smi-ted/training/pubchem_canon_script.py

import logging
from dataclasses import dataclass
import pyarrow as pa

import datasets


logger = logging.getLogger(__name__)


FEATURES = datasets.Features(
    {
        "text": datasets.Value("string"),
    }
)


@dataclass
class PubChemConfig(datasets.BuilderConfig):
    """BuilderConfig for text files."""

    encoding: str = "utf-8"
    chunksize: int = 10 << 20  # 10MB


class PubChem(datasets.ArrowBasedBuilder):

    BUILDER_CONFIG_CLASS = PubChemConfig

    def _info(self):
        return datasets.DatasetInfo(features=FEATURES)

    def _split_generators(self, dl_manager):
        """The `data_files` kwarg in load_dataset() can be a str, List[str], Dict[str,str], or Dict[str,List[str]].

        If str or List[str], then the dataset returns only the 'train' split.
        If dict, then keys should be from the `datasets.Split` enum.
        """
        if not self.config.data_files:
            raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
        data_files = dl_manager.download_and_extract(self.config.data_files)
        if isinstance(data_files, (str, list, tuple)):
            files = data_files
            if isinstance(files, str):
                files = [files]
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": files})]
        splits = []
        for split_name, files in data_files.items():
            if isinstance(files, str):
                files = [files]
            splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files}))
        return splits

    def _generate_tables(self, files):

        for file_idx, file in enumerate(files):
            batch_idx = 0
            with open(file, "r", encoding=self.config.encoding) as f:
                while True:
                    batch = f.read(self.config.chunksize)
                    if not batch:
                        break
                    batch += f.readline()  # finish current line
                    batch = batch.splitlines()
                    #batch = [word.split()[-1] for word in batch]
                    pa_table = pa.Table.from_arrays([pa.array(batch)], schema=pa.schema({"text": pa.string()}))
                    # Uncomment for debugging (will print the Arrow table size and elements)
                    #logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                    #logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                    yield (file_idx, batch_idx), pa_table
                    batch_idx += 1