"""Deterministic source/task-balanced training pilot; no evaluation examples."""
import argparse,json
from pathlib import Path
import duckdb,pyarrow as pa
from assemble import allocate

def main():
    p=argparse.ArgumentParser();p.add_argument('--root',required=True);p.add_argument('--output',required=True);p.add_argument('--rows',type=int,default=1000);a=p.parse_args()
    db=duckdb.connect();db.execute("SET memory_limit='16GB'")
    pattern=str(Path(a.root)/'selected'/'part-*.parquet')
    db.execute("CREATE TABLE pool AS SELECT *,provenance.repository AS source FROM read_parquet(?) WHERE split='train' AND array_length(options) BETWEEN 2 AND 20",[pattern])
    available=dict(db.execute('SELECT source,count(*) FROM pool GROUP BY source').fetchall())
    quotas=allocate(available,a.rows,{k:1 for k in available});caps=[]
    for source,quota in quotas.items():
        strata=dict(db.execute('SELECT task,count(*) FROM pool WHERE source=? GROUP BY task',[source]).fetchall())
        for task,cap in allocate(strata,quota,{k:1 for k in strata}).items():caps.append({'source':source,'task':task,'cap':cap})
    db.register('caps',pa.Table.from_pylist(caps))
    db.execute('CREATE TABLE picked AS SELECT p.* FROM pool p JOIN caps c USING(source,task) QUALIFY row_number() OVER(PARTITION BY source,task ORDER BY sha256(id))<=c.cap')
    # Round-robin source order means the first 19 records touch all 19 sources.
    table=db.execute('SELECT * EXCLUDE(source) FROM picked QUALIFY true ORDER BY row_number() OVER(PARTITION BY source ORDER BY sha256(id)),source').fetch_arrow_table()
    with open(a.output,'w') as f:
        for row in table.to_pylist():f.write(json.dumps(row,ensure_ascii=False)+'\n')
    print(json.dumps({'rows':len(table),'sources':quotas,'split':'train','max_options':20}),flush=True)

if __name__=='__main__':main()