# /// script
# requires-python = ">=3.11"
# dependencies = ["huggingface-hub>=1.0", "datasets>=4.0", "transformers>=5.0", "torch>=2.6", "pillow>=11", "psutil>=6", "nvidia-ml-py>=12.560", "jiwer>=4.0"]
# ///
from __future__ import annotations
import argparse, atexit, json, os, threading, time
from pathlib import Path
import psutil
from datasets import load_dataset
from huggingface_hub import HfApi, hf_hub_download

def emit(p,s,m): print(json.dumps({'event':'progress','percent':p,'stage':s,'message':m}),flush=True)
def gpu_sample():
 try:
  import pynvml; pynvml.nvmlInit(); count=pynvml.nvmlDeviceGetCount(); names=[]; utils=[]; used=total=0; temps=[]
  for i in range(count):
   h=pynvml.nvmlDeviceGetHandleByIndex(i); n=pynvml.nvmlDeviceGetName(h); names.append(n.decode() if isinstance(n,bytes) else str(n)); u=pynvml.nvmlDeviceGetUtilizationRates(h); utils.append(float(u.gpu)); m=pynvml.nvmlDeviceGetMemoryInfo(h); used+=m.used; total+=m.total
   try: temps.append(float(pynvml.nvmlDeviceGetTemperature(h,pynvml.NVML_TEMPERATURE_GPU)))
   except Exception: pass
  pynvml.nvmlShutdown(); return {'gpu_count':count,'gpu_name':' + '.join(names) or None,'gpu_util_percent':sum(utils)/len(utils) if utils else None,'vram_used_gb':used/1024**3 if total else None,'vram_total_gb':total/1024**3 if total else None,'vram_percent':100*used/total if total else None,'gpu_temperature_c':sum(temps)/len(temps) if temps else None}
 except Exception:return {'gpu_count':0,'gpu_name':None,'gpu_util_percent':None,'vram_used_gb':None,'vram_total_gb':None,'vram_percent':None,'gpu_temperature_c':None}

class Telemetry:
 def __init__(self): self.stop=threading.Event(); self.t=threading.Thread(target=self.run,daemon=True)
 def start(self): psutil.cpu_percent(None); self.t.start(); atexit.register(self.stop.set)
 def run(self):
  while not self.stop.wait(1):
   m=psutil.virtual_memory(); payload={'event':'telemetry','timestamp':time.time(),'cpu_percent':psutil.cpu_percent(None),'ram_percent':m.percent,'ram_used_gb':round((m.total-m.available)/1024**3,3),'ram_total_gb':round(m.total/1024**3,3)}; payload.update(gpu_sample()); print(json.dumps(payload),flush=True)
def main():
 ap=argparse.ArgumentParser(); ap.add_argument('--source',required=True); ap.add_argument('--dataset-id',required=True); ap.add_argument('--split',default='validation'); ap.add_argument('--metric',default='auto'); ap.add_argument('--max-samples',type=int,default=128); ap.add_argument('--prompt-column',default='prompt'); ap.add_argument('--reference-column',default='text'); ap.add_argument('--prediction-column',default='prediction'); ap.add_argument('--image-column',default='image'); ap.add_argument('--output-repo',default=''); ap.add_argument('--private',action='store_true'); ap.add_argument('--dry-run',action='store_true'); a=ap.parse_args(); token=os.environ['HF_TOKEN']; Telemetry().start()
 emit(5,'dataset',f'Loading {a.dataset_id}:{a.split}')
 try:
  ds=load_dataset(a.dataset_id,split=a.split,token=token)
 except Exception:
  files=HfApi(token=token).list_repo_files(a.dataset_id,repo_type='dataset',token=token)
  wanted=[f for f in files if f.lower().endswith(('.jsonl','.json')) and a.split.lower() in f.lower()]
  if not wanted: wanted=[f for f in files if f.lower().endswith(('.jsonl','.json')) and 'validation' in f.lower()]
  if not wanted: wanted=[f for f in files if f.lower().endswith(('.jsonl','.json')) and 'train' in f.lower()]
  if not wanted: raise
  local=hf_hub_download(a.dataset_id,wanted[0],repo_type='dataset',token=token)
  ds=load_dataset('json',data_files=local,split='train')
 if a.max_samples>0: ds=ds.select(range(min(a.max_samples,len(ds))))
 columns=list(ds.column_names); plan=vars(a)|{'rows':len(ds),'columns':columns}; out=Path('/cache/eval')/str(int(time.time())); out.mkdir(parents=True,exist_ok=True); (out/'eval_plan.json').write_text(json.dumps(plan,indent=2))
 if a.dry_run: emit(100,'completed',f'EvalJob dry-run validated on {len(ds)} row(s)'); return
 predictions=[]; references=[]
 if a.prediction_column in columns and a.reference_column in columns:
  predictions=[str(x or '') for x in ds[a.prediction_column]]; references=[str(x or '') for x in ds[a.reference_column]]; emit(55,'score','Using prediction and reference columns')
 else:
  from transformers import pipeline
  task='image-to-text' if a.image_column in columns else 'text-generation'; source=str(Path('/cache')/a.source.lstrip('/')) if (Path('/cache')/a.source.lstrip('/')).exists() else a.source
  pipe=pipeline(task,model=source,token=token,trust_remote_code=True,device_map='auto'); emit(30,'inference',f'Loaded {task} pipeline')
  for index,row in enumerate(ds):
   inp=row.get(a.image_column) if task=='image-to-text' else row.get(a.prompt_column) or row.get('text') or ''
   result=pipe(inp,max_new_tokens=256); text=result[0].get('generated_text') or result[0].get('text') or str(result[0]); predictions.append(str(text)); references.append(str(row.get(a.reference_column) or ''))
   if index%max(1,len(ds)//10)==0: emit(30+55*(index+1)/max(1,len(ds)),'inference',f'{index+1}/{len(ds)} samples')
 from jiwer import wer, cer
 exact=sum(p.strip()==r.strip() for p,r in zip(predictions,references))/max(1,len(references)); report={'source':a.source,'dataset':a.dataset_id,'rows':len(references),'exact_match':exact,'wer':wer(references,predictions),'cer':cer(references,predictions),'samples':[{'prediction':p,'reference':r} for p,r in list(zip(predictions,references))[:20]]}; (out/'evaluation.json').write_text(json.dumps(report,indent=2,ensure_ascii=False))
 if a.output_repo:
  api=HfApi(token=token); api.create_repo(a.output_repo,repo_type='model',private=a.private,exist_ok=True,token=token); api.upload_folder(folder_path=out,repo_id=a.output_repo,repo_type='model',path_in_repo='evaluations',token=token,commit_message='Add EvalJob report')
 emit(100,'completed',f"EvalJob completed: exact {exact:.3f}, CER {report['cer']:.3f}")
if __name__=='__main__': main()