Tensorflow pretrained FlauBERT mixed precision error
## Environment info
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- `transformers` version: 4.0.0
- Python version: 3.7.6
- Tensorflow version (GPU?):  2.3.0
- Using GPU in script?: Yes  - GPU Tesla V100-SXM2-16GB, compute capability 7.0
- Using distributed or parallel set-up in script?: No

### Who can help
@jplu 

## Information

Model I am using (Bert, XLNet ...):  "jplu/tf-flaubert-small-cased"

The problem arises when using:
* [ x] 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: 
* [x ] my own task or dataset: text classification

## To reproduce

Steps to reproduce the behavior:

1. Set the dtype policies to mixed precision "float16" with tensorflow
2. Load pre-trained tensorflow flaubert model ("jplu/tf-flaubert-small-cased")

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**After executing the following code:**
```python
from transformers import TFFlaubertModel
import tensorflow as tf
from tensorflow.keras.mixed_precision import experimental as mixed_precision

policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_policy(policy)

model_name = "jplu/tf-flaubert-small-cased"

model = TFFlaubertModel.from_pretrained(model_name)
```

**I got the following error:** 
```
InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a half tensor but is a float tensor [Op:AddV2]
```

## Expected behavior
There should not be any problem. When I run "bert-base-case" pretrained model it works perfectly (the bottom code does not return any error) 
```python
from transformers import TFBertModel
import tensorflow as tf
from tensorflow.keras.mixed_precision import experimental as mixed_precision

policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_policy(policy)

model_name = "bert-base-cased"

model = TFBertModel.from_pretrained(model_name)
```
Maybe there is an issue about hard-coded uses of float32 in FlauBERT ? and it is not fixed yet unlike other models ?

Hello!

Unfortunately the TF models are not yet compliant with the "float16" mixed precision. This is our main goal for the next release (the one after 4.2.X) as we are actively working on this.

Sorry the the inconvenience. I will update this post once done. <|||||>This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.

If you think this still needs to be addressed please comment on this thread.