Tokenization slows towards end of dataset
### Describe the bug

Mapped tokenization slows down substantially towards end of dataset.

train set started off very slow, caught up to 20k then tapered off til the end.

what's particularly strange is that the tokenization crashed a few times before due to errors with invalid tokens somewhere or corrupted downloads, and the speed ups/downs consistently happened the same times

```bash
Running tokenizer on dataset (num_proc=48):   0%|          | 847000/881416735 [12:18<252:45:45, 967.72 examples/s]
Running tokenizer on dataset (num_proc=48):   0%|          | 848000/881416735 [12:19<224:16:10, 1090.66 examples/s]

Running tokenizer on dataset (num_proc=48):  10%|▉         | 84964000/881416735 [3:48:00<11:21:34, 19476.01 examples/s]
Running tokenizer on dataset (num_proc=48):  10%|▉         | 84967000/881416735 [3:48:00<12:04:01, 18333.79 examples/s]

Running tokenizer on dataset (num_proc=48):  61%|██████    | 538631977/881416735 [13:46:40<27:50:04, 3420.84 examples/s]
Running tokenizer on dataset (num_proc=48):  61%|██████    | 538632977/881416735 [13:46:40<23:48:20, 3999.77 examples/s]

Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881365886/881416735 [38:30:19<04:34, 185.10 examples/s]
Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881366886/881416735 [38:30:25<04:36, 180.57 examples/s]
```

and validation set as well

```bash
Running tokenizer on dataset (num_proc=48):  90%|████████▉ | 41544000/46390354 [28:44<02:37, 30798.76 examples/s]
Running tokenizer on dataset (num_proc=48):  90%|████████▉ | 41550000/46390354 [28:44<02:08, 37698.08 examples/s]

Running tokenizer on dataset (num_proc=48):  96%|█████████▋| 44747422/46390354 [2:15:48<12:22:44, 36.87 examples/s]
Running tokenizer on dataset (num_proc=48):  96%|█████████▋| 44747422/46390354 [2:16:00<12:22:44, 36.87 examples/s]

```



### Steps to reproduce the bug

    using the following kwargs
    
```python
with accelerator.main_process_first():
        lm_datasets = tokenized_datasets.map(
            group_texts,
            batched=True,
            num_proc=48
            load_from_cache_file=True,
            desc=f"Grouping texts in chunks of {block_size}",
        )
```

running through slurm script 
```bash
#SBATCH --partition=gpu-nvidia-a100
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --gpus-per-task=8
#SBATCH --cpus-per-task=96
```

using this dataset https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T





### Expected behavior

Constant speed throughout

### Environment info

- `datasets` version: 2.15.0
- Platform: Linux-5.15.0-1049-aws-x86_64-with-glibc2.10
- Python version: 3.8.18
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0
Hi ! First note that if the dataset is not heterogeneous / shuffled, there might be places in the data with shorter texts that are faster to tokenize.

Moreover, the way `num_proc` works is by slicing the dataset and passing each slice to a process to run the `map()` function. So at the very end of `map()`, some processes might have finished transforming their slice of data while others are still running, causing the throughput to become lower. I did see some comments about how num_proc=None could help and outputting numpy arrays can also help in the docs, but this seems quite odd now dropping down to 1it/s

```bash
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46048888/46390354 [12:33:30<4:20:32, 21.84 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46049888/46390354 [12:36:11<8:37:59, 10.95 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46050888/46390354 [12:46:35<24:56:56,  3.78 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46051888/46390354 [12:56:43<35:08:10,  2.68 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46052888/46390354 [13:06:58<42:05:41,  2.23 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46053888/46390354 [13:16:01<44:40:18,  2.09 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46054888/46390354 [13:25:11<46:35:28,  2.00 examples/s]
Running tokenizer on dataset (num_proc=48):  99%|█████████▉| 46055888/46390354 [13:34:23<47:55:34,  1.94 examples/s]
```

 @ethansmith2000 Hi, did you solve this problem? I'm strugging with the same problem now.