/opt/imh-python/lib/python3.9/site-packages/astroid/brain
Edit: /opt/imh-python/lib/python3.9/site-packages/astroid/brain/brain_random.py (3170B)
# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
# For details: https://github.com/pylint-dev/astroid/blob/main/LICENSE
# Copyright (c) https://github.com/pylint-dev/astroid/blob/main/CONTRIBUTORS.txt
from __future__ import annotations
import random
from astroid.context import InferenceContext
from astroid.exceptions import UseInferenceDefault
from astroid.inference_tip import inference_tip
from astroid.manager import AstroidManager
from astroid.nodes.node_classes import (
Attribute,
Call,
Const,
EvaluatedObject,
List,
Name,
Set,
Tuple,
)
from astroid.util import safe_infer
ACCEPTED_ITERABLES_FOR_SAMPLE = (List, Set, Tuple)
def _clone_node_with_lineno(node, parent, lineno):
if isinstance(node, EvaluatedObject):
node = node.original
cls = node.__class__
other_fields = node._other_fields
_astroid_fields = node._astroid_fields
init_params = {
"lineno": lineno,
"col_offset": node.col_offset,
"parent": parent,
"end_lineno": node.end_lineno,
"end_col_offset": node.end_col_offset,
}
postinit_params = {param: getattr(node, param) for param in _astroid_fields}
if other_fields:
init_params.update({param: getattr(node, param) for param in other_fields})
new_node = cls(**init_params)
if hasattr(node, "postinit") and _astroid_fields:
new_node.postinit(**postinit_params)
return new_node
def infer_random_sample(node, context: InferenceContext | None = None):
if len(node.args) != 2:
raise UseInferenceDefault
inferred_length = safe_infer(node.args[1], context=context)
if not isinstance(inferred_length, Const):
raise UseInferenceDefault
if not isinstance(inferred_length.value, int):
raise UseInferenceDefault
inferred_sequence = safe_infer(node.args[0], context=context)
if not inferred_sequence:
raise UseInferenceDefault
if not isinstance(inferred_sequence, ACCEPTED_ITERABLES_FOR_SAMPLE):
raise UseInferenceDefault
if inferred_length.value > len(inferred_sequence.elts):
# In this case, this will raise a ValueError
raise UseInferenceDefault
try:
elts = random.sample(inferred_sequence.elts, inferred_length.value)
except ValueError as exc:
raise UseInferenceDefault from exc
new_node = List(
lineno=node.lineno,
col_offset=node.col_offset,
parent=node.scope(),
end_lineno=node.end_lineno,
end_col_offset=node.end_col_offset,
)
new_elts = [
_clone_node_with_lineno(elt, parent=new_node, lineno=new_node.lineno)
for elt in elts
]
new_node.postinit(new_elts)
return iter((new_node,))
def _looks_like_random_sample(node) -> bool:
func = node.func
if isinstance(func, Attribute):
return func.attrname == "sample"
if isinstance(func, Name):
return func.name == "sample"
return False
def register(manager: AstroidManager) -> None:
manager.register_transform(
Call, inference_tip(infer_random_sample), _looks_like_random_sample
)