diff --git "a/ImageTransformers.ipynb" "b/ImageTransformers.ipynb"
--- "a/ImageTransformers.ipynb"
+++ "b/ImageTransformers.ipynb"
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "1083a759",
+   "id": "3d92f83f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "d9fe5dd0",
+   "id": "615814f9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "55c7438a",
+   "id": "769e62db",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "b07f08d1",
+   "id": "df9802cc",
    "metadata": {},
    "outputs": [
     {
@@ -129,29 +129,10 @@
     "im = ex['image']"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "1b9ee123",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "3c7218c0",
+   "id": "216812d3",
    "metadata": {},
    "outputs": [
     {
@@ -184,7 +165,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "94fb78f0",
+   "id": "3e02c68b",
    "metadata": {},
    "outputs": [
     {
@@ -232,21 +213,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "8897c4a6",
+   "execution_count": 64,
+   "id": "3bfa6291",
    "metadata": {},
    "outputs": [],
    "source": [
     "from transformers import ViTImageProcessor\n",
     "\n",
     "model_name_or_path = 'google/vit-base-patch16-224-in21k'\n",
-    "processor = ViTImageProcessor.from_pretrained(model_name_or_path)  # returns (1, 3, 224, 224) \n"
+    "processsor = ViTImageProcessor.from_pretrained(model_name_or_path)  # returns (1, 3, 224, 224) \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "a3bec518",
+   "execution_count": 65,
+   "id": "12819321",
    "metadata": {},
    "outputs": [
     {
@@ -255,7 +236,7 @@
        "'bean_rust'"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -266,14 +247,381 @@
     "    inputs['labels'] = example['labels']\n",
     "    return inputs\n",
     "\n",
-    "out = process_example(ex)\n",
+    "out = process_example(ex)  # you could call ds.map with this function but itd be slow\n",
     "labels.int2str(out.labels)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "986a77dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image_file_path', 'image', 'labels'],\n",
+       "        num_rows: 1034\n",
+       "    })\n",
+       "    validation: Dataset({\n",
+       "        features: ['image_file_path', 'image', 'labels'],\n",
+       "        num_rows: 133\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image_file_path', 'image', 'labels'],\n",
+       "        num_rows: 128\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = load_dataset('beans')\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "d2684663",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transform_batch(example):\n",
+    "    \n",
+    "    inputs = processsor([image for image in example['image']], return_tensors='pt')\n",
+    "    \n",
+    "    inputs['labels'] = example['labels']\n",
+    "    \n",
+    "    return inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "197d3121",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prepared_ds = ds.with_transform(transform_batch)\n",
+    "# now whenever i use the dataset, it will be transformed in real time\n",
+    "ex1 = prepared_ds['train'][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "b6b3b4bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "angular_leaf_spot\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "ex0 = ds['train'][0:2]  # og dataset\n",
+    "\n",
+    "labels.int2str(ex1['labels'])\n",
+    "\n",
+    "\n",
+    "# img = ex1['image']\n",
+    "# Image.show(img)\n",
+    "img = ex1['pixel_values']\n",
+    "\n",
+    "image_np = np.transpose(img, (1, 2, 0))\n",
+    "\n",
+    "plt.imshow(image_np)\n",
+    "plt.axis('off')  # Optional: to not show axis values\n",
+    "plt.show()\n",
+    "print(labels.int2str(ex1['labels']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "eef4b855",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "def collate_fn(batch):\n",
+    "    return {\n",
+    "        'pixel_values': torch.stack([x['pixel_values'] for x in batch]),\n",
+    "        'labels': torch.tensor([x['labels'] for x in batch])\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "3c47d1d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import ViTForImageClassification\n",
+    "\n",
+    "\n",
+    "model_name_or_path = 'google/vit-base-patch16-224-in21k'\n",
+    "\n",
+    "labels = ds['train'].features['labels'].names\n",
+    "\n",
+    "model = ViTForImageClassification.from_pretrained(\n",
+    "    model_name_or_path,\n",
+    "    num_labels=len(labels),\n",
+    "    id2label={str(i): c for i, c in enumerate(labels)},\n",
+    "    label2id={c: str(i) for i, c in enumerate(labels)}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "0bcbc561",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from datasets import load_metric\n",
+    "\n",
+    "metric = load_metric(\"accuracy\",  trust_remote_code=True)\n",
+    "def compute_metrics(p):\n",
+    "    return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "22c42d5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import TrainingArguments\n",
+    "\n",
+    "training_args = TrainingArguments(\n",
+    "  output_dir=\"./vit-base-beans\",\n",
+    "  per_device_train_batch_size=16,\n",
+    "  evaluation_strategy=\"steps\",\n",
+    "  num_train_epochs=4,\n",
+    "  fp16=False,\n",
+    "  save_steps=100,\n",
+    "  eval_steps=100,\n",
+    "  logging_steps=10,\n",
+    "  learning_rate=2e-4,\n",
+    "  save_total_limit=2,\n",
+    "  remove_unused_columns=False,\n",
+    "  push_to_hub=False,\n",
+    "#   report_to='tensorboard',\n",
+    "  load_best_model_at_end=True,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "c7326444",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import Trainer\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    data_collator=collate_fn,\n",
+    "    compute_metrics=compute_metrics,\n",
+    "    train_dataset=prepared_ds[\"train\"],\n",
+    "    eval_dataset=prepared_ds[\"validation\"],\n",
+    "    tokenizer=processor,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "03def7b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mdetroitnatif\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.16.3"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/Users/tylerklimas/Desktop/MovieSentiment/moviesentiment/wandb/run-20240302_102828-movlajxw</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/detroitnatif/huggingface/runs/movlajxw' target=\"_blank\">magic-dawn-2</a></strong> to <a href='https://wandb.ai/detroitnatif/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/detroitnatif/huggingface' target=\"_blank\">https://wandb.ai/detroitnatif/huggingface</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/detroitnatif/huggingface/runs/movlajxw' target=\"_blank\">https://wandb.ai/detroitnatif/huggingface/runs/movlajxw</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='5' max='260' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [  5/260 00:25 < 36:35, 0.12 it/s, Epoch 0.06/4]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [112]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_results \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m trainer\u001b[38;5;241m.\u001b[39msave_model()\n\u001b[1;32m      3\u001b[0m trainer\u001b[38;5;241m.\u001b[39mlog_metrics(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m, train_results\u001b[38;5;241m.\u001b[39mmetrics)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/trainer.py:1624\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1622\u001b[0m         hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m   1623\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1624\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1625\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1626\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1627\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1628\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1629\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/trainer.py:1961\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1958\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_step_begin(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[1;32m   1960\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39maccumulate(model):\n\u001b[0;32m-> 1961\u001b[0m     tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1963\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   1964\u001b[0m     args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m   1965\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_tpu_available()\n\u001b[1;32m   1966\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m   1967\u001b[0m ):\n\u001b[1;32m   1968\u001b[0m     \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m   1969\u001b[0m     tr_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/trainer.py:2902\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2899\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m loss_mb\u001b[38;5;241m.\u001b[39mreduce_mean()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m   2901\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_loss_context_manager():\n\u001b[0;32m-> 2902\u001b[0m     loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2904\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mn_gpu \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   2905\u001b[0m     loss \u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mmean()  \u001b[38;5;66;03m# mean() to average on multi-gpu parallel training\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/trainer.py:2925\u001b[0m, in \u001b[0;36mTrainer.compute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m   2923\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2924\u001b[0m     labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 2925\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2926\u001b[0m \u001b[38;5;66;03m# Save past state if it exists\u001b[39;00m\n\u001b[1;32m   2927\u001b[0m \u001b[38;5;66;03m# TODO: this needs to be fixed and made cleaner later.\u001b[39;00m\n\u001b[1;32m   2928\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mpast_index \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/vit/modeling_vit.py:794\u001b[0m, in \u001b[0;36mViTForImageClassification.forward\u001b[0;34m(self, pixel_values, head_mask, labels, output_attentions, output_hidden_states, interpolate_pos_encoding, return_dict)\u001b[0m\n\u001b[1;32m    786\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    787\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\u001b[39;00m\n\u001b[1;32m    788\u001b[0m \u001b[38;5;124;03m    Labels for computing the image classification/regression loss. Indices should be in `[0, ...,\u001b[39;00m\n\u001b[1;32m    789\u001b[0m \u001b[38;5;124;03m    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\u001b[39;00m\n\u001b[1;32m    790\u001b[0m \u001b[38;5;124;03m    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\u001b[39;00m\n\u001b[1;32m    791\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    792\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m--> 794\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[43minterpolate_pos_encoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolate_pos_encoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    803\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    805\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclassifier(sequence_output[:, \u001b[38;5;241m0\u001b[39m, :])\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/vit/modeling_vit.py:577\u001b[0m, in \u001b[0;36mViTModel.forward\u001b[0;34m(self, pixel_values, bool_masked_pos, head_mask, output_attentions, output_hidden_states, interpolate_pos_encoding, return_dict)\u001b[0m\n\u001b[1;32m    571\u001b[0m     pixel_values \u001b[38;5;241m=\u001b[39m pixel_values\u001b[38;5;241m.\u001b[39mto(expected_dtype)\n\u001b[1;32m    573\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(\n\u001b[1;32m    574\u001b[0m     pixel_values, bool_masked_pos\u001b[38;5;241m=\u001b[39mbool_masked_pos, interpolate_pos_encoding\u001b[38;5;241m=\u001b[39minterpolate_pos_encoding\n\u001b[1;32m    575\u001b[0m )\n\u001b[0;32m--> 577\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    578\u001b[0m \u001b[43m    \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    579\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    580\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    581\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    582\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    583\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    584\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    585\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayernorm(sequence_output)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/vit/modeling_vit.py:407\u001b[0m, in \u001b[0;36mViTEncoder.forward\u001b[0;34m(self, hidden_states, head_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    400\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m    401\u001b[0m         layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m    402\u001b[0m         hidden_states,\n\u001b[1;32m    403\u001b[0m         layer_head_mask,\n\u001b[1;32m    404\u001b[0m         output_attentions,\n\u001b[1;32m    405\u001b[0m     )\n\u001b[1;32m    406\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 407\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    409\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/vit/modeling_vit.py:352\u001b[0m, in \u001b[0;36mViTLayer.forward\u001b[0;34m(self, hidden_states, head_mask, output_attentions)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    348\u001b[0m     hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor,\n\u001b[1;32m    349\u001b[0m     head_mask: Optional[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    350\u001b[0m     output_attentions: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    351\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[Tuple[torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor], Tuple[torch\u001b[38;5;241m.\u001b[39mTensor]]:\n\u001b[0;32m--> 352\u001b[0m     self_attention_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    353\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayernorm_before\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# in ViT, layernorm is applied before self-attention\u001b[39;49;00m\n\u001b[1;32m    354\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    355\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    356\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    357\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m self_attention_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    358\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m self_attention_outputs[\u001b[38;5;241m1\u001b[39m:]  \u001b[38;5;66;03m# add self attentions if we output attention weights\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/vit/modeling_vit.py:294\u001b[0m, in \u001b[0;36mViTAttention.forward\u001b[0;34m(self, hidden_states, head_mask, output_attentions)\u001b[0m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    289\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    290\u001b[0m     hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor,\n\u001b[1;32m    291\u001b[0m     head_mask: Optional[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    292\u001b[0m     output_attentions: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    293\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[Tuple[torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor], Tuple[torch\u001b[38;5;241m.\u001b[39mTensor]]:\n\u001b[0;32m--> 294\u001b[0m     self_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattention\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    296\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(self_outputs[\u001b[38;5;241m0\u001b[39m], hidden_states)\n\u001b[1;32m    298\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (attention_output,) \u001b[38;5;241m+\u001b[39m self_outputs[\u001b[38;5;241m1\u001b[39m:]  \u001b[38;5;66;03m# add attentions if we output them\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/transformers/models/vit/modeling_vit.py:234\u001b[0m, in \u001b[0;36mViTSelfAttention.forward\u001b[0;34m(self, hidden_states, head_mask, output_attentions)\u001b[0m\n\u001b[1;32m    231\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    232\u001b[0m     attention_probs \u001b[38;5;241m=\u001b[39m attention_probs \u001b[38;5;241m*\u001b[39m head_mask\n\u001b[0;32m--> 234\u001b[0m context_layer \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattention_probs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue_layer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    236\u001b[0m context_layer \u001b[38;5;241m=\u001b[39m context_layer\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m)\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[1;32m    237\u001b[0m new_context_layer_shape \u001b[38;5;241m=\u001b[39m context_layer\u001b[38;5;241m.\u001b[39msize()[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mall_head_size,)\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "train_results = trainer.train()\n",
+    "trainer.save_model()\n",
+    "trainer.log_metrics(\"train\", train_results.metrics)\n",
+    "trainer.save_metrics(\"train\", train_results.metrics)\n",
+    "trainer.save_state()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ecfe4314",
+   "id": "1b040f3c",
    "metadata": {},
    "outputs": [],
    "source": []