import re
import numpy as np
import psutil
import os
from tqdm.auto import tqdm
import logging
logger = logging.getLogger(__name__)
def get_current_ram_usage():
ram = psutil.virtual_memory()
return ram.available / 1024 / 1024 / 1024, ram.total / 1024 / 1024 / 1024
def download_models(models):
for model in tqdm(models, desc="Downloading models"):
logger.info(f"Downloading {model}")
for i in range(0, 5):
curr_dir = f"{model}/train_{i}/best_model/"
os.makedirs(curr_dir)
os.system(
f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/config.json -P {curr_dir}"
)
os.system(
f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/pytorch_model.bin -P {curr_dir}"
)
os.system(
f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/special_tokens_map.json -P {curr_dir}"
)
os.system(
f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/tokenizer_config.json -P {curr_dir}"
)
os.system(
f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/training_args.bin -P {curr_dir}"
)
os.system(
f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/vocab.txt -P {curr_dir}"
)
def softmax(x):
return np.exp(x) / sum(np.exp(x))
def ga(file):
code = """
"""
a = os.path.dirname(file) + "/static/index.html"
with open(a, "r") as f:
data = f.read()
if len(re.findall("G-", data)) == 0:
with open(a, "w") as ff:
newdata = re.sub("", "" + code, data)
ff.write(newdata)