ahmedfarazsyk/datasets-github-issues huggingface dataset:
Attempting to return a rank 3 grayscale image from dataset.map results in extreme slowdown 
Author: navidmafi
State: open

### Describe the bug

Background: Digital images are often represented as a  (Height, Width, Channel) tensor. This is the same for huggingface datasets that contain images. These images are loaded in Pillow containers which offer, for example, the `.convert` method.

I can convert an image from a (H,W,3) shape to a grayscale (H,W) image and I have no problems with this. But when attempting to return a (H,W,1) shaped matrix from a map function, it never completes and sometimes even results in an OOM from the OS.


I've used various methods to expand a (H,W) shaped array to a (H,W,1) array. But they all resulted in extremely long map operations consuming a lot of CPU and RAM.

### Steps to reproduce the bug

Below is a minimal example using two methods to get the desired output. Both of which don't work
```py
import tensorflow as tf
import datasets
import numpy as np

ds = datasets.load_dataset("project-sloth/captcha-images")
to_gray_pillow = lambda sample: {'image': np.expand_dims(sample['image'].convert("L"), axis=-1)}
ds_gray = ds.map(to_gray_pillow)

# Alternatively
ds = datasets.load_dataset("project-sloth/captcha-images").with_format("tensorflow")
to_gray_tf = lambda sample: {'image': tf.expand_dims(tf.image.rgb_to_grayscale(sample['image']), axis=-1)}
ds_gray = ds.map(to_gray_tf)
```

### Expected behavior

I expect the map operation to complete and return a new dataset containing grayscale images in a (H,W,1) shape.

### Environment info

datasets 2.21.0
python tested with both 3.11 and 3.12
host os : linux
Inconsistent output in documentation example: `num_classes` not displayed in `ClassLabel` output
Author: sergiopaniego
State: closed

In the documentation for [ClassLabel](https://huggingface.co/docs/datasets/v2.21.0/en/package_reference/main_classes#datasets.ClassLabel), there is an example of usage with the following code:

````
from datasets import Features
features = Features({'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'])})
features
````

which expects to output (as stated in the documentation):

````
{'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'], id=None)}
````

but it generates the following

````
{'label': ClassLabel(names=['bad', 'ok', 'good'], id=None)}
````

If my understanding is correct, this happens because although num_classes is used during the init of the object, it is afterward ignored:

https://github.com/huggingface/datasets/blob/be5cff059a2a5b89d7a97bc04739c4919ab8089f/src/datasets/features/features.py#L975

I would like to work on this issue if this is something needed 😄
Caching shuffles by np.random.Generator results in unintiutive behavior
Author: el-hult
State: open

### Describe the bug

Create a dataset. Save it to disk. Load from disk. Shuffle, usning a `np.random.Generator`. Iterate. Shuffle again. Iterate. The iterates are different since the supplied np.random.Generator has progressed between the shuffles.

Load dataset from disk again. Shuffle and Iterate. See same result as before. Shuffle and iterate, and this time it does not have the same shuffling as ion previous run.

The motivation is I have a deep learning loop with 

```
for epoch in range(10):
    for batch in dataset.shuffle(generator=generator).iter(batch_size=32):
        .... # do stuff
```
where I want a new shuffling at every epoch. Instead I get the same shuffling.

### Steps to reproduce the bug

Run the code below two times. 

```python
import datasets
import numpy as np

generator = np.random.default_rng(0)
ds = datasets.Dataset.from_dict(mapping={"X":range(1000)})
ds.save_to_disk("tmp")
print("First loop: ", end="")
for _ in range(10):
    print(next(ds.shuffle(generator=generator).iter(batch_size=1))['X'], end=", ")
print("")

print("Second loop: ", end="")
ds = datasets.Dataset.load_from_disk("tmp")
for _ in range(10):
    print(next(ds.shuffle(generator=generator).iter(batch_size=1))['X'], end=", ")
print("")
```

The output is:

```
$ python main.py 
Saving the dataset (1/1 shards): 100%|███████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 495019.95 examples/s]
First loop: 459, 739, 72, 943, 241, 181, 845, 830, 896, 334, 
Second loop: 741, 847, 944, 795, 483, 842, 717, 865, 231, 840,
$ python main.py 
Saving the dataset (1/1 shards): 100%|████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 22243.40 examples/s]
First loop: 459, 739, 72, 943, 241, 181, 845, 830, 896, 334, 
Second loop: 741, 741, 741, 741, 741, 741, 741, 741, 741, 741, 
```

The second loop, on the second run, only spits out "741, 741, 741...." which is *not* the desired output

### Expected behavior

I want the dataset to shuffle at every epoch since I provide it with a generator for shuffling. 

### Environment info

Datasets version 2.21.0
Ubuntu linux.

Comments: I first thought this was a mistake of mine, and also posted on stack overflow. https://stackoverflow.com/questions/78913797/iterating-a-huggingface-dataset-from-disk-using-generator-seems-broken-how-to-d 

It seems to me the issue is the caching step in 

https://github.com/huggingface/datasets/blob/be5cff059a2a5b89d7a97bc04739c4919ab8089f/src/datasets/arrow_dataset.py#L4306-L4316

because the shuffle happens after checking the cache, the rng state won't advance if the cache is used. This is VERY confusing. Also not documented.

My proposal is that you remove the API for using a Generator, and only keep the seed-based API since that is functional and cache-compatible.

Second that, its very confusing.
[interleave_dataset] sample batches from a single source at a time
Author: memray
State: open
Labels: name: enhancement

### Feature request

interleave_dataset and [RandomlyCyclingMultiSourcesExamplesIterable](https://github.com/huggingface/datasets/blob/3813ce846e52824b38e53895810682f0a496a2e3/src/datasets/iterable_dataset.py#L816) enable us to sample data examples from different sources. But can we also sample batches in a similar manner (each batch only contains data from a single source)?


### Motivation

Some recent research [[1](https://blog.salesforceairesearch.com/sfr-embedded-mistral/), [2](https://arxiv.org/pdf/2310.07554)] shows that source homogenous batching can be helpful for contrastive learning. Can we add a function called `RandomlyCyclingMultiSourcesBatchesIterable` to support this functionality?

### Your contribution

I can contribute a PR. But I wonder what the best way is to test its correctness and robustness.
datasets cannot handle nested json if features is given.
Author: ljw20180420
State: closed

### Describe the bug

I have a json named temp.json.
```json
{"ref1": "ABC", "ref2": "DEF", "cuts":[{"cut1": 3, "cut2": 5}]}
```
I want to load it.
```python
ds = datasets.load_dataset('json', data_files="./temp.json", features=datasets.Features({
    'ref1': datasets.Value('string'),
    'ref2': datasets.Value('string'),
    'cuts': datasets.Sequence({
        "cut1": datasets.Value("uint16"),
        "cut2": datasets.Value("uint16")
    })
}))
```
The above code does not work. However, I can load it without giving features.
```python
ds = datasets.load_dataset('json', data_files="./temp.json")
```
Is it possible to load integers as uint16 to save some memory?

### Steps to reproduce the bug

As in the bug description.

### Expected behavior

The data are loaded and integers are uint16.

### Environment info

Copy-and-paste the text below in your GitHub issue.

- `datasets` version: 2.21.0
- Platform: Linux-5.15.0-118-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.24.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0

Comments: Hi ! `Sequence` has a weird behavior for dictionaries (from tensorflow-datasets), use a regular list instead:

```python
ds = datasets.load_dataset('json', data_files="./temp.json", features=datasets.Features({
    'ref1': datasets.Value('string'),
    'ref2': datasets.Value('string'),
    'cuts': [{
        "cut1": datasets.Value("uint16"),
        "cut2": datasets.Value("uint16")
    }]
}))
```

> Hi ! `Sequence` has a weird behavior for dictionaries (from tensorflow-datasets), use a regular list instead:
> 
> ```python
> ds = datasets.load_dataset('json', data_files="./temp.json", features=datasets.Features({
>     'ref1': datasets.Value('string'),
>     'ref2': datasets.Value('string'),
>     'cuts': [{
>         "cut1": datasets.Value("uint16"),
>         "cut2": datasets.Value("uint16")
>     }]
> }))
> ```
Thank you!


It works.
Stream dataset does not iterate if the batch size is larger than the dataset size (related to drop_last_batch)
Author: memray
State: closed

### Describe the bug

Hi there,

I use streaming and interleaving to combine multiple datasets saved in jsonl files. The size of dataset can vary (from 100ish to 100k-ish). I use dataset.map() and a big batch size to reduce the IO cost. It was working fine with datasets-2.16.1 but this problem shows up after I upgraded to datasets-2.19.2. With  2.21.0 the problem remains.

Please see the code below to reproduce the problem.

The dataset can iterate correctly if we set either streaming=False or drop_last_batch=False.

I have to use drop_last_batch=True since it's for distributed training.

### Steps to reproduce the bug

```python
# datasets==2.21.0
import datasets
def data_prepare(examples):
    print(examples["sentence1"][0])
    return examples

batch_size = 101
# the size of the dataset is 100
# the dataset iterates correctly if we set either streaming=False or drop_last_batch=False 
dataset = datasets.load_dataset("mteb/biosses-sts", split="test", streaming=True)
dataset = dataset.map(lambda x: data_prepare(x),
                      drop_last_batch=True,
                      batched=True, batch_size=batch_size)
for ex in dataset:
    print(ex)
    pass

```

### Expected behavior

The dataset iterates regardless of the batch size.

### Environment info

- `datasets` version: 2.21.0
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.24.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0


Comments: That's expected behavior, it's also the same in `torch`:

```python
>>> list(DataLoader(list(range(5)), batch_size=10, drop_last=True))
[]
```
cudf-cu12 24.4.1, ibis-framework 8.0.0 requires pyarrow<15.0.0a0,>=14.0.1,pyarrow<16,>=2 and datasets 2.21.0 requires pyarrow>=15.0.0
Author: SoumyaMB10
State: open

### Describe the bug

!pip install accelerate>=0.16.0 torchvision transformers>=4.25.1 datasets>=2.19.1 ftfy tensorboard Jinja2 peft==0.7.0

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.
ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.

to solve above error
!pip install pyarrow==14.0.1

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
datasets 2.21.0 requires pyarrow>=15.0.0, but you have pyarrow 14.0.1 which is incompatible.

### Steps to reproduce the bug

!pip install datasets>=2.19.1

### Expected behavior

run without dependency error

### Environment info

Diffusers version: 0.31.0.dev0

Platform: Linux-6.1.85+-x86_64-with-glibc2.35
Running on Google Colab?: Yes
Python version: 3.10.12
PyTorch version (GPU?): 2.3.1+cu121 (True)
Flax version (CPU?/GPU?/TPU?): 0.8.4 (gpu)
Jax version: 0.4.26
JaxLib version: 0.4.26
Huggingface_hub version: 0.23.5
Transformers version: 4.42.4
Accelerate version: 0.32.1
PEFT version: 0.7.0
Bitsandbytes version: not installed
Safetensors version: 0.4.4
xFormers version: not installed
Accelerator: Tesla T4, 15360 MiB
Using GPU in script?:
Using distributed or parallel set-up in script?:

Comments: @sayakpaul  please advice 

Hits the same dependency conflict
CI is broken for numpy-2: Failed to fetch wheel: llvmlite==0.34.0
Author: albertvillanova
State: closed

Ci is broken with error `Failed to fetch wheel: llvmlite==0.34.0`: https://github.com/huggingface/datasets/actions/runs/10466825281/job/28984414269
```
Run uv pip install --system "datasets[tests_numpy2] @ ."
Resolved 150 packages in 4.42s
error: Failed to prepare distributions
  Caused by: Failed to fetch wheel: llvmlite==0.34.0
  Caused by: Build backend failed to build wheel through `build_wheel()` with exit status: 1
--- stdout:
running bdist_wheel
/home/runner/.cache/uv/builds-v0/.tmpcyKh8S/bin/python /home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py
LLVM version...
--- stderr:
Traceback (most recent call last):
  File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 105, in main_posix
    out = subprocess.check_output([llvm_config, '--version'])
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 421, in check_output
    return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 503, in run
    with Popen(*popenargs, **kwargs) as process:
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 971, in __init__
    self._execute_child(args, executable, preexec_fn, close_fds,
  File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 1863, in _execute_child
    raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'llvm-config'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 191, in <module>
    main()
  File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 181, in main
    main_posix('linux', '.so')
  File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 107, in main_posix
    raise RuntimeError("%s failed executing, please point LLVM_CONFIG "
RuntimeError: llvm-config failed executing, please point LLVM_CONFIG to the path for llvm-config
error: command '/home/runner/.cache/uv/builds-v0/.tmpcyKh8S/bin/python' failed with exit code 1
```

Comments: Note that the CI before was using:
- llvmlite: 0.43.0
- numba: 0.60.0

Now it tries to use:
- llvmlite: 0.34.0
- numba: 0.51.2

The issue is because numba-0.60.0 pins numpy<2.1 and `uv` tries to install latest numpy-2.1.0 with an old numba-0.51.0 version (and llvmlite-0.34.0). See discussion in their repo:
- https://github.com/numba/numba/issues/9708

Latest numpy-2.1.0 will be supported by the next numba-0.61.0 release in September.

Note that our CI requires numba with the "audio" extra:
- librosa > numba
ConnectionError for gated datasets and unauthenticated users
Author: albertvillanova
State: closed

Since the Hub returns dataset info for gated datasets and unauthenticated users, there is dead code: https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/load.py#L1846-L1852

We should remove the dead code and properly handle this case: currently we are raising a `ConnectionError` instead of a `DatasetNotFoundError` (as before).

See:
- https://github.com/huggingface/dataset-viewer/issues/3025
- https://github.com/huggingface/huggingface_hub/issues/2457
website broken: Create a new dataset repository, doesn't create a new repo in Firefox
Author: neoneye
State: closed

### Describe the bug

This issue is also reported here:
https://discuss.huggingface.co/t/create-a-new-dataset-repository-broken-page/102644

This page is broken.
https://huggingface.co/new-dataset

I fill in the form with my text, and click `Create Dataset`.
![Screenshot 2024-08-16 at 15 55 37](https://github.com/user-attachments/assets/de16627b-7a55-4bcf-9f0b-a48227aabfe6)

Then the form gets wiped. And no repo got created. No error message visible in the developer console.
![Screenshot 2024-08-16 at 15 56 54](https://github.com/user-attachments/assets/0520164b-431c-40a5-9634-11fd62c4f4c3)

# Idea for improvement

For better UX, if the repo cannot be created, then show an error message, that something went wrong.

# Work around, that works for me

```python
from huggingface_hub import HfApi, HfFolder
repo_id = 'simon-arc-solve-fractal-v3'
api = HfApi()
username = api.whoami()['name']
repo_url = api.create_repo(repo_id=repo_id, exist_ok=True, private=True, repo_type="dataset")
```

### Steps to reproduce the bug

Go https://huggingface.co/new-dataset

Fill in the form.

Click `Create dataset`.

Now the form is cleared. And the page doesn't jump anywhere.

### Expected behavior

The moment the user clicks `Create dataset`, the repo gets created and the page jumps to the created repo.


### Environment info

Firefox 128.0.3 (64-bit)
macOS Sonoma 14.5


Comments: I don't reproduce, I was able to create a new repo: https://huggingface.co/datasets/severo/reproduce-datasets-issues-7108. Can you confirm it's still broken?

I have just tried again.

Firefox: The `Create dataset` doesn't work. It has worked in the past. It's my preferred browser.

Chrome: The `Create dataset` works.

It seems to be a Firefox specific issue.

I have updated Firefox 129.0 (64 bit), and now the `Create dataset` is working again in Firefox.

UX: It would be nice with better error messages on HuggingFace.

maybe an issue with the cookie. cc @Wauplin @coyotte508
load_dataset broken in 2.21.0
Author: anjor
State: closed

### Describe the bug

`eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)` 
used to work till 2.20.0 but doesn't work in 2.21.0

In 2.20.0:
![Screenshot 2024-08-16 at 3 57 10 PM](https://github.com/user-attachments/assets/0516489b-8187-486d-bee8-88af3381dee9)


in 2.21.0:
![Screenshot 2024-08-16 at 3 57 24 PM](https://github.com/user-attachments/assets/bc257570-f461-41e4-8717-90a69ed7c24f)


### Steps to reproduce the bug

1. Spin up a new google collab
2. `pip install datasets==2.21.0`
3. `import datasets`
4. `eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)` 
5. Will throw an error.



### Expected behavior

Try steps 1-5 again but replace datasets version with 2.20.0, it will work

### Environment info



- `datasets` version: 2.21.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.5
- PyArrow version: 17.0.0
- Pandas version: 2.1.4
- `fsspec` version: 2024.5.0


Comments: There seems to be a PR related to the load_dataset path that went into 2.21.0 -- https://github.com/huggingface/datasets/pull/6862/files

Taking a look at it now

+1

Downgrading to 2.20.0 fixed my issue, hopefully helpful for others.

I tried adding a simple test to `test_load.py` with the alpaca eval dataset but the test didn't fail :(. 

So looks like this might have something to do with the environment? 

There was an issue with the script of the "tatsu-lab/alpaca_eval" dataset.

I was fixed with this PR: 
- [Fix FileNotFoundError](https://huggingface.co/datasets/tatsu-lab/alpaca_eval/discussions/2)

It should work now if you retry to load the dataset.
IterableDataset: cannot resolve features from list of numpy arrays
Author: VeryLazyBoy
State: open

### Describe the bug

when resolve features of `IterableDataset`, got `pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values` error.

```
Traceback (most recent call last):
  File "test.py", line 6
    iter_ds = iter_ds._resolve_features()
  File "lib/python3.10/site-packages/datasets/iterable_dataset.py", line 2876, in _resolve_features
    features = _infer_features_from_batch(self.with_format(None)._head())
  File "lib/python3.10/site-packages/datasets/iterable_dataset.py", line 63, in _infer_features_from_batch
    pa_table = pa.Table.from_pydict(batch)
  File "pyarrow/table.pxi", line 1813, in pyarrow.lib._Tabular.from_pydict
  File "pyarrow/table.pxi", line 5339, in pyarrow.lib._from_pydict
  File "pyarrow/array.pxi", line 374, in pyarrow.lib.asarray
  File "pyarrow/array.pxi", line 344, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array
  File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values
```

### Steps to reproduce the bug

```python
from datasets import Dataset
import numpy as np

# create list of numpy
iter_ds = Dataset.from_dict({'a': [[[1, 2, 3], [1, 2, 3]]]}).to_iterable_dataset().map(lambda x: {'a': [np.array(x['a'])]})
iter_ds = iter_ds._resolve_features()  # errors here
```

### Expected behavior

features can be successfully resolved

### Environment info

- `datasets` version: 2.21.0
- Platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0

Comments: Assign this issue to me under Hacktoberfest with hacktoberfest label inserted on the issue
Some of DownloadConfig's properties are always being overridden in load.py
Author: ductai199x
State: open

### Describe the bug

The `extract_compressed_file` and `force_extract` properties of DownloadConfig are always being set to True in the function `dataset_module_factory` in the `load.py` file. This behavior is very annoying because data extracted will just be ignored the next time the dataset is loaded.

See this image below:
![image](https://github.com/user-attachments/assets/9e76ebb7-09b1-4c95-adc8-a959b536f93c)


### Steps to reproduce the bug

1. Have a local dataset that contains archived files (zip, tar.gz, etc)
2. Build a dataset loading script to download and extract these files
3. Run the load_dataset function with a DownloadConfig that specifically set `force_extract` to False
4. The extraction process will start no matter if the archives was extracted previously

### Expected behavior

The extraction process should not run when the archives were previously extracted and `force_extract` is set to False.

### Environment info

datasets==2.20.0
python3.9
Add Arabic Docs to datasets
Author: AhmedAlmaghz
State: open
Labels: name: enhancement

### Feature request

Add Arabic Docs to datasets
[Datasets Arabic](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx)

### Motivation

@AhmedAlmaghz
https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx

### Your contribution

@AhmedAlmaghz
https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx