405 lines
16 KiB
Python
405 lines
16 KiB
Python
"""Translate WolfDawn extraction JSON.
|
||
|
||
This module consumes the JSON produced by the vendored WolfDawn ``wolf`` CLI
|
||
(``strings-extract`` / ``names-extract``) and fills in the ``text`` fields with
|
||
translations, leaving ``source`` untouched so WolfDawn's inject drift-guard can
|
||
match each line back to its original.
|
||
|
||
Supported document ``kind``s (all share the ``{source, text}`` leaf pattern):
|
||
map / common -> scenes[].lines[]
|
||
db -> groups[].lines[]
|
||
gamedat -> lines[]
|
||
txt -> lines[]
|
||
txt-dir -> files[].lines[]
|
||
names -> names[]
|
||
|
||
Only entries whose ``source`` contains target-language (Japanese by default)
|
||
text are sent to the model; everything else keeps ``text == source`` so inject
|
||
is a no-op for it.
|
||
|
||
names.json safety: WolfDawn tags every value name with a static ``safety`` badge
|
||
(``safe``, ``refs``, or ``verify``). For ``kind == "names"`` documents, only
|
||
entries whose badge is ``safe`` or ``refs`` are translated; ``verify`` names and
|
||
legacy entries without a badge keep ``text == source``. After translating
|
||
names.json, translatable entries are harvested into ``vocab.txt`` (grouped by
|
||
``note``, with bilingual headers) so later phases keep item / skill / term names
|
||
consistent.
|
||
|
||
Text wrapping: optional advanced ``wolfWrap`` / ``wolfWidth`` (via :mod:`util.dazedwrap`)
|
||
can still re-wrap dialogue bodies at translate time, but the normal Wolf workflow
|
||
leaves JSON line breaks alone and runs WolfDawn ``relayout`` after inject (Step 5)
|
||
to fit the message box. Defaults to off (``wolfWrap=false``).
|
||
|
||
names.json category wrap (Step 3): selected ``note`` categories can be re-wrapped
|
||
with dazedwrap at a dedicated width (``.env``: ``wolfNameWrap``,
|
||
``wolfNameWrapWidth``, ``wolfNameWrapNotes``). Other name categories keep the
|
||
model output verbatim.
|
||
|
||
Speakers: WolfDawn tags each line with ``speaker`` / ``speaker_src``. For the
|
||
first-line formats (``literal_line1`` / ``literal_line1_lowconf``) the speaker
|
||
name is baked into line 1 of ``source``. Those lines are reshaped into the shared
|
||
``[Speaker]: line`` convention (which the prompt already translates) and restored
|
||
to WOLF's native ``Speaker\nline`` layout on write-back. See ``util.speakers``.
|
||
Detection is WolfDawn's, so the reliable nameplate (``literal_line1``) is always
|
||
reshaped; only the low-confidence guess (``literal_line1_lowconf``) is gated by a
|
||
per-game, AI-recommended setting from the workflow.
|
||
"""
|
||
|
||
import json
|
||
import os
|
||
import re
|
||
import threading
|
||
import time
|
||
import traceback
|
||
|
||
from colorama import Fore
|
||
from tqdm import tqdm
|
||
|
||
import util.dazedwrap as dazedwrap
|
||
from util.paths import PROMPT_PATH, VOCAB_PATH
|
||
from util.translation import (
|
||
TranslationConfig,
|
||
translateAI as sharedtranslateAI,
|
||
getPricingConfig,
|
||
calculateCost,
|
||
)
|
||
from util import speakers as wolf_speakers
|
||
from util import vocab as wolf_vocab
|
||
from util import wolf_names
|
||
|
||
# Globals (mirror the other engine modules; populated from .env at import time)
|
||
MODEL = os.getenv("model")
|
||
TIMEOUT = int(os.getenv("timeout"))
|
||
LANGUAGE = os.getenv("language").capitalize()
|
||
|
||
PROMPT = PROMPT_PATH.read_text(encoding="utf-8")
|
||
VOCAB = VOCAB_PATH.read_text(encoding="utf-8")
|
||
LOCK = threading.Lock()
|
||
MAXHISTORY = 10
|
||
ESTIMATE = ""
|
||
TOKENS = [0, 0]
|
||
MISMATCH = [] # Files that hit a length-mismatch during translation
|
||
FILENAME = None
|
||
|
||
# Regex - default matches Japanese (kanji, kana, full-width forms).
|
||
LANGREGEX = r"[一-龠ぁ-ゔァ-ヴーａ-ｚＡ-Ｚ０-９\uFF61-\uFF9F]+"
|
||
|
||
# Speaker handling: for first-line-speaker formats, reshape the line into the
|
||
# shared "[Speaker]: line" transport before translating and restore WOLF's
|
||
# native "Speaker\nline" layout on write-back. Which formats are reshaped is
|
||
# configurable from the workflow (data/wolf_speakers.json).
|
||
SPEAKER_CONFIG = wolf_speakers.load_config()
|
||
|
||
# names.json: translate per-entry safe/refs badges only (see util.wolf_names).
|
||
|
||
# Text wrapping: optional advanced rewrap via dazedwrap (wolfWrap/wolfWidth). Defaults
|
||
# off - the workflow uses WolfDawn relayout after inject instead.
|
||
WRAP = (os.getenv("wolfWrap", "false").strip().lower() == "true")
|
||
try:
|
||
WRAPWIDTH = int(os.getenv("wolfWidth") or 0)
|
||
except ValueError:
|
||
WRAPWIDTH = 0
|
||
|
||
# names.json: optional per-category dazedwrap (Step 3 workflow UI).
|
||
NAME_WRAP_ENABLED, NAME_WRAP_WIDTH, NAME_WRAP_NOTES = wolf_names.load_name_wrap_config()
|
||
|
||
|
||
def reload_name_wrap_config() -> None:
|
||
"""Re-read names.json category wrap settings from ``.env``."""
|
||
global NAME_WRAP_ENABLED, NAME_WRAP_WIDTH, NAME_WRAP_NOTES
|
||
NAME_WRAP_ENABLED, NAME_WRAP_WIDTH, NAME_WRAP_NOTES = wolf_names.load_name_wrap_config()
|
||
|
||
# Pricing / batching from the configured model
|
||
PRICING_CONFIG = getPricingConfig(MODEL)
|
||
INPUTAPICOST = PRICING_CONFIG["inputAPICost"]
|
||
OUTPUTAPICOST = PRICING_CONFIG["outputAPICost"]
|
||
BATCHSIZE = PRICING_CONFIG["batchSize"]
|
||
FREQUENCY_PENALTY = PRICING_CONFIG["frequencyPenalty"]
|
||
|
||
# tqdm / progress globals (PBAR is polled by util/subprocess_runner.py)
|
||
BAR_FORMAT = "{l_bar}{bar:10}{r_bar}{bar:-10b}"
|
||
POSITION = 0
|
||
LEAVE = False
|
||
PBAR = None
|
||
|
||
TRANSLATION_CONFIG = TranslationConfig(
|
||
model=MODEL,
|
||
language=LANGUAGE,
|
||
prompt=PROMPT,
|
||
vocab=VOCAB,
|
||
langRegex=LANGREGEX,
|
||
batchSize=BATCHSIZE,
|
||
maxHistory=MAXHISTORY,
|
||
estimateMode=False,
|
||
)
|
||
|
||
|
||
def handleWolfDawn(filename, estimate):
|
||
"""Entry point used by the CLI/GUI dispatchers. Returns a summary string or 'Fail'."""
|
||
global ESTIMATE, TOKENS, FILENAME, SPEAKER_CONFIG, VOCAB
|
||
ESTIMATE = estimate
|
||
FILENAME = filename
|
||
# Re-read workflow-configured settings so edits made this session take effect
|
||
# even when translation runs in-process (the module import is cached).
|
||
SPEAKER_CONFIG = wolf_speakers.load_config()
|
||
reload_name_wrap_config()
|
||
# Reload the glossary so a later phase (DB text / dialogue) picks up names
|
||
# that an earlier Phase 0 (names) harvested into vocab.txt.
|
||
VOCAB = VOCAB_PATH.read_text(encoding="utf-8")
|
||
TRANSLATION_CONFIG.vocab = VOCAB
|
||
|
||
start = time.time()
|
||
translatedData = openFiles(filename)
|
||
|
||
if not estimate:
|
||
try:
|
||
with open("translated/" + filename, "w", encoding="utf-8", newline="\n") as outFile:
|
||
json.dump(translatedData[0], outFile, ensure_ascii=False, indent=4)
|
||
except Exception:
|
||
traceback.print_exc()
|
||
return "Fail"
|
||
|
||
end = time.time()
|
||
tqdm.write(getResultString(translatedData, end - start, filename))
|
||
with LOCK:
|
||
TOKENS[0] += translatedData[1][0]
|
||
TOKENS[1] += translatedData[1][1]
|
||
|
||
totalString = getResultString(["", TOKENS, None], end - start, "TOTAL")
|
||
if len(MISMATCH) > 0:
|
||
return totalString + Fore.RED + f"\nMismatch Errors: {MISMATCH}" + Fore.RESET
|
||
return totalString
|
||
|
||
|
||
def openFiles(filename):
|
||
"""Load the extraction JSON, translate it in place, and return [data, tokens, error]."""
|
||
with open("files/" + filename, "r", encoding="utf-8-sig") as f:
|
||
data = json.load(f)
|
||
|
||
kind = data.get("kind")
|
||
if kind not in ("map", "common", "db", "gamedat", "txt", "txt-dir", "names"):
|
||
raise NameError(
|
||
f"{filename}: unrecognised WolfDawn document (kind={kind!r}). "
|
||
"Expected a strings-extract or names-extract JSON."
|
||
)
|
||
return parseDocument(data, filename)
|
||
|
||
|
||
def collectEntries(data):
|
||
"""Return the list of leaf {source, text} dicts for a WolfDawn document.
|
||
|
||
The returned dicts are live references into ``data`` so mutating ``text``
|
||
updates the document that gets written back out.
|
||
"""
|
||
kind = data.get("kind")
|
||
entries = []
|
||
|
||
if kind in ("map", "common"):
|
||
for scene in data.get("scenes") or []:
|
||
for line in scene.get("lines") or []:
|
||
entries.append(line)
|
||
elif kind == "db":
|
||
for group in data.get("groups") or []:
|
||
for line in group.get("lines") or []:
|
||
entries.append(line)
|
||
elif kind in ("gamedat", "txt"):
|
||
for line in data.get("lines") or []:
|
||
entries.append(line)
|
||
elif kind == "txt-dir":
|
||
for fileDoc in data.get("files") or []:
|
||
for line in fileDoc.get("lines") or []:
|
||
entries.append(line)
|
||
elif kind == "names":
|
||
for name in data.get("names") or []:
|
||
entries.append(name)
|
||
|
||
return entries
|
||
|
||
|
||
def _wrap_body(body):
|
||
"""Word-wrap a dialogue body to WRAPWIDTH (no-op when wrapping is disabled)."""
|
||
if not WRAP or WRAPWIDTH <= 0 or not isinstance(body, str) or not body:
|
||
return body
|
||
return dazedwrap.wrapText(body, WRAPWIDTH)
|
||
|
||
|
||
def _wrap_names_entry(text, entry):
|
||
"""Apply dazedwrap to a names.json entry when its ``note`` is selected in Step 3."""
|
||
if not NAME_WRAP_ENABLED or NAME_WRAP_WIDTH <= 0:
|
||
return text
|
||
if not isinstance(text, str) or not text:
|
||
return text
|
||
note = str(entry.get("note", ""))
|
||
if note not in NAME_WRAP_NOTES:
|
||
return text
|
||
return dazedwrap.wrapText(text, NAME_WRAP_WIDTH)
|
||
|
||
|
||
def _wrap_plain(text, is_firstline):
|
||
"""Wrap a non-reshaped entry, protecting a nameplate first line if present.
|
||
|
||
``is_firstline`` is True for first-line-speaker formats that are turned off in
|
||
the config: their model output is still ``Speaker\\nbody``, so line 1 (the
|
||
name) is kept intact and only the body is wrapped. Any leading ``@<option>``
|
||
window prefix is also preserved.
|
||
"""
|
||
if not WRAP or WRAPWIDTH <= 0 or not isinstance(text, str) or not text:
|
||
return text
|
||
prefix, rest = wolf_speakers.split_window_prefix(text)
|
||
if is_firstline and "\n" in rest:
|
||
name, body = rest.split("\n", 1)
|
||
return prefix + name + "\n" + _wrap_body(body)
|
||
return prefix + _wrap_body(rest)
|
||
|
||
|
||
def parseDocument(data, filename):
|
||
"""Translate every translatable leaf entry and return [data, tokens, error]."""
|
||
global PBAR
|
||
totalTokens = [0, 0]
|
||
|
||
entries = collectEntries(data)
|
||
is_names = data.get("kind") == "names"
|
||
|
||
def _translatable(e):
|
||
src = e.get("source")
|
||
if not (isinstance(src, str) and re.search(LANGREGEX, src)):
|
||
return False
|
||
# names.json: only translate WolfDawn safe/refs entries (per-name badge).
|
||
if is_names and not wolf_names.is_name_translatable(e):
|
||
return False
|
||
return True
|
||
|
||
# Only translate entries that actually contain target-language text (and, for
|
||
# names.json, carry a translatable safety badge); the rest keep text == source so
|
||
# WolfDawn treats them as untouched on inject.
|
||
translatable = [e for e in entries if _translatable(e)]
|
||
|
||
with tqdm(bar_format=BAR_FORMAT, position=POSITION, total=len(translatable), leave=LEAVE) as pbar:
|
||
pbar.desc = filename
|
||
PBAR = pbar
|
||
if not translatable:
|
||
return [data, totalTokens, None]
|
||
|
||
# Reshape first-line-speaker lines into the shared "[Speaker]: line"
|
||
# transport format. plans[i] carries what is needed to restore each
|
||
# entry after translation.
|
||
sources = []
|
||
plans = [] # (entry, prefix, has_speaker, is_firstline)
|
||
for entry in translatable:
|
||
src = entry["source"]
|
||
is_firstline = entry.get("speaker_src", "") in wolf_speakers.FIRSTLINE_SRCS
|
||
split = wolf_speakers.split_source(src, entry.get("speaker_src", ""), SPEAKER_CONFIG)
|
||
if split is not None:
|
||
prefix, speaker, body = split
|
||
sources.append(wolf_speakers.to_prefixed(speaker, body))
|
||
plans.append((entry, prefix, True, is_firstline))
|
||
else:
|
||
sources.append(src)
|
||
plans.append((entry, "", False, is_firstline))
|
||
|
||
try:
|
||
response = translateAI(sources, [])
|
||
except Exception as e:
|
||
return [data, totalTokens, e]
|
||
|
||
translated, tokens = response[0], response[1]
|
||
totalTokens[0] += tokens[0]
|
||
totalTokens[1] += tokens[1]
|
||
|
||
# Write translations back (skip in estimate mode: translated == sources).
|
||
if not ESTIMATE and isinstance(translated, list) and len(translated) == len(plans):
|
||
for (entry, prefix, has_speaker, is_firstline), text in zip(plans, translated):
|
||
if not isinstance(text, str):
|
||
continue
|
||
if is_names:
|
||
entry["text"] = _wrap_names_entry(text, entry)
|
||
elif has_speaker:
|
||
speaker_en, body_en = wolf_speakers.parse_prefixed(text)
|
||
if speaker_en is not None:
|
||
# Wrap only the body; the name stays on its own line.
|
||
entry["text"] = wolf_speakers.restore_source(
|
||
prefix, speaker_en, _wrap_body(body_en)
|
||
)
|
||
else:
|
||
# Model dropped the [Speaker]: prefix; keep its output as-is.
|
||
entry["text"] = prefix + _wrap_body(text)
|
||
else:
|
||
entry["text"] = _wrap_plain(text, is_firstline)
|
||
|
||
# Phase 0 feeds the glossary: harvest the just-translated safe name values
|
||
# into vocab.txt (grouped by note) so the DB-text and dialogue phases keep
|
||
# item/skill/term names consistent. Mirrors the RPGMaker DB-first strategy.
|
||
if is_names and not ESTIMATE:
|
||
_harvest_names_to_vocab(data)
|
||
|
||
return [data, totalTokens, None]
|
||
|
||
|
||
def _harvest_names_to_vocab(data):
|
||
"""Write short translated name labels into vocab.txt, grouped by note.
|
||
|
||
Profile blurbs and other content-shaped names are translated in names.json but
|
||
skipped here. Categories with no harvestable terms remove any stale section.
|
||
"""
|
||
try:
|
||
db_labels = wolf_names.derive_db_labels("files")
|
||
by_note: dict[str, list] = {}
|
||
touched_notes: set[str] = set()
|
||
for entry in data.get("names") or []:
|
||
if not wolf_names.is_name_translatable(entry):
|
||
continue
|
||
note = str(entry.get("note", ""))
|
||
touched_notes.add(note)
|
||
if not wolf_names.is_vocab_harvest_candidate(entry):
|
||
continue
|
||
src, dst = entry.get("source"), entry.get("text")
|
||
if not isinstance(src, str) or not isinstance(dst, str):
|
||
continue
|
||
by_note.setdefault(note, []).append((src, dst))
|
||
for note in touched_notes:
|
||
header = wolf_names.note_header(note, db_labels)
|
||
pairs = by_note.get(note, [])
|
||
if pairs:
|
||
wolf_vocab.update_vocab_section(header, pairs)
|
||
else:
|
||
wolf_vocab.remove_vocab_section(header)
|
||
except Exception:
|
||
traceback.print_exc()
|
||
|
||
|
||
def getResultString(translatedData, translationTime, filename):
|
||
"""Format the per-file / total cost + status line for the console log."""
|
||
cost = calculateCost(translatedData[1][0], translatedData[1][1], MODEL)
|
||
totalTokenstring = (
|
||
Fore.YELLOW + "[Input: " + str(translatedData[1][0]) + "]"
|
||
"[Output: " + str(translatedData[1][1]) + "]"
|
||
"[Cost: ${:,.4f}".format(cost) + "]"
|
||
)
|
||
timeString = Fore.BLUE + "[" + str(round(translationTime, 1)) + "s]"
|
||
|
||
if translatedData[2] is None:
|
||
return filename + ": " + totalTokenstring + timeString + Fore.GREEN + " \u2713 " + Fore.RESET
|
||
try:
|
||
raise translatedData[2]
|
||
except Exception as e:
|
||
traceback.print_exc()
|
||
errorString = str(e) + Fore.RED
|
||
return (
|
||
filename + ": " + totalTokenstring + timeString
|
||
+ Fore.RED + " \u2717 " + errorString + Fore.RESET
|
||
)
|
||
|
||
|
||
def translateAI(text, history, history_ctx=None):
|
||
"""Thin wrapper around the shared translation entry point."""
|
||
global PBAR, MISMATCH, FILENAME
|
||
TRANSLATION_CONFIG.estimateMode = bool(ESTIMATE)
|
||
return sharedtranslateAI(
|
||
text=text,
|
||
history=history,
|
||
config=TRANSLATION_CONFIG,
|
||
filename=FILENAME,
|
||
pbar=PBAR,
|
||
lock=LOCK,
|
||
mismatchList=MISMATCH,
|
||
)
|