second commit

env/lib/python3.11/site-packages/pydantic/_internal/_config.py

@@ -0,0 +1,345 @@
+from __future__ import annotations as _annotations
+
+import warnings
+from contextlib import contextmanager
+from re import Pattern
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    cast,
+)
+
+from pydantic_core import core_schema
+from typing_extensions import (
+    Literal,
+    Self,
+)
+
+from ..aliases import AliasGenerator
+from ..config import ConfigDict, ExtraValues, JsonDict, JsonEncoder, JsonSchemaExtraCallable
+from ..errors import PydanticUserError
+from ..warnings import PydanticDeprecatedSince20, PydanticDeprecatedSince210
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+if TYPE_CHECKING:
+    from .._internal._schema_generation_shared import GenerateSchema
+    from ..fields import ComputedFieldInfo, FieldInfo
+
+DEPRECATION_MESSAGE = 'Support for class-based `config` is deprecated, use ConfigDict instead.'
+
+
+class ConfigWrapper:
+    """Internal wrapper for Config which exposes ConfigDict items as attributes."""
+
+    __slots__ = ('config_dict',)
+
+    config_dict: ConfigDict
+
+    # all annotations are copied directly from ConfigDict, and should be kept up to date, a test will fail if they
+    # stop matching
+    title: str | None
+    str_to_lower: bool
+    str_to_upper: bool
+    str_strip_whitespace: bool
+    str_min_length: int
+    str_max_length: int | None
+    extra: ExtraValues | None
+    frozen: bool
+    populate_by_name: bool
+    use_enum_values: bool
+    validate_assignment: bool
+    arbitrary_types_allowed: bool
+    from_attributes: bool
+    # whether to use the actual key provided in the data (e.g. alias or first alias for "field required" errors) instead of field_names
+    # to construct error `loc`s, default `True`
+    loc_by_alias: bool
+    alias_generator: Callable[[str], str] | AliasGenerator | None
+    model_title_generator: Callable[[type], str] | None
+    field_title_generator: Callable[[str, FieldInfo | ComputedFieldInfo], str] | None
+    ignored_types: tuple[type, ...]
+    allow_inf_nan: bool
+    json_schema_extra: JsonDict | JsonSchemaExtraCallable | None
+    json_encoders: dict[type[object], JsonEncoder] | None
+
+    # new in V2
+    strict: bool
+    # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never'
+    revalidate_instances: Literal['always', 'never', 'subclass-instances']
+    ser_json_timedelta: Literal['iso8601', 'float']
+    ser_json_bytes: Literal['utf8', 'base64', 'hex']
+    val_json_bytes: Literal['utf8', 'base64', 'hex']
+    ser_json_inf_nan: Literal['null', 'constants', 'strings']
+    # whether to validate default values during validation, default False
+    validate_default: bool
+    validate_return: bool
+    protected_namespaces: tuple[str | Pattern[str], ...]
+    hide_input_in_errors: bool
+    defer_build: bool
+    plugin_settings: dict[str, object] | None
+    schema_generator: type[GenerateSchema] | None
+    json_schema_serialization_defaults_required: bool
+    json_schema_mode_override: Literal['validation', 'serialization', None]
+    coerce_numbers_to_str: bool
+    regex_engine: Literal['rust-regex', 'python-re']
+    validation_error_cause: bool
+    use_attribute_docstrings: bool
+    cache_strings: bool | Literal['all', 'keys', 'none']
+
+    def __init__(self, config: ConfigDict | dict[str, Any] | type[Any] | None, *, check: bool = True):
+        if check:
+            self.config_dict = prepare_config(config)
+        else:
+            self.config_dict = cast(ConfigDict, config)
+
+    @classmethod
+    def for_model(cls, bases: tuple[type[Any], ...], namespace: dict[str, Any], kwargs: dict[str, Any]) -> Self:
+        """Build a new `ConfigWrapper` instance for a `BaseModel`.
+
+        The config wrapper built based on (in descending order of priority):
+        - options from `kwargs`
+        - options from the `namespace`
+        - options from the base classes (`bases`)
+
+        Args:
+            bases: A tuple of base classes.
+            namespace: The namespace of the class being created.
+            kwargs: The kwargs passed to the class being created.
+
+        Returns:
+            A `ConfigWrapper` instance for `BaseModel`.
+        """
+        config_new = ConfigDict()
+        for base in bases:
+            config = getattr(base, 'model_config', None)
+            if config:
+                config_new.update(config.copy())
+
+        config_class_from_namespace = namespace.get('Config')
+        config_dict_from_namespace = namespace.get('model_config')
+
+        raw_annotations = namespace.get('__annotations__', {})
+        if raw_annotations.get('model_config') and config_dict_from_namespace is None:
+            raise PydanticUserError(
+                '`model_config` cannot be used as a model field name. Use `model_config` for model configuration.',
+                code='model-config-invalid-field-name',
+            )
+
+        if config_class_from_namespace and config_dict_from_namespace:
+            raise PydanticUserError('"Config" and "model_config" cannot be used together', code='config-both')
+
+        config_from_namespace = config_dict_from_namespace or prepare_config(config_class_from_namespace)
+
+        config_new.update(config_from_namespace)
+
+        for k in list(kwargs.keys()):
+            if k in config_keys:
+                config_new[k] = kwargs.pop(k)
+
+        return cls(config_new)
+
+    # we don't show `__getattr__` to type checkers so missing attributes cause errors
+    if not TYPE_CHECKING:  # pragma: no branch
+
+        def __getattr__(self, name: str) -> Any:
+            try:
+                return self.config_dict[name]
+            except KeyError:
+                try:
+                    return config_defaults[name]
+                except KeyError:
+                    raise AttributeError(f'Config has no attribute {name!r}') from None
+
+    def core_config(self, title: str | None) -> core_schema.CoreConfig:
+        """Create a pydantic-core config.
+
+        We don't use getattr here since we don't want to populate with defaults.
+
+        Args:
+            title: The title to use if not set in config.
+
+        Returns:
+            A `CoreConfig` object created from config.
+        """
+        config = self.config_dict
+
+        if config.get('schema_generator') is not None:
+            warnings.warn(
+                'The `schema_generator` setting has been deprecated since v2.10. This setting no longer has any effect.',
+                PydanticDeprecatedSince210,
+                stacklevel=2,
+            )
+
+        core_config_values = {
+            'title': config.get('title') or title or None,
+            'extra_fields_behavior': config.get('extra'),
+            'allow_inf_nan': config.get('allow_inf_nan'),
+            'populate_by_name': config.get('populate_by_name'),
+            'str_strip_whitespace': config.get('str_strip_whitespace'),
+            'str_to_lower': config.get('str_to_lower'),
+            'str_to_upper': config.get('str_to_upper'),
+            'strict': config.get('strict'),
+            'ser_json_timedelta': config.get('ser_json_timedelta'),
+            'ser_json_bytes': config.get('ser_json_bytes'),
+            'val_json_bytes': config.get('val_json_bytes'),
+            'ser_json_inf_nan': config.get('ser_json_inf_nan'),
+            'from_attributes': config.get('from_attributes'),
+            'loc_by_alias': config.get('loc_by_alias'),
+            'revalidate_instances': config.get('revalidate_instances'),
+            'validate_default': config.get('validate_default'),
+            'str_max_length': config.get('str_max_length'),
+            'str_min_length': config.get('str_min_length'),
+            'hide_input_in_errors': config.get('hide_input_in_errors'),
+            'coerce_numbers_to_str': config.get('coerce_numbers_to_str'),
+            'regex_engine': config.get('regex_engine'),
+            'validation_error_cause': config.get('validation_error_cause'),
+            'cache_strings': config.get('cache_strings'),
+        }
+
+        return core_schema.CoreConfig(**{k: v for k, v in core_config_values.items() if v is not None})
+
+    def __repr__(self):
+        c = ', '.join(f'{k}={v!r}' for k, v in self.config_dict.items())
+        return f'ConfigWrapper({c})'
+
+
+class ConfigWrapperStack:
+    """A stack of `ConfigWrapper` instances."""
+
+    def __init__(self, config_wrapper: ConfigWrapper):
+        self._config_wrapper_stack: list[ConfigWrapper] = [config_wrapper]
+
+    @property
+    def tail(self) -> ConfigWrapper:
+        return self._config_wrapper_stack[-1]
+
+    @contextmanager
+    def push(self, config_wrapper: ConfigWrapper | ConfigDict | None):
+        if config_wrapper is None:
+            yield
+            return
+
+        if not isinstance(config_wrapper, ConfigWrapper):
+            config_wrapper = ConfigWrapper(config_wrapper, check=False)
+
+        self._config_wrapper_stack.append(config_wrapper)
+        try:
+            yield
+        finally:
+            self._config_wrapper_stack.pop()
+
+
+config_defaults = ConfigDict(
+    title=None,
+    str_to_lower=False,
+    str_to_upper=False,
+    str_strip_whitespace=False,
+    str_min_length=0,
+    str_max_length=None,
+    # let the model / dataclass decide how to handle it
+    extra=None,
+    frozen=False,
+    populate_by_name=False,
+    use_enum_values=False,
+    validate_assignment=False,
+    arbitrary_types_allowed=False,
+    from_attributes=False,
+    loc_by_alias=True,
+    alias_generator=None,
+    model_title_generator=None,
+    field_title_generator=None,
+    ignored_types=(),
+    allow_inf_nan=True,
+    json_schema_extra=None,
+    strict=False,
+    revalidate_instances='never',
+    ser_json_timedelta='iso8601',
+    ser_json_bytes='utf8',
+    val_json_bytes='utf8',
+    ser_json_inf_nan='null',
+    validate_default=False,
+    validate_return=False,
+    protected_namespaces=('model_validate', 'model_dump'),
+    hide_input_in_errors=False,
+    json_encoders=None,
+    defer_build=False,
+    schema_generator=None,
+    plugin_settings=None,
+    json_schema_serialization_defaults_required=False,
+    json_schema_mode_override=None,
+    coerce_numbers_to_str=False,
+    regex_engine='rust-regex',
+    validation_error_cause=False,
+    use_attribute_docstrings=False,
+    cache_strings=True,
+)
+
+
+def prepare_config(config: ConfigDict | dict[str, Any] | type[Any] | None) -> ConfigDict:
+    """Create a `ConfigDict` instance from an existing dict, a class (e.g. old class-based config) or None.
+
+    Args:
+        config: The input config.
+
+    Returns:
+        A ConfigDict object created from config.
+    """
+    if config is None:
+        return ConfigDict()
+
+    if not isinstance(config, dict):
+        warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning)
+        config = {k: getattr(config, k) for k in dir(config) if not k.startswith('__')}
+
+    config_dict = cast(ConfigDict, config)
+    check_deprecated(config_dict)
+    return config_dict
+
+
+config_keys = set(ConfigDict.__annotations__.keys())
+
+
+V2_REMOVED_KEYS = {
+    'allow_mutation',
+    'error_msg_templates',
+    'fields',
+    'getter_dict',
+    'smart_union',
+    'underscore_attrs_are_private',
+    'json_loads',
+    'json_dumps',
+    'copy_on_model_validation',
+    'post_init_call',
+}
+V2_RENAMED_KEYS = {
+    'allow_population_by_field_name': 'populate_by_name',
+    'anystr_lower': 'str_to_lower',
+    'anystr_strip_whitespace': 'str_strip_whitespace',
+    'anystr_upper': 'str_to_upper',
+    'keep_untouched': 'ignored_types',
+    'max_anystr_length': 'str_max_length',
+    'min_anystr_length': 'str_min_length',
+    'orm_mode': 'from_attributes',
+    'schema_extra': 'json_schema_extra',
+    'validate_all': 'validate_default',
+}
+
+
+def check_deprecated(config_dict: ConfigDict) -> None:
+    """Check for deprecated config keys and warn the user.
+
+    Args:
+        config_dict: The input config.
+    """
+    deprecated_removed_keys = V2_REMOVED_KEYS & config_dict.keys()
+    deprecated_renamed_keys = V2_RENAMED_KEYS.keys() & config_dict.keys()
+    if deprecated_removed_keys or deprecated_renamed_keys:
+        renamings = {k: V2_RENAMED_KEYS[k] for k in sorted(deprecated_renamed_keys)}
+        renamed_bullets = [f'* {k!r} has been renamed to {v!r}' for k, v in renamings.items()]
+        removed_bullets = [f'* {k!r} has been removed' for k in sorted(deprecated_removed_keys)]
+        message = '\n'.join(['Valid config keys have changed in V2:'] + renamed_bullets + removed_bullets)
+        warnings.warn(message, UserWarning)

env/lib/python3.11/site-packages/pydantic/_internal/_core_metadata.py

@@ -0,0 +1,91 @@
+from __future__ import annotations as _annotations
+
+from typing import TYPE_CHECKING, Any, TypedDict, cast
+from warnings import warn
+
+if TYPE_CHECKING:
+    from ..config import JsonDict, JsonSchemaExtraCallable
+    from ._schema_generation_shared import (
+        GetJsonSchemaFunction,
+    )
+
+
+class CoreMetadata(TypedDict, total=False):
+    """A `TypedDict` for holding the metadata dict of the schema.
+
+    Attributes:
+        pydantic_js_functions: List of JSON schema functions that resolve refs during application.
+        pydantic_js_annotation_functions: List of JSON schema functions that don't resolve refs during application.
+        pydantic_js_prefer_positional_arguments: Whether JSON schema generator will
+            prefer positional over keyword arguments for an 'arguments' schema.
+            custom validation function. Only applies to before, plain, and wrap validators.
+        pydantic_js_udpates: key / value pair updates to apply to the JSON schema for a type.
+        pydantic_js_extra: WIP, either key/value pair updates to apply to the JSON schema, or a custom callable.
+
+    TODO: Perhaps we should move this structure to pydantic-core. At the moment, though,
+    it's easier to iterate on if we leave it in pydantic until we feel there is a semi-stable API.
+
+    TODO: It's unfortunate how functionally oriented JSON schema generation is, especially that which occurs during
+    the core schema generation process. It's inevitable that we need to store some json schema related information
+    on core schemas, given that we generate JSON schemas directly from core schemas. That being said, debugging related
+    issues is quite difficult when JSON schema information is disguised via dynamically defined functions.
+    """
+
+    pydantic_js_functions: list[GetJsonSchemaFunction]
+    pydantic_js_annotation_functions: list[GetJsonSchemaFunction]
+    pydantic_js_prefer_positional_arguments: bool
+    pydantic_js_updates: JsonDict
+    pydantic_js_extra: JsonDict | JsonSchemaExtraCallable
+
+
+def update_core_metadata(
+    core_metadata: Any,
+    /,
+    *,
+    pydantic_js_functions: list[GetJsonSchemaFunction] | None = None,
+    pydantic_js_annotation_functions: list[GetJsonSchemaFunction] | None = None,
+    pydantic_js_updates: JsonDict | None = None,
+    pydantic_js_extra: JsonDict | JsonSchemaExtraCallable | None = None,
+) -> None:
+    from ..json_schema import PydanticJsonSchemaWarning
+
+    """Update CoreMetadata instance in place. When we make modifications in this function, they
+    take effect on the `core_metadata` reference passed in as the first (and only) positional argument.
+
+    First, cast to `CoreMetadata`, then finish with a cast to `dict[str, Any]` for core schema compatibility.
+    We do this here, instead of before / after each call to this function so that this typing hack
+    can be easily removed if/when we move `CoreMetadata` to `pydantic-core`.
+
+    For parameter descriptions, see `CoreMetadata` above.
+    """
+    core_metadata = cast(CoreMetadata, core_metadata)
+
+    if pydantic_js_functions:
+        core_metadata.setdefault('pydantic_js_functions', []).extend(pydantic_js_functions)
+
+    if pydantic_js_annotation_functions:
+        core_metadata.setdefault('pydantic_js_annotation_functions', []).extend(pydantic_js_annotation_functions)
+
+    if pydantic_js_updates:
+        if (existing_updates := core_metadata.get('pydantic_js_updates')) is not None:
+            core_metadata['pydantic_js_updates'] = {**existing_updates, **pydantic_js_updates}
+        else:
+            core_metadata['pydantic_js_updates'] = pydantic_js_updates
+
+    if pydantic_js_extra is not None:
+        existing_pydantic_js_extra = core_metadata.get('pydantic_js_extra')
+        if existing_pydantic_js_extra is None:
+            core_metadata['pydantic_js_extra'] = pydantic_js_extra
+        if isinstance(existing_pydantic_js_extra, dict):
+            if isinstance(pydantic_js_extra, dict):
+                core_metadata['pydantic_js_extra'] = {**existing_pydantic_js_extra, **pydantic_js_extra}
+            if callable(pydantic_js_extra):
+                warn(
+                    'Composing `dict` and `callable` type `json_schema_extra` is not supported.'
+                    'The `callable` type is being ignored.'
+                    "If you'd like support for this behavior, please open an issue on pydantic.",
+                    PydanticJsonSchemaWarning,
+                )
+        if callable(existing_pydantic_js_extra):
+            # if ever there's a case of a callable, we'll just keep the last json schema extra spec
+            core_metadata['pydantic_js_extra'] = pydantic_js_extra

env/lib/python3.11/site-packages/pydantic/_internal/_core_utils.py

@@ -0,0 +1,610 @@
+from __future__ import annotations
+
+import os
+from collections import defaultdict
+from typing import Any, Callable, Hashable, TypeVar, Union
+
+from pydantic_core import CoreSchema, core_schema
+from pydantic_core import validate_core_schema as _validate_core_schema
+from typing_extensions import TypeGuard, get_args, get_origin
+
+from ..errors import PydanticUserError
+from . import _repr
+from ._core_metadata import CoreMetadata
+from ._typing_extra import is_generic_alias, is_type_alias_type
+
+AnyFunctionSchema = Union[
+    core_schema.AfterValidatorFunctionSchema,
+    core_schema.BeforeValidatorFunctionSchema,
+    core_schema.WrapValidatorFunctionSchema,
+    core_schema.PlainValidatorFunctionSchema,
+]
+
+
+FunctionSchemaWithInnerSchema = Union[
+    core_schema.AfterValidatorFunctionSchema,
+    core_schema.BeforeValidatorFunctionSchema,
+    core_schema.WrapValidatorFunctionSchema,
+]
+
+CoreSchemaField = Union[
+    core_schema.ModelField, core_schema.DataclassField, core_schema.TypedDictField, core_schema.ComputedField
+]
+CoreSchemaOrField = Union[core_schema.CoreSchema, CoreSchemaField]
+
+_CORE_SCHEMA_FIELD_TYPES = {'typed-dict-field', 'dataclass-field', 'model-field', 'computed-field'}
+_FUNCTION_WITH_INNER_SCHEMA_TYPES = {'function-before', 'function-after', 'function-wrap'}
+_LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES = {'list', 'set', 'frozenset'}
+
+TAGGED_UNION_TAG_KEY = 'pydantic.internal.tagged_union_tag'
+"""
+Used in a `Tag` schema to specify the tag used for a discriminated union.
+"""
+
+
+def is_core_schema(
+    schema: CoreSchemaOrField,
+) -> TypeGuard[CoreSchema]:
+    return schema['type'] not in _CORE_SCHEMA_FIELD_TYPES
+
+
+def is_core_schema_field(
+    schema: CoreSchemaOrField,
+) -> TypeGuard[CoreSchemaField]:
+    return schema['type'] in _CORE_SCHEMA_FIELD_TYPES
+
+
+def is_function_with_inner_schema(
+    schema: CoreSchemaOrField,
+) -> TypeGuard[FunctionSchemaWithInnerSchema]:
+    return schema['type'] in _FUNCTION_WITH_INNER_SCHEMA_TYPES
+
+
+def is_list_like_schema_with_items_schema(
+    schema: CoreSchema,
+) -> TypeGuard[core_schema.ListSchema | core_schema.SetSchema | core_schema.FrozenSetSchema]:
+    return schema['type'] in _LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES
+
+
+def get_type_ref(type_: type[Any], args_override: tuple[type[Any], ...] | None = None) -> str:
+    """Produces the ref to be used for this type by pydantic_core's core schemas.
+
+    This `args_override` argument was added for the purpose of creating valid recursive references
+    when creating generic models without needing to create a concrete class.
+    """
+    origin = get_origin(type_) or type_
+
+    args = get_args(type_) if is_generic_alias(type_) else (args_override or ())
+    generic_metadata = getattr(type_, '__pydantic_generic_metadata__', None)
+    if generic_metadata:
+        origin = generic_metadata['origin'] or origin
+        args = generic_metadata['args'] or args
+
+    module_name = getattr(origin, '__module__', '<No __module__>')
+    if is_type_alias_type(origin):
+        type_ref = f'{module_name}.{origin.__name__}:{id(origin)}'
+    else:
+        try:
+            qualname = getattr(origin, '__qualname__', f'<No __qualname__: {origin}>')
+        except Exception:
+            qualname = getattr(origin, '__qualname__', '<No __qualname__>')
+        type_ref = f'{module_name}.{qualname}:{id(origin)}'
+
+    arg_refs: list[str] = []
+    for arg in args:
+        if isinstance(arg, str):
+            # Handle string literals as a special case; we may be able to remove this special handling if we
+            # wrap them in a ForwardRef at some point.
+            arg_ref = f'{arg}:str-{id(arg)}'
+        else:
+            arg_ref = f'{_repr.display_as_type(arg)}:{id(arg)}'
+        arg_refs.append(arg_ref)
+    if arg_refs:
+        type_ref = f'{type_ref}[{",".join(arg_refs)}]'
+    return type_ref
+
+
+def get_ref(s: core_schema.CoreSchema) -> None | str:
+    """Get the ref from the schema if it has one.
+    This exists just for type checking to work correctly.
+    """
+    return s.get('ref', None)
+
+
+def collect_definitions(schema: core_schema.CoreSchema) -> dict[str, core_schema.CoreSchema]:
+    defs: dict[str, CoreSchema] = {}
+
+    def _record_valid_refs(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.CoreSchema:
+        ref = get_ref(s)
+        if ref:
+            defs[ref] = s
+        return recurse(s, _record_valid_refs)
+
+    walk_core_schema(schema, _record_valid_refs, copy=False)
+
+    return defs
+
+
+def define_expected_missing_refs(
+    schema: core_schema.CoreSchema, allowed_missing_refs: set[str]
+) -> core_schema.CoreSchema | None:
+    if not allowed_missing_refs:
+        # in this case, there are no missing refs to potentially substitute, so there's no need to walk the schema
+        # this is a common case (will be hit for all non-generic models), so it's worth optimizing for
+        return None
+
+    refs = collect_definitions(schema).keys()
+
+    expected_missing_refs = allowed_missing_refs.difference(refs)
+    if expected_missing_refs:
+        definitions: list[core_schema.CoreSchema] = [
+            core_schema.invalid_schema(ref=ref) for ref in expected_missing_refs
+        ]
+        return core_schema.definitions_schema(schema, definitions)
+    return None
+
+
+def collect_invalid_schemas(schema: core_schema.CoreSchema) -> bool:
+    invalid = False
+
+    def _is_schema_valid(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.CoreSchema:
+        nonlocal invalid
+
+        if s['type'] == 'invalid':
+            invalid = True
+            return s
+
+        return recurse(s, _is_schema_valid)
+
+    walk_core_schema(schema, _is_schema_valid, copy=False)
+    return invalid
+
+
+T = TypeVar('T')
+
+
+Recurse = Callable[[core_schema.CoreSchema, 'Walk'], core_schema.CoreSchema]
+Walk = Callable[[core_schema.CoreSchema, Recurse], core_schema.CoreSchema]
+
+# TODO: Should we move _WalkCoreSchema into pydantic_core proper?
+#   Issue: https://github.com/pydantic/pydantic-core/issues/615
+
+CoreSchemaT = TypeVar('CoreSchemaT')
+
+
+class _WalkCoreSchema:
+    def __init__(self, *, copy: bool = True):
+        self._schema_type_to_method = self._build_schema_type_to_method()
+        self._copy = copy
+
+    def _copy_schema(self, schema: CoreSchemaT) -> CoreSchemaT:
+        return schema.copy() if self._copy else schema  # pyright: ignore[reportAttributeAccessIssue]
+
+    def _build_schema_type_to_method(self) -> dict[core_schema.CoreSchemaType, Recurse]:
+        mapping: dict[core_schema.CoreSchemaType, Recurse] = {}
+        key: core_schema.CoreSchemaType
+        for key in get_args(core_schema.CoreSchemaType):
+            method_name = f"handle_{key.replace('-', '_')}_schema"
+            mapping[key] = getattr(self, method_name, self._handle_other_schemas)
+        return mapping
+
+    def walk(self, schema: core_schema.CoreSchema, f: Walk) -> core_schema.CoreSchema:
+        return f(schema, self._walk)
+
+    def _walk(self, schema: core_schema.CoreSchema, f: Walk) -> core_schema.CoreSchema:
+        schema = self._schema_type_to_method[schema['type']](self._copy_schema(schema), f)
+        ser_schema: core_schema.SerSchema | None = schema.get('serialization')  # type: ignore
+        if ser_schema:
+            schema['serialization'] = self._handle_ser_schemas(ser_schema, f)
+        return schema
+
+    def _handle_other_schemas(self, schema: core_schema.CoreSchema, f: Walk) -> core_schema.CoreSchema:
+        sub_schema = schema.get('schema', None)
+        if sub_schema is not None:
+            schema['schema'] = self.walk(sub_schema, f)  # type: ignore
+        return schema
+
+    def _handle_ser_schemas(self, ser_schema: core_schema.SerSchema, f: Walk) -> core_schema.SerSchema:
+        schema: core_schema.CoreSchema | None = ser_schema.get('schema', None)
+        return_schema: core_schema.CoreSchema | None = ser_schema.get('return_schema', None)
+        if schema is not None or return_schema is not None:
+            ser_schema = self._copy_schema(ser_schema)
+            if schema is not None:
+                ser_schema['schema'] = self.walk(schema, f)  # type: ignore
+            if return_schema is not None:
+                ser_schema['return_schema'] = self.walk(return_schema, f)  # type: ignore
+        return ser_schema
+
+    def handle_definitions_schema(self, schema: core_schema.DefinitionsSchema, f: Walk) -> core_schema.CoreSchema:
+        new_definitions: list[core_schema.CoreSchema] = []
+        for definition in schema['definitions']:
+            if 'schema_ref' in definition and 'ref' in definition:
+                # This indicates a purposely indirect reference
+                # We want to keep such references around for implications related to JSON schema, etc.:
+                new_definitions.append(definition)
+                # However, we still need to walk the referenced definition:
+                self.walk(definition, f)
+                continue
+
+            updated_definition = self.walk(definition, f)
+            if 'ref' in updated_definition:
+                # If the updated definition schema doesn't have a 'ref', it shouldn't go in the definitions
+                # This is most likely to happen due to replacing something with a definition reference, in
+                # which case it should certainly not go in the definitions list
+                new_definitions.append(updated_definition)
+        new_inner_schema = self.walk(schema['schema'], f)
+
+        if not new_definitions and len(schema) == 3:
+            # This means we'd be returning a "trivial" definitions schema that just wrapped the inner schema
+            return new_inner_schema
+
+        new_schema = self._copy_schema(schema)
+        new_schema['schema'] = new_inner_schema
+        new_schema['definitions'] = new_definitions
+        return new_schema
+
+    def handle_list_schema(self, schema: core_schema.ListSchema, f: Walk) -> core_schema.CoreSchema:
+        items_schema = schema.get('items_schema')
+        if items_schema is not None:
+            schema['items_schema'] = self.walk(items_schema, f)
+        return schema
+
+    def handle_set_schema(self, schema: core_schema.SetSchema, f: Walk) -> core_schema.CoreSchema:
+        items_schema = schema.get('items_schema')
+        if items_schema is not None:
+            schema['items_schema'] = self.walk(items_schema, f)
+        return schema
+
+    def handle_frozenset_schema(self, schema: core_schema.FrozenSetSchema, f: Walk) -> core_schema.CoreSchema:
+        items_schema = schema.get('items_schema')
+        if items_schema is not None:
+            schema['items_schema'] = self.walk(items_schema, f)
+        return schema
+
+    def handle_generator_schema(self, schema: core_schema.GeneratorSchema, f: Walk) -> core_schema.CoreSchema:
+        items_schema = schema.get('items_schema')
+        if items_schema is not None:
+            schema['items_schema'] = self.walk(items_schema, f)
+        return schema
+
+    def handle_tuple_schema(self, schema: core_schema.TupleSchema, f: Walk) -> core_schema.CoreSchema:
+        schema['items_schema'] = [self.walk(v, f) for v in schema['items_schema']]
+        return schema
+
+    def handle_dict_schema(self, schema: core_schema.DictSchema, f: Walk) -> core_schema.CoreSchema:
+        keys_schema = schema.get('keys_schema')
+        if keys_schema is not None:
+            schema['keys_schema'] = self.walk(keys_schema, f)
+        values_schema = schema.get('values_schema')
+        if values_schema:
+            schema['values_schema'] = self.walk(values_schema, f)
+        return schema
+
+    def handle_function_after_schema(
+        self, schema: core_schema.AfterValidatorFunctionSchema, f: Walk
+    ) -> core_schema.CoreSchema:
+        schema['schema'] = self.walk(schema['schema'], f)
+        return schema
+
+    def handle_function_before_schema(
+        self, schema: core_schema.BeforeValidatorFunctionSchema, f: Walk
+    ) -> core_schema.CoreSchema:
+        schema['schema'] = self.walk(schema['schema'], f)
+        if 'json_schema_input_schema' in schema:
+            schema['json_schema_input_schema'] = self.walk(schema['json_schema_input_schema'], f)
+        return schema
+
+    # TODO duplicate schema types for serializers and validators, needs to be deduplicated:
+    def handle_function_plain_schema(
+        self, schema: core_schema.PlainValidatorFunctionSchema | core_schema.PlainSerializerFunctionSerSchema, f: Walk
+    ) -> core_schema.CoreSchema:
+        if 'json_schema_input_schema' in schema:
+            schema['json_schema_input_schema'] = self.walk(schema['json_schema_input_schema'], f)
+        return schema  # pyright: ignore[reportReturnType]
+
+    # TODO duplicate schema types for serializers and validators, needs to be deduplicated:
+    def handle_function_wrap_schema(
+        self, schema: core_schema.WrapValidatorFunctionSchema | core_schema.WrapSerializerFunctionSerSchema, f: Walk
+    ) -> core_schema.CoreSchema:
+        if 'schema' in schema:
+            schema['schema'] = self.walk(schema['schema'], f)
+        if 'json_schema_input_schema' in schema:
+            schema['json_schema_input_schema'] = self.walk(schema['json_schema_input_schema'], f)
+        return schema  # pyright: ignore[reportReturnType]
+
+    def handle_union_schema(self, schema: core_schema.UnionSchema, f: Walk) -> core_schema.CoreSchema:
+        new_choices: list[CoreSchema | tuple[CoreSchema, str]] = []
+        for v in schema['choices']:
+            if isinstance(v, tuple):
+                new_choices.append((self.walk(v[0], f), v[1]))
+            else:
+                new_choices.append(self.walk(v, f))
+        schema['choices'] = new_choices
+        return schema
+
+    def handle_tagged_union_schema(self, schema: core_schema.TaggedUnionSchema, f: Walk) -> core_schema.CoreSchema:
+        new_choices: dict[Hashable, core_schema.CoreSchema] = {}
+        for k, v in schema['choices'].items():
+            new_choices[k] = v if isinstance(v, (str, int)) else self.walk(v, f)
+        schema['choices'] = new_choices
+        return schema
+
+    def handle_chain_schema(self, schema: core_schema.ChainSchema, f: Walk) -> core_schema.CoreSchema:
+        schema['steps'] = [self.walk(v, f) for v in schema['steps']]
+        return schema
+
+    def handle_lax_or_strict_schema(self, schema: core_schema.LaxOrStrictSchema, f: Walk) -> core_schema.CoreSchema:
+        schema['lax_schema'] = self.walk(schema['lax_schema'], f)
+        schema['strict_schema'] = self.walk(schema['strict_schema'], f)
+        return schema
+
+    def handle_json_or_python_schema(self, schema: core_schema.JsonOrPythonSchema, f: Walk) -> core_schema.CoreSchema:
+        schema['json_schema'] = self.walk(schema['json_schema'], f)
+        schema['python_schema'] = self.walk(schema['python_schema'], f)
+        return schema
+
+    def handle_model_fields_schema(self, schema: core_schema.ModelFieldsSchema, f: Walk) -> core_schema.CoreSchema:
+        extras_schema = schema.get('extras_schema')
+        if extras_schema is not None:
+            schema['extras_schema'] = self.walk(extras_schema, f)
+        replaced_fields: dict[str, core_schema.ModelField] = {}
+        replaced_computed_fields: list[core_schema.ComputedField] = []
+        for computed_field in schema.get('computed_fields', ()):
+            replaced_field = self._copy_schema(computed_field)
+            replaced_field['return_schema'] = self.walk(computed_field['return_schema'], f)
+            replaced_computed_fields.append(replaced_field)
+        if replaced_computed_fields:
+            schema['computed_fields'] = replaced_computed_fields
+        for k, v in schema['fields'].items():
+            replaced_field = self._copy_schema(v)
+            replaced_field['schema'] = self.walk(v['schema'], f)
+            replaced_fields[k] = replaced_field
+        schema['fields'] = replaced_fields
+        return schema
+
+    def handle_typed_dict_schema(self, schema: core_schema.TypedDictSchema, f: Walk) -> core_schema.CoreSchema:
+        extras_schema = schema.get('extras_schema')
+        if extras_schema is not None:
+            schema['extras_schema'] = self.walk(extras_schema, f)
+        replaced_computed_fields: list[core_schema.ComputedField] = []
+        for computed_field in schema.get('computed_fields', ()):
+            replaced_field = self._copy_schema(computed_field)
+            replaced_field['return_schema'] = self.walk(computed_field['return_schema'], f)
+            replaced_computed_fields.append(replaced_field)
+        if replaced_computed_fields:
+            schema['computed_fields'] = replaced_computed_fields
+        replaced_fields: dict[str, core_schema.TypedDictField] = {}
+        for k, v in schema['fields'].items():
+            replaced_field = self._copy_schema(v)
+            replaced_field['schema'] = self.walk(v['schema'], f)
+            replaced_fields[k] = replaced_field
+        schema['fields'] = replaced_fields
+        return schema
+
+    def handle_dataclass_args_schema(self, schema: core_schema.DataclassArgsSchema, f: Walk) -> core_schema.CoreSchema:
+        replaced_fields: list[core_schema.DataclassField] = []
+        replaced_computed_fields: list[core_schema.ComputedField] = []
+        for computed_field in schema.get('computed_fields', ()):
+            replaced_field = self._copy_schema(computed_field)
+            replaced_field['return_schema'] = self.walk(computed_field['return_schema'], f)
+            replaced_computed_fields.append(replaced_field)
+        if replaced_computed_fields:
+            schema['computed_fields'] = replaced_computed_fields
+        for field in schema['fields']:
+            replaced_field = self._copy_schema(field)
+            replaced_field['schema'] = self.walk(field['schema'], f)
+            replaced_fields.append(replaced_field)
+        schema['fields'] = replaced_fields
+        return schema
+
+    def handle_arguments_schema(self, schema: core_schema.ArgumentsSchema, f: Walk) -> core_schema.CoreSchema:
+        replaced_arguments_schema: list[core_schema.ArgumentsParameter] = []
+        for param in schema['arguments_schema']:
+            replaced_param = self._copy_schema(param)
+            replaced_param['schema'] = self.walk(param['schema'], f)
+            replaced_arguments_schema.append(replaced_param)
+        schema['arguments_schema'] = replaced_arguments_schema
+        if 'var_args_schema' in schema:
+            schema['var_args_schema'] = self.walk(schema['var_args_schema'], f)
+        if 'var_kwargs_schema' in schema:
+            schema['var_kwargs_schema'] = self.walk(schema['var_kwargs_schema'], f)
+        return schema
+
+    def handle_call_schema(self, schema: core_schema.CallSchema, f: Walk) -> core_schema.CoreSchema:
+        schema['arguments_schema'] = self.walk(schema['arguments_schema'], f)
+        if 'return_schema' in schema:
+            schema['return_schema'] = self.walk(schema['return_schema'], f)
+        return schema
+
+
+_dispatch = _WalkCoreSchema().walk
+_dispatch_no_copy = _WalkCoreSchema(copy=False).walk
+
+
+def walk_core_schema(schema: core_schema.CoreSchema, f: Walk, *, copy: bool = True) -> core_schema.CoreSchema:
+    """Recursively traverse a CoreSchema.
+
+    Args:
+        schema (core_schema.CoreSchema): The CoreSchema to process, it will not be modified.
+        f (Walk): A function to apply. This function takes two arguments:
+          1. The current CoreSchema that is being processed
+             (not the same one you passed into this function, one level down).
+          2. The "next" `f` to call. This lets you for example use `f=functools.partial(some_method, some_context)`
+             to pass data down the recursive calls without using globals or other mutable state.
+        copy: Whether schema should be recursively copied.
+
+    Returns:
+        core_schema.CoreSchema: A processed CoreSchema.
+    """
+    return f(schema.copy() if copy else schema, _dispatch if copy else _dispatch_no_copy)
+
+
+def simplify_schema_references(schema: core_schema.CoreSchema) -> core_schema.CoreSchema:  # noqa: C901
+    definitions: dict[str, core_schema.CoreSchema] = {}
+    ref_counts: dict[str, int] = defaultdict(int)
+    involved_in_recursion: dict[str, bool] = {}
+    current_recursion_ref_count: dict[str, int] = defaultdict(int)
+
+    def collect_refs(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.CoreSchema:
+        if s['type'] == 'definitions':
+            for definition in s['definitions']:
+                ref = get_ref(definition)
+                assert ref is not None
+                if ref not in definitions:
+                    definitions[ref] = definition
+                recurse(definition, collect_refs)
+            return recurse(s['schema'], collect_refs)
+        else:
+            ref = get_ref(s)
+            if ref is not None:
+                new = recurse(s, collect_refs)
+                new_ref = get_ref(new)
+                if new_ref:
+                    definitions[new_ref] = new
+                return core_schema.definition_reference_schema(schema_ref=ref)
+            else:
+                return recurse(s, collect_refs)
+
+    schema = walk_core_schema(schema, collect_refs)
+
+    def count_refs(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.CoreSchema:
+        if s['type'] != 'definition-ref':
+            return recurse(s, count_refs)
+        ref = s['schema_ref']
+        ref_counts[ref] += 1
+
+        if ref_counts[ref] >= 2:
+            # If this model is involved in a recursion this should be detected
+            # on its second encounter, we can safely stop the walk here.
+            if current_recursion_ref_count[ref] != 0:
+                involved_in_recursion[ref] = True
+            return s
+
+        current_recursion_ref_count[ref] += 1
+        if 'serialization' in s:
+            # Even though this is a `'definition-ref'` schema, there might
+            # be more references inside the serialization schema:
+            recurse(s, count_refs)
+
+        next_s = definitions[ref]
+        visited: set[str] = set()
+        while next_s['type'] == 'definition-ref':
+            if next_s['schema_ref'] in visited:
+                raise PydanticUserError(
+                    f'{ref} contains a circular reference to itself.', code='circular-reference-schema'
+                )
+
+            visited.add(next_s['schema_ref'])
+            ref_counts[next_s['schema_ref']] += 1
+            next_s = definitions[next_s['schema_ref']]
+
+        recurse(next_s, count_refs)
+        current_recursion_ref_count[ref] -= 1
+        return s
+
+    schema = walk_core_schema(schema, count_refs, copy=False)
+
+    assert all(c == 0 for c in current_recursion_ref_count.values()), 'this is a bug! please report it'
+
+    def can_be_inlined(s: core_schema.DefinitionReferenceSchema, ref: str) -> bool:
+        if ref_counts[ref] > 1:
+            return False
+        if involved_in_recursion.get(ref, False):
+            return False
+        if 'serialization' in s:
+            return False
+        if 'metadata' in s:
+            metadata = s['metadata']
+            for k in [
+                *CoreMetadata.__annotations__.keys(),
+                'pydantic.internal.union_discriminator',
+                'pydantic.internal.tagged_union_tag',
+            ]:
+                if k in metadata:
+                    # we need to keep this as a ref
+                    return False
+        return True
+
+    def inline_refs(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.CoreSchema:
+        # Assume there are no infinite loops, because we already checked for that in `count_refs`
+        while s['type'] == 'definition-ref':
+            ref = s['schema_ref']
+
+            # Check if the reference is only used once, not involved in recursion and does not have
+            # any extra keys (like 'serialization')
+            if can_be_inlined(s, ref):
+                # Inline the reference by replacing the reference with the actual schema
+                new = definitions.pop(ref)
+                ref_counts[ref] -= 1  # because we just replaced it!
+                # put all other keys that were on the def-ref schema into the inlined version
+                # in particular this is needed for `serialization`
+                if 'serialization' in s:
+                    new['serialization'] = s['serialization']
+                s = new
+            else:
+                break
+        return recurse(s, inline_refs)
+
+    schema = walk_core_schema(schema, inline_refs, copy=False)
+
+    def_values = [v for v in definitions.values() if ref_counts[v['ref']] > 0]  # type: ignore
+
+    if def_values:
+        schema = core_schema.definitions_schema(schema=schema, definitions=def_values)
+    return schema
+
+
+def _strip_metadata(schema: CoreSchema) -> CoreSchema:
+    def strip_metadata(s: CoreSchema, recurse: Recurse) -> CoreSchema:
+        s = s.copy()
+        s.pop('metadata', None)
+        if s['type'] == 'model-fields':
+            s = s.copy()
+            s['fields'] = {k: v.copy() for k, v in s['fields'].items()}
+            for field_name, field_schema in s['fields'].items():
+                field_schema.pop('metadata', None)
+                s['fields'][field_name] = field_schema
+            computed_fields = s.get('computed_fields', None)
+            if computed_fields:
+                s['computed_fields'] = [cf.copy() for cf in computed_fields]
+                for cf in computed_fields:
+                    cf.pop('metadata', None)
+            else:
+                s.pop('computed_fields', None)
+        elif s['type'] == 'model':
+            # remove some defaults
+            if s.get('custom_init', True) is False:
+                s.pop('custom_init')
+            if s.get('root_model', True) is False:
+                s.pop('root_model')
+            if {'title'}.issuperset(s.get('config', {}).keys()):
+                s.pop('config', None)
+
+        return recurse(s, strip_metadata)
+
+    return walk_core_schema(schema, strip_metadata)
+
+
+def pretty_print_core_schema(
+    schema: CoreSchema,
+    include_metadata: bool = False,
+) -> None:
+    """Pretty print a CoreSchema using rich.
+    This is intended for debugging purposes.
+
+    Args:
+        schema: The CoreSchema to print.
+        include_metadata: Whether to include metadata in the output. Defaults to `False`.
+    """
+    from rich import print  # type: ignore  # install it manually in your dev env
+
+    if not include_metadata:
+        schema = _strip_metadata(schema)
+
+    return print(schema)
+
+
+def validate_core_schema(schema: CoreSchema) -> CoreSchema:
+    if 'PYDANTIC_SKIP_VALIDATING_CORE_SCHEMAS' in os.environ:
+        return schema
+    return _validate_core_schema(schema)

env/lib/python3.11/site-packages/pydantic/_internal/_dataclasses.py

@@ -0,0 +1,246 @@
+"""Private logic for creating pydantic dataclasses."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import typing
+import warnings
+from functools import partial, wraps
+from typing import Any, ClassVar
+
+from pydantic_core import (
+    ArgsKwargs,
+    SchemaSerializer,
+    SchemaValidator,
+    core_schema,
+)
+from typing_extensions import TypeGuard
+
+from ..errors import PydanticUndefinedAnnotation
+from ..plugin._schema_validator import PluggableSchemaValidator, create_schema_validator
+from ..warnings import PydanticDeprecatedSince20
+from . import _config, _decorators
+from ._fields import collect_dataclass_fields
+from ._generate_schema import GenerateSchema
+from ._generics import get_standard_typevars_map
+from ._mock_val_ser import set_dataclass_mocks
+from ._namespace_utils import NsResolver
+from ._schema_generation_shared import CallbackGetCoreSchemaHandler
+from ._signature import generate_pydantic_signature
+from ._utils import LazyClassAttribute
+
+if typing.TYPE_CHECKING:
+    from _typeshed import DataclassInstance as StandardDataclass
+
+    from ..config import ConfigDict
+    from ..fields import FieldInfo
+
+    class PydanticDataclass(StandardDataclass, typing.Protocol):
+        """A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass.
+
+        Attributes:
+            __pydantic_config__: Pydantic-specific configuration settings for the dataclass.
+            __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields.
+            __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
+            __pydantic_decorators__: Metadata containing the decorators defined on the dataclass.
+            __pydantic_fields__: Metadata about the fields defined on the dataclass.
+            __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass.
+            __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass.
+        """
+
+        __pydantic_config__: ClassVar[ConfigDict]
+        __pydantic_complete__: ClassVar[bool]
+        __pydantic_core_schema__: ClassVar[core_schema.CoreSchema]
+        __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos]
+        __pydantic_fields__: ClassVar[dict[str, FieldInfo]]
+        __pydantic_serializer__: ClassVar[SchemaSerializer]
+        __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator]
+
+else:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+
+def set_dataclass_fields(
+    cls: type[StandardDataclass],
+    ns_resolver: NsResolver | None = None,
+    config_wrapper: _config.ConfigWrapper | None = None,
+) -> None:
+    """Collect and set `cls.__pydantic_fields__`.
+
+    Args:
+        cls: The class.
+        ns_resolver: Namespace resolver to use when getting dataclass annotations.
+        config_wrapper: The config wrapper instance, defaults to `None`.
+    """
+    typevars_map = get_standard_typevars_map(cls)
+    fields = collect_dataclass_fields(
+        cls, ns_resolver=ns_resolver, typevars_map=typevars_map, config_wrapper=config_wrapper
+    )
+
+    cls.__pydantic_fields__ = fields  # type: ignore
+
+
+def complete_dataclass(
+    cls: type[Any],
+    config_wrapper: _config.ConfigWrapper,
+    *,
+    raise_errors: bool = True,
+    ns_resolver: NsResolver | None = None,
+    _force_build: bool = False,
+) -> bool:
+    """Finish building a pydantic dataclass.
+
+    This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`.
+
+    This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`.
+
+    Args:
+        cls: The class.
+        config_wrapper: The config wrapper instance.
+        raise_errors: Whether to raise errors, defaults to `True`.
+        ns_resolver: The namespace resolver instance to use when collecting dataclass fields
+            and during schema building.
+        _force_build: Whether to force building the dataclass, no matter if
+            [`defer_build`][pydantic.config.ConfigDict.defer_build] is set.
+
+    Returns:
+        `True` if building a pydantic dataclass is successfully completed, `False` otherwise.
+
+    Raises:
+        PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations.
+    """
+    original_init = cls.__init__
+
+    # dataclass.__init__ must be defined here so its `__qualname__` can be changed since functions can't be copied,
+    # and so that the mock validator is used if building was deferred:
+    def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) -> None:
+        __tracebackhide__ = True
+        s = __dataclass_self__
+        s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
+
+    __init__.__qualname__ = f'{cls.__qualname__}.__init__'
+
+    cls.__init__ = __init__  # type: ignore
+    cls.__pydantic_config__ = config_wrapper.config_dict  # type: ignore
+
+    set_dataclass_fields(cls, ns_resolver, config_wrapper=config_wrapper)
+
+    if not _force_build and config_wrapper.defer_build:
+        set_dataclass_mocks(cls, cls.__name__)
+        return False
+
+    if hasattr(cls, '__post_init_post_parse__'):
+        warnings.warn(
+            'Support for `__post_init_post_parse__` has been dropped, the method will not be called', DeprecationWarning
+        )
+
+    typevars_map = get_standard_typevars_map(cls)
+    gen_schema = GenerateSchema(
+        config_wrapper,
+        ns_resolver=ns_resolver,
+        typevars_map=typevars_map,
+    )
+
+    # set __signature__ attr only for the class, but not for its instances
+    # (because instances can define `__call__`, and `inspect.signature` shouldn't
+    # use the `__signature__` attribute and instead generate from `__call__`).
+    cls.__signature__ = LazyClassAttribute(
+        '__signature__',
+        partial(
+            generate_pydantic_signature,
+            # It's important that we reference the `original_init` here,
+            # as it is the one synthesized by the stdlib `dataclass` module:
+            init=original_init,
+            fields=cls.__pydantic_fields__,  # type: ignore
+            populate_by_name=config_wrapper.populate_by_name,
+            extra=config_wrapper.extra,
+            is_dataclass=True,
+        ),
+    )
+    get_core_schema = getattr(cls, '__get_pydantic_core_schema__', None)
+    try:
+        if get_core_schema:
+            schema = get_core_schema(
+                cls,
+                CallbackGetCoreSchemaHandler(
+                    partial(gen_schema.generate_schema, from_dunder_get_core_schema=False),
+                    gen_schema,
+                    ref_mode='unpack',
+                ),
+            )
+        else:
+            schema = gen_schema.generate_schema(cls, from_dunder_get_core_schema=False)
+    except PydanticUndefinedAnnotation as e:
+        if raise_errors:
+            raise
+        set_dataclass_mocks(cls, cls.__name__, f'`{e.name}`')
+        return False
+
+    core_config = config_wrapper.core_config(title=cls.__name__)
+
+    try:
+        schema = gen_schema.clean_schema(schema)
+    except gen_schema.CollectedInvalid:
+        set_dataclass_mocks(cls, cls.__name__, 'all referenced types')
+        return False
+
+    # We are about to set all the remaining required properties expected for this cast;
+    # __pydantic_decorators__ and __pydantic_fields__ should already be set
+    cls = typing.cast('type[PydanticDataclass]', cls)
+    # debug(schema)
+
+    cls.__pydantic_core_schema__ = schema
+    cls.__pydantic_validator__ = validator = create_schema_validator(
+        schema, cls, cls.__module__, cls.__qualname__, 'dataclass', core_config, config_wrapper.plugin_settings
+    )
+    cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config)
+
+    if config_wrapper.validate_assignment:
+
+        @wraps(cls.__setattr__)
+        def validated_setattr(instance: Any, field: str, value: str, /) -> None:
+            validator.validate_assignment(instance, field, value)
+
+        cls.__setattr__ = validated_setattr.__get__(None, cls)  # type: ignore
+
+    cls.__pydantic_complete__ = True
+    return True
+
+
+def is_builtin_dataclass(_cls: type[Any]) -> TypeGuard[type[StandardDataclass]]:
+    """Returns True if a class is a stdlib dataclass and *not* a pydantic dataclass.
+
+    We check that
+    - `_cls` is a dataclass
+    - `_cls` does not inherit from a processed pydantic dataclass (and thus have a `__pydantic_validator__`)
+    - `_cls` does not have any annotations that are not dataclass fields
+    e.g.
+    ```python
+    import dataclasses
+
+    import pydantic.dataclasses
+
+    @dataclasses.dataclass
+    class A:
+        x: int
+
+    @pydantic.dataclasses.dataclass
+    class B(A):
+        y: int
+    ```
+    In this case, when we first check `B`, we make an extra check and look at the annotations ('y'),
+    which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x')
+
+    Args:
+        cls: The class.
+
+    Returns:
+        `True` if the class is a stdlib dataclass, `False` otherwise.
+    """
+    return (
+        dataclasses.is_dataclass(_cls)
+        and not hasattr(_cls, '__pydantic_validator__')
+        and set(_cls.__dataclass_fields__).issuperset(set(getattr(_cls, '__annotations__', {})))
+    )

env/lib/python3.11/site-packages/pydantic/_internal/_decorators.py

@@ -0,0 +1,823 @@
+"""Logic related to validators applied to models etc. via the `@field_validator` and `@model_validator` decorators."""
+
+from __future__ import annotations as _annotations
+
+from collections import deque
+from dataclasses import dataclass, field
+from functools import cached_property, partial, partialmethod
+from inspect import Parameter, Signature, isdatadescriptor, ismethoddescriptor, signature
+from itertools import islice
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Generic, Iterable, TypeVar, Union
+
+from pydantic_core import PydanticUndefined, core_schema
+from typing_extensions import Literal, TypeAlias, is_typeddict
+
+from ..errors import PydanticUserError
+from ._core_utils import get_type_ref
+from ._internal_dataclass import slots_true
+from ._namespace_utils import GlobalsNamespace, MappingNamespace
+from ._typing_extra import get_function_type_hints
+from ._utils import can_be_positional
+
+if TYPE_CHECKING:
+    from ..fields import ComputedFieldInfo
+    from ..functional_validators import FieldValidatorModes
+
+
+@dataclass(**slots_true)
+class ValidatorDecoratorInfo:
+    """A container for data from `@validator` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@validator'.
+        fields: A tuple of field names the validator should be called on.
+        mode: The proposed validator mode.
+        each_item: For complex objects (sets, lists etc.) whether to validate individual
+            elements rather than the whole object.
+        always: Whether this method and other validators should be called even if the value is missing.
+        check_fields: Whether to check that the fields actually exist on the model.
+    """
+
+    decorator_repr: ClassVar[str] = '@validator'
+
+    fields: tuple[str, ...]
+    mode: Literal['before', 'after']
+    each_item: bool
+    always: bool
+    check_fields: bool | None
+
+
+@dataclass(**slots_true)
+class FieldValidatorDecoratorInfo:
+    """A container for data from `@field_validator` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@field_validator'.
+        fields: A tuple of field names the validator should be called on.
+        mode: The proposed validator mode.
+        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'`.
+    """
+
+    decorator_repr: ClassVar[str] = '@field_validator'
+
+    fields: tuple[str, ...]
+    mode: FieldValidatorModes
+    check_fields: bool | None
+    json_schema_input_type: Any
+
+
+@dataclass(**slots_true)
+class RootValidatorDecoratorInfo:
+    """A container for data from `@root_validator` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@root_validator'.
+        mode: The proposed validator mode.
+    """
+
+    decorator_repr: ClassVar[str] = '@root_validator'
+    mode: Literal['before', 'after']
+
+
+@dataclass(**slots_true)
+class FieldSerializerDecoratorInfo:
+    """A container for data from `@field_serializer` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@field_serializer'.
+        fields: A tuple of field names the serializer should be called on.
+        mode: The proposed serializer mode.
+        return_type: The type of the serializer's return value.
+        when_used: The serialization condition. Accepts a string with values `'always'`, `'unless-none'`, `'json'`,
+            and `'json-unless-none'`.
+        check_fields: Whether to check that the fields actually exist on the model.
+    """
+
+    decorator_repr: ClassVar[str] = '@field_serializer'
+    fields: tuple[str, ...]
+    mode: Literal['plain', 'wrap']
+    return_type: Any
+    when_used: core_schema.WhenUsed
+    check_fields: bool | None
+
+
+@dataclass(**slots_true)
+class ModelSerializerDecoratorInfo:
+    """A container for data from `@model_serializer` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@model_serializer'.
+        mode: The proposed serializer mode.
+        return_type: The type of the serializer's return value.
+        when_used: The serialization condition. Accepts a string with values `'always'`, `'unless-none'`, `'json'`,
+            and `'json-unless-none'`.
+    """
+
+    decorator_repr: ClassVar[str] = '@model_serializer'
+    mode: Literal['plain', 'wrap']
+    return_type: Any
+    when_used: core_schema.WhenUsed
+
+
+@dataclass(**slots_true)
+class ModelValidatorDecoratorInfo:
+    """A container for data from `@model_validator` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@model_validator'.
+        mode: The proposed serializer mode.
+    """
+
+    decorator_repr: ClassVar[str] = '@model_validator'
+    mode: Literal['wrap', 'before', 'after']
+
+
+DecoratorInfo: TypeAlias = """Union[
+    ValidatorDecoratorInfo,
+    FieldValidatorDecoratorInfo,
+    RootValidatorDecoratorInfo,
+    FieldSerializerDecoratorInfo,
+    ModelSerializerDecoratorInfo,
+    ModelValidatorDecoratorInfo,
+    ComputedFieldInfo,
+]"""
+
+ReturnType = TypeVar('ReturnType')
+DecoratedType: TypeAlias = (
+    'Union[classmethod[Any, Any, ReturnType], staticmethod[Any, ReturnType], Callable[..., ReturnType], property]'
+)
+
+
+@dataclass  # can't use slots here since we set attributes on `__post_init__`
+class PydanticDescriptorProxy(Generic[ReturnType]):
+    """Wrap a classmethod, staticmethod, property or unbound function
+    and act as a descriptor that allows us to detect decorated items
+    from the class' attributes.
+
+    This class' __get__ returns the wrapped item's __get__ result,
+    which makes it transparent for classmethods and staticmethods.
+
+    Attributes:
+        wrapped: The decorator that has to be wrapped.
+        decorator_info: The decorator info.
+        shim: A wrapper function to wrap V1 style function.
+    """
+
+    wrapped: DecoratedType[ReturnType]
+    decorator_info: DecoratorInfo
+    shim: Callable[[Callable[..., Any]], Callable[..., Any]] | None = None
+
+    def __post_init__(self):
+        for attr in 'setter', 'deleter':
+            if hasattr(self.wrapped, attr):
+                f = partial(self._call_wrapped_attr, name=attr)
+                setattr(self, attr, f)
+
+    def _call_wrapped_attr(self, func: Callable[[Any], None], *, name: str) -> PydanticDescriptorProxy[ReturnType]:
+        self.wrapped = getattr(self.wrapped, name)(func)
+        if isinstance(self.wrapped, property):
+            # update ComputedFieldInfo.wrapped_property
+            from ..fields import ComputedFieldInfo
+
+            if isinstance(self.decorator_info, ComputedFieldInfo):
+                self.decorator_info.wrapped_property = self.wrapped
+        return self
+
+    def __get__(self, obj: object | None, obj_type: type[object] | None = None) -> PydanticDescriptorProxy[ReturnType]:
+        try:
+            return self.wrapped.__get__(obj, obj_type)
+        except AttributeError:
+            # not a descriptor, e.g. a partial object
+            return self.wrapped  # type: ignore[return-value]
+
+    def __set_name__(self, instance: Any, name: str) -> None:
+        if hasattr(self.wrapped, '__set_name__'):
+            self.wrapped.__set_name__(instance, name)  # pyright: ignore[reportFunctionMemberAccess]
+
+    def __getattr__(self, __name: str) -> Any:
+        """Forward checks for __isabstractmethod__ and such."""
+        return getattr(self.wrapped, __name)
+
+
+DecoratorInfoType = TypeVar('DecoratorInfoType', bound=DecoratorInfo)
+
+
+@dataclass(**slots_true)
+class Decorator(Generic[DecoratorInfoType]):
+    """A generic container class to join together the decorator metadata
+    (metadata from decorator itself, which we have when the
+    decorator is called but not when we are building the core-schema)
+    and the bound function (which we have after the class itself is created).
+
+    Attributes:
+        cls_ref: The class ref.
+        cls_var_name: The decorated function name.
+        func: The decorated function.
+        shim: A wrapper function to wrap V1 style function.
+        info: The decorator info.
+    """
+
+    cls_ref: str
+    cls_var_name: str
+    func: Callable[..., Any]
+    shim: Callable[[Any], Any] | None
+    info: DecoratorInfoType
+
+    @staticmethod
+    def build(
+        cls_: Any,
+        *,
+        cls_var_name: str,
+        shim: Callable[[Any], Any] | None,
+        info: DecoratorInfoType,
+    ) -> Decorator[DecoratorInfoType]:
+        """Build a new decorator.
+
+        Args:
+            cls_: The class.
+            cls_var_name: The decorated function name.
+            shim: A wrapper function to wrap V1 style function.
+            info: The decorator info.
+
+        Returns:
+            The new decorator instance.
+        """
+        func = get_attribute_from_bases(cls_, cls_var_name)
+        if shim is not None:
+            func = shim(func)
+        func = unwrap_wrapped_function(func, unwrap_partial=False)
+        if not callable(func):
+            # This branch will get hit for classmethod properties
+            attribute = get_attribute_from_base_dicts(cls_, cls_var_name)  # prevents the binding call to `__get__`
+            if isinstance(attribute, PydanticDescriptorProxy):
+                func = unwrap_wrapped_function(attribute.wrapped)
+        return Decorator(
+            cls_ref=get_type_ref(cls_),
+            cls_var_name=cls_var_name,
+            func=func,
+            shim=shim,
+            info=info,
+        )
+
+    def bind_to_cls(self, cls: Any) -> Decorator[DecoratorInfoType]:
+        """Bind the decorator to a class.
+
+        Args:
+            cls: the class.
+
+        Returns:
+            The new decorator instance.
+        """
+        return self.build(
+            cls,
+            cls_var_name=self.cls_var_name,
+            shim=self.shim,
+            info=self.info,
+        )
+
+
+def get_bases(tp: type[Any]) -> tuple[type[Any], ...]:
+    """Get the base classes of a class or typeddict.
+
+    Args:
+        tp: The type or class to get the bases.
+
+    Returns:
+        The base classes.
+    """
+    if is_typeddict(tp):
+        return tp.__orig_bases__  # type: ignore
+    try:
+        return tp.__bases__
+    except AttributeError:
+        return ()
+
+
+def mro(tp: type[Any]) -> tuple[type[Any], ...]:
+    """Calculate the Method Resolution Order of bases using the C3 algorithm.
+
+    See https://www.python.org/download/releases/2.3/mro/
+    """
+    # try to use the existing mro, for performance mainly
+    # but also because it helps verify the implementation below
+    if not is_typeddict(tp):
+        try:
+            return tp.__mro__
+        except AttributeError:
+            # GenericAlias and some other cases
+            pass
+
+    bases = get_bases(tp)
+    return (tp,) + mro_for_bases(bases)
+
+
+def mro_for_bases(bases: tuple[type[Any], ...]) -> tuple[type[Any], ...]:
+    def merge_seqs(seqs: list[deque[type[Any]]]) -> Iterable[type[Any]]:
+        while True:
+            non_empty = [seq for seq in seqs if seq]
+            if not non_empty:
+                # Nothing left to process, we're done.
+                return
+            candidate: type[Any] | None = None
+            for seq in non_empty:  # Find merge candidates among seq heads.
+                candidate = seq[0]
+                not_head = [s for s in non_empty if candidate in islice(s, 1, None)]
+                if not_head:
+                    # Reject the candidate.
+                    candidate = None
+                else:
+                    break
+            if not candidate:
+                raise TypeError('Inconsistent hierarchy, no C3 MRO is possible')
+            yield candidate
+            for seq in non_empty:
+                # Remove candidate.
+                if seq[0] == candidate:
+                    seq.popleft()
+
+    seqs = [deque(mro(base)) for base in bases] + [deque(bases)]
+    return tuple(merge_seqs(seqs))
+
+
+_sentinel = object()
+
+
+def get_attribute_from_bases(tp: type[Any] | tuple[type[Any], ...], name: str) -> Any:
+    """Get the attribute from the next class in the MRO that has it,
+    aiming to simulate calling the method on the actual class.
+
+    The reason for iterating over the mro instead of just getting
+    the attribute (which would do that for us) is to support TypedDict,
+    which lacks a real __mro__, but can have a virtual one constructed
+    from its bases (as done here).
+
+    Args:
+        tp: The type or class to search for the attribute. If a tuple, this is treated as a set of base classes.
+        name: The name of the attribute to retrieve.
+
+    Returns:
+        Any: The attribute value, if found.
+
+    Raises:
+        AttributeError: If the attribute is not found in any class in the MRO.
+    """
+    if isinstance(tp, tuple):
+        for base in mro_for_bases(tp):
+            attribute = base.__dict__.get(name, _sentinel)
+            if attribute is not _sentinel:
+                attribute_get = getattr(attribute, '__get__', None)
+                if attribute_get is not None:
+                    return attribute_get(None, tp)
+                return attribute
+        raise AttributeError(f'{name} not found in {tp}')
+    else:
+        try:
+            return getattr(tp, name)
+        except AttributeError:
+            return get_attribute_from_bases(mro(tp), name)
+
+
+def get_attribute_from_base_dicts(tp: type[Any], name: str) -> Any:
+    """Get an attribute out of the `__dict__` following the MRO.
+    This prevents the call to `__get__` on the descriptor, and allows
+    us to get the original function for classmethod properties.
+
+    Args:
+        tp: The type or class to search for the attribute.
+        name: The name of the attribute to retrieve.
+
+    Returns:
+        Any: The attribute value, if found.
+
+    Raises:
+        KeyError: If the attribute is not found in any class's `__dict__` in the MRO.
+    """
+    for base in reversed(mro(tp)):
+        if name in base.__dict__:
+            return base.__dict__[name]
+    return tp.__dict__[name]  # raise the error
+
+
+@dataclass(**slots_true)
+class DecoratorInfos:
+    """Mapping of name in the class namespace to decorator info.
+
+    note that the name in the class namespace is the function or attribute name
+    not the field name!
+    """
+
+    validators: dict[str, Decorator[ValidatorDecoratorInfo]] = field(default_factory=dict)
+    field_validators: dict[str, Decorator[FieldValidatorDecoratorInfo]] = field(default_factory=dict)
+    root_validators: dict[str, Decorator[RootValidatorDecoratorInfo]] = field(default_factory=dict)
+    field_serializers: dict[str, Decorator[FieldSerializerDecoratorInfo]] = field(default_factory=dict)
+    model_serializers: dict[str, Decorator[ModelSerializerDecoratorInfo]] = field(default_factory=dict)
+    model_validators: dict[str, Decorator[ModelValidatorDecoratorInfo]] = field(default_factory=dict)
+    computed_fields: dict[str, Decorator[ComputedFieldInfo]] = field(default_factory=dict)
+
+    @staticmethod
+    def build(model_dc: type[Any]) -> DecoratorInfos:  # noqa: C901 (ignore complexity)
+        """We want to collect all DecFunc instances that exist as
+        attributes in the namespace of the class (a BaseModel or dataclass)
+        that called us
+        But we want to collect these in the order of the bases
+        So instead of getting them all from the leaf class (the class that called us),
+        we traverse the bases from root (the oldest ancestor class) to leaf
+        and collect all of the instances as we go, taking care to replace
+        any duplicate ones with the last one we see to mimic how function overriding
+        works with inheritance.
+        If we do replace any functions we put the replacement into the position
+        the replaced function was in; that is, we maintain the order.
+        """
+        # reminder: dicts are ordered and replacement does not alter the order
+        res = DecoratorInfos()
+        for base in reversed(mro(model_dc)[1:]):
+            existing: DecoratorInfos | None = base.__dict__.get('__pydantic_decorators__')
+            if existing is None:
+                existing = DecoratorInfos.build(base)
+            res.validators.update({k: v.bind_to_cls(model_dc) for k, v in existing.validators.items()})
+            res.field_validators.update({k: v.bind_to_cls(model_dc) for k, v in existing.field_validators.items()})
+            res.root_validators.update({k: v.bind_to_cls(model_dc) for k, v in existing.root_validators.items()})
+            res.field_serializers.update({k: v.bind_to_cls(model_dc) for k, v in existing.field_serializers.items()})
+            res.model_serializers.update({k: v.bind_to_cls(model_dc) for k, v in existing.model_serializers.items()})
+            res.model_validators.update({k: v.bind_to_cls(model_dc) for k, v in existing.model_validators.items()})
+            res.computed_fields.update({k: v.bind_to_cls(model_dc) for k, v in existing.computed_fields.items()})
+
+        to_replace: list[tuple[str, Any]] = []
+
+        for var_name, var_value in vars(model_dc).items():
+            if isinstance(var_value, PydanticDescriptorProxy):
+                info = var_value.decorator_info
+                if isinstance(info, ValidatorDecoratorInfo):
+                    res.validators[var_name] = Decorator.build(
+                        model_dc, cls_var_name=var_name, shim=var_value.shim, info=info
+                    )
+                elif isinstance(info, FieldValidatorDecoratorInfo):
+                    res.field_validators[var_name] = Decorator.build(
+                        model_dc, cls_var_name=var_name, shim=var_value.shim, info=info
+                    )
+                elif isinstance(info, RootValidatorDecoratorInfo):
+                    res.root_validators[var_name] = Decorator.build(
+                        model_dc, cls_var_name=var_name, shim=var_value.shim, info=info
+                    )
+                elif isinstance(info, FieldSerializerDecoratorInfo):
+                    # check whether a serializer function is already registered for fields
+                    for field_serializer_decorator in res.field_serializers.values():
+                        # check that each field has at most one serializer function.
+                        # serializer functions for the same field in subclasses are allowed,
+                        # and are treated as overrides
+                        if field_serializer_decorator.cls_var_name == var_name:
+                            continue
+                        for f in info.fields:
+                            if f in field_serializer_decorator.info.fields:
+                                raise PydanticUserError(
+                                    'Multiple field serializer functions were defined '
+                                    f'for field {f!r}, this is not allowed.',
+                                    code='multiple-field-serializers',
+                                )
+                    res.field_serializers[var_name] = Decorator.build(
+                        model_dc, cls_var_name=var_name, shim=var_value.shim, info=info
+                    )
+                elif isinstance(info, ModelValidatorDecoratorInfo):
+                    res.model_validators[var_name] = Decorator.build(
+                        model_dc, cls_var_name=var_name, shim=var_value.shim, info=info
+                    )
+                elif isinstance(info, ModelSerializerDecoratorInfo):
+                    res.model_serializers[var_name] = Decorator.build(
+                        model_dc, cls_var_name=var_name, shim=var_value.shim, info=info
+                    )
+                else:
+                    from ..fields import ComputedFieldInfo
+
+                    isinstance(var_value, ComputedFieldInfo)
+                    res.computed_fields[var_name] = Decorator.build(
+                        model_dc, cls_var_name=var_name, shim=None, info=info
+                    )
+                to_replace.append((var_name, var_value.wrapped))
+        if to_replace:
+            # If we can save `__pydantic_decorators__` on the class we'll be able to check for it above
+            # so then we don't need to re-process the type, which means we can discard our descriptor wrappers
+            # and replace them with the thing they are wrapping (see the other setattr call below)
+            # which allows validator class methods to also function as regular class methods
+            model_dc.__pydantic_decorators__ = res
+            for name, value in to_replace:
+                setattr(model_dc, name, value)
+        return res
+
+
+def inspect_validator(validator: Callable[..., Any], mode: FieldValidatorModes) -> bool:
+    """Look at a field or model validator function and determine whether it takes an info argument.
+
+    An error is raised if the function has an invalid signature.
+
+    Args:
+        validator: The validator function to inspect.
+        mode: The proposed validator mode.
+
+    Returns:
+        Whether the validator takes an info argument.
+    """
+    try:
+        sig = signature(validator)
+    except (ValueError, TypeError):
+        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
+        # In this case, we assume no info argument is present:
+        return False
+    n_positional = count_positional_required_params(sig)
+    if mode == 'wrap':
+        if n_positional == 3:
+            return True
+        elif n_positional == 2:
+            return False
+    else:
+        assert mode in {'before', 'after', 'plain'}, f"invalid mode: {mode!r}, expected 'before', 'after' or 'plain"
+        if n_positional == 2:
+            return True
+        elif n_positional == 1:
+            return False
+
+    raise PydanticUserError(
+        f'Unrecognized field_validator function signature for {validator} with `mode={mode}`:{sig}',
+        code='validator-signature',
+    )
+
+
+def inspect_field_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> tuple[bool, bool]:
+    """Look at a field serializer function and determine if it is a field serializer,
+    and whether it takes an info argument.
+
+    An error is raised if the function has an invalid signature.
+
+    Args:
+        serializer: The serializer function to inspect.
+        mode: The serializer mode, either 'plain' or 'wrap'.
+
+    Returns:
+        Tuple of (is_field_serializer, info_arg).
+    """
+    try:
+        sig = signature(serializer)
+    except (ValueError, TypeError):
+        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
+        # In this case, we assume no info argument is present and this is not a method:
+        return (False, False)
+
+    first = next(iter(sig.parameters.values()), None)
+    is_field_serializer = first is not None and first.name == 'self'
+
+    n_positional = count_positional_required_params(sig)
+    if is_field_serializer:
+        # -1 to correct for self parameter
+        info_arg = _serializer_info_arg(mode, n_positional - 1)
+    else:
+        info_arg = _serializer_info_arg(mode, n_positional)
+
+    if info_arg is None:
+        raise PydanticUserError(
+            f'Unrecognized field_serializer function signature for {serializer} with `mode={mode}`:{sig}',
+            code='field-serializer-signature',
+        )
+
+    return is_field_serializer, info_arg
+
+
+def inspect_annotated_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> bool:
+    """Look at a serializer function used via `Annotated` and determine whether it takes an info argument.
+
+    An error is raised if the function has an invalid signature.
+
+    Args:
+        serializer: The serializer function to check.
+        mode: The serializer mode, either 'plain' or 'wrap'.
+
+    Returns:
+        info_arg
+    """
+    try:
+        sig = signature(serializer)
+    except (ValueError, TypeError):
+        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
+        # In this case, we assume no info argument is present:
+        return False
+    info_arg = _serializer_info_arg(mode, count_positional_required_params(sig))
+    if info_arg is None:
+        raise PydanticUserError(
+            f'Unrecognized field_serializer function signature for {serializer} with `mode={mode}`:{sig}',
+            code='field-serializer-signature',
+        )
+    else:
+        return info_arg
+
+
+def inspect_model_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> bool:
+    """Look at a model serializer function and determine whether it takes an info argument.
+
+    An error is raised if the function has an invalid signature.
+
+    Args:
+        serializer: The serializer function to check.
+        mode: The serializer mode, either 'plain' or 'wrap'.
+
+    Returns:
+        `info_arg` - whether the function expects an info argument.
+    """
+    if isinstance(serializer, (staticmethod, classmethod)) or not is_instance_method_from_sig(serializer):
+        raise PydanticUserError(
+            '`@model_serializer` must be applied to instance methods', code='model-serializer-instance-method'
+        )
+
+    sig = signature(serializer)
+    info_arg = _serializer_info_arg(mode, count_positional_required_params(sig))
+    if info_arg is None:
+        raise PydanticUserError(
+            f'Unrecognized model_serializer function signature for {serializer} with `mode={mode}`:{sig}',
+            code='model-serializer-signature',
+        )
+    else:
+        return info_arg
+
+
+def _serializer_info_arg(mode: Literal['plain', 'wrap'], n_positional: int) -> bool | None:
+    if mode == 'plain':
+        if n_positional == 1:
+            # (input_value: Any, /) -> Any
+            return False
+        elif n_positional == 2:
+            # (model: Any, input_value: Any, /) -> Any
+            return True
+    else:
+        assert mode == 'wrap', f"invalid mode: {mode!r}, expected 'plain' or 'wrap'"
+        if n_positional == 2:
+            # (input_value: Any, serializer: SerializerFunctionWrapHandler, /) -> Any
+            return False
+        elif n_positional == 3:
+            # (input_value: Any, serializer: SerializerFunctionWrapHandler, info: SerializationInfo, /) -> Any
+            return True
+
+    return None
+
+
+AnyDecoratorCallable: TypeAlias = (
+    'Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any], Callable[..., Any]]'
+)
+
+
+def is_instance_method_from_sig(function: AnyDecoratorCallable) -> bool:
+    """Whether the function is an instance method.
+
+    It will consider a function as instance method if the first parameter of
+    function is `self`.
+
+    Args:
+        function: The function to check.
+
+    Returns:
+        `True` if the function is an instance method, `False` otherwise.
+    """
+    sig = signature(unwrap_wrapped_function(function))
+    first = next(iter(sig.parameters.values()), None)
+    if first and first.name == 'self':
+        return True
+    return False
+
+
+def ensure_classmethod_based_on_signature(function: AnyDecoratorCallable) -> Any:
+    """Apply the `@classmethod` decorator on the function.
+
+    Args:
+        function: The function to apply the decorator on.
+
+    Return:
+        The `@classmethod` decorator applied function.
+    """
+    if not isinstance(
+        unwrap_wrapped_function(function, unwrap_class_static_method=False), classmethod
+    ) and _is_classmethod_from_sig(function):
+        return classmethod(function)  # type: ignore[arg-type]
+    return function
+
+
+def _is_classmethod_from_sig(function: AnyDecoratorCallable) -> bool:
+    sig = signature(unwrap_wrapped_function(function))
+    first = next(iter(sig.parameters.values()), None)
+    if first and first.name == 'cls':
+        return True
+    return False
+
+
+def unwrap_wrapped_function(
+    func: Any,
+    *,
+    unwrap_partial: bool = True,
+    unwrap_class_static_method: bool = True,
+) -> Any:
+    """Recursively unwraps a wrapped function until the underlying function is reached.
+    This handles property, functools.partial, functools.partialmethod, staticmethod, and classmethod.
+
+    Args:
+        func: The function to unwrap.
+        unwrap_partial: If True (default), unwrap partial and partialmethod decorators.
+        unwrap_class_static_method: If True (default), also unwrap classmethod and staticmethod
+            decorators. If False, only unwrap partial and partialmethod decorators.
+
+    Returns:
+        The underlying function of the wrapped function.
+    """
+    # Define the types we want to check against as a single tuple.
+    unwrap_types = (
+        (property, cached_property)
+        + ((partial, partialmethod) if unwrap_partial else ())
+        + ((staticmethod, classmethod) if unwrap_class_static_method else ())
+    )
+
+    while isinstance(func, unwrap_types):
+        if unwrap_class_static_method and isinstance(func, (classmethod, staticmethod)):
+            func = func.__func__
+        elif isinstance(func, (partial, partialmethod)):
+            func = func.func
+        elif isinstance(func, property):
+            func = func.fget  # arbitrary choice, convenient for computed fields
+        else:
+            # Make coverage happy as it can only get here in the last possible case
+            assert isinstance(func, cached_property)
+            func = func.func  # type: ignore
+
+    return func
+
+
+def get_function_return_type(
+    func: Any,
+    explicit_return_type: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any:
+    """Get the function return type.
+
+    It gets the return type from the type annotation if `explicit_return_type` is `None`.
+    Otherwise, it returns `explicit_return_type`.
+
+    Args:
+        func: The function to get its return type.
+        explicit_return_type: The explicit return type.
+        globalns: The globals namespace to use during type annotation evaluation.
+        localns: The locals namespace to use during type annotation evaluation.
+
+    Returns:
+        The function return type.
+    """
+    if explicit_return_type is PydanticUndefined:
+        # try to get it from the type annotation
+        hints = get_function_type_hints(
+            unwrap_wrapped_function(func),
+            include_keys={'return'},
+            globalns=globalns,
+            localns=localns,
+        )
+        return hints.get('return', PydanticUndefined)
+    else:
+        return explicit_return_type
+
+
+def count_positional_required_params(sig: Signature) -> int:
+    """Get the number of positional (required) arguments of a signature.
+
+    This function should only be used to inspect signatures of validation and serialization functions.
+    The first argument (the value being serialized or validated) is counted as a required argument
+    even if a default value exists.
+
+    Returns:
+        The number of positional arguments of a signature.
+    """
+    parameters = list(sig.parameters.values())
+    return sum(
+        1
+        for param in parameters
+        if can_be_positional(param)
+        # First argument is the value being validated/serialized, and can have a default value
+        # (e.g. `float`, which has signature `(x=0, /)`). We assume other parameters (the info arg
+        # for instance) should be required, and thus without any default value.
+        and (param.default is Parameter.empty or param is parameters[0])
+    )
+
+
+def ensure_property(f: Any) -> Any:
+    """Ensure that a function is a `property` or `cached_property`, or is a valid descriptor.
+
+    Args:
+        f: The function to check.
+
+    Returns:
+        The function, or a `property` or `cached_property` instance wrapping the function.
+    """
+    if ismethoddescriptor(f) or isdatadescriptor(f):
+        return f
+    else:
+        return property(f)

env/lib/python3.11/site-packages/pydantic/_internal/_decorators_v1.py

@@ -0,0 +1,174 @@
+"""Logic for V1 validators, e.g. `@validator` and `@root_validator`."""
+
+from __future__ import annotations as _annotations
+
+from inspect import Parameter, signature
+from typing import Any, Dict, Tuple, Union, cast
+
+from pydantic_core import core_schema
+from typing_extensions import Protocol
+
+from ..errors import PydanticUserError
+from ._utils import can_be_positional
+
+
+class V1OnlyValueValidator(Protocol):
+    """A simple validator, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any) -> Any: ...
+
+
+class V1ValidatorWithValues(Protocol):
+    """A validator with `values` argument, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any, values: dict[str, Any]) -> Any: ...
+
+
+class V1ValidatorWithValuesKwOnly(Protocol):
+    """A validator with keyword only `values` argument, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any, *, values: dict[str, Any]) -> Any: ...
+
+
+class V1ValidatorWithKwargs(Protocol):
+    """A validator with `kwargs` argument, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any, **kwargs: Any) -> Any: ...
+
+
+class V1ValidatorWithValuesAndKwargs(Protocol):
+    """A validator with `values` and `kwargs` arguments, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any, values: dict[str, Any], **kwargs: Any) -> Any: ...
+
+
+V1Validator = Union[
+    V1ValidatorWithValues, V1ValidatorWithValuesKwOnly, V1ValidatorWithKwargs, V1ValidatorWithValuesAndKwargs
+]
+
+
+def can_be_keyword(param: Parameter) -> bool:
+    return param.kind in (Parameter.POSITIONAL_OR_KEYWORD, Parameter.KEYWORD_ONLY)
+
+
+def make_generic_v1_field_validator(validator: V1Validator) -> core_schema.WithInfoValidatorFunction:
+    """Wrap a V1 style field validator for V2 compatibility.
+
+    Args:
+        validator: The V1 style field validator.
+
+    Returns:
+        A wrapped V2 style field validator.
+
+    Raises:
+        PydanticUserError: If the signature is not supported or the parameters are
+            not available in Pydantic V2.
+    """
+    sig = signature(validator)
+
+    needs_values_kw = False
+
+    for param_num, (param_name, parameter) in enumerate(sig.parameters.items()):
+        if can_be_keyword(parameter) and param_name in ('field', 'config'):
+            raise PydanticUserError(
+                'The `field` and `config` parameters are not available in Pydantic V2, '
+                'please use the `info` parameter instead.',
+                code='validator-field-config-info',
+            )
+        if parameter.kind is Parameter.VAR_KEYWORD:
+            needs_values_kw = True
+        elif can_be_keyword(parameter) and param_name == 'values':
+            needs_values_kw = True
+        elif can_be_positional(parameter) and param_num == 0:
+            # value
+            continue
+        elif parameter.default is Parameter.empty:  # ignore params with defaults e.g. bound by functools.partial
+            raise PydanticUserError(
+                f'Unsupported signature for V1 style validator {validator}: {sig} is not supported.',
+                code='validator-v1-signature',
+            )
+
+    if needs_values_kw:
+        # (v, **kwargs), (v, values, **kwargs), (v, *, values, **kwargs) or (v, *, values)
+        val1 = cast(V1ValidatorWithValues, validator)
+
+        def wrapper1(value: Any, info: core_schema.ValidationInfo) -> Any:
+            return val1(value, values=info.data)
+
+        return wrapper1
+    else:
+        val2 = cast(V1OnlyValueValidator, validator)
+
+        def wrapper2(value: Any, _: core_schema.ValidationInfo) -> Any:
+            return val2(value)
+
+        return wrapper2
+
+
+RootValidatorValues = Dict[str, Any]
+# technically tuple[model_dict, model_extra, fields_set] | tuple[dataclass_dict, init_vars]
+RootValidatorFieldsTuple = Tuple[Any, ...]
+
+
+class V1RootValidatorFunction(Protocol):
+    """A simple root validator, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __values: RootValidatorValues) -> RootValidatorValues: ...
+
+
+class V2CoreBeforeRootValidator(Protocol):
+    """V2 validator with mode='before'."""
+
+    def __call__(self, __values: RootValidatorValues, __info: core_schema.ValidationInfo) -> RootValidatorValues: ...
+
+
+class V2CoreAfterRootValidator(Protocol):
+    """V2 validator with mode='after'."""
+
+    def __call__(
+        self, __fields_tuple: RootValidatorFieldsTuple, __info: core_schema.ValidationInfo
+    ) -> RootValidatorFieldsTuple: ...
+
+
+def make_v1_generic_root_validator(
+    validator: V1RootValidatorFunction, pre: bool
+) -> V2CoreBeforeRootValidator | V2CoreAfterRootValidator:
+    """Wrap a V1 style root validator for V2 compatibility.
+
+    Args:
+        validator: The V1 style field validator.
+        pre: Whether the validator is a pre validator.
+
+    Returns:
+        A wrapped V2 style validator.
+    """
+    if pre is True:
+        # mode='before' for pydantic-core
+        def _wrapper1(values: RootValidatorValues, _: core_schema.ValidationInfo) -> RootValidatorValues:
+            return validator(values)
+
+        return _wrapper1
+
+    # mode='after' for pydantic-core
+    def _wrapper2(fields_tuple: RootValidatorFieldsTuple, _: core_schema.ValidationInfo) -> RootValidatorFieldsTuple:
+        if len(fields_tuple) == 2:
+            # dataclass, this is easy
+            values, init_vars = fields_tuple
+            values = validator(values)
+            return values, init_vars
+        else:
+            # ugly hack: to match v1 behaviour, we merge values and model_extra, then split them up based on fields
+            # afterwards
+            model_dict, model_extra, fields_set = fields_tuple
+            if model_extra:
+                fields = set(model_dict.keys())
+                model_dict.update(model_extra)
+                model_dict_new = validator(model_dict)
+                for k in list(model_dict_new.keys()):
+                    if k not in fields:
+                        model_extra[k] = model_dict_new.pop(k)
+            else:
+                model_dict_new = validator(model_dict)
+            return model_dict_new, model_extra, fields_set
+
+    return _wrapper2

env/lib/python3.11/site-packages/pydantic/_internal/_discriminated_union.py

@@ -0,0 +1,503 @@
+from __future__ import annotations as _annotations
+
+from typing import TYPE_CHECKING, Any, Hashable, Sequence
+
+from pydantic_core import CoreSchema, core_schema
+
+from ..errors import PydanticUserError
+from . import _core_utils
+from ._core_utils import (
+    CoreSchemaField,
+    collect_definitions,
+)
+
+if TYPE_CHECKING:
+    from ..types import Discriminator
+
+CORE_SCHEMA_METADATA_DISCRIMINATOR_PLACEHOLDER_KEY = 'pydantic.internal.union_discriminator'
+
+
+class MissingDefinitionForUnionRef(Exception):
+    """Raised when applying a discriminated union discriminator to a schema
+    requires a definition that is not yet defined
+    """
+
+    def __init__(self, ref: str) -> None:
+        self.ref = ref
+        super().__init__(f'Missing definition for ref {self.ref!r}')
+
+
+def set_discriminator_in_metadata(schema: CoreSchema, discriminator: Any) -> None:
+    schema.setdefault('metadata', {})
+    metadata = schema.get('metadata')
+    assert metadata is not None
+    metadata[CORE_SCHEMA_METADATA_DISCRIMINATOR_PLACEHOLDER_KEY] = discriminator
+
+
+def apply_discriminators(schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
+    # We recursively walk through the `schema` passed to `apply_discriminators`, applying discriminators
+    # where necessary at each level. During this recursion, we allow references to be resolved from the definitions
+    # that are originally present on the original, outermost `schema`. Before `apply_discriminators` is called,
+    # `simplify_schema_references` is called on the schema (in the `clean_schema` function),
+    # which often puts the definitions in the outermost schema.
+    global_definitions: dict[str, CoreSchema] = collect_definitions(schema)
+
+    def inner(s: core_schema.CoreSchema, recurse: _core_utils.Recurse) -> core_schema.CoreSchema:
+        nonlocal global_definitions
+
+        s = recurse(s, inner)
+        if s['type'] == 'tagged-union':
+            return s
+
+        metadata = s.get('metadata', {})
+        discriminator = metadata.pop(CORE_SCHEMA_METADATA_DISCRIMINATOR_PLACEHOLDER_KEY, None)
+        if discriminator is not None:
+            s = apply_discriminator(s, discriminator, global_definitions)
+        return s
+
+    return _core_utils.walk_core_schema(schema, inner, copy=False)
+
+
+def apply_discriminator(
+    schema: core_schema.CoreSchema,
+    discriminator: str | Discriminator,
+    definitions: dict[str, core_schema.CoreSchema] | None = None,
+) -> core_schema.CoreSchema:
+    """Applies the discriminator and returns a new core schema.
+
+    Args:
+        schema: The input schema.
+        discriminator: The name of the field which will serve as the discriminator.
+        definitions: A mapping of schema ref to schema.
+
+    Returns:
+        The new core schema.
+
+    Raises:
+        TypeError:
+            - If `discriminator` is used with invalid union variant.
+            - If `discriminator` is used with `Union` type with one variant.
+            - If `discriminator` value mapped to multiple choices.
+        MissingDefinitionForUnionRef:
+            If the definition for ref is missing.
+        PydanticUserError:
+            - If a model in union doesn't have a discriminator field.
+            - If discriminator field has a non-string alias.
+            - If discriminator fields have different aliases.
+            - If discriminator field not of type `Literal`.
+    """
+    from ..types import Discriminator
+
+    if isinstance(discriminator, Discriminator):
+        if isinstance(discriminator.discriminator, str):
+            discriminator = discriminator.discriminator
+        else:
+            return discriminator._convert_schema(schema)
+
+    return _ApplyInferredDiscriminator(discriminator, definitions or {}).apply(schema)
+
+
+class _ApplyInferredDiscriminator:
+    """This class is used to convert an input schema containing a union schema into one where that union is
+    replaced with a tagged-union, with all the associated debugging and performance benefits.
+
+    This is done by:
+    * Validating that the input schema is compatible with the provided discriminator
+    * Introspecting the schema to determine which discriminator values should map to which union choices
+    * Handling various edge cases such as 'definitions', 'default', 'nullable' schemas, and more
+
+    I have chosen to implement the conversion algorithm in this class, rather than a function,
+    to make it easier to maintain state while recursively walking the provided CoreSchema.
+    """
+
+    def __init__(self, discriminator: str, definitions: dict[str, core_schema.CoreSchema]):
+        # `discriminator` should be the name of the field which will serve as the discriminator.
+        # It must be the python name of the field, and *not* the field's alias. Note that as of now,
+        # all members of a discriminated union _must_ use a field with the same name as the discriminator.
+        # This may change if/when we expose a way to manually specify the TaggedUnionSchema's choices.
+        self.discriminator = discriminator
+
+        # `definitions` should contain a mapping of schema ref to schema for all schemas which might
+        # be referenced by some choice
+        self.definitions = definitions
+
+        # `_discriminator_alias` will hold the value, if present, of the alias for the discriminator
+        #
+        # Note: following the v1 implementation, we currently disallow the use of different aliases
+        # for different choices. This is not a limitation of pydantic_core, but if we try to handle
+        # this, the inference logic gets complicated very quickly, and could result in confusing
+        # debugging challenges for users making subtle mistakes.
+        #
+        # Rather than trying to do the most powerful inference possible, I think we should eventually
+        # expose a way to more-manually control the way the TaggedUnionSchema is constructed through
+        # the use of a new type which would be placed as an Annotation on the Union type. This would
+        # provide the full flexibility/power of pydantic_core's TaggedUnionSchema where necessary for
+        # more complex cases, without over-complicating the inference logic for the common cases.
+        self._discriminator_alias: str | None = None
+
+        # `_should_be_nullable` indicates whether the converted union has `None` as an allowed value.
+        # If `None` is an acceptable value of the (possibly-wrapped) union, we ignore it while
+        # constructing the TaggedUnionSchema, but set the `_should_be_nullable` attribute to True.
+        # Once we have constructed the TaggedUnionSchema, if `_should_be_nullable` is True, we ensure
+        # that the final schema gets wrapped as a NullableSchema. This has the same semantics on the
+        # python side, but resolves the issue that `None` cannot correspond to any discriminator values.
+        self._should_be_nullable = False
+
+        # `_is_nullable` is used to track if the final produced schema will definitely be nullable;
+        # we set it to True if the input schema is wrapped in a nullable schema that we know will be preserved
+        # as an indication that, even if None is discovered as one of the union choices, we will not need to wrap
+        # the final value in another nullable schema.
+        #
+        # This is more complicated than just checking for the final outermost schema having type 'nullable' thanks
+        # to the possible presence of other wrapper schemas such as DefinitionsSchema, WithDefaultSchema, etc.
+        self._is_nullable = False
+
+        # `_choices_to_handle` serves as a stack of choices to add to the tagged union. Initially, choices
+        # from the union in the wrapped schema will be appended to this list, and the recursive choice-handling
+        # algorithm may add more choices to this stack as (nested) unions are encountered.
+        self._choices_to_handle: list[core_schema.CoreSchema] = []
+
+        # `_tagged_union_choices` is built during the call to `apply`, and will hold the choices to be included
+        # in the output TaggedUnionSchema that will replace the union from the input schema
+        self._tagged_union_choices: dict[Hashable, core_schema.CoreSchema] = {}
+
+        # `_used` is changed to True after applying the discriminator to prevent accidental reuse
+        self._used = False
+
+    def apply(self, schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
+        """Return a new CoreSchema based on `schema` that uses a tagged-union with the discriminator provided
+        to this class.
+
+        Args:
+            schema: The input schema.
+
+        Returns:
+            The new core schema.
+
+        Raises:
+            TypeError:
+                - If `discriminator` is used with invalid union variant.
+                - If `discriminator` is used with `Union` type with one variant.
+                - If `discriminator` value mapped to multiple choices.
+            ValueError:
+                If the definition for ref is missing.
+            PydanticUserError:
+                - If a model in union doesn't have a discriminator field.
+                - If discriminator field has a non-string alias.
+                - If discriminator fields have different aliases.
+                - If discriminator field not of type `Literal`.
+        """
+        assert not self._used
+        schema = self._apply_to_root(schema)
+        if self._should_be_nullable and not self._is_nullable:
+            schema = core_schema.nullable_schema(schema)
+        self._used = True
+        return schema
+
+    def _apply_to_root(self, schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
+        """This method handles the outer-most stage of recursion over the input schema:
+        unwrapping nullable or definitions schemas, and calling the `_handle_choice`
+        method iteratively on the choices extracted (recursively) from the possibly-wrapped union.
+        """
+        if schema['type'] == 'nullable':
+            self._is_nullable = True
+            wrapped = self._apply_to_root(schema['schema'])
+            nullable_wrapper = schema.copy()
+            nullable_wrapper['schema'] = wrapped
+            return nullable_wrapper
+
+        if schema['type'] == 'definitions':
+            wrapped = self._apply_to_root(schema['schema'])
+            definitions_wrapper = schema.copy()
+            definitions_wrapper['schema'] = wrapped
+            return definitions_wrapper
+
+        if schema['type'] != 'union':
+            # If the schema is not a union, it probably means it just had a single member and
+            # was flattened by pydantic_core.
+            # However, it still may make sense to apply the discriminator to this schema,
+            # as a way to get discriminated-union-style error messages, so we allow this here.
+            schema = core_schema.union_schema([schema])
+
+        # Reverse the choices list before extending the stack so that they get handled in the order they occur
+        choices_schemas = [v[0] if isinstance(v, tuple) else v for v in schema['choices'][::-1]]
+        self._choices_to_handle.extend(choices_schemas)
+        while self._choices_to_handle:
+            choice = self._choices_to_handle.pop()
+            self._handle_choice(choice)
+
+        if self._discriminator_alias is not None and self._discriminator_alias != self.discriminator:
+            # * We need to annotate `discriminator` as a union here to handle both branches of this conditional
+            # * We need to annotate `discriminator` as list[list[str | int]] and not list[list[str]] due to the
+            #   invariance of list, and because list[list[str | int]] is the type of the discriminator argument
+            #   to tagged_union_schema below
+            # * See the docstring of pydantic_core.core_schema.tagged_union_schema for more details about how to
+            #   interpret the value of the discriminator argument to tagged_union_schema. (The list[list[str]] here
+            #   is the appropriate way to provide a list of fallback attributes to check for a discriminator value.)
+            discriminator: str | list[list[str | int]] = [[self.discriminator], [self._discriminator_alias]]
+        else:
+            discriminator = self.discriminator
+        return core_schema.tagged_union_schema(
+            choices=self._tagged_union_choices,
+            discriminator=discriminator,
+            custom_error_type=schema.get('custom_error_type'),
+            custom_error_message=schema.get('custom_error_message'),
+            custom_error_context=schema.get('custom_error_context'),
+            strict=False,
+            from_attributes=True,
+            ref=schema.get('ref'),
+            metadata=schema.get('metadata'),
+            serialization=schema.get('serialization'),
+        )
+
+    def _handle_choice(self, choice: core_schema.CoreSchema) -> None:
+        """This method handles the "middle" stage of recursion over the input schema.
+        Specifically, it is responsible for handling each choice of the outermost union
+        (and any "coalesced" choices obtained from inner unions).
+
+        Here, "handling" entails:
+        * Coalescing nested unions and compatible tagged-unions
+        * Tracking the presence of 'none' and 'nullable' schemas occurring as choices
+        * Validating that each allowed discriminator value maps to a unique choice
+        * Updating the _tagged_union_choices mapping that will ultimately be used to build the TaggedUnionSchema.
+        """
+        if choice['type'] == 'definition-ref':
+            if choice['schema_ref'] not in self.definitions:
+                raise MissingDefinitionForUnionRef(choice['schema_ref'])
+
+        if choice['type'] == 'none':
+            self._should_be_nullable = True
+        elif choice['type'] == 'definitions':
+            self._handle_choice(choice['schema'])
+        elif choice['type'] == 'nullable':
+            self._should_be_nullable = True
+            self._handle_choice(choice['schema'])  # unwrap the nullable schema
+        elif choice['type'] == 'union':
+            # Reverse the choices list before extending the stack so that they get handled in the order they occur
+            choices_schemas = [v[0] if isinstance(v, tuple) else v for v in choice['choices'][::-1]]
+            self._choices_to_handle.extend(choices_schemas)
+        elif choice['type'] not in {
+            'model',
+            'typed-dict',
+            'tagged-union',
+            'lax-or-strict',
+            'dataclass',
+            'dataclass-args',
+            'definition-ref',
+        } and not _core_utils.is_function_with_inner_schema(choice):
+            # We should eventually handle 'definition-ref' as well
+            raise TypeError(
+                f'{choice["type"]!r} is not a valid discriminated union variant;'
+                ' should be a `BaseModel` or `dataclass`'
+            )
+        else:
+            if choice['type'] == 'tagged-union' and self._is_discriminator_shared(choice):
+                # In this case, this inner tagged-union is compatible with the outer tagged-union,
+                # and its choices can be coalesced into the outer TaggedUnionSchema.
+                subchoices = [x for x in choice['choices'].values() if not isinstance(x, (str, int))]
+                # Reverse the choices list before extending the stack so that they get handled in the order they occur
+                self._choices_to_handle.extend(subchoices[::-1])
+                return
+
+            inferred_discriminator_values = self._infer_discriminator_values_for_choice(choice, source_name=None)
+            self._set_unique_choice_for_values(choice, inferred_discriminator_values)
+
+    def _is_discriminator_shared(self, choice: core_schema.TaggedUnionSchema) -> bool:
+        """This method returns a boolean indicating whether the discriminator for the `choice`
+        is the same as that being used for the outermost tagged union. This is used to
+        determine whether this TaggedUnionSchema choice should be "coalesced" into the top level,
+        or whether it should be treated as a separate (nested) choice.
+        """
+        inner_discriminator = choice['discriminator']
+        return inner_discriminator == self.discriminator or (
+            isinstance(inner_discriminator, list)
+            and (self.discriminator in inner_discriminator or [self.discriminator] in inner_discriminator)
+        )
+
+    def _infer_discriminator_values_for_choice(  # noqa C901
+        self, choice: core_schema.CoreSchema, source_name: str | None
+    ) -> list[str | int]:
+        """This function recurses over `choice`, extracting all discriminator values that should map to this choice.
+
+        `model_name` is accepted for the purpose of producing useful error messages.
+        """
+        if choice['type'] == 'definitions':
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=source_name)
+        elif choice['type'] == 'function-plain':
+            raise TypeError(
+                f'{choice["type"]!r} is not a valid discriminated union variant;'
+                ' should be a `BaseModel` or `dataclass`'
+            )
+        elif _core_utils.is_function_with_inner_schema(choice):
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=source_name)
+        elif choice['type'] == 'lax-or-strict':
+            return sorted(
+                set(
+                    self._infer_discriminator_values_for_choice(choice['lax_schema'], source_name=None)
+                    + self._infer_discriminator_values_for_choice(choice['strict_schema'], source_name=None)
+                )
+            )
+
+        elif choice['type'] == 'tagged-union':
+            values: list[str | int] = []
+            # Ignore str/int "choices" since these are just references to other choices
+            subchoices = [x for x in choice['choices'].values() if not isinstance(x, (str, int))]
+            for subchoice in subchoices:
+                subchoice_values = self._infer_discriminator_values_for_choice(subchoice, source_name=None)
+                values.extend(subchoice_values)
+            return values
+
+        elif choice['type'] == 'union':
+            values = []
+            for subchoice in choice['choices']:
+                subchoice_schema = subchoice[0] if isinstance(subchoice, tuple) else subchoice
+                subchoice_values = self._infer_discriminator_values_for_choice(subchoice_schema, source_name=None)
+                values.extend(subchoice_values)
+            return values
+
+        elif choice['type'] == 'nullable':
+            self._should_be_nullable = True
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=None)
+
+        elif choice['type'] == 'model':
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=choice['cls'].__name__)
+
+        elif choice['type'] == 'dataclass':
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=choice['cls'].__name__)
+
+        elif choice['type'] == 'model-fields':
+            return self._infer_discriminator_values_for_model_choice(choice, source_name=source_name)
+
+        elif choice['type'] == 'dataclass-args':
+            return self._infer_discriminator_values_for_dataclass_choice(choice, source_name=source_name)
+
+        elif choice['type'] == 'typed-dict':
+            return self._infer_discriminator_values_for_typed_dict_choice(choice, source_name=source_name)
+
+        elif choice['type'] == 'definition-ref':
+            schema_ref = choice['schema_ref']
+            if schema_ref not in self.definitions:
+                raise MissingDefinitionForUnionRef(schema_ref)
+            return self._infer_discriminator_values_for_choice(self.definitions[schema_ref], source_name=source_name)
+        else:
+            raise TypeError(
+                f'{choice["type"]!r} is not a valid discriminated union variant;'
+                ' should be a `BaseModel` or `dataclass`'
+            )
+
+    def _infer_discriminator_values_for_typed_dict_choice(
+        self, choice: core_schema.TypedDictSchema, source_name: str | None = None
+    ) -> list[str | int]:
+        """This method just extracts the _infer_discriminator_values_for_choice logic specific to TypedDictSchema
+        for the sake of readability.
+        """
+        source = 'TypedDict' if source_name is None else f'TypedDict {source_name!r}'
+        field = choice['fields'].get(self.discriminator)
+        if field is None:
+            raise PydanticUserError(
+                f'{source} needs a discriminator field for key {self.discriminator!r}', code='discriminator-no-field'
+            )
+        return self._infer_discriminator_values_for_field(field, source)
+
+    def _infer_discriminator_values_for_model_choice(
+        self, choice: core_schema.ModelFieldsSchema, source_name: str | None = None
+    ) -> list[str | int]:
+        source = 'ModelFields' if source_name is None else f'Model {source_name!r}'
+        field = choice['fields'].get(self.discriminator)
+        if field is None:
+            raise PydanticUserError(
+                f'{source} needs a discriminator field for key {self.discriminator!r}', code='discriminator-no-field'
+            )
+        return self._infer_discriminator_values_for_field(field, source)
+
+    def _infer_discriminator_values_for_dataclass_choice(
+        self, choice: core_schema.DataclassArgsSchema, source_name: str | None = None
+    ) -> list[str | int]:
+        source = 'DataclassArgs' if source_name is None else f'Dataclass {source_name!r}'
+        for field in choice['fields']:
+            if field['name'] == self.discriminator:
+                break
+        else:
+            raise PydanticUserError(
+                f'{source} needs a discriminator field for key {self.discriminator!r}', code='discriminator-no-field'
+            )
+        return self._infer_discriminator_values_for_field(field, source)
+
+    def _infer_discriminator_values_for_field(self, field: CoreSchemaField, source: str) -> list[str | int]:
+        if field['type'] == 'computed-field':
+            # This should never occur as a discriminator, as it is only relevant to serialization
+            return []
+        alias = field.get('validation_alias', self.discriminator)
+        if not isinstance(alias, str):
+            raise PydanticUserError(
+                f'Alias {alias!r} is not supported in a discriminated union', code='discriminator-alias-type'
+            )
+        if self._discriminator_alias is None:
+            self._discriminator_alias = alias
+        elif self._discriminator_alias != alias:
+            raise PydanticUserError(
+                f'Aliases for discriminator {self.discriminator!r} must be the same '
+                f'(got {alias}, {self._discriminator_alias})',
+                code='discriminator-alias',
+            )
+        return self._infer_discriminator_values_for_inner_schema(field['schema'], source)
+
+    def _infer_discriminator_values_for_inner_schema(
+        self, schema: core_schema.CoreSchema, source: str
+    ) -> list[str | int]:
+        """When inferring discriminator values for a field, we typically extract the expected values from a literal
+        schema. This function does that, but also handles nested unions and defaults.
+        """
+        if schema['type'] == 'literal':
+            return schema['expected']
+
+        elif schema['type'] == 'union':
+            # Generally when multiple values are allowed they should be placed in a single `Literal`, but
+            # we add this case to handle the situation where a field is annotated as a `Union` of `Literal`s.
+            # For example, this lets us handle `Union[Literal['key'], Union[Literal['Key'], Literal['KEY']]]`
+            values: list[Any] = []
+            for choice in schema['choices']:
+                choice_schema = choice[0] if isinstance(choice, tuple) else choice
+                choice_values = self._infer_discriminator_values_for_inner_schema(choice_schema, source)
+                values.extend(choice_values)
+            return values
+
+        elif schema['type'] == 'default':
+            # This will happen if the field has a default value; we ignore it while extracting the discriminator values
+            return self._infer_discriminator_values_for_inner_schema(schema['schema'], source)
+
+        elif schema['type'] == 'function-after':
+            # After validators don't affect the discriminator values
+            return self._infer_discriminator_values_for_inner_schema(schema['schema'], source)
+
+        elif schema['type'] in {'function-before', 'function-wrap', 'function-plain'}:
+            validator_type = repr(schema['type'].split('-')[1])
+            raise PydanticUserError(
+                f'Cannot use a mode={validator_type} validator in the'
+                f' discriminator field {self.discriminator!r} of {source}',
+                code='discriminator-validator',
+            )
+
+        else:
+            raise PydanticUserError(
+                f'{source} needs field {self.discriminator!r} to be of type `Literal`',
+                code='discriminator-needs-literal',
+            )
+
+    def _set_unique_choice_for_values(self, choice: core_schema.CoreSchema, values: Sequence[str | int]) -> None:
+        """This method updates `self.tagged_union_choices` so that all provided (discriminator) `values` map to the
+        provided `choice`, validating that none of these values already map to another (different) choice.
+        """
+        for discriminator_value in values:
+            if discriminator_value in self._tagged_union_choices:
+                # It is okay if `value` is already in tagged_union_choices as long as it maps to the same value.
+                # Because tagged_union_choices may map values to other values, we need to walk the choices dict
+                # until we get to a "real" choice, and confirm that is equal to the one assigned.
+                existing_choice = self._tagged_union_choices[discriminator_value]
+                if existing_choice != choice:
+                    raise TypeError(
+                        f'Value {discriminator_value!r} for discriminator '
+                        f'{self.discriminator!r} mapped to multiple choices'
+                    )
+            else:
+                self._tagged_union_choices[discriminator_value] = choice

env/lib/python3.11/site-packages/pydantic/_internal/_docs_extraction.py

@@ -0,0 +1,108 @@
+"""Utilities related to attribute docstring extraction."""
+
+from __future__ import annotations
+
+import ast
+import inspect
+import textwrap
+from typing import Any
+
+
+class DocstringVisitor(ast.NodeVisitor):
+    def __init__(self) -> None:
+        super().__init__()
+
+        self.target: str | None = None
+        self.attrs: dict[str, str] = {}
+        self.previous_node_type: type[ast.AST] | None = None
+
+    def visit(self, node: ast.AST) -> Any:
+        node_result = super().visit(node)
+        self.previous_node_type = type(node)
+        return node_result
+
+    def visit_AnnAssign(self, node: ast.AnnAssign) -> Any:
+        if isinstance(node.target, ast.Name):
+            self.target = node.target.id
+
+    def visit_Expr(self, node: ast.Expr) -> Any:
+        if (
+            isinstance(node.value, ast.Constant)
+            and isinstance(node.value.value, str)
+            and self.previous_node_type is ast.AnnAssign
+        ):
+            docstring = inspect.cleandoc(node.value.value)
+            if self.target:
+                self.attrs[self.target] = docstring
+            self.target = None
+
+
+def _dedent_source_lines(source: list[str]) -> str:
+    # Required for nested class definitions, e.g. in a function block
+    dedent_source = textwrap.dedent(''.join(source))
+    if dedent_source.startswith((' ', '\t')):
+        # We are in the case where there's a dedented (usually multiline) string
+        # at a lower indentation level than the class itself. We wrap our class
+        # in a function as a workaround.
+        dedent_source = f'def dedent_workaround():\n{dedent_source}'
+    return dedent_source
+
+
+def _extract_source_from_frame(cls: type[Any]) -> list[str] | None:
+    frame = inspect.currentframe()
+
+    while frame:
+        if inspect.getmodule(frame) is inspect.getmodule(cls):
+            lnum = frame.f_lineno
+            try:
+                lines, _ = inspect.findsource(frame)
+            except OSError:
+                # Source can't be retrieved (maybe because running in an interactive terminal),
+                # we don't want to error here.
+                pass
+            else:
+                block_lines = inspect.getblock(lines[lnum - 1 :])
+                dedent_source = _dedent_source_lines(block_lines)
+                try:
+                    block_tree = ast.parse(dedent_source)
+                except SyntaxError:
+                    pass
+                else:
+                    stmt = block_tree.body[0]
+                    if isinstance(stmt, ast.FunctionDef) and stmt.name == 'dedent_workaround':
+                        # `_dedent_source_lines` wrapped the class around the workaround function
+                        stmt = stmt.body[0]
+                    if isinstance(stmt, ast.ClassDef) and stmt.name == cls.__name__:
+                        return block_lines
+
+        frame = frame.f_back
+
+
+def extract_docstrings_from_cls(cls: type[Any], use_inspect: bool = False) -> dict[str, str]:
+    """Map model attributes and their corresponding docstring.
+
+    Args:
+        cls: The class of the Pydantic model to inspect.
+        use_inspect: Whether to skip usage of frames to find the object and use
+            the `inspect` module instead.
+
+    Returns:
+        A mapping containing attribute names and their corresponding docstring.
+    """
+    if use_inspect:
+        # Might not work as expected if two classes have the same name in the same source file.
+        try:
+            source, _ = inspect.getsourcelines(cls)
+        except OSError:
+            return {}
+    else:
+        source = _extract_source_from_frame(cls)
+
+    if not source:
+        return {}
+
+    dedent_source = _dedent_source_lines(source)
+
+    visitor = DocstringVisitor()
+    visitor.visit(ast.parse(dedent_source))
+    return visitor.attrs

env/lib/python3.11/site-packages/pydantic/_internal/_fields.py

@@ -0,0 +1,392 @@
+"""Private logic related to fields (the `Field()` function and `FieldInfo` class), and arguments to `Annotated`."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import warnings
+from copy import copy
+from functools import lru_cache
+from inspect import Parameter, ismethoddescriptor, signature
+from typing import TYPE_CHECKING, Any, Callable, Pattern
+
+from pydantic_core import PydanticUndefined
+from typing_extensions import TypeIs
+
+from pydantic.errors import PydanticUserError
+
+from . import _typing_extra
+from ._config import ConfigWrapper
+from ._docs_extraction import extract_docstrings_from_cls
+from ._import_utils import import_cached_base_model, import_cached_field_info
+from ._namespace_utils import NsResolver
+from ._repr import Representation
+from ._utils import can_be_positional
+
+if TYPE_CHECKING:
+    from annotated_types import BaseMetadata
+
+    from ..fields import FieldInfo
+    from ..main import BaseModel
+    from ._dataclasses import StandardDataclass
+    from ._decorators import DecoratorInfos
+
+
+class PydanticMetadata(Representation):
+    """Base class for annotation markers like `Strict`."""
+
+    __slots__ = ()
+
+
+def pydantic_general_metadata(**metadata: Any) -> BaseMetadata:
+    """Create a new `_PydanticGeneralMetadata` class with the given metadata.
+
+    Args:
+        **metadata: The metadata to add.
+
+    Returns:
+        The new `_PydanticGeneralMetadata` class.
+    """
+    return _general_metadata_cls()(metadata)  # type: ignore
+
+
+@lru_cache(maxsize=None)
+def _general_metadata_cls() -> type[BaseMetadata]:
+    """Do it this way to avoid importing `annotated_types` at import time."""
+    from annotated_types import BaseMetadata
+
+    class _PydanticGeneralMetadata(PydanticMetadata, BaseMetadata):
+        """Pydantic general metadata like `max_digits`."""
+
+        def __init__(self, metadata: Any):
+            self.__dict__ = metadata
+
+    return _PydanticGeneralMetadata  # type: ignore
+
+
+def _update_fields_from_docstrings(cls: type[Any], fields: dict[str, FieldInfo], config_wrapper: ConfigWrapper) -> None:
+    if config_wrapper.use_attribute_docstrings:
+        fields_docs = extract_docstrings_from_cls(cls)
+        for ann_name, field_info in fields.items():
+            if field_info.description is None and ann_name in fields_docs:
+                field_info.description = fields_docs[ann_name]
+
+
+def collect_model_fields(  # noqa: C901
+    cls: type[BaseModel],
+    bases: tuple[type[Any], ...],
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver | None,
+    *,
+    typevars_map: dict[Any, Any] | None = None,
+) -> tuple[dict[str, FieldInfo], set[str]]:
+    """Collect the fields of a nascent pydantic model.
+
+    Also collect the names of any ClassVars present in the type hints.
+
+    The returned value is a tuple of two items: the fields dict, and the set of ClassVar names.
+
+    Args:
+        cls: BaseModel or dataclass.
+        bases: Parents of the class, generally `cls.__bases__`.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: Namespace resolver to use when getting model annotations.
+        typevars_map: A dictionary mapping type variables to their concrete types.
+
+    Returns:
+        A tuple contains fields and class variables.
+
+    Raises:
+        NameError:
+            - If there is a conflict between a field name and protected namespaces.
+            - If there is a field other than `root` in `RootModel`.
+            - If a field shadows an attribute in the parent model.
+    """
+    BaseModel = import_cached_base_model()
+    FieldInfo_ = import_cached_field_info()
+
+    parent_fields_lookup: dict[str, FieldInfo] = {}
+    for base in reversed(bases):
+        if model_fields := getattr(base, '__pydantic_fields__', None):
+            parent_fields_lookup.update(model_fields)
+
+    type_hints = _typing_extra.get_model_type_hints(cls, ns_resolver=ns_resolver)
+
+    # https://docs.python.org/3/howto/annotations.html#accessing-the-annotations-dict-of-an-object-in-python-3-9-and-older
+    # annotations is only used for finding fields in parent classes
+    annotations = cls.__dict__.get('__annotations__', {})
+    fields: dict[str, FieldInfo] = {}
+
+    class_vars: set[str] = set()
+    for ann_name, (ann_type, evaluated) in type_hints.items():
+        if ann_name == 'model_config':
+            # We never want to treat `model_config` as a field
+            # Note: we may need to change this logic if/when we introduce a `BareModel` class with no
+            # protected namespaces (where `model_config` might be allowed as a field name)
+            continue
+
+        for protected_namespace in config_wrapper.protected_namespaces:
+            ns_violation: bool = False
+            if isinstance(protected_namespace, Pattern):
+                ns_violation = protected_namespace.match(ann_name) is not None
+            elif isinstance(protected_namespace, str):
+                ns_violation = ann_name.startswith(protected_namespace)
+
+            if ns_violation:
+                for b in bases:
+                    if hasattr(b, ann_name):
+                        if not (issubclass(b, BaseModel) and ann_name in getattr(b, '__pydantic_fields__', {})):
+                            raise NameError(
+                                f'Field "{ann_name}" conflicts with member {getattr(b, ann_name)}'
+                                f' of protected namespace "{protected_namespace}".'
+                            )
+                else:
+                    valid_namespaces = ()
+                    for pn in config_wrapper.protected_namespaces:
+                        if isinstance(pn, Pattern):
+                            if not pn.match(ann_name):
+                                valid_namespaces += (f're.compile({pn.pattern})',)
+                        else:
+                            if not ann_name.startswith(pn):
+                                valid_namespaces += (pn,)
+
+                    warnings.warn(
+                        f'Field "{ann_name}" in {cls.__name__} has conflict with protected namespace "{protected_namespace}".'
+                        '\n\nYou may be able to resolve this warning by setting'
+                        f" `model_config['protected_namespaces'] = {valid_namespaces}`.",
+                        UserWarning,
+                    )
+        if _typing_extra.is_classvar_annotation(ann_type):
+            class_vars.add(ann_name)
+            continue
+        if _is_finalvar_with_default_val(ann_type, getattr(cls, ann_name, PydanticUndefined)):
+            class_vars.add(ann_name)
+            continue
+        if not is_valid_field_name(ann_name):
+            continue
+        if cls.__pydantic_root_model__ and ann_name != 'root':
+            raise NameError(
+                f"Unexpected field with name {ann_name!r}; only 'root' is allowed as a field of a `RootModel`"
+            )
+
+        # when building a generic model with `MyModel[int]`, the generic_origin check makes sure we don't get
+        # "... shadows an attribute" warnings
+        generic_origin = getattr(cls, '__pydantic_generic_metadata__', {}).get('origin')
+        for base in bases:
+            dataclass_fields = {
+                field.name for field in (dataclasses.fields(base) if dataclasses.is_dataclass(base) else ())
+            }
+            if hasattr(base, ann_name):
+                if base is generic_origin:
+                    # Don't warn when "shadowing" of attributes in parametrized generics
+                    continue
+
+                if ann_name in dataclass_fields:
+                    # Don't warn when inheriting stdlib dataclasses whose fields are "shadowed" by defaults being set
+                    # on the class instance.
+                    continue
+
+                if ann_name not in annotations:
+                    # Don't warn when a field exists in a parent class but has not been defined in the current class
+                    continue
+
+                warnings.warn(
+                    f'Field name "{ann_name}" in "{cls.__qualname__}" shadows an attribute in parent '
+                    f'"{base.__qualname__}"',
+                    UserWarning,
+                )
+
+        try:
+            default = getattr(cls, ann_name, PydanticUndefined)
+            if default is PydanticUndefined:
+                raise AttributeError
+        except AttributeError:
+            if ann_name in annotations:
+                field_info = FieldInfo_.from_annotation(ann_type)
+                field_info.evaluated = evaluated
+            else:
+                # if field has no default value and is not in __annotations__ this means that it is
+                # defined in a base class and we can take it from there
+                if ann_name in parent_fields_lookup:
+                    # The field was present on one of the (possibly multiple) base classes
+                    # copy the field to make sure typevar substitutions don't cause issues with the base classes
+                    field_info = copy(parent_fields_lookup[ann_name])
+                else:
+                    # The field was not found on any base classes; this seems to be caused by fields not getting
+                    # generated thanks to models not being fully defined while initializing recursive models.
+                    # Nothing stops us from just creating a new FieldInfo for this type hint, so we do this.
+                    field_info = FieldInfo_.from_annotation(ann_type)
+                    field_info.evaluated = evaluated
+        else:
+            _warn_on_nested_alias_in_annotation(ann_type, ann_name)
+            if isinstance(default, FieldInfo_) and ismethoddescriptor(default.default):
+                # the `getattr` call above triggers a call to `__get__` for descriptors, so we do
+                # the same if the `= field(default=...)` form is used. Note that we only do this
+                # for method descriptors for now, we might want to extend this to any descriptor
+                # in the future (by simply checking for `hasattr(default.default, '__get__')`).
+                default.default = default.default.__get__(None, cls)
+
+            field_info = FieldInfo_.from_annotated_attribute(ann_type, default)
+            field_info.evaluated = evaluated
+            # attributes which are fields are removed from the class namespace:
+            # 1. To match the behaviour of annotation-only fields
+            # 2. To avoid false positives in the NameError check above
+            try:
+                delattr(cls, ann_name)
+            except AttributeError:
+                pass  # indicates the attribute was on a parent class
+
+        # Use cls.__dict__['__pydantic_decorators__'] instead of cls.__pydantic_decorators__
+        # to make sure the decorators have already been built for this exact class
+        decorators: DecoratorInfos = cls.__dict__['__pydantic_decorators__']
+        if ann_name in decorators.computed_fields:
+            raise ValueError("you can't override a field with a computed field")
+        fields[ann_name] = field_info
+
+    if typevars_map:
+        for field in fields.values():
+            field.apply_typevars_map(typevars_map)
+
+    _update_fields_from_docstrings(cls, fields, config_wrapper)
+    return fields, class_vars
+
+
+def _warn_on_nested_alias_in_annotation(ann_type: type[Any], ann_name: str) -> None:
+    FieldInfo = import_cached_field_info()
+
+    args = getattr(ann_type, '__args__', None)
+    if args:
+        for anno_arg in args:
+            if _typing_extra.is_annotated(anno_arg):
+                for anno_type_arg in _typing_extra.get_args(anno_arg):
+                    if isinstance(anno_type_arg, FieldInfo) and anno_type_arg.alias is not None:
+                        warnings.warn(
+                            f'`alias` specification on field "{ann_name}" must be set on outermost annotation to take effect.',
+                            UserWarning,
+                        )
+                        return
+
+
+def _is_finalvar_with_default_val(type_: type[Any], val: Any) -> bool:
+    FieldInfo = import_cached_field_info()
+
+    if not _typing_extra.is_finalvar(type_):
+        return False
+    elif val is PydanticUndefined:
+        return False
+    elif isinstance(val, FieldInfo) and (val.default is PydanticUndefined and val.default_factory is None):
+        return False
+    else:
+        return True
+
+
+def collect_dataclass_fields(
+    cls: type[StandardDataclass],
+    *,
+    ns_resolver: NsResolver | None = None,
+    typevars_map: dict[Any, Any] | None = None,
+    config_wrapper: ConfigWrapper | None = None,
+) -> dict[str, FieldInfo]:
+    """Collect the fields of a dataclass.
+
+    Args:
+        cls: dataclass.
+        ns_resolver: Namespace resolver to use when getting dataclass annotations.
+            Defaults to an empty instance.
+        typevars_map: A dictionary mapping type variables to their concrete types.
+        config_wrapper: The config wrapper instance.
+
+    Returns:
+        The dataclass fields.
+    """
+    FieldInfo_ = import_cached_field_info()
+
+    fields: dict[str, FieldInfo] = {}
+    ns_resolver = ns_resolver or NsResolver()
+    dataclass_fields = cls.__dataclass_fields__
+
+    # The logic here is similar to `_typing_extra.get_cls_type_hints`,
+    # although we do it manually as stdlib dataclasses already have annotations
+    # collected in each class:
+    for base in reversed(cls.__mro__):
+        if not dataclasses.is_dataclass(base):
+            continue
+
+        with ns_resolver.push(base):
+            for ann_name, dataclass_field in dataclass_fields.items():
+                if ann_name not in base.__dict__.get('__annotations__', {}):
+                    # `__dataclass_fields__`contains every field, even the ones from base classes.
+                    # Only collect the ones defined on `base`.
+                    continue
+
+                globalns, localns = ns_resolver.types_namespace
+                ann_type, _ = _typing_extra.try_eval_type(dataclass_field.type, globalns, localns)
+
+                if _typing_extra.is_classvar_annotation(ann_type):
+                    continue
+
+                if (
+                    not dataclass_field.init
+                    and dataclass_field.default is dataclasses.MISSING
+                    and dataclass_field.default_factory is dataclasses.MISSING
+                ):
+                    # TODO: We should probably do something with this so that validate_assignment behaves properly
+                    #   Issue: https://github.com/pydantic/pydantic/issues/5470
+                    continue
+
+                if isinstance(dataclass_field.default, FieldInfo_):
+                    if dataclass_field.default.init_var:
+                        if dataclass_field.default.init is False:
+                            raise PydanticUserError(
+                                f'Dataclass field {ann_name} has init=False and init_var=True, but these are mutually exclusive.',
+                                code='clashing-init-and-init-var',
+                            )
+
+                        # TODO: same note as above re validate_assignment
+                        continue
+                    field_info = FieldInfo_.from_annotated_attribute(ann_type, dataclass_field.default)
+                else:
+                    field_info = FieldInfo_.from_annotated_attribute(ann_type, dataclass_field)
+
+                fields[ann_name] = field_info
+
+                if field_info.default is not PydanticUndefined and isinstance(
+                    getattr(cls, ann_name, field_info), FieldInfo_
+                ):
+                    # We need this to fix the default when the "default" from __dataclass_fields__ is a pydantic.FieldInfo
+                    setattr(cls, ann_name, field_info.default)
+
+    if typevars_map:
+        for field in fields.values():
+            # We don't pass any ns, as `field.annotation`
+            # was already evaluated. TODO: is this method relevant?
+            # Can't we juste use `_generics.replace_types`?
+            field.apply_typevars_map(typevars_map)
+
+    if config_wrapper is not None:
+        _update_fields_from_docstrings(cls, fields, config_wrapper)
+
+    return fields
+
+
+def is_valid_field_name(name: str) -> bool:
+    return not name.startswith('_')
+
+
+def is_valid_privateattr_name(name: str) -> bool:
+    return name.startswith('_') and not name.startswith('__')
+
+
+def takes_validated_data_argument(
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any],
+) -> TypeIs[Callable[[dict[str, Any]], Any]]:
+    """Whether the provided default factory callable has a validated data parameter."""
+    try:
+        sig = signature(default_factory)
+    except (ValueError, TypeError):
+        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
+        # In this case, we assume no data argument is present:
+        return False
+
+    parameters = list(sig.parameters.values())
+
+    return len(parameters) == 1 and can_be_positional(parameters[0]) and parameters[0].default is Parameter.empty

env/lib/python3.11/site-packages/pydantic/_internal/_forward_ref.py

@@ -0,0 +1,23 @@
+from __future__ import annotations as _annotations
+
+from dataclasses import dataclass
+from typing import Union
+
+
+@dataclass
+class PydanticRecursiveRef:
+    type_ref: str
+
+    __name__ = 'PydanticRecursiveRef'
+    __hash__ = object.__hash__
+
+    def __call__(self) -> None:
+        """Defining __call__ is necessary for the `typing` module to let you use an instance of
+        this class as the result of resolving a standard ForwardRef.
+        """
+
+    def __or__(self, other):
+        return Union[self, other]  # type: ignore
+
+    def __ror__(self, other):
+        return Union[other, self]  # type: ignore

env/lib/python3.11/site-packages/pydantic/_internal/_generics.py

@@ -0,0 +1,536 @@
+from __future__ import annotations
+
+import sys
+import types
+import typing
+from collections import ChainMap
+from contextlib import contextmanager
+from contextvars import ContextVar
+from types import prepare_class
+from typing import TYPE_CHECKING, Any, Iterator, Mapping, MutableMapping, Tuple, TypeVar
+from weakref import WeakValueDictionary
+
+import typing_extensions
+
+from . import _typing_extra
+from ._core_utils import get_type_ref
+from ._forward_ref import PydanticRecursiveRef
+from ._utils import all_identical, is_model_class
+
+if sys.version_info >= (3, 10):
+    from typing import _UnionGenericAlias  # type: ignore[attr-defined]
+
+if TYPE_CHECKING:
+    from ..main import BaseModel
+
+GenericTypesCacheKey = Tuple[Any, Any, Tuple[Any, ...]]
+
+# Note: We want to remove LimitedDict, but to do this, we'd need to improve the handling of generics caching.
+#   Right now, to handle recursive generics, we some types must remain cached for brief periods without references.
+#   By chaining the WeakValuesDict with a LimitedDict, we have a way to retain caching for all types with references,
+#   while also retaining a limited number of types even without references. This is generally enough to build
+#   specific recursive generic models without losing required items out of the cache.
+
+KT = TypeVar('KT')
+VT = TypeVar('VT')
+_LIMITED_DICT_SIZE = 100
+if TYPE_CHECKING:
+
+    class LimitedDict(dict, MutableMapping[KT, VT]):
+        def __init__(self, size_limit: int = _LIMITED_DICT_SIZE): ...
+
+else:
+
+    class LimitedDict(dict):
+        """Limit the size/length of a dict used for caching to avoid unlimited increase in memory usage.
+
+        Since the dict is ordered, and we always remove elements from the beginning, this is effectively a FIFO cache.
+        """
+
+        def __init__(self, size_limit: int = _LIMITED_DICT_SIZE):
+            self.size_limit = size_limit
+            super().__init__()
+
+        def __setitem__(self, key: Any, value: Any, /) -> None:
+            super().__setitem__(key, value)
+            if len(self) > self.size_limit:
+                excess = len(self) - self.size_limit + self.size_limit // 10
+                to_remove = list(self.keys())[:excess]
+                for k in to_remove:
+                    del self[k]
+
+
+# weak dictionaries allow the dynamically created parametrized versions of generic models to get collected
+# once they are no longer referenced by the caller.
+if sys.version_info >= (3, 9):  # Typing for weak dictionaries available at 3.9
+    GenericTypesCache = WeakValueDictionary[GenericTypesCacheKey, 'type[BaseModel]']
+else:
+    GenericTypesCache = WeakValueDictionary
+
+if TYPE_CHECKING:
+
+    class DeepChainMap(ChainMap[KT, VT]):  # type: ignore
+        ...
+
+else:
+
+    class DeepChainMap(ChainMap):
+        """Variant of ChainMap that allows direct updates to inner scopes.
+
+        Taken from https://docs.python.org/3/library/collections.html#collections.ChainMap,
+        with some light modifications for this use case.
+        """
+
+        def clear(self) -> None:
+            for mapping in self.maps:
+                mapping.clear()
+
+        def __setitem__(self, key: KT, value: VT) -> None:
+            for mapping in self.maps:
+                mapping[key] = value
+
+        def __delitem__(self, key: KT) -> None:
+            hit = False
+            for mapping in self.maps:
+                if key in mapping:
+                    del mapping[key]
+                    hit = True
+            if not hit:
+                raise KeyError(key)
+
+
+# Despite the fact that LimitedDict _seems_ no longer necessary, I'm very nervous to actually remove it
+# and discover later on that we need to re-add all this infrastructure...
+# _GENERIC_TYPES_CACHE = DeepChainMap(GenericTypesCache(), LimitedDict())
+
+_GENERIC_TYPES_CACHE = GenericTypesCache()
+
+
+class PydanticGenericMetadata(typing_extensions.TypedDict):
+    origin: type[BaseModel] | None  # analogous to typing._GenericAlias.__origin__
+    args: tuple[Any, ...]  # analogous to typing._GenericAlias.__args__
+    parameters: tuple[TypeVar, ...]  # analogous to typing.Generic.__parameters__
+
+
+def create_generic_submodel(
+    model_name: str, origin: type[BaseModel], args: tuple[Any, ...], params: tuple[Any, ...]
+) -> type[BaseModel]:
+    """Dynamically create a submodel of a provided (generic) BaseModel.
+
+    This is used when producing concrete parametrizations of generic models. This function
+    only *creates* the new subclass; the schema/validators/serialization must be updated to
+    reflect a concrete parametrization elsewhere.
+
+    Args:
+        model_name: The name of the newly created model.
+        origin: The base class for the new model to inherit from.
+        args: A tuple of generic metadata arguments.
+        params: A tuple of generic metadata parameters.
+
+    Returns:
+        The created submodel.
+    """
+    namespace: dict[str, Any] = {'__module__': origin.__module__}
+    bases = (origin,)
+    meta, ns, kwds = prepare_class(model_name, bases)
+    namespace.update(ns)
+    created_model = meta(
+        model_name,
+        bases,
+        namespace,
+        __pydantic_generic_metadata__={
+            'origin': origin,
+            'args': args,
+            'parameters': params,
+        },
+        __pydantic_reset_parent_namespace__=False,
+        **kwds,
+    )
+
+    model_module, called_globally = _get_caller_frame_info(depth=3)
+    if called_globally:  # create global reference and therefore allow pickling
+        object_by_reference = None
+        reference_name = model_name
+        reference_module_globals = sys.modules[created_model.__module__].__dict__
+        while object_by_reference is not created_model:
+            object_by_reference = reference_module_globals.setdefault(reference_name, created_model)
+            reference_name += '_'
+
+    return created_model
+
+
+def _get_caller_frame_info(depth: int = 2) -> tuple[str | None, bool]:
+    """Used inside a function to check whether it was called globally.
+
+    Args:
+        depth: The depth to get the frame.
+
+    Returns:
+        A tuple contains `module_name` and `called_globally`.
+
+    Raises:
+        RuntimeError: If the function is not called inside a function.
+    """
+    try:
+        previous_caller_frame = sys._getframe(depth)
+    except ValueError as e:
+        raise RuntimeError('This function must be used inside another function') from e
+    except AttributeError:  # sys module does not have _getframe function, so there's nothing we can do about it
+        return None, False
+    frame_globals = previous_caller_frame.f_globals
+    return frame_globals.get('__name__'), previous_caller_frame.f_locals is frame_globals
+
+
+DictValues: type[Any] = {}.values().__class__
+
+
+def iter_contained_typevars(v: Any) -> Iterator[TypeVar]:
+    """Recursively iterate through all subtypes and type args of `v` and yield any typevars that are found.
+
+    This is inspired as an alternative to directly accessing the `__parameters__` attribute of a GenericAlias,
+    since __parameters__ of (nested) generic BaseModel subclasses won't show up in that list.
+    """
+    if isinstance(v, TypeVar):
+        yield v
+    elif is_model_class(v):
+        yield from v.__pydantic_generic_metadata__['parameters']
+    elif isinstance(v, (DictValues, list)):
+        for var in v:
+            yield from iter_contained_typevars(var)
+    else:
+        args = get_args(v)
+        for arg in args:
+            yield from iter_contained_typevars(arg)
+
+
+def get_args(v: Any) -> Any:
+    pydantic_generic_metadata: PydanticGenericMetadata | None = getattr(v, '__pydantic_generic_metadata__', None)
+    if pydantic_generic_metadata:
+        return pydantic_generic_metadata.get('args')
+    return typing_extensions.get_args(v)
+
+
+def get_origin(v: Any) -> Any:
+    pydantic_generic_metadata: PydanticGenericMetadata | None = getattr(v, '__pydantic_generic_metadata__', None)
+    if pydantic_generic_metadata:
+        return pydantic_generic_metadata.get('origin')
+    return typing_extensions.get_origin(v)
+
+
+def get_standard_typevars_map(cls: Any) -> dict[TypeVar, Any] | None:
+    """Package a generic type's typevars and parametrization (if present) into a dictionary compatible with the
+    `replace_types` function. Specifically, this works with standard typing generics and typing._GenericAlias.
+    """
+    origin = get_origin(cls)
+    if origin is None:
+        return None
+    if not hasattr(origin, '__parameters__'):
+        return None
+
+    # In this case, we know that cls is a _GenericAlias, and origin is the generic type
+    # So it is safe to access cls.__args__ and origin.__parameters__
+    args: tuple[Any, ...] = cls.__args__  # type: ignore
+    parameters: tuple[TypeVar, ...] = origin.__parameters__
+    return dict(zip(parameters, args))
+
+
+def get_model_typevars_map(cls: type[BaseModel]) -> dict[TypeVar, Any] | None:
+    """Package a generic BaseModel's typevars and concrete parametrization (if present) into a dictionary compatible
+    with the `replace_types` function.
+
+    Since BaseModel.__class_getitem__ does not produce a typing._GenericAlias, and the BaseModel generic info is
+    stored in the __pydantic_generic_metadata__ attribute, we need special handling here.
+    """
+    # TODO: This could be unified with `get_standard_typevars_map` if we stored the generic metadata
+    #   in the __origin__, __args__, and __parameters__ attributes of the model.
+    generic_metadata = cls.__pydantic_generic_metadata__
+    origin = generic_metadata['origin']
+    args = generic_metadata['args']
+    return dict(zip(iter_contained_typevars(origin), args))
+
+
+def replace_types(type_: Any, type_map: Mapping[Any, Any] | None) -> Any:
+    """Return type with all occurrences of `type_map` keys recursively replaced with their values.
+
+    Args:
+        type_: The class or generic alias.
+        type_map: Mapping from `TypeVar` instance to concrete types.
+
+    Returns:
+        A new type representing the basic structure of `type_` with all
+        `typevar_map` keys recursively replaced.
+
+    Example:
+        ```python
+        from typing import List, Tuple, Union
+
+        from pydantic._internal._generics import replace_types
+
+        replace_types(Tuple[str, Union[List[str], float]], {str: int})
+        #> Tuple[int, Union[List[int], float]]
+        ```
+    """
+    if not type_map:
+        return type_
+
+    type_args = get_args(type_)
+
+    if _typing_extra.is_annotated(type_):
+        annotated_type, *annotations = type_args
+        annotated = replace_types(annotated_type, type_map)
+        for annotation in annotations:
+            annotated = typing_extensions.Annotated[annotated, annotation]
+        return annotated
+
+    origin_type = get_origin(type_)
+
+    # Having type args is a good indicator that this is a typing special form
+    # instance or a generic alias of some sort.
+    if type_args:
+        resolved_type_args = tuple(replace_types(arg, type_map) for arg in type_args)
+        if all_identical(type_args, resolved_type_args):
+            # If all arguments are the same, there is no need to modify the
+            # type or create a new object at all
+            return type_
+
+        if (
+            origin_type is not None
+            and isinstance(type_, _typing_extra.typing_base)
+            and not isinstance(origin_type, _typing_extra.typing_base)
+            and getattr(type_, '_name', None) is not None
+        ):
+            # In python < 3.9 generic aliases don't exist so any of these like `list`,
+            # `type` or `collections.abc.Callable` need to be translated.
+            # See: https://www.python.org/dev/peps/pep-0585
+            origin_type = getattr(typing, type_._name)
+        assert origin_type is not None
+
+        if _typing_extra.origin_is_union(origin_type):
+            if any(_typing_extra.is_any(arg) for arg in resolved_type_args):
+                # `Any | T` ~ `Any`:
+                resolved_type_args = (Any,)
+            # `Never | T` ~ `T`:
+            resolved_type_args = tuple(
+                arg
+                for arg in resolved_type_args
+                if not (_typing_extra.is_no_return(arg) or _typing_extra.is_never(arg))
+            )
+
+        # PEP-604 syntax (Ex.: list | str) is represented with a types.UnionType object that does not have __getitem__.
+        # We also cannot use isinstance() since we have to compare types.
+        if sys.version_info >= (3, 10) and origin_type is types.UnionType:
+            return _UnionGenericAlias(origin_type, resolved_type_args)
+        # NotRequired[T] and Required[T] don't support tuple type resolved_type_args, hence the condition below
+        return origin_type[resolved_type_args[0] if len(resolved_type_args) == 1 else resolved_type_args]
+
+    # We handle pydantic generic models separately as they don't have the same
+    # semantics as "typing" classes or generic aliases
+
+    if not origin_type and is_model_class(type_):
+        parameters = type_.__pydantic_generic_metadata__['parameters']
+        if not parameters:
+            return type_
+        resolved_type_args = tuple(replace_types(t, type_map) for t in parameters)
+        if all_identical(parameters, resolved_type_args):
+            return type_
+        return type_[resolved_type_args]
+
+    # Handle special case for typehints that can have lists as arguments.
+    # `typing.Callable[[int, str], int]` is an example for this.
+    if isinstance(type_, list):
+        resolved_list = [replace_types(element, type_map) for element in type_]
+        if all_identical(type_, resolved_list):
+            return type_
+        return resolved_list
+
+    # If all else fails, we try to resolve the type directly and otherwise just
+    # return the input with no modifications.
+    return type_map.get(type_, type_)
+
+
+def has_instance_in_type(type_: Any, isinstance_target: Any) -> bool:
+    """Checks if the type, or any of its arbitrary nested args, satisfy
+    `isinstance(<type>, isinstance_target)`.
+    """
+    if isinstance(type_, isinstance_target):
+        return True
+    if _typing_extra.is_annotated(type_):
+        return has_instance_in_type(type_.__origin__, isinstance_target)
+    if _typing_extra.is_literal(type_):
+        return False
+
+    type_args = get_args(type_)
+
+    # Having type args is a good indicator that this is a typing module
+    # class instantiation or a generic alias of some sort.
+    for arg in type_args:
+        if has_instance_in_type(arg, isinstance_target):
+            return True
+
+    # Handle special case for typehints that can have lists as arguments.
+    # `typing.Callable[[int, str], int]` is an example for this.
+    if (
+        isinstance(type_, list)
+        # On Python < 3.10, typing_extensions implements `ParamSpec` as a subclass of `list`:
+        and not isinstance(type_, typing_extensions.ParamSpec)
+    ):
+        for element in type_:
+            if has_instance_in_type(element, isinstance_target):
+                return True
+
+    return False
+
+
+def check_parameters_count(cls: type[BaseModel], parameters: tuple[Any, ...]) -> None:
+    """Check the generic model parameters count is equal.
+
+    Args:
+        cls: The generic model.
+        parameters: A tuple of passed parameters to the generic model.
+
+    Raises:
+        TypeError: If the passed parameters count is not equal to generic model parameters count.
+    """
+    actual = len(parameters)
+    expected = len(cls.__pydantic_generic_metadata__['parameters'])
+    if actual != expected:
+        description = 'many' if actual > expected else 'few'
+        raise TypeError(f'Too {description} parameters for {cls}; actual {actual}, expected {expected}')
+
+
+_generic_recursion_cache: ContextVar[set[str] | None] = ContextVar('_generic_recursion_cache', default=None)
+
+
+@contextmanager
+def generic_recursion_self_type(
+    origin: type[BaseModel], args: tuple[Any, ...]
+) -> Iterator[PydanticRecursiveRef | None]:
+    """This contextmanager should be placed around the recursive calls used to build a generic type,
+    and accept as arguments the generic origin type and the type arguments being passed to it.
+
+    If the same origin and arguments are observed twice, it implies that a self-reference placeholder
+    can be used while building the core schema, and will produce a schema_ref that will be valid in the
+    final parent schema.
+    """
+    previously_seen_type_refs = _generic_recursion_cache.get()
+    if previously_seen_type_refs is None:
+        previously_seen_type_refs = set()
+        token = _generic_recursion_cache.set(previously_seen_type_refs)
+    else:
+        token = None
+
+    try:
+        type_ref = get_type_ref(origin, args_override=args)
+        if type_ref in previously_seen_type_refs:
+            self_type = PydanticRecursiveRef(type_ref=type_ref)
+            yield self_type
+        else:
+            previously_seen_type_refs.add(type_ref)
+            yield
+            previously_seen_type_refs.remove(type_ref)
+    finally:
+        if token:
+            _generic_recursion_cache.reset(token)
+
+
+def recursively_defined_type_refs() -> set[str]:
+    visited = _generic_recursion_cache.get()
+    if not visited:
+        return set()  # not in a generic recursion, so there are no types
+
+    return visited.copy()  # don't allow modifications
+
+
+def get_cached_generic_type_early(parent: type[BaseModel], typevar_values: Any) -> type[BaseModel] | None:
+    """The use of a two-stage cache lookup approach was necessary to have the highest performance possible for
+    repeated calls to `__class_getitem__` on generic types (which may happen in tighter loops during runtime),
+    while still ensuring that certain alternative parametrizations ultimately resolve to the same type.
+
+    As a concrete example, this approach was necessary to make Model[List[T]][int] equal to Model[List[int]].
+    The approach could be modified to not use two different cache keys at different points, but the
+    _early_cache_key is optimized to be as quick to compute as possible (for repeated-access speed), and the
+    _late_cache_key is optimized to be as "correct" as possible, so that two types that will ultimately be the
+    same after resolving the type arguments will always produce cache hits.
+
+    If we wanted to move to only using a single cache key per type, we would either need to always use the
+    slower/more computationally intensive logic associated with _late_cache_key, or would need to accept
+    that Model[List[T]][int] is a different type than Model[List[T]][int]. Because we rely on subclass relationships
+    during validation, I think it is worthwhile to ensure that types that are functionally equivalent are actually
+    equal.
+    """
+    return _GENERIC_TYPES_CACHE.get(_early_cache_key(parent, typevar_values))
+
+
+def get_cached_generic_type_late(
+    parent: type[BaseModel], typevar_values: Any, origin: type[BaseModel], args: tuple[Any, ...]
+) -> type[BaseModel] | None:
+    """See the docstring of `get_cached_generic_type_early` for more information about the two-stage cache lookup."""
+    cached = _GENERIC_TYPES_CACHE.get(_late_cache_key(origin, args, typevar_values))
+    if cached is not None:
+        set_cached_generic_type(parent, typevar_values, cached, origin, args)
+    return cached
+
+
+def set_cached_generic_type(
+    parent: type[BaseModel],
+    typevar_values: tuple[Any, ...],
+    type_: type[BaseModel],
+    origin: type[BaseModel] | None = None,
+    args: tuple[Any, ...] | None = None,
+) -> None:
+    """See the docstring of `get_cached_generic_type_early` for more information about why items are cached with
+    two different keys.
+    """
+    _GENERIC_TYPES_CACHE[_early_cache_key(parent, typevar_values)] = type_
+    if len(typevar_values) == 1:
+        _GENERIC_TYPES_CACHE[_early_cache_key(parent, typevar_values[0])] = type_
+    if origin and args:
+        _GENERIC_TYPES_CACHE[_late_cache_key(origin, args, typevar_values)] = type_
+
+
+def _union_orderings_key(typevar_values: Any) -> Any:
+    """This is intended to help differentiate between Union types with the same arguments in different order.
+
+    Thanks to caching internal to the `typing` module, it is not possible to distinguish between
+    List[Union[int, float]] and List[Union[float, int]] (and similarly for other "parent" origins besides List)
+    because `typing` considers Union[int, float] to be equal to Union[float, int].
+
+    However, you _can_ distinguish between (top-level) Union[int, float] vs. Union[float, int].
+    Because we parse items as the first Union type that is successful, we get slightly more consistent behavior
+    if we make an effort to distinguish the ordering of items in a union. It would be best if we could _always_
+    get the exact-correct order of items in the union, but that would require a change to the `typing` module itself.
+    (See https://github.com/python/cpython/issues/86483 for reference.)
+    """
+    if isinstance(typevar_values, tuple):
+        args_data = []
+        for value in typevar_values:
+            args_data.append(_union_orderings_key(value))
+        return tuple(args_data)
+    elif _typing_extra.is_union(typevar_values):
+        return get_args(typevar_values)
+    else:
+        return ()
+
+
+def _early_cache_key(cls: type[BaseModel], typevar_values: Any) -> GenericTypesCacheKey:
+    """This is intended for minimal computational overhead during lookups of cached types.
+
+    Note that this is overly simplistic, and it's possible that two different cls/typevar_values
+    inputs would ultimately result in the same type being created in BaseModel.__class_getitem__.
+    To handle this, we have a fallback _late_cache_key that is checked later if the _early_cache_key
+    lookup fails, and should result in a cache hit _precisely_ when the inputs to __class_getitem__
+    would result in the same type.
+    """
+    return cls, typevar_values, _union_orderings_key(typevar_values)
+
+
+def _late_cache_key(origin: type[BaseModel], args: tuple[Any, ...], typevar_values: Any) -> GenericTypesCacheKey:
+    """This is intended for use later in the process of creating a new type, when we have more information
+    about the exact args that will be passed. If it turns out that a different set of inputs to
+    __class_getitem__ resulted in the same inputs to the generic type creation process, we can still
+    return the cached type, and update the cache with the _early_cache_key as well.
+    """
+    # The _union_orderings_key is placed at the start here to ensure there cannot be a collision with an
+    # _early_cache_key, as that function will always produce a BaseModel subclass as the first item in the key,
+    # whereas this function will always produce a tuple as the first item in the key.
+    return _union_orderings_key(typevar_values), origin, args

env/lib/python3.11/site-packages/pydantic/_internal/_git.py

@@ -0,0 +1,27 @@
+"""Git utilities, adopted from mypy's git utilities (https://github.com/python/mypy/blob/master/mypy/git.py)."""
+
+from __future__ import annotations
+
+import os
+import subprocess
+
+
+def is_git_repo(dir: str) -> bool:
+    """Is the given directory version-controlled with git?"""
+    return os.path.exists(os.path.join(dir, '.git'))
+
+
+def have_git() -> bool:
+    """Can we run the git executable?"""
+    try:
+        subprocess.check_output(['git', '--help'])
+        return True
+    except subprocess.CalledProcessError:
+        return False
+    except OSError:
+        return False
+
+
+def git_revision(dir: str) -> str:
+    """Get the SHA-1 of the HEAD of a git repository."""
+    return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], cwd=dir).decode('utf-8').strip()

env/lib/python3.11/site-packages/pydantic/_internal/_import_utils.py

@@ -0,0 +1,20 @@
+from functools import lru_cache
+from typing import TYPE_CHECKING, Type
+
+if TYPE_CHECKING:
+    from pydantic import BaseModel
+    from pydantic.fields import FieldInfo
+
+
+@lru_cache(maxsize=None)
+def import_cached_base_model() -> Type['BaseModel']:
+    from pydantic import BaseModel
+
+    return BaseModel
+
+
+@lru_cache(maxsize=None)
+def import_cached_field_info() -> Type['FieldInfo']:
+    from pydantic.fields import FieldInfo
+
+    return FieldInfo

env/lib/python3.11/site-packages/pydantic/_internal/_internal_dataclass.py

@@ -0,0 +1,7 @@
+import sys
+
+# `slots` is available on Python >= 3.10
+if sys.version_info >= (3, 10):
+    slots_true = {'slots': True}
+else:
+    slots_true = {}

env/lib/python3.11/site-packages/pydantic/_internal/_known_annotated_metadata.py

@@ -0,0 +1,392 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from copy import copy
+from functools import lru_cache, partial
+from typing import TYPE_CHECKING, Any, Iterable
+
+from pydantic_core import CoreSchema, PydanticCustomError, ValidationError, to_jsonable_python
+from pydantic_core import core_schema as cs
+
+from ._fields import PydanticMetadata
+from ._import_utils import import_cached_field_info
+
+if TYPE_CHECKING:
+    pass
+
+STRICT = {'strict'}
+FAIL_FAST = {'fail_fast'}
+LENGTH_CONSTRAINTS = {'min_length', 'max_length'}
+INEQUALITY = {'le', 'ge', 'lt', 'gt'}
+NUMERIC_CONSTRAINTS = {'multiple_of', *INEQUALITY}
+ALLOW_INF_NAN = {'allow_inf_nan'}
+
+STR_CONSTRAINTS = {
+    *LENGTH_CONSTRAINTS,
+    *STRICT,
+    'strip_whitespace',
+    'to_lower',
+    'to_upper',
+    'pattern',
+    'coerce_numbers_to_str',
+}
+BYTES_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT}
+
+LIST_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST}
+TUPLE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST}
+SET_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST}
+DICT_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT}
+GENERATOR_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT}
+SEQUENCE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *FAIL_FAST}
+
+FLOAT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT}
+DECIMAL_CONSTRAINTS = {'max_digits', 'decimal_places', *FLOAT_CONSTRAINTS}
+INT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT}
+BOOL_CONSTRAINTS = STRICT
+UUID_CONSTRAINTS = STRICT
+
+DATE_TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT}
+TIMEDELTA_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT}
+TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT}
+LAX_OR_STRICT_CONSTRAINTS = STRICT
+ENUM_CONSTRAINTS = STRICT
+COMPLEX_CONSTRAINTS = STRICT
+
+UNION_CONSTRAINTS = {'union_mode'}
+URL_CONSTRAINTS = {
+    'max_length',
+    'allowed_schemes',
+    'host_required',
+    'default_host',
+    'default_port',
+    'default_path',
+}
+
+TEXT_SCHEMA_TYPES = ('str', 'bytes', 'url', 'multi-host-url')
+SEQUENCE_SCHEMA_TYPES = ('list', 'tuple', 'set', 'frozenset', 'generator', *TEXT_SCHEMA_TYPES)
+NUMERIC_SCHEMA_TYPES = ('float', 'int', 'date', 'time', 'timedelta', 'datetime')
+
+CONSTRAINTS_TO_ALLOWED_SCHEMAS: dict[str, set[str]] = defaultdict(set)
+
+constraint_schema_pairings: list[tuple[set[str], tuple[str, ...]]] = [
+    (STR_CONSTRAINTS, TEXT_SCHEMA_TYPES),
+    (BYTES_CONSTRAINTS, ('bytes',)),
+    (LIST_CONSTRAINTS, ('list',)),
+    (TUPLE_CONSTRAINTS, ('tuple',)),
+    (SET_CONSTRAINTS, ('set', 'frozenset')),
+    (DICT_CONSTRAINTS, ('dict',)),
+    (GENERATOR_CONSTRAINTS, ('generator',)),
+    (FLOAT_CONSTRAINTS, ('float',)),
+    (INT_CONSTRAINTS, ('int',)),
+    (DATE_TIME_CONSTRAINTS, ('date', 'time', 'datetime', 'timedelta')),
+    # TODO: this is a bit redundant, we could probably avoid some of these
+    (STRICT, (*TEXT_SCHEMA_TYPES, *SEQUENCE_SCHEMA_TYPES, *NUMERIC_SCHEMA_TYPES, 'typed-dict', 'model')),
+    (UNION_CONSTRAINTS, ('union',)),
+    (URL_CONSTRAINTS, ('url', 'multi-host-url')),
+    (BOOL_CONSTRAINTS, ('bool',)),
+    (UUID_CONSTRAINTS, ('uuid',)),
+    (LAX_OR_STRICT_CONSTRAINTS, ('lax-or-strict',)),
+    (ENUM_CONSTRAINTS, ('enum',)),
+    (DECIMAL_CONSTRAINTS, ('decimal',)),
+    (COMPLEX_CONSTRAINTS, ('complex',)),
+]
+
+for constraints, schemas in constraint_schema_pairings:
+    for c in constraints:
+        CONSTRAINTS_TO_ALLOWED_SCHEMAS[c].update(schemas)
+
+
+def as_jsonable_value(v: Any) -> Any:
+    if type(v) not in (int, str, float, bytes, bool, type(None)):
+        return to_jsonable_python(v)
+    return v
+
+
+def expand_grouped_metadata(annotations: Iterable[Any]) -> Iterable[Any]:
+    """Expand the annotations.
+
+    Args:
+        annotations: An iterable of annotations.
+
+    Returns:
+        An iterable of expanded annotations.
+
+    Example:
+        ```python
+        from annotated_types import Ge, Len
+
+        from pydantic._internal._known_annotated_metadata import expand_grouped_metadata
+
+        print(list(expand_grouped_metadata([Ge(4), Len(5)])))
+        #> [Ge(ge=4), MinLen(min_length=5)]
+        ```
+    """
+    import annotated_types as at
+
+    FieldInfo = import_cached_field_info()
+
+    for annotation in annotations:
+        if isinstance(annotation, at.GroupedMetadata):
+            yield from annotation
+        elif isinstance(annotation, FieldInfo):
+            yield from annotation.metadata
+            # this is a bit problematic in that it results in duplicate metadata
+            # all of our "consumers" can handle it, but it is not ideal
+            # we probably should split up FieldInfo into:
+            # - annotated types metadata
+            # - individual metadata known only to Pydantic
+            annotation = copy(annotation)
+            annotation.metadata = []
+            yield annotation
+        else:
+            yield annotation
+
+
+@lru_cache
+def _get_at_to_constraint_map() -> dict[type, str]:
+    """Return a mapping of annotated types to constraints.
+
+    Normally, we would define a mapping like this in the module scope, but we can't do that
+    because we don't permit module level imports of `annotated_types`, in an attempt to speed up
+    the import time of `pydantic`. We still only want to have this dictionary defined in one place,
+    so we use this function to cache the result.
+    """
+    import annotated_types as at
+
+    return {
+        at.Gt: 'gt',
+        at.Ge: 'ge',
+        at.Lt: 'lt',
+        at.Le: 'le',
+        at.MultipleOf: 'multiple_of',
+        at.MinLen: 'min_length',
+        at.MaxLen: 'max_length',
+    }
+
+
+def apply_known_metadata(annotation: Any, schema: CoreSchema) -> CoreSchema | None:  # noqa: C901
+    """Apply `annotation` to `schema` if it is an annotation we know about (Gt, Le, etc.).
+    Otherwise return `None`.
+
+    This does not handle all known annotations. If / when it does, it can always
+    return a CoreSchema and return the unmodified schema if the annotation should be ignored.
+
+    Assumes that GroupedMetadata has already been expanded via `expand_grouped_metadata`.
+
+    Args:
+        annotation: The annotation.
+        schema: The schema.
+
+    Returns:
+        An updated schema with annotation if it is an annotation we know about, `None` otherwise.
+
+    Raises:
+        PydanticCustomError: If `Predicate` fails.
+    """
+    import annotated_types as at
+
+    from ._validators import NUMERIC_VALIDATOR_LOOKUP, forbid_inf_nan_check
+
+    schema = schema.copy()
+    schema_update, other_metadata = collect_known_metadata([annotation])
+    schema_type = schema['type']
+
+    chain_schema_constraints: set[str] = {
+        'pattern',
+        'strip_whitespace',
+        'to_lower',
+        'to_upper',
+        'coerce_numbers_to_str',
+    }
+    chain_schema_steps: list[CoreSchema] = []
+
+    for constraint, value in schema_update.items():
+        if constraint not in CONSTRAINTS_TO_ALLOWED_SCHEMAS:
+            raise ValueError(f'Unknown constraint {constraint}')
+        allowed_schemas = CONSTRAINTS_TO_ALLOWED_SCHEMAS[constraint]
+
+        # if it becomes necessary to handle more than one constraint
+        # in this recursive case with function-after or function-wrap, we should refactor
+        # this is a bit challenging because we sometimes want to apply constraints to the inner schema,
+        # whereas other times we want to wrap the existing schema with a new one that enforces a new constraint.
+        if schema_type in {'function-before', 'function-wrap', 'function-after'} and constraint == 'strict':
+            schema['schema'] = apply_known_metadata(annotation, schema['schema'])  # type: ignore  # schema is function schema
+            return schema
+
+        # if we're allowed to apply constraint directly to the schema, like le to int, do that
+        if schema_type in allowed_schemas:
+            if constraint == 'union_mode' and schema_type == 'union':
+                schema['mode'] = value  # type: ignore  # schema is UnionSchema
+            else:
+                schema[constraint] = value
+            continue
+
+        #  else, apply a function after validator to the schema to enforce the corresponding constraint
+        if constraint in chain_schema_constraints:
+
+            def _apply_constraint_with_incompatibility_info(
+                value: Any, handler: cs.ValidatorFunctionWrapHandler
+            ) -> Any:
+                try:
+                    x = handler(value)
+                except ValidationError as ve:
+                    # if the error is about the type, it's likely that the constraint is incompatible the type of the field
+                    # for example, the following invalid schema wouldn't be caught during schema build, but rather at this point
+                    # with a cryptic 'string_type' error coming from the string validator,
+                    # that we'd rather express as a constraint incompatibility error (TypeError)
+                    # Annotated[list[int], Field(pattern='abc')]
+                    if 'type' in ve.errors()[0]['type']:
+                        raise TypeError(
+                            f"Unable to apply constraint '{constraint}' to supplied value {value} for schema of type '{schema_type}'"  # noqa: B023
+                        )
+                    raise ve
+                return x
+
+            chain_schema_steps.append(
+                cs.no_info_wrap_validator_function(
+                    _apply_constraint_with_incompatibility_info, cs.str_schema(**{constraint: value})
+                )
+            )
+        elif constraint in NUMERIC_VALIDATOR_LOOKUP:
+            if constraint in LENGTH_CONSTRAINTS:
+                inner_schema = schema
+                while inner_schema['type'] in {'function-before', 'function-wrap', 'function-after'}:
+                    inner_schema = inner_schema['schema']  # type: ignore
+                inner_schema_type = inner_schema['type']
+                if inner_schema_type == 'list' or (
+                    inner_schema_type == 'json-or-python' and inner_schema['json_schema']['type'] == 'list'  # type: ignore
+                ):
+                    js_constraint_key = 'minItems' if constraint == 'min_length' else 'maxItems'
+                else:
+                    js_constraint_key = 'minLength' if constraint == 'min_length' else 'maxLength'
+            else:
+                js_constraint_key = constraint
+
+            schema = cs.no_info_after_validator_function(
+                partial(NUMERIC_VALIDATOR_LOOKUP[constraint], **{constraint: value}), schema
+            )
+            metadata = schema.get('metadata', {})
+            if (existing_json_schema_updates := metadata.get('pydantic_js_updates')) is not None:
+                metadata['pydantic_js_updates'] = {
+                    **existing_json_schema_updates,
+                    **{js_constraint_key: as_jsonable_value(value)},
+                }
+            else:
+                metadata['pydantic_js_updates'] = {js_constraint_key: as_jsonable_value(value)}
+            schema['metadata'] = metadata
+        elif constraint == 'allow_inf_nan' and value is False:
+            schema = cs.no_info_after_validator_function(
+                forbid_inf_nan_check,
+                schema,
+            )
+        else:
+            # It's rare that we'd get here, but it's possible if we add a new constraint and forget to handle it
+            # Most constraint errors are caught at runtime during attempted application
+            raise RuntimeError(f"Unable to apply constraint '{constraint}' to schema of type '{schema_type}'")
+
+    for annotation in other_metadata:
+        if (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()):
+            constraint = at_to_constraint_map[annotation_type]
+            validator = NUMERIC_VALIDATOR_LOOKUP.get(constraint)
+            if validator is None:
+                raise ValueError(f'Unknown constraint {constraint}')
+            schema = cs.no_info_after_validator_function(
+                partial(validator, {constraint: getattr(annotation, constraint)}), schema
+            )
+            continue
+        elif isinstance(annotation, (at.Predicate, at.Not)):
+            predicate_name = f'{annotation.func.__qualname__}' if hasattr(annotation.func, '__qualname__') else ''
+
+            def val_func(v: Any) -> Any:
+                predicate_satisfied = annotation.func(v)  # noqa: B023
+
+                # annotation.func may also raise an exception, let it pass through
+                if isinstance(annotation, at.Predicate):  # noqa: B023
+                    if not predicate_satisfied:
+                        raise PydanticCustomError(
+                            'predicate_failed',
+                            f'Predicate {predicate_name} failed',  # type: ignore  # noqa: B023
+                        )
+                else:
+                    if predicate_satisfied:
+                        raise PydanticCustomError(
+                            'not_operation_failed',
+                            f'Not of {predicate_name} failed',  # type: ignore  # noqa: B023
+                        )
+
+                return v
+
+            schema = cs.no_info_after_validator_function(val_func, schema)
+        else:
+            # ignore any other unknown metadata
+            return None
+
+    if chain_schema_steps:
+        chain_schema_steps = [schema] + chain_schema_steps
+        return cs.chain_schema(chain_schema_steps)
+
+    return schema
+
+
+def collect_known_metadata(annotations: Iterable[Any]) -> tuple[dict[str, Any], list[Any]]:
+    """Split `annotations` into known metadata and unknown annotations.
+
+    Args:
+        annotations: An iterable of annotations.
+
+    Returns:
+        A tuple contains a dict of known metadata and a list of unknown annotations.
+
+    Example:
+        ```python
+        from annotated_types import Gt, Len
+
+        from pydantic._internal._known_annotated_metadata import collect_known_metadata
+
+        print(collect_known_metadata([Gt(1), Len(42), ...]))
+        #> ({'gt': 1, 'min_length': 42}, [Ellipsis])
+        ```
+    """
+    annotations = expand_grouped_metadata(annotations)
+
+    res: dict[str, Any] = {}
+    remaining: list[Any] = []
+
+    for annotation in annotations:
+        # isinstance(annotation, PydanticMetadata) also covers ._fields:_PydanticGeneralMetadata
+        if isinstance(annotation, PydanticMetadata):
+            res.update(annotation.__dict__)
+        # we don't use dataclasses.asdict because that recursively calls asdict on the field values
+        elif (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()):
+            constraint = at_to_constraint_map[annotation_type]
+            res[constraint] = getattr(annotation, constraint)
+        elif isinstance(annotation, type) and issubclass(annotation, PydanticMetadata):
+            # also support PydanticMetadata classes being used without initialisation,
+            # e.g. `Annotated[int, Strict]` as well as `Annotated[int, Strict()]`
+            res.update({k: v for k, v in vars(annotation).items() if not k.startswith('_')})
+        else:
+            remaining.append(annotation)
+    # Nones can sneak in but pydantic-core will reject them
+    # it'd be nice to clean things up so we don't put in None (we probably don't _need_ to, it was just easier)
+    # but this is simple enough to kick that can down the road
+    res = {k: v for k, v in res.items() if v is not None}
+    return res, remaining
+
+
+def check_metadata(metadata: dict[str, Any], allowed: Iterable[str], source_type: Any) -> None:
+    """A small utility function to validate that the given metadata can be applied to the target.
+    More than saving lines of code, this gives us a consistent error message for all of our internal implementations.
+
+    Args:
+        metadata: A dict of metadata.
+        allowed: An iterable of allowed metadata.
+        source_type: The source type.
+
+    Raises:
+        TypeError: If there is metadatas that can't be applied on source type.
+    """
+    unknown = metadata.keys() - set(allowed)
+    if unknown:
+        raise TypeError(
+            f'The following constraints cannot be applied to {source_type!r}: {", ".join([f"{k!r}" for k in unknown])}'
+        )

env/lib/python3.11/site-packages/pydantic/_internal/_mock_val_ser.py

@@ -0,0 +1,235 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable, Generic, Iterator, Mapping, TypeVar, Union
+
+from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator
+from typing_extensions import Literal
+
+from ..errors import PydanticErrorCodes, PydanticUserError
+from ..plugin._schema_validator import PluggableSchemaValidator
+
+if TYPE_CHECKING:
+    from ..dataclasses import PydanticDataclass
+    from ..main import BaseModel
+    from ..type_adapter import TypeAdapter
+
+
+ValSer = TypeVar('ValSer', bound=Union[SchemaValidator, PluggableSchemaValidator, SchemaSerializer])
+T = TypeVar('T')
+
+
+class MockCoreSchema(Mapping[str, Any]):
+    """Mocker for `pydantic_core.CoreSchema` which optionally attempts to
+    rebuild the thing it's mocking when one of its methods is accessed and raises an error if that fails.
+    """
+
+    __slots__ = '_error_message', '_code', '_attempt_rebuild', '_built_memo'
+
+    def __init__(
+        self,
+        error_message: str,
+        *,
+        code: PydanticErrorCodes,
+        attempt_rebuild: Callable[[], CoreSchema | None] | None = None,
+    ) -> None:
+        self._error_message = error_message
+        self._code: PydanticErrorCodes = code
+        self._attempt_rebuild = attempt_rebuild
+        self._built_memo: CoreSchema | None = None
+
+    def __getitem__(self, key: str) -> Any:
+        return self._get_built().__getitem__(key)
+
+    def __len__(self) -> int:
+        return self._get_built().__len__()
+
+    def __iter__(self) -> Iterator[str]:
+        return self._get_built().__iter__()
+
+    def _get_built(self) -> CoreSchema:
+        if self._built_memo is not None:
+            return self._built_memo
+
+        if self._attempt_rebuild:
+            schema = self._attempt_rebuild()
+            if schema is not None:
+                self._built_memo = schema
+                return schema
+        raise PydanticUserError(self._error_message, code=self._code)
+
+    def rebuild(self) -> CoreSchema | None:
+        self._built_memo = None
+        if self._attempt_rebuild:
+            schema = self._attempt_rebuild()
+            if schema is not None:
+                return schema
+            else:
+                raise PydanticUserError(self._error_message, code=self._code)
+        return None
+
+
+class MockValSer(Generic[ValSer]):
+    """Mocker for `pydantic_core.SchemaValidator` or `pydantic_core.SchemaSerializer` which optionally attempts to
+    rebuild the thing it's mocking when one of its methods is accessed and raises an error if that fails.
+    """
+
+    __slots__ = '_error_message', '_code', '_val_or_ser', '_attempt_rebuild'
+
+    def __init__(
+        self,
+        error_message: str,
+        *,
+        code: PydanticErrorCodes,
+        val_or_ser: Literal['validator', 'serializer'],
+        attempt_rebuild: Callable[[], ValSer | None] | None = None,
+    ) -> None:
+        self._error_message = error_message
+        self._val_or_ser = SchemaValidator if val_or_ser == 'validator' else SchemaSerializer
+        self._code: PydanticErrorCodes = code
+        self._attempt_rebuild = attempt_rebuild
+
+    def __getattr__(self, item: str) -> None:
+        __tracebackhide__ = True
+        if self._attempt_rebuild:
+            val_ser = self._attempt_rebuild()
+            if val_ser is not None:
+                return getattr(val_ser, item)
+
+        # raise an AttributeError if `item` doesn't exist
+        getattr(self._val_or_ser, item)
+        raise PydanticUserError(self._error_message, code=self._code)
+
+    def rebuild(self) -> ValSer | None:
+        if self._attempt_rebuild:
+            val_ser = self._attempt_rebuild()
+            if val_ser is not None:
+                return val_ser
+            else:
+                raise PydanticUserError(self._error_message, code=self._code)
+        return None
+
+
+def set_type_adapter_mocks(adapter: TypeAdapter, type_repr: str) -> None:
+    """Set `core_schema`, `validator` and `serializer` to mock core types on a type adapter instance.
+
+    Args:
+        adapter: The type adapter instance to set the mocks on
+        type_repr: Name of the type used in the adapter, used in error messages
+    """
+    undefined_type_error_message = (
+        f'`TypeAdapter[{type_repr}]` is not fully defined; you should define `{type_repr}` and all referenced types,'
+        f' then call `.rebuild()` on the instance.'
+    )
+
+    def attempt_rebuild_fn(attr_fn: Callable[[TypeAdapter], T]) -> Callable[[], T | None]:
+        def handler() -> T | None:
+            if adapter.rebuild(raise_errors=False, _parent_namespace_depth=5) is not False:
+                return attr_fn(adapter)
+            else:
+                return None
+
+        return handler
+
+    adapter.core_schema = MockCoreSchema(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.core_schema),
+    )
+    adapter.validator = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='validator',
+        attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.validator),
+    )
+    adapter.serializer = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='serializer',
+        attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.serializer),
+    )
+
+
+def set_model_mocks(cls: type[BaseModel], cls_name: str, undefined_name: str = 'all referenced types') -> None:
+    """Set `__pydantic_core_schema__`, `__pydantic_validator__` and `__pydantic_serializer__` to mock core types on a model.
+
+    Args:
+        cls: The model class to set the mocks on
+        cls_name: Name of the model class, used in error messages
+        undefined_name: Name of the undefined thing, used in error messages
+    """
+    undefined_type_error_message = (
+        f'`{cls_name}` is not fully defined; you should define {undefined_name},'
+        f' then call `{cls_name}.model_rebuild()`.'
+    )
+
+    def attempt_rebuild_fn(attr_fn: Callable[[type[BaseModel]], T]) -> Callable[[], T | None]:
+        def handler() -> T | None:
+            if cls.model_rebuild(raise_errors=False, _parent_namespace_depth=5) is not False:
+                return attr_fn(cls)
+            else:
+                return None
+
+        return handler
+
+    cls.__pydantic_core_schema__ = MockCoreSchema(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__),
+    )
+    cls.__pydantic_validator__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='validator',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__),
+    )
+    cls.__pydantic_serializer__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='serializer',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_serializer__),
+    )
+
+
+def set_dataclass_mocks(
+    cls: type[PydanticDataclass], cls_name: str, undefined_name: str = 'all referenced types'
+) -> None:
+    """Set `__pydantic_validator__` and `__pydantic_serializer__` to `MockValSer`s on a dataclass.
+
+    Args:
+        cls: The model class to set the mocks on
+        cls_name: Name of the model class, used in error messages
+        undefined_name: Name of the undefined thing, used in error messages
+    """
+    from ..dataclasses import rebuild_dataclass
+
+    undefined_type_error_message = (
+        f'`{cls_name}` is not fully defined; you should define {undefined_name},'
+        f' then call `pydantic.dataclasses.rebuild_dataclass({cls_name})`.'
+    )
+
+    def attempt_rebuild_fn(attr_fn: Callable[[type[PydanticDataclass]], T]) -> Callable[[], T | None]:
+        def handler() -> T | None:
+            if rebuild_dataclass(cls, raise_errors=False, _parent_namespace_depth=5) is not False:
+                return attr_fn(cls)
+            else:
+                return None
+
+        return handler
+
+    cls.__pydantic_core_schema__ = MockCoreSchema(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__),
+    )
+    cls.__pydantic_validator__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='validator',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__),
+    )
+    cls.__pydantic_serializer__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='serializer',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_serializer__),
+    )

env/lib/python3.11/site-packages/pydantic/_internal/_model_construction.py

@@ -0,0 +1,848 @@
+"""Private logic for creating models."""
+
+from __future__ import annotations as _annotations
+
+import builtins
+import operator
+import sys
+import typing
+import warnings
+import weakref
+from abc import ABCMeta
+from functools import lru_cache, partial
+from types import FunctionType
+from typing import Any, Callable, Generic, Literal, NoReturn, TypeVar, cast
+
+from pydantic_core import PydanticUndefined, SchemaSerializer
+from typing_extensions import TypeAliasType, dataclass_transform, deprecated, get_args
+
+from ..errors import PydanticUndefinedAnnotation, PydanticUserError
+from ..plugin._schema_validator import create_schema_validator
+from ..warnings import GenericBeforeBaseModelWarning, PydanticDeprecatedSince20
+from ._config import ConfigWrapper
+from ._decorators import DecoratorInfos, PydanticDescriptorProxy, get_attribute_from_bases, unwrap_wrapped_function
+from ._fields import collect_model_fields, is_valid_field_name, is_valid_privateattr_name
+from ._generate_schema import GenerateSchema
+from ._generics import PydanticGenericMetadata, get_model_typevars_map
+from ._import_utils import import_cached_base_model, import_cached_field_info
+from ._mock_val_ser import set_model_mocks
+from ._namespace_utils import NsResolver
+from ._schema_generation_shared import CallbackGetCoreSchemaHandler
+from ._signature import generate_pydantic_signature
+from ._typing_extra import (
+    _make_forward_ref,
+    eval_type_backport,
+    is_annotated,
+    is_classvar_annotation,
+    parent_frame_namespace,
+)
+from ._utils import LazyClassAttribute, SafeGetItemProxy
+
+if typing.TYPE_CHECKING:
+    from ..fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr
+    from ..fields import Field as PydanticModelField
+    from ..fields import PrivateAttr as PydanticModelPrivateAttr
+    from ..main import BaseModel
+else:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+    PydanticModelField = object()
+    PydanticModelPrivateAttr = object()
+
+object_setattr = object.__setattr__
+
+
+class _ModelNamespaceDict(dict):
+    """A dictionary subclass that intercepts attribute setting on model classes and
+    warns about overriding of decorators.
+    """
+
+    def __setitem__(self, k: str, v: object) -> None:
+        existing: Any = self.get(k, None)
+        if existing and v is not existing and isinstance(existing, PydanticDescriptorProxy):
+            warnings.warn(f'`{k}` overrides an existing Pydantic `{existing.decorator_info.decorator_repr}` decorator')
+
+        return super().__setitem__(k, v)
+
+
+def NoInitField(
+    *,
+    init: Literal[False] = False,
+) -> Any:
+    """Only for typing purposes. Used as default value of `__pydantic_fields_set__`,
+    `__pydantic_extra__`, `__pydantic_private__`, so they could be ignored when
+    synthesizing the `__init__` signature.
+    """
+
+
+@dataclass_transform(kw_only_default=True, field_specifiers=(PydanticModelField, PydanticModelPrivateAttr, NoInitField))
+class ModelMetaclass(ABCMeta):
+    def __new__(
+        mcs,
+        cls_name: str,
+        bases: tuple[type[Any], ...],
+        namespace: dict[str, Any],
+        __pydantic_generic_metadata__: PydanticGenericMetadata | None = None,
+        __pydantic_reset_parent_namespace__: bool = True,
+        _create_model_module: str | None = None,
+        **kwargs: Any,
+    ) -> type:
+        """Metaclass for creating Pydantic models.
+
+        Args:
+            cls_name: The name of the class to be created.
+            bases: The base classes of the class to be created.
+            namespace: The attribute dictionary of the class to be created.
+            __pydantic_generic_metadata__: Metadata for generic models.
+            __pydantic_reset_parent_namespace__: Reset parent namespace.
+            _create_model_module: The module of the class to be created, if created by `create_model`.
+            **kwargs: Catch-all for any other keyword arguments.
+
+        Returns:
+            The new class created by the metaclass.
+        """
+        # Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we rely on the fact
+        # that `BaseModel` itself won't have any bases, but any subclass of it will, to determine whether the `__new__`
+        # call we're in the middle of is for the `BaseModel` class.
+        if bases:
+            base_field_names, class_vars, base_private_attributes = mcs._collect_bases_data(bases)
+
+            config_wrapper = ConfigWrapper.for_model(bases, namespace, kwargs)
+            namespace['model_config'] = config_wrapper.config_dict
+            private_attributes = inspect_namespace(
+                namespace, config_wrapper.ignored_types, class_vars, base_field_names
+            )
+            if private_attributes or base_private_attributes:
+                original_model_post_init = get_model_post_init(namespace, bases)
+                if original_model_post_init is not None:
+                    # if there are private_attributes and a model_post_init function, we handle both
+
+                    def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None:
+                        """We need to both initialize private attributes and call the user-defined model_post_init
+                        method.
+                        """
+                        init_private_attributes(self, context)
+                        original_model_post_init(self, context)
+
+                    namespace['model_post_init'] = wrapped_model_post_init
+                else:
+                    namespace['model_post_init'] = init_private_attributes
+
+            namespace['__class_vars__'] = class_vars
+            namespace['__private_attributes__'] = {**base_private_attributes, **private_attributes}
+            if __pydantic_generic_metadata__:
+                namespace['__pydantic_generic_metadata__'] = __pydantic_generic_metadata__
+
+            cls = cast('type[BaseModel]', super().__new__(mcs, cls_name, bases, namespace, **kwargs))
+            BaseModel_ = import_cached_base_model()
+
+            mro = cls.__mro__
+            if Generic in mro and mro.index(Generic) < mro.index(BaseModel_):
+                warnings.warn(
+                    GenericBeforeBaseModelWarning(
+                        'Classes should inherit from `BaseModel` before generic classes (e.g. `typing.Generic[T]`) '
+                        'for pydantic generics to work properly.'
+                    ),
+                    stacklevel=2,
+                )
+
+            cls.__pydantic_custom_init__ = not getattr(cls.__init__, '__pydantic_base_init__', False)
+            cls.__pydantic_post_init__ = (
+                None if cls.model_post_init is BaseModel_.model_post_init else 'model_post_init'
+            )
+
+            cls.__pydantic_decorators__ = DecoratorInfos.build(cls)
+
+            # Use the getattr below to grab the __parameters__ from the `typing.Generic` parent class
+            if __pydantic_generic_metadata__:
+                cls.__pydantic_generic_metadata__ = __pydantic_generic_metadata__
+            else:
+                parent_parameters = getattr(cls, '__pydantic_generic_metadata__', {}).get('parameters', ())
+                parameters = getattr(cls, '__parameters__', None) or parent_parameters
+                if parameters and parent_parameters and not all(x in parameters for x in parent_parameters):
+                    from ..root_model import RootModelRootType
+
+                    missing_parameters = tuple(x for x in parameters if x not in parent_parameters)
+                    if RootModelRootType in parent_parameters and RootModelRootType not in parameters:
+                        # This is a special case where the user has subclassed `RootModel`, but has not parametrized
+                        # RootModel with the generic type identifiers being used. Ex:
+                        # class MyModel(RootModel, Generic[T]):
+                        #    root: T
+                        # Should instead just be:
+                        # class MyModel(RootModel[T]):
+                        #   root: T
+                        parameters_str = ', '.join([x.__name__ for x in missing_parameters])
+                        error_message = (
+                            f'{cls.__name__} is a subclass of `RootModel`, but does not include the generic type identifier(s) '
+                            f'{parameters_str} in its parameters. '
+                            f'You should parametrize RootModel directly, e.g., `class {cls.__name__}(RootModel[{parameters_str}]): ...`.'
+                        )
+                    else:
+                        combined_parameters = parent_parameters + missing_parameters
+                        parameters_str = ', '.join([str(x) for x in combined_parameters])
+                        generic_type_label = f'typing.Generic[{parameters_str}]'
+                        error_message = (
+                            f'All parameters must be present on typing.Generic;'
+                            f' you should inherit from {generic_type_label}.'
+                        )
+                        if Generic not in bases:  # pragma: no cover
+                            # We raise an error here not because it is desirable, but because some cases are mishandled.
+                            # It would be nice to remove this error and still have things behave as expected, it's just
+                            # challenging because we are using a custom `__class_getitem__` to parametrize generic models,
+                            # and not returning a typing._GenericAlias from it.
+                            bases_str = ', '.join([x.__name__ for x in bases] + [generic_type_label])
+                            error_message += (
+                                f' Note: `typing.Generic` must go last: `class {cls.__name__}({bases_str}): ...`)'
+                            )
+                    raise TypeError(error_message)
+
+                cls.__pydantic_generic_metadata__ = {
+                    'origin': None,
+                    'args': (),
+                    'parameters': parameters,
+                }
+
+            cls.__pydantic_complete__ = False  # Ensure this specific class gets completed
+
+            # preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487
+            # for attributes not in `new_namespace` (e.g. private attributes)
+            for name, obj in private_attributes.items():
+                obj.__set_name__(cls, name)
+
+            if __pydantic_reset_parent_namespace__:
+                cls.__pydantic_parent_namespace__ = build_lenient_weakvaluedict(parent_frame_namespace())
+            parent_namespace: dict[str, Any] | None = getattr(cls, '__pydantic_parent_namespace__', None)
+            if isinstance(parent_namespace, dict):
+                parent_namespace = unpack_lenient_weakvaluedict(parent_namespace)
+
+            ns_resolver = NsResolver(parent_namespace=parent_namespace)
+
+            set_model_fields(cls, bases, config_wrapper, ns_resolver)
+
+            if config_wrapper.frozen and '__hash__' not in namespace:
+                set_default_hash_func(cls, bases)
+
+            complete_model_class(
+                cls,
+                cls_name,
+                config_wrapper,
+                raise_errors=False,
+                ns_resolver=ns_resolver,
+                create_model_module=_create_model_module,
+            )
+
+            # If this is placed before the complete_model_class call above,
+            # the generic computed fields return type is set to PydanticUndefined
+            cls.__pydantic_computed_fields__ = {
+                k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items()
+            }
+
+            set_deprecated_descriptors(cls)
+
+            # using super(cls, cls) on the next line ensures we only call the parent class's __pydantic_init_subclass__
+            # I believe the `type: ignore` is only necessary because mypy doesn't realize that this code branch is
+            # only hit for _proper_ subclasses of BaseModel
+            super(cls, cls).__pydantic_init_subclass__(**kwargs)  # type: ignore[misc]
+            return cls
+        else:
+            # These are instance variables, but have been assigned to `NoInitField` to trick the type checker.
+            for instance_slot in '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__':
+                namespace.pop(
+                    instance_slot,
+                    None,  # In case the metaclass is used with a class other than `BaseModel`.
+                )
+            namespace.get('__annotations__', {}).clear()
+            return super().__new__(mcs, cls_name, bases, namespace, **kwargs)
+
+    def mro(cls) -> list[type[Any]]:
+        original_mro = super().mro()
+
+        if cls.__bases__ == (object,):
+            return original_mro
+
+        generic_metadata: PydanticGenericMetadata | None = cls.__dict__.get('__pydantic_generic_metadata__')
+        if not generic_metadata:
+            return original_mro
+
+        assert_err_msg = 'Unexpected error occurred when generating MRO of generic subclass. Please report this issue on GitHub: https://github.com/pydantic/pydantic/issues.'
+
+        origin: type[BaseModel] | None
+        origin, args = (
+            generic_metadata['origin'],
+            generic_metadata['args'],
+        )
+        if not origin:
+            return original_mro
+
+        target_params = origin.__pydantic_generic_metadata__['parameters']
+        param_dict = dict(zip(target_params, args))
+
+        indexed_origins = {origin}
+
+        new_mro: list[type[Any]] = [cls]
+        for base in original_mro[1:]:
+            base_origin: type[BaseModel] | None = getattr(base, '__pydantic_generic_metadata__', {}).get('origin')
+            base_params: tuple[TypeVar, ...] = getattr(base, '__pydantic_generic_metadata__', {}).get('parameters', ())
+
+            if base_origin in indexed_origins:
+                continue
+            elif base not in indexed_origins and base_params:
+                assert set(base_params) <= param_dict.keys(), assert_err_msg
+                new_base_args = tuple(param_dict[param] for param in base_params)
+                new_base = base[new_base_args]  # type: ignore
+                new_mro.append(new_base)
+
+                indexed_origins.add(base_origin or base)
+
+                if base_origin is not None:
+                    # dropped previous indexed origins
+                    continue
+            else:
+                indexed_origins.add(base_origin or base)
+
+            # Avoid redundunt case such as
+            # class A(BaseModel, Generic[T]): ...
+            # A[T] is A  # True
+            if base is not new_mro[-1]:
+                new_mro.append(base)
+
+        return new_mro
+
+    if not typing.TYPE_CHECKING:  # pragma: no branch
+        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
+
+        def __getattr__(self, item: str) -> Any:
+            """This is necessary to keep attribute access working for class attribute access."""
+            private_attributes = self.__dict__.get('__private_attributes__')
+            if private_attributes and item in private_attributes:
+                return private_attributes[item]
+            raise AttributeError(item)
+
+    @classmethod
+    def __prepare__(cls, *args: Any, **kwargs: Any) -> dict[str, object]:
+        return _ModelNamespaceDict()
+
+    def __instancecheck__(self, instance: Any) -> bool:
+        """Avoid calling ABC _abc_subclasscheck unless we're pretty sure.
+
+        See #3829 and python/cpython#92810
+        """
+        return hasattr(instance, '__pydantic_validator__') and super().__instancecheck__(instance)
+
+    @staticmethod
+    def _collect_bases_data(bases: tuple[type[Any], ...]) -> tuple[set[str], set[str], dict[str, ModelPrivateAttr]]:
+        BaseModel = import_cached_base_model()
+
+        field_names: set[str] = set()
+        class_vars: set[str] = set()
+        private_attributes: dict[str, ModelPrivateAttr] = {}
+        for base in bases:
+            if issubclass(base, BaseModel) and base is not BaseModel:
+                # model_fields might not be defined yet in the case of generics, so we use getattr here:
+                field_names.update(getattr(base, '__pydantic_fields__', {}).keys())
+                class_vars.update(base.__class_vars__)
+                private_attributes.update(base.__private_attributes__)
+        return field_names, class_vars, private_attributes
+
+    @property
+    @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.',
+            PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return self.model_fields
+
+    @property
+    def model_fields(self) -> dict[str, FieldInfo]:
+        """Get metadata about the fields defined on the model.
+
+        Returns:
+            A mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects.
+        """
+        return getattr(self, '__pydantic_fields__', {})
+
+    @property
+    def model_computed_fields(self) -> dict[str, ComputedFieldInfo]:
+        """Get metadata about the computed fields defined on the model.
+
+        Returns:
+            A mapping of computed field names to [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.
+        """
+        return getattr(self, '__pydantic_computed_fields__', {})
+
+    def __dir__(self) -> list[str]:
+        attributes = list(super().__dir__())
+        if '__fields__' in attributes:
+            attributes.remove('__fields__')
+        return attributes
+
+
+def init_private_attributes(self: BaseModel, context: Any, /) -> None:
+    """This function is meant to behave like a BaseModel method to initialise private attributes.
+
+    It takes context as an argument since that's what pydantic-core passes when calling it.
+
+    Args:
+        self: The BaseModel instance.
+        context: The context.
+    """
+    if getattr(self, '__pydantic_private__', None) is None:
+        pydantic_private = {}
+        for name, private_attr in self.__private_attributes__.items():
+            default = private_attr.get_default()
+            if default is not PydanticUndefined:
+                pydantic_private[name] = default
+        object_setattr(self, '__pydantic_private__', pydantic_private)
+
+
+def get_model_post_init(namespace: dict[str, Any], bases: tuple[type[Any], ...]) -> Callable[..., Any] | None:
+    """Get the `model_post_init` method from the namespace or the class bases, or `None` if not defined."""
+    if 'model_post_init' in namespace:
+        return namespace['model_post_init']
+
+    BaseModel = import_cached_base_model()
+
+    model_post_init = get_attribute_from_bases(bases, 'model_post_init')
+    if model_post_init is not BaseModel.model_post_init:
+        return model_post_init
+
+
+def inspect_namespace(  # noqa C901
+    namespace: dict[str, Any],
+    ignored_types: tuple[type[Any], ...],
+    base_class_vars: set[str],
+    base_class_fields: set[str],
+) -> dict[str, ModelPrivateAttr]:
+    """Iterate over the namespace and:
+    * gather private attributes
+    * check for items which look like fields but are not (e.g. have no annotation) and warn.
+
+    Args:
+        namespace: The attribute dictionary of the class to be created.
+        ignored_types: A tuple of ignore types.
+        base_class_vars: A set of base class class variables.
+        base_class_fields: A set of base class fields.
+
+    Returns:
+        A dict contains private attributes info.
+
+    Raises:
+        TypeError: If there is a `__root__` field in model.
+        NameError: If private attribute name is invalid.
+        PydanticUserError:
+            - If a field does not have a type annotation.
+            - If a field on base class was overridden by a non-annotated attribute.
+    """
+    from ..fields import ModelPrivateAttr, PrivateAttr
+
+    FieldInfo = import_cached_field_info()
+
+    all_ignored_types = ignored_types + default_ignored_types()
+
+    private_attributes: dict[str, ModelPrivateAttr] = {}
+    raw_annotations = namespace.get('__annotations__', {})
+
+    if '__root__' in raw_annotations or '__root__' in namespace:
+        raise TypeError("To define root models, use `pydantic.RootModel` rather than a field called '__root__'")
+
+    ignored_names: set[str] = set()
+    for var_name, value in list(namespace.items()):
+        if var_name == 'model_config' or var_name == '__pydantic_extra__':
+            continue
+        elif (
+            isinstance(value, type)
+            and value.__module__ == namespace['__module__']
+            and '__qualname__' in namespace
+            and value.__qualname__.startswith(namespace['__qualname__'])
+        ):
+            # `value` is a nested type defined in this namespace; don't error
+            continue
+        elif isinstance(value, all_ignored_types) or value.__class__.__module__ == 'functools':
+            ignored_names.add(var_name)
+            continue
+        elif isinstance(value, ModelPrivateAttr):
+            if var_name.startswith('__'):
+                raise NameError(
+                    'Private attributes must not use dunder names;'
+                    f' use a single underscore prefix instead of {var_name!r}.'
+                )
+            elif is_valid_field_name(var_name):
+                raise NameError(
+                    'Private attributes must not use valid field names;'
+                    f' use sunder names, e.g. {"_" + var_name!r} instead of {var_name!r}.'
+                )
+            private_attributes[var_name] = value
+            del namespace[var_name]
+        elif isinstance(value, FieldInfo) and not is_valid_field_name(var_name):
+            suggested_name = var_name.lstrip('_') or 'my_field'  # don't suggest '' for all-underscore name
+            raise NameError(
+                f'Fields must not use names with leading underscores;'
+                f' e.g., use {suggested_name!r} instead of {var_name!r}.'
+            )
+
+        elif var_name.startswith('__'):
+            continue
+        elif is_valid_privateattr_name(var_name):
+            if var_name not in raw_annotations or not is_classvar_annotation(raw_annotations[var_name]):
+                private_attributes[var_name] = cast(ModelPrivateAttr, PrivateAttr(default=value))
+                del namespace[var_name]
+        elif var_name in base_class_vars:
+            continue
+        elif var_name not in raw_annotations:
+            if var_name in base_class_fields:
+                raise PydanticUserError(
+                    f'Field {var_name!r} defined on a base class was overridden by a non-annotated attribute. '
+                    f'All field definitions, including overrides, require a type annotation.',
+                    code='model-field-overridden',
+                )
+            elif isinstance(value, FieldInfo):
+                raise PydanticUserError(
+                    f'Field {var_name!r} requires a type annotation', code='model-field-missing-annotation'
+                )
+            else:
+                raise PydanticUserError(
+                    f'A non-annotated attribute was detected: `{var_name} = {value!r}`. All model fields require a '
+                    f'type annotation; if `{var_name}` is not meant to be a field, you may be able to resolve this '
+                    f"error by annotating it as a `ClassVar` or updating `model_config['ignored_types']`.",
+                    code='model-field-missing-annotation',
+                )
+
+    for ann_name, ann_type in raw_annotations.items():
+        if (
+            is_valid_privateattr_name(ann_name)
+            and ann_name not in private_attributes
+            and ann_name not in ignored_names
+            # This condition can be a false negative when `ann_type` is stringified,
+            # but it is handled in most cases in `set_model_fields`:
+            and not is_classvar_annotation(ann_type)
+            and ann_type not in all_ignored_types
+            and getattr(ann_type, '__module__', None) != 'functools'
+        ):
+            if isinstance(ann_type, str):
+                # Walking up the frames to get the module namespace where the model is defined
+                # (as the model class wasn't created yet, we unfortunately can't use `cls.__module__`):
+                frame = sys._getframe(2)
+                if frame is not None:
+                    try:
+                        ann_type = eval_type_backport(
+                            _make_forward_ref(ann_type, is_argument=False, is_class=True),
+                            globalns=frame.f_globals,
+                            localns=frame.f_locals,
+                        )
+                    except (NameError, TypeError):
+                        pass
+
+            if is_annotated(ann_type):
+                _, *metadata = get_args(ann_type)
+                private_attr = next((v for v in metadata if isinstance(v, ModelPrivateAttr)), None)
+                if private_attr is not None:
+                    private_attributes[ann_name] = private_attr
+                    continue
+            private_attributes[ann_name] = PrivateAttr()
+
+    return private_attributes
+
+
+def set_default_hash_func(cls: type[BaseModel], bases: tuple[type[Any], ...]) -> None:
+    base_hash_func = get_attribute_from_bases(bases, '__hash__')
+    new_hash_func = make_hash_func(cls)
+    if base_hash_func in {None, object.__hash__} or getattr(base_hash_func, '__code__', None) == new_hash_func.__code__:
+        # If `__hash__` is some default, we generate a hash function.
+        # It will be `None` if not overridden from BaseModel.
+        # It may be `object.__hash__` if there is another
+        # parent class earlier in the bases which doesn't override `__hash__` (e.g. `typing.Generic`).
+        # It may be a value set by `set_default_hash_func` if `cls` is a subclass of another frozen model.
+        # In the last case we still need a new hash function to account for new `model_fields`.
+        cls.__hash__ = new_hash_func
+
+
+def make_hash_func(cls: type[BaseModel]) -> Any:
+    getter = operator.itemgetter(*cls.__pydantic_fields__.keys()) if cls.__pydantic_fields__ else lambda _: 0
+
+    def hash_func(self: Any) -> int:
+        try:
+            return hash(getter(self.__dict__))
+        except KeyError:
+            # In rare cases (such as when using the deprecated 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.
+            return hash(getter(SafeGetItemProxy(self.__dict__)))
+
+    return hash_func
+
+
+def set_model_fields(
+    cls: type[BaseModel],
+    bases: tuple[type[Any], ...],
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver | None,
+) -> None:
+    """Collect and set `cls.__pydantic_fields__` and `cls.__class_vars__`.
+
+    Args:
+        cls: BaseModel or dataclass.
+        bases: Parents of the class, generally `cls.__bases__`.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: Namespace resolver to use when getting model annotations.
+    """
+    typevars_map = get_model_typevars_map(cls)
+    fields, class_vars = collect_model_fields(cls, bases, config_wrapper, ns_resolver, typevars_map=typevars_map)
+
+    cls.__pydantic_fields__ = fields
+    cls.__class_vars__.update(class_vars)
+
+    for k in class_vars:
+        # Class vars should not be private attributes
+        #     We remove them _here_ and not earlier because we rely on inspecting the class to determine its classvars,
+        #     but private attributes are determined by inspecting the namespace _prior_ to class creation.
+        #     In the case that a classvar with a leading-'_' is defined via a ForwardRef (e.g., when using
+        #     `__future__.annotations`), we want to remove the private attribute which was detected _before_ we knew it
+        #     evaluated to a classvar
+
+        value = cls.__private_attributes__.pop(k, None)
+        if value is not None and value.default is not PydanticUndefined:
+            setattr(cls, k, value.default)
+
+
+def complete_model_class(
+    cls: type[BaseModel],
+    cls_name: str,
+    config_wrapper: ConfigWrapper,
+    *,
+    raise_errors: bool = True,
+    ns_resolver: NsResolver | None = None,
+    create_model_module: str | None = None,
+) -> bool:
+    """Finish building a model class.
+
+    This logic must be called after class has been created since validation functions must be bound
+    and `get_type_hints` requires a class object.
+
+    Args:
+        cls: BaseModel or dataclass.
+        cls_name: The model or dataclass name.
+        config_wrapper: The config wrapper instance.
+        raise_errors: Whether to raise errors.
+        ns_resolver: The namespace resolver instance to use during schema building.
+        create_model_module: The module of the class to be created, if created by `create_model`.
+
+    Returns:
+        `True` if the model is successfully completed, else `False`.
+
+    Raises:
+        PydanticUndefinedAnnotation: If `PydanticUndefinedAnnotation` occurs in`__get_pydantic_core_schema__`
+            and `raise_errors=True`.
+    """
+    if config_wrapper.defer_build:
+        set_model_mocks(cls, cls_name)
+        return False
+
+    typevars_map = get_model_typevars_map(cls)
+    gen_schema = GenerateSchema(
+        config_wrapper,
+        ns_resolver,
+        typevars_map,
+    )
+
+    handler = CallbackGetCoreSchemaHandler(
+        partial(gen_schema.generate_schema, from_dunder_get_core_schema=False),
+        gen_schema,
+        ref_mode='unpack',
+    )
+
+    try:
+        schema = cls.__get_pydantic_core_schema__(cls, handler)
+    except PydanticUndefinedAnnotation as e:
+        if raise_errors:
+            raise
+        set_model_mocks(cls, cls_name, f'`{e.name}`')
+        return False
+
+    core_config = config_wrapper.core_config(title=cls.__name__)
+
+    try:
+        schema = gen_schema.clean_schema(schema)
+    except gen_schema.CollectedInvalid:
+        set_model_mocks(cls, cls_name)
+        return False
+
+    # debug(schema)
+    cls.__pydantic_core_schema__ = schema
+
+    cls.__pydantic_validator__ = create_schema_validator(
+        schema,
+        cls,
+        create_model_module or cls.__module__,
+        cls.__qualname__,
+        'create_model' if create_model_module else 'BaseModel',
+        core_config,
+        config_wrapper.plugin_settings,
+    )
+    cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config)
+    cls.__pydantic_complete__ = True
+
+    # set __signature__ attr only for model class, but not for its instances
+    # (because instances can define `__call__`, and `inspect.signature` shouldn't
+    # use the `__signature__` attribute and instead generate from `__call__`).
+    cls.__signature__ = LazyClassAttribute(
+        '__signature__',
+        partial(
+            generate_pydantic_signature,
+            init=cls.__init__,
+            fields=cls.__pydantic_fields__,
+            populate_by_name=config_wrapper.populate_by_name,
+            extra=config_wrapper.extra,
+        ),
+    )
+    return True
+
+
+def set_deprecated_descriptors(cls: type[BaseModel]) -> None:
+    """Set data descriptors on the class for deprecated fields."""
+    for field, field_info in cls.__pydantic_fields__.items():
+        if (msg := field_info.deprecation_message) is not None:
+            desc = _DeprecatedFieldDescriptor(msg)
+            desc.__set_name__(cls, field)
+            setattr(cls, field, desc)
+
+    for field, computed_field_info in cls.__pydantic_computed_fields__.items():
+        if (
+            (msg := computed_field_info.deprecation_message) is not None
+            # Avoid having two warnings emitted:
+            and not hasattr(unwrap_wrapped_function(computed_field_info.wrapped_property), '__deprecated__')
+        ):
+            desc = _DeprecatedFieldDescriptor(msg, computed_field_info.wrapped_property)
+            desc.__set_name__(cls, field)
+            setattr(cls, field, desc)
+
+
+class _DeprecatedFieldDescriptor:
+    """Read-only data descriptor used to emit a runtime deprecation warning before accessing a deprecated field.
+
+    Attributes:
+        msg: The deprecation message to be emitted.
+        wrapped_property: The property instance if the deprecated field is a computed field, or `None`.
+        field_name: The name of the field being deprecated.
+    """
+
+    field_name: str
+
+    def __init__(self, msg: str, wrapped_property: property | None = None) -> None:
+        self.msg = msg
+        self.wrapped_property = wrapped_property
+
+    def __set_name__(self, cls: type[BaseModel], name: str) -> None:
+        self.field_name = name
+
+    def __get__(self, obj: BaseModel | None, obj_type: type[BaseModel] | None = None) -> Any:
+        if obj is None:
+            if self.wrapped_property is not None:
+                return self.wrapped_property.__get__(None, obj_type)
+            raise AttributeError(self.field_name)
+
+        warnings.warn(self.msg, builtins.DeprecationWarning, stacklevel=2)
+
+        if self.wrapped_property is not None:
+            return self.wrapped_property.__get__(obj, obj_type)
+        return obj.__dict__[self.field_name]
+
+    # Defined to make it a data descriptor and take precedence over the instance's dictionary.
+    # Note that it will not be called when setting a value on a model instance
+    # as `BaseModel.__setattr__` is defined and takes priority.
+    def __set__(self, obj: Any, value: Any) -> NoReturn:
+        raise AttributeError(self.field_name)
+
+
+class _PydanticWeakRef:
+    """Wrapper for `weakref.ref` that enables `pickle` serialization.
+
+    Cloudpickle fails to serialize `weakref.ref` objects due to an arcane error related
+    to abstract base classes (`abc.ABC`). This class works around the issue by wrapping
+    `weakref.ref` instead of subclassing it.
+
+    See https://github.com/pydantic/pydantic/issues/6763 for context.
+
+    Semantics:
+        - If not pickled, behaves the same as a `weakref.ref`.
+        - If pickled along with the referenced object, the same `weakref.ref` behavior
+          will be maintained between them after unpickling.
+        - If pickled without the referenced object, after unpickling the underlying
+          reference will be cleared (`__call__` will always return `None`).
+    """
+
+    def __init__(self, obj: Any):
+        if obj is None:
+            # The object will be `None` upon deserialization if the serialized weakref
+            # had lost its underlying object.
+            self._wr = None
+        else:
+            self._wr = weakref.ref(obj)
+
+    def __call__(self) -> Any:
+        if self._wr is None:
+            return None
+        else:
+            return self._wr()
+
+    def __reduce__(self) -> tuple[Callable, tuple[weakref.ReferenceType | None]]:
+        return _PydanticWeakRef, (self(),)
+
+
+def build_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None:
+    """Takes an input dictionary, and produces a new value that (invertibly) replaces the values with weakrefs.
+
+    We can't just use a WeakValueDictionary because many types (including int, str, etc.) can't be stored as values
+    in a WeakValueDictionary.
+
+    The `unpack_lenient_weakvaluedict` function can be used to reverse this operation.
+    """
+    if d is None:
+        return None
+    result = {}
+    for k, v in d.items():
+        try:
+            proxy = _PydanticWeakRef(v)
+        except TypeError:
+            proxy = v
+        result[k] = proxy
+    return result
+
+
+def unpack_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None:
+    """Inverts the transform performed by `build_lenient_weakvaluedict`."""
+    if d is None:
+        return None
+
+    result = {}
+    for k, v in d.items():
+        if isinstance(v, _PydanticWeakRef):
+            v = v()
+            if v is not None:
+                result[k] = v
+        else:
+            result[k] = v
+    return result
+
+
+@lru_cache(maxsize=None)
+def default_ignored_types() -> tuple[type[Any], ...]:
+    from ..fields import ComputedFieldInfo
+
+    ignored_types = [
+        FunctionType,
+        property,
+        classmethod,
+        staticmethod,
+        PydanticDescriptorProxy,
+        ComputedFieldInfo,
+        TypeAliasType,  # from `typing_extensions`
+    ]
+
+    if sys.version_info >= (3, 12):
+        ignored_types.append(typing.TypeAliasType)
+
+    return tuple(ignored_types)

env/lib/python3.11/site-packages/pydantic/_internal/_namespace_utils.py

@@ -0,0 +1,284 @@
+from __future__ import annotations
+
+import sys
+from collections.abc import Generator
+from contextlib import contextmanager
+from functools import cached_property
+from typing import Any, Callable, Iterator, Mapping, NamedTuple, TypeVar
+
+from typing_extensions import ParamSpec, TypeAlias, TypeAliasType, TypeVarTuple
+
+GlobalsNamespace: TypeAlias = 'dict[str, Any]'
+"""A global namespace.
+
+In most cases, this is a reference to the `__dict__` attribute of a module.
+This namespace type is expected as the `globals` argument during annotations evaluation.
+"""
+
+MappingNamespace: TypeAlias = Mapping[str, Any]
+"""Any kind of namespace.
+
+In most cases, this is a local namespace (e.g. the `__dict__` attribute of a class,
+the [`f_locals`][frame.f_locals] attribute of a frame object, when dealing with types
+defined inside functions).
+This namespace type is expected as the `locals` argument during annotations evaluation.
+"""
+
+_TypeVarLike: TypeAlias = 'TypeVar | ParamSpec | TypeVarTuple'
+
+
+class NamespacesTuple(NamedTuple):
+    """A tuple of globals and locals to be used during annotations evaluation.
+
+    This datastructure is defined as a named tuple so that it can easily be unpacked:
+
+    ```python {lint="skip" test="skip"}
+    def eval_type(typ: type[Any], ns: NamespacesTuple) -> None:
+        return eval(typ, *ns)
+    ```
+    """
+
+    globals: GlobalsNamespace
+    """The namespace to be used as the `globals` argument during annotations evaluation."""
+
+    locals: MappingNamespace
+    """The namespace to be used as the `locals` argument during annotations evaluation."""
+
+
+def get_module_ns_of(obj: Any) -> dict[str, Any]:
+    """Get the namespace of the module where the object is defined.
+
+    Caution: this function does not return a copy of the module namespace, so the result
+    should not be mutated. The burden of enforcing this is on the caller.
+    """
+    module_name = getattr(obj, '__module__', None)
+    if module_name:
+        try:
+            return sys.modules[module_name].__dict__
+        except KeyError:
+            # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363
+            return {}
+    return {}
+
+
+# Note that this class is almost identical to `collections.ChainMap`, but need to enforce
+# immutable mappings here:
+class LazyLocalNamespace(Mapping[str, Any]):
+    """A lazily evaluated mapping, to be used as the `locals` argument during annotations evaluation.
+
+    While the [`eval`][eval] function expects a mapping as the `locals` argument, it only
+    performs `__getitem__` calls. The [`Mapping`][collections.abc.Mapping] abstract base class
+    is fully implemented only for type checking purposes.
+
+    Args:
+        *namespaces: The namespaces to consider, in ascending order of priority.
+
+    Example:
+        ```python {lint="skip" test="skip"}
+        ns = LazyLocalNamespace({'a': 1, 'b': 2}, {'a': 3})
+        ns['a']
+        #> 3
+        ns['b']
+        #> 2
+        ```
+    """
+
+    def __init__(self, *namespaces: MappingNamespace) -> None:
+        self._namespaces = namespaces
+
+    @cached_property
+    def data(self) -> dict[str, Any]:
+        return {k: v for ns in self._namespaces for k, v in ns.items()}
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, key: str) -> Any:
+        return self.data[key]
+
+    def __contains__(self, key: object) -> bool:
+        return key in self.data
+
+    def __iter__(self) -> Iterator[str]:
+        return iter(self.data)
+
+
+def ns_for_function(obj: Callable[..., Any], parent_namespace: MappingNamespace | None = None) -> NamespacesTuple:
+    """Return the global and local namespaces to be used when evaluating annotations for the provided function.
+
+    The global namespace will be the `__dict__` attribute of the module the function was defined in.
+    The local namespace will contain the `__type_params__` introduced by PEP 695.
+
+    Args:
+        obj: The object to use when building namespaces.
+        parent_namespace: Optional namespace to be added with the lowest priority in the local namespace.
+            If the passed function is a method, the `parent_namespace` will be the namespace of the class
+            the method is defined in. Thus, we also fetch type `__type_params__` from there (i.e. the
+            class-scoped type variables).
+    """
+    locals_list: list[MappingNamespace] = []
+    if parent_namespace is not None:
+        locals_list.append(parent_namespace)
+
+    # Get the `__type_params__` attribute introduced by PEP 695.
+    # Note that the `typing._eval_type` function expects type params to be
+    # passed as a separate argument. However, internally, `_eval_type` calls
+    # `ForwardRef._evaluate` which will merge type params with the localns,
+    # essentially mimicking what we do here.
+    type_params: tuple[_TypeVarLike, ...]
+    if hasattr(obj, '__type_params__'):
+        type_params = obj.__type_params__
+    else:
+        type_params = ()
+    if parent_namespace is not None:
+        # We also fetch type params from the parent namespace. If present, it probably
+        # means the function was defined in a class. This is to support the following:
+        # https://github.com/python/cpython/issues/124089.
+        type_params += parent_namespace.get('__type_params__', ())
+
+    locals_list.append({t.__name__: t for t in type_params})
+
+    # What about short-cirtuiting to `obj.__globals__`?
+    globalns = get_module_ns_of(obj)
+
+    return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list))
+
+
+class NsResolver:
+    """A class responsible for the namespaces resolving logic for annotations evaluation.
+
+    This class handles the namespace logic when evaluating annotations mainly for class objects.
+
+    It holds a stack of classes that are being inspected during the core schema building,
+    and the `types_namespace` property exposes the globals and locals to be used for
+    type annotation evaluation. Additionally -- if no class is present in the stack -- a
+    fallback globals and locals can be provided using the `namespaces_tuple` argument
+    (this is useful when generating a schema for a simple annotation, e.g. when using
+    `TypeAdapter`).
+
+    The namespace creation logic is unfortunately flawed in some cases, for backwards
+    compatibility reasons and to better support valid edge cases. See the description
+    for the `parent_namespace` argument and the example for more details.
+
+    Args:
+        namespaces_tuple: The default globals and locals to use if no class is present
+            on the stack. This can be useful when using the `GenerateSchema` class
+            with `TypeAdapter`, where the "type" being analyzed is a simple annotation.
+        parent_namespace: An optional parent namespace that will be added to the locals
+            with the lowest priority. For a given class defined in a function, the locals
+            of this function are usually used as the parent namespace:
+
+            ```python {lint="skip" test="skip"}
+            from pydantic import BaseModel
+
+            def func() -> None:
+                SomeType = int
+
+                class Model(BaseModel):
+                    f: 'SomeType'
+
+                # when collecting fields, an namespace resolver instance will be created
+                # this way:
+                # ns_resolver = NsResolver(parent_namespace={'SomeType': SomeType})
+            ```
+
+            For backwards compatibility reasons and to support valid edge cases, this parent
+            namespace will be used for *every* type being pushed to the stack. In the future,
+            we might want to be smarter by only doing so when the type being pushed is defined
+            in the same module as the parent namespace.
+
+    Example:
+        ```python {lint="skip" test="skip"}
+        ns_resolver = NsResolver(
+            parent_namespace={'fallback': 1},
+        )
+
+        class Sub:
+            m: 'Model'
+
+        class Model:
+            some_local = 1
+            sub: Sub
+
+        ns_resolver = NsResolver()
+
+        # This is roughly what happens when we build a core schema for `Model`:
+        with ns_resolver.push(Model):
+            ns_resolver.types_namespace
+            #> NamespacesTuple({'Sub': Sub}, {'Model': Model, 'some_local': 1})
+            # First thing to notice here, the model being pushed is added to the locals.
+            # Because `NsResolver` is being used during the model definition, it is not
+            # yet added to the globals. This is useful when resolving self-referencing annotations.
+
+            with ns_resolver.push(Sub):
+                ns_resolver.types_namespace
+                #> NamespacesTuple({'Sub': Sub}, {'Sub': Sub, 'Model': Model})
+                # Second thing to notice: `Sub` is present in both the globals and locals.
+                # This is not an issue, just that as described above, the model being pushed
+                # is added to the locals, but it happens to be present in the globals as well
+                # because it is already defined.
+                # Third thing to notice: `Model` is also added in locals. This is a backwards
+                # compatibility workaround that allows for `Sub` to be able to resolve `'Model'`
+                # correctly (as otherwise models would have to be rebuilt even though this
+                # doesn't look necessary).
+        ```
+    """
+
+    def __init__(
+        self,
+        namespaces_tuple: NamespacesTuple | None = None,
+        parent_namespace: MappingNamespace | None = None,
+    ) -> None:
+        self._base_ns_tuple = namespaces_tuple or NamespacesTuple({}, {})
+        self._parent_ns = parent_namespace
+        self._types_stack: list[type[Any] | TypeAliasType] = []
+
+    @cached_property
+    def types_namespace(self) -> NamespacesTuple:
+        """The current global and local namespaces to be used for annotations evaluation."""
+        if not self._types_stack:
+            # TODO: should we merge the parent namespace here?
+            # This is relevant for TypeAdapter, where there are no types on the stack, and we might
+            # need access to the parent_ns. Right now, we sidestep this in `type_adapter.py` by passing
+            # locals to both parent_ns and the base_ns_tuple, but this is a bit hacky.
+            # we might consider something like:
+            # if self._parent_ns is not None:
+            #     # Hacky workarounds, see class docstring:
+            #     # An optional parent namespace that will be added to the locals with the lowest priority
+            #     locals_list: list[MappingNamespace] = [self._parent_ns, self._base_ns_tuple.locals]
+            #     return NamespacesTuple(self._base_ns_tuple.globals, LazyLocalNamespace(*locals_list))
+            return self._base_ns_tuple
+
+        typ = self._types_stack[-1]
+
+        globalns = get_module_ns_of(typ)
+
+        locals_list: list[MappingNamespace] = []
+        # Hacky workarounds, see class docstring:
+        # An optional parent namespace that will be added to the locals with the lowest priority
+        if self._parent_ns is not None:
+            locals_list.append(self._parent_ns)
+        if len(self._types_stack) > 1:
+            first_type = self._types_stack[0]
+            locals_list.append({first_type.__name__: first_type})
+
+        if hasattr(typ, '__dict__'):
+            # TypeAliasType is the exception.
+            locals_list.append(vars(typ))
+
+        # The len check above presents this from being added twice:
+        locals_list.append({typ.__name__: typ})
+
+        return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list))
+
+    @contextmanager
+    def push(self, typ: type[Any] | TypeAliasType, /) -> Generator[None]:
+        """Push a type to the stack."""
+        self._types_stack.append(typ)
+        # Reset the cached property:
+        self.__dict__.pop('types_namespace', None)
+        try:
+            yield
+        finally:
+            self._types_stack.pop()
+            self.__dict__.pop('types_namespace', None)

env/lib/python3.11/site-packages/pydantic/_internal/_repr.py

@@ -0,0 +1,123 @@
+"""Tools to provide pretty/human-readable display of objects."""
+
+from __future__ import annotations as _annotations
+
+import types
+import typing
+from typing import Any
+
+import typing_extensions
+
+from . import _typing_extra
+
+if typing.TYPE_CHECKING:
+    ReprArgs: typing_extensions.TypeAlias = 'typing.Iterable[tuple[str | None, Any]]'
+    RichReprResult: typing_extensions.TypeAlias = (
+        'typing.Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]'
+    )
+
+
+class PlainRepr(str):
+    """String class where repr doesn't include quotes. Useful with Representation when you want to return a string
+    representation of something that is valid (or pseudo-valid) python.
+    """
+
+    def __repr__(self) -> str:
+        return str(self)
+
+
+class Representation:
+    # Mixin to provide `__str__`, `__repr__`, and `__pretty__` and `__rich_repr__` methods.
+    # `__pretty__` is used by [devtools](https://python-devtools.helpmanual.io/).
+    # `__rich_repr__` is used by [rich](https://rich.readthedocs.io/en/stable/pretty.html).
+    # (this is not a docstring to avoid adding a docstring to classes which inherit from Representation)
+
+    # we don't want to use a type annotation here as it can break get_type_hints
+    __slots__ = ()  # type: typing.Collection[str]
+
+    def __repr_args__(self) -> ReprArgs:
+        """Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
+
+        Can either return:
+        * name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
+        * or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]`
+        """
+        attrs_names = self.__slots__
+        if not attrs_names and hasattr(self, '__dict__'):
+            attrs_names = self.__dict__.keys()
+        attrs = ((s, getattr(self, s)) for s in attrs_names)
+        return [(a, v if v is not self else self.__repr_recursion__(v)) for a, v in attrs if v is not None]
+
+    def __repr_name__(self) -> str:
+        """Name of the instance's class, used in __repr__."""
+        return self.__class__.__name__
+
+    def __repr_recursion__(self, object: Any) -> str:
+        """Returns the string representation of a recursive object."""
+        # This is copied over from the stdlib `pprint` module:
+        return f'<Recursion on {type(object).__name__} with id={id(object)}>'
+
+    def __repr_str__(self, join_str: str) -> str:
+        return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__())
+
+    def __pretty__(self, fmt: typing.Callable[[Any], Any], **kwargs: Any) -> typing.Generator[Any, None, None]:
+        """Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects."""
+        yield self.__repr_name__() + '('
+        yield 1
+        for name, value in self.__repr_args__():
+            if name is not None:
+                yield name + '='
+            yield fmt(value)
+            yield ','
+            yield 0
+        yield -1
+        yield ')'
+
+    def __rich_repr__(self) -> RichReprResult:
+        """Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects."""
+        for name, field_repr in self.__repr_args__():
+            if name is None:
+                yield field_repr
+            else:
+                yield name, field_repr
+
+    def __str__(self) -> str:
+        return self.__repr_str__(' ')
+
+    def __repr__(self) -> str:
+        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
+
+
+def display_as_type(obj: Any) -> str:
+    """Pretty representation of a type, should be as close as possible to the original type definition string.
+
+    Takes some logic from `typing._type_repr`.
+    """
+    if isinstance(obj, (types.FunctionType, types.BuiltinFunctionType)):
+        return obj.__name__
+    elif obj is ...:
+        return '...'
+    elif isinstance(obj, Representation):
+        return repr(obj)
+    elif isinstance(obj, typing.ForwardRef) or _typing_extra.is_type_alias_type(obj):
+        return str(obj)
+
+    if not isinstance(obj, (_typing_extra.typing_base, _typing_extra.WithArgsTypes, type)):
+        obj = obj.__class__
+
+    if _typing_extra.origin_is_union(typing_extensions.get_origin(obj)):
+        args = ', '.join(map(display_as_type, typing_extensions.get_args(obj)))
+        return f'Union[{args}]'
+    elif isinstance(obj, _typing_extra.WithArgsTypes):
+        if _typing_extra.is_literal(obj):
+            args = ', '.join(map(repr, typing_extensions.get_args(obj)))
+        else:
+            args = ', '.join(map(display_as_type, typing_extensions.get_args(obj)))
+        try:
+            return f'{obj.__qualname__}[{args}]'
+        except AttributeError:
+            return str(obj).replace('typing.', '').replace('typing_extensions.', '')  # handles TypeAliasType in 3.12
+    elif isinstance(obj, type):
+        return obj.__qualname__
+    else:
+        return repr(obj).replace('typing.', '').replace('typing_extensions.', '')

env/lib/python3.11/site-packages/pydantic/_internal/_schema_generation_shared.py

@@ -0,0 +1,126 @@
+"""Types and utility functions used by various other internal tools."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable
+
+from pydantic_core import core_schema
+from typing_extensions import Literal
+
+from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
+
+if TYPE_CHECKING:
+    from ..json_schema import GenerateJsonSchema, JsonSchemaValue
+    from ._core_utils import CoreSchemaOrField
+    from ._generate_schema import GenerateSchema
+    from ._namespace_utils import NamespacesTuple
+
+    GetJsonSchemaFunction = Callable[[CoreSchemaOrField, GetJsonSchemaHandler], JsonSchemaValue]
+    HandlerOverride = Callable[[CoreSchemaOrField], JsonSchemaValue]
+
+
+class GenerateJsonSchemaHandler(GetJsonSchemaHandler):
+    """JsonSchemaHandler implementation that doesn't do ref unwrapping by default.
+
+    This is used for any Annotated metadata so that we don't end up with conflicting
+    modifications to the definition schema.
+
+    Used internally by Pydantic, please do not rely on this implementation.
+    See `GetJsonSchemaHandler` for the handler API.
+    """
+
+    def __init__(self, generate_json_schema: GenerateJsonSchema, handler_override: HandlerOverride | None) -> None:
+        self.generate_json_schema = generate_json_schema
+        self.handler = handler_override or generate_json_schema.generate_inner
+        self.mode = generate_json_schema.mode
+
+    def __call__(self, core_schema: CoreSchemaOrField, /) -> JsonSchemaValue:
+        return self.handler(core_schema)
+
+    def resolve_ref_schema(self, maybe_ref_json_schema: JsonSchemaValue) -> JsonSchemaValue:
+        """Resolves `$ref` in the json schema.
+
+        This returns the input json schema if there is no `$ref` in json schema.
+
+        Args:
+            maybe_ref_json_schema: The input json schema that may contains `$ref`.
+
+        Returns:
+            Resolved json schema.
+
+        Raises:
+            LookupError: If it can't find the definition for `$ref`.
+        """
+        if '$ref' not in maybe_ref_json_schema:
+            return maybe_ref_json_schema
+        ref = maybe_ref_json_schema['$ref']
+        json_schema = self.generate_json_schema.get_schema_from_definitions(ref)
+        if json_schema is None:
+            raise LookupError(
+                f'Could not find a ref for {ref}.'
+                ' Maybe you tried to call resolve_ref_schema from within a recursive model?'
+            )
+        return json_schema
+
+
+class CallbackGetCoreSchemaHandler(GetCoreSchemaHandler):
+    """Wrapper to use an arbitrary function as a `GetCoreSchemaHandler`.
+
+    Used internally by Pydantic, please do not rely on this implementation.
+    See `GetCoreSchemaHandler` for the handler API.
+    """
+
+    def __init__(
+        self,
+        handler: Callable[[Any], core_schema.CoreSchema],
+        generate_schema: GenerateSchema,
+        ref_mode: Literal['to-def', 'unpack'] = 'to-def',
+    ) -> None:
+        self._handler = handler
+        self._generate_schema = generate_schema
+        self._ref_mode = ref_mode
+
+    def __call__(self, source_type: Any, /) -> core_schema.CoreSchema:
+        schema = self._handler(source_type)
+        ref = schema.get('ref')
+        if self._ref_mode == 'to-def':
+            if ref is not None:
+                self._generate_schema.defs.definitions[ref] = schema
+                return core_schema.definition_reference_schema(ref)
+            return schema
+        else:  # ref_mode = 'unpack
+            return self.resolve_ref_schema(schema)
+
+    def _get_types_namespace(self) -> NamespacesTuple:
+        return self._generate_schema._types_namespace
+
+    def generate_schema(self, source_type: Any, /) -> core_schema.CoreSchema:
+        return self._generate_schema.generate_schema(source_type)
+
+    @property
+    def field_name(self) -> str | None:
+        return self._generate_schema.field_name_stack.get()
+
+    def resolve_ref_schema(self, maybe_ref_schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
+        """Resolves reference in the core schema.
+
+        Args:
+            maybe_ref_schema: The input core schema that may contains reference.
+
+        Returns:
+            Resolved core schema.
+
+        Raises:
+            LookupError: If it can't find the definition for reference.
+        """
+        if maybe_ref_schema['type'] == 'definition-ref':
+            ref = maybe_ref_schema['schema_ref']
+            if ref not in self._generate_schema.defs.definitions:
+                raise LookupError(
+                    f'Could not find a ref for {ref}.'
+                    ' Maybe you tried to call resolve_ref_schema from within a recursive model?'
+                )
+            return self._generate_schema.defs.definitions[ref]
+        elif maybe_ref_schema['type'] == 'definitions':
+            return self.resolve_ref_schema(maybe_ref_schema['schema'])
+        return maybe_ref_schema

env/lib/python3.11/site-packages/pydantic/_internal/_serializers.py

@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+import collections
+import collections.abc
+import typing
+from typing import Any
+
+from pydantic_core import PydanticOmit, core_schema
+
+SEQUENCE_ORIGIN_MAP: dict[Any, Any] = {
+    typing.Deque: collections.deque,
+    collections.deque: collections.deque,
+    list: list,
+    typing.List: list,
+    set: set,
+    typing.AbstractSet: set,
+    typing.Set: set,
+    frozenset: frozenset,
+    typing.FrozenSet: frozenset,
+    typing.Sequence: list,
+    typing.MutableSequence: list,
+    typing.MutableSet: set,
+    # this doesn't handle subclasses of these
+    # parametrized typing.Set creates one of these
+    collections.abc.MutableSet: set,
+    collections.abc.Set: frozenset,
+}
+
+
+def serialize_sequence_via_list(
+    v: Any, handler: core_schema.SerializerFunctionWrapHandler, info: core_schema.SerializationInfo
+) -> Any:
+    items: list[Any] = []
+
+    mapped_origin = SEQUENCE_ORIGIN_MAP.get(type(v), None)
+    if mapped_origin is None:
+        # we shouldn't hit this branch, should probably add a serialization error or something
+        return v
+
+    for index, item in enumerate(v):
+        try:
+            v = handler(item, index)
+        except PydanticOmit:
+            pass
+        else:
+            items.append(v)
+
+    if info.mode_is_json():
+        return items
+    else:
+        return mapped_origin(items)

env/lib/python3.11/site-packages/pydantic/_internal/_signature.py

@@ -0,0 +1,188 @@
+from __future__ import annotations
+
+import dataclasses
+from inspect import Parameter, Signature, signature
+from typing import TYPE_CHECKING, Any, Callable
+
+from pydantic_core import PydanticUndefined
+
+from ._utils import is_valid_identifier
+
+if TYPE_CHECKING:
+    from ..config import ExtraValues
+    from ..fields import FieldInfo
+
+
+# Copied over from stdlib dataclasses
+class _HAS_DEFAULT_FACTORY_CLASS:
+    def __repr__(self):
+        return '<factory>'
+
+
+_HAS_DEFAULT_FACTORY = _HAS_DEFAULT_FACTORY_CLASS()
+
+
+def _field_name_for_signature(field_name: str, field_info: FieldInfo) -> str:
+    """Extract the correct name to use for the field when generating a signature.
+
+    Assuming the field has a valid alias, this will return the alias. Otherwise, it will return the field name.
+    First priority is given to the alias, then the validation_alias, then the field name.
+
+    Args:
+        field_name: The name of the field
+        field_info: The corresponding FieldInfo object.
+
+    Returns:
+        The correct name to use when generating a signature.
+    """
+    if isinstance(field_info.alias, str) and is_valid_identifier(field_info.alias):
+        return field_info.alias
+    if isinstance(field_info.validation_alias, str) and is_valid_identifier(field_info.validation_alias):
+        return field_info.validation_alias
+
+    return field_name
+
+
+def _process_param_defaults(param: Parameter) -> Parameter:
+    """Modify the signature for a parameter in a dataclass where the default value is a FieldInfo instance.
+
+    Args:
+        param (Parameter): The parameter
+
+    Returns:
+        Parameter: The custom processed parameter
+    """
+    from ..fields import FieldInfo
+
+    param_default = param.default
+    if isinstance(param_default, FieldInfo):
+        annotation = param.annotation
+        # Replace the annotation if appropriate
+        # inspect does "clever" things to show annotations as strings because we have
+        # `from __future__ import annotations` in main, we don't want that
+        if annotation == 'Any':
+            annotation = Any
+
+        # Replace the field default
+        default = param_default.default
+        if default is PydanticUndefined:
+            if param_default.default_factory is PydanticUndefined:
+                default = Signature.empty
+            else:
+                # this is used by dataclasses to indicate a factory exists:
+                default = dataclasses._HAS_DEFAULT_FACTORY  # type: ignore
+        return param.replace(
+            annotation=annotation, name=_field_name_for_signature(param.name, param_default), default=default
+        )
+    return param
+
+
+def _generate_signature_parameters(  # noqa: C901 (ignore complexity, could use a refactor)
+    init: Callable[..., None],
+    fields: dict[str, FieldInfo],
+    populate_by_name: bool,
+    extra: ExtraValues | None,
+) -> dict[str, Parameter]:
+    """Generate a mapping of parameter names to Parameter objects for a pydantic BaseModel or dataclass."""
+    from itertools import islice
+
+    present_params = signature(init).parameters.values()
+    merged_params: dict[str, Parameter] = {}
+    var_kw = None
+    use_var_kw = False
+
+    for param in islice(present_params, 1, None):  # skip self arg
+        # inspect does "clever" things to show annotations as strings because we have
+        # `from __future__ import annotations` in main, we don't want that
+        if fields.get(param.name):
+            # exclude params with init=False
+            if getattr(fields[param.name], 'init', True) is False:
+                continue
+            param = param.replace(name=_field_name_for_signature(param.name, fields[param.name]))
+        if param.annotation == 'Any':
+            param = param.replace(annotation=Any)
+        if param.kind is param.VAR_KEYWORD:
+            var_kw = param
+            continue
+        merged_params[param.name] = param
+
+    if var_kw:  # if custom init has no var_kw, fields which are not declared in it cannot be passed through
+        allow_names = populate_by_name
+        for field_name, field in fields.items():
+            # when alias is a str it should be used for signature generation
+            param_name = _field_name_for_signature(field_name, field)
+
+            if field_name in merged_params or param_name in merged_params:
+                continue
+
+            if not is_valid_identifier(param_name):
+                if allow_names:
+                    param_name = field_name
+                else:
+                    use_var_kw = True
+                    continue
+
+            if field.is_required():
+                default = Parameter.empty
+            elif field.default_factory is not None:
+                # Mimics stdlib dataclasses:
+                default = _HAS_DEFAULT_FACTORY
+            else:
+                default = field.default
+            merged_params[param_name] = Parameter(
+                param_name,
+                Parameter.KEYWORD_ONLY,
+                annotation=field.rebuild_annotation(),
+                default=default,
+            )
+
+    if extra == 'allow':
+        use_var_kw = True
+
+    if var_kw and use_var_kw:
+        # Make sure the parameter for extra kwargs
+        # does not have the same name as a field
+        default_model_signature = [
+            ('self', Parameter.POSITIONAL_ONLY),
+            ('data', Parameter.VAR_KEYWORD),
+        ]
+        if [(p.name, p.kind) for p in present_params] == default_model_signature:
+            # if this is the standard model signature, use extra_data as the extra args name
+            var_kw_name = 'extra_data'
+        else:
+            # else start from var_kw
+            var_kw_name = var_kw.name
+
+        # generate a name that's definitely unique
+        while var_kw_name in fields:
+            var_kw_name += '_'
+        merged_params[var_kw_name] = var_kw.replace(name=var_kw_name)
+
+    return merged_params
+
+
+def generate_pydantic_signature(
+    init: Callable[..., None],
+    fields: dict[str, FieldInfo],
+    populate_by_name: bool,
+    extra: ExtraValues | None,
+    is_dataclass: bool = False,
+) -> Signature:
+    """Generate signature for a pydantic BaseModel or dataclass.
+
+    Args:
+        init: The class init.
+        fields: The model fields.
+        populate_by_name: The `populate_by_name` value of the config.
+        extra: The `extra` value of the config.
+        is_dataclass: Whether the model is a dataclass.
+
+    Returns:
+        The dataclass/BaseModel subclass signature.
+    """
+    merged_params = _generate_signature_parameters(init, fields, populate_by_name, extra)
+
+    if is_dataclass:
+        merged_params = {k: _process_param_defaults(v) for k, v in merged_params.items()}
+
+    return Signature(parameters=list(merged_params.values()), return_annotation=None)

env/lib/python3.11/site-packages/pydantic/_internal/_std_types_schema.py

@@ -0,0 +1,404 @@
+"""Logic for generating pydantic-core schemas for standard library types.
+
+Import of this module is deferred since it contains imports of many standard library modules.
+"""
+
+# TODO: eventually, we'd like to move all of the types handled here to have pydantic-core validators
+# so that we can avoid this annotation injection and just use the standard pydantic-core schema generation
+
+from __future__ import annotations as _annotations
+
+import collections
+import collections.abc
+import dataclasses
+import os
+import typing
+from functools import partial
+from typing import Any, Callable, Iterable, Tuple, TypeVar, cast
+
+import typing_extensions
+from pydantic_core import (
+    CoreSchema,
+    PydanticCustomError,
+    core_schema,
+)
+from typing_extensions import get_args, get_origin
+
+from pydantic._internal._serializers import serialize_sequence_via_list
+from pydantic.errors import PydanticSchemaGenerationError
+from pydantic.types import Strict
+
+from ..json_schema import JsonSchemaValue
+from . import _known_annotated_metadata, _typing_extra
+from ._import_utils import import_cached_field_info
+from ._internal_dataclass import slots_true
+from ._schema_generation_shared import GetCoreSchemaHandler, GetJsonSchemaHandler
+
+FieldInfo = import_cached_field_info()
+
+if typing.TYPE_CHECKING:
+    from ._generate_schema import GenerateSchema
+
+    StdSchemaFunction = Callable[[GenerateSchema, type[Any]], core_schema.CoreSchema]
+
+
+@dataclasses.dataclass(**slots_true)
+class InnerSchemaValidator:
+    """Use a fixed CoreSchema, avoiding interference from outward annotations."""
+
+    core_schema: CoreSchema
+    js_schema: JsonSchemaValue | None = None
+    js_core_schema: CoreSchema | None = None
+    js_schema_update: JsonSchemaValue | None = None
+
+    def __get_pydantic_json_schema__(self, _schema: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
+        if self.js_schema is not None:
+            return self.js_schema
+        js_schema = handler(self.js_core_schema or self.core_schema)
+        if self.js_schema_update is not None:
+            js_schema.update(self.js_schema_update)
+        return js_schema
+
+    def __get_pydantic_core_schema__(self, _source_type: Any, _handler: GetCoreSchemaHandler) -> CoreSchema:
+        return self.core_schema
+
+
+def path_schema_prepare_pydantic_annotations(
+    source_type: Any, annotations: Iterable[Any]
+) -> tuple[Any, list[Any]] | None:
+    import pathlib
+
+    orig_source_type: Any = get_origin(source_type) or source_type
+    if (
+        (source_type_args := get_args(source_type))
+        and orig_source_type is os.PathLike
+        and source_type_args[0] not in {str, bytes, Any}
+    ):
+        return None
+
+    if orig_source_type not in {
+        os.PathLike,
+        pathlib.Path,
+        pathlib.PurePath,
+        pathlib.PosixPath,
+        pathlib.PurePosixPath,
+        pathlib.PureWindowsPath,
+    }:
+        return None
+
+    metadata, remaining_annotations = _known_annotated_metadata.collect_known_metadata(annotations)
+    _known_annotated_metadata.check_metadata(metadata, _known_annotated_metadata.STR_CONSTRAINTS, orig_source_type)
+
+    is_first_arg_byte = source_type_args and source_type_args[0] is bytes
+    construct_path = pathlib.PurePath if orig_source_type is os.PathLike else orig_source_type
+    constrained_schema = (
+        core_schema.bytes_schema(**metadata) if is_first_arg_byte else core_schema.str_schema(**metadata)
+    )
+
+    def path_validator(input_value: str | bytes) -> os.PathLike[Any]:  # type: ignore
+        try:
+            if is_first_arg_byte:
+                if isinstance(input_value, bytes):
+                    try:
+                        input_value = input_value.decode()
+                    except UnicodeDecodeError as e:
+                        raise PydanticCustomError('bytes_type', 'Input must be valid bytes') from e
+                else:
+                    raise PydanticCustomError('bytes_type', 'Input must be bytes')
+            elif not isinstance(input_value, str):
+                raise PydanticCustomError('path_type', 'Input is not a valid path')
+
+            return construct_path(input_value)
+        except TypeError as e:
+            raise PydanticCustomError('path_type', 'Input is not a valid path') from e
+
+    instance_schema = core_schema.json_or_python_schema(
+        json_schema=core_schema.no_info_after_validator_function(path_validator, constrained_schema),
+        python_schema=core_schema.is_instance_schema(orig_source_type),
+    )
+
+    strict: bool | None = None
+    for annotation in annotations:
+        if isinstance(annotation, Strict):
+            strict = annotation.strict
+
+    schema = core_schema.lax_or_strict_schema(
+        lax_schema=core_schema.union_schema(
+            [
+                instance_schema,
+                core_schema.no_info_after_validator_function(path_validator, constrained_schema),
+            ],
+            custom_error_type='path_type',
+            custom_error_message=f'Input is not a valid path for {orig_source_type}',
+            strict=True,
+        ),
+        strict_schema=instance_schema,
+        serialization=core_schema.to_string_ser_schema(),
+        strict=strict,
+    )
+
+    return (
+        orig_source_type,
+        [
+            InnerSchemaValidator(schema, js_core_schema=constrained_schema, js_schema_update={'format': 'path'}),
+            *remaining_annotations,
+        ],
+    )
+
+
+def deque_validator(
+    input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler, maxlen: None | int
+) -> collections.deque[Any]:
+    if isinstance(input_value, collections.deque):
+        maxlens = [v for v in (input_value.maxlen, maxlen) if v is not None]
+        if maxlens:
+            maxlen = min(maxlens)
+        return collections.deque(handler(input_value), maxlen=maxlen)
+    else:
+        return collections.deque(handler(input_value), maxlen=maxlen)
+
+
+@dataclasses.dataclass(**slots_true)
+class DequeValidator:
+    item_source_type: type[Any]
+    metadata: dict[str, Any]
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
+        if _typing_extra.is_any(self.item_source_type):
+            items_schema = None
+        else:
+            items_schema = handler.generate_schema(self.item_source_type)
+
+        # if we have a MaxLen annotation might as well set that as the default maxlen on the deque
+        # this lets us reuse existing metadata annotations to let users set the maxlen on a dequeue
+        # that e.g. comes from JSON
+        coerce_instance_wrap = partial(
+            core_schema.no_info_wrap_validator_function,
+            partial(deque_validator, maxlen=self.metadata.get('max_length', None)),
+        )
+
+        # we have to use a lax list schema here, because we need to validate the deque's
+        # items via a list schema, but it's ok if the deque itself is not a list
+        metadata_with_strict_override = {**self.metadata, 'strict': False}
+        constrained_schema = core_schema.list_schema(items_schema, **metadata_with_strict_override)
+
+        check_instance = core_schema.json_or_python_schema(
+            json_schema=core_schema.list_schema(),
+            python_schema=core_schema.is_instance_schema(collections.deque),
+        )
+
+        serialization = core_schema.wrap_serializer_function_ser_schema(
+            serialize_sequence_via_list, schema=items_schema or core_schema.any_schema(), info_arg=True
+        )
+
+        strict = core_schema.chain_schema([check_instance, coerce_instance_wrap(constrained_schema)])
+
+        if self.metadata.get('strict', False):
+            schema = strict
+        else:
+            lax = coerce_instance_wrap(constrained_schema)
+            schema = core_schema.lax_or_strict_schema(lax_schema=lax, strict_schema=strict)
+        schema['serialization'] = serialization
+
+        return schema
+
+
+def deque_schema_prepare_pydantic_annotations(
+    source_type: Any, annotations: Iterable[Any]
+) -> tuple[Any, list[Any]] | None:
+    args = get_args(source_type)
+
+    if not args:
+        args = typing.cast(Tuple[Any], (Any,))
+    elif len(args) != 1:
+        raise ValueError('Expected deque to have exactly 1 generic parameter')
+
+    item_source_type = args[0]
+
+    metadata, remaining_annotations = _known_annotated_metadata.collect_known_metadata(annotations)
+    _known_annotated_metadata.check_metadata(metadata, _known_annotated_metadata.SEQUENCE_CONSTRAINTS, source_type)
+
+    return (source_type, [DequeValidator(item_source_type, metadata), *remaining_annotations])
+
+
+MAPPING_ORIGIN_MAP: dict[Any, Any] = {
+    typing.DefaultDict: collections.defaultdict,
+    collections.defaultdict: collections.defaultdict,
+    collections.OrderedDict: collections.OrderedDict,
+    typing_extensions.OrderedDict: collections.OrderedDict,
+    dict: dict,
+    typing.Dict: dict,
+    collections.Counter: collections.Counter,
+    typing.Counter: collections.Counter,
+    # this doesn't handle subclasses of these
+    typing.Mapping: dict,
+    typing.MutableMapping: dict,
+    # parametrized typing.{Mutable}Mapping creates one of these
+    collections.abc.MutableMapping: dict,
+    collections.abc.Mapping: dict,
+}
+
+
+def defaultdict_validator(
+    input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler, default_default_factory: Callable[[], Any]
+) -> collections.defaultdict[Any, Any]:
+    if isinstance(input_value, collections.defaultdict):
+        default_factory = input_value.default_factory
+        return collections.defaultdict(default_factory, handler(input_value))
+    else:
+        return collections.defaultdict(default_default_factory, handler(input_value))
+
+
+def get_defaultdict_default_default_factory(values_source_type: Any) -> Callable[[], Any]:
+    def infer_default() -> Callable[[], Any]:
+        allowed_default_types: dict[Any, Any] = {
+            typing.Tuple: tuple,
+            tuple: tuple,
+            collections.abc.Sequence: tuple,
+            collections.abc.MutableSequence: list,
+            typing.List: list,
+            list: list,
+            typing.Sequence: list,
+            typing.Set: set,
+            set: set,
+            typing.MutableSet: set,
+            collections.abc.MutableSet: set,
+            collections.abc.Set: frozenset,
+            typing.MutableMapping: dict,
+            typing.Mapping: dict,
+            collections.abc.Mapping: dict,
+            collections.abc.MutableMapping: dict,
+            float: float,
+            int: int,
+            str: str,
+            bool: bool,
+        }
+        values_type_origin = get_origin(values_source_type) or values_source_type
+        instructions = 'set using `DefaultDict[..., Annotated[..., Field(default_factory=...)]]`'
+        if isinstance(values_type_origin, TypeVar):
+
+            def type_var_default_factory() -> None:
+                raise RuntimeError(
+                    'Generic defaultdict cannot be used without a concrete value type or an'
+                    ' explicit default factory, ' + instructions
+                )
+
+            return type_var_default_factory
+        elif values_type_origin not in allowed_default_types:
+            # a somewhat subjective set of types that have reasonable default values
+            allowed_msg = ', '.join([t.__name__ for t in set(allowed_default_types.values())])
+            raise PydanticSchemaGenerationError(
+                f'Unable to infer a default factory for keys of type {values_source_type}.'
+                f' Only {allowed_msg} are supported, other types require an explicit default factory'
+                ' ' + instructions
+            )
+        return allowed_default_types[values_type_origin]
+
+    # Assume Annotated[..., Field(...)]
+    if _typing_extra.is_annotated(values_source_type):
+        field_info = next((v for v in get_args(values_source_type) if isinstance(v, FieldInfo)), None)
+    else:
+        field_info = None
+    if field_info and field_info.default_factory:
+        # Assume the default factory does not take any argument:
+        default_default_factory = cast(Callable[[], Any], field_info.default_factory)
+    else:
+        default_default_factory = infer_default()
+    return default_default_factory
+
+
+@dataclasses.dataclass(**slots_true)
+class MappingValidator:
+    mapped_origin: type[Any]
+    keys_source_type: type[Any]
+    values_source_type: type[Any]
+    min_length: int | None = None
+    max_length: int | None = None
+    strict: bool = False
+
+    def serialize_mapping_via_dict(self, v: Any, handler: core_schema.SerializerFunctionWrapHandler) -> Any:
+        return handler(v)
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
+        if _typing_extra.is_any(self.keys_source_type):
+            keys_schema = None
+        else:
+            keys_schema = handler.generate_schema(self.keys_source_type)
+        if _typing_extra.is_any(self.values_source_type):
+            values_schema = None
+        else:
+            values_schema = handler.generate_schema(self.values_source_type)
+
+        metadata = {'min_length': self.min_length, 'max_length': self.max_length, 'strict': self.strict}
+
+        if self.mapped_origin is dict:
+            schema = core_schema.dict_schema(keys_schema, values_schema, **metadata)
+        else:
+            constrained_schema = core_schema.dict_schema(keys_schema, values_schema, **metadata)
+            check_instance = core_schema.json_or_python_schema(
+                json_schema=core_schema.dict_schema(),
+                python_schema=core_schema.is_instance_schema(self.mapped_origin),
+            )
+
+            if self.mapped_origin is collections.defaultdict:
+                default_default_factory = get_defaultdict_default_default_factory(self.values_source_type)
+                coerce_instance_wrap = partial(
+                    core_schema.no_info_wrap_validator_function,
+                    partial(defaultdict_validator, default_default_factory=default_default_factory),
+                )
+            else:
+                coerce_instance_wrap = partial(core_schema.no_info_after_validator_function, self.mapped_origin)
+
+            serialization = core_schema.wrap_serializer_function_ser_schema(
+                self.serialize_mapping_via_dict,
+                schema=core_schema.dict_schema(
+                    keys_schema or core_schema.any_schema(), values_schema or core_schema.any_schema()
+                ),
+                info_arg=False,
+            )
+
+            strict = core_schema.chain_schema([check_instance, coerce_instance_wrap(constrained_schema)])
+
+            if metadata.get('strict', False):
+                schema = strict
+            else:
+                lax = coerce_instance_wrap(constrained_schema)
+                schema = core_schema.lax_or_strict_schema(lax_schema=lax, strict_schema=strict)
+                schema['serialization'] = serialization
+
+        return schema
+
+
+def mapping_like_prepare_pydantic_annotations(
+    source_type: Any, annotations: Iterable[Any]
+) -> tuple[Any, list[Any]] | None:
+    origin: Any = get_origin(source_type)
+
+    mapped_origin = MAPPING_ORIGIN_MAP.get(origin, None) if origin else MAPPING_ORIGIN_MAP.get(source_type, None)
+    if mapped_origin is None:
+        return None
+
+    args = get_args(source_type)
+
+    if not args:
+        args = typing.cast(Tuple[Any, Any], (Any, Any))
+    elif mapped_origin is collections.Counter:
+        # a single generic
+        if len(args) != 1:
+            raise ValueError('Expected Counter to have exactly 1 generic parameter')
+        args = (args[0], int)  # keys are always an int
+    elif len(args) != 2:
+        raise ValueError('Expected mapping to have exactly 2 generic parameters')
+
+    keys_source_type, values_source_type = args
+
+    metadata, remaining_annotations = _known_annotated_metadata.collect_known_metadata(annotations)
+    _known_annotated_metadata.check_metadata(metadata, _known_annotated_metadata.SEQUENCE_CONSTRAINTS, source_type)
+
+    return (
+        source_type,
+        [
+            MappingValidator(mapped_origin, keys_source_type, values_source_type, **metadata),
+            *remaining_annotations,
+        ],
+    )

env/lib/python3.11/site-packages/pydantic/_internal/_typing_extra.py

@@ -0,0 +1,893 @@
+"""Logic for interacting with type annotations, mostly extensions, shims and hacks to wrap Python's typing module."""
+
+from __future__ import annotations
+
+import collections.abc
+import re
+import sys
+import types
+import typing
+import warnings
+from functools import lru_cache, partial
+from typing import TYPE_CHECKING, Any, Callable
+
+import typing_extensions
+from typing_extensions import TypeIs, deprecated, get_args, get_origin
+
+from ._namespace_utils import GlobalsNamespace, MappingNamespace, NsResolver, get_module_ns_of
+
+if sys.version_info < (3, 10):
+    NoneType = type(None)
+    EllipsisType = type(Ellipsis)
+else:
+    from types import EllipsisType as EllipsisType
+    from types import NoneType as NoneType
+
+if TYPE_CHECKING:
+    from pydantic import BaseModel
+
+# See https://typing-extensions.readthedocs.io/en/latest/#runtime-use-of-types:
+
+
+@lru_cache(maxsize=None)
+def _get_typing_objects_by_name_of(name: str) -> tuple[Any, ...]:
+    """Get the member named `name` from both `typing` and `typing-extensions` (if it exists)."""
+    result = tuple(getattr(module, name) for module in (typing, typing_extensions) if hasattr(module, name))
+    if not result:
+        raise ValueError(f'Neither `typing` nor `typing_extensions` has an object called {name!r}')
+    return result
+
+
+# As suggested by the `typing-extensions` documentation, we could apply caching to this method,
+# but it doesn't seem to improve performance. This also requires `obj` to be hashable, which
+# might not be always the case:
+def _is_typing_name(obj: object, name: str) -> bool:
+    """Return whether `obj` is the member of the typing modules (includes the `typing-extensions` one) named `name`."""
+    # Using `any()` is slower:
+    for thing in _get_typing_objects_by_name_of(name):
+        if obj is thing:
+            return True
+    return False
+
+
+def is_any(tp: Any, /) -> bool:
+    """Return whether the provided argument is the `Any` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_any(Any)
+    #> True
+    ```
+    """
+    return _is_typing_name(tp, name='Any')
+
+
+def is_union(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Union` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_union(Union[int, str])
+    #> True
+    is_union(int | str)
+    #> False
+    ```
+    """
+    return _is_typing_name(get_origin(tp), name='Union')
+
+
+def is_literal(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Literal` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_literal(Literal[42])
+    #> True
+    ```
+    """
+    return _is_typing_name(get_origin(tp), name='Literal')
+
+
+# TODO remove and replace with `get_args` when we drop support for Python 3.8
+# (see https://docs.python.org/3/whatsnew/3.9.html#id4).
+def literal_values(tp: Any, /) -> list[Any]:
+    """Return the values contained in the provided `Literal` special form."""
+    if not is_literal(tp):
+        return [tp]
+
+    values = get_args(tp)
+    return [x for value in values for x in literal_values(value)]
+
+
+def is_annotated(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Annotated` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_annotated(Annotated[int, ...])
+    #> True
+    ```
+    """
+    return _is_typing_name(get_origin(tp), name='Annotated')
+
+
+def annotated_type(tp: Any, /) -> Any | None:
+    """Return the type of the `Annotated` special form, or `None`."""
+    return get_args(tp)[0] if is_annotated(tp) else None
+
+
+def is_unpack(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Unpack` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_unpack(Unpack[Ts])
+    #> True
+    ```
+    """
+    return _is_typing_name(get_origin(tp), name='Unpack')
+
+
+def unpack_type(tp: Any, /) -> Any | None:
+    """Return the type wrapped by the `Unpack` special form, or `None`."""
+    return get_args(tp)[0] if is_unpack(tp) else None
+
+
+def is_self(tp: Any, /) -> bool:
+    """Return whether the provided argument is the `Self` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_self(Self)
+    #> True
+    ```
+    """
+    return _is_typing_name(tp, name='Self')
+
+
+def is_new_type(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `NewType`.
+
+    ```python {test="skip" lint="skip"}
+    is_new_type(NewType('MyInt', int))
+    #> True
+    ```
+    """
+    if sys.version_info < (3, 10):
+        # On Python < 3.10, `typing.NewType` is a function
+        return hasattr(tp, '__supertype__')
+    else:
+        return _is_typing_name(type(tp), name='NewType')
+
+
+def is_hashable(tp: Any, /) -> bool:
+    """Return whether the provided argument is the `Hashable` class.
+
+    ```python {test="skip" lint="skip"}
+    is_hashable(Hashable)
+    #> True
+    ```
+    """
+    # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes,
+    # hence the second check:
+    return tp is collections.abc.Hashable or get_origin(tp) is collections.abc.Hashable
+
+
+def is_callable(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Callable`, parametrized or not.
+
+    ```python {test="skip" lint="skip"}
+    is_callable(Callable[[int], str])
+    #> True
+    is_callable(typing.Callable)
+    #> True
+    is_callable(collections.abc.Callable)
+    #> True
+    ```
+    """
+    # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes,
+    # hence the second check:
+    return tp is collections.abc.Callable or get_origin(tp) is collections.abc.Callable
+
+
+_PARAMSPEC_TYPES: tuple[type[typing_extensions.ParamSpec], ...] = (typing_extensions.ParamSpec,)
+if sys.version_info >= (3, 10):
+    _PARAMSPEC_TYPES = (*_PARAMSPEC_TYPES, typing.ParamSpec)  # pyright: ignore[reportAssignmentType]
+
+
+def is_paramspec(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `ParamSpec`.
+
+    ```python {test="skip" lint="skip"}
+    P = ParamSpec('P')
+    is_paramspec(P)
+    #> True
+    ```
+    """
+    return isinstance(tp, _PARAMSPEC_TYPES)
+
+
+_TYPE_ALIAS_TYPES: tuple[type[typing_extensions.TypeAliasType], ...] = (typing_extensions.TypeAliasType,)
+if sys.version_info >= (3, 12):
+    _TYPE_ALIAS_TYPES = (*_TYPE_ALIAS_TYPES, typing.TypeAliasType)
+
+
+def is_type_alias_type(tp: Any, /) -> TypeIs[typing_extensions.TypeAliasType]:
+    """Return whether the provided argument is an instance of `TypeAliasType`.
+
+    ```python {test="skip" lint="skip"}
+    type Int = int
+    is_type_alias_type(Int)
+    #> True
+    Str = TypeAliasType('Str', str)
+    is_type_alias_type(Str)
+    #> True
+    ```
+    """
+    return isinstance(tp, _TYPE_ALIAS_TYPES)
+
+
+def is_classvar(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `ClassVar` special form, parametrized or not.
+
+    Note that in most cases, you will want to use the `is_classvar_annotation` function,
+    which is used to check if an annotation (in the context of a Pydantic model or dataclass)
+    should be treated as being a class variable.
+
+    ```python {test="skip" lint="skip"}
+    is_classvar(ClassVar[int])
+    #> True
+    is_classvar(ClassVar)
+    #> True
+    """
+    # ClassVar is not necessarily parametrized:
+    return _is_typing_name(tp, name='ClassVar') or _is_typing_name(get_origin(tp), name='ClassVar')
+
+
+_classvar_re = re.compile(r'((\w+\.)?Annotated\[)?(\w+\.)?ClassVar\[')
+
+
+def is_classvar_annotation(tp: Any, /) -> bool:
+    """Return whether the provided argument represents a class variable annotation.
+
+    Although not explicitly stated by the typing specification, `ClassVar` can be used
+    inside `Annotated` and as such, this function checks for this specific scenario.
+
+    Because this function is used to detect class variables before evaluating forward references
+    (or because evaluation failed), we also implement a naive regex match implementation. This is
+    required because class variables are inspected before fields are collected, so we try to be
+    as accurate as possible.
+    """
+    if is_classvar(tp) or (anntp := annotated_type(tp)) is not None and is_classvar(anntp):
+        return True
+
+    str_ann: str | None = None
+    if isinstance(tp, typing.ForwardRef):
+        str_ann = tp.__forward_arg__
+    if isinstance(tp, str):
+        str_ann = tp
+
+    if str_ann is not None and _classvar_re.match(str_ann):
+        # stdlib dataclasses do something similar, although a bit more advanced
+        # (see `dataclass._is_type`).
+        return True
+
+    return False
+
+
+# TODO implement `is_finalvar_annotation` as Final can be wrapped with other special forms:
+def is_finalvar(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Final` special form, parametrized or not.
+
+    ```python {test="skip" lint="skip"}
+    is_finalvar(Final[int])
+    #> True
+    is_finalvar(Final)
+    #> True
+    """
+    # Final is not necessarily parametrized:
+    return _is_typing_name(tp, name='Final') or _is_typing_name(get_origin(tp), name='Final')
+
+
+def is_required(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Required` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_required(Required[int])
+    #> True
+    """
+    return _is_typing_name(get_origin(tp), name='Required')
+
+
+def is_not_required(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `NotRequired` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_required(Required[int])
+    #> True
+    """
+    return _is_typing_name(get_origin(tp), name='NotRequired')
+
+
+def is_no_return(tp: Any, /) -> bool:
+    """Return whether the provided argument is the `NoReturn` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_no_return(NoReturn)
+    #> True
+    ```
+    """
+    return _is_typing_name(tp, name='NoReturn')
+
+
+def is_never(tp: Any, /) -> bool:
+    """Return whether the provided argument is the `Never` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_never(Never)
+    #> True
+    ```
+    """
+    return _is_typing_name(tp, name='Never')
+
+
+_DEPRECATED_TYPES: tuple[type[typing_extensions.deprecated], ...] = (typing_extensions.deprecated,)
+if hasattr(warnings, 'deprecated'):
+    _DEPRECATED_TYPES = (*_DEPRECATED_TYPES, warnings.deprecated)  # pyright: ignore[reportAttributeAccessIssue]
+
+
+def is_deprecated_instance(obj: Any, /) -> TypeIs[deprecated]:
+    """Return whether the argument is an instance of the `warnings.deprecated` class or the `typing_extensions` backport."""
+    return isinstance(obj, _DEPRECATED_TYPES)
+
+
+_NONE_TYPES: tuple[Any, ...] = (None, NoneType, typing.Literal[None], typing_extensions.Literal[None])
+
+
+def is_none_type(tp: Any, /) -> bool:
+    """Return whether the argument represents the `None` type as part of an annotation.
+
+    ```python {test="skip" lint="skip"}
+    is_none_type(None)
+    #> True
+    is_none_type(NoneType)
+    #> True
+    is_none_type(Literal[None])
+    #> True
+    is_none_type(type[None])
+    #> False
+    """
+    return tp in _NONE_TYPES
+
+
+def is_namedtuple(tp: Any, /) -> bool:
+    """Return whether the provided argument is a named tuple class.
+
+    The class can be created using `typing.NamedTuple` or `collections.namedtuple`.
+    Parametrized generic classes are *not* assumed to be named tuples.
+    """
+    from ._utils import lenient_issubclass  # circ. import
+
+    return lenient_issubclass(tp, tuple) and hasattr(tp, '_fields')
+
+
+if sys.version_info < (3, 9):
+
+    def is_zoneinfo_type(tp: Any, /) -> bool:
+        """Return whether the provided argument is the `zoneinfo.ZoneInfo` type."""
+        return False
+
+else:
+    from zoneinfo import ZoneInfo
+
+    def is_zoneinfo_type(tp: Any, /) -> TypeIs[type[ZoneInfo]]:
+        """Return whether the provided argument is the `zoneinfo.ZoneInfo` type."""
+        return tp is ZoneInfo
+
+
+if sys.version_info < (3, 10):
+
+    def origin_is_union(tp: Any, /) -> bool:
+        """Return whether the provided argument is the `Union` special form."""
+        return _is_typing_name(tp, name='Union')
+
+    def is_generic_alias(type_: type[Any]) -> bool:
+        return isinstance(type_, typing._GenericAlias)  # pyright: ignore[reportAttributeAccessIssue]
+
+else:
+
+    def origin_is_union(tp: Any, /) -> bool:
+        """Return whether the provided argument is the `Union` special form or the `UnionType`."""
+        return _is_typing_name(tp, name='Union') or tp is types.UnionType
+
+    def is_generic_alias(tp: Any, /) -> bool:
+        return isinstance(tp, (types.GenericAlias, typing._GenericAlias))  # pyright: ignore[reportAttributeAccessIssue]
+
+
+# TODO: Ideally, we should avoid relying on the private `typing` constructs:
+
+if sys.version_info < (3, 9):
+    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias,)  # pyright: ignore[reportAttributeAccessIssue]
+elif sys.version_info < (3, 10):
+    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias)  # pyright: ignore[reportAttributeAccessIssue]
+else:
+    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias, types.UnionType)  # pyright: ignore[reportAttributeAccessIssue]
+
+
+# Similarly, we shouldn't rely on this `_Final` class, which is even more private than `_GenericAlias`:
+typing_base: Any = typing._Final  # pyright: ignore[reportAttributeAccessIssue]
+
+
+### Annotation evaluations functions:
+
+
+def parent_frame_namespace(*, parent_depth: int = 2, force: bool = False) -> dict[str, Any] | None:
+    """We allow use of items in parent namespace to get around the issue with `get_type_hints` only looking in the
+    global module namespace. See https://github.com/pydantic/pydantic/issues/2678#issuecomment-1008139014 -> Scope
+    and suggestion at the end of the next comment by @gvanrossum.
+
+    WARNING 1: it matters exactly where this is called. By default, this function will build a namespace from the
+    parent of where it is called.
+
+    WARNING 2: this only looks in the parent namespace, not other parents since (AFAIK) there's no way to collect a
+    dict of exactly what's in scope. Using `f_back` would work sometimes but would be very wrong and confusing in many
+    other cases. See https://discuss.python.org/t/is-there-a-way-to-access-parent-nested-namespaces/20659.
+
+    There are some cases where we want to force fetching the parent namespace, ex: during a `model_rebuild` call.
+    In this case, we want both the namespace of the class' module, if applicable, and the parent namespace of the
+    module where the rebuild is called.
+
+    In other cases, like during initial schema build, if a class is defined at the top module level, we don't need to
+    fetch that module's namespace, because the class' __module__ attribute can be used to access the parent namespace.
+    This is done in `_namespace_utils.get_module_ns_of`. Thus, there's no need to cache the parent frame namespace in this case.
+    """
+    frame = sys._getframe(parent_depth)
+
+    # note, we don't copy frame.f_locals here (or during the last return call), because we don't expect the namespace to be modified down the line
+    # if this becomes a problem, we could implement some sort of frozen mapping structure to enforce this
+    if force:
+        return frame.f_locals
+
+    # if either of the following conditions are true, the class is defined at the top module level
+    # to better understand why we need both of these checks, see
+    # https://github.com/pydantic/pydantic/pull/10113#discussion_r1714981531
+    if frame.f_back is None or frame.f_code.co_name == '<module>':
+        return None
+
+    return frame.f_locals
+
+
+def _type_convert(arg: Any) -> Any:
+    """Convert `None` to `NoneType` and strings to `ForwardRef` instances.
+
+    This is a backport of the private `typing._type_convert` function. When
+    evaluating a type, `ForwardRef._evaluate` ends up being called, and is
+    responsible for making this conversion. However, we still have to apply
+    it for the first argument passed to our type evaluation functions, similarly
+    to the `typing.get_type_hints` function.
+    """
+    if arg is None:
+        return NoneType
+    if isinstance(arg, str):
+        # Like `typing.get_type_hints`, assume the arg can be in any context,
+        # hence the proper `is_argument` and `is_class` args:
+        return _make_forward_ref(arg, is_argument=False, is_class=True)
+    return arg
+
+
+def get_model_type_hints(
+    obj: type[BaseModel],
+    *,
+    ns_resolver: NsResolver | None = None,
+) -> dict[str, tuple[Any, bool]]:
+    """Collect annotations from a Pydantic model class, including those from parent classes.
+
+    Args:
+        obj: The Pydantic model to inspect.
+        ns_resolver: A namespace resolver instance to use. Defaults to an empty instance.
+
+    Returns:
+        A dictionary mapping annotation names to a two-tuple: the first element is the evaluated
+        type or the original annotation if a `NameError` occurred, the second element is a boolean
+        indicating if whether the evaluation succeeded.
+    """
+    hints: dict[str, Any] | dict[str, tuple[Any, bool]] = {}
+    ns_resolver = ns_resolver or NsResolver()
+
+    for base in reversed(obj.__mro__):
+        ann: dict[str, Any] | None = base.__dict__.get('__annotations__')
+        if not ann or isinstance(ann, types.GetSetDescriptorType):
+            continue
+        with ns_resolver.push(base):
+            globalns, localns = ns_resolver.types_namespace
+            for name, value in ann.items():
+                if name.startswith('_'):
+                    # For private attributes, we only need the annotation to detect the `ClassVar` special form.
+                    # For this reason, we still try to evaluate it, but we also catch any possible exception (on
+                    # top of the `NameError`s caught in `try_eval_type`) that could happen so that users are free
+                    # to use any kind of forward annotation for private fields (e.g. circular imports, new typing
+                    # syntax, etc).
+                    try:
+                        hints[name] = try_eval_type(value, globalns, localns)
+                    except Exception:
+                        hints[name] = (value, False)
+                else:
+                    hints[name] = try_eval_type(value, globalns, localns)
+    return hints
+
+
+def get_cls_type_hints(
+    obj: type[Any],
+    *,
+    ns_resolver: NsResolver | None = None,
+) -> dict[str, Any]:
+    """Collect annotations from a class, including those from parent classes.
+
+    Args:
+        obj: The class to inspect.
+        ns_resolver: A namespace resolver instance to use. Defaults to an empty instance.
+    """
+    hints: dict[str, Any] | dict[str, tuple[Any, bool]] = {}
+    ns_resolver = ns_resolver or NsResolver()
+
+    for base in reversed(obj.__mro__):
+        ann: dict[str, Any] | None = base.__dict__.get('__annotations__')
+        if not ann or isinstance(ann, types.GetSetDescriptorType):
+            continue
+        with ns_resolver.push(base):
+            globalns, localns = ns_resolver.types_namespace
+            for name, value in ann.items():
+                hints[name] = eval_type(value, globalns, localns)
+    return hints
+
+
+def try_eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> tuple[Any, bool]:
+    """Try evaluating the annotation using the provided namespaces.
+
+    Args:
+        value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance
+            of `str`, it will be converted to a `ForwardRef`.
+        localns: The global namespace to use during annotation evaluation.
+        globalns: The local namespace to use during annotation evaluation.
+
+    Returns:
+        A two-tuple containing the possibly evaluated type and a boolean indicating
+            whether the evaluation succeeded or not.
+    """
+    value = _type_convert(value)
+
+    try:
+        return eval_type_backport(value, globalns, localns), True
+    except NameError:
+        return value, False
+
+
+def eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any:
+    """Evaluate the annotation using the provided namespaces.
+
+    Args:
+        value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance
+            of `str`, it will be converted to a `ForwardRef`.
+        localns: The global namespace to use during annotation evaluation.
+        globalns: The local namespace to use during annotation evaluation.
+    """
+    value = _type_convert(value)
+    return eval_type_backport(value, globalns, localns)
+
+
+@deprecated(
+    '`eval_type_lenient` is deprecated, use `try_eval_type` instead.',
+    category=None,
+)
+def eval_type_lenient(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any:
+    ev, _ = try_eval_type(value, globalns, localns)
+    return ev
+
+
+def eval_type_backport(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    """An enhanced version of `typing._eval_type` which will fall back to using the `eval_type_backport`
+    package if it's installed to let older Python versions use newer typing constructs.
+
+    Specifically, this transforms `X | Y` into `typing.Union[X, Y]` and `list[X]` into `typing.List[X]`
+    (as well as all the types made generic in PEP 585) if the original syntax is not supported in the
+    current Python version.
+
+    This function will also display a helpful error if the value passed fails to evaluate.
+    """
+    try:
+        return _eval_type_backport(value, globalns, localns, type_params)
+    except TypeError as e:
+        if 'Unable to evaluate type annotation' in str(e):
+            raise
+
+        # If it is a `TypeError` and value isn't a `ForwardRef`, it would have failed during annotation definition.
+        # Thus we assert here for type checking purposes:
+        assert isinstance(value, typing.ForwardRef)
+
+        message = f'Unable to evaluate type annotation {value.__forward_arg__!r}.'
+        if sys.version_info >= (3, 11):
+            e.add_note(message)
+            raise
+        else:
+            raise TypeError(message) from e
+
+
+def _eval_type_backport(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    try:
+        return _eval_type(value, globalns, localns, type_params)
+    except TypeError as e:
+        if not (isinstance(value, typing.ForwardRef) and is_backport_fixable_error(e)):
+            raise
+
+        try:
+            from eval_type_backport import eval_type_backport
+        except ImportError:
+            raise TypeError(
+                f'Unable to evaluate type annotation {value.__forward_arg__!r}. If you are making use '
+                'of the new typing syntax (unions using `|` since Python 3.10 or builtins subscripting '
+                'since Python 3.9), you should either replace the use of new syntax with the existing '
+                '`typing` constructs or install the `eval_type_backport` package.'
+            ) from e
+
+        return eval_type_backport(
+            value,
+            globalns,
+            localns,  # pyright: ignore[reportArgumentType], waiting on a new `eval_type_backport` release.
+            try_default=False,
+        )
+
+
+def _eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    if sys.version_info >= (3, 13):
+        return typing._eval_type(  # type: ignore
+            value, globalns, localns, type_params=type_params
+        )
+    else:
+        return typing._eval_type(  # type: ignore
+            value, globalns, localns
+        )
+
+
+def is_backport_fixable_error(e: TypeError) -> bool:
+    msg = str(e)
+
+    return (
+        sys.version_info < (3, 10)
+        and msg.startswith('unsupported operand type(s) for |: ')
+        or sys.version_info < (3, 9)
+        and "' object is not subscriptable" in msg
+    )
+
+
+def get_function_type_hints(
+    function: Callable[..., Any],
+    *,
+    include_keys: set[str] | None = None,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> dict[str, Any]:
+    """Return type hints for a function.
+
+    This is similar to the `typing.get_type_hints` function, with a few differences:
+    - Support `functools.partial` by using the underlying `func` attribute.
+    - If `function` happens to be a built-in type (e.g. `int`), assume it doesn't have annotations
+      but specify the `return` key as being the actual type.
+    - Do not wrap type annotation of a parameter with `Optional` if it has a default value of `None`
+      (related bug: https://github.com/python/cpython/issues/90353, only fixed in 3.11+).
+    """
+    try:
+        if isinstance(function, partial):
+            annotations = function.func.__annotations__
+        else:
+            annotations = function.__annotations__
+    except AttributeError:
+        type_hints = get_type_hints(function)
+        if isinstance(function, type):
+            # `type[...]` is a callable, which returns an instance of itself.
+            # At some point, we might even look into the return type of `__new__`
+            # if it returns something else.
+            type_hints.setdefault('return', function)
+        return type_hints
+
+    if globalns is None:
+        globalns = get_module_ns_of(function)
+    type_params: tuple[Any, ...] | None = None
+    if localns is None:
+        # If localns was specified, it is assumed to already contain type params. This is because
+        # Pydantic has more advanced logic to do so (see `_namespace_utils.ns_for_function`).
+        type_params = getattr(function, '__type_params__', ())
+
+    type_hints = {}
+    for name, value in annotations.items():
+        if include_keys is not None and name not in include_keys:
+            continue
+        if value is None:
+            value = NoneType
+        elif isinstance(value, str):
+            value = _make_forward_ref(value)
+
+        type_hints[name] = eval_type_backport(value, globalns, localns, type_params)
+
+    return type_hints
+
+
+if sys.version_info < (3, 9, 8) or (3, 10) <= sys.version_info < (3, 10, 1):
+
+    def _make_forward_ref(
+        arg: Any,
+        is_argument: bool = True,
+        *,
+        is_class: bool = False,
+    ) -> typing.ForwardRef:
+        """Wrapper for ForwardRef that accounts for the `is_class` argument missing in older versions.
+        The `module` argument is omitted as it breaks <3.9.8, =3.10.0 and isn't used in the calls below.
+
+        See https://github.com/python/cpython/pull/28560 for some background.
+        The backport happened on 3.9.8, see:
+        https://github.com/pydantic/pydantic/discussions/6244#discussioncomment-6275458,
+        and on 3.10.1 for the 3.10 branch, see:
+        https://github.com/pydantic/pydantic/issues/6912
+
+        Implemented as EAFP with memory.
+        """
+        return typing.ForwardRef(arg, is_argument)
+
+else:
+    _make_forward_ref = typing.ForwardRef
+
+
+if sys.version_info >= (3, 10):
+    get_type_hints = typing.get_type_hints
+
+else:
+    """
+    For older versions of python, we have a custom implementation of `get_type_hints` which is a close as possible to
+    the implementation in CPython 3.10.8.
+    """
+
+    @typing.no_type_check
+    def get_type_hints(  # noqa: C901
+        obj: Any,
+        globalns: dict[str, Any] | None = None,
+        localns: dict[str, Any] | None = None,
+        include_extras: bool = False,
+    ) -> dict[str, Any]:  # pragma: no cover
+        """Taken verbatim from python 3.10.8 unchanged, except:
+        * type annotations of the function definition above.
+        * prefixing `typing.` where appropriate
+        * Use `_make_forward_ref` instead of `typing.ForwardRef` to handle the `is_class` argument.
+
+        https://github.com/python/cpython/blob/aaaf5174241496afca7ce4d4584570190ff972fe/Lib/typing.py#L1773-L1875
+
+        DO NOT CHANGE THIS METHOD UNLESS ABSOLUTELY NECESSARY.
+        ======================================================
+
+        Return type hints for an object.
+
+        This is often the same as obj.__annotations__, but it handles
+        forward references encoded as string literals, adds Optional[t] if a
+        default value equal to None is set and recursively replaces all
+        'Annotated[T, ...]' with 'T' (unless 'include_extras=True').
+
+        The argument may be a module, class, method, or function. The annotations
+        are returned as a dictionary. For classes, annotations include also
+        inherited members.
+
+        TypeError is raised if the argument is not of a type that can contain
+        annotations, and an empty dictionary is returned if no annotations are
+        present.
+
+        BEWARE -- the behavior of globalns and localns is counterintuitive
+        (unless you are familiar with how eval() and exec() work).  The
+        search order is locals first, then globals.
+
+        - If no dict arguments are passed, an attempt is made to use the
+          globals from obj (or the respective module's globals for classes),
+          and these are also used as the locals.  If the object does not appear
+          to have globals, an empty dictionary is used.  For classes, the search
+          order is globals first then locals.
+
+        - If one dict argument is passed, it is used for both globals and
+          locals.
+
+        - If two dict arguments are passed, they specify globals and
+          locals, respectively.
+        """
+        if getattr(obj, '__no_type_check__', None):
+            return {}
+        # Classes require a special treatment.
+        if isinstance(obj, type):
+            hints = {}
+            for base in reversed(obj.__mro__):
+                if globalns is None:
+                    base_globals = getattr(sys.modules.get(base.__module__, None), '__dict__', {})
+                else:
+                    base_globals = globalns
+                ann = base.__dict__.get('__annotations__', {})
+                if isinstance(ann, types.GetSetDescriptorType):
+                    ann = {}
+                base_locals = dict(vars(base)) if localns is None else localns
+                if localns is None and globalns is None:
+                    # This is surprising, but required.  Before Python 3.10,
+                    # get_type_hints only evaluated the globalns of
+                    # a class.  To maintain backwards compatibility, we reverse
+                    # the globalns and localns order so that eval() looks into
+                    # *base_globals* first rather than *base_locals*.
+                    # This only affects ForwardRefs.
+                    base_globals, base_locals = base_locals, base_globals
+                for name, value in ann.items():
+                    if value is None:
+                        value = type(None)
+                    if isinstance(value, str):
+                        value = _make_forward_ref(value, is_argument=False, is_class=True)
+
+                    value = eval_type_backport(value, base_globals, base_locals)
+                    hints[name] = value
+            if not include_extras and hasattr(typing, '_strip_annotations'):
+                return {
+                    k: typing._strip_annotations(t)  # type: ignore
+                    for k, t in hints.items()
+                }
+            else:
+                return hints
+
+        if globalns is None:
+            if isinstance(obj, types.ModuleType):
+                globalns = obj.__dict__
+            else:
+                nsobj = obj
+                # Find globalns for the unwrapped object.
+                while hasattr(nsobj, '__wrapped__'):
+                    nsobj = nsobj.__wrapped__
+                globalns = getattr(nsobj, '__globals__', {})
+            if localns is None:
+                localns = globalns
+        elif localns is None:
+            localns = globalns
+        hints = getattr(obj, '__annotations__', None)
+        if hints is None:
+            # Return empty annotations for something that _could_ have them.
+            if isinstance(obj, typing._allowed_types):  # type: ignore
+                return {}
+            else:
+                raise TypeError(f'{obj!r} is not a module, class, method, ' 'or function.')
+        defaults = typing._get_defaults(obj)  # type: ignore
+        hints = dict(hints)
+        for name, value in hints.items():
+            if value is None:
+                value = type(None)
+            if isinstance(value, str):
+                # class-level forward refs were handled above, this must be either
+                # a module-level annotation or a function argument annotation
+
+                value = _make_forward_ref(
+                    value,
+                    is_argument=not isinstance(obj, types.ModuleType),
+                    is_class=False,
+                )
+            value = eval_type_backport(value, globalns, localns)
+            if name in defaults and defaults[name] is None:
+                value = typing.Optional[value]
+            hints[name] = value
+        return hints if include_extras else {k: typing._strip_annotations(t) for k, t in hints.items()}  # type: ignore

env/lib/python3.11/site-packages/pydantic/_internal/_utils.py

@@ -0,0 +1,389 @@
+"""Bucket of reusable internal utilities.
+
+This should be reduced as much as possible with functions only used in one place, moved to that place.
+"""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import keyword
+import typing
+import weakref
+from collections import OrderedDict, defaultdict, deque
+from copy import deepcopy
+from functools import cached_property
+from inspect import Parameter
+from itertools import zip_longest
+from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType
+from typing import Any, Callable, Mapping, TypeVar
+
+from typing_extensions import TypeAlias, TypeGuard
+
+from . import _repr, _typing_extra
+from ._import_utils import import_cached_base_model
+
+if typing.TYPE_CHECKING:
+    MappingIntStrAny: TypeAlias = 'typing.Mapping[int, Any] | typing.Mapping[str, Any]'
+    AbstractSetIntStr: TypeAlias = 'typing.AbstractSet[int] | typing.AbstractSet[str]'
+    from ..main import BaseModel
+
+
+# these are types that are returned unchanged by deepcopy
+IMMUTABLE_NON_COLLECTIONS_TYPES: set[type[Any]] = {
+    int,
+    float,
+    complex,
+    str,
+    bool,
+    bytes,
+    type,
+    _typing_extra.NoneType,
+    FunctionType,
+    BuiltinFunctionType,
+    LambdaType,
+    weakref.ref,
+    CodeType,
+    # note: including ModuleType will differ from behaviour of deepcopy by not producing error.
+    # It might be not a good idea in general, but considering that this function used only internally
+    # against default values of fields, this will allow to actually have a field with module as default value
+    ModuleType,
+    NotImplemented.__class__,
+    Ellipsis.__class__,
+}
+
+# these are types that if empty, might be copied with simple copy() instead of deepcopy()
+BUILTIN_COLLECTIONS: set[type[Any]] = {
+    list,
+    set,
+    tuple,
+    frozenset,
+    dict,
+    OrderedDict,
+    defaultdict,
+    deque,
+}
+
+
+def can_be_positional(param: Parameter) -> bool:
+    """Return whether the parameter accepts a positional argument.
+
+    ```python {test="skip" lint="skip"}
+    def func(a, /, b, *, c):
+        pass
+
+    params = inspect.signature(func).parameters
+    can_be_positional(params['a'])
+    #> True
+    can_be_positional(params['b'])
+    #> True
+    can_be_positional(params['c'])
+    #> False
+    ```
+    """
+    return param.kind in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD)
+
+
+def sequence_like(v: Any) -> bool:
+    return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque))
+
+
+def lenient_isinstance(o: Any, class_or_tuple: type[Any] | tuple[type[Any], ...] | None) -> bool:  # pragma: no cover
+    try:
+        return isinstance(o, class_or_tuple)  # type: ignore[arg-type]
+    except TypeError:
+        return False
+
+
+def lenient_issubclass(cls: Any, class_or_tuple: Any) -> bool:  # pragma: no cover
+    try:
+        return isinstance(cls, type) and issubclass(cls, class_or_tuple)
+    except TypeError:
+        if isinstance(cls, _typing_extra.WithArgsTypes):
+            return False
+        raise  # pragma: no cover
+
+
+def is_model_class(cls: Any) -> TypeGuard[type[BaseModel]]:
+    """Returns true if cls is a _proper_ subclass of BaseModel, and provides proper type-checking,
+    unlike raw calls to lenient_issubclass.
+    """
+    BaseModel = import_cached_base_model()
+
+    return lenient_issubclass(cls, BaseModel) and cls is not BaseModel
+
+
+def is_valid_identifier(identifier: str) -> bool:
+    """Checks that a string is a valid identifier and not a Python keyword.
+    :param identifier: The identifier to test.
+    :return: True if the identifier is valid.
+    """
+    return identifier.isidentifier() and not keyword.iskeyword(identifier)
+
+
+KeyType = TypeVar('KeyType')
+
+
+def deep_update(mapping: dict[KeyType, Any], *updating_mappings: dict[KeyType, Any]) -> dict[KeyType, Any]:
+    updated_mapping = mapping.copy()
+    for updating_mapping in updating_mappings:
+        for k, v in updating_mapping.items():
+            if k in updated_mapping and isinstance(updated_mapping[k], dict) and isinstance(v, dict):
+                updated_mapping[k] = deep_update(updated_mapping[k], v)
+            else:
+                updated_mapping[k] = v
+    return updated_mapping
+
+
+def update_not_none(mapping: dict[Any, Any], **update: Any) -> None:
+    mapping.update({k: v for k, v in update.items() if v is not None})
+
+
+T = TypeVar('T')
+
+
+def unique_list(
+    input_list: list[T] | tuple[T, ...],
+    *,
+    name_factory: typing.Callable[[T], str] = str,
+) -> list[T]:
+    """Make a list unique while maintaining order.
+    We update the list if another one with the same name is set
+    (e.g. model validator overridden in subclass).
+    """
+    result: list[T] = []
+    result_names: list[str] = []
+    for v in input_list:
+        v_name = name_factory(v)
+        if v_name not in result_names:
+            result_names.append(v_name)
+            result.append(v)
+        else:
+            result[result_names.index(v_name)] = v
+
+    return result
+
+
+class ValueItems(_repr.Representation):
+    """Class for more convenient calculation of excluded or included fields on values."""
+
+    __slots__ = ('_items', '_type')
+
+    def __init__(self, value: Any, items: AbstractSetIntStr | MappingIntStrAny) -> None:
+        items = self._coerce_items(items)
+
+        if isinstance(value, (list, tuple)):
+            items = self._normalize_indexes(items, len(value))  # type: ignore
+
+        self._items: MappingIntStrAny = items  # type: ignore
+
+    def is_excluded(self, item: Any) -> bool:
+        """Check if item is fully excluded.
+
+        :param item: key or index of a value
+        """
+        return self.is_true(self._items.get(item))
+
+    def is_included(self, item: Any) -> bool:
+        """Check if value is contained in self._items.
+
+        :param item: key or index of value
+        """
+        return item in self._items
+
+    def for_element(self, e: int | str) -> AbstractSetIntStr | MappingIntStrAny | None:
+        """:param e: key or index of element on value
+        :return: raw values for element if self._items is dict and contain needed element
+        """
+        item = self._items.get(e)  # type: ignore
+        return item if not self.is_true(item) else None
+
+    def _normalize_indexes(self, items: MappingIntStrAny, v_length: int) -> dict[int | str, Any]:
+        """:param items: dict or set of indexes which will be normalized
+        :param v_length: length of sequence indexes of which will be
+
+        >>> self._normalize_indexes({0: True, -2: True, -1: True}, 4)
+        {0: True, 2: True, 3: True}
+        >>> self._normalize_indexes({'__all__': True}, 4)
+        {0: True, 1: True, 2: True, 3: True}
+        """
+        normalized_items: dict[int | str, Any] = {}
+        all_items = None
+        for i, v in items.items():
+            if not (isinstance(v, typing.Mapping) or isinstance(v, typing.AbstractSet) or self.is_true(v)):
+                raise TypeError(f'Unexpected type of exclude value for index "{i}" {v.__class__}')
+            if i == '__all__':
+                all_items = self._coerce_value(v)
+                continue
+            if not isinstance(i, int):
+                raise TypeError(
+                    'Excluding fields from a sequence of sub-models or dicts must be performed index-wise: '
+                    'expected integer keys or keyword "__all__"'
+                )
+            normalized_i = v_length + i if i < 0 else i
+            normalized_items[normalized_i] = self.merge(v, normalized_items.get(normalized_i))
+
+        if not all_items:
+            return normalized_items
+        if self.is_true(all_items):
+            for i in range(v_length):
+                normalized_items.setdefault(i, ...)
+            return normalized_items
+        for i in range(v_length):
+            normalized_item = normalized_items.setdefault(i, {})
+            if not self.is_true(normalized_item):
+                normalized_items[i] = self.merge(all_items, normalized_item)
+        return normalized_items
+
+    @classmethod
+    def merge(cls, base: Any, override: Any, intersect: bool = False) -> Any:
+        """Merge a `base` item with an `override` item.
+
+        Both `base` and `override` are converted to dictionaries if possible.
+        Sets are converted to dictionaries with the sets entries as keys and
+        Ellipsis as values.
+
+        Each key-value pair existing in `base` is merged with `override`,
+        while the rest of the key-value pairs are updated recursively with this function.
+
+        Merging takes place based on the "union" of keys if `intersect` is
+        set to `False` (default) and on the intersection of keys if
+        `intersect` is set to `True`.
+        """
+        override = cls._coerce_value(override)
+        base = cls._coerce_value(base)
+        if override is None:
+            return base
+        if cls.is_true(base) or base is None:
+            return override
+        if cls.is_true(override):
+            return base if intersect else override
+
+        # intersection or union of keys while preserving ordering:
+        if intersect:
+            merge_keys = [k for k in base if k in override] + [k for k in override if k in base]
+        else:
+            merge_keys = list(base) + [k for k in override if k not in base]
+
+        merged: dict[int | str, Any] = {}
+        for k in merge_keys:
+            merged_item = cls.merge(base.get(k), override.get(k), intersect=intersect)
+            if merged_item is not None:
+                merged[k] = merged_item
+
+        return merged
+
+    @staticmethod
+    def _coerce_items(items: AbstractSetIntStr | MappingIntStrAny) -> MappingIntStrAny:
+        if isinstance(items, typing.Mapping):
+            pass
+        elif isinstance(items, typing.AbstractSet):
+            items = dict.fromkeys(items, ...)  # type: ignore
+        else:
+            class_name = getattr(items, '__class__', '???')
+            raise TypeError(f'Unexpected type of exclude value {class_name}')
+        return items  # type: ignore
+
+    @classmethod
+    def _coerce_value(cls, value: Any) -> Any:
+        if value is None or cls.is_true(value):
+            return value
+        return cls._coerce_items(value)
+
+    @staticmethod
+    def is_true(v: Any) -> bool:
+        return v is True or v is ...
+
+    def __repr_args__(self) -> _repr.ReprArgs:
+        return [(None, self._items)]
+
+
+if typing.TYPE_CHECKING:
+
+    def LazyClassAttribute(name: str, get_value: Callable[[], T]) -> T: ...
+
+else:
+
+    class LazyClassAttribute:
+        """A descriptor exposing an attribute only accessible on a class (hidden from instances).
+
+        The attribute is lazily computed and cached during the first access.
+        """
+
+        def __init__(self, name: str, get_value: Callable[[], Any]) -> None:
+            self.name = name
+            self.get_value = get_value
+
+        @cached_property
+        def value(self) -> Any:
+            return self.get_value()
+
+        def __get__(self, instance: Any, owner: type[Any]) -> None:
+            if instance is None:
+                return self.value
+            raise AttributeError(f'{self.name!r} attribute of {owner.__name__!r} is class-only')
+
+
+Obj = TypeVar('Obj')
+
+
+def smart_deepcopy(obj: Obj) -> Obj:
+    """Return type as is for immutable built-in types
+    Use obj.copy() for built-in empty collections
+    Use copy.deepcopy() for non-empty collections and unknown objects.
+    """
+    obj_type = obj.__class__
+    if obj_type in IMMUTABLE_NON_COLLECTIONS_TYPES:
+        return obj  # fastest case: obj is immutable and not collection therefore will not be copied anyway
+    try:
+        if not obj and obj_type in BUILTIN_COLLECTIONS:
+            # faster way for empty collections, no need to copy its members
+            return obj if obj_type is tuple else obj.copy()  # tuple doesn't have copy method  # type: ignore
+    except (TypeError, ValueError, RuntimeError):
+        # do we really dare to catch ALL errors? Seems a bit risky
+        pass
+
+    return deepcopy(obj)  # slowest way when we actually might need a deepcopy
+
+
+_SENTINEL = object()
+
+
+def all_identical(left: typing.Iterable[Any], right: typing.Iterable[Any]) -> bool:
+    """Check that the items of `left` are the same objects as those in `right`.
+
+    >>> a, b = object(), object()
+    >>> all_identical([a, b, a], [a, b, a])
+    True
+    >>> all_identical([a, b, [a]], [a, b, [a]])  # new list object, while "equal" is not "identical"
+    False
+    """
+    for left_item, right_item in zip_longest(left, right, fillvalue=_SENTINEL):
+        if left_item is not right_item:
+            return False
+    return True
+
+
+@dataclasses.dataclass(frozen=True)
+class SafeGetItemProxy:
+    """Wrapper redirecting `__getitem__` to `get` with a sentinel value as default
+
+    This makes is safe to use in `operator.itemgetter` when some keys may be missing
+    """
+
+    # Define __slots__manually for performances
+    # @dataclasses.dataclass() only support slots=True in python>=3.10
+    __slots__ = ('wrapped',)
+
+    wrapped: Mapping[str, Any]
+
+    def __getitem__(self, key: str, /) -> Any:
+        return self.wrapped.get(key, _SENTINEL)
+
+    # required to pass the object to operator.itemgetter() instances due to a quirk of typeshed
+    # https://github.com/python/mypy/issues/13713
+    # https://github.com/python/typeshed/pull/8785
+    # Since this is typing-only, hide it in a typing.TYPE_CHECKING block
+    if typing.TYPE_CHECKING:
+
+        def __contains__(self, key: str, /) -> bool:
+            return self.wrapped.__contains__(key)

env/lib/python3.11/site-packages/pydantic/_internal/_validate_call.py

@@ -0,0 +1,115 @@
+from __future__ import annotations as _annotations
+
+import functools
+import inspect
+from functools import partial
+from typing import Any, Awaitable, Callable
+
+import pydantic_core
+
+from ..config import ConfigDict
+from ..plugin._schema_validator import create_schema_validator
+from ._config import ConfigWrapper
+from ._generate_schema import GenerateSchema, ValidateCallSupportedTypes
+from ._namespace_utils import MappingNamespace, NsResolver, ns_for_function
+
+
+def extract_function_name(func: ValidateCallSupportedTypes) -> str:
+    """Extract the name of a `ValidateCallSupportedTypes` object."""
+    return f'partial({func.func.__name__})' if isinstance(func, functools.partial) else func.__name__
+
+
+def extract_function_qualname(func: ValidateCallSupportedTypes) -> str:
+    """Extract the qualname of a `ValidateCallSupportedTypes` object."""
+    return f'partial({func.func.__qualname__})' if isinstance(func, functools.partial) else func.__qualname__
+
+
+def update_wrapper_attributes(wrapped: ValidateCallSupportedTypes, wrapper: Callable[..., Any]):
+    """Update the `wrapper` function with the attributes of the `wrapped` function. Return the updated function."""
+    if inspect.iscoroutinefunction(wrapped):
+
+        @functools.wraps(wrapped)
+        async def wrapper_function(*args, **kwargs):  # type: ignore
+            return await wrapper(*args, **kwargs)
+    else:
+
+        @functools.wraps(wrapped)
+        def wrapper_function(*args, **kwargs):
+            return wrapper(*args, **kwargs)
+
+    # We need to manually update this because `partial` object has no `__name__` and `__qualname__`.
+    wrapper_function.__name__ = extract_function_name(wrapped)
+    wrapper_function.__qualname__ = extract_function_qualname(wrapped)
+    wrapper_function.raw_function = wrapped  # type: ignore
+
+    return wrapper_function
+
+
+class ValidateCallWrapper:
+    """This is a wrapper around a function that validates the arguments passed to it, and optionally the return value."""
+
+    __slots__ = ('__pydantic_validator__', '__return_pydantic_validator__')
+
+    def __init__(
+        self,
+        function: ValidateCallSupportedTypes,
+        config: ConfigDict | None,
+        validate_return: bool,
+        parent_namespace: MappingNamespace | None,
+    ) -> None:
+        if isinstance(function, partial):
+            schema_type = function.func
+            module = function.func.__module__
+        else:
+            schema_type = function
+            module = function.__module__
+        qualname = extract_function_qualname(function)
+
+        ns_resolver = NsResolver(namespaces_tuple=ns_for_function(schema_type, parent_namespace=parent_namespace))
+
+        config_wrapper = ConfigWrapper(config)
+        gen_schema = GenerateSchema(config_wrapper, ns_resolver)
+        schema = gen_schema.clean_schema(gen_schema.generate_schema(function))
+        core_config = config_wrapper.core_config(title=qualname)
+
+        self.__pydantic_validator__ = create_schema_validator(
+            schema,
+            schema_type,
+            module,
+            qualname,
+            'validate_call',
+            core_config,
+            config_wrapper.plugin_settings,
+        )
+
+        if validate_return:
+            signature = inspect.signature(function)
+            return_type = signature.return_annotation if signature.return_annotation is not signature.empty else Any
+            gen_schema = GenerateSchema(config_wrapper, ns_resolver)
+            schema = gen_schema.clean_schema(gen_schema.generate_schema(return_type))
+            validator = create_schema_validator(
+                schema,
+                schema_type,
+                module,
+                qualname,
+                'validate_call',
+                core_config,
+                config_wrapper.plugin_settings,
+            )
+            if inspect.iscoroutinefunction(function):
+
+                async def return_val_wrapper(aw: Awaitable[Any]) -> None:
+                    return validator.validate_python(await aw)
+
+                self.__return_pydantic_validator__ = return_val_wrapper
+            else:
+                self.__return_pydantic_validator__ = validator.validate_python
+        else:
+            self.__return_pydantic_validator__ = None
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))
+        if self.__return_pydantic_validator__:
+            return self.__return_pydantic_validator__(res)
+        else:
+            return res

env/lib/python3.11/site-packages/pydantic/_internal/_validators.py

@@ -0,0 +1,424 @@
+"""Validator functions for standard library types.
+
+Import of this module is deferred since it contains imports of many standard library modules.
+"""
+
+from __future__ import annotations as _annotations
+
+import math
+import re
+import typing
+from decimal import Decimal
+from fractions import Fraction
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from typing import Any, Callable, Union
+
+from pydantic_core import PydanticCustomError, core_schema
+from pydantic_core._pydantic_core import PydanticKnownError
+
+
+def sequence_validator(
+    input_value: typing.Sequence[Any],
+    /,
+    validator: core_schema.ValidatorFunctionWrapHandler,
+) -> typing.Sequence[Any]:
+    """Validator for `Sequence` types, isinstance(v, Sequence) has already been called."""
+    value_type = type(input_value)
+
+    # We don't accept any plain string as a sequence
+    # Relevant issue: https://github.com/pydantic/pydantic/issues/5595
+    if issubclass(value_type, (str, bytes)):
+        raise PydanticCustomError(
+            'sequence_str',
+            "'{type_name}' instances are not allowed as a Sequence value",
+            {'type_name': value_type.__name__},
+        )
+
+    # TODO: refactor sequence validation to validate with either a list or a tuple
+    # schema, depending on the type of the value.
+    # Additionally, we should be able to remove one of either this validator or the
+    # SequenceValidator in _std_types_schema.py (preferably this one, while porting over some logic).
+    # Effectively, a refactor for sequence validation is needed.
+    if value_type is tuple:
+        input_value = list(input_value)
+
+    v_list = validator(input_value)
+
+    # the rest of the logic is just re-creating the original type from `v_list`
+    if value_type is list:
+        return v_list
+    elif issubclass(value_type, range):
+        # return the list as we probably can't re-create the range
+        return v_list
+    elif value_type is tuple:
+        return tuple(v_list)
+    else:
+        # best guess at how to re-create the original type, more custom construction logic might be required
+        return value_type(v_list)  # type: ignore[call-arg]
+
+
+def import_string(value: Any) -> Any:
+    if isinstance(value, str):
+        try:
+            return _import_string_logic(value)
+        except ImportError as e:
+            raise PydanticCustomError('import_error', 'Invalid python path: {error}', {'error': str(e)}) from e
+    else:
+        # otherwise we just return the value and let the next validator do the rest of the work
+        return value
+
+
+def _import_string_logic(dotted_path: str) -> Any:
+    """Inspired by uvicorn — dotted paths should include a colon before the final item if that item is not a module.
+    (This is necessary to distinguish between a submodule and an attribute when there is a conflict.).
+
+    If the dotted path does not include a colon and the final item is not a valid module, importing as an attribute
+    rather than a submodule will be attempted automatically.
+
+    So, for example, the following values of `dotted_path` result in the following returned values:
+    * 'collections': <module 'collections'>
+    * 'collections.abc': <module 'collections.abc'>
+    * 'collections.abc:Mapping': <class 'collections.abc.Mapping'>
+    * `collections.abc.Mapping`: <class 'collections.abc.Mapping'> (though this is a bit slower than the previous line)
+
+    An error will be raised under any of the following scenarios:
+    * `dotted_path` contains more than one colon (e.g., 'collections:abc:Mapping')
+    * the substring of `dotted_path` before the colon is not a valid module in the environment (e.g., '123:Mapping')
+    * the substring of `dotted_path` after the colon is not an attribute of the module (e.g., 'collections:abc123')
+    """
+    from importlib import import_module
+
+    components = dotted_path.strip().split(':')
+    if len(components) > 2:
+        raise ImportError(f"Import strings should have at most one ':'; received {dotted_path!r}")
+
+    module_path = components[0]
+    if not module_path:
+        raise ImportError(f'Import strings should have a nonempty module name; received {dotted_path!r}')
+
+    try:
+        module = import_module(module_path)
+    except ModuleNotFoundError as e:
+        if '.' in module_path:
+            # Check if it would be valid if the final item was separated from its module with a `:`
+            maybe_module_path, maybe_attribute = dotted_path.strip().rsplit('.', 1)
+            try:
+                return _import_string_logic(f'{maybe_module_path}:{maybe_attribute}')
+            except ImportError:
+                pass
+            raise ImportError(f'No module named {module_path!r}') from e
+        raise e
+
+    if len(components) > 1:
+        attribute = components[1]
+        try:
+            return getattr(module, attribute)
+        except AttributeError as e:
+            raise ImportError(f'cannot import name {attribute!r} from {module_path!r}') from e
+    else:
+        return module
+
+
+def pattern_either_validator(input_value: Any, /) -> typing.Pattern[Any]:
+    if isinstance(input_value, typing.Pattern):
+        return input_value
+    elif isinstance(input_value, (str, bytes)):
+        # todo strict mode
+        return compile_pattern(input_value)  # type: ignore
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+def pattern_str_validator(input_value: Any, /) -> typing.Pattern[str]:
+    if isinstance(input_value, typing.Pattern):
+        if isinstance(input_value.pattern, str):
+            return input_value
+        else:
+            raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern')
+    elif isinstance(input_value, str):
+        return compile_pattern(input_value)
+    elif isinstance(input_value, bytes):
+        raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern')
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+def pattern_bytes_validator(input_value: Any, /) -> typing.Pattern[bytes]:
+    if isinstance(input_value, typing.Pattern):
+        if isinstance(input_value.pattern, bytes):
+            return input_value
+        else:
+            raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern')
+    elif isinstance(input_value, bytes):
+        return compile_pattern(input_value)
+    elif isinstance(input_value, str):
+        raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern')
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+PatternType = typing.TypeVar('PatternType', str, bytes)
+
+
+def compile_pattern(pattern: PatternType) -> typing.Pattern[PatternType]:
+    try:
+        return re.compile(pattern)
+    except re.error:
+        raise PydanticCustomError('pattern_regex', 'Input should be a valid regular expression')
+
+
+def ip_v4_address_validator(input_value: Any, /) -> IPv4Address:
+    if isinstance(input_value, IPv4Address):
+        return input_value
+
+    try:
+        return IPv4Address(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_address', 'Input is not a valid IPv4 address')
+
+
+def ip_v6_address_validator(input_value: Any, /) -> IPv6Address:
+    if isinstance(input_value, IPv6Address):
+        return input_value
+
+    try:
+        return IPv6Address(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_address', 'Input is not a valid IPv6 address')
+
+
+def ip_v4_network_validator(input_value: Any, /) -> IPv4Network:
+    """Assume IPv4Network initialised with a default `strict` argument.
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv4Network
+    """
+    if isinstance(input_value, IPv4Network):
+        return input_value
+
+    try:
+        return IPv4Network(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_network', 'Input is not a valid IPv4 network')
+
+
+def ip_v6_network_validator(input_value: Any, /) -> IPv6Network:
+    """Assume IPv6Network initialised with a default `strict` argument.
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv6Network
+    """
+    if isinstance(input_value, IPv6Network):
+        return input_value
+
+    try:
+        return IPv6Network(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_network', 'Input is not a valid IPv6 network')
+
+
+def ip_v4_interface_validator(input_value: Any, /) -> IPv4Interface:
+    if isinstance(input_value, IPv4Interface):
+        return input_value
+
+    try:
+        return IPv4Interface(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_interface', 'Input is not a valid IPv4 interface')
+
+
+def ip_v6_interface_validator(input_value: Any, /) -> IPv6Interface:
+    if isinstance(input_value, IPv6Interface):
+        return input_value
+
+    try:
+        return IPv6Interface(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_interface', 'Input is not a valid IPv6 interface')
+
+
+def fraction_validator(input_value: Any, /) -> Fraction:
+    if isinstance(input_value, Fraction):
+        return input_value
+
+    try:
+        return Fraction(input_value)
+    except ValueError:
+        raise PydanticCustomError('fraction_parsing', 'Input is not a valid fraction')
+
+
+def forbid_inf_nan_check(x: Any) -> Any:
+    if not math.isfinite(x):
+        raise PydanticKnownError('finite_number')
+    return x
+
+
+def _safe_repr(v: Any) -> int | float | str:
+    """The context argument for `PydanticKnownError` requires a number or str type, so we do a simple repr() coercion for types like timedelta.
+
+    See tests/test_types.py::test_annotated_metadata_any_order for some context.
+    """
+    if isinstance(v, (int, float, str)):
+        return v
+    return repr(v)
+
+
+def greater_than_validator(x: Any, gt: Any) -> Any:
+    try:
+        if not (x > gt):
+            raise PydanticKnownError('greater_than', {'gt': _safe_repr(gt)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'gt' to supplied value {x}")
+
+
+def greater_than_or_equal_validator(x: Any, ge: Any) -> Any:
+    try:
+        if not (x >= ge):
+            raise PydanticKnownError('greater_than_equal', {'ge': _safe_repr(ge)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'ge' to supplied value {x}")
+
+
+def less_than_validator(x: Any, lt: Any) -> Any:
+    try:
+        if not (x < lt):
+            raise PydanticKnownError('less_than', {'lt': _safe_repr(lt)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'lt' to supplied value {x}")
+
+
+def less_than_or_equal_validator(x: Any, le: Any) -> Any:
+    try:
+        if not (x <= le):
+            raise PydanticKnownError('less_than_equal', {'le': _safe_repr(le)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'le' to supplied value {x}")
+
+
+def multiple_of_validator(x: Any, multiple_of: Any) -> Any:
+    try:
+        if x % multiple_of:
+            raise PydanticKnownError('multiple_of', {'multiple_of': _safe_repr(multiple_of)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'multiple_of' to supplied value {x}")
+
+
+def min_length_validator(x: Any, min_length: Any) -> Any:
+    try:
+        if not (len(x) >= min_length):
+            raise PydanticKnownError(
+                'too_short', {'field_type': 'Value', 'min_length': min_length, 'actual_length': len(x)}
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'min_length' to supplied value {x}")
+
+
+def max_length_validator(x: Any, max_length: Any) -> Any:
+    try:
+        if len(x) > max_length:
+            raise PydanticKnownError(
+                'too_long',
+                {'field_type': 'Value', 'max_length': max_length, 'actual_length': len(x)},
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'max_length' to supplied value {x}")
+
+
+def _extract_decimal_digits_info(decimal: Decimal) -> tuple[int, int]:
+    """Compute the total number of digits and decimal places for a given [`Decimal`][decimal.Decimal] instance.
+
+    This function handles both normalized and non-normalized Decimal instances.
+    Example: Decimal('1.230') -> 4 digits, 3 decimal places
+
+    Args:
+        decimal (Decimal): The decimal number to analyze.
+
+    Returns:
+        tuple[int, int]: A tuple containing the number of decimal places and total digits.
+
+    Though this could be divided into two separate functions, the logic is easier to follow if we couple the computation
+    of the number of decimals and digits together.
+    """
+    decimal_tuple = decimal.as_tuple()
+    if not isinstance(decimal_tuple.exponent, int):
+        raise TypeError(f'Unable to extract decimal digits info from supplied value {decimal}')
+    exponent = decimal_tuple.exponent
+    num_digits = len(decimal_tuple.digits)
+
+    if exponent >= 0:
+        # A positive exponent adds that many trailing zeros
+        # Ex: digit_tuple=(1, 2, 3), exponent=2 -> 12300 -> 0 decimal places, 5 digits
+        num_digits += exponent
+        decimal_places = 0
+    else:
+        # If the absolute value of the negative exponent is larger than the
+        # number of digits, then it's the same as the number of digits,
+        # because it'll consume all the digits in digit_tuple and then
+        # add abs(exponent) - len(digit_tuple) leading zeros after the decimal point.
+        # Ex: digit_tuple=(1, 2, 3), exponent=-2 -> 1.23 -> 2 decimal places, 3 digits
+        # Ex: digit_tuple=(1, 2, 3), exponent=-4 -> 0.0123 -> 4 decimal places, 4 digits
+        decimal_places = abs(exponent)
+        num_digits = max(num_digits, decimal_places)
+
+    return decimal_places, num_digits
+
+
+def max_digits_validator(x: Any, max_digits: Any) -> Any:
+    _, num_digits = _extract_decimal_digits_info(x)
+    _, normalized_num_digits = _extract_decimal_digits_info(x.normalize())
+
+    try:
+        if (num_digits > max_digits) and (normalized_num_digits > max_digits):
+            raise PydanticKnownError(
+                'decimal_max_digits',
+                {'max_digits': max_digits},
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'max_digits' to supplied value {x}")
+
+
+def decimal_places_validator(x: Any, decimal_places: Any) -> Any:
+    decimal_places_, _ = _extract_decimal_digits_info(x)
+    normalized_decimal_places, _ = _extract_decimal_digits_info(x.normalize())
+
+    try:
+        if (decimal_places_ > decimal_places) and (normalized_decimal_places > decimal_places):
+            raise PydanticKnownError(
+                'decimal_max_places',
+                {'decimal_places': decimal_places},
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'decimal_places' to supplied value {x}")
+
+
+NUMERIC_VALIDATOR_LOOKUP: dict[str, Callable] = {
+    'gt': greater_than_validator,
+    'ge': greater_than_or_equal_validator,
+    'lt': less_than_validator,
+    'le': less_than_or_equal_validator,
+    'multiple_of': multiple_of_validator,
+    'min_length': min_length_validator,
+    'max_length': max_length_validator,
+    'max_digits': max_digits_validator,
+    'decimal_places': decimal_places_validator,
+}
+
+IpType = Union[IPv4Address, IPv6Address, IPv4Network, IPv6Network, IPv4Interface, IPv6Interface]
+
+IP_VALIDATOR_LOOKUP: dict[type[IpType], Callable] = {
+    IPv4Address: ip_v4_address_validator,
+    IPv6Address: ip_v6_address_validator,
+    IPv4Network: ip_v4_network_validator,
+    IPv6Network: ip_v6_network_validator,
+    IPv4Interface: ip_v4_interface_validator,
+    IPv6Interface: ip_v6_interface_validator,
+}