[`run_(clm|mlm).py` examples] add streaming dataset support
This PR adds streaming dataset support. It's fine if it remains a PR for those who might need it. if it's to be merged should probably check when streaming was added to `datasets` and require that version.

1. API-wise everything is as before but need to pass `--streaming`  to load the dataset in a streaming mode. 
2. and also since `IterableDataset` has no `__len__` this makes the `--max_steps` flag required (possibly can make this more clear by asserting earlier if `--streaming` and `not --max_steps`)

This should make a huge speed up to start working with large datasets. So that work can start immediately and the data gets loaded progressively (which overall is likely to be slower - haven't measured yet - but it makes starting much easier).


Example run:
```
python examples/pytorch/language-modeling/run_clm.py --bf16 --seed 42 \
--model_name_or_path facebook/opt-1.3b --dataset_name wikitext \
--dataset_config_name wikitext-103-raw-v1 --per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 --gradient_accumulation_steps 1 --do_train \
--do_eval --logging_steps 10 --save_steps 1000 --eval_steps 100 --weight_decay \
0.1 --num_train_epochs 1 --adam_beta1 0.9 --adam_beta2 0.95 --learning_rate \
0.0002 --lr_scheduler_type linear --warmup_steps 500 --report_to tensorboard \
--output_dir save_dir --max_steps 1_000_000 --streaming
```

Ported this to `run_mlm.py` as well.

Thank you, @lhoestq for helping with this
_The documentation is not available anymore as the PR was closed or merged._<|||||>Reworked w/o `kwargs` and copying the code where needed, please let me know when it's good for you and I can replicate to mlm<|||||>should I figure out when streaming was added and put a assert if an earlier datasets is used?<|||||>Good point. Streaming was introduced a while ago, but I think it stabilized with the 2.0 version, so maybe use this one as a minimal requirement for streaming?<|||||>- added version check
- ported to mlm
- added a doc entry

This is good to go for a final review, Sylvain. Thank you.<|||||>And thank you for reviewing my work, Sylvain!