Download androidflux_tools/export_repaired_interchange.py from Gyubeum/AndroidFlux_RL_Train: direct link, hf CLI and curl.
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- Download file 2.65 kB
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https://huggingface.co/datasets/Gyubeum/AndroidFlux_RL_Train/resolve/main/androidflux_tools/export_repaired_interchange.py
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hf download hf://datasets/Gyubeum/AndroidFlux_RL_Train/androidflux_tools/export_repaired_interchange.py
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curl -L -o export_repaired_interchange.py https://huggingface.co/datasets/Gyubeum/AndroidFlux_RL_Train/resolve/main/androidflux_tools/export_repaired_interchange.py
2.65 kB
| """Convert a repaired AndroidFlux Parquet slice to af_recovery_prompt_v1. | |
| Usage: python export_repaired_interchange.py DOWNLOADED_SLICE NEW_PACKAGE_DIR | |
| No access to the original machine or source trajectories is required. | |
| """ | |
| import argparse | |
| import hashlib | |
| import json | |
| from pathlib import Path | |
| import pyarrow.parquet as pq | |
| def export(source, output): | |
| output.mkdir(parents=True,exist_ok=False) | |
| (output/'images').mkdir() | |
| rows=pq.read_table(source/'samples.parquet').to_pylist() | |
| required=set(); records=[] | |
| for row in rows: | |
| k=row['generation_step_idx'];screens=row['screen_image_ids'] | |
| required.update(screens[max(0,k-4):]) | |
| records.append(dict(schema_version='af_recovery_prompt_v1',sample_id=row['sample_id'], | |
| source=dict(dataset='Gyubeum/AndroidFlux_RL_Train',revision=None,domain=row['source_config'], | |
| trajectory_id=row['trajectory_id'],checkpoint_step=k),goal=row['goal'], | |
| history=[dict(step=i+1,summary=s) for i,s in enumerate(json.loads(row['qwen_history_entries_json']))], | |
| screens=[f'images/{h}.png' if i>=max(0,k-4) else None for i,h in enumerate(screens)], | |
| annotations={key:row[key] for key in ('reference_action_type','reference_action_json', | |
| 'reference_action_source','reference_action_semantics','reference_action_coordinate_space', | |
| 'reference_action_available','history_summary_complete','first_error_step_idx','source_model')})) | |
| found=set() | |
| for batch in pq.ParquetFile(source/'images.parquet').iter_batches(batch_size=8): | |
| for im in batch.to_pylist(): | |
| h=im['image_id'] | |
| if h in required: | |
| assert hashlib.sha256(im['encoded_bytes']).hexdigest()==h | |
| (output/'images'/f'{h}.png').write_bytes(im['encoded_bytes']);found.add(h) | |
| assert found==required | |
| with (output/'samples.jsonl').open('w') as f: | |
| for r in records:f.write(json.dumps(r,ensure_ascii=False)+'\n') | |
| report=dict(samples=len(rows),images=len(found),source_slice=str(source), | |
| source_samples_sha256=hashlib.sha256((source/'samples.parquet').read_bytes()).hexdigest(), | |
| unavailable_reference=sum(not r['reference_action_available'] for r in rows), | |
| incomplete_history=sum(not r['history_summary_complete'] for r in rows)) | |
| (output/'BUILD_REPORT.json').write_text(json.dumps(report,indent=2)+'\n') | |
| return report | |
| if __name__=='__main__': | |
| ap=argparse.ArgumentParser();ap.add_argument('source',type=Path);ap.add_argument('output',type=Path) | |
| args=ap.parse_args();print(json.dumps(export(args.source,args.output),indent=2)) | |