TypeError when trying to load pretrained ALBERT model in BertTokenizer 
## Environment info
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- `transformers` version: 4.6.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyTorch version (GPU?): 1.8.1+cu101 (False)
- Tensorflow version (GPU?): 2.5.0 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
@LysandreJik 
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## Information

Model I am using (Bert, XLNet ...): BertTokenizer

The problem arises when using:
* [ *] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [* ] my own task or dataset: (give details below)

Trying to tokenize a dataset of tweets  
## To reproduce

Steps to reproduce the behavior:


from transformers import BertTokenizer

tokenizerr = BertTokenizer.from_pretrained("albert-base-v2")
->> The error message is ->
TypeError                                 Traceback (most recent call last)
<ipython-input-11-f632f8d4de7e> in <module>()
      1 from transformers import BertTokenizer
----> 2 tokenizerr = BertTokenizer.from_pretrained("albert-base-v2")

3 frames
/usr/lib/python3.7/genericpath.py in isfile(path)
     28     """Test whether a path is a regular file"""
     29     try:
---> 30         st = os.stat(path)
     31     except OSError:
     32         return False

TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType

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## Expected behavior
The code should load the pre-trained BertTokenizer model for the Albert-base-v2  model. The same thing happened with Albert-base-v1
<!-- A clear and concise description of what you would expect to happen. -->

Hello! Why don't you try to load the ALBERT tokenizer in an ALBERT tokenizer?
```py
from transformers import AlbertTokenizer

tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
```<|||||>Hey!
I was not aware of its existence to be honest, but shouldnt loading it in the BertTokenizer work?<|||||>ALBERT and BERT are different models, and the ALBERT tokenizer isn't related to BERT's tokenizer at all. They're not based on the same algorithms: BERT's tokenizer is using WordPiece, ALBERT's using Unigram.

If you're looking for a tokenizer to encompass all other tokenizers, take a look at the [`Auto*` classes](https://huggingface.co/transformers/model_doc/auto.html):

```py
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
```<|||||>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.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.