codingutf8 2019present the huggingface inc team 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 orglicenseslicense2 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 preprocessing script before training the distilled model coding utf 8 2019 present the huggingface inc team 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 preprocessing script before training the distilled model
import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
logger = logging.getLogger(__name__)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
    )
    parser.add_argument(
        "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
    )
    parser.add_argument(
        "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
    )
    parser.add_argument("--vocab_size", default=30522, type=int)
    args = parser.parse_args()

    logger.info(f"Loading data from {args.data_file}")
    with open(args.data_file, "rb") as fp:
        data = pickle.load(fp)

    logger.info("Counting occurrences for MLM.")
    counter = Counter()
    for tk_ids in data:
        counter.update(tk_ids)
    counts = [0] * args.vocab_size
    for k, v in counter.items():
        counts[k] = v

    logger.info(f"Dump to {args.token_counts_dump}")
    with open(args.token_counts_dump, "wb") as handle:
        pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)