Загрузить файлы в «venv/Lib/site-packages/pydantic/v1»

venv/Lib/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"')

venv/Lib/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))

venv/Lib/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_)

venv/Lib/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)

venv/Lib/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