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remove merge markers from preload_models.py

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Lincoln Stein 2022-11-02 22:39:35 -04:00
parent 9666f466ab
commit 0196571a12
1 changed files with 1 additions and 25 deletions
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26
scripts/preload_models.py
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@ -17,6 +17,7 @@ from omegaconf import OmegaConf
from huggingface_hub import HfFolder, hf_hub_url from huggingface_hub import HfFolder, hf_hub_url
from pathlib import Path from pathlib import Path
from getpass_asterisk import getpass_asterisk from getpass_asterisk import getpass_asterisk
from transformers import CLIPTokenizer, CLIPTextModel
import traceback import traceback
import requests import requests
import clip import clip
@ -223,12 +224,7 @@ This involves a few easy steps.
''' '''
) )
input('Press <enter> when you are ready to continue:') input('Press <enter> when you are ready to continue:')
<<<<<<< HEAD
from huggingface_hub import HfFolder
=======
print('(Fetching Hugging Face token from cache...',end='') print('(Fetching Hugging Face token from cache...',end='')
>>>>>>> spezialspezial-patch-9
access_token = HfFolder.get_token() access_token = HfFolder.get_token()
if access_token is not None: if access_token is not None:
print('found') print('found')
@ -293,11 +289,6 @@ def download_weight_datasets(models:dict, access_token:str):
#--------------------------------------------- #---------------------------------------------
def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool: def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool:
<<<<<<< HEAD
from huggingface_hub import hf_hub_url
=======
>>>>>>> spezialspezial-patch-9
model_dest = os.path.join(Model_dir, model_name) model_dest = os.path.join(Model_dir, model_name)
os.makedirs(os.path.dirname(model_dest), exist_ok=True) os.makedirs(os.path.dirname(model_dest), exist_ok=True)
url = hf_hub_url(repo_id, model_name) url = hf_hub_url(repo_id, model_name)
@ -353,11 +344,7 @@ def update_config_file(successfully_downloaded:dict,opt:dict):
try: try:
if os.path.exists(Config_file): if os.path.exists(Config_file):
<<<<<<< HEAD
print(f'** {Config_file} exists. Renaming to {Config_file}.orig') print(f'** {Config_file} exists. Renaming to {Config_file}.orig')
=======
print(f'* {Config_file} exists. Renaming to {Config_file}.orig')
>>>>>>> spezialspezial-patch-9
os.rename(Config_file,f'{Config_file}.orig') os.rename(Config_file,f'{Config_file}.orig')
tmpfile = os.path.join(os.path.dirname(Config_file),'new_config.tmp') tmpfile = os.path.join(os.path.dirname(Config_file),'new_config.tmp')
with open(tmpfile, 'w') as outfile: with open(tmpfile, 'w') as outfile:
@ -411,11 +398,7 @@ def new_config_file_contents(successfully_downloaded:dict, Config_file:str)->str
# this will preload the Bert tokenizer fles # this will preload the Bert tokenizer fles
def download_bert(): def download_bert():
print('Installing bert tokenizer (ignore deprecation errors)...', end='') print('Installing bert tokenizer (ignore deprecation errors)...', end='')
<<<<<<< HEAD
from transformers import BertTokenizerFast, AutoFeatureExtractor
=======
sys.stdout.flush() sys.stdout.flush()
>>>>>>> spezialspezial-patch-9
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning) warnings.filterwarnings('ignore', category=DeprecationWarning)
from transformers import BertTokenizerFast, AutoFeatureExtractor from transformers import BertTokenizerFast, AutoFeatureExtractor
@ -433,13 +416,6 @@ def download_kornia():
#--------------------------------------------- #---------------------------------------------
def download_clip(): def download_clip():
print('Loading CLIP model...',end='') print('Loading CLIP model...',end='')
<<<<<<< HEAD
from transformers import CLIPTokenizer, CLIPTextModel
=======
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
from transformers import CLIPTokenizer, CLIPTextModel
>>>>>>> spezialspezial-patch-9
sys.stdout.flush() sys.stdout.flush()
version = 'openai/clip-vit-large-patch14' version = 'openai/clip-vit-large-patch14'
tokenizer = CLIPTokenizer.from_pretrained(version) tokenizer = CLIPTokenizer.from_pretrained(version)