noamaanMulla-03/datasets-issues huggingface dataset:
Update docs around audio and vision

As part of the strategy to center the docs around the different modalities, this PR updates the quickstart to include audio and vision examples. This improves the developer experience by making audio and vision content more discoverable, enabling users working in these modalities to also quickly get started without digging too deeply into the docs.

Other changes include:

- Moved the installation guide to the Get Started section because it should be part of a user's onboarding to the library before exploring tutorials or how-to's.
- Updated the native TF code at creating a `tf.data.Dataset` because it was throwing an error. The `to_tensor()` bit was redundant and removing it fixed the error (please double-check me here!).
- Added some UI components to the quickstart so it's easier for users to navigate directly to the relevant section with context about what to expect.
- Reverted to the code tabs for content that don't have any framework-specific text. I think this saves space compared to the code blocks. We'll still use the code blocks if the `torch` text is different from the `tf` text.

Let me know what you think, especially if we should include some code samples for training a model in the audio/vision sections. I left this out since we already showed it in the NLP section. I want to keep the focus on using Datasets to load and process a dataset, and not so much the training part. Maybe we can add links to the Transformers docs instead?

Labels: name: documentation

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

> Let me know what you think, especially if we should include some code samples for training a model in the audio/vision sections. I left this out since we already showed it in the NLP section. I want to keep the focus on using Datasets to load and process a dataset, and not so much the training part. Maybe we can add links to the Transformers docs instead?

We plan to address this with end-to-end examples (for each modality) more focused on preprocessing than the ones in the Transformers docs.
Update Google Cloud Storage documentation and add Azure Blob Storage example

While I was going through the 🤗 Datasets documentation of the Cloud storage filesystems at https://huggingface.co/docs/datasets/filesystems, I realized that the Google Cloud Storage documentation could be improved e.g. bullet point says "Load your dataset" when the actual call was to "Save your dataset", in-line code comment was mentioning "s3 bucket" instead of "gcs bucket", and some more in-line comments could be included.

Also, I think that mixing Google Cloud Storage documentation with AWS S3's one was a little bit confusing, so I moved all those to the end of the document under an h2 tab named "Other filesystems", with an h3 for "Google Cloud Storage".

Besides that, I was currently working with Azure Blob Storage and found out that the URL to [adlfs](https://github.com/fsspec/adlfs) was common for both filesystems Azure Blob Storage and Azure DataLake Storage, as well as the URL, which was updated even though the redirect was working fine, so I decided to group those under the same row in the column of supported filesystems.

And took also the change to add a small documentation entry as for Google Cloud Storage but for Azure Blob Storage, as I assume that AWS S3, GCP Cloud Storage, and Azure Blob Storage, are the most used cloud storage providers.

Let me know if you're OK with these changes, or whether you want me to roll back some of those! :hugs:

Labels: name: documentation

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

Hi @stevhliu, I've kept the `>>>` before all the in-line code comments as it was done like that in the default S3 example that was already there, I assume that it's done like that just for readiness, let me know whether we should remove the `>>>` in the Python blocks before the in-line code comments or keep them.

![image](https://user-images.githubusercontent.com/36760800/174254663-b68d28d2-eae1-40f3-8695-dc4b0c3b479a.png)


Comments are ignored by doctest, so I think we can remove the `>>>` :)

Cool I'll remove those now 👍🏻

Sure @lhoestq, I just kept that structure as that was the more similar one to the one that was already there, but we can go with that approach, just let me know whether I should change the headers so as to leave all those providers in the same level (`h2`). Thanks!
Fix WMT dataset loading issue and docs update

This PR is a fix for #4354 

Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets.

As I am on a M1 Mac, I am not able to create a virtual `dev` environment using `pip install -e ".[dev]"`. Issue is with `tensorflow-text` not supported on M1s and there is no supporting repo by Apple or Google. So, if I was needed to perform local testing, I am not able to do that.

Let me know, if any additional changes are required.

Thanks


The PR branch now has some commits unrelated to the changes, probably due to rebasing. Can you please close this PR and open a new one from a new branch? You can use  `git cherry-pick` to preserve the relevant changes:
```bash
git checkout master
git remote add upstream git@github.com:huggingface/datasets.git
git pull --ff-only upstream master
git checkout -b wmt-datasets-fix2
git cherry-pick f2d6c995d5153131168f64fc60fe33a7813739a4 a9fdead5f435aeb88c237600be28eb8d4fde4c55
```

Closing this PR due to unwanted commit changes. Will be opening new PR for the same issue.
Dataset Viewer issue for conll2003

### Link

https://huggingface.co/datasets/conll2003/viewer/conll2003/test

### Description

Seems like a cache problem with this config / split:

```
Server error
Status code:   400
Exception:     FileNotFoundError
Message:       [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/conll2003/__init__.py'
```

### Owner

No

Labels: name: dataset-viewer

Fixed, thanks.
Dataset Viewer issue for xtreme

### Link

https://huggingface.co/datasets/xtreme/viewer/PAN-X.de/test

### Description

There seems to be a problem with the cache of this config / split:

```
Server error
Status code:   400
Exception:     FileNotFoundError
Message:       [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/xtreme/349258adc25bb45e47de193222f95e68a44f7a7ab53c4283b3f007208a11bf7e/xtreme.py'
```

### Owner

No

Labels: name: dataset-viewer

Fixed, thanks.
Tell users to upload on the hub directly

As noted in https://github.com/huggingface/datasets/pull/4534, it is still not clear that it is recommended to add datasets on the Hugging Face Hub directly instead of GitHub, so I updated some docs.

Moreover since users won't be able to get reviews from us on the Hub, I added a paragraph to tell users that they can open a discussion and tag `datasets` maintainers for reviews.

Finally I removed the _previous good reasons_ to add a dataset on GitHub to only keep this one:

> In some rare cases it makes more sense to open a PR on GitHub. For example when you are not the author of the dataset and there is no clear organization / namespace that you can put the dataset under.

Does it sound good to you @albertvillanova @julien-c ?


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

Thanks ! I updated the two remaining files
Dataset Viewer issue for sst2

### Link

https://huggingface.co/datasets/sst2

### Description

Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):

```
Status code:   400
Exception:     Exception
Message:       Give up after 5 attempts with ConnectionError
```

### Owner

No

Labels: name: dataset-viewer

Hi @lewtun, thanks for reporting.

I have checked locally and refreshed the preview and it seems working smooth now:
```python
In [8]: ds
Out[8]: 
DatasetDict({
    train: Dataset({
        features: ['idx', 'sentence', 'label'],
        num_rows: 67349
    })
    validation: Dataset({
        features: ['idx', 'sentence', 'label'],
        num_rows: 872
    })
    test: Dataset({
        features: ['idx', 'sentence', 'label'],
        num_rows: 1821
    })
})
```

Could you confirm? 

Thanks @albertvillanova - it is indeed working now (not sure what caused the error in the first place). Closing this :)
Dataset Viewer issue for allocine

### Link

https://huggingface.co/datasets/allocine

### Description

Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:

```
Status code:   400
Exception:     AttributeError
Message:       'TarContainedFile' object has no attribute 'readable'
```

### Owner

No

Labels: name: dataset-viewer

I removed my assignment as @huggingface/datasets should be able to answer better than me


Let me have a look...

Thanks for the quick fix @albertvillanova 

Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content *sequentially* (no random access).

> Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content _sequentially_ (no random access).

Ah thanks for the clarification! I'll look out for this next time and implement the fix myself :)
Dataset Viewer issue for Pile of Law

### Link

https://huggingface.co/datasets/pile-of-law/pile-of-law

### Description

Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?

Thanks so much! 

### Owner

Yes

Labels: name: dataset-viewer

Hi @Breakend, yes – we'll propose a solution today

Thanks so much, I appreciate it!

Thanks so much for adding the docs. I was able to successfully hide the viewer using the 
```
viewer: false
```
flag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks again!

Awesome! Thanks for confirming. cc @severo 

Just for the record:

- the doc
  
<img width="1430" alt="Capture d’écran 2022-06-27 à 09 29 27" src="https://user-images.githubusercontent.com/1676121/175884089-bca6c0d5-6387-473e-98ca-86a910ede4bd.png">

- the dataset main page

<img width="1134" alt="Capture d’écran 2022-06-27 à 09 29 05" src="https://user-images.githubusercontent.com/1676121/175884152-5f285bf0-3471-45de-927a-e141b00ebb33.png">

- the dataset viewer page

<img width="567" alt="Capture d’écran 2022-06-27 à 09 29 16" src="https://user-images.githubusercontent.com/1676121/175884191-ab6a297b-1c11-417e-bbde-0b7623278a79.png">
`dataset = dataset.map()` causes faiss index lost

## Describe the bug
assigning the resulted dataset to original dataset causes lost of the faiss index

## Steps to reproduce the bug
`my_dataset` is a regular loaded dataset. It's a part of a customed dataset structure
```python
self.dataset.add_faiss_index('embeddings')
self.dataset.list_indexes()
# ['embeddings']

dataset2 = my_dataset.map(
    lambda x: self._get_nearest_examples_batch(x['text']), batch=True
)

# the unexpected result:
dataset2.list_indexes()
# []

self.dataset.list_indexes()
# ['embeddings']
```

in case something wrong with my `_get_nearest_examples_batch()`, it's like this
```python
def _get_nearest_examples_batch(self, examples, k=5):
    queries = embed(examples)
    scores_batch, retrievals_batch = self.dataset.get_nearest_examples_batch(self.faiss_column, queries, k)
    return {
        'neighbors': [batch['text'] for batch in retrievals_batch],
        'scores': scores_batch
    }
```

## Expected results
`map` shouldn't drop the indexes, in another word, indexes should be carried to the generated dataset

## Actual results
map drops the indexes

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.8.12
- PyArrow version: 7.0.0


Labels: name: bug

Hi ! Indeed `map` is dropping the index right now, because one can create a dataset with more or fewer rows using `map` (and therefore the index might not be relevant anymore)

I guess we could check the resulting dataset length, and if the user hasn't changed the dataset size we could keep the index, what do you think ?

doing `.add_column("x",x_data)` also removes the index. the new column might be irrelevant to the index so I don't think it should drop. 

Minimal example

```python
from datasets import load_dataset
import numpy as np

data=load_dataset("ceyda/cats_vs_dogs_sample") #just a test dataset
data=data["train"]
embd_data=data.map(lambda x: {"emb":np.random.uniform(-1,0,50).astype(np.float32)})
embd_data.add_faiss_index(column="emb")
print(embd_data.list_indexes())
embd_data=embd_data.add_column("x",[0]*data.num_rows)
print(embd_data.list_indexes())
```

I agree `add_column` shouldn't drop the index indeed ! Is it something you'd like to contribute ? I think it's just a matter of copying the `self._indexes` dictionary to the output dataset
Add `batch_size` parameter when calling `add_faiss_index` and `add_faiss_index_from_external_arrays`

Currently, even though the `batch_size` when adding vectors to the FAISS index can be tweaked in `FaissIndex.add_vectors()`, the function `ArrowDataset.add_faiss_index` doesn't have either the parameter `batch_size` to be propagated to the nested `FaissIndex.add_vectors` function or `*args, **kwargs`, so on, this PR adds the `batch_size` parameter to both `ArrowDataset.add_faiss_index` and `ArrowDataset.add_faiss_index_from_external_arrays`.

This is useful so as to tweak the `batch_size` according to the VM specifications.


Also, I had a doubt while checking the code related to the indices... 

@lhoestq, there's a value in `config.py` named `DATASET_INDICES_FILENAME` which has the arrow extension (which I assume it should be `indices.faiss`, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an `ArrowDataset` in disk, but since those indices are never stored AFAIK, is that actually required?

https://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/config.py#L183

https://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/arrow_dataset.py#L1079-L1092

So should I also remove that?

P.S. I also edited the following code comment which I found misleading as it's not actually storing the indices.

https://github.com/huggingface/datasets/blob/8ddc4bbeb1e2bd307b21f5d21f884649aa2bf640/src/datasets/arrow_dataset.py#L1122

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

> @lhoestq, there's a value in config.py named DATASET_INDICES_FILENAME which has the arrow extension (which I assume it should be indices.faiss, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an ArrowDataset in disk, but since those indices are never stored AFAIK, is that actually required?

The arrow file is used to store an indices mapping (when you shuffle the dataset for example) - not for a faiss index ;)

Ok cool thanks a lot for the explanation @lhoestq I was not sure about that :+1: I'll also add it there as you suggested!

CI failures are unrelated to this PR and fixed on master, merging
Better DuplicateKeysError error to help the user debug the issue

As mentioned in https://github.com/huggingface/datasets/issues/2552 it would be nice to improve the error message when a dataset fails to build because there are duplicate example keys.

The current one is
```python
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys should be unique and deterministic in nature
```

and we could have something that guides the user to debugging the issue:
```python
DuplicateKeysError: both 42th and 1337th examples have the same keys `48`.
Please fix the dataset script at <path/to/the/dataset/script>
```

Labels: name: enhancement

name: good first issue

excuse me, my `datasets` version is `2.2.2`, but I also just see the error info like 
```
DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 0
Keys should be unique and deterministic in nature
```

Hi ! for which dataset do you have this error ?

Also note that this issue is just about improving the error message, which is not very friendly x)

@lhoestq  I would like to take a hit at improving the error message. Will open a draft PR and will reach out to you for review


> DuplicateKeysError: both 42th and 1337th examples have the same keys `48`.

@lhoestq  when you mention 42th and 1337th in the above case , are these values the examples' "id" or are they the examples' index ? 

Hi ! Thanks @VijayKalmath :)

In the general case, examples don't have an "id" field, so I think it should correspond to the index

@lhoestq  , I have opened a draft PR for this Issue. 

I wanted to check with you if there is a way to get `<path/to/the/dataset/script>` currently or do I need to add extra code to find that. 

If I need to find the script , I can assume that the generator function will always be in `datasets/{dataset_name}/{dataset_name}.py`. 

Thanks !

> I wanted to check with you if there is a way to get <path/to/the/dataset/script> currently or do I need to add extra code to find that.

You don't have access to this info inside the ArrowWriter unfortunately. This info is available in builder.py in the DatasetBuilder code that uses the ArrowWriter though, maybe a try-catch there can do the job
Make DuplicateKeysError more user friendly [For Issue #2556]

# What does this PR do?

## Summary

*DuplicateKeysError error does not provide any information regarding the examples which have the same the key.*

*This information is very helpful for debugging the dataset generator script.*

## Additions
-

## Changes
- Changed `DuplicateKeysError Class` in `src/datasets/keyhash.py` to add current index and duplicate_key_indices to error message.
- Changed `check_duplicate_keys` function in `src/datasets/arrow_writer.py` to find indices of examples with duplicate hash if duplicate keys are found.

## Deletions
-

## To do  : 
- [x] Find way to find and print path `<Path to Dataset>` in Error message 

## Issues Addressed : 

Fixes #2556  


> Nice thanks !
> 
> After your changes feel free to mark this PR as "ready for review" ;)

Marking PR ready for review.

@lhoestq Let me know if there is anything else required or if we are good to go ahead and merge.

_The documentation is not available anymore as the PR was closed or merged._
Properly raise FileNotFound even if the dataset is private

`tests/test_load.py::test_load_streaming_private_dataset` was failing because the hub now returns 401 when getting the HfApi.dataset_info of a dataset without authentication. `load_dataset` was raising ConnectionError, while it should be FileNoteFoundError since it first checks for local files before checking the Hub.

Moreover when use_auth_token is not set (default is False), we should not pass `token=None` to HfApi.dataset_info, or it will use the local token by default - instead it should use no token. It's currently not possible to ask for no token to be used, so as a workaround I simply set token="no-token"


_The documentation is not available anymore as the PR was closed or merged._
Fix hashing for python 3.9

In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function.

Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9

To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic.

Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now.

Fix https://github.com/huggingface/datasets/issues/4506


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

What do you think @albertvillanova ?
split cache used when processing different split

## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through

```
class myDataModule:

    def train_dataloader(self):
         ds = load_dataset('squad', split='train')
         ds = ds.map(some_function)
         return [ds]

    def val_dataloader(self):
         ds = load_dataset('squad', split="validation")
         ds = ds.map(some_function)
         return [ds]
```
I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue.

If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first!

Labels: name: bug

I was not able to reproduce this behavior (I tried without using pytorch lightning though, since I don't know what code you ran in pytorch lightning to get this).

If you can provide a MWE that would be perfect ! :)

Hi, I think the issue happened because I was loading datasets under an `if` ... `else` statement and the condition would change the dataset I would need to load but instead the cached one was always returned. However, I believe that is expected behaviour, if so I'll close the issue.

Otherwise I will try to provide a MWE
Dataset Viewer issue for multi_news

### Link

https://huggingface.co/datasets/multi_news

### Description

Not sure what the index error is referring to here:

```
Status code:   400
Exception:     IndexError
Message:       list index out of range
```

### Owner

No

Labels: name: dataset-viewer

Thanks for reporting, @lewtun.

I forced the refreshing of the preview and it worked OK for train and validation splits.

I guess the error has to do with the data files being hosted at Google Drive: this gives errors when requested automatically using scripts.
We should host them to fix the error. Let's see if the license allows that.

I guess we can host the data: https://github.com/Alex-Fabbri/Multi-News/blob/master/LICENSE.txt