Use random_attention_mask for TF tests
# What does this PR do?

- Change TF's `random_attention_mask` to match its PT/Flax equivalence.
- Use `random_attention_mask` defined in `test_modeling_tf_common.py` to generate attention mask in TF tests.

  - so TF code has the same logic as in PT/Flax tests (regarding this attention mask part in tests)
  - avoid large difference between PT/TF outputs. (In particular, `TFGPT2EncoderDecoderModelTest` in [here](https://github.com/huggingface/transformers/issues/16497))
    - In the case of ``TFBERTEncoderDecoderModelTest`` or `TFGPT2EncoderDecoderModelTest`, it is caused by some sequence in a batch which gets all 0s as attention mask (generated by `ids_tensor`) - may happens on both encoder and decoder (especially after combining with the causal mask).

## More context

Currently, most of TF tests still uses
```python
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
```
while in PT/Flax tests, they call
```
input_mask = random_attention_mask([self.batch_size, self.seq_length])
```
(defined in the comment test file).

In particular, `random_attention_mask` has
```
    # make sure that at least one token is attended to for each batch
    attn_mask[:, -1] = 1
```


_The documentation is not available anymore as the PR was closed or merged._<|||||>> Interesting. It moves the column of 1s for the start to the end and now becomes like a left-padded input. It could help with GPT-2, indeed

(If you are interested to know a bit more the detail, @gante )

Actually, moving 1 to the end will cause problem (when a model uses causal mask.). This is why I needed to update the code in `TFCLIPModelTest`.

In general, current library has a bit issue when the final attention mask (after combining the causal mask if any) received by the attention layer has a sequence (in the batch) having all 0s as mask. One thing (but maybe not only) involved is the different values (-1e4, -1e9, -1e30, -inf) used.

Put 1 at the start will avoid this situation (when combining the causal mask).
(But I don't want to change the PT/Flax logic in this PR. This should be addressed in a separate PR after discussion.)

Regarding the tests like `TFGPT2EncoderDecoderModelTest`, this PR only helps partially (the encoder part). The decoder part needs extra logic for now (to address the above situation regarding the causal mask)
<|||||>Hi, @Rocketknight1,

Yes, all the points are right -- except

- I am not sure about this statement `and slightly increases the expected number of unmasked tokens in each input.`: I would say not this case, but I might misunderstand the sentence.

- `but guarantees that at least one token will have a value of 1`:
  - Yes, but not guarantee the same thing for the final attention mask used by attention layers to compute the softmax - because the final mask might be the one after **combining the causal mask** (for decoder models).
  - Some more future PRs to improve these kinds of things. 
<|||||>That makes sense! And my comment about "increases the expected number of unmasked tokens" was just an irrelevant observation - the average number of unmasked tokens is very slightly larger since we guarantee that one of them will have value 1. Ignore me!