 CLIP - Mismatch tokenizer_config.json of CLIPTokenizer.from_pretrained() and huggingface_hub
### System Info

Model ID : `"openai/clip-vit-base-patch32"`
Transformer Version : 4.23.0
In the huggingface hub, the file https://huggingface.co/openai/clip-vit-base-patch32/blob/main/tokenizer_config.json doesn't have the parameter `model_max_length:77`.
On the contrary, if I use the following:
`tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")`
`tokenizer.save_pretrained('./test_tokenizer/')`
The tokenizer_config.json saved under **test_tokenizer** has the the parameter `model_max_length:77`.

So, when I load tokenizer from the downloaded files from https://huggingface.co/openai/clip-vit-base-patch32/tree/main , the `tokenizer.model_max_length` is different from the `CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")`.
The former one has a `model_max_length=1000000000000000019884624838656`
The later one has a `model_max_length=77`



### Who can help?

@patil-suraj , @SaulLu 

### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

1. Download the files directly from CLIP huggingface hub: https://huggingface.co/openai/clip-vit-base-patch32/tree/main and store them under a folder such as './hub_model_clip/'
2.  Run the following code in a notebook
```
from transformers import CLIPTokenizer
tokenizer_1 = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
tokenizer_2 = CLIPTokenizer.from_pretrained("./hub_model_clip/")
```
3. Check the following two:
```
print(tokenizer_1.model_max_length) # 77
print(tokenizer_2.model_max_length) # 1000000000000000019884624838656
```

### Expected behavior

Sync up the model_max_len from different source of the CLIP tokenizer
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