josteece/github-issues huggingface dataset:
load_from_disk with remote filesystem fails due to a wrong temporary local folder path
Opened by bruno-hays (open)

## Describe the bug

The function load_from_disk fails when using a remote filesystem because of a wrong temporary path generation in the load_from_disk method of arrow_dataset.py:

```python
if is_remote_filesystem(fs):
    src_dataset_path = extract_path_from_uri(dataset_path)
    dataset_path = Dataset._build_local_temp_path(src_dataset_path)
    fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)
```
If _dataset_path_ is `gs://speech/mydataset/train`, then _src_dataset_path_ will be `speech/mydataset/train` and _dataset_path_ will be something like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train`

Then, after downloading the **folder** _src_dataset_path_, you will get a path like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train/train/state.json` (notice we have train twice)

Instead of downloading the remote folder we should be downloading all the files in the folder for the path to be right:

```python
fs.download(os.path.join(src_dataset_path,*), dataset_path.as_posix(), recursive=True)
```

## Steps to reproduce the bug
```python
fs = gcsfs.GCSFileSystem(**storage_options)
dataset = load_from_disk("common_voice_processed") # loading local dataset previously saved locally, works fine
dataset.save_to_disk(output_dir, fs=fs) #works fine
dataset = load_from_disk(output_dir, fs=fs) # crashes
```

## Expected results
The dataset is loaded

## Actual results

FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/9s/gf0b9jz15d517yrf7m3nvlxr0000gn/T/tmp6t5e221_/speech/datasets/tests/common_voice_processed/train/state.json'

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets-2.6.1.dev0
- Platform: mac os monterey 12.5.1
- Python version: 3.8.13
- PyArrow version:pyarrow==9.0.0


Comments:
Hi Hubert! Could you please probably create a publicly available `gs://` dataset link? I think this would be easier for others to directly start to debug.

What seems to work is to change the line to:
```
fs.download(src_dataset_path, dataset_path.parent.as_posix(), recursive=True)
```
Bug with filtered indices
Opened by albertvillanova (closed)

## Describe the bug
As reported by @PartiallyTyped (and by @Muennighoff):
- https://github.com/huggingface/datasets/issues/5111#issuecomment-1278652524

There is an issue with the indices of a filtered dataset.

## Steps to reproduce the bug
```python
ds = Dataset.from_dict({"num": [0, 1, 2, 3]})
ds = ds.filter(lambda num: num % 2 == 0, input_columns="num", batch_size=2)
assert all(item["num"] % 2 == 0 for item in ds)
```

## Expected results
The indices of the filtered dataset should correspond to the examples with "language" equals to "english".

## Actual results
Indices to items with other languages are included in the filtered dataset indices

## Preliminar investigation
It seems a bug introduced by:
- #5030


Comments:
The issue is here:
https://github.com/huggingface/datasets/blob/3ad9644b9a2e4558dd1d0f1e43c67658674e6228/src/datasets/arrow_dataset.py#L2964

@PartiallyTyped, @Muennighoff: the issue is fixed.

We are planning to make a patch release today.

Thanks a lot for the swift response! For a brief moment yesterday I thought I had gone insane 🤣On 14 Oct 2022, at 15:44, Albert Villanova del Moral ***@***.***> wrote:﻿
@PartiallyTyped, @Muennighoff: the issue is fixed.
We are planning to make a patch release today.

—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you were mentioned.Message ID: ***@***.***>
Map caching not working for some class methods
Opened by Mouhanedg56 (closed)

## Describe the bug
The cache loading is not working as expected for some class methods with a model stored in an attribute.
The new fingerprint for `_map_single` is not the same at each run. The hasher generate a different hash for the class method.
This comes from `dumps` function in `datasets.utils.py_utils` which generates a different dump at each run.

## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import AutoConfig, AutoModel, AutoTokenizer

dataset = load_dataset("ethos", "binary")
BASE_MODELNAME = "sentence-transformers/all-MiniLM-L6-v2"

class Object:
    def __init__(self):
        config = AutoConfig.from_pretrained(BASE_MODELNAME)
        self.bert = AutoModel.from_config(config=config, add_pooling_layer=False)
        self.tok = AutoTokenizer.from_pretrained(BASE_MODELNAME)

    def tokenize(self, examples):

        tokenized_texts = self.tok(
            examples["text"],
            padding="max_length",
            truncation=True,
            max_length=256,
        )
        return tokenized_texts

instance = Object()
result = dict()
for phase in ["train"]:
    result[phase] = dataset[phase].map(instance.tokenize, batched=True, load_from_cache_file=True, num_proc=2)

```

## Expected results
Load cache instead of recompute result.

## Actual results
Result recomputed from scratch at each run.
The cache works fine when deleting `bert` attribute.

## Environment info
- `datasets` version: 2.5.3.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.13
- PyArrow version: 7.0.0
- Pandas version: 1.5.0



Comments:
The hash used for caching is computed by pickling recursively the function passed to `map`. Maybe some objects don't have the same hash across sessions.  In particular you can check the hash of your model using
```python
from datasets.fingerprint import Hasher
obj = AutoModel.from_config(config=config, add_pooling_layer=False)
print(Hasher.hash(obj))
```

You can find mode info here: https://huggingface.co/docs/datasets/about_cache

You can also provide your own unique hash in `map` if you want, with the `new_fingerprint` argument

Indeed, the hash is changing. The `dumps` function serialize the model object in different ways because the model object is not deterministic
```python
from datasets.utils.py_utils import dumps
obj1 = AutoModel.from_config(config=config, add_pooling_layer=False)
obj2 = AutoModel.from_config(config=config, add_pooling_layer=False)

dumps(bert) == dumps(bert2). # False
```

> You can find mode info here: https://huggingface.co/docs/datasets/about_cache
> 
> You can also provide your own unique hash in map if you want, with the new_fingerprint argument


Thanks, the doc is so helpful. Indeed, we can fix the hash and get cache hit using `new_fingerprint`. Closing the issue.
Fix task template reload from dict
Opened by lhoestq (closed)

Since #4926 the JSON dumps are simplified and it made task template dicts empty by default.

I fixed this by always including the task name which is needed to reload a task from a dict

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

> Just wondering if there might be other data classes default values missed that could cause an issue... Apart from feature-like classes and tasks, I don't see any others though...

I think we're good ! `asdict` is used on the DatasetInfo attributes like features, tasks etc. and they all support dict conversion properly now

> And a question: but this information about the tasks is no longer being saved as YAML tags in the dataset card; won't be a problem with current datasets using task templates (with this information in their metadata JSON) once we replace the JSON by the YAML tags (which do not have this information about the task templates)?

In the long run we'll use the train_eval_index YAML tags instead, but I agree when removing the JSON files we should try to not break existing code that may rely on this
Error in create a dataset from a Python generator
Opened by yangxuhui (closed)

## Describe the bug
In HOW-TO-GUIDES > Load > [Python generator](https://huggingface.co/docs/datasets/v2.5.2/en/loading#python-generator), the code example defines the `my_gen` function, but when creating the dataset, an undefined `my_dict` is passed in.  

```Python
>>> from datasets import Dataset
>>> def my_gen():
...    for i in range(1, 4):
...        yield {"a": i}
>>> dataset = Dataset.from_generator(my_dict)
```

Comments:
Hi, thanks for reporting! The last line should be `dataset = Dataset.from_generator(my_gen)`.

Can I work on this one?
Classes label error when loading symbolic links using imagefolder
Opened by horizon86 (closed)

**Is your feature request related to a problem? Please describe.**
Like this: #4015
When there are **symbolic links** to pictures in the data folder, the parent folder name of the **real file** will be used as the class name instead of the parent folder of the symbolic link itself. Can you give an option to decide whether to enable symbolic link tracking?

This is inconsistent with the `torchvision.datasets.ImageFolder` behavior.

For example:
![image](https://user-images.githubusercontent.com/49552732/195008591-3cce644e-aabe-4f39-90b9-832861cadb3d.png)
![image](https://user-images.githubusercontent.com/49552732/195008841-0b0c2289-eb7f-411a-977b-37426f23a277.png)

It use `others` in green circle as class label but not `abnormal`, I wish `load_dataset` not use the real file parent as label.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.

**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.

**Additional context**
Add any other context about the feature request here.


Comments:
It can be solved temporarily  by remove `resolve` in 
https://github.com/huggingface/datasets/blob/bef23be3d9543b1ca2da87ab2f05070201044ddc/src/datasets/data_files.py#L278

Hi, thanks for reporting and suggesting a fix! We still need to account for `.`/`..` in the file path, so a more robust fix would be `Path(os.path.abspath(filepath))`.

> Hi, thanks for reporting and suggesting a fix! We still need to account for `.`/`..` in the file path, so a more robust fix would be `Path(os.path.abspath(filepath))`.

Thanks for your reply!
Mismatch between tutoriel and doc
Opened by clefourrier (closed)

## Describe the bug
In the "Process text data" tutorial, [`map` has `return_tensors` as kwarg](https://huggingface.co/docs/datasets/main/en/nlp_process#map). It does not seem to appear in the [function documentation](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.map), nor to work.

## Steps to reproduce the bug
MWE:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")

from datasets import load_dataset
dataset  = load_dataset("lhoestq/demo1", split="train")

dataset = dataset.map(lambda examples: tokenizer(examples["review"]), batched=True, return_tensors="pt")

```

## Expected results
return_tensors to be a valid kwarg :smiley: 

## Actual results
```python
>> TypeError: map() got an unexpected keyword argument 'return_tensors'
```

## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.14.0-1052-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3



Comments:
Hi, thanks for reporting! This line should be replaced with 
```python
dataset = dataset.map(lambda examples: tokenizer(examples["text"], return_tensors="np"), batched=True)
```
for it to work (the `return_tensors` part inside the `tokenizer` call).

Can I work on this?

Fixed in https://github.com/huggingface/datasets/pull/5095
Use HTML relative paths for tiles in the docs
Opened by lewtun (closed)

This PR replaces the absolute paths in the landing page tiles with relative ones so that one can test navigation both locally in and in future PRs (see [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5084/en/index) for an example PR where the links don't work).

I encountered this while working on the `optimum` docs and figured I'd fix it elsewhere too :)

Internal Slack thread: https://huggingface.slack.com/archives/C02GLJ5S0E9/p1665129710176619

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

> Good catch, @lewtun. Thanks for the fix.
> 
> Do you know if there are other absolute paths in the docs that should be fixed as well?

I found a few more in [0d4796b](https://github.com/huggingface/datasets/pull/5092/commits/0d4796b747e6620d9fcc17a8f74acc5cf4bba7be).

However, I noticed that none of the cross-references (e.g. to API classes / methods) work locally, but that is probably just a limitation of the local build

Thanks.
Allow connection objects in `from_sql` + small doc improvement
Opened by mariosasko (closed)

Allow connection objects in `from_sql` (emit a warning that they are cachable) and add a tip that explains the format of the con parameter when provided as a URI string.

PS: ~~This PR contains a parameter link, so https://github.com/huggingface/doc-builder/pull/311 needs to be merged before it's "ready for review".~~ Done!

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Review sync issues from GitHub to Hub
Opened by albertvillanova (closed)

## Describe the bug
We have discovered that sometimes there were sync issues between GitHub and Hub datasets, after a merge commit to main branch.

For example:
- this merge commit: https://github.com/huggingface/datasets/commit/d74a9e8e4bfff1fed03a4cab99180a841d7caf4b
- was not properly synced with the Hub: https://github.com/huggingface/datasets/actions/runs/3002495269/jobs/4819769684
```
[main 9e641de]  Add Papers with Code ID to scifact dataset (#4941)
 Author: Albert Villanova del Moral <albertvillanova@users.noreply.huggingface.co>
 1 file changed, 42 insertions(+), 14 deletions(-)
push failed !
GitCommandError(['git', 'push'], 1, b'remote: ----------------------------------------------------------        \nremote: Sorry, your push was rejected during YAML metadata verification:        \nremote: - Error: "license" does not match any of the allowed types        \nremote: ----------------------------------------------------------        \nremote: Please find the documentation at:        \nremote: https://huggingface.co/docs/hub/models-cards#model-card-metadata        \nremote: ----------------------------------------------------------        \nTo [https://huggingface.co/datasets/scifact.git\n](https://huggingface.co/datasets/scifact.git/n) ! [remote rejected] main -> main (pre-receive hook declined)\nerror: failed to push some refs to \'[https://huggingface.co/datasets/scifact.git\](https://huggingface.co/datasets/scifact.git/)'', b'')
```
We are reviewing sync issues in previous commits to recover them and repushing to the Hub.

TODO: Review
- [x] #4941
  - scifact
- [x] #4931
  - scifact
- [x] #4753
  - wikipedia 
- [x] #4554
  - wmt17, wmt19, wmt_t2t
  - Fixed with "Release 2.4.0" commit: https://github.com/huggingface/datasets/commit/401d4c4f9b9594cb6527c599c0e7a72ce1a0ea49
    - https://huggingface.co/datasets/wmt17/commit/5c0afa83fbbd3508ff7627c07f1b27756d1379ea
    - https://huggingface.co/datasets/wmt19/commit/b8ad5bf1960208a376a0ab20bc8eac9638f7b400
    - https://huggingface.co/datasets/wmt_t2t/commit/b6d67191804dd0933476fede36754a436b48d1fc
- [x] #4607
- [x] #4416
  - lccc
  - Fixed with "Release 2.3.0" commit: https://huggingface.co/datasets/lccc/commit/8b1f8cf425b5653a0a4357a53205aac82ce038d1
- [x] #4367



Comments:
Nice!!
Resume failed process
Opened by felix-schneider (open)

**Is your feature request related to a problem? Please describe.**
When a process (`map`, `filter`, etc.) crashes part-way through, you lose all progress.

**Describe the solution you'd like**
It would be good if the cache reflected the partial progress, so that after we restart the script, the process can restart where it left off.

**Describe alternatives you've considered**
Doing processing outside of `datasets`, by writing the dataset to json files and building a restart mechanism myself.

**Additional context**
N/A


Comments:
Filtering on an empty dataset returns a corrupted dataset.
Opened by gabegma (closed)

## Describe the bug
When filtering a dataset twice, where the first result is an empty dataset, the second dataset seems corrupted.

## Steps to reproduce the bug
```python
  datasets = load_dataset("glue", "sst2")
  dataset_split = datasets['validation']
  ds_filter_1 = dataset_split.filter(lambda x: False)  # Some filtering condition that leads to an empty dataset
  assert ds_filter_1.num_rows == 0
  sentences = ds_filter_1['sentence']
  assert len(sentences) == 0
  ds_filter_2 = ds_filter_1.filter(lambda x: False)  # Some other filtering condition
  assert ds_filter_2.num_rows == 0
  assert 'sentence' in ds_filter_2.column_names
  sentences = ds_filter_2['sentence']
```

## Expected results
The last line should be returning an empty list, same as 4 lines above.

## Actual results
The last line currently raises `IndexError: index out of bounds`.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-11.6.6-x86_64-i386-64bit
- Python version: 3.9.11
- PyArrow version: 7.0.0
- Pandas version: 1.4.1


Comments:
~~It seems like #5043 fix (merged recently) is the root cause of such behaviour. When we empty indices mapping (because the dataset length equals to zero), we can no longer get column item like: `ds_filter_2['sentence']` which uses
`ds_filter_1._indices.column(0)`~~

**UPDATE:**
Empty datasets are returned without going through partial function on `map` method, which will not work to get indices for `filter`: we need to run `get_indices_from_mask_function` partial function on the dataset to get output = `{"indices": []}`. But this is complicated since functions used in args, in particular `get_indices_from_mask_function`, do not support empty datasets.
We can just handle empty datasets aside on filter method.

#self-assign

Thank you for solving this amazingly quickly!
IterableDataset formatting in numpy/torch/tf/jax
Opened by lhoestq (closed)

This code now returns a numpy array:
```python
from datasets import load_dataset

ds = load_dataset("imagenet-1k", split="train", streaming=True).with_format("np")
print(next(iter(ds))["image"])
```

It also works with "arrow", "pandas", "torch", "tf" and "jax"

### Implementation details:

I'm using the existing code to format an Arrow Table to the right output format for simplicity.
Therefore it's probbaly not the most optimized approach.

For example to output PyTorch tensors it does this for every example:

python data -> arrow table -> numpy extracted data -> pytorch formatted data

### Releasing this feature

Even though I consider this as a bug/inconsistency, this change is a breaking change.
And I'm sure some users were relying on the torch iterable dataset to return PIL Image and used data collators to convert to pytorch.

So I guess this is `datasets` 3.0 ?

### TODO

- [x] merge https://github.com/huggingface/datasets/pull/5072
- [ ] docs
- [ ] tests

Close https://github.com/huggingface/datasets/issues/5083

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5084). All of your documentation changes will be reflected on that endpoint.

Actually I'm not happy with this implementation. It always require the iterable dataset to have definite `features`, which removes a lot of flexibility. So I think we need an actual formatting from python objects, not from arrow data.

Closing this one since it has too many conflicts and still require some work - it will be easier to open a new PR
Support numpy/torch/tf/jax formatting for IterableDataset
Opened by lhoestq (closed)

Right now `IterableDataset` doesn't do any formatting.

In particular this code should return a numpy array:
```python
from datasets import load_dataset

ds = load_dataset("imagenet-1k", split="train", streaming=True).with_format("np")
print(next(iter(ds))["image"])
```

Right now it returns a PIL.Image.

Setting `streaming=False` does return a numpy array after #5072 

Comments:
hii @lhoestq, can you assign this issue to me? Though i am new to open source still I would love to put my best foot forward. I can see there isn't anyone right now assigned to this issue.

Hi @zutarich ! This issue was fixed by #5852 - sorry I forgot to close it

Feel free to look for other issues and ping me or @mariosasko if you have questions :)
Also let us know if we can help find an issue that can correspond to what you're looking for
adding keep in memory
Opened by Mustapha-AJEGHRIR (closed)

Fixing #514 .

Hello @mariosasko 👋, I have implemented what you have recommanded to fix the keep in memory problem for shuffle on the issue #514 . 

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

Hi @mariosasko , I have added a test for the `keep_in_memory` version. I have also removed the `Compatible with temp_seed` part in the scope of `dset_shuffled`, please verify if that makes sense.
Use hfh for caching
Opened by albertvillanova (open)

## Is your feature request related to a problem?

As previously discussed in our meeting with @Wauplin and agreed on our last datasets team sync meeting, I'm investigating how `datasets` can use `hfh` for caching.

## Describe the solution you'd like

Due to the peculiarities of the `datasets` cache, I would propose adopting `hfh` caching system in stages.

First, we could easily start using `hfh` caching for:
- dataset Python scripts
- dataset READMEs
- dataset infos JSON files (now deprecated)

Second, we could also use `hfh` caching for data files downloaded from the Hub.

Further investigation is needed for:
- files downloaded from non-Hub hosts
- extracted files from downloaded archive/compressed files
- generated Arrow files

## Additional context

Docs about the `hfh` caching system:
- [Manage huggingface_hub cache-system](https://huggingface.co/docs/huggingface_hub/main/en/how-to-cache)
- [Cache-system reference](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/cache)

The `transformers` library has already adopted `hfh` for caching. See:
- huggingface/transformers#18438
- huggingface/transformers#18857
- huggingface/transformers#18966


Comments:
There is some discussion in https://github.com/huggingface/huggingface_hub/pull/1088 if it can help :)
refactor: replace AssertionError with more meaningful exceptions (#5074)
Opened by galbwe (closed)

Closes #5074

Replaces `AssertionError` in the following files with more descriptive exceptions:

- `src/datasets/arrow_reader.py`
- `src/datasets/builder.py`
- `src/datasets/utils/version.py`

The issue listed more files that needed to be fixed, but the rest of them were contained in the top-level `datasets` directory, which was removed when #4974 was merged

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Fix header level in Audio docs
Opened by stevhliu (closed)

Fixes header level so `Dataset features` is the doc title instead of `The Audio type`:

![Screen Shot 2022-10-05 at 1 22 02 PM](https://user-images.githubusercontent.com/59462357/194155840-eeb5d62f-f4eb-411e-b281-8494c5fffdce.png)

Comments:
_The documentation is not available anymore as the PR was closed or merged._
fix: update exception throw from OSError to EnvironmentError in `push…
Opened by rahulXs (closed)

Status:
Ready for review

Description of Changes:
Fixes #5075

Changes proposed in this pull request:
- Throw EnvironmentError instead of OSError in `push_to_hub` when the Hub token is not present.

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