| """Reproduce TermiGen v2. Requires pyarrow and huggingface_hub.""" |
| import argparse,hashlib,json |
| from pathlib import Path |
| import pyarrow as pa |
| import pyarrow.parquet as pq |
| from huggingface_hub import hf_hub_download |
| p=argparse.ArgumentParser(description=__doc__) |
| p.add_argument('--out',type=Path,required=True) |
| p.add_argument('--source',type=Path) |
| a=p.parse_args() |
| m=json.loads((Path(__file__).parent/'metadata/selection.json').read_text()) |
| a.out.mkdir(parents=True,exist_ok=True) |
| f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache')) |
| assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256'] |
| t=pq.read_table(f);rows=[] |
| for r in t.to_pylist(): |
| s=m['tasks'][r['path']];assert r['content_sha256']==s['content_sha256'] |
| if s['kept']:rows.append(dict(r,coder_solved=s['coder_solved'],instruct_successes=s['instruct_successes'],instruct_missing_rewards=s['instruct_missing_rewards'],attempts_per_model=4)) |
| assert len(rows)==2277 |
| schema=t.schema.append(pa.field('coder_solved',pa.bool_())).append(pa.field('instruct_successes',pa.int64())).append(pa.field('instruct_missing_rewards',pa.int64())).append(pa.field('attempts_per_model',pa.int64())) |
| out=pa.Table.from_pylist(rows,schema=schema).replace_schema_metadata({b'ot.release.v2':json.dumps({k:v for k,v in m.items() if k!='tasks'}).encode()}) |
| pq.write_table(out,a.out/'tasks.parquet',compression='zstd') |
|
|
