Skipping shard in the remote repo and resume upload
### 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

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