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attrs.py
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attrs.py
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"""Plugin for supporting the attrs library (http://www.attrs.org)"""
from __future__ import annotations
from collections import defaultdict
from functools import reduce
from typing import Final, Iterable, List, Mapping, cast
from typing_extensions import Literal
import mypy.plugin # To avoid circular imports.
from mypy.applytype import apply_generic_arguments
from mypy.errorcodes import LITERAL_REQ
from mypy.expandtype import expand_type, expand_type_by_instance
from mypy.exprtotype import TypeTranslationError, expr_to_unanalyzed_type
from mypy.meet import meet_types
from mypy.messages import format_type_bare
from mypy.nodes import (
ARG_NAMED,
ARG_NAMED_OPT,
ARG_OPT,
ARG_POS,
MDEF,
Argument,
AssignmentStmt,
CallExpr,
Context,
Decorator,
Expression,
FuncDef,
IndexExpr,
JsonDict,
LambdaExpr,
ListExpr,
MemberExpr,
NameExpr,
OverloadedFuncDef,
PlaceholderNode,
RefExpr,
SymbolTableNode,
TempNode,
TupleExpr,
TypeApplication,
TypeInfo,
TypeVarExpr,
Var,
is_class_var,
)
from mypy.plugin import SemanticAnalyzerPluginInterface
from mypy.plugins.common import (
_get_argument,
_get_bool_argument,
_get_decorator_bool_argument,
add_attribute_to_class,
add_method_to_class,
deserialize_and_fixup_type,
)
from mypy.server.trigger import make_wildcard_trigger
from mypy.typeops import get_type_vars, make_simplified_union, map_type_from_supertype
from mypy.types import (
AnyType,
CallableType,
FunctionLike,
Instance,
LiteralType,
NoneType,
Overloaded,
ProperType,
TupleType,
Type,
TypeOfAny,
TypeType,
TypeVarType,
UninhabitedType,
UnionType,
get_proper_type,
)
from mypy.typevars import fill_typevars
from mypy.util import unmangle
# The names of the different functions that create classes or arguments.
attr_class_makers: Final = {"attr.s", "attr.attrs", "attr.attributes"}
attr_dataclass_makers: Final = {"attr.dataclass"}
attr_frozen_makers: Final = {"attr.frozen", "attrs.frozen"}
attr_define_makers: Final = {"attr.define", "attr.mutable", "attrs.define", "attrs.mutable"}
attr_attrib_makers: Final = {"attr.ib", "attr.attrib", "attr.attr", "attr.field", "attrs.field"}
attr_optional_converters: Final = {"attr.converters.optional", "attrs.converters.optional"}
SELF_TVAR_NAME: Final = "_AT"
MAGIC_ATTR_NAME: Final = "__attrs_attrs__"
MAGIC_ATTR_CLS_NAME_TEMPLATE: Final = "__{}_AttrsAttributes__" # The tuple subclass pattern.
ATTRS_INIT_NAME: Final = "__attrs_init__"
class Converter:
"""Holds information about a `converter=` argument"""
def __init__(self, init_type: Type | None = None, ret_type: Type | None = None) -> None:
self.init_type = init_type
self.ret_type = ret_type
class Attribute:
"""The value of an attr.ib() call."""
def __init__(
self,
name: str,
alias: str | None,
info: TypeInfo,
has_default: bool,
init: bool,
kw_only: bool,
converter: Converter | None,
context: Context,
init_type: Type | None,
) -> None:
self.name = name
self.alias = alias
self.info = info
self.has_default = has_default
self.init = init
self.kw_only = kw_only
self.converter = converter
self.context = context
self.init_type = init_type
def argument(self, ctx: mypy.plugin.ClassDefContext) -> Argument:
"""Return this attribute as an argument to __init__."""
assert self.init
init_type: Type | None = None
if self.converter:
if self.converter.init_type:
init_type = self.converter.init_type
if init_type and self.init_type and self.converter.ret_type:
# The converter return type should be the same type as the attribute type.
# Copy type vars from attr type to converter.
converter_vars = get_type_vars(self.converter.ret_type)
init_vars = get_type_vars(self.init_type)
if converter_vars and len(converter_vars) == len(init_vars):
variables = {
binder.id: arg for binder, arg in zip(converter_vars, init_vars)
}
init_type = expand_type(init_type, variables)
else:
ctx.api.fail("Cannot determine __init__ type from converter", self.context)
init_type = AnyType(TypeOfAny.from_error)
else: # There is no converter, the init type is the normal type.
init_type = self.init_type or self.info[self.name].type
unannotated = False
if init_type is None:
unannotated = True
# Convert type not set to Any.
init_type = AnyType(TypeOfAny.unannotated)
else:
proper_type = get_proper_type(init_type)
if isinstance(proper_type, AnyType):
if proper_type.type_of_any == TypeOfAny.unannotated:
unannotated = True
if unannotated and ctx.api.options.disallow_untyped_defs:
# This is a compromise. If you don't have a type here then the
# __init__ will be untyped. But since the __init__ is added it's
# pointing at the decorator. So instead we also show the error in the
# assignment, which is where you would fix the issue.
node = self.info[self.name].node
assert node is not None
ctx.api.msg.need_annotation_for_var(node, self.context)
if self.kw_only:
arg_kind = ARG_NAMED_OPT if self.has_default else ARG_NAMED
else:
arg_kind = ARG_OPT if self.has_default else ARG_POS
# Attrs removes leading underscores when creating the __init__ arguments.
name = self.alias or self.name.lstrip("_")
return Argument(Var(name, init_type), init_type, None, arg_kind)
def serialize(self) -> JsonDict:
"""Serialize this object so it can be saved and restored."""
return {
"name": self.name,
"alias": self.alias,
"has_default": self.has_default,
"init": self.init,
"kw_only": self.kw_only,
"has_converter": self.converter is not None,
"converter_init_type": self.converter.init_type.serialize()
if self.converter and self.converter.init_type
else None,
"context_line": self.context.line,
"context_column": self.context.column,
"init_type": self.init_type.serialize() if self.init_type else None,
}
@classmethod
def deserialize(
cls, info: TypeInfo, data: JsonDict, api: SemanticAnalyzerPluginInterface
) -> Attribute:
"""Return the Attribute that was serialized."""
raw_init_type = data["init_type"]
init_type = deserialize_and_fixup_type(raw_init_type, api) if raw_init_type else None
raw_converter_init_type = data["converter_init_type"]
converter_init_type = (
deserialize_and_fixup_type(raw_converter_init_type, api)
if raw_converter_init_type
else None
)
return Attribute(
data["name"],
data["alias"],
info,
data["has_default"],
data["init"],
data["kw_only"],
Converter(converter_init_type) if data["has_converter"] else None,
Context(line=data["context_line"], column=data["context_column"]),
init_type,
)
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.init_type:
self.init_type = map_type_from_supertype(self.init_type, sub_type, self.info)
else:
self.init_type = None
def _determine_eq_order(ctx: mypy.plugin.ClassDefContext) -> bool:
"""
Validate the combination of *cmp*, *eq*, and *order*. Derive the effective
value of order.
"""
cmp = _get_decorator_optional_bool_argument(ctx, "cmp")
eq = _get_decorator_optional_bool_argument(ctx, "eq")
order = _get_decorator_optional_bool_argument(ctx, "order")
if cmp is not None and any((eq is not None, order is not None)):
ctx.api.fail('Don\'t mix "cmp" with "eq" and "order"', ctx.reason)
# cmp takes precedence due to bw-compatibility.
if cmp is not None:
return cmp
# If left None, equality is on and ordering mirrors equality.
if eq is None:
eq = True
if order is None:
order = eq
if eq is False and order is True:
ctx.api.fail("eq must be True if order is True", ctx.reason)
return order
def _get_decorator_optional_bool_argument(
ctx: mypy.plugin.ClassDefContext, name: str, default: bool | None = None
) -> bool | None:
"""Return the Optional[bool] argument for the decorator.
This handles both @decorator(...) and @decorator.
"""
if isinstance(ctx.reason, CallExpr):
attr_value = _get_argument(ctx.reason, name)
if attr_value:
if isinstance(attr_value, NameExpr):
if attr_value.fullname == "builtins.True":
return True
if attr_value.fullname == "builtins.False":
return False
if attr_value.fullname == "builtins.None":
return None
ctx.api.fail(
f'"{name}" argument must be a True, False, or None literal',
ctx.reason,
code=LITERAL_REQ,
)
return default
return default
else:
return default
def attr_tag_callback(ctx: mypy.plugin.ClassDefContext) -> None:
"""Record that we have an attrs class in the main semantic analysis pass.
The later pass implemented by attr_class_maker_callback will use this
to detect attrs classes in base classes.
"""
# The value is ignored, only the existence matters.
ctx.cls.info.metadata["attrs_tag"] = {}
def attr_class_maker_callback(
ctx: mypy.plugin.ClassDefContext,
auto_attribs_default: bool | None = False,
frozen_default: bool = False,
slots_default: bool = False,
) -> bool:
"""Add necessary dunder methods to classes decorated with attr.s.
attrs is a package that lets you define classes without writing dull boilerplate code.
At a quick glance, the decorator searches the class body for assignments of `attr.ib`s (or
annotated variables if auto_attribs=True), then depending on how the decorator is called,
it will add an __init__ or all the compare methods.
For frozen=True it will turn the attrs into properties.
See https://www.attrs.org/en/stable/how-does-it-work.html for information on how attrs works.
If this returns False, some required metadata was not ready yet and we need another
pass.
"""
info = ctx.cls.info
init = _get_decorator_bool_argument(ctx, "init", True)
frozen = _get_frozen(ctx, frozen_default)
order = _determine_eq_order(ctx)
slots = _get_decorator_bool_argument(ctx, "slots", slots_default)
auto_attribs = _get_decorator_optional_bool_argument(ctx, "auto_attribs", auto_attribs_default)
kw_only = _get_decorator_bool_argument(ctx, "kw_only", False)
match_args = _get_decorator_bool_argument(ctx, "match_args", True)
for super_info in ctx.cls.info.mro[1:-1]:
if "attrs_tag" in super_info.metadata and "attrs" not in super_info.metadata:
# Super class is not ready yet. Request another pass.
return False
attributes = _analyze_class(ctx, auto_attribs, kw_only)
# Check if attribute types are ready.
for attr in attributes:
node = info.get(attr.name)
if node is None:
# This name is likely blocked by some semantic analysis error that
# should have been reported already.
_add_empty_metadata(info)
return True
_add_attrs_magic_attribute(ctx, [(attr.name, info[attr.name].type) for attr in attributes])
if slots:
_add_slots(ctx, attributes)
if match_args and ctx.api.options.python_version[:2] >= (3, 10):
# `.__match_args__` is only added for python3.10+, but the argument
# exists for earlier versions as well.
_add_match_args(ctx, attributes)
# Save the attributes so that subclasses can reuse them.
ctx.cls.info.metadata["attrs"] = {
"attributes": [attr.serialize() for attr in attributes],
"frozen": frozen,
}
adder = MethodAdder(ctx)
# If __init__ is not being generated, attrs still generates it as __attrs_init__ instead.
_add_init(ctx, attributes, adder, "__init__" if init else ATTRS_INIT_NAME)
if order:
_add_order(ctx, adder)
if frozen:
_make_frozen(ctx, attributes)
return True
def _get_frozen(ctx: mypy.plugin.ClassDefContext, frozen_default: bool) -> bool:
"""Return whether this class is frozen."""
if _get_decorator_bool_argument(ctx, "frozen", frozen_default):
return True
# Subclasses of frozen classes are frozen so check that.
for super_info in ctx.cls.info.mro[1:-1]:
if "attrs" in super_info.metadata and super_info.metadata["attrs"]["frozen"]:
return True
return False
def _analyze_class(
ctx: mypy.plugin.ClassDefContext, auto_attribs: bool | None, kw_only: bool
) -> list[Attribute]:
"""Analyze the class body of an attr maker, its parents, and return the Attributes found.
auto_attribs=True means we'll generate attributes from type annotations also.
auto_attribs=None means we'll detect which mode to use.
kw_only=True means that all attributes created here will be keyword only args in __init__.
"""
own_attrs: dict[str, Attribute] = {}
if auto_attribs is None:
auto_attribs = _detect_auto_attribs(ctx)
# Walk the body looking for assignments and decorators.
for stmt in ctx.cls.defs.body:
if isinstance(stmt, AssignmentStmt):
for attr in _attributes_from_assignment(ctx, stmt, auto_attribs, kw_only):
# When attrs are defined twice in the same body we want to use the 2nd definition
# in the 2nd location. So remove it from the OrderedDict.
# Unless it's auto_attribs in which case we want the 2nd definition in the
# 1st location.
if not auto_attribs and attr.name in own_attrs:
del own_attrs[attr.name]
own_attrs[attr.name] = attr
elif isinstance(stmt, Decorator):
_cleanup_decorator(stmt, own_attrs)
for attribute in own_attrs.values():
# Even though these look like class level assignments we want them to look like
# instance level assignments.
if attribute.name in ctx.cls.info.names:
node = ctx.cls.info.names[attribute.name].node
if isinstance(node, PlaceholderNode):
# This node is not ready yet.
continue
assert isinstance(node, Var)
node.is_initialized_in_class = False
# Traverse the MRO and collect attributes from the parents.
taken_attr_names = set(own_attrs)
super_attrs = []
for super_info in ctx.cls.info.mro[1:-1]:
if "attrs" in super_info.metadata:
# Each class depends on the set of attributes in its attrs ancestors.
ctx.api.add_plugin_dependency(make_wildcard_trigger(super_info.fullname))
for data in super_info.metadata["attrs"]["attributes"]:
# Only add an attribute if it hasn't been defined before. This
# allows for overwriting attribute definitions by subclassing.
if data["name"] not in taken_attr_names:
a = Attribute.deserialize(super_info, data, ctx.api)
a.expand_typevar_from_subtype(ctx.cls.info)
super_attrs.append(a)
taken_attr_names.add(a.name)
attributes = super_attrs + list(own_attrs.values())
# Check the init args for correct default-ness. Note: This has to be done after all the
# attributes for all classes have been read, because subclasses can override parents.
last_default = False
for i, attribute in enumerate(attributes):
if not attribute.init:
continue
if attribute.kw_only:
# Keyword-only attributes don't care whether they are default or not.
continue
# If the issue comes from merging different classes, report it
# at the class definition point.
context = attribute.context if i >= len(super_attrs) else ctx.cls
if not attribute.has_default and last_default:
ctx.api.fail("Non-default attributes not allowed after default attributes.", context)
last_default |= attribute.has_default
return attributes
def _add_empty_metadata(info: TypeInfo) -> None:
"""Add empty metadata to mark that we've finished processing this class."""
info.metadata["attrs"] = {"attributes": [], "frozen": False}
def _detect_auto_attribs(ctx: mypy.plugin.ClassDefContext) -> bool:
"""Return whether auto_attribs should be enabled or disabled.
It's disabled if there are any unannotated attribs()
"""
for stmt in ctx.cls.defs.body:
if isinstance(stmt, AssignmentStmt):
for lvalue in stmt.lvalues:
lvalues, rvalues = _parse_assignments(lvalue, stmt)
if len(lvalues) != len(rvalues):
# This means we have some assignment that isn't 1 to 1.
# It can't be an attrib.
continue
for lhs, rvalue in zip(lvalues, rvalues):
# Check if the right hand side is a call to an attribute maker.
if (
isinstance(rvalue, CallExpr)
and isinstance(rvalue.callee, RefExpr)
and rvalue.callee.fullname in attr_attrib_makers
and not stmt.new_syntax
):
# This means we have an attrib without an annotation and so
# we can't do auto_attribs=True
return False
return True
def _attributes_from_assignment(
ctx: mypy.plugin.ClassDefContext, stmt: AssignmentStmt, auto_attribs: bool, kw_only: bool
) -> Iterable[Attribute]:
"""Return Attribute objects that are created by this assignment.
The assignments can look like this:
x = attr.ib()
x = y = attr.ib()
x, y = attr.ib(), attr.ib()
or if auto_attribs is enabled also like this:
x: type
x: type = default_value
x: type = attr.ib(...)
"""
for lvalue in stmt.lvalues:
lvalues, rvalues = _parse_assignments(lvalue, stmt)
if len(lvalues) != len(rvalues):
# This means we have some assignment that isn't 1 to 1.
# It can't be an attrib.
continue
for lhs, rvalue in zip(lvalues, rvalues):
# Check if the right hand side is a call to an attribute maker.
if (
isinstance(rvalue, CallExpr)
and isinstance(rvalue.callee, RefExpr)
and rvalue.callee.fullname in attr_attrib_makers
):
attr = _attribute_from_attrib_maker(ctx, auto_attribs, kw_only, lhs, rvalue, stmt)
if attr:
yield attr
elif auto_attribs and stmt.type and stmt.new_syntax and not is_class_var(lhs):
yield _attribute_from_auto_attrib(ctx, kw_only, lhs, rvalue, stmt)
def _cleanup_decorator(stmt: Decorator, attr_map: dict[str, Attribute]) -> None:
"""Handle decorators in class bodies.
`x.default` will set a default value on x
`x.validator` and `x.default` will get removed to avoid throwing a type error.
"""
remove_me = []
for func_decorator in stmt.decorators:
if (
isinstance(func_decorator, MemberExpr)
and isinstance(func_decorator.expr, NameExpr)
and func_decorator.expr.name in attr_map
):
if func_decorator.name == "default":
attr_map[func_decorator.expr.name].has_default = True
if func_decorator.name in ("default", "validator"):
# These are decorators on the attrib object that only exist during
# class creation time. In order to not trigger a type error later we
# just remove them. This might leave us with a Decorator with no
# decorators (Emperor's new clothes?)
# TODO: It would be nice to type-check these rather than remove them.
# default should be Callable[[], T]
# validator should be Callable[[Any, 'Attribute', T], Any]
# where T is the type of the attribute.
remove_me.append(func_decorator)
for dec in remove_me:
stmt.decorators.remove(dec)
def _attribute_from_auto_attrib(
ctx: mypy.plugin.ClassDefContext,
kw_only: bool,
lhs: NameExpr,
rvalue: Expression,
stmt: AssignmentStmt,
) -> Attribute:
"""Return an Attribute for a new type assignment."""
name = unmangle(lhs.name)
# `x: int` (without equal sign) assigns rvalue to TempNode(AnyType())
has_rhs = not isinstance(rvalue, TempNode)
sym = ctx.cls.info.names.get(name)
init_type = sym.type if sym else None
return Attribute(name, None, ctx.cls.info, has_rhs, True, kw_only, None, stmt, init_type)
def _attribute_from_attrib_maker(
ctx: mypy.plugin.ClassDefContext,
auto_attribs: bool,
kw_only: bool,
lhs: NameExpr,
rvalue: CallExpr,
stmt: AssignmentStmt,
) -> Attribute | None:
"""Return an Attribute from the assignment or None if you can't make one."""
if auto_attribs and not stmt.new_syntax:
# auto_attribs requires an annotation on *every* attr.ib.
assert lhs.node is not None
ctx.api.msg.need_annotation_for_var(lhs.node, stmt)
return None
if len(stmt.lvalues) > 1:
ctx.api.fail("Too many names for one attribute", stmt)
return None
# This is the type that belongs in the __init__ method for this attrib.
init_type = stmt.type
# Read all the arguments from the call.
init = _get_bool_argument(ctx, rvalue, "init", True)
# Note: If the class decorator says kw_only=True the attribute is ignored.
# See https://github.com/python-attrs/attrs/issues/481 for explanation.
kw_only |= _get_bool_argument(ctx, rvalue, "kw_only", False)
# TODO: Check for attr.NOTHING
attr_has_default = bool(_get_argument(rvalue, "default"))
attr_has_factory = bool(_get_argument(rvalue, "factory"))
if attr_has_default and attr_has_factory:
ctx.api.fail('Can\'t pass both "default" and "factory".', rvalue)
elif attr_has_factory:
attr_has_default = True
# If the type isn't set through annotation but is passed through `type=` use that.
type_arg = _get_argument(rvalue, "type")
if type_arg and not init_type:
try:
un_type = expr_to_unanalyzed_type(type_arg, ctx.api.options, ctx.api.is_stub_file)
except TypeTranslationError:
ctx.api.fail("Invalid argument to type", type_arg)
else:
init_type = ctx.api.anal_type(un_type)
if init_type and isinstance(lhs.node, Var) and not lhs.node.type:
# If there is no annotation, add one.
lhs.node.type = init_type
lhs.is_inferred_def = False
# Note: convert is deprecated but works the same as converter.
converter = _get_argument(rvalue, "converter")
convert = _get_argument(rvalue, "convert")
if convert and converter:
ctx.api.fail('Can\'t pass both "convert" and "converter".', rvalue)
elif convert:
ctx.api.fail("convert is deprecated, use converter", rvalue)
converter = convert
converter_info = _parse_converter(ctx, converter)
# Custom alias might be defined:
alias = None
alias_expr = _get_argument(rvalue, "alias")
if alias_expr:
alias = ctx.api.parse_str_literal(alias_expr)
if alias is None:
ctx.api.fail(
'"alias" argument to attrs field must be a string literal',
rvalue,
code=LITERAL_REQ,
)
name = unmangle(lhs.name)
return Attribute(
name, alias, ctx.cls.info, attr_has_default, init, kw_only, converter_info, stmt, init_type
)
def _parse_converter(
ctx: mypy.plugin.ClassDefContext, converter_expr: Expression | None
) -> Converter | None:
"""Return the Converter object from an Expression."""
# TODO: Support complex converters, e.g. lambdas, calls, etc.
if not converter_expr:
return None
converter_info = Converter()
if (
isinstance(converter_expr, CallExpr)
and isinstance(converter_expr.callee, RefExpr)
and converter_expr.callee.fullname in attr_optional_converters
and converter_expr.args
and converter_expr.args[0]
):
# Special handling for attr.converters.optional(type)
# We extract the type and add make the init_args Optional in Attribute.argument
converter_expr = converter_expr.args[0]
is_attr_converters_optional = True
else:
is_attr_converters_optional = False
converter_type: Type | None = None
if isinstance(converter_expr, RefExpr) and converter_expr.node:
if isinstance(converter_expr.node, FuncDef):
if converter_expr.node.type and isinstance(converter_expr.node.type, FunctionLike):
converter_type = converter_expr.node.type
else: # The converter is an unannotated function.
converter_info.init_type = AnyType(TypeOfAny.unannotated)
return converter_info
elif isinstance(converter_expr.node, OverloadedFuncDef) and is_valid_overloaded_converter(
converter_expr.node
):
converter_type = converter_expr.node.type
elif isinstance(converter_expr.node, TypeInfo):
from mypy.checkmember import type_object_type # To avoid import cycle.
converter_type = type_object_type(converter_expr.node, ctx.api.named_type)
elif (
isinstance(converter_expr, IndexExpr)
and isinstance(converter_expr.analyzed, TypeApplication)
and isinstance(converter_expr.base, RefExpr)
and isinstance(converter_expr.base.node, TypeInfo)
):
# The converter is a generic type.
from mypy.checkmember import type_object_type # To avoid import cycle.
converter_type = type_object_type(converter_expr.base.node, ctx.api.named_type)
if isinstance(converter_type, CallableType):
converter_type = apply_generic_arguments(
converter_type,
converter_expr.analyzed.types,
ctx.api.msg.incompatible_typevar_value,
converter_type,
)
else:
converter_type = None
if isinstance(converter_expr, LambdaExpr):
# TODO: should we send a fail if converter_expr.min_args > 1?
converter_info.init_type = AnyType(TypeOfAny.unannotated)
return converter_info
if not converter_type:
# Signal that we have an unsupported converter.
ctx.api.fail(
"Unsupported converter, only named functions, types and lambdas are currently "
"supported",
converter_expr,
)
converter_info.init_type = AnyType(TypeOfAny.from_error)
return converter_info
converter_type = get_proper_type(converter_type)
if isinstance(converter_type, CallableType) and converter_type.arg_types:
converter_info.init_type = converter_type.arg_types[0]
if not is_attr_converters_optional:
converter_info.ret_type = converter_type.ret_type
elif isinstance(converter_type, Overloaded):
types: list[Type] = []
for item in converter_type.items:
# Walk the overloads looking for methods that can accept one argument.
num_arg_types = len(item.arg_types)
if not num_arg_types:
continue
if num_arg_types > 1 and any(kind == ARG_POS for kind in item.arg_kinds[1:]):
continue
types.append(item.arg_types[0])
# Make a union of all the valid types.
if types:
converter_info.init_type = make_simplified_union(types)
if is_attr_converters_optional and converter_info.init_type:
# If the converter was attr.converter.optional(type) then add None to
# the allowed init_type.
converter_info.init_type = UnionType.make_union([converter_info.init_type, NoneType()])
return converter_info
def is_valid_overloaded_converter(defn: OverloadedFuncDef) -> bool:
return all(
(not isinstance(item, Decorator) or isinstance(item.func.type, FunctionLike))
for item in defn.items
)
def _parse_assignments(
lvalue: Expression, stmt: AssignmentStmt
) -> tuple[list[NameExpr], list[Expression]]:
"""Convert a possibly complex assignment expression into lists of lvalues and rvalues."""
lvalues: list[NameExpr] = []
rvalues: list[Expression] = []
if isinstance(lvalue, (TupleExpr, ListExpr)):
if all(isinstance(item, NameExpr) for item in lvalue.items):
lvalues = cast(List[NameExpr], lvalue.items)
if isinstance(stmt.rvalue, (TupleExpr, ListExpr)):
rvalues = stmt.rvalue.items
elif isinstance(lvalue, NameExpr):
lvalues = [lvalue]
rvalues = [stmt.rvalue]
return lvalues, rvalues
def _add_order(ctx: mypy.plugin.ClassDefContext, adder: MethodAdder) -> None:
"""Generate all the ordering methods for this class."""
bool_type = ctx.api.named_type("builtins.bool")
object_type = ctx.api.named_type("builtins.object")
# Make the types be:
# AT = TypeVar('AT')
# def __lt__(self: AT, other: AT) -> bool
# This way comparisons with subclasses will work correctly.
tvd = TypeVarType(
SELF_TVAR_NAME,
ctx.cls.info.fullname + "." + SELF_TVAR_NAME,
id=-1,
values=[],
upper_bound=object_type,
default=AnyType(TypeOfAny.from_omitted_generics),
)
self_tvar_expr = TypeVarExpr(
SELF_TVAR_NAME,
ctx.cls.info.fullname + "." + SELF_TVAR_NAME,
[],
object_type,
AnyType(TypeOfAny.from_omitted_generics),
)
ctx.cls.info.names[SELF_TVAR_NAME] = SymbolTableNode(MDEF, self_tvar_expr)
args = [Argument(Var("other", tvd), tvd, None, ARG_POS)]
for method in ["__lt__", "__le__", "__gt__", "__ge__"]:
adder.add_method(method, args, bool_type, self_type=tvd, tvd=tvd)
def _make_frozen(ctx: mypy.plugin.ClassDefContext, attributes: list[Attribute]) -> None:
"""Turn all the attributes into properties to simulate frozen classes."""
for attribute in attributes:
if attribute.name in ctx.cls.info.names:
# This variable belongs to this class so we can modify it.
node = ctx.cls.info.names[attribute.name].node
if not isinstance(node, Var):
# The superclass attribute was overridden with a non-variable.
# No need to do anything here, override will be verified during
# type checking.
continue
node.is_property = True
else:
# This variable belongs to a super class so create new Var so we
# can modify it.
var = Var(attribute.name, attribute.init_type)
var.info = ctx.cls.info
var._fullname = f"{ctx.cls.info.fullname}.{var.name}"
ctx.cls.info.names[var.name] = SymbolTableNode(MDEF, var)
var.is_property = True
def _add_init(
ctx: mypy.plugin.ClassDefContext,
attributes: list[Attribute],
adder: MethodAdder,
method_name: Literal["__init__", "__attrs_init__"],
) -> None:
"""Generate an __init__ method for the attributes and add it to the class."""
# Convert attributes to arguments with kw_only arguments at the end of
# the argument list
pos_args = []
kw_only_args = []
sym_table = ctx.cls.info.names
for attribute in attributes:
if not attribute.init:
continue
if attribute.kw_only:
kw_only_args.append(attribute.argument(ctx))
else:
pos_args.append(attribute.argument(ctx))
# If the attribute is Final, present in `__init__` and has
# no default, make sure it doesn't error later.
if not attribute.has_default and attribute.name in sym_table:
sym_node = sym_table[attribute.name].node
if isinstance(sym_node, Var) and sym_node.is_final:
sym_node.final_set_in_init = True
args = pos_args + kw_only_args
if all(
# We use getattr rather than instance checks because the variable.type
# might be wrapped into a Union or some other type, but even non-Any
# types reliably track the fact that the argument was not annotated.
getattr(arg.variable.type, "type_of_any", None) == TypeOfAny.unannotated
for arg in args
):
# This workaround makes --disallow-incomplete-defs usable with attrs,
# but is definitely suboptimal as a long-term solution.
# See https://github.com/python/mypy/issues/5954 for discussion.
for a in args:
a.variable.type = AnyType(TypeOfAny.implementation_artifact)
a.type_annotation = AnyType(TypeOfAny.implementation_artifact)
adder.add_method(method_name, args, NoneType())
def _add_attrs_magic_attribute(
ctx: mypy.plugin.ClassDefContext, attrs: list[tuple[str, Type | None]]
) -> None:
any_type = AnyType(TypeOfAny.explicit)
attributes_types: list[Type] = [
ctx.api.named_type_or_none("attr.Attribute", [attr_type or any_type]) or any_type
for _, attr_type in attrs
]
fallback_type = ctx.api.named_type(
"builtins.tuple", [ctx.api.named_type_or_none("attr.Attribute", [any_type]) or any_type]
)
attr_name = MAGIC_ATTR_CLS_NAME_TEMPLATE.format(ctx.cls.fullname.replace(".", "_"))
ti = ctx.api.basic_new_typeinfo(attr_name, fallback_type, 0)
for (name, _), attr_type in zip(attrs, attributes_types):
var = Var(name, attr_type)
var._fullname = name
var.is_property = True
proper_type = get_proper_type(attr_type)
if isinstance(proper_type, Instance):
var.info = proper_type.type
ti.names[name] = SymbolTableNode(MDEF, var, plugin_generated=True)
attributes_type = Instance(ti, [])
# We need to stash the type of the magic attribute so it can be
# loaded on cached runs.
ctx.cls.info.names[attr_name] = SymbolTableNode(MDEF, ti, plugin_generated=True)
add_attribute_to_class(
ctx.api,
ctx.cls,
MAGIC_ATTR_NAME,
TupleType(attributes_types, fallback=attributes_type),
fullname=f"{ctx.cls.fullname}.{MAGIC_ATTR_NAME}",
override_allow_incompatible=True,
is_classvar=True,
)
def _add_slots(ctx: mypy.plugin.ClassDefContext, attributes: list[Attribute]) -> None:
if any(p.slots is None for p in ctx.cls.info.mro[1:-1]):
# At least one type in mro (excluding `self` and `object`)
# does not have concrete `__slots__` defined. Ignoring.
return
# Unlike `@dataclasses.dataclass`, `__slots__` is rewritten here.
ctx.cls.info.slots = {attr.name for attr in attributes}
# Also, inject `__slots__` attribute to class namespace:
slots_type = TupleType(
[ctx.api.named_type("builtins.str") for _ in attributes],
fallback=ctx.api.named_type("builtins.tuple"),
)
add_attribute_to_class(api=ctx.api, cls=ctx.cls, name="__slots__", typ=slots_type)
def _add_match_args(ctx: mypy.plugin.ClassDefContext, attributes: list[Attribute]) -> None:
if (
"__match_args__" not in ctx.cls.info.names
or ctx.cls.info.names["__match_args__"].plugin_generated
):
str_type = ctx.api.named_type("builtins.str")
match_args = TupleType(
[
str_type.copy_modified(last_known_value=LiteralType(attr.name, fallback=str_type))
for attr in attributes
if not attr.kw_only and attr.init
],
fallback=ctx.api.named_type("builtins.tuple"),
)
add_attribute_to_class(api=ctx.api, cls=ctx.cls, name="__match_args__", typ=match_args)
class MethodAdder:
"""Helper to add methods to a TypeInfo.
ctx: The ClassDefCtx we are using on which we will add methods.
"""
# TODO: Combine this with the code build_namedtuple_typeinfo to support both.
def __init__(self, ctx: mypy.plugin.ClassDefContext) -> None:
self.ctx = ctx
self.self_type = fill_typevars(ctx.cls.info)
def add_method(
self,
method_name: str,
args: list[Argument],
ret_type: Type,
self_type: Type | None = None,
tvd: TypeVarType | None = None,
) -> None:
"""Add a method: def <method_name>(self, <args>) -> <ret_type>): ... to info.
self_type: The type to use for the self argument or None to use the inferred self type.
tvd: If the method is generic these should be the type variables.
"""
self_type = self_type if self_type is not None else self.self_type
add_method_to_class(
self.ctx.api, self.ctx.cls, method_name, args, ret_type, self_type, tvd
)
def _get_attrs_init_type(typ: Instance) -> CallableType | None:
"""
If `typ` refers to an attrs class, get the type of its initializer method.
"""
magic_attr = typ.type.get(MAGIC_ATTR_NAME)
if magic_attr is None or not magic_attr.plugin_generated:
return None
init_method = typ.type.get_method("__init__") or typ.type.get_method(ATTRS_INIT_NAME)
if not isinstance(init_method, FuncDef) or not isinstance(init_method.type, CallableType):
return None
return init_method.type
def _fail_not_attrs_class(ctx: mypy.plugin.FunctionSigContext, t: Type, parent_t: Type) -> None:
t_name = format_type_bare(t, ctx.api.options)
if parent_t is t:
msg = (
f'Argument 1 to "evolve" has a variable type "{t_name}" not bound to an attrs class'
if isinstance(t, TypeVarType)
else f'Argument 1 to "evolve" has incompatible type "{t_name}"; expected an attrs class'
)
else:
pt_name = format_type_bare(parent_t, ctx.api.options)
msg = (