Minimartzz/github-issues huggingface dataset:
Fix None issue with Sequence of dict
State: closed

`Features.encode_example` currently fails if it contains a sequence if dict like `Sequence({"subcolumn": Value("int32")})` and if `None` is passed instead of the dict.

```python
  File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/features/features.py", line 1310, in encode_example
    return encode_nested_example(self, example)
  File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/features/features.py", line 973, in encode_nested_example
    return {k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in zip_dict(schema, obj)}
  File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/features/features.py", line 973, in <dictcomp>
    return {k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in zip_dict(schema, obj)}
  File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/features/features.py", line 998, in encode_nested_example
    for k, (sub_schema, sub_objs) in zip_dict(schema.feature, obj):
  File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/utils/py_utils.py", line 207, in zip_dict
    yield key, tuple(d[key] for d in dicts)
  File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/utils/py_utils.py", line 207, in <genexpr>
    yield key, tuple(d[key] for d in dicts)
TypeError: 'NoneType' object is not subscriptable
```

I fixed this issue and updated the tests (this case was missing in the tests)

Comments:
_The documentation is not available anymore as the PR was closed or merged._

Merging since I'd like do do a patch release soon for this one
AMI load_dataset error: sndfile library not found
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
Getting error message when loading AMI dataset.

## Steps to reproduce the bug
`python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"

`

## Expected results
A clear and concise description of the expected results.

## Actual results
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
    use_auth_token=use_auth_token,
  File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
    ) from None
OSError: Cannot find data file. 
Original error:
sndfile library not found

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0


Comments:
Issue unresolved, see [4000](https://github.com/huggingface/datasets/issues/4009#issue-1179658611)
set_format does not work with multi dimension tensor
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
set_format only transforms the last dimension of a multi-dimension list to tensor

## Steps to reproduce the bug
```python
import torch
from datasets import Dataset

ds = Dataset.from_dict({"A": [torch.rand((2, 2))]})
# ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]})  # => same result
ds = ds.with_format("torch")
print(ds[0])
```

## Expected results
```
{'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]}
```

## Actual results
```
{'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]}
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- datasets version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0

Comments:
Hi! Use the `ArrayXD` feature type (where X is the number of dimensions) to get correctly formated tensors. So in your case, define the dataset as follows :
```python
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}, features=Features({"A": Array2D(shape=(2, 2), dtype="float32")}))
```


Hi @mariosasko I'm facing the same issue and the only work around I've found so far is to convert my `DatasetDict` to a dictionary and then create new objects with `Dataset.from_dict`.
```
dataset = load_dataset("my_dataset.py")
dataset = dataset.map(lambda example: blabla(example))
dict_dataset_test = dataset["test"].to_dict()
...
dataset_test = Dataset.from_dict(dict_dataset_test, features=Features(features))
```
However, converting a `Dataset` object to a dict takes quite a lot of time and memory... Is there a way to directly create an `Array2D` without having to transform the original `Dataset` to a dict?

Hi! Yes, you can directly pass the `Features` dictionary as `features` in `map` to cast the column to `Array2D`:
```python
dataset = dataset.map(lambda example: blabla(example), features=Features(features))
```
Or you can use `cast` after `map` to do that:
```python
dataset = dataset.map(lambda example: blabla(example))
dataset = dataset.cast(Features(features))
```

Fantastic thank you @mariosasko
The first option you suggested is indeed way faster 😃
Use audio feature in ASR task template
State: closed

The AutomaticSpeechRecognition task template is outdated: it still uses the file path column as input instead of the audio column.

I changed that and updated all the datasets as well as the tests.

The only community dataset that will need to be updated is `facebook/multilingual_librispeech`. It has almost zero usage unfortunately (probably because users load the duplicate `multilingual_librispeech` directly instead), but it means we can update it.

(this makes me think that we should deprecate `multilingual_librispeech` it and redirect users to `facebook/multilingual_librispeech`).

This PR is also useful for the AudioFolder in https://github.com/huggingface/datasets/pull/3963

Comments:
_The documentation is not available anymore as the PR was closed or merged._
How to use generate this multitask dataset for SQUAD? I am getting a value error. 
State: closed

## Dataset viewer issue for 'squad_multitask*'

**Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask

*short description of the issue*

I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine. 
I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format')
Error:
Status code:   400
Exception:     TypeError
Message:       argument of type 'Value' is not iterable

Kindly advice. 


Comments:
Hi! Replacing `nlp.<obj>` with `datasets.<obj>` in the script should fix the problem. `nlp` has been renamed to `datasets` more than a year ago, so please use `datasets` instead to avoid weird issues.

Thank You! Was able to solve with the help of this.

But I request you to please fix the same in the dataset hub explorer as well...

May I ask how to get this dataset?
Fix Audio.encode_example() when writing an array
State: closed

Closes  #3996

Comments:
_The documentation is not available anymore as the PR was closed or merged._

@albertvillanova do you think [this line](https://github.com/huggingface/datasets/pull/3998/files#diff-58e348f6e4deaa5f3119e420a5d48ebb82875a78c28628831748fb54f59b2c78R67) is enough? that's why we missed this bug, we didn't check this case
Sync Features dictionaries
State: closed

This PR adds a wrapper to the `Features` class to keep the secondary dict, `_column_requires_decoding`, aligned with the main dict (as discussed in https://github.com/huggingface/datasets/pull/3723#discussion_r806912731).

A more elegant approach would be to subclass `UserDict` and override `__setitem__` and `__delitem__`, but this PR doesn't implement it for the following reasons:
* it requires replacing all occurrences of `isinstance(obj, dict)` with `isinstance(obj, Mapping)`, which is five times slower than `isinstance(obj, dict)` on my machine, in `features.py`
* is a breaking change, i.e., `isinstance(Features(...), dict)` would return `False` after it
* IMO, it makes sense to be consistent in the user-facing API and subclass either `dict` or `UserDict`. The problem with the latter is that it can't be used for `DatasetDict` because `DatasetDict` exposes the `data` property, which is also used by `UserDict`, so this would result in a collision.



Comments:
_The documentation is not available anymore as the PR was closed or merged._
Close `PIL.Image` file handler in `Image.decode_example`
State: closed

Closes the file handler of the PIL image object in `Image.decode_example` to avoid the `Too many open files` error.

To pass [the image equality checks](https://app.circleci.com/pipelines/github/huggingface/datasets/10774/workflows/d56670e6-16bb-4c64-b601-a152c5acf5ed/jobs/65825) in CI, `Image.decode_example` calls `image.load()` regardless of how the image object is created (not only for the `PIL.Image.open(local_path)` case). This is needed because `load()` sets the `readonly` attribute of a `PIL.Image` object to 0 (it's 1 after `PIL.Image.open(file_like)`), and in the older PIL versions (only fixed on main), that attribute is considered in `PIL.Image.__eq__`. More info can be found here: https://github.com/python-pillow/Pillow/issues/5926.

Fix #3985 





Comments:
_The documentation is not available anymore as the PR was closed or merged._
Streaming dataset + interleave + DataLoader hangs with multiple workers
State: open
Labels: description: Something isn't working
name: bug

## Describe the bug
Interleaving multiple iterable datasets that use `load_dataset` on streaming mode hangs when passed to `torch.utils.data.DataLoader` with multiple workers.

## Steps to reproduce the bug
```python
from datasets import interleave_datasets, load_dataset
from torch.utils.data import DataLoader

en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
fr_dataset = load_dataset('oscar', "unshuffled_deduplicated_fr", split='train', streaming=True)
it_dataset = load_dataset('oscar', "unshuffled_deduplicated_it", split='train', streaming=True)
de_dataset = load_dataset('oscar', "unshuffled_deduplicated_de", split='train', streaming=True)
multilingual_dataset = interleave_datasets([en_dataset, fr_dataset, de_dataset, it_dataset])
multilingual_dataset = multilingual_dataset.with_format('torch')

next(iter(multilingual_dataset))  # works fairly fast

dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=4)
for batch in dataloader:
    print(len(batch))  # prints nothing after 30 min of waiting

dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=0)
for batch in dataloader:
    print(len(batch))  # prints right away

```

## Expected results
It should be able to iterate the dataset with multiple workers.

## Actual results
Prints with results with `next(iter(multilingual_dataset)) ` and `num_workers=0`  but it prints nothing with `num_workers=4` or any number above 0.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.1.dev0
- `pytorch` version: 1.10.0+cu113
- Python version: 3.7
- PyArrow version: 6.0.1


Comments:
Same thing occurs when streaming files loaded from disk.

Hi ! Thanks for reporting, could this be related to https://github.com/huggingface/datasets/issues/3950 ?

Currently streaming datasets only works in single process, but we're working on having in work in distributed setups as well :) (EDIT: done)

Hi, thanks for your reply. It seems related :)

+1

Please update `datasets` if you're having this issue. What version are you using ?
Image column is not decoded in map when using with with_transform
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
Image column is not _decoded_ in **map** when using with `with_transform`

## Steps to reproduce the bug
```python
from datasets import Image, Dataset

def add_C(batch):
    batch["C"] = batch["A"]
    return batch

ds = Dataset.from_dict({"A": ["image.png"]}).cast_column("A",  Image())
ds = ds.with_transform(lambda x: x)  # <= This line causes the problem
ds = ds.map(add_C, batched=True)
print(ds[0])
```

## Expected results
```
{'C': <PIL.PngImagePlugin.PngImageFile>,  ...}
```

## Actual results
```
{'C': {'bytes': None, 'path': 'image.png'}, ...}
```

If we remove the `with_transform` line, we get the expected result.

## Environment info
- `datasets` version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0


Comments:
Hi! This behavior stems from this line: https://github.com/huggingface/datasets/blob/799b817d97590ddc97cbd38d07469403e030de8c/src/datasets/arrow_dataset.py#L1919
Basically, the `Image`/`Audio` columns are decoded only if the `format_type` attribute is `None` (`set_format`/`with_format` and `set_transform`/`with_transform` assign a non-`None` value to it) and the  `input_columns` param is not specified (see https://github.com/huggingface/datasets/issues/3756). We will remove these limitations soon.
Add Lung Image Database Consortium image collection (LIDC-IDRI) dataset
State: open
Labels: description: Requesting to add a new dataset
name: dataset request

description: Vision datasets
name: vision

## Adding a Dataset

- **Name:** *Lung Image Database Consortium image collection (LIDC-IDRI)*
- **Description:** *Consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.*
- **Data:** *[link to the Github repository or current dataset location](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)*
- **Motivation:** *Key dataset in the healthcare community*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

FYI @osanseviero @abidlabs 

Comments:
Improve AutomaticSpeechRecognition task template
State: closed
Labels: description: New feature or request
name: enhancement

**Is your feature request related to a problem? Please describe.**
[AutomaticSpeechRecognition task template](https://github.com/huggingface/datasets/blob/master/src/datasets/tasks/automatic_speech_recognition.py) is outdated as it uses path to audiofile as an audio column instead of a Audio feature itself (I guess it's because Audio feature didn't exist at the time this template was created).

**Describe the solution you'd like**
Change audio columns from string path to Audio feature.


Comments:
There is an open PR to do that: #3364. I just haven't had time to finish it... 

> There is an open PR to do that: #3364. I just haven't had time to finish it...

😬 thanks...
Remove old wikipedia leftovers
State: closed

After updating Wikipedia dataset, remove old wikipedia leftovers from doc.


Comments:
_The documentation is not available anymore as the PR was closed or merged._

> This makes me think we shouldn't advise the use of load_dataset in dataset scripts, since it doesn't guarantee that the cache will work as expected (the cache directory is not set correctly, and the required disk space for downloaded files is not recorded)

@lhoestq, do you think it could be a good idea to add a comment in this script WARNING that using load_dataset in a script is not good practice and that people should avoid using that script as a template to create other scripts? 

good idea ! :)
[image feature] Too many files open error when image feature is returned as a path
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug

PR in context: #3967. If I load the dataset in this PR (TextVQA), and do a simple list comprehension on the dataset, I get `Too many open files error`. This is happening due to the way we are loading the image feature when a str path is returned from the `_generate_examples`. Specifically at https://github.com/huggingface/datasets/blob/508eb4ab5d52f590baa677b4f64b1cc069139f7b/src/datasets/features/image.py#L110, we are open the file handle to the image but never closing it. This in my understanding is causing the issue.

## Steps to reproduce the bug
Pull the PR locally and run the following code
```python
from datasets import load_dataset

dataset = load_dataset("./datasets/textvqa")["train"]
data = [item for item in dataset]

# Error happens
```

## Expected results

List comprehension should work smoothly

## Actual results

`Too many open files error`

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.1.dev0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.10.0
- PyArrow version: 7.0.0
- Pandas version: 1.4.1


Comments:
Exclude Google Drive tests of the CI
State: closed

These tests make the CI spam the Google Drive API, the CI now gets banned by Google Drive very often.

I think we can just skip these tests from the CI for now.

In the future we could have a CI job that runs only once a day or once a week for such cases

cc @albertvillanova @mariosasko @severo 

Close #3415

![image](https://user-images.githubusercontent.com/42851186/159283608-fdeca1ac-b57f-4fa3-bf09-6fa5361c494f.png)


Comments:
_The documentation is not available anymore as the PR was closed or merged._

I was thinking exactly the same: running unit tests that request continuously a third-party API is not a good idea.
Add tip on how to speed up loading with ImageFolder
State: closed

This PR does two things:
* adds a tip on how to speed up loading of a large number of files with ImageFolder (motivated by [this issue](https://github.com/huggingface/datasets/issues/3960))
* replaces the current references to the `Dataset` methods in the Image Processing doc with their fully qualified counterparts (to align it with the Audio Processing doc)

cc @stevhliu 

Comments:
_The documentation is not available anymore as the PR was closed or merged._

Thanks for adding that tip! 👍 

For the docs syntax, it might be better if we hide the package name/full path to the class or function and only show the name of it. I think it's easier for users to read the function name (eg,`cast_column`) instead of the full path which can be a bit lengthy for some functions like `datasets.IterableDataset.remove_columns` (and if we like this idea, we can align the rest of the docs on it). 

> For the docs syntax, it might be better if we hide the package name/full path to the class or function and only show the name of it. I think it's easier for users to read the function name (eg,cast_column) instead of the full path which can be a bit lengthy for some functions like datasets.IterableDataset.remove_columns (and if we like this idea, we can align the rest of the docs on it).

That's also OK, as long as we are consistent.

@lhoestq @albertvillanova @polinaeterna Which one of these two styles do you prefer?

Agree on hiding `datasets` name. Not sure about hiding class name as it's anyway not visible for users if they use `Dataset.cast_column` or  `IterableDataset.cast_column` when working with their datasets. But I agree that the most important thing is to be consistent :)

Good points! :)

I think it'll be good to show the class name since some functions have different parameters. For example, if users click on `IterableDataset.map` and then `Dataset.map`, they'll see different parameters and have to figure out why (which isn't too difficult I guess lol). But showing the class name avoids any confusion upfront.
Fix google drive streaming for small files
State: closed

Google drive did another change recently, following #3787 #3843 .

In particular Google Drive now returns 403 for GET requests with `confirm=t` when a files doesn't have a virus warning message. I fixed this by passing `confirm=t` if and only if when there is one (i.e. when status code is 200 for HEAD)

Comments:
_The documentation is not available anymore as the PR was closed or merged._

Actually the CI fails because of this
![image](https://user-images.githubusercontent.com/42851186/159281771-78e611b1-6b04-4a87-8324-b6ba2d8c6a6a.png)

It looks like we can't have a proper way to test google drive in the CI right now. Though it seems to work locally if you're not banned. I think I'll just disable those tests for now

this fix will not be included?

No we can't do anything except stop using google drive when possible
Fix main classes reference in docs
State: closed

Currently the section index (on the page's right side) of the [main classes reference](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes) incorrectly displays `Tensor returned:`, this PR fixes this issue by wrapping code examples in this page with markdown code block.

There are other examples in datasets library having this issue.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3976). All of your documentation changes will be reflected on that endpoint.

Not sure why some section titles end with `[[datasets.xxx]]`, like this: https://moon-ci-docs.huggingface.co/docs/datasets/pr_3976/en/package_reference/main_classes#datasetdict[[datasets.datasetdict]]

Thanks ! I think this has been fixed already in https://github.com/huggingface/datasets/pull/3925 though

I'm closing this one then if it's fine for you
Update many missing tags to dataset README's
State: closed

I've started to go through the datasets available and noticed that there are 127 datasets that does not have all the tags so I started filling them in; starting with some of the most common and QA datasets

Not 100% certain that the task_id is correct for SuperGLUE

If anyone is browsing the issues and would like to help make Hugging face datasets even more feature complete and awesome, feel free to use this tool I wrote to find the missing tags in the [datacards](https://github.com/Hugging-Face-Supporter/datacards)

Comments: