Files
context-continuity/venv.old-py38/lib/python3.9/site-packages/markdown/preprocessors.py
T
Master 943acbc573 feat: referentiel projets dynamique + validation nommage by design
Durcit la convention de nommage des projets (dérive constatée : 'Sliding
Automation', 'code_versioning'... au lieu des formes canoniques).

- trilium_api.py : projets_canoniques() lit le référentiel = valeurs du label
  projet sur les notes de type=projet (source unique, pas de constante en dur).
  Note-projet CodeVersioning créée (manquait).
- mcp_server.py : _valider_projet() branché dans les 6 tools de création
  (add_decision/history/backlog, new_conversation, create_entite, add_skill).
  Refuse un projet non canonique (suggestion si faute) ou inconnu (renvoi au
  processus de création de projet). Ne verrouille pas si référentiel illisible.
- lint_audit.py : VAL-nommage aligné sur le référentiel (attrape casse, espace
  ET snake_case ; l'ancien 'contient un espace' ratait code_versioning).
- Données : 79 notes ré-étiquetées vers les 3 formes canoniques.

Quality by design : l'erreur de nommage devient impossible à l'écriture, le
Lint n'est plus que le filet de sécurité.
2026-07-17 14:59:33 +02:00

92 lines
3.1 KiB
Python

# Python Markdown
# A Python implementation of John Gruber's Markdown.
# Documentation: https://python-markdown.github.io/
# GitHub: https://github.com/Python-Markdown/markdown/
# PyPI: https://pypi.org/project/Markdown/
# Started by Manfred Stienstra (http://www.dwerg.net/).
# Maintained for a few years by Yuri Takhteyev (http://www.freewisdom.org).
# Currently maintained by Waylan Limberg (https://github.com/waylan),
# Dmitry Shachnev (https://github.com/mitya57) and Isaac Muse (https://github.com/facelessuser).
# Copyright 2007-2023 The Python Markdown Project (v. 1.7 and later)
# Copyright 2004, 2005, 2006 Yuri Takhteyev (v. 0.2-1.6b)
# Copyright 2004 Manfred Stienstra (the original version)
# License: BSD (see LICENSE.md for details).
"""
Preprocessors work on source text before it is broken down into its individual parts.
This is an excellent place to clean up bad characters or to extract portions for later
processing that the parser may otherwise choke on.
"""
from __future__ import annotations
from typing import TYPE_CHECKING, Any
from . import util
from .htmlparser import HTMLExtractor
import re
if TYPE_CHECKING: # pragma: no cover
from markdown import Markdown
def build_preprocessors(md: Markdown, **kwargs: Any) -> util.Registry[Preprocessor]:
""" Build and return the default set of preprocessors used by Markdown. """
preprocessors = util.Registry()
preprocessors.register(NormalizeWhitespace(md), 'normalize_whitespace', 30)
preprocessors.register(HtmlBlockPreprocessor(md), 'html_block', 20)
return preprocessors
class Preprocessor(util.Processor):
"""
Preprocessors are run after the text is broken into lines.
Each preprocessor implements a `run` method that takes a pointer to a
list of lines of the document, modifies it as necessary and returns
either the same pointer or a pointer to a new list.
Preprocessors must extend `Preprocessor`.
"""
def run(self, lines: list[str]) -> list[str]:
"""
Each subclass of `Preprocessor` should override the `run` method, which
takes the document as a list of strings split by newlines and returns
the (possibly modified) list of lines.
"""
pass # pragma: no cover
class NormalizeWhitespace(Preprocessor):
""" Normalize whitespace for consistent parsing. """
def run(self, lines: list[str]) -> list[str]:
source = '\n'.join(lines)
source = source.replace(util.STX, "").replace(util.ETX, "")
source = source.replace("\r\n", "\n").replace("\r", "\n") + "\n\n"
source = source.expandtabs(self.md.tab_length)
source = re.sub(r'(?<=\n) +\n', '\n', source)
return source.split('\n')
class HtmlBlockPreprocessor(Preprocessor):
"""
Remove html blocks from the text and store them for later retrieval.
The raw HTML is stored in the [`htmlStash`][markdown.util.HtmlStash] of the
[`Markdown`][markdown.Markdown] instance.
"""
def run(self, lines: list[str]) -> list[str]:
source = '\n'.join(lines)
parser = HTMLExtractor(self.md)
parser.feed(source)
parser.close()
return ''.join(parser.cleandoc).split('\n')