xyx138/github-issues huggingface dataset:
cast_column to Sequence(subfeatures_dict) has err

### Describe the bug

I am working with the following demo code:
```
from datasets import load_dataset
from datasets.features import Sequence, Value, ClassLabel, Features

ais_dataset = load_dataset("/data/ryan.gao/ais_dataset_cache/raw/1978/")
ais_dataset = ais_dataset["train"]

def add_class(example):
    example["my_labeled_bbox"] = {"bbox": [100,100,200,200], "label": "cat"}
    return example

ais_dataset = ais_dataset.map(add_class, batched=False, num_proc=32)
ais_dataset = ais_dataset.cast_column("my_labeled_bbox", Sequence(
                                                        {
                                                            "bbox": Sequence(Value(dtype="int64")),
                                                            "label": ClassLabel(names=["cat", "dog"])
                                                        }))

print(ais_dataset[0])
```
However, executing this code results in an error:

```
  File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature
    raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
int64
to
Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)
```
Upon examining the source code in datasets/table.py at line 2035:

```
        if isinstance(feature, Sequence) and isinstance(feature.feature, dict):
            feature = {
                name: Sequence(subfeature, length=feature.length) for name, subfeature in feature.feature.items()
            }
```

I noticed that if subfeature is of type Sequence, the code results in Sequence(Sequence(...), ...) and Sequence(ClassLabel(...), ...), which appears to be the source of the error.


### Steps to reproduce the bug

run my demo code

### Expected behavior

no exception

### Environment info

python 3.9 
datasets: 2.16.1
Wrong path for cache directory in offline mode

### Describe the bug

Dear huggingfacers,

I'm trying to use a subset of the-stack dataset. When I run the command the first time

```
dataset = load_dataset(
    path='bigcode/the-stack',
    data_dir='data/fortran',
    split='train' )
```

It downloads the files and caches them normally.

Nevertheless, since my compute nodes are not online (`HF_DATASETS_OFFLINE=1`) . Whenever I try to run the command again, the library is passing the wrong cache path:


`Cache directory for the-stack doesn't exist at /Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data%2Ffortran-data_dir=data%2Ffortran`

when the right path is:

`'/Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data\%2Ffortran`

Not sure why those redundancies are included in the path.  If I try adding the correct path through the the cache_dir argument it throws an error:

ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'bigcode/the-stack': Offline mode is enabled.


Your help with this issue is greatly appreciated. Thanks a lot for the great work.


### Steps to reproduce the bug

1: 

`dataset = load_dataset(
    path='bigcode/the-stack',
    data_dir='data/fortran',
    split='train' )`


2: 

`HF_DATASETS_OFFLINE=1`

3:

`dataset = load_dataset(
    path='bigcode/the-stack',
    data_dir='data/fortran',
    split='train' )`


### Expected behavior

being able to use the cached data

### Environment info

several different systems
Transform fingerprint collisions due to setting fixed random seed

### Describe the bug

The transform fingerprinting logic relies on the `random` library for random bits when the function is not hashable (e.g. bound methods as used in `trl`: https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py#L356). This causes collisions when the training code sets a fixed random seed, which is common practice: https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_full.yaml#L45.

This results in fingerprint collisions which leads to silently loading incorrect cache files corresponding to completely different datasets.

### Steps to reproduce the bug

n/a

### Expected behavior

Use `uuid` v4 instead of `random.getrandbits()`

### Environment info

`datasets` main branch
Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace

While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.

## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
    "caner",
    config_name="default",
    commit_message="Convert dataset to Parquet",
    commit_description="Convert dataset to Parquet.",
    create_pr=True,
    token=token,
)
```
creates the additional dataset `albertvillanova/caner`.
Allow concatenation of datasets with mixed structs

Fixes #6466 

The idea is to do a recursive check for structs. PyArrow handles it well enough.


For a demo you can do:

```python
from datasets import Dataset, concatenate_datasets

ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]})
ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]})
print(concatenate_datasets([ds, ds2]).features)
print(concatenate_datasets([ds, ds2]).to_dict())
```

Now both the features and the rows are fixed.

I note that Sequence suffers from the same problem, so I can fix that in a future PR once this one is merged.
losing DatasetInfo in Dataset.map when num_proc > 1

### Describe the bug

Hello and thanks for developing this package!

When I process a Dataset with the map function using multiple processors some set attributes of the DatasetInfo get lost and are None in the resulting Dataset.    

### Steps to reproduce the bug

```python
from datasets import Dataset, DatasetInfo


def run_map(num_proc):
    dataset = Dataset.from_dict(
        {"col1": [0, 1], "col2": [3, 4]},
        info=DatasetInfo(
            dataset_name="my_dataset",
        ),
    )

    ds = dataset.map(lambda x: x, num_proc=num_proc)
    print(ds.info.dataset_name)


run_map(1)
run_map(2)
```

This puts out:

```bash
Map: 100%|██████████| 2/2 [00:00<00:00, 724.66 examples/s]
my_dataset
Map (num_proc=2): 100%|██████████| 2/2 [00:00<00:00, 18.25 examples/s]
None
```

### Expected behavior

I expect the DatasetInfo to be kept as it was and there should be no difference in the output of running map with num_proc=1 and num_proc=2. 

Expected output:
```bash
Map: 100%|██████████| 2/2 [00:00<00:00, 724.66 examples/s]
my_dataset
Map (num_proc=2): 100%|██████████| 2/2 [00:00<00:00, 18.25 examples/s]
my_dataset
```

### Environment info

- `datasets` version: 2.16.1
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.17
- Python version: 3.8.18
- `huggingface_hub` version: 0.20.2
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.9.2
np.fromfile not supported

How to do np.fromfile  to use it like np.load


```python
def xnumpy_fromfile(filepath_or_buffer, *args, download_config: Optional[DownloadConfig] = None, **kwargs):
    import numpy as np

    if hasattr(filepath_or_buffer, "read"):
        return np.fromfile(filepath_or_buffer, *args, **kwargs)
    else:
        filepath_or_buffer = str(filepath_or_buffer)
        return np.fromfile(xopen(filepath_or_buffer, "rb", download_config=download_config).read(), *args, **kwargs)
```
this is not work
Fix for Incorrect ex_iterable used with multi num_worker

Corrects an issue where `self._ex_iterable` was erroneously used instead of `ex_iterable`, when both Distributed Data Parallel (DDP) and multi num_worker are used concurrently. This improper usage led to the generation of incorrect `shards_indices`, subsequently causing issues with the control flow responsible for worker creation. The fix ensures the appropriate iterable is used, thus providing a more accurate determination of whether a new worker should be instantiated or not.
fix os.listdir return name is empty string

fix #6588

xlistdir return name is empty string

for example：
`
from datasets.download.streaming_download_manager import xjoin
from datasets.download.streaming_download_manager import xlistdir
config = DownloadConfig(storage_options=options)
manger = StreamingDownloadManager("ILSVRC2012",download_config=config)
input_path = "lakefs://datalab/main/imagenet/ILSVRC2012.zip"


download_files = manger.download_and_extract(input_path)

current_dir = xjoin(download_files,"ILSVRC2012/Images/ILSVRC2012_img_train")
folder_list = xlistdir(current_dir)

`
Unable to load `eli5` dataset with streaming

### Describe the bug

Unable to load `eli5` dataset with streaming.

### Steps to reproduce the bug

This fails with FileNotFoundError: https://files.pushshift.io/reddit/submissions 
```
from datasets import load_dataset
load_dataset("eli5", streaming=True)
```

This works correctly.
```
from datasets import load_dataset
load_dataset("eli5")
```

### Expected behavior

- Loading `eli5` dataset should not raise an error under the streaming mode.
- Or at the very least, show a warning that streaming mode is not supported with `eli5` dataset.

### Environment info

- `datasets` version: 2.16.1
- Platform: Linux-6.2.0-39-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.6.0
document page 404 not found after redirection

### Describe the bug

The redirected page encountered 404 not found.

### Steps to reproduce the bug

1. In this tutorial: https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt 
original md: https://github.com/huggingface/course/blob/2c733c2246b8b7e0e6f19a9e5d15bb12df43b2a3/chapters/en/chapter5/4.mdx#L49

```
By default, 🤗 Datasets will decompress the files needed to load a dataset. If you want to preserve hard drive space, you can pass `DownloadConfig(delete_extracted=True)` to the `download_config` argument of `load_dataset()`. See the [documentation](https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig) for more details.
```
The documentation points to `https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig`

it shows `The documentation page PACKAGE_REFERENCE/BUILDER_CLASSES.HTML doesn’t exist in v2.16.1, but exists on the main version. Click [here](https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html) to redirect to the main version of the documentation.`

But the redirected website `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html` is 404 not found.

### Expected behavior

I Guess the redirected webisite should be 
`https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes`    (without `.html`)
or `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes#datasets.DownloadConfig`.

### Environment info

Datasets main
No online docs for 2.16 release

We do not have the online docs for the latest minor release 2.16 (2.16.0 nor 2.16.1).

In the online docs, the latest version appearing is 2.15.0: https://huggingface.co/docs/datasets/index

![Screenshot from 2024-01-09 08-43-08](https://github.com/huggingface/datasets/assets/8515462/83613222-867f-41f4-8833-7a4a76582f44)
AttributeError: 'str' object has no attribute 'to'

### Describe the bug

```
--------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
[<ipython-input-6-80c6086794e8>](https://localhost:8080/#) in <cell line: 10>()
      8 		  report_to="wandb")
      9 
---> 10 trainer = Trainer(
     11     model=model,
     12     args=training_args,

1 frames
[/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in _move_model_to_device(self, model, device)
    688 
    689     def _move_model_to_device(self, model, device):
--> 690         model = model.to(device)
    691         # Moving a model to an XLA device disconnects the tied weights, so we have to retie them.
    692         if self.args.parallel_mode == ParallelMode.TPU and hasattr(model, "tie_weights"):

AttributeError: 'str' object has no attribute 'to'
```

### Steps to reproduce the bug

here is the notebook:
```
https://colab.research.google.com/drive/10JDBNsLlYrQdnI2FWfDK3F5M8wvVUDXG?usp=sharing

```


### Expected behavior

run the Training 

### Environment info

Colab Notebook , T4
I train controlnet_sdxl in bf16 datatype, got unsupported ERROR in datasets

### Describe the bug

```
Traceback (most recent call last):
  File "train_controlnet_sdxl.py", line 1252, in <module>
    main(args)
  File "train_controlnet_sdxl.py", line 1013, in main
    train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
  File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 592, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3093, in map
    for rank, done, content in Dataset._map_single(**dataset_kwargs):
  File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3489, in _map_single
    writer.write_batch(batch)
  File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_writer.py", line 557, in write_batch
    arrays.append(pa.array(typed_sequence))
  File "pyarrow/array.pxi", line 248, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 113, in pyarrow.lib._handle_arrow_array_protocol
  File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_writer.py", line 191, in __arrow_array__
    out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
  File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/features/features.py", line 447, in cast_to_python_objects
    return _cast_to_python_objects(
  File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/features/features.py", line 324, in _cast_to_python_objects
    for x in obj.detach().cpu().numpy()
TypeError: Got unsupported ScalarType BFloat16
```

### Steps to reproduce the bug

Here is my train script I use BF16 type，I use diffusers train my model
```
export MODEL_DIR="/home/mhh/sd_models/stable-diffusion-xl-base-1.0"
export OUTPUT_DIR="./control_net"
export VAE_NAME="/home/mhh/sd_models/sdxl-vae-fp16-fix"

accelerate launch train_controlnet_sdxl.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --pretrained_vae_model_name_or_path=$VAE_NAME \
 --dataset_name=/home/mhh/sd_datasets/fusing/fill50k \
 --mixed_precision="bf16" \
 --resolution=1024 \
 --learning_rate=1e-5 \
 --max_train_steps=200 \
 --validation_image "/home/mhh/sd_datasets/controlnet_image/conditioning_image_1.png" "/home/mhh/sd_datasets/controlnet_image/conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --validation_steps=50 \
 --train_batch_size=1 \
 --gradient_accumulation_steps=4 \
 --report_to="wandb" \
 --seed=42 \
```

### Expected behavior

When I changed the data type to fp16, it worked.

### Environment info

datasets                2.16.1
numpy                   1.24.4
`drop_last_batch=True` for IterableDataset map function is ignored with multiprocessing DataLoader 

### Describe the bug

Scenario:
- Interleaving two iterable datasets of unequal lengths (`all_exhausted`), followed by a batch mapping with batch size 2 to effectively merge the two datasets and get a sample from each dataset in a single batch, with `drop_last_batch=True` to skip the last batch in case it doesn't have two samples.

What works:
- Using DataLoader with `num_workers=0`

What does not work:
- Using DataLoader with `num_workers=1`, errors in the last batch.

Basically, `drop_last_batch=True` is ignored when using multiple dataloading workers.

Please take a look at the minimal repro script below.

### Steps to reproduce the bug

```python
from datasets import Dataset, interleave_datasets
from torch.utils.data import DataLoader


def merge_samples(batch):
    assert len(batch['a']) == 2, "Batch size must be 2"
    batch['c'] = [batch['a'][0]]
    batch['d'] = [batch['a'][1]]
    return batch


def gen1():
    for ii in range(1, 8385):
        yield {"a": ii}


def gen2():
    for ii in range(1, 5302):
        yield {"a": ii}


if __name__ == '__main__':

    dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=1024)
    dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=1024)

    interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy="all_exhausted")
    mapped = interleaved.map(merge_samples, batched=True, batch_size=2, remove_columns=interleaved.column_names,
                             drop_last_batch=True)

    # Works
    loader = DataLoader(mapped, batch_size=32, num_workers=0)
    i = 0
    for b in loader:
        print(i, b['c'].shape, b['d'].shape)
        i += 1

    print("DataLoader with num_workers=0 works")

    # Doesn't work
    loader = DataLoader(mapped, batch_size=32, num_workers=1)
    i = 0
    for b in loader:
        print(i, b['c'].shape, b['d'].shape)
        i += 1


```

### Expected behavior

 `drop_last_batch=True` should have same behaviour for `num_workers=0` and `num_workers>=1`

### Environment info

- `datasets` version: 2.16.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.10.12
- `huggingface_hub` version: 0.20.2
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.6.0

I have also tested on Linux and got the same behavior.
`Dataset.filter` missing `with_rank` parameter

### Describe the bug

The issue shall be open: https://github.com/huggingface/datasets/issues/6435

When i try to pass `with_rank` to `Dataset.filter()`, i get this:

`Dataset.filter() got an unexpected keyword argument 'with_rank'`

### Steps to reproduce the bug

Run notebook:

https://colab.research.google.com/drive/1WUNKph8BdP0on5ve3gQnh_PE0cFLQqTn?usp=sharing

### Expected behavior

Should work?

### Environment info

NVIDIA RTX 4090
Latest version 2.16.1, when load dataset error occurs. ValueError: BuilderConfig 'allenai--c4' not found. Available: ['default']

### Describe the bug

python script is:
```

from datasets import load_dataset
cache_dir = 'path/to/your/cache/directory'
dataset = load_dataset('allenai/c4','allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train',  use_auth_token=False, cache_dir=cache_dir)
```


the script success when datasets version is 2.14.7. 
when using 2.16.1, error occurs
`
ValueError: BuilderConfig 'allenai--c4' not found. Available: ['default']`

### Steps to reproduce the bug

1. pip install datasets==2.16.1
2. run python script:
```

from datasets import load_dataset
cache_dir = 'path/to/your/cache/directory'
dataset = load_dataset('allenai/c4','allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train',  use_auth_token=False, cache_dir=cache_dir)
```

### Expected behavior

the dataset should be loaded successful in the latest version. 

### Environment info

datasets 2.16.1
Fix imagefolder with one image

A dataset repository with one image and one metadata file was considered a JSON dataset instead of an ImageFolder dataset. This is because we pick the dataset type with the most compatible data file extensions present in the repository and it results in a tie in this case.

e.g. for https://huggingface.co/datasets/multimodalart/repro_1_image

I fixed this by deprioritizing metadata files in the count.

fix https://github.com/huggingface/datasets/issues/6545
Loading a dataset from Google Colab hangs at "Resolving data files".

### Describe the bug

Hello,

I'm trying to load a dataset from Google Colab but the process hangs at `Resolving data files`:

![image](https://github.com/huggingface/datasets/assets/99779/7175ad85-e571-46ed-9f87-92653985777d)

It is happening when the `_get_origin_metadata` definition is invoked:

```python
def _get_origin_metadata(
    data_files: List[str],
    max_workers=64,
    download_config: Optional[DownloadConfig] = None,
) -> Tuple[str]:
    return thread_map(
        partial(_get_single_origin_metadata, download_config=download_config),
        data_files,
        max_workers=max_workers,
        tqdm_class=hf_tqdm,
        desc="Resolving data files",
        disable=len(data_files) <= 16,
```

The thread is then stuck at `waiter.acquire()` in the builtin `threading.py` file.

I can load the dataset just fine on my machine.

Cheers,

Thomas


### Steps to reproduce the bug

In Google Colab:

```python
!pip install datasets
from datasets import load_dataset

dataset = load_dataset("colour-science/color-checker-detection-dataset")
```

### Expected behavior

The dataset should be loaded.

### Environment info

- `datasets` version: 2.16.1
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.20.1
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0
Fix parallel downloads for datasets without scripts

Enable parallel downloads using multiprocessing when `num_proc` is passed to `load_dataset`.

It was enabled for datasets with scripts already (if they passed lists to `dl_manager.download`) but not for no-script datasets (we pass dicts {split: [list of files]} to `dl_manager.download` for those ones).

I fixed this by parallelising on the lists contained in the data files dicts when possible.

I also added a context manager `stack_multiprocessing_download_progress_bars` in `DownloadManager` to stack the progress bard of the downloads (from `cached_path(...)` calls). Otherwise the progress bars overlap each other with an annoying flickering effect.
Loading from hf hub with clearer error message

### Feature request

Shouldn't this kinda work ?
```
Dataset.from_json("hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json")
```
I got an error
```
File ~/miniconda3/envs/datatrove/lib/python3.10/site-packages/datasets/data_files.py:380, in resolve_pattern(pattern, base_path, allowed_extensions, download_config)
    378     if allowed_extensions is not None:
    379         error_msg += f" with any supported extension {list(allowed_extensions)}"
--> 380     raise FileNotFoundError(error_msg)
    381 return out

FileNotFoundError: Unable to find 'hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json'
(I'm logged in)
```

Fix: the correct path is
```
hf://datasets/HuggingFaceTB/eval_data/eval_data_context_and_answers.json
```

Proposal: raise a clearer error

### Motivation

Clearer error message

### Your contribution

Can open a PR
Skip if a dataset has issues

### Describe the bug

Hello everyone,
I'm using **load_datasets** from **huggingface** to download the datasets and I'm facing an issue, the download starts but it reaches some state and then  fails with the following error:
Couldn't reach https://huggingface.co/datasets/wikimedia/wikipedia/resolve/4cb9b0d719291f1a10f96f67d609c5d442980dc9/20231101.ext/train-00000-of-00001.parquet

Failed to resolve \'huggingface.co\' ([Errno -3] Temporary failure in name resolution)"))')))


![image](https://github.com/huggingface/datasets/assets/143214684/8847d9cb-529e-4eda-9c76-282713dfa3af)

so I was wondering is there a parameter to be passed to load_dataset() to skip files that can't be downloaded??

### Steps to reproduce the bug

Parameter to be passed to load_dataset() of huggingface to skip files that can't be downloaded??

### Expected behavior

load_dataset() finishes without error

### Environment info

None
Datasets : wikipedia 20220301.en error 

### Describe the bug

When I used load_dataset to download this data set, the following error occurred. The main problem was that the target data did not exist.

### Steps to reproduce the bug

1.I tried downloading directly.
```python
wiki_dataset = load_dataset("wikipedia", "20220301.en")
```
An exception occurred
```
MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). 
Example of usage: 
	`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
```
2.I modified the code as prompted.
```python
wiki_dataset = load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
```
An exception occurred:
```
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
```


### Expected behavior

I searched in the parent directory of the corresponding URL, but there was no corresponding "20220301" directory.
I really need this data set and hope to provide a download method.

### Environment info

python 3.8
datasets 2.16.0
apache-beam 2.52.0
dill 0.3.7
Extreme inefficiency for `save_to_disk` when merging datasets

### Describe the bug

Hi, I tried to merge in total 22M sequences of data, where each sequence is of maximum length 2000. I found that merging these datasets and then `save_to_disk` is extremely slow because of flattening the indices. Wondering if you have any suggestions or guidance on this. Thank you very much! 

### Steps to reproduce the bug

The source data is too big to demonstrate

### Expected behavior

The source data is too big to demonstrate

### Environment info

python                     3.9.0
datasets                  2.7.0
pytorch                   2.0.0 
tokenizers                0.13.1
transformers              4.31.0
Adding support for netCDF (*.nc) files

### Feature request

netCDF (*.nc) is a file format for storing multidimensional scientific data, which is used by packages like `xarray` (labelled multi-dimensional arrays in Python). It would be nice to have native support for netCDF in `datasets`.

### Motivation

When uploading *.nc files onto Huggingface Hub through the `datasets` API, I would like to be able to preview the dataset without converting it to another format.

### Your contribution

I can submit a PR, provided I have the time.
ted_talks_iwslt | Error: Config name is missing

### Describe the bug

Running load_dataset using the newest `datasets` library like below on the ted_talks_iwslt using year pair data will throw an error "Config name is missing"

see also:
https://huggingface.co/datasets/ted_talks_iwslt/discussions/3

likely caused by #6493, where the `and not config_kwargs` part in the if logic was removed 
 https://github.com/huggingface/datasets/blob/ef3b5dd3633995c95d77f35fb17f89ff44990bc4/src/datasets/builder.py#L512

### Steps to reproduce the bug

run: 
```python
load_dataset("ted_talks_iwslt", language_pair=("ja", "en"), year="2015")
```

### Expected behavior

Load the data without error

### Environment info

datasets 2.16.0
[Feature request] Indexing datasets by a customly-defined id field to enable random access dataset items via the id

### Feature request

Some datasets may contain an id-like field, for example the `id` field in [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) and the `_id` field in [BeIR/dbpedia-entity](https://huggingface.co/datasets/BeIR/dbpedia-entity). HF datasets support efficient random access via row, but not via this kinds of id fields. I wonder if it is possible to add support for indexing by a custom "id-like" field to enable random access via such ids. The ids may be numbers or strings.

### Motivation

In some cases, especially during inference/evaluation, I may want to find out the item that has a specified id, defined by the dataset itself.

For example, in a typical re-ranking setting in information retrieval, the user may want to re-rank the set of candidate documents of each query. The input is usually presented in a TREC-style run file, with the following format:

```
<qid> Q0 <docno> <rank> <score> <tag>
```

The re-ranking program should be able to fetch the queries and documents according to the `<qid>` and `<docno>`, which are the original id defined in the query/document datasets. To accomplish this, I have to iterate over the whole HF dataset to get the mapping from real ids to row ids every time I start the program, which is time-consuming. Thus I want HF dataset to provide options for users to index by a custom id column, not by row.

### Your contribution

I'm not an expert in this project and I'm afraid that I'm not able to make contributions on the code.
Impossible to save a mapped dataset to disk

### Describe the bug

I want to play around with different hyperparameters when training but don't want to re-map my dataset with 3 million samples each time for tens of hours when I [fully fine-tune SDXL](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py).

After I do the mapping like this:
```
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True)
train_dataset = train_dataset.map(
    compute_vae_encodings_fn,
    batched=True,
    batch_size=16,
)
```
and try to save it like this:
`train_dataset.save_to_disk("test")`
i get this error ([full traceback](https://pastebin.com/kq3vt739)):
```
TypeError: Object of type function is not JSON serializable
The format kwargs must be JSON serializable, but key 'transform' isn't.
```

But what is interesting is that pushing to hub works like that:
`train_dataset.push_to_hub("kopyl/mapped-833-icons-sdxl-1024-dataset", token=True)`
Here is the link of the pushed dataset: https://huggingface.co/datasets/kopyl/mapped-833-icons-sdxl-1024-dataset

### Steps to reproduce the bug

Here is the self-contained notebook:

https://colab.research.google.com/drive/1RtCsEMVcwWcMwlWURk_cj_9xUBHz065M?usp=sharing

### Expected behavior

It should be easily saved to disk

### Environment info

NVIDIA A100, Linux (NC24ads A100 v4 from Azure), CUDA 12.2.

[pip freeze](https://pastebin.com/QTNb6iru)
Impossible to only download a test split

I've spent a significant amount of time trying to locate the split object inside my _split_generators() custom function.
Then after diving [in the code](https://github.com/huggingface/datasets/blob/5ff3670c18ed34fa8ddfa70a9aa403ae6cc9ad54/src/datasets/load.py#L2558) I realized that `download_and_prepare` is executed before! split is passed to the dataset builder in `as_dataset`.

If I'm not missing something, this seems like bad design, for the following use case:

> Imagine there is a huge dataset that has an evaluation test set and you want to just download and run just to compare your method.

Is there a current workaround that can help me achieve the same result?

Thank you,
BBox type

see [internal discussion](https://huggingface.slack.com/archives/C02EK7C3SHW/p1703097195609209)

Draft to get some feedback on a possible `BBox` feature type that can be used to get object detection bounding boxes data in one format or another.

```python
>>> from datasets import load_dataset, BBox
>>> ds = load_dataset("svhn", "full_numbers", split="train")
>>> ds[0]
{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x126409BE0>,
  'digits': {'bbox': [[38, 1, 21, 40], [57, 3, 16, 40]], 'label': [4, 6]}
}
>>> ds = ds.rename_column("digits", "annotations").cast_column("annotations", BBox(format="coco"))
>>> ds[0]
{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x147730070>,
  'annotations': [{'bbox': [38, 1, 21, 40], 'category_id': 4}, {'bbox': [57, 3, 16, 40], 'category_id': 6}]
}
```

note that it's a type for a list of bounding boxes, not just one - which would be needed to switch from a format to another using type casting.
Streaming the Pile: Missing Files

### Describe the bug

The pile does not stream, a "File not Found error" is returned. It looks like the Pile's files have been moved.

### Steps to reproduce the bug

To reproduce run the following code:

```
from datasets import load_dataset

dataset = load_dataset('EleutherAI/pile', 'en', split='train', streaming=True)
next(iter(dataset))
```

I get the following error:
`FileNotFoundError: https://the-eye.eu/public/AI/pile/train/00.jsonl.zst`

### Expected behavior

Return the data in a stream.

### Environment info

- `datasets` version: 2.12.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.11.5
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.3
The order of the splits is not preserved

We had a regression and the order of the splits is not preserved. They are alphabetically sorted, instead of preserving original "train", "validation", "test" order.

Check: In branch "main"
```python
In [9]: dataset = load_dataset("adversarial_qa", '"adversarialQA")

In [10]: dataset
Out[10]: 
DatasetDict({
    test: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 3000
    })
    train: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 30000
    })
    validation: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 3000
    })
})
```

Before (2.15.0) it was:
```python
DatasetDict({
    train: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 30000
    })
    validation: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 3000
    })
    test: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers', 'metadata'],
        num_rows: 3000
    })
})
```

See issues: 
- https://huggingface.co/datasets/adversarial_qa/discussions/3
- https://huggingface.co/datasets/beans/discussions/4

This is a regression because it was previously fixed. See:
- #6196
- #5728
Cache backward compatibility with 2.15.0

...for datasets without scripts

It takes into account the changes in cache from
- https://github.com/huggingface/datasets/pull/6493: switch to `config/version/commit_sha` schema
- https://github.com/huggingface/datasets/pull/6454: fix `DataFilesDict` keys ordering when hashing

requires https://github.com/huggingface/datasets/pull/6493 to be merged
Implement get dataset default config name

Implement `get_dataset_default_config_name`.

Now that we support setting a configuration as default in `push_to_hub` (see #6500), we need a programmatically way to know in advance which is the default configuration. This will be used in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet

Follow-up of:
- #6500

CC: @severo
Read GeoParquet files using parquet reader

Let GeoParquet files with the file extension `*.geoparquet` or `*.gpq` be readable by the default parquet reader.

Those two file extensions are the ones most commonly used for GeoParquet files, and is included in the `gpq` validator tool at https://github.com/planetlabs/gpq/blob/e5576b4ee7306b4d2259d56c879465a9364dab90/cmd/gpq/command/convert.go#L73-L75

Addresses https://github.com/huggingface/datasets/issues/6438
Incorrect test set labels for RTE and CoLA datasets via load_dataset

### Describe the bug

The test set labels for the RTE and CoLA datasets when loading via datasets load_dataset are all -1.
Edit: It appears this is also the case for every other dataset except for MRPC (stsb, sst2, qqp, mnli (both matched and mismatched), qnli, wnli, ax). Is this intended behavior to safeguard the test set for evaluation purposes?

### Steps to reproduce the bug

!pip install datasets
from datasets import load_dataset

rte_data = load_dataset('glue', 'rte')
cola_data = load_dataset('glue', 'cola')
print(rte_data['test'][0:30]['label'])
print(cola_data['test'][0:30]['label'])

Output:
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]

The non-label test data seems to be fine:
e.g. rte_data['test'][1] is:
{'sentence1': "Authorities in Brazil say that more than 200 people are being held hostage in a prison in the country's remote, Amazonian-jungle state of Rondonia.",
 'sentence2': 'Authorities in Brazil hold 200 people as hostage.',
 'label': -1,
 'idx': 1}
Training and validation data are also fine:
e.g. rte_data['train][0] is:
{'sentence1': 'No Weapons of Mass Destruction Found in Iraq Yet.',
 'sentence2': 'Weapons of Mass Destruction Found in Iraq.',
 'label': 1,
 'idx': 0}


### Expected behavior

Expected the labels to be binary 0/1 values; Got all -1s instead

### Environment info

- `datasets` version: 2.15.0
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0
Fix streaming xnli

This code was failing

```python

In [1]: from datasets import load_dataset

In [2]: 
   ...:         ds = load_dataset("xnli", "all_languages", split="test", streaming=True)
   ...: 
   ...:         sample_data = next(iter(ds))["premise"]  # pick up one data
   ...:         input_text = list(sample_data.values())
```

```
File ~/hf/datasets/src/datasets/features/translation.py:104, in TranslationVariableLanguages.encode_example(self, translation_dict)
    102     return translation_dict
    103 elif self.languages and set(translation_dict) - lang_set:
--> 104     raise ValueError(
    105         f'Some languages in example ({", ".join(sorted(set(translation_dict) - lang_set))}) are not in valid set ({", ".join(lang_set)}).'
    106     )
    108 # Convert dictionary into tuples, splitting out cases where there are
    109 # multiple translations for a single language.
    110 translation_tuples = []

ValueError: Some languages in example (language, translation) are not in valid set (ur, fr, hi, sw, vi, el, de, th, en, tr, zh, ar, bg, ru, es).
```

because in streaming mode we expect features encode methods to be no-ops if the example is already encoded.

I fixed `TranslationVariableLanguages` to account for that
Fallback on dataset script if user wants to load default config

Right now this code is failing on `main`:

```python
load_dataset("openbookqa")
```

This is because it tries to load the dataset from the Parquet export but the dataset has multiple configurations and the Parquet export doesn't know which one is the default one.

I fixed this by simply falling back on using the dataset script (which tells the user to pass `trust_remote_code=True`):

```python
load_dataset("openbookqa", trust_remote_code=True)
```

Note that if the user happened to specify a config name I don't fall back on the script since we can use the Parquet export in this case (no need to know which config is the default)


```python
load_dataset("openbookqa", "main")
```
Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.

**Describe the bug**

Getting a `412 Client Error: Precondition Failed` when trying to write a dataset to the HF hub.

```
huggingface_hub.utils._errors.HfHubHTTPError: 412 Client Error: Precondition Failed for url: https://huggingface.co/api/datasets/GLorr/test-dask/commit/main (Request ID: Root=1-657ae26f-3bd92bf861bb254b2cc0826c;50a09ab7-9347-406a-ba49-69f98abee9cc)

A commit has happened since. Please refresh and try again.
```

**Steps to reproduce the bug**
This is a minimal reproducer:
```
import dask.dataframe as dd
import pandas as pd
import random
import os

import huggingface_hub

import datasets

huggingface_hub.login(token=os.getenv("HF_TOKEN"))

data = {"number": [random.randint(0,10) for _ in range(1000)]}
df = pd.DataFrame.from_dict(data)
dataframe = dd.from_pandas(df, npartitions=1)
dataframe = dataframe.repartition(npartitions=3)

schema = datasets.Features({"number": datasets.Value("int64")}).arrow_schema

repo_id = "GLorr/test-dask"
repo_path = f"hf://datasets/{repo_id}"
huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True)
dd.to_parquet(dataframe, path=f"{repo_path}/data", schema=schema)
```

**Expected behavior**
Would expect to write to the hub without any problem.

**Environment info**
```
datasets==2.15.0
huggingface-hub==0.19.4
```
Lazy data files resolution and offline cache reload

Includes both https://github.com/huggingface/datasets/pull/6458 and https://github.com/huggingface/datasets/pull/6459

This PR should be merged instead of the two individually, since they are conflicting

## Offline cache reload

 it can reload datasets that were pushed to hub if they exist in the cache.

example:

```python
>>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp")
>>> load_dataset("lhoestq/tmp")
DatasetDict({
    train: Dataset({
        features: ['a'],
        num_rows: 2
    })
})
```

and later, without connection:

```python
>>> load_dataset("lhoestq/tmp")
Using the latest cached version of the dataset since lhoestq/tmp couldn't be found on the Hugging Face Hub
Found the latest cached dataset configuration 'default' at /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/default/0.0.0/da0e902a945afeb9 (last modified on Wed Dec 13 14:55:52 2023).
DatasetDict({
    train: Dataset({
        features: ['a'],
        num_rows: 2
    })
})
```

- Updated `CachedDatasetModuleFactory` to look for datasets in the cache at `<namespace>___<dataset_name>/<config_id>`
- Since the metadata configs parameters are not available in offline mode, we don't know which folder to load (config_id and hash change), so I simply load the latest one
  - I instantiate a BuilderConfig even if there is no metadata config with the right config_name
  - Its config_id is equal to the config_name to be able to retrieve it in the cache (no more suffix for configs from metadata configs)
  - We can reload this config if offline mode by specifying the right config_name (same as online !)
- Consequences of this change:
  - Only when there are user's parameters it creates a custom builder config with config_id = config_name + user parameters hash
  - the hash used to name the cache folder takes into account the metadata config and the dataset info, so that the right cache can be reloaded when there is internet connection without redownloading the data or resolving the data files. For local directories I hash the builder configs and dataset info, and for datasets on the hub I use the commit sha as hash.
  - cache directories now look like `config/version/commit_sha` for hub datasets which is clean :)

Fix https://github.com/huggingface/datasets/issues/3547

## Lazy data files resolution

this makes this code run in 2sec instead of >10sec

```python
from datasets import load_dataset

ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False)
```

For some datasets with many configs and files it can be up to 100x faster.
This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts.

The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict
`load_dataset(...,save_infos=True)` not working without loading script

### Describe the bug

It seems that saving a dataset infos back into the card file is not working for datasets without a loading script.

After tracking the problem a bit it looks like saving the infos uses `Builder.get_imported_module_dir()` as its destination directory.
Internally this is a call to `inspect.getfile()` but since the actual builder class used is dynamically created (cf. `datasets.load.configure_builder_class`) this method actually return te path to the parent builder class (e.g. `datasets.packaged_modules.json.JSON`). 



### Steps to reproduce the bug

1. Have a local dataset without any loading script
2. Make sure there are no dataset infos in the README.md
3. Load with `save_infos=True`
4. No change in the dataset README.md
5. A new README.md file is created in the directory of the parent builder class (e.g. for json in `.../site-packages/datasets/packaged_modules/json/README.md`)

### Expected behavior

The dataset README.md should be updated and no file should be created in the python environment.

### Environment info

- `datasets` version: 2.15.0
- Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.6.0
429 Client Error

Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it?
Thanks

Dataset:
https://huggingface.co/datasets/cerebras/SlimPajama-627B

Error:
`requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`
FileNotFoundError: [Errno 2] No such file or directory: 'nul'

### Describe the bug

it seems that sth wrong with my terrible "bug body" life, When i run this code, "import datasets"
i meet this error FileNotFoundError: [Errno 2] No such file or directory: 'nul'
![image](https://github.com/huggingface/datasets/assets/73683903/3973c120-ebb1-42b7-bede-b9de053e861d)
![image](https://github.com/huggingface/datasets/assets/73683903/0496adff-a7a7-4dcb-929e-ec11ede71f04)


### Steps to reproduce the bug

1.import datasets

### Expected behavior

i just run a single line code and stuct in this bug 

### Environment info

OS: Windows10
Datasets==2.15.0
python=3.10
Add IterableDataset `__repr__`

Example for glue sst2:

Dataset

```
DatasetDict({
    test: Dataset({
        features: ['sentence', 'label', 'idx'],
        num_rows: 1821
    })
    train: Dataset({
        features: ['sentence', 'label', 'idx'],
        num_rows: 67349
    })
    validation: Dataset({
        features: ['sentence', 'label', 'idx'],
        num_rows: 872
    })
})
```

IterableDataset (new)

```
IterableDatasetDict({
    test: IterableDataset({
        features: ['sentence', 'label', 'idx'],
        n_shards: 1
    })
    train: IterableDataset({
        features: ['sentence', 'label', 'idx'],
        n_shards: 1
    })
    validation: IterableDataset({
        features: ['sentence', 'label', 'idx'],
        n_shards: 1
    })
})
```

IterableDataset (before)

```
{'test': <datasets.iterable_dataset.IterableDataset object at 0x130d421f0>, 'train': <datasets.iterable_dataset.IterableDataset object at 0x136f3aaf0>, 'validation': <datasets.iterable_dataset.IterableDataset object at 0x136f4b100>}
{'sentence': 'hide new secretions from the parental units ', 'label': 0, 'idx': 0}
```