noamaanMulla-03/datasets-issues huggingface dataset:
Datasets Unknown Keyword Argument Error - task_templates

### Describe the bug

Issue

```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```

Gives error

```
TypeError: DatasetInfo.__init__() got an unexpected keyword argument 'task_templates'
```

A simple downgrade to lower `datasets v 2.21.0` solves it.




### Steps to reproduce the bug

1. `pip install datsets`
2.
```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```



### Expected behavior

Should load the dataset correctly.

### Environment info

- Datasets version  `3.0.0`
- `transformers` version: 4.45.0.dev0
- Platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35
- Python version: 3.12.4
- Huggingface_hub version: 0.24.6
- Safetensors version: 0.4.5
- Accelerate version: 0.35.0.dev0
- Accelerate config:    not found
- PyTorch version (GPU?): 2.4.1+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes



Thanks, for reporting.

We have been fixing most Hub datasets to remove the deprecated (and now non-supported) task templates, but we missed the "facebook/winoground".

It is fixed now: https://huggingface.co/datasets/facebook/winoground/discussions/8



Hello @albertvillanova 

I got the same error while loading this dataset: https://huggingface.co/datasets/alaleye/aloresb...

How can I fix it ? 
Thanks

I am getting the same error on the below code, any fix to this ?

```
from datasets import load_dataset

minds = load_dataset("PolyAI/minds14", name="en-AU", split="train")
minds
```
Fix multi gpu process example

to is not an inplace function.

But i am not sure about this code anyway, i think this is modifying the global variable `model` everytime the function is called? Which is on every batch? So it is juggling the same model on every gpu right? Isnt that very inefficient? 


Okay nevermind looks like to works both ways for models. but my doubt still remains, isnt this changing the device of the model every batch?
Dynamic adjusting dataloader sampling weight

Hi,
Thanks for your wonderful work! I'm wondering is there a way to dynamically adjust the sampling weight of each data in the dataset during training? Looking forward to your reply, thanks again.


You mean based on a condition that has to be checked on-the-fly during training ? Otherwise if you know in advance after how many samples you need to change the sampling you can simply concatenate the two mixes

Yes, like during training, if one data sample's prediction is consistently wrong, its sampling weight gets higher and higher, and if one data sample's prediction is already correct, then we rarely sample it

it's not possible to use `interleave_datasets()` and modify the probabilities while iterating on the dataset at the moment, so you'd have to implement your own torch `Sampler` or your own`IterableDataset` to implement this logic
Add with_split to DatasetDict.map

#7356


Can you check this out, @lhoestq?

cc @lhoestq @albertvillanova 

@lhoestq


@lhoestq


@lhoestq

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7368). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@lhoestq 

@lhoestq please...

Thank you so much for reviewing this PR! Have a great weekend~
Stuck in "Resolving data files..."

### Describe the bug

I have an image dataset with 300k images, the size of image is 768 * 768.

When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going on in this part?

From my understand, after Arrow files been created in the first run, the second run should not take time longer than one or two minutes.

### Steps to reproduce the bug

# Run following code two times
dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')

### Expected behavior

Fast dataset building

### Environment info

- `datasets` version: 2.14.5
- Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35
- Python version: 3.10.11
- Huggingface_hub version: 0.17.3
- PyArrow version: 10.0.1
- Pandas version: 1.5.3


Most likely, the data file inference logic is the problem here.

You can run the following code to verify this:
```python
import time
from datasets.data_files import get_data_patterns
start_time = time.time()
get_data_patterns("/path/to/img_dir")
end_time = time.time()
print(f"Elapsed time: {end_time - start_time:.2f}s")
```
 
We plan to optimize this for the next version (or version after that). In the meantime, specifying the split patterns manually should give better performance:
```python
ds = load_dataset("imagefolder", data_files={"train": "path/to/img_dir/train/**", ...}, split="train")
```

Hi, @mariosasko, you are right; data file inference logic is extremely slow.

I have done a similar test, that is I modify the source code of datasets/load.py to measure the cost of two suspicious operations:
```python
def get_module(self) -> DatasetModule:
        base_path = Path(self.data_dir or "").expanduser().resolve().as_posix()
        start = time.time()
        patterns = sanitize_patterns(self.data_files) if self.data_files is not None else get_data_patterns(base_path)
        print(f"patterns: {time.time() - start}")
        start = time.time()
        data_files = DataFilesDict.from_patterns(
            patterns,
            download_config=self.download_config,
            base_path=base_path,
        )
        print(f"data_files: {time.time() - start}")
```
It gaves:
patterns: 3062.2050700187683
data_files: 413.9576675891876

Thus, these two operations contribute to almost all of load time. What's going on in them?

Furthermore, what's my current workaround about this problem? Should I save it by `save_to_disk()` and load dataset through `load_from_disk`?

were you able to solve this issue?, I am facing the same issue

Still suffering from this issue. For me, I cannot download Emilia dataset, and stucked at 'Resolving data files' forever.
``` e = load_dataset('amphion/Emilia-Dataset', token=my_token)
Resolving data files:   0%|                                                                                  | 1/4343 [00:00<11:27,  6.32it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████| 4343/4343 [00:01<00:00, 3844.15it/s]
```
index error when num_shards > len(dataset)

In `ds.push_to_hub()` and `ds.save_to_disk()`, `num_shards` must be smaller than or equal to the number of rows in the dataset, but currently this is not checked anywhere inside these functions. Attempting to invoke these functions with `num_shards > len(dataset)` should raise an informative `ValueError`.

I frequently work with datasets with a small number of rows where each row is pretty large, so I often encounter this issue, where the function runs until the shard index in `ds.shard(num_shards, indx)` goes out of bounds. Ideally, a `ValueError` should be raised before reaching this point (i.e. as soon as `ds.push_to_hub()` or `ds.save_to_disk()` is invoked with `num_shards > len(dataset)`).

It seems that adding something like:
```python
if len(self) < num_shards:
   raise ValueError(f"num_shards ({num_shards}) must be smaller than or equal to the number of rows in the dataset ({len(self)}). Please either reduce num_shards or increase max_shard_size to make sure num_shards <= len(dataset).")
```
to the beginning of the definition of the `ds.shard()` function [here](https://github.com/huggingface/datasets/blob/f693f4e93aabafa878470c80fd42ddb10ec550d6/src/datasets/arrow_dataset.py#L4728) would deal with this issue for both `ds.push_to_hub()` and `ds.save_to_disk()`, but I'm not exactly sure if this is the best place to raise the `ValueError` (it seems that a more correct way to do it would be to write separate checks for `ds.push_to_hub()` and `ds.save_to_disk()`). I'd be happy to submit a PR if you think something along these lines would be acceptable.


Actually, looking at the code a bit more carefully, maybe an even better solution is to explicitly set `num_shards=len(self)` somewhere inside both `push_to_hub()` and `save_to_disk()` when these functions are invoked with `num_shards > len(dataset)`.
Refactor `string_to_dict` to return `None` if there is no match instead of raising `ValueError`

Making this change, as encouraged here:

* https://github.com/huggingface/datasets/pull/7434#discussion_r1979933054

instead of having the pattern of using `try`-`except` to handle when there is no match, we can instead check if the return value is `None`; we can also assert that the return value should not be `None` if we know that should be true


cc: @lhoestq 

I am going to rebase #7434 onto this branch. Then we can merge this one first if you approve, and then #7434.

@lhoestq any thoughts here?

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7435). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

It looks like I was unsafely asserting that `source_url_fields is not None` in `image.py`, `video.py` and `audio.py` (which did not correspond to the `except ValueError` like was there previously). I've changed it to handle `source_url_fields is None`.

Can we re-run CI on this one?

Sweet! These failures are looking spurious due to connectivity issues. Can the failing run be retried?

@lhoestq Sorry to double ping, but can this PR be reviewed? I think it is ready!
Fix small bugs with async map

helpful for the next PR to enable parallel image/audio/video decoding and make multimodal datasets go brr (e.g. for lerobot)

- fix with_indices
- fix resuming with save_state_dict() / load_state_dict() - omg that wasn't easy
- remove unnecessary decoding in map() to enable parallelism in FormattedExampleIterable later

small bonus: keeping features in batch()


The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7445). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
fix: dill default for version bigger 0.3.8

Fixes def log for dill version >= 0.3.9 
https://pypi.org/project/dill/

This project uses dill with the release of version 0.3.9 the datasets lib. 


`datasets` doesn't support `dill` 0.3.9 yet afaik since `dill` made some changes related to the determinism of dumps

It would be cool to investigate (maybe run the `datasets` test) with recent `dill` to see excactly what breaks and if we can make `dill` 0.3.9 work with `datasets`
Add IterableDataset.decode with multithreading

Useful for dataset streaming for multimodal datasets, and especially for lerobot.
It speeds up streaming up to 20 times.

When decoding is enabled (default), media types are decoded:

* audio -> dict of "array" and "sampling_rate" and "path"
* image -> PIL.Image
* video -> torchvision.io.VideoReader

You can enable multithreading using `num_threads`. This is especially useful to speed up remote
data streaming. However it can be slower than `num_threads=0` for local data on fast disks.

PS: Disabling decoding is useful if you want to iterate on the paths or bytes of the media files
without actually decoding their content.

Example: Speed up streaming with multithreading:

```py
>>> import os
>>> from datasets import load_dataset
>>> from tqdm import tqdm
>>> ds = load_dataset("sshh12/planet-textures", split="train", streaming=True)
>>> num_threads = min(32, (os.cpu_count() or 1) + 4)
>>> ds = ds.decode(num_threads=num_threads)
>>> for _ in tqdm(ds):  # 20 times faster !
...     ...
```

why not multiprocessing ? decoding is done with the GIL released in soundfile/PIL/torchvision so multiprocessing would just use more memory

TODO
- [x] test
- [x] add to docs


The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7450). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Problems with local dataset after upgrade from 3.3.2 to 3.4.0

### Describe the bug

I was not able to open a local saved dataset anymore that was created using an older datasets version after the upgrade yesterday from datasets 3.3.2 to 3.4.0

The traceback is 

```
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 67, in _generate_tables
    batches = pa.ipc.open_stream(f)
  File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 190, in open_stream
    return RecordBatchStreamReader(source, options=options,
  File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 52, in __init__
    self._open(source, options=options, memory_pool=memory_pool)
  File "pyarrow/ipc.pxi", line 1006, in pyarrow.lib._RecordBatchStreamReader._open
  File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 538970747 metadata bytes, but only read 2126

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1855, in _prepare_split_single
    for _, table in generator:
  File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 69, in _generate_tables
    reader = pa.ipc.open_file(f)
  File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 234, in open_file
    return RecordBatchFileReader(
  File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 110, in __init__
    self._open(source, footer_offset=footer_offset,
  File "pyarrow/ipc.pxi", line 1090, in pyarrow.lib._RecordBatchFileReader._open
  File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Not an Arrow file
```

### Steps to reproduce the bug

Load a dataset from a local folder with 

```
dataset = load_dataset(
                args.train_data_dir,
                cache_dir=args.cache_dir,
            )
```
as it is done for example in the training script for SD3 controlnet.

This is the minimal script to test it: 

```
from datasets import load_dataset

def main():
    dataset = load_dataset(
        "local_dataset",  
    )
    print(dataset)
    print("Sample data:", dataset["train"][0])

if __name__ == "__main__":
    main()
````

### Expected behavior

Work in 3.4.0 like in 3.3.2

### Environment info

- `datasets` version: 3.4.0
- Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0


Hi ! I just released 3.4.1 with a fix, let me know if it's working now !
Add repeat() for iterable datasets

### Feature request

It would be useful to be able to straightforwardly repeat iterable datasets indefinitely, to provide complete control over starting and ending of iteration to the user.

An IterableDataset.repeat(n) function could do this automatically

### Motivation

This feature was discussed in this issue https://github.com/huggingface/datasets/issues/7147, and would resolve the need to use the hack of interleave datasets with probability 0 as a simple way to achieve this functionality.

An additional benefit might be the simplification of the use of iterable datasets in a distributed setting:
If the user can assume that datasets will repeat indefinitely, then issues around different numbers of samples appearing on different devices (e.g. https://github.com/huggingface/datasets/issues/6437, https://github.com/huggingface/datasets/issues/6594, https://github.com/huggingface/datasets/issues/6623, https://github.com/huggingface/datasets/issues/6719) can potentially be straightforwardly resolved by simply doing:

ids.repeat(None).take(n_samples_per_epoch)


### Your contribution

I'm not familiar enough with the codebase to assess how straightforward this would be to implement.

If it might be very straightforward, I could possibly have a go.

Labels: name: enhancement

perhaps concatenate_datasets can already be used to achieve almost the same effect? 

`concatenate_datasets` does the job when there is a finite number of repetitions, but in case of `.repeat()` forever we need a new logic in `iterable_dataset.py`

done in https://github.com/huggingface/datasets/pull/7198
interleave_datasets resets shuffle state

### Describe the bug

```
import datasets
import torch.utils.data


def gen(shards):
    yield {"shards": shards}


def main():
    dataset = datasets.IterableDataset.from_generator(
        gen,
        gen_kwargs={'shards': list(range(25))}
    )
    dataset = dataset.shuffle(buffer_size=1)
    dataset = datasets.interleave_datasets(
        [dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted"
    )

    dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=8,
        num_workers=8,
    )

    for i, batch in enumerate(dataloader):
        print(batch)
        if i >= 10:
            break


if __name__ == "__main__":
     main()
```

### Steps to reproduce the bug

Run the script, it will output

```
{'shards': [tensor([ 0,  8, 16, 24,  0,  8, 16, 24])]}
{'shards': [tensor([ 1,  9, 17,  1,  9, 17,  1,  9])]}
{'shards': [tensor([ 2, 10, 18,  2, 10, 18,  2, 10])]}
{'shards': [tensor([ 3, 11, 19,  3, 11, 19,  3, 11])]}
{'shards': [tensor([ 4, 12, 20,  4, 12, 20,  4, 12])]}
{'shards': [tensor([ 5, 13, 21,  5, 13, 21,  5, 13])]}
{'shards': [tensor([ 6, 14, 22,  6, 14, 22,  6, 14])]}
{'shards': [tensor([ 7, 15, 23,  7, 15, 23,  7, 15])]}
{'shards': [tensor([ 0,  8, 16, 24,  0,  8, 16, 24])]}
{'shards': [tensor([17,  1,  9, 17,  1,  9, 17,  1])]}
{'shards': [tensor([18,  2, 10, 18,  2, 10, 18,  2])]}
```

### Expected behavior

The shards should be shuffled.

### Environment info

- `datasets` version: 3.0.0
- Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.25.0
- PyArrow version: 17.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.6.0


It also does preserve `split_by_node`, so in the meantime you should call `shuffle` or `split_by_node` AFTER `interleave_datasets` or `concatenate_datasets`
Introduce pdf support (#7318)

First implementation of the Pdf feature to support pdfs (#7318) . Using [pdfplumber](https://github.com/jsvine/pdfplumber?tab=readme-ov-file#python-library) as the default library to work with pdfs. 
@lhoestq and @AndreaFrancis 


The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7325). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Hi @AndreaFrancis and @lhoestq ! Thanks for looking at the code and for all the changes and suggestions. I have worked on all your suggestions. I need to work a bit more on the tests, but I created a first version and uploaded a simple pdf to use for testing. The pdf has a couple of pages some of which have images, figures, and tables (this is exactly the pdfs for which I think keeping the pdf format is very interesting because they have multiple types of content). Will try to finish the tests as soon as possible.

(I updated the CI a bit, I took the liberty to update your branch and re-run it on your PR @yabramuvdi)
Fix local pdf loading

fir this error when accessing a local pdf

```
File ~/.pyenv/versions/3.12.2/envs/hf-datasets/lib/python3.12/site-packages/pdfminer/psparser.py:220, in PSBaseParser.seek(self, pos)
    218 """Seeks the parser to the given position."""
    219 log.debug("seek: %r", pos)
--> 220 self.fp.seek(pos)
    221 # reset the status for nextline()
    222 self.bufpos = pos

ValueError: seek of closed file
```


The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7466). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Add IterableDataset.shard()

Will be useful to distribute a dataset across workers (other than pytorch) like spark

I also renamed `.n_shards` -> `.num_shards` for consistency and kept the old name for backward compatibility. And a few changes in internal functions for consistency as well (rank, world_size -> num_shards, index)

Breaking change: the new default for `contiguous` in `Dataset.shard()` is `True`, but imo not a big deal since I couldn't find any usage of `contiguous=False` internally (we always do contiguous=True for map-style datasets since its more optimized) or in the wild


The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7252). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Is there some way to get this to work for pytorch dataloader workers?

eg. start with a single sharded IterableDataset.from_generator(), then reshard before calling map() to do expensive processing over multiple workers
Custom split name with the web interface

### Describe the bug

According the doc here: https://huggingface.co/docs/hub/datasets-file-names-and-splits#custom-split-name
it should infer the split name from the subdir of data or the beg of the name of the files in data.
When doing this manually through web upload it does not work. it uses "train" as a unique split.
example: https://huggingface.co/datasets/eole-nlp/estimator_chatml

### Steps to reproduce the bug

follow the link above

### Expected behavior

there should be two splits "mlqe" and "1720_da"

### Environment info

website
Webdataset data format problem

### Describe the bug

Please see https://huggingface.co/datasets/ejschwartz/idioms/discussions/1

Error code: FileFormatMismatchBetweenSplitsError

All three splits, train, test, and validation, use webdataset. But only the train split has more than one file. How can I force the other two splits to also be interpreted as being the webdataset format?  (I don't think there is currently a way, but happy to be told that I am wrong.)

### Steps to reproduce the bug

```
import datasets
datasets.load_dataset("ejschwartz/idioms")

### Expected behavior

The dataset loads.  Alternatively, there is a YAML syntax for manually specifying the format.

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0


I was able to work around it