//  [python]
def test_processor_case_4(self):
        # case 4: visual question answering (inference), apply_ocr=True

        image_processor = LayoutLMv2ImageProcessor()
        tokenizers = self.get_tokenizers
        images = self.get_images

        for tokenizer in tokenizers:
            processor = LayoutLMv2Processor(image_processor=image_processor, tokenizer=tokenizer)

            # not batched
            question = "What's his name?"
            input_processor = processor(images[0], question, return_tensors="pt")

            # verify keys
            expected_keys = ["attention_mask", "bbox", "image", "input_ids", "token_type_ids"]
            actual_keys = sorted(input_processor.keys())
            self.assertListEqual(actual_keys, expected_keys)

            # verify input_ids
            # this was obtained with Tesseract 4.1.1
            # fmt: off
            expected_decoding = "[CLS] what's his name? [SEP] 11 : 14 to 11 : 39 a. m 11 : 39 to 11 : 44 a. m. 11 : 44 a. m. to 12 : 25 p. m. 12 : 25 to 12 : 58 p. m. 12 : 58 to 4 : 00 p. m. 2 : 00 to 5 : 00 p. m. coffee break coffee will be served for men and women in the lobby adjacent to exhibit area. please move into exhibit area. ( exhibits open ) trrf general session ( part | ) presiding : lee a. waller trrf vice president “ introductory remarks ” lee a. waller, trrf vice presi - dent individual interviews with trrf public board members and sci - entific advisory council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public refrigerated warehousing industry is looking for. plus questions from the floor. dr. emil m. mrak, university of cal - ifornia, chairman, trrf board ; sam r. cecil, university of georgia college of agriculture ; dr. stanley charm, tufts university school of medicine ; dr. robert h. cotton, itt continental baking company ; dr. owen fennema, university of wis - consin ; dr. robert e. hardenburg, usda. questions and answers exhibits open capt. jack stoney room trrf scientific advisory council meeting ballroom foyer [SEP]"  # noqa: E231
            # fmt: on
            decoding = processor.decode(input_processor.input_ids.squeeze().tolist())
            self.assertSequenceEqual(decoding, expected_decoding)

            # batched
            questions = ["How old is he?", "what's the time"]
            input_processor = processor(
                images, questions, padding="max_length", max_length=20, truncation=True, return_tensors="pt"
            )

            # verify keys
            expected_keys = ["attention_mask", "bbox", "image", "input_ids", "token_type_ids"]
            actual_keys = sorted(input_processor.keys())
            self.assertListEqual(actual_keys, expected_keys)

            # verify input_ids
            # this was obtained with Tesseract 4.1.1
            expected_decoding = "[CLS] what's the time [SEP] 7 itc limited report and accounts 2013 itc ’ s [SEP]"
            decoding = processor.decode(input_processor.input_ids[1].tolist())
            self.assertSequenceEqual(decoding, expected_decoding)

            # verify bbox
            # fmt: off
            expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [74, 136, 161, 158], [74, 136, 161, 158], [74, 136, 161, 158], [1000, 1000, 1000, 1000]]  # noqa: E231
            # fmt: on
            self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox)