Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
State: closed

## Describe the bug
A clear and concise description of what the bug is.

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple

import datasets
import pandas as pd

_CITATION = """\
"""

_DATASETNAME = "jadi_ide"

_DESCRIPTION = """\
"""

_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
    _DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"


class JaDi_Ide(datasets.GeneratorBasedBuilder):

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)

    BUILDER_CONFIGS = [
        NusantaraConfig(
            name="jadi_ide_source",
            version=SOURCE_VERSION,
            description="JaDi-Ide source schema",
            schema="source",
            subset_id="jadi_ide",
        ),
    ]

    DEFAULT_CONFIG_NAME = "source"

    def _info(self) -> datasets.DatasetInfo:
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"), 
                    "text": datasets.Value("string"), 
                    "label": datasets.Value("string")
                }
            )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        """Returns SplitGenerators."""
        # Dataset does not have predetermined split, putting all as TRAIN
        urls = _URLS[_DATASETNAME]
        base_dir = Path(dl_manager.download_and_extract(urls))
        data_files = {"train": base_dir}

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_files["train"],
                    "split": "train",
                },
            ),
        ]

    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        df = pd.read_excel(filepath, engine='openpyxl')
        df.columns = ["id", "text", "label"]

        if self.config.schema == "source":
            for row in df.itertuples():
                ex = {
                    "id": str(row.id),
                    "text": row.text,
                    "label": row.label,
                }
                yield row.id, ex

```

## Expected results
Expecting to load the dataset smoothly.

## Actual results
  File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
    use_auth_token=use_auth_token,
  File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
    super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
  File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
    desc=f"Generating {split_info.name} split",
  File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
    for obj in iterable:
  File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
    df = pd.read_excel(filepath, engine='openpyxl')
  File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
    return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1


What's more, the downloaded data is actually a folder instead of an excel file.

Hi hi, instead of using `download_and_extract` function, I only use `download` function: `base_dir = Path(dl_manager.download(urls))`. It turns out that the code works for `datasets==2.2.2`, however, it doesn't work with `datasets==2.4.0`. 

Hi @yana-xuyan, thanks for reporting.

Indeed you already found the answer: an Excel file should be just downloaded and not downloaded-and-extracted.

The reason why is that if you call also extract, our library will try to infer the compression format (and extract it). And Excel files are viewed as ZIP files and extracted as so (into a directory). This is because the Office Open XML is indeed a zipped file under the hood): https://en.wikipedia.org/wiki/Office_Open_XML
> Office Open XML (also informally known as OOXML) is a **zipped**, XML-based file format
```python
import zipfile

zipfile.is_zipfile("filename.xlsx")
```
returns `True`.

Hi @albertvillanova, thank you for your reply! Do you have any clue on why the same error still exists with `datasets==2.4.0` even after I don't extract the downloaded file? FYI, if I downgrade to `datasets==2.2.2`, the code works fine.

I guess this has to do with the cache: you should remove the previously-wrongly generated directory from the cache; otherwise `datasets` tries to re-use it.