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Waylon Walker 2022-03-31 20:20:07 -05:00
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"""Plugin that provides support for dataclasses."""
from typing import Dict, List, Set, Tuple, Optional
from typing_extensions import Final
from mypy.nodes import (
ARG_OPT, ARG_NAMED, ARG_NAMED_OPT, ARG_POS, ARG_STAR, ARG_STAR2, MDEF,
Argument, AssignmentStmt, CallExpr, Context, Expression, JsonDict,
NameExpr, RefExpr, SymbolTableNode, TempNode, TypeInfo, Var, TypeVarExpr,
PlaceholderNode
)
from mypy.plugin import ClassDefContext, SemanticAnalyzerPluginInterface
from mypy.plugins.common import (
add_method, _get_decorator_bool_argument, deserialize_and_fixup_type, add_attribute_to_class,
)
from mypy.typeops import map_type_from_supertype
from mypy.types import (
Type, Instance, NoneType, TypeVarType, CallableType, TupleType, LiteralType,
get_proper_type, AnyType, TypeOfAny,
)
from mypy.server.trigger import make_wildcard_trigger
# 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: Optional[Type],
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 not isinstance(self.type, TypeVarType):
return
self.type = map_type_from_supertype(self.type, sub_type, self.info)
class DataclassTransformer:
def __init__(self, ctx: ClassDefContext) -> None:
self._ctx = ctx
def transform(self) -> None:
"""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, defer() should be already called.
return
for attr in attributes:
if attr.type is None:
ctx.api.defer()
return
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(
'You may not have a custom %s method when order=True' % method_name,
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._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):
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'],
}
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) -> Optional[List[DataclassAttribute]]:
"""Collect all attributes declared in the dataclass and its parents.
All assignments of the form
a: SomeType
b: SomeOtherType = ...
are collected.
"""
# 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:
# This name is likely blocked by a star import. We don't need to defer because
# defer() is already called by mark_incomplete().
continue
node = sym.node
if isinstance(node, PlaceholderNode):
# This node is not ready yet.
return None
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' 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)
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]) -> 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 = True
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_class_maker_callback(ctx: ClassDefContext) -> None:
"""Hooks into the class typechecking process to add support for dataclasses.
"""
transformer = DataclassTransformer(ctx)
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, {}