ahmedfarazsyk/datasets-github-issues huggingface dataset:
datasets.config.PYARROW_VERSION has no attribute 'major'
Author: bwang482
State: closed
Labels: name: bug

In the test_dataset_common.py script, line 288-289

```
if datasets.config.PYARROW_VERSION.major < 3:
   packaged_datasets = [pd for pd in packaged_datasets if pd["dataset_name"] != "parquet"]
```

which throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.

```
import datasets
datasets.config.PYARROW_VERSION.major
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/var/folders/1f/0wqmlgp90qjd5mpj53fnjq440000gn/T/ipykernel_73361/2547517336.py in <module>
      1 import datasets
----> 2 datasets.config.PYARROW_VERSION.major

AttributeError: 'str' object has no attribute 'major'
```

## Environment info
- `datasets` version: 1.11.0
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 4.0.1


Comments: I have changed line 288 to `if int(datasets.config.PYARROW_VERSION.split(".")[0]) < 3:` just to get around it.

Hi @bwang482,

I'm sorry but I'm not able to reproduce your bug.

Please note that in our current master branch, we made a commit (d03223d4d64b89e76b48b00602aba5aa2f817f1e) that simultaneously modified:
- test_dataset_common.py: https://github.com/huggingface/datasets/commit/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-a1bc225bd9a5bade373d1f140e24d09cbbdc97971c2f73bb627daaa803ada002L289 that introduces the usage of `datasets.config.PYARROW_VERSION.major`
- but also changed config.py: https://github.com/huggingface/datasets/commit/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-e021fcfc41811fb970fab889b8d245e68382bca8208e63eaafc9a396a336f8f2L40, so that `datasets.config.PYARROW_VERSION.major` exists


Sorted. Thanks!

Reopening this. Although the `test_dataset_common.py` script works fine now.

Has this got something to do with my pull request not passing `ci/circleci: run_dataset_script_tests_pyarrow` tests?

https://github.com/huggingface/datasets/pull/2873

Hi @bwang482,

If you click on `Details` (on the right of your non passing CI test names: `ci/circleci: run_dataset_script_tests_pyarrow`), you can have more information about the non-passing tests.

For example, for ["ci/circleci: run_dataset_script_tests_pyarrow_1" details](https://circleci.com/gh/huggingface/datasets/46324?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link), you can see that the only non-passing test has to do with the dataset card (missing information in the `README.md` file): `test_changed_dataset_card`
```
=========================== short test summary info ============================
FAILED tests/test_dataset_cards.py::test_changed_dataset_card[swedish_medical_ner]
= 1 failed, 3214 passed, 2874 skipped, 2 xfailed, 1 xpassed, 15 warnings in 175.59s (0:02:55) =
```

Therefore, your PR non-passing test has nothing to do with this issue.
Add Common Objects in 3D (CO3D)
Author: nateraw
State: open
Labels: name: dataset request

name: vision

## Adding a Dataset
- **Name:** *Common Objects in 3D (CO3D)*
- **Description:** *See blog post [here](https://ai.facebook.com/blog/common-objects-in-3d-dataset-for-3d-reconstruction)*
- **Paper:** *[link to paper](https://arxiv.org/abs/2109.00512)*
- **Data:** *[link to data](https://ai.facebook.com/datasets/co3d-downloads/)*
- **Motivation:** *excerpt from above blog post:*

> As the first data set of its kind, CO3D will aptly enable reconstruction of real-life 3D objects. Indeed, CO3D already provides training data to enable our NeRFormer to tackle the new-view synthesis (NVS) task. Here, photorealistic NVS is a major step on the path to fully immersive AR/VR effects, where objects can be virtually transported across different environments, which will allow connecting users by sharing or recollecting their experiences.
> 
> Besides practical applications in AR/VR, we hope that the data set will become a standard testbed for the recent proliferation of methods (including NeRFormer, Implicit Differentiable Renderer, NeRF, and others) that reconstruct 3D scenes by means of an implicit shape model.
> 

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Cannot download TOTTO dataset
Author: mrm8488
State: closed
Labels: name: bug

Error: Couldn't find file at https://storage.googleapis.com/totto/totto_data.zip

`datasets version: 1.11.0`
# How to reproduce:

```py
from datasets import load_dataset
dataset = load_dataset('totto')
```




Comments: Hola @mrm8488, thanks for reporting.

Apparently, the data source host changed their URL one week ago: https://github.com/google-research-datasets/ToTTo/commit/cebeb430ec2a97747e704d16a9354f7d9073ff8f

I'm fixing it.
Loading allenai/c4 in streaming mode does too many HEAD requests
Author: lhoestq
State: closed
Labels: name: enhancement

name: streaming

This does 60,000+ HEAD requests to get all the ETags of all the data files:
```python
from datasets import load_dataset
load_dataset("allenai/c4", streaming=True)
```
It makes loading the dataset completely impractical.

The ETags are used to compute the config id (it must depend on the data files being used).
Instead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository)

Comments: https://github.com/huggingface/datasets/blob/6c766f9115d686182d76b1b937cb27e099c45d68/src/datasets/builder.py#L179-L186

Thanks a lot!!!
Wound segmentation datasets
Author: osanseviero
State: open
Labels: name: dataset request

name: vision

## Adding a Dataset
- **Name:** Wound segmentation datasets
- **Description:** annotated wound image dataset 
- **Paper:** https://www.nature.com/articles/s41598-020-78799-w
- **Data:** https://github.com/uwm-bigdata/wound-segmentation
- **Motivation:** Interesting simple image dataset, useful for segmentation, with visibility due to http://www.miccai.org/special-interest-groups/challenges/ and https://fusc.grand-challenge.org/

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Add Open Catalyst Project Dataset
Author: osanseviero
State: open
Labels: name: dataset request

## Adding a Dataset
- **Name:**  Open Catalyst 2020 (OC20) Dataset
- **Website:** https://opencatalystproject.org/
- **Data:** https://github.com/Open-Catalyst-Project/ocp/blob/master/DATASET.md

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Negative timezone
Author: jadermcs
State: closed
Labels: name: bug

## Describe the bug
The load_dataset method do not accept a parquet file with a negative timezone, as it has the following regex:
```
"^(s|ms|us|ns),\s*tz=([a-zA-Z0-9/_+:]*)$"
```
So a valid timestap ```timestamp[us, tz=-03:00]``` returns an error when loading parquet files.

## Steps to reproduce the bug
```python
# Where the timestamp column has a tz of -03:00
datasets = load_dataset('parquet', data_files={'train': train_files, 'validation': validation_files,
                                        'test': test_files}, cache_dir="./cache_teste/")
```

## Expected results
The -03:00 is a valid tz so the regex should accept this without raising an error.

## Actual results
As this regex disaproves a valid tz it raises the following error:
```python
raise ValueError(
                f"{datasets_dtype} is not a validly formatted string representation of a pyarrow timestamp."
                f"Examples include timestamp[us] or timestamp[us, tz=America/New_York]"
                f"See: https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp"
            )
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 5.0.0


Comments: Fixed by #2847.
[feature request] adding easy to remember `datasets.cache_dataset()` + `datasets.is_dataset_cached()`
Author: stas00
State: open
Labels: name: enhancement

Often, there is a need to prepare a dataset but not use it immediately, e.g. think tests suite setup, so it'd be really useful to be able to do:

``` 
if not datasets.is_dataset_cached(ds): datasets.cache_dataset(ds)
```

This can already be done with:
```
builder = load_dataset_builder(ds)
if not os.path.idsir(builder.cache_dir):
    builder.download_and_prepare()
```

but the current way is a way less intuitive and much harder to remember than the proposed API, IMHO. 

One more way is to do:

```
_ = load_dataset(ds)
```
but it wastes resources loading the dataset when it's not needed.

this has been discussed at https://huggingface.slack.com/archives/C01229B19EX/p1630021912025800

Thank you!

@lhoestq
always requiring the username in the dataset name when there is one
Author: stas00
State: closed
Labels: name: enhancement

Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.

So both of us started with `stas/openwebtext-10k`, somewhere along the lines lost `stas/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`

So this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w/o it.

The same in code:

```
# first run
python -c "from datasets import load_dataset; load_dataset('stas/openwebtext-10k')"
# now run immediately
python -c "from datasets import load_dataset; load_dataset('openwebtext-10k')"
# the second command should fail, but it doesn't fail now.
```

Please let me know if I explained myself clearly.

Thank you!

Comments: From what I can understand, you want the saved arrow file directory to have username as well instead of just dataset name if it was downloaded with the user prefix?

I don't think the user cares of how this is done, but the 2nd command should fail, IMHO, as its dataset name is invalid:
```
# first run
python -c "from datasets import load_dataset; load_dataset('stas/openwebtext-10k')"
# now run immediately
python -c "from datasets import load_dataset; load_dataset('openwebtext-10k')"
# the second command should fail, but it doesn't fail now.
```

Moreover, if someone were to create `openwebtext-10k` w/o the prefix, they will now get the wrong dataset, if they previously downloaded `stas/openwebtext-10k`.

And if there are 2 users with the same dataset name `foo/ds` and `bar/ds` - currently this won't work to get the correct dataset.

So really there 3 unrelated issues hiding in the current behavior.

This has been fixed now, and we'll do a new release of the library today.

Now the stas/openwebtext-10k dataset is cached at `.cache/huggingface/datasets/stas___openwebtext10k` and openwebtext-10k would be at `.cache/huggingface/datasets/openwebtext10k`. Since they are different, the cache won't fall back on loading the wrong one anymore.

Same for the python script used to generate the dataset: stas/openwebtext-10k is cached at `.cache/huggingface/modules/datasets_modules/datasets/stas___openwebtext10k` and openwebtext-10k would be at `.cache/huggingface/modules/datasets_modules/datasets/openwebtext10k`

Amazing! Thank you for adding this improvement, @lhoestq!

(can be closed?)

Yes indeed :) thanks
Adding GLUECoS Hinglish and Spanglish code-switching bemchmark
Author: yjernite
State: open
Labels: name: dataset request

## Adding a Dataset
- **Name:** GLUECoS
- **Description:** a Microsoft Benchmark to evaluate code-switching for only two language pairs but a variety of tasks
- **Paper:** https://aclanthology.org/2020.acl-main.329/
- **Data:** https://github.com/microsoft/GLUECoS
- **Motivation:** We currently only have [one other](https://huggingface.co/datasets/lince) dataset for code-switching

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments: Hi @yjernite I am interested in adding this dataset. 
In the repo they have also added a code mixed MT task from English to Hinglish [here](https://github.com/microsoft/GLUECoS#code-mixed-machine-translation-task). I think this could be a good dataset addition in itself and then I can add the rest of the GLUECoS tasks as one dataset. What do you think?
OpenWebText: NonMatchingSplitsSizesError
Author: thomasw21
State: closed
Labels: name: bug

## Describe the bug

When downloading `openwebtext`, I'm getting:
```
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]
```

I suspect that the file we download from has changed since the size doesn't look like to match with documentation

`Downloading:   0%|          | 0.00/12.9G [00:00<?, ?B/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.

## Steps to reproduce the bug
```python
from datasets import load_dataset

load_dataset("openwebtext", download_mode="force_redownload")
```

## Expected results

Loading is successful

## Actual results

Loading throws above error.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: linux (Redhat version 8.1)
- Python version: 3.8
- PyArrow version: 4.0.1


Comments: Thanks for reporting, I'm updating the verifications metadata

I just regenerated the verifications metadata and noticed that nothing changed: the data file is fine (the checksum didn't change), and the number of examples is still 8013769. Not sure how you managed to get 7982430 examples.

Can you try to delete your cache ( by default at `~/.cache/huggingface/datasets`) and try again please ?
Also, on which platform are you (linux/macos/windows) ?

I'll try without deleting the whole cache (we have large datasets already stored). I was under the impression that `download_mode="force_redownload"` would bypass cache.
Sorry plateform should be linux (Redhat version 8.1)

Hi @thomasw21 , are you still having this issue after clearing your cache ?

Sorry I haven't had time to work on this. I'll close and re-open if I can't figure out why I'm having this issue. Thanks for taking a look !
prepare_module issue when loading from read-only fs
Author: Dref360
State: closed
Labels: name: bug

## Describe the bug

When we use prepare_module from a readonly file system, we create a FileLock using the `local_path`.
This path is not necessarily writable.

`lock_path = local_path + ".lock"`


## Steps to reproduce the bug

Run `load_dataset` on a readonly python loader file.
```python
ds = load_dataset(
        python_loader, data_files={"train": train_path, "test": test_path}
    )
```

where `python_loader` is a path to a file located in a readonly folder.

## Expected results
This should work I think?

## Actual results

```python
    return load_dataset(
  File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 711, in load_dataset
    module_path, hash, resolved_file_path = prepare_module(
  File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 465, in prepare_module
    with FileLock(lock_path):
  File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 314, in __enter__
    self.acquire()
  File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 263, in acquire
    self._acquire()
  File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 378, in _acquire
    fd = os.open(self._lock_file, open_mode)
OSError: [Errno 30] Read-only file system: 'YOUR_FILE.py.lock'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 3.0.0


Comments: Hello, I opened #2887 to fix this.
IndexError when accessing first element of a Dataset if first RecordBatch is empty
Author: lhoestq
State: closed

The computation of the offsets of the underlying Table of a Dataset has some issues if the first RecordBatch is empty.

```python
from datasets import Dataset
import pyarrow as pa

pa_table = pa.Table.from_pydict({"a": [1]})
pa_table2 = pa.Table.from_pydict({"a": []}, schema=pa_table.schema)
ds_table = pa.concat_tables([pa_table2, pa_table])

dataset = Dataset(ds_table)

print([len(b) for b in dataset.data._batches])
# [0, 1]

print(dataset.data._offsets)
# [0 0 1] (should be [0, 1])

dataset[0]
```
raises
```python
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/table.py in _interpolation_search(arr, x)
     90         else:
     91             i, j = i, k
---> 92     raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.")
     93 
     94 

IndexError: Invalid query '0' for size 1.
```

This can be fixed by ignoring empty batches when computing `table._batches` and `table._offsets`

cc @SaulLu
Logging levels not taken into account
Author: LysandreJik
State: closed
Labels: name: bug

## Describe the bug

The `logging` module isn't working as intended relative to the levels to set.

## Steps to reproduce the bug

```python
from datasets import logging

logging.set_verbosity_debug()
logger = logging.get_logger()

logger.error("ERROR")
logger.warning("WARNING")
logger.info("INFO")
logger.debug("DEBUG"
```

## Expected results

I expect all logs to be output since I'm putting a `debug` level.

## Actual results

Only the two first logs are output.

## Environment info

- `datasets` version: 1.11.0
- Platform: Linux-5.13.9-arch1-1-x86_64-with-glibc2.33
- Python version: 3.9.6
- PyArrow version: 5.0.0

## To go further

This logging issue appears in `datasets` but not in `transformers`. It happens because there is no handler defined for the logger. When no handler is defined, the `logging` library will output a one-off error to stderr, using a `StderrHandler` with level `WARNING`.

`transformers` sets a default `StreamHandler` [here](https://github.com/huggingface/transformers/blob/5c6eca71a983bae2589eed01e5c04fcf88ba5690/src/transformers/utils/logging.py#L86)

Comments: I just take a look at all the outputs produced by `datasets` using the different log-levels.
As far as i can tell using `datasets==1.17.0` they overall issue seems to be fixed.

However, I noticed that there is one tqdm based progress indicator appearing on STDERR that I can simply not suppress.
```
Resolving data files: 100%|██████████| 652/652 [00:00<00:00, 1604.52it/s]
```

According to _get_origin_metadata_locally_or_by_urls it shold be supressable by using the `NOTSET` log-level
https://github.com/huggingface/datasets/blob/1406a04c3e911cec2680d8bc513653e0cafcaaa4/src/datasets/data_files.py#L491-L501
Sadly when specifiing the log-level `NOTSET` it seems to has no effect.

But appart from it not having any effect I must admit that it seems unintuitive to me.
I would suggest changing this such that it is only shown when the log-level is greater or equal to INFO.

This would conform better to INFO according to the [documentation](https://huggingface.co/docs/datasets/v1.0.0/package_reference/logging_methods.html#datasets.logging.set_verbosity_info).
> This will display most of the logging information and tqdm bars.

Any inputs on this?
I will be happy to supply a PR if desired 👍 

Hi! This should disable the tqdm output:
```python
import datasets
datasets.set_progress_bar_enabled(False)
```

On a side note: I believe the issue with logging (not tqdm) is still relevant on master.
ArrowInvalid when mapping dataset with missing values
Author: uniquefine
State: open
Labels: name: bug

## Describe the bug
I encountered an `ArrowInvalid` when mapping dataset with missing values. 
Here are the files for a minimal example. The exception is only thrown when the first line in the csv has a missing value (if you move the last line to the top it isn't thrown).
[data_small.csv](https://github.com/huggingface/datasets/files/7037838/data_small.csv)
[data.csv](https://github.com/huggingface/datasets/files/7037842/data.csv)

## Steps to reproduce the bug
```python
from datasets import load_dataset

datasets = load_dataset("csv", data_files=['data_small.csv'])

datasets = datasets.map(lambda e: {'labels': e['match']},
                        remove_columns=['id'])
```

## Expected results
No error

## Actual results
```
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Invalid null value
```

## Environment info
- `datasets` version: 1.5.0
- Platform: Linux-5.11.0-25-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyTorch version (GPU?): 1.7.1+cpu (False)
- Tensorflow version (GPU?): 2.4.1 (False)
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no


Comments: Hi ! It fails because of the feature type inference.

Because the first 1000 examples all have null values in the "match" field, then it infers that the type for this field is `null` type before writing the data on disk. But as soon as it tries to map an example with a non-null "match" field, then it fails.

To fix that you can either:
- increase the writer_batch_size to >2000 (default is 1000) so that some non-null values will be in the first batch written to disk
```python
datasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], writer_batch_size=2000)
```
- OR force the feature type with:
```python
from datasets import Features, Value

features = Features({
    'conflict': Value('int64'),
    'date': Value('string'),
    'headline': Value('string'),
    'match': Value('float64'),
    'label': Value('float64')
})
datasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], features=features)
```
Optimize streaming from TAR archives
Author: lhoestq
State: closed
Labels: name: enhancement

name: streaming

Hi ! As you know TAR has some constraints for data streaming. While it is optimized for buffering, the files in the TAR archive **need to be streamed in order**. It means that we can't choose which file to stream from, and this notation is to be avoided for TAR archives:
```
tar://books_large_p1.txt::https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2
```
Instead, I suggest we implement `iter_archive` for the `StreamingDownloadManager`.
The regular `DownloadManager` already has it.

Then we will have to update the json/txt/csv/etc. loaders to make them use `iter_archive` on TAR archives.

That's also what Tensorflow Datasets is doing in this case.
See this [dataset](https://github.com/tensorflow/datasets/blob/93895059c80a9e05805e8f32a2e310f66a23fc98/tensorflow_datasets/image_classification/flowers.py) for example.

Therefore instead of doing
```python
uncompressed = dl_manager.extract(tar_archive)
filename = "books_large_p1.txt"
with open(os.path.join(uncompressed, filename)) as f:
    for line in f:
        ...
```
we'll do
```python
for filename, f in dl_manager.iter_archive(tar_archive):
    for line in f:
        ...
```

Comments: Closed by: 
- #3066