Add dynamic resolution input/interpolate position embedding to SigLIP (#30719)

* Add interpolate positional encoding to siglip

* Change # of patches for siglip interpolation test

* fix formatting

* Apply nit suggestions from code review

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

---------

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

tests/models/siglip/test_modeling_siglip.py

@@ -687,3 +687,25 @@ class SiglipModelIntegrationTest(unittest.TestCase):
         probs = torch.sigmoid(logits_per_image)  # these are the probabilities
         expected_probs = torch.tensor([[3.1937e-01, 3.2463e-05]], device=torch_device)
         self.assertTrue(torch.allclose(probs, expected_probs, atol=1e-3))
+
+    @slow
+    def test_inference_interpolate_pos_encoding(self):
+        model_name = "google/siglip-base-patch16-224"
+        model = SiglipModel.from_pretrained(model_name).to(torch_device)
+
+        # 640 x 480 image
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        processor = SiglipProcessor.from_pretrained(model_name, do_resize=False, size={"height": 480, "width": 640})
+
+        inputs = processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, interpolate_pos_encoding=True)
+
+        # verify the shape
+        # patch size = 16
+        # batch size 1, (640/16) * (480/16) = 1200 patches, 768 hidden size
+        expected_shape = torch.Size((1, 1200, 768))
+
+        self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)