upgrade sentencepiece version (#13564)

examples/research_projects/visual_bert/demo.ipynb

@@ -37,10 +37,10 @@
     "OBJ_URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/objects_vocab.txt\"\n",
     "ATTR_URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/attributes_vocab.txt\"\n",
     "VQA_URL = \"https://dl.fbaipublicfiles.com/pythia/data/answers_vqa.txt\"\n",
-    "    \n",
+    "\n",
     "\n",
     "# for visualizing output\n",
-    "def showarray(a, fmt='jpeg'):\n",
+    "def showarray(a, fmt=\"jpeg\"):\n",
     "    a = np.uint8(np.clip(a, 0, 255))\n",
     "    f = io.BytesIO()\n",
     "    PIL.Image.fromarray(a).save(f, fmt)\n",
@@ -82,7 +82,7 @@
     "image_preprocess = Preprocess(frcnn_cfg)\n",
     "\n",
     "bert_tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\")\n",
-    "visualbert_vqa = VisualBertForQuestionAnswering.from_pretrained(\"uclanlp/visualbert-vqa\")\n"
+    "visualbert_vqa = VisualBertForQuestionAnswering.from_pretrained(\"uclanlp/visualbert-vqa\")"
    ],
    "outputs": [
     {
@@ -104,17 +104,17 @@
    "cell_type": "code",
    "execution_count": 5,
    "source": [
-    "#image viz\n",
+    "# image viz\n",
     "frcnn_visualizer = SingleImageViz(URL, id2obj=objids, id2attr=attrids)\n",
     "# run frcnn\n",
     "images, sizes, scales_yx = image_preprocess(URL)\n",
     "output_dict = frcnn(\n",
-    "    images, \n",
-    "    sizes, \n",
-    "    scales_yx=scales_yx, \n",
+    "    images,\n",
+    "    sizes,\n",
+    "    scales_yx=scales_yx,\n",
     "    padding=\"max_detections\",\n",
     "    max_detections=frcnn_cfg.max_detections,\n",
-    "    return_tensors=\"pt\"\n",
+    "    return_tensors=\"pt\",\n",
     ")\n",
     "# add boxes and labels to the image\n",
     "\n",
@@ -167,7 +167,7 @@
     "    \"What is the shape of the monitor?\",\n",
     "]\n",
     "\n",
-    "#Very important that the boxes are normalized\n",
+    "# Very important that the boxes are normalized\n",
     "# normalized_boxes = output_dict.get(\"normalized_boxes\")\n",
     "features = output_dict.get(\"roi_features\")"
    ],
@@ -189,7 +189,7 @@
     "        return_token_type_ids=True,\n",
     "        return_attention_mask=True,\n",
     "        add_special_tokens=True,\n",
-    "        return_tensors=\"pt\"\n",
+    "        return_tensors=\"pt\",\n",
     "    )\n",
     "\n",
     "    output_vqa = visualbert_vqa(\n",