Initial commit

venv/lib/python3.8/site-packages/astroid/brain/brain_random.py

@@ -0,0 +1,75 @@
+# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
+# For details: https://github.com/PyCQA/astroid/blob/master/COPYING.LESSER
+import random
+
+import astroid
+from astroid import helpers
+from astroid import MANAGER
+
+
+ACCEPTED_ITERABLES_FOR_SAMPLE = (astroid.List, astroid.Set, astroid.Tuple)
+
+
+def _clone_node_with_lineno(node, parent, lineno):
+    cls = node.__class__
+    other_fields = node._other_fields
+    _astroid_fields = node._astroid_fields
+    init_params = {"lineno": lineno, "col_offset": node.col_offset, "parent": parent}
+    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=None):
+    if len(node.args) != 2:
+        raise astroid.UseInferenceDefault
+
+    length = node.args[1]
+    if not isinstance(length, astroid.Const):
+        raise astroid.UseInferenceDefault
+    if not isinstance(length.value, int):
+        raise astroid.UseInferenceDefault
+
+    inferred_sequence = helpers.safe_infer(node.args[0], context=context)
+    if not inferred_sequence:
+        raise astroid.UseInferenceDefault
+
+    if not isinstance(inferred_sequence, ACCEPTED_ITERABLES_FOR_SAMPLE):
+        raise astroid.UseInferenceDefault
+
+    if length.value > len(inferred_sequence.elts):
+        # In this case, this will raise a ValueError
+        raise astroid.UseInferenceDefault
+
+    try:
+        elts = random.sample(inferred_sequence.elts, length.value)
+    except ValueError:
+        raise astroid.UseInferenceDefault
+
+    new_node = astroid.List(
+        lineno=node.lineno, col_offset=node.col_offset, parent=node.scope()
+    )
+    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):
+    func = node.func
+    if isinstance(func, astroid.Attribute):
+        return func.attrname == "sample"
+    if isinstance(func, astroid.Name):
+        return func.name == "sample"
+    return False
+
+
+MANAGER.register_transform(
+    astroid.Call, astroid.inference_tip(infer_random_sample), _looks_like_random_sample
+)