Offset overflow when slicing a big dataset with an array of indices in Pyarrow >= 1.0.0
Related: https://issues.apache.org/jira/browse/ARROW-9773

It's definitely a size thing. I took a smaller dataset with 87000 rows and did:
```
for i in range(10,1000,20):
    table = pa.concat_tables([dset._data]*i)
    table.take([0])
```
and it broke at around i=300.

Also when `_indices` is not None, this breaks indexing by slice. E.g. `dset.shuffle()[:1]` breaks.

Luckily so far I haven't seen `_indices.column(0).take` break, which means it doesn't break `select` or anything like that which is where the speed really matters, it's just `_getitem`. So I'm currently working around it by just doing the arrow v0 method in `_getitem`:
```
#if PYARROW_V0:
data_subset = pa.concat_tables(
    self._data.slice(indices_array[i].as_py(), 1) for i in range(len(indices_array))
)
#else:
    #data_subset = self._data.take(indices_array)
``` Let me know if you meet other offset overflow issues @joeddav  Will this problem be solved in newer version? This specific issue has been fixed in https://github.com/huggingface/datasets/pull/645

If you still have this error, could you open a new issue and explain how to reproduce the error ? same error here  in version 2.1.0 Facing the same issue. 
Steps to reproduce: (dataset is a few GB big so try in colab maybe)
Datasets version - 2.11.0
```
import datasets
import re

ds = datasets.load_dataset('nishanthc/dnd_map_dataset_v0.1', split = 'train')

def get_text_caption(example):
    regex_pattern = r'\s\d+x\d+|,\sLQ|,\sgrid|\.\w+$'
    example['text_caption'] = re.sub(regex_pattern, '', example['picture_text'])
    return example

ds = ds.map(get_text_caption)
```

I am trying to apply a regex to remove certain patterns from a text column. Not sure why this error is showing up. Got this error on a very large data set (900m rows, 35 cols) performing a similar batch map operation. There is a solution that has been proposed here: https://github.com/huggingface/datasets/issues/5783 @lhoestq  I ran into this problem with load_dataset. What should I do
 What version of `datasets` are you using ? Feel free to open a new issue with some details (e.g. what dataset you loaded, what code you ran etc) @lhoestq It's been solved,thanks
How to reproduce:

```python
from datasets import load_dataset

wiki = load_dataset("wikipedia", "20200501.en", split="train")
wiki[[0]]

---------------------------------------------------------------------------
ArrowInvalid                              Traceback (most recent call last)
<ipython-input-13-381aedc9811b> in <module>
----> 1 wikipedia[[0]]

~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in __getitem__(self, key)
   1069             format_columns=self._format_columns,
   1070             output_all_columns=self._output_all_columns,
-> 1071             format_kwargs=self._format_kwargs,
   1072         )
   1073 

~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)
   1037                 )
   1038             else:
-> 1039                 data_subset = self._data.take(indices_array)
   1040 
   1041             if format_type is not None:

~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.take()

~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/compute.py in take(data, indices, boundscheck)
    266     """
    267     options = TakeOptions(boundscheck)
--> 268     return call_function('take', [data, indices], options)
    269 
    270 

~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()

~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()

~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()

~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()

ArrowInvalid: offset overflow while concatenating arrays
```

It seems to work fine with small datasets or with pyarrow 0.17.1