Your IP : 18.222.110.231
"""Convert python types to pydantic-core schema."""
from __future__ import annotations as _annotations
import collections.abc
import dataclasses
import inspect
import re
import sys
import typing
import warnings
from contextlib import ExitStack, contextmanager
from copy import copy, deepcopy
from enum import Enum
from functools import partial
from inspect import Parameter, _ParameterKind, signature
from itertools import chain
from operator import attrgetter
from types import FunctionType, LambdaType, MethodType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Final,
ForwardRef,
Iterable,
Iterator,
Mapping,
Type,
TypeVar,
Union,
cast,
overload,
)
from warnings import warn
from pydantic_core import CoreSchema, PydanticUndefined, core_schema, to_jsonable_python
from typing_extensions import Annotated, Literal, TypeAliasType, TypedDict, get_args, get_origin, is_typeddict
from ..aliases import AliasGenerator
from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
from ..config import ConfigDict, JsonDict, JsonEncoder
from ..errors import PydanticSchemaGenerationError, PydanticUndefinedAnnotation, PydanticUserError
from ..json_schema import JsonSchemaValue
from ..version import version_short
from ..warnings import PydanticDeprecatedSince20
from . import _core_utils, _decorators, _discriminated_union, _known_annotated_metadata, _typing_extra
from ._config import ConfigWrapper, ConfigWrapperStack
from ._core_metadata import CoreMetadataHandler, build_metadata_dict
from ._core_utils import (
CoreSchemaOrField,
collect_invalid_schemas,
define_expected_missing_refs,
get_ref,
get_type_ref,
is_function_with_inner_schema,
is_list_like_schema_with_items_schema,
simplify_schema_references,
validate_core_schema,
)
from ._decorators import (
Decorator,
DecoratorInfos,
FieldSerializerDecoratorInfo,
FieldValidatorDecoratorInfo,
ModelSerializerDecoratorInfo,
ModelValidatorDecoratorInfo,
RootValidatorDecoratorInfo,
ValidatorDecoratorInfo,
get_attribute_from_bases,
inspect_field_serializer,
inspect_model_serializer,
inspect_validator,
)
from ._docs_extraction import extract_docstrings_from_cls
from ._fields import collect_dataclass_fields, get_type_hints_infer_globalns
from ._forward_ref import PydanticRecursiveRef
from ._generics import get_standard_typevars_map, has_instance_in_type, recursively_defined_type_refs, replace_types
from ._mock_val_ser import MockCoreSchema
from ._schema_generation_shared import CallbackGetCoreSchemaHandler
from ._typing_extra import is_finalvar, is_self_type
from ._utils import lenient_issubclass
if TYPE_CHECKING:
from ..fields import ComputedFieldInfo, FieldInfo
from ..main import BaseModel
from ..types import Discriminator
from ..validators import FieldValidatorModes
from ._dataclasses import StandardDataclass
from ._schema_generation_shared import GetJsonSchemaFunction
_SUPPORTS_TYPEDDICT = sys.version_info >= (3, 12)
_AnnotatedType = type(Annotated[int, 123])
FieldDecoratorInfo = Union[ValidatorDecoratorInfo, FieldValidatorDecoratorInfo, FieldSerializerDecoratorInfo]
FieldDecoratorInfoType = TypeVar('FieldDecoratorInfoType', bound=FieldDecoratorInfo)
AnyFieldDecorator = Union[
Decorator[ValidatorDecoratorInfo],
Decorator[FieldValidatorDecoratorInfo],
Decorator[FieldSerializerDecoratorInfo],
]
ModifyCoreSchemaWrapHandler = GetCoreSchemaHandler
GetCoreSchemaFunction = Callable[[Any, ModifyCoreSchemaWrapHandler], core_schema.CoreSchema]
TUPLE_TYPES: list[type] = [tuple, typing.Tuple]
LIST_TYPES: list[type] = [list, typing.List, collections.abc.MutableSequence]
SET_TYPES: list[type] = [set, typing.Set, collections.abc.MutableSet]
FROZEN_SET_TYPES: list[type] = [frozenset, typing.FrozenSet, collections.abc.Set]
DICT_TYPES: list[type] = [dict, typing.Dict, collections.abc.MutableMapping, collections.abc.Mapping]
def check_validator_fields_against_field_name(
info: FieldDecoratorInfo,
field: str,
) -> bool:
"""Check if field name is in validator fields.
Args:
info: The field info.
field: The field name to check.
Returns:
`True` if field name is in validator fields, `False` otherwise.
"""
if '*' in info.fields:
return True
for v_field_name in info.fields:
if v_field_name == field:
return True
return False
def check_decorator_fields_exist(decorators: Iterable[AnyFieldDecorator], fields: Iterable[str]) -> None:
"""Check if the defined fields in decorators exist in `fields` param.
It ignores the check for a decorator if the decorator has `*` as field or `check_fields=False`.
Args:
decorators: An iterable of decorators.
fields: An iterable of fields name.
Raises:
PydanticUserError: If one of the field names does not exist in `fields` param.
"""
fields = set(fields)
for dec in decorators:
if '*' in dec.info.fields:
continue
if dec.info.check_fields is False:
continue
for field in dec.info.fields:
if field not in fields:
raise PydanticUserError(
f'Decorators defined with incorrect fields: {dec.cls_ref}.{dec.cls_var_name}'
" (use check_fields=False if you're inheriting from the model and intended this)",
code='decorator-missing-field',
)
def filter_field_decorator_info_by_field(
validator_functions: Iterable[Decorator[FieldDecoratorInfoType]], field: str
) -> list[Decorator[FieldDecoratorInfoType]]:
return [dec for dec in validator_functions if check_validator_fields_against_field_name(dec.info, field)]
def apply_each_item_validators(
schema: core_schema.CoreSchema,
each_item_validators: list[Decorator[ValidatorDecoratorInfo]],
field_name: str | None,
) -> core_schema.CoreSchema:
# This V1 compatibility shim should eventually be removed
# push down any `each_item=True` validators
# note that this won't work for any Annotated types that get wrapped by a function validator
# but that's okay because that didn't exist in V1
if schema['type'] == 'nullable':
schema['schema'] = apply_each_item_validators(schema['schema'], each_item_validators, field_name)
return schema
elif schema['type'] == 'tuple':
if (variadic_item_index := schema.get('variadic_item_index')) is not None:
schema['items_schema'][variadic_item_index] = apply_validators(
schema['items_schema'][variadic_item_index], each_item_validators, field_name
)
elif is_list_like_schema_with_items_schema(schema):
inner_schema = schema.get('items_schema', None)
if inner_schema is None:
inner_schema = core_schema.any_schema()
schema['items_schema'] = apply_validators(inner_schema, each_item_validators, field_name)
elif schema['type'] == 'dict':
# push down any `each_item=True` validators onto dict _values_
# this is super arbitrary but it's the V1 behavior
inner_schema = schema.get('values_schema', None)
if inner_schema is None:
inner_schema = core_schema.any_schema()
schema['values_schema'] = apply_validators(inner_schema, each_item_validators, field_name)
elif each_item_validators:
raise TypeError(
f"`@validator(..., each_item=True)` cannot be applied to fields with a schema of {schema['type']}"
)
return schema
def modify_model_json_schema(
schema_or_field: CoreSchemaOrField,
handler: GetJsonSchemaHandler,
*,
cls: Any,
title: str | None = None,
) -> JsonSchemaValue:
"""Add title and description for model-like classes' JSON schema.
Args:
schema_or_field: The schema data to generate a JSON schema from.
handler: The `GetCoreSchemaHandler` instance.
cls: The model-like class.
title: The title to set for the model's schema, defaults to the model's name
Returns:
JsonSchemaValue: The updated JSON schema.
"""
from ..dataclasses import is_pydantic_dataclass
from ..main import BaseModel
from ..root_model import RootModel
from ._dataclasses import is_builtin_dataclass
json_schema = handler(schema_or_field)
original_schema = handler.resolve_ref_schema(json_schema)
# Preserve the fact that definitions schemas should never have sibling keys:
if '$ref' in original_schema:
ref = original_schema['$ref']
original_schema.clear()
original_schema['allOf'] = [{'$ref': ref}]
if title is not None:
original_schema['title'] = title
elif 'title' not in original_schema:
original_schema['title'] = cls.__name__
# BaseModel + Dataclass; don't use cls.__doc__ as it will contain the verbose class signature by default
docstring = None if cls is BaseModel or is_builtin_dataclass(cls) or is_pydantic_dataclass(cls) else cls.__doc__
if docstring and 'description' not in original_schema:
original_schema['description'] = inspect.cleandoc(docstring)
elif issubclass(cls, RootModel) and cls.model_fields['root'].description:
original_schema['description'] = cls.model_fields['root'].description
return json_schema
JsonEncoders = Dict[Type[Any], JsonEncoder]
def _add_custom_serialization_from_json_encoders(
json_encoders: JsonEncoders | None, tp: Any, schema: CoreSchema
) -> CoreSchema:
"""Iterate over the json_encoders and add the first matching encoder to the schema.
Args:
json_encoders: A dictionary of types and their encoder functions.
tp: The type to check for a matching encoder.
schema: The schema to add the encoder to.
"""
if not json_encoders:
return schema
if 'serialization' in schema:
return schema
# Check the class type and its superclasses for a matching encoder
# Decimal.__class__.__mro__ (and probably other cases) doesn't include Decimal itself
# if the type is a GenericAlias (e.g. from list[int]) we need to use __class__ instead of .__mro__
for base in (tp, *getattr(tp, '__mro__', tp.__class__.__mro__)[:-1]):
encoder = json_encoders.get(base)
if encoder is None:
continue
warnings.warn(
f'`json_encoders` is deprecated. See https://docs.pydantic.dev/{version_short()}/concepts/serialization/#custom-serializers for alternatives',
PydanticDeprecatedSince20,
)
# TODO: in theory we should check that the schema accepts a serialization key
schema['serialization'] = core_schema.plain_serializer_function_ser_schema(encoder, when_used='json')
return schema
return schema
TypesNamespace = Union[Dict[str, Any], None]
class TypesNamespaceStack:
"""A stack of types namespaces."""
def __init__(self, types_namespace: TypesNamespace):
self._types_namespace_stack: list[TypesNamespace] = [types_namespace]
@property
def tail(self) -> TypesNamespace:
return self._types_namespace_stack[-1]
@contextmanager
def push(self, for_type: type[Any]):
types_namespace = {**_typing_extra.get_cls_types_namespace(for_type), **(self.tail or {})}
self._types_namespace_stack.append(types_namespace)
try:
yield
finally:
self._types_namespace_stack.pop()
def _get_first_non_null(a: Any, b: Any) -> Any:
"""Return the first argument if it is not None, otherwise return the second argument.
Use case: serialization_alias (argument a) and alias (argument b) are both defined, and serialization_alias is ''.
This function will return serialization_alias, which is the first argument, even though it is an empty string.
"""
return a if a is not None else b
class GenerateSchema:
"""Generate core schema for a Pydantic model, dataclass and types like `str`, `datetime`, ... ."""
__slots__ = (
'_config_wrapper_stack',
'_types_namespace_stack',
'_typevars_map',
'field_name_stack',
'model_type_stack',
'defs',
)
def __init__(
self,
config_wrapper: ConfigWrapper,
types_namespace: dict[str, Any] | None,
typevars_map: dict[Any, Any] | None = None,
) -> None:
# we need a stack for recursing into child models
self._config_wrapper_stack = ConfigWrapperStack(config_wrapper)
self._types_namespace_stack = TypesNamespaceStack(types_namespace)
self._typevars_map = typevars_map
self.field_name_stack = _FieldNameStack()
self.model_type_stack = _ModelTypeStack()
self.defs = _Definitions()
@classmethod
def __from_parent(
cls,
config_wrapper_stack: ConfigWrapperStack,
types_namespace_stack: TypesNamespaceStack,
model_type_stack: _ModelTypeStack,
typevars_map: dict[Any, Any] | None,
defs: _Definitions,
) -> GenerateSchema:
obj = cls.__new__(cls)
obj._config_wrapper_stack = config_wrapper_stack
obj._types_namespace_stack = types_namespace_stack
obj.model_type_stack = model_type_stack
obj._typevars_map = typevars_map
obj.field_name_stack = _FieldNameStack()
obj.defs = defs
return obj
@property
def _config_wrapper(self) -> ConfigWrapper:
return self._config_wrapper_stack.tail
@property
def _types_namespace(self) -> dict[str, Any] | None:
return self._types_namespace_stack.tail
@property
def _current_generate_schema(self) -> GenerateSchema:
cls = self._config_wrapper.schema_generator or GenerateSchema
return cls.__from_parent(
self._config_wrapper_stack,
self._types_namespace_stack,
self.model_type_stack,
self._typevars_map,
self.defs,
)
@property
def _arbitrary_types(self) -> bool:
return self._config_wrapper.arbitrary_types_allowed
def str_schema(self) -> CoreSchema:
"""Generate a CoreSchema for `str`"""
return core_schema.str_schema()
# the following methods can be overridden but should be considered
# unstable / private APIs
def _list_schema(self, tp: Any, items_type: Any) -> CoreSchema:
return core_schema.list_schema(self.generate_schema(items_type))
def _dict_schema(self, tp: Any, keys_type: Any, values_type: Any) -> CoreSchema:
return core_schema.dict_schema(self.generate_schema(keys_type), self.generate_schema(values_type))
def _set_schema(self, tp: Any, items_type: Any) -> CoreSchema:
return core_schema.set_schema(self.generate_schema(items_type))
def _frozenset_schema(self, tp: Any, items_type: Any) -> CoreSchema:
return core_schema.frozenset_schema(self.generate_schema(items_type))
def _arbitrary_type_schema(self, tp: Any) -> CoreSchema:
if not isinstance(tp, type):
warn(
f'{tp!r} is not a Python type (it may be an instance of an object),'
' Pydantic will allow any object with no validation since we cannot even'
' enforce that the input is an instance of the given type.'
' To get rid of this error wrap the type with `pydantic.SkipValidation`.',
UserWarning,
)
return core_schema.any_schema()
return core_schema.is_instance_schema(tp)
def _unknown_type_schema(self, obj: Any) -> CoreSchema:
raise PydanticSchemaGenerationError(
f'Unable to generate pydantic-core schema for {obj!r}. '
'Set `arbitrary_types_allowed=True` in the model_config to ignore this error'
' or implement `__get_pydantic_core_schema__` on your type to fully support it.'
'\n\nIf you got this error by calling handler(<some type>) within'
' `__get_pydantic_core_schema__` then you likely need to call'
' `handler.generate_schema(<some type>)` since we do not call'
' `__get_pydantic_core_schema__` on `<some type>` otherwise to avoid infinite recursion.'
)
def _apply_discriminator_to_union(
self, schema: CoreSchema, discriminator: str | Discriminator | None
) -> CoreSchema:
if discriminator is None:
return schema
try:
return _discriminated_union.apply_discriminator(
schema,
discriminator,
)
except _discriminated_union.MissingDefinitionForUnionRef:
# defer until defs are resolved
_discriminated_union.set_discriminator_in_metadata(
schema,
discriminator,
)
return schema
class CollectedInvalid(Exception):
pass
def clean_schema(self, schema: CoreSchema) -> CoreSchema:
schema = self.collect_definitions(schema)
schema = simplify_schema_references(schema)
if collect_invalid_schemas(schema):
raise self.CollectedInvalid()
schema = _discriminated_union.apply_discriminators(schema)
schema = validate_core_schema(schema)
return schema
def collect_definitions(self, schema: CoreSchema) -> CoreSchema:
ref = cast('str | None', schema.get('ref', None))
if ref:
self.defs.definitions[ref] = schema
if 'ref' in schema:
schema = core_schema.definition_reference_schema(schema['ref'])
return core_schema.definitions_schema(
schema,
list(self.defs.definitions.values()),
)
def _add_js_function(self, metadata_schema: CoreSchema, js_function: Callable[..., Any]) -> None:
metadata = CoreMetadataHandler(metadata_schema).metadata
pydantic_js_functions = metadata.setdefault('pydantic_js_functions', [])
# because of how we generate core schemas for nested generic models
# we can end up adding `BaseModel.__get_pydantic_json_schema__` multiple times
# this check may fail to catch duplicates if the function is a `functools.partial`
# or something like that
# but if it does it'll fail by inserting the duplicate
if js_function not in pydantic_js_functions:
pydantic_js_functions.append(js_function)
def generate_schema(
self,
obj: Any,
from_dunder_get_core_schema: bool = True,
) -> core_schema.CoreSchema:
"""Generate core schema.
Args:
obj: The object to generate core schema for.
from_dunder_get_core_schema: Whether to generate schema from either the
`__get_pydantic_core_schema__` function or `__pydantic_core_schema__` property.
Returns:
The generated core schema.
Raises:
PydanticUndefinedAnnotation:
If it is not possible to evaluate forward reference.
PydanticSchemaGenerationError:
If it is not possible to generate pydantic-core schema.
TypeError:
- If `alias_generator` returns a disallowed type (must be str, AliasPath or AliasChoices).
- If V1 style validator with `each_item=True` applied on a wrong field.
PydanticUserError:
- If `typing.TypedDict` is used instead of `typing_extensions.TypedDict` on Python < 3.12.
- If `__modify_schema__` method is used instead of `__get_pydantic_json_schema__`.
"""
schema: CoreSchema | None = None
if from_dunder_get_core_schema:
from_property = self._generate_schema_from_property(obj, obj)
if from_property is not None:
schema = from_property
if schema is None:
schema = self._generate_schema_inner(obj)
metadata_js_function = _extract_get_pydantic_json_schema(obj, schema)
if metadata_js_function is not None:
metadata_schema = resolve_original_schema(schema, self.defs.definitions)
if metadata_schema:
self._add_js_function(metadata_schema, metadata_js_function)
schema = _add_custom_serialization_from_json_encoders(self._config_wrapper.json_encoders, obj, schema)
return schema
def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema:
"""Generate schema for a Pydantic model."""
with self.defs.get_schema_or_ref(cls) as (model_ref, maybe_schema):
if maybe_schema is not None:
return maybe_schema
fields = cls.model_fields
decorators = cls.__pydantic_decorators__
computed_fields = decorators.computed_fields
check_decorator_fields_exist(
chain(
decorators.field_validators.values(),
decorators.field_serializers.values(),
decorators.validators.values(),
),
{*fields.keys(), *computed_fields.keys()},
)
config_wrapper = ConfigWrapper(cls.model_config, check=False)
core_config = config_wrapper.core_config(cls)
title = self._get_model_title_from_config(cls, config_wrapper)
metadata = build_metadata_dict(js_functions=[partial(modify_model_json_schema, cls=cls, title=title)])
model_validators = decorators.model_validators.values()
extras_schema = None
if core_config.get('extra_fields_behavior') == 'allow':
assert cls.__mro__[0] is cls
assert cls.__mro__[-1] is object
for candidate_cls in cls.__mro__[:-1]:
extras_annotation = getattr(candidate_cls, '__annotations__', {}).get('__pydantic_extra__', None)
if extras_annotation is not None:
if isinstance(extras_annotation, str):
extras_annotation = _typing_extra.eval_type_backport(
_typing_extra._make_forward_ref(extras_annotation, is_argument=False, is_class=True),
self._types_namespace,
)
tp = get_origin(extras_annotation)
if tp not in (Dict, dict):
raise PydanticSchemaGenerationError(
'The type annotation for `__pydantic_extra__` must be `Dict[str, ...]`'
)
extra_items_type = self._get_args_resolving_forward_refs(
extras_annotation,
required=True,
)[1]
if extra_items_type is not Any:
extras_schema = self.generate_schema(extra_items_type)
break
with self._config_wrapper_stack.push(config_wrapper), self._types_namespace_stack.push(cls):
self = self._current_generate_schema
if cls.__pydantic_root_model__:
root_field = self._common_field_schema('root', fields['root'], decorators)
inner_schema = root_field['schema']
inner_schema = apply_model_validators(inner_schema, model_validators, 'inner')
model_schema = core_schema.model_schema(
cls,
inner_schema,
custom_init=getattr(cls, '__pydantic_custom_init__', None),
root_model=True,
post_init=getattr(cls, '__pydantic_post_init__', None),
config=core_config,
ref=model_ref,
metadata=metadata,
)
else:
fields_schema: core_schema.CoreSchema = core_schema.model_fields_schema(
{k: self._generate_md_field_schema(k, v, decorators) for k, v in fields.items()},
computed_fields=[
self._computed_field_schema(d, decorators.field_serializers)
for d in computed_fields.values()
],
extras_schema=extras_schema,
model_name=cls.__name__,
)
inner_schema = apply_validators(fields_schema, decorators.root_validators.values(), None)
new_inner_schema = define_expected_missing_refs(inner_schema, recursively_defined_type_refs())
if new_inner_schema is not None:
inner_schema = new_inner_schema
inner_schema = apply_model_validators(inner_schema, model_validators, 'inner')
model_schema = core_schema.model_schema(
cls,
inner_schema,
custom_init=getattr(cls, '__pydantic_custom_init__', None),
root_model=False,
post_init=getattr(cls, '__pydantic_post_init__', None),
config=core_config,
ref=model_ref,
metadata=metadata,
)
schema = self._apply_model_serializers(model_schema, decorators.model_serializers.values())
schema = apply_model_validators(schema, model_validators, 'outer')
self.defs.definitions[model_ref] = schema
return core_schema.definition_reference_schema(model_ref)
@staticmethod
def _get_model_title_from_config(
model: type[BaseModel | StandardDataclass], config_wrapper: ConfigWrapper | None = None
) -> str | None:
"""Get the title of a model if `model_title_generator` or `title` are set in the config, else return None"""
if config_wrapper is None:
return None
if config_wrapper.title:
return config_wrapper.title
model_title_generator = config_wrapper.model_title_generator
if model_title_generator:
title = model_title_generator(model)
if not isinstance(title, str):
raise TypeError(f'model_title_generator {model_title_generator} must return str, not {title.__class__}')
return title
return None
def _unpack_refs_defs(self, schema: CoreSchema) -> CoreSchema:
"""Unpack all 'definitions' schemas into `GenerateSchema.defs.definitions`
and return the inner schema.
"""
def get_ref(s: CoreSchema) -> str:
return s['ref'] # type: ignore
if schema['type'] == 'definitions':
self.defs.definitions.update({get_ref(s): s for s in schema['definitions']})
schema = schema['schema']
return schema
def _generate_schema_from_property(self, obj: Any, source: Any) -> core_schema.CoreSchema | None:
"""Try to generate schema from either the `__get_pydantic_core_schema__` function or
`__pydantic_core_schema__` property.
Note: `__get_pydantic_core_schema__` takes priority so it can
decide whether to use a `__pydantic_core_schema__` attribute, or generate a fresh schema.
"""
# avoid calling `__get_pydantic_core_schema__` if we've already visited this object
if is_self_type(obj):
obj = self.model_type_stack.get()
with self.defs.get_schema_or_ref(obj) as (_, maybe_schema):
if maybe_schema is not None:
return maybe_schema
if obj is source:
ref_mode = 'unpack'
else:
ref_mode = 'to-def'
schema: CoreSchema
if (get_schema := getattr(obj, '__get_pydantic_core_schema__', None)) is not None:
if len(inspect.signature(get_schema).parameters) == 1:
# (source) -> CoreSchema
schema = get_schema(source)
else:
schema = get_schema(
source, CallbackGetCoreSchemaHandler(self._generate_schema_inner, self, ref_mode=ref_mode)
)
# fmt: off
elif (
(existing_schema := getattr(obj, '__pydantic_core_schema__', None)) is not None
and not isinstance(existing_schema, MockCoreSchema)
and existing_schema.get('cls', None) == obj
):
schema = existing_schema
# fmt: on
elif (validators := getattr(obj, '__get_validators__', None)) is not None:
warn(
'`__get_validators__` is deprecated and will be removed, use `__get_pydantic_core_schema__` instead.',
PydanticDeprecatedSince20,
)
schema = core_schema.chain_schema([core_schema.with_info_plain_validator_function(v) for v in validators()])
else:
# we have no existing schema information on the property, exit early so that we can go generate a schema
return None
schema = self._unpack_refs_defs(schema)
if is_function_with_inner_schema(schema):
ref = schema['schema'].pop('ref', None) # pyright: ignore[reportCallIssue, reportArgumentType]
if ref:
schema['ref'] = ref
else:
ref = get_ref(schema)
if ref:
self.defs.definitions[ref] = schema
return core_schema.definition_reference_schema(ref)
return schema
def _resolve_forward_ref(self, obj: Any) -> Any:
# we assume that types_namespace has the target of forward references in its scope,
# but this could fail, for example, if calling Validator on an imported type which contains
# forward references to other types only defined in the module from which it was imported
# `Validator(SomeImportedTypeAliasWithAForwardReference)`
# or the equivalent for BaseModel
# class Model(BaseModel):
# x: SomeImportedTypeAliasWithAForwardReference
try:
obj = _typing_extra.eval_type_backport(obj, globalns=self._types_namespace)
except NameError as e:
raise PydanticUndefinedAnnotation.from_name_error(e) from e
# if obj is still a ForwardRef, it means we can't evaluate it, raise PydanticUndefinedAnnotation
if isinstance(obj, ForwardRef):
raise PydanticUndefinedAnnotation(obj.__forward_arg__, f'Unable to evaluate forward reference {obj}')
if self._typevars_map:
obj = replace_types(obj, self._typevars_map)
return obj
@overload
def _get_args_resolving_forward_refs(self, obj: Any, required: Literal[True]) -> tuple[Any, ...]: ...
@overload
def _get_args_resolving_forward_refs(self, obj: Any) -> tuple[Any, ...] | None: ...
def _get_args_resolving_forward_refs(self, obj: Any, required: bool = False) -> tuple[Any, ...] | None:
args = get_args(obj)
if args:
args = tuple([self._resolve_forward_ref(a) if isinstance(a, ForwardRef) else a for a in args])
elif required: # pragma: no cover
raise TypeError(f'Expected {obj} to have generic parameters but it had none')
return args
def _get_first_arg_or_any(self, obj: Any) -> Any:
args = self._get_args_resolving_forward_refs(obj)
if not args:
return Any
return args[0]
def _get_first_two_args_or_any(self, obj: Any) -> tuple[Any, Any]:
args = self._get_args_resolving_forward_refs(obj)
if not args:
return (Any, Any)
if len(args) < 2:
origin = get_origin(obj)
raise TypeError(f'Expected two type arguments for {origin}, got 1')
return args[0], args[1]
def _generate_schema_inner(self, obj: Any) -> core_schema.CoreSchema:
if isinstance(obj, _AnnotatedType):
return self._annotated_schema(obj)
if isinstance(obj, dict):
# we assume this is already a valid schema
return obj # type: ignore[return-value]
if isinstance(obj, str):
obj = ForwardRef(obj)
if isinstance(obj, ForwardRef):
return self.generate_schema(self._resolve_forward_ref(obj))
from ..main import BaseModel
if lenient_issubclass(obj, BaseModel):
with self.model_type_stack.push(obj):
return self._model_schema(obj)
if isinstance(obj, PydanticRecursiveRef):
return core_schema.definition_reference_schema(schema_ref=obj.type_ref)
return self.match_type(obj)
def match_type(self, obj: Any) -> core_schema.CoreSchema: # noqa: C901
"""Main mapping of types to schemas.
The general structure is a series of if statements starting with the simple cases
(non-generic primitive types) and then handling generics and other more complex cases.
Each case either generates a schema directly, calls into a public user-overridable method
(like `GenerateSchema.tuple_variable_schema`) or calls into a private method that handles some
boilerplate before calling into the user-facing method (e.g. `GenerateSchema._tuple_schema`).
The idea is that we'll evolve this into adding more and more user facing methods over time
as they get requested and we figure out what the right API for them is.
"""
if obj is str:
return self.str_schema()
elif obj is bytes:
return core_schema.bytes_schema()
elif obj is int:
return core_schema.int_schema()
elif obj is float:
return core_schema.float_schema()
elif obj is bool:
return core_schema.bool_schema()
elif obj is Any or obj is object:
return core_schema.any_schema()
elif obj is None or obj is _typing_extra.NoneType:
return core_schema.none_schema()
elif obj in TUPLE_TYPES:
return self._tuple_schema(obj)
elif obj in LIST_TYPES:
return self._list_schema(obj, self._get_first_arg_or_any(obj))
elif obj in SET_TYPES:
return self._set_schema(obj, self._get_first_arg_or_any(obj))
elif obj in FROZEN_SET_TYPES:
return self._frozenset_schema(obj, self._get_first_arg_or_any(obj))
elif obj in DICT_TYPES:
return self._dict_schema(obj, *self._get_first_two_args_or_any(obj))
elif isinstance(obj, TypeAliasType):
return self._type_alias_type_schema(obj)
elif obj is type:
return self._type_schema()
elif _typing_extra.is_callable_type(obj):
return core_schema.callable_schema()
elif _typing_extra.is_literal_type(obj):
return self._literal_schema(obj)
elif is_typeddict(obj):
return self._typed_dict_schema(obj, None)
elif _typing_extra.is_namedtuple(obj):
return self._namedtuple_schema(obj, None)
elif _typing_extra.is_new_type(obj):
# NewType, can't use isinstance because it fails <3.10
return self.generate_schema(obj.__supertype__)
elif obj == re.Pattern:
return self._pattern_schema(obj)
elif obj is collections.abc.Hashable or obj is typing.Hashable:
return self._hashable_schema()
elif isinstance(obj, typing.TypeVar):
return self._unsubstituted_typevar_schema(obj)
elif is_finalvar(obj):
if obj is Final:
return core_schema.any_schema()
return self.generate_schema(
self._get_first_arg_or_any(obj),
)
elif isinstance(obj, (FunctionType, LambdaType, MethodType, partial)):
return self._callable_schema(obj)
elif inspect.isclass(obj) and issubclass(obj, Enum):
from ._std_types_schema import get_enum_core_schema
return get_enum_core_schema(obj, self._config_wrapper.config_dict)
if _typing_extra.is_dataclass(obj):
return self._dataclass_schema(obj, None)
res = self._get_prepare_pydantic_annotations_for_known_type(obj, ())
if res is not None:
source_type, annotations = res
return self._apply_annotations(source_type, annotations)
origin = get_origin(obj)
if origin is not None:
return self._match_generic_type(obj, origin)
if self._arbitrary_types:
return self._arbitrary_type_schema(obj)
return self._unknown_type_schema(obj)
def _match_generic_type(self, obj: Any, origin: Any) -> CoreSchema: # noqa: C901
if isinstance(origin, TypeAliasType):
return self._type_alias_type_schema(obj)
# Need to handle generic dataclasses before looking for the schema properties because attribute accesses
# on _GenericAlias delegate to the origin type, so lose the information about the concrete parametrization
# As a result, currently, there is no way to cache the schema for generic dataclasses. This may be possible
# to resolve by modifying the value returned by `Generic.__class_getitem__`, but that is a dangerous game.
if _typing_extra.is_dataclass(origin):
return self._dataclass_schema(obj, origin)
if _typing_extra.is_namedtuple(origin):
return self._namedtuple_schema(obj, origin)
from_property = self._generate_schema_from_property(origin, obj)
if from_property is not None:
return from_property
if _typing_extra.origin_is_union(origin):
return self._union_schema(obj)
elif origin in TUPLE_TYPES:
return self._tuple_schema(obj)
elif origin in LIST_TYPES:
return self._list_schema(obj, self._get_first_arg_or_any(obj))
elif origin in SET_TYPES:
return self._set_schema(obj, self._get_first_arg_or_any(obj))
elif origin in FROZEN_SET_TYPES:
return self._frozenset_schema(obj, self._get_first_arg_or_any(obj))
elif origin in DICT_TYPES:
return self._dict_schema(obj, *self._get_first_two_args_or_any(obj))
elif is_typeddict(origin):
return self._typed_dict_schema(obj, origin)
elif origin in (typing.Type, type):
return self._subclass_schema(obj)
elif origin in {typing.Sequence, collections.abc.Sequence}:
return self._sequence_schema(obj)
elif origin in {typing.Iterable, collections.abc.Iterable, typing.Generator, collections.abc.Generator}:
return self._iterable_schema(obj)
elif origin in (re.Pattern, typing.Pattern):
return self._pattern_schema(obj)
if self._arbitrary_types:
return self._arbitrary_type_schema(origin)
return self._unknown_type_schema(obj)
def _generate_td_field_schema(
self,
name: str,
field_info: FieldInfo,
decorators: DecoratorInfos,
*,
required: bool = True,
) -> core_schema.TypedDictField:
"""Prepare a TypedDictField to represent a model or typeddict field."""
common_field = self._common_field_schema(name, field_info, decorators)
return core_schema.typed_dict_field(
common_field['schema'],
required=False if not field_info.is_required() else required,
serialization_exclude=common_field['serialization_exclude'],
validation_alias=common_field['validation_alias'],
serialization_alias=common_field['serialization_alias'],
metadata=common_field['metadata'],
)
def _generate_md_field_schema(
self,
name: str,
field_info: FieldInfo,
decorators: DecoratorInfos,
) -> core_schema.ModelField:
"""Prepare a ModelField to represent a model field."""
common_field = self._common_field_schema(name, field_info, decorators)
return core_schema.model_field(
common_field['schema'],
serialization_exclude=common_field['serialization_exclude'],
validation_alias=common_field['validation_alias'],
serialization_alias=common_field['serialization_alias'],
frozen=common_field['frozen'],
metadata=common_field['metadata'],
)
def _generate_dc_field_schema(
self,
name: str,
field_info: FieldInfo,
decorators: DecoratorInfos,
) -> core_schema.DataclassField:
"""Prepare a DataclassField to represent the parameter/field, of a dataclass."""
common_field = self._common_field_schema(name, field_info, decorators)
return core_schema.dataclass_field(
name,
common_field['schema'],
init=field_info.init,
init_only=field_info.init_var or None,
kw_only=None if field_info.kw_only else False,
serialization_exclude=common_field['serialization_exclude'],
validation_alias=common_field['validation_alias'],
serialization_alias=common_field['serialization_alias'],
frozen=common_field['frozen'],
metadata=common_field['metadata'],
)
@staticmethod
def _apply_alias_generator_to_field_info(
alias_generator: Callable[[str], str] | AliasGenerator, field_info: FieldInfo, field_name: str
) -> None:
"""Apply an alias_generator to aliases on a FieldInfo instance if appropriate.
Args:
alias_generator: A callable that takes a string and returns a string, or an AliasGenerator instance.
field_info: The FieldInfo instance to which the alias_generator is (maybe) applied.
field_name: The name of the field from which to generate the alias.
"""
# Apply an alias_generator if
# 1. An alias is not specified
# 2. An alias is specified, but the priority is <= 1
if (
field_info.alias_priority is None
or field_info.alias_priority <= 1
or field_info.alias is None
or field_info.validation_alias is None
or field_info.serialization_alias is None
):
alias, validation_alias, serialization_alias = None, None, None
if isinstance(alias_generator, AliasGenerator):
alias, validation_alias, serialization_alias = alias_generator.generate_aliases(field_name)
elif isinstance(alias_generator, Callable):
alias = alias_generator(field_name)
if not isinstance(alias, str):
raise TypeError(f'alias_generator {alias_generator} must return str, not {alias.__class__}')
# if priority is not set, we set to 1
# which supports the case where the alias_generator from a child class is used
# to generate an alias for a field in a parent class
if field_info.alias_priority is None or field_info.alias_priority <= 1:
field_info.alias_priority = 1
# if the priority is 1, then we set the aliases to the generated alias
if field_info.alias_priority == 1:
field_info.serialization_alias = _get_first_non_null(serialization_alias, alias)
field_info.validation_alias = _get_first_non_null(validation_alias, alias)
field_info.alias = alias
# if any of the aliases are not set, then we set them to the corresponding generated alias
if field_info.alias is None:
field_info.alias = alias
if field_info.serialization_alias is None:
field_info.serialization_alias = _get_first_non_null(serialization_alias, alias)
if field_info.validation_alias is None:
field_info.validation_alias = _get_first_non_null(validation_alias, alias)
@staticmethod
def _apply_alias_generator_to_computed_field_info(
alias_generator: Callable[[str], str] | AliasGenerator,
computed_field_info: ComputedFieldInfo,
computed_field_name: str,
):
"""Apply an alias_generator to alias on a ComputedFieldInfo instance if appropriate.
Args:
alias_generator: A callable that takes a string and returns a string, or an AliasGenerator instance.
computed_field_info: The ComputedFieldInfo instance to which the alias_generator is (maybe) applied.
computed_field_name: The name of the computed field from which to generate the alias.
"""
# Apply an alias_generator if
# 1. An alias is not specified
# 2. An alias is specified, but the priority is <= 1
if (
computed_field_info.alias_priority is None
or computed_field_info.alias_priority <= 1
or computed_field_info.alias is None
):
alias, validation_alias, serialization_alias = None, None, None
if isinstance(alias_generator, AliasGenerator):
alias, validation_alias, serialization_alias = alias_generator.generate_aliases(computed_field_name)
elif isinstance(alias_generator, Callable):
alias = alias_generator(computed_field_name)
if not isinstance(alias, str):
raise TypeError(f'alias_generator {alias_generator} must return str, not {alias.__class__}')
# if priority is not set, we set to 1
# which supports the case where the alias_generator from a child class is used
# to generate an alias for a field in a parent class
if computed_field_info.alias_priority is None or computed_field_info.alias_priority <= 1:
computed_field_info.alias_priority = 1
# if the priority is 1, then we set the aliases to the generated alias
# note that we use the serialization_alias with priority over alias, as computed_field
# aliases are used for serialization only (not validation)
if computed_field_info.alias_priority == 1:
computed_field_info.alias = _get_first_non_null(serialization_alias, alias)
@staticmethod
def _apply_field_title_generator_to_field_info(
config_wrapper: ConfigWrapper, field_info: FieldInfo | ComputedFieldInfo, field_name: str
) -> None:
"""Apply a field_title_generator on a FieldInfo or ComputedFieldInfo instance if appropriate
Args:
config_wrapper: The config of the model
field_info: The FieldInfo or ComputedField instance to which the title_generator is (maybe) applied.
field_name: The name of the field from which to generate the title.
"""
field_title_generator = field_info.field_title_generator or config_wrapper.field_title_generator
if field_title_generator is None:
return
if field_info.title is None:
title = field_title_generator(field_name, field_info) # type: ignore
if not isinstance(title, str):
raise TypeError(f'field_title_generator {field_title_generator} must return str, not {title.__class__}')
field_info.title = title
def _common_field_schema( # C901
self, name: str, field_info: FieldInfo, decorators: DecoratorInfos
) -> _CommonField:
# Update FieldInfo annotation if appropriate:
from .. import AliasChoices, AliasPath
from ..fields import FieldInfo
if has_instance_in_type(field_info.annotation, (ForwardRef, str)):
types_namespace = self._types_namespace
if self._typevars_map:
types_namespace = (types_namespace or {}).copy()
# Ensure that typevars get mapped to their concrete types:
types_namespace.update({k.__name__: v for k, v in self._typevars_map.items()})
evaluated = _typing_extra.eval_type_lenient(field_info.annotation, types_namespace)
if evaluated is not field_info.annotation and not has_instance_in_type(evaluated, PydanticRecursiveRef):
new_field_info = FieldInfo.from_annotation(evaluated)
field_info.annotation = new_field_info.annotation
# Handle any field info attributes that may have been obtained from now-resolved annotations
for k, v in new_field_info._attributes_set.items():
# If an attribute is already set, it means it was set by assigning to a call to Field (or just a
# default value), and that should take the highest priority. So don't overwrite existing attributes.
# We skip over "attributes" that are present in the metadata_lookup dict because these won't
# actually end up as attributes of the `FieldInfo` instance.
if k not in field_info._attributes_set and k not in field_info.metadata_lookup:
setattr(field_info, k, v)
# Finally, ensure the field info also reflects all the `_attributes_set` that are actually metadata.
field_info.metadata = [*new_field_info.metadata, *field_info.metadata]
source_type, annotations = field_info.annotation, field_info.metadata
def set_discriminator(schema: CoreSchema) -> CoreSchema:
schema = self._apply_discriminator_to_union(schema, field_info.discriminator)
return schema
with self.field_name_stack.push(name):
if field_info.discriminator is not None:
schema = self._apply_annotations(source_type, annotations, transform_inner_schema=set_discriminator)
else:
schema = self._apply_annotations(
source_type,
annotations,
)
# This V1 compatibility shim should eventually be removed
# push down any `each_item=True` validators
# note that this won't work for any Annotated types that get wrapped by a function validator
# but that's okay because that didn't exist in V1
this_field_validators = filter_field_decorator_info_by_field(decorators.validators.values(), name)
if _validators_require_validate_default(this_field_validators):
field_info.validate_default = True
each_item_validators = [v for v in this_field_validators if v.info.each_item is True]
this_field_validators = [v for v in this_field_validators if v not in each_item_validators]
schema = apply_each_item_validators(schema, each_item_validators, name)
schema = apply_validators(schema, filter_field_decorator_info_by_field(this_field_validators, name), name)
schema = apply_validators(
schema, filter_field_decorator_info_by_field(decorators.field_validators.values(), name), name
)
# the default validator needs to go outside of any other validators
# so that it is the topmost validator for the field validator
# which uses it to check if the field has a default value or not
if not field_info.is_required():
schema = wrap_default(field_info, schema)
schema = self._apply_field_serializers(
schema, filter_field_decorator_info_by_field(decorators.field_serializers.values(), name)
)
self._apply_field_title_generator_to_field_info(self._config_wrapper, field_info, name)
json_schema_updates = {
'title': field_info.title,
'description': field_info.description,
'deprecated': bool(field_info.deprecated) or field_info.deprecated == '' or None,
'examples': to_jsonable_python(field_info.examples),
}
json_schema_updates = {k: v for k, v in json_schema_updates.items() if v is not None}
json_schema_extra = field_info.json_schema_extra
metadata = build_metadata_dict(
js_annotation_functions=[get_json_schema_update_func(json_schema_updates, json_schema_extra)]
)
alias_generator = self._config_wrapper.alias_generator
if alias_generator is not None:
self._apply_alias_generator_to_field_info(alias_generator, field_info, name)
if isinstance(field_info.validation_alias, (AliasChoices, AliasPath)):
validation_alias = field_info.validation_alias.convert_to_aliases()
else:
validation_alias = field_info.validation_alias
return _common_field(
schema,
serialization_exclude=True if field_info.exclude else None,
validation_alias=validation_alias,
serialization_alias=field_info.serialization_alias,
frozen=field_info.frozen,
metadata=metadata,
)
def _union_schema(self, union_type: Any) -> core_schema.CoreSchema:
"""Generate schema for a Union."""
args = self._get_args_resolving_forward_refs(union_type, required=True)
choices: list[CoreSchema] = []
nullable = False
for arg in args:
if arg is None or arg is _typing_extra.NoneType:
nullable = True
else:
choices.append(self.generate_schema(arg))
if len(choices) == 1:
s = choices[0]
else:
choices_with_tags: list[CoreSchema | tuple[CoreSchema, str]] = []
for choice in choices:
tag = choice.get('metadata', {}).get(_core_utils.TAGGED_UNION_TAG_KEY)
if tag is not None:
choices_with_tags.append((choice, tag))
else:
choices_with_tags.append(choice)
s = core_schema.union_schema(choices_with_tags)
if nullable:
s = core_schema.nullable_schema(s)
return s
def _type_alias_type_schema(
self,
obj: Any, # TypeAliasType
) -> CoreSchema:
with self.defs.get_schema_or_ref(obj) as (ref, maybe_schema):
if maybe_schema is not None:
return maybe_schema
origin = get_origin(obj) or obj
annotation = origin.__value__
typevars_map = get_standard_typevars_map(obj)
with self._types_namespace_stack.push(origin):
annotation = _typing_extra.eval_type_lenient(annotation, self._types_namespace)
annotation = replace_types(annotation, typevars_map)
schema = self.generate_schema(annotation)
assert schema['type'] != 'definitions'
schema['ref'] = ref # type: ignore
self.defs.definitions[ref] = schema
return core_schema.definition_reference_schema(ref)
def _literal_schema(self, literal_type: Any) -> CoreSchema:
"""Generate schema for a Literal."""
expected = _typing_extra.all_literal_values(literal_type)
assert expected, f'literal "expected" cannot be empty, obj={literal_type}'
return core_schema.literal_schema(expected)
def _typed_dict_schema(self, typed_dict_cls: Any, origin: Any) -> core_schema.CoreSchema:
"""Generate schema for a TypedDict.
It is not possible to track required/optional keys in TypedDict without __required_keys__
since TypedDict.__new__ erases the base classes (it replaces them with just `dict`)
and thus we can track usage of total=True/False
__required_keys__ was added in Python 3.9
(https://github.com/miss-islington/cpython/blob/1e9939657dd1f8eb9f596f77c1084d2d351172fc/Doc/library/typing.rst?plain=1#L1546-L1548)
however it is buggy
(https://github.com/python/typing_extensions/blob/ac52ac5f2cb0e00e7988bae1e2a1b8257ac88d6d/src/typing_extensions.py#L657-L666).
On 3.11 but < 3.12 TypedDict does not preserve inheritance information.
Hence to avoid creating validators that do not do what users expect we only
support typing.TypedDict on Python >= 3.12 or typing_extension.TypedDict on all versions
"""
from ..fields import FieldInfo
with self.model_type_stack.push(typed_dict_cls), self.defs.get_schema_or_ref(typed_dict_cls) as (
typed_dict_ref,
maybe_schema,
):
if maybe_schema is not None:
return maybe_schema
typevars_map = get_standard_typevars_map(typed_dict_cls)
if origin is not None:
typed_dict_cls = origin
if not _SUPPORTS_TYPEDDICT and type(typed_dict_cls).__module__ == 'typing':
raise PydanticUserError(
'Please use `typing_extensions.TypedDict` instead of `typing.TypedDict` on Python < 3.12.',
code='typed-dict-version',
)
try:
config: ConfigDict | None = get_attribute_from_bases(typed_dict_cls, '__pydantic_config__')
except AttributeError:
config = None
with self._config_wrapper_stack.push(config), self._types_namespace_stack.push(typed_dict_cls):
core_config = self._config_wrapper.core_config(typed_dict_cls)
self = self._current_generate_schema
required_keys: frozenset[str] = typed_dict_cls.__required_keys__
fields: dict[str, core_schema.TypedDictField] = {}
decorators = DecoratorInfos.build(typed_dict_cls)
if self._config_wrapper.use_attribute_docstrings:
field_docstrings = extract_docstrings_from_cls(typed_dict_cls, use_inspect=True)
else:
field_docstrings = None
for field_name, annotation in get_type_hints_infer_globalns(
typed_dict_cls, localns=self._types_namespace, include_extras=True
).items():
annotation = replace_types(annotation, typevars_map)
required = field_name in required_keys
if get_origin(annotation) == _typing_extra.Required:
required = True
annotation = self._get_args_resolving_forward_refs(
annotation,
required=True,
)[0]
elif get_origin(annotation) == _typing_extra.NotRequired:
required = False
annotation = self._get_args_resolving_forward_refs(
annotation,
required=True,
)[0]
field_info = FieldInfo.from_annotation(annotation)
if (
field_docstrings is not None
and field_info.description is None
and field_name in field_docstrings
):
field_info.description = field_docstrings[field_name]
self._apply_field_title_generator_to_field_info(self._config_wrapper, field_info, field_name)
fields[field_name] = self._generate_td_field_schema(
field_name, field_info, decorators, required=required
)
title = self._get_model_title_from_config(typed_dict_cls, ConfigWrapper(config))
metadata = build_metadata_dict(
js_functions=[partial(modify_model_json_schema, cls=typed_dict_cls, title=title)],
typed_dict_cls=typed_dict_cls,
)
td_schema = core_schema.typed_dict_schema(
fields,
computed_fields=[
self._computed_field_schema(d, decorators.field_serializers)
for d in decorators.computed_fields.values()
],
ref=typed_dict_ref,
metadata=metadata,
config=core_config,
)
schema = self._apply_model_serializers(td_schema, decorators.model_serializers.values())
schema = apply_model_validators(schema, decorators.model_validators.values(), 'all')
self.defs.definitions[typed_dict_ref] = schema
return core_schema.definition_reference_schema(typed_dict_ref)
def _namedtuple_schema(self, namedtuple_cls: Any, origin: Any) -> core_schema.CoreSchema:
"""Generate schema for a NamedTuple."""
with self.model_type_stack.push(namedtuple_cls), self.defs.get_schema_or_ref(namedtuple_cls) as (
namedtuple_ref,
maybe_schema,
):
if maybe_schema is not None:
return maybe_schema
typevars_map = get_standard_typevars_map(namedtuple_cls)
if origin is not None:
namedtuple_cls = origin
annotations: dict[str, Any] = get_type_hints_infer_globalns(
namedtuple_cls, include_extras=True, localns=self._types_namespace
)
if not annotations:
# annotations is empty, happens if namedtuple_cls defined via collections.namedtuple(...)
annotations = {k: Any for k in namedtuple_cls._fields}
if typevars_map:
annotations = {
field_name: replace_types(annotation, typevars_map)
for field_name, annotation in annotations.items()
}
arguments_schema = core_schema.arguments_schema(
[
self._generate_parameter_schema(
field_name, annotation, default=namedtuple_cls._field_defaults.get(field_name, Parameter.empty)
)
for field_name, annotation in annotations.items()
],
metadata=build_metadata_dict(js_prefer_positional_arguments=True),
)
return core_schema.call_schema(arguments_schema, namedtuple_cls, ref=namedtuple_ref)
def _generate_parameter_schema(
self,
name: str,
annotation: type[Any],
default: Any = Parameter.empty,
mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only'] | None = None,
) -> core_schema.ArgumentsParameter:
"""Prepare a ArgumentsParameter to represent a field in a namedtuple or function signature."""
from ..fields import FieldInfo
if default is Parameter.empty:
field = FieldInfo.from_annotation(annotation)
else:
field = FieldInfo.from_annotated_attribute(annotation, default)
assert field.annotation is not None, 'field.annotation should not be None when generating a schema'
source_type, annotations = field.annotation, field.metadata
with self.field_name_stack.push(name):
schema = self._apply_annotations(source_type, annotations)
if not field.is_required():
schema = wrap_default(field, schema)
parameter_schema = core_schema.arguments_parameter(name, schema)
if mode is not None:
parameter_schema['mode'] = mode
if field.alias is not None:
parameter_schema['alias'] = field.alias
else:
alias_generator = self._config_wrapper.alias_generator
if isinstance(alias_generator, AliasGenerator) and alias_generator.alias is not None:
parameter_schema['alias'] = alias_generator.alias(name)
elif isinstance(alias_generator, Callable):
parameter_schema['alias'] = alias_generator(name)
return parameter_schema
def _tuple_schema(self, tuple_type: Any) -> core_schema.CoreSchema:
"""Generate schema for a Tuple, e.g. `tuple[int, str]` or `tuple[int, ...]`."""
# TODO: do we really need to resolve type vars here?
typevars_map = get_standard_typevars_map(tuple_type)
params = self._get_args_resolving_forward_refs(tuple_type)
if typevars_map and params:
params = tuple(replace_types(param, typevars_map) for param in params)
# NOTE: subtle difference: `tuple[()]` gives `params=()`, whereas `typing.Tuple[()]` gives `params=((),)`
# This is only true for <3.11, on Python 3.11+ `typing.Tuple[()]` gives `params=()`
if not params:
if tuple_type in TUPLE_TYPES:
return core_schema.tuple_schema([core_schema.any_schema()], variadic_item_index=0)
else:
# special case for `tuple[()]` which means `tuple[]` - an empty tuple
return core_schema.tuple_schema([])
elif params[-1] is Ellipsis:
if len(params) == 2:
return core_schema.tuple_schema([self.generate_schema(params[0])], variadic_item_index=0)
else:
# TODO: something like https://github.com/pydantic/pydantic/issues/5952
raise ValueError('Variable tuples can only have one type')
elif len(params) == 1 and params[0] == ():
# special case for `Tuple[()]` which means `Tuple[]` - an empty tuple
# NOTE: This conditional can be removed when we drop support for Python 3.10.
return core_schema.tuple_schema([])
else:
return core_schema.tuple_schema([self.generate_schema(param) for param in params])
def _type_schema(self) -> core_schema.CoreSchema:
return core_schema.custom_error_schema(
core_schema.is_instance_schema(type),
custom_error_type='is_type',
custom_error_message='Input should be a type',
)
def _union_is_subclass_schema(self, union_type: Any) -> core_schema.CoreSchema:
"""Generate schema for `Type[Union[X, ...]]`."""
args = self._get_args_resolving_forward_refs(union_type, required=True)
return core_schema.union_schema([self.generate_schema(typing.Type[args]) for args in args])
def _subclass_schema(self, type_: Any) -> core_schema.CoreSchema:
"""Generate schema for a Type, e.g. `Type[int]`."""
type_param = self._get_first_arg_or_any(type_)
if type_param == Any:
return self._type_schema()
elif isinstance(type_param, typing.TypeVar):
if type_param.__bound__:
if _typing_extra.origin_is_union(get_origin(type_param.__bound__)):
return self._union_is_subclass_schema(type_param.__bound__)
return core_schema.is_subclass_schema(type_param.__bound__)
elif type_param.__constraints__:
return core_schema.union_schema(
[self.generate_schema(typing.Type[c]) for c in type_param.__constraints__]
)
else:
return self._type_schema()
elif _typing_extra.origin_is_union(get_origin(type_param)):
return self._union_is_subclass_schema(type_param)
else:
return core_schema.is_subclass_schema(type_param)
def _sequence_schema(self, sequence_type: Any) -> core_schema.CoreSchema:
"""Generate schema for a Sequence, e.g. `Sequence[int]`."""
from ._std_types_schema import serialize_sequence_via_list
item_type = self._get_first_arg_or_any(sequence_type)
item_type_schema = self.generate_schema(item_type)
list_schema = core_schema.list_schema(item_type_schema)
python_schema = core_schema.is_instance_schema(typing.Sequence, cls_repr='Sequence')
if item_type != Any:
from ._validators import sequence_validator
python_schema = core_schema.chain_schema(
[python_schema, core_schema.no_info_wrap_validator_function(sequence_validator, list_schema)],
)
serialization = core_schema.wrap_serializer_function_ser_schema(
serialize_sequence_via_list, schema=item_type_schema, info_arg=True
)
return core_schema.json_or_python_schema(
json_schema=list_schema, python_schema=python_schema, serialization=serialization
)
def _iterable_schema(self, type_: Any) -> core_schema.GeneratorSchema:
"""Generate a schema for an `Iterable`."""
item_type = self._get_first_arg_or_any(type_)
return core_schema.generator_schema(self.generate_schema(item_type))
def _pattern_schema(self, pattern_type: Any) -> core_schema.CoreSchema:
from . import _validators
metadata = build_metadata_dict(js_functions=[lambda _1, _2: {'type': 'string', 'format': 'regex'}])
ser = core_schema.plain_serializer_function_ser_schema(
attrgetter('pattern'), when_used='json', return_schema=core_schema.str_schema()
)
if pattern_type == typing.Pattern or pattern_type == re.Pattern:
# bare type
return core_schema.no_info_plain_validator_function(
_validators.pattern_either_validator, serialization=ser, metadata=metadata
)
param = self._get_args_resolving_forward_refs(
pattern_type,
required=True,
)[0]
if param is str:
return core_schema.no_info_plain_validator_function(
_validators.pattern_str_validator, serialization=ser, metadata=metadata
)
elif param is bytes:
return core_schema.no_info_plain_validator_function(
_validators.pattern_bytes_validator, serialization=ser, metadata=metadata
)
else:
raise PydanticSchemaGenerationError(f'Unable to generate pydantic-core schema for {pattern_type!r}.')
def _hashable_schema(self) -> core_schema.CoreSchema:
return core_schema.custom_error_schema(
core_schema.is_instance_schema(collections.abc.Hashable),
custom_error_type='is_hashable',
custom_error_message='Input should be hashable',
)
def _dataclass_schema(
self, dataclass: type[StandardDataclass], origin: type[StandardDataclass] | None
) -> core_schema.CoreSchema:
"""Generate schema for a dataclass."""
with self.model_type_stack.push(dataclass), self.defs.get_schema_or_ref(dataclass) as (
dataclass_ref,
maybe_schema,
):
if maybe_schema is not None:
return maybe_schema
typevars_map = get_standard_typevars_map(dataclass)
if origin is not None:
dataclass = origin
with ExitStack() as dataclass_bases_stack:
# Pushing a namespace prioritises items already in the stack, so iterate though the MRO forwards
for dataclass_base in dataclass.__mro__:
if dataclasses.is_dataclass(dataclass_base):
dataclass_bases_stack.enter_context(self._types_namespace_stack.push(dataclass_base))
# Pushing a config overwrites the previous config, so iterate though the MRO backwards
config = None
for dataclass_base in reversed(dataclass.__mro__):
if dataclasses.is_dataclass(dataclass_base):
config = getattr(dataclass_base, '__pydantic_config__', None)
dataclass_bases_stack.enter_context(self._config_wrapper_stack.push(config))
core_config = self._config_wrapper.core_config(dataclass)
self = self._current_generate_schema
from ..dataclasses import is_pydantic_dataclass
if is_pydantic_dataclass(dataclass):
fields = deepcopy(dataclass.__pydantic_fields__)
if typevars_map:
for field in fields.values():
field.apply_typevars_map(typevars_map, self._types_namespace)
else:
fields = collect_dataclass_fields(
dataclass,
self._types_namespace,
typevars_map=typevars_map,
)
# disallow combination of init=False on a dataclass field and extra='allow' on a dataclass
if self._config_wrapper_stack.tail.extra == 'allow':
# disallow combination of init=False on a dataclass field and extra='allow' on a dataclass
for field_name, field in fields.items():
if field.init is False:
raise PydanticUserError(
f'Field {field_name} has `init=False` and dataclass has config setting `extra="allow"`. '
f'This combination is not allowed.',
code='dataclass-init-false-extra-allow',
)
decorators = dataclass.__dict__.get('__pydantic_decorators__') or DecoratorInfos.build(dataclass)
# Move kw_only=False args to the start of the list, as this is how vanilla dataclasses work.
# Note that when kw_only is missing or None, it is treated as equivalent to kw_only=True
args = sorted(
(self._generate_dc_field_schema(k, v, decorators) for k, v in fields.items()),
key=lambda a: a.get('kw_only') is not False,
)
has_post_init = hasattr(dataclass, '__post_init__')
has_slots = hasattr(dataclass, '__slots__')
args_schema = core_schema.dataclass_args_schema(
dataclass.__name__,
args,
computed_fields=[
self._computed_field_schema(d, decorators.field_serializers)
for d in decorators.computed_fields.values()
],
collect_init_only=has_post_init,
)
inner_schema = apply_validators(args_schema, decorators.root_validators.values(), None)
model_validators = decorators.model_validators.values()
inner_schema = apply_model_validators(inner_schema, model_validators, 'inner')
title = self._get_model_title_from_config(dataclass, ConfigWrapper(config))
metadata = build_metadata_dict(
js_functions=[partial(modify_model_json_schema, cls=dataclass, title=title)]
)
dc_schema = core_schema.dataclass_schema(
dataclass,
inner_schema,
post_init=has_post_init,
ref=dataclass_ref,
fields=[field.name for field in dataclasses.fields(dataclass)],
slots=has_slots,
config=core_config,
metadata=metadata,
)
schema = self._apply_model_serializers(dc_schema, decorators.model_serializers.values())
schema = apply_model_validators(schema, model_validators, 'outer')
self.defs.definitions[dataclass_ref] = schema
return core_schema.definition_reference_schema(dataclass_ref)
# Type checkers seem to assume ExitStack may suppress exceptions and therefore
# control flow can exit the `with` block without returning.
assert False, 'Unreachable'
def _callable_schema(self, function: Callable[..., Any]) -> core_schema.CallSchema:
"""Generate schema for a Callable.
TODO support functional validators once we support them in Config
"""
sig = signature(function)
type_hints = _typing_extra.get_function_type_hints(function, types_namespace=self._types_namespace)
mode_lookup: dict[_ParameterKind, Literal['positional_only', 'positional_or_keyword', 'keyword_only']] = {
Parameter.POSITIONAL_ONLY: 'positional_only',
Parameter.POSITIONAL_OR_KEYWORD: 'positional_or_keyword',
Parameter.KEYWORD_ONLY: 'keyword_only',
}
arguments_list: list[core_schema.ArgumentsParameter] = []
var_args_schema: core_schema.CoreSchema | None = None
var_kwargs_schema: core_schema.CoreSchema | None = None
for name, p in sig.parameters.items():
if p.annotation is sig.empty:
annotation = typing.cast(Any, Any)
else:
annotation = type_hints[name]
parameter_mode = mode_lookup.get(p.kind)
if parameter_mode is not None:
arg_schema = self._generate_parameter_schema(name, annotation, p.default, parameter_mode)
arguments_list.append(arg_schema)
elif p.kind == Parameter.VAR_POSITIONAL:
var_args_schema = self.generate_schema(annotation)
else:
assert p.kind == Parameter.VAR_KEYWORD, p.kind
var_kwargs_schema = self.generate_schema(annotation)
return_schema: core_schema.CoreSchema | None = None
config_wrapper = self._config_wrapper
if config_wrapper.validate_return:
return_hint = type_hints.get('return')
if return_hint is not None:
return_schema = self.generate_schema(return_hint)
return core_schema.call_schema(
core_schema.arguments_schema(
arguments_list,
var_args_schema=var_args_schema,
var_kwargs_schema=var_kwargs_schema,
populate_by_name=config_wrapper.populate_by_name,
),
function,
return_schema=return_schema,
)
def _unsubstituted_typevar_schema(self, typevar: typing.TypeVar) -> core_schema.CoreSchema:
assert isinstance(typevar, typing.TypeVar)
bound = typevar.__bound__
constraints = typevar.__constraints__
try:
typevar_has_default = typevar.has_default() # type: ignore
except AttributeError:
# could still have a default if it's an old version of typing_extensions.TypeVar
typevar_has_default = getattr(typevar, '__default__', None) is not None
if (bound is not None) + (len(constraints) != 0) + typevar_has_default > 1:
raise NotImplementedError(
'Pydantic does not support mixing more than one of TypeVar bounds, constraints and defaults'
)
if typevar_has_default:
return self.generate_schema(typevar.__default__) # type: ignore
elif constraints:
return self._union_schema(typing.Union[constraints]) # type: ignore
elif bound:
schema = self.generate_schema(bound)
schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
lambda x, h: h(x), schema=core_schema.any_schema()
)
return schema
else:
return core_schema.any_schema()
def _computed_field_schema(
self,
d: Decorator[ComputedFieldInfo],
field_serializers: dict[str, Decorator[FieldSerializerDecoratorInfo]],
) -> core_schema.ComputedField:
try:
return_type = _decorators.get_function_return_type(d.func, d.info.return_type, self._types_namespace)
except NameError as e:
raise PydanticUndefinedAnnotation.from_name_error(e) from e
if return_type is PydanticUndefined:
raise PydanticUserError(
'Computed field is missing return type annotation or specifying `return_type`'
' to the `@computed_field` decorator (e.g. `@computed_field(return_type=int|str)`)',
code='model-field-missing-annotation',
)
return_type = replace_types(return_type, self._typevars_map)
# Create a new ComputedFieldInfo so that different type parametrizations of the same
# generic model's computed field can have different return types.
d.info = dataclasses.replace(d.info, return_type=return_type)
return_type_schema = self.generate_schema(return_type)
# Apply serializers to computed field if there exist
return_type_schema = self._apply_field_serializers(
return_type_schema,
filter_field_decorator_info_by_field(field_serializers.values(), d.cls_var_name),
computed_field=True,
)
alias_generator = self._config_wrapper.alias_generator
if alias_generator is not None:
self._apply_alias_generator_to_computed_field_info(
alias_generator=alias_generator, computed_field_info=d.info, computed_field_name=d.cls_var_name
)
self._apply_field_title_generator_to_field_info(self._config_wrapper, d.info, d.cls_var_name)
def set_computed_field_metadata(schema: CoreSchemaOrField, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
json_schema = handler(schema)
json_schema['readOnly'] = True
title = d.info.title
if title is not None:
json_schema['title'] = title
description = d.info.description
if description is not None:
json_schema['description'] = description
if d.info.deprecated or d.info.deprecated == '':
json_schema['deprecated'] = True
examples = d.info.examples
if examples is not None:
json_schema['examples'] = to_jsonable_python(examples)
json_schema_extra = d.info.json_schema_extra
if json_schema_extra is not None:
add_json_schema_extra(json_schema, json_schema_extra)
return json_schema
metadata = build_metadata_dict(js_annotation_functions=[set_computed_field_metadata])
return core_schema.computed_field(
d.cls_var_name, return_schema=return_type_schema, alias=d.info.alias, metadata=metadata
)
def _annotated_schema(self, annotated_type: Any) -> core_schema.CoreSchema:
"""Generate schema for an Annotated type, e.g. `Annotated[int, Field(...)]` or `Annotated[int, Gt(0)]`."""
from ..fields import FieldInfo
source_type, *annotations = self._get_args_resolving_forward_refs(
annotated_type,
required=True,
)
schema = self._apply_annotations(source_type, annotations)
# put the default validator last so that TypeAdapter.get_default_value() works
# even if there are function validators involved
for annotation in annotations:
if isinstance(annotation, FieldInfo):
schema = wrap_default(annotation, schema)
return schema
def _get_prepare_pydantic_annotations_for_known_type(
self, obj: Any, annotations: tuple[Any, ...]
) -> tuple[Any, list[Any]] | None:
from ._std_types_schema import PREPARE_METHODS
# Check for hashability
try:
hash(obj)
except TypeError:
# obj is definitely not a known type if this fails
return None
for gen in PREPARE_METHODS:
res = gen(obj, annotations, self._config_wrapper.config_dict)
if res is not None:
return res
return None
def _apply_annotations(
self,
source_type: Any,
annotations: list[Any],
transform_inner_schema: Callable[[CoreSchema], CoreSchema] = lambda x: x,
) -> CoreSchema:
"""Apply arguments from `Annotated` or from `FieldInfo` to a schema.
This gets called by `GenerateSchema._annotated_schema` but differs from it in that it does
not expect `source_type` to be an `Annotated` object, it expects it to be the first argument of that
(in other words, `GenerateSchema._annotated_schema` just unpacks `Annotated`, this process it).
"""
annotations = list(_known_annotated_metadata.expand_grouped_metadata(annotations))
res = self._get_prepare_pydantic_annotations_for_known_type(source_type, tuple(annotations))
if res is not None:
source_type, annotations = res
pydantic_js_annotation_functions: list[GetJsonSchemaFunction] = []
def inner_handler(obj: Any) -> CoreSchema:
from_property = self._generate_schema_from_property(obj, source_type)
if from_property is None:
schema = self._generate_schema_inner(obj)
else:
schema = from_property
metadata_js_function = _extract_get_pydantic_json_schema(obj, schema)
if metadata_js_function is not None:
metadata_schema = resolve_original_schema(schema, self.defs.definitions)
if metadata_schema is not None:
self._add_js_function(metadata_schema, metadata_js_function)
return transform_inner_schema(schema)
get_inner_schema = CallbackGetCoreSchemaHandler(inner_handler, self)
for annotation in annotations:
if annotation is None:
continue
get_inner_schema = self._get_wrapped_inner_schema(
get_inner_schema, annotation, pydantic_js_annotation_functions
)
schema = get_inner_schema(source_type)
if pydantic_js_annotation_functions:
metadata = CoreMetadataHandler(schema).metadata
metadata.setdefault('pydantic_js_annotation_functions', []).extend(pydantic_js_annotation_functions)
return _add_custom_serialization_from_json_encoders(self._config_wrapper.json_encoders, source_type, schema)
def _apply_single_annotation(self, schema: core_schema.CoreSchema, metadata: Any) -> core_schema.CoreSchema:
from ..fields import FieldInfo
if isinstance(metadata, FieldInfo):
for field_metadata in metadata.metadata:
schema = self._apply_single_annotation(schema, field_metadata)
if metadata.discriminator is not None:
schema = self._apply_discriminator_to_union(schema, metadata.discriminator)
return schema
if schema['type'] == 'nullable':
# for nullable schemas, metadata is automatically applied to the inner schema
inner = schema.get('schema', core_schema.any_schema())
inner = self._apply_single_annotation(inner, metadata)
if inner:
schema['schema'] = inner
return schema
original_schema = schema
ref = schema.get('ref', None)
if ref is not None:
schema = schema.copy()
new_ref = ref + f'_{repr(metadata)}'
if new_ref in self.defs.definitions:
return self.defs.definitions[new_ref]
schema['ref'] = new_ref # type: ignore
elif schema['type'] == 'definition-ref':
ref = schema['schema_ref']
if ref in self.defs.definitions:
schema = self.defs.definitions[ref].copy()
new_ref = ref + f'_{repr(metadata)}'
if new_ref in self.defs.definitions:
return self.defs.definitions[new_ref]
schema['ref'] = new_ref # type: ignore
maybe_updated_schema = _known_annotated_metadata.apply_known_metadata(metadata, schema.copy())
if maybe_updated_schema is not None:
return maybe_updated_schema
return original_schema
def _apply_single_annotation_json_schema(
self, schema: core_schema.CoreSchema, metadata: Any
) -> core_schema.CoreSchema:
from ..fields import FieldInfo
if isinstance(metadata, FieldInfo):
for field_metadata in metadata.metadata:
schema = self._apply_single_annotation_json_schema(schema, field_metadata)
json_schema_update: JsonSchemaValue = {}
if metadata.title:
json_schema_update['title'] = metadata.title
if metadata.description:
json_schema_update['description'] = metadata.description
if metadata.examples:
json_schema_update['examples'] = to_jsonable_python(metadata.examples)
json_schema_extra = metadata.json_schema_extra
if json_schema_update or json_schema_extra:
CoreMetadataHandler(schema).metadata.setdefault('pydantic_js_annotation_functions', []).append(
get_json_schema_update_func(json_schema_update, json_schema_extra)
)
return schema
def _get_wrapped_inner_schema(
self,
get_inner_schema: GetCoreSchemaHandler,
annotation: Any,
pydantic_js_annotation_functions: list[GetJsonSchemaFunction],
) -> CallbackGetCoreSchemaHandler:
metadata_get_schema: GetCoreSchemaFunction = getattr(annotation, '__get_pydantic_core_schema__', None) or (
lambda source, handler: handler(source)
)
def new_handler(source: Any) -> core_schema.CoreSchema:
schema = metadata_get_schema(source, get_inner_schema)
schema = self._apply_single_annotation(schema, annotation)
schema = self._apply_single_annotation_json_schema(schema, annotation)
metadata_js_function = _extract_get_pydantic_json_schema(annotation, schema)
if metadata_js_function is not None:
pydantic_js_annotation_functions.append(metadata_js_function)
return schema
return CallbackGetCoreSchemaHandler(new_handler, self)
def _apply_field_serializers(
self,
schema: core_schema.CoreSchema,
serializers: list[Decorator[FieldSerializerDecoratorInfo]],
computed_field: bool = False,
) -> core_schema.CoreSchema:
"""Apply field serializers to a schema."""
if serializers:
schema = copy(schema)
if schema['type'] == 'definitions':
inner_schema = schema['schema']
schema['schema'] = self._apply_field_serializers(inner_schema, serializers)
return schema
else:
ref = typing.cast('str|None', schema.get('ref', None))
if ref is not None:
schema = core_schema.definition_reference_schema(ref)
# use the last serializer to make it easy to override a serializer set on a parent model
serializer = serializers[-1]
is_field_serializer, info_arg = inspect_field_serializer(
serializer.func, serializer.info.mode, computed_field=computed_field
)
try:
return_type = _decorators.get_function_return_type(
serializer.func, serializer.info.return_type, self._types_namespace
)
except NameError as e:
raise PydanticUndefinedAnnotation.from_name_error(e) from e
if return_type is PydanticUndefined:
return_schema = None
else:
return_schema = self.generate_schema(return_type)
if serializer.info.mode == 'wrap':
schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
serializer.func,
is_field_serializer=is_field_serializer,
info_arg=info_arg,
return_schema=return_schema,
when_used=serializer.info.when_used,
)
else:
assert serializer.info.mode == 'plain'
schema['serialization'] = core_schema.plain_serializer_function_ser_schema(
serializer.func,
is_field_serializer=is_field_serializer,
info_arg=info_arg,
return_schema=return_schema,
when_used=serializer.info.when_used,
)
return schema
def _apply_model_serializers(
self, schema: core_schema.CoreSchema, serializers: Iterable[Decorator[ModelSerializerDecoratorInfo]]
) -> core_schema.CoreSchema:
"""Apply model serializers to a schema."""
ref: str | None = schema.pop('ref', None) # type: ignore
if serializers:
serializer = list(serializers)[-1]
info_arg = inspect_model_serializer(serializer.func, serializer.info.mode)
try:
return_type = _decorators.get_function_return_type(
serializer.func, serializer.info.return_type, self._types_namespace
)
except NameError as e:
raise PydanticUndefinedAnnotation.from_name_error(e) from e
if return_type is PydanticUndefined:
return_schema = None
else:
return_schema = self.generate_schema(return_type)
if serializer.info.mode == 'wrap':
ser_schema: core_schema.SerSchema = core_schema.wrap_serializer_function_ser_schema(
serializer.func,
info_arg=info_arg,
return_schema=return_schema,
when_used=serializer.info.when_used,
)
else:
# plain
ser_schema = core_schema.plain_serializer_function_ser_schema(
serializer.func,
info_arg=info_arg,
return_schema=return_schema,
when_used=serializer.info.when_used,
)
schema['serialization'] = ser_schema
if ref:
schema['ref'] = ref # type: ignore
return schema
_VALIDATOR_F_MATCH: Mapping[
tuple[FieldValidatorModes, Literal['no-info', 'with-info']],
Callable[[Callable[..., Any], core_schema.CoreSchema, str | None], core_schema.CoreSchema],
] = {
('before', 'no-info'): lambda f, schema, _: core_schema.no_info_before_validator_function(f, schema),
('after', 'no-info'): lambda f, schema, _: core_schema.no_info_after_validator_function(f, schema),
('plain', 'no-info'): lambda f, _1, _2: core_schema.no_info_plain_validator_function(f),
('wrap', 'no-info'): lambda f, schema, _: core_schema.no_info_wrap_validator_function(f, schema),
('before', 'with-info'): lambda f, schema, field_name: core_schema.with_info_before_validator_function(
f, schema, field_name=field_name
),
('after', 'with-info'): lambda f, schema, field_name: core_schema.with_info_after_validator_function(
f, schema, field_name=field_name
),
('plain', 'with-info'): lambda f, _, field_name: core_schema.with_info_plain_validator_function(
f, field_name=field_name
),
('wrap', 'with-info'): lambda f, schema, field_name: core_schema.with_info_wrap_validator_function(
f, schema, field_name=field_name
),
}
def apply_validators(
schema: core_schema.CoreSchema,
validators: Iterable[Decorator[RootValidatorDecoratorInfo]]
| Iterable[Decorator[ValidatorDecoratorInfo]]
| Iterable[Decorator[FieldValidatorDecoratorInfo]],
field_name: str | None,
) -> core_schema.CoreSchema:
"""Apply validators to a schema.
Args:
schema: The schema to apply validators on.
validators: An iterable of validators.
field_name: The name of the field if validators are being applied to a model field.
Returns:
The updated schema.
"""
for validator in validators:
info_arg = inspect_validator(validator.func, validator.info.mode)
val_type = 'with-info' if info_arg else 'no-info'
schema = _VALIDATOR_F_MATCH[(validator.info.mode, val_type)](validator.func, schema, field_name)
return schema
def _validators_require_validate_default(validators: Iterable[Decorator[ValidatorDecoratorInfo]]) -> bool:
"""In v1, if any of the validators for a field had `always=True`, the default value would be validated.
This serves as an auxiliary function for re-implementing that logic, by looping over a provided
collection of (v1-style) ValidatorDecoratorInfo's and checking if any of them have `always=True`.
We should be able to drop this function and the associated logic calling it once we drop support
for v1-style validator decorators. (Or we can extend it and keep it if we add something equivalent
to the v1-validator `always` kwarg to `field_validator`.)
"""
for validator in validators:
if validator.info.always:
return True
return False
def apply_model_validators(
schema: core_schema.CoreSchema,
validators: Iterable[Decorator[ModelValidatorDecoratorInfo]],
mode: Literal['inner', 'outer', 'all'],
) -> core_schema.CoreSchema:
"""Apply model validators to a schema.
If mode == 'inner', only "before" validators are applied
If mode == 'outer', validators other than "before" are applied
If mode == 'all', all validators are applied
Args:
schema: The schema to apply validators on.
validators: An iterable of validators.
mode: The validator mode.
Returns:
The updated schema.
"""
ref: str | None = schema.pop('ref', None) # type: ignore
for validator in validators:
if mode == 'inner' and validator.info.mode != 'before':
continue
if mode == 'outer' and validator.info.mode == 'before':
continue
info_arg = inspect_validator(validator.func, validator.info.mode)
if validator.info.mode == 'wrap':
if info_arg:
schema = core_schema.with_info_wrap_validator_function(function=validator.func, schema=schema)
else:
schema = core_schema.no_info_wrap_validator_function(function=validator.func, schema=schema)
elif validator.info.mode == 'before':
if info_arg:
schema = core_schema.with_info_before_validator_function(function=validator.func, schema=schema)
else:
schema = core_schema.no_info_before_validator_function(function=validator.func, schema=schema)
else:
assert validator.info.mode == 'after'
if info_arg:
schema = core_schema.with_info_after_validator_function(function=validator.func, schema=schema)
else:
schema = core_schema.no_info_after_validator_function(function=validator.func, schema=schema)
if ref:
schema['ref'] = ref # type: ignore
return schema
def wrap_default(field_info: FieldInfo, schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
"""Wrap schema with default schema if default value or `default_factory` are available.
Args:
field_info: The field info object.
schema: The schema to apply default on.
Returns:
Updated schema by default value or `default_factory`.
"""
if field_info.default_factory:
return core_schema.with_default_schema(
schema, default_factory=field_info.default_factory, validate_default=field_info.validate_default
)
elif field_info.default is not PydanticUndefined:
return core_schema.with_default_schema(
schema, default=field_info.default, validate_default=field_info.validate_default
)
else:
return schema
def _extract_get_pydantic_json_schema(tp: Any, schema: CoreSchema) -> GetJsonSchemaFunction | None:
"""Extract `__get_pydantic_json_schema__` from a type, handling the deprecated `__modify_schema__`."""
js_modify_function = getattr(tp, '__get_pydantic_json_schema__', None)
if hasattr(tp, '__modify_schema__'):
from pydantic import BaseModel # circular reference
has_custom_v2_modify_js_func = (
js_modify_function is not None
and BaseModel.__get_pydantic_json_schema__.__func__ # type: ignore
not in (js_modify_function, getattr(js_modify_function, '__func__', None))
)
if not has_custom_v2_modify_js_func:
cls_name = getattr(tp, '__name__', None)
raise PydanticUserError(
f'The `__modify_schema__` method is not supported in Pydantic v2. '
f'Use `__get_pydantic_json_schema__` instead{f" in class `{cls_name}`" if cls_name else ""}.',
code='custom-json-schema',
)
# handle GenericAlias' but ignore Annotated which "lies" about its origin (in this case it would be `int`)
if hasattr(tp, '__origin__') and not isinstance(tp, type(Annotated[int, 'placeholder'])):
return _extract_get_pydantic_json_schema(tp.__origin__, schema)
if js_modify_function is None:
return None
return js_modify_function
def get_json_schema_update_func(
json_schema_update: JsonSchemaValue, json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None
) -> GetJsonSchemaFunction:
def json_schema_update_func(
core_schema_or_field: CoreSchemaOrField, handler: GetJsonSchemaHandler
) -> JsonSchemaValue:
json_schema = {**handler(core_schema_or_field), **json_schema_update}
add_json_schema_extra(json_schema, json_schema_extra)
return json_schema
return json_schema_update_func
def add_json_schema_extra(
json_schema: JsonSchemaValue, json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None
):
if isinstance(json_schema_extra, dict):
json_schema.update(to_jsonable_python(json_schema_extra))
elif callable(json_schema_extra):
json_schema_extra(json_schema)
class _CommonField(TypedDict):
schema: core_schema.CoreSchema
validation_alias: str | list[str | int] | list[list[str | int]] | None
serialization_alias: str | None
serialization_exclude: bool | None
frozen: bool | None
metadata: dict[str, Any]
def _common_field(
schema: core_schema.CoreSchema,
*,
validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
serialization_alias: str | None = None,
serialization_exclude: bool | None = None,
frozen: bool | None = None,
metadata: Any = None,
) -> _CommonField:
return {
'schema': schema,
'validation_alias': validation_alias,
'serialization_alias': serialization_alias,
'serialization_exclude': serialization_exclude,
'frozen': frozen,
'metadata': metadata,
}
class _Definitions:
"""Keeps track of references and definitions."""
def __init__(self) -> None:
self.seen: set[str] = set()
self.definitions: dict[str, core_schema.CoreSchema] = {}
@contextmanager
def get_schema_or_ref(self, tp: Any) -> Iterator[tuple[str, None] | tuple[str, CoreSchema]]:
"""Get a definition for `tp` if one exists.
If a definition exists, a tuple of `(ref_string, CoreSchema)` is returned.
If no definition exists yet, a tuple of `(ref_string, None)` is returned.
Note that the returned `CoreSchema` will always be a `DefinitionReferenceSchema`,
not the actual definition itself.
This should be called for any type that can be identified by reference.
This includes any recursive types.
At present the following types can be named/recursive:
- BaseModel
- Dataclasses
- TypedDict
- TypeAliasType
"""
ref = get_type_ref(tp)
# return the reference if we're either (1) in a cycle or (2) it was already defined
if ref in self.seen or ref in self.definitions:
yield (ref, core_schema.definition_reference_schema(ref))
else:
self.seen.add(ref)
try:
yield (ref, None)
finally:
self.seen.discard(ref)
def resolve_original_schema(schema: CoreSchema, definitions: dict[str, CoreSchema]) -> CoreSchema | None:
if schema['type'] == 'definition-ref':
return definitions.get(schema['schema_ref'], None)
elif schema['type'] == 'definitions':
return schema['schema']
else:
return schema
class _FieldNameStack:
__slots__ = ('_stack',)
def __init__(self) -> None:
self._stack: list[str] = []
@contextmanager
def push(self, field_name: str) -> Iterator[None]:
self._stack.append(field_name)
yield
self._stack.pop()
def get(self) -> str | None:
if self._stack:
return self._stack[-1]
else:
return None
class _ModelTypeStack:
__slots__ = ('_stack',)
def __init__(self) -> None:
self._stack: list[type] = []
@contextmanager
def push(self, type_obj: type) -> Iterator[None]:
self._stack.append(type_obj)
yield
self._stack.pop()
def get(self) -> type | None:
if self._stack:
return self._stack[-1]
else:
return None