Add dynamic resolution input/interpolate position embedding to deit (#31131)

* Added interpolate pos encoding feature and test to deit

* Added interpolate pos encoding feature and test for deit TF model

* readded accidentally delted test for multi_gpu

* storing only patch_size instead of entire config and removed commented code

* Update modeling_tf_deit.py to remove extra line

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

tests/models/deit/test_modeling_deit.py

@@ -423,6 +423,28 @@ class DeiTModelIntegrationTest(unittest.TestCase):
 
         self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
 
+    @slow
+    def test_inference_interpolate_pos_encoding(self):
+        model = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224").to(
+            torch_device
+        )
+
+        image_processor = self.default_image_processor
+
+        # image size is {"height": 480, "width": 640}
+        image = prepare_img()
+        image_processor.size = {"height": 480, "width": 640}
+        # center crop set to False so image is not center cropped to 224x224
+        inputs = image_processor(images=image, return_tensors="pt", do_center_crop=False).to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, interpolate_pos_encoding=True)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 1000))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
     @slow
     @require_accelerate
     @require_torch_accelerator