gusornu/isuee_gus huggingface dataset:
Title: Safely load datasets by disabling execution of dataset loading script
Author: irenedea
State: closed
Labels: name: enhancement

### Feature request

Is there a way to disable execution of dataset loading script using `load_dataset`? This is a security vulnerability that could lead to arbitrary code execution. 

Any suggested workarounds are welcome as well. 

### Motivation

This is a security vulnerability that could lead to arbitrary code execution. 

### Your contribution

n/a
Title: TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array
Author: y-hwang
State: open

### Describe the bug

Hi, I am preprocessing a large custom dataset with numpy arrays. I am running into this TypeError during writing in a dataset.map() function. I've tried decreasing writer batch size, but this error persists. This error does not occur for smaller datasets. 



Thank you!



### Steps to reproduce the bug

Traceback (most recent call last):
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/multiprocess/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1354, in _write_generator_to_queue
    for i, result in enumerate(func(**kwargs)):
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3493, in _map_single
    writer.write_batch(batch)
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 555, in write_batch
    arrays.append(pa.array(typed_sequence))
  File "pyarrow/array.pxi", line 243, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 184, in __arrow_array__
    out = numpy_to_pyarrow_listarray(data)
  File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/features/features.py", line 1394, in numpy_to_pyarrow_listarray
    values = pa.ListArray.from_arrays(offsets, values)
  File "pyarrow/array.pxi", line 2004, in pyarrow.lib.ListArray.from_arrays
TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array

### Expected behavior

Type should not be a ChunkedArray

### Environment info

datasets v2.14.5
arrow v1.2.3
pyarrow v12.0.1
Title: Raise a different exception for inexisting dataset vs files without known extension
Author: severo
State: closed

See https://github.com/huggingface/datasets-server/issues/2082#issuecomment-1805716557

We have the same error for:
- https://huggingface.co/datasets/severo/a_dataset_that_does_not_exist: a dataset that does not exist
- https://huggingface.co/datasets/severo/test_files_without_extension: a dataset with files without a known extension

```
>>> import datasets
>>> datasets.get_dataset_config_names('severo/a_dataset_that_does_not_exist')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
    dataset_module = dataset_module_factory(
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory
    raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/a_dataset_that_does_not_exist/a_dataset_that_does_not_exist.py or any data file in the same directory. Couldn't find 'severo/a_dataset_that_does_not_exist' on the Hugging Face Hub either: FileNotFoundError: Dataset 'severo/a_dataset_that_does_not_exist' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.
>>> datasets.get_dataset_config_names('severo/test_files_without_extension')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
    dataset_module = dataset_module_factory(
  File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory
    raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/test_files_without_extension/test_files_without_extension.py or any data file in the same directory. Couldn't find 'severo/test_files_without_extension' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in severo/test_files_without_extension.
```

To differentiate, we must parse the error message (only the end is different). We should have a different exception for these two errors.
Title: Add ability to set lock type
Author: leoleoasd
State: closed
Labels: name: enhancement

### Feature request

Allow setting file lock type, maybe from an environment variable

Currently, it only depends on whether fnctl is available:

https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/utils/filelock.py#L463-L470C16

### Motivation

In my environment, flock isn't supported on a network attached drive

### Your contribution

I'll be happy to submit a pr.
Title: TorchFormatter images (H, W, C) instead of (C, H, W) format
Author: Modexus
State: closed

### Describe the bug

Using .set_format("torch") leads to images having shape (H, W, C), the same as in numpy. 
However, pytorch normally uses (C, H, W) format.

Maybe I'm missing something but this makes the format a lot less useful as I then have to permute it anyways.
If not using the format it is possible to directly use torchvision transforms but any non-transformed value will not be a tensor.

Is there a reason for this choice?

### Steps to reproduce the bug

```python
from datasets import Dataset, Features, Audio, Image
images = ["path/to/image.png"] * 10
features = Features({"image": Image()})
ds = Dataset.from_dict({"image": images}, features=features) 
ds = ds.with_format("torch")
ds[0]["image"].shape
```
```python
torch.Size([512, 512, 4])
```

### Expected behavior

```python
from datasets import Dataset, Features, Audio, Image
images = ["path/to/image.png"] * 10
features = Features({"image": Image()})
ds = Dataset.from_dict({"image": images}, features=features) 
ds = ds.with_format("torch")
ds[0]["image"].shape
```
```python
torch.Size([4, 512, 512])
```

### Environment info

- `datasets` version: 2.14.6
- Platform: Linux-6.5.9-100.fc37.x86_64-x86_64-with-glibc2.31
- Python version: 3.11.6
- Huggingface_hub version: 0.18.0
- PyArrow version: 14.0.1
- Pandas version: 2.1.2
Title: Filter occasionally hangs
Author: dakinggg
State: open

### Describe the bug

A call to `.filter` occasionally hangs (after the filter is complete, according to tqdm)

There is a trace produced
```
Exception ignored in: <function Dataset.__del__ at 0x7efb48130c10>
Traceback (most recent call last):
  File "/usr/lib/python3/dist-packages/datasets/arrow_dataset.py", line 1366, in __del__
    if hasattr(self, "_indices"):
  File "/usr/lib/python3/dist-packages/composer/core/engine.py", line 123, in sigterm_handler
    sys.exit(128 + signal)
SystemExit: 143
```

but I'm not sure if the trace is actually from `datasets`, or from surrounding code that is trying to clean up after datasets gets stuck.

Unfortunately I can't reproduce this issue anywhere close to reliably. It happens infrequently when using `num_procs > 1`. Anecdotally I started seeing it when using larger datasets (~10M samples).

### Steps to reproduce the bug

N/A see description

### Expected behavior

map/filter calls always complete sucessfully

### Environment info

- `datasets` version: 2.14.6
- Platform: Linux-5.4.0-137-generic-x86_64-with-glibc2.31
- Python version: 3.10.13
- Huggingface_hub version: 0.17.3
- PyArrow version: 13.0.0
- Pandas version: 2.1.2
Title: Webdataset dataset builder
Author: lhoestq
State: closed

Allow `load_dataset` to support the Webdataset format.

It allows users to download/stream data from local files or from the Hugging Face Hub.

Moreover it will enable the Dataset Viewer for Webdataset datasets on HF.

## Implementation details

- I added a new Webdataset builder
- dataset with TAR files are now read using the Webdataset builder
- Basic decoding from `webdataset` is used by default, except unsafe ones like pickle
- HF authentication support is done with `xopen`

## TODOS

- [x] tests
- [x] docs
Title: handle future deprecation argument
Author: winglian
State: closed

getting this error:
```
/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/datasets/table.py:1387: FutureWarning: promote has been superseded by mode='default'.
  return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0)
```

Since datasets supports arrow greater than 8.0.0, we need to handle both cases.

[Arrow v14 docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html)
[Arrow v13 docs](https://arrow.apache.org/docs/13.0/python/generated/pyarrow.concat_tables.html)
Title: How to create 3d medical imgae dataset?
Author: QingYunA
State: open
Labels: name: enhancement

### Feature request

I am newer to huggingface, after i look up `datasets` docs, I can't find how to create the dataset contains 3d medical image (ends with '.mhd', '.dcm', '.nii')

### Motivation

help us to upload 3d medical dataset to huggingface!

### Your contribution

I'll submit a PR if I find a way to add this feature
Title: How to load existing downloaded dataset ?
Author: liming-ai
State: closed
Labels: name: enhancement

Hi @mariosasko @lhoestq  @katielink 

Thanks for your contribution and hard work.

### Feature request

First, I download a dataset as normal by:
```
from datasets import load_dataset

dataset = load_dataset('username/data_name', cache_dir='data')
```

The dataset format in `data` directory will be:
```
-data
  |-data_name
    |-test-00000-of-00001-bf4c733542e35fcb.parquet
    |-train-00000-of-00001-2a1df75c6bce91ab.parquet
```


Then I use SCP to clone this dataset into another machine, and then try:
```
from datasets import load_dataset

dataset = load_dataset('data/data_name')  # load from local path
```

This leads to re-generating training and validation split for each time, and the disk quota will be duplicated occupation.

How can I just load the dataset without generating and saving these splits again?

### Motivation

I do not want to download the same dataset in two machines, scp is much faster and better than HuggingFace API. I hope we can directly load the downloaded datasets (.parquest)

### Your contribution

Please refer to the feature
Title: Formatting overhead
Author: d-miketa
State: closed

### Describe the bug

Hi! I very recently noticed that my training time is dominated by batch formatting. Using Lightning's profilers, I located the bottleneck within `datasets.formatting.formatting` and then narrowed it down with `line-profiler`. It turns out that almost all of the overhead is due to creating new instances of `self.python_arrow_extractor`. I admit I'm confused why that could be the case - as far as I can tell there's no complex `__init__` logic to execute.

![image](https://github.com/huggingface/datasets/assets/320321/5e022e0b-0d21-43d0-8e6f-9e641142e96b)

### Steps to reproduce the bug

1. Set up a dataset `ds` with potentially several (4+) columns (not sure if this is necessary, but it did at one point of the investigation make overhead worse)
2. Process it using a custom transform, `ds = ds.with_transform(transform_func)`
3. Decorate this function https://github.com/huggingface/datasets/blob/main/src/datasets/formatting/formatting.py#L512 with `@profile` from https://pypi.org/project/line-profiler/
4. Profile with `$ kernprof -l script_to_profile.py`

### Expected behavior

Batch formatting should have acceptable overhead.

### Environment info

```
datasets=2.14.6
pyarrow=14.0.0
```
Title: Get an error when i try to concatenate the squad dataset with my own dataset
Author: CCDXDX
State: closed

### Describe the bug

Hello,

I'm new here and I need to concatenate the squad dataset with my own dataset i created. I find the following error when i try to do it: Traceback (most recent call last):

  Cell In[9], line 1
    concatenated_dataset = concatenate_datasets([train_dataset, dataset1])

  File ~\anaconda3\Lib\site-packages\datasets\combine.py:213 in concatenate_datasets
    return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)

  File ~\anaconda3\Lib\site-packages\datasets\arrow_dataset.py:6002 in _concatenate_map_style_datasets
    _check_if_features_can_be_aligned([dset.features for dset in dsets])

  File ~\anaconda3\Lib\site-packages\datasets\features\features.py:2122 in _check_if_features_can_be_aligned
    raise ValueError(

ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Value(dtype='string', id=None)} or Value("null").

### Steps to reproduce the bug

```python

from huggingface_hub import notebook_login
from datasets import load_dataset



notebook_login("mymailadresse", "mypassword")
squad = load_dataset("squad", split="train[:5000]")
squad = squad.train_test_split(test_size=0.2)
dataset1 = squad["train"]




import json

mybase = [
    {
        "id": "1",
        "context": "She lives in Nantes",
        "question": "Where does she live?",
        "answers": {
            "text": "Nantes",
            "answer_start": [13],
        }
    }
]




# Save the data to a JSON file
json_file_path = r"C:\Users\mypath\thefile.json"
with open(json_file_path, "w", encoding= "utf-8") as json_file:
    json.dump(mybase, json_file, indent=4)




# Load the JSON file as a dataset
custom_dataset = load_dataset("json", data_files=json_file_path)


# Access the train split
train_dataset = custom_dataset["train"]


from datasets import concatenate_datasets


# Concatenate the datasets
concatenated_dataset = concatenate_datasets([train_dataset, dataset1])
```

### Expected behavior

I would expect the two datasets to be concatenated without error. The len(dataset1) is equal to 4000 and the len(train_dataset) is equal to 1 so I would exepect concatenated_dataset to be created and having lenght 4001.

### Environment info

Python 3.11.4 and using windows

Thank you for your help
Title: imagenet-1k downloads over and over
Author: seann999
State: closed

### Describe the bug

What could be causing this?
```
$ python3
Python 3.8.13 (default, Mar 28 2022, 11:38:47) 
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> load_dataset("imagenet-1k")
Downloading builder script: 100%|██████████| 4.72k/4.72k [00:00<00:00, 7.51MB/s]
Downloading readme: 100%|███████████████████| 85.4k/85.4k [00:00<00:00, 510kB/s]
Downloading extra modules: 100%|████████████| 46.4k/46.4k [00:00<00:00, 300kB/s]
Downloading data: 100%|████████████████████| 29.1G/29.1G [19:36<00:00, 24.8MB/s]
Downloading data: 100%|████████████████████| 29.3G/29.3G [08:38<00:00, 56.5MB/s]
Downloading data: 100%|████████████████████| 29.0G/29.0G [09:26<00:00, 51.2MB/s]
Downloading data: 100%|████████████████████| 29.2G/29.2G [09:38<00:00, 50.6MB/s]
Downloading data: 100%|███████████████████▉| 29.2G/29.2G [09:37<00:00, 44.1MB/s^Downloading data:   0%|                     | 106M/29.1G [00:05<23:49, 20.3MB/s]
```

### Steps to reproduce the bug

See above commands/code

### Expected behavior

imagenet-1k is downloaded

### Environment info

- `datasets` version: 2.14.6
- Platform: Linux-6.2.0-34-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- Huggingface_hub version: 0.15.1
- PyArrow version: 14.0.0
- Pandas version: 1.5.2
Title: Add CheXpert dataset for vision
Author: SauravMaheshkar
State: open
Labels: name: enhancement, name: dataset request

### Feature request

### Name 

**CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison**

### Paper

https://arxiv.org/abs/1901.07031

### Data

https://stanfordaimi.azurewebsites.net/datasets/8cbd9ed4-2eb9-4565-affc-111cf4f7ebe2

### Motivation

CheXpert is one of the fundamental models in medical image classification and can serve as a viable pre-training dataset for radiology classification or low-scale ablation / exploratory studies.

This could also serve as a good pre-training dataset for Kaggle competitions.

### Your contribution

Would love to make a PR and pre-process / get this into 🤗
Title: Add my dataset
Author: keyur536
State: closed

## medical data

**Description:**
This dataset, named "medical data," is a collection of text data from various sources, carefully curated and cleaned for use in natural language processing (NLP) tasks. It consists of a diverse range of text, including articles, books, and online content, covering topics from science to literature.

**Citation:**
If applicable, please include a citation for this dataset to give credit to the original sources or contributors.

**Key Features:**
- Language: The text is primarily in English, but it may include content in other languages as well.
- Use Cases: This dataset is suitable for text classification, language modeling, sentiment analysis, and other NLP tasks.

**Usage:**
To access this dataset, use the `load_your_dataset` function provided in the `your_dataset.py` script within this repository. You can specify the dataset split you need, such as "train," "test," or "validation," to get the data for your specific task.

**Contributors:**
- [Keyur Chaudhari]

**Contact:**
If you have any questions or need assistance regarding this dataset, please feel free to contact [keyurchaudhari536@gmail.com].

Please note that this dataset is shared under a specific license, which can be found in the [LICENSE](link to your dataset's license) file. Make sure to review and adhere to the terms of the license when using this dataset for your projects.
Title: Fix for continuation behaviour on broken dataset archives due to starving download connections via HTTP-GET
Author: RuntimeRacer
State: open

This PR proposes a (slightly hacky) fix for an Issue that can occur when downloading large dataset parts over unstable connections.
The underlying issue is also being discussed in https://github.com/huggingface/datasets/issues/5594.

Issue Symptoms & Behaviour:
- Download of a large archive file during dataset download via HTTP-GET fails.
- An silent net exception (which I was unable to identify) is thrown within the `tqdm` download progress.
- Due to missing exception catch code, the above process just continues processing, assuming `http_get` completed successfully.
- Pending Archive file gets renamed to remove the `.incomplete` extension, despite not all data has been downloaded. 
- Also, for reasons I did not investigate, there seems to be no real integrity check for the downloaded files; or it does not detect this problem. This is especially problematic, since the downloader script won't retry downloading this archive after CRC-Checking, even if it is being manually restarted / executed again after running into errors on extraction.

Fix proposal: Adding a retry mechanic for HTTP-GET downloads, which adds the following behaviour:
- Download Progress Thread checks for download size validity in case the HTTP connection starves mid download. If the check fails, a RuntimeError is thrown
- Cache Downloader code with retry mechanic monitors for an exception thrown by the download progress thread, and retries download with updated `resume_size`.
- Cache Downloader will not mark incomplete files which have thrown an exception during download, and exceeded retries, as complete.
Title: Caching problem when deleting a dataset 
Author: clefourrier
State: closed

### Describe the bug

Pushing a dataset with n + m features to a repo which was deleted, but contained n features, will fail.

### Steps to reproduce the bug

1. Create a dataset with n features per row
2. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)`
3. Go on the hub, delete the repo at `YOUR_PATH`
4. Update your local dataset to have n + m features per row
5. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` will fail because of a mismatch in features number


### Expected behavior

Step 5 should work or display a message to indicate the cache has not been cleared

### Environment info

- `datasets` version: 2.12.0
- Platform: Linux-5.15.0-88-generic-x86_64-with-glibc2.31
- Python version: 3.10.10
- Huggingface_hub version: 0.16.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0
Title: CI is broken: TypeError: Couldn't cast array
Author: albertvillanova
State: closed

See: https://github.com/huggingface/datasets/actions/runs/6730567226/job/18293518039
```
FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type
fixed_size_list<item: int32>[3]
to
Sequence(feature=Value(dtype='int64', id=None), length=3, id=None)
```
Title: `Dataset.from_generator` should not try to download from HF GCS
Author: yundai424
State: closed

### Describe the bug

When using [`Dataset.from_generator`](https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/arrow_dataset.py#L1072) with `streaming=False`, the internal logic will call [`download_and_prepare`](https://github.com/huggingface/datasets/blob/main/src/datasets/io/generator.py#L47) which will attempt to download from HF GCS which is redundant, because user has already provided the generator from which the data should be drawn. 

If someone attempts to call `Dataset.from_generator` from an environment that doesn't have external internet access (for example internal production machine) and doesn't set `HF_DATASETS_OFFLINE=1`, this will result in process being stuck at building connection.

### Steps to reproduce the bug

```python
import datasets
def gen():
    for _ in range(100):
        yield {"text": "dummy text"}

dataset = datasets.Dataset.from_generator(gen)
```

A minimum example executed on any environment that doesn't have access to HF GCS can result in the error

### Expected behavior

`try_from_hf_gcs` should be set to False here https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/io/generator.py#L51

### Environment info

- `datasets` version: 2.14.4
- Platform: Linux-3.10.0-1160.90.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.12
- Huggingface_hub version: 0.17.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
Title: TensorDataset format does not work with Trainer from transformers
Author: jinzzasol
State: closed

### Describe the bug

The model was built to do fine tunning on BERT model for relation extraction.

trainer.train() returns an error message ```TypeError: vars() argument must have __dict__ attribute``` when it has `train_dataset` generated from `torch.utils.data.TensorDataset`

However, in the document, the required data format is `torch.utils.data.TensorDataset`. 
![image](https://github.com/huggingface/datasets/assets/49014051/36fa34ac-3127-4c64-9580-9ab736136d83)

Transformers trainer is supposed to accept the train_dataset in the format of torch.utils.data.TensorDataset, but it returns error message *"TypeError: vars() argument must have __dict__ attribute"*

```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-30-5df728c929a2> in <cell line: 1>()
----> 1 trainer.train()
      2 trainer.evaluate(test_dataset)

9 frames
/usr/local/lib/python3.10/dist-packages/transformers/data/data_collator.py in <listcomp>(.0)
    107 
    108     if not isinstance(features[0], Mapping):
--> 109         features = [vars(f) for f in features]
    110     first = features[0]
    111     batch = {}

TypeError: vars() argument must have __dict__ attribute
```

### Steps to reproduce the bug

Create train_dataset using `torch.utils.data.TensorDataset`, for instance,

```train_dataset = torch.utils.data.TensorDataset(train_input_ids, train_attention_masks, train_labels)```

Feed this `train_dataset` to your trainer and run trainer.train

```
trainer = Trainer(model,
                  training_args,
                  train_dataset=train_dataset,
                  eval_dataset=dev_dataset,
                  compute_metrics=compute_metrics,
                  )
```

### Expected behavior

Trainer should start training

### Environment info

It is running on Google Colab

- `datasets` version: 2.14.6
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.17.3
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
Title: Multi process map did not load cache file correctly
Author: enze5088
State: closed

### Describe the bug

When I  was training model on Multiple GPUs by DDP, the dataset is tokenized multiple times after main process. 
![1698820541284](https://github.com/huggingface/datasets/assets/14285786/0b2fe054-54d8-4e00-96e6-6ca5b69e662b)

![1698820501568](https://github.com/huggingface/datasets/assets/14285786/dd62bf6f-a58f-41bf-9848-ea4fb3b62b9b)

Code is modified from  [run_clm.py](https://github.com/huggingface/transformers/blob/7d8ff3629b2725ec43ace99c1a6e87ac1978d433/examples/pytorch/language-modeling/run_clm.py#L484)


### Steps to reproduce the bug

```
block_size = data_args.block_size
IGNORE_INDEX = -100
Ignore_Input = False

def tokenize_function(examples):
    sources = []
    targets = []
    for instruction, inputs, output in zip(examples['instruction'], examples['input'], examples['output']):

        source = instruction + inputs 

        target = f"{output}{tokenizer.eos_token}"

        sources.append(source)
        targets.append(target)

    tokenized_sources = tokenizer(sources, return_attention_mask=False)

    tokenized_targets = tokenizer(targets, return_attention_mask=False,
                                  add_special_tokens=False
                                  )

    all_input_ids = []
    all_labels = []
    for s, t in zip(tokenized_sources['input_ids'], tokenized_targets['input_ids']):
        if len(s) > block_size and Ignore_Input == False:
            # print(s)
            continue
        input_ids = torch.LongTensor(s + t)[:block_size]
        if Ignore_Input:
            labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:block_size]
        else:
            labels = input_ids
        assert len(input_ids) == len(labels)
        all_input_ids.append(input_ids)
        all_labels.append(labels)

    results = {
        'input_ids': all_input_ids,
        'labels': all_labels,

    }
    return results



with training_args.main_process_first(desc="dataset map tokenization ", local=False):
    # print('local_rank',training_args.local_rank)
    if not data_args.streaming:
        tokenized_datasets = raw_datasets.map(
            tokenize_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
            desc="Running tokenizer on dataset ",
        )
    else:
        tokenized_datasets = raw_datasets.map(
            tokenize_function,
            batched=True,
            remove_columns=column_names,
            desc="Running tokenizer on dataset "
        )
```

### Expected behavior

This code should only tokenize the dataset in the main process, and the other processes load the dataset after waiting

### Environment info

transformers == 4.34.1
datasets == 2.14.5
Title: with_format() function returns bytes instead of PIL images even when image column is not part of "columns"
Author: leot13
State: closed

### Describe the bug

When using the with_format() function on a dataset containing images, even if the image column is not part of the columns provided in the function, its type will be changed to bytes.
Here is a minimal reproduction of the bug:
https://colab.research.google.com/drive/1hyaOspgyhB41oiR1-tXE3k_gJCdJUQCf?usp=sharing

### Steps to reproduce the bug

1. Load the image dataset
2. apply with_format(columns=["text"])
3. Check the type of images in the "image" column before and after applying with_format

### Expected behavior

The type should stay the same, but it does not

### Environment info

datasets==2.14.6
Title: Parquet size grows exponential for categorical data
Author: aseganti
State: closed

### Describe the bug

It seems that when saving a data frame with a categorical column inside the size can grow exponentially.

This seems to happen because when we save the categorical data to parquet, we are saving the data + all the categories existing in the original data. This happens even when the categories are not present in the original data.


### Steps to reproduce the bug

To reproduce the bug, it is enough to run this script:

```
import pandas as pd
import os

if __name__ == "__main__":
    for n in [10, 1e2, 1e3, 1e4, 1e5]:
        for n_col in [1, 10, 100, 1000, 10000]:
            input = pd.DataFrame([{"{i}": f"{i}_cat" for col in range(n_col)} for i in range(int(n))])
            input.iloc[0:100].to_parquet("a.parquet")
            for col in input.columns:
                input[col] = input[col].astype("category")
            input.iloc[0:100].to_parquet("b.parquet")
            a_size_mb = os.stat("a.parquet").st_size / (1024 * 1024)
            b_size_mb = os.stat("b.parquet").st_size / (1024 * 1024)
            print(f"{n} {n_col} {a_size_mb} {b_size_mb} {100*b_size_mb/a_size_mb:.2f}")
```

That produces this output:

<img width="464" alt="Screenshot 2023-10-31 at 11 25 25" src="https://github.com/huggingface/datasets/assets/82567957/2b8a9284-7f9e-4c10-a006-0a27236ebd15">



### Expected behavior

In my opinion either:

1. The two file should have (almost) the same size
2. There should be warning telling the user that such difference in size is possible

### Environment info

Python 3.8.18
pandas==2.0.3
numpy==1.24.4
Title:  Add support for `Sequence(Audio/Image)` feature in `push_to_hub`
Author: Laurent2916
State: closed
Labels: name: enhancement

### Feature request

Allow for `Sequence` of `Image` (or `Audio`) to be embedded inside the shards.

### Motivation

Currently, thanks to #3685, when `embed_external_files` is set to True (which is the default) in `push_to_hub`, features of type `Image` and `Audio` are embedded inside the arrow/parquet shards, instead of only storing paths to the files.

I've noticed that this behavior does not extend to `Sequence` of `Image`, when working with a [dataset of timelapse images](https://huggingface.co/datasets/1aurent/Human-Embryo-Timelapse).

### Your contribution

I'll submit a PR if I find a way to add this feature
Title: Stuck in "Resolving data files..."
Author: Luciennnnnnn
State: open

### 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
Title: Mounting datasets cache fails due to absolute paths.
Author: charliebudd
State: closed

### Describe the bug

Creating a datasets cache and mounting this into, for example, a docker container, renders the data unreadable due to absolute paths written into the cache.

### Steps to reproduce the bug

1. Create a datasets cache by downloading some data
2. Mount the dataset folder into a docker container or remote system.
3. (Edit) Set `HF_HOME` or `HF_DATASET_CACHE` to point to the mounted cache.
4. Attempt to access the data from within the docker container.
5. An error is thrown saying no file exists at \<absolute path to original cache location\>

### Expected behavior

The data is loaded without error

### Environment info

- `datasets` version: 2.14.4
- Platform: Linux-5.4.0-162-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.16.4
- PyArrow version: 13.0.0
- Pandas version: 2.0.3
Title: Allow passing a multiprocessing context to functions that support `num_proc`
Author: bryant1410
State: open
Labels: name: enhancement

### Feature request

Allow specifying [a multiprocessing context](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods) to functions that support `num_proc` or use multiprocessing pools. For example, the following could be done:

```python
dataset = dataset.map(_func, num_proc=2, mp_context=multiprocess.get_context("spawn"))
```

Or at least the multiprocessing start method ("fork", "spawn", "fork_server" or `None`):

```python
dataset = dataset.map(_func, num_proc=2, mp_start_method="spawn")
```

Another option could be passing the `pool` as an argument.

### Motivation

By default, `multiprocess` (the `multiprocessing`-fork library that this repo uses) uses the "fork" start method for multiprocessing pools (for the default context). It could be changed by using `set_start_method`. However, this conditions the multiprocessing start method from all processing in a Python program that uses the default context, because [you can't call that function more than once](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods:~:text=set_start_method()%20should%20not%20be%20used%20more%20than%20once%20in%20the%20program.). My proposal is to allow using a different multiprocessing context, not to condition the whole Python program.

One reason to change the start method is that "fork" (the default) makes child processes likely deadlock if thread pools were created before (and also this is not supported by POSIX). For example, this happens when using PyTorch because OpenMP threads are used for CPU intra-op parallelism, which is enabled by default (e.g., for context see [`torch.set_num_threads`](https://pytorch.org/docs/stable/generated/torch.set_num_threads.html)). This can also be fixed by setting `torch.set_num_threads(1)` (or similarly by other methods) but this conditions the whole Python program as it can only be set once to guarantee its behavior (similarly to). There are noticeable performance differences when setting this number to 1 even when using GPU(s). Using, e.g., a "spawn" start method would solve this issue.

For more context, see:

* https://discuss.huggingface.co/t/dataset-map-stuck-with-torch-set-num-threads-set-to-2-or-larger/37984
* https://discuss.huggingface.co/t/using-num-proc-1-in-dataset-map-hangs/44310

### Your contribution

I'd be happy to review a PR that makes such a change. And if you really don't have the bandwidth for it, I'd consider creating one.
Title: More hub centric docs
Author: lhoestq
State: closed

Let's have more hub-centric documentation in the datasets docs

Tutorials
- Add “Configure the dataset viewer” page
- Change order:
  - Overview
    - and more focused on the Hub rather than the library
  - Then all the hub related things
    - and mention how to read/write with other tools like pandas
  - Then all the datasets lib related things in a subsection

Also:
- Rename “know your dataset” page to “Explore your dataset”
- Remove “Evaluate Predictions” page since it's 'evaluate' stuff (or move to legacy section ?)

TODO:
- [ ] write the “Configure the dataset viewer” page
Title: load_dataset save_to_disk load_from_disk error
Author: brisker
State: closed

### Describe the bug

datasets version： 2.10.1
I `load_dataset `and `save_to_disk` sucessfully on windows10( **and I `load_from_disk(/LLM/data/wiki)` succcesfully on windows10**), and I copy the dataset `/LLM/data/wiki`
into a ubuntu system, but when I `load_from_disk(/LLM/data/wiki)` on ubuntu,  something weird happens:


```
load_from_disk('/LLM/data/wiki')
  File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1874, in load_from_disk
    return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)
  File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1309, in load_from_disk
    dataset_dict[k] = Dataset.load_from_disk(
  File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1543, in load_from_disk
    fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options)
  File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 610, in get_fs_token_paths
    chain = _un_chain(urlpath0, storage_options or {})
  File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 325, in _un_chain
    cls = get_filesystem_class(protocol)
  File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/registry.py", line 232, in get_filesystem_class
    raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: /LLM/data/wiki
```
It seems that something went wrong on the arrow file?
How can I solve this , since currently I can not save_to_disk on ubuntu system

### Steps to reproduce the bug

datasets version： 2.10.1

### Expected behavior

datasets version： 2.10.1

### Environment info

datasets version： 2.10.1
Title: Error loading wikitext data    raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
Author: Ahmed-Roushdy
State: closed

I was trying to load  the wiki dataset, but i got this error 

    traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')
  File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/load.py", line 1804, in load_dataset
    ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
  File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/builder.py", line 1108, in as_dataset
    raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.
Title: Different objects are returned from calls that should be returning the same kind of object.
Author: phalexo
State: open

### Describe the bug

        1.  dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir, split='train[:1%]')
        2.  dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir)

The only difference I would expect these calls to have is the size of the dataset.

But, while 2. returns a dictionary with "train" key in it, 1. returns a dataset WITHOUT any initial "train" keyword.

Both calls are to be used within exactly the same context. They should return identically structured datasets of different size.

### Steps to reproduce the bug

See above.

### Expected behavior

Expect both calls to return the same structured Dataset structure but with different number of elements, i.e. call 1. should have 1% of the data of the call 2.0

### Environment info

Ubuntu 20.04
gcc 9.x.x.

It is really irrelevant.
Title: Can't load ds = load_dataset("imdb")
Author: vivianc2
State: closed

### Describe the bug

I did `from datasets import load_dataset, load_metric` and then `ds = load_dataset("imdb")` and it gave me the error:
ExpectedMoreDownloadedFiles: {'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'}

I tried doing `ds = load_dataset("imdb",download_mode="force_redownload")` as well as reinstalling dataset. I still face this problem.

### Steps to reproduce the bug

1. from datasets import load_dataset, load_metric
2. ds = load_dataset("imdb")

### Expected behavior

It should load and give me this when I run `ds`

DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 25000
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 25000
    })
    unsupervised: Dataset({
        features: ['text', 'label'],
        num_rows: 50000
    })
})

### Environment info

- `datasets` version: 2.14.6
- Platform: Linux-5.4.0-164-generic-x86_64-with-glibc2.17
- Python version: 3.8.18
- Huggingface_hub version: 0.16.2
- PyArrow version: 13.0.0
- Pandas version: 2.0.2
Title: Incorrect example code in 'Create a dataset' docs
Author: rwood-97
State: closed

### Describe the bug

On [this](https://huggingface.co/docs/datasets/create_dataset) page, the example code for loading in images and audio is incorrect.

Currently, examples are:

``` python
from datasets import ImageFolder
dataset = load_dataset("imagefolder", data_dir="/path/to/pokemon")
```
and 
``` python
from datasets import AudioFolder
dataset = load_dataset("audiofolder", data_dir="/path/to/folder")
```

I'm pretty sure the imports are wrong and should be:

``` python 
from datasets import load_dataset
dataset = load_dataset("audiofolder", data_dir="/path/to/folder")
```

I am happy to update this if this is right but just wanted to check before making any changes.

### Steps to reproduce the bug

Go to https://huggingface.co/docs/datasets/create_dataset

### Expected behavior

N/A

### Environment info

N/A
Title: Fix UnboundLocalError if preprocessing returns an empty list
Author: cwallenwein
State: closed

If this tokenization function is used with IterableDatasets and no sample is as big as the context length, `input_batch` will be an empty list.
```
def tokenize(batch, tokenizer, context_length):
    outputs = tokenizer(
        batch["text"],
        truncation=True,
        max_length=context_length,
        return_overflowing_tokens=True,
        return_length=True
    )
    input_batch = []
    for length, input_ids in zip(outputs["length"], outputs["input_ids"]):
        if length == context_length:
            input_batch.append(input_ids)
    return {"input_ids": input_batch}

dataset.map(tokenize, batched=True, batch_size=batch_size, fn_kwargs={"context_length": context_length, "tokenizer": tokenizer}, remove_columns=dataset.column_names)
```

This will throw the following error: UnboundLocalError: local variable 'batch_idx' referenced before assignment, because the for loop was not executed a single time

```
for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)):
     yield new_key, example
current_idx += batch_idx + 1
```

Some of the possible solutions

```
for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)):
     yield new_key, example
try:
     current_idx += batch_idx + 1
except:
     current_idx += 1
```

or 

```
batch_idx = 0
for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)):
     yield new_key, example
current_idx += batch_idx + 1
```
Title: support squad structure datasets using a YAML parameter
Author: MajdTannous1
State: open
Labels: name: enhancement

### Feature request

Since the squad structure is widely used, I think it could be beneficial to support it using a YAML parameter.
could you implement automatic data loading of squad-like data using squad JSON format, to read it from JSON files and view it in the correct squad structure.
The dataset structure should be like this:
https://huggingface.co/datasets/squad
Columns:id,title,context,question,answers

### Motivation

Dataset repo requires arbitrary Python code execution

### Your contribution

The dataset structure should be like this:
https://huggingface.co/datasets/squad
Columns:id,title,context,question,answers
train and dev sets in squad structure JSON files
Title: Pin supported upper version of fsspec
Author: albertvillanova
State: closed

Pin upper version of `fsspec` to avoid disruptions introduced by breaking changes (and the need of urgent patch releases with hotfixes) on each release on their side. See:
- #6331
- #6210 
- #5731
- #5617
- #5447

I propose that we explicitly test, introduce fixes and support each new `fsspec` version release.

CC: @LysandreJik
Title: Replace deprecated license_file in setup.cfg
Author: albertvillanova
State: closed

Replace deprecated license_file in `setup.cfg`.

See: https://github.com/huggingface/datasets/actions/runs/6610930650/job/17953825724?pr=6331
```
      /tmp/pip-build-env-a51hls20/overlay/lib/python3.8/site-packages/setuptools/config/setupcfg.py:293: _DeprecatedConfig: Deprecated config in `setup.cfg`
      !!
      
              ********************************************************************************
              The license_file parameter is deprecated, use license_files instead.
      
              By 2023-Oct-30, you need to update your project and remove deprecated calls
              or your builds will no longer be supported.
      
              See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.
              ********************************************************************************
      
      !!
```
Title: Temporarily pin fsspec < 2023.10.0
Author: albertvillanova
State: closed

Temporarily pin fsspec < 2023.10.0 until permanent solution is found.

Hot fix #6330.

See: https://github.com/huggingface/datasets/actions/runs/6610904287/job/17953774987
```
...
ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.
= 373 failed, 2055 passed, 17 skipped, 8 warnings, 6 errors in 228.14s (0:03:48) =
```
Title: Latest fsspec==2023.10.0 issue with streaming datasets
Author: ZachNagengast
State: closed

### Describe the bug

Loading a streaming dataset with this version of fsspec fails with the following error:

`NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet.`

I suspect the issue is with this PR 

https://github.com/fsspec/filesystem_spec/pull/1381

### Steps to reproduce the bug

1. Upgrade fsspec to version `2023.10.0`
2. Attempt to load a streaming dataset e.g. `load_dataset("laion/gpt4v-emotion-dataset", split="train", streaming=True)`
3. Observe the following exception:

```
  File "/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/datasets/load.py", line 2146, in load_dataset
    return builder_instance.as_streaming_dataset(split=split)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/datasets/builder.py", line 1318, in as_streaming_dataset
    raise NotImplementedError(
NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet.
```

### Expected behavior

Should stream the dataset as normal.

### Environment info

datasets@main
fsspec==2023.10.0