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Þ'¯°ÌME[YéïĵÂCå½ Ué©Áû'Ê9%eÔðNU”ë‘ÌsD3/®+UI˜9h.WC”빓$#:pz:YÓ ¿xž* ³$Í +$kñAŠ‹†¢ Uê>¸)_š¬÷©ßAÂÔb9ÇU ¯¾á•9¯ÏÏ÷O÷¼¼Fähal1‰3Ì[Ïr•´UCksNÐ] R‘¸¥H+§Šé†c©vÖÞ0iÓ76s†î!§=ß ¼~Ô'°Ãmäoäš³ªøi1úÉ)³yV8 CLÄØÁ‘WYïi€H6ÖÑiámø^ÈY´°Ñ7¥Û*—Ñ©L«Qƒï—Ùrÿ ›£Ð*š¸ˆL©ˆ$ˆ ÷¾D§9È®«qbqC)–ˆïv´çñsÑVT­Ø, <àïºÀO«Jý·õ àfPìð .wFšir´þ’2_Y *Æ€x\« ì€9š@ Ž|F⇥ˆkZ@hÖÄ0t¿-<“‹qµ¾*ZL¤Ú)&BJpÓF5=$„at*Zš$’ÑtdûÝRI1 2Ž‰$€$I$#‰SÞ’Hë¬ï;Á$¡t$’`<(ñÇt)$‡Ð.Êf¢X’Kt=Éé$‚ˆªè¢oÝëòI%Rgcª÷ŠyI%¡‰ÿ !ñ)´õ $¤ Ô’IIGÿÙ# 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) 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: self.schemamarks_by_line[line_num].append(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), ) # vi: ts=4 expandtab