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fuchsia / third_party / github.com / python / mypy / 8c219539380208bf5b8d189aafd4dec10f941f98 / . / mypy / nodes.py
blob: ebd222f4f2539cbcd306eec62defa129252c7d8d [file] [log] [blame]
"""Abstract syntax tree node classes (i.e. parse tree)."""
from __future__ import annotations
import os
from abc import abstractmethod
from collections import defaultdict
from enum import Enum, unique
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Final,
Iterator,
List,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
cast,
)
from typing_extensions import TypeAlias as _TypeAlias, TypeGuard
from mypy_extensions import trait
import mypy.strconv
from mypy.options import Options
from mypy.util import short_type
from mypy.visitor import ExpressionVisitor, NodeVisitor, StatementVisitor
if TYPE_CHECKING:
from mypy.patterns import Pattern
class Context:
"""Base type for objects that are valid as error message locations."""
__slots__ = ("line", "column", "end_line", "end_column")
def __init__(self, line: int = -1, column: int = -1) -> None:
self.line = line
self.column = column
self.end_line: int | None = None
self.end_column: int | None = None
def set_line(
self,
target: Context | int,
column: int | None = None,
end_line: int | None = None,
end_column: int | None = None,
) -> None:
"""If target is a node, pull line (and column) information
into this node. If column is specified, this will override any column
information coming from a node.
"""
if isinstance(target, int):
self.line = target
else:
self.line = target.line
self.column = target.column
self.end_line = target.end_line
self.end_column = target.end_column
if column is not None:
self.column = column
if end_line is not None:
self.end_line = end_line
if end_column is not None:
self.end_column = end_column
if TYPE_CHECKING:
# break import cycle only needed for mypy
import mypy.types
T = TypeVar("T")
JsonDict: _TypeAlias = Dict[str, Any]
# Symbol table node kinds
#
# TODO rename to use more descriptive names
LDEF: Final = 0
GDEF: Final = 1
MDEF: Final = 2
# Placeholder for a name imported via 'from ... import'. Second phase of
# semantic will replace this the actual imported reference. This is
# needed so that we can detect whether a name has been imported during
# XXX what?
UNBOUND_IMPORTED: Final = 3
# RevealExpr node kinds
REVEAL_TYPE: Final = 0
REVEAL_LOCALS: Final = 1
LITERAL_YES: Final = 2
LITERAL_TYPE: Final = 1
LITERAL_NO: Final = 0
node_kinds: Final = {LDEF: "Ldef", GDEF: "Gdef", MDEF: "Mdef", UNBOUND_IMPORTED: "UnboundImported"}
inverse_node_kinds: Final = {_kind: _name for _name, _kind in node_kinds.items()}
implicit_module_attrs: Final = {
"__name__": "__builtins__.str",
"__doc__": None, # depends on Python version, see semanal.py
"__path__": None, # depends on if the module is a package
"__file__": "__builtins__.str",
"__package__": "__builtins__.str",
"__annotations__": None, # dict[str, Any] bounded in add_implicit_module_attrs()
}
# These aliases exist because built-in class objects are not subscriptable.
# For example `list[int]` fails at runtime. Instead List[int] should be used.
type_aliases: Final = {
"typing.List": "builtins.list",
"typing.Dict": "builtins.dict",
"typing.Set": "builtins.set",
"typing.FrozenSet": "builtins.frozenset",
"typing.ChainMap": "collections.ChainMap",
"typing.Counter": "collections.Counter",
"typing.DefaultDict": "collections.defaultdict",
"typing.Deque": "collections.deque",
"typing.OrderedDict": "collections.OrderedDict",
# HACK: a lie in lieu of actual support for PEP 675
"typing.LiteralString": "builtins.str",
}
# This keeps track of the oldest supported Python version where the corresponding
# alias source is available.
type_aliases_source_versions: Final = {
"typing.List": (2, 7),
"typing.Dict": (2, 7),
"typing.Set": (2, 7),
"typing.FrozenSet": (2, 7),
"typing.ChainMap": (3, 3),
"typing.Counter": (2, 7),
"typing.DefaultDict": (2, 7),
"typing.Deque": (2, 7),
"typing.OrderedDict": (3, 7),
"typing.LiteralString": (3, 11),
}
# This keeps track of aliases in `typing_extensions`, which we treat specially.
typing_extensions_aliases: Final = {
# See: https://github.com/python/mypy/issues/11528
"typing_extensions.OrderedDict": "collections.OrderedDict",
# HACK: a lie in lieu of actual support for PEP 675
"typing_extensions.LiteralString": "builtins.str",
}
reverse_builtin_aliases: Final = {
"builtins.list": "typing.List",
"builtins.dict": "typing.Dict",
"builtins.set": "typing.Set",
"builtins.frozenset": "typing.FrozenSet",
}
_nongen_builtins: Final = {"builtins.tuple": "typing.Tuple", "builtins.enumerate": ""}
_nongen_builtins.update((name, alias) for alias, name in type_aliases.items())
# Drop OrderedDict from this for backward compatibility
del _nongen_builtins["collections.OrderedDict"]
# HACK: consequence of hackily treating LiteralString as an alias for str
del _nongen_builtins["builtins.str"]
def get_nongen_builtins(python_version: tuple[int, int]) -> dict[str, str]:
# After 3.9 with pep585 generic builtins are allowed
return _nongen_builtins if python_version < (3, 9) else {}
RUNTIME_PROTOCOL_DECOS: Final = (
"typing.runtime_checkable",
"typing_extensions.runtime",
"typing_extensions.runtime_checkable",
)
class Node(Context):
"""Common base class for all non-type parse tree nodes."""
__slots__ = ()
def __str__(self) -> str:
ans = self.accept(mypy.strconv.StrConv(options=Options()))
if ans is None:
return repr(self)
return ans
def str_with_options(self, options: Options) -> str:
ans = self.accept(mypy.strconv.StrConv(options=options))
assert ans
return ans
def accept(self, visitor: NodeVisitor[T]) -> T:
raise RuntimeError("Not implemented", type(self))
@trait
class Statement(Node):
"""A statement node."""
__slots__ = ()
def accept(self, visitor: StatementVisitor[T]) -> T:
raise RuntimeError("Not implemented", type(self))
@trait
class Expression(Node):
"""An expression node."""
__slots__ = ()
def accept(self, visitor: ExpressionVisitor[T]) -> T:
raise RuntimeError("Not implemented", type(self))
class FakeExpression(Expression):
"""A dummy expression.
We need a dummy expression in one place, and can't instantiate Expression
because it is a trait and mypyc barfs.
"""
__slots__ = ()
# TODO:
# Lvalue = Union['NameExpr', 'MemberExpr', 'IndexExpr', 'SuperExpr', 'StarExpr'
# 'TupleExpr']; see #1783.
Lvalue: _TypeAlias = Expression
@trait
class SymbolNode(Node):
"""Nodes that can be stored in a symbol table."""
__slots__ = ()
@property
@abstractmethod
def name(self) -> str:
pass
# Fully qualified name
@property
@abstractmethod
def fullname(self) -> str:
pass
@abstractmethod
def serialize(self) -> JsonDict:
pass
@classmethod
def deserialize(cls, data: JsonDict) -> SymbolNode:
classname = data[".class"]
method = deserialize_map.get(classname)
if method is not None:
return method(data)
raise NotImplementedError(f"unexpected .class {classname}")
# Items: fullname, related symbol table node, surrounding type (if any)
Definition: _TypeAlias = Tuple[str, "SymbolTableNode", Optional["TypeInfo"]]
class MypyFile(SymbolNode):
"""The abstract syntax tree of a single source file."""
__slots__ = (
"_fullname",
"path",
"defs",
"alias_deps",
"is_bom",
"names",
"imports",
"ignored_lines",
"skipped_lines",
"is_stub",
"is_cache_skeleton",
"is_partial_stub_package",
"plugin_deps",
"future_import_flags",
)
__match_args__ = ("name", "path", "defs")
# Fully qualified module name
_fullname: str
# Path to the file (empty string if not known)
path: str
# Top-level definitions and statements
defs: list[Statement]
# Type alias dependencies as mapping from target to set of alias full names
alias_deps: defaultdict[str, set[str]]
# Is there a UTF-8 BOM at the start?
is_bom: bool
names: SymbolTable
# All import nodes within the file (also ones within functions etc.)
imports: list[ImportBase]
# Lines on which to ignore certain errors when checking.
# If the value is empty, ignore all errors; otherwise, the list contains all
# error codes to ignore.
ignored_lines: dict[int, list[str]]
# Lines that were skipped during semantic analysis e.g. due to ALWAYS_FALSE, MYPY_FALSE,
# or platform/version checks. Those lines would not be type-checked.
skipped_lines: set[int]
# Is this file represented by a stub file (.pyi)?
is_stub: bool
# Is this loaded from the cache and thus missing the actual body of the file?
is_cache_skeleton: bool
# Does this represent an __init__.pyi stub with a module __getattr__
# (i.e. a partial stub package), for such packages we suppress any missing
# module errors in addition to missing attribute errors.
is_partial_stub_package: bool
# Plugin-created dependencies
plugin_deps: dict[str, set[str]]
# Future imports defined in this file. Populated during semantic analysis.
future_import_flags: set[str]
def __init__(
self,
defs: list[Statement],
imports: list[ImportBase],
is_bom: bool = False,
ignored_lines: dict[int, list[str]] | None = None,
) -> None:
super().__init__()
self.defs = defs
self.line = 1 # Dummy line number
self.column = 0 # Dummy column
self.imports = imports
self.is_bom = is_bom
self.alias_deps = defaultdict(set)
self.plugin_deps = {}
if ignored_lines:
self.ignored_lines = ignored_lines
else:
self.ignored_lines = {}
self.skipped_lines = set()
self.path = ""
self.is_stub = False
self.is_cache_skeleton = False
self.is_partial_stub_package = False
self.future_import_flags = set()
def local_definitions(self) -> Iterator[Definition]:
"""Return all definitions within the module (including nested).
This doesn't include imported definitions.
"""
return local_definitions(self.names, self.fullname)
@property
def name(self) -> str:
return "" if not self._fullname else self._fullname.split(".")[-1]
@property
def fullname(self) -> str:
return self._fullname
def accept(self, visitor: NodeVisitor[T]) -> T:
return visitor.visit_mypy_file(self)
def is_package_init_file(self) -> bool:
return len(self.path) != 0 and os.path.basename(self.path).startswith("__init__.")
def is_future_flag_set(self, flag: str) -> bool:
return flag in self.future_import_flags
def serialize(self) -> JsonDict:
return {
".class": "MypyFile",
"_fullname": self._fullname,
"names": self.names.serialize(self._fullname),
"is_stub": self.is_stub,
"path": self.path,
"is_partial_stub_package": self.is_partial_stub_package,
"future_import_flags": list(self.future_import_flags),
}
@classmethod
def deserialize(cls, data: JsonDict) -> MypyFile:
assert data[".class"] == "MypyFile", data
tree = MypyFile([], [])
tree._fullname = data["_fullname"]
tree.names = SymbolTable.deserialize(data["names"])
tree.is_stub = data["is_stub"]
tree.path = data["path"]
tree.is_partial_stub_package = data["is_partial_stub_package"]
tree.is_cache_skeleton = True
tree.future_import_flags = set(data["future_import_flags"])
return tree
class ImportBase(Statement):
"""Base class for all import statements."""
__slots__ = ("is_unreachable", "is_top_level", "is_mypy_only", "assignments")
is_unreachable: bool # Set by semanal.SemanticAnalyzerPass1 if inside `if False` etc.
is_top_level: bool # Ditto if outside any class or def
is_mypy_only: bool # Ditto if inside `if TYPE_CHECKING` or `if MYPY`
# If an import replaces existing definitions, we construct dummy assignment
# statements that assign the imported names to the names in the current scope,
# for type checking purposes. Example:
#
# x = 1
# from m import x <-- add assignment representing "x = m.x"
assignments: list[AssignmentStmt]
def __init__(self) -> None:
super().__init__()
self.assignments = []
self.is_unreachable = False
self.is_top_level = False
self.is_mypy_only = False
class Import(ImportBase):
"""import m [as n]"""
__slots__ = ("ids",)
__match_args__ = ("ids",)
ids: list[tuple[str, str | None]] # (module id, as id)
def __init__(self, ids: list[tuple[str, str | None]]) -> None:
super().__init__()
self.ids = ids
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_import(self)
class ImportFrom(ImportBase):
"""from m import x [as y], ..."""
__slots__ = ("id", "names", "relative")
__match_args__ = ("id", "names", "relative")
id: str
relative: int
names: list[tuple[str, str | None]] # Tuples (name, as name)
def __init__(self, id: str, relative: int, names: list[tuple[str, str | None]]) -> None:
super().__init__()
self.id = id
self.names = names
self.relative = relative
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_import_from(self)
class ImportAll(ImportBase):
"""from m import *"""
__slots__ = ("id", "relative")
__match_args__ = ("id", "relative")
id: str
relative: int
def __init__(self, id: str, relative: int) -> None:
super().__init__()
self.id = id
self.relative = relative
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_import_all(self)
FUNCBASE_FLAGS: Final = ["is_property", "is_class", "is_static", "is_final"]
class FuncBase(Node):
"""Abstract base class for function-like nodes.
N.B: Although this has SymbolNode subclasses (FuncDef,
OverloadedFuncDef), avoid calling isinstance(..., FuncBase) on
something that is typed as SymbolNode. This is to work around
mypy bug #3603, in which mypy doesn't understand multiple
inheritance very well, and will assume that a SymbolNode
cannot be a FuncBase.
Instead, test against SYMBOL_FUNCBASE_TYPES, which enumerates
SymbolNode subclasses that are also FuncBase subclasses.
"""
__slots__ = (
"type",
"unanalyzed_type",
"info",
"is_property",
"is_class", # Uses "@classmethod" (explicit or implicit)
"is_static", # Uses "@staticmethod" (explicit or implicit)
"is_final", # Uses "@final"
"is_explicit_override", # Uses "@override"
"_fullname",
)
def __init__(self) -> None:
super().__init__()
# Type signature. This is usually CallableType or Overloaded, but it can be
# something else for decorated functions.
self.type: mypy.types.ProperType | None = None
# Original, not semantically analyzed type (used for reprocessing)
self.unanalyzed_type: mypy.types.ProperType | None = None
# If method, reference to TypeInfo
# TODO: Type should be Optional[TypeInfo]
self.info = FUNC_NO_INFO
self.is_property = False
self.is_class = False
self.is_static = False
self.is_final = False
self.is_explicit_override = False
# Name with module prefix
self._fullname = ""
@property
@abstractmethod
def name(self) -> str:
pass
@property
def fullname(self) -> str:
return self._fullname
OverloadPart: _TypeAlias = Union["FuncDef", "Decorator"]
class OverloadedFuncDef(FuncBase, SymbolNode, Statement):
"""A logical node representing all the variants of a multi-declaration function.
A multi-declaration function is often an @overload, but can also be a
@property with a setter and a/or a deleter.
This node has no explicit representation in the source program.
Overloaded variants must be consecutive in the source file.
"""
__slots__ = ("items", "unanalyzed_items", "impl")
items: list[OverloadPart]
unanalyzed_items: list[OverloadPart]
impl: OverloadPart | None
def __init__(self, items: list[OverloadPart]) -> None:
super().__init__()
self.items = items
self.unanalyzed_items = items.copy()
self.impl = None
if items:
# TODO: figure out how to reliably set end position (we don't know the impl here).
self.set_line(items[0].line, items[0].column)
self.is_final = False
@property
def name(self) -> str:
if self.items:
return self.items[0].name
else:
# This may happen for malformed overload
assert self.impl is not None
return self.impl.name
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_overloaded_func_def(self)
def serialize(self) -> JsonDict:
return {
".class": "OverloadedFuncDef",
"items": [i.serialize() for i in self.items],
"type": None if self.type is None else self.type.serialize(),
"fullname": self._fullname,
"impl": None if self.impl is None else self.impl.serialize(),
"flags": get_flags(self, FUNCBASE_FLAGS),
}
@classmethod
def deserialize(cls, data: JsonDict) -> OverloadedFuncDef:
assert data[".class"] == "OverloadedFuncDef"
res = OverloadedFuncDef(
[cast(OverloadPart, SymbolNode.deserialize(d)) for d in data["items"]]
)
if data.get("impl") is not None:
res.impl = cast(OverloadPart, SymbolNode.deserialize(data["impl"]))
# set line for empty overload items, as not set in __init__
if len(res.items) > 0:
res.set_line(res.impl.line)
if data.get("type") is not None:
typ = mypy.types.deserialize_type(data["type"])
assert isinstance(typ, mypy.types.ProperType)
res.type = typ
res._fullname = data["fullname"]
set_flags(res, data["flags"])
# NOTE: res.info will be set in the fixup phase.
return res
class Argument(Node):
"""A single argument in a FuncItem."""
__slots__ = ("variable", "type_annotation", "initializer", "kind", "pos_only")
__match_args__ = ("variable", "type_annotation", "initializer", "kind", "pos_only")
def __init__(
self,
variable: Var,
type_annotation: mypy.types.Type | None,
initializer: Expression | None,
kind: ArgKind,
pos_only: bool = False,
) -> None:
super().__init__()
self.variable = variable
self.type_annotation = type_annotation
self.initializer = initializer
self.kind = kind # must be an ARG_* constant
self.pos_only = pos_only
def set_line(
self,
target: Context | int,
column: int | None = None,
end_line: int | None = None,
end_column: int | None = None,
) -> None:
super().set_line(target, column, end_line, end_column)
if self.initializer and self.initializer.line < 0:
self.initializer.set_line(self.line, self.column, self.end_line, self.end_column)
self.variable.set_line(self.line, self.column, self.end_line, self.end_column)
FUNCITEM_FLAGS: Final = FUNCBASE_FLAGS + [
"is_overload",
"is_generator",
"is_coroutine",
"is_async_generator",
"is_awaitable_coroutine",
]
class FuncItem(FuncBase):
"""Base class for nodes usable as overloaded function items."""
__slots__ = (
"arguments", # Note that can be unset if deserialized (type is a lie!)
"arg_names", # Names of arguments
"arg_kinds", # Kinds of arguments
"min_args", # Minimum number of arguments
"max_pos", # Maximum number of positional arguments, -1 if no explicit
# limit (*args not included)
"body", # Body of the function
"is_overload", # Is this an overload variant of function with more than
# one overload variant?
"is_generator", # Contains a yield statement?
"is_coroutine", # Defined using 'async def' syntax?
"is_async_generator", # Is an async def generator?
"is_awaitable_coroutine", # Decorated with '@{typing,asyncio}.coroutine'?
"expanded", # Variants of function with type variables with values expanded
)
__deletable__ = ("arguments", "max_pos", "min_args")
def __init__(
self,
arguments: list[Argument] | None = None,
body: Block | None = None,
typ: mypy.types.FunctionLike | None = None,
) -> None:
super().__init__()
self.arguments = arguments or []
self.arg_names = [None if arg.pos_only else arg.variable.name for arg in self.arguments]
self.arg_kinds: list[ArgKind] = [arg.kind for arg in self.arguments]
self.max_pos: int = self.arg_kinds.count(ARG_POS) + self.arg_kinds.count(ARG_OPT)
self.body: Block = body or Block([])
self.type = typ
self.unanalyzed_type = typ
self.is_overload: bool = False
self.is_generator: bool = False
self.is_coroutine: bool = False
self.is_async_generator: bool = False
self.is_awaitable_coroutine: bool = False
self.expanded: list[FuncItem] = []
self.min_args = 0
for i in range(len(self.arguments)):
if self.arguments[i] is None and i < self.max_fixed_argc():
self.min_args = i + 1
def max_fixed_argc(self) -> int:
return self.max_pos
def is_dynamic(self) -> bool:
return self.type is None
FUNCDEF_FLAGS: Final = FUNCITEM_FLAGS + [
"is_decorated",
"is_conditional",
"is_trivial_body",
"is_mypy_only",
]
# Abstract status of a function
NOT_ABSTRACT: Final = 0
# Explicitly abstract (with @abstractmethod or overload without implementation)
IS_ABSTRACT: Final = 1
# Implicitly abstract: used for functions with trivial bodies defined in Protocols
IMPLICITLY_ABSTRACT: Final = 2
class FuncDef(FuncItem, SymbolNode, Statement):
"""Function definition.
This is a non-lambda function defined using 'def'.
"""
__slots__ = (
"_name",
"is_decorated",
"is_conditional",
"abstract_status",
"original_def",
"deco_line",
"is_trivial_body",
"is_mypy_only",
# Present only when a function is decorated with @typing.datasclass_transform or similar
"dataclass_transform_spec",
)
__match_args__ = ("name", "arguments", "type", "body")
# Note that all __init__ args must have default values
def __init__(
self,
name: str = "", # Function name
arguments: list[Argument] | None = None,
body: Block | None = None,
typ: mypy.types.FunctionLike | None = None,
) -> None:
super().__init__(arguments, body, typ)
self._name = name
self.is_decorated = False
self.is_conditional = False # Defined conditionally (within block)?
self.abstract_status = NOT_ABSTRACT
# Is this an abstract method with trivial body?
# Such methods can't be called via super().
self.is_trivial_body = False
self.is_final = False
# Original conditional definition
self.original_def: None | FuncDef | Var | Decorator = None
# Used for error reporting (to keep backward compatibility with pre-3.8)
self.deco_line: int | None = None
# Definitions that appear in if TYPE_CHECKING are marked with this flag.
self.is_mypy_only = False
self.dataclass_transform_spec: DataclassTransformSpec | None = None
@property
def name(self) -> str:
return self._name
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_func_def(self)
def serialize(self) -> JsonDict:
# We're deliberating omitting arguments and storing only arg_names and
# arg_kinds for space-saving reasons (arguments is not used in later
# stages of mypy).
# TODO: After a FuncDef is deserialized, the only time we use `arg_names`
# and `arg_kinds` is when `type` is None and we need to infer a type. Can
# we store the inferred type ahead of time?
return {
".class": "FuncDef",
"name": self._name,
"fullname": self._fullname,
"arg_names": self.arg_names,
"arg_kinds": [int(x.value) for x in self.arg_kinds],
"type": None if self.type is None else self.type.serialize(),
"flags": get_flags(self, FUNCDEF_FLAGS),
"abstract_status": self.abstract_status,
# TODO: Do we need expanded, original_def?
"dataclass_transform_spec": (
None
if self.dataclass_transform_spec is None
else self.dataclass_transform_spec.serialize()
),
}
@classmethod
def deserialize(cls, data: JsonDict) -> FuncDef:
assert data[".class"] == "FuncDef"
body = Block([])
ret = FuncDef(
data["name"],
[],
body,
(
None
if data["type"] is None
else cast(mypy.types.FunctionLike, mypy.types.deserialize_type(data["type"]))
),
)
ret._fullname = data["fullname"]
set_flags(ret, data["flags"])
# NOTE: ret.info is set in the fixup phase.
ret.arg_names = data["arg_names"]
ret.arg_kinds = [ArgKind(x) for x in data["arg_kinds"]]
ret.abstract_status = data["abstract_status"]
ret.dataclass_transform_spec = (
DataclassTransformSpec.deserialize(data["dataclass_transform_spec"])
if data["dataclass_transform_spec"] is not None
else None
)
# Leave these uninitialized so that future uses will trigger an error
del ret.arguments
del ret.max_pos
del ret.min_args
return ret
# All types that are both SymbolNodes and FuncBases. See the FuncBase
# docstring for the rationale.
SYMBOL_FUNCBASE_TYPES = (OverloadedFuncDef, FuncDef)
class Decorator(SymbolNode, Statement):
"""A decorated function.
A single Decorator object can include any number of function decorators.
"""
__slots__ = ("func", "decorators", "original_decorators", "var", "is_overload")
__match_args__ = ("decorators", "var", "func")
func: FuncDef # Decorated function
decorators: list[Expression] # Decorators (may be empty)
# Some decorators are removed by semanal, keep the original here.
original_decorators: list[Expression]
# TODO: This is mostly used for the type; consider replacing with a 'type' attribute
var: Var # Represents the decorated function obj
is_overload: bool
def __init__(self, func: FuncDef, decorators: list[Expression], var: Var) -> None:
super().__init__()
self.func = func
self.decorators = decorators
self.original_decorators = decorators.copy()
self.var = var
self.is_overload = False
@property
def name(self) -> str:
return self.func.name
@property
def fullname(self) -> str:
return self.func.fullname
@property
def is_final(self) -> bool:
return self.func.is_final
@property
def info(self) -> TypeInfo:
return self.func.info
@property
def type(self) -> mypy.types.Type | None:
return self.var.type
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_decorator(self)
def serialize(self) -> JsonDict:
return {
".class": "Decorator",
"func": self.func.serialize(),
"var": self.var.serialize(),
"is_overload": self.is_overload,
}
@classmethod
def deserialize(cls, data: JsonDict) -> Decorator:
assert data[".class"] == "Decorator"
dec = Decorator(FuncDef.deserialize(data["func"]), [], Var.deserialize(data["var"]))
dec.is_overload = data["is_overload"]
return dec
VAR_FLAGS: Final = [
"is_self",
"is_cls",
"is_initialized_in_class",
"is_staticmethod",
"is_classmethod",
"is_property",
"is_settable_property",
"is_suppressed_import",
"is_classvar",
"is_abstract_var",
"is_final",
"final_unset_in_class",
"final_set_in_init",
"explicit_self_type",
"is_ready",
"is_inferred",
"invalid_partial_type",
"from_module_getattr",
"has_explicit_value",
"allow_incompatible_override",
]
class Var(SymbolNode):
"""A variable.
It can refer to global/local variable or a data attribute.
"""
__slots__ = (
"_name",
"_fullname",
"info",
"type",
"final_value",
"is_self",
"is_cls",
"is_ready",
"is_inferred",
"is_initialized_in_class",
"is_staticmethod",
"is_classmethod",
"is_property",
"is_settable_property",
"is_classvar",
"is_abstract_var",
"is_final",
"final_unset_in_class",
"final_set_in_init",
"is_suppressed_import",
"explicit_self_type",
"from_module_getattr",
"has_explicit_value",
"allow_incompatible_override",
"invalid_partial_type",
)
__match_args__ = ("name", "type", "final_value")
def __init__(self, name: str, type: mypy.types.Type | None = None) -> None:
super().__init__()
self._name = name # Name without module prefix
# TODO: Should be Optional[str]
self._fullname = "" # Name with module prefix
# TODO: Should be Optional[TypeInfo]
self.info = VAR_NO_INFO
self.type: mypy.types.Type | None = type # Declared or inferred type, or None
# Is this the first argument to an ordinary method (usually "self")?
self.is_self = False
# Is this the first argument to a classmethod (typically "cls")?
self.is_cls = False
self.is_ready = True # If inferred, is the inferred type available?
self.is_inferred = self.type is None
# Is this initialized explicitly to a non-None value in class body?
self.is_initialized_in_class = False
self.is_staticmethod = False
self.is_classmethod = False
self.is_property = False
self.is_settable_property = False
self.is_classvar = False
self.is_abstract_var = False
# Set to true when this variable refers to a module we were unable to
# parse for some reason (eg a silenced module)
self.is_suppressed_import = False
# Was this "variable" (rather a constant) defined as Final[...]?
self.is_final = False
# If constant value is a simple literal,
# store the literal value (unboxed) for the benefit of
# tools like mypyc.
self.final_value: int | float | complex | bool | str | None = None
# Where the value was set (only for class attributes)
self.final_unset_in_class = False
self.final_set_in_init = False
# This is True for a variable that was declared on self with an explicit type:
# class C:
# def __init__(self) -> None:
# self.x: int
# This case is important because this defines a new Var, even if there is one
# present in a superclass (without explicit type this doesn't create a new Var).
# See SemanticAnalyzer.analyze_member_lvalue() for details.
self.explicit_self_type = False
# If True, this is an implicit Var created due to module-level __getattr__.
self.from_module_getattr = False
# Var can be created with an explicit value `a = 1` or without one `a: int`,
# we need a way to tell which one is which.
self.has_explicit_value = False
# If True, subclasses can override this with an incompatible type.
self.allow_incompatible_override = False
# If True, this means we didn't manage to infer full type and fall back to
# something like list[Any]. We may decide to not use such types as context.
self.invalid_partial_type = False
@property
def name(self) -> str:
return self._name
@property
def fullname(self) -> str:
return self._fullname
def accept(self, visitor: NodeVisitor[T]) -> T:
return visitor.visit_var(self)
def serialize(self) -> JsonDict:
# TODO: Leave default values out?
# NOTE: Sometimes self.is_ready is False here, but we don't care.
data: JsonDict = {
".class": "Var",
"name": self._name,
"fullname": self._fullname,
"type": None if self.type is None else self.type.serialize(),
"flags": get_flags(self, VAR_FLAGS),
}
if self.final_value is not None:
data["final_value"] = self.final_value
return data
@classmethod
def deserialize(cls, data: JsonDict) -> Var:
assert data[".class"] == "Var"
name = data["name"]
type = None if data["type"] is None else mypy.types.deserialize_type(data["type"])
v = Var(name, type)
v.is_ready = False # Override True default set in __init__
v._fullname = data["fullname"]
set_flags(v, data["flags"])
v.final_value = data.get("final_value")
return v
class ClassDef(Statement):
"""Class definition"""
__slots__ = (
"name",
"_fullname",
"defs",
"type_vars",
"base_type_exprs",
"removed_base_type_exprs",
"info",
"metaclass",
"decorators",
"keywords",
"analyzed",
"has_incompatible_baseclass",
"deco_line",
"removed_statements",
)
__match_args__ = ("name", "defs")
name: str # Name of the class without module prefix
_fullname: str # Fully qualified name of the class
defs: Block
type_vars: list[mypy.types.TypeVarLikeType]
# Base class expressions (not semantically analyzed -- can be arbitrary expressions)
base_type_exprs: list[Expression]
# Special base classes like Generic[...] get moved here during semantic analysis
removed_base_type_exprs: list[Expression]
info: TypeInfo # Related TypeInfo
metaclass: Expression | None
decorators: list[Expression]
keywords: dict[str, Expression]
analyzed: Expression | None
has_incompatible_baseclass: bool
# Used by special forms like NamedTuple and TypedDict to store invalid statements
removed_statements: list[Statement]
def __init__(
self,
name: str,
defs: Block,
type_vars: list[mypy.types.TypeVarLikeType] | None = None,
base_type_exprs: list[Expression] | None = None,
metaclass: Expression | None = None,
keywords: list[tuple[str, Expression]] | None = None,
) -> None:
super().__init__()
self.name = name
self._fullname = ""
self.defs = defs
self.type_vars = type_vars or []
self.base_type_exprs = base_type_exprs or []
self.removed_base_type_exprs = []
self.info = CLASSDEF_NO_INFO
self.metaclass = metaclass
self.decorators = []
self.keywords = dict(keywords) if keywords else {}
self.analyzed = None
self.has_incompatible_baseclass = False
# Used for error reporting (to keep backwad compatibility with pre-3.8)
self.deco_line: int | None = None
self.removed_statements = []
@property
def fullname(self) -> str:
return self._fullname
@fullname.setter
def fullname(self, v: str) -> None:
self._fullname = v
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_class_def(self)
def is_generic(self) -> bool:
return self.info.is_generic()
def serialize(self) -> JsonDict:
# Not serialized: defs, base_type_exprs, metaclass, decorators,
# analyzed (for named tuples etc.)
return {
".class": "ClassDef",
"name": self.name,
"fullname": self.fullname,
"type_vars": [v.serialize() for v in self.type_vars],
}
@classmethod
def deserialize(self, data: JsonDict) -> ClassDef:
assert data[".class"] == "ClassDef"
res = ClassDef(
data["name"],
Block([]),
# https://github.com/python/mypy/issues/12257
[
cast(mypy.types.TypeVarLikeType, mypy.types.deserialize_type(v))
for v in data["type_vars"]
],
)
res.fullname = data["fullname"]
return res
class GlobalDecl(Statement):
"""Declaration global x, y, ..."""
__slots__ = ("names",)
__match_args__ = ("names",)
names: list[str]
def __init__(self, names: list[str]) -> None:
super().__init__()
self.names = names
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_global_decl(self)
class NonlocalDecl(Statement):
"""Declaration nonlocal x, y, ..."""
__slots__ = ("names",)
__match_args__ = ("names",)
names: list[str]
def __init__(self, names: list[str]) -> None:
super().__init__()
self.names = names
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_nonlocal_decl(self)
class Block(Statement):
__slots__ = ("body", "is_unreachable")
__match_args__ = ("body", "is_unreachable")
def __init__(self, body: list[Statement]) -> None:
super().__init__()
self.body = body
# True if we can determine that this block is not executed during semantic
# analysis. For example, this applies to blocks that are protected by
# something like "if PY3:" when using Python 2. However, some code is
# only considered unreachable during type checking and this is not true
# in those cases.
self.is_unreachable = False
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_block(self)
# Statements
class ExpressionStmt(Statement):
"""An expression as a statement, such as print(s)."""
__slots__ = ("expr",)
__match_args__ = ("expr",)
expr: Expression
def __init__(self, expr: Expression) -> None:
super().__init__()
self.expr = expr
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_expression_stmt(self)
class AssignmentStmt(Statement):
"""Assignment statement.
The same node class is used for single assignment, multiple assignment
(e.g. x, y = z) and chained assignment (e.g. x = y = z), assignments
that define new names, and assignments with explicit types ("# type: t"
or "x: t [= ...]").
An lvalue can be NameExpr, TupleExpr, ListExpr, MemberExpr, or IndexExpr.
"""
__slots__ = (
"lvalues",
"rvalue",
"type",
"unanalyzed_type",
"new_syntax",
"is_alias_def",
"is_final_def",
"invalid_recursive_alias",
)
__match_args__ = ("lvalues", "rvalues", "type")
lvalues: list[Lvalue]
# This is a TempNode if and only if no rvalue (x: t).
rvalue: Expression
# Declared type in a comment, may be None.
type: mypy.types.Type | None
# Original, not semantically analyzed type in annotation (used for reprocessing)
unanalyzed_type: mypy.types.Type | None
# This indicates usage of PEP 526 type annotation syntax in assignment.
new_syntax: bool
# Does this assignment define a type alias?
is_alias_def: bool
# Is this a final definition?
# Final attributes can't be re-assigned once set, and can't be overridden
# in a subclass. This flag is not set if an attempted declaration was found to
# be invalid during semantic analysis. It is still set to `True` if
# a final declaration overrides another final declaration (this is checked
# during type checking when MROs are known).
is_final_def: bool
# Stop further processing of this assignment, to prevent flipping back and forth
# during semantic analysis passes.
invalid_recursive_alias: bool
def __init__(
self,
lvalues: list[Lvalue],
rvalue: Expression,
type: mypy.types.Type | None = None,
new_syntax: bool = False,
) -> None:
super().__init__()
self.lvalues = lvalues
self.rvalue = rvalue
self.type = type
self.unanalyzed_type = type
self.new_syntax = new_syntax
self.is_alias_def = False
self.is_final_def = False
self.invalid_recursive_alias = False
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_assignment_stmt(self)
class OperatorAssignmentStmt(Statement):
"""Operator assignment statement such as x += 1"""
__slots__ = ("op", "lvalue", "rvalue")
__match_args__ = ("lvalue", "op", "rvalue")
op: str # TODO: Enum?
lvalue: Lvalue
rvalue: Expression
def __init__(self, op: str, lvalue: Lvalue, rvalue: Expression) -> None:
super().__init__()
self.op = op
self.lvalue = lvalue
self.rvalue = rvalue
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_operator_assignment_stmt(self)
class WhileStmt(Statement):
__slots__ = ("expr", "body", "else_body")
__match_args__ = ("expr", "body", "else_body")
expr: Expression
body: Block
else_body: Block | None
def __init__(self, expr: Expression, body: Block, else_body: Block | None) -> None:
super().__init__()
self.expr = expr
self.body = body
self.else_body = else_body
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_while_stmt(self)
class ForStmt(Statement):
__slots__ = (
"index",
"index_type",
"unanalyzed_index_type",
"inferred_item_type",
"inferred_iterator_type",
"expr",
"body",
"else_body",
"is_async",
)
__match_args__ = ("index", "index_type", "expr", "body", "else_body")
# Index variables
index: Lvalue
# Type given by type comments for index, can be None
index_type: mypy.types.Type | None
# Original, not semantically analyzed type in annotation (used for reprocessing)
unanalyzed_index_type: mypy.types.Type | None
# Inferred iterable item type
inferred_item_type: mypy.types.Type | None
# Inferred iterator type
inferred_iterator_type: mypy.types.Type | None
# Expression to iterate
expr: Expression
body: Block
else_body: Block | None
is_async: bool # True if `async for ...` (PEP 492, Python 3.5)
def __init__(
self,
index: Lvalue,
expr: Expression,
body: Block,
else_body: Block | None,
index_type: mypy.types.Type | None = None,
) -> None:
super().__init__()
self.index = index
self.index_type = index_type
self.unanalyzed_index_type = index_type
self.inferred_item_type = None
self.inferred_iterator_type = None
self.expr = expr
self.body = body
self.else_body = else_body
self.is_async = False
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_for_stmt(self)
class ReturnStmt(Statement):
__slots__ = ("expr",)
__match_args__ = ("expr",)
expr: Expression | None
def __init__(self, expr: Expression | None) -> None:
super().__init__()
self.expr = expr
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_return_stmt(self)
class AssertStmt(Statement):
__slots__ = ("expr", "msg")
__match_args__ = ("expr", "msg")
expr: Expression
msg: Expression | None
def __init__(self, expr: Expression, msg: Expression | None = None) -> None:
super().__init__()
self.expr = expr
self.msg = msg
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_assert_stmt(self)
class DelStmt(Statement):
__slots__ = ("expr",)
__match_args__ = ("expr",)
expr: Lvalue
def __init__(self, expr: Lvalue) -> None:
super().__init__()
self.expr = expr
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_del_stmt(self)
class BreakStmt(Statement):
__slots__ = ()
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_break_stmt(self)
class ContinueStmt(Statement):
__slots__ = ()
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_continue_stmt(self)
class PassStmt(Statement):
__slots__ = ()
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_pass_stmt(self)
class IfStmt(Statement):
__slots__ = ("expr", "body", "else_body")
__match_args__ = ("expr", "body", "else_body")
expr: list[Expression]
body: list[Block]
else_body: Block | None
def __init__(self, expr: list[Expression], body: list[Block], else_body: Block | None) -> None:
super().__init__()
self.expr = expr
self.body = body
self.else_body = else_body
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_if_stmt(self)
class RaiseStmt(Statement):
__slots__ = ("expr", "from_expr")
__match_args__ = ("expr", "from_expr")
# Plain 'raise' is a valid statement.
expr: Expression | None
from_expr: Expression | None
def __init__(self, expr: Expression | None, from_expr: Expression | None) -> None:
super().__init__()
self.expr = expr
self.from_expr = from_expr
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_raise_stmt(self)
class TryStmt(Statement):
__slots__ = ("body", "types", "vars", "handlers", "else_body", "finally_body", "is_star")
__match_args__ = ("body", "types", "vars", "handlers", "else_body", "finally_body", "is_star")
body: Block # Try body
# Plain 'except:' also possible
types: list[Expression | None] # Except type expressions
vars: list[NameExpr | None] # Except variable names
handlers: list[Block] # Except bodies
else_body: Block | None
finally_body: Block | None
# Whether this is try ... except* (added in Python 3.11)
is_star: bool
def __init__(
self,
body: Block,
vars: list[NameExpr | None],
types: list[Expression | None],
handlers: list[Block],
else_body: Block | None,
finally_body: Block | None,
) -> None:
super().__init__()
self.body = body
self.vars = vars
self.types = types
self.handlers = handlers
self.else_body = else_body
self.finally_body = finally_body
self.is_star = False
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_try_stmt(self)
class WithStmt(Statement):
__slots__ = ("expr", "target", "unanalyzed_type", "analyzed_types", "body", "is_async")
__match_args__ = ("expr", "target", "body")
expr: list[Expression]
target: list[Lvalue | None]
# Type given by type comments for target, can be None
unanalyzed_type: mypy.types.Type | None
# Semantically analyzed types from type comment (TypeList type expanded)
analyzed_types: list[mypy.types.Type]
body: Block
is_async: bool # True if `async with ...` (PEP 492, Python 3.5)
def __init__(
self,
expr: list[Expression],
target: list[Lvalue | None],
body: Block,
target_type: mypy.types.Type | None = None,
) -> None:
super().__init__()
self.expr = expr
self.target = target
self.unanalyzed_type = target_type
self.analyzed_types = []
self.body = body
self.is_async = False
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_with_stmt(self)
class MatchStmt(Statement):
__slots__ = ("subject", "patterns", "guards", "bodies")
__match_args__ = ("subject", "patterns", "guards", "bodies")
subject: Expression
patterns: list[Pattern]
guards: list[Expression | None]
bodies: list[Block]
def __init__(
self,
subject: Expression,
patterns: list[Pattern],
guards: list[Expression | None],
bodies: list[Block],
) -> None:
super().__init__()
assert len(patterns) == len(guards) == len(bodies)
self.subject = subject
self.patterns = patterns
self.guards = guards
self.bodies = bodies
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_match_stmt(self)
# Expressions
class IntExpr(Expression):
"""Integer literal"""
__slots__ = ("value",)
__match_args__ = ("value",)
value: int # 0 by default
def __init__(self, value: int) -> None:
super().__init__()
self.value = value
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_int_expr(self)
# How mypy uses StrExpr and BytesExpr:
#
# b'x' -> BytesExpr
# 'x', u'x' -> StrExpr
class StrExpr(Expression):
"""String literal"""
__slots__ = ("value",)
__match_args__ = ("value",)
value: str # '' by default
def __init__(self, value: str) -> None:
super().__init__()
self.value = value
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_str_expr(self)
def is_StrExpr_list(seq: list[Expression]) -> TypeGuard[list[StrExpr]]:
return all(isinstance(item, StrExpr) for item in seq)
class BytesExpr(Expression):
"""Bytes literal"""
__slots__ = ("value",)
__match_args__ = ("value",)
# Note: we deliberately do NOT use bytes here because it ends up
# unnecessarily complicating a lot of the result logic. For example,
# we'd have to worry about converting the bytes into a format we can
# easily serialize/deserialize to and from JSON, would have to worry
# about turning the bytes into a human-readable representation in
# error messages...
#
# It's more convenient to just store the human-readable representation
# from the very start.
value: str
def __init__(self, value: str) -> None:
super().__init__()
self.value = value
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_bytes_expr(self)
class FloatExpr(Expression):
"""Float literal"""
__slots__ = ("value",)
__match_args__ = ("value",)
value: float # 0.0 by default
def __init__(self, value: float) -> None:
super().__init__()
self.value = value
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_float_expr(self)
class ComplexExpr(Expression):
"""Complex literal"""
__slots__ = ("value",)
__match_args__ = ("value",)
value: complex
def __init__(self, value: complex) -> None:
super().__init__()
self.value = value
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_complex_expr(self)
class EllipsisExpr(Expression):
"""Ellipsis (...)"""
__slots__ = ()
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_ellipsis(self)
class StarExpr(Expression):
"""Star expression"""
__slots__ = ("expr", "valid")
__match_args__ = ("expr", "valid")
expr: Expression
valid: bool
def __init__(self, expr: Expression) -> None:
super().__init__()
self.expr = expr
# Whether this starred expression is used in a tuple/list and as lvalue
self.valid = False
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_star_expr(self)
class RefExpr(Expression):
"""Abstract base class for name-like constructs"""
__slots__ = (
"kind",
"node",
"_fullname",
"is_new_def",
"is_inferred_def",
"is_alias_rvalue",
"type_guard",
)
def __init__(self) -> None:
super().__init__()
# LDEF/GDEF/MDEF/... (None if not available)
self.kind: int | None = None
# Var, FuncDef or TypeInfo that describes this
self.node: SymbolNode | None = None
# Fully qualified name (or name if not global)
self._fullname = ""
# Does this define a new name?
self.is_new_def = False
# Does this define a new name with inferred type?
#
# For members, after semantic analysis, this does not take base
# classes into consideration at all; the type checker deals with these.
self.is_inferred_def = False
# Is this expression appears as an rvalue of a valid type alias definition?
self.is_alias_rvalue = False
# Cache type guard from callable_type.type_guard
self.type_guard: mypy.types.Type | None = None
@property
def fullname(self) -> str:
return self._fullname
@fullname.setter
def fullname(self, v: str) -> None:
self._fullname = v
class NameExpr(RefExpr):
"""Name expression
This refers to a local name, global name or a module.
"""
__slots__ = ("name", "is_special_form")
__match_args__ = ("name", "node")
def __init__(self, name: str) -> None:
super().__init__()
self.name = name # Name referred to
# Is this a l.h.s. of a special form assignment like typed dict or type variable?
self.is_special_form = False
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_name_expr(self)
def serialize(self) -> JsonDict:
assert False, f"Serializing NameExpr: {self}"
class MemberExpr(RefExpr):
"""Member access expression x.y"""
__slots__ = ("expr", "name", "def_var")
__match_args__ = ("expr", "name", "node")
def __init__(self, expr: Expression, name: str) -> None:
super().__init__()
self.expr = expr
self.name = name
# The variable node related to a definition through 'self.x = <initializer>'.
# The nodes of other kinds of member expressions are resolved during type checking.
self.def_var: Var | None = None
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_member_expr(self)
# Kinds of arguments
@unique
class ArgKind(Enum):
# Positional argument
ARG_POS = 0
# Positional, optional argument (functions only, not calls)
ARG_OPT = 1
# *arg argument
ARG_STAR = 2
# Keyword argument x=y in call, or keyword-only function arg
ARG_NAMED = 3
# **arg argument
ARG_STAR2 = 4
# In an argument list, keyword-only and also optional
ARG_NAMED_OPT = 5
def is_positional(self, star: bool = False) -> bool:
return self == ARG_POS or self == ARG_OPT or (star and self == ARG_STAR)
def is_named(self, star: bool = False) -> bool:
return self == ARG_NAMED or self == ARG_NAMED_OPT or (star and self == ARG_STAR2)
def is_required(self) -> bool:
return self == ARG_POS or self == ARG_NAMED
def is_optional(self) -> bool:
return self == ARG_OPT or self == ARG_NAMED_OPT
def is_star(self) -> bool:
return self == ARG_STAR or self == ARG_STAR2
ARG_POS: Final = ArgKind.ARG_POS
ARG_OPT: Final = ArgKind.ARG_OPT
ARG_STAR: Final = ArgKind.ARG_STAR
ARG_NAMED: Final = ArgKind.ARG_NAMED
ARG_STAR2: Final = ArgKind.ARG_STAR2
ARG_NAMED_OPT: Final = ArgKind.ARG_NAMED_OPT
class CallExpr(Expression):
"""Call expression.
This can also represent several special forms that are syntactically calls
such as cast(...) and None # type: ....
"""
__slots__ = ("callee", "args", "arg_kinds", "arg_names", "analyzed")
__match_args__ = ("callee", "args", "arg_kinds", "arg_names")
def __init__(
self,
callee: Expression,
args: list[Expression],
arg_kinds: list[ArgKind],
arg_names: list[str | None],
analyzed: Expression | None = None,
) -> None:
super().__init__()
if not arg_names:
arg_names = [None] * len(args)
self.callee = callee
self.args = args
self.arg_kinds = arg_kinds # ARG_ constants
# Each name can be None if not a keyword argument.
self.arg_names: list[str | None] = arg_names
# If not None, the node that represents the meaning of the CallExpr. For
# cast(...) this is a CastExpr.
self.analyzed = analyzed
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_call_expr(self)
class YieldFromExpr(Expression):
__slots__ = ("expr",)
__match_args__ = ("expr",)
expr: Expression
def __init__(self, expr: Expression) -> None:
super().__init__()
self.expr = expr
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_yield_from_expr(self)
class YieldExpr(Expression):
__slots__ = ("expr",)
__match_args__ = ("expr",)
expr: Expression | None
def __init__(self, expr: Expression | None) -> None:
super().__init__()
self.expr = expr
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_yield_expr(self)
class IndexExpr(Expression):
"""Index expression x[y].
Also wraps type application such as List[int] as a special form.
"""
__slots__ = ("base", "index", "method_type", "analyzed")
__match_args__ = ("base", "index")
base: Expression
index: Expression
# Inferred __getitem__ method type
method_type: mypy.types.Type | None
# If not None, this is actually semantically a type application
# Class[type, ...] or a type alias initializer.
analyzed: TypeApplication | TypeAliasExpr | None
def __init__(self, base: Expression, index: Expression) -> None:
super().__init__()
self.base = base
self.index = index
self.method_type = None
self.analyzed = None
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_index_expr(self)
class UnaryExpr(Expression):
"""Unary operation"""
__slots__ = ("op", "expr", "method_type")
__match_args__ = ("op", "expr")
op: str # TODO: Enum?
expr: Expression
# Inferred operator method type
method_type: mypy.types.Type | None
def __init__(self, op: str, expr: Expression) -> None:
super().__init__()
self.op = op
self.expr = expr
self.method_type = None
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_unary_expr(self)
class AssignmentExpr(Expression):
"""Assignment expressions in Python 3.8+, like "a := 2"."""
__slots__ = ("target", "value")
__match_args__ = ("target", "value")
def __init__(self, target: Expression, value: Expression) -> None:
super().__init__()
self.target = target
self.value = value
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_assignment_expr(self)
class OpExpr(Expression):
"""Binary operation.
The dot (.), [] and comparison operators have more specific nodes.
"""
__slots__ = (
"op",
"left",
"right",
"method_type",
"right_always",
"right_unreachable",
"analyzed",
)
__match_args__ = ("left", "op", "right")
op: str # TODO: Enum?
left: Expression
right: Expression
# Inferred type for the operator method type (when relevant).
method_type: mypy.types.Type | None
# Per static analysis only: Is the right side going to be evaluated every time?
right_always: bool
# Per static analysis only: Is the right side unreachable?
right_unreachable: bool
# Used for expressions that represent a type "X | Y" in some contexts
analyzed: TypeAliasExpr | None
def __init__(
self, op: str, left: Expression, right: Expression, analyzed: TypeAliasExpr | None = None
) -> None:
super().__init__()
self.op = op
self.left = left
self.right = right
self.method_type = None
self.right_always = False
self.right_unreachable = False
self.analyzed = analyzed
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_op_expr(self)
class ComparisonExpr(Expression):
"""Comparison expression (e.g. a < b > c < d)."""
__slots__ = ("operators", "operands", "method_types")
__match_args__ = ("operands", "operators")
operators: list[str]
operands: list[Expression]
# Inferred type for the operator methods (when relevant; None for 'is').
method_types: list[mypy.types.Type | None]
def __init__(self, operators: list[str], operands: list[Expression]) -> None:
super().__init__()
self.operators = operators
self.operands = operands
self.method_types = []
def pairwise(self) -> Iterator[tuple[str, Expression, Expression]]:
"""If this comparison expr is "a < b is c == d", yields the sequence
("<", a, b), ("is", b, c), ("==", c, d)
"""
for i, operator in enumerate(self.operators):
yield operator, self.operands[i], self.operands[i + 1]
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_comparison_expr(self)
class SliceExpr(Expression):
"""Slice expression (e.g. 'x:y', 'x:', '::2' or ':').
This is only valid as index in index expressions.
"""
__slots__ = ("begin_index", "end_index", "stride")
__match_args__ = ("begin_index", "end_index", "stride")
begin_index: Expression | None
end_index: Expression | None
stride: Expression | None
def __init__(
self,
begin_index: Expression | None,
end_index: Expression | None,
stride: Expression | None,
) -> None:
super().__init__()
self.begin_index = begin_index
self.end_index = end_index
self.stride = stride
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_slice_expr(self)
class CastExpr(Expression):
"""Cast expression cast(type, expr)."""
__slots__ = ("expr", "type")
__match_args__ = ("expr", "type")
expr: Expression
type: mypy.types.Type
def __init__(self, expr: Expression, typ: mypy.types.Type) -> None:
super().__init__()
self.expr = expr
self.type = typ
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_cast_expr(self)
class AssertTypeExpr(Expression):
"""Represents a typing.assert_type(expr, type) call."""
__slots__ = ("expr", "type")
__match_args__ = ("expr", "type")
expr: Expression
type: mypy.types.Type
def __init__(self, expr: Expression, typ: mypy.types.Type) -> None:
super().__init__()
self.expr = expr
self.type = typ
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_assert_type_expr(self)
class RevealExpr(Expression):
"""Reveal type expression reveal_type(expr) or reveal_locals() expression."""
__slots__ = ("expr", "kind", "local_nodes")
__match_args__ = ("expr", "kind", "local_nodes")
expr: Expression | None
kind: int
local_nodes: list[Var] | None
def __init__(
self, kind: int, expr: Expression | None = None, local_nodes: list[Var] | None = None
) -> None:
super().__init__()
self.expr = expr
self.kind = kind
self.local_nodes = local_nodes
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_reveal_expr(self)
class SuperExpr(Expression):
"""Expression super().name"""
__slots__ = ("name", "info", "call")
__match_args__ = ("name", "call", "info")
name: str
info: TypeInfo | None # Type that contains this super expression
call: CallExpr # The expression super(...)
def __init__(self, name: str, call: CallExpr) -> None:
super().__init__()
self.name = name
self.call = call
self.info = None
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_super_expr(self)
class LambdaExpr(FuncItem, Expression):
"""Lambda expression"""
__match_args__ = ("arguments", "arg_names", "arg_kinds", "body")
@property
def name(self) -> str:
return "<lambda>"
def expr(self) -> Expression:
"""Return the expression (the body) of the lambda."""
ret = self.body.body[-1]
assert isinstance(ret, ReturnStmt)
expr = ret.expr
assert expr is not None # lambda can't have empty body
return expr
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_lambda_expr(self)
def is_dynamic(self) -> bool:
return False
class ListExpr(Expression):
"""List literal expression [...]."""
__slots__ = ("items",)
__match_args__ = ("items",)
items: list[Expression]
def __init__(self, items: list[Expression]) -> None:
super().__init__()
self.items = items
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_list_expr(self)
class DictExpr(Expression):
"""Dictionary literal expression {key: value, ...}."""
__slots__ = ("items",)
__match_args__ = ("items",)
items: list[tuple[Expression | None, Expression]]
def __init__(self, items: list[tuple[Expression | None, Expression]]) -> None:
super().__init__()
self.items = items
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_dict_expr(self)
class TupleExpr(Expression):
"""Tuple literal expression (..., ...)
Also lvalue sequences (..., ...) and [..., ...]"""
__slots__ = ("items",)
__match_args__ = ("items",)
items: list[Expression]
def __init__(self, items: list[Expression]) -> None:
super().__init__()
self.items = items
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_tuple_expr(self)
class SetExpr(Expression):
"""Set literal expression {value, ...}."""
__slots__ = ("items",)
__match_args__ = ("items",)
items: list[Expression]
def __init__(self, items: list[Expression]) -> None:
super().__init__()
self.items = items
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_set_expr(self)
class GeneratorExpr(Expression):
"""Generator expression ... for ... in ... [ for ... in ... ] [ if ... ]."""
__slots__ = ("left_expr", "sequences", "condlists", "is_async", "indices")
__match_args__ = ("left_expr", "indices", "sequences", "condlists")
left_expr: Expression
sequences: list[Expression]
condlists: list[list[Expression]]
is_async: list[bool]
indices: list[Lvalue]
def __init__(
self,
left_expr: Expression,
indices: list[Lvalue],
sequences: list[Expression],
condlists: list[list[Expression]],
is_async: list[bool],
) -> None:
super().__init__()
self.left_expr = left_expr
self.sequences = sequences
self.condlists = condlists
self.indices = indices
self.is_async = is_async
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_generator_expr(self)
class ListComprehension(Expression):
"""List comprehension (e.g. [x + 1 for x in a])"""
__slots__ = ("generator",)
__match_args__ = ("generator",)
generator: GeneratorExpr
def __init__(self, generator: GeneratorExpr) -> None:
super().__init__()
self.generator = generator
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_list_comprehension(self)
class SetComprehension(Expression):
"""Set comprehension (e.g. {x + 1 for x in a})"""
__slots__ = ("generator",)
__match_args__ = ("generator",)
generator: GeneratorExpr
def __init__(self, generator: GeneratorExpr) -> None:
super().__init__()
self.generator = generator
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_set_comprehension(self)
class DictionaryComprehension(Expression):
"""Dictionary comprehension (e.g. {k: v for k, v in a}"""
__slots__ = ("key", "value", "sequences", "condlists", "is_async", "indices")
__match_args__ = ("key", "value", "indices", "sequences", "condlists")
key: Expression
value: Expression
sequences: list[Expression]
condlists: list[list[Expression]]
is_async: list[bool]
indices: list[Lvalue]
def __init__(
self,
key: Expression,
value: Expression,
indices: list[Lvalue],
sequences: list[Expression],
condlists: list[list[Expression]],
is_async: list[bool],
) -> None:
super().__init__()
self.key = key
self.value = value
self.sequences = sequences
self.condlists = condlists
self.indices = indices
self.is_async = is_async
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_dictionary_comprehension(self)
class ConditionalExpr(Expression):
"""Conditional expression (e.g. x if y else z)"""
__slots__ = ("cond", "if_expr", "else_expr")
__match_args__ = ("if_expr", "cond", "else_expr")
cond: Expression
if_expr: Expression
else_expr: Expression
def __init__(self, cond: Expression, if_expr: Expression, else_expr: Expression) -> None:
super().__init__()
self.cond = cond
self.if_expr = if_expr
self.else_expr = else_expr
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_conditional_expr(self)
class TypeApplication(Expression):
"""Type application expr[type, ...]"""
__slots__ = ("expr", "types")
__match_args__ = ("expr", "types")
expr: Expression
types: list[mypy.types.Type]
def __init__(self, expr: Expression, types: list[mypy.types.Type]) -> None:
super().__init__()
self.expr = expr
self.types = types
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_type_application(self)
# Variance of a type variable. For example, T in the definition of
# List[T] is invariant, so List[int] is not a subtype of List[object],
# and also List[object] is not a subtype of List[int].
#
# The T in Iterable[T] is covariant, so Iterable[int] is a subtype of
# Iterable[object], but not vice versa.
#
# If T is contravariant in Foo[T], Foo[object] is a subtype of
# Foo[int], but not vice versa.
INVARIANT: Final = 0
COVARIANT: Final = 1
CONTRAVARIANT: Final = 2
class TypeVarLikeExpr(SymbolNode, Expression):
"""Base class for TypeVarExpr, ParamSpecExpr and TypeVarTupleExpr.
Note that they are constructed by the semantic analyzer.
"""
__slots__ = ("_name", "_fullname", "upper_bound", "default", "variance")
_name: str
_fullname: str
# Upper bound: only subtypes of upper_bound are valid as values. By default
# this is 'object', meaning no restriction.
upper_bound: mypy.types.Type
# Default: used to resolve the TypeVar if the default is not explicitly given.
# By default this is 'AnyType(TypeOfAny.from_omitted_generics)'. See PEP 696.
default: mypy.types.Type
# Variance of the type variable. Invariant is the default.
# TypeVar(..., covariant=True) defines a covariant type variable.
# TypeVar(..., contravariant=True) defines a contravariant type
# variable.
variance: int
def __init__(
self,
name: str,
fullname: str,
upper_bound: mypy.types.Type,
default: mypy.types.Type,
variance: int = INVARIANT,
) -> None:
super().__init__()
self._name = name
self._fullname = fullname
self.upper_bound = upper_bound
self.default = default
self.variance = variance
@property
def name(self) -> str:
return self._name
@property
def fullname(self) -> str:
return self._fullname
class TypeVarExpr(TypeVarLikeExpr):
"""Type variable expression TypeVar(...).
This is also used to represent type variables in symbol tables.
A type variable is not valid as a type unless bound in a TypeVarLikeScope.
That happens within:
1. a generic class that uses the type variable as a type argument or
2. a generic function that refers to the type variable in its signature.
"""
__slots__ = ("values",)
__match_args__ = ("name", "values", "upper_bound", "default")
# Value restriction: only types in the list are valid as values. If the
# list is empty, there is no restriction.
values: list[mypy.types.Type]
def __init__(
self,
name: str,
fullname: str,
values: list[mypy.types.Type],
upper_bound: mypy.types.Type,
default: mypy.types.Type,
variance: int = INVARIANT,
) -> None:
super().__init__(name, fullname, upper_bound, default, variance)
self.values = values
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_type_var_expr(self)
def serialize(self) -> JsonDict:
return {
".class": "TypeVarExpr",
"name": self._name,
"fullname": self._fullname,
"values": [t.serialize() for t in self.values],
"upper_bound": self.upper_bound.serialize(),
"default": self.default.serialize(),
"variance": self.variance,
}
@classmethod
def deserialize(cls, data: JsonDict) -> TypeVarExpr:
assert data[".class"] == "TypeVarExpr"
return TypeVarExpr(
data["name"],
data["fullname"],
[mypy.types.deserialize_type(v) for v in data["values"]],
mypy.types.deserialize_type(data["upper_bound"]),
mypy.types.deserialize_type(data["default"]),
data["variance"],
)
class ParamSpecExpr(TypeVarLikeExpr):
__slots__ = ()
__match_args__ = ("name", "upper_bound", "default")
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_paramspec_expr(self)
def serialize(self) -> JsonDict:
return {
".class": "ParamSpecExpr",
"name": self._name,
"fullname": self._fullname,
"upper_bound": self.upper_bound.serialize(),
"default": self.default.serialize(),
"variance": self.variance,
}
@classmethod
def deserialize(cls, data: JsonDict) -> ParamSpecExpr:
assert data[".class"] == "ParamSpecExpr"
return ParamSpecExpr(
data["name"],
data["fullname"],
mypy.types.deserialize_type(data["upper_bound"]),
mypy.types.deserialize_type(data["default"]),
data["variance"],
)
class TypeVarTupleExpr(TypeVarLikeExpr):
"""Type variable tuple expression TypeVarTuple(...)."""
__slots__ = "tuple_fallback"
tuple_fallback: mypy.types.Instance
__match_args__ = ("name", "upper_bound", "default")
def __init__(
self,
name: str,
fullname: str,
upper_bound: mypy.types.Type,
tuple_fallback: mypy.types.Instance,
default: mypy.types.Type,
variance: int = INVARIANT,
) -> None:
super().__init__(name, fullname, upper_bound, default, variance)
self.tuple_fallback = tuple_fallback
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_type_var_tuple_expr(self)
def serialize(self) -> JsonDict:
return {
".class": "TypeVarTupleExpr",
"name": self._name,
"fullname": self._fullname,
"upper_bound": self.upper_bound.serialize(),
"tuple_fallback": self.tuple_fallback.serialize(),
"default": self.default.serialize(),
"variance": self.variance,
}
@classmethod
def deserialize(cls, data: JsonDict) -> TypeVarTupleExpr:
assert data[".class"] == "TypeVarTupleExpr"
return TypeVarTupleExpr(
data["name"],
data["fullname"],
mypy.types.deserialize_type(data["upper_bound"]),
mypy.types.Instance.deserialize(data["tuple_fallback"]),
mypy.types.deserialize_type(data["default"]),
data["variance"],
)
class TypeAliasExpr(Expression):
"""Type alias expression (rvalue)."""
__slots__ = ("type", "tvars", "no_args", "node")
__match_args__ = ("type", "tvars", "no_args", "node")
# The target type.
type: mypy.types.Type
# Names of type variables used to define the alias
tvars: list[str]
# Whether this alias was defined in bare form. Used to distinguish
# between
# A = List
# and
# A = List[Any]
no_args: bool
node: TypeAlias
def __init__(self, node: TypeAlias) -> None:
super().__init__()
self.type = node.target
self.tvars = [v.name for v in node.alias_tvars]
self.no_args = node.no_args
self.node = node
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_type_alias_expr(self)
class NamedTupleExpr(Expression):
"""Named tuple expression namedtuple(...) or NamedTuple(...)."""
__slots__ = ("info", "is_typed")
__match_args__ = ("info",)
# The class representation of this named tuple (its tuple_type attribute contains
# the tuple item types)
info: TypeInfo
is_typed: bool # whether this class was created with typing(_extensions).NamedTuple
def __init__(self, info: TypeInfo, is_typed: bool = False) -> None:
super().__init__()
self.info = info
self.is_typed = is_typed
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_namedtuple_expr(self)
class TypedDictExpr(Expression):
"""Typed dict expression TypedDict(...)."""
__slots__ = ("info",)
__match_args__ = ("info",)
# The class representation of this typed dict
info: TypeInfo
def __init__(self, info: TypeInfo) -> None:
super().__init__()
self.info = info
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_typeddict_expr(self)
class EnumCallExpr(Expression):
"""Named tuple expression Enum('name', 'val1 val2 ...')."""
__slots__ = ("info", "items", "values")
__match_args__ = ("info", "items", "values")
# The class representation of this enumerated type
info: TypeInfo
# The item names (for debugging)
items: list[str]
values: list[Expression | None]
def __init__(self, info: TypeInfo, items: list[str], values: list[Expression | None]) -> None:
super().__init__()
self.info = info
self.items = items
self.values = values
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_enum_call_expr(self)
class PromoteExpr(Expression):
"""Ducktype class decorator expression _promote(...)."""
__slots__ = ("type",)
type: mypy.types.ProperType
def __init__(self, type: mypy.types.ProperType) -> None:
super().__init__()
self.type = type
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit__promote_expr(self)
class NewTypeExpr(Expression):
"""NewType expression NewType(...)."""
__slots__ = ("name", "old_type", "info")
__match_args__ = ("name", "old_type", "info")
name: str
# The base type (the second argument to NewType)
old_type: mypy.types.Type | None
# The synthesized class representing the new type (inherits old_type)
info: TypeInfo | None
def __init__(
self, name: str, old_type: mypy.types.Type | None, line: int, column: int
) -> None:
super().__init__(line=line, column=column)
self.name = name
self.old_type = old_type
self.info = None
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_newtype_expr(self)
class AwaitExpr(Expression):
"""Await expression (await ...)."""
__slots__ = ("expr",)
__match_args__ = ("expr",)
expr: Expression
def __init__(self, expr: Expression) -> None:
super().__init__()
self.expr = expr
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_await_expr(self)
# Constants
class TempNode(Expression):
"""Temporary dummy node used during type checking.
This node is not present in the original program; it is just an artifact
of the type checker implementation. It only represents an opaque node with
some fixed type.
"""
__slots__ = ("type", "no_rhs")
type: mypy.types.Type
# Is this TempNode used to indicate absence of a right hand side in an annotated assignment?
# (e.g. for 'x: int' the rvalue is TempNode(AnyType(TypeOfAny.special_form), no_rhs=True))
no_rhs: bool
def __init__(
self, typ: mypy.types.Type, no_rhs: bool = False, *, context: Context | None = None
) -> None:
"""Construct a dummy node; optionally borrow line/column from context object."""
super().__init__()
self.type = typ
self.no_rhs = no_rhs
if context is not None:
self.line = context.line
self.column = context.column
def __repr__(self) -> str:
return "TempNode:%d(%s)" % (self.line, str(self.type))
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_temp_node(self)
# Special attributes not collected as protocol members by Python 3.12
# See typing._SPECIAL_NAMES
EXCLUDED_PROTOCOL_ATTRIBUTES: Final = frozenset(
{
"__abstractmethods__",
"__annotations__",
"__dict__",
"__doc__",
"__init__",
"__module__",
"__new__",
"__slots__",
"__subclasshook__",
"__weakref__",
"__class_getitem__", # Since Python 3.9
}
)
class TypeInfo(SymbolNode):
"""The type structure of a single class.
Each TypeInfo corresponds one-to-one to a ClassDef, which
represents the AST of the class.
In type-theory terms, this is a "type constructor", and if the
class is generic then it will be a type constructor of higher kind.
Where the class is used in an actual type, it's in the form of an
Instance, which amounts to a type application of the tycon to
the appropriate number of arguments.
"""
__slots__ = (
"_fullname",
"module_name",
"defn",
"mro",
"_mro_refs",
"bad_mro",
"is_final",
"declared_metaclass",
"metaclass_type",
"names",
"is_abstract",
"is_protocol",
"runtime_protocol",
"abstract_attributes",
"deletable_attributes",
"slots",
"assuming",
"assuming_proper",
"inferring",
"is_enum",
"fallback_to_any",
"meta_fallback_to_any",
"type_vars",
"has_param_spec_type",
"bases",
"_promote",
"tuple_type",
"special_alias",
"is_named_tuple",
"typeddict_type",
"is_newtype",
"is_intersection",
"metadata",
"alt_promote",
"has_type_var_tuple_type",
"type_var_tuple_prefix",
"type_var_tuple_suffix",
"self_type",
"dataclass_transform_spec",
)
_fullname: str # Fully qualified name
# Fully qualified name for the module this type was defined in. This
# information is also in the fullname, but is harder to extract in the
# case of nested class definitions.
module_name: str
defn: ClassDef # Corresponding ClassDef
# Method Resolution Order: the order of looking up attributes. The first
# value always to refers to this class.
mro: list[TypeInfo]
# Used to stash the names of the mro classes temporarily between
# deserialization and fixup. See deserialize() for why.
_mro_refs: list[str] | None
bad_mro: bool # Could not construct full MRO
is_final: bool
declared_metaclass: mypy.types.Instance | None
metaclass_type: mypy.types.Instance | None
names: SymbolTable # Names defined directly in this type
is_abstract: bool # Does the class have any abstract attributes?
is_protocol: bool # Is this a protocol class?
runtime_protocol: bool # Does this protocol support isinstance checks?
# List of names of abstract attributes together with their abstract status.
# The abstract status must be one of `NOT_ABSTRACT`, `IS_ABSTRACT`, `IMPLICITLY_ABSTRACT`.
abstract_attributes: list[tuple[str, int]]
deletable_attributes: list[str] # Used by mypyc only
# Does this type have concrete `__slots__` defined?
# If class does not have `__slots__` defined then it is `None`,
# if it has empty `__slots__` then it is an empty set.
slots: set[str] | None
# The attributes 'assuming' and 'assuming_proper' represent structural subtype matrices.
#
# In languages with structural subtyping, one can keep a global subtype matrix like this:
# . A B C .
# A 1 0 0
# B 1 1 1
# C 1 0 1
# .
# where 1 indicates that the type in corresponding row is a subtype of the type
# in corresponding column. This matrix typically starts filled with all 1's and
# a typechecker tries to "disprove" every subtyping relation using atomic (or nominal) types.
# However, we don't want to keep this huge global state. Instead, we keep the subtype
# information in the form of list of pairs (subtype, supertype) shared by all Instances
# with given supertype's TypeInfo. When we enter a subtype check we push a pair in this list
# thus assuming that we started with 1 in corresponding matrix element. Such algorithm allows
# to treat recursive and mutually recursive protocols and other kinds of complex situations.
#
# If concurrent/parallel type checking will be added in future,
# then there should be one matrix per thread/process to avoid false negatives
# during the type checking phase.
assuming: list[tuple[mypy.types.Instance, mypy.types.Instance]]
assuming_proper: list[tuple[mypy.types.Instance, mypy.types.Instance]]
# Ditto for temporary 'inferring' stack of recursive constraint inference.
# It contains Instances of protocol types that appeared as an argument to
# constraints.infer_constraints(). We need 'inferring' to avoid infinite recursion for
# recursive and mutually recursive protocols.
#
# We make 'assuming' and 'inferring' attributes here instead of passing they as kwargs,
# since this would require to pass them in many dozens of calls. In particular,
# there is a dependency infer_constraint -> is_subtype -> is_callable_subtype ->
# -> infer_constraints.
inferring: list[mypy.types.Instance]
# 'inferring' and 'assuming' can't be made sets, since we need to use
# is_same_type to correctly treat unions.
# Classes inheriting from Enum shadow their true members with a __getattr__, so we
# have to treat them as a special case.
is_enum: bool
# If true, any unknown attributes should have type 'Any' instead
# of generating a type error. This would be true if there is a
# base class with type 'Any', but other use cases may be
# possible. This is similar to having __getattr__ that returns Any
# (and __setattr__), but without the __getattr__ method.
fallback_to_any: bool
# Same as above but for cases where metaclass has type Any. This will suppress
# all attribute errors only for *class object* access.
meta_fallback_to_any: bool
# Information related to type annotations.
# Generic type variable names (full names)
type_vars: list[str]
# Whether this class has a ParamSpec type variable
has_param_spec_type: bool
# Direct base classes.
bases: list[mypy.types.Instance]
# Another type which this type will be treated as a subtype of,
# even though it's not a subclass in Python. The non-standard
# `@_promote` decorator introduces this, and there are also
# several builtin examples, in particular `int` -> `float`.
_promote: list[mypy.types.ProperType]
# This is used for promoting native integer types such as 'i64' to
# 'int'. (_promote is used for the other direction.) This only
# supports one-step promotions (e.g., i64 -> int, not
# i64 -> int -> float, and this isn't used to promote in joins.
#
# This results in some unintuitive results, such as that even
# though i64 is compatible with int and int is compatible with
# float, i64 is *not* compatible with float.
alt_promote: mypy.types.Instance | None
# Representation of a Tuple[...] base class, if the class has any
# (e.g., for named tuples). If this is not None, the actual Type
# object used for this class is not an Instance but a TupleType;
# the corresponding Instance is set as the fallback type of the
# tuple type.
tuple_type: mypy.types.TupleType | None
# Is this a named tuple type?
is_named_tuple: bool
# If this class is defined by the TypedDict type constructor,
# then this is not None.
typeddict_type: mypy.types.TypedDictType | None
# Is this a newtype type?
is_newtype: bool
# Is this a synthesized intersection type?
is_intersection: bool
# This is a dictionary that will be serialized and un-serialized as is.
# It is useful for plugins to add their data to save in the cache.
metadata: dict[str, JsonDict]
# Store type alias representing this type (for named tuples and TypedDicts).
# Although definitions of these types are stored in symbol tables as TypeInfo,
# when a type analyzer will find them, it should construct a TupleType, or
# a TypedDict type. However, we can't use the plain types, since if the definition
# is recursive, this will create an actual recursive structure of types (i.e. as
# internal Python objects) causing infinite recursions everywhere during type checking.
# To overcome this, we create a TypeAlias node, that will point to these types.
# We store this node in the `special_alias` attribute, because it must be the same node
# in case we are doing multiple semantic analysis passes.
special_alias: TypeAlias | None
# Shared type variable for typing.Self in this class (if used, otherwise None).
self_type: mypy.types.TypeVarType | None
# Added if the corresponding class is directly decorated with `typing.dataclass_transform`
dataclass_transform_spec: DataclassTransformSpec | None
FLAGS: Final = [
"is_abstract",
"is_enum",
"fallback_to_any",
"meta_fallback_to_any",
"is_named_tuple",
"is_newtype",
"is_protocol",
"runtime_protocol",
"is_final",
"is_intersection",
]
def __init__(self, names: SymbolTable, defn: ClassDef, module_name: str) -> None:
"""Initialize a TypeInfo."""
super().__init__()
self._fullname = defn.fullname
self.names = names
self.defn = defn
self.module_name = module_name
self.type_vars = []
self.has_param_spec_type = False
self.has_type_var_tuple_type = False
self.bases = []
self.mro = []
self._mro_refs = None
self.bad_mro = False
self.declared_metaclass = None
self.metaclass_type = None
self.is_abstract = False
self.abstract_attributes = []
self.deletable_attributes = []
self.slots = None
self.assuming = []
self.assuming_proper = []
self.inferring = []
self.is_protocol = False
self.runtime_protocol = False
self.type_var_tuple_prefix: int | None = None
self.type_var_tuple_suffix: int | None = None
self.add_type_vars()
self.is_final = False
self.is_enum = False
self.fallback_to_any = False
self.meta_fallback_to_any = False
self._promote = []
self.alt_promote = None
self.tuple_type = None
self.special_alias = None
self.is_named_tuple = False
self.typeddict_type = None
self.is_newtype = False
self.is_intersection = False
self.metadata = {}
self.self_type = None
self.dataclass_transform_spec = None
def add_type_vars(self) -> None:
self.has_type_var_tuple_type = False
if self.defn.type_vars:
for i, vd in enumerate(self.defn.type_vars):
if isinstance(vd, mypy.types.ParamSpecType):
self.has_param_spec_type = True
if isinstance(vd, mypy.types.TypeVarTupleType):
assert not self.has_type_var_tuple_type
self.has_type_var_tuple_type = True
self.type_var_tuple_prefix = i
self.type_var_tuple_suffix = len(self.defn.type_vars) - i - 1
self.type_vars.append(vd.name)
assert not (
self.has_param_spec_type and self.has_type_var_tuple_type
), "Mixing type var tuples and param specs not supported yet"
@property
def name(self) -> str:
"""Short name."""
return self.defn.name
@property
def fullname(self) -> str:
return self._fullname
def is_generic(self) -> bool:
"""Is the type generic (i.e. does it have type variables)?"""
return len(self.type_vars) > 0
def get(self, name: str) -> SymbolTableNode | None:
for cls in self.mro:
n = cls.names.get(name)
if n:
return n
return None
def get_containing_type_info(self, name: str) -> TypeInfo | None:
for cls in self.mro:
if name in cls.names:
return cls
return None
@property
def protocol_members(self) -> list[str]:
# Protocol members are names of all attributes/methods defined in a protocol
# and in all its supertypes (except for 'object').
members: set[str] = set()
assert self.mro, "This property can be only accessed after MRO is (re-)calculated"
for base in self.mro[:-1]: # we skip "object" since everyone implements it
if base.is_protocol:
for name, node in base.names.items():
if isinstance(node.node, (TypeAlias, TypeVarExpr, MypyFile)):
# These are auxiliary definitions (and type aliases are prohibited).
continue
if name in EXCLUDED_PROTOCOL_ATTRIBUTES:
continue
members.add(name)
return sorted(list(members))
def __getitem__(self, name: str) -> SymbolTableNode:
n = self.get(name)
if n:
return n
else:
raise KeyError(name)
def __repr__(self) -> str:
return f"<TypeInfo {self.fullname}>"
def __bool__(self) -> bool:
# We defined this here instead of just overriding it in
# FakeInfo so that mypyc can generate a direct call instead of
# using the generic bool handling.
return not isinstance(self, FakeInfo)
def has_readable_member(self, name: str) -> bool:
return self.get(name) is not None
def get_method(self, name: str) -> FuncBase | Decorator | None:
for cls in self.mro:
if name in cls.names:
node = cls.names[name].node
if isinstance(node, FuncBase):
return node
elif isinstance(node, Decorator): # Two `if`s make `mypyc` happy
return node
else:
return None
return None
def calculate_metaclass_type(self) -> mypy.types.Instance | None:
declared = self.declared_metaclass
if declared is not None and not declared.type.has_base("builtins.type"):
return declared
if self._fullname == "builtins.type":
return mypy.types.Instance(self, [])
candidates = [
s.declared_metaclass
for s in self.mro
if s.declared_metaclass is not None and s.declared_metaclass.type is not None
]
for c in candidates:
if all(other.type in c.type.mro for other in candidates):
return c
return None
def is_metaclass(self) -> bool:
return (
self.has_base("builtins.type")
or self.fullname == "abc.ABCMeta"
or self.fallback_to_any
)
def has_base(self, fullname: str) -> bool:
"""Return True if type has a base type with the specified name.
This can be either via extension or via implementation.
"""
for cls in self.mro:
if cls.fullname == fullname:
return True
return False
def direct_base_classes(self) -> list[TypeInfo]:
"""Return a direct base classes.
Omit base classes of other base classes.
"""
return [base.type for base in self.bases]
def update_tuple_type(self, typ: mypy.types.TupleType) -> None:
"""Update tuple_type and special_alias as needed."""
self.tuple_type = typ
alias = TypeAlias.from_tuple_type(self)
if not self.special_alias:
self.special_alias = alias
else:
self.special_alias.target = alias.target
def update_typeddict_type(self, typ: mypy.types.TypedDictType) -> None:
"""Update typeddict_type and special_alias as needed."""
self.typeddict_type = typ
alias = TypeAlias.from_typeddict_type(self)
if not self.special_alias:
self.special_alias = alias
else:
self.special_alias.target = alias.target
def __str__(self) -> str:
"""Return a string representation of the type.
This includes the most important information about the type.
"""
options = Options()
return self.dump(
str_conv=mypy.strconv.StrConv(options=options),
type_str_conv=mypy.types.TypeStrVisitor(options=options),
)
def dump(
self, str_conv: mypy.strconv.StrConv, type_str_conv: mypy.types.TypeStrVisitor
) -> str:
"""Return a string dump of the contents of the TypeInfo."""
base: str = ""
def type_str(typ: mypy.types.Type) -> str:
return typ.accept(type_str_conv)
head = "TypeInfo" + str_conv.format_id(self)
if self.bases:
base = f"Bases({', '.join(type_str(base) for base in self.bases)})"
mro = "Mro({})".format(
", ".join(item.fullname + str_conv.format_id(item) for item in self.mro)
)
names = []
for name in sorted(self.names):
description = name + str_conv.format_id(self.names[name].node)
node = self.names[name].node
if isinstance(node, Var) and node.type:
description += f" ({type_str(node.type)})"
names.append(description)
items = [f"Name({self.fullname})", base, mro, ("Names", names)]
if self.declared_metaclass:
items.append(f"DeclaredMetaclass({type_str(self.declared_metaclass)})")
if self.metaclass_type:
items.append(f"MetaclassType({type_str(self.metaclass_type)})")
return mypy.strconv.dump_tagged(items, head, str_conv=str_conv)
def serialize(self) -> JsonDict:
# NOTE: This is where all ClassDefs originate, so there shouldn't be duplicates.
data = {
".class": "TypeInfo",
"module_name": self.module_name,
"fullname": self.fullname,
"names": self.names.serialize(self.fullname),
"defn": self.defn.serialize(),
"abstract_attributes": self.abstract_attributes,
"type_vars": self.type_vars,
"has_param_spec_type": self.has_param_spec_type,
"bases": [b.serialize() for b in self.bases],
"mro": [c.fullname for c in self.mro],
"_promote": [p.serialize() for p in self._promote],
"alt_promote": None if self.alt_promote is None else self.alt_promote.serialize(),
"declared_metaclass": (
None if self.declared_metaclass is None else self.declared_metaclass.serialize()
),
"metaclass_type": None
if self.metaclass_type is None
else self.metaclass_type.serialize(),
"tuple_type": None if self.tuple_type is None else self.tuple_type.serialize(),
"typeddict_type": None
if self.typeddict_type is None
else self.typeddict_type.serialize(),
"flags": get_flags(self, TypeInfo.FLAGS),
"metadata": self.metadata,
"slots": list(sorted(self.slots)) if self.slots is not None else None,
"deletable_attributes": self.deletable_attributes,
"self_type": self.self_type.serialize() if self.self_type is not None else None,
"dataclass_transform_spec": (
self.dataclass_transform_spec.serialize()
if self.dataclass_transform_spec is not None
else None
),
}
return data
@classmethod
def deserialize(cls, data: JsonDict) -> TypeInfo:
names = SymbolTable.deserialize(data["names"])
defn = ClassDef.deserialize(data["defn"])
module_name = data["module_name"]
ti = TypeInfo(names, defn, module_name)
ti._fullname = data["fullname"]
# TODO: Is there a reason to reconstruct ti.subtypes?
ti.abstract_attributes = [(attr[0], attr[1]) for attr in data["abstract_attributes"]]
ti.type_vars = data["type_vars"]
ti.has_param_spec_type = data["has_param_spec_type"]
ti.bases = [mypy.types.Instance.deserialize(b) for b in data["bases"]]
_promote = []
for p in data["_promote"]:
t = mypy.types.deserialize_type(p)
assert isinstance(t, mypy.types.ProperType)
_promote.append(t)
ti._promote = _promote
ti.alt_promote = (
None
if data["alt_promote"] is None
else mypy.types.Instance.deserialize(data["alt_promote"])
)
ti.declared_metaclass = (
None
if data["declared_metaclass"] is None
else mypy.types.Instance.deserialize(data["declared_metaclass"])
)
ti.metaclass_type = (
None
if data["metaclass_type"] is None
else mypy.types.Instance.deserialize(data["metaclass_type"])
)
# NOTE: ti.mro will be set in the fixup phase based on these
# names. The reason we need to store the mro instead of just
# recomputing it from base classes has to do with a subtle
# point about fine-grained incremental: the cache files might
# not be loaded until after a class in the mro has changed its
# bases, which causes the mro to change. If we recomputed our
# mro, we would compute the *new* mro, which leaves us with no
# way to detect that the mro has changed! Thus we need to make
# sure to load the original mro so that once the class is
# rechecked, it can tell that the mro has changed.
ti._mro_refs = data["mro"]
ti.tuple_type = (
None
if data["tuple_type"] is None
else mypy.types.TupleType.deserialize(data["tuple_type"])
)
ti.typeddict_type = (
None
if data["typeddict_type"] is None
else mypy.types.TypedDictType.deserialize(data["typeddict_type"])
)
ti.metadata = data["metadata"]
ti.slots = set(data["slots"]) if data["slots"] is not None else None
ti.deletable_attributes = data["deletable_attributes"]
set_flags(ti, data["flags"])
st = data["self_type"]
ti.self_type = mypy.types.TypeVarType.deserialize(st) if st is not None else None
if data.get("dataclass_transform_spec") is not None:
ti.dataclass_transform_spec = DataclassTransformSpec.deserialize(
data["dataclass_transform_spec"]
)
return ti
class FakeInfo(TypeInfo):
__slots__ = ("msg",)
# types.py defines a single instance of this class, called types.NOT_READY.
# This instance is used as a temporary placeholder in the process of de-serialization
# of 'Instance' types. The de-serialization happens in two steps: In the first step,
# Instance.type is set to NOT_READY. In the second step (in fixup.py) it is replaced by
# an actual TypeInfo. If you see the assertion error below, then most probably something
# went wrong during the second step and an 'Instance' that raised this error was not fixed.
# Note:
# 'None' is not used as a dummy value for two reasons:
# 1. This will require around 80-100 asserts to make 'mypy --strict-optional mypy'
# pass cleanly.
# 2. If NOT_READY value is accidentally used somewhere, it will be obvious where the value
# is from, whereas a 'None' value could come from anywhere.
#
# Additionally, this serves as a more general-purpose placeholder
# for missing TypeInfos in a number of places where the excuses
# for not being Optional are a little weaker.
#
# TypeInfo defines a __bool__ method that returns False for FakeInfo
# so that it can be conveniently tested against in the same way that it
# would be if things were properly optional.
def __init__(self, msg: str) -> None:
self.msg = msg
def __getattribute__(self, attr: str) -> type:
# Handle __class__ so that isinstance still works...
if attr == "__class__":
return object.__getattribute__(self, attr) # type: ignore[no-any-return]
raise AssertionError(object.__getattribute__(self, "msg"))
VAR_NO_INFO: Final[TypeInfo] = FakeInfo("Var is lacking info")
CLASSDEF_NO_INFO: Final[TypeInfo] = FakeInfo("ClassDef is lacking info")
FUNC_NO_INFO: Final[TypeInfo] = FakeInfo("FuncBase for non-methods lack info")
class TypeAlias(SymbolNode):
"""
A symbol node representing a type alias.
Type alias is a static concept, in contrast to variables with types
like Type[...]. Namely:
* type aliases
- can be used in type context (annotations)
- cannot be re-assigned
* variables with type Type[...]
- cannot be used in type context
- but can be re-assigned
An alias can be defined only by an assignment to a name (not any other lvalues).
Such assignment defines an alias by default. To define a variable,
an explicit Type[...] annotation is required. As an exception,
at non-global scope non-subscripted rvalue creates a variable even without
an annotation. This exception exists to accommodate the common use case of
class-valued attributes. See SemanticAnalyzerPass2.check_and_set_up_type_alias
for details.
Aliases can be generic. We use bound type variables for generic aliases, similar
to classes. Essentially, type aliases work as macros that expand textually.
The definition and expansion rules are following:
1. An alias targeting a generic class without explicit variables act as
the given class (this doesn't apply to TypedDict, Tuple and Callable, which
are not proper classes but special type constructors):
A = List
AA = List[Any]
x: A # same as List[Any]
x: A[int] # same as List[int]
x: AA # same as List[Any]
x: AA[int] # Error!
C = Callable # Same as Callable[..., Any]
T = Tuple # Same as Tuple[Any, ...]
2. An alias using explicit type variables in its rvalue expects
replacements (type arguments) for these variables. If missing, they
are treated as Any, like for other generics:
B = List[Tuple[T, T]]
x: B # same as List[Tuple[Any, Any]]
x: B[int] # same as List[Tuple[int, int]]
def f(x: B[T]) -> T: ... # without T, Any would be used here
3. An alias can be defined using another aliases. In the definition
rvalue the Any substitution doesn't happen for top level unsubscripted
generic classes:
A = List
B = A # here A is expanded to List, _not_ List[Any],
# to match the Python runtime behaviour
x: B[int] # same as List[int]
C = List[A] # this expands to List[List[Any]]
AA = List[T]
D = AA # here AA expands to List[Any]
x: D[int] # Error!
Note: the fact that we support aliases like `A = List` means that the target
type will be initially an instance type with wrong number of type arguments.
Such instances are all fixed either during or after main semantic analysis passes.
We therefore store the difference between `List` and `List[Any]` rvalues (targets)
using the `no_args` flag. See also TypeAliasExpr.no_args.
Meaning of other fields:
target: The target type. For generic aliases contains bound type variables
as nested types (currently TypeVar and ParamSpec are supported).
_fullname: Qualified name of this type alias. This is used in particular
to track fine grained dependencies from aliases.
alias_tvars: Type variables used to define this alias.
normalized: Used to distinguish between `A = List`, and `A = list`. Both
are internally stored using `builtins.list` (because `typing.List` is
itself an alias), while the second cannot be subscripted because of
Python runtime limitation.
line and column: Line and column on the original alias definition.
eager: If True, immediately expand alias when referred to (useful for aliases
within functions that can't be looked up from the symbol table)
"""
__slots__ = (
"target",
"_fullname",
"alias_tvars",
"no_args",
"normalized",
"_is_recursive",
"eager",
"tvar_tuple_index",
)
__match_args__ = ("name", "target", "alias_tvars", "no_args")
def __init__(
self,
target: mypy.types.Type,
fullname: str,
line: int,
column: int,
*,
alias_tvars: list[mypy.types.TypeVarLikeType] | None = None,
no_args: bool = False,
normalized: bool = False,
eager: bool = False,
) -> None:
self._fullname = fullname
self.target = target
if alias_tvars is None:
alias_tvars = []
self.alias_tvars = alias_tvars
self.no_args = no_args
self.normalized = normalized
# This attribute is manipulated by TypeAliasType. If non-None,
# it is the cached value.
self._is_recursive: bool | None = None
self.eager = eager
self.tvar_tuple_index = None
for i, t in enumerate(alias_tvars):
if isinstance(t, mypy.types.TypeVarTupleType):
self.tvar_tuple_index = i
super().__init__(line, column)
@classmethod
def from_tuple_type(cls, info: TypeInfo) -> TypeAlias:
"""Generate an alias to the tuple type described by a given TypeInfo.
NOTE: this doesn't set type alias type variables (for generic tuple types),
they must be set by the caller (when fully analyzed).
"""
assert info.tuple_type
# TODO: is it possible to refactor this to set the correct type vars here?
return TypeAlias(
info.tuple_type.copy_modified(fallback=mypy.types.Instance(info, info.defn.type_vars)),
info.fullname,
info.line,
info.column,
)
@classmethod
def from_typeddict_type(cls, info: TypeInfo) -> TypeAlias:
"""Generate an alias to the TypedDict type described by a given TypeInfo.
NOTE: this doesn't set type alias type variables (for generic TypedDicts),
they must be set by the caller (when fully analyzed).
"""
assert info.typeddict_type
# TODO: is it possible to refactor this to set the correct type vars here?
return TypeAlias(
info.typeddict_type.copy_modified(
fallback=mypy.types.Instance(info, info.defn.type_vars)
),
info.fullname,
info.line,
info.column,
)
@property
def name(self) -> str:
return self._fullname.split(".")[-1]
@property
def fullname(self) -> str:
return self._fullname
@property
def has_param_spec_type(self) -> bool:
return any(isinstance(v, mypy.types.ParamSpecType) for v in self.alias_tvars)
def serialize(self) -> JsonDict:
data: JsonDict = {
".class": "TypeAlias",
"fullname": self._fullname,
"target": self.target.serialize(),
"alias_tvars": [v.serialize() for v in self.alias_tvars],
"no_args": self.no_args,
"normalized": self.normalized,
"line": self.line,
"column": self.column,
}
return data
def accept(self, visitor: NodeVisitor[T]) -> T:
return visitor.visit_type_alias(self)
@classmethod
def deserialize(cls, data: JsonDict) -> TypeAlias:
assert data[".class"] == "TypeAlias"
fullname = data["fullname"]
alias_tvars = [mypy.types.deserialize_type(v) for v in data["alias_tvars"]]
assert all(isinstance(t, mypy.types.TypeVarLikeType) for t in alias_tvars)
target = mypy.types.deserialize_type(data["target"])
no_args = data["no_args"]
normalized = data["normalized"]
line = data["line"]
column = data["column"]
return cls(
target,
fullname,
line,
column,
alias_tvars=cast(List[mypy.types.TypeVarLikeType], alias_tvars),
no_args=no_args,
normalized=normalized,
)
class PlaceholderNode(SymbolNode):
"""Temporary symbol node that will later become a real SymbolNode.
These are only present during semantic analysis when using the new
semantic analyzer. These are created if some essential dependencies
of a definition are not yet complete.
A typical use is for names imported from a module which is still
incomplete (within an import cycle):
from m import f # Initially may create PlaceholderNode
This is particularly important if the imported shadows a name from
an enclosing scope or builtins:
from m import int # Placeholder avoids mixups with builtins.int
Another case where this is useful is when there is another definition
or assignment:
from m import f
def f() -> None: ...
In the above example, the presence of PlaceholderNode allows us to
handle the second definition as a redefinition.
They are also used to create PlaceholderType instances for types
that refer to incomplete types. Example:
class C(Sequence[C]): ...
We create a PlaceholderNode (with becomes_typeinfo=True) for C so
that the type C in Sequence[C] can be bound.
Attributes:
fullname: Full name of the PlaceholderNode.
node: AST node that contains the definition that caused this to
be created. This is useful for tracking order of incomplete definitions
and for debugging.
becomes_typeinfo: If True, this refers something that could later
become a TypeInfo. It can't be used with type variables, in
particular, as this would cause issues with class type variable
detection.
The long-term purpose of placeholder nodes/types is to evolve into
something that can support general recursive types.
"""
__slots__ = ("_fullname", "node", "becomes_typeinfo")
def __init__(
self, fullname: str, node: Node, line: int, *, becomes_typeinfo: bool = False
) -> None:
self._fullname = fullname
self.node = node
self.becomes_typeinfo = becomes_typeinfo
self.line = line
@property
def name(self) -> str:
return self._fullname.split(".")[-1]
@property
def fullname(self) -> str:
return self._fullname
def serialize(self) -> JsonDict:
assert False, "PlaceholderNode can't be serialized"
def accept(self, visitor: NodeVisitor[T]) -> T:
return visitor.visit_placeholder_node(self)
class SymbolTableNode:
"""Description of a name binding in a symbol table.
These are only used as values in module (global), function (local)
and class symbol tables (see SymbolTable). The name that is bound is
the key in SymbolTable.
Symbol tables don't contain direct references to AST nodes primarily
because there can be multiple symbol table references to a single
AST node (due to imports and aliases), and different references can
behave differently. This class describes the unique properties of
each reference.
The most fundamental attribute is 'node', which is the AST node that
the name refers to.
The kind is usually one of LDEF, GDEF or MDEF, depending on the scope
of the definition. These three kinds can usually be used
interchangeably and the difference between local, global and class
scopes is mostly descriptive, with no semantic significance.
However, some tools that consume mypy ASTs may care about these so
they should be correct.
Attributes:
node: AST node of definition. Among others, this can be one of
FuncDef, Var, TypeInfo, TypeVarExpr or MypyFile -- or None
for cross_ref that hasn't been fixed up yet.
kind: Kind of node. Possible values:
- LDEF: local definition
- GDEF: global (module-level) definition
- MDEF: class member definition
- UNBOUND_IMPORTED: temporary kind for imported names (we
don't know the final kind yet)
module_public: If False, this name won't be imported via
'from <module> import *'. This has no effect on names within
classes.
module_hidden: If True, the name will be never exported (needed for
stub files)
cross_ref: For deserialized MypyFile nodes, the referenced module
name; for other nodes, optionally the name of the referenced object.
implicit: Was this defined by assignment to self attribute?
plugin_generated: Was this symbol generated by a plugin?
(And therefore needs to be removed in aststrip.)
no_serialize: Do not serialize this node if True. This is used to prevent
keys in the cache that refer to modules on which this file does not
depend. Currently this can happen if there is a module not in build
used e.g. like this:
import a.b.c # type: ignore
This will add a submodule symbol to parent module `a` symbol table,
but `a.b` is _not_ added as its dependency. Therefore, we should
not serialize these symbols as they may not be found during fixup
phase, instead they will be re-added during subsequent patch parents
phase.
TODO: Refactor build.py to make dependency tracking more transparent
and/or refactor look-up functions to not require parent patching.
NOTE: No other attributes should be added to this class unless they
are shared by all node kinds.
"""
__slots__ = (
"kind",
"node",
"module_public",
"module_hidden",
"cross_ref",
"implicit",
"plugin_generated",
"no_serialize",
)
def __init__(
self,
kind: int,
node: SymbolNode | None,
module_public: bool = True,
implicit: bool = False,
module_hidden: bool = False,
*,
plugin_generated: bool = False,
no_serialize: bool = False,
) -> None:
self.kind = kind
self.node = node
self.module_public = module_public
self.implicit = implicit
self.module_hidden = module_hidden
self.cross_ref: str | None = None
self.plugin_generated = plugin_generated
self.no_serialize = no_serialize
@property
def fullname(self) -> str | None:
if self.node is not None:
return self.node.fullname
else:
return None
@property
def type(self) -> mypy.types.Type | None:
node = self.node
if isinstance(node, (Var, SYMBOL_FUNCBASE_TYPES)) and node.type is not None:
return node.type
elif isinstance(node, Decorator):
return node.var.type
else:
return None
def copy(self) -> SymbolTableNode:
new = SymbolTableNode(
self.kind, self.node, self.module_public, self.implicit, self.module_hidden
)
new.cross_ref = self.cross_ref
return new
def __str__(self) -> str:
s = f"{node_kinds[self.kind]}/{short_type(self.node)}"
if isinstance(self.node, SymbolNode):
s += f" ({self.node.fullname})"
# Include declared type of variables and functions.
if self.type is not None:
s += f" : {self.type}"
return s
def serialize(self, prefix: str, name: str) -> JsonDict:
"""Serialize a SymbolTableNode.
Args:
prefix: full name of the containing module or class; or None
name: name of this object relative to the containing object
"""
data: JsonDict = {".class": "SymbolTableNode", "kind": node_kinds[self.kind]}
if self.module_hidden:
data["module_hidden"] = True
if not self.module_public:
data["module_public"] = False
if self.implicit:
data["implicit"] = True
if self.plugin_generated:
data["plugin_generated"] = True
if isinstance(self.node, MypyFile):
data["cross_ref"] = self.node.fullname
else:
assert self.node is not None, f"{prefix}:{name}"
if prefix is not None:
fullname = self.node.fullname
if (
"." in fullname
and fullname != prefix + "." + name
and not (isinstance(self.node, Var) and self.node.from_module_getattr)
):
assert not isinstance(
self.node, PlaceholderNode
), f"Definition of {fullname} is unexpectedly incomplete"
data["cross_ref"] = fullname
return data
data["node"] = self.node.serialize()
return data
@classmethod
def deserialize(cls, data: JsonDict) -> SymbolTableNode:
assert data[".class"] == "SymbolTableNode"
kind = inverse_node_kinds[data["kind"]]
if "cross_ref" in data:
# This will be fixed up later.
stnode = SymbolTableNode(kind, None)
stnode.cross_ref = data["cross_ref"]
else:
assert "node" in data, data
node = SymbolNode.deserialize(data["node"])
stnode = SymbolTableNode(kind, node)
if "module_hidden" in data:
stnode.module_hidden = data["module_hidden"]
if "module_public" in data:
stnode.module_public = data["module_public"]
if "implicit" in data:
stnode.implicit = data["implicit"]
if "plugin_generated" in data:
stnode.plugin_generated = data["plugin_generated"]
return stnode
class SymbolTable(Dict[str, SymbolTableNode]):
"""Static representation of a namespace dictionary.
This is used for module, class and function namespaces.
"""
__slots__ = ()
def __str__(self) -> str:
a: list[str] = []
for key, value in self.items():
# Filter out the implicit import of builtins.
if isinstance(value, SymbolTableNode):
if (
value.fullname != "builtins"
and (value.fullname or "").split(".")[-1] not in implicit_module_attrs
):
a.append(" " + str(key) + " : " + str(value))
else:
a.append(" <invalid item>")
a = sorted(a)
a.insert(0, "SymbolTable(")
a[-1] += ")"
return "\n".join(a)
def copy(self) -> SymbolTable:
return SymbolTable([(key, node.copy()) for key, node in self.items()])
def serialize(self, fullname: str) -> JsonDict:
data: JsonDict = {".class": "SymbolTable"}
for key, value in self.items():
# Skip __builtins__: it's a reference to the builtins
# module that gets added to every module by
# SemanticAnalyzerPass2.visit_file(), but it shouldn't be
# accessed by users of the module.
if key == "__builtins__" or value.no_serialize:
continue
data[key] = value.serialize(fullname, key)
return data
@classmethod
def deserialize(cls, data: JsonDict) -> SymbolTable:
assert data[".class"] == "SymbolTable"
st = SymbolTable()
for key, value in data.items():
if key != ".class":
st[key] = SymbolTableNode.deserialize(value)
return st
class DataclassTransformSpec:
"""Specifies how a dataclass-like transform should be applied. The fields here are based on the
parameters accepted by `typing.dataclass_transform`."""
__slots__ = (
"eq_default",
"order_default",
"kw_only_default",
"frozen_default",
"field_specifiers",
)
def __init__(
self,
*,
eq_default: bool | None = None,
order_default: bool | None = None,
kw_only_default: bool | None = None,
field_specifiers: tuple[str, ...] | None = None,
# Specified outside of PEP 681:
# frozen_default was added to CPythonin https://github.com/python/cpython/pull/99958 citing
# positive discussion in typing-sig
frozen_default: bool | None = None,
):
self.eq_default = eq_default if eq_default is not None else True
self.order_default = order_default if order_default is not None else False
self.kw_only_default = kw_only_default if kw_only_default is not None else False
self.frozen_default = frozen_default if frozen_default is not None else False
self.field_specifiers = field_specifiers if field_specifiers is not None else ()
def serialize(self) -> JsonDict:
return {
"eq_default": self.eq_default,
"order_default": self.order_default,
"kw_only_default": self.kw_only_default,
"frozen_default": self.frozen_default,
"field_specifiers": list(self.field_specifiers),
}
@classmethod
def deserialize(cls, data: JsonDict) -> DataclassTransformSpec:
return DataclassTransformSpec(
eq_default=data.get("eq_default"),
order_default=data.get("order_default"),
kw_only_default=data.get("kw_only_default"),
frozen_default=data.get("frozen_default"),
field_specifiers=tuple(data.get("field_specifiers", [])),
)
def get_flags(node: Node, names: list[str]) -> list[str]:
return [name for name in names if getattr(node, name)]
def set_flags(node: Node, flags: list[str]) -> None:
for name in flags:
setattr(node, name, True)
def get_member_expr_fullname(expr: MemberExpr) -> str | None:
"""Return the qualified name representation of a member expression.
Return a string of form foo.bar, foo.bar.baz, or similar, or None if the
argument cannot be represented in this form.
"""
initial: str | None = None
if isinstance(expr.expr, NameExpr):
initial = expr.expr.name
elif isinstance(expr.expr, MemberExpr):
initial = get_member_expr_fullname(expr.expr)
else:
return None
return f"{initial}.{expr.name}"
deserialize_map: Final = {
key: obj.deserialize
for key, obj in globals().items()
if type(obj) is not FakeInfo
and isinstance(obj, type)
and issubclass(obj, SymbolNode)
and obj is not SymbolNode
}
def check_arg_kinds(
arg_kinds: list[ArgKind], nodes: list[T], fail: Callable[[str, T], None]
) -> None:
is_var_arg = False
is_kw_arg = False
seen_named = False
seen_opt = False
for kind, node in zip(arg_kinds, nodes):
if kind == ARG_POS:
if is_var_arg or is_kw_arg or seen_named or seen_opt:
fail(
"Required positional args may not appear after default, named or var args",
node,
)
break
elif kind == ARG_OPT:
if is_var_arg or is_kw_arg or seen_named:
fail("Positional default args may not appear after named or var args", node)
break
seen_opt = True
elif kind == ARG_STAR:
if is_var_arg or is_kw_arg or seen_named:
fail("Var args may not appear after named or var args", node)
break
is_var_arg = True
elif kind == ARG_NAMED or kind == ARG_NAMED_OPT:
seen_named = True
if is_kw_arg:
fail("A **kwargs argument must be the last argument", node)
break
elif kind == ARG_STAR2:
if is_kw_arg:
fail("You may only have one **kwargs argument", node)
break
is_kw_arg = True
def check_arg_names(
names: Sequence[str | None],
nodes: list[T],
fail: Callable[[str, T], None],
description: str = "function definition",
) -> None:
seen_names: set[str | None] = set()
for name, node in zip(names, nodes):
if name is not None and name in seen_names:
fail(f'Duplicate argument "{name}" in {description}', node)
break
seen_names.add(name)
def is_class_var(expr: NameExpr) -> bool:
"""Return whether the expression is ClassVar[...]"""
if isinstance(expr.node, Var):
return expr.node.is_classvar
return False
def is_final_node(node: SymbolNode | None) -> bool:
"""Check whether `node` corresponds to a final attribute."""
return isinstance(node, (Var, FuncDef, OverloadedFuncDef, Decorator)) and node.is_final
def local_definitions(
names: SymbolTable, name_prefix: str, info: TypeInfo | None = None
) -> Iterator[Definition]:
"""Iterate over local definitions (not imported) in a symbol table.
Recursively iterate over class members and nested classes.
"""
# TODO: What should the name be? Or maybe remove it?
for name, symnode in names.items():
shortname = name
if "-redef" in name:
# Restore original name from mangled name of multiply defined function
shortname = name.split("-redef")[0]
fullname = name_prefix + "." + shortname
node = symnode.node
if node and node.fullname == fullname:
yield fullname, symnode, info
if isinstance(node, TypeInfo):
yield from local_definitions(node.names, fullname, node)