KoboldAI-Client/utils.py

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from threading import Timer
import re
import shutil
import json
import subprocess
import tempfile
import requests
import os
vars = None
#==================================================================#
# Decorator to prevent a function's actions from being run until
# at least x seconds have passed without the function being called
#==================================================================#
def debounce(wait):
def decorator(fun):
def debounced(*args, **kwargs):
def call_it():
fun(*args, **kwargs)
try:
debounced.t.cancel()
except AttributeError:
pass
debounced.t = Timer(wait, call_it)
debounced.t.start()
return debounced
return decorator
#==================================================================#
# Replace fancy quotes and apostrope's with standard ones
#==================================================================#
def fixquotes(txt):
txt = txt.replace("", '"')
txt = txt.replace("", '"')
txt = txt.replace("", "'")
txt = txt.replace("`", "'")
return txt
#==================================================================#
#
#==================================================================#
def trimincompletesentence(txt):
# Cache length of text
ln = len(txt)
# Find last instance of punctuation (Borrowed from Clover-Edition by cloveranon)
lastpunc = max(txt.rfind("."), txt.rfind("!"), txt.rfind("?"))
# Is this the end of a quote?
if(lastpunc < ln-1):
if(txt[lastpunc+1] == '"'):
lastpunc = lastpunc + 1
if(lastpunc >= 0):
txt = txt[:lastpunc+1]
return txt
#==================================================================#
#
#==================================================================#
def replaceblanklines(txt):
txt = txt.replace("\n\n", "\n")
return txt
#==================================================================#
#
#==================================================================#
def removespecialchars(txt, vars=None):
if vars is None or vars.actionmode == 0:
txt = re.sub(r"[#/@%<>{}+=~|\^]", "", txt)
else:
txt = re.sub(r"[#/@%{}+=~|\^]", "", txt)
return txt
#==================================================================#
# If the next action follows a sentence closure, add a space
#==================================================================#
def addsentencespacing(txt, vars):
# Get last character of last action
if(len(vars.actions) > 0):
if(len(vars.actions[vars.actions.get_last_key()]) > 0):
action = vars.actions[vars.actions.get_last_key()]
lastchar = action[-1] if len(action) else ""
else:
# Last action is blank, this should never happen, but
# since it did let's bail out.
return txt
else:
action = vars.prompt
lastchar = action[-1] if len(action) else ""
if(lastchar == "." or lastchar == "!" or lastchar == "?" or lastchar == "," or lastchar == ";" or lastchar == ":"):
txt = " " + txt
return txt
def singlelineprocessing(txt, vars):
txt = vars.regex_sl.sub('', txt)
if(len(vars.actions) > 0):
if(len(vars.actions[vars.actions.get_last_key()]) > 0):
action = vars.actions[vars.actions.get_last_key()]
lastchar = action[-1] if len(action) else ""
else:
# Last action is blank, this should never happen, but
# since it did let's bail out.
return txt
else:
action = vars.prompt
lastchar = action[-1] if len(action) else ""
if(lastchar != "\n"):
txt = txt + "\n"
return txt
#==================================================================#
# Cleans string for use in file name
#==================================================================#
def cleanfilename(filename):
filteredcharacters = ('/','\\')
filename = "".join(c for c in filename if c not in filteredcharacters).rstrip()
return filename
#==================================================================#
# Newline substitution for fairseq models
#==================================================================#
def encodenewlines(txt):
if(vars.newlinemode == "s"):
return txt.replace('\n', "</s>")
return txt
def decodenewlines(txt):
if(vars.newlinemode == "s"):
return txt.replace("</s>", '\n')
return txt
#==================================================================#
# Downloads sharded huggingface checkpoints using aria2c if possible
#==================================================================#
def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_dir=None, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, mirror=None, **kwargs):
import transformers
import transformers.modeling_utils
from huggingface_hub import HfFolder
if shutil.which("aria2c") is None: # Don't do anything if aria2 is not installed
return
if local_files_only: # If local_files_only is true, we obviously don't need to download anything
return
if os.path.isdir(pretrained_model_name_or_path) or os.path.isfile(pretrained_model_name_or_path) or os.path.isfile(pretrained_model_name_or_path + ".index") or transformers.modeling_utils.is_remote_url(pretrained_model_name_or_path):
return
if proxies:
print("WARNING: KoboldAI does not support using aria2 to download models from huggingface.co through a proxy. Disabling aria2 download mode.")
return
if use_auth_token:
if isinstance(use_auth_token, str):
token = use_auth_token
else:
token = HfFolder.get_token()
if token is None:
raise EnvironmentError("You specified use_auth_token=True, but a huggingface token was not found.")
_cache_dir = str(cache_dir) if cache_dir is not None else transformers.TRANSFORMERS_CACHE
sharded = False
headers = {"user-agent": transformers.file_utils.http_user_agent(user_agent)}
if use_auth_token:
headers["authorization"] = f"Bearer {use_auth_token}"
def is_cached(url):
try:
transformers.file_utils.get_from_cache(url, cache_dir=cache_dir, local_files_only=True)
except FileNotFoundError:
return False
return True
while True: # Try to get the huggingface.co URL of the model's pytorch_model.bin or pytorch_model.bin.index.json file
try:
filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME
except AttributeError:
return
url = transformers.file_utils.hf_bucket_url(pretrained_model_name_or_path, filename, revision=revision, mirror=mirror)
if is_cached(url) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
break
if sharded:
return
else:
sharded = True
if not sharded: # If the model has a pytorch_model.bin file, that's the only file to download
filenames = [transformers.modeling_utils.WEIGHTS_NAME]
else: # Otherwise download the pytorch_model.bin.index.json and then let aria2 download all the pytorch_model-#####-of-#####.bin files mentioned inside it
map_filename = transformers.file_utils.cached_path(url, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, use_auth_token=use_auth_token, user_agent=user_agent)
with open(map_filename) as f:
map_data = json.load(f)
filenames = set(map_data["weight_map"].values())
urls = [transformers.file_utils.hf_bucket_url(pretrained_model_name_or_path, n, revision=revision, mirror=mirror) for n in filenames]
if not force_download:
urls = [u for u in urls if not is_cached(u)]
if not urls:
return
etags = [h.get("X-Linked-Etag") or h.get("ETag") for u in urls for h in [requests.head(u, headers=headers, allow_redirects=False, proxies=proxies, timeout=10).headers]]
filenames = [transformers.file_utils.url_to_filename(u, t) for u, t in zip(urls, etags)]
for n in filenames:
path = os.path.join(_cache_dir, "kai-tempfile." + n + ".aria2")
if os.path.exists(path):
os.remove(path)
path = os.path.join(_cache_dir, "kai-tempfile." + n)
if os.path.exists(path):
os.remove(path)
if force_download:
path = os.path.join(_cache_dir, n + ".json")
if os.path.exists(path):
os.remove(path)
path = os.path.join(_cache_dir, n)
if os.path.exists(path):
os.remove(path)
aria2_config = "\n".join(f"{u}\n out=kai-tempfile.{n}" for u, n in zip(urls, filenames)).encode()
with tempfile.NamedTemporaryFile("w+b", delete=False) as f:
f.write(aria2_config)
f.flush()
p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming", "false", "-d", _cache_dir, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {token}'"] if use_auth_token else []), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
for line in p.stdout:
print(line.decode(), end="", flush=True)
path = f.name
try:
os.remove(path)
except OSError:
pass
for u, t, n in zip(urls, etags, filenames):
os.rename(os.path.join(_cache_dir, "kai-tempfile." + n), os.path.join(_cache_dir, n))
with open(os.path.join(_cache_dir, n + ".json"), "w") as f:
json.dump({"url": u, "etag": t}, f)