843 lines
34 KiB
Python
843 lines
34 KiB
Python
"""Plugin for supporting the attrs library (http://www.attrs.org)"""
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from mypy.backports import OrderedDict
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from typing import Optional, Dict, List, cast, Tuple, Iterable
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from typing_extensions import Final
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import mypy.plugin # To avoid circular imports.
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from mypy.exprtotype import expr_to_unanalyzed_type, TypeTranslationError
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from mypy.nodes import (
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Context, Argument, Var, ARG_OPT, ARG_POS, TypeInfo, AssignmentStmt,
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TupleExpr, ListExpr, NameExpr, CallExpr, RefExpr, FuncDef,
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is_class_var, TempNode, Decorator, MemberExpr, Expression,
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SymbolTableNode, MDEF, JsonDict, OverloadedFuncDef, ARG_NAMED_OPT, ARG_NAMED,
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TypeVarExpr, PlaceholderNode, LambdaExpr
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)
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from mypy.plugin import SemanticAnalyzerPluginInterface
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from mypy.plugins.common import (
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_get_argument, _get_bool_argument, _get_decorator_bool_argument, add_method,
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deserialize_and_fixup_type, add_attribute_to_class,
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)
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from mypy.types import (
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TupleType, Type, AnyType, TypeOfAny, CallableType, NoneType, TypeVarType,
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Overloaded, UnionType, FunctionLike, Instance, get_proper_type,
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LiteralType,
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)
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from mypy.typeops import make_simplified_union, map_type_from_supertype
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from mypy.typevars import fill_typevars
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from mypy.util import unmangle
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from mypy.server.trigger import make_wildcard_trigger
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KW_ONLY_PYTHON_2_UNSUPPORTED: Final = "kw_only is not supported in Python 2"
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# The names of the different functions that create classes or arguments.
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attr_class_makers: Final = {
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'attr.s',
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'attr.attrs',
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'attr.attributes',
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}
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attr_dataclass_makers: Final = {
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'attr.dataclass',
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}
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attr_frozen_makers: Final = {"attr.frozen", "attrs.frozen"}
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attr_define_makers: Final = {"attr.define", "attr.mutable", "attrs.define", "attrs.mutable"}
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attr_attrib_makers: Final = {
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'attr.ib',
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'attr.attrib',
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'attr.attr',
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'attr.field',
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'attrs.field',
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}
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attr_optional_converters: Final = {'attr.converters.optional', 'attrs.converters.optional'}
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SELF_TVAR_NAME: Final = "_AT"
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MAGIC_ATTR_NAME: Final = "__attrs_attrs__"
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MAGIC_ATTR_CLS_NAME: Final = "_AttrsAttributes" # The namedtuple subclass name.
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class Converter:
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"""Holds information about a `converter=` argument"""
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def __init__(self,
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init_type: Optional[Type] = None,
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) -> None:
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self.init_type = init_type
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class Attribute:
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"""The value of an attr.ib() call."""
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def __init__(self, name: str, info: TypeInfo,
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has_default: bool, init: bool, kw_only: bool, converter: Optional[Converter],
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context: Context,
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init_type: Optional[Type]) -> None:
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self.name = name
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self.info = info
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self.has_default = has_default
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self.init = init
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self.kw_only = kw_only
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self.converter = converter
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self.context = context
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self.init_type = init_type
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def argument(self, ctx: 'mypy.plugin.ClassDefContext') -> Argument:
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"""Return this attribute as an argument to __init__."""
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assert self.init
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init_type: Optional[Type] = None
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if self.converter:
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if self.converter.init_type:
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init_type = self.converter.init_type
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else:
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ctx.api.fail("Cannot determine __init__ type from converter", self.context)
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init_type = AnyType(TypeOfAny.from_error)
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else: # There is no converter, the init type is the normal type.
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init_type = self.init_type or self.info[self.name].type
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unannotated = False
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if init_type is None:
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unannotated = True
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# Convert type not set to Any.
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init_type = AnyType(TypeOfAny.unannotated)
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else:
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proper_type = get_proper_type(init_type)
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if isinstance(proper_type, AnyType):
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if proper_type.type_of_any == TypeOfAny.unannotated:
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unannotated = True
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if unannotated and ctx.api.options.disallow_untyped_defs:
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# This is a compromise. If you don't have a type here then the
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# __init__ will be untyped. But since the __init__ is added it's
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# pointing at the decorator. So instead we also show the error in the
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# assignment, which is where you would fix the issue.
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node = self.info[self.name].node
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assert node is not None
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ctx.api.msg.need_annotation_for_var(node, self.context)
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if self.kw_only:
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arg_kind = ARG_NAMED_OPT if self.has_default else ARG_NAMED
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else:
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arg_kind = ARG_OPT if self.has_default else ARG_POS
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# Attrs removes leading underscores when creating the __init__ arguments.
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return Argument(Var(self.name.lstrip("_"), init_type), init_type,
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None,
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arg_kind)
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def serialize(self) -> JsonDict:
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"""Serialize this object so it can be saved and restored."""
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return {
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'name': self.name,
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'has_default': self.has_default,
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'init': self.init,
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'kw_only': self.kw_only,
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'has_converter': self.converter is not None,
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'converter_init_type': self.converter.init_type.serialize()
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if self.converter and self.converter.init_type else None,
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'context_line': self.context.line,
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'context_column': self.context.column,
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'init_type': self.init_type.serialize() if self.init_type else None,
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}
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@classmethod
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def deserialize(cls, info: TypeInfo,
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data: JsonDict,
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api: SemanticAnalyzerPluginInterface) -> 'Attribute':
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"""Return the Attribute that was serialized."""
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raw_init_type = data['init_type']
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init_type = deserialize_and_fixup_type(raw_init_type, api) if raw_init_type else None
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raw_converter_init_type = data['converter_init_type']
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converter_init_type = (deserialize_and_fixup_type(raw_converter_init_type, api)
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if raw_converter_init_type else None)
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return Attribute(data['name'],
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info,
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data['has_default'],
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data['init'],
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data['kw_only'],
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Converter(converter_init_type) if data['has_converter'] else None,
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Context(line=data['context_line'], column=data['context_column']),
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init_type)
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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.init_type:
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self.init_type = map_type_from_supertype(self.init_type, sub_type, self.info)
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else:
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self.init_type = None
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def _determine_eq_order(ctx: 'mypy.plugin.ClassDefContext') -> bool:
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"""
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Validate the combination of *cmp*, *eq*, and *order*. Derive the effective
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value of order.
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"""
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cmp = _get_decorator_optional_bool_argument(ctx, 'cmp')
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eq = _get_decorator_optional_bool_argument(ctx, 'eq')
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order = _get_decorator_optional_bool_argument(ctx, 'order')
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if cmp is not None and any((eq is not None, order is not None)):
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ctx.api.fail('Don\'t mix "cmp" with "eq" and "order"', ctx.reason)
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# cmp takes precedence due to bw-compatibility.
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if cmp is not None:
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return cmp
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# If left None, equality is on and ordering mirrors equality.
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if eq is None:
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eq = True
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if order is None:
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order = eq
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if eq is False and order is True:
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ctx.api.fail('eq must be True if order is True', ctx.reason)
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return order
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def _get_decorator_optional_bool_argument(
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ctx: 'mypy.plugin.ClassDefContext',
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name: str,
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default: Optional[bool] = None,
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) -> Optional[bool]:
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"""Return the Optional[bool] argument for the decorator.
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This handles both @decorator(...) and @decorator.
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"""
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if isinstance(ctx.reason, CallExpr):
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attr_value = _get_argument(ctx.reason, name)
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if attr_value:
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if isinstance(attr_value, NameExpr):
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if attr_value.fullname == 'builtins.True':
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return True
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if attr_value.fullname == 'builtins.False':
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return False
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if attr_value.fullname == 'builtins.None':
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return None
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ctx.api.fail(f'"{name}" argument must be True or False.', ctx.reason)
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return default
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return default
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else:
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return default
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def attr_tag_callback(ctx: 'mypy.plugin.ClassDefContext') -> None:
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"""Record that we have an attrs class in the main semantic analysis pass.
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The later pass implemented by attr_class_maker_callback will use this
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to detect attrs lasses in base classes.
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"""
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# The value is ignored, only the existence matters.
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ctx.cls.info.metadata['attrs_tag'] = {}
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def attr_class_maker_callback(ctx: 'mypy.plugin.ClassDefContext',
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auto_attribs_default: Optional[bool] = False,
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frozen_default: bool = False) -> bool:
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"""Add necessary dunder methods to classes decorated with attr.s.
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attrs is a package that lets you define classes without writing dull boilerplate code.
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At a quick glance, the decorator searches the class body for assignments of `attr.ib`s (or
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annotated variables if auto_attribs=True), then depending on how the decorator is called,
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it will add an __init__ or all the __cmp__ methods. For frozen=True it will turn the attrs
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into properties.
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See http://www.attrs.org/en/stable/how-does-it-work.html for information on how attrs works.
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If this returns False, some required metadata was not ready yet and we need another
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pass.
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"""
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info = ctx.cls.info
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init = _get_decorator_bool_argument(ctx, 'init', True)
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frozen = _get_frozen(ctx, frozen_default)
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order = _determine_eq_order(ctx)
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slots = _get_decorator_bool_argument(ctx, 'slots', False)
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auto_attribs = _get_decorator_optional_bool_argument(ctx, 'auto_attribs', auto_attribs_default)
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kw_only = _get_decorator_bool_argument(ctx, 'kw_only', False)
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match_args = _get_decorator_bool_argument(ctx, 'match_args', True)
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early_fail = False
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if ctx.api.options.python_version[0] < 3:
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if auto_attribs:
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ctx.api.fail("auto_attribs is not supported in Python 2", ctx.reason)
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early_fail = True
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if not info.defn.base_type_exprs:
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# Note: This will not catch subclassing old-style classes.
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ctx.api.fail("attrs only works with new-style classes", info.defn)
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early_fail = True
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if kw_only:
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ctx.api.fail(KW_ONLY_PYTHON_2_UNSUPPORTED, ctx.reason)
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early_fail = True
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if early_fail:
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_add_empty_metadata(info)
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return True
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for super_info in ctx.cls.info.mro[1:-1]:
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if 'attrs_tag' in super_info.metadata and 'attrs' not in super_info.metadata:
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# Super class is not ready yet. Request another pass.
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return False
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attributes = _analyze_class(ctx, auto_attribs, kw_only)
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# Check if attribute types are ready.
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for attr in attributes:
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node = info.get(attr.name)
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if node is None:
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# This name is likely blocked by some semantic analysis error that
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# should have been reported already.
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_add_empty_metadata(info)
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return True
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_add_attrs_magic_attribute(ctx, [(attr.name, info[attr.name].type) for attr in attributes])
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if slots:
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_add_slots(ctx, attributes)
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if match_args and ctx.api.options.python_version[:2] >= (3, 10):
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# `.__match_args__` is only added for python3.10+, but the argument
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# exists for earlier versions as well.
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_add_match_args(ctx, attributes)
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# Save the attributes so that subclasses can reuse them.
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ctx.cls.info.metadata['attrs'] = {
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'attributes': [attr.serialize() for attr in attributes],
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'frozen': frozen,
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}
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adder = MethodAdder(ctx)
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if init:
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_add_init(ctx, attributes, adder)
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if order:
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_add_order(ctx, adder)
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if frozen:
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_make_frozen(ctx, attributes)
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return True
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def _get_frozen(ctx: 'mypy.plugin.ClassDefContext', frozen_default: bool) -> bool:
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"""Return whether this class is frozen."""
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if _get_decorator_bool_argument(ctx, 'frozen', frozen_default):
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return True
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# Subclasses of frozen classes are frozen so check that.
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for super_info in ctx.cls.info.mro[1:-1]:
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if 'attrs' in super_info.metadata and super_info.metadata['attrs']['frozen']:
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return True
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return False
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def _analyze_class(ctx: 'mypy.plugin.ClassDefContext',
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auto_attribs: Optional[bool],
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kw_only: bool) -> List[Attribute]:
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"""Analyze the class body of an attr maker, its parents, and return the Attributes found.
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auto_attribs=True means we'll generate attributes from type annotations also.
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auto_attribs=None means we'll detect which mode to use.
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kw_only=True means that all attributes created here will be keyword only args in __init__.
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"""
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own_attrs: OrderedDict[str, Attribute] = OrderedDict()
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if auto_attribs is None:
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auto_attribs = _detect_auto_attribs(ctx)
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# Walk the body looking for assignments and decorators.
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for stmt in ctx.cls.defs.body:
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if isinstance(stmt, AssignmentStmt):
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for attr in _attributes_from_assignment(ctx, stmt, auto_attribs, kw_only):
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# When attrs are defined twice in the same body we want to use the 2nd definition
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# in the 2nd location. So remove it from the OrderedDict.
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# Unless it's auto_attribs in which case we want the 2nd definition in the
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# 1st location.
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if not auto_attribs and attr.name in own_attrs:
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del own_attrs[attr.name]
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own_attrs[attr.name] = attr
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elif isinstance(stmt, Decorator):
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_cleanup_decorator(stmt, own_attrs)
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for attribute in own_attrs.values():
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# Even though these look like class level assignments we want them to look like
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# instance level assignments.
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if attribute.name in ctx.cls.info.names:
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node = ctx.cls.info.names[attribute.name].node
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if isinstance(node, PlaceholderNode):
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# This node is not ready yet.
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continue
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assert isinstance(node, Var)
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node.is_initialized_in_class = False
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# Traverse the MRO and collect attributes from the parents.
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taken_attr_names = set(own_attrs)
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super_attrs = []
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for super_info in ctx.cls.info.mro[1:-1]:
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if 'attrs' in super_info.metadata:
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# Each class depends on the set of attributes in its attrs ancestors.
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ctx.api.add_plugin_dependency(make_wildcard_trigger(super_info.fullname))
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for data in super_info.metadata['attrs']['attributes']:
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# Only add an attribute if it hasn't been defined before. This
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# allows for overwriting attribute definitions by subclassing.
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if data['name'] not in taken_attr_names:
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a = Attribute.deserialize(super_info, data, ctx.api)
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a.expand_typevar_from_subtype(ctx.cls.info)
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super_attrs.append(a)
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taken_attr_names.add(a.name)
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attributes = super_attrs + list(own_attrs.values())
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# Check the init args for correct default-ness. Note: This has to be done after all the
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# attributes for all classes have been read, because subclasses can override parents.
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last_default = False
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for i, attribute in enumerate(attributes):
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if not attribute.init:
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continue
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if attribute.kw_only:
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# Keyword-only attributes don't care whether they are default or not.
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continue
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# If the issue comes from merging different classes, report it
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# at the class definition point.
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context = attribute.context if i >= len(super_attrs) else ctx.cls
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if not attribute.has_default and last_default:
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ctx.api.fail(
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"Non-default attributes not allowed after default attributes.",
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context)
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last_default |= attribute.has_default
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return attributes
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def _add_empty_metadata(info: TypeInfo) -> None:
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"""Add empty metadata to mark that we've finished processing this class."""
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info.metadata['attrs'] = {
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'attributes': [],
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'frozen': False,
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}
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def _detect_auto_attribs(ctx: 'mypy.plugin.ClassDefContext') -> bool:
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"""Return whether auto_attribs should be enabled or disabled.
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It's disabled if there are any unannotated attribs()
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"""
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for stmt in ctx.cls.defs.body:
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if isinstance(stmt, AssignmentStmt):
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for lvalue in stmt.lvalues:
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lvalues, rvalues = _parse_assignments(lvalue, stmt)
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if len(lvalues) != len(rvalues):
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# This means we have some assignment that isn't 1 to 1.
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# It can't be an attrib.
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continue
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for lhs, rvalue in zip(lvalues, rvalues):
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# Check if the right hand side is a call to an attribute maker.
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if (isinstance(rvalue, CallExpr)
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and isinstance(rvalue.callee, RefExpr)
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and rvalue.callee.fullname in attr_attrib_makers
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and not stmt.new_syntax):
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# This means we have an attrib without an annotation and so
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# we can't do auto_attribs=True
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return False
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return True
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def _attributes_from_assignment(ctx: 'mypy.plugin.ClassDefContext',
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stmt: AssignmentStmt, auto_attribs: bool,
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kw_only: bool) -> Iterable[Attribute]:
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"""Return Attribute objects that are created by this assignment.
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The assignments can look like this:
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x = attr.ib()
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x = y = attr.ib()
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x, y = attr.ib(), attr.ib()
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or if auto_attribs is enabled also like this:
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x: type
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x: type = default_value
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"""
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for lvalue in stmt.lvalues:
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lvalues, rvalues = _parse_assignments(lvalue, stmt)
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if len(lvalues) != len(rvalues):
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# This means we have some assignment that isn't 1 to 1.
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# It can't be an attrib.
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continue
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for lhs, rvalue in zip(lvalues, rvalues):
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# Check if the right hand side is a call to an attribute maker.
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if (isinstance(rvalue, CallExpr)
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and isinstance(rvalue.callee, RefExpr)
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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, 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) -> Optional[Attribute]:
|
|
"""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)
|
|
if kw_only and ctx.api.options.python_version[0] < 3:
|
|
ctx.api.fail(KW_ONLY_PYTHON_2_UNSUPPORTED, stmt)
|
|
return None
|
|
|
|
# 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)
|
|
|
|
name = unmangle(lhs.name)
|
|
return Attribute(name, ctx.cls.info, attr_has_default, init,
|
|
kw_only, converter_info, stmt, init_type)
|
|
|
|
|
|
def _parse_converter(ctx: 'mypy.plugin.ClassDefContext',
|
|
converter_expr: Optional[Expression]) -> Optional[Converter]:
|
|
"""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: Optional[Type] = 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)
|
|
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]
|
|
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,
|
|
-1, [], object_type)
|
|
self_tvar_expr = TypeVarExpr(SELF_TVAR_NAME, ctx.cls.info.fullname + '.' + SELF_TVAR_NAME,
|
|
[], object_type)
|
|
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
|
|
assert isinstance(node, Var)
|
|
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, ctx.cls.info[attribute.name].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') -> 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 = []
|
|
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))
|
|
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('__init__', args, NoneType())
|
|
|
|
|
|
def _add_attrs_magic_attribute(ctx: 'mypy.plugin.ClassDefContext',
|
|
attrs: 'List[Tuple[str, Optional[Type]]]') -> 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,
|
|
])
|
|
|
|
ti = ctx.api.basic_new_typeinfo(MAGIC_ATTR_CLS_NAME, fallback_type, 0)
|
|
ti.is_named_tuple = True
|
|
for (name, _), attr_type in zip(attrs, attributes_types):
|
|
var = Var(name, attr_type)
|
|
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, [])
|
|
|
|
# TODO: refactor using `add_attribute_to_class`
|
|
var = Var(name=MAGIC_ATTR_NAME, type=TupleType(attributes_types, fallback=attributes_type))
|
|
var.info = ctx.cls.info
|
|
var.is_classvar = True
|
|
var._fullname = f"{ctx.cls.fullname}.{MAGIC_ATTR_CLS_NAME}"
|
|
var.allow_incompatible_override = True
|
|
ctx.cls.info.names[MAGIC_ATTR_NAME] = SymbolTableNode(
|
|
kind=MDEF,
|
|
node=var,
|
|
plugin_generated=True,
|
|
no_serialize=True,
|
|
)
|
|
|
|
|
|
def _add_slots(ctx: 'mypy.plugin.ClassDefContext',
|
|
attributes: List[Attribute]) -> None:
|
|
# Unlike `@dataclasses.dataclass`, `__slots__` is rewritten here.
|
|
ctx.cls.info.slots = {attr.name for attr in attributes}
|
|
|
|
|
|
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: Optional[Type] = None,
|
|
tvd: Optional[TypeVarType] = 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(self.ctx, method_name, args, ret_type, self_type, tvd)
|