Check TF ops for ONNX compliance (#10025)

* Add check-ops script

* Finish to implement check_tf_ops and start the test

* Make the test mandatory only for BERT

* Update tf_ops folder

* Remove useless classes

* Add the ONNX test for GPT2 and BART

* Add a onnxruntime slow test + better opset flexibility

* Fix test + apply style

* fix tests

* Switch min opset from 12 to 10

* Update src/transformers/file_utils.py

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Fix GPT2

* Remove extra shape_list usage

* Fix GPT2

* Address Morgan's comments

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

src/transformers/file_utils.py

@@ -151,6 +151,16 @@ except importlib_metadata.PackageNotFoundError:
         _faiss_available = False
 
 
+_onnx_available = (
+    importlib.util.find_spec("keras2onnx") is not None and importlib.util.find_spec("onnxruntime") is not None
+)
+try:
+    _onxx_version = importlib_metadata.version("onnx")
+    logger.debug(f"Successfully imported onnx version {_onxx_version}")
+except importlib_metadata.PackageNotFoundError:
+    _onnx_available = False
+
+
 _scatter_available = importlib.util.find_spec("torch_scatter") is not None
 try:
     _scatter_version = importlib_metadata.version("torch_scatter")
@@ -230,6 +240,10 @@ def is_tf_available():
     return _tf_available
 
 
+def is_onnx_available():
+    return _onnx_available
+
+
 def is_flax_available():
     return _flax_available