* use torch.testing.assertclose instead to get more details about error in cis
* fix
* style
* test_all
* revert for I bert
* fixes and updates
* more image processing fixes
* more image processors
* fix mamba and co
* style
* less strick
* ok I won't be strict
* skip and be done
* up
* Un-skip tests
* Add aliasing support to tf_to_pt_weight_rename
* Refactor tf-to-pt weight rename for simplicity
* Patch mobilebert
* Let us pray that the transfo-xl one works
* Add XGLM rename
* Expand the test to see if we can get more models to break
* Expand the test to see if we can get more models to break
* Fix MPNet (it was actually an unrelated bug)
* Fix MPNet (it was actually an unrelated bug)
* Add speech2text fix
* Update src/transformers/modeling_tf_pytorch_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/mobilebert/modeling_tf_mobilebert.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update to always return a tuple from tf_to_pt_weight_rename
* reformat
* Add a couple of missing tuples
* Remove the extra test for tie_word_embeddings since it didn't cause any unexpected failures anyway
* Revert changes to modeling_tf_mpnet.py
* Skip MPNet test and add explanation
* Add weight link for BART
* Add TODO to clean this up a bit
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* hidden layers, huh, what are they good for (absolutely nothing)
* Some tests break with 1 hidden layer, use 2
* Use 1 hidden layer in a few slow models
* Use num_hidden_layers=2 everywhere
* Slightly higher tol for groupvit
* Slightly higher tol for groupvit
* Rework TF type hints to use | None instead of Optional[] for tf.Tensor
* Rework TF type hints to use | None instead of Optional[] for tf.Tensor
* Don't forget the imports
* Add the imports to tests too
* make fixup
* Refactor tests that depended on get_type_hints
* Better test refactor
* Fix an old hidden bug in the test_keras_fit input creation code
* Fix for the Deit tests
* Result of black 23.1
* Update target to Python 3.7
* Switch flake8 to ruff
* Configure isort
* Configure isort
* Apply isort with line limit
* Put the right black version
* adapt black in check copies
* Fix copies
