encoder: Callable[[Any], Any] | None = PydanticUndefined,  # type: ignore[assignment]
        models_as_dict: bool = PydanticUndefined,  # type: ignore[assignment]
        **dumps_kwargs: Any,
    ) -> str:
        warnings.warn(
            'The `json` method is deprecated; use `model_dump_json` instead.', category=PydanticDeprecatedSince20
        )
        if encoder is not PydanticUndefined:
            raise TypeError('The `encoder` argument is no longer supported; use field serializers instead.')
        if models_as_dict is not PydanticUndefined:
            raise TypeError('The `models_as_dict` argument is no longer supported; use a model serializer instead.')
        if dumps_kwargs:
            raise TypeError('`dumps_kwargs` keyword arguments are no longer supported.')
        return self.model_dump_json(
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )

    @classmethod
    @typing_extensions.deprecated('The `parse_obj` method is deprecated; use `model_validate` instead.', category=None)
    def parse_obj(cls, obj: Any) -> Self:  # noqa: D102
        warnings.warn(
            'The `parse_obj` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
        )
        return cls.model_validate(obj)

    @classmethod
    @typing_extensions.deprecated(
        'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
        'otherwise load the data then use `model_validate` instead.',
        category=None,
    )
    def parse_raw(  # noqa: D102
        cls,
        b: str | bytes,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: DeprecatedParseProtocol | None = None,
        allow_pickle: bool = False,
    ) -> Self:  # pragma: no cover
        warnings.warn(
            'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
            'otherwise load the data then use `model_validate` instead.',
            category=PydanticDeprecatedSince20,
        )
        from .deprecated import parse

        try:
            obj = parse.load_str_bytes(
                b,
                proto=proto,
                content_type=content_type,
                encoding=encoding,
                allow_pickle=allow_pickle,
            )
        except (ValueError, TypeError) as exc:
            import json

            # try to match V1
            if isinstance(exc, UnicodeDecodeError):
                type_str = 'value_error.unicodedecode'
            elif isinstance(exc, json.JSONDecodeError):
                type_str = 'value_error.jsondecode'
            elif isinstance(exc, ValueError):
                type_str = 'value_error'
            else:
                type_str = 'type_error'

            # ctx is missing here, but since we've added `input` to the error, we're not pretending it's the same
            error: pydantic_core.InitErrorDetails = {
                # The type: ignore on the next line is to ignore the requirement of LiteralString
                'type': pydantic_core.PydanticCustomError(type_str, str(exc)),  # type: ignore
                'loc': ('__root__',),
                'input': b,
            }
            raise pydantic_core.ValidationError.from_exception_data(cls.__name__, [error])
        return cls.model_validate(obj)

    @classmethod
    @typing_extensions.deprecated(
        'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
        'use `model_validate_json`, otherwise `model_validate` instead.',
        category=None,
    )
    def parse_file(  # noqa: D102
        cls,
        path: str | Path,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: DeprecatedParseProtocol | None = None,
        allow_pickle: bool = False,
    ) -> Self:
        warnings.warn(
            'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
            'use `model_validate_json`, otherwise `model_validate` instead.',
            category=PydanticDeprecatedSince20,
        )
        from .deprecated import parse

        obj = parse.load_file(
            path,
            proto=proto,
            content_type=content_type,
            encoding=encoding,
            allow_pickle=allow_pickle,
        )
        return cls.parse_obj(obj)

    @classmethod
    @typing_extensions.deprecated(
        'The `from_orm` method is deprecated; set '
        "`model_config['from_attributes']=True` and use `model_validate` instead.",
        category=None,
    )
    def from_orm(cls, obj: Any) -> Self:  # noqa: D102
        warnings.warn(
            'The `from_orm` method is deprecated; set '
            "`model_config['from_attributes']=True` and use `model_validate` instead.",
            category=PydanticDeprecatedSince20,
        )
        if not cls.model_config.get('from_attributes', None):
            raise PydanticUserError(
                'You must set the config attribute `from_attributes=True` to use from_orm', code=None
            )
        return cls.model_validate(obj)

    @classmethod
    @typing_extensions.deprecated('The `construct` method is deprecated; use `model_construct` instead.', category=None)
    def construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: D102
        warnings.warn(
            'The `construct` method is deprecated; use `model_construct` instead.', category=PydanticDeprecatedSince20
        )
        return cls.model_construct(_fields_set=_fields_set, **values)

    @typing_extensions.deprecated(
        'The `copy` method is deprecated; use `model_copy` instead. '
        'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
        category=None,
    )
    def copy(
        self,
        *,
        include: AbstractSetIntStr | MappingIntStrAny | None = None,
        exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
        update: Dict[str, Any] | None = None,  # noqa UP006
        deep: bool = False,
    ) -> Self:  # pragma: no cover
        """Returns a copy of the model.

        !!! warning "Deprecated"
            This method is now deprecated; use `model_copy` instead.

        If you need `include` or `exclude`, use:

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

        Args:
            include: Optional set or mapping specifying which fields to include in the copied model.
            exclude: Optional set or mapping specifying which fields to exclude in the copied model.
            update: Optional dictionary of field-value pairs to override field values in the copied model.
            deep: If True, the values of fields that are Pydantic models will be deep-copied.

        Returns:
            A copy of the model with included, excluded and updated fields as specified.
        """
        warnings.warn(
            'The `copy` method is deprecated; use `model_copy` instead. '
            'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
            category=PydanticDeprecatedSince20,
        )
        from .deprecated import copy_internals

        values = dict(
            copy_internals._iter(
                self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
            ),
            **(update or {}),
        )
        if self.__pydantic_private__ is None:
            private = None
        else:
            private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}

        if self.__pydantic_extra__ is None:
            extra: dict[str, Any] | None = None
        else:
            extra = self.__pydantic_extra__.copy()
            for k in list(self.__pydantic_extra__):
                if k not in values:  # k was in the exclude
                    extra.pop(k)
            for k in list(values):
                if k in self.__pydantic_extra__:  # k must have come from extra
                    extra[k] = values.pop(k)

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

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

        return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)

    @classmethod
    @typing_extensions.deprecated('The `schema` method is deprecated; use `model_json_schema` instead.', category=None)
    def schema(  # noqa: D102
        cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
    ) -> Dict[str, Any]:  # noqa UP006
        warnings.warn(
            'The `schema` method is deprecated; use `model_json_schema` instead.', category=PydanticDeprecatedSince20
        )
        return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template)

    @classmethod
    @typing_extensions.deprecated(
        'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
        category=None,
    )
    def schema_json(  # noqa: D102
        cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
    ) -> str:  # pragma: no cover
        warnings.warn(
            'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
            category=PydanticDeprecatedSince20,
        )
        import json

        from .deprecated.json import pydantic_encoder

        return json.dumps(
            cls.model_json_schema(by_alias=by_alias, ref_template=ref_template),
            default=pydantic_encoder,
            **dumps_kwargs,
        )

    @classmethod
    @typing_extensions.deprecated('The `validate` method is deprecated; use `model_validate` instead.', category=None)
    def validate(cls, value: Any) -> Self:  # noqa: D102
        warnings.warn(
            'The `validate` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
        )
        return cls.model_validate(value)

    @classmethod
    @typing_extensions.deprecated(
        'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
        category=None,
    )
    def update_forward_refs(cls, **localns: Any) -> None:  # noqa: D102
        warnings.warn(
            'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
            category=PydanticDeprecatedSince20,
        )
        if localns:  # pragma: no cover
            raise TypeError('`localns` arguments are not longer accepted.')
        cls.model_rebuild(force=True)

    @typing_extensions.deprecated(
        'The private method `_iter` will be removed and should no longer be used.', category=None
    )
    def _iter(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method `_iter` will be removed and should no longer be used.',
            category=PydanticDeprecatedSince20,
        )
        from .deprecated import copy_internals

        return copy_internals._iter(self, *args, **kwargs)

    @typing_extensions.deprecated(
        'The private method `_copy_and_set_values` will be removed and should no longer be used.',
        category=None,
    )
    def _copy_and_set_values(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method `_copy_and_set_values` will be removed and should no longer be used.',
            category=PydanticDeprecatedSince20,
        )
        from .deprecated import copy_internals

        return copy_internals._copy_and_set_values(self, *args, **kwargs)

    @classmethod
    @typing_extensions.deprecated(
        'The private method `_get_value` will be removed and should no longer be used.',
        category=None,
    )
    def _get_value(cls, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method `_get_value` will be removed and should no longer be used.',
            category=PydanticDeprecatedSince20,
        )
        from .deprecated import copy_internals

        return copy_internals._get_value(cls, *args, **kwargs)

    @typing_extensions.deprecated(
        'The private method `_calculate_keys` will be removed and should no longer be used.',
        category=None,
    )
    def _calculate_keys(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method `_calculate_keys` will be removed and should no longer be used.',
            category=PydanticDeprecatedSince20,
        )
        from .deprecated import copy_internals

        return copy_internals._calculate_keys(self, *args, **kwargs)

Subclasses

• Request
• Components
• Contact
• Discriminator
• Encoding
• Example
• ExternalDocumentation
• Info
• License
• Link
• MediaType
• OAuthFlow
• OAuthFlows
• OpenAPIExtensions
• ParameterBase
• PathItem
• Reference
• RequestBody
• Response
• Schema
• ServerVariable
• Tag
• XML
• OAuth2Config
• AlbModel
• AlbRequestContext
• AlbRequestContextData
• APIGatewayEventAuthorizer
• APIGatewayEventIdentity
• APIGatewayEventRequestContext
• APIGatewayProxyEventModel
• ApiGatewayAuthorizerToken
• ApiGatewayUserCert
• ApiGatewayUserCertValidity
• APIGatewayProxyEventV2Model
• RequestContextV2
• RequestContextV2Authorizer
• RequestContextV2AuthorizerIam
• RequestContextV2AuthorizerIamCognito
• RequestContextV2AuthorizerJwt
• RequestContextV2Http
• BedrockAgentEventModel
• BedrockAgentModel
• BedrockAgentPropertyModel
• BedrockAgentRequestBodyModel
• BedrockAgentRequestMediaModel
• CloudFormationCustomResourceBaseModel
• CloudWatchLogsData
• CloudWatchLogsDecode
• CloudWatchLogsLogEvent
• CloudWatchLogsModel
• DynamoDBStreamChangedRecordModel
• DynamoDBStreamModel
• DynamoDBStreamRecordModel
• UserIdentity
• EventBridgeModel
• KafkaBaseEventModel
• KafkaRecordModel
• KinesisDataStreamModel
• KinesisDataStreamRecord
• KinesisDataStreamRecordPayload
• KinesisFirehoseModel
• KinesisFirehoseRecord
• KinesisFirehoseRecordMetadata
• KinesisFirehoseSqsModel
• KinesisFirehoseSqsRecord
• S3Bucket
• S3EventNotificationEventBridgeBucketModel
• S3EventNotificationEventBridgeDetailModel
• S3EventNotificationObjectModel
• S3EventRecordGlacierEventData
• S3EventRecordGlacierRestoreEventData
• S3Identity
• S3Message
• S3Model
• S3Object
• S3OwnerIdentify
• S3RecordModel
• S3RequestParameters
• S3ResponseElements
• S3BatchOperationJobModel
• S3BatchOperationModel
• S3BatchOperationTaskModel
• S3ObjectConfiguration
• S3ObjectContext
• S3ObjectLambdaEvent
• S3ObjectSessionAttributes
• S3ObjectSessionContext
• S3ObjectSessionIssuer
• S3ObjectUserIdentity
• S3ObjectUserRequest
• SesMail
• SesMailCommonHeaders
• SesMailHeaders
• SesMessage
• SesModel
• SesReceipt
• SesReceiptAction
• SesReceiptVerdict
• SesRecordModel
• SnsModel
• SnsMsgAttributeModel
• SnsNotificationModel
• SnsRecordModel
• SqsAttributesModel
• SqsModel
• SqsMsgAttributeModel
• SqsRecordModel
• VpcLatticeModel
• VpcLatticeV2Model
• VpcLatticeV2RequestContext
• VpcLatticeV2RequestContextIdentity

Class variables

var model_computed_fields

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

var model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

var model_fields

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

Static methods

def construct(**values: Any) ‑> Self

def from_orm(obj: Any) ‑> Self

def model_construct(**values: Any) ‑> Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

Note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance's __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Args

_fields_set

A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

values

Trusted or pre-validated data dictionary.

Returns

A new instance of the Model class with validated data.

def model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[GenerateJsonSchema] = pydantic.json_schema.GenerateJsonSchema, mode: JsonSchemaMode = 'validation') ‑> dict[str, typing.Any]

Generates a JSON schema for a model class.

Args

by_alias

Whether to use attribute aliases or not.

ref_template

The reference template.

schema_generator

To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

mode

The mode in which to generate the schema.

Returns

The JSON schema for the given model class.

def model_parametrized_name(params: tuple[type[Any], ...]) ‑> str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Args

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.

Returns

String representing the new class where params are passed to cls as type variables.

Raises

TypeError

Raised when trying to generate concrete names for non-generic models.

def model_rebuild(*, force: bool = False, raise_errors: bool = True) ‑> bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Args

force

Whether to force the rebuilding of the model schema, defaults to False.

raise_errors

Whether to raise errors, defaults to True.

_parent_namespace_depth

The depth level of the parent namespace, defaults to 2.

_types_namespace

The types namespace, defaults to None.

Returns

Returns None if the schema is already "complete" and rebuilding was not required. If rebuilding was required, returns True if rebuilding was successful, otherwise False.

def model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: Any | None = None) ‑> Self

Validate a pydantic model instance.

Args

obj

The object to validate.

strict

Whether to enforce types strictly.

from_attributes

Whether to extract data from object attributes.

context

Additional context to pass to the validator.

Raises

ValidationError

If the object could not be validated.

Returns

The validated model instance.

def model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: Any | None = None) ‑> Self

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args

json_data

The JSON data to validate.

strict

Whether to enforce types strictly.

context

Extra variables to pass to the validator.

Returns

The validated Pydantic model.

Raises

ValidationError

If json_data is not a JSON string or the object could not be validated.

def model_validate_strings(obj: Any, *, strict: bool | None = None, context: Any | None = None) ‑> Self

Validate the given object with string data against the Pydantic model.

Args

obj

The object containing string data to validate.

strict

Whether to enforce types strictly.

context

Extra variables to pass to the validator.

Returns

The validated Pydantic model.

def parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False)

def parse_obj(obj: Any) ‑> Self

def parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False)

def schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') ‑> Dict[str, Any]

def schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) ‑> str

def update_forward_refs(**localns: Any) ‑> None

def validate(value: Any) ‑> Self

Instance variables

prop model_extra : dict[str, Any] | None

Get extra fields set during validation.

Returns

A dictionary of extra fields, or None if config.extra is not set to "allow".

Expand source code

@property
def model_extra(self) -> dict[str, Any] | None:
    """Get extra fields set during validation.

    Returns:
        A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
    """
    return self.__pydantic_extra__

prop model_fields_set : set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns

A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

Expand source code

@property
def model_fields_set(self) -> set[str]:
    """Returns the set of fields that have been explicitly set on this model instance.

    Returns:
        A set of strings representing the fields that have been set,
            i.e. that were not filled from defaults.
    """
    return self.__pydantic_fields_set__

Methods

def copy(self, *, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False)

Returns a copy of the model.

Deprecated

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

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

Args

include

Optional set or mapping specifying which fields to include in the copied model.

exclude

Optional set or mapping specifying which fields to exclude in the copied model.

update

Optional dictionary of field-value pairs to override field values in the copied model.

deep

If True, the values of fields that are Pydantic models will be deep-copied.

Returns

A copy of the model with included, excluded and updated fields as specified.

def dict(self, *, include: IncEx | None = None, exclude: IncEx | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) ‑> Dict[str, Any]

def json(self, *, include: IncEx | None = None, exclude: IncEx | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) ‑> str

def model_copy(self, *, update: dict[str, Any] | None = None, deep: bool = False) ‑> Self

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Args

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.

deep

Set to True to make a deep copy of the model.

Returns

New model instance.

def model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: IncEx | None = None, exclude: IncEx | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, serialize_as_any: bool = False) ‑> dict[str, typing.Any]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args

mode

The mode in which to_python should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects.

include

A set of fields to include in the output.

exclude

A set of fields to exclude from the output.

context

Additional context to pass to the serializer.

by_alias

Whether to use the field's alias in the dictionary key if defined.

exclude_unset

Whether to exclude fields that have not been explicitly set.

exclude_defaults

Whether to exclude fields that are set to their default value.

exclude_none

Whether to exclude fields that have a value of None.

round_trip

If True, dumped values should be valid as input for non-idempotent types such as Json[T].

warnings

How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

serialize_as_any

Whether to serialize fields with duck-typing serialization behavior.

Returns

A dictionary representation of the model.

def model_dump_json(self, *, indent: int | None = None, include: IncEx | None = None, exclude: IncEx | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, serialize_as_any: bool = False) ‑> str

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic's to_json method.

Args

indent

Indentation to use in the JSON output. If None is passed, the output will be compact.

include

Field(s) to include in the JSON output.

exclude

Field(s) to exclude from the JSON output.

context

Additional context to pass to the serializer.

by_alias

Whether to serialize using field aliases.

exclude_unset

Whether to exclude fields that have not been explicitly set.

exclude_defaults

Whether to exclude fields that are set to their default value.

exclude_none

Whether to exclude fields that have a value of None.

round_trip

If True, dumped values should be valid as input for non-idempotent types such as Json[T].

warnings

How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

serialize_as_any

Whether to serialize fields with duck-typing serialization behavior.

Returns

A JSON string representation of the model.

def model_post_init(self, _BaseModel__context: Any) ‑> None

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

class ValidationError (*args, **kwargs)

Inappropriate argument value (of correct type).

Ancestors

• builtins.ValueError
• builtins.Exception
• builtins.BaseException

Static methods

def from_exception_data(title, line_errors, input_type='python', hide_input=False)

Instance variables

var title

Methods

def error_count(self, /)

def errors(self, /, *, include_url=True, include_context=True, include_input=True)

def json(self, /, *, indent=None, include_url=True, include_context=True, include_input=True)