helmo/github-issues huggingface dataset:
Arrow map type in parquet files unsupported
State: open | Author: TevenLeScao (CONTRIBUTOR)

### Describe the bug

When I try to load parquet files that were processed with Spark, I get the following issue:

`ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.`

Strangely, loading the dataset with `streaming=True` solves the issue.

### Steps to reproduce the bug

The dataset is private, but this can be reproduced with any dataset that has Arrow maps.

### Expected behavior

Loading the dataset no matter whether streaming is True or not.

### Environment info

- `datasets` version: 2.10.1
- Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31
- Python version: 3.10.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2

Comments: I'm attaching a minimal reproducible example:
```python
from datasets import load_dataset
import pyarrow as pa
import pyarrow.parquet as pq

table_with_map = pa.Table.from_pydict(
    {"a": [1, 2], "b": [[("a", 2)], [("b", 4)]]},
    schema=pa.schema({"a": pa.int32(), "b": pa.map_(pa.string(), pa.int32())})
)
pq.write_table(table_with_map, "parquet_with_map.parquet")
dset = load_dataset("parquet", data_files="parquet_with_map.parquet", split="train") # error unless streaming=True
``` 

For a dataset generated with the packaged loaders (CSV, JSON, Parquet), `streaming=True` sets the dataset's features to `None` (unless explicitly provided in `load_dataset`), hence no error will be thrown as long as the features stay "unresolved" (resolving the features with `_resolve_features` will lead to an error).

I've also been wondering about datasets support for Arrow Map datatypes. I had a situation where I had a pandas series of dict[str, float] with hundreds of different possible key values (ie. not bounded), and this got converted to a sequence of structs where every single struct had the entire set of keys.

I worked around it, by explicitly creating a sequence of [str, float], but given that pyarrow has an explicit Map datatype, it would be good to be able to explicitly cast/force this data type combination.

(feel free to ignore) polars will not support this type: https://github.com/pola-rs/polars/issues/3942#issuecomment-1202331210

> Polars will not add the map dtype. It's benefit do not outweigh the extra complexity. Maybe we can investigate conversion of maps to struct. But I will have to explore that.

Looks like they chose to convert every instance with https://github.com/pola-rs/polars/pull/4226
`load_from_disk` vs `load_dataset` performance.
State: open | Author: davidgilbertson (NONE)

### Describe the bug

I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices:
1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering.
2. `save_to_disk` and then use `load_from_disk` to load the filtered version.

The performance of these two approaches is wildly different:
* Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching)
* Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM)

I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should  not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it?

Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260).

### Steps to reproduce the bug

See above

### Expected behavior

Load times should be about the same.

### Environment info

- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3

Comments: Hi! We've recently made some improvements to `save_to_disk`/`list_to_disk` (100x faster in some scenarios), so it would help if you could install `datasets` directly from `main` (`pip install git+https://github.com/huggingface/datasets.git`) and re-run the "benchmark".

Great to hear! I'll give it a try when I've got a moment.

@mariosasko is that fix released to pip in the meantime? Asking cause im facing still the same issue (regarding loading images from local paths):
```
dataset = load_dataset("csv", cache_dir="cache", data_files=["/STORAGE/DATA/mijam/vit/code/list_filtered.csv"], num_proc=16, split="train").cast_column("image", Image())
dataset = dataset.class_encode_column("label")
```
quite fast. 

Then I do `save_to_disk()` and some time later:
```
dataset = load_from_disk('/STORAGE/DATA/mijam/accel/saved_arrow_big')
```
really slow. In theory it should be quicked since it only loads arrow files, no conversions and so on.


@mjamroz I assume your CSV file stores image file paths. This means `save_to_disk` needs to embed the image bytes resulting in a much bigger Arrow file (than the initial one). Maybe specifying `num_shards` to make the Arrow files smaller can help (large Arrow files on some systems take a long time to load).
audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files. 
State: closed | Author: jcho19 (NONE)

### Describe the bug

x = load_dataset("audiofolder", data_dir="x")
When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.)


### Steps to reproduce the bug

x = load_dataset("audiofolder", data_dir="x")


### Expected behavior

x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files).

### Environment info

- `datasets` version: 2.9.0
- Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3

Comments: Hi!

> naming convention of mp3 files

Yes, this could be the problem. MP3 files should end with  `.mp3`/`.MP3` to be recognized as audio files.

If the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree "x"` command)?

Hi! I'm sorry, I don't want to reveal my entire dataset, but here's a snippet (all of the mp3 files below are some of the ones not being recognized by audiofolder. Also, for another dataset, audiofolder loaded zero mp3 files because "train" was in the name of one of the mp3 files. 
my_dataset
├── data
│   ├── VHA_Innovation_Stories_-_Day_2-123.mp3
│   ├── VHA_Innovation_Stories_-_Day_2-124.mp3
│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-93.mp3
│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-94.mp3
│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-95.mp3
│   ├── Your_Impact\357\274\232_Neurosurgery_equipment-5.mp3
│   └── Your_Impact\357\274\232_Neurosurgery_equipment-6.mp3
└── metadata.csv

Here's a few of the 13 files recognized by the dataset:
British_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-1.mp3
British_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-2.mp3
British_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-3.mp3
IVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-1.mp3
IVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-2.mp3
Add `Dataset.to_list` to the API
State: closed | Author: mariosasko (COLLABORATOR)
Labels: name: enhancement

name: good first issue

Since there is `Dataset.from_list` in the API, we should also add `Dataset.to_list` to be consistent.
 
Regarding the implementation, we can re-use `Dataset.to_dict`'s code and replace the `to_pydict` calls with `to_pylist`.

Comments: Hello, I have an interest in this issue.
Is the `Dataset.to_dict` you are describing correct in the code here?

https://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/arrow_dataset.py#L4633-L4667

Yes, this is where `Dataset.to_dict` is defined.

#self-assign
Authorization error
State: closed | Author: OleksandrKorovii (NONE)

### Describe the bug

Get `Authorization error` when try to push data into hugginface datasets hub.

### Steps to reproduce the bug

I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share), 

1. `huggingface-cli login` with WRITE token
2. `git lfs install`
3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name`
4.
  ```
  cp /somewhere/data/*.json .
  git lfs track *.json
  git add .gitattributes
  git add *.json
  git commit -m "add json files"
  ```

but when I execute `git push` I got the error:

```
Uploading LFS objects:   0% (0/1), 0 B | 0 B/s, done.
batch response: Authorization error.
error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news'
```

Size of data ~100Gb.  I have five json files - different parts.

### Expected behavior

All my data pushed into hub

### Environment info

- `datasets` version: 2.10.1
- Platform: macOS-13.2.1-arm64-arm-64bit
- Python version: 3.10.10
- PyArrow version: 11.0.0
- Pandas version: 1.5.3

Comments: Hi! 

It's better to report this kind of issue in the `huggingface_hub` repo, so if you still haven't resolved it, I suggest you open an issue there.

Yeah,  I solved it. Problem was in osxkeychain.  When I do `hugginface-cli login` it's add token with default account (username)`hg_user` but my repo contain other username.  When I changed username in keychain -  it works now.
Dataloader getitem not working for DreamboothDatasets
State: closed | Author: salahiguiliz (NONE)

### Describe the bug

 Dataloader getitem is not working as before (see example of [DreamboothDatasets](https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py#L451C14-L529))
moving Datasets to 2.8.0 solved the issue.

### Steps to reproduce the bug

1- using DreamBoothDataset to load some images 
2- error after loading when trying to visualise the images 

### Expected behavior

I was expecting a numpy array of the image

### Environment info

- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5

Comments: Hi! 

> (see example of DreamboothDatasets)


Could you please provide a link to it?  If you are referring to the example in the `diffusers` repo, your issue is unrelated to `datasets` as that example uses `Dataset` from PyTorch to load data.
in-place dataset update
State: closed | Author: speedcell4 (NONE)
Labels: name: wontfix

### Motivation

For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this.

```python
from datasets import Dataset

ds = Dataset.from_list([])

ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>>     features: [],
>>>     num_rows: 0
>>> })

ds = ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>>     features: ['a', 'b'],
>>>     num_rows: 1
>>> })
```

### Feature request

Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`.

```python
from datasets import Dataset

ds = Dataset.from_list([])

ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>>     features: [],
>>>     num_rows: 0
>>> })

ds.add_item_({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>>     features: ['a', 'b'],
>>>     num_rows: 1
>>> })
```

### Related Functions

* `.map`
* `.filter`
* `.add_item`

Comments: We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications.

In your case the old dataset is garbage collected pretty quickly so you won't have memory issues.

Note that datasets loaded from disk (memory mapped) are not loaded in memory, and therefore the new dataset actually use the same buffers as the old one.

Thank you for your detailed reply.

> In your case the old dataset is garbage collected pretty quickly so you won't have memory issues.

I understand this, but it still copies the old dataset to create the new one, is this correct? So maybe it is not memory-consuming, but time-consuming?

Indeed, and because of that it is more efficient to add multiple rows at once instead of one by one, using `concatenate_datasets` for example.
Unpins sqlAlchemy
State: closed | Author: lazarust (NONE)

Closes #5477 

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5595). All of your documentation changes will be reflected on that endpoint.

It looks like this issue hasn't been fixed yet, so let's wait a bit more.

@lazarust thanks for your work, but unfortunately we cannot merge it.

See my comment in: https://github.com/huggingface/datasets/issues/5477#issuecomment-1495512688

The fix was released yesterday (2023-04-03) only in `pandas-2.0.0`:
- https://github.com/pandas-dev/pandas/releases/tag/v2.0.0

but it will not be back-ported to `pandas-1`:
- https://github.com/pandas-dev/pandas/pull/48576#issuecomment-1466467159

Also note that `pandas-2.0.0` dropped support for Python 3.7:
- https://github.com/pandas-dev/pandas/issues/41678
- https://github.com/pandas-dev/pandas/pull/41989

Therefore, we cannot unpin `sqlalchemy` until we drop support for Python 3.7 (these Python users cannot use `pandas-2`). See our latest CI checks below:
- "CI / test" fails because it runs on Python 3.7
- "CI / test_py310" succeeds because it runs on Python 3.10
.sort() is broken when used after .filter(), only in 2.10.0
State: closed | Author: MattYoon (NONE)
Labels: name: bug

### Describe the bug

Hi, thank you for your support!

It seems like the addition of multiple key sort (#5502) in 2.10.0 broke the `.sort()` method.

After filtering a dataset with `.filter()`, the `.sort()` seems to refer to the query_table index of the previous unfiltered dataset, resulting in an IndexError.

This only happens with the 2.10.0 release.

### Steps to reproduce the bug

```Python
from datasets import load_dataset

# dataset with length of 1104
ds = load_dataset('glue', 'ax')['test']
ds = ds.filter(lambda x: x['idx'] > 1100)
ds.sort('premise')
print('Done')
```

  File "/home/dongkeun/datasets_test/test.py", line 5, in <module>
    ds.sort('premise')
  File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 528, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 511, in wrapper
    out = func(dataset, *args, **kwargs)
  File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3959, in sort
    sort_table = query_table(
  File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 588, in query_table
    _check_valid_index_key(key, size)
  File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 537, in _check_valid_index_key
    _check_valid_index_key(max(key), size=size)
  File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 531, in _check_valid_index_key
    raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 1103 is out of bounds for size 3

### Expected behavior

It should sort the dataset and print "Done". Which it does on 2.9.0.

### Environment info

- `datasets` version: 2.10.0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3

Comments: Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix
Cache is not transportable
State: closed | Author: davidgilbertson (NONE)

### Describe the bug

I would like to share cache between two machines (a Windows host machine and a WSL instance).

I run most my code in WSL. I have just run out of space in the virtual drive. Rather than expand the drive size, I plan to move to cache to the host Windows machine, thereby sharing the downloads.

I'm hoping that I can just copy/paste the cache files, but I notice that a lot of the file names start with the path name, e.g. `_home_davidg_.cache_huggingface_datasets_conll2003_default-451...98.lock` where `home/davidg` is where the cache is in WSL. 

This seems to suggest that the cache is not portable/cannot be centralised or shared. Is this the case, or are the files that start with path names not integral to the caching mechanism? Because copying the cache files _seems_ to work, but I'm not filled with confidence that something isn't going to break.

A related issue, when trying to load a dataset that should come from cache (running in WSL, pointing to cache on the Windows host) it seemed to work fine, but it still uses a WSL directory for `.cache\huggingface\modules\datasets_modules`. I see nothing in the docs about this, or how to point it to a different place.

I have asked a related question on the forum: https://discuss.huggingface.co/t/is-datasets-cache-operating-system-agnostic/32656

### Steps to reproduce the bug

View the cache directory in WSL/Windows.

### Expected behavior

Cache can be shared between (virtual) machines and be transportable.

It would be nice to have a simple way to say "Dear Hugging Face packages, please put ALL your cache in `blah/de/blah`" and have all the Hugging Face packages respect that single location.

### Environment info

```
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- ```

Comments: Hi ! No the cache is not transportable in general. It will work on a shared filesystem if you use the same python environment, but not across machines/os/environments.

In particular, reloading cached datasets does work, but reloading cached processed datasets (e.g. from `map`) may not work. This is because some hashes used by caching are based on pickle dumps of the function you pass to `map`.

Finally you may copy the cache to another machine, but all the `cached-*.arrow` files are unlikely to be reloaded.

OK good to know. Thanks @lhoestq !
Unable to load coyo700M dataset
State: closed | Author: manuaero (NONE)

### Describe the bug

Seeing this error when downloading https://huggingface.co/datasets/kakaobrain/coyo-700m:
```ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.```


Full stack trace

```Downloading and preparing dataset parquet/kakaobrain--coyo-700m to /root/.cache/huggingface/datasets/kakaobrain___parquet/kakaobrain--coyo-700m-ae729692ae3e0073/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 100%
1/1 [00:00<00:00, 63.35it/s]
Extracting data files: 100%
1/1 [00:00<00:00, 5.00it/s]
---------------------------------------------------------------------------
ArrowInvalid                              Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
   1859                 _time = time.time()
-> 1860                 for _, table in generator:
   1861                     if max_shard_size is not None and writer._num_bytes > max_shard_size:

9 frames
ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.

The above exception was the direct cause of the following exception:

DatasetGenerationError                    Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
   1890             if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
   1891                 e = e.__context__
-> 1892             raise DatasetGenerationError("An error occurred while generating the dataset") from e
   1893 
   1894         yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)

DatasetGenerationError: An error occurred while generating the dataset```

### Steps to reproduce the bug

```
from datasets import load_dataset
hf_dataset = load_dataset("kakaobrain/coyo-700m")
```

### Expected behavior

The above commands load the dataset successfully. Or handles exception and continue loading the remainder.

### Environment info

colab. any

Comments: Hi @manuaero 

Thank you for your interest in the COYO dataset.

Our dataset provides the img-url and alt-text in the form of a parquet, so to utilize the coyo dataset you will need to download it directly.

We provide a [guide](https://github.com/kakaobrain/coyo-dataset/blob/main/download/README.md) to download, so check it out.

Thank you.
[DOC] Mistaken docs on set_format
State: closed | Author: NightMachinery (CONTRIBUTOR)
Labels: name: good first issue

### Describe the bug

https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format
<img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png">

While actually running it will result in:
<img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png">


### Steps to reproduce the bug

_

### Expected behavior

_

### Environment info

- `datasets` version: 2.10.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5

Comments: Thanks for reporting!
Add instructions to create `DataLoader` from augmented dataset in object detection guide
State: closed | Author: Laurent2916 (CONTRIBUTOR)

The following adds instructions on how to create a `DataLoader` from the guide on how to use object detection with augmentations (#4710). I am open to hearing any suggestions for improvement !

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5579). All of your documentation changes will be reflected on that endpoint.

I'm not sure we need this part as we provide a link to the notebook that shows how to train an object detection model, and this notebook instantiates a `DataLoader` before training the model. I'd like to hear what @stevhliu thinks.

PS: Your `collate_fn` calls `torch.stack` on the `bbox` tensors, which don't have the same shape, so this will fail.

I agree with @mariosasko; we also have a [Use with PyTorch](https://huggingface.co/docs/datasets/use_with_pytorch) guide that shows how you can create a `DataLoader`.