593 lines
24 KiB
Python
593 lines
24 KiB
Python
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"""Plugin that provides support for dataclasses."""
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from typing import Dict, List, Set, Tuple, Optional
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from typing_extensions import Final
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from mypy.nodes import (
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ARG_OPT, ARG_NAMED, ARG_NAMED_OPT, ARG_POS, ARG_STAR, ARG_STAR2, MDEF,
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Argument, AssignmentStmt, CallExpr, TypeAlias, Context, Expression, JsonDict,
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NameExpr, RefExpr, SymbolTableNode, TempNode, TypeInfo, Var, TypeVarExpr,
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PlaceholderNode
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)
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from mypy.plugin import ClassDefContext, SemanticAnalyzerPluginInterface
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from mypy.plugins.common import (
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add_method, _get_decorator_bool_argument, deserialize_and_fixup_type, add_attribute_to_class,
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)
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from mypy.typeops import map_type_from_supertype
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from mypy.types import (
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Type, Instance, NoneType, TypeVarType, CallableType, TupleType, LiteralType,
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get_proper_type, AnyType, TypeOfAny,
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)
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from mypy.server.trigger import make_wildcard_trigger
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from mypy.state import state
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# The set of decorators that generate dataclasses.
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dataclass_makers: Final = {
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'dataclass',
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'dataclasses.dataclass',
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}
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# The set of functions that generate dataclass fields.
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field_makers: Final = {
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'dataclasses.field',
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}
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SELF_TVAR_NAME: Final = "_DT"
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class DataclassAttribute:
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def __init__(
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self,
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name: str,
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is_in_init: bool,
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is_init_var: bool,
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has_default: bool,
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line: int,
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column: int,
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type: Optional[Type],
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info: TypeInfo,
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kw_only: bool,
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) -> None:
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self.name = name
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self.is_in_init = is_in_init
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self.is_init_var = is_init_var
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self.has_default = has_default
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self.line = line
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self.column = column
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self.type = type
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self.info = info
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self.kw_only = kw_only
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def to_argument(self) -> Argument:
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arg_kind = ARG_POS
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if self.kw_only and self.has_default:
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arg_kind = ARG_NAMED_OPT
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elif self.kw_only and not self.has_default:
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arg_kind = ARG_NAMED
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elif not self.kw_only and self.has_default:
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arg_kind = ARG_OPT
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return Argument(
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variable=self.to_var(),
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type_annotation=self.type,
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initializer=None,
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kind=arg_kind,
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)
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def to_var(self) -> Var:
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return Var(self.name, self.type)
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def serialize(self) -> JsonDict:
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assert self.type
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return {
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'name': self.name,
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'is_in_init': self.is_in_init,
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'is_init_var': self.is_init_var,
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'has_default': self.has_default,
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'line': self.line,
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'column': self.column,
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'type': self.type.serialize(),
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'kw_only': self.kw_only,
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}
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@classmethod
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def deserialize(
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cls, info: TypeInfo, data: JsonDict, api: SemanticAnalyzerPluginInterface
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) -> 'DataclassAttribute':
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data = data.copy()
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if data.get('kw_only') is None:
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data['kw_only'] = False
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typ = deserialize_and_fixup_type(data.pop('type'), api)
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return cls(type=typ, info=info, **data)
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def expand_typevar_from_subtype(self, sub_type: TypeInfo) -> None:
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"""Expands type vars in the context of a subtype when an attribute is inherited
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from a generic super type."""
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if self.type is not None:
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self.type = map_type_from_supertype(self.type, sub_type, self.info)
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class DataclassTransformer:
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"""Implement the behavior of @dataclass.
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Note that this may be executed multiple times on the same class, so
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everything here must be idempotent.
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This runs after the main semantic analysis pass, so you can assume that
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there are no placeholders.
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"""
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def __init__(self, ctx: ClassDefContext) -> None:
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self._ctx = ctx
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def transform(self) -> bool:
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"""Apply all the necessary transformations to the underlying
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dataclass so as to ensure it is fully type checked according
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to the rules in PEP 557.
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"""
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ctx = self._ctx
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info = self._ctx.cls.info
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attributes = self.collect_attributes()
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if attributes is None:
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# Some definitions are not ready. We need another pass.
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return False
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for attr in attributes:
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if attr.type is None:
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return False
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decorator_arguments = {
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'init': _get_decorator_bool_argument(self._ctx, 'init', True),
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'eq': _get_decorator_bool_argument(self._ctx, 'eq', True),
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'order': _get_decorator_bool_argument(self._ctx, 'order', False),
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'frozen': _get_decorator_bool_argument(self._ctx, 'frozen', False),
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'slots': _get_decorator_bool_argument(self._ctx, 'slots', False),
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'match_args': _get_decorator_bool_argument(self._ctx, 'match_args', True),
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}
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py_version = self._ctx.api.options.python_version
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# If there are no attributes, it may be that the semantic analyzer has not
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# processed them yet. In order to work around this, we can simply skip generating
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# __init__ if there are no attributes, because if the user truly did not define any,
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# then the object default __init__ with an empty signature will be present anyway.
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if (decorator_arguments['init'] and
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('__init__' not in info.names or info.names['__init__'].plugin_generated) and
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attributes):
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args = [attr.to_argument() for attr in attributes if attr.is_in_init
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and not self._is_kw_only_type(attr.type)]
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if info.fallback_to_any:
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# Make positional args optional since we don't know their order.
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# This will at least allow us to typecheck them if they are called
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# as kwargs
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for arg in args:
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if arg.kind == ARG_POS:
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arg.kind = ARG_OPT
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nameless_var = Var('')
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args = [Argument(nameless_var, AnyType(TypeOfAny.explicit), None, ARG_STAR),
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*args,
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Argument(nameless_var, AnyType(TypeOfAny.explicit), None, ARG_STAR2),
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]
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add_method(
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ctx,
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'__init__',
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args=args,
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return_type=NoneType(),
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)
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if (decorator_arguments['eq'] and info.get('__eq__') is None or
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decorator_arguments['order']):
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# Type variable for self types in generated methods.
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obj_type = ctx.api.named_type('builtins.object')
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self_tvar_expr = TypeVarExpr(SELF_TVAR_NAME, info.fullname + '.' + SELF_TVAR_NAME,
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[], obj_type)
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info.names[SELF_TVAR_NAME] = SymbolTableNode(MDEF, self_tvar_expr)
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# Add <, >, <=, >=, but only if the class has an eq method.
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if decorator_arguments['order']:
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if not decorator_arguments['eq']:
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ctx.api.fail('eq must be True if order is True', ctx.cls)
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for method_name in ['__lt__', '__gt__', '__le__', '__ge__']:
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# Like for __eq__ and __ne__, we want "other" to match
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# the self type.
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obj_type = ctx.api.named_type('builtins.object')
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order_tvar_def = TypeVarType(SELF_TVAR_NAME, info.fullname + '.' + SELF_TVAR_NAME,
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-1, [], obj_type)
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order_return_type = ctx.api.named_type('builtins.bool')
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order_args = [
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Argument(Var('other', order_tvar_def), order_tvar_def, None, ARG_POS)
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]
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existing_method = info.get(method_name)
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if existing_method is not None and not existing_method.plugin_generated:
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assert existing_method.node
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ctx.api.fail(
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f'You may not have a custom {method_name} method when order=True',
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existing_method.node,
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)
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add_method(
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ctx,
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method_name,
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args=order_args,
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return_type=order_return_type,
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self_type=order_tvar_def,
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tvar_def=order_tvar_def,
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)
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if decorator_arguments['frozen']:
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self._propertize_callables(attributes, settable=False)
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self._freeze(attributes)
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else:
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self._propertize_callables(attributes)
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if decorator_arguments['slots']:
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self.add_slots(info, attributes, correct_version=py_version >= (3, 10))
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self.reset_init_only_vars(info, attributes)
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if (decorator_arguments['match_args'] and
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('__match_args__' not in info.names or
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info.names['__match_args__'].plugin_generated) and
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attributes and
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py_version >= (3, 10)):
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str_type = ctx.api.named_type("builtins.str")
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literals: List[Type] = [LiteralType(attr.name, str_type)
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for attr in attributes if attr.is_in_init]
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match_args_type = TupleType(literals, ctx.api.named_type("builtins.tuple"))
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add_attribute_to_class(ctx.api, ctx.cls, "__match_args__", match_args_type)
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self._add_dataclass_fields_magic_attribute()
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info.metadata['dataclass'] = {
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'attributes': [attr.serialize() for attr in attributes],
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'frozen': decorator_arguments['frozen'],
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}
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return True
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def add_slots(self,
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info: TypeInfo,
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attributes: List[DataclassAttribute],
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*,
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correct_version: bool) -> None:
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if not correct_version:
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# This means that version is lower than `3.10`,
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# it is just a non-existent argument for `dataclass` function.
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self._ctx.api.fail(
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'Keyword argument "slots" for "dataclass" '
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'is only valid in Python 3.10 and higher',
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self._ctx.reason,
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)
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return
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generated_slots = {attr.name for attr in attributes}
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if ((info.slots is not None and info.slots != generated_slots)
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or info.names.get('__slots__')):
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# This means we have a slots conflict.
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# Class explicitly specifies a different `__slots__` field.
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# And `@dataclass(slots=True)` is used.
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# In runtime this raises a type error.
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self._ctx.api.fail(
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'"{}" both defines "__slots__" and is used with "slots=True"'.format(
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self._ctx.cls.name,
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),
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self._ctx.cls,
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)
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return
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info.slots = generated_slots
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def reset_init_only_vars(self, info: TypeInfo, attributes: List[DataclassAttribute]) -> None:
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"""Remove init-only vars from the class and reset init var declarations."""
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for attr in attributes:
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if attr.is_init_var:
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if attr.name in info.names:
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del info.names[attr.name]
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else:
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# Nodes of superclass InitVars not used in __init__ cannot be reached.
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assert attr.is_init_var
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for stmt in info.defn.defs.body:
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if isinstance(stmt, AssignmentStmt) and stmt.unanalyzed_type:
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lvalue = stmt.lvalues[0]
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if isinstance(lvalue, NameExpr) and lvalue.name == attr.name:
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# Reset node so that another semantic analysis pass will
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# recreate a symbol node for this attribute.
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lvalue.node = None
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def collect_attributes(self) -> Optional[List[DataclassAttribute]]:
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"""Collect all attributes declared in the dataclass and its parents.
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All assignments of the form
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a: SomeType
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b: SomeOtherType = ...
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are collected.
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Return None if some dataclass base class hasn't been processed
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yet and thus we'll need to ask for another pass.
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"""
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# First, collect attributes belonging to the current class.
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ctx = self._ctx
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cls = self._ctx.cls
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attrs: List[DataclassAttribute] = []
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known_attrs: Set[str] = set()
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kw_only = _get_decorator_bool_argument(ctx, 'kw_only', False)
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for stmt in cls.defs.body:
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# Any assignment that doesn't use the new type declaration
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# syntax can be ignored out of hand.
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if not (isinstance(stmt, AssignmentStmt) and stmt.new_syntax):
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continue
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# a: int, b: str = 1, 'foo' is not supported syntax so we
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# don't have to worry about it.
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lhs = stmt.lvalues[0]
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if not isinstance(lhs, NameExpr):
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continue
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sym = cls.info.names.get(lhs.name)
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if sym is None:
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# There was probably a semantic analysis error.
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continue
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node = sym.node
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assert not isinstance(node, PlaceholderNode)
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if isinstance(node, TypeAlias):
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ctx.api.fail(
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(
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'Type aliases inside dataclass definitions '
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'are not supported at runtime'
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),
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node
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)
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# Skip processing this node. This doesn't match the runtime behaviour,
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# but the only alternative would be to modify the SymbolTable,
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# and it's a little hairy to do that in a plugin.
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continue
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assert isinstance(node, Var)
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# x: ClassVar[int] is ignored by dataclasses.
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if node.is_classvar:
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continue
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# x: InitVar[int] is turned into x: int and is removed from the class.
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is_init_var = False
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node_type = get_proper_type(node.type)
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if (isinstance(node_type, Instance) and
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node_type.type.fullname == 'dataclasses.InitVar'):
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||
|
|
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: Optional[Type]) -> 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, {}
|