[WIP] Ensure TF model configs can be converted to proper JSON
# What does this PR do?

This is an extension to https://github.com/huggingface/transformers/pull/14361/files, which hopefully will prevent errors such as https://github.com/huggingface/transformers/issues/14403 from going unnoticed.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

@Rocketknight1 I assume this test (if run) will fail for quite some architectures, I will try to see if I can provide a fix on this PR, feel free to review/comment.
Thanks for this! I see a few failing tests, but I think something like this should work. One thing I'd suggest: I think the `from_config()` method should probably work whether a dict or a true config object is passed. Can we check the type of the input, and only do the conversion if it's actually a dict?<|||||>Yes, I think these here need to be fixed individually, as the config indeed is not JSONifiable. Regarding your last comment, I'll try to see if I can add that and a test case for it later.
```

FAILED tests/test_modeling_tf_distilbert.py::TFDistilBertModelTest::test_save_load_config
FAILED tests/test_modeling_tf_funnel.py::TFFunnelModelTest::test_save_load_config
FAILED tests/test_modeling_tf_gpt2.py::TFGPT2ModelTest::test_save_load_config
FAILED tests/test_modeling_tf_flaubert.py::TFFlaubertModelTest::test_save_load_config
FAILED tests/test_modeling_tf_funnel.py::TFFunnelBaseModelTest::test_save_load_config
FAILED tests/test_modeling_tf_led.py::TFLEDModelTest::test_save_load_config
FAILED tests/test_modeling_tf_xlm.py::TFXLMModelTest::test_save_load_config
FAILED tests/test_modeling_tf_xlnet.py::TFXLNetModelTest::test_save_load_config
```
<|||||>That's strange, the config for at least some of them seems to convert fine for me. For example, this works (after installing from master):

```
from transformers import TFAutoModel
import json

model = TFAutoModel.from_pretrained('distilbert-base-cased')
json.dumps(model.get_config().to_dict())
```<|||||>It seems the problem rather is on loading the config: When running PretrainedConfig.from_dict(), the returned config will be of class PretrainedConfig, NOT of the specific models class; hence getattr calls to non-standard properties fail.

Is there a way to get the correct config class when calling from_config()? Otherwise, we would need to save the class name as part of the get_config(), and when calling from_config() use this to map to the correct class.

EDIT: I think we can use cls.config_class. Lets see if the tests go through.

```
src/transformers/models/distilbert/modeling_tf_distilbert.py:347: in __init__
    self.num_hidden_layers = config.num_hidden_layers
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = PretrainedConfig {
  "activation": "gelu",
  "attention_dropout": 0.1,
  "dim": 32,
  "dropout": 0.1,
  "hidden_act": ..._classif_dropout": 0.2,
  "sinusoidal_pos_embds": false,
  "transformers_version": "4.13.0.dev0",
  "vocab_size": 99
}

key = 'num_hidden_layers'

    def __getattribute__(self, key):
        if key != "attribute_map" and key in super().__getattribute__("attribute_map"):
            key = super().__getattribute__("attribute_map")[key]
>       return super().__getattribute__(key)
E       AttributeError: 'PretrainedConfig' object has no attribute 'num_hidden_layers'
```<|||||>@Zahlii Since we know things are broken, we're going to merge this PR urgently, and then quickly work on testing to follow it up. I'll tag you in the PR - we're planning a revamp to TF testing, since the tests that would have caught this were marked as tooslow. <|||||>@Zahlii Another update, and a change of plan! We're going to revert the last commit, do the fixes in this PR, and I might add some testing to this PR before it's merged. Is that okay with you?<|||||>Sure, go ahead and let me know if I can support further.<|||||>Cool, thank you!<|||||>Small comment without having checked the code - I observed that the saved model format per default traces all functions. For my use cases, I always disabled that because it added an enormous overhead, and with a correct config handling it wasn't required. On the one hand this rids the requirement for the config stuff, on the other hand it is much slower. How is this currently handled , both inside tests and others? https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model<|||||>> Small comment without having checked the code - I observed that the saved model format per default traces all functions. For my use cases, I always disabled that because it added an enormous overhead, and with a correct config handling it wasn't required. On the one hand this rids the requirement for the config stuff, on the other hand it is much slower. How is this currently handled , both inside tests and others? https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model

This is a good point - the short answer is that we want it to work when people save their model like that, but like you we found it was much too slow to test every model with it in the CI. The solution we went with in this PR was to keep it as a 'core' test only for the most commonly used model classes (BERT, GPT2 and BART), and hope that if there's a problem with it that it shows up there.