"""Bounded, synthetic model tuning; exclusive shared scheduler, no foreign process control."""
import copy,json,threading,time,uuid,socket
from pathlib import Path
from profiles import SCHEMAS,CHAT_GPU_DEFAULTS
from image_test import probe
from inference import InferenceError

CONTEXT_STEPS=(2048,4096,8192,16384,32768,65536,131072,160000,192000,262144)
MAX_CANDIDATES=300
MAX_SECONDS=7200
GPU_SPLITS=([98,2],[95,5],[90,10],[85,15],[75,25],[50,50])

def model_layers(path,mtp):
 """Read bounded GGUF metadata only; never load tensors or tokenizer strings."""
 import struct
 formats={0:'B',1:'b',2:'H',3:'h',4:'I',5:'i',6:'f',7:'?',10:'Q',11:'q',12:'d'}
 with open(path,'rb') as f:
  size=Path(path).stat().st_size
  def read(n):
   b=f.read(n)
   if len(b)!=n:raise ValueError('Unvollständige GGUF-Metadaten.')
   return b
  def num(fmt):return struct.unpack('<'+fmt,read(struct.calcsize('<'+fmt)))[0]
  def string(keep=False):
   n=num('Q')
   if n>size-f.tell() or (keep and n>65536):raise ValueError('Ungültige GGUF-Zeichenfolge.')
   if keep:return read(n).decode('utf-8')
   f.seek(n,1)
  def value(t,keep=False,depth=0):
   if t in formats:return num(formats[t])
   if t==8:return string(keep)
   if t==9 and depth<2:
    subtype=num('I');count=num('Q')
    if count>10000000:raise ValueError('GGUF-Array zu groß.')
    if subtype in formats:
     length=struct.calcsize('<'+formats[subtype])*count
     if length>size-f.tell():raise ValueError('Ungültiges GGUF-Array.')
     f.seek(length,1)
    else:
     for _ in range(count):value(subtype,False,depth+1)
    return None
   raise ValueError('Unbekannter GGUF-Metadatentyp.')
  if read(4)!=b'GGUF' or num('I') not in (2,3):raise ValueError('GGUF-Version nicht unterstützt.')
  num('Q');count=num('Q');fields={}
  if count>100000:raise ValueError('Zu viele GGUF-Felder.')
  for _ in range(count):
   key=string(True);t=num('I');wanted=key=='general.architecture' or key.endswith(('.block_count','.nextn_predict_layers'))
   v=value(t,wanted)
   if wanted:fields[key]=v
  arch=fields.get('general.architecture');blocks=fields.get(str(arch)+'.block_count');draft=fields.get(str(arch)+'.nextn_predict_layers',0)
  if type(blocks)!=int or type(draft)!=int or not 1<=blocks<=4096 or not 0<=draft<blocks:raise ValueError('Keine verlässliche Layerzahl im GGUF.')
  return blocks-(0 if mtp else draft)+1 # output layer also participates in layer split

def fine_splits(total,ratio,upper):
 """llama layer split uses ceil(fraction * total) layers on the first GPU."""
 import math
 first=math.ceil(total*ratio[0]/sum(ratio))
 stop=min(total,math.ceil(total*upper/100))
 return [(k,[100*(k-.5)/total,100-100*(k-.5)/total]) for k in range(first+1,stop)]

class TestBudget(Exception):
 pass

class AutoTests:
 def __init__(self,path,profiles,worker,scheduler):
  self.path=Path(path);self.profiles=profiles;self.worker=worker;self.scheduler=scheduler;self.lock=threading.RLock();self.cancel=threading.Event();self.conn=None;self.thread=None
  self.job=json.loads(self.path.read_text()) if self.path.exists() else None
  if self.job and self.job['state']=='running':self.job.update(state='interrupted',phase='Durch Neustart unterbrochen');self.persist()
 def persist(self):
  self.path.parent.mkdir(parents=True,exist_ok=True);p=self.path.with_suffix('.tmp');p.write_text(json.dumps(self.job));p.replace(self.path)
 def status(self):
  with self.lock:return {'job':copy.deepcopy(self.job)}
 def update(self,**kw):
  with self.lock:self.job.update(kw);self.persist()
 def start(self,data):
  fields=set(data);base={'model_id','max_context','mtp'}
  if fields-base-{'start_context','cache_type_k','cache_type_v'} or not base<=fields or (('cache_type_k' in fields)!=('cache_type_v' in fields)):raise ValueError('Modell, Start- und Endkontext, MTP und beide KV-Cache-Typen auswählen.')
  start_context=data.get('start_context',2048)
  if type(start_context)!=int or start_context not in CONTEXT_STEPS or type(data['max_context'])!=int or data['max_context'] not in CONTEXT_STEPS or start_context>data['max_context'] or type(data['mtp'])!=bool:raise ValueError('Start- und Endkontext müssen gültige Stufen in aufsteigender Reihenfolge sein.')
  if data.get('cache_type_k','q4_0') not in ('f16','q8_0','q4_0') or data.get('cache_type_v','q4_0') not in ('f16','q8_0','q4_0'):raise ValueError('KV-Cache: nur f16, q8_0 oder q4_0 unterstützt.')
  model=self.worker.catalog.entry(data['model_id'])
  if model['kind']!='chat' or not model['file'].endswith('.gguf') or not model['profile_eligible']:raise ValueError('Ein heruntergeladenes Chat-GGUF auswählen.')
  devices=probe();primary=next((g for g in devices if '5080' in g['name']),None);secondary=next((g for g in devices if '3060' in g['name']),None)
  if not primary or not secondary:raise ValueError('Dieser Auto-Test benötigt RTX 5080 und RTX 3060. Keine stillschweigende andere GPU-Zuordnung.')
  with self.lock:
   if self.thread and self.thread.is_alive():raise ValueError('Ein Auto-Test läuft bereits.')
   self.cancel.clear();self.job=dict(id=uuid.uuid4().hex,state='running',phase='Wartet auf exklusive Modellreservierung',model_id=model['id'],model_file=model['file'],start_context=start_context,max_context=data['max_context'],mtp=data['mtp'],cache_type_k=data.get('cache_type_k','q4_0'),cache_type_v=data.get('cache_type_v','q4_0'),results=[],attempts=0,max_candidates=MAX_CANDIDATES,completed_contexts=[],started_at=time.time(),error=None);self.persist()
   self.thread=threading.Thread(target=self.run,args=(primary['uuid'],secondary['uuid']),daemon=True);self.thread.start()
   return self.status()
 def stop(self):
  self.cancel.set()
  with self.lock:conn=self.conn
  if conn and conn.sock:
   try:conn.sock.shutdown(socket.SHUT_RDWR)
   except OSError:pass
  return {'cancellation_requested':True}
 def request(self,path,data):
  if self.cancel.is_set():raise InterruptedError()
  conn,key=self.worker.connect();conn.timeout=180
  with self.lock:self.conn=conn
  try:
   conn.request('POST',path,json.dumps(data).encode(),{'Content-Type':'application/json','Authorization':'Bearer '+key})
   response=conn.getresponse();raw=response.read(8*1024*1024)
   if response.status!=200:raise InferenceError('Synthetischer Test abgelehnt: Kontext oder Modell prüfen.')
   return json.loads(raw)
  finally:
   conn.close()
   with self.lock:self.conn=None
 def benchmark(self,context):
  # Tokenize synthetic text with this model; do not estimate token count from characters.
  # Keep room for 64 output tokens and template overhead, but verify nearly the full context.
  target=context-max(128,context//100);unit='alpha beta gamma delta epsilon zeta eta theta 0123456789. '
  text=unit*(target//4+1);tokens=self.request('/tokenize',{'content':text,'add_special':True})['tokens']
  if len(tokens)<target:raise InferenceError('Synthetischer Kontext konnte nicht gefüllt werden.')
  result=self.request('/completion',dict(prompt=tokens[:target],n_predict=64,temperature=0,seed=1234,cache_prompt=False,stream=False))
  timing=result.get('timings',{})
  if not timing.get('predicted_n') or timing.get('prompt_n',0)<target*.99:raise InferenceError('Keine vollständige Kontext-/Ausgabemessung zurückgegeben.')
  return {k:timing[k] for k in ('prompt_n','prompt_ms','prompt_per_second','predicted_n','predicted_ms','predicted_per_second') if k in timing}
 def test_candidate(self,context,tier,devices,split,ratio,offload,layer_count=None):
  successes=[]
  for micro in (128,64,256):
   if self.cancel.is_set():raise InterruptedError()
   if self.job['attempts']>=self.job.get('max_candidates',MAX_CANDIDATES):raise TestBudget('Kandidatenlimit erreicht')
   if time.time()-self.job['started_at']>MAX_SECONDS:raise TestBudget('Zeitlimit erreicht')
   params={**{k:v[2] for k,v in SCHEMAS['chat'].items()},**CHAT_GPU_DEFAULTS,'context':context,'slots':1,'batch':2048,'ubatch':micro,'gpu_devices':devices,'split_mode':split,'tensor_split':ratio,'gpu_offload':offload,'mtp':self.job['mtp'],'cache_type_k':self.job.get('cache_type_k','q4_0'),'cache_type_v':self.job.get('cache_type_v','q4_0')}
   p=dict(id='auto-'+self.job['id'],revision=self.job['attempts']+1,name='deck-auto-test',kind='chat',model_id=self.job['model_id'],parameters=params)
   self.update(attempts=p['revision'],phase=f'{context} Kontext · {tier} · Microbatch {micro}')
   row=dict(context=context,tier=tier,parameters=params,success=False,primary_gpu_layers=layer_count)
   self.worker.stop()
   stage='prediction'
   try:
    self.worker.plan(p,cancel=self.cancel.is_set)
    stage='model_start'
    self.worker.ensure(p,cancel=self.cancel.is_set)
    # Small warm-up, then populated-context measurement, bounded output.
    stage='measurement'
    self.request('/completion',dict(prompt='Synthetic warmup.',n_predict=16,temperature=0,cache_prompt=False))
    timings=self.benchmark(context)
    row.update(success=True,timings=timings,memory_plan=copy.deepcopy(self.worker.memory_plan),tested_context_fraction=timings.get('prompt_n',0)/context)
    successes.append(row)
   except Exception as exc:
    if self.cancel.is_set():raise InterruptedError()
    row['failure_stage']=stage
    row['error']=str(exc) if isinstance(exc,ValueError) else 'Testprozess oder Verbindung fehlgeschlagen; kein eindeutiger OOM-Nachweis.'
   finally:self.worker.stop()
   with self.lock:self.job['results'].append(row);self.persist()
   # Smaller microbatch is useful after rejection; do not skip it.
  return successes
 def run(self,primary,secondary):
  try:
   with self.scheduler.lease(('auto-test',self.job['id']),timeout=0,allowed=lambda:not self.cancel.is_set()):
    try:
     contexts=[x for x in CONTEXT_STEPS if self.job.get('start_context',2048)<=x<=self.job['max_context']]
     fine_search=[]
     for context in contexts:
      self.update(phase=f'{context} Kontext · Grundverteilungen',current_context=context)
      primary_success=self.test_candidate(context,'5080',[primary],'none',[],'full')
      dual_success=[]
      for ratio in GPU_SPLITS:
       rows=self.test_candidate(context,'5080 + 3060',[primary,secondary],'layer',ratio,'full')
       if rows:dual_success.append(ratio)
      if not primary_success and not dual_success:
       self.test_candidate(context,'5080 + 3060 + CPU',[primary,secondary],'layer',[85,15],'auto')
      self.update(completed_contexts=self.job.get('completed_contexts',[])+[context])
      if dual_success and not primary_success:fine_search.append((context,dual_success[0]))
     # Run one-layer refinement only after every requested context has its
     # coarse 5080/3060 splits, so tuning cannot starve later context levels.
     for context,ratio in reversed(fine_search):
      try:
       entry=self.worker.catalog.entry(self.job['model_id'])
       total=model_layers(self.worker.catalog.root/entry['id']/('model'+Path(entry['file']).suffix),self.job['mtp'])
      except (OSError,ValueError,TypeError) as exc:
       self.update(fine_search_note='Layer-Feinsuche nicht verfügbar: '+str(exc));continue
      for layers,finer in fine_splits(total,ratio,100):
       result=self.test_candidate(context,'5080 + 3060 · Layer-Feinsuche',[primary,secondary],'layer',finer,'full',layers)
       if not result:break
     self.update(state='complete',phase='Testreihe abgeschlossen · Ergebnisse sind keine Garantie für beliebige Last',finished_at=time.time())
    finally:self.worker.stop()
  except TestBudget as exc:self.update(state='partial',phase=f'{exc} · Testreihe unvollständig, Teilresultate verfügbar',finished_at=time.time())
  except Exception as exc:self.update(state='cancelled' if self.cancel.is_set() else 'failed',phase='Abgebrochen' if self.cancel.is_set() else 'Test beendet',error=None if self.cancel.is_set() else str(exc),finished_at=time.time())
 def save(self,data):
  with self.lock:
   if set(data)!={'job_id','index','name'} or not self.job or data['job_id']!=self.job['id'] or type(data['index'])!=int or not 0<=data['index']<len(self.job['results']):raise ValueError('Testergebnis nicht gefunden.')
   row=copy.deepcopy(self.job['results'][data['index']]);model_id=self.job['model_id']
  if not row['success']:raise ValueError('Nur erfolgreich getestete Kandidaten speichern.')
  return self.profiles.save(dict(id=None,revision=0,name=data['name'],kind='chat',model_id=model_id,parameters=row['parameters']))