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Co-authored-by: Robert Brennan <accounts@rbren.io> Co-authored-by: openhands <openhands@all-hands.dev> Co-authored-by: Engel Nyst <enyst@users.noreply.github.com> Co-authored-by: Graham Neubig <neubig@gmail.com>
33 lines
997 B
Python
33 lines
997 B
Python
import argparse
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import pandas as pd
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from datasets import load_dataset
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parser = argparse.ArgumentParser()
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parser.add_argument('output_filepath', type=str, help='Path to save the output file')
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parser.add_argument(
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'--dataset_name',
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type=str,
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help='Name of the dataset to download',
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default='princeton-nlp/SWE-bench_Verified',
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)
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parser.add_argument('--split', type=str, help='Split to download', default='test')
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args = parser.parse_args()
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dataset = load_dataset(args.dataset_name, split=args.split)
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output_filepath = args.output_filepath
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print(
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f'Downloading gold patches from {args.dataset_name} (split: {args.split}) to {output_filepath}'
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)
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patches = [
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{
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'instance_id': row['instance_id'],
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'model_patch': row['patch'],
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'model_name_or_path': 'gold',
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}
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for row in dataset
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]
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print(f'{len(patches)} gold patches loaded')
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pd.DataFrame(patches).to_json(output_filepath, lines=True, orient='records')
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print(f'Patches saved to {output_filepath}')
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