Edit: brain_dataclasses.py
# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html # For details: https://github.com/PyCQA/astroid/blob/main/LICENSE # Copyright (c) https://github.com/PyCQA/astroid/blob/main/CONTRIBUTORS.txt """ Astroid hook for the dataclasses library. Support built-in dataclasses, pydantic.dataclasses, and marshmallow_dataclass-annotated dataclasses. References: - https://docs.python.org/3/library/dataclasses.html - https://pydantic-docs.helpmanual.io/usage/dataclasses/ - https://lovasoa.github.io/marshmallow_dataclass/ """ from __future__ import annotations import sys from collections.abc import Iterator from typing import Tuple, Union from astroid import bases, context, helpers, nodes from astroid.builder import parse from astroid.const import PY39_PLUS, PY310_PLUS from astroid.exceptions import AstroidSyntaxError, InferenceError, UseInferenceDefault from astroid.inference_tip import inference_tip from astroid.manager import AstroidManager from astroid.typing import InferenceResult from astroid.util import Uninferable, UninferableBase if sys.version_info >= (3, 8): from typing import Literal else: from typing_extensions import Literal _FieldDefaultReturn = Union[ None, Tuple[Literal["default"], nodes.NodeNG], Tuple[Literal["default_factory"], nodes.Call], ] DATACLASSES_DECORATORS = frozenset(("dataclass",)) FIELD_NAME = "field" DATACLASS_MODULES = frozenset( ("dataclasses", "marshmallow_dataclass", "pydantic.dataclasses") ) DEFAULT_FACTORY = "_HAS_DEFAULT_FACTORY" # based on typing.py def is_decorated_with_dataclass( node: nodes.ClassDef, decorator_names: frozenset[str] = DATACLASSES_DECORATORS ) -> bool: """Return True if a decorated node has a `dataclass` decorator applied.""" if not isinstance(node, nodes.ClassDef) or not node.decorators: return False return any( _looks_like_dataclass_decorator(decorator_attribute, decorator_names) for decorator_attribute in node.decorators.nodes ) def dataclass_transform(node: nodes.ClassDef) -> None: """Rewrite a dataclass to be easily understood by pylint.""" node.is_dataclass = True for assign_node in _get_dataclass_attributes(node): name = assign_node.target.name rhs_node = nodes.Unknown( lineno=assign_node.lineno, col_offset=assign_node.col_offset, parent=assign_node, ) rhs_node = AstroidManager().visit_transforms(rhs_node) node.instance_attrs[name] = [rhs_node] if not _check_generate_dataclass_init(node): return kw_only_decorated = False if PY310_PLUS and node.decorators.nodes: for decorator in node.decorators.nodes: if not isinstance(decorator, nodes.Call): kw_only_decorated = False break for keyword in decorator.keywords: if keyword.arg == "kw_only": kw_only_decorated = keyword.value.bool_value() init_str = _generate_dataclass_init( node, list(_get_dataclass_attributes(node, init=True)), kw_only_decorated, ) try: init_node = parse(init_str)["__init__"] except AstroidSyntaxError: pass else: init_node.parent = node init_node.lineno, init_node.col_offset = None, None node.locals["__init__"] = [init_node] root = node.root() if DEFAULT_FACTORY not in root.locals: new_assign = parse(f"{DEFAULT_FACTORY} = object()").body[0] new_assign.parent = root root.locals[DEFAULT_FACTORY] = [new_assign.targets[0]] def _get_dataclass_attributes( node: nodes.ClassDef, init: bool = False ) -> Iterator[nodes.AnnAssign]: """Yield the AnnAssign nodes of dataclass attributes for the node. If init is True, also include InitVars. """ for assign_node in node.body: if not isinstance(assign_node, nodes.AnnAssign) or not isinstance( assign_node.target, nodes.AssignName ): continue # Annotation is never None if _is_class_var(assign_node.annotation): # type: ignore[arg-type] continue if _is_keyword_only_sentinel(assign_node.annotation): continue # Annotation is never None if not init and _is_init_var(assign_node.annotation): # type: ignore[arg-type] continue yield assign_node def _check_generate_dataclass_init(node: nodes.ClassDef) -> bool: """Return True if we should generate an __init__ method for node. This is True when: - node doesn't define its own __init__ method - the dataclass decorator was called *without* the keyword argument init=False """ if "__init__" in node.locals: return False found = None for decorator_attribute in node.decorators.nodes: if not isinstance(decorator_attribute, nodes.Call): continue if _looks_like_dataclass_decorator(decorator_attribute): found = decorator_attribute if found is None: return True # Check for keyword arguments of the form init=False return not any( keyword.arg == "init" and not keyword.value.bool_value() # type: ignore[union-attr] # value is never None for keyword in found.keywords ) def _find_arguments_from_base_classes( node: nodes.ClassDef, ) -> tuple[ dict[str, tuple[str | None, str | None]], dict[str, tuple[str | None, str | None]] ]: """Iterate through all bases and get their typing and defaults.""" pos_only_store: dict[str, tuple[str | None, str | None]] = {} kw_only_store: dict[str, tuple[str | None, str | None]] = {} # See TODO down below # all_have_defaults = True for base in reversed(node.mro()): if not base.is_dataclass: continue try: base_init: nodes.FunctionDef = base.locals["__init__"][0] except KeyError: continue pos_only, kw_only = base_init.args._get_arguments_data() for posarg, data in pos_only.items(): # if data[1] is None: # if all_have_defaults and pos_only_store: # # TODO: This should return an Uninferable as this would raise # # a TypeError at runtime. However, transforms can't return # # Uninferables currently. # pass # all_have_defaults = False pos_only_store[posarg] = data for kwarg, data in kw_only.items(): kw_only_store[kwarg] = data return pos_only_store, kw_only_store def _parse_arguments_into_strings( pos_only_store: dict[str, tuple[str | None, str | None]], kw_only_store: dict[str, tuple[str | None, str | None]], ) -> tuple[str, str]: """Parse positional and keyword arguments into strings for an __init__ method.""" pos_only, kw_only = "", "" for pos_arg, data in pos_only_store.items(): pos_only += pos_arg if data[0]: pos_only += ": " + data[0] if data[1]: pos_only += " = " + data[1] pos_only += ", " for kw_arg, data in kw_only_store.items(): kw_only += kw_arg if data[0]: kw_only += ": " + data[0] if data[1]: kw_only += " = " + data[1] kw_only += ", " return pos_only, kw_only def _get_previous_field_default(node: nodes.ClassDef, name: str) -> nodes.NodeNG | None: """Get the default value of a previously defined field.""" for base in reversed(node.mro()): if not base.is_dataclass: continue if name in base.locals: for assign in base.locals[name]: if ( isinstance(assign.parent, nodes.AnnAssign) and assign.parent.value and isinstance(assign.parent.value, nodes.Call) and _looks_like_dataclass_field_call(assign.parent.value) ): default = _get_field_default(assign.parent.value) if default: return default[1] return None def _generate_dataclass_init( # pylint: disable=too-many-locals node: nodes.ClassDef, assigns: list[nodes.AnnAssign], kw_only_decorated: bool ) -> str: """Return an init method for a dataclass given the targets.""" params: list[str] = [] kw_only_params: list[str] = [] assignments: list[str] = [] prev_pos_only_store, prev_kw_only_store = _find_arguments_from_base_classes(node) for assign in assigns: name, annotation, value = assign.target.name, assign.annotation, assign.value # Check whether this assign is overriden by a property assignment property_node: nodes.FunctionDef | None = None for additional_assign in node.locals[name]: if not isinstance(additional_assign, nodes.FunctionDef): continue if not additional_assign.decorators: continue if "builtins.property" in additional_assign.decoratornames(): property_node = additional_assign break is_field = isinstance(value, nodes.Call) and _looks_like_dataclass_field_call( value, check_scope=False ) if is_field: # Skip any fields that have `init=False` if any( keyword.arg == "init" and not keyword.value.bool_value() for keyword in value.keywords # type: ignore[union-attr] # value is never None ): # Also remove the name from the previous arguments to be inserted later prev_pos_only_store.pop(name, None) prev_kw_only_store.pop(name, None) continue if _is_init_var(annotation): # type: ignore[arg-type] # annotation is never None init_var = True if isinstance(annotation, nodes.Subscript): annotation = annotation.slice else: # Cannot determine type annotation for parameter from InitVar annotation = None assignment_str = "" else: init_var = False assignment_str = f"self.{name} = {name}" ann_str, default_str = None, None if annotation is not None: ann_str = annotation.as_string() if value: if is_field: result = _get_field_default(value) # type: ignore[arg-type] if result: default_type, default_node = result if default_type == "default": default_str = default_node.as_string() elif default_type == "default_factory": default_str = DEFAULT_FACTORY assignment_str = ( f"self.{name} = {default_node.as_string()} " f"if {name} is {DEFAULT_FACTORY} else {name}" ) else: default_str = value.as_string() elif property_node: # We set the result of the property call as default # This hides the fact that this would normally be a 'property object' # But we can't represent those as string try: # Call str to make sure also Uninferable gets stringified default_str = str(next(property_node.infer_call_result()).as_string()) except (InferenceError, StopIteration): pass else: # Even with `init=False` the default value still can be propogated to # later assignments. Creating weird signatures like: # (self, a: str = 1) -> None previous_default = _get_previous_field_default(node, name) if previous_default: default_str = previous_default.as_string() # Construct the param string to add to the init if necessary param_str = name if ann_str is not None: param_str += f": {ann_str}" if default_str is not None: param_str += f" = {default_str}" # If the field is a kw_only field, we need to add it to the kw_only_params # This overwrites whether or not the class is kw_only decorated if is_field: kw_only = [k for k in value.keywords if k.arg == "kw_only"] # type: ignore[union-attr] if kw_only: if kw_only[0].value.bool_value(): kw_only_params.append(param_str) else: params.append(param_str) continue # If kw_only decorated, we need to add all parameters to the kw_only_params if kw_only_decorated: if name in prev_kw_only_store: prev_kw_only_store[name] = (ann_str, default_str) else: kw_only_params.append(param_str) else: # If the name was previously seen, overwrite that data # pylint: disable-next=else-if-used if name in prev_pos_only_store: prev_pos_only_store[name] = (ann_str, default_str) elif name in prev_kw_only_store: params = [name] + params prev_kw_only_store.pop(name) else: params.append(param_str) if not init_var: assignments.append(assignment_str) prev_pos_only, prev_kw_only = _parse_arguments_into_strings( prev_pos_only_store, prev_kw_only_store ) # Construct the new init method paramter string # First we do the positional only parameters, making sure to add the # the self parameter and the comma to allow adding keyword only parameters params_string = "" if "self" in prev_pos_only else "self, " params_string += prev_pos_only + ", ".join(params) if not params_string.endswith(", "): params_string += ", " # Then we add the keyword only parameters if prev_kw_only or kw_only_params: params_string += "*, " params_string += f"{prev_kw_only}{', '.join(kw_only_params)}" assignments_string = "\n ".join(assignments) if assignments else "pass" return f"def __init__({params_string}) -> None:\n {assignments_string}" def infer_dataclass_attribute( node: nodes.Unknown, ctx: context.InferenceContext | None = None ) -> Iterator[InferenceResult]: """Inference tip for an Unknown node that was dynamically generated to represent a dataclass attribute. In the case that a default value is provided, that is inferred first. Then, an Instance of the annotated class is yielded. """ assign = node.parent if not isinstance(assign, nodes.AnnAssign): yield Uninferable return annotation, value = assign.annotation, assign.value if value is not None: yield from value.infer(context=ctx) if annotation is not None: yield from _infer_instance_from_annotation(annotation, ctx=ctx) else: yield Uninferable def infer_dataclass_field_call( node: nodes.Call, ctx: context.InferenceContext | None = None ) -> Iterator[InferenceResult]: """Inference tip for dataclass field calls.""" if not isinstance(node.parent, (nodes.AnnAssign, nodes.Assign)): raise UseInferenceDefault result = _get_field_default(node) if not result: yield Uninferable else: default_type, default = result if default_type == "default": yield from default.infer(context=ctx) else: new_call = parse(default.as_string()).body[0].value new_call.parent = node.parent yield from new_call.infer(context=ctx) def _looks_like_dataclass_decorator( node: nodes.NodeNG, decorator_names: frozenset[str] = DATACLASSES_DECORATORS ) -> bool: """Return True if node looks like a dataclass decorator. Uses inference to lookup the value of the node, and if that fails, matches against specific names. """ if isinstance(node, nodes.Call): # decorator with arguments node = node.func try: inferred = next(node.infer()) except (InferenceError, StopIteration): inferred = Uninferable if isinstance(inferred, UninferableBase): if isinstance(node, nodes.Name): return node.name in decorator_names if isinstance(node, nodes.Attribute): return node.attrname in decorator_names return False return ( isinstance(inferred, nodes.FunctionDef) and inferred.name in decorator_names and inferred.root().name in DATACLASS_MODULES ) def _looks_like_dataclass_attribute(node: nodes.Unknown) -> bool: """Return True if node was dynamically generated as the child of an AnnAssign statement. """ parent = node.parent if not parent: return False scope = parent.scope() return ( isinstance(parent, nodes.AnnAssign) and isinstance(scope, nodes.ClassDef) and is_decorated_with_dataclass(scope) ) def _looks_like_dataclass_field_call( node: nodes.Call, check_scope: bool = True ) -> bool: """Return True if node is calling dataclasses field or Field from an AnnAssign statement directly in the body of a ClassDef. If check_scope is False, skips checking the statement and body. """ if check_scope: stmt = node.statement(future=True) scope = stmt.scope() if not ( isinstance(stmt, nodes.AnnAssign) and stmt.value is not None and isinstance(scope, nodes.ClassDef) and is_decorated_with_dataclass(scope) ): return False try: inferred = next(node.func.infer()) except (InferenceError, StopIteration): return False if not isinstance(inferred, nodes.FunctionDef): return False return inferred.name == FIELD_NAME and inferred.root().name in DATACLASS_MODULES def _get_field_default(field_call: nodes.Call) -> _FieldDefaultReturn: """Return a the default value of a field call, and the corresponding keyword argument name. field(default=...) results in the ... node field(default_factory=...) results in a Call node with func ... and no arguments If neither or both arguments are present, return ("", None) instead, indicating that there is not a valid default value. """ default, default_factory = None, None for keyword in field_call.keywords: if keyword.arg == "default": default = keyword.value elif keyword.arg == "default_factory": default_factory = keyword.value if default is not None and default_factory is None: return "default", default if default is None and default_factory is not None: new_call = nodes.Call( lineno=field_call.lineno, col_offset=field_call.col_offset, parent=field_call.parent, ) new_call.postinit(func=default_factory) return "default_factory", new_call return None def _is_class_var(node: nodes.NodeNG) -> bool: """Return True if node is a ClassVar, with or without subscripting.""" if PY39_PLUS: try: inferred = next(node.infer()) except (InferenceError, StopIteration): return False return getattr(inferred, "name", "") == "ClassVar" # Before Python 3.9, inference returns typing._SpecialForm instead of ClassVar. # Our backup is to inspect the node's structure. return isinstance(node, nodes.Subscript) and ( isinstance(node.value, nodes.Name) and node.value.name == "ClassVar" or isinstance(node.value, nodes.Attribute) and node.value.attrname == "ClassVar" ) def _is_keyword_only_sentinel(node: nodes.NodeNG) -> bool: """Return True if node is the KW_ONLY sentinel.""" if not PY310_PLUS: return False inferred = helpers.safe_infer(node) return ( isinstance(inferred, bases.Instance) and inferred.qname() == "dataclasses._KW_ONLY_TYPE" ) def _is_init_var(node: nodes.NodeNG) -> bool: """Return True if node is an InitVar, with or without subscripting.""" try: inferred = next(node.infer()) except (InferenceError, StopIteration): return False return getattr(inferred, "name", "") == "InitVar" # Allowed typing classes for which we support inferring instances _INFERABLE_TYPING_TYPES = frozenset( ( "Dict", "FrozenSet", "List", "Set", "Tuple", ) ) def _infer_instance_from_annotation( node: nodes.NodeNG, ctx: context.InferenceContext | None = None ) -> Iterator[UninferableBase | bases.Instance]: """Infer an instance corresponding to the type annotation represented by node. Currently has limited support for the typing module. """ klass = None try: klass = next(node.infer(context=ctx)) except (InferenceError, StopIteration): yield Uninferable if not isinstance(klass, nodes.ClassDef): yield Uninferable elif klass.root().name in { "typing", "_collections_abc", "", }: # "" because of synthetic nodes in brain_typing.py if klass.name in _INFERABLE_TYPING_TYPES: yield klass.instantiate_class() else: yield Uninferable else: yield klass.instantiate_class() AstroidManager().register_transform( nodes.ClassDef, dataclass_transform, is_decorated_with_dataclass ) AstroidManager().register_transform( nodes.Call, inference_tip(infer_dataclass_field_call, raise_on_overwrite=True), _looks_like_dataclass_field_call, ) AstroidManager().register_transform( nodes.Unknown, inference_tip(infer_dataclass_attribute, raise_on_overwrite=True), _looks_like_dataclass_attribute, )
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