MarouaneSanhaji/github-issues huggingface dataset:
Modify add_column() to optionally accept a FeatureType as param
State: closed

Fix #7142.

**Before (Add + Cast)**:

```
from datasets import load_dataset, Value
ds = load_dataset("rotten_tomatoes", split="test")
lst = [i for i in range(len(ds))]

ds = ds.add_column("new_col", lst)
# Assigns int64 to new_col by default
print(ds.features)

ds = ds.cast_column("new_col", Value(dtype="uint16", id=None))
print(ds.features)
```


**Before (Numpy Workaround)**:

```
from datasets import load_dataset
import numpy as np
ds = load_dataset("rotten_tomatoes", split="test")
lst = [i for i in range(len(ds))]

ds = ds.add_column("new_col", np.array(lst, dtype=np.uint16))
print(ds.features)
```

**After**:
```
from datasets import load_dataset, Value
ds = load_dataset("rotten_tomatoes", split="test")
lst = [i for i in range(len(ds))]
val = Value(dtype="uint16", id=None))
ds = ds.add_column("new_col", lst, feature=val)
print(ds.features)
```

Requesting review @lhoestq 
I will also update the docs if this looks good.

Cool ! maybe you can rename the argument `feature` and with type `FeatureType` ? This way it would work the same way as `.cast_column()` ?

@lhoestq Since there is no way to get a `pyarrow.Schema` from a `FeatureType`, I had to go via `Features`. How does this look?

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7143). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@lhoestq done!

@lhoestq anything pending on this?
Specifying datatype when adding a column to a dataset.
State: closed

### Feature request

There should be a way to specify the datatype of a column in `datasets.add_column()`. 

### Motivation

To specify a custom datatype, we have to use `datasets.add_column()` followed by `datasets.cast_column()` which is slow for large datasets. Another workaround is to pass a `numpy.array()` of desired type to the `datasets.add_column()` function.

IMO this functionality should be natively supported.

https://discuss.huggingface.co/t/add-column-with-a-particular-type-in-datasets/95674



### Your contribution

I can submit a PR for this.

#self-assign
Use load_dataset to load imagenet-1K But find a empty dataset
State: open

### Describe the bug

```python
def get_dataset(data_path, train_folder="train", val_folder="val"):
    traindir = os.path.join(data_path, train_folder)
    valdir = os.path.join(data_path, val_folder)

    def transform_val_examples(examples):
        transform = Compose([
            Resize(256),
            CenterCrop(224),
            ToTensor(),
        ])
        examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]]
        return examples

    def transform_train_examples(examples):
        transform = Compose([
            RandomResizedCrop(224),
            RandomHorizontalFlip(),
            ToTensor(),
        ])
        examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]]
        return examples

    # @fengsicheng: This way is very slow for big dataset like ImageNet-1K (but can pass the network problem using local dataset)
    # train_set = load_dataset("imagefolder", data_dir=traindir, num_proc=4)
    # test_set = load_dataset("imagefolder", data_dir=valdir, num_proc=4)

    train_set = load_dataset("imagenet-1K", split="train", trust_remote_code=True)                                                                                                                                                                                                            
    test_set = load_dataset("imagenet-1K", split="test", trust_remote_code=True)

    print(train_set["label"])

    train_set.set_transform(transform_train_examples)
    test_set.set_transform(transform_val_examples)

    return train_set, test_set
```
    above the code, but output of the print is a list of None:
    
<img width="952" alt="image" src="https://github.com/user-attachments/assets/c4e2fdd8-3b8f-481e-8f86-9bbeb49d79fb">


### Steps to reproduce the bug

1. just ran the code 
2. see the print


### Expected behavior

I do not know how to fix this, can anyone provide help or something? It is hurry for me

### Environment info

- `datasets` version: 2.21.0
- Platform: Linux-5.4.0-190-generic-x86_64-with-glibc2.31
- Python version: 3.10.14
- `huggingface_hub` version: 0.24.6
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1

Imagenet-1k  is a gated dataset which means you’ll have to agree to share your contact info to access it. Have you tried this yet? Once you have, you can sign in with your user token (you can find this in your Hugging Face account settings) when prompted by running.

```
huggingface-cli login
train_set  = load_dataset('imagenet-1k', split='train', use_auth_token=True)
``` 

Thanks a lot! It helps me
Cache only changed columns?
State: open

### Feature request

Cache only the actual changes to the dataset i.e. changed columns.

### Motivation

I realized that caching actually saves the complete dataset again.
This is especially problematic for image datasets if one wants to only change another column e.g. some metadata and then has to save 5 TB again.

### Your contribution

Is this even viable in the current architecture of the package?
I quickly looked into it and it seems it would require significant changes.

I would spend some time looking into this but maybe somebody could help with the feasibility and some plan to implement before spending too much time on it?

so I guess a workaround to this is to simply remove all columns except the ones to cache and then add them back with `concatenate_datasets(..., axis=1)`.

yes this is the right workaround. We're keeping the cache like this to make it easier for people to delete intermediate cache files
remove filecheck to enable symlinks
State: closed

Enables streaming from local symlinks #7083 

@lhoestq

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7133). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

The CI is failing, looks like it breaks imagefolder loading.

I just checked fsspec internals and maybe instead we can detect symlink by checking `islink` and `size` to make sure it's a file
```python
if info["type"] == "file" or (info.get("islink") and info["size"])
```


hmm actually `size` doesn't seem to filter symlinked directories, we need another way

Does fsspec perhaps allow resolving symlinks? Something like https://docs.python.org/3/library/pathlib.html#pathlib.Path.resolve

there is `info["destination"]` in case of a symlink, so maybe


```python
if info["type"] == "file" or (info.get("islink") and info.get("destination") and os.path.isfile(info["destination"]))
```

I've added a fix which works with some temporary test files locally 

`(info["type"] == "file" or (info.get("islink") and os.path.isfile(os.path.realpath(filepath))))`
Fix data file module inference
State: open

I saved a dataset with two splits to disk with `DatasetDict.save_to_disk`. The train is bigger and ended up in 10 shards, whereas the test split only resulted in 1 split.

Now when trying to load the dataset, an error is raised that not all splits have the same data format:

> ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}

This is not expected because both splits are saved as arrow files.

I did some debugging and found that this is the case because the list of data_files includes a `state.json` file. 

Now this means for train split I get 10 ".arrow" and 1 ".json" file. Since datasets picks based on the most common extension this is correctly inferred as "arrow". In the test split, there is 1 .arrow and 1 .json file. Given the function description:

> It picks the module based on the most common file extension.
    In case of a draw ".parquet" is the favorite, and then alphabetical order.

This is not quite true though, because in a tie the extensions are actually based on reverse-alphabetical order:
```
for (ext, _), _ in sorted(extensions_counter.items(), key=sort_key, *reverse=True*):
```

Which thus leads to the module wrongly inferred as "json", whereas it should be "arrow", matching the train split.

I first thought about adding "state.json" in the list of excluded files for the inference: https://github.com/huggingface/datasets/blob/main/src/datasets/load.py#L513. However, I think from digging into the code it looks like the right thing to do is to exclude it in the list of `data_files` to start with, because it is more of a metadata than a data file.

Hi ! datasets saved using `save_to_disk` should be loaded with `load_from_disk` ;)

It is convienient to just pass in a path to a local dataset or one from the hub and use the same function to load it. Is it not possible to get this fix merged in to allow this? 

We can modify `save_to_disk` to write the dataset in a structure supported by the Hub in this case, it's kind of a legacy function anyway
Caching shuffles by np.random.Generator results in unintiutive behavior
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.

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
State: open

### 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.
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

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)
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


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
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?:

@sayakpaul  please advice 

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

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
website broken: Create a new dataset repository, doesn't create a new repo in Firefox
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


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
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


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
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

Assign this issue to me under Hacktoberfest with hacktoberfest label inserted on the issue
Add Arabic Docs to datasets
State: open

### 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
Unable to create dataset card for Lushootseed language
State: closed

### Feature request

While I was creating the dataset which contained all documents from the Lushootseed Wikipedia, the dataset card asked me to enter which language the dataset was in. Since Lushootseed is a critically endangered language, it was not available as one of the options. Is it possible to allow entering languages that aren't available in the options?

### Motivation

I'd like to add more information about my dataset in the dataset card, and the language is one of the most important pieces of information, since the entire dataset is primarily concerned collecting Lushootseed documents.

### Your contribution

I can submit a pull request

Thanks for reporting.

It is weird, because the language entry is in the list. See: https://github.com/huggingface/huggingface.js/blob/98e32f0ed4ee057a596f66a1dec738e5db9643d5/packages/languages/src/languages_iso_639_3.ts#L15186-L15189

I have reported the issue:
- https://github.com/huggingface/huggingface.js/issues/834



As explained in the reported issue above, the problem only appears in the autocomplete field: you can still enter the `lut` language directly in the markdown editor window.
load_dataset ignores cached datasets and tries to hit HF Hub, resulting in API rate limit errors
State: open

### Describe the bug

I have been running lm-eval-harness a lot which has results in an API rate limit. This seems strange, since all of the data should be cached locally. I have in fact verified this.

### Steps to reproduce the bug

1. Be Me
2. Run `load_dataset("TAUR-Lab/MuSR")`
3. Hit rate limit error
4. Dataset is in .cache/huggingface/datasets
5. ???

### Expected behavior

We should not run into API rate limits if we have cached the dataset

### Environment info

datasets 2.16.0
python 3.10.4

I'm having the same issue - running into rate limits when doing hyperparameter tuning even though the dataset is supposed to be cached. I feel like this behaviour should at the very least be documented, but honestly you should just not be running into rate limits in the first place when the dataset is cached. It even happens when specifying a specific revision for the dataset, in which case AFAIK there should be no reason to be doing API requests if it's already cached (besides maybe a quick hash check but hitting rate limits for that in ~200 requests across 10 hours of use seems a bit ridiculous).
More easily support streaming local files
State: open

### Feature request

Simplify downloading and streaming datasets locally. Specifically, perhaps add an option to `load_dataset(..., streaming="download_first")` or add better support for streaming symlinked or arrow files.

### Motivation

I have downloaded FineWeb-edu locally and currently trying to stream the dataset from the local files. I have both the raw parquet files using `hugginface-cli download --repo-type dataset HuggingFaceFW/fineweb-edu` and the processed arrow files using `load_dataset("HuggingFaceFW/fineweb-edu")`. 

Streaming the files locally does not work well for both file types for two different reasons.

**Arrow files**

When running `load_dataset("arrow", data_files={"train": "~/.cache/huggingface/datasets/HuggingFaceFW___fineweb-edu/default/0.0.0/5b89d1ea9319fe101b3cbdacd89a903aca1d6052/fineweb-edu-train-*.arrow"})` resolving the data files is fast, but because `arrow` is not included in the known [extensions file list](https://github.com/huggingface/datasets/blob/ce4a0c573920607bc6c814605734091b06b860e7/src/datasets/utils/file_utils.py#L738) , all files are opened and scanned to determine the compression type. Adding `arrow` to the known extension types resolves this issue. 

**Parquet files**

When running `load_dataset("arrow", data_files={"train": "~/.cache/huggingface/hub/dataset-HuggingFaceFW___fineweb-edu/snapshots/5b89d1ea9319fe101b3cbdacd89a903aca1d6052/data/CC-MAIN-*/train-*.parquet"})` the paths do not get resolved because the parquet files are symlinked from the blobs (which contain all files in case there are different versions). This occurs because the [pattern matching](https://github.com/huggingface/datasets/blob/ce4a0c573920607bc6c814605734091b06b860e7/src/datasets/data_files.py#L389) checks if the path is a file and does not check for symlinks. Symlinks (at least on my machine) are of type "other". 


### Your contribution

I have created a PR for fixing arrow file streaming and symlinks. However, I have not checked locally if the tests work or new tests need to be added.

IMO, the easiest option would be to add a `streaming=download_first` option, but I'm afraid that exceeds my current knowledge of how the datasets library works. https://github.com/huggingface/datasets/pull/7083
Generating train split takes a long time
State: open

### Describe the bug

Loading a simple webdataset takes ~45 minutes.

### Steps to reproduce the bug

```
from datasets import load_dataset
dataset = load_dataset("PixArt-alpha/SAM-LLaVA-Captions10M")
```

### Expected behavior

The dataset should load immediately as it does when loaded through a normal indexed WebDataset loader. Generating splits should be optional and there should be a message showing how to disable it. 

### Environment info

- `datasets` version: 2.20.0
- Platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.14
- `huggingface_hub` version: 0.24.1
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0

@alexanderswerdlow 
When no specific split is mentioned, the load_dataset library will load all available splits of the dataset. For example, if a dataset has "train" and "test" splits, the load_dataset function will load both into the DatasetDict object.

![image](https://github.com/user-attachments/assets/379e6f57-7e1b-4cc3-bc36-dae3e878a51c)


The dataset PixArt-alpha/SAM-LLaVA-Captions10M may have been uploaded with different predefined splits (e.g., "train", "test", etc.), and by default, Hugging Face will load all splits unless you specifically request only one.

### If you want to load only a specific split (e.g., only the "train" set), you can specify it in the split parameter like this:
```python
from datasets import load_dataset
dataset = load_dataset("PixArt-alpha/SAM-LLaVA-Captions10M", split="train")
```

### You can also load multiple splits if needed:
```python
dataset = load_dataset("PixArt-alpha/SAM-LLaVA-Captions10M", split=["train", "test"])
```



@alexanderswerdlow, I will now work on this..

## Idea:
Whenever this code has ran:
```python
from datasets import load_dataset
dataset = load_dataset("PixArt-alpha/SAM-LLaVA-Captions10M")
```

It should show all the splits of the datasets, and user has to choose which one should be loaded before generating a split like this,,

![image](https://github.com/user-attachments/assets/8fbc604f-f0a5-4a59-a63e-aa4c26442c83)
column_names ignored by load_dataset() when loading CSV file
State: open

### Describe the bug

load_dataset() ignores the column_names kwarg when loading a CSV file.  Instead, it uses whatever values are on the first line of the file.


### Steps to reproduce the bug

Call `load_dataset` to load data from a CSV file and specify `column_names` kwarg.

### Expected behavior

The resulting dataset should have the specified column names **and** the first line of the file should be considered as data values.

### Environment info

- `datasets` version: 2.20.0
- Platform: Linux-5.10.0-30-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- `huggingface_hub` version: 0.24.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0

I confirm that `column_names` values are not copied to `names` variable because in this case `CsvConfig.__post_init__` is not called: `CsvConfig` is instantiated with default values and afterwards the `config_kwargs` are used to overwrite its attributes.

@luismsgomes in the meantime, you can avoid the bug if you pass `names` instead of `column_names`.
Convert_to_parquet fails for datasets with multiple configs
State: closed

If the dataset has multiple configs, when using the `datasets-cli convert_to_parquet` command to avoid issues with the data viewer caused by loading scripts, the conversion process only successfully converts the data corresponding to the first config. When it starts converting the second config, it throws an error:

```
Traceback (most recent call last):
  File "/opt/anaconda3/envs/dl/bin/datasets-cli", line 8, in <module>
    sys.exit(main())
  File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/datasets_cli.py", line 41, in main
    service.run()
  File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/convert_to_parquet.py", line 83, in run
    dataset.push_to_hub(
  File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/dataset_dict.py", line 1713, in push_to_hub
    api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True)
  File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
    return fn(*args, **kwargs)
  File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
    hf_raise_for_status(response)
  File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 358, in hf_raise_for_status
    raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError:  (Request ID: Root=1-669fc665-7c2e80d75f4337496ee95402;731fcdc7-0950-4eec-99cf-ce047b8d003f)

Bad request:
Invalid reference for a branch: refs/pr/1
```

Many users have encountered the same issue, which has caused inconvenience.

https://discuss.huggingface.co/t/convert-to-parquet-fails-for-datasets-with-multiple-configs/86733


Thanks for reporting.

I will make the code more robust.

I have opened an issue in the huggingface-hub repo:
- https://github.com/huggingface/huggingface_hub/issues/2419

I am opening a PR to avoid calling `create_branch` if the branch already exists.
One subset per file in repo ?
State: open

Right now we consider all the files of a dataset to be the same data, e.g.
```
single_subset_dataset/
├── train0.jsonl
├── train1.jsonl
└── train2.jsonl
```
but in cases like this, each file is actually a different subset of the dataset and should be loaded separately
```
many_subsets_dataset/
├── animals.jsonl
├── trees.jsonl
└── metadata.jsonl
```

It would be nice to detect those subsets automatically using a simple heuristic. For example we can group files together if their paths names are the same except some digits ?

Hi @lhoestq! I’ve opened a PR that addresses this issue
Datasets.datafiles resolve_pattern `TypeError: can only concatenate tuple (not "str") to tuple`
State: closed

### Describe the bug

in data_files.py, line 332, 
`fs,  _, _ = get_fs_token_paths(pattern, storage_options=storage_options)`
If we run the code on AWS, as fs.protocol will be a tuple like: `('file', 'local')`
So,  `isinstance(fs.protocol, str) == False` and
`protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""` will raise 
`TypeError: can only concatenate tuple (not "str") to tuple`.

### Steps to reproduce the bug

Steps to reproduce:
1. Run on a cloud server like AWS,
2. `import datasets.data_files as datafile`
3. datafile.resolve_pattern('path/to/dataset', '.')
4. `TypeError: can only concatenate tuple (not "str") to tuple`

### Expected behavior

Should return path of the dataset, with fs.protocol at the beginning

### Environment info

- `datasets` version: 2.14.0
- Platform: Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.19
- Huggingface_hub version: 0.23.5
- PyArrow version: 16.1.0
- Pandas version: 1.1.5

Hi,

This issue was fixed in `datasets` 2.15.0:
- #6105

You will need to update your `datasets`:
```
pip install -U datasets
```

Duplicate of:
- #6100
Save nparray as list
State: closed

### Describe the bug

When I use the `map` function to convert images into features, datasets saves nparray as a list. Some people use the `set_format` function to convert the column back, but doesn't this lose precision?

### Steps to reproduce the bug

the map function
```python
def convert_image_to_features(inst, processor, image_dir):
    image_file = inst["image_url"]
    file = image_file.split("/")[-1]
    image_path = os.path.join(image_dir, file)
    image = Image.open(image_path)
    image = image.convert("RGBA")

    inst["pixel_values"] = processor(images=image, return_tensors="np")["pixel_values"]
    return inst
```

main function
```python
map_fun = partial(
      convert_image_to_features, processor=processor, image_dir=image_dir
  )
ds = ds.map(map_fun, batched=False, num_proc=20)
print(type(ds[0]["pixel_values"])
```

### Expected behavior

(type < list>) 

### Environment info

- `datasets` version: 2.16.1
- Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.23.4
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0

In addition, when I use `set_format ` and index the ds, the following error occurs:
the code
```python
ds.set_format(type="np", colums="pixel_values")
```
error
<img width="918" alt="image" src="https://github.com/user-attachments/assets/b28bbff2-20ea-4d28-ab62-b4ed2d944996">


>  Some people use the set_format function to convert the column back, but doesn't this lose precision?

Under the hood the data is saved in Arrow format using the same precision as your numpy arrays?
By default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision

(you can fix your second issue by fixing the typo `colums` -> `columns`)

> (you can fix your second issue by fixing the typo `colums` -> `columns`)

You are right, I was careless. Thank you.

> > Some people use the set_format function to convert the column back, but doesn't this lose precision?
> 
> Under the hood the data is saved in Arrow format using the same precision as your numpy arrays? By default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision

Yes, after testing I found that there was no loss of precision. Thanks again for your answer.
ImportError: numpy.core.multiarray when using `filter`
State: closed

### Describe the bug

I can't apply the filter method on my dataset. 

### Steps to reproduce the bug

The following snippet generates a bug: 

```python
from datasets import load_dataset 

ami = load_dataset('kamilakesbi/ami', 'ihm')

ami['train'].filter(
    lambda example: example["file_name"] == 'EN2001a'
)
```

I get the following error: 

`ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).`

### Expected behavior

It should work properly! 

### Environment info

- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0

Could you please check your `numpy` version?

I got this issue while using numpy version 2.0. 

I solved it by switching back to numpy 1.26.0 :) 

We recently added support for numpy 2.0, but it is not released yet.

Ok I see, thanks! I think we can close this issue for now as switching back to version 1.26.0 solves the problem :)
`sort` after `filter` unreasonably  slow
State: closed

### Describe the bug

as the tittle says ...

### Steps to reproduce the bug

`sort` seems to be normal.

```python
from datasets import Dataset
import random

nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]

ds = Dataset.from_list(nums)

print("start sort")

ds = ds.sort("k")

print("finish sort")
```

but `sort` after `filter` is extremely slow.

```python
from datasets import Dataset
import random

nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]

ds = Dataset.from_list(nums)

ds = ds.filter(lambda x:x > 100, input_columns="k")

print("start sort")

ds = ds.sort("k")

print("finish sort")
```


### Expected behavior

Is this a bug, or is it a misuse of the `sort` function?

### Environment info

- `datasets` version: 2.20.0
- Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0

`filter` add an indices mapping on top of the dataset, so `sort` has to gather all the rows that are kept to form a new Arrow table and sort the table. Gathering all the rows can take some time, but is a necessary step. You can try calling `ds = ds.flatten_indices()` before sorting to remove the indices mapping.

> `filter` add an indices mapping on top of the dataset, so `sort` has to gather all the rows that are kept to form a new Arrow table and sort the table. Gathering all the rows can take some time, but is a necessary step. You can try calling `ds = ds.flatten_indices()` before sorting to remove the indices mapping.

This worked, thank you so much.
load `streaming=True` dataset with downloaded cache
State: open

### Describe the bug

We build a dataset which contains several hdf5 files and write a script using `h5py` to generate the dataset. The hdf5 files are large and the processed dataset cache takes more disk space. So we hope to try streaming iterable dataset. Unfortunately, `h5py` can't convert a remote URL into a hdf5 file descriptor. So we use `fsspec` as an interface like below:

```python
def _generate_examples(self, filepath, split):
        for file in filepath:
            with fsspec.open(file, "rb") as fs:
                with h5py.File(fs, "r") as fp:
                    # for event_id in sorted(list(fp.keys())):
                    event_ids = list(fp.keys())
                    ......
```

### Steps to reproduce the bug

The `fsspec` works, but it takes 10+ min to print the first 10 examples, which is even longer than the downloading time. I'm not sure if it just caches the whole hdf5 file and generates the examples.

### Expected behavior

So does the following make sense so far?

1. download the files
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True)
```

2. load the iterable dataset faster (using the raw file cache at path `.cache/huggingface/datasets/downloads`)
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True, streaming=true)
```

I made some tests, but the code above can't get the expected result. I'm not sure if this is supported. I also find the issue #6327 . It seemed similar to mine, but I couldn't find a solution.

### Environment info

- `datasets` = 2.18.0
- `h5py` = 3.10.0
- `fsspec` = 2023.10.0

When you pass `streaming=True`, the cache is ignored. The remote data URL is used instead and the data is streamed from the remote server.

Thanks for your reply! So is there any solution to get my expected behavior besides clone the whole repo ? Or could I adjust my script to load the downloaded arrow files and generate the dataset streamingly?