611 lines
29 KiB
Python
611 lines
29 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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from urllib.error import HTTPError
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import requests
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import requests.adapters
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import time
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from transformers import __version__ as transformers_version
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from transformers import PreTrainedModel
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import packaging.version
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from tqdm.auto import tqdm
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import os
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import itertools
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import hashlib
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import huggingface_hub
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import packaging.version
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from pathlib import Path
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from typing import List, Optional
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HAS_ACCELERATE = packaging.version.parse(transformers_version) >= packaging.version.parse("4.20.0.dev0")
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try:
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import accelerate
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except ImportError:
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HAS_ACCELERATE = False
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vars = None
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args = None
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num_shards: Optional[int] = None
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current_shard = 0
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from_pretrained_model_name = ""
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from_pretrained_index_filename: Optional[str] = None
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from_pretrained_kwargs = {}
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bar = None
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layers_module_names: Optional[List[str]] = None
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module_names: Optional[List[str]] = None
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named_buffers: Optional[List[tuple]] = None
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default_sampler_order = [6, 0, 1, 2, 3, 4, 5]
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emit = 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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# Don't add sentence spacing if submission is empty or starts with whitespace
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if(len(txt) == 0 or len(txt) != len(txt.lstrip())):
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return txt
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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 != " "):
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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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if(vars.newlinemode == "ns"):
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return txt.replace("</s>", '')
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return txt
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#==================================================================#
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# Returns number of layers given an HF model config
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#==================================================================#
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def num_layers(config):
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return config["n_layer"] if isinstance(config, dict) else config.num_layers if hasattr(config, "num_layers") else config.n_layer if hasattr(config, "n_layer") else config.num_hidden_layers if hasattr(config, 'num_hidden_layers') else None
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#==================================================================#
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# Downloads huggingface checkpoints using aria2c if possible
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#==================================================================#
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from flask_socketio import emit
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def _download_with_aria2(aria2_config: str, total_length: int, directory: str = ".", user_agent=None, force_download=False, use_auth_token=None):
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class Send_to_socketio(object):
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def write(self, bar):
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bar = bar.replace("\r", "").replace("\n", "")
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if bar != "":
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try:
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print('\r' + bar, end='')
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try:
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emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True)
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except:
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pass
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eventlet.sleep(seconds=0)
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except:
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pass
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def flush(self):
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pass
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import transformers
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aria2_port = 6799 if vars is None else vars.aria2_port
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lengths = {}
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s = requests.Session()
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s.mount("http://", requests.adapters.HTTPAdapter(max_retries=requests.adapters.Retry(total=120, backoff_factor=1)))
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bar = None
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done = False
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secret = os.urandom(17).hex()
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try:
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with tempfile.NamedTemporaryFile("w+b", delete=False) as f:
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f.write(aria2_config)
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f.flush()
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p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--enable-rpc=true", f"--rpc-secret={secret}", "--rpc-listen-port", str(aria2_port), "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming=false", "-d", directory, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {use_auth_token}'"] if use_auth_token else []), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
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while p.poll() is None:
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r = s.post(f"http://localhost:{aria2_port}/jsonrpc", json={"jsonrpc": "2.0", "id": "kai", "method": "aria2.tellActive", "params": [f"token:{secret}"]}).json()["result"]
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if not r:
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s.close()
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if bar is not None:
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bar.n = bar.total
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bar.close()
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p.terminate()
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done = True
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break
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if bar is None:
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bar = tqdm(total=total_length, desc=f"[aria2] Downloading model", unit="B", unit_scale=True, unit_divisor=1000, file=Send_to_socketio())
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visited = set()
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for x in r:
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filename = x["files"][0]["path"]
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lengths[filename] = (int(x["completedLength"]), int(x["totalLength"]))
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visited.add(filename)
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for k, v in lengths.items():
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if k not in visited:
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lengths[k] = (v[1], v[1])
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bar.n = sum(v[0] for v in lengths.values())
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bar.update()
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time.sleep(0.1)
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path = f.name
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except Exception as e:
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p.terminate()
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raise e
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finally:
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try:
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os.remove(path)
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except OSError:
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pass
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code = p.wait()
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if not done and code:
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raise OSError(f"aria2 exited with exit code {code}")
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def _transformers22_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, **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 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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_revision = args.revision if args.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
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sharded = 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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storage_folder = os.path.join(_cache_dir, huggingface_hub.file_download.repo_folder_name(repo_id=pretrained_model_name_or_path, repo_type="model"))
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os.makedirs(storage_folder, exist_ok=True)
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def is_cached(filename):
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try:
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huggingface_hub.hf_hub_download(pretrained_model_name_or_path, filename, cache_dir=cache_dir, local_files_only=True, revision=_revision)
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except ValueError:
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return False
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return True
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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 = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=_revision)
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if is_cached(filename) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
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break
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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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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 = huggingface_hub.hf_hub_download(pretrained_model_name_or_path, filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, 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 = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=_revision) for n in filenames]
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if not force_download:
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urls = [u for u, n in zip(urls, filenames) if not is_cached(n)]
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if not urls:
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return
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blob_paths = []
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# This section is a modified version of hf_hub_download from huggingface_hub
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# See https://github.com/huggingface/huggingface_hub/blob/main/LICENSE for license
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for u, n in zip(urls, filenames):
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relative_filename = os.path.join(*n.split("/"))
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if not local_files_only:
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try:
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r = huggingface_hub.file_download._request_wrapper(
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method="HEAD",
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url=u,
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headers=headers,
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allow_redirects=False,
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follow_relative_redirects=True,
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proxies=proxies,
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timeout=10,
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)
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try:
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r.raise_for_status()
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except HTTPError as e:
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error_code = r.headers.get("X-Error-Code")
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if error_code != "EntryNotFound":
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raise RuntimeError(f"HEAD {u} failed with error code {r.status_code}")
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commit_hash = r.headers.get(huggingface_hub.file_download.HUGGINGFACE_HEADER_X_REPO_COMMIT)
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if commit_hash is not None:
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no_exist_file_path = (
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Path(storage_folder)
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/ ".no_exist"
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/ commit_hash
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/ relative_filename
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)
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no_exist_file_path.parent.mkdir(parents=True, exist_ok=True)
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no_exist_file_path.touch()
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huggingface_hub.file_download._cache_commit_hash_for_specific_revision(
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storage_folder, _revision, commit_hash
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)
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raise
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commit_hash = r.headers[huggingface_hub.file_download.HUGGINGFACE_HEADER_X_REPO_COMMIT]
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if commit_hash is None:
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raise OSError(
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"Distant resource does not seem to be on huggingface.co (missing"
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" commit header)."
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)
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etag = r.headers.get(huggingface_hub.file_download.HUGGINGFACE_HEADER_X_LINKED_ETAG) or r.headers.get(
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"ETag"
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)
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# We favor a custom header indicating the etag of the linked resource, and
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# we fallback to the regular etag header.
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# If we don't have any of those, raise an error.
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if etag is None:
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raise OSError(
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"Distant resource does not have an ETag, we won't be able to"
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" reliably ensure reproducibility."
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)
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etag = huggingface_hub.file_download._normalize_etag(etag)
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# In case of a redirect, save an extra redirect on the request.get call,
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# and ensure we download the exact atomic version even if it changed
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# between the HEAD and the GET (unlikely, but hey).
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# Useful for lfs blobs that are stored on a CDN.
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if 300 <= r.status_code <= 399:
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url_to_download = r.headers["Location"]
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if (
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"lfs.huggingface.co" in url_to_download
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or "lfs-staging.huggingface.co" in url_to_download
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):
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# Remove authorization header when downloading a LFS blob
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headers.pop("authorization", None)
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except (requests.exceptions.SSLError, requests.exceptions.ProxyError):
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# Actually raise for those subclasses of ConnectionError
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raise
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except (
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requests.exceptions.ConnectionError,
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requests.exceptions.Timeout,
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huggingface_hub.file_download.OfflineModeIsEnabled,
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):
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# Otherwise, our Internet connection is down.
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# etag is None
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pass
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if etag is None:
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# In those cases, we cannot force download.
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if force_download:
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raise ValueError(
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"We have no connection or you passed local_files_only, so"
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" force_download is not an accepted option."
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)
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if huggingface_hub.file_download.REGEX_COMMIT_HASH.match(_revision):
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commit_hash = _revision
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else:
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ref_path = os.path.join(storage_folder, "refs", _revision)
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with open(ref_path) as f:
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commit_hash = f.read()
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pointer_path = os.path.join(
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storage_folder, "snapshots", commit_hash, relative_filename
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)
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if os.path.exists(pointer_path):
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return pointer_path
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# If we couldn't find an appropriate file on disk,
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# raise an error.
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# If files cannot be found and local_files_only=True,
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# the models might've been found if local_files_only=False
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# Notify the user about that
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if local_files_only:
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raise huggingface_hub.file_download.LocalEntryNotFoundError(
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"Cannot find the requested files in the disk cache and"
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" outgoing traffic has been disabled. To enable hf.co look-ups"
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" and downloads online, set 'local_files_only' to False."
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)
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else:
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raise huggingface_hub.file_download.LocalEntryNotFoundError(
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"Connection error, and we cannot find the requested files in"
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" the disk cache. Please try again or make sure your Internet"
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" connection is on."
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)
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# From now on, etag and commit_hash are not None.
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blob_path = os.path.join(storage_folder, "blobs", etag)
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pointer_path = os.path.join(
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storage_folder, "snapshots", commit_hash, relative_filename
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)
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os.makedirs(os.path.dirname(blob_path), exist_ok=True)
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os.makedirs(os.path.dirname(pointer_path), exist_ok=True)
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# if passed revision is not identical to commit_hash
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# then revision has to be a branch name or tag name.
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# In that case store a ref.
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huggingface_hub.file_download._cache_commit_hash_for_specific_revision(storage_folder, _revision, commit_hash)
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if os.path.exists(pointer_path) and not force_download:
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return pointer_path
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if os.path.exists(blob_path) and not force_download:
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# we have the blob already, but not the pointer
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huggingface_hub.file_download.logger.info("creating pointer to %s from %s", blob_path, pointer_path)
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huggingface_hub.file_download._create_relative_symlink(blob_path, pointer_path)
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return pointer_path
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# Some Windows versions do not allow for paths longer than 255 characters.
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# In this case, we must specify it is an extended path by using the "\\?\" prefix.
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if os.name == "nt" and len(os.path.abspath(blob_path)) > 255:
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blob_path = "\\\\?\\" + os.path.abspath(blob_path)
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blob_paths.append(blob_path)
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filenames = blob_paths
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headers = [requests.head(u, headers=headers, allow_redirects=True, proxies=proxies, timeout=10).headers for u in urls]
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for n in filenames:
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prefix, suffix = n.rsplit(os.sep, 1)
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path = os.path.join(prefix, "kai-tempfile." + suffix + ".aria2")
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if os.path.exists(path):
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os.remove(path)
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path = os.path.join(prefix, "kai-tempfile." + suffix)
|
||
if os.path.exists(path):
|
||
os.remove(path)
|
||
total_length = sum(int(h["Content-Length"]) for h in headers)
|
||
aria2_config = "\n".join(f"{u}\n out={os.path.join(prefix, 'kai-tempfile.' + suffix)}" for u, n in zip(urls, filenames) for prefix, suffix in [n.rsplit(os.sep, 1)]).encode()
|
||
_download_with_aria2(aria2_config, total_length, use_auth_token=token if use_auth_token else None, user_agent=user_agent, force_download=force_download)
|
||
for u, n in zip(urls, filenames):
|
||
prefix, suffix = n.rsplit(os.sep, 1)
|
||
os.rename(os.path.join(prefix, "kai-tempfile." + suffix), os.path.join(prefix, suffix))
|
||
|
||
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, **kwargs):
|
||
import transformers
|
||
import transformers.modeling_utils
|
||
from huggingface_hub import HfFolder
|
||
_revision = args.revision if args.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
|
||
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 packaging.version.parse(transformers.__version__) >= packaging.version.parse("4.22.0.dev0"):
|
||
return _transformers22_aria2_hook(pretrained_model_name_or_path, force_download=force_download, cache_dir=cache_dir, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, **kwargs)
|
||
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:
|
||
huggingface_hub.cached_download(url, cache_dir=cache_dir, local_files_only=True)
|
||
except ValueError:
|
||
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 = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=_revision)
|
||
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 = huggingface_hub.cached_download(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 = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=_revision) 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]]
|
||
headers = [requests.head(u, headers=headers, allow_redirects=True, proxies=proxies, timeout=10).headers for u in urls]
|
||
filenames = [hashlib.sha256(u.encode("utf-8")).hexdigest() + "." + hashlib.sha256(t.encode("utf-8")).hexdigest() 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)
|
||
total_length = sum(int(h["Content-Length"]) for h in headers)
|
||
aria2_config = "\n".join(f"{u}\n out=kai-tempfile.{n}" for u, n in zip(urls, filenames)).encode()
|
||
_download_with_aria2(aria2_config, total_length, directory=_cache_dir, use_auth_token=token if use_auth_token else None, user_agent=user_agent, force_download=force_download)
|
||
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)
|
||
|
||
#==================================================================#
|
||
# Given the path to a pytorch_model.bin.index.json, returns how many
|
||
# shards there are in the model
|
||
#==================================================================#
|
||
def get_num_shards(filename):
|
||
with open(filename) as f:
|
||
map_data = json.load(f)
|
||
return len(set(map_data["weight_map"].values()))
|
||
|
||
#==================================================================#
|
||
# Given the name/path of a sharded model and the path to a
|
||
# pytorch_model.bin.index.json, returns a list of weight names in the
|
||
# sharded model. Requires lazy loader to be enabled to work properl
|
||
#==================================================================#
|
||
def get_sharded_checkpoint_num_tensors(pretrained_model_name_or_path, filename, cache_dir=None, force_download=False, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, **kwargs):
|
||
import transformers.modeling_utils
|
||
import torch
|
||
_revision = args.revision if args.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
|
||
shard_paths, _ = transformers.modeling_utils.get_checkpoint_shard_files(pretrained_model_name_or_path, filename, 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, revision=_revision)
|
||
return list(itertools.chain(*(torch.load(p, map_location="cpu").keys() for p in shard_paths)))
|
||
|
||
#==================================================================#
|
||
# Given a PreTrainedModel, returns the list of module names that correspond
|
||
# to the model's hidden layers.
|
||
#==================================================================#
|
||
def get_layers_module_names(model: PreTrainedModel) -> List[str]:
|
||
names: List[str] = []
|
||
def recurse(module, head=""):
|
||
for c in module.named_children():
|
||
name = head + c[0]
|
||
if c[0].isnumeric() and any(c[1].__class__.__name__.endswith(suffix) for suffix in ("Block", "Layer")):
|
||
names.append(name)
|
||
else:
|
||
recurse(c[1], head=name + ".")
|
||
recurse(model)
|
||
return names
|
||
|
||
#==================================================================#
|
||
# Given a PreTrainedModel, returns the module name that corresponds
|
||
# to the model's input embeddings.
|
||
#==================================================================#
|
||
def get_input_embeddings_module_name(model: PreTrainedModel) -> str:
|
||
embeddings = model.get_input_embeddings()
|
||
def recurse(module, head=""):
|
||
for c in module.named_children():
|
||
name = head + c[0]
|
||
if c[1] is embeddings:
|
||
return name
|
||
else:
|
||
return recurse(c[1], head=name + ".")
|
||
return recurse(model)
|
||
|
||
#==================================================================#
|
||
# Given a PreTrainedModel and a list of module names, returns a list
|
||
# of module names such that the union of the set of modules given as input
|
||
# and the set of modules returned as output contains all modules in the model.
|
||
#==================================================================#
|
||
def get_missing_module_names(model: PreTrainedModel, names: List[str]) -> List[str]:
|
||
missing_names: List[str] = []
|
||
def recurse(module, head=""):
|
||
for c in module.named_children():
|
||
name = head + c[0]
|
||
if any(name.startswith(n) for n in names):
|
||
continue
|
||
if next(c[1].named_children(), None) is None:
|
||
missing_names.append(name)
|
||
else:
|
||
recurse(c[1], head=name + ".")
|
||
recurse(model)
|
||
return missing_names |