import json, sys
print('HF-JOBS-LANE-OK', sys.version.split()[0])
import torch, transformers
print('torch', torch.__version__, 'transformers', transformers.__version__)
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL = 'uctnlp/mzansilm-125m'
tok = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL)
model.eval()
SA = [
 'Umuntu wethu umnikazi wosazibeli ngokuqondisa umhlaba wonke umzimba wezokwelapha.',
 'Intsapha ye-AI ingenza ulunikezelo lwamanzi ngokuchanekileyo kakhulu.',
 'Die plugins moet spoedig ingelewer word.',
 'Re a lokwa feela haholo holimo ho ya haholo.',
]
tot = 0.0; n = 0
for s in SA * 3:
    ids = tok(s, return_tensors='pt').input_ids
    with torch.no_grad():
        o = model(ids, labels=ids)
    tot += float(o.loss); n += 1
res = {'event':'checkpoint','gate':'mzansilm_probe','status':'recorded',
       'metrics':{'mean_loss':round(tot/n,4),'samples':n,'vocab':tok.vocab_size,
                  'params':sum(p.numel() for p in model.parameters())},
       'model':MODEL,'compute':'hf-jobs-cpu'}
print('RESULT_JSON=' + json.dumps(res))
open('probe_result.json','w').write(json.dumps(res) + '\n')