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""" |
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Translate this project to other languages (experimental, please open an issue if there is any bug) |
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Usage: |
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1. modify config.py, set your LLM_MODEL and API_KEY(s) to provide access to OPENAI (or any other LLM model provider) |
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|
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2. modify LANG (below ↓) |
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LANG = "English" |
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|
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3. modify TransPrompt (below ↓) |
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TransPrompt = f"Replace each json value `#` with translated results in English, e.g., \"原始文本\":\"TranslatedText\". Keep Json format. Do not answer #." |
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|
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4. Run `python multi_language.py`. |
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Note: You need to run it multiple times to increase translation coverage because GPT makes mistakes sometimes. |
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|
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5. Find the translated program in `multi-language\English\*` |
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P.S. |
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- The translation mapping will be stored in `docs/translation_xxxx.json`, you can revised mistaken translation there. |
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- If you would like to share your `docs/translation_xxxx.json`, (so that everyone can use the cached & revised translation mapping), please open a Pull Request |
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- If there is any translation error in `docs/translation_xxxx.json`, please open a Pull Request |
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- Welcome any Pull Request, regardless of language |
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""" |
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|
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import os |
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import json |
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import functools |
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import re |
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import pickle |
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import time |
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|
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CACHE_FOLDER = "gpt_log" |
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blacklist = ['multi-language', 'gpt_log', '.git', 'private_upload', 'multi_language.py', 'build', '.github', '.vscode', '__pycache__', 'venv'] |
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LANG = "English" |
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TransPrompt = f"Replace each json value `#` with translated results in English, e.g., \"原始文本\":\"TranslatedText\". Keep Json format. Do not answer #." |
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if not os.path.exists(CACHE_FOLDER): |
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os.makedirs(CACHE_FOLDER) |
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|
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def lru_file_cache(maxsize=128, ttl=None, filename=None): |
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""" |
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Decorator that caches a function's return value after being called with given arguments. |
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It uses a Least Recently Used (LRU) cache strategy to limit the size of the cache. |
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maxsize: Maximum size of the cache. Defaults to 128. |
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ttl: Time-to-Live of the cache. If a value hasn't been accessed for `ttl` seconds, it will be evicted from the cache. |
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filename: Name of the file to store the cache in. If not supplied, the function name + ".cache" will be used. |
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""" |
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cache_path = os.path.join(CACHE_FOLDER, f"{filename}.cache") if filename is not None else None |
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|
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def decorator_function(func): |
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cache = {} |
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_cache_info = { |
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"hits": 0, |
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"misses": 0, |
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"maxsize": maxsize, |
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"currsize": 0, |
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"ttl": ttl, |
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"filename": cache_path, |
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} |
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|
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@functools.wraps(func) |
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def wrapper_function(*args, **kwargs): |
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key = str((args, frozenset(kwargs))) |
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if key in cache: |
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if _cache_info["ttl"] is None or (cache[key][1] + _cache_info["ttl"]) >= time.time(): |
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_cache_info["hits"] += 1 |
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print(f'Warning, reading cache, last read {(time.time()-cache[key][1])//60} minutes ago'); time.sleep(2) |
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cache[key][1] = time.time() |
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return cache[key][0] |
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else: |
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del cache[key] |
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result = func(*args, **kwargs) |
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cache[key] = [result, time.time()] |
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_cache_info["misses"] += 1 |
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_cache_info["currsize"] += 1 |
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|
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if _cache_info["currsize"] > _cache_info["maxsize"]: |
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oldest_key = None |
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for k in cache: |
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if oldest_key is None: |
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oldest_key = k |
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elif cache[k][1] < cache[oldest_key][1]: |
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oldest_key = k |
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del cache[oldest_key] |
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_cache_info["currsize"] -= 1 |
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if cache_path is not None: |
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with open(cache_path, "wb") as f: |
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pickle.dump(cache, f) |
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return result |
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def cache_info(): |
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return _cache_info |
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wrapper_function.cache_info = cache_info |
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if cache_path is not None and os.path.exists(cache_path): |
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with open(cache_path, "rb") as f: |
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cache = pickle.load(f) |
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_cache_info["currsize"] = len(cache) |
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return wrapper_function |
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return decorator_function |
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|
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def contains_chinese(string): |
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""" |
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Returns True if the given string contains Chinese characters, False otherwise. |
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""" |
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chinese_regex = re.compile(u'[\u4e00-\u9fff]+') |
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return chinese_regex.search(string) is not None |
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|
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def split_list(lst, n_each_req): |
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""" |
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Split a list into smaller lists, each with a maximum number of elements. |
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:param lst: the list to split |
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:param n_each_req: the maximum number of elements in each sub-list |
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:return: a list of sub-lists |
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""" |
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result = [] |
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for i in range(0, len(lst), n_each_req): |
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result.append(lst[i:i + n_each_req]) |
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return result |
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|
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def map_to_json(map, language): |
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dict_ = read_map_from_json(language) |
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dict_.update(map) |
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with open(f'docs/translate_{language.lower()}.json', 'w', encoding='utf8') as f: |
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json.dump(dict_, f, indent=4, ensure_ascii=False) |
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|
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def read_map_from_json(language): |
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if os.path.exists(f'docs/translate_{language.lower()}.json'): |
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with open(f'docs/translate_{language.lower()}.json', 'r', encoding='utf8') as f: |
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res = json.load(f) |
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res = {k:v for k, v in res.items() if v is not None and contains_chinese(k)} |
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return res |
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return {} |
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|
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def advanced_split(splitted_string, spliter, include_spliter=False): |
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splitted_string_tmp = [] |
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for string_ in splitted_string: |
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if spliter in string_: |
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splitted = string_.split(spliter) |
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for i, s in enumerate(splitted): |
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if include_spliter: |
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if i != len(splitted)-1: |
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splitted[i] += spliter |
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splitted[i] = splitted[i].strip() |
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for i in reversed(range(len(splitted))): |
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if not contains_chinese(splitted[i]): |
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splitted.pop(i) |
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splitted_string_tmp.extend(splitted) |
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else: |
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splitted_string_tmp.append(string_) |
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splitted_string = splitted_string_tmp |
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return splitted_string_tmp |
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cached_translation = {} |
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cached_translation = read_map_from_json(language=LANG) |
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|
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def trans(word_to_translate, language, special=False): |
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if len(word_to_translate) == 0: return {} |
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from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency |
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from toolbox import get_conf, ChatBotWithCookies |
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \ |
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get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY') |
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llm_kwargs = { |
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'api_key': API_KEY, |
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'llm_model': LLM_MODEL, |
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'top_p':1.0, |
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'max_length': None, |
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'temperature':0.4, |
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} |
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import random |
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N_EACH_REQ = random.randint(16, 32) |
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word_to_translate_split = split_list(word_to_translate, N_EACH_REQ) |
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inputs_array = [str(s) for s in word_to_translate_split] |
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inputs_show_user_array = inputs_array |
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history_array = [[] for _ in inputs_array] |
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if special: |
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sys_prompt_array = [f"Translate following names to English with CamelCase naming convention. Keep original format" for _ in inputs_array] |
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else: |
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sys_prompt_array = [f"Translate following sentences to {LANG}. E.g., You should translate sentences to the following format ['translation of sentence 1', 'translation of sentence 2']. Do NOT answer with Chinese!" for _ in inputs_array] |
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chatbot = ChatBotWithCookies(llm_kwargs) |
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gpt_say_generator = request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( |
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inputs_array, |
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inputs_show_user_array, |
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llm_kwargs, |
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chatbot, |
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history_array, |
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sys_prompt_array, |
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) |
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while True: |
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try: |
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gpt_say = next(gpt_say_generator) |
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print(gpt_say[1][0][1]) |
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except StopIteration as e: |
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result = e.value |
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break |
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translated_result = {} |
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for i, r in enumerate(result): |
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if i%2 == 1: |
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try: |
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res_before_trans = eval(result[i-1]) |
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res_after_trans = eval(result[i]) |
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if len(res_before_trans) != len(res_after_trans): |
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raise RuntimeError |
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for a,b in zip(res_before_trans, res_after_trans): |
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translated_result[a] = b |
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except: |
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print('GPT answers with unexpected format, some words may not be translated, but you can try again later to increase translation coverage.') |
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res_before_trans = eval(result[i-1]) |
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for a in res_before_trans: |
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translated_result[a] = None |
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return translated_result |
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def trans_json(word_to_translate, language, special=False): |
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if len(word_to_translate) == 0: return {} |
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from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency |
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from toolbox import get_conf, ChatBotWithCookies |
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \ |
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get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY') |
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llm_kwargs = { |
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'api_key': API_KEY, |
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'llm_model': LLM_MODEL, |
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'top_p':1.0, |
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'max_length': None, |
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'temperature':0.1, |
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} |
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import random |
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N_EACH_REQ = random.randint(16, 32) |
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random.shuffle(word_to_translate) |
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word_to_translate_split = split_list(word_to_translate, N_EACH_REQ) |
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inputs_array = [{k:"#" for k in s} for s in word_to_translate_split] |
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inputs_array = [ json.dumps(i, ensure_ascii=False) for i in inputs_array] |
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inputs_show_user_array = inputs_array |
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history_array = [[] for _ in inputs_array] |
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sys_prompt_array = [TransPrompt for _ in inputs_array] |
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chatbot = ChatBotWithCookies(llm_kwargs) |
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gpt_say_generator = request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( |
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inputs_array, |
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inputs_show_user_array, |
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llm_kwargs, |
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chatbot, |
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history_array, |
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sys_prompt_array, |
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) |
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while True: |
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try: |
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gpt_say = next(gpt_say_generator) |
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print(gpt_say[1][0][1]) |
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except StopIteration as e: |
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result = e.value |
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break |
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translated_result = {} |
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for i, r in enumerate(result): |
|
if i%2 == 1: |
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try: |
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translated_result.update(json.loads(result[i])) |
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except: |
|
print(result[i]) |
|
print(result) |
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return translated_result |
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def step_1_core_key_translate(): |
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LANG_STD = 'std' |
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def extract_chinese_characters(file_path): |
|
syntax = [] |
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with open(file_path, 'r', encoding='utf-8') as f: |
|
content = f.read() |
|
import ast |
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root = ast.parse(content) |
|
for node in ast.walk(root): |
|
if isinstance(node, ast.Name): |
|
if contains_chinese(node.id): syntax.append(node.id) |
|
if isinstance(node, ast.Import): |
|
for n in node.names: |
|
if contains_chinese(n.name): syntax.append(n.name) |
|
elif isinstance(node, ast.ImportFrom): |
|
for n in node.names: |
|
if contains_chinese(n.name): syntax.append(n.name) |
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|
|
for k in node.module.split('.'): |
|
if contains_chinese(k): syntax.append(k) |
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return syntax |
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|
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def extract_chinese_characters_from_directory(directory_path): |
|
chinese_characters = [] |
|
for root, dirs, files in os.walk(directory_path): |
|
if any([b in root for b in blacklist]): |
|
continue |
|
print(files) |
|
for file in files: |
|
if file.endswith('.py'): |
|
file_path = os.path.join(root, file) |
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chinese_characters.extend(extract_chinese_characters(file_path)) |
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return chinese_characters |
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|
|
directory_path = './' |
|
chinese_core_names = extract_chinese_characters_from_directory(directory_path) |
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chinese_core_keys = [name for name in chinese_core_names] |
|
chinese_core_keys_norepeat = [] |
|
for d in chinese_core_keys: |
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if d not in chinese_core_keys_norepeat: chinese_core_keys_norepeat.append(d) |
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need_translate = [] |
|
cached_translation = read_map_from_json(language=LANG_STD) |
|
cached_translation_keys = list(cached_translation.keys()) |
|
for d in chinese_core_keys_norepeat: |
|
if d not in cached_translation_keys: |
|
need_translate.append(d) |
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|
|
need_translate_mapping = trans(need_translate, language=LANG_STD, special=True) |
|
map_to_json(need_translate_mapping, language=LANG_STD) |
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cached_translation = read_map_from_json(language=LANG_STD) |
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cached_translation = dict(sorted(cached_translation.items(), key=lambda x: -len(x[0]))) |
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|
|
chinese_core_keys_norepeat_mapping = {} |
|
for k in chinese_core_keys_norepeat: |
|
chinese_core_keys_norepeat_mapping.update({k:cached_translation[k]}) |
|
chinese_core_keys_norepeat_mapping = dict(sorted(chinese_core_keys_norepeat_mapping.items(), key=lambda x: -len(x[0]))) |
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def copy_source_code(): |
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|
|
from toolbox import get_conf |
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import shutil |
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import os |
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try: shutil.rmtree(f'./multi-language/{LANG}/') |
|
except: pass |
|
os.makedirs(f'./multi-language', exist_ok=True) |
|
backup_dir = f'./multi-language/{LANG}/' |
|
shutil.copytree('./', backup_dir, ignore=lambda x, y: blacklist) |
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copy_source_code() |
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|
directory_path = f'./multi-language/{LANG}/' |
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for root, dirs, files in os.walk(directory_path): |
|
for file in files: |
|
if file.endswith('.py'): |
|
file_path = os.path.join(root, file) |
|
syntax = [] |
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|
|
with open(file_path, 'r', encoding='utf-8') as f: |
|
content = f.read() |
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|
|
for k, v in chinese_core_keys_norepeat_mapping.items(): |
|
content = content.replace(k, v) |
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|
|
with open(file_path, 'w', encoding='utf-8') as f: |
|
f.write(content) |
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|
|
def step_2_core_key_translate(): |
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|
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def load_string(strings, string_input): |
|
string_ = string_input.strip().strip(',').strip().strip('.').strip() |
|
if string_.startswith('[Local Message]'): |
|
string_ = string_.replace('[Local Message]', '') |
|
string_ = string_.strip().strip(',').strip().strip('.').strip() |
|
splitted_string = [string_] |
|
|
|
splitted_string = advanced_split(splitted_string, spliter=",", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="。", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter=")", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="(", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="(", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter=")", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="<", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter=">", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="[", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="]", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="【", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="】", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="?", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter=":", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter=":", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter=",", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="#", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="\n", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter=";", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="`", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter=" ", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="- ", include_spliter=False) |
|
splitted_string = advanced_split(splitted_string, spliter="---", include_spliter=False) |
|
|
|
|
|
for j, s in enumerate(splitted_string): |
|
if '.com' in s: continue |
|
if '\'' in s: continue |
|
if '\"' in s: continue |
|
strings.append([s,0]) |
|
|
|
|
|
def get_strings(node): |
|
strings = [] |
|
|
|
for child in ast.iter_child_nodes(node): |
|
node = child |
|
if isinstance(child, ast.Str): |
|
if contains_chinese(child.s): |
|
load_string(strings=strings, string_input=child.s) |
|
elif isinstance(child, ast.AST): |
|
strings.extend(get_strings(child)) |
|
return strings |
|
|
|
string_literals = [] |
|
directory_path = f'./multi-language/{LANG}/' |
|
for root, dirs, files in os.walk(directory_path): |
|
for file in files: |
|
if file.endswith('.py'): |
|
file_path = os.path.join(root, file) |
|
syntax = [] |
|
with open(file_path, 'r', encoding='utf-8') as f: |
|
content = f.read() |
|
|
|
comments_arr = [] |
|
for code_sp in content.splitlines(): |
|
comments = re.findall(r'#.*$', code_sp) |
|
for comment in comments: |
|
load_string(strings=comments_arr, string_input=comment) |
|
string_literals.extend(comments_arr) |
|
|
|
|
|
import ast |
|
tree = ast.parse(content) |
|
res = get_strings(tree, ) |
|
string_literals.extend(res) |
|
|
|
[print(s) for s in string_literals] |
|
chinese_literal_names = [] |
|
chinese_literal_names_norepeat = [] |
|
for string, offset in string_literals: |
|
chinese_literal_names.append(string) |
|
chinese_literal_names_norepeat = [] |
|
for d in chinese_literal_names: |
|
if d not in chinese_literal_names_norepeat: chinese_literal_names_norepeat.append(d) |
|
need_translate = [] |
|
cached_translation = read_map_from_json(language=LANG) |
|
cached_translation_keys = list(cached_translation.keys()) |
|
for d in chinese_literal_names_norepeat: |
|
if d not in cached_translation_keys: |
|
need_translate.append(d) |
|
|
|
|
|
up = trans_json(need_translate, language=LANG, special=False) |
|
map_to_json(up, language=LANG) |
|
cached_translation = read_map_from_json(language=LANG) |
|
LANG_STD = 'std' |
|
cached_translation.update(read_map_from_json(language=LANG_STD)) |
|
cached_translation = dict(sorted(cached_translation.items(), key=lambda x: -len(x[0]))) |
|
|
|
|
|
|
|
|
|
directory_path = f'./multi-language/{LANG}/' |
|
for root, dirs, files in os.walk(directory_path): |
|
for file in files: |
|
if file.endswith('.py'): |
|
file_path = os.path.join(root, file) |
|
syntax = [] |
|
|
|
with open(file_path, 'r', encoding='utf-8') as f: |
|
content = f.read() |
|
|
|
for k, v in cached_translation.items(): |
|
if v is None: continue |
|
if '"' in v: |
|
v = v.replace('"', "`") |
|
if '\'' in v: |
|
v = v.replace('\'', "`") |
|
content = content.replace(k, v) |
|
|
|
with open(file_path, 'w', encoding='utf-8') as f: |
|
f.write(content) |
|
|
|
if file.strip('.py') in cached_translation: |
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file_new = cached_translation[file.strip('.py')] + '.py' |
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file_path_new = os.path.join(root, file_new) |
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with open(file_path_new, 'w', encoding='utf-8') as f: |
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f.write(content) |
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os.remove(file_path) |
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step_1_core_key_translate() |
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step_2_core_key_translate() |
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print('Finished, checkout generated results at ./multi-language/') |