dataset.shuffle(keep_in_memory=True) is never allowed

As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`

The commit added the lines
```python
# lines 994-996 in src/nlp/arrow_dataset.py
       assert (
            not keep_in_memory or cache_file_name is None
        ), "Please use either `keep_in_memory` or `cache_file_name` but not both."
```

This affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. 

I'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.

This seems to be fixed in #513 for the filter function, replacing `cache_file_name` with `indices_cache_file_name` in the assert. Although not for the `map()` function @thomwolf 

Maybe I'm a bit tired but I fail to see the issue here.

Since `cache_file_name` is `None` by default, if you set `keep_in_memory` to `True`, the assert should pass, no?

I failed to realise that this only applies to `shuffle()`. Whenever `keep_in_memory` is set to True, this is passed on to the `select()` function. However, if `cache_file_name` is None, it will be defined in the `shuffle()` function before it is passed on to `select()`. 

Thus, `select()` is called with `keep_in_memory=True` and a not None value for `cache_file_name`. 
This is essentially fixed in #513 

Easily reproducible:
```python
>>> import nlp
>>> data = nlp.load_dataset("cosmos_qa", split="train")
Using custom data configuration default
>>> data.shuffle(keep_in_memory=True)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 1398, in shuffle
    verbose=verbose,
  File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 1178, in select
    ), "Please use either `keep_in_memory` or `cache_file_name` but not both."
AssertionError: Please use either `keep_in_memory` or `cache_file_name` but not both.
>>>data.select([0], keep_in_memory=True)
# No error
```

Oh yes ok got it thanks. Should be fixed if we are happy with #513 indeed.

My bad. This is actually not fixed in #513. Sorry about that...
The new `indices_cache_file_name` is set to a non-None value in the new `shuffle()` as well. 

The buffer and caching mechanisms used in the `select()` function are too intricate for me to understand why the check is there at all. I've removed it in my local build and it seems to be working fine for my project, without really considering other implications of the change. 



Ok I'll investigate and add a series of tests on the `keep_in_memory=True` settings which is under-tested atm

Hey, still seeing this issue with the latest version.

The same :(

These are the steps needed to fix this issue:
1. add the following check to `Dataset.shuffle`:
```python
if keep_in_memory and indices_cache_file_name is not None:
    raise ValueError("Please use either `keep_in_memory` or `indices_cache_file_name` but not both.")
```
2. set `indices_cache_file_name` to `None` if `keep_in_memory` is True in the call to `select`
3. add a test with `shuffle(keep_in_memory=True)`

Hi @mariosasko , I have opened this PR #5082