helmo/github-issues huggingface dataset:
load `streaming=True` dataset with downloaded cache
State: open | Author: wanghaoyucn (NONE)

### Describe the bug

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

### Steps to reproduce the bug

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

### Expected behavior

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

### Environment info

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

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

Thanks for your reply! So is there any solution to get my expected behavior besides clone the whole repo ? Or could I adjust my script to load the downloaded arrow files and generate the dataset streamingly?
Fix export to JSON when dataset larger than batch size
State: open | Author: albertvillanova (MEMBER)

Fix export to JSON (`lines=False`) when dataset larger than batch size.

Fix #7037.

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7039). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

The test before confirms the bug.

There are different possible solutions to this issue:
- the easiest would be to write multiple JSON files, one for each batch; this solution can be done in parallel if `num_proc` is passed
- alternatively, we could tweak the writing and remove the extra `[` and `]` characters; this solution will only be valid if `orient="records"`
- others?

@albertvillanova I was planning to approach it in the second way for `orient="records"` , `orient="values"` and `orient="index"`. For `orient="split"`, the columns and index can be written in one go and the data can be written in streaming manner. For `orient="columns"`, each column can be written in a streaming way.  LMK if I should go ahead with this.

> The test before confirms the bug.
> 
> There are different possible solutions to this issue:
> 
>     * the easiest would be to write multiple JSON files, one for each batch; this solution can be done in parallel if `num_proc` is passed
> 
>     * alternatively, we could tweak the writing and remove the extra `[` and `]` characters; this solution will only be valid if `orient="records"`
> 
>     * others?
A bug of Dataset.to_json() function
State: open | Author: LinglingGreat (NONE)
Labels: name: bug

### Describe the bug

When using the Dataset.to_json() function, an unexpected error occurs if the parameter is set to lines=False. The stored data should be in the form of a list, but it actually turns into multiple lists, which causes an error when reading the data again.
The reason is that to_json() writes to the file in several segments based on the batch size. This is not a problem when lines=True, but it is incorrect when lines=False, because writing in several times will produce multiple lists(when len(dataset) > batch_size).

### Steps to reproduce the bug

try this code:
```python
from datasets import load_dataset
import json

train_dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")["train"]
output_path = "./harmless-base_hftojs.json"
print(len(train_dataset))
train_dataset.to_json(output_path, lines=False, force_ascii=False, indent=2)

with open(output_path, encoding="utf-8") as f:
    data = json.loads(f.read())

```
it raise error: json.decoder.JSONDecodeError: Extra data: line 4003 column 1 (char 1373709)

Extra square brackets have appeared here:
<img width="265" alt="image" src="https://github.com/huggingface/datasets/assets/26499566/81492332-386d-42e8-88d1-b6d4ae3682cc">


### Expected behavior

The code runs normally.

### Environment info

datasets=2.20.0

Comments: Thanks for reporting, @LinglingGreat.

I confirm this is a bug.

@albertvillanova I would like to take a shot at this if you aren't working on it currently. Let me know!
Docs are not generated when a parameter defaults to a NamedSplit value
State: closed | Author: albertvillanova (MEMBER)
Labels: name: maintenance

While generating the docs, we get an error when some parameter defaults to a `NamedSplit` value, like:
```python
def call_function(split=Split.TRAIN):
    ...
```
The error is: ValueError: Equality not supported between split train and <class 'inspect._empty'>

See: https://github.com/huggingface/datasets/actions/runs/9869660902/job/27254359863?pr=7015
```
Building the MDX files:  97%|█████████▋| 58/60 [00:00<00:00, 91.94it/s] 
Traceback (most recent call last):
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 197, in build_mdx_files
    content, new_anchors, source_files, errors = resolve_autodoc(
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 123, in resolve_autodoc
    doc = autodoc(
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 499, in autodoc
    method_doc, check = document_object(
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 395, in document_object
    signature = format_signature(obj)
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 126, in format_signature
    if param.default != inspect._empty:
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 136, in __ne__
    return not self.__eq__(other)
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 379, in __eq__
    raise ValueError(f"Equality not supported between split {self} and {other}")
ValueError: Equality not supported between split train and <class 'inspect._empty'>

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/runner/work/datasets/datasets/.venv/bin/doc-builder", line 8, in <module>
    sys.exit(main())
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/doc_builder_cli.py", line 47, in main
    args.func(args)
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/build.py", line 102, in build_command
    build_doc(
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 367, in build_doc
    anchors_mapping, source_files_mapping = build_mdx_files(
  File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 230, in build_mdx_files
    raise type(e)(f"There was an error when converting {file} to the MDX format.\n" + e.args[0]) from e
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Equality not supported between split train and <class 'inspect._empty'>
```
`from_generator` does not allow to specify the split name
State: closed | Author: pminervini (CONTRIBUTOR)

### Describe the bug

I'm building train, dev, and test using `from_generator`; however, in all three cases, the logger prints `Generating train split:`
It's not possible to change the split name since it seems to be hardcoded: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/generator/generator.py

### Steps to reproduce the bug

```
In [1]: from datasets import Dataset

In [2]: def gen():
   ...:     yield {"pokemon": "bulbasaur", "type": "grass"}
   ...: 

In [3]: ds = Dataset.from_generator(gen)
Generating train split: 1 examples [00:00, 133.89 examples/s]
```

### Expected behavior

It should be possible to specify any split name

### Environment info

- `datasets` version: 2.19.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- `huggingface_hub` version: 0.23.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0

Comments: Thanks for reporting, @pminervini.

I agree we should give the option to define the split name.

Indeed, there is a PR that addresses precisely this issue:
- #7015

I am reviewing it.

Booom! thank you guys :)
Register `.zstd` extension for zstd-compressed files
State: closed | Author: polinaeterna (CONTRIBUTOR)

For example, https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0 dataset files have `.zstd` extension which is currently ignored (only `.zst` is registered).

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7032). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@albertvillanova hm I don't know tbh, it's just that "mlfoundations/dclm-baseline-1.0" dataset contains [files](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0/tree/main/global-shard_01_of_10/local-shard_0_of_10) with this extension and these files seem to be valid 

not sure why CI is failing but seems to be unrelated to this pr? can I merge @lhoestq @albertvillanova ?

yes you can merge, the CI failure is unrelated (surely an issue with hub-ci)

ah why not, you could try opening a PR

btw there is a channel with them at (internal) https://app.slack.com/client/T1RCG4490/C079AKTV11P if you want to let them know

@lhoestq, your previous comment was addressed to me or Polina?

@polinaeterna let me know if it is OK for you.

I opened https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0/discussions/7

Should we close this PR then?
Add option to disable progress bar when reading a dataset ("Loading dataset from disk")
State: closed | Author: yuvalkirstain (NONE)
Labels: name: enhancement

### Feature request

Add an option in load_from_disk to disable the progress bar even if the number of files is larger than 16.

### Motivation

I am reading a lot of datasets that it creates lots of logs.

<img width="1432" alt="image" src="https://github.com/huggingface/datasets/assets/57996478/8d4bbf03-6b89-44b6-937c-932f01b4eb2a">


### Your contribution

Seems like an easy fix to make.  I can create a PR if necessary.

Comments: You can disable progress bars for all of `datasets` with `disable_progress_bars`. [Link](https://huggingface.co/docs/datasets/en/package_reference/utilities#datasets.enable_progress_bars)

So you could do something like:

```python
from datasets import load_from_disk, enable_progress_bars, disable_progress_bars

disable_progress_bars()
# Your code
load_from_disk(....)

enable_progress_bars()
```


Thank you! Closing the issue.
load_dataset on AWS lambda throws OSError(30, 'Read-only file system') error
State: open | Author: sugam-nexusflow (NONE)

### Describe the bug

I'm using AWS lambda to run a python application. I run the `load_dataset` function with cache_dir="/tmp" and is still throws the OSError(30, 'Read-only file system') error. Is even updated all the HF envs to point to /tmp dir but the issue still persists. I can confirm that the I can write to /tmp directory. 

### Steps to reproduce the bug

```python
d = load_dataset(
      path=hugging_face_link,
      split=split,
      token=token,
      cache_dir="/tmp/hugging_face_cache",
 )
```

### Expected behavior

Everything written to the file system as part of the load_datasets function should be in the /tmp directory. 

### Environment info

datasets version: 2.16.1
Platform: Linux-5.10.216-225.855.amzn2.x86_64-x86_64-with-glibc2.26
Python version: 3.11.9
huggingface_hub version: 0.19.4
PyArrow version: 16.1.0
Pandas version: 2.2.2
fsspec version: 2023.10.0

Comments: hi ! can you share the full stack trace ? this should help locate what files is not written in the cache_dir
Streaming dataset not returning data
State: open | Author: johnwee1 (NONE)

### Describe the bug

I'm deciding to post here because I'm still not sure what the issue is, or if I am using IterableDatasets wrongly.
I'm following the guide on here https://huggingface.co/learn/cookbook/en/fine_tuning_code_llm_on_single_gpu pretty much to a tee and have verified that it works when I'm fine-tuning on the provided dataset.

However, I'm doing some data preprocessing steps (filtering out entries), when I try to swap out the dataset for mine, it fails to train. However, I eventually fixed this by simply setting `stream=False` in `load_dataset`.

Coud this be some sort of network / firewall issue I'm facing?

### Steps to reproduce the bug

I made a post with greater description about how I reproduced this problem before I found my workaround: https://discuss.huggingface.co/t/problem-with-custom-iterator-of-streaming-dataset-not-returning-anything/94551

Here is the problematic dataset snippet, which works when streaming=False (and with buffer keyword removed from shuffle)

```
commitpackft = load_dataset(
    "chargoddard/commitpack-ft-instruct", split="train", streaming=True
).filter(lambda example: example["language"] == "Python")
 
 
def form_template(example):
    """Forms a template for each example following the alpaca format for CommitPack"""
    example["content"] = (
        "### Human: " + example["instruction"] + " " + example["input"] + " ### Assistant: " + example["output"]
    )
    return example
 
 
dataset = commitpackft.map(
    form_template,
    remove_columns=["id", "language", "license", "instruction", "input", "output"],
).shuffle(
    seed=42, buffer_size=10000
)  # remove everything since its all inside "content" now
validation_data = dataset.take(4000)
train_data = dataset.skip(4000)
```

The annoying part about this is that it only fails during training and I don't know when it will fail, except that it always fails during evaluation.

### Expected behavior

The expected behavior is that I should be able to get something from the iterator when called instead of getting nothing / stuck in a loop somewhere.

### Environment info

- `datasets` version: 2.20.0
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.31
- Python version: 3.11.7
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
Casting list array to fixed size list raises error
State: closed | Author: albertvillanova (MEMBER)
Labels: name: bug

When trying to cast a list array to fixed size list, an AttributeError is raised:
> AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'

Steps to reproduce the bug:
```python
import pyarrow as pa
from datasets.table import array_cast

arr = pa.array([[0, 1]])
array_cast(arr, pa.list_(pa.int64(), 2))
```

Stack trace:
```
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-12-6cb90a1d8216> in <module>
      3 
      4 arr = pa.array([[0, 1]])
----> 5 array_cast(arr, pa.list_(pa.int64(), 2))

~/huggingface/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
   1802             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
   1803         else:
-> 1804             return func(array, *args, **kwargs)
   1805 
   1806     return wrapper

~/huggingface/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str)
   1920                 else:
   1921                     array_values = array.values[
-> 1922                         array.offset * pa_type.length : (array.offset + len(array)) * pa_type.length
   1923                     ]
   1924                     return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size)

AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
```
`load_dataset` fails to load dataset saved by `save_to_disk`
State: open | Author: sliedes (NONE)

### Describe the bug

This code fails to load the dataset it just saved:

```python
from datasets import load_dataset
from transformers import AutoTokenizer

MODEL = "google-bert/bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(MODEL)

dataset = load_dataset("yelp_review_full")

def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets.save_to_disk("dataset")

tokenized_datasets = load_dataset("dataset/")  # raises
```

It raises `ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}`.

I believe this bug is caused by the [logic that tries to infer dataset format](https://github.com/huggingface/datasets/blob/9af8dd3de7626183a9a9ec8973cebc672d690400/src/datasets/load.py#L556). It counts the most common file extension. However, a small dataset can fit in a single `.arrow` file and have two JSON metadata files, causing the format to be inferred as JSON:

```shell
$ ls -l dataset/test
-rw-r--r-- 1 sliedes sliedes 191498784 Jul  1 13:55 data-00000-of-00001.arrow
-rw-r--r-- 1 sliedes sliedes      1730 Jul  1 13:55 dataset_info.json
-rw-r--r-- 1 sliedes sliedes       249 Jul  1 13:55 state.json
```

### Steps to reproduce the bug

Execute the code above.

### Expected behavior

The dataset is loaded successfully.

### Environment info

- `datasets` version: 2.20.0
- Platform: Linux-6.9.3-arch1-1-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0


Comments: In my case the error was:
```
ValueError: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.
```
Did you try `load_from_disk`?

More generally, any reason there is no API consistency between save_to_disk and push_to_hub ? 

Would be nice to be able to save_to_disk and then upload manually to the hub and load_dataset (which works in some situations but not all)...

I have the exact same problem !

`load_from_disk` managed to load the dataset, but the bug with `load_dataset` needs to be fixed. 

any update ? I need some function from load dataset like num_proc, or streaming to optimize ram, ... but got this error
`drop_duplicates` method
State: open | Author: MohamedAliRashad (NONE)
Labels: name: duplicate

name: enhancement

### Feature request

`drop_duplicates` method for huggingface datasets (similiar in simplicity to the `pandas` one)

### Motivation

Ease of use

### Your contribution

I don't think i am good enough to help

Comments: There is an open issue #2514 about this which also proposes solutions.
CI is broken for faiss tests on Windows: node down: Not properly terminated
State: closed | Author: albertvillanova (MEMBER)
Labels: name: maintenance

Faiss tests on Windows make the CI run indefinitely until maximum execution time (360 minutes) is reached.

See: https://github.com/huggingface/datasets/actions/runs/9712659783
```
test (integration, windows-latest, deps-minimum)
The job running on runner GitHub Actions 60 has exceeded the maximum execution time of 360 minutes.
test (integration, windows-latest, deps-latest)
The job running on runner GitHub Actions 238 has exceeded the maximum execution time of 360 minutes.
```
```
____________________________ tests/test_search.py _____________________________
[gw1] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw1' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
____________________________ tests/test_search.py _____________________________
[gw2] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw2' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
```
```
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 
[gw0] node down: Not properly terminated
[gw0] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 

replacing crashed worker gw0

tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 
[gw1] node down: Not properly terminated
[gw1] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 

replacing crashed worker gw1

tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 
[gw2] node down: Not properly terminated
[gw2] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 

replacing crashed worker gw2
```
Re-enable raising error from huggingface-hub FutureWarning in CI
State: closed | Author: albertvillanova (MEMBER)
Labels: name: maintenance

Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR:
- #6876

Note that this can only be done once transformers releases the fix:
- https://github.com/huggingface/transformers/pull/31007
CI is broken after ruff-0.5.0: E721
State: closed | Author: albertvillanova (MEMBER)
Labels: name: maintenance

After ruff-0.5.0 release (https://github.com/astral-sh/ruff/releases/tag/0.5.0), our CI is broken due to E721 rule. 

See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983
> src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks