hf-dev-train/transformers-main/examples/legacy/seq2seq/pack_dataset.py

#!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fill examples with bitext up to max_tokens without breaking up examples.
[['I went', 'yo fui'],
['to the store', 'a la tienda']
]
=> ['I went to the store', 'yo fui a la tienda']
"""

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def pack_examples(tok, src_examples, tgt_examples, max_tokens=1024):
    finished_src, finished_tgt = [], []

    sorted_examples = list(zip(src_examples, tgt_examples))
    new_src, new_tgt = sorted_examples[0]

    def is_too_big(strang):
        return tok(strang, return_tensors="pt").input_ids.shape[1] > max_tokens

    for src, tgt in tqdm(sorted_examples[1:]):
        cand_src = new_src + " " + src
        cand_tgt = new_tgt + " " + tgt
        if is_too_big(cand_src) or is_too_big(cand_tgt):  # cant fit, finalize example
            finished_src.append(new_src)
            finished_tgt.append(new_tgt)
            new_src, new_tgt = src, tgt
        else:  # can fit, keep adding
            new_src, new_tgt = cand_src, cand_tgt

    # cleanup
    if new_src:
        assert new_tgt
        finished_src.append(new_src)
        finished_tgt.append(new_tgt)
    return finished_src, finished_tgt


def pack_data_dir(tok, data_dir: Path, max_tokens, save_path):
    save_path = Path(save_path)
    save_path.mkdir(exist_ok=True)
    for split in ["train"]:
        src_path, tgt_path = data_dir / f"{split}.source", data_dir / f"{split}.target"
        src_docs = [x.rstrip() for x in Path(src_path).open().readlines()]
        tgt_docs = [x.rstrip() for x in Path(tgt_path).open().readlines()]
        packed_src, packed_tgt = pack_examples(tok, src_docs, tgt_docs, max_tokens)
        print(f"packed {split} split from {len(src_docs)} examples -> {len(packed_src)}.")
        Path(save_path / f"{split}.source").open("w").write("\n".join(packed_src))
        Path(save_path / f"{split}.target").open("w").write("\n".join(packed_tgt))
    for split in ["val", "test"]:
        src_path, tgt_path = data_dir / f"{split}.source", data_dir / f"{split}.target"
        shutil.copyfile(src_path, save_path / f"{split}.source")
        shutil.copyfile(tgt_path, save_path / f"{split}.target")


def packer_cli():
    parser = argparse.ArgumentParser()
    parser.add_argument("--tok_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
    parser.add_argument("--max_seq_len", type=int, default=128)
    parser.add_argument("--data_dir", type=str)
    parser.add_argument("--save_path", type=str)
    args = parser.parse_args()
    tokenizer = AutoTokenizer.from_pretrained(args.tok_name)
    return pack_data_dir(tokenizer, Path(args.data_dir), args.max_seq_len, args.save_path)


if __name__ == "__main__":
    packer_cli()