# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # 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. """TODO""" from collections import defaultdict from pathlib import Path import datasets import pyarrow as pa import pyarrow.parquet as pq from datasets import Sequence, Value from datasets.config import PYARROW_VERSION from datasets.utils.logging import get_logger from huggingface_hub import hf_api logger = get_logger(__name__) if PYARROW_VERSION.major <= 6: msg = f"pyarrow version >= 7.0.0 required for this loading script, you have {PYARROW_VERSION}" logger.warning(msg) raise RuntimeError(msg) _DESCRIPTION = "TODO" _HOMEPAGE = "TODO" api = hf_api.HfApi() files = api.list_repo_files("biglam/europeana_newspapers", repo_type="dataset") data = defaultdict(dict) parquet_files = (f for f in files if f.endswith(".parquet")) for file in parquet_files: lang, decade = Path(file).stem.split("-") data[lang].update({decade: file}) _DATA = dict(data) _LANG_CONFIGS = set(_DATA.keys()) class EuropeanaNewspapersConfig(datasets.BuilderConfig): """BuilderConfig for the Europeana Newspapers dataset.""" def __init__( self, *args, languages=None, min_decade=None, max_decade=None, **kwargs ): """BuilderConfig for the Europeana Newspapers dataset. Args: languages (:obj:`List[str]`): List of languages to load. **kwargs: keyword arguments forwarded to super. """ super().__init__( *args, name="+".join(languages), **kwargs, ) for lang in languages: if lang not in _LANG_CONFIGS: raise ValueError( f"{lang} not a valid language key for this dataset, valid keys are {_LANG_CONFIGS}" ) self.languages = languages self.min_decade = min_decade self.max_decade = max_decade class EuropeanaNewspapers(datasets.GeneratorBasedBuilder): """TODO.""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIG_CLASS = EuropeanaNewspapersConfig BUILDER_CONFIGS = [ EuropeanaNewspapersConfig(languages=[lang]) for lang in _LANG_CONFIGS ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "text": Value(dtype="string"), "mean_ocr": Value(dtype="float64"), "std_ocr": Value(dtype="float64"), "bounding_boxes": Sequence( feature=Sequence( feature=Value(dtype="float64", id=None), length=-1, ), ), "title": Value(dtype="string"), "date": Value(dtype="string"), "language": Sequence( feature=Value(dtype="string", id=None), ), "item_iiif_url": Value( dtype="string", ), # "multi_language": Value(dtype="bool"), "issue_uri": Value(dtype="string"), "id": Value(dtype="string"), } ), supervised_keys=None, homepage=_HOMEPAGE, license="Multiple: see the 'license' field of each sample.", ) def _split_generators(self, dl_manager): # parquet_files = list(Path(".").rglob("*.parquet")) languages = self.config.languages min_decade = self.config.min_decade max_decade = self.config.max_decade data_files = [] for language in languages: for decade, file in _DATA[language].items(): decade = int(decade) if max_decade is None and min_decade is None: data_files.append(file) if ( max_decade is not None and min_decade is not None and min_decade <= decade <= max_decade ): data_files.append(file) if ( min_decade is not None and max_decade is None and decade >= min_decade ): data_files.append(file) if ( min_decade is None and max_decade is not None and decade <= max_decade ): data_files.append(file) files = dl_manager.download(data_files) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": files, }, ), ] def _generate_examples(self, files): key = 0 for file in files: with open(file, "rb") as f: parquet_file = pq.ParquetFile(f) for record_batch in parquet_file.iter_batches(batch_size=10_000): pa_table = pa.Table.from_batches([record_batch]) rows = pa_table.to_pylist() for row in rows: row.pop("multi_language") yield key, row key += 1