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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)])})"
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