AniketTec/github-issues huggingface dataset:
Title: Installing `datasets` on M1 computers
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
I wanted to install `datasets` dependencies on my M1 (in order to start contributing to the project). However, I got an error regarding `tensorflow`.

On M1, `tensorflow-macos` needs to be installed instead. Can we add a conditional requirement, so that `tensorflow-macos` would be installed on M1?

## Steps to reproduce the bug
Fresh clone this project (on m1), create a virtualenv and run this:
```python
pip install -e ".[dev]"
```

## Expected results
Installation should be smooth, and all the dependencies should be installed on M1.

## Actual results
You should receive an error, saying pip couldn't find a version that matches this pattern:
```
tensorflow>=2.3,!=2.6.0,!=2.6.1
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.6.2.dev0
- Platform: macOS-12.6-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.0



Comments:
Thanks for reporting, @david1542.
Title: Progress bars have color red and never completed to 100%
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
Progress bars after transformative operations turn in red and never be completed to 100%

## Steps to reproduce the bug
```python
from datasets import load_dataset

load_dataset('rotten_tomatoes', split='test').filter(lambda o: True)
```

## Expected results
Progress bar should be 100% and green

## Actual results
Progress  bar turn in red and never completed to 100%

## Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.14
- PyArrow version: 6.0.1
- Pandas version: 1.3.5

Comments:
Hi @echatzikyriakidis, thanks for submitting the issue.
Which shell are you using exactly? I tried to run the command you sent, but I don't see colors at all 🧐

I tried from bash and zsh as well.

Hi @david1542 ,

I use Google Colab.


Got it. I [created a PR](https://github.com/huggingface/datasets/pull/5120) that fixes this issue. Turns out that the wrapping logic for the inner loop was slightly incorrect.

Thank you!

Hello @mariosasko 

I am still facing this issue. Was this problem fixed?

![Screen Shot 2024-06-19 at 11 35 57 AM](https://github.com/huggingface/datasets/assets/66180831/cd9c6ac4-178c-4297-821c-a56411d34c89)

I cleared the hugging face cache before running, and no error message was given. Let me know if you need a minimal repro of my code.
Title: Fix iter_batches
State: closed

The `pa.Table.to_reader()` method available in `pyarrow>=8.0.0` may return chunks of size < `max_chunksize`, therefore `iter_batches` can return batches smaller than the `batch_size` specified by the user

Therefore batched `map` couldn't always use batches of the right size, e.g. this fails because it runs only on one batch of one element:
```python

from datasets import Dataset, concatenate_datasets

ds = concatenate_datasets([Dataset.from_dict({"a": [i]}) for i in range(10)])

ds2 = ds.map(lambda _: {}, batched=True)
assert list(ds2) == list(ds)
```

This was introduced in https://github.com/huggingface/datasets/pull/5030

Close https://github.com/huggingface/datasets/issues/5111

This will require a patch release along with https://github.com/huggingface/datasets/pull/5113

TODO:
- [x] fix tests
- [x] add more tests

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

I also ran the code in https://github.com/huggingface/datasets/issues/5111 and it works fine now :)

This is ready for review :)
Title: load_from_disk with remote filesystem fails due to a wrong temporary local folder path
State: open
Labels: description: Something isn't working
name: bug

## 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)
```
Title: Bug with filtered indices
State: closed
Labels: description: Something isn't working
name: bug

## 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: ***@***.***>
Title: Map caching not working for some class methods
State: closed
Labels: description: Something isn't working
name: bug

## 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.
Title: Fix task template reload from dict
State: 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
Title: Error in create a dataset from a Python generator
State: closed
Labels: description: Something isn't working
name: bug

description: Good for newcomers
name: good first issue

name: hacktoberfest

## 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?
Title: Classes label error when loading symbolic links using imagefolder
State: closed
Labels: description: New feature or request
name: enhancement

description: Good for newcomers
name: good first issue

name: hacktoberfest

**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!
Title: Mismatch between tutoriel and doc
State: closed
Labels: description: Something isn't working
name: bug

description: Good for newcomers
name: good first issue

name: hacktoberfest

## 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
Title: Use HTML relative paths for tiles in the docs
State: 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.
Title: Allow connection objects in `from_sql` + small doc improvement
State: 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._
Title: Review sync issues from GitHub to Hub
State: closed
Labels: description: Something isn't working
name: bug

## 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!!
Title: Resume failed process
State: open
Labels: description: New feature or request
name: enhancement

**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:
Title: Filtering on an empty dataset returns a corrupted dataset.
State: closed
Labels: description: Something isn't working
name: bug

name: hacktoberfest

## 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!