Issue: `to_tf_dataset` consumes too much memory

### Describe the bug

Hi, I'm using `to_tf_dataset` to convert a _large_ dataset to `tf.data.Dataset`. I observed that the data loading *before* training took a lot of time and memory, even with `batch_size=1`.

After some digging, i believe the reason lies in the shuffle behavior. The [source code](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/tf_utils.py#L185) uses `len(dataset)` as the `buffer_size`, which may load all the data into the memory, and the [tf.data doc](https://www.tensorflow.org/guide/data#randomly_shuffling_input_data) also states that "While large buffer_sizes shuffle more thoroughly, they can take a lot of memory, and significant time to fill".

### Steps to reproduce the bug

```python
from datasets import Dataset

def gen():  # some large data
    for i in range(50000000):
        yield {"data": i}

ds = Dataset.from_generator(gen, cache_dir="./huggingface")

tf_ds = ds.to_tf_dataset(
    batch_size=64,
    shuffle=False,  # no shuffle
    drop_remainder=False,
    prefetch=True,
)

# fast and memory friendly 🤗
for batch in tf_ds: 
    ...

tf_ds_shuffle = ds.to_tf_dataset(
    batch_size=64,
    shuffle=True,
    drop_remainder=False,
    prefetch=True,
)

# slow and memory hungry for simple iteration 😱
for batch in tf_ds_shuffle: 
    ...

```

### Expected behavior

Shuffling should not load all the data into the memory. Would adding a `buffer_size` parameter in the `to_tf_dataset` API alleviate the problem?

### Environment info

- `datasets` version: 2.11.0
- Platform: Linux-5.17.1-051701-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 1.4.3


Comments:
Cc @amyeroberts @Rocketknight1 

Indded I think it's because it does something like this under the hood when there's no multiprocessing:

```python
tf_dataset = tf_dataset.shuffle(len(dataset))
```

PS: with multiprocessing it appears to be different:

```python
indices = np.arange(len(dataset))
if shuffle:
    np.random.shuffle(indices)
```

Hi @massquantity, the dataset being shuffled there is not the full dataset. If you look at [the line above](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/tf_utils.py#L182), the dataset is actually just a single indices array at that point, and that array is the only thing that gets fully loaded into memory and shuffled. We then load samples from the dataset by applying a transform function to the shuffled dataset, which fetches samples based on the indices it receives.

If your dataset is **really** gigantic, then this index tensor might be a memory issue, but since it's just an int64 tensor it will only use 1GB of memory per 125 million samples.

Still, if you're encountering memory issues, there might be another cause here - can you share some code to reproduce the error, or does it depend on some internal/proprietary dataset?

Hi @Rocketknight1, you're right and I also noticed that only indices are used in shuffling. My data has shape (50000000, 10), but really the problem doesn't relate to a specific dataset. Simply running the following code costs me 10GB of memory.

```python
from datasets import Dataset

def gen():
    for i in range(50000000):
        yield {"data": i}

ds = Dataset.from_generator(gen, cache_dir="./huggingface")

tf_ds = ds.to_tf_dataset(
    batch_size=1,
    shuffle=True,
    drop_remainder=False,
    prefetch=True,
)
tf_ds = iter(tf_ds)
next(tf_ds)
# {'data': <tf.Tensor: shape=(1,), dtype=int64, numpy=array([0])>}
```

I just realized maybe it was an issue from tensorflow (I'm using tf 2.12). So I tried the following code, and it used 10GB of memory too.
```python
import numpy as np
import tensorflow as tf

data_size = 50000000
tf_dataset = tf.data.Dataset.from_tensor_slices(np.arange(data_size))
tf_dataset = iter(tf_dataset.shuffle(data_size))
next(tf_dataset)
# <tf.Tensor: shape=(), dtype=int64, numpy=24774043>
```

By the way, as @lhoestq mentioned, multiprocessing uses numpy shuffling, and it uses less than 1 GB of memory:
```python
tf_ds_mp = ds.to_tf_dataset(
    batch_size=1,
    shuffle=True,
    drop_remainder=False,
    prefetch=True,
    num_workers=2,
)
```

Thanks for that reproduction script - I've confirmed the same issue is occurring for me. Investigating it now!

Update: The memory usage is occurring in creation of the index and shuffle buffer. You can reproduce it very simply with:

```python
import tensorflow as tf
indices = tf.range(50_000_000, dtype=tf.int64)
dataset = tf.data.Dataset.from_tensor_slices(indices)
dataset = dataset.shuffle(len(dataset))
print(next(iter(dataset))
```
When I wrote this code I thought `tf.data` had an optimization for shuffling an entire tensor that wouldn't create the entire shuffle buffer, but evidently it's just creating the enormous buffer in memory. I'll see if I can find a more efficient way to do this - we might end up moving everything to the `numpy` multiprocessing path to avoid it.

I opened a PR to fix this - will continue the discussion there!