Current File : //opt/cloudlinux/venv/lib/python3.11/site-packages/astroid/brain/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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