support loading pretrained model from fsspec paths
### Feature request

Make `from_pretrained` works with fsspec paths.

```
tokenizer = BartTokenizerFast.from_pretrained("hdfs://..../bert-base-uncased")
```

### Motivation

To make transformers suitable for usage in the cloud or in clusters where files should be stored in HDFS or S3.

### Your contribution

I'll be willing to submit a PR but `from_pretrained` is a very complicated function so I may need assistance.
Thank you, but we're not interested in expanding support to other tools than the Hugging Face Hub :-)<|||||>I'd like to use Hub too, but our cluster have no public internet access...<|||||>> I'd like to use Hub too, but our cluster have no public internet access...

hai, can you  help  how  to load the pretrain model  from hdfs path<|||||>Still no way to do that. I have to use pytorch_lightning's ModelCheckpoint callback to load & save.
You can pre-process your pretrained model into a pytorch `.py` file then load it with `torch.load(fsspec.open('xxx'))`.<|||||>> 

thanks so much for your reply ,  can you teach more detail please ,thanks<|||||>hha, I have got an simple solution without changing  the source code ,  just use spark submit :
--conf spark.yarn.dist.archives=hdfs://your_own_hdfs_path/your_pretraining_model.zip#your_pretraining_model
 

