two version of R2R are here HEAD master

105 files changed, 43561 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/pydantic/__init__.py b/.venv/lib/python3.12/site-packages/pydantic/__init__.py
new file mode 100644
index 00000000..7663c976
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/__init__.py
@@ -0,0 +1,431 @@
+import typing
+from importlib import import_module
+from warnings import warn
+
+from ._migration import getattr_migration
+from .version import VERSION
+
+if typing.TYPE_CHECKING:
+    # import of virtually everything is supported via `__getattr__` below,
+    # but we need them here for type checking and IDE support
+    import pydantic_core
+    from pydantic_core.core_schema import (
+        FieldSerializationInfo,
+        SerializationInfo,
+        SerializerFunctionWrapHandler,
+        ValidationInfo,
+        ValidatorFunctionWrapHandler,
+    )
+
+    from . import dataclasses
+    from .aliases import AliasChoices, AliasGenerator, AliasPath
+    from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
+    from .config import ConfigDict, with_config
+    from .errors import *
+    from .fields import Field, PrivateAttr, computed_field
+    from .functional_serializers import (
+        PlainSerializer,
+        SerializeAsAny,
+        WrapSerializer,
+        field_serializer,
+        model_serializer,
+    )
+    from .functional_validators import (
+        AfterValidator,
+        BeforeValidator,
+        InstanceOf,
+        ModelWrapValidatorHandler,
+        PlainValidator,
+        SkipValidation,
+        WrapValidator,
+        field_validator,
+        model_validator,
+    )
+    from .json_schema import WithJsonSchema
+    from .main import *
+    from .networks import *
+    from .type_adapter import TypeAdapter
+    from .types import *
+    from .validate_call_decorator import validate_call
+    from .warnings import (
+        PydanticDeprecatedSince20,
+        PydanticDeprecatedSince26,
+        PydanticDeprecatedSince29,
+        PydanticDeprecationWarning,
+        PydanticExperimentalWarning,
+    )
+
+    # this encourages pycharm to import `ValidationError` from here, not pydantic_core
+    ValidationError = pydantic_core.ValidationError
+    from .deprecated.class_validators import root_validator, validator
+    from .deprecated.config import BaseConfig, Extra
+    from .deprecated.tools import *
+    from .root_model import RootModel
+
+__version__ = VERSION
+__all__ = (
+    # dataclasses
+    'dataclasses',
+    # functional validators
+    'field_validator',
+    'model_validator',
+    'AfterValidator',
+    'BeforeValidator',
+    'PlainValidator',
+    'WrapValidator',
+    'SkipValidation',
+    'InstanceOf',
+    'ModelWrapValidatorHandler',
+    # JSON Schema
+    'WithJsonSchema',
+    # deprecated V1 functional validators, these are imported via `__getattr__` below
+    'root_validator',
+    'validator',
+    # functional serializers
+    'field_serializer',
+    'model_serializer',
+    'PlainSerializer',
+    'SerializeAsAny',
+    'WrapSerializer',
+    # config
+    'ConfigDict',
+    'with_config',
+    # deprecated V1 config, these are imported via `__getattr__` below
+    'BaseConfig',
+    'Extra',
+    # validate_call
+    'validate_call',
+    # errors
+    'PydanticErrorCodes',
+    'PydanticUserError',
+    'PydanticSchemaGenerationError',
+    'PydanticImportError',
+    'PydanticUndefinedAnnotation',
+    'PydanticInvalidForJsonSchema',
+    # fields
+    'Field',
+    'computed_field',
+    'PrivateAttr',
+    # alias
+    'AliasChoices',
+    'AliasGenerator',
+    'AliasPath',
+    # main
+    'BaseModel',
+    'create_model',
+    # network
+    'AnyUrl',
+    'AnyHttpUrl',
+    'FileUrl',
+    'HttpUrl',
+    'FtpUrl',
+    'WebsocketUrl',
+    'AnyWebsocketUrl',
+    'UrlConstraints',
+    'EmailStr',
+    'NameEmail',
+    'IPvAnyAddress',
+    'IPvAnyInterface',
+    'IPvAnyNetwork',
+    'PostgresDsn',
+    'CockroachDsn',
+    'AmqpDsn',
+    'RedisDsn',
+    'MongoDsn',
+    'KafkaDsn',
+    'NatsDsn',
+    'MySQLDsn',
+    'MariaDBDsn',
+    'ClickHouseDsn',
+    'SnowflakeDsn',
+    'validate_email',
+    # root_model
+    'RootModel',
+    # deprecated tools, these are imported via `__getattr__` below
+    'parse_obj_as',
+    'schema_of',
+    'schema_json_of',
+    # types
+    'Strict',
+    'StrictStr',
+    'conbytes',
+    'conlist',
+    'conset',
+    'confrozenset',
+    'constr',
+    'StringConstraints',
+    'ImportString',
+    'conint',
+    'PositiveInt',
+    'NegativeInt',
+    'NonNegativeInt',
+    'NonPositiveInt',
+    'confloat',
+    'PositiveFloat',
+    'NegativeFloat',
+    'NonNegativeFloat',
+    'NonPositiveFloat',
+    'FiniteFloat',
+    'condecimal',
+    'condate',
+    'UUID1',
+    'UUID3',
+    'UUID4',
+    'UUID5',
+    'FilePath',
+    'DirectoryPath',
+    'NewPath',
+    'Json',
+    'Secret',
+    'SecretStr',
+    'SecretBytes',
+    'SocketPath',
+    'StrictBool',
+    'StrictBytes',
+    'StrictInt',
+    'StrictFloat',
+    'PaymentCardNumber',
+    'ByteSize',
+    'PastDate',
+    'FutureDate',
+    'PastDatetime',
+    'FutureDatetime',
+    'AwareDatetime',
+    'NaiveDatetime',
+    'AllowInfNan',
+    'EncoderProtocol',
+    'EncodedBytes',
+    'EncodedStr',
+    'Base64Encoder',
+    'Base64Bytes',
+    'Base64Str',
+    'Base64UrlBytes',
+    'Base64UrlStr',
+    'GetPydanticSchema',
+    'Tag',
+    'Discriminator',
+    'JsonValue',
+    'FailFast',
+    # type_adapter
+    'TypeAdapter',
+    # version
+    '__version__',
+    'VERSION',
+    # warnings
+    'PydanticDeprecatedSince20',
+    'PydanticDeprecatedSince26',
+    'PydanticDeprecatedSince29',
+    'PydanticDeprecationWarning',
+    'PydanticExperimentalWarning',
+    # annotated handlers
+    'GetCoreSchemaHandler',
+    'GetJsonSchemaHandler',
+    # pydantic_core
+    'ValidationError',
+    'ValidationInfo',
+    'SerializationInfo',
+    'ValidatorFunctionWrapHandler',
+    'FieldSerializationInfo',
+    'SerializerFunctionWrapHandler',
+    'OnErrorOmit',
+)
+
+# A mapping of {<member name>: (package, <module name>)} defining dynamic imports
+_dynamic_imports: 'dict[str, tuple[str, str]]' = {
+    'dataclasses': (__spec__.parent, '__module__'),
+    # functional validators
+    'field_validator': (__spec__.parent, '.functional_validators'),
+    'model_validator': (__spec__.parent, '.functional_validators'),
+    'AfterValidator': (__spec__.parent, '.functional_validators'),
+    'BeforeValidator': (__spec__.parent, '.functional_validators'),
+    'PlainValidator': (__spec__.parent, '.functional_validators'),
+    'WrapValidator': (__spec__.parent, '.functional_validators'),
+    'SkipValidation': (__spec__.parent, '.functional_validators'),
+    'InstanceOf': (__spec__.parent, '.functional_validators'),
+    'ModelWrapValidatorHandler': (__spec__.parent, '.functional_validators'),
+    # JSON Schema
+    'WithJsonSchema': (__spec__.parent, '.json_schema'),
+    # functional serializers
+    'field_serializer': (__spec__.parent, '.functional_serializers'),
+    'model_serializer': (__spec__.parent, '.functional_serializers'),
+    'PlainSerializer': (__spec__.parent, '.functional_serializers'),
+    'SerializeAsAny': (__spec__.parent, '.functional_serializers'),
+    'WrapSerializer': (__spec__.parent, '.functional_serializers'),
+    # config
+    'ConfigDict': (__spec__.parent, '.config'),
+    'with_config': (__spec__.parent, '.config'),
+    # validate call
+    'validate_call': (__spec__.parent, '.validate_call_decorator'),
+    # errors
+    'PydanticErrorCodes': (__spec__.parent, '.errors'),
+    'PydanticUserError': (__spec__.parent, '.errors'),
+    'PydanticSchemaGenerationError': (__spec__.parent, '.errors'),
+    'PydanticImportError': (__spec__.parent, '.errors'),
+    'PydanticUndefinedAnnotation': (__spec__.parent, '.errors'),
+    'PydanticInvalidForJsonSchema': (__spec__.parent, '.errors'),
+    # fields
+    'Field': (__spec__.parent, '.fields'),
+    'computed_field': (__spec__.parent, '.fields'),
+    'PrivateAttr': (__spec__.parent, '.fields'),
+    # alias
+    'AliasChoices': (__spec__.parent, '.aliases'),
+    'AliasGenerator': (__spec__.parent, '.aliases'),
+    'AliasPath': (__spec__.parent, '.aliases'),
+    # main
+    'BaseModel': (__spec__.parent, '.main'),
+    'create_model': (__spec__.parent, '.main'),
+    # network
+    'AnyUrl': (__spec__.parent, '.networks'),
+    'AnyHttpUrl': (__spec__.parent, '.networks'),
+    'FileUrl': (__spec__.parent, '.networks'),
+    'HttpUrl': (__spec__.parent, '.networks'),
+    'FtpUrl': (__spec__.parent, '.networks'),
+    'WebsocketUrl': (__spec__.parent, '.networks'),
+    'AnyWebsocketUrl': (__spec__.parent, '.networks'),
+    'UrlConstraints': (__spec__.parent, '.networks'),
+    'EmailStr': (__spec__.parent, '.networks'),
+    'NameEmail': (__spec__.parent, '.networks'),
+    'IPvAnyAddress': (__spec__.parent, '.networks'),
+    'IPvAnyInterface': (__spec__.parent, '.networks'),
+    'IPvAnyNetwork': (__spec__.parent, '.networks'),
+    'PostgresDsn': (__spec__.parent, '.networks'),
+    'CockroachDsn': (__spec__.parent, '.networks'),
+    'AmqpDsn': (__spec__.parent, '.networks'),
+    'RedisDsn': (__spec__.parent, '.networks'),
+    'MongoDsn': (__spec__.parent, '.networks'),
+    'KafkaDsn': (__spec__.parent, '.networks'),
+    'NatsDsn': (__spec__.parent, '.networks'),
+    'MySQLDsn': (__spec__.parent, '.networks'),
+    'MariaDBDsn': (__spec__.parent, '.networks'),
+    'ClickHouseDsn': (__spec__.parent, '.networks'),
+    'SnowflakeDsn': (__spec__.parent, '.networks'),
+    'validate_email': (__spec__.parent, '.networks'),
+    # root_model
+    'RootModel': (__spec__.parent, '.root_model'),
+    # types
+    'Strict': (__spec__.parent, '.types'),
+    'StrictStr': (__spec__.parent, '.types'),
+    'conbytes': (__spec__.parent, '.types'),
+    'conlist': (__spec__.parent, '.types'),
+    'conset': (__spec__.parent, '.types'),
+    'confrozenset': (__spec__.parent, '.types'),
+    'constr': (__spec__.parent, '.types'),
+    'StringConstraints': (__spec__.parent, '.types'),
+    'ImportString': (__spec__.parent, '.types'),
+    'conint': (__spec__.parent, '.types'),
+    'PositiveInt': (__spec__.parent, '.types'),
+    'NegativeInt': (__spec__.parent, '.types'),
+    'NonNegativeInt': (__spec__.parent, '.types'),
+    'NonPositiveInt': (__spec__.parent, '.types'),
+    'confloat': (__spec__.parent, '.types'),
+    'PositiveFloat': (__spec__.parent, '.types'),
+    'NegativeFloat': (__spec__.parent, '.types'),
+    'NonNegativeFloat': (__spec__.parent, '.types'),
+    'NonPositiveFloat': (__spec__.parent, '.types'),
+    'FiniteFloat': (__spec__.parent, '.types'),
+    'condecimal': (__spec__.parent, '.types'),
+    'condate': (__spec__.parent, '.types'),
+    'UUID1': (__spec__.parent, '.types'),
+    'UUID3': (__spec__.parent, '.types'),
+    'UUID4': (__spec__.parent, '.types'),
+    'UUID5': (__spec__.parent, '.types'),
+    'FilePath': (__spec__.parent, '.types'),
+    'DirectoryPath': (__spec__.parent, '.types'),
+    'NewPath': (__spec__.parent, '.types'),
+    'Json': (__spec__.parent, '.types'),
+    'Secret': (__spec__.parent, '.types'),
+    'SecretStr': (__spec__.parent, '.types'),
+    'SecretBytes': (__spec__.parent, '.types'),
+    'StrictBool': (__spec__.parent, '.types'),
+    'StrictBytes': (__spec__.parent, '.types'),
+    'StrictInt': (__spec__.parent, '.types'),
+    'StrictFloat': (__spec__.parent, '.types'),
+    'PaymentCardNumber': (__spec__.parent, '.types'),
+    'ByteSize': (__spec__.parent, '.types'),
+    'PastDate': (__spec__.parent, '.types'),
+    'SocketPath': (__spec__.parent, '.types'),
+    'FutureDate': (__spec__.parent, '.types'),
+    'PastDatetime': (__spec__.parent, '.types'),
+    'FutureDatetime': (__spec__.parent, '.types'),
+    'AwareDatetime': (__spec__.parent, '.types'),
+    'NaiveDatetime': (__spec__.parent, '.types'),
+    'AllowInfNan': (__spec__.parent, '.types'),
+    'EncoderProtocol': (__spec__.parent, '.types'),
+    'EncodedBytes': (__spec__.parent, '.types'),
+    'EncodedStr': (__spec__.parent, '.types'),
+    'Base64Encoder': (__spec__.parent, '.types'),
+    'Base64Bytes': (__spec__.parent, '.types'),
+    'Base64Str': (__spec__.parent, '.types'),
+    'Base64UrlBytes': (__spec__.parent, '.types'),
+    'Base64UrlStr': (__spec__.parent, '.types'),
+    'GetPydanticSchema': (__spec__.parent, '.types'),
+    'Tag': (__spec__.parent, '.types'),
+    'Discriminator': (__spec__.parent, '.types'),
+    'JsonValue': (__spec__.parent, '.types'),
+    'OnErrorOmit': (__spec__.parent, '.types'),
+    'FailFast': (__spec__.parent, '.types'),
+    # type_adapter
+    'TypeAdapter': (__spec__.parent, '.type_adapter'),
+    # warnings
+    'PydanticDeprecatedSince20': (__spec__.parent, '.warnings'),
+    'PydanticDeprecatedSince26': (__spec__.parent, '.warnings'),
+    'PydanticDeprecatedSince29': (__spec__.parent, '.warnings'),
+    'PydanticDeprecationWarning': (__spec__.parent, '.warnings'),
+    'PydanticExperimentalWarning': (__spec__.parent, '.warnings'),
+    # annotated handlers
+    'GetCoreSchemaHandler': (__spec__.parent, '.annotated_handlers'),
+    'GetJsonSchemaHandler': (__spec__.parent, '.annotated_handlers'),
+    # pydantic_core stuff
+    'ValidationError': ('pydantic_core', '.'),
+    'ValidationInfo': ('pydantic_core', '.core_schema'),
+    'SerializationInfo': ('pydantic_core', '.core_schema'),
+    'ValidatorFunctionWrapHandler': ('pydantic_core', '.core_schema'),
+    'FieldSerializationInfo': ('pydantic_core', '.core_schema'),
+    'SerializerFunctionWrapHandler': ('pydantic_core', '.core_schema'),
+    # deprecated, mostly not included in __all__
+    'root_validator': (__spec__.parent, '.deprecated.class_validators'),
+    'validator': (__spec__.parent, '.deprecated.class_validators'),
+    'BaseConfig': (__spec__.parent, '.deprecated.config'),
+    'Extra': (__spec__.parent, '.deprecated.config'),
+    'parse_obj_as': (__spec__.parent, '.deprecated.tools'),
+    'schema_of': (__spec__.parent, '.deprecated.tools'),
+    'schema_json_of': (__spec__.parent, '.deprecated.tools'),
+    # deprecated dynamic imports
+    'FieldValidationInfo': ('pydantic_core', '.core_schema'),
+    'GenerateSchema': (__spec__.parent, '._internal._generate_schema'),
+}
+_deprecated_dynamic_imports = {'FieldValidationInfo', 'GenerateSchema'}
+
+_getattr_migration = getattr_migration(__name__)
+
+
+def __getattr__(attr_name: str) -> object:
+    if attr_name in _deprecated_dynamic_imports:
+        warn(
+            f'Importing {attr_name} from `pydantic` is deprecated. This feature is either no longer supported, or is not public.',
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
+    dynamic_attr = _dynamic_imports.get(attr_name)
+    if dynamic_attr is None:
+        return _getattr_migration(attr_name)
+
+    package, module_name = dynamic_attr
+
+    if module_name == '__module__':
+        result = import_module(f'.{attr_name}', package=package)
+        globals()[attr_name] = result
+        return result
+    else:
+        module = import_module(module_name, package=package)
+        result = getattr(module, attr_name)
+        g = globals()
+        for k, (_, v_module_name) in _dynamic_imports.items():
+            if v_module_name == module_name and k not in _deprecated_dynamic_imports:
+                g[k] = getattr(module, k)
+        return result
+
+
+def __dir__() -> 'list[str]':
+    return list(__all__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/__init__.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/__init__.py
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/_internal/__init__.py
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_config.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_config.py
new file mode 100644
index 00000000..6d491c29
--- /dev/null
+++ b/.venv/lib/python3.12/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)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_core_metadata.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_core_metadata.py
new file mode 100644
index 00000000..89e3e788
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_core_utils.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_core_utils.py
new file mode 100644
index 00000000..f6ab20e4
--- /dev/null
+++ b/.venv/lib/python3.12/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)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_dataclasses.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_dataclasses.py
new file mode 100644
index 00000000..f2e6f22f
--- /dev/null
+++ b/.venv/lib/python3.12/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__', {})))
+    )
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_decorators.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_decorators.py
new file mode 100644
index 00000000..bb0f7207
--- /dev/null
+++ b/.venv/lib/python3.12/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)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_decorators_v1.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_decorators_v1.py
new file mode 100644
index 00000000..2dfa3f2a
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_discriminated_union.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_discriminated_union.py
new file mode 100644
index 00000000..29a50a5a
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_docs_extraction.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_docs_extraction.py
new file mode 100644
index 00000000..685a6d06
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py
new file mode 100644
index 00000000..5c760abc
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_forward_ref.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_forward_ref.py
new file mode 100644
index 00000000..231f81d1
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_generate_schema.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_generate_schema.py
new file mode 100644
index 00000000..4d4a6a63
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/_internal/_generate_schema.py
@@ -0,0 +1,2522 @@
+"""Convert python types to pydantic-core schema."""
+
+from __future__ import annotations as _annotations
+
+import collections.abc
+import dataclasses
+import datetime
+import inspect
+import os
+import pathlib
+import re
+import sys
+import typing
+import warnings
+from contextlib import contextmanager
+from copy import copy, deepcopy
+from decimal import Decimal
+from enum import Enum
+from fractions import Fraction
+from functools import partial
+from inspect import Parameter, _ParameterKind, signature
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from itertools import chain
+from operator import attrgetter
+from types import FunctionType, LambdaType, MethodType
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Final,
+    ForwardRef,
+    Iterable,
+    Iterator,
+    Mapping,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+from uuid import UUID
+from warnings import warn
+
+import typing_extensions
+from pydantic_core import (
+    CoreSchema,
+    MultiHostUrl,
+    PydanticCustomError,
+    PydanticSerializationUnexpectedValue,
+    PydanticUndefined,
+    Url,
+    core_schema,
+    to_jsonable_python,
+)
+from typing_extensions import Literal, TypeAliasType, TypedDict, get_args, get_origin, is_typeddict
+
+from ..aliases import AliasChoices, AliasGenerator, AliasPath
+from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
+from ..config import ConfigDict, JsonDict, JsonEncoder, JsonSchemaExtraCallable
+from ..errors import PydanticSchemaGenerationError, PydanticUndefinedAnnotation, PydanticUserError
+from ..functional_validators import AfterValidator, BeforeValidator, FieldValidatorModes, PlainValidator, WrapValidator
+from ..json_schema import JsonSchemaValue
+from ..version import version_short
+from ..warnings import PydanticDeprecatedSince20
+from . import _core_utils, _decorators, _discriminated_union, _known_annotated_metadata, _typing_extra
+from ._config import ConfigWrapper, ConfigWrapperStack
+from ._core_metadata import update_core_metadata
+from ._core_utils import (
+    collect_invalid_schemas,
+    define_expected_missing_refs,
+    get_ref,
+    get_type_ref,
+    is_function_with_inner_schema,
+    is_list_like_schema_with_items_schema,
+    simplify_schema_references,
+    validate_core_schema,
+)
+from ._decorators import (
+    Decorator,
+    DecoratorInfos,
+    FieldSerializerDecoratorInfo,
+    FieldValidatorDecoratorInfo,
+    ModelSerializerDecoratorInfo,
+    ModelValidatorDecoratorInfo,
+    RootValidatorDecoratorInfo,
+    ValidatorDecoratorInfo,
+    get_attribute_from_bases,
+    inspect_field_serializer,
+    inspect_model_serializer,
+    inspect_validator,
+)
+from ._docs_extraction import extract_docstrings_from_cls
+from ._fields import collect_dataclass_fields, takes_validated_data_argument
+from ._forward_ref import PydanticRecursiveRef
+from ._generics import get_standard_typevars_map, has_instance_in_type, recursively_defined_type_refs, replace_types
+from ._import_utils import import_cached_base_model, import_cached_field_info
+from ._mock_val_ser import MockCoreSchema
+from ._namespace_utils import NamespacesTuple, NsResolver
+from ._schema_generation_shared import CallbackGetCoreSchemaHandler
+from ._utils import lenient_issubclass, smart_deepcopy
+
+if TYPE_CHECKING:
+    from ..fields import ComputedFieldInfo, FieldInfo
+    from ..main import BaseModel
+    from ..types import Discriminator
+    from ._dataclasses import StandardDataclass
+    from ._schema_generation_shared import GetJsonSchemaFunction
+
+_SUPPORTS_TYPEDDICT = sys.version_info >= (3, 12)
+
+FieldDecoratorInfo = Union[ValidatorDecoratorInfo, FieldValidatorDecoratorInfo, FieldSerializerDecoratorInfo]
+FieldDecoratorInfoType = TypeVar('FieldDecoratorInfoType', bound=FieldDecoratorInfo)
+AnyFieldDecorator = Union[
+    Decorator[ValidatorDecoratorInfo],
+    Decorator[FieldValidatorDecoratorInfo],
+    Decorator[FieldSerializerDecoratorInfo],
+]
+
+ModifyCoreSchemaWrapHandler = GetCoreSchemaHandler
+GetCoreSchemaFunction = Callable[[Any, ModifyCoreSchemaWrapHandler], core_schema.CoreSchema]
+
+TUPLE_TYPES: list[type] = [tuple, typing.Tuple]
+LIST_TYPES: list[type] = [list, typing.List, collections.abc.MutableSequence]
+SET_TYPES: list[type] = [set, typing.Set, collections.abc.MutableSet]
+FROZEN_SET_TYPES: list[type] = [frozenset, typing.FrozenSet, collections.abc.Set]
+DICT_TYPES: list[type] = [dict, typing.Dict]
+IP_TYPES: list[type] = [IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network]
+SEQUENCE_TYPES: list[type] = [typing.Sequence, collections.abc.Sequence]
+PATH_TYPES: list[type] = [
+    os.PathLike,
+    pathlib.Path,
+    pathlib.PurePath,
+    pathlib.PosixPath,
+    pathlib.PurePosixPath,
+    pathlib.PureWindowsPath,
+]
+MAPPING_TYPES = [
+    typing.Mapping,
+    typing.MutableMapping,
+    collections.abc.Mapping,
+    collections.abc.MutableMapping,
+    collections.OrderedDict,
+    typing_extensions.OrderedDict,
+    typing.DefaultDict,
+    collections.defaultdict,
+    collections.Counter,
+    typing.Counter,
+]
+DEQUE_TYPES: list[type] = [collections.deque, typing.Deque]
+
+# Note: This does not play very well with type checkers. For example,
+# `a: LambdaType = lambda x: x` will raise a type error by Pyright.
+ValidateCallSupportedTypes = Union[
+    LambdaType,
+    FunctionType,
+    MethodType,
+    partial,
+]
+
+VALIDATE_CALL_SUPPORTED_TYPES = get_args(ValidateCallSupportedTypes)
+
+_mode_to_validator: dict[
+    FieldValidatorModes, type[BeforeValidator | AfterValidator | PlainValidator | WrapValidator]
+] = {'before': BeforeValidator, 'after': AfterValidator, 'plain': PlainValidator, 'wrap': WrapValidator}
+
+
+def check_validator_fields_against_field_name(
+    info: FieldDecoratorInfo,
+    field: str,
+) -> bool:
+    """Check if field name is in validator fields.
+
+    Args:
+        info: The field info.
+        field: The field name to check.
+
+    Returns:
+        `True` if field name is in validator fields, `False` otherwise.
+    """
+    if '*' in info.fields:
+        return True
+    for v_field_name in info.fields:
+        if v_field_name == field:
+            return True
+    return False
+
+
+def check_decorator_fields_exist(decorators: Iterable[AnyFieldDecorator], fields: Iterable[str]) -> None:
+    """Check if the defined fields in decorators exist in `fields` param.
+
+    It ignores the check for a decorator if the decorator has `*` as field or `check_fields=False`.
+
+    Args:
+        decorators: An iterable of decorators.
+        fields: An iterable of fields name.
+
+    Raises:
+        PydanticUserError: If one of the field names does not exist in `fields` param.
+    """
+    fields = set(fields)
+    for dec in decorators:
+        if '*' in dec.info.fields:
+            continue
+        if dec.info.check_fields is False:
+            continue
+        for field in dec.info.fields:
+            if field not in fields:
+                raise PydanticUserError(
+                    f'Decorators defined with incorrect fields: {dec.cls_ref}.{dec.cls_var_name}'
+                    " (use check_fields=False if you're inheriting from the model and intended this)",
+                    code='decorator-missing-field',
+                )
+
+
+def filter_field_decorator_info_by_field(
+    validator_functions: Iterable[Decorator[FieldDecoratorInfoType]], field: str
+) -> list[Decorator[FieldDecoratorInfoType]]:
+    return [dec for dec in validator_functions if check_validator_fields_against_field_name(dec.info, field)]
+
+
+def apply_each_item_validators(
+    schema: core_schema.CoreSchema,
+    each_item_validators: list[Decorator[ValidatorDecoratorInfo]],
+    field_name: str | None,
+) -> core_schema.CoreSchema:
+    # This V1 compatibility shim should eventually be removed
+
+    # fail early if each_item_validators is empty
+    if not each_item_validators:
+        return schema
+
+    # push down any `each_item=True` validators
+    # note that this won't work for any Annotated types that get wrapped by a function validator
+    # but that's okay because that didn't exist in V1
+    if schema['type'] == 'nullable':
+        schema['schema'] = apply_each_item_validators(schema['schema'], each_item_validators, field_name)
+        return schema
+    elif schema['type'] == 'tuple':
+        if (variadic_item_index := schema.get('variadic_item_index')) is not None:
+            schema['items_schema'][variadic_item_index] = apply_validators(
+                schema['items_schema'][variadic_item_index],
+                each_item_validators,
+                field_name,
+            )
+    elif is_list_like_schema_with_items_schema(schema):
+        inner_schema = schema.get('items_schema', core_schema.any_schema())
+        schema['items_schema'] = apply_validators(inner_schema, each_item_validators, field_name)
+    elif schema['type'] == 'dict':
+        inner_schema = schema.get('values_schema', core_schema.any_schema())
+        schema['values_schema'] = apply_validators(inner_schema, each_item_validators, field_name)
+    else:
+        raise TypeError(
+            f"`@validator(..., each_item=True)` cannot be applied to fields with a schema of {schema['type']}"
+        )
+    return schema
+
+
+def _extract_json_schema_info_from_field_info(
+    info: FieldInfo | ComputedFieldInfo,
+) -> tuple[JsonDict | None, JsonDict | JsonSchemaExtraCallable | None]:
+    json_schema_updates = {
+        'title': info.title,
+        'description': info.description,
+        'deprecated': bool(info.deprecated) or info.deprecated == '' or None,
+        'examples': to_jsonable_python(info.examples),
+    }
+    json_schema_updates = {k: v for k, v in json_schema_updates.items() if v is not None}
+    return (json_schema_updates or None, info.json_schema_extra)
+
+
+JsonEncoders = Dict[Type[Any], JsonEncoder]
+
+
+def _add_custom_serialization_from_json_encoders(
+    json_encoders: JsonEncoders | None, tp: Any, schema: CoreSchema
+) -> CoreSchema:
+    """Iterate over the json_encoders and add the first matching encoder to the schema.
+
+    Args:
+        json_encoders: A dictionary of types and their encoder functions.
+        tp: The type to check for a matching encoder.
+        schema: The schema to add the encoder to.
+    """
+    if not json_encoders:
+        return schema
+    if 'serialization' in schema:
+        return schema
+    # Check the class type and its superclasses for a matching encoder
+    # Decimal.__class__.__mro__ (and probably other cases) doesn't include Decimal itself
+    # if the type is a GenericAlias (e.g. from list[int]) we need to use __class__ instead of .__mro__
+    for base in (tp, *getattr(tp, '__mro__', tp.__class__.__mro__)[:-1]):
+        encoder = json_encoders.get(base)
+        if encoder is None:
+            continue
+
+        warnings.warn(
+            f'`json_encoders` is deprecated. See https://docs.pydantic.dev/{version_short()}/concepts/serialization/#custom-serializers for alternatives',
+            PydanticDeprecatedSince20,
+        )
+
+        # TODO: in theory we should check that the schema accepts a serialization key
+        schema['serialization'] = core_schema.plain_serializer_function_ser_schema(encoder, when_used='json')
+        return schema
+
+    return schema
+
+
+def _get_first_non_null(a: Any, b: Any) -> Any:
+    """Return the first argument if it is not None, otherwise return the second argument.
+
+    Use case: serialization_alias (argument a) and alias (argument b) are both defined, and serialization_alias is ''.
+    This function will return serialization_alias, which is the first argument, even though it is an empty string.
+    """
+    return a if a is not None else b
+
+
+class GenerateSchema:
+    """Generate core schema for a Pydantic model, dataclass and types like `str`, `datetime`, ... ."""
+
+    __slots__ = (
+        '_config_wrapper_stack',
+        '_ns_resolver',
+        '_typevars_map',
+        'field_name_stack',
+        'model_type_stack',
+        'defs',
+    )
+
+    def __init__(
+        self,
+        config_wrapper: ConfigWrapper,
+        ns_resolver: NsResolver | None = None,
+        typevars_map: dict[Any, Any] | None = None,
+    ) -> None:
+        # we need a stack for recursing into nested models
+        self._config_wrapper_stack = ConfigWrapperStack(config_wrapper)
+        self._ns_resolver = ns_resolver or NsResolver()
+        self._typevars_map = typevars_map
+        self.field_name_stack = _FieldNameStack()
+        self.model_type_stack = _ModelTypeStack()
+        self.defs = _Definitions()
+
+    def __init_subclass__(cls) -> None:
+        super().__init_subclass__()
+        warnings.warn(
+            'Subclassing `GenerateSchema` is not supported. The API is highly subject to change in minor versions.',
+            UserWarning,
+            stacklevel=2,
+        )
+
+    @property
+    def _config_wrapper(self) -> ConfigWrapper:
+        return self._config_wrapper_stack.tail
+
+    @property
+    def _types_namespace(self) -> NamespacesTuple:
+        return self._ns_resolver.types_namespace
+
+    @property
+    def _arbitrary_types(self) -> bool:
+        return self._config_wrapper.arbitrary_types_allowed
+
+    # the following methods can be overridden but should be considered
+    # unstable / private APIs
+    def _list_schema(self, items_type: Any) -> CoreSchema:
+        return core_schema.list_schema(self.generate_schema(items_type))
+
+    def _dict_schema(self, keys_type: Any, values_type: Any) -> CoreSchema:
+        return core_schema.dict_schema(self.generate_schema(keys_type), self.generate_schema(values_type))
+
+    def _set_schema(self, items_type: Any) -> CoreSchema:
+        return core_schema.set_schema(self.generate_schema(items_type))
+
+    def _frozenset_schema(self, items_type: Any) -> CoreSchema:
+        return core_schema.frozenset_schema(self.generate_schema(items_type))
+
+    def _enum_schema(self, enum_type: type[Enum]) -> CoreSchema:
+        cases: list[Any] = list(enum_type.__members__.values())
+
+        enum_ref = get_type_ref(enum_type)
+        description = None if not enum_type.__doc__ else inspect.cleandoc(enum_type.__doc__)
+        if (
+            description == 'An enumeration.'
+        ):  # This is the default value provided by enum.EnumMeta.__new__; don't use it
+            description = None
+        js_updates = {'title': enum_type.__name__, 'description': description}
+        js_updates = {k: v for k, v in js_updates.items() if v is not None}
+
+        sub_type: Literal['str', 'int', 'float'] | None = None
+        if issubclass(enum_type, int):
+            sub_type = 'int'
+            value_ser_type: core_schema.SerSchema = core_schema.simple_ser_schema('int')
+        elif issubclass(enum_type, str):
+            # this handles `StrEnum` (3.11 only), and also `Foobar(str, Enum)`
+            sub_type = 'str'
+            value_ser_type = core_schema.simple_ser_schema('str')
+        elif issubclass(enum_type, float):
+            sub_type = 'float'
+            value_ser_type = core_schema.simple_ser_schema('float')
+        else:
+            # TODO this is an ugly hack, how do we trigger an Any schema for serialization?
+            value_ser_type = core_schema.plain_serializer_function_ser_schema(lambda x: x)
+
+        if cases:
+
+            def get_json_schema(schema: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
+                json_schema = handler(schema)
+                original_schema = handler.resolve_ref_schema(json_schema)
+                original_schema.update(js_updates)
+                return json_schema
+
+            # we don't want to add the missing to the schema if it's the default one
+            default_missing = getattr(enum_type._missing_, '__func__', None) is Enum._missing_.__func__  # pyright: ignore[reportFunctionMemberAccess]
+            enum_schema = core_schema.enum_schema(
+                enum_type,
+                cases,
+                sub_type=sub_type,
+                missing=None if default_missing else enum_type._missing_,
+                ref=enum_ref,
+                metadata={'pydantic_js_functions': [get_json_schema]},
+            )
+
+            if self._config_wrapper.use_enum_values:
+                enum_schema = core_schema.no_info_after_validator_function(
+                    attrgetter('value'), enum_schema, serialization=value_ser_type
+                )
+
+            return enum_schema
+
+        else:
+
+            def get_json_schema_no_cases(_, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
+                json_schema = handler(core_schema.enum_schema(enum_type, cases, sub_type=sub_type, ref=enum_ref))
+                original_schema = handler.resolve_ref_schema(json_schema)
+                original_schema.update(js_updates)
+                return json_schema
+
+            # Use an isinstance check for enums with no cases.
+            # The most important use case for this is creating TypeVar bounds for generics that should
+            # be restricted to enums. This is more consistent than it might seem at first, since you can only
+            # subclass enum.Enum (or subclasses of enum.Enum) if all parent classes have no cases.
+            # We use the get_json_schema function when an Enum subclass has been declared with no cases
+            # so that we can still generate a valid json schema.
+            return core_schema.is_instance_schema(
+                enum_type,
+                metadata={'pydantic_js_functions': [get_json_schema_no_cases]},
+            )
+
+    def _ip_schema(self, tp: Any) -> CoreSchema:
+        from ._validators import IP_VALIDATOR_LOOKUP, IpType
+
+        ip_type_json_schema_format: dict[type[IpType], str] = {
+            IPv4Address: 'ipv4',
+            IPv4Network: 'ipv4network',
+            IPv4Interface: 'ipv4interface',
+            IPv6Address: 'ipv6',
+            IPv6Network: 'ipv6network',
+            IPv6Interface: 'ipv6interface',
+        }
+
+        def ser_ip(ip: Any, info: core_schema.SerializationInfo) -> str | IpType:
+            if not isinstance(ip, (tp, str)):
+                raise PydanticSerializationUnexpectedValue(
+                    f"Expected `{tp}` but got `{type(ip)}` with value `'{ip}'` - serialized value may not be as expected."
+                )
+            if info.mode == 'python':
+                return ip
+            return str(ip)
+
+        return core_schema.lax_or_strict_schema(
+            lax_schema=core_schema.no_info_plain_validator_function(IP_VALIDATOR_LOOKUP[tp]),
+            strict_schema=core_schema.json_or_python_schema(
+                json_schema=core_schema.no_info_after_validator_function(tp, core_schema.str_schema()),
+                python_schema=core_schema.is_instance_schema(tp),
+            ),
+            serialization=core_schema.plain_serializer_function_ser_schema(ser_ip, info_arg=True, when_used='always'),
+            metadata={
+                'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': ip_type_json_schema_format[tp]}]
+            },
+        )
+
+    def _fraction_schema(self) -> CoreSchema:
+        """Support for [`fractions.Fraction`][fractions.Fraction]."""
+        from ._validators import fraction_validator
+
+        # TODO: note, this is a fairly common pattern, re lax / strict for attempted type coercion,
+        # can we use a helper function to reduce boilerplate?
+        return core_schema.lax_or_strict_schema(
+            lax_schema=core_schema.no_info_plain_validator_function(fraction_validator),
+            strict_schema=core_schema.json_or_python_schema(
+                json_schema=core_schema.no_info_plain_validator_function(fraction_validator),
+                python_schema=core_schema.is_instance_schema(Fraction),
+            ),
+            # use str serialization to guarantee round trip behavior
+            serialization=core_schema.to_string_ser_schema(when_used='always'),
+            metadata={'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': 'fraction'}]},
+        )
+
+    def _arbitrary_type_schema(self, tp: Any) -> CoreSchema:
+        if not isinstance(tp, type):
+            warn(
+                f'{tp!r} is not a Python type (it may be an instance of an object),'
+                ' Pydantic will allow any object with no validation since we cannot even'
+                ' enforce that the input is an instance of the given type.'
+                ' To get rid of this error wrap the type with `pydantic.SkipValidation`.',
+                UserWarning,
+            )
+            return core_schema.any_schema()
+        return core_schema.is_instance_schema(tp)
+
+    def _unknown_type_schema(self, obj: Any) -> CoreSchema:
+        raise PydanticSchemaGenerationError(
+            f'Unable to generate pydantic-core schema for {obj!r}. '
+            'Set `arbitrary_types_allowed=True` in the model_config to ignore this error'
+            ' or implement `__get_pydantic_core_schema__` on your type to fully support it.'
+            '\n\nIf you got this error by calling handler(<some type>) within'
+            ' `__get_pydantic_core_schema__` then you likely need to call'
+            ' `handler.generate_schema(<some type>)` since we do not call'
+            ' `__get_pydantic_core_schema__` on `<some type>` otherwise to avoid infinite recursion.'
+        )
+
+    def _apply_discriminator_to_union(
+        self, schema: CoreSchema, discriminator: str | Discriminator | None
+    ) -> CoreSchema:
+        if discriminator is None:
+            return schema
+        try:
+            return _discriminated_union.apply_discriminator(
+                schema,
+                discriminator,
+            )
+        except _discriminated_union.MissingDefinitionForUnionRef:
+            # defer until defs are resolved
+            _discriminated_union.set_discriminator_in_metadata(
+                schema,
+                discriminator,
+            )
+            return schema
+
+    class CollectedInvalid(Exception):
+        pass
+
+    def clean_schema(self, schema: CoreSchema) -> CoreSchema:
+        schema = self.collect_definitions(schema)
+        schema = simplify_schema_references(schema)
+        if collect_invalid_schemas(schema):
+            raise self.CollectedInvalid()
+        schema = _discriminated_union.apply_discriminators(schema)
+        schema = validate_core_schema(schema)
+        return schema
+
+    def collect_definitions(self, schema: CoreSchema) -> CoreSchema:
+        ref = cast('str | None', schema.get('ref', None))
+        if ref:
+            self.defs.definitions[ref] = schema
+        if 'ref' in schema:
+            schema = core_schema.definition_reference_schema(schema['ref'])
+        return core_schema.definitions_schema(
+            schema,
+            list(self.defs.definitions.values()),
+        )
+
+    def _add_js_function(self, metadata_schema: CoreSchema, js_function: Callable[..., Any]) -> None:
+        metadata = metadata_schema.get('metadata', {})
+        pydantic_js_functions = metadata.setdefault('pydantic_js_functions', [])
+        # because of how we generate core schemas for nested generic models
+        # we can end up adding `BaseModel.__get_pydantic_json_schema__` multiple times
+        # this check may fail to catch duplicates if the function is a `functools.partial`
+        # or something like that, but if it does it'll fail by inserting the duplicate
+        if js_function not in pydantic_js_functions:
+            pydantic_js_functions.append(js_function)
+        metadata_schema['metadata'] = metadata
+
+    def generate_schema(
+        self,
+        obj: Any,
+        from_dunder_get_core_schema: bool = True,
+    ) -> core_schema.CoreSchema:
+        """Generate core schema.
+
+        Args:
+            obj: The object to generate core schema for.
+            from_dunder_get_core_schema: Whether to generate schema from either the
+                `__get_pydantic_core_schema__` function or `__pydantic_core_schema__` property.
+
+        Returns:
+            The generated core schema.
+
+        Raises:
+            PydanticUndefinedAnnotation:
+                If it is not possible to evaluate forward reference.
+            PydanticSchemaGenerationError:
+                If it is not possible to generate pydantic-core schema.
+            TypeError:
+                - If `alias_generator` returns a disallowed type (must be str, AliasPath or AliasChoices).
+                - If V1 style validator with `each_item=True` applied on a wrong field.
+            PydanticUserError:
+                - If `typing.TypedDict` is used instead of `typing_extensions.TypedDict` on Python < 3.12.
+                - If `__modify_schema__` method is used instead of `__get_pydantic_json_schema__`.
+        """
+        schema: CoreSchema | None = None
+
+        if from_dunder_get_core_schema:
+            from_property = self._generate_schema_from_property(obj, obj)
+            if from_property is not None:
+                schema = from_property
+
+        if schema is None:
+            schema = self._generate_schema_inner(obj)
+
+        metadata_js_function = _extract_get_pydantic_json_schema(obj, schema)
+        if metadata_js_function is not None:
+            metadata_schema = resolve_original_schema(schema, self.defs.definitions)
+            if metadata_schema:
+                self._add_js_function(metadata_schema, metadata_js_function)
+
+        schema = _add_custom_serialization_from_json_encoders(self._config_wrapper.json_encoders, obj, schema)
+
+        return schema
+
+    def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema:
+        """Generate schema for a Pydantic model."""
+        with self.defs.get_schema_or_ref(cls) as (model_ref, maybe_schema):
+            if maybe_schema is not None:
+                return maybe_schema
+
+            fields = getattr(cls, '__pydantic_fields__', {})
+            decorators = cls.__pydantic_decorators__
+            computed_fields = decorators.computed_fields
+            check_decorator_fields_exist(
+                chain(
+                    decorators.field_validators.values(),
+                    decorators.field_serializers.values(),
+                    decorators.validators.values(),
+                ),
+                {*fields.keys(), *computed_fields.keys()},
+            )
+            config_wrapper = ConfigWrapper(cls.model_config, check=False)
+            core_config = config_wrapper.core_config(title=cls.__name__)
+            model_validators = decorators.model_validators.values()
+
+            with self._config_wrapper_stack.push(config_wrapper), self._ns_resolver.push(cls):
+                extras_schema = None
+                if core_config.get('extra_fields_behavior') == 'allow':
+                    assert cls.__mro__[0] is cls
+                    assert cls.__mro__[-1] is object
+                    for candidate_cls in cls.__mro__[:-1]:
+                        extras_annotation = getattr(candidate_cls, '__annotations__', {}).get(
+                            '__pydantic_extra__', None
+                        )
+                        if extras_annotation is not None:
+                            if isinstance(extras_annotation, str):
+                                extras_annotation = _typing_extra.eval_type_backport(
+                                    _typing_extra._make_forward_ref(
+                                        extras_annotation, is_argument=False, is_class=True
+                                    ),
+                                    *self._types_namespace,
+                                )
+                            tp = get_origin(extras_annotation)
+                            if tp not in (Dict, dict):
+                                raise PydanticSchemaGenerationError(
+                                    'The type annotation for `__pydantic_extra__` must be `Dict[str, ...]`'
+                                )
+                            extra_items_type = self._get_args_resolving_forward_refs(
+                                extras_annotation,
+                                required=True,
+                            )[1]
+                            if not _typing_extra.is_any(extra_items_type):
+                                extras_schema = self.generate_schema(extra_items_type)
+                                break
+
+                generic_origin: type[BaseModel] | None = getattr(cls, '__pydantic_generic_metadata__', {}).get('origin')
+
+                if cls.__pydantic_root_model__:
+                    root_field = self._common_field_schema('root', fields['root'], decorators)
+                    inner_schema = root_field['schema']
+                    inner_schema = apply_model_validators(inner_schema, model_validators, 'inner')
+                    model_schema = core_schema.model_schema(
+                        cls,
+                        inner_schema,
+                        generic_origin=generic_origin,
+                        custom_init=getattr(cls, '__pydantic_custom_init__', None),
+                        root_model=True,
+                        post_init=getattr(cls, '__pydantic_post_init__', None),
+                        config=core_config,
+                        ref=model_ref,
+                    )
+                else:
+                    fields_schema: core_schema.CoreSchema = core_schema.model_fields_schema(
+                        {k: self._generate_md_field_schema(k, v, decorators) for k, v in fields.items()},
+                        computed_fields=[
+                            self._computed_field_schema(d, decorators.field_serializers)
+                            for d in computed_fields.values()
+                        ],
+                        extras_schema=extras_schema,
+                        model_name=cls.__name__,
+                    )
+                    inner_schema = apply_validators(fields_schema, decorators.root_validators.values(), None)
+                    new_inner_schema = define_expected_missing_refs(inner_schema, recursively_defined_type_refs())
+                    if new_inner_schema is not None:
+                        inner_schema = new_inner_schema
+                    inner_schema = apply_model_validators(inner_schema, model_validators, 'inner')
+
+                    model_schema = core_schema.model_schema(
+                        cls,
+                        inner_schema,
+                        generic_origin=generic_origin,
+                        custom_init=getattr(cls, '__pydantic_custom_init__', None),
+                        root_model=False,
+                        post_init=getattr(cls, '__pydantic_post_init__', None),
+                        config=core_config,
+                        ref=model_ref,
+                    )
+
+                schema = self._apply_model_serializers(model_schema, decorators.model_serializers.values())
+                schema = apply_model_validators(schema, model_validators, 'outer')
+                self.defs.definitions[model_ref] = schema
+                return core_schema.definition_reference_schema(model_ref)
+
+    def _unpack_refs_defs(self, schema: CoreSchema) -> CoreSchema:
+        """Unpack all 'definitions' schemas into `GenerateSchema.defs.definitions`
+        and return the inner schema.
+        """
+        if schema['type'] == 'definitions':
+            definitions = self.defs.definitions
+            for s in schema['definitions']:
+                definitions[s['ref']] = s  # type: ignore
+            return schema['schema']
+        return schema
+
+    def _resolve_self_type(self, obj: Any) -> Any:
+        obj = self.model_type_stack.get()
+        if obj is None:
+            raise PydanticUserError('`typing.Self` is invalid in this context', code='invalid-self-type')
+        return obj
+
+    def _generate_schema_from_property(self, obj: Any, source: Any) -> core_schema.CoreSchema | None:
+        """Try to generate schema from either the `__get_pydantic_core_schema__` function or
+        `__pydantic_core_schema__` property.
+
+        Note: `__get_pydantic_core_schema__` takes priority so it can
+        decide whether to use a `__pydantic_core_schema__` attribute, or generate a fresh schema.
+        """
+        # avoid calling `__get_pydantic_core_schema__` if we've already visited this object
+        if _typing_extra.is_self(obj):
+            obj = self._resolve_self_type(obj)
+        with self.defs.get_schema_or_ref(obj) as (_, maybe_schema):
+            if maybe_schema is not None:
+                return maybe_schema
+        if obj is source:
+            ref_mode = 'unpack'
+        else:
+            ref_mode = 'to-def'
+
+        schema: CoreSchema
+
+        if (get_schema := getattr(obj, '__get_pydantic_core_schema__', None)) is not None:
+            schema = get_schema(
+                source, CallbackGetCoreSchemaHandler(self._generate_schema_inner, self, ref_mode=ref_mode)
+            )
+        elif (
+            hasattr(obj, '__dict__')
+            # In some cases (e.g. a stdlib dataclass subclassing a Pydantic dataclass),
+            # doing an attribute access to get the schema will result in the parent schema
+            # being fetched. Thus, only look for the current obj's dict:
+            and (existing_schema := obj.__dict__.get('__pydantic_core_schema__')) is not None
+            and not isinstance(existing_schema, MockCoreSchema)
+        ):
+            schema = existing_schema
+        elif (validators := getattr(obj, '__get_validators__', None)) is not None:
+            from pydantic.v1 import BaseModel as BaseModelV1
+
+            if issubclass(obj, BaseModelV1):
+                warn(
+                    f'Mixing V1 models and V2 models (or constructs, like `TypeAdapter`) is not supported. Please upgrade `{obj.__name__}` to V2.',
+                    UserWarning,
+                )
+            else:
+                warn(
+                    '`__get_validators__` is deprecated and will be removed, use `__get_pydantic_core_schema__` instead.',
+                    PydanticDeprecatedSince20,
+                )
+            schema = core_schema.chain_schema([core_schema.with_info_plain_validator_function(v) for v in validators()])
+        else:
+            # we have no existing schema information on the property, exit early so that we can go generate a schema
+            return None
+
+        schema = self._unpack_refs_defs(schema)
+
+        if is_function_with_inner_schema(schema):
+            ref = schema['schema'].pop('ref', None)  # pyright: ignore[reportCallIssue, reportArgumentType]
+            if ref:
+                schema['ref'] = ref
+        else:
+            ref = get_ref(schema)
+
+        if ref:
+            self.defs.definitions[ref] = schema
+            return core_schema.definition_reference_schema(ref)
+
+        return schema
+
+    def _resolve_forward_ref(self, obj: Any) -> Any:
+        # we assume that types_namespace has the target of forward references in its scope,
+        # but this could fail, for example, if calling Validator on an imported type which contains
+        # forward references to other types only defined in the module from which it was imported
+        # `Validator(SomeImportedTypeAliasWithAForwardReference)`
+        # or the equivalent for BaseModel
+        # class Model(BaseModel):
+        #   x: SomeImportedTypeAliasWithAForwardReference
+        try:
+            obj = _typing_extra.eval_type_backport(obj, *self._types_namespace)
+        except NameError as e:
+            raise PydanticUndefinedAnnotation.from_name_error(e) from e
+
+        # if obj is still a ForwardRef, it means we can't evaluate it, raise PydanticUndefinedAnnotation
+        if isinstance(obj, ForwardRef):
+            raise PydanticUndefinedAnnotation(obj.__forward_arg__, f'Unable to evaluate forward reference {obj}')
+
+        if self._typevars_map:
+            obj = replace_types(obj, self._typevars_map)
+
+        return obj
+
+    @overload
+    def _get_args_resolving_forward_refs(self, obj: Any, required: Literal[True]) -> tuple[Any, ...]: ...
+
+    @overload
+    def _get_args_resolving_forward_refs(self, obj: Any) -> tuple[Any, ...] | None: ...
+
+    def _get_args_resolving_forward_refs(self, obj: Any, required: bool = False) -> tuple[Any, ...] | None:
+        args = get_args(obj)
+        if args:
+            if sys.version_info >= (3, 9):
+                from types import GenericAlias
+
+                if isinstance(obj, GenericAlias):
+                    # PEP 585 generic aliases don't convert args to ForwardRefs, unlike `typing.List/Dict` etc.
+                    args = (_typing_extra._make_forward_ref(a) if isinstance(a, str) else a for a in args)
+            args = tuple(self._resolve_forward_ref(a) if isinstance(a, ForwardRef) else a for a in args)
+        elif required:  # pragma: no cover
+            raise TypeError(f'Expected {obj} to have generic parameters but it had none')
+        return args
+
+    def _get_first_arg_or_any(self, obj: Any) -> Any:
+        args = self._get_args_resolving_forward_refs(obj)
+        if not args:
+            return Any
+        return args[0]
+
+    def _get_first_two_args_or_any(self, obj: Any) -> tuple[Any, Any]:
+        args = self._get_args_resolving_forward_refs(obj)
+        if not args:
+            return (Any, Any)
+        if len(args) < 2:
+            origin = get_origin(obj)
+            raise TypeError(f'Expected two type arguments for {origin}, got 1')
+        return args[0], args[1]
+
+    def _generate_schema_inner(self, obj: Any) -> core_schema.CoreSchema:
+        if _typing_extra.is_annotated(obj):
+            return self._annotated_schema(obj)
+
+        if isinstance(obj, dict):
+            # we assume this is already a valid schema
+            return obj  # type: ignore[return-value]
+
+        if isinstance(obj, str):
+            obj = ForwardRef(obj)
+
+        if isinstance(obj, ForwardRef):
+            return self.generate_schema(self._resolve_forward_ref(obj))
+
+        BaseModel = import_cached_base_model()
+
+        if lenient_issubclass(obj, BaseModel):
+            with self.model_type_stack.push(obj):
+                return self._model_schema(obj)
+
+        if isinstance(obj, PydanticRecursiveRef):
+            return core_schema.definition_reference_schema(schema_ref=obj.type_ref)
+
+        return self.match_type(obj)
+
+    def match_type(self, obj: Any) -> core_schema.CoreSchema:  # noqa: C901
+        """Main mapping of types to schemas.
+
+        The general structure is a series of if statements starting with the simple cases
+        (non-generic primitive types) and then handling generics and other more complex cases.
+
+        Each case either generates a schema directly, calls into a public user-overridable method
+        (like `GenerateSchema.tuple_variable_schema`) or calls into a private method that handles some
+        boilerplate before calling into the user-facing method (e.g. `GenerateSchema._tuple_schema`).
+
+        The idea is that we'll evolve this into adding more and more user facing methods over time
+        as they get requested and we figure out what the right API for them is.
+        """
+        if obj is str:
+            return core_schema.str_schema()
+        elif obj is bytes:
+            return core_schema.bytes_schema()
+        elif obj is int:
+            return core_schema.int_schema()
+        elif obj is float:
+            return core_schema.float_schema()
+        elif obj is bool:
+            return core_schema.bool_schema()
+        elif obj is complex:
+            return core_schema.complex_schema()
+        elif _typing_extra.is_any(obj) or obj is object:
+            return core_schema.any_schema()
+        elif obj is datetime.date:
+            return core_schema.date_schema()
+        elif obj is datetime.datetime:
+            return core_schema.datetime_schema()
+        elif obj is datetime.time:
+            return core_schema.time_schema()
+        elif obj is datetime.timedelta:
+            return core_schema.timedelta_schema()
+        elif obj is Decimal:
+            return core_schema.decimal_schema()
+        elif obj is UUID:
+            return core_schema.uuid_schema()
+        elif obj is Url:
+            return core_schema.url_schema()
+        elif obj is Fraction:
+            return self._fraction_schema()
+        elif obj is MultiHostUrl:
+            return core_schema.multi_host_url_schema()
+        elif obj is None or obj is _typing_extra.NoneType:
+            return core_schema.none_schema()
+        elif obj in IP_TYPES:
+            return self._ip_schema(obj)
+        elif obj in TUPLE_TYPES:
+            return self._tuple_schema(obj)
+        elif obj in LIST_TYPES:
+            return self._list_schema(Any)
+        elif obj in SET_TYPES:
+            return self._set_schema(Any)
+        elif obj in FROZEN_SET_TYPES:
+            return self._frozenset_schema(Any)
+        elif obj in SEQUENCE_TYPES:
+            return self._sequence_schema(Any)
+        elif obj in DICT_TYPES:
+            return self._dict_schema(Any, Any)
+        elif _typing_extra.is_type_alias_type(obj):
+            return self._type_alias_type_schema(obj)
+        elif obj is type:
+            return self._type_schema()
+        elif _typing_extra.is_callable(obj):
+            return core_schema.callable_schema()
+        elif _typing_extra.is_literal(obj):
+            return self._literal_schema(obj)
+        elif is_typeddict(obj):
+            return self._typed_dict_schema(obj, None)
+        elif _typing_extra.is_namedtuple(obj):
+            return self._namedtuple_schema(obj, None)
+        elif _typing_extra.is_new_type(obj):
+            # NewType, can't use isinstance because it fails <3.10
+            return self.generate_schema(obj.__supertype__)
+        elif obj is re.Pattern:
+            return self._pattern_schema(obj)
+        elif _typing_extra.is_hashable(obj):
+            return self._hashable_schema()
+        elif isinstance(obj, typing.TypeVar):
+            return self._unsubstituted_typevar_schema(obj)
+        elif _typing_extra.is_finalvar(obj):
+            if obj is Final:
+                return core_schema.any_schema()
+            return self.generate_schema(
+                self._get_first_arg_or_any(obj),
+            )
+        elif isinstance(obj, VALIDATE_CALL_SUPPORTED_TYPES):
+            return self._call_schema(obj)
+        elif inspect.isclass(obj) and issubclass(obj, Enum):
+            return self._enum_schema(obj)
+        elif _typing_extra.is_zoneinfo_type(obj):
+            return self._zoneinfo_schema()
+
+        if dataclasses.is_dataclass(obj):
+            return self._dataclass_schema(obj, None)
+
+        origin = get_origin(obj)
+        if origin is not None:
+            return self._match_generic_type(obj, origin)
+
+        res = self._get_prepare_pydantic_annotations_for_known_type(obj, ())
+        if res is not None:
+            source_type, annotations = res
+            return self._apply_annotations(source_type, annotations)
+
+        if self._arbitrary_types:
+            return self._arbitrary_type_schema(obj)
+        return self._unknown_type_schema(obj)
+
+    def _match_generic_type(self, obj: Any, origin: Any) -> CoreSchema:  # noqa: C901
+        # Need to handle generic dataclasses before looking for the schema properties because attribute accesses
+        # on _GenericAlias delegate to the origin type, so lose the information about the concrete parametrization
+        # As a result, currently, there is no way to cache the schema for generic dataclasses. This may be possible
+        # to resolve by modifying the value returned by `Generic.__class_getitem__`, but that is a dangerous game.
+        if dataclasses.is_dataclass(origin):
+            return self._dataclass_schema(obj, origin)  # pyright: ignore[reportArgumentType]
+        if _typing_extra.is_namedtuple(origin):
+            return self._namedtuple_schema(obj, origin)
+
+        from_property = self._generate_schema_from_property(origin, obj)
+        if from_property is not None:
+            return from_property
+
+        if _typing_extra.is_type_alias_type(origin):
+            return self._type_alias_type_schema(obj)
+        elif _typing_extra.origin_is_union(origin):
+            return self._union_schema(obj)
+        elif origin in TUPLE_TYPES:
+            return self._tuple_schema(obj)
+        elif origin in LIST_TYPES:
+            return self._list_schema(self._get_first_arg_or_any(obj))
+        elif origin in SET_TYPES:
+            return self._set_schema(self._get_first_arg_or_any(obj))
+        elif origin in FROZEN_SET_TYPES:
+            return self._frozenset_schema(self._get_first_arg_or_any(obj))
+        elif origin in DICT_TYPES:
+            return self._dict_schema(*self._get_first_two_args_or_any(obj))
+        elif is_typeddict(origin):
+            return self._typed_dict_schema(obj, origin)
+        elif origin in (typing.Type, type):
+            return self._subclass_schema(obj)
+        elif origin in SEQUENCE_TYPES:
+            return self._sequence_schema(self._get_first_arg_or_any(obj))
+        elif origin in {typing.Iterable, collections.abc.Iterable, typing.Generator, collections.abc.Generator}:
+            return self._iterable_schema(obj)
+        elif origin in (re.Pattern, typing.Pattern):
+            return self._pattern_schema(obj)
+
+        res = self._get_prepare_pydantic_annotations_for_known_type(obj, ())
+        if res is not None:
+            source_type, annotations = res
+            return self._apply_annotations(source_type, annotations)
+
+        if self._arbitrary_types:
+            return self._arbitrary_type_schema(origin)
+        return self._unknown_type_schema(obj)
+
+    def _generate_td_field_schema(
+        self,
+        name: str,
+        field_info: FieldInfo,
+        decorators: DecoratorInfos,
+        *,
+        required: bool = True,
+    ) -> core_schema.TypedDictField:
+        """Prepare a TypedDictField to represent a model or typeddict field."""
+        common_field = self._common_field_schema(name, field_info, decorators)
+        return core_schema.typed_dict_field(
+            common_field['schema'],
+            required=False if not field_info.is_required() else required,
+            serialization_exclude=common_field['serialization_exclude'],
+            validation_alias=common_field['validation_alias'],
+            serialization_alias=common_field['serialization_alias'],
+            metadata=common_field['metadata'],
+        )
+
+    def _generate_md_field_schema(
+        self,
+        name: str,
+        field_info: FieldInfo,
+        decorators: DecoratorInfos,
+    ) -> core_schema.ModelField:
+        """Prepare a ModelField to represent a model field."""
+        common_field = self._common_field_schema(name, field_info, decorators)
+        return core_schema.model_field(
+            common_field['schema'],
+            serialization_exclude=common_field['serialization_exclude'],
+            validation_alias=common_field['validation_alias'],
+            serialization_alias=common_field['serialization_alias'],
+            frozen=common_field['frozen'],
+            metadata=common_field['metadata'],
+        )
+
+    def _generate_dc_field_schema(
+        self,
+        name: str,
+        field_info: FieldInfo,
+        decorators: DecoratorInfos,
+    ) -> core_schema.DataclassField:
+        """Prepare a DataclassField to represent the parameter/field, of a dataclass."""
+        common_field = self._common_field_schema(name, field_info, decorators)
+        return core_schema.dataclass_field(
+            name,
+            common_field['schema'],
+            init=field_info.init,
+            init_only=field_info.init_var or None,
+            kw_only=None if field_info.kw_only else False,
+            serialization_exclude=common_field['serialization_exclude'],
+            validation_alias=common_field['validation_alias'],
+            serialization_alias=common_field['serialization_alias'],
+            frozen=common_field['frozen'],
+            metadata=common_field['metadata'],
+        )
+
+    @staticmethod
+    def _apply_alias_generator_to_field_info(
+        alias_generator: Callable[[str], str] | AliasGenerator, field_info: FieldInfo, field_name: str
+    ) -> None:
+        """Apply an alias_generator to aliases on a FieldInfo instance if appropriate.
+
+        Args:
+            alias_generator: A callable that takes a string and returns a string, or an AliasGenerator instance.
+            field_info: The FieldInfo instance to which the alias_generator is (maybe) applied.
+            field_name: The name of the field from which to generate the alias.
+        """
+        # Apply an alias_generator if
+        # 1. An alias is not specified
+        # 2. An alias is specified, but the priority is <= 1
+        if (
+            field_info.alias_priority is None
+            or field_info.alias_priority <= 1
+            or field_info.alias is None
+            or field_info.validation_alias is None
+            or field_info.serialization_alias is None
+        ):
+            alias, validation_alias, serialization_alias = None, None, None
+
+            if isinstance(alias_generator, AliasGenerator):
+                alias, validation_alias, serialization_alias = alias_generator.generate_aliases(field_name)
+            elif isinstance(alias_generator, Callable):
+                alias = alias_generator(field_name)
+                if not isinstance(alias, str):
+                    raise TypeError(f'alias_generator {alias_generator} must return str, not {alias.__class__}')
+
+            # if priority is not set, we set to 1
+            # which supports the case where the alias_generator from a child class is used
+            # to generate an alias for a field in a parent class
+            if field_info.alias_priority is None or field_info.alias_priority <= 1:
+                field_info.alias_priority = 1
+
+            # if the priority is 1, then we set the aliases to the generated alias
+            if field_info.alias_priority == 1:
+                field_info.serialization_alias = _get_first_non_null(serialization_alias, alias)
+                field_info.validation_alias = _get_first_non_null(validation_alias, alias)
+                field_info.alias = alias
+
+            # if any of the aliases are not set, then we set them to the corresponding generated alias
+            if field_info.alias is None:
+                field_info.alias = alias
+            if field_info.serialization_alias is None:
+                field_info.serialization_alias = _get_first_non_null(serialization_alias, alias)
+            if field_info.validation_alias is None:
+                field_info.validation_alias = _get_first_non_null(validation_alias, alias)
+
+    @staticmethod
+    def _apply_alias_generator_to_computed_field_info(
+        alias_generator: Callable[[str], str] | AliasGenerator,
+        computed_field_info: ComputedFieldInfo,
+        computed_field_name: str,
+    ):
+        """Apply an alias_generator to alias on a ComputedFieldInfo instance if appropriate.
+
+        Args:
+            alias_generator: A callable that takes a string and returns a string, or an AliasGenerator instance.
+            computed_field_info: The ComputedFieldInfo instance to which the alias_generator is (maybe) applied.
+            computed_field_name: The name of the computed field from which to generate the alias.
+        """
+        # Apply an alias_generator if
+        # 1. An alias is not specified
+        # 2. An alias is specified, but the priority is <= 1
+
+        if (
+            computed_field_info.alias_priority is None
+            or computed_field_info.alias_priority <= 1
+            or computed_field_info.alias is None
+        ):
+            alias, validation_alias, serialization_alias = None, None, None
+
+            if isinstance(alias_generator, AliasGenerator):
+                alias, validation_alias, serialization_alias = alias_generator.generate_aliases(computed_field_name)
+            elif isinstance(alias_generator, Callable):
+                alias = alias_generator(computed_field_name)
+                if not isinstance(alias, str):
+                    raise TypeError(f'alias_generator {alias_generator} must return str, not {alias.__class__}')
+
+            # if priority is not set, we set to 1
+            # which supports the case where the alias_generator from a child class is used
+            # to generate an alias for a field in a parent class
+            if computed_field_info.alias_priority is None or computed_field_info.alias_priority <= 1:
+                computed_field_info.alias_priority = 1
+
+            # if the priority is 1, then we set the aliases to the generated alias
+            # note that we use the serialization_alias with priority over alias, as computed_field
+            # aliases are used for serialization only (not validation)
+            if computed_field_info.alias_priority == 1:
+                computed_field_info.alias = _get_first_non_null(serialization_alias, alias)
+
+    @staticmethod
+    def _apply_field_title_generator_to_field_info(
+        config_wrapper: ConfigWrapper, field_info: FieldInfo | ComputedFieldInfo, field_name: str
+    ) -> None:
+        """Apply a field_title_generator on a FieldInfo or ComputedFieldInfo instance if appropriate
+        Args:
+            config_wrapper: The config of the model
+            field_info: The FieldInfo or ComputedField instance to which the title_generator is (maybe) applied.
+            field_name: The name of the field from which to generate the title.
+        """
+        field_title_generator = field_info.field_title_generator or config_wrapper.field_title_generator
+
+        if field_title_generator is None:
+            return
+
+        if field_info.title is None:
+            title = field_title_generator(field_name, field_info)  # type: ignore
+            if not isinstance(title, str):
+                raise TypeError(f'field_title_generator {field_title_generator} must return str, not {title.__class__}')
+
+            field_info.title = title
+
+    def _common_field_schema(  # C901
+        self, name: str, field_info: FieldInfo, decorators: DecoratorInfos
+    ) -> _CommonField:
+        # Update FieldInfo annotation if appropriate:
+        FieldInfo = import_cached_field_info()
+        if not field_info.evaluated:
+            # TODO Can we use field_info.apply_typevars_map here?
+            try:
+                evaluated_type = _typing_extra.eval_type(field_info.annotation, *self._types_namespace)
+            except NameError as e:
+                raise PydanticUndefinedAnnotation.from_name_error(e) from e
+            evaluated_type = replace_types(evaluated_type, self._typevars_map)
+            field_info.evaluated = True
+            if not has_instance_in_type(evaluated_type, PydanticRecursiveRef):
+                new_field_info = FieldInfo.from_annotation(evaluated_type)
+                field_info.annotation = new_field_info.annotation
+
+                # Handle any field info attributes that may have been obtained from now-resolved annotations
+                for k, v in new_field_info._attributes_set.items():
+                    # If an attribute is already set, it means it was set by assigning to a call to Field (or just a
+                    # default value), and that should take the highest priority. So don't overwrite existing attributes.
+                    # We skip over "attributes" that are present in the metadata_lookup dict because these won't
+                    # actually end up as attributes of the `FieldInfo` instance.
+                    if k not in field_info._attributes_set and k not in field_info.metadata_lookup:
+                        setattr(field_info, k, v)
+
+                # Finally, ensure the field info also reflects all the `_attributes_set` that are actually metadata.
+                field_info.metadata = [*new_field_info.metadata, *field_info.metadata]
+
+        source_type, annotations = field_info.annotation, field_info.metadata
+
+        def set_discriminator(schema: CoreSchema) -> CoreSchema:
+            schema = self._apply_discriminator_to_union(schema, field_info.discriminator)
+            return schema
+
+        # Convert `@field_validator` decorators to `Before/After/Plain/WrapValidator` instances:
+        validators_from_decorators = []
+        for decorator in filter_field_decorator_info_by_field(decorators.field_validators.values(), name):
+            validators_from_decorators.append(_mode_to_validator[decorator.info.mode]._from_decorator(decorator))
+
+        with self.field_name_stack.push(name):
+            if field_info.discriminator is not None:
+                schema = self._apply_annotations(
+                    source_type, annotations + validators_from_decorators, transform_inner_schema=set_discriminator
+                )
+            else:
+                schema = self._apply_annotations(
+                    source_type,
+                    annotations + validators_from_decorators,
+                )
+
+        # This V1 compatibility shim should eventually be removed
+        # push down any `each_item=True` validators
+        # note that this won't work for any Annotated types that get wrapped by a function validator
+        # but that's okay because that didn't exist in V1
+        this_field_validators = filter_field_decorator_info_by_field(decorators.validators.values(), name)
+        if _validators_require_validate_default(this_field_validators):
+            field_info.validate_default = True
+        each_item_validators = [v for v in this_field_validators if v.info.each_item is True]
+        this_field_validators = [v for v in this_field_validators if v not in each_item_validators]
+        schema = apply_each_item_validators(schema, each_item_validators, name)
+
+        schema = apply_validators(schema, this_field_validators, name)
+
+        # the default validator needs to go outside of any other validators
+        # so that it is the topmost validator for the field validator
+        # which uses it to check if the field has a default value or not
+        if not field_info.is_required():
+            schema = wrap_default(field_info, schema)
+
+        schema = self._apply_field_serializers(
+            schema, filter_field_decorator_info_by_field(decorators.field_serializers.values(), name)
+        )
+        self._apply_field_title_generator_to_field_info(self._config_wrapper, field_info, name)
+
+        pydantic_js_updates, pydantic_js_extra = _extract_json_schema_info_from_field_info(field_info)
+        core_metadata: dict[str, Any] = {}
+        update_core_metadata(
+            core_metadata, pydantic_js_updates=pydantic_js_updates, pydantic_js_extra=pydantic_js_extra
+        )
+
+        alias_generator = self._config_wrapper.alias_generator
+        if alias_generator is not None:
+            self._apply_alias_generator_to_field_info(alias_generator, field_info, name)
+
+        if isinstance(field_info.validation_alias, (AliasChoices, AliasPath)):
+            validation_alias = field_info.validation_alias.convert_to_aliases()
+        else:
+            validation_alias = field_info.validation_alias
+
+        return _common_field(
+            schema,
+            serialization_exclude=True if field_info.exclude else None,
+            validation_alias=validation_alias,
+            serialization_alias=field_info.serialization_alias,
+            frozen=field_info.frozen,
+            metadata=core_metadata,
+        )
+
+    def _union_schema(self, union_type: Any) -> core_schema.CoreSchema:
+        """Generate schema for a Union."""
+        args = self._get_args_resolving_forward_refs(union_type, required=True)
+        choices: list[CoreSchema] = []
+        nullable = False
+        for arg in args:
+            if arg is None or arg is _typing_extra.NoneType:
+                nullable = True
+            else:
+                choices.append(self.generate_schema(arg))
+
+        if len(choices) == 1:
+            s = choices[0]
+        else:
+            choices_with_tags: list[CoreSchema | tuple[CoreSchema, str]] = []
+            for choice in choices:
+                tag = choice.get('metadata', {}).get(_core_utils.TAGGED_UNION_TAG_KEY)
+                if tag is not None:
+                    choices_with_tags.append((choice, tag))
+                else:
+                    choices_with_tags.append(choice)
+            s = core_schema.union_schema(choices_with_tags)
+
+        if nullable:
+            s = core_schema.nullable_schema(s)
+        return s
+
+    def _type_alias_type_schema(self, obj: TypeAliasType) -> CoreSchema:
+        with self.defs.get_schema_or_ref(obj) as (ref, maybe_schema):
+            if maybe_schema is not None:
+                return maybe_schema
+
+            origin: TypeAliasType = get_origin(obj) or obj
+            typevars_map = get_standard_typevars_map(obj)
+
+            with self._ns_resolver.push(origin):
+                try:
+                    annotation = _typing_extra.eval_type(origin.__value__, *self._types_namespace)
+                except NameError as e:
+                    raise PydanticUndefinedAnnotation.from_name_error(e) from e
+                annotation = replace_types(annotation, typevars_map)
+                schema = self.generate_schema(annotation)
+                assert schema['type'] != 'definitions'
+                schema['ref'] = ref  # type: ignore
+            self.defs.definitions[ref] = schema
+            return core_schema.definition_reference_schema(ref)
+
+    def _literal_schema(self, literal_type: Any) -> CoreSchema:
+        """Generate schema for a Literal."""
+        expected = _typing_extra.literal_values(literal_type)
+        assert expected, f'literal "expected" cannot be empty, obj={literal_type}'
+        schema = core_schema.literal_schema(expected)
+
+        if self._config_wrapper.use_enum_values and any(isinstance(v, Enum) for v in expected):
+            schema = core_schema.no_info_after_validator_function(
+                lambda v: v.value if isinstance(v, Enum) else v, schema
+            )
+
+        return schema
+
+    def _typed_dict_schema(self, typed_dict_cls: Any, origin: Any) -> core_schema.CoreSchema:
+        """Generate schema for a TypedDict.
+
+        It is not possible to track required/optional keys in TypedDict without __required_keys__
+        since TypedDict.__new__ erases the base classes (it replaces them with just `dict`)
+        and thus we can track usage of total=True/False
+        __required_keys__ was added in Python 3.9
+        (https://github.com/miss-islington/cpython/blob/1e9939657dd1f8eb9f596f77c1084d2d351172fc/Doc/library/typing.rst?plain=1#L1546-L1548)
+        however it is buggy
+        (https://github.com/python/typing_extensions/blob/ac52ac5f2cb0e00e7988bae1e2a1b8257ac88d6d/src/typing_extensions.py#L657-L666).
+
+        On 3.11 but < 3.12 TypedDict does not preserve inheritance information.
+
+        Hence to avoid creating validators that do not do what users expect we only
+        support typing.TypedDict on Python >= 3.12 or typing_extension.TypedDict on all versions
+        """
+        FieldInfo = import_cached_field_info()
+
+        with self.model_type_stack.push(typed_dict_cls), self.defs.get_schema_or_ref(typed_dict_cls) as (
+            typed_dict_ref,
+            maybe_schema,
+        ):
+            if maybe_schema is not None:
+                return maybe_schema
+
+            typevars_map = get_standard_typevars_map(typed_dict_cls)
+            if origin is not None:
+                typed_dict_cls = origin
+
+            if not _SUPPORTS_TYPEDDICT and type(typed_dict_cls).__module__ == 'typing':
+                raise PydanticUserError(
+                    'Please use `typing_extensions.TypedDict` instead of `typing.TypedDict` on Python < 3.12.',
+                    code='typed-dict-version',
+                )
+
+            try:
+                # if a typed dictionary class doesn't have config, we use the parent's config, hence a default of `None`
+                # see https://github.com/pydantic/pydantic/issues/10917
+                config: ConfigDict | None = get_attribute_from_bases(typed_dict_cls, '__pydantic_config__')
+            except AttributeError:
+                config = None
+
+            with self._config_wrapper_stack.push(config):
+                core_config = self._config_wrapper.core_config(title=typed_dict_cls.__name__)
+
+                required_keys: frozenset[str] = typed_dict_cls.__required_keys__
+
+                fields: dict[str, core_schema.TypedDictField] = {}
+
+                decorators = DecoratorInfos.build(typed_dict_cls)
+
+                if self._config_wrapper.use_attribute_docstrings:
+                    field_docstrings = extract_docstrings_from_cls(typed_dict_cls, use_inspect=True)
+                else:
+                    field_docstrings = None
+
+                try:
+                    annotations = _typing_extra.get_cls_type_hints(typed_dict_cls, ns_resolver=self._ns_resolver)
+                except NameError as e:
+                    raise PydanticUndefinedAnnotation.from_name_error(e) from e
+
+                for field_name, annotation in annotations.items():
+                    annotation = replace_types(annotation, typevars_map)
+                    required = field_name in required_keys
+
+                    if _typing_extra.is_required(annotation):
+                        required = True
+                        annotation = self._get_args_resolving_forward_refs(
+                            annotation,
+                            required=True,
+                        )[0]
+                    elif _typing_extra.is_not_required(annotation):
+                        required = False
+                        annotation = self._get_args_resolving_forward_refs(
+                            annotation,
+                            required=True,
+                        )[0]
+
+                    field_info = FieldInfo.from_annotation(annotation)
+                    if (
+                        field_docstrings is not None
+                        and field_info.description is None
+                        and field_name in field_docstrings
+                    ):
+                        field_info.description = field_docstrings[field_name]
+                    self._apply_field_title_generator_to_field_info(self._config_wrapper, field_info, field_name)
+                    fields[field_name] = self._generate_td_field_schema(
+                        field_name, field_info, decorators, required=required
+                    )
+
+                td_schema = core_schema.typed_dict_schema(
+                    fields,
+                    cls=typed_dict_cls,
+                    computed_fields=[
+                        self._computed_field_schema(d, decorators.field_serializers)
+                        for d in decorators.computed_fields.values()
+                    ],
+                    ref=typed_dict_ref,
+                    config=core_config,
+                )
+
+                schema = self._apply_model_serializers(td_schema, decorators.model_serializers.values())
+                schema = apply_model_validators(schema, decorators.model_validators.values(), 'all')
+                self.defs.definitions[typed_dict_ref] = schema
+                return core_schema.definition_reference_schema(typed_dict_ref)
+
+    def _namedtuple_schema(self, namedtuple_cls: Any, origin: Any) -> core_schema.CoreSchema:
+        """Generate schema for a NamedTuple."""
+        with self.model_type_stack.push(namedtuple_cls), self.defs.get_schema_or_ref(namedtuple_cls) as (
+            namedtuple_ref,
+            maybe_schema,
+        ):
+            if maybe_schema is not None:
+                return maybe_schema
+            typevars_map = get_standard_typevars_map(namedtuple_cls)
+            if origin is not None:
+                namedtuple_cls = origin
+
+            try:
+                annotations = _typing_extra.get_cls_type_hints(namedtuple_cls, ns_resolver=self._ns_resolver)
+            except NameError as e:
+                raise PydanticUndefinedAnnotation.from_name_error(e) from e
+            if not annotations:
+                # annotations is empty, happens if namedtuple_cls defined via collections.namedtuple(...)
+                annotations: dict[str, Any] = {k: Any for k in namedtuple_cls._fields}
+
+            if typevars_map:
+                annotations = {
+                    field_name: replace_types(annotation, typevars_map)
+                    for field_name, annotation in annotations.items()
+                }
+
+            arguments_schema = core_schema.arguments_schema(
+                [
+                    self._generate_parameter_schema(
+                        field_name,
+                        annotation,
+                        default=namedtuple_cls._field_defaults.get(field_name, Parameter.empty),
+                    )
+                    for field_name, annotation in annotations.items()
+                ],
+                metadata={'pydantic_js_prefer_positional_arguments': True},
+            )
+            return core_schema.call_schema(arguments_schema, namedtuple_cls, ref=namedtuple_ref)
+
+    def _generate_parameter_schema(
+        self,
+        name: str,
+        annotation: type[Any],
+        default: Any = Parameter.empty,
+        mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only'] | None = None,
+    ) -> core_schema.ArgumentsParameter:
+        """Prepare a ArgumentsParameter to represent a field in a namedtuple or function signature."""
+        FieldInfo = import_cached_field_info()
+
+        if default is Parameter.empty:
+            field = FieldInfo.from_annotation(annotation)
+        else:
+            field = FieldInfo.from_annotated_attribute(annotation, default)
+        assert field.annotation is not None, 'field.annotation should not be None when generating a schema'
+        with self.field_name_stack.push(name):
+            schema = self._apply_annotations(field.annotation, [field])
+
+        if not field.is_required():
+            schema = wrap_default(field, schema)
+
+        parameter_schema = core_schema.arguments_parameter(name, schema)
+        if mode is not None:
+            parameter_schema['mode'] = mode
+        if field.alias is not None:
+            parameter_schema['alias'] = field.alias
+        else:
+            alias_generator = self._config_wrapper.alias_generator
+            if isinstance(alias_generator, AliasGenerator) and alias_generator.alias is not None:
+                parameter_schema['alias'] = alias_generator.alias(name)
+            elif isinstance(alias_generator, Callable):
+                parameter_schema['alias'] = alias_generator(name)
+        return parameter_schema
+
+    def _tuple_schema(self, tuple_type: Any) -> core_schema.CoreSchema:
+        """Generate schema for a Tuple, e.g. `tuple[int, str]` or `tuple[int, ...]`."""
+        # TODO: do we really need to resolve type vars here?
+        typevars_map = get_standard_typevars_map(tuple_type)
+        params = self._get_args_resolving_forward_refs(tuple_type)
+
+        if typevars_map and params:
+            params = tuple(replace_types(param, typevars_map) for param in params)
+
+        # NOTE: subtle difference: `tuple[()]` gives `params=()`, whereas `typing.Tuple[()]` gives `params=((),)`
+        # This is only true for <3.11, on Python 3.11+ `typing.Tuple[()]` gives `params=()`
+        if not params:
+            if tuple_type in TUPLE_TYPES:
+                return core_schema.tuple_schema([core_schema.any_schema()], variadic_item_index=0)
+            else:
+                # special case for `tuple[()]` which means `tuple[]` - an empty tuple
+                return core_schema.tuple_schema([])
+        elif params[-1] is Ellipsis:
+            if len(params) == 2:
+                return core_schema.tuple_schema([self.generate_schema(params[0])], variadic_item_index=0)
+            else:
+                # TODO: something like https://github.com/pydantic/pydantic/issues/5952
+                raise ValueError('Variable tuples can only have one type')
+        elif len(params) == 1 and params[0] == ():
+            # special case for `Tuple[()]` which means `Tuple[]` - an empty tuple
+            # NOTE: This conditional can be removed when we drop support for Python 3.10.
+            return core_schema.tuple_schema([])
+        else:
+            return core_schema.tuple_schema([self.generate_schema(param) for param in params])
+
+    def _type_schema(self) -> core_schema.CoreSchema:
+        return core_schema.custom_error_schema(
+            core_schema.is_instance_schema(type),
+            custom_error_type='is_type',
+            custom_error_message='Input should be a type',
+        )
+
+    def _zoneinfo_schema(self) -> core_schema.CoreSchema:
+        """Generate schema for a zone_info.ZoneInfo object"""
+        # we're def >=py3.9 if ZoneInfo was included in input
+        if sys.version_info < (3, 9):
+            assert False, 'Unreachable'
+
+        # import in this path is safe
+        from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
+
+        def validate_str_is_valid_iana_tz(value: Any, /) -> ZoneInfo:
+            if isinstance(value, ZoneInfo):
+                return value
+            try:
+                return ZoneInfo(value)
+            except (ZoneInfoNotFoundError, ValueError, TypeError):
+                raise PydanticCustomError('zoneinfo_str', 'invalid timezone: {value}', {'value': value})
+
+        metadata = {'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': 'zoneinfo'}]}
+        return core_schema.no_info_plain_validator_function(
+            validate_str_is_valid_iana_tz,
+            serialization=core_schema.to_string_ser_schema(),
+            metadata=metadata,
+        )
+
+    def _union_is_subclass_schema(self, union_type: Any) -> core_schema.CoreSchema:
+        """Generate schema for `Type[Union[X, ...]]`."""
+        args = self._get_args_resolving_forward_refs(union_type, required=True)
+        return core_schema.union_schema([self.generate_schema(typing.Type[args]) for args in args])
+
+    def _subclass_schema(self, type_: Any) -> core_schema.CoreSchema:
+        """Generate schema for a Type, e.g. `Type[int]`."""
+        type_param = self._get_first_arg_or_any(type_)
+
+        # Assume `type[Annotated[<typ>, ...]]` is equivalent to `type[<typ>]`:
+        type_param = _typing_extra.annotated_type(type_param) or type_param
+
+        if _typing_extra.is_any(type_param):
+            return self._type_schema()
+        elif _typing_extra.is_type_alias_type(type_param):
+            return self.generate_schema(typing.Type[type_param.__value__])
+        elif isinstance(type_param, typing.TypeVar):
+            if type_param.__bound__:
+                if _typing_extra.origin_is_union(get_origin(type_param.__bound__)):
+                    return self._union_is_subclass_schema(type_param.__bound__)
+                return core_schema.is_subclass_schema(type_param.__bound__)
+            elif type_param.__constraints__:
+                return core_schema.union_schema(
+                    [self.generate_schema(typing.Type[c]) for c in type_param.__constraints__]
+                )
+            else:
+                return self._type_schema()
+        elif _typing_extra.origin_is_union(get_origin(type_param)):
+            return self._union_is_subclass_schema(type_param)
+        else:
+            if _typing_extra.is_self(type_param):
+                type_param = self._resolve_self_type(type_param)
+
+            if not inspect.isclass(type_param):
+                raise TypeError(f'Expected a class, got {type_param!r}')
+            return core_schema.is_subclass_schema(type_param)
+
+    def _sequence_schema(self, items_type: Any) -> core_schema.CoreSchema:
+        """Generate schema for a Sequence, e.g. `Sequence[int]`."""
+        from ._serializers import serialize_sequence_via_list
+
+        item_type_schema = self.generate_schema(items_type)
+        list_schema = core_schema.list_schema(item_type_schema)
+
+        json_schema = smart_deepcopy(list_schema)
+        python_schema = core_schema.is_instance_schema(typing.Sequence, cls_repr='Sequence')
+        if not _typing_extra.is_any(items_type):
+            from ._validators import sequence_validator
+
+            python_schema = core_schema.chain_schema(
+                [python_schema, core_schema.no_info_wrap_validator_function(sequence_validator, list_schema)],
+            )
+
+        serialization = core_schema.wrap_serializer_function_ser_schema(
+            serialize_sequence_via_list, schema=item_type_schema, info_arg=True
+        )
+        return core_schema.json_or_python_schema(
+            json_schema=json_schema, python_schema=python_schema, serialization=serialization
+        )
+
+    def _iterable_schema(self, type_: Any) -> core_schema.GeneratorSchema:
+        """Generate a schema for an `Iterable`."""
+        item_type = self._get_first_arg_or_any(type_)
+
+        return core_schema.generator_schema(self.generate_schema(item_type))
+
+    def _pattern_schema(self, pattern_type: Any) -> core_schema.CoreSchema:
+        from . import _validators
+
+        metadata = {'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': 'regex'}]}
+        ser = core_schema.plain_serializer_function_ser_schema(
+            attrgetter('pattern'), when_used='json', return_schema=core_schema.str_schema()
+        )
+        if pattern_type is typing.Pattern or pattern_type is re.Pattern:
+            # bare type
+            return core_schema.no_info_plain_validator_function(
+                _validators.pattern_either_validator, serialization=ser, metadata=metadata
+            )
+
+        param = self._get_args_resolving_forward_refs(
+            pattern_type,
+            required=True,
+        )[0]
+        if param is str:
+            return core_schema.no_info_plain_validator_function(
+                _validators.pattern_str_validator, serialization=ser, metadata=metadata
+            )
+        elif param is bytes:
+            return core_schema.no_info_plain_validator_function(
+                _validators.pattern_bytes_validator, serialization=ser, metadata=metadata
+            )
+        else:
+            raise PydanticSchemaGenerationError(f'Unable to generate pydantic-core schema for {pattern_type!r}.')
+
+    def _hashable_schema(self) -> core_schema.CoreSchema:
+        return core_schema.custom_error_schema(
+            schema=core_schema.json_or_python_schema(
+                json_schema=core_schema.chain_schema(
+                    [core_schema.any_schema(), core_schema.is_instance_schema(collections.abc.Hashable)]
+                ),
+                python_schema=core_schema.is_instance_schema(collections.abc.Hashable),
+            ),
+            custom_error_type='is_hashable',
+            custom_error_message='Input should be hashable',
+        )
+
+    def _dataclass_schema(
+        self, dataclass: type[StandardDataclass], origin: type[StandardDataclass] | None
+    ) -> core_schema.CoreSchema:
+        """Generate schema for a dataclass."""
+        with self.model_type_stack.push(dataclass), self.defs.get_schema_or_ref(dataclass) as (
+            dataclass_ref,
+            maybe_schema,
+        ):
+            if maybe_schema is not None:
+                return maybe_schema
+
+            typevars_map = get_standard_typevars_map(dataclass)
+            if origin is not None:
+                dataclass = origin
+
+            # if (plain) dataclass doesn't have config, we use the parent's config, hence a default of `None`
+            # (Pydantic dataclasses have an empty dict config by default).
+            # see https://github.com/pydantic/pydantic/issues/10917
+            config = getattr(dataclass, '__pydantic_config__', None)
+
+            from ..dataclasses import is_pydantic_dataclass
+
+            with self._ns_resolver.push(dataclass), self._config_wrapper_stack.push(config):
+                if is_pydantic_dataclass(dataclass):
+                    fields = deepcopy(dataclass.__pydantic_fields__)
+                    if typevars_map:
+                        for field in fields.values():
+                            field.apply_typevars_map(typevars_map, *self._types_namespace)
+                else:
+                    fields = collect_dataclass_fields(
+                        dataclass,
+                        typevars_map=typevars_map,
+                    )
+
+                if self._config_wrapper.extra == 'allow':
+                    # disallow combination of init=False on a dataclass field and extra='allow' on a dataclass
+                    for field_name, field in fields.items():
+                        if field.init is False:
+                            raise PydanticUserError(
+                                f'Field {field_name} has `init=False` and dataclass has config setting `extra="allow"`. '
+                                f'This combination is not allowed.',
+                                code='dataclass-init-false-extra-allow',
+                            )
+
+                decorators = dataclass.__dict__.get('__pydantic_decorators__') or DecoratorInfos.build(dataclass)
+                # Move kw_only=False args to the start of the list, as this is how vanilla dataclasses work.
+                # Note that when kw_only is missing or None, it is treated as equivalent to kw_only=True
+                args = sorted(
+                    (self._generate_dc_field_schema(k, v, decorators) for k, v in fields.items()),
+                    key=lambda a: a.get('kw_only') is not False,
+                )
+                has_post_init = hasattr(dataclass, '__post_init__')
+                has_slots = hasattr(dataclass, '__slots__')
+
+                args_schema = core_schema.dataclass_args_schema(
+                    dataclass.__name__,
+                    args,
+                    computed_fields=[
+                        self._computed_field_schema(d, decorators.field_serializers)
+                        for d in decorators.computed_fields.values()
+                    ],
+                    collect_init_only=has_post_init,
+                )
+
+                inner_schema = apply_validators(args_schema, decorators.root_validators.values(), None)
+
+                model_validators = decorators.model_validators.values()
+                inner_schema = apply_model_validators(inner_schema, model_validators, 'inner')
+
+                core_config = self._config_wrapper.core_config(title=dataclass.__name__)
+
+                dc_schema = core_schema.dataclass_schema(
+                    dataclass,
+                    inner_schema,
+                    generic_origin=origin,
+                    post_init=has_post_init,
+                    ref=dataclass_ref,
+                    fields=[field.name for field in dataclasses.fields(dataclass)],
+                    slots=has_slots,
+                    config=core_config,
+                    # we don't use a custom __setattr__ for dataclasses, so we must
+                    # pass along the frozen config setting to the pydantic-core schema
+                    frozen=self._config_wrapper_stack.tail.frozen,
+                )
+                schema = self._apply_model_serializers(dc_schema, decorators.model_serializers.values())
+                schema = apply_model_validators(schema, model_validators, 'outer')
+                self.defs.definitions[dataclass_ref] = schema
+                return core_schema.definition_reference_schema(dataclass_ref)
+
+    def _call_schema(self, function: ValidateCallSupportedTypes) -> core_schema.CallSchema:
+        """Generate schema for a Callable.
+
+        TODO support functional validators once we support them in Config
+        """
+        sig = signature(function)
+        globalns, localns = self._types_namespace
+        type_hints = _typing_extra.get_function_type_hints(function, globalns=globalns, localns=localns)
+
+        mode_lookup: dict[_ParameterKind, Literal['positional_only', 'positional_or_keyword', 'keyword_only']] = {
+            Parameter.POSITIONAL_ONLY: 'positional_only',
+            Parameter.POSITIONAL_OR_KEYWORD: 'positional_or_keyword',
+            Parameter.KEYWORD_ONLY: 'keyword_only',
+        }
+
+        arguments_list: list[core_schema.ArgumentsParameter] = []
+        var_args_schema: core_schema.CoreSchema | None = None
+        var_kwargs_schema: core_schema.CoreSchema | None = None
+        var_kwargs_mode: core_schema.VarKwargsMode | None = None
+
+        for name, p in sig.parameters.items():
+            if p.annotation is sig.empty:
+                annotation = typing.cast(Any, Any)
+            else:
+                annotation = type_hints[name]
+
+            parameter_mode = mode_lookup.get(p.kind)
+            if parameter_mode is not None:
+                arg_schema = self._generate_parameter_schema(name, annotation, p.default, parameter_mode)
+                arguments_list.append(arg_schema)
+            elif p.kind == Parameter.VAR_POSITIONAL:
+                var_args_schema = self.generate_schema(annotation)
+            else:
+                assert p.kind == Parameter.VAR_KEYWORD, p.kind
+
+                unpack_type = _typing_extra.unpack_type(annotation)
+                if unpack_type is not None:
+                    if not is_typeddict(unpack_type):
+                        raise PydanticUserError(
+                            f'Expected a `TypedDict` class, got {unpack_type.__name__!r}', code='unpack-typed-dict'
+                        )
+                    non_pos_only_param_names = {
+                        name for name, p in sig.parameters.items() if p.kind != Parameter.POSITIONAL_ONLY
+                    }
+                    overlapping_params = non_pos_only_param_names.intersection(unpack_type.__annotations__)
+                    if overlapping_params:
+                        raise PydanticUserError(
+                            f'Typed dictionary {unpack_type.__name__!r} overlaps with parameter'
+                            f"{'s' if len(overlapping_params) >= 2 else ''} "
+                            f"{', '.join(repr(p) for p in sorted(overlapping_params))}",
+                            code='overlapping-unpack-typed-dict',
+                        )
+
+                    var_kwargs_mode = 'unpacked-typed-dict'
+                    var_kwargs_schema = self._typed_dict_schema(unpack_type, None)
+                else:
+                    var_kwargs_mode = 'uniform'
+                    var_kwargs_schema = self.generate_schema(annotation)
+
+        return_schema: core_schema.CoreSchema | None = None
+        config_wrapper = self._config_wrapper
+        if config_wrapper.validate_return:
+            return_hint = sig.return_annotation
+            if return_hint is not sig.empty:
+                return_schema = self.generate_schema(return_hint)
+
+        return core_schema.call_schema(
+            core_schema.arguments_schema(
+                arguments_list,
+                var_args_schema=var_args_schema,
+                var_kwargs_mode=var_kwargs_mode,
+                var_kwargs_schema=var_kwargs_schema,
+                populate_by_name=config_wrapper.populate_by_name,
+            ),
+            function,
+            return_schema=return_schema,
+        )
+
+    def _unsubstituted_typevar_schema(self, typevar: typing.TypeVar) -> core_schema.CoreSchema:
+        assert isinstance(typevar, typing.TypeVar)
+
+        bound = typevar.__bound__
+        constraints = typevar.__constraints__
+
+        try:
+            typevar_has_default = typevar.has_default()  # type: ignore
+        except AttributeError:
+            # could still have a default if it's an old version of typing_extensions.TypeVar
+            typevar_has_default = getattr(typevar, '__default__', None) is not None
+
+        if (bound is not None) + (len(constraints) != 0) + typevar_has_default > 1:
+            raise NotImplementedError(
+                'Pydantic does not support mixing more than one of TypeVar bounds, constraints and defaults'
+            )
+
+        if typevar_has_default:
+            return self.generate_schema(typevar.__default__)  # type: ignore
+        elif constraints:
+            return self._union_schema(typing.Union[constraints])  # type: ignore
+        elif bound:
+            schema = self.generate_schema(bound)
+            schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
+                lambda x, h: h(x), schema=core_schema.any_schema()
+            )
+            return schema
+        else:
+            return core_schema.any_schema()
+
+    def _computed_field_schema(
+        self,
+        d: Decorator[ComputedFieldInfo],
+        field_serializers: dict[str, Decorator[FieldSerializerDecoratorInfo]],
+    ) -> core_schema.ComputedField:
+        try:
+            # Do not pass in globals as the function could be defined in a different module.
+            # Instead, let `get_function_return_type` infer the globals to use, but still pass
+            # in locals that may contain a parent/rebuild namespace:
+            return_type = _decorators.get_function_return_type(
+                d.func, d.info.return_type, localns=self._types_namespace.locals
+            )
+        except NameError as e:
+            raise PydanticUndefinedAnnotation.from_name_error(e) from e
+        if return_type is PydanticUndefined:
+            raise PydanticUserError(
+                'Computed field is missing return type annotation or specifying `return_type`'
+                ' to the `@computed_field` decorator (e.g. `@computed_field(return_type=int|str)`)',
+                code='model-field-missing-annotation',
+            )
+
+        return_type = replace_types(return_type, self._typevars_map)
+        # Create a new ComputedFieldInfo so that different type parametrizations of the same
+        # generic model's computed field can have different return types.
+        d.info = dataclasses.replace(d.info, return_type=return_type)
+        return_type_schema = self.generate_schema(return_type)
+        # Apply serializers to computed field if there exist
+        return_type_schema = self._apply_field_serializers(
+            return_type_schema,
+            filter_field_decorator_info_by_field(field_serializers.values(), d.cls_var_name),
+        )
+
+        alias_generator = self._config_wrapper.alias_generator
+        if alias_generator is not None:
+            self._apply_alias_generator_to_computed_field_info(
+                alias_generator=alias_generator, computed_field_info=d.info, computed_field_name=d.cls_var_name
+            )
+        self._apply_field_title_generator_to_field_info(self._config_wrapper, d.info, d.cls_var_name)
+
+        pydantic_js_updates, pydantic_js_extra = _extract_json_schema_info_from_field_info(d.info)
+        core_metadata: dict[str, Any] = {}
+        update_core_metadata(
+            core_metadata,
+            pydantic_js_updates={'readOnly': True, **(pydantic_js_updates if pydantic_js_updates else {})},
+            pydantic_js_extra=pydantic_js_extra,
+        )
+        return core_schema.computed_field(
+            d.cls_var_name, return_schema=return_type_schema, alias=d.info.alias, metadata=core_metadata
+        )
+
+    def _annotated_schema(self, annotated_type: Any) -> core_schema.CoreSchema:
+        """Generate schema for an Annotated type, e.g. `Annotated[int, Field(...)]` or `Annotated[int, Gt(0)]`."""
+        FieldInfo = import_cached_field_info()
+
+        source_type, *annotations = self._get_args_resolving_forward_refs(
+            annotated_type,
+            required=True,
+        )
+        schema = self._apply_annotations(source_type, annotations)
+        # put the default validator last so that TypeAdapter.get_default_value() works
+        # even if there are function validators involved
+        for annotation in annotations:
+            if isinstance(annotation, FieldInfo):
+                schema = wrap_default(annotation, schema)
+        return schema
+
+    def _get_prepare_pydantic_annotations_for_known_type(
+        self, obj: Any, annotations: tuple[Any, ...]
+    ) -> tuple[Any, list[Any]] | None:
+        from ._std_types_schema import (
+            deque_schema_prepare_pydantic_annotations,
+            mapping_like_prepare_pydantic_annotations,
+            path_schema_prepare_pydantic_annotations,
+        )
+
+        # Check for hashability
+        try:
+            hash(obj)
+        except TypeError:
+            # obj is definitely not a known type if this fails
+            return None
+
+        # TODO: I'd rather we didn't handle the generic nature in the annotations prep, but the same way we do other
+        # generic types like list[str] via _match_generic_type, but I'm not sure if we can do that because this is
+        # not always called from match_type, but sometimes from _apply_annotations
+        obj_origin = get_origin(obj) or obj
+
+        if obj_origin in PATH_TYPES:
+            return path_schema_prepare_pydantic_annotations(obj, annotations)
+        elif obj_origin in DEQUE_TYPES:
+            return deque_schema_prepare_pydantic_annotations(obj, annotations)
+        elif obj_origin in MAPPING_TYPES:
+            return mapping_like_prepare_pydantic_annotations(obj, annotations)
+        else:
+            return None
+
+    def _apply_annotations(
+        self,
+        source_type: Any,
+        annotations: list[Any],
+        transform_inner_schema: Callable[[CoreSchema], CoreSchema] = lambda x: x,
+    ) -> CoreSchema:
+        """Apply arguments from `Annotated` or from `FieldInfo` to a schema.
+
+        This gets called by `GenerateSchema._annotated_schema` but differs from it in that it does
+        not expect `source_type` to be an `Annotated` object, it expects it to be  the first argument of that
+        (in other words, `GenerateSchema._annotated_schema` just unpacks `Annotated`, this process it).
+        """
+        annotations = list(_known_annotated_metadata.expand_grouped_metadata(annotations))
+        res = self._get_prepare_pydantic_annotations_for_known_type(source_type, tuple(annotations))
+        if res is not None:
+            source_type, annotations = res
+
+        pydantic_js_annotation_functions: list[GetJsonSchemaFunction] = []
+
+        def inner_handler(obj: Any) -> CoreSchema:
+            from_property = self._generate_schema_from_property(obj, source_type)
+            if from_property is None:
+                schema = self._generate_schema_inner(obj)
+            else:
+                schema = from_property
+            metadata_js_function = _extract_get_pydantic_json_schema(obj, schema)
+            if metadata_js_function is not None:
+                metadata_schema = resolve_original_schema(schema, self.defs.definitions)
+                if metadata_schema is not None:
+                    self._add_js_function(metadata_schema, metadata_js_function)
+            return transform_inner_schema(schema)
+
+        get_inner_schema = CallbackGetCoreSchemaHandler(inner_handler, self)
+
+        for annotation in annotations:
+            if annotation is None:
+                continue
+            get_inner_schema = self._get_wrapped_inner_schema(
+                get_inner_schema, annotation, pydantic_js_annotation_functions
+            )
+
+        schema = get_inner_schema(source_type)
+        if pydantic_js_annotation_functions:
+            core_metadata = schema.setdefault('metadata', {})
+            update_core_metadata(core_metadata, pydantic_js_annotation_functions=pydantic_js_annotation_functions)
+        return _add_custom_serialization_from_json_encoders(self._config_wrapper.json_encoders, source_type, schema)
+
+    def _apply_single_annotation(self, schema: core_schema.CoreSchema, metadata: Any) -> core_schema.CoreSchema:
+        FieldInfo = import_cached_field_info()
+
+        if isinstance(metadata, FieldInfo):
+            for field_metadata in metadata.metadata:
+                schema = self._apply_single_annotation(schema, field_metadata)
+
+            if metadata.discriminator is not None:
+                schema = self._apply_discriminator_to_union(schema, metadata.discriminator)
+            return schema
+
+        if schema['type'] == 'nullable':
+            # for nullable schemas, metadata is automatically applied to the inner schema
+            inner = schema.get('schema', core_schema.any_schema())
+            inner = self._apply_single_annotation(inner, metadata)
+            if inner:
+                schema['schema'] = inner
+            return schema
+
+        original_schema = schema
+        ref = schema.get('ref', None)
+        if ref is not None:
+            schema = schema.copy()
+            new_ref = ref + f'_{repr(metadata)}'
+            if new_ref in self.defs.definitions:
+                return self.defs.definitions[new_ref]
+            schema['ref'] = new_ref  # type: ignore
+        elif schema['type'] == 'definition-ref':
+            ref = schema['schema_ref']
+            if ref in self.defs.definitions:
+                schema = self.defs.definitions[ref].copy()
+                new_ref = ref + f'_{repr(metadata)}'
+                if new_ref in self.defs.definitions:
+                    return self.defs.definitions[new_ref]
+                schema['ref'] = new_ref  # type: ignore
+
+        maybe_updated_schema = _known_annotated_metadata.apply_known_metadata(metadata, schema.copy())
+
+        if maybe_updated_schema is not None:
+            return maybe_updated_schema
+        return original_schema
+
+    def _apply_single_annotation_json_schema(
+        self, schema: core_schema.CoreSchema, metadata: Any
+    ) -> core_schema.CoreSchema:
+        FieldInfo = import_cached_field_info()
+
+        if isinstance(metadata, FieldInfo):
+            for field_metadata in metadata.metadata:
+                schema = self._apply_single_annotation_json_schema(schema, field_metadata)
+
+            pydantic_js_updates, pydantic_js_extra = _extract_json_schema_info_from_field_info(metadata)
+            core_metadata = schema.setdefault('metadata', {})
+            update_core_metadata(
+                core_metadata, pydantic_js_updates=pydantic_js_updates, pydantic_js_extra=pydantic_js_extra
+            )
+        return schema
+
+    def _get_wrapped_inner_schema(
+        self,
+        get_inner_schema: GetCoreSchemaHandler,
+        annotation: Any,
+        pydantic_js_annotation_functions: list[GetJsonSchemaFunction],
+    ) -> CallbackGetCoreSchemaHandler:
+        metadata_get_schema: GetCoreSchemaFunction = getattr(annotation, '__get_pydantic_core_schema__', None) or (
+            lambda source, handler: handler(source)
+        )
+
+        def new_handler(source: Any) -> core_schema.CoreSchema:
+            schema = metadata_get_schema(source, get_inner_schema)
+            schema = self._apply_single_annotation(schema, annotation)
+            schema = self._apply_single_annotation_json_schema(schema, annotation)
+
+            metadata_js_function = _extract_get_pydantic_json_schema(annotation, schema)
+            if metadata_js_function is not None:
+                pydantic_js_annotation_functions.append(metadata_js_function)
+            return schema
+
+        return CallbackGetCoreSchemaHandler(new_handler, self)
+
+    def _apply_field_serializers(
+        self,
+        schema: core_schema.CoreSchema,
+        serializers: list[Decorator[FieldSerializerDecoratorInfo]],
+    ) -> core_schema.CoreSchema:
+        """Apply field serializers to a schema."""
+        if serializers:
+            schema = copy(schema)
+            if schema['type'] == 'definitions':
+                inner_schema = schema['schema']
+                schema['schema'] = self._apply_field_serializers(inner_schema, serializers)
+                return schema
+            else:
+                ref = typing.cast('str|None', schema.get('ref', None))
+                if ref is not None:
+                    self.defs.definitions[ref] = schema
+                    schema = core_schema.definition_reference_schema(ref)
+
+            # use the last serializer to make it easy to override a serializer set on a parent model
+            serializer = serializers[-1]
+            is_field_serializer, info_arg = inspect_field_serializer(serializer.func, serializer.info.mode)
+
+            try:
+                # Do not pass in globals as the function could be defined in a different module.
+                # Instead, let `get_function_return_type` infer the globals to use, but still pass
+                # in locals that may contain a parent/rebuild namespace:
+                return_type = _decorators.get_function_return_type(
+                    serializer.func, serializer.info.return_type, localns=self._types_namespace.locals
+                )
+            except NameError as e:
+                raise PydanticUndefinedAnnotation.from_name_error(e) from e
+
+            if return_type is PydanticUndefined:
+                return_schema = None
+            else:
+                return_schema = self.generate_schema(return_type)
+
+            if serializer.info.mode == 'wrap':
+                schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
+                    serializer.func,
+                    is_field_serializer=is_field_serializer,
+                    info_arg=info_arg,
+                    return_schema=return_schema,
+                    when_used=serializer.info.when_used,
+                )
+            else:
+                assert serializer.info.mode == 'plain'
+                schema['serialization'] = core_schema.plain_serializer_function_ser_schema(
+                    serializer.func,
+                    is_field_serializer=is_field_serializer,
+                    info_arg=info_arg,
+                    return_schema=return_schema,
+                    when_used=serializer.info.when_used,
+                )
+        return schema
+
+    def _apply_model_serializers(
+        self, schema: core_schema.CoreSchema, serializers: Iterable[Decorator[ModelSerializerDecoratorInfo]]
+    ) -> core_schema.CoreSchema:
+        """Apply model serializers to a schema."""
+        ref: str | None = schema.pop('ref', None)  # type: ignore
+        if serializers:
+            serializer = list(serializers)[-1]
+            info_arg = inspect_model_serializer(serializer.func, serializer.info.mode)
+
+            try:
+                # Do not pass in globals as the function could be defined in a different module.
+                # Instead, let `get_function_return_type` infer the globals to use, but still pass
+                # in locals that may contain a parent/rebuild namespace:
+                return_type = _decorators.get_function_return_type(
+                    serializer.func, serializer.info.return_type, localns=self._types_namespace.locals
+                )
+            except NameError as e:
+                raise PydanticUndefinedAnnotation.from_name_error(e) from e
+            if return_type is PydanticUndefined:
+                return_schema = None
+            else:
+                return_schema = self.generate_schema(return_type)
+
+            if serializer.info.mode == 'wrap':
+                ser_schema: core_schema.SerSchema = core_schema.wrap_serializer_function_ser_schema(
+                    serializer.func,
+                    info_arg=info_arg,
+                    return_schema=return_schema,
+                    when_used=serializer.info.when_used,
+                )
+            else:
+                # plain
+                ser_schema = core_schema.plain_serializer_function_ser_schema(
+                    serializer.func,
+                    info_arg=info_arg,
+                    return_schema=return_schema,
+                    when_used=serializer.info.when_used,
+                )
+            schema['serialization'] = ser_schema
+        if ref:
+            schema['ref'] = ref  # type: ignore
+        return schema
+
+
+_VALIDATOR_F_MATCH: Mapping[
+    tuple[FieldValidatorModes, Literal['no-info', 'with-info']],
+    Callable[[Callable[..., Any], core_schema.CoreSchema, str | None], core_schema.CoreSchema],
+] = {
+    ('before', 'no-info'): lambda f, schema, _: core_schema.no_info_before_validator_function(f, schema),
+    ('after', 'no-info'): lambda f, schema, _: core_schema.no_info_after_validator_function(f, schema),
+    ('plain', 'no-info'): lambda f, _1, _2: core_schema.no_info_plain_validator_function(f),
+    ('wrap', 'no-info'): lambda f, schema, _: core_schema.no_info_wrap_validator_function(f, schema),
+    ('before', 'with-info'): lambda f, schema, field_name: core_schema.with_info_before_validator_function(
+        f, schema, field_name=field_name
+    ),
+    ('after', 'with-info'): lambda f, schema, field_name: core_schema.with_info_after_validator_function(
+        f, schema, field_name=field_name
+    ),
+    ('plain', 'with-info'): lambda f, _, field_name: core_schema.with_info_plain_validator_function(
+        f, field_name=field_name
+    ),
+    ('wrap', 'with-info'): lambda f, schema, field_name: core_schema.with_info_wrap_validator_function(
+        f, schema, field_name=field_name
+    ),
+}
+
+
+# TODO V3: this function is only used for deprecated decorators. It should
+# be removed once we drop support for those.
+def apply_validators(
+    schema: core_schema.CoreSchema,
+    validators: Iterable[Decorator[RootValidatorDecoratorInfo]]
+    | Iterable[Decorator[ValidatorDecoratorInfo]]
+    | Iterable[Decorator[FieldValidatorDecoratorInfo]],
+    field_name: str | None,
+) -> core_schema.CoreSchema:
+    """Apply validators to a schema.
+
+    Args:
+        schema: The schema to apply validators on.
+        validators: An iterable of validators.
+        field_name: The name of the field if validators are being applied to a model field.
+
+    Returns:
+        The updated schema.
+    """
+    for validator in validators:
+        info_arg = inspect_validator(validator.func, validator.info.mode)
+        val_type = 'with-info' if info_arg else 'no-info'
+
+        schema = _VALIDATOR_F_MATCH[(validator.info.mode, val_type)](validator.func, schema, field_name)
+    return schema
+
+
+def _validators_require_validate_default(validators: Iterable[Decorator[ValidatorDecoratorInfo]]) -> bool:
+    """In v1, if any of the validators for a field had `always=True`, the default value would be validated.
+
+    This serves as an auxiliary function for re-implementing that logic, by looping over a provided
+    collection of (v1-style) ValidatorDecoratorInfo's and checking if any of them have `always=True`.
+
+    We should be able to drop this function and the associated logic calling it once we drop support
+    for v1-style validator decorators. (Or we can extend it and keep it if we add something equivalent
+    to the v1-validator `always` kwarg to `field_validator`.)
+    """
+    for validator in validators:
+        if validator.info.always:
+            return True
+    return False
+
+
+def apply_model_validators(
+    schema: core_schema.CoreSchema,
+    validators: Iterable[Decorator[ModelValidatorDecoratorInfo]],
+    mode: Literal['inner', 'outer', 'all'],
+) -> core_schema.CoreSchema:
+    """Apply model validators to a schema.
+
+    If mode == 'inner', only "before" validators are applied
+    If mode == 'outer', validators other than "before" are applied
+    If mode == 'all', all validators are applied
+
+    Args:
+        schema: The schema to apply validators on.
+        validators: An iterable of validators.
+        mode: The validator mode.
+
+    Returns:
+        The updated schema.
+    """
+    ref: str | None = schema.pop('ref', None)  # type: ignore
+    for validator in validators:
+        if mode == 'inner' and validator.info.mode != 'before':
+            continue
+        if mode == 'outer' and validator.info.mode == 'before':
+            continue
+        info_arg = inspect_validator(validator.func, validator.info.mode)
+        if validator.info.mode == 'wrap':
+            if info_arg:
+                schema = core_schema.with_info_wrap_validator_function(function=validator.func, schema=schema)
+            else:
+                schema = core_schema.no_info_wrap_validator_function(function=validator.func, schema=schema)
+        elif validator.info.mode == 'before':
+            if info_arg:
+                schema = core_schema.with_info_before_validator_function(function=validator.func, schema=schema)
+            else:
+                schema = core_schema.no_info_before_validator_function(function=validator.func, schema=schema)
+        else:
+            assert validator.info.mode == 'after'
+            if info_arg:
+                schema = core_schema.with_info_after_validator_function(function=validator.func, schema=schema)
+            else:
+                schema = core_schema.no_info_after_validator_function(function=validator.func, schema=schema)
+    if ref:
+        schema['ref'] = ref  # type: ignore
+    return schema
+
+
+def wrap_default(field_info: FieldInfo, schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
+    """Wrap schema with default schema if default value or `default_factory` are available.
+
+    Args:
+        field_info: The field info object.
+        schema: The schema to apply default on.
+
+    Returns:
+        Updated schema by default value or `default_factory`.
+    """
+    if field_info.default_factory:
+        return core_schema.with_default_schema(
+            schema,
+            default_factory=field_info.default_factory,
+            default_factory_takes_data=takes_validated_data_argument(field_info.default_factory),
+            validate_default=field_info.validate_default,
+        )
+    elif field_info.default is not PydanticUndefined:
+        return core_schema.with_default_schema(
+            schema, default=field_info.default, validate_default=field_info.validate_default
+        )
+    else:
+        return schema
+
+
+def _extract_get_pydantic_json_schema(tp: Any, schema: CoreSchema) -> GetJsonSchemaFunction | None:
+    """Extract `__get_pydantic_json_schema__` from a type, handling the deprecated `__modify_schema__`."""
+    js_modify_function = getattr(tp, '__get_pydantic_json_schema__', None)
+
+    if hasattr(tp, '__modify_schema__'):
+        BaseModel = import_cached_base_model()
+
+        has_custom_v2_modify_js_func = (
+            js_modify_function is not None
+            and BaseModel.__get_pydantic_json_schema__.__func__  # type: ignore
+            not in (js_modify_function, getattr(js_modify_function, '__func__', None))
+        )
+
+        if not has_custom_v2_modify_js_func:
+            cls_name = getattr(tp, '__name__', None)
+            raise PydanticUserError(
+                f'The `__modify_schema__` method is not supported in Pydantic v2. '
+                f'Use `__get_pydantic_json_schema__` instead{f" in class `{cls_name}`" if cls_name else ""}.',
+                code='custom-json-schema',
+            )
+
+    # handle GenericAlias' but ignore Annotated which "lies" about its origin (in this case it would be `int`)
+    if hasattr(tp, '__origin__') and not _typing_extra.is_annotated(tp):
+        return _extract_get_pydantic_json_schema(tp.__origin__, schema)
+
+    if js_modify_function is None:
+        return None
+
+    return js_modify_function
+
+
+class _CommonField(TypedDict):
+    schema: core_schema.CoreSchema
+    validation_alias: str | list[str | int] | list[list[str | int]] | None
+    serialization_alias: str | None
+    serialization_exclude: bool | None
+    frozen: bool | None
+    metadata: dict[str, Any]
+
+
+def _common_field(
+    schema: core_schema.CoreSchema,
+    *,
+    validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
+    serialization_alias: str | None = None,
+    serialization_exclude: bool | None = None,
+    frozen: bool | None = None,
+    metadata: Any = None,
+) -> _CommonField:
+    return {
+        'schema': schema,
+        'validation_alias': validation_alias,
+        'serialization_alias': serialization_alias,
+        'serialization_exclude': serialization_exclude,
+        'frozen': frozen,
+        'metadata': metadata,
+    }
+
+
+class _Definitions:
+    """Keeps track of references and definitions."""
+
+    def __init__(self) -> None:
+        self.seen: set[str] = set()
+        self.definitions: dict[str, core_schema.CoreSchema] = {}
+
+    @contextmanager
+    def get_schema_or_ref(self, tp: Any) -> Iterator[tuple[str, None] | tuple[str, CoreSchema]]:
+        """Get a definition for `tp` if one exists.
+
+        If a definition exists, a tuple of `(ref_string, CoreSchema)` is returned.
+        If no definition exists yet, a tuple of `(ref_string, None)` is returned.
+
+        Note that the returned `CoreSchema` will always be a `DefinitionReferenceSchema`,
+        not the actual definition itself.
+
+        This should be called for any type that can be identified by reference.
+        This includes any recursive types.
+
+        At present the following types can be named/recursive:
+
+        - BaseModel
+        - Dataclasses
+        - TypedDict
+        - TypeAliasType
+        """
+        ref = get_type_ref(tp)
+        # return the reference if we're either (1) in a cycle or (2) it was already defined
+        if ref in self.seen or ref in self.definitions:
+            yield (ref, core_schema.definition_reference_schema(ref))
+        else:
+            self.seen.add(ref)
+            try:
+                yield (ref, None)
+            finally:
+                self.seen.discard(ref)
+
+
+def resolve_original_schema(schema: CoreSchema, definitions: dict[str, CoreSchema]) -> CoreSchema | None:
+    if schema['type'] == 'definition-ref':
+        return definitions.get(schema['schema_ref'], None)
+    elif schema['type'] == 'definitions':
+        return schema['schema']
+    else:
+        return schema
+
+
+class _FieldNameStack:
+    __slots__ = ('_stack',)
+
+    def __init__(self) -> None:
+        self._stack: list[str] = []
+
+    @contextmanager
+    def push(self, field_name: str) -> Iterator[None]:
+        self._stack.append(field_name)
+        yield
+        self._stack.pop()
+
+    def get(self) -> str | None:
+        if self._stack:
+            return self._stack[-1]
+        else:
+            return None
+
+
+class _ModelTypeStack:
+    __slots__ = ('_stack',)
+
+    def __init__(self) -> None:
+        self._stack: list[type] = []
+
+    @contextmanager
+    def push(self, type_obj: type) -> Iterator[None]:
+        self._stack.append(type_obj)
+        yield
+        self._stack.pop()
+
+    def get(self) -> type | None:
+        if self._stack:
+            return self._stack[-1]
+        else:
+            return None
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_generics.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_generics.py
new file mode 100644
index 00000000..8a9de221
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_git.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_git.py
new file mode 100644
index 00000000..bff0dca3
--- /dev/null
+++ b/.venv/lib/python3.12/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()
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_import_utils.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_import_utils.py
new file mode 100644
index 00000000..29748eca
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_internal_dataclass.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_internal_dataclass.py
new file mode 100644
index 00000000..33e152cc
--- /dev/null
+++ b/.venv/lib/python3.12/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 = {}
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_known_annotated_metadata.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_known_annotated_metadata.py
new file mode 100644
index 00000000..78de89ec
--- /dev/null
+++ b/.venv/lib/python3.12/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])}'
+        )
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_mock_val_ser.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_mock_val_ser.py
new file mode 100644
index 00000000..624c68e4
--- /dev/null
+++ b/.venv/lib/python3.12/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__),
+    )
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_model_construction.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_model_construction.py
new file mode 100644
index 00000000..7670ff80
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/_internal/_model_construction.py
@@ -0,0 +1,792 @@
+"""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, 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}
+
+            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)
+
+    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)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_namespace_utils.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_namespace_utils.py
new file mode 100644
index 00000000..799c4c4e
--- /dev/null
+++ b/.venv/lib/python3.12/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)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_repr.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_repr.py
new file mode 100644
index 00000000..de81c8bd
--- /dev/null
+++ b/.venv/lib/python3.12/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.', '')
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_schema_generation_shared.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_schema_generation_shared.py
new file mode 100644
index 00000000..a6e3391d
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_serializers.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_serializers.py
new file mode 100644
index 00000000..3e459cf1
--- /dev/null
+++ b/.venv/lib/python3.12/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)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_signature.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_signature.py
new file mode 100644
index 00000000..2273577c
--- /dev/null
+++ b/.venv/lib/python3.12/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)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_std_types_schema.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_std_types_schema.py
new file mode 100644
index 00000000..84a7a4a4
--- /dev/null
+++ b/.venv/lib/python3.12/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,
+        ],
+    )
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_typing_extra.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_typing_extra.py
new file mode 100644
index 00000000..399c8c46
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_utils.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_utils.py
new file mode 100644
index 00000000..861271b7
--- /dev/null
+++ b/.venv/lib/python3.12/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)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py
new file mode 100644
index 00000000..f04da826
--- /dev/null
+++ b/.venv/lib/python3.12/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
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_internal/_validators.py b/.venv/lib/python3.12/site-packages/pydantic/_internal/_validators.py
new file mode 100644
index 00000000..5d165c04
--- /dev/null
+++ b/.venv/lib/python3.12/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,
+}
diff --git a/.venv/lib/python3.12/site-packages/pydantic/_migration.py b/.venv/lib/python3.12/site-packages/pydantic/_migration.py
new file mode 100644
index 00000000..c8478a62
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/_migration.py
@@ -0,0 +1,308 @@
+import sys
+from typing import Any, Callable, Dict
+
+from .version import version_short
+
+MOVED_IN_V2 = {
+    'pydantic.utils:version_info': 'pydantic.version:version_info',
+    'pydantic.error_wrappers:ValidationError': 'pydantic:ValidationError',
+    'pydantic.utils:to_camel': 'pydantic.alias_generators:to_pascal',
+    'pydantic.utils:to_lower_camel': 'pydantic.alias_generators:to_camel',
+    'pydantic:PyObject': 'pydantic.types:ImportString',
+    'pydantic.types:PyObject': 'pydantic.types:ImportString',
+    'pydantic.generics:GenericModel': 'pydantic.BaseModel',
+}
+
+DEPRECATED_MOVED_IN_V2 = {
+    'pydantic.tools:schema_of': 'pydantic.deprecated.tools:schema_of',
+    'pydantic.tools:parse_obj_as': 'pydantic.deprecated.tools:parse_obj_as',
+    'pydantic.tools:schema_json_of': 'pydantic.deprecated.tools:schema_json_of',
+    'pydantic.json:pydantic_encoder': 'pydantic.deprecated.json:pydantic_encoder',
+    'pydantic:validate_arguments': 'pydantic.deprecated.decorator:validate_arguments',
+    'pydantic.json:custom_pydantic_encoder': 'pydantic.deprecated.json:custom_pydantic_encoder',
+    'pydantic.json:timedelta_isoformat': 'pydantic.deprecated.json:timedelta_isoformat',
+    'pydantic.decorator:validate_arguments': 'pydantic.deprecated.decorator:validate_arguments',
+    'pydantic.class_validators:validator': 'pydantic.deprecated.class_validators:validator',
+    'pydantic.class_validators:root_validator': 'pydantic.deprecated.class_validators:root_validator',
+    'pydantic.config:BaseConfig': 'pydantic.deprecated.config:BaseConfig',
+    'pydantic.config:Extra': 'pydantic.deprecated.config:Extra',
+}
+
+REDIRECT_TO_V1 = {
+    f'pydantic.utils:{obj}': f'pydantic.v1.utils:{obj}'
+    for obj in (
+        'deep_update',
+        'GetterDict',
+        'lenient_issubclass',
+        'lenient_isinstance',
+        'is_valid_field',
+        'update_not_none',
+        'import_string',
+        'Representation',
+        'ROOT_KEY',
+        'smart_deepcopy',
+        'sequence_like',
+    )
+}
+
+
+REMOVED_IN_V2 = {
+    'pydantic:ConstrainedBytes',
+    'pydantic:ConstrainedDate',
+    'pydantic:ConstrainedDecimal',
+    'pydantic:ConstrainedFloat',
+    'pydantic:ConstrainedFrozenSet',
+    'pydantic:ConstrainedInt',
+    'pydantic:ConstrainedList',
+    'pydantic:ConstrainedSet',
+    'pydantic:ConstrainedStr',
+    'pydantic:JsonWrapper',
+    'pydantic:NoneBytes',
+    'pydantic:NoneStr',
+    'pydantic:NoneStrBytes',
+    'pydantic:Protocol',
+    'pydantic:Required',
+    'pydantic:StrBytes',
+    'pydantic:compiled',
+    'pydantic.config:get_config',
+    'pydantic.config:inherit_config',
+    'pydantic.config:prepare_config',
+    'pydantic:create_model_from_namedtuple',
+    'pydantic:create_model_from_typeddict',
+    'pydantic.dataclasses:create_pydantic_model_from_dataclass',
+    'pydantic.dataclasses:make_dataclass_validator',
+    'pydantic.dataclasses:set_validation',
+    'pydantic.datetime_parse:parse_date',
+    'pydantic.datetime_parse:parse_time',
+    'pydantic.datetime_parse:parse_datetime',
+    'pydantic.datetime_parse:parse_duration',
+    'pydantic.error_wrappers:ErrorWrapper',
+    'pydantic.errors:AnyStrMaxLengthError',
+    'pydantic.errors:AnyStrMinLengthError',
+    'pydantic.errors:ArbitraryTypeError',
+    'pydantic.errors:BoolError',
+    'pydantic.errors:BytesError',
+    'pydantic.errors:CallableError',
+    'pydantic.errors:ClassError',
+    'pydantic.errors:ColorError',
+    'pydantic.errors:ConfigError',
+    'pydantic.errors:DataclassTypeError',
+    'pydantic.errors:DateError',
+    'pydantic.errors:DateNotInTheFutureError',
+    'pydantic.errors:DateNotInThePastError',
+    'pydantic.errors:DateTimeError',
+    'pydantic.errors:DecimalError',
+    'pydantic.errors:DecimalIsNotFiniteError',
+    'pydantic.errors:DecimalMaxDigitsError',
+    'pydantic.errors:DecimalMaxPlacesError',
+    'pydantic.errors:DecimalWholeDigitsError',
+    'pydantic.errors:DictError',
+    'pydantic.errors:DurationError',
+    'pydantic.errors:EmailError',
+    'pydantic.errors:EnumError',
+    'pydantic.errors:EnumMemberError',
+    'pydantic.errors:ExtraError',
+    'pydantic.errors:FloatError',
+    'pydantic.errors:FrozenSetError',
+    'pydantic.errors:FrozenSetMaxLengthError',
+    'pydantic.errors:FrozenSetMinLengthError',
+    'pydantic.errors:HashableError',
+    'pydantic.errors:IPv4AddressError',
+    'pydantic.errors:IPv4InterfaceError',
+    'pydantic.errors:IPv4NetworkError',
+    'pydantic.errors:IPv6AddressError',
+    'pydantic.errors:IPv6InterfaceError',
+    'pydantic.errors:IPv6NetworkError',
+    'pydantic.errors:IPvAnyAddressError',
+    'pydantic.errors:IPvAnyInterfaceError',
+    'pydantic.errors:IPvAnyNetworkError',
+    'pydantic.errors:IntEnumError',
+    'pydantic.errors:IntegerError',
+    'pydantic.errors:InvalidByteSize',
+    'pydantic.errors:InvalidByteSizeUnit',
+    'pydantic.errors:InvalidDiscriminator',
+    'pydantic.errors:InvalidLengthForBrand',
+    'pydantic.errors:JsonError',
+    'pydantic.errors:JsonTypeError',
+    'pydantic.errors:ListError',
+    'pydantic.errors:ListMaxLengthError',
+    'pydantic.errors:ListMinLengthError',
+    'pydantic.errors:ListUniqueItemsError',
+    'pydantic.errors:LuhnValidationError',
+    'pydantic.errors:MissingDiscriminator',
+    'pydantic.errors:MissingError',
+    'pydantic.errors:NoneIsAllowedError',
+    'pydantic.errors:NoneIsNotAllowedError',
+    'pydantic.errors:NotDigitError',
+    'pydantic.errors:NotNoneError',
+    'pydantic.errors:NumberNotGeError',
+    'pydantic.errors:NumberNotGtError',
+    'pydantic.errors:NumberNotLeError',
+    'pydantic.errors:NumberNotLtError',
+    'pydantic.errors:NumberNotMultipleError',
+    'pydantic.errors:PathError',
+    'pydantic.errors:PathNotADirectoryError',
+    'pydantic.errors:PathNotAFileError',
+    'pydantic.errors:PathNotExistsError',
+    'pydantic.errors:PatternError',
+    'pydantic.errors:PyObjectError',
+    'pydantic.errors:PydanticTypeError',
+    'pydantic.errors:PydanticValueError',
+    'pydantic.errors:SequenceError',
+    'pydantic.errors:SetError',
+    'pydantic.errors:SetMaxLengthError',
+    'pydantic.errors:SetMinLengthError',
+    'pydantic.errors:StrError',
+    'pydantic.errors:StrRegexError',
+    'pydantic.errors:StrictBoolError',
+    'pydantic.errors:SubclassError',
+    'pydantic.errors:TimeError',
+    'pydantic.errors:TupleError',
+    'pydantic.errors:TupleLengthError',
+    'pydantic.errors:UUIDError',
+    'pydantic.errors:UUIDVersionError',
+    'pydantic.errors:UrlError',
+    'pydantic.errors:UrlExtraError',
+    'pydantic.errors:UrlHostError',
+    'pydantic.errors:UrlHostTldError',
+    'pydantic.errors:UrlPortError',
+    'pydantic.errors:UrlSchemeError',
+    'pydantic.errors:UrlSchemePermittedError',
+    'pydantic.errors:UrlUserInfoError',
+    'pydantic.errors:WrongConstantError',
+    'pydantic.main:validate_model',
+    'pydantic.networks:stricturl',
+    'pydantic:parse_file_as',
+    'pydantic:parse_raw_as',
+    'pydantic:stricturl',
+    'pydantic.tools:parse_file_as',
+    'pydantic.tools:parse_raw_as',
+    'pydantic.types:ConstrainedBytes',
+    'pydantic.types:ConstrainedDate',
+    'pydantic.types:ConstrainedDecimal',
+    'pydantic.types:ConstrainedFloat',
+    'pydantic.types:ConstrainedFrozenSet',
+    'pydantic.types:ConstrainedInt',
+    'pydantic.types:ConstrainedList',
+    'pydantic.types:ConstrainedSet',
+    'pydantic.types:ConstrainedStr',
+    'pydantic.types:JsonWrapper',
+    'pydantic.types:NoneBytes',
+    'pydantic.types:NoneStr',
+    'pydantic.types:NoneStrBytes',
+    'pydantic.types:StrBytes',
+    'pydantic.typing:evaluate_forwardref',
+    'pydantic.typing:AbstractSetIntStr',
+    'pydantic.typing:AnyCallable',
+    'pydantic.typing:AnyClassMethod',
+    'pydantic.typing:CallableGenerator',
+    'pydantic.typing:DictAny',
+    'pydantic.typing:DictIntStrAny',
+    'pydantic.typing:DictStrAny',
+    'pydantic.typing:IntStr',
+    'pydantic.typing:ListStr',
+    'pydantic.typing:MappingIntStrAny',
+    'pydantic.typing:NoArgAnyCallable',
+    'pydantic.typing:NoneType',
+    'pydantic.typing:ReprArgs',
+    'pydantic.typing:SetStr',
+    'pydantic.typing:StrPath',
+    'pydantic.typing:TupleGenerator',
+    'pydantic.typing:WithArgsTypes',
+    'pydantic.typing:all_literal_values',
+    'pydantic.typing:display_as_type',
+    'pydantic.typing:get_all_type_hints',
+    'pydantic.typing:get_args',
+    'pydantic.typing:get_origin',
+    'pydantic.typing:get_sub_types',
+    'pydantic.typing:is_callable_type',
+    'pydantic.typing:is_classvar',
+    'pydantic.typing:is_finalvar',
+    'pydantic.typing:is_literal_type',
+    'pydantic.typing:is_namedtuple',
+    'pydantic.typing:is_new_type',
+    'pydantic.typing:is_none_type',
+    'pydantic.typing:is_typeddict',
+    'pydantic.typing:is_typeddict_special',
+    'pydantic.typing:is_union',
+    'pydantic.typing:new_type_supertype',
+    'pydantic.typing:resolve_annotations',
+    'pydantic.typing:typing_base',
+    'pydantic.typing:update_field_forward_refs',
+    'pydantic.typing:update_model_forward_refs',
+    'pydantic.utils:ClassAttribute',
+    'pydantic.utils:DUNDER_ATTRIBUTES',
+    'pydantic.utils:PyObjectStr',
+    'pydantic.utils:ValueItems',
+    'pydantic.utils:almost_equal_floats',
+    'pydantic.utils:get_discriminator_alias_and_values',
+    'pydantic.utils:get_model',
+    'pydantic.utils:get_unique_discriminator_alias',
+    'pydantic.utils:in_ipython',
+    'pydantic.utils:is_valid_identifier',
+    'pydantic.utils:path_type',
+    'pydantic.utils:validate_field_name',
+    'pydantic:validate_model',
+}
+
+
+def getattr_migration(module: str) -> Callable[[str], Any]:
+    """Implement PEP 562 for objects that were either moved or removed on the migration
+    to V2.
+
+    Args:
+        module: The module name.
+
+    Returns:
+        A callable that will raise an error if the object is not found.
+    """
+    # This avoids circular import with errors.py.
+    from .errors import PydanticImportError
+
+    def wrapper(name: str) -> object:
+        """Raise an error if the object is not found, or warn if it was moved.
+
+        In case it was moved, it still returns the object.
+
+        Args:
+            name: The object name.
+
+        Returns:
+            The object.
+        """
+        if name == '__path__':
+            raise AttributeError(f'module {module!r} has no attribute {name!r}')
+
+        import warnings
+
+        from ._internal._validators import import_string
+
+        import_path = f'{module}:{name}'
+        if import_path in MOVED_IN_V2.keys():
+            new_location = MOVED_IN_V2[import_path]
+            warnings.warn(f'`{import_path}` has been moved to `{new_location}`.')
+            return import_string(MOVED_IN_V2[import_path])
+        if import_path in DEPRECATED_MOVED_IN_V2:
+            # skip the warning here because a deprecation warning will be raised elsewhere
+            return import_string(DEPRECATED_MOVED_IN_V2[import_path])
+        if import_path in REDIRECT_TO_V1:
+            new_location = REDIRECT_TO_V1[import_path]
+            warnings.warn(
+                f'`{import_path}` has been removed. We are importing from `{new_location}` instead.'
+                'See the migration guide for more details: https://docs.pydantic.dev/latest/migration/'
+            )
+            return import_string(REDIRECT_TO_V1[import_path])
+        if import_path == 'pydantic:BaseSettings':
+            raise PydanticImportError(
+                '`BaseSettings` has been moved to the `pydantic-settings` package. '
+                f'See https://docs.pydantic.dev/{version_short()}/migration/#basesettings-has-moved-to-pydantic-settings '
+                'for more details.'
+            )
+        if import_path in REMOVED_IN_V2:
+            raise PydanticImportError(f'`{import_path}` has been removed in V2.')
+        globals: Dict[str, Any] = sys.modules[module].__dict__
+        if name in globals:
+            return globals[name]
+        raise AttributeError(f'module {module!r} has no attribute {name!r}')
+
+    return wrapper
diff --git a/.venv/lib/python3.12/site-packages/pydantic/alias_generators.py b/.venv/lib/python3.12/site-packages/pydantic/alias_generators.py
new file mode 100644
index 00000000..0b7653f5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/alias_generators.py
@@ -0,0 +1,62 @@
+"""Alias generators for converting between different capitalization conventions."""
+
+import re
+
+__all__ = ('to_pascal', 'to_camel', 'to_snake')
+
+# TODO: in V3, change the argument names to be more descriptive
+# Generally, don't only convert from snake_case, or name the functions
+# more specifically like snake_to_camel.
+
+
+def to_pascal(snake: str) -> str:
+    """Convert a snake_case string to PascalCase.
+
+    Args:
+        snake: The string to convert.
+
+    Returns:
+        The PascalCase string.
+    """
+    camel = snake.title()
+    return re.sub('([0-9A-Za-z])_(?=[0-9A-Z])', lambda m: m.group(1), camel)
+
+
+def to_camel(snake: str) -> str:
+    """Convert a snake_case string to camelCase.
+
+    Args:
+        snake: The string to convert.
+
+    Returns:
+        The converted camelCase string.
+    """
+    # If the string is already in camelCase and does not contain a digit followed
+    # by a lowercase letter, return it as it is
+    if re.match('^[a-z]+[A-Za-z0-9]*$', snake) and not re.search(r'\d[a-z]', snake):
+        return snake
+
+    camel = to_pascal(snake)
+    return re.sub('(^_*[A-Z])', lambda m: m.group(1).lower(), camel)
+
+
+def to_snake(camel: str) -> str:
+    """Convert a PascalCase, camelCase, or kebab-case string to snake_case.
+
+    Args:
+        camel: The string to convert.
+
+    Returns:
+        The converted string in snake_case.
+    """
+    # Handle the sequence of uppercase letters followed by a lowercase letter
+    snake = re.sub(r'([A-Z]+)([A-Z][a-z])', lambda m: f'{m.group(1)}_{m.group(2)}', camel)
+    # Insert an underscore between a lowercase letter and an uppercase letter
+    snake = re.sub(r'([a-z])([A-Z])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
+    # Insert an underscore between a digit and an uppercase letter
+    snake = re.sub(r'([0-9])([A-Z])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
+    # Insert an underscore between a lowercase letter and a digit
+    snake = re.sub(r'([a-z])([0-9])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
+    # Replace hyphens with underscores to handle kebab-case
+    snake = snake.replace('-', '_')
+    return snake.lower()
diff --git a/.venv/lib/python3.12/site-packages/pydantic/aliases.py b/.venv/lib/python3.12/site-packages/pydantic/aliases.py
new file mode 100644
index 00000000..a9b806da
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/aliases.py
@@ -0,0 +1,132 @@
+"""Support for alias configurations."""
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, Callable, Literal
+
+from pydantic_core import PydanticUndefined
+
+from ._internal import _internal_dataclass
+
+__all__ = ('AliasGenerator', 'AliasPath', 'AliasChoices')
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class AliasPath:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/alias#aliaspath-and-aliaschoices
+
+    A data class used by `validation_alias` as a convenience to create aliases.
+
+    Attributes:
+        path: A list of string or integer aliases.
+    """
+
+    path: list[int | str]
+
+    def __init__(self, first_arg: str, *args: str | int) -> None:
+        self.path = [first_arg] + list(args)
+
+    def convert_to_aliases(self) -> list[str | int]:
+        """Converts arguments to a list of string or integer aliases.
+
+        Returns:
+            The list of aliases.
+        """
+        return self.path
+
+    def search_dict_for_path(self, d: dict) -> Any:
+        """Searches a dictionary for the path specified by the alias.
+
+        Returns:
+            The value at the specified path, or `PydanticUndefined` if the path is not found.
+        """
+        v = d
+        for k in self.path:
+            if isinstance(v, str):
+                # disallow indexing into a str, like for AliasPath('x', 0) and x='abc'
+                return PydanticUndefined
+            try:
+                v = v[k]
+            except (KeyError, IndexError, TypeError):
+                return PydanticUndefined
+        return v
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class AliasChoices:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/alias#aliaspath-and-aliaschoices
+
+    A data class used by `validation_alias` as a convenience to create aliases.
+
+    Attributes:
+        choices: A list containing a string or `AliasPath`.
+    """
+
+    choices: list[str | AliasPath]
+
+    def __init__(self, first_choice: str | AliasPath, *choices: str | AliasPath) -> None:
+        self.choices = [first_choice] + list(choices)
+
+    def convert_to_aliases(self) -> list[list[str | int]]:
+        """Converts arguments to a list of lists containing string or integer aliases.
+
+        Returns:
+            The list of aliases.
+        """
+        aliases: list[list[str | int]] = []
+        for c in self.choices:
+            if isinstance(c, AliasPath):
+                aliases.append(c.convert_to_aliases())
+            else:
+                aliases.append([c])
+        return aliases
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class AliasGenerator:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/alias#using-an-aliasgenerator
+
+    A data class used by `alias_generator` as a convenience to create various aliases.
+
+    Attributes:
+        alias: A callable that takes a field name and returns an alias for it.
+        validation_alias: A callable that takes a field name and returns a validation alias for it.
+        serialization_alias: A callable that takes a field name and returns a serialization alias for it.
+    """
+
+    alias: Callable[[str], str] | None = None
+    validation_alias: Callable[[str], str | AliasPath | AliasChoices] | None = None
+    serialization_alias: Callable[[str], str] | None = None
+
+    def _generate_alias(
+        self,
+        alias_kind: Literal['alias', 'validation_alias', 'serialization_alias'],
+        allowed_types: tuple[type[str] | type[AliasPath] | type[AliasChoices], ...],
+        field_name: str,
+    ) -> str | AliasPath | AliasChoices | None:
+        """Generate an alias of the specified kind. Returns None if the alias generator is None.
+
+        Raises:
+            TypeError: If the alias generator produces an invalid type.
+        """
+        alias = None
+        if alias_generator := getattr(self, alias_kind):
+            alias = alias_generator(field_name)
+            if alias and not isinstance(alias, allowed_types):
+                raise TypeError(
+                    f'Invalid `{alias_kind}` type. `{alias_kind}` generator must produce one of `{allowed_types}`'
+                )
+        return alias
+
+    def generate_aliases(self, field_name: str) -> tuple[str | None, str | AliasPath | AliasChoices | None, str | None]:
+        """Generate `alias`, `validation_alias`, and `serialization_alias` for a field.
+
+        Returns:
+            A tuple of three aliases - validation, alias, and serialization.
+        """
+        alias = self._generate_alias('alias', (str,), field_name)
+        validation_alias = self._generate_alias('validation_alias', (str, AliasChoices, AliasPath), field_name)
+        serialization_alias = self._generate_alias('serialization_alias', (str,), field_name)
+
+        return alias, validation_alias, serialization_alias  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/pydantic/annotated_handlers.py b/.venv/lib/python3.12/site-packages/pydantic/annotated_handlers.py
new file mode 100644
index 00000000..d0cb5d3d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/annotated_handlers.py
@@ -0,0 +1,122 @@
+"""Type annotations to use with `__get_pydantic_core_schema__` and `__get_pydantic_json_schema__`."""
+
+from __future__ import annotations as _annotations
+
+from typing import TYPE_CHECKING, Any, Union
+
+from pydantic_core import core_schema
+
+if TYPE_CHECKING:
+    from ._internal._namespace_utils import NamespacesTuple
+    from .json_schema import JsonSchemaMode, JsonSchemaValue
+
+    CoreSchemaOrField = Union[
+        core_schema.CoreSchema,
+        core_schema.ModelField,
+        core_schema.DataclassField,
+        core_schema.TypedDictField,
+        core_schema.ComputedField,
+    ]
+
+__all__ = 'GetJsonSchemaHandler', 'GetCoreSchemaHandler'
+
+
+class GetJsonSchemaHandler:
+    """Handler to call into the next JSON schema generation function.
+
+    Attributes:
+        mode: Json schema mode, can be `validation` or `serialization`.
+    """
+
+    mode: JsonSchemaMode
+
+    def __call__(self, core_schema: CoreSchemaOrField, /) -> JsonSchemaValue:
+        """Call the inner handler and get the JsonSchemaValue it returns.
+        This will call the next JSON schema modifying function up until it calls
+        into `pydantic.json_schema.GenerateJsonSchema`, which will raise a
+        `pydantic.errors.PydanticInvalidForJsonSchema` error if it cannot generate
+        a JSON schema.
+
+        Args:
+            core_schema: A `pydantic_core.core_schema.CoreSchema`.
+
+        Returns:
+            JsonSchemaValue: The JSON schema generated by the inner JSON schema modify
+            functions.
+        """
+        raise NotImplementedError
+
+    def resolve_ref_schema(self, maybe_ref_json_schema: JsonSchemaValue, /) -> JsonSchemaValue:
+        """Get the real schema for a `{"$ref": ...}` schema.
+        If the schema given is not a `$ref` schema, it will be returned as is.
+        This means you don't have to check before calling this function.
+
+        Args:
+            maybe_ref_json_schema: A JsonSchemaValue which may be a `$ref` schema.
+
+        Raises:
+            LookupError: If the ref is not found.
+
+        Returns:
+            JsonSchemaValue: A JsonSchemaValue that has no `$ref`.
+        """
+        raise NotImplementedError
+
+
+class GetCoreSchemaHandler:
+    """Handler to call into the next CoreSchema schema generation function."""
+
+    def __call__(self, source_type: Any, /) -> core_schema.CoreSchema:
+        """Call the inner handler and get the CoreSchema it returns.
+        This will call the next CoreSchema modifying function up until it calls
+        into Pydantic's internal schema generation machinery, which will raise a
+        `pydantic.errors.PydanticSchemaGenerationError` error if it cannot generate
+        a CoreSchema for the given source type.
+
+        Args:
+            source_type: The input type.
+
+        Returns:
+            CoreSchema: The `pydantic-core` CoreSchema generated.
+        """
+        raise NotImplementedError
+
+    def generate_schema(self, source_type: Any, /) -> core_schema.CoreSchema:
+        """Generate a schema unrelated to the current context.
+        Use this function if e.g. you are handling schema generation for a sequence
+        and want to generate a schema for its items.
+        Otherwise, you may end up doing something like applying a `min_length` constraint
+        that was intended for the sequence itself to its items!
+
+        Args:
+            source_type: The input type.
+
+        Returns:
+            CoreSchema: The `pydantic-core` CoreSchema generated.
+        """
+        raise NotImplementedError
+
+    def resolve_ref_schema(self, maybe_ref_schema: core_schema.CoreSchema, /) -> core_schema.CoreSchema:
+        """Get the real schema for a `definition-ref` schema.
+        If the schema given is not a `definition-ref` schema, it will be returned as is.
+        This means you don't have to check before calling this function.
+
+        Args:
+            maybe_ref_schema: A `CoreSchema`, `ref`-based or not.
+
+        Raises:
+            LookupError: If the `ref` is not found.
+
+        Returns:
+            A concrete `CoreSchema`.
+        """
+        raise NotImplementedError
+
+    @property
+    def field_name(self) -> str | None:
+        """Get the name of the closest field to this validator."""
+        raise NotImplementedError
+
+    def _get_types_namespace(self) -> NamespacesTuple:
+        """Internal method used during type resolution for serializer annotations."""
+        raise NotImplementedError
diff --git a/.venv/lib/python3.12/site-packages/pydantic/class_validators.py b/.venv/lib/python3.12/site-packages/pydantic/class_validators.py
new file mode 100644
index 00000000..9977150c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/class_validators.py
@@ -0,0 +1,5 @@
+"""`class_validators` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/color.py b/.venv/lib/python3.12/site-packages/pydantic/color.py
new file mode 100644
index 00000000..c702fc63
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/color.py
@@ -0,0 +1,604 @@
+"""Color definitions are used as per the CSS3
+[CSS Color Module Level 3](http://www.w3.org/TR/css3-color/#svg-color) specification.
+
+A few colors have multiple names referring to the sames colors, eg. `grey` and `gray` or `aqua` and `cyan`.
+
+In these cases the _last_ color when sorted alphabetically takes preferences,
+eg. `Color((0, 255, 255)).as_named() == 'cyan'` because "cyan" comes after "aqua".
+
+Warning: Deprecated
+    The `Color` class is deprecated, use `pydantic_extra_types` instead.
+    See [`pydantic-extra-types.Color`](../usage/types/extra_types/color_types.md)
+    for more information.
+"""
+
+import math
+import re
+from colorsys import hls_to_rgb, rgb_to_hls
+from typing import Any, Callable, Optional, Tuple, Type, Union, cast
+
+from pydantic_core import CoreSchema, PydanticCustomError, core_schema
+from typing_extensions import deprecated
+
+from ._internal import _repr
+from ._internal._schema_generation_shared import GetJsonSchemaHandler as _GetJsonSchemaHandler
+from .json_schema import JsonSchemaValue
+from .warnings import PydanticDeprecatedSince20
+
+ColorTuple = Union[Tuple[int, int, int], Tuple[int, int, int, float]]
+ColorType = Union[ColorTuple, str]
+HslColorTuple = Union[Tuple[float, float, float], Tuple[float, float, float, float]]
+
+
+class RGBA:
+    """Internal use only as a representation of a color."""
+
+    __slots__ = 'r', 'g', 'b', 'alpha', '_tuple'
+
+    def __init__(self, r: float, g: float, b: float, alpha: Optional[float]):
+        self.r = r
+        self.g = g
+        self.b = b
+        self.alpha = alpha
+
+        self._tuple: Tuple[float, float, float, Optional[float]] = (r, g, b, alpha)
+
+    def __getitem__(self, item: Any) -> Any:
+        return self._tuple[item]
+
+
+# these are not compiled here to avoid import slowdown, they'll be compiled the first time they're used, then cached
+_r_255 = r'(\d{1,3}(?:\.\d+)?)'
+_r_comma = r'\s*,\s*'
+_r_alpha = r'(\d(?:\.\d+)?|\.\d+|\d{1,2}%)'
+_r_h = r'(-?\d+(?:\.\d+)?|-?\.\d+)(deg|rad|turn)?'
+_r_sl = r'(\d{1,3}(?:\.\d+)?)%'
+r_hex_short = r'\s*(?:#|0x)?([0-9a-f])([0-9a-f])([0-9a-f])([0-9a-f])?\s*'
+r_hex_long = r'\s*(?:#|0x)?([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})?\s*'
+# CSS3 RGB examples: rgb(0, 0, 0), rgba(0, 0, 0, 0.5), rgba(0, 0, 0, 50%)
+r_rgb = rf'\s*rgba?\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}(?:{_r_comma}{_r_alpha})?\s*\)\s*'
+# CSS3 HSL examples: hsl(270, 60%, 50%), hsla(270, 60%, 50%, 0.5), hsla(270, 60%, 50%, 50%)
+r_hsl = rf'\s*hsla?\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}(?:{_r_comma}{_r_alpha})?\s*\)\s*'
+# CSS4 RGB examples: rgb(0 0 0), rgb(0 0 0 / 0.5), rgb(0 0 0 / 50%), rgba(0 0 0 / 50%)
+r_rgb_v4_style = rf'\s*rgba?\(\s*{_r_255}\s+{_r_255}\s+{_r_255}(?:\s*/\s*{_r_alpha})?\s*\)\s*'
+# CSS4 HSL examples: hsl(270 60% 50%), hsl(270 60% 50% / 0.5), hsl(270 60% 50% / 50%), hsla(270 60% 50% / 50%)
+r_hsl_v4_style = rf'\s*hsla?\(\s*{_r_h}\s+{_r_sl}\s+{_r_sl}(?:\s*/\s*{_r_alpha})?\s*\)\s*'
+
+# colors where the two hex characters are the same, if all colors match this the short version of hex colors can be used
+repeat_colors = {int(c * 2, 16) for c in '0123456789abcdef'}
+rads = 2 * math.pi
+
+
+@deprecated(
+    'The `Color` class is deprecated, use `pydantic_extra_types` instead. '
+    'See https://docs.pydantic.dev/latest/api/pydantic_extra_types_color/.',
+    category=PydanticDeprecatedSince20,
+)
+class Color(_repr.Representation):
+    """Represents a color."""
+
+    __slots__ = '_original', '_rgba'
+
+    def __init__(self, value: ColorType) -> None:
+        self._rgba: RGBA
+        self._original: ColorType
+        if isinstance(value, (tuple, list)):
+            self._rgba = parse_tuple(value)
+        elif isinstance(value, str):
+            self._rgba = parse_str(value)
+        elif isinstance(value, Color):
+            self._rgba = value._rgba
+            value = value._original
+        else:
+            raise PydanticCustomError(
+                'color_error', 'value is not a valid color: value must be a tuple, list or string'
+            )
+
+        # if we've got here value must be a valid color
+        self._original = value
+
+    @classmethod
+    def __get_pydantic_json_schema__(
+        cls, core_schema: core_schema.CoreSchema, handler: _GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        field_schema = {}
+        field_schema.update(type='string', format='color')
+        return field_schema
+
+    def original(self) -> ColorType:
+        """Original value passed to `Color`."""
+        return self._original
+
+    def as_named(self, *, fallback: bool = False) -> str:
+        """Returns the name of the color if it can be found in `COLORS_BY_VALUE` dictionary,
+        otherwise returns the hexadecimal representation of the color or raises `ValueError`.
+
+        Args:
+            fallback: If True, falls back to returning the hexadecimal representation of
+                the color instead of raising a ValueError when no named color is found.
+
+        Returns:
+            The name of the color, or the hexadecimal representation of the color.
+
+        Raises:
+            ValueError: When no named color is found and fallback is `False`.
+        """
+        if self._rgba.alpha is None:
+            rgb = cast(Tuple[int, int, int], self.as_rgb_tuple())
+            try:
+                return COLORS_BY_VALUE[rgb]
+            except KeyError as e:
+                if fallback:
+                    return self.as_hex()
+                else:
+                    raise ValueError('no named color found, use fallback=True, as_hex() or as_rgb()') from e
+        else:
+            return self.as_hex()
+
+    def as_hex(self) -> str:
+        """Returns the hexadecimal representation of the color.
+
+        Hex string representing the color can be 3, 4, 6, or 8 characters depending on whether the string
+        a "short" representation of the color is possible and whether there's an alpha channel.
+
+        Returns:
+            The hexadecimal representation of the color.
+        """
+        values = [float_to_255(c) for c in self._rgba[:3]]
+        if self._rgba.alpha is not None:
+            values.append(float_to_255(self._rgba.alpha))
+
+        as_hex = ''.join(f'{v:02x}' for v in values)
+        if all(c in repeat_colors for c in values):
+            as_hex = ''.join(as_hex[c] for c in range(0, len(as_hex), 2))
+        return '#' + as_hex
+
+    def as_rgb(self) -> str:
+        """Color as an `rgb(<r>, <g>, <b>)` or `rgba(<r>, <g>, <b>, <a>)` string."""
+        if self._rgba.alpha is None:
+            return f'rgb({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)})'
+        else:
+            return (
+                f'rgba({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)}, '
+                f'{round(self._alpha_float(), 2)})'
+            )
+
+    def as_rgb_tuple(self, *, alpha: Optional[bool] = None) -> ColorTuple:
+        """Returns the color as an RGB or RGBA tuple.
+
+        Args:
+            alpha: Whether to include the alpha channel. There are three options for this input:
+
+                - `None` (default): Include alpha only if it's set. (e.g. not `None`)
+                - `True`: Always include alpha.
+                - `False`: Always omit alpha.
+
+        Returns:
+            A tuple that contains the values of the red, green, and blue channels in the range 0 to 255.
+                If alpha is included, it is in the range 0 to 1.
+        """
+        r, g, b = (float_to_255(c) for c in self._rgba[:3])
+        if alpha is None:
+            if self._rgba.alpha is None:
+                return r, g, b
+            else:
+                return r, g, b, self._alpha_float()
+        elif alpha:
+            return r, g, b, self._alpha_float()
+        else:
+            # alpha is False
+            return r, g, b
+
+    def as_hsl(self) -> str:
+        """Color as an `hsl(<h>, <s>, <l>)` or `hsl(<h>, <s>, <l>, <a>)` string."""
+        if self._rgba.alpha is None:
+            h, s, li = self.as_hsl_tuple(alpha=False)  # type: ignore
+            return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%})'
+        else:
+            h, s, li, a = self.as_hsl_tuple(alpha=True)  # type: ignore
+            return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%}, {round(a, 2)})'
+
+    def as_hsl_tuple(self, *, alpha: Optional[bool] = None) -> HslColorTuple:
+        """Returns the color as an HSL or HSLA tuple.
+
+        Args:
+            alpha: Whether to include the alpha channel.
+
+                - `None` (default): Include the alpha channel only if it's set (e.g. not `None`).
+                - `True`: Always include alpha.
+                - `False`: Always omit alpha.
+
+        Returns:
+            The color as a tuple of hue, saturation, lightness, and alpha (if included).
+                All elements are in the range 0 to 1.
+
+        Note:
+            This is HSL as used in HTML and most other places, not HLS as used in Python's `colorsys`.
+        """
+        h, l, s = rgb_to_hls(self._rgba.r, self._rgba.g, self._rgba.b)  # noqa: E741
+        if alpha is None:
+            if self._rgba.alpha is None:
+                return h, s, l
+            else:
+                return h, s, l, self._alpha_float()
+        if alpha:
+            return h, s, l, self._alpha_float()
+        else:
+            # alpha is False
+            return h, s, l
+
+    def _alpha_float(self) -> float:
+        return 1 if self._rgba.alpha is None else self._rgba.alpha
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, source: Type[Any], handler: Callable[[Any], CoreSchema]
+    ) -> core_schema.CoreSchema:
+        return core_schema.with_info_plain_validator_function(
+            cls._validate, serialization=core_schema.to_string_ser_schema()
+        )
+
+    @classmethod
+    def _validate(cls, __input_value: Any, _: Any) -> 'Color':
+        return cls(__input_value)
+
+    def __str__(self) -> str:
+        return self.as_named(fallback=True)
+
+    def __repr_args__(self) -> '_repr.ReprArgs':
+        return [(None, self.as_named(fallback=True))] + [('rgb', self.as_rgb_tuple())]
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, Color) and self.as_rgb_tuple() == other.as_rgb_tuple()
+
+    def __hash__(self) -> int:
+        return hash(self.as_rgb_tuple())
+
+
+def parse_tuple(value: Tuple[Any, ...]) -> RGBA:
+    """Parse a tuple or list to get RGBA values.
+
+    Args:
+        value: A tuple or list.
+
+    Returns:
+        An `RGBA` tuple parsed from the input tuple.
+
+    Raises:
+        PydanticCustomError: If tuple is not valid.
+    """
+    if len(value) == 3:
+        r, g, b = (parse_color_value(v) for v in value)
+        return RGBA(r, g, b, None)
+    elif len(value) == 4:
+        r, g, b = (parse_color_value(v) for v in value[:3])
+        return RGBA(r, g, b, parse_float_alpha(value[3]))
+    else:
+        raise PydanticCustomError('color_error', 'value is not a valid color: tuples must have length 3 or 4')
+
+
+def parse_str(value: str) -> RGBA:
+    """Parse a string representing a color to an RGBA tuple.
+
+    Possible formats for the input string include:
+
+    * named color, see `COLORS_BY_NAME`
+    * hex short eg. `<prefix>fff` (prefix can be `#`, `0x` or nothing)
+    * hex long eg. `<prefix>ffffff` (prefix can be `#`, `0x` or nothing)
+    * `rgb(<r>, <g>, <b>)`
+    * `rgba(<r>, <g>, <b>, <a>)`
+
+    Args:
+        value: A string representing a color.
+
+    Returns:
+        An `RGBA` tuple parsed from the input string.
+
+    Raises:
+        ValueError: If the input string cannot be parsed to an RGBA tuple.
+    """
+    value_lower = value.lower()
+    try:
+        r, g, b = COLORS_BY_NAME[value_lower]
+    except KeyError:
+        pass
+    else:
+        return ints_to_rgba(r, g, b, None)
+
+    m = re.fullmatch(r_hex_short, value_lower)
+    if m:
+        *rgb, a = m.groups()
+        r, g, b = (int(v * 2, 16) for v in rgb)
+        if a:
+            alpha: Optional[float] = int(a * 2, 16) / 255
+        else:
+            alpha = None
+        return ints_to_rgba(r, g, b, alpha)
+
+    m = re.fullmatch(r_hex_long, value_lower)
+    if m:
+        *rgb, a = m.groups()
+        r, g, b = (int(v, 16) for v in rgb)
+        if a:
+            alpha = int(a, 16) / 255
+        else:
+            alpha = None
+        return ints_to_rgba(r, g, b, alpha)
+
+    m = re.fullmatch(r_rgb, value_lower) or re.fullmatch(r_rgb_v4_style, value_lower)
+    if m:
+        return ints_to_rgba(*m.groups())  # type: ignore
+
+    m = re.fullmatch(r_hsl, value_lower) or re.fullmatch(r_hsl_v4_style, value_lower)
+    if m:
+        return parse_hsl(*m.groups())  # type: ignore
+
+    raise PydanticCustomError('color_error', 'value is not a valid color: string not recognised as a valid color')
+
+
+def ints_to_rgba(r: Union[int, str], g: Union[int, str], b: Union[int, str], alpha: Optional[float] = None) -> RGBA:
+    """Converts integer or string values for RGB color and an optional alpha value to an `RGBA` object.
+
+    Args:
+        r: An integer or string representing the red color value.
+        g: An integer or string representing the green color value.
+        b: An integer or string representing the blue color value.
+        alpha: A float representing the alpha value. Defaults to None.
+
+    Returns:
+        An instance of the `RGBA` class with the corresponding color and alpha values.
+    """
+    return RGBA(parse_color_value(r), parse_color_value(g), parse_color_value(b), parse_float_alpha(alpha))
+
+
+def parse_color_value(value: Union[int, str], max_val: int = 255) -> float:
+    """Parse the color value provided and return a number between 0 and 1.
+
+    Args:
+        value: An integer or string color value.
+        max_val: Maximum range value. Defaults to 255.
+
+    Raises:
+        PydanticCustomError: If the value is not a valid color.
+
+    Returns:
+        A number between 0 and 1.
+    """
+    try:
+        color = float(value)
+    except ValueError:
+        raise PydanticCustomError('color_error', 'value is not a valid color: color values must be a valid number')
+    if 0 <= color <= max_val:
+        return color / max_val
+    else:
+        raise PydanticCustomError(
+            'color_error',
+            'value is not a valid color: color values must be in the range 0 to {max_val}',
+            {'max_val': max_val},
+        )
+
+
+def parse_float_alpha(value: Union[None, str, float, int]) -> Optional[float]:
+    """Parse an alpha value checking it's a valid float in the range 0 to 1.
+
+    Args:
+        value: The input value to parse.
+
+    Returns:
+        The parsed value as a float, or `None` if the value was None or equal 1.
+
+    Raises:
+        PydanticCustomError: If the input value cannot be successfully parsed as a float in the expected range.
+    """
+    if value is None:
+        return None
+    try:
+        if isinstance(value, str) and value.endswith('%'):
+            alpha = float(value[:-1]) / 100
+        else:
+            alpha = float(value)
+    except ValueError:
+        raise PydanticCustomError('color_error', 'value is not a valid color: alpha values must be a valid float')
+
+    if math.isclose(alpha, 1):
+        return None
+    elif 0 <= alpha <= 1:
+        return alpha
+    else:
+        raise PydanticCustomError('color_error', 'value is not a valid color: alpha values must be in the range 0 to 1')
+
+
+def parse_hsl(h: str, h_units: str, sat: str, light: str, alpha: Optional[float] = None) -> RGBA:
+    """Parse raw hue, saturation, lightness, and alpha values and convert to RGBA.
+
+    Args:
+        h: The hue value.
+        h_units: The unit for hue value.
+        sat: The saturation value.
+        light: The lightness value.
+        alpha: Alpha value.
+
+    Returns:
+        An instance of `RGBA`.
+    """
+    s_value, l_value = parse_color_value(sat, 100), parse_color_value(light, 100)
+
+    h_value = float(h)
+    if h_units in {None, 'deg'}:
+        h_value = h_value % 360 / 360
+    elif h_units == 'rad':
+        h_value = h_value % rads / rads
+    else:
+        # turns
+        h_value = h_value % 1
+
+    r, g, b = hls_to_rgb(h_value, l_value, s_value)
+    return RGBA(r, g, b, parse_float_alpha(alpha))
+
+
+def float_to_255(c: float) -> int:
+    """Converts a float value between 0 and 1 (inclusive) to an integer between 0 and 255 (inclusive).
+
+    Args:
+        c: The float value to be converted. Must be between 0 and 1 (inclusive).
+
+    Returns:
+        The integer equivalent of the given float value rounded to the nearest whole number.
+
+    Raises:
+        ValueError: If the given float value is outside the acceptable range of 0 to 1 (inclusive).
+    """
+    return int(round(c * 255))
+
+
+COLORS_BY_NAME = {
+    'aliceblue': (240, 248, 255),
+    'antiquewhite': (250, 235, 215),
+    'aqua': (0, 255, 255),
+    'aquamarine': (127, 255, 212),
+    'azure': (240, 255, 255),
+    'beige': (245, 245, 220),
+    'bisque': (255, 228, 196),
+    'black': (0, 0, 0),
+    'blanchedalmond': (255, 235, 205),
+    'blue': (0, 0, 255),
+    'blueviolet': (138, 43, 226),
+    'brown': (165, 42, 42),
+    'burlywood': (222, 184, 135),
+    'cadetblue': (95, 158, 160),
+    'chartreuse': (127, 255, 0),
+    'chocolate': (210, 105, 30),
+    'coral': (255, 127, 80),
+    'cornflowerblue': (100, 149, 237),
+    'cornsilk': (255, 248, 220),
+    'crimson': (220, 20, 60),
+    'cyan': (0, 255, 255),
+    'darkblue': (0, 0, 139),
+    'darkcyan': (0, 139, 139),
+    'darkgoldenrod': (184, 134, 11),
+    'darkgray': (169, 169, 169),
+    'darkgreen': (0, 100, 0),
+    'darkgrey': (169, 169, 169),
+    'darkkhaki': (189, 183, 107),
+    'darkmagenta': (139, 0, 139),
+    'darkolivegreen': (85, 107, 47),
+    'darkorange': (255, 140, 0),
+    'darkorchid': (153, 50, 204),
+    'darkred': (139, 0, 0),
+    'darksalmon': (233, 150, 122),
+    'darkseagreen': (143, 188, 143),
+    'darkslateblue': (72, 61, 139),
+    'darkslategray': (47, 79, 79),
+    'darkslategrey': (47, 79, 79),
+    'darkturquoise': (0, 206, 209),
+    'darkviolet': (148, 0, 211),
+    'deeppink': (255, 20, 147),
+    'deepskyblue': (0, 191, 255),
+    'dimgray': (105, 105, 105),
+    'dimgrey': (105, 105, 105),
+    'dodgerblue': (30, 144, 255),
+    'firebrick': (178, 34, 34),
+    'floralwhite': (255, 250, 240),
+    'forestgreen': (34, 139, 34),
+    'fuchsia': (255, 0, 255),
+    'gainsboro': (220, 220, 220),
+    'ghostwhite': (248, 248, 255),
+    'gold': (255, 215, 0),
+    'goldenrod': (218, 165, 32),
+    'gray': (128, 128, 128),
+    'green': (0, 128, 0),
+    'greenyellow': (173, 255, 47),
+    'grey': (128, 128, 128),
+    'honeydew': (240, 255, 240),
+    'hotpink': (255, 105, 180),
+    'indianred': (205, 92, 92),
+    'indigo': (75, 0, 130),
+    'ivory': (255, 255, 240),
+    'khaki': (240, 230, 140),
+    'lavender': (230, 230, 250),
+    'lavenderblush': (255, 240, 245),
+    'lawngreen': (124, 252, 0),
+    'lemonchiffon': (255, 250, 205),
+    'lightblue': (173, 216, 230),
+    'lightcoral': (240, 128, 128),
+    'lightcyan': (224, 255, 255),
+    'lightgoldenrodyellow': (250, 250, 210),
+    'lightgray': (211, 211, 211),
+    'lightgreen': (144, 238, 144),
+    'lightgrey': (211, 211, 211),
+    'lightpink': (255, 182, 193),
+    'lightsalmon': (255, 160, 122),
+    'lightseagreen': (32, 178, 170),
+    'lightskyblue': (135, 206, 250),
+    'lightslategray': (119, 136, 153),
+    'lightslategrey': (119, 136, 153),
+    'lightsteelblue': (176, 196, 222),
+    'lightyellow': (255, 255, 224),
+    'lime': (0, 255, 0),
+    'limegreen': (50, 205, 50),
+    'linen': (250, 240, 230),
+    'magenta': (255, 0, 255),
+    'maroon': (128, 0, 0),
+    'mediumaquamarine': (102, 205, 170),
+    'mediumblue': (0, 0, 205),
+    'mediumorchid': (186, 85, 211),
+    'mediumpurple': (147, 112, 219),
+    'mediumseagreen': (60, 179, 113),
+    'mediumslateblue': (123, 104, 238),
+    'mediumspringgreen': (0, 250, 154),
+    'mediumturquoise': (72, 209, 204),
+    'mediumvioletred': (199, 21, 133),
+    'midnightblue': (25, 25, 112),
+    'mintcream': (245, 255, 250),
+    'mistyrose': (255, 228, 225),
+    'moccasin': (255, 228, 181),
+    'navajowhite': (255, 222, 173),
+    'navy': (0, 0, 128),
+    'oldlace': (253, 245, 230),
+    'olive': (128, 128, 0),
+    'olivedrab': (107, 142, 35),
+    'orange': (255, 165, 0),
+    'orangered': (255, 69, 0),
+    'orchid': (218, 112, 214),
+    'palegoldenrod': (238, 232, 170),
+    'palegreen': (152, 251, 152),
+    'paleturquoise': (175, 238, 238),
+    'palevioletred': (219, 112, 147),
+    'papayawhip': (255, 239, 213),
+    'peachpuff': (255, 218, 185),
+    'peru': (205, 133, 63),
+    'pink': (255, 192, 203),
+    'plum': (221, 160, 221),
+    'powderblue': (176, 224, 230),
+    'purple': (128, 0, 128),
+    'red': (255, 0, 0),
+    'rosybrown': (188, 143, 143),
+    'royalblue': (65, 105, 225),
+    'saddlebrown': (139, 69, 19),
+    'salmon': (250, 128, 114),
+    'sandybrown': (244, 164, 96),
+    'seagreen': (46, 139, 87),
+    'seashell': (255, 245, 238),
+    'sienna': (160, 82, 45),
+    'silver': (192, 192, 192),
+    'skyblue': (135, 206, 235),
+    'slateblue': (106, 90, 205),
+    'slategray': (112, 128, 144),
+    'slategrey': (112, 128, 144),
+    'snow': (255, 250, 250),
+    'springgreen': (0, 255, 127),
+    'steelblue': (70, 130, 180),
+    'tan': (210, 180, 140),
+    'teal': (0, 128, 128),
+    'thistle': (216, 191, 216),
+    'tomato': (255, 99, 71),
+    'turquoise': (64, 224, 208),
+    'violet': (238, 130, 238),
+    'wheat': (245, 222, 179),
+    'white': (255, 255, 255),
+    'whitesmoke': (245, 245, 245),
+    'yellow': (255, 255, 0),
+    'yellowgreen': (154, 205, 50),
+}
+
+COLORS_BY_VALUE = {v: k for k, v in COLORS_BY_NAME.items()}
diff --git a/.venv/lib/python3.12/site-packages/pydantic/config.py b/.venv/lib/python3.12/site-packages/pydantic/config.py
new file mode 100644
index 00000000..e5e5e7c5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/config.py
@@ -0,0 +1,1049 @@
+"""Configuration for Pydantic models."""
+
+from __future__ import annotations as _annotations
+
+from re import Pattern
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Type, TypeVar, Union
+
+from typing_extensions import Literal, TypeAlias, TypedDict
+
+from ._migration import getattr_migration
+from .aliases import AliasGenerator
+from .errors import PydanticUserError
+
+if TYPE_CHECKING:
+    from ._internal._generate_schema import GenerateSchema as _GenerateSchema
+    from .fields import ComputedFieldInfo, FieldInfo
+
+__all__ = ('ConfigDict', 'with_config')
+
+
+JsonValue: TypeAlias = Union[int, float, str, bool, None, List['JsonValue'], 'JsonDict']
+JsonDict: TypeAlias = Dict[str, JsonValue]
+
+JsonEncoder = Callable[[Any], Any]
+
+JsonSchemaExtraCallable: TypeAlias = Union[
+    Callable[[JsonDict], None],
+    Callable[[JsonDict, Type[Any]], None],
+]
+
+ExtraValues = Literal['allow', 'ignore', 'forbid']
+
+
+class ConfigDict(TypedDict, total=False):
+    """A TypedDict for configuring Pydantic behaviour."""
+
+    title: str | None
+    """The title for the generated JSON schema, defaults to the model's name"""
+
+    model_title_generator: Callable[[type], str] | None
+    """A callable that takes a model class and returns the title for it. Defaults to `None`."""
+
+    field_title_generator: Callable[[str, FieldInfo | ComputedFieldInfo], str] | None
+    """A callable that takes a field's name and info and returns title for it. Defaults to `None`."""
+
+    str_to_lower: bool
+    """Whether to convert all characters to lowercase for str types. Defaults to `False`."""
+
+    str_to_upper: bool
+    """Whether to convert all characters to uppercase for str types. Defaults to `False`."""
+
+    str_strip_whitespace: bool
+    """Whether to strip leading and trailing whitespace for str types."""
+
+    str_min_length: int
+    """The minimum length for str types. Defaults to `None`."""
+
+    str_max_length: int | None
+    """The maximum length for str types. Defaults to `None`."""
+
+    extra: ExtraValues | None
+    """
+    Whether to ignore, allow, or forbid extra attributes during model initialization. Defaults to `'ignore'`.
+
+    You can configure how pydantic handles the attributes that are not defined in the model:
+
+    * `allow` - Allow any extra attributes.
+    * `forbid` - Forbid any extra attributes.
+    * `ignore` - Ignore any extra attributes.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+
+    class User(BaseModel):
+        model_config = ConfigDict(extra='ignore')  # (1)!
+
+        name: str
+
+    user = User(name='John Doe', age=20)  # (2)!
+    print(user)
+    #> name='John Doe'
+    ```
+
+    1. This is the default behaviour.
+    2. The `age` argument is ignored.
+
+    Instead, with `extra='allow'`, the `age` argument is included:
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+
+    class User(BaseModel):
+        model_config = ConfigDict(extra='allow')
+
+        name: str
+
+    user = User(name='John Doe', age=20)  # (1)!
+    print(user)
+    #> name='John Doe' age=20
+    ```
+
+    1. The `age` argument is included.
+
+    With `extra='forbid'`, an error is raised:
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, ValidationError
+
+    class User(BaseModel):
+        model_config = ConfigDict(extra='forbid')
+
+        name: str
+
+    try:
+        User(name='John Doe', age=20)
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for User
+        age
+          Extra inputs are not permitted [type=extra_forbidden, input_value=20, input_type=int]
+        '''
+    ```
+    """
+
+    frozen: bool
+    """
+    Whether models are faux-immutable, i.e. whether `__setattr__` is allowed, and also generates
+    a `__hash__()` method for the model. This makes instances of the model potentially hashable if all the
+    attributes are hashable. Defaults to `False`.
+
+    Note:
+        On V1, the inverse of this setting was called `allow_mutation`, and was `True` by default.
+    """
+
+    populate_by_name: bool
+    """
+    Whether an aliased field may be populated by its name as given by the model
+    attribute, as well as the alias. Defaults to `False`.
+
+    Note:
+        The name of this configuration setting was changed in **v2.0** from
+        `allow_population_by_field_name` to `populate_by_name`.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, Field
+
+    class User(BaseModel):
+        model_config = ConfigDict(populate_by_name=True)
+
+        name: str = Field(alias='full_name')  # (1)!
+        age: int
+
+    user = User(full_name='John Doe', age=20)  # (2)!
+    print(user)
+    #> name='John Doe' age=20
+    user = User(name='John Doe', age=20)  # (3)!
+    print(user)
+    #> name='John Doe' age=20
+    ```
+
+    1. The field `'name'` has an alias `'full_name'`.
+    2. The model is populated by the alias `'full_name'`.
+    3. The model is populated by the field name `'name'`.
+    """
+
+    use_enum_values: bool
+    """
+    Whether to populate models with the `value` property of enums, rather than the raw enum.
+    This may be useful if you want to serialize `model.model_dump()` later. Defaults to `False`.
+
+    !!! note
+        If you have an `Optional[Enum]` value that you set a default for, you need to use `validate_default=True`
+        for said Field to ensure that the `use_enum_values` flag takes effect on the default, as extracting an
+        enum's value occurs during validation, not serialization.
+
+    ```python
+    from enum import Enum
+    from typing import Optional
+
+    from pydantic import BaseModel, ConfigDict, Field
+
+    class SomeEnum(Enum):
+        FOO = 'foo'
+        BAR = 'bar'
+        BAZ = 'baz'
+
+    class SomeModel(BaseModel):
+        model_config = ConfigDict(use_enum_values=True)
+
+        some_enum: SomeEnum
+        another_enum: Optional[SomeEnum] = Field(
+            default=SomeEnum.FOO, validate_default=True
+        )
+
+    model1 = SomeModel(some_enum=SomeEnum.BAR)
+    print(model1.model_dump())
+    #> {'some_enum': 'bar', 'another_enum': 'foo'}
+
+    model2 = SomeModel(some_enum=SomeEnum.BAR, another_enum=SomeEnum.BAZ)
+    print(model2.model_dump())
+    #> {'some_enum': 'bar', 'another_enum': 'baz'}
+    ```
+    """
+
+    validate_assignment: bool
+    """
+    Whether to validate the data when the model is changed. Defaults to `False`.
+
+    The default behavior of Pydantic is to validate the data when the model is created.
+
+    In case the user changes the data after the model is created, the model is _not_ revalidated.
+
+    ```python
+    from pydantic import BaseModel
+
+    class User(BaseModel):
+        name: str
+
+    user = User(name='John Doe')  # (1)!
+    print(user)
+    #> name='John Doe'
+    user.name = 123  # (1)!
+    print(user)
+    #> name=123
+    ```
+
+    1. The validation happens only when the model is created.
+    2. The validation does not happen when the data is changed.
+
+    In case you want to revalidate the model when the data is changed, you can use `validate_assignment=True`:
+
+    ```python
+    from pydantic import BaseModel, ValidationError
+
+    class User(BaseModel, validate_assignment=True):  # (1)!
+        name: str
+
+    user = User(name='John Doe')  # (2)!
+    print(user)
+    #> name='John Doe'
+    try:
+        user.name = 123  # (3)!
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for User
+        name
+          Input should be a valid string [type=string_type, input_value=123, input_type=int]
+        '''
+    ```
+
+    1. You can either use class keyword arguments, or `model_config` to set `validate_assignment=True`.
+    2. The validation happens when the model is created.
+    3. The validation _also_ happens when the data is changed.
+    """
+
+    arbitrary_types_allowed: bool
+    """
+    Whether arbitrary types are allowed for field types. Defaults to `False`.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, ValidationError
+
+    # This is not a pydantic model, it's an arbitrary class
+    class Pet:
+        def __init__(self, name: str):
+            self.name = name
+
+    class Model(BaseModel):
+        model_config = ConfigDict(arbitrary_types_allowed=True)
+
+        pet: Pet
+        owner: str
+
+    pet = Pet(name='Hedwig')
+    # A simple check of instance type is used to validate the data
+    model = Model(owner='Harry', pet=pet)
+    print(model)
+    #> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry'
+    print(model.pet)
+    #> <__main__.Pet object at 0x0123456789ab>
+    print(model.pet.name)
+    #> Hedwig
+    print(type(model.pet))
+    #> <class '__main__.Pet'>
+    try:
+        # If the value is not an instance of the type, it's invalid
+        Model(owner='Harry', pet='Hedwig')
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        pet
+          Input should be an instance of Pet [type=is_instance_of, input_value='Hedwig', input_type=str]
+        '''
+
+    # Nothing in the instance of the arbitrary type is checked
+    # Here name probably should have been a str, but it's not validated
+    pet2 = Pet(name=42)
+    model2 = Model(owner='Harry', pet=pet2)
+    print(model2)
+    #> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry'
+    print(model2.pet)
+    #> <__main__.Pet object at 0x0123456789ab>
+    print(model2.pet.name)
+    #> 42
+    print(type(model2.pet))
+    #> <class '__main__.Pet'>
+    ```
+    """
+
+    from_attributes: bool
+    """
+    Whether to build models and look up discriminators of tagged unions using python object attributes.
+    """
+
+    loc_by_alias: bool
+    """Whether to use the actual key provided in the data (e.g. alias) for error `loc`s rather than the field's name. Defaults to `True`."""
+
+    alias_generator: Callable[[str], str] | AliasGenerator | None
+    """
+    A callable that takes a field name and returns an alias for it
+    or an instance of [`AliasGenerator`][pydantic.aliases.AliasGenerator]. Defaults to `None`.
+
+    When using a callable, the alias generator is used for both validation and serialization.
+    If you want to use different alias generators for validation and serialization, you can use
+    [`AliasGenerator`][pydantic.aliases.AliasGenerator] instead.
+
+    If data source field names do not match your code style (e. g. CamelCase fields),
+    you can automatically generate aliases using `alias_generator`. Here's an example with
+    a basic callable:
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+    from pydantic.alias_generators import to_pascal
+
+    class Voice(BaseModel):
+        model_config = ConfigDict(alias_generator=to_pascal)
+
+        name: str
+        language_code: str
+
+    voice = Voice(Name='Filiz', LanguageCode='tr-TR')
+    print(voice.language_code)
+    #> tr-TR
+    print(voice.model_dump(by_alias=True))
+    #> {'Name': 'Filiz', 'LanguageCode': 'tr-TR'}
+    ```
+
+    If you want to use different alias generators for validation and serialization, you can use
+    [`AliasGenerator`][pydantic.aliases.AliasGenerator].
+
+    ```python
+    from pydantic import AliasGenerator, BaseModel, ConfigDict
+    from pydantic.alias_generators import to_camel, to_pascal
+
+    class Athlete(BaseModel):
+        first_name: str
+        last_name: str
+        sport: str
+
+        model_config = ConfigDict(
+            alias_generator=AliasGenerator(
+                validation_alias=to_camel,
+                serialization_alias=to_pascal,
+            )
+        )
+
+    athlete = Athlete(firstName='John', lastName='Doe', sport='track')
+    print(athlete.model_dump(by_alias=True))
+    #> {'FirstName': 'John', 'LastName': 'Doe', 'Sport': 'track'}
+    ```
+
+    Note:
+        Pydantic offers three built-in alias generators: [`to_pascal`][pydantic.alias_generators.to_pascal],
+        [`to_camel`][pydantic.alias_generators.to_camel], and [`to_snake`][pydantic.alias_generators.to_snake].
+    """
+
+    ignored_types: tuple[type, ...]
+    """A tuple of types that may occur as values of class attributes without annotations. This is
+    typically used for custom descriptors (classes that behave like `property`). If an attribute is set on a
+    class without an annotation and has a type that is not in this tuple (or otherwise recognized by
+    _pydantic_), an error will be raised. Defaults to `()`.
+    """
+
+    allow_inf_nan: bool
+    """Whether to allow infinity (`+inf` an `-inf`) and NaN values to float and decimal fields. Defaults to `True`."""
+
+    json_schema_extra: JsonDict | JsonSchemaExtraCallable | None
+    """A dict or callable to provide extra JSON schema properties. Defaults to `None`."""
+
+    json_encoders: dict[type[object], JsonEncoder] | None
+    """
+    A `dict` of custom JSON encoders for specific types. Defaults to `None`.
+
+    !!! warning "Deprecated"
+        This config option is a carryover from v1.
+        We originally planned to remove it in v2 but didn't have a 1:1 replacement so we are keeping it for now.
+        It is still deprecated and will likely be removed in the future.
+    """
+
+    # new in V2
+    strict: bool
+    """
+    _(new in V2)_ If `True`, strict validation is applied to all fields on the model.
+
+    By default, Pydantic attempts to coerce values to the correct type, when possible.
+
+    There are situations in which you may want to disable this behavior, and instead raise an error if a value's type
+    does not match the field's type annotation.
+
+    To configure strict mode for all fields on a model, you can set `strict=True` on the model.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+
+    class Model(BaseModel):
+        model_config = ConfigDict(strict=True)
+
+        name: str
+        age: int
+    ```
+
+    See [Strict Mode](../concepts/strict_mode.md) for more details.
+
+    See the [Conversion Table](../concepts/conversion_table.md) for more details on how Pydantic converts data in both
+    strict and lax modes.
+    """
+    # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never'
+    revalidate_instances: Literal['always', 'never', 'subclass-instances']
+    """
+    When and how to revalidate models and dataclasses during validation. Accepts the string
+    values of `'never'`, `'always'` and `'subclass-instances'`. Defaults to `'never'`.
+
+    - `'never'` will not revalidate models and dataclasses during validation
+    - `'always'` will revalidate models and dataclasses during validation
+    - `'subclass-instances'` will revalidate models and dataclasses during validation if the instance is a
+        subclass of the model or dataclass
+
+    By default, model and dataclass instances are not revalidated during validation.
+
+    ```python
+    from typing import List
+
+    from pydantic import BaseModel
+
+    class User(BaseModel, revalidate_instances='never'):  # (1)!
+        hobbies: List[str]
+
+    class SubUser(User):
+        sins: List[str]
+
+    class Transaction(BaseModel):
+        user: User
+
+    my_user = User(hobbies=['reading'])
+    t = Transaction(user=my_user)
+    print(t)
+    #> user=User(hobbies=['reading'])
+
+    my_user.hobbies = [1]  # (2)!
+    t = Transaction(user=my_user)  # (3)!
+    print(t)
+    #> user=User(hobbies=[1])
+
+    my_sub_user = SubUser(hobbies=['scuba diving'], sins=['lying'])
+    t = Transaction(user=my_sub_user)
+    print(t)
+    #> user=SubUser(hobbies=['scuba diving'], sins=['lying'])
+    ```
+
+    1. `revalidate_instances` is set to `'never'` by **default.
+    2. The assignment is not validated, unless you set `validate_assignment` to `True` in the model's config.
+    3. Since `revalidate_instances` is set to `never`, this is not revalidated.
+
+    If you want to revalidate instances during validation, you can set `revalidate_instances` to `'always'`
+    in the model's config.
+
+    ```python
+    from typing import List
+
+    from pydantic import BaseModel, ValidationError
+
+    class User(BaseModel, revalidate_instances='always'):  # (1)!
+        hobbies: List[str]
+
+    class SubUser(User):
+        sins: List[str]
+
+    class Transaction(BaseModel):
+        user: User
+
+    my_user = User(hobbies=['reading'])
+    t = Transaction(user=my_user)
+    print(t)
+    #> user=User(hobbies=['reading'])
+
+    my_user.hobbies = [1]
+    try:
+        t = Transaction(user=my_user)  # (2)!
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Transaction
+        user.hobbies.0
+          Input should be a valid string [type=string_type, input_value=1, input_type=int]
+        '''
+
+    my_sub_user = SubUser(hobbies=['scuba diving'], sins=['lying'])
+    t = Transaction(user=my_sub_user)
+    print(t)  # (3)!
+    #> user=User(hobbies=['scuba diving'])
+    ```
+
+    1. `revalidate_instances` is set to `'always'`.
+    2. The model is revalidated, since `revalidate_instances` is set to `'always'`.
+    3. Using `'never'` we would have gotten `user=SubUser(hobbies=['scuba diving'], sins=['lying'])`.
+
+    It's also possible to set `revalidate_instances` to `'subclass-instances'` to only revalidate instances
+    of subclasses of the model.
+
+    ```python
+    from typing import List
+
+    from pydantic import BaseModel
+
+    class User(BaseModel, revalidate_instances='subclass-instances'):  # (1)!
+        hobbies: List[str]
+
+    class SubUser(User):
+        sins: List[str]
+
+    class Transaction(BaseModel):
+        user: User
+
+    my_user = User(hobbies=['reading'])
+    t = Transaction(user=my_user)
+    print(t)
+    #> user=User(hobbies=['reading'])
+
+    my_user.hobbies = [1]
+    t = Transaction(user=my_user)  # (2)!
+    print(t)
+    #> user=User(hobbies=[1])
+
+    my_sub_user = SubUser(hobbies=['scuba diving'], sins=['lying'])
+    t = Transaction(user=my_sub_user)
+    print(t)  # (3)!
+    #> user=User(hobbies=['scuba diving'])
+    ```
+
+    1. `revalidate_instances` is set to `'subclass-instances'`.
+    2. This is not revalidated, since `my_user` is not a subclass of `User`.
+    3. Using `'never'` we would have gotten `user=SubUser(hobbies=['scuba diving'], sins=['lying'])`.
+    """
+
+    ser_json_timedelta: Literal['iso8601', 'float']
+    """
+    The format of JSON serialized timedeltas. Accepts the string values of `'iso8601'` and
+    `'float'`. Defaults to `'iso8601'`.
+
+    - `'iso8601'` will serialize timedeltas to ISO 8601 durations.
+    - `'float'` will serialize timedeltas to the total number of seconds.
+    """
+
+    ser_json_bytes: Literal['utf8', 'base64', 'hex']
+    """
+    The encoding of JSON serialized bytes. Defaults to `'utf8'`.
+    Set equal to `val_json_bytes` to get back an equal value after serialization round trip.
+
+    - `'utf8'` will serialize bytes to UTF-8 strings.
+    - `'base64'` will serialize bytes to URL safe base64 strings.
+    - `'hex'` will serialize bytes to hexadecimal strings.
+    """
+
+    val_json_bytes: Literal['utf8', 'base64', 'hex']
+    """
+    The encoding of JSON serialized bytes to decode. Defaults to `'utf8'`.
+    Set equal to `ser_json_bytes` to get back an equal value after serialization round trip.
+
+    - `'utf8'` will deserialize UTF-8 strings to bytes.
+    - `'base64'` will deserialize URL safe base64 strings to bytes.
+    - `'hex'` will deserialize hexadecimal strings to bytes.
+    """
+
+    ser_json_inf_nan: Literal['null', 'constants', 'strings']
+    """
+    The encoding of JSON serialized infinity and NaN float values. Defaults to `'null'`.
+
+    - `'null'` will serialize infinity and NaN values as `null`.
+    - `'constants'` will serialize infinity and NaN values as `Infinity` and `NaN`.
+    - `'strings'` will serialize infinity as string `"Infinity"` and NaN as string `"NaN"`.
+    """
+
+    # whether to validate default values during validation, default False
+    validate_default: bool
+    """Whether to validate default values during validation. Defaults to `False`."""
+
+    validate_return: bool
+    """Whether to validate the return value from call validators. Defaults to `False`."""
+
+    protected_namespaces: tuple[str | Pattern[str], ...]
+    """
+    A `tuple` of strings and/or patterns that prevent models from having fields with names that conflict with them.
+    For strings, we match on a prefix basis. Ex, if 'dog' is in the protected namespace, 'dog_name' will be protected.
+    For patterns, we match on the entire field name. Ex, if `re.compile(r'^dog$')` is in the protected namespace, 'dog' will be protected, but 'dog_name' will not be.
+    Defaults to `('model_validate', 'model_dump',)`.
+
+    The reason we've selected these is to prevent collisions with other validation / dumping formats
+    in the future - ex, `model_validate_{some_newly_supported_format}`.
+
+    Before v2.10, Pydantic used `('model_',)` as the default value for this setting to
+    prevent collisions between model attributes and `BaseModel`'s own methods. This was changed
+    in v2.10 given feedback that this restriction was limiting in AI and data science contexts,
+    where it is common to have fields with names like `model_id`, `model_input`, `model_output`, etc.
+
+    For more details, see https://github.com/pydantic/pydantic/issues/10315.
+
+    ```python
+    import warnings
+
+    from pydantic import BaseModel
+
+    warnings.filterwarnings('error')  # Raise warnings as errors
+
+    try:
+
+        class Model(BaseModel):
+            model_dump_something: str
+
+    except UserWarning as e:
+        print(e)
+        '''
+        Field "model_dump_something" in Model has conflict with protected namespace "model_dump".
+
+        You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('model_validate',)`.
+        '''
+    ```
+
+    You can customize this behavior using the `protected_namespaces` setting:
+
+    ```python {test="skip"}
+    import re
+    import warnings
+
+    from pydantic import BaseModel, ConfigDict
+
+    with warnings.catch_warnings(record=True) as caught_warnings:
+        warnings.simplefilter('always')  # Catch all warnings
+
+        class Model(BaseModel):
+            safe_field: str
+            also_protect_field: str
+            protect_this: str
+
+            model_config = ConfigDict(
+                protected_namespaces=(
+                    'protect_me_',
+                    'also_protect_',
+                    re.compile('^protect_this$'),
+                )
+            )
+
+    for warning in caught_warnings:
+        print(f'{warning.message}')
+        '''
+        Field "also_protect_field" in Model has conflict with protected namespace "also_protect_".
+        You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('protect_me_', re.compile('^protect_this$'))`.
+
+        Field "protect_this" in Model has conflict with protected namespace "re.compile('^protect_this$')".
+        You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('protect_me_', 'also_protect_')`.
+        '''
+    ```
+
+    While Pydantic will only emit a warning when an item is in a protected namespace but does not actually have a collision,
+    an error _is_ raised if there is an actual collision with an existing attribute:
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+
+    try:
+
+        class Model(BaseModel):
+            model_validate: str
+
+            model_config = ConfigDict(protected_namespaces=('model_',))
+
+    except NameError as e:
+        print(e)
+        '''
+        Field "model_validate" conflicts with member <bound method BaseModel.model_validate of <class 'pydantic.main.BaseModel'>> of protected namespace "model_".
+        '''
+    ```
+    """
+
+    hide_input_in_errors: bool
+    """
+    Whether to hide inputs when printing errors. Defaults to `False`.
+
+    Pydantic shows the input value and type when it raises `ValidationError` during the validation.
+
+    ```python
+    from pydantic import BaseModel, ValidationError
+
+    class Model(BaseModel):
+        a: str
+
+    try:
+        Model(a=123)
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        a
+          Input should be a valid string [type=string_type, input_value=123, input_type=int]
+        '''
+    ```
+
+    You can hide the input value and type by setting the `hide_input_in_errors` config to `True`.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, ValidationError
+
+    class Model(BaseModel):
+        a: str
+        model_config = ConfigDict(hide_input_in_errors=True)
+
+    try:
+        Model(a=123)
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        a
+          Input should be a valid string [type=string_type]
+        '''
+    ```
+    """
+
+    defer_build: bool
+    """
+    Whether to defer model validator and serializer construction until the first model validation. Defaults to False.
+
+    This can be useful to avoid the overhead of building models which are only
+    used nested within other models, or when you want to manually define type namespace via
+    [`Model.model_rebuild(_types_namespace=...)`][pydantic.BaseModel.model_rebuild].
+
+    Since v2.10, this setting also applies to pydantic dataclasses and TypeAdapter instances.
+    """
+
+    plugin_settings: dict[str, object] | None
+    """A `dict` of settings for plugins. Defaults to `None`."""
+
+    schema_generator: type[_GenerateSchema] | None
+    """
+    !!! warning
+        `schema_generator` is deprecated in v2.10.
+
+        Prior to v2.10, this setting was advertised as highly subject to change.
+        It's possible that this interface may once again become public once the internal core schema generation
+        API is more stable, but that will likely come after significant performance improvements have been made.
+    """
+
+    json_schema_serialization_defaults_required: bool
+    """
+    Whether fields with default values should be marked as required in the serialization schema. Defaults to `False`.
+
+    This ensures that the serialization schema will reflect the fact a field with a default will always be present
+    when serializing the model, even though it is not required for validation.
+
+    However, there are scenarios where this may be undesirable — in particular, if you want to share the schema
+    between validation and serialization, and don't mind fields with defaults being marked as not required during
+    serialization. See [#7209](https://github.com/pydantic/pydantic/issues/7209) for more details.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+
+    class Model(BaseModel):
+        a: str = 'a'
+
+        model_config = ConfigDict(json_schema_serialization_defaults_required=True)
+
+    print(Model.model_json_schema(mode='validation'))
+    '''
+    {
+        'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}},
+        'title': 'Model',
+        'type': 'object',
+    }
+    '''
+    print(Model.model_json_schema(mode='serialization'))
+    '''
+    {
+        'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}},
+        'required': ['a'],
+        'title': 'Model',
+        'type': 'object',
+    }
+    '''
+    ```
+    """
+
+    json_schema_mode_override: Literal['validation', 'serialization', None]
+    """
+    If not `None`, the specified mode will be used to generate the JSON schema regardless of what `mode` was passed to
+    the function call. Defaults to `None`.
+
+    This provides a way to force the JSON schema generation to reflect a specific mode, e.g., to always use the
+    validation schema.
+
+    It can be useful when using frameworks (such as FastAPI) that may generate different schemas for validation
+    and serialization that must both be referenced from the same schema; when this happens, we automatically append
+    `-Input` to the definition reference for the validation schema and `-Output` to the definition reference for the
+    serialization schema. By specifying a `json_schema_mode_override` though, this prevents the conflict between
+    the validation and serialization schemas (since both will use the specified schema), and so prevents the suffixes
+    from being added to the definition references.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, Json
+
+    class Model(BaseModel):
+        a: Json[int]  # requires a string to validate, but will dump an int
+
+    print(Model.model_json_schema(mode='serialization'))
+    '''
+    {
+        'properties': {'a': {'title': 'A', 'type': 'integer'}},
+        'required': ['a'],
+        'title': 'Model',
+        'type': 'object',
+    }
+    '''
+
+    class ForceInputModel(Model):
+        # the following ensures that even with mode='serialization', we
+        # will get the schema that would be generated for validation.
+        model_config = ConfigDict(json_schema_mode_override='validation')
+
+    print(ForceInputModel.model_json_schema(mode='serialization'))
+    '''
+    {
+        'properties': {
+            'a': {
+                'contentMediaType': 'application/json',
+                'contentSchema': {'type': 'integer'},
+                'title': 'A',
+                'type': 'string',
+            }
+        },
+        'required': ['a'],
+        'title': 'ForceInputModel',
+        'type': 'object',
+    }
+    '''
+    ```
+    """
+
+    coerce_numbers_to_str: bool
+    """
+    If `True`, enables automatic coercion of any `Number` type to `str` in "lax" (non-strict) mode. Defaults to `False`.
+
+    Pydantic doesn't allow number types (`int`, `float`, `Decimal`) to be coerced as type `str` by default.
+
+    ```python
+    from decimal import Decimal
+
+    from pydantic import BaseModel, ConfigDict, ValidationError
+
+    class Model(BaseModel):
+        value: str
+
+    try:
+        print(Model(value=42))
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        value
+          Input should be a valid string [type=string_type, input_value=42, input_type=int]
+        '''
+
+    class Model(BaseModel):
+        model_config = ConfigDict(coerce_numbers_to_str=True)
+
+        value: str
+
+    repr(Model(value=42).value)
+    #> "42"
+    repr(Model(value=42.13).value)
+    #> "42.13"
+    repr(Model(value=Decimal('42.13')).value)
+    #> "42.13"
+    ```
+    """
+
+    regex_engine: Literal['rust-regex', 'python-re']
+    """
+    The regex engine to be used for pattern validation.
+    Defaults to `'rust-regex'`.
+
+    - `rust-regex` uses the [`regex`](https://docs.rs/regex) Rust crate,
+      which is non-backtracking and therefore more DDoS resistant, but does not support all regex features.
+    - `python-re` use the [`re`](https://docs.python.org/3/library/re.html) module,
+      which supports all regex features, but may be slower.
+
+    !!! note
+        If you use a compiled regex pattern, the python-re engine will be used regardless of this setting.
+        This is so that flags such as `re.IGNORECASE` are respected.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, Field, ValidationError
+
+    class Model(BaseModel):
+        model_config = ConfigDict(regex_engine='python-re')
+
+        value: str = Field(pattern=r'^abc(?=def)')
+
+    print(Model(value='abcdef').value)
+    #> abcdef
+
+    try:
+        print(Model(value='abxyzcdef'))
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        value
+          String should match pattern '^abc(?=def)' [type=string_pattern_mismatch, input_value='abxyzcdef', input_type=str]
+        '''
+    ```
+    """
+
+    validation_error_cause: bool
+    """
+    If `True`, Python exceptions that were part of a validation failure will be shown as an exception group as a cause. Can be useful for debugging. Defaults to `False`.
+
+    Note:
+        Python 3.10 and older don't support exception groups natively. <=3.10, backport must be installed: `pip install exceptiongroup`.
+
+    Note:
+        The structure of validation errors are likely to change in future Pydantic versions. Pydantic offers no guarantees about their structure. Should be used for visual traceback debugging only.
+    """
+
+    use_attribute_docstrings: bool
+    '''
+    Whether docstrings of attributes (bare string literals immediately following the attribute declaration)
+    should be used for field descriptions. Defaults to `False`.
+
+    Available in Pydantic v2.7+.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, Field
+
+
+    class Model(BaseModel):
+        model_config = ConfigDict(use_attribute_docstrings=True)
+
+        x: str
+        """
+        Example of an attribute docstring
+        """
+
+        y: int = Field(description="Description in Field")
+        """
+        Description in Field overrides attribute docstring
+        """
+
+
+    print(Model.model_fields["x"].description)
+    # > Example of an attribute docstring
+    print(Model.model_fields["y"].description)
+    # > Description in Field
+    ```
+    This requires the source code of the class to be available at runtime.
+
+    !!! warning "Usage with `TypedDict`"
+        Due to current limitations, attribute docstrings detection may not work as expected when using `TypedDict`
+        (in particular when multiple `TypedDict` classes have the same name in the same source file). The behavior
+        can be different depending on the Python version used.
+    '''
+
+    cache_strings: bool | Literal['all', 'keys', 'none']
+    """
+    Whether to cache strings to avoid constructing new Python objects. Defaults to True.
+
+    Enabling this setting should significantly improve validation performance while increasing memory usage slightly.
+
+    - `True` or `'all'` (the default): cache all strings
+    - `'keys'`: cache only dictionary keys
+    - `False` or `'none'`: no caching
+
+    !!! note
+        `True` or `'all'` is required to cache strings during general validation because
+        validators don't know if they're in a key or a value.
+
+    !!! tip
+        If repeated strings are rare, it's recommended to use `'keys'` or `'none'` to reduce memory usage,
+        as the performance difference is minimal if repeated strings are rare.
+    """
+
+
+_TypeT = TypeVar('_TypeT', bound=type)
+
+
+def with_config(config: ConfigDict) -> Callable[[_TypeT], _TypeT]:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/config/#configuration-with-dataclass-from-the-standard-library-or-typeddict
+
+    A convenience decorator to set a [Pydantic configuration](config.md) on a `TypedDict` or a `dataclass` from the standard library.
+
+    Although the configuration can be set using the `__pydantic_config__` attribute, it does not play well with type checkers,
+    especially with `TypedDict`.
+
+    !!! example "Usage"
+
+        ```python
+        from typing_extensions import TypedDict
+
+        from pydantic import ConfigDict, TypeAdapter, with_config
+
+        @with_config(ConfigDict(str_to_lower=True))
+        class Model(TypedDict):
+            x: str
+
+        ta = TypeAdapter(Model)
+
+        print(ta.validate_python({'x': 'ABC'}))
+        #> {'x': 'abc'}
+        ```
+    """
+
+    def inner(class_: _TypeT, /) -> _TypeT:
+        # Ideally, we would check for `class_` to either be a `TypedDict` or a stdlib dataclass.
+        # However, the `@with_config` decorator can be applied *after* `@dataclass`. To avoid
+        # common mistakes, we at least check for `class_` to not be a Pydantic model.
+        from ._internal._utils import is_model_class
+
+        if is_model_class(class_):
+            raise PydanticUserError(
+                f'Cannot use `with_config` on {class_.__name__} as it is a Pydantic model',
+                code='with-config-on-model',
+            )
+        class_.__pydantic_config__ = config
+        return class_
+
+    return inner
+
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/dataclasses.py b/.venv/lib/python3.12/site-packages/pydantic/dataclasses.py
new file mode 100644
index 00000000..fe7709f8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/dataclasses.py
@@ -0,0 +1,366 @@
+"""Provide an enhanced dataclass that performs validation."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import sys
+import types
+from typing import TYPE_CHECKING, Any, Callable, Generic, NoReturn, TypeVar, overload
+from warnings import warn
+
+from typing_extensions import Literal, TypeGuard, dataclass_transform
+
+from ._internal import _config, _decorators, _namespace_utils, _typing_extra
+from ._internal import _dataclasses as _pydantic_dataclasses
+from ._migration import getattr_migration
+from .config import ConfigDict
+from .errors import PydanticUserError
+from .fields import Field, FieldInfo, PrivateAttr
+
+if TYPE_CHECKING:
+    from ._internal._dataclasses import PydanticDataclass
+    from ._internal._namespace_utils import MappingNamespace
+
+__all__ = 'dataclass', 'rebuild_dataclass'
+
+_T = TypeVar('_T')
+
+if sys.version_info >= (3, 10):
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+    @overload
+    def dataclass(
+        *,
+        init: Literal[False] = False,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: ConfigDict | type[object] | None = None,
+        validate_on_init: bool | None = None,
+        kw_only: bool = ...,
+        slots: bool = ...,
+    ) -> Callable[[type[_T]], type[PydanticDataclass]]:  # type: ignore
+        ...
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+    @overload
+    def dataclass(
+        _cls: type[_T],  # type: ignore
+        *,
+        init: Literal[False] = False,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool | None = None,
+        config: ConfigDict | type[object] | None = None,
+        validate_on_init: bool | None = None,
+        kw_only: bool = ...,
+        slots: bool = ...,
+    ) -> type[PydanticDataclass]: ...
+
+else:
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+    @overload
+    def dataclass(
+        *,
+        init: Literal[False] = False,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool | None = None,
+        config: ConfigDict | type[object] | None = None,
+        validate_on_init: bool | None = None,
+    ) -> Callable[[type[_T]], type[PydanticDataclass]]:  # type: ignore
+        ...
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+    @overload
+    def dataclass(
+        _cls: type[_T],  # type: ignore
+        *,
+        init: Literal[False] = False,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool | None = None,
+        config: ConfigDict | type[object] | None = None,
+        validate_on_init: bool | None = None,
+    ) -> type[PydanticDataclass]: ...
+
+
+@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+def dataclass(
+    _cls: type[_T] | None = None,
+    *,
+    init: Literal[False] = False,
+    repr: bool = True,
+    eq: bool = True,
+    order: bool = False,
+    unsafe_hash: bool = False,
+    frozen: bool | None = None,
+    config: ConfigDict | type[object] | None = None,
+    validate_on_init: bool | None = None,
+    kw_only: bool = False,
+    slots: bool = False,
+) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/dataclasses/
+
+    A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`,
+    but with added validation.
+
+    This function should be used similarly to `dataclasses.dataclass`.
+
+    Args:
+        _cls: The target `dataclass`.
+        init: Included for signature compatibility with `dataclasses.dataclass`, and is passed through to
+            `dataclasses.dataclass` when appropriate. If specified, must be set to `False`, as pydantic inserts its
+            own  `__init__` function.
+        repr: A boolean indicating whether to include the field in the `__repr__` output.
+        eq: Determines if a `__eq__` method should be generated for the class.
+        order: Determines if comparison magic methods should be generated, such as `__lt__`, but not `__eq__`.
+        unsafe_hash: Determines if a `__hash__` method should be included in the class, as in `dataclasses.dataclass`.
+        frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its
+            attributes to be modified after it has been initialized. If not set, the value from the provided `config` argument will be used (and will default to `False` otherwise).
+        config: The Pydantic config to use for the `dataclass`.
+        validate_on_init: A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses
+            are validated on init.
+        kw_only: Determines if `__init__` method parameters must be specified by keyword only. Defaults to `False`.
+        slots: Determines if the generated class should be a 'slots' `dataclass`, which does not allow the addition of
+            new attributes after instantiation.
+
+    Returns:
+        A decorator that accepts a class as its argument and returns a Pydantic `dataclass`.
+
+    Raises:
+        AssertionError: Raised if `init` is not `False` or `validate_on_init` is `False`.
+    """
+    assert init is False, 'pydantic.dataclasses.dataclass only supports init=False'
+    assert validate_on_init is not False, 'validate_on_init=False is no longer supported'
+
+    if sys.version_info >= (3, 10):
+        kwargs = {'kw_only': kw_only, 'slots': slots}
+    else:
+        kwargs = {}
+
+    def make_pydantic_fields_compatible(cls: type[Any]) -> None:
+        """Make sure that stdlib `dataclasses` understands `Field` kwargs like `kw_only`
+        To do that, we simply change
+          `x: int = pydantic.Field(..., kw_only=True)`
+        into
+          `x: int = dataclasses.field(default=pydantic.Field(..., kw_only=True), kw_only=True)`
+        """
+        for annotation_cls in cls.__mro__:
+            # In Python < 3.9, `__annotations__` might not be present if there are no fields.
+            # we therefore need to use `getattr` to avoid an `AttributeError`.
+            annotations = getattr(annotation_cls, '__annotations__', [])
+            for field_name in annotations:
+                field_value = getattr(cls, field_name, None)
+                # Process only if this is an instance of `FieldInfo`.
+                if not isinstance(field_value, FieldInfo):
+                    continue
+
+                # Initialize arguments for the standard `dataclasses.field`.
+                field_args: dict = {'default': field_value}
+
+                # Handle `kw_only` for Python 3.10+
+                if sys.version_info >= (3, 10) and field_value.kw_only:
+                    field_args['kw_only'] = True
+
+                # Set `repr` attribute if it's explicitly specified to be not `True`.
+                if field_value.repr is not True:
+                    field_args['repr'] = field_value.repr
+
+                setattr(cls, field_name, dataclasses.field(**field_args))
+                # In Python 3.8, dataclasses checks cls.__dict__['__annotations__'] for annotations,
+                # so we must make sure it's initialized before we add to it.
+                if cls.__dict__.get('__annotations__') is None:
+                    cls.__annotations__ = {}
+                cls.__annotations__[field_name] = annotations[field_name]
+
+    def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]:
+        """Create a Pydantic dataclass from a regular dataclass.
+
+        Args:
+            cls: The class to create the Pydantic dataclass from.
+
+        Returns:
+            A Pydantic dataclass.
+        """
+        from ._internal._utils import is_model_class
+
+        if is_model_class(cls):
+            raise PydanticUserError(
+                f'Cannot create a Pydantic dataclass from {cls.__name__} as it is already a Pydantic model',
+                code='dataclass-on-model',
+            )
+
+        original_cls = cls
+
+        # we warn on conflicting config specifications, but only if the class doesn't have a dataclass base
+        # because a dataclass base might provide a __pydantic_config__ attribute that we don't want to warn about
+        has_dataclass_base = any(dataclasses.is_dataclass(base) for base in cls.__bases__)
+        if not has_dataclass_base and config is not None and hasattr(cls, '__pydantic_config__'):
+            warn(
+                f'`config` is set via both the `dataclass` decorator and `__pydantic_config__` for dataclass {cls.__name__}. '
+                f'The `config` specification from `dataclass` decorator will take priority.',
+                category=UserWarning,
+                stacklevel=2,
+            )
+
+        # if config is not explicitly provided, try to read it from the type
+        config_dict = config if config is not None else getattr(cls, '__pydantic_config__', None)
+        config_wrapper = _config.ConfigWrapper(config_dict)
+        decorators = _decorators.DecoratorInfos.build(cls)
+
+        # Keep track of the original __doc__ so that we can restore it after applying the dataclasses decorator
+        # Otherwise, classes with no __doc__ will have their signature added into the JSON schema description,
+        # since dataclasses.dataclass will set this as the __doc__
+        original_doc = cls.__doc__
+
+        if _pydantic_dataclasses.is_builtin_dataclass(cls):
+            # Don't preserve the docstring for vanilla dataclasses, as it may include the signature
+            # This matches v1 behavior, and there was an explicit test for it
+            original_doc = None
+
+            # We don't want to add validation to the existing std lib dataclass, so we will subclass it
+            #   If the class is generic, we need to make sure the subclass also inherits from Generic
+            #   with all the same parameters.
+            bases = (cls,)
+            if issubclass(cls, Generic):
+                generic_base = Generic[cls.__parameters__]  # type: ignore
+                bases = bases + (generic_base,)
+            cls = types.new_class(cls.__name__, bases)
+
+        make_pydantic_fields_compatible(cls)
+
+        # Respect frozen setting from dataclass constructor and fallback to config setting if not provided
+        if frozen is not None:
+            frozen_ = frozen
+            if config_wrapper.frozen:
+                # It's not recommended to define both, as the setting from the dataclass decorator will take priority.
+                warn(
+                    f'`frozen` is set via both the `dataclass` decorator and `config` for dataclass {cls.__name__!r}.'
+                    'This is not recommended. The `frozen` specification on `dataclass` will take priority.',
+                    category=UserWarning,
+                    stacklevel=2,
+                )
+        else:
+            frozen_ = config_wrapper.frozen or False
+
+        cls = dataclasses.dataclass(  # type: ignore[call-overload]
+            cls,
+            # the value of init here doesn't affect anything except that it makes it easier to generate a signature
+            init=True,
+            repr=repr,
+            eq=eq,
+            order=order,
+            unsafe_hash=unsafe_hash,
+            frozen=frozen_,
+            **kwargs,
+        )
+
+        cls.__pydantic_decorators__ = decorators  # type: ignore
+        cls.__doc__ = original_doc
+        cls.__module__ = original_cls.__module__
+        cls.__qualname__ = original_cls.__qualname__
+        cls.__pydantic_complete__ = False  # `complete_dataclass` will set it to `True` if successful.
+        # TODO `parent_namespace` is currently None, but we could do the same thing as Pydantic models:
+        # fetch the parent ns using `parent_frame_namespace` (if the dataclass was defined in a function),
+        # and possibly cache it (see the `__pydantic_parent_namespace__` logic for models).
+        _pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False)
+        return cls
+
+    return create_dataclass if _cls is None else create_dataclass(_cls)
+
+
+__getattr__ = getattr_migration(__name__)
+
+if (3, 8) <= sys.version_info < (3, 11):
+    # Monkeypatch dataclasses.InitVar so that typing doesn't error if it occurs as a type when evaluating type hints
+    # Starting in 3.11, typing.get_type_hints will not raise an error if the retrieved type hints are not callable.
+
+    def _call_initvar(*args: Any, **kwargs: Any) -> NoReturn:
+        """This function does nothing but raise an error that is as similar as possible to what you'd get
+        if you were to try calling `InitVar[int]()` without this monkeypatch. The whole purpose is just
+        to ensure typing._type_check does not error if the type hint evaluates to `InitVar[<parameter>]`.
+        """
+        raise TypeError("'InitVar' object is not callable")
+
+    dataclasses.InitVar.__call__ = _call_initvar
+
+
+def rebuild_dataclass(
+    cls: type[PydanticDataclass],
+    *,
+    force: bool = False,
+    raise_errors: bool = True,
+    _parent_namespace_depth: int = 2,
+    _types_namespace: MappingNamespace | None = None,
+) -> bool | None:
+    """Try to rebuild the pydantic-core schema for the dataclass.
+
+    This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
+    the initial attempt to build the schema, and automatic rebuilding fails.
+
+    This is analogous to `BaseModel.model_rebuild`.
+
+    Args:
+        cls: The class to rebuild the pydantic-core schema for.
+        force: Whether to force the rebuilding of the schema, defaults to `False`.
+        raise_errors: Whether to raise errors, defaults to `True`.
+        _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
+        _types_namespace: The types namespace, defaults to `None`.
+
+    Returns:
+        Returns `None` if the schema is already "complete" and rebuilding was not required.
+        If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
+    """
+    if not force and cls.__pydantic_complete__:
+        return None
+
+    if '__pydantic_core_schema__' in cls.__dict__:
+        delattr(cls, '__pydantic_core_schema__')  # delete cached value to ensure full rebuild happens
+
+    if _types_namespace is not None:
+        rebuild_ns = _types_namespace
+    elif _parent_namespace_depth > 0:
+        rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
+    else:
+        rebuild_ns = {}
+
+    ns_resolver = _namespace_utils.NsResolver(
+        parent_namespace=rebuild_ns,
+    )
+
+    return _pydantic_dataclasses.complete_dataclass(
+        cls,
+        _config.ConfigWrapper(cls.__pydantic_config__, check=False),
+        raise_errors=raise_errors,
+        ns_resolver=ns_resolver,
+        # We could provide a different config instead (with `'defer_build'` set to `True`)
+        # of this explicit `_force_build` argument, but because config can come from the
+        # decorator parameter or the `__pydantic_config__` attribute, `complete_dataclass`
+        # will overwrite `__pydantic_config__` with the provided config above:
+        _force_build=True,
+    )
+
+
+def is_pydantic_dataclass(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]:
+    """Whether a class is a pydantic dataclass.
+
+    Args:
+        class_: The class.
+
+    Returns:
+        `True` if the class is a pydantic dataclass, `False` otherwise.
+    """
+    try:
+        return '__pydantic_validator__' in class_.__dict__ and dataclasses.is_dataclass(class_)
+    except AttributeError:
+        return False
diff --git a/.venv/lib/python3.12/site-packages/pydantic/datetime_parse.py b/.venv/lib/python3.12/site-packages/pydantic/datetime_parse.py
new file mode 100644
index 00000000..53d52649
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/datetime_parse.py
@@ -0,0 +1,5 @@
+"""The `datetime_parse` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/decorator.py b/.venv/lib/python3.12/site-packages/pydantic/decorator.py
new file mode 100644
index 00000000..0d97560c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/decorator.py
@@ -0,0 +1,5 @@
+"""The `decorator` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/deprecated/__init__.py b/.venv/lib/python3.12/site-packages/pydantic/deprecated/__init__.py
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/deprecated/__init__.py
diff --git a/.venv/lib/python3.12/site-packages/pydantic/deprecated/class_validators.py b/.venv/lib/python3.12/site-packages/pydantic/deprecated/class_validators.py
new file mode 100644
index 00000000..810b4687
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/deprecated/class_validators.py
@@ -0,0 +1,256 @@
+"""Old `@validator` and `@root_validator` function validators from V1."""
+
+from __future__ import annotations as _annotations
+
+from functools import partial, partialmethod
+from types import FunctionType
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union, overload
+from warnings import warn
+
+from typing_extensions import Literal, Protocol, TypeAlias, deprecated
+
+from .._internal import _decorators, _decorators_v1
+from ..errors import PydanticUserError
+from ..warnings import PydanticDeprecatedSince20
+
+_ALLOW_REUSE_WARNING_MESSAGE = '`allow_reuse` is deprecated and will be ignored; it should no longer be necessary'
+
+
+if TYPE_CHECKING:
+
+    class _OnlyValueValidatorClsMethod(Protocol):
+        def __call__(self, __cls: Any, __value: Any) -> Any: ...
+
+    class _V1ValidatorWithValuesClsMethod(Protocol):
+        def __call__(self, __cls: Any, __value: Any, values: dict[str, Any]) -> Any: ...
+
+    class _V1ValidatorWithValuesKwOnlyClsMethod(Protocol):
+        def __call__(self, __cls: Any, __value: Any, *, values: dict[str, Any]) -> Any: ...
+
+    class _V1ValidatorWithKwargsClsMethod(Protocol):
+        def __call__(self, __cls: Any, **kwargs: Any) -> Any: ...
+
+    class _V1ValidatorWithValuesAndKwargsClsMethod(Protocol):
+        def __call__(self, __cls: Any, values: dict[str, Any], **kwargs: Any) -> Any: ...
+
+    class _V1RootValidatorClsMethod(Protocol):
+        def __call__(
+            self, __cls: Any, __values: _decorators_v1.RootValidatorValues
+        ) -> _decorators_v1.RootValidatorValues: ...
+
+    V1Validator = Union[
+        _OnlyValueValidatorClsMethod,
+        _V1ValidatorWithValuesClsMethod,
+        _V1ValidatorWithValuesKwOnlyClsMethod,
+        _V1ValidatorWithKwargsClsMethod,
+        _V1ValidatorWithValuesAndKwargsClsMethod,
+        _decorators_v1.V1ValidatorWithValues,
+        _decorators_v1.V1ValidatorWithValuesKwOnly,
+        _decorators_v1.V1ValidatorWithKwargs,
+        _decorators_v1.V1ValidatorWithValuesAndKwargs,
+    ]
+
+    V1RootValidator = Union[
+        _V1RootValidatorClsMethod,
+        _decorators_v1.V1RootValidatorFunction,
+    ]
+
+    _PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]]
+
+    # Allow both a V1 (assumed pre=False) or V2 (assumed mode='after') validator
+    # We lie to type checkers and say we return the same thing we get
+    # but in reality we return a proxy object that _mostly_ behaves like the wrapped thing
+    _V1ValidatorType = TypeVar('_V1ValidatorType', V1Validator, _PartialClsOrStaticMethod)
+    _V1RootValidatorFunctionType = TypeVar(
+        '_V1RootValidatorFunctionType',
+        _decorators_v1.V1RootValidatorFunction,
+        _V1RootValidatorClsMethod,
+        _PartialClsOrStaticMethod,
+    )
+else:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+
+@deprecated(
+    'Pydantic V1 style `@validator` validators are deprecated.'
+    ' You should migrate to Pydantic V2 style `@field_validator` validators,'
+    ' see the migration guide for more details',
+    category=None,
+)
+def validator(
+    __field: str,
+    *fields: str,
+    pre: bool = False,
+    each_item: bool = False,
+    always: bool = False,
+    check_fields: bool | None = None,
+    allow_reuse: bool = False,
+) -> Callable[[_V1ValidatorType], _V1ValidatorType]:
+    """Decorate methods on the class indicating that they should be used to validate fields.
+
+    Args:
+        __field (str): The first field the validator should be called on; this is separate
+            from `fields` to ensure an error is raised if you don't pass at least one.
+        *fields (str): Additional field(s) the validator should be called on.
+        pre (bool, optional): Whether this validator should be called before the standard
+            validators (else after). Defaults to False.
+        each_item (bool, optional): For complex objects (sets, lists etc.) whether to validate
+            individual elements rather than the whole object. Defaults to False.
+        always (bool, optional): Whether this method and other validators should be called even if
+            the value is missing. Defaults to False.
+        check_fields (bool | None, optional): Whether to check that the fields actually exist on the model.
+            Defaults to None.
+        allow_reuse (bool, optional): Whether to track and raise an error if another validator refers to
+            the decorated function. Defaults to False.
+
+    Returns:
+        Callable: A decorator that can be used to decorate a
+            function to be used as a validator.
+    """
+    warn(
+        'Pydantic V1 style `@validator` validators are deprecated.'
+        ' You should migrate to Pydantic V2 style `@field_validator` validators,'
+        ' see the migration guide for more details',
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    if allow_reuse is True:  # pragma: no cover
+        warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning)
+    fields = __field, *fields
+    if isinstance(fields[0], FunctionType):
+        raise PydanticUserError(
+            '`@validator` should be used with fields and keyword arguments, not bare. '
+            "E.g. usage should be `@validator('<field_name>', ...)`",
+            code='validator-no-fields',
+        )
+    elif not all(isinstance(field, str) for field in fields):
+        raise PydanticUserError(
+            '`@validator` fields should be passed as separate string args. '
+            "E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`",
+            code='validator-invalid-fields',
+        )
+
+    mode: Literal['before', 'after'] = 'before' if pre is True else 'after'
+
+    def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]:
+        if _decorators.is_instance_method_from_sig(f):
+            raise PydanticUserError(
+                '`@validator` cannot be applied to instance methods', code='validator-instance-method'
+            )
+        # auto apply the @classmethod decorator
+        f = _decorators.ensure_classmethod_based_on_signature(f)
+        wrap = _decorators_v1.make_generic_v1_field_validator
+        validator_wrapper_info = _decorators.ValidatorDecoratorInfo(
+            fields=fields,
+            mode=mode,
+            each_item=each_item,
+            always=always,
+            check_fields=check_fields,
+        )
+        return _decorators.PydanticDescriptorProxy(f, validator_wrapper_info, shim=wrap)
+
+    return dec  # type: ignore[return-value]
+
+
+@overload
+def root_validator(
+    *,
+    # if you don't specify `pre` the default is `pre=False`
+    # which means you need to specify `skip_on_failure=True`
+    skip_on_failure: Literal[True],
+    allow_reuse: bool = ...,
+) -> Callable[
+    [_V1RootValidatorFunctionType],
+    _V1RootValidatorFunctionType,
+]: ...
+
+
+@overload
+def root_validator(
+    *,
+    # if you specify `pre=True` then you don't need to specify
+    # `skip_on_failure`, in fact it is not allowed as an argument!
+    pre: Literal[True],
+    allow_reuse: bool = ...,
+) -> Callable[
+    [_V1RootValidatorFunctionType],
+    _V1RootValidatorFunctionType,
+]: ...
+
+
+@overload
+def root_validator(
+    *,
+    # if you explicitly specify `pre=False` then you
+    # MUST specify `skip_on_failure=True`
+    pre: Literal[False],
+    skip_on_failure: Literal[True],
+    allow_reuse: bool = ...,
+) -> Callable[
+    [_V1RootValidatorFunctionType],
+    _V1RootValidatorFunctionType,
+]: ...
+
+
+@deprecated(
+    'Pydantic V1 style `@root_validator` validators are deprecated.'
+    ' You should migrate to Pydantic V2 style `@model_validator` validators,'
+    ' see the migration guide for more details',
+    category=None,
+)
+def root_validator(
+    *__args,
+    pre: bool = False,
+    skip_on_failure: bool = False,
+    allow_reuse: bool = False,
+) -> Any:
+    """Decorate methods on a model indicating that they should be used to validate (and perhaps
+    modify) data either before or after standard model parsing/validation is performed.
+
+    Args:
+        pre (bool, optional): Whether this validator should be called before the standard
+            validators (else after). Defaults to False.
+        skip_on_failure (bool, optional): Whether to stop validation and return as soon as a
+            failure is encountered. Defaults to False.
+        allow_reuse (bool, optional): Whether to track and raise an error if another validator
+            refers to the decorated function. Defaults to False.
+
+    Returns:
+        Any: A decorator that can be used to decorate a function to be used as a root_validator.
+    """
+    warn(
+        'Pydantic V1 style `@root_validator` validators are deprecated.'
+        ' You should migrate to Pydantic V2 style `@model_validator` validators,'
+        ' see the migration guide for more details',
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    if __args:
+        # Ensure a nice error is raised if someone attempts to use the bare decorator
+        return root_validator()(*__args)  # type: ignore
+
+    if allow_reuse is True:  # pragma: no cover
+        warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning)
+    mode: Literal['before', 'after'] = 'before' if pre is True else 'after'
+    if pre is False and skip_on_failure is not True:
+        raise PydanticUserError(
+            'If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`.'
+            ' Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.',
+            code='root-validator-pre-skip',
+        )
+
+    wrap = partial(_decorators_v1.make_v1_generic_root_validator, pre=pre)
+
+    def dec(f: Callable[..., Any] | classmethod[Any, Any, Any] | staticmethod[Any, Any]) -> Any:
+        if _decorators.is_instance_method_from_sig(f):
+            raise TypeError('`@root_validator` cannot be applied to instance methods')
+        # auto apply the @classmethod decorator
+        res = _decorators.ensure_classmethod_based_on_signature(f)
+        dec_info = _decorators.RootValidatorDecoratorInfo(mode=mode)
+        return _decorators.PydanticDescriptorProxy(res, dec_info, shim=wrap)
+
+    return dec
diff --git a/.venv/lib/python3.12/site-packages/pydantic/deprecated/config.py b/.venv/lib/python3.12/site-packages/pydantic/deprecated/config.py
new file mode 100644
index 00000000..45400c65
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/deprecated/config.py
@@ -0,0 +1,72 @@
+from __future__ import annotations as _annotations
+
+import warnings
+from typing import TYPE_CHECKING, Any
+
+from typing_extensions import Literal, deprecated
+
+from .._internal import _config
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'BaseConfig', 'Extra'
+
+
+class _ConfigMetaclass(type):
+    def __getattr__(self, item: str) -> Any:
+        try:
+            obj = _config.config_defaults[item]
+            warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
+            return obj
+        except KeyError as exc:
+            raise AttributeError(f"type object '{self.__name__}' has no attribute {exc}") from exc
+
+
+@deprecated('BaseConfig is deprecated. Use the `pydantic.ConfigDict` instead.', category=PydanticDeprecatedSince20)
+class BaseConfig(metaclass=_ConfigMetaclass):
+    """This class is only retained for backwards compatibility.
+
+    !!! Warning "Deprecated"
+        BaseConfig is deprecated. Use the [`pydantic.ConfigDict`][pydantic.ConfigDict] instead.
+    """
+
+    def __getattr__(self, item: str) -> Any:
+        try:
+            obj = super().__getattribute__(item)
+            warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
+            return obj
+        except AttributeError as exc:
+            try:
+                return getattr(type(self), item)
+            except AttributeError:
+                # re-raising changes the displayed text to reflect that `self` is not a type
+                raise AttributeError(str(exc)) from exc
+
+    def __init_subclass__(cls, **kwargs: Any) -> None:
+        warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
+        return super().__init_subclass__(**kwargs)
+
+
+class _ExtraMeta(type):
+    def __getattribute__(self, __name: str) -> Any:
+        # The @deprecated decorator accesses other attributes, so we only emit a warning for the expected ones
+        if __name in {'allow', 'ignore', 'forbid'}:
+            warnings.warn(
+                "`pydantic.config.Extra` is deprecated, use literal values instead (e.g. `extra='allow'`)",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+        return super().__getattribute__(__name)
+
+
+@deprecated(
+    "Extra is deprecated. Use literal values instead (e.g. `extra='allow'`)", category=PydanticDeprecatedSince20
+)
+class Extra(metaclass=_ExtraMeta):
+    allow: Literal['allow'] = 'allow'
+    ignore: Literal['ignore'] = 'ignore'
+    forbid: Literal['forbid'] = 'forbid'
diff --git a/.venv/lib/python3.12/site-packages/pydantic/deprecated/copy_internals.py b/.venv/lib/python3.12/site-packages/pydantic/deprecated/copy_internals.py
new file mode 100644
index 00000000..00e0a8a9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/deprecated/copy_internals.py
@@ -0,0 +1,224 @@
+from __future__ import annotations as _annotations
+
+import typing
+from copy import deepcopy
+from enum import Enum
+from typing import Any, Tuple
+
+import typing_extensions
+
+from .._internal import (
+    _model_construction,
+    _typing_extra,
+    _utils,
+)
+
+if typing.TYPE_CHECKING:
+    from .. import BaseModel
+    from .._internal._utils import AbstractSetIntStr, MappingIntStrAny
+
+    AnyClassMethod = classmethod[Any, Any, Any]
+    TupleGenerator = typing.Generator[Tuple[str, Any], None, None]
+    Model = typing.TypeVar('Model', bound='BaseModel')
+    # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
+    IncEx: typing_extensions.TypeAlias = 'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None'
+
+_object_setattr = _model_construction.object_setattr
+
+
+def _iter(
+    self: BaseModel,
+    to_dict: bool = False,
+    by_alias: bool = False,
+    include: AbstractSetIntStr | MappingIntStrAny | None = None,
+    exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
+    exclude_unset: bool = False,
+    exclude_defaults: bool = False,
+    exclude_none: bool = False,
+) -> TupleGenerator:
+    # Merge field set excludes with explicit exclude parameter with explicit overriding field set options.
+    # The extra "is not None" guards are not logically necessary but optimizes performance for the simple case.
+    if exclude is not None:
+        exclude = _utils.ValueItems.merge(
+            {k: v.exclude for k, v in self.__pydantic_fields__.items() if v.exclude is not None}, exclude
+        )
+
+    if include is not None:
+        include = _utils.ValueItems.merge({k: True for k in self.__pydantic_fields__}, include, intersect=True)
+
+    allowed_keys = _calculate_keys(self, include=include, exclude=exclude, exclude_unset=exclude_unset)  # type: ignore
+    if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none):
+        # huge boost for plain _iter()
+        yield from self.__dict__.items()
+        if self.__pydantic_extra__:
+            yield from self.__pydantic_extra__.items()
+        return
+
+    value_exclude = _utils.ValueItems(self, exclude) if exclude is not None else None
+    value_include = _utils.ValueItems(self, include) if include is not None else None
+
+    if self.__pydantic_extra__ is None:
+        items = self.__dict__.items()
+    else:
+        items = list(self.__dict__.items()) + list(self.__pydantic_extra__.items())
+
+    for field_key, v in items:
+        if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None):
+            continue
+
+        if exclude_defaults:
+            try:
+                field = self.__pydantic_fields__[field_key]
+            except KeyError:
+                pass
+            else:
+                if not field.is_required() and field.default == v:
+                    continue
+
+        if by_alias and field_key in self.__pydantic_fields__:
+            dict_key = self.__pydantic_fields__[field_key].alias or field_key
+        else:
+            dict_key = field_key
+
+        if to_dict or value_include or value_exclude:
+            v = _get_value(
+                type(self),
+                v,
+                to_dict=to_dict,
+                by_alias=by_alias,
+                include=value_include and value_include.for_element(field_key),
+                exclude=value_exclude and value_exclude.for_element(field_key),
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                exclude_none=exclude_none,
+            )
+        yield dict_key, v
+
+
+def _copy_and_set_values(
+    self: Model,
+    values: dict[str, Any],
+    fields_set: set[str],
+    extra: dict[str, Any] | None = None,
+    private: dict[str, Any] | None = None,
+    *,
+    deep: bool,  # UP006
+) -> Model:
+    if deep:
+        # chances of having empty dict here are quite low for using smart_deepcopy
+        values = deepcopy(values)
+        extra = deepcopy(extra)
+        private = deepcopy(private)
+
+    cls = self.__class__
+    m = cls.__new__(cls)
+    _object_setattr(m, '__dict__', values)
+    _object_setattr(m, '__pydantic_extra__', extra)
+    _object_setattr(m, '__pydantic_fields_set__', fields_set)
+    _object_setattr(m, '__pydantic_private__', private)
+
+    return m
+
+
+@typing.no_type_check
+def _get_value(
+    cls: type[BaseModel],
+    v: Any,
+    to_dict: bool,
+    by_alias: bool,
+    include: AbstractSetIntStr | MappingIntStrAny | None,
+    exclude: AbstractSetIntStr | MappingIntStrAny | None,
+    exclude_unset: bool,
+    exclude_defaults: bool,
+    exclude_none: bool,
+) -> Any:
+    from .. import BaseModel
+
+    if isinstance(v, BaseModel):
+        if to_dict:
+            return v.model_dump(
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                include=include,  # type: ignore
+                exclude=exclude,  # type: ignore
+                exclude_none=exclude_none,
+            )
+        else:
+            return v.copy(include=include, exclude=exclude)
+
+    value_exclude = _utils.ValueItems(v, exclude) if exclude else None
+    value_include = _utils.ValueItems(v, include) if include else None
+
+    if isinstance(v, dict):
+        return {
+            k_: _get_value(
+                cls,
+                v_,
+                to_dict=to_dict,
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                include=value_include and value_include.for_element(k_),
+                exclude=value_exclude and value_exclude.for_element(k_),
+                exclude_none=exclude_none,
+            )
+            for k_, v_ in v.items()
+            if (not value_exclude or not value_exclude.is_excluded(k_))
+            and (not value_include or value_include.is_included(k_))
+        }
+
+    elif _utils.sequence_like(v):
+        seq_args = (
+            _get_value(
+                cls,
+                v_,
+                to_dict=to_dict,
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                include=value_include and value_include.for_element(i),
+                exclude=value_exclude and value_exclude.for_element(i),
+                exclude_none=exclude_none,
+            )
+            for i, v_ in enumerate(v)
+            if (not value_exclude or not value_exclude.is_excluded(i))
+            and (not value_include or value_include.is_included(i))
+        )
+
+        return v.__class__(*seq_args) if _typing_extra.is_namedtuple(v.__class__) else v.__class__(seq_args)
+
+    elif isinstance(v, Enum) and getattr(cls.model_config, 'use_enum_values', False):
+        return v.value
+
+    else:
+        return v
+
+
+def _calculate_keys(
+    self: BaseModel,
+    include: MappingIntStrAny | None,
+    exclude: MappingIntStrAny | None,
+    exclude_unset: bool,
+    update: typing.Dict[str, Any] | None = None,  # noqa UP006
+) -> typing.AbstractSet[str] | None:
+    if include is None and exclude is None and exclude_unset is False:
+        return None
+
+    keys: typing.AbstractSet[str]
+    if exclude_unset:
+        keys = self.__pydantic_fields_set__.copy()
+    else:
+        keys = set(self.__dict__.keys())
+        keys = keys | (self.__pydantic_extra__ or {}).keys()
+
+    if include is not None:
+        keys &= include.keys()
+
+    if update:
+        keys -= update.keys()
+
+    if exclude:
+        keys -= {k for k, v in exclude.items() if _utils.ValueItems.is_true(v)}
+
+    return keys
diff --git a/.venv/lib/python3.12/site-packages/pydantic/deprecated/decorator.py b/.venv/lib/python3.12/site-packages/pydantic/deprecated/decorator.py
new file mode 100644
index 00000000..27ee4603
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/deprecated/decorator.py
@@ -0,0 +1,283 @@
+import warnings
+from functools import wraps
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional, Tuple, Type, TypeVar, Union, overload
+
+from typing_extensions import deprecated
+
+from .._internal import _config, _typing_extra
+from ..alias_generators import to_pascal
+from ..errors import PydanticUserError
+from ..functional_validators import field_validator
+from ..main import BaseModel, create_model
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = ('validate_arguments',)
+
+if TYPE_CHECKING:
+    AnyCallable = Callable[..., Any]
+
+    AnyCallableT = TypeVar('AnyCallableT', bound=AnyCallable)
+    ConfigType = Union[None, Type[Any], Dict[str, Any]]
+
+
+@overload
+def validate_arguments(
+    func: None = None, *, config: 'ConfigType' = None
+) -> Callable[['AnyCallableT'], 'AnyCallableT']: ...
+
+
+@overload
+def validate_arguments(func: 'AnyCallableT') -> 'AnyCallableT': ...
+
+
+@deprecated(
+    'The `validate_arguments` method is deprecated; use `validate_call` instead.',
+    category=None,
+)
+def validate_arguments(func: Optional['AnyCallableT'] = None, *, config: 'ConfigType' = None) -> Any:
+    """Decorator to validate the arguments passed to a function."""
+    warnings.warn(
+        'The `validate_arguments` method is deprecated; use `validate_call` instead.',
+        PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+
+    def validate(_func: 'AnyCallable') -> 'AnyCallable':
+        vd = ValidatedFunction(_func, config)
+
+        @wraps(_func)
+        def wrapper_function(*args: Any, **kwargs: Any) -> Any:
+            return vd.call(*args, **kwargs)
+
+        wrapper_function.vd = vd  # type: ignore
+        wrapper_function.validate = vd.init_model_instance  # type: ignore
+        wrapper_function.raw_function = vd.raw_function  # type: ignore
+        wrapper_function.model = vd.model  # type: ignore
+        return wrapper_function
+
+    if func:
+        return validate(func)
+    else:
+        return validate
+
+
+ALT_V_ARGS = 'v__args'
+ALT_V_KWARGS = 'v__kwargs'
+V_POSITIONAL_ONLY_NAME = 'v__positional_only'
+V_DUPLICATE_KWARGS = 'v__duplicate_kwargs'
+
+
+class ValidatedFunction:
+    def __init__(self, function: 'AnyCallable', config: 'ConfigType'):
+        from inspect import Parameter, signature
+
+        parameters: Mapping[str, Parameter] = signature(function).parameters
+
+        if parameters.keys() & {ALT_V_ARGS, ALT_V_KWARGS, V_POSITIONAL_ONLY_NAME, V_DUPLICATE_KWARGS}:
+            raise PydanticUserError(
+                f'"{ALT_V_ARGS}", "{ALT_V_KWARGS}", "{V_POSITIONAL_ONLY_NAME}" and "{V_DUPLICATE_KWARGS}" '
+                f'are not permitted as argument names when using the "{validate_arguments.__name__}" decorator',
+                code=None,
+            )
+
+        self.raw_function = function
+        self.arg_mapping: Dict[int, str] = {}
+        self.positional_only_args: set[str] = set()
+        self.v_args_name = 'args'
+        self.v_kwargs_name = 'kwargs'
+
+        type_hints = _typing_extra.get_type_hints(function, include_extras=True)
+        takes_args = False
+        takes_kwargs = False
+        fields: Dict[str, Tuple[Any, Any]] = {}
+        for i, (name, p) in enumerate(parameters.items()):
+            if p.annotation is p.empty:
+                annotation = Any
+            else:
+                annotation = type_hints[name]
+
+            default = ... if p.default is p.empty else p.default
+            if p.kind == Parameter.POSITIONAL_ONLY:
+                self.arg_mapping[i] = name
+                fields[name] = annotation, default
+                fields[V_POSITIONAL_ONLY_NAME] = List[str], None
+                self.positional_only_args.add(name)
+            elif p.kind == Parameter.POSITIONAL_OR_KEYWORD:
+                self.arg_mapping[i] = name
+                fields[name] = annotation, default
+                fields[V_DUPLICATE_KWARGS] = List[str], None
+            elif p.kind == Parameter.KEYWORD_ONLY:
+                fields[name] = annotation, default
+            elif p.kind == Parameter.VAR_POSITIONAL:
+                self.v_args_name = name
+                fields[name] = Tuple[annotation, ...], None
+                takes_args = True
+            else:
+                assert p.kind == Parameter.VAR_KEYWORD, p.kind
+                self.v_kwargs_name = name
+                fields[name] = Dict[str, annotation], None
+                takes_kwargs = True
+
+        # these checks avoid a clash between "args" and a field with that name
+        if not takes_args and self.v_args_name in fields:
+            self.v_args_name = ALT_V_ARGS
+
+        # same with "kwargs"
+        if not takes_kwargs and self.v_kwargs_name in fields:
+            self.v_kwargs_name = ALT_V_KWARGS
+
+        if not takes_args:
+            # we add the field so validation below can raise the correct exception
+            fields[self.v_args_name] = List[Any], None
+
+        if not takes_kwargs:
+            # same with kwargs
+            fields[self.v_kwargs_name] = Dict[Any, Any], None
+
+        self.create_model(fields, takes_args, takes_kwargs, config)
+
+    def init_model_instance(self, *args: Any, **kwargs: Any) -> BaseModel:
+        values = self.build_values(args, kwargs)
+        return self.model(**values)
+
+    def call(self, *args: Any, **kwargs: Any) -> Any:
+        m = self.init_model_instance(*args, **kwargs)
+        return self.execute(m)
+
+    def build_values(self, args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> Dict[str, Any]:
+        values: Dict[str, Any] = {}
+        if args:
+            arg_iter = enumerate(args)
+            while True:
+                try:
+                    i, a = next(arg_iter)
+                except StopIteration:
+                    break
+                arg_name = self.arg_mapping.get(i)
+                if arg_name is not None:
+                    values[arg_name] = a
+                else:
+                    values[self.v_args_name] = [a] + [a for _, a in arg_iter]
+                    break
+
+        var_kwargs: Dict[str, Any] = {}
+        wrong_positional_args = []
+        duplicate_kwargs = []
+        fields_alias = [
+            field.alias
+            for name, field in self.model.__pydantic_fields__.items()
+            if name not in (self.v_args_name, self.v_kwargs_name)
+        ]
+        non_var_fields = set(self.model.__pydantic_fields__) - {self.v_args_name, self.v_kwargs_name}
+        for k, v in kwargs.items():
+            if k in non_var_fields or k in fields_alias:
+                if k in self.positional_only_args:
+                    wrong_positional_args.append(k)
+                if k in values:
+                    duplicate_kwargs.append(k)
+                values[k] = v
+            else:
+                var_kwargs[k] = v
+
+        if var_kwargs:
+            values[self.v_kwargs_name] = var_kwargs
+        if wrong_positional_args:
+            values[V_POSITIONAL_ONLY_NAME] = wrong_positional_args
+        if duplicate_kwargs:
+            values[V_DUPLICATE_KWARGS] = duplicate_kwargs
+        return values
+
+    def execute(self, m: BaseModel) -> Any:
+        d = {
+            k: v
+            for k, v in m.__dict__.items()
+            if k in m.__pydantic_fields_set__ or m.__pydantic_fields__[k].default_factory
+        }
+        var_kwargs = d.pop(self.v_kwargs_name, {})
+
+        if self.v_args_name in d:
+            args_: List[Any] = []
+            in_kwargs = False
+            kwargs = {}
+            for name, value in d.items():
+                if in_kwargs:
+                    kwargs[name] = value
+                elif name == self.v_args_name:
+                    args_ += value
+                    in_kwargs = True
+                else:
+                    args_.append(value)
+            return self.raw_function(*args_, **kwargs, **var_kwargs)
+        elif self.positional_only_args:
+            args_ = []
+            kwargs = {}
+            for name, value in d.items():
+                if name in self.positional_only_args:
+                    args_.append(value)
+                else:
+                    kwargs[name] = value
+            return self.raw_function(*args_, **kwargs, **var_kwargs)
+        else:
+            return self.raw_function(**d, **var_kwargs)
+
+    def create_model(self, fields: Dict[str, Any], takes_args: bool, takes_kwargs: bool, config: 'ConfigType') -> None:
+        pos_args = len(self.arg_mapping)
+
+        config_wrapper = _config.ConfigWrapper(config)
+
+        if config_wrapper.alias_generator:
+            raise PydanticUserError(
+                'Setting the "alias_generator" property on custom Config for '
+                '@validate_arguments is not yet supported, please remove.',
+                code=None,
+            )
+        if config_wrapper.extra is None:
+            config_wrapper.config_dict['extra'] = 'forbid'
+
+        class DecoratorBaseModel(BaseModel):
+            @field_validator(self.v_args_name, check_fields=False)
+            @classmethod
+            def check_args(cls, v: Optional[List[Any]]) -> Optional[List[Any]]:
+                if takes_args or v is None:
+                    return v
+
+                raise TypeError(f'{pos_args} positional arguments expected but {pos_args + len(v)} given')
+
+            @field_validator(self.v_kwargs_name, check_fields=False)
+            @classmethod
+            def check_kwargs(cls, v: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
+                if takes_kwargs or v is None:
+                    return v
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v.keys()))
+                raise TypeError(f'unexpected keyword argument{plural}: {keys}')
+
+            @field_validator(V_POSITIONAL_ONLY_NAME, check_fields=False)
+            @classmethod
+            def check_positional_only(cls, v: Optional[List[str]]) -> None:
+                if v is None:
+                    return
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v))
+                raise TypeError(f'positional-only argument{plural} passed as keyword argument{plural}: {keys}')
+
+            @field_validator(V_DUPLICATE_KWARGS, check_fields=False)
+            @classmethod
+            def check_duplicate_kwargs(cls, v: Optional[List[str]]) -> None:
+                if v is None:
+                    return
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v))
+                raise TypeError(f'multiple values for argument{plural}: {keys}')
+
+            model_config = config_wrapper.config_dict
+
+        self.model = create_model(to_pascal(self.raw_function.__name__), __base__=DecoratorBaseModel, **fields)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/deprecated/json.py b/.venv/lib/python3.12/site-packages/pydantic/deprecated/json.py
new file mode 100644
index 00000000..d031aa5b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/deprecated/json.py
@@ -0,0 +1,141 @@
+import datetime
+import warnings
+from collections import deque
+from decimal import Decimal
+from enum import Enum
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from pathlib import Path
+from re import Pattern
+from types import GeneratorType
+from typing import TYPE_CHECKING, Any, Callable, Dict, Type, Union
+from uuid import UUID
+
+from typing_extensions import deprecated
+
+from .._internal._import_utils import import_cached_base_model
+from ..color import Color
+from ..networks import NameEmail
+from ..types import SecretBytes, SecretStr
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'pydantic_encoder', 'custom_pydantic_encoder', 'timedelta_isoformat'
+
+
+def isoformat(o: Union[datetime.date, datetime.time]) -> str:
+    return o.isoformat()
+
+
+def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
+    """Encodes a Decimal as int of there's no exponent, otherwise float.
+
+    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
+    where a integer (but not int typed) is used. Encoding this as a float
+    results in failed round-tripping between encode and parse.
+    Our Id type is a prime example of this.
+
+    >>> decimal_encoder(Decimal("1.0"))
+    1.0
+
+    >>> decimal_encoder(Decimal("1"))
+    1
+    """
+    exponent = dec_value.as_tuple().exponent
+    if isinstance(exponent, int) and exponent >= 0:
+        return int(dec_value)
+    else:
+        return float(dec_value)
+
+
+ENCODERS_BY_TYPE: Dict[Type[Any], Callable[[Any], Any]] = {
+    bytes: lambda o: o.decode(),
+    Color: str,
+    datetime.date: isoformat,
+    datetime.datetime: isoformat,
+    datetime.time: isoformat,
+    datetime.timedelta: lambda td: td.total_seconds(),
+    Decimal: decimal_encoder,
+    Enum: lambda o: o.value,
+    frozenset: list,
+    deque: list,
+    GeneratorType: list,
+    IPv4Address: str,
+    IPv4Interface: str,
+    IPv4Network: str,
+    IPv6Address: str,
+    IPv6Interface: str,
+    IPv6Network: str,
+    NameEmail: str,
+    Path: str,
+    Pattern: lambda o: o.pattern,
+    SecretBytes: str,
+    SecretStr: str,
+    set: list,
+    UUID: str,
+}
+
+
+@deprecated(
+    '`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.',
+    category=None,
+)
+def pydantic_encoder(obj: Any) -> Any:
+    warnings.warn(
+        '`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    from dataclasses import asdict, is_dataclass
+
+    BaseModel = import_cached_base_model()
+
+    if isinstance(obj, BaseModel):
+        return obj.model_dump()
+    elif is_dataclass(obj):
+        return asdict(obj)  # type: ignore
+
+    # Check the class type and its superclasses for a matching encoder
+    for base in obj.__class__.__mro__[:-1]:
+        try:
+            encoder = ENCODERS_BY_TYPE[base]
+        except KeyError:
+            continue
+        return encoder(obj)
+    else:  # We have exited the for loop without finding a suitable encoder
+        raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable")
+
+
+# TODO: Add a suggested migration path once there is a way to use custom encoders
+@deprecated(
+    '`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.',
+    category=None,
+)
+def custom_pydantic_encoder(type_encoders: Dict[Any, Callable[[Type[Any]], Any]], obj: Any) -> Any:
+    warnings.warn(
+        '`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    # Check the class type and its superclasses for a matching encoder
+    for base in obj.__class__.__mro__[:-1]:
+        try:
+            encoder = type_encoders[base]
+        except KeyError:
+            continue
+
+        return encoder(obj)
+    else:  # We have exited the for loop without finding a suitable encoder
+        return pydantic_encoder(obj)
+
+
+@deprecated('`timedelta_isoformat` is deprecated.', category=None)
+def timedelta_isoformat(td: datetime.timedelta) -> str:
+    """ISO 8601 encoding for Python timedelta object."""
+    warnings.warn('`timedelta_isoformat` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
+    minutes, seconds = divmod(td.seconds, 60)
+    hours, minutes = divmod(minutes, 60)
+    return f'{"-" if td.days < 0 else ""}P{abs(td.days)}DT{hours:d}H{minutes:d}M{seconds:d}.{td.microseconds:06d}S'
diff --git a/.venv/lib/python3.12/site-packages/pydantic/deprecated/parse.py b/.venv/lib/python3.12/site-packages/pydantic/deprecated/parse.py
new file mode 100644
index 00000000..2a92e62b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/deprecated/parse.py
@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+import json
+import pickle
+import warnings
+from enum import Enum
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable
+
+from typing_extensions import deprecated
+
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+
+class Protocol(str, Enum):
+    json = 'json'
+    pickle = 'pickle'
+
+
+@deprecated('`load_str_bytes` is deprecated.', category=None)
+def load_str_bytes(
+    b: str | bytes,
+    *,
+    content_type: str | None = None,
+    encoding: str = 'utf8',
+    proto: Protocol | None = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    warnings.warn('`load_str_bytes` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
+    if proto is None and content_type:
+        if content_type.endswith(('json', 'javascript')):
+            pass
+        elif allow_pickle and content_type.endswith('pickle'):
+            proto = Protocol.pickle
+        else:
+            raise TypeError(f'Unknown content-type: {content_type}')
+
+    proto = proto or Protocol.json
+
+    if proto == Protocol.json:
+        if isinstance(b, bytes):
+            b = b.decode(encoding)
+        return json_loads(b)  # type: ignore
+    elif proto == Protocol.pickle:
+        if not allow_pickle:
+            raise RuntimeError('Trying to decode with pickle with allow_pickle=False')
+        bb = b if isinstance(b, bytes) else b.encode()  # type: ignore
+        return pickle.loads(bb)
+    else:
+        raise TypeError(f'Unknown protocol: {proto}')
+
+
+@deprecated('`load_file` is deprecated.', category=None)
+def load_file(
+    path: str | Path,
+    *,
+    content_type: str | None = None,
+    encoding: str = 'utf8',
+    proto: Protocol | None = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    warnings.warn('`load_file` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
+    path = Path(path)
+    b = path.read_bytes()
+    if content_type is None:
+        if path.suffix in ('.js', '.json'):
+            proto = Protocol.json
+        elif path.suffix == '.pkl':
+            proto = Protocol.pickle
+
+    return load_str_bytes(
+        b, proto=proto, content_type=content_type, encoding=encoding, allow_pickle=allow_pickle, json_loads=json_loads
+    )
diff --git a/.venv/lib/python3.12/site-packages/pydantic/deprecated/tools.py b/.venv/lib/python3.12/site-packages/pydantic/deprecated/tools.py
new file mode 100644
index 00000000..b04eae40
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/deprecated/tools.py
@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import json
+import warnings
+from typing import TYPE_CHECKING, Any, Callable, Type, TypeVar, Union
+
+from typing_extensions import deprecated
+
+from ..json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema
+from ..type_adapter import TypeAdapter
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'parse_obj_as', 'schema_of', 'schema_json_of'
+
+NameFactory = Union[str, Callable[[Type[Any]], str]]
+
+
+T = TypeVar('T')
+
+
+@deprecated(
+    '`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.',
+    category=None,
+)
+def parse_obj_as(type_: type[T], obj: Any, type_name: NameFactory | None = None) -> T:
+    warnings.warn(
+        '`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    if type_name is not None:  # pragma: no cover
+        warnings.warn(
+            'The type_name parameter is deprecated. parse_obj_as no longer creates temporary models',
+            DeprecationWarning,
+            stacklevel=2,
+        )
+    return TypeAdapter(type_).validate_python(obj)
+
+
+@deprecated(
+    '`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+    category=None,
+)
+def schema_of(
+    type_: Any,
+    *,
+    title: NameFactory | None = None,
+    by_alias: bool = True,
+    ref_template: str = DEFAULT_REF_TEMPLATE,
+    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+) -> dict[str, Any]:
+    """Generate a JSON schema (as dict) for the passed model or dynamically generated one."""
+    warnings.warn(
+        '`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    res = TypeAdapter(type_).json_schema(
+        by_alias=by_alias,
+        schema_generator=schema_generator,
+        ref_template=ref_template,
+    )
+    if title is not None:
+        if isinstance(title, str):
+            res['title'] = title
+        else:
+            warnings.warn(
+                'Passing a callable for the `title` parameter is deprecated and no longer supported',
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            res['title'] = title(type_)
+    return res
+
+
+@deprecated(
+    '`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+    category=None,
+)
+def schema_json_of(
+    type_: Any,
+    *,
+    title: NameFactory | None = None,
+    by_alias: bool = True,
+    ref_template: str = DEFAULT_REF_TEMPLATE,
+    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+    **dumps_kwargs: Any,
+) -> str:
+    """Generate a JSON schema (as JSON) for the passed model or dynamically generated one."""
+    warnings.warn(
+        '`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    return json.dumps(
+        schema_of(type_, title=title, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator),
+        **dumps_kwargs,
+    )
diff --git a/.venv/lib/python3.12/site-packages/pydantic/env_settings.py b/.venv/lib/python3.12/site-packages/pydantic/env_settings.py
new file mode 100644
index 00000000..cd0b04e6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/env_settings.py
@@ -0,0 +1,5 @@
+"""The `env_settings` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/error_wrappers.py b/.venv/lib/python3.12/site-packages/pydantic/error_wrappers.py
new file mode 100644
index 00000000..2985419a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/error_wrappers.py
@@ -0,0 +1,5 @@
+"""The `error_wrappers` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/errors.py b/.venv/lib/python3.12/site-packages/pydantic/errors.py
new file mode 100644
index 00000000..c7d46842
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/errors.py
@@ -0,0 +1,162 @@
+"""Pydantic-specific errors."""
+
+from __future__ import annotations as _annotations
+
+import re
+
+from typing_extensions import Literal, Self
+
+from ._migration import getattr_migration
+from .version import version_short
+
+__all__ = (
+    'PydanticUserError',
+    'PydanticUndefinedAnnotation',
+    'PydanticImportError',
+    'PydanticSchemaGenerationError',
+    'PydanticInvalidForJsonSchema',
+    'PydanticErrorCodes',
+)
+
+# We use this URL to allow for future flexibility about how we host the docs, while allowing for Pydantic
+# code in the while with "old" URLs to still work.
+# 'u' refers to "user errors" - e.g. errors caused by developers using pydantic, as opposed to validation errors.
+DEV_ERROR_DOCS_URL = f'https://errors.pydantic.dev/{version_short()}/u/'
+PydanticErrorCodes = Literal[
+    'class-not-fully-defined',
+    'custom-json-schema',
+    'decorator-missing-field',
+    'discriminator-no-field',
+    'discriminator-alias-type',
+    'discriminator-needs-literal',
+    'discriminator-alias',
+    'discriminator-validator',
+    'callable-discriminator-no-tag',
+    'typed-dict-version',
+    'model-field-overridden',
+    'model-field-missing-annotation',
+    'config-both',
+    'removed-kwargs',
+    'circular-reference-schema',
+    'invalid-for-json-schema',
+    'json-schema-already-used',
+    'base-model-instantiated',
+    'undefined-annotation',
+    'schema-for-unknown-type',
+    'import-error',
+    'create-model-field-definitions',
+    'create-model-config-base',
+    'validator-no-fields',
+    'validator-invalid-fields',
+    'validator-instance-method',
+    'validator-input-type',
+    'root-validator-pre-skip',
+    'model-serializer-instance-method',
+    'validator-field-config-info',
+    'validator-v1-signature',
+    'validator-signature',
+    'field-serializer-signature',
+    'model-serializer-signature',
+    'multiple-field-serializers',
+    'invalid-annotated-type',
+    'type-adapter-config-unused',
+    'root-model-extra',
+    'unevaluable-type-annotation',
+    'dataclass-init-false-extra-allow',
+    'clashing-init-and-init-var',
+    'model-config-invalid-field-name',
+    'with-config-on-model',
+    'dataclass-on-model',
+    'validate-call-type',
+    'unpack-typed-dict',
+    'overlapping-unpack-typed-dict',
+    'invalid-self-type',
+]
+
+
+class PydanticErrorMixin:
+    """A mixin class for common functionality shared by all Pydantic-specific errors.
+
+    Attributes:
+        message: A message describing the error.
+        code: An optional error code from PydanticErrorCodes enum.
+    """
+
+    def __init__(self, message: str, *, code: PydanticErrorCodes | None) -> None:
+        self.message = message
+        self.code = code
+
+    def __str__(self) -> str:
+        if self.code is None:
+            return self.message
+        else:
+            return f'{self.message}\n\nFor further information visit {DEV_ERROR_DOCS_URL}{self.code}'
+
+
+class PydanticUserError(PydanticErrorMixin, TypeError):
+    """An error raised due to incorrect use of Pydantic."""
+
+
+class PydanticUndefinedAnnotation(PydanticErrorMixin, NameError):
+    """A subclass of `NameError` raised when handling undefined annotations during `CoreSchema` generation.
+
+    Attributes:
+        name: Name of the error.
+        message: Description of the error.
+    """
+
+    def __init__(self, name: str, message: str) -> None:
+        self.name = name
+        super().__init__(message=message, code='undefined-annotation')
+
+    @classmethod
+    def from_name_error(cls, name_error: NameError) -> Self:
+        """Convert a `NameError` to a `PydanticUndefinedAnnotation` error.
+
+        Args:
+            name_error: `NameError` to be converted.
+
+        Returns:
+            Converted `PydanticUndefinedAnnotation` error.
+        """
+        try:
+            name = name_error.name  # type: ignore  # python > 3.10
+        except AttributeError:
+            name = re.search(r".*'(.+?)'", str(name_error)).group(1)  # type: ignore[union-attr]
+        return cls(name=name, message=str(name_error))
+
+
+class PydanticImportError(PydanticErrorMixin, ImportError):
+    """An error raised when an import fails due to module changes between V1 and V2.
+
+    Attributes:
+        message: Description of the error.
+    """
+
+    def __init__(self, message: str) -> None:
+        super().__init__(message, code='import-error')
+
+
+class PydanticSchemaGenerationError(PydanticUserError):
+    """An error raised during failures to generate a `CoreSchema` for some type.
+
+    Attributes:
+        message: Description of the error.
+    """
+
+    def __init__(self, message: str) -> None:
+        super().__init__(message, code='schema-for-unknown-type')
+
+
+class PydanticInvalidForJsonSchema(PydanticUserError):
+    """An error raised during failures to generate a JSON schema for some `CoreSchema`.
+
+    Attributes:
+        message: Description of the error.
+    """
+
+    def __init__(self, message: str) -> None:
+        super().__init__(message, code='invalid-for-json-schema')
+
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/experimental/__init__.py b/.venv/lib/python3.12/site-packages/pydantic/experimental/__init__.py
new file mode 100644
index 00000000..4aa58c6a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/experimental/__init__.py
@@ -0,0 +1,10 @@
+"""The "experimental" module of pydantic contains potential new features that are subject to change."""
+
+import warnings
+
+from pydantic.warnings import PydanticExperimentalWarning
+
+warnings.warn(
+    'This module is experimental, its contents are subject to change and deprecation.',
+    category=PydanticExperimentalWarning,
+)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/experimental/pipeline.py b/.venv/lib/python3.12/site-packages/pydantic/experimental/pipeline.py
new file mode 100644
index 00000000..29da979e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/experimental/pipeline.py
@@ -0,0 +1,669 @@
+"""Experimental pipeline API functionality. Be careful with this API, it's subject to change."""
+
+from __future__ import annotations
+
+import datetime
+import operator
+import re
+import sys
+from collections import deque
+from collections.abc import Container
+from dataclasses import dataclass
+from decimal import Decimal
+from functools import cached_property, partial
+from typing import TYPE_CHECKING, Any, Callable, Generic, Pattern, Protocol, TypeVar, Union, overload
+
+import annotated_types
+from typing_extensions import Annotated
+
+if TYPE_CHECKING:
+    from pydantic_core import core_schema as cs
+
+    from pydantic import GetCoreSchemaHandler
+
+from pydantic._internal._internal_dataclass import slots_true as _slots_true
+
+if sys.version_info < (3, 10):
+    EllipsisType = type(Ellipsis)
+else:
+    from types import EllipsisType
+
+__all__ = ['validate_as', 'validate_as_deferred', 'transform']
+
+_slots_frozen = {**_slots_true, 'frozen': True}
+
+
+@dataclass(**_slots_frozen)
+class _ValidateAs:
+    tp: type[Any]
+    strict: bool = False
+
+
+@dataclass
+class _ValidateAsDefer:
+    func: Callable[[], type[Any]]
+
+    @cached_property
+    def tp(self) -> type[Any]:
+        return self.func()
+
+
+@dataclass(**_slots_frozen)
+class _Transform:
+    func: Callable[[Any], Any]
+
+
+@dataclass(**_slots_frozen)
+class _PipelineOr:
+    left: _Pipeline[Any, Any]
+    right: _Pipeline[Any, Any]
+
+
+@dataclass(**_slots_frozen)
+class _PipelineAnd:
+    left: _Pipeline[Any, Any]
+    right: _Pipeline[Any, Any]
+
+
+@dataclass(**_slots_frozen)
+class _Eq:
+    value: Any
+
+
+@dataclass(**_slots_frozen)
+class _NotEq:
+    value: Any
+
+
+@dataclass(**_slots_frozen)
+class _In:
+    values: Container[Any]
+
+
+@dataclass(**_slots_frozen)
+class _NotIn:
+    values: Container[Any]
+
+
+_ConstraintAnnotation = Union[
+    annotated_types.Le,
+    annotated_types.Ge,
+    annotated_types.Lt,
+    annotated_types.Gt,
+    annotated_types.Len,
+    annotated_types.MultipleOf,
+    annotated_types.Timezone,
+    annotated_types.Interval,
+    annotated_types.Predicate,
+    # common predicates not included in annotated_types
+    _Eq,
+    _NotEq,
+    _In,
+    _NotIn,
+    # regular expressions
+    Pattern[str],
+]
+
+
+@dataclass(**_slots_frozen)
+class _Constraint:
+    constraint: _ConstraintAnnotation
+
+
+_Step = Union[_ValidateAs, _ValidateAsDefer, _Transform, _PipelineOr, _PipelineAnd, _Constraint]
+
+_InT = TypeVar('_InT')
+_OutT = TypeVar('_OutT')
+_NewOutT = TypeVar('_NewOutT')
+
+
+class _FieldTypeMarker:
+    pass
+
+
+# TODO: ultimately, make this public, see https://github.com/pydantic/pydantic/pull/9459#discussion_r1628197626
+# Also, make this frozen eventually, but that doesn't work right now because of the generic base
+# Which attempts to modify __orig_base__ and such.
+# We could go with a manual freeze, but that seems overkill for now.
+@dataclass(**_slots_true)
+class _Pipeline(Generic[_InT, _OutT]):
+    """Abstract representation of a chain of validation, transformation, and parsing steps."""
+
+    _steps: tuple[_Step, ...]
+
+    def transform(
+        self,
+        func: Callable[[_OutT], _NewOutT],
+    ) -> _Pipeline[_InT, _NewOutT]:
+        """Transform the output of the previous step.
+
+        If used as the first step in a pipeline, the type of the field is used.
+        That is, the transformation is applied to after the value is parsed to the field's type.
+        """
+        return _Pipeline[_InT, _NewOutT](self._steps + (_Transform(func),))
+
+    @overload
+    def validate_as(self, tp: type[_NewOutT], *, strict: bool = ...) -> _Pipeline[_InT, _NewOutT]: ...
+
+    @overload
+    def validate_as(self, tp: EllipsisType, *, strict: bool = ...) -> _Pipeline[_InT, Any]:  # type: ignore
+        ...
+
+    def validate_as(self, tp: type[_NewOutT] | EllipsisType, *, strict: bool = False) -> _Pipeline[_InT, Any]:  # type: ignore
+        """Validate / parse the input into a new type.
+
+        If no type is provided, the type of the field is used.
+
+        Types are parsed in Pydantic's `lax` mode by default,
+        but you can enable `strict` mode by passing `strict=True`.
+        """
+        if isinstance(tp, EllipsisType):
+            return _Pipeline[_InT, Any](self._steps + (_ValidateAs(_FieldTypeMarker, strict=strict),))
+        return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAs(tp, strict=strict),))
+
+    def validate_as_deferred(self, func: Callable[[], type[_NewOutT]]) -> _Pipeline[_InT, _NewOutT]:
+        """Parse the input into a new type, deferring resolution of the type until the current class
+        is fully defined.
+
+        This is useful when you need to reference the class in it's own type annotations.
+        """
+        return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAsDefer(func),))
+
+    # constraints
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutGe], constraint: annotated_types.Ge) -> _Pipeline[_InT, _NewOutGe]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutGt], constraint: annotated_types.Gt) -> _Pipeline[_InT, _NewOutGt]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutLe], constraint: annotated_types.Le) -> _Pipeline[_InT, _NewOutLe]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutLt], constraint: annotated_types.Lt) -> _Pipeline[_InT, _NewOutLt]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutLen], constraint: annotated_types.Len
+    ) -> _Pipeline[_InT, _NewOutLen]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutT], constraint: annotated_types.MultipleOf
+    ) -> _Pipeline[_InT, _NewOutT]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutDatetime], constraint: annotated_types.Timezone
+    ) -> _Pipeline[_InT, _NewOutDatetime]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: annotated_types.Predicate) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutInterval], constraint: annotated_types.Interval
+    ) -> _Pipeline[_InT, _NewOutInterval]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _Eq) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _NotEq) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _In) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _NotIn) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutT], constraint: Pattern[str]) -> _Pipeline[_InT, _NewOutT]: ...
+
+    def constrain(self, constraint: _ConstraintAnnotation) -> Any:
+        """Constrain a value to meet a certain condition.
+
+        We support most conditions from `annotated_types`, as well as regular expressions.
+
+        Most of the time you'll be calling a shortcut method like `gt`, `lt`, `len`, etc
+        so you don't need to call this directly.
+        """
+        return _Pipeline[_InT, _OutT](self._steps + (_Constraint(constraint),))
+
+    def predicate(self: _Pipeline[_InT, _NewOutT], func: Callable[[_NewOutT], bool]) -> _Pipeline[_InT, _NewOutT]:
+        """Constrain a value to meet a certain predicate."""
+        return self.constrain(annotated_types.Predicate(func))
+
+    def gt(self: _Pipeline[_InT, _NewOutGt], gt: _NewOutGt) -> _Pipeline[_InT, _NewOutGt]:
+        """Constrain a value to be greater than a certain value."""
+        return self.constrain(annotated_types.Gt(gt))
+
+    def lt(self: _Pipeline[_InT, _NewOutLt], lt: _NewOutLt) -> _Pipeline[_InT, _NewOutLt]:
+        """Constrain a value to be less than a certain value."""
+        return self.constrain(annotated_types.Lt(lt))
+
+    def ge(self: _Pipeline[_InT, _NewOutGe], ge: _NewOutGe) -> _Pipeline[_InT, _NewOutGe]:
+        """Constrain a value to be greater than or equal to a certain value."""
+        return self.constrain(annotated_types.Ge(ge))
+
+    def le(self: _Pipeline[_InT, _NewOutLe], le: _NewOutLe) -> _Pipeline[_InT, _NewOutLe]:
+        """Constrain a value to be less than or equal to a certain value."""
+        return self.constrain(annotated_types.Le(le))
+
+    def len(self: _Pipeline[_InT, _NewOutLen], min_len: int, max_len: int | None = None) -> _Pipeline[_InT, _NewOutLen]:
+        """Constrain a value to have a certain length."""
+        return self.constrain(annotated_types.Len(min_len, max_len))
+
+    @overload
+    def multiple_of(self: _Pipeline[_InT, _NewOutDiv], multiple_of: _NewOutDiv) -> _Pipeline[_InT, _NewOutDiv]: ...
+
+    @overload
+    def multiple_of(self: _Pipeline[_InT, _NewOutMod], multiple_of: _NewOutMod) -> _Pipeline[_InT, _NewOutMod]: ...
+
+    def multiple_of(self: _Pipeline[_InT, Any], multiple_of: Any) -> _Pipeline[_InT, Any]:
+        """Constrain a value to be a multiple of a certain number."""
+        return self.constrain(annotated_types.MultipleOf(multiple_of))
+
+    def eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to be equal to a certain value."""
+        return self.constrain(_Eq(value))
+
+    def not_eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to not be equal to a certain value."""
+        return self.constrain(_NotEq(value))
+
+    def in_(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to be in a certain set."""
+        return self.constrain(_In(values))
+
+    def not_in(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to not be in a certain set."""
+        return self.constrain(_NotIn(values))
+
+    # timezone methods
+    def datetime_tz_naive(self: _Pipeline[_InT, datetime.datetime]) -> _Pipeline[_InT, datetime.datetime]:
+        return self.constrain(annotated_types.Timezone(None))
+
+    def datetime_tz_aware(self: _Pipeline[_InT, datetime.datetime]) -> _Pipeline[_InT, datetime.datetime]:
+        return self.constrain(annotated_types.Timezone(...))
+
+    def datetime_tz(
+        self: _Pipeline[_InT, datetime.datetime], tz: datetime.tzinfo
+    ) -> _Pipeline[_InT, datetime.datetime]:
+        return self.constrain(annotated_types.Timezone(tz))  # type: ignore
+
+    def datetime_with_tz(
+        self: _Pipeline[_InT, datetime.datetime], tz: datetime.tzinfo | None
+    ) -> _Pipeline[_InT, datetime.datetime]:
+        return self.transform(partial(datetime.datetime.replace, tzinfo=tz))
+
+    # string methods
+    def str_lower(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.lower)
+
+    def str_upper(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.upper)
+
+    def str_title(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.title)
+
+    def str_strip(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.strip)
+
+    def str_pattern(self: _Pipeline[_InT, str], pattern: str) -> _Pipeline[_InT, str]:
+        return self.constrain(re.compile(pattern))
+
+    def str_contains(self: _Pipeline[_InT, str], substring: str) -> _Pipeline[_InT, str]:
+        return self.predicate(lambda v: substring in v)
+
+    def str_starts_with(self: _Pipeline[_InT, str], prefix: str) -> _Pipeline[_InT, str]:
+        return self.predicate(lambda v: v.startswith(prefix))
+
+    def str_ends_with(self: _Pipeline[_InT, str], suffix: str) -> _Pipeline[_InT, str]:
+        return self.predicate(lambda v: v.endswith(suffix))
+
+    # operators
+    def otherwise(self, other: _Pipeline[_OtherIn, _OtherOut]) -> _Pipeline[_InT | _OtherIn, _OutT | _OtherOut]:
+        """Combine two validation chains, returning the result of the first chain if it succeeds, and the second chain if it fails."""
+        return _Pipeline((_PipelineOr(self, other),))
+
+    __or__ = otherwise
+
+    def then(self, other: _Pipeline[_OutT, _OtherOut]) -> _Pipeline[_InT, _OtherOut]:
+        """Pipe the result of one validation chain into another."""
+        return _Pipeline((_PipelineAnd(self, other),))
+
+    __and__ = then
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> cs.CoreSchema:
+        from pydantic_core import core_schema as cs
+
+        queue = deque(self._steps)
+
+        s = None
+
+        while queue:
+            step = queue.popleft()
+            s = _apply_step(step, s, handler, source_type)
+
+        s = s or cs.any_schema()
+        return s
+
+    def __supports_type__(self, _: _OutT) -> bool:
+        raise NotImplementedError
+
+
+validate_as = _Pipeline[Any, Any](()).validate_as
+validate_as_deferred = _Pipeline[Any, Any](()).validate_as_deferred
+transform = _Pipeline[Any, Any]((_ValidateAs(_FieldTypeMarker),)).transform
+
+
+def _check_func(
+    func: Callable[[Any], bool], predicate_err: str | Callable[[], str], s: cs.CoreSchema | None
+) -> cs.CoreSchema:
+    from pydantic_core import core_schema as cs
+
+    def handler(v: Any) -> Any:
+        if func(v):
+            return v
+        raise ValueError(f'Expected {predicate_err if isinstance(predicate_err, str) else predicate_err()}')
+
+    if s is None:
+        return cs.no_info_plain_validator_function(handler)
+    else:
+        return cs.no_info_after_validator_function(handler, s)
+
+
+def _apply_step(step: _Step, s: cs.CoreSchema | None, handler: GetCoreSchemaHandler, source_type: Any) -> cs.CoreSchema:
+    from pydantic_core import core_schema as cs
+
+    if isinstance(step, _ValidateAs):
+        s = _apply_parse(s, step.tp, step.strict, handler, source_type)
+    elif isinstance(step, _ValidateAsDefer):
+        s = _apply_parse(s, step.tp, False, handler, source_type)
+    elif isinstance(step, _Transform):
+        s = _apply_transform(s, step.func, handler)
+    elif isinstance(step, _Constraint):
+        s = _apply_constraint(s, step.constraint)
+    elif isinstance(step, _PipelineOr):
+        s = cs.union_schema([handler(step.left), handler(step.right)])
+    else:
+        assert isinstance(step, _PipelineAnd)
+        s = cs.chain_schema([handler(step.left), handler(step.right)])
+    return s
+
+
+def _apply_parse(
+    s: cs.CoreSchema | None,
+    tp: type[Any],
+    strict: bool,
+    handler: GetCoreSchemaHandler,
+    source_type: Any,
+) -> cs.CoreSchema:
+    from pydantic_core import core_schema as cs
+
+    from pydantic import Strict
+
+    if tp is _FieldTypeMarker:
+        return handler(source_type)
+
+    if strict:
+        tp = Annotated[tp, Strict()]  # type: ignore
+
+    if s and s['type'] == 'any':
+        return handler(tp)
+    else:
+        return cs.chain_schema([s, handler(tp)]) if s else handler(tp)
+
+
+def _apply_transform(
+    s: cs.CoreSchema | None, func: Callable[[Any], Any], handler: GetCoreSchemaHandler
+) -> cs.CoreSchema:
+    from pydantic_core import core_schema as cs
+
+    if s is None:
+        return cs.no_info_plain_validator_function(func)
+
+    if s['type'] == 'str':
+        if func is str.strip:
+            s = s.copy()
+            s['strip_whitespace'] = True
+            return s
+        elif func is str.lower:
+            s = s.copy()
+            s['to_lower'] = True
+            return s
+        elif func is str.upper:
+            s = s.copy()
+            s['to_upper'] = True
+            return s
+
+    return cs.no_info_after_validator_function(func, s)
+
+
+def _apply_constraint(  # noqa: C901
+    s: cs.CoreSchema | None, constraint: _ConstraintAnnotation
+) -> cs.CoreSchema:
+    """Apply a single constraint to a schema."""
+    if isinstance(constraint, annotated_types.Gt):
+        gt = constraint.gt
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(gt, int):
+                s['gt'] = gt
+            elif s['type'] == 'float' and isinstance(gt, float):
+                s['gt'] = gt
+            elif s['type'] == 'decimal' and isinstance(gt, Decimal):
+                s['gt'] = gt
+        else:
+
+            def check_gt(v: Any) -> bool:
+                return v > gt
+
+            s = _check_func(check_gt, f'> {gt}', s)
+    elif isinstance(constraint, annotated_types.Ge):
+        ge = constraint.ge
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(ge, int):
+                s['ge'] = ge
+            elif s['type'] == 'float' and isinstance(ge, float):
+                s['ge'] = ge
+            elif s['type'] == 'decimal' and isinstance(ge, Decimal):
+                s['ge'] = ge
+
+        def check_ge(v: Any) -> bool:
+            return v >= ge
+
+        s = _check_func(check_ge, f'>= {ge}', s)
+    elif isinstance(constraint, annotated_types.Lt):
+        lt = constraint.lt
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(lt, int):
+                s['lt'] = lt
+            elif s['type'] == 'float' and isinstance(lt, float):
+                s['lt'] = lt
+            elif s['type'] == 'decimal' and isinstance(lt, Decimal):
+                s['lt'] = lt
+
+        def check_lt(v: Any) -> bool:
+            return v < lt
+
+        s = _check_func(check_lt, f'< {lt}', s)
+    elif isinstance(constraint, annotated_types.Le):
+        le = constraint.le
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(le, int):
+                s['le'] = le
+            elif s['type'] == 'float' and isinstance(le, float):
+                s['le'] = le
+            elif s['type'] == 'decimal' and isinstance(le, Decimal):
+                s['le'] = le
+
+        def check_le(v: Any) -> bool:
+            return v <= le
+
+        s = _check_func(check_le, f'<= {le}', s)
+    elif isinstance(constraint, annotated_types.Len):
+        min_len = constraint.min_length
+        max_len = constraint.max_length
+
+        if s and s['type'] in {'str', 'list', 'tuple', 'set', 'frozenset', 'dict'}:
+            assert (
+                s['type'] == 'str'
+                or s['type'] == 'list'
+                or s['type'] == 'tuple'
+                or s['type'] == 'set'
+                or s['type'] == 'dict'
+                or s['type'] == 'frozenset'
+            )
+            s = s.copy()
+            if min_len != 0:
+                s['min_length'] = min_len
+            if max_len is not None:
+                s['max_length'] = max_len
+
+        def check_len(v: Any) -> bool:
+            if max_len is not None:
+                return (min_len <= len(v)) and (len(v) <= max_len)
+            return min_len <= len(v)
+
+        s = _check_func(check_len, f'length >= {min_len} and length <= {max_len}', s)
+    elif isinstance(constraint, annotated_types.MultipleOf):
+        multiple_of = constraint.multiple_of
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(multiple_of, int):
+                s['multiple_of'] = multiple_of
+            elif s['type'] == 'float' and isinstance(multiple_of, float):
+                s['multiple_of'] = multiple_of
+            elif s['type'] == 'decimal' and isinstance(multiple_of, Decimal):
+                s['multiple_of'] = multiple_of
+
+        def check_multiple_of(v: Any) -> bool:
+            return v % multiple_of == 0
+
+        s = _check_func(check_multiple_of, f'% {multiple_of} == 0', s)
+    elif isinstance(constraint, annotated_types.Timezone):
+        tz = constraint.tz
+
+        if tz is ...:
+            if s and s['type'] == 'datetime':
+                s = s.copy()
+                s['tz_constraint'] = 'aware'
+            else:
+
+                def check_tz_aware(v: object) -> bool:
+                    assert isinstance(v, datetime.datetime)
+                    return v.tzinfo is not None
+
+                s = _check_func(check_tz_aware, 'timezone aware', s)
+        elif tz is None:
+            if s and s['type'] == 'datetime':
+                s = s.copy()
+                s['tz_constraint'] = 'naive'
+            else:
+
+                def check_tz_naive(v: object) -> bool:
+                    assert isinstance(v, datetime.datetime)
+                    return v.tzinfo is None
+
+                s = _check_func(check_tz_naive, 'timezone naive', s)
+        else:
+            raise NotImplementedError('Constraining to a specific timezone is not yet supported')
+    elif isinstance(constraint, annotated_types.Interval):
+        if constraint.ge:
+            s = _apply_constraint(s, annotated_types.Ge(constraint.ge))
+        if constraint.gt:
+            s = _apply_constraint(s, annotated_types.Gt(constraint.gt))
+        if constraint.le:
+            s = _apply_constraint(s, annotated_types.Le(constraint.le))
+        if constraint.lt:
+            s = _apply_constraint(s, annotated_types.Lt(constraint.lt))
+        assert s is not None
+    elif isinstance(constraint, annotated_types.Predicate):
+        func = constraint.func
+
+        if func.__name__ == '<lambda>':
+            # attempt to extract the source code for a lambda function
+            # to use as the function name in error messages
+            # TODO: is there a better way? should we just not do this?
+            import inspect
+
+            try:
+                # remove ')' suffix, can use removesuffix once we drop 3.8
+                source = inspect.getsource(func).strip()
+                if source.endswith(')'):
+                    source = source[:-1]
+                lambda_source_code = '`' + ''.join(''.join(source.split('lambda ')[1:]).split(':')[1:]).strip() + '`'
+            except OSError:
+                # stringified annotations
+                lambda_source_code = 'lambda'
+
+            s = _check_func(func, lambda_source_code, s)
+        else:
+            s = _check_func(func, func.__name__, s)
+    elif isinstance(constraint, _NotEq):
+        value = constraint.value
+
+        def check_not_eq(v: Any) -> bool:
+            return operator.__ne__(v, value)
+
+        s = _check_func(check_not_eq, f'!= {value}', s)
+    elif isinstance(constraint, _Eq):
+        value = constraint.value
+
+        def check_eq(v: Any) -> bool:
+            return operator.__eq__(v, value)
+
+        s = _check_func(check_eq, f'== {value}', s)
+    elif isinstance(constraint, _In):
+        values = constraint.values
+
+        def check_in(v: Any) -> bool:
+            return operator.__contains__(values, v)
+
+        s = _check_func(check_in, f'in {values}', s)
+    elif isinstance(constraint, _NotIn):
+        values = constraint.values
+
+        def check_not_in(v: Any) -> bool:
+            return operator.__not__(operator.__contains__(values, v))
+
+        s = _check_func(check_not_in, f'not in {values}', s)
+    else:
+        assert isinstance(constraint, Pattern)
+        if s and s['type'] == 'str':
+            s = s.copy()
+            s['pattern'] = constraint.pattern
+        else:
+
+            def check_pattern(v: object) -> bool:
+                assert isinstance(v, str)
+                return constraint.match(v) is not None
+
+            s = _check_func(check_pattern, f'~ {constraint.pattern}', s)
+    return s
+
+
+class _SupportsRange(annotated_types.SupportsLe, annotated_types.SupportsGe, Protocol):
+    pass
+
+
+class _SupportsLen(Protocol):
+    def __len__(self) -> int: ...
+
+
+_NewOutGt = TypeVar('_NewOutGt', bound=annotated_types.SupportsGt)
+_NewOutGe = TypeVar('_NewOutGe', bound=annotated_types.SupportsGe)
+_NewOutLt = TypeVar('_NewOutLt', bound=annotated_types.SupportsLt)
+_NewOutLe = TypeVar('_NewOutLe', bound=annotated_types.SupportsLe)
+_NewOutLen = TypeVar('_NewOutLen', bound=_SupportsLen)
+_NewOutDiv = TypeVar('_NewOutDiv', bound=annotated_types.SupportsDiv)
+_NewOutMod = TypeVar('_NewOutMod', bound=annotated_types.SupportsMod)
+_NewOutDatetime = TypeVar('_NewOutDatetime', bound=datetime.datetime)
+_NewOutInterval = TypeVar('_NewOutInterval', bound=_SupportsRange)
+_OtherIn = TypeVar('_OtherIn')
+_OtherOut = TypeVar('_OtherOut')
diff --git a/.venv/lib/python3.12/site-packages/pydantic/fields.py b/.venv/lib/python3.12/site-packages/pydantic/fields.py
new file mode 100644
index 00000000..9582685c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/fields.py
@@ -0,0 +1,1504 @@
+"""Defining fields on models."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import inspect
+import sys
+import typing
+from copy import copy
+from dataclasses import Field as DataclassField
+from functools import cached_property
+from typing import Any, Callable, ClassVar, TypeVar, cast, overload
+from warnings import warn
+
+import annotated_types
+import typing_extensions
+from pydantic_core import PydanticUndefined
+from typing_extensions import Literal, TypeAlias, Unpack, deprecated
+
+from . import types
+from ._internal import _decorators, _fields, _generics, _internal_dataclass, _repr, _typing_extra, _utils
+from ._internal._namespace_utils import GlobalsNamespace, MappingNamespace
+from .aliases import AliasChoices, AliasPath
+from .config import JsonDict
+from .errors import PydanticUserError
+from .json_schema import PydanticJsonSchemaWarning
+from .warnings import PydanticDeprecatedSince20
+
+if typing.TYPE_CHECKING:
+    from ._internal._repr import ReprArgs
+else:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'Field', 'PrivateAttr', 'computed_field'
+
+
+_Unset: Any = PydanticUndefined
+
+if sys.version_info >= (3, 13):
+    import warnings
+
+    Deprecated: TypeAlias = warnings.deprecated | deprecated
+else:
+    Deprecated: TypeAlias = deprecated
+
+
+class _FromFieldInfoInputs(typing_extensions.TypedDict, total=False):
+    """This class exists solely to add type checking for the `**kwargs` in `FieldInfo.from_field`."""
+
+    annotation: type[Any] | None
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None
+    alias: str | None
+    alias_priority: int | None
+    validation_alias: str | AliasPath | AliasChoices | None
+    serialization_alias: str | None
+    title: str | None
+    field_title_generator: Callable[[str, FieldInfo], str] | None
+    description: str | None
+    examples: list[Any] | None
+    exclude: bool | None
+    gt: annotated_types.SupportsGt | None
+    ge: annotated_types.SupportsGe | None
+    lt: annotated_types.SupportsLt | None
+    le: annotated_types.SupportsLe | None
+    multiple_of: float | None
+    strict: bool | None
+    min_length: int | None
+    max_length: int | None
+    pattern: str | typing.Pattern[str] | None
+    allow_inf_nan: bool | None
+    max_digits: int | None
+    decimal_places: int | None
+    union_mode: Literal['smart', 'left_to_right'] | None
+    discriminator: str | types.Discriminator | None
+    deprecated: Deprecated | str | bool | None
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None
+    frozen: bool | None
+    validate_default: bool | None
+    repr: bool
+    init: bool | None
+    init_var: bool | None
+    kw_only: bool | None
+    coerce_numbers_to_str: bool | None
+    fail_fast: bool | None
+
+
+class _FieldInfoInputs(_FromFieldInfoInputs, total=False):
+    """This class exists solely to add type checking for the `**kwargs` in `FieldInfo.__init__`."""
+
+    default: Any
+
+
+class FieldInfo(_repr.Representation):
+    """This class holds information about a field.
+
+    `FieldInfo` is used for any field definition regardless of whether the [`Field()`][pydantic.fields.Field]
+    function is explicitly used.
+
+    !!! warning
+        You generally shouldn't be creating `FieldInfo` directly, you'll only need to use it when accessing
+        [`BaseModel`][pydantic.main.BaseModel] `.model_fields` internals.
+
+    Attributes:
+        annotation: The type annotation of the field.
+        default: The default value of the field.
+        default_factory: A callable to generate the default value. The callable can either take 0 arguments
+            (in which case it is called as is) or a single argument containing the already validated data.
+        alias: The alias name of the field.
+        alias_priority: The priority of the field's alias.
+        validation_alias: The validation alias of the field.
+        serialization_alias: The serialization alias of the field.
+        title: The title of the field.
+        field_title_generator: A callable that takes a field name and returns title for it.
+        description: The description of the field.
+        examples: List of examples of the field.
+        exclude: Whether to exclude the field from the model serialization.
+        discriminator: Field name or Discriminator for discriminating the type in a tagged union.
+        deprecated: A deprecation message, an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport,
+            or a boolean. If `True`, a default deprecation message will be emitted when accessing the field.
+        json_schema_extra: A dict or callable to provide extra JSON schema properties.
+        frozen: Whether the field is frozen.
+        validate_default: Whether to validate the default value of the field.
+        repr: Whether to include the field in representation of the model.
+        init: Whether the field should be included in the constructor of the dataclass.
+        init_var: Whether the field should _only_ be included in the constructor of the dataclass, and not stored.
+        kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass.
+        metadata: List of metadata constraints.
+    """
+
+    annotation: type[Any] | None
+    default: Any
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None
+    alias: str | None
+    alias_priority: int | None
+    validation_alias: str | AliasPath | AliasChoices | None
+    serialization_alias: str | None
+    title: str | None
+    field_title_generator: Callable[[str, FieldInfo], str] | None
+    description: str | None
+    examples: list[Any] | None
+    exclude: bool | None
+    discriminator: str | types.Discriminator | None
+    deprecated: Deprecated | str | bool | None
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None
+    frozen: bool | None
+    validate_default: bool | None
+    repr: bool
+    init: bool | None
+    init_var: bool | None
+    kw_only: bool | None
+    metadata: list[Any]
+
+    __slots__ = (
+        'annotation',
+        'evaluated',
+        'default',
+        'default_factory',
+        'alias',
+        'alias_priority',
+        'validation_alias',
+        'serialization_alias',
+        'title',
+        'field_title_generator',
+        'description',
+        'examples',
+        'exclude',
+        'discriminator',
+        'deprecated',
+        'json_schema_extra',
+        'frozen',
+        'validate_default',
+        'repr',
+        'init',
+        'init_var',
+        'kw_only',
+        'metadata',
+        '_attributes_set',
+    )
+
+    # used to convert kwargs to metadata/constraints,
+    # None has a special meaning - these items are collected into a `PydanticGeneralMetadata`
+    metadata_lookup: ClassVar[dict[str, typing.Callable[[Any], Any] | None]] = {
+        'strict': types.Strict,
+        'gt': annotated_types.Gt,
+        'ge': annotated_types.Ge,
+        'lt': annotated_types.Lt,
+        'le': annotated_types.Le,
+        'multiple_of': annotated_types.MultipleOf,
+        'min_length': annotated_types.MinLen,
+        'max_length': annotated_types.MaxLen,
+        'pattern': None,
+        'allow_inf_nan': None,
+        'max_digits': None,
+        'decimal_places': None,
+        'union_mode': None,
+        'coerce_numbers_to_str': None,
+        'fail_fast': types.FailFast,
+    }
+
+    def __init__(self, **kwargs: Unpack[_FieldInfoInputs]) -> None:
+        """This class should generally not be initialized directly; instead, use the `pydantic.fields.Field` function
+        or one of the constructor classmethods.
+
+        See the signature of `pydantic.fields.Field` for more details about the expected arguments.
+        """
+        self._attributes_set = {k: v for k, v in kwargs.items() if v is not _Unset}
+        kwargs = {k: _DefaultValues.get(k) if v is _Unset else v for k, v in kwargs.items()}  # type: ignore
+        self.annotation, annotation_metadata = self._extract_metadata(kwargs.get('annotation'))
+        self.evaluated = False
+
+        default = kwargs.pop('default', PydanticUndefined)
+        if default is Ellipsis:
+            self.default = PydanticUndefined
+            # Also remove it from the attributes set, otherwise
+            # `GenerateSchema._common_field_schema` mistakenly
+            # uses it:
+            self._attributes_set.pop('default', None)
+        else:
+            self.default = default
+
+        self.default_factory = kwargs.pop('default_factory', None)
+
+        if self.default is not PydanticUndefined and self.default_factory is not None:
+            raise TypeError('cannot specify both default and default_factory')
+
+        self.alias = kwargs.pop('alias', None)
+        self.validation_alias = kwargs.pop('validation_alias', None)
+        self.serialization_alias = kwargs.pop('serialization_alias', None)
+        alias_is_set = any(alias is not None for alias in (self.alias, self.validation_alias, self.serialization_alias))
+        self.alias_priority = kwargs.pop('alias_priority', None) or 2 if alias_is_set else None
+        self.title = kwargs.pop('title', None)
+        self.field_title_generator = kwargs.pop('field_title_generator', None)
+        self.description = kwargs.pop('description', None)
+        self.examples = kwargs.pop('examples', None)
+        self.exclude = kwargs.pop('exclude', None)
+        self.discriminator = kwargs.pop('discriminator', None)
+        # For compatibility with FastAPI<=0.110.0, we preserve the existing value if it is not overridden
+        self.deprecated = kwargs.pop('deprecated', getattr(self, 'deprecated', None))
+        self.repr = kwargs.pop('repr', True)
+        self.json_schema_extra = kwargs.pop('json_schema_extra', None)
+        self.validate_default = kwargs.pop('validate_default', None)
+        self.frozen = kwargs.pop('frozen', None)
+        # currently only used on dataclasses
+        self.init = kwargs.pop('init', None)
+        self.init_var = kwargs.pop('init_var', None)
+        self.kw_only = kwargs.pop('kw_only', None)
+
+        self.metadata = self._collect_metadata(kwargs) + annotation_metadata  # type: ignore
+
+    @staticmethod
+    def from_field(default: Any = PydanticUndefined, **kwargs: Unpack[_FromFieldInfoInputs]) -> FieldInfo:
+        """Create a new `FieldInfo` object with the `Field` function.
+
+        Args:
+            default: The default value for the field. Defaults to Undefined.
+            **kwargs: Additional arguments dictionary.
+
+        Raises:
+            TypeError: If 'annotation' is passed as a keyword argument.
+
+        Returns:
+            A new FieldInfo object with the given parameters.
+
+        Example:
+            This is how you can create a field with default value like this:
+
+            ```python
+            import pydantic
+
+            class MyModel(pydantic.BaseModel):
+                foo: int = pydantic.Field(4)
+            ```
+        """
+        if 'annotation' in kwargs:
+            raise TypeError('"annotation" is not permitted as a Field keyword argument')
+        return FieldInfo(default=default, **kwargs)
+
+    @staticmethod
+    def from_annotation(annotation: type[Any]) -> FieldInfo:
+        """Creates a `FieldInfo` instance from a bare annotation.
+
+        This function is used internally to create a `FieldInfo` from a bare annotation like this:
+
+        ```python
+        import pydantic
+
+        class MyModel(pydantic.BaseModel):
+            foo: int  # <-- like this
+        ```
+
+        We also account for the case where the annotation can be an instance of `Annotated` and where
+        one of the (not first) arguments in `Annotated` is an instance of `FieldInfo`, e.g.:
+
+        ```python
+        import annotated_types
+        from typing_extensions import Annotated
+
+        import pydantic
+
+        class MyModel(pydantic.BaseModel):
+            foo: Annotated[int, annotated_types.Gt(42)]
+            bar: Annotated[int, pydantic.Field(gt=42)]
+        ```
+
+        Args:
+            annotation: An annotation object.
+
+        Returns:
+            An instance of the field metadata.
+        """
+        final = False
+        if _typing_extra.is_finalvar(annotation):
+            final = True
+            if annotation is not typing_extensions.Final:
+                annotation = typing_extensions.get_args(annotation)[0]
+
+        if _typing_extra.is_annotated(annotation):
+            first_arg, *extra_args = typing_extensions.get_args(annotation)
+            if _typing_extra.is_finalvar(first_arg):
+                final = True
+            field_info_annotations = [a for a in extra_args if isinstance(a, FieldInfo)]
+            field_info = FieldInfo.merge_field_infos(*field_info_annotations, annotation=first_arg)
+            if field_info:
+                new_field_info = copy(field_info)
+                new_field_info.annotation = first_arg
+                new_field_info.frozen = final or field_info.frozen
+                metadata: list[Any] = []
+                for a in extra_args:
+                    if _typing_extra.is_deprecated_instance(a):
+                        new_field_info.deprecated = a.message
+                    elif not isinstance(a, FieldInfo):
+                        metadata.append(a)
+                    else:
+                        metadata.extend(a.metadata)
+                new_field_info.metadata = metadata
+                return new_field_info
+
+        return FieldInfo(annotation=annotation, frozen=final or None)  # pyright: ignore[reportArgumentType]
+
+    @staticmethod
+    def from_annotated_attribute(annotation: type[Any], default: Any) -> FieldInfo:
+        """Create `FieldInfo` from an annotation with a default value.
+
+        This is used in cases like the following:
+
+        ```python
+        import annotated_types
+        from typing_extensions import Annotated
+
+        import pydantic
+
+        class MyModel(pydantic.BaseModel):
+            foo: int = 4  # <-- like this
+            bar: Annotated[int, annotated_types.Gt(4)] = 4  # <-- or this
+            spam: Annotated[int, pydantic.Field(gt=4)] = 4  # <-- or this
+        ```
+
+        Args:
+            annotation: The type annotation of the field.
+            default: The default value of the field.
+
+        Returns:
+            A field object with the passed values.
+        """
+        if annotation is default:
+            raise PydanticUserError(
+                'Error when building FieldInfo from annotated attribute. '
+                "Make sure you don't have any field name clashing with a type annotation ",
+                code='unevaluable-type-annotation',
+            )
+
+        final = _typing_extra.is_finalvar(annotation)
+        if final and annotation is not typing_extensions.Final:
+            annotation = typing_extensions.get_args(annotation)[0]
+
+        if isinstance(default, FieldInfo):
+            default.annotation, annotation_metadata = FieldInfo._extract_metadata(annotation)  # pyright: ignore[reportArgumentType]
+            default.metadata += annotation_metadata
+            default = default.merge_field_infos(
+                *[x for x in annotation_metadata if isinstance(x, FieldInfo)], default, annotation=default.annotation
+            )
+            default.frozen = final or default.frozen
+            return default
+
+        if isinstance(default, dataclasses.Field):
+            init_var = False
+            if annotation is dataclasses.InitVar:
+                init_var = True
+                annotation = typing.cast(Any, Any)
+            elif isinstance(annotation, dataclasses.InitVar):
+                init_var = True
+                annotation = annotation.type
+
+            pydantic_field = FieldInfo._from_dataclass_field(default)
+            pydantic_field.annotation, annotation_metadata = FieldInfo._extract_metadata(annotation)  # pyright: ignore[reportArgumentType]
+            pydantic_field.metadata += annotation_metadata
+            pydantic_field = pydantic_field.merge_field_infos(
+                *[x for x in annotation_metadata if isinstance(x, FieldInfo)],
+                pydantic_field,
+                annotation=pydantic_field.annotation,
+            )
+            pydantic_field.frozen = final or pydantic_field.frozen
+            pydantic_field.init_var = init_var
+            pydantic_field.init = getattr(default, 'init', None)
+            pydantic_field.kw_only = getattr(default, 'kw_only', None)
+            return pydantic_field
+
+        if _typing_extra.is_annotated(annotation):
+            first_arg, *extra_args = typing_extensions.get_args(annotation)
+            field_infos = [a for a in extra_args if isinstance(a, FieldInfo)]
+            field_info = FieldInfo.merge_field_infos(*field_infos, annotation=first_arg, default=default)
+            metadata: list[Any] = []
+            for a in extra_args:
+                if _typing_extra.is_deprecated_instance(a):
+                    field_info.deprecated = a.message
+                elif not isinstance(a, FieldInfo):
+                    metadata.append(a)
+                else:
+                    metadata.extend(a.metadata)
+            field_info.metadata = metadata
+            return field_info
+
+        return FieldInfo(annotation=annotation, default=default, frozen=final or None)  # pyright: ignore[reportArgumentType]
+
+    @staticmethod
+    def merge_field_infos(*field_infos: FieldInfo, **overrides: Any) -> FieldInfo:
+        """Merge `FieldInfo` instances keeping only explicitly set attributes.
+
+        Later `FieldInfo` instances override earlier ones.
+
+        Returns:
+            FieldInfo: A merged FieldInfo instance.
+        """
+        if len(field_infos) == 1:
+            # No merging necessary, but we still need to make a copy and apply the overrides
+            field_info = copy(field_infos[0])
+            field_info._attributes_set.update(overrides)
+
+            default_override = overrides.pop('default', PydanticUndefined)
+            if default_override is Ellipsis:
+                default_override = PydanticUndefined
+            if default_override is not PydanticUndefined:
+                field_info.default = default_override
+
+            for k, v in overrides.items():
+                setattr(field_info, k, v)
+            return field_info  # type: ignore
+
+        merged_field_info_kwargs: dict[str, Any] = {}
+        metadata = {}
+        for field_info in field_infos:
+            attributes_set = field_info._attributes_set.copy()
+
+            try:
+                json_schema_extra = attributes_set.pop('json_schema_extra')
+                existing_json_schema_extra = merged_field_info_kwargs.get('json_schema_extra')
+
+                if existing_json_schema_extra is None:
+                    merged_field_info_kwargs['json_schema_extra'] = json_schema_extra
+                if isinstance(existing_json_schema_extra, dict):
+                    if isinstance(json_schema_extra, dict):
+                        merged_field_info_kwargs['json_schema_extra'] = {
+                            **existing_json_schema_extra,
+                            **json_schema_extra,
+                        }
+                    if callable(json_schema_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,
+                        )
+                elif callable(json_schema_extra):
+                    # if ever there's a case of a callable, we'll just keep the last json schema extra spec
+                    merged_field_info_kwargs['json_schema_extra'] = json_schema_extra
+            except KeyError:
+                pass
+
+            # later FieldInfo instances override everything except json_schema_extra from earlier FieldInfo instances
+            merged_field_info_kwargs.update(attributes_set)
+
+            for x in field_info.metadata:
+                if not isinstance(x, FieldInfo):
+                    metadata[type(x)] = x
+
+        merged_field_info_kwargs.update(overrides)
+        field_info = FieldInfo(**merged_field_info_kwargs)
+        field_info.metadata = list(metadata.values())
+        return field_info
+
+    @staticmethod
+    def _from_dataclass_field(dc_field: DataclassField[Any]) -> FieldInfo:
+        """Return a new `FieldInfo` instance from a `dataclasses.Field` instance.
+
+        Args:
+            dc_field: The `dataclasses.Field` instance to convert.
+
+        Returns:
+            The corresponding `FieldInfo` instance.
+
+        Raises:
+            TypeError: If any of the `FieldInfo` kwargs does not match the `dataclass.Field` kwargs.
+        """
+        default = dc_field.default
+        if default is dataclasses.MISSING:
+            default = _Unset
+
+        if dc_field.default_factory is dataclasses.MISSING:
+            default_factory = _Unset
+        else:
+            default_factory = dc_field.default_factory
+
+        # use the `Field` function so in correct kwargs raise the correct `TypeError`
+        dc_field_metadata = {k: v for k, v in dc_field.metadata.items() if k in _FIELD_ARG_NAMES}
+        return Field(default=default, default_factory=default_factory, repr=dc_field.repr, **dc_field_metadata)  # pyright: ignore[reportCallIssue]
+
+    @staticmethod
+    def _extract_metadata(annotation: type[Any] | None) -> tuple[type[Any] | None, list[Any]]:
+        """Tries to extract metadata/constraints from an annotation if it uses `Annotated`.
+
+        Args:
+            annotation: The type hint annotation for which metadata has to be extracted.
+
+        Returns:
+            A tuple containing the extracted metadata type and the list of extra arguments.
+        """
+        if annotation is not None:
+            if _typing_extra.is_annotated(annotation):
+                first_arg, *extra_args = typing_extensions.get_args(annotation)
+                return first_arg, list(extra_args)
+
+        return annotation, []
+
+    @staticmethod
+    def _collect_metadata(kwargs: dict[str, Any]) -> list[Any]:
+        """Collect annotations from kwargs.
+
+        Args:
+            kwargs: Keyword arguments passed to the function.
+
+        Returns:
+            A list of metadata objects - a combination of `annotated_types.BaseMetadata` and
+                `PydanticMetadata`.
+        """
+        metadata: list[Any] = []
+        general_metadata = {}
+        for key, value in list(kwargs.items()):
+            try:
+                marker = FieldInfo.metadata_lookup[key]
+            except KeyError:
+                continue
+
+            del kwargs[key]
+            if value is not None:
+                if marker is None:
+                    general_metadata[key] = value
+                else:
+                    metadata.append(marker(value))
+        if general_metadata:
+            metadata.append(_fields.pydantic_general_metadata(**general_metadata))
+        return metadata
+
+    @property
+    def deprecation_message(self) -> str | None:
+        """The deprecation message to be emitted, or `None` if not set."""
+        if self.deprecated is None:
+            return None
+        if isinstance(self.deprecated, bool):
+            return 'deprecated' if self.deprecated else None
+        return self.deprecated if isinstance(self.deprecated, str) else self.deprecated.message
+
+    @property
+    def default_factory_takes_validated_data(self) -> bool | None:
+        """Whether the provided default factory callable has a validated data parameter.
+
+        Returns `None` if no default factory is set.
+        """
+        if self.default_factory is not None:
+            return _fields.takes_validated_data_argument(self.default_factory)
+
+    @overload
+    def get_default(
+        self, *, call_default_factory: Literal[True], validated_data: dict[str, Any] | None = None
+    ) -> Any: ...
+
+    @overload
+    def get_default(self, *, call_default_factory: Literal[False] = ...) -> Any: ...
+
+    def get_default(self, *, call_default_factory: bool = False, validated_data: dict[str, Any] | None = None) -> Any:
+        """Get the default value.
+
+        We expose an option for whether to call the default_factory (if present), as calling it may
+        result in side effects that we want to avoid. However, there are times when it really should
+        be called (namely, when instantiating a model via `model_construct`).
+
+        Args:
+            call_default_factory: Whether to call the default factory or not.
+            validated_data: The already validated data to be passed to the default factory.
+
+        Returns:
+            The default value, calling the default factory if requested or `None` if not set.
+        """
+        if self.default_factory is None:
+            return _utils.smart_deepcopy(self.default)
+        elif call_default_factory:
+            if self.default_factory_takes_validated_data:
+                fac = cast('Callable[[dict[str, Any]], Any]', self.default_factory)
+                if validated_data is None:
+                    raise ValueError(
+                        "The default factory requires the 'validated_data' argument, which was not provided when calling 'get_default'."
+                    )
+                return fac(validated_data)
+            else:
+                fac = cast('Callable[[], Any]', self.default_factory)
+                return fac()
+        else:
+            return None
+
+    def is_required(self) -> bool:
+        """Check if the field is required (i.e., does not have a default value or factory).
+
+        Returns:
+            `True` if the field is required, `False` otherwise.
+        """
+        return self.default is PydanticUndefined and self.default_factory is None
+
+    def rebuild_annotation(self) -> Any:
+        """Attempts to rebuild the original annotation for use in function signatures.
+
+        If metadata is present, it adds it to the original annotation using
+        `Annotated`. Otherwise, it returns the original annotation as-is.
+
+        Note that because the metadata has been flattened, the original annotation
+        may not be reconstructed exactly as originally provided, e.g. if the original
+        type had unrecognized annotations, or was annotated with a call to `pydantic.Field`.
+
+        Returns:
+            The rebuilt annotation.
+        """
+        if not self.metadata:
+            return self.annotation
+        else:
+            # Annotated arguments must be a tuple
+            return typing_extensions.Annotated[(self.annotation, *self.metadata)]  # type: ignore
+
+    def apply_typevars_map(
+        self,
+        typevars_map: dict[Any, Any] | None,
+        globalns: GlobalsNamespace | None = None,
+        localns: MappingNamespace | None = None,
+    ) -> None:
+        """Apply a `typevars_map` to the annotation.
+
+        This method is used when analyzing parametrized generic types to replace typevars with their concrete types.
+
+        This method applies the `typevars_map` to the annotation in place.
+
+        Args:
+            typevars_map: A dictionary mapping type variables to their concrete types.
+            globalns: The globals namespace to use during type annotation evaluation.
+            localns: The locals namespace to use during type annotation evaluation.
+
+        See Also:
+            pydantic._internal._generics.replace_types is used for replacing the typevars with
+                their concrete types.
+        """
+        annotation, _ = _typing_extra.try_eval_type(self.annotation, globalns, localns)
+        self.annotation = _generics.replace_types(annotation, typevars_map)
+
+    def __repr_args__(self) -> ReprArgs:
+        yield 'annotation', _repr.PlainRepr(_repr.display_as_type(self.annotation))
+        yield 'required', self.is_required()
+
+        for s in self.__slots__:
+            # TODO: properly make use of the protocol (https://rich.readthedocs.io/en/stable/pretty.html#rich-repr-protocol)
+            # By yielding a three-tuple:
+            if s in ('_attributes_set', 'annotation', 'evaluated'):
+                continue
+            elif s == 'metadata' and not self.metadata:
+                continue
+            elif s == 'repr' and self.repr is True:
+                continue
+            if s == 'frozen' and self.frozen is False:
+                continue
+            if s == 'validation_alias' and self.validation_alias == self.alias:
+                continue
+            if s == 'serialization_alias' and self.serialization_alias == self.alias:
+                continue
+            if s == 'default' and self.default is not PydanticUndefined:
+                yield 'default', self.default
+            elif s == 'default_factory' and self.default_factory is not None:
+                yield 'default_factory', _repr.PlainRepr(_repr.display_as_type(self.default_factory))
+            else:
+                value = getattr(self, s)
+                if value is not None and value is not PydanticUndefined:
+                    yield s, value
+
+
+class _EmptyKwargs(typing_extensions.TypedDict):
+    """This class exists solely to ensure that type checking warns about passing `**extra` in `Field`."""
+
+
+_DefaultValues = {
+    'default': ...,
+    'default_factory': None,
+    'alias': None,
+    'alias_priority': None,
+    'validation_alias': None,
+    'serialization_alias': None,
+    'title': None,
+    'description': None,
+    'examples': None,
+    'exclude': None,
+    'discriminator': None,
+    'json_schema_extra': None,
+    'frozen': None,
+    'validate_default': None,
+    'repr': True,
+    'init': None,
+    'init_var': None,
+    'kw_only': None,
+    'pattern': None,
+    'strict': None,
+    'gt': None,
+    'ge': None,
+    'lt': None,
+    'le': None,
+    'multiple_of': None,
+    'allow_inf_nan': None,
+    'max_digits': None,
+    'decimal_places': None,
+    'min_length': None,
+    'max_length': None,
+    'coerce_numbers_to_str': None,
+}
+
+
+_T = TypeVar('_T')
+
+
+# NOTE: Actual return type is 'FieldInfo', but we want to help type checkers
+# to understand the magic that happens at runtime with the following overloads:
+@overload  # type hint the return value as `Any` to avoid type checking regressions when using `...`.
+def Field(
+    default: ellipsis,  # noqa: F821  # TODO: use `_typing_extra.EllipsisType` when we drop Py3.9
+    *,
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: bool | None = _Unset,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | typing.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> Any: ...
+@overload  # `default` argument set
+def Field(
+    default: _T,
+    *,
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: bool | None = _Unset,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | typing.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> _T: ...
+@overload  # `default_factory` argument set
+def Field(
+    *,
+    default_factory: Callable[[], _T] | Callable[[dict[str, Any]], _T],
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: bool | None = _Unset,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | typing.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> _T: ...
+@overload
+def Field(  # No default set
+    *,
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: bool | None = _Unset,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | typing.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> Any: ...
+def Field(  # noqa: C901
+    default: Any = PydanticUndefined,
+    *,
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None = _Unset,
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: bool | None = _Unset,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | typing.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> Any:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/fields
+
+    Create a field for objects that can be configured.
+
+    Used to provide extra information about a field, either for the model schema or complex validation. Some arguments
+    apply only to number fields (`int`, `float`, `Decimal`) and some apply only to `str`.
+
+    Note:
+        - Any `_Unset` objects will be replaced by the corresponding value defined in the `_DefaultValues` dictionary. If a key for the `_Unset` object is not found in the `_DefaultValues` dictionary, it will default to `None`
+
+    Args:
+        default: Default value if the field is not set.
+        default_factory: A callable to generate the default value. The callable can either take 0 arguments
+            (in which case it is called as is) or a single argument containing the already validated data.
+        alias: The name to use for the attribute when validating or serializing by alias.
+            This is often used for things like converting between snake and camel case.
+        alias_priority: Priority of the alias. This affects whether an alias generator is used.
+        validation_alias: Like `alias`, but only affects validation, not serialization.
+        serialization_alias: Like `alias`, but only affects serialization, not validation.
+        title: Human-readable title.
+        field_title_generator: A callable that takes a field name and returns title for it.
+        description: Human-readable description.
+        examples: Example values for this field.
+        exclude: Whether to exclude the field from the model serialization.
+        discriminator: Field name or Discriminator for discriminating the type in a tagged union.
+        deprecated: A deprecation message, an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport,
+            or a boolean. If `True`, a default deprecation message will be emitted when accessing the field.
+        json_schema_extra: A dict or callable to provide extra JSON schema properties.
+        frozen: Whether the field is frozen. If true, attempts to change the value on an instance will raise an error.
+        validate_default: If `True`, apply validation to the default value every time you create an instance.
+            Otherwise, for performance reasons, the default value of the field is trusted and not validated.
+        repr: A boolean indicating whether to include the field in the `__repr__` output.
+        init: Whether the field should be included in the constructor of the dataclass.
+            (Only applies to dataclasses.)
+        init_var: Whether the field should _only_ be included in the constructor of the dataclass.
+            (Only applies to dataclasses.)
+        kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass.
+            (Only applies to dataclasses.)
+        coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode).
+        strict: If `True`, strict validation is applied to the field.
+            See [Strict Mode](../concepts/strict_mode.md) for details.
+        gt: Greater than. If set, value must be greater than this. Only applicable to numbers.
+        ge: Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers.
+        lt: Less than. If set, value must be less than this. Only applicable to numbers.
+        le: Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers.
+        multiple_of: Value must be a multiple of this. Only applicable to numbers.
+        min_length: Minimum length for iterables.
+        max_length: Maximum length for iterables.
+        pattern: Pattern for strings (a regular expression).
+        allow_inf_nan: Allow `inf`, `-inf`, `nan`. Only applicable to numbers.
+        max_digits: Maximum number of allow digits for strings.
+        decimal_places: Maximum number of decimal places allowed for numbers.
+        union_mode: The strategy to apply when validating a union. Can be `smart` (the default), or `left_to_right`.
+            See [Union Mode](../concepts/unions.md#union-modes) for details.
+        fail_fast: If `True`, validation will stop on the first error. If `False`, all validation errors will be collected.
+            This option can be applied only to iterable types (list, tuple, set, and frozenset).
+        extra: (Deprecated) Extra fields that will be included in the JSON schema.
+
+            !!! warning Deprecated
+                The `extra` kwargs is deprecated. Use `json_schema_extra` instead.
+
+    Returns:
+        A new [`FieldInfo`][pydantic.fields.FieldInfo]. The return annotation is `Any` so `Field` can be used on
+            type-annotated fields without causing a type error.
+    """
+    # Check deprecated and removed params from V1. This logic should eventually be removed.
+    const = extra.pop('const', None)  # type: ignore
+    if const is not None:
+        raise PydanticUserError('`const` is removed, use `Literal` instead', code='removed-kwargs')
+
+    min_items = extra.pop('min_items', None)  # type: ignore
+    if min_items is not None:
+        warn('`min_items` is deprecated and will be removed, use `min_length` instead', DeprecationWarning)
+        if min_length in (None, _Unset):
+            min_length = min_items  # type: ignore
+
+    max_items = extra.pop('max_items', None)  # type: ignore
+    if max_items is not None:
+        warn('`max_items` is deprecated and will be removed, use `max_length` instead', DeprecationWarning)
+        if max_length in (None, _Unset):
+            max_length = max_items  # type: ignore
+
+    unique_items = extra.pop('unique_items', None)  # type: ignore
+    if unique_items is not None:
+        raise PydanticUserError(
+            (
+                '`unique_items` is removed, use `Set` instead'
+                '(this feature is discussed in https://github.com/pydantic/pydantic-core/issues/296)'
+            ),
+            code='removed-kwargs',
+        )
+
+    allow_mutation = extra.pop('allow_mutation', None)  # type: ignore
+    if allow_mutation is not None:
+        warn('`allow_mutation` is deprecated and will be removed. use `frozen` instead', DeprecationWarning)
+        if allow_mutation is False:
+            frozen = True
+
+    regex = extra.pop('regex', None)  # type: ignore
+    if regex is not None:
+        raise PydanticUserError('`regex` is removed. use `pattern` instead', code='removed-kwargs')
+
+    if extra:
+        warn(
+            'Using extra keyword arguments on `Field` is deprecated and will be removed.'
+            ' Use `json_schema_extra` instead.'
+            f' (Extra keys: {", ".join(k.__repr__() for k in extra.keys())})',
+            DeprecationWarning,
+        )
+        if not json_schema_extra or json_schema_extra is _Unset:
+            json_schema_extra = extra  # type: ignore
+
+    if (
+        validation_alias
+        and validation_alias is not _Unset
+        and not isinstance(validation_alias, (str, AliasChoices, AliasPath))
+    ):
+        raise TypeError('Invalid `validation_alias` type. it should be `str`, `AliasChoices`, or `AliasPath`')
+
+    if serialization_alias in (_Unset, None) and isinstance(alias, str):
+        serialization_alias = alias
+
+    if validation_alias in (_Unset, None):
+        validation_alias = alias
+
+    include = extra.pop('include', None)  # type: ignore
+    if include is not None:
+        warn('`include` is deprecated and does nothing. It will be removed, use `exclude` instead', DeprecationWarning)
+
+    return FieldInfo.from_field(
+        default,
+        default_factory=default_factory,
+        alias=alias,
+        alias_priority=alias_priority,
+        validation_alias=validation_alias,
+        serialization_alias=serialization_alias,
+        title=title,
+        field_title_generator=field_title_generator,
+        description=description,
+        examples=examples,
+        exclude=exclude,
+        discriminator=discriminator,
+        deprecated=deprecated,
+        json_schema_extra=json_schema_extra,
+        frozen=frozen,
+        pattern=pattern,
+        validate_default=validate_default,
+        repr=repr,
+        init=init,
+        init_var=init_var,
+        kw_only=kw_only,
+        coerce_numbers_to_str=coerce_numbers_to_str,
+        strict=strict,
+        gt=gt,
+        ge=ge,
+        lt=lt,
+        le=le,
+        multiple_of=multiple_of,
+        min_length=min_length,
+        max_length=max_length,
+        allow_inf_nan=allow_inf_nan,
+        max_digits=max_digits,
+        decimal_places=decimal_places,
+        union_mode=union_mode,
+        fail_fast=fail_fast,
+    )
+
+
+_FIELD_ARG_NAMES = set(inspect.signature(Field).parameters)
+_FIELD_ARG_NAMES.remove('extra')  # do not include the varkwargs parameter
+
+
+class ModelPrivateAttr(_repr.Representation):
+    """A descriptor for private attributes in class models.
+
+    !!! warning
+        You generally shouldn't be creating `ModelPrivateAttr` instances directly, instead use
+        `pydantic.fields.PrivateAttr`. (This is similar to `FieldInfo` vs. `Field`.)
+
+    Attributes:
+        default: The default value of the attribute if not provided.
+        default_factory: A callable function that generates the default value of the
+            attribute if not provided.
+    """
+
+    __slots__ = ('default', 'default_factory')
+
+    def __init__(
+        self, default: Any = PydanticUndefined, *, default_factory: typing.Callable[[], Any] | None = None
+    ) -> None:
+        if default is Ellipsis:
+            self.default = PydanticUndefined
+        else:
+            self.default = default
+        self.default_factory = default_factory
+
+    if not typing.TYPE_CHECKING:
+        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
+
+        def __getattr__(self, item: str) -> Any:
+            """This function improves compatibility with custom descriptors by ensuring delegation happens
+            as expected when the default value of a private attribute is a descriptor.
+            """
+            if item in {'__get__', '__set__', '__delete__'}:
+                if hasattr(self.default, item):
+                    return getattr(self.default, item)
+            raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
+
+    def __set_name__(self, cls: type[Any], name: str) -> None:
+        """Preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487."""
+        default = self.default
+        if default is PydanticUndefined:
+            return
+        set_name = getattr(default, '__set_name__', None)
+        if callable(set_name):
+            set_name(cls, name)
+
+    def get_default(self) -> Any:
+        """Retrieve the default value of the object.
+
+        If `self.default_factory` is `None`, the method will return a deep copy of the `self.default` object.
+
+        If `self.default_factory` is not `None`, it will call `self.default_factory` and return the value returned.
+
+        Returns:
+            The default value of the object.
+        """
+        return _utils.smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, self.__class__) and (self.default, self.default_factory) == (
+            other.default,
+            other.default_factory,
+        )
+
+
+# NOTE: Actual return type is 'ModelPrivateAttr', but we want to help type checkers
+# to understand the magic that happens at runtime.
+@overload  # `default` argument set
+def PrivateAttr(
+    default: _T,
+    *,
+    init: Literal[False] = False,
+) -> _T: ...
+@overload  # `default_factory` argument set
+def PrivateAttr(
+    *,
+    default_factory: Callable[[], _T],
+    init: Literal[False] = False,
+) -> _T: ...
+@overload  # No default set
+def PrivateAttr(
+    *,
+    init: Literal[False] = False,
+) -> Any: ...
+def PrivateAttr(
+    default: Any = PydanticUndefined,
+    *,
+    default_factory: Callable[[], Any] | None = None,
+    init: Literal[False] = False,
+) -> Any:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/#private-model-attributes
+
+    Indicates that an attribute is intended for private use and not handled during normal validation/serialization.
+
+    Private attributes are not validated by Pydantic, so it's up to you to ensure they are used in a type-safe manner.
+
+    Private attributes are stored in `__private_attributes__` on the model.
+
+    Args:
+        default: The attribute's default value. Defaults to Undefined.
+        default_factory: Callable that will be
+            called when a default value is needed for this attribute.
+            If both `default` and `default_factory` are set, an error will be raised.
+        init: Whether the attribute should be included in the constructor of the dataclass. Always `False`.
+
+    Returns:
+        An instance of [`ModelPrivateAttr`][pydantic.fields.ModelPrivateAttr] class.
+
+    Raises:
+        ValueError: If both `default` and `default_factory` are set.
+    """
+    if default is not PydanticUndefined and default_factory is not None:
+        raise TypeError('cannot specify both default and default_factory')
+
+    return ModelPrivateAttr(
+        default,
+        default_factory=default_factory,
+    )
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class ComputedFieldInfo:
+    """A container for data from `@computed_field` so that we can access it while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@computed_field'.
+        wrapped_property: The wrapped computed field property.
+        return_type: The type of the computed field property's return value.
+        alias: The alias of the property to be used during serialization.
+        alias_priority: The priority of the alias. This affects whether an alias generator is used.
+        title: Title of the computed field to include in the serialization JSON schema.
+        field_title_generator: A callable that takes a field name and returns title for it.
+        description: Description of the computed field to include in the serialization JSON schema.
+        deprecated: A deprecation message, an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport,
+            or a boolean. If `True`, a default deprecation message will be emitted when accessing the field.
+        examples: Example values of the computed field to include in the serialization JSON schema.
+        json_schema_extra: A dict or callable to provide extra JSON schema properties.
+        repr: A boolean indicating whether to include the field in the __repr__ output.
+    """
+
+    decorator_repr: ClassVar[str] = '@computed_field'
+    wrapped_property: property
+    return_type: Any
+    alias: str | None
+    alias_priority: int | None
+    title: str | None
+    field_title_generator: typing.Callable[[str, ComputedFieldInfo], str] | None
+    description: str | None
+    deprecated: Deprecated | str | bool | None
+    examples: list[Any] | None
+    json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None
+    repr: bool
+
+    @property
+    def deprecation_message(self) -> str | None:
+        """The deprecation message to be emitted, or `None` if not set."""
+        if self.deprecated is None:
+            return None
+        if isinstance(self.deprecated, bool):
+            return 'deprecated' if self.deprecated else None
+        return self.deprecated if isinstance(self.deprecated, str) else self.deprecated.message
+
+
+def _wrapped_property_is_private(property_: cached_property | property) -> bool:  # type: ignore
+    """Returns true if provided property is private, False otherwise."""
+    wrapped_name: str = ''
+
+    if isinstance(property_, property):
+        wrapped_name = getattr(property_.fget, '__name__', '')
+    elif isinstance(property_, cached_property):  # type: ignore
+        wrapped_name = getattr(property_.func, '__name__', '')  # type: ignore
+
+    return wrapped_name.startswith('_') and not wrapped_name.startswith('__')
+
+
+# this should really be `property[T], cached_property[T]` but property is not generic unlike cached_property
+# See https://github.com/python/typing/issues/985 and linked issues
+PropertyT = typing.TypeVar('PropertyT')
+
+
+@typing.overload
+def computed_field(
+    *,
+    alias: str | None = None,
+    alias_priority: int | None = None,
+    title: str | None = None,
+    field_title_generator: typing.Callable[[str, ComputedFieldInfo], str] | None = None,
+    description: str | None = None,
+    deprecated: Deprecated | str | bool | None = None,
+    examples: list[Any] | None = None,
+    json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None = None,
+    repr: bool = True,
+    return_type: Any = PydanticUndefined,
+) -> typing.Callable[[PropertyT], PropertyT]: ...
+
+
+@typing.overload
+def computed_field(__func: PropertyT) -> PropertyT: ...
+
+
+def computed_field(
+    func: PropertyT | None = None,
+    /,
+    *,
+    alias: str | None = None,
+    alias_priority: int | None = None,
+    title: str | None = None,
+    field_title_generator: typing.Callable[[str, ComputedFieldInfo], str] | None = None,
+    description: str | None = None,
+    deprecated: Deprecated | str | bool | None = None,
+    examples: list[Any] | None = None,
+    json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None = None,
+    repr: bool | None = None,
+    return_type: Any = PydanticUndefined,
+) -> PropertyT | typing.Callable[[PropertyT], PropertyT]:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/fields#the-computed_field-decorator
+
+    Decorator to include `property` and `cached_property` when serializing models or dataclasses.
+
+    This is useful for fields that are computed from other fields, or for fields that are expensive to compute and should be cached.
+
+    ```python
+    from pydantic import BaseModel, computed_field
+
+    class Rectangle(BaseModel):
+        width: int
+        length: int
+
+        @computed_field
+        @property
+        def area(self) -> int:
+            return self.width * self.length
+
+    print(Rectangle(width=3, length=2).model_dump())
+    #> {'width': 3, 'length': 2, 'area': 6}
+    ```
+
+    If applied to functions not yet decorated with `@property` or `@cached_property`, the function is
+    automatically wrapped with `property`. Although this is more concise, you will lose IntelliSense in your IDE,
+    and confuse static type checkers, thus explicit use of `@property` is recommended.
+
+    !!! warning "Mypy Warning"
+        Even with the `@property` or `@cached_property` applied to your function before `@computed_field`,
+        mypy may throw a `Decorated property not supported` error.
+        See [mypy issue #1362](https://github.com/python/mypy/issues/1362), for more information.
+        To avoid this error message, add `# type: ignore[misc]` to the `@computed_field` line.
+
+        [pyright](https://github.com/microsoft/pyright) supports `@computed_field` without error.
+
+    ```python
+    import random
+
+    from pydantic import BaseModel, computed_field
+
+    class Square(BaseModel):
+        width: float
+
+        @computed_field
+        def area(self) -> float:  # converted to a `property` by `computed_field`
+            return round(self.width**2, 2)
+
+        @area.setter
+        def area(self, new_area: float) -> None:
+            self.width = new_area**0.5
+
+        @computed_field(alias='the magic number', repr=False)
+        def random_number(self) -> int:
+            return random.randint(0, 1_000)
+
+    square = Square(width=1.3)
+
+    # `random_number` does not appear in representation
+    print(repr(square))
+    #> Square(width=1.3, area=1.69)
+
+    print(square.random_number)
+    #> 3
+
+    square.area = 4
+
+    print(square.model_dump_json(by_alias=True))
+    #> {"width":2.0,"area":4.0,"the magic number":3}
+    ```
+
+    !!! warning "Overriding with `computed_field`"
+        You can't override a field from a parent class with a `computed_field` in the child class.
+        `mypy` complains about this behavior if allowed, and `dataclasses` doesn't allow this pattern either.
+        See the example below:
+
+    ```python
+    from pydantic import BaseModel, computed_field
+
+    class Parent(BaseModel):
+        a: str
+
+    try:
+
+        class Child(Parent):
+            @computed_field
+            @property
+            def a(self) -> str:
+                return 'new a'
+
+    except ValueError as e:
+        print(repr(e))
+        #> ValueError("you can't override a field with a computed field")
+    ```
+
+    Private properties decorated with `@computed_field` have `repr=False` by default.
+
+    ```python
+    from functools import cached_property
+
+    from pydantic import BaseModel, computed_field
+
+    class Model(BaseModel):
+        foo: int
+
+        @computed_field
+        @cached_property
+        def _private_cached_property(self) -> int:
+            return -self.foo
+
+        @computed_field
+        @property
+        def _private_property(self) -> int:
+            return -self.foo
+
+    m = Model(foo=1)
+    print(repr(m))
+    #> Model(foo=1)
+    ```
+
+    Args:
+        func: the function to wrap.
+        alias: alias to use when serializing this computed field, only used when `by_alias=True`
+        alias_priority: priority of the alias. This affects whether an alias generator is used
+        title: Title to use when including this computed field in JSON Schema
+        field_title_generator: A callable that takes a field name and returns title for it.
+        description: Description to use when including this computed field in JSON Schema, defaults to the function's
+            docstring
+        deprecated: A deprecation message (or an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport).
+            to be emitted when accessing the field. Or a boolean. This will automatically be set if the property is decorated with the
+            `deprecated` decorator.
+        examples: Example values to use when including this computed field in JSON Schema
+        json_schema_extra: A dict or callable to provide extra JSON schema properties.
+        repr: whether to include this computed field in model repr.
+            Default is `False` for private properties and `True` for public properties.
+        return_type: optional return for serialization logic to expect when serializing to JSON, if included
+            this must be correct, otherwise a `TypeError` is raised.
+            If you don't include a return type Any is used, which does runtime introspection to handle arbitrary
+            objects.
+
+    Returns:
+        A proxy wrapper for the property.
+    """
+
+    def dec(f: Any) -> Any:
+        nonlocal description, deprecated, return_type, alias_priority
+        unwrapped = _decorators.unwrap_wrapped_function(f)
+
+        if description is None and unwrapped.__doc__:
+            description = inspect.cleandoc(unwrapped.__doc__)
+
+        if deprecated is None and hasattr(unwrapped, '__deprecated__'):
+            deprecated = unwrapped.__deprecated__
+
+        # if the function isn't already decorated with `@property` (or another descriptor), then we wrap it now
+        f = _decorators.ensure_property(f)
+        alias_priority = (alias_priority or 2) if alias is not None else None
+
+        if repr is None:
+            repr_: bool = not _wrapped_property_is_private(property_=f)
+        else:
+            repr_ = repr
+
+        dec_info = ComputedFieldInfo(
+            f,
+            return_type,
+            alias,
+            alias_priority,
+            title,
+            field_title_generator,
+            description,
+            deprecated,
+            examples,
+            json_schema_extra,
+            repr_,
+        )
+        return _decorators.PydanticDescriptorProxy(f, dec_info)
+
+    if func is None:
+        return dec
+    else:
+        return dec(func)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/functional_serializers.py b/.venv/lib/python3.12/site-packages/pydantic/functional_serializers.py
new file mode 100644
index 00000000..9f850295
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/functional_serializers.py
@@ -0,0 +1,449 @@
+"""This module contains related classes and functions for serialization."""
+
+from __future__ import annotations
+
+import dataclasses
+from functools import partial, partialmethod
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, overload
+
+from pydantic_core import PydanticUndefined, core_schema
+from pydantic_core.core_schema import SerializationInfo, SerializerFunctionWrapHandler, WhenUsed
+from typing_extensions import Annotated, Literal, TypeAlias
+
+from . import PydanticUndefinedAnnotation
+from ._internal import _decorators, _internal_dataclass
+from .annotated_handlers import GetCoreSchemaHandler
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
+class PlainSerializer:
+    """Plain serializers use a function to modify the output of serialization.
+
+    This is particularly helpful when you want to customize the serialization for annotated types.
+    Consider an input of `list`, which will be serialized into a space-delimited string.
+
+    ```python
+    from typing import List
+
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, PlainSerializer
+
+    CustomStr = Annotated[
+        List, PlainSerializer(lambda x: ' '.join(x), return_type=str)
+    ]
+
+    class StudentModel(BaseModel):
+        courses: CustomStr
+
+    student = StudentModel(courses=['Math', 'Chemistry', 'English'])
+    print(student.model_dump())
+    #> {'courses': 'Math Chemistry English'}
+    ```
+
+    Attributes:
+        func: The serializer function.
+        return_type: The return type for the function. If omitted it will be inferred from the type annotation.
+        when_used: Determines when this serializer should be used. Accepts a string with values `'always'`,
+            `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'.
+    """
+
+    func: core_schema.SerializerFunction
+    return_type: Any = PydanticUndefined
+    when_used: WhenUsed = 'always'
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        """Gets the Pydantic core schema.
+
+        Args:
+            source_type: The source type.
+            handler: The `GetCoreSchemaHandler` instance.
+
+        Returns:
+            The Pydantic core schema.
+        """
+        schema = handler(source_type)
+        try:
+            # Do not pass in globals as the function could be defined in a different module.
+            # Instead, let `get_function_return_type` infer the globals to use, but still pass
+            # in locals that may contain a parent/rebuild namespace:
+            return_type = _decorators.get_function_return_type(
+                self.func,
+                self.return_type,
+                localns=handler._get_types_namespace().locals,
+            )
+        except NameError as e:
+            raise PydanticUndefinedAnnotation.from_name_error(e) from e
+        return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type)
+        schema['serialization'] = core_schema.plain_serializer_function_ser_schema(
+            function=self.func,
+            info_arg=_decorators.inspect_annotated_serializer(self.func, 'plain'),
+            return_schema=return_schema,
+            when_used=self.when_used,
+        )
+        return schema
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
+class WrapSerializer:
+    """Wrap serializers receive the raw inputs along with a handler function that applies the standard serialization
+    logic, and can modify the resulting value before returning it as the final output of serialization.
+
+    For example, here's a scenario in which a wrap serializer transforms timezones to UTC **and** utilizes the existing `datetime` serialization logic.
+
+    ```python
+    from datetime import datetime, timezone
+    from typing import Any, Dict
+
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, WrapSerializer
+
+    class EventDatetime(BaseModel):
+        start: datetime
+        end: datetime
+
+    def convert_to_utc(value: Any, handler, info) -> Dict[str, datetime]:
+        # Note that `handler` can actually help serialize the `value` for
+        # further custom serialization in case it's a subclass.
+        partial_result = handler(value, info)
+        if info.mode == 'json':
+            return {
+                k: datetime.fromisoformat(v).astimezone(timezone.utc)
+                for k, v in partial_result.items()
+            }
+        return {k: v.astimezone(timezone.utc) for k, v in partial_result.items()}
+
+    UTCEventDatetime = Annotated[EventDatetime, WrapSerializer(convert_to_utc)]
+
+    class EventModel(BaseModel):
+        event_datetime: UTCEventDatetime
+
+    dt = EventDatetime(
+        start='2024-01-01T07:00:00-08:00', end='2024-01-03T20:00:00+06:00'
+    )
+    event = EventModel(event_datetime=dt)
+    print(event.model_dump())
+    '''
+    {
+        'event_datetime': {
+            'start': datetime.datetime(
+                2024, 1, 1, 15, 0, tzinfo=datetime.timezone.utc
+            ),
+            'end': datetime.datetime(
+                2024, 1, 3, 14, 0, tzinfo=datetime.timezone.utc
+            ),
+        }
+    }
+    '''
+
+    print(event.model_dump_json())
+    '''
+    {"event_datetime":{"start":"2024-01-01T15:00:00Z","end":"2024-01-03T14:00:00Z"}}
+    '''
+    ```
+
+    Attributes:
+        func: The serializer function to be wrapped.
+        return_type: The return type for the function. If omitted it will be inferred from the type annotation.
+        when_used: Determines when this serializer should be used. Accepts a string with values `'always'`,
+            `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'.
+    """
+
+    func: core_schema.WrapSerializerFunction
+    return_type: Any = PydanticUndefined
+    when_used: WhenUsed = 'always'
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        """This method is used to get the Pydantic core schema of the class.
+
+        Args:
+            source_type: Source type.
+            handler: Core schema handler.
+
+        Returns:
+            The generated core schema of the class.
+        """
+        schema = handler(source_type)
+        globalns, localns = handler._get_types_namespace()
+        try:
+            # Do not pass in globals as the function could be defined in a different module.
+            # Instead, let `get_function_return_type` infer the globals to use, but still pass
+            # in locals that may contain a parent/rebuild namespace:
+            return_type = _decorators.get_function_return_type(
+                self.func,
+                self.return_type,
+                localns=handler._get_types_namespace().locals,
+            )
+        except NameError as e:
+            raise PydanticUndefinedAnnotation.from_name_error(e) from e
+        return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type)
+        schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
+            function=self.func,
+            info_arg=_decorators.inspect_annotated_serializer(self.func, 'wrap'),
+            return_schema=return_schema,
+            when_used=self.when_used,
+        )
+        return schema
+
+
+if TYPE_CHECKING:
+    _Partial: TypeAlias = 'partial[Any] | partialmethod[Any]'
+
+    FieldPlainSerializer: TypeAlias = 'core_schema.SerializerFunction | _Partial'
+    """A field serializer method or function in `plain` mode."""
+
+    FieldWrapSerializer: TypeAlias = 'core_schema.WrapSerializerFunction | _Partial'
+    """A field serializer method or function in `wrap` mode."""
+
+    FieldSerializer: TypeAlias = 'FieldPlainSerializer | FieldWrapSerializer'
+    """A field serializer method or function."""
+
+    _FieldPlainSerializerT = TypeVar('_FieldPlainSerializerT', bound=FieldPlainSerializer)
+    _FieldWrapSerializerT = TypeVar('_FieldWrapSerializerT', bound=FieldWrapSerializer)
+
+
+@overload
+def field_serializer(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['wrap'],
+    return_type: Any = ...,
+    when_used: WhenUsed = ...,
+    check_fields: bool | None = ...,
+) -> Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]: ...
+
+
+@overload
+def field_serializer(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['plain'] = ...,
+    return_type: Any = ...,
+    when_used: WhenUsed = ...,
+    check_fields: bool | None = ...,
+) -> Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]: ...
+
+
+def field_serializer(
+    *fields: str,
+    mode: Literal['plain', 'wrap'] = 'plain',
+    return_type: Any = PydanticUndefined,
+    when_used: WhenUsed = 'always',
+    check_fields: bool | None = None,
+) -> (
+    Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]
+    | Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]
+):
+    """Decorator that enables custom field serialization.
+
+    In the below example, a field of type `set` is used to mitigate duplication. A `field_serializer` is used to serialize the data as a sorted list.
+
+    ```python
+    from typing import Set
+
+    from pydantic import BaseModel, field_serializer
+
+    class StudentModel(BaseModel):
+        name: str = 'Jane'
+        courses: Set[str]
+
+        @field_serializer('courses', when_used='json')
+        def serialize_courses_in_order(self, courses: Set[str]):
+            return sorted(courses)
+
+    student = StudentModel(courses={'Math', 'Chemistry', 'English'})
+    print(student.model_dump_json())
+    #> {"name":"Jane","courses":["Chemistry","English","Math"]}
+    ```
+
+    See [Custom serializers](../concepts/serialization.md#custom-serializers) for more information.
+
+    Four signatures are supported:
+
+    - `(self, value: Any, info: FieldSerializationInfo)`
+    - `(self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)`
+    - `(value: Any, info: SerializationInfo)`
+    - `(value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)`
+
+    Args:
+        fields: Which field(s) the method should be called on.
+        mode: The serialization mode.
+
+            - `plain` means the function will be called instead of the default serialization logic,
+            - `wrap` means the function will be called with an argument to optionally call the
+               default serialization logic.
+        return_type: Optional return type for the function, if omitted it will be inferred from the type annotation.
+        when_used: Determines the serializer will be used for serialization.
+        check_fields: Whether to check that the fields actually exist on the model.
+
+    Returns:
+        The decorator function.
+    """
+
+    def dec(f: FieldSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
+        dec_info = _decorators.FieldSerializerDecoratorInfo(
+            fields=fields,
+            mode=mode,
+            return_type=return_type,
+            when_used=when_used,
+            check_fields=check_fields,
+        )
+        return _decorators.PydanticDescriptorProxy(f, dec_info)  # pyright: ignore[reportArgumentType]
+
+    return dec  # pyright: ignore[reportReturnType]
+
+
+if TYPE_CHECKING:
+    # The first argument in the following callables represent the `self` type:
+
+    ModelPlainSerializerWithInfo: TypeAlias = Callable[[Any, SerializationInfo], Any]
+    """A model serializer method with the `info` argument, in `plain` mode."""
+
+    ModelPlainSerializerWithoutInfo: TypeAlias = Callable[[Any], Any]
+    """A model serializer method without the `info` argument, in `plain` mode."""
+
+    ModelPlainSerializer: TypeAlias = 'ModelPlainSerializerWithInfo | ModelPlainSerializerWithoutInfo'
+    """A model serializer method in `plain` mode."""
+
+    ModelWrapSerializerWithInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler, SerializationInfo], Any]
+    """A model serializer method with the `info` argument, in `wrap` mode."""
+
+    ModelWrapSerializerWithoutInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler], Any]
+    """A model serializer method without the `info` argument, in `wrap` mode."""
+
+    ModelWrapSerializer: TypeAlias = 'ModelWrapSerializerWithInfo | ModelWrapSerializerWithoutInfo'
+    """A model serializer method in `wrap` mode."""
+
+    ModelSerializer: TypeAlias = 'ModelPlainSerializer | ModelWrapSerializer'
+
+    _ModelPlainSerializerT = TypeVar('_ModelPlainSerializerT', bound=ModelPlainSerializer)
+    _ModelWrapSerializerT = TypeVar('_ModelWrapSerializerT', bound=ModelWrapSerializer)
+
+
+@overload
+def model_serializer(f: _ModelPlainSerializerT, /) -> _ModelPlainSerializerT: ...
+
+
+@overload
+def model_serializer(
+    *, mode: Literal['wrap'], when_used: WhenUsed = 'always', return_type: Any = ...
+) -> Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]: ...
+
+
+@overload
+def model_serializer(
+    *,
+    mode: Literal['plain'] = ...,
+    when_used: WhenUsed = 'always',
+    return_type: Any = ...,
+) -> Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]: ...
+
+
+def model_serializer(
+    f: _ModelPlainSerializerT | _ModelWrapSerializerT | None = None,
+    /,
+    *,
+    mode: Literal['plain', 'wrap'] = 'plain',
+    when_used: WhenUsed = 'always',
+    return_type: Any = PydanticUndefined,
+) -> (
+    _ModelPlainSerializerT
+    | Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]
+    | Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]
+):
+    """Decorator that enables custom model serialization.
+
+    This is useful when a model need to be serialized in a customized manner, allowing for flexibility beyond just specific fields.
+
+    An example would be to serialize temperature to the same temperature scale, such as degrees Celsius.
+
+    ```python
+    from typing import Literal
+
+    from pydantic import BaseModel, model_serializer
+
+    class TemperatureModel(BaseModel):
+        unit: Literal['C', 'F']
+        value: int
+
+        @model_serializer()
+        def serialize_model(self):
+            if self.unit == 'F':
+                return {'unit': 'C', 'value': int((self.value - 32) / 1.8)}
+            return {'unit': self.unit, 'value': self.value}
+
+    temperature = TemperatureModel(unit='F', value=212)
+    print(temperature.model_dump())
+    #> {'unit': 'C', 'value': 100}
+    ```
+
+    Two signatures are supported for `mode='plain'`, which is the default:
+
+    - `(self)`
+    - `(self, info: SerializationInfo)`
+
+    And two other signatures for `mode='wrap'`:
+
+    - `(self, nxt: SerializerFunctionWrapHandler)`
+    - `(self, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)`
+
+        See [Custom serializers](../concepts/serialization.md#custom-serializers) for more information.
+
+    Args:
+        f: The function to be decorated.
+        mode: The serialization mode.
+
+            - `'plain'` means the function will be called instead of the default serialization logic
+            - `'wrap'` means the function will be called with an argument to optionally call the default
+                serialization logic.
+        when_used: Determines when this serializer should be used.
+        return_type: The return type for the function. If omitted it will be inferred from the type annotation.
+
+    Returns:
+        The decorator function.
+    """
+
+    def dec(f: ModelSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
+        dec_info = _decorators.ModelSerializerDecoratorInfo(mode=mode, return_type=return_type, when_used=when_used)
+        return _decorators.PydanticDescriptorProxy(f, dec_info)
+
+    if f is None:
+        return dec  # pyright: ignore[reportReturnType]
+    else:
+        return dec(f)  # pyright: ignore[reportReturnType]
+
+
+AnyType = TypeVar('AnyType')
+
+
+if TYPE_CHECKING:
+    SerializeAsAny = Annotated[AnyType, ...]  # SerializeAsAny[list[str]] will be treated by type checkers as list[str]
+    """Force serialization to ignore whatever is defined in the schema and instead ask the object
+    itself how it should be serialized.
+    In particular, this means that when model subclasses are serialized, fields present in the subclass
+    but not in the original schema will be included.
+    """
+else:
+
+    @dataclasses.dataclass(**_internal_dataclass.slots_true)
+    class SerializeAsAny:  # noqa: D101
+        def __class_getitem__(cls, item: Any) -> Any:
+            return Annotated[item, SerializeAsAny()]
+
+        def __get_pydantic_core_schema__(
+            self, source_type: Any, handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            schema = handler(source_type)
+            schema_to_update = schema
+            while schema_to_update['type'] == 'definitions':
+                schema_to_update = schema_to_update.copy()
+                schema_to_update = schema_to_update['schema']
+            schema_to_update['serialization'] = core_schema.wrap_serializer_function_ser_schema(
+                lambda x, h: h(x), schema=core_schema.any_schema()
+            )
+            return schema
+
+        __hash__ = object.__hash__
diff --git a/.venv/lib/python3.12/site-packages/pydantic/functional_validators.py b/.venv/lib/python3.12/site-packages/pydantic/functional_validators.py
new file mode 100644
index 00000000..aa3da8c2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/functional_validators.py
@@ -0,0 +1,825 @@
+"""This module contains related classes and functions for validation."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import sys
+from functools import partialmethod
+from types import FunctionType
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union, cast, overload
+
+from pydantic_core import PydanticUndefined, core_schema
+from pydantic_core import core_schema as _core_schema
+from typing_extensions import Annotated, Literal, Self, TypeAlias
+
+from ._internal import _decorators, _generics, _internal_dataclass
+from .annotated_handlers import GetCoreSchemaHandler
+from .errors import PydanticUserError
+
+if sys.version_info < (3, 11):
+    from typing_extensions import Protocol
+else:
+    from typing import Protocol
+
+_inspect_validator = _decorators.inspect_validator
+
+
+@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
+class AfterValidator:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-validators
+
+    A metadata class that indicates that a validation should be applied **after** the inner validation logic.
+
+    Attributes:
+        func: The validator function.
+
+    Example:
+        ```python
+        from typing_extensions import Annotated
+
+        from pydantic import AfterValidator, BaseModel, ValidationError
+
+        MyInt = Annotated[int, AfterValidator(lambda v: v + 1)]
+
+        class Model(BaseModel):
+            a: MyInt
+
+        print(Model(a=1).a)
+        #> 2
+
+        try:
+            Model(a='a')
+        except ValidationError as e:
+            print(e.json(indent=2))
+            '''
+            [
+              {
+                "type": "int_parsing",
+                "loc": [
+                  "a"
+                ],
+                "msg": "Input should be a valid integer, unable to parse string as an integer",
+                "input": "a",
+                "url": "https://errors.pydantic.dev/2/v/int_parsing"
+              }
+            ]
+            '''
+        ```
+    """
+
+    func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(source_type)
+        info_arg = _inspect_validator(self.func, 'after')
+        if info_arg:
+            func = cast(core_schema.WithInfoValidatorFunction, self.func)
+            return core_schema.with_info_after_validator_function(func, schema=schema, field_name=handler.field_name)
+        else:
+            func = cast(core_schema.NoInfoValidatorFunction, self.func)
+            return core_schema.no_info_after_validator_function(func, schema=schema)
+
+    @classmethod
+    def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
+        return cls(func=decorator.func)
+
+
+@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
+class BeforeValidator:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-validators
+
+    A metadata class that indicates that a validation should be applied **before** the inner validation logic.
+
+    Attributes:
+        func: The validator function.
+        json_schema_input_type: The input type of the function. This is only used to generate the appropriate
+            JSON Schema (in validation mode).
+
+    Example:
+        ```python
+        from typing_extensions import Annotated
+
+        from pydantic import BaseModel, BeforeValidator
+
+        MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)]
+
+        class Model(BaseModel):
+            a: MyInt
+
+        print(Model(a=1).a)
+        #> 2
+
+        try:
+            Model(a='a')
+        except TypeError as e:
+            print(e)
+            #> can only concatenate str (not "int") to str
+        ```
+    """
+
+    func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
+    json_schema_input_type: Any = PydanticUndefined
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(source_type)
+        input_schema = (
+            None
+            if self.json_schema_input_type is PydanticUndefined
+            else handler.generate_schema(self.json_schema_input_type)
+        )
+
+        info_arg = _inspect_validator(self.func, 'before')
+        if info_arg:
+            func = cast(core_schema.WithInfoValidatorFunction, self.func)
+            return core_schema.with_info_before_validator_function(
+                func,
+                schema=schema,
+                field_name=handler.field_name,
+                json_schema_input_schema=input_schema,
+            )
+        else:
+            func = cast(core_schema.NoInfoValidatorFunction, self.func)
+            return core_schema.no_info_before_validator_function(
+                func, schema=schema, json_schema_input_schema=input_schema
+            )
+
+    @classmethod
+    def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
+        return cls(
+            func=decorator.func,
+            json_schema_input_type=decorator.info.json_schema_input_type,
+        )
+
+
+@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
+class PlainValidator:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-validators
+
+    A metadata class that indicates that a validation should be applied **instead** of the inner validation logic.
+
+    !!! note
+        Before v2.9, `PlainValidator` wasn't always compatible with JSON Schema generation for `mode='validation'`.
+        You can now use the `json_schema_input_type` argument to specify the input type of the function
+        to be used in the JSON schema when `mode='validation'` (the default). See the example below for more details.
+
+    Attributes:
+        func: The validator function.
+        json_schema_input_type: The input type of the function. This is only used to generate the appropriate
+            JSON Schema (in validation mode). If not provided, will default to `Any`.
+
+    Example:
+        ```python
+        from typing import Union
+
+        from typing_extensions import Annotated
+
+        from pydantic import BaseModel, PlainValidator
+
+        MyInt = Annotated[
+            int,
+            PlainValidator(
+                lambda v: int(v) + 1, json_schema_input_type=Union[str, int]  # (1)!
+            ),
+        ]
+
+        class Model(BaseModel):
+            a: MyInt
+
+        print(Model(a='1').a)
+        #> 2
+
+        print(Model(a=1).a)
+        #> 2
+        ```
+
+        1. In this example, we've specified the `json_schema_input_type` as `Union[str, int]` which indicates to the JSON schema
+        generator that in validation mode, the input type for the `a` field can be either a `str` or an `int`.
+    """
+
+    func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
+    json_schema_input_type: Any = Any
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        # Note that for some valid uses of PlainValidator, it is not possible to generate a core schema for the
+        # source_type, so calling `handler(source_type)` will error, which prevents us from generating a proper
+        # serialization schema. To work around this for use cases that will not involve serialization, we simply
+        # catch any PydanticSchemaGenerationError that may be raised while attempting to build the serialization schema
+        # and abort any attempts to handle special serialization.
+        from pydantic import PydanticSchemaGenerationError
+
+        try:
+            schema = handler(source_type)
+            # TODO if `schema['serialization']` is one of `'include-exclude-dict/sequence',
+            # schema validation will fail. That's why we use 'type ignore' comments below.
+            serialization = schema.get(
+                'serialization',
+                core_schema.wrap_serializer_function_ser_schema(
+                    function=lambda v, h: h(v),
+                    schema=schema,
+                    return_schema=handler.generate_schema(source_type),
+                ),
+            )
+        except PydanticSchemaGenerationError:
+            serialization = None
+
+        input_schema = handler.generate_schema(self.json_schema_input_type)
+
+        info_arg = _inspect_validator(self.func, 'plain')
+        if info_arg:
+            func = cast(core_schema.WithInfoValidatorFunction, self.func)
+            return core_schema.with_info_plain_validator_function(
+                func,
+                field_name=handler.field_name,
+                serialization=serialization,  # pyright: ignore[reportArgumentType]
+                json_schema_input_schema=input_schema,
+            )
+        else:
+            func = cast(core_schema.NoInfoValidatorFunction, self.func)
+            return core_schema.no_info_plain_validator_function(
+                func,
+                serialization=serialization,  # pyright: ignore[reportArgumentType]
+                json_schema_input_schema=input_schema,
+            )
+
+    @classmethod
+    def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
+        return cls(
+            func=decorator.func,
+            json_schema_input_type=decorator.info.json_schema_input_type,
+        )
+
+
+@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
+class WrapValidator:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-validators
+
+    A metadata class that indicates that a validation should be applied **around** the inner validation logic.
+
+    Attributes:
+        func: The validator function.
+        json_schema_input_type: The input type of the function. This is only used to generate the appropriate
+            JSON Schema (in validation mode).
+
+    ```python
+    from datetime import datetime
+
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, ValidationError, WrapValidator
+
+    def validate_timestamp(v, handler):
+        if v == 'now':
+            # we don't want to bother with further validation, just return the new value
+            return datetime.now()
+        try:
+            return handler(v)
+        except ValidationError:
+            # validation failed, in this case we want to return a default value
+            return datetime(2000, 1, 1)
+
+    MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)]
+
+    class Model(BaseModel):
+        a: MyTimestamp
+
+    print(Model(a='now').a)
+    #> 2032-01-02 03:04:05.000006
+    print(Model(a='invalid').a)
+    #> 2000-01-01 00:00:00
+    ```
+    """
+
+    func: core_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunction
+    json_schema_input_type: Any = PydanticUndefined
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(source_type)
+        input_schema = (
+            None
+            if self.json_schema_input_type is PydanticUndefined
+            else handler.generate_schema(self.json_schema_input_type)
+        )
+
+        info_arg = _inspect_validator(self.func, 'wrap')
+        if info_arg:
+            func = cast(core_schema.WithInfoWrapValidatorFunction, self.func)
+            return core_schema.with_info_wrap_validator_function(
+                func,
+                schema=schema,
+                field_name=handler.field_name,
+                json_schema_input_schema=input_schema,
+            )
+        else:
+            func = cast(core_schema.NoInfoWrapValidatorFunction, self.func)
+            return core_schema.no_info_wrap_validator_function(
+                func,
+                schema=schema,
+                json_schema_input_schema=input_schema,
+            )
+
+    @classmethod
+    def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
+        return cls(
+            func=decorator.func,
+            json_schema_input_type=decorator.info.json_schema_input_type,
+        )
+
+
+if TYPE_CHECKING:
+
+    class _OnlyValueValidatorClsMethod(Protocol):
+        def __call__(self, cls: Any, value: Any, /) -> Any: ...
+
+    class _V2ValidatorClsMethod(Protocol):
+        def __call__(self, cls: Any, value: Any, info: _core_schema.ValidationInfo, /) -> Any: ...
+
+    class _OnlyValueWrapValidatorClsMethod(Protocol):
+        def __call__(self, cls: Any, value: Any, handler: _core_schema.ValidatorFunctionWrapHandler, /) -> Any: ...
+
+    class _V2WrapValidatorClsMethod(Protocol):
+        def __call__(
+            self,
+            cls: Any,
+            value: Any,
+            handler: _core_schema.ValidatorFunctionWrapHandler,
+            info: _core_schema.ValidationInfo,
+            /,
+        ) -> Any: ...
+
+    _V2Validator = Union[
+        _V2ValidatorClsMethod,
+        _core_schema.WithInfoValidatorFunction,
+        _OnlyValueValidatorClsMethod,
+        _core_schema.NoInfoValidatorFunction,
+    ]
+
+    _V2WrapValidator = Union[
+        _V2WrapValidatorClsMethod,
+        _core_schema.WithInfoWrapValidatorFunction,
+        _OnlyValueWrapValidatorClsMethod,
+        _core_schema.NoInfoWrapValidatorFunction,
+    ]
+
+    _PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]]
+
+    _V2BeforeAfterOrPlainValidatorType = TypeVar(
+        '_V2BeforeAfterOrPlainValidatorType',
+        bound=Union[_V2Validator, _PartialClsOrStaticMethod],
+    )
+    _V2WrapValidatorType = TypeVar('_V2WrapValidatorType', bound=Union[_V2WrapValidator, _PartialClsOrStaticMethod])
+
+FieldValidatorModes: TypeAlias = Literal['before', 'after', 'wrap', 'plain']
+
+
+@overload
+def field_validator(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['wrap'],
+    check_fields: bool | None = ...,
+    json_schema_input_type: Any = ...,
+) -> Callable[[_V2WrapValidatorType], _V2WrapValidatorType]: ...
+
+
+@overload
+def field_validator(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['before', 'plain'],
+    check_fields: bool | None = ...,
+    json_schema_input_type: Any = ...,
+) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ...
+
+
+@overload
+def field_validator(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['after'] = ...,
+    check_fields: bool | None = ...,
+) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ...
+
+
+def field_validator(
+    field: str,
+    /,
+    *fields: str,
+    mode: FieldValidatorModes = 'after',
+    check_fields: bool | None = None,
+    json_schema_input_type: Any = PydanticUndefined,
+) -> Callable[[Any], Any]:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-validators
+
+    Decorate methods on the class indicating that they should be used to validate fields.
+
+    Example usage:
+    ```python
+    from typing import Any
+
+    from pydantic import (
+        BaseModel,
+        ValidationError,
+        field_validator,
+    )
+
+    class Model(BaseModel):
+        a: str
+
+        @field_validator('a')
+        @classmethod
+        def ensure_foobar(cls, v: Any):
+            if 'foobar' not in v:
+                raise ValueError('"foobar" not found in a')
+            return v
+
+    print(repr(Model(a='this is foobar good')))
+    #> Model(a='this is foobar good')
+
+    try:
+        Model(a='snap')
+    except ValidationError as exc_info:
+        print(exc_info)
+        '''
+        1 validation error for Model
+        a
+          Value error, "foobar" not found in a [type=value_error, input_value='snap', input_type=str]
+        '''
+    ```
+
+    For more in depth examples, see [Field Validators](../concepts/validators.md#field-validators).
+
+    Args:
+        field: The first field the `field_validator` should be called on; this is separate
+            from `fields` to ensure an error is raised if you don't pass at least one.
+        *fields: Additional field(s) the `field_validator` should be called on.
+        mode: Specifies whether to validate the fields before or after validation.
+        check_fields: Whether to check that the fields actually exist on the model.
+        json_schema_input_type: The input type of the function. This is only used to generate
+            the appropriate JSON Schema (in validation mode) and can only specified
+            when `mode` is either `'before'`, `'plain'` or `'wrap'`.
+
+    Returns:
+        A decorator that can be used to decorate a function to be used as a field_validator.
+
+    Raises:
+        PydanticUserError:
+            - If `@field_validator` is used bare (with no fields).
+            - If the args passed to `@field_validator` as fields are not strings.
+            - If `@field_validator` applied to instance methods.
+    """
+    if isinstance(field, FunctionType):
+        raise PydanticUserError(
+            '`@field_validator` should be used with fields and keyword arguments, not bare. '
+            "E.g. usage should be `@validator('<field_name>', ...)`",
+            code='validator-no-fields',
+        )
+
+    if mode not in ('before', 'plain', 'wrap') and json_schema_input_type is not PydanticUndefined:
+        raise PydanticUserError(
+            f"`json_schema_input_type` can't be used when mode is set to {mode!r}",
+            code='validator-input-type',
+        )
+
+    if json_schema_input_type is PydanticUndefined and mode == 'plain':
+        json_schema_input_type = Any
+
+    fields = field, *fields
+    if not all(isinstance(field, str) for field in fields):
+        raise PydanticUserError(
+            '`@field_validator` fields should be passed as separate string args. '
+            "E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`",
+            code='validator-invalid-fields',
+        )
+
+    def dec(
+        f: Callable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any],
+    ) -> _decorators.PydanticDescriptorProxy[Any]:
+        if _decorators.is_instance_method_from_sig(f):
+            raise PydanticUserError(
+                '`@field_validator` cannot be applied to instance methods', code='validator-instance-method'
+            )
+
+        # auto apply the @classmethod decorator
+        f = _decorators.ensure_classmethod_based_on_signature(f)
+
+        dec_info = _decorators.FieldValidatorDecoratorInfo(
+            fields=fields, mode=mode, check_fields=check_fields, json_schema_input_type=json_schema_input_type
+        )
+        return _decorators.PydanticDescriptorProxy(f, dec_info)
+
+    return dec
+
+
+_ModelType = TypeVar('_ModelType')
+_ModelTypeCo = TypeVar('_ModelTypeCo', covariant=True)
+
+
+class ModelWrapValidatorHandler(_core_schema.ValidatorFunctionWrapHandler, Protocol[_ModelTypeCo]):
+    """`@model_validator` decorated function handler argument type. This is used when `mode='wrap'`."""
+
+    def __call__(  # noqa: D102
+        self,
+        value: Any,
+        outer_location: str | int | None = None,
+        /,
+    ) -> _ModelTypeCo:  # pragma: no cover
+        ...
+
+
+class ModelWrapValidatorWithoutInfo(Protocol[_ModelType]):
+    """A `@model_validator` decorated function signature.
+    This is used when `mode='wrap'` and the function does not have info argument.
+    """
+
+    def __call__(  # noqa: D102
+        self,
+        cls: type[_ModelType],
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        handler: ModelWrapValidatorHandler[_ModelType],
+        /,
+    ) -> _ModelType: ...
+
+
+class ModelWrapValidator(Protocol[_ModelType]):
+    """A `@model_validator` decorated function signature. This is used when `mode='wrap'`."""
+
+    def __call__(  # noqa: D102
+        self,
+        cls: type[_ModelType],
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        handler: ModelWrapValidatorHandler[_ModelType],
+        info: _core_schema.ValidationInfo,
+        /,
+    ) -> _ModelType: ...
+
+
+class FreeModelBeforeValidatorWithoutInfo(Protocol):
+    """A `@model_validator` decorated function signature.
+    This is used when `mode='before'` and the function does not have info argument.
+    """
+
+    def __call__(  # noqa: D102
+        self,
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        /,
+    ) -> Any: ...
+
+
+class ModelBeforeValidatorWithoutInfo(Protocol):
+    """A `@model_validator` decorated function signature.
+    This is used when `mode='before'` and the function does not have info argument.
+    """
+
+    def __call__(  # noqa: D102
+        self,
+        cls: Any,
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        /,
+    ) -> Any: ...
+
+
+class FreeModelBeforeValidator(Protocol):
+    """A `@model_validator` decorated function signature. This is used when `mode='before'`."""
+
+    def __call__(  # noqa: D102
+        self,
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        info: _core_schema.ValidationInfo,
+        /,
+    ) -> Any: ...
+
+
+class ModelBeforeValidator(Protocol):
+    """A `@model_validator` decorated function signature. This is used when `mode='before'`."""
+
+    def __call__(  # noqa: D102
+        self,
+        cls: Any,
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        info: _core_schema.ValidationInfo,
+        /,
+    ) -> Any: ...
+
+
+ModelAfterValidatorWithoutInfo = Callable[[_ModelType], _ModelType]
+"""A `@model_validator` decorated function signature. This is used when `mode='after'` and the function does not
+have info argument.
+"""
+
+ModelAfterValidator = Callable[[_ModelType, _core_schema.ValidationInfo], _ModelType]
+"""A `@model_validator` decorated function signature. This is used when `mode='after'`."""
+
+_AnyModelWrapValidator = Union[ModelWrapValidator[_ModelType], ModelWrapValidatorWithoutInfo[_ModelType]]
+_AnyModelBeforeValidator = Union[
+    FreeModelBeforeValidator, ModelBeforeValidator, FreeModelBeforeValidatorWithoutInfo, ModelBeforeValidatorWithoutInfo
+]
+_AnyModelAfterValidator = Union[ModelAfterValidator[_ModelType], ModelAfterValidatorWithoutInfo[_ModelType]]
+
+
+@overload
+def model_validator(
+    *,
+    mode: Literal['wrap'],
+) -> Callable[
+    [_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
+]: ...
+
+
+@overload
+def model_validator(
+    *,
+    mode: Literal['before'],
+) -> Callable[
+    [_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
+]: ...
+
+
+@overload
+def model_validator(
+    *,
+    mode: Literal['after'],
+) -> Callable[
+    [_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
+]: ...
+
+
+def model_validator(
+    *,
+    mode: Literal['wrap', 'before', 'after'],
+) -> Any:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#model-validators
+
+    Decorate model methods for validation purposes.
+
+    Example usage:
+    ```python
+    from typing_extensions import Self
+
+    from pydantic import BaseModel, ValidationError, model_validator
+
+    class Square(BaseModel):
+        width: float
+        height: float
+
+        @model_validator(mode='after')
+        def verify_square(self) -> Self:
+            if self.width != self.height:
+                raise ValueError('width and height do not match')
+            return self
+
+    s = Square(width=1, height=1)
+    print(repr(s))
+    #> Square(width=1.0, height=1.0)
+
+    try:
+        Square(width=1, height=2)
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Square
+          Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict]
+        '''
+    ```
+
+    For more in depth examples, see [Model Validators](../concepts/validators.md#model-validators).
+
+    Args:
+        mode: A required string literal that specifies the validation mode.
+            It can be one of the following: 'wrap', 'before', or 'after'.
+
+    Returns:
+        A decorator that can be used to decorate a function to be used as a model validator.
+    """
+
+    def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]:
+        # auto apply the @classmethod decorator
+        f = _decorators.ensure_classmethod_based_on_signature(f)
+        dec_info = _decorators.ModelValidatorDecoratorInfo(mode=mode)
+        return _decorators.PydanticDescriptorProxy(f, dec_info)
+
+    return dec
+
+
+AnyType = TypeVar('AnyType')
+
+
+if TYPE_CHECKING:
+    # If we add configurable attributes to IsInstance, we'd probably need to stop hiding it from type checkers like this
+    InstanceOf = Annotated[AnyType, ...]  # `IsInstance[Sequence]` will be recognized by type checkers as `Sequence`
+
+else:
+
+    @dataclasses.dataclass(**_internal_dataclass.slots_true)
+    class InstanceOf:
+        '''Generic type for annotating a type that is an instance of a given class.
+
+        Example:
+            ```python
+            from pydantic import BaseModel, InstanceOf
+
+            class Foo:
+                ...
+
+            class Bar(BaseModel):
+                foo: InstanceOf[Foo]
+
+            Bar(foo=Foo())
+            try:
+                Bar(foo=42)
+            except ValidationError as e:
+                print(e)
+                """
+                [
+                │   {
+                │   │   'type': 'is_instance_of',
+                │   │   'loc': ('foo',),
+                │   │   'msg': 'Input should be an instance of Foo',
+                │   │   'input': 42,
+                │   │   'ctx': {'class': 'Foo'},
+                │   │   'url': 'https://errors.pydantic.dev/0.38.0/v/is_instance_of'
+                │   }
+                ]
+                """
+            ```
+        '''
+
+        @classmethod
+        def __class_getitem__(cls, item: AnyType) -> AnyType:
+            return Annotated[item, cls()]
+
+        @classmethod
+        def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+            from pydantic import PydanticSchemaGenerationError
+
+            # use the generic _origin_ as the second argument to isinstance when appropriate
+            instance_of_schema = core_schema.is_instance_schema(_generics.get_origin(source) or source)
+
+            try:
+                # Try to generate the "standard" schema, which will be used when loading from JSON
+                original_schema = handler(source)
+            except PydanticSchemaGenerationError:
+                # If that fails, just produce a schema that can validate from python
+                return instance_of_schema
+            else:
+                # Use the "original" approach to serialization
+                instance_of_schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
+                    function=lambda v, h: h(v), schema=original_schema
+                )
+                return core_schema.json_or_python_schema(python_schema=instance_of_schema, json_schema=original_schema)
+
+        __hash__ = object.__hash__
+
+
+if TYPE_CHECKING:
+    SkipValidation = Annotated[AnyType, ...]  # SkipValidation[list[str]] will be treated by type checkers as list[str]
+else:
+
+    @dataclasses.dataclass(**_internal_dataclass.slots_true)
+    class SkipValidation:
+        """If this is applied as an annotation (e.g., via `x: Annotated[int, SkipValidation]`), validation will be
+            skipped. You can also use `SkipValidation[int]` as a shorthand for `Annotated[int, SkipValidation]`.
+
+        This can be useful if you want to use a type annotation for documentation/IDE/type-checking purposes,
+        and know that it is safe to skip validation for one or more of the fields.
+
+        Because this converts the validation schema to `any_schema`, subsequent annotation-applied transformations
+        may not have the expected effects. Therefore, when used, this annotation should generally be the final
+        annotation applied to a type.
+        """
+
+        def __class_getitem__(cls, item: Any) -> Any:
+            return Annotated[item, SkipValidation()]
+
+        @classmethod
+        def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+            original_schema = handler(source)
+            metadata = {'pydantic_js_annotation_functions': [lambda _c, h: h(original_schema)]}
+            return core_schema.any_schema(
+                metadata=metadata,
+                serialization=core_schema.wrap_serializer_function_ser_schema(
+                    function=lambda v, h: h(v), schema=original_schema
+                ),
+            )
+
+        __hash__ = object.__hash__
diff --git a/.venv/lib/python3.12/site-packages/pydantic/generics.py b/.venv/lib/python3.12/site-packages/pydantic/generics.py
new file mode 100644
index 00000000..3f1070d0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/generics.py
@@ -0,0 +1,5 @@
+"""The `generics` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/json.py b/.venv/lib/python3.12/site-packages/pydantic/json.py
new file mode 100644
index 00000000..bcaff9f5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/json.py
@@ -0,0 +1,5 @@
+"""The `json` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/json_schema.py b/.venv/lib/python3.12/site-packages/pydantic/json_schema.py
new file mode 100644
index 00000000..de59d6c4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/json_schema.py
@@ -0,0 +1,2649 @@
+"""
+Usage docs: https://docs.pydantic.dev/2.5/concepts/json_schema/
+
+The `json_schema` module contains classes and functions to allow the way [JSON Schema](https://json-schema.org/)
+is generated to be customized.
+
+In general you shouldn't need to use this module directly; instead, you can use
+[`BaseModel.model_json_schema`][pydantic.BaseModel.model_json_schema] and
+[`TypeAdapter.json_schema`][pydantic.TypeAdapter.json_schema].
+"""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import inspect
+import math
+import os
+import re
+import warnings
+from collections import defaultdict
+from copy import deepcopy
+from enum import Enum
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Counter,
+    Dict,
+    Hashable,
+    Iterable,
+    NewType,
+    Pattern,
+    Sequence,
+    Tuple,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+
+import pydantic_core
+from pydantic_core import CoreSchema, PydanticOmit, core_schema, to_jsonable_python
+from pydantic_core.core_schema import ComputedField
+from typing_extensions import Annotated, Literal, TypeAlias, assert_never, deprecated, final
+
+from pydantic.warnings import PydanticDeprecatedSince26, PydanticDeprecatedSince29
+
+from ._internal import (
+    _config,
+    _core_metadata,
+    _core_utils,
+    _decorators,
+    _internal_dataclass,
+    _mock_val_ser,
+    _schema_generation_shared,
+    _typing_extra,
+)
+from .annotated_handlers import GetJsonSchemaHandler
+from .config import JsonDict, JsonValue
+from .errors import PydanticInvalidForJsonSchema, PydanticSchemaGenerationError, PydanticUserError
+
+if TYPE_CHECKING:
+    from . import ConfigDict
+    from ._internal._core_utils import CoreSchemaField, CoreSchemaOrField
+    from ._internal._dataclasses import PydanticDataclass
+    from ._internal._schema_generation_shared import GetJsonSchemaFunction
+    from .main import BaseModel
+
+
+CoreSchemaOrFieldType = Literal[core_schema.CoreSchemaType, core_schema.CoreSchemaFieldType]
+"""
+A type alias for defined schema types that represents a union of
+`core_schema.CoreSchemaType` and
+`core_schema.CoreSchemaFieldType`.
+"""
+
+JsonSchemaValue = Dict[str, Any]
+"""
+A type alias for a JSON schema value. This is a dictionary of string keys to arbitrary JSON values.
+"""
+
+JsonSchemaMode = Literal['validation', 'serialization']
+"""
+A type alias that represents the mode of a JSON schema; either 'validation' or 'serialization'.
+
+For some types, the inputs to validation differ from the outputs of serialization. For example,
+computed fields will only be present when serializing, and should not be provided when
+validating. This flag provides a way to indicate whether you want the JSON schema required
+for validation inputs, or that will be matched by serialization outputs.
+"""
+
+_MODE_TITLE_MAPPING: dict[JsonSchemaMode, str] = {'validation': 'Input', 'serialization': 'Output'}
+
+
+JsonSchemaWarningKind = Literal['skipped-choice', 'non-serializable-default', 'skipped-discriminator']
+"""
+A type alias representing the kinds of warnings that can be emitted during JSON schema generation.
+
+See [`GenerateJsonSchema.render_warning_message`][pydantic.json_schema.GenerateJsonSchema.render_warning_message]
+for more details.
+"""
+
+
+class PydanticJsonSchemaWarning(UserWarning):
+    """This class is used to emit warnings produced during JSON schema generation.
+    See the [`GenerateJsonSchema.emit_warning`][pydantic.json_schema.GenerateJsonSchema.emit_warning] and
+    [`GenerateJsonSchema.render_warning_message`][pydantic.json_schema.GenerateJsonSchema.render_warning_message]
+    methods for more details; these can be overridden to control warning behavior.
+    """
+
+
+# ##### JSON Schema Generation #####
+DEFAULT_REF_TEMPLATE = '#/$defs/{model}'
+"""The default format string used to generate reference names."""
+
+# There are three types of references relevant to building JSON schemas:
+#   1. core_schema "ref" values; these are not exposed as part of the JSON schema
+#       * these might look like the fully qualified path of a model, its id, or something similar
+CoreRef = NewType('CoreRef', str)
+#   2. keys of the "definitions" object that will eventually go into the JSON schema
+#       * by default, these look like "MyModel", though may change in the presence of collisions
+#       * eventually, we may want to make it easier to modify the way these names are generated
+DefsRef = NewType('DefsRef', str)
+#   3. the values corresponding to the "$ref" key in the schema
+#       * By default, these look like "#/$defs/MyModel", as in {"$ref": "#/$defs/MyModel"}
+JsonRef = NewType('JsonRef', str)
+
+CoreModeRef = Tuple[CoreRef, JsonSchemaMode]
+JsonSchemaKeyT = TypeVar('JsonSchemaKeyT', bound=Hashable)
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class _DefinitionsRemapping:
+    defs_remapping: dict[DefsRef, DefsRef]
+    json_remapping: dict[JsonRef, JsonRef]
+
+    @staticmethod
+    def from_prioritized_choices(
+        prioritized_choices: dict[DefsRef, list[DefsRef]],
+        defs_to_json: dict[DefsRef, JsonRef],
+        definitions: dict[DefsRef, JsonSchemaValue],
+    ) -> _DefinitionsRemapping:
+        """
+        This function should produce a remapping that replaces complex DefsRef with the simpler ones from the
+        prioritized_choices such that applying the name remapping would result in an equivalent JSON schema.
+        """
+        # We need to iteratively simplify the definitions until we reach a fixed point.
+        # The reason for this is that outer definitions may reference inner definitions that get simplified
+        # into an equivalent reference, and the outer definitions won't be equivalent until we've simplified
+        # the inner definitions.
+        copied_definitions = deepcopy(definitions)
+        definitions_schema = {'$defs': copied_definitions}
+        for _iter in range(100):  # prevent an infinite loop in the case of a bug, 100 iterations should be enough
+            # For every possible remapped DefsRef, collect all schemas that that DefsRef might be used for:
+            schemas_for_alternatives: dict[DefsRef, list[JsonSchemaValue]] = defaultdict(list)
+            for defs_ref in copied_definitions:
+                alternatives = prioritized_choices[defs_ref]
+                for alternative in alternatives:
+                    schemas_for_alternatives[alternative].append(copied_definitions[defs_ref])
+
+            # Deduplicate the schemas for each alternative; the idea is that we only want to remap to a new DefsRef
+            # if it introduces no ambiguity, i.e., there is only one distinct schema for that DefsRef.
+            for defs_ref in schemas_for_alternatives:
+                schemas_for_alternatives[defs_ref] = _deduplicate_schemas(schemas_for_alternatives[defs_ref])
+
+            # Build the remapping
+            defs_remapping: dict[DefsRef, DefsRef] = {}
+            json_remapping: dict[JsonRef, JsonRef] = {}
+            for original_defs_ref in definitions:
+                alternatives = prioritized_choices[original_defs_ref]
+                # Pick the first alternative that has only one schema, since that means there is no collision
+                remapped_defs_ref = next(x for x in alternatives if len(schemas_for_alternatives[x]) == 1)
+                defs_remapping[original_defs_ref] = remapped_defs_ref
+                json_remapping[defs_to_json[original_defs_ref]] = defs_to_json[remapped_defs_ref]
+            remapping = _DefinitionsRemapping(defs_remapping, json_remapping)
+            new_definitions_schema = remapping.remap_json_schema({'$defs': copied_definitions})
+            if definitions_schema == new_definitions_schema:
+                # We've reached the fixed point
+                return remapping
+            definitions_schema = new_definitions_schema
+
+        raise PydanticInvalidForJsonSchema('Failed to simplify the JSON schema definitions')
+
+    def remap_defs_ref(self, ref: DefsRef) -> DefsRef:
+        return self.defs_remapping.get(ref, ref)
+
+    def remap_json_ref(self, ref: JsonRef) -> JsonRef:
+        return self.json_remapping.get(ref, ref)
+
+    def remap_json_schema(self, schema: Any) -> Any:
+        """
+        Recursively update the JSON schema replacing all $refs
+        """
+        if isinstance(schema, str):
+            # Note: this may not really be a JsonRef; we rely on having no collisions between JsonRefs and other strings
+            return self.remap_json_ref(JsonRef(schema))
+        elif isinstance(schema, list):
+            return [self.remap_json_schema(item) for item in schema]
+        elif isinstance(schema, dict):
+            for key, value in schema.items():
+                if key == '$ref' and isinstance(value, str):
+                    schema['$ref'] = self.remap_json_ref(JsonRef(value))
+                elif key == '$defs':
+                    schema['$defs'] = {
+                        self.remap_defs_ref(DefsRef(key)): self.remap_json_schema(value)
+                        for key, value in schema['$defs'].items()
+                    }
+                else:
+                    schema[key] = self.remap_json_schema(value)
+        return schema
+
+
+class GenerateJsonSchema:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/json_schema/#customizing-the-json-schema-generation-process
+
+    A class for generating JSON schemas.
+
+    This class generates JSON schemas based on configured parameters. The default schema dialect
+    is [https://json-schema.org/draft/2020-12/schema](https://json-schema.org/draft/2020-12/schema).
+    The class uses `by_alias` to configure how fields with
+    multiple names are handled and `ref_template` to format reference names.
+
+    Attributes:
+        schema_dialect: The JSON schema dialect used to generate the schema. See
+            [Declaring a Dialect](https://json-schema.org/understanding-json-schema/reference/schema.html#id4)
+            in the JSON Schema documentation for more information about dialects.
+        ignored_warning_kinds: Warnings to ignore when generating the schema. `self.render_warning_message` will
+            do nothing if its argument `kind` is in `ignored_warning_kinds`;
+            this value can be modified on subclasses to easily control which warnings are emitted.
+        by_alias: Whether to use field aliases when generating the schema.
+        ref_template: The format string used when generating reference names.
+        core_to_json_refs: A mapping of core refs to JSON refs.
+        core_to_defs_refs: A mapping of core refs to definition refs.
+        defs_to_core_refs: A mapping of definition refs to core refs.
+        json_to_defs_refs: A mapping of JSON refs to definition refs.
+        definitions: Definitions in the schema.
+
+    Args:
+        by_alias: Whether to use field aliases in the generated schemas.
+        ref_template: The format string to use when generating reference names.
+
+    Raises:
+        JsonSchemaError: If the instance of the class is inadvertently reused after generating a schema.
+    """
+
+    schema_dialect = 'https://json-schema.org/draft/2020-12/schema'
+
+    # `self.render_warning_message` will do nothing if its argument `kind` is in `ignored_warning_kinds`;
+    # this value can be modified on subclasses to easily control which warnings are emitted
+    ignored_warning_kinds: set[JsonSchemaWarningKind] = {'skipped-choice'}
+
+    def __init__(self, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE):
+        self.by_alias = by_alias
+        self.ref_template = ref_template
+
+        self.core_to_json_refs: dict[CoreModeRef, JsonRef] = {}
+        self.core_to_defs_refs: dict[CoreModeRef, DefsRef] = {}
+        self.defs_to_core_refs: dict[DefsRef, CoreModeRef] = {}
+        self.json_to_defs_refs: dict[JsonRef, DefsRef] = {}
+
+        self.definitions: dict[DefsRef, JsonSchemaValue] = {}
+        self._config_wrapper_stack = _config.ConfigWrapperStack(_config.ConfigWrapper({}))
+
+        self._mode: JsonSchemaMode = 'validation'
+
+        # The following includes a mapping of a fully-unique defs ref choice to a list of preferred
+        # alternatives, which are generally simpler, such as only including the class name.
+        # At the end of schema generation, we use these to produce a JSON schema with more human-readable
+        # definitions, which would also work better in a generated OpenAPI client, etc.
+        self._prioritized_defsref_choices: dict[DefsRef, list[DefsRef]] = {}
+        self._collision_counter: dict[str, int] = defaultdict(int)
+        self._collision_index: dict[str, int] = {}
+
+        self._schema_type_to_method = self.build_schema_type_to_method()
+
+        # When we encounter definitions we need to try to build them immediately
+        # so that they are available schemas that reference them
+        # But it's possible that CoreSchema was never going to be used
+        # (e.g. because the CoreSchema that references short circuits is JSON schema generation without needing
+        #  the reference) so instead of failing altogether if we can't build a definition we
+        # store the error raised and re-throw it if we end up needing that def
+        self._core_defs_invalid_for_json_schema: dict[DefsRef, PydanticInvalidForJsonSchema] = {}
+
+        # This changes to True after generating a schema, to prevent issues caused by accidental reuse
+        # of a single instance of a schema generator
+        self._used = False
+
+    @property
+    def _config(self) -> _config.ConfigWrapper:
+        return self._config_wrapper_stack.tail
+
+    @property
+    def mode(self) -> JsonSchemaMode:
+        if self._config.json_schema_mode_override is not None:
+            return self._config.json_schema_mode_override
+        else:
+            return self._mode
+
+    def build_schema_type_to_method(
+        self,
+    ) -> dict[CoreSchemaOrFieldType, Callable[[CoreSchemaOrField], JsonSchemaValue]]:
+        """Builds a dictionary mapping fields to methods for generating JSON schemas.
+
+        Returns:
+            A dictionary containing the mapping of `CoreSchemaOrFieldType` to a handler method.
+
+        Raises:
+            TypeError: If no method has been defined for generating a JSON schema for a given pydantic core schema type.
+        """
+        mapping: dict[CoreSchemaOrFieldType, Callable[[CoreSchemaOrField], JsonSchemaValue]] = {}
+        core_schema_types: list[CoreSchemaOrFieldType] = _typing_extra.literal_values(
+            CoreSchemaOrFieldType  # type: ignore
+        )
+        for key in core_schema_types:
+            method_name = f"{key.replace('-', '_')}_schema"
+            try:
+                mapping[key] = getattr(self, method_name)
+            except AttributeError as e:  # pragma: no cover
+                if os.environ['PYDANTIC_PRIVATE_ALLOW_UNHANDLED_SCHEMA_TYPES'] == '1':
+                    continue
+                raise TypeError(
+                    f'No method for generating JsonSchema for core_schema.type={key!r} '
+                    f'(expected: {type(self).__name__}.{method_name})'
+                ) from e
+        return mapping
+
+    def generate_definitions(
+        self, inputs: Sequence[tuple[JsonSchemaKeyT, JsonSchemaMode, core_schema.CoreSchema]]
+    ) -> tuple[dict[tuple[JsonSchemaKeyT, JsonSchemaMode], JsonSchemaValue], dict[DefsRef, JsonSchemaValue]]:
+        """Generates JSON schema definitions from a list of core schemas, pairing the generated definitions with a
+        mapping that links the input keys to the definition references.
+
+        Args:
+            inputs: A sequence of tuples, where:
+
+                - The first element is a JSON schema key type.
+                - The second element is the JSON mode: either 'validation' or 'serialization'.
+                - The third element is a core schema.
+
+        Returns:
+            A tuple where:
+
+                - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and
+                    whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have
+                    JsonRef references to definitions that are defined in the second returned element.)
+                - The second element is a dictionary whose keys are definition references for the JSON schemas
+                    from the first returned element, and whose values are the actual JSON schema definitions.
+
+        Raises:
+            PydanticUserError: Raised if the JSON schema generator has already been used to generate a JSON schema.
+        """
+        if self._used:
+            raise PydanticUserError(
+                'This JSON schema generator has already been used to generate a JSON schema. '
+                f'You must create a new instance of {type(self).__name__} to generate a new JSON schema.',
+                code='json-schema-already-used',
+            )
+
+        for _, mode, schema in inputs:
+            self._mode = mode
+            self.generate_inner(schema)
+
+        definitions_remapping = self._build_definitions_remapping()
+
+        json_schemas_map: dict[tuple[JsonSchemaKeyT, JsonSchemaMode], DefsRef] = {}
+        for key, mode, schema in inputs:
+            self._mode = mode
+            json_schema = self.generate_inner(schema)
+            json_schemas_map[(key, mode)] = definitions_remapping.remap_json_schema(json_schema)
+
+        json_schema = {'$defs': self.definitions}
+        json_schema = definitions_remapping.remap_json_schema(json_schema)
+        self._used = True
+        return json_schemas_map, self.sort(json_schema['$defs'])  # type: ignore
+
+    def generate(self, schema: CoreSchema, mode: JsonSchemaMode = 'validation') -> JsonSchemaValue:
+        """Generates a JSON schema for a specified schema in a specified mode.
+
+        Args:
+            schema: A Pydantic model.
+            mode: The mode in which to generate the schema. Defaults to 'validation'.
+
+        Returns:
+            A JSON schema representing the specified schema.
+
+        Raises:
+            PydanticUserError: If the JSON schema generator has already been used to generate a JSON schema.
+        """
+        self._mode = mode
+        if self._used:
+            raise PydanticUserError(
+                'This JSON schema generator has already been used to generate a JSON schema. '
+                f'You must create a new instance of {type(self).__name__} to generate a new JSON schema.',
+                code='json-schema-already-used',
+            )
+
+        json_schema: JsonSchemaValue = self.generate_inner(schema)
+        json_ref_counts = self.get_json_ref_counts(json_schema)
+
+        ref = cast(JsonRef, json_schema.get('$ref'))
+        while ref is not None:  # may need to unpack multiple levels
+            ref_json_schema = self.get_schema_from_definitions(ref)
+            if json_ref_counts[ref] == 1 and ref_json_schema is not None and len(json_schema) == 1:
+                # "Unpack" the ref since this is the only reference and there are no sibling keys
+                json_schema = ref_json_schema.copy()  # copy to prevent recursive dict reference
+                json_ref_counts[ref] -= 1
+                ref = cast(JsonRef, json_schema.get('$ref'))
+            ref = None
+
+        self._garbage_collect_definitions(json_schema)
+        definitions_remapping = self._build_definitions_remapping()
+
+        if self.definitions:
+            json_schema['$defs'] = self.definitions
+
+        json_schema = definitions_remapping.remap_json_schema(json_schema)
+
+        # For now, we will not set the $schema key. However, if desired, this can be easily added by overriding
+        # this method and adding the following line after a call to super().generate(schema):
+        # json_schema['$schema'] = self.schema_dialect
+
+        self._used = True
+        return self.sort(json_schema)
+
+    def generate_inner(self, schema: CoreSchemaOrField) -> JsonSchemaValue:  # noqa: C901
+        """Generates a JSON schema for a given core schema.
+
+        Args:
+            schema: The given core schema.
+
+        Returns:
+            The generated JSON schema.
+
+        TODO: the nested function definitions here seem like bad practice, I'd like to unpack these
+        in a future PR. It'd be great if we could shorten the call stack a bit for JSON schema generation,
+        and I think there's potential for that here.
+        """
+        # If a schema with the same CoreRef has been handled, just return a reference to it
+        # Note that this assumes that it will _never_ be the case that the same CoreRef is used
+        # on types that should have different JSON schemas
+        if 'ref' in schema:
+            core_ref = CoreRef(schema['ref'])  # type: ignore[typeddict-item]
+            core_mode_ref = (core_ref, self.mode)
+            if core_mode_ref in self.core_to_defs_refs and self.core_to_defs_refs[core_mode_ref] in self.definitions:
+                return {'$ref': self.core_to_json_refs[core_mode_ref]}
+
+        def populate_defs(core_schema: CoreSchema, json_schema: JsonSchemaValue) -> JsonSchemaValue:
+            if 'ref' in core_schema:
+                core_ref = CoreRef(core_schema['ref'])  # type: ignore[typeddict-item]
+                defs_ref, ref_json_schema = self.get_cache_defs_ref_schema(core_ref)
+                json_ref = JsonRef(ref_json_schema['$ref'])
+                # Replace the schema if it's not a reference to itself
+                # What we want to avoid is having the def be just a ref to itself
+                # which is what would happen if we blindly assigned any
+                if json_schema.get('$ref', None) != json_ref:
+                    self.definitions[defs_ref] = json_schema
+                    self._core_defs_invalid_for_json_schema.pop(defs_ref, None)
+                json_schema = ref_json_schema
+            return json_schema
+
+        def handler_func(schema_or_field: CoreSchemaOrField) -> JsonSchemaValue:
+            """Generate a JSON schema based on the input schema.
+
+            Args:
+                schema_or_field: The core schema to generate a JSON schema from.
+
+            Returns:
+                The generated JSON schema.
+
+            Raises:
+                TypeError: If an unexpected schema type is encountered.
+            """
+            # Generate the core-schema-type-specific bits of the schema generation:
+            json_schema: JsonSchemaValue | None = None
+            if self.mode == 'serialization' and 'serialization' in schema_or_field:
+                # In this case, we skip the JSON Schema generation of the schema
+                # and use the `'serialization'` schema instead (canonical example:
+                # `Annotated[int, PlainSerializer(str)]`).
+                ser_schema = schema_or_field['serialization']  # type: ignore
+                json_schema = self.ser_schema(ser_schema)
+
+                # It might be that the 'serialization'` is skipped depending on `when_used`.
+                # This is only relevant for `nullable` schemas though, so we special case here.
+                if (
+                    json_schema is not None
+                    and ser_schema.get('when_used') in ('unless-none', 'json-unless-none')
+                    and schema_or_field['type'] == 'nullable'
+                ):
+                    json_schema = self.get_flattened_anyof([{'type': 'null'}, json_schema])
+            if json_schema is None:
+                if _core_utils.is_core_schema(schema_or_field) or _core_utils.is_core_schema_field(schema_or_field):
+                    generate_for_schema_type = self._schema_type_to_method[schema_or_field['type']]
+                    json_schema = generate_for_schema_type(schema_or_field)
+                else:
+                    raise TypeError(f'Unexpected schema type: schema={schema_or_field}')
+            if _core_utils.is_core_schema(schema_or_field):
+                json_schema = populate_defs(schema_or_field, json_schema)
+            return json_schema
+
+        current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, handler_func)
+
+        metadata = cast(_core_metadata.CoreMetadata, schema.get('metadata', {}))
+
+        # TODO: I dislike that we have to wrap these basic dict updates in callables, is there any way around this?
+
+        if js_updates := metadata.get('pydantic_js_updates'):
+
+            def js_updates_handler_func(
+                schema_or_field: CoreSchemaOrField,
+                current_handler: GetJsonSchemaHandler = current_handler,
+            ) -> JsonSchemaValue:
+                json_schema = {**current_handler(schema_or_field), **js_updates}
+                return json_schema
+
+            current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, js_updates_handler_func)
+
+        if js_extra := metadata.get('pydantic_js_extra'):
+
+            def js_extra_handler_func(
+                schema_or_field: CoreSchemaOrField,
+                current_handler: GetJsonSchemaHandler = current_handler,
+            ) -> JsonSchemaValue:
+                json_schema = current_handler(schema_or_field)
+                if isinstance(js_extra, dict):
+                    json_schema.update(to_jsonable_python(js_extra))
+                elif callable(js_extra):
+                    # similar to typing issue in _update_class_schema when we're working with callable js extra
+                    js_extra(json_schema)  # type: ignore
+                return json_schema
+
+            current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, js_extra_handler_func)
+
+        for js_modify_function in metadata.get('pydantic_js_functions', ()):
+
+            def new_handler_func(
+                schema_or_field: CoreSchemaOrField,
+                current_handler: GetJsonSchemaHandler = current_handler,
+                js_modify_function: GetJsonSchemaFunction = js_modify_function,
+            ) -> JsonSchemaValue:
+                json_schema = js_modify_function(schema_or_field, current_handler)
+                if _core_utils.is_core_schema(schema_or_field):
+                    json_schema = populate_defs(schema_or_field, json_schema)
+                original_schema = current_handler.resolve_ref_schema(json_schema)
+                ref = json_schema.pop('$ref', None)
+                if ref and json_schema:
+                    original_schema.update(json_schema)
+                return original_schema
+
+            current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, new_handler_func)
+
+        for js_modify_function in metadata.get('pydantic_js_annotation_functions', ()):
+
+            def new_handler_func(
+                schema_or_field: CoreSchemaOrField,
+                current_handler: GetJsonSchemaHandler = current_handler,
+                js_modify_function: GetJsonSchemaFunction = js_modify_function,
+            ) -> JsonSchemaValue:
+                json_schema = js_modify_function(schema_or_field, current_handler)
+                if _core_utils.is_core_schema(schema_or_field):
+                    json_schema = populate_defs(schema_or_field, json_schema)
+                return json_schema
+
+            current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, new_handler_func)
+
+        json_schema = current_handler(schema)
+        if _core_utils.is_core_schema(schema):
+            json_schema = populate_defs(schema, json_schema)
+        return json_schema
+
+    def sort(self, value: JsonSchemaValue, parent_key: str | None = None) -> JsonSchemaValue:
+        """Override this method to customize the sorting of the JSON schema (e.g., don't sort at all, sort all keys unconditionally, etc.)
+
+        By default, alphabetically sort the keys in the JSON schema, skipping the 'properties' and 'default' keys to preserve field definition order.
+        This sort is recursive, so it will sort all nested dictionaries as well.
+        """
+        sorted_dict: dict[str, JsonSchemaValue] = {}
+        keys = value.keys()
+        if parent_key not in ('properties', 'default'):
+            keys = sorted(keys)
+        for key in keys:
+            sorted_dict[key] = self._sort_recursive(value[key], parent_key=key)
+        return sorted_dict
+
+    def _sort_recursive(self, value: Any, parent_key: str | None = None) -> Any:
+        """Recursively sort a JSON schema value."""
+        if isinstance(value, dict):
+            sorted_dict: dict[str, JsonSchemaValue] = {}
+            keys = value.keys()
+            if parent_key not in ('properties', 'default'):
+                keys = sorted(keys)
+            for key in keys:
+                sorted_dict[key] = self._sort_recursive(value[key], parent_key=key)
+            return sorted_dict
+        elif isinstance(value, list):
+            sorted_list: list[JsonSchemaValue] = []
+            for item in value:
+                sorted_list.append(self._sort_recursive(item, parent_key))
+            return sorted_list
+        else:
+            return value
+
+    # ### Schema generation methods
+
+    def invalid_schema(self, schema: core_schema.InvalidSchema) -> JsonSchemaValue:
+        """Placeholder - should never be called."""
+
+        raise RuntimeError('Cannot generate schema for invalid_schema. This is a bug! Please report it.')
+
+    def any_schema(self, schema: core_schema.AnySchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches any value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return {}
+
+    def none_schema(self, schema: core_schema.NoneSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches `None`.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return {'type': 'null'}
+
+    def bool_schema(self, schema: core_schema.BoolSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a bool value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return {'type': 'boolean'}
+
+    def int_schema(self, schema: core_schema.IntSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches an int value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        json_schema: dict[str, Any] = {'type': 'integer'}
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.numeric)
+        json_schema = {k: v for k, v in json_schema.items() if v not in {math.inf, -math.inf}}
+        return json_schema
+
+    def float_schema(self, schema: core_schema.FloatSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a float value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        json_schema: dict[str, Any] = {'type': 'number'}
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.numeric)
+        json_schema = {k: v for k, v in json_schema.items() if v not in {math.inf, -math.inf}}
+        return json_schema
+
+    def decimal_schema(self, schema: core_schema.DecimalSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a decimal value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        json_schema = self.str_schema(core_schema.str_schema())
+        if self.mode == 'validation':
+            multiple_of = schema.get('multiple_of')
+            le = schema.get('le')
+            ge = schema.get('ge')
+            lt = schema.get('lt')
+            gt = schema.get('gt')
+            json_schema = {
+                'anyOf': [
+                    self.float_schema(
+                        core_schema.float_schema(
+                            allow_inf_nan=schema.get('allow_inf_nan'),
+                            multiple_of=None if multiple_of is None else float(multiple_of),
+                            le=None if le is None else float(le),
+                            ge=None if ge is None else float(ge),
+                            lt=None if lt is None else float(lt),
+                            gt=None if gt is None else float(gt),
+                        )
+                    ),
+                    json_schema,
+                ],
+            }
+        return json_schema
+
+    def str_schema(self, schema: core_schema.StringSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a string value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        json_schema = {'type': 'string'}
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.string)
+        if isinstance(json_schema.get('pattern'), Pattern):
+            # TODO: should we add regex flags to the pattern?
+            json_schema['pattern'] = json_schema.get('pattern').pattern  # type: ignore
+        return json_schema
+
+    def bytes_schema(self, schema: core_schema.BytesSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a bytes value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        json_schema = {'type': 'string', 'format': 'base64url' if self._config.ser_json_bytes == 'base64' else 'binary'}
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.bytes)
+        return json_schema
+
+    def date_schema(self, schema: core_schema.DateSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a date value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return {'type': 'string', 'format': 'date'}
+
+    def time_schema(self, schema: core_schema.TimeSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a time value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return {'type': 'string', 'format': 'time'}
+
+    def datetime_schema(self, schema: core_schema.DatetimeSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a datetime value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return {'type': 'string', 'format': 'date-time'}
+
+    def timedelta_schema(self, schema: core_schema.TimedeltaSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a timedelta value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        if self._config.ser_json_timedelta == 'float':
+            return {'type': 'number'}
+        return {'type': 'string', 'format': 'duration'}
+
+    def literal_schema(self, schema: core_schema.LiteralSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a literal value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        expected = [v.value if isinstance(v, Enum) else v for v in schema['expected']]
+        # jsonify the expected values
+        expected = [to_jsonable_python(v) for v in expected]
+
+        result: dict[str, Any] = {}
+        if len(expected) == 1:
+            result['const'] = expected[0]
+        else:
+            result['enum'] = expected
+
+        types = {type(e) for e in expected}
+        if types == {str}:
+            result['type'] = 'string'
+        elif types == {int}:
+            result['type'] = 'integer'
+        elif types == {float}:
+            result['type'] = 'number'
+        elif types == {bool}:
+            result['type'] = 'boolean'
+        elif types == {list}:
+            result['type'] = 'array'
+        elif types == {type(None)}:
+            result['type'] = 'null'
+        return result
+
+    def enum_schema(self, schema: core_schema.EnumSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches an Enum value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        enum_type = schema['cls']
+        description = None if not enum_type.__doc__ else inspect.cleandoc(enum_type.__doc__)
+        if (
+            description == 'An enumeration.'
+        ):  # This is the default value provided by enum.EnumMeta.__new__; don't use it
+            description = None
+        result: dict[str, Any] = {'title': enum_type.__name__, 'description': description}
+        result = {k: v for k, v in result.items() if v is not None}
+
+        expected = [to_jsonable_python(v.value) for v in schema['members']]
+
+        result['enum'] = expected
+
+        types = {type(e) for e in expected}
+        if isinstance(enum_type, str) or types == {str}:
+            result['type'] = 'string'
+        elif isinstance(enum_type, int) or types == {int}:
+            result['type'] = 'integer'
+        elif isinstance(enum_type, float) or types == {float}:
+            result['type'] = 'number'
+        elif types == {bool}:
+            result['type'] = 'boolean'
+        elif types == {list}:
+            result['type'] = 'array'
+
+        return result
+
+    def is_instance_schema(self, schema: core_schema.IsInstanceSchema) -> JsonSchemaValue:
+        """Handles JSON schema generation for a core schema that checks if a value is an instance of a class.
+
+        Unless overridden in a subclass, this raises an error.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.handle_invalid_for_json_schema(schema, f'core_schema.IsInstanceSchema ({schema["cls"]})')
+
+    def is_subclass_schema(self, schema: core_schema.IsSubclassSchema) -> JsonSchemaValue:
+        """Handles JSON schema generation for a core schema that checks if a value is a subclass of a class.
+
+        For backwards compatibility with v1, this does not raise an error, but can be overridden to change this.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        # Note: This is for compatibility with V1; you can override if you want different behavior.
+        return {}
+
+    def callable_schema(self, schema: core_schema.CallableSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a callable value.
+
+        Unless overridden in a subclass, this raises an error.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.handle_invalid_for_json_schema(schema, 'core_schema.CallableSchema')
+
+    def list_schema(self, schema: core_schema.ListSchema) -> JsonSchemaValue:
+        """Returns a schema that matches a list schema.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        items_schema = {} if 'items_schema' not in schema else self.generate_inner(schema['items_schema'])
+        json_schema = {'type': 'array', 'items': items_schema}
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.array)
+        return json_schema
+
+    @deprecated('`tuple_positional_schema` is deprecated. Use `tuple_schema` instead.', category=None)
+    @final
+    def tuple_positional_schema(self, schema: core_schema.TupleSchema) -> JsonSchemaValue:
+        """Replaced by `tuple_schema`."""
+        warnings.warn(
+            '`tuple_positional_schema` is deprecated. Use `tuple_schema` instead.',
+            PydanticDeprecatedSince26,
+            stacklevel=2,
+        )
+        return self.tuple_schema(schema)
+
+    @deprecated('`tuple_variable_schema` is deprecated. Use `tuple_schema` instead.', category=None)
+    @final
+    def tuple_variable_schema(self, schema: core_schema.TupleSchema) -> JsonSchemaValue:
+        """Replaced by `tuple_schema`."""
+        warnings.warn(
+            '`tuple_variable_schema` is deprecated. Use `tuple_schema` instead.',
+            PydanticDeprecatedSince26,
+            stacklevel=2,
+        )
+        return self.tuple_schema(schema)
+
+    def tuple_schema(self, schema: core_schema.TupleSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a tuple schema e.g. `Tuple[int,
+        str, bool]` or `Tuple[int, ...]`.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        json_schema: JsonSchemaValue = {'type': 'array'}
+        if 'variadic_item_index' in schema:
+            variadic_item_index = schema['variadic_item_index']
+            if variadic_item_index > 0:
+                json_schema['minItems'] = variadic_item_index
+                json_schema['prefixItems'] = [
+                    self.generate_inner(item) for item in schema['items_schema'][:variadic_item_index]
+                ]
+            if variadic_item_index + 1 == len(schema['items_schema']):
+                # if the variadic item is the last item, then represent it faithfully
+                json_schema['items'] = self.generate_inner(schema['items_schema'][variadic_item_index])
+            else:
+                # otherwise, 'items' represents the schema for the variadic
+                # item plus the suffix, so just allow anything for simplicity
+                # for now
+                json_schema['items'] = True
+        else:
+            prefixItems = [self.generate_inner(item) for item in schema['items_schema']]
+            if prefixItems:
+                json_schema['prefixItems'] = prefixItems
+            json_schema['minItems'] = len(prefixItems)
+            json_schema['maxItems'] = len(prefixItems)
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.array)
+        return json_schema
+
+    def set_schema(self, schema: core_schema.SetSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a set schema.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self._common_set_schema(schema)
+
+    def frozenset_schema(self, schema: core_schema.FrozenSetSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a frozenset schema.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self._common_set_schema(schema)
+
+    def _common_set_schema(self, schema: core_schema.SetSchema | core_schema.FrozenSetSchema) -> JsonSchemaValue:
+        items_schema = {} if 'items_schema' not in schema else self.generate_inner(schema['items_schema'])
+        json_schema = {'type': 'array', 'uniqueItems': True, 'items': items_schema}
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.array)
+        return json_schema
+
+    def generator_schema(self, schema: core_schema.GeneratorSchema) -> JsonSchemaValue:
+        """Returns a JSON schema that represents the provided GeneratorSchema.
+
+        Args:
+            schema: The schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        items_schema = {} if 'items_schema' not in schema else self.generate_inner(schema['items_schema'])
+        json_schema = {'type': 'array', 'items': items_schema}
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.array)
+        return json_schema
+
+    def dict_schema(self, schema: core_schema.DictSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a dict schema.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        json_schema: JsonSchemaValue = {'type': 'object'}
+
+        keys_schema = self.generate_inner(schema['keys_schema']).copy() if 'keys_schema' in schema else {}
+        if '$ref' not in keys_schema:
+            keys_pattern = keys_schema.pop('pattern', None)
+            # Don't give a title to patternProperties/propertyNames:
+            keys_schema.pop('title', None)
+        else:
+            # Here, we assume that if the keys schema is a definition reference,
+            # it can't be a simple string core schema (and thus no pattern can exist).
+            # However, this is only in practice (in theory, a definition reference core
+            # schema could be generated for a simple string schema).
+            # Note that we avoid calling `self.resolve_ref_schema`, as it might not exist yet.
+            keys_pattern = None
+
+        values_schema = self.generate_inner(schema['values_schema']).copy() if 'values_schema' in schema else {}
+        # don't give a title to additionalProperties:
+        values_schema.pop('title', None)
+
+        if values_schema or keys_pattern is not None:  # don't add additionalProperties if it's empty
+            if keys_pattern is None:
+                json_schema['additionalProperties'] = values_schema
+            else:
+                json_schema['patternProperties'] = {keys_pattern: values_schema}
+
+        if (
+            # The len check indicates that constraints are probably present:
+            (keys_schema.get('type') == 'string' and len(keys_schema) > 1)
+            # If this is a definition reference schema, it most likely has constraints:
+            or '$ref' in keys_schema
+        ):
+            keys_schema.pop('type', None)
+            json_schema['propertyNames'] = keys_schema
+
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.object)
+        return json_schema
+
+    def function_before_schema(self, schema: core_schema.BeforeValidatorFunctionSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a function-before schema.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        if self._mode == 'validation' and (input_schema := schema.get('json_schema_input_schema')):
+            return self.generate_inner(input_schema)
+
+        return self.generate_inner(schema['schema'])
+
+    def function_after_schema(self, schema: core_schema.AfterValidatorFunctionSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a function-after schema.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.generate_inner(schema['schema'])
+
+    def function_plain_schema(self, schema: core_schema.PlainValidatorFunctionSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a function-plain schema.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        if self._mode == 'validation' and (input_schema := schema.get('json_schema_input_schema')):
+            return self.generate_inner(input_schema)
+
+        return self.handle_invalid_for_json_schema(
+            schema, f'core_schema.PlainValidatorFunctionSchema ({schema["function"]})'
+        )
+
+    def function_wrap_schema(self, schema: core_schema.WrapValidatorFunctionSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a function-wrap schema.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        if self._mode == 'validation' and (input_schema := schema.get('json_schema_input_schema')):
+            return self.generate_inner(input_schema)
+
+        return self.generate_inner(schema['schema'])
+
+    def default_schema(self, schema: core_schema.WithDefaultSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema with a default value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        json_schema = self.generate_inner(schema['schema'])
+
+        if 'default' not in schema:
+            return json_schema
+        default = schema['default']
+        # Note: if you want to include the value returned by the default_factory,
+        # override this method and replace the code above with:
+        # if 'default' in schema:
+        #     default = schema['default']
+        # elif 'default_factory' in schema:
+        #     default = schema['default_factory']()
+        # else:
+        #     return json_schema
+
+        # we reflect the application of custom plain, no-info serializers to defaults for
+        # JSON Schemas viewed in serialization mode:
+        # TODO: improvements along with https://github.com/pydantic/pydantic/issues/8208
+        if (
+            self.mode == 'serialization'
+            and (ser_schema := schema['schema'].get('serialization'))
+            and (ser_func := ser_schema.get('function'))
+            and ser_schema.get('type') == 'function-plain'
+            and not ser_schema.get('info_arg')
+            and not (default is None and ser_schema.get('when_used') in ('unless-none', 'json-unless-none'))
+        ):
+            try:
+                default = ser_func(default)  # type: ignore
+            except Exception:
+                # It might be that the provided default needs to be validated (read: parsed) first
+                # (assuming `validate_default` is enabled). However, we can't perform
+                # such validation during JSON Schema generation so we don't support
+                # this pattern for now.
+                # (One example is when using `foo: ByteSize = '1MB'`, which validates and
+                # serializes as an int. In this case, `ser_func` is `int` and `int('1MB')` fails).
+                self.emit_warning(
+                    'non-serializable-default',
+                    f'Unable to serialize value {default!r} with the plain serializer; excluding default from JSON schema',
+                )
+                return json_schema
+
+        try:
+            encoded_default = self.encode_default(default)
+        except pydantic_core.PydanticSerializationError:
+            self.emit_warning(
+                'non-serializable-default',
+                f'Default value {default} is not JSON serializable; excluding default from JSON schema',
+            )
+            # Return the inner schema, as though there was no default
+            return json_schema
+
+        json_schema['default'] = encoded_default
+        return json_schema
+
+    def nullable_schema(self, schema: core_schema.NullableSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that allows null values.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        null_schema = {'type': 'null'}
+        inner_json_schema = self.generate_inner(schema['schema'])
+
+        if inner_json_schema == null_schema:
+            return null_schema
+        else:
+            # Thanks to the equality check against `null_schema` above, I think 'oneOf' would also be valid here;
+            # I'll use 'anyOf' for now, but it could be changed it if it would work better with some external tooling
+            return self.get_flattened_anyof([inner_json_schema, null_schema])
+
+    def union_schema(self, schema: core_schema.UnionSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that allows values matching any of the given schemas.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        generated: list[JsonSchemaValue] = []
+
+        choices = schema['choices']
+        for choice in choices:
+            # choice will be a tuple if an explicit label was provided
+            choice_schema = choice[0] if isinstance(choice, tuple) else choice
+            try:
+                generated.append(self.generate_inner(choice_schema))
+            except PydanticOmit:
+                continue
+            except PydanticInvalidForJsonSchema as exc:
+                self.emit_warning('skipped-choice', exc.message)
+        if len(generated) == 1:
+            return generated[0]
+        return self.get_flattened_anyof(generated)
+
+    def tagged_union_schema(self, schema: core_schema.TaggedUnionSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that allows values matching any of the given schemas, where
+        the schemas are tagged with a discriminator field that indicates which schema should be used to validate
+        the value.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        generated: dict[str, JsonSchemaValue] = {}
+        for k, v in schema['choices'].items():
+            if isinstance(k, Enum):
+                k = k.value
+            try:
+                # Use str(k) since keys must be strings for json; while not technically correct,
+                # it's the closest that can be represented in valid JSON
+                generated[str(k)] = self.generate_inner(v).copy()
+            except PydanticOmit:
+                continue
+            except PydanticInvalidForJsonSchema as exc:
+                self.emit_warning('skipped-choice', exc.message)
+
+        one_of_choices = _deduplicate_schemas(generated.values())
+        json_schema: JsonSchemaValue = {'oneOf': one_of_choices}
+
+        # This reflects the v1 behavior; TODO: we should make it possible to exclude OpenAPI stuff from the JSON schema
+        openapi_discriminator = self._extract_discriminator(schema, one_of_choices)
+        if openapi_discriminator is not None:
+            json_schema['discriminator'] = {
+                'propertyName': openapi_discriminator,
+                'mapping': {k: v.get('$ref', v) for k, v in generated.items()},
+            }
+
+        return json_schema
+
+    def _extract_discriminator(
+        self, schema: core_schema.TaggedUnionSchema, one_of_choices: list[JsonDict]
+    ) -> str | None:
+        """Extract a compatible OpenAPI discriminator from the schema and one_of choices that end up in the final
+        schema."""
+        openapi_discriminator: str | None = None
+
+        if isinstance(schema['discriminator'], str):
+            return schema['discriminator']
+
+        if isinstance(schema['discriminator'], list):
+            # If the discriminator is a single item list containing a string, that is equivalent to the string case
+            if len(schema['discriminator']) == 1 and isinstance(schema['discriminator'][0], str):
+                return schema['discriminator'][0]
+            # When an alias is used that is different from the field name, the discriminator will be a list of single
+            # str lists, one for the attribute and one for the actual alias. The logic here will work even if there is
+            # more than one possible attribute, and looks for whether a single alias choice is present as a documented
+            # property on all choices. If so, that property will be used as the OpenAPI discriminator.
+            for alias_path in schema['discriminator']:
+                if not isinstance(alias_path, list):
+                    break  # this means that the discriminator is not a list of alias paths
+                if len(alias_path) != 1:
+                    continue  # this means that the "alias" does not represent a single field
+                alias = alias_path[0]
+                if not isinstance(alias, str):
+                    continue  # this means that the "alias" does not represent a field
+                alias_is_present_on_all_choices = True
+                for choice in one_of_choices:
+                    try:
+                        choice = self.resolve_ref_schema(choice)
+                    except RuntimeError as exc:
+                        # TODO: fixme - this is a workaround for the fact that we can't always resolve refs
+                        # for tagged union choices at this point in the schema gen process, we might need to do
+                        # another pass at the end like we do for core schemas
+                        self.emit_warning('skipped-discriminator', str(exc))
+                        choice = {}
+                    properties = choice.get('properties', {})
+                    if not isinstance(properties, dict) or alias not in properties:
+                        alias_is_present_on_all_choices = False
+                        break
+                if alias_is_present_on_all_choices:
+                    openapi_discriminator = alias
+                    break
+        return openapi_discriminator
+
+    def chain_schema(self, schema: core_schema.ChainSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a core_schema.ChainSchema.
+
+        When generating a schema for validation, we return the validation JSON schema for the first step in the chain.
+        For serialization, we return the serialization JSON schema for the last step in the chain.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        step_index = 0 if self.mode == 'validation' else -1  # use first step for validation, last for serialization
+        return self.generate_inner(schema['steps'][step_index])
+
+    def lax_or_strict_schema(self, schema: core_schema.LaxOrStrictSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that allows values matching either the lax schema or the
+        strict schema.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        # TODO: Need to read the default value off of model config or whatever
+        use_strict = schema.get('strict', False)  # TODO: replace this default False
+        # If your JSON schema fails to generate it is probably
+        # because one of the following two branches failed.
+        if use_strict:
+            return self.generate_inner(schema['strict_schema'])
+        else:
+            return self.generate_inner(schema['lax_schema'])
+
+    def json_or_python_schema(self, schema: core_schema.JsonOrPythonSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that allows values matching either the JSON schema or the
+        Python schema.
+
+        The JSON schema is used instead of the Python schema. If you want to use the Python schema, you should override
+        this method.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.generate_inner(schema['json_schema'])
+
+    def typed_dict_schema(self, schema: core_schema.TypedDictSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a typed dict.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        total = schema.get('total', True)
+        named_required_fields: list[tuple[str, bool, CoreSchemaField]] = [
+            (name, self.field_is_required(field, total), field)
+            for name, field in schema['fields'].items()
+            if self.field_is_present(field)
+        ]
+        if self.mode == 'serialization':
+            named_required_fields.extend(self._name_required_computed_fields(schema.get('computed_fields', [])))
+        cls = schema.get('cls')
+        config = _get_typed_dict_config(cls)
+        with self._config_wrapper_stack.push(config):
+            json_schema = self._named_required_fields_schema(named_required_fields)
+
+        if cls is not None:
+            self._update_class_schema(json_schema, cls, config)
+        else:
+            extra = config.get('extra')
+            if extra == 'forbid':
+                json_schema['additionalProperties'] = False
+            elif extra == 'allow':
+                json_schema['additionalProperties'] = True
+
+        return json_schema
+
+    @staticmethod
+    def _name_required_computed_fields(
+        computed_fields: list[ComputedField],
+    ) -> list[tuple[str, bool, core_schema.ComputedField]]:
+        return [(field['property_name'], True, field) for field in computed_fields]
+
+    def _named_required_fields_schema(
+        self, named_required_fields: Sequence[tuple[str, bool, CoreSchemaField]]
+    ) -> JsonSchemaValue:
+        properties: dict[str, JsonSchemaValue] = {}
+        required_fields: list[str] = []
+        for name, required, field in named_required_fields:
+            if self.by_alias:
+                name = self._get_alias_name(field, name)
+            try:
+                field_json_schema = self.generate_inner(field).copy()
+            except PydanticOmit:
+                continue
+            if 'title' not in field_json_schema and self.field_title_should_be_set(field):
+                title = self.get_title_from_name(name)
+                field_json_schema['title'] = title
+            field_json_schema = self.handle_ref_overrides(field_json_schema)
+            properties[name] = field_json_schema
+            if required:
+                required_fields.append(name)
+
+        json_schema = {'type': 'object', 'properties': properties}
+        if required_fields:
+            json_schema['required'] = required_fields
+        return json_schema
+
+    def _get_alias_name(self, field: CoreSchemaField, name: str) -> str:
+        if field['type'] == 'computed-field':
+            alias: Any = field.get('alias', name)
+        elif self.mode == 'validation':
+            alias = field.get('validation_alias', name)
+        else:
+            alias = field.get('serialization_alias', name)
+        if isinstance(alias, str):
+            name = alias
+        elif isinstance(alias, list):
+            alias = cast('list[str] | str', alias)
+            for path in alias:
+                if isinstance(path, list) and len(path) == 1 and isinstance(path[0], str):
+                    # Use the first valid single-item string path; the code that constructs the alias array
+                    # should ensure the first such item is what belongs in the JSON schema
+                    name = path[0]
+                    break
+        else:
+            assert_never(alias)
+        return name
+
+    def typed_dict_field_schema(self, schema: core_schema.TypedDictField) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a typed dict field.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.generate_inner(schema['schema'])
+
+    def dataclass_field_schema(self, schema: core_schema.DataclassField) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a dataclass field.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.generate_inner(schema['schema'])
+
+    def model_field_schema(self, schema: core_schema.ModelField) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a model field.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.generate_inner(schema['schema'])
+
+    def computed_field_schema(self, schema: core_schema.ComputedField) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a computed field.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.generate_inner(schema['return_schema'])
+
+    def model_schema(self, schema: core_schema.ModelSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a model.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        # We do not use schema['model'].model_json_schema() here
+        # because it could lead to inconsistent refs handling, etc.
+        cls = cast('type[BaseModel]', schema['cls'])
+        config = cls.model_config
+
+        with self._config_wrapper_stack.push(config):
+            json_schema = self.generate_inner(schema['schema'])
+
+        self._update_class_schema(json_schema, cls, config)
+
+        return json_schema
+
+    def _update_class_schema(self, json_schema: JsonSchemaValue, cls: type[Any], config: ConfigDict) -> None:
+        """Update json_schema with the following, extracted from `config` and `cls`:
+
+        * title
+        * description
+        * additional properties
+        * json_schema_extra
+        * deprecated
+
+        Done in place, hence there's no return value as the original json_schema is mutated.
+        No ref resolving is involved here, as that's not appropriate for simple updates.
+        """
+        from .main import BaseModel
+        from .root_model import RootModel
+
+        if (config_title := config.get('title')) is not None:
+            json_schema.setdefault('title', config_title)
+        elif model_title_generator := config.get('model_title_generator'):
+            title = model_title_generator(cls)
+            if not isinstance(title, str):
+                raise TypeError(f'model_title_generator {model_title_generator} must return str, not {title.__class__}')
+            json_schema.setdefault('title', title)
+        if 'title' not in json_schema:
+            json_schema['title'] = cls.__name__
+
+        # BaseModel and dataclasses; don't use cls.__doc__ as it will contain the verbose class signature by default
+        docstring = None if cls is BaseModel or dataclasses.is_dataclass(cls) else cls.__doc__
+
+        if docstring:
+            json_schema.setdefault('description', inspect.cleandoc(docstring))
+        elif issubclass(cls, RootModel) and (root_description := cls.__pydantic_fields__['root'].description):
+            json_schema.setdefault('description', root_description)
+
+        extra = config.get('extra')
+        if 'additionalProperties' not in json_schema:
+            if extra == 'allow':
+                json_schema['additionalProperties'] = True
+            elif extra == 'forbid':
+                json_schema['additionalProperties'] = False
+
+        json_schema_extra = config.get('json_schema_extra')
+        if issubclass(cls, BaseModel) and cls.__pydantic_root_model__:
+            root_json_schema_extra = cls.model_fields['root'].json_schema_extra
+            if json_schema_extra and root_json_schema_extra:
+                raise ValueError(
+                    '"model_config[\'json_schema_extra\']" and "Field.json_schema_extra" on "RootModel.root"'
+                    ' field must not be set simultaneously'
+                )
+            if root_json_schema_extra:
+                json_schema_extra = root_json_schema_extra
+
+        if isinstance(json_schema_extra, (staticmethod, classmethod)):
+            # In older versions of python, this is necessary to ensure staticmethod/classmethods are callable
+            json_schema_extra = json_schema_extra.__get__(cls)
+
+        if isinstance(json_schema_extra, dict):
+            json_schema.update(json_schema_extra)
+        elif callable(json_schema_extra):
+            # FIXME: why are there type ignores here? We support two signatures for json_schema_extra callables...
+            if len(inspect.signature(json_schema_extra).parameters) > 1:
+                json_schema_extra(json_schema, cls)  # type: ignore
+            else:
+                json_schema_extra(json_schema)  # type: ignore
+        elif json_schema_extra is not None:
+            raise ValueError(
+                f"model_config['json_schema_extra']={json_schema_extra} should be a dict, callable, or None"
+            )
+
+        if hasattr(cls, '__deprecated__'):
+            json_schema['deprecated'] = True
+
+    def resolve_ref_schema(self, json_schema: JsonSchemaValue) -> JsonSchemaValue:
+        """Resolve a JsonSchemaValue to the non-ref schema if it is a $ref schema.
+
+        Args:
+            json_schema: The schema to resolve.
+
+        Returns:
+            The resolved schema.
+
+        Raises:
+            RuntimeError: If the schema reference can't be found in definitions.
+        """
+        if '$ref' not in json_schema:
+            return json_schema
+
+        ref = json_schema['$ref']
+        schema_to_update = self.get_schema_from_definitions(JsonRef(ref))
+        if schema_to_update is None:
+            raise RuntimeError(f'Cannot update undefined schema for $ref={ref}')
+        return self.resolve_ref_schema(schema_to_update)
+
+    def model_fields_schema(self, schema: core_schema.ModelFieldsSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a model's fields.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        named_required_fields: list[tuple[str, bool, CoreSchemaField]] = [
+            (name, self.field_is_required(field, total=True), field)
+            for name, field in schema['fields'].items()
+            if self.field_is_present(field)
+        ]
+        if self.mode == 'serialization':
+            named_required_fields.extend(self._name_required_computed_fields(schema.get('computed_fields', [])))
+        json_schema = self._named_required_fields_schema(named_required_fields)
+        extras_schema = schema.get('extras_schema', None)
+        if extras_schema is not None:
+            schema_to_update = self.resolve_ref_schema(json_schema)
+            schema_to_update['additionalProperties'] = self.generate_inner(extras_schema)
+        return json_schema
+
+    def field_is_present(self, field: CoreSchemaField) -> bool:
+        """Whether the field should be included in the generated JSON schema.
+
+        Args:
+            field: The schema for the field itself.
+
+        Returns:
+            `True` if the field should be included in the generated JSON schema, `False` otherwise.
+        """
+        if self.mode == 'serialization':
+            # If you still want to include the field in the generated JSON schema,
+            # override this method and return True
+            return not field.get('serialization_exclude')
+        elif self.mode == 'validation':
+            return True
+        else:
+            assert_never(self.mode)
+
+    def field_is_required(
+        self,
+        field: core_schema.ModelField | core_schema.DataclassField | core_schema.TypedDictField,
+        total: bool,
+    ) -> bool:
+        """Whether the field should be marked as required in the generated JSON schema.
+        (Note that this is irrelevant if the field is not present in the JSON schema.).
+
+        Args:
+            field: The schema for the field itself.
+            total: Only applies to `TypedDictField`s.
+                Indicates if the `TypedDict` this field belongs to is total, in which case any fields that don't
+                explicitly specify `required=False` are required.
+
+        Returns:
+            `True` if the field should be marked as required in the generated JSON schema, `False` otherwise.
+        """
+        if self.mode == 'serialization' and self._config.json_schema_serialization_defaults_required:
+            return not field.get('serialization_exclude')
+        else:
+            if field['type'] == 'typed-dict-field':
+                return field.get('required', total)
+            else:
+                return field['schema']['type'] != 'default'
+
+    def dataclass_args_schema(self, schema: core_schema.DataclassArgsSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a dataclass's constructor arguments.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        named_required_fields: list[tuple[str, bool, CoreSchemaField]] = [
+            (field['name'], self.field_is_required(field, total=True), field)
+            for field in schema['fields']
+            if self.field_is_present(field)
+        ]
+        if self.mode == 'serialization':
+            named_required_fields.extend(self._name_required_computed_fields(schema.get('computed_fields', [])))
+        return self._named_required_fields_schema(named_required_fields)
+
+    def dataclass_schema(self, schema: core_schema.DataclassSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a dataclass.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        from ._internal._dataclasses import is_builtin_dataclass
+
+        cls = schema['cls']
+        config: ConfigDict = getattr(cls, '__pydantic_config__', cast('ConfigDict', {}))
+
+        with self._config_wrapper_stack.push(config):
+            json_schema = self.generate_inner(schema['schema']).copy()
+
+        self._update_class_schema(json_schema, cls, config)
+
+        # Dataclass-specific handling of description
+        if is_builtin_dataclass(cls):
+            # vanilla dataclass; don't use cls.__doc__ as it will contain the class signature by default
+            description = None
+        else:
+            description = None if cls.__doc__ is None else inspect.cleandoc(cls.__doc__)
+        if description:
+            json_schema['description'] = description
+
+        return json_schema
+
+    def arguments_schema(self, schema: core_schema.ArgumentsSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a function's arguments.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        prefer_positional = schema.get('metadata', {}).get('pydantic_js_prefer_positional_arguments')
+
+        arguments = schema['arguments_schema']
+        kw_only_arguments = [a for a in arguments if a.get('mode') == 'keyword_only']
+        kw_or_p_arguments = [a for a in arguments if a.get('mode') in {'positional_or_keyword', None}]
+        p_only_arguments = [a for a in arguments if a.get('mode') == 'positional_only']
+        var_args_schema = schema.get('var_args_schema')
+        var_kwargs_schema = schema.get('var_kwargs_schema')
+
+        if prefer_positional:
+            positional_possible = not kw_only_arguments and not var_kwargs_schema
+            if positional_possible:
+                return self.p_arguments_schema(p_only_arguments + kw_or_p_arguments, var_args_schema)
+
+        keyword_possible = not p_only_arguments and not var_args_schema
+        if keyword_possible:
+            return self.kw_arguments_schema(kw_or_p_arguments + kw_only_arguments, var_kwargs_schema)
+
+        if not prefer_positional:
+            positional_possible = not kw_only_arguments and not var_kwargs_schema
+            if positional_possible:
+                return self.p_arguments_schema(p_only_arguments + kw_or_p_arguments, var_args_schema)
+
+        raise PydanticInvalidForJsonSchema(
+            'Unable to generate JSON schema for arguments validator with positional-only and keyword-only arguments'
+        )
+
+    def kw_arguments_schema(
+        self, arguments: list[core_schema.ArgumentsParameter], var_kwargs_schema: CoreSchema | None
+    ) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a function's keyword arguments.
+
+        Args:
+            arguments: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        properties: dict[str, JsonSchemaValue] = {}
+        required: list[str] = []
+        for argument in arguments:
+            name = self.get_argument_name(argument)
+            argument_schema = self.generate_inner(argument['schema']).copy()
+            argument_schema['title'] = self.get_title_from_name(name)
+            properties[name] = argument_schema
+
+            if argument['schema']['type'] != 'default':
+                # This assumes that if the argument has a default value,
+                # the inner schema must be of type WithDefaultSchema.
+                # I believe this is true, but I am not 100% sure
+                required.append(name)
+
+        json_schema: JsonSchemaValue = {'type': 'object', 'properties': properties}
+        if required:
+            json_schema['required'] = required
+
+        if var_kwargs_schema:
+            additional_properties_schema = self.generate_inner(var_kwargs_schema)
+            if additional_properties_schema:
+                json_schema['additionalProperties'] = additional_properties_schema
+        else:
+            json_schema['additionalProperties'] = False
+        return json_schema
+
+    def p_arguments_schema(
+        self, arguments: list[core_schema.ArgumentsParameter], var_args_schema: CoreSchema | None
+    ) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a function's positional arguments.
+
+        Args:
+            arguments: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        prefix_items: list[JsonSchemaValue] = []
+        min_items = 0
+
+        for argument in arguments:
+            name = self.get_argument_name(argument)
+
+            argument_schema = self.generate_inner(argument['schema']).copy()
+            argument_schema['title'] = self.get_title_from_name(name)
+            prefix_items.append(argument_schema)
+
+            if argument['schema']['type'] != 'default':
+                # This assumes that if the argument has a default value,
+                # the inner schema must be of type WithDefaultSchema.
+                # I believe this is true, but I am not 100% sure
+                min_items += 1
+
+        json_schema: JsonSchemaValue = {'type': 'array'}
+        if prefix_items:
+            json_schema['prefixItems'] = prefix_items
+        if min_items:
+            json_schema['minItems'] = min_items
+
+        if var_args_schema:
+            items_schema = self.generate_inner(var_args_schema)
+            if items_schema:
+                json_schema['items'] = items_schema
+        else:
+            json_schema['maxItems'] = len(prefix_items)
+
+        return json_schema
+
+    def get_argument_name(self, argument: core_schema.ArgumentsParameter) -> str:
+        """Retrieves the name of an argument.
+
+        Args:
+            argument: The core schema.
+
+        Returns:
+            The name of the argument.
+        """
+        name = argument['name']
+        if self.by_alias:
+            alias = argument.get('alias')
+            if isinstance(alias, str):
+                name = alias
+            else:
+                pass  # might want to do something else?
+        return name
+
+    def call_schema(self, schema: core_schema.CallSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a function call.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.generate_inner(schema['arguments_schema'])
+
+    def custom_error_schema(self, schema: core_schema.CustomErrorSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a custom error.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return self.generate_inner(schema['schema'])
+
+    def json_schema(self, schema: core_schema.JsonSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a JSON object.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        content_core_schema = schema.get('schema') or core_schema.any_schema()
+        content_json_schema = self.generate_inner(content_core_schema)
+        if self.mode == 'validation':
+            return {'type': 'string', 'contentMediaType': 'application/json', 'contentSchema': content_json_schema}
+        else:
+            # self.mode == 'serialization'
+            return content_json_schema
+
+    def url_schema(self, schema: core_schema.UrlSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a URL.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        json_schema = {'type': 'string', 'format': 'uri', 'minLength': 1}
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.string)
+        return json_schema
+
+    def multi_host_url_schema(self, schema: core_schema.MultiHostUrlSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a URL that can be used with multiple hosts.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        # Note: 'multi-host-uri' is a custom/pydantic-specific format, not part of the JSON Schema spec
+        json_schema = {'type': 'string', 'format': 'multi-host-uri', 'minLength': 1}
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.string)
+        return json_schema
+
+    def uuid_schema(self, schema: core_schema.UuidSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a UUID.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return {'type': 'string', 'format': 'uuid'}
+
+    def definitions_schema(self, schema: core_schema.DefinitionsSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that defines a JSON object with definitions.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        for definition in schema['definitions']:
+            try:
+                self.generate_inner(definition)
+            except PydanticInvalidForJsonSchema as e:
+                core_ref: CoreRef = CoreRef(definition['ref'])  # type: ignore
+                self._core_defs_invalid_for_json_schema[self.get_defs_ref((core_ref, self.mode))] = e
+                continue
+        return self.generate_inner(schema['schema'])
+
+    def definition_ref_schema(self, schema: core_schema.DefinitionReferenceSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a schema that references a definition.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        core_ref = CoreRef(schema['schema_ref'])
+        _, ref_json_schema = self.get_cache_defs_ref_schema(core_ref)
+        return ref_json_schema
+
+    def ser_schema(
+        self, schema: core_schema.SerSchema | core_schema.IncExSeqSerSchema | core_schema.IncExDictSerSchema
+    ) -> JsonSchemaValue | None:
+        """Generates a JSON schema that matches a schema that defines a serialized object.
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        schema_type = schema['type']
+        if schema_type == 'function-plain' or schema_type == 'function-wrap':
+            # PlainSerializerFunctionSerSchema or WrapSerializerFunctionSerSchema
+            return_schema = schema.get('return_schema')
+            if return_schema is not None:
+                return self.generate_inner(return_schema)
+        elif schema_type == 'format' or schema_type == 'to-string':
+            # FormatSerSchema or ToStringSerSchema
+            return self.str_schema(core_schema.str_schema())
+        elif schema['type'] == 'model':
+            # ModelSerSchema
+            return self.generate_inner(schema['schema'])
+        return None
+
+    def complex_schema(self, schema: core_schema.ComplexSchema) -> JsonSchemaValue:
+        """Generates a JSON schema that matches a complex number.
+
+        JSON has no standard way to represent complex numbers. Complex number is not a numeric
+        type. Here we represent complex number as strings following the rule defined by Python.
+        For instance, '1+2j' is an accepted complex string. Details can be found in
+        [Python's `complex` documentation][complex].
+
+        Args:
+            schema: The core schema.
+
+        Returns:
+            The generated JSON schema.
+        """
+        return {'type': 'string'}
+
+    # ### Utility methods
+
+    def get_title_from_name(self, name: str) -> str:
+        """Retrieves a title from a name.
+
+        Args:
+            name: The name to retrieve a title from.
+
+        Returns:
+            The title.
+        """
+        return name.title().replace('_', ' ').strip()
+
+    def field_title_should_be_set(self, schema: CoreSchemaOrField) -> bool:
+        """Returns true if a field with the given schema should have a title set based on the field name.
+
+        Intuitively, we want this to return true for schemas that wouldn't otherwise provide their own title
+        (e.g., int, float, str), and false for those that would (e.g., BaseModel subclasses).
+
+        Args:
+            schema: The schema to check.
+
+        Returns:
+            `True` if the field should have a title set, `False` otherwise.
+        """
+        if _core_utils.is_core_schema_field(schema):
+            if schema['type'] == 'computed-field':
+                field_schema = schema['return_schema']
+            else:
+                field_schema = schema['schema']
+            return self.field_title_should_be_set(field_schema)
+
+        elif _core_utils.is_core_schema(schema):
+            if schema.get('ref'):  # things with refs, such as models and enums, should not have titles set
+                return False
+            if schema['type'] in {'default', 'nullable', 'definitions'}:
+                return self.field_title_should_be_set(schema['schema'])  # type: ignore[typeddict-item]
+            if _core_utils.is_function_with_inner_schema(schema):
+                return self.field_title_should_be_set(schema['schema'])
+            if schema['type'] == 'definition-ref':
+                # Referenced schemas should not have titles set for the same reason
+                # schemas with refs should not
+                return False
+            return True  # anything else should have title set
+
+        else:
+            raise PydanticInvalidForJsonSchema(f'Unexpected schema type: schema={schema}')  # pragma: no cover
+
+    def normalize_name(self, name: str) -> str:
+        """Normalizes a name to be used as a key in a dictionary.
+
+        Args:
+            name: The name to normalize.
+
+        Returns:
+            The normalized name.
+        """
+        return re.sub(r'[^a-zA-Z0-9.\-_]', '_', name).replace('.', '__')
+
+    def get_defs_ref(self, core_mode_ref: CoreModeRef) -> DefsRef:
+        """Override this method to change the way that definitions keys are generated from a core reference.
+
+        Args:
+            core_mode_ref: The core reference.
+
+        Returns:
+            The definitions key.
+        """
+        # Split the core ref into "components"; generic origins and arguments are each separate components
+        core_ref, mode = core_mode_ref
+        components = re.split(r'([\][,])', core_ref)
+        # Remove IDs from each component
+        components = [x.rsplit(':', 1)[0] for x in components]
+        core_ref_no_id = ''.join(components)
+        # Remove everything before the last period from each "component"
+        components = [re.sub(r'(?:[^.[\]]+\.)+((?:[^.[\]]+))', r'\1', x) for x in components]
+        short_ref = ''.join(components)
+
+        mode_title = _MODE_TITLE_MAPPING[mode]
+
+        # It is important that the generated defs_ref values be such that at least one choice will not
+        # be generated for any other core_ref. Currently, this should be the case because we include
+        # the id of the source type in the core_ref
+        name = DefsRef(self.normalize_name(short_ref))
+        name_mode = DefsRef(self.normalize_name(short_ref) + f'-{mode_title}')
+        module_qualname = DefsRef(self.normalize_name(core_ref_no_id))
+        module_qualname_mode = DefsRef(f'{module_qualname}-{mode_title}')
+        module_qualname_id = DefsRef(self.normalize_name(core_ref))
+        occurrence_index = self._collision_index.get(module_qualname_id)
+        if occurrence_index is None:
+            self._collision_counter[module_qualname] += 1
+            occurrence_index = self._collision_index[module_qualname_id] = self._collision_counter[module_qualname]
+
+        module_qualname_occurrence = DefsRef(f'{module_qualname}__{occurrence_index}')
+        module_qualname_occurrence_mode = DefsRef(f'{module_qualname_mode}__{occurrence_index}')
+
+        self._prioritized_defsref_choices[module_qualname_occurrence_mode] = [
+            name,
+            name_mode,
+            module_qualname,
+            module_qualname_mode,
+            module_qualname_occurrence,
+            module_qualname_occurrence_mode,
+        ]
+
+        return module_qualname_occurrence_mode
+
+    def get_cache_defs_ref_schema(self, core_ref: CoreRef) -> tuple[DefsRef, JsonSchemaValue]:
+        """This method wraps the get_defs_ref method with some cache-lookup/population logic,
+        and returns both the produced defs_ref and the JSON schema that will refer to the right definition.
+
+        Args:
+            core_ref: The core reference to get the definitions reference for.
+
+        Returns:
+            A tuple of the definitions reference and the JSON schema that will refer to it.
+        """
+        core_mode_ref = (core_ref, self.mode)
+        maybe_defs_ref = self.core_to_defs_refs.get(core_mode_ref)
+        if maybe_defs_ref is not None:
+            json_ref = self.core_to_json_refs[core_mode_ref]
+            return maybe_defs_ref, {'$ref': json_ref}
+
+        defs_ref = self.get_defs_ref(core_mode_ref)
+
+        # populate the ref translation mappings
+        self.core_to_defs_refs[core_mode_ref] = defs_ref
+        self.defs_to_core_refs[defs_ref] = core_mode_ref
+
+        json_ref = JsonRef(self.ref_template.format(model=defs_ref))
+        self.core_to_json_refs[core_mode_ref] = json_ref
+        self.json_to_defs_refs[json_ref] = defs_ref
+        ref_json_schema = {'$ref': json_ref}
+        return defs_ref, ref_json_schema
+
+    def handle_ref_overrides(self, json_schema: JsonSchemaValue) -> JsonSchemaValue:
+        """Remove any sibling keys that are redundant with the referenced schema.
+
+        Args:
+            json_schema: The schema to remove redundant sibling keys from.
+
+        Returns:
+            The schema with redundant sibling keys removed.
+        """
+        if '$ref' in json_schema:
+            # prevent modifications to the input; this copy may be safe to drop if there is significant overhead
+            json_schema = json_schema.copy()
+
+            referenced_json_schema = self.get_schema_from_definitions(JsonRef(json_schema['$ref']))
+            if referenced_json_schema is None:
+                # This can happen when building schemas for models with not-yet-defined references.
+                # It may be a good idea to do a recursive pass at the end of the generation to remove
+                # any redundant override keys.
+                return json_schema
+            for k, v in list(json_schema.items()):
+                if k == '$ref':
+                    continue
+                if k in referenced_json_schema and referenced_json_schema[k] == v:
+                    del json_schema[k]  # redundant key
+
+        return json_schema
+
+    def get_schema_from_definitions(self, json_ref: JsonRef) -> JsonSchemaValue | None:
+        try:
+            def_ref = self.json_to_defs_refs[json_ref]
+            if def_ref in self._core_defs_invalid_for_json_schema:
+                raise self._core_defs_invalid_for_json_schema[def_ref]
+            return self.definitions.get(def_ref, None)
+        except KeyError:
+            if json_ref.startswith(('http://', 'https://')):
+                return None
+            raise
+
+    def encode_default(self, dft: Any) -> Any:
+        """Encode a default value to a JSON-serializable value.
+
+        This is used to encode default values for fields in the generated JSON schema.
+
+        Args:
+            dft: The default value to encode.
+
+        Returns:
+            The encoded default value.
+        """
+        from .type_adapter import TypeAdapter, _type_has_config
+
+        config = self._config
+        try:
+            default = (
+                dft
+                if _type_has_config(type(dft))
+                else TypeAdapter(type(dft), config=config.config_dict).dump_python(dft, mode='json')
+            )
+        except PydanticSchemaGenerationError:
+            raise pydantic_core.PydanticSerializationError(f'Unable to encode default value {dft}')
+
+        return pydantic_core.to_jsonable_python(
+            default,
+            timedelta_mode=config.ser_json_timedelta,
+            bytes_mode=config.ser_json_bytes,
+        )
+
+    def update_with_validations(
+        self, json_schema: JsonSchemaValue, core_schema: CoreSchema, mapping: dict[str, str]
+    ) -> None:
+        """Update the json_schema with the corresponding validations specified in the core_schema,
+        using the provided mapping to translate keys in core_schema to the appropriate keys for a JSON schema.
+
+        Args:
+            json_schema: The JSON schema to update.
+            core_schema: The core schema to get the validations from.
+            mapping: A mapping from core_schema attribute names to the corresponding JSON schema attribute names.
+        """
+        for core_key, json_schema_key in mapping.items():
+            if core_key in core_schema:
+                json_schema[json_schema_key] = core_schema[core_key]
+
+    class ValidationsMapping:
+        """This class just contains mappings from core_schema attribute names to the corresponding
+        JSON schema attribute names. While I suspect it is unlikely to be necessary, you can in
+        principle override this class in a subclass of GenerateJsonSchema (by inheriting from
+        GenerateJsonSchema.ValidationsMapping) to change these mappings.
+        """
+
+        numeric = {
+            'multiple_of': 'multipleOf',
+            'le': 'maximum',
+            'ge': 'minimum',
+            'lt': 'exclusiveMaximum',
+            'gt': 'exclusiveMinimum',
+        }
+        bytes = {
+            'min_length': 'minLength',
+            'max_length': 'maxLength',
+        }
+        string = {
+            'min_length': 'minLength',
+            'max_length': 'maxLength',
+            'pattern': 'pattern',
+        }
+        array = {
+            'min_length': 'minItems',
+            'max_length': 'maxItems',
+        }
+        object = {
+            'min_length': 'minProperties',
+            'max_length': 'maxProperties',
+        }
+
+    def get_flattened_anyof(self, schemas: list[JsonSchemaValue]) -> JsonSchemaValue:
+        members = []
+        for schema in schemas:
+            if len(schema) == 1 and 'anyOf' in schema:
+                members.extend(schema['anyOf'])
+            else:
+                members.append(schema)
+        members = _deduplicate_schemas(members)
+        if len(members) == 1:
+            return members[0]
+        return {'anyOf': members}
+
+    def get_json_ref_counts(self, json_schema: JsonSchemaValue) -> dict[JsonRef, int]:
+        """Get all values corresponding to the key '$ref' anywhere in the json_schema."""
+        json_refs: dict[JsonRef, int] = Counter()
+
+        def _add_json_refs(schema: Any) -> None:
+            if isinstance(schema, dict):
+                if '$ref' in schema:
+                    json_ref = JsonRef(schema['$ref'])
+                    if not isinstance(json_ref, str):
+                        return  # in this case, '$ref' might have been the name of a property
+                    already_visited = json_ref in json_refs
+                    json_refs[json_ref] += 1
+                    if already_visited:
+                        return  # prevent recursion on a definition that was already visited
+                    try:
+                        defs_ref = self.json_to_defs_refs[json_ref]
+                        if defs_ref in self._core_defs_invalid_for_json_schema:
+                            raise self._core_defs_invalid_for_json_schema[defs_ref]
+                        _add_json_refs(self.definitions[defs_ref])
+                    except KeyError:
+                        if not json_ref.startswith(('http://', 'https://')):
+                            raise
+
+                for k, v in schema.items():
+                    if k == 'examples':
+                        continue  # skip refs processing for examples, allow arbitrary values / refs
+                    _add_json_refs(v)
+            elif isinstance(schema, list):
+                for v in schema:
+                    _add_json_refs(v)
+
+        _add_json_refs(json_schema)
+        return json_refs
+
+    def handle_invalid_for_json_schema(self, schema: CoreSchemaOrField, error_info: str) -> JsonSchemaValue:
+        raise PydanticInvalidForJsonSchema(f'Cannot generate a JsonSchema for {error_info}')
+
+    def emit_warning(self, kind: JsonSchemaWarningKind, detail: str) -> None:
+        """This method simply emits PydanticJsonSchemaWarnings based on handling in the `warning_message` method."""
+        message = self.render_warning_message(kind, detail)
+        if message is not None:
+            warnings.warn(message, PydanticJsonSchemaWarning)
+
+    def render_warning_message(self, kind: JsonSchemaWarningKind, detail: str) -> str | None:
+        """This method is responsible for ignoring warnings as desired, and for formatting the warning messages.
+
+        You can override the value of `ignored_warning_kinds` in a subclass of GenerateJsonSchema
+        to modify what warnings are generated. If you want more control, you can override this method;
+        just return None in situations where you don't want warnings to be emitted.
+
+        Args:
+            kind: The kind of warning to render. It can be one of the following:
+
+                - 'skipped-choice': A choice field was skipped because it had no valid choices.
+                - 'non-serializable-default': A default value was skipped because it was not JSON-serializable.
+            detail: A string with additional details about the warning.
+
+        Returns:
+            The formatted warning message, or `None` if no warning should be emitted.
+        """
+        if kind in self.ignored_warning_kinds:
+            return None
+        return f'{detail} [{kind}]'
+
+    def _build_definitions_remapping(self) -> _DefinitionsRemapping:
+        defs_to_json: dict[DefsRef, JsonRef] = {}
+        for defs_refs in self._prioritized_defsref_choices.values():
+            for defs_ref in defs_refs:
+                json_ref = JsonRef(self.ref_template.format(model=defs_ref))
+                defs_to_json[defs_ref] = json_ref
+
+        return _DefinitionsRemapping.from_prioritized_choices(
+            self._prioritized_defsref_choices, defs_to_json, self.definitions
+        )
+
+    def _garbage_collect_definitions(self, schema: JsonSchemaValue) -> None:
+        visited_defs_refs: set[DefsRef] = set()
+        unvisited_json_refs = _get_all_json_refs(schema)
+        while unvisited_json_refs:
+            next_json_ref = unvisited_json_refs.pop()
+            try:
+                next_defs_ref = self.json_to_defs_refs[next_json_ref]
+                if next_defs_ref in visited_defs_refs:
+                    continue
+                visited_defs_refs.add(next_defs_ref)
+                unvisited_json_refs.update(_get_all_json_refs(self.definitions[next_defs_ref]))
+            except KeyError:
+                if not next_json_ref.startswith(('http://', 'https://')):
+                    raise
+
+        self.definitions = {k: v for k, v in self.definitions.items() if k in visited_defs_refs}
+
+
+# ##### Start JSON Schema Generation Functions #####
+
+
+def model_json_schema(
+    cls: type[BaseModel] | type[PydanticDataclass],
+    by_alias: bool = True,
+    ref_template: str = DEFAULT_REF_TEMPLATE,
+    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+    mode: JsonSchemaMode = 'validation',
+) -> dict[str, Any]:
+    """Utility function to generate a JSON Schema for a model.
+
+    Args:
+        cls: The model class to generate a JSON Schema for.
+        by_alias: If `True` (the default), fields will be serialized according to their alias.
+            If `False`, fields will be serialized according to their attribute name.
+        ref_template: The template to use for generating JSON Schema references.
+        schema_generator: The class to use for generating the JSON Schema.
+        mode: The mode to use for generating the JSON Schema. It can be one of the following:
+
+            - 'validation': Generate a JSON Schema for validating data.
+            - 'serialization': Generate a JSON Schema for serializing data.
+
+    Returns:
+        The generated JSON Schema.
+    """
+    from .main import BaseModel
+
+    schema_generator_instance = schema_generator(by_alias=by_alias, ref_template=ref_template)
+
+    if isinstance(cls.__pydantic_core_schema__, _mock_val_ser.MockCoreSchema):
+        cls.__pydantic_core_schema__.rebuild()
+
+    if cls is BaseModel:
+        raise AttributeError('model_json_schema() must be called on a subclass of BaseModel, not BaseModel itself.')
+
+    assert not isinstance(cls.__pydantic_core_schema__, _mock_val_ser.MockCoreSchema), 'this is a bug! please report it'
+    return schema_generator_instance.generate(cls.__pydantic_core_schema__, mode=mode)
+
+
+def models_json_schema(
+    models: Sequence[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode]],
+    *,
+    by_alias: bool = True,
+    title: str | None = None,
+    description: str | None = None,
+    ref_template: str = DEFAULT_REF_TEMPLATE,
+    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+) -> tuple[dict[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode], JsonSchemaValue], JsonSchemaValue]:
+    """Utility function to generate a JSON Schema for multiple models.
+
+    Args:
+        models: A sequence of tuples of the form (model, mode).
+        by_alias: Whether field aliases should be used as keys in the generated JSON Schema.
+        title: The title of the generated JSON Schema.
+        description: The description of the generated JSON Schema.
+        ref_template: The reference template to use for generating JSON Schema references.
+        schema_generator: The schema generator to use for generating the JSON Schema.
+
+    Returns:
+        A tuple where:
+            - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and
+                whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have
+                JsonRef references to definitions that are defined in the second returned element.)
+            - The second element is a JSON schema containing all definitions referenced in the first returned
+                    element, along with the optional title and description keys.
+    """
+    for cls, _ in models:
+        if isinstance(cls.__pydantic_core_schema__, _mock_val_ser.MockCoreSchema):
+            cls.__pydantic_core_schema__.rebuild()
+
+    instance = schema_generator(by_alias=by_alias, ref_template=ref_template)
+    inputs: list[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode, CoreSchema]] = [
+        (m, mode, m.__pydantic_core_schema__) for m, mode in models
+    ]
+    json_schemas_map, definitions = instance.generate_definitions(inputs)
+
+    json_schema: dict[str, Any] = {}
+    if definitions:
+        json_schema['$defs'] = definitions
+    if title:
+        json_schema['title'] = title
+    if description:
+        json_schema['description'] = description
+
+    return json_schemas_map, json_schema
+
+
+# ##### End JSON Schema Generation Functions #####
+
+
+_HashableJsonValue: TypeAlias = Union[
+    int, float, str, bool, None, Tuple['_HashableJsonValue', ...], Tuple[Tuple[str, '_HashableJsonValue'], ...]
+]
+
+
+def _deduplicate_schemas(schemas: Iterable[JsonDict]) -> list[JsonDict]:
+    return list({_make_json_hashable(schema): schema for schema in schemas}.values())
+
+
+def _make_json_hashable(value: JsonValue) -> _HashableJsonValue:
+    if isinstance(value, dict):
+        return tuple(sorted((k, _make_json_hashable(v)) for k, v in value.items()))
+    elif isinstance(value, list):
+        return tuple(_make_json_hashable(v) for v in value)
+    else:
+        return value
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class WithJsonSchema:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/json_schema/#withjsonschema-annotation
+
+    Add this as an annotation on a field to override the (base) JSON schema that would be generated for that field.
+    This provides a way to set a JSON schema for types that would otherwise raise errors when producing a JSON schema,
+    such as Callable, or types that have an is-instance core schema, without needing to go so far as creating a
+    custom subclass of pydantic.json_schema.GenerateJsonSchema.
+    Note that any _modifications_ to the schema that would normally be made (such as setting the title for model fields)
+    will still be performed.
+
+    If `mode` is set this will only apply to that schema generation mode, allowing you
+    to set different json schemas for validation and serialization.
+    """
+
+    json_schema: JsonSchemaValue | None
+    mode: Literal['validation', 'serialization'] | None = None
+
+    def __get_pydantic_json_schema__(
+        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        mode = self.mode or handler.mode
+        if mode != handler.mode:
+            return handler(core_schema)
+        if self.json_schema is None:
+            # This exception is handled in pydantic.json_schema.GenerateJsonSchema._named_required_fields_schema
+            raise PydanticOmit
+        else:
+            return self.json_schema
+
+    def __hash__(self) -> int:
+        return hash(type(self.mode))
+
+
+class Examples:
+    """Add examples to a JSON schema.
+
+    If the JSON Schema already contains examples, the provided examples
+    will be appended.
+
+    If `mode` is set this will only apply to that schema generation mode,
+    allowing you to add different examples for validation and serialization.
+    """
+
+    @overload
+    @deprecated('Using a dict for `examples` is deprecated since v2.9 and will be removed in v3.0. Use a list instead.')
+    def __init__(
+        self, examples: dict[str, Any], mode: Literal['validation', 'serialization'] | None = None
+    ) -> None: ...
+
+    @overload
+    def __init__(self, examples: list[Any], mode: Literal['validation', 'serialization'] | None = None) -> None: ...
+
+    def __init__(
+        self, examples: dict[str, Any] | list[Any], mode: Literal['validation', 'serialization'] | None = None
+    ) -> None:
+        if isinstance(examples, dict):
+            warnings.warn(
+                'Using a dict for `examples` is deprecated, use a list instead.',
+                PydanticDeprecatedSince29,
+                stacklevel=2,
+            )
+        self.examples = examples
+        self.mode = mode
+
+    def __get_pydantic_json_schema__(
+        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        mode = self.mode or handler.mode
+        json_schema = handler(core_schema)
+        if mode != handler.mode:
+            return json_schema
+        examples = json_schema.get('examples')
+        if examples is None:
+            json_schema['examples'] = to_jsonable_python(self.examples)
+        if isinstance(examples, dict):
+            if isinstance(self.examples, list):
+                warnings.warn(
+                    'Updating existing JSON Schema examples of type dict with examples of type list. '
+                    'Only the existing examples values will be retained. Note that dict support for '
+                    'examples is deprecated and will be removed in v3.0.',
+                    UserWarning,
+                )
+                json_schema['examples'] = to_jsonable_python(
+                    [ex for value in examples.values() for ex in value] + self.examples
+                )
+            else:
+                json_schema['examples'] = to_jsonable_python({**examples, **self.examples})
+        if isinstance(examples, list):
+            if isinstance(self.examples, list):
+                json_schema['examples'] = to_jsonable_python(examples + self.examples)
+            elif isinstance(self.examples, dict):
+                warnings.warn(
+                    'Updating existing JSON Schema examples of type list with examples of type dict. '
+                    'Only the examples values will be retained. Note that dict support for '
+                    'examples is deprecated and will be removed in v3.0.',
+                    UserWarning,
+                )
+                json_schema['examples'] = to_jsonable_python(
+                    examples + [ex for value in self.examples.values() for ex in value]
+                )
+
+        return json_schema
+
+    def __hash__(self) -> int:
+        return hash(type(self.mode))
+
+
+def _get_all_json_refs(item: Any) -> set[JsonRef]:
+    """Get all the definitions references from a JSON schema."""
+    refs: set[JsonRef] = set()
+    stack = [item]
+
+    while stack:
+        current = stack.pop()
+        if isinstance(current, dict):
+            for key, value in current.items():
+                if key == 'examples' and isinstance(value, list):
+                    # Skip examples that may contain arbitrary values and references
+                    # (e.g. `{"examples": [{"$ref": "..."}]}`). Note: checking for value
+                    # of type list is necessary to avoid skipping valid portions of the schema,
+                    # for instance when "examples" is used as a property key. A more robust solution
+                    # could be found, but would require more advanced JSON Schema parsing logic.
+                    continue
+                if key == '$ref' and isinstance(value, str):
+                    refs.add(JsonRef(value))
+                elif isinstance(value, dict):
+                    stack.append(value)
+                elif isinstance(value, list):
+                    stack.extend(value)
+        elif isinstance(current, list):
+            stack.extend(current)
+
+    return refs
+
+
+AnyType = TypeVar('AnyType')
+
+if TYPE_CHECKING:
+    SkipJsonSchema = Annotated[AnyType, ...]
+else:
+
+    @dataclasses.dataclass(**_internal_dataclass.slots_true)
+    class SkipJsonSchema:
+        """Usage docs: https://docs.pydantic.dev/2.10/concepts/json_schema/#skipjsonschema-annotation
+
+        Add this as an annotation on a field to skip generating a JSON schema for that field.
+
+        Example:
+            ```python
+            from typing import Union
+
+            from pydantic import BaseModel
+            from pydantic.json_schema import SkipJsonSchema
+
+            from pprint import pprint
+
+            class Model(BaseModel):
+                a: Union[int, None] = None  # (1)!
+                b: Union[int, SkipJsonSchema[None]] = None  # (2)!
+                c: SkipJsonSchema[Union[int, None]] = None  # (3)!
+
+            pprint(Model.model_json_schema())
+            '''
+            {
+                'properties': {
+                    'a': {
+                        'anyOf': [
+                            {'type': 'integer'},
+                            {'type': 'null'}
+                        ],
+                        'default': None,
+                        'title': 'A'
+                    },
+                    'b': {
+                        'default': None,
+                        'title': 'B',
+                        'type': 'integer'
+                    }
+                },
+                'title': 'Model',
+                'type': 'object'
+            }
+            '''
+            ```
+
+            1. The integer and null types are both included in the schema for `a`.
+            2. The integer type is the only type included in the schema for `b`.
+            3. The entirety of the `c` field is omitted from the schema.
+        """
+
+        def __class_getitem__(cls, item: AnyType) -> AnyType:
+            return Annotated[item, cls()]
+
+        def __get_pydantic_json_schema__(
+            self, core_schema: CoreSchema, handler: GetJsonSchemaHandler
+        ) -> JsonSchemaValue:
+            raise PydanticOmit
+
+        def __hash__(self) -> int:
+            return hash(type(self))
+
+
+def _get_typed_dict_config(cls: type[Any] | None) -> ConfigDict:
+    if cls is not None:
+        try:
+            return _decorators.get_attribute_from_bases(cls, '__pydantic_config__')
+        except AttributeError:
+            pass
+    return {}
diff --git a/.venv/lib/python3.12/site-packages/pydantic/main.py b/.venv/lib/python3.12/site-packages/pydantic/main.py
new file mode 100644
index 00000000..b1be872e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/main.py
@@ -0,0 +1,1688 @@
+"""Logic for creating models."""
+
+from __future__ import annotations as _annotations
+
+import operator
+import sys
+import types
+import typing
+import warnings
+from copy import copy, deepcopy
+from functools import cached_property
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    Generator,
+    Literal,
+    Mapping,
+    Set,
+    Tuple,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+
+import pydantic_core
+import typing_extensions
+from pydantic_core import PydanticUndefined
+from typing_extensions import Self, TypeAlias, Unpack
+
+from ._internal import (
+    _config,
+    _decorators,
+    _fields,
+    _forward_ref,
+    _generics,
+    _import_utils,
+    _mock_val_ser,
+    _model_construction,
+    _namespace_utils,
+    _repr,
+    _typing_extra,
+    _utils,
+)
+from ._migration import getattr_migration
+from .aliases import AliasChoices, AliasPath
+from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
+from .config import ConfigDict
+from .errors import PydanticUndefinedAnnotation, PydanticUserError
+from .json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema, JsonSchemaMode, JsonSchemaValue, model_json_schema
+from .plugin._schema_validator import PluggableSchemaValidator
+from .warnings import PydanticDeprecatedSince20
+
+if TYPE_CHECKING:
+    from inspect import Signature
+    from pathlib import Path
+
+    from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator
+
+    from ._internal._namespace_utils import MappingNamespace
+    from ._internal._utils import AbstractSetIntStr, MappingIntStrAny
+    from .deprecated.parse import Protocol as DeprecatedParseProtocol
+    from .fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr
+else:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'BaseModel', 'create_model'
+
+# Keep these type aliases available at runtime:
+TupleGenerator: TypeAlias = Generator[Tuple[str, Any], None, None]
+# NOTE: In reality, `bool` should be replaced by `Literal[True]` but mypy fails to correctly apply bidirectional
+# type inference (e.g. when using `{'a': {'b': True}}`):
+# NOTE: Keep this type alias in sync with the stub definition in `pydantic-core`:
+IncEx: TypeAlias = Union[Set[int], Set[str], Mapping[int, Union['IncEx', bool]], Mapping[str, Union['IncEx', bool]]]
+
+_object_setattr = _model_construction.object_setattr
+
+
+class BaseModel(metaclass=_model_construction.ModelMetaclass):
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/
+
+    A base class for creating Pydantic models.
+
+    Attributes:
+        __class_vars__: The names of the class variables defined on the model.
+        __private_attributes__: Metadata about the private attributes of the model.
+        __signature__: The synthesized `__init__` [`Signature`][inspect.Signature] of the model.
+
+        __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
+        __pydantic_core_schema__: The core schema of the model.
+        __pydantic_custom_init__: Whether the model has a custom `__init__` function.
+        __pydantic_decorators__: Metadata containing the decorators defined on the model.
+            This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
+        __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
+            __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
+        __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
+        __pydantic_post_init__: The name of the post-init method for the model, if defined.
+        __pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].
+        __pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.
+        __pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.
+
+        __pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.
+        __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.
+
+        __pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
+            is set to `'allow'`.
+        __pydantic_fields_set__: The names of fields explicitly set during instantiation.
+        __pydantic_private__: Values of private attributes set on the model instance.
+    """
+
+    # Note: Many of the below class vars are defined in the metaclass, but we define them here for type checking purposes.
+
+    model_config: ClassVar[ConfigDict] = ConfigDict()
+    """
+    Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
+    """
+
+    # Because `dict` is in the local namespace of the `BaseModel` class, we use `Dict` for annotations.
+    # TODO v3 fallback to `dict` when the deprecated `dict` method gets removed.
+    __class_vars__: ClassVar[set[str]]
+    """The names of the class variables defined on the model."""
+
+    __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]]  # noqa: UP006
+    """Metadata about the private attributes of the model."""
+
+    __signature__: ClassVar[Signature]
+    """The synthesized `__init__` [`Signature`][inspect.Signature] of the model."""
+
+    __pydantic_complete__: ClassVar[bool] = False
+    """Whether model building is completed, or if there are still undefined fields."""
+
+    __pydantic_core_schema__: ClassVar[CoreSchema]
+    """The core schema of the model."""
+
+    __pydantic_custom_init__: ClassVar[bool]
+    """Whether the model has a custom `__init__` method."""
+
+    # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation.
+    __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = _decorators.DecoratorInfos()
+    """Metadata containing the decorators defined on the model.
+    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1."""
+
+    __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata]
+    """Metadata for generic models; contains data used for a similar purpose to
+    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these."""
+
+    __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None  # noqa: UP006
+    """Parent namespace of the model, used for automatic rebuilding of models."""
+
+    __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
+    """The name of the post-init method for the model, if defined."""
+
+    __pydantic_root_model__: ClassVar[bool] = False
+    """Whether the model is a [`RootModel`][pydantic.root_model.RootModel]."""
+
+    __pydantic_serializer__: ClassVar[SchemaSerializer]
+    """The `pydantic-core` `SchemaSerializer` used to dump instances of the model."""
+
+    __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator]
+    """The `pydantic-core` `SchemaValidator` used to validate instances of the model."""
+
+    __pydantic_fields__: ClassVar[Dict[str, FieldInfo]]  # noqa: UP006
+    """A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.
+    This replaces `Model.__fields__` from Pydantic V1.
+    """
+
+    __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]]  # noqa: UP006
+    """A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects."""
+
+    __pydantic_extra__: dict[str, Any] | None = _model_construction.NoInitField(init=False)
+    """A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`."""
+
+    __pydantic_fields_set__: set[str] = _model_construction.NoInitField(init=False)
+    """The names of fields explicitly set during instantiation."""
+
+    __pydantic_private__: dict[str, Any] | None = _model_construction.NoInitField(init=False)
+    """Values of private attributes set on the model instance."""
+
+    if not TYPE_CHECKING:
+        # Prevent `BaseModel` from being instantiated directly
+        # (defined in an `if not TYPE_CHECKING` block for clarity and to avoid type checking errors):
+        __pydantic_core_schema__ = _mock_val_ser.MockCoreSchema(
+            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
+            code='base-model-instantiated',
+        )
+        __pydantic_validator__ = _mock_val_ser.MockValSer(
+            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
+            val_or_ser='validator',
+            code='base-model-instantiated',
+        )
+        __pydantic_serializer__ = _mock_val_ser.MockValSer(
+            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
+            val_or_ser='serializer',
+            code='base-model-instantiated',
+        )
+
+    __slots__ = '__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__'
+
+    def __init__(self, /, **data: Any) -> None:
+        """Create a new model by parsing and validating input data from keyword arguments.
+
+        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
+        validated to form a valid model.
+
+        `self` is explicitly positional-only to allow `self` as a field name.
+        """
+        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
+        __tracebackhide__ = True
+        validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
+        if self is not validated_self:
+            warnings.warn(
+                'A custom validator is returning a value other than `self`.\n'
+                "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
+                'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
+                stacklevel=2,
+            )
+
+    # The following line sets a flag that we use to determine when `__init__` gets overridden by the user
+    __init__.__pydantic_base_init__ = True  # pyright: ignore[reportFunctionMemberAccess]
+
+    if TYPE_CHECKING:
+        model_fields: ClassVar[dict[str, FieldInfo]]
+        model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]]
+    else:
+        # TODO: V3 - remove `model_fields` and `model_computed_fields` properties from the `BaseModel` class - they should only
+        # be accessible on the model class, not on instances. We have these purely for backwards compatibility with Pydantic <v2.10.
+        # This is similar to the fact that we have __fields__ defined here (on `BaseModel`) and on `ModelMetaClass`.
+        # Another problem here is that there's no great way to have class properties :(
+        @property
+        def model_fields(self) -> dict[str, FieldInfo]:
+            """Get metadata about the fields defined on the model.
+
+            Deprecation warning: you should be getting this information from the model class, not from an instance.
+            In V3, this property will be removed from the `BaseModel` class.
+
+            Returns:
+                A mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects.
+            """
+            # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation, hence the default here.
+            return getattr(self, '__pydantic_fields__', {})
+
+        @property
+        def model_computed_fields(self) -> dict[str, ComputedFieldInfo]:
+            """Get metadata about the computed fields defined on the model.
+
+            Deprecation warning: you should be getting this information from the model class, not from an instance.
+            In V3, this property will be removed from the `BaseModel` class.
+
+            Returns:
+                A mapping of computed field names to [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.
+            """
+            # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation, hence the default here.
+            return getattr(self, '__pydantic_computed_fields__', {})
+
+    @property
+    def model_extra(self) -> dict[str, Any] | None:
+        """Get extra fields set during validation.
+
+        Returns:
+            A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
+        """
+        return self.__pydantic_extra__
+
+    @property
+    def model_fields_set(self) -> set[str]:
+        """Returns the set of fields that have been explicitly set on this model instance.
+
+        Returns:
+            A set of strings representing the fields that have been set,
+                i.e. that were not filled from defaults.
+        """
+        return self.__pydantic_fields_set__
+
+    @classmethod
+    def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: C901
+        """Creates a new instance of the `Model` class with validated data.
+
+        Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
+        Default values are respected, but no other validation is performed.
+
+        !!! note
+            `model_construct()` generally respects the `model_config.extra` setting on the provided model.
+            That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
+            and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
+            Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
+            an error if extra values are passed, but they will be ignored.
+
+        Args:
+            _fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
+                this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
+                Otherwise, the field names from the `values` argument will be used.
+            values: Trusted or pre-validated data dictionary.
+
+        Returns:
+            A new instance of the `Model` class with validated data.
+        """
+        m = cls.__new__(cls)
+        fields_values: dict[str, Any] = {}
+        fields_set = set()
+
+        for name, field in cls.__pydantic_fields__.items():
+            if field.alias is not None and field.alias in values:
+                fields_values[name] = values.pop(field.alias)
+                fields_set.add(name)
+
+            if (name not in fields_set) and (field.validation_alias is not None):
+                validation_aliases: list[str | AliasPath] = (
+                    field.validation_alias.choices
+                    if isinstance(field.validation_alias, AliasChoices)
+                    else [field.validation_alias]
+                )
+
+                for alias in validation_aliases:
+                    if isinstance(alias, str) and alias in values:
+                        fields_values[name] = values.pop(alias)
+                        fields_set.add(name)
+                        break
+                    elif isinstance(alias, AliasPath):
+                        value = alias.search_dict_for_path(values)
+                        if value is not PydanticUndefined:
+                            fields_values[name] = value
+                            fields_set.add(name)
+                            break
+
+            if name not in fields_set:
+                if name in values:
+                    fields_values[name] = values.pop(name)
+                    fields_set.add(name)
+                elif not field.is_required():
+                    fields_values[name] = field.get_default(call_default_factory=True, validated_data=fields_values)
+        if _fields_set is None:
+            _fields_set = fields_set
+
+        _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None
+        _object_setattr(m, '__dict__', fields_values)
+        _object_setattr(m, '__pydantic_fields_set__', _fields_set)
+        if not cls.__pydantic_root_model__:
+            _object_setattr(m, '__pydantic_extra__', _extra)
+
+        if cls.__pydantic_post_init__:
+            m.model_post_init(None)
+            # update private attributes with values set
+            if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
+                for k, v in values.items():
+                    if k in m.__private_attributes__:
+                        m.__pydantic_private__[k] = v
+
+        elif not cls.__pydantic_root_model__:
+            # Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
+            # Since it doesn't, that means that `__pydantic_private__` should be set to None
+            _object_setattr(m, '__pydantic_private__', None)
+
+        return m
+
+    def model_copy(self, *, update: Mapping[str, Any] | None = None, deep: bool = False) -> Self:
+        """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
+
+        Returns a copy of the model.
+
+        Args:
+            update: Values to change/add in the new model. Note: the data is not validated
+                before creating the new model. You should trust this data.
+            deep: Set to `True` to make a deep copy of the model.
+
+        Returns:
+            New model instance.
+        """
+        copied = self.__deepcopy__() if deep else self.__copy__()
+        if update:
+            if self.model_config.get('extra') == 'allow':
+                for k, v in update.items():
+                    if k in self.__pydantic_fields__:
+                        copied.__dict__[k] = v
+                    else:
+                        if copied.__pydantic_extra__ is None:
+                            copied.__pydantic_extra__ = {}
+                        copied.__pydantic_extra__[k] = v
+            else:
+                copied.__dict__.update(update)
+            copied.__pydantic_fields_set__.update(update.keys())
+        return copied
+
+    def model_dump(
+        self,
+        *,
+        mode: Literal['json', 'python'] | str = 'python',
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        context: Any | None = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        round_trip: bool = False,
+        warnings: bool | Literal['none', 'warn', 'error'] = True,
+        serialize_as_any: bool = False,
+    ) -> dict[str, Any]:
+        """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
+
+        Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
+
+        Args:
+            mode: The mode in which `to_python` should run.
+                If mode is 'json', the output will only contain JSON serializable types.
+                If mode is 'python', the output may contain non-JSON-serializable Python objects.
+            include: A set of fields to include in the output.
+            exclude: A set of fields to exclude from the output.
+            context: Additional context to pass to the serializer.
+            by_alias: Whether to use the field's alias in the dictionary key if defined.
+            exclude_unset: Whether to exclude fields that have not been explicitly set.
+            exclude_defaults: Whether to exclude fields that are set to their default value.
+            exclude_none: Whether to exclude fields that have a value of `None`.
+            round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
+            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
+                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
+            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
+
+        Returns:
+            A dictionary representation of the model.
+        """
+        return self.__pydantic_serializer__.to_python(
+            self,
+            mode=mode,
+            by_alias=by_alias,
+            include=include,
+            exclude=exclude,
+            context=context,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            round_trip=round_trip,
+            warnings=warnings,
+            serialize_as_any=serialize_as_any,
+        )
+
+    def model_dump_json(
+        self,
+        *,
+        indent: int | None = None,
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        context: Any | None = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        round_trip: bool = False,
+        warnings: bool | Literal['none', 'warn', 'error'] = True,
+        serialize_as_any: bool = False,
+    ) -> str:
+        """Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
+
+        Generates a JSON representation of the model using Pydantic's `to_json` method.
+
+        Args:
+            indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
+            include: Field(s) to include in the JSON output.
+            exclude: Field(s) to exclude from the JSON output.
+            context: Additional context to pass to the serializer.
+            by_alias: Whether to serialize using field aliases.
+            exclude_unset: Whether to exclude fields that have not been explicitly set.
+            exclude_defaults: Whether to exclude fields that are set to their default value.
+            exclude_none: Whether to exclude fields that have a value of `None`.
+            round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
+            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
+                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
+            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
+
+        Returns:
+            A JSON string representation of the model.
+        """
+        return self.__pydantic_serializer__.to_json(
+            self,
+            indent=indent,
+            include=include,
+            exclude=exclude,
+            context=context,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            round_trip=round_trip,
+            warnings=warnings,
+            serialize_as_any=serialize_as_any,
+        ).decode()
+
+    @classmethod
+    def model_json_schema(
+        cls,
+        by_alias: bool = True,
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+        mode: JsonSchemaMode = 'validation',
+    ) -> dict[str, Any]:
+        """Generates a JSON schema for a model class.
+
+        Args:
+            by_alias: Whether to use attribute aliases or not.
+            ref_template: The reference template.
+            schema_generator: To override the logic used to generate the JSON schema, as a subclass of
+                `GenerateJsonSchema` with your desired modifications
+            mode: The mode in which to generate the schema.
+
+        Returns:
+            The JSON schema for the given model class.
+        """
+        return model_json_schema(
+            cls, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator, mode=mode
+        )
+
+    @classmethod
+    def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str:
+        """Compute the class name for parametrizations of generic classes.
+
+        This method can be overridden to achieve a custom naming scheme for generic BaseModels.
+
+        Args:
+            params: Tuple of types of the class. Given a generic class
+                `Model` with 2 type variables and a concrete model `Model[str, int]`,
+                the value `(str, int)` would be passed to `params`.
+
+        Returns:
+            String representing the new class where `params` are passed to `cls` as type variables.
+
+        Raises:
+            TypeError: Raised when trying to generate concrete names for non-generic models.
+        """
+        if not issubclass(cls, typing.Generic):
+            raise TypeError('Concrete names should only be generated for generic models.')
+
+        # Any strings received should represent forward references, so we handle them specially below.
+        # If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
+        # we may be able to remove this special case.
+        param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
+        params_component = ', '.join(param_names)
+        return f'{cls.__name__}[{params_component}]'
+
+    def model_post_init(self, __context: Any) -> None:
+        """Override this method to perform additional initialization after `__init__` and `model_construct`.
+        This is useful if you want to do some validation that requires the entire model to be initialized.
+        """
+        pass
+
+    @classmethod
+    def model_rebuild(
+        cls,
+        *,
+        force: bool = False,
+        raise_errors: bool = True,
+        _parent_namespace_depth: int = 2,
+        _types_namespace: MappingNamespace | None = None,
+    ) -> bool | None:
+        """Try to rebuild the pydantic-core schema for the model.
+
+        This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
+        the initial attempt to build the schema, and automatic rebuilding fails.
+
+        Args:
+            force: Whether to force the rebuilding of the model schema, defaults to `False`.
+            raise_errors: Whether to raise errors, defaults to `True`.
+            _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
+            _types_namespace: The types namespace, defaults to `None`.
+
+        Returns:
+            Returns `None` if the schema is already "complete" and rebuilding was not required.
+            If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
+        """
+        if not force and cls.__pydantic_complete__:
+            return None
+
+        if '__pydantic_core_schema__' in cls.__dict__:
+            delattr(cls, '__pydantic_core_schema__')  # delete cached value to ensure full rebuild happens
+
+        if _types_namespace is not None:
+            rebuild_ns = _types_namespace
+        elif _parent_namespace_depth > 0:
+            rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
+        else:
+            rebuild_ns = {}
+
+        parent_ns = _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}
+
+        ns_resolver = _namespace_utils.NsResolver(
+            parent_namespace={**rebuild_ns, **parent_ns},
+        )
+
+        # manually override defer_build so complete_model_class doesn't skip building the model again
+        config = {**cls.model_config, 'defer_build': False}
+        return _model_construction.complete_model_class(
+            cls,
+            cls.__name__,
+            _config.ConfigWrapper(config, check=False),
+            raise_errors=raise_errors,
+            ns_resolver=ns_resolver,
+        )
+
+    @classmethod
+    def model_validate(
+        cls,
+        obj: Any,
+        *,
+        strict: bool | None = None,
+        from_attributes: bool | None = None,
+        context: Any | None = None,
+    ) -> Self:
+        """Validate a pydantic model instance.
+
+        Args:
+            obj: The object to validate.
+            strict: Whether to enforce types strictly.
+            from_attributes: Whether to extract data from object attributes.
+            context: Additional context to pass to the validator.
+
+        Raises:
+            ValidationError: If the object could not be validated.
+
+        Returns:
+            The validated model instance.
+        """
+        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
+        __tracebackhide__ = True
+        return cls.__pydantic_validator__.validate_python(
+            obj, strict=strict, from_attributes=from_attributes, context=context
+        )
+
+    @classmethod
+    def model_validate_json(
+        cls,
+        json_data: str | bytes | bytearray,
+        *,
+        strict: bool | None = None,
+        context: Any | None = None,
+    ) -> Self:
+        """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
+
+        Validate the given JSON data against the Pydantic model.
+
+        Args:
+            json_data: The JSON data to validate.
+            strict: Whether to enforce types strictly.
+            context: Extra variables to pass to the validator.
+
+        Returns:
+            The validated Pydantic model.
+
+        Raises:
+            ValidationError: If `json_data` is not a JSON string or the object could not be validated.
+        """
+        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
+        __tracebackhide__ = True
+        return cls.__pydantic_validator__.validate_json(json_data, strict=strict, context=context)
+
+    @classmethod
+    def model_validate_strings(
+        cls,
+        obj: Any,
+        *,
+        strict: bool | None = None,
+        context: Any | None = None,
+    ) -> Self:
+        """Validate the given object with string data against the Pydantic model.
+
+        Args:
+            obj: The object containing string data to validate.
+            strict: Whether to enforce types strictly.
+            context: Extra variables to pass to the validator.
+
+        Returns:
+            The validated Pydantic model.
+        """
+        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
+        __tracebackhide__ = True
+        return cls.__pydantic_validator__.validate_strings(obj, strict=strict, context=context)
+
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source: type[BaseModel], handler: GetCoreSchemaHandler, /) -> CoreSchema:
+        """Hook into generating the model's CoreSchema.
+
+        Args:
+            source: The class we are generating a schema for.
+                This will generally be the same as the `cls` argument if this is a classmethod.
+            handler: A callable that calls into Pydantic's internal CoreSchema generation logic.
+
+        Returns:
+            A `pydantic-core` `CoreSchema`.
+        """
+        # Only use the cached value from this _exact_ class; we don't want one from a parent class
+        # This is why we check `cls.__dict__` and don't use `cls.__pydantic_core_schema__` or similar.
+        schema = cls.__dict__.get('__pydantic_core_schema__')
+        if schema is not None and not isinstance(schema, _mock_val_ser.MockCoreSchema):
+            # Due to the way generic classes are built, it's possible that an invalid schema may be temporarily
+            # set on generic classes. I think we could resolve this to ensure that we get proper schema caching
+            # for generics, but for simplicity for now, we just always rebuild if the class has a generic origin.
+            if not cls.__pydantic_generic_metadata__['origin']:
+                return cls.__pydantic_core_schema__
+
+        return handler(source)
+
+    @classmethod
+    def __get_pydantic_json_schema__(
+        cls,
+        core_schema: CoreSchema,
+        handler: GetJsonSchemaHandler,
+        /,
+    ) -> JsonSchemaValue:
+        """Hook into generating the model's JSON schema.
+
+        Args:
+            core_schema: A `pydantic-core` CoreSchema.
+                You can ignore this argument and call the handler with a new CoreSchema,
+                wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
+                or just call the handler with the original schema.
+            handler: Call into Pydantic's internal JSON schema generation.
+                This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
+                generation fails.
+                Since this gets called by `BaseModel.model_json_schema` you can override the
+                `schema_generator` argument to that function to change JSON schema generation globally
+                for a type.
+
+        Returns:
+            A JSON schema, as a Python object.
+        """
+        return handler(core_schema)
+
+    @classmethod
+    def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:
+        """This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
+        only after the class is actually fully initialized. In particular, attributes like `model_fields` will
+        be present when this is called.
+
+        This is necessary because `__init_subclass__` will always be called by `type.__new__`,
+        and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
+        `type.__new__` was called in such a manner that the class would already be sufficiently initialized.
+
+        This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
+        any kwargs passed to the class definition that aren't used internally by pydantic.
+
+        Args:
+            **kwargs: Any keyword arguments passed to the class definition that aren't used internally
+                by pydantic.
+        """
+        pass
+
+    def __class_getitem__(
+        cls, typevar_values: type[Any] | tuple[type[Any], ...]
+    ) -> type[BaseModel] | _forward_ref.PydanticRecursiveRef:
+        cached = _generics.get_cached_generic_type_early(cls, typevar_values)
+        if cached is not None:
+            return cached
+
+        if cls is BaseModel:
+            raise TypeError('Type parameters should be placed on typing.Generic, not BaseModel')
+        if not hasattr(cls, '__parameters__'):
+            raise TypeError(f'{cls} cannot be parametrized because it does not inherit from typing.Generic')
+        if not cls.__pydantic_generic_metadata__['parameters'] and typing.Generic not in cls.__bases__:
+            raise TypeError(f'{cls} is not a generic class')
+
+        if not isinstance(typevar_values, tuple):
+            typevar_values = (typevar_values,)
+        _generics.check_parameters_count(cls, typevar_values)
+
+        # Build map from generic typevars to passed params
+        typevars_map: dict[TypeVar, type[Any]] = dict(
+            zip(cls.__pydantic_generic_metadata__['parameters'], typevar_values)
+        )
+
+        if _utils.all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map:
+            submodel = cls  # if arguments are equal to parameters it's the same object
+            _generics.set_cached_generic_type(cls, typevar_values, submodel)
+        else:
+            parent_args = cls.__pydantic_generic_metadata__['args']
+            if not parent_args:
+                args = typevar_values
+            else:
+                args = tuple(_generics.replace_types(arg, typevars_map) for arg in parent_args)
+
+            origin = cls.__pydantic_generic_metadata__['origin'] or cls
+            model_name = origin.model_parametrized_name(args)
+            params = tuple(
+                {param: None for param in _generics.iter_contained_typevars(typevars_map.values())}
+            )  # use dict as ordered set
+
+            with _generics.generic_recursion_self_type(origin, args) as maybe_self_type:
+                cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args)
+                if cached is not None:
+                    return cached
+
+                if maybe_self_type is not None:
+                    return maybe_self_type
+
+                # Attempt to rebuild the origin in case new types have been defined
+                try:
+                    # depth 2 gets you above this __class_getitem__ call.
+                    # Note that we explicitly provide the parent ns, otherwise
+                    # `model_rebuild` will use the parent ns no matter if it is the ns of a module.
+                    # We don't want this here, as this has unexpected effects when a model
+                    # is being parametrized during a forward annotation evaluation.
+                    parent_ns = _typing_extra.parent_frame_namespace(parent_depth=2) or {}
+                    origin.model_rebuild(_types_namespace=parent_ns)
+                except PydanticUndefinedAnnotation:
+                    # It's okay if it fails, it just means there are still undefined types
+                    # that could be evaluated later.
+                    pass
+
+                submodel = _generics.create_generic_submodel(model_name, origin, args, params)
+
+                # Update cache
+                _generics.set_cached_generic_type(cls, typevar_values, submodel, origin, args)
+
+        return submodel
+
+    def __copy__(self) -> Self:
+        """Returns a shallow copy of the model."""
+        cls = type(self)
+        m = cls.__new__(cls)
+        _object_setattr(m, '__dict__', copy(self.__dict__))
+        _object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__))
+        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
+
+        if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
+            _object_setattr(m, '__pydantic_private__', None)
+        else:
+            _object_setattr(
+                m,
+                '__pydantic_private__',
+                {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined},
+            )
+
+        return m
+
+    def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
+        """Returns a deep copy of the model."""
+        cls = type(self)
+        m = cls.__new__(cls)
+        _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
+        _object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo))
+        # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
+        # and attempting a deepcopy would be marginally slower.
+        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
+
+        if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
+            _object_setattr(m, '__pydantic_private__', None)
+        else:
+            _object_setattr(
+                m,
+                '__pydantic_private__',
+                deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo),
+            )
+
+        return m
+
+    if not TYPE_CHECKING:
+        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
+        # The same goes for __setattr__ and __delattr__, see: https://github.com/pydantic/pydantic/issues/8643
+
+        def __getattr__(self, item: str) -> Any:
+            private_attributes = object.__getattribute__(self, '__private_attributes__')
+            if item in private_attributes:
+                attribute = private_attributes[item]
+                if hasattr(attribute, '__get__'):
+                    return attribute.__get__(self, type(self))  # type: ignore
+
+                try:
+                    # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
+                    return self.__pydantic_private__[item]  # type: ignore
+                except KeyError as exc:
+                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
+            else:
+                # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.
+                # See `BaseModel.__repr_args__` for more details
+                try:
+                    pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
+                except AttributeError:
+                    pydantic_extra = None
+
+                if pydantic_extra:
+                    try:
+                        return pydantic_extra[item]
+                    except KeyError as exc:
+                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
+                else:
+                    if hasattr(self.__class__, item):
+                        return super().__getattribute__(item)  # Raises AttributeError if appropriate
+                    else:
+                        # this is the current error
+                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
+
+        def __setattr__(self, name: str, value: Any) -> None:
+            if name in self.__class_vars__:
+                raise AttributeError(
+                    f'{name!r} is a ClassVar of `{self.__class__.__name__}` and cannot be set on an instance. '
+                    f'If you want to set a value on the class, use `{self.__class__.__name__}.{name} = value`.'
+                )
+            elif not _fields.is_valid_field_name(name):
+                if self.__pydantic_private__ is None or name not in self.__private_attributes__:
+                    _object_setattr(self, name, value)
+                else:
+                    attribute = self.__private_attributes__[name]
+                    if hasattr(attribute, '__set__'):
+                        attribute.__set__(self, value)  # type: ignore
+                    else:
+                        self.__pydantic_private__[name] = value
+                return
+
+            self._check_frozen(name, value)
+
+            attr = getattr(self.__class__, name, None)
+            # NOTE: We currently special case properties and `cached_property`, but we might need
+            # to generalize this to all data/non-data descriptors at some point. For non-data descriptors
+            # (such as `cached_property`), it isn't obvious though. `cached_property` caches the value
+            # to the instance's `__dict__`, but other non-data descriptors might do things differently.
+            if isinstance(attr, property):
+                attr.__set__(self, value)
+            elif isinstance(attr, cached_property):
+                self.__dict__[name] = value
+            elif self.model_config.get('validate_assignment', None):
+                self.__pydantic_validator__.validate_assignment(self, name, value)
+            elif self.model_config.get('extra') != 'allow' and name not in self.__pydantic_fields__:
+                # TODO - matching error
+                raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
+            elif self.model_config.get('extra') == 'allow' and name not in self.__pydantic_fields__:
+                if self.model_extra and name in self.model_extra:
+                    self.__pydantic_extra__[name] = value  # type: ignore
+                else:
+                    try:
+                        getattr(self, name)
+                    except AttributeError:
+                        # attribute does not already exist on instance, so put it in extra
+                        self.__pydantic_extra__[name] = value  # type: ignore
+                    else:
+                        # attribute _does_ already exist on instance, and was not in extra, so update it
+                        _object_setattr(self, name, value)
+            else:
+                self.__dict__[name] = value
+                self.__pydantic_fields_set__.add(name)
+
+        def __delattr__(self, item: str) -> Any:
+            if item in self.__private_attributes__:
+                attribute = self.__private_attributes__[item]
+                if hasattr(attribute, '__delete__'):
+                    attribute.__delete__(self)  # type: ignore
+                    return
+
+                try:
+                    # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
+                    del self.__pydantic_private__[item]  # type: ignore
+                    return
+                except KeyError as exc:
+                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
+
+            self._check_frozen(item, None)
+
+            if item in self.__pydantic_fields__:
+                object.__delattr__(self, item)
+            elif self.__pydantic_extra__ is not None and item in self.__pydantic_extra__:
+                del self.__pydantic_extra__[item]
+            else:
+                try:
+                    object.__delattr__(self, item)
+                except AttributeError:
+                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
+
+        # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
+        # type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
+        def __replace__(self, **changes: Any) -> Self:
+            return self.model_copy(update=changes)
+
+    def _check_frozen(self, name: str, value: Any) -> None:
+        if self.model_config.get('frozen', None):
+            typ = 'frozen_instance'
+        elif getattr(self.__pydantic_fields__.get(name), 'frozen', False):
+            typ = 'frozen_field'
+        else:
+            return
+        error: pydantic_core.InitErrorDetails = {
+            'type': typ,
+            'loc': (name,),
+            'input': value,
+        }
+        raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])
+
+    def __getstate__(self) -> dict[Any, Any]:
+        private = self.__pydantic_private__
+        if private:
+            private = {k: v for k, v in private.items() if v is not PydanticUndefined}
+        return {
+            '__dict__': self.__dict__,
+            '__pydantic_extra__': self.__pydantic_extra__,
+            '__pydantic_fields_set__': self.__pydantic_fields_set__,
+            '__pydantic_private__': private,
+        }
+
+    def __setstate__(self, state: dict[Any, Any]) -> None:
+        _object_setattr(self, '__pydantic_fields_set__', state.get('__pydantic_fields_set__', {}))
+        _object_setattr(self, '__pydantic_extra__', state.get('__pydantic_extra__', {}))
+        _object_setattr(self, '__pydantic_private__', state.get('__pydantic_private__', {}))
+        _object_setattr(self, '__dict__', state.get('__dict__', {}))
+
+    if not TYPE_CHECKING:
+
+        def __eq__(self, other: Any) -> bool:
+            if isinstance(other, BaseModel):
+                # When comparing instances of generic types for equality, as long as all field values are equal,
+                # only require their generic origin types to be equal, rather than exact type equality.
+                # This prevents headaches like MyGeneric(x=1) != MyGeneric[Any](x=1).
+                self_type = self.__pydantic_generic_metadata__['origin'] or self.__class__
+                other_type = other.__pydantic_generic_metadata__['origin'] or other.__class__
+
+                # Perform common checks first
+                if not (
+                    self_type == other_type
+                    and getattr(self, '__pydantic_private__', None) == getattr(other, '__pydantic_private__', None)
+                    and self.__pydantic_extra__ == other.__pydantic_extra__
+                ):
+                    return False
+
+                # We only want to compare pydantic fields but ignoring fields is costly.
+                # We'll perform a fast check first, and fallback only when needed
+                # See GH-7444 and GH-7825 for rationale and a performance benchmark
+
+                # First, do the fast (and sometimes faulty) __dict__ comparison
+                if self.__dict__ == other.__dict__:
+                    # If the check above passes, then pydantic fields are equal, we can return early
+                    return True
+
+                # We don't want to trigger unnecessary costly filtering of __dict__ on all unequal objects, so we return
+                # early if there are no keys to ignore (we would just return False later on anyway)
+                model_fields = type(self).__pydantic_fields__.keys()
+                if self.__dict__.keys() <= model_fields and other.__dict__.keys() <= model_fields:
+                    return False
+
+                # If we reach here, there are non-pydantic-fields keys, mapped to unequal values, that we need to ignore
+                # Resort to costly filtering of the __dict__ objects
+                # We use operator.itemgetter because it is much faster than dict comprehensions
+                # NOTE: Contrary to standard python class and instances, when the Model class has a default value for an
+                # attribute and the model instance doesn't have a corresponding attribute, accessing the missing attribute
+                # raises an error in BaseModel.__getattr__ instead of returning the class attribute
+                # So we can use operator.itemgetter() instead of operator.attrgetter()
+                getter = operator.itemgetter(*model_fields) if model_fields else lambda _: _utils._SENTINEL
+                try:
+                    return getter(self.__dict__) == getter(other.__dict__)
+                except KeyError:
+                    # In rare cases (such as when using the deprecated BaseModel.copy() method),
+                    # the __dict__ may not contain all model fields, which is how we can get here.
+                    # getter(self.__dict__) is much faster than any 'safe' method that accounts
+                    # for missing keys, and wrapping it in a `try` doesn't slow things down much
+                    # in the common case.
+                    self_fields_proxy = _utils.SafeGetItemProxy(self.__dict__)
+                    other_fields_proxy = _utils.SafeGetItemProxy(other.__dict__)
+                    return getter(self_fields_proxy) == getter(other_fields_proxy)
+
+            # other instance is not a BaseModel
+            else:
+                return NotImplemented  # delegate to the other item in the comparison
+
+    if TYPE_CHECKING:
+        # We put `__init_subclass__` in a TYPE_CHECKING block because, even though we want the type-checking benefits
+        # described in the signature of `__init_subclass__` below, we don't want to modify the default behavior of
+        # subclass initialization.
+
+        def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]):
+            """This signature is included purely to help type-checkers check arguments to class declaration, which
+            provides a way to conveniently set model_config key/value pairs.
+
+            ```python
+            from pydantic import BaseModel
+
+            class MyModel(BaseModel, extra='allow'): ...
+            ```
+
+            However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any
+            of the config arguments, and will only receive any keyword arguments passed during class initialization
+            that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.)
+
+            Args:
+                **kwargs: Keyword arguments passed to the class definition, which set model_config
+
+            Note:
+                You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called
+                *after* the class is fully initialized.
+            """
+
+    def __iter__(self) -> TupleGenerator:
+        """So `dict(model)` works."""
+        yield from [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')]
+        extra = self.__pydantic_extra__
+        if extra:
+            yield from extra.items()
+
+    def __repr__(self) -> str:
+        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
+
+    def __repr_args__(self) -> _repr.ReprArgs:
+        for k, v in self.__dict__.items():
+            field = self.__pydantic_fields__.get(k)
+            if field and field.repr:
+                if v is not self:
+                    yield k, v
+                else:
+                    yield k, self.__repr_recursion__(v)
+        # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.
+        # This can happen if a `ValidationError` is raised during initialization and the instance's
+        # repr is generated as part of the exception handling. Therefore, we use `getattr` here
+        # with a fallback, even though the type hints indicate the attribute will always be present.
+        try:
+            pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
+        except AttributeError:
+            pydantic_extra = None
+
+        if pydantic_extra is not None:
+            yield from ((k, v) for k, v in pydantic_extra.items())
+        yield from ((k, getattr(self, k)) for k, v in self.__pydantic_computed_fields__.items() if v.repr)
+
+    # take logic from `_repr.Representation` without the side effects of inheritance, see #5740
+    __repr_name__ = _repr.Representation.__repr_name__
+    __repr_recursion__ = _repr.Representation.__repr_recursion__
+    __repr_str__ = _repr.Representation.__repr_str__
+    __pretty__ = _repr.Representation.__pretty__
+    __rich_repr__ = _repr.Representation.__rich_repr__
+
+    def __str__(self) -> str:
+        return self.__repr_str__(' ')
+
+    # ##### Deprecated methods from v1 #####
+    @property
+    @typing_extensions.deprecated(
+        'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=None
+    )
+    def __fields__(self) -> dict[str, FieldInfo]:
+        warnings.warn(
+            'The `__fields__` attribute is deprecated, use `model_fields` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return self.model_fields
+
+    @property
+    @typing_extensions.deprecated(
+        'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
+        category=None,
+    )
+    def __fields_set__(self) -> set[str]:
+        warnings.warn(
+            'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return self.__pydantic_fields_set__
+
+    @typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.', category=None)
+    def dict(  # noqa: D102
+        self,
+        *,
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+    ) -> Dict[str, Any]:  # noqa UP006
+        warnings.warn(
+            'The `dict` method is deprecated; use `model_dump` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return self.model_dump(
+            include=include,
+            exclude=exclude,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+        )
+
+    @typing_extensions.deprecated('The `json` method is deprecated; use `model_dump_json` instead.', category=None)
+    def json(  # noqa: D102
+        self,
+        *,
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        encoder: Callable[[Any], Any] | None = PydanticUndefined,  # type: ignore[assignment]
+        models_as_dict: bool = PydanticUndefined,  # type: ignore[assignment]
+        **dumps_kwargs: Any,
+    ) -> str:
+        warnings.warn(
+            'The `json` method is deprecated; use `model_dump_json` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        if encoder is not PydanticUndefined:
+            raise TypeError('The `encoder` argument is no longer supported; use field serializers instead.')
+        if models_as_dict is not PydanticUndefined:
+            raise TypeError('The `models_as_dict` argument is no longer supported; use a model serializer instead.')
+        if dumps_kwargs:
+            raise TypeError('`dumps_kwargs` keyword arguments are no longer supported.')
+        return self.model_dump_json(
+            include=include,
+            exclude=exclude,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+        )
+
+    @classmethod
+    @typing_extensions.deprecated('The `parse_obj` method is deprecated; use `model_validate` instead.', category=None)
+    def parse_obj(cls, obj: Any) -> Self:  # noqa: D102
+        warnings.warn(
+            'The `parse_obj` method is deprecated; use `model_validate` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return cls.model_validate(obj)
+
+    @classmethod
+    @typing_extensions.deprecated(
+        'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
+        'otherwise load the data then use `model_validate` instead.',
+        category=None,
+    )
+    def parse_raw(  # noqa: D102
+        cls,
+        b: str | bytes,
+        *,
+        content_type: str | None = None,
+        encoding: str = 'utf8',
+        proto: DeprecatedParseProtocol | None = None,
+        allow_pickle: bool = False,
+    ) -> Self:  # pragma: no cover
+        warnings.warn(
+            'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
+            'otherwise load the data then use `model_validate` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        from .deprecated import parse
+
+        try:
+            obj = parse.load_str_bytes(
+                b,
+                proto=proto,
+                content_type=content_type,
+                encoding=encoding,
+                allow_pickle=allow_pickle,
+            )
+        except (ValueError, TypeError) as exc:
+            import json
+
+            # try to match V1
+            if isinstance(exc, UnicodeDecodeError):
+                type_str = 'value_error.unicodedecode'
+            elif isinstance(exc, json.JSONDecodeError):
+                type_str = 'value_error.jsondecode'
+            elif isinstance(exc, ValueError):
+                type_str = 'value_error'
+            else:
+                type_str = 'type_error'
+
+            # ctx is missing here, but since we've added `input` to the error, we're not pretending it's the same
+            error: pydantic_core.InitErrorDetails = {
+                # The type: ignore on the next line is to ignore the requirement of LiteralString
+                'type': pydantic_core.PydanticCustomError(type_str, str(exc)),  # type: ignore
+                'loc': ('__root__',),
+                'input': b,
+            }
+            raise pydantic_core.ValidationError.from_exception_data(cls.__name__, [error])
+        return cls.model_validate(obj)
+
+    @classmethod
+    @typing_extensions.deprecated(
+        'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
+        'use `model_validate_json`, otherwise `model_validate` instead.',
+        category=None,
+    )
+    def parse_file(  # noqa: D102
+        cls,
+        path: str | Path,
+        *,
+        content_type: str | None = None,
+        encoding: str = 'utf8',
+        proto: DeprecatedParseProtocol | None = None,
+        allow_pickle: bool = False,
+    ) -> Self:
+        warnings.warn(
+            'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
+            'use `model_validate_json`, otherwise `model_validate` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        from .deprecated import parse
+
+        obj = parse.load_file(
+            path,
+            proto=proto,
+            content_type=content_type,
+            encoding=encoding,
+            allow_pickle=allow_pickle,
+        )
+        return cls.parse_obj(obj)
+
+    @classmethod
+    @typing_extensions.deprecated(
+        'The `from_orm` method is deprecated; set '
+        "`model_config['from_attributes']=True` and use `model_validate` instead.",
+        category=None,
+    )
+    def from_orm(cls, obj: Any) -> Self:  # noqa: D102
+        warnings.warn(
+            'The `from_orm` method is deprecated; set '
+            "`model_config['from_attributes']=True` and use `model_validate` instead.",
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        if not cls.model_config.get('from_attributes', None):
+            raise PydanticUserError(
+                'You must set the config attribute `from_attributes=True` to use from_orm', code=None
+            )
+        return cls.model_validate(obj)
+
+    @classmethod
+    @typing_extensions.deprecated('The `construct` method is deprecated; use `model_construct` instead.', category=None)
+    def construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: D102
+        warnings.warn(
+            'The `construct` method is deprecated; use `model_construct` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return cls.model_construct(_fields_set=_fields_set, **values)
+
+    @typing_extensions.deprecated(
+        'The `copy` method is deprecated; use `model_copy` instead. '
+        'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
+        category=None,
+    )
+    def copy(
+        self,
+        *,
+        include: AbstractSetIntStr | MappingIntStrAny | None = None,
+        exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
+        update: Dict[str, Any] | None = None,  # noqa UP006
+        deep: bool = False,
+    ) -> Self:  # pragma: no cover
+        """Returns a copy of the model.
+
+        !!! warning "Deprecated"
+            This method is now deprecated; use `model_copy` instead.
+
+        If you need `include` or `exclude`, use:
+
+        ```python {test="skip" lint="skip"}
+        data = self.model_dump(include=include, exclude=exclude, round_trip=True)
+        data = {**data, **(update or {})}
+        copied = self.model_validate(data)
+        ```
+
+        Args:
+            include: Optional set or mapping specifying which fields to include in the copied model.
+            exclude: Optional set or mapping specifying which fields to exclude in the copied model.
+            update: Optional dictionary of field-value pairs to override field values in the copied model.
+            deep: If True, the values of fields that are Pydantic models will be deep-copied.
+
+        Returns:
+            A copy of the model with included, excluded and updated fields as specified.
+        """
+        warnings.warn(
+            'The `copy` method is deprecated; use `model_copy` instead. '
+            'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        from .deprecated import copy_internals
+
+        values = dict(
+            copy_internals._iter(
+                self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
+            ),
+            **(update or {}),
+        )
+        if self.__pydantic_private__ is None:
+            private = None
+        else:
+            private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}
+
+        if self.__pydantic_extra__ is None:
+            extra: dict[str, Any] | None = None
+        else:
+            extra = self.__pydantic_extra__.copy()
+            for k in list(self.__pydantic_extra__):
+                if k not in values:  # k was in the exclude
+                    extra.pop(k)
+            for k in list(values):
+                if k in self.__pydantic_extra__:  # k must have come from extra
+                    extra[k] = values.pop(k)
+
+        # new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
+        if update:
+            fields_set = self.__pydantic_fields_set__ | update.keys()
+        else:
+            fields_set = set(self.__pydantic_fields_set__)
+
+        # removing excluded fields from `__pydantic_fields_set__`
+        if exclude:
+            fields_set -= set(exclude)
+
+        return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)
+
+    @classmethod
+    @typing_extensions.deprecated('The `schema` method is deprecated; use `model_json_schema` instead.', category=None)
+    def schema(  # noqa: D102
+        cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
+    ) -> Dict[str, Any]:  # noqa UP006
+        warnings.warn(
+            'The `schema` method is deprecated; use `model_json_schema` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template)
+
+    @classmethod
+    @typing_extensions.deprecated(
+        'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
+        category=None,
+    )
+    def schema_json(  # noqa: D102
+        cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
+    ) -> str:  # pragma: no cover
+        warnings.warn(
+            'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        import json
+
+        from .deprecated.json import pydantic_encoder
+
+        return json.dumps(
+            cls.model_json_schema(by_alias=by_alias, ref_template=ref_template),
+            default=pydantic_encoder,
+            **dumps_kwargs,
+        )
+
+    @classmethod
+    @typing_extensions.deprecated('The `validate` method is deprecated; use `model_validate` instead.', category=None)
+    def validate(cls, value: Any) -> Self:  # noqa: D102
+        warnings.warn(
+            'The `validate` method is deprecated; use `model_validate` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return cls.model_validate(value)
+
+    @classmethod
+    @typing_extensions.deprecated(
+        'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
+        category=None,
+    )
+    def update_forward_refs(cls, **localns: Any) -> None:  # noqa: D102
+        warnings.warn(
+            'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        if localns:  # pragma: no cover
+            raise TypeError('`localns` arguments are not longer accepted.')
+        cls.model_rebuild(force=True)
+
+    @typing_extensions.deprecated(
+        'The private method `_iter` will be removed and should no longer be used.', category=None
+    )
+    def _iter(self, *args: Any, **kwargs: Any) -> Any:
+        warnings.warn(
+            'The private method `_iter` will be removed and should no longer be used.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        from .deprecated import copy_internals
+
+        return copy_internals._iter(self, *args, **kwargs)
+
+    @typing_extensions.deprecated(
+        'The private method `_copy_and_set_values` will be removed and should no longer be used.',
+        category=None,
+    )
+    def _copy_and_set_values(self, *args: Any, **kwargs: Any) -> Any:
+        warnings.warn(
+            'The private method `_copy_and_set_values` will be removed and should no longer be used.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        from .deprecated import copy_internals
+
+        return copy_internals._copy_and_set_values(self, *args, **kwargs)
+
+    @classmethod
+    @typing_extensions.deprecated(
+        'The private method `_get_value` will be removed and should no longer be used.',
+        category=None,
+    )
+    def _get_value(cls, *args: Any, **kwargs: Any) -> Any:
+        warnings.warn(
+            'The private method `_get_value` will be removed and should no longer be used.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        from .deprecated import copy_internals
+
+        return copy_internals._get_value(cls, *args, **kwargs)
+
+    @typing_extensions.deprecated(
+        'The private method `_calculate_keys` will be removed and should no longer be used.',
+        category=None,
+    )
+    def _calculate_keys(self, *args: Any, **kwargs: Any) -> Any:
+        warnings.warn(
+            'The private method `_calculate_keys` will be removed and should no longer be used.',
+            category=PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        from .deprecated import copy_internals
+
+        return copy_internals._calculate_keys(self, *args, **kwargs)
+
+
+ModelT = TypeVar('ModelT', bound=BaseModel)
+
+
+@overload
+def create_model(
+    model_name: str,
+    /,
+    *,
+    __config__: ConfigDict | None = None,
+    __doc__: str | None = None,
+    __base__: None = None,
+    __module__: str = __name__,
+    __validators__: dict[str, Callable[..., Any]] | None = None,
+    __cls_kwargs__: dict[str, Any] | None = None,
+    **field_definitions: Any,
+) -> type[BaseModel]: ...
+
+
+@overload
+def create_model(
+    model_name: str,
+    /,
+    *,
+    __config__: ConfigDict | None = None,
+    __doc__: str | None = None,
+    __base__: type[ModelT] | tuple[type[ModelT], ...],
+    __module__: str = __name__,
+    __validators__: dict[str, Callable[..., Any]] | None = None,
+    __cls_kwargs__: dict[str, Any] | None = None,
+    **field_definitions: Any,
+) -> type[ModelT]: ...
+
+
+def create_model(  # noqa: C901
+    model_name: str,
+    /,
+    *,
+    __config__: ConfigDict | None = None,
+    __doc__: str | None = None,
+    __base__: type[ModelT] | tuple[type[ModelT], ...] | None = None,
+    __module__: str | None = None,
+    __validators__: dict[str, Callable[..., Any]] | None = None,
+    __cls_kwargs__: dict[str, Any] | None = None,
+    __slots__: tuple[str, ...] | None = None,
+    **field_definitions: Any,
+) -> type[ModelT]:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/#dynamic-model-creation
+
+    Dynamically creates and returns a new Pydantic model, in other words, `create_model` dynamically creates a
+    subclass of [`BaseModel`][pydantic.BaseModel].
+
+    Args:
+        model_name: The name of the newly created model.
+        __config__: The configuration of the new model.
+        __doc__: The docstring of the new model.
+        __base__: The base class or classes for the new model.
+        __module__: The name of the module that the model belongs to;
+            if `None`, the value is taken from `sys._getframe(1)`
+        __validators__: A dictionary of methods that validate fields. The keys are the names of the validation methods to
+            be added to the model, and the values are the validation methods themselves. You can read more about functional
+            validators [here](https://docs.pydantic.dev/2.9/concepts/validators/#field-validators).
+        __cls_kwargs__: A dictionary of keyword arguments for class creation, such as `metaclass`.
+        __slots__: Deprecated. Should not be passed to `create_model`.
+        **field_definitions: Attributes of the new model. They should be passed in the format:
+            `<name>=(<type>, <default value>)`, `<name>=(<type>, <FieldInfo>)`, or `typing.Annotated[<type>, <FieldInfo>]`.
+            Any additional metadata in `typing.Annotated[<type>, <FieldInfo>, ...]` will be ignored.
+            Note, `FieldInfo` instances should be created via `pydantic.Field(...)`.
+            Initializing `FieldInfo` instances directly is not supported.
+
+    Returns:
+        The new [model][pydantic.BaseModel].
+
+    Raises:
+        PydanticUserError: If `__base__` and `__config__` are both passed.
+    """
+    if __slots__ is not None:
+        # __slots__ will be ignored from here on
+        warnings.warn('__slots__ should not be passed to create_model', RuntimeWarning)
+
+    if __base__ is not None:
+        if __config__ is not None:
+            raise PydanticUserError(
+                'to avoid confusion `__config__` and `__base__` cannot be used together',
+                code='create-model-config-base',
+            )
+        if not isinstance(__base__, tuple):
+            __base__ = (__base__,)
+    else:
+        __base__ = (cast('type[ModelT]', BaseModel),)
+
+    __cls_kwargs__ = __cls_kwargs__ or {}
+
+    fields = {}
+    annotations = {}
+
+    for f_name, f_def in field_definitions.items():
+        if not _fields.is_valid_field_name(f_name):
+            warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning)
+        if isinstance(f_def, tuple):
+            f_def = cast('tuple[str, Any]', f_def)
+            try:
+                f_annotation, f_value = f_def
+            except ValueError as e:
+                raise PydanticUserError(
+                    'Field definitions should be a `(<type>, <default>)`.',
+                    code='create-model-field-definitions',
+                ) from e
+
+        elif _typing_extra.is_annotated(f_def):
+            (f_annotation, f_value, *_) = typing_extensions.get_args(
+                f_def
+            )  # first two input are expected from Annotated, refer to https://docs.python.org/3/library/typing.html#typing.Annotated
+            FieldInfo = _import_utils.import_cached_field_info()
+
+            if not isinstance(f_value, FieldInfo):
+                raise PydanticUserError(
+                    'Field definitions should be a Annotated[<type>, <FieldInfo>]',
+                    code='create-model-field-definitions',
+                )
+
+        else:
+            f_annotation, f_value = None, f_def
+
+        if f_annotation:
+            annotations[f_name] = f_annotation
+        fields[f_name] = f_value
+
+    if __module__ is None:
+        f = sys._getframe(1)
+        __module__ = f.f_globals['__name__']
+
+    namespace: dict[str, Any] = {'__annotations__': annotations, '__module__': __module__}
+    if __doc__:
+        namespace.update({'__doc__': __doc__})
+    if __validators__:
+        namespace.update(__validators__)
+    namespace.update(fields)
+    if __config__:
+        namespace['model_config'] = _config.ConfigWrapper(__config__).config_dict
+    resolved_bases = types.resolve_bases(__base__)
+    meta, ns, kwds = types.prepare_class(model_name, resolved_bases, kwds=__cls_kwargs__)
+    if resolved_bases is not __base__:
+        ns['__orig_bases__'] = __base__
+    namespace.update(ns)
+
+    return meta(
+        model_name,
+        resolved_bases,
+        namespace,
+        __pydantic_reset_parent_namespace__=False,
+        _create_model_module=__module__,
+        **kwds,
+    )
+
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/mypy.py b/.venv/lib/python3.12/site-packages/pydantic/mypy.py
new file mode 100644
index 00000000..9ad3a3ff
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/mypy.py
@@ -0,0 +1,1320 @@
+"""This module includes classes and functions designed specifically for use with the mypy plugin."""
+
+from __future__ import annotations
+
+import sys
+from configparser import ConfigParser
+from typing import Any, Callable, Iterator
+
+from mypy.errorcodes import ErrorCode
+from mypy.expandtype import expand_type, expand_type_by_instance
+from mypy.nodes import (
+    ARG_NAMED,
+    ARG_NAMED_OPT,
+    ARG_OPT,
+    ARG_POS,
+    ARG_STAR2,
+    INVARIANT,
+    MDEF,
+    Argument,
+    AssignmentStmt,
+    Block,
+    CallExpr,
+    ClassDef,
+    Context,
+    Decorator,
+    DictExpr,
+    EllipsisExpr,
+    Expression,
+    FuncDef,
+    IfStmt,
+    JsonDict,
+    MemberExpr,
+    NameExpr,
+    PassStmt,
+    PlaceholderNode,
+    RefExpr,
+    Statement,
+    StrExpr,
+    SymbolTableNode,
+    TempNode,
+    TypeAlias,
+    TypeInfo,
+    Var,
+)
+from mypy.options import Options
+from mypy.plugin import (
+    CheckerPluginInterface,
+    ClassDefContext,
+    MethodContext,
+    Plugin,
+    ReportConfigContext,
+    SemanticAnalyzerPluginInterface,
+)
+from mypy.plugins.common import (
+    deserialize_and_fixup_type,
+)
+from mypy.semanal import set_callable_name
+from mypy.server.trigger import make_wildcard_trigger
+from mypy.state import state
+from mypy.typeops import map_type_from_supertype
+from mypy.types import (
+    AnyType,
+    CallableType,
+    Instance,
+    NoneType,
+    Type,
+    TypeOfAny,
+    TypeType,
+    TypeVarType,
+    UnionType,
+    get_proper_type,
+)
+from mypy.typevars import fill_typevars
+from mypy.util import get_unique_redefinition_name
+from mypy.version import __version__ as mypy_version
+
+from pydantic._internal import _fields
+from pydantic.version import parse_mypy_version
+
+CONFIGFILE_KEY = 'pydantic-mypy'
+METADATA_KEY = 'pydantic-mypy-metadata'
+BASEMODEL_FULLNAME = 'pydantic.main.BaseModel'
+BASESETTINGS_FULLNAME = 'pydantic_settings.main.BaseSettings'
+ROOT_MODEL_FULLNAME = 'pydantic.root_model.RootModel'
+MODEL_METACLASS_FULLNAME = 'pydantic._internal._model_construction.ModelMetaclass'
+FIELD_FULLNAME = 'pydantic.fields.Field'
+DATACLASS_FULLNAME = 'pydantic.dataclasses.dataclass'
+MODEL_VALIDATOR_FULLNAME = 'pydantic.functional_validators.model_validator'
+DECORATOR_FULLNAMES = {
+    'pydantic.functional_validators.field_validator',
+    'pydantic.functional_validators.model_validator',
+    'pydantic.functional_serializers.serializer',
+    'pydantic.functional_serializers.model_serializer',
+    'pydantic.deprecated.class_validators.validator',
+    'pydantic.deprecated.class_validators.root_validator',
+}
+
+
+MYPY_VERSION_TUPLE = parse_mypy_version(mypy_version)
+BUILTINS_NAME = 'builtins'
+
+# Increment version if plugin changes and mypy caches should be invalidated
+__version__ = 2
+
+
+def plugin(version: str) -> type[Plugin]:
+    """`version` is the mypy version string.
+
+    We might want to use this to print a warning if the mypy version being used is
+    newer, or especially older, than we expect (or need).
+
+    Args:
+        version: The mypy version string.
+
+    Return:
+        The Pydantic mypy plugin type.
+    """
+    return PydanticPlugin
+
+
+class PydanticPlugin(Plugin):
+    """The Pydantic mypy plugin."""
+
+    def __init__(self, options: Options) -> None:
+        self.plugin_config = PydanticPluginConfig(options)
+        self._plugin_data = self.plugin_config.to_data()
+        super().__init__(options)
+
+    def get_base_class_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:
+        """Update Pydantic model class."""
+        sym = self.lookup_fully_qualified(fullname)
+        if sym and isinstance(sym.node, TypeInfo):  # pragma: no branch
+            # No branching may occur if the mypy cache has not been cleared
+            if any(base.fullname == BASEMODEL_FULLNAME for base in sym.node.mro):
+                return self._pydantic_model_class_maker_callback
+        return None
+
+    def get_metaclass_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:
+        """Update Pydantic `ModelMetaclass` definition."""
+        if fullname == MODEL_METACLASS_FULLNAME:
+            return self._pydantic_model_metaclass_marker_callback
+        return None
+
+    def get_method_hook(self, fullname: str) -> Callable[[MethodContext], Type] | None:
+        """Adjust return type of `from_orm` method call."""
+        if fullname.endswith('.from_orm'):
+            return from_attributes_callback
+        return None
+
+    def report_config_data(self, ctx: ReportConfigContext) -> dict[str, Any]:
+        """Return all plugin config data.
+
+        Used by mypy to determine if cache needs to be discarded.
+        """
+        return self._plugin_data
+
+    def _pydantic_model_class_maker_callback(self, ctx: ClassDefContext) -> None:
+        transformer = PydanticModelTransformer(ctx.cls, ctx.reason, ctx.api, self.plugin_config)
+        transformer.transform()
+
+    def _pydantic_model_metaclass_marker_callback(self, ctx: ClassDefContext) -> None:
+        """Reset dataclass_transform_spec attribute of ModelMetaclass.
+
+        Let the plugin handle it. This behavior can be disabled
+        if 'debug_dataclass_transform' is set to True', for testing purposes.
+        """
+        if self.plugin_config.debug_dataclass_transform:
+            return
+        info_metaclass = ctx.cls.info.declared_metaclass
+        assert info_metaclass, "callback not passed from 'get_metaclass_hook'"
+        if getattr(info_metaclass.type, 'dataclass_transform_spec', None):
+            info_metaclass.type.dataclass_transform_spec = None
+
+
+class PydanticPluginConfig:
+    """A Pydantic mypy plugin config holder.
+
+    Attributes:
+        init_forbid_extra: Whether to add a `**kwargs` at the end of the generated `__init__` signature.
+        init_typed: Whether to annotate fields in the generated `__init__`.
+        warn_required_dynamic_aliases: Whether to raise required dynamic aliases error.
+        debug_dataclass_transform: Whether to not reset `dataclass_transform_spec` attribute
+            of `ModelMetaclass` for testing purposes.
+    """
+
+    __slots__ = (
+        'init_forbid_extra',
+        'init_typed',
+        'warn_required_dynamic_aliases',
+        'debug_dataclass_transform',
+    )
+    init_forbid_extra: bool
+    init_typed: bool
+    warn_required_dynamic_aliases: bool
+    debug_dataclass_transform: bool  # undocumented
+
+    def __init__(self, options: Options) -> None:
+        if options.config_file is None:  # pragma: no cover
+            return
+
+        toml_config = parse_toml(options.config_file)
+        if toml_config is not None:
+            config = toml_config.get('tool', {}).get('pydantic-mypy', {})
+            for key in self.__slots__:
+                setting = config.get(key, False)
+                if not isinstance(setting, bool):
+                    raise ValueError(f'Configuration value must be a boolean for key: {key}')
+                setattr(self, key, setting)
+        else:
+            plugin_config = ConfigParser()
+            plugin_config.read(options.config_file)
+            for key in self.__slots__:
+                setting = plugin_config.getboolean(CONFIGFILE_KEY, key, fallback=False)
+                setattr(self, key, setting)
+
+    def to_data(self) -> dict[str, Any]:
+        """Returns a dict of config names to their values."""
+        return {key: getattr(self, key) for key in self.__slots__}
+
+
+def from_attributes_callback(ctx: MethodContext) -> Type:
+    """Raise an error if from_attributes is not enabled."""
+    model_type: Instance
+    ctx_type = ctx.type
+    if isinstance(ctx_type, TypeType):
+        ctx_type = ctx_type.item
+    if isinstance(ctx_type, CallableType) and isinstance(ctx_type.ret_type, Instance):
+        model_type = ctx_type.ret_type  # called on the class
+    elif isinstance(ctx_type, Instance):
+        model_type = ctx_type  # called on an instance (unusual, but still valid)
+    else:  # pragma: no cover
+        detail = f'ctx.type: {ctx_type} (of type {ctx_type.__class__.__name__})'
+        error_unexpected_behavior(detail, ctx.api, ctx.context)
+        return ctx.default_return_type
+    pydantic_metadata = model_type.type.metadata.get(METADATA_KEY)
+    if pydantic_metadata is None:
+        return ctx.default_return_type
+    if not any(base.fullname == BASEMODEL_FULLNAME for base in model_type.type.mro):
+        # not a Pydantic v2 model
+        return ctx.default_return_type
+    from_attributes = pydantic_metadata.get('config', {}).get('from_attributes')
+    if from_attributes is not True:
+        error_from_attributes(model_type.type.name, ctx.api, ctx.context)
+    return ctx.default_return_type
+
+
+class PydanticModelField:
+    """Based on mypy.plugins.dataclasses.DataclassAttribute."""
+
+    def __init__(
+        self,
+        name: str,
+        alias: str | None,
+        is_frozen: bool,
+        has_dynamic_alias: bool,
+        has_default: bool,
+        strict: bool | None,
+        line: int,
+        column: int,
+        type: Type | None,
+        info: TypeInfo,
+    ):
+        self.name = name
+        self.alias = alias
+        self.is_frozen = is_frozen
+        self.has_dynamic_alias = has_dynamic_alias
+        self.has_default = has_default
+        self.strict = strict
+        self.line = line
+        self.column = column
+        self.type = type
+        self.info = info
+
+    def to_argument(
+        self,
+        current_info: TypeInfo,
+        typed: bool,
+        model_strict: bool,
+        force_optional: bool,
+        use_alias: bool,
+        api: SemanticAnalyzerPluginInterface,
+        force_typevars_invariant: bool,
+        is_root_model_root: bool,
+    ) -> Argument:
+        """Based on mypy.plugins.dataclasses.DataclassAttribute.to_argument."""
+        variable = self.to_var(current_info, api, use_alias, force_typevars_invariant)
+
+        strict = model_strict if self.strict is None else self.strict
+        if typed or strict:
+            type_annotation = self.expand_type(current_info, api)
+        else:
+            type_annotation = AnyType(TypeOfAny.explicit)
+
+        return Argument(
+            variable=variable,
+            type_annotation=type_annotation,
+            initializer=None,
+            kind=ARG_OPT
+            if is_root_model_root
+            else (ARG_NAMED_OPT if force_optional or self.has_default else ARG_NAMED),
+        )
+
+    def expand_type(
+        self, current_info: TypeInfo, api: SemanticAnalyzerPluginInterface, force_typevars_invariant: bool = False
+    ) -> Type | None:
+        """Based on mypy.plugins.dataclasses.DataclassAttribute.expand_type."""
+        if force_typevars_invariant:
+            # In some cases, mypy will emit an error "Cannot use a covariant type variable as a parameter"
+            # To prevent that, we add an option to replace typevars with invariant ones while building certain
+            # method signatures (in particular, `__init__`). There may be a better way to do this, if this causes
+            # us problems in the future, we should look into why the dataclasses plugin doesn't have this issue.
+            if isinstance(self.type, TypeVarType):
+                modified_type = self.type.copy_modified()
+                modified_type.variance = INVARIANT
+                self.type = modified_type
+
+        if self.type is not None and self.info.self_type is not None:
+            # In general, it is not safe to call `expand_type()` during semantic analysis,
+            # however this plugin is called very late, so all types should be fully ready.
+            # Also, it is tricky to avoid eager expansion of Self types here (e.g. because
+            # we serialize attributes).
+            with state.strict_optional_set(api.options.strict_optional):
+                filled_with_typevars = fill_typevars(current_info)
+                # Cannot be TupleType as current_info represents a Pydantic model:
+                assert isinstance(filled_with_typevars, Instance)
+                if force_typevars_invariant:
+                    for arg in filled_with_typevars.args:
+                        if isinstance(arg, TypeVarType):
+                            arg.variance = INVARIANT
+                return expand_type(self.type, {self.info.self_type.id: filled_with_typevars})
+        return self.type
+
+    def to_var(
+        self,
+        current_info: TypeInfo,
+        api: SemanticAnalyzerPluginInterface,
+        use_alias: bool,
+        force_typevars_invariant: bool = False,
+    ) -> Var:
+        """Based on mypy.plugins.dataclasses.DataclassAttribute.to_var."""
+        if use_alias and self.alias is not None:
+            name = self.alias
+        else:
+            name = self.name
+
+        return Var(name, self.expand_type(current_info, api, force_typevars_invariant))
+
+    def serialize(self) -> JsonDict:
+        """Based on mypy.plugins.dataclasses.DataclassAttribute.serialize."""
+        assert self.type
+        return {
+            'name': self.name,
+            'alias': self.alias,
+            'is_frozen': self.is_frozen,
+            'has_dynamic_alias': self.has_dynamic_alias,
+            'has_default': self.has_default,
+            'strict': self.strict,
+            'line': self.line,
+            'column': self.column,
+            'type': self.type.serialize(),
+        }
+
+    @classmethod
+    def deserialize(cls, info: TypeInfo, data: JsonDict, api: SemanticAnalyzerPluginInterface) -> PydanticModelField:
+        """Based on mypy.plugins.dataclasses.DataclassAttribute.deserialize."""
+        data = data.copy()
+        typ = deserialize_and_fixup_type(data.pop('type'), api)
+        return cls(type=typ, info=info, **data)
+
+    def expand_typevar_from_subtype(self, sub_type: TypeInfo, api: SemanticAnalyzerPluginInterface) -> None:
+        """Expands type vars in the context of a subtype when an attribute is inherited
+        from a generic super type.
+        """
+        if self.type is not None:
+            with state.strict_optional_set(api.options.strict_optional):
+                self.type = map_type_from_supertype(self.type, sub_type, self.info)
+
+
+class PydanticModelClassVar:
+    """Based on mypy.plugins.dataclasses.DataclassAttribute.
+
+    ClassVars are ignored by subclasses.
+
+    Attributes:
+        name: the ClassVar name
+    """
+
+    def __init__(self, name):
+        self.name = name
+
+    @classmethod
+    def deserialize(cls, data: JsonDict) -> PydanticModelClassVar:
+        """Based on mypy.plugins.dataclasses.DataclassAttribute.deserialize."""
+        data = data.copy()
+        return cls(**data)
+
+    def serialize(self) -> JsonDict:
+        """Based on mypy.plugins.dataclasses.DataclassAttribute.serialize."""
+        return {
+            'name': self.name,
+        }
+
+
+class PydanticModelTransformer:
+    """Transform the BaseModel subclass according to the plugin settings.
+
+    Attributes:
+        tracked_config_fields: A set of field configs that the plugin has to track their value.
+    """
+
+    tracked_config_fields: set[str] = {
+        'extra',
+        'frozen',
+        'from_attributes',
+        'populate_by_name',
+        'alias_generator',
+        'strict',
+    }
+
+    def __init__(
+        self,
+        cls: ClassDef,
+        reason: Expression | Statement,
+        api: SemanticAnalyzerPluginInterface,
+        plugin_config: PydanticPluginConfig,
+    ) -> None:
+        self._cls = cls
+        self._reason = reason
+        self._api = api
+
+        self.plugin_config = plugin_config
+
+    def transform(self) -> bool:
+        """Configures the BaseModel subclass according to the plugin settings.
+
+        In particular:
+
+        * determines the model config and fields,
+        * adds a fields-aware signature for the initializer and construct methods
+        * freezes the class if frozen = True
+        * stores the fields, config, and if the class is settings in the mypy metadata for access by subclasses
+        """
+        info = self._cls.info
+        is_root_model = any(ROOT_MODEL_FULLNAME in base.fullname for base in info.mro[:-1])
+        config = self.collect_config()
+        fields, class_vars = self.collect_fields_and_class_vars(config, is_root_model)
+        if fields is None or class_vars is None:
+            # Some definitions are not ready. We need another pass.
+            return False
+        for field in fields:
+            if field.type is None:
+                return False
+
+        is_settings = any(base.fullname == BASESETTINGS_FULLNAME for base in info.mro[:-1])
+        self.add_initializer(fields, config, is_settings, is_root_model)
+        self.add_model_construct_method(fields, config, is_settings, is_root_model)
+        self.set_frozen(fields, self._api, frozen=config.frozen is True)
+
+        self.adjust_decorator_signatures()
+
+        info.metadata[METADATA_KEY] = {
+            'fields': {field.name: field.serialize() for field in fields},
+            'class_vars': {class_var.name: class_var.serialize() for class_var in class_vars},
+            'config': config.get_values_dict(),
+        }
+
+        return True
+
+    def adjust_decorator_signatures(self) -> None:
+        """When we decorate a function `f` with `pydantic.validator(...)`, `pydantic.field_validator`
+        or `pydantic.serializer(...)`, mypy sees `f` as a regular method taking a `self` instance,
+        even though pydantic internally wraps `f` with `classmethod` if necessary.
+
+        Teach mypy this by marking any function whose outermost decorator is a `validator()`,
+        `field_validator()` or `serializer()` call as a `classmethod`.
+        """
+        for sym in self._cls.info.names.values():
+            if isinstance(sym.node, Decorator):
+                first_dec = sym.node.original_decorators[0]
+                if (
+                    isinstance(first_dec, CallExpr)
+                    and isinstance(first_dec.callee, NameExpr)
+                    and first_dec.callee.fullname in DECORATOR_FULLNAMES
+                    # @model_validator(mode="after") is an exception, it expects a regular method
+                    and not (
+                        first_dec.callee.fullname == MODEL_VALIDATOR_FULLNAME
+                        and any(
+                            first_dec.arg_names[i] == 'mode' and isinstance(arg, StrExpr) and arg.value == 'after'
+                            for i, arg in enumerate(first_dec.args)
+                        )
+                    )
+                ):
+                    # TODO: Only do this if the first argument of the decorated function is `cls`
+                    sym.node.func.is_class = True
+
+    def collect_config(self) -> ModelConfigData:  # noqa: C901 (ignore complexity)
+        """Collects the values of the config attributes that are used by the plugin, accounting for parent classes."""
+        cls = self._cls
+        config = ModelConfigData()
+
+        has_config_kwargs = False
+        has_config_from_namespace = False
+
+        # Handle `class MyModel(BaseModel, <name>=<expr>, ...):`
+        for name, expr in cls.keywords.items():
+            config_data = self.get_config_update(name, expr)
+            if config_data:
+                has_config_kwargs = True
+                config.update(config_data)
+
+        # Handle `model_config`
+        stmt: Statement | None = None
+        for stmt in cls.defs.body:
+            if not isinstance(stmt, (AssignmentStmt, ClassDef)):
+                continue
+
+            if isinstance(stmt, AssignmentStmt):
+                lhs = stmt.lvalues[0]
+                if not isinstance(lhs, NameExpr) or lhs.name != 'model_config':
+                    continue
+
+                if isinstance(stmt.rvalue, CallExpr):  # calls to `dict` or `ConfigDict`
+                    for arg_name, arg in zip(stmt.rvalue.arg_names, stmt.rvalue.args):
+                        if arg_name is None:
+                            continue
+                        config.update(self.get_config_update(arg_name, arg, lax_extra=True))
+                elif isinstance(stmt.rvalue, DictExpr):  # dict literals
+                    for key_expr, value_expr in stmt.rvalue.items:
+                        if not isinstance(key_expr, StrExpr):
+                            continue
+                        config.update(self.get_config_update(key_expr.value, value_expr))
+
+            elif isinstance(stmt, ClassDef):
+                if stmt.name != 'Config':  # 'deprecated' Config-class
+                    continue
+                for substmt in stmt.defs.body:
+                    if not isinstance(substmt, AssignmentStmt):
+                        continue
+                    lhs = substmt.lvalues[0]
+                    if not isinstance(lhs, NameExpr):
+                        continue
+                    config.update(self.get_config_update(lhs.name, substmt.rvalue))
+
+            if has_config_kwargs:
+                self._api.fail(
+                    'Specifying config in two places is ambiguous, use either Config attribute or class kwargs',
+                    cls,
+                )
+                break
+
+            has_config_from_namespace = True
+
+        if has_config_kwargs or has_config_from_namespace:
+            if (
+                stmt
+                and config.has_alias_generator
+                and not config.populate_by_name
+                and self.plugin_config.warn_required_dynamic_aliases
+            ):
+                error_required_dynamic_aliases(self._api, stmt)
+
+        for info in cls.info.mro[1:]:  # 0 is the current class
+            if METADATA_KEY not in info.metadata:
+                continue
+
+            # Each class depends on the set of fields in its ancestors
+            self._api.add_plugin_dependency(make_wildcard_trigger(info.fullname))
+            for name, value in info.metadata[METADATA_KEY]['config'].items():
+                config.setdefault(name, value)
+        return config
+
+    def collect_fields_and_class_vars(
+        self, model_config: ModelConfigData, is_root_model: bool
+    ) -> tuple[list[PydanticModelField] | None, list[PydanticModelClassVar] | None]:
+        """Collects the fields for the model, accounting for parent classes."""
+        cls = self._cls
+
+        # First, collect fields and ClassVars belonging to any class in the MRO, ignoring duplicates.
+        #
+        # We iterate through the MRO in reverse because attrs defined in the parent must appear
+        # earlier in the attributes list than attrs defined in the child. See:
+        # https://docs.python.org/3/library/dataclasses.html#inheritance
+        #
+        # However, we also want fields defined in the subtype to override ones defined
+        # in the parent. We can implement this via a dict without disrupting the attr order
+        # because dicts preserve insertion order in Python 3.7+.
+        found_fields: dict[str, PydanticModelField] = {}
+        found_class_vars: dict[str, PydanticModelClassVar] = {}
+        for info in reversed(cls.info.mro[1:-1]):  # 0 is the current class, -2 is BaseModel, -1 is object
+            # if BASEMODEL_METADATA_TAG_KEY in info.metadata and BASEMODEL_METADATA_KEY not in info.metadata:
+            #     # We haven't processed the base class yet. Need another pass.
+            #     return None, None
+            if METADATA_KEY not in info.metadata:
+                continue
+
+            # Each class depends on the set of attributes in its dataclass ancestors.
+            self._api.add_plugin_dependency(make_wildcard_trigger(info.fullname))
+
+            for name, data in info.metadata[METADATA_KEY]['fields'].items():
+                field = PydanticModelField.deserialize(info, data, self._api)
+                # (The following comment comes directly from the dataclasses plugin)
+                # TODO: We shouldn't be performing type operations during the main
+                #       semantic analysis pass, since some TypeInfo attributes might
+                #       still be in flux. This should be performed in a later phase.
+                field.expand_typevar_from_subtype(cls.info, self._api)
+                found_fields[name] = field
+
+                sym_node = cls.info.names.get(name)
+                if sym_node and sym_node.node and not isinstance(sym_node.node, Var):
+                    self._api.fail(
+                        'BaseModel field may only be overridden by another field',
+                        sym_node.node,
+                    )
+            # Collect ClassVars
+            for name, data in info.metadata[METADATA_KEY]['class_vars'].items():
+                found_class_vars[name] = PydanticModelClassVar.deserialize(data)
+
+        # Second, collect fields and ClassVars belonging to the current class.
+        current_field_names: set[str] = set()
+        current_class_vars_names: set[str] = set()
+        for stmt in self._get_assignment_statements_from_block(cls.defs):
+            maybe_field = self.collect_field_or_class_var_from_stmt(stmt, model_config, found_class_vars)
+            if maybe_field is None:
+                continue
+
+            lhs = stmt.lvalues[0]
+            assert isinstance(lhs, NameExpr)  # collect_field_or_class_var_from_stmt guarantees this
+            if isinstance(maybe_field, PydanticModelField):
+                if is_root_model and lhs.name != 'root':
+                    error_extra_fields_on_root_model(self._api, stmt)
+                else:
+                    current_field_names.add(lhs.name)
+                    found_fields[lhs.name] = maybe_field
+            elif isinstance(maybe_field, PydanticModelClassVar):
+                current_class_vars_names.add(lhs.name)
+                found_class_vars[lhs.name] = maybe_field
+
+        return list(found_fields.values()), list(found_class_vars.values())
+
+    def _get_assignment_statements_from_if_statement(self, stmt: IfStmt) -> Iterator[AssignmentStmt]:
+        for body in stmt.body:
+            if not body.is_unreachable:
+                yield from self._get_assignment_statements_from_block(body)
+        if stmt.else_body is not None and not stmt.else_body.is_unreachable:
+            yield from self._get_assignment_statements_from_block(stmt.else_body)
+
+    def _get_assignment_statements_from_block(self, block: Block) -> Iterator[AssignmentStmt]:
+        for stmt in block.body:
+            if isinstance(stmt, AssignmentStmt):
+                yield stmt
+            elif isinstance(stmt, IfStmt):
+                yield from self._get_assignment_statements_from_if_statement(stmt)
+
+    def collect_field_or_class_var_from_stmt(  # noqa C901
+        self, stmt: AssignmentStmt, model_config: ModelConfigData, class_vars: dict[str, PydanticModelClassVar]
+    ) -> PydanticModelField | PydanticModelClassVar | None:
+        """Get pydantic model field from statement.
+
+        Args:
+            stmt: The statement.
+            model_config: Configuration settings for the model.
+            class_vars: ClassVars already known to be defined on the model.
+
+        Returns:
+            A pydantic model field if it could find the field in statement. Otherwise, `None`.
+        """
+        cls = self._cls
+
+        lhs = stmt.lvalues[0]
+        if not isinstance(lhs, NameExpr) or not _fields.is_valid_field_name(lhs.name) or lhs.name == 'model_config':
+            return None
+
+        if not stmt.new_syntax:
+            if (
+                isinstance(stmt.rvalue, CallExpr)
+                and isinstance(stmt.rvalue.callee, CallExpr)
+                and isinstance(stmt.rvalue.callee.callee, NameExpr)
+                and stmt.rvalue.callee.callee.fullname in DECORATOR_FULLNAMES
+            ):
+                # This is a (possibly-reused) validator or serializer, not a field
+                # In particular, it looks something like: my_validator = validator('my_field')(f)
+                # Eventually, we may want to attempt to respect model_config['ignored_types']
+                return None
+
+            if lhs.name in class_vars:
+                # Class vars are not fields and are not required to be annotated
+                return None
+
+            # The assignment does not have an annotation, and it's not anything else we recognize
+            error_untyped_fields(self._api, stmt)
+            return None
+
+        lhs = stmt.lvalues[0]
+        if not isinstance(lhs, NameExpr):
+            return None
+
+        if not _fields.is_valid_field_name(lhs.name) or lhs.name == 'model_config':
+            return None
+
+        sym = cls.info.names.get(lhs.name)
+        if sym is None:  # pragma: no cover
+            # This is likely due to a star import (see the dataclasses plugin for a more detailed explanation)
+            # This is the same logic used in the dataclasses plugin
+            return None
+
+        node = sym.node
+        if isinstance(node, PlaceholderNode):  # pragma: no cover
+            # See the PlaceholderNode docstring for more detail about how this can occur
+            # Basically, it is an edge case when dealing with complex import logic
+
+            # The dataclasses plugin now asserts this cannot happen, but I'd rather not error if it does..
+            return None
+
+        if isinstance(node, TypeAlias):
+            self._api.fail(
+                'Type aliases inside BaseModel definitions are not supported at runtime',
+                node,
+            )
+            # Skip processing this node. This doesn't match the runtime behaviour,
+            # but the only alternative would be to modify the SymbolTable,
+            # and it's a little hairy to do that in a plugin.
+            return None
+
+        if not isinstance(node, Var):  # pragma: no cover
+            # Don't know if this edge case still happens with the `is_valid_field` check above
+            # but better safe than sorry
+
+            # The dataclasses plugin now asserts this cannot happen, but I'd rather not error if it does..
+            return None
+
+        # x: ClassVar[int] is not a field
+        if node.is_classvar:
+            return PydanticModelClassVar(lhs.name)
+
+        # x: InitVar[int] is not supported in BaseModel
+        node_type = get_proper_type(node.type)
+        if isinstance(node_type, Instance) and node_type.type.fullname == 'dataclasses.InitVar':
+            self._api.fail(
+                'InitVar is not supported in BaseModel',
+                node,
+            )
+
+        has_default = self.get_has_default(stmt)
+        strict = self.get_strict(stmt)
+
+        if sym.type is None and node.is_final and node.is_inferred:
+            # This follows the logic from the dataclasses plugin. The following comment is taken verbatim:
+            #
+            # This is a special case, assignment like x: Final = 42 is classified
+            # annotated above, but mypy strips the `Final` turning it into x = 42.
+            # We do not support inferred types in dataclasses, so we can try inferring
+            # type for simple literals, and otherwise require an explicit type
+            # argument for Final[...].
+            typ = self._api.analyze_simple_literal_type(stmt.rvalue, is_final=True)
+            if typ:
+                node.type = typ
+            else:
+                self._api.fail(
+                    'Need type argument for Final[...] with non-literal default in BaseModel',
+                    stmt,
+                )
+                node.type = AnyType(TypeOfAny.from_error)
+
+        alias, has_dynamic_alias = self.get_alias_info(stmt)
+        if has_dynamic_alias and not model_config.populate_by_name and self.plugin_config.warn_required_dynamic_aliases:
+            error_required_dynamic_aliases(self._api, stmt)
+        is_frozen = self.is_field_frozen(stmt)
+
+        init_type = self._infer_dataclass_attr_init_type(sym, lhs.name, stmt)
+        return PydanticModelField(
+            name=lhs.name,
+            has_dynamic_alias=has_dynamic_alias,
+            has_default=has_default,
+            strict=strict,
+            alias=alias,
+            is_frozen=is_frozen,
+            line=stmt.line,
+            column=stmt.column,
+            type=init_type,
+            info=cls.info,
+        )
+
+    def _infer_dataclass_attr_init_type(self, sym: SymbolTableNode, name: str, context: Context) -> Type | None:
+        """Infer __init__ argument type for an attribute.
+
+        In particular, possibly use the signature of __set__.
+        """
+        default = sym.type
+        if sym.implicit:
+            return default
+        t = get_proper_type(sym.type)
+
+        # Perform a simple-minded inference from the signature of __set__, if present.
+        # We can't use mypy.checkmember here, since this plugin runs before type checking.
+        # We only support some basic scanerios here, which is hopefully sufficient for
+        # the vast majority of use cases.
+        if not isinstance(t, Instance):
+            return default
+        setter = t.type.get('__set__')
+        if setter:
+            if isinstance(setter.node, FuncDef):
+                super_info = t.type.get_containing_type_info('__set__')
+                assert super_info
+                if setter.type:
+                    setter_type = get_proper_type(map_type_from_supertype(setter.type, t.type, super_info))
+                else:
+                    return AnyType(TypeOfAny.unannotated)
+                if isinstance(setter_type, CallableType) and setter_type.arg_kinds == [
+                    ARG_POS,
+                    ARG_POS,
+                    ARG_POS,
+                ]:
+                    return expand_type_by_instance(setter_type.arg_types[2], t)
+                else:
+                    self._api.fail(f'Unsupported signature for "__set__" in "{t.type.name}"', context)
+            else:
+                self._api.fail(f'Unsupported "__set__" in "{t.type.name}"', context)
+
+        return default
+
+    def add_initializer(
+        self, fields: list[PydanticModelField], config: ModelConfigData, is_settings: bool, is_root_model: bool
+    ) -> None:
+        """Adds a fields-aware `__init__` method to the class.
+
+        The added `__init__` will be annotated with types vs. all `Any` depending on the plugin settings.
+        """
+        if '__init__' in self._cls.info.names and not self._cls.info.names['__init__'].plugin_generated:
+            return  # Don't generate an __init__ if one already exists
+
+        typed = self.plugin_config.init_typed
+        model_strict = bool(config.strict)
+        use_alias = config.populate_by_name is not True
+        requires_dynamic_aliases = bool(config.has_alias_generator and not config.populate_by_name)
+        args = self.get_field_arguments(
+            fields,
+            typed=typed,
+            model_strict=model_strict,
+            requires_dynamic_aliases=requires_dynamic_aliases,
+            use_alias=use_alias,
+            is_settings=is_settings,
+            is_root_model=is_root_model,
+            force_typevars_invariant=True,
+        )
+
+        if is_settings:
+            base_settings_node = self._api.lookup_fully_qualified(BASESETTINGS_FULLNAME).node
+            assert isinstance(base_settings_node, TypeInfo)
+            if '__init__' in base_settings_node.names:
+                base_settings_init_node = base_settings_node.names['__init__'].node
+                assert isinstance(base_settings_init_node, FuncDef)
+                if base_settings_init_node is not None and base_settings_init_node.type is not None:
+                    func_type = base_settings_init_node.type
+                    assert isinstance(func_type, CallableType)
+                    for arg_idx, arg_name in enumerate(func_type.arg_names):
+                        if arg_name is None or arg_name.startswith('__') or not arg_name.startswith('_'):
+                            continue
+                        analyzed_variable_type = self._api.anal_type(func_type.arg_types[arg_idx])
+                        variable = Var(arg_name, analyzed_variable_type)
+                        args.append(Argument(variable, analyzed_variable_type, None, ARG_OPT))
+
+        if not self.should_init_forbid_extra(fields, config):
+            var = Var('kwargs')
+            args.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2))
+
+        add_method(self._api, self._cls, '__init__', args=args, return_type=NoneType())
+
+    def add_model_construct_method(
+        self,
+        fields: list[PydanticModelField],
+        config: ModelConfigData,
+        is_settings: bool,
+        is_root_model: bool,
+    ) -> None:
+        """Adds a fully typed `model_construct` classmethod to the class.
+
+        Similar to the fields-aware __init__ method, but always uses the field names (not aliases),
+        and does not treat settings fields as optional.
+        """
+        set_str = self._api.named_type(f'{BUILTINS_NAME}.set', [self._api.named_type(f'{BUILTINS_NAME}.str')])
+        optional_set_str = UnionType([set_str, NoneType()])
+        fields_set_argument = Argument(Var('_fields_set', optional_set_str), optional_set_str, None, ARG_OPT)
+        with state.strict_optional_set(self._api.options.strict_optional):
+            args = self.get_field_arguments(
+                fields,
+                typed=True,
+                model_strict=bool(config.strict),
+                requires_dynamic_aliases=False,
+                use_alias=False,
+                is_settings=is_settings,
+                is_root_model=is_root_model,
+            )
+        if not self.should_init_forbid_extra(fields, config):
+            var = Var('kwargs')
+            args.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2))
+
+        args = args + [fields_set_argument] if is_root_model else [fields_set_argument] + args
+
+        add_method(
+            self._api,
+            self._cls,
+            'model_construct',
+            args=args,
+            return_type=fill_typevars(self._cls.info),
+            is_classmethod=True,
+        )
+
+    def set_frozen(self, fields: list[PydanticModelField], api: SemanticAnalyzerPluginInterface, frozen: bool) -> None:
+        """Marks all fields as properties so that attempts to set them trigger mypy errors.
+
+        This is the same approach used by the attrs and dataclasses plugins.
+        """
+        info = self._cls.info
+        for field in fields:
+            sym_node = info.names.get(field.name)
+            if sym_node is not None:
+                var = sym_node.node
+                if isinstance(var, Var):
+                    var.is_property = frozen or field.is_frozen
+                elif isinstance(var, PlaceholderNode) and not self._api.final_iteration:
+                    # See https://github.com/pydantic/pydantic/issues/5191 to hit this branch for test coverage
+                    self._api.defer()
+                else:  # pragma: no cover
+                    # I don't know whether it's possible to hit this branch, but I've added it for safety
+                    try:
+                        var_str = str(var)
+                    except TypeError:
+                        # This happens for PlaceholderNode; perhaps it will happen for other types in the future..
+                        var_str = repr(var)
+                    detail = f'sym_node.node: {var_str} (of type {var.__class__})'
+                    error_unexpected_behavior(detail, self._api, self._cls)
+            else:
+                var = field.to_var(info, api, use_alias=False)
+                var.info = info
+                var.is_property = frozen
+                var._fullname = info.fullname + '.' + var.name
+                info.names[var.name] = SymbolTableNode(MDEF, var)
+
+    def get_config_update(self, name: str, arg: Expression, lax_extra: bool = False) -> ModelConfigData | None:
+        """Determines the config update due to a single kwarg in the ConfigDict definition.
+
+        Warns if a tracked config attribute is set to a value the plugin doesn't know how to interpret (e.g., an int)
+        """
+        if name not in self.tracked_config_fields:
+            return None
+        if name == 'extra':
+            if isinstance(arg, StrExpr):
+                forbid_extra = arg.value == 'forbid'
+            elif isinstance(arg, MemberExpr):
+                forbid_extra = arg.name == 'forbid'
+            else:
+                if not lax_extra:
+                    # Only emit an error for other types of `arg` (e.g., `NameExpr`, `ConditionalExpr`, etc.) when
+                    # reading from a config class, etc. If a ConfigDict is used, then we don't want to emit an error
+                    # because you'll get type checking from the ConfigDict itself.
+                    #
+                    # It would be nice if we could introspect the types better otherwise, but I don't know what the API
+                    # is to evaluate an expr into its type and then check if that type is compatible with the expected
+                    # type. Note that you can still get proper type checking via: `model_config = ConfigDict(...)`, just
+                    # if you don't use an explicit string, the plugin won't be able to infer whether extra is forbidden.
+                    error_invalid_config_value(name, self._api, arg)
+                return None
+            return ModelConfigData(forbid_extra=forbid_extra)
+        if name == 'alias_generator':
+            has_alias_generator = True
+            if isinstance(arg, NameExpr) and arg.fullname == 'builtins.None':
+                has_alias_generator = False
+            return ModelConfigData(has_alias_generator=has_alias_generator)
+        if isinstance(arg, NameExpr) and arg.fullname in ('builtins.True', 'builtins.False'):
+            return ModelConfigData(**{name: arg.fullname == 'builtins.True'})
+        error_invalid_config_value(name, self._api, arg)
+        return None
+
+    @staticmethod
+    def get_has_default(stmt: AssignmentStmt) -> bool:
+        """Returns a boolean indicating whether the field defined in `stmt` is a required field."""
+        expr = stmt.rvalue
+        if isinstance(expr, TempNode):
+            # TempNode means annotation-only, so has no default
+            return False
+        if isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME:
+            # The "default value" is a call to `Field`; at this point, the field has a default if and only if:
+            # * there is a positional argument that is not `...`
+            # * there is a keyword argument named "default" that is not `...`
+            # * there is a "default_factory" that is not `None`
+            for arg, name in zip(expr.args, expr.arg_names):
+                # If name is None, then this arg is the default because it is the only positional argument.
+                if name is None or name == 'default':
+                    return arg.__class__ is not EllipsisExpr
+                if name == 'default_factory':
+                    return not (isinstance(arg, NameExpr) and arg.fullname == 'builtins.None')
+            return False
+        # Has no default if the "default value" is Ellipsis (i.e., `field_name: Annotation = ...`)
+        return not isinstance(expr, EllipsisExpr)
+
+    @staticmethod
+    def get_strict(stmt: AssignmentStmt) -> bool | None:
+        """Returns a the `strict` value of a field if defined, otherwise `None`."""
+        expr = stmt.rvalue
+        if isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME:
+            for arg, name in zip(expr.args, expr.arg_names):
+                if name != 'strict':
+                    continue
+                if isinstance(arg, NameExpr):
+                    if arg.fullname == 'builtins.True':
+                        return True
+                    elif arg.fullname == 'builtins.False':
+                        return False
+                return None
+        return None
+
+    @staticmethod
+    def get_alias_info(stmt: AssignmentStmt) -> tuple[str | None, bool]:
+        """Returns a pair (alias, has_dynamic_alias), extracted from the declaration of the field defined in `stmt`.
+
+        `has_dynamic_alias` is True if and only if an alias is provided, but not as a string literal.
+        If `has_dynamic_alias` is True, `alias` will be None.
+        """
+        expr = stmt.rvalue
+        if isinstance(expr, TempNode):
+            # TempNode means annotation-only
+            return None, False
+
+        if not (
+            isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME
+        ):
+            # Assigned value is not a call to pydantic.fields.Field
+            return None, False
+
+        for i, arg_name in enumerate(expr.arg_names):
+            if arg_name != 'alias':
+                continue
+            arg = expr.args[i]
+            if isinstance(arg, StrExpr):
+                return arg.value, False
+            else:
+                return None, True
+        return None, False
+
+    @staticmethod
+    def is_field_frozen(stmt: AssignmentStmt) -> bool:
+        """Returns whether the field is frozen, extracted from the declaration of the field defined in `stmt`.
+
+        Note that this is only whether the field was declared to be frozen in a `<field_name> = Field(frozen=True)`
+        sense; this does not determine whether the field is frozen because the entire model is frozen; that is
+        handled separately.
+        """
+        expr = stmt.rvalue
+        if isinstance(expr, TempNode):
+            # TempNode means annotation-only
+            return False
+
+        if not (
+            isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME
+        ):
+            # Assigned value is not a call to pydantic.fields.Field
+            return False
+
+        for i, arg_name in enumerate(expr.arg_names):
+            if arg_name == 'frozen':
+                arg = expr.args[i]
+                return isinstance(arg, NameExpr) and arg.fullname == 'builtins.True'
+        return False
+
+    def get_field_arguments(
+        self,
+        fields: list[PydanticModelField],
+        typed: bool,
+        model_strict: bool,
+        use_alias: bool,
+        requires_dynamic_aliases: bool,
+        is_settings: bool,
+        is_root_model: bool,
+        force_typevars_invariant: bool = False,
+    ) -> list[Argument]:
+        """Helper function used during the construction of the `__init__` and `model_construct` method signatures.
+
+        Returns a list of mypy Argument instances for use in the generated signatures.
+        """
+        info = self._cls.info
+        arguments = [
+            field.to_argument(
+                info,
+                typed=typed,
+                model_strict=model_strict,
+                force_optional=requires_dynamic_aliases or is_settings,
+                use_alias=use_alias,
+                api=self._api,
+                force_typevars_invariant=force_typevars_invariant,
+                is_root_model_root=is_root_model and field.name == 'root',
+            )
+            for field in fields
+            if not (use_alias and field.has_dynamic_alias)
+        ]
+        return arguments
+
+    def should_init_forbid_extra(self, fields: list[PydanticModelField], config: ModelConfigData) -> bool:
+        """Indicates whether the generated `__init__` should get a `**kwargs` at the end of its signature.
+
+        We disallow arbitrary kwargs if the extra config setting is "forbid", or if the plugin config says to,
+        *unless* a required dynamic alias is present (since then we can't determine a valid signature).
+        """
+        if not config.populate_by_name:
+            if self.is_dynamic_alias_present(fields, bool(config.has_alias_generator)):
+                return False
+        if config.forbid_extra:
+            return True
+        return self.plugin_config.init_forbid_extra
+
+    @staticmethod
+    def is_dynamic_alias_present(fields: list[PydanticModelField], has_alias_generator: bool) -> bool:
+        """Returns whether any fields on the model have a "dynamic alias", i.e., an alias that cannot be
+        determined during static analysis.
+        """
+        for field in fields:
+            if field.has_dynamic_alias:
+                return True
+        if has_alias_generator:
+            for field in fields:
+                if field.alias is None:
+                    return True
+        return False
+
+
+class ModelConfigData:
+    """Pydantic mypy plugin model config class."""
+
+    def __init__(
+        self,
+        forbid_extra: bool | None = None,
+        frozen: bool | None = None,
+        from_attributes: bool | None = None,
+        populate_by_name: bool | None = None,
+        has_alias_generator: bool | None = None,
+        strict: bool | None = None,
+    ):
+        self.forbid_extra = forbid_extra
+        self.frozen = frozen
+        self.from_attributes = from_attributes
+        self.populate_by_name = populate_by_name
+        self.has_alias_generator = has_alias_generator
+        self.strict = strict
+
+    def get_values_dict(self) -> dict[str, Any]:
+        """Returns a dict of Pydantic model config names to their values.
+
+        It includes the config if config value is not `None`.
+        """
+        return {k: v for k, v in self.__dict__.items() if v is not None}
+
+    def update(self, config: ModelConfigData | None) -> None:
+        """Update Pydantic model config values."""
+        if config is None:
+            return
+        for k, v in config.get_values_dict().items():
+            setattr(self, k, v)
+
+    def setdefault(self, key: str, value: Any) -> None:
+        """Set default value for Pydantic model config if config value is `None`."""
+        if getattr(self, key) is None:
+            setattr(self, key, value)
+
+
+ERROR_ORM = ErrorCode('pydantic-orm', 'Invalid from_attributes call', 'Pydantic')
+ERROR_CONFIG = ErrorCode('pydantic-config', 'Invalid config value', 'Pydantic')
+ERROR_ALIAS = ErrorCode('pydantic-alias', 'Dynamic alias disallowed', 'Pydantic')
+ERROR_UNEXPECTED = ErrorCode('pydantic-unexpected', 'Unexpected behavior', 'Pydantic')
+ERROR_UNTYPED = ErrorCode('pydantic-field', 'Untyped field disallowed', 'Pydantic')
+ERROR_FIELD_DEFAULTS = ErrorCode('pydantic-field', 'Invalid Field defaults', 'Pydantic')
+ERROR_EXTRA_FIELD_ROOT_MODEL = ErrorCode('pydantic-field', 'Extra field on RootModel subclass', 'Pydantic')
+
+
+def error_from_attributes(model_name: str, api: CheckerPluginInterface, context: Context) -> None:
+    """Emits an error when the model does not have `from_attributes=True`."""
+    api.fail(f'"{model_name}" does not have from_attributes=True', context, code=ERROR_ORM)
+
+
+def error_invalid_config_value(name: str, api: SemanticAnalyzerPluginInterface, context: Context) -> None:
+    """Emits an error when the config value is invalid."""
+    api.fail(f'Invalid value for "Config.{name}"', context, code=ERROR_CONFIG)
+
+
+def error_required_dynamic_aliases(api: SemanticAnalyzerPluginInterface, context: Context) -> None:
+    """Emits required dynamic aliases error.
+
+    This will be called when `warn_required_dynamic_aliases=True`.
+    """
+    api.fail('Required dynamic aliases disallowed', context, code=ERROR_ALIAS)
+
+
+def error_unexpected_behavior(
+    detail: str, api: CheckerPluginInterface | SemanticAnalyzerPluginInterface, context: Context
+) -> None:  # pragma: no cover
+    """Emits unexpected behavior error."""
+    # Can't think of a good way to test this, but I confirmed it renders as desired by adding to a non-error path
+    link = 'https://github.com/pydantic/pydantic/issues/new/choose'
+    full_message = f'The pydantic mypy plugin ran into unexpected behavior: {detail}\n'
+    full_message += f'Please consider reporting this bug at {link} so we can try to fix it!'
+    api.fail(full_message, context, code=ERROR_UNEXPECTED)
+
+
+def error_untyped_fields(api: SemanticAnalyzerPluginInterface, context: Context) -> None:
+    """Emits an error when there is an untyped field in the model."""
+    api.fail('Untyped fields disallowed', context, code=ERROR_UNTYPED)
+
+
+def error_extra_fields_on_root_model(api: CheckerPluginInterface, context: Context) -> None:
+    """Emits an error when there is more than just a root field defined for a subclass of RootModel."""
+    api.fail('Only `root` is allowed as a field of a `RootModel`', context, code=ERROR_EXTRA_FIELD_ROOT_MODEL)
+
+
+def add_method(
+    api: SemanticAnalyzerPluginInterface | CheckerPluginInterface,
+    cls: ClassDef,
+    name: str,
+    args: list[Argument],
+    return_type: Type,
+    self_type: Type | None = None,
+    tvar_def: TypeVarType | None = None,
+    is_classmethod: bool = False,
+) -> None:
+    """Very closely related to `mypy.plugins.common.add_method_to_class`, with a few pydantic-specific changes."""
+    info = cls.info
+
+    # First remove any previously generated methods with the same name
+    # to avoid clashes and problems in the semantic analyzer.
+    if name in info.names:
+        sym = info.names[name]
+        if sym.plugin_generated and isinstance(sym.node, FuncDef):
+            cls.defs.body.remove(sym.node)  # pragma: no cover
+
+    if isinstance(api, SemanticAnalyzerPluginInterface):
+        function_type = api.named_type('builtins.function')
+    else:
+        function_type = api.named_generic_type('builtins.function', [])
+
+    if is_classmethod:
+        self_type = self_type or TypeType(fill_typevars(info))
+        first = [Argument(Var('_cls'), self_type, None, ARG_POS, True)]
+    else:
+        self_type = self_type or fill_typevars(info)
+        # `self` is positional *ONLY* here, but this can't be expressed
+        # fully in the mypy internal API. ARG_POS is the closest we can get.
+        # Using ARG_POS will, however, give mypy errors if a `self` field
+        # is present on a model:
+        #
+        #     Name "self" already defined (possibly by an import)  [no-redef]
+        #
+        # As a workaround, we give this argument a name that will
+        # never conflict. By its positional nature, this name will not
+        # be used or exposed to users.
+        first = [Argument(Var('__pydantic_self__'), self_type, None, ARG_POS)]
+    args = first + args
+
+    arg_types, arg_names, arg_kinds = [], [], []
+    for arg in args:
+        assert arg.type_annotation, 'All arguments must be fully typed.'
+        arg_types.append(arg.type_annotation)
+        arg_names.append(arg.variable.name)
+        arg_kinds.append(arg.kind)
+
+    signature = CallableType(arg_types, arg_kinds, arg_names, return_type, function_type)
+    if tvar_def:
+        signature.variables = [tvar_def]
+
+    func = FuncDef(name, args, Block([PassStmt()]))
+    func.info = info
+    func.type = set_callable_name(signature, func)
+    func.is_class = is_classmethod
+    func._fullname = info.fullname + '.' + name
+    func.line = info.line
+
+    # NOTE: we would like the plugin generated node to dominate, but we still
+    # need to keep any existing definitions so they get semantically analyzed.
+    if name in info.names:
+        # Get a nice unique name instead.
+        r_name = get_unique_redefinition_name(name, info.names)
+        info.names[r_name] = info.names[name]
+
+    # Add decorator for is_classmethod
+    # The dataclasses plugin claims this is unnecessary for classmethods, but not including it results in a
+    # signature incompatible with the superclass, which causes mypy errors to occur for every subclass of BaseModel.
+    if is_classmethod:
+        func.is_decorated = True
+        v = Var(name, func.type)
+        v.info = info
+        v._fullname = func._fullname
+        v.is_classmethod = True
+        dec = Decorator(func, [NameExpr('classmethod')], v)
+        dec.line = info.line
+        sym = SymbolTableNode(MDEF, dec)
+    else:
+        sym = SymbolTableNode(MDEF, func)
+    sym.plugin_generated = True
+    info.names[name] = sym
+
+    info.defn.defs.body.append(func)
+
+
+def parse_toml(config_file: str) -> dict[str, Any] | None:
+    """Returns a dict of config keys to values.
+
+    It reads configs from toml file and returns `None` if the file is not a toml file.
+    """
+    if not config_file.endswith('.toml'):
+        return None
+
+    if sys.version_info >= (3, 11):
+        import tomllib as toml_
+    else:
+        try:
+            import tomli as toml_
+        except ImportError:  # pragma: no cover
+            import warnings
+
+            warnings.warn('No TOML parser installed, cannot read configuration from `pyproject.toml`.')
+            return None
+
+    with open(config_file, 'rb') as rf:
+        return toml_.load(rf)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/networks.py b/.venv/lib/python3.12/site-packages/pydantic/networks.py
new file mode 100644
index 00000000..3d2b842d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/networks.py
@@ -0,0 +1,1291 @@
+"""The networks module contains types for common network-related fields."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses as _dataclasses
+import re
+from dataclasses import fields
+from functools import lru_cache
+from importlib.metadata import version
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from typing import TYPE_CHECKING, Any, ClassVar
+
+from pydantic_core import (
+    MultiHostHost,
+    PydanticCustomError,
+    PydanticSerializationUnexpectedValue,
+    SchemaSerializer,
+    core_schema,
+)
+from pydantic_core import MultiHostUrl as _CoreMultiHostUrl
+from pydantic_core import Url as _CoreUrl
+from typing_extensions import Annotated, Self, TypeAlias
+
+from pydantic.errors import PydanticUserError
+
+from ._internal import _repr, _schema_generation_shared
+from ._migration import getattr_migration
+from .annotated_handlers import GetCoreSchemaHandler
+from .json_schema import JsonSchemaValue
+from .type_adapter import TypeAdapter
+
+if TYPE_CHECKING:
+    import email_validator
+
+    NetworkType: TypeAlias = 'str | bytes | int | tuple[str | bytes | int, str | int]'
+
+else:
+    email_validator = None
+
+
+__all__ = [
+    'AnyUrl',
+    'AnyHttpUrl',
+    'FileUrl',
+    'FtpUrl',
+    'HttpUrl',
+    'WebsocketUrl',
+    'AnyWebsocketUrl',
+    'UrlConstraints',
+    'EmailStr',
+    'NameEmail',
+    'IPvAnyAddress',
+    'IPvAnyInterface',
+    'IPvAnyNetwork',
+    'PostgresDsn',
+    'CockroachDsn',
+    'AmqpDsn',
+    'RedisDsn',
+    'MongoDsn',
+    'KafkaDsn',
+    'NatsDsn',
+    'validate_email',
+    'MySQLDsn',
+    'MariaDBDsn',
+    'ClickHouseDsn',
+    'SnowflakeDsn',
+]
+
+
+@_dataclasses.dataclass
+class UrlConstraints:
+    """Url constraints.
+
+    Attributes:
+        max_length: The maximum length of the url. Defaults to `None`.
+        allowed_schemes: The allowed schemes. Defaults to `None`.
+        host_required: Whether the host is required. Defaults to `None`.
+        default_host: The default host. Defaults to `None`.
+        default_port: The default port. Defaults to `None`.
+        default_path: The default path. Defaults to `None`.
+    """
+
+    max_length: int | None = None
+    allowed_schemes: list[str] | None = None
+    host_required: bool | None = None
+    default_host: str | None = None
+    default_port: int | None = None
+    default_path: str | None = None
+
+    def __hash__(self) -> int:
+        return hash(
+            (
+                self.max_length,
+                tuple(self.allowed_schemes) if self.allowed_schemes is not None else None,
+                self.host_required,
+                self.default_host,
+                self.default_port,
+                self.default_path,
+            )
+        )
+
+    @property
+    def defined_constraints(self) -> dict[str, Any]:
+        """Fetch a key / value mapping of constraints to values that are not None. Used for core schema updates."""
+        return {field.name: value for field in fields(self) if (value := getattr(self, field.name)) is not None}
+
+    def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(source)
+
+        # for function-wrap schemas, url constraints is applied to the inner schema
+        # because when we generate schemas for urls, we wrap a core_schema.url_schema() with a function-wrap schema
+        # that helps with validation on initialization, see _BaseUrl and _BaseMultiHostUrl below.
+        schema_to_mutate = schema['schema'] if schema['type'] == 'function-wrap' else schema
+        if annotated_type := schema_to_mutate['type'] not in ('url', 'multi-host-url'):
+            raise PydanticUserError(
+                f"'UrlConstraints' cannot annotate '{annotated_type}'.", code='invalid-annotated-type'
+            )
+        for constraint_key, constraint_value in self.defined_constraints.items():
+            schema_to_mutate[constraint_key] = constraint_value
+        return schema
+
+
+class _BaseUrl:
+    _constraints: ClassVar[UrlConstraints] = UrlConstraints()
+    _url: _CoreUrl
+
+    def __init__(self, url: str | _CoreUrl | _BaseUrl) -> None:
+        self._url = _build_type_adapter(self.__class__).validate_python(url)._url
+
+    @property
+    def scheme(self) -> str:
+        """The scheme part of the URL.
+
+        e.g. `https` in `https://user:pass@host:port/path?query#fragment`
+        """
+        return self._url.scheme
+
+    @property
+    def username(self) -> str | None:
+        """The username part of the URL, or `None`.
+
+        e.g. `user` in `https://user:pass@host:port/path?query#fragment`
+        """
+        return self._url.username
+
+    @property
+    def password(self) -> str | None:
+        """The password part of the URL, or `None`.
+
+        e.g. `pass` in `https://user:pass@host:port/path?query#fragment`
+        """
+        return self._url.password
+
+    @property
+    def host(self) -> str | None:
+        """The host part of the URL, or `None`.
+
+        If the URL must be punycode encoded, this is the encoded host, e.g if the input URL is `https://£££.com`,
+        `host` will be `xn--9aaa.com`
+        """
+        return self._url.host
+
+    def unicode_host(self) -> str | None:
+        """The host part of the URL as a unicode string, or `None`.
+
+        e.g. `host` in `https://user:pass@host:port/path?query#fragment`
+
+        If the URL must be punycode encoded, this is the decoded host, e.g if the input URL is `https://£££.com`,
+        `unicode_host()` will be `£££.com`
+        """
+        return self._url.unicode_host()
+
+    @property
+    def port(self) -> int | None:
+        """The port part of the URL, or `None`.
+
+        e.g. `port` in `https://user:pass@host:port/path?query#fragment`
+        """
+        return self._url.port
+
+    @property
+    def path(self) -> str | None:
+        """The path part of the URL, or `None`.
+
+        e.g. `/path` in `https://user:pass@host:port/path?query#fragment`
+        """
+        return self._url.path
+
+    @property
+    def query(self) -> str | None:
+        """The query part of the URL, or `None`.
+
+        e.g. `query` in `https://user:pass@host:port/path?query#fragment`
+        """
+        return self._url.query
+
+    def query_params(self) -> list[tuple[str, str]]:
+        """The query part of the URL as a list of key-value pairs.
+
+        e.g. `[('foo', 'bar')]` in `https://user:pass@host:port/path?foo=bar#fragment`
+        """
+        return self._url.query_params()
+
+    @property
+    def fragment(self) -> str | None:
+        """The fragment part of the URL, or `None`.
+
+        e.g. `fragment` in `https://user:pass@host:port/path?query#fragment`
+        """
+        return self._url.fragment
+
+    def unicode_string(self) -> str:
+        """The URL as a unicode string, unlike `__str__()` this will not punycode encode the host.
+
+        If the URL must be punycode encoded, this is the decoded string, e.g if the input URL is `https://£££.com`,
+        `unicode_string()` will be `https://£££.com`
+        """
+        return self._url.unicode_string()
+
+    def __str__(self) -> str:
+        """The URL as a string, this will punycode encode the host if required."""
+        return str(self._url)
+
+    def __repr__(self) -> str:
+        return f'{self.__class__.__name__}({str(self._url)!r})'
+
+    def __deepcopy__(self, memo: dict) -> Self:
+        return self.__class__(self._url)
+
+    def __eq__(self, other: Any) -> bool:
+        return self.__class__ is other.__class__ and self._url == other._url
+
+    def __lt__(self, other: Any) -> bool:
+        return self.__class__ is other.__class__ and self._url < other._url
+
+    def __gt__(self, other: Any) -> bool:
+        return self.__class__ is other.__class__ and self._url > other._url
+
+    def __le__(self, other: Any) -> bool:
+        return self.__class__ is other.__class__ and self._url <= other._url
+
+    def __ge__(self, other: Any) -> bool:
+        return self.__class__ is other.__class__ and self._url >= other._url
+
+    def __hash__(self) -> int:
+        return hash(self._url)
+
+    def __len__(self) -> int:
+        return len(str(self._url))
+
+    @classmethod
+    def build(
+        cls,
+        *,
+        scheme: str,
+        username: str | None = None,
+        password: str | None = None,
+        host: str,
+        port: int | None = None,
+        path: str | None = None,
+        query: str | None = None,
+        fragment: str | None = None,
+    ) -> Self:
+        """Build a new `Url` instance from its component parts.
+
+        Args:
+            scheme: The scheme part of the URL.
+            username: The username part of the URL, or omit for no username.
+            password: The password part of the URL, or omit for no password.
+            host: The host part of the URL.
+            port: The port part of the URL, or omit for no port.
+            path: The path part of the URL, or omit for no path.
+            query: The query part of the URL, or omit for no query.
+            fragment: The fragment part of the URL, or omit for no fragment.
+
+        Returns:
+            An instance of URL
+        """
+        return cls(
+            _CoreUrl.build(
+                scheme=scheme,
+                username=username,
+                password=password,
+                host=host,
+                port=port,
+                path=path,
+                query=query,
+                fragment=fragment,
+            )
+        )
+
+    @classmethod
+    def serialize_url(cls, url: Any, info: core_schema.SerializationInfo) -> str | Self:
+        if not isinstance(url, cls):
+            raise PydanticSerializationUnexpectedValue(
+                f"Expected `{cls}` but got `{type(url)}` with value `'{url}'` - serialized value may not be as expected."
+            )
+        if info.mode == 'json':
+            return str(url)
+        return url
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, source: type[_BaseUrl], handler: GetCoreSchemaHandler
+    ) -> core_schema.CoreSchema:
+        def wrap_val(v, h):
+            if isinstance(v, source):
+                return v
+            if isinstance(v, _BaseUrl):
+                v = str(v)
+            core_url = h(v)
+            instance = source.__new__(source)
+            instance._url = core_url
+            return instance
+
+        return core_schema.no_info_wrap_validator_function(
+            wrap_val,
+            schema=core_schema.url_schema(**cls._constraints.defined_constraints),
+            serialization=core_schema.plain_serializer_function_ser_schema(
+                cls.serialize_url, info_arg=True, when_used='always'
+            ),
+        )
+
+    @classmethod
+    def __get_pydantic_json_schema__(
+        cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        # we use the url schema for json schema generation, but we might have to extract it from
+        # the function-wrap schema we use as a tool for validation on initialization
+        inner_schema = core_schema['schema'] if core_schema['type'] == 'function-wrap' else core_schema
+        return handler(inner_schema)
+
+    __pydantic_serializer__ = SchemaSerializer(core_schema.any_schema(serialization=core_schema.to_string_ser_schema()))
+
+
+class _BaseMultiHostUrl:
+    _constraints: ClassVar[UrlConstraints] = UrlConstraints()
+    _url: _CoreMultiHostUrl
+
+    def __init__(self, url: str | _CoreMultiHostUrl | _BaseMultiHostUrl) -> None:
+        self._url = _build_type_adapter(self.__class__).validate_python(url)._url
+
+    @property
+    def scheme(self) -> str:
+        """The scheme part of the URL.
+
+        e.g. `https` in `https://foo.com,bar.com/path?query#fragment`
+        """
+        return self._url.scheme
+
+    @property
+    def path(self) -> str | None:
+        """The path part of the URL, or `None`.
+
+        e.g. `/path` in `https://foo.com,bar.com/path?query#fragment`
+        """
+        return self._url.path
+
+    @property
+    def query(self) -> str | None:
+        """The query part of the URL, or `None`.
+
+        e.g. `query` in `https://foo.com,bar.com/path?query#fragment`
+        """
+        return self._url.query
+
+    def query_params(self) -> list[tuple[str, str]]:
+        """The query part of the URL as a list of key-value pairs.
+
+        e.g. `[('foo', 'bar')]` in `https://foo.com,bar.com/path?query#fragment`
+        """
+        return self._url.query_params()
+
+    @property
+    def fragment(self) -> str | None:
+        """The fragment part of the URL, or `None`.
+
+        e.g. `fragment` in `https://foo.com,bar.com/path?query#fragment`
+        """
+        return self._url.fragment
+
+    def hosts(self) -> list[MultiHostHost]:
+        '''The hosts of the `MultiHostUrl` as [`MultiHostHost`][pydantic_core.MultiHostHost] typed dicts.
+
+        ```python
+        from pydantic_core import MultiHostUrl
+
+        mhu = MultiHostUrl('https://foo.com:123,foo:bar@bar.com/path')
+        print(mhu.hosts())
+        """
+        [
+            {'username': None, 'password': None, 'host': 'foo.com', 'port': 123},
+            {'username': 'foo', 'password': 'bar', 'host': 'bar.com', 'port': 443}
+        ]
+        ```
+        Returns:
+            A list of dicts, each representing a host.
+        '''
+        return self._url.hosts()
+
+    def unicode_string(self) -> str:
+        """The URL as a unicode string, unlike `__str__()` this will not punycode encode the hosts."""
+        return self._url.unicode_string()
+
+    def __str__(self) -> str:
+        """The URL as a string, this will punycode encode the host if required."""
+        return str(self._url)
+
+    def __repr__(self) -> str:
+        return f'{self.__class__.__name__}({str(self._url)!r})'
+
+    def __deepcopy__(self, memo: dict) -> Self:
+        return self.__class__(self._url)
+
+    def __eq__(self, other: Any) -> bool:
+        return self.__class__ is other.__class__ and self._url == other._url
+
+    def __hash__(self) -> int:
+        return hash(self._url)
+
+    def __len__(self) -> int:
+        return len(str(self._url))
+
+    @classmethod
+    def build(
+        cls,
+        *,
+        scheme: str,
+        hosts: list[MultiHostHost] | None = None,
+        username: str | None = None,
+        password: str | None = None,
+        host: str | None = None,
+        port: int | None = None,
+        path: str | None = None,
+        query: str | None = None,
+        fragment: str | None = None,
+    ) -> Self:
+        """Build a new `MultiHostUrl` instance from its component parts.
+
+        This method takes either `hosts` - a list of `MultiHostHost` typed dicts, or the individual components
+        `username`, `password`, `host` and `port`.
+
+        Args:
+            scheme: The scheme part of the URL.
+            hosts: Multiple hosts to build the URL from.
+            username: The username part of the URL.
+            password: The password part of the URL.
+            host: The host part of the URL.
+            port: The port part of the URL.
+            path: The path part of the URL.
+            query: The query part of the URL, or omit for no query.
+            fragment: The fragment part of the URL, or omit for no fragment.
+
+        Returns:
+            An instance of `MultiHostUrl`
+        """
+        return cls(
+            _CoreMultiHostUrl.build(
+                scheme=scheme,
+                hosts=hosts,
+                username=username,
+                password=password,
+                host=host,
+                port=port,
+                path=path,
+                query=query,
+                fragment=fragment,
+            )
+        )
+
+    @classmethod
+    def serialize_url(cls, url: Any, info: core_schema.SerializationInfo) -> str | Self:
+        if not isinstance(url, cls):
+            raise PydanticSerializationUnexpectedValue(
+                f"Expected `{cls}` but got `{type(url)}` with value `'{url}'` - serialized value may not be as expected."
+            )
+        if info.mode == 'json':
+            return str(url)
+        return url
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, source: type[_BaseMultiHostUrl], handler: GetCoreSchemaHandler
+    ) -> core_schema.CoreSchema:
+        def wrap_val(v, h):
+            if isinstance(v, source):
+                return v
+            if isinstance(v, _BaseMultiHostUrl):
+                v = str(v)
+            core_url = h(v)
+            instance = source.__new__(source)
+            instance._url = core_url
+            return instance
+
+        return core_schema.no_info_wrap_validator_function(
+            wrap_val,
+            schema=core_schema.multi_host_url_schema(**cls._constraints.defined_constraints),
+            serialization=core_schema.plain_serializer_function_ser_schema(
+                cls.serialize_url, info_arg=True, when_used='always'
+            ),
+        )
+
+    @classmethod
+    def __get_pydantic_json_schema__(
+        cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        # we use the url schema for json schema generation, but we might have to extract it from
+        # the function-wrap schema we use as a tool for validation on initialization
+        inner_schema = core_schema['schema'] if core_schema['type'] == 'function-wrap' else core_schema
+        return handler(inner_schema)
+
+    __pydantic_serializer__ = SchemaSerializer(core_schema.any_schema(serialization=core_schema.to_string_ser_schema()))
+
+
+@lru_cache
+def _build_type_adapter(cls: type[_BaseUrl | _BaseMultiHostUrl]) -> TypeAdapter:
+    return TypeAdapter(cls)
+
+
+class AnyUrl(_BaseUrl):
+    """Base type for all URLs.
+
+    * Any scheme allowed
+    * Top-level domain (TLD) not required
+    * Host not required
+
+    Assuming an input URL of `http://samuel:pass@example.com:8000/the/path/?query=here#fragment=is;this=bit`,
+    the types export the following properties:
+
+    - `scheme`: the URL scheme (`http`), always set.
+    - `host`: the URL host (`example.com`).
+    - `username`: optional username if included (`samuel`).
+    - `password`: optional password if included (`pass`).
+    - `port`: optional port (`8000`).
+    - `path`: optional path (`/the/path/`).
+    - `query`: optional URL query (for example, `GET` arguments or "search string", such as `query=here`).
+    - `fragment`: optional fragment (`fragment=is;this=bit`).
+    """
+
+
+# Note: all single host urls inherit from `AnyUrl` to preserve compatibility with pre-v2.10 code
+# Where urls were annotated variants of `AnyUrl`, which was an alias to `pydantic_core.Url`
+
+
+class AnyHttpUrl(AnyUrl):
+    """A type that will accept any http or https URL.
+
+    * TLD not required
+    * Host not required
+    """
+
+    _constraints = UrlConstraints(allowed_schemes=['http', 'https'])
+
+
+class HttpUrl(AnyUrl):
+    """A type that will accept any http or https URL.
+
+    * TLD not required
+    * Host not required
+    * Max length 2083
+
+    ```python
+    from pydantic import BaseModel, HttpUrl, ValidationError
+
+    class MyModel(BaseModel):
+        url: HttpUrl
+
+    m = MyModel(url='http://www.example.com')  # (1)!
+    print(m.url)
+    #> http://www.example.com/
+
+    try:
+        MyModel(url='ftp://invalid.url')
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for MyModel
+        url
+          URL scheme should be 'http' or 'https' [type=url_scheme, input_value='ftp://invalid.url', input_type=str]
+        '''
+
+    try:
+        MyModel(url='not a url')
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for MyModel
+        url
+          Input should be a valid URL, relative URL without a base [type=url_parsing, input_value='not a url', input_type=str]
+        '''
+    ```
+
+    1. Note: mypy would prefer `m = MyModel(url=HttpUrl('http://www.example.com'))`, but Pydantic will convert the string to an HttpUrl instance anyway.
+
+    "International domains" (e.g. a URL where the host or TLD includes non-ascii characters) will be encoded via
+    [punycode](https://en.wikipedia.org/wiki/Punycode) (see
+    [this article](https://www.xudongz.com/blog/2017/idn-phishing/) for a good description of why this is important):
+
+    ```python
+    from pydantic import BaseModel, HttpUrl
+
+    class MyModel(BaseModel):
+        url: HttpUrl
+
+    m1 = MyModel(url='http://puny£code.com')
+    print(m1.url)
+    #> http://xn--punycode-eja.com/
+    m2 = MyModel(url='https://www.аррӏе.com/')
+    print(m2.url)
+    #> https://www.xn--80ak6aa92e.com/
+    m3 = MyModel(url='https://www.example.珠宝/')
+    print(m3.url)
+    #> https://www.example.xn--pbt977c/
+    ```
+
+
+    !!! warning "Underscores in Hostnames"
+        In Pydantic, underscores are allowed in all parts of a domain except the TLD.
+        Technically this might be wrong - in theory the hostname cannot have underscores, but subdomains can.
+
+        To explain this; consider the following two cases:
+
+        - `exam_ple.co.uk`: the hostname is `exam_ple`, which should not be allowed since it contains an underscore.
+        - `foo_bar.example.com` the hostname is `example`, which should be allowed since the underscore is in the subdomain.
+
+        Without having an exhaustive list of TLDs, it would be impossible to differentiate between these two. Therefore
+        underscores are allowed, but you can always do further validation in a validator if desired.
+
+        Also, Chrome, Firefox, and Safari all currently accept `http://exam_ple.com` as a URL, so we're in good
+        (or at least big) company.
+    """
+
+    _constraints = UrlConstraints(max_length=2083, allowed_schemes=['http', 'https'])
+
+
+class AnyWebsocketUrl(AnyUrl):
+    """A type that will accept any ws or wss URL.
+
+    * TLD not required
+    * Host not required
+    """
+
+    _constraints = UrlConstraints(allowed_schemes=['ws', 'wss'])
+
+
+class WebsocketUrl(AnyUrl):
+    """A type that will accept any ws or wss URL.
+
+    * TLD not required
+    * Host not required
+    * Max length 2083
+    """
+
+    _constraints = UrlConstraints(max_length=2083, allowed_schemes=['ws', 'wss'])
+
+
+class FileUrl(AnyUrl):
+    """A type that will accept any file URL.
+
+    * Host not required
+    """
+
+    _constraints = UrlConstraints(allowed_schemes=['file'])
+
+
+class FtpUrl(AnyUrl):
+    """A type that will accept ftp URL.
+
+    * TLD not required
+    * Host not required
+    """
+
+    _constraints = UrlConstraints(allowed_schemes=['ftp'])
+
+
+class PostgresDsn(_BaseMultiHostUrl):
+    """A type that will accept any Postgres DSN.
+
+    * User info required
+    * TLD not required
+    * Host required
+    * Supports multiple hosts
+
+    If further validation is required, these properties can be used by validators to enforce specific behaviour:
+
+    ```python
+    from pydantic import (
+        BaseModel,
+        HttpUrl,
+        PostgresDsn,
+        ValidationError,
+        field_validator,
+    )
+
+    class MyModel(BaseModel):
+        url: HttpUrl
+
+    m = MyModel(url='http://www.example.com')
+
+    # the repr() method for a url will display all properties of the url
+    print(repr(m.url))
+    #> HttpUrl('http://www.example.com/')
+    print(m.url.scheme)
+    #> http
+    print(m.url.host)
+    #> www.example.com
+    print(m.url.port)
+    #> 80
+
+    class MyDatabaseModel(BaseModel):
+        db: PostgresDsn
+
+        @field_validator('db')
+        def check_db_name(cls, v):
+            assert v.path and len(v.path) > 1, 'database must be provided'
+            return v
+
+    m = MyDatabaseModel(db='postgres://user:pass@localhost:5432/foobar')
+    print(m.db)
+    #> postgres://user:pass@localhost:5432/foobar
+
+    try:
+        MyDatabaseModel(db='postgres://user:pass@localhost:5432')
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for MyDatabaseModel
+        db
+          Assertion failed, database must be provided
+        assert (None)
+         +  where None = PostgresDsn('postgres://user:pass@localhost:5432').path [type=assertion_error, input_value='postgres://user:pass@localhost:5432', input_type=str]
+        '''
+    ```
+    """
+
+    _constraints = UrlConstraints(
+        host_required=True,
+        allowed_schemes=[
+            'postgres',
+            'postgresql',
+            'postgresql+asyncpg',
+            'postgresql+pg8000',
+            'postgresql+psycopg',
+            'postgresql+psycopg2',
+            'postgresql+psycopg2cffi',
+            'postgresql+py-postgresql',
+            'postgresql+pygresql',
+        ],
+    )
+
+    @property
+    def host(self) -> str:
+        """The required URL host."""
+        return self._url.host  # pyright: ignore[reportAttributeAccessIssue]
+
+
+class CockroachDsn(AnyUrl):
+    """A type that will accept any Cockroach DSN.
+
+    * User info required
+    * TLD not required
+    * Host required
+    """
+
+    _constraints = UrlConstraints(
+        host_required=True,
+        allowed_schemes=[
+            'cockroachdb',
+            'cockroachdb+psycopg2',
+            'cockroachdb+asyncpg',
+        ],
+    )
+
+    @property
+    def host(self) -> str:
+        """The required URL host."""
+        return self._url.host  # pyright: ignore[reportReturnType]
+
+
+class AmqpDsn(AnyUrl):
+    """A type that will accept any AMQP DSN.
+
+    * User info required
+    * TLD not required
+    * Host not required
+    """
+
+    _constraints = UrlConstraints(allowed_schemes=['amqp', 'amqps'])
+
+
+class RedisDsn(AnyUrl):
+    """A type that will accept any Redis DSN.
+
+    * User info required
+    * TLD not required
+    * Host required (e.g., `rediss://:pass@localhost`)
+    """
+
+    _constraints = UrlConstraints(
+        allowed_schemes=['redis', 'rediss'],
+        default_host='localhost',
+        default_port=6379,
+        default_path='/0',
+        host_required=True,
+    )
+
+    @property
+    def host(self) -> str:
+        """The required URL host."""
+        return self._url.host  # pyright: ignore[reportReturnType]
+
+
+class MongoDsn(_BaseMultiHostUrl):
+    """A type that will accept any MongoDB DSN.
+
+    * User info not required
+    * Database name not required
+    * Port not required
+    * User info may be passed without user part (e.g., `mongodb://mongodb0.example.com:27017`).
+    """
+
+    _constraints = UrlConstraints(allowed_schemes=['mongodb', 'mongodb+srv'], default_port=27017)
+
+
+class KafkaDsn(AnyUrl):
+    """A type that will accept any Kafka DSN.
+
+    * User info required
+    * TLD not required
+    * Host not required
+    """
+
+    _constraints = UrlConstraints(allowed_schemes=['kafka'], default_host='localhost', default_port=9092)
+
+
+class NatsDsn(_BaseMultiHostUrl):
+    """A type that will accept any NATS DSN.
+
+    NATS is a connective technology built for the ever increasingly hyper-connected world.
+    It is a single technology that enables applications to securely communicate across
+    any combination of cloud vendors, on-premise, edge, web and mobile, and devices.
+    More: https://nats.io
+    """
+
+    _constraints = UrlConstraints(
+        allowed_schemes=['nats', 'tls', 'ws', 'wss'], default_host='localhost', default_port=4222
+    )
+
+
+class MySQLDsn(AnyUrl):
+    """A type that will accept any MySQL DSN.
+
+    * User info required
+    * TLD not required
+    * Host not required
+    """
+
+    _constraints = UrlConstraints(
+        allowed_schemes=[
+            'mysql',
+            'mysql+mysqlconnector',
+            'mysql+aiomysql',
+            'mysql+asyncmy',
+            'mysql+mysqldb',
+            'mysql+pymysql',
+            'mysql+cymysql',
+            'mysql+pyodbc',
+        ],
+        default_port=3306,
+        host_required=True,
+    )
+
+
+class MariaDBDsn(AnyUrl):
+    """A type that will accept any MariaDB DSN.
+
+    * User info required
+    * TLD not required
+    * Host not required
+    """
+
+    _constraints = UrlConstraints(
+        allowed_schemes=['mariadb', 'mariadb+mariadbconnector', 'mariadb+pymysql'],
+        default_port=3306,
+    )
+
+
+class ClickHouseDsn(AnyUrl):
+    """A type that will accept any ClickHouse DSN.
+
+    * User info required
+    * TLD not required
+    * Host not required
+    """
+
+    _constraints = UrlConstraints(
+        allowed_schemes=['clickhouse+native', 'clickhouse+asynch'],
+        default_host='localhost',
+        default_port=9000,
+    )
+
+
+class SnowflakeDsn(AnyUrl):
+    """A type that will accept any Snowflake DSN.
+
+    * User info required
+    * TLD not required
+    * Host required
+    """
+
+    _constraints = UrlConstraints(
+        allowed_schemes=['snowflake'],
+        host_required=True,
+    )
+
+    @property
+    def host(self) -> str:
+        """The required URL host."""
+        return self._url.host  # pyright: ignore[reportReturnType]
+
+
+def import_email_validator() -> None:
+    global email_validator
+    try:
+        import email_validator
+    except ImportError as e:
+        raise ImportError('email-validator is not installed, run `pip install pydantic[email]`') from e
+    if not version('email-validator').partition('.')[0] == '2':
+        raise ImportError('email-validator version >= 2.0 required, run pip install -U email-validator')
+
+
+if TYPE_CHECKING:
+    EmailStr = Annotated[str, ...]
+else:
+
+    class EmailStr:
+        """
+        Info:
+            To use this type, you need to install the optional
+            [`email-validator`](https://github.com/JoshData/python-email-validator) package:
+
+            ```bash
+            pip install email-validator
+            ```
+
+        Validate email addresses.
+
+        ```python
+        from pydantic import BaseModel, EmailStr
+
+        class Model(BaseModel):
+            email: EmailStr
+
+        print(Model(email='contact@mail.com'))
+        #> email='contact@mail.com'
+        ```
+        """  # noqa: D212
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls,
+            _source: type[Any],
+            _handler: GetCoreSchemaHandler,
+        ) -> core_schema.CoreSchema:
+            import_email_validator()
+            return core_schema.no_info_after_validator_function(cls._validate, core_schema.str_schema())
+
+        @classmethod
+        def __get_pydantic_json_schema__(
+            cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler
+        ) -> JsonSchemaValue:
+            field_schema = handler(core_schema)
+            field_schema.update(type='string', format='email')
+            return field_schema
+
+        @classmethod
+        def _validate(cls, input_value: str, /) -> str:
+            return validate_email(input_value)[1]
+
+
+class NameEmail(_repr.Representation):
+    """
+    Info:
+        To use this type, you need to install the optional
+        [`email-validator`](https://github.com/JoshData/python-email-validator) package:
+
+        ```bash
+        pip install email-validator
+        ```
+
+    Validate a name and email address combination, as specified by
+    [RFC 5322](https://datatracker.ietf.org/doc/html/rfc5322#section-3.4).
+
+    The `NameEmail` has two properties: `name` and `email`.
+    In case the `name` is not provided, it's inferred from the email address.
+
+    ```python
+    from pydantic import BaseModel, NameEmail
+
+    class User(BaseModel):
+        email: NameEmail
+
+    user = User(email='Fred Bloggs <fred.bloggs@example.com>')
+    print(user.email)
+    #> Fred Bloggs <fred.bloggs@example.com>
+    print(user.email.name)
+    #> Fred Bloggs
+
+    user = User(email='fred.bloggs@example.com')
+    print(user.email)
+    #> fred.bloggs <fred.bloggs@example.com>
+    print(user.email.name)
+    #> fred.bloggs
+    ```
+    """  # noqa: D212
+
+    __slots__ = 'name', 'email'
+
+    def __init__(self, name: str, email: str):
+        self.name = name
+        self.email = email
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, NameEmail) and (self.name, self.email) == (other.name, other.email)
+
+    @classmethod
+    def __get_pydantic_json_schema__(
+        cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        field_schema = handler(core_schema)
+        field_schema.update(type='string', format='name-email')
+        return field_schema
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls,
+        _source: type[Any],
+        _handler: GetCoreSchemaHandler,
+    ) -> core_schema.CoreSchema:
+        import_email_validator()
+
+        return core_schema.no_info_after_validator_function(
+            cls._validate,
+            core_schema.json_or_python_schema(
+                json_schema=core_schema.str_schema(),
+                python_schema=core_schema.union_schema(
+                    [core_schema.is_instance_schema(cls), core_schema.str_schema()],
+                    custom_error_type='name_email_type',
+                    custom_error_message='Input is not a valid NameEmail',
+                ),
+                serialization=core_schema.to_string_ser_schema(),
+            ),
+        )
+
+    @classmethod
+    def _validate(cls, input_value: Self | str, /) -> Self:
+        if isinstance(input_value, str):
+            name, email = validate_email(input_value)
+            return cls(name, email)
+        else:
+            return input_value
+
+    def __str__(self) -> str:
+        if '@' in self.name:
+            return f'"{self.name}" <{self.email}>'
+
+        return f'{self.name} <{self.email}>'
+
+
+IPvAnyAddressType: TypeAlias = 'IPv4Address | IPv6Address'
+IPvAnyInterfaceType: TypeAlias = 'IPv4Interface | IPv6Interface'
+IPvAnyNetworkType: TypeAlias = 'IPv4Network | IPv6Network'
+
+if TYPE_CHECKING:
+    IPvAnyAddress = IPvAnyAddressType
+    IPvAnyInterface = IPvAnyInterfaceType
+    IPvAnyNetwork = IPvAnyNetworkType
+else:
+
+    class IPvAnyAddress:
+        """Validate an IPv4 or IPv6 address.
+
+        ```python
+        from pydantic import BaseModel
+        from pydantic.networks import IPvAnyAddress
+
+        class IpModel(BaseModel):
+            ip: IPvAnyAddress
+
+        print(IpModel(ip='127.0.0.1'))
+        #> ip=IPv4Address('127.0.0.1')
+
+        try:
+            IpModel(ip='http://www.example.com')
+        except ValueError as e:
+            print(e.errors())
+            '''
+            [
+                {
+                    'type': 'ip_any_address',
+                    'loc': ('ip',),
+                    'msg': 'value is not a valid IPv4 or IPv6 address',
+                    'input': 'http://www.example.com',
+                }
+            ]
+            '''
+        ```
+        """
+
+        __slots__ = ()
+
+        def __new__(cls, value: Any) -> IPvAnyAddressType:
+            """Validate an IPv4 or IPv6 address."""
+            try:
+                return IPv4Address(value)
+            except ValueError:
+                pass
+
+            try:
+                return IPv6Address(value)
+            except ValueError:
+                raise PydanticCustomError('ip_any_address', 'value is not a valid IPv4 or IPv6 address')
+
+        @classmethod
+        def __get_pydantic_json_schema__(
+            cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler
+        ) -> JsonSchemaValue:
+            field_schema = {}
+            field_schema.update(type='string', format='ipvanyaddress')
+            return field_schema
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls,
+            _source: type[Any],
+            _handler: GetCoreSchemaHandler,
+        ) -> core_schema.CoreSchema:
+            return core_schema.no_info_plain_validator_function(
+                cls._validate, serialization=core_schema.to_string_ser_schema()
+            )
+
+        @classmethod
+        def _validate(cls, input_value: Any, /) -> IPvAnyAddressType:
+            return cls(input_value)  # type: ignore[return-value]
+
+    class IPvAnyInterface:
+        """Validate an IPv4 or IPv6 interface."""
+
+        __slots__ = ()
+
+        def __new__(cls, value: NetworkType) -> IPvAnyInterfaceType:
+            """Validate an IPv4 or IPv6 interface."""
+            try:
+                return IPv4Interface(value)
+            except ValueError:
+                pass
+
+            try:
+                return IPv6Interface(value)
+            except ValueError:
+                raise PydanticCustomError('ip_any_interface', 'value is not a valid IPv4 or IPv6 interface')
+
+        @classmethod
+        def __get_pydantic_json_schema__(
+            cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler
+        ) -> JsonSchemaValue:
+            field_schema = {}
+            field_schema.update(type='string', format='ipvanyinterface')
+            return field_schema
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls,
+            _source: type[Any],
+            _handler: GetCoreSchemaHandler,
+        ) -> core_schema.CoreSchema:
+            return core_schema.no_info_plain_validator_function(
+                cls._validate, serialization=core_schema.to_string_ser_schema()
+            )
+
+        @classmethod
+        def _validate(cls, input_value: NetworkType, /) -> IPvAnyInterfaceType:
+            return cls(input_value)  # type: ignore[return-value]
+
+    class IPvAnyNetwork:
+        """Validate an IPv4 or IPv6 network."""
+
+        __slots__ = ()
+
+        def __new__(cls, value: NetworkType) -> IPvAnyNetworkType:
+            """Validate an IPv4 or IPv6 network."""
+            # Assume IP Network is defined with a default value for `strict` argument.
+            # Define your own class if you want to specify network address check strictness.
+            try:
+                return IPv4Network(value)
+            except ValueError:
+                pass
+
+            try:
+                return IPv6Network(value)
+            except ValueError:
+                raise PydanticCustomError('ip_any_network', 'value is not a valid IPv4 or IPv6 network')
+
+        @classmethod
+        def __get_pydantic_json_schema__(
+            cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler
+        ) -> JsonSchemaValue:
+            field_schema = {}
+            field_schema.update(type='string', format='ipvanynetwork')
+            return field_schema
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls,
+            _source: type[Any],
+            _handler: GetCoreSchemaHandler,
+        ) -> core_schema.CoreSchema:
+            return core_schema.no_info_plain_validator_function(
+                cls._validate, serialization=core_schema.to_string_ser_schema()
+            )
+
+        @classmethod
+        def _validate(cls, input_value: NetworkType, /) -> IPvAnyNetworkType:
+            return cls(input_value)  # type: ignore[return-value]
+
+
+def _build_pretty_email_regex() -> re.Pattern[str]:
+    name_chars = r'[\w!#$%&\'*+\-/=?^_`{|}~]'
+    unquoted_name_group = rf'((?:{name_chars}+\s+)*{name_chars}+)'
+    quoted_name_group = r'"((?:[^"]|\")+)"'
+    email_group = r'<(.+)>'
+    return re.compile(rf'\s*(?:{unquoted_name_group}|{quoted_name_group})?\s*{email_group}\s*')
+
+
+pretty_email_regex = _build_pretty_email_regex()
+
+MAX_EMAIL_LENGTH = 2048
+"""Maximum length for an email.
+A somewhat arbitrary but very generous number compared to what is allowed by most implementations.
+"""
+
+
+def validate_email(value: str) -> tuple[str, str]:
+    """Email address validation using [email-validator](https://pypi.org/project/email-validator/).
+
+    Returns:
+        A tuple containing the local part of the email (or the name for "pretty" email addresses)
+            and the normalized email.
+
+    Raises:
+        PydanticCustomError: If the email is invalid.
+
+    Note:
+        Note that:
+
+        * Raw IP address (literal) domain parts are not allowed.
+        * `"John Doe <local_part@domain.com>"` style "pretty" email addresses are processed.
+        * Spaces are striped from the beginning and end of addresses, but no error is raised.
+    """
+    if email_validator is None:
+        import_email_validator()
+
+    if len(value) > MAX_EMAIL_LENGTH:
+        raise PydanticCustomError(
+            'value_error',
+            'value is not a valid email address: {reason}',
+            {'reason': f'Length must not exceed {MAX_EMAIL_LENGTH} characters'},
+        )
+
+    m = pretty_email_regex.fullmatch(value)
+    name: str | None = None
+    if m:
+        unquoted_name, quoted_name, value = m.groups()
+        name = unquoted_name or quoted_name
+
+    email = value.strip()
+
+    try:
+        parts = email_validator.validate_email(email, check_deliverability=False)
+    except email_validator.EmailNotValidError as e:
+        raise PydanticCustomError(
+            'value_error', 'value is not a valid email address: {reason}', {'reason': str(e.args[0])}
+        ) from e
+
+    email = parts.normalized
+    assert email is not None
+    name = name or parts.local_part
+    return name, email
+
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/parse.py b/.venv/lib/python3.12/site-packages/pydantic/parse.py
new file mode 100644
index 00000000..68b7f046
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/parse.py
@@ -0,0 +1,5 @@
+"""The `parse` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/plugin/__init__.py b/.venv/lib/python3.12/site-packages/pydantic/plugin/__init__.py
new file mode 100644
index 00000000..d8215660
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/plugin/__init__.py
@@ -0,0 +1,171 @@
+"""Usage docs: https://docs.pydantic.dev/2.10/concepts/plugins#build-a-plugin
+
+Plugin interface for Pydantic plugins, and related types.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Callable, NamedTuple
+
+from pydantic_core import CoreConfig, CoreSchema, ValidationError
+from typing_extensions import Literal, Protocol, TypeAlias
+
+__all__ = (
+    'PydanticPluginProtocol',
+    'BaseValidateHandlerProtocol',
+    'ValidatePythonHandlerProtocol',
+    'ValidateJsonHandlerProtocol',
+    'ValidateStringsHandlerProtocol',
+    'NewSchemaReturns',
+    'SchemaTypePath',
+    'SchemaKind',
+)
+
+NewSchemaReturns: TypeAlias = 'tuple[ValidatePythonHandlerProtocol | None, ValidateJsonHandlerProtocol | None, ValidateStringsHandlerProtocol | None]'
+
+
+class SchemaTypePath(NamedTuple):
+    """Path defining where `schema_type` was defined, or where `TypeAdapter` was called."""
+
+    module: str
+    name: str
+
+
+SchemaKind: TypeAlias = Literal['BaseModel', 'TypeAdapter', 'dataclass', 'create_model', 'validate_call']
+
+
+class PydanticPluginProtocol(Protocol):
+    """Protocol defining the interface for Pydantic plugins."""
+
+    def new_schema_validator(
+        self,
+        schema: CoreSchema,
+        schema_type: Any,
+        schema_type_path: SchemaTypePath,
+        schema_kind: SchemaKind,
+        config: CoreConfig | None,
+        plugin_settings: dict[str, object],
+    ) -> tuple[
+        ValidatePythonHandlerProtocol | None, ValidateJsonHandlerProtocol | None, ValidateStringsHandlerProtocol | None
+    ]:
+        """This method is called for each plugin every time a new [`SchemaValidator`][pydantic_core.SchemaValidator]
+        is created.
+
+        It should return an event handler for each of the three validation methods, or `None` if the plugin does not
+        implement that method.
+
+        Args:
+            schema: The schema to validate against.
+            schema_type: The original type which the schema was created from, e.g. the model class.
+            schema_type_path: Path defining where `schema_type` was defined, or where `TypeAdapter` was called.
+            schema_kind: The kind of schema to validate against.
+            config: The config to use for validation.
+            plugin_settings: Any plugin settings.
+
+        Returns:
+            A tuple of optional event handlers for each of the three validation methods -
+                `validate_python`, `validate_json`, `validate_strings`.
+        """
+        raise NotImplementedError('Pydantic plugins should implement `new_schema_validator`.')
+
+
+class BaseValidateHandlerProtocol(Protocol):
+    """Base class for plugin callbacks protocols.
+
+    You shouldn't implement this protocol directly, instead use one of the subclasses with adds the correctly
+    typed `on_error` method.
+    """
+
+    on_enter: Callable[..., None]
+    """`on_enter` is changed to be more specific on all subclasses"""
+
+    def on_success(self, result: Any) -> None:
+        """Callback to be notified of successful validation.
+
+        Args:
+            result: The result of the validation.
+        """
+        return
+
+    def on_error(self, error: ValidationError) -> None:
+        """Callback to be notified of validation errors.
+
+        Args:
+            error: The validation error.
+        """
+        return
+
+    def on_exception(self, exception: Exception) -> None:
+        """Callback to be notified of validation exceptions.
+
+        Args:
+            exception: The exception raised during validation.
+        """
+        return
+
+
+class ValidatePythonHandlerProtocol(BaseValidateHandlerProtocol, Protocol):
+    """Event handler for `SchemaValidator.validate_python`."""
+
+    def on_enter(
+        self,
+        input: Any,
+        *,
+        strict: bool | None = None,
+        from_attributes: bool | None = None,
+        context: dict[str, Any] | None = None,
+        self_instance: Any | None = None,
+    ) -> None:
+        """Callback to be notified of validation start, and create an instance of the event handler.
+
+        Args:
+            input: The input to be validated.
+            strict: Whether to validate the object in strict mode.
+            from_attributes: Whether to validate objects as inputs by extracting attributes.
+            context: The context to use for validation, this is passed to functional validators.
+            self_instance: An instance of a model to set attributes on from validation, this is used when running
+                validation from the `__init__` method of a model.
+        """
+        pass
+
+
+class ValidateJsonHandlerProtocol(BaseValidateHandlerProtocol, Protocol):
+    """Event handler for `SchemaValidator.validate_json`."""
+
+    def on_enter(
+        self,
+        input: str | bytes | bytearray,
+        *,
+        strict: bool | None = None,
+        context: dict[str, Any] | None = None,
+        self_instance: Any | None = None,
+    ) -> None:
+        """Callback to be notified of validation start, and create an instance of the event handler.
+
+        Args:
+            input: The JSON data to be validated.
+            strict: Whether to validate the object in strict mode.
+            context: The context to use for validation, this is passed to functional validators.
+            self_instance: An instance of a model to set attributes on from validation, this is used when running
+                validation from the `__init__` method of a model.
+        """
+        pass
+
+
+StringInput: TypeAlias = 'dict[str, StringInput]'
+
+
+class ValidateStringsHandlerProtocol(BaseValidateHandlerProtocol, Protocol):
+    """Event handler for `SchemaValidator.validate_strings`."""
+
+    def on_enter(
+        self, input: StringInput, *, strict: bool | None = None, context: dict[str, Any] | None = None
+    ) -> None:
+        """Callback to be notified of validation start, and create an instance of the event handler.
+
+        Args:
+            input: The string data to be validated.
+            strict: Whether to validate the object in strict mode.
+            context: The context to use for validation, this is passed to functional validators.
+        """
+        pass
diff --git a/.venv/lib/python3.12/site-packages/pydantic/plugin/_loader.py b/.venv/lib/python3.12/site-packages/pydantic/plugin/_loader.py
new file mode 100644
index 00000000..2f90dc54
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/plugin/_loader.py
@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+import importlib.metadata as importlib_metadata
+import os
+import warnings
+from typing import TYPE_CHECKING, Final, Iterable
+
+if TYPE_CHECKING:
+    from . import PydanticPluginProtocol
+
+
+PYDANTIC_ENTRY_POINT_GROUP: Final[str] = 'pydantic'
+
+# cache of plugins
+_plugins: dict[str, PydanticPluginProtocol] | None = None
+# return no plugins while loading plugins to avoid recursion and errors while import plugins
+# this means that if plugins use pydantic
+_loading_plugins: bool = False
+
+
+def get_plugins() -> Iterable[PydanticPluginProtocol]:
+    """Load plugins for Pydantic.
+
+    Inspired by: https://github.com/pytest-dev/pluggy/blob/1.3.0/src/pluggy/_manager.py#L376-L402
+    """
+    disabled_plugins = os.getenv('PYDANTIC_DISABLE_PLUGINS')
+    global _plugins, _loading_plugins
+    if _loading_plugins:
+        # this happens when plugins themselves use pydantic, we return no plugins
+        return ()
+    elif disabled_plugins in ('__all__', '1', 'true'):
+        return ()
+    elif _plugins is None:
+        _plugins = {}
+        # set _loading_plugins so any plugins that use pydantic don't themselves use plugins
+        _loading_plugins = True
+        try:
+            for dist in importlib_metadata.distributions():
+                for entry_point in dist.entry_points:
+                    if entry_point.group != PYDANTIC_ENTRY_POINT_GROUP:
+                        continue
+                    if entry_point.value in _plugins:
+                        continue
+                    if disabled_plugins is not None and entry_point.name in disabled_plugins.split(','):
+                        continue
+                    try:
+                        _plugins[entry_point.value] = entry_point.load()
+                    except (ImportError, AttributeError) as e:
+                        warnings.warn(
+                            f'{e.__class__.__name__} while loading the `{entry_point.name}` Pydantic plugin, '
+                            f'this plugin will not be installed.\n\n{e!r}'
+                        )
+        finally:
+            _loading_plugins = False
+
+    return _plugins.values()
diff --git a/.venv/lib/python3.12/site-packages/pydantic/plugin/_schema_validator.py b/.venv/lib/python3.12/site-packages/pydantic/plugin/_schema_validator.py
new file mode 100644
index 00000000..21287f44
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/plugin/_schema_validator.py
@@ -0,0 +1,139 @@
+"""Pluggable schema validator for pydantic."""
+
+from __future__ import annotations
+
+import functools
+from typing import TYPE_CHECKING, Any, Callable, Iterable, TypeVar
+
+from pydantic_core import CoreConfig, CoreSchema, SchemaValidator, ValidationError
+from typing_extensions import Literal, ParamSpec
+
+if TYPE_CHECKING:
+    from . import BaseValidateHandlerProtocol, PydanticPluginProtocol, SchemaKind, SchemaTypePath
+
+
+P = ParamSpec('P')
+R = TypeVar('R')
+Event = Literal['on_validate_python', 'on_validate_json', 'on_validate_strings']
+events: list[Event] = list(Event.__args__)  # type: ignore
+
+
+def create_schema_validator(
+    schema: CoreSchema,
+    schema_type: Any,
+    schema_type_module: str,
+    schema_type_name: str,
+    schema_kind: SchemaKind,
+    config: CoreConfig | None = None,
+    plugin_settings: dict[str, Any] | None = None,
+) -> SchemaValidator | PluggableSchemaValidator:
+    """Create a `SchemaValidator` or `PluggableSchemaValidator` if plugins are installed.
+
+    Returns:
+        If plugins are installed then return `PluggableSchemaValidator`, otherwise return `SchemaValidator`.
+    """
+    from . import SchemaTypePath
+    from ._loader import get_plugins
+
+    plugins = get_plugins()
+    if plugins:
+        return PluggableSchemaValidator(
+            schema,
+            schema_type,
+            SchemaTypePath(schema_type_module, schema_type_name),
+            schema_kind,
+            config,
+            plugins,
+            plugin_settings or {},
+        )
+    else:
+        return SchemaValidator(schema, config)
+
+
+class PluggableSchemaValidator:
+    """Pluggable schema validator."""
+
+    __slots__ = '_schema_validator', 'validate_json', 'validate_python', 'validate_strings'
+
+    def __init__(
+        self,
+        schema: CoreSchema,
+        schema_type: Any,
+        schema_type_path: SchemaTypePath,
+        schema_kind: SchemaKind,
+        config: CoreConfig | None,
+        plugins: Iterable[PydanticPluginProtocol],
+        plugin_settings: dict[str, Any],
+    ) -> None:
+        self._schema_validator = SchemaValidator(schema, config)
+
+        python_event_handlers: list[BaseValidateHandlerProtocol] = []
+        json_event_handlers: list[BaseValidateHandlerProtocol] = []
+        strings_event_handlers: list[BaseValidateHandlerProtocol] = []
+        for plugin in plugins:
+            try:
+                p, j, s = plugin.new_schema_validator(
+                    schema, schema_type, schema_type_path, schema_kind, config, plugin_settings
+                )
+            except TypeError as e:  # pragma: no cover
+                raise TypeError(f'Error using plugin `{plugin.__module__}:{plugin.__class__.__name__}`: {e}') from e
+            if p is not None:
+                python_event_handlers.append(p)
+            if j is not None:
+                json_event_handlers.append(j)
+            if s is not None:
+                strings_event_handlers.append(s)
+
+        self.validate_python = build_wrapper(self._schema_validator.validate_python, python_event_handlers)
+        self.validate_json = build_wrapper(self._schema_validator.validate_json, json_event_handlers)
+        self.validate_strings = build_wrapper(self._schema_validator.validate_strings, strings_event_handlers)
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._schema_validator, name)
+
+
+def build_wrapper(func: Callable[P, R], event_handlers: list[BaseValidateHandlerProtocol]) -> Callable[P, R]:
+    if not event_handlers:
+        return func
+    else:
+        on_enters = tuple(h.on_enter for h in event_handlers if filter_handlers(h, 'on_enter'))
+        on_successes = tuple(h.on_success for h in event_handlers if filter_handlers(h, 'on_success'))
+        on_errors = tuple(h.on_error for h in event_handlers if filter_handlers(h, 'on_error'))
+        on_exceptions = tuple(h.on_exception for h in event_handlers if filter_handlers(h, 'on_exception'))
+
+        @functools.wraps(func)
+        def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            for on_enter_handler in on_enters:
+                on_enter_handler(*args, **kwargs)
+
+            try:
+                result = func(*args, **kwargs)
+            except ValidationError as error:
+                for on_error_handler in on_errors:
+                    on_error_handler(error)
+                raise
+            except Exception as exception:
+                for on_exception_handler in on_exceptions:
+                    on_exception_handler(exception)
+                raise
+            else:
+                for on_success_handler in on_successes:
+                    on_success_handler(result)
+                return result
+
+        return wrapper
+
+
+def filter_handlers(handler_cls: BaseValidateHandlerProtocol, method_name: str) -> bool:
+    """Filter out handler methods which are not implemented by the plugin directly - e.g. are missing
+    or are inherited from the protocol.
+    """
+    handler = getattr(handler_cls, method_name, None)
+    if handler is None:
+        return False
+    elif handler.__module__ == 'pydantic.plugin':
+        # this is the original handler, from the protocol due to runtime inheritance
+        # we don't want to call it
+        return False
+    else:
+        return True
diff --git a/.venv/lib/python3.12/site-packages/pydantic/py.typed b/.venv/lib/python3.12/site-packages/pydantic/py.typed
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/py.typed
diff --git a/.venv/lib/python3.12/site-packages/pydantic/root_model.py b/.venv/lib/python3.12/site-packages/pydantic/root_model.py
new file mode 100644
index 00000000..8351651d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/root_model.py
@@ -0,0 +1,156 @@
+"""RootModel class and type definitions."""
+
+from __future__ import annotations as _annotations
+
+import typing
+from copy import copy, deepcopy
+
+from pydantic_core import PydanticUndefined
+
+from . import PydanticUserError
+from ._internal import _model_construction, _repr
+from .main import BaseModel, _object_setattr
+
+if typing.TYPE_CHECKING:
+    from typing import Any
+
+    from typing_extensions import Literal, Self, dataclass_transform
+
+    from .fields import Field as PydanticModelField
+    from .fields import PrivateAttr as PydanticModelPrivateAttr
+
+    # dataclass_transform could be applied to RootModel directly, but `ModelMetaclass`'s dataclass_transform
+    # takes priority (at least with pyright). We trick type checkers into thinking we apply dataclass_transform
+    # on a new metaclass.
+    @dataclass_transform(kw_only_default=False, field_specifiers=(PydanticModelField, PydanticModelPrivateAttr))
+    class _RootModelMetaclass(_model_construction.ModelMetaclass): ...
+else:
+    _RootModelMetaclass = _model_construction.ModelMetaclass
+
+__all__ = ('RootModel',)
+
+RootModelRootType = typing.TypeVar('RootModelRootType')
+
+
+class RootModel(BaseModel, typing.Generic[RootModelRootType], metaclass=_RootModelMetaclass):
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/models/#rootmodel-and-custom-root-types
+
+    A Pydantic `BaseModel` for the root object of the model.
+
+    Attributes:
+        root: The root object of the model.
+        __pydantic_root_model__: Whether the model is a RootModel.
+        __pydantic_private__: Private fields in the model.
+        __pydantic_extra__: Extra fields in the model.
+
+    """
+
+    __pydantic_root_model__ = True
+    __pydantic_private__ = None
+    __pydantic_extra__ = None
+
+    root: RootModelRootType
+
+    def __init_subclass__(cls, **kwargs):
+        extra = cls.model_config.get('extra')
+        if extra is not None:
+            raise PydanticUserError(
+                "`RootModel` does not support setting `model_config['extra']`", code='root-model-extra'
+            )
+        super().__init_subclass__(**kwargs)
+
+    def __init__(self, /, root: RootModelRootType = PydanticUndefined, **data) -> None:  # type: ignore
+        __tracebackhide__ = True
+        if data:
+            if root is not PydanticUndefined:
+                raise ValueError(
+                    '"RootModel.__init__" accepts either a single positional argument or arbitrary keyword arguments'
+                )
+            root = data  # type: ignore
+        self.__pydantic_validator__.validate_python(root, self_instance=self)
+
+    __init__.__pydantic_base_init__ = True  # pyright: ignore[reportFunctionMemberAccess]
+
+    @classmethod
+    def model_construct(cls, root: RootModelRootType, _fields_set: set[str] | None = None) -> Self:  # type: ignore
+        """Create a new model using the provided root object and update fields set.
+
+        Args:
+            root: The root object of the model.
+            _fields_set: The set of fields to be updated.
+
+        Returns:
+            The new model.
+
+        Raises:
+            NotImplemented: If the model is not a subclass of `RootModel`.
+        """
+        return super().model_construct(root=root, _fields_set=_fields_set)
+
+    def __getstate__(self) -> dict[Any, Any]:
+        return {
+            '__dict__': self.__dict__,
+            '__pydantic_fields_set__': self.__pydantic_fields_set__,
+        }
+
+    def __setstate__(self, state: dict[Any, Any]) -> None:
+        _object_setattr(self, '__pydantic_fields_set__', state['__pydantic_fields_set__'])
+        _object_setattr(self, '__dict__', state['__dict__'])
+
+    def __copy__(self) -> Self:
+        """Returns a shallow copy of the model."""
+        cls = type(self)
+        m = cls.__new__(cls)
+        _object_setattr(m, '__dict__', copy(self.__dict__))
+        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
+        return m
+
+    def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
+        """Returns a deep copy of the model."""
+        cls = type(self)
+        m = cls.__new__(cls)
+        _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
+        # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
+        # and attempting a deepcopy would be marginally slower.
+        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
+        return m
+
+    if typing.TYPE_CHECKING:
+
+        def model_dump(  # type: ignore
+            self,
+            *,
+            mode: Literal['json', 'python'] | str = 'python',
+            include: Any = None,
+            exclude: Any = None,
+            context: dict[str, Any] | None = None,
+            by_alias: bool = False,
+            exclude_unset: bool = False,
+            exclude_defaults: bool = False,
+            exclude_none: bool = False,
+            round_trip: bool = False,
+            warnings: bool | Literal['none', 'warn', 'error'] = True,
+            serialize_as_any: bool = False,
+        ) -> Any:
+            """This method is included just to get a more accurate return type for type checkers.
+            It is included in this `if TYPE_CHECKING:` block since no override is actually necessary.
+
+            See the documentation of `BaseModel.model_dump` for more details about the arguments.
+
+            Generally, this method will have a return type of `RootModelRootType`, assuming that `RootModelRootType` is
+            not a `BaseModel` subclass. If `RootModelRootType` is a `BaseModel` subclass, then the return
+            type will likely be `dict[str, Any]`, as `model_dump` calls are recursive. The return type could
+            even be something different, in the case of a custom serializer.
+            Thus, `Any` is used here to catch all of these cases.
+            """
+            ...
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, RootModel):
+            return NotImplemented
+        return self.__pydantic_fields__['root'].annotation == other.__pydantic_fields__[
+            'root'
+        ].annotation and super().__eq__(other)
+
+    def __repr_args__(self) -> _repr.ReprArgs:
+        yield 'root', self.root
diff --git a/.venv/lib/python3.12/site-packages/pydantic/schema.py b/.venv/lib/python3.12/site-packages/pydantic/schema.py
new file mode 100644
index 00000000..a3245a61
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/schema.py
@@ -0,0 +1,5 @@
+"""The `schema` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/tools.py b/.venv/lib/python3.12/site-packages/pydantic/tools.py
new file mode 100644
index 00000000..fdc68c4f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/tools.py
@@ -0,0 +1,5 @@
+"""The `tools` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/type_adapter.py b/.venv/lib/python3.12/site-packages/pydantic/type_adapter.py
new file mode 100644
index 00000000..b32c05cd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/type_adapter.py
@@ -0,0 +1,676 @@
+"""Type adapter specification."""
+
+from __future__ import annotations as _annotations
+
+import sys
+from dataclasses import is_dataclass
+from types import FrameType
+from typing import (
+    Any,
+    Generic,
+    Iterable,
+    Literal,
+    TypeVar,
+    cast,
+    final,
+    overload,
+)
+
+from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator, Some
+from typing_extensions import ParamSpec, is_typeddict
+
+from pydantic.errors import PydanticUserError
+from pydantic.main import BaseModel, IncEx
+
+from ._internal import _config, _generate_schema, _mock_val_ser, _namespace_utils, _repr, _typing_extra, _utils
+from .config import ConfigDict
+from .errors import PydanticUndefinedAnnotation
+from .json_schema import (
+    DEFAULT_REF_TEMPLATE,
+    GenerateJsonSchema,
+    JsonSchemaKeyT,
+    JsonSchemaMode,
+    JsonSchemaValue,
+)
+from .plugin._schema_validator import PluggableSchemaValidator, create_schema_validator
+
+T = TypeVar('T')
+R = TypeVar('R')
+P = ParamSpec('P')
+TypeAdapterT = TypeVar('TypeAdapterT', bound='TypeAdapter')
+
+
+def _getattr_no_parents(obj: Any, attribute: str) -> Any:
+    """Returns the attribute value without attempting to look up attributes from parent types."""
+    if hasattr(obj, '__dict__'):
+        try:
+            return obj.__dict__[attribute]
+        except KeyError:
+            pass
+
+    slots = getattr(obj, '__slots__', None)
+    if slots is not None and attribute in slots:
+        return getattr(obj, attribute)
+    else:
+        raise AttributeError(attribute)
+
+
+def _type_has_config(type_: Any) -> bool:
+    """Returns whether the type has config."""
+    type_ = _typing_extra.annotated_type(type_) or type_
+    try:
+        return issubclass(type_, BaseModel) or is_dataclass(type_) or is_typeddict(type_)
+    except TypeError:
+        # type is not a class
+        return False
+
+
+@final
+class TypeAdapter(Generic[T]):
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/type_adapter/
+
+    Type adapters provide a flexible way to perform validation and serialization based on a Python type.
+
+    A `TypeAdapter` instance exposes some of the functionality from `BaseModel` instance methods
+    for types that do not have such methods (such as dataclasses, primitive types, and more).
+
+    **Note:** `TypeAdapter` instances are not types, and cannot be used as type annotations for fields.
+
+    Args:
+        type: The type associated with the `TypeAdapter`.
+        config: Configuration for the `TypeAdapter`, should be a dictionary conforming to
+            [`ConfigDict`][pydantic.config.ConfigDict].
+
+            !!! note
+                You cannot provide a configuration when instantiating a `TypeAdapter` if the type you're using
+                has its own config that cannot be overridden (ex: `BaseModel`, `TypedDict`, and `dataclass`). A
+                [`type-adapter-config-unused`](../errors/usage_errors.md#type-adapter-config-unused) error will
+                be raised in this case.
+        _parent_depth: Depth at which to search for the [parent frame][frame-objects]. This frame is used when
+            resolving forward annotations during schema building, by looking for the globals and locals of this
+            frame. Defaults to 2, which will result in the frame where the `TypeAdapter` was instantiated.
+
+            !!! note
+                This parameter is named with an underscore to suggest its private nature and discourage use.
+                It may be deprecated in a minor version, so we only recommend using it if you're comfortable
+                with potential change in behavior/support. It's default value is 2 because internally,
+                the `TypeAdapter` class makes another call to fetch the frame.
+        module: The module that passes to plugin if provided.
+
+    Attributes:
+        core_schema: The core schema for the type.
+        validator: The schema validator for the type.
+        serializer: The schema serializer for the type.
+        pydantic_complete: Whether the core schema for the type is successfully built.
+
+    ??? tip "Compatibility with `mypy`"
+        Depending on the type used, `mypy` might raise an error when instantiating a `TypeAdapter`. As a workaround, you can explicitly
+        annotate your variable:
+
+        ```py
+        from typing import Union
+
+        from pydantic import TypeAdapter
+
+        ta: TypeAdapter[Union[str, int]] = TypeAdapter(Union[str, int])  # type: ignore[arg-type]
+        ```
+
+    ??? info "Namespace management nuances and implementation details"
+
+        Here, we collect some notes on namespace management, and subtle differences from `BaseModel`:
+
+        `BaseModel` uses its own `__module__` to find out where it was defined
+        and then looks for symbols to resolve forward references in those globals.
+        On the other hand, `TypeAdapter` can be initialized with arbitrary objects,
+        which may not be types and thus do not have a `__module__` available.
+        So instead we look at the globals in our parent stack frame.
+
+        It is expected that the `ns_resolver` passed to this function will have the correct
+        namespace for the type we're adapting. See the source code for `TypeAdapter.__init__`
+        and `TypeAdapter.rebuild` for various ways to construct this namespace.
+
+        This works for the case where this function is called in a module that
+        has the target of forward references in its scope, but
+        does not always work for more complex cases.
+
+        For example, take the following:
+
+        ```python {title="a.py"}
+        from typing import Dict, List
+
+        IntList = List[int]
+        OuterDict = Dict[str, 'IntList']
+        ```
+
+        ```python {test="skip" title="b.py"}
+        from a import OuterDict
+
+        from pydantic import TypeAdapter
+
+        IntList = int  # replaces the symbol the forward reference is looking for
+        v = TypeAdapter(OuterDict)
+        v({'x': 1})  # should fail but doesn't
+        ```
+
+        If `OuterDict` were a `BaseModel`, this would work because it would resolve
+        the forward reference within the `a.py` namespace.
+        But `TypeAdapter(OuterDict)` can't determine what module `OuterDict` came from.
+
+        In other words, the assumption that _all_ forward references exist in the
+        module we are being called from is not technically always true.
+        Although most of the time it is and it works fine for recursive models and such,
+        `BaseModel`'s behavior isn't perfect either and _can_ break in similar ways,
+        so there is no right or wrong between the two.
+
+        But at the very least this behavior is _subtly_ different from `BaseModel`'s.
+    """
+
+    core_schema: CoreSchema
+    validator: SchemaValidator | PluggableSchemaValidator
+    serializer: SchemaSerializer
+    pydantic_complete: bool
+
+    @overload
+    def __init__(
+        self,
+        type: type[T],
+        *,
+        config: ConfigDict | None = ...,
+        _parent_depth: int = ...,
+        module: str | None = ...,
+    ) -> None: ...
+
+    # This second overload is for unsupported special forms (such as Annotated, Union, etc.)
+    # Currently there is no way to type this correctly
+    # See https://github.com/python/typing/pull/1618
+    @overload
+    def __init__(
+        self,
+        type: Any,
+        *,
+        config: ConfigDict | None = ...,
+        _parent_depth: int = ...,
+        module: str | None = ...,
+    ) -> None: ...
+
+    def __init__(
+        self,
+        type: Any,
+        *,
+        config: ConfigDict | None = None,
+        _parent_depth: int = 2,
+        module: str | None = None,
+    ) -> None:
+        if _type_has_config(type) and config is not None:
+            raise PydanticUserError(
+                'Cannot use `config` when the type is a BaseModel, dataclass or TypedDict.'
+                ' These types can have their own config and setting the config via the `config`'
+                ' parameter to TypeAdapter will not override it, thus the `config` you passed to'
+                ' TypeAdapter becomes meaningless, which is probably not what you want.',
+                code='type-adapter-config-unused',
+            )
+
+        self._type = type
+        self._config = config
+        self._parent_depth = _parent_depth
+        self.pydantic_complete = False
+
+        parent_frame = self._fetch_parent_frame()
+        if parent_frame is not None:
+            globalns = parent_frame.f_globals
+            # Do not provide a local ns if the type adapter happens to be instantiated at the module level:
+            localns = parent_frame.f_locals if parent_frame.f_locals is not globalns else {}
+        else:
+            globalns = {}
+            localns = {}
+
+        self._module_name = module or cast(str, globalns.get('__name__', ''))
+        self._init_core_attrs(
+            ns_resolver=_namespace_utils.NsResolver(
+                namespaces_tuple=_namespace_utils.NamespacesTuple(locals=localns, globals=globalns),
+                parent_namespace=localns,
+            ),
+            force=False,
+        )
+
+    def _fetch_parent_frame(self) -> FrameType | None:
+        frame = sys._getframe(self._parent_depth)
+        if frame.f_globals.get('__name__') == 'typing':
+            # Because `TypeAdapter` is generic, explicitly parametrizing the class results
+            # in a `typing._GenericAlias` instance, which proxies instantiation calls to the
+            # "real" `TypeAdapter` class and thus adding an extra frame to the call. To avoid
+            # pulling anything from the `typing` module, use the correct frame (the one before):
+            return frame.f_back
+
+        return frame
+
+    def _init_core_attrs(
+        self, ns_resolver: _namespace_utils.NsResolver, force: bool, raise_errors: bool = False
+    ) -> bool:
+        """Initialize the core schema, validator, and serializer for the type.
+
+        Args:
+            ns_resolver: The namespace resolver to use when building the core schema for the adapted type.
+            force: Whether to force the construction of the core schema, validator, and serializer.
+                If `force` is set to `False` and `_defer_build` is `True`, the core schema, validator, and serializer will be set to mocks.
+            raise_errors: Whether to raise errors if initializing any of the core attrs fails.
+
+        Returns:
+            `True` if the core schema, validator, and serializer were successfully initialized, otherwise `False`.
+
+        Raises:
+            PydanticUndefinedAnnotation: If `PydanticUndefinedAnnotation` occurs in`__get_pydantic_core_schema__`
+                and `raise_errors=True`.
+        """
+        if not force and self._defer_build:
+            _mock_val_ser.set_type_adapter_mocks(self, str(self._type))
+            self.pydantic_complete = False
+            return False
+
+        try:
+            self.core_schema = _getattr_no_parents(self._type, '__pydantic_core_schema__')
+            self.validator = _getattr_no_parents(self._type, '__pydantic_validator__')
+            self.serializer = _getattr_no_parents(self._type, '__pydantic_serializer__')
+
+            # TODO: we don't go through the rebuild logic here directly because we don't want
+            # to repeat all of the namespace fetching logic that we've already done
+            # so we simply skip to the block below that does the actual schema generation
+            if (
+                isinstance(self.core_schema, _mock_val_ser.MockCoreSchema)
+                or isinstance(self.validator, _mock_val_ser.MockValSer)
+                or isinstance(self.serializer, _mock_val_ser.MockValSer)
+            ):
+                raise AttributeError()
+        except AttributeError:
+            config_wrapper = _config.ConfigWrapper(self._config)
+
+            schema_generator = _generate_schema.GenerateSchema(config_wrapper, ns_resolver=ns_resolver)
+
+            try:
+                core_schema = schema_generator.generate_schema(self._type)
+            except PydanticUndefinedAnnotation:
+                if raise_errors:
+                    raise
+                _mock_val_ser.set_type_adapter_mocks(self, str(self._type))
+                return False
+
+            try:
+                self.core_schema = schema_generator.clean_schema(core_schema)
+            except schema_generator.CollectedInvalid:
+                _mock_val_ser.set_type_adapter_mocks(self, str(self._type))
+                return False
+
+            core_config = config_wrapper.core_config(None)
+
+            self.validator = create_schema_validator(
+                schema=self.core_schema,
+                schema_type=self._type,
+                schema_type_module=self._module_name,
+                schema_type_name=str(self._type),
+                schema_kind='TypeAdapter',
+                config=core_config,
+                plugin_settings=config_wrapper.plugin_settings,
+            )
+            self.serializer = SchemaSerializer(self.core_schema, core_config)
+
+        self.pydantic_complete = True
+        return True
+
+    @property
+    def _defer_build(self) -> bool:
+        config = self._config if self._config is not None else self._model_config
+        if config:
+            return config.get('defer_build') is True
+        return False
+
+    @property
+    def _model_config(self) -> ConfigDict | None:
+        type_: Any = _typing_extra.annotated_type(self._type) or self._type  # Eg FastAPI heavily uses Annotated
+        if _utils.lenient_issubclass(type_, BaseModel):
+            return type_.model_config
+        return getattr(type_, '__pydantic_config__', None)
+
+    def __repr__(self) -> str:
+        return f'TypeAdapter({_repr.display_as_type(self._type)})'
+
+    def rebuild(
+        self,
+        *,
+        force: bool = False,
+        raise_errors: bool = True,
+        _parent_namespace_depth: int = 2,
+        _types_namespace: _namespace_utils.MappingNamespace | None = None,
+    ) -> bool | None:
+        """Try to rebuild the pydantic-core schema for the adapter's type.
+
+        This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
+        the initial attempt to build the schema, and automatic rebuilding fails.
+
+        Args:
+            force: Whether to force the rebuilding of the type adapter's schema, defaults to `False`.
+            raise_errors: Whether to raise errors, defaults to `True`.
+            _parent_namespace_depth: Depth at which to search for the [parent frame][frame-objects]. This
+                frame is used when resolving forward annotations during schema rebuilding, by looking for
+                the locals of this frame. Defaults to 2, which will result in the frame where the method
+                was called.
+            _types_namespace: An explicit types namespace to use, instead of using the local namespace
+                from the parent frame. Defaults to `None`.
+
+        Returns:
+            Returns `None` if the schema is already "complete" and rebuilding was not required.
+            If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
+        """
+        if not force and self.pydantic_complete:
+            return None
+
+        if _types_namespace is not None:
+            rebuild_ns = _types_namespace
+        elif _parent_namespace_depth > 0:
+            rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
+        else:
+            rebuild_ns = {}
+
+        # we have to manually fetch globals here because there's no type on the stack of the NsResolver
+        # and so we skip the globalns = get_module_ns_of(typ) call that would normally happen
+        globalns = sys._getframe(max(_parent_namespace_depth - 1, 1)).f_globals
+        ns_resolver = _namespace_utils.NsResolver(
+            namespaces_tuple=_namespace_utils.NamespacesTuple(locals=rebuild_ns, globals=globalns),
+            parent_namespace=rebuild_ns,
+        )
+        return self._init_core_attrs(ns_resolver=ns_resolver, force=True, raise_errors=raise_errors)
+
+    def validate_python(
+        self,
+        object: Any,
+        /,
+        *,
+        strict: bool | None = None,
+        from_attributes: bool | None = None,
+        context: dict[str, Any] | None = None,
+        experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False,
+    ) -> T:
+        """Validate a Python object against the model.
+
+        Args:
+            object: The Python object to validate against the model.
+            strict: Whether to strictly check types.
+            from_attributes: Whether to extract data from object attributes.
+            context: Additional context to pass to the validator.
+            experimental_allow_partial: **Experimental** whether to enable
+                [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.
+                * False / 'off': Default behavior, no partial validation.
+                * True / 'on': Enable partial validation.
+                * 'trailing-strings': Enable partial validation and allow trailing strings in the input.
+
+        !!! note
+            When using `TypeAdapter` with a Pydantic `dataclass`, the use of the `from_attributes`
+            argument is not supported.
+
+        Returns:
+            The validated object.
+        """
+        return self.validator.validate_python(
+            object,
+            strict=strict,
+            from_attributes=from_attributes,
+            context=context,
+            allow_partial=experimental_allow_partial,
+        )
+
+    def validate_json(
+        self,
+        data: str | bytes | bytearray,
+        /,
+        *,
+        strict: bool | None = None,
+        context: dict[str, Any] | None = None,
+        experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False,
+    ) -> T:
+        """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
+
+        Validate a JSON string or bytes against the model.
+
+        Args:
+            data: The JSON data to validate against the model.
+            strict: Whether to strictly check types.
+            context: Additional context to use during validation.
+            experimental_allow_partial: **Experimental** whether to enable
+                [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.
+                * False / 'off': Default behavior, no partial validation.
+                * True / 'on': Enable partial validation.
+                * 'trailing-strings': Enable partial validation and allow trailing strings in the input.
+
+        Returns:
+            The validated object.
+        """
+        return self.validator.validate_json(
+            data, strict=strict, context=context, allow_partial=experimental_allow_partial
+        )
+
+    def validate_strings(
+        self,
+        obj: Any,
+        /,
+        *,
+        strict: bool | None = None,
+        context: dict[str, Any] | None = None,
+        experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False,
+    ) -> T:
+        """Validate object contains string data against the model.
+
+        Args:
+            obj: The object contains string data to validate.
+            strict: Whether to strictly check types.
+            context: Additional context to use during validation.
+            experimental_allow_partial: **Experimental** whether to enable
+                [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.
+                * False / 'off': Default behavior, no partial validation.
+                * True / 'on': Enable partial validation.
+                * 'trailing-strings': Enable partial validation and allow trailing strings in the input.
+
+        Returns:
+            The validated object.
+        """
+        return self.validator.validate_strings(
+            obj, strict=strict, context=context, allow_partial=experimental_allow_partial
+        )
+
+    def get_default_value(self, *, strict: bool | None = None, context: dict[str, Any] | None = None) -> Some[T] | None:
+        """Get the default value for the wrapped type.
+
+        Args:
+            strict: Whether to strictly check types.
+            context: Additional context to pass to the validator.
+
+        Returns:
+            The default value wrapped in a `Some` if there is one or None if not.
+        """
+        return self.validator.get_default_value(strict=strict, context=context)
+
+    def dump_python(
+        self,
+        instance: T,
+        /,
+        *,
+        mode: Literal['json', 'python'] = 'python',
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        round_trip: bool = False,
+        warnings: bool | Literal['none', 'warn', 'error'] = True,
+        serialize_as_any: bool = False,
+        context: dict[str, Any] | None = None,
+    ) -> Any:
+        """Dump an instance of the adapted type to a Python object.
+
+        Args:
+            instance: The Python object to serialize.
+            mode: The output format.
+            include: Fields to include in the output.
+            exclude: Fields to exclude from the output.
+            by_alias: Whether to use alias names for field names.
+            exclude_unset: Whether to exclude unset fields.
+            exclude_defaults: Whether to exclude fields with default values.
+            exclude_none: Whether to exclude fields with None values.
+            round_trip: Whether to output the serialized data in a way that is compatible with deserialization.
+            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
+                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
+            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
+            context: Additional context to pass to the serializer.
+
+        Returns:
+            The serialized object.
+        """
+        return self.serializer.to_python(
+            instance,
+            mode=mode,
+            by_alias=by_alias,
+            include=include,
+            exclude=exclude,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            round_trip=round_trip,
+            warnings=warnings,
+            serialize_as_any=serialize_as_any,
+            context=context,
+        )
+
+    def dump_json(
+        self,
+        instance: T,
+        /,
+        *,
+        indent: int | None = None,
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        round_trip: bool = False,
+        warnings: bool | Literal['none', 'warn', 'error'] = True,
+        serialize_as_any: bool = False,
+        context: dict[str, Any] | None = None,
+    ) -> bytes:
+        """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-serialization
+
+        Serialize an instance of the adapted type to JSON.
+
+        Args:
+            instance: The instance to be serialized.
+            indent: Number of spaces for JSON indentation.
+            include: Fields to include.
+            exclude: Fields to exclude.
+            by_alias: Whether to use alias names for field names.
+            exclude_unset: Whether to exclude unset fields.
+            exclude_defaults: Whether to exclude fields with default values.
+            exclude_none: Whether to exclude fields with a value of `None`.
+            round_trip: Whether to serialize and deserialize the instance to ensure round-tripping.
+            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
+                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
+            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
+            context: Additional context to pass to the serializer.
+
+        Returns:
+            The JSON representation of the given instance as bytes.
+        """
+        return self.serializer.to_json(
+            instance,
+            indent=indent,
+            include=include,
+            exclude=exclude,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            round_trip=round_trip,
+            warnings=warnings,
+            serialize_as_any=serialize_as_any,
+            context=context,
+        )
+
+    def json_schema(
+        self,
+        *,
+        by_alias: bool = True,
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+        mode: JsonSchemaMode = 'validation',
+    ) -> dict[str, Any]:
+        """Generate a JSON schema for the adapted type.
+
+        Args:
+            by_alias: Whether to use alias names for field names.
+            ref_template: The format string used for generating $ref strings.
+            schema_generator: The generator class used for creating the schema.
+            mode: The mode to use for schema generation.
+
+        Returns:
+            The JSON schema for the model as a dictionary.
+        """
+        schema_generator_instance = schema_generator(by_alias=by_alias, ref_template=ref_template)
+        if isinstance(self.core_schema, _mock_val_ser.MockCoreSchema):
+            self.core_schema.rebuild()
+            assert not isinstance(self.core_schema, _mock_val_ser.MockCoreSchema), 'this is a bug! please report it'
+        return schema_generator_instance.generate(self.core_schema, mode=mode)
+
+    @staticmethod
+    def json_schemas(
+        inputs: Iterable[tuple[JsonSchemaKeyT, JsonSchemaMode, TypeAdapter[Any]]],
+        /,
+        *,
+        by_alias: bool = True,
+        title: str | None = None,
+        description: str | None = None,
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+    ) -> tuple[dict[tuple[JsonSchemaKeyT, JsonSchemaMode], JsonSchemaValue], JsonSchemaValue]:
+        """Generate a JSON schema including definitions from multiple type adapters.
+
+        Args:
+            inputs: Inputs to schema generation. The first two items will form the keys of the (first)
+                output mapping; the type adapters will provide the core schemas that get converted into
+                definitions in the output JSON schema.
+            by_alias: Whether to use alias names.
+            title: The title for the schema.
+            description: The description for the schema.
+            ref_template: The format string used for generating $ref strings.
+            schema_generator: The generator class used for creating the schema.
+
+        Returns:
+            A tuple where:
+
+                - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and
+                    whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have
+                    JsonRef references to definitions that are defined in the second returned element.)
+                - The second element is a JSON schema containing all definitions referenced in the first returned
+                    element, along with the optional title and description keys.
+
+        """
+        schema_generator_instance = schema_generator(by_alias=by_alias, ref_template=ref_template)
+
+        inputs_ = []
+        for key, mode, adapter in inputs:
+            # This is the same pattern we follow for model json schemas - we attempt a core schema rebuild if we detect a mock
+            if isinstance(adapter.core_schema, _mock_val_ser.MockCoreSchema):
+                adapter.core_schema.rebuild()
+                assert not isinstance(
+                    adapter.core_schema, _mock_val_ser.MockCoreSchema
+                ), 'this is a bug! please report it'
+            inputs_.append((key, mode, adapter.core_schema))
+
+        json_schemas_map, definitions = schema_generator_instance.generate_definitions(inputs_)
+
+        json_schema: dict[str, Any] = {}
+        if definitions:
+            json_schema['$defs'] = definitions
+        if title:
+            json_schema['title'] = title
+        if description:
+            json_schema['description'] = description
+
+        return json_schemas_map, json_schema
diff --git a/.venv/lib/python3.12/site-packages/pydantic/types.py b/.venv/lib/python3.12/site-packages/pydantic/types.py
new file mode 100644
index 00000000..5ba60605
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/types.py
@@ -0,0 +1,3255 @@
+"""The types module contains custom types used by pydantic."""
+
+from __future__ import annotations as _annotations
+
+import base64
+import dataclasses as _dataclasses
+import re
+from datetime import date, datetime
+from decimal import Decimal
+from enum import Enum
+from pathlib import Path
+from types import ModuleType
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    FrozenSet,
+    Generic,
+    Hashable,
+    Iterator,
+    List,
+    Pattern,
+    Set,
+    TypeVar,
+    Union,
+    cast,
+    get_args,
+    get_origin,
+)
+from uuid import UUID
+
+import annotated_types
+from annotated_types import BaseMetadata, MaxLen, MinLen
+from pydantic_core import CoreSchema, PydanticCustomError, SchemaSerializer, core_schema
+from typing_extensions import Annotated, Literal, Protocol, TypeAlias, TypeAliasType, deprecated
+
+from ._internal import _core_utils, _fields, _internal_dataclass, _typing_extra, _utils, _validators
+from ._migration import getattr_migration
+from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
+from .errors import PydanticUserError
+from .json_schema import JsonSchemaValue
+from .warnings import PydanticDeprecatedSince20
+
+__all__ = (
+    'Strict',
+    'StrictStr',
+    'SocketPath',
+    'conbytes',
+    'conlist',
+    'conset',
+    'confrozenset',
+    'constr',
+    'ImportString',
+    'conint',
+    'PositiveInt',
+    'NegativeInt',
+    'NonNegativeInt',
+    'NonPositiveInt',
+    'confloat',
+    'PositiveFloat',
+    'NegativeFloat',
+    'NonNegativeFloat',
+    'NonPositiveFloat',
+    'FiniteFloat',
+    'condecimal',
+    'UUID1',
+    'UUID3',
+    'UUID4',
+    'UUID5',
+    'FilePath',
+    'DirectoryPath',
+    'NewPath',
+    'Json',
+    'Secret',
+    'SecretStr',
+    'SecretBytes',
+    'StrictBool',
+    'StrictBytes',
+    'StrictInt',
+    'StrictFloat',
+    'PaymentCardNumber',
+    'ByteSize',
+    'PastDate',
+    'FutureDate',
+    'PastDatetime',
+    'FutureDatetime',
+    'condate',
+    'AwareDatetime',
+    'NaiveDatetime',
+    'AllowInfNan',
+    'EncoderProtocol',
+    'EncodedBytes',
+    'EncodedStr',
+    'Base64Encoder',
+    'Base64Bytes',
+    'Base64Str',
+    'Base64UrlBytes',
+    'Base64UrlStr',
+    'GetPydanticSchema',
+    'StringConstraints',
+    'Tag',
+    'Discriminator',
+    'JsonValue',
+    'OnErrorOmit',
+    'FailFast',
+)
+
+
+T = TypeVar('T')
+
+
+@_dataclasses.dataclass
+class Strict(_fields.PydanticMetadata, BaseMetadata):
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/strict_mode/#strict-mode-with-annotated-strict
+
+    A field metadata class to indicate that a field should be validated in strict mode.
+    Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below.
+
+    Attributes:
+        strict: Whether to validate the field in strict mode.
+
+    Example:
+        ```python
+        from typing_extensions import Annotated
+
+        from pydantic.types import Strict
+
+        StrictBool = Annotated[bool, Strict()]
+        ```
+    """
+
+    strict: bool = True
+
+    def __hash__(self) -> int:
+        return hash(self.strict)
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BOOLEAN TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+StrictBool = Annotated[bool, Strict()]
+"""A boolean that must be either ``True`` or ``False``."""
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ INTEGER TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+def conint(
+    *,
+    strict: bool | None = None,
+    gt: int | None = None,
+    ge: int | None = None,
+    lt: int | None = None,
+    le: int | None = None,
+    multiple_of: int | None = None,
+) -> type[int]:
+    """
+    !!! warning "Discouraged"
+        This function is **discouraged** in favor of using
+        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
+        [`Field`][pydantic.fields.Field] instead.
+
+        This function will be **deprecated** in Pydantic 3.0.
+
+        The reason is that `conint` returns a type, which doesn't play well with static analysis tools.
+
+        === ":x: Don't do this"
+            ```python
+            from pydantic import BaseModel, conint
+
+            class Foo(BaseModel):
+                bar: conint(strict=True, gt=0)
+            ```
+
+        === ":white_check_mark: Do this"
+            ```python
+            from typing_extensions import Annotated
+
+            from pydantic import BaseModel, Field
+
+            class Foo(BaseModel):
+                bar: Annotated[int, Field(strict=True, gt=0)]
+            ```
+
+    A wrapper around `int` that allows for additional constraints.
+
+    Args:
+        strict: Whether to validate the integer in strict mode. Defaults to `None`.
+        gt: The value must be greater than this.
+        ge: The value must be greater than or equal to this.
+        lt: The value must be less than this.
+        le: The value must be less than or equal to this.
+        multiple_of: The value must be a multiple of this.
+
+    Returns:
+        The wrapped integer type.
+
+    ```python
+    from pydantic import BaseModel, ValidationError, conint
+
+    class ConstrainedExample(BaseModel):
+        constrained_int: conint(gt=1)
+
+    m = ConstrainedExample(constrained_int=2)
+    print(repr(m))
+    #> ConstrainedExample(constrained_int=2)
+
+    try:
+        ConstrainedExample(constrained_int=0)
+    except ValidationError as e:
+        print(e.errors())
+        '''
+        [
+            {
+                'type': 'greater_than',
+                'loc': ('constrained_int',),
+                'msg': 'Input should be greater than 1',
+                'input': 0,
+                'ctx': {'gt': 1},
+                'url': 'https://errors.pydantic.dev/2/v/greater_than',
+            }
+        ]
+        '''
+    ```
+
+    """  # noqa: D212
+    return Annotated[  # pyright: ignore[reportReturnType]
+        int,
+        Strict(strict) if strict is not None else None,
+        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
+        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
+    ]
+
+
+PositiveInt = Annotated[int, annotated_types.Gt(0)]
+"""An integer that must be greater than zero.
+
+```python
+from pydantic import BaseModel, PositiveInt, ValidationError
+
+class Model(BaseModel):
+    positive_int: PositiveInt
+
+m = Model(positive_int=1)
+print(repr(m))
+#> Model(positive_int=1)
+
+try:
+    Model(positive_int=-1)
+except ValidationError as e:
+    print(e.errors())
+    '''
+    [
+        {
+            'type': 'greater_than',
+            'loc': ('positive_int',),
+            'msg': 'Input should be greater than 0',
+            'input': -1,
+            'ctx': {'gt': 0},
+            'url': 'https://errors.pydantic.dev/2/v/greater_than',
+        }
+    ]
+    '''
+```
+"""
+NegativeInt = Annotated[int, annotated_types.Lt(0)]
+"""An integer that must be less than zero.
+
+```python
+from pydantic import BaseModel, NegativeInt, ValidationError
+
+class Model(BaseModel):
+    negative_int: NegativeInt
+
+m = Model(negative_int=-1)
+print(repr(m))
+#> Model(negative_int=-1)
+
+try:
+    Model(negative_int=1)
+except ValidationError as e:
+    print(e.errors())
+    '''
+    [
+        {
+            'type': 'less_than',
+            'loc': ('negative_int',),
+            'msg': 'Input should be less than 0',
+            'input': 1,
+            'ctx': {'lt': 0},
+            'url': 'https://errors.pydantic.dev/2/v/less_than',
+        }
+    ]
+    '''
+```
+"""
+NonPositiveInt = Annotated[int, annotated_types.Le(0)]
+"""An integer that must be less than or equal to zero.
+
+```python
+from pydantic import BaseModel, NonPositiveInt, ValidationError
+
+class Model(BaseModel):
+    non_positive_int: NonPositiveInt
+
+m = Model(non_positive_int=0)
+print(repr(m))
+#> Model(non_positive_int=0)
+
+try:
+    Model(non_positive_int=1)
+except ValidationError as e:
+    print(e.errors())
+    '''
+    [
+        {
+            'type': 'less_than_equal',
+            'loc': ('non_positive_int',),
+            'msg': 'Input should be less than or equal to 0',
+            'input': 1,
+            'ctx': {'le': 0},
+            'url': 'https://errors.pydantic.dev/2/v/less_than_equal',
+        }
+    ]
+    '''
+```
+"""
+NonNegativeInt = Annotated[int, annotated_types.Ge(0)]
+"""An integer that must be greater than or equal to zero.
+
+```python
+from pydantic import BaseModel, NonNegativeInt, ValidationError
+
+class Model(BaseModel):
+    non_negative_int: NonNegativeInt
+
+m = Model(non_negative_int=0)
+print(repr(m))
+#> Model(non_negative_int=0)
+
+try:
+    Model(non_negative_int=-1)
+except ValidationError as e:
+    print(e.errors())
+    '''
+    [
+        {
+            'type': 'greater_than_equal',
+            'loc': ('non_negative_int',),
+            'msg': 'Input should be greater than or equal to 0',
+            'input': -1,
+            'ctx': {'ge': 0},
+            'url': 'https://errors.pydantic.dev/2/v/greater_than_equal',
+        }
+    ]
+    '''
+```
+"""
+StrictInt = Annotated[int, Strict()]
+"""An integer that must be validated in strict mode.
+
+```python
+from pydantic import BaseModel, StrictInt, ValidationError
+
+class StrictIntModel(BaseModel):
+    strict_int: StrictInt
+
+try:
+    StrictIntModel(strict_int=3.14159)
+except ValidationError as e:
+    print(e)
+    '''
+    1 validation error for StrictIntModel
+    strict_int
+      Input should be a valid integer [type=int_type, input_value=3.14159, input_type=float]
+    '''
+```
+"""
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FLOAT TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+@_dataclasses.dataclass
+class AllowInfNan(_fields.PydanticMetadata):
+    """A field metadata class to indicate that a field should allow `-inf`, `inf`, and `nan`.
+
+    Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below.
+
+    Attributes:
+        allow_inf_nan: Whether to allow `-inf`, `inf`, and `nan`. Defaults to `True`.
+
+    Example:
+        ```python
+        from typing_extensions import Annotated
+
+        from pydantic.types import AllowInfNan
+
+        LaxFloat = Annotated[float, AllowInfNan()]
+    """
+
+    allow_inf_nan: bool = True
+
+    def __hash__(self) -> int:
+        return hash(self.allow_inf_nan)
+
+
+def confloat(
+    *,
+    strict: bool | None = None,
+    gt: float | None = None,
+    ge: float | None = None,
+    lt: float | None = None,
+    le: float | None = None,
+    multiple_of: float | None = None,
+    allow_inf_nan: bool | None = None,
+) -> type[float]:
+    """
+    !!! warning "Discouraged"
+        This function is **discouraged** in favor of using
+        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
+        [`Field`][pydantic.fields.Field] instead.
+
+        This function will be **deprecated** in Pydantic 3.0.
+
+        The reason is that `confloat` returns a type, which doesn't play well with static analysis tools.
+
+        === ":x: Don't do this"
+            ```python
+            from pydantic import BaseModel, confloat
+
+            class Foo(BaseModel):
+                bar: confloat(strict=True, gt=0)
+            ```
+
+        === ":white_check_mark: Do this"
+            ```python
+            from typing_extensions import Annotated
+
+            from pydantic import BaseModel, Field
+
+            class Foo(BaseModel):
+                bar: Annotated[float, Field(strict=True, gt=0)]
+            ```
+
+    A wrapper around `float` that allows for additional constraints.
+
+    Args:
+        strict: Whether to validate the float in strict mode.
+        gt: The value must be greater than this.
+        ge: The value must be greater than or equal to this.
+        lt: The value must be less than this.
+        le: The value must be less than or equal to this.
+        multiple_of: The value must be a multiple of this.
+        allow_inf_nan: Whether to allow `-inf`, `inf`, and `nan`.
+
+    Returns:
+        The wrapped float type.
+
+    ```python
+    from pydantic import BaseModel, ValidationError, confloat
+
+    class ConstrainedExample(BaseModel):
+        constrained_float: confloat(gt=1.0)
+
+    m = ConstrainedExample(constrained_float=1.1)
+    print(repr(m))
+    #> ConstrainedExample(constrained_float=1.1)
+
+    try:
+        ConstrainedExample(constrained_float=0.9)
+    except ValidationError as e:
+        print(e.errors())
+        '''
+        [
+            {
+                'type': 'greater_than',
+                'loc': ('constrained_float',),
+                'msg': 'Input should be greater than 1',
+                'input': 0.9,
+                'ctx': {'gt': 1.0},
+                'url': 'https://errors.pydantic.dev/2/v/greater_than',
+            }
+        ]
+        '''
+    ```
+    """  # noqa: D212
+    return Annotated[  # pyright: ignore[reportReturnType]
+        float,
+        Strict(strict) if strict is not None else None,
+        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
+        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
+        AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None,
+    ]
+
+
+PositiveFloat = Annotated[float, annotated_types.Gt(0)]
+"""A float that must be greater than zero.
+
+```python
+from pydantic import BaseModel, PositiveFloat, ValidationError
+
+class Model(BaseModel):
+    positive_float: PositiveFloat
+
+m = Model(positive_float=1.0)
+print(repr(m))
+#> Model(positive_float=1.0)
+
+try:
+    Model(positive_float=-1.0)
+except ValidationError as e:
+    print(e.errors())
+    '''
+    [
+        {
+            'type': 'greater_than',
+            'loc': ('positive_float',),
+            'msg': 'Input should be greater than 0',
+            'input': -1.0,
+            'ctx': {'gt': 0.0},
+            'url': 'https://errors.pydantic.dev/2/v/greater_than',
+        }
+    ]
+    '''
+```
+"""
+NegativeFloat = Annotated[float, annotated_types.Lt(0)]
+"""A float that must be less than zero.
+
+```python
+from pydantic import BaseModel, NegativeFloat, ValidationError
+
+class Model(BaseModel):
+    negative_float: NegativeFloat
+
+m = Model(negative_float=-1.0)
+print(repr(m))
+#> Model(negative_float=-1.0)
+
+try:
+    Model(negative_float=1.0)
+except ValidationError as e:
+    print(e.errors())
+    '''
+    [
+        {
+            'type': 'less_than',
+            'loc': ('negative_float',),
+            'msg': 'Input should be less than 0',
+            'input': 1.0,
+            'ctx': {'lt': 0.0},
+            'url': 'https://errors.pydantic.dev/2/v/less_than',
+        }
+    ]
+    '''
+```
+"""
+NonPositiveFloat = Annotated[float, annotated_types.Le(0)]
+"""A float that must be less than or equal to zero.
+
+```python
+from pydantic import BaseModel, NonPositiveFloat, ValidationError
+
+class Model(BaseModel):
+    non_positive_float: NonPositiveFloat
+
+m = Model(non_positive_float=0.0)
+print(repr(m))
+#> Model(non_positive_float=0.0)
+
+try:
+    Model(non_positive_float=1.0)
+except ValidationError as e:
+    print(e.errors())
+    '''
+    [
+        {
+            'type': 'less_than_equal',
+            'loc': ('non_positive_float',),
+            'msg': 'Input should be less than or equal to 0',
+            'input': 1.0,
+            'ctx': {'le': 0.0},
+            'url': 'https://errors.pydantic.dev/2/v/less_than_equal',
+        }
+    ]
+    '''
+```
+"""
+NonNegativeFloat = Annotated[float, annotated_types.Ge(0)]
+"""A float that must be greater than or equal to zero.
+
+```python
+from pydantic import BaseModel, NonNegativeFloat, ValidationError
+
+class Model(BaseModel):
+    non_negative_float: NonNegativeFloat
+
+m = Model(non_negative_float=0.0)
+print(repr(m))
+#> Model(non_negative_float=0.0)
+
+try:
+    Model(non_negative_float=-1.0)
+except ValidationError as e:
+    print(e.errors())
+    '''
+    [
+        {
+            'type': 'greater_than_equal',
+            'loc': ('non_negative_float',),
+            'msg': 'Input should be greater than or equal to 0',
+            'input': -1.0,
+            'ctx': {'ge': 0.0},
+            'url': 'https://errors.pydantic.dev/2/v/greater_than_equal',
+        }
+    ]
+    '''
+```
+"""
+StrictFloat = Annotated[float, Strict(True)]
+"""A float that must be validated in strict mode.
+
+```python
+from pydantic import BaseModel, StrictFloat, ValidationError
+
+class StrictFloatModel(BaseModel):
+    strict_float: StrictFloat
+
+try:
+    StrictFloatModel(strict_float='1.0')
+except ValidationError as e:
+    print(e)
+    '''
+    1 validation error for StrictFloatModel
+    strict_float
+      Input should be a valid number [type=float_type, input_value='1.0', input_type=str]
+    '''
+```
+"""
+FiniteFloat = Annotated[float, AllowInfNan(False)]
+"""A float that must be finite (not ``-inf``, ``inf``, or ``nan``).
+
+```python
+from pydantic import BaseModel, FiniteFloat
+
+class Model(BaseModel):
+    finite: FiniteFloat
+
+m = Model(finite=1.0)
+print(m)
+#> finite=1.0
+```
+"""
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTES TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+def conbytes(
+    *,
+    min_length: int | None = None,
+    max_length: int | None = None,
+    strict: bool | None = None,
+) -> type[bytes]:
+    """A wrapper around `bytes` that allows for additional constraints.
+
+    Args:
+        min_length: The minimum length of the bytes.
+        max_length: The maximum length of the bytes.
+        strict: Whether to validate the bytes in strict mode.
+
+    Returns:
+        The wrapped bytes type.
+    """
+    return Annotated[  # pyright: ignore[reportReturnType]
+        bytes,
+        Strict(strict) if strict is not None else None,
+        annotated_types.Len(min_length or 0, max_length),
+    ]
+
+
+StrictBytes = Annotated[bytes, Strict()]
+"""A bytes that must be validated in strict mode."""
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ STRING TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+@_dataclasses.dataclass(frozen=True)
+class StringConstraints(annotated_types.GroupedMetadata):
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/fields/#string-constraints
+
+    A field metadata class to apply constraints to `str` types.
+    Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below.
+
+    Attributes:
+        strip_whitespace: Whether to remove leading and trailing whitespace.
+        to_upper: Whether to convert the string to uppercase.
+        to_lower: Whether to convert the string to lowercase.
+        strict: Whether to validate the string in strict mode.
+        min_length: The minimum length of the string.
+        max_length: The maximum length of the string.
+        pattern: A regex pattern that the string must match.
+
+    Example:
+        ```python
+        from typing_extensions import Annotated
+
+        from pydantic.types import StringConstraints
+
+        ConstrainedStr = Annotated[str, StringConstraints(min_length=1, max_length=10)]
+        ```
+    """
+
+    strip_whitespace: bool | None = None
+    to_upper: bool | None = None
+    to_lower: bool | None = None
+    strict: bool | None = None
+    min_length: int | None = None
+    max_length: int | None = None
+    pattern: str | Pattern[str] | None = None
+
+    def __iter__(self) -> Iterator[BaseMetadata]:
+        if self.min_length is not None:
+            yield MinLen(self.min_length)
+        if self.max_length is not None:
+            yield MaxLen(self.max_length)
+        if self.strict is not None:
+            yield Strict(self.strict)
+        if (
+            self.strip_whitespace is not None
+            or self.pattern is not None
+            or self.to_lower is not None
+            or self.to_upper is not None
+        ):
+            yield _fields.pydantic_general_metadata(
+                strip_whitespace=self.strip_whitespace,
+                to_upper=self.to_upper,
+                to_lower=self.to_lower,
+                pattern=self.pattern,
+            )
+
+
+def constr(
+    *,
+    strip_whitespace: bool | None = None,
+    to_upper: bool | None = None,
+    to_lower: bool | None = None,
+    strict: bool | None = None,
+    min_length: int | None = None,
+    max_length: int | None = None,
+    pattern: str | Pattern[str] | None = None,
+) -> type[str]:
+    """
+    !!! warning "Discouraged"
+        This function is **discouraged** in favor of using
+        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
+        [`StringConstraints`][pydantic.types.StringConstraints] instead.
+
+        This function will be **deprecated** in Pydantic 3.0.
+
+        The reason is that `constr` returns a type, which doesn't play well with static analysis tools.
+
+        === ":x: Don't do this"
+            ```python
+            from pydantic import BaseModel, constr
+
+            class Foo(BaseModel):
+                bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')
+            ```
+
+        === ":white_check_mark: Do this"
+            ```python
+            from typing_extensions import Annotated
+
+            from pydantic import BaseModel, StringConstraints
+
+            class Foo(BaseModel):
+                bar: Annotated[
+                    str,
+                    StringConstraints(
+                        strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$'
+                    ),
+                ]
+            ```
+
+    A wrapper around `str` that allows for additional constraints.
+
+    ```python
+    from pydantic import BaseModel, constr
+
+    class Foo(BaseModel):
+        bar: constr(strip_whitespace=True, to_upper=True)
+
+    foo = Foo(bar='  hello  ')
+    print(foo)
+    #> bar='HELLO'
+    ```
+
+    Args:
+        strip_whitespace: Whether to remove leading and trailing whitespace.
+        to_upper: Whether to turn all characters to uppercase.
+        to_lower: Whether to turn all characters to lowercase.
+        strict: Whether to validate the string in strict mode.
+        min_length: The minimum length of the string.
+        max_length: The maximum length of the string.
+        pattern: A regex pattern to validate the string against.
+
+    Returns:
+        The wrapped string type.
+    """  # noqa: D212
+    return Annotated[  # pyright: ignore[reportReturnType]
+        str,
+        StringConstraints(
+            strip_whitespace=strip_whitespace,
+            to_upper=to_upper,
+            to_lower=to_lower,
+            strict=strict,
+            min_length=min_length,
+            max_length=max_length,
+            pattern=pattern,
+        ),
+    ]
+
+
+StrictStr = Annotated[str, Strict()]
+"""A string that must be validated in strict mode."""
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~ COLLECTION TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+HashableItemType = TypeVar('HashableItemType', bound=Hashable)
+
+
+def conset(
+    item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None
+) -> type[set[HashableItemType]]:
+    """A wrapper around `typing.Set` that allows for additional constraints.
+
+    Args:
+        item_type: The type of the items in the set.
+        min_length: The minimum length of the set.
+        max_length: The maximum length of the set.
+
+    Returns:
+        The wrapped set type.
+    """
+    return Annotated[Set[item_type], annotated_types.Len(min_length or 0, max_length)]  # pyright: ignore[reportReturnType]
+
+
+def confrozenset(
+    item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None
+) -> type[frozenset[HashableItemType]]:
+    """A wrapper around `typing.FrozenSet` that allows for additional constraints.
+
+    Args:
+        item_type: The type of the items in the frozenset.
+        min_length: The minimum length of the frozenset.
+        max_length: The maximum length of the frozenset.
+
+    Returns:
+        The wrapped frozenset type.
+    """
+    return Annotated[FrozenSet[item_type], annotated_types.Len(min_length or 0, max_length)]  # pyright: ignore[reportReturnType]
+
+
+AnyItemType = TypeVar('AnyItemType')
+
+
+def conlist(
+    item_type: type[AnyItemType],
+    *,
+    min_length: int | None = None,
+    max_length: int | None = None,
+    unique_items: bool | None = None,
+) -> type[list[AnyItemType]]:
+    """A wrapper around typing.List that adds validation.
+
+    Args:
+        item_type: The type of the items in the list.
+        min_length: The minimum length of the list. Defaults to None.
+        max_length: The maximum length of the list. Defaults to None.
+        unique_items: Whether the items in the list must be unique. Defaults to None.
+            !!! warning Deprecated
+                The `unique_items` parameter is deprecated, use `Set` instead.
+                See [this issue](https://github.com/pydantic/pydantic-core/issues/296) for more details.
+
+    Returns:
+        The wrapped list type.
+    """
+    if unique_items is not None:
+        raise PydanticUserError(
+            (
+                '`unique_items` is removed, use `Set` instead'
+                '(this feature is discussed in https://github.com/pydantic/pydantic-core/issues/296)'
+            ),
+            code='removed-kwargs',
+        )
+    return Annotated[List[item_type], annotated_types.Len(min_length or 0, max_length)]  # pyright: ignore[reportReturnType]
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~ IMPORT STRING TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+AnyType = TypeVar('AnyType')
+if TYPE_CHECKING:
+    ImportString = Annotated[AnyType, ...]
+else:
+
+    class ImportString:
+        """A type that can be used to import a Python object from a string.
+
+        `ImportString` expects a string and loads the Python object importable at that dotted path.
+        Attributes of modules may be separated from the module by `:` or `.`, e.g. if `'math:cos'` is provided,
+        the resulting field value would be the function `cos`. If a `.` is used and both an attribute and submodule
+        are present at the same path, the module will be preferred.
+
+        On model instantiation, pointers will be evaluated and imported. There is
+        some nuance to this behavior, demonstrated in the examples below.
+
+        ```python
+        import math
+
+        from pydantic import BaseModel, Field, ImportString, ValidationError
+
+        class ImportThings(BaseModel):
+            obj: ImportString
+
+        # A string value will cause an automatic import
+        my_cos = ImportThings(obj='math.cos')
+
+        # You can use the imported function as you would expect
+        cos_of_0 = my_cos.obj(0)
+        assert cos_of_0 == 1
+
+        # A string whose value cannot be imported will raise an error
+        try:
+            ImportThings(obj='foo.bar')
+        except ValidationError as e:
+            print(e)
+            '''
+            1 validation error for ImportThings
+            obj
+              Invalid python path: No module named 'foo.bar' [type=import_error, input_value='foo.bar', input_type=str]
+            '''
+
+        # Actual python objects can be assigned as well
+        my_cos = ImportThings(obj=math.cos)
+        my_cos_2 = ImportThings(obj='math.cos')
+        my_cos_3 = ImportThings(obj='math:cos')
+        assert my_cos == my_cos_2 == my_cos_3
+
+        # You can set default field value either as Python object:
+        class ImportThingsDefaultPyObj(BaseModel):
+            obj: ImportString = math.cos
+
+        # or as a string value (but only if used with `validate_default=True`)
+        class ImportThingsDefaultString(BaseModel):
+            obj: ImportString = Field(default='math.cos', validate_default=True)
+
+        my_cos_default1 = ImportThingsDefaultPyObj()
+        my_cos_default2 = ImportThingsDefaultString()
+        assert my_cos_default1.obj == my_cos_default2.obj == math.cos
+
+        # note: this will not work!
+        class ImportThingsMissingValidateDefault(BaseModel):
+            obj: ImportString = 'math.cos'
+
+        my_cos_default3 = ImportThingsMissingValidateDefault()
+        assert my_cos_default3.obj == 'math.cos'  # just string, not evaluated
+        ```
+
+        Serializing an `ImportString` type to json is also possible.
+
+        ```python
+        from pydantic import BaseModel, ImportString
+
+        class ImportThings(BaseModel):
+            obj: ImportString
+
+        # Create an instance
+        m = ImportThings(obj='math.cos')
+        print(m)
+        #> obj=<built-in function cos>
+        print(m.model_dump_json())
+        #> {"obj":"math.cos"}
+        ```
+        """
+
+        @classmethod
+        def __class_getitem__(cls, item: AnyType) -> AnyType:
+            return Annotated[item, cls()]
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, source: type[Any], handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            serializer = core_schema.plain_serializer_function_ser_schema(cls._serialize, when_used='json')
+            if cls is source:
+                # Treat bare usage of ImportString (`schema is None`) as the same as ImportString[Any]
+                return core_schema.no_info_plain_validator_function(
+                    function=_validators.import_string, serialization=serializer
+                )
+            else:
+                return core_schema.no_info_before_validator_function(
+                    function=_validators.import_string, schema=handler(source), serialization=serializer
+                )
+
+        @classmethod
+        def __get_pydantic_json_schema__(cls, cs: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
+            return handler(core_schema.str_schema())
+
+        @staticmethod
+        def _serialize(v: Any) -> str:
+            if isinstance(v, ModuleType):
+                return v.__name__
+            elif hasattr(v, '__module__') and hasattr(v, '__name__'):
+                return f'{v.__module__}.{v.__name__}'
+            # Handle special cases for sys.XXX streams
+            # if we see more of these, we should consider a more general solution
+            elif hasattr(v, 'name'):
+                if v.name == '<stdout>':
+                    return 'sys.stdout'
+                elif v.name == '<stdin>':
+                    return 'sys.stdin'
+                elif v.name == '<stderr>':
+                    return 'sys.stderr'
+            else:
+                return v
+
+        def __repr__(self) -> str:
+            return 'ImportString'
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DECIMAL TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+def condecimal(
+    *,
+    strict: bool | None = None,
+    gt: int | Decimal | None = None,
+    ge: int | Decimal | None = None,
+    lt: int | Decimal | None = None,
+    le: int | Decimal | None = None,
+    multiple_of: int | Decimal | None = None,
+    max_digits: int | None = None,
+    decimal_places: int | None = None,
+    allow_inf_nan: bool | None = None,
+) -> type[Decimal]:
+    """
+    !!! warning "Discouraged"
+        This function is **discouraged** in favor of using
+        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
+        [`Field`][pydantic.fields.Field] instead.
+
+        This function will be **deprecated** in Pydantic 3.0.
+
+        The reason is that `condecimal` returns a type, which doesn't play well with static analysis tools.
+
+        === ":x: Don't do this"
+            ```python
+            from pydantic import BaseModel, condecimal
+
+            class Foo(BaseModel):
+                bar: condecimal(strict=True, allow_inf_nan=True)
+            ```
+
+        === ":white_check_mark: Do this"
+            ```python
+            from decimal import Decimal
+
+            from typing_extensions import Annotated
+
+            from pydantic import BaseModel, Field
+
+            class Foo(BaseModel):
+                bar: Annotated[Decimal, Field(strict=True, allow_inf_nan=True)]
+            ```
+
+    A wrapper around Decimal that adds validation.
+
+    Args:
+        strict: Whether to validate the value in strict mode. Defaults to `None`.
+        gt: The value must be greater than this. Defaults to `None`.
+        ge: The value must be greater than or equal to this. Defaults to `None`.
+        lt: The value must be less than this. Defaults to `None`.
+        le: The value must be less than or equal to this. Defaults to `None`.
+        multiple_of: The value must be a multiple of this. Defaults to `None`.
+        max_digits: The maximum number of digits. Defaults to `None`.
+        decimal_places: The number of decimal places. Defaults to `None`.
+        allow_inf_nan: Whether to allow infinity and NaN. Defaults to `None`.
+
+    ```python
+    from decimal import Decimal
+
+    from pydantic import BaseModel, ValidationError, condecimal
+
+    class ConstrainedExample(BaseModel):
+        constrained_decimal: condecimal(gt=Decimal('1.0'))
+
+    m = ConstrainedExample(constrained_decimal=Decimal('1.1'))
+    print(repr(m))
+    #> ConstrainedExample(constrained_decimal=Decimal('1.1'))
+
+    try:
+        ConstrainedExample(constrained_decimal=Decimal('0.9'))
+    except ValidationError as e:
+        print(e.errors())
+        '''
+        [
+            {
+                'type': 'greater_than',
+                'loc': ('constrained_decimal',),
+                'msg': 'Input should be greater than 1.0',
+                'input': Decimal('0.9'),
+                'ctx': {'gt': Decimal('1.0')},
+                'url': 'https://errors.pydantic.dev/2/v/greater_than',
+            }
+        ]
+        '''
+    ```
+    """  # noqa: D212
+    return Annotated[  # pyright: ignore[reportReturnType]
+        Decimal,
+        Strict(strict) if strict is not None else None,
+        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
+        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
+        _fields.pydantic_general_metadata(max_digits=max_digits, decimal_places=decimal_places),
+        AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None,
+    ]
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ UUID TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+@_dataclasses.dataclass(**_internal_dataclass.slots_true)
+class UuidVersion:
+    """A field metadata class to indicate a [UUID](https://docs.python.org/3/library/uuid.html) version.
+
+    Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below.
+
+    Attributes:
+        uuid_version: The version of the UUID. Must be one of 1, 3, 4, or 5.
+
+    Example:
+        ```python
+        from uuid import UUID
+
+        from typing_extensions import Annotated
+
+        from pydantic.types import UuidVersion
+
+        UUID1 = Annotated[UUID, UuidVersion(1)]
+        ```
+    """
+
+    uuid_version: Literal[1, 3, 4, 5]
+
+    def __get_pydantic_json_schema__(
+        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        field_schema = handler(core_schema)
+        field_schema.pop('anyOf', None)  # remove the bytes/str union
+        field_schema.update(type='string', format=f'uuid{self.uuid_version}')
+        return field_schema
+
+    def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        if isinstance(self, source):
+            # used directly as a type
+            return core_schema.uuid_schema(version=self.uuid_version)
+        else:
+            # update existing schema with self.uuid_version
+            schema = handler(source)
+            _check_annotated_type(schema['type'], 'uuid', self.__class__.__name__)
+            schema['version'] = self.uuid_version  # type: ignore
+            return schema
+
+    def __hash__(self) -> int:
+        return hash(type(self.uuid_version))
+
+
+UUID1 = Annotated[UUID, UuidVersion(1)]
+"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 1.
+
+```python
+import uuid
+
+from pydantic import UUID1, BaseModel
+
+class Model(BaseModel):
+    uuid1: UUID1
+
+Model(uuid1=uuid.uuid1())
+```
+"""
+UUID3 = Annotated[UUID, UuidVersion(3)]
+"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 3.
+
+```python
+import uuid
+
+from pydantic import UUID3, BaseModel
+
+class Model(BaseModel):
+    uuid3: UUID3
+
+Model(uuid3=uuid.uuid3(uuid.NAMESPACE_DNS, 'pydantic.org'))
+```
+"""
+UUID4 = Annotated[UUID, UuidVersion(4)]
+"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 4.
+
+```python
+import uuid
+
+from pydantic import UUID4, BaseModel
+
+class Model(BaseModel):
+    uuid4: UUID4
+
+Model(uuid4=uuid.uuid4())
+```
+"""
+UUID5 = Annotated[UUID, UuidVersion(5)]
+"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 5.
+
+```python
+import uuid
+
+from pydantic import UUID5, BaseModel
+
+class Model(BaseModel):
+    uuid5: UUID5
+
+Model(uuid5=uuid.uuid5(uuid.NAMESPACE_DNS, 'pydantic.org'))
+```
+"""
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PATH TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+@_dataclasses.dataclass
+class PathType:
+    path_type: Literal['file', 'dir', 'new', 'socket']
+
+    def __get_pydantic_json_schema__(
+        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        field_schema = handler(core_schema)
+        format_conversion = {'file': 'file-path', 'dir': 'directory-path'}
+        field_schema.update(format=format_conversion.get(self.path_type, 'path'), type='string')
+        return field_schema
+
+    def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        function_lookup = {
+            'file': cast(core_schema.WithInfoValidatorFunction, self.validate_file),
+            'dir': cast(core_schema.WithInfoValidatorFunction, self.validate_directory),
+            'new': cast(core_schema.WithInfoValidatorFunction, self.validate_new),
+            'socket': cast(core_schema.WithInfoValidatorFunction, self.validate_socket),
+        }
+
+        return core_schema.with_info_after_validator_function(
+            function_lookup[self.path_type],
+            handler(source),
+        )
+
+    @staticmethod
+    def validate_file(path: Path, _: core_schema.ValidationInfo) -> Path:
+        if path.is_file():
+            return path
+        else:
+            raise PydanticCustomError('path_not_file', 'Path does not point to a file')
+
+    @staticmethod
+    def validate_socket(path: Path, _: core_schema.ValidationInfo) -> Path:
+        if path.is_socket():
+            return path
+        else:
+            raise PydanticCustomError('path_not_socket', 'Path does not point to a socket')
+
+    @staticmethod
+    def validate_directory(path: Path, _: core_schema.ValidationInfo) -> Path:
+        if path.is_dir():
+            return path
+        else:
+            raise PydanticCustomError('path_not_directory', 'Path does not point to a directory')
+
+    @staticmethod
+    def validate_new(path: Path, _: core_schema.ValidationInfo) -> Path:
+        if path.exists():
+            raise PydanticCustomError('path_exists', 'Path already exists')
+        elif not path.parent.exists():
+            raise PydanticCustomError('parent_does_not_exist', 'Parent directory does not exist')
+        else:
+            return path
+
+    def __hash__(self) -> int:
+        return hash(type(self.path_type))
+
+
+FilePath = Annotated[Path, PathType('file')]
+"""A path that must point to a file.
+
+```python
+from pathlib import Path
+
+from pydantic import BaseModel, FilePath, ValidationError
+
+class Model(BaseModel):
+    f: FilePath
+
+path = Path('text.txt')
+path.touch()
+m = Model(f='text.txt')
+print(m.model_dump())
+#> {'f': PosixPath('text.txt')}
+path.unlink()
+
+path = Path('directory')
+path.mkdir(exist_ok=True)
+try:
+    Model(f='directory')  # directory
+except ValidationError as e:
+    print(e)
+    '''
+    1 validation error for Model
+    f
+      Path does not point to a file [type=path_not_file, input_value='directory', input_type=str]
+    '''
+path.rmdir()
+
+try:
+    Model(f='not-exists-file')
+except ValidationError as e:
+    print(e)
+    '''
+    1 validation error for Model
+    f
+      Path does not point to a file [type=path_not_file, input_value='not-exists-file', input_type=str]
+    '''
+```
+"""
+DirectoryPath = Annotated[Path, PathType('dir')]
+"""A path that must point to a directory.
+
+```python
+from pathlib import Path
+
+from pydantic import BaseModel, DirectoryPath, ValidationError
+
+class Model(BaseModel):
+    f: DirectoryPath
+
+path = Path('directory/')
+path.mkdir()
+m = Model(f='directory/')
+print(m.model_dump())
+#> {'f': PosixPath('directory')}
+path.rmdir()
+
+path = Path('file.txt')
+path.touch()
+try:
+    Model(f='file.txt')  # file
+except ValidationError as e:
+    print(e)
+    '''
+    1 validation error for Model
+    f
+      Path does not point to a directory [type=path_not_directory, input_value='file.txt', input_type=str]
+    '''
+path.unlink()
+
+try:
+    Model(f='not-exists-directory')
+except ValidationError as e:
+    print(e)
+    '''
+    1 validation error for Model
+    f
+      Path does not point to a directory [type=path_not_directory, input_value='not-exists-directory', input_type=str]
+    '''
+```
+"""
+NewPath = Annotated[Path, PathType('new')]
+"""A path for a new file or directory that must not already exist. The parent directory must already exist."""
+
+SocketPath = Annotated[Path, PathType('socket')]
+"""A path to an existing socket file"""
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ JSON TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    # Json[list[str]] will be recognized by type checkers as list[str]
+    Json = Annotated[AnyType, ...]
+
+else:
+
+    class Json:
+        """A special type wrapper which loads JSON before parsing.
+
+        You can use the `Json` data type to make Pydantic first load a raw JSON string before
+        validating the loaded data into the parametrized type:
+
+        ```python
+        from typing import Any, List
+
+        from pydantic import BaseModel, Json, ValidationError
+
+        class AnyJsonModel(BaseModel):
+            json_obj: Json[Any]
+
+        class ConstrainedJsonModel(BaseModel):
+            json_obj: Json[List[int]]
+
+        print(AnyJsonModel(json_obj='{"b": 1}'))
+        #> json_obj={'b': 1}
+        print(ConstrainedJsonModel(json_obj='[1, 2, 3]'))
+        #> json_obj=[1, 2, 3]
+
+        try:
+            ConstrainedJsonModel(json_obj=12)
+        except ValidationError as e:
+            print(e)
+            '''
+            1 validation error for ConstrainedJsonModel
+            json_obj
+              JSON input should be string, bytes or bytearray [type=json_type, input_value=12, input_type=int]
+            '''
+
+        try:
+            ConstrainedJsonModel(json_obj='[a, b]')
+        except ValidationError as e:
+            print(e)
+            '''
+            1 validation error for ConstrainedJsonModel
+            json_obj
+              Invalid JSON: expected value at line 1 column 2 [type=json_invalid, input_value='[a, b]', input_type=str]
+            '''
+
+        try:
+            ConstrainedJsonModel(json_obj='["a", "b"]')
+        except ValidationError as e:
+            print(e)
+            '''
+            2 validation errors for ConstrainedJsonModel
+            json_obj.0
+              Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
+            json_obj.1
+              Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='b', input_type=str]
+            '''
+        ```
+
+        When you dump the model using `model_dump` or `model_dump_json`, the dumped value will be the result of validation,
+        not the original JSON string. However, you can use the argument `round_trip=True` to get the original JSON string back:
+
+        ```python
+        from typing import List
+
+        from pydantic import BaseModel, Json
+
+        class ConstrainedJsonModel(BaseModel):
+            json_obj: Json[List[int]]
+
+        print(ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json())
+        #> {"json_obj":[1,2,3]}
+        print(
+            ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json(round_trip=True)
+        )
+        #> {"json_obj":"[1,2,3]"}
+        ```
+        """
+
+        @classmethod
+        def __class_getitem__(cls, item: AnyType) -> AnyType:
+            return Annotated[item, cls()]
+
+        @classmethod
+        def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+            if cls is source:
+                return core_schema.json_schema(None)
+            else:
+                return core_schema.json_schema(handler(source))
+
+        def __repr__(self) -> str:
+            return 'Json'
+
+        def __hash__(self) -> int:
+            return hash(type(self))
+
+        def __eq__(self, other: Any) -> bool:
+            return type(other) is type(self)
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SECRET TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+SecretType = TypeVar('SecretType')
+
+
+class _SecretBase(Generic[SecretType]):
+    def __init__(self, secret_value: SecretType) -> None:
+        self._secret_value: SecretType = secret_value
+
+    def get_secret_value(self) -> SecretType:
+        """Get the secret value.
+
+        Returns:
+            The secret value.
+        """
+        return self._secret_value
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, self.__class__) and self.get_secret_value() == other.get_secret_value()
+
+    def __hash__(self) -> int:
+        return hash(self.get_secret_value())
+
+    def __str__(self) -> str:
+        return str(self._display())
+
+    def __repr__(self) -> str:
+        return f'{self.__class__.__name__}({self._display()!r})'
+
+    def _display(self) -> str | bytes:
+        raise NotImplementedError
+
+
+def _serialize_secret(value: Secret[SecretType], info: core_schema.SerializationInfo) -> str | Secret[SecretType]:
+    if info.mode == 'json':
+        return str(value)
+    else:
+        return value
+
+
+class Secret(_SecretBase[SecretType]):
+    """A generic base class used for defining a field with sensitive information that you do not want to be visible in logging or tracebacks.
+
+    You may either directly parametrize `Secret` with a type, or subclass from `Secret` with a parametrized type. The benefit of subclassing
+    is that you can define a custom `_display` method, which will be used for `repr()` and `str()` methods. The examples below demonstrate both
+    ways of using `Secret` to create a new secret type.
+
+    1. Directly parametrizing `Secret` with a type:
+
+    ```python
+    from pydantic import BaseModel, Secret
+
+    SecretBool = Secret[bool]
+
+    class Model(BaseModel):
+        secret_bool: SecretBool
+
+    m = Model(secret_bool=True)
+    print(m.model_dump())
+    #> {'secret_bool': Secret('**********')}
+
+    print(m.model_dump_json())
+    #> {"secret_bool":"**********"}
+
+    print(m.secret_bool.get_secret_value())
+    #> True
+    ```
+
+    2. Subclassing from parametrized `Secret`:
+
+    ```python
+    from datetime import date
+
+    from pydantic import BaseModel, Secret
+
+    class SecretDate(Secret[date]):
+        def _display(self) -> str:
+            return '****/**/**'
+
+    class Model(BaseModel):
+        secret_date: SecretDate
+
+    m = Model(secret_date=date(2022, 1, 1))
+    print(m.model_dump())
+    #> {'secret_date': SecretDate('****/**/**')}
+
+    print(m.model_dump_json())
+    #> {"secret_date":"****/**/**"}
+
+    print(m.secret_date.get_secret_value())
+    #> 2022-01-01
+    ```
+
+    The value returned by the `_display` method will be used for `repr()` and `str()`.
+
+    You can enforce constraints on the underlying type through annotations:
+    For example:
+
+    ```python
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, Field, Secret, ValidationError
+
+    SecretPosInt = Secret[Annotated[int, Field(gt=0, strict=True)]]
+
+    class Model(BaseModel):
+        sensitive_int: SecretPosInt
+
+    m = Model(sensitive_int=42)
+    print(m.model_dump())
+    #> {'sensitive_int': Secret('**********')}
+
+    try:
+        m = Model(sensitive_int=-42)  # (1)!
+    except ValidationError as exc_info:
+        print(exc_info.errors(include_url=False, include_input=False))
+        '''
+        [
+            {
+                'type': 'greater_than',
+                'loc': ('sensitive_int',),
+                'msg': 'Input should be greater than 0',
+                'ctx': {'gt': 0},
+            }
+        ]
+        '''
+
+    try:
+        m = Model(sensitive_int='42')  # (2)!
+    except ValidationError as exc_info:
+        print(exc_info.errors(include_url=False, include_input=False))
+        '''
+        [
+            {
+                'type': 'int_type',
+                'loc': ('sensitive_int',),
+                'msg': 'Input should be a valid integer',
+            }
+        ]
+        '''
+    ```
+
+    1. The input value is not greater than 0, so it raises a validation error.
+    2. The input value is not an integer, so it raises a validation error because the `SecretPosInt` type has strict mode enabled.
+    """
+
+    def _display(self) -> str | bytes:
+        return '**********' if self.get_secret_value() else ''
+
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        inner_type = None
+        # if origin_type is Secret, then cls is a GenericAlias, and we can extract the inner type directly
+        origin_type = get_origin(source)
+        if origin_type is not None:
+            inner_type = get_args(source)[0]
+        # otherwise, we need to get the inner type from the base class
+        else:
+            bases = getattr(cls, '__orig_bases__', getattr(cls, '__bases__', []))
+            for base in bases:
+                if get_origin(base) is Secret:
+                    inner_type = get_args(base)[0]
+            if bases == [] or inner_type is None:
+                raise TypeError(
+                    f"Can't get secret type from {cls.__name__}. "
+                    'Please use Secret[<type>], or subclass from Secret[<type>] instead.'
+                )
+
+        inner_schema = handler.generate_schema(inner_type)  # type: ignore
+
+        def validate_secret_value(value, handler) -> Secret[SecretType]:
+            if isinstance(value, Secret):
+                value = value.get_secret_value()
+            validated_inner = handler(value)
+            return cls(validated_inner)
+
+        return core_schema.json_or_python_schema(
+            python_schema=core_schema.no_info_wrap_validator_function(
+                validate_secret_value,
+                inner_schema,
+            ),
+            json_schema=core_schema.no_info_after_validator_function(lambda x: cls(x), inner_schema),
+            serialization=core_schema.plain_serializer_function_ser_schema(
+                _serialize_secret,
+                info_arg=True,
+                when_used='always',
+            ),
+        )
+
+    __pydantic_serializer__ = SchemaSerializer(
+        core_schema.any_schema(
+            serialization=core_schema.plain_serializer_function_ser_schema(
+                _serialize_secret,
+                info_arg=True,
+                when_used='always',
+            )
+        )
+    )
+
+
+def _secret_display(value: SecretType) -> str:  # type: ignore
+    return '**********' if value else ''
+
+
+def _serialize_secret_field(
+    value: _SecretField[SecretType], info: core_schema.SerializationInfo
+) -> str | _SecretField[SecretType]:
+    if info.mode == 'json':
+        # we want the output to always be string without the `b'` prefix for bytes,
+        # hence we just use `secret_display`
+        return _secret_display(value.get_secret_value())
+    else:
+        return value
+
+
+class _SecretField(_SecretBase[SecretType]):
+    _inner_schema: ClassVar[CoreSchema]
+    _error_kind: ClassVar[str]
+
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        def get_json_schema(_core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
+            json_schema = handler(cls._inner_schema)
+            _utils.update_not_none(
+                json_schema,
+                type='string',
+                writeOnly=True,
+                format='password',
+            )
+            return json_schema
+
+        json_schema = core_schema.no_info_after_validator_function(
+            source,  # construct the type
+            cls._inner_schema,
+        )
+
+        def get_secret_schema(strict: bool) -> CoreSchema:
+            return core_schema.json_or_python_schema(
+                python_schema=core_schema.union_schema(
+                    [
+                        core_schema.is_instance_schema(source),
+                        json_schema,
+                    ],
+                    custom_error_type=cls._error_kind,
+                    strict=strict,
+                ),
+                json_schema=json_schema,
+                serialization=core_schema.plain_serializer_function_ser_schema(
+                    _serialize_secret_field,
+                    info_arg=True,
+                    when_used='always',
+                ),
+            )
+
+        return core_schema.lax_or_strict_schema(
+            lax_schema=get_secret_schema(strict=False),
+            strict_schema=get_secret_schema(strict=True),
+            metadata={'pydantic_js_functions': [get_json_schema]},
+        )
+
+    __pydantic_serializer__ = SchemaSerializer(
+        core_schema.any_schema(
+            serialization=core_schema.plain_serializer_function_ser_schema(
+                _serialize_secret_field,
+                info_arg=True,
+                when_used='always',
+            )
+        )
+    )
+
+
+class SecretStr(_SecretField[str]):
+    """A string used for storing sensitive information that you do not want to be visible in logging or tracebacks.
+
+    When the secret value is nonempty, it is displayed as `'**********'` instead of the underlying value in
+    calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `''`.
+
+    ```python
+    from pydantic import BaseModel, SecretStr
+
+    class User(BaseModel):
+        username: str
+        password: SecretStr
+
+    user = User(username='scolvin', password='password1')
+
+    print(user)
+    #> username='scolvin' password=SecretStr('**********')
+    print(user.password.get_secret_value())
+    #> password1
+    print((SecretStr('password'), SecretStr('')))
+    #> (SecretStr('**********'), SecretStr(''))
+    ```
+
+    As seen above, by default, [`SecretStr`][pydantic.types.SecretStr] (and [`SecretBytes`][pydantic.types.SecretBytes])
+    will be serialized as `**********` when serializing to json.
+
+    You can use the [`field_serializer`][pydantic.functional_serializers.field_serializer] to dump the
+    secret as plain-text when serializing to json.
+
+    ```python
+    from pydantic import BaseModel, SecretBytes, SecretStr, field_serializer
+
+    class Model(BaseModel):
+        password: SecretStr
+        password_bytes: SecretBytes
+
+        @field_serializer('password', 'password_bytes', when_used='json')
+        def dump_secret(self, v):
+            return v.get_secret_value()
+
+    model = Model(password='IAmSensitive', password_bytes=b'IAmSensitiveBytes')
+    print(model)
+    #> password=SecretStr('**********') password_bytes=SecretBytes(b'**********')
+    print(model.password)
+    #> **********
+    print(model.model_dump())
+    '''
+    {
+        'password': SecretStr('**********'),
+        'password_bytes': SecretBytes(b'**********'),
+    }
+    '''
+    print(model.model_dump_json())
+    #> {"password":"IAmSensitive","password_bytes":"IAmSensitiveBytes"}
+    ```
+    """
+
+    _inner_schema: ClassVar[CoreSchema] = core_schema.str_schema()
+    _error_kind: ClassVar[str] = 'string_type'
+
+    def __len__(self) -> int:
+        return len(self._secret_value)
+
+    def _display(self) -> str:
+        return _secret_display(self._secret_value)
+
+
+class SecretBytes(_SecretField[bytes]):
+    """A bytes used for storing sensitive information that you do not want to be visible in logging or tracebacks.
+
+    It displays `b'**********'` instead of the string value on `repr()` and `str()` calls.
+    When the secret value is nonempty, it is displayed as `b'**********'` instead of the underlying value in
+    calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `b''`.
+
+    ```python
+    from pydantic import BaseModel, SecretBytes
+
+    class User(BaseModel):
+        username: str
+        password: SecretBytes
+
+    user = User(username='scolvin', password=b'password1')
+    #> username='scolvin' password=SecretBytes(b'**********')
+    print(user.password.get_secret_value())
+    #> b'password1'
+    print((SecretBytes(b'password'), SecretBytes(b'')))
+    #> (SecretBytes(b'**********'), SecretBytes(b''))
+    ```
+    """
+
+    _inner_schema: ClassVar[CoreSchema] = core_schema.bytes_schema()
+    _error_kind: ClassVar[str] = 'bytes_type'
+
+    def __len__(self) -> int:
+        return len(self._secret_value)
+
+    def _display(self) -> bytes:
+        return _secret_display(self._secret_value).encode()
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PAYMENT CARD TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class PaymentCardBrand(str, Enum):
+    amex = 'American Express'
+    mastercard = 'Mastercard'
+    visa = 'Visa'
+    other = 'other'
+
+    def __str__(self) -> str:
+        return self.value
+
+
+@deprecated(
+    'The `PaymentCardNumber` class is deprecated, use `pydantic_extra_types` instead. '
+    'See https://docs.pydantic.dev/latest/api/pydantic_extra_types_payment/#pydantic_extra_types.payment.PaymentCardNumber.',
+    category=PydanticDeprecatedSince20,
+)
+class PaymentCardNumber(str):
+    """Based on: https://en.wikipedia.org/wiki/Payment_card_number."""
+
+    strip_whitespace: ClassVar[bool] = True
+    min_length: ClassVar[int] = 12
+    max_length: ClassVar[int] = 19
+    bin: str
+    last4: str
+    brand: PaymentCardBrand
+
+    def __init__(self, card_number: str):
+        self.validate_digits(card_number)
+
+        card_number = self.validate_luhn_check_digit(card_number)
+
+        self.bin = card_number[:6]
+        self.last4 = card_number[-4:]
+        self.brand = self.validate_brand(card_number)
+
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        return core_schema.with_info_after_validator_function(
+            cls.validate,
+            core_schema.str_schema(
+                min_length=cls.min_length, max_length=cls.max_length, strip_whitespace=cls.strip_whitespace
+            ),
+        )
+
+    @classmethod
+    def validate(cls, input_value: str, /, _: core_schema.ValidationInfo) -> PaymentCardNumber:
+        """Validate the card number and return a `PaymentCardNumber` instance."""
+        return cls(input_value)
+
+    @property
+    def masked(self) -> str:
+        """Mask all but the last 4 digits of the card number.
+
+        Returns:
+            A masked card number string.
+        """
+        num_masked = len(self) - 10  # len(bin) + len(last4) == 10
+        return f'{self.bin}{"*" * num_masked}{self.last4}'
+
+    @classmethod
+    def validate_digits(cls, card_number: str) -> None:
+        """Validate that the card number is all digits."""
+        if not card_number.isdigit():
+            raise PydanticCustomError('payment_card_number_digits', 'Card number is not all digits')
+
+    @classmethod
+    def validate_luhn_check_digit(cls, card_number: str) -> str:
+        """Based on: https://en.wikipedia.org/wiki/Luhn_algorithm."""
+        sum_ = int(card_number[-1])
+        length = len(card_number)
+        parity = length % 2
+        for i in range(length - 1):
+            digit = int(card_number[i])
+            if i % 2 == parity:
+                digit *= 2
+            if digit > 9:
+                digit -= 9
+            sum_ += digit
+        valid = sum_ % 10 == 0
+        if not valid:
+            raise PydanticCustomError('payment_card_number_luhn', 'Card number is not luhn valid')
+        return card_number
+
+    @staticmethod
+    def validate_brand(card_number: str) -> PaymentCardBrand:
+        """Validate length based on BIN for major brands:
+        https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN).
+        """
+        if card_number[0] == '4':
+            brand = PaymentCardBrand.visa
+        elif 51 <= int(card_number[:2]) <= 55:
+            brand = PaymentCardBrand.mastercard
+        elif card_number[:2] in {'34', '37'}:
+            brand = PaymentCardBrand.amex
+        else:
+            brand = PaymentCardBrand.other
+
+        required_length: None | int | str = None
+        if brand in PaymentCardBrand.mastercard:
+            required_length = 16
+            valid = len(card_number) == required_length
+        elif brand == PaymentCardBrand.visa:
+            required_length = '13, 16 or 19'
+            valid = len(card_number) in {13, 16, 19}
+        elif brand == PaymentCardBrand.amex:
+            required_length = 15
+            valid = len(card_number) == required_length
+        else:
+            valid = True
+
+        if not valid:
+            raise PydanticCustomError(
+                'payment_card_number_brand',
+                'Length for a {brand} card must be {required_length}',
+                {'brand': brand, 'required_length': required_length},
+            )
+        return brand
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTE SIZE TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ByteSize(int):
+    """Converts a string representing a number of bytes with units (such as `'1KB'` or `'11.5MiB'`) into an integer.
+
+    You can use the `ByteSize` data type to (case-insensitively) convert a string representation of a number of bytes into
+    an integer, and also to print out human-readable strings representing a number of bytes.
+
+    In conformance with [IEC 80000-13 Standard](https://en.wikipedia.org/wiki/ISO/IEC_80000) we interpret `'1KB'` to mean 1000 bytes,
+    and `'1KiB'` to mean 1024 bytes. In general, including a middle `'i'` will cause the unit to be interpreted as a power of 2,
+    rather than a power of 10 (so, for example, `'1 MB'` is treated as `1_000_000` bytes, whereas `'1 MiB'` is treated as `1_048_576` bytes).
+
+    !!! info
+        Note that `1b` will be parsed as "1 byte" and not "1 bit".
+
+    ```python
+    from pydantic import BaseModel, ByteSize
+
+    class MyModel(BaseModel):
+        size: ByteSize
+
+    print(MyModel(size=52000).size)
+    #> 52000
+    print(MyModel(size='3000 KiB').size)
+    #> 3072000
+
+    m = MyModel(size='50 PB')
+    print(m.size.human_readable())
+    #> 44.4PiB
+    print(m.size.human_readable(decimal=True))
+    #> 50.0PB
+    print(m.size.human_readable(separator=' '))
+    #> 44.4 PiB
+
+    print(m.size.to('TiB'))
+    #> 45474.73508864641
+    ```
+    """
+
+    byte_sizes = {
+        'b': 1,
+        'kb': 10**3,
+        'mb': 10**6,
+        'gb': 10**9,
+        'tb': 10**12,
+        'pb': 10**15,
+        'eb': 10**18,
+        'kib': 2**10,
+        'mib': 2**20,
+        'gib': 2**30,
+        'tib': 2**40,
+        'pib': 2**50,
+        'eib': 2**60,
+        'bit': 1 / 8,
+        'kbit': 10**3 / 8,
+        'mbit': 10**6 / 8,
+        'gbit': 10**9 / 8,
+        'tbit': 10**12 / 8,
+        'pbit': 10**15 / 8,
+        'ebit': 10**18 / 8,
+        'kibit': 2**10 / 8,
+        'mibit': 2**20 / 8,
+        'gibit': 2**30 / 8,
+        'tibit': 2**40 / 8,
+        'pibit': 2**50 / 8,
+        'eibit': 2**60 / 8,
+    }
+    byte_sizes.update({k.lower()[0]: v for k, v in byte_sizes.items() if 'i' not in k})
+
+    byte_string_pattern = r'^\s*(\d*\.?\d+)\s*(\w+)?'
+    byte_string_re = re.compile(byte_string_pattern, re.IGNORECASE)
+
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        return core_schema.with_info_after_validator_function(
+            function=cls._validate,
+            schema=core_schema.union_schema(
+                [
+                    core_schema.str_schema(pattern=cls.byte_string_pattern),
+                    core_schema.int_schema(ge=0),
+                ],
+                custom_error_type='byte_size',
+                custom_error_message='could not parse value and unit from byte string',
+            ),
+            serialization=core_schema.plain_serializer_function_ser_schema(
+                int, return_schema=core_schema.int_schema(ge=0)
+            ),
+        )
+
+    @classmethod
+    def _validate(cls, input_value: Any, /, _: core_schema.ValidationInfo) -> ByteSize:
+        try:
+            return cls(int(input_value))
+        except ValueError:
+            pass
+
+        str_match = cls.byte_string_re.match(str(input_value))
+        if str_match is None:
+            raise PydanticCustomError('byte_size', 'could not parse value and unit from byte string')
+
+        scalar, unit = str_match.groups()
+        if unit is None:
+            unit = 'b'
+
+        try:
+            unit_mult = cls.byte_sizes[unit.lower()]
+        except KeyError:
+            raise PydanticCustomError('byte_size_unit', 'could not interpret byte unit: {unit}', {'unit': unit})
+
+        return cls(int(float(scalar) * unit_mult))
+
+    def human_readable(self, decimal: bool = False, separator: str = '') -> str:
+        """Converts a byte size to a human readable string.
+
+        Args:
+            decimal: If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units
+                (e.g. 1024 bytes per KiB).
+            separator: A string used to split the value and unit. Defaults to an empty string ('').
+
+        Returns:
+            A human readable string representation of the byte size.
+        """
+        if decimal:
+            divisor = 1000
+            units = 'B', 'KB', 'MB', 'GB', 'TB', 'PB'
+            final_unit = 'EB'
+        else:
+            divisor = 1024
+            units = 'B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'
+            final_unit = 'EiB'
+
+        num = float(self)
+        for unit in units:
+            if abs(num) < divisor:
+                if unit == 'B':
+                    return f'{num:0.0f}{separator}{unit}'
+                else:
+                    return f'{num:0.1f}{separator}{unit}'
+            num /= divisor
+
+        return f'{num:0.1f}{separator}{final_unit}'
+
+    def to(self, unit: str) -> float:
+        """Converts a byte size to another unit, including both byte and bit units.
+
+        Args:
+            unit: The unit to convert to. Must be one of the following: B, KB, MB, GB, TB, PB, EB,
+                KiB, MiB, GiB, TiB, PiB, EiB (byte units) and
+                bit, kbit, mbit, gbit, tbit, pbit, ebit,
+                kibit, mibit, gibit, tibit, pibit, eibit (bit units).
+
+        Returns:
+            The byte size in the new unit.
+        """
+        try:
+            unit_div = self.byte_sizes[unit.lower()]
+        except KeyError:
+            raise PydanticCustomError('byte_size_unit', 'Could not interpret byte unit: {unit}', {'unit': unit})
+
+        return self / unit_div
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DATE TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+def _check_annotated_type(annotated_type: str, expected_type: str, annotation: str) -> None:
+    if annotated_type != expected_type:
+        raise PydanticUserError(f"'{annotation}' cannot annotate '{annotated_type}'.", code='invalid-annotated-type')
+
+
+if TYPE_CHECKING:
+    PastDate = Annotated[date, ...]
+    FutureDate = Annotated[date, ...]
+else:
+
+    class PastDate:
+        """A date in the past."""
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, source: type[Any], handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            if cls is source:
+                # used directly as a type
+                return core_schema.date_schema(now_op='past')
+            else:
+                schema = handler(source)
+                _check_annotated_type(schema['type'], 'date', cls.__name__)
+                schema['now_op'] = 'past'
+                return schema
+
+        def __repr__(self) -> str:
+            return 'PastDate'
+
+    class FutureDate:
+        """A date in the future."""
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, source: type[Any], handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            if cls is source:
+                # used directly as a type
+                return core_schema.date_schema(now_op='future')
+            else:
+                schema = handler(source)
+                _check_annotated_type(schema['type'], 'date', cls.__name__)
+                schema['now_op'] = 'future'
+                return schema
+
+        def __repr__(self) -> str:
+            return 'FutureDate'
+
+
+def condate(
+    *,
+    strict: bool | None = None,
+    gt: date | None = None,
+    ge: date | None = None,
+    lt: date | None = None,
+    le: date | None = None,
+) -> type[date]:
+    """A wrapper for date that adds constraints.
+
+    Args:
+        strict: Whether to validate the date value in strict mode. Defaults to `None`.
+        gt: The value must be greater than this. Defaults to `None`.
+        ge: The value must be greater than or equal to this. Defaults to `None`.
+        lt: The value must be less than this. Defaults to `None`.
+        le: The value must be less than or equal to this. Defaults to `None`.
+
+    Returns:
+        A date type with the specified constraints.
+    """
+    return Annotated[  # pyright: ignore[reportReturnType]
+        date,
+        Strict(strict) if strict is not None else None,
+        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
+    ]
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DATETIME TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    AwareDatetime = Annotated[datetime, ...]
+    NaiveDatetime = Annotated[datetime, ...]
+    PastDatetime = Annotated[datetime, ...]
+    FutureDatetime = Annotated[datetime, ...]
+
+else:
+
+    class AwareDatetime:
+        """A datetime that requires timezone info."""
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, source: type[Any], handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            if cls is source:
+                # used directly as a type
+                return core_schema.datetime_schema(tz_constraint='aware')
+            else:
+                schema = handler(source)
+                _check_annotated_type(schema['type'], 'datetime', cls.__name__)
+                schema['tz_constraint'] = 'aware'
+                return schema
+
+        def __repr__(self) -> str:
+            return 'AwareDatetime'
+
+    class NaiveDatetime:
+        """A datetime that doesn't require timezone info."""
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, source: type[Any], handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            if cls is source:
+                # used directly as a type
+                return core_schema.datetime_schema(tz_constraint='naive')
+            else:
+                schema = handler(source)
+                _check_annotated_type(schema['type'], 'datetime', cls.__name__)
+                schema['tz_constraint'] = 'naive'
+                return schema
+
+        def __repr__(self) -> str:
+            return 'NaiveDatetime'
+
+    class PastDatetime:
+        """A datetime that must be in the past."""
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, source: type[Any], handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            if cls is source:
+                # used directly as a type
+                return core_schema.datetime_schema(now_op='past')
+            else:
+                schema = handler(source)
+                _check_annotated_type(schema['type'], 'datetime', cls.__name__)
+                schema['now_op'] = 'past'
+                return schema
+
+        def __repr__(self) -> str:
+            return 'PastDatetime'
+
+    class FutureDatetime:
+        """A datetime that must be in the future."""
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, source: type[Any], handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            if cls is source:
+                # used directly as a type
+                return core_schema.datetime_schema(now_op='future')
+            else:
+                schema = handler(source)
+                _check_annotated_type(schema['type'], 'datetime', cls.__name__)
+                schema['now_op'] = 'future'
+                return schema
+
+        def __repr__(self) -> str:
+            return 'FutureDatetime'
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Encoded TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class EncoderProtocol(Protocol):
+    """Protocol for encoding and decoding data to and from bytes."""
+
+    @classmethod
+    def decode(cls, data: bytes) -> bytes:
+        """Decode the data using the encoder.
+
+        Args:
+            data: The data to decode.
+
+        Returns:
+            The decoded data.
+        """
+        ...
+
+    @classmethod
+    def encode(cls, value: bytes) -> bytes:
+        """Encode the data using the encoder.
+
+        Args:
+            value: The data to encode.
+
+        Returns:
+            The encoded data.
+        """
+        ...
+
+    @classmethod
+    def get_json_format(cls) -> str:
+        """Get the JSON format for the encoded data.
+
+        Returns:
+            The JSON format for the encoded data.
+        """
+        ...
+
+
+class Base64Encoder(EncoderProtocol):
+    """Standard (non-URL-safe) Base64 encoder."""
+
+    @classmethod
+    def decode(cls, data: bytes) -> bytes:
+        """Decode the data from base64 encoded bytes to original bytes data.
+
+        Args:
+            data: The data to decode.
+
+        Returns:
+            The decoded data.
+        """
+        try:
+            return base64.b64decode(data)
+        except ValueError as e:
+            raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})
+
+    @classmethod
+    def encode(cls, value: bytes) -> bytes:
+        """Encode the data from bytes to a base64 encoded bytes.
+
+        Args:
+            value: The data to encode.
+
+        Returns:
+            The encoded data.
+        """
+        return base64.b64encode(value)
+
+    @classmethod
+    def get_json_format(cls) -> Literal['base64']:
+        """Get the JSON format for the encoded data.
+
+        Returns:
+            The JSON format for the encoded data.
+        """
+        return 'base64'
+
+
+class Base64UrlEncoder(EncoderProtocol):
+    """URL-safe Base64 encoder."""
+
+    @classmethod
+    def decode(cls, data: bytes) -> bytes:
+        """Decode the data from base64 encoded bytes to original bytes data.
+
+        Args:
+            data: The data to decode.
+
+        Returns:
+            The decoded data.
+        """
+        try:
+            return base64.urlsafe_b64decode(data)
+        except ValueError as e:
+            raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})
+
+    @classmethod
+    def encode(cls, value: bytes) -> bytes:
+        """Encode the data from bytes to a base64 encoded bytes.
+
+        Args:
+            value: The data to encode.
+
+        Returns:
+            The encoded data.
+        """
+        return base64.urlsafe_b64encode(value)
+
+    @classmethod
+    def get_json_format(cls) -> Literal['base64url']:
+        """Get the JSON format for the encoded data.
+
+        Returns:
+            The JSON format for the encoded data.
+        """
+        return 'base64url'
+
+
+@_dataclasses.dataclass(**_internal_dataclass.slots_true)
+class EncodedBytes:
+    """A bytes type that is encoded and decoded using the specified encoder.
+
+    `EncodedBytes` needs an encoder that implements `EncoderProtocol` to operate.
+
+    ```python
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, EncodedBytes, EncoderProtocol, ValidationError
+
+    class MyEncoder(EncoderProtocol):
+        @classmethod
+        def decode(cls, data: bytes) -> bytes:
+            if data == b'**undecodable**':
+                raise ValueError('Cannot decode data')
+            return data[13:]
+
+        @classmethod
+        def encode(cls, value: bytes) -> bytes:
+            return b'**encoded**: ' + value
+
+        @classmethod
+        def get_json_format(cls) -> str:
+            return 'my-encoder'
+
+    MyEncodedBytes = Annotated[bytes, EncodedBytes(encoder=MyEncoder)]
+
+    class Model(BaseModel):
+        my_encoded_bytes: MyEncodedBytes
+
+    # Initialize the model with encoded data
+    m = Model(my_encoded_bytes=b'**encoded**: some bytes')
+
+    # Access decoded value
+    print(m.my_encoded_bytes)
+    #> b'some bytes'
+
+    # Serialize into the encoded form
+    print(m.model_dump())
+    #> {'my_encoded_bytes': b'**encoded**: some bytes'}
+
+    # Validate encoded data
+    try:
+        Model(my_encoded_bytes=b'**undecodable**')
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        my_encoded_bytes
+          Value error, Cannot decode data [type=value_error, input_value=b'**undecodable**', input_type=bytes]
+        '''
+    ```
+    """
+
+    encoder: type[EncoderProtocol]
+
+    def __get_pydantic_json_schema__(
+        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        field_schema = handler(core_schema)
+        field_schema.update(type='string', format=self.encoder.get_json_format())
+        return field_schema
+
+    def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(source)
+        _check_annotated_type(schema['type'], 'bytes', self.__class__.__name__)
+        return core_schema.with_info_after_validator_function(
+            function=self.decode,
+            schema=schema,
+            serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode),
+        )
+
+    def decode(self, data: bytes, _: core_schema.ValidationInfo) -> bytes:
+        """Decode the data using the specified encoder.
+
+        Args:
+            data: The data to decode.
+
+        Returns:
+            The decoded data.
+        """
+        return self.encoder.decode(data)
+
+    def encode(self, value: bytes) -> bytes:
+        """Encode the data using the specified encoder.
+
+        Args:
+            value: The data to encode.
+
+        Returns:
+            The encoded data.
+        """
+        return self.encoder.encode(value)
+
+    def __hash__(self) -> int:
+        return hash(self.encoder)
+
+
+@_dataclasses.dataclass(**_internal_dataclass.slots_true)
+class EncodedStr:
+    """A str type that is encoded and decoded using the specified encoder.
+
+    `EncodedStr` needs an encoder that implements `EncoderProtocol` to operate.
+
+    ```python
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, EncodedStr, EncoderProtocol, ValidationError
+
+    class MyEncoder(EncoderProtocol):
+        @classmethod
+        def decode(cls, data: bytes) -> bytes:
+            if data == b'**undecodable**':
+                raise ValueError('Cannot decode data')
+            return data[13:]
+
+        @classmethod
+        def encode(cls, value: bytes) -> bytes:
+            return b'**encoded**: ' + value
+
+        @classmethod
+        def get_json_format(cls) -> str:
+            return 'my-encoder'
+
+    MyEncodedStr = Annotated[str, EncodedStr(encoder=MyEncoder)]
+
+    class Model(BaseModel):
+        my_encoded_str: MyEncodedStr
+
+    # Initialize the model with encoded data
+    m = Model(my_encoded_str='**encoded**: some str')
+
+    # Access decoded value
+    print(m.my_encoded_str)
+    #> some str
+
+    # Serialize into the encoded form
+    print(m.model_dump())
+    #> {'my_encoded_str': '**encoded**: some str'}
+
+    # Validate encoded data
+    try:
+        Model(my_encoded_str='**undecodable**')
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        my_encoded_str
+          Value error, Cannot decode data [type=value_error, input_value='**undecodable**', input_type=str]
+        '''
+    ```
+    """
+
+    encoder: type[EncoderProtocol]
+
+    def __get_pydantic_json_schema__(
+        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        field_schema = handler(core_schema)
+        field_schema.update(type='string', format=self.encoder.get_json_format())
+        return field_schema
+
+    def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(source)
+        _check_annotated_type(schema['type'], 'str', self.__class__.__name__)
+        return core_schema.with_info_after_validator_function(
+            function=self.decode_str,
+            schema=schema,
+            serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode_str),
+        )
+
+    def decode_str(self, data: str, _: core_schema.ValidationInfo) -> str:
+        """Decode the data using the specified encoder.
+
+        Args:
+            data: The data to decode.
+
+        Returns:
+            The decoded data.
+        """
+        return self.encoder.decode(data.encode()).decode()
+
+    def encode_str(self, value: str) -> str:
+        """Encode the data using the specified encoder.
+
+        Args:
+            value: The data to encode.
+
+        Returns:
+            The encoded data.
+        """
+        return self.encoder.encode(value.encode()).decode()  # noqa: UP008
+
+    def __hash__(self) -> int:
+        return hash(self.encoder)
+
+
+Base64Bytes = Annotated[bytes, EncodedBytes(encoder=Base64Encoder)]
+"""A bytes type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.
+
+Note:
+    Under the hood, `Base64Bytes` uses the standard library `base64.b64encode` and `base64.b64decode` functions.
+
+    As a result, attempting to decode url-safe base64 data using the `Base64Bytes` type may fail or produce an incorrect
+    decoding.
+
+Warning:
+    In versions of Pydantic prior to v2.10, `Base64Bytes` used [`base64.encodebytes`][base64.encodebytes]
+    and [`base64.decodebytes`][base64.decodebytes] functions. According to the [base64 documentation](https://docs.python.org/3/library/base64.html),
+    these methods are considered legacy implementation, and thus, Pydantic v2.10+ now uses the modern
+    [`base64.b64encode`][base64.b64encode] and [`base64.b64decode`][base64.b64decode] functions.
+
+    If you'd still like to use these legacy encoders / decoders, you can achieve this by creating a custom annotated type,
+    like follows:
+
+    ```python
+    import base64
+    from typing import Literal
+
+    from pydantic_core import PydanticCustomError
+    from typing_extensions import Annotated
+
+    from pydantic import EncodedBytes, EncoderProtocol
+
+    class LegacyBase64Encoder(EncoderProtocol):
+        @classmethod
+        def decode(cls, data: bytes) -> bytes:
+            try:
+                return base64.decodebytes(data)
+            except ValueError as e:
+                raise PydanticCustomError(
+                    'base64_decode',
+                    "Base64 decoding error: '{error}'",
+                    {'error': str(e)},
+                )
+
+        @classmethod
+        def encode(cls, value: bytes) -> bytes:
+            return base64.encodebytes(value)
+
+        @classmethod
+        def get_json_format(cls) -> Literal['base64']:
+            return 'base64'
+
+    LegacyBase64Bytes = Annotated[bytes, EncodedBytes(encoder=LegacyBase64Encoder)]
+    ```
+
+```python
+from pydantic import Base64Bytes, BaseModel, ValidationError
+
+class Model(BaseModel):
+    base64_bytes: Base64Bytes
+
+# Initialize the model with base64 data
+m = Model(base64_bytes=b'VGhpcyBpcyB0aGUgd2F5')
+
+# Access decoded value
+print(m.base64_bytes)
+#> b'This is the way'
+
+# Serialize into the base64 form
+print(m.model_dump())
+#> {'base64_bytes': b'VGhpcyBpcyB0aGUgd2F5'}
+
+# Validate base64 data
+try:
+    print(Model(base64_bytes=b'undecodable').base64_bytes)
+except ValidationError as e:
+    print(e)
+    '''
+    1 validation error for Model
+    base64_bytes
+      Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value=b'undecodable', input_type=bytes]
+    '''
+```
+"""
+Base64Str = Annotated[str, EncodedStr(encoder=Base64Encoder)]
+"""A str type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.
+
+Note:
+    Under the hood, `Base64Str` uses the standard library `base64.b64encode` and `base64.b64decode` functions.
+
+    As a result, attempting to decode url-safe base64 data using the `Base64Str` type may fail or produce an incorrect
+    decoding.
+
+Warning:
+    In versions of Pydantic prior to v2.10, `Base64Str` used [`base64.encodebytes`][base64.encodebytes]
+    and [`base64.decodebytes`][base64.decodebytes] functions. According to the [base64 documentation](https://docs.python.org/3/library/base64.html),
+    these methods are considered legacy implementation, and thus, Pydantic v2.10+ now uses the modern
+    [`base64.b64encode`][base64.b64encode] and [`base64.b64decode`][base64.b64decode] functions.
+
+    See the [`Base64Bytes`][pydantic.types.Base64Bytes] type for more information on how to
+    replicate the old behavior with the legacy encoders / decoders.
+
+```python
+from pydantic import Base64Str, BaseModel, ValidationError
+
+class Model(BaseModel):
+    base64_str: Base64Str
+
+# Initialize the model with base64 data
+m = Model(base64_str='VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y')
+
+# Access decoded value
+print(m.base64_str)
+#> These aren't the droids you're looking for
+
+# Serialize into the base64 form
+print(m.model_dump())
+#> {'base64_str': 'VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y'}
+
+# Validate base64 data
+try:
+    print(Model(base64_str='undecodable').base64_str)
+except ValidationError as e:
+    print(e)
+    '''
+    1 validation error for Model
+    base64_str
+      Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value='undecodable', input_type=str]
+    '''
+```
+"""
+Base64UrlBytes = Annotated[bytes, EncodedBytes(encoder=Base64UrlEncoder)]
+"""A bytes type that is encoded and decoded using the URL-safe base64 encoder.
+
+Note:
+    Under the hood, `Base64UrlBytes` use standard library `base64.urlsafe_b64encode` and `base64.urlsafe_b64decode`
+    functions.
+
+    As a result, the `Base64UrlBytes` type can be used to faithfully decode "vanilla" base64 data
+    (using `'+'` and `'/'`).
+
+```python
+from pydantic import Base64UrlBytes, BaseModel
+
+class Model(BaseModel):
+    base64url_bytes: Base64UrlBytes
+
+# Initialize the model with base64 data
+m = Model(base64url_bytes=b'SHc_dHc-TXc==')
+print(m)
+#> base64url_bytes=b'Hw?tw>Mw'
+```
+"""
+Base64UrlStr = Annotated[str, EncodedStr(encoder=Base64UrlEncoder)]
+"""A str type that is encoded and decoded using the URL-safe base64 encoder.
+
+Note:
+    Under the hood, `Base64UrlStr` use standard library `base64.urlsafe_b64encode` and `base64.urlsafe_b64decode`
+    functions.
+
+    As a result, the `Base64UrlStr` type can be used to faithfully decode "vanilla" base64 data (using `'+'` and `'/'`).
+
+```python
+from pydantic import Base64UrlStr, BaseModel
+
+class Model(BaseModel):
+    base64url_str: Base64UrlStr
+
+# Initialize the model with base64 data
+m = Model(base64url_str='SHc_dHc-TXc==')
+print(m)
+#> base64url_str='Hw?tw>Mw'
+```
+"""
+
+
+__getattr__ = getattr_migration(__name__)
+
+
+@_dataclasses.dataclass(**_internal_dataclass.slots_true)
+class GetPydanticSchema:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/types/#using-getpydanticschema-to-reduce-boilerplate
+
+    A convenience class for creating an annotation that provides pydantic custom type hooks.
+
+    This class is intended to eliminate the need to create a custom "marker" which defines the
+     `__get_pydantic_core_schema__` and `__get_pydantic_json_schema__` custom hook methods.
+
+    For example, to have a field treated by type checkers as `int`, but by pydantic as `Any`, you can do:
+    ```python
+    from typing import Any
+
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, GetPydanticSchema
+
+    HandleAsAny = GetPydanticSchema(lambda _s, h: h(Any))
+
+    class Model(BaseModel):
+        x: Annotated[int, HandleAsAny]  # pydantic sees `x: Any`
+
+    print(repr(Model(x='abc').x))
+    #> 'abc'
+    ```
+    """
+
+    get_pydantic_core_schema: Callable[[Any, GetCoreSchemaHandler], CoreSchema] | None = None
+    get_pydantic_json_schema: Callable[[Any, GetJsonSchemaHandler], JsonSchemaValue] | None = None
+
+    # Note: we may want to consider adding a convenience staticmethod `def for_type(type_: Any) -> GetPydanticSchema:`
+    #   which returns `GetPydanticSchema(lambda _s, h: h(type_))`
+
+    if not TYPE_CHECKING:
+        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
+
+        def __getattr__(self, item: str) -> Any:
+            """Use this rather than defining `__get_pydantic_core_schema__` etc. to reduce the number of nested calls."""
+            if item == '__get_pydantic_core_schema__' and self.get_pydantic_core_schema:
+                return self.get_pydantic_core_schema
+            elif item == '__get_pydantic_json_schema__' and self.get_pydantic_json_schema:
+                return self.get_pydantic_json_schema
+            else:
+                return object.__getattribute__(self, item)
+
+    __hash__ = object.__hash__
+
+
+@_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
+class Tag:
+    """Provides a way to specify the expected tag to use for a case of a (callable) discriminated union.
+
+    Also provides a way to label a union case in error messages.
+
+    When using a callable `Discriminator`, attach a `Tag` to each case in the `Union` to specify the tag that
+    should be used to identify that case. For example, in the below example, the `Tag` is used to specify that
+    if `get_discriminator_value` returns `'apple'`, the input should be validated as an `ApplePie`, and if it
+    returns `'pumpkin'`, the input should be validated as a `PumpkinPie`.
+
+    The primary role of the `Tag` here is to map the return value from the callable `Discriminator` function to
+    the appropriate member of the `Union` in question.
+
+    ```python
+    from typing import Any, Union
+
+    from typing_extensions import Annotated, Literal
+
+    from pydantic import BaseModel, Discriminator, Tag
+
+    class Pie(BaseModel):
+        time_to_cook: int
+        num_ingredients: int
+
+    class ApplePie(Pie):
+        fruit: Literal['apple'] = 'apple'
+
+    class PumpkinPie(Pie):
+        filling: Literal['pumpkin'] = 'pumpkin'
+
+    def get_discriminator_value(v: Any) -> str:
+        if isinstance(v, dict):
+            return v.get('fruit', v.get('filling'))
+        return getattr(v, 'fruit', getattr(v, 'filling', None))
+
+    class ThanksgivingDinner(BaseModel):
+        dessert: Annotated[
+            Union[
+                Annotated[ApplePie, Tag('apple')],
+                Annotated[PumpkinPie, Tag('pumpkin')],
+            ],
+            Discriminator(get_discriminator_value),
+        ]
+
+    apple_variation = ThanksgivingDinner.model_validate(
+        {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
+    )
+    print(repr(apple_variation))
+    '''
+    ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
+    '''
+
+    pumpkin_variation = ThanksgivingDinner.model_validate(
+        {
+            'dessert': {
+                'filling': 'pumpkin',
+                'time_to_cook': 40,
+                'num_ingredients': 6,
+            }
+        }
+    )
+    print(repr(pumpkin_variation))
+    '''
+    ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
+    '''
+    ```
+
+    !!! note
+        You must specify a `Tag` for every case in a `Tag` that is associated with a
+        callable `Discriminator`. Failing to do so will result in a `PydanticUserError` with code
+        [`callable-discriminator-no-tag`](../errors/usage_errors.md#callable-discriminator-no-tag).
+
+    See the [Discriminated Unions] concepts docs for more details on how to use `Tag`s.
+
+    [Discriminated Unions]: ../concepts/unions.md#discriminated-unions
+    """
+
+    tag: str
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
+        schema = handler(source_type)
+        metadata = schema.setdefault('metadata', {})
+        assert isinstance(metadata, dict)
+        metadata[_core_utils.TAGGED_UNION_TAG_KEY] = self.tag
+        return schema
+
+
+@_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
+class Discriminator:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/unions/#discriminated-unions-with-callable-discriminator
+
+    Provides a way to use a custom callable as the way to extract the value of a union discriminator.
+
+    This allows you to get validation behavior like you'd get from `Field(discriminator=<field_name>)`,
+    but without needing to have a single shared field across all the union choices. This also makes it
+    possible to handle unions of models and primitive types with discriminated-union-style validation errors.
+    Finally, this allows you to use a custom callable as the way to identify which member of a union a value
+    belongs to, while still seeing all the performance benefits of a discriminated union.
+
+    Consider this example, which is much more performant with the use of `Discriminator` and thus a `TaggedUnion`
+    than it would be as a normal `Union`.
+
+    ```python
+    from typing import Any, Union
+
+    from typing_extensions import Annotated, Literal
+
+    from pydantic import BaseModel, Discriminator, Tag
+
+    class Pie(BaseModel):
+        time_to_cook: int
+        num_ingredients: int
+
+    class ApplePie(Pie):
+        fruit: Literal['apple'] = 'apple'
+
+    class PumpkinPie(Pie):
+        filling: Literal['pumpkin'] = 'pumpkin'
+
+    def get_discriminator_value(v: Any) -> str:
+        if isinstance(v, dict):
+            return v.get('fruit', v.get('filling'))
+        return getattr(v, 'fruit', getattr(v, 'filling', None))
+
+    class ThanksgivingDinner(BaseModel):
+        dessert: Annotated[
+            Union[
+                Annotated[ApplePie, Tag('apple')],
+                Annotated[PumpkinPie, Tag('pumpkin')],
+            ],
+            Discriminator(get_discriminator_value),
+        ]
+
+    apple_variation = ThanksgivingDinner.model_validate(
+        {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
+    )
+    print(repr(apple_variation))
+    '''
+    ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
+    '''
+
+    pumpkin_variation = ThanksgivingDinner.model_validate(
+        {
+            'dessert': {
+                'filling': 'pumpkin',
+                'time_to_cook': 40,
+                'num_ingredients': 6,
+            }
+        }
+    )
+    print(repr(pumpkin_variation))
+    '''
+    ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
+    '''
+    ```
+
+    See the [Discriminated Unions] concepts docs for more details on how to use `Discriminator`s.
+
+    [Discriminated Unions]: ../concepts/unions.md#discriminated-unions
+    """
+
+    discriminator: str | Callable[[Any], Hashable]
+    """The callable or field name for discriminating the type in a tagged union.
+
+    A `Callable` discriminator must extract the value of the discriminator from the input.
+    A `str` discriminator must be the name of a field to discriminate against.
+    """
+    custom_error_type: str | None = None
+    """Type to use in [custom errors](../errors/errors.md#custom-errors) replacing the standard discriminated union
+    validation errors.
+    """
+    custom_error_message: str | None = None
+    """Message to use in custom errors."""
+    custom_error_context: dict[str, int | str | float] | None = None
+    """Context to use in custom errors."""
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
+        origin = _typing_extra.get_origin(source_type)
+        if not origin or not _typing_extra.origin_is_union(origin):
+            raise TypeError(f'{type(self).__name__} must be used with a Union type, not {source_type}')
+
+        if isinstance(self.discriminator, str):
+            from pydantic import Field
+
+            return handler(Annotated[source_type, Field(discriminator=self.discriminator)])
+        else:
+            original_schema = handler(source_type)
+            return self._convert_schema(original_schema)
+
+    def _convert_schema(self, original_schema: core_schema.CoreSchema) -> core_schema.TaggedUnionSchema:
+        if original_schema['type'] != 'union':
+            # This likely indicates that the schema was a single-item union that was simplified.
+            # In this case, we do the same thing we do in
+            # `pydantic._internal._discriminated_union._ApplyInferredDiscriminator._apply_to_root`, namely,
+            # package the generated schema back into a single-item union.
+            original_schema = core_schema.union_schema([original_schema])
+
+        tagged_union_choices = {}
+        for choice in original_schema['choices']:
+            tag = None
+            if isinstance(choice, tuple):
+                choice, tag = choice
+            metadata = choice.get('metadata')
+            if metadata is not None:
+                metadata_tag = metadata.get(_core_utils.TAGGED_UNION_TAG_KEY)
+                if metadata_tag is not None:
+                    tag = metadata_tag
+            if tag is None:
+                raise PydanticUserError(
+                    f'`Tag` not provided for choice {choice} used with `Discriminator`',
+                    code='callable-discriminator-no-tag',
+                )
+            tagged_union_choices[tag] = choice
+
+        # Have to do these verbose checks to ensure falsy values ('' and {}) don't get ignored
+        custom_error_type = self.custom_error_type
+        if custom_error_type is None:
+            custom_error_type = original_schema.get('custom_error_type')
+
+        custom_error_message = self.custom_error_message
+        if custom_error_message is None:
+            custom_error_message = original_schema.get('custom_error_message')
+
+        custom_error_context = self.custom_error_context
+        if custom_error_context is None:
+            custom_error_context = original_schema.get('custom_error_context')
+
+        custom_error_type = original_schema.get('custom_error_type') if custom_error_type is None else custom_error_type
+        return core_schema.tagged_union_schema(
+            tagged_union_choices,
+            self.discriminator,
+            custom_error_type=custom_error_type,
+            custom_error_message=custom_error_message,
+            custom_error_context=custom_error_context,
+            strict=original_schema.get('strict'),
+            ref=original_schema.get('ref'),
+            metadata=original_schema.get('metadata'),
+            serialization=original_schema.get('serialization'),
+        )
+
+
+_JSON_TYPES = {int, float, str, bool, list, dict, type(None)}
+
+
+def _get_type_name(x: Any) -> str:
+    type_ = type(x)
+    if type_ in _JSON_TYPES:
+        return type_.__name__
+
+    # Handle proper subclasses; note we don't need to handle None or bool here
+    if isinstance(x, int):
+        return 'int'
+    if isinstance(x, float):
+        return 'float'
+    if isinstance(x, str):
+        return 'str'
+    if isinstance(x, list):
+        return 'list'
+    if isinstance(x, dict):
+        return 'dict'
+
+    # Fail by returning the type's actual name
+    return getattr(type_, '__name__', '<no type name>')
+
+
+class _AllowAnyJson:
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
+        python_schema = handler(source_type)
+        return core_schema.json_or_python_schema(json_schema=core_schema.any_schema(), python_schema=python_schema)
+
+
+if TYPE_CHECKING:
+    # This seems to only be necessary for mypy
+    JsonValue: TypeAlias = Union[
+        List['JsonValue'],
+        Dict[str, 'JsonValue'],
+        str,
+        bool,
+        int,
+        float,
+        None,
+    ]
+    """A `JsonValue` is used to represent a value that can be serialized to JSON.
+
+    It may be one of:
+
+    * `List['JsonValue']`
+    * `Dict[str, 'JsonValue']`
+    * `str`
+    * `bool`
+    * `int`
+    * `float`
+    * `None`
+
+    The following example demonstrates how to use `JsonValue` to validate JSON data,
+    and what kind of errors to expect when input data is not json serializable.
+
+    ```python
+    import json
+
+    from pydantic import BaseModel, JsonValue, ValidationError
+
+    class Model(BaseModel):
+        j: JsonValue
+
+    valid_json_data = {'j': {'a': {'b': {'c': 1, 'd': [2, None]}}}}
+    invalid_json_data = {'j': {'a': {'b': ...}}}
+
+    print(repr(Model.model_validate(valid_json_data)))
+    #> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}})
+    print(repr(Model.model_validate_json(json.dumps(valid_json_data))))
+    #> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}})
+
+    try:
+        Model.model_validate(invalid_json_data)
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        j.dict.a.dict.b
+          input was not a valid JSON value [type=invalid-json-value, input_value=Ellipsis, input_type=ellipsis]
+        '''
+    ```
+    """
+
+else:
+    JsonValue = TypeAliasType(
+        'JsonValue',
+        Annotated[
+            Union[
+                Annotated[List['JsonValue'], Tag('list')],
+                Annotated[Dict[str, 'JsonValue'], Tag('dict')],
+                Annotated[str, Tag('str')],
+                Annotated[bool, Tag('bool')],
+                Annotated[int, Tag('int')],
+                Annotated[float, Tag('float')],
+                Annotated[None, Tag('NoneType')],
+            ],
+            Discriminator(
+                _get_type_name,
+                custom_error_type='invalid-json-value',
+                custom_error_message='input was not a valid JSON value',
+            ),
+            _AllowAnyJson,
+        ],
+    )
+
+
+class _OnErrorOmit:
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
+        # there is no actual default value here but we use with_default_schema since it already has the on_error
+        # behavior implemented and it would be no more efficient to implement it on every other validator
+        # or as a standalone validator
+        return core_schema.with_default_schema(schema=handler(source_type), on_error='omit')
+
+
+OnErrorOmit = Annotated[T, _OnErrorOmit]
+"""
+When used as an item in a list, the key type in a dict, optional values of a TypedDict, etc.
+this annotation omits the item from the iteration if there is any error validating it.
+That is, instead of a [`ValidationError`][pydantic_core.ValidationError] being propagated up and the entire iterable being discarded
+any invalid items are discarded and the valid ones are returned.
+"""
+
+
+@_dataclasses.dataclass
+class FailFast(_fields.PydanticMetadata, BaseMetadata):
+    """A `FailFast` annotation can be used to specify that validation should stop at the first error.
+
+    This can be useful when you want to validate a large amount of data and you only need to know if it's valid or not.
+
+    You might want to enable this setting if you want to validate your data faster (basically, if you use this,
+    validation will be more performant with the caveat that you get less information).
+
+    ```python
+    from typing import List
+
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, FailFast, ValidationError
+
+    class Model(BaseModel):
+        x: Annotated[List[int], FailFast()]
+
+    # This will raise a single error for the first invalid value and stop validation
+    try:
+        obj = Model(x=[1, 2, 'a', 4, 5, 'b', 7, 8, 9, 'c'])
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        x.2
+          Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
+        '''
+    ```
+    """
+
+    fail_fast: bool = True
diff --git a/.venv/lib/python3.12/site-packages/pydantic/typing.py b/.venv/lib/python3.12/site-packages/pydantic/typing.py
new file mode 100644
index 00000000..0bda22d0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/typing.py
@@ -0,0 +1,5 @@
+"""`typing` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/utils.py b/.venv/lib/python3.12/site-packages/pydantic/utils.py
new file mode 100644
index 00000000..8d1e2a81
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/utils.py
@@ -0,0 +1,5 @@
+"""The `utils` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/__init__.py b/.venv/lib/python3.12/site-packages/pydantic/v1/__init__.py
new file mode 100644
index 00000000..6ad3f466
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/__init__.py
@@ -0,0 +1,131 @@
+# flake8: noqa
+from pydantic.v1 import dataclasses
+from pydantic.v1.annotated_types import create_model_from_namedtuple, create_model_from_typeddict
+from pydantic.v1.class_validators import root_validator, validator
+from pydantic.v1.config import BaseConfig, ConfigDict, Extra
+from pydantic.v1.decorator import validate_arguments
+from pydantic.v1.env_settings import BaseSettings
+from pydantic.v1.error_wrappers import ValidationError
+from pydantic.v1.errors import *
+from pydantic.v1.fields import Field, PrivateAttr, Required
+from pydantic.v1.main import *
+from pydantic.v1.networks import *
+from pydantic.v1.parse import Protocol
+from pydantic.v1.tools import *
+from pydantic.v1.types import *
+from pydantic.v1.version import VERSION, compiled
+
+__version__ = VERSION
+
+# WARNING __all__ from pydantic.errors is not included here, it will be removed as an export here in v2
+# please use "from pydantic.v1.errors import ..." instead
+__all__ = [
+    # annotated types utils
+    'create_model_from_namedtuple',
+    'create_model_from_typeddict',
+    # dataclasses
+    'dataclasses',
+    # class_validators
+    'root_validator',
+    'validator',
+    # config
+    'BaseConfig',
+    'ConfigDict',
+    'Extra',
+    # decorator
+    'validate_arguments',
+    # env_settings
+    'BaseSettings',
+    # error_wrappers
+    'ValidationError',
+    # fields
+    'Field',
+    'Required',
+    # main
+    'BaseModel',
+    'create_model',
+    'validate_model',
+    # network
+    'AnyUrl',
+    'AnyHttpUrl',
+    'FileUrl',
+    'HttpUrl',
+    'stricturl',
+    'EmailStr',
+    'NameEmail',
+    'IPvAnyAddress',
+    'IPvAnyInterface',
+    'IPvAnyNetwork',
+    'PostgresDsn',
+    'CockroachDsn',
+    'AmqpDsn',
+    'RedisDsn',
+    'MongoDsn',
+    'KafkaDsn',
+    'validate_email',
+    # parse
+    'Protocol',
+    # tools
+    'parse_file_as',
+    'parse_obj_as',
+    'parse_raw_as',
+    'schema_of',
+    'schema_json_of',
+    # types
+    'NoneStr',
+    'NoneBytes',
+    'StrBytes',
+    'NoneStrBytes',
+    'StrictStr',
+    'ConstrainedBytes',
+    'conbytes',
+    'ConstrainedList',
+    'conlist',
+    'ConstrainedSet',
+    'conset',
+    'ConstrainedFrozenSet',
+    'confrozenset',
+    'ConstrainedStr',
+    'constr',
+    'PyObject',
+    'ConstrainedInt',
+    'conint',
+    'PositiveInt',
+    'NegativeInt',
+    'NonNegativeInt',
+    'NonPositiveInt',
+    'ConstrainedFloat',
+    'confloat',
+    'PositiveFloat',
+    'NegativeFloat',
+    'NonNegativeFloat',
+    'NonPositiveFloat',
+    'FiniteFloat',
+    'ConstrainedDecimal',
+    'condecimal',
+    'ConstrainedDate',
+    'condate',
+    'UUID1',
+    'UUID3',
+    'UUID4',
+    'UUID5',
+    'FilePath',
+    'DirectoryPath',
+    'Json',
+    'JsonWrapper',
+    'SecretField',
+    'SecretStr',
+    'SecretBytes',
+    'StrictBool',
+    'StrictBytes',
+    'StrictInt',
+    'StrictFloat',
+    'PaymentCardNumber',
+    'PrivateAttr',
+    'ByteSize',
+    'PastDate',
+    'FutureDate',
+    # version
+    'compiled',
+    'VERSION',
+]
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/_hypothesis_plugin.py b/.venv/lib/python3.12/site-packages/pydantic/v1/_hypothesis_plugin.py
new file mode 100644
index 00000000..b62234d5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/_hypothesis_plugin.py
@@ -0,0 +1,391 @@
+"""
+Register Hypothesis strategies for Pydantic custom types.
+
+This enables fully-automatic generation of test data for most Pydantic classes.
+
+Note that this module has *no* runtime impact on Pydantic itself; instead it
+is registered as a setuptools entry point and Hypothesis will import it if
+Pydantic is installed.  See also:
+
+https://hypothesis.readthedocs.io/en/latest/strategies.html#registering-strategies-via-setuptools-entry-points
+https://hypothesis.readthedocs.io/en/latest/data.html#hypothesis.strategies.register_type_strategy
+https://hypothesis.readthedocs.io/en/latest/strategies.html#interaction-with-pytest-cov
+https://docs.pydantic.dev/usage/types/#pydantic-types
+
+Note that because our motivation is to *improve user experience*, the strategies
+are always sound (never generate invalid data) but sacrifice completeness for
+maintainability (ie may be unable to generate some tricky but valid data).
+
+Finally, this module makes liberal use of `# type: ignore[<code>]` pragmas.
+This is because Hypothesis annotates `register_type_strategy()` with
+`(T, SearchStrategy[T])`, but in most cases we register e.g. `ConstrainedInt`
+to generate instances of the builtin `int` type which match the constraints.
+"""
+
+import contextlib
+import datetime
+import ipaddress
+import json
+import math
+from fractions import Fraction
+from typing import Callable, Dict, Type, Union, cast, overload
+
+import hypothesis.strategies as st
+
+import pydantic
+import pydantic.color
+import pydantic.types
+from pydantic.v1.utils import lenient_issubclass
+
+# FilePath and DirectoryPath are explicitly unsupported, as we'd have to create
+# them on-disk, and that's unsafe in general without being told *where* to do so.
+#
+# URLs are unsupported because it's easy for users to define their own strategy for
+# "normal" URLs, and hard for us to define a general strategy which includes "weird"
+# URLs but doesn't also have unpredictable performance problems.
+#
+# conlist() and conset() are unsupported for now, because the workarounds for
+# Cython and Hypothesis to handle parametrized generic types are incompatible.
+# We are rethinking Hypothesis compatibility in Pydantic v2.
+
+# Emails
+try:
+    import email_validator
+except ImportError:  # pragma: no cover
+    pass
+else:
+
+    def is_valid_email(s: str) -> bool:
+        # Hypothesis' st.emails() occasionally generates emails like 0@A0--0.ac
+        # that are invalid according to email-validator, so we filter those out.
+        try:
+            email_validator.validate_email(s, check_deliverability=False)
+            return True
+        except email_validator.EmailNotValidError:  # pragma: no cover
+            return False
+
+    # Note that these strategies deliberately stay away from any tricky Unicode
+    # or other encoding issues; we're just trying to generate *something* valid.
+    st.register_type_strategy(pydantic.EmailStr, st.emails().filter(is_valid_email))  # type: ignore[arg-type]
+    st.register_type_strategy(
+        pydantic.NameEmail,
+        st.builds(
+            '{} <{}>'.format,  # type: ignore[arg-type]
+            st.from_regex('[A-Za-z0-9_]+( [A-Za-z0-9_]+){0,5}', fullmatch=True),
+            st.emails().filter(is_valid_email),
+        ),
+    )
+
+# PyObject - dotted names, in this case taken from the math module.
+st.register_type_strategy(
+    pydantic.PyObject,  # type: ignore[arg-type]
+    st.sampled_from(
+        [cast(pydantic.PyObject, f'math.{name}') for name in sorted(vars(math)) if not name.startswith('_')]
+    ),
+)
+
+# CSS3 Colors; as name, hex, rgb(a) tuples or strings, or hsl strings
+_color_regexes = (
+    '|'.join(
+        (
+            pydantic.color.r_hex_short,
+            pydantic.color.r_hex_long,
+            pydantic.color.r_rgb,
+            pydantic.color.r_rgba,
+            pydantic.color.r_hsl,
+            pydantic.color.r_hsla,
+        )
+    )
+    # Use more precise regex patterns to avoid value-out-of-range errors
+    .replace(pydantic.color._r_sl, r'(?:(\d\d?(?:\.\d+)?|100(?:\.0+)?)%)')
+    .replace(pydantic.color._r_alpha, r'(?:(0(?:\.\d+)?|1(?:\.0+)?|\.\d+|\d{1,2}%))')
+    .replace(pydantic.color._r_255, r'(?:((?:\d|\d\d|[01]\d\d|2[0-4]\d|25[0-4])(?:\.\d+)?|255(?:\.0+)?))')
+)
+st.register_type_strategy(
+    pydantic.color.Color,
+    st.one_of(
+        st.sampled_from(sorted(pydantic.color.COLORS_BY_NAME)),
+        st.tuples(
+            st.integers(0, 255),
+            st.integers(0, 255),
+            st.integers(0, 255),
+            st.none() | st.floats(0, 1) | st.floats(0, 100).map('{}%'.format),
+        ),
+        st.from_regex(_color_regexes, fullmatch=True),
+    ),
+)
+
+
+# Card numbers, valid according to the Luhn algorithm
+
+
+def add_luhn_digit(card_number: str) -> str:
+    # See https://en.wikipedia.org/wiki/Luhn_algorithm
+    for digit in '0123456789':
+        with contextlib.suppress(Exception):
+            pydantic.PaymentCardNumber.validate_luhn_check_digit(card_number + digit)
+            return card_number + digit
+    raise AssertionError('Unreachable')  # pragma: no cover
+
+
+card_patterns = (
+    # Note that these patterns omit the Luhn check digit; that's added by the function above
+    '4[0-9]{14}',  # Visa
+    '5[12345][0-9]{13}',  # Mastercard
+    '3[47][0-9]{12}',  # American Express
+    '[0-26-9][0-9]{10,17}',  # other (incomplete to avoid overlap)
+)
+st.register_type_strategy(
+    pydantic.PaymentCardNumber,
+    st.from_regex('|'.join(card_patterns), fullmatch=True).map(add_luhn_digit),  # type: ignore[arg-type]
+)
+
+# UUIDs
+st.register_type_strategy(pydantic.UUID1, st.uuids(version=1))
+st.register_type_strategy(pydantic.UUID3, st.uuids(version=3))
+st.register_type_strategy(pydantic.UUID4, st.uuids(version=4))
+st.register_type_strategy(pydantic.UUID5, st.uuids(version=5))
+
+# Secrets
+st.register_type_strategy(pydantic.SecretBytes, st.binary().map(pydantic.SecretBytes))
+st.register_type_strategy(pydantic.SecretStr, st.text().map(pydantic.SecretStr))
+
+# IP addresses, networks, and interfaces
+st.register_type_strategy(pydantic.IPvAnyAddress, st.ip_addresses())  # type: ignore[arg-type]
+st.register_type_strategy(
+    pydantic.IPvAnyInterface,
+    st.from_type(ipaddress.IPv4Interface) | st.from_type(ipaddress.IPv6Interface),  # type: ignore[arg-type]
+)
+st.register_type_strategy(
+    pydantic.IPvAnyNetwork,
+    st.from_type(ipaddress.IPv4Network) | st.from_type(ipaddress.IPv6Network),  # type: ignore[arg-type]
+)
+
+# We hook into the con***() functions and the ConstrainedNumberMeta metaclass,
+# so here we only have to register subclasses for other constrained types which
+# don't go via those mechanisms.  Then there are the registration hooks below.
+st.register_type_strategy(pydantic.StrictBool, st.booleans())
+st.register_type_strategy(pydantic.StrictStr, st.text())
+
+
+# FutureDate, PastDate
+st.register_type_strategy(pydantic.FutureDate, st.dates(min_value=datetime.date.today() + datetime.timedelta(days=1)))
+st.register_type_strategy(pydantic.PastDate, st.dates(max_value=datetime.date.today() - datetime.timedelta(days=1)))
+
+
+# Constrained-type resolver functions
+#
+# For these ones, we actually want to inspect the type in order to work out a
+# satisfying strategy.  First up, the machinery for tracking resolver functions:
+
+RESOLVERS: Dict[type, Callable[[type], st.SearchStrategy]] = {}  # type: ignore[type-arg]
+
+
+@overload
+def _registered(typ: Type[pydantic.types.T]) -> Type[pydantic.types.T]:
+    pass
+
+
+@overload
+def _registered(typ: pydantic.types.ConstrainedNumberMeta) -> pydantic.types.ConstrainedNumberMeta:
+    pass
+
+
+def _registered(
+    typ: Union[Type[pydantic.types.T], pydantic.types.ConstrainedNumberMeta]
+) -> Union[Type[pydantic.types.T], pydantic.types.ConstrainedNumberMeta]:
+    # This function replaces the version in `pydantic.types`, in order to
+    # effect the registration of new constrained types so that Hypothesis
+    # can generate valid examples.
+    pydantic.types._DEFINED_TYPES.add(typ)
+    for supertype, resolver in RESOLVERS.items():
+        if issubclass(typ, supertype):
+            st.register_type_strategy(typ, resolver(typ))  # type: ignore
+            return typ
+    raise NotImplementedError(f'Unknown type {typ!r} has no resolver to register')  # pragma: no cover
+
+
+def resolves(
+    typ: Union[type, pydantic.types.ConstrainedNumberMeta]
+) -> Callable[[Callable[..., st.SearchStrategy]], Callable[..., st.SearchStrategy]]:  # type: ignore[type-arg]
+    def inner(f):  # type: ignore
+        assert f not in RESOLVERS
+        RESOLVERS[typ] = f
+        return f
+
+    return inner
+
+
+# Type-to-strategy resolver functions
+
+
+@resolves(pydantic.JsonWrapper)
+def resolve_json(cls):  # type: ignore[no-untyped-def]
+    try:
+        inner = st.none() if cls.inner_type is None else st.from_type(cls.inner_type)
+    except Exception:  # pragma: no cover
+        finite = st.floats(allow_infinity=False, allow_nan=False)
+        inner = st.recursive(
+            base=st.one_of(st.none(), st.booleans(), st.integers(), finite, st.text()),
+            extend=lambda x: st.lists(x) | st.dictionaries(st.text(), x),  # type: ignore
+        )
+    inner_type = getattr(cls, 'inner_type', None)
+    return st.builds(
+        cls.inner_type.json if lenient_issubclass(inner_type, pydantic.BaseModel) else json.dumps,
+        inner,
+        ensure_ascii=st.booleans(),
+        indent=st.none() | st.integers(0, 16),
+        sort_keys=st.booleans(),
+    )
+
+
+@resolves(pydantic.ConstrainedBytes)
+def resolve_conbytes(cls):  # type: ignore[no-untyped-def]  # pragma: no cover
+    min_size = cls.min_length or 0
+    max_size = cls.max_length
+    if not cls.strip_whitespace:
+        return st.binary(min_size=min_size, max_size=max_size)
+    # Fun with regex to ensure we neither start nor end with whitespace
+    repeats = '{{{},{}}}'.format(
+        min_size - 2 if min_size > 2 else 0,
+        max_size - 2 if (max_size or 0) > 2 else '',
+    )
+    if min_size >= 2:
+        pattern = rf'\W.{repeats}\W'
+    elif min_size == 1:
+        pattern = rf'\W(.{repeats}\W)?'
+    else:
+        assert min_size == 0
+        pattern = rf'(\W(.{repeats}\W)?)?'
+    return st.from_regex(pattern.encode(), fullmatch=True)
+
+
+@resolves(pydantic.ConstrainedDecimal)
+def resolve_condecimal(cls):  # type: ignore[no-untyped-def]
+    min_value = cls.ge
+    max_value = cls.le
+    if cls.gt is not None:
+        assert min_value is None, 'Set `gt` or `ge`, but not both'
+        min_value = cls.gt
+    if cls.lt is not None:
+        assert max_value is None, 'Set `lt` or `le`, but not both'
+        max_value = cls.lt
+    s = st.decimals(min_value, max_value, allow_nan=False, places=cls.decimal_places)
+    if cls.lt is not None:
+        s = s.filter(lambda d: d < cls.lt)
+    if cls.gt is not None:
+        s = s.filter(lambda d: cls.gt < d)
+    return s
+
+
+@resolves(pydantic.ConstrainedFloat)
+def resolve_confloat(cls):  # type: ignore[no-untyped-def]
+    min_value = cls.ge
+    max_value = cls.le
+    exclude_min = False
+    exclude_max = False
+
+    if cls.gt is not None:
+        assert min_value is None, 'Set `gt` or `ge`, but not both'
+        min_value = cls.gt
+        exclude_min = True
+    if cls.lt is not None:
+        assert max_value is None, 'Set `lt` or `le`, but not both'
+        max_value = cls.lt
+        exclude_max = True
+
+    if cls.multiple_of is None:
+        return st.floats(min_value, max_value, exclude_min=exclude_min, exclude_max=exclude_max, allow_nan=False)
+
+    if min_value is not None:
+        min_value = math.ceil(min_value / cls.multiple_of)
+        if exclude_min:
+            min_value = min_value + 1
+    if max_value is not None:
+        assert max_value >= cls.multiple_of, 'Cannot build model with max value smaller than multiple of'
+        max_value = math.floor(max_value / cls.multiple_of)
+        if exclude_max:
+            max_value = max_value - 1
+
+    return st.integers(min_value, max_value).map(lambda x: x * cls.multiple_of)
+
+
+@resolves(pydantic.ConstrainedInt)
+def resolve_conint(cls):  # type: ignore[no-untyped-def]
+    min_value = cls.ge
+    max_value = cls.le
+    if cls.gt is not None:
+        assert min_value is None, 'Set `gt` or `ge`, but not both'
+        min_value = cls.gt + 1
+    if cls.lt is not None:
+        assert max_value is None, 'Set `lt` or `le`, but not both'
+        max_value = cls.lt - 1
+
+    if cls.multiple_of is None or cls.multiple_of == 1:
+        return st.integers(min_value, max_value)
+
+    # These adjustments and the .map handle integer-valued multiples, while the
+    # .filter handles trickier cases as for confloat.
+    if min_value is not None:
+        min_value = math.ceil(Fraction(min_value) / Fraction(cls.multiple_of))
+    if max_value is not None:
+        max_value = math.floor(Fraction(max_value) / Fraction(cls.multiple_of))
+    return st.integers(min_value, max_value).map(lambda x: x * cls.multiple_of)
+
+
+@resolves(pydantic.ConstrainedDate)
+def resolve_condate(cls):  # type: ignore[no-untyped-def]
+    if cls.ge is not None:
+        assert cls.gt is None, 'Set `gt` or `ge`, but not both'
+        min_value = cls.ge
+    elif cls.gt is not None:
+        min_value = cls.gt + datetime.timedelta(days=1)
+    else:
+        min_value = datetime.date.min
+    if cls.le is not None:
+        assert cls.lt is None, 'Set `lt` or `le`, but not both'
+        max_value = cls.le
+    elif cls.lt is not None:
+        max_value = cls.lt - datetime.timedelta(days=1)
+    else:
+        max_value = datetime.date.max
+    return st.dates(min_value, max_value)
+
+
+@resolves(pydantic.ConstrainedStr)
+def resolve_constr(cls):  # type: ignore[no-untyped-def]  # pragma: no cover
+    min_size = cls.min_length or 0
+    max_size = cls.max_length
+
+    if cls.regex is None and not cls.strip_whitespace:
+        return st.text(min_size=min_size, max_size=max_size)
+
+    if cls.regex is not None:
+        strategy = st.from_regex(cls.regex)
+        if cls.strip_whitespace:
+            strategy = strategy.filter(lambda s: s == s.strip())
+    elif cls.strip_whitespace:
+        repeats = '{{{},{}}}'.format(
+            min_size - 2 if min_size > 2 else 0,
+            max_size - 2 if (max_size or 0) > 2 else '',
+        )
+        if min_size >= 2:
+            strategy = st.from_regex(rf'\W.{repeats}\W')
+        elif min_size == 1:
+            strategy = st.from_regex(rf'\W(.{repeats}\W)?')
+        else:
+            assert min_size == 0
+            strategy = st.from_regex(rf'(\W(.{repeats}\W)?)?')
+
+    if min_size == 0 and max_size is None:
+        return strategy
+    elif max_size is None:
+        return strategy.filter(lambda s: min_size <= len(s))
+    return strategy.filter(lambda s: min_size <= len(s) <= max_size)
+
+
+# Finally, register all previously-defined types, and patch in our new function
+for typ in list(pydantic.types._DEFINED_TYPES):
+    _registered(typ)
+pydantic.types._registered = _registered
+st.register_type_strategy(pydantic.Json, resolve_json)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/annotated_types.py b/.venv/lib/python3.12/site-packages/pydantic/v1/annotated_types.py
new file mode 100644
index 00000000..d9eaaafd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/annotated_types.py
@@ -0,0 +1,72 @@
+import sys
+from typing import TYPE_CHECKING, Any, Dict, FrozenSet, NamedTuple, Type
+
+from pydantic.v1.fields import Required
+from pydantic.v1.main import BaseModel, create_model
+from pydantic.v1.typing import is_typeddict, is_typeddict_special
+
+if TYPE_CHECKING:
+    from typing_extensions import TypedDict
+
+if sys.version_info < (3, 11):
+
+    def is_legacy_typeddict(typeddict_cls: Type['TypedDict']) -> bool:  # type: ignore[valid-type]
+        return is_typeddict(typeddict_cls) and type(typeddict_cls).__module__ == 'typing'
+
+else:
+
+    def is_legacy_typeddict(_: Any) -> Any:
+        return False
+
+
+def create_model_from_typeddict(
+    # Mypy bug: `Type[TypedDict]` is resolved as `Any` https://github.com/python/mypy/issues/11030
+    typeddict_cls: Type['TypedDict'],  # type: ignore[valid-type]
+    **kwargs: Any,
+) -> Type['BaseModel']:
+    """
+    Create a `BaseModel` based on the fields of a `TypedDict`.
+    Since `typing.TypedDict` in Python 3.8 does not store runtime information about optional keys,
+    we raise an error if this happens (see https://bugs.python.org/issue38834).
+    """
+    field_definitions: Dict[str, Any]
+
+    # Best case scenario: with python 3.9+ or when `TypedDict` is imported from `typing_extensions`
+    if not hasattr(typeddict_cls, '__required_keys__'):
+        raise TypeError(
+            'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.9.2. '
+            'Without it, there is no way to differentiate required and optional fields when subclassed.'
+        )
+
+    if is_legacy_typeddict(typeddict_cls) and any(
+        is_typeddict_special(t) for t in typeddict_cls.__annotations__.values()
+    ):
+        raise TypeError(
+            'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.11. '
+            'Without it, there is no way to reflect Required/NotRequired keys.'
+        )
+
+    required_keys: FrozenSet[str] = typeddict_cls.__required_keys__  # type: ignore[attr-defined]
+    field_definitions = {
+        field_name: (field_type, Required if field_name in required_keys else None)
+        for field_name, field_type in typeddict_cls.__annotations__.items()
+    }
+
+    return create_model(typeddict_cls.__name__, **kwargs, **field_definitions)
+
+
+def create_model_from_namedtuple(namedtuple_cls: Type['NamedTuple'], **kwargs: Any) -> Type['BaseModel']:
+    """
+    Create a `BaseModel` based on the fields of a named tuple.
+    A named tuple can be created with `typing.NamedTuple` and declared annotations
+    but also with `collections.namedtuple`, in this case we consider all fields
+    to have type `Any`.
+    """
+    # With python 3.10+, `__annotations__` always exists but can be empty hence the `getattr... or...` logic
+    namedtuple_annotations: Dict[str, Type[Any]] = getattr(namedtuple_cls, '__annotations__', None) or {
+        k: Any for k in namedtuple_cls._fields
+    }
+    field_definitions: Dict[str, Any] = {
+        field_name: (field_type, Required) for field_name, field_type in namedtuple_annotations.items()
+    }
+    return create_model(namedtuple_cls.__name__, **kwargs, **field_definitions)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/class_validators.py b/.venv/lib/python3.12/site-packages/pydantic/v1/class_validators.py
new file mode 100644
index 00000000..2f68fc86
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/class_validators.py
@@ -0,0 +1,361 @@
+import warnings
+from collections import ChainMap
+from functools import partial, partialmethod, wraps
+from itertools import chain
+from types import FunctionType
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, overload
+
+from pydantic.v1.errors import ConfigError
+from pydantic.v1.typing import AnyCallable
+from pydantic.v1.utils import ROOT_KEY, in_ipython
+
+if TYPE_CHECKING:
+    from pydantic.v1.typing import AnyClassMethod
+
+
+class Validator:
+    __slots__ = 'func', 'pre', 'each_item', 'always', 'check_fields', 'skip_on_failure'
+
+    def __init__(
+        self,
+        func: AnyCallable,
+        pre: bool = False,
+        each_item: bool = False,
+        always: bool = False,
+        check_fields: bool = False,
+        skip_on_failure: bool = False,
+    ):
+        self.func = func
+        self.pre = pre
+        self.each_item = each_item
+        self.always = always
+        self.check_fields = check_fields
+        self.skip_on_failure = skip_on_failure
+
+
+if TYPE_CHECKING:
+    from inspect import Signature
+
+    from pydantic.v1.config import BaseConfig
+    from pydantic.v1.fields import ModelField
+    from pydantic.v1.types import ModelOrDc
+
+    ValidatorCallable = Callable[[Optional[ModelOrDc], Any, Dict[str, Any], ModelField, Type[BaseConfig]], Any]
+    ValidatorsList = List[ValidatorCallable]
+    ValidatorListDict = Dict[str, List[Validator]]
+
+_FUNCS: Set[str] = set()
+VALIDATOR_CONFIG_KEY = '__validator_config__'
+ROOT_VALIDATOR_CONFIG_KEY = '__root_validator_config__'
+
+
+def validator(
+    *fields: str,
+    pre: bool = False,
+    each_item: bool = False,
+    always: bool = False,
+    check_fields: bool = True,
+    whole: Optional[bool] = None,
+    allow_reuse: bool = False,
+) -> Callable[[AnyCallable], 'AnyClassMethod']:
+    """
+    Decorate methods on the class indicating that they should be used to validate fields
+    :param fields: which field(s) the method should be called on
+    :param pre: whether or not this validator should be called before the standard validators (else after)
+    :param each_item: for complex objects (sets, lists etc.) whether to validate individual elements rather than the
+      whole object
+    :param always: whether this method and other validators should be called even if the value is missing
+    :param check_fields: whether to check that the fields actually exist on the model
+    :param allow_reuse: whether to track and raise an error if another validator refers to the decorated function
+    """
+    if not fields:
+        raise ConfigError('validator with no fields specified')
+    elif isinstance(fields[0], FunctionType):
+        raise ConfigError(
+            "validators should be used with fields and keyword arguments, not bare. "  # noqa: Q000
+            "E.g. usage should be `@validator('<field_name>', ...)`"
+        )
+    elif not all(isinstance(field, str) for field in fields):
+        raise ConfigError(
+            "validator fields should be passed as separate string args. "  # noqa: Q000
+            "E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`"
+        )
+
+    if whole is not None:
+        warnings.warn(
+            'The "whole" keyword argument is deprecated, use "each_item" (inverse meaning, default False) instead',
+            DeprecationWarning,
+        )
+        assert each_item is False, '"each_item" and "whole" conflict, remove "whole"'
+        each_item = not whole
+
+    def dec(f: AnyCallable) -> 'AnyClassMethod':
+        f_cls = _prepare_validator(f, allow_reuse)
+        setattr(
+            f_cls,
+            VALIDATOR_CONFIG_KEY,
+            (
+                fields,
+                Validator(func=f_cls.__func__, pre=pre, each_item=each_item, always=always, check_fields=check_fields),
+            ),
+        )
+        return f_cls
+
+    return dec
+
+
+@overload
+def root_validator(_func: AnyCallable) -> 'AnyClassMethod':
+    ...
+
+
+@overload
+def root_validator(
+    *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False
+) -> Callable[[AnyCallable], 'AnyClassMethod']:
+    ...
+
+
+def root_validator(
+    _func: Optional[AnyCallable] = None, *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False
+) -> Union['AnyClassMethod', Callable[[AnyCallable], 'AnyClassMethod']]:
+    """
+    Decorate methods on a model indicating that they should be used to validate (and perhaps modify) data either
+    before or after standard model parsing/validation is performed.
+    """
+    if _func:
+        f_cls = _prepare_validator(_func, allow_reuse)
+        setattr(
+            f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)
+        )
+        return f_cls
+
+    def dec(f: AnyCallable) -> 'AnyClassMethod':
+        f_cls = _prepare_validator(f, allow_reuse)
+        setattr(
+            f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)
+        )
+        return f_cls
+
+    return dec
+
+
+def _prepare_validator(function: AnyCallable, allow_reuse: bool) -> 'AnyClassMethod':
+    """
+    Avoid validators with duplicated names since without this, validators can be overwritten silently
+    which generally isn't the intended behaviour, don't run in ipython (see #312) or if allow_reuse is False.
+    """
+    f_cls = function if isinstance(function, classmethod) else classmethod(function)
+    if not in_ipython() and not allow_reuse:
+        ref = (
+            getattr(f_cls.__func__, '__module__', '<No __module__>')
+            + '.'
+            + getattr(f_cls.__func__, '__qualname__', f'<No __qualname__: id:{id(f_cls.__func__)}>')
+        )
+        if ref in _FUNCS:
+            raise ConfigError(f'duplicate validator function "{ref}"; if this is intended, set `allow_reuse=True`')
+        _FUNCS.add(ref)
+    return f_cls
+
+
+class ValidatorGroup:
+    def __init__(self, validators: 'ValidatorListDict') -> None:
+        self.validators = validators
+        self.used_validators = {'*'}
+
+    def get_validators(self, name: str) -> Optional[Dict[str, Validator]]:
+        self.used_validators.add(name)
+        validators = self.validators.get(name, [])
+        if name != ROOT_KEY:
+            validators += self.validators.get('*', [])
+        if validators:
+            return {getattr(v.func, '__name__', f'<No __name__: id:{id(v.func)}>'): v for v in validators}
+        else:
+            return None
+
+    def check_for_unused(self) -> None:
+        unused_validators = set(
+            chain.from_iterable(
+                (
+                    getattr(v.func, '__name__', f'<No __name__: id:{id(v.func)}>')
+                    for v in self.validators[f]
+                    if v.check_fields
+                )
+                for f in (self.validators.keys() - self.used_validators)
+            )
+        )
+        if unused_validators:
+            fn = ', '.join(unused_validators)
+            raise ConfigError(
+                f"Validators defined with incorrect fields: {fn} "  # noqa: Q000
+                f"(use check_fields=False if you're inheriting from the model and intended this)"
+            )
+
+
+def extract_validators(namespace: Dict[str, Any]) -> Dict[str, List[Validator]]:
+    validators: Dict[str, List[Validator]] = {}
+    for var_name, value in namespace.items():
+        validator_config = getattr(value, VALIDATOR_CONFIG_KEY, None)
+        if validator_config:
+            fields, v = validator_config
+            for field in fields:
+                if field in validators:
+                    validators[field].append(v)
+                else:
+                    validators[field] = [v]
+    return validators
+
+
+def extract_root_validators(namespace: Dict[str, Any]) -> Tuple[List[AnyCallable], List[Tuple[bool, AnyCallable]]]:
+    from inspect import signature
+
+    pre_validators: List[AnyCallable] = []
+    post_validators: List[Tuple[bool, AnyCallable]] = []
+    for name, value in namespace.items():
+        validator_config: Optional[Validator] = getattr(value, ROOT_VALIDATOR_CONFIG_KEY, None)
+        if validator_config:
+            sig = signature(validator_config.func)
+            args = list(sig.parameters.keys())
+            if args[0] == 'self':
+                raise ConfigError(
+                    f'Invalid signature for root validator {name}: {sig}, "self" not permitted as first argument, '
+                    f'should be: (cls, values).'
+                )
+            if len(args) != 2:
+                raise ConfigError(f'Invalid signature for root validator {name}: {sig}, should be: (cls, values).')
+            # check function signature
+            if validator_config.pre:
+                pre_validators.append(validator_config.func)
+            else:
+                post_validators.append((validator_config.skip_on_failure, validator_config.func))
+    return pre_validators, post_validators
+
+
+def inherit_validators(base_validators: 'ValidatorListDict', validators: 'ValidatorListDict') -> 'ValidatorListDict':
+    for field, field_validators in base_validators.items():
+        if field not in validators:
+            validators[field] = []
+        validators[field] += field_validators
+    return validators
+
+
+def make_generic_validator(validator: AnyCallable) -> 'ValidatorCallable':
+    """
+    Make a generic function which calls a validator with the right arguments.
+
+    Unfortunately other approaches (eg. return a partial of a function that builds the arguments) is slow,
+    hence this laborious way of doing things.
+
+    It's done like this so validators don't all need **kwargs in their signature, eg. any combination of
+    the arguments "values", "fields" and/or "config" are permitted.
+    """
+    from inspect import signature
+
+    if not isinstance(validator, (partial, partialmethod)):
+        # This should be the default case, so overhead is reduced
+        sig = signature(validator)
+        args = list(sig.parameters.keys())
+    else:
+        # Fix the generated argument lists of partial methods
+        sig = signature(validator.func)
+        args = [
+            k
+            for k in signature(validator.func).parameters.keys()
+            if k not in validator.args | validator.keywords.keys()
+        ]
+
+    first_arg = args.pop(0)
+    if first_arg == 'self':
+        raise ConfigError(
+            f'Invalid signature for validator {validator}: {sig}, "self" not permitted as first argument, '
+            f'should be: (cls, value, values, config, field), "values", "config" and "field" are all optional.'
+        )
+    elif first_arg == 'cls':
+        # assume the second argument is value
+        return wraps(validator)(_generic_validator_cls(validator, sig, set(args[1:])))
+    else:
+        # assume the first argument was value which has already been removed
+        return wraps(validator)(_generic_validator_basic(validator, sig, set(args)))
+
+
+def prep_validators(v_funcs: Iterable[AnyCallable]) -> 'ValidatorsList':
+    return [make_generic_validator(f) for f in v_funcs if f]
+
+
+all_kwargs = {'values', 'field', 'config'}
+
+
+def _generic_validator_cls(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable':
+    # assume the first argument is value
+    has_kwargs = False
+    if 'kwargs' in args:
+        has_kwargs = True
+        args -= {'kwargs'}
+
+    if not args.issubset(all_kwargs):
+        raise ConfigError(
+            f'Invalid signature for validator {validator}: {sig}, should be: '
+            f'(cls, value, values, config, field), "values", "config" and "field" are all optional.'
+        )
+
+    if has_kwargs:
+        return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)
+    elif args == set():
+        return lambda cls, v, values, field, config: validator(cls, v)
+    elif args == {'values'}:
+        return lambda cls, v, values, field, config: validator(cls, v, values=values)
+    elif args == {'field'}:
+        return lambda cls, v, values, field, config: validator(cls, v, field=field)
+    elif args == {'config'}:
+        return lambda cls, v, values, field, config: validator(cls, v, config=config)
+    elif args == {'values', 'field'}:
+        return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field)
+    elif args == {'values', 'config'}:
+        return lambda cls, v, values, field, config: validator(cls, v, values=values, config=config)
+    elif args == {'field', 'config'}:
+        return lambda cls, v, values, field, config: validator(cls, v, field=field, config=config)
+    else:
+        # args == {'values', 'field', 'config'}
+        return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)
+
+
+def _generic_validator_basic(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable':
+    has_kwargs = False
+    if 'kwargs' in args:
+        has_kwargs = True
+        args -= {'kwargs'}
+
+    if not args.issubset(all_kwargs):
+        raise ConfigError(
+            f'Invalid signature for validator {validator}: {sig}, should be: '
+            f'(value, values, config, field), "values", "config" and "field" are all optional.'
+        )
+
+    if has_kwargs:
+        return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)
+    elif args == set():
+        return lambda cls, v, values, field, config: validator(v)
+    elif args == {'values'}:
+        return lambda cls, v, values, field, config: validator(v, values=values)
+    elif args == {'field'}:
+        return lambda cls, v, values, field, config: validator(v, field=field)
+    elif args == {'config'}:
+        return lambda cls, v, values, field, config: validator(v, config=config)
+    elif args == {'values', 'field'}:
+        return lambda cls, v, values, field, config: validator(v, values=values, field=field)
+    elif args == {'values', 'config'}:
+        return lambda cls, v, values, field, config: validator(v, values=values, config=config)
+    elif args == {'field', 'config'}:
+        return lambda cls, v, values, field, config: validator(v, field=field, config=config)
+    else:
+        # args == {'values', 'field', 'config'}
+        return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)
+
+
+def gather_all_validators(type_: 'ModelOrDc') -> Dict[str, 'AnyClassMethod']:
+    all_attributes = ChainMap(*[cls.__dict__ for cls in type_.__mro__])  # type: ignore[arg-type,var-annotated]
+    return {
+        k: v
+        for k, v in all_attributes.items()
+        if hasattr(v, VALIDATOR_CONFIG_KEY) or hasattr(v, ROOT_VALIDATOR_CONFIG_KEY)
+    }
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/color.py b/.venv/lib/python3.12/site-packages/pydantic/v1/color.py
new file mode 100644
index 00000000..b0bbf78f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/color.py
@@ -0,0 +1,494 @@
+"""
+Color definitions are  used as per CSS3 specification:
+http://www.w3.org/TR/css3-color/#svg-color
+
+A few colors have multiple names referring to the sames colors, eg. `grey` and `gray` or `aqua` and `cyan`.
+
+In these cases the LAST color when sorted alphabetically takes preferences,
+eg. Color((0, 255, 255)).as_named() == 'cyan' because "cyan" comes after "aqua".
+"""
+import math
+import re
+from colorsys import hls_to_rgb, rgb_to_hls
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union, cast
+
+from pydantic.v1.errors import ColorError
+from pydantic.v1.utils import Representation, almost_equal_floats
+
+if TYPE_CHECKING:
+    from pydantic.v1.typing import CallableGenerator, ReprArgs
+
+ColorTuple = Union[Tuple[int, int, int], Tuple[int, int, int, float]]
+ColorType = Union[ColorTuple, str]
+HslColorTuple = Union[Tuple[float, float, float], Tuple[float, float, float, float]]
+
+
+class RGBA:
+    """
+    Internal use only as a representation of a color.
+    """
+
+    __slots__ = 'r', 'g', 'b', 'alpha', '_tuple'
+
+    def __init__(self, r: float, g: float, b: float, alpha: Optional[float]):
+        self.r = r
+        self.g = g
+        self.b = b
+        self.alpha = alpha
+
+        self._tuple: Tuple[float, float, float, Optional[float]] = (r, g, b, alpha)
+
+    def __getitem__(self, item: Any) -> Any:
+        return self._tuple[item]
+
+
+# these are not compiled here to avoid import slowdown, they'll be compiled the first time they're used, then cached
+r_hex_short = r'\s*(?:#|0x)?([0-9a-f])([0-9a-f])([0-9a-f])([0-9a-f])?\s*'
+r_hex_long = r'\s*(?:#|0x)?([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})?\s*'
+_r_255 = r'(\d{1,3}(?:\.\d+)?)'
+_r_comma = r'\s*,\s*'
+r_rgb = fr'\s*rgb\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}\)\s*'
+_r_alpha = r'(\d(?:\.\d+)?|\.\d+|\d{1,2}%)'
+r_rgba = fr'\s*rgba\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_alpha}\s*\)\s*'
+_r_h = r'(-?\d+(?:\.\d+)?|-?\.\d+)(deg|rad|turn)?'
+_r_sl = r'(\d{1,3}(?:\.\d+)?)%'
+r_hsl = fr'\s*hsl\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}\s*\)\s*'
+r_hsla = fr'\s*hsl\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}{_r_comma}{_r_alpha}\s*\)\s*'
+
+# colors where the two hex characters are the same, if all colors match this the short version of hex colors can be used
+repeat_colors = {int(c * 2, 16) for c in '0123456789abcdef'}
+rads = 2 * math.pi
+
+
+class Color(Representation):
+    __slots__ = '_original', '_rgba'
+
+    def __init__(self, value: ColorType) -> None:
+        self._rgba: RGBA
+        self._original: ColorType
+        if isinstance(value, (tuple, list)):
+            self._rgba = parse_tuple(value)
+        elif isinstance(value, str):
+            self._rgba = parse_str(value)
+        elif isinstance(value, Color):
+            self._rgba = value._rgba
+            value = value._original
+        else:
+            raise ColorError(reason='value must be a tuple, list or string')
+
+        # if we've got here value must be a valid color
+        self._original = value
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type='string', format='color')
+
+    def original(self) -> ColorType:
+        """
+        Original value passed to Color
+        """
+        return self._original
+
+    def as_named(self, *, fallback: bool = False) -> str:
+        if self._rgba.alpha is None:
+            rgb = cast(Tuple[int, int, int], self.as_rgb_tuple())
+            try:
+                return COLORS_BY_VALUE[rgb]
+            except KeyError as e:
+                if fallback:
+                    return self.as_hex()
+                else:
+                    raise ValueError('no named color found, use fallback=True, as_hex() or as_rgb()') from e
+        else:
+            return self.as_hex()
+
+    def as_hex(self) -> str:
+        """
+        Hex string representing the color can be 3, 4, 6 or 8 characters depending on whether the string
+        a "short" representation of the color is possible and whether there's an alpha channel.
+        """
+        values = [float_to_255(c) for c in self._rgba[:3]]
+        if self._rgba.alpha is not None:
+            values.append(float_to_255(self._rgba.alpha))
+
+        as_hex = ''.join(f'{v:02x}' for v in values)
+        if all(c in repeat_colors for c in values):
+            as_hex = ''.join(as_hex[c] for c in range(0, len(as_hex), 2))
+        return '#' + as_hex
+
+    def as_rgb(self) -> str:
+        """
+        Color as an rgb(<r>, <g>, <b>) or rgba(<r>, <g>, <b>, <a>) string.
+        """
+        if self._rgba.alpha is None:
+            return f'rgb({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)})'
+        else:
+            return (
+                f'rgba({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)}, '
+                f'{round(self._alpha_float(), 2)})'
+            )
+
+    def as_rgb_tuple(self, *, alpha: Optional[bool] = None) -> ColorTuple:
+        """
+        Color as an RGB or RGBA tuple; red, green and blue are in the range 0 to 255, alpha if included is
+        in the range 0 to 1.
+
+        :param alpha: whether to include the alpha channel, options are
+          None - (default) include alpha only if it's set (e.g. not None)
+          True - always include alpha,
+          False - always omit alpha,
+        """
+        r, g, b = (float_to_255(c) for c in self._rgba[:3])
+        if alpha is None:
+            if self._rgba.alpha is None:
+                return r, g, b
+            else:
+                return r, g, b, self._alpha_float()
+        elif alpha:
+            return r, g, b, self._alpha_float()
+        else:
+            # alpha is False
+            return r, g, b
+
+    def as_hsl(self) -> str:
+        """
+        Color as an hsl(<h>, <s>, <l>) or hsl(<h>, <s>, <l>, <a>) string.
+        """
+        if self._rgba.alpha is None:
+            h, s, li = self.as_hsl_tuple(alpha=False)  # type: ignore
+            return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%})'
+        else:
+            h, s, li, a = self.as_hsl_tuple(alpha=True)  # type: ignore
+            return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%}, {round(a, 2)})'
+
+    def as_hsl_tuple(self, *, alpha: Optional[bool] = None) -> HslColorTuple:
+        """
+        Color as an HSL or HSLA tuple, e.g. hue, saturation, lightness and optionally alpha; all elements are in
+        the range 0 to 1.
+
+        NOTE: this is HSL as used in HTML and most other places, not HLS as used in python's colorsys.
+
+        :param alpha: whether to include the alpha channel, options are
+          None - (default) include alpha only if it's set (e.g. not None)
+          True - always include alpha,
+          False - always omit alpha,
+        """
+        h, l, s = rgb_to_hls(self._rgba.r, self._rgba.g, self._rgba.b)
+        if alpha is None:
+            if self._rgba.alpha is None:
+                return h, s, l
+            else:
+                return h, s, l, self._alpha_float()
+        if alpha:
+            return h, s, l, self._alpha_float()
+        else:
+            # alpha is False
+            return h, s, l
+
+    def _alpha_float(self) -> float:
+        return 1 if self._rgba.alpha is None else self._rgba.alpha
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls
+
+    def __str__(self) -> str:
+        return self.as_named(fallback=True)
+
+    def __repr_args__(self) -> 'ReprArgs':
+        return [(None, self.as_named(fallback=True))] + [('rgb', self.as_rgb_tuple())]  # type: ignore
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, Color) and self.as_rgb_tuple() == other.as_rgb_tuple()
+
+    def __hash__(self) -> int:
+        return hash(self.as_rgb_tuple())
+
+
+def parse_tuple(value: Tuple[Any, ...]) -> RGBA:
+    """
+    Parse a tuple or list as a color.
+    """
+    if len(value) == 3:
+        r, g, b = (parse_color_value(v) for v in value)
+        return RGBA(r, g, b, None)
+    elif len(value) == 4:
+        r, g, b = (parse_color_value(v) for v in value[:3])
+        return RGBA(r, g, b, parse_float_alpha(value[3]))
+    else:
+        raise ColorError(reason='tuples must have length 3 or 4')
+
+
+def parse_str(value: str) -> RGBA:
+    """
+    Parse a string to an RGBA tuple, trying the following formats (in this order):
+    * named color, see COLORS_BY_NAME below
+    * hex short eg. `<prefix>fff` (prefix can be `#`, `0x` or nothing)
+    * hex long eg. `<prefix>ffffff` (prefix can be `#`, `0x` or nothing)
+    * `rgb(<r>, <g>, <b>) `
+    * `rgba(<r>, <g>, <b>, <a>)`
+    """
+    value_lower = value.lower()
+    try:
+        r, g, b = COLORS_BY_NAME[value_lower]
+    except KeyError:
+        pass
+    else:
+        return ints_to_rgba(r, g, b, None)
+
+    m = re.fullmatch(r_hex_short, value_lower)
+    if m:
+        *rgb, a = m.groups()
+        r, g, b = (int(v * 2, 16) for v in rgb)
+        if a:
+            alpha: Optional[float] = int(a * 2, 16) / 255
+        else:
+            alpha = None
+        return ints_to_rgba(r, g, b, alpha)
+
+    m = re.fullmatch(r_hex_long, value_lower)
+    if m:
+        *rgb, a = m.groups()
+        r, g, b = (int(v, 16) for v in rgb)
+        if a:
+            alpha = int(a, 16) / 255
+        else:
+            alpha = None
+        return ints_to_rgba(r, g, b, alpha)
+
+    m = re.fullmatch(r_rgb, value_lower)
+    if m:
+        return ints_to_rgba(*m.groups(), None)  # type: ignore
+
+    m = re.fullmatch(r_rgba, value_lower)
+    if m:
+        return ints_to_rgba(*m.groups())  # type: ignore
+
+    m = re.fullmatch(r_hsl, value_lower)
+    if m:
+        h, h_units, s, l_ = m.groups()
+        return parse_hsl(h, h_units, s, l_)
+
+    m = re.fullmatch(r_hsla, value_lower)
+    if m:
+        h, h_units, s, l_, a = m.groups()
+        return parse_hsl(h, h_units, s, l_, parse_float_alpha(a))
+
+    raise ColorError(reason='string not recognised as a valid color')
+
+
+def ints_to_rgba(r: Union[int, str], g: Union[int, str], b: Union[int, str], alpha: Optional[float]) -> RGBA:
+    return RGBA(parse_color_value(r), parse_color_value(g), parse_color_value(b), parse_float_alpha(alpha))
+
+
+def parse_color_value(value: Union[int, str], max_val: int = 255) -> float:
+    """
+    Parse a value checking it's a valid int in the range 0 to max_val and divide by max_val to give a number
+    in the range 0 to 1
+    """
+    try:
+        color = float(value)
+    except ValueError:
+        raise ColorError(reason='color values must be a valid number')
+    if 0 <= color <= max_val:
+        return color / max_val
+    else:
+        raise ColorError(reason=f'color values must be in the range 0 to {max_val}')
+
+
+def parse_float_alpha(value: Union[None, str, float, int]) -> Optional[float]:
+    """
+    Parse a value checking it's a valid float in the range 0 to 1
+    """
+    if value is None:
+        return None
+    try:
+        if isinstance(value, str) and value.endswith('%'):
+            alpha = float(value[:-1]) / 100
+        else:
+            alpha = float(value)
+    except ValueError:
+        raise ColorError(reason='alpha values must be a valid float')
+
+    if almost_equal_floats(alpha, 1):
+        return None
+    elif 0 <= alpha <= 1:
+        return alpha
+    else:
+        raise ColorError(reason='alpha values must be in the range 0 to 1')
+
+
+def parse_hsl(h: str, h_units: str, sat: str, light: str, alpha: Optional[float] = None) -> RGBA:
+    """
+    Parse raw hue, saturation, lightness and alpha values and convert to RGBA.
+    """
+    s_value, l_value = parse_color_value(sat, 100), parse_color_value(light, 100)
+
+    h_value = float(h)
+    if h_units in {None, 'deg'}:
+        h_value = h_value % 360 / 360
+    elif h_units == 'rad':
+        h_value = h_value % rads / rads
+    else:
+        # turns
+        h_value = h_value % 1
+
+    r, g, b = hls_to_rgb(h_value, l_value, s_value)
+    return RGBA(r, g, b, alpha)
+
+
+def float_to_255(c: float) -> int:
+    return int(round(c * 255))
+
+
+COLORS_BY_NAME = {
+    'aliceblue': (240, 248, 255),
+    'antiquewhite': (250, 235, 215),
+    'aqua': (0, 255, 255),
+    'aquamarine': (127, 255, 212),
+    'azure': (240, 255, 255),
+    'beige': (245, 245, 220),
+    'bisque': (255, 228, 196),
+    'black': (0, 0, 0),
+    'blanchedalmond': (255, 235, 205),
+    'blue': (0, 0, 255),
+    'blueviolet': (138, 43, 226),
+    'brown': (165, 42, 42),
+    'burlywood': (222, 184, 135),
+    'cadetblue': (95, 158, 160),
+    'chartreuse': (127, 255, 0),
+    'chocolate': (210, 105, 30),
+    'coral': (255, 127, 80),
+    'cornflowerblue': (100, 149, 237),
+    'cornsilk': (255, 248, 220),
+    'crimson': (220, 20, 60),
+    'cyan': (0, 255, 255),
+    'darkblue': (0, 0, 139),
+    'darkcyan': (0, 139, 139),
+    'darkgoldenrod': (184, 134, 11),
+    'darkgray': (169, 169, 169),
+    'darkgreen': (0, 100, 0),
+    'darkgrey': (169, 169, 169),
+    'darkkhaki': (189, 183, 107),
+    'darkmagenta': (139, 0, 139),
+    'darkolivegreen': (85, 107, 47),
+    'darkorange': (255, 140, 0),
+    'darkorchid': (153, 50, 204),
+    'darkred': (139, 0, 0),
+    'darksalmon': (233, 150, 122),
+    'darkseagreen': (143, 188, 143),
+    'darkslateblue': (72, 61, 139),
+    'darkslategray': (47, 79, 79),
+    'darkslategrey': (47, 79, 79),
+    'darkturquoise': (0, 206, 209),
+    'darkviolet': (148, 0, 211),
+    'deeppink': (255, 20, 147),
+    'deepskyblue': (0, 191, 255),
+    'dimgray': (105, 105, 105),
+    'dimgrey': (105, 105, 105),
+    'dodgerblue': (30, 144, 255),
+    'firebrick': (178, 34, 34),
+    'floralwhite': (255, 250, 240),
+    'forestgreen': (34, 139, 34),
+    'fuchsia': (255, 0, 255),
+    'gainsboro': (220, 220, 220),
+    'ghostwhite': (248, 248, 255),
+    'gold': (255, 215, 0),
+    'goldenrod': (218, 165, 32),
+    'gray': (128, 128, 128),
+    'green': (0, 128, 0),
+    'greenyellow': (173, 255, 47),
+    'grey': (128, 128, 128),
+    'honeydew': (240, 255, 240),
+    'hotpink': (255, 105, 180),
+    'indianred': (205, 92, 92),
+    'indigo': (75, 0, 130),
+    'ivory': (255, 255, 240),
+    'khaki': (240, 230, 140),
+    'lavender': (230, 230, 250),
+    'lavenderblush': (255, 240, 245),
+    'lawngreen': (124, 252, 0),
+    'lemonchiffon': (255, 250, 205),
+    'lightblue': (173, 216, 230),
+    'lightcoral': (240, 128, 128),
+    'lightcyan': (224, 255, 255),
+    'lightgoldenrodyellow': (250, 250, 210),
+    'lightgray': (211, 211, 211),
+    'lightgreen': (144, 238, 144),
+    'lightgrey': (211, 211, 211),
+    'lightpink': (255, 182, 193),
+    'lightsalmon': (255, 160, 122),
+    'lightseagreen': (32, 178, 170),
+    'lightskyblue': (135, 206, 250),
+    'lightslategray': (119, 136, 153),
+    'lightslategrey': (119, 136, 153),
+    'lightsteelblue': (176, 196, 222),
+    'lightyellow': (255, 255, 224),
+    'lime': (0, 255, 0),
+    'limegreen': (50, 205, 50),
+    'linen': (250, 240, 230),
+    'magenta': (255, 0, 255),
+    'maroon': (128, 0, 0),
+    'mediumaquamarine': (102, 205, 170),
+    'mediumblue': (0, 0, 205),
+    'mediumorchid': (186, 85, 211),
+    'mediumpurple': (147, 112, 219),
+    'mediumseagreen': (60, 179, 113),
+    'mediumslateblue': (123, 104, 238),
+    'mediumspringgreen': (0, 250, 154),
+    'mediumturquoise': (72, 209, 204),
+    'mediumvioletred': (199, 21, 133),
+    'midnightblue': (25, 25, 112),
+    'mintcream': (245, 255, 250),
+    'mistyrose': (255, 228, 225),
+    'moccasin': (255, 228, 181),
+    'navajowhite': (255, 222, 173),
+    'navy': (0, 0, 128),
+    'oldlace': (253, 245, 230),
+    'olive': (128, 128, 0),
+    'olivedrab': (107, 142, 35),
+    'orange': (255, 165, 0),
+    'orangered': (255, 69, 0),
+    'orchid': (218, 112, 214),
+    'palegoldenrod': (238, 232, 170),
+    'palegreen': (152, 251, 152),
+    'paleturquoise': (175, 238, 238),
+    'palevioletred': (219, 112, 147),
+    'papayawhip': (255, 239, 213),
+    'peachpuff': (255, 218, 185),
+    'peru': (205, 133, 63),
+    'pink': (255, 192, 203),
+    'plum': (221, 160, 221),
+    'powderblue': (176, 224, 230),
+    'purple': (128, 0, 128),
+    'red': (255, 0, 0),
+    'rosybrown': (188, 143, 143),
+    'royalblue': (65, 105, 225),
+    'saddlebrown': (139, 69, 19),
+    'salmon': (250, 128, 114),
+    'sandybrown': (244, 164, 96),
+    'seagreen': (46, 139, 87),
+    'seashell': (255, 245, 238),
+    'sienna': (160, 82, 45),
+    'silver': (192, 192, 192),
+    'skyblue': (135, 206, 235),
+    'slateblue': (106, 90, 205),
+    'slategray': (112, 128, 144),
+    'slategrey': (112, 128, 144),
+    'snow': (255, 250, 250),
+    'springgreen': (0, 255, 127),
+    'steelblue': (70, 130, 180),
+    'tan': (210, 180, 140),
+    'teal': (0, 128, 128),
+    'thistle': (216, 191, 216),
+    'tomato': (255, 99, 71),
+    'turquoise': (64, 224, 208),
+    'violet': (238, 130, 238),
+    'wheat': (245, 222, 179),
+    'white': (255, 255, 255),
+    'whitesmoke': (245, 245, 245),
+    'yellow': (255, 255, 0),
+    'yellowgreen': (154, 205, 50),
+}
+
+COLORS_BY_VALUE = {v: k for k, v in COLORS_BY_NAME.items()}
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/config.py b/.venv/lib/python3.12/site-packages/pydantic/v1/config.py
new file mode 100644
index 00000000..18f7c999
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/config.py
@@ -0,0 +1,191 @@
+import json
+from enum import Enum
+from typing import TYPE_CHECKING, Any, Callable, Dict, ForwardRef, Optional, Tuple, Type, Union
+
+from typing_extensions import Literal, Protocol
+
+from pydantic.v1.typing import AnyArgTCallable, AnyCallable
+from pydantic.v1.utils import GetterDict
+from pydantic.v1.version import compiled
+
+if TYPE_CHECKING:
+    from typing import overload
+
+    from pydantic.v1.fields import ModelField
+    from pydantic.v1.main import BaseModel
+
+    ConfigType = Type['BaseConfig']
+
+    class SchemaExtraCallable(Protocol):
+        @overload
+        def __call__(self, schema: Dict[str, Any]) -> None:
+            pass
+
+        @overload
+        def __call__(self, schema: Dict[str, Any], model_class: Type[BaseModel]) -> None:
+            pass
+
+else:
+    SchemaExtraCallable = Callable[..., None]
+
+__all__ = 'BaseConfig', 'ConfigDict', 'get_config', 'Extra', 'inherit_config', 'prepare_config'
+
+
+class Extra(str, Enum):
+    allow = 'allow'
+    ignore = 'ignore'
+    forbid = 'forbid'
+
+
+# https://github.com/cython/cython/issues/4003
+# Fixed in Cython 3 and Pydantic v1 won't support Cython 3.
+# Pydantic v2 doesn't depend on Cython at all.
+if not compiled:
+    from typing_extensions import TypedDict
+
+    class ConfigDict(TypedDict, total=False):
+        title: Optional[str]
+        anystr_lower: bool
+        anystr_strip_whitespace: bool
+        min_anystr_length: int
+        max_anystr_length: Optional[int]
+        validate_all: bool
+        extra: Extra
+        allow_mutation: bool
+        frozen: bool
+        allow_population_by_field_name: bool
+        use_enum_values: bool
+        fields: Dict[str, Union[str, Dict[str, str]]]
+        validate_assignment: bool
+        error_msg_templates: Dict[str, str]
+        arbitrary_types_allowed: bool
+        orm_mode: bool
+        getter_dict: Type[GetterDict]
+        alias_generator: Optional[Callable[[str], str]]
+        keep_untouched: Tuple[type, ...]
+        schema_extra: Union[Dict[str, object], 'SchemaExtraCallable']
+        json_loads: Callable[[str], object]
+        json_dumps: AnyArgTCallable[str]
+        json_encoders: Dict[Type[object], AnyCallable]
+        underscore_attrs_are_private: bool
+        allow_inf_nan: bool
+        copy_on_model_validation: Literal['none', 'deep', 'shallow']
+        # whether dataclass `__post_init__` should be run after validation
+        post_init_call: Literal['before_validation', 'after_validation']
+
+else:
+    ConfigDict = dict  # type: ignore
+
+
+class BaseConfig:
+    title: Optional[str] = None
+    anystr_lower: bool = False
+    anystr_upper: bool = False
+    anystr_strip_whitespace: bool = False
+    min_anystr_length: int = 0
+    max_anystr_length: Optional[int] = None
+    validate_all: bool = False
+    extra: Extra = Extra.ignore
+    allow_mutation: bool = True
+    frozen: bool = False
+    allow_population_by_field_name: bool = False
+    use_enum_values: bool = False
+    fields: Dict[str, Union[str, Dict[str, str]]] = {}
+    validate_assignment: bool = False
+    error_msg_templates: Dict[str, str] = {}
+    arbitrary_types_allowed: bool = False
+    orm_mode: bool = False
+    getter_dict: Type[GetterDict] = GetterDict
+    alias_generator: Optional[Callable[[str], str]] = None
+    keep_untouched: Tuple[type, ...] = ()
+    schema_extra: Union[Dict[str, Any], 'SchemaExtraCallable'] = {}
+    json_loads: Callable[[str], Any] = json.loads
+    json_dumps: Callable[..., str] = json.dumps
+    json_encoders: Dict[Union[Type[Any], str, ForwardRef], AnyCallable] = {}
+    underscore_attrs_are_private: bool = False
+    allow_inf_nan: bool = True
+
+    # whether inherited models as fields should be reconstructed as base model,
+    # and whether such a copy should be shallow or deep
+    copy_on_model_validation: Literal['none', 'deep', 'shallow'] = 'shallow'
+
+    # whether `Union` should check all allowed types before even trying to coerce
+    smart_union: bool = False
+    # whether dataclass `__post_init__` should be run before or after validation
+    post_init_call: Literal['before_validation', 'after_validation'] = 'before_validation'
+
+    @classmethod
+    def get_field_info(cls, name: str) -> Dict[str, Any]:
+        """
+        Get properties of FieldInfo from the `fields` property of the config class.
+        """
+
+        fields_value = cls.fields.get(name)
+
+        if isinstance(fields_value, str):
+            field_info: Dict[str, Any] = {'alias': fields_value}
+        elif isinstance(fields_value, dict):
+            field_info = fields_value
+        else:
+            field_info = {}
+
+        if 'alias' in field_info:
+            field_info.setdefault('alias_priority', 2)
+
+        if field_info.get('alias_priority', 0) <= 1 and cls.alias_generator:
+            alias = cls.alias_generator(name)
+            if not isinstance(alias, str):
+                raise TypeError(f'Config.alias_generator must return str, not {alias.__class__}')
+            field_info.update(alias=alias, alias_priority=1)
+        return field_info
+
+    @classmethod
+    def prepare_field(cls, field: 'ModelField') -> None:
+        """
+        Optional hook to check or modify fields during model creation.
+        """
+        pass
+
+
+def get_config(config: Union[ConfigDict, Type[object], None]) -> Type[BaseConfig]:
+    if config is None:
+        return BaseConfig
+
+    else:
+        config_dict = (
+            config
+            if isinstance(config, dict)
+            else {k: getattr(config, k) for k in dir(config) if not k.startswith('__')}
+        )
+
+        class Config(BaseConfig):
+            ...
+
+        for k, v in config_dict.items():
+            setattr(Config, k, v)
+        return Config
+
+
+def inherit_config(self_config: 'ConfigType', parent_config: 'ConfigType', **namespace: Any) -> 'ConfigType':
+    if not self_config:
+        base_classes: Tuple['ConfigType', ...] = (parent_config,)
+    elif self_config == parent_config:
+        base_classes = (self_config,)
+    else:
+        base_classes = self_config, parent_config
+
+    namespace['json_encoders'] = {
+        **getattr(parent_config, 'json_encoders', {}),
+        **getattr(self_config, 'json_encoders', {}),
+        **namespace.get('json_encoders', {}),
+    }
+
+    return type('Config', base_classes, namespace)
+
+
+def prepare_config(config: Type[BaseConfig], cls_name: str) -> None:
+    if not isinstance(config.extra, Extra):
+        try:
+            config.extra = Extra(config.extra)
+        except ValueError:
+            raise ValueError(f'"{cls_name}": {config.extra} is not a valid value for "extra"')
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/dataclasses.py b/.venv/lib/python3.12/site-packages/pydantic/v1/dataclasses.py
new file mode 100644
index 00000000..bd167029
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/dataclasses.py
@@ -0,0 +1,500 @@
+"""
+The main purpose is to enhance stdlib dataclasses by adding validation
+A pydantic dataclass can be generated from scratch or from a stdlib one.
+
+Behind the scene, a pydantic dataclass is just like a regular one on which we attach
+a `BaseModel` and magic methods to trigger the validation of the data.
+`__init__` and `__post_init__` are hence overridden and have extra logic to be
+able to validate input data.
+
+When a pydantic dataclass is generated from scratch, it's just a plain dataclass
+with validation triggered at initialization
+
+The tricky part if for stdlib dataclasses that are converted after into pydantic ones e.g.
+
+```py
+@dataclasses.dataclass
+class M:
+    x: int
+
+ValidatedM = pydantic.dataclasses.dataclass(M)
+```
+
+We indeed still want to support equality, hashing, repr, ... as if it was the stdlib one!
+
+```py
+assert isinstance(ValidatedM(x=1), M)
+assert ValidatedM(x=1) == M(x=1)
+```
+
+This means we **don't want to create a new dataclass that inherits from it**
+The trick is to create a wrapper around `M` that will act as a proxy to trigger
+validation without altering default `M` behaviour.
+"""
+import copy
+import dataclasses
+import sys
+from contextlib import contextmanager
+from functools import wraps
+
+try:
+    from functools import cached_property
+except ImportError:
+    # cached_property available only for python3.8+
+    pass
+
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Dict, Generator, Optional, Type, TypeVar, Union, overload
+
+from typing_extensions import dataclass_transform
+
+from pydantic.v1.class_validators import gather_all_validators
+from pydantic.v1.config import BaseConfig, ConfigDict, Extra, get_config
+from pydantic.v1.error_wrappers import ValidationError
+from pydantic.v1.errors import DataclassTypeError
+from pydantic.v1.fields import Field, FieldInfo, Required, Undefined
+from pydantic.v1.main import create_model, validate_model
+from pydantic.v1.utils import ClassAttribute
+
+if TYPE_CHECKING:
+    from pydantic.v1.main import BaseModel
+    from pydantic.v1.typing import CallableGenerator, NoArgAnyCallable
+
+    DataclassT = TypeVar('DataclassT', bound='Dataclass')
+
+    DataclassClassOrWrapper = Union[Type['Dataclass'], 'DataclassProxy']
+
+    class Dataclass:
+        # stdlib attributes
+        __dataclass_fields__: ClassVar[Dict[str, Any]]
+        __dataclass_params__: ClassVar[Any]  # in reality `dataclasses._DataclassParams`
+        __post_init__: ClassVar[Callable[..., None]]
+
+        # Added by pydantic
+        __pydantic_run_validation__: ClassVar[bool]
+        __post_init_post_parse__: ClassVar[Callable[..., None]]
+        __pydantic_initialised__: ClassVar[bool]
+        __pydantic_model__: ClassVar[Type[BaseModel]]
+        __pydantic_validate_values__: ClassVar[Callable[['Dataclass'], None]]
+        __pydantic_has_field_info_default__: ClassVar[bool]  # whether a `pydantic.Field` is used as default value
+
+        def __init__(self, *args: object, **kwargs: object) -> None:
+            pass
+
+        @classmethod
+        def __get_validators__(cls: Type['Dataclass']) -> 'CallableGenerator':
+            pass
+
+        @classmethod
+        def __validate__(cls: Type['DataclassT'], v: Any) -> 'DataclassT':
+            pass
+
+
+__all__ = [
+    'dataclass',
+    'set_validation',
+    'create_pydantic_model_from_dataclass',
+    'is_builtin_dataclass',
+    'make_dataclass_validator',
+]
+
+_T = TypeVar('_T')
+
+if sys.version_info >= (3, 10):
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
+    @overload
+    def dataclass(
+        *,
+        init: bool = True,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: Union[ConfigDict, Type[object], None] = None,
+        validate_on_init: Optional[bool] = None,
+        use_proxy: Optional[bool] = None,
+        kw_only: bool = ...,
+    ) -> Callable[[Type[_T]], 'DataclassClassOrWrapper']:
+        ...
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
+    @overload
+    def dataclass(
+        _cls: Type[_T],
+        *,
+        init: bool = True,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: Union[ConfigDict, Type[object], None] = None,
+        validate_on_init: Optional[bool] = None,
+        use_proxy: Optional[bool] = None,
+        kw_only: bool = ...,
+    ) -> 'DataclassClassOrWrapper':
+        ...
+
+else:
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
+    @overload
+    def dataclass(
+        *,
+        init: bool = True,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: Union[ConfigDict, Type[object], None] = None,
+        validate_on_init: Optional[bool] = None,
+        use_proxy: Optional[bool] = None,
+    ) -> Callable[[Type[_T]], 'DataclassClassOrWrapper']:
+        ...
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
+    @overload
+    def dataclass(
+        _cls: Type[_T],
+        *,
+        init: bool = True,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: Union[ConfigDict, Type[object], None] = None,
+        validate_on_init: Optional[bool] = None,
+        use_proxy: Optional[bool] = None,
+    ) -> 'DataclassClassOrWrapper':
+        ...
+
+
+@dataclass_transform(field_specifiers=(dataclasses.field, Field))
+def dataclass(
+    _cls: Optional[Type[_T]] = None,
+    *,
+    init: bool = True,
+    repr: bool = True,
+    eq: bool = True,
+    order: bool = False,
+    unsafe_hash: bool = False,
+    frozen: bool = False,
+    config: Union[ConfigDict, Type[object], None] = None,
+    validate_on_init: Optional[bool] = None,
+    use_proxy: Optional[bool] = None,
+    kw_only: bool = False,
+) -> Union[Callable[[Type[_T]], 'DataclassClassOrWrapper'], 'DataclassClassOrWrapper']:
+    """
+    Like the python standard lib dataclasses but with type validation.
+    The result is either a pydantic dataclass that will validate input data
+    or a wrapper that will trigger validation around a stdlib dataclass
+    to avoid modifying it directly
+    """
+    the_config = get_config(config)
+
+    def wrap(cls: Type[Any]) -> 'DataclassClassOrWrapper':
+        should_use_proxy = (
+            use_proxy
+            if use_proxy is not None
+            else (
+                is_builtin_dataclass(cls)
+                and (cls.__bases__[0] is object or set(dir(cls)) == set(dir(cls.__bases__[0])))
+            )
+        )
+        if should_use_proxy:
+            dc_cls_doc = ''
+            dc_cls = DataclassProxy(cls)
+            default_validate_on_init = False
+        else:
+            dc_cls_doc = cls.__doc__ or ''  # needs to be done before generating dataclass
+            if sys.version_info >= (3, 10):
+                dc_cls = dataclasses.dataclass(
+                    cls,
+                    init=init,
+                    repr=repr,
+                    eq=eq,
+                    order=order,
+                    unsafe_hash=unsafe_hash,
+                    frozen=frozen,
+                    kw_only=kw_only,
+                )
+            else:
+                dc_cls = dataclasses.dataclass(  # type: ignore
+                    cls, init=init, repr=repr, eq=eq, order=order, unsafe_hash=unsafe_hash, frozen=frozen
+                )
+            default_validate_on_init = True
+
+        should_validate_on_init = default_validate_on_init if validate_on_init is None else validate_on_init
+        _add_pydantic_validation_attributes(cls, the_config, should_validate_on_init, dc_cls_doc)
+        dc_cls.__pydantic_model__.__try_update_forward_refs__(**{cls.__name__: cls})
+        return dc_cls
+
+    if _cls is None:
+        return wrap
+
+    return wrap(_cls)
+
+
+@contextmanager
+def set_validation(cls: Type['DataclassT'], value: bool) -> Generator[Type['DataclassT'], None, None]:
+    original_run_validation = cls.__pydantic_run_validation__
+    try:
+        cls.__pydantic_run_validation__ = value
+        yield cls
+    finally:
+        cls.__pydantic_run_validation__ = original_run_validation
+
+
+class DataclassProxy:
+    __slots__ = '__dataclass__'
+
+    def __init__(self, dc_cls: Type['Dataclass']) -> None:
+        object.__setattr__(self, '__dataclass__', dc_cls)
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        with set_validation(self.__dataclass__, True):
+            return self.__dataclass__(*args, **kwargs)
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self.__dataclass__, name)
+
+    def __setattr__(self, __name: str, __value: Any) -> None:
+        return setattr(self.__dataclass__, __name, __value)
+
+    def __instancecheck__(self, instance: Any) -> bool:
+        return isinstance(instance, self.__dataclass__)
+
+    def __copy__(self) -> 'DataclassProxy':
+        return DataclassProxy(copy.copy(self.__dataclass__))
+
+    def __deepcopy__(self, memo: Any) -> 'DataclassProxy':
+        return DataclassProxy(copy.deepcopy(self.__dataclass__, memo))
+
+
+def _add_pydantic_validation_attributes(  # noqa: C901 (ignore complexity)
+    dc_cls: Type['Dataclass'],
+    config: Type[BaseConfig],
+    validate_on_init: bool,
+    dc_cls_doc: str,
+) -> None:
+    """
+    We need to replace the right method. If no `__post_init__` has been set in the stdlib dataclass
+    it won't even exist (code is generated on the fly by `dataclasses`)
+    By default, we run validation after `__init__` or `__post_init__` if defined
+    """
+    init = dc_cls.__init__
+
+    @wraps(init)
+    def handle_extra_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None:
+        if config.extra == Extra.ignore:
+            init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__})
+
+        elif config.extra == Extra.allow:
+            for k, v in kwargs.items():
+                self.__dict__.setdefault(k, v)
+            init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__})
+
+        else:
+            init(self, *args, **kwargs)
+
+    if hasattr(dc_cls, '__post_init__'):
+        try:
+            post_init = dc_cls.__post_init__.__wrapped__  # type: ignore[attr-defined]
+        except AttributeError:
+            post_init = dc_cls.__post_init__
+
+        @wraps(post_init)
+        def new_post_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None:
+            if config.post_init_call == 'before_validation':
+                post_init(self, *args, **kwargs)
+
+            if self.__class__.__pydantic_run_validation__:
+                self.__pydantic_validate_values__()
+                if hasattr(self, '__post_init_post_parse__'):
+                    self.__post_init_post_parse__(*args, **kwargs)
+
+            if config.post_init_call == 'after_validation':
+                post_init(self, *args, **kwargs)
+
+        setattr(dc_cls, '__init__', handle_extra_init)
+        setattr(dc_cls, '__post_init__', new_post_init)
+
+    else:
+
+        @wraps(init)
+        def new_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None:
+            handle_extra_init(self, *args, **kwargs)
+
+            if self.__class__.__pydantic_run_validation__:
+                self.__pydantic_validate_values__()
+
+            if hasattr(self, '__post_init_post_parse__'):
+                # We need to find again the initvars. To do that we use `__dataclass_fields__` instead of
+                # public method `dataclasses.fields`
+
+                # get all initvars and their default values
+                initvars_and_values: Dict[str, Any] = {}
+                for i, f in enumerate(self.__class__.__dataclass_fields__.values()):
+                    if f._field_type is dataclasses._FIELD_INITVAR:  # type: ignore[attr-defined]
+                        try:
+                            # set arg value by default
+                            initvars_and_values[f.name] = args[i]
+                        except IndexError:
+                            initvars_and_values[f.name] = kwargs.get(f.name, f.default)
+
+                self.__post_init_post_parse__(**initvars_and_values)
+
+        setattr(dc_cls, '__init__', new_init)
+
+    setattr(dc_cls, '__pydantic_run_validation__', ClassAttribute('__pydantic_run_validation__', validate_on_init))
+    setattr(dc_cls, '__pydantic_initialised__', False)
+    setattr(dc_cls, '__pydantic_model__', create_pydantic_model_from_dataclass(dc_cls, config, dc_cls_doc))
+    setattr(dc_cls, '__pydantic_validate_values__', _dataclass_validate_values)
+    setattr(dc_cls, '__validate__', classmethod(_validate_dataclass))
+    setattr(dc_cls, '__get_validators__', classmethod(_get_validators))
+
+    if dc_cls.__pydantic_model__.__config__.validate_assignment and not dc_cls.__dataclass_params__.frozen:
+        setattr(dc_cls, '__setattr__', _dataclass_validate_assignment_setattr)
+
+
+def _get_validators(cls: 'DataclassClassOrWrapper') -> 'CallableGenerator':
+    yield cls.__validate__
+
+
+def _validate_dataclass(cls: Type['DataclassT'], v: Any) -> 'DataclassT':
+    with set_validation(cls, True):
+        if isinstance(v, cls):
+            v.__pydantic_validate_values__()
+            return v
+        elif isinstance(v, (list, tuple)):
+            return cls(*v)
+        elif isinstance(v, dict):
+            return cls(**v)
+        else:
+            raise DataclassTypeError(class_name=cls.__name__)
+
+
+def create_pydantic_model_from_dataclass(
+    dc_cls: Type['Dataclass'],
+    config: Type[Any] = BaseConfig,
+    dc_cls_doc: Optional[str] = None,
+) -> Type['BaseModel']:
+    field_definitions: Dict[str, Any] = {}
+    for field in dataclasses.fields(dc_cls):
+        default: Any = Undefined
+        default_factory: Optional['NoArgAnyCallable'] = None
+        field_info: FieldInfo
+
+        if field.default is not dataclasses.MISSING:
+            default = field.default
+        elif field.default_factory is not dataclasses.MISSING:
+            default_factory = field.default_factory
+        else:
+            default = Required
+
+        if isinstance(default, FieldInfo):
+            field_info = default
+            dc_cls.__pydantic_has_field_info_default__ = True
+        else:
+            field_info = Field(default=default, default_factory=default_factory, **field.metadata)
+
+        field_definitions[field.name] = (field.type, field_info)
+
+    validators = gather_all_validators(dc_cls)
+    model: Type['BaseModel'] = create_model(
+        dc_cls.__name__,
+        __config__=config,
+        __module__=dc_cls.__module__,
+        __validators__=validators,
+        __cls_kwargs__={'__resolve_forward_refs__': False},
+        **field_definitions,
+    )
+    model.__doc__ = dc_cls_doc if dc_cls_doc is not None else dc_cls.__doc__ or ''
+    return model
+
+
+if sys.version_info >= (3, 8):
+
+    def _is_field_cached_property(obj: 'Dataclass', k: str) -> bool:
+        return isinstance(getattr(type(obj), k, None), cached_property)
+
+else:
+
+    def _is_field_cached_property(obj: 'Dataclass', k: str) -> bool:
+        return False
+
+
+def _dataclass_validate_values(self: 'Dataclass') -> None:
+    # validation errors can occur if this function is called twice on an already initialised dataclass.
+    # for example if Extra.forbid is enabled, it would consider __pydantic_initialised__ an invalid extra property
+    if getattr(self, '__pydantic_initialised__'):
+        return
+    if getattr(self, '__pydantic_has_field_info_default__', False):
+        # We need to remove `FieldInfo` values since they are not valid as input
+        # It's ok to do that because they are obviously the default values!
+        input_data = {
+            k: v
+            for k, v in self.__dict__.items()
+            if not (isinstance(v, FieldInfo) or _is_field_cached_property(self, k))
+        }
+    else:
+        input_data = {k: v for k, v in self.__dict__.items() if not _is_field_cached_property(self, k)}
+    d, _, validation_error = validate_model(self.__pydantic_model__, input_data, cls=self.__class__)
+    if validation_error:
+        raise validation_error
+    self.__dict__.update(d)
+    object.__setattr__(self, '__pydantic_initialised__', True)
+
+
+def _dataclass_validate_assignment_setattr(self: 'Dataclass', name: str, value: Any) -> None:
+    if self.__pydantic_initialised__:
+        d = dict(self.__dict__)
+        d.pop(name, None)
+        known_field = self.__pydantic_model__.__fields__.get(name, None)
+        if known_field:
+            value, error_ = known_field.validate(value, d, loc=name, cls=self.__class__)
+            if error_:
+                raise ValidationError([error_], self.__class__)
+
+    object.__setattr__(self, name, value)
+
+
+def is_builtin_dataclass(_cls: Type[Any]) -> bool:
+    """
+    Whether a class is a stdlib dataclass
+    (useful to discriminated a pydantic dataclass that is actually a wrapper around a stdlib dataclass)
+
+    we check that
+    - `_cls` is a dataclass
+    - `_cls` is not a processed pydantic dataclass (with a basemodel attached)
+    - `_cls` is not a pydantic dataclass inheriting directly from a stdlib dataclass
+    e.g.
+    ```
+    @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')
+    """
+    return (
+        dataclasses.is_dataclass(_cls)
+        and not hasattr(_cls, '__pydantic_model__')
+        and set(_cls.__dataclass_fields__).issuperset(set(getattr(_cls, '__annotations__', {})))
+    )
+
+
+def make_dataclass_validator(dc_cls: Type['Dataclass'], config: Type[BaseConfig]) -> 'CallableGenerator':
+    """
+    Create a pydantic.dataclass from a builtin dataclass to add type validation
+    and yield the validators
+    It retrieves the parameters of the dataclass and forwards them to the newly created dataclass
+    """
+    yield from _get_validators(dataclass(dc_cls, config=config, use_proxy=True))
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/datetime_parse.py b/.venv/lib/python3.12/site-packages/pydantic/v1/datetime_parse.py
new file mode 100644
index 00000000..a7598fc6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/datetime_parse.py
@@ -0,0 +1,248 @@
+"""
+Functions to parse datetime objects.
+
+We're using regular expressions rather than time.strptime because:
+- They provide both validation and parsing.
+- They're more flexible for datetimes.
+- The date/datetime/time constructors produce friendlier error messages.
+
+Stolen from https://raw.githubusercontent.com/django/django/main/django/utils/dateparse.py at
+9718fa2e8abe430c3526a9278dd976443d4ae3c6
+
+Changed to:
+* use standard python datetime types not django.utils.timezone
+* raise ValueError when regex doesn't match rather than returning None
+* support parsing unix timestamps for dates and datetimes
+"""
+import re
+from datetime import date, datetime, time, timedelta, timezone
+from typing import Dict, Optional, Type, Union
+
+from pydantic.v1 import errors
+
+date_expr = r'(?P<year>\d{4})-(?P<month>\d{1,2})-(?P<day>\d{1,2})'
+time_expr = (
+    r'(?P<hour>\d{1,2}):(?P<minute>\d{1,2})'
+    r'(?::(?P<second>\d{1,2})(?:\.(?P<microsecond>\d{1,6})\d{0,6})?)?'
+    r'(?P<tzinfo>Z|[+-]\d{2}(?::?\d{2})?)?$'
+)
+
+date_re = re.compile(f'{date_expr}$')
+time_re = re.compile(time_expr)
+datetime_re = re.compile(f'{date_expr}[T ]{time_expr}')
+
+standard_duration_re = re.compile(
+    r'^'
+    r'(?:(?P<days>-?\d+) (days?, )?)?'
+    r'((?:(?P<hours>-?\d+):)(?=\d+:\d+))?'
+    r'(?:(?P<minutes>-?\d+):)?'
+    r'(?P<seconds>-?\d+)'
+    r'(?:\.(?P<microseconds>\d{1,6})\d{0,6})?'
+    r'$'
+)
+
+# Support the sections of ISO 8601 date representation that are accepted by timedelta
+iso8601_duration_re = re.compile(
+    r'^(?P<sign>[-+]?)'
+    r'P'
+    r'(?:(?P<days>\d+(.\d+)?)D)?'
+    r'(?:T'
+    r'(?:(?P<hours>\d+(.\d+)?)H)?'
+    r'(?:(?P<minutes>\d+(.\d+)?)M)?'
+    r'(?:(?P<seconds>\d+(.\d+)?)S)?'
+    r')?'
+    r'$'
+)
+
+EPOCH = datetime(1970, 1, 1)
+# if greater than this, the number is in ms, if less than or equal it's in seconds
+# (in seconds this is 11th October 2603, in ms it's 20th August 1970)
+MS_WATERSHED = int(2e10)
+# slightly more than datetime.max in ns - (datetime.max - EPOCH).total_seconds() * 1e9
+MAX_NUMBER = int(3e20)
+StrBytesIntFloat = Union[str, bytes, int, float]
+
+
+def get_numeric(value: StrBytesIntFloat, native_expected_type: str) -> Union[None, int, float]:
+    if isinstance(value, (int, float)):
+        return value
+    try:
+        return float(value)
+    except ValueError:
+        return None
+    except TypeError:
+        raise TypeError(f'invalid type; expected {native_expected_type}, string, bytes, int or float')
+
+
+def from_unix_seconds(seconds: Union[int, float]) -> datetime:
+    if seconds > MAX_NUMBER:
+        return datetime.max
+    elif seconds < -MAX_NUMBER:
+        return datetime.min
+
+    while abs(seconds) > MS_WATERSHED:
+        seconds /= 1000
+    dt = EPOCH + timedelta(seconds=seconds)
+    return dt.replace(tzinfo=timezone.utc)
+
+
+def _parse_timezone(value: Optional[str], error: Type[Exception]) -> Union[None, int, timezone]:
+    if value == 'Z':
+        return timezone.utc
+    elif value is not None:
+        offset_mins = int(value[-2:]) if len(value) > 3 else 0
+        offset = 60 * int(value[1:3]) + offset_mins
+        if value[0] == '-':
+            offset = -offset
+        try:
+            return timezone(timedelta(minutes=offset))
+        except ValueError:
+            raise error()
+    else:
+        return None
+
+
+def parse_date(value: Union[date, StrBytesIntFloat]) -> date:
+    """
+    Parse a date/int/float/string and return a datetime.date.
+
+    Raise ValueError if the input is well formatted but not a valid date.
+    Raise ValueError if the input isn't well formatted.
+    """
+    if isinstance(value, date):
+        if isinstance(value, datetime):
+            return value.date()
+        else:
+            return value
+
+    number = get_numeric(value, 'date')
+    if number is not None:
+        return from_unix_seconds(number).date()
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    match = date_re.match(value)  # type: ignore
+    if match is None:
+        raise errors.DateError()
+
+    kw = {k: int(v) for k, v in match.groupdict().items()}
+
+    try:
+        return date(**kw)
+    except ValueError:
+        raise errors.DateError()
+
+
+def parse_time(value: Union[time, StrBytesIntFloat]) -> time:
+    """
+    Parse a time/string and return a datetime.time.
+
+    Raise ValueError if the input is well formatted but not a valid time.
+    Raise ValueError if the input isn't well formatted, in particular if it contains an offset.
+    """
+    if isinstance(value, time):
+        return value
+
+    number = get_numeric(value, 'time')
+    if number is not None:
+        if number >= 86400:
+            # doesn't make sense since the time time loop back around to 0
+            raise errors.TimeError()
+        return (datetime.min + timedelta(seconds=number)).time()
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    match = time_re.match(value)  # type: ignore
+    if match is None:
+        raise errors.TimeError()
+
+    kw = match.groupdict()
+    if kw['microsecond']:
+        kw['microsecond'] = kw['microsecond'].ljust(6, '0')
+
+    tzinfo = _parse_timezone(kw.pop('tzinfo'), errors.TimeError)
+    kw_: Dict[str, Union[None, int, timezone]] = {k: int(v) for k, v in kw.items() if v is not None}
+    kw_['tzinfo'] = tzinfo
+
+    try:
+        return time(**kw_)  # type: ignore
+    except ValueError:
+        raise errors.TimeError()
+
+
+def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime:
+    """
+    Parse a datetime/int/float/string and return a datetime.datetime.
+
+    This function supports time zone offsets. When the input contains one,
+    the output uses a timezone with a fixed offset from UTC.
+
+    Raise ValueError if the input is well formatted but not a valid datetime.
+    Raise ValueError if the input isn't well formatted.
+    """
+    if isinstance(value, datetime):
+        return value
+
+    number = get_numeric(value, 'datetime')
+    if number is not None:
+        return from_unix_seconds(number)
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    match = datetime_re.match(value)  # type: ignore
+    if match is None:
+        raise errors.DateTimeError()
+
+    kw = match.groupdict()
+    if kw['microsecond']:
+        kw['microsecond'] = kw['microsecond'].ljust(6, '0')
+
+    tzinfo = _parse_timezone(kw.pop('tzinfo'), errors.DateTimeError)
+    kw_: Dict[str, Union[None, int, timezone]] = {k: int(v) for k, v in kw.items() if v is not None}
+    kw_['tzinfo'] = tzinfo
+
+    try:
+        return datetime(**kw_)  # type: ignore
+    except ValueError:
+        raise errors.DateTimeError()
+
+
+def parse_duration(value: StrBytesIntFloat) -> timedelta:
+    """
+    Parse a duration int/float/string and return a datetime.timedelta.
+
+    The preferred format for durations in Django is '%d %H:%M:%S.%f'.
+
+    Also supports ISO 8601 representation.
+    """
+    if isinstance(value, timedelta):
+        return value
+
+    if isinstance(value, (int, float)):
+        # below code requires a string
+        value = f'{value:f}'
+    elif isinstance(value, bytes):
+        value = value.decode()
+
+    try:
+        match = standard_duration_re.match(value) or iso8601_duration_re.match(value)
+    except TypeError:
+        raise TypeError('invalid type; expected timedelta, string, bytes, int or float')
+
+    if not match:
+        raise errors.DurationError()
+
+    kw = match.groupdict()
+    sign = -1 if kw.pop('sign', '+') == '-' else 1
+    if kw.get('microseconds'):
+        kw['microseconds'] = kw['microseconds'].ljust(6, '0')
+
+    if kw.get('seconds') and kw.get('microseconds') and kw['seconds'].startswith('-'):
+        kw['microseconds'] = '-' + kw['microseconds']
+
+    kw_ = {k: float(v) for k, v in kw.items() if v is not None}
+
+    return sign * timedelta(**kw_)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/decorator.py b/.venv/lib/python3.12/site-packages/pydantic/v1/decorator.py
new file mode 100644
index 00000000..2c7c2c2f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/decorator.py
@@ -0,0 +1,264 @@
+from functools import wraps
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional, Tuple, Type, TypeVar, Union, overload
+
+from pydantic.v1 import validator
+from pydantic.v1.config import Extra
+from pydantic.v1.errors import ConfigError
+from pydantic.v1.main import BaseModel, create_model
+from pydantic.v1.typing import get_all_type_hints
+from pydantic.v1.utils import to_camel
+
+__all__ = ('validate_arguments',)
+
+if TYPE_CHECKING:
+    from pydantic.v1.typing import AnyCallable
+
+    AnyCallableT = TypeVar('AnyCallableT', bound=AnyCallable)
+    ConfigType = Union[None, Type[Any], Dict[str, Any]]
+
+
+@overload
+def validate_arguments(func: None = None, *, config: 'ConfigType' = None) -> Callable[['AnyCallableT'], 'AnyCallableT']:
+    ...
+
+
+@overload
+def validate_arguments(func: 'AnyCallableT') -> 'AnyCallableT':
+    ...
+
+
+def validate_arguments(func: Optional['AnyCallableT'] = None, *, config: 'ConfigType' = None) -> Any:
+    """
+    Decorator to validate the arguments passed to a function.
+    """
+
+    def validate(_func: 'AnyCallable') -> 'AnyCallable':
+        vd = ValidatedFunction(_func, config)
+
+        @wraps(_func)
+        def wrapper_function(*args: Any, **kwargs: Any) -> Any:
+            return vd.call(*args, **kwargs)
+
+        wrapper_function.vd = vd  # type: ignore
+        wrapper_function.validate = vd.init_model_instance  # type: ignore
+        wrapper_function.raw_function = vd.raw_function  # type: ignore
+        wrapper_function.model = vd.model  # type: ignore
+        return wrapper_function
+
+    if func:
+        return validate(func)
+    else:
+        return validate
+
+
+ALT_V_ARGS = 'v__args'
+ALT_V_KWARGS = 'v__kwargs'
+V_POSITIONAL_ONLY_NAME = 'v__positional_only'
+V_DUPLICATE_KWARGS = 'v__duplicate_kwargs'
+
+
+class ValidatedFunction:
+    def __init__(self, function: 'AnyCallableT', config: 'ConfigType'):  # noqa C901
+        from inspect import Parameter, signature
+
+        parameters: Mapping[str, Parameter] = signature(function).parameters
+
+        if parameters.keys() & {ALT_V_ARGS, ALT_V_KWARGS, V_POSITIONAL_ONLY_NAME, V_DUPLICATE_KWARGS}:
+            raise ConfigError(
+                f'"{ALT_V_ARGS}", "{ALT_V_KWARGS}", "{V_POSITIONAL_ONLY_NAME}" and "{V_DUPLICATE_KWARGS}" '
+                f'are not permitted as argument names when using the "{validate_arguments.__name__}" decorator'
+            )
+
+        self.raw_function = function
+        self.arg_mapping: Dict[int, str] = {}
+        self.positional_only_args = set()
+        self.v_args_name = 'args'
+        self.v_kwargs_name = 'kwargs'
+
+        type_hints = get_all_type_hints(function)
+        takes_args = False
+        takes_kwargs = False
+        fields: Dict[str, Tuple[Any, Any]] = {}
+        for i, (name, p) in enumerate(parameters.items()):
+            if p.annotation is p.empty:
+                annotation = Any
+            else:
+                annotation = type_hints[name]
+
+            default = ... if p.default is p.empty else p.default
+            if p.kind == Parameter.POSITIONAL_ONLY:
+                self.arg_mapping[i] = name
+                fields[name] = annotation, default
+                fields[V_POSITIONAL_ONLY_NAME] = List[str], None
+                self.positional_only_args.add(name)
+            elif p.kind == Parameter.POSITIONAL_OR_KEYWORD:
+                self.arg_mapping[i] = name
+                fields[name] = annotation, default
+                fields[V_DUPLICATE_KWARGS] = List[str], None
+            elif p.kind == Parameter.KEYWORD_ONLY:
+                fields[name] = annotation, default
+            elif p.kind == Parameter.VAR_POSITIONAL:
+                self.v_args_name = name
+                fields[name] = Tuple[annotation, ...], None
+                takes_args = True
+            else:
+                assert p.kind == Parameter.VAR_KEYWORD, p.kind
+                self.v_kwargs_name = name
+                fields[name] = Dict[str, annotation], None  # type: ignore
+                takes_kwargs = True
+
+        # these checks avoid a clash between "args" and a field with that name
+        if not takes_args and self.v_args_name in fields:
+            self.v_args_name = ALT_V_ARGS
+
+        # same with "kwargs"
+        if not takes_kwargs and self.v_kwargs_name in fields:
+            self.v_kwargs_name = ALT_V_KWARGS
+
+        if not takes_args:
+            # we add the field so validation below can raise the correct exception
+            fields[self.v_args_name] = List[Any], None
+
+        if not takes_kwargs:
+            # same with kwargs
+            fields[self.v_kwargs_name] = Dict[Any, Any], None
+
+        self.create_model(fields, takes_args, takes_kwargs, config)
+
+    def init_model_instance(self, *args: Any, **kwargs: Any) -> BaseModel:
+        values = self.build_values(args, kwargs)
+        return self.model(**values)
+
+    def call(self, *args: Any, **kwargs: Any) -> Any:
+        m = self.init_model_instance(*args, **kwargs)
+        return self.execute(m)
+
+    def build_values(self, args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> Dict[str, Any]:
+        values: Dict[str, Any] = {}
+        if args:
+            arg_iter = enumerate(args)
+            while True:
+                try:
+                    i, a = next(arg_iter)
+                except StopIteration:
+                    break
+                arg_name = self.arg_mapping.get(i)
+                if arg_name is not None:
+                    values[arg_name] = a
+                else:
+                    values[self.v_args_name] = [a] + [a for _, a in arg_iter]
+                    break
+
+        var_kwargs: Dict[str, Any] = {}
+        wrong_positional_args = []
+        duplicate_kwargs = []
+        fields_alias = [
+            field.alias
+            for name, field in self.model.__fields__.items()
+            if name not in (self.v_args_name, self.v_kwargs_name)
+        ]
+        non_var_fields = set(self.model.__fields__) - {self.v_args_name, self.v_kwargs_name}
+        for k, v in kwargs.items():
+            if k in non_var_fields or k in fields_alias:
+                if k in self.positional_only_args:
+                    wrong_positional_args.append(k)
+                if k in values:
+                    duplicate_kwargs.append(k)
+                values[k] = v
+            else:
+                var_kwargs[k] = v
+
+        if var_kwargs:
+            values[self.v_kwargs_name] = var_kwargs
+        if wrong_positional_args:
+            values[V_POSITIONAL_ONLY_NAME] = wrong_positional_args
+        if duplicate_kwargs:
+            values[V_DUPLICATE_KWARGS] = duplicate_kwargs
+        return values
+
+    def execute(self, m: BaseModel) -> Any:
+        d = {k: v for k, v in m._iter() if k in m.__fields_set__ or m.__fields__[k].default_factory}
+        var_kwargs = d.pop(self.v_kwargs_name, {})
+
+        if self.v_args_name in d:
+            args_: List[Any] = []
+            in_kwargs = False
+            kwargs = {}
+            for name, value in d.items():
+                if in_kwargs:
+                    kwargs[name] = value
+                elif name == self.v_args_name:
+                    args_ += value
+                    in_kwargs = True
+                else:
+                    args_.append(value)
+            return self.raw_function(*args_, **kwargs, **var_kwargs)
+        elif self.positional_only_args:
+            args_ = []
+            kwargs = {}
+            for name, value in d.items():
+                if name in self.positional_only_args:
+                    args_.append(value)
+                else:
+                    kwargs[name] = value
+            return self.raw_function(*args_, **kwargs, **var_kwargs)
+        else:
+            return self.raw_function(**d, **var_kwargs)
+
+    def create_model(self, fields: Dict[str, Any], takes_args: bool, takes_kwargs: bool, config: 'ConfigType') -> None:
+        pos_args = len(self.arg_mapping)
+
+        class CustomConfig:
+            pass
+
+        if not TYPE_CHECKING:  # pragma: no branch
+            if isinstance(config, dict):
+                CustomConfig = type('Config', (), config)  # noqa: F811
+            elif config is not None:
+                CustomConfig = config  # noqa: F811
+
+        if hasattr(CustomConfig, 'fields') or hasattr(CustomConfig, 'alias_generator'):
+            raise ConfigError(
+                'Setting the "fields" and "alias_generator" property on custom Config for '
+                '@validate_arguments is not yet supported, please remove.'
+            )
+
+        class DecoratorBaseModel(BaseModel):
+            @validator(self.v_args_name, check_fields=False, allow_reuse=True)
+            def check_args(cls, v: Optional[List[Any]]) -> Optional[List[Any]]:
+                if takes_args or v is None:
+                    return v
+
+                raise TypeError(f'{pos_args} positional arguments expected but {pos_args + len(v)} given')
+
+            @validator(self.v_kwargs_name, check_fields=False, allow_reuse=True)
+            def check_kwargs(cls, v: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
+                if takes_kwargs or v is None:
+                    return v
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v.keys()))
+                raise TypeError(f'unexpected keyword argument{plural}: {keys}')
+
+            @validator(V_POSITIONAL_ONLY_NAME, check_fields=False, allow_reuse=True)
+            def check_positional_only(cls, v: Optional[List[str]]) -> None:
+                if v is None:
+                    return
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v))
+                raise TypeError(f'positional-only argument{plural} passed as keyword argument{plural}: {keys}')
+
+            @validator(V_DUPLICATE_KWARGS, check_fields=False, allow_reuse=True)
+            def check_duplicate_kwargs(cls, v: Optional[List[str]]) -> None:
+                if v is None:
+                    return
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v))
+                raise TypeError(f'multiple values for argument{plural}: {keys}')
+
+            class Config(CustomConfig):
+                extra = getattr(CustomConfig, 'extra', Extra.forbid)
+
+        self.model = create_model(to_camel(self.raw_function.__name__), __base__=DecoratorBaseModel, **fields)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/env_settings.py b/.venv/lib/python3.12/site-packages/pydantic/v1/env_settings.py
new file mode 100644
index 00000000..5f6f2175
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/env_settings.py
@@ -0,0 +1,350 @@
+import os
+import warnings
+from pathlib import Path
+from typing import AbstractSet, Any, Callable, ClassVar, Dict, List, Mapping, Optional, Tuple, Type, Union
+
+from pydantic.v1.config import BaseConfig, Extra
+from pydantic.v1.fields import ModelField
+from pydantic.v1.main import BaseModel
+from pydantic.v1.types import JsonWrapper
+from pydantic.v1.typing import StrPath, display_as_type, get_origin, is_union
+from pydantic.v1.utils import deep_update, lenient_issubclass, path_type, sequence_like
+
+env_file_sentinel = str(object())
+
+SettingsSourceCallable = Callable[['BaseSettings'], Dict[str, Any]]
+DotenvType = Union[StrPath, List[StrPath], Tuple[StrPath, ...]]
+
+
+class SettingsError(ValueError):
+    pass
+
+
+class BaseSettings(BaseModel):
+    """
+    Base class for settings, allowing values to be overridden by environment variables.
+
+    This is useful in production for secrets you do not wish to save in code, it plays nicely with docker(-compose),
+    Heroku and any 12 factor app design.
+    """
+
+    def __init__(
+        __pydantic_self__,
+        _env_file: Optional[DotenvType] = env_file_sentinel,
+        _env_file_encoding: Optional[str] = None,
+        _env_nested_delimiter: Optional[str] = None,
+        _secrets_dir: Optional[StrPath] = None,
+        **values: Any,
+    ) -> None:
+        # Uses something other than `self` the first arg to allow "self" as a settable attribute
+        super().__init__(
+            **__pydantic_self__._build_values(
+                values,
+                _env_file=_env_file,
+                _env_file_encoding=_env_file_encoding,
+                _env_nested_delimiter=_env_nested_delimiter,
+                _secrets_dir=_secrets_dir,
+            )
+        )
+
+    def _build_values(
+        self,
+        init_kwargs: Dict[str, Any],
+        _env_file: Optional[DotenvType] = None,
+        _env_file_encoding: Optional[str] = None,
+        _env_nested_delimiter: Optional[str] = None,
+        _secrets_dir: Optional[StrPath] = None,
+    ) -> Dict[str, Any]:
+        # Configure built-in sources
+        init_settings = InitSettingsSource(init_kwargs=init_kwargs)
+        env_settings = EnvSettingsSource(
+            env_file=(_env_file if _env_file != env_file_sentinel else self.__config__.env_file),
+            env_file_encoding=(
+                _env_file_encoding if _env_file_encoding is not None else self.__config__.env_file_encoding
+            ),
+            env_nested_delimiter=(
+                _env_nested_delimiter if _env_nested_delimiter is not None else self.__config__.env_nested_delimiter
+            ),
+            env_prefix_len=len(self.__config__.env_prefix),
+        )
+        file_secret_settings = SecretsSettingsSource(secrets_dir=_secrets_dir or self.__config__.secrets_dir)
+        # Provide a hook to set built-in sources priority and add / remove sources
+        sources = self.__config__.customise_sources(
+            init_settings=init_settings, env_settings=env_settings, file_secret_settings=file_secret_settings
+        )
+        if sources:
+            return deep_update(*reversed([source(self) for source in sources]))
+        else:
+            # no one should mean to do this, but I think returning an empty dict is marginally preferable
+            # to an informative error and much better than a confusing error
+            return {}
+
+    class Config(BaseConfig):
+        env_prefix: str = ''
+        env_file: Optional[DotenvType] = None
+        env_file_encoding: Optional[str] = None
+        env_nested_delimiter: Optional[str] = None
+        secrets_dir: Optional[StrPath] = None
+        validate_all: bool = True
+        extra: Extra = Extra.forbid
+        arbitrary_types_allowed: bool = True
+        case_sensitive: bool = False
+
+        @classmethod
+        def prepare_field(cls, field: ModelField) -> None:
+            env_names: Union[List[str], AbstractSet[str]]
+            field_info_from_config = cls.get_field_info(field.name)
+
+            env = field_info_from_config.get('env') or field.field_info.extra.get('env')
+            if env is None:
+                if field.has_alias:
+                    warnings.warn(
+                        'aliases are no longer used by BaseSettings to define which environment variables to read. '
+                        'Instead use the "env" field setting. '
+                        'See https://pydantic-docs.helpmanual.io/usage/settings/#environment-variable-names',
+                        FutureWarning,
+                    )
+                env_names = {cls.env_prefix + field.name}
+            elif isinstance(env, str):
+                env_names = {env}
+            elif isinstance(env, (set, frozenset)):
+                env_names = env
+            elif sequence_like(env):
+                env_names = list(env)
+            else:
+                raise TypeError(f'invalid field env: {env!r} ({display_as_type(env)}); should be string, list or set')
+
+            if not cls.case_sensitive:
+                env_names = env_names.__class__(n.lower() for n in env_names)
+            field.field_info.extra['env_names'] = env_names
+
+        @classmethod
+        def customise_sources(
+            cls,
+            init_settings: SettingsSourceCallable,
+            env_settings: SettingsSourceCallable,
+            file_secret_settings: SettingsSourceCallable,
+        ) -> Tuple[SettingsSourceCallable, ...]:
+            return init_settings, env_settings, file_secret_settings
+
+        @classmethod
+        def parse_env_var(cls, field_name: str, raw_val: str) -> Any:
+            return cls.json_loads(raw_val)
+
+    # populated by the metaclass using the Config class defined above, annotated here to help IDEs only
+    __config__: ClassVar[Type[Config]]
+
+
+class InitSettingsSource:
+    __slots__ = ('init_kwargs',)
+
+    def __init__(self, init_kwargs: Dict[str, Any]):
+        self.init_kwargs = init_kwargs
+
+    def __call__(self, settings: BaseSettings) -> Dict[str, Any]:
+        return self.init_kwargs
+
+    def __repr__(self) -> str:
+        return f'InitSettingsSource(init_kwargs={self.init_kwargs!r})'
+
+
+class EnvSettingsSource:
+    __slots__ = ('env_file', 'env_file_encoding', 'env_nested_delimiter', 'env_prefix_len')
+
+    def __init__(
+        self,
+        env_file: Optional[DotenvType],
+        env_file_encoding: Optional[str],
+        env_nested_delimiter: Optional[str] = None,
+        env_prefix_len: int = 0,
+    ):
+        self.env_file: Optional[DotenvType] = env_file
+        self.env_file_encoding: Optional[str] = env_file_encoding
+        self.env_nested_delimiter: Optional[str] = env_nested_delimiter
+        self.env_prefix_len: int = env_prefix_len
+
+    def __call__(self, settings: BaseSettings) -> Dict[str, Any]:  # noqa C901
+        """
+        Build environment variables suitable for passing to the Model.
+        """
+        d: Dict[str, Any] = {}
+
+        if settings.__config__.case_sensitive:
+            env_vars: Mapping[str, Optional[str]] = os.environ
+        else:
+            env_vars = {k.lower(): v for k, v in os.environ.items()}
+
+        dotenv_vars = self._read_env_files(settings.__config__.case_sensitive)
+        if dotenv_vars:
+            env_vars = {**dotenv_vars, **env_vars}
+
+        for field in settings.__fields__.values():
+            env_val: Optional[str] = None
+            for env_name in field.field_info.extra['env_names']:
+                env_val = env_vars.get(env_name)
+                if env_val is not None:
+                    break
+
+            is_complex, allow_parse_failure = self.field_is_complex(field)
+            if is_complex:
+                if env_val is None:
+                    # field is complex but no value found so far, try explode_env_vars
+                    env_val_built = self.explode_env_vars(field, env_vars)
+                    if env_val_built:
+                        d[field.alias] = env_val_built
+                else:
+                    # field is complex and there's a value, decode that as JSON, then add explode_env_vars
+                    try:
+                        env_val = settings.__config__.parse_env_var(field.name, env_val)
+                    except ValueError as e:
+                        if not allow_parse_failure:
+                            raise SettingsError(f'error parsing env var "{env_name}"') from e
+
+                    if isinstance(env_val, dict):
+                        d[field.alias] = deep_update(env_val, self.explode_env_vars(field, env_vars))
+                    else:
+                        d[field.alias] = env_val
+            elif env_val is not None:
+                # simplest case, field is not complex, we only need to add the value if it was found
+                d[field.alias] = env_val
+
+        return d
+
+    def _read_env_files(self, case_sensitive: bool) -> Dict[str, Optional[str]]:
+        env_files = self.env_file
+        if env_files is None:
+            return {}
+
+        if isinstance(env_files, (str, os.PathLike)):
+            env_files = [env_files]
+
+        dotenv_vars = {}
+        for env_file in env_files:
+            env_path = Path(env_file).expanduser()
+            if env_path.is_file():
+                dotenv_vars.update(
+                    read_env_file(env_path, encoding=self.env_file_encoding, case_sensitive=case_sensitive)
+                )
+
+        return dotenv_vars
+
+    def field_is_complex(self, field: ModelField) -> Tuple[bool, bool]:
+        """
+        Find out if a field is complex, and if so whether JSON errors should be ignored
+        """
+        if lenient_issubclass(field.annotation, JsonWrapper):
+            return False, False
+
+        if field.is_complex():
+            allow_parse_failure = False
+        elif is_union(get_origin(field.type_)) and field.sub_fields and any(f.is_complex() for f in field.sub_fields):
+            allow_parse_failure = True
+        else:
+            return False, False
+
+        return True, allow_parse_failure
+
+    def explode_env_vars(self, field: ModelField, env_vars: Mapping[str, Optional[str]]) -> Dict[str, Any]:
+        """
+        Process env_vars and extract the values of keys containing env_nested_delimiter into nested dictionaries.
+
+        This is applied to a single field, hence filtering by env_var prefix.
+        """
+        prefixes = [f'{env_name}{self.env_nested_delimiter}' for env_name in field.field_info.extra['env_names']]
+        result: Dict[str, Any] = {}
+        for env_name, env_val in env_vars.items():
+            if not any(env_name.startswith(prefix) for prefix in prefixes):
+                continue
+            # we remove the prefix before splitting in case the prefix has characters in common with the delimiter
+            env_name_without_prefix = env_name[self.env_prefix_len :]
+            _, *keys, last_key = env_name_without_prefix.split(self.env_nested_delimiter)
+            env_var = result
+            for key in keys:
+                env_var = env_var.setdefault(key, {})
+            env_var[last_key] = env_val
+
+        return result
+
+    def __repr__(self) -> str:
+        return (
+            f'EnvSettingsSource(env_file={self.env_file!r}, env_file_encoding={self.env_file_encoding!r}, '
+            f'env_nested_delimiter={self.env_nested_delimiter!r})'
+        )
+
+
+class SecretsSettingsSource:
+    __slots__ = ('secrets_dir',)
+
+    def __init__(self, secrets_dir: Optional[StrPath]):
+        self.secrets_dir: Optional[StrPath] = secrets_dir
+
+    def __call__(self, settings: BaseSettings) -> Dict[str, Any]:
+        """
+        Build fields from "secrets" files.
+        """
+        secrets: Dict[str, Optional[str]] = {}
+
+        if self.secrets_dir is None:
+            return secrets
+
+        secrets_path = Path(self.secrets_dir).expanduser()
+
+        if not secrets_path.exists():
+            warnings.warn(f'directory "{secrets_path}" does not exist')
+            return secrets
+
+        if not secrets_path.is_dir():
+            raise SettingsError(f'secrets_dir must reference a directory, not a {path_type(secrets_path)}')
+
+        for field in settings.__fields__.values():
+            for env_name in field.field_info.extra['env_names']:
+                path = find_case_path(secrets_path, env_name, settings.__config__.case_sensitive)
+                if not path:
+                    # path does not exist, we currently don't return a warning for this
+                    continue
+
+                if path.is_file():
+                    secret_value = path.read_text().strip()
+                    if field.is_complex():
+                        try:
+                            secret_value = settings.__config__.parse_env_var(field.name, secret_value)
+                        except ValueError as e:
+                            raise SettingsError(f'error parsing env var "{env_name}"') from e
+
+                    secrets[field.alias] = secret_value
+                else:
+                    warnings.warn(
+                        f'attempted to load secret file "{path}" but found a {path_type(path)} instead.',
+                        stacklevel=4,
+                    )
+        return secrets
+
+    def __repr__(self) -> str:
+        return f'SecretsSettingsSource(secrets_dir={self.secrets_dir!r})'
+
+
+def read_env_file(
+    file_path: StrPath, *, encoding: str = None, case_sensitive: bool = False
+) -> Dict[str, Optional[str]]:
+    try:
+        from dotenv import dotenv_values
+    except ImportError as e:
+        raise ImportError('python-dotenv is not installed, run `pip install pydantic[dotenv]`') from e
+
+    file_vars: Dict[str, Optional[str]] = dotenv_values(file_path, encoding=encoding or 'utf8')
+    if not case_sensitive:
+        return {k.lower(): v for k, v in file_vars.items()}
+    else:
+        return file_vars
+
+
+def find_case_path(dir_path: Path, file_name: str, case_sensitive: bool) -> Optional[Path]:
+    """
+    Find a file within path's directory matching filename, optionally ignoring case.
+    """
+    for f in dir_path.iterdir():
+        if f.name == file_name:
+            return f
+        elif not case_sensitive and f.name.lower() == file_name.lower():
+            return f
+    return None
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/error_wrappers.py b/.venv/lib/python3.12/site-packages/pydantic/v1/error_wrappers.py
new file mode 100644
index 00000000..bc7f2631
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/error_wrappers.py
@@ -0,0 +1,161 @@
+import json
+from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Sequence, Tuple, Type, Union
+
+from pydantic.v1.json import pydantic_encoder
+from pydantic.v1.utils import Representation
+
+if TYPE_CHECKING:
+    from typing_extensions import TypedDict
+
+    from pydantic.v1.config import BaseConfig
+    from pydantic.v1.types import ModelOrDc
+    from pydantic.v1.typing import ReprArgs
+
+    Loc = Tuple[Union[int, str], ...]
+
+    class _ErrorDictRequired(TypedDict):
+        loc: Loc
+        msg: str
+        type: str
+
+    class ErrorDict(_ErrorDictRequired, total=False):
+        ctx: Dict[str, Any]
+
+
+__all__ = 'ErrorWrapper', 'ValidationError'
+
+
+class ErrorWrapper(Representation):
+    __slots__ = 'exc', '_loc'
+
+    def __init__(self, exc: Exception, loc: Union[str, 'Loc']) -> None:
+        self.exc = exc
+        self._loc = loc
+
+    def loc_tuple(self) -> 'Loc':
+        if isinstance(self._loc, tuple):
+            return self._loc
+        else:
+            return (self._loc,)
+
+    def __repr_args__(self) -> 'ReprArgs':
+        return [('exc', self.exc), ('loc', self.loc_tuple())]
+
+
+# ErrorList is something like Union[List[Union[List[ErrorWrapper], ErrorWrapper]], ErrorWrapper]
+# but recursive, therefore just use:
+ErrorList = Union[Sequence[Any], ErrorWrapper]
+
+
+class ValidationError(Representation, ValueError):
+    __slots__ = 'raw_errors', 'model', '_error_cache'
+
+    def __init__(self, errors: Sequence[ErrorList], model: 'ModelOrDc') -> None:
+        self.raw_errors = errors
+        self.model = model
+        self._error_cache: Optional[List['ErrorDict']] = None
+
+    def errors(self) -> List['ErrorDict']:
+        if self._error_cache is None:
+            try:
+                config = self.model.__config__  # type: ignore
+            except AttributeError:
+                config = self.model.__pydantic_model__.__config__  # type: ignore
+            self._error_cache = list(flatten_errors(self.raw_errors, config))
+        return self._error_cache
+
+    def json(self, *, indent: Union[None, int, str] = 2) -> str:
+        return json.dumps(self.errors(), indent=indent, default=pydantic_encoder)
+
+    def __str__(self) -> str:
+        errors = self.errors()
+        no_errors = len(errors)
+        return (
+            f'{no_errors} validation error{"" if no_errors == 1 else "s"} for {self.model.__name__}\n'
+            f'{display_errors(errors)}'
+        )
+
+    def __repr_args__(self) -> 'ReprArgs':
+        return [('model', self.model.__name__), ('errors', self.errors())]
+
+
+def display_errors(errors: List['ErrorDict']) -> str:
+    return '\n'.join(f'{_display_error_loc(e)}\n  {e["msg"]} ({_display_error_type_and_ctx(e)})' for e in errors)
+
+
+def _display_error_loc(error: 'ErrorDict') -> str:
+    return ' -> '.join(str(e) for e in error['loc'])
+
+
+def _display_error_type_and_ctx(error: 'ErrorDict') -> str:
+    t = 'type=' + error['type']
+    ctx = error.get('ctx')
+    if ctx:
+        return t + ''.join(f'; {k}={v}' for k, v in ctx.items())
+    else:
+        return t
+
+
+def flatten_errors(
+    errors: Sequence[Any], config: Type['BaseConfig'], loc: Optional['Loc'] = None
+) -> Generator['ErrorDict', None, None]:
+    for error in errors:
+        if isinstance(error, ErrorWrapper):
+            if loc:
+                error_loc = loc + error.loc_tuple()
+            else:
+                error_loc = error.loc_tuple()
+
+            if isinstance(error.exc, ValidationError):
+                yield from flatten_errors(error.exc.raw_errors, config, error_loc)
+            else:
+                yield error_dict(error.exc, config, error_loc)
+        elif isinstance(error, list):
+            yield from flatten_errors(error, config, loc=loc)
+        else:
+            raise RuntimeError(f'Unknown error object: {error}')
+
+
+def error_dict(exc: Exception, config: Type['BaseConfig'], loc: 'Loc') -> 'ErrorDict':
+    type_ = get_exc_type(exc.__class__)
+    msg_template = config.error_msg_templates.get(type_) or getattr(exc, 'msg_template', None)
+    ctx = exc.__dict__
+    if msg_template:
+        msg = msg_template.format(**ctx)
+    else:
+        msg = str(exc)
+
+    d: 'ErrorDict' = {'loc': loc, 'msg': msg, 'type': type_}
+
+    if ctx:
+        d['ctx'] = ctx
+
+    return d
+
+
+_EXC_TYPE_CACHE: Dict[Type[Exception], str] = {}
+
+
+def get_exc_type(cls: Type[Exception]) -> str:
+    # slightly more efficient than using lru_cache since we don't need to worry about the cache filling up
+    try:
+        return _EXC_TYPE_CACHE[cls]
+    except KeyError:
+        r = _get_exc_type(cls)
+        _EXC_TYPE_CACHE[cls] = r
+        return r
+
+
+def _get_exc_type(cls: Type[Exception]) -> str:
+    if issubclass(cls, AssertionError):
+        return 'assertion_error'
+
+    base_name = 'type_error' if issubclass(cls, TypeError) else 'value_error'
+    if cls in (TypeError, ValueError):
+        # just TypeError or ValueError, no extra code
+        return base_name
+
+    # if it's not a TypeError or ValueError, we just take the lowercase of the exception name
+    # no chaining or snake case logic, use "code" for more complex error types.
+    code = getattr(cls, 'code', None) or cls.__name__.replace('Error', '').lower()
+    return base_name + '.' + code
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/errors.py b/.venv/lib/python3.12/site-packages/pydantic/v1/errors.py
new file mode 100644
index 00000000..6e864425
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/errors.py
@@ -0,0 +1,646 @@
+from decimal import Decimal
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable, Sequence, Set, Tuple, Type, Union
+
+from pydantic.v1.typing import display_as_type
+
+if TYPE_CHECKING:
+    from pydantic.v1.typing import DictStrAny
+
+# explicitly state exports to avoid "from pydantic.v1.errors import *" also importing Decimal, Path etc.
+__all__ = (
+    'PydanticTypeError',
+    'PydanticValueError',
+    'ConfigError',
+    'MissingError',
+    'ExtraError',
+    'NoneIsNotAllowedError',
+    'NoneIsAllowedError',
+    'WrongConstantError',
+    'NotNoneError',
+    'BoolError',
+    'BytesError',
+    'DictError',
+    'EmailError',
+    'UrlError',
+    'UrlSchemeError',
+    'UrlSchemePermittedError',
+    'UrlUserInfoError',
+    'UrlHostError',
+    'UrlHostTldError',
+    'UrlPortError',
+    'UrlExtraError',
+    'EnumError',
+    'IntEnumError',
+    'EnumMemberError',
+    'IntegerError',
+    'FloatError',
+    'PathError',
+    'PathNotExistsError',
+    'PathNotAFileError',
+    'PathNotADirectoryError',
+    'PyObjectError',
+    'SequenceError',
+    'ListError',
+    'SetError',
+    'FrozenSetError',
+    'TupleError',
+    'TupleLengthError',
+    'ListMinLengthError',
+    'ListMaxLengthError',
+    'ListUniqueItemsError',
+    'SetMinLengthError',
+    'SetMaxLengthError',
+    'FrozenSetMinLengthError',
+    'FrozenSetMaxLengthError',
+    'AnyStrMinLengthError',
+    'AnyStrMaxLengthError',
+    'StrError',
+    'StrRegexError',
+    'NumberNotGtError',
+    'NumberNotGeError',
+    'NumberNotLtError',
+    'NumberNotLeError',
+    'NumberNotMultipleError',
+    'DecimalError',
+    'DecimalIsNotFiniteError',
+    'DecimalMaxDigitsError',
+    'DecimalMaxPlacesError',
+    'DecimalWholeDigitsError',
+    'DateTimeError',
+    'DateError',
+    'DateNotInThePastError',
+    'DateNotInTheFutureError',
+    'TimeError',
+    'DurationError',
+    'HashableError',
+    'UUIDError',
+    'UUIDVersionError',
+    'ArbitraryTypeError',
+    'ClassError',
+    'SubclassError',
+    'JsonError',
+    'JsonTypeError',
+    'PatternError',
+    'DataclassTypeError',
+    'CallableError',
+    'IPvAnyAddressError',
+    'IPvAnyInterfaceError',
+    'IPvAnyNetworkError',
+    'IPv4AddressError',
+    'IPv6AddressError',
+    'IPv4NetworkError',
+    'IPv6NetworkError',
+    'IPv4InterfaceError',
+    'IPv6InterfaceError',
+    'ColorError',
+    'StrictBoolError',
+    'NotDigitError',
+    'LuhnValidationError',
+    'InvalidLengthForBrand',
+    'InvalidByteSize',
+    'InvalidByteSizeUnit',
+    'MissingDiscriminator',
+    'InvalidDiscriminator',
+)
+
+
+def cls_kwargs(cls: Type['PydanticErrorMixin'], ctx: 'DictStrAny') -> 'PydanticErrorMixin':
+    """
+    For built-in exceptions like ValueError or TypeError, we need to implement
+    __reduce__ to override the default behaviour (instead of __getstate__/__setstate__)
+    By default pickle protocol 2 calls `cls.__new__(cls, *args)`.
+    Since we only use kwargs, we need a little constructor to change that.
+    Note: the callable can't be a lambda as pickle looks in the namespace to find it
+    """
+    return cls(**ctx)
+
+
+class PydanticErrorMixin:
+    code: str
+    msg_template: str
+
+    def __init__(self, **ctx: Any) -> None:
+        self.__dict__ = ctx
+
+    def __str__(self) -> str:
+        return self.msg_template.format(**self.__dict__)
+
+    def __reduce__(self) -> Tuple[Callable[..., 'PydanticErrorMixin'], Tuple[Type['PydanticErrorMixin'], 'DictStrAny']]:
+        return cls_kwargs, (self.__class__, self.__dict__)
+
+
+class PydanticTypeError(PydanticErrorMixin, TypeError):
+    pass
+
+
+class PydanticValueError(PydanticErrorMixin, ValueError):
+    pass
+
+
+class ConfigError(RuntimeError):
+    pass
+
+
+class MissingError(PydanticValueError):
+    msg_template = 'field required'
+
+
+class ExtraError(PydanticValueError):
+    msg_template = 'extra fields not permitted'
+
+
+class NoneIsNotAllowedError(PydanticTypeError):
+    code = 'none.not_allowed'
+    msg_template = 'none is not an allowed value'
+
+
+class NoneIsAllowedError(PydanticTypeError):
+    code = 'none.allowed'
+    msg_template = 'value is not none'
+
+
+class WrongConstantError(PydanticValueError):
+    code = 'const'
+
+    def __str__(self) -> str:
+        permitted = ', '.join(repr(v) for v in self.permitted)  # type: ignore
+        return f'unexpected value; permitted: {permitted}'
+
+
+class NotNoneError(PydanticTypeError):
+    code = 'not_none'
+    msg_template = 'value is not None'
+
+
+class BoolError(PydanticTypeError):
+    msg_template = 'value could not be parsed to a boolean'
+
+
+class BytesError(PydanticTypeError):
+    msg_template = 'byte type expected'
+
+
+class DictError(PydanticTypeError):
+    msg_template = 'value is not a valid dict'
+
+
+class EmailError(PydanticValueError):
+    msg_template = 'value is not a valid email address'
+
+
+class UrlError(PydanticValueError):
+    code = 'url'
+
+
+class UrlSchemeError(UrlError):
+    code = 'url.scheme'
+    msg_template = 'invalid or missing URL scheme'
+
+
+class UrlSchemePermittedError(UrlError):
+    code = 'url.scheme'
+    msg_template = 'URL scheme not permitted'
+
+    def __init__(self, allowed_schemes: Set[str]):
+        super().__init__(allowed_schemes=allowed_schemes)
+
+
+class UrlUserInfoError(UrlError):
+    code = 'url.userinfo'
+    msg_template = 'userinfo required in URL but missing'
+
+
+class UrlHostError(UrlError):
+    code = 'url.host'
+    msg_template = 'URL host invalid'
+
+
+class UrlHostTldError(UrlError):
+    code = 'url.host'
+    msg_template = 'URL host invalid, top level domain required'
+
+
+class UrlPortError(UrlError):
+    code = 'url.port'
+    msg_template = 'URL port invalid, port cannot exceed 65535'
+
+
+class UrlExtraError(UrlError):
+    code = 'url.extra'
+    msg_template = 'URL invalid, extra characters found after valid URL: {extra!r}'
+
+
+class EnumMemberError(PydanticTypeError):
+    code = 'enum'
+
+    def __str__(self) -> str:
+        permitted = ', '.join(repr(v.value) for v in self.enum_values)  # type: ignore
+        return f'value is not a valid enumeration member; permitted: {permitted}'
+
+
+class IntegerError(PydanticTypeError):
+    msg_template = 'value is not a valid integer'
+
+
+class FloatError(PydanticTypeError):
+    msg_template = 'value is not a valid float'
+
+
+class PathError(PydanticTypeError):
+    msg_template = 'value is not a valid path'
+
+
+class _PathValueError(PydanticValueError):
+    def __init__(self, *, path: Path) -> None:
+        super().__init__(path=str(path))
+
+
+class PathNotExistsError(_PathValueError):
+    code = 'path.not_exists'
+    msg_template = 'file or directory at path "{path}" does not exist'
+
+
+class PathNotAFileError(_PathValueError):
+    code = 'path.not_a_file'
+    msg_template = 'path "{path}" does not point to a file'
+
+
+class PathNotADirectoryError(_PathValueError):
+    code = 'path.not_a_directory'
+    msg_template = 'path "{path}" does not point to a directory'
+
+
+class PyObjectError(PydanticTypeError):
+    msg_template = 'ensure this value contains valid import path or valid callable: {error_message}'
+
+
+class SequenceError(PydanticTypeError):
+    msg_template = 'value is not a valid sequence'
+
+
+class IterableError(PydanticTypeError):
+    msg_template = 'value is not a valid iterable'
+
+
+class ListError(PydanticTypeError):
+    msg_template = 'value is not a valid list'
+
+
+class SetError(PydanticTypeError):
+    msg_template = 'value is not a valid set'
+
+
+class FrozenSetError(PydanticTypeError):
+    msg_template = 'value is not a valid frozenset'
+
+
+class DequeError(PydanticTypeError):
+    msg_template = 'value is not a valid deque'
+
+
+class TupleError(PydanticTypeError):
+    msg_template = 'value is not a valid tuple'
+
+
+class TupleLengthError(PydanticValueError):
+    code = 'tuple.length'
+    msg_template = 'wrong tuple length {actual_length}, expected {expected_length}'
+
+    def __init__(self, *, actual_length: int, expected_length: int) -> None:
+        super().__init__(actual_length=actual_length, expected_length=expected_length)
+
+
+class ListMinLengthError(PydanticValueError):
+    code = 'list.min_items'
+    msg_template = 'ensure this value has at least {limit_value} items'
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class ListMaxLengthError(PydanticValueError):
+    code = 'list.max_items'
+    msg_template = 'ensure this value has at most {limit_value} items'
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class ListUniqueItemsError(PydanticValueError):
+    code = 'list.unique_items'
+    msg_template = 'the list has duplicated items'
+
+
+class SetMinLengthError(PydanticValueError):
+    code = 'set.min_items'
+    msg_template = 'ensure this value has at least {limit_value} items'
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class SetMaxLengthError(PydanticValueError):
+    code = 'set.max_items'
+    msg_template = 'ensure this value has at most {limit_value} items'
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class FrozenSetMinLengthError(PydanticValueError):
+    code = 'frozenset.min_items'
+    msg_template = 'ensure this value has at least {limit_value} items'
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class FrozenSetMaxLengthError(PydanticValueError):
+    code = 'frozenset.max_items'
+    msg_template = 'ensure this value has at most {limit_value} items'
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class AnyStrMinLengthError(PydanticValueError):
+    code = 'any_str.min_length'
+    msg_template = 'ensure this value has at least {limit_value} characters'
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class AnyStrMaxLengthError(PydanticValueError):
+    code = 'any_str.max_length'
+    msg_template = 'ensure this value has at most {limit_value} characters'
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class StrError(PydanticTypeError):
+    msg_template = 'str type expected'
+
+
+class StrRegexError(PydanticValueError):
+    code = 'str.regex'
+    msg_template = 'string does not match regex "{pattern}"'
+
+    def __init__(self, *, pattern: str) -> None:
+        super().__init__(pattern=pattern)
+
+
+class _NumberBoundError(PydanticValueError):
+    def __init__(self, *, limit_value: Union[int, float, Decimal]) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class NumberNotGtError(_NumberBoundError):
+    code = 'number.not_gt'
+    msg_template = 'ensure this value is greater than {limit_value}'
+
+
+class NumberNotGeError(_NumberBoundError):
+    code = 'number.not_ge'
+    msg_template = 'ensure this value is greater than or equal to {limit_value}'
+
+
+class NumberNotLtError(_NumberBoundError):
+    code = 'number.not_lt'
+    msg_template = 'ensure this value is less than {limit_value}'
+
+
+class NumberNotLeError(_NumberBoundError):
+    code = 'number.not_le'
+    msg_template = 'ensure this value is less than or equal to {limit_value}'
+
+
+class NumberNotFiniteError(PydanticValueError):
+    code = 'number.not_finite_number'
+    msg_template = 'ensure this value is a finite number'
+
+
+class NumberNotMultipleError(PydanticValueError):
+    code = 'number.not_multiple'
+    msg_template = 'ensure this value is a multiple of {multiple_of}'
+
+    def __init__(self, *, multiple_of: Union[int, float, Decimal]) -> None:
+        super().__init__(multiple_of=multiple_of)
+
+
+class DecimalError(PydanticTypeError):
+    msg_template = 'value is not a valid decimal'
+
+
+class DecimalIsNotFiniteError(PydanticValueError):
+    code = 'decimal.not_finite'
+    msg_template = 'value is not a valid decimal'
+
+
+class DecimalMaxDigitsError(PydanticValueError):
+    code = 'decimal.max_digits'
+    msg_template = 'ensure that there are no more than {max_digits} digits in total'
+
+    def __init__(self, *, max_digits: int) -> None:
+        super().__init__(max_digits=max_digits)
+
+
+class DecimalMaxPlacesError(PydanticValueError):
+    code = 'decimal.max_places'
+    msg_template = 'ensure that there are no more than {decimal_places} decimal places'
+
+    def __init__(self, *, decimal_places: int) -> None:
+        super().__init__(decimal_places=decimal_places)
+
+
+class DecimalWholeDigitsError(PydanticValueError):
+    code = 'decimal.whole_digits'
+    msg_template = 'ensure that there are no more than {whole_digits} digits before the decimal point'
+
+    def __init__(self, *, whole_digits: int) -> None:
+        super().__init__(whole_digits=whole_digits)
+
+
+class DateTimeError(PydanticValueError):
+    msg_template = 'invalid datetime format'
+
+
+class DateError(PydanticValueError):
+    msg_template = 'invalid date format'
+
+
+class DateNotInThePastError(PydanticValueError):
+    code = 'date.not_in_the_past'
+    msg_template = 'date is not in the past'
+
+
+class DateNotInTheFutureError(PydanticValueError):
+    code = 'date.not_in_the_future'
+    msg_template = 'date is not in the future'
+
+
+class TimeError(PydanticValueError):
+    msg_template = 'invalid time format'
+
+
+class DurationError(PydanticValueError):
+    msg_template = 'invalid duration format'
+
+
+class HashableError(PydanticTypeError):
+    msg_template = 'value is not a valid hashable'
+
+
+class UUIDError(PydanticTypeError):
+    msg_template = 'value is not a valid uuid'
+
+
+class UUIDVersionError(PydanticValueError):
+    code = 'uuid.version'
+    msg_template = 'uuid version {required_version} expected'
+
+    def __init__(self, *, required_version: int) -> None:
+        super().__init__(required_version=required_version)
+
+
+class ArbitraryTypeError(PydanticTypeError):
+    code = 'arbitrary_type'
+    msg_template = 'instance of {expected_arbitrary_type} expected'
+
+    def __init__(self, *, expected_arbitrary_type: Type[Any]) -> None:
+        super().__init__(expected_arbitrary_type=display_as_type(expected_arbitrary_type))
+
+
+class ClassError(PydanticTypeError):
+    code = 'class'
+    msg_template = 'a class is expected'
+
+
+class SubclassError(PydanticTypeError):
+    code = 'subclass'
+    msg_template = 'subclass of {expected_class} expected'
+
+    def __init__(self, *, expected_class: Type[Any]) -> None:
+        super().__init__(expected_class=display_as_type(expected_class))
+
+
+class JsonError(PydanticValueError):
+    msg_template = 'Invalid JSON'
+
+
+class JsonTypeError(PydanticTypeError):
+    code = 'json'
+    msg_template = 'JSON object must be str, bytes or bytearray'
+
+
+class PatternError(PydanticValueError):
+    code = 'regex_pattern'
+    msg_template = 'Invalid regular expression'
+
+
+class DataclassTypeError(PydanticTypeError):
+    code = 'dataclass'
+    msg_template = 'instance of {class_name}, tuple or dict expected'
+
+
+class CallableError(PydanticTypeError):
+    msg_template = '{value} is not callable'
+
+
+class EnumError(PydanticTypeError):
+    code = 'enum_instance'
+    msg_template = '{value} is not a valid Enum instance'
+
+
+class IntEnumError(PydanticTypeError):
+    code = 'int_enum_instance'
+    msg_template = '{value} is not a valid IntEnum instance'
+
+
+class IPvAnyAddressError(PydanticValueError):
+    msg_template = 'value is not a valid IPv4 or IPv6 address'
+
+
+class IPvAnyInterfaceError(PydanticValueError):
+    msg_template = 'value is not a valid IPv4 or IPv6 interface'
+
+
+class IPvAnyNetworkError(PydanticValueError):
+    msg_template = 'value is not a valid IPv4 or IPv6 network'
+
+
+class IPv4AddressError(PydanticValueError):
+    msg_template = 'value is not a valid IPv4 address'
+
+
+class IPv6AddressError(PydanticValueError):
+    msg_template = 'value is not a valid IPv6 address'
+
+
+class IPv4NetworkError(PydanticValueError):
+    msg_template = 'value is not a valid IPv4 network'
+
+
+class IPv6NetworkError(PydanticValueError):
+    msg_template = 'value is not a valid IPv6 network'
+
+
+class IPv4InterfaceError(PydanticValueError):
+    msg_template = 'value is not a valid IPv4 interface'
+
+
+class IPv6InterfaceError(PydanticValueError):
+    msg_template = 'value is not a valid IPv6 interface'
+
+
+class ColorError(PydanticValueError):
+    msg_template = 'value is not a valid color: {reason}'
+
+
+class StrictBoolError(PydanticValueError):
+    msg_template = 'value is not a valid boolean'
+
+
+class NotDigitError(PydanticValueError):
+    code = 'payment_card_number.digits'
+    msg_template = 'card number is not all digits'
+
+
+class LuhnValidationError(PydanticValueError):
+    code = 'payment_card_number.luhn_check'
+    msg_template = 'card number is not luhn valid'
+
+
+class InvalidLengthForBrand(PydanticValueError):
+    code = 'payment_card_number.invalid_length_for_brand'
+    msg_template = 'Length for a {brand} card must be {required_length}'
+
+
+class InvalidByteSize(PydanticValueError):
+    msg_template = 'could not parse value and unit from byte string'
+
+
+class InvalidByteSizeUnit(PydanticValueError):
+    msg_template = 'could not interpret byte unit: {unit}'
+
+
+class MissingDiscriminator(PydanticValueError):
+    code = 'discriminated_union.missing_discriminator'
+    msg_template = 'Discriminator {discriminator_key!r} is missing in value'
+
+
+class InvalidDiscriminator(PydanticValueError):
+    code = 'discriminated_union.invalid_discriminator'
+    msg_template = (
+        'No match for discriminator {discriminator_key!r} and value {discriminator_value!r} '
+        '(allowed values: {allowed_values})'
+    )
+
+    def __init__(self, *, discriminator_key: str, discriminator_value: Any, allowed_values: Sequence[Any]) -> None:
+        super().__init__(
+            discriminator_key=discriminator_key,
+            discriminator_value=discriminator_value,
+            allowed_values=', '.join(map(repr, allowed_values)),
+        )
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/fields.py b/.venv/lib/python3.12/site-packages/pydantic/v1/fields.py
new file mode 100644
index 00000000..002b60cd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/fields.py
@@ -0,0 +1,1253 @@
+import copy
+import re
+from collections import Counter as CollectionCounter, defaultdict, deque
+from collections.abc import Callable, Hashable as CollectionsHashable, Iterable as CollectionsIterable
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Counter,
+    DefaultDict,
+    Deque,
+    Dict,
+    ForwardRef,
+    FrozenSet,
+    Generator,
+    Iterable,
+    Iterator,
+    List,
+    Mapping,
+    Optional,
+    Pattern,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+
+from typing_extensions import Annotated, Final
+
+from pydantic.v1 import errors as errors_
+from pydantic.v1.class_validators import Validator, make_generic_validator, prep_validators
+from pydantic.v1.error_wrappers import ErrorWrapper
+from pydantic.v1.errors import ConfigError, InvalidDiscriminator, MissingDiscriminator, NoneIsNotAllowedError
+from pydantic.v1.types import Json, JsonWrapper
+from pydantic.v1.typing import (
+    NoArgAnyCallable,
+    convert_generics,
+    display_as_type,
+    get_args,
+    get_origin,
+    is_finalvar,
+    is_literal_type,
+    is_new_type,
+    is_none_type,
+    is_typeddict,
+    is_typeddict_special,
+    is_union,
+    new_type_supertype,
+)
+from pydantic.v1.utils import (
+    PyObjectStr,
+    Representation,
+    ValueItems,
+    get_discriminator_alias_and_values,
+    get_unique_discriminator_alias,
+    lenient_isinstance,
+    lenient_issubclass,
+    sequence_like,
+    smart_deepcopy,
+)
+from pydantic.v1.validators import constant_validator, dict_validator, find_validators, validate_json
+
+Required: Any = Ellipsis
+
+T = TypeVar('T')
+
+
+class UndefinedType:
+    def __repr__(self) -> str:
+        return 'PydanticUndefined'
+
+    def __copy__(self: T) -> T:
+        return self
+
+    def __reduce__(self) -> str:
+        return 'Undefined'
+
+    def __deepcopy__(self: T, _: Any) -> T:
+        return self
+
+
+Undefined = UndefinedType()
+
+if TYPE_CHECKING:
+    from pydantic.v1.class_validators import ValidatorsList
+    from pydantic.v1.config import BaseConfig
+    from pydantic.v1.error_wrappers import ErrorList
+    from pydantic.v1.types import ModelOrDc
+    from pydantic.v1.typing import AbstractSetIntStr, MappingIntStrAny, ReprArgs
+
+    ValidateReturn = Tuple[Optional[Any], Optional[ErrorList]]
+    LocStr = Union[Tuple[Union[int, str], ...], str]
+    BoolUndefined = Union[bool, UndefinedType]
+
+
+class FieldInfo(Representation):
+    """
+    Captures extra information about a field.
+    """
+
+    __slots__ = (
+        'default',
+        'default_factory',
+        'alias',
+        'alias_priority',
+        'title',
+        'description',
+        'exclude',
+        'include',
+        'const',
+        'gt',
+        'ge',
+        'lt',
+        'le',
+        'multiple_of',
+        'allow_inf_nan',
+        'max_digits',
+        'decimal_places',
+        'min_items',
+        'max_items',
+        'unique_items',
+        'min_length',
+        'max_length',
+        'allow_mutation',
+        'repr',
+        'regex',
+        'discriminator',
+        'extra',
+    )
+
+    # field constraints with the default value, it's also used in update_from_config below
+    __field_constraints__ = {
+        'min_length': None,
+        'max_length': None,
+        'regex': None,
+        'gt': None,
+        'lt': None,
+        'ge': None,
+        'le': None,
+        'multiple_of': None,
+        'allow_inf_nan': None,
+        'max_digits': None,
+        'decimal_places': None,
+        'min_items': None,
+        'max_items': None,
+        'unique_items': None,
+        'allow_mutation': True,
+    }
+
+    def __init__(self, default: Any = Undefined, **kwargs: Any) -> None:
+        self.default = default
+        self.default_factory = kwargs.pop('default_factory', None)
+        self.alias = kwargs.pop('alias', None)
+        self.alias_priority = kwargs.pop('alias_priority', 2 if self.alias is not None else None)
+        self.title = kwargs.pop('title', None)
+        self.description = kwargs.pop('description', None)
+        self.exclude = kwargs.pop('exclude', None)
+        self.include = kwargs.pop('include', None)
+        self.const = kwargs.pop('const', None)
+        self.gt = kwargs.pop('gt', None)
+        self.ge = kwargs.pop('ge', None)
+        self.lt = kwargs.pop('lt', None)
+        self.le = kwargs.pop('le', None)
+        self.multiple_of = kwargs.pop('multiple_of', None)
+        self.allow_inf_nan = kwargs.pop('allow_inf_nan', None)
+        self.max_digits = kwargs.pop('max_digits', None)
+        self.decimal_places = kwargs.pop('decimal_places', None)
+        self.min_items = kwargs.pop('min_items', None)
+        self.max_items = kwargs.pop('max_items', None)
+        self.unique_items = kwargs.pop('unique_items', None)
+        self.min_length = kwargs.pop('min_length', None)
+        self.max_length = kwargs.pop('max_length', None)
+        self.allow_mutation = kwargs.pop('allow_mutation', True)
+        self.regex = kwargs.pop('regex', None)
+        self.discriminator = kwargs.pop('discriminator', None)
+        self.repr = kwargs.pop('repr', True)
+        self.extra = kwargs
+
+    def __repr_args__(self) -> 'ReprArgs':
+        field_defaults_to_hide: Dict[str, Any] = {
+            'repr': True,
+            **self.__field_constraints__,
+        }
+
+        attrs = ((s, getattr(self, s)) for s in self.__slots__)
+        return [(a, v) for a, v in attrs if v != field_defaults_to_hide.get(a, None)]
+
+    def get_constraints(self) -> Set[str]:
+        """
+        Gets the constraints set on the field by comparing the constraint value with its default value
+
+        :return: the constraints set on field_info
+        """
+        return {attr for attr, default in self.__field_constraints__.items() if getattr(self, attr) != default}
+
+    def update_from_config(self, from_config: Dict[str, Any]) -> None:
+        """
+        Update this FieldInfo based on a dict from get_field_info, only fields which have not been set are dated.
+        """
+        for attr_name, value in from_config.items():
+            try:
+                current_value = getattr(self, attr_name)
+            except AttributeError:
+                # attr_name is not an attribute of FieldInfo, it should therefore be added to extra
+                # (except if extra already has this value!)
+                self.extra.setdefault(attr_name, value)
+            else:
+                if current_value is self.__field_constraints__.get(attr_name, None):
+                    setattr(self, attr_name, value)
+                elif attr_name == 'exclude':
+                    self.exclude = ValueItems.merge(value, current_value)
+                elif attr_name == 'include':
+                    self.include = ValueItems.merge(value, current_value, intersect=True)
+
+    def _validate(self) -> None:
+        if self.default is not Undefined and self.default_factory is not None:
+            raise ValueError('cannot specify both default and default_factory')
+
+
+def Field(
+    default: Any = Undefined,
+    *,
+    default_factory: Optional[NoArgAnyCallable] = None,
+    alias: Optional[str] = None,
+    title: Optional[str] = None,
+    description: Optional[str] = None,
+    exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny', Any]] = None,
+    include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny', Any]] = None,
+    const: Optional[bool] = None,
+    gt: Optional[float] = None,
+    ge: Optional[float] = None,
+    lt: Optional[float] = None,
+    le: Optional[float] = None,
+    multiple_of: Optional[float] = None,
+    allow_inf_nan: Optional[bool] = None,
+    max_digits: Optional[int] = None,
+    decimal_places: Optional[int] = None,
+    min_items: Optional[int] = None,
+    max_items: Optional[int] = None,
+    unique_items: Optional[bool] = None,
+    min_length: Optional[int] = None,
+    max_length: Optional[int] = None,
+    allow_mutation: bool = True,
+    regex: Optional[str] = None,
+    discriminator: Optional[str] = None,
+    repr: bool = True,
+    **extra: Any,
+) -> Any:
+    """
+    Used to provide extra information about a field, either for the model schema or complex validation. Some arguments
+    apply only to number fields (``int``, ``float``, ``Decimal``) and some apply only to ``str``.
+
+    :param default: since this is replacing the field’s default, its first argument is used
+      to set the default, use ellipsis (``...``) to indicate the field is required
+    :param default_factory: callable that will be called when a default value is needed for this field
+      If both `default` and `default_factory` are set, an error is raised.
+    :param alias: the public name of the field
+    :param title: can be any string, used in the schema
+    :param description: can be any string, used in the schema
+    :param exclude: exclude this field while dumping.
+      Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method.
+    :param include: include this field while dumping.
+      Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method.
+    :param const: this field is required and *must* take it's default value
+    :param gt: only applies to numbers, requires the field to be "greater than". The schema
+      will have an ``exclusiveMinimum`` validation keyword
+    :param ge: only applies to numbers, requires the field to be "greater than or equal to". The
+      schema will have a ``minimum`` validation keyword
+    :param lt: only applies to numbers, requires the field to be "less than". The schema
+      will have an ``exclusiveMaximum`` validation keyword
+    :param le: only applies to numbers, requires the field to be "less than or equal to". The
+      schema will have a ``maximum`` validation keyword
+    :param multiple_of: only applies to numbers, requires the field to be "a multiple of". The
+      schema will have a ``multipleOf`` validation keyword
+    :param allow_inf_nan: only applies to numbers, allows the field to be NaN or infinity (+inf or -inf),
+        which is a valid Python float. Default True, set to False for compatibility with JSON.
+    :param max_digits: only applies to Decimals, requires the field to have a maximum number
+      of digits within the decimal. It does not include a zero before the decimal point or trailing decimal zeroes.
+    :param decimal_places: only applies to Decimals, requires the field to have at most a number of decimal places
+      allowed. It does not include trailing decimal zeroes.
+    :param min_items: only applies to lists, requires the field to have a minimum number of
+      elements. The schema will have a ``minItems`` validation keyword
+    :param max_items: only applies to lists, requires the field to have a maximum number of
+      elements. The schema will have a ``maxItems`` validation keyword
+    :param unique_items: only applies to lists, requires the field not to have duplicated
+      elements. The schema will have a ``uniqueItems`` validation keyword
+    :param min_length: only applies to strings, requires the field to have a minimum length. The
+      schema will have a ``minLength`` validation keyword
+    :param max_length: only applies to strings, requires the field to have a maximum length. The
+      schema will have a ``maxLength`` validation keyword
+    :param allow_mutation: a boolean which defaults to True. When False, the field raises a TypeError if the field is
+      assigned on an instance.  The BaseModel Config must set validate_assignment to True
+    :param regex: only applies to strings, requires the field match against a regular expression
+      pattern string. The schema will have a ``pattern`` validation keyword
+    :param discriminator: only useful with a (discriminated a.k.a. tagged) `Union` of sub models with a common field.
+      The `discriminator` is the name of this common field to shorten validation and improve generated schema
+    :param repr: show this field in the representation
+    :param **extra: any additional keyword arguments will be added as is to the schema
+    """
+    field_info = FieldInfo(
+        default,
+        default_factory=default_factory,
+        alias=alias,
+        title=title,
+        description=description,
+        exclude=exclude,
+        include=include,
+        const=const,
+        gt=gt,
+        ge=ge,
+        lt=lt,
+        le=le,
+        multiple_of=multiple_of,
+        allow_inf_nan=allow_inf_nan,
+        max_digits=max_digits,
+        decimal_places=decimal_places,
+        min_items=min_items,
+        max_items=max_items,
+        unique_items=unique_items,
+        min_length=min_length,
+        max_length=max_length,
+        allow_mutation=allow_mutation,
+        regex=regex,
+        discriminator=discriminator,
+        repr=repr,
+        **extra,
+    )
+    field_info._validate()
+    return field_info
+
+
+# used to be an enum but changed to int's for small performance improvement as less access overhead
+SHAPE_SINGLETON = 1
+SHAPE_LIST = 2
+SHAPE_SET = 3
+SHAPE_MAPPING = 4
+SHAPE_TUPLE = 5
+SHAPE_TUPLE_ELLIPSIS = 6
+SHAPE_SEQUENCE = 7
+SHAPE_FROZENSET = 8
+SHAPE_ITERABLE = 9
+SHAPE_GENERIC = 10
+SHAPE_DEQUE = 11
+SHAPE_DICT = 12
+SHAPE_DEFAULTDICT = 13
+SHAPE_COUNTER = 14
+SHAPE_NAME_LOOKUP = {
+    SHAPE_LIST: 'List[{}]',
+    SHAPE_SET: 'Set[{}]',
+    SHAPE_TUPLE_ELLIPSIS: 'Tuple[{}, ...]',
+    SHAPE_SEQUENCE: 'Sequence[{}]',
+    SHAPE_FROZENSET: 'FrozenSet[{}]',
+    SHAPE_ITERABLE: 'Iterable[{}]',
+    SHAPE_DEQUE: 'Deque[{}]',
+    SHAPE_DICT: 'Dict[{}]',
+    SHAPE_DEFAULTDICT: 'DefaultDict[{}]',
+    SHAPE_COUNTER: 'Counter[{}]',
+}
+
+MAPPING_LIKE_SHAPES: Set[int] = {SHAPE_DEFAULTDICT, SHAPE_DICT, SHAPE_MAPPING, SHAPE_COUNTER}
+
+
+class ModelField(Representation):
+    __slots__ = (
+        'type_',
+        'outer_type_',
+        'annotation',
+        'sub_fields',
+        'sub_fields_mapping',
+        'key_field',
+        'validators',
+        'pre_validators',
+        'post_validators',
+        'default',
+        'default_factory',
+        'required',
+        'final',
+        'model_config',
+        'name',
+        'alias',
+        'has_alias',
+        'field_info',
+        'discriminator_key',
+        'discriminator_alias',
+        'validate_always',
+        'allow_none',
+        'shape',
+        'class_validators',
+        'parse_json',
+    )
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        type_: Type[Any],
+        class_validators: Optional[Dict[str, Validator]],
+        model_config: Type['BaseConfig'],
+        default: Any = None,
+        default_factory: Optional[NoArgAnyCallable] = None,
+        required: 'BoolUndefined' = Undefined,
+        final: bool = False,
+        alias: Optional[str] = None,
+        field_info: Optional[FieldInfo] = None,
+    ) -> None:
+        self.name: str = name
+        self.has_alias: bool = alias is not None
+        self.alias: str = alias if alias is not None else name
+        self.annotation = type_
+        self.type_: Any = convert_generics(type_)
+        self.outer_type_: Any = type_
+        self.class_validators = class_validators or {}
+        self.default: Any = default
+        self.default_factory: Optional[NoArgAnyCallable] = default_factory
+        self.required: 'BoolUndefined' = required
+        self.final: bool = final
+        self.model_config = model_config
+        self.field_info: FieldInfo = field_info or FieldInfo(default)
+        self.discriminator_key: Optional[str] = self.field_info.discriminator
+        self.discriminator_alias: Optional[str] = self.discriminator_key
+
+        self.allow_none: bool = False
+        self.validate_always: bool = False
+        self.sub_fields: Optional[List[ModelField]] = None
+        self.sub_fields_mapping: Optional[Dict[str, 'ModelField']] = None  # used for discriminated union
+        self.key_field: Optional[ModelField] = None
+        self.validators: 'ValidatorsList' = []
+        self.pre_validators: Optional['ValidatorsList'] = None
+        self.post_validators: Optional['ValidatorsList'] = None
+        self.parse_json: bool = False
+        self.shape: int = SHAPE_SINGLETON
+        self.model_config.prepare_field(self)
+        self.prepare()
+
+    def get_default(self) -> Any:
+        return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
+
+    @staticmethod
+    def _get_field_info(
+        field_name: str, annotation: Any, value: Any, config: Type['BaseConfig']
+    ) -> Tuple[FieldInfo, Any]:
+        """
+        Get a FieldInfo from a root typing.Annotated annotation, value, or config default.
+
+        The FieldInfo may be set in typing.Annotated or the value, but not both. If neither contain
+        a FieldInfo, a new one will be created using the config.
+
+        :param field_name: name of the field for use in error messages
+        :param annotation: a type hint such as `str` or `Annotated[str, Field(..., min_length=5)]`
+        :param value: the field's assigned value
+        :param config: the model's config object
+        :return: the FieldInfo contained in the `annotation`, the value, or a new one from the config.
+        """
+        field_info_from_config = config.get_field_info(field_name)
+
+        field_info = None
+        if get_origin(annotation) is Annotated:
+            field_infos = [arg for arg in get_args(annotation)[1:] if isinstance(arg, FieldInfo)]
+            if len(field_infos) > 1:
+                raise ValueError(f'cannot specify multiple `Annotated` `Field`s for {field_name!r}')
+            field_info = next(iter(field_infos), None)
+            if field_info is not None:
+                field_info = copy.copy(field_info)
+                field_info.update_from_config(field_info_from_config)
+                if field_info.default not in (Undefined, Required):
+                    raise ValueError(f'`Field` default cannot be set in `Annotated` for {field_name!r}')
+                if value is not Undefined and value is not Required:
+                    # check also `Required` because of `validate_arguments` that sets `...` as default value
+                    field_info.default = value
+
+        if isinstance(value, FieldInfo):
+            if field_info is not None:
+                raise ValueError(f'cannot specify `Annotated` and value `Field`s together for {field_name!r}')
+            field_info = value
+            field_info.update_from_config(field_info_from_config)
+        elif field_info is None:
+            field_info = FieldInfo(value, **field_info_from_config)
+        value = None if field_info.default_factory is not None else field_info.default
+        field_info._validate()
+        return field_info, value
+
+    @classmethod
+    def infer(
+        cls,
+        *,
+        name: str,
+        value: Any,
+        annotation: Any,
+        class_validators: Optional[Dict[str, Validator]],
+        config: Type['BaseConfig'],
+    ) -> 'ModelField':
+        from pydantic.v1.schema import get_annotation_from_field_info
+
+        field_info, value = cls._get_field_info(name, annotation, value, config)
+        required: 'BoolUndefined' = Undefined
+        if value is Required:
+            required = True
+            value = None
+        elif value is not Undefined:
+            required = False
+        annotation = get_annotation_from_field_info(annotation, field_info, name, config.validate_assignment)
+
+        return cls(
+            name=name,
+            type_=annotation,
+            alias=field_info.alias,
+            class_validators=class_validators,
+            default=value,
+            default_factory=field_info.default_factory,
+            required=required,
+            model_config=config,
+            field_info=field_info,
+        )
+
+    def set_config(self, config: Type['BaseConfig']) -> None:
+        self.model_config = config
+        info_from_config = config.get_field_info(self.name)
+        config.prepare_field(self)
+        new_alias = info_from_config.get('alias')
+        new_alias_priority = info_from_config.get('alias_priority') or 0
+        if new_alias and new_alias_priority >= (self.field_info.alias_priority or 0):
+            self.field_info.alias = new_alias
+            self.field_info.alias_priority = new_alias_priority
+            self.alias = new_alias
+        new_exclude = info_from_config.get('exclude')
+        if new_exclude is not None:
+            self.field_info.exclude = ValueItems.merge(self.field_info.exclude, new_exclude)
+        new_include = info_from_config.get('include')
+        if new_include is not None:
+            self.field_info.include = ValueItems.merge(self.field_info.include, new_include, intersect=True)
+
+    @property
+    def alt_alias(self) -> bool:
+        return self.name != self.alias
+
+    def prepare(self) -> None:
+        """
+        Prepare the field but inspecting self.default, self.type_ etc.
+
+        Note: this method is **not** idempotent (because _type_analysis is not idempotent),
+        e.g. calling it it multiple times may modify the field and configure it incorrectly.
+        """
+        self._set_default_and_type()
+        if self.type_.__class__ is ForwardRef or self.type_.__class__ is DeferredType:
+            # self.type_ is currently a ForwardRef and there's nothing we can do now,
+            # user will need to call model.update_forward_refs()
+            return
+
+        self._type_analysis()
+        if self.required is Undefined:
+            self.required = True
+        if self.default is Undefined and self.default_factory is None:
+            self.default = None
+        self.populate_validators()
+
+    def _set_default_and_type(self) -> None:
+        """
+        Set the default value, infer the type if needed and check if `None` value is valid.
+        """
+        if self.default_factory is not None:
+            if self.type_ is Undefined:
+                raise errors_.ConfigError(
+                    f'you need to set the type of field {self.name!r} when using `default_factory`'
+                )
+            return
+
+        default_value = self.get_default()
+
+        if default_value is not None and self.type_ is Undefined:
+            self.type_ = default_value.__class__
+            self.outer_type_ = self.type_
+            self.annotation = self.type_
+
+        if self.type_ is Undefined:
+            raise errors_.ConfigError(f'unable to infer type for attribute "{self.name}"')
+
+        if self.required is False and default_value is None:
+            self.allow_none = True
+
+    def _type_analysis(self) -> None:  # noqa: C901 (ignore complexity)
+        # typing interface is horrible, we have to do some ugly checks
+        if lenient_issubclass(self.type_, JsonWrapper):
+            self.type_ = self.type_.inner_type
+            self.parse_json = True
+        elif lenient_issubclass(self.type_, Json):
+            self.type_ = Any
+            self.parse_json = True
+        elif isinstance(self.type_, TypeVar):
+            if self.type_.__bound__:
+                self.type_ = self.type_.__bound__
+            elif self.type_.__constraints__:
+                self.type_ = Union[self.type_.__constraints__]
+            else:
+                self.type_ = Any
+        elif is_new_type(self.type_):
+            self.type_ = new_type_supertype(self.type_)
+
+        if self.type_ is Any or self.type_ is object:
+            if self.required is Undefined:
+                self.required = False
+            self.allow_none = True
+            return
+        elif self.type_ is Pattern or self.type_ is re.Pattern:
+            # python 3.7 only, Pattern is a typing object but without sub fields
+            return
+        elif is_literal_type(self.type_):
+            return
+        elif is_typeddict(self.type_):
+            return
+
+        if is_finalvar(self.type_):
+            self.final = True
+
+            if self.type_ is Final:
+                self.type_ = Any
+            else:
+                self.type_ = get_args(self.type_)[0]
+
+            self._type_analysis()
+            return
+
+        origin = get_origin(self.type_)
+
+        if origin is Annotated or is_typeddict_special(origin):
+            self.type_ = get_args(self.type_)[0]
+            self._type_analysis()
+            return
+
+        if self.discriminator_key is not None and not is_union(origin):
+            raise TypeError('`discriminator` can only be used with `Union` type with more than one variant')
+
+        # add extra check for `collections.abc.Hashable` for python 3.10+ where origin is not `None`
+        if origin is None or origin is CollectionsHashable:
+            # field is not "typing" object eg. Union, Dict, List etc.
+            # allow None for virtual superclasses of NoneType, e.g. Hashable
+            if isinstance(self.type_, type) and isinstance(None, self.type_):
+                self.allow_none = True
+            return
+        elif origin is Callable:
+            return
+        elif is_union(origin):
+            types_ = []
+            for type_ in get_args(self.type_):
+                if is_none_type(type_) or type_ is Any or type_ is object:
+                    if self.required is Undefined:
+                        self.required = False
+                    self.allow_none = True
+                if is_none_type(type_):
+                    continue
+                types_.append(type_)
+
+            if len(types_) == 1:
+                # Optional[]
+                self.type_ = types_[0]
+                # this is the one case where the "outer type" isn't just the original type
+                self.outer_type_ = self.type_
+                # re-run to correctly interpret the new self.type_
+                self._type_analysis()
+            else:
+                self.sub_fields = [self._create_sub_type(t, f'{self.name}_{display_as_type(t)}') for t in types_]
+
+                if self.discriminator_key is not None:
+                    self.prepare_discriminated_union_sub_fields()
+            return
+        elif issubclass(origin, Tuple):  # type: ignore
+            # origin == Tuple without item type
+            args = get_args(self.type_)
+            if not args:  # plain tuple
+                self.type_ = Any
+                self.shape = SHAPE_TUPLE_ELLIPSIS
+            elif len(args) == 2 and args[1] is Ellipsis:  # e.g. Tuple[int, ...]
+                self.type_ = args[0]
+                self.shape = SHAPE_TUPLE_ELLIPSIS
+                self.sub_fields = [self._create_sub_type(args[0], f'{self.name}_0')]
+            elif args == ((),):  # Tuple[()] means empty tuple
+                self.shape = SHAPE_TUPLE
+                self.type_ = Any
+                self.sub_fields = []
+            else:
+                self.shape = SHAPE_TUPLE
+                self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(args)]
+            return
+        elif issubclass(origin, List):
+            # Create self validators
+            get_validators = getattr(self.type_, '__get_validators__', None)
+            if get_validators:
+                self.class_validators.update(
+                    {f'list_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
+                )
+
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_LIST
+        elif issubclass(origin, Set):
+            # Create self validators
+            get_validators = getattr(self.type_, '__get_validators__', None)
+            if get_validators:
+                self.class_validators.update(
+                    {f'set_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
+                )
+
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_SET
+        elif issubclass(origin, FrozenSet):
+            # Create self validators
+            get_validators = getattr(self.type_, '__get_validators__', None)
+            if get_validators:
+                self.class_validators.update(
+                    {f'frozenset_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
+                )
+
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_FROZENSET
+        elif issubclass(origin, Deque):
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_DEQUE
+        elif issubclass(origin, Sequence):
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_SEQUENCE
+        # priority to most common mapping: dict
+        elif origin is dict or origin is Dict:
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
+            self.type_ = get_args(self.type_)[1]
+            self.shape = SHAPE_DICT
+        elif issubclass(origin, DefaultDict):
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
+            self.type_ = get_args(self.type_)[1]
+            self.shape = SHAPE_DEFAULTDICT
+        elif issubclass(origin, Counter):
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
+            self.type_ = int
+            self.shape = SHAPE_COUNTER
+        elif issubclass(origin, Mapping):
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True)
+            self.type_ = get_args(self.type_)[1]
+            self.shape = SHAPE_MAPPING
+        # Equality check as almost everything inherits form Iterable, including str
+        # check for Iterable and CollectionsIterable, as it could receive one even when declared with the other
+        elif origin in {Iterable, CollectionsIterable}:
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_ITERABLE
+            self.sub_fields = [self._create_sub_type(self.type_, f'{self.name}_type')]
+        elif issubclass(origin, Type):  # type: ignore
+            return
+        elif hasattr(origin, '__get_validators__') or self.model_config.arbitrary_types_allowed:
+            # Is a Pydantic-compatible generic that handles itself
+            # or we have arbitrary_types_allowed = True
+            self.shape = SHAPE_GENERIC
+            self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(get_args(self.type_))]
+            self.type_ = origin
+            return
+        else:
+            raise TypeError(f'Fields of type "{origin}" are not supported.')
+
+        # type_ has been refined eg. as the type of a List and sub_fields needs to be populated
+        self.sub_fields = [self._create_sub_type(self.type_, '_' + self.name)]
+
+    def prepare_discriminated_union_sub_fields(self) -> None:
+        """
+        Prepare the mapping <discriminator key> -> <ModelField> and update `sub_fields`
+        Note that this process can be aborted if a `ForwardRef` is encountered
+        """
+        assert self.discriminator_key is not None
+
+        if self.type_.__class__ is DeferredType:
+            return
+
+        assert self.sub_fields is not None
+        sub_fields_mapping: Dict[str, 'ModelField'] = {}
+        all_aliases: Set[str] = set()
+
+        for sub_field in self.sub_fields:
+            t = sub_field.type_
+            if t.__class__ is ForwardRef:
+                # Stopping everything...will need to call `update_forward_refs`
+                return
+
+            alias, discriminator_values = get_discriminator_alias_and_values(t, self.discriminator_key)
+            all_aliases.add(alias)
+            for discriminator_value in discriminator_values:
+                sub_fields_mapping[discriminator_value] = sub_field
+
+        self.sub_fields_mapping = sub_fields_mapping
+        self.discriminator_alias = get_unique_discriminator_alias(all_aliases, self.discriminator_key)
+
+    def _create_sub_type(self, type_: Type[Any], name: str, *, for_keys: bool = False) -> 'ModelField':
+        if for_keys:
+            class_validators = None
+        else:
+            # validators for sub items should not have `each_item` as we want to check only the first sublevel
+            class_validators = {
+                k: Validator(
+                    func=v.func,
+                    pre=v.pre,
+                    each_item=False,
+                    always=v.always,
+                    check_fields=v.check_fields,
+                    skip_on_failure=v.skip_on_failure,
+                )
+                for k, v in self.class_validators.items()
+                if v.each_item
+            }
+
+        field_info, _ = self._get_field_info(name, type_, None, self.model_config)
+
+        return self.__class__(
+            type_=type_,
+            name=name,
+            class_validators=class_validators,
+            model_config=self.model_config,
+            field_info=field_info,
+        )
+
+    def populate_validators(self) -> None:
+        """
+        Prepare self.pre_validators, self.validators, and self.post_validators based on self.type_'s  __get_validators__
+        and class validators. This method should be idempotent, e.g. it should be safe to call multiple times
+        without mis-configuring the field.
+        """
+        self.validate_always = getattr(self.type_, 'validate_always', False) or any(
+            v.always for v in self.class_validators.values()
+        )
+
+        class_validators_ = self.class_validators.values()
+        if not self.sub_fields or self.shape == SHAPE_GENERIC:
+            get_validators = getattr(self.type_, '__get_validators__', None)
+            v_funcs = (
+                *[v.func for v in class_validators_ if v.each_item and v.pre],
+                *(get_validators() if get_validators else list(find_validators(self.type_, self.model_config))),
+                *[v.func for v in class_validators_ if v.each_item and not v.pre],
+            )
+            self.validators = prep_validators(v_funcs)
+
+        self.pre_validators = []
+        self.post_validators = []
+
+        if self.field_info and self.field_info.const:
+            self.post_validators.append(make_generic_validator(constant_validator))
+
+        if class_validators_:
+            self.pre_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and v.pre)
+            self.post_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and not v.pre)
+
+        if self.parse_json:
+            self.pre_validators.append(make_generic_validator(validate_json))
+
+        self.pre_validators = self.pre_validators or None
+        self.post_validators = self.post_validators or None
+
+    def validate(
+        self, v: Any, values: Dict[str, Any], *, loc: 'LocStr', cls: Optional['ModelOrDc'] = None
+    ) -> 'ValidateReturn':
+        assert self.type_.__class__ is not DeferredType
+
+        if self.type_.__class__ is ForwardRef:
+            assert cls is not None
+            raise ConfigError(
+                f'field "{self.name}" not yet prepared so type is still a ForwardRef, '
+                f'you might need to call {cls.__name__}.update_forward_refs().'
+            )
+
+        errors: Optional['ErrorList']
+        if self.pre_validators:
+            v, errors = self._apply_validators(v, values, loc, cls, self.pre_validators)
+            if errors:
+                return v, errors
+
+        if v is None:
+            if is_none_type(self.type_):
+                # keep validating
+                pass
+            elif self.allow_none:
+                if self.post_validators:
+                    return self._apply_validators(v, values, loc, cls, self.post_validators)
+                else:
+                    return None, None
+            else:
+                return v, ErrorWrapper(NoneIsNotAllowedError(), loc)
+
+        if self.shape == SHAPE_SINGLETON:
+            v, errors = self._validate_singleton(v, values, loc, cls)
+        elif self.shape in MAPPING_LIKE_SHAPES:
+            v, errors = self._validate_mapping_like(v, values, loc, cls)
+        elif self.shape == SHAPE_TUPLE:
+            v, errors = self._validate_tuple(v, values, loc, cls)
+        elif self.shape == SHAPE_ITERABLE:
+            v, errors = self._validate_iterable(v, values, loc, cls)
+        elif self.shape == SHAPE_GENERIC:
+            v, errors = self._apply_validators(v, values, loc, cls, self.validators)
+        else:
+            #  sequence, list, set, generator, tuple with ellipsis, frozen set
+            v, errors = self._validate_sequence_like(v, values, loc, cls)
+
+        if not errors and self.post_validators:
+            v, errors = self._apply_validators(v, values, loc, cls, self.post_validators)
+        return v, errors
+
+    def _validate_sequence_like(  # noqa: C901 (ignore complexity)
+        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
+    ) -> 'ValidateReturn':
+        """
+        Validate sequence-like containers: lists, tuples, sets and generators
+        Note that large if-else blocks are necessary to enable Cython
+        optimization, which is why we disable the complexity check above.
+        """
+        if not sequence_like(v):
+            e: errors_.PydanticTypeError
+            if self.shape == SHAPE_LIST:
+                e = errors_.ListError()
+            elif self.shape in (SHAPE_TUPLE, SHAPE_TUPLE_ELLIPSIS):
+                e = errors_.TupleError()
+            elif self.shape == SHAPE_SET:
+                e = errors_.SetError()
+            elif self.shape == SHAPE_FROZENSET:
+                e = errors_.FrozenSetError()
+            else:
+                e = errors_.SequenceError()
+            return v, ErrorWrapper(e, loc)
+
+        loc = loc if isinstance(loc, tuple) else (loc,)
+        result = []
+        errors: List[ErrorList] = []
+        for i, v_ in enumerate(v):
+            v_loc = *loc, i
+            r, ee = self._validate_singleton(v_, values, v_loc, cls)
+            if ee:
+                errors.append(ee)
+            else:
+                result.append(r)
+
+        if errors:
+            return v, errors
+
+        converted: Union[List[Any], Set[Any], FrozenSet[Any], Tuple[Any, ...], Iterator[Any], Deque[Any]] = result
+
+        if self.shape == SHAPE_SET:
+            converted = set(result)
+        elif self.shape == SHAPE_FROZENSET:
+            converted = frozenset(result)
+        elif self.shape == SHAPE_TUPLE_ELLIPSIS:
+            converted = tuple(result)
+        elif self.shape == SHAPE_DEQUE:
+            converted = deque(result, maxlen=getattr(v, 'maxlen', None))
+        elif self.shape == SHAPE_SEQUENCE:
+            if isinstance(v, tuple):
+                converted = tuple(result)
+            elif isinstance(v, set):
+                converted = set(result)
+            elif isinstance(v, Generator):
+                converted = iter(result)
+            elif isinstance(v, deque):
+                converted = deque(result, maxlen=getattr(v, 'maxlen', None))
+        return converted, None
+
+    def _validate_iterable(
+        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
+    ) -> 'ValidateReturn':
+        """
+        Validate Iterables.
+
+        This intentionally doesn't validate values to allow infinite generators.
+        """
+
+        try:
+            iterable = iter(v)
+        except TypeError:
+            return v, ErrorWrapper(errors_.IterableError(), loc)
+        return iterable, None
+
+    def _validate_tuple(
+        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
+    ) -> 'ValidateReturn':
+        e: Optional[Exception] = None
+        if not sequence_like(v):
+            e = errors_.TupleError()
+        else:
+            actual_length, expected_length = len(v), len(self.sub_fields)  # type: ignore
+            if actual_length != expected_length:
+                e = errors_.TupleLengthError(actual_length=actual_length, expected_length=expected_length)
+
+        if e:
+            return v, ErrorWrapper(e, loc)
+
+        loc = loc if isinstance(loc, tuple) else (loc,)
+        result = []
+        errors: List[ErrorList] = []
+        for i, (v_, field) in enumerate(zip(v, self.sub_fields)):  # type: ignore
+            v_loc = *loc, i
+            r, ee = field.validate(v_, values, loc=v_loc, cls=cls)
+            if ee:
+                errors.append(ee)
+            else:
+                result.append(r)
+
+        if errors:
+            return v, errors
+        else:
+            return tuple(result), None
+
+    def _validate_mapping_like(
+        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
+    ) -> 'ValidateReturn':
+        try:
+            v_iter = dict_validator(v)
+        except TypeError as exc:
+            return v, ErrorWrapper(exc, loc)
+
+        loc = loc if isinstance(loc, tuple) else (loc,)
+        result, errors = {}, []
+        for k, v_ in v_iter.items():
+            v_loc = *loc, '__key__'
+            key_result, key_errors = self.key_field.validate(k, values, loc=v_loc, cls=cls)  # type: ignore
+            if key_errors:
+                errors.append(key_errors)
+                continue
+
+            v_loc = *loc, k
+            value_result, value_errors = self._validate_singleton(v_, values, v_loc, cls)
+            if value_errors:
+                errors.append(value_errors)
+                continue
+
+            result[key_result] = value_result
+        if errors:
+            return v, errors
+        elif self.shape == SHAPE_DICT:
+            return result, None
+        elif self.shape == SHAPE_DEFAULTDICT:
+            return defaultdict(self.type_, result), None
+        elif self.shape == SHAPE_COUNTER:
+            return CollectionCounter(result), None
+        else:
+            return self._get_mapping_value(v, result), None
+
+    def _get_mapping_value(self, original: T, converted: Dict[Any, Any]) -> Union[T, Dict[Any, Any]]:
+        """
+        When type is `Mapping[KT, KV]` (or another unsupported mapping), we try to avoid
+        coercing to `dict` unwillingly.
+        """
+        original_cls = original.__class__
+
+        if original_cls == dict or original_cls == Dict:
+            return converted
+        elif original_cls in {defaultdict, DefaultDict}:
+            return defaultdict(self.type_, converted)
+        else:
+            try:
+                # Counter, OrderedDict, UserDict, ...
+                return original_cls(converted)  # type: ignore
+            except TypeError:
+                raise RuntimeError(f'Could not convert dictionary to {original_cls.__name__!r}') from None
+
+    def _validate_singleton(
+        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
+    ) -> 'ValidateReturn':
+        if self.sub_fields:
+            if self.discriminator_key is not None:
+                return self._validate_discriminated_union(v, values, loc, cls)
+
+            errors = []
+
+            if self.model_config.smart_union and is_union(get_origin(self.type_)):
+                # 1st pass: check if the value is an exact instance of one of the Union types
+                # (e.g. to avoid coercing a bool into an int)
+                for field in self.sub_fields:
+                    if v.__class__ is field.outer_type_:
+                        return v, None
+
+                # 2nd pass: check if the value is an instance of any subclass of the Union types
+                for field in self.sub_fields:
+                    # This whole logic will be improved later on to support more complex `isinstance` checks
+                    # It will probably be done once a strict mode is added and be something like:
+                    # ```
+                    #     value, error = field.validate(v, values, strict=True)
+                    #     if error is None:
+                    #         return value, None
+                    # ```
+                    try:
+                        if isinstance(v, field.outer_type_):
+                            return v, None
+                    except TypeError:
+                        # compound type
+                        if lenient_isinstance(v, get_origin(field.outer_type_)):
+                            value, error = field.validate(v, values, loc=loc, cls=cls)
+                            if not error:
+                                return value, None
+
+            # 1st pass by default or 3rd pass with `smart_union` enabled:
+            # check if the value can be coerced into one of the Union types
+            for field in self.sub_fields:
+                value, error = field.validate(v, values, loc=loc, cls=cls)
+                if error:
+                    errors.append(error)
+                else:
+                    return value, None
+            return v, errors
+        else:
+            return self._apply_validators(v, values, loc, cls, self.validators)
+
+    def _validate_discriminated_union(
+        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc']
+    ) -> 'ValidateReturn':
+        assert self.discriminator_key is not None
+        assert self.discriminator_alias is not None
+
+        try:
+            try:
+                discriminator_value = v[self.discriminator_alias]
+            except KeyError:
+                if self.model_config.allow_population_by_field_name:
+                    discriminator_value = v[self.discriminator_key]
+                else:
+                    raise
+        except KeyError:
+            return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc)
+        except TypeError:
+            try:
+                # BaseModel or dataclass
+                discriminator_value = getattr(v, self.discriminator_key)
+            except (AttributeError, TypeError):
+                return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc)
+
+        if self.sub_fields_mapping is None:
+            assert cls is not None
+            raise ConfigError(
+                f'field "{self.name}" not yet prepared so type is still a ForwardRef, '
+                f'you might need to call {cls.__name__}.update_forward_refs().'
+            )
+
+        try:
+            sub_field = self.sub_fields_mapping[discriminator_value]
+        except (KeyError, TypeError):
+            # KeyError: `discriminator_value` is not in the dictionary.
+            # TypeError: `discriminator_value` is unhashable.
+            assert self.sub_fields_mapping is not None
+            return v, ErrorWrapper(
+                InvalidDiscriminator(
+                    discriminator_key=self.discriminator_key,
+                    discriminator_value=discriminator_value,
+                    allowed_values=list(self.sub_fields_mapping),
+                ),
+                loc,
+            )
+        else:
+            if not isinstance(loc, tuple):
+                loc = (loc,)
+            return sub_field.validate(v, values, loc=(*loc, display_as_type(sub_field.type_)), cls=cls)
+
+    def _apply_validators(
+        self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'], validators: 'ValidatorsList'
+    ) -> 'ValidateReturn':
+        for validator in validators:
+            try:
+                v = validator(cls, v, values, self, self.model_config)
+            except (ValueError, TypeError, AssertionError) as exc:
+                return v, ErrorWrapper(exc, loc)
+        return v, None
+
+    def is_complex(self) -> bool:
+        """
+        Whether the field is "complex" eg. env variables should be parsed as JSON.
+        """
+        from pydantic.v1.main import BaseModel
+
+        return (
+            self.shape != SHAPE_SINGLETON
+            or hasattr(self.type_, '__pydantic_model__')
+            or lenient_issubclass(self.type_, (BaseModel, list, set, frozenset, dict))
+        )
+
+    def _type_display(self) -> PyObjectStr:
+        t = display_as_type(self.type_)
+
+        if self.shape in MAPPING_LIKE_SHAPES:
+            t = f'Mapping[{display_as_type(self.key_field.type_)}, {t}]'  # type: ignore
+        elif self.shape == SHAPE_TUPLE:
+            t = 'Tuple[{}]'.format(', '.join(display_as_type(f.type_) for f in self.sub_fields))  # type: ignore
+        elif self.shape == SHAPE_GENERIC:
+            assert self.sub_fields
+            t = '{}[{}]'.format(
+                display_as_type(self.type_), ', '.join(display_as_type(f.type_) for f in self.sub_fields)
+            )
+        elif self.shape != SHAPE_SINGLETON:
+            t = SHAPE_NAME_LOOKUP[self.shape].format(t)
+
+        if self.allow_none and (self.shape != SHAPE_SINGLETON or not self.sub_fields):
+            t = f'Optional[{t}]'
+        return PyObjectStr(t)
+
+    def __repr_args__(self) -> 'ReprArgs':
+        args = [('name', self.name), ('type', self._type_display()), ('required', self.required)]
+
+        if not self.required:
+            if self.default_factory is not None:
+                args.append(('default_factory', f'<function {self.default_factory.__name__}>'))
+            else:
+                args.append(('default', self.default))
+
+        if self.alt_alias:
+            args.append(('alias', self.alias))
+        return args
+
+
+class ModelPrivateAttr(Representation):
+    __slots__ = ('default', 'default_factory')
+
+    def __init__(self, default: Any = Undefined, *, default_factory: Optional[NoArgAnyCallable] = None) -> None:
+        self.default = default
+        self.default_factory = default_factory
+
+    def get_default(self) -> Any:
+        return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, self.__class__) and (self.default, self.default_factory) == (
+            other.default,
+            other.default_factory,
+        )
+
+
+def PrivateAttr(
+    default: Any = Undefined,
+    *,
+    default_factory: Optional[NoArgAnyCallable] = None,
+) -> Any:
+    """
+    Indicates that attribute is only used internally and never mixed with regular fields.
+
+    Types or values of private attrs are not checked by pydantic and it's up to you to keep them relevant.
+
+    Private attrs are stored in model __slots__.
+
+    :param default: the attribute’s default value
+    :param default_factory: callable that will be called when a default value is needed for this attribute
+      If both `default` and `default_factory` are set, an error is raised.
+    """
+    if default is not Undefined and default_factory is not None:
+        raise ValueError('cannot specify both default and default_factory')
+
+    return ModelPrivateAttr(
+        default,
+        default_factory=default_factory,
+    )
+
+
+class DeferredType:
+    """
+    Used to postpone field preparation, while creating recursive generic models.
+    """
+
+
+def is_finalvar_with_default_val(type_: Type[Any], val: Any) -> bool:
+    return is_finalvar(type_) and val is not Undefined and not isinstance(val, FieldInfo)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/generics.py b/.venv/lib/python3.12/site-packages/pydantic/v1/generics.py
new file mode 100644
index 00000000..9a69f2b3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/generics.py
@@ -0,0 +1,400 @@
+import sys
+import types
+import typing
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    ClassVar,
+    Dict,
+    ForwardRef,
+    Generic,
+    Iterator,
+    List,
+    Mapping,
+    Optional,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+from weakref import WeakKeyDictionary, WeakValueDictionary
+
+from typing_extensions import Annotated, Literal as ExtLiteral
+
+from pydantic.v1.class_validators import gather_all_validators
+from pydantic.v1.fields import DeferredType
+from pydantic.v1.main import BaseModel, create_model
+from pydantic.v1.types import JsonWrapper
+from pydantic.v1.typing import display_as_type, get_all_type_hints, get_args, get_origin, typing_base
+from pydantic.v1.utils import all_identical, lenient_issubclass
+
+if sys.version_info >= (3, 10):
+    from typing import _UnionGenericAlias
+if sys.version_info >= (3, 8):
+    from typing import Literal
+
+GenericModelT = TypeVar('GenericModelT', bound='GenericModel')
+TypeVarType = Any  # since mypy doesn't allow the use of TypeVar as a type
+
+CacheKey = Tuple[Type[Any], Any, Tuple[Any, ...]]
+Parametrization = Mapping[TypeVarType, Type[Any]]
+
+# 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[CacheKey, Type[BaseModel]]
+    AssignedParameters = WeakKeyDictionary[Type[BaseModel], Parametrization]
+else:
+    GenericTypesCache = WeakValueDictionary
+    AssignedParameters = WeakKeyDictionary
+
+# _generic_types_cache is a Mapping from __class_getitem__ arguments to the parametrized version of generic models.
+# This ensures multiple calls of e.g. A[B] return always the same class.
+_generic_types_cache = GenericTypesCache()
+
+# _assigned_parameters is a Mapping from parametrized version of generic models to assigned types of parametrizations
+# as captured during construction of the class (not instances).
+# E.g., for generic model `Model[A, B]`, when parametrized model `Model[int, str]` is created,
+# `Model[int, str]`: {A: int, B: str}` will be stored in `_assigned_parameters`.
+# (This information is only otherwise available after creation from the class name string).
+_assigned_parameters = AssignedParameters()
+
+
+class GenericModel(BaseModel):
+    __slots__ = ()
+    __concrete__: ClassVar[bool] = False
+
+    if TYPE_CHECKING:
+        # Putting this in a TYPE_CHECKING block allows us to replace `if Generic not in cls.__bases__` with
+        # `not hasattr(cls, "__parameters__")`. This means we don't need to force non-concrete subclasses of
+        # `GenericModel` to also inherit from `Generic`, which would require changes to the use of `create_model` below.
+        __parameters__: ClassVar[Tuple[TypeVarType, ...]]
+
+    # Setting the return type as Type[Any] instead of Type[BaseModel] prevents PyCharm warnings
+    def __class_getitem__(cls: Type[GenericModelT], params: Union[Type[Any], Tuple[Type[Any], ...]]) -> Type[Any]:
+        """Instantiates a new class from a generic class `cls` and type variables `params`.
+
+        :param params: Tuple of types the class . Given a generic class
+            `Model` with 2 type variables and a concrete model `Model[str, int]`,
+            the value `(str, int)` would be passed to `params`.
+        :return: New model class inheriting from `cls` with instantiated
+            types described by `params`. If no parameters are given, `cls` is
+            returned as is.
+
+        """
+
+        def _cache_key(_params: Any) -> CacheKey:
+            args = get_args(_params)
+            # python returns a list for Callables, which is not hashable
+            if len(args) == 2 and isinstance(args[0], list):
+                args = (tuple(args[0]), args[1])
+            return cls, _params, args
+
+        cached = _generic_types_cache.get(_cache_key(params))
+        if cached is not None:
+            return cached
+        if cls.__concrete__ and Generic not in cls.__bases__:
+            raise TypeError('Cannot parameterize a concrete instantiation of a generic model')
+        if not isinstance(params, tuple):
+            params = (params,)
+        if cls is GenericModel and any(isinstance(param, TypeVar) for param in params):
+            raise TypeError('Type parameters should be placed on typing.Generic, not GenericModel')
+        if not hasattr(cls, '__parameters__'):
+            raise TypeError(f'Type {cls.__name__} must inherit from typing.Generic before being parameterized')
+
+        check_parameters_count(cls, params)
+        # Build map from generic typevars to passed params
+        typevars_map: Dict[TypeVarType, Type[Any]] = dict(zip(cls.__parameters__, params))
+        if all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map:
+            return cls  # if arguments are equal to parameters it's the same object
+
+        # Create new model with original model as parent inserting fields with DeferredType.
+        model_name = cls.__concrete_name__(params)
+        validators = gather_all_validators(cls)
+
+        type_hints = get_all_type_hints(cls).items()
+        instance_type_hints = {k: v for k, v in type_hints if get_origin(v) is not ClassVar}
+
+        fields = {k: (DeferredType(), cls.__fields__[k].field_info) for k in instance_type_hints if k in cls.__fields__}
+
+        model_module, called_globally = get_caller_frame_info()
+        created_model = cast(
+            Type[GenericModel],  # casting ensures mypy is aware of the __concrete__ and __parameters__ attributes
+            create_model(
+                model_name,
+                __module__=model_module or cls.__module__,
+                __base__=(cls,) + tuple(cls.__parameterized_bases__(typevars_map)),
+                __config__=None,
+                __validators__=validators,
+                __cls_kwargs__=None,
+                **fields,
+            ),
+        )
+
+        _assigned_parameters[created_model] = typevars_map
+
+        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 += '_'
+
+        created_model.Config = cls.Config
+
+        # Find any typevars that are still present in the model.
+        # If none are left, the model is fully "concrete", otherwise the new
+        # class is a generic class as well taking the found typevars as
+        # parameters.
+        new_params = tuple(
+            {param: None for param in iter_contained_typevars(typevars_map.values())}
+        )  # use dict as ordered set
+        created_model.__concrete__ = not new_params
+        if new_params:
+            created_model.__parameters__ = new_params
+
+        # Save created model in cache so we don't end up creating duplicate
+        # models that should be identical.
+        _generic_types_cache[_cache_key(params)] = created_model
+        if len(params) == 1:
+            _generic_types_cache[_cache_key(params[0])] = created_model
+
+        # Recursively walk class type hints and replace generic typevars
+        # with concrete types that were passed.
+        _prepare_model_fields(created_model, fields, instance_type_hints, typevars_map)
+
+        return created_model
+
+    @classmethod
+    def __concrete_name__(cls: Type[Any], params: Tuple[Type[Any], ...]) -> str:
+        """Compute class name for child classes.
+
+        :param params: Tuple of types the class . Given a generic class
+            `Model` with 2 type variables and a concrete model `Model[str, int]`,
+            the value `(str, int)` would be passed to `params`.
+        :return: String representing a the new class where `params` are
+            passed to `cls` as type variables.
+
+        This method can be overridden to achieve a custom naming scheme for GenericModels.
+        """
+        param_names = [display_as_type(param) for param in params]
+        params_component = ', '.join(param_names)
+        return f'{cls.__name__}[{params_component}]'
+
+    @classmethod
+    def __parameterized_bases__(cls, typevars_map: Parametrization) -> Iterator[Type[Any]]:
+        """
+        Returns unbound bases of cls parameterised to given type variables
+
+        :param typevars_map: Dictionary of type applications for binding subclasses.
+            Given a generic class `Model` with 2 type variables [S, T]
+            and a concrete model `Model[str, int]`,
+            the value `{S: str, T: int}` would be passed to `typevars_map`.
+        :return: an iterator of generic sub classes, parameterised by `typevars_map`
+            and other assigned parameters of `cls`
+
+        e.g.:
+        ```
+        class A(GenericModel, Generic[T]):
+            ...
+
+        class B(A[V], Generic[V]):
+            ...
+
+        assert A[int] in B.__parameterized_bases__({V: int})
+        ```
+        """
+
+        def build_base_model(
+            base_model: Type[GenericModel], mapped_types: Parametrization
+        ) -> Iterator[Type[GenericModel]]:
+            base_parameters = tuple(mapped_types[param] for param in base_model.__parameters__)
+            parameterized_base = base_model.__class_getitem__(base_parameters)
+            if parameterized_base is base_model or parameterized_base is cls:
+                # Avoid duplication in MRO
+                return
+            yield parameterized_base
+
+        for base_model in cls.__bases__:
+            if not issubclass(base_model, GenericModel):
+                # not a class that can be meaningfully parameterized
+                continue
+            elif not getattr(base_model, '__parameters__', None):
+                # base_model is "GenericModel"  (and has no __parameters__)
+                # or
+                # base_model is already concrete, and will be included transitively via cls.
+                continue
+            elif cls in _assigned_parameters:
+                if base_model in _assigned_parameters:
+                    # cls is partially parameterised but not from base_model
+                    # e.g. cls = B[S], base_model = A[S]
+                    # B[S][int] should subclass A[int],  (and will be transitively via B[int])
+                    # but it's not viable to consistently subclass types with arbitrary construction
+                    # So don't attempt to include A[S][int]
+                    continue
+                else:  # base_model not in _assigned_parameters:
+                    # cls is partially parameterized, base_model is original generic
+                    # e.g.  cls = B[str, T], base_model = B[S, T]
+                    # Need to determine the mapping for the base_model parameters
+                    mapped_types: Parametrization = {
+                        key: typevars_map.get(value, value) for key, value in _assigned_parameters[cls].items()
+                    }
+                    yield from build_base_model(base_model, mapped_types)
+            else:
+                # cls is base generic, so base_class has a distinct base
+                # can construct the Parameterised base model using typevars_map directly
+                yield from build_base_model(base_model, typevars_map)
+
+
+def replace_types(type_: Any, type_map: Mapping[Any, Any]) -> Any:
+    """Return type with all occurrences of `type_map` keys recursively replaced with their values.
+
+    :param type_: Any type, class or generic alias
+    :param type_map: Mapping from `TypeVar` instance to concrete types.
+    :return: New type representing the basic structure of `type_` with all
+        `typevar_map` keys recursively replaced.
+
+    >>> 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_)
+    origin_type = get_origin(type_)
+
+    if origin_type is Annotated:
+        annotated_type, *annotations = type_args
+        return Annotated[replace_types(annotated_type, type_map), tuple(annotations)]
+
+    if (origin_type is ExtLiteral) or (sys.version_info >= (3, 8) and origin_type is Literal):
+        return type_map.get(type_, type_)
+    # Having type args is a good indicator that this is a typing module
+    # class instantiation 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_base)
+            and not isinstance(origin_type, 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
+        # 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:  # noqa: E721
+            return _UnionGenericAlias(origin_type, resolved_type_args)
+        return origin_type[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 lenient_issubclass(type_, GenericModel) and not type_.__concrete__:
+        type_args = type_.__parameters__
+        resolved_type_args = tuple(replace_types(t, type_map) for t in type_args)
+        if all_identical(type_args, 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, list)):
+        resolved_list = list(replace_types(element, type_map) for element in type_)
+        if all_identical(type_, resolved_list):
+            return type_
+        return resolved_list
+
+    # For JsonWrapperValue, need to handle its inner type to allow correct parsing
+    # of generic Json arguments like Json[T]
+    if not origin_type and lenient_issubclass(type_, JsonWrapper):
+        type_.inner_type = replace_types(type_.inner_type, type_map)
+        return type_
+
+    # If all else fails, we try to resolve the type directly and otherwise just
+    # return the input with no modifications.
+    new_type = type_map.get(type_, type_)
+    # Convert string to ForwardRef
+    if isinstance(new_type, str):
+        return ForwardRef(new_type)
+    else:
+        return new_type
+
+
+def check_parameters_count(cls: Type[GenericModel], parameters: Tuple[Any, ...]) -> None:
+    actual = len(parameters)
+    expected = len(cls.__parameters__)
+    if actual != expected:
+        description = 'many' if actual > expected else 'few'
+        raise TypeError(f'Too {description} parameters for {cls.__name__}; actual {actual}, expected {expected}')
+
+
+DictValues: Type[Any] = {}.values().__class__
+
+
+def iter_contained_typevars(v: Any) -> Iterator[TypeVarType]:
+    """Recursively iterate through all subtypes and type args of `v` and yield any typevars that are found."""
+    if isinstance(v, TypeVar):
+        yield v
+    elif hasattr(v, '__parameters__') and not get_origin(v) and lenient_issubclass(v, GenericModel):
+        yield from v.__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_caller_frame_info() -> Tuple[Optional[str], bool]:
+    """
+    Used inside a function to check whether it was called globally
+
+    Will only work against non-compiled code, therefore used only in pydantic.generics
+
+    :returns Tuple[module_name, called_globally]
+    """
+    try:
+        previous_caller_frame = sys._getframe(2)
+    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
+
+
+def _prepare_model_fields(
+    created_model: Type[GenericModel],
+    fields: Mapping[str, Any],
+    instance_type_hints: Mapping[str, type],
+    typevars_map: Mapping[Any, type],
+) -> None:
+    """
+    Replace DeferredType fields with concrete type hints and prepare them.
+    """
+
+    for key, field in created_model.__fields__.items():
+        if key not in fields:
+            assert field.type_.__class__ is not DeferredType
+            # https://github.com/nedbat/coveragepy/issues/198
+            continue  # pragma: no cover
+
+        assert field.type_.__class__ is DeferredType, field.type_.__class__
+
+        field_type_hint = instance_type_hints[key]
+        concrete_type = replace_types(field_type_hint, typevars_map)
+        field.type_ = concrete_type
+        field.outer_type_ = concrete_type
+        field.prepare()
+        created_model.__annotations__[key] = concrete_type
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/json.py b/.venv/lib/python3.12/site-packages/pydantic/v1/json.py
new file mode 100644
index 00000000..41d0d5fc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/json.py
@@ -0,0 +1,112 @@
+import datetime
+from collections import deque
+from decimal import Decimal
+from enum import Enum
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from pathlib import Path
+from re import Pattern
+from types import GeneratorType
+from typing import Any, Callable, Dict, Type, Union
+from uuid import UUID
+
+from pydantic.v1.color import Color
+from pydantic.v1.networks import NameEmail
+from pydantic.v1.types import SecretBytes, SecretStr
+
+__all__ = 'pydantic_encoder', 'custom_pydantic_encoder', 'timedelta_isoformat'
+
+
+def isoformat(o: Union[datetime.date, datetime.time]) -> str:
+    return o.isoformat()
+
+
+def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
+    """
+    Encodes a Decimal as int of there's no exponent, otherwise float
+
+    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
+    where a integer (but not int typed) is used. Encoding this as a float
+    results in failed round-tripping between encode and parse.
+    Our Id type is a prime example of this.
+
+    >>> decimal_encoder(Decimal("1.0"))
+    1.0
+
+    >>> decimal_encoder(Decimal("1"))
+    1
+    """
+    if dec_value.as_tuple().exponent >= 0:
+        return int(dec_value)
+    else:
+        return float(dec_value)
+
+
+ENCODERS_BY_TYPE: Dict[Type[Any], Callable[[Any], Any]] = {
+    bytes: lambda o: o.decode(),
+    Color: str,
+    datetime.date: isoformat,
+    datetime.datetime: isoformat,
+    datetime.time: isoformat,
+    datetime.timedelta: lambda td: td.total_seconds(),
+    Decimal: decimal_encoder,
+    Enum: lambda o: o.value,
+    frozenset: list,
+    deque: list,
+    GeneratorType: list,
+    IPv4Address: str,
+    IPv4Interface: str,
+    IPv4Network: str,
+    IPv6Address: str,
+    IPv6Interface: str,
+    IPv6Network: str,
+    NameEmail: str,
+    Path: str,
+    Pattern: lambda o: o.pattern,
+    SecretBytes: str,
+    SecretStr: str,
+    set: list,
+    UUID: str,
+}
+
+
+def pydantic_encoder(obj: Any) -> Any:
+    from dataclasses import asdict, is_dataclass
+
+    from pydantic.v1.main import BaseModel
+
+    if isinstance(obj, BaseModel):
+        return obj.dict()
+    elif is_dataclass(obj):
+        return asdict(obj)
+
+    # Check the class type and its superclasses for a matching encoder
+    for base in obj.__class__.__mro__[:-1]:
+        try:
+            encoder = ENCODERS_BY_TYPE[base]
+        except KeyError:
+            continue
+        return encoder(obj)
+    else:  # We have exited the for loop without finding a suitable encoder
+        raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable")
+
+
+def custom_pydantic_encoder(type_encoders: Dict[Any, Callable[[Type[Any]], Any]], obj: Any) -> Any:
+    # Check the class type and its superclasses for a matching encoder
+    for base in obj.__class__.__mro__[:-1]:
+        try:
+            encoder = type_encoders[base]
+        except KeyError:
+            continue
+
+        return encoder(obj)
+    else:  # We have exited the for loop without finding a suitable encoder
+        return pydantic_encoder(obj)
+
+
+def timedelta_isoformat(td: datetime.timedelta) -> str:
+    """
+    ISO 8601 encoding for Python timedelta object.
+    """
+    minutes, seconds = divmod(td.seconds, 60)
+    hours, minutes = divmod(minutes, 60)
+    return f'{"-" if td.days < 0 else ""}P{abs(td.days)}DT{hours:d}H{minutes:d}M{seconds:d}.{td.microseconds:06d}S'
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/main.py b/.venv/lib/python3.12/site-packages/pydantic/v1/main.py
new file mode 100644
index 00000000..68af6f55
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/main.py
@@ -0,0 +1,1107 @@
+import warnings
+from abc import ABCMeta
+from copy import deepcopy
+from enum import Enum
+from functools import partial
+from pathlib import Path
+from types import FunctionType, prepare_class, resolve_bases
+from typing import (
+    TYPE_CHECKING,
+    AbstractSet,
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    List,
+    Mapping,
+    Optional,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+    no_type_check,
+    overload,
+)
+
+from typing_extensions import dataclass_transform
+
+from pydantic.v1.class_validators import ValidatorGroup, extract_root_validators, extract_validators, inherit_validators
+from pydantic.v1.config import BaseConfig, Extra, inherit_config, prepare_config
+from pydantic.v1.error_wrappers import ErrorWrapper, ValidationError
+from pydantic.v1.errors import ConfigError, DictError, ExtraError, MissingError
+from pydantic.v1.fields import (
+    MAPPING_LIKE_SHAPES,
+    Field,
+    ModelField,
+    ModelPrivateAttr,
+    PrivateAttr,
+    Undefined,
+    is_finalvar_with_default_val,
+)
+from pydantic.v1.json import custom_pydantic_encoder, pydantic_encoder
+from pydantic.v1.parse import Protocol, load_file, load_str_bytes
+from pydantic.v1.schema import default_ref_template, model_schema
+from pydantic.v1.types import PyObject, StrBytes
+from pydantic.v1.typing import (
+    AnyCallable,
+    get_args,
+    get_origin,
+    is_classvar,
+    is_namedtuple,
+    is_union,
+    resolve_annotations,
+    update_model_forward_refs,
+)
+from pydantic.v1.utils import (
+    DUNDER_ATTRIBUTES,
+    ROOT_KEY,
+    ClassAttribute,
+    GetterDict,
+    Representation,
+    ValueItems,
+    generate_model_signature,
+    is_valid_field,
+    is_valid_private_name,
+    lenient_issubclass,
+    sequence_like,
+    smart_deepcopy,
+    unique_list,
+    validate_field_name,
+)
+
+if TYPE_CHECKING:
+    from inspect import Signature
+
+    from pydantic.v1.class_validators import ValidatorListDict
+    from pydantic.v1.types import ModelOrDc
+    from pydantic.v1.typing import (
+        AbstractSetIntStr,
+        AnyClassMethod,
+        CallableGenerator,
+        DictAny,
+        DictStrAny,
+        MappingIntStrAny,
+        ReprArgs,
+        SetStr,
+        TupleGenerator,
+    )
+
+    Model = TypeVar('Model', bound='BaseModel')
+
+__all__ = 'BaseModel', 'create_model', 'validate_model'
+
+_T = TypeVar('_T')
+
+
+def validate_custom_root_type(fields: Dict[str, ModelField]) -> None:
+    if len(fields) > 1:
+        raise ValueError(f'{ROOT_KEY} cannot be mixed with other fields')
+
+
+def generate_hash_function(frozen: bool) -> Optional[Callable[[Any], int]]:
+    def hash_function(self_: Any) -> int:
+        return hash(self_.__class__) + hash(tuple(self_.__dict__.values()))
+
+    return hash_function if frozen else None
+
+
+# If a field is of type `Callable`, its default value should be a function and cannot to ignored.
+ANNOTATED_FIELD_UNTOUCHED_TYPES: Tuple[Any, ...] = (property, type, classmethod, staticmethod)
+# When creating a `BaseModel` instance, we bypass all the methods, properties... added to the model
+UNTOUCHED_TYPES: Tuple[Any, ...] = (FunctionType,) + ANNOTATED_FIELD_UNTOUCHED_TYPES
+# Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we need to add this extra
+# (somewhat hacky) boolean to keep track of whether we've created the `BaseModel` class yet, and therefore whether it's
+# safe to refer to it. If it *hasn't* been created, we assume that the `__new__` call we're in the middle of is for
+# the `BaseModel` class, since that's defined immediately after the metaclass.
+_is_base_model_class_defined = False
+
+
+@dataclass_transform(kw_only_default=True, field_specifiers=(Field,))
+class ModelMetaclass(ABCMeta):
+    @no_type_check  # noqa C901
+    def __new__(mcs, name, bases, namespace, **kwargs):  # noqa C901
+        fields: Dict[str, ModelField] = {}
+        config = BaseConfig
+        validators: 'ValidatorListDict' = {}
+
+        pre_root_validators, post_root_validators = [], []
+        private_attributes: Dict[str, ModelPrivateAttr] = {}
+        base_private_attributes: Dict[str, ModelPrivateAttr] = {}
+        slots: SetStr = namespace.get('__slots__', ())
+        slots = {slots} if isinstance(slots, str) else set(slots)
+        class_vars: SetStr = set()
+        hash_func: Optional[Callable[[Any], int]] = None
+
+        for base in reversed(bases):
+            if _is_base_model_class_defined and issubclass(base, BaseModel) and base != BaseModel:
+                fields.update(smart_deepcopy(base.__fields__))
+                config = inherit_config(base.__config__, config)
+                validators = inherit_validators(base.__validators__, validators)
+                pre_root_validators += base.__pre_root_validators__
+                post_root_validators += base.__post_root_validators__
+                base_private_attributes.update(base.__private_attributes__)
+                class_vars.update(base.__class_vars__)
+                hash_func = base.__hash__
+
+        resolve_forward_refs = kwargs.pop('__resolve_forward_refs__', True)
+        allowed_config_kwargs: SetStr = {
+            key
+            for key in dir(config)
+            if not (key.startswith('__') and key.endswith('__'))  # skip dunder methods and attributes
+        }
+        config_kwargs = {key: kwargs.pop(key) for key in kwargs.keys() & allowed_config_kwargs}
+        config_from_namespace = namespace.get('Config')
+        if config_kwargs and config_from_namespace:
+            raise TypeError('Specifying config in two places is ambiguous, use either Config attribute or class kwargs')
+        config = inherit_config(config_from_namespace, config, **config_kwargs)
+
+        validators = inherit_validators(extract_validators(namespace), validators)
+        vg = ValidatorGroup(validators)
+
+        for f in fields.values():
+            f.set_config(config)
+            extra_validators = vg.get_validators(f.name)
+            if extra_validators:
+                f.class_validators.update(extra_validators)
+                # re-run prepare to add extra validators
+                f.populate_validators()
+
+        prepare_config(config, name)
+
+        untouched_types = ANNOTATED_FIELD_UNTOUCHED_TYPES
+
+        def is_untouched(v: Any) -> bool:
+            return isinstance(v, untouched_types) or v.__class__.__name__ == 'cython_function_or_method'
+
+        if (namespace.get('__module__'), namespace.get('__qualname__')) != ('pydantic.main', 'BaseModel'):
+            annotations = resolve_annotations(namespace.get('__annotations__', {}), namespace.get('__module__', None))
+            # annotation only fields need to come first in fields
+            for ann_name, ann_type in annotations.items():
+                if is_classvar(ann_type):
+                    class_vars.add(ann_name)
+                elif is_finalvar_with_default_val(ann_type, namespace.get(ann_name, Undefined)):
+                    class_vars.add(ann_name)
+                elif is_valid_field(ann_name):
+                    validate_field_name(bases, ann_name)
+                    value = namespace.get(ann_name, Undefined)
+                    allowed_types = get_args(ann_type) if is_union(get_origin(ann_type)) else (ann_type,)
+                    if (
+                        is_untouched(value)
+                        and ann_type != PyObject
+                        and not any(
+                            lenient_issubclass(get_origin(allowed_type), Type) for allowed_type in allowed_types
+                        )
+                    ):
+                        continue
+                    fields[ann_name] = ModelField.infer(
+                        name=ann_name,
+                        value=value,
+                        annotation=ann_type,
+                        class_validators=vg.get_validators(ann_name),
+                        config=config,
+                    )
+                elif ann_name not in namespace and config.underscore_attrs_are_private:
+                    private_attributes[ann_name] = PrivateAttr()
+
+            untouched_types = UNTOUCHED_TYPES + config.keep_untouched
+            for var_name, value in namespace.items():
+                can_be_changed = var_name not in class_vars and not is_untouched(value)
+                if isinstance(value, ModelPrivateAttr):
+                    if not is_valid_private_name(var_name):
+                        raise NameError(
+                            f'Private attributes "{var_name}" must not be a valid field name; '
+                            f'Use sunder or dunder names, e. g. "_{var_name}" or "__{var_name}__"'
+                        )
+                    private_attributes[var_name] = value
+                elif config.underscore_attrs_are_private and is_valid_private_name(var_name) and can_be_changed:
+                    private_attributes[var_name] = PrivateAttr(default=value)
+                elif is_valid_field(var_name) and var_name not in annotations and can_be_changed:
+                    validate_field_name(bases, var_name)
+                    inferred = ModelField.infer(
+                        name=var_name,
+                        value=value,
+                        annotation=annotations.get(var_name, Undefined),
+                        class_validators=vg.get_validators(var_name),
+                        config=config,
+                    )
+                    if var_name in fields:
+                        if lenient_issubclass(inferred.type_, fields[var_name].type_):
+                            inferred.type_ = fields[var_name].type_
+                        else:
+                            raise TypeError(
+                                f'The type of {name}.{var_name} differs from the new default value; '
+                                f'if you wish to change the type of this field, please use a type annotation'
+                            )
+                    fields[var_name] = inferred
+
+        _custom_root_type = ROOT_KEY in fields
+        if _custom_root_type:
+            validate_custom_root_type(fields)
+        vg.check_for_unused()
+        if config.json_encoders:
+            json_encoder = partial(custom_pydantic_encoder, config.json_encoders)
+        else:
+            json_encoder = pydantic_encoder
+        pre_rv_new, post_rv_new = extract_root_validators(namespace)
+
+        if hash_func is None:
+            hash_func = generate_hash_function(config.frozen)
+
+        exclude_from_namespace = fields | private_attributes.keys() | {'__slots__'}
+        new_namespace = {
+            '__config__': config,
+            '__fields__': fields,
+            '__exclude_fields__': {
+                name: field.field_info.exclude for name, field in fields.items() if field.field_info.exclude is not None
+            }
+            or None,
+            '__include_fields__': {
+                name: field.field_info.include for name, field in fields.items() if field.field_info.include is not None
+            }
+            or None,
+            '__validators__': vg.validators,
+            '__pre_root_validators__': unique_list(
+                pre_root_validators + pre_rv_new,
+                name_factory=lambda v: v.__name__,
+            ),
+            '__post_root_validators__': unique_list(
+                post_root_validators + post_rv_new,
+                name_factory=lambda skip_on_failure_and_v: skip_on_failure_and_v[1].__name__,
+            ),
+            '__schema_cache__': {},
+            '__json_encoder__': staticmethod(json_encoder),
+            '__custom_root_type__': _custom_root_type,
+            '__private_attributes__': {**base_private_attributes, **private_attributes},
+            '__slots__': slots | private_attributes.keys(),
+            '__hash__': hash_func,
+            '__class_vars__': class_vars,
+            **{n: v for n, v in namespace.items() if n not in exclude_from_namespace},
+        }
+
+        cls = super().__new__(mcs, name, bases, new_namespace, **kwargs)
+        # set __signature__ attr only for model class, but not for its instances
+        cls.__signature__ = ClassAttribute('__signature__', generate_model_signature(cls.__init__, fields, config))
+        if resolve_forward_refs:
+            cls.__try_update_forward_refs__()
+
+        # 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 namespace.items():
+            if name not in new_namespace:
+                set_name = getattr(obj, '__set_name__', None)
+                if callable(set_name):
+                    set_name(cls, name)
+
+        return cls
+
+    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, '__post_root_validators__') and super().__instancecheck__(instance)
+
+
+object_setattr = object.__setattr__
+
+
+class BaseModel(Representation, metaclass=ModelMetaclass):
+    if TYPE_CHECKING:
+        # populated by the metaclass, defined here to help IDEs only
+        __fields__: ClassVar[Dict[str, ModelField]] = {}
+        __include_fields__: ClassVar[Optional[Mapping[str, Any]]] = None
+        __exclude_fields__: ClassVar[Optional[Mapping[str, Any]]] = None
+        __validators__: ClassVar[Dict[str, AnyCallable]] = {}
+        __pre_root_validators__: ClassVar[List[AnyCallable]]
+        __post_root_validators__: ClassVar[List[Tuple[bool, AnyCallable]]]
+        __config__: ClassVar[Type[BaseConfig]] = BaseConfig
+        __json_encoder__: ClassVar[Callable[[Any], Any]] = lambda x: x
+        __schema_cache__: ClassVar['DictAny'] = {}
+        __custom_root_type__: ClassVar[bool] = False
+        __signature__: ClassVar['Signature']
+        __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]]
+        __class_vars__: ClassVar[SetStr]
+        __fields_set__: ClassVar[SetStr] = set()
+
+    Config = BaseConfig
+    __slots__ = ('__dict__', '__fields_set__')
+    __doc__ = ''  # Null out the Representation docstring
+
+    def __init__(__pydantic_self__, **data: Any) -> None:
+        """
+        Create a new model by parsing and validating input data from keyword arguments.
+
+        Raises ValidationError if the input data cannot be parsed to form a valid model.
+        """
+        # Uses something other than `self` the first arg to allow "self" as a settable attribute
+        values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
+        if validation_error:
+            raise validation_error
+        try:
+            object_setattr(__pydantic_self__, '__dict__', values)
+        except TypeError as e:
+            raise TypeError(
+                'Model values must be a dict; you may not have returned a dictionary from a root validator'
+            ) from e
+        object_setattr(__pydantic_self__, '__fields_set__', fields_set)
+        __pydantic_self__._init_private_attributes()
+
+    @no_type_check
+    def __setattr__(self, name, value):  # noqa: C901 (ignore complexity)
+        if name in self.__private_attributes__ or name in DUNDER_ATTRIBUTES:
+            return object_setattr(self, name, value)
+
+        if self.__config__.extra is not Extra.allow and name not in self.__fields__:
+            raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
+        elif not self.__config__.allow_mutation or self.__config__.frozen:
+            raise TypeError(f'"{self.__class__.__name__}" is immutable and does not support item assignment')
+        elif name in self.__fields__ and self.__fields__[name].final:
+            raise TypeError(
+                f'"{self.__class__.__name__}" object "{name}" field is final and does not support reassignment'
+            )
+        elif self.__config__.validate_assignment:
+            new_values = {**self.__dict__, name: value}
+
+            for validator in self.__pre_root_validators__:
+                try:
+                    new_values = validator(self.__class__, new_values)
+                except (ValueError, TypeError, AssertionError) as exc:
+                    raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], self.__class__)
+
+            known_field = self.__fields__.get(name, None)
+            if known_field:
+                # We want to
+                # - make sure validators are called without the current value for this field inside `values`
+                # - keep other values (e.g. submodels) untouched (using `BaseModel.dict()` will change them into dicts)
+                # - keep the order of the fields
+                if not known_field.field_info.allow_mutation:
+                    raise TypeError(f'"{known_field.name}" has allow_mutation set to False and cannot be assigned')
+                dict_without_original_value = {k: v for k, v in self.__dict__.items() if k != name}
+                value, error_ = known_field.validate(value, dict_without_original_value, loc=name, cls=self.__class__)
+                if error_:
+                    raise ValidationError([error_], self.__class__)
+                else:
+                    new_values[name] = value
+
+            errors = []
+            for skip_on_failure, validator in self.__post_root_validators__:
+                if skip_on_failure and errors:
+                    continue
+                try:
+                    new_values = validator(self.__class__, new_values)
+                except (ValueError, TypeError, AssertionError) as exc:
+                    errors.append(ErrorWrapper(exc, loc=ROOT_KEY))
+            if errors:
+                raise ValidationError(errors, self.__class__)
+
+            # update the whole __dict__ as other values than just `value`
+            # may be changed (e.g. with `root_validator`)
+            object_setattr(self, '__dict__', new_values)
+        else:
+            self.__dict__[name] = value
+
+        self.__fields_set__.add(name)
+
+    def __getstate__(self) -> 'DictAny':
+        private_attrs = ((k, getattr(self, k, Undefined)) for k in self.__private_attributes__)
+        return {
+            '__dict__': self.__dict__,
+            '__fields_set__': self.__fields_set__,
+            '__private_attribute_values__': {k: v for k, v in private_attrs if v is not Undefined},
+        }
+
+    def __setstate__(self, state: 'DictAny') -> None:
+        object_setattr(self, '__dict__', state['__dict__'])
+        object_setattr(self, '__fields_set__', state['__fields_set__'])
+        for name, value in state.get('__private_attribute_values__', {}).items():
+            object_setattr(self, name, value)
+
+    def _init_private_attributes(self) -> None:
+        for name, private_attr in self.__private_attributes__.items():
+            default = private_attr.get_default()
+            if default is not Undefined:
+                object_setattr(self, name, default)
+
+    def dict(
+        self,
+        *,
+        include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
+        exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
+        by_alias: bool = False,
+        skip_defaults: Optional[bool] = None,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+    ) -> 'DictStrAny':
+        """
+        Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
+
+        """
+        if skip_defaults is not None:
+            warnings.warn(
+                f'{self.__class__.__name__}.dict(): "skip_defaults" is deprecated and replaced by "exclude_unset"',
+                DeprecationWarning,
+            )
+            exclude_unset = skip_defaults
+
+        return dict(
+            self._iter(
+                to_dict=True,
+                by_alias=by_alias,
+                include=include,
+                exclude=exclude,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                exclude_none=exclude_none,
+            )
+        )
+
+    def json(
+        self,
+        *,
+        include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
+        exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
+        by_alias: bool = False,
+        skip_defaults: Optional[bool] = None,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        encoder: Optional[Callable[[Any], Any]] = None,
+        models_as_dict: bool = True,
+        **dumps_kwargs: Any,
+    ) -> str:
+        """
+        Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`.
+
+        `encoder` is an optional function to supply as `default` to json.dumps(), other arguments as per `json.dumps()`.
+        """
+        if skip_defaults is not None:
+            warnings.warn(
+                f'{self.__class__.__name__}.json(): "skip_defaults" is deprecated and replaced by "exclude_unset"',
+                DeprecationWarning,
+            )
+            exclude_unset = skip_defaults
+        encoder = cast(Callable[[Any], Any], encoder or self.__json_encoder__)
+
+        # We don't directly call `self.dict()`, which does exactly this with `to_dict=True`
+        # because we want to be able to keep raw `BaseModel` instances and not as `dict`.
+        # This allows users to write custom JSON encoders for given `BaseModel` classes.
+        data = dict(
+            self._iter(
+                to_dict=models_as_dict,
+                by_alias=by_alias,
+                include=include,
+                exclude=exclude,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                exclude_none=exclude_none,
+            )
+        )
+        if self.__custom_root_type__:
+            data = data[ROOT_KEY]
+        return self.__config__.json_dumps(data, default=encoder, **dumps_kwargs)
+
+    @classmethod
+    def _enforce_dict_if_root(cls, obj: Any) -> Any:
+        if cls.__custom_root_type__ and (
+            not (isinstance(obj, dict) and obj.keys() == {ROOT_KEY})
+            and not (isinstance(obj, BaseModel) and obj.__fields__.keys() == {ROOT_KEY})
+            or cls.__fields__[ROOT_KEY].shape in MAPPING_LIKE_SHAPES
+        ):
+            return {ROOT_KEY: obj}
+        else:
+            return obj
+
+    @classmethod
+    def parse_obj(cls: Type['Model'], obj: Any) -> 'Model':
+        obj = cls._enforce_dict_if_root(obj)
+        if not isinstance(obj, dict):
+            try:
+                obj = dict(obj)
+            except (TypeError, ValueError) as e:
+                exc = TypeError(f'{cls.__name__} expected dict not {obj.__class__.__name__}')
+                raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls) from e
+        return cls(**obj)
+
+    @classmethod
+    def parse_raw(
+        cls: Type['Model'],
+        b: StrBytes,
+        *,
+        content_type: str = None,
+        encoding: str = 'utf8',
+        proto: Protocol = None,
+        allow_pickle: bool = False,
+    ) -> 'Model':
+        try:
+            obj = load_str_bytes(
+                b,
+                proto=proto,
+                content_type=content_type,
+                encoding=encoding,
+                allow_pickle=allow_pickle,
+                json_loads=cls.__config__.json_loads,
+            )
+        except (ValueError, TypeError, UnicodeDecodeError) as e:
+            raise ValidationError([ErrorWrapper(e, loc=ROOT_KEY)], cls)
+        return cls.parse_obj(obj)
+
+    @classmethod
+    def parse_file(
+        cls: Type['Model'],
+        path: Union[str, Path],
+        *,
+        content_type: str = None,
+        encoding: str = 'utf8',
+        proto: Protocol = None,
+        allow_pickle: bool = False,
+    ) -> 'Model':
+        obj = load_file(
+            path,
+            proto=proto,
+            content_type=content_type,
+            encoding=encoding,
+            allow_pickle=allow_pickle,
+            json_loads=cls.__config__.json_loads,
+        )
+        return cls.parse_obj(obj)
+
+    @classmethod
+    def from_orm(cls: Type['Model'], obj: Any) -> 'Model':
+        if not cls.__config__.orm_mode:
+            raise ConfigError('You must have the config attribute orm_mode=True to use from_orm')
+        obj = {ROOT_KEY: obj} if cls.__custom_root_type__ else cls._decompose_class(obj)
+        m = cls.__new__(cls)
+        values, fields_set, validation_error = validate_model(cls, obj)
+        if validation_error:
+            raise validation_error
+        object_setattr(m, '__dict__', values)
+        object_setattr(m, '__fields_set__', fields_set)
+        m._init_private_attributes()
+        return m
+
+    @classmethod
+    def construct(cls: Type['Model'], _fields_set: Optional['SetStr'] = None, **values: Any) -> 'Model':
+        """
+        Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
+        Default values are respected, but no other validation is performed.
+        Behaves as if `Config.extra = 'allow'` was set since it adds all passed values
+        """
+        m = cls.__new__(cls)
+        fields_values: Dict[str, Any] = {}
+        for name, field in cls.__fields__.items():
+            if field.alt_alias and field.alias in values:
+                fields_values[name] = values[field.alias]
+            elif name in values:
+                fields_values[name] = values[name]
+            elif not field.required:
+                fields_values[name] = field.get_default()
+        fields_values.update(values)
+        object_setattr(m, '__dict__', fields_values)
+        if _fields_set is None:
+            _fields_set = set(values.keys())
+        object_setattr(m, '__fields_set__', _fields_set)
+        m._init_private_attributes()
+        return m
+
+    def _copy_and_set_values(self: 'Model', values: 'DictStrAny', fields_set: 'SetStr', *, deep: bool) -> 'Model':
+        if deep:
+            # chances of having empty dict here are quite low for using smart_deepcopy
+            values = deepcopy(values)
+
+        cls = self.__class__
+        m = cls.__new__(cls)
+        object_setattr(m, '__dict__', values)
+        object_setattr(m, '__fields_set__', fields_set)
+        for name in self.__private_attributes__:
+            value = getattr(self, name, Undefined)
+            if value is not Undefined:
+                if deep:
+                    value = deepcopy(value)
+                object_setattr(m, name, value)
+
+        return m
+
+    def copy(
+        self: 'Model',
+        *,
+        include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
+        exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
+        update: Optional['DictStrAny'] = None,
+        deep: bool = False,
+    ) -> 'Model':
+        """
+        Duplicate a model, optionally choose which fields to include, exclude and change.
+
+        :param include: fields to include in new model
+        :param exclude: fields to exclude from new model, as with values this takes precedence over include
+        :param update: values to change/add in the new model. Note: the data is not validated before creating
+            the new model: you should trust this data
+        :param deep: set to `True` to make a deep copy of the model
+        :return: new model instance
+        """
+
+        values = dict(
+            self._iter(to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False),
+            **(update or {}),
+        )
+
+        # new `__fields_set__` can have unset optional fields with a set value in `update` kwarg
+        if update:
+            fields_set = self.__fields_set__ | update.keys()
+        else:
+            fields_set = set(self.__fields_set__)
+
+        return self._copy_and_set_values(values, fields_set, deep=deep)
+
+    @classmethod
+    def schema(cls, by_alias: bool = True, ref_template: str = default_ref_template) -> 'DictStrAny':
+        cached = cls.__schema_cache__.get((by_alias, ref_template))
+        if cached is not None:
+            return cached
+        s = model_schema(cls, by_alias=by_alias, ref_template=ref_template)
+        cls.__schema_cache__[(by_alias, ref_template)] = s
+        return s
+
+    @classmethod
+    def schema_json(
+        cls, *, by_alias: bool = True, ref_template: str = default_ref_template, **dumps_kwargs: Any
+    ) -> str:
+        from pydantic.v1.json import pydantic_encoder
+
+        return cls.__config__.json_dumps(
+            cls.schema(by_alias=by_alias, ref_template=ref_template), default=pydantic_encoder, **dumps_kwargs
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.validate
+
+    @classmethod
+    def validate(cls: Type['Model'], value: Any) -> 'Model':
+        if isinstance(value, cls):
+            copy_on_model_validation = cls.__config__.copy_on_model_validation
+            # whether to deep or shallow copy the model on validation, None means do not copy
+            deep_copy: Optional[bool] = None
+            if copy_on_model_validation not in {'deep', 'shallow', 'none'}:
+                # Warn about deprecated behavior
+                warnings.warn(
+                    "`copy_on_model_validation` should be a string: 'deep', 'shallow' or 'none'", DeprecationWarning
+                )
+                if copy_on_model_validation:
+                    deep_copy = False
+
+            if copy_on_model_validation == 'shallow':
+                # shallow copy
+                deep_copy = False
+            elif copy_on_model_validation == 'deep':
+                # deep copy
+                deep_copy = True
+
+            if deep_copy is None:
+                return value
+            else:
+                return value._copy_and_set_values(value.__dict__, value.__fields_set__, deep=deep_copy)
+
+        value = cls._enforce_dict_if_root(value)
+
+        if isinstance(value, dict):
+            return cls(**value)
+        elif cls.__config__.orm_mode:
+            return cls.from_orm(value)
+        else:
+            try:
+                value_as_dict = dict(value)
+            except (TypeError, ValueError) as e:
+                raise DictError() from e
+            return cls(**value_as_dict)
+
+    @classmethod
+    def _decompose_class(cls: Type['Model'], obj: Any) -> GetterDict:
+        if isinstance(obj, GetterDict):
+            return obj
+        return cls.__config__.getter_dict(obj)
+
+    @classmethod
+    @no_type_check
+    def _get_value(
+        cls,
+        v: Any,
+        to_dict: bool,
+        by_alias: bool,
+        include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']],
+        exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']],
+        exclude_unset: bool,
+        exclude_defaults: bool,
+        exclude_none: bool,
+    ) -> Any:
+        if isinstance(v, BaseModel):
+            if to_dict:
+                v_dict = v.dict(
+                    by_alias=by_alias,
+                    exclude_unset=exclude_unset,
+                    exclude_defaults=exclude_defaults,
+                    include=include,
+                    exclude=exclude,
+                    exclude_none=exclude_none,
+                )
+                if ROOT_KEY in v_dict:
+                    return v_dict[ROOT_KEY]
+                return v_dict
+            else:
+                return v.copy(include=include, exclude=exclude)
+
+        value_exclude = ValueItems(v, exclude) if exclude else None
+        value_include = ValueItems(v, include) if include else None
+
+        if isinstance(v, dict):
+            return {
+                k_: cls._get_value(
+                    v_,
+                    to_dict=to_dict,
+                    by_alias=by_alias,
+                    exclude_unset=exclude_unset,
+                    exclude_defaults=exclude_defaults,
+                    include=value_include and value_include.for_element(k_),
+                    exclude=value_exclude and value_exclude.for_element(k_),
+                    exclude_none=exclude_none,
+                )
+                for k_, v_ in v.items()
+                if (not value_exclude or not value_exclude.is_excluded(k_))
+                and (not value_include or value_include.is_included(k_))
+            }
+
+        elif sequence_like(v):
+            seq_args = (
+                cls._get_value(
+                    v_,
+                    to_dict=to_dict,
+                    by_alias=by_alias,
+                    exclude_unset=exclude_unset,
+                    exclude_defaults=exclude_defaults,
+                    include=value_include and value_include.for_element(i),
+                    exclude=value_exclude and value_exclude.for_element(i),
+                    exclude_none=exclude_none,
+                )
+                for i, v_ in enumerate(v)
+                if (not value_exclude or not value_exclude.is_excluded(i))
+                and (not value_include or value_include.is_included(i))
+            )
+
+            return v.__class__(*seq_args) if is_namedtuple(v.__class__) else v.__class__(seq_args)
+
+        elif isinstance(v, Enum) and getattr(cls.Config, 'use_enum_values', False):
+            return v.value
+
+        else:
+            return v
+
+    @classmethod
+    def __try_update_forward_refs__(cls, **localns: Any) -> None:
+        """
+        Same as update_forward_refs but will not raise exception
+        when forward references are not defined.
+        """
+        update_model_forward_refs(cls, cls.__fields__.values(), cls.__config__.json_encoders, localns, (NameError,))
+
+    @classmethod
+    def update_forward_refs(cls, **localns: Any) -> None:
+        """
+        Try to update ForwardRefs on fields based on this Model, globalns and localns.
+        """
+        update_model_forward_refs(cls, cls.__fields__.values(), cls.__config__.json_encoders, localns)
+
+    def __iter__(self) -> 'TupleGenerator':
+        """
+        so `dict(model)` works
+        """
+        yield from self.__dict__.items()
+
+    def _iter(
+        self,
+        to_dict: bool = False,
+        by_alias: bool = False,
+        include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
+        exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+    ) -> 'TupleGenerator':
+        # Merge field set excludes with explicit exclude parameter with explicit overriding field set options.
+        # The extra "is not None" guards are not logically necessary but optimizes performance for the simple case.
+        if exclude is not None or self.__exclude_fields__ is not None:
+            exclude = ValueItems.merge(self.__exclude_fields__, exclude)
+
+        if include is not None or self.__include_fields__ is not None:
+            include = ValueItems.merge(self.__include_fields__, include, intersect=True)
+
+        allowed_keys = self._calculate_keys(
+            include=include, exclude=exclude, exclude_unset=exclude_unset  # type: ignore
+        )
+        if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none):
+            # huge boost for plain _iter()
+            yield from self.__dict__.items()
+            return
+
+        value_exclude = ValueItems(self, exclude) if exclude is not None else None
+        value_include = ValueItems(self, include) if include is not None else None
+
+        for field_key, v in self.__dict__.items():
+            if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None):
+                continue
+
+            if exclude_defaults:
+                model_field = self.__fields__.get(field_key)
+                if not getattr(model_field, 'required', True) and getattr(model_field, 'default', _missing) == v:
+                    continue
+
+            if by_alias and field_key in self.__fields__:
+                dict_key = self.__fields__[field_key].alias
+            else:
+                dict_key = field_key
+
+            if to_dict or value_include or value_exclude:
+                v = self._get_value(
+                    v,
+                    to_dict=to_dict,
+                    by_alias=by_alias,
+                    include=value_include and value_include.for_element(field_key),
+                    exclude=value_exclude and value_exclude.for_element(field_key),
+                    exclude_unset=exclude_unset,
+                    exclude_defaults=exclude_defaults,
+                    exclude_none=exclude_none,
+                )
+            yield dict_key, v
+
+    def _calculate_keys(
+        self,
+        include: Optional['MappingIntStrAny'],
+        exclude: Optional['MappingIntStrAny'],
+        exclude_unset: bool,
+        update: Optional['DictStrAny'] = None,
+    ) -> Optional[AbstractSet[str]]:
+        if include is None and exclude is None and exclude_unset is False:
+            return None
+
+        keys: AbstractSet[str]
+        if exclude_unset:
+            keys = self.__fields_set__.copy()
+        else:
+            keys = self.__dict__.keys()
+
+        if include is not None:
+            keys &= include.keys()
+
+        if update:
+            keys -= update.keys()
+
+        if exclude:
+            keys -= {k for k, v in exclude.items() if ValueItems.is_true(v)}
+
+        return keys
+
+    def __eq__(self, other: Any) -> bool:
+        if isinstance(other, BaseModel):
+            return self.dict() == other.dict()
+        else:
+            return self.dict() == other
+
+    def __repr_args__(self) -> 'ReprArgs':
+        return [
+            (k, v)
+            for k, v in self.__dict__.items()
+            if k not in DUNDER_ATTRIBUTES and (k not in self.__fields__ or self.__fields__[k].field_info.repr)
+        ]
+
+
+_is_base_model_class_defined = True
+
+
+@overload
+def create_model(
+    __model_name: str,
+    *,
+    __config__: Optional[Type[BaseConfig]] = None,
+    __base__: None = None,
+    __module__: str = __name__,
+    __validators__: Dict[str, 'AnyClassMethod'] = None,
+    __cls_kwargs__: Dict[str, Any] = None,
+    **field_definitions: Any,
+) -> Type['BaseModel']:
+    ...
+
+
+@overload
+def create_model(
+    __model_name: str,
+    *,
+    __config__: Optional[Type[BaseConfig]] = None,
+    __base__: Union[Type['Model'], Tuple[Type['Model'], ...]],
+    __module__: str = __name__,
+    __validators__: Dict[str, 'AnyClassMethod'] = None,
+    __cls_kwargs__: Dict[str, Any] = None,
+    **field_definitions: Any,
+) -> Type['Model']:
+    ...
+
+
+def create_model(
+    __model_name: str,
+    *,
+    __config__: Optional[Type[BaseConfig]] = None,
+    __base__: Union[None, Type['Model'], Tuple[Type['Model'], ...]] = None,
+    __module__: str = __name__,
+    __validators__: Dict[str, 'AnyClassMethod'] = None,
+    __cls_kwargs__: Dict[str, Any] = None,
+    __slots__: Optional[Tuple[str, ...]] = None,
+    **field_definitions: Any,
+) -> Type['Model']:
+    """
+    Dynamically create a model.
+    :param __model_name: name of the created model
+    :param __config__: config class to use for the new model
+    :param __base__: base class for the new model to inherit from
+    :param __module__: module of the created model
+    :param __validators__: a dict of method names and @validator class methods
+    :param __cls_kwargs__: a dict for class creation
+    :param __slots__: Deprecated, `__slots__` should not be passed to `create_model`
+    :param field_definitions: fields of the model (or extra fields if a base is supplied)
+        in the format `<name>=(<type>, <default default>)` or `<name>=<default value>, e.g.
+        `foobar=(str, ...)` or `foobar=123`, or, for complex use-cases, in the format
+        `<name>=<Field>` or `<name>=(<type>, <FieldInfo>)`, e.g.
+        `foo=Field(datetime, default_factory=datetime.utcnow, alias='bar')` or
+        `foo=(str, FieldInfo(title='Foo'))`
+    """
+    if __slots__ is not None:
+        # __slots__ will be ignored from here on
+        warnings.warn('__slots__ should not be passed to create_model', RuntimeWarning)
+
+    if __base__ is not None:
+        if __config__ is not None:
+            raise ConfigError('to avoid confusion __config__ and __base__ cannot be used together')
+        if not isinstance(__base__, tuple):
+            __base__ = (__base__,)
+    else:
+        __base__ = (cast(Type['Model'], BaseModel),)
+
+    __cls_kwargs__ = __cls_kwargs__ or {}
+
+    fields = {}
+    annotations = {}
+
+    for f_name, f_def in field_definitions.items():
+        if not is_valid_field(f_name):
+            warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning)
+        if isinstance(f_def, tuple):
+            try:
+                f_annotation, f_value = f_def
+            except ValueError as e:
+                raise ConfigError(
+                    'field definitions should either be a tuple of (<type>, <default>) or just a '
+                    'default value, unfortunately this means tuples as '
+                    'default values are not allowed'
+                ) from e
+        else:
+            f_annotation, f_value = None, f_def
+
+        if f_annotation:
+            annotations[f_name] = f_annotation
+        fields[f_name] = f_value
+
+    namespace: 'DictStrAny' = {'__annotations__': annotations, '__module__': __module__}
+    if __validators__:
+        namespace.update(__validators__)
+    namespace.update(fields)
+    if __config__:
+        namespace['Config'] = inherit_config(__config__, BaseConfig)
+    resolved_bases = resolve_bases(__base__)
+    meta, ns, kwds = prepare_class(__model_name, resolved_bases, kwds=__cls_kwargs__)
+    if resolved_bases is not __base__:
+        ns['__orig_bases__'] = __base__
+    namespace.update(ns)
+    return meta(__model_name, resolved_bases, namespace, **kwds)
+
+
+_missing = object()
+
+
+def validate_model(  # noqa: C901 (ignore complexity)
+    model: Type[BaseModel], input_data: 'DictStrAny', cls: 'ModelOrDc' = None
+) -> Tuple['DictStrAny', 'SetStr', Optional[ValidationError]]:
+    """
+    validate data against a model.
+    """
+    values = {}
+    errors = []
+    # input_data names, possibly alias
+    names_used = set()
+    # field names, never aliases
+    fields_set = set()
+    config = model.__config__
+    check_extra = config.extra is not Extra.ignore
+    cls_ = cls or model
+
+    for validator in model.__pre_root_validators__:
+        try:
+            input_data = validator(cls_, input_data)
+        except (ValueError, TypeError, AssertionError) as exc:
+            return {}, set(), ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls_)
+
+    for name, field in model.__fields__.items():
+        value = input_data.get(field.alias, _missing)
+        using_name = False
+        if value is _missing and config.allow_population_by_field_name and field.alt_alias:
+            value = input_data.get(field.name, _missing)
+            using_name = True
+
+        if value is _missing:
+            if field.required:
+                errors.append(ErrorWrapper(MissingError(), loc=field.alias))
+                continue
+
+            value = field.get_default()
+
+            if not config.validate_all and not field.validate_always:
+                values[name] = value
+                continue
+        else:
+            fields_set.add(name)
+            if check_extra:
+                names_used.add(field.name if using_name else field.alias)
+
+        v_, errors_ = field.validate(value, values, loc=field.alias, cls=cls_)
+        if isinstance(errors_, ErrorWrapper):
+            errors.append(errors_)
+        elif isinstance(errors_, list):
+            errors.extend(errors_)
+        else:
+            values[name] = v_
+
+    if check_extra:
+        if isinstance(input_data, GetterDict):
+            extra = input_data.extra_keys() - names_used
+        else:
+            extra = input_data.keys() - names_used
+        if extra:
+            fields_set |= extra
+            if config.extra is Extra.allow:
+                for f in extra:
+                    values[f] = input_data[f]
+            else:
+                for f in sorted(extra):
+                    errors.append(ErrorWrapper(ExtraError(), loc=f))
+
+    for skip_on_failure, validator in model.__post_root_validators__:
+        if skip_on_failure and errors:
+            continue
+        try:
+            values = validator(cls_, values)
+        except (ValueError, TypeError, AssertionError) as exc:
+            errors.append(ErrorWrapper(exc, loc=ROOT_KEY))
+
+    if errors:
+        return values, fields_set, ValidationError(errors, cls_)
+    else:
+        return values, fields_set, None
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/mypy.py b/.venv/lib/python3.12/site-packages/pydantic/v1/mypy.py
new file mode 100644
index 00000000..f4e27ab4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/mypy.py
@@ -0,0 +1,949 @@
+import sys
+from configparser import ConfigParser
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type as TypingType, Union
+
+from mypy.errorcodes import ErrorCode
+from mypy.nodes import (
+    ARG_NAMED,
+    ARG_NAMED_OPT,
+    ARG_OPT,
+    ARG_POS,
+    ARG_STAR2,
+    MDEF,
+    Argument,
+    AssignmentStmt,
+    Block,
+    CallExpr,
+    ClassDef,
+    Context,
+    Decorator,
+    EllipsisExpr,
+    FuncBase,
+    FuncDef,
+    JsonDict,
+    MemberExpr,
+    NameExpr,
+    PassStmt,
+    PlaceholderNode,
+    RefExpr,
+    StrExpr,
+    SymbolNode,
+    SymbolTableNode,
+    TempNode,
+    TypeInfo,
+    TypeVarExpr,
+    Var,
+)
+from mypy.options import Options
+from mypy.plugin import (
+    CheckerPluginInterface,
+    ClassDefContext,
+    FunctionContext,
+    MethodContext,
+    Plugin,
+    ReportConfigContext,
+    SemanticAnalyzerPluginInterface,
+)
+from mypy.plugins import dataclasses
+from mypy.semanal import set_callable_name  # type: ignore
+from mypy.server.trigger import make_wildcard_trigger
+from mypy.types import (
+    AnyType,
+    CallableType,
+    Instance,
+    NoneType,
+    Overloaded,
+    ProperType,
+    Type,
+    TypeOfAny,
+    TypeType,
+    TypeVarId,
+    TypeVarType,
+    UnionType,
+    get_proper_type,
+)
+from mypy.typevars import fill_typevars
+from mypy.util import get_unique_redefinition_name
+from mypy.version import __version__ as mypy_version
+
+from pydantic.v1.utils import is_valid_field
+
+try:
+    from mypy.types import TypeVarDef  # type: ignore[attr-defined]
+except ImportError:  # pragma: no cover
+    # Backward-compatible with TypeVarDef from Mypy 0.910.
+    from mypy.types import TypeVarType as TypeVarDef
+
+CONFIGFILE_KEY = 'pydantic-mypy'
+METADATA_KEY = 'pydantic-mypy-metadata'
+_NAMESPACE = __name__[:-5]  # 'pydantic' in 1.10.X, 'pydantic.v1' in v2.X
+BASEMODEL_FULLNAME = f'{_NAMESPACE}.main.BaseModel'
+BASESETTINGS_FULLNAME = f'{_NAMESPACE}.env_settings.BaseSettings'
+MODEL_METACLASS_FULLNAME = f'{_NAMESPACE}.main.ModelMetaclass'
+FIELD_FULLNAME = f'{_NAMESPACE}.fields.Field'
+DATACLASS_FULLNAME = f'{_NAMESPACE}.dataclasses.dataclass'
+
+
+def parse_mypy_version(version: str) -> Tuple[int, ...]:
+    return tuple(map(int, version.partition('+')[0].split('.')))
+
+
+MYPY_VERSION_TUPLE = parse_mypy_version(mypy_version)
+BUILTINS_NAME = 'builtins' if MYPY_VERSION_TUPLE >= (0, 930) else '__builtins__'
+
+# Increment version if plugin changes and mypy caches should be invalidated
+__version__ = 2
+
+
+def plugin(version: str) -> 'TypingType[Plugin]':
+    """
+    `version` is the mypy version string
+
+    We might want to use this to print a warning if the mypy version being used is
+    newer, or especially older, than we expect (or need).
+    """
+    return PydanticPlugin
+
+
+class PydanticPlugin(Plugin):
+    def __init__(self, options: Options) -> None:
+        self.plugin_config = PydanticPluginConfig(options)
+        self._plugin_data = self.plugin_config.to_data()
+        super().__init__(options)
+
+    def get_base_class_hook(self, fullname: str) -> 'Optional[Callable[[ClassDefContext], None]]':
+        sym = self.lookup_fully_qualified(fullname)
+        if sym and isinstance(sym.node, TypeInfo):  # pragma: no branch
+            # No branching may occur if the mypy cache has not been cleared
+            if any(get_fullname(base) == BASEMODEL_FULLNAME for base in sym.node.mro):
+                return self._pydantic_model_class_maker_callback
+        return None
+
+    def get_metaclass_hook(self, fullname: str) -> Optional[Callable[[ClassDefContext], None]]:
+        if fullname == MODEL_METACLASS_FULLNAME:
+            return self._pydantic_model_metaclass_marker_callback
+        return None
+
+    def get_function_hook(self, fullname: str) -> 'Optional[Callable[[FunctionContext], Type]]':
+        sym = self.lookup_fully_qualified(fullname)
+        if sym and sym.fullname == FIELD_FULLNAME:
+            return self._pydantic_field_callback
+        return None
+
+    def get_method_hook(self, fullname: str) -> Optional[Callable[[MethodContext], Type]]:
+        if fullname.endswith('.from_orm'):
+            return from_orm_callback
+        return None
+
+    def get_class_decorator_hook(self, fullname: str) -> Optional[Callable[[ClassDefContext], None]]:
+        """Mark pydantic.dataclasses as dataclass.
+
+        Mypy version 1.1.1 added support for `@dataclass_transform` decorator.
+        """
+        if fullname == DATACLASS_FULLNAME and MYPY_VERSION_TUPLE < (1, 1):
+            return dataclasses.dataclass_class_maker_callback  # type: ignore[return-value]
+        return None
+
+    def report_config_data(self, ctx: ReportConfigContext) -> Dict[str, Any]:
+        """Return all plugin config data.
+
+        Used by mypy to determine if cache needs to be discarded.
+        """
+        return self._plugin_data
+
+    def _pydantic_model_class_maker_callback(self, ctx: ClassDefContext) -> None:
+        transformer = PydanticModelTransformer(ctx, self.plugin_config)
+        transformer.transform()
+
+    def _pydantic_model_metaclass_marker_callback(self, ctx: ClassDefContext) -> None:
+        """Reset dataclass_transform_spec attribute of ModelMetaclass.
+
+        Let the plugin handle it. This behavior can be disabled
+        if 'debug_dataclass_transform' is set to True', for testing purposes.
+        """
+        if self.plugin_config.debug_dataclass_transform:
+            return
+        info_metaclass = ctx.cls.info.declared_metaclass
+        assert info_metaclass, "callback not passed from 'get_metaclass_hook'"
+        if getattr(info_metaclass.type, 'dataclass_transform_spec', None):
+            info_metaclass.type.dataclass_transform_spec = None  # type: ignore[attr-defined]
+
+    def _pydantic_field_callback(self, ctx: FunctionContext) -> 'Type':
+        """
+        Extract the type of the `default` argument from the Field function, and use it as the return type.
+
+        In particular:
+        * Check whether the default and default_factory argument is specified.
+        * Output an error if both are specified.
+        * Retrieve the type of the argument which is specified, and use it as return type for the function.
+        """
+        default_any_type = ctx.default_return_type
+
+        assert ctx.callee_arg_names[0] == 'default', '"default" is no longer first argument in Field()'
+        assert ctx.callee_arg_names[1] == 'default_factory', '"default_factory" is no longer second argument in Field()'
+        default_args = ctx.args[0]
+        default_factory_args = ctx.args[1]
+
+        if default_args and default_factory_args:
+            error_default_and_default_factory_specified(ctx.api, ctx.context)
+            return default_any_type
+
+        if default_args:
+            default_type = ctx.arg_types[0][0]
+            default_arg = default_args[0]
+
+            # Fallback to default Any type if the field is required
+            if not isinstance(default_arg, EllipsisExpr):
+                return default_type
+
+        elif default_factory_args:
+            default_factory_type = ctx.arg_types[1][0]
+
+            # Functions which use `ParamSpec` can be overloaded, exposing the callable's types as a parameter
+            # Pydantic calls the default factory without any argument, so we retrieve the first item
+            if isinstance(default_factory_type, Overloaded):
+                if MYPY_VERSION_TUPLE > (0, 910):
+                    default_factory_type = default_factory_type.items[0]
+                else:
+                    # Mypy0.910 exposes the items of overloaded types in a function
+                    default_factory_type = default_factory_type.items()[0]  # type: ignore[operator]
+
+            if isinstance(default_factory_type, CallableType):
+                ret_type = default_factory_type.ret_type
+                # mypy doesn't think `ret_type` has `args`, you'd think mypy should know,
+                # add this check in case it varies by version
+                args = getattr(ret_type, 'args', None)
+                if args:
+                    if all(isinstance(arg, TypeVarType) for arg in args):
+                        # Looks like the default factory is a type like `list` or `dict`, replace all args with `Any`
+                        ret_type.args = tuple(default_any_type for _ in args)  # type: ignore[attr-defined]
+                return ret_type
+
+        return default_any_type
+
+
+class PydanticPluginConfig:
+    __slots__ = (
+        'init_forbid_extra',
+        'init_typed',
+        'warn_required_dynamic_aliases',
+        'warn_untyped_fields',
+        'debug_dataclass_transform',
+    )
+    init_forbid_extra: bool
+    init_typed: bool
+    warn_required_dynamic_aliases: bool
+    warn_untyped_fields: bool
+    debug_dataclass_transform: bool  # undocumented
+
+    def __init__(self, options: Options) -> None:
+        if options.config_file is None:  # pragma: no cover
+            return
+
+        toml_config = parse_toml(options.config_file)
+        if toml_config is not None:
+            config = toml_config.get('tool', {}).get('pydantic-mypy', {})
+            for key in self.__slots__:
+                setting = config.get(key, False)
+                if not isinstance(setting, bool):
+                    raise ValueError(f'Configuration value must be a boolean for key: {key}')
+                setattr(self, key, setting)
+        else:
+            plugin_config = ConfigParser()
+            plugin_config.read(options.config_file)
+            for key in self.__slots__:
+                setting = plugin_config.getboolean(CONFIGFILE_KEY, key, fallback=False)
+                setattr(self, key, setting)
+
+    def to_data(self) -> Dict[str, Any]:
+        return {key: getattr(self, key) for key in self.__slots__}
+
+
+def from_orm_callback(ctx: MethodContext) -> Type:
+    """
+    Raise an error if orm_mode is not enabled
+    """
+    model_type: Instance
+    ctx_type = ctx.type
+    if isinstance(ctx_type, TypeType):
+        ctx_type = ctx_type.item
+    if isinstance(ctx_type, CallableType) and isinstance(ctx_type.ret_type, Instance):
+        model_type = ctx_type.ret_type  # called on the class
+    elif isinstance(ctx_type, Instance):
+        model_type = ctx_type  # called on an instance (unusual, but still valid)
+    else:  # pragma: no cover
+        detail = f'ctx.type: {ctx_type} (of type {ctx_type.__class__.__name__})'
+        error_unexpected_behavior(detail, ctx.api, ctx.context)
+        return ctx.default_return_type
+    pydantic_metadata = model_type.type.metadata.get(METADATA_KEY)
+    if pydantic_metadata is None:
+        return ctx.default_return_type
+    orm_mode = pydantic_metadata.get('config', {}).get('orm_mode')
+    if orm_mode is not True:
+        error_from_orm(get_name(model_type.type), ctx.api, ctx.context)
+    return ctx.default_return_type
+
+
+class PydanticModelTransformer:
+    tracked_config_fields: Set[str] = {
+        'extra',
+        'allow_mutation',
+        'frozen',
+        'orm_mode',
+        'allow_population_by_field_name',
+        'alias_generator',
+    }
+
+    def __init__(self, ctx: ClassDefContext, plugin_config: PydanticPluginConfig) -> None:
+        self._ctx = ctx
+        self.plugin_config = plugin_config
+
+    def transform(self) -> None:
+        """
+        Configures the BaseModel subclass according to the plugin settings.
+
+        In particular:
+        * determines the model config and fields,
+        * adds a fields-aware signature for the initializer and construct methods
+        * freezes the class if allow_mutation = False or frozen = True
+        * stores the fields, config, and if the class is settings in the mypy metadata for access by subclasses
+        """
+        ctx = self._ctx
+        info = ctx.cls.info
+
+        self.adjust_validator_signatures()
+        config = self.collect_config()
+        fields = self.collect_fields(config)
+        is_settings = any(get_fullname(base) == BASESETTINGS_FULLNAME for base in info.mro[:-1])
+        self.add_initializer(fields, config, is_settings)
+        self.add_construct_method(fields)
+        self.set_frozen(fields, frozen=config.allow_mutation is False or config.frozen is True)
+        info.metadata[METADATA_KEY] = {
+            'fields': {field.name: field.serialize() for field in fields},
+            'config': config.set_values_dict(),
+        }
+
+    def adjust_validator_signatures(self) -> None:
+        """When we decorate a function `f` with `pydantic.validator(...), mypy sees
+        `f` as a regular method taking a `self` instance, even though pydantic
+        internally wraps `f` with `classmethod` if necessary.
+
+        Teach mypy this by marking any function whose outermost decorator is a
+        `validator()` call as a classmethod.
+        """
+        for name, sym in self._ctx.cls.info.names.items():
+            if isinstance(sym.node, Decorator):
+                first_dec = sym.node.original_decorators[0]
+                if (
+                    isinstance(first_dec, CallExpr)
+                    and isinstance(first_dec.callee, NameExpr)
+                    and first_dec.callee.fullname == f'{_NAMESPACE}.class_validators.validator'
+                ):
+                    sym.node.func.is_class = True
+
+    def collect_config(self) -> 'ModelConfigData':
+        """
+        Collects the values of the config attributes that are used by the plugin, accounting for parent classes.
+        """
+        ctx = self._ctx
+        cls = ctx.cls
+        config = ModelConfigData()
+        for stmt in cls.defs.body:
+            if not isinstance(stmt, ClassDef):
+                continue
+            if stmt.name == 'Config':
+                for substmt in stmt.defs.body:
+                    if not isinstance(substmt, AssignmentStmt):
+                        continue
+                    config.update(self.get_config_update(substmt))
+                if (
+                    config.has_alias_generator
+                    and not config.allow_population_by_field_name
+                    and self.plugin_config.warn_required_dynamic_aliases
+                ):
+                    error_required_dynamic_aliases(ctx.api, stmt)
+        for info in cls.info.mro[1:]:  # 0 is the current class
+            if METADATA_KEY not in info.metadata:
+                continue
+
+            # Each class depends on the set of fields in its ancestors
+            ctx.api.add_plugin_dependency(make_wildcard_trigger(get_fullname(info)))
+            for name, value in info.metadata[METADATA_KEY]['config'].items():
+                config.setdefault(name, value)
+        return config
+
+    def collect_fields(self, model_config: 'ModelConfigData') -> List['PydanticModelField']:
+        """
+        Collects the fields for the model, accounting for parent classes
+        """
+        # First, collect fields belonging to the current class.
+        ctx = self._ctx
+        cls = self._ctx.cls
+        fields = []  # type: List[PydanticModelField]
+        known_fields = set()  # type: Set[str]
+        for stmt in cls.defs.body:
+            if not isinstance(stmt, AssignmentStmt):  # `and stmt.new_syntax` to require annotation
+                continue
+
+            lhs = stmt.lvalues[0]
+            if not isinstance(lhs, NameExpr) or not is_valid_field(lhs.name):
+                continue
+
+            if not stmt.new_syntax and self.plugin_config.warn_untyped_fields:
+                error_untyped_fields(ctx.api, stmt)
+
+            # if lhs.name == '__config__':  # BaseConfig not well handled; I'm not sure why yet
+            #     continue
+
+            sym = cls.info.names.get(lhs.name)
+            if sym is None:  # pragma: no cover
+                # This is likely due to a star import (see the dataclasses plugin for a more detailed explanation)
+                # This is the same logic used in the dataclasses plugin
+                continue
+
+            node = sym.node
+            if isinstance(node, PlaceholderNode):  # pragma: no cover
+                # See the PlaceholderNode docstring for more detail about how this can occur
+                # Basically, it is an edge case when dealing with complex import logic
+                # This is the same logic used in the dataclasses plugin
+                continue
+            if not isinstance(node, Var):  # pragma: no cover
+                # Don't know if this edge case still happens with the `is_valid_field` check above
+                # but better safe than sorry
+                continue
+
+            # x: ClassVar[int] is ignored by dataclasses.
+            if node.is_classvar:
+                continue
+
+            is_required = self.get_is_required(cls, stmt, lhs)
+            alias, has_dynamic_alias = self.get_alias_info(stmt)
+            if (
+                has_dynamic_alias
+                and not model_config.allow_population_by_field_name
+                and self.plugin_config.warn_required_dynamic_aliases
+            ):
+                error_required_dynamic_aliases(ctx.api, stmt)
+            fields.append(
+                PydanticModelField(
+                    name=lhs.name,
+                    is_required=is_required,
+                    alias=alias,
+                    has_dynamic_alias=has_dynamic_alias,
+                    line=stmt.line,
+                    column=stmt.column,
+                )
+            )
+            known_fields.add(lhs.name)
+        all_fields = fields.copy()
+        for info in cls.info.mro[1:]:  # 0 is the current class, -2 is BaseModel, -1 is object
+            if METADATA_KEY not in info.metadata:
+                continue
+
+            superclass_fields = []
+            # Each class depends on the set of fields in its ancestors
+            ctx.api.add_plugin_dependency(make_wildcard_trigger(get_fullname(info)))
+
+            for name, data in info.metadata[METADATA_KEY]['fields'].items():
+                if name not in known_fields:
+                    field = PydanticModelField.deserialize(info, data)
+                    known_fields.add(name)
+                    superclass_fields.append(field)
+                else:
+                    (field,) = (a for a in all_fields if a.name == name)
+                    all_fields.remove(field)
+                    superclass_fields.append(field)
+            all_fields = superclass_fields + all_fields
+        return all_fields
+
+    def add_initializer(self, fields: List['PydanticModelField'], config: 'ModelConfigData', is_settings: bool) -> None:
+        """
+        Adds a fields-aware `__init__` method to the class.
+
+        The added `__init__` will be annotated with types vs. all `Any` depending on the plugin settings.
+        """
+        ctx = self._ctx
+        typed = self.plugin_config.init_typed
+        use_alias = config.allow_population_by_field_name is not True
+        force_all_optional = is_settings or bool(
+            config.has_alias_generator and not config.allow_population_by_field_name
+        )
+        init_arguments = self.get_field_arguments(
+            fields, typed=typed, force_all_optional=force_all_optional, use_alias=use_alias
+        )
+        if not self.should_init_forbid_extra(fields, config):
+            var = Var('kwargs')
+            init_arguments.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2))
+
+        if '__init__' not in ctx.cls.info.names:
+            add_method(ctx, '__init__', init_arguments, NoneType())
+
+    def add_construct_method(self, fields: List['PydanticModelField']) -> None:
+        """
+        Adds a fully typed `construct` classmethod to the class.
+
+        Similar to the fields-aware __init__ method, but always uses the field names (not aliases),
+        and does not treat settings fields as optional.
+        """
+        ctx = self._ctx
+        set_str = ctx.api.named_type(f'{BUILTINS_NAME}.set', [ctx.api.named_type(f'{BUILTINS_NAME}.str')])
+        optional_set_str = UnionType([set_str, NoneType()])
+        fields_set_argument = Argument(Var('_fields_set', optional_set_str), optional_set_str, None, ARG_OPT)
+        construct_arguments = self.get_field_arguments(fields, typed=True, force_all_optional=False, use_alias=False)
+        construct_arguments = [fields_set_argument] + construct_arguments
+
+        obj_type = ctx.api.named_type(f'{BUILTINS_NAME}.object')
+        self_tvar_name = '_PydanticBaseModel'  # Make sure it does not conflict with other names in the class
+        tvar_fullname = ctx.cls.fullname + '.' + self_tvar_name
+        if MYPY_VERSION_TUPLE >= (1, 4):
+            tvd = TypeVarType(
+                self_tvar_name,
+                tvar_fullname,
+                (
+                    TypeVarId(-1, namespace=ctx.cls.fullname + '.construct')
+                    if MYPY_VERSION_TUPLE >= (1, 11)
+                    else TypeVarId(-1)
+                ),
+                [],
+                obj_type,
+                AnyType(TypeOfAny.from_omitted_generics),  # type: ignore[arg-type]
+            )
+            self_tvar_expr = TypeVarExpr(
+                self_tvar_name,
+                tvar_fullname,
+                [],
+                obj_type,
+                AnyType(TypeOfAny.from_omitted_generics),  # type: ignore[arg-type]
+            )
+        else:
+            tvd = TypeVarDef(self_tvar_name, tvar_fullname, -1, [], obj_type)
+            self_tvar_expr = TypeVarExpr(self_tvar_name, tvar_fullname, [], obj_type)
+        ctx.cls.info.names[self_tvar_name] = SymbolTableNode(MDEF, self_tvar_expr)
+
+        # Backward-compatible with TypeVarDef from Mypy 0.910.
+        if isinstance(tvd, TypeVarType):
+            self_type = tvd
+        else:
+            self_type = TypeVarType(tvd)
+
+        add_method(
+            ctx,
+            'construct',
+            construct_arguments,
+            return_type=self_type,
+            self_type=self_type,
+            tvar_def=tvd,
+            is_classmethod=True,
+        )
+
+    def set_frozen(self, fields: List['PydanticModelField'], frozen: bool) -> None:
+        """
+        Marks all fields as properties so that attempts to set them trigger mypy errors.
+
+        This is the same approach used by the attrs and dataclasses plugins.
+        """
+        ctx = self._ctx
+        info = ctx.cls.info
+        for field in fields:
+            sym_node = info.names.get(field.name)
+            if sym_node is not None:
+                var = sym_node.node
+                if isinstance(var, Var):
+                    var.is_property = frozen
+                elif isinstance(var, PlaceholderNode) and not ctx.api.final_iteration:
+                    # See https://github.com/pydantic/pydantic/issues/5191 to hit this branch for test coverage
+                    ctx.api.defer()
+                else:  # pragma: no cover
+                    # I don't know whether it's possible to hit this branch, but I've added it for safety
+                    try:
+                        var_str = str(var)
+                    except TypeError:
+                        # This happens for PlaceholderNode; perhaps it will happen for other types in the future..
+                        var_str = repr(var)
+                    detail = f'sym_node.node: {var_str} (of type {var.__class__})'
+                    error_unexpected_behavior(detail, ctx.api, ctx.cls)
+            else:
+                var = field.to_var(info, use_alias=False)
+                var.info = info
+                var.is_property = frozen
+                var._fullname = get_fullname(info) + '.' + get_name(var)
+                info.names[get_name(var)] = SymbolTableNode(MDEF, var)
+
+    def get_config_update(self, substmt: AssignmentStmt) -> Optional['ModelConfigData']:
+        """
+        Determines the config update due to a single statement in the Config class definition.
+
+        Warns if a tracked config attribute is set to a value the plugin doesn't know how to interpret (e.g., an int)
+        """
+        lhs = substmt.lvalues[0]
+        if not (isinstance(lhs, NameExpr) and lhs.name in self.tracked_config_fields):
+            return None
+        if lhs.name == 'extra':
+            if isinstance(substmt.rvalue, StrExpr):
+                forbid_extra = substmt.rvalue.value == 'forbid'
+            elif isinstance(substmt.rvalue, MemberExpr):
+                forbid_extra = substmt.rvalue.name == 'forbid'
+            else:
+                error_invalid_config_value(lhs.name, self._ctx.api, substmt)
+                return None
+            return ModelConfigData(forbid_extra=forbid_extra)
+        if lhs.name == 'alias_generator':
+            has_alias_generator = True
+            if isinstance(substmt.rvalue, NameExpr) and substmt.rvalue.fullname == 'builtins.None':
+                has_alias_generator = False
+            return ModelConfigData(has_alias_generator=has_alias_generator)
+        if isinstance(substmt.rvalue, NameExpr) and substmt.rvalue.fullname in ('builtins.True', 'builtins.False'):
+            return ModelConfigData(**{lhs.name: substmt.rvalue.fullname == 'builtins.True'})
+        error_invalid_config_value(lhs.name, self._ctx.api, substmt)
+        return None
+
+    @staticmethod
+    def get_is_required(cls: ClassDef, stmt: AssignmentStmt, lhs: NameExpr) -> bool:
+        """
+        Returns a boolean indicating whether the field defined in `stmt` is a required field.
+        """
+        expr = stmt.rvalue
+        if isinstance(expr, TempNode):
+            # TempNode means annotation-only, so only non-required if Optional
+            value_type = get_proper_type(cls.info[lhs.name].type)
+            return not PydanticModelTransformer.type_has_implicit_default(value_type)
+        if isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME:
+            # The "default value" is a call to `Field`; at this point, the field is
+            # only required if default is Ellipsis (i.e., `field_name: Annotation = Field(...)`) or if default_factory
+            # is specified.
+            for arg, name in zip(expr.args, expr.arg_names):
+                # If name is None, then this arg is the default because it is the only positional argument.
+                if name is None or name == 'default':
+                    return arg.__class__ is EllipsisExpr
+                if name == 'default_factory':
+                    return False
+            # In this case, default and default_factory are not specified, so we need to look at the annotation
+            value_type = get_proper_type(cls.info[lhs.name].type)
+            return not PydanticModelTransformer.type_has_implicit_default(value_type)
+        # Only required if the "default value" is Ellipsis (i.e., `field_name: Annotation = ...`)
+        return isinstance(expr, EllipsisExpr)
+
+    @staticmethod
+    def type_has_implicit_default(type_: Optional[ProperType]) -> bool:
+        """
+        Returns True if the passed type will be given an implicit default value.
+
+        In pydantic v1, this is the case for Optional types and Any (with default value None).
+        """
+        if isinstance(type_, AnyType):
+            # Annotated as Any
+            return True
+        if isinstance(type_, UnionType) and any(
+            isinstance(item, NoneType) or isinstance(item, AnyType) for item in type_.items
+        ):
+            # Annotated as Optional, or otherwise having NoneType or AnyType in the union
+            return True
+        return False
+
+    @staticmethod
+    def get_alias_info(stmt: AssignmentStmt) -> Tuple[Optional[str], bool]:
+        """
+        Returns a pair (alias, has_dynamic_alias), extracted from the declaration of the field defined in `stmt`.
+
+        `has_dynamic_alias` is True if and only if an alias is provided, but not as a string literal.
+        If `has_dynamic_alias` is True, `alias` will be None.
+        """
+        expr = stmt.rvalue
+        if isinstance(expr, TempNode):
+            # TempNode means annotation-only
+            return None, False
+
+        if not (
+            isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME
+        ):
+            # Assigned value is not a call to pydantic.fields.Field
+            return None, False
+
+        for i, arg_name in enumerate(expr.arg_names):
+            if arg_name != 'alias':
+                continue
+            arg = expr.args[i]
+            if isinstance(arg, StrExpr):
+                return arg.value, False
+            else:
+                return None, True
+        return None, False
+
+    def get_field_arguments(
+        self, fields: List['PydanticModelField'], typed: bool, force_all_optional: bool, use_alias: bool
+    ) -> List[Argument]:
+        """
+        Helper function used during the construction of the `__init__` and `construct` method signatures.
+
+        Returns a list of mypy Argument instances for use in the generated signatures.
+        """
+        info = self._ctx.cls.info
+        arguments = [
+            field.to_argument(info, typed=typed, force_optional=force_all_optional, use_alias=use_alias)
+            for field in fields
+            if not (use_alias and field.has_dynamic_alias)
+        ]
+        return arguments
+
+    def should_init_forbid_extra(self, fields: List['PydanticModelField'], config: 'ModelConfigData') -> bool:
+        """
+        Indicates whether the generated `__init__` should get a `**kwargs` at the end of its signature
+
+        We disallow arbitrary kwargs if the extra config setting is "forbid", or if the plugin config says to,
+        *unless* a required dynamic alias is present (since then we can't determine a valid signature).
+        """
+        if not config.allow_population_by_field_name:
+            if self.is_dynamic_alias_present(fields, bool(config.has_alias_generator)):
+                return False
+        if config.forbid_extra:
+            return True
+        return self.plugin_config.init_forbid_extra
+
+    @staticmethod
+    def is_dynamic_alias_present(fields: List['PydanticModelField'], has_alias_generator: bool) -> bool:
+        """
+        Returns whether any fields on the model have a "dynamic alias", i.e., an alias that cannot be
+        determined during static analysis.
+        """
+        for field in fields:
+            if field.has_dynamic_alias:
+                return True
+        if has_alias_generator:
+            for field in fields:
+                if field.alias is None:
+                    return True
+        return False
+
+
+class PydanticModelField:
+    def __init__(
+        self, name: str, is_required: bool, alias: Optional[str], has_dynamic_alias: bool, line: int, column: int
+    ):
+        self.name = name
+        self.is_required = is_required
+        self.alias = alias
+        self.has_dynamic_alias = has_dynamic_alias
+        self.line = line
+        self.column = column
+
+    def to_var(self, info: TypeInfo, use_alias: bool) -> Var:
+        name = self.name
+        if use_alias and self.alias is not None:
+            name = self.alias
+        return Var(name, info[self.name].type)
+
+    def to_argument(self, info: TypeInfo, typed: bool, force_optional: bool, use_alias: bool) -> Argument:
+        if typed and info[self.name].type is not None:
+            type_annotation = info[self.name].type
+        else:
+            type_annotation = AnyType(TypeOfAny.explicit)
+        return Argument(
+            variable=self.to_var(info, use_alias),
+            type_annotation=type_annotation,
+            initializer=None,
+            kind=ARG_NAMED_OPT if force_optional or not self.is_required else ARG_NAMED,
+        )
+
+    def serialize(self) -> JsonDict:
+        return self.__dict__
+
+    @classmethod
+    def deserialize(cls, info: TypeInfo, data: JsonDict) -> 'PydanticModelField':
+        return cls(**data)
+
+
+class ModelConfigData:
+    def __init__(
+        self,
+        forbid_extra: Optional[bool] = None,
+        allow_mutation: Optional[bool] = None,
+        frozen: Optional[bool] = None,
+        orm_mode: Optional[bool] = None,
+        allow_population_by_field_name: Optional[bool] = None,
+        has_alias_generator: Optional[bool] = None,
+    ):
+        self.forbid_extra = forbid_extra
+        self.allow_mutation = allow_mutation
+        self.frozen = frozen
+        self.orm_mode = orm_mode
+        self.allow_population_by_field_name = allow_population_by_field_name
+        self.has_alias_generator = has_alias_generator
+
+    def set_values_dict(self) -> Dict[str, Any]:
+        return {k: v for k, v in self.__dict__.items() if v is not None}
+
+    def update(self, config: Optional['ModelConfigData']) -> None:
+        if config is None:
+            return
+        for k, v in config.set_values_dict().items():
+            setattr(self, k, v)
+
+    def setdefault(self, key: str, value: Any) -> None:
+        if getattr(self, key) is None:
+            setattr(self, key, value)
+
+
+ERROR_ORM = ErrorCode('pydantic-orm', 'Invalid from_orm call', 'Pydantic')
+ERROR_CONFIG = ErrorCode('pydantic-config', 'Invalid config value', 'Pydantic')
+ERROR_ALIAS = ErrorCode('pydantic-alias', 'Dynamic alias disallowed', 'Pydantic')
+ERROR_UNEXPECTED = ErrorCode('pydantic-unexpected', 'Unexpected behavior', 'Pydantic')
+ERROR_UNTYPED = ErrorCode('pydantic-field', 'Untyped field disallowed', 'Pydantic')
+ERROR_FIELD_DEFAULTS = ErrorCode('pydantic-field', 'Invalid Field defaults', 'Pydantic')
+
+
+def error_from_orm(model_name: str, api: CheckerPluginInterface, context: Context) -> None:
+    api.fail(f'"{model_name}" does not have orm_mode=True', context, code=ERROR_ORM)
+
+
+def error_invalid_config_value(name: str, api: SemanticAnalyzerPluginInterface, context: Context) -> None:
+    api.fail(f'Invalid value for "Config.{name}"', context, code=ERROR_CONFIG)
+
+
+def error_required_dynamic_aliases(api: SemanticAnalyzerPluginInterface, context: Context) -> None:
+    api.fail('Required dynamic aliases disallowed', context, code=ERROR_ALIAS)
+
+
+def error_unexpected_behavior(
+    detail: str, api: Union[CheckerPluginInterface, SemanticAnalyzerPluginInterface], context: Context
+) -> None:  # pragma: no cover
+    # Can't think of a good way to test this, but I confirmed it renders as desired by adding to a non-error path
+    link = 'https://github.com/pydantic/pydantic/issues/new/choose'
+    full_message = f'The pydantic mypy plugin ran into unexpected behavior: {detail}\n'
+    full_message += f'Please consider reporting this bug at {link} so we can try to fix it!'
+    api.fail(full_message, context, code=ERROR_UNEXPECTED)
+
+
+def error_untyped_fields(api: SemanticAnalyzerPluginInterface, context: Context) -> None:
+    api.fail('Untyped fields disallowed', context, code=ERROR_UNTYPED)
+
+
+def error_default_and_default_factory_specified(api: CheckerPluginInterface, context: Context) -> None:
+    api.fail('Field default and default_factory cannot be specified together', context, code=ERROR_FIELD_DEFAULTS)
+
+
+def add_method(
+    ctx: ClassDefContext,
+    name: str,
+    args: List[Argument],
+    return_type: Type,
+    self_type: Optional[Type] = None,
+    tvar_def: Optional[TypeVarDef] = None,
+    is_classmethod: bool = False,
+    is_new: bool = False,
+    # is_staticmethod: bool = False,
+) -> None:
+    """
+    Adds a new method to a class.
+
+    This can be dropped if/when https://github.com/python/mypy/issues/7301 is merged
+    """
+    info = ctx.cls.info
+
+    # First remove any previously generated methods with the same name
+    # to avoid clashes and problems in the semantic analyzer.
+    if name in info.names:
+        sym = info.names[name]
+        if sym.plugin_generated and isinstance(sym.node, FuncDef):
+            ctx.cls.defs.body.remove(sym.node)  # pragma: no cover
+
+    self_type = self_type or fill_typevars(info)
+    if is_classmethod or is_new:
+        first = [Argument(Var('_cls'), TypeType.make_normalized(self_type), None, ARG_POS)]
+    # elif is_staticmethod:
+    #     first = []
+    else:
+        self_type = self_type or fill_typevars(info)
+        first = [Argument(Var('__pydantic_self__'), self_type, None, ARG_POS)]
+    args = first + args
+    arg_types, arg_names, arg_kinds = [], [], []
+    for arg in args:
+        assert arg.type_annotation, 'All arguments must be fully typed.'
+        arg_types.append(arg.type_annotation)
+        arg_names.append(get_name(arg.variable))
+        arg_kinds.append(arg.kind)
+
+    function_type = ctx.api.named_type(f'{BUILTINS_NAME}.function')
+    signature = CallableType(arg_types, arg_kinds, arg_names, return_type, function_type)
+    if tvar_def:
+        signature.variables = [tvar_def]
+
+    func = FuncDef(name, args, Block([PassStmt()]))
+    func.info = info
+    func.type = set_callable_name(signature, func)
+    func.is_class = is_classmethod
+    # func.is_static = is_staticmethod
+    func._fullname = get_fullname(info) + '.' + name
+    func.line = info.line
+
+    # NOTE: we would like the plugin generated node to dominate, but we still
+    # need to keep any existing definitions so they get semantically analyzed.
+    if name in info.names:
+        # Get a nice unique name instead.
+        r_name = get_unique_redefinition_name(name, info.names)
+        info.names[r_name] = info.names[name]
+
+    if is_classmethod:  # or is_staticmethod:
+        func.is_decorated = True
+        v = Var(name, func.type)
+        v.info = info
+        v._fullname = func._fullname
+        # if is_classmethod:
+        v.is_classmethod = True
+        dec = Decorator(func, [NameExpr('classmethod')], v)
+        # else:
+        #     v.is_staticmethod = True
+        #     dec = Decorator(func, [NameExpr('staticmethod')], v)
+
+        dec.line = info.line
+        sym = SymbolTableNode(MDEF, dec)
+    else:
+        sym = SymbolTableNode(MDEF, func)
+    sym.plugin_generated = True
+
+    info.names[name] = sym
+    info.defn.defs.body.append(func)
+
+
+def get_fullname(x: Union[FuncBase, SymbolNode]) -> str:
+    """
+    Used for compatibility with mypy 0.740; can be dropped once support for 0.740 is dropped.
+    """
+    fn = x.fullname
+    if callable(fn):  # pragma: no cover
+        return fn()
+    return fn
+
+
+def get_name(x: Union[FuncBase, SymbolNode]) -> str:
+    """
+    Used for compatibility with mypy 0.740; can be dropped once support for 0.740 is dropped.
+    """
+    fn = x.name
+    if callable(fn):  # pragma: no cover
+        return fn()
+    return fn
+
+
+def parse_toml(config_file: str) -> Optional[Dict[str, Any]]:
+    if not config_file.endswith('.toml'):
+        return None
+
+    read_mode = 'rb'
+    if sys.version_info >= (3, 11):
+        import tomllib as toml_
+    else:
+        try:
+            import tomli as toml_
+        except ImportError:
+            # older versions of mypy have toml as a dependency, not tomli
+            read_mode = 'r'
+            try:
+                import toml as toml_  # type: ignore[no-redef]
+            except ImportError:  # pragma: no cover
+                import warnings
+
+                warnings.warn('No TOML parser installed, cannot read configuration from `pyproject.toml`.')
+                return None
+
+    with open(config_file, read_mode) as rf:
+        return toml_.load(rf)  # type: ignore[arg-type]
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/networks.py b/.venv/lib/python3.12/site-packages/pydantic/v1/networks.py
new file mode 100644
index 00000000..ba07b748
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/networks.py
@@ -0,0 +1,747 @@
+import re
+from ipaddress import (
+    IPv4Address,
+    IPv4Interface,
+    IPv4Network,
+    IPv6Address,
+    IPv6Interface,
+    IPv6Network,
+    _BaseAddress,
+    _BaseNetwork,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Collection,
+    Dict,
+    Generator,
+    List,
+    Match,
+    Optional,
+    Pattern,
+    Set,
+    Tuple,
+    Type,
+    Union,
+    cast,
+    no_type_check,
+)
+
+from pydantic.v1 import errors
+from pydantic.v1.utils import Representation, update_not_none
+from pydantic.v1.validators import constr_length_validator, str_validator
+
+if TYPE_CHECKING:
+    import email_validator
+    from typing_extensions import TypedDict
+
+    from pydantic.v1.config import BaseConfig
+    from pydantic.v1.fields import ModelField
+    from pydantic.v1.typing import AnyCallable
+
+    CallableGenerator = Generator[AnyCallable, None, None]
+
+    class Parts(TypedDict, total=False):
+        scheme: str
+        user: Optional[str]
+        password: Optional[str]
+        ipv4: Optional[str]
+        ipv6: Optional[str]
+        domain: Optional[str]
+        port: Optional[str]
+        path: Optional[str]
+        query: Optional[str]
+        fragment: Optional[str]
+
+    class HostParts(TypedDict, total=False):
+        host: str
+        tld: Optional[str]
+        host_type: Optional[str]
+        port: Optional[str]
+        rebuild: bool
+
+else:
+    email_validator = None
+
+    class Parts(dict):
+        pass
+
+
+NetworkType = Union[str, bytes, int, Tuple[Union[str, bytes, int], Union[str, int]]]
+
+__all__ = [
+    'AnyUrl',
+    'AnyHttpUrl',
+    'FileUrl',
+    'HttpUrl',
+    'stricturl',
+    'EmailStr',
+    'NameEmail',
+    'IPvAnyAddress',
+    'IPvAnyInterface',
+    'IPvAnyNetwork',
+    'PostgresDsn',
+    'CockroachDsn',
+    'AmqpDsn',
+    'RedisDsn',
+    'MongoDsn',
+    'KafkaDsn',
+    'validate_email',
+]
+
+_url_regex_cache = None
+_multi_host_url_regex_cache = None
+_ascii_domain_regex_cache = None
+_int_domain_regex_cache = None
+_host_regex_cache = None
+
+_host_regex = (
+    r'(?:'
+    r'(?P<ipv4>(?:\d{1,3}\.){3}\d{1,3})(?=$|[/:#?])|'  # ipv4
+    r'(?P<ipv6>\[[A-F0-9]*:[A-F0-9:]+\])(?=$|[/:#?])|'  # ipv6
+    r'(?P<domain>[^\s/:?#]+)'  # domain, validation occurs later
+    r')?'
+    r'(?::(?P<port>\d+))?'  # port
+)
+_scheme_regex = r'(?:(?P<scheme>[a-z][a-z0-9+\-.]+)://)?'  # scheme https://tools.ietf.org/html/rfc3986#appendix-A
+_user_info_regex = r'(?:(?P<user>[^\s:/]*)(?::(?P<password>[^\s/]*))?@)?'
+_path_regex = r'(?P<path>/[^\s?#]*)?'
+_query_regex = r'(?:\?(?P<query>[^\s#]*))?'
+_fragment_regex = r'(?:#(?P<fragment>[^\s#]*))?'
+
+
+def url_regex() -> Pattern[str]:
+    global _url_regex_cache
+    if _url_regex_cache is None:
+        _url_regex_cache = re.compile(
+            rf'{_scheme_regex}{_user_info_regex}{_host_regex}{_path_regex}{_query_regex}{_fragment_regex}',
+            re.IGNORECASE,
+        )
+    return _url_regex_cache
+
+
+def multi_host_url_regex() -> Pattern[str]:
+    """
+    Compiled multi host url regex.
+
+    Additionally to `url_regex` it allows to match multiple hosts.
+    E.g. host1.db.net,host2.db.net
+    """
+    global _multi_host_url_regex_cache
+    if _multi_host_url_regex_cache is None:
+        _multi_host_url_regex_cache = re.compile(
+            rf'{_scheme_regex}{_user_info_regex}'
+            r'(?P<hosts>([^/]*))'  # validation occurs later
+            rf'{_path_regex}{_query_regex}{_fragment_regex}',
+            re.IGNORECASE,
+        )
+    return _multi_host_url_regex_cache
+
+
+def ascii_domain_regex() -> Pattern[str]:
+    global _ascii_domain_regex_cache
+    if _ascii_domain_regex_cache is None:
+        ascii_chunk = r'[_0-9a-z](?:[-_0-9a-z]{0,61}[_0-9a-z])?'
+        ascii_domain_ending = r'(?P<tld>\.[a-z]{2,63})?\.?'
+        _ascii_domain_regex_cache = re.compile(
+            fr'(?:{ascii_chunk}\.)*?{ascii_chunk}{ascii_domain_ending}', re.IGNORECASE
+        )
+    return _ascii_domain_regex_cache
+
+
+def int_domain_regex() -> Pattern[str]:
+    global _int_domain_regex_cache
+    if _int_domain_regex_cache is None:
+        int_chunk = r'[_0-9a-\U00040000](?:[-_0-9a-\U00040000]{0,61}[_0-9a-\U00040000])?'
+        int_domain_ending = r'(?P<tld>(\.[^\W\d_]{2,63})|(\.(?:xn--)[_0-9a-z-]{2,63}))?\.?'
+        _int_domain_regex_cache = re.compile(fr'(?:{int_chunk}\.)*?{int_chunk}{int_domain_ending}', re.IGNORECASE)
+    return _int_domain_regex_cache
+
+
+def host_regex() -> Pattern[str]:
+    global _host_regex_cache
+    if _host_regex_cache is None:
+        _host_regex_cache = re.compile(
+            _host_regex,
+            re.IGNORECASE,
+        )
+    return _host_regex_cache
+
+
+class AnyUrl(str):
+    strip_whitespace = True
+    min_length = 1
+    max_length = 2**16
+    allowed_schemes: Optional[Collection[str]] = None
+    tld_required: bool = False
+    user_required: bool = False
+    host_required: bool = True
+    hidden_parts: Set[str] = set()
+
+    __slots__ = ('scheme', 'user', 'password', 'host', 'tld', 'host_type', 'port', 'path', 'query', 'fragment')
+
+    @no_type_check
+    def __new__(cls, url: Optional[str], **kwargs) -> object:
+        return str.__new__(cls, cls.build(**kwargs) if url is None else url)
+
+    def __init__(
+        self,
+        url: str,
+        *,
+        scheme: str,
+        user: Optional[str] = None,
+        password: Optional[str] = None,
+        host: Optional[str] = None,
+        tld: Optional[str] = None,
+        host_type: str = 'domain',
+        port: Optional[str] = None,
+        path: Optional[str] = None,
+        query: Optional[str] = None,
+        fragment: Optional[str] = None,
+    ) -> None:
+        str.__init__(url)
+        self.scheme = scheme
+        self.user = user
+        self.password = password
+        self.host = host
+        self.tld = tld
+        self.host_type = host_type
+        self.port = port
+        self.path = path
+        self.query = query
+        self.fragment = fragment
+
+    @classmethod
+    def build(
+        cls,
+        *,
+        scheme: str,
+        user: Optional[str] = None,
+        password: Optional[str] = None,
+        host: str,
+        port: Optional[str] = None,
+        path: Optional[str] = None,
+        query: Optional[str] = None,
+        fragment: Optional[str] = None,
+        **_kwargs: str,
+    ) -> str:
+        parts = Parts(
+            scheme=scheme,
+            user=user,
+            password=password,
+            host=host,
+            port=port,
+            path=path,
+            query=query,
+            fragment=fragment,
+            **_kwargs,  # type: ignore[misc]
+        )
+
+        url = scheme + '://'
+        if user:
+            url += user
+        if password:
+            url += ':' + password
+        if user or password:
+            url += '@'
+        url += host
+        if port and ('port' not in cls.hidden_parts or cls.get_default_parts(parts).get('port') != port):
+            url += ':' + port
+        if path:
+            url += path
+        if query:
+            url += '?' + query
+        if fragment:
+            url += '#' + fragment
+        return url
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minLength=cls.min_length, maxLength=cls.max_length, format='uri')
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Any, field: 'ModelField', config: 'BaseConfig') -> 'AnyUrl':
+        if value.__class__ == cls:
+            return value
+        value = str_validator(value)
+        if cls.strip_whitespace:
+            value = value.strip()
+        url: str = cast(str, constr_length_validator(value, field, config))
+
+        m = cls._match_url(url)
+        # the regex should always match, if it doesn't please report with details of the URL tried
+        assert m, 'URL regex failed unexpectedly'
+
+        original_parts = cast('Parts', m.groupdict())
+        parts = cls.apply_default_parts(original_parts)
+        parts = cls.validate_parts(parts)
+
+        if m.end() != len(url):
+            raise errors.UrlExtraError(extra=url[m.end() :])
+
+        return cls._build_url(m, url, parts)
+
+    @classmethod
+    def _build_url(cls, m: Match[str], url: str, parts: 'Parts') -> 'AnyUrl':
+        """
+        Validate hosts and build the AnyUrl object. Split from `validate` so this method
+        can be altered in `MultiHostDsn`.
+        """
+        host, tld, host_type, rebuild = cls.validate_host(parts)
+
+        return cls(
+            None if rebuild else url,
+            scheme=parts['scheme'],
+            user=parts['user'],
+            password=parts['password'],
+            host=host,
+            tld=tld,
+            host_type=host_type,
+            port=parts['port'],
+            path=parts['path'],
+            query=parts['query'],
+            fragment=parts['fragment'],
+        )
+
+    @staticmethod
+    def _match_url(url: str) -> Optional[Match[str]]:
+        return url_regex().match(url)
+
+    @staticmethod
+    def _validate_port(port: Optional[str]) -> None:
+        if port is not None and int(port) > 65_535:
+            raise errors.UrlPortError()
+
+    @classmethod
+    def validate_parts(cls, parts: 'Parts', validate_port: bool = True) -> 'Parts':
+        """
+        A method used to validate parts of a URL.
+        Could be overridden to set default values for parts if missing
+        """
+        scheme = parts['scheme']
+        if scheme is None:
+            raise errors.UrlSchemeError()
+
+        if cls.allowed_schemes and scheme.lower() not in cls.allowed_schemes:
+            raise errors.UrlSchemePermittedError(set(cls.allowed_schemes))
+
+        if validate_port:
+            cls._validate_port(parts['port'])
+
+        user = parts['user']
+        if cls.user_required and user is None:
+            raise errors.UrlUserInfoError()
+
+        return parts
+
+    @classmethod
+    def validate_host(cls, parts: 'Parts') -> Tuple[str, Optional[str], str, bool]:
+        tld, host_type, rebuild = None, None, False
+        for f in ('domain', 'ipv4', 'ipv6'):
+            host = parts[f]  # type: ignore[literal-required]
+            if host:
+                host_type = f
+                break
+
+        if host is None:
+            if cls.host_required:
+                raise errors.UrlHostError()
+        elif host_type == 'domain':
+            is_international = False
+            d = ascii_domain_regex().fullmatch(host)
+            if d is None:
+                d = int_domain_regex().fullmatch(host)
+                if d is None:
+                    raise errors.UrlHostError()
+                is_international = True
+
+            tld = d.group('tld')
+            if tld is None and not is_international:
+                d = int_domain_regex().fullmatch(host)
+                assert d is not None
+                tld = d.group('tld')
+                is_international = True
+
+            if tld is not None:
+                tld = tld[1:]
+            elif cls.tld_required:
+                raise errors.UrlHostTldError()
+
+            if is_international:
+                host_type = 'int_domain'
+                rebuild = True
+                host = host.encode('idna').decode('ascii')
+                if tld is not None:
+                    tld = tld.encode('idna').decode('ascii')
+
+        return host, tld, host_type, rebuild  # type: ignore
+
+    @staticmethod
+    def get_default_parts(parts: 'Parts') -> 'Parts':
+        return {}
+
+    @classmethod
+    def apply_default_parts(cls, parts: 'Parts') -> 'Parts':
+        for key, value in cls.get_default_parts(parts).items():
+            if not parts[key]:  # type: ignore[literal-required]
+                parts[key] = value  # type: ignore[literal-required]
+        return parts
+
+    def __repr__(self) -> str:
+        extra = ', '.join(f'{n}={getattr(self, n)!r}' for n in self.__slots__ if getattr(self, n) is not None)
+        return f'{self.__class__.__name__}({super().__repr__()}, {extra})'
+
+
+class AnyHttpUrl(AnyUrl):
+    allowed_schemes = {'http', 'https'}
+
+    __slots__ = ()
+
+
+class HttpUrl(AnyHttpUrl):
+    tld_required = True
+    # https://stackoverflow.com/questions/417142/what-is-the-maximum-length-of-a-url-in-different-browsers
+    max_length = 2083
+    hidden_parts = {'port'}
+
+    @staticmethod
+    def get_default_parts(parts: 'Parts') -> 'Parts':
+        return {'port': '80' if parts['scheme'] == 'http' else '443'}
+
+
+class FileUrl(AnyUrl):
+    allowed_schemes = {'file'}
+    host_required = False
+
+    __slots__ = ()
+
+
+class MultiHostDsn(AnyUrl):
+    __slots__ = AnyUrl.__slots__ + ('hosts',)
+
+    def __init__(self, *args: Any, hosts: Optional[List['HostParts']] = None, **kwargs: Any):
+        super().__init__(*args, **kwargs)
+        self.hosts = hosts
+
+    @staticmethod
+    def _match_url(url: str) -> Optional[Match[str]]:
+        return multi_host_url_regex().match(url)
+
+    @classmethod
+    def validate_parts(cls, parts: 'Parts', validate_port: bool = True) -> 'Parts':
+        return super().validate_parts(parts, validate_port=False)
+
+    @classmethod
+    def _build_url(cls, m: Match[str], url: str, parts: 'Parts') -> 'MultiHostDsn':
+        hosts_parts: List['HostParts'] = []
+        host_re = host_regex()
+        for host in m.groupdict()['hosts'].split(','):
+            d: Parts = host_re.match(host).groupdict()  # type: ignore
+            host, tld, host_type, rebuild = cls.validate_host(d)
+            port = d.get('port')
+            cls._validate_port(port)
+            hosts_parts.append(
+                {
+                    'host': host,
+                    'host_type': host_type,
+                    'tld': tld,
+                    'rebuild': rebuild,
+                    'port': port,
+                }
+            )
+
+        if len(hosts_parts) > 1:
+            return cls(
+                None if any([hp['rebuild'] for hp in hosts_parts]) else url,
+                scheme=parts['scheme'],
+                user=parts['user'],
+                password=parts['password'],
+                path=parts['path'],
+                query=parts['query'],
+                fragment=parts['fragment'],
+                host_type=None,
+                hosts=hosts_parts,
+            )
+        else:
+            # backwards compatibility with single host
+            host_part = hosts_parts[0]
+            return cls(
+                None if host_part['rebuild'] else url,
+                scheme=parts['scheme'],
+                user=parts['user'],
+                password=parts['password'],
+                host=host_part['host'],
+                tld=host_part['tld'],
+                host_type=host_part['host_type'],
+                port=host_part.get('port'),
+                path=parts['path'],
+                query=parts['query'],
+                fragment=parts['fragment'],
+            )
+
+
+class PostgresDsn(MultiHostDsn):
+    allowed_schemes = {
+        'postgres',
+        'postgresql',
+        'postgresql+asyncpg',
+        'postgresql+pg8000',
+        'postgresql+psycopg',
+        'postgresql+psycopg2',
+        'postgresql+psycopg2cffi',
+        'postgresql+py-postgresql',
+        'postgresql+pygresql',
+    }
+    user_required = True
+
+    __slots__ = ()
+
+
+class CockroachDsn(AnyUrl):
+    allowed_schemes = {
+        'cockroachdb',
+        'cockroachdb+psycopg2',
+        'cockroachdb+asyncpg',
+    }
+    user_required = True
+
+
+class AmqpDsn(AnyUrl):
+    allowed_schemes = {'amqp', 'amqps'}
+    host_required = False
+
+
+class RedisDsn(AnyUrl):
+    __slots__ = ()
+    allowed_schemes = {'redis', 'rediss'}
+    host_required = False
+
+    @staticmethod
+    def get_default_parts(parts: 'Parts') -> 'Parts':
+        return {
+            'domain': 'localhost' if not (parts['ipv4'] or parts['ipv6']) else '',
+            'port': '6379',
+            'path': '/0',
+        }
+
+
+class MongoDsn(AnyUrl):
+    allowed_schemes = {'mongodb'}
+
+    # TODO: Needed to generic "Parts" for "Replica Set", "Sharded Cluster", and other mongodb deployment modes
+    @staticmethod
+    def get_default_parts(parts: 'Parts') -> 'Parts':
+        return {
+            'port': '27017',
+        }
+
+
+class KafkaDsn(AnyUrl):
+    allowed_schemes = {'kafka'}
+
+    @staticmethod
+    def get_default_parts(parts: 'Parts') -> 'Parts':
+        return {
+            'domain': 'localhost',
+            'port': '9092',
+        }
+
+
+def stricturl(
+    *,
+    strip_whitespace: bool = True,
+    min_length: int = 1,
+    max_length: int = 2**16,
+    tld_required: bool = True,
+    host_required: bool = True,
+    allowed_schemes: Optional[Collection[str]] = None,
+) -> Type[AnyUrl]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(
+        strip_whitespace=strip_whitespace,
+        min_length=min_length,
+        max_length=max_length,
+        tld_required=tld_required,
+        host_required=host_required,
+        allowed_schemes=allowed_schemes,
+    )
+    return type('UrlValue', (AnyUrl,), namespace)
+
+
+def import_email_validator() -> None:
+    global email_validator
+    try:
+        import email_validator
+    except ImportError as e:
+        raise ImportError('email-validator is not installed, run `pip install pydantic[email]`') from e
+
+
+class EmailStr(str):
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type='string', format='email')
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        # included here and below so the error happens straight away
+        import_email_validator()
+
+        yield str_validator
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Union[str]) -> str:
+        return validate_email(value)[1]
+
+
+class NameEmail(Representation):
+    __slots__ = 'name', 'email'
+
+    def __init__(self, name: str, email: str):
+        self.name = name
+        self.email = email
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, NameEmail) and (self.name, self.email) == (other.name, other.email)
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type='string', format='name-email')
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        import_email_validator()
+
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Any) -> 'NameEmail':
+        if value.__class__ == cls:
+            return value
+        value = str_validator(value)
+        return cls(*validate_email(value))
+
+    def __str__(self) -> str:
+        return f'{self.name} <{self.email}>'
+
+
+class IPvAnyAddress(_BaseAddress):
+    __slots__ = ()
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type='string', format='ipvanyaddress')
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Union[str, bytes, int]) -> Union[IPv4Address, IPv6Address]:
+        try:
+            return IPv4Address(value)
+        except ValueError:
+            pass
+
+        try:
+            return IPv6Address(value)
+        except ValueError:
+            raise errors.IPvAnyAddressError()
+
+
+class IPvAnyInterface(_BaseAddress):
+    __slots__ = ()
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type='string', format='ipvanyinterface')
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: NetworkType) -> Union[IPv4Interface, IPv6Interface]:
+        try:
+            return IPv4Interface(value)
+        except ValueError:
+            pass
+
+        try:
+            return IPv6Interface(value)
+        except ValueError:
+            raise errors.IPvAnyInterfaceError()
+
+
+class IPvAnyNetwork(_BaseNetwork):  # type: ignore
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type='string', format='ipvanynetwork')
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: NetworkType) -> Union[IPv4Network, IPv6Network]:
+        # Assume IP Network is defined with a default value for ``strict`` argument.
+        # Define your own class if you want to specify network address check strictness.
+        try:
+            return IPv4Network(value)
+        except ValueError:
+            pass
+
+        try:
+            return IPv6Network(value)
+        except ValueError:
+            raise errors.IPvAnyNetworkError()
+
+
+pretty_email_regex = re.compile(r'([\w ]*?) *<(.*)> *')
+MAX_EMAIL_LENGTH = 2048
+"""Maximum length for an email.
+A somewhat arbitrary but very generous number compared to what is allowed by most implementations.
+"""
+
+
+def validate_email(value: Union[str]) -> Tuple[str, str]:
+    """
+    Email address validation using https://pypi.org/project/email-validator/
+    Notes:
+    * raw ip address (literal) domain parts are not allowed.
+    * "John Doe <local_part@domain.com>" style "pretty" email addresses are processed
+    * spaces are striped from the beginning and end of addresses but no error is raised
+    """
+    if email_validator is None:
+        import_email_validator()
+
+    if len(value) > MAX_EMAIL_LENGTH:
+        raise errors.EmailError()
+
+    m = pretty_email_regex.fullmatch(value)
+    name: Union[str, None] = None
+    if m:
+        name, value = m.groups()
+    email = value.strip()
+    try:
+        parts = email_validator.validate_email(email, check_deliverability=False)
+    except email_validator.EmailNotValidError as e:
+        raise errors.EmailError from e
+
+    if hasattr(parts, 'normalized'):
+        # email-validator >= 2
+        email = parts.normalized
+        assert email is not None
+        name = name or parts.local_part
+        return name, email
+    else:
+        # email-validator >1, <2
+        at_index = email.index('@')
+        local_part = email[:at_index]  # RFC 5321, local part must be case-sensitive.
+        global_part = email[at_index:].lower()
+
+        return name or local_part, local_part + global_part
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/parse.py b/.venv/lib/python3.12/site-packages/pydantic/v1/parse.py
new file mode 100644
index 00000000..431d75a6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/parse.py
@@ -0,0 +1,66 @@
+import json
+import pickle
+from enum import Enum
+from pathlib import Path
+from typing import Any, Callable, Union
+
+from pydantic.v1.types import StrBytes
+
+
+class Protocol(str, Enum):
+    json = 'json'
+    pickle = 'pickle'
+
+
+def load_str_bytes(
+    b: StrBytes,
+    *,
+    content_type: str = None,
+    encoding: str = 'utf8',
+    proto: Protocol = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    if proto is None and content_type:
+        if content_type.endswith(('json', 'javascript')):
+            pass
+        elif allow_pickle and content_type.endswith('pickle'):
+            proto = Protocol.pickle
+        else:
+            raise TypeError(f'Unknown content-type: {content_type}')
+
+    proto = proto or Protocol.json
+
+    if proto == Protocol.json:
+        if isinstance(b, bytes):
+            b = b.decode(encoding)
+        return json_loads(b)
+    elif proto == Protocol.pickle:
+        if not allow_pickle:
+            raise RuntimeError('Trying to decode with pickle with allow_pickle=False')
+        bb = b if isinstance(b, bytes) else b.encode()
+        return pickle.loads(bb)
+    else:
+        raise TypeError(f'Unknown protocol: {proto}')
+
+
+def load_file(
+    path: Union[str, Path],
+    *,
+    content_type: str = None,
+    encoding: str = 'utf8',
+    proto: Protocol = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    path = Path(path)
+    b = path.read_bytes()
+    if content_type is None:
+        if path.suffix in ('.js', '.json'):
+            proto = Protocol.json
+        elif path.suffix == '.pkl':
+            proto = Protocol.pickle
+
+    return load_str_bytes(
+        b, proto=proto, content_type=content_type, encoding=encoding, allow_pickle=allow_pickle, json_loads=json_loads
+    )
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/py.typed b/.venv/lib/python3.12/site-packages/pydantic/v1/py.typed
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/py.typed
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/schema.py b/.venv/lib/python3.12/site-packages/pydantic/v1/schema.py
new file mode 100644
index 00000000..a91fe2cd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/schema.py
@@ -0,0 +1,1163 @@
+import re
+import warnings
+from collections import defaultdict
+from dataclasses import is_dataclass
+from datetime import date, datetime, time, timedelta
+from decimal import Decimal
+from enum import Enum
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    ForwardRef,
+    FrozenSet,
+    Generic,
+    Iterable,
+    List,
+    Optional,
+    Pattern,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+from uuid import UUID
+
+from typing_extensions import Annotated, Literal
+
+from pydantic.v1.fields import (
+    MAPPING_LIKE_SHAPES,
+    SHAPE_DEQUE,
+    SHAPE_FROZENSET,
+    SHAPE_GENERIC,
+    SHAPE_ITERABLE,
+    SHAPE_LIST,
+    SHAPE_SEQUENCE,
+    SHAPE_SET,
+    SHAPE_SINGLETON,
+    SHAPE_TUPLE,
+    SHAPE_TUPLE_ELLIPSIS,
+    FieldInfo,
+    ModelField,
+)
+from pydantic.v1.json import pydantic_encoder
+from pydantic.v1.networks import AnyUrl, EmailStr
+from pydantic.v1.types import (
+    ConstrainedDecimal,
+    ConstrainedFloat,
+    ConstrainedFrozenSet,
+    ConstrainedInt,
+    ConstrainedList,
+    ConstrainedSet,
+    ConstrainedStr,
+    SecretBytes,
+    SecretStr,
+    StrictBytes,
+    StrictStr,
+    conbytes,
+    condecimal,
+    confloat,
+    confrozenset,
+    conint,
+    conlist,
+    conset,
+    constr,
+)
+from pydantic.v1.typing import (
+    all_literal_values,
+    get_args,
+    get_origin,
+    get_sub_types,
+    is_callable_type,
+    is_literal_type,
+    is_namedtuple,
+    is_none_type,
+    is_union,
+)
+from pydantic.v1.utils import ROOT_KEY, get_model, lenient_issubclass
+
+if TYPE_CHECKING:
+    from pydantic.v1.dataclasses import Dataclass
+    from pydantic.v1.main import BaseModel
+
+default_prefix = '#/definitions/'
+default_ref_template = '#/definitions/{model}'
+
+TypeModelOrEnum = Union[Type['BaseModel'], Type[Enum]]
+TypeModelSet = Set[TypeModelOrEnum]
+
+
+def _apply_modify_schema(
+    modify_schema: Callable[..., None], field: Optional[ModelField], field_schema: Dict[str, Any]
+) -> None:
+    from inspect import signature
+
+    sig = signature(modify_schema)
+    args = set(sig.parameters.keys())
+    if 'field' in args or 'kwargs' in args:
+        modify_schema(field_schema, field=field)
+    else:
+        modify_schema(field_schema)
+
+
+def schema(
+    models: Sequence[Union[Type['BaseModel'], Type['Dataclass']]],
+    *,
+    by_alias: bool = True,
+    title: Optional[str] = None,
+    description: Optional[str] = None,
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+) -> Dict[str, Any]:
+    """
+    Process a list of models and generate a single JSON Schema with all of them defined in the ``definitions``
+    top-level JSON key, including their sub-models.
+
+    :param models: a list of models to include in the generated JSON Schema
+    :param by_alias: generate the schemas using the aliases defined, if any
+    :param title: title for the generated schema that includes the definitions
+    :param description: description for the generated schema
+    :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
+      default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
+      else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
+      top-level key ``definitions``, so you can extract them from there. But all the references will have the set
+      prefix.
+    :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful
+      for references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For
+      a sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
+    :return: dict with the JSON Schema with a ``definitions`` top-level key including the schema definitions for
+      the models and sub-models passed in ``models``.
+    """
+    clean_models = [get_model(model) for model in models]
+    flat_models = get_flat_models_from_models(clean_models)
+    model_name_map = get_model_name_map(flat_models)
+    definitions = {}
+    output_schema: Dict[str, Any] = {}
+    if title:
+        output_schema['title'] = title
+    if description:
+        output_schema['description'] = description
+    for model in clean_models:
+        m_schema, m_definitions, m_nested_models = model_process_schema(
+            model,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+        )
+        definitions.update(m_definitions)
+        model_name = model_name_map[model]
+        definitions[model_name] = m_schema
+    if definitions:
+        output_schema['definitions'] = definitions
+    return output_schema
+
+
+def model_schema(
+    model: Union[Type['BaseModel'], Type['Dataclass']],
+    by_alias: bool = True,
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+) -> Dict[str, Any]:
+    """
+    Generate a JSON Schema for one model. With all the sub-models defined in the ``definitions`` top-level
+    JSON key.
+
+    :param model: a Pydantic model (a class that inherits from BaseModel)
+    :param by_alias: generate the schemas using the aliases defined, if any
+    :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
+      default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
+      else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
+      top-level key ``definitions``, so you can extract them from there. But all the references will have the set
+      prefix.
+    :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
+      references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
+      sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
+    :return: dict with the JSON Schema for the passed ``model``
+    """
+    model = get_model(model)
+    flat_models = get_flat_models_from_model(model)
+    model_name_map = get_model_name_map(flat_models)
+    model_name = model_name_map[model]
+    m_schema, m_definitions, nested_models = model_process_schema(
+        model, by_alias=by_alias, model_name_map=model_name_map, ref_prefix=ref_prefix, ref_template=ref_template
+    )
+    if model_name in nested_models:
+        # model_name is in Nested models, it has circular references
+        m_definitions[model_name] = m_schema
+        m_schema = get_schema_ref(model_name, ref_prefix, ref_template, False)
+    if m_definitions:
+        m_schema.update({'definitions': m_definitions})
+    return m_schema
+
+
+def get_field_info_schema(field: ModelField, schema_overrides: bool = False) -> Tuple[Dict[str, Any], bool]:
+    # If no title is explicitly set, we don't set title in the schema for enums.
+    # The behaviour is the same as `BaseModel` reference, where the default title
+    # is in the definitions part of the schema.
+    schema_: Dict[str, Any] = {}
+    if field.field_info.title or not lenient_issubclass(field.type_, Enum):
+        schema_['title'] = field.field_info.title or field.alias.title().replace('_', ' ')
+
+    if field.field_info.title:
+        schema_overrides = True
+
+    if field.field_info.description:
+        schema_['description'] = field.field_info.description
+        schema_overrides = True
+
+    if not field.required and field.default is not None and not is_callable_type(field.outer_type_):
+        schema_['default'] = encode_default(field.default)
+        schema_overrides = True
+
+    return schema_, schema_overrides
+
+
+def field_schema(
+    field: ModelField,
+    *,
+    by_alias: bool = True,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+    known_models: Optional[TypeModelSet] = None,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    Process a Pydantic field and return a tuple with a JSON Schema for it as the first item.
+    Also return a dictionary of definitions with models as keys and their schemas as values. If the passed field
+    is a model and has sub-models, and those sub-models don't have overrides (as ``title``, ``default``, etc), they
+    will be included in the definitions and referenced in the schema instead of included recursively.
+
+    :param field: a Pydantic ``ModelField``
+    :param by_alias: use the defined alias (if any) in the returned schema
+    :param model_name_map: used to generate the JSON Schema references to other models included in the definitions
+    :param ref_prefix: the JSON Pointer prefix to use for references to other schemas, if None, the default of
+      #/definitions/ will be used
+    :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
+      references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
+      sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
+    :param known_models: used to solve circular references
+    :return: tuple of the schema for this field and additional definitions
+    """
+    s, schema_overrides = get_field_info_schema(field)
+
+    validation_schema = get_field_schema_validations(field)
+    if validation_schema:
+        s.update(validation_schema)
+        schema_overrides = True
+
+    f_schema, f_definitions, f_nested_models = field_type_schema(
+        field,
+        by_alias=by_alias,
+        model_name_map=model_name_map,
+        schema_overrides=schema_overrides,
+        ref_prefix=ref_prefix,
+        ref_template=ref_template,
+        known_models=known_models or set(),
+    )
+
+    # $ref will only be returned when there are no schema_overrides
+    if '$ref' in f_schema:
+        return f_schema, f_definitions, f_nested_models
+    else:
+        s.update(f_schema)
+        return s, f_definitions, f_nested_models
+
+
+numeric_types = (int, float, Decimal)
+_str_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
+    ('max_length', numeric_types, 'maxLength'),
+    ('min_length', numeric_types, 'minLength'),
+    ('regex', str, 'pattern'),
+)
+
+_numeric_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
+    ('gt', numeric_types, 'exclusiveMinimum'),
+    ('lt', numeric_types, 'exclusiveMaximum'),
+    ('ge', numeric_types, 'minimum'),
+    ('le', numeric_types, 'maximum'),
+    ('multiple_of', numeric_types, 'multipleOf'),
+)
+
+
+def get_field_schema_validations(field: ModelField) -> Dict[str, Any]:
+    """
+    Get the JSON Schema validation keywords for a ``field`` with an annotation of
+    a Pydantic ``FieldInfo`` with validation arguments.
+    """
+    f_schema: Dict[str, Any] = {}
+
+    if lenient_issubclass(field.type_, Enum):
+        # schema is already updated by `enum_process_schema`; just update with field extra
+        if field.field_info.extra:
+            f_schema.update(field.field_info.extra)
+        return f_schema
+
+    if lenient_issubclass(field.type_, (str, bytes)):
+        for attr_name, t, keyword in _str_types_attrs:
+            attr = getattr(field.field_info, attr_name, None)
+            if isinstance(attr, t):
+                f_schema[keyword] = attr
+    if lenient_issubclass(field.type_, numeric_types) and not issubclass(field.type_, bool):
+        for attr_name, t, keyword in _numeric_types_attrs:
+            attr = getattr(field.field_info, attr_name, None)
+            if isinstance(attr, t):
+                f_schema[keyword] = attr
+    if field.field_info is not None and field.field_info.const:
+        f_schema['const'] = field.default
+    if field.field_info.extra:
+        f_schema.update(field.field_info.extra)
+    modify_schema = getattr(field.outer_type_, '__modify_schema__', None)
+    if modify_schema:
+        _apply_modify_schema(modify_schema, field, f_schema)
+    return f_schema
+
+
+def get_model_name_map(unique_models: TypeModelSet) -> Dict[TypeModelOrEnum, str]:
+    """
+    Process a set of models and generate unique names for them to be used as keys in the JSON Schema
+    definitions. By default the names are the same as the class name. But if two models in different Python
+    modules have the same name (e.g. "users.Model" and "items.Model"), the generated names will be
+    based on the Python module path for those conflicting models to prevent name collisions.
+
+    :param unique_models: a Python set of models
+    :return: dict mapping models to names
+    """
+    name_model_map = {}
+    conflicting_names: Set[str] = set()
+    for model in unique_models:
+        model_name = normalize_name(model.__name__)
+        if model_name in conflicting_names:
+            model_name = get_long_model_name(model)
+            name_model_map[model_name] = model
+        elif model_name in name_model_map:
+            conflicting_names.add(model_name)
+            conflicting_model = name_model_map.pop(model_name)
+            name_model_map[get_long_model_name(conflicting_model)] = conflicting_model
+            name_model_map[get_long_model_name(model)] = model
+        else:
+            name_model_map[model_name] = model
+    return {v: k for k, v in name_model_map.items()}
+
+
+def get_flat_models_from_model(model: Type['BaseModel'], known_models: Optional[TypeModelSet] = None) -> TypeModelSet:
+    """
+    Take a single ``model`` and generate a set with itself and all the sub-models in the tree. I.e. if you pass
+    model ``Foo`` (subclass of Pydantic ``BaseModel``) as ``model``, and it has a field of type ``Bar`` (also
+    subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also subclass of ``BaseModel``),
+    the return value will be ``set([Foo, Bar, Baz])``.
+
+    :param model: a Pydantic ``BaseModel`` subclass
+    :param known_models: used to solve circular references
+    :return: a set with the initial model and all its sub-models
+    """
+    known_models = known_models or set()
+    flat_models: TypeModelSet = set()
+    flat_models.add(model)
+    known_models |= flat_models
+    fields = cast(Sequence[ModelField], model.__fields__.values())
+    flat_models |= get_flat_models_from_fields(fields, known_models=known_models)
+    return flat_models
+
+
+def get_flat_models_from_field(field: ModelField, known_models: TypeModelSet) -> TypeModelSet:
+    """
+    Take a single Pydantic ``ModelField`` (from a model) that could have been declared as a subclass of BaseModel
+    (so, it could be a submodel), and generate a set with its model and all the sub-models in the tree.
+    I.e. if you pass a field that was declared to be of type ``Foo`` (subclass of BaseModel) as ``field``, and that
+    model ``Foo`` has a field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of
+    type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
+
+    :param field: a Pydantic ``ModelField``
+    :param known_models: used to solve circular references
+    :return: a set with the model used in the declaration for this field, if any, and all its sub-models
+    """
+    from pydantic.v1.main import BaseModel
+
+    flat_models: TypeModelSet = set()
+
+    field_type = field.type_
+    if lenient_issubclass(getattr(field_type, '__pydantic_model__', None), BaseModel):
+        field_type = field_type.__pydantic_model__
+
+    if field.sub_fields and not lenient_issubclass(field_type, BaseModel):
+        flat_models |= get_flat_models_from_fields(field.sub_fields, known_models=known_models)
+    elif lenient_issubclass(field_type, BaseModel) and field_type not in known_models:
+        flat_models |= get_flat_models_from_model(field_type, known_models=known_models)
+    elif lenient_issubclass(field_type, Enum):
+        flat_models.add(field_type)
+    return flat_models
+
+
+def get_flat_models_from_fields(fields: Sequence[ModelField], known_models: TypeModelSet) -> TypeModelSet:
+    """
+    Take a list of Pydantic  ``ModelField``s (from a model) that could have been declared as subclasses of ``BaseModel``
+    (so, any of them could be a submodel), and generate a set with their models and all the sub-models in the tree.
+    I.e. if you pass a the fields of a model ``Foo`` (subclass of ``BaseModel``) as ``fields``, and on of them has a
+    field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also
+    subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
+
+    :param fields: a list of Pydantic ``ModelField``s
+    :param known_models: used to solve circular references
+    :return: a set with any model declared in the fields, and all their sub-models
+    """
+    flat_models: TypeModelSet = set()
+    for field in fields:
+        flat_models |= get_flat_models_from_field(field, known_models=known_models)
+    return flat_models
+
+
+def get_flat_models_from_models(models: Sequence[Type['BaseModel']]) -> TypeModelSet:
+    """
+    Take a list of ``models`` and generate a set with them and all their sub-models in their trees. I.e. if you pass
+    a list of two models, ``Foo`` and ``Bar``, both subclasses of Pydantic ``BaseModel`` as models, and ``Bar`` has
+    a field of type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
+    """
+    flat_models: TypeModelSet = set()
+    for model in models:
+        flat_models |= get_flat_models_from_model(model)
+    return flat_models
+
+
+def get_long_model_name(model: TypeModelOrEnum) -> str:
+    return f'{model.__module__}__{model.__qualname__}'.replace('.', '__')
+
+
+def field_type_schema(
+    field: ModelField,
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    schema_overrides: bool = False,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    Used by ``field_schema()``, you probably should be using that function.
+
+    Take a single ``field`` and generate the schema for its type only, not including additional
+    information as title, etc. Also return additional schema definitions, from sub-models.
+    """
+    from pydantic.v1.main import BaseModel  # noqa: F811
+
+    definitions = {}
+    nested_models: Set[str] = set()
+    f_schema: Dict[str, Any]
+    if field.shape in {
+        SHAPE_LIST,
+        SHAPE_TUPLE_ELLIPSIS,
+        SHAPE_SEQUENCE,
+        SHAPE_SET,
+        SHAPE_FROZENSET,
+        SHAPE_ITERABLE,
+        SHAPE_DEQUE,
+    }:
+        items_schema, f_definitions, f_nested_models = field_singleton_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+        f_schema = {'type': 'array', 'items': items_schema}
+        if field.shape in {SHAPE_SET, SHAPE_FROZENSET}:
+            f_schema['uniqueItems'] = True
+
+    elif field.shape in MAPPING_LIKE_SHAPES:
+        f_schema = {'type': 'object'}
+        key_field = cast(ModelField, field.key_field)
+        regex = getattr(key_field.type_, 'regex', None)
+        items_schema, f_definitions, f_nested_models = field_singleton_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+        if regex:
+            # Dict keys have a regex pattern
+            # items_schema might be a schema or empty dict, add it either way
+            f_schema['patternProperties'] = {ConstrainedStr._get_pattern(regex): items_schema}
+        if items_schema:
+            # The dict values are not simply Any, so they need a schema
+            f_schema['additionalProperties'] = items_schema
+    elif field.shape == SHAPE_TUPLE or (field.shape == SHAPE_GENERIC and not issubclass(field.type_, BaseModel)):
+        sub_schema = []
+        sub_fields = cast(List[ModelField], field.sub_fields)
+        for sf in sub_fields:
+            sf_schema, sf_definitions, sf_nested_models = field_type_schema(
+                sf,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+            definitions.update(sf_definitions)
+            nested_models.update(sf_nested_models)
+            sub_schema.append(sf_schema)
+
+        sub_fields_len = len(sub_fields)
+        if field.shape == SHAPE_GENERIC:
+            all_of_schemas = sub_schema[0] if sub_fields_len == 1 else {'type': 'array', 'items': sub_schema}
+            f_schema = {'allOf': [all_of_schemas]}
+        else:
+            f_schema = {
+                'type': 'array',
+                'minItems': sub_fields_len,
+                'maxItems': sub_fields_len,
+            }
+            if sub_fields_len >= 1:
+                f_schema['items'] = sub_schema
+    else:
+        assert field.shape in {SHAPE_SINGLETON, SHAPE_GENERIC}, field.shape
+        f_schema, f_definitions, f_nested_models = field_singleton_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            schema_overrides=schema_overrides,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+
+    # check field type to avoid repeated calls to the same __modify_schema__ method
+    if field.type_ != field.outer_type_:
+        if field.shape == SHAPE_GENERIC:
+            field_type = field.type_
+        else:
+            field_type = field.outer_type_
+        modify_schema = getattr(field_type, '__modify_schema__', None)
+        if modify_schema:
+            _apply_modify_schema(modify_schema, field, f_schema)
+    return f_schema, definitions, nested_models
+
+
+def model_process_schema(
+    model: TypeModelOrEnum,
+    *,
+    by_alias: bool = True,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+    known_models: Optional[TypeModelSet] = None,
+    field: Optional[ModelField] = None,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    Used by ``model_schema()``, you probably should be using that function.
+
+    Take a single ``model`` and generate its schema. Also return additional schema definitions, from sub-models. The
+    sub-models of the returned schema will be referenced, but their definitions will not be included in the schema. All
+    the definitions are returned as the second value.
+    """
+    from inspect import getdoc, signature
+
+    known_models = known_models or set()
+    if lenient_issubclass(model, Enum):
+        model = cast(Type[Enum], model)
+        s = enum_process_schema(model, field=field)
+        return s, {}, set()
+    model = cast(Type['BaseModel'], model)
+    s = {'title': model.__config__.title or model.__name__}
+    doc = getdoc(model)
+    if doc:
+        s['description'] = doc
+    known_models.add(model)
+    m_schema, m_definitions, nested_models = model_type_schema(
+        model,
+        by_alias=by_alias,
+        model_name_map=model_name_map,
+        ref_prefix=ref_prefix,
+        ref_template=ref_template,
+        known_models=known_models,
+    )
+    s.update(m_schema)
+    schema_extra = model.__config__.schema_extra
+    if callable(schema_extra):
+        if len(signature(schema_extra).parameters) == 1:
+            schema_extra(s)
+        else:
+            schema_extra(s, model)
+    else:
+        s.update(schema_extra)
+    return s, m_definitions, nested_models
+
+
+def model_type_schema(
+    model: Type['BaseModel'],
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    You probably should be using ``model_schema()``, this function is indirectly used by that function.
+
+    Take a single ``model`` and generate the schema for its type only, not including additional
+    information as title, etc. Also return additional schema definitions, from sub-models.
+    """
+    properties = {}
+    required = []
+    definitions: Dict[str, Any] = {}
+    nested_models: Set[str] = set()
+    for k, f in model.__fields__.items():
+        try:
+            f_schema, f_definitions, f_nested_models = field_schema(
+                f,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+        except SkipField as skip:
+            warnings.warn(skip.message, UserWarning)
+            continue
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+        if by_alias:
+            properties[f.alias] = f_schema
+            if f.required:
+                required.append(f.alias)
+        else:
+            properties[k] = f_schema
+            if f.required:
+                required.append(k)
+    if ROOT_KEY in properties:
+        out_schema = properties[ROOT_KEY]
+        out_schema['title'] = model.__config__.title or model.__name__
+    else:
+        out_schema = {'type': 'object', 'properties': properties}
+        if required:
+            out_schema['required'] = required
+    if model.__config__.extra == 'forbid':
+        out_schema['additionalProperties'] = False
+    return out_schema, definitions, nested_models
+
+
+def enum_process_schema(enum: Type[Enum], *, field: Optional[ModelField] = None) -> Dict[str, Any]:
+    """
+    Take a single `enum` and generate its schema.
+
+    This is similar to the `model_process_schema` function, but applies to ``Enum`` objects.
+    """
+    import inspect
+
+    schema_: Dict[str, Any] = {
+        'title': enum.__name__,
+        # Python assigns all enums a default docstring value of 'An enumeration', so
+        # all enums will have a description field even if not explicitly provided.
+        'description': inspect.cleandoc(enum.__doc__ or 'An enumeration.'),
+        # Add enum values and the enum field type to the schema.
+        'enum': [item.value for item in cast(Iterable[Enum], enum)],
+    }
+
+    add_field_type_to_schema(enum, schema_)
+
+    modify_schema = getattr(enum, '__modify_schema__', None)
+    if modify_schema:
+        _apply_modify_schema(modify_schema, field, schema_)
+
+    return schema_
+
+
+def field_singleton_sub_fields_schema(
+    field: ModelField,
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    schema_overrides: bool = False,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    This function is indirectly used by ``field_schema()``, you probably should be using that function.
+
+    Take a list of Pydantic ``ModelField`` from the declaration of a type with parameters, and generate their
+    schema. I.e., fields used as "type parameters", like ``str`` and ``int`` in ``Tuple[str, int]``.
+    """
+    sub_fields = cast(List[ModelField], field.sub_fields)
+    definitions = {}
+    nested_models: Set[str] = set()
+    if len(sub_fields) == 1:
+        return field_type_schema(
+            sub_fields[0],
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            schema_overrides=schema_overrides,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+    else:
+        s: Dict[str, Any] = {}
+        # https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md#discriminator-object
+        field_has_discriminator: bool = field.discriminator_key is not None
+        if field_has_discriminator:
+            assert field.sub_fields_mapping is not None
+
+            discriminator_models_refs: Dict[str, Union[str, Dict[str, Any]]] = {}
+
+            for discriminator_value, sub_field in field.sub_fields_mapping.items():
+                if isinstance(discriminator_value, Enum):
+                    discriminator_value = str(discriminator_value.value)
+                # sub_field is either a `BaseModel` or directly an `Annotated` `Union` of many
+                if is_union(get_origin(sub_field.type_)):
+                    sub_models = get_sub_types(sub_field.type_)
+                    discriminator_models_refs[discriminator_value] = {
+                        model_name_map[sub_model]: get_schema_ref(
+                            model_name_map[sub_model], ref_prefix, ref_template, False
+                        )
+                        for sub_model in sub_models
+                    }
+                else:
+                    sub_field_type = sub_field.type_
+                    if hasattr(sub_field_type, '__pydantic_model__'):
+                        sub_field_type = sub_field_type.__pydantic_model__
+
+                    discriminator_model_name = model_name_map[sub_field_type]
+                    discriminator_model_ref = get_schema_ref(discriminator_model_name, ref_prefix, ref_template, False)
+                    discriminator_models_refs[discriminator_value] = discriminator_model_ref['$ref']
+
+            s['discriminator'] = {
+                'propertyName': field.discriminator_alias if by_alias else field.discriminator_key,
+                'mapping': discriminator_models_refs,
+            }
+
+        sub_field_schemas = []
+        for sf in sub_fields:
+            sub_schema, sub_definitions, sub_nested_models = field_type_schema(
+                sf,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                schema_overrides=schema_overrides,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+            definitions.update(sub_definitions)
+            if schema_overrides and 'allOf' in sub_schema:
+                # if the sub_field is a referenced schema we only need the referenced
+                # object. Otherwise we will end up with several allOf inside anyOf/oneOf.
+                # See https://github.com/pydantic/pydantic/issues/1209
+                sub_schema = sub_schema['allOf'][0]
+
+            if sub_schema.keys() == {'discriminator', 'oneOf'}:
+                # we don't want discriminator information inside oneOf choices, this is dealt with elsewhere
+                sub_schema.pop('discriminator')
+            sub_field_schemas.append(sub_schema)
+            nested_models.update(sub_nested_models)
+        s['oneOf' if field_has_discriminator else 'anyOf'] = sub_field_schemas
+        return s, definitions, nested_models
+
+
+# Order is important, e.g. subclasses of str must go before str
+# this is used only for standard library types, custom types should use __modify_schema__ instead
+field_class_to_schema: Tuple[Tuple[Any, Dict[str, Any]], ...] = (
+    (Path, {'type': 'string', 'format': 'path'}),
+    (datetime, {'type': 'string', 'format': 'date-time'}),
+    (date, {'type': 'string', 'format': 'date'}),
+    (time, {'type': 'string', 'format': 'time'}),
+    (timedelta, {'type': 'number', 'format': 'time-delta'}),
+    (IPv4Network, {'type': 'string', 'format': 'ipv4network'}),
+    (IPv6Network, {'type': 'string', 'format': 'ipv6network'}),
+    (IPv4Interface, {'type': 'string', 'format': 'ipv4interface'}),
+    (IPv6Interface, {'type': 'string', 'format': 'ipv6interface'}),
+    (IPv4Address, {'type': 'string', 'format': 'ipv4'}),
+    (IPv6Address, {'type': 'string', 'format': 'ipv6'}),
+    (Pattern, {'type': 'string', 'format': 'regex'}),
+    (str, {'type': 'string'}),
+    (bytes, {'type': 'string', 'format': 'binary'}),
+    (bool, {'type': 'boolean'}),
+    (int, {'type': 'integer'}),
+    (float, {'type': 'number'}),
+    (Decimal, {'type': 'number'}),
+    (UUID, {'type': 'string', 'format': 'uuid'}),
+    (dict, {'type': 'object'}),
+    (list, {'type': 'array', 'items': {}}),
+    (tuple, {'type': 'array', 'items': {}}),
+    (set, {'type': 'array', 'items': {}, 'uniqueItems': True}),
+    (frozenset, {'type': 'array', 'items': {}, 'uniqueItems': True}),
+)
+
+json_scheme = {'type': 'string', 'format': 'json-string'}
+
+
+def add_field_type_to_schema(field_type: Any, schema_: Dict[str, Any]) -> None:
+    """
+    Update the given `schema` with the type-specific metadata for the given `field_type`.
+
+    This function looks through `field_class_to_schema` for a class that matches the given `field_type`,
+    and then modifies the given `schema` with the information from that type.
+    """
+    for type_, t_schema in field_class_to_schema:
+        # Fallback for `typing.Pattern` and `re.Pattern` as they are not a valid class
+        if lenient_issubclass(field_type, type_) or field_type is type_ is Pattern:
+            schema_.update(t_schema)
+            break
+
+
+def get_schema_ref(name: str, ref_prefix: Optional[str], ref_template: str, schema_overrides: bool) -> Dict[str, Any]:
+    if ref_prefix:
+        schema_ref = {'$ref': ref_prefix + name}
+    else:
+        schema_ref = {'$ref': ref_template.format(model=name)}
+    return {'allOf': [schema_ref]} if schema_overrides else schema_ref
+
+
+def field_singleton_schema(  # noqa: C901 (ignore complexity)
+    field: ModelField,
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    schema_overrides: bool = False,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    This function is indirectly used by ``field_schema()``, you should probably be using that function.
+
+    Take a single Pydantic ``ModelField``, and return its schema and any additional definitions from sub-models.
+    """
+    from pydantic.v1.main import BaseModel
+
+    definitions: Dict[str, Any] = {}
+    nested_models: Set[str] = set()
+    field_type = field.type_
+
+    # Recurse into this field if it contains sub_fields and is NOT a
+    # BaseModel OR that BaseModel is a const
+    if field.sub_fields and (
+        (field.field_info and field.field_info.const) or not lenient_issubclass(field_type, BaseModel)
+    ):
+        return field_singleton_sub_fields_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            schema_overrides=schema_overrides,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+    if field_type is Any or field_type is object or field_type.__class__ == TypeVar or get_origin(field_type) is type:
+        return {}, definitions, nested_models  # no restrictions
+    if is_none_type(field_type):
+        return {'type': 'null'}, definitions, nested_models
+    if is_callable_type(field_type):
+        raise SkipField(f'Callable {field.name} was excluded from schema since JSON schema has no equivalent type.')
+    f_schema: Dict[str, Any] = {}
+    if field.field_info is not None and field.field_info.const:
+        f_schema['const'] = field.default
+
+    if is_literal_type(field_type):
+        values = tuple(x.value if isinstance(x, Enum) else x for x in all_literal_values(field_type))
+
+        if len({v.__class__ for v in values}) > 1:
+            return field_schema(
+                multitypes_literal_field_for_schema(values, field),
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+
+        # All values have the same type
+        field_type = values[0].__class__
+        f_schema['enum'] = list(values)
+        add_field_type_to_schema(field_type, f_schema)
+    elif lenient_issubclass(field_type, Enum):
+        enum_name = model_name_map[field_type]
+        f_schema, schema_overrides = get_field_info_schema(field, schema_overrides)
+        f_schema.update(get_schema_ref(enum_name, ref_prefix, ref_template, schema_overrides))
+        definitions[enum_name] = enum_process_schema(field_type, field=field)
+    elif is_namedtuple(field_type):
+        sub_schema, *_ = model_process_schema(
+            field_type.__pydantic_model__,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+            field=field,
+        )
+        items_schemas = list(sub_schema['properties'].values())
+        f_schema.update(
+            {
+                'type': 'array',
+                'items': items_schemas,
+                'minItems': len(items_schemas),
+                'maxItems': len(items_schemas),
+            }
+        )
+    elif not hasattr(field_type, '__pydantic_model__'):
+        add_field_type_to_schema(field_type, f_schema)
+
+        modify_schema = getattr(field_type, '__modify_schema__', None)
+        if modify_schema:
+            _apply_modify_schema(modify_schema, field, f_schema)
+
+    if f_schema:
+        return f_schema, definitions, nested_models
+
+    # Handle dataclass-based models
+    if lenient_issubclass(getattr(field_type, '__pydantic_model__', None), BaseModel):
+        field_type = field_type.__pydantic_model__
+
+    if issubclass(field_type, BaseModel):
+        model_name = model_name_map[field_type]
+        if field_type not in known_models:
+            sub_schema, sub_definitions, sub_nested_models = model_process_schema(
+                field_type,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+                field=field,
+            )
+            definitions.update(sub_definitions)
+            definitions[model_name] = sub_schema
+            nested_models.update(sub_nested_models)
+        else:
+            nested_models.add(model_name)
+        schema_ref = get_schema_ref(model_name, ref_prefix, ref_template, schema_overrides)
+        return schema_ref, definitions, nested_models
+
+    # For generics with no args
+    args = get_args(field_type)
+    if args is not None and not args and Generic in field_type.__bases__:
+        return f_schema, definitions, nested_models
+
+    raise ValueError(f'Value not declarable with JSON Schema, field: {field}')
+
+
+def multitypes_literal_field_for_schema(values: Tuple[Any, ...], field: ModelField) -> ModelField:
+    """
+    To support `Literal` with values of different types, we split it into multiple `Literal` with same type
+    e.g. `Literal['qwe', 'asd', 1, 2]` becomes `Union[Literal['qwe', 'asd'], Literal[1, 2]]`
+    """
+    literal_distinct_types = defaultdict(list)
+    for v in values:
+        literal_distinct_types[v.__class__].append(v)
+    distinct_literals = (Literal[tuple(same_type_values)] for same_type_values in literal_distinct_types.values())
+
+    return ModelField(
+        name=field.name,
+        type_=Union[tuple(distinct_literals)],  # type: ignore
+        class_validators=field.class_validators,
+        model_config=field.model_config,
+        default=field.default,
+        required=field.required,
+        alias=field.alias,
+        field_info=field.field_info,
+    )
+
+
+def encode_default(dft: Any) -> Any:
+    from pydantic.v1.main import BaseModel
+
+    if isinstance(dft, BaseModel) or is_dataclass(dft):
+        dft = cast('dict[str, Any]', pydantic_encoder(dft))
+
+    if isinstance(dft, dict):
+        return {encode_default(k): encode_default(v) for k, v in dft.items()}
+    elif isinstance(dft, Enum):
+        return dft.value
+    elif isinstance(dft, (int, float, str)):
+        return dft
+    elif isinstance(dft, (list, tuple)):
+        t = dft.__class__
+        seq_args = (encode_default(v) for v in dft)
+        return t(*seq_args) if is_namedtuple(t) else t(seq_args)
+    elif dft is None:
+        return None
+    else:
+        return pydantic_encoder(dft)
+
+
+_map_types_constraint: Dict[Any, Callable[..., type]] = {int: conint, float: confloat, Decimal: condecimal}
+
+
+def get_annotation_from_field_info(
+    annotation: Any, field_info: FieldInfo, field_name: str, validate_assignment: bool = False
+) -> Type[Any]:
+    """
+    Get an annotation with validation implemented for numbers and strings based on the field_info.
+    :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr``
+    :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema
+    :param field_name: name of the field for use in error messages
+    :param validate_assignment: default False, flag for BaseModel Config value of validate_assignment
+    :return: the same ``annotation`` if unmodified or a new annotation with validation in place
+    """
+    constraints = field_info.get_constraints()
+    used_constraints: Set[str] = set()
+    if constraints:
+        annotation, used_constraints = get_annotation_with_constraints(annotation, field_info)
+    if validate_assignment:
+        used_constraints.add('allow_mutation')
+
+    unused_constraints = constraints - used_constraints
+    if unused_constraints:
+        raise ValueError(
+            f'On field "{field_name}" the following field constraints are set but not enforced: '
+            f'{", ".join(unused_constraints)}. '
+            f'\nFor more details see https://docs.pydantic.dev/usage/schema/#unenforced-field-constraints'
+        )
+
+    return annotation
+
+
+def get_annotation_with_constraints(annotation: Any, field_info: FieldInfo) -> Tuple[Type[Any], Set[str]]:  # noqa: C901
+    """
+    Get an annotation with used constraints implemented for numbers and strings based on the field_info.
+
+    :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr``
+    :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema
+    :return: the same ``annotation`` if unmodified or a new annotation along with the used constraints.
+    """
+    used_constraints: Set[str] = set()
+
+    def go(type_: Any) -> Type[Any]:
+        if (
+            is_literal_type(type_)
+            or isinstance(type_, ForwardRef)
+            or lenient_issubclass(type_, (ConstrainedList, ConstrainedSet, ConstrainedFrozenSet))
+        ):
+            return type_
+        origin = get_origin(type_)
+        if origin is not None:
+            args: Tuple[Any, ...] = get_args(type_)
+            if any(isinstance(a, ForwardRef) for a in args):
+                # forward refs cause infinite recursion below
+                return type_
+
+            if origin is Annotated:
+                return go(args[0])
+            if is_union(origin):
+                return Union[tuple(go(a) for a in args)]  # type: ignore
+
+            if issubclass(origin, List) and (
+                field_info.min_items is not None
+                or field_info.max_items is not None
+                or field_info.unique_items is not None
+            ):
+                used_constraints.update({'min_items', 'max_items', 'unique_items'})
+                return conlist(
+                    go(args[0]),
+                    min_items=field_info.min_items,
+                    max_items=field_info.max_items,
+                    unique_items=field_info.unique_items,
+                )
+
+            if issubclass(origin, Set) and (field_info.min_items is not None or field_info.max_items is not None):
+                used_constraints.update({'min_items', 'max_items'})
+                return conset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items)
+
+            if issubclass(origin, FrozenSet) and (field_info.min_items is not None or field_info.max_items is not None):
+                used_constraints.update({'min_items', 'max_items'})
+                return confrozenset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items)
+
+            for t in (Tuple, List, Set, FrozenSet, Sequence):
+                if issubclass(origin, t):  # type: ignore
+                    return t[tuple(go(a) for a in args)]  # type: ignore
+
+            if issubclass(origin, Dict):
+                return Dict[args[0], go(args[1])]  # type: ignore
+
+        attrs: Optional[Tuple[str, ...]] = None
+        constraint_func: Optional[Callable[..., type]] = None
+        if isinstance(type_, type):
+            if issubclass(type_, (SecretStr, SecretBytes)):
+                attrs = ('max_length', 'min_length')
+
+                def constraint_func(**kw: Any) -> Type[Any]:  # noqa: F811
+                    return type(type_.__name__, (type_,), kw)
+
+            elif issubclass(type_, str) and not issubclass(type_, (EmailStr, AnyUrl)):
+                attrs = ('max_length', 'min_length', 'regex')
+                if issubclass(type_, StrictStr):
+
+                    def constraint_func(**kw: Any) -> Type[Any]:
+                        return type(type_.__name__, (type_,), kw)
+
+                else:
+                    constraint_func = constr
+            elif issubclass(type_, bytes):
+                attrs = ('max_length', 'min_length', 'regex')
+                if issubclass(type_, StrictBytes):
+
+                    def constraint_func(**kw: Any) -> Type[Any]:
+                        return type(type_.__name__, (type_,), kw)
+
+                else:
+                    constraint_func = conbytes
+            elif issubclass(type_, numeric_types) and not issubclass(
+                type_,
+                (
+                    ConstrainedInt,
+                    ConstrainedFloat,
+                    ConstrainedDecimal,
+                    ConstrainedList,
+                    ConstrainedSet,
+                    ConstrainedFrozenSet,
+                    bool,
+                ),
+            ):
+                # Is numeric type
+                attrs = ('gt', 'lt', 'ge', 'le', 'multiple_of')
+                if issubclass(type_, float):
+                    attrs += ('allow_inf_nan',)
+                if issubclass(type_, Decimal):
+                    attrs += ('max_digits', 'decimal_places')
+                numeric_type = next(t for t in numeric_types if issubclass(type_, t))  # pragma: no branch
+                constraint_func = _map_types_constraint[numeric_type]
+
+        if attrs:
+            used_constraints.update(set(attrs))
+            kwargs = {
+                attr_name: attr
+                for attr_name, attr in ((attr_name, getattr(field_info, attr_name)) for attr_name in attrs)
+                if attr is not None
+            }
+            if kwargs:
+                constraint_func = cast(Callable[..., type], constraint_func)
+                return constraint_func(**kwargs)
+        return type_
+
+    return go(annotation), used_constraints
+
+
+def normalize_name(name: str) -> str:
+    """
+    Normalizes the given name. This can be applied to either a model *or* enum.
+    """
+    return re.sub(r'[^a-zA-Z0-9.\-_]', '_', name)
+
+
+class SkipField(Exception):
+    """
+    Utility exception used to exclude fields from schema.
+    """
+
+    def __init__(self, message: str) -> None:
+        self.message = message
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/tools.py b/.venv/lib/python3.12/site-packages/pydantic/v1/tools.py
new file mode 100644
index 00000000..6838a23e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/tools.py
@@ -0,0 +1,92 @@
+import json
+from functools import lru_cache
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable, Optional, Type, TypeVar, Union
+
+from pydantic.v1.parse import Protocol, load_file, load_str_bytes
+from pydantic.v1.types import StrBytes
+from pydantic.v1.typing import display_as_type
+
+__all__ = ('parse_file_as', 'parse_obj_as', 'parse_raw_as', 'schema_of', 'schema_json_of')
+
+NameFactory = Union[str, Callable[[Type[Any]], str]]
+
+if TYPE_CHECKING:
+    from pydantic.v1.typing import DictStrAny
+
+
+def _generate_parsing_type_name(type_: Any) -> str:
+    return f'ParsingModel[{display_as_type(type_)}]'
+
+
+@lru_cache(maxsize=2048)
+def _get_parsing_type(type_: Any, *, type_name: Optional[NameFactory] = None) -> Any:
+    from pydantic.v1.main import create_model
+
+    if type_name is None:
+        type_name = _generate_parsing_type_name
+    if not isinstance(type_name, str):
+        type_name = type_name(type_)
+    return create_model(type_name, __root__=(type_, ...))
+
+
+T = TypeVar('T')
+
+
+def parse_obj_as(type_: Type[T], obj: Any, *, type_name: Optional[NameFactory] = None) -> T:
+    model_type = _get_parsing_type(type_, type_name=type_name)  # type: ignore[arg-type]
+    return model_type(__root__=obj).__root__
+
+
+def parse_file_as(
+    type_: Type[T],
+    path: Union[str, Path],
+    *,
+    content_type: str = None,
+    encoding: str = 'utf8',
+    proto: Protocol = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+    type_name: Optional[NameFactory] = None,
+) -> T:
+    obj = load_file(
+        path,
+        proto=proto,
+        content_type=content_type,
+        encoding=encoding,
+        allow_pickle=allow_pickle,
+        json_loads=json_loads,
+    )
+    return parse_obj_as(type_, obj, type_name=type_name)
+
+
+def parse_raw_as(
+    type_: Type[T],
+    b: StrBytes,
+    *,
+    content_type: str = None,
+    encoding: str = 'utf8',
+    proto: Protocol = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+    type_name: Optional[NameFactory] = None,
+) -> T:
+    obj = load_str_bytes(
+        b,
+        proto=proto,
+        content_type=content_type,
+        encoding=encoding,
+        allow_pickle=allow_pickle,
+        json_loads=json_loads,
+    )
+    return parse_obj_as(type_, obj, type_name=type_name)
+
+
+def schema_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_kwargs: Any) -> 'DictStrAny':
+    """Generate a JSON schema (as dict) for the passed model or dynamically generated one"""
+    return _get_parsing_type(type_, type_name=title).schema(**schema_kwargs)
+
+
+def schema_json_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_json_kwargs: Any) -> str:
+    """Generate a JSON schema (as JSON) for the passed model or dynamically generated one"""
+    return _get_parsing_type(type_, type_name=title).schema_json(**schema_json_kwargs)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/types.py b/.venv/lib/python3.12/site-packages/pydantic/v1/types.py
new file mode 100644
index 00000000..0cd789a4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/types.py
@@ -0,0 +1,1205 @@
+import abc
+import math
+import re
+import warnings
+from datetime import date
+from decimal import Decimal, InvalidOperation
+from enum import Enum
+from pathlib import Path
+from types import new_class
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    FrozenSet,
+    List,
+    Optional,
+    Pattern,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+from uuid import UUID
+from weakref import WeakSet
+
+from pydantic.v1 import errors
+from pydantic.v1.datetime_parse import parse_date
+from pydantic.v1.utils import import_string, update_not_none
+from pydantic.v1.validators import (
+    bytes_validator,
+    constr_length_validator,
+    constr_lower,
+    constr_strip_whitespace,
+    constr_upper,
+    decimal_validator,
+    float_finite_validator,
+    float_validator,
+    frozenset_validator,
+    int_validator,
+    list_validator,
+    number_multiple_validator,
+    number_size_validator,
+    path_exists_validator,
+    path_validator,
+    set_validator,
+    str_validator,
+    strict_bytes_validator,
+    strict_float_validator,
+    strict_int_validator,
+    strict_str_validator,
+)
+
+__all__ = [
+    'NoneStr',
+    'NoneBytes',
+    'StrBytes',
+    'NoneStrBytes',
+    'StrictStr',
+    'ConstrainedBytes',
+    'conbytes',
+    'ConstrainedList',
+    'conlist',
+    'ConstrainedSet',
+    'conset',
+    'ConstrainedFrozenSet',
+    'confrozenset',
+    'ConstrainedStr',
+    'constr',
+    'PyObject',
+    'ConstrainedInt',
+    'conint',
+    'PositiveInt',
+    'NegativeInt',
+    'NonNegativeInt',
+    'NonPositiveInt',
+    'ConstrainedFloat',
+    'confloat',
+    'PositiveFloat',
+    'NegativeFloat',
+    'NonNegativeFloat',
+    'NonPositiveFloat',
+    'FiniteFloat',
+    'ConstrainedDecimal',
+    'condecimal',
+    'UUID1',
+    'UUID3',
+    'UUID4',
+    'UUID5',
+    'FilePath',
+    'DirectoryPath',
+    'Json',
+    'JsonWrapper',
+    'SecretField',
+    'SecretStr',
+    'SecretBytes',
+    'StrictBool',
+    'StrictBytes',
+    'StrictInt',
+    'StrictFloat',
+    'PaymentCardNumber',
+    'ByteSize',
+    'PastDate',
+    'FutureDate',
+    'ConstrainedDate',
+    'condate',
+]
+
+NoneStr = Optional[str]
+NoneBytes = Optional[bytes]
+StrBytes = Union[str, bytes]
+NoneStrBytes = Optional[StrBytes]
+OptionalInt = Optional[int]
+OptionalIntFloat = Union[OptionalInt, float]
+OptionalIntFloatDecimal = Union[OptionalIntFloat, Decimal]
+OptionalDate = Optional[date]
+StrIntFloat = Union[str, int, float]
+
+if TYPE_CHECKING:
+    from typing_extensions import Annotated
+
+    from pydantic.v1.dataclasses import Dataclass
+    from pydantic.v1.main import BaseModel
+    from pydantic.v1.typing import CallableGenerator
+
+    ModelOrDc = Type[Union[BaseModel, Dataclass]]
+
+T = TypeVar('T')
+_DEFINED_TYPES: 'WeakSet[type]' = WeakSet()
+
+
+@overload
+def _registered(typ: Type[T]) -> Type[T]:
+    pass
+
+
+@overload
+def _registered(typ: 'ConstrainedNumberMeta') -> 'ConstrainedNumberMeta':
+    pass
+
+
+def _registered(typ: Union[Type[T], 'ConstrainedNumberMeta']) -> Union[Type[T], 'ConstrainedNumberMeta']:
+    # In order to generate valid examples of constrained types, Hypothesis needs
+    # to inspect the type object - so we keep a weakref to each contype object
+    # until it can be registered.  When (or if) our Hypothesis plugin is loaded,
+    # it monkeypatches this function.
+    # If Hypothesis is never used, the total effect is to keep a weak reference
+    # which has minimal memory usage and doesn't even affect garbage collection.
+    _DEFINED_TYPES.add(typ)
+    return typ
+
+
+class ConstrainedNumberMeta(type):
+    def __new__(cls, name: str, bases: Any, dct: Dict[str, Any]) -> 'ConstrainedInt':  # type: ignore
+        new_cls = cast('ConstrainedInt', type.__new__(cls, name, bases, dct))
+
+        if new_cls.gt is not None and new_cls.ge is not None:
+            raise errors.ConfigError('bounds gt and ge cannot be specified at the same time')
+        if new_cls.lt is not None and new_cls.le is not None:
+            raise errors.ConfigError('bounds lt and le cannot be specified at the same time')
+
+        return _registered(new_cls)  # type: ignore
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BOOLEAN TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    StrictBool = bool
+else:
+
+    class StrictBool(int):
+        """
+        StrictBool to allow for bools which are not type-coerced.
+        """
+
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(type='boolean')
+
+        @classmethod
+        def __get_validators__(cls) -> 'CallableGenerator':
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: Any) -> bool:
+            """
+            Ensure that we only allow bools.
+            """
+            if isinstance(value, bool):
+                return value
+
+            raise errors.StrictBoolError()
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ INTEGER TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedInt(int, metaclass=ConstrainedNumberMeta):
+    strict: bool = False
+    gt: OptionalInt = None
+    ge: OptionalInt = None
+    lt: OptionalInt = None
+    le: OptionalInt = None
+    multiple_of: OptionalInt = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            exclusiveMinimum=cls.gt,
+            exclusiveMaximum=cls.lt,
+            minimum=cls.ge,
+            maximum=cls.le,
+            multipleOf=cls.multiple_of,
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield strict_int_validator if cls.strict else int_validator
+        yield number_size_validator
+        yield number_multiple_validator
+
+
+def conint(
+    *,
+    strict: bool = False,
+    gt: Optional[int] = None,
+    ge: Optional[int] = None,
+    lt: Optional[int] = None,
+    le: Optional[int] = None,
+    multiple_of: Optional[int] = None,
+) -> Type[int]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of)
+    return type('ConstrainedIntValue', (ConstrainedInt,), namespace)
+
+
+if TYPE_CHECKING:
+    PositiveInt = int
+    NegativeInt = int
+    NonPositiveInt = int
+    NonNegativeInt = int
+    StrictInt = int
+else:
+
+    class PositiveInt(ConstrainedInt):
+        gt = 0
+
+    class NegativeInt(ConstrainedInt):
+        lt = 0
+
+    class NonPositiveInt(ConstrainedInt):
+        le = 0
+
+    class NonNegativeInt(ConstrainedInt):
+        ge = 0
+
+    class StrictInt(ConstrainedInt):
+        strict = True
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FLOAT TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedFloat(float, metaclass=ConstrainedNumberMeta):
+    strict: bool = False
+    gt: OptionalIntFloat = None
+    ge: OptionalIntFloat = None
+    lt: OptionalIntFloat = None
+    le: OptionalIntFloat = None
+    multiple_of: OptionalIntFloat = None
+    allow_inf_nan: Optional[bool] = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            exclusiveMinimum=cls.gt,
+            exclusiveMaximum=cls.lt,
+            minimum=cls.ge,
+            maximum=cls.le,
+            multipleOf=cls.multiple_of,
+        )
+        # Modify constraints to account for differences between IEEE floats and JSON
+        if field_schema.get('exclusiveMinimum') == -math.inf:
+            del field_schema['exclusiveMinimum']
+        if field_schema.get('minimum') == -math.inf:
+            del field_schema['minimum']
+        if field_schema.get('exclusiveMaximum') == math.inf:
+            del field_schema['exclusiveMaximum']
+        if field_schema.get('maximum') == math.inf:
+            del field_schema['maximum']
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield strict_float_validator if cls.strict else float_validator
+        yield number_size_validator
+        yield number_multiple_validator
+        yield float_finite_validator
+
+
+def confloat(
+    *,
+    strict: bool = False,
+    gt: float = None,
+    ge: float = None,
+    lt: float = None,
+    le: float = None,
+    multiple_of: float = None,
+    allow_inf_nan: Optional[bool] = None,
+) -> Type[float]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of, allow_inf_nan=allow_inf_nan)
+    return type('ConstrainedFloatValue', (ConstrainedFloat,), namespace)
+
+
+if TYPE_CHECKING:
+    PositiveFloat = float
+    NegativeFloat = float
+    NonPositiveFloat = float
+    NonNegativeFloat = float
+    StrictFloat = float
+    FiniteFloat = float
+else:
+
+    class PositiveFloat(ConstrainedFloat):
+        gt = 0
+
+    class NegativeFloat(ConstrainedFloat):
+        lt = 0
+
+    class NonPositiveFloat(ConstrainedFloat):
+        le = 0
+
+    class NonNegativeFloat(ConstrainedFloat):
+        ge = 0
+
+    class StrictFloat(ConstrainedFloat):
+        strict = True
+
+    class FiniteFloat(ConstrainedFloat):
+        allow_inf_nan = False
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTES TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedBytes(bytes):
+    strip_whitespace = False
+    to_upper = False
+    to_lower = False
+    min_length: OptionalInt = None
+    max_length: OptionalInt = None
+    strict: bool = False
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minLength=cls.min_length, maxLength=cls.max_length)
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield strict_bytes_validator if cls.strict else bytes_validator
+        yield constr_strip_whitespace
+        yield constr_upper
+        yield constr_lower
+        yield constr_length_validator
+
+
+def conbytes(
+    *,
+    strip_whitespace: bool = False,
+    to_upper: bool = False,
+    to_lower: bool = False,
+    min_length: Optional[int] = None,
+    max_length: Optional[int] = None,
+    strict: bool = False,
+) -> Type[bytes]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(
+        strip_whitespace=strip_whitespace,
+        to_upper=to_upper,
+        to_lower=to_lower,
+        min_length=min_length,
+        max_length=max_length,
+        strict=strict,
+    )
+    return _registered(type('ConstrainedBytesValue', (ConstrainedBytes,), namespace))
+
+
+if TYPE_CHECKING:
+    StrictBytes = bytes
+else:
+
+    class StrictBytes(ConstrainedBytes):
+        strict = True
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ STRING TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedStr(str):
+    strip_whitespace = False
+    to_upper = False
+    to_lower = False
+    min_length: OptionalInt = None
+    max_length: OptionalInt = None
+    curtail_length: OptionalInt = None
+    regex: Optional[Union[str, Pattern[str]]] = None
+    strict = False
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            minLength=cls.min_length,
+            maxLength=cls.max_length,
+            pattern=cls.regex and cls._get_pattern(cls.regex),
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield strict_str_validator if cls.strict else str_validator
+        yield constr_strip_whitespace
+        yield constr_upper
+        yield constr_lower
+        yield constr_length_validator
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Union[str]) -> Union[str]:
+        if cls.curtail_length and len(value) > cls.curtail_length:
+            value = value[: cls.curtail_length]
+
+        if cls.regex:
+            if not re.match(cls.regex, value):
+                raise errors.StrRegexError(pattern=cls._get_pattern(cls.regex))
+
+        return value
+
+    @staticmethod
+    def _get_pattern(regex: Union[str, Pattern[str]]) -> str:
+        return regex if isinstance(regex, str) else regex.pattern
+
+
+def constr(
+    *,
+    strip_whitespace: bool = False,
+    to_upper: bool = False,
+    to_lower: bool = False,
+    strict: bool = False,
+    min_length: Optional[int] = None,
+    max_length: Optional[int] = None,
+    curtail_length: Optional[int] = None,
+    regex: Optional[str] = None,
+) -> Type[str]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(
+        strip_whitespace=strip_whitespace,
+        to_upper=to_upper,
+        to_lower=to_lower,
+        strict=strict,
+        min_length=min_length,
+        max_length=max_length,
+        curtail_length=curtail_length,
+        regex=regex and re.compile(regex),
+    )
+    return _registered(type('ConstrainedStrValue', (ConstrainedStr,), namespace))
+
+
+if TYPE_CHECKING:
+    StrictStr = str
+else:
+
+    class StrictStr(ConstrainedStr):
+        strict = True
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SET TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+# This types superclass should be Set[T], but cython chokes on that...
+class ConstrainedSet(set):  # type: ignore
+    # Needed for pydantic to detect that this is a set
+    __origin__ = set
+    __args__: Set[Type[T]]  # type: ignore
+
+    min_items: Optional[int] = None
+    max_items: Optional[int] = None
+    item_type: Type[T]  # type: ignore
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.set_length_validator
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items)
+
+    @classmethod
+    def set_length_validator(cls, v: 'Optional[Set[T]]') -> 'Optional[Set[T]]':
+        if v is None:
+            return None
+
+        v = set_validator(v)
+        v_len = len(v)
+
+        if cls.min_items is not None and v_len < cls.min_items:
+            raise errors.SetMinLengthError(limit_value=cls.min_items)
+
+        if cls.max_items is not None and v_len > cls.max_items:
+            raise errors.SetMaxLengthError(limit_value=cls.max_items)
+
+        return v
+
+
+def conset(item_type: Type[T], *, min_items: Optional[int] = None, max_items: Optional[int] = None) -> Type[Set[T]]:
+    # __args__ is needed to conform to typing generics api
+    namespace = {'min_items': min_items, 'max_items': max_items, 'item_type': item_type, '__args__': [item_type]}
+    # We use new_class to be able to deal with Generic types
+    return new_class('ConstrainedSetValue', (ConstrainedSet,), {}, lambda ns: ns.update(namespace))
+
+
+# This types superclass should be FrozenSet[T], but cython chokes on that...
+class ConstrainedFrozenSet(frozenset):  # type: ignore
+    # Needed for pydantic to detect that this is a set
+    __origin__ = frozenset
+    __args__: FrozenSet[Type[T]]  # type: ignore
+
+    min_items: Optional[int] = None
+    max_items: Optional[int] = None
+    item_type: Type[T]  # type: ignore
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.frozenset_length_validator
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items)
+
+    @classmethod
+    def frozenset_length_validator(cls, v: 'Optional[FrozenSet[T]]') -> 'Optional[FrozenSet[T]]':
+        if v is None:
+            return None
+
+        v = frozenset_validator(v)
+        v_len = len(v)
+
+        if cls.min_items is not None and v_len < cls.min_items:
+            raise errors.FrozenSetMinLengthError(limit_value=cls.min_items)
+
+        if cls.max_items is not None and v_len > cls.max_items:
+            raise errors.FrozenSetMaxLengthError(limit_value=cls.max_items)
+
+        return v
+
+
+def confrozenset(
+    item_type: Type[T], *, min_items: Optional[int] = None, max_items: Optional[int] = None
+) -> Type[FrozenSet[T]]:
+    # __args__ is needed to conform to typing generics api
+    namespace = {'min_items': min_items, 'max_items': max_items, 'item_type': item_type, '__args__': [item_type]}
+    # We use new_class to be able to deal with Generic types
+    return new_class('ConstrainedFrozenSetValue', (ConstrainedFrozenSet,), {}, lambda ns: ns.update(namespace))
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ LIST TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+# This types superclass should be List[T], but cython chokes on that...
+class ConstrainedList(list):  # type: ignore
+    # Needed for pydantic to detect that this is a list
+    __origin__ = list
+    __args__: Tuple[Type[T], ...]  # type: ignore
+
+    min_items: Optional[int] = None
+    max_items: Optional[int] = None
+    unique_items: Optional[bool] = None
+    item_type: Type[T]  # type: ignore
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.list_length_validator
+        if cls.unique_items:
+            yield cls.unique_items_validator
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items, uniqueItems=cls.unique_items)
+
+    @classmethod
+    def list_length_validator(cls, v: 'Optional[List[T]]') -> 'Optional[List[T]]':
+        if v is None:
+            return None
+
+        v = list_validator(v)
+        v_len = len(v)
+
+        if cls.min_items is not None and v_len < cls.min_items:
+            raise errors.ListMinLengthError(limit_value=cls.min_items)
+
+        if cls.max_items is not None and v_len > cls.max_items:
+            raise errors.ListMaxLengthError(limit_value=cls.max_items)
+
+        return v
+
+    @classmethod
+    def unique_items_validator(cls, v: 'Optional[List[T]]') -> 'Optional[List[T]]':
+        if v is None:
+            return None
+
+        for i, value in enumerate(v, start=1):
+            if value in v[i:]:
+                raise errors.ListUniqueItemsError()
+
+        return v
+
+
+def conlist(
+    item_type: Type[T], *, min_items: Optional[int] = None, max_items: Optional[int] = None, unique_items: bool = None
+) -> Type[List[T]]:
+    # __args__ is needed to conform to typing generics api
+    namespace = dict(
+        min_items=min_items, max_items=max_items, unique_items=unique_items, item_type=item_type, __args__=(item_type,)
+    )
+    # We use new_class to be able to deal with Generic types
+    return new_class('ConstrainedListValue', (ConstrainedList,), {}, lambda ns: ns.update(namespace))
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PYOBJECT TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+if TYPE_CHECKING:
+    PyObject = Callable[..., Any]
+else:
+
+    class PyObject:
+        validate_always = True
+
+        @classmethod
+        def __get_validators__(cls) -> 'CallableGenerator':
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: Any) -> Any:
+            if isinstance(value, Callable):
+                return value
+
+            try:
+                value = str_validator(value)
+            except errors.StrError:
+                raise errors.PyObjectError(error_message='value is neither a valid import path not a valid callable')
+
+            try:
+                return import_string(value)
+            except ImportError as e:
+                raise errors.PyObjectError(error_message=str(e))
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DECIMAL TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedDecimal(Decimal, metaclass=ConstrainedNumberMeta):
+    gt: OptionalIntFloatDecimal = None
+    ge: OptionalIntFloatDecimal = None
+    lt: OptionalIntFloatDecimal = None
+    le: OptionalIntFloatDecimal = None
+    max_digits: OptionalInt = None
+    decimal_places: OptionalInt = None
+    multiple_of: OptionalIntFloatDecimal = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            exclusiveMinimum=cls.gt,
+            exclusiveMaximum=cls.lt,
+            minimum=cls.ge,
+            maximum=cls.le,
+            multipleOf=cls.multiple_of,
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield decimal_validator
+        yield number_size_validator
+        yield number_multiple_validator
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Decimal) -> Decimal:
+        try:
+            normalized_value = value.normalize()
+        except InvalidOperation:
+            normalized_value = value
+        digit_tuple, exponent = normalized_value.as_tuple()[1:]
+        if exponent in {'F', 'n', 'N'}:
+            raise errors.DecimalIsNotFiniteError()
+
+        if exponent >= 0:
+            # A positive exponent adds that many trailing zeros.
+            digits = len(digit_tuple) + exponent
+            decimals = 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 of the digits in digit_tuple and then
+            # add abs(exponent) - len(digit_tuple) leading zeros after the
+            # decimal point.
+            if abs(exponent) > len(digit_tuple):
+                digits = decimals = abs(exponent)
+            else:
+                digits = len(digit_tuple)
+                decimals = abs(exponent)
+        whole_digits = digits - decimals
+
+        if cls.max_digits is not None and digits > cls.max_digits:
+            raise errors.DecimalMaxDigitsError(max_digits=cls.max_digits)
+
+        if cls.decimal_places is not None and decimals > cls.decimal_places:
+            raise errors.DecimalMaxPlacesError(decimal_places=cls.decimal_places)
+
+        if cls.max_digits is not None and cls.decimal_places is not None:
+            expected = cls.max_digits - cls.decimal_places
+            if whole_digits > expected:
+                raise errors.DecimalWholeDigitsError(whole_digits=expected)
+
+        return value
+
+
+def condecimal(
+    *,
+    gt: Decimal = None,
+    ge: Decimal = None,
+    lt: Decimal = None,
+    le: Decimal = None,
+    max_digits: Optional[int] = None,
+    decimal_places: Optional[int] = None,
+    multiple_of: Decimal = None,
+) -> Type[Decimal]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(
+        gt=gt, ge=ge, lt=lt, le=le, max_digits=max_digits, decimal_places=decimal_places, multiple_of=multiple_of
+    )
+    return type('ConstrainedDecimalValue', (ConstrainedDecimal,), namespace)
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ UUID TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    UUID1 = UUID
+    UUID3 = UUID
+    UUID4 = UUID
+    UUID5 = UUID
+else:
+
+    class UUID1(UUID):
+        _required_version = 1
+
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(type='string', format=f'uuid{cls._required_version}')
+
+    class UUID3(UUID1):
+        _required_version = 3
+
+    class UUID4(UUID1):
+        _required_version = 4
+
+    class UUID5(UUID1):
+        _required_version = 5
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PATH TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    FilePath = Path
+    DirectoryPath = Path
+else:
+
+    class FilePath(Path):
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(format='file-path')
+
+        @classmethod
+        def __get_validators__(cls) -> 'CallableGenerator':
+            yield path_validator
+            yield path_exists_validator
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: Path) -> Path:
+            if not value.is_file():
+                raise errors.PathNotAFileError(path=value)
+
+            return value
+
+    class DirectoryPath(Path):
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(format='directory-path')
+
+        @classmethod
+        def __get_validators__(cls) -> 'CallableGenerator':
+            yield path_validator
+            yield path_exists_validator
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: Path) -> Path:
+            if not value.is_dir():
+                raise errors.PathNotADirectoryError(path=value)
+
+            return value
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ JSON TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class JsonWrapper:
+    pass
+
+
+class JsonMeta(type):
+    def __getitem__(self, t: Type[Any]) -> Type[JsonWrapper]:
+        if t is Any:
+            return Json  # allow Json[Any] to replecate plain Json
+        return _registered(type('JsonWrapperValue', (JsonWrapper,), {'inner_type': t}))
+
+
+if TYPE_CHECKING:
+    Json = Annotated[T, ...]  # Json[list[str]] will be recognized by type checkers as list[str]
+
+else:
+
+    class Json(metaclass=JsonMeta):
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(type='string', format='json-string')
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SECRET TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class SecretField(abc.ABC):
+    """
+    Note: this should be implemented as a generic like `SecretField(ABC, Generic[T])`,
+          the `__init__()` should be part of the abstract class and the
+          `get_secret_value()` method should use the generic `T` type.
+
+          However Cython doesn't support very well generics at the moment and
+          the generated code fails to be imported (see
+          https://github.com/cython/cython/issues/2753).
+    """
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, self.__class__) and self.get_secret_value() == other.get_secret_value()
+
+    def __str__(self) -> str:
+        return '**********' if self.get_secret_value() else ''
+
+    def __hash__(self) -> int:
+        return hash(self.get_secret_value())
+
+    @abc.abstractmethod
+    def get_secret_value(self) -> Any:  # pragma: no cover
+        ...
+
+
+class SecretStr(SecretField):
+    min_length: OptionalInt = None
+    max_length: OptionalInt = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            type='string',
+            writeOnly=True,
+            format='password',
+            minLength=cls.min_length,
+            maxLength=cls.max_length,
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.validate
+        yield constr_length_validator
+
+    @classmethod
+    def validate(cls, value: Any) -> 'SecretStr':
+        if isinstance(value, cls):
+            return value
+        value = str_validator(value)
+        return cls(value)
+
+    def __init__(self, value: str):
+        self._secret_value = value
+
+    def __repr__(self) -> str:
+        return f"SecretStr('{self}')"
+
+    def __len__(self) -> int:
+        return len(self._secret_value)
+
+    def display(self) -> str:
+        warnings.warn('`secret_str.display()` is deprecated, use `str(secret_str)` instead', DeprecationWarning)
+        return str(self)
+
+    def get_secret_value(self) -> str:
+        return self._secret_value
+
+
+class SecretBytes(SecretField):
+    min_length: OptionalInt = None
+    max_length: OptionalInt = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            type='string',
+            writeOnly=True,
+            format='password',
+            minLength=cls.min_length,
+            maxLength=cls.max_length,
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.validate
+        yield constr_length_validator
+
+    @classmethod
+    def validate(cls, value: Any) -> 'SecretBytes':
+        if isinstance(value, cls):
+            return value
+        value = bytes_validator(value)
+        return cls(value)
+
+    def __init__(self, value: bytes):
+        self._secret_value = value
+
+    def __repr__(self) -> str:
+        return f"SecretBytes(b'{self}')"
+
+    def __len__(self) -> int:
+        return len(self._secret_value)
+
+    def display(self) -> str:
+        warnings.warn('`secret_bytes.display()` is deprecated, use `str(secret_bytes)` instead', DeprecationWarning)
+        return str(self)
+
+    def get_secret_value(self) -> bytes:
+        return self._secret_value
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PAYMENT CARD TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class PaymentCardBrand(str, Enum):
+    # If you add another card type, please also add it to the
+    # Hypothesis strategy in `pydantic._hypothesis_plugin`.
+    amex = 'American Express'
+    mastercard = 'Mastercard'
+    visa = 'Visa'
+    other = 'other'
+
+    def __str__(self) -> str:
+        return self.value
+
+
+class PaymentCardNumber(str):
+    """
+    Based on: https://en.wikipedia.org/wiki/Payment_card_number
+    """
+
+    strip_whitespace: ClassVar[bool] = True
+    min_length: ClassVar[int] = 12
+    max_length: ClassVar[int] = 19
+    bin: str
+    last4: str
+    brand: PaymentCardBrand
+
+    def __init__(self, card_number: str):
+        self.bin = card_number[:6]
+        self.last4 = card_number[-4:]
+        self.brand = self._get_brand(card_number)
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield str_validator
+        yield constr_strip_whitespace
+        yield constr_length_validator
+        yield cls.validate_digits
+        yield cls.validate_luhn_check_digit
+        yield cls
+        yield cls.validate_length_for_brand
+
+    @property
+    def masked(self) -> str:
+        num_masked = len(self) - 10  # len(bin) + len(last4) == 10
+        return f'{self.bin}{"*" * num_masked}{self.last4}'
+
+    @classmethod
+    def validate_digits(cls, card_number: str) -> str:
+        if not card_number.isdigit():
+            raise errors.NotDigitError
+        return card_number
+
+    @classmethod
+    def validate_luhn_check_digit(cls, card_number: str) -> str:
+        """
+        Based on: https://en.wikipedia.org/wiki/Luhn_algorithm
+        """
+        sum_ = int(card_number[-1])
+        length = len(card_number)
+        parity = length % 2
+        for i in range(length - 1):
+            digit = int(card_number[i])
+            if i % 2 == parity:
+                digit *= 2
+            if digit > 9:
+                digit -= 9
+            sum_ += digit
+        valid = sum_ % 10 == 0
+        if not valid:
+            raise errors.LuhnValidationError
+        return card_number
+
+    @classmethod
+    def validate_length_for_brand(cls, card_number: 'PaymentCardNumber') -> 'PaymentCardNumber':
+        """
+        Validate length based on BIN for major brands:
+        https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN)
+        """
+        required_length: Union[None, int, str] = None
+        if card_number.brand in PaymentCardBrand.mastercard:
+            required_length = 16
+            valid = len(card_number) == required_length
+        elif card_number.brand == PaymentCardBrand.visa:
+            required_length = '13, 16 or 19'
+            valid = len(card_number) in {13, 16, 19}
+        elif card_number.brand == PaymentCardBrand.amex:
+            required_length = 15
+            valid = len(card_number) == required_length
+        else:
+            valid = True
+        if not valid:
+            raise errors.InvalidLengthForBrand(brand=card_number.brand, required_length=required_length)
+        return card_number
+
+    @staticmethod
+    def _get_brand(card_number: str) -> PaymentCardBrand:
+        if card_number[0] == '4':
+            brand = PaymentCardBrand.visa
+        elif 51 <= int(card_number[:2]) <= 55:
+            brand = PaymentCardBrand.mastercard
+        elif card_number[:2] in {'34', '37'}:
+            brand = PaymentCardBrand.amex
+        else:
+            brand = PaymentCardBrand.other
+        return brand
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTE SIZE TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+BYTE_SIZES = {
+    'b': 1,
+    'kb': 10**3,
+    'mb': 10**6,
+    'gb': 10**9,
+    'tb': 10**12,
+    'pb': 10**15,
+    'eb': 10**18,
+    'kib': 2**10,
+    'mib': 2**20,
+    'gib': 2**30,
+    'tib': 2**40,
+    'pib': 2**50,
+    'eib': 2**60,
+}
+BYTE_SIZES.update({k.lower()[0]: v for k, v in BYTE_SIZES.items() if 'i' not in k})
+byte_string_re = re.compile(r'^\s*(\d*\.?\d+)\s*(\w+)?', re.IGNORECASE)
+
+
+class ByteSize(int):
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, v: StrIntFloat) -> 'ByteSize':
+        try:
+            return cls(int(v))
+        except ValueError:
+            pass
+
+        str_match = byte_string_re.match(str(v))
+        if str_match is None:
+            raise errors.InvalidByteSize()
+
+        scalar, unit = str_match.groups()
+        if unit is None:
+            unit = 'b'
+
+        try:
+            unit_mult = BYTE_SIZES[unit.lower()]
+        except KeyError:
+            raise errors.InvalidByteSizeUnit(unit=unit)
+
+        return cls(int(float(scalar) * unit_mult))
+
+    def human_readable(self, decimal: bool = False) -> str:
+        if decimal:
+            divisor = 1000
+            units = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']
+            final_unit = 'EB'
+        else:
+            divisor = 1024
+            units = ['B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB']
+            final_unit = 'EiB'
+
+        num = float(self)
+        for unit in units:
+            if abs(num) < divisor:
+                return f'{num:0.1f}{unit}'
+            num /= divisor
+
+        return f'{num:0.1f}{final_unit}'
+
+    def to(self, unit: str) -> float:
+        try:
+            unit_div = BYTE_SIZES[unit.lower()]
+        except KeyError:
+            raise errors.InvalidByteSizeUnit(unit=unit)
+
+        return self / unit_div
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DATE TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    PastDate = date
+    FutureDate = date
+else:
+
+    class PastDate(date):
+        @classmethod
+        def __get_validators__(cls) -> 'CallableGenerator':
+            yield parse_date
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: date) -> date:
+            if value >= date.today():
+                raise errors.DateNotInThePastError()
+
+            return value
+
+    class FutureDate(date):
+        @classmethod
+        def __get_validators__(cls) -> 'CallableGenerator':
+            yield parse_date
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: date) -> date:
+            if value <= date.today():
+                raise errors.DateNotInTheFutureError()
+
+            return value
+
+
+class ConstrainedDate(date, metaclass=ConstrainedNumberMeta):
+    gt: OptionalDate = None
+    ge: OptionalDate = None
+    lt: OptionalDate = None
+    le: OptionalDate = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, exclusiveMinimum=cls.gt, exclusiveMaximum=cls.lt, minimum=cls.ge, maximum=cls.le)
+
+    @classmethod
+    def __get_validators__(cls) -> 'CallableGenerator':
+        yield parse_date
+        yield number_size_validator
+
+
+def condate(
+    *,
+    gt: date = None,
+    ge: date = None,
+    lt: date = None,
+    le: date = None,
+) -> Type[date]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(gt=gt, ge=ge, lt=lt, le=le)
+    return type('ConstrainedDateValue', (ConstrainedDate,), namespace)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/typing.py b/.venv/lib/python3.12/site-packages/pydantic/v1/typing.py
new file mode 100644
index 00000000..7dd341ce
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/typing.py
@@ -0,0 +1,608 @@
+import sys
+import typing
+from collections.abc import Callable
+from os import PathLike
+from typing import (  # type: ignore
+    TYPE_CHECKING,
+    AbstractSet,
+    Any,
+    Callable as TypingCallable,
+    ClassVar,
+    Dict,
+    ForwardRef,
+    Generator,
+    Iterable,
+    List,
+    Mapping,
+    NewType,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    _eval_type,
+    cast,
+    get_type_hints,
+)
+
+from typing_extensions import (
+    Annotated,
+    Final,
+    Literal,
+    NotRequired as TypedDictNotRequired,
+    Required as TypedDictRequired,
+)
+
+try:
+    from typing import _TypingBase as typing_base  # type: ignore
+except ImportError:
+    from typing import _Final as typing_base  # type: ignore
+
+try:
+    from typing import GenericAlias as TypingGenericAlias  # type: ignore
+except ImportError:
+    # python < 3.9 does not have GenericAlias (list[int], tuple[str, ...] and so on)
+    TypingGenericAlias = ()
+
+try:
+    from types import UnionType as TypesUnionType  # type: ignore
+except ImportError:
+    # python < 3.10 does not have UnionType (str | int, byte | bool and so on)
+    TypesUnionType = ()
+
+
+if sys.version_info < (3, 9):
+
+    def evaluate_forwardref(type_: ForwardRef, globalns: Any, localns: Any) -> Any:
+        return type_._evaluate(globalns, localns)
+
+else:
+
+    def evaluate_forwardref(type_: ForwardRef, globalns: Any, localns: Any) -> Any:
+        # Even though it is the right signature for python 3.9, mypy complains with
+        # `error: Too many arguments for "_evaluate" of "ForwardRef"` hence the cast...
+        # Python 3.13/3.12.4+ made `recursive_guard` a kwarg, so name it explicitly to avoid:
+        # TypeError: ForwardRef._evaluate() missing 1 required keyword-only argument: 'recursive_guard'
+        return cast(Any, type_)._evaluate(globalns, localns, recursive_guard=set())
+
+
+if sys.version_info < (3, 9):
+    # Ensure we always get all the whole `Annotated` hint, not just the annotated type.
+    # For 3.7 to 3.8, `get_type_hints` doesn't recognize `typing_extensions.Annotated`,
+    # so it already returns the full annotation
+    get_all_type_hints = get_type_hints
+
+else:
+
+    def get_all_type_hints(obj: Any, globalns: Any = None, localns: Any = None) -> Any:
+        return get_type_hints(obj, globalns, localns, include_extras=True)
+
+
+_T = TypeVar('_T')
+
+AnyCallable = TypingCallable[..., Any]
+NoArgAnyCallable = TypingCallable[[], Any]
+
+# workaround for https://github.com/python/mypy/issues/9496
+AnyArgTCallable = TypingCallable[..., _T]
+
+
+# Annotated[...] is implemented by returning an instance of one of these classes, depending on
+# python/typing_extensions version.
+AnnotatedTypeNames = {'AnnotatedMeta', '_AnnotatedAlias'}
+
+
+LITERAL_TYPES: Set[Any] = {Literal}
+if hasattr(typing, 'Literal'):
+    LITERAL_TYPES.add(typing.Literal)
+
+
+if sys.version_info < (3, 8):
+
+    def get_origin(t: Type[Any]) -> Optional[Type[Any]]:
+        if type(t).__name__ in AnnotatedTypeNames:
+            # weirdly this is a runtime requirement, as well as for mypy
+            return cast(Type[Any], Annotated)
+        return getattr(t, '__origin__', None)
+
+else:
+    from typing import get_origin as _typing_get_origin
+
+    def get_origin(tp: Type[Any]) -> Optional[Type[Any]]:
+        """
+        We can't directly use `typing.get_origin` since we need a fallback to support
+        custom generic classes like `ConstrainedList`
+        It should be useless once https://github.com/cython/cython/issues/3537 is
+        solved and https://github.com/pydantic/pydantic/pull/1753 is merged.
+        """
+        if type(tp).__name__ in AnnotatedTypeNames:
+            return cast(Type[Any], Annotated)  # mypy complains about _SpecialForm
+        return _typing_get_origin(tp) or getattr(tp, '__origin__', None)
+
+
+if sys.version_info < (3, 8):
+    from typing import _GenericAlias
+
+    def get_args(t: Type[Any]) -> Tuple[Any, ...]:
+        """Compatibility version of get_args for python 3.7.
+
+        Mostly compatible with the python 3.8 `typing` module version
+        and able to handle almost all use cases.
+        """
+        if type(t).__name__ in AnnotatedTypeNames:
+            return t.__args__ + t.__metadata__
+        if isinstance(t, _GenericAlias):
+            res = t.__args__
+            if t.__origin__ is Callable and res and res[0] is not Ellipsis:
+                res = (list(res[:-1]), res[-1])
+            return res
+        return getattr(t, '__args__', ())
+
+else:
+    from typing import get_args as _typing_get_args
+
+    def _generic_get_args(tp: Type[Any]) -> Tuple[Any, ...]:
+        """
+        In python 3.9, `typing.Dict`, `typing.List`, ...
+        do have an empty `__args__` by default (instead of the generic ~T for example).
+        In order to still support `Dict` for example and consider it as `Dict[Any, Any]`,
+        we retrieve the `_nparams` value that tells us how many parameters it needs.
+        """
+        if hasattr(tp, '_nparams'):
+            return (Any,) * tp._nparams
+        # Special case for `tuple[()]`, which used to return ((),) with `typing.Tuple`
+        # in python 3.10- but now returns () for `tuple` and `Tuple`.
+        # This will probably be clarified in pydantic v2
+        try:
+            if tp == Tuple[()] or sys.version_info >= (3, 9) and tp == tuple[()]:  # type: ignore[misc]
+                return ((),)
+        # there is a TypeError when compiled with cython
+        except TypeError:  # pragma: no cover
+            pass
+        return ()
+
+    def get_args(tp: Type[Any]) -> Tuple[Any, ...]:
+        """Get type arguments with all substitutions performed.
+
+        For unions, basic simplifications used by Union constructor are performed.
+        Examples::
+            get_args(Dict[str, int]) == (str, int)
+            get_args(int) == ()
+            get_args(Union[int, Union[T, int], str][int]) == (int, str)
+            get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int])
+            get_args(Callable[[], T][int]) == ([], int)
+        """
+        if type(tp).__name__ in AnnotatedTypeNames:
+            return tp.__args__ + tp.__metadata__
+        # the fallback is needed for the same reasons as `get_origin` (see above)
+        return _typing_get_args(tp) or getattr(tp, '__args__', ()) or _generic_get_args(tp)
+
+
+if sys.version_info < (3, 9):
+
+    def convert_generics(tp: Type[Any]) -> Type[Any]:
+        """Python 3.9 and older only supports generics from `typing` module.
+        They convert strings to ForwardRef automatically.
+
+        Examples::
+            typing.List['Hero'] == typing.List[ForwardRef('Hero')]
+        """
+        return tp
+
+else:
+    from typing import _UnionGenericAlias  # type: ignore
+
+    from typing_extensions import _AnnotatedAlias
+
+    def convert_generics(tp: Type[Any]) -> Type[Any]:
+        """
+        Recursively searches for `str` type hints and replaces them with ForwardRef.
+
+        Examples::
+            convert_generics(list['Hero']) == list[ForwardRef('Hero')]
+            convert_generics(dict['Hero', 'Team']) == dict[ForwardRef('Hero'), ForwardRef('Team')]
+            convert_generics(typing.Dict['Hero', 'Team']) == typing.Dict[ForwardRef('Hero'), ForwardRef('Team')]
+            convert_generics(list[str | 'Hero'] | int) == list[str | ForwardRef('Hero')] | int
+        """
+        origin = get_origin(tp)
+        if not origin or not hasattr(tp, '__args__'):
+            return tp
+
+        args = get_args(tp)
+
+        # typing.Annotated needs special treatment
+        if origin is Annotated:
+            return _AnnotatedAlias(convert_generics(args[0]), args[1:])
+
+        # recursively replace `str` instances inside of `GenericAlias` with `ForwardRef(arg)`
+        converted = tuple(
+            ForwardRef(arg) if isinstance(arg, str) and isinstance(tp, TypingGenericAlias) else convert_generics(arg)
+            for arg in args
+        )
+
+        if converted == args:
+            return tp
+        elif isinstance(tp, TypingGenericAlias):
+            return TypingGenericAlias(origin, converted)
+        elif isinstance(tp, TypesUnionType):
+            # recreate types.UnionType (PEP604, Python >= 3.10)
+            return _UnionGenericAlias(origin, converted)
+        else:
+            try:
+                setattr(tp, '__args__', converted)
+            except AttributeError:
+                pass
+            return tp
+
+
+if sys.version_info < (3, 10):
+
+    def is_union(tp: Optional[Type[Any]]) -> bool:
+        return tp is Union
+
+    WithArgsTypes = (TypingGenericAlias,)
+
+else:
+    import types
+    import typing
+
+    def is_union(tp: Optional[Type[Any]]) -> bool:
+        return tp is Union or tp is types.UnionType  # noqa: E721
+
+    WithArgsTypes = (typing._GenericAlias, types.GenericAlias, types.UnionType)
+
+
+StrPath = Union[str, PathLike]
+
+
+if TYPE_CHECKING:
+    from pydantic.v1.fields import ModelField
+
+    TupleGenerator = Generator[Tuple[str, Any], None, None]
+    DictStrAny = Dict[str, Any]
+    DictAny = Dict[Any, Any]
+    SetStr = Set[str]
+    ListStr = List[str]
+    IntStr = Union[int, str]
+    AbstractSetIntStr = AbstractSet[IntStr]
+    DictIntStrAny = Dict[IntStr, Any]
+    MappingIntStrAny = Mapping[IntStr, Any]
+    CallableGenerator = Generator[AnyCallable, None, None]
+    ReprArgs = Sequence[Tuple[Optional[str], Any]]
+
+    MYPY = False
+    if MYPY:
+        AnyClassMethod = classmethod[Any]
+    else:
+        # classmethod[TargetType, CallableParamSpecType, CallableReturnType]
+        AnyClassMethod = classmethod[Any, Any, Any]
+
+__all__ = (
+    'AnyCallable',
+    'NoArgAnyCallable',
+    'NoneType',
+    'is_none_type',
+    'display_as_type',
+    'resolve_annotations',
+    'is_callable_type',
+    'is_literal_type',
+    'all_literal_values',
+    'is_namedtuple',
+    'is_typeddict',
+    'is_typeddict_special',
+    'is_new_type',
+    'new_type_supertype',
+    'is_classvar',
+    'is_finalvar',
+    'update_field_forward_refs',
+    'update_model_forward_refs',
+    'TupleGenerator',
+    'DictStrAny',
+    'DictAny',
+    'SetStr',
+    'ListStr',
+    'IntStr',
+    'AbstractSetIntStr',
+    'DictIntStrAny',
+    'CallableGenerator',
+    'ReprArgs',
+    'AnyClassMethod',
+    'CallableGenerator',
+    'WithArgsTypes',
+    'get_args',
+    'get_origin',
+    'get_sub_types',
+    'typing_base',
+    'get_all_type_hints',
+    'is_union',
+    'StrPath',
+    'MappingIntStrAny',
+)
+
+
+NoneType = None.__class__
+
+
+NONE_TYPES: Tuple[Any, Any, Any] = (None, NoneType, Literal[None])
+
+
+if sys.version_info < (3, 8):
+    # Even though this implementation is slower, we need it for python 3.7:
+    # In python 3.7 "Literal" is not a builtin type and uses a different
+    # mechanism.
+    # for this reason `Literal[None] is Literal[None]` evaluates to `False`,
+    # breaking the faster implementation used for the other python versions.
+
+    def is_none_type(type_: Any) -> bool:
+        return type_ in NONE_TYPES
+
+elif sys.version_info[:2] == (3, 8):
+
+    def is_none_type(type_: Any) -> bool:
+        for none_type in NONE_TYPES:
+            if type_ is none_type:
+                return True
+        # With python 3.8, specifically 3.8.10, Literal "is" check sare very flakey
+        # can change on very subtle changes like use of types in other modules,
+        # hopefully this check avoids that issue.
+        if is_literal_type(type_):  # pragma: no cover
+            return all_literal_values(type_) == (None,)
+        return False
+
+else:
+
+    def is_none_type(type_: Any) -> bool:
+        return type_ in NONE_TYPES
+
+
+def display_as_type(v: Type[Any]) -> str:
+    if not isinstance(v, typing_base) and not isinstance(v, WithArgsTypes) and not isinstance(v, type):
+        v = v.__class__
+
+    if is_union(get_origin(v)):
+        return f'Union[{", ".join(map(display_as_type, get_args(v)))}]'
+
+    if isinstance(v, WithArgsTypes):
+        # Generic alias are constructs like `list[int]`
+        return str(v).replace('typing.', '')
+
+    try:
+        return v.__name__
+    except AttributeError:
+        # happens with typing objects
+        return str(v).replace('typing.', '')
+
+
+def resolve_annotations(raw_annotations: Dict[str, Type[Any]], module_name: Optional[str]) -> Dict[str, Type[Any]]:
+    """
+    Partially taken from typing.get_type_hints.
+
+    Resolve string or ForwardRef annotations into type objects if possible.
+    """
+    base_globals: Optional[Dict[str, Any]] = None
+    if module_name:
+        try:
+            module = sys.modules[module_name]
+        except KeyError:
+            # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363
+            pass
+        else:
+            base_globals = module.__dict__
+
+    annotations = {}
+    for name, value in raw_annotations.items():
+        if isinstance(value, str):
+            if (3, 10) > sys.version_info >= (3, 9, 8) or sys.version_info >= (3, 10, 1):
+                value = ForwardRef(value, is_argument=False, is_class=True)
+            else:
+                value = ForwardRef(value, is_argument=False)
+        try:
+            if sys.version_info >= (3, 13):
+                value = _eval_type(value, base_globals, None, type_params=())
+            else:
+                value = _eval_type(value, base_globals, None)
+        except NameError:
+            # this is ok, it can be fixed with update_forward_refs
+            pass
+        annotations[name] = value
+    return annotations
+
+
+def is_callable_type(type_: Type[Any]) -> bool:
+    return type_ is Callable or get_origin(type_) is Callable
+
+
+def is_literal_type(type_: Type[Any]) -> bool:
+    return Literal is not None and get_origin(type_) in LITERAL_TYPES
+
+
+def literal_values(type_: Type[Any]) -> Tuple[Any, ...]:
+    return get_args(type_)
+
+
+def all_literal_values(type_: Type[Any]) -> Tuple[Any, ...]:
+    """
+    This method is used to retrieve all Literal values as
+    Literal can be used recursively (see https://www.python.org/dev/peps/pep-0586)
+    e.g. `Literal[Literal[Literal[1, 2, 3], "foo"], 5, None]`
+    """
+    if not is_literal_type(type_):
+        return (type_,)
+
+    values = literal_values(type_)
+    return tuple(x for value in values for x in all_literal_values(value))
+
+
+def is_namedtuple(type_: Type[Any]) -> bool:
+    """
+    Check if a given class is a named tuple.
+    It can be either a `typing.NamedTuple` or `collections.namedtuple`
+    """
+    from pydantic.v1.utils import lenient_issubclass
+
+    return lenient_issubclass(type_, tuple) and hasattr(type_, '_fields')
+
+
+def is_typeddict(type_: Type[Any]) -> bool:
+    """
+    Check if a given class is a typed dict (from `typing` or `typing_extensions`)
+    In 3.10, there will be a public method (https://docs.python.org/3.10/library/typing.html#typing.is_typeddict)
+    """
+    from pydantic.v1.utils import lenient_issubclass
+
+    return lenient_issubclass(type_, dict) and hasattr(type_, '__total__')
+
+
+def _check_typeddict_special(type_: Any) -> bool:
+    return type_ is TypedDictRequired or type_ is TypedDictNotRequired
+
+
+def is_typeddict_special(type_: Any) -> bool:
+    """
+    Check if type is a TypedDict special form (Required or NotRequired).
+    """
+    return _check_typeddict_special(type_) or _check_typeddict_special(get_origin(type_))
+
+
+test_type = NewType('test_type', str)
+
+
+def is_new_type(type_: Type[Any]) -> bool:
+    """
+    Check whether type_ was created using typing.NewType
+    """
+    return isinstance(type_, test_type.__class__) and hasattr(type_, '__supertype__')  # type: ignore
+
+
+def new_type_supertype(type_: Type[Any]) -> Type[Any]:
+    while hasattr(type_, '__supertype__'):
+        type_ = type_.__supertype__
+    return type_
+
+
+def _check_classvar(v: Optional[Type[Any]]) -> bool:
+    if v is None:
+        return False
+
+    return v.__class__ == ClassVar.__class__ and getattr(v, '_name', None) == 'ClassVar'
+
+
+def _check_finalvar(v: Optional[Type[Any]]) -> bool:
+    """
+    Check if a given type is a `typing.Final` type.
+    """
+    if v is None:
+        return False
+
+    return v.__class__ == Final.__class__ and (sys.version_info < (3, 8) or getattr(v, '_name', None) == 'Final')
+
+
+def is_classvar(ann_type: Type[Any]) -> bool:
+    if _check_classvar(ann_type) or _check_classvar(get_origin(ann_type)):
+        return True
+
+    # this is an ugly workaround for class vars that contain forward references and are therefore themselves
+    # forward references, see #3679
+    if ann_type.__class__ == ForwardRef and ann_type.__forward_arg__.startswith('ClassVar['):
+        return True
+
+    return False
+
+
+def is_finalvar(ann_type: Type[Any]) -> bool:
+    return _check_finalvar(ann_type) or _check_finalvar(get_origin(ann_type))
+
+
+def update_field_forward_refs(field: 'ModelField', globalns: Any, localns: Any) -> None:
+    """
+    Try to update ForwardRefs on fields based on this ModelField, globalns and localns.
+    """
+    prepare = False
+    if field.type_.__class__ == ForwardRef:
+        prepare = True
+        field.type_ = evaluate_forwardref(field.type_, globalns, localns or None)
+    if field.outer_type_.__class__ == ForwardRef:
+        prepare = True
+        field.outer_type_ = evaluate_forwardref(field.outer_type_, globalns, localns or None)
+    if prepare:
+        field.prepare()
+
+    if field.sub_fields:
+        for sub_f in field.sub_fields:
+            update_field_forward_refs(sub_f, globalns=globalns, localns=localns)
+
+    if field.discriminator_key is not None:
+        field.prepare_discriminated_union_sub_fields()
+
+
+def update_model_forward_refs(
+    model: Type[Any],
+    fields: Iterable['ModelField'],
+    json_encoders: Dict[Union[Type[Any], str, ForwardRef], AnyCallable],
+    localns: 'DictStrAny',
+    exc_to_suppress: Tuple[Type[BaseException], ...] = (),
+) -> None:
+    """
+    Try to update model fields ForwardRefs based on model and localns.
+    """
+    if model.__module__ in sys.modules:
+        globalns = sys.modules[model.__module__].__dict__.copy()
+    else:
+        globalns = {}
+
+    globalns.setdefault(model.__name__, model)
+
+    for f in fields:
+        try:
+            update_field_forward_refs(f, globalns=globalns, localns=localns)
+        except exc_to_suppress:
+            pass
+
+    for key in set(json_encoders.keys()):
+        if isinstance(key, str):
+            fr: ForwardRef = ForwardRef(key)
+        elif isinstance(key, ForwardRef):
+            fr = key
+        else:
+            continue
+
+        try:
+            new_key = evaluate_forwardref(fr, globalns, localns or None)
+        except exc_to_suppress:  # pragma: no cover
+            continue
+
+        json_encoders[new_key] = json_encoders.pop(key)
+
+
+def get_class(type_: Type[Any]) -> Union[None, bool, Type[Any]]:
+    """
+    Tries to get the class of a Type[T] annotation. Returns True if Type is used
+    without brackets. Otherwise returns None.
+    """
+    if type_ is type:
+        return True
+
+    if get_origin(type_) is None:
+        return None
+
+    args = get_args(type_)
+    if not args or not isinstance(args[0], type):
+        return True
+    else:
+        return args[0]
+
+
+def get_sub_types(tp: Any) -> List[Any]:
+    """
+    Return all the types that are allowed by type `tp`
+    `tp` can be a `Union` of allowed types or an `Annotated` type
+    """
+    origin = get_origin(tp)
+    if origin is Annotated:
+        return get_sub_types(get_args(tp)[0])
+    elif is_union(origin):
+        return [x for t in get_args(tp) for x in get_sub_types(t)]
+    else:
+        return [tp]
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/utils.py b/.venv/lib/python3.12/site-packages/pydantic/v1/utils.py
new file mode 100644
index 00000000..0bd238ee
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/utils.py
@@ -0,0 +1,804 @@
+import keyword
+import warnings
+import weakref
+from collections import OrderedDict, defaultdict, deque
+from copy import deepcopy
+from itertools import islice, zip_longest
+from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType
+from typing import (
+    TYPE_CHECKING,
+    AbstractSet,
+    Any,
+    Callable,
+    Collection,
+    Dict,
+    Generator,
+    Iterable,
+    Iterator,
+    List,
+    Mapping,
+    NoReturn,
+    Optional,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+
+from typing_extensions import Annotated
+
+from pydantic.v1.errors import ConfigError
+from pydantic.v1.typing import (
+    NoneType,
+    WithArgsTypes,
+    all_literal_values,
+    display_as_type,
+    get_args,
+    get_origin,
+    is_literal_type,
+    is_union,
+)
+from pydantic.v1.version import version_info
+
+if TYPE_CHECKING:
+    from inspect import Signature
+    from pathlib import Path
+
+    from pydantic.v1.config import BaseConfig
+    from pydantic.v1.dataclasses import Dataclass
+    from pydantic.v1.fields import ModelField
+    from pydantic.v1.main import BaseModel
+    from pydantic.v1.typing import AbstractSetIntStr, DictIntStrAny, IntStr, MappingIntStrAny, ReprArgs
+
+    RichReprResult = Iterable[Union[Any, Tuple[Any], Tuple[str, Any], Tuple[str, Any, Any]]]
+
+__all__ = (
+    'import_string',
+    'sequence_like',
+    'validate_field_name',
+    'lenient_isinstance',
+    'lenient_issubclass',
+    'in_ipython',
+    'is_valid_identifier',
+    'deep_update',
+    'update_not_none',
+    'almost_equal_floats',
+    'get_model',
+    'to_camel',
+    'to_lower_camel',
+    'is_valid_field',
+    'smart_deepcopy',
+    'PyObjectStr',
+    'Representation',
+    'GetterDict',
+    'ValueItems',
+    'version_info',  # required here to match behaviour in v1.3
+    'ClassAttribute',
+    'path_type',
+    'ROOT_KEY',
+    'get_unique_discriminator_alias',
+    'get_discriminator_alias_and_values',
+    'DUNDER_ATTRIBUTES',
+)
+
+ROOT_KEY = '__root__'
+# these are types that are returned unchanged by deepcopy
+IMMUTABLE_NON_COLLECTIONS_TYPES: Set[Type[Any]] = {
+    int,
+    float,
+    complex,
+    str,
+    bool,
+    bytes,
+    type,
+    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 import_string(dotted_path: str) -> Any:
+    """
+    Stolen approximately from django. Import a dotted module path and return the attribute/class designated by the
+    last name in the path. Raise ImportError if the import fails.
+    """
+    from importlib import import_module
+
+    try:
+        module_path, class_name = dotted_path.strip(' ').rsplit('.', 1)
+    except ValueError as e:
+        raise ImportError(f'"{dotted_path}" doesn\'t look like a module path') from e
+
+    module = import_module(module_path)
+    try:
+        return getattr(module, class_name)
+    except AttributeError as e:
+        raise ImportError(f'Module "{module_path}" does not define a "{class_name}" attribute') from e
+
+
+def truncate(v: Union[str], *, max_len: int = 80) -> str:
+    """
+    Truncate a value and add a unicode ellipsis (three dots) to the end if it was too long
+    """
+    warnings.warn('`truncate` is no-longer used by pydantic and is deprecated', DeprecationWarning)
+    if isinstance(v, str) and len(v) > (max_len - 2):
+        # -3 so quote + string + … + quote has correct length
+        return (v[: (max_len - 3)] + '…').__repr__()
+    try:
+        v = v.__repr__()
+    except TypeError:
+        v = v.__class__.__repr__(v)  # in case v is a type
+    if len(v) > max_len:
+        v = v[: max_len - 1] + '…'
+    return v
+
+
+def sequence_like(v: Any) -> bool:
+    return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque))
+
+
+def validate_field_name(bases: List[Type['BaseModel']], field_name: str) -> None:
+    """
+    Ensure that the field's name does not shadow an existing attribute of the model.
+    """
+    for base in bases:
+        if getattr(base, field_name, None):
+            raise NameError(
+                f'Field name "{field_name}" shadows a BaseModel attribute; '
+                f'use a different field name with "alias=\'{field_name}\'".'
+            )
+
+
+def lenient_isinstance(o: Any, class_or_tuple: Union[Type[Any], Tuple[Type[Any], ...], None]) -> bool:
+    try:
+        return isinstance(o, class_or_tuple)  # type: ignore[arg-type]
+    except TypeError:
+        return False
+
+
+def lenient_issubclass(cls: Any, class_or_tuple: Union[Type[Any], Tuple[Type[Any], ...], None]) -> bool:
+    try:
+        return isinstance(cls, type) and issubclass(cls, class_or_tuple)  # type: ignore[arg-type]
+    except TypeError:
+        if isinstance(cls, WithArgsTypes):
+            return False
+        raise  # pragma: no cover
+
+
+def in_ipython() -> bool:
+    """
+    Check whether we're in an ipython environment, including jupyter notebooks.
+    """
+    try:
+        eval('__IPYTHON__')
+    except NameError:
+        return False
+    else:  # pragma: no cover
+        return True
+
+
+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})
+
+
+def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:
+    """
+    Return True if two floats are almost equal
+    """
+    return abs(value_1 - value_2) <= delta
+
+
+def generate_model_signature(
+    init: Callable[..., None], fields: Dict[str, 'ModelField'], config: Type['BaseConfig']
+) -> 'Signature':
+    """
+    Generate signature for model based on its fields
+    """
+    from inspect import Parameter, Signature, signature
+
+    from pydantic.v1.config import Extra
+
+    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
+        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 = config.allow_population_by_field_name
+        for field_name, field in fields.items():
+            param_name = field.alias
+            if field_name in merged_params or param_name in merged_params:
+                continue
+            elif not is_valid_identifier(param_name):
+                if allow_names and is_valid_identifier(field_name):
+                    param_name = field_name
+                else:
+                    use_var_kw = True
+                    continue
+
+            # TODO: replace annotation with actual expected types once #1055 solved
+            kwargs = {'default': field.default} if not field.required else {}
+            merged_params[param_name] = Parameter(
+                param_name, Parameter.KEYWORD_ONLY, annotation=field.annotation, **kwargs
+            )
+
+    if config.extra is 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 = [
+            ('__pydantic_self__', Parameter.POSITIONAL_OR_KEYWORD),
+            ('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 Signature(parameters=list(merged_params.values()), return_annotation=None)
+
+
+def get_model(obj: Union[Type['BaseModel'], Type['Dataclass']]) -> Type['BaseModel']:
+    from pydantic.v1.main import BaseModel
+
+    try:
+        model_cls = obj.__pydantic_model__  # type: ignore
+    except AttributeError:
+        model_cls = obj
+
+    if not issubclass(model_cls, BaseModel):
+        raise TypeError('Unsupported type, must be either BaseModel or dataclass')
+    return model_cls
+
+
+def to_camel(string: str) -> str:
+    return ''.join(word.capitalize() for word in string.split('_'))
+
+
+def to_lower_camel(string: str) -> str:
+    if len(string) >= 1:
+        pascal_string = to_camel(string)
+        return pascal_string[0].lower() + pascal_string[1:]
+    return string.lower()
+
+
+T = TypeVar('T')
+
+
+def unique_list(
+    input_list: Union[List[T], Tuple[T, ...]],
+    *,
+    name_factory: 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. root 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 PyObjectStr(str):
+    """
+    String class where repr doesn't include quotes. Useful with Representation when you want to return a string
+    representation of something that valid (or pseudo-valid) python.
+    """
+
+    def __repr__(self) -> str:
+        return str(self)
+
+
+class Representation:
+    """
+    Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.
+
+    __pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations
+    of objects.
+    """
+
+    __slots__: Tuple[str, ...] = tuple()
+
+    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 = ((s, getattr(self, s)) for s in self.__slots__)
+        return [(a, 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_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: Callable[[Any], Any], **kwargs: Any) -> Generator[Any, None, None]:
+        """
+        Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of 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 __str__(self) -> str:
+        return self.__repr_str__(' ')
+
+    def __repr__(self) -> str:
+        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
+
+    def __rich_repr__(self) -> 'RichReprResult':
+        """Get fields for Rich library"""
+        for name, field_repr in self.__repr_args__():
+            if name is None:
+                yield field_repr
+            else:
+                yield name, field_repr
+
+
+class GetterDict(Representation):
+    """
+    Hack to make object's smell just enough like dicts for validate_model.
+
+    We can't inherit from Mapping[str, Any] because it upsets cython so we have to implement all methods ourselves.
+    """
+
+    __slots__ = ('_obj',)
+
+    def __init__(self, obj: Any):
+        self._obj = obj
+
+    def __getitem__(self, key: str) -> Any:
+        try:
+            return getattr(self._obj, key)
+        except AttributeError as e:
+            raise KeyError(key) from e
+
+    def get(self, key: Any, default: Any = None) -> Any:
+        return getattr(self._obj, key, default)
+
+    def extra_keys(self) -> Set[Any]:
+        """
+        We don't want to get any other attributes of obj if the model didn't explicitly ask for them
+        """
+        return set()
+
+    def keys(self) -> List[Any]:
+        """
+        Keys of the pseudo dictionary, uses a list not set so order information can be maintained like python
+        dictionaries.
+        """
+        return list(self)
+
+    def values(self) -> List[Any]:
+        return [self[k] for k in self]
+
+    def items(self) -> Iterator[Tuple[str, Any]]:
+        for k in self:
+            yield k, self.get(k)
+
+    def __iter__(self) -> Iterator[str]:
+        for name in dir(self._obj):
+            if not name.startswith('_'):
+                yield name
+
+    def __len__(self) -> int:
+        return sum(1 for _ in self)
+
+    def __contains__(self, item: Any) -> bool:
+        return item in self.keys()
+
+    def __eq__(self, other: Any) -> bool:
+        return dict(self) == dict(other.items())
+
+    def __repr_args__(self) -> 'ReprArgs':
+        return [(None, dict(self))]
+
+    def __repr_name__(self) -> str:
+        return f'GetterDict[{display_as_type(self._obj)}]'
+
+
+class ValueItems(Representation):
+    """
+    Class for more convenient calculation of excluded or included fields on values.
+    """
+
+    __slots__ = ('_items', '_type')
+
+    def __init__(self, value: Any, items: Union['AbstractSetIntStr', 'MappingIntStrAny']) -> None:
+        items = self._coerce_items(items)
+
+        if isinstance(value, (list, tuple)):
+            items = self._normalize_indexes(items, len(value))
+
+        self._items: 'MappingIntStrAny' = items
+
+    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: 'IntStr') -> Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']]:
+        """
+        :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)
+        return item if not self.is_true(item) else None
+
+    def _normalize_indexes(self, items: 'MappingIntStrAny', v_length: int) -> 'DictIntStrAny':
+        """
+        :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: 'DictIntStrAny' = {}
+        all_items = None
+        for i, v in items.items():
+            if not (isinstance(v, Mapping) or isinstance(v, 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: 'DictIntStrAny' = {}
+        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: Union['AbstractSetIntStr', 'MappingIntStrAny']) -> 'MappingIntStrAny':
+        if isinstance(items, Mapping):
+            pass
+        elif isinstance(items, AbstractSet):
+            items = dict.fromkeys(items, ...)
+        else:
+            class_name = getattr(items, '__class__', '???')
+            assert_never(
+                items,
+                f'Unexpected type of exclude value {class_name}',
+            )
+        return items
+
+    @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) -> 'ReprArgs':
+        return [(None, self._items)]
+
+
+class ClassAttribute:
+    """
+    Hide class attribute from its instances
+    """
+
+    __slots__ = (
+        'name',
+        'value',
+    )
+
+    def __init__(self, name: str, value: Any) -> None:
+        self.name = name
+        self.value = 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')
+
+
+path_types = {
+    'is_dir': 'directory',
+    'is_file': 'file',
+    'is_mount': 'mount point',
+    'is_symlink': 'symlink',
+    'is_block_device': 'block device',
+    'is_char_device': 'char device',
+    'is_fifo': 'FIFO',
+    'is_socket': 'socket',
+}
+
+
+def path_type(p: 'Path') -> str:
+    """
+    Find out what sort of thing a path is.
+    """
+    assert p.exists(), 'path does not exist'
+    for method, name in path_types.items():
+        if getattr(p, method)():
+            return name
+
+    return 'unknown'
+
+
+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()  # type: ignore  # tuple doesn't have copy method
+    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
+
+
+def is_valid_field(name: str) -> bool:
+    if not name.startswith('_'):
+        return True
+    return ROOT_KEY == name
+
+
+DUNDER_ATTRIBUTES = {
+    '__annotations__',
+    '__classcell__',
+    '__doc__',
+    '__module__',
+    '__orig_bases__',
+    '__orig_class__',
+    '__qualname__',
+}
+
+
+def is_valid_private_name(name: str) -> bool:
+    return not is_valid_field(name) and name not in DUNDER_ATTRIBUTES
+
+
+_EMPTY = object()
+
+
+def all_identical(left: Iterable[Any], right: 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=_EMPTY):
+        if left_item is not right_item:
+            return False
+    return True
+
+
+def assert_never(obj: NoReturn, msg: str) -> NoReturn:
+    """
+    Helper to make sure that we have covered all possible types.
+
+    This is mostly useful for ``mypy``, docs:
+    https://mypy.readthedocs.io/en/latest/literal_types.html#exhaustive-checks
+    """
+    raise TypeError(msg)
+
+
+def get_unique_discriminator_alias(all_aliases: Collection[str], discriminator_key: str) -> str:
+    """Validate that all aliases are the same and if that's the case return the alias"""
+    unique_aliases = set(all_aliases)
+    if len(unique_aliases) > 1:
+        raise ConfigError(
+            f'Aliases for discriminator {discriminator_key!r} must be the same (got {", ".join(sorted(all_aliases))})'
+        )
+    return unique_aliases.pop()
+
+
+def get_discriminator_alias_and_values(tp: Any, discriminator_key: str) -> Tuple[str, Tuple[str, ...]]:
+    """
+    Get alias and all valid values in the `Literal` type of the discriminator field
+    `tp` can be a `BaseModel` class or directly an `Annotated` `Union` of many.
+    """
+    is_root_model = getattr(tp, '__custom_root_type__', False)
+
+    if get_origin(tp) is Annotated:
+        tp = get_args(tp)[0]
+
+    if hasattr(tp, '__pydantic_model__'):
+        tp = tp.__pydantic_model__
+
+    if is_union(get_origin(tp)):
+        alias, all_values = _get_union_alias_and_all_values(tp, discriminator_key)
+        return alias, tuple(v for values in all_values for v in values)
+    elif is_root_model:
+        union_type = tp.__fields__[ROOT_KEY].type_
+        alias, all_values = _get_union_alias_and_all_values(union_type, discriminator_key)
+
+        if len(set(all_values)) > 1:
+            raise ConfigError(
+                f'Field {discriminator_key!r} is not the same for all submodels of {display_as_type(tp)!r}'
+            )
+
+        return alias, all_values[0]
+
+    else:
+        try:
+            t_discriminator_type = tp.__fields__[discriminator_key].type_
+        except AttributeError as e:
+            raise TypeError(f'Type {tp.__name__!r} is not a valid `BaseModel` or `dataclass`') from e
+        except KeyError as e:
+            raise ConfigError(f'Model {tp.__name__!r} needs a discriminator field for key {discriminator_key!r}') from e
+
+        if not is_literal_type(t_discriminator_type):
+            raise ConfigError(f'Field {discriminator_key!r} of model {tp.__name__!r} needs to be a `Literal`')
+
+        return tp.__fields__[discriminator_key].alias, all_literal_values(t_discriminator_type)
+
+
+def _get_union_alias_and_all_values(
+    union_type: Type[Any], discriminator_key: str
+) -> Tuple[str, Tuple[Tuple[str, ...], ...]]:
+    zipped_aliases_values = [get_discriminator_alias_and_values(t, discriminator_key) for t in get_args(union_type)]
+    # unzip: [('alias_a',('v1', 'v2)), ('alias_b', ('v3',))] => [('alias_a', 'alias_b'), (('v1', 'v2'), ('v3',))]
+    all_aliases, all_values = zip(*zipped_aliases_values)
+    return get_unique_discriminator_alias(all_aliases, discriminator_key), all_values
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/validators.py b/.venv/lib/python3.12/site-packages/pydantic/v1/validators.py
new file mode 100644
index 00000000..c0940e81
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/validators.py
@@ -0,0 +1,768 @@
+import math
+import re
+from collections import OrderedDict, deque
+from collections.abc import Hashable as CollectionsHashable
+from datetime import date, datetime, time, timedelta
+from decimal import Decimal, DecimalException
+from enum import Enum, IntEnum
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Deque,
+    Dict,
+    ForwardRef,
+    FrozenSet,
+    Generator,
+    Hashable,
+    List,
+    NamedTuple,
+    Pattern,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+from uuid import UUID
+from warnings import warn
+
+from pydantic.v1 import errors
+from pydantic.v1.datetime_parse import parse_date, parse_datetime, parse_duration, parse_time
+from pydantic.v1.typing import (
+    AnyCallable,
+    all_literal_values,
+    display_as_type,
+    get_class,
+    is_callable_type,
+    is_literal_type,
+    is_namedtuple,
+    is_none_type,
+    is_typeddict,
+)
+from pydantic.v1.utils import almost_equal_floats, lenient_issubclass, sequence_like
+
+if TYPE_CHECKING:
+    from typing_extensions import Literal, TypedDict
+
+    from pydantic.v1.config import BaseConfig
+    from pydantic.v1.fields import ModelField
+    from pydantic.v1.types import ConstrainedDecimal, ConstrainedFloat, ConstrainedInt
+
+    ConstrainedNumber = Union[ConstrainedDecimal, ConstrainedFloat, ConstrainedInt]
+    AnyOrderedDict = OrderedDict[Any, Any]
+    Number = Union[int, float, Decimal]
+    StrBytes = Union[str, bytes]
+
+
+def str_validator(v: Any) -> Union[str]:
+    if isinstance(v, str):
+        if isinstance(v, Enum):
+            return v.value
+        else:
+            return v
+    elif isinstance(v, (float, int, Decimal)):
+        # is there anything else we want to add here? If you think so, create an issue.
+        return str(v)
+    elif isinstance(v, (bytes, bytearray)):
+        return v.decode()
+    else:
+        raise errors.StrError()
+
+
+def strict_str_validator(v: Any) -> Union[str]:
+    if isinstance(v, str) and not isinstance(v, Enum):
+        return v
+    raise errors.StrError()
+
+
+def bytes_validator(v: Any) -> Union[bytes]:
+    if isinstance(v, bytes):
+        return v
+    elif isinstance(v, bytearray):
+        return bytes(v)
+    elif isinstance(v, str):
+        return v.encode()
+    elif isinstance(v, (float, int, Decimal)):
+        return str(v).encode()
+    else:
+        raise errors.BytesError()
+
+
+def strict_bytes_validator(v: Any) -> Union[bytes]:
+    if isinstance(v, bytes):
+        return v
+    elif isinstance(v, bytearray):
+        return bytes(v)
+    else:
+        raise errors.BytesError()
+
+
+BOOL_FALSE = {0, '0', 'off', 'f', 'false', 'n', 'no'}
+BOOL_TRUE = {1, '1', 'on', 't', 'true', 'y', 'yes'}
+
+
+def bool_validator(v: Any) -> bool:
+    if v is True or v is False:
+        return v
+    if isinstance(v, bytes):
+        v = v.decode()
+    if isinstance(v, str):
+        v = v.lower()
+    try:
+        if v in BOOL_TRUE:
+            return True
+        if v in BOOL_FALSE:
+            return False
+    except TypeError:
+        raise errors.BoolError()
+    raise errors.BoolError()
+
+
+# matches the default limit cpython, see https://github.com/python/cpython/pull/96500
+max_str_int = 4_300
+
+
+def int_validator(v: Any) -> int:
+    if isinstance(v, int) and not (v is True or v is False):
+        return v
+
+    # see https://github.com/pydantic/pydantic/issues/1477 and in turn, https://github.com/python/cpython/issues/95778
+    # this check should be unnecessary once patch releases are out for 3.7, 3.8, 3.9 and 3.10
+    # but better to check here until then.
+    # NOTICE: this does not fully protect user from the DOS risk since the standard library JSON implementation
+    # (and other std lib modules like xml) use `int()` and are likely called before this, the best workaround is to
+    # 1. update to the latest patch release of python once released, 2. use a different JSON library like ujson
+    if isinstance(v, (str, bytes, bytearray)) and len(v) > max_str_int:
+        raise errors.IntegerError()
+
+    try:
+        return int(v)
+    except (TypeError, ValueError, OverflowError):
+        raise errors.IntegerError()
+
+
+def strict_int_validator(v: Any) -> int:
+    if isinstance(v, int) and not (v is True or v is False):
+        return v
+    raise errors.IntegerError()
+
+
+def float_validator(v: Any) -> float:
+    if isinstance(v, float):
+        return v
+
+    try:
+        return float(v)
+    except (TypeError, ValueError):
+        raise errors.FloatError()
+
+
+def strict_float_validator(v: Any) -> float:
+    if isinstance(v, float):
+        return v
+    raise errors.FloatError()
+
+
+def float_finite_validator(v: 'Number', field: 'ModelField', config: 'BaseConfig') -> 'Number':
+    allow_inf_nan = getattr(field.type_, 'allow_inf_nan', None)
+    if allow_inf_nan is None:
+        allow_inf_nan = config.allow_inf_nan
+
+    if allow_inf_nan is False and (math.isnan(v) or math.isinf(v)):
+        raise errors.NumberNotFiniteError()
+    return v
+
+
+def number_multiple_validator(v: 'Number', field: 'ModelField') -> 'Number':
+    field_type: ConstrainedNumber = field.type_
+    if field_type.multiple_of is not None:
+        mod = float(v) / float(field_type.multiple_of) % 1
+        if not almost_equal_floats(mod, 0.0) and not almost_equal_floats(mod, 1.0):
+            raise errors.NumberNotMultipleError(multiple_of=field_type.multiple_of)
+    return v
+
+
+def number_size_validator(v: 'Number', field: 'ModelField') -> 'Number':
+    field_type: ConstrainedNumber = field.type_
+    if field_type.gt is not None and not v > field_type.gt:
+        raise errors.NumberNotGtError(limit_value=field_type.gt)
+    elif field_type.ge is not None and not v >= field_type.ge:
+        raise errors.NumberNotGeError(limit_value=field_type.ge)
+
+    if field_type.lt is not None and not v < field_type.lt:
+        raise errors.NumberNotLtError(limit_value=field_type.lt)
+    if field_type.le is not None and not v <= field_type.le:
+        raise errors.NumberNotLeError(limit_value=field_type.le)
+
+    return v
+
+
+def constant_validator(v: 'Any', field: 'ModelField') -> 'Any':
+    """Validate ``const`` fields.
+
+    The value provided for a ``const`` field must be equal to the default value
+    of the field. This is to support the keyword of the same name in JSON
+    Schema.
+    """
+    if v != field.default:
+        raise errors.WrongConstantError(given=v, permitted=[field.default])
+
+    return v
+
+
+def anystr_length_validator(v: 'StrBytes', config: 'BaseConfig') -> 'StrBytes':
+    v_len = len(v)
+
+    min_length = config.min_anystr_length
+    if v_len < min_length:
+        raise errors.AnyStrMinLengthError(limit_value=min_length)
+
+    max_length = config.max_anystr_length
+    if max_length is not None and v_len > max_length:
+        raise errors.AnyStrMaxLengthError(limit_value=max_length)
+
+    return v
+
+
+def anystr_strip_whitespace(v: 'StrBytes') -> 'StrBytes':
+    return v.strip()
+
+
+def anystr_upper(v: 'StrBytes') -> 'StrBytes':
+    return v.upper()
+
+
+def anystr_lower(v: 'StrBytes') -> 'StrBytes':
+    return v.lower()
+
+
+def ordered_dict_validator(v: Any) -> 'AnyOrderedDict':
+    if isinstance(v, OrderedDict):
+        return v
+
+    try:
+        return OrderedDict(v)
+    except (TypeError, ValueError):
+        raise errors.DictError()
+
+
+def dict_validator(v: Any) -> Dict[Any, Any]:
+    if isinstance(v, dict):
+        return v
+
+    try:
+        return dict(v)
+    except (TypeError, ValueError):
+        raise errors.DictError()
+
+
+def list_validator(v: Any) -> List[Any]:
+    if isinstance(v, list):
+        return v
+    elif sequence_like(v):
+        return list(v)
+    else:
+        raise errors.ListError()
+
+
+def tuple_validator(v: Any) -> Tuple[Any, ...]:
+    if isinstance(v, tuple):
+        return v
+    elif sequence_like(v):
+        return tuple(v)
+    else:
+        raise errors.TupleError()
+
+
+def set_validator(v: Any) -> Set[Any]:
+    if isinstance(v, set):
+        return v
+    elif sequence_like(v):
+        return set(v)
+    else:
+        raise errors.SetError()
+
+
+def frozenset_validator(v: Any) -> FrozenSet[Any]:
+    if isinstance(v, frozenset):
+        return v
+    elif sequence_like(v):
+        return frozenset(v)
+    else:
+        raise errors.FrozenSetError()
+
+
+def deque_validator(v: Any) -> Deque[Any]:
+    if isinstance(v, deque):
+        return v
+    elif sequence_like(v):
+        return deque(v)
+    else:
+        raise errors.DequeError()
+
+
+def enum_member_validator(v: Any, field: 'ModelField', config: 'BaseConfig') -> Enum:
+    try:
+        enum_v = field.type_(v)
+    except ValueError:
+        # field.type_ should be an enum, so will be iterable
+        raise errors.EnumMemberError(enum_values=list(field.type_))
+    return enum_v.value if config.use_enum_values else enum_v
+
+
+def uuid_validator(v: Any, field: 'ModelField') -> UUID:
+    try:
+        if isinstance(v, str):
+            v = UUID(v)
+        elif isinstance(v, (bytes, bytearray)):
+            try:
+                v = UUID(v.decode())
+            except ValueError:
+                # 16 bytes in big-endian order as the bytes argument fail
+                # the above check
+                v = UUID(bytes=v)
+    except ValueError:
+        raise errors.UUIDError()
+
+    if not isinstance(v, UUID):
+        raise errors.UUIDError()
+
+    required_version = getattr(field.type_, '_required_version', None)
+    if required_version and v.version != required_version:
+        raise errors.UUIDVersionError(required_version=required_version)
+
+    return v
+
+
+def decimal_validator(v: Any) -> Decimal:
+    if isinstance(v, Decimal):
+        return v
+    elif isinstance(v, (bytes, bytearray)):
+        v = v.decode()
+
+    v = str(v).strip()
+
+    try:
+        v = Decimal(v)
+    except DecimalException:
+        raise errors.DecimalError()
+
+    if not v.is_finite():
+        raise errors.DecimalIsNotFiniteError()
+
+    return v
+
+
+def hashable_validator(v: Any) -> Hashable:
+    if isinstance(v, Hashable):
+        return v
+
+    raise errors.HashableError()
+
+
+def ip_v4_address_validator(v: Any) -> IPv4Address:
+    if isinstance(v, IPv4Address):
+        return v
+
+    try:
+        return IPv4Address(v)
+    except ValueError:
+        raise errors.IPv4AddressError()
+
+
+def ip_v6_address_validator(v: Any) -> IPv6Address:
+    if isinstance(v, IPv6Address):
+        return v
+
+    try:
+        return IPv6Address(v)
+    except ValueError:
+        raise errors.IPv6AddressError()
+
+
+def ip_v4_network_validator(v: Any) -> IPv4Network:
+    """
+    Assume IPv4Network initialised with a default ``strict`` argument
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv4Network
+    """
+    if isinstance(v, IPv4Network):
+        return v
+
+    try:
+        return IPv4Network(v)
+    except ValueError:
+        raise errors.IPv4NetworkError()
+
+
+def ip_v6_network_validator(v: Any) -> IPv6Network:
+    """
+    Assume IPv6Network initialised with a default ``strict`` argument
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv6Network
+    """
+    if isinstance(v, IPv6Network):
+        return v
+
+    try:
+        return IPv6Network(v)
+    except ValueError:
+        raise errors.IPv6NetworkError()
+
+
+def ip_v4_interface_validator(v: Any) -> IPv4Interface:
+    if isinstance(v, IPv4Interface):
+        return v
+
+    try:
+        return IPv4Interface(v)
+    except ValueError:
+        raise errors.IPv4InterfaceError()
+
+
+def ip_v6_interface_validator(v: Any) -> IPv6Interface:
+    if isinstance(v, IPv6Interface):
+        return v
+
+    try:
+        return IPv6Interface(v)
+    except ValueError:
+        raise errors.IPv6InterfaceError()
+
+
+def path_validator(v: Any) -> Path:
+    if isinstance(v, Path):
+        return v
+
+    try:
+        return Path(v)
+    except TypeError:
+        raise errors.PathError()
+
+
+def path_exists_validator(v: Any) -> Path:
+    if not v.exists():
+        raise errors.PathNotExistsError(path=v)
+
+    return v
+
+
+def callable_validator(v: Any) -> AnyCallable:
+    """
+    Perform a simple check if the value is callable.
+
+    Note: complete matching of argument type hints and return types is not performed
+    """
+    if callable(v):
+        return v
+
+    raise errors.CallableError(value=v)
+
+
+def enum_validator(v: Any) -> Enum:
+    if isinstance(v, Enum):
+        return v
+
+    raise errors.EnumError(value=v)
+
+
+def int_enum_validator(v: Any) -> IntEnum:
+    if isinstance(v, IntEnum):
+        return v
+
+    raise errors.IntEnumError(value=v)
+
+
+def make_literal_validator(type_: Any) -> Callable[[Any], Any]:
+    permitted_choices = all_literal_values(type_)
+
+    # To have a O(1) complexity and still return one of the values set inside the `Literal`,
+    # we create a dict with the set values (a set causes some problems with the way intersection works).
+    # In some cases the set value and checked value can indeed be different (see `test_literal_validator_str_enum`)
+    allowed_choices = {v: v for v in permitted_choices}
+
+    def literal_validator(v: Any) -> Any:
+        try:
+            return allowed_choices[v]
+        except (KeyError, TypeError):
+            raise errors.WrongConstantError(given=v, permitted=permitted_choices)
+
+    return literal_validator
+
+
+def constr_length_validator(v: 'StrBytes', field: 'ModelField', config: 'BaseConfig') -> 'StrBytes':
+    v_len = len(v)
+
+    min_length = field.type_.min_length if field.type_.min_length is not None else config.min_anystr_length
+    if v_len < min_length:
+        raise errors.AnyStrMinLengthError(limit_value=min_length)
+
+    max_length = field.type_.max_length if field.type_.max_length is not None else config.max_anystr_length
+    if max_length is not None and v_len > max_length:
+        raise errors.AnyStrMaxLengthError(limit_value=max_length)
+
+    return v
+
+
+def constr_strip_whitespace(v: 'StrBytes', field: 'ModelField', config: 'BaseConfig') -> 'StrBytes':
+    strip_whitespace = field.type_.strip_whitespace or config.anystr_strip_whitespace
+    if strip_whitespace:
+        v = v.strip()
+
+    return v
+
+
+def constr_upper(v: 'StrBytes', field: 'ModelField', config: 'BaseConfig') -> 'StrBytes':
+    upper = field.type_.to_upper or config.anystr_upper
+    if upper:
+        v = v.upper()
+
+    return v
+
+
+def constr_lower(v: 'StrBytes', field: 'ModelField', config: 'BaseConfig') -> 'StrBytes':
+    lower = field.type_.to_lower or config.anystr_lower
+    if lower:
+        v = v.lower()
+    return v
+
+
+def validate_json(v: Any, config: 'BaseConfig') -> Any:
+    if v is None:
+        # pass None through to other validators
+        return v
+    try:
+        return config.json_loads(v)  # type: ignore
+    except ValueError:
+        raise errors.JsonError()
+    except TypeError:
+        raise errors.JsonTypeError()
+
+
+T = TypeVar('T')
+
+
+def make_arbitrary_type_validator(type_: Type[T]) -> Callable[[T], T]:
+    def arbitrary_type_validator(v: Any) -> T:
+        if isinstance(v, type_):
+            return v
+        raise errors.ArbitraryTypeError(expected_arbitrary_type=type_)
+
+    return arbitrary_type_validator
+
+
+def make_class_validator(type_: Type[T]) -> Callable[[Any], Type[T]]:
+    def class_validator(v: Any) -> Type[T]:
+        if lenient_issubclass(v, type_):
+            return v
+        raise errors.SubclassError(expected_class=type_)
+
+    return class_validator
+
+
+def any_class_validator(v: Any) -> Type[T]:
+    if isinstance(v, type):
+        return v
+    raise errors.ClassError()
+
+
+def none_validator(v: Any) -> 'Literal[None]':
+    if v is None:
+        return v
+    raise errors.NotNoneError()
+
+
+def pattern_validator(v: Any) -> Pattern[str]:
+    if isinstance(v, Pattern):
+        return v
+
+    str_value = str_validator(v)
+
+    try:
+        return re.compile(str_value)
+    except re.error:
+        raise errors.PatternError()
+
+
+NamedTupleT = TypeVar('NamedTupleT', bound=NamedTuple)
+
+
+def make_namedtuple_validator(
+    namedtuple_cls: Type[NamedTupleT], config: Type['BaseConfig']
+) -> Callable[[Tuple[Any, ...]], NamedTupleT]:
+    from pydantic.v1.annotated_types import create_model_from_namedtuple
+
+    NamedTupleModel = create_model_from_namedtuple(
+        namedtuple_cls,
+        __config__=config,
+        __module__=namedtuple_cls.__module__,
+    )
+    namedtuple_cls.__pydantic_model__ = NamedTupleModel  # type: ignore[attr-defined]
+
+    def namedtuple_validator(values: Tuple[Any, ...]) -> NamedTupleT:
+        annotations = NamedTupleModel.__annotations__
+
+        if len(values) > len(annotations):
+            raise errors.ListMaxLengthError(limit_value=len(annotations))
+
+        dict_values: Dict[str, Any] = dict(zip(annotations, values))
+        validated_dict_values: Dict[str, Any] = dict(NamedTupleModel(**dict_values))
+        return namedtuple_cls(**validated_dict_values)
+
+    return namedtuple_validator
+
+
+def make_typeddict_validator(
+    typeddict_cls: Type['TypedDict'], config: Type['BaseConfig']  # type: ignore[valid-type]
+) -> Callable[[Any], Dict[str, Any]]:
+    from pydantic.v1.annotated_types import create_model_from_typeddict
+
+    TypedDictModel = create_model_from_typeddict(
+        typeddict_cls,
+        __config__=config,
+        __module__=typeddict_cls.__module__,
+    )
+    typeddict_cls.__pydantic_model__ = TypedDictModel  # type: ignore[attr-defined]
+
+    def typeddict_validator(values: 'TypedDict') -> Dict[str, Any]:  # type: ignore[valid-type]
+        return TypedDictModel.parse_obj(values).dict(exclude_unset=True)
+
+    return typeddict_validator
+
+
+class IfConfig:
+    def __init__(self, validator: AnyCallable, *config_attr_names: str, ignored_value: Any = False) -> None:
+        self.validator = validator
+        self.config_attr_names = config_attr_names
+        self.ignored_value = ignored_value
+
+    def check(self, config: Type['BaseConfig']) -> bool:
+        return any(getattr(config, name) not in {None, self.ignored_value} for name in self.config_attr_names)
+
+
+# order is important here, for example: bool is a subclass of int so has to come first, datetime before date same,
+# IPv4Interface before IPv4Address, etc
+_VALIDATORS: List[Tuple[Type[Any], List[Any]]] = [
+    (IntEnum, [int_validator, enum_member_validator]),
+    (Enum, [enum_member_validator]),
+    (
+        str,
+        [
+            str_validator,
+            IfConfig(anystr_strip_whitespace, 'anystr_strip_whitespace'),
+            IfConfig(anystr_upper, 'anystr_upper'),
+            IfConfig(anystr_lower, 'anystr_lower'),
+            IfConfig(anystr_length_validator, 'min_anystr_length', 'max_anystr_length'),
+        ],
+    ),
+    (
+        bytes,
+        [
+            bytes_validator,
+            IfConfig(anystr_strip_whitespace, 'anystr_strip_whitespace'),
+            IfConfig(anystr_upper, 'anystr_upper'),
+            IfConfig(anystr_lower, 'anystr_lower'),
+            IfConfig(anystr_length_validator, 'min_anystr_length', 'max_anystr_length'),
+        ],
+    ),
+    (bool, [bool_validator]),
+    (int, [int_validator]),
+    (float, [float_validator, IfConfig(float_finite_validator, 'allow_inf_nan', ignored_value=True)]),
+    (Path, [path_validator]),
+    (datetime, [parse_datetime]),
+    (date, [parse_date]),
+    (time, [parse_time]),
+    (timedelta, [parse_duration]),
+    (OrderedDict, [ordered_dict_validator]),
+    (dict, [dict_validator]),
+    (list, [list_validator]),
+    (tuple, [tuple_validator]),
+    (set, [set_validator]),
+    (frozenset, [frozenset_validator]),
+    (deque, [deque_validator]),
+    (UUID, [uuid_validator]),
+    (Decimal, [decimal_validator]),
+    (IPv4Interface, [ip_v4_interface_validator]),
+    (IPv6Interface, [ip_v6_interface_validator]),
+    (IPv4Address, [ip_v4_address_validator]),
+    (IPv6Address, [ip_v6_address_validator]),
+    (IPv4Network, [ip_v4_network_validator]),
+    (IPv6Network, [ip_v6_network_validator]),
+]
+
+
+def find_validators(  # noqa: C901 (ignore complexity)
+    type_: Type[Any], config: Type['BaseConfig']
+) -> Generator[AnyCallable, None, None]:
+    from pydantic.v1.dataclasses import is_builtin_dataclass, make_dataclass_validator
+
+    if type_ is Any or type_ is object:
+        return
+    type_type = type_.__class__
+    if type_type == ForwardRef or type_type == TypeVar:
+        return
+
+    if is_none_type(type_):
+        yield none_validator
+        return
+    if type_ is Pattern or type_ is re.Pattern:
+        yield pattern_validator
+        return
+    if type_ is Hashable or type_ is CollectionsHashable:
+        yield hashable_validator
+        return
+    if is_callable_type(type_):
+        yield callable_validator
+        return
+    if is_literal_type(type_):
+        yield make_literal_validator(type_)
+        return
+    if is_builtin_dataclass(type_):
+        yield from make_dataclass_validator(type_, config)
+        return
+    if type_ is Enum:
+        yield enum_validator
+        return
+    if type_ is IntEnum:
+        yield int_enum_validator
+        return
+    if is_namedtuple(type_):
+        yield tuple_validator
+        yield make_namedtuple_validator(type_, config)
+        return
+    if is_typeddict(type_):
+        yield make_typeddict_validator(type_, config)
+        return
+
+    class_ = get_class(type_)
+    if class_ is not None:
+        if class_ is not Any and isinstance(class_, type):
+            yield make_class_validator(class_)
+        else:
+            yield any_class_validator
+        return
+
+    for val_type, validators in _VALIDATORS:
+        try:
+            if issubclass(type_, val_type):
+                for v in validators:
+                    if isinstance(v, IfConfig):
+                        if v.check(config):
+                            yield v.validator
+                    else:
+                        yield v
+                return
+        except TypeError:
+            raise RuntimeError(f'error checking inheritance of {type_!r} (type: {display_as_type(type_)})')
+
+    if config.arbitrary_types_allowed:
+        yield make_arbitrary_type_validator(type_)
+    else:
+        if hasattr(type_, '__pydantic_core_schema__'):
+            warn(f'Mixing V1 and V2 models is not supported. `{type_.__name__}` is a V2 model.', UserWarning)
+        raise RuntimeError(f'no validator found for {type_}, see `arbitrary_types_allowed` in Config')
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/version.py b/.venv/lib/python3.12/site-packages/pydantic/v1/version.py
new file mode 100644
index 00000000..47be0b75
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/version.py
@@ -0,0 +1,38 @@
+__all__ = 'compiled', 'VERSION', 'version_info'
+
+VERSION = '1.10.19'
+
+try:
+    import cython  # type: ignore
+except ImportError:
+    compiled: bool = False
+else:  # pragma: no cover
+    try:
+        compiled = cython.compiled
+    except AttributeError:
+        compiled = False
+
+
+def version_info() -> str:
+    import platform
+    import sys
+    from importlib import import_module
+    from pathlib import Path
+
+    optional_deps = []
+    for p in ('devtools', 'dotenv', 'email-validator', 'typing-extensions'):
+        try:
+            import_module(p.replace('-', '_'))
+        except ImportError:
+            continue
+        optional_deps.append(p)
+
+    info = {
+        'pydantic version': VERSION,
+        'pydantic compiled': compiled,
+        'install path': Path(__file__).resolve().parent,
+        'python version': sys.version,
+        'platform': platform.platform(),
+        'optional deps. installed': optional_deps,
+    }
+    return '\n'.join('{:>30} {}'.format(k + ':', str(v).replace('\n', ' ')) for k, v in info.items())
diff --git a/.venv/lib/python3.12/site-packages/pydantic/validate_call_decorator.py b/.venv/lib/python3.12/site-packages/pydantic/validate_call_decorator.py
new file mode 100644
index 00000000..b5ac03c1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/validate_call_decorator.py
@@ -0,0 +1,115 @@
+"""Decorator for validating function calls."""
+
+from __future__ import annotations as _annotations
+
+import inspect
+from functools import partial
+from types import BuiltinFunctionType
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, cast, overload
+
+from ._internal import _generate_schema, _typing_extra, _validate_call
+from .errors import PydanticUserError
+
+__all__ = ('validate_call',)
+
+if TYPE_CHECKING:
+    from .config import ConfigDict
+
+    AnyCallableT = TypeVar('AnyCallableT', bound=Callable[..., Any])
+
+
+_INVALID_TYPE_ERROR_CODE = 'validate-call-type'
+
+
+def _check_function_type(function: object) -> None:
+    """Check if the input function is a supported type for `validate_call`."""
+    if isinstance(function, _generate_schema.VALIDATE_CALL_SUPPORTED_TYPES):
+        try:
+            inspect.signature(cast(_generate_schema.ValidateCallSupportedTypes, function))
+        except ValueError:
+            raise PydanticUserError(
+                f"Input function `{function}` doesn't have a valid signature", code=_INVALID_TYPE_ERROR_CODE
+            )
+
+        if isinstance(function, partial):
+            try:
+                assert not isinstance(partial.func, partial), 'Partial of partial'
+                _check_function_type(function.func)
+            except PydanticUserError as e:
+                raise PydanticUserError(
+                    f'Partial of `{function.func}` is invalid because the type of `{function.func}` is not supported by `validate_call`',
+                    code=_INVALID_TYPE_ERROR_CODE,
+                ) from e
+
+        return
+
+    if isinstance(function, BuiltinFunctionType):
+        raise PydanticUserError(f'Input built-in function `{function}` is not supported', code=_INVALID_TYPE_ERROR_CODE)
+    if isinstance(function, (classmethod, staticmethod, property)):
+        name = type(function).__name__
+        raise PydanticUserError(
+            f'The `@{name}` decorator should be applied after `@validate_call` (put `@{name}` on top)',
+            code=_INVALID_TYPE_ERROR_CODE,
+        )
+
+    if inspect.isclass(function):
+        raise PydanticUserError(
+            f'Unable to validate {function}: `validate_call` should be applied to functions, not classes (put `@validate_call` on top of `__init__` or `__new__` instead)',
+            code=_INVALID_TYPE_ERROR_CODE,
+        )
+    if callable(function):
+        raise PydanticUserError(
+            f'Unable to validate {function}: `validate_call` should be applied to functions, not instances or other callables. Use `validate_call` explicitly on `__call__` instead.',
+            code=_INVALID_TYPE_ERROR_CODE,
+        )
+
+    raise PydanticUserError(
+        f'Unable to validate {function}: `validate_call` should be applied to one of the following: function, method, partial, or lambda',
+        code=_INVALID_TYPE_ERROR_CODE,
+    )
+
+
+@overload
+def validate_call(
+    *, config: ConfigDict | None = None, validate_return: bool = False
+) -> Callable[[AnyCallableT], AnyCallableT]: ...
+
+
+@overload
+def validate_call(func: AnyCallableT, /) -> AnyCallableT: ...
+
+
+def validate_call(
+    func: AnyCallableT | None = None,
+    /,
+    *,
+    config: ConfigDict | None = None,
+    validate_return: bool = False,
+) -> AnyCallableT | Callable[[AnyCallableT], AnyCallableT]:
+    """Usage docs: https://docs.pydantic.dev/2.10/concepts/validation_decorator/
+
+    Returns a decorated wrapper around the function that validates the arguments and, optionally, the return value.
+
+    Usage may be either as a plain decorator `@validate_call` or with arguments `@validate_call(...)`.
+
+    Args:
+        func: The function to be decorated.
+        config: The configuration dictionary.
+        validate_return: Whether to validate the return value.
+
+    Returns:
+        The decorated function.
+    """
+    parent_namespace = _typing_extra.parent_frame_namespace()
+
+    def validate(function: AnyCallableT) -> AnyCallableT:
+        _check_function_type(function)
+        validate_call_wrapper = _validate_call.ValidateCallWrapper(
+            cast(_generate_schema.ValidateCallSupportedTypes, function), config, validate_return, parent_namespace
+        )
+        return _validate_call.update_wrapper_attributes(function, validate_call_wrapper.__call__)  # type: ignore
+
+    if func is not None:
+        return validate(func)
+    else:
+        return validate
diff --git a/.venv/lib/python3.12/site-packages/pydantic/validators.py b/.venv/lib/python3.12/site-packages/pydantic/validators.py
new file mode 100644
index 00000000..7921b04f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/validators.py
@@ -0,0 +1,5 @@
+"""The `validators` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)
diff --git a/.venv/lib/python3.12/site-packages/pydantic/version.py b/.venv/lib/python3.12/site-packages/pydantic/version.py
new file mode 100644
index 00000000..da645480
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/version.py
@@ -0,0 +1,78 @@
+"""The `version` module holds the version information for Pydantic."""
+
+from __future__ import annotations as _annotations
+
+__all__ = 'VERSION', 'version_info'
+
+VERSION = '2.10.6'
+"""The version of Pydantic."""
+
+
+def version_short() -> str:
+    """Return the `major.minor` part of Pydantic version.
+
+    It returns '2.1' if Pydantic version is '2.1.1'.
+    """
+    return '.'.join(VERSION.split('.')[:2])
+
+
+def version_info() -> str:
+    """Return complete version information for Pydantic and its dependencies."""
+    import importlib.metadata as importlib_metadata
+    import os
+    import platform
+    import sys
+    from pathlib import Path
+
+    import pydantic_core._pydantic_core as pdc
+
+    from ._internal import _git as git
+
+    # get data about packages that are closely related to pydantic, use pydantic or often conflict with pydantic
+    package_names = {
+        'email-validator',
+        'fastapi',
+        'mypy',
+        'pydantic-extra-types',
+        'pydantic-settings',
+        'pyright',
+        'typing_extensions',
+    }
+    related_packages = []
+
+    for dist in importlib_metadata.distributions():
+        name = dist.metadata['Name']
+        if name in package_names:
+            related_packages.append(f'{name}-{dist.version}')
+
+    pydantic_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
+    most_recent_commit = (
+        git.git_revision(pydantic_dir) if git.is_git_repo(pydantic_dir) and git.have_git() else 'unknown'
+    )
+
+    info = {
+        'pydantic version': VERSION,
+        'pydantic-core version': pdc.__version__,
+        'pydantic-core build': getattr(pdc, 'build_info', None) or pdc.build_profile,
+        'install path': Path(__file__).resolve().parent,
+        'python version': sys.version,
+        'platform': platform.platform(),
+        'related packages': ' '.join(related_packages),
+        'commit': most_recent_commit,
+    }
+    return '\n'.join('{:>30} {}'.format(k + ':', str(v).replace('\n', ' ')) for k, v in info.items())
+
+
+def parse_mypy_version(version: str) -> tuple[int, int, int]:
+    """Parse `mypy` string version to a 3-tuple of ints.
+
+    It parses normal version like `1.11.0` and extra info followed by a `+` sign
+    like `1.11.0+dev.d6d9d8cd4f27c52edac1f537e236ec48a01e54cb.dirty`.
+
+    Args:
+        version: The mypy version string.
+
+    Returns:
+        A triple of ints, e.g. `(1, 11, 0)`.
+    """
+    return tuple(map(int, version.partition('+')[0].split('.')))  # pyright: ignore[reportReturnType]
diff --git a/.venv/lib/python3.12/site-packages/pydantic/warnings.py b/.venv/lib/python3.12/site-packages/pydantic/warnings.py
new file mode 100644
index 00000000..4cbd52da
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/warnings.py
@@ -0,0 +1,86 @@
+"""Pydantic-specific warnings."""
+
+from __future__ import annotations as _annotations
+
+from .version import version_short
+
+__all__ = (
+    'PydanticDeprecatedSince20',
+    'PydanticDeprecationWarning',
+    'PydanticDeprecatedSince26',
+    'PydanticExperimentalWarning',
+)
+
+
+class PydanticDeprecationWarning(DeprecationWarning):
+    """A Pydantic specific deprecation warning.
+
+    This warning is raised when using deprecated functionality in Pydantic. It provides information on when the
+    deprecation was introduced and the expected version in which the corresponding functionality will be removed.
+
+    Attributes:
+        message: Description of the warning.
+        since: Pydantic version in what the deprecation was introduced.
+        expected_removal: Pydantic version in what the corresponding functionality expected to be removed.
+    """
+
+    message: str
+    since: tuple[int, int]
+    expected_removal: tuple[int, int]
+
+    def __init__(
+        self, message: str, *args: object, since: tuple[int, int], expected_removal: tuple[int, int] | None = None
+    ) -> None:
+        super().__init__(message, *args)
+        self.message = message.rstrip('.')
+        self.since = since
+        self.expected_removal = expected_removal if expected_removal is not None else (since[0] + 1, 0)
+
+    def __str__(self) -> str:
+        message = (
+            f'{self.message}. Deprecated in Pydantic V{self.since[0]}.{self.since[1]}'
+            f' to be removed in V{self.expected_removal[0]}.{self.expected_removal[1]}.'
+        )
+        if self.since == (2, 0):
+            message += f' See Pydantic V2 Migration Guide at https://errors.pydantic.dev/{version_short()}/migration/'
+        return message
+
+
+class PydanticDeprecatedSince20(PydanticDeprecationWarning):
+    """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.0."""
+
+    def __init__(self, message: str, *args: object) -> None:
+        super().__init__(message, *args, since=(2, 0), expected_removal=(3, 0))
+
+
+class PydanticDeprecatedSince26(PydanticDeprecationWarning):
+    """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.6."""
+
+    def __init__(self, message: str, *args: object) -> None:
+        super().__init__(message, *args, since=(2, 6), expected_removal=(3, 0))
+
+
+class PydanticDeprecatedSince29(PydanticDeprecationWarning):
+    """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.9."""
+
+    def __init__(self, message: str, *args: object) -> None:
+        super().__init__(message, *args, since=(2, 9), expected_removal=(3, 0))
+
+
+class PydanticDeprecatedSince210(PydanticDeprecationWarning):
+    """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.10."""
+
+    def __init__(self, message: str, *args: object) -> None:
+        super().__init__(message, *args, since=(2, 10), expected_removal=(3, 0))
+
+
+class GenericBeforeBaseModelWarning(Warning):
+    pass
+
+
+class PydanticExperimentalWarning(Warning):
+    """A Pydantic specific experimental functionality warning.
+
+    This warning is raised when using experimental functionality in Pydantic.
+    It is raised to warn users that the functionality may change or be removed in future versions of Pydantic.
+    """