"""Plugin that provides support for dataclasses.""" from __future__ import annotations from typing_extensions import Final from mypy.nodes import ( ARG_NAMED, ARG_NAMED_OPT, ARG_OPT, ARG_POS, ARG_STAR, ARG_STAR2, MDEF, Argument, AssignmentStmt, CallExpr, Context, Expression, JsonDict, NameExpr, PlaceholderNode, RefExpr, SymbolTableNode, TempNode, TypeAlias, TypeInfo, TypeVarExpr, Var, ) from mypy.plugin import ClassDefContext, SemanticAnalyzerPluginInterface from mypy.plugins.common import ( _get_decorator_bool_argument, add_attribute_to_class, add_method, deserialize_and_fixup_type, ) from mypy.server.trigger import make_wildcard_trigger from mypy.state import state from mypy.typeops import map_type_from_supertype from mypy.types import ( AnyType, CallableType, Instance, LiteralType, NoneType, TupleType, Type, TypeOfAny, TypeVarType, get_proper_type, ) # The set of decorators that generate dataclasses. dataclass_makers: Final = {"dataclass", "dataclasses.dataclass"} # The set of functions that generate dataclass fields. field_makers: Final = {"dataclasses.field"} SELF_TVAR_NAME: Final = "_DT" class DataclassAttribute: def __init__( self, name: str, is_in_init: bool, is_init_var: bool, has_default: bool, line: int, column: int, type: Type | None, info: TypeInfo, kw_only: bool, ) -> None: self.name = name self.is_in_init = is_in_init self.is_init_var = is_init_var self.has_default = has_default self.line = line self.column = column self.type = type self.info = info self.kw_only = kw_only def to_argument(self) -> Argument: arg_kind = ARG_POS if self.kw_only and self.has_default: arg_kind = ARG_NAMED_OPT elif self.kw_only and not self.has_default: arg_kind = ARG_NAMED elif not self.kw_only and self.has_default: arg_kind = ARG_OPT return Argument( variable=self.to_var(), type_annotation=self.type, initializer=None, kind=arg_kind ) def to_var(self) -> Var: return Var(self.name, self.type) def serialize(self) -> JsonDict: assert self.type return { "name": self.name, "is_in_init": self.is_in_init, "is_init_var": self.is_init_var, "has_default": self.has_default, "line": self.line, "column": self.column, "type": self.type.serialize(), "kw_only": self.kw_only, } @classmethod def deserialize( cls, info: TypeInfo, data: JsonDict, api: SemanticAnalyzerPluginInterface ) -> DataclassAttribute: data = data.copy() if data.get("kw_only") is None: data["kw_only"] = False typ = deserialize_and_fixup_type(data.pop("type"), api) return cls(type=typ, info=info, **data) def expand_typevar_from_subtype(self, sub_type: TypeInfo) -> None: """Expands type vars in the context of a subtype when an attribute is inherited from a generic super type.""" if self.type is not None: self.type = map_type_from_supertype(self.type, sub_type, self.info) class DataclassTransformer: """Implement the behavior of @dataclass. Note that this may be executed multiple times on the same class, so everything here must be idempotent. This runs after the main semantic analysis pass, so you can assume that there are no placeholders. """ def __init__(self, ctx: ClassDefContext) -> None: self._ctx = ctx def transform(self) -> bool: """Apply all the necessary transformations to the underlying dataclass so as to ensure it is fully type checked according to the rules in PEP 557. """ ctx = self._ctx info = self._ctx.cls.info attributes = self.collect_attributes() if attributes is None: # Some definitions are not ready. We need another pass. return False for attr in attributes: if attr.type is None: return False decorator_arguments = { "init": _get_decorator_bool_argument(self._ctx, "init", True), "eq": _get_decorator_bool_argument(self._ctx, "eq", True), "order": _get_decorator_bool_argument(self._ctx, "order", False), "frozen": _get_decorator_bool_argument(self._ctx, "frozen", False), "slots": _get_decorator_bool_argument(self._ctx, "slots", False), "match_args": _get_decorator_bool_argument(self._ctx, "match_args", True), } py_version = self._ctx.api.options.python_version # If there are no attributes, it may be that the semantic analyzer has not # processed them yet. In order to work around this, we can simply skip generating # __init__ if there are no attributes, because if the user truly did not define any, # then the object default __init__ with an empty signature will be present anyway. if ( decorator_arguments["init"] and ("__init__" not in info.names or info.names["__init__"].plugin_generated) and attributes ): args = [ attr.to_argument() for attr in attributes if attr.is_in_init and not self._is_kw_only_type(attr.type) ] if info.fallback_to_any: # Make positional args optional since we don't know their order. # This will at least allow us to typecheck them if they are called # as kwargs for arg in args: if arg.kind == ARG_POS: arg.kind = ARG_OPT nameless_var = Var("") args = [ Argument(nameless_var, AnyType(TypeOfAny.explicit), None, ARG_STAR), *args, Argument(nameless_var, AnyType(TypeOfAny.explicit), None, ARG_STAR2), ] add_method(ctx, "__init__", args=args, return_type=NoneType()) if ( decorator_arguments["eq"] and info.get("__eq__") is None or decorator_arguments["order"] ): # Type variable for self types in generated methods. obj_type = ctx.api.named_type("builtins.object") self_tvar_expr = TypeVarExpr( SELF_TVAR_NAME, info.fullname + "." + SELF_TVAR_NAME, [], obj_type ) info.names[SELF_TVAR_NAME] = SymbolTableNode(MDEF, self_tvar_expr) # Add <, >, <=, >=, but only if the class has an eq method. if decorator_arguments["order"]: if not decorator_arguments["eq"]: ctx.api.fail("eq must be True if order is True", ctx.cls) for method_name in ["__lt__", "__gt__", "__le__", "__ge__"]: # Like for __eq__ and __ne__, we want "other" to match # the self type. obj_type = ctx.api.named_type("builtins.object") order_tvar_def = TypeVarType( SELF_TVAR_NAME, info.fullname + "." + SELF_TVAR_NAME, -1, [], obj_type ) order_return_type = ctx.api.named_type("builtins.bool") order_args = [ Argument(Var("other", order_tvar_def), order_tvar_def, None, ARG_POS) ] existing_method = info.get(method_name) if existing_method is not None and not existing_method.plugin_generated: assert existing_method.node ctx.api.fail( f"You may not have a custom {method_name} method when order=True", existing_method.node, ) add_method( ctx, method_name, args=order_args, return_type=order_return_type, self_type=order_tvar_def, tvar_def=order_tvar_def, ) if decorator_arguments["frozen"]: self._propertize_callables(attributes, settable=False) self._freeze(attributes) else: self._propertize_callables(attributes) if decorator_arguments["slots"]: self.add_slots(info, attributes, correct_version=py_version >= (3, 10)) self.reset_init_only_vars(info, attributes) if ( decorator_arguments["match_args"] and ( "__match_args__" not in info.names or info.names["__match_args__"].plugin_generated ) and attributes and py_version >= (3, 10) ): str_type = ctx.api.named_type("builtins.str") literals: list[Type] = [ LiteralType(attr.name, str_type) for attr in attributes if attr.is_in_init ] match_args_type = TupleType(literals, ctx.api.named_type("builtins.tuple")) add_attribute_to_class(ctx.api, ctx.cls, "__match_args__", match_args_type) self._add_dataclass_fields_magic_attribute() info.metadata["dataclass"] = { "attributes": [attr.serialize() for attr in attributes], "frozen": decorator_arguments["frozen"], } return True def add_slots( self, info: TypeInfo, attributes: list[DataclassAttribute], *, correct_version: bool ) -> None: if not correct_version: # This means that version is lower than `3.10`, # it is just a non-existent argument for `dataclass` function. self._ctx.api.fail( 'Keyword argument "slots" for "dataclass" ' "is only valid in Python 3.10 and higher", self._ctx.reason, ) return generated_slots = {attr.name for attr in attributes} if (info.slots is not None and info.slots != generated_slots) or info.names.get( "__slots__" ): # This means we have a slots conflict. # Class explicitly specifies a different `__slots__` field. # And `@dataclass(slots=True)` is used. # In runtime this raises a type error. self._ctx.api.fail( '"{}" both defines "__slots__" and is used with "slots=True"'.format( self._ctx.cls.name ), self._ctx.cls, ) return info.slots = generated_slots def reset_init_only_vars(self, info: TypeInfo, attributes: list[DataclassAttribute]) -> None: """Remove init-only vars from the class and reset init var declarations.""" for attr in attributes: if attr.is_init_var: if attr.name in info.names: del info.names[attr.name] else: # Nodes of superclass InitVars not used in __init__ cannot be reached. assert attr.is_init_var for stmt in info.defn.defs.body: if isinstance(stmt, AssignmentStmt) and stmt.unanalyzed_type: lvalue = stmt.lvalues[0] if isinstance(lvalue, NameExpr) and lvalue.name == attr.name: # Reset node so that another semantic analysis pass will # recreate a symbol node for this attribute. lvalue.node = None def collect_attributes(self) -> list[DataclassAttribute] | None: """Collect all attributes declared in the dataclass and its parents. All assignments of the form a: SomeType b: SomeOtherType = ... are collected. Return None if some dataclass base class hasn't been processed yet and thus we'll need to ask for another pass. """ # First, collect attributes belonging to the current class. ctx = self._ctx cls = self._ctx.cls attrs: list[DataclassAttribute] = [] known_attrs: set[str] = set() kw_only = _get_decorator_bool_argument(ctx, "kw_only", False) for stmt in cls.defs.body: # Any assignment that doesn't use the new type declaration # syntax can be ignored out of hand. if not (isinstance(stmt, AssignmentStmt) and stmt.new_syntax): continue # a: int, b: str = 1, 'foo' is not supported syntax so we # don't have to worry about it. lhs = stmt.lvalues[0] if not isinstance(lhs, NameExpr): continue sym = cls.info.names.get(lhs.name) if sym is None: # There was probably a semantic analysis error. continue node = sym.node assert not isinstance(node, PlaceholderNode) if isinstance(node, TypeAlias): ctx.api.fail( ("Type aliases inside dataclass definitions " "are not supported at runtime"), node, ) # Skip processing this node. This doesn't match the runtime behaviour, # but the only alternative would be to modify the SymbolTable, # and it's a little hairy to do that in a plugin. continue assert isinstance(node, Var) # x: ClassVar[int] is ignored by dataclasses. if node.is_classvar: continue # x: InitVar[int] is turned into x: int and is removed from the class. is_init_var = False node_type = get_proper_type(node.type) if ( isinstance(node_type, Instance) and node_type.type.fullname == "dataclasses.InitVar" ): is_init_var = True node.type = node_type.args[0] if self._is_kw_only_type(node_type): kw_only = True has_field_call, field_args = _collect_field_args(stmt.rvalue, ctx) is_in_init_param = field_args.get("init") if is_in_init_param is None: is_in_init = True else: is_in_init = bool(ctx.api.parse_bool(is_in_init_param)) has_default = False # Ensure that something like x: int = field() is rejected # after an attribute with a default. if has_field_call: has_default = "default" in field_args or "default_factory" in field_args # All other assignments are already type checked. elif not isinstance(stmt.rvalue, TempNode): has_default = True if not has_default: # Make all non-default attributes implicit because they are de-facto set # on self in the generated __init__(), not in the class body. sym.implicit = True is_kw_only = kw_only # Use the kw_only field arg if it is provided. Otherwise use the # kw_only value from the decorator parameter. field_kw_only_param = field_args.get("kw_only") if field_kw_only_param is not None: is_kw_only = bool(ctx.api.parse_bool(field_kw_only_param)) known_attrs.add(lhs.name) attrs.append( DataclassAttribute( name=lhs.name, is_in_init=is_in_init, is_init_var=is_init_var, has_default=has_default, line=stmt.line, column=stmt.column, type=sym.type, info=cls.info, kw_only=is_kw_only, ) ) # Next, collect attributes belonging to any class in the MRO # as long as those attributes weren't already collected. This # makes it possible to overwrite attributes in subclasses. # copy() because we potentially modify all_attrs below and if this code requires debugging # we'll have unmodified attrs laying around. all_attrs = attrs.copy() for info in cls.info.mro[1:-1]: if "dataclass_tag" in info.metadata and "dataclass" not in info.metadata: # We haven't processed the base class yet. Need another pass. return None if "dataclass" not in info.metadata: continue super_attrs = [] # Each class depends on the set of attributes in its dataclass ancestors. ctx.api.add_plugin_dependency(make_wildcard_trigger(info.fullname)) for data in info.metadata["dataclass"]["attributes"]: name: str = data["name"] if name not in known_attrs: attr = DataclassAttribute.deserialize(info, data, ctx.api) # TODO: We shouldn't be performing type operations during the main # semantic analysis pass, since some TypeInfo attributes might # still be in flux. This should be performed in a later phase. with state.strict_optional_set(ctx.api.options.strict_optional): attr.expand_typevar_from_subtype(ctx.cls.info) known_attrs.add(name) super_attrs.append(attr) elif all_attrs: # How early in the attribute list an attribute appears is determined by the # reverse MRO, not simply MRO. # See https://docs.python.org/3/library/dataclasses.html#inheritance for # details. for attr in all_attrs: if attr.name == name: all_attrs.remove(attr) super_attrs.append(attr) break all_attrs = super_attrs + all_attrs all_attrs.sort(key=lambda a: a.kw_only) # Ensure that arguments without a default don't follow # arguments that have a default. found_default = False # Ensure that the KW_ONLY sentinel is only provided once found_kw_sentinel = False for attr in all_attrs: # If we find any attribute that is_in_init, not kw_only, and that # doesn't have a default after one that does have one, # then that's an error. if found_default and attr.is_in_init and not attr.has_default and not attr.kw_only: # If the issue comes from merging different classes, report it # at the class definition point. context = Context(line=attr.line, column=attr.column) if attr in attrs else ctx.cls ctx.api.fail( "Attributes without a default cannot follow attributes with one", context ) found_default = found_default or (attr.has_default and attr.is_in_init) if found_kw_sentinel and self._is_kw_only_type(attr.type): context = Context(line=attr.line, column=attr.column) if attr in attrs else ctx.cls ctx.api.fail("There may not be more than one field with the KW_ONLY type", context) found_kw_sentinel = found_kw_sentinel or self._is_kw_only_type(attr.type) return all_attrs def _freeze(self, attributes: list[DataclassAttribute]) -> None: """Converts all attributes to @property methods in order to emulate frozen classes. """ info = self._ctx.cls.info for attr in attributes: sym_node = info.names.get(attr.name) if sym_node is not None: var = sym_node.node assert isinstance(var, Var) var.is_property = True else: var = attr.to_var() var.info = info var.is_property = True var._fullname = info.fullname + "." + var.name info.names[var.name] = SymbolTableNode(MDEF, var) def _propertize_callables( self, attributes: list[DataclassAttribute], settable: bool = True ) -> None: """Converts all attributes with callable types to @property methods. This avoids the typechecker getting confused and thinking that `my_dataclass_instance.callable_attr(foo)` is going to receive a `self` argument (it is not). """ info = self._ctx.cls.info for attr in attributes: if isinstance(get_proper_type(attr.type), CallableType): var = attr.to_var() var.info = info var.is_property = True var.is_settable_property = settable var._fullname = info.fullname + "." + var.name info.names[var.name] = SymbolTableNode(MDEF, var) def _is_kw_only_type(self, node: Type | None) -> bool: """Checks if the type of the node is the KW_ONLY sentinel value.""" if node is None: return False node_type = get_proper_type(node) if not isinstance(node_type, Instance): return False return node_type.type.fullname == "dataclasses.KW_ONLY" def _add_dataclass_fields_magic_attribute(self) -> None: attr_name = "__dataclass_fields__" any_type = AnyType(TypeOfAny.explicit) field_type = self._ctx.api.named_type_or_none("dataclasses.Field", [any_type]) or any_type attr_type = self._ctx.api.named_type( "builtins.dict", [self._ctx.api.named_type("builtins.str"), field_type] ) var = Var(name=attr_name, type=attr_type) var.info = self._ctx.cls.info var._fullname = self._ctx.cls.info.fullname + "." + attr_name self._ctx.cls.info.names[attr_name] = SymbolTableNode( kind=MDEF, node=var, plugin_generated=True ) def dataclass_tag_callback(ctx: ClassDefContext) -> None: """Record that we have a dataclass in the main semantic analysis pass. The later pass implemented by DataclassTransformer will use this to detect dataclasses in base classes. """ # The value is ignored, only the existence matters. ctx.cls.info.metadata["dataclass_tag"] = {} def dataclass_class_maker_callback(ctx: ClassDefContext) -> bool: """Hooks into the class typechecking process to add support for dataclasses.""" transformer = DataclassTransformer(ctx) return transformer.transform() def _collect_field_args( expr: Expression, ctx: ClassDefContext ) -> tuple[bool, dict[str, Expression]]: """Returns a tuple where the first value represents whether or not the expression is a call to dataclass.field and the second is a dictionary of the keyword arguments that field() was called with. """ if ( isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname in field_makers ): # field() only takes keyword arguments. args = {} for name, arg, kind in zip(expr.arg_names, expr.args, expr.arg_kinds): if not kind.is_named(): if kind.is_named(star=True): # This means that `field` is used with `**` unpacking, # the best we can do for now is not to fail. # TODO: we can infer what's inside `**` and try to collect it. message = 'Unpacking **kwargs in "field()" is not supported' else: message = '"field()" does not accept positional arguments' ctx.api.fail(message, expr) return True, {} assert name is not None args[name] = arg return True, args return False, {}