Super slow iteration with trivial custom transform
State: open

### Describe the bug

Dataset is 10X slower when applying trivial transforms:

```
import time
import numpy as np
from datasets import Dataset, Features, Array2D

a = np.zeros((800, 800))
a = np.stack([a] * 1000)
features = Features({"a": Array2D(shape=(800, 800), dtype="uint8")})

ds1 = Dataset.from_dict({"a": a}, features=features).with_format('numpy')

def transform(batch):
	return batch

ds2 = ds1.with_transform(transform)

%time sum(1 for _ in ds1)
%time sum(1 for _ in ds2)
```

```
CPU times: user 472 ms, sys: 319 ms, total: 791 ms
Wall time: 794 ms
CPU times: user 9.32 s, sys: 443 ms, total: 9.76 s
Wall time: 9.78 s
```

In my real code I'm using set_transform to apply some post-processing on-the-fly for the 2d array, but it significantly slows down the dataset even if the transform itself is trivial.

Related issue: https://github.com/huggingface/datasets/issues/5841

### Steps to reproduce the bug

Use code in the description to reproduce.

### Expected behavior

Trivial custom transform in the example should not slowdown the dataset iteration.

### Environment info

- `datasets` version: 2.18.0
- Platform: Linux-5.15.0-79-generic-x86_64-with-glibc2.35
- Python version: 3.11.4
- `huggingface_hub` version: 0.20.2
- PyArrow version: 15.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.12.2

Similar issue in text process 

```python

tokenizer=AutoTokenizer.from_pretrained(model_dir[args.model])
train_dataset=datasets.load_from_disk(dataset_dir[args.dataset],keep_in_memory=True)['train']
train_dataset=train_dataset.map(partial(dname2func[args.dataset],tokenizer=tokenizer),batched=True,num_proc =50,remove_columns=train_dataset.features.keys(),desc='tokenize',keep_in_memory=True)

```
After this train_dataset will be like
```python
Dataset({
    features: ['input_ids', 'labels'],
    num_rows: 51760
})
```
In which input_ids and labels are both List[int]
However, per iter on dataset cost 7.412479639053345s ……？
```python
for j in tqdm(range(len(train_dataset)),desc='first stage'):
    input_id,label=train_dataset['input_ids'][j],train_dataset['labels'][j]

``` 

The transform currently replaces the numpy formatting.

So you're back to copying data to long python lists which is super slow.

It would be cool for the transform to not remove the formatting in this case, but this requires a few changes in the lib

This also (somewhat surprisingly) affects iterable datasets, making map very challenging to use for data with large arrays, unless there is some workaround?

For iterable datasets you should be able to do this without slow downs
```python
ds = ds.with_format("arrow").map(...)
```

I haven't tried with "numpy" though, maybe there is a step that does Arrow -> List -> NumPy instead of Arrow -> NumPy directly. If it's the case it would be cool to avoid that

Thanks! this works for me

However, it raises an error if batched=False and map batch_size isn't explicitly set to 1 due to map's default batch_size affecting the batch size of the RebatchedArrowExamplesIterable - is this a bug?

Thanks for the fix @alex-hh !

opened a new issue for the numpy slowdown #7206