sunny2309/githug-issues huggingface dataset:
Implement ability to define splits in metadata section of dataset card

### Feature request

If you go here: https://huggingface.co/datasets/inria-soda/tabular-benchmark/tree/main you will see bunch of folders that has various CSV files. I’d like dataset viewer to show these files instead of only one dataset like it currently does. (and also people to be able to load them as splits instead of loading through `data_files`) 
e.g GLUE has various splits on viewer but it’s too overkill to ask people to implement loading script, so it would be better to let them define these in the README file instead.

Also pinging @polinaeterna @lhoestq @adrinjalali
Use logging instead of printing to console

### Describe the bug

Some logs ([here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L778), [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L786), and [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L830)) generated by the `DatasetBuilder` are printed to the console instead of passed to `datasets` logger.


### Steps to reproduce the bug

```python
>> import datasets

>> datasets.load_dataset("some-dataset")

Downloading and preparing dataset csv/data to <path>...
Downloading data files: 100%|██████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 7729.06it/s]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 527.23it/s]
Dataset csv downloaded and prepared to <path>. Subsequent calls will reuse this data.
```

### Expected behavior

The logs should not be printed to the console directly but passed to the logger so that the user can redirect them wherever he wants.

### Environment info

- `datasets` version: 2.6.1
- Platform: macOS-13.0-x86_64-i386-64bit
- Python version: 3.9.15
- PyArrow version: 10.0.0
- Pandas version: 1.5.1
`push_to_hub` not propagating `token` through `DownloadConfig`

### Describe the bug

When trying to upload a new 🤗 Dataset to the Hub via Python, and providing the `token` as a parameter to the `Dataset.push_to_hub` function, it just works for the first time, assuming that the dataset didn't exist before.

But when trying to run `Dataset.push_to_hub` again over the same dataset, instead of updating it, it throws a `ConnectionError` when trying to retrieve the `README.md` that may contain some metadata about the dataset, so as to also update it, but since the `token` is not propagated, the `DownloadConfig` provided to the `datasets.utils.file_utils.get_from_cache` function doesn't contain the `use_auth_token` value set to `token`, it's just using the default one which is None/False.

So on, when uploading a dataset via Python with `push_to_hub` with the `token` as a parameter with the HuggingFace API Token as value, it can just be uploaded when the dataset is new, otherwise it fails with to `ConnectionError` due to the `token` not being propagated as `use_auth_token`.

### Steps to reproduce the bug

Let's create a new dataset in our HF account via Python as:

```python
from datasets import Dataset

data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
```

When we create the `Dataset` for the first time it works and there are no issues, but when trying to actually upload a new version of the same dataset (same name under the same username), we encounter the following issue:

```python
from datasets import Dataset

data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
>>> ConnectionError: Couldn't reach https://huggingface.co/datasets/alvarobartt/demo/resolve/main/README.md (ConnectionError('Unauthorized for URL https://huggingface.co/datasets/<HF_USERNAME>/<HF_DATASET>/resolve/main/README.md. Please use the parameter `use_auth_token=True` after logging in with `huggingface-cli login`'))
```

### Expected behavior

Ideally, the `token` parameter provided to `push_to_hub` should be propagated and used to download the `README.md` when trying to update a `Dataset`, instead of throwing that exception, so that the authentication can be done directly through code without running `huggingface-cli login`as mentioned at https://huggingface.co/docs/datasets/upload_dataset#upload-with-python.

### Environment info

- `datasets` version: 2.6.1
- Platform: macOS-13.0-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.0
- Pandas version: 1.5.1
[zstd] Use max window log size

ZstdDecompressor has a parameter `max_window_size` to limit max memory usage when decompressing zstd files. The default `max_window_size` is not enough when files are compressed by `zstd --ultra` flags.

Change `max_window_size` to the zstd's max window size. NOTE, the `zstd.WINDOWLOG_MAX` is the log_2 value of the max window size.
Use hfh hf_hub_url function

Small refactoring to use `hf_hub_url` function from `huggingface_hub`.

This PR also creates the `hub` module that will contain all `huggingface_hub` functionalities relevant to `datasets`.

This is a necessary stage before implementing the use of the `hfh` caching system (which uses its `hf_hub_url` under the hood).

EDIT:
~~Finally, we use our `config.HUB_DATASETS_URL` when using `hfh.hf_hub_url`~~
There is a breaking change:  the `hfh` `hf_hub_url` function uses
- `hfh` `HUGGINGFACE_CO_URL_TEMPLATE` URL template, different from the `datasets` `config.HUB_DATASETS_URL`
- also, `hfh` `DEFAULT_REVISION`, instead of `datasets` `config.HUB_DEFAULT_VERSION`
Drop labels in Image and Audio folders if files are on different levels in directory or if there is only one label

Will close https://github.com/huggingface/datasets/issues/5153

Drop labels by default (`drop_labels=None`) when:
* there are files on different levels of directory hierarchy by checking their path depth
* all files are in the same directory (=only one label was inferred)

First one fixes cases like this:
```
repo
   image3.jpg
   image4.jpg
   data
      image1.jpg
      image2.jpg
```
Second one fixes cases like this:
```
repo
   image1.jpg
   image2.jpg
   image3.jpg
```
This is mostly to fix the viewer for people who just drop images in the Hub interface into the root dir.

I added tests for both of the cases on local and remote files. **I also changed data files for old test on drop_labels** (`test_generate_examples_drop_labels`). The files I provide to `test_generate_examples_drop_labels` now has "canonical" classification structure (two dirs) in order not to change the logic of the test (=not to check these two cases addressed in the PR).
Make torch.Tensor and spacy models cacheable

Override `Pickler.save` to implement deterministic reduction (lazily registered; inspired by https://github.com/uqfoundation/dill/blob/master/dill/_dill.py#L343) functions for `torch.Tensor` and spaCy models.

Fix https://github.com/huggingface/datasets/issues/5170, fix https://github.com/huggingface/datasets/issues/3178
`path` is `None` when downloading a custom audio dataset from the Hub

### Describe the bug

I've created an [audio dataset](https://huggingface.co/datasets/lewtun/audio-test-push) using the `audiofolder` feature desribed in the [docs](https://huggingface.co/docs/datasets/audio_dataset#audiofolder) and then pushed it to the Hub. 

Locally, I can see the `audio.path` feature is of the expected form `path/to/data_dir`, but when I download the dataset from the Hub, I see `audio.path` is `None`

Here's an example:

```python
from datasets import load_dataset

ds = load_dataset("lewtun/audio-test-push")
ds["train"][0]
# {
#     "audio": {
#         "path": None,    <-- Is this expected?
#         "array": array(
#             [
#                 3.97140226e-07,
#                 7.30310290e-07,
#                 7.56406735e-07,
#                 ...,
#                 -1.19636677e-01,
#                 -1.16811886e-01,
#                 -1.12441722e-01,
#             ]
#         ),
#         "sampling_rate": 44100,
#     },
#     "song_id": 0,
#     "genre_id": 0,
#     "genre": "Electronic",
# }
```

Is this expected behaviour? If yes, feel free to close this issue as it's not a true bug then :)

### Steps to reproduce the bug

1. Create an audio dataset with the `audiofolder` feature
2. Push the dataset to the Hub with `push_to_hub()`
3. Download the Hub dataset and inspect the `audio.path` feature

### Expected behavior

`audio.path` points to the file associated with the audio data

### Environment info

- `datasets` version: 2.6.2.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1
Reduce friction in tabular dataset workflow by eliminating having splits when dataset is loaded

### Feature request

Sorry for cryptic name but I'd like to explain using code itself. When I want to load a specific dataset from a repository (for instance, this: https://huggingface.co/datasets/inria-soda/tabular-benchmark)

```python
from datasets import load_dataset
dataset = load_dataset("inria-soda/tabular-benchmark", data_files=["reg_cat/house_sales.csv"], streaming=True)
print(next(iter(dataset["train"])))
```

`datasets` library is essentially designed for people who'd like to use benchmark datasets on various modalities to fine-tune their models, and these benchmark datasets usually have pre-defined train and test splits. However, for tabular workflows, having train and test splits usually ends up model overfitting to validation split so usually the users would like to do validation techniques like `StratifiedKFoldCrossValidation` or when they tune for hyperparameters they do `GridSearchCrossValidation` so often the behavior is to create their own splits. Even [in this paper](https://hal.archives-ouvertes.fr/hal-03723551) a benchmark is introduced but the split is done by authors.
It's a bit confusing for average tabular user to try and load a dataset and see `"train"` so it would be nice if we would not load dataset into a split called `train `by default.

```diff
from datasets import load_dataset
dataset = load_dataset("inria-soda/tabular-benchmark", data_files=["reg_cat/house_sales.csv"], streaming=True)
-print(next(iter(dataset["train"])))
+print(next(iter(dataset)))
``` 

### Motivation

I explained it above 😅

### Your contribution

I think this is quite a big change that seems small (e.g. how to determine datasets that will not be load to train split?), it's best if we discuss first!
add: segmentation guide.

Closes #5181 

I have opened a PR on Hub (https://huggingface.co/datasets/huggingface/documentation-images/discussions/5) to include the images in our central Hub repository. Once the PR is merged I will edit the image links. 

I have also prepared a [Colab Notebook](https://colab.research.google.com/drive/1BMDCfOTBnyshoME5RSxn5iQy-TWeFbOA?usp=sharing) in case anyone wants to play. 

- [x] Replace the image links
Incorrect error message when Dataset.from_sql fails and sqlalchemy not installed

### Describe the bug

When calling `Dataset.from_sql` (in my case, with sqlite3), it fails with a message ```ValueError: Please pass `features` or at least one example when writing data``` when I don't have `sqlalchemy` installed. 


### Steps to reproduce the bug

Make a new sqlite db with `sqlite3` and `pandas` from a remote [URL](https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv).

```python
import sqlite3
import pandas as pd
from datasets import Dataset

conn = sqlite3.connect('us_covid_data.db')
df = pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv')
df.to_sql('states', conn, if_exists='replace')
```

Then if you try to query this DB like this:

```python
ds = Dataset.from_sql('''SELECT * from states WHERE state=="New York";''', "sqlite:///us_covid_data.db")
```

You run into the error I described above:

```ValueError: Please pass `features` or at least one example when writing data```

However, if you try to pass features, as the error suggests, then you get an error that tells you the underlying problem...

```python
from datasets import Dataset, Features, Value

features = Features({
    'date': Value('date32'),
    'label': Value('string'),
    'fips': Value('int32'),
    'cases': Value('int32'),
    'deaths': Value('int32')
})
ds = Dataset.from_sql(
    '''SELECT * from states WHERE state=="New York";''',
    "sqlite:///us_covid_data.db",
    features=features
)
```

Which results in the actual underlying error: `ImportError: Using URI string without sqlalchemy installed.`

### Expected behavior

Instead of `ValueError` about needing to pass features, we should provide the actual underlying error about not having SQLAlchemy installed when it isn't found in the environment. 

### Environment info

- `datasets` version: 2.6.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.10
- PyArrow version: 10.0.0
- Pandas version: 1.2.5
Add notebook / other resource links to the task-specific data loading guides

Does it make sense to include links to notebooks / scripts that show how to use a dataset for training / fine-tuning a model? 

For example, here in [https://huggingface.co/docs/datasets/image_classification] we could include a mention of https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb.

Applies to https://huggingface.co/docs/datasets/object_detection as well.

Cc: @osanseviero @nateraw
An example or recommendations for creating large image datasets?

I know that Apache Beam and `datasets` have [some connector utilities](https://huggingface.co/docs/datasets/beam). But it's a little unclear what we mean by "But if you want to run your own Beam pipeline with Dataflow, here is how:". What does that pipeline do? 

As a user, I was wondering if we have this support for creating large image datasets. If so, we should mention that [here](https://huggingface.co/docs/datasets/image_dataset). 

Cc @lhoestq
Raise ffmpeg warnings only once

Our warnings looks nice now.

`librosa` warning that was raised at each decoding: 
```
/usr/local/lib/python3.7/dist-packages/librosa/core/audio.py:165: UserWarning: PySoundFile failed. Trying audioread instead.
  warnings.warn("PySoundFile failed. Trying audioread instead.")
```
is suppressed with `filterwarnings("ignore")` in a context manager. That means the first warning is also ignored (setting `filterwarnings("once")` didn't work!), so I added info that audioread is used for decoding to our message. Hope it's enough.


Tests failed at first because they used to check if the warning was raised at (each) decoding in `librosa` case but now we throw only one warning (at first decoding). I removed this check for warnings, do you think it's fine?
Fix CI require beam

This PR:
- Fixes the CI `require_beam`: before it was requiring PyTorch instead
  ```python
  def require_beam(test_case):
      if not config.TORCH_AVAILABLE:
          test_case = unittest.skip("test requires PyTorch")(test_case)
      return test_case
  ```
- Fixes a missing `require_beam` in `test_beam_based_builder_download_and_prepare_as_parquet`
- Refactors `require_beam` to use `pytest` (`skipif`) instead
Add ffmpeg4 installation instructions in warnings

Adds instructions on how to install `ffmpeg=4` on Linux (relevant for Colab users).

Looks pretty ugly because I didn't find a way to check `ffmpeg` version from python (without `subprocess.call()`; `ctypes.util.find_library` doesn't work`), so the warning is raised on each decoding. Any suggestions on how to make it look nice are welcome!

This is how it looks on Colab:
![image](https://user-images.githubusercontent.com/16348744/198052412-d48018d1-4416-4aa5-9114-f7f9b4af031f.png)
Support dill 0.3.6

This PR:
- ~~Unpins dill to allow installing dill>=0.3.6~~
- ~~Removes the fix on dill for >=0.3.6 because they implemented a deterministic mode (to be confirmed by @anivegesana)~~
- Pins dill<0.3.7 to allow latest dill 0.3.6
- Implements a fix for dill `save_function` for dill 0.3.6
- Additionally had to implement a fix for dill `save_code` and `_save_regex` for dill 0.3.6
- Fixes the CI so that the latest dill version is tested (besides the minimum 0.3.1.1 required by apache-beam 2.42.0)

Fix #5162.
Pip-compile: Could not find a version that matches dill<0.3.6,>=0.3.6

### Describe the bug

When using `pip-compile` (part of `pip-tools`) to generate a pinned requirements file that includes `datasets`, a version conflict of `dill` appears.

It is caused by a transitive dependency conflict between `datasets` and `multiprocess`.

### Steps to reproduce the bug

```bash
$ echo "datasets" > requirements.in
$ pip install pip-tools
$ pip-compile requirements.in 
Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets==2.6.1->-r requirements.in (line 1))
Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6
Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1
There are incompatible versions in the resolved dependencies:
  dill<0.3.6 (from datasets==2.6.1->-r requirements.in (line 1))
  dill>=0.3.6 (from multiprocess==0.70.14->datasets==2.6.1->-r requirements.in (line 1))
```

### Expected behavior

A correctly generated file `requirements.txt` with pinned dependencies

### Environment info

Tested with versions `2.6.1, 2.6.0, 2.5.2` on Python 3.8 and 3.10 on Ubuntu 20.04LTS and Python 3.10 on MacOS 12.6 (M1).
Dataset can’t cache model’s outputs

### Describe the bug

Hi,

I try to cache some outputs of teacher model( Knowledge Distillation ) by using map function of Dataset library, while every time I run my code, I still recompute all the sequences. I tested Bert Model like this, I got different hash every single run, so any idea to deal with this?

### Steps to reproduce the bug

1. run below code
2. get different hash
```
from transformers import BertModel
from transformers import AutoTokenizer
import torch
token = ['hello']
model = BertModel.from_pretrained("bert-base-uncased").eval()
tok = AutoTokenizer.from_pretrained("bert-base-uncased")
def abcd():
    with torch.no_grad():
        
        out = model(**tok(token,return_tensors='pt'))[0]
        # out = tok(token)
    return out
from datasets.fingerprint import Hasher
my_func = abcd
print(Hasher.hash(my_func))

print(abcd())
```

### Expected behavior

I wanna cache all the model output 

### Environment info

datasets:2.5.0
Automatically add filename for image/audio folder

### Feature request

When creating a custom audio of image dataset, it would be great to automatically have access to the filename. It should be both:
a) Automatically displayed in the viewer
b) Automatically added as a column to the dataset when doing `load_dataset`

In `diffusers` our test rely quite heavily on images and audio files now and it's a bit tedious at the moment to download specific images from a datasets repo. 

E.g. we have a dataset of images for tests in `diffusers`: https://huggingface.co/datasets/hf-internal-testing/diffusers-images
where it would be extremely nice to have direct access to the filename both visually on the datasets page (@severo ) as well as via the `load_datasets` function. We currently have some akward functionality to download images by path name: https://github.com/huggingface/diffusers/blob/2fb8fafa4b761f6fc144cf75a6f6f0ea6af3a1c1/src/diffusers/utils/testing_utils.py#L131 
It would be much nicer to just go over `load_dataset(...)` 

### Motivation

Intuitively the filename is something people understand directly. E.g if you upload a folder of images online, it's nice if you recognize the image as well as the filename next to it directly and that you're able to use it right away. 

The label on the other hand is less intuitive to understand as you haven't added it yourself.

### Your contribution

Not sure if I have the time to add it myself anytime soon, but it would help us a lot for `diffusers`.
Fix language and license tag names in all Hub datasets

While working on this:
- #5137

we realized there are still many datasets with deprecated "languages" and "licenses" tag names (instead of "language" and "license").

This is a blocking issue: no subsequent PR can be opened to modify their metadata: a ValueError will be thrown.

We should fix the "language" and "license" tag names in all Hub datasets.

TODO:
- [x] Fix language and license tag names in 402 Hub datasets

CC: @julien-c
Consistent caching between python and jupyter

### Feature request

I hope this is not my mistake, currently if I use `load_dataset` from a python session on a custom dataset to do the preprocessing, it will be saved in the cache and in other python sessions it will be loaded from the cache, however calling the same from a jupyter notebook does not work, meaning the preprocessing starts from scratch.

If adjusting the hashes is impossible, is there a way to manually set dataset fingerprint to "force" this behaviour?

### Motivation

If this is not already the case and I am doing something wrong, it would be useful to have the two fingerprints consistent so one can create the dataset once and then try small things on jupyter without preprocessing everything again.


### Your contribution

I am happy to try a PR if you give me some pointers where the changes should happen