Added tokenizer kwargs for fill mask pipeline
Added tokenizer kwargs for the fill mask pipeline, which enables to truncate/pad/specify max length etc for the tokenizer. Pipeline can be used as follows following the edit:

`from transformers import pipeline`
`fill_mask_pipeline = pipeline(`
`    'fill-mask', `
`    model=model, `
`    tokenizer=tokenizer, `
`    device=0`
`)`

`tokenizer_kwargs = {'truncation':True, 'max_length':2048}`
`output = fill_mask_pipeline("Text to predict <mask>", **tokenizer_kwargs)`

cc @Narsil <|||||>The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_22995). All of your documentation changes will be reflected on that endpoint.<|||||>Can we refactor in order to do :

```python
 output = fill_mask_pipeline("Text to predict <mask>", tokenizer_kwargs=tokenizer_kwargs)
 ```
 Instead ? Accepting kwargs directly is very hard to maintain down the line because of clashing arguments (for instance `max_length` is one that pops up often enough).
  
 We can also whiteliste some parameters like `truncation` or `padding` to make them more convenient. but enabling all the kwargs directly is really not something we want I think.
 
 Thanks for the contribution though, it's a step in the good direction ! <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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