Download Tools/generate_tokenizer_baselines.py from pcuenq/tokenizer-conformance: direct link, hf CLI and curl.
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https://huggingface.co/datasets/pcuenq/tokenizer-conformance/resolve/main/Tools/generate_tokenizer_baselines.py
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curl -L -o generate_tokenizer_baselines.py https://huggingface.co/datasets/pcuenq/tokenizer-conformance/resolve/main/Tools/generate_tokenizer_baselines.py
3.07 kB
| #!/usr/bin/env python3 | |
| """Generate or check fast-tokenizer references at the revisions in manifest.json. | |
| Adapted from apocryphx/swift-transformers PR #360; see README.md for attribution. | |
| Only Python reference outputs are written; Swift output never becomes a golden. | |
| """ | |
| import argparse | |
| import datetime | |
| import json | |
| from pathlib import Path | |
| import tokenizers | |
| import transformers | |
| from transformers import AutoTokenizer | |
| ROOT = Path(__file__).resolve().parent.parent | |
| def main(): | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument('--check', action='store_true', help='Compare reference entries without writing files') | |
| args = parser.parse_args() | |
| manifest = json.loads((ROOT / 'manifest.json').read_text(encoding='utf-8')) | |
| failed = False | |
| for corpus in manifest['corpora']: | |
| inputs = json.loads((ROOT / corpus['inputs']).read_text(encoding='utf-8')) | |
| assert inputs and len({i['id'] for i in inputs}) == len(inputs), 'Empty corpus or duplicate IDs' | |
| for spec in corpus['baselines']: | |
| model = spec['model_id'] | |
| revision = spec['revision'] | |
| assert len(revision) == 40 and all(c in '0123456789abcdef' for c in revision) | |
| print(f'{corpus["id"]}: {model}@{revision}', flush=True) | |
| tokenizer = AutoTokenizer.from_pretrained(model, revision=revision, use_fast=True) | |
| assert tokenizer.is_fast, 'References must use the fast tokenizer' | |
| entries = [] | |
| for row in inputs: | |
| ids = tokenizer(row['text'], add_special_tokens=True)['input_ids'] | |
| entries.append({ | |
| 'id': row['id'], | |
| 'input_ids': ids, | |
| 'tokens': tokenizer.convert_ids_to_tokens(ids), | |
| 'decoded_with_special': tokenizer.decode(ids, skip_special_tokens=False), | |
| 'decoded_skip_special': tokenizer.decode(ids, skip_special_tokens=True), | |
| }) | |
| path = ROOT / spec['path'] | |
| if args.check: | |
| previous = json.loads(path.read_text(encoding='utf-8'))['entries'] | |
| matches = previous == entries | |
| print(f' {len(entries)} entries: {"MATCH" if matches else "DIFFER"}', flush=True) | |
| failed |= not matches | |
| continue | |
| payload = { | |
| 'metadata': { | |
| 'model_id': model, 'model_revision': revision, | |
| 'transformers_version': transformers.__version__, | |
| 'tokenizers_version': tokenizers.__version__, | |
| 'generated_at': datetime.datetime.now(datetime.timezone.utc).replace(microsecond=0).isoformat(), | |
| 'input_count': len(entries), | |
| }, | |
| 'entries': entries, | |
| } | |
| path.parent.mkdir(parents=True, exist_ok=True) | |
| path.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + '\n', encoding='utf-8') | |
| raise SystemExit(1 if failed else 0) | |
| if __name__ == '__main__': | |
| main() | |