Feature to use the PreTrainedTokenizerFast class as a stand-alone tokenizer
Author: SaulLu
Pull request: yes

# What does this PR do?

In this PR, I propose to add the features needed to use `PreTrainedTokenizerFast` as a standalone tokenizer. These features include:

1. The ability to save a `PreTrainedTokenizerFast` tokenizer. Until now, it was not possible to `save_pretrained` ( with default values in the method) a `PreTrainedTokenizerFast` initialized from a folder containing only the `tokenizer.json`, `tokenizer_config.json` and `special_tokens_map.json` files. An error was previously returned because the `save_vocabulary` method was not implemented, which is normal when trying to use a fast tokenizer alone as it has no slow version. This feature allows this kind of use: 
```
from transformers import PreTrainedTokenizerFast

tokenizer = PreTrainedTokenizerFast.from_pretrained("SaulLu/bengali-tokenizer-v2")
tokenizer.save_pretrained("./local_tokenizer")
```
2. The ability to specify in the `config.json` file that the type of tokenizer to be loaded is `PreTrainedTokenizerFast` in order to be able to load a `PreTrainedTokenizerFast` with `AutoTokenizer`.

In this PR, I also propose the modification/addition of 3 types of tests:
- Modifications: This design change required the modification of common tests for tokenizers stored in the `tests/test_tokenization_common.py` file. To my knowledge, this is quite a different use as this is the first time a tokenizer will not have a slow/legacy version. The changes to `tests/test_tokenization_common.py` allow a test class derived from `TokenizerTesterMixin` to leave the `tokenizer_class` attribute set to None and to only set the `rust_tokenizer_class` attribute. In other words, the derived class will allow to test a tokenizer which would not have an associated slow/legacy version. As there were several possibilities to modify these tests, if you ever think that it is easier to develop these tests in another PR, I can remove this part from this PR.
- Added : Added tests for using a standalone `PreTrainedTokenizerFast`  in the `tests/test_tokenization_fast.py` file. I have created a tokenizer for this and stored it [here](https://huggingface.co/robot-test/dummy-tokenizer-fast).
- Added : Added tests to load a standalone `PreTrainedTokenizerFast` via `AutoTokenizer` in the `tests/test_tokenization_auto.py ` file. I have created a tokenizer for this and stored it [here](https://huggingface.co/robot-test/dummy-tokenizer-fast-with-model-config).

This PR should make it easy to use a fast tokenizer created with the `Tokenizers` library in the `Transformers` library. A typical use case would be : 
1. Create a tokenizer with `Tokenizers` library
```
from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from tokenizers.pre_tokenizers import Whitespace

tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])

tokenizer.pre_tokenizer = Whitespace()
files = [...]
tokenizer.train(files, trainer)
```
2. Adapt the tokenizer to `Transformers` library 
At the end of this step, the tokenizer will be saved in a folder named `brand_new_tokenizer` and containing `tokenizer.json`, `tokenizer_config.json` and `special_tokens_map.json` files.
a. Save and initialize `PreTrainedTokenizerFast` with json file
```
tokenizer.save("tokenizer.json")
```
```
from transformers import PreTrainedTokenizerFast
from transformers.tokenization_utils import AddedToken

fast_tokenizer = PreTrainedTokenizerFast(
               tokenizer_file="tokenizer.json", 
               model_max_length=512,
               padding_side="right",
               mask_token=AddedToken("[MASK]", lstrip=True, rstrip=False
         )
fast_tokenizer.save_pretrained("brand_new_tokenizer")
```
   b. Initialize `PreTrainedTokenizerFast` from the tokenizer object
```
from transformers import PreTrainedTokenizerFast
from transformers.tokenization_utils import AddedToken

fast_tokenizer = PreTrainedTokenizerFast(
               tokenizer_object=tokenizer, 
               model_max_length=512,
               padding_side="right",
               mask_token=AddedToken("[MASK]", lstrip=True, rstrip=False
         )
fast_tokenizer.save_pretrained("brand_new_tokenizer")
```
3. Load tokenizer with `PreTrainedTokenizerFast`
```
from transformers import PreTrainedTokenizerFast

tokenizer = PreTrainedTokenizerFast.from_pretrained("brand_new_tokenizer")
```
4. (Temporary solution before a next PR) Create a `config.json` file in `brand_new_tokenizer` folder and initialize a tokenizer with `AutoTokenizer`.
Config file:
```
{ 
   "model_type": "albert", 
   "tokenizer_class": "PreTrainedTokenizerFast" 
}
```
```
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("brand_new_tokenizer")
```
In one (or more)  next PRs, we would still have to :
- disassociate the tokenizer from the `config.json` file so that `AutoTokenizer` can load a saved tokenizer without a model
- if necessary adjust the documentation (for example [here](https://huggingface.co/transformers/fast_tokenizers.html) )


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## Who can review?

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Comments:
Could this also be a fallback for `AutoTokenizer` when none of the children classes match?