"""CPU-only EmbeddingGemma worker; independent of GPU mode and chat leases."""
import http.client
import json
import os
from pathlib import Path
import secrets
import signal
import socket
import subprocess
import threading
import time

REPO='ggml-org/embeddinggemma-300m-qat-q8_0-GGUF'
FILE='embeddinggemma-300m-qat-Q8_0.gguf'
def supported(model):
 return bool(model and model.get('kind')=='embeddings' and model.get('repo')==REPO and model.get('file')==FILE)

class Embeddings:
 def __init__(self,root,catalog,worker):
  self.root=Path(root);self.catalog=catalog;self.worker=worker;self.lock=threading.RLock();self.process=None;self.identity=None;self.port=None;self.key=None
 def blockers(self,p):
  if not supported(p.get('model')):return ['Noch kein geprüftes Embedding-Rezept für diese Datei. Unterstützt: EmbeddingGemma 300M QAT Q8_0.']
  try:self.worker.build()
  except ValueError as exc:return [str(exc)]
  return []
 def stop(self):
  with self.lock:
   process=self.process;self.process=None;self.identity=None
   if process and process.poll() is None:
    os.killpg(process.pid,signal.SIGTERM)
    try:process.wait(timeout=10)
    except subprocess.TimeoutExpired:os.killpg(process.pid,signal.SIGKILL);process.wait()
   (self.root/'key').unlink(missing_ok=True)
 def ensure(self,p):
  identity=(p['id'],p['revision'],self.worker.runtime.status()['active'])
  if self.process and self.process.poll() is None and self.identity==identity:return
  self.stop();errors=self.blockers(p)
  if errors:raise ValueError(errors[0])
  entry=self.catalog.entry(p['model_id']);model=self.catalog.root/entry['id']/'model.gguf';directory=self.worker.build()
  self.root.mkdir(parents=True,exist_ok=True,mode=0o700)
  with socket.socket() as sock:sock.bind(('127.0.0.1',0));self.port=sock.getsockname()[1]
  self.key=secrets.token_urlsafe(32);keyfile=self.root/'key'
  fd=os.open(keyfile,os.O_WRONLY|os.O_CREAT|os.O_TRUNC,0o600)
  with os.fdopen(fd,'w') as out:out.write(self.key+'\n')
  env=dict(os.environ,CUDA_VISIBLE_DEVICES='')
  args=[str(directory/'build/bin/llama-server'),'--model',str(model),'--alias',p['name'],'--embedding','--pooling','mean','--ctx-size','4096','--batch-size','4096','--ubatch-size','1024','--parallel','4','--threads','8','--threads-batch','8','--n-gpu-layers','0','--host','127.0.0.1','--port',str(self.port),'--api-key-file',str(keyfile),'--no-ui']
  self.process=subprocess.Popen(args,env=env,stdout=subprocess.DEVNULL,stderr=subprocess.DEVNULL,start_new_session=True)
  try:
   deadline=time.monotonic()+120
   while time.monotonic()<deadline:
    if self.process.poll() is not None:raise ValueError('EmbeddingGemma konnte nicht gestartet werden. llama.cpp-Build und Modell prüfen.')
    conn=http.client.HTTPConnection('127.0.0.1',self.port,timeout=2)
    try:
     conn.request('GET','/health',headers={'Authorization':'Bearer '+self.key})
     if conn.getresponse().status==200:self.identity=identity;keyfile.unlink(missing_ok=True);return
    except OSError:pass
    finally:conn.close()
    time.sleep(.25)
   raise ValueError('Embedding-Start hat das Zeitlimit überschritten.')
  except Exception:self.stop();raise
 def generate(self,p,data):
  with self.lock:
   self.ensure(p);conn=http.client.HTTPConnection('127.0.0.1',self.port,timeout=120)
   try:
    conn.request('POST','/v1/embeddings',body=json.dumps(data).encode(),headers={'Content-Type':'application/json','Authorization':'Bearer '+self.key})
    response=conn.getresponse();raw=response.read(4*1024*1024+1)
    if response.status!=200:raise ValueError('Embedding-Anfrage abgelehnt. Textlänge und Eingabe prüfen (maximal ca. 1024 Token je Text).')
    if len(raw)>4*1024*1024:raise ValueError('Embedding-Antwort zu groß.')
    result=json.loads(raw)
    if not result.get('data'):raise ValueError('Keine Embedding-Vektoren zurückgegeben.')
    return result
   except (OSError,ValueError):raise ValueError('Embedding-Anfrage fehlgeschlagen. Eingabelänge und Laufzeit prüfen.') from None
   finally:conn.close()