Add Unity Parser
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3 changed files with 579 additions and 2 deletions
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@ -49,6 +49,7 @@ from modules.wolf2 import handleWOLF2
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from modules.javascript import handleJavascript
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from modules.irissoft import handleIris
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from modules.regex import handleRegex
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from modules.unity import handleUnity
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from modules.images import handleImages
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from modules.rpgmakerplugin import handlePlugin
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@ -76,6 +77,7 @@ MODULES = [
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["Javascript", "js", handleJavascript],
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["Iris", "txt", handleIris],
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["Regex", "txt", handleRegex],
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["Unity", "txt", handleUnity],
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["Images", "", handleImages],
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]
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575
modules/unity.py
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575
modules/unity.py
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@ -0,0 +1,575 @@
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# Libraries
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import json
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import os
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import re
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import textwrap
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import threading
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import time
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import traceback
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import tiktoken
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import openai
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from pathlib import Path
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from colorama import Fore
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from dotenv import load_dotenv
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from retry import retry
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from tqdm import tqdm
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# Open AI
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load_dotenv()
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if os.getenv("api").replace(" ", "") != "":
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openai.base_url = os.getenv("api")
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openai.organization = os.getenv("org")
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openai.api_key = os.getenv("key")
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# Globals
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MODEL = os.getenv("model")
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TIMEOUT = int(os.getenv("timeout"))
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LANGUAGE = os.getenv("language").capitalize()
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PROMPT = Path("prompt.txt").read_text(encoding="utf-8")
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VOCAB = Path("vocab.txt").read_text(encoding="utf-8")
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THREADS = int(os.getenv("threads"))
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LOCK = threading.Lock()
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WIDTH = int(os.getenv("width"))
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LISTWIDTH = int(os.getenv("listWidth"))
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NOTEWIDTH = 70
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MAXHISTORY = 10
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ESTIMATE = ""
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TOKENS = [0, 0]
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NAMESLIST = []
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NAMES = False # Output a list of all the character names found
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BRFLAG = False # If the game uses <br> instead
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FIXTEXTWRAP = True # Overwrites textwrap
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IGNORETLTEXT = False # Ignores all translated text.
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MISMATCH = [] # Lists files that throw a mismatch error (Length of GPT list response is wrong)
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# tqdm Globals
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BAR_FORMAT = "{l_bar}{bar:10}{r_bar}{bar:-10b}"
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POSITION = 0
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LEAVE = False
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PBAR = None
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FILENAME = None
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# Full Width
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ascii_to_wide = dict((i, chr(i + 0xFEE0)) for i in range(0x21, 0x7F))
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ascii_to_wide.update({0x20: "\u3000", 0x2D: "\u2212"}) # space and minus
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wide_to_ascii = dict((i, chr(i - 0xFEE0)) for i in range(0xFF01, 0xFF5F))
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wide_to_ascii.update({0x3000: " ", 0x2212: "-"}) # space and minus
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# Pricing - Depends on the model https://openai.com/pricing
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# Batch Size - GPT 3.5 Struggles past 15 lines per request. GPT4 struggles past 50 lines per request
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# If you are getting a MISMATCH LENGTH error, lower the batch size.
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if "gpt-3.5" in MODEL:
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INPUTAPICOST = 0.002
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OUTPUTAPICOST = 0.002
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BATCHSIZE = 10
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elif "gpt-4" in MODEL:
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INPUTAPICOST = 0.005
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OUTPUTAPICOST = 0.015
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BATCHSIZE = 40
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def handleUnity(filename, estimate):
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global ESTIMATE, FILENAME
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ESTIMATE = estimate
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FILENAME = filename
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if ESTIMATE:
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start = time.time()
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translatedData = openFiles(filename)
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# Print Result
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end = time.time()
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tqdm.write(getResultString(translatedData, end - start, filename))
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with LOCK:
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TOKENS[0] += translatedData[1][0]
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TOKENS[1] += translatedData[1][1]
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# Print Total
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totalString = getResultString(["", TOKENS, None], end - start, "TOTAL")
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# Print any errors on maps
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if len(MISMATCH) > 0:
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return (
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totalString + Fore.RED + f"\nMismatch Errors: {MISMATCH}" + Fore.RESET
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)
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else:
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return totalString
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else:
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try:
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with open(
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"translated/" + filename, "w", encoding="utf8", errors="ignore"
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) as outFile:
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start = time.time()
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translatedData = openFiles(filename)
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# Print Result
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end = time.time()
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outFile.writelines(translatedData[0])
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tqdm.write(getResultString(translatedData, end - start, filename))
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with LOCK:
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TOKENS[0] += translatedData[1][0]
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TOKENS[1] += translatedData[1][1]
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except Exception:
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traceback.print_exc()
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return "Fail"
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return getResultString(["", TOKENS, None], end - start, "TOTAL")
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def getResultString(translatedData, translationTime, filename):
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# File Print String
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totalTokenstring = (
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Fore.YELLOW + "[Input: " + str(translatedData[1][0]) + "]"
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"[Output: "
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+ str(translatedData[1][1])
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+ "]" "[Cost: ${:,.4f}".format(
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(translatedData[1][0] * 0.001 * INPUTAPICOST)
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+ (translatedData[1][1] * 0.001 * OUTPUTAPICOST)
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)
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+ "]"
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)
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timeString = Fore.BLUE + "[" + str(round(translationTime, 1)) + "s]"
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if translatedData[2] == None:
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# Success
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return (
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filename
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+ ": "
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+ totalTokenstring
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+ timeString
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+ Fore.GREEN
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+ " \u2713 "
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+ Fore.RESET
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)
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else:
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# Fail
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try:
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raise translatedData[2]
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except Exception as e:
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traceback.print_exc()
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errorString = str(e) + Fore.RED
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return (
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filename
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+ ": "
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+ totalTokenstring
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+ timeString
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+ Fore.RED
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+ " \u2717 "
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+ errorString
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+ Fore.RESET
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)
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def openFiles(filename):
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with open("files/" + filename, "r", encoding="utf8") as readFile:
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translatedData = parseUnity(readFile, filename)
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# Delete lines marked for deletion
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finalData = []
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for line in translatedData[0]:
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if line != "\\d\n":
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finalData.append(line)
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translatedData[0] = finalData
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return translatedData
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def parseUnity(readFile, filename):
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totalTokens = [0, 0]
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# Read File into data
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data = readFile.readlines()
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# Create Progress Bar
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with tqdm(bar_format=BAR_FORMAT, position=POSITION, leave=LEAVE) as pbar:
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pbar.desc = filename
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try:
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result = translateUnity(data, pbar, filename, [])
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totalTokens[0] += result[0]
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totalTokens[1] += result[1]
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except Exception as e:
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traceback.print_exc()
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return [data, totalTokens, e]
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return [data, totalTokens, None]
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def translateUnity(data, pbar, filename, translatedList):
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stringList = []
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currentGroup = []
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tokens = [0, 0]
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speaker = ""
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voice = False
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global LOCK, ESTIMATE, PBAR
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PBAR = pbar
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i = 0
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# Dialogue
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while i < len(data):
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# Lines
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regex = r".*?=(.*)"
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match = re.search(regex, data[i])
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if match != None and match.group(1) != "":
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originalString = match.group(1)
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# Pass 1
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if translatedList == []:
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# Grab Consecutive Strings
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jaString = match.group(1)
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# Remove textwrap
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jaString = jaString.replace("\n", "")
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# Add String
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stringList.append(jaString.strip())
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# Pass 2
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else:
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# Get Text
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if translatedList:
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# Grab and Pop
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translatedText = translatedList[0]
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translatedList.pop(0)
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# Set to None if empty list
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if len(translatedList) <= 0:
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translatedList = None
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# Textwrap
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translatedText = textwrap.fill(translatedText, width=WIDTH)
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translatedText = translatedText.replace('\n', '\\n')
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# Remove Double Spaces and =
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translatedText = translatedText.replace(" ", " ")
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translatedText = translatedText.replace("=", "->")
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# Set Data
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data[i] = f'{originalString}={translatedText}\n'
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i += 1
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# Nothing relevant. Skip Line.
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else:
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i += 1
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# EOF
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if len(stringList) > 0:
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# Set Progress
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pbar.total = len(stringList)
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pbar.refresh()
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# Translate
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response = translateGPT(stringList, "", True)
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tokens[0] += response[1][0]
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tokens[1] += response[1][1]
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translatedList = response[0]
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# Set Strings
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if len(stringList) == len(translatedList):
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translateUnity(data, pbar, filename, translatedList)
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# Mismatch
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else:
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with LOCK:
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if filename not in MISMATCH:
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MISMATCH.append(filename)
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return tokens
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# Save some money and enter the character before translation
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def getSpeaker(speaker):
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if speaker not in str(NAMESLIST):
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response = translateGPT(
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speaker,
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"Reply with the " + LANGUAGE + " translation of the NPC name.",
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True,
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)
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response[0] = response[0].title()
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response[0] = response[0].replace("'S", "'s")
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# Retry if name doesn't translate for some reason
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if re.search(r"([a-zA-Z？?])", response[0]) == None:
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response = translateGPT(
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speaker,
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"Reply with the " + LANGUAGE + " translation of the NPC name.",
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False,
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)
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response[0] = response[0].title()
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response[0] = response[0].replace("'S", "'s")
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speakerList = [speaker, response[0]]
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NAMESLIST.append(speakerList)
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return response
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# Find Speaker
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else:
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for i in range(len(NAMESLIST)):
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if speaker == NAMESLIST[i][0]:
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return [NAMESLIST[i][1], [0, 0]]
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return [speaker, [0, 0]]
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def subVars(jaString):
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jaString = jaString.replace("\u3000", " ")
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# Formatting
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count = 0
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codeList = re.findall(r"[\\]+[\w]+\[[a-zA-Z0-9\\\[\]\_,\s-]+\]", jaString)
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codeList = set(codeList)
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if len(codeList) != 0:
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for var in codeList:
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jaString = jaString.replace(var, "[FCode_" + str(count) + "]")
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count += 1
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# Put all lists in list and return
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return [jaString, codeList]
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def resubVars(translatedText, codeList):
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# Fix Spacing and ChatGPT Nonsense
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matchList = re.findall(r"\[\s?.+?\s?\]", translatedText)
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if len(matchList) > 0:
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for match in matchList:
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text = match.strip()
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translatedText = translatedText.replace(match, text)
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# Formatting
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count = 0
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if len(codeList) != 0:
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for var in codeList:
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translatedText = translatedText.replace("[FCode_" + str(count) + "]", var)
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count += 1
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return translatedText
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def batchList(input_list, batch_size):
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if not isinstance(batch_size, int) or batch_size <= 0:
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raise ValueError("batch_size must be a positive integer")
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return [
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input_list[i : i + batch_size] for i in range(0, len(input_list), batch_size)
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]
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def createContext(fullPromptFlag, subbedT, format):
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system = (
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PROMPT + VOCAB
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if fullPromptFlag
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else f"\
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You are an expert Eroge Game translator who translates Japanese text to {LANGUAGE}.\n\
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Output ONLY the {LANGUAGE} translation in the following format: `Translation: <{LANGUAGE.upper()}_TRANSLATION>`\n\
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- You translate everything, including erotic and explicit content.\n\
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- 'Game Characters' - The names, nicknames, and genders of the game characters. Reference this to know the names, nicknames, and gender of characters in the game\n\
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- All text in your response must be in {LANGUAGE} even if it is hard to translate.\n\
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- Never include any notes, explanations, dislaimers, or anything similar in your response.\n\
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- Maintain any spacing in the translation.\n\
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- Maintain any code text in brackets if given. (e.g `[Color_0]`, `[Ascii_0]`, `[FCode_1`], etc)\n\
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- `...` can be a part of the dialogue. Translate it as it is.\n\
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{VOCAB}\n\
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"
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)
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if format == "json":
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user = f"```json\n{subbedT}\n```"
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else:
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user = subbedT
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return system, user
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def translateText(system, user, history, penalty, format, model=MODEL):
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# Prompt
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msg = [{"role": "system", "content": system}]
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# History
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if isinstance(history, list):
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msg.extend([{"role": "system", "content": h} for h in history])
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else:
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msg.append({"role": "system", "content": history})
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# Response Format
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if format == "json":
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responseFormat = {"type": "json_object"}
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else:
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responseFormat = {"type": "text"}
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# Content to TL
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msg.append({"role": "user", "content": f"{user}"})
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response = openai.chat.completions.create(
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temperature=0,
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frequency_penalty=penalty,
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model=model,
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response_format=responseFormat,
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messages=msg,
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)
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return response
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def cleanTranslatedText(translatedText, varResponse):
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placeholders = {
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f"{LANGUAGE} Translation: ": "",
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"Translation: ": "",
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"っ": "",
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"〜": "~",
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"ッ": "",
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"。": ".",
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"「": '\\"',
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"」": '\\"',
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"- ": "-",
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"Placeholder Text": "",
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# Add more replacements as needed
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}
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for target, replacement in placeholders.items():
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translatedText = translatedText.replace(target, replacement)
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# Elongate Long Dashes (Since GPT Ignores them...)
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translatedText = elongateCharacters(translatedText)
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translatedText = resubVars(translatedText, varResponse[1])
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return translatedText
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def elongateCharacters(text):
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# Define a pattern to match one character followed by one or more `ー` characters
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# Using a positive lookbehind assertion to capture the preceding character
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pattern = r"(?<=(.))ー+"
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# Define a replacement function that elongates the captured character
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def repl(match):
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char = match.group(1) # The character before the ー sequence
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count = len(match.group(0)) - 1 # Number of ー characters
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return char * count # Replace ー sequence with the character repeated
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# Use re.sub() to replace the pattern in the text
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return re.sub(pattern, repl, text)
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def extractTranslation(translatedTextList, is_list):
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try:
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line_dict = json.loads(translatedTextList)
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# If it's a batch (i.e., list), extract with tags; otherwise, return the single item.
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string_list = list(line_dict.values())
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if is_list:
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return string_list
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else:
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return string_list[0]
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except Exception as e:
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print(f"extractTranslation Error: {e}")
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return None
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def countTokens(system, user, history):
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inputTotalTokens = 0
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outputTotalTokens = 0
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enc = tiktoken.encoding_for_model("gpt-4")
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# Input
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if isinstance(history, list):
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for line in history:
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inputTotalTokens += len(enc.encode(line))
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else:
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inputTotalTokens += len(enc.encode(history))
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inputTotalTokens += len(enc.encode(system))
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inputTotalTokens += len(enc.encode(user))
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# Output
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outputTotalTokens += round(len(enc.encode(user)) * 3)
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return [inputTotalTokens, outputTotalTokens]
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def combineList(tlist, text):
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if isinstance(text, list):
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return [t for sublist in tlist for t in sublist]
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return tlist[0]
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@retry(exceptions=Exception, tries=5, delay=5)
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def translateGPT(text, history, fullPromptFlag):
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global PBAR, MISMATCH, FILENAME
|
||||
|
||||
mismatch = False
|
||||
totalTokens = [0, 0]
|
||||
if isinstance(text, list):
|
||||
format = "json"
|
||||
tList = batchList(text, BATCHSIZE)
|
||||
else:
|
||||
format = "text"
|
||||
tList = [text]
|
||||
|
||||
for index, tItem in enumerate(tList):
|
||||
# Before sending to translation, if we have a list of items, add the formatting
|
||||
if isinstance(tItem, list):
|
||||
payload = {f"Line{i+1}": string for i, string in enumerate(tItem)}
|
||||
payload = json.dumps(payload, indent=4, ensure_ascii=False)
|
||||
varResponse = subVars(payload)
|
||||
subbedT = varResponse[0]
|
||||
else:
|
||||
varResponse = subVars(tItem)
|
||||
subbedT = varResponse[0]
|
||||
|
||||
# Things to Check before starting translation
|
||||
if not re.search(r"[一-龠ぁ-ゔァ-ヴーａ-ｚＡ-Ｚ０-９\uFF61-\uFF9F]+", subbedT):
|
||||
if PBAR is not None:
|
||||
PBAR.update(len(tItem))
|
||||
continue
|
||||
|
||||
# Create Message
|
||||
system, user = createContext(fullPromptFlag, subbedT, format)
|
||||
|
||||
# Calculate Estimate
|
||||
if ESTIMATE:
|
||||
estimate = countTokens(system, user, history)
|
||||
totalTokens[0] += estimate[0]
|
||||
totalTokens[1] += estimate[1]
|
||||
continue
|
||||
|
||||
# Translating
|
||||
response = translateText(system, user, history, 0.05, format)
|
||||
translatedText = response.choices[0].message.content
|
||||
totalTokens[0] += response.usage.prompt_tokens
|
||||
totalTokens[1] += response.usage.completion_tokens
|
||||
|
||||
# Check Translation
|
||||
translatedText = cleanTranslatedText(translatedText, varResponse)
|
||||
if isinstance(tItem, list):
|
||||
extractedTranslations = extractTranslation(translatedText, True)
|
||||
if extractedTranslations == None or len(tItem) != len(
|
||||
extractedTranslations
|
||||
):
|
||||
# Mismatch. Try Again
|
||||
response = translateText(system, user, history, 0.05, format, "gpt-4o")
|
||||
translatedText = response.choices[0].message.content
|
||||
totalTokens[0] += response.usage.prompt_tokens
|
||||
totalTokens[1] += response.usage.completion_tokens
|
||||
|
||||
# Formatting
|
||||
translatedText = cleanTranslatedText(translatedText, varResponse)
|
||||
if isinstance(tItem, list):
|
||||
extractedTranslations = extractTranslation(translatedText, True)
|
||||
if extractedTranslations == None or len(tItem) != len(
|
||||
extractedTranslations
|
||||
):
|
||||
mismatch = True # Just here for breakpoint
|
||||
|
||||
# Set if no mismatch
|
||||
if mismatch == False:
|
||||
tList[index] = extractedTranslations
|
||||
history = extractedTranslations[
|
||||
-10:
|
||||
] # Update history if we have a list
|
||||
else:
|
||||
history = text[-10:]
|
||||
mismatch = False
|
||||
if FILENAME not in MISMATCH:
|
||||
MISMATCH.append(FILENAME)
|
||||
|
||||
# Update Loading Bar
|
||||
with LOCK:
|
||||
if PBAR is not None:
|
||||
PBAR.update(len(tItem))
|
||||
else:
|
||||
# Ensure we're passing a single string to extractTranslation
|
||||
tList[index] = translatedText
|
||||
|
||||
finalList = combineList(tList, text)
|
||||
return [finalList, totalTokens]
|
||||
|
|
@ -964,7 +964,7 @@ def searchDB(events, pbar, jobList, filename):
|
|||
scenarioList[2].pop(0)
|
||||
|
||||
# Grab Items
|
||||
if table["name"] == "種族" and ITEMFLAG == True:
|
||||
if table["name"] == "シーン回想" and ITEMFLAG == True:
|
||||
with open("translations.txt", "a", encoding="utf-8") as file:
|
||||
for item in table["data"]:
|
||||
dataList = item["data"]
|
||||
|
|
@ -972,7 +972,7 @@ def searchDB(events, pbar, jobList, filename):
|
|||
# Parse #
|
||||
for j in range(len(dataList)):
|
||||
# Name
|
||||
if dataList[j].get("name") == "種族":
|
||||
if dataList[j].get("name") == "キャラ名":
|
||||
# Pass 1 (Grab Data)
|
||||
if setData == False:
|
||||
if dataList[j].get("value") != "":
|
||||
|
|
|
|||
Loading…
Reference in a new issue