Add `LayoutXLMTokenizer` and `LayoutXLMTokenizerFast`
# What does this PR do?

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Fixes #13972 (issue)


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Models:

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Nice! Let me know if you need any help.<|||||>> Nice! Let me know if you need any help.

Thanks!

The tokenizer in `'microsoft/layoutxlm-base'` was registered with `XLMRobertaTokenizer`, so the user will get a  warning that there is a mismatch when they do

```
tokenizer = LayoutXLMTokenizer.from_pretrained('microsoft/layoutxlm-base') 
```

Is there a way to avoid this?

Also, I keep getting a `ModuleNotFoundError: No module named 'sentencepiece'` error in `tests/test_processor_layoutlmv2.py`. I can't figure out what is wrong.

<|||||>> The tokenizer in 'microsoft/layoutxlm-base' was registered with XLMRobertaTokenizer, so the user will get a warning

Yeah, that's because the config currently has an attribute `tokenizer_class` which is set to `XLMRobertaTokenizer` as can be seen [here](https://huggingface.co/microsoft/layoutxlm-base/blob/main/config.json). Once `LayoutXLMTokenizer`/`LayoutXLMTokenizerFast` are ready, we will upload the vocab files to the model repo, and remove the `tokenizer_class` attribute.<|||||>> > The tokenizer in 'microsoft/layoutxlm-base' was registered with XLMRobertaTokenizer, so the user will get a warning
> 
> Yeah, that's because the config currently has an attribute `tokenizer_class` which is set to `XLMRobertaTokenizer` as can be seen [here](https://huggingface.co/microsoft/layoutxlm-base/blob/main/config.json). Once `LayoutXLMTokenizer`/`LayoutXLMTokenizerFast` are ready, we will upload the vocab files to the model repo, and remove the `tokenizer_class` attribute.

Got it. I just have a failing test and a docstring error in the CI. Any advice on how to fix them?<|||||>Ok, I'll take a look later today.<|||||>@kingyiusuen I've fixed the docs issue, currently checking out the tests. One is failing, the boxes created between the slow and fast tokenizer aren't equal:

```
from transformers import LayoutXLMTokenizer, LayoutXLMTokenizerFast

tokenizer_p = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base")
tokenizer_r = LayoutXLMTokenizerFast.from_pretrained("microsoft/layoutxlm-base")
question = "what's his name?"
words = ["a", "weirdly", "test"]
boxes = [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]]

encoding_p = tokenizer_p(question, words, boxes, padding="max_length", max_length=20)
encoding_r = tokenizer_r(question, words, boxes, padding="max_length", max_length=20)
for x,y in zip(encoding_p.bbox, encoding_r.bbox): print(x,y)

# this prints:
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
[1000, 1000, 1000, 1000] [1000, 1000, 1000, 1000]
[423, 237, 440, 251] [1000, 1000, 1000, 1000]
[427, 272, 441, 287] [423, 237, 440, 251]
[427, 272, 441, 287] [427, 272, 441, 287]
[419, 115, 437, 129] [427, 272, 441, 287]
[1000, 1000, 1000, 1000] [419, 115, 437, 129]
[0, 0, 0, 0] [1000, 1000, 1000, 1000]
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
[0, 0, 0, 0] [0, 0, 0, 0]
```

The `input_ids` and `attention_mask` are equal. Decoding the `input_ids`, it seems that one adds 2 special </s> tokens in between  the question and the context? So the fast tokenizer seems correct.

UPDATE: fixed it in the slow tokenizer.<|||||>It might make sense to create a separate LayoutXLMProcessor. Will do this.<|||||>I've made all necessary changes, fixed all tests, implemented a new `LayoutXLMProcessor` and created new tests for it accordingly.

You can find my branch here: https://github.com/NielsRogge/transformers/tree/add-layoutxlm-fast-tokenizer

Should I open a PR on your branch? Or should I directly open a PR to HuggingFace Transformers?<|||||>> I've made all necessary changes, fixed all tests, implemented a new `LayoutXLMProcessor` and created new tests for it accordingly.
> 
> You can find my branch here: https://github.com/NielsRogge/transformers/tree/add-layoutxlm-fast-tokenizer
> 
> Should I open a PR on your branch? Or should I directly open a PR to HuggingFace Transformers?

Maybe you should directly open a PR to HuggingFace Transformers. You've done most of the heavy-lifting. This should be counted as your contribution. 😃 