"""Owned audio-separator environment and explicitly supported model packages."""
import hashlib,json,os,shutil,sys,time
from pathlib import Path
from ltx_original_runtime import LTXOriginalRuntime
VERSION='0.47.0'
MODELS={
 'vocals_mel_band_roformer.ckpt':('MelBand RoFormer · Gesang / Instrumental','vocals_mel_band_roformer.yaml'),
 'model_bs_roformer_ep_317_sdr_12.9755.ckpt':('BS-RoFormer · Gesang / Instrumental','model_bs_roformer_ep_317_sdr_12.9755.yaml')}
class SeparatorRuntime(LTXOriginalRuntime):
 name='Audio Separator'
 def __init__(self,root):super().__init__(root,None)
 def paths(self):
  marker=self.root/'active.json'
  if marker.is_file():
   ident=json.loads(marker.read_text()).get('id','')
   if len(ident)==32 and all(c in '0123456789abcdef' for c in ident):
    base=self.root/ident
    if (base/'ready').is_file():return base/'python/bin/python',base
  return self.root/'missing-python',self.root/'missing-runtime'
 def status(self):
  python,base=self.paths()
  return dict(installed=python.is_file() and (base/'ready').is_file(),version=VERSION,required_disk_gib=15,job=dict(self.job) if self.job else None,models=[dict(id=name,name=label,configuration=config,installed=self.model_ready(name)) for name,(label,config) in MODELS.items()])
 def model_ready(self,name):return name in MODELS and (self.root/'models'/(name+'.ready')).is_file() and all((self.root/'models'/f).is_file() and (self.root/'models'/f).stat().st_size>0 for f in (name,MODELS[name][1]))
 def start(self):
  if not shutil.which('ffmpeg'):raise ValueError('FFmpeg fehlt. Deck-Systemvoraussetzungen installieren.')
  with self.lock:
   if self.job and self.job['state']=='running':raise ValueError('Einrichtungsauftrag läuft bereits.')
   self.requested_model=None
   return super().start()
 def download(self,name):
  import threading,uuid
  if not isinstance(name,str) or name not in MODELS:raise ValueError('Dieses Modellpaket ist nicht unterstützt.')
  with self.lock:
   if not self.status()['installed']:raise ValueError('Zuerst Audio Separator installieren.')
   if self.job and self.job['state']=='running':raise ValueError('Einrichtungsauftrag läuft bereits.')
   if shutil.disk_usage(self.root).free<3*1024**3:raise ValueError('Mindestens 3 GiB freier Speicher erforderlich.')
   self.cancel.clear();self.requested_model=name;self.job=dict(id=uuid.uuid4().hex,state='running',phase='Modellpaket herunterladen',started_at=time.time());self._save()
   self.worker=threading.Thread(target=self._run,daemon=True);self.worker.start();return self.status()
 def _run(self):
  base=self.root/self.job['id'];state='failed';phase='Einrichtung fehlgeschlagen.';model=getattr(self,'requested_model',None)
  try:
   if model:
    directory=base;directory.mkdir(mode=0o700)
    self._command([self.paths()[0],'-c','from audio_separator.separator import Separator; import sys; Separator(model_file_dir=sys.argv[1],info_only=True).download_model_files(sys.argv[2])',directory,model])
    filenames=(model,MODELS[model][1])
    if any(not (directory/f).is_file() or (directory/f).stat().st_size==0 for f in filenames):raise ValueError('Modell oder YAML-Konfiguration fehlt.')
    if self.cancel.is_set():raise InterruptedError()
    destination=self.root/'models';destination.mkdir(exist_ok=True);hashes={}
    for f in filenames:
     digest=hashlib.sha256()
     with (directory/f).open('rb') as source:
      for chunk in iter(lambda:source.read(1024**2),b''):digest.update(chunk)
     hashes[f]=digest.hexdigest();(directory/f).replace(destination/f)
    (destination/(model+'.ready')).write_text(json.dumps(hashes))
    state='complete';phase='Modell und passende Konfiguration heruntergeladen. Kein Modell geladen.'
   else:
    base.mkdir(mode=0o700);tmp=base/'tmp';tmp.mkdir()
    env=dict(os.environ,TMPDIR=str(tmp),PIP_NO_INPUT='1',PIP_DISABLE_PIP_VERSION_CHECK='1')
    python=base/'python/bin/python';self._phase('Eigene Python-Umgebung erstellen');self._command([sys.executable,'-m','venv',base/'python'],env)
    self._phase('CUDA-PyTorch herunterladen · mehrere GiB');self._command([python,'-m','pip','install','--no-cache-dir','torch==2.11.0','torchaudio==2.11.0','--index-url','https://download.pytorch.org/whl/cu128'],env)
    self._phase('Audio Separator und GPU-Abhängigkeiten installieren');self._command([python,'-m','pip','install','--no-cache-dir',f'audio-separator[gpu]=={VERSION}','onnxruntime-gpu==1.22.0','audioread==3.1.0'],env)
    self._phase('Paketkonsistenz und CUDA-Unterstützung prüfen');self._command([python,'-m','pip','check'],env)
    self._command([python,'-c','import torch; from audio_separator.separator import Separator; assert torch.version.cuda'],env)
    if self.cancel.is_set():raise InterruptedError()
    (base/'ready').write_text(VERSION);marker=self.root/'active.tmp';marker.write_text(json.dumps({'id':base.name}));marker.replace(self.root/'active.json')
    state='complete';phase='Audio Separator installiert. Jetzt Modellpaket auswählen.'
  except InterruptedError:state='cancelled';phase='Einrichtung abgebrochen.'
  except Exception as exc:phase=str(exc)
  finally:
   if model or state!='complete':shutil.rmtree(base,ignore_errors=True)
   with self.lock:self.process=None;self.job.update(state=state,phase=phase,finished_at=time.time());self._save()