Misc. fixes for Pytorch QA examples:
Author: searchivarius
Pull request: yes

Thank you for the great library! This fixes a number of issues with Pytorch QA examples. All numbers are either the same or went up. However, there are still some issues, which I wasn't able to fix (in one example). Please, see the notes and benchmark results below.

# What does this PR do?
1. Fixes evaluation errors popping up when you train/eval on squad v2 (one was newly encountered and one that was previously reported Running SQuAD 1.0 sample command raises IndexError #15401 but not completely fixed).
2. Removes boolean arguments that don't use store_true. Please, don't use these: **ANY** non-empty string is being converted to `True` in this case. This is clearly an **undesired**behavior, which creates a LOT of confusion.
3. All no-trainer test scripts are now saving metric values in the same way (with the right prefix eval_), which is consistent with the trainer-based versions.
4. Adds forgotten model.eval() in the no-trainer versions. This improved some results, but not everything (see the discussion in the end). Please, see the F1 scores and the discussion below.

- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [X] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case. This is a **reduced** PR [as discussed here](https://github.com/huggingface/transformers/pull/16926#issuecomment-1108479241).
- [ ] You make sure to update the documentation with your changes? **I believe examples aren't covered by the documentation** 
- [X] Did you write any new necessary tests? **I trained squad and squad v2 models and compared results (see the discussion below)**, but I am not sure if running more QA tests automatically will be feasible. Do note that the existing "unit-test" is very crude and does not permit detecting small regressions in model quality.

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Perhaps, this can be of most interest for  @sgugger, who reviewed a prior version of this PR.

## Comparing old and new performance + some potential issues

Some remaining issues:
1. Despite the fixes & improvements, there's still a discrepancy between no-trainer and original version for SQuAD v2 or the beam-search version.
2. In particular, for SQuAD v2 and the beam-search variant **without trainer**, both old and new numbers look very wrong to me.

Please note that to be able to run SQuAD v2 tests, **I had to apply utils_qa.py fixes to the old code as well**. Otherwise, it would have just failed:

The metric is F1, the exact scores have the same pattern:

|                                   | previous |  new |
|-----------------------------------|:--------:|:----:|
| squad v1                          |   88.4   | 88.4 |
| squad v1 (no trainer)             |   86.7   | 88.5 |
| squad v2                          |    N/A   | 75.2 |
| squad v2 (no trainer)             |    N/A   | 77.1 |
| squad v1 (beam search)            |   92.1   | 92.1 |
| squad v1 (beam search no trainer) |   90.2   | 91.0 |
| squad v2 (beam search)            |   83.2   | 83.2 |
| squad v2 (beam search no trainer) |    4.9   | 50.1 |

Comments:
_The documentation is not available anymore as the PR was closed or merged._