Sulak2020/github-issues-multirepo-datasets-Sulakshana huggingface dataset:
Title: HTTPError 403 raised by CLI convert_to_parquet when creating script branch on 3rd party repos
State: closed
Labels: name: bug
description: Something isn't working

CLI convert_to_parquet cannot create "script" branch on 3rd party repos.

It can only create it on repos where the user executing the script has write access.

Otherwise, a 403 Forbidden HTTPError is raised:
```
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py", line 304, in hf_raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.10/dist-packages/requests/models.py", line 1021, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/ORG/DATASET/branch/script

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/bin/datasets-cli", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.10/dist-packages/datasets/commands/datasets_cli.py", line 41, in main
    service.run()
  File "/usr/local/lib/python3.10/dist-packages/datasets/commands/convert_to_parquet.py", line 92, in run
    create_branch(dataset_id, branch="script", repo_type="dataset", token=token, exist_ok=True)
  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
    hf_raise_for_status(response)
  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py", line 367, in hf_raise_for_status
    raise HfHubHTTPError(message, response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError:  (Request ID: Root=1-6645ee0d-4db1ed8a1fbe04956be15897;139a6e23-df7d-4f62-b5ba-adb6d8e6e696)

403 Forbidden: Forbidden: cannot write to script.
Cannot access content at: https://huggingface.co/api/datasets/ORG/DATASET/branch/script.
If you are trying to create or update content,make sure you have a token with the `write` role.
```
Title: [WebDataset] KeyError with user-defined `Features` when a field is missing in an example
State: closed

reported at https://huggingface.co/datasets/ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions/discussions/1

```
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 109, in _generate_examples
    example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
```

Comments: @lhoestq How difficult of fix is this?It shouldn't be difficult, I think it's just a matter of adding the missing fields from `self.config.features` in `example` here: before it iterates on image_field_names and audio_field_names. A missing field should have a value set to None

https://github.com/huggingface/datasets/blob/768cb35ede5a6c35fa7545aa3671f3e321c96440/src/datasets/packaged_modules/webdataset/webdataset.py#L113-L116@lhoestq So like this then?

```    
def _generate_examples(self, tar_paths, tar_iterators):
        image_field_names = [
            field_name for field_name, feature in self.info.features.items() if isinstance(feature, datasets.Image)
        ]
        audio_field_names = [
            field_name for field_name, feature in self.info.features.items() if isinstance(feature, datasets.Audio)
        ]
	
        all_field_names = list(self.config.features.keys())
    
        for tar_idx, (tar_path, tar_iterator) in enumerate(zip(tar_paths, tar_iterators)):
            for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
                for field_name in all_field_names:
                    if field_name not in example:
                        if field_name in self.config.features:
                            example[field_name] = self.config.features[field_name]
                        else:
                            example[field_name] = None
    
                # Process image and audio fields
                for field_name in image_field_names + audio_field_names:
                    if example[field_name] is not None:
                        example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
    
                yield f"{tar_idx}_{example_idx}", example
```

Or should we avoid trying add the missing values and just set them to None?

```
            for field_name in all_field_names:
                if field_name not in example:
                    example[field_name] = None
```Yup this is the solution !

```python
            for field_name in all_field_names:
                if field_name not in example:
                    example[field_name] = None
```@lhoestq Awesome, thanks! I made a PR with the fixes
Title: List of dictionary features get standardized
State: open

### Describe the bug

Hi, i’m trying to create a HF dataset from a list using Dataset.from_list.

Each sample in the list is a dict with the same keys (which will be my features). The values for each feature are a list of dictionaries, and each such dictionary has a different set of keys. However, the datasets library standardizes all dictionaries under a feature and adds all possible keys (with None value) from all the dictionaries under that feature.

How can I keep the same set of keys as in the original list for each dictionary under a feature?

### Steps to reproduce the bug

```
from datasets import Dataset

# Define a function to generate a sample with "tools" feature
def generate_sample():
    # Generate random sample data
    sample_data = {
        "text": "Sample text",
        "feature_1": []
    }
    
    # Add feature_1 with random keys for this sample
    feature_1 = [{"key1": "value1"}, {"key2": "value2"}]  # Example feature_1 with random keys
    sample_data["feature_1"].extend(feature_1)
    
    return sample_data

# Generate multiple samples
num_samples = 10
samples = [generate_sample() for _ in range(num_samples)]

# Create a Hugging Face Dataset
dataset = Dataset.from_list(samples)
dataset[0]
```

```{'text': 'Sample text', 'feature_1': [{'key1': 'value1', 'key2': None}, {'key1': None, 'key2': 'value2'}]}```

### Expected behavior

```{'text': 'Sample text', 'feature_1': [{'key1': 'value1'}, {'key2': 'value2'}]}```

### Environment info

- `datasets` version: 2.19.1
- Platform: Linux-5.15.0-1040-nvidia-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.0
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0

Comments: I think this may be a limitation of the arrow formatDupe of #5950
Title: datasets template guide :: issue in documentation YAML
State: closed

### Describe the bug

There is a YAML error at the top of the page, and I don't think it's supposed to be there 

### Steps to reproduce the bug

1. Browse to [this tutorial document](https://github.com/huggingface/datasets/blob/main/templates/README_guide.md)
2. Observe a big red error at the top
3. The rest of the document remains functional

### Expected behavior

I think the YAML block should be displayed or ignored.

### Environment info

N/A

Comments: Hello, @bghira.

Thanks for reporting. Please note that the text originating the error is not supposed to be valid YAML: it contains the instructions to generate the actual YAML content, that should replace the instructions comment.

On the other hand, I agree that it is not nice to have that YAML error message at the top of the page: 
![Screenshot from 2024-05-14 06-58-02](https://github.com/huggingface/datasets/assets/8515462/28409eb4-99e7-4b24-8eaa-21a65a8f23b2)

I am proposing a change to make the YAML error disappear.thanks albert! i looked at it for a while to figure it out. i think the `raw` view option is the correct way to look at it?
Title: Unable to load JSON saved using `to_json`
State: closed

### Describe the bug

Datasets stored in the JSON format cannot be loaded using `json.load()`

### Steps to reproduce the bug

```
import json
from datasets import load_dataset

dataset = load_dataset("squad")
train_dataset, test_dataset = dataset["train"], dataset["validation"]
test_dataset.to_json("full_dataset.json")

# This works
loaded_test = load_dataset("json", data_files="full_dataset.json")

# This fails
loaded_test = json.load(open("full_dataset.json", "r"))
```

### Expected behavior

The JSON should be correctly formatted when writing so that it can be loaded using `json.load()`.

### Environment info

Colab: https://colab.research.google.com/drive/1st1iStFUVgu9ZPvnzSzL4vDeYWDwYpUm?usp=sharing

Comments: Hi @DarshanDeshpande,

Please note that the default format of the method `Dataset.to_json` is [JSON-Lines](https://jsonlines.org/): it passes `orient="records", lines=True` to `pandas.DataFrame.to_json`. This format is specially useful for large datasets, since unlike regular JSON files, it does not require loading all the data into memory at once, but can be done iteratively by batches.

In order to read this file using the `json` library, you should parse line by line:
```python
with open("full_dataset.json", "r") as f:
    data = [json.loads(line) for line in f]
len(data)
```
Maybe we should explain this better in our docs.Now we explain this better in out docs:
- #6895
Title: add `with_transform` and/or `set_transform` to IterableDataset
State: open
Labels: name: enhancement
description: New feature or request

### Feature request

when working with a really large dataset it would save us a lot of time (and compute resources) to use either with_transform or the set_transform from the Dataset class instead of waiting for the entire dataset to map

### Motivation

don't want to wait for a really long dataset to map, this would give IterableDataset an extra advantage over the Dataset class.
reducing time and resources

### Your contribution

I am a little busy with my job search lately, but would post about this feature in my social media.
Apologies again (dad going to kick me out soon), if I ever have some free time I will contribute to making this a reality, but that's going to be hard
      / (┬┬﹏┬┬)\
Title: Support WebDataset containing file basenames with dots
State: closed

Support WebDataset containing file basenames with dots.

Fix #6880.

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6888). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.I think webdataset splits the file name and extension using the first dot no ?

https://github.com/webdataset/webdataset/blob/945b251a872ec0d337be8f9ea17a9c5b0d017ff3/webdataset/tariterators.py#L226

links to this function that splits on first dot

```python

def base_plus_ext(path):
    """Split off all file extensions.

    Returns base, allext.

    Args:
        path: path with extensions

    Returns:
        path with all extensions removed
    """
    match = re.match(r"^((?:.*/|)[^.]+)[.]([^/]*)$", path)
    if not match:
        return None, None
    return match.group(1), match.group(2)
```So maybe the original issue is actually due to one of the files containing a dot in its file name that is not for the extension

```python
>>> base_plus_ext("15_Cohen_1-s2.0-S0929664620300449-gr3_lrg-b.png")
('15_Cohen_1-s2', '0-S0929664620300449-gr3_lrg-b.png')
```Thanks for your review, @lhoestq.

I was not aware that `webdataset` requires filenames without dots in their basenames.I they can have dots for the extension (that becomes the column name) but not in the key used to group files into samples
Title: FAISS load to None
State: open

### Describe the bug

I've use FAISS with Datasets and save to FAISS.

Then load to save FAISS then no error, then ds to None

```python
ds.load_faiss_index('embeddings', 'my_index.faiss')
```

### Steps to reproduce the bug

# 1.
```python
ds_with_embeddings = ds.map(lambda example: {'embeddings': model(transforms(example['image']).unsqueeze(0)).squeeze()}, batch_size=64)

ds_with_embeddings.add_faiss_index(column='embeddings')

ds_with_embeddings.save_faiss_index('embeddings', 'index.faiss')
```

# 2.
```python
ds.load_faiss_index('embeddings', 'my_index.faiss')
```

### Expected behavior

Add column in Datasets.

### Environment info

Google Colab, SageMaker Notebook

Comments: Hello,

I'm not sure I understand. 
The return value of `ds.load_faiss_index` is None as expected.

I see that loading an Index on a dataset that doesn't have an `embedding` column doesn't raise an Issue. Is that the issue?

So `ds` doesn't have an `embedding` column, but we load an index that looks for it. But this will raise an issue only when calling `ds.search`.
Title: load_dataset with data_dir and cache_dir set fail with not supported
State: open

### Describe the bug

with python 3.11 I execute:
```py
from transformers import Wav2Vec2Processor, Data2VecAudioModel
import torch
from torch import nn
from datasets import load_dataset, concatenate_datasets

# load demo audio and set processor
dataset_clean = load_dataset("librispeech_asr", "clean", split="validation", data_dir="data", cache_dir="cache")
```
This fails in the last line with 
```log
Found cached dataset librispeech_asr (file:///Users/as/Documents/Project/git/audio2vec/cache/librispeech_asr/clean-data_dir=data/2.1.0/cff5df6e7955c80a67f80e27e7e655de71c689e2d2364bece785b972acb37fe7)
Traceback (most recent call last):
  File "/Users/as/Documents/Project/git/audio2vec/src/music2vec-v1.py", line 7, in <module>
    dataset_clean = load_dataset("librispeech_asr", "clean", split="validation", data_dir="data", cache_dir="cache")
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/as/anaconda3/lib/python3.11/site-packages/datasets/load.py", line 1810, in load_dataset
    ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/as/anaconda3/lib/python3.11/site-packages/datasets/builder.py", line 1113, 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.
```

### Steps to reproduce the bug

I setup an venv with requirements.txt
```txt
transformers==4.40.2
torch==2.2.2
datasets==2.16.0
fsspec==2023.9.2
```

pip freeze is:
```
aiohttp==3.9.5
aiosignal==1.3.1
attrs==23.2.0
certifi==2024.2.2
charset-normalizer==3.3.2
datasets==2.16.0
dill==0.3.7
filelock==3.14.0
frozenlist==1.4.1
fsspec==2023.9.2
huggingface-hub==0.23.0
idna==3.7
Jinja2==3.1.4
MarkupSafe==2.1.5
mpmath==1.3.0
multidict==6.0.5
multiprocess==0.70.15
networkx==3.3
numpy==1.26.4
packaging==24.0
pandas==2.2.2
pyarrow==16.0.0
pyarrow-hotfix==0.6
python-dateutil==2.9.0.post0
pytz==2024.1
PyYAML==6.0.1
regex==2024.4.28
requests==2.31.0
safetensors==0.4.3
six==1.16.0
sympy==1.12
tokenizers==0.19.1
torch==2.2.2
tqdm==4.66.4
transformers==4.40.2
typing_extensions==4.11.0
tzdata==2024.1
urllib3==2.2.1
xxhash==3.4.1
yarl==1.9.4

```

I execute this on a M1 Mac.


### Expected behavior

I don't understand the error message. Why is "local" caching not supported. Would it possible to give some additional hint with the error message how to solve this issue?


### Environment info

source ....
python -u example.py
Title: CI is broken after jax-0.4.27 release: AttributeError: 'jaxlib.xla_extension.DeviceList' object has no attribute 'device'
State: closed
Labels: name: bug
description: Something isn't working

After jax-0.4.27 release (https://github.com/google/jax/releases/tag/jax-v0.4.27), our CI is broken with the error:
```Python traceback
AttributeError: 'jaxlib.xla_extension.DeviceList' object has no attribute 'device'. Did you mean: 'devices'?
```

See: https://github.com/huggingface/datasets/actions/runs/8997488610/job/24715736153
```Python traceback
___________________ FormatterTest.test_jax_formatter_device ____________________
[gw1] linux -- Python 3.10.14 /opt/hostedtoolcache/Python/3.10.14/x64/bin/python

self = <tests.test_formatting.FormatterTest testMethod=test_jax_formatter_device>

    @require_jax
    def test_jax_formatter_device(self):
        import jax
    
        from datasets.formatting import JaxFormatter
    
        pa_table = self._create_dummy_table()
        device = jax.devices()[0]
        formatter = JaxFormatter(device=str(device))
        row = formatter.format_row(pa_table)
>       assert row["a"].device() == device
E       AttributeError: 'jaxlib.xla_extension.DeviceList' object has no attribute 'device'. Did you mean: 'devices'?

tests/test_formatting.py:630: AttributeError
```
Title: Batched mapping does not raise an error if values for an existing column are empty
State: open

### Describe the bug

Using `Dataset.map(fn, batched=True)` allows resizing the dataset by returning a dict of lists, all of which must be the same size. If they are not the same size, an error like `pyarrow.lib.ArrowInvalid: Column 1 named x expected length 1 but got length 0` is raised.

This is not the case if the function returns an empty list for an existing column in the dataset. In that case, the dataset is silently resized to 0 rows.

### Steps to reproduce the bug

MWE:

```
import datasets
data = datasets.Dataset.from_dict({"test": [1]})

def mapping_fn(examples):
    return {"test": [], "y": [1]}

data = data.map(mapping_fn, batched=True)
print(len(data))
```

Note  that when returning `"x": []`, the error is raised correctly, also when returning `"test": [1,2]`.

### Expected behavior

Expected an exception: `pyarrow.lib.ArrowInvalid: Column 1 named test expected length 1 but got length 0` or `pyarrow.lib.ArrowInvalid: Column 2 named y expected length 0 but got length 1`.

Any exception would be acceptable.

### Environment info

- `datasets` version: 2.19.1
- Platform: Linux-5.4.0-153-generic-x86_64-with-glibc2.31
- Python version: 3.11.8
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0
Title: Example on Semantic segmentation contains bug
State: open

### Describe the bug

https://huggingface.co/docs/datasets/en/semantic_segmentation shows wrong example with torchvision transforms.
Specifically, as one can see in screenshot below, the object boundaries have weird colors. 

<img width="689" alt="image" src="https://github.com/huggingface/datasets/assets/4803565/59aa0e2c-2e3e-415b-9d42-2314044c5aee">


Original example with `albumentations` is correct 
<img width="705" alt="image" src="https://github.com/huggingface/datasets/assets/4803565/27dbd725-cea5-4e48-ba59-7050c3ce17b3">


That is because `torch vision.transforms.Resize` interpolates with bilinear everything which is wrong when used for segmentation labels - you just cannot mix them. Overall, `torchvision.transforms` is designed for classification only and cannot be used to images and masks together, unless you write two separate branches of augmentations.


The correct way would be to use `v2` version of transforms and convert the segmentation labels to https://pytorch.org/vision/main/generated/torchvision.tv_tensors.Mask.html#torchvision.tv_tensors.Mask object




### Steps to reproduce the bug

Go to the website.
<img width="689" alt="image" src="https://github.com/huggingface/datasets/assets/4803565/ea1276d0-d69a-48cf-b9c2-cd61217815ef">

https://huggingface.co/docs/datasets/en/semantic_segmentation

### Expected behavior

Results, similar to `albumentation`. Or remove the torch vision part altogether. Or use `kornia` instead.

### Environment info

Irrelevant
Title: Dataset 'rewardsignal/reddit_writing_prompts' doesn't exist on the Hub
State: closed

### Describe the bug

The dataset `rewardsignal/reddit_writing_prompts` is missing in Huggingface Hub.

### Steps to reproduce the bug

```
from datasets import load_dataset

prompt_response_dataset = load_dataset("rewardsignal/reddit_writing_prompts", data_files="prompt_responses_full.csv", split='train[:80%]')
```

### Expected behavior

DatasetNotFoundError: Dataset 'rewardsignal/reddit_writing_prompts' doesn't exist on the Hub or cannot be accessed

### Environment info

Nothing to do with versions

Comments: Hi @vinodrajendran001, thanks for reporting.

Indeed the dataset no longer exists on the Hub. The URL https://huggingface.co/datasets/rewardsignal/reddit_writing_prompts gives 404 Not Found error.
Title: CI fails after huggingface_hub-0.23.0 release: FutureWarning: "resume_download"
State: closed
Labels: name: bug
description: Something isn't working

CI fails after latest huggingface_hub-0.23.0 release: https://github.com/huggingface/huggingface_hub/releases/tag/v0.23.0

```
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_bertscore - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_perplexity - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_tokenizer - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_tokenizer_with_cache - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
FAILED tests/test_arrow_dataset.py::MiscellaneousDatasetTest::test_set_format_encode - FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
```

Comments: I think this needs to be fixed on transformers.

CC: @Wauplin See:
- https://github.com/huggingface/transformers/issues/30618Opened https://github.com/huggingface/transformers/pull/30620
Title: Support folder-based datasets with large metadata.jsonl
State: open

I tried creating an `imagefolder` dataset with a 714MB `metadata.jsonl` but got the error below.  This pull request fixes the problem by increasing the block size like the message suggests.
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("imagefolder", data_dir="data-for-upload")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/path/to/datasets/load.py", line 2609, in load_dataset
    builder_instance.download_and_prepare(
      ...
  File "/path/to/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 245, in _read_metadata
    return paj.read_json(f)
  File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
  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: straddling object straddles two block boundaries (try to increase block size?)
```