Owlvit docs test (#18257)

* fix docs and add owlvit docs test

* fix minor bug in post_process, add to processor

* improve owlvit code examples

* fix hardcoded image size

docs/source/en/model_doc/owlvit.mdx

@@ -39,19 +39,26 @@ OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses [CL
 
 >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 >>> image = Image.open(requests.get(url, stream=True).raw)
-
->>> inputs = processor(text=[["a photo of a cat", "a photo of a dog"]], images=image, return_tensors="pt")
-
+>>> texts = [["a photo of a cat", "a photo of a dog"]]
+>>> inputs = processor(text=texts, images=image, return_tensors="pt")
 >>> outputs = model(**inputs)
->>> logits = outputs["logits"]  # Prediction logits of shape [batch_size, num_patches, num_max_text_queries]
->>> boxes = outputs["pred_boxes"]  # Object box boundaries of shape [batch_size, num_patches, 4]
 
->>> batch_size = boxes.shape[0]
->>> for i in range(batch_size):  # Loop over sets of images and text queries
-...     boxes = outputs["pred_boxes"][i]
-...     logits = torch.max(outputs["logits"][i], dim=-1)
-...     scores = torch.sigmoid(logits.values)
-...     labels = logits.indices
+>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
+>>> target_sizes = torch.Tensor([image.size[::-1]])
+>>> # Convert outputs (bounding boxes and class logits) to COCO API
+>>> results = processor.post_process(outputs=outputs, target_sizes=target_sizes)
+
+>>> i = 0  # Retrieve predictions for the first image for the corresponding text queries
+>>> text = texts[i]
+>>> boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
+
+>>> score_threshold = 0.1
+>>> for box, score, label in zip(boxes, scores, labels):
+...     box = [round(i, 2) for i in box.tolist()]
+...     if score >= score_threshold:
+...         print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
+Detected a photo of a cat with confidence 0.243 at location [1.42, 50.69, 308.58, 370.48]
+Detected a photo of a cat with confidence 0.298 at location [348.06, 20.56, 642.33, 372.61]
 ```
 
 This model was contributed by [adirik](https://huggingface.co/adirik). The original code can be found [here](https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit).