Add common test for torch.export and fix some vision models (#35124)

* Add is_torch_greater_or_equal test decorator

* Add common test for torch.export

* Fix bit

* Fix focalnet

* Fix imagegpt

* Fix seggpt

* Fix swin2sr

* Enable torch.export test for vision models

* Enable test for video models

* Remove json

* Enable for hiera

* Enable for ijepa

* Fix detr

* Fic conditional_detr

* Fix maskformer

* Enable test maskformer

* Fix test for deformable detr

* Fix custom kernels for export in rt-detr and deformable-detr

* Enable test for all DPT

* Remove custom test for deformable detr

* Simplify test to use only kwargs for export

* Add comment

* Move compile_compatible_method_lru_cache to utils

* Fix beit export

* Fix deformable detr

* Fix copies data2vec<->beit

* Fix typos, update test to work with dict

* Add seed to the test

* Enable test for vit_mae

* Fix beit tests

* [run-slow] beit, bit, conditional_detr, data2vec, deformable_detr, detr, focalnet, imagegpt, maskformer, rt_detr, seggpt, swin2sr

* Add vitpose test

* Add textnet test

* Add dinov2 with registers

* Update tests/test_modeling_common.py

* Switch to torch.testing.assert_close

* Fix masformer

* Remove save-load from test

* Add dab_detr

* Add depth_pro

* Fix and test RT-DETRv2

* Fix dab_detr

tests/models/beit/test_modeling_beit.py

@@ -271,6 +271,7 @@ class BeitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     test_pruning = False
     test_resize_embeddings = False
     test_head_masking = False
+    test_torch_exportable = True
 
     def setUp(self):
         self.model_tester = BeitModelTester(self)
@@ -292,6 +293,10 @@ class BeitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     def test_feed_forward_chunking(self):
         pass
 
+    @unittest.skip(reason="BEiT can't compile dynamic")
+    def test_sdpa_can_compile_dynamic(self):
+        pass
+
     def test_model_get_set_embeddings(self):
         config, _ = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -764,13 +769,6 @@ class BeitModelIntegrationTest(unittest.TestCase):
         inputs = processor(images=image, return_tensors="pt", size={"height": 480, "width": 480})
         pixel_values = inputs.pixel_values.to(torch_device)
 
-        # with interpolate_pos_encoding being False an exception should be raised with higher resolution
-        # images than what the model supports.
-        self.assertFalse(processor.do_center_crop)
-        with torch.no_grad():
-            with self.assertRaises(ValueError, msg="doesn't match model"):
-                model(pixel_values, interpolate_pos_encoding=False)
-
         # with interpolate_pos_encoding being True the model should process the higher resolution image
         # successfully and produce the expected output.
         with torch.no_grad():