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import inspect
from dataclasses import _MISSING_TYPE, MISSING, Field, field, fields
from functools import wraps
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Tuple,
Type,
TypeVar,
Union,
get_args,
get_origin,
overload,
)
from .errors import (
StrictDataclassClassValidationError,
StrictDataclassDefinitionError,
StrictDataclassFieldValidationError,
)
Validator_T = Callable[[Any], None]
T = TypeVar("T")
# The overload decorator helps type checkers understand the different return types
@overload
def strict(cls: Type[T]) -> Type[T]: ...
@overload
def strict(*, accept_kwargs: bool = False) -> Callable[[Type[T]], Type[T]]: ...
def strict(
cls: Optional[Type[T]] = None, *, accept_kwargs: bool = False
) -> Union[Type[T], Callable[[Type[T]], Type[T]]]:
"""
Decorator to add strict validation to a dataclass.
This decorator must be used on top of `@dataclass` to ensure IDEs and static typing tools
recognize the class as a dataclass.
Can be used with or without arguments:
- `@strict`
- `@strict(accept_kwargs=True)`
Args:
cls:
The class to convert to a strict dataclass.
accept_kwargs (`bool`, *optional*):
If True, allows arbitrary keyword arguments in `__init__`. Defaults to False.
Returns:
The enhanced dataclass with strict validation on field assignment.
Example:
```py
>>> from dataclasses import dataclass
>>> from huggingface_hub.dataclasses import as_validated_field, strict, validated_field
>>> @as_validated_field
>>> def positive_int(value: int):
... if not value >= 0:
... raise ValueError(f"Value must be positive, got {value}")
>>> @strict(accept_kwargs=True)
... @dataclass
... class User:
... name: str
... age: int = positive_int(default=10)
# Initialize
>>> User(name="John")
User(name='John', age=10)
# Extra kwargs are accepted
>>> User(name="John", age=30, lastname="Doe")
User(name='John', age=30, *lastname='Doe')
# Invalid type => raises
>>> User(name="John", age="30")
huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
TypeError: Field 'age' expected int, got str (value: '30')
# Invalid value => raises
>>> User(name="John", age=-1)
huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
ValueError: Value must be positive, got -1
```
"""
def wrap(cls: Type[T]) -> Type[T]:
if not hasattr(cls, "__dataclass_fields__"):
raise StrictDataclassDefinitionError(
f"Class '{cls.__name__}' must be a dataclass before applying @strict."
)
# List and store validators
field_validators: Dict[str, List[Validator_T]] = {}
for f in fields(cls): # type: ignore [arg-type]
validators = []
validators.append(_create_type_validator(f))
custom_validator = f.metadata.get("validator")
if custom_validator is not None:
if not isinstance(custom_validator, list):
custom_validator = [custom_validator]
for validator in custom_validator:
if not _is_validator(validator):
raise StrictDataclassDefinitionError(
f"Invalid validator for field '{f.name}': {validator}. Must be a callable taking a single argument."
)
validators.extend(custom_validator)
field_validators[f.name] = validators
cls.__validators__ = field_validators # type: ignore
# Override __setattr__ to validate fields on assignment
original_setattr = cls.__setattr__
def __strict_setattr__(self: Any, name: str, value: Any) -> None:
"""Custom __setattr__ method for strict dataclasses."""
# Run all validators
for validator in self.__validators__.get(name, []):
try:
validator(value)
except (ValueError, TypeError) as e:
raise StrictDataclassFieldValidationError(field=name, cause=e) from e
# If validation passed, set the attribute
original_setattr(self, name, value)
cls.__setattr__ = __strict_setattr__ # type: ignore[method-assign]
if accept_kwargs:
# (optional) Override __init__ to accept arbitrary keyword arguments
original_init = cls.__init__
@wraps(original_init)
def __init__(self, **kwargs: Any) -> None:
# Extract only the fields that are part of the dataclass
dataclass_fields = {f.name for f in fields(cls)} # type: ignore [arg-type]
standard_kwargs = {k: v for k, v in kwargs.items() if k in dataclass_fields}
# Call the original __init__ with standard fields
original_init(self, **standard_kwargs)
# Add any additional kwargs as attributes
for name, value in kwargs.items():
if name not in dataclass_fields:
self.__setattr__(name, value)
cls.__init__ = __init__ # type: ignore[method-assign]
# (optional) Override __repr__ to include additional kwargs
original_repr = cls.__repr__
@wraps(original_repr)
def __repr__(self) -> str:
# Call the original __repr__ to get the standard fields
standard_repr = original_repr(self)
# Get additional kwargs
additional_kwargs = [
# add a '*' in front of additional kwargs to let the user know they are not part of the dataclass
f"*{k}={v!r}"
for k, v in self.__dict__.items()
if k not in cls.__dataclass_fields__ # type: ignore [attr-defined]
]
additional_repr = ", ".join(additional_kwargs)
# Combine both representations
return f"{standard_repr[:-1]}, {additional_repr})" if additional_kwargs else standard_repr
cls.__repr__ = __repr__ # type: ignore [method-assign]
# List all public methods starting with `validate_` => class validators.
class_validators = []
for name in dir(cls):
if not name.startswith("validate_"):
continue
method = getattr(cls, name)
if not callable(method):
continue
if len(inspect.signature(method).parameters) != 1:
raise StrictDataclassDefinitionError(
f"Class '{cls.__name__}' has a class validator '{name}' that takes more than one argument."
" Class validators must take only 'self' as an argument. Methods starting with 'validate_'"
" are considered to be class validators."
)
class_validators.append(method)
cls.__class_validators__ = class_validators # type: ignore [attr-defined]
# Add `validate` method to the class, but first check if it already exists
def validate(self: T) -> None:
"""Run class validators on the instance."""
for validator in cls.__class_validators__: # type: ignore [attr-defined]
try:
validator(self)
except (ValueError, TypeError) as e:
raise StrictDataclassClassValidationError(validator=validator.__name__, cause=e) from e
# Hack to be able to raise if `.validate()` already exists except if it was created by this decorator on a parent class
# (in which case we just override it)
validate.__is_defined_by_strict_decorator__ = True # type: ignore [attr-defined]
if hasattr(cls, "validate"):
if not getattr(cls.validate, "__is_defined_by_strict_decorator__", False): # type: ignore [attr-defined]
raise StrictDataclassDefinitionError(
f"Class '{cls.__name__}' already implements a method called 'validate'."
" This method name is reserved when using the @strict decorator on a dataclass."
" If you want to keep your own method, please rename it."
)
cls.validate = validate # type: ignore
# Run class validators after initialization
initial_init = cls.__init__
@wraps(initial_init)
def init_with_validate(self, *args, **kwargs) -> None:
"""Run class validators after initialization."""
initial_init(self, *args, **kwargs) # type: ignore [call-arg]
cls.validate(self) # type: ignore [attr-defined]
setattr(cls, "__init__", init_with_validate)
return cls
# Return wrapped class or the decorator itself
return wrap(cls) if cls is not None else wrap
def validated_field(
validator: Union[List[Validator_T], Validator_T],
default: Union[Any, _MISSING_TYPE] = MISSING,
default_factory: Union[Callable[[], Any], _MISSING_TYPE] = MISSING,
init: bool = True,
repr: bool = True,
hash: Optional[bool] = None,
compare: bool = True,
metadata: Optional[Dict] = None,
**kwargs: Any,
) -> Any:
"""
Create a dataclass field with a custom validator.
Useful to apply several checks to a field. If only applying one rule, check out the [`as_validated_field`] decorator.
Args:
validator (`Callable` or `List[Callable]`):
A method that takes a value as input and raises ValueError/TypeError if the value is invalid.
Can be a list of validators to apply multiple checks.
**kwargs:
Additional arguments to pass to `dataclasses.field()`.
Returns:
A field with the validator attached in metadata
"""
if not isinstance(validator, list):
validator = [validator]
if metadata is None:
metadata = {}
metadata["validator"] = validator
return field( # type: ignore
default=default, # type: ignore [arg-type]
default_factory=default_factory, # type: ignore [arg-type]
init=init,
repr=repr,
hash=hash,
compare=compare,
metadata=metadata,
**kwargs,
)
def as_validated_field(validator: Validator_T):
"""
Decorates a validator function as a [`validated_field`] (i.e. a dataclass field with a custom validator).
Args:
validator (`Callable`):
A method that takes a value as input and raises ValueError/TypeError if the value is invalid.
"""
def _inner(
default: Union[Any, _MISSING_TYPE] = MISSING,
default_factory: Union[Callable[[], Any], _MISSING_TYPE] = MISSING,
init: bool = True,
repr: bool = True,
hash: Optional[bool] = None,
compare: bool = True,
metadata: Optional[Dict] = None,
**kwargs: Any,
):
return validated_field(
validator,
default=default,
default_factory=default_factory,
init=init,
repr=repr,
hash=hash,
compare=compare,
metadata=metadata,
**kwargs,
)
return _inner
def type_validator(name: str, value: Any, expected_type: Any) -> None:
"""Validate that 'value' matches 'expected_type'."""
origin = get_origin(expected_type)
args = get_args(expected_type)
if expected_type is Any:
return
elif validator := _BASIC_TYPE_VALIDATORS.get(origin):
validator(name, value, args)
elif isinstance(expected_type, type): # simple types
_validate_simple_type(name, value, expected_type)
else:
raise TypeError(f"Unsupported type for field '{name}': {expected_type}")
def _validate_union(name: str, value: Any, args: Tuple[Any, ...]) -> None:
"""Validate that value matches one of the types in a Union."""
errors = []
for t in args:
try:
type_validator(name, value, t)
return # Valid if any type matches
except TypeError as e:
errors.append(str(e))
raise TypeError(
f"Field '{name}' with value {repr(value)} doesn't match any type in {args}. Errors: {'; '.join(errors)}"
)
def _validate_literal(name: str, value: Any, args: Tuple[Any, ...]) -> None:
"""Validate Literal type."""
if value not in args:
raise TypeError(f"Field '{name}' expected one of {args}, got {value}")
def _validate_list(name: str, value: Any, args: Tuple[Any, ...]) -> None:
"""Validate List[T] type."""
if not isinstance(value, list):
raise TypeError(f"Field '{name}' expected a list, got {type(value).__name__}")
# Validate each item in the list
item_type = args[0]
for i, item in enumerate(value):
try:
type_validator(f"{name}[{i}]", item, item_type)
except TypeError as e:
raise TypeError(f"Invalid item at index {i} in list '{name}'") from e
def _validate_dict(name: str, value: Any, args: Tuple[Any, ...]) -> None:
"""Validate Dict[K, V] type."""
if not isinstance(value, dict):
raise TypeError(f"Field '{name}' expected a dict, got {type(value).__name__}")
# Validate keys and values
key_type, value_type = args
for k, v in value.items():
try:
type_validator(f"{name}.key", k, key_type)
type_validator(f"{name}[{k!r}]", v, value_type)
except TypeError as e:
raise TypeError(f"Invalid key or value in dict '{name}'") from e
def _validate_tuple(name: str, value: Any, args: Tuple[Any, ...]) -> None:
"""Validate Tuple type."""
if not isinstance(value, tuple):
raise TypeError(f"Field '{name}' expected a tuple, got {type(value).__name__}")
# Handle variable-length tuples: Tuple[T, ...]
if len(args) == 2 and args[1] is Ellipsis:
for i, item in enumerate(value):
try:
type_validator(f"{name}[{i}]", item, args[0])
except TypeError as e:
raise TypeError(f"Invalid item at index {i} in tuple '{name}'") from e
# Handle fixed-length tuples: Tuple[T1, T2, ...]
elif len(args) != len(value):
raise TypeError(f"Field '{name}' expected a tuple of length {len(args)}, got {len(value)}")
else:
for i, (item, expected) in enumerate(zip(value, args)):
try:
type_validator(f"{name}[{i}]", item, expected)
except TypeError as e:
raise TypeError(f"Invalid item at index {i} in tuple '{name}'") from e
def _validate_set(name: str, value: Any, args: Tuple[Any, ...]) -> None:
"""Validate Set[T] type."""
if not isinstance(value, set):
raise TypeError(f"Field '{name}' expected a set, got {type(value).__name__}")
# Validate each item in the set
item_type = args[0]
for i, item in enumerate(value):
try:
type_validator(f"{name} item", item, item_type)
except TypeError as e:
raise TypeError(f"Invalid item in set '{name}'") from e
def _validate_simple_type(name: str, value: Any, expected_type: type) -> None:
"""Validate simple type (int, str, etc.)."""
if not isinstance(value, expected_type):
raise TypeError(
f"Field '{name}' expected {expected_type.__name__}, got {type(value).__name__} (value: {repr(value)})"
)
def _create_type_validator(field: Field) -> Validator_T:
"""Create a type validator function for a field."""
# Hacky: we cannot use a lambda here because of reference issues
def validator(value: Any) -> None:
type_validator(field.name, value, field.type)
return validator
def _is_validator(validator: Any) -> bool:
"""Check if a function is a validator.
A validator is a Callable that can be called with a single positional argument.
The validator can have more arguments with default values.
Basically, returns True if `validator(value)` is possible.
"""
if not callable(validator):
return False
signature = inspect.signature(validator)
parameters = list(signature.parameters.values())
if len(parameters) == 0:
return False
if parameters[0].kind not in (
inspect.Parameter.POSITIONAL_OR_KEYWORD,
inspect.Parameter.POSITIONAL_ONLY,
inspect.Parameter.VAR_POSITIONAL,
):
return False
for parameter in parameters[1:]:
if parameter.default == inspect.Parameter.empty:
return False
return True
_BASIC_TYPE_VALIDATORS = {
Union: _validate_union,
Literal: _validate_literal,
list: _validate_list,
dict: _validate_dict,
tuple: _validate_tuple,
set: _validate_set,
}
__all__ = [
"strict",
"validated_field",
"Validator_T",
"StrictDataclassClassValidationError",
"StrictDataclassDefinitionError",
"StrictDataclassFieldValidationError",
]
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