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import re |
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import numpy as np |
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import psutil |
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import os |
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from tqdm.auto import tqdm |
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import logging |
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logger = logging.getLogger(__name__) |
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def get_current_ram_usage(): |
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ram = psutil.virtual_memory() |
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return ram.available / 1024 / 1024 / 1024, ram.total / 1024 / 1024 / 1024 |
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def download_models(models): |
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for model in tqdm(models, desc="Downloading models"): |
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logger.info(f"Downloading {model}") |
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for i in range(0, 5): |
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curr_dir = f"{model}/train_{i}/best_model/" |
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os.makedirs(curr_dir, exist_ok=True) |
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os.system( |
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f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/config.json -P {curr_dir}" |
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) |
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os.system( |
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f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/pytorch_model.bin -P {curr_dir}" |
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) |
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os.system( |
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f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/special_tokens_map.json -P {curr_dir}" |
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) |
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os.system( |
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f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/tokenizer_config.json -P {curr_dir}" |
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) |
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os.system( |
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f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/training_args.bin -P {curr_dir}" |
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) |
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os.system( |
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f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/vocab.txt -P {curr_dir}" |
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) |
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def softmax(x): |
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return np.exp(x) / sum(np.exp(x)) |
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def ga(file): |
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code = """ |
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<!-- Global site tag (gtag.js) - Google Analytics --> |
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<script async src="https://www.googletagmanager.com/gtag/js?id=G-NH9HWCW08F"></script> |
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<script> |
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window.dataLayer = window.dataLayer || []; |
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function gtag(){dataLayer.push(arguments);} |
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gtag('js', new Date()); |
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gtag('config', 'G-NH9HWCW08F'); |
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</script> |
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""" |
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a = os.path.dirname(file) + "/static/index.html" |
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with open(a, "r") as f: |
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data = f.read() |
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if len(re.findall("G-", data)) == 0: |
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with open(a, "w") as ff: |
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newdata = re.sub("<head>", "<head>" + code, data) |
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ff.write(newdata) |
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