Movement pruning
This PR adds the code to reproduce the results of our recent work on Movement pruning.
Some supplemental treats:
- A notebook showcasing how to efficiently store an extremely sparse model
- Sharing a couple of fine-pruned checkpoints (PruneBERT)
- Details of all hyper-parameters and results

Thanks for the valuable inputs on quantization @mfuntowicz! --> `Saving_PruneBERT.ipynb`<|||||>> This is great. Didn't you want to include the `MaskedBertXXX` directly in the library, as was done with `DistilBERT`?
> 
> We can also do it at a later date, as we'll need to write the tests (kudos on the docs, looks nice!)

Good question. I think it's fair for the moment to leave it outside of the library itself: once a pre-trained model has been fine-pruned, it can be pruned once for all and you end up with a standard `BertForSequenceClassification` for instance. So I see it more as an "intermediate tool".<|||||># [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4637?src=pr&el=h1) Report
> Merging [#4637](https://codecov.io/gh/huggingface/transformers/pull/4637?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ec4cdfdd05d89b243d6d842fce019959291dd92a&el=desc) will **decrease** coverage by `0.00%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/4637/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/4637?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #4637      +/-   ##
==========================================
- Coverage   78.04%   78.04%   -0.01%     
==========================================
  Files         124      124              
  Lines       20676    20676              
==========================================
- Hits        16137    16136       -1     
- Misses       4539     4540       +1     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/4637?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/modeling\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/4637/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `88.66% <0.00%> (-0.17%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/4637?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/4637?src=pr&el=footer). Last update [ec4cdfd...bee5496](https://codecov.io/gh/huggingface/transformers/pull/4637?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
<|||||>> Looks great. Well organized & clearly communicated.
> 
> Would it be worthwhile to add the released prunebert encoders to exBERT? I don't think we have any fine-tuned models there right now but it'd be cool to let people see how the movement pruning process affects the attention distributions.

Good point! I'll have a look.