rIsHu009/github-issues-updated huggingface dataset:
Title: [BUG] Dataset initialized from in-memory data does not create cache.
Author: MattYoon (NONE)
State: closed
Created: 2023-08-01T11:58:58Z

### Describe the bug

`Dataset` initialized from in-memory data (dictionary in my case, haven't tested with other types) does not create cache when processed with the `map` method, unlike `Dataset` initialized by other methods such as `load_dataset`.

### Steps to reproduce the bug

```python
# below code was run the second time so the map function can be loaded from cache if exists

from datasets import load_dataset, Dataset

dataset = load_dataset("tatsu-lab/alpaca")['train']

dataset = dataset.map(lambda x: {'input': x['input'] + 'hi'}) # some random map
print(len(dataset.cache_files))
# 1

# copy the exact same data but initialize from a dictionary
memory_dataset = Dataset.from_dict({
    'instruction': dataset['instruction'],
    'input': dataset['input'],
    'output': dataset['output'],
    'text': dataset['text']})

memory_dataset = memory_dataset.map(lambda x: {'input': x['input'] + 'hi'}) # exact same map
print(len(memory_dataset.cache_files))
# Map: 100%|██████████| 52002[/52002]
# 0
```

### Expected behavior

The `map` function should create cache regardless of the method the `Dataset` was created.

### Environment info

- `datasets` version: 2.14.2
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3

Comments:
This is expected behavior. You must provide `cache_file_name` when performing `.map` on an in-memory dataset for the result to be cached.
Title: load local json_file as dataset
Author: CiaoHe (NONE)
State: closed
Created: 2023-07-31T12:53:49Z

### Describe the bug

I tried to load local json file as dataset but failed to parsing json file because some columns are 'float' type.

### Steps to reproduce the bug

1. load json file with certain columns are 'float' type. For example `data = load_data("json", data_files=JSON_PATH)`
2. Then, the error will be triggered like `ArrowInvalid: Could not convert '-0.2253' with type str: tried to convert to double

### Expected behavior

Should allow some columns are 'float' type, at least it should convert those columns to str type.
I tried to avoid the error by naively convert the float item to str:
```python
# if col type is not str, we need to convert it to str
mapping = {}
for col in keys:
    if isinstance(dataset[0][col], str):
        mapping[col] = [row.get(col) for row in dataset]
    else:
        mapping[col] = [str(row.get(col)) for row in dataset]
```

### Environment info

- `datasets` version: 2.14.2
- Platform: Linux-5.4.0-52-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.0
- Pandas version: 2.0.1

Comments:
Hi! We use PyArrow to read JSON files, and PyArrow doesn't allow different value types in the same column. #5776 should address this.

In the meantime, you can combine `Dataset.from_generator` with the above code to cast the values to the same type. 

Thanks for your help!
Title: HF Datasets data access is extremely slow even when in memory
Author: NightMachinery (CONTRIBUTOR)
State: open
Created: 2023-07-31T11:12:19Z

### Describe the bug

Doing a simple `some_dataset[:10]` can take more than a minute.

Profiling it:
<img width="1280" alt="image" src="https://github.com/huggingface/datasets/assets/36224762/e641fb95-ff02-4072-9016-5416a65f75ab">

`some_dataset` is completely in memory with no disk cache.

This is proving fatal to my usage of HF Datasets. Is there a way I can forgo the arrow format and store the dataset as PyTorch tensors so that `_tensorize` is not needed? And is `_consolidate` supposed to take this long?

It's faster to produce the dataset from scratch than to access it from HF Datasets!

### Steps to reproduce the bug

I have uploaded the dataset that causes this problem [here](https://huggingface.co/datasets/NightMachinery/hf_datasets_bug1).

```python
#!/usr/bin/env python3
import sys
import time
import torch
from datasets import load_dataset


def main(dataset_name):
    # Start the timer
    start_time = time.time()

    # Load the dataset from Hugging Face Hub
    dataset = load_dataset(dataset_name)

    # Set the dataset format as torch
    dataset.set_format(type="torch")

    # Perform an identity map
    dataset = dataset.map(lambda example: example, batched=True, batch_size=20)

    # End the timer
    end_time = time.time()

    # Print the time taken
    print(f"Time taken: {end_time - start_time:.2f} seconds")


if __name__ == "__main__":
    dataset_name = "NightMachinery/hf_datasets_bug1"
    print(f"dataset_name: {dataset_name}")
    main(dataset_name)
```

### Expected behavior

_

### Environment info

- `datasets` version: 2.13.1
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3

Comments:
Possibly related:
- https://github.com/pytorch/pytorch/issues/22462
Title: TypeError when loading from GCP bucket
Author: bilelomrani1 (NONE)
State: closed
Created: 2023-07-30T23:03:00Z

### Describe the bug

Loading a dataset from a GCP bucket raises a type error. This bug was introduced recently (either in 2.14 or 2.14.1), and appeared during a migration from 2.13.1.

### Steps to reproduce the bug

Load any file from a GCP bucket:

```python
import datasets

datasets.load_dataset("json", data_files=["gs://..."])
```

The following exception is raised:

```python
Traceback (most recent call last):
...
packages/datasets/data_files.py", line 335, in resolve_pattern
    protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""
TypeError: can only concatenate tuple (not "str") to tuple
```

With a `GoogleFileSystem`, the attribute `fs.protocol` is a tuple `('gs', 'gcs')` and hence cannot be concatenated with a string.


### Expected behavior

The file should be loaded without exception.

### Environment info

- `datasets` version: 2.14.1
- Platform: macOS-13.2.1-x86_64-i386-64bit
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3

Comments:
Thanks for reporting, @bilelomrani1.

We are fixing it. 

We have fixed it. We are planning to do a patch release today.
Title: Dataset.get_nearest_examples does not return all feature values for the k most similar datapoints - side effect of Dataset.set_format
Author: aschoenauer-sebag (NONE)
State: closed
Created: 2023-07-28T20:31:59Z

### Describe the bug

Hi team!

I observe that there seems to be a side effect of `Dataset.set_format`: after setting a format and creating a FAISS index, the method `get_nearest_examples` from the `Dataset` class, fails to retrieve anything else but the embeddings themselves - not super useful. This is not the case if not using the `set_format` method: you can also retrieve any other feature value, such as an index/id/etc.

Are you able to reproduce what I observe?

### Steps to reproduce the bug

```python

from datasets import Dataset
import numpy as np

foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]}
foo = Dataset.from_dict(foo)
foo.set_format('numpy', ['vectors'])
foo.add_faiss_index('vectors')
new_vector = np.random.random(1024)
scores, res = foo.get_nearest_examples('vectors', new_vector, k=3)
```
This will return, for the resulting most similar vectors to `new_vector` - in particular it will not return the `ids` feature:
```
{'vectors': array([[random values ...]])}
```

### Expected behavior

The expected behavior happens when the `set_format` method is not called:
```python

from datasets import Dataset
import numpy as np

foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]}
foo = Dataset.from_dict(foo)
# foo.set_format('numpy', ['vectors'])
foo.add_faiss_index('vectors')
new_vector = np.random.random(1024)
scores, res = foo.get_nearest_examples('vectors', new_vector, k=3)
```
This *will*  return the `ids` of the similar vectors - with unfortunately a list of lists in lieu of the array I think for caching reasons - read it elsewhere
```
{'vectors': [[random values on multiple lines...]], 'ids': ['x', 'y', 'z']}
```

### Environment info

- `datasets` version: 2.12.0
- Platform: Linux-5.4.0-155-generic-x86_64-with-glibc2.31
- Python version: 3.10.6
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3

Comments:
Actually, my bad -- specifying
```python
foo.set_format('numpy', ['vectors'], output_all_columns=True)
```
fixes it.
Title: FilesIterable skips all the files after a hidden file
Author: dkrivosic (NONE)
State: closed
Created: 2023-07-28T07:25:57Z

### Describe the bug

When initializing `FilesIterable` with a list of file paths using `FilesIterable.from_paths`, it will discard all the files after a hidden file.
The problem is in [this line](https://github.com/huggingface/datasets/blob/88896a7b28610ace95e444b94f9a4bc332cc1ee3/src/datasets/download/download_manager.py#L233C26-L233C26) where `return` should be replaced by `continue`. 

### Steps to reproduce the bug

https://colab.research.google.com/drive/1SQlxs4y_LSo1Q89KnFoYDSyyKEISun_J#scrollTo=93K4_blkW-8-

### Expected behavior

The script should print all the files except the hidden one.

### Environment info

- `datasets` version: 2.14.1
- Platform: Linux-5.15.109+-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.16.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.3

Comments:
Thanks for reporting. We've merged a PR with a fix.
Title: Loading local data files initiates web requests
Author: lytning98 (NONE)
State: closed
Created: 2023-07-28T04:06:26Z

As documented in the [official docs](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/loading_methods#datasets.load_dataset.example-2), I tried to load datasets from local files by
```python
# Load a JSON file
from datasets import load_dataset
ds = load_dataset('json', data_files='path/to/local/my_dataset.json')
```
But this failed on a web request because I'm executing the script on a machine without Internet access. Stacktrace shows
```
in PackagedDatasetModuleFactory.__init__(self, name, data_dir, data_files, download_config, download_mode)
    940 self.download_config = download_config
    941 self.download_mode = download_mode
--> 942 increase_load_count(name, resource_type="dataset")
```
I've read from the source code that this can be fixed by setting environment variable to run in offline mode. I'm just wondering that is this an expected behaviour that even loading a LOCAL JSON file requires Internet access by default? And what's the point of requesting to `increase_load_count` on some server when loading just LOCAL data files?

Comments:
Title: fsspec dependency is set too low
Author: iXce (NONE)
State: closed
Created: 2023-07-27T20:08:22Z

### Describe the bug

fsspec.callbacks.TqdmCallback (used in https://github.com/huggingface/datasets/blob/73bed12ecda17d1573fd3bf73ed5db24d3622f86/src/datasets/utils/file_utils.py#L338) was first released in fsspec [2022.3.0](https://github.com/fsspec/filesystem_spec/releases/tag/2022.3.0, commit where it was added: https://github.com/fsspec/filesystem_spec/commit/9577c8a482eb0a69092913b81580942a68d66a76#diff-906155c7e926a9ff58b9f23369bb513b09b445f5b0f41fa2a84015d0b471c68cR180), however the dependency is set to 2021.11.1 https://github.com/huggingface/datasets/blob/main/setup.py#L129



### Steps to reproduce the bug

1. Install fsspec==2021.11.1
2. Install latest datasets==2.14.1
3. Import datasets, import fails due to lack of `fsspec.callbacks.TqdmCallback`

### Expected behavior

No import issue

### Environment info

N/A

Comments:
Thanks for reporting! A PR with a fix has just been merged.
Title: Support `fsspec` in `Dataset.to_<format>` methods
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2023-07-27T19:08:37Z
Labels: enhancement

Supporting this should be fairly easy.

Requested on the forum [here](https://discuss.huggingface.co/t/how-can-i-convert-a-loaded-dataset-in-to-a-parquet-file-and-save-it-to-the-s3/48353).

Comments:
Hi @mariosasko unless someone's already working on it, I guess I can tackle it!

Hi! Sure, feel free to tackle this.

#self-assign

I'm assuming this should just cover `to_csv`, `to_parquet`, and `to_json`, right? As `to_list` and `to_dict` just return Python objects, `to_pandas` returns a `pandas.DataFrame` and `to_sql` just inserts into a SQL DB, is that right?

Fixed by #6096.
Title: Changing pixel values of images in the Winoground dataset
Author: ZitengWangNYU (NONE)
State: open
Created: 2023-07-27T17:55:35Z

Hi, as I followed the instructions, with lasted "datasets" version:
"
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
"
I got slightly different datasets in colab and in my hpc environment. Specifically, the pixel values of images are slightly different. 
I thought it was due to the package version difference, but today's morning I found out that my winoground dataset in colab became the same with the one in my hpc environment. The dataset in colab can produce the correct result but now it is gone as well. 

Can you help me with this? What causes the datasets to have the wrong pixel values?

Comments:
Title: resume_download with streaming=True
Author: NicolasMICAUX (NONE)
State: closed
Created: 2023-07-26T14:08:22Z

### Describe the bug

I used:
```
dataset = load_dataset(
    "oscar-corpus/OSCAR-2201",
    token=True,
    language="fr",
    streaming=True,
    split="train"
)
```
Unfortunately, the server had a problem during the training process. I saved the step my training stopped at.
But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset?

`download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True.

### Steps to reproduce the bug

```
from datasets import load_dataset, DownloadConfig
dataset = load_dataset(
    "oscar-corpus/OSCAR-2201",
    token=True,
    language="fr",
    streaming=True,  # optional
    split="train",
    download_config=DownloadConfig(resume_download=True)
)
# interupt the run and try to relaunch it => this restart from scratch
```

### Expected behavior

I would expect a parameter to start streaming from a given index in the dataset.

### Environment info

- `datasets` version: 2.14.0
- Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.0

Comments:
Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380). 

Ok thank you for your answer

I'm closing this as a duplicate of #5380
Title: Why is the speed difference of gen example so big?
Author: pixeli99 (NONE)
State: closed
Created: 2023-07-21T03:34:49Z

```python
def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
        with open(metadata_path, 'r') as file:
            metadata = json.load(file)

        for idx, item in enumerate(metadata):
            image_path = item.get('image_path')
            text_content = item.get('text_content')
            image_data = open(image_path, "rb").read()
            yield idx, {
                "text": text_content,
                "image": {
                    "path": image_path,
                    "bytes": image_data,
                },
                "conditioning_image": {
                    "path": image_path,
                    "bytes": image_data,
                },
            }
```
Hello, 

I use the above function to deal with my local data set, but I am very surprised that the speed at which I generate example is very different. When I start a training task, **sometimes 1000examples/s, sometimes only 10examples/s.**

![image](https://github.com/huggingface/datasets/assets/46072190/cdc17661-8267-4fd8-b30c-b74d505efd9b)

I'm not saying that speed is changing all the time. I mean, the reading speed is different in different training, which will cause me to start training over and over again until the speed of this generation of examples is normal.

Comments:
Hi!

It's hard to explain this behavior without more information. Can you profile the slower version with the following code
```python
import cProfile, pstats
from datasets import load_dataset

with cProfile.Profile() as profiler:
    ds = load_dataset(...)

stats = pstats.Stats(profiler).sort_stats("cumtime")
stats.print_stats()
```
and share the output?
Title: Fix host URL in The Pile datasets 
Author: nickovchinnikov (NONE)
State: open
Created: 2023-07-20T09:08:52Z

### Describe the bug

In  #3627 and  #5543, you tried to fix the host URL in The Pile datasets. But both URLs are not working now:

`HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst`
And
`ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))`

### Steps to reproduce the bug

```
from datasets import load_dataset

# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://mystic.the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```

Result:
`ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))`

And

```
from datasets import load_dataset

# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```

Result:
`HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst`

### Expected behavior

Downloading as normal.

### Environment info

Environment info

    `datasets` version: 2.9.0
    Platform: Windows
    Python version: 3.9.13

Comments:
Title: Multi-processed `Dataset.map` slows down a lot when `import torch`
Author: ShinoharaHare (NONE)
State: closed
Created: 2023-07-20T06:36:14Z
Labels: duplicate

### Describe the bug

When using `Dataset.map` with `num_proc > 1`, the speed slows down much if I add `import torch` to the start of the script even though I don't use it.

I'm not sure if it's `torch` only or if any other package that is "large" will also cause the same result.
BTW, `import lightning` also slows it down.

Below are the progress bars of `Dataset.map`, the only difference between them is with or without `import torch`, but the speed varies by 6-7 times.

- without `import torch` ![image](https://github.com/huggingface/datasets/assets/47121592/0233055a-ced4-424a-9f0f-32a2afd802c2)
- with `import torch` ![image](https://github.com/huggingface/datasets/assets/47121592/463eafb7-b81e-4eb9-91ca-fd7fe20f3d59)


### Steps to reproduce the bug

Below is the code I used, but I don't think the dataset and the mapping function have much to do with the phenomenon.

```python3
from datasets import load_from_disk, disable_caching
from transformers import AutoTokenizer
# import torch
# import lightning

def rearrange_datapoints(
    batch,
    tokenizer,
    sequence_length,
):
    datapoints = []
    
    input_ids = []
    for x in batch['input_ids']:
        input_ids += x
        while len(input_ids) >= sequence_length:
            datapoint = input_ids[:sequence_length]
            datapoints.append(datapoint)
            input_ids[:sequence_length] = []

    if input_ids:
        paddings = [-1] * (sequence_length - len(input_ids))
        datapoint = paddings + input_ids if tokenizer.padding_side == 'left' else input_ids + paddings
        datapoints.append(datapoint)
    
    batch['input_ids'] = datapoints
    return batch


if __name__ == '__main__':
    disable_caching()

    tokenizer = AutoTokenizer.from_pretrained('...', use_fast=False)
    dataset = load_from_disk('...')
    
    dataset = dataset.map(
        rearrange_datapoints,
        fn_kwargs=dict(
            tokenizer=tokenizer,
            sequence_length=2048,
        ),
        batched=True,
        num_proc=8,
    )

```

### Expected behavior

The multi-processed `Dataset.map` function speed between with and without `import torch` should be the same.

### Environment info

- `datasets` version: 2.13.1
- Platform: Linux-3.10.0-1127.el7.x86_64-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1

Comments:
A duplicate of https://github.com/huggingface/datasets/issues/5929
Title: Change package name from "datasets" to something less generic
Author: jack-jjm (NONE)
State: closed
Created: 2023-07-19T19:53:28Z
Labels: enhancement

### Feature request

I'm repeatedly finding myself in situations where I want to have a package called `datasets.py` or `evaluate.py` in my code and can't because those names are being taken up by Huggingface packages. While I can understand how (even from the user's perspective) it's aesthetically pleasing to have nice terse library names, ultimately a library hogging simple names like this is something I find short-sighted, impractical and at my most irritable, frankly rude.

My preference would be a pattern like what you get with all the other big libraries like numpy or pandas:

```
import huggingface as hf
# hf.transformers, hf.datasets, hf.evaluate
```

or things like

```
import huggingface.transformers as tf
# tf.load_model(), etc
```

If this isn't possible for some technical reason, at least just call the packages something like `hf_transformers` and so on.

I realize this is a very big change that's probably been discussed internally already, but I'm making this issue and sister issues on each huggingface project just to start the conversation and begin tracking community feeling on the matter, since I suspect I'm not the only one who feels like this.

Sorry if this has been requested already on this issue tracker, I couldn't find anything looking for terms like "package name".

Sister issues:
- [transformers](https://github.com/huggingface/transformers/issues/24934)
- **datasets**
- [evaluate](https://github.com/huggingface/evaluate/issues/476)

### Motivation

Not taking up package names the user is likely to want to use.

### Your contribution

No - more a matter of internal discussion among core library authors.

Comments:
This would break a lot of existing code, so we can't really do this.

I encountered this issue while working on a large project with 6+ years history. We have a submodule named datasets in the backend, and face a big challenge incorporating huggingface datasets into the project, especially considering django app renaming and other issues.
It would be nice if the authors at least provide a recipe on how to avoid name conflict in this situation.
Title: Skipping shard in the remote repo and resume upload
Author: rs9000 (NONE)
State: closed
Created: 2023-07-19T09:25:26Z

### Describe the bug

For some reason when I try to resume the upload of my dataset, it is very slow to reach the index of the shard from which to resume the uploading.

From my understanding, the problem is in this part of the code:

arrow_dataset.py
```python
for index, shard in logging.tqdm(
            enumerate(itertools.chain([first_shard], shards_iter)),
            desc="Pushing dataset shards to the dataset hub",
            total=num_shards,
            disable=not logging.is_progress_bar_enabled(),
        ):
            shard_path_in_repo = path_in_repo(index, shard)
            # Upload a shard only if it doesn't already exist in the repository
            if shard_path_in_repo not in data_files:
```

In particular, iterating the generator is slow during the call:
```python
self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
```

I wonder if it is possible to avoid calling this function for shards that are already uploaded and just start from the correct shard index.

### Steps to reproduce the bug

1. Start the upload
```python
dataset = load_dataset("imagefolder", data_dir=DATA_DIR, split="train", drop_labels=True)    
dataset.push_to_hub("repo/name")
```
2. Stop and restart the upload after hundreds of shards

### Expected behavior

Skip the uploaded shards faster.

### Environment info

- `datasets` version: 2.5.1
- Platform: Linux-4.18.0-193.el8.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.16
- PyArrow version: 12.0.1
- Pandas version: 2.0.2

Comments:
Hi! `_select_contiguous` fetches a (zero-copy) slice of the dataset's Arrow table to build a shard, so I don't think this part is the problem. To me, the issue seems to be the step where we embed external image files' bytes (a lot of file reads). You can use `.map` with multiprocessing to perform this step before `push_to_hub` in a faster manner and cache it to disk:
```python
from datasets.table import embed_table_storage
# load_dataset(...)
format = dataset.format
dataset = dataset.with_format("arrow")
dataset = dataset.map(embed_table_storage, batched=True)
dataset = dataset.with_format(**format)
# push_to_hub(...)
```

(In Datasets 3.0, these external bytes will be written to an Arrow file when generating a dataset to avoid this "embed" step)

Hi, thanks, this solution saves some time.
But can't we avoid embedding all external image files bytes with each push, skipping the images that have already been pushed into the repo?

Edit: Ok I missed the part of cache it manually on the disk the first time, this solves the problem. Thank you
Title: Update `ruff` version in pre-commit config 
Author: polinaeterna (CONTRIBUTOR)
State: closed
Created: 2023-07-18T17:13:50Z

so that it corresponds to the one that is being run in CI

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6049). All of your documentation changes will be reflected on that endpoint.

I've updated the `ruff`'s pre-commit version as part of https://github.com/huggingface/datasets/pull/6434, so feel free to close this PR.
Title: when i use datasets.load_dataset, i encounter the http connect error!
Author: yangy1992 (NONE)
State: closed
Created: 2023-07-18T10:16:34Z

### Describe the bug

`common_voice_test = load_dataset("audiofolder", data_dir="./dataset/",cache_dir="./cache",split=datasets.Split.TEST)`
when i run the code above, i got the error as below:
--------------------------------------------
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f299ed082e0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))")))


--------------------------------------------------
My all data is on local machine, why does it need to connect the internet? how can i fix it, because my machine cannot connect the internet.

### Steps to reproduce the bug

1

### Expected behavior

no error when i use the load_dataset func

### Environment info

python=3.8.15

Comments:
The `audiofolder` loader is not available in version `2.3.2`, hence the error. Please run the `pip install -U datasets` command to update the `datasets` installation to make `load_dataset("audiofolder", ...)` work.
Title: Support proxy and user-agent in fsspec calls
Author: lhoestq (MEMBER)
State: open
Created: 2023-07-17T16:39:26Z
Labels: enhancement

good second issue

Since we switched to the new HfFileSystem we no longer apply user's proxy and user-agent.

Using the HTTP_PROXY and HTTPS_PROXY environment variables works though since we use aiohttp to call the HF Hub.

This can be implemented in `_prepare_single_hop_path_and_storage_options`.

Though ideally the `HfFileSystem` could support passing at least the proxies

Comments:
hii @lhoestq can you assign this issue to me?


You can reply "#self-assign" to this issue to automatically get assigned to it :)
Let me know if you have any questions or if I can help

#2289 

Actually i am quite new to figure it out how everything goes and done 

> You can reply "#self-assign" to this issue to automatically get assigned to it :)
> Let me know if you have any questions or if I can help

when i wrote #self-assign it automatically got converted to some number is it correct or i have done it some wrong way, I am quite new to open source thus wanna try to learn and explore it

#2289 #self-assign 

Ah yea github tries to replace the #self-assign with an issue link. I guess you can try to copy-paste instead to see if it works

Anyway let me assign you manually

thanks a lot @lhoestq ! though i have a very lil idea of the issue, i am new. as i said before,  but gonna try my best shot to do it.
can you please suggest some tips or anything from your side, how basically we approach it will be really helpfull.
Will try my best!

The HfFileSystem from the `huggingface_hub` package can already read the HTTP_PROXY and HTTPS_PROXY environment variables. So the remaining thing missing is the `user_agent` that the user may include in a `DownloadConfig` object. The user agent can be used for regular http calls but also calls to the HfFileSystem.

- for http, the `user_agent` isn't passed from `DownloadConfig` to `get_datasets_user_agent` in `_prepare_single_hop_path_and_storage_options` in `streaming_download_manager.py` so we need to include it
- for HfFileSystem I think it requires a PR in https://github.com/huggingface/huggingface_hub to include it in the `HfFileSystem.__init__`

Hi @lhoestq 👋🏼

Is anyone currently working on this? If not, I'd like to pick it up.

As I understand it:
- The `user_agent` from `DownloadConfig` isn't currently passed to `get_datasets_user_agent()` inside `_prepare_single_hop_path_and_storage_options`.
- I'll update that function to include the correct `user-agent` in the `headers`.
- For full support, we may also need a change in `huggingface_hub` to let `HfFileSystem` accept custom headers.

Please let me know if this approach sounds good or if you’d prefer it handled differently 🙌
Title: Loading column subset from parquet file produces error since version 2.13
Author: kklemon (NONE)
State: closed
Created: 2023-07-16T09:13:07Z

### Describe the bug

`load_dataset` allows loading a subset of columns from a parquet file with the `columns` argument. Since version 2.13, this produces the following error:

```
Traceback (most recent call last):                      
  File "/usr/lib/python3.10/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
    for _, table in generator:
  File "/usr/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
    raise ValueError(
ValueError: Tried to load parquet data with columns '['sepal_length']' with mismatching features '{'sepal_length': Value(dtype='float64', id=None), 'sepal_width': Value(dtype='float64', id=None), 'petal_length': Value(dtype='float64', id=None), 'petal_width': Value(dtype='float64', id=None), 'species': Value(dtype='string', id=None)}'
```

This seems to occur because `datasets` is checking whether the columns in the schema exactly match the provided list of columns, instead of whether they are a subset.

### Steps to reproduce the bug

```python
# Prepare some sample data

import pandas as pd

iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
iris.to_parquet('iris.parquet')

# ['sepal_length', 'sepal_width', 'petal_length', 'petal_width',  'species']
print(iris.columns)

# Load data with datasets

from datasets import load_dataset

# Load full parquet file
dataset = load_dataset('parquet', data_files='iris.parquet')

# Load column subset; throws error for datasets>=2.13
dataset = load_dataset('parquet', data_files='iris.parquet', columns=['sepal_length'])
```

### Expected behavior

No error should be thrown and the given column subset should be loaded.

### Environment info

- `datasets` version: 2.13.0
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.9
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3

Comments:
Title: Documentation links to examples are broken
Author: david-waterworth (NONE)
State: closed
Created: 2023-07-15T04:54:50Z

### Describe the bug

The links at the bottom of [add_dataset](https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html) to examples of specific datasets are all broken, for example

- text classification: [ag_news](https://github.com/huggingface/datasets/blob/master/datasets/ag_news/ag_news.py) (original data are in csv files)

### Steps to reproduce the bug

Click on links to examples from latest documentation

### Expected behavior

Links should be up to date - it might be more stable to link to https://huggingface.co/datasets/ag_news/blob/main/ag_news.py

### Environment info

dataset v1.2.1

Comments:
These docs are outdated (version 1.2.1 is over two years old). Please refer to [this](https://huggingface.co/docs/datasets/dataset_script) version instead.

Initially, we hosted datasets in this repo, but now you can find them [on the HF Hub](https://huggingface.co/datasets) (e.g. the [`ag_news`](https://huggingface.co/datasets/ag_news/blob/main/ag_news.py) script)

Sorry I thought I'd selected the latest version.
Title: Dataset representation
Author: Ganryuu (NONE)
State: open
Created: 2023-07-14T15:42:37Z

__repr__ and _repr_html_ now both are similar to that of Polars

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6035). All of your documentation changes will be reflected on that endpoint.
Title: load_dataset hangs on WSL
Author: Andy-Zhou2 (NONE)
State: closed
Created: 2023-07-14T09:03:10Z

### Describe the bug

load_dataset simply hangs. It happens once every ~5 times, and interestingly hangs for a multiple of 5 minutes (hangs for 5/10/15 minutes). Using the profiler in PyCharm shows that it spends the time at <method 'connect' of '_socket.socket' objects>. However, a local cache is available so I am not sure why socket is needed. ([profiler result](https://ibb.co/0Btbbp8))

It only happens on WSL for me. It works for native Windows and my MacBook. (cache quickly recognized and loaded within a second).



### Steps to reproduce the bug

I am using Ubuntu 22.04.2 LTS (GNU/Linux 5.15.90.1-microsoft-standard-WSL2 x86_64)

Python 3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0] on linux
>>> import datasets
>>> datasets.load_dataset('ai2_arc', 'ARC-Challenge')  # hangs for 5/10/15 minutes

### Expected behavior

cache quickly recognized and loaded within a second

### Environment info

Please let me know if I should provide more environment information.

Comments:
Even if a dataset is cached, we still make requests to check whether the cache is up-to-date. [This](https://huggingface.co/docs/datasets/v2.13.1/en/loading#offline) section in the docs explains how to avoid them and directly load the cached version.

Thanks - that works! However it doesn't resolve the original issue (but I am not sure if it is a WSL problem)

We use `requests` to make HTTP requests (and `aiohttp` in the streaming mode), so I don't think we can provide much help regarding the socket issue (it probably has something to do with WSL).
Title: `map` function doesn't fully utilize `input_columns`.
Author: kwonmha (NONE)
State: closed
Created: 2023-07-14T08:49:28Z

### Describe the bug

I wanted to select only some columns of data.
And I thought that's why the argument `input_columns` exists.
What I expected is like this:
If there are ["a", "b", "c", "d"] columns, and if I set `input_columns=["a", "d"]`, the data will have only ["a", "d"] columns.

But it doesn't select columns.
It preserves existing columns.
The main cause is `update` function of `dictionary` type `transformed_batch`.

https://github.com/huggingface/datasets/blob/682d21e94ab1e64c11b583de39dc4c93f0101c5a/src/datasets/iterable_dataset.py#L687-L691

`transformed_batch` gets all the columns by `transformed_batch = dict(batch)`.
Even `function_args` selects `input_columns`, `update` preserves columns other than `input_columns`.
I think it should take a new dictionary with columns in `input_columns` like this:
```
# transformed_batch = dict(batch)
# transformed_batch.update(self.function(*function_args, **self.fn_kwargs)

# This is what I think correct.
transformed_batch = self.function(*function_args, **self.fn_kwargs)
```

Let me know how to use `input_columns`.

### Steps to reproduce the bug

Described all above.

### Expected behavior

Described all above.

### Environment info

datasets: 2.12
python: 3.8

Comments:
Title: Argument type for map function changes when using `input_columns` for `IterableDataset`
Author: kwonmha (NONE)
State: closed
Created: 2023-07-14T05:11:14Z

### Describe the bug

I wrote `tokenize(examples)` function as an argument for `map` function for `IterableDataset`.
It process dictionary type `examples` as a parameter.
It is used in `train_dataset = train_dataset.map(tokenize, batched=True)`
No error is raised.

And then, I found some unnecessary keys and values in `examples` so I added `input_columns` argument to `map` function to select keys and values.
It gives me an error saying
```
TypeError: tokenize() takes 1 positional argument but 3 were given.
```

The code below matters.
https://github.com/huggingface/datasets/blob/406b2212263c0d33f267e35b917f410ff6b3bc00/src/datasets/iterable_dataset.py#L687

For example, `inputs = {"a":1, "b":2, "c":3}`.
If `self.input_coluns` is `None`,
`inputs` is a dictionary type variable and `function_args` becomes a `list` of a single `dict` variable.
`function_args` becomes `[{"a":1, "b":2, "c":3}]`

Otherwise, lets say `self.input_columns = ["a", "c"]`
`[inputs[col] for col in self.input_columns]` results in `[1, 3]`.
I think it should be `[{"a":1, "c":3}]`.

I want to ask if the resulting format is intended.
Maybe I can modify `tokenize()` to have 2 parameters in this case instead of having 1 dictionary.
But this is confusing to me.
Or it should be fixed as `[{col:inputs[col] for col in self.input_columns}]`

### Steps to reproduce the bug

Run `map` function of `IterableDataset` with `input_columns` argument.

### Expected behavior

`function_args` looks better to have same format.
I think it should be `[{"a":1, "c":3}]`.

### Environment info

dataset version: 2.12
python: 3.8

Comments:
Yes, this is intended.
Title: Switch to huggingface_hub's HfFileSystem
Author: lhoestq (MEMBER)
State: closed
Created: 2023-07-11T16:24:40Z
Labels: enhancement

instead of the current datasets.filesystems.hffilesystem.HfFileSystem which can be slow in some cases

related to https://github.com/huggingface/datasets/issues/5846 and https://github.com/huggingface/datasets/pull/5919

Comments:
Title: Dataset string representation enhancement
Author: Ganryuu (NONE)
State: open
Created: 2023-07-11T13:38:25Z

my attempt at #6010  
not sure if this is the right way to go about it, I will wait for your feedback

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6016). All of your documentation changes will be reflected on that endpoint.

It we could have something similar to Polars, that would be great.

This is what Polars outputs: 
* `__repr__`/`__str__` :
```
shape: (67_349, 3)
┌───────┬───────────────────────────────────┬───────┐
│ idx   ┆ sentence                          ┆ label │
│ ---   ┆ ---                               ┆ ---   │
│ i32   ┆ str                               ┆ i64   │
╞═══════╪═══════════════════════════════════╪═══════╡
│ 0     ┆ hide new secretions from the par… ┆ 0     │
│ 1     ┆ contains no wit , only labored g… ┆ 0     │
│ 2     ┆ that loves its characters and co… ┆ 1     │
│ 3     ┆ remains utterly satisfied to rem… ┆ 0     │
│ …     ┆ …                                 ┆ …     │
│ 67345 ┆ anguish , anger and frustration   ┆ 0     │
│ 67346 ┆ at achieving the modest , crowd-… ┆ 1     │
│ 67347 ┆ a patient viewer                  ┆ 1     │
│ 67348 ┆ this new jangle of noise , mayhe… ┆ 0     │
└───────┴───────────────────────────────────┴───────┘
```

* `_repr_html_`:
<img width="251" alt="Screenshot 2023-07-12 at 18 25 58" src="https://github.com/huggingface/datasets/assets/47462742/5d04519d-f302-4411-9fbc-7445bdf53b23">
Title: [FR] `map` should reuse unchanged columns from the previous dataset to avoid disk usage
Author: NightMachinery (CONTRIBUTOR)
State: open
Created: 2023-07-10T06:42:20Z
Labels: enhancement

good second issue

### Feature request

Currently adding a new column with `map` will cause all the data in the dataset to be duplicated and stored/cached on the disk again. It should reuse unchanged columns. 

### Motivation

This allows having datasets with different columns but sharing some basic columns. Currently, these datasets would become too expensive to store and one would need some kind of on-the-fly join; which also doesn't seem implemented.

### Your contribution

_

Comments:
You can use the `remove_columns` parameter in `map` to avoid duplicating the columns (and save disk space) and then concatenate the original dataset with the map result:
```python
from datasets import concatenate_datasets
# dummy example
ds_new = ds.map(lambda x: {"new_col": x["col"] + 2}, remove_columns=ds.column_names)
ds_combined = concatenate_datasets([ds, ds_new], axis=1)
```

Doing this automatically is hard to implement efficiently unless we know ahead of time which existing columns will be modified by a `map` transform. We have this info when `input_columns` are specified, so I think this is the only case we can optimize.

Hi @mariosasko 👋 I’d like to start working on this issue.
Title: Improve `Dataset`'s string representation
Author: mariosasko (COLLABORATOR)
State: open
Created: 2023-07-07T16:38:03Z
Labels: enhancement

Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows.

We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit.

Comments:
I want to take a shot at this if possible 

Yes, feel free to work on this.

You can check the PyArrow Table `__repr__`  and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas.

@mariosasko are there any other similar issues that I could work on? I see this has been already solved.
Title: Dataset.from_generator consistently freezes at ~1000 rows
Author: andreemic (NONE)
State: closed
Created: 2023-07-05T16:06:48Z

### Describe the bug

Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I

Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset.

I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset.

Let me know if you have ideas how to resolve it!

### Steps to reproduce the bug

```python
from datasets import Dataset
import numpy as np

def gen():
    for row in range(10000):
        yield {"i": np.random.rand(512, 512, 3)}
        
Dataset.from_generator(gen)
# -> 90% of the time gets stuck around 1000 rows
```

### Expected behavior

Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on.

### Environment info

- `datasets` version: 2.8.0
- Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 12.0.1
- Pandas version: 1.5.1

Comments:
By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({"i": datasets.Array3D(shape=(512,512,3), dtype="float32")})` should be faster).

Our support for multi-dim arrays could be better, and we plan to improve it as part of https://github.com/huggingface/datasets/issues/5272.

> By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({"i": datasets.Array3D(shape=(512,512,3), dtype="float32")})` should be faster).
> 
> Our support for multi-dim arrays could be better, and we plan to improve it as part of #5272.

Thanks for the explanation! The Image array was just for demonstration, I use PIL Images in practice. Does that make a difference? What's the best approach for a dataset with PIL Images as rows?

It's best to use the `datasets.Image()` feature type for PIL images (to save space) :)
Title: Add KLUE-MRC metrics
Author: ingyuseong (NONE)
State: closed
Created: 2023-07-03T12:11:10Z

## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension)

Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue).
KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC.

Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC.

- [x] All tests passed
- [x] Added a metric card (referred the metric card of SQuAD 2.0)
- [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed

### References
- [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf)
- [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue)
- #2416

Comments:
The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing).