import os,json,time,sys,concurrent.futures as cf
from openai import OpenAI
from items import ITEMS
from native import NATIVE
from run import SUFFIX
c=OpenAI(base_url="https://router.huggingface.co/v1",api_key=os.environ.get("HF_TOKEN","x"),timeout=180)
jobs=[]
for it in NATIVE:
    for s in range(3): jobs.append((it,"ta",s,None,"native"))
for it in ITEMS:
    for l in ("en","ta","tl"): jobs.append((it,l,0,"minimal","parallel"))
def call(j):
    it,l,s,eff,split=j; err=None
    kw={"extra_body":{"reasoning_effort":eff}} if eff else {}
    for a in range(8):
        try:
            t=time.time()
            r=c.chat.completions.create(model="reflection/Beam-1.0-501B-A23B",messages=[{"role":"user","content":it[l]+SUFFIX[l]}],max_tokens=6000,**kw)
            m=r.choices[0].message;u=r.usage
            return dict(split=split,effort=eff or "default",id=it["id"],cat=it["cat"],lang=l,sample=s,answer=it["answer"],aliases=it.get("aliases",[]),output=m.content,
              reasoning=getattr(m,"reasoning_content",None),prompt_tokens=u.prompt_tokens,completion_tokens=u.completion_tokens,
              reasoning_tokens=(u.completion_tokens_details.reasoning_tokens if u.completion_tokens_details else 0),latency=round(time.time()-t,2))
        except Exception as e: err=str(e); time.sleep(5*(a+1))
    return dict(split=split,id=it["id"],lang=l,sample=s,error=err)
with cf.ThreadPoolExecutor(4) as ex: out=list(ex.map(call,jobs))
json.dump(out,open("raw_results2.json","w"),ensure_ascii=False,indent=1)
print(len(out),sum('error' in r for r in out))