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import os |
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from datasets import load_dataset |
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from datasets import config |
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from datasets.utils.py_utils import convert_file_size_to_int |
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from datasets.table import embed_table_storage |
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from tqdm import tqdm |
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def build_parquet(split): |
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dataset = load_dataset("./src/LADaS.py", split=split, trust_remote_code=True) |
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max_shard_size = '500MB' |
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dataset_nbytes = dataset._estimate_nbytes() |
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max_shard_size = convert_file_size_to_int(max_shard_size or config.MAX_SHARD_SIZE) |
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num_shards = int(dataset_nbytes / max_shard_size) + 1 |
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num_shards = max(num_shards, 1) |
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shards = (dataset.shard(num_shards=num_shards, index=i, contiguous=True) for i in range(num_shards)) |
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def shards_with_embedded_external_files(shards): |
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for shard in shards: |
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format = shard.format |
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shard = shard.with_format("arrow") |
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shard = shard.map( |
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embed_table_storage, |
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batched=True, |
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batch_size=1000, |
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keep_in_memory=True, |
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) |
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shard = shard.with_format(**format) |
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yield shard |
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shards = shards_with_embedded_external_files(shards) |
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os.makedirs("data", exist_ok=True) |
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for index, shard in tqdm( |
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enumerate(shards), |
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desc="Save the dataset shards", |
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total=num_shards, |
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): |
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shard_path = f"data/{split}-{index:05d}-of-{num_shards:05d}.parquet" |
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shard.to_parquet(shard_path) |
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if __name__ == "__main__": |
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build_parquet("train") |
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build_parquet("validation") |
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build_parquet("test") |
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