# Inspect loads task files outside their package; make the repository namespace importable.
import sys
from pathlib import Path
_ROOT = str(Path(__file__).resolve().parents[2])
if _ROOT not in sys.path:
    sys.path.insert(0, _ROOT)

from inspect_ai import Task,task
from inspect_ai.dataset import Sample,MemoryDataset
from inspect_ai.model import get_model,GenerateConfig,ChatMessageUser
from inspect_ai.solver import solver
from inspect_ai.scorer import scorer,Score,mean
from encoding_tax.common import ROOT,load_config,digest
from encoding_tax.prompts import serialize,reasoning_stops,continue_answer
from encoding_tax.scoring import probability_score
from encoding_tax.data import load_rows
from extensions.e6_exploratory_v2 import provider
from extensions.e6_exploratory_v2.design import require_extension,cases,case_id,truth,body,score_diagnostic,diagnostic_answer

@solver
def extension_solver():
    base=load_config()
    async def solve(state,generate):
        m=state.metadata;p=base['models'][m['reader']]
        text,trace=body(m,base);raw=serialize(p,text,'scratchpad')
        output=await get_model().generate([ChatMessageUser(content=raw)],config=GenerateConfig(max_tokens=m['budget'],stop_seqs=reasoning_stops(p),temperature=0,top_p=1,max_retries=0))
        m.update(supplied_trace=trace,scratchpad=output.completion,requests=[output.metadata],reasoning_end=output.metadata['finish_reason'])
        answer_prompt=continue_answer(p,raw,output.completion)+('[' if m['mode']=='diagnostic' else '')
        m['answer_prefill']='[' if m['mode']=='diagnostic' else ''
        state.output=await get_model().generate([ChatMessageUser(content=answer_prompt)],config=GenerateConfig(max_tokens=48,stop_seqs=[']'] if m['mode']=='diagnostic' else ['\n'],temperature=0,top_p=1,max_retries=0))
        m['requests'].append(state.output.metadata)
        state.messages=[ChatMessageUser(content=answer_prompt),state.output.message];state.completed=True
        return state
    return solve

@scorer(metrics=[mean()])
def extension_score():
    async def score(state,target):
        m=state.metadata;raw=state.output.completion
        result=probability_score(raw,m['label']) if m['mode']=='monitor' else score_diagnostic(diagnostic_answer(raw) if state.output.metadata.get('finish_reason')=='stop' else '',truth(m))
        result['raw_suffix']=raw
        if m['mode']=='diagnostic':result['reconstructed_answer']=diagnostic_answer(raw)
        m['outcome']=result
        return Score(value=result['probability'] if m['mode']=='monitor' else result['all_correct'],answer=raw,metadata=result)
    return score

def build(reader):
    cfg,base,pin=require_extension()
    rows=load_rows({'datasets':{'synthetic':cfg['dataset']}},'synthetic','pilot')
    data=[Sample(id=case_id(m),input=m['problem'],target=str(m['label']),metadata=m) for m in cases(rows,reader)]
    model=get_model('encoding_tax_e6x2/'+reader,study_config=cfg['baseline_config'],audit_dir='results/e6-exploratory-v2/pilot/transport',config=GenerateConfig(max_connections=16,max_retries=0))
    return Task(name='E6X2_pilot',dataset=MemoryDataset(data),solver=extension_solver(),scorer=extension_score(),model=model,config=GenerateConfig(max_connections=16,max_retries=0,temperature=0,top_p=1),fail_on_error=False,metadata={'exploratory':True,'run_version':'e6x2','reader':reader,'split':'pilot','extension_freeze':pin,'extension_freeze_sha256':digest(ROOT/'extensions/e6_exploratory_v2/freeze.json'),'answer_cap':48,'dataset':cfg['dataset'],'concurrency':16})

@task
def gemma():return build('gemma')
@task
def gptoss():return build('gptoss')
@task
def nemotron():return build('nemotron')
@task
def qwen27():return build('qwen27')
@task
def qwen2t():return build('qwen2t')