Disabling Eager Mode Prevents Loading Pre-Trained BERT
# 🐛 Bug

## Information

Model I am using (Bert, XLNet ...):
**BERT**
Language I am using the model on (English, Chinese ...):
**English**

## To reproduce

Steps to reproduce the behavior:

1. Disable Tensorflow eager mode `tf.compat.v1.disable_v2_behavior()`
2. Create a pretrained BERT instance `model = transformers.TFBertModel.from_pretrained("bert-base-uncased")`

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## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
`model` contains a pre-trained BERT model

## Environment info
<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version:
- Platform: Windows-10-10.0.18362-SP0
- Python version: 3.7.6
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): 2.0.0 (True)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

TFBertModel can auto use gpu?  i find model(np.array(input_ids)) is slow,cost 100+ms
In [14]: %timeit model_outputs = model(np.array(input_ids))
133 ms ± 3.4 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [15]: %time model_outputs = model(np.array(input_ids))
CPU times: user 330 ms, sys: 14.4 ms, total: 344 ms
Wall time: 158 ms
<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>I have a similar issue. Did you manage to find a fix for this @msandrewhoang03 ?