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unittest doesn't support pytest's super-handy `@pytest.mark.parametrize`, I researched and there are many proposed workarounds, most tedious at best. If we include https://pypi.org/project/parameterized/ in dev dependencies - it will provide a very easy to write parameterization in tests. Same as pytest's fixture, plus quite a few other ways. 

Example:
```
from parameterized import parameterized
@parameterized([
    (2, 2, 4),
    (2, 3, 8),
    (1, 9, 1),
    (0, 9, 0),
])
def test_pow(base, exponent, expected):
   assert_equal(math.pow(base, exponent), expected)
```
(extra `self`var if inside a test class)

To remind the pytest style is slightly different:
```
    @pytest.mark.parametrize("test_input,expected", [("3+5", 8), ("2+4", 6), ("6*9", 42)])
    def test_eval(test_input, expected):
```
More examples here: https://pypi.org/project/parameterized

May I suggest that it will make it much easier to write some types of tests?

* [docstring] missing arg

add the missing `tie_word_embeddings` entry

* cleanup

* Update src/transformers/configuration_reformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

there is no var `decoder_input_ids`, but there is `input_ids` for decoder :)

* Add warning for gradient accumulation

* Formatting

cc @jplu

* feat: allow padding_text for any generative model

* docs(pipelines.py): correct typo

* Update src/transformers/pipelines.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* feat: rename padding_text to prefix

* fix: cannot tokenize empty text

* fix: pass prefix arg to pipeline

* test: add prefix to text-generetation pipeline

* style: fix style

* style: clean code and variable name more explicit

* set arg docstring to optional
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* create model card for astroGPT

* Hotlink to actual image file
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Create Readme.MD for KanBERTo

KanBERTo language model readme for Kannada language.

* Update model_cards/Naveen-k/KanBERTo/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* remove the implied defaults to :obj:`None`

* fix bug in the original

* replace to :obj:`True`, :obj:`False`

* correct bool types

fix docstring s/int/bool/

* fix description

* fix num_labels to match reality

* Remove hard-coded uses of float32 to fix mixed precision use in TF Distilbert

* fix style

* fix gelu dtype issue in TF Distilbert

* fix numeric overflow while using half precision

* move wandb/comet logger init to train() to allow parallel logging

* Setup wandb/comet loggers on first call to log()

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Loodos model cards had errors on "Usage" section. It is fixed. Also "electra-base-turkish-uncased" model removed from s3 and re-uploaded as "electra-base-turkish-uncased-discriminator". Its README added. (#6921)
Co-authored-by: Abdullah Oluk <abdullaholuk123@gmail.com>

cc @psorianom @rachelker

Adding the LXMERT pretraining model (MultiModal  languageXvision)  to HuggingFace's suite of models (#5793)

* added template files for LXMERT and competed the configuration_lxmert.py

* added modeling, tokization, testing, and finishing touched for lxmert [yet to be tested]

* added model card for lxmert

* cleaning up lxmert code

* Update src/transformers/modeling_lxmert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/modeling_tf_lxmert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/modeling_tf_lxmert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/modeling_lxmert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* tested torch lxmert, changed documtention, updated outputs, and other small fixes

* Update src/transformers/convert_pytorch_checkpoint_to_tf2.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/convert_pytorch_checkpoint_to_tf2.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/convert_pytorch_checkpoint_to_tf2.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* renaming, other small issues, did not change TF code in this commit

* added lxmert question answering model in pytorch

* added capability to edit number of qa labels for lxmert

* made answer optional for lxmert question answering

* add option to return hidden_states for lxmert

* changed default qa labels for lxmert

* changed config archive path

* squshing 3 commits: merged UI + testing improvments + more UI and testing

* changed some variable names for lxmert

* TF LXMERT

* Various fixes to LXMERT

* Final touches to LXMERT

* AutoTokenizer order

* Add LXMERT to index.rst and README.md

* Merge commit test fixes + Style update

* TensorFlow 2.3.0 sequential model changes variable names

Remove inherited test

* Update src/transformers/modeling_tf_pytorch_utils.py

* Update docs/source/model_doc/lxmert.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update docs/source/model_doc/lxmert.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_tf_lxmert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* added suggestions

* Fixes

* Final fixes for TF model

* Fix docs
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Since `generate()` does:
```
        num_beams = num_beams if num_beams is not None else self.config.num_beams
```
This test fails if `model.config.num_beams > 1` (which is the case in the model I'm porting).

This fix makes the test setup unambiguous by passing an explicit `num_beams=1` to `generate()`.

Thanks.

* Fix output_attention -> output_attentions

* Formatting

* One unsaved file

* add Text2TextGenerationPipeline

* remove max length warning

* remove comments

* remove input_length

* fix typo

* add tests

* use TFAutoModelForSeq2SeqLM

* doc

* typo

* add the doc below TextGenerationPipeline

* doc nit

* style

* delete comment

* [doc] typos

fixed typos

* Update README.md