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dataclasses module

Native, in-sandbox @dataclass: sandboxed code can define its own dataclasses, executed entirely inside the sandbox (unlike host-supplied class instances, which enter via the ClassInstance wrapper and dispatch back to the host — see classes.md).

Host-supplied instances and this module barely interact: dataclasses.is_dataclass(x) honours the flag the host sent, but fields() and asdict() do not work on host instances (they are not native dataclasses). Bare dataclasses are NOT accepted as inputs — the host must wrap them in ClassInstance explicitly.

Unsupported

@dataclass, @dataclass(...) with eq and/or frozen, and is_dataclass exist. Everything below raises at decoration time rather than producing a subtly wrong class.

Each raises NotImplementedError, marking a feature Monty has not built yet rather than a mistake in the calling code. CPython accepts all of them, so the exception type is a divergence in its own right: code catching TypeError around a decoration will not catch these.

  • Every @dataclass(...) option except eq and frozen — init, repr, order, unsafe_hash, match_args, kw_only, slots and weakref_slot. Setting one away from its CPython default raises NotImplementedError: dataclass() does not yet support the <name> option; each is named individually rather than reported as an unknown keyword. Ordering dunders therefore do not exist, and hashing is whatever eq/frozen imply.
  • __post_init__ — raises NotImplementedError: dataclass() does not yet support __post_init__ in a class body, which would be silently skipped.
  • InitVar[...] — raises NotImplementedError: dataclass() does not yet support InitVar (field <name>), which would become an ordinary field. Detected textually, since annotations are never evaluated: the name need not be imported to be rejected.
  • field() / default_factory / MISSING — field(...) in a class body raises NameError. There is no MISSING object, so the Field attributes whose value would be one raise NotImplementedError: Field.default is not yet supported, dataclasses.MISSING is not implemented (likewise default_factory, and default only for a field that has none). Field.metadata and Field._field_type raise the same way, for types.MappingProxyType and dataclasses._FIELD.
  • Module helpers — fields, asdict, astuple and replace do not exist: accessing them raises AttributeError, not NotImplementedError, since the module has no such attribute.

Mutable defaults are rejected as CPython rejects them (ValueError: mutable default <class 'list'> for field xs is not allowed: use default_factory), and so is a non-default field after a defaulted one (TypeError: non-default argument 'b' follows default argument 'a').

Divergences from CPython

  • Annotations are stringized. Fields come from the class’s __annotations__, which Monty stores as never-evaluated source text (always PEP 563); see typing.md. Field discovery and the generated methods are unaffected, the field type being inert metadata, but C.__dataclass_fields__['x'].type is the string 'int', not the int type object.
  • __dataclass_fields__ holds only real fields. CPython keeps ClassVar (and InitVar) entries in the mapping, marked _FIELD_CLASSVAR, and filters them in fields(). Monty has no field kinds, so the mapping is the field list and class variables never appear in it.
  • Field renders differently. repr(field) follows CPython’s layout but writes MISSING where CPython writes <dataclasses._MISSING_TYPE object at 0x..>, and the stringized type. repr(type(field)) is <class 'Field'>, not <class 'dataclasses.Field'> (Field.__name__ matches either way, so attribute errors read the same).
  • Overwriting __dataclass_fields__ un-marks the class. Every dunder reads the mapping from the class namespace, so C.__dataclass_fields__ = 5 makes is_dataclass(C) false and C(...) construct like a plain class. CPython keeps its generated methods and still calls C a dataclass.
  • ClassVar / InitVar detection is purely textual. Monty matches the annotation text (bare, dotted, subscripted, or quoted) without checking that the name is actually imported, where CPython resolves a string annotation through the defining module’s namespace. So c: "ClassVar[int]" without ClassVar in scope is excluded by Monty but is an ordinary field to CPython. Conversely any dotted spelling matches, so a same-named attribute on an unrelated module (mymod.ClassVar) is treated as typing.ClassVar.
  • A field holding a function or bound method reprs differently, since Monty’s own repr for those differs (see classes.md). Only the text differs; the value and its equality match CPython.
  • A class-body __setattr__ never runs for the synthesized __init__, which writes fields straight into the instance __dict__. This is the never-dispatched attribute hook described in classes.md rather than something dataclass-specific, so @dataclass does not reject it.
  • @dataclass on a non-class (e.g. dataclasses.dataclass(5)) raises TypeError: dataclass() should be called on a class, not '<type>'. CPython instead raises an incidental AttributeError about __module__ from its implementation. The @deco syntax only ever targets a class, so this affects only direct calls.
  • dataclass(...) returns a native callable, not a Python function. CPython builds a closure, which Monty cannot: a native function has nowhere to keep the bound options but its own value. Applying it to a class is identical, and it reprs as <function dataclass at 0x..>, but type() says builtin_function_or_method where CPython says function, and CPython’s repr names the closure (dataclass.<locals>.wrap). Having nowhere to live but the value, the options are the value: dataclass(frozen=True) is dataclass(frozen=True) is True, where each CPython call builds a fresh closure. Fixable only if Monty gains closures over native functions; nothing else depends on that, so it is not planned.
  • del obj.field on a frozen instance never raises cannot delete field, because Monty’s parser has no del statement at all. (Assignment matches CPython, message included, and dataclasses.FrozenInstanceError is importable.)
  • Re-decorating a dataclass rebuilds it. C = dataclass(frozen=True)(C) gives Monty a fully frozen class, where CPython keeps the __init__ its first decoration generated — one that writes fields through the new frozen __setattr__, so CPython’s re-decorated class raises FrozenInstanceError the moment you construct it. Monty synthesizes from the current metadata, so it constructs normally.
  • __dataclass_params__ reads back normalised. C.__dataclass_params__ exists, reprs like CPython’s and answers all ten flags, but each is the bool Monty acted on: @dataclass(frozen=1) reports frozen=True where CPython echoes the 1 you passed. As in CPython the object only reports the options — the class acts on what it was decorated with — so assigning another one changes what you read back and nothing else.

Architectural gaps (cannot match)

  • No inheritance, so field inheritance across base dataclasses is unsupported (Monty has no class inheritance at all).
  • slots=True / weakref_slot=True — no __slots__, no weakrefs.