import copy import dataclasses from abc import ABCMeta from copy import deepcopy from typing import Any, final _FIELDS = "__fields__" @dataclasses.dataclass class Field: """ An alternative to dataclasses.dataclass decorator for a more flexible field definition. Attributes: default (Any, optional): Default value for the field. Defaults to None. name (str, optional): Name of the field. Defaults to None. type (type, optional): Type of the field. Defaults to None. default_factory (Any, optional): A function that returns the default value. Defaults to None. final (bool, optional): A boolean indicating if the field is final (cannot be overridden). Defaults to False. abstract (bool, optional): A boolean indicating if the field is abstract (must be implemented by subclasses). Defaults to False. required (bool, optional): A boolean indicating if the field is required. Defaults to False. origin_cls (type, optional): The original class that defined the field. Defaults to None. """ default: Any = None name: str = None type: type = None init: bool = True default_factory: Any = None final: bool = False abstract: bool = False required: bool = False origin_cls: type = None def get_default(self): if self.default_factory is not None: return self.default_factory() else: return self.default @dataclasses.dataclass class FinalField(Field): def __post_init__(self): self.final = True @dataclasses.dataclass class RequiredField(Field): def __post_init__(self): self.required = True @dataclasses.dataclass class AbstractField(Field): def __post_init__(self): self.abstract = True class FinalFieldError(TypeError): pass class RequiredFieldError(TypeError): pass class AbstractFieldError(TypeError): pass class TypeMismatchError(TypeError): pass standart_variables = dir(object) def is_possible_field(field_name, field_value): """ Check if a name-value pair can potentially represent a field. Args: field_name (str): The name of the field. field_value: The value of the field. Returns: bool: True if the name-value pair can represent a field, False otherwise. """ return field_name not in standart_variables and not field_name.startswith("__") and not callable(field_value) def get_fields(cls, attrs): """ Get the fields for a class based on its attributes. Args: cls (type): The class to get the fields for. attrs (dict): The attributes of the class. Returns: dict: A dictionary mapping field names to Field instances. """ fields = {**getattr(cls, _FIELDS, {})} annotations = {**attrs.get("__annotations__", {})} for attr_name, attr_value in attrs.items(): if attr_name not in annotations and is_possible_field(attr_name, attr_value): if attr_name in fields: if not isinstance(attr_value, fields[attr_name].type): raise TypeMismatchError( f"Type mismatch for field '{attr_name}' of class '{fields[attr_name].origin_cls}'. Expected {fields[attr_name].type}, got {type(attr_value)}" ) annotations[attr_name] = fields[attr_name].type for field_name, field_type in annotations.items(): if field_name in fields and fields[field_name].final: raise FinalFieldError( f"Final field {field_name} defined in {fields[field_name].origin_cls} overridden in {cls}" ) args = { "name": field_name, "type": field_type, "origin_cls": attrs["__qualname__"], } if field_name in attrs: field = attrs[field_name] if isinstance(field, Field): args = {**dataclasses.asdict(field), **args} elif isinstance(field, dataclasses.Field): args = { "default": field.default, "name": field.name, "type": field.type, "init": field.init, "default_factory": field.default_factory, **args, } else: args["default"] = field else: args["default"] = dataclasses.MISSING args["default_factory"] = None args["required"] = True field_instance = Field(**args) fields[field_name] = field_instance return fields def is_dataclass(obj): """Returns True if obj is a dataclass or an instance of a dataclass.""" cls = obj if isinstance(obj, type) else type(obj) return hasattr(cls, _FIELDS) def class_fields(obj): all_fields = fields(obj) return [field for field in all_fields if field.origin_cls == obj.__class__.__qualname__] def fields(cls): return list(getattr(cls, _FIELDS).values()) def fields_names(cls): return list(getattr(cls, _FIELDS).keys()) def final_fields(cls): return [field for field in fields(cls) if field.final] def required_fields(cls): return [field for field in fields(cls) if field.required] def abstract_fields(cls): return [field for field in fields(cls) if field.abstract] def is_abstract_field(field): return field.abstract def is_final_field(field): return field.final def get_field_default(field): if field.default_factory is not None: return field.default_factory() else: return field.default def asdict(obj): assert is_dataclass(obj), f"{obj} must be a dataclass, got {type(obj)} with bases {obj.__class__.__bases__}" return _asdict_inner(obj) def _asdict_inner(obj): if is_dataclass(obj): result = {} for field in fields(obj): v = getattr(obj, field.name) result[field.name] = _asdict_inner(v) return result elif isinstance(obj, tuple) and hasattr(obj, "_fields"): # named tuple return type(obj)(*[_asdict_inner(v) for v in obj]) elif isinstance(obj, (list, tuple)): return type(obj)([_asdict_inner(v) for v in obj]) elif isinstance(obj, dict): return type(obj)({_asdict_inner(k): _asdict_inner(v) for k, v in obj.items()}) else: return copy.deepcopy(obj) class DataclassMeta(ABCMeta): """ Metaclass for Dataclass. Checks for final fields when a subclass is created. """ @final def __init__(cls, name, bases, attrs): super().__init__(name, bases, attrs) setattr(cls, _FIELDS, get_fields(cls, attrs)) class Dataclass(metaclass=DataclassMeta): """ Base class for data-like classes that provides additional functionality and control over Python's built-in @dataclasses.dataclass decorator. Other classes can inherit from this class to get the benefits of this implementation. As a base class, it ensures that all subclasses will automatically be data classes. The usage and field definitions are similar to Python's built-in @dataclasses.dataclass decorator. However, this implementation provides additional classes for defining "final", "required", and "abstract" fields. Key enhancements of this custom implementation: 1. Automatic Data Class Creation: All subclasses automatically become data classes, without needing to use the @dataclasses.dataclass decorator. 2. Field Immutability: Supports creation of "final" fields (using FinalField class) that cannot be overridden by subclasses. This functionality is not natively supported in Python or in the built-in dataclasses module. 3. Required Fields: Supports creation of "required" fields (using RequiredField class) that must be provided when creating an instance of the class, adding a level of validation not present in the built-in dataclasses module. 4. Abstract Fields: Supports creation of "abstract" fields (using AbstractField class) that must be overridden by any non-abstract subclass. This is similar to abstract methods in an abc.ABC class, but applied to fields. 5. Type Checking: Performs type checking to ensure that if a field is redefined in a subclass, the type of the field remains consistent, adding static type checking not natively supported in Python. 6. Error Definitions: Defines specific error types (FinalFieldError, RequiredFieldError, AbstractFieldError, TypeMismatchError) for providing detailed error information during debugging. 7. MetaClass Usage: Uses a metaclass (DataclassMeta) for customization of class creation, allowing checks and alterations to be made at the time of class creation, providing more control. Example: ``` class Parent(Dataclass): final_field: int = FinalField(1) # this field cannot be overridden required_field: str = RequiredField() also_required_field: float abstract_field: int = AbstractField() class Child(Parent): abstract_field = 3 # now once overridden, this is no longer abstract required_field = Field(name="required_field", default="provided", type=str) class Mixin(Dataclass): mixin_field = Field(name="mixin_field", default="mixin", type=str) class GrandChild(Child, Mixin): pass grand_child = GrandChild() print(grand_child.to_dict()) ``` """ @final def __init__(self, *args, **kwargs): """ Initialize fields based on kwargs. Checks for abstract fields when an instance is created. """ init_fields = [field for field in fields(self) if field.init] for field, arg in zip(init_fields, args): kwargs[field.name] = arg for field in abstract_fields(self): raise AbstractFieldError( f"Abstract field '{field.name}' of class {field.origin_cls} not implemented in {self.__class__.__name__}" ) for field in required_fields(self): if field.name not in kwargs: raise RequiredFieldError( f"Required field '{field.name}' of class {field.origin_cls} not set in {self.__class__.__name__}" ) for field in fields(self): if field.name in kwargs: setattr(self, field.name, kwargs[field.name]) else: setattr(self, field.name, get_field_default(field)) self.__post_init__() @property def __is_dataclass__(self) -> bool: return True def __post_init__(self): """ Post initialization hook. """ pass def to_dict(self): """ Convert to dict. """ return asdict(self) def __repr__(self) -> str: """ String representation. """ return f"{self.__class__.__name__}({', '.join([f'{field.name}={repr(getattr(self, field.name))}' for field in fields(self)])})"