Repository: huggingface/datasets
File: src/datasets/packaged_modules/arrow/arrow.py (Python)

Code before:
    def _info(self):
        return datasets.DatasetInfo(features=self.config.features)

    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(self.config.data_files)
        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 is they are stoed in the arrow schema
            if self.info.features is None:
                for file in itertools.chain.from_iterable(files):
                    self.info.features = datasets.Features.from_arrow_schema(read_schema_from_file(file))
                    # with open(file, "rb") as f:
                    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):
        schema = self.info.features.arrow_schema if self.info.features is not None else None
        if self.info.features is not None and self.config.columns is not None:
            if sorted(field.name for field in 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)):
            for batch_idx, record_batch in enumerate(_memory_mapped_record_batch_reader_from_file(file)):
                pa_table = pa.Table.from_batches([record_batch])
                yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)


Review comment (suggestion):
Same here, we can't use read_schema_from_file. But using `open()` and pyarrow:
```suggestion
                    with open(file, "rb") as f:
                        self.info.features = datasets.Features.from_arrow_schema(pa.ipc.open_stream(f).schema)
```

Code after:

    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(self.config.data_files)
        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 is they are stoed in the arrow schema
            if self.info.features is None:
                for file in itertools.chain.from_iterable(files):
                    with open(file, "rb") as f:
                        self.info.features = datasets.Features.from_arrow_schema(pa.ipc.open_stream(f).schema)
                    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):
        for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
            with open(file, "rb") as f:
                try:
                    for batch_idx, record_batch in enumerate(pa.ipc.open_stream(f)):
                        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