#
Strict Dataclasses

The `huggingface_hub` package provides a utility to create strict dataclasses. These are enhanced versions of Python’s standard `dataclass` with additional validation features. Strict dataclasses ensure that fields are validated both during initialization and assignment, making them ideal for scenarios where data integrity is critical.

##
Overview

Strict dataclasses are created using the `@strict` decorator. They extend the functionality of regular dataclasses by:

- Validating field types based on type hints
- Supporting custom validators for additional checks
- Optionally allowing arbitrary keyword arguments in the constructor
- Validating fields both at initialization and during assignment

##
Benefits

- Data Integrity: Ensures fields always contain valid data
- Ease of Use: Integrates seamlessly with Python’s `dataclass` module
- Flexibility: Supports custom validators for complex validation logic
- Lightweight: Requires no additional dependencies such as Pydantic, attrs, or similar libraries

##
Usage

###
Basic Example

    from dataclasses import dataclass
    from huggingface_hub.dataclasses import strict, as_validated_field

    # Custom validator to ensure a value is positive
    @as_validated_field
    def positive_int(value: int):
        if not value > 0:
            raise ValueError(f"Value must be positive, got {value}")

    @strict
    @dataclass
    class Config:
        model_type: str
        hidden_size: int = positive_int(default=16)
        vocab_size: int = 32  # Default value

        # Methods named `validate_xxx` are treated as class-wise validators
        def validate_big_enough_vocab(self):
            if self.vocab_size < self.hidden_size:
                raise ValueError(f"vocab_size ({self.vocab_size}) must be greater than hidden_size ({self.hidden_size})")

Fields are validated during initialization:

    config = Config(model_type="bert", hidden_size=24)   # Valid
    config = Config(model_type="bert", hidden_size=-1)   # Raises StrictDataclassFieldValidationError

Consistency between fields is also validated during initialization (class-wise validation):

    # `vocab_size` too small compared to `hidden_size`
    config = Config(model_type="bert", hidden_size=32, vocab_size=16)   # Raises StrictDataclassClassValidationError

Fields are also validated during assignment:

    config.hidden_size = 512  # Valid
    config.hidden_size = -1   # Raises StrictDataclassFieldValidationError

To re-run class-wide validation after assignment, you must call `.validate` explicitly:

    config.validate()  # Runs all class validators

###
Custom Validators

You can attach multiple custom validators to fields using `validated_field`. A validator is a callable that takes a single argument and raises an exception if the value is invalid.

    from dataclasses import dataclass
    from huggingface_hub.dataclasses import strict, validated_field

    def multiple_of_64(value: int):
        if value % 64 != 0:
            raise ValueError(f"Value must be a multiple of 64, got {value}")

    @strict
    @dataclass
    class Config:
        hidden_size: int = validated_field(validator=[positive_int, multiple_of_64])

In this example, both validators are applied to the `hidden_size` field.

###
Additional Keyword Arguments

By default, strict dataclasses only accept fields defined in the class. You can allow additional keyword arguments by setting `accept_kwargs=True` in the `@strict` decorator.

    from dataclasses import dataclass
    from huggingface_hub.dataclasses import strict

    @strict(accept_kwargs=True)
    @dataclass
    class ConfigWithKwargs:
        model_type: str
        vocab_size: int = 16

    config = ConfigWithKwargs(model_type="bert", vocab_size=30000, extra_field="extra_value")
    print(config)  # ConfigWithKwargs(model_type='bert', vocab_size=30000, *extra_field='extra_value')

Additional keyword arguments appear in the string representation of the dataclass but are prefixed with `*` to highlight that they are not validated.

###
Integration with Type Hints

Strict dataclasses respect type hints and validate them automatically. For example:

    from typing import List
    from dataclasses import dataclass
    from huggingface_hub.dataclasses import strict

    @strict
    @dataclass
    class Config:
        layers: List[int]

    config = Config(layers=[64, 128])  # Valid
    config = Config(layers="not_a_list")  # Raises StrictDataclassFieldValidationError

Supported types include:

- Any
- Union
- Optional
- Literal
- List
- Dict
- Tuple
- Set

And any combination of these types. If your need more complex type validation, you can do it through a custom validator.

###
Class validators

Methods named `validate_xxx` are treated as class validators. These methods must only take `self` as an argument. Class validators are run once during initialization, right after `__post_init__`. You can define as many of them as needed—they’ll be executed sequentially in the order they appear.

Note that class validators are not automatically re-run when a field is updated after initialization. To manually re-validate the object, you need to call `obj.validate()`.

    from dataclasses import dataclass
    from huggingface_hub.dataclasses import strict

    @strict
    @dataclass
    class Config:
        foo: str
        foo_length: int
        upper_case: bool = False

        def validate_foo_length(self):
            if len(self.foo) != self.foo_length:
                raise ValueError(f"foo must be {self.foo_length} characters long, got {len(self.foo)}")

        def validate_foo_casing(self):
            if self.upper_case and self.foo.upper() != self.foo:
                raise ValueError(f"foo must be uppercase, got {self.foo}")

    config = Config(foo="bar", foo_length=3) # ok

    config.upper_case = True
    config.validate() # Raises StrictDataclassClassValidationError

    Config(foo="abcd", foo_length=3) # Raises StrictDataclassFieldValidationError
    Config(foo="Bar", foo_length=3, upper_case=True) # Raises StrictDataclassFieldValidationError

Method `.validate()` is a reserved name on strict dataclasses. To prevent unexpected behaviors, a `StrictDataclassDefinitionError` error will be raised if your class already defines one.

##
API Reference

###
@strict

The `@strict` decorator enhances a dataclass with strict validation.

####
huggingface_hub.dataclasses.strict

< source >

( accept_kwargs: bool = False )

Parameters

- cls — The class to convert to a strict dataclass.
- accept_kwargs (`bool`, optional) — If True, allows arbitrary keyword arguments in `__init__`. Defaults to False.

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)`

Example:

    >>> 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

###
as_validated_field

Decorator to create a `validated_field`. Recommended for fields with a single validator to avoid boilerplate code.

####
huggingface_hub.dataclasses.as_validated_field

< source >

( validator: typing.Callable[[typing.Any], NoneType] )

Parameters

- validator (`Callable`) — A method that takes a value as input and raises ValueError/TypeError if the value is invalid.

Decorates a validator function as a `validated_field` (i.e. a dataclass field with a custom validator).

###
validated_field

Creates a dataclass field with custom validation.

####
huggingface_hub.dataclasses.validated_field

< source >

( validator: typing.Union[typing.List[typing.Callable[[typing.Any], NoneType]], typing.Callable[[typing.Any], NoneType]] default: typing.Union[typing.Any, dataclasses._MISSING_TYPE] = <dataclasses._MISSING_TYPE object at 0x7f1ee9517580> default_factory: typing.Union[typing.Callable[[], typing.Any], dataclasses._MISSING_TYPE] = <dataclasses._MISSING_TYPE object at 0x7f1ee9517580> init: bool = True repr: bool = True hash: typing.Optional[bool] = None compare: bool = True metadata: typing.Optional[typing.Dict] = None **kwargs: typing.Any )

Parameters

- 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()`.

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.

###
Errors

###
class huggingface_hub.errors.StrictDataclassError

< source >

( )

Base exception for strict dataclasses.

###
class huggingface_hub.errors.StrictDataclassDefinitionError

< source >

( )

Exception thrown when a strict dataclass is defined incorrectly.

###
class huggingface_hub.errors.StrictDataclassFieldValidationError

< source >

( field: str cause: Exception )

Exception thrown when a strict dataclass fails validation for a given field.

##
Why Not Use pydantic ? (or attrs ? or marshmallow_dataclass ?)

- See discussion in https://github.com/huggingface/transformers/issues/36329 regarding adding Pydantic as a dependency. It would be a heavy addition and require careful logic to support both v1 and v2.
- We don’t need most of Pydantic’s features, especially those related to automatic casting, jsonschema, serialization, aliases, etc.
- We don’t need the ability to instantiate a class from a dictionary.
- We don’t want to mutate data. In `@strict`, “validation” means “checking if a value is valid.” In Pydantic, “validation” means “casting a value, possibly mutating it, and then checking if it’s valid.”
- We don’t need blazing-fast validation. `@strict` isn’t designed for heavy loads where performance is critical. Common use cases involve validating a model configuration (performed once and negligible compared to running a model). This allows us to keep the code minimal.

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