/snap/core18/2979/usr/lib/python3/dist-packages/cloudinit
Edit: /snap/core18/2979/usr/lib/python3/dist-packages/cloudinit/safeyaml.py (7905B)
# Copyright (C) 2012 Canonical Ltd.
#
# Author: Scott Moser
#
# This file is part of cloud-init. See LICENSE file for license information.
from collections import namedtuple
from itertools import chain
from typing import Any, Dict, List, Tuple
import yaml
YAMLError = yaml.YAMLError
# SchemaPathMarks track the path to an element within a loaded YAML file.
# The start_mark and end_mark contain the row and column indicators
# which represent the coordinates where the schema element begins and ends.
SchemaPathMarks = namedtuple(
"SchemaPathMarks", ("path", "start_mark", "end_mark")
)
class _CustomSafeLoader(yaml.SafeLoader):
def construct_python_unicode(self, node):
return super().construct_scalar(node)
def _fix_nested_map_index(new_key_path, marks):
new_marks = []
for mark in marks:
if "." not in mark.path:
new_marks.append(mark)
continue
path_prefix, _path_idx = mark.path.rsplit(".", 1)
if new_key_path not in mark.path and path_prefix in mark.path:
new_marks.append(
SchemaPathMarks(
# Replace only the first match of path_prefix
mark.path.replace(path_prefix, new_key_path, 1),
mark.start_mark,
mark.end_mark,
)
)
else:
new_marks.append(mark)
return new_marks
class _CustomSafeLoaderWithMarks(yaml.SafeLoader):
"""A loader which provides line and column start and end marks for YAML.
If the YAML loaded represents a dictionary, get_single_data will inject
a top-level "schemamarks" key in that dictionary which can be used at
call-sites to process YAML paths schemamark metadata when annotating
YAML files for errors.
The schemamarks key is dictionary where each key is a dot-delimited path
into the YAML object. Each dot represents an element that is nested under
a parent and list items are represented with the format
`.`.
The values in schemamarks will be the line number in the original content
where YAML element begins to aid in annotation when encountering schema
errors.
The example YAML shows expected schemamarks for both dicts and lists:
one: val1
two:
subtwo: val2
three: [val3, val4]
schemamarks == {
"one": 1, "two": 2, "two.subtwo": 3, "three": 4, "three.0": 4,
"three.1": 4
}
"""
def __init__(self, stream):
super().__init__(stream)
self.schemamarks_by_line: Dict[int, List[SchemaPathMarks]] = {}
def _get_nested_path_prefix(self, node):
if node.start_mark.line in self.schemamarks_by_line:
return f"{self.schemamarks_by_line[node.start_mark.line][0][0]}."
for _line_num, schema_marks in sorted(
self.schemamarks_by_line.items(), reverse=True
):
for mark in schema_marks[::-1]:
if ( # Is the node within the scope of the furthest mark
node.start_mark.line >= mark.start_mark.line
and node.start_mark.column >= mark.start_mark.column
and node.end_mark.line <= mark.end_mark.line
and node.end_mark.column <= mark.end_mark.column
):
return f"{mark.path}."
return ""
def construct_mapping(self, node):
mapping = super().construct_mapping(node)
nested_path_prefix = self._get_nested_path_prefix(node)
for key_node, value_node in node.value:
node_key_path = f"{nested_path_prefix}{key_node.value}"
line_num = key_node.start_mark.line
mark = SchemaPathMarks(
node_key_path, key_node.start_mark, value_node.end_mark
)
if line_num not in self.schemamarks_by_line:
self.schemamarks_by_line[line_num] = [mark]
else:
self.schemamarks_by_line[line_num].append(mark)
return mapping
def construct_sequence(self, node, deep=False):
sequence = super().construct_sequence(node, deep=True)
nested_path_prefix = self._get_nested_path_prefix(node)
for index, sequence_item in enumerate(node.value):
line_num = sequence_item.start_mark.line
node_key_path = f"{nested_path_prefix}{index}"
marks = SchemaPathMarks(
node_key_path, sequence_item.start_mark, sequence_item.end_mark
)
if line_num not in self.schemamarks_by_line:
self.schemamarks_by_line[line_num] = [marks]
else:
if line_num == sequence_item.end_mark.line:
self.schemamarks_by_line[line_num].append(marks)
else: # Incorrect multi-line mapping or sequence object.
for inner_line in range(
line_num, sequence_item.end_mark.line
):
if inner_line in self.schemamarks_by_line:
schema_marks = self.schemamarks_by_line[inner_line]
new_marks = _fix_nested_map_index(
node_key_path, schema_marks
)
if (
inner_line == line_num
and schema_marks[0].path != node_key_path
):
new_marks.insert(
0,
SchemaPathMarks(
node_key_path,
schema_marks[0].start_mark,
schema_marks[-1].end_mark,
),
)
self.schemamarks_by_line[inner_line] = new_marks
return sequence
def get_single_data(self):
data = super().get_single_data()
if isinstance(data, dict): # valid cloud-config schema is a dict
data["schemamarks"] = dict(
[
(v.path, v.start_mark.line + 1) # 1-based human-readable
for v in chain(*self.schemamarks_by_line.values())
]
)
return data
_CustomSafeLoader.add_constructor(
"tag:yaml.org,2002:python/unicode",
_CustomSafeLoader.construct_python_unicode,
)
class NoAliasSafeDumper(yaml.dumper.SafeDumper):
"""A class which avoids constructing anchors/aliases on yaml dump"""
def ignore_aliases(self, data):
return True
def load_with_marks(blob) -> Tuple[Any, Dict[str, int]]:
"""Perform YAML SafeLoad and track start and end marks during parse.
JSON schema errors come with an encoded object path such as:
..
YAML loader needs to preserve a mapping of schema path to line and column
marks to annotate original content with JSON schema error marks for the
command:
cloud-init devel schema --annotate
"""
result = yaml.load(blob, Loader=_CustomSafeLoaderWithMarks)
if not isinstance(result, dict):
schemamarks = {}
else:
schemamarks = result.pop("schemamarks")
return result, schemamarks
def load(blob):
return yaml.load(blob, Loader=_CustomSafeLoader)
def dumps(obj, explicit_start=True, explicit_end=True, noalias=False):
"""Return data in nicely formatted yaml."""
return yaml.dump(
obj,
line_break="\n",
indent=4,
explicit_start=explicit_start,
explicit_end=explicit_end,
default_flow_style=False,
Dumper=(NoAliasSafeDumper if noalias else yaml.dumper.SafeDumper),
)
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