219 lines
10 KiB
Python
219 lines
10 KiB
Python
from threading import Timer
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import re
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import shutil
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import json
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import subprocess
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import tempfile
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import requests
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import os
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vars = None
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#==================================================================#
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# Decorator to prevent a function's actions from being run until
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# at least x seconds have passed without the function being called
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#==================================================================#
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def debounce(wait):
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def decorator(fun):
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def debounced(*args, **kwargs):
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def call_it():
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fun(*args, **kwargs)
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try:
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debounced.t.cancel()
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except AttributeError:
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pass
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debounced.t = Timer(wait, call_it)
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debounced.t.start()
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return debounced
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return decorator
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#==================================================================#
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# Replace fancy quotes and apostrope's with standard ones
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#==================================================================#
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def fixquotes(txt):
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txt = txt.replace("“", '"')
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txt = txt.replace("”", '"')
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txt = txt.replace("’", "'")
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txt = txt.replace("`", "'")
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return txt
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#==================================================================#
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#
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#==================================================================#
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def trimincompletesentence(txt):
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# Cache length of text
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ln = len(txt)
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# Find last instance of punctuation (Borrowed from Clover-Edition by cloveranon)
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lastpunc = max(txt.rfind("."), txt.rfind("!"), txt.rfind("?"))
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# Is this the end of a quote?
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if(lastpunc < ln-1):
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if(txt[lastpunc+1] == '"'):
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lastpunc = lastpunc + 1
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if(lastpunc >= 0):
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txt = txt[:lastpunc+1]
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return txt
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#==================================================================#
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#
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#==================================================================#
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def replaceblanklines(txt):
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txt = txt.replace("\n\n", "\n")
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return txt
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#==================================================================#
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#
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#==================================================================#
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def removespecialchars(txt, vars=None):
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if vars is None or vars.actionmode == 0:
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txt = re.sub(r"[#/@%<>{}+=~|\^]", "", txt)
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else:
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txt = re.sub(r"[#/@%{}+=~|\^]", "", txt)
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return txt
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#==================================================================#
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# If the next action follows a sentence closure, add a space
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#==================================================================#
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def addsentencespacing(txt, vars):
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# Get last character of last action
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if(len(vars.actions) > 0):
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if(len(vars.actions[vars.actions.get_last_key()]) > 0):
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action = vars.actions[vars.actions.get_last_key()]
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lastchar = action[-1] if len(action) else ""
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else:
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# Last action is blank, this should never happen, but
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# since it did let's bail out.
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return txt
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else:
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action = vars.prompt
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lastchar = action[-1] if len(action) else ""
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if(lastchar == "." or lastchar == "!" or lastchar == "?" or lastchar == "," or lastchar == ";" or lastchar == ":"):
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txt = " " + txt
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return txt
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def singlelineprocessing(txt, vars):
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txt = vars.regex_sl.sub('', txt)
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if(len(vars.actions) > 0):
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if(len(vars.actions[vars.actions.get_last_key()]) > 0):
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action = vars.actions[vars.actions.get_last_key()]
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lastchar = action[-1] if len(action) else ""
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else:
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# Last action is blank, this should never happen, but
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# since it did let's bail out.
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return txt
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else:
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action = vars.prompt
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lastchar = action[-1] if len(action) else ""
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if(lastchar != "\n"):
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txt = txt + "\n"
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return txt
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#==================================================================#
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# Cleans string for use in file name
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#==================================================================#
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def cleanfilename(filename):
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filteredcharacters = ('/','\\')
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filename = "".join(c for c in filename if c not in filteredcharacters).rstrip()
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return filename
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#==================================================================#
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# Newline substitution for fairseq models
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#==================================================================#
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def encodenewlines(txt):
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if(vars.newlinemode == "s"):
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return txt.replace('\n', "</s>")
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return txt
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def decodenewlines(txt):
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if(vars.newlinemode == "s"):
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return txt.replace("</s>", '\n')
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return txt
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#==================================================================#
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# Downloads sharded huggingface checkpoints using aria2c if possible
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#==================================================================#
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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):
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import transformers
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import transformers.modeling_utils
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from huggingface_hub import HfFolder
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if shutil.which("aria2c") is None: # Don't do anything if aria2 is not installed
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return
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if os.path.isdir(pretrained_model_name_or_path) or os.path.isfile(pretrained_model_name_or_path) or transformers.modeling_utils.is_remote_url(pretrained_model_name_or_path) or os.path.isfile(pretrained_model_name_or_path + ".index"):
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return
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if proxies:
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print("WARNING: KoboldAI does not support using aria2 to download models from huggingface.co through a proxy. Disabling aria2 download mode.")
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return
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if use_auth_token:
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if isinstance(use_auth_token, str):
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token = use_auth_token
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else:
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token = HfFolder.get_token()
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if token is None:
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raise EnvironmentError("You specified use_auth_token=True, but a huggingface token was not found.")
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_cache_dir = str(cache_dir) if cache_dir is not None else transformers.TRANSFORMERS_CACHE
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sharded = False
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while True: # Try to get the huggingface.co URL of the model's pytorch_model.bin or pytorch_model.bin.index.json file
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try:
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filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME
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except AttributeError:
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return
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url = transformers.file_utils.hf_bucket_url(pretrained_model_name_or_path, filename, revision=revision, mirror=mirror)
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try:
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transformers.file_utils.get_from_cache(url, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent)
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except transformers.file_utils.RepositoryNotFoundError:
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raise EnvironmentError(
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f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
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"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
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"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
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"login` and pass `use_auth_token=True`."
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)
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except transformers.file_utils.RevisionNotFoundError:
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raise EnvironmentError(
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f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
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"this model name. Check the model page at "
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f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
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)
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except transformers.file_utils.EntryNotFoundError:
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if sharded:
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return
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else:
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sharded = True
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else:
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break
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if not sharded: # If the model has a pytorch_model.bin file, that's the only file to download
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filenames = [transformers.modeling_utils.WEIGHTS_NAME]
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else: # Otherwise download the pytorch_model.bin.index.json and then let aria2 download all the pytorch_model-#####-of-#####.bin files mentioned inside it
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map_filename = transformers.file_utils.cached_path(url, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent)
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with open(map_filename) as f:
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map_data = json.load(f)
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filenames = set(map_data["weight_map"].values())
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urls = [transformers.file_utils.hf_bucket_url(pretrained_model_name_or_path, n, revision=revision, mirror=mirror) for n in filenames]
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def is_cached(url):
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try:
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transformers.file_utils.get_from_cache(url, cache_dir=cache_dir, local_files_only=True)
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except FileNotFoundError:
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return False
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return True
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if not force_download:
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if all(is_cached(u) for u in urls):
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return
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elif local_files_only:
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raise FileNotFoundError("Cannot find the requested files in the cached path and outgoing traffic has been disabled. To enable model look-ups and downloads online, set 'local_files_only' to False.")
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headers = {"user-agent": transformers.file_utils.http_user_agent(user_agent)}
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if use_auth_token:
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headers["authorization"] = f"Bearer {use_auth_token}"
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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]]
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filenames = [transformers.file_utils.url_to_filename(u, t) for u, t in zip(urls, etags)]
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aria2_config = "\n".join(f"{u}\n out={n}" for u, n in zip(urls, filenames)).encode()
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with tempfile.NamedTemporaryFile("w+b", delete=True) as f:
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f.write(aria2_config)
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p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "-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)
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for line in p.stdout:
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print(line.decode(), end="", flush=True)
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for u, t, n in zip(urls, etags, filenames):
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with open(os.path.join(_cache_dir, n + ".json"), "w") as f:
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json.dump({"url": u, "etag": t}, f)
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