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_quiet.py
---------
Silences the noisy tqdm / verbose logging that floods notebook output when
`!python -m ...` is run in a non-TTY subprocess (Kaggle / Colab).
Import this module at the **very top** of train.py / evaluate.py, *before*
any `transformers` / `huggingface_hub` / `bitsandbytes` import, because those
libraries read these env vars once at import time.
We do not suppress tqdm globally — HF Trainer's training-loop progress is
useful. We only kill:
• HF Hub per-shard download bars (the `pytorch_model-00001-of-00002.bin`
spam you see during first model load).
• transformers' info-level logging.
• bitsandbytes welcome banner.
• HF tokenizers' multiprocessing fork warning.
"""
import os
# ── env vars read at import time by downstream libs ──────────────────────
os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
os.environ.setdefault("TRANSFORMERS_VERBOSITY", "warning")
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
os.environ.setdefault("BITSANDBYTES_NOWELCOME", "1")
os.environ.setdefault("PYTHONUNBUFFERED", "1")
# ── httpx 0.28+ compat patch (must apply BEFORE transformers imports) ────
# transformers ≤4.50 calls httpx.Client.head(allow_redirects=...) which
# httpx 0.28 removed. The shim translates kwarg → follow_redirects.
from . import _httpx_compat # noqa: F401
# ── torch 2.6+ compat patch (must apply BEFORE Trainer.train() resumes) ──
# torch 2.6 made `torch.load(..., weights_only=True)` the default; the
# rng_state.pth that transformers ≤4.50 loads during resume contains numpy
# objects and trips the safe unpickler. Restore the legacy default.
from . import _torch_load_compat # noqa: F401
# ── programmatic disable (belt-and-braces) ───────────────────────────────
try:
from huggingface_hub.utils import disable_progress_bars
disable_progress_bars()
except Exception:
pass
try:
import transformers
transformers.logging.set_verbosity_warning()
transformers.logging.disable_progress_bar()
except Exception:
pass
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