Delete french-bench-grammar-vocab-reading.py

french-bench-grammar-vocab-reading.py

@@ -1,95 +0,0 @@
-"""FrenchBench dataset."""
-import itertools
-from dataclasses import dataclass
-from typing import List, Optional
-
-import pyarrow as pa
-import pyarrow.parquet as pq
-
-import datasets
-from datasets.table import table_cast
-from datasets import SplitInfo
-
-
-logger = datasets.utils.logging.get_logger(__name__)
-
-URLS = ["https://huggingface.co/datasets/manu/french-bench-grammar-vocab-reading/resolve/main/data/Grammar-00000-of-00001-1b3667841d70df82.parquet",
-       "https://huggingface.co/datasets/manu/french-bench-grammar-vocab-reading/resolve/main/data/Vocabulary-00000-of-00001-8b228c3d484c6cf3.parquet",
-       "https://huggingface.co/datasets/manu/french-bench-grammar-vocab-reading/resolve/main/data/Reading-00000-of-00001-6ec75512c22ddcfa.parquet"]
-
-@dataclass
-class ParquetConfig(datasets.BuilderConfig):
-    """BuilderConfig for Parquet."""
-
-    batch_size: int = 10_000
-    columns: Optional[List[str]] = None
-    features: Optional[datasets.Features] = None
-
-
-class FrenchBenchGrammarVocabReading(datasets.ArrowBasedBuilder):
-    BUILDER_CONFIG_CLASS = ParquetConfig
-
-    def _info(self):
-        splits = {'Grammar': SplitInfo(name='Grammar', num_bytes=29094, num_examples=119, shard_lengths=None, dataset_name=None), 'Vocabulary': SplitInfo(name='Vocabulary', num_bytes=30944, num_examples=119, shard_lengths=None, dataset_name=None), 'Reading': SplitInfo(name='Reading', num_bytes=115507, num_examples=71, shard_lengths=None, dataset_name=None)}
-        return datasets.DatasetInfo(features=self.config.features, splits=splits)
-
-    def _split_generators(self, dl_manager):
-        """We handle string, list and dicts in datafiles"""
-        # 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(URLS)
-        if isinstance(data_files, (str, list, tuple)):
-            files = data_files
-            if isinstance(files, str):
-                files = [files]
-            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
-            files = [dl_manager.iter_files(file) for file in 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]
-            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
-            files = [dl_manager.iter_files(file) for file in files]
-            # Infer features if they are stored in the arrow schema
-            if self.info.features is None:
-                for file in itertools.chain.from_iterable(files):
-                    with open(file, "rb") as f:
-                        features = datasets.Features.from_arrow_schema(pq.read_schema(f))
-                        if self.config.columns is not None:
-                            features = datasets.Features(
-                                {col: feat for col, feat in features.items() if col in self.config.columns}
-                            )
-                        self.info.features = features
-                    break
-            splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files}))
-        return splits
-
-    def _cast_table(self, pa_table: pa.Table) -> pa.Table:
-        if self.info.features is not None:
-            # more expensive cast to support nested features with keys in a different order
-            # allows str <-> int/float or str to Audio for example
-            pa_table = table_cast(pa_table, self.info.features.arrow_schema)
-        return pa_table
-
-    def _generate_tables(self, files):
-        if self.config.features is not None and self.config.columns is not None:
-            if sorted(field.name for field in self.info.features.arrow_schema) != sorted(self.config.columns):
-                raise ValueError(
-                    f"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"
-                )
-        for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
-            with open(file, "rb") as f:
-                parquet_file = pq.ParquetFile(f)
-                try:
-                    for batch_idx, record_batch in enumerate(
-                        parquet_file.iter_batches(batch_size=self.config.batch_size, columns=self.config.columns)
-                    ):
-                        pa_table = pa.Table.from_batches([record_batch])
-                        # 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 f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
-                except ValueError as e:
-                    logger.error(f"Failed to read file '{file}' with error {type(e)}: {e}")
-                    raise