import enum
import inspect
from abc import ABC, abstractmethod  # pylint: disable=[no-name-in-module]
from typing import Any, Callable, Dict, Generic, List, Optional, Set, Tuple, Type, TypeVar, Union, cast

import yaml

from knot_resolver.utils.functional import all_matches

from .base_generic_type_wrapper import BaseGenericTypeWrapper
from .base_value_type import BaseValueType
from .exceptions import AggregateDataValidationError, DataDescriptionError, DataValidationError
from .renaming import Renamed, renamed
from .types import (
    get_generic_type_argument,
    get_generic_type_arguments,
    get_generic_type_wrapper_argument,
    get_optional_inner_type,
    is_dict,
    is_enum,
    is_generic_type_wrapper,
    is_internal_field_name,
    is_list,
    is_literal,
    is_none_type,
    is_optional,
    is_tuple,
    is_union,
)

T = TypeVar("T")


def is_obj_type(obj: Any, types: Union[type, Tuple[Any, ...], Tuple[type, ...]]) -> bool:
    # To check specific type we are using 'type()' instead of 'isinstance()'
    # because for example 'bool' is instance of 'int', 'isinstance(False, int)' returns True.
    # pylint: disable=unidiomatic-typecheck
    if isinstance(types, tuple):
        return type(obj) in types
    return type(obj) is types


class Serializable(ABC):
    """
    An interface for making classes serializable to a dictionary (and in turn into a JSON).
    """

    @abstractmethod
    def to_dict(self) -> Dict[Any, Any]:
        raise NotImplementedError(f"...for class {self.__class__.__name__}")

    @staticmethod
    def is_serializable(typ: Type[Any]) -> bool:
        return (
            typ in {str, bool, int, float}
            or is_none_type(typ)
            or is_literal(typ)
            or is_dict(typ)
            or is_list(typ)
            or is_generic_type_wrapper(typ)
            or (inspect.isclass(typ) and issubclass(typ, Serializable))
            or (inspect.isclass(typ) and issubclass(typ, BaseValueType))
            or (inspect.isclass(typ) and issubclass(typ, BaseSchema))
            or (is_optional(typ) and Serializable.is_serializable(get_optional_inner_type(typ)))
            or (is_union(typ) and all_matches(Serializable.is_serializable, get_generic_type_arguments(typ)))
        )

    @staticmethod
    def serialize(obj: Any) -> Any:
        if isinstance(obj, Serializable):
            return obj.to_dict()

        if isinstance(obj, (BaseValueType, BaseGenericTypeWrapper)):
            o = obj.serialize()
            # if Serializable.is_serializable(o):
            return Serializable.serialize(o)
            # return o

        if isinstance(obj, list):
            res: List[Any] = [Serializable.serialize(i) for i in cast(List[Any], obj)]
            return res

        return obj


class _LazyDefault(Generic[T], Serializable):
    """
    Wrapper for default values BaseSchema classes which deffers their instantiation until the schema
    itself is being instantiated
    """

    def __init__(self, constructor: Callable[..., T], *args: Any, **kwargs: Any) -> None:
        # pylint: disable=[super-init-not-called]
        self._func = constructor
        self._args = args
        self._kwargs = kwargs

    def instantiate(self) -> T:
        return self._func(*self._args, **self._kwargs)

    def to_dict(self) -> Dict[Any, Any]:
        return Serializable.serialize(self.instantiate())


def lazy_default(constructor: Callable[..., T], *args: Any, **kwargs: Any) -> T:
    """We use a factory function because you can't lie about the return type in `__new__`"""
    return _LazyDefault(constructor, *args, **kwargs)  # type: ignore


def _split_docstring(docstring: str) -> Tuple[str, Optional[str]]:
    """
    Splits docstring into description of the class and description of attributes
    """

    if "---" not in docstring:
        return ("\n".join([s.strip() for s in docstring.splitlines()]).strip(), None)

    doc, attrs_doc = docstring.split("---", maxsplit=1)
    return (
        "\n".join([s.strip() for s in doc.splitlines()]).strip(),
        attrs_doc,
    )


def _parse_attrs_docstrings(docstring: str) -> Optional[Dict[str, str]]:
    """
    Given a docstring of a BaseSchema, return a dict with descriptions of individual attributes.
    """

    _, attrs_doc = _split_docstring(docstring)
    if attrs_doc is None:
        return None

    # try to parse it as yaml:
    data = yaml.safe_load(attrs_doc)
    assert isinstance(data, dict), "Invalid format of attribute description"
    return cast(Dict[str, str], data)


def _get_properties_schema(typ: Type[Any]) -> Dict[Any, Any]:
    schema: Dict[Any, Any] = {}
    annot: Dict[str, Any] = typ.__dict__.get("__annotations__", {})
    docstring: str = typ.__dict__.get("__doc__", "") or ""
    attribute_documentation = _parse_attrs_docstrings(docstring)
    for field_name, python_type in annot.items():
        name = field_name.replace("_", "-")
        schema[name] = _describe_type(python_type)

        # description
        if attribute_documentation is not None:
            if field_name not in attribute_documentation:
                raise DataDescriptionError(f"The docstring does not describe field '{field_name}'", str(typ))
            schema[name]["description"] = attribute_documentation[field_name]
            del attribute_documentation[field_name]

        # default value
        if hasattr(typ, field_name):
            assert Serializable.is_serializable(
                python_type
            ), f"Type '{python_type}' does not appear to be JSON serializable"
            schema[name]["default"] = Serializable.serialize(getattr(typ, field_name))

    if attribute_documentation is not None and len(attribute_documentation) > 0:
        raise DataDescriptionError(
            f"The docstring describes attributes which are not present - {tuple(attribute_documentation.keys())}",
            str(typ),
        )

    return schema


def _describe_type(typ: Type[Any]) -> Dict[Any, Any]:  # noqa: PLR0911, PLR0912
    # pylint: disable=too-many-branches

    if inspect.isclass(typ) and issubclass(typ, BaseSchema):
        return typ.json_schema(include_schema_definition=False)

    if inspect.isclass(typ) and issubclass(typ, BaseValueType):
        return typ.json_schema()

    if is_generic_type_wrapper(typ):
        wrapped = get_generic_type_wrapper_argument(typ)
        return _describe_type(wrapped)

    if is_none_type(typ):
        return {"type": "null"}

    if typ is int:
        return {"type": "integer"}

    if typ is bool:
        return {"type": "boolean"}

    if typ is str:
        return {"type": "string"}

    if is_literal(typ):
        lit = get_generic_type_arguments(typ)
        return {"type": "string", "enum": lit}

    if is_optional(typ):
        desc = _describe_type(get_optional_inner_type(typ))
        if "type" in desc:
            desc["type"] = [desc["type"], "null"]
            return desc
        return {"anyOf": [{"type": "null"}, desc]}

    if is_union(typ):
        variants = get_generic_type_arguments(typ)
        return {"anyOf": [_describe_type(v) for v in variants]}

    if is_list(typ):
        return {"type": "array", "items": _describe_type(get_generic_type_argument(typ))}

    if is_dict(typ):
        key, val = get_generic_type_arguments(typ)

        if inspect.isclass(key) and issubclass(key, BaseValueType):
            assert (
                key.__str__ is not BaseValueType.__str__
            ), "To support derived 'BaseValueType', __str__ must be implemented."
        else:
            assert key is str, "We currently do not support any other keys then strings"

        return {"type": "object", "additionalProperties": _describe_type(val)}

    if inspect.isclass(typ) and issubclass(typ, enum.Enum):  # same as our is_enum(typ), but inlined for type checker
        return {"type": "string", "enum": [str(v) for v in typ]}

    raise NotImplementedError(f"Trying to get JSON schema for type '{typ}', which is not implemented")


TSource = Union[None, "BaseSchema", Dict[str, Any]]


def _create_untouchable(name: str) -> object:
    class _Untouchable:
        def __getattribute__(self, item_name: str) -> Any:
            raise RuntimeError(f"You are not supposed to access object '{name}'.")

        def __setattr__(self, item_name: str, value: Any) -> None:
            raise RuntimeError(f"You are not supposed to access object '{name}'.")

    return _Untouchable()


class ObjectMapper:
    def _create_tuple(self, tp: Type[Any], obj: Tuple[Any, ...], object_path: str) -> Tuple[Any, ...]:
        types = get_generic_type_arguments(tp)
        errs: List[DataValidationError] = []
        res: List[Any] = []
        for i, (t, val) in enumerate(zip(types, obj)):
            try:
                res.append(self.map_object(t, val, object_path=f"{object_path}[{i}]"))
            except DataValidationError as e:
                errs.append(e)
        if len(errs) == 1:
            raise errs[0]
        if len(errs) > 1:
            raise AggregateDataValidationError(object_path, child_exceptions=errs)
        return tuple(res)

    def _create_dict(self, tp: Type[Any], obj: Dict[Any, Any], object_path: str) -> Dict[Any, Any]:
        key_type, val_type = get_generic_type_arguments(tp)
        try:
            errs: List[DataValidationError] = []
            res: Dict[Any, Any] = {}
            for key, val in obj.items():
                try:
                    nkey = self.map_object(key_type, key, object_path=f"{object_path}[{key}]")
                    nval = self.map_object(val_type, val, object_path=f"{object_path}[{key}]")
                    res[nkey] = nval
                except DataValidationError as e:
                    errs.append(e)
            if len(errs) == 1:
                raise errs[0]
            if len(errs) > 1:
                raise AggregateDataValidationError(object_path, child_exceptions=errs)
            return res
        except AttributeError as e:
            raise DataValidationError(
                f"Expected dict-like object, but failed to access its .items() method. Value was {obj}", object_path
            ) from e

    def _create_list(self, tp: Type[Any], obj: List[Any], object_path: str) -> List[Any]:
        if isinstance(obj, str):
            raise DataValidationError("expected list, got string", object_path)

        inner_type = get_generic_type_argument(tp)
        errs: List[DataValidationError] = []
        res: List[Any] = []

        try:
            for i, val in enumerate(obj):
                res.append(self.map_object(inner_type, val, object_path=f"{object_path}[{i}]"))
            if len(res) == 0:
                raise DataValidationError("empty list is not allowed", object_path)
        except DataValidationError as e:
            errs.append(e)
        except TypeError as e:
            errs.append(DataValidationError(str(e), object_path))

        if len(errs) == 1:
            raise errs[0]
        if len(errs) > 1:
            raise AggregateDataValidationError(object_path, child_exceptions=errs)
        return res

    def _create_str(self, obj: Any, object_path: str) -> str:
        # we are willing to cast any primitive value to string, but no compound values are allowed
        if is_obj_type(obj, (str, float, int)) or isinstance(obj, BaseValueType):
            return str(obj)
        if is_obj_type(obj, bool):
            raise DataValidationError(
                "Expected str, found bool. Be careful, that YAML parsers consider even"
                ' "no" and "yes" as a bool. Search for the Norway Problem for more'
                " details. And please use quotes explicitly.",
                object_path,
            )
        raise DataValidationError(
            f"expected str (or number that would be cast to string), but found type {type(obj)}", object_path
        )

    def _create_int(self, obj: Any, object_path: str) -> int:
        # we don't want to make an int out of anything else than other int
        # except for BaseValueType class instances
        if is_obj_type(obj, int) or isinstance(obj, BaseValueType):
            return int(obj)
        raise DataValidationError(f"expected int, found {type(obj)}", object_path)

    def _create_union(self, tp: Type[T], obj: Any, object_path: str) -> T:
        variants = get_generic_type_arguments(tp)
        errs: List[DataValidationError] = []
        for v in variants:
            try:
                return self.map_object(v, obj, object_path=object_path)
            except DataValidationError as e:
                errs.append(e)

        raise DataValidationError("could not parse any of the possible variants", object_path, child_exceptions=errs)

    def _create_optional(self, tp: Type[Optional[T]], obj: Any, object_path: str) -> Optional[T]:
        inner: Type[Any] = get_optional_inner_type(tp)
        if obj is None:
            return None
        return self.map_object(inner, obj, object_path=object_path)

    def _create_bool(self, obj: Any, object_path: str) -> bool:
        if is_obj_type(obj, bool):
            return obj
        raise DataValidationError(f"expected bool, found {type(obj)}", object_path)

    def _create_literal(self, tp: Type[Any], obj: Any, object_path: str) -> Any:
        expected = get_generic_type_arguments(tp)
        if obj in expected:
            return obj
        raise DataValidationError(f"'{obj}' does not match any of the expected values {expected}", object_path)

    def _create_base_schema_object(self, tp: Type[Any], obj: Any, object_path: str) -> "BaseSchema":
        if isinstance(obj, (dict, BaseSchema)):
            return tp(obj, object_path=object_path)
        raise DataValidationError(f"expected 'dict' or 'NoRenameBaseSchema' object, found '{type(obj)}'", object_path)

    def create_value_type_object(self, tp: Type[Any], obj: Any, object_path: str) -> "BaseValueType":
        if isinstance(obj, tp):
            # if we already have a custom value type, just pass it through
            return obj
        # no validation performed, the implementation does it in the constuctor
        try:
            return tp(obj, object_path=object_path)
        except ValueError as e:
            if len(e.args) > 0 and isinstance(e.args[0], str):
                msg = e.args[0]
            else:
                msg = f"Failed to validate value against {tp} type"
            raise DataValidationError(msg, object_path) from e

    def _create_default(self, obj: Any) -> Any:
        if isinstance(obj, _LazyDefault):
            return obj.instantiate()  # type: ignore
        return obj

    def map_object(  # noqa: PLR0911, PLR0912
        self,
        tp: Type[Any],
        obj: Any,
        default: Any = ...,
        use_default: bool = False,
        object_path: str = "/",
    ) -> Any:
        """
        Given an expected type `cls` and a value object `obj`, return a new object of the given type and map fields of `obj` into it. During the mapping procedure,
        runtime type checking is performed.
        """

        # Disabling these checks, because I think it's much more readable as a single function
        # and it's not that large at this point. If it got larger, then we should definitely split it
        # pylint: disable=too-many-branches,too-many-locals,too-many-statements

        # default values
        if obj is None and use_default:
            return self._create_default(default)

        # NoneType
        if is_none_type(tp):
            if obj is None:
                return None
            raise DataValidationError(f"expected None, found '{obj}'.", object_path)

        # Optional[T]  (could be technically handled by Union[*variants], but this way we have better error reporting)
        if is_optional(tp):
            return self._create_optional(tp, obj, object_path)

        # Union[*variants]
        if is_union(tp):
            return self._create_union(tp, obj, object_path)

        # after this, there is no place for a None object
        if obj is None:
            raise DataValidationError(f"unexpected value 'None' for type {tp}", object_path)

        # int
        if tp is int:
            return self._create_int(obj, object_path)

        # str
        if tp is str:
            return self._create_str(obj, object_path)

        # bool
        if tp is bool:
            return self._create_bool(obj, object_path)

        # float
        if tp is float:
            raise NotImplementedError(
                "Floating point values are not supported in the object mapper."
                " Please implement them and be careful with type coercions"
            )

        # Literal[T]
        if is_literal(tp):
            return self._create_literal(tp, obj, object_path)

        # Dict[K,V]
        if is_dict(tp):
            return self._create_dict(tp, obj, object_path)

        # any Enums (probably used only internally in DataValidator)
        if is_enum(tp):
            if isinstance(obj, tp):
                return obj
            raise DataValidationError(f"unexpected value '{obj}' for enum '{tp}'", object_path)

        # List[T]
        if is_list(tp):
            return self._create_list(tp, obj, object_path)

        # Tuple[A,B,C,D,...]
        if is_tuple(tp):
            return self._create_tuple(tp, obj, object_path)

        # type of obj and cls type match
        if is_obj_type(obj, tp):
            return obj

        # when the specified type is Any, just return the given value
        # on mypy version 1.11.0 comparison-overlap error started popping up
        # https://github.com/python/mypy/issues/17665
        if tp == Any:  # type: ignore[comparison-overlap]
            return obj

        # BaseValueType subclasses
        if inspect.isclass(tp) and issubclass(tp, BaseValueType):
            return self.create_value_type_object(tp, obj, object_path)

        # BaseGenericTypeWrapper subclasses
        if is_generic_type_wrapper(tp):
            inner_type = get_generic_type_wrapper_argument(tp)
            obj_valid = self.map_object(inner_type, obj, object_path)
            return tp(obj_valid, object_path=object_path)  # type: ignore

        # nested BaseSchema subclasses
        if inspect.isclass(tp) and issubclass(tp, BaseSchema):
            return self._create_base_schema_object(tp, obj, object_path)

        # if the object matches, just pass it through
        if inspect.isclass(tp) and isinstance(obj, tp):
            return obj

        # default error handler
        raise DataValidationError(
            f"Type {tp} cannot be parsed. This is a implementation error. "
            "Please fix your types in the class or improve the parser/validator.",
            object_path,
        )

    def is_obj_type_valid(self, obj: Any, tp: Type[Any]) -> bool:
        """
        Runtime type checking. Validate, that a given object is of a given type.
        """

        try:
            self.map_object(tp, obj)
            return True
        except (DataValidationError, ValueError):
            return False

    def _assign_default(self, obj: Any, name: str, python_type: Any, object_path: str) -> None:
        cls = obj.__class__

        try:
            default = self._create_default(getattr(cls, name, None))
        except ValueError as e:
            raise DataValidationError(str(e), f"{object_path}/{name}") from e

        value = self.map_object(python_type, default, object_path=f"{object_path}/{name}")
        setattr(obj, name, value)

    def _assign_field(self, obj: Any, name: str, python_type: Any, value: Any, object_path: str) -> None:
        value = self.map_object(python_type, value, object_path=f"{object_path}/{name}")
        setattr(obj, name, value)

    def _assign_fields(self, obj: Any, source: Union[Dict[str, Any], "BaseSchema", None], object_path: str) -> Set[str]:
        """
        Order of assignment:
          1. all direct assignments
          2. assignments with conversion method
        """
        cls = obj.__class__
        annot = cls.__dict__.get("__annotations__", {})
        errs: List[DataValidationError] = []

        used_keys: Set[str] = set()
        for name, python_type in annot.items():
            try:
                if is_internal_field_name(name):
                    continue

                # populate field
                if source is None:
                    self._assign_default(obj, name, python_type, object_path)

                # check for invalid configuration with both transformation function and default value
                elif hasattr(obj, f"_{name}") and hasattr(obj, name):
                    raise RuntimeError(
                        f"Field '{obj.__class__.__name__}.{name}' has default value and transformation function at"
                        " the same time. That is now allowed. Store the default in the transformation function."
                    )

                # there is a transformation function to create the value
                elif hasattr(obj, f"_{name}") and callable(getattr(obj, f"_{name}")):
                    val = self._get_converted_value(obj, name, source, object_path)
                    self._assign_field(obj, name, python_type, val, object_path)
                    used_keys.add(name)

                # source just contains the value
                elif name in source:
                    val = source[name]
                    self._assign_field(obj, name, python_type, val, object_path)
                    used_keys.add(name)

                # there is a default value, or the type is optional => store the default or null
                elif hasattr(obj, name) or is_optional(python_type):
                    self._assign_default(obj, name, python_type, object_path)

                # we expected a value but it was not there
                else:
                    errs.append(DataValidationError(f"missing attribute '{name}'.", object_path))
            except DataValidationError as e:
                errs.append(e)

        if len(errs) == 1:
            raise errs[0]
        if len(errs) > 1:
            raise AggregateDataValidationError(object_path, errs)
        return used_keys

    def _get_converted_value(self, obj: Any, key: str, source: TSource, object_path: str) -> Any:
        """
        Get a value of a field by invoking appropriate transformation function.
        """
        try:
            func = getattr(obj.__class__, f"_{key}")
            argc = len(inspect.signature(func).parameters)
            if argc == 1:
                # it is a static method
                return func(source)
            if argc == 2:
                # it is a instance method
                return func(_create_untouchable("obj"), source)
            raise RuntimeError("Transformation function has wrong number of arguments")
        except ValueError as e:
            if len(e.args) > 0 and isinstance(e.args[0], str):
                msg = e.args[0]
            else:
                msg = "Failed to validate value type"
            raise DataValidationError(msg, object_path) from e

    def object_constructor(self, obj: Any, source: Union["BaseSchema", Dict[Any, Any]], object_path: str) -> None:
        """
        Delegated constructor for the NoRenameBaseSchema class.

        The reason this method is delegated to the mapper is due to renaming. Like this, we don't have to
        worry about a different BaseSchema class, when we want to have dynamically renamed fields.
        """
        # As this is a delegated constructor, we must ignore protected access warnings

        # sanity check
        if not isinstance(source, (BaseSchema, dict)):  # type: ignore
            raise DataValidationError(f"expected dict-like object, found '{type(source)}'", object_path)

        # construct lower level schema first if configured to do so
        if obj._LAYER is not None:  # noqa: SLF001
            source = obj._LAYER(source, object_path=object_path)  # pylint: disable=not-callable  # noqa: SLF001

        # assign fields
        used_keys = self._assign_fields(obj, source, object_path)

        # check for unused keys in the source object
        if source and not isinstance(source, BaseSchema):
            unused = source.keys() - used_keys
            if len(unused) > 0:
                keys = ", ".join((f"'{u}'" for u in unused))
                raise DataValidationError(
                    f"unexpected extra key(s) {keys}",
                    object_path,
                )

        # validate the constructed value
        try:
            obj._validate()  # noqa: SLF001
        except ValueError as e:
            raise DataValidationError(e.args[0] if len(e.args) > 0 else "Validation error", object_path or "/") from e


class BaseSchema(Serializable):
    """
    Base class for modeling configuration schema. It somewhat resembles standard dataclasses with additional
    functionality:

    * type validation
    * data conversion

    To create an instance of this class, you have to provide source data in the form of dict-like object.
    Generally, raw dict or another `BaseSchema` instance. The provided data object is traversed, transformed
    and validated before assigned to the appropriate fields (attributes).

    Fields (attributes)
    ===================

    The fields (or attributes) of the class are defined the same way as in a dataclass by creating a class-level
    type-annotated fields. An example of that is:

    class A(BaseSchema):
        awesome_number: int

    If your `BaseSchema` instance has a field with type of a BaseSchema, its value is recursively created
    from the nested input data. This way, you can specify a complex tree of BaseSchema's and use the root
    BaseSchema to create instance of everything.

    Transformation
    ==============

    You can provide the BaseSchema class with a field and a function with the same name, but starting with
    underscore ('_'). For example, you could have field called `awesome_number` and function called
    `_awesome_number(self, source)`. The function takes one argument - the source data (optionally with self,
    but you are not supposed to touch that). It can read any data from the source object and return a value of
    an appropriate type, which will be assigned to the field `awesome_number`. If you want to report an error
    during validation, raise a `ValueError` exception.

    Using this, you can convert any input values into any type and field you want. To make the conversion easier
    to write, you could also specify a special class variable called `_LAYER` pointing to another
    BaseSchema class. This causes the source object to be first parsed as the specified additional layer of BaseSchema and after that
    used a source for this class. This therefore allows nesting of transformation functions.

    Validation
    ==========

    All assignments to fields during object construction are checked at runtime for proper types. This means,
    you are free to use an untrusted source object and turn it into a data structure, where you are sure what
    is what.

    You can also define a `_validate` method, which will be called once the whole data structure is built. You
    can validate the data in there and raise a `ValueError`, if they are invalid.

    Default values
    ==============

    If you create a field with a value, it will be used as a default value whenever the data in source object
    are not present. As a special case, default value for Optional type is None if not specified otherwise. You
    are not allowed to have a field with a default value and a transformation function at once.

    Example
    =======

    See tests/utils/test_modelling.py for example usage.
    """

    _LAYER: Optional[Type["BaseSchema"]] = None
    _MAPPER: ObjectMapper = ObjectMapper()

    def __init__(self, source: TSource = None, object_path: str = ""):  # pylint: disable=[super-init-not-called]
        # save source data (and drop information about nullness)
        source = source or {}
        self.__source: Union[Dict[str, Any], BaseSchema] = source

        # delegate the rest of the constructor
        self._MAPPER.object_constructor(self, source, object_path)

    def get_unparsed_data(self) -> Dict[str, Any]:
        if isinstance(self.__source, BaseSchema):
            return self.__source.get_unparsed_data()
        if isinstance(self.__source, Renamed):
            return self.__source.original()
        return self.__source

    def __getitem__(self, key: str) -> Any:
        if not hasattr(self, key):
            raise RuntimeError(f"Object '{self}' of type '{type(self)}' does not have field named '{key}'")
        return getattr(self, key)

    def __contains__(self, item: Any) -> bool:
        return hasattr(self, item)

    def _validate(self) -> None:
        """
        Validation procedure called after all field are assigned. Should throw a ValueError in case of failure.
        """

    def __eq__(self, o: object) -> bool:
        cls = self.__class__
        if not isinstance(o, cls):
            return False

        annot = cls.__dict__.get("__annotations__", {})
        for name in annot.keys():
            if getattr(self, name) != getattr(o, name):
                return False

        return True

    @classmethod
    def json_schema(
        cls: Type["BaseSchema"],
        schema_id: Optional[str] = None,
        title: Optional[str] = None,
        description: Optional[str] = None,
        include_schema_definition: bool = True,
    ) -> Dict[Any, Any]:
        if cls._LAYER is not None:
            return cls._LAYER.json_schema(
                schema_id=schema_id,
                title=title,
                description=description,
                include_schema_definition=include_schema_definition,
            )

        schema: Dict[Any, Any] = {}
        if include_schema_definition:
            schema["$schema"] = "https://json-schema.org/draft/2020-12/schema"
        if schema_id is not None:
            schema["$id"] = schema_id
        if title is not None:
            schema["title"] = title
        if description is not None:
            schema["description"] = description
        elif cls.__doc__ is not None:
            schema["description"] = _split_docstring(cls.__doc__)[0]
        schema["type"] = "object"
        schema["properties"] = _get_properties_schema(cls)

        return schema

    def to_dict(self) -> Dict[Any, Any]:
        res: Dict[Any, Any] = {}
        cls = self.__class__
        annot = cls.__dict__.get("__annotations__", {})

        for name in annot:
            res[name] = Serializable.serialize(getattr(self, name))
        return res


class RenamingObjectMapper(ObjectMapper):
    """
    Same as object mapper, but it uses collection wrappers from the module `renamed` to perform dynamic field renaming.

    More specifically:
    - it renames all properties in (nested) objects
    - it does not rename keys in dictionaries
    """

    def _create_dict(self, tp: Type[Any], obj: Dict[Any, Any], object_path: str) -> Dict[Any, Any]:
        if isinstance(obj, Renamed):
            obj = obj.original()
        return super()._create_dict(tp, obj, object_path)

    def _create_base_schema_object(self, tp: Type[Any], obj: Any, object_path: str) -> "BaseSchema":
        if isinstance(obj, dict):
            obj = renamed(obj)
        return super()._create_base_schema_object(tp, obj, object_path)

    def object_constructor(self, obj: Any, source: Union["BaseSchema", Dict[Any, Any]], object_path: str) -> None:
        if isinstance(source, dict):
            source = renamed(source)
        return super().object_constructor(obj, source, object_path)


# export as a standalone functions for simplicity compatibility
is_obj_type_valid = ObjectMapper().is_obj_type_valid
map_object = ObjectMapper().map_object


class ConfigSchema(BaseSchema):
    """
    Same as BaseSchema, but maps with RenamingObjectMapper
    """

    _MAPPER: ObjectMapper = RenamingObjectMapper()